Environmental Health Criteria 214


    This report contains the collective views of an international group of
    experts and does not necessarily represent the decisions or the stated
    policy of the United Nations Environment Programme, the International
    Labour Organization, or the World Health Organization.

    First draft prepared by Dr D. L. MacIntosh, University of Georgia,
    Athens, GA, USA and Professor J. D. Spengler, Harvard University,
    Boston, MA, USA

    Published under the joint sponsorship of the United Nations
    Environment Programme, the International Labour Organization, and the
    World Health Organization, and produced within the framework of the
    Inter-Organization Programme for the Sound Management of Chemicals.

    World Health Organization
    Geneva, 2000

         The International Programme on Chemical Safety (IPCS),
    established in 1980, is a joint venture of the United Nations
    Environment Programme (UNEP), the International Labour Organization
    (ILO), and the World Health Organization (WHO).  The overall
    objectives of the IPCS are to establish the scientific basis for
    assessment of the risk to human health and the environment from
    exposure to chemicals, through international peer review processes, as
    a prerequisite for the promotion of chemical safety, and to provide
    technical assistance in strengthening national capacities for the
    sound management of chemicals.

         The Inter-Organization Programme for the Sound Management of
    Chemicals (IOMC) was established in 1995 by UNEP, ILO, the Food and
    Agriculture Organization of the United Nations, WHO, the United
    Nations Industrial Development Organization, the United Nations
    Institute for Training and Research, and the Organisation for Economic
    Co-operation and Development (Participating Organizations), following
    recommendations made by the 1992 UN Conference on Environment and
    Development to strengthen cooperation and increase coordination in the
    field of chemical safety.  The purpose of the IOMC is to promote
    coordination of the policies and activities pursued by the
    Participating Organizations, jointly or separately, to achieve the
    sound management of chemicals in relation to human health and the

    WHO Library Cataloguing-in-Publication Data

    Human exposure assessment.

    (Environmental health criteria ; 214)

         1.Environmental monitoring - methods   2.Environmental exposure
         3.Models, theoretical   4.Data collection - methods    
         5.Toxicity tests
         I.International Programme on Chemical Safety II.Series

         ISBN 92 4 157214 0                  (NLM Classification: QT 162)
         ISSN 0250-863X

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    that are not mentioned. Errors and omissions excepted, the names of
    proprietary products are distinguished by initial capital letters.







         1.1. Introduction
         1.2. Defining exposure
              1.2.1. Exposure and exposure concentration
              1.2.2. Exposure estimation by integration and averaging
              1.2.3. Exposure measurements and models
              1.2.4. Exposure in the context of an environmental health
         1.3. Elements of exposure assessment
         1.4. Approaches to quantitative exposure assessment
         1.5. Linking exposure events and dose events
         1.6. Summary


         2.1. Introduction
         2.2. Human exposure information in environmental epidemiology
         2.3. Human exposure information in risk assessment
              2.3.1. Risk allocation for population subgroups or
              2.3.2. Population at higher or highest risk
         2.4. Human exposure information in risk management
         2.5. Human exposure information in status and trend analysis
         2.6. Summary


         3.1. Introduction
         3.2. Study design
         3.3. Sampling and generalization
         3.4. Types of study design
              3.4.1. Comprehensive samples
              3.4.2. Probability samples
              3.4.3. Other sample types
         3.5. Exposure assessment approaches
              3.5.1. Direct approaches to exposure assessment
              Personal monitoring of inhalation exposures
              Personal monitoring of dietary exposures
              Personal monitoring of dermal absorption

              3.5.2. Indirect approaches to exposure assessment
              Environmental monitoring
              Models as an indirect approach to assessing
              Questionnaires as an indirect approach to
                               assessing exposure
         3.6. Summary


         4.1. Introduction
         4.2. Descriptive statistics
              4.2.1. Numerical summaries
              4.2.2. Graphical summaries
              Cumulative frequency diagrams
              Box plots
              Quantile-quantile plots
              Scatter plots
         4.3. Probability distributions
              4.3.1. Normal distribution
              4.3.2. Lognormal distribution
              4.3.3. Binomial distribution
              4.3.4. Poisson distribution
         4.4. Parametric inferential statistics
              4.4.1. Estimation
              4.4.2. Measurement error and reliability
              4.4.3. Hypothesis testing and two-sample problems
              4.4.4. Statistical models
              Analysis of variance and linear regression
              Logistic regression
              4.4.5. Sample size determination
         4.5. Non-parametric inferential statistics
         4.6. Other topics
         4.7. Summary


         5.1. Introduction
         5.2. Methods
              5.2.1. Activity pattern concepts
              Time allocation parameters
              Microenvironment parameters
              Intensity of contact
              5.2.2. Surrogates of time-activity patterns
              5.2.3. Data collection methods
         5.3. Potential limitations
              5.3.1. Activity representativeness
              5.3.2. Validity and reliability
              5.3.3. Inter- and intra-person variability
         5.4. Summary


         6.1. Introduction
         6.2. General types of exposure model
         6.3. Environmental media and exposure media
         6.4. Single-medium models
              6.4.1. Outdoor and indoor air
              6.4.2. Potable water
              6.4.3. Surface waters
              6.4.4. Groundwater
              6.4.5. Soil
         6.5. Multiple-media modelling
              6.5.1. Inter-media transfer factors
              Diffusive partition coefficients
              Advective partition coefficients
              6.5.2. Exposure factors
              6.5.3. Multiple-media/multiple-pathway models
         6.6. Probabilistic exposure models
              6.6.1. Variability
              6.6.2. Uncertainty
              6.6.3. Implementing probabilistic exposure models
         6.7. A generalized dose model
         6.8. Physiologically based pharmacokinetic models
         6.9. Validation and generalization
         6.10. Summary


         7.1. Introduction
         7.2. Air monitoring
              7.2.1. Gases and vapours
              Passive samplers
              Active samplers
              Direct-reading instruments
              7.2.2. Aerosols
              7.2.3. Semivolatile compounds
              7.2.4. Reactive gas monitoring
         7.3. Water
              7.3.1. Factors influencing water quality
              7.3.2. Water quality monitoring strategies
              7.3.3. Sample collection
         7.4. Assessing exposures through food
              7.4.1. Duplicate diet surveys
              7.4.2. Market basket or total diet surveys
              7.4.3. Food consumption
              Food diaries
              24-h recall
              Food frequency questionnaires
              Meal-based diet history
              Food habit questionnaires
              7.4.4. Contaminants in food
         7.5. Summary


         8.1. Introduction
         8.2. Selected sampling methods
              8.2.1. Soil
              Surface soil collection
              Soil contact and intake measurements
              8.2.2. Settled dust
              Wipe sampling methods
              Vacuum methods
              Sedimentation methods
         8.3. Sampling design considerations
              8.3.1. Concentration and loading
              8.3.2. Collection efficiency
         8.4. Sampling strategies
         8.5. Summary


         9.1. Introduction
         9.2. House dust mites
              9.2.1. Air sampling for house dust mites
              9.2.2. Dust sampling for house dust mites
              9.2.3. Available methods of analysis for house dust mites
              Mite counts
              Immunochemical assays of dust mite
              Guanine determination
              9.2.4. Mite allergens
         9.3. Allergens from pets and cockroaches
              9.3.1. Air sampling for allergens from pets and cockroaches
              9.3.2. Dust sampling for allergens from pets and
              9.3.3. Available methods of analysis
              9.3.4. Typical allergen concentrations
         9.4. Fungi
              9.4.1. Air sampling for fungi
              9.4.2. Settled dust for fungi
              9.4.3. Available methods of analysis for fungi in air
              Total counts of viable and non-viable
                               fungal particles
              9.4.4. General considerations for fungi
         9.5. Bacteria (including actinomycetes)
              9.5.1. Air sampling for bacteria
              9.5.2. Dust sampling for bacteria
              9.5.3. Available methods of analysis for bacteria
              Total count of viable and non-viable
              Viable bacteria

         9.6. Pollen
              9.6.1. Air sampling for pollen
              9.6.2. Dust sampling for pollen
              9.6.3. Available methods of analysis for pollen in air
              9.6.4. General considerations for pollen sampling
         9.7. Summary


         10.1. Introduction
         10.2. General characteristics
         10.3. Considerations for use in environmental exposure assessment
              10.3.1. Toxicokinetics and toxicodynamics
              10.3.2. Biological variability
              10.3.3. Validation of biological markers
              10.3.4. Normative data
         10.4. Advantages of biological markers for exposure assessment
              10.4.1. Characterizing inter-individual variability
              10.4.2. Efficacy of use
              10.4.3. Internal exposure sources
         10.5. Limitations of biological markers for exposure assessment
              10.5.1. Source identification
              10.5.2. Biological variability and altered exposure response
              10.5.3. Participant burden
              10.5.4. Biosafety
         10.6. Media available for use
              10.6.1. Blood
              10.6.2. Urine
              10.6.3. Exhaled breath
              10.6.4. Saliva
              10.6.5. Keratinized tissue (hair and nails)
              10.6.6. Ossified tissue
              10.6.7. Breast milk
              10.6.8. Adipose tissue
              10.6.9. Faeces
              10.6.10. Other media
         10.7. Summary


         11.1. Introduction
         11.2. Quality assurance and quality control
         11.3. Elements of a quality assurance programme
         11.4. Quality assurance programme
              11.4.1. Organization and personnel
              11.4.2. Record-keeping and data recording
              11.4.3. Study plan and standard operating procedures
              11.4.4. Collection of samples
              11.4.5. Equipment maintenance and calibration
              11.4.6. Internal audit and corrective action

         11.5. Quality control/quality assurance for sample measurement
              11.5.1. Method selection and validation
             Detection limits
              11.5.2. Internal quality control
             Control charts
              11.5.3. External quality control
              11.5.4. Reference materials
         11.6. Quality assurance and control issues in population-based
         11.7. Summary


         12.1. Introduction
         12.2. Exposure studies
         12.3. Air pollution exposure studies
              12.3.1. Particle studies
              12.3.2. Carbon monoxide
              12.3.3. Nitrogen dioxide
              12.3.4. Ozone
              12.3.5. Combined exposure studies
              12.3.6. Assessing ambient pollution impacts indoors
              12.3.7. Volatile organic compounds
              12.3.8. Commuter exposures
         12.4. Exposures and biomarkers
              12.4.1. Exposure to lead and cadmium
              12.4.2. Exposure to furans, dioxins and polychlorinated
              12.4.3. Exposure to volatile organic compounds and urinary
         12.5. Exposure to contaminants in drinking-water
         12.6. Exposure to microbes
         12.7. Exposure studies and risk assessment
              12.7.1. The German Environmental Survey
              12.7.2. The National Human Exposure Assessment Survey
              12.7.3. Windsor, Canada exposure and risk study
              12.7.4. Pesticide exposure study
              12.7.5. Czech study of air pollution impact on human health





         Every effort has been made to present information in the criteria
    monographs as accurately as possible without unduly delaying their
    publication.  In the interest of all users of the Environmental Health
    Criteria monographs, readers are requested to communicate any errors
    that may have occurred to the Director of the International Programme
    on Chemical Safety, World Health Organization, Geneva, Switzerland, in
    order that they may be included in corrigenda.

                               *     *     *

         A detailed data profile and a legal file can be obtained from the
    International Register of Potentially Toxic Chemicals, Case postale
    356, 1219 Châtelaine, Geneva, Switzerland (telephone no.
    + 41 22 - 9799111, fax no. + 41 22 - 7973460, E-mail irptc@unep.ch).

                               *     *     *

         This publication was made possible by grant number
    5 U01 ES02617-15 from the National Institute of Environmental Health
    Sciences, National Institutes of Health, USA, and by financial support
    from the European Commission.

    Environmental Health Criteria



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    Selection of chemicals

         Since the inception of the EHC Programme, the IPCS has organized
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    Dr J. Alexander, Department of Environmental Medicine, National
         Institute of Public Health, Folkehelsa, Torshov, Oslo, Norway

    Dr M. Berglund, Institute of Environmental Medicine, Division of
         Metals and Health, Karolinska Institute, Stockholm, Sweden

    Dr M. Dellarco, US Environmental Protection Agency,
         Washington, DC, USA

    Mrs B. Genthe, Environmentek, CSIR, Stellenbosch, South Africa

    Dr L. Gil, Department of Biochemistry, University of Chile -
         Faculty of Medicine, Casilla, Santiago, Chile

    Dr S. Goto, Department of Community Environmental Sciences,
         Institute of Public Health, Minato-ku, Tokyo, Japan

    Professor M. Jantunen, Department of Environmental Hygiene and
         Toxicology, National Public Health Institute, Kuopio, Finland

    Dr N. Künzli, Department of Environment and Health, Institute of
         Social and Preventive Medicine, University of Basel, Basel,

    Dr D. MacIntosh, Environmental Health Science, University of
         Georgia, Athens, GA, USA

    Dr M. Morandi, Environmental Sciences, Houston School of Public
         Health, Houston Health Science Center, University of Texas,
         Houston, TX, USA

    Dr S. Pavittranon, National Institute of Health, Department of
         Medical Sciences, Bamrasnaradura Hospital, Nonthari, Thailand

    Dr N. Rees, Risk Assessment, Management and International
         Coordination Branch, Ministry of Agriculture, Fisheries and Food,
         London, United Kingdom

    Dr B. Schoket, Department of Biochemistry, National Institute of
         Environmental Health, "Fodor József" National Public Health
         Centre, Budapest, Hungary

    Dr L. Sheldon, US Environmental Protection Agency, National
         Research Laboratory, Research Triangle Park, NC, USA

    Professor J. D. Spengler, School of Public Health, Harvard
         University, Boston, MA, USA

    Dr P. Straehl, Swiss Federal Agency for Environment, Forestry and
         Landscape, Swiss Department of the Interior, Bern, Switzerland


    Mrs S. Munn, European Commission, European Chemicals Bureau,
         Environment Institute, Ispra (VA), Italy


    Mr C. Corvalan, Office of Global and Integrated Environmental
         Health, World Health Organization, Geneva, Switzerland

    Dr K. Gutschmidt, International Programme on Chemical Safety,
         World Health Organization, Geneva, Switzerland

    Dr M. Krzyzanowski, European Centre for Environment and
         Health, World Health Organization, Regional Office for Europe,
         Bilthoven Division, De Bilt, Netherlands

    Dr G. Moy, Food Safety, World Health Organization, Geneva,

    Dr H. Tamashiro, Office of Global and Integrated Environmental
         Health, World Health Organization, Geneva, Switzerland

    Dr M. Younes, International Programme on Chemical Safety,
         World Health Organization, Geneva, Switzerland


         A Task Group on the Environmental Health Criteria for Human
    Exposure Assessment met in Glion-sur-Montreux, Switzerland, from 16 to
    20 February 1998. Dr M. Younes, IPCS, welcomed the participants on
    behalf of the Manager, IPCS, and the three IPCS cooperating
    organizations (UNEP/ILO/WHO). The Task Group reviewed and revised the
    final draft of the monograph. In preparation for the final draft a
    review meeting was held at the National Institute of Health Sciences
    (NIHS), Tokyo, from 17 to 19 July 1996.

         The first draft was prepared by Dr D. L. MacIntosh, University of
    Georgia, USA and Professor J. D. Spengler, Harvard University, USA.

         Dr K. Gutschmidt was responsible officer in IPCS for the overall
    scientific content of the monograph and the organization for the
    meetings, and Ms K. Lyle (Sheffield, United Kingdom) was responsible
    for the technical editing of the monograph.

         The efforts of all who helped in the preparation and finalization
    of the monograph are gratefully acknowledged.


    ACGIH     American Conference of Governmental Industrial Hygienists
    ADD       average daily dose
    AI        acceptance intervals
    ALAD      Delta-aminolaevulinic acid dehydratase
    AMIS      Air Monitoring Information System
    ANOVA     analysis of variance
    AOAC      Association of Official Analytical Chemists
    ASTM      American Society for Testing of Materials
    CDF       chlorinated dibenzofurans; cumulative distribution function
    CFU       colony-forming units
    CI        confidence interval
    DG18      dichloran 18% diglycerol agar
    DVM       dust vacuum method
    EDTA      ethylenediamine tetra-acetic acid
    ELISA     enzyme-linked immunosorbent assays
    EPS       extracellular polysaccharides
    ETS       environmental tobacco smoke (exposure)
    EU        endotoxin unit
    FDA       US Food and Drug Administration
    FFQ       food frequency questionnaire
    GEMS      Global Environment Monitoring System
    GerES     German Environmental Survey
    GM        geometric mean
    GSD       geometric standard deviation
    HEAL      Human Exposure Assessment Location
    HPLC      high-pressure liquid chromatography
    HUD       US Department of Housing and Urban Development
    IAEA      International Atomic Energy Agency
    IAQ       internal air quality
    ISEA      International Society of Exposure Analysis
    ISO       International Organization for Standardization
    LADD      lifetime average daily dose
    LAL        Limulus amoebocyte lysate
    LOD       limit of detection
    LOQ       limit of quantification
    LWW       Lioy-Weisel-Wainman
    MAD       maximum allowable deviations
    MCS       multiple chemical sensitivity
    MDL       method detection limit
    MEA       malt extract agar
    NAAQS     National Ambient Air Quality Standard
    NHEXAS    National Human Exposure Assessment Survey
    NIOSH     National Institute for Occupational Safety and Health
    NTA       nitriloacetic acid
    OR        odds ratio
    PAH       polycyclic aromatic hydrocarbons
    PBPK      physiologically based pharmacokinetic (method)
    PCB       polychlorinated biphenyls
    PCDD      polychlorinated dibenzo- p-dioxin
    PCP       pentachlorophenol
    PDF       probability distribution function

    PEM       personal exposure monitor
    PMn       particulate matter with aerodynamic diameter <  n µm
    PTEAM     particle total exposure assessment methodology
    QA        quality assurance
    QC        quality control
    RAST      radioallergosorbent tests
    RIA       radioimmunoassay
    RSP       respirable particulate matter
    SAM       stationary outdoor monitor
    SBS       sick building syndrome
    SD        standard deviation
    SEM       scanning electron microscope
    SIM       stationary indoor monitor
    SOP       standard operating procedure
    SVOC      semivolatile organic compound
    TCCD      2,3,7,8-tetrachloro dibenzo- p-dioxin
    TDS       US FDA Total Diet Study
    TEQ       TCCD toxic equivalents
    TSP       total suspended particulates
    TWI       tolerable weekly intake
    UNEP      United Nations Environment Programme
    VOC       volatile organic compound
    XRF       X-ray fluorescence


         The International Programme on Chemical Safety (IPCS), launched
    in 1980, is a joint collaborative programme of the International Labor
    Organization (ILO), the United Nations Environment Programme (UNEP),
    and the World Health Organization (WHO); WHO is the Administrating
    Organization of the Programme. The two main roles of the IPCS are to
    establish the scientific health and environmental risk assessment
    basis for safe use of chemicals  (normative function) and to
    strengthen national capabilities for chemical safety  (technical 
     cooperation). In the field of methodology, the work of the IPCS aims
    at promoting the development, improvement, validation, harmonization
    and use of generally acceptable, scientifically sound methodologies
    for the evaluation of risks to human health and the environment from
    exposure to chemicals. The work encompasses the development of
    Environmental Health Criteria monographs on general principles of
    various areas of risk assessment covering various aspects related to
    risk assessment such as, in this publication, on exposure assessment.

         The WHO and the World Meteorological Organization coordinate the
    assessment of climate, urban air and water pollution, and health
    status of populations. These measures provide the indicator of trends
    and status.

         Until 1995, the basic source for internationally comparable urban
    air pollution data was the Global Environment Monitoring System
    (GEMS/Air) of UNEP and WHO. Started in 1974, shortly after the
    Stockholm Environment Conference, GEMS had built up a system that
    collected comparable ambient air pollution data in about 50 cities of
    35 countries, varied in geography and income (UNEP/WHO, 1988, 1992).
    Typically, sulfur dioxide and total suspended particulates (TSP) had
    been monitored in three stations of each city, one each in industrial,
    commercial, and residential zones. Later, GEMS also collected
    monitoring data for carbon monoxide, nitrogen dioxide, and lead, and
    made emissions estimates for all five pollutants. The results were
    published periodically by GEMS, and also often appeared in other
    periodic international data sets, such as those of the World Bank
    (World Bank, 1992), the World Resources Institute (World Resources
    Institute, 1992), the United Nations (UN ESCAP, 1990) and UNEP itself
    (UNEP, 1991).

         More recently, WHO created with the Air Management Information
    System (AMIS) the successor of GEMS/Air. Like GEMS/Air, AMIS provides
    air quality data for major and megacities. Data on sulfur dioxide,
    nitrogen dioxide, carbon monoxide, ozone, black smoke, suspended
    particulate matter, PM10, lead and others are available. AMIS also
    includes information on air quality management (WHO, 1997).

         Much of what is known about contaminants in food, soils, water
    and air has become available through WHO and UNEP publications. For
    more than 20 years WHO/UNEP has been promoting an appreciation for
    improved assessments of human exposures through training sessions,

    workshops, demonstration projects, and published methodologies and
    reports. Through a series of WHO-sponsored studies in every populated
    continent, the principles of human exposure assessment have been
    illustrated for indoor and outdoor air pollutants, food contamination
    and water. In 1984, after some background reports (e.g., UNEP/WHO,
    1982), WHO and UNEP conducted the Human Exposure Assessment Location
    (HEAL) Project, which facilitates research and information sharing
    among 10-15 institutions worldwide concerned with exposure assessment
    for a limited number of pollutants (Ozolins, 1989). Unfortunately,
    although providing important functions, the HEAL project has not had
    the mandate or anything approaching the resources required to actually
    make comparable international estimates of population exposures. HEAL
    projects, for the most part, have investigated exposures to
    conventional inorganic air pollutants such as carbon monoxide,
    nitrogen dioxide and general undifferentiated particle mass where
    inhalation is the primary route of exposures. However, the HEAL
    programme does offer examples of lead, cadmium and pesticide studies
    which illustrate multiple exposure pathways and demonstrate the
    necessity of extensive analytical training and quality programmes. An
    analytical quality control programme which involved all participating
    laboratories enabled reliable international comparisons of exposure
    despite differences in methodologies applied by the different

         Preceding this criteria document the UNEP, FAO and WHO have been
    actively advancing the concepts and methodologies for human exposures.
    GEMS/Air, GEMS/Water and GEMS/Food are establishing the uniformity
    among data collected worldwide to establish national and international
    status and trends. These efforts, together with others, such as the
    Codex Committee on Pesticide Residues, the several Joint FAO/WHO
    Consultations on food consumption, pesticide residues, veterinary
    drugs, additives and chemical contaminants, have been developing the
    basis of quantitative assessment of human exposures and risk. Table 38
    (pg. 279) provides a listing of pertinent publications related to
    assessment of air, water and food contamination.


         This current criteria document on human exposure assessment
    presents in one publication the concepts, rationale, and statistical
    and procedural methodologies for human exposure assessment. The
    underpinnings of exposure assessment are the basic environmental and
    biological measurements found in the more familiar specialties of air
    and water pollution and food and soil sciences. Therefore, throughout
    this document readers are referred to other publications for technical
    details on instrumental and laboratory methods. This criteria document
    is intended for the community of scientific investigators inquiring
    about the human health consequences of contaminants in our
    environment. As such, this text will be of interest to physical
    scientists, engineers and epidemiologists. It is intended also for
    those professions involved in devising, evaluating and implementing
    policy with respect to managing the quality of environmental health,
    inclusive of air, water, food and soil. By necessity environment is

    defined broadly to include place, media, and activities where we
    humans encounter contaminants.

         Of primary concern in this document are those environmental
    contaminants that exist in various media as a consequence of direct or
    indirect human intention. We have included some biological agents that
    are "natural" but, through actions of irritation and allergy, can
    contribute to or cause morbidity and mortality as a result of
    inadequate building design and maintenance. We recognize that viral,
    bacterial and other biological agents in air, food, soil and water
    contribute significantly to the burden of disease worldwide. However,
    in the context of environmental exposure assessment the focus is on
    chemical contaminants and a few specific allergens that might
    contribute directly to disease or, in combination with biopathogens,
    alter susceptibility and expression of disease.

         To say that exposure assessment of environmental contaminants is
    exclusive of any population or location is, in principle, a
    contradiction. There are practical considerations, however, for
    identifying the industrial workplace as a separate domain.
    Administratively, many nations handle occupational health and safety
    concerns separately from the environment. The management of workplace
    hazards through well-established industrial hygiene practices of
    source control, ventilation and worker protection are widely
    recognized. This separation of workplace exposures from the general
    environmental exposure focus in this document is not hard and fast.
    Occupationally acquired contaminants can expose family members not
    working in the specific industry. Industrial control strategies that
    increase ventilation can adversely affect the neighbouring community.
    In many societies, commercial and residential use of property are
    integrated. Family operated business along congested streets means
    that contaminants generated in outdoors, indoors and workplaces are
    intermingled. Even where commercial and residential property are
    distinct, chemical and biological contaminants can lead to non-worker

         Information on human exposures has a well-recognized role as a
    corollary to epidemiology. But it is more than this, because
    understanding human exposures to environmental contaminants is
    fundamental to public policy. The adequacy of environmental mitigation
    strategies is predicated on improving or safeguarding human and
    ecological health. The public mandate for and acceptance of controls
    on emissions is first based on sensory awareness of pollution.
    Irritated airways, foul-smelling exhaust, obscuring plumes, oil slicks
    on water, dirty and foul-tasting water, and medical waste and debris
    on beaches are readily interpreted as transgressions against us and
    threaten commonly shared natural resources. As we enter the
    twenty-first century, we recognize that we, humans have had profound
    but often subtle impacts on the chemistry of the biosphere and
    lithosphere. Metals, organic compounds, particulate matter, and
    photochemically produced gases are widely dispersed, recognizing no
    geographic or political boundaries. Global markets, urbanization, and
    increased mobility have environmental contamination as a consequence.

    Assessing the quantities and distribution of potentially harmful
    contaminant exposures to human populations is a critical component of
    risk management. As long as disease prevention and health promotion
    are the principal tenets of public health, then assessing the levels
    of contaminant exposures in environmental and biological samples will
    be necessary.

         This book presents the methodologies for surveying exposures,
    analysing data and integrating findings with the ongoing national and
    global debate defining natural limits to human behaviour. It serves
    the cross-disciplinary needs of environmental managers, risk assessors
    and epidemiologists to learn something about the design, conduct,
    interpretation and value of human exposure studies of multimedia
    environmental contaminants. For investigators considering exposure
    studies, this book guides them to contemporary information on
    measurement of analysis methods and strategies.

         In Chapter 1 of the document the basic terms and concepts used in
    exposure assessment are defined. Similar understanding of terms used
    commonly among health assessors working in the different fields of
    air, water, soil and food sciences is a critical starting point in
    defining the emerging specialist area of exposure assessment.
    Application of exposure research and routine assessments to the
    information needs of risk managers, policy-makers and epidemiologists
    is established in Chapter 2. Discussion of these information needs is
    developed in Chapter 3, which presents the objectives for various
    study designs.

         Chapter 4 covers basic statistical concepts used in exposure
    assessment. The intent is to inform the reader of how statistical
    analysis is vital to all components of an exposure assessment. By
    examples and references the reader is directed to more substantial
    texts on study design, data analysis, modelling and quality control.

         Chapter 5 is devoted to a component of exposure assessment
    related to the collection and interpretation of human activity
    patterns. Information on how, where and when people contact
    potentially contaminant media is useful for data interpretation,
    establishing risk scenarios and identifying activities, locations and
    populations at differential risk. The emphasis here is primarily
    related to air pollution exposure studies. In the conduct of total
    multimedia exposure investigations or modelling analogous information
    is needed for the ingestion of water and food, as well as for dermal

         Chapter 6 extends the concepts of the preceding chapters in
    discussing models for human exposure assessment. The data requirements
    for various pathways and various modelling approaches are presented.

         Chapter 7 separates the conceptual first half of the text from
    the pragmatic guidelines offered in the rest of the document. The
    chapter contains a discussion of air monitoring, water monitoring and
    food sampling. These particular fields are rather well developed

    individually, if not well integrated into multimedia studies. The
    reader is referred to many other resources that can guide the
    investigator to details on instruments, sampling methods and
    laboratory analysis.

         In Chapter 8, proportionally more emphasis is placed on soil and
    settled dust sampling. Again, the laboratory methods for metals,
    organics and various chemical compounds are readily available in the
    published literature. This chapter, then, focuses on relatively new
    sampling techniques to quantify in a standardized way the contaminant
    levels in soil and settled dust.

         In Chapter 9, on microbiological agents, assessment techniques
    for commonly encountered allergens, mycotoxins, fungal and pollen
    spores, microbiological bacteria and endotoxins are presented. These
    agents have been included because of their imputed contribution to
    respiratory disease and potential interactions with chemical
    pollutants. There is growing recognition that exposure to these agents
    in schools, homes, hospitals and office buildings constitutes a
    specific risk to atopic, asthmatic and compromised individuals.

         The use of biomarkers for exposure assessments is presented in
    Chapter 10. Biological samples derived from human tissue or fluids
    have been used as markers of both effects as well as exposure (dose)
    to a variety of occupational and environmental contaminants. The
    chapter describes the applications of biomarkers in exposure studies.

         The quality assurance (QA) activities that should be considered
    in conducting and evaluating exposure studies are addressed in Chapter
    11. Contributors to this document intended to impart their experiences
    to improve future exposure study. It is emphasized that QA aspects
    must be considered in all components of exposure studies, to enhance
    comparability and interpretation.

         Chapter 12 presents brief synopses of exposure studies.
    Selections illustrate a variety of study designs with different
    objectives and target pollutants and populations. Relatively more
    emphasis has been given to particles and passive exposure to cigarette
    smoke. The evidence is that cigarette consumption has increased almost
    worldwide, suggesting that greater attention be given to
    characterizing and reducing exposures to non-smokers, in particular,
    infants and young children. Epidemiological studies conducted over the
    last 15 years indicate that ambient particulate matter is adversely
    affecting human health at levels well below many of the established
    standards. Exposure assessment along with toxicology and epidemiology
    will be needed to answer many of the remaining unresolved issues about
    ambient and indoor suspended particles.

         Other studies summarized show how exposure assessment is
    supportive of epidemiology and risk management. The reader should
    recognize that Chapter 12 is not comprehensive but is intended to help
    educate the research community and others about the application, use
    and limitations of exposure assessment methodologies.


    1.1  Introduction

         People are exposed to a variety of potentially harmful agents in
    the air they breathe, the liquids they drink, the food they eat, the
    surfaces they touch and the products they use. An important aspect of
    public health protection is the prevention or reduction of exposures
    to environmental agents that contribute, either directly or
    indirectly, to increased rates of premature death, disease, discomfort
    or disability. It is usually not possible, however, to measure the
    effectiveness of mitigation strategies directly in terms of prevented
    disease, reduced premature death, or avoided dysfunction. Instead,
    measurement or estimation of actual human exposure, coupled with
    appropriate assumptions about associated health effects or safety
    limits (e.g., acceptable daily intake, tolerable daily intake), is the
    standard method used for determining whether intervention is necessary
    to protect and promote public health, which forms of intervention will
    be most effective in meeting public health goals, and whether past
    intervention efforts have been successful (Ott & Roberts, 1998).

         The purpose of this chapter is to define the concept of exposure,
    and the direct and indirect method of exposure assessment. A brief
    discussion of exposure in the environmental health paradigm and its
    relationship to dose is presented.

    1.2  Defining exposure

         Exposure is defined as contact over time and space between a
    person and one or more biological, chemical or physical agents (US
    NRC, 1991a). Exposure assessment is to identify and define the
    exposures that occur, or are anticipated to occur, in human
    populations (IPCS, 1993). This can be a complex endeavour requiring
    analysis of many different aspects of the contact between people and
    hazardous substances (see Table 1). Although exposure is a
    well-established concept familiar to all environmental health
    scientists, its meaning often varies depending on the context of the
    discussion. It is important however, that exposure and related terms
    be defined precisely. In the following sections, we describe and
    define important exposure-related terms used in this document. The
    definitions are consistent with the US EPA's Exposure Assessment
    Guidelines and related WHO publications (WHO, 1987, 1996a; US EPA,
    1992a; IPCS, 1994). It is important to recognize, however, that
    terminology and definitions vary among organizations and nations.
    Thus, the reader is advised to concentrate on the concepts, rather
    than the specific terms, as they represent the crux of exposure

    Table 1.  Different aspects of the contact between people and pollution
              that are potentially important in exposure analysis
              (Sexton et al., 1995b)

    Agent(s)                    biological, chemical, physical, single
                                agent, multiple agents, mixtures

    Source(s)                   anthropogenic/non-anthropogenic, area/point,
                                stationary/mobile, indoor/outdoor

    Transport/carrier medium    air, water, soil, dust, food, product/item

    Exposure pathways(s)        eating contaminated food,
                                breathing contaminated workplace air
                                touching residential surface

    Exposure concentration      mg/kg (food), mg/litre (water), µg/m3 (air),
                                µg/cm2 contaminated surface), % by weight,
                                fibres/m3 (air)

    Exposure route(s)           inhalation, dermal contact, ingestion,
                                multiple routes

    Exposure duration           seconds, minutes, hours, days, weeks,
                                months, years, lifetime

    Exposure frequency          continuous, intermittent, cyclic, random,

    Exposure setting(s)         occupational/non-occupational,
                                residential/non-residential, indoors/outdoors

    Exposed population          general population, population subgroups,

    Geographic scope            site/source specific, local, regional,
                                national, international, global

    Time frame                  past, present, future, trends

    1.2.1  Exposure and exposure concentration

         Exposure, as defined earlier, is the contact of a biological,
    chemical, or physical agent with the outer part of the human body,
    such as the skin, mouth or nostrils. Although there are many instances
    where contact occurs with an undiluted chemical (e.g., use of
    degreasing chemicals for cleaning hands), contact more often occurs
    with a carrier medium (air, water, food, dust or soil) that contains
    dilute amounts of the agent. "Exposure concentration" (e.g., mg/litre,
    mg/kg, µg/m3) is defined as the concentration of an environmental
    agent in the carrier medium at the point of contact with the body.

    1.2.2  Exposure estimation by integration and averaging

         A minimal description of exposure for a particular route must
    include exposure concentration and the duration of contact. If the
    exposure concentration is integrated over the duration of contact
    (Table 2), the area under the resulting curve is the magnitude of the
    exposure in units of concentration multiplied by time (e.g.,
    mg/litreÊday, mg/kgÊday, µg/m3Êh). This is the method of choice to
    describe and estimate short-term doses, where integration times are of
    the order of minutes, hours or days.

         Over periods of months, years or decades, exposures to most
    environmental agents occur intermittently rather than continuously.
    Yet long-term health effects, such as cancer, are customarily
    evaluated based on an average dose over the period of interest
    (typically years), rather than as a series of intermittent exposures.
    Consequently, long-term doses are usually estimated by summing doses
    across discrete exposure episodes and then calculating an average dose
    for the period of interest (e.g., year, lifetime). Although the
    integration approach can also be used to estimate long-term exposures
    or doses, its application to time periods longer than about a week is
    usually difficult and inconvenient.

    1.2.3  Exposure measurements and models

         Direct measurements are the only way to establish unequivocally
    whether and to what extent individuals are exposed to specific
    environmental agents. But it is neither affordable nor technically
    feasible to measure exposures for everyone in all populations of
    interest. Models, which are mathematical abstractions of physical
    reality, may obviate the need for such extensive monitoring programmes
    by providing estimates of population exposures (and doses) that are
    based on a smaller number of representative measurements (Fig. 1). The
    challenge is to develop appropriate and robust models that allow for
    extrapolation from relatively few measurements to estimates of
    exposures and doses for a much larger population (US NRC, 1991b).

         For relatively small groups, measurements or estimates can be
    made for some or all of the individuals separately, and then combined
    as necessary to estimate the exposure (or dose) distribution. For
    larger groups, exposure models and statistics can sometimes be used to
    derive an estimate of the distribution of population exposures,
    depending on the quantity and quality of existing data. Monte Carlo
    and other statistical techniques are increasingly being used to
    generate and analyse exposure distributions for large groups (US EPA,

    1.2.4  Exposure in the context of an environmental health paradigm

         The presence of hazardous substances in our environment does not
    necessarily imply a risk to human health or to the ecosystem. Exposure
    is an integral and necessary component in a sequence of events having
    potential health consequences. An expanded and more detailed version

    TABLE 2

    of the environmental health paradigm also showing the role of exposure
    is depicted in Fig. 2. The role of exposure assessment in the risk
    assessment framework applied by EU and US EPA is shown in Fig. 3.

         The release of an agent into the environment, its ensuing
    transport, transformation and fate in various environmental media, and
    its ultimate contact with people are critical events in understanding
    how and why exposures occur. Definitions for key events in the
    continuum are summarized below. They were compiled from three sources:
    Ott (1990); US EPA (1992a); Sexton et al. (1995a).

    *   Sources. The point or area of origin for an environmental agent
       is known as a source. Agents are released into the environment from
       a wide variety of sources, which are often categorized as
        primary sources including point sources (e.g., incinerator)
       versus area sources (e.g., urban runoff), stationary sources (e.g.,
       refinery) versus mobile sources (e.g., automobile) and
       anthropogenic sources (e.g., landfill) versus non-anthropogenic
       sources (e.g., natural vegetation) and  secondary sources 
       including condensation of vapours into particles and chemical
       reactions of precursors producing new pollutants.

    *   Exposure pathway. An exposure pathway is the physical course
       taken by an agent as it moves from a source to a point of contact
       with a person. The substance present in the media is quantified as
       its concentration.

    FIGURE 1

    FIGURE 2

    FIGURE 3

    *   Exposure concentration. As discussed in 1.2.1, exposure is the
       concentration of an agent in a carrier medium at the point of
       contact with the outer boundary of the human body. The
       concentration is the amount (mass) of a substance or contaminant
       that is present in a medium such as air, water, food or soil
       expressed per volume or mass. Assessments are often not at exposure
       or exposure concentration, since that information alone is not very
       useful unless it is converted to dose or risk. Assessments
       therefore usually estimate how much of an agent is expected to
       enter the body. This transfer of an environmental agent from the
       exterior to the interior of the body can occur by either or both of
       two basic processes: intake and uptake.

    *   Exposure route. Exposure route denotes the different ways the
       substance may enter the body. The route may be dermal, ingestion or

    *   Intake. Intake is associated with ingestion and inhalation routes
       of exposure. The agent, which is likely to be part of a carrier
       medium (air, water, soil, dust, food), enters the body by bulk
       transport, usually through the nose or mouth. The amount of the
       agent that crosses the boundary per unit time can be referred to as
       the "intake rate", which is the product of the exposure
       concentration times the rate of either ingestion or inhalation. For
       inhalation, intake may be calculated for any time period. For
       ingestion, intake is usually expressed as the amount of food or
       water consumed times the pollutant concentration in that medium
       during a certain time period.

    *   Uptake. Uptake is associated with the dermal route of exposure,
       as well as with ingestion and inhalation after intake has occurred.
       The agent, as with intake, is likely to be part of a carrier medium
       (e.g., water, soil, consumer product), but enters the body by
       crossing an absorption barrier, such as the skin, respiratory tract
       or gastrointestinal tract. The rates of bulk transport across the
       absorption barriers are generally not the same for the agent and
       the carrier medium. The amount of the agent that crosses the
       barrier per unit time can be referred to as the  uptake rate. This
       rate is a function of the exposure concentration, as well as of the
       permeability and surface area of the exposed barrier. The uptake
       rate is also called a  flux. 

    *   Dose. Once the agent enters the body by either intake or uptake,
       it is described as a dose. Several different types of dose are
       relevant to exposure estimation. All these different dose measures
       are approximations of the target or biological effective dose.

       -   Potential (administered) dose. Potential or administered dose
          is the amount of the agent that is actually ingested, inhaled or
          applied to the skin. The concept of potential dose is
          straightforward for inhalation and ingestion, where it is
          analogous to the dose administered in a dose-response
          experiment. For the dermal route, however, it is important to

          keep in mind that potential (or administered) dose refers to the
          amount of the agent, whether in pure form or as part of a
          carrier medium, that is applied to the surface of the skin. In
          cases where the agent is in diluted form as part of a carrier
          medium, not all of the potential dose will actually be touching
          the skin.

       -   Applied dose. Applied dose is the amount of the agent directly
          in contact with the body's absorption barriers, such as the
          skin, respiratory tract and gastrointestinal tract, and
          therefore available for absorption. Information is rarely
          available on applied dose, so it is calculated from potential
          dose based on factors such as bioavailability (Fig. 2).

       -   Internal (absorbed) dose. The amount of the agent absorbed,
          and therefore available to undergo metabolism, transport,
          storage or elimination, is referred to as the  internal or
           absorbed dose (Fig. 2). Bioavailability has been used to
          describe absorbed dose.

       -   Delivered dose. The delivered dose is the portion of the
          internal (absorbed) dose that reaches a tissue of interest.

       -   Biologically effective (target) dose. The biologically
          effective dose is the portion of the delivered dose that reaches
          the site or sites of toxic action.

         The link, if any, between biologically effective (target) dose
    and subsequent disease or illness depends on the relationship between
    dose and response (e.g., shape of the dose-response curve), underlying
    pharmacodynamic mechanisms (e.g., compensation, damage, repair), and
    important susceptibility factors (e.g., health status, nutrition,
    stress, genetic predisposition).

    *   Biological effect. A measurable response to dose in a molecule,
       cell or tissue is termed a biological effect. The significance of a
       biological effect, whether it is an indicator or a precursor for
       subsequent adverse health effects, may not be known.

    *   Adverse effect. A biological effect that causes change in
       morphology, physiology, growth, development or life span which
       results in impairment of functional capacity to compensate for
       additional stress or increase in susceptibility to the harmful
       effects of other environmental influences (IPCS, 1994).

    1.3  Elements of exposure assessment

         Assessing human exposure to an environmental agent involves the
    qualitative description and the quantitative estimation of the agent's
    contact with (exposure) and entry into (dose) the body. Although no
    two exposure assessments are exactly the same, all have several common
    elements: the number of people exposed at specific concentrations for
    the time period of interest; the resulting dose; and the contribution

    of important sources, pathways and behavioural factors to exposure or
    dose. A list of the types of estimates that might comprise a
    comprehensive exposure assessment could include the following (as
    described in part by Brown (1987) and Sexton et al. (1995a)):

    *   Exposure 
       -  routes, pathways and frequencies
       -  duration of interest (short-term, long-term, intermittent or
          peak exposures)
       -  distribution (e.g., mean, variance, 90th percentile) --
          population, important subpopulations (e.g., more exposed, more
       -  individuals -- average, upper tail of distribution, most exposed
          in population.

    *   Dose 
       -  link with exposures
       -  distribution (e.g., mean, variance, 90th percentile) --
          population important subpopulations (e.g., higher doses, more
       -  individuals -- average, upper tail of distribution, highest dose
          in population.

    *   Causes 
       -  relative contribution of important sources
       -  relative contribution of important environmental media
       -  contribution of important exposure pathways
       -  relative contribution of important routes of exposure.

    *   Variability 
       -  within individuals (e.g., changes in exposure from day to day
          for the same person)
       -  between individuals (e.g., differences in exposure on the same
          day for two different people)
       -  between groups (e.g., different socio-economic classes or
          residential locations)
       -  over time (e.g., changes in exposure from one season to another)
       -  across space (e.g., changes in exposure/dose from one region of
          a city, country to another).

    *   Uncertainty 
       -  lack of data (e.g., statistical error in measurements, model
          parameters, etc.; misidentification of hazards and causal
       -  lack of understanding (e.g., mistakes in functional form of
          models, misuses of proxy data from analogous contexts).

         Although comprehensive exposure assessments could be considered
    the ideal, they are very costly; decisions therefore need to be made
    on the most important elements for inclusion. For any study, the
    purpose must first be defined. Possible purposes include environmental
    epidemiology, risk assessment, risk management or status and trend
    analysis (see Chapter 2). The data elements and measuring approaches

    that are needed for this purpose are then determined. Table 3
    summarizes the basic information that is required for each study. It
    should be mentioned that different elements of the exposure assessment
    framework might be selected to meet different study requirements.

    Table 3. Basic information needed for exposure assessments in 
             different contexts

                              Information required

    Risk assessment           Point estimates or distributions of 
                              exposure and dose
                              Duration of exposure and dose

    Risk management           Pollutant source contributing to 
    (conducted once hazard    exposure and dose
    is identified)            Personal activities contributing 
                              to exposure and dose
                              Effectiveness of intervention measures

    Status and trend          Change of exposure and dose of 
                              populations over time

    Epidemiology              Individual and population exposures and 
                              doses, exposure dose categories

    1.4  Approaches to quantitative exposure assessment

         Quantitative estimation of exposure is often the central feature
    of assessment activities. The quantitative estimation of exposure can
    be approached in two general ways:  direct assessment, including
    point-of-contact measurements and biological indicators of exposure;
    and  indirect assessment, including environmental monitoring,
    modelling, questionnaires (US NRC, 1991b) (see Chapter 3.5). These two
    generic approaches to quantitative estimation of exposure are
    independent and complementary. Each relies on different kinds of data
    and has different strengths and weaknesses. It is potentially useful,
    therefore, to employ multiple approaches as a way of checking the
    robustness of results. Among other factors, the choice of which method
    to use will depend on the purpose of the assessment and the
    availability of suitable methods, measurements and models.

         Direct approaches for air, water and food include personal air
    monitors, measurements of water at the point of use and measurement of
    the food being consumed. Indirect approaches include
    microenvironmental air monitoring and measurements of the water supply
    and food supply (contents of a typical food basket, for instance).

         Exposure models are constructed to assess or predict personal
    exposures or population exposure distributions from indirect
    measurements and other relevant information.

         Measures of contaminants in biological material (biomarkers)
    afford a direct measure of exposure modified by and integrated over
    some time in the past which depends on physiological factors that
    control metabolism and excretion. Such measures give no direct
    information about the exposure pathways. Examples of the type of
    biomarkers measured in human material that can be used for
    reconstructing internal dose and their relevance to exposure
    assessment are discussed in Chapter 10.

    1.5  Linking exposure events and dose events

         The schematic framework in Fig. 2 shows how the
    interrelationships among significant exposure- and dose-related events
    in the paradigm can be conceived.

         It is important to keep in mind that, although events along the
    continuum are correlated, the relative position of a particular
    individual within a distribution may change dramatically from one
    event to the next as the agent or its metabolite/derivative moves
    through the various stages from exposure concentration to biologically
    effective dose.

         To make realistic estimates for a specific event (e.g., an
    internal dose), it is necessary to have at least one of two types of
    information: measurements of the event itself (e.g., internal dose),
    or measurements of an earlier (e.g., potential dose) or later (e.g.,
    delivered dose) event in the continuum. It is also necessary to
    understand the critical intervening mechanisms and processes (e.g.,
    pharmacokinetics) that govern the relationship between the event
    measured and the event of interest (e.g., internal dose). Unless such
    data are on hand, extrapolating from one event to another, moving
    either from exposure to dose (downwards in Fig. 2) or from dose to
    exposure (upwards in Fig. 2) is problematic.

         Suitable data and adequate understanding are seldom, if ever,
    available to describe and estimate all of the significant events for
    the groups and individuals of interest. Generally speaking,
    measurement of exposure concentration and delivered dose  (body 
     burden) is in many cases relatively straightforward, whereas
    measurement of potential (administered) dose and internal (absorbed)
    dose is usually possible only with substantially greater effort.
    Measurement of biologically effective (target) dose may also be
    possible in some cases, although it is usually impossible to measure
    the applied dose.

         This situation presents us with a conundrum. We would like to
    have realistic estimates of exposure concentrations of an agent for
    all important pathways, and the resulting biologically effective dose.
    Typically, however, if relevant data are available at all, they are

    related to exposure concentrations for one pathway or route of
    exposure. In the few cases where data on dose are also available,
    these data usually reflect delivered dose (body burden) rather than
    biologically effective dose. Even if suitable measurements of both
    exposure concentration and delivered or target dose are on hand, the
    absence of pharmacokinetic understanding to relate these measurements
    to each other, as well as to other significant events along the
    continuum, seriously impairs efforts to establish the link between
    exposure and dose.

         We are thus left with a situation in which we can measure
    specific events on either side of the body's absorption boundaries,
    but we can relate them to each other only by using a series of
    unsubstantiated assumptions. Yet it is this relationship between
    exposure and dose that is critical to, for example, establishing cause
    and effect relationships between exposure and diseases.

    1.6  Summary

         Exposure requires the occurrence of the presence of an
    environmental toxicant at a particular point in space and time; and
    the presence of a person or persons at the same location and time. In
    addition, the amount which comes in contact with the outer boundary of
    the human body is required.

         As the intrinsic value of exposure-related information has become
    recognized, "exposure analysis" has emerged as an important field of
    scientific investigation, complementing such traditional public health
    disciplines as epidemiology and toxicology, and is an essential
    component in informed environmental health decision-making (Goldman et
    al., 1992; Sexton et al., 1992, 1994; Wagener et al., 1995).


    2.1  Introduction

         Exposure assessments collect data on the route magnitude,
    duration, frequency and distributions of exposures to hazardous agents
    for individuals and populations. Human exposure data have been used
    for the evaluation and protection of environmental health in four
    interrelated disciplines: epidemiology, risk assessment, risk
    management, and status and trends analysis. The fundamental goal of
    exposure assessment studies is to reduce the uncertainty of the
    exposure estimates that are used within each discipline to make public
    policy decisions or reach research conclusions.

          Epidemiology is the examination of the link between human
    exposures and health outcomes (Sexton et al., 1992).  Risk 
     assessment is the estimation of the likelihood, magnitude and
    uncertainty of population health risks associated with exposures. In
    contrast,  risk management is the determination of the source and
    level of health risks and which health risks are acceptable and what
    to do about them. Status and trends analysis comprises the evaluation
    of historical patterns, current status and possible future changes in
    human exposures.

         The purpose of this chapter is to describe the disciplines from
    environmental epidemiology through risk assessment. It also describes
    how human exposure assessment data are used in each of these

    2.2  Human exposure information in environmental epidemiology

         Epidemiology is the study of the determinants and distribution of
    health status (or health-related events) in human populations.
    Environmental epidemiology searches for statistical associations
    between environmental exposures and adverse health effects (presumed)
    to be caused by such exposures. It is a scientific tool that can
    sometimes detect environmentally induced health effects in
    populations, and it may offer opportunities to link actual exposures
    with adverse health outcomes (US NRC, 1991c, 1994; Matanoski et al.,
    1992; Beaglehole et al., 1993).

         Exposure assessment methods can be used for identifying and
    defining the low or high exposure groups. They can also be used for
    devising more accurate exposure data from measured environmental
    contaminant levels and personal questionnaire or time-activity diary
    data, or estimating population exposure differences between days of
    high and low pollution, or between high and low pollution in
    communities using measured environmental and population behavioural
    data (see also Chapters 3 and 5).

         In particular, to establish long-term health effects of "low
    dose" environmental exposures, epidemiological methods are the
    predominant, if not only, tools at hand for health-effect assessment.
    However, the excess risk of most environmentally related health
    effects is small, with relative risks and odds ratios usually being
    less than 2 across the observed range of exposure experienced by
    populations. Furthermore, there are usually no "non-exposed"
    comparison groups, and the factors contributing to the development of
    diseases are numerous. As a consequence, environmental epidemiology
    faces considerable methodological challenges. Adequate exposure
    assessment is one key issue, as well as the need for studies conducted
    with large populations.

    2.3  Human exposure information in risk assessment

         Risk assessment is a formalized process for estimating the
    magnitude, likelihood and uncertainty of environmentally induced
    health effects in populations. Exposure assessment (e.g., exposure
    concentrations and related dose for specific pathways) and effects
    assessment (i.e., hazard identification, dose-response evaluation) are
    integral parts of the risk assessment process. The goal is to use the
    best available information and knowledge to estimate health risks for
    the subject population, important subgroups within the population
    (e.g., children, pregnant women and the elderly), and individuals in
    the middle and at the "high end" of the exposure distribution (US NRC,
    1983; Graham et al., 1992; Sexton et al., 1992).

         Environmental health policy decisions should be based on
    established links among emission sources, human exposures and adverse
    health effects. The chain of events depicted in Fig. 4 is an
    "environmental health paradigm": a simplified representation of the
    key steps between emission of toxic agents into the environment and
    the final outcome as potential disease or dysfunction in humans. This
    sequential series of events serves as a useful framework for
    understanding and evaluating environmental health risks (Sexton, 1992;
    Sexton et al., 1992, 1993). It is directly related to the risk
    assessment process.

    *   Exposure assessment in the risk assessment framework focuses on
       the initial portion of the environmental health paradigm: from
       sources, to environmental concentrations, to exposure, to dose. The
       major goal of exposure assessment is to develop a qualitative and
       quantitative description of the environmental agent's contact with
       (exposure) and entry into (dose) the human body. Emphasis is placed
       on estimating the magnitude, duration and frequency of exposures,
       as well as estimating the number of people exposed to various
       concentrations of the agent in question (US NRC, 1983, 1991a;
       Callahan & Bryan, 1994).

    FIGURE 4

    *   Effects assessment examines the latter portion of the events
       continuum: from dose to adverse health effects (Fig. 4). The goals
       are to determine the intrinsic hazards associated with the agent
       (hazard identification) and to quantify the relationship between
       dose to the target tissue and related harmful outcomes
       (dose-response/effect assessment). The overlap between exposure
       assessment and effects assessment reflects the importance of the
       exposure-dose relationship to both activities (Sexton et al.,

    *   Risk characterization is the last phase of the risk assessment
       process. The results of the actual exposure assessment and the
       effects assessments are combined to estimate the human health risks
       from the exposures.

         Systemic (non-cancer) toxicants are usually assumed to have
    thresholds below which no effects occur. For these toxicants, safety
    assessments are performed with establishment of  tolerable intakes 
    (IPCS, 1993) or  reference concentrations/doses (USEPA). From these,
    guidelines are derived and standards designed to protect public
    health. Ambient concentration standards, and workplace personal
    exposure limits, are often established at or below threshold levels
    determined as part of the risk assessment process. Although these
    standards are set with safety margins, exposures that exceed these
    reference levels raise concerns about potentially elevated health
    risks for the exposed population (Fig. 5a).

         Quantitative risk assessment for carcinogens is a well
    established, albeit controversial, procedure. As part of the
    guidelines developed by the WHO, it is common practice to extrapolate
    from high to low dose by assuming a linear, non-threshold model for
    carcinogenicity. Under this assumption, cancer risk for individuals
    can be estimated directly from the exposure or dose distribution, and
    the number of excess cancer cases (i.e., the increase above background
    rates) in the exposed population can usually be estimated by
    multiplying the average dose by both the total number of people
    exposed and the dose-response slope factor (Fig. 5b). Although
    individual risk is assumed to increase with increasing exposure and
    dose all along the distribution, exposures of concern are typically
    defined to be those above some minimal level of risk (e.g., WHO
    considers this to be a 1 in 105 or 106 excess lifetime risk of
    developing cancer). Unit cancer risk numbers are given in inverse
    concentration units for food, water and air as (ppm)-1, (ppb)-1 or
    mg-1m-3). Expressed in inverse dose units (mg kg-1day-1), the cancer
    slope risk factor is multiplied by ingestion or inhalation rates and
    adjusted for body weight. Individual cancer risk is calculated by
    assuming a lifetime of exposure at a given level of contamination.
    When exposure data are available, it is then possible to approximate
    the cancer risk of the typical or average person in the population or
    one who might be at maximum risk due to a greater level of exposure.

    FIGURE 5

         In regulatory applications of risk assessments, exposure
    estimates are often constructed using existing data or single point
    measurements to estimate the risk of a facility, hazardous waste site
    or chemical waste site, or even the use of a chemical product. This
    approach can result in large errors in the exposure assessment and
    hence the risk assessment. Exposure assessment studies are used to
    obtain a more accurate determination of the exposure associated with a
    health impact outcome of concern. Population-based risk assessments
    benefit from the use of population-based measurements derived from
    surveys or models (see Chapter 3) to estimate the distribution of
    health effect outcomes in the total exposed population over a
    specified time period.

    2.3.1  Risk allocation for population subgroups or activities

         Exposure studies may also be conducted to provide more realistic
    and location-specific information for use in human health risk
    assessments. Measurement data on pollutant concentrations and exposure
    factors, such as contact rates, can be used instead of relying on
    assumed "default" values for an "averaged" or representative
    individual. An example of an exposure study designed to collect data
    for the purpose of allocating risk to locations, sources and
    activities is the Windsor Air Quality Study conducted in Windsor,
    Ontario, Canada (Bell et al., 1994).

         The Windsor Air Quality Study was designed to investigate the
    Windsor airshed characteristics with respect to airborne toxic
    compounds and to determine personal inhalation exposures to these
    compounds. Data were then used as inputs for a multimedia assessment
    of risk due to total pollutant exposure. The air quality study
    examined just one aspect, the inhalation route. It was designed to
    separately attribute risk to several airborne contaminants by indoor
    and outdoor locations. Statistical analysis and inference were used to
    impute source contributions to population risk (i.e., the waste
    incinerator across the river in Detroit, Michigan, USA) for selected
    volatile organic compounds (VOCs), carbonyls and trace metals (see
    Table 4) based on microenvironmental and personal measurements and
    time activity patterns. In general, air quality was determined to be
    relatively poor in recreation halls, new office buildings, cars and
    garages when compared to outdoor air quality standards and criteria.
    Although high contaminant concentrations were detected in various
    microenvironments, population exposures (defined as the product of
    concentration and time) were relatively low because the study subjects
    did not spend any appreciable time in those microenvironments. This
    point is illustrated in Fig. 6. For all of the VOCs, the highest
    concentrations were measured during the commuting periods, with
    comparable concentrations being measured indoors at the office and
    home and the lowest outdoors (Table 3). When time in each
    microenvironment is considered, exposure in the home accounted for
    over 70% of the total exposure profile for that individual.

        Table 4.  Target analytes in the Windsor air quality study

    Volatile organic compounds

    Propane, chloromethane, 2-methylpropane, chloroethene, 1,3-butadiene, butane, 
    2-methylbutane, pentane, isoprene, 1,1-dichloroethene, dichloromethane, allyl chloride, 
    hexane trichloromethane, 1,2-dichloroethane, 1,1,1-trichloroethane, benzene, 
    tetrachloromethane, xylenes, styrene, o-xylene, 1,1,2,2-tetrachloroethane, nonane, 
    1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, 1,4-dichlorobenzene; decane, 
    1,2-dichlorobenzene, undecane, 1,2,4-trichlorobenzene, dodecane, tridecane


    Formaldehyde, acetaldehyde, acrolein, acetone, propianaldehyde, crotonaldehyde, methyl 
    ethyl ketone, benzaldehyde, isovaleraldehyde, 2-pentanone, valeraldehyde,  o-tolualdehyde, 
     m-tolualdehyde,  p-tolualdehyde, methyl isobutyl ketone, hexanal, 2,5-dimethylbenzaldehyde

    Trace metals

    Beryllium, chromium, manganese, nickel, arsenic, selenium, cadmium, lead

         Results of the study emphasize the importance of exposure
    assessments for policy decisions. For this community, changes in
    lifestyle, consumer product formulations, cleaning of indoor air and
    increased ventilation would probably have more impact on reducing
    health risks from exposures to VOCs than reliance on
    government-mandated abatement strategies for ambient sources.

    2.3.2  Population at higher or highest risk

         Risk assessment may be used to identify and evaluate those
    populations, subpopulations and individuals at potentially greater
    risk so that, if warranted, appropriate mitigation actions can be
    implemented. Individuals and groups are deemed to be at potentially
    higher risk because they are exposed to high concentrations of
    hazardous pollutants (Sexton et al., 1993). Individuals and groups can
    also be at increased risk because they are more susceptible to the
    adverse effects of a given exposure. Among the potential causes of
    enhanced susceptibility are inherent genetic variability, age, gender,
    pre-existing disease (e.g., diabetes, asthma), inadequate diet,
    environmental or lifestyle factors (e.g., smoking), stress and
    inadequate access to health care. As far as possible, it is important
    to identify these susceptible individuals and groups so that we can
    understand their exposures and take account of this information in
    assessing and managing risks. Exposure and risk information for
    susceptible populations is critical since health standards and
    regulations are often developed with the intent of protecting these

         Exposure studies provide valuable information for the risk
    assessment by quantifying the distribution of exposures in a
    population and identifying those subpopulations or individuals who
    have the highest exposures. Information is also gathered on
    characteristics of the populations and factors that could contribute
    to elevated exposures. In these studies, measures of central tendency,
    such as the median and average, along with expressions of variability,
    such as the standard deviation, are commonly used to describe the
    distribution of exposures for a population (Fig. 7). Often, the
    relative position of an individual or group in the exposure
    distribution is of primary interest to the exposure assessor. Among
    the most frequently used descriptors for individual and subgroup
    exposures are values near the middle of the distribution, values above
    the 90th percentile and values at the extreme upper end, such as for
    the most exposed person in the population. Exposure studies that are
    targeted on susceptible populations are used with the same type of
    inputs in risk assessment for these groups.

    2.4  Human exposure information in risk management

         Risk management decisions carried out by policy-makers are of
    four basic types: priority setting, selection of the most
    cost-effective method to prevent or reduce unacceptable risks, setting
    and evaluating compliance with standards or guidelines, and the
    evaluation of the success of risk mitigation efforts. Exposure
    information is crucial to these decisions. In addition to data on
    exposures and related health effects, decision-makers also must
    account for the economic, engineering, legal, social and political
    aspects of the problem (Burke et al., 1992; Sexton et al., 1992).

         Conceptually, as shown in Fig. 8, estimating and prioritizing
    health risks are seemingly straightforward. Risk is a combination of
    effects estimates, where "highest" priorities can be thought of as
    those that entail both "high" toxicity for the agent of interest
    (adverse effects are likely to occur in humans at relatively low
    exposures or doses), and "high" exposures for the population,
    subpopulation or individuals of interest (exposures or doses are above
    a health-based standard). Conversely, "lowest" priority risks involve
    "low" toxicity and "low" exposures. "Medium" priority risks are those
    for which either toxicity or exposure is "low" while the other is
    "high" (Sexton, 1993). The Windsor Air Quality Study, for example,
    showed that incinerator emissions contributed little to total human
    exposure for VOCs. Despite the fact that the pollutants were of high
    toxicity, incinerator emissions were considered to be of relatively
    low risk to the population. In contrast, studies show that second-hand
    smoke has both high toxicity and high human exposures, and should
    therefore be identified as a high priority risk.

    FIGURE 6

    FIGURE 7

    FIGURE 8

         Risk mitigation proceeds from first determining that an exposure
    is a hazard (risk assessment) to identifying and quantifying the route
    and the environmental pathways for a contaminant. Where a contaminant
    has multiple sources or routes of exposure, relative contributions to
    individual and population risk must be determined. Exposure
    assessments are crucial for developing this information, and may rely
    on both measurements and modelling. Once this information is obtained,
    then effort can be directed toward the most effective mitigation

         In fact, intervention studies are implicitly or explicitly
    predicated on the sequence of risk assessment and mitigation.
    Intervention at the source, transmission or receptor (receiving
    person) is intended to reduce the effect or risk of an effect.
    Prohibiting smoking in public buildings or sections of restaurants is
    designed to separate sources from receptors. Specific ventilation
    requirements for operating theatres or isolation rooms of infectious
    patients are designed to dilute potential contaminants and pathogens.
    On a larger scale, substitution of cleaner fuels (e.g., reformulated
    or unleaded gasoline, cleaner coal, low-sulfur oil, natural gas)
    radiation of food or ozonation of drinking-water are examples of risk
    mitigation interventions based on the assumption that contaminant
    reductions experienced in the environmental media will result in a
    corresponding reduction in actual exposures and hence risk. It is
    essential, then, to understand the efficacy of mitigation strategies
    with respect to their effect on human exposures.

         The combined use of total exposure assessment for air,
    receptor-source modelling and economic principles can assist
    environmental policy and regulation in developing risk mitigation
    strategies. The hybridization of these well-developed models can be
    used to assist in the identification of priority sources to target
    regulatory programmes, and in the development of cost-effective
    strategies for air pollution control to bring about the greatest and
    earliest reduction in pollutant exposures.

         Epidemiological information about the health effects of
    relatively low levels of air pollutants now raises controversial
    policy issues for risk management. On the one hand, the economic
    consequences of these health effects may be substantial; on the other
    hand, for some pollutants, control measures may become very expensive.
    For pollutants such as VOCs, for example, exposure monitoring rather
    than ambient air monitoring may lead to more rapid and cost-effective
    risk reduction policies.

         Developed countries have experimented with regulatory reforms
    that include emission trading. Basically, the concept calls for
    emission reduction at one source to be credited to the emission levels
    at another source. These trading schemes are based on the assumption
    that equal mass emission reduction of a pollutant would result in
    equal health or ecological benefits. Thinking in terms of total 
    exposure assessment reorients the relative importance of sources and

    their impacts on different populations. Accordingly, control options
    for reducing exposures can be broadened (Smith, 1995).

    2.5  Human exposure information in status and trend analysis

         Evaluating the current status of exposures and doses in the
    context of historical trends is an important tool for both risk
    assessment and risk management. In many cases it requires collecting
    exposure data over a relatively long period of time (e.g., 5-20
    years). This can only be done through an exposure assessment study and
    often when the contaminant has a long residence time in the
    environment or biological tissue. If concentrations of a contaminant
    exhibit high variability in environmental media, the study may require
    relatively large sample sizes, the use of probability samples and/or
    extensive follow-up to observe trends. Data on status and trends can
    be invaluable for identifying new or emerging problems, recognizing
    the relative importance of emission sources and exposure pathways,
    assessing the effectiveness of pollution controls, distinguishing
    opportunities for epidemiological research and predicting future
    changes in exposures and effects (Goldman et al., 1992; Sexton et al.,

         Exposure studies may be conducted to document the status and
    trends of human exposure (e.g., Kemper, 1993; Noren, 1993). A good
    example of a study designed for this purpose is the German
    Environmental Survey (GerES). The nationwide representative survey was
    conducted for the first time in 1985-1986, on behalf of the Federal
    Ministry for the Environment, Nature Conservation and Reactor Safety.
    In 1990-1991 the survey was repeated in West Germany (the FRG before
    reunification) and in 1991-1992 it was extended to East Germany
    (former GDR) (Krause et al., 1992; Schulz et al., 1995).

         The purpose of the survey was to establish a representative
    database on the body burden of the general population. Biological
    monitoring was used to characterize exposure to pollutants
    (predominantly heavy metals). In addition, the occurrence of a number
    of pollutants in the domestic area likely to contribute to total
    exposure (house dust and drinking-water) was studied. The design of
    the study is summarized as follows:

    *   Population samples. Cross-sectional samples using a stratified
       two-step random sampling procedure according to the size of the
       community, gender and age. The final set included 2731 West Germans
       in 1985-1986 and 4287 adults from East and West Germany in
       1990-1992 (aged 25-79 years). In addition about 700 children (aged
       6-14 years) living in the same households were included in

    *   Human biomonitoring. Analysis of blood (lead, cadmium, copper,
       mercury), spot urine (arsenic, cadmium, copper, chromium, mercury)
       and scalp hair (aluminium, barium, cadmium, chromium, copper,
       magnesium, phosphorus, lead, strontium and zinc).

    *   Questionnaires. Questions about social factors, smoking habits,
       potential sources of exposure in the domestic, working, and general
       environment, and nutritional behaviour.

    *   Domestic environment. Concentration of trace elements in dust
       deposit indoors, in vacuum cleaner bags (pentachlorophenol [PCP],
       lindane and pyrethroids) and in household tap water; determination
       of VOCs in homes of a subsample of 479 participants (passive
       sampling) in 1985-1986.

    *   Personal sampling. Determination of VOCs by personal sampling
       using a subsample of 113 people in 1991.

    *   Dietary intake. A 24-h duplicate study in 1990-1992 with a
       subsample of 318 people.

         Characteristics of the frequency distributions (percentiles) and
    other statistical parameters of the concentration of elements and
    pollutants in the different media were calculated. As an example, the
    concentrations of elements and compounds in blood and urine of the
    German adult population analysed in 1990-1992 are shown in Table 5.
    The 1990-1991 and 1991-1992 surveys showed differences between East
    and West Germany. The mercury concentrations in blood and urine as
    well as the cadmium, chromium and copper concentrations in urine were
    significantly higher ( p < 0.001) in East Germany than in West
    Germany. The blood lead level was identical in both study populations
    (geometric mean 45 µg/litre).

         The comparison of the results for the biological, personal and
    microenvironmental exposure measurements taken in East Germany in
    1985-1986 and in 1990-1992 permits an analysis of trends over time.
    The success of abatement measures could be shown in a number of cases:
    the reduction of lead concentrations in petrol and of industrial
    cadmium emissions resulted in decreased lead and cadmium
    concentrations in the blood of the general population. The ban on PCP
    led to a decrease of PCP in house dust. The results of the GerES have
    provided a useful set of reference data to characterize and to assess
    exposures of the general population. They have also been useful for a
    number of risk assessments, for example the role of copper in
    drinking-water and liver cirrhosis in early childhood, and presence of
    mercury in amalgam fillings.

    2.6  Summary

         The specifics of any particular exposure analysis hinge on its
    intended use or uses. For example, the pertinent aspects of exposure
    to be considered, the nature of the information required and the
    necessary quantity and quality of the data will depend on whether the
    exposure assessment is being conducted in the context of an
    epidemiological investigation (Matanoski et al., 1992), risk
    assessment (Graham et al., 1992), risk management (Burke et al., 1992)
    or status and trend analysis (Goldman et al., 1992) (see also Chapter
    1, Table 1).

        Table 5.  Elements and compounds in blood and urine of the German population (aged 25-69 years, 1990-1992)
    (Krause et al., 1992)

                                 QL      N       <QL    10      50      90      95      98      MAX     AM      GM      CI GM

    Lead (µg/litre)              15      3966    61     24.0    45.3    86.8    105.6   134.2   708.0   52.4    45.3    44.5-46.0
    Cadmium (µg/litre)           0.1     3965    231    0.1     0.3     1.9     2.6     3.6     11.3    0.7     0.4     0.4-0.4
    Copper (mg/litre)            0.1     3968    0      0.8     0.9     1.2     1.3     1.5     2.5     1.0     0.9     0.9-1.0
    Mercury (µg/litre)           0.2     3958    632    <0.2    0.6     1.6     2.1     3.0     12.2    0.8     0.5     0.5-0.5

    Arsenic (µg/litre)           0.6     4001    210    1.8     7.1     19.8    29.9    56.7    205.5   10.5    6.3     6.1-6.5
    Arsenic (µg/g creatinine)            4001           1.4     4.9     15.3    24.1    40.0    147.6   7.6     4.6     4.5-4.8
    Cadmium (µg/litre)           0.1     4002    150    0.1     0.3     0.9     1.3     1.7     6.9     0.4     0.3     0.3-0.3
    Cadmium (µg/g creatinine)            4002           0.1     0.2     0.7     0.9     1.3     6.1     0.3     0.2     0.2-0.2
    Chromium (µg/litre)          0.2     4002    1716   0.15    0.2     0.4     0.6     1.0     21.2    0.3     0.2     0.2-0.2
    Chromium (µg/g creatinine)           4002           0.0     0.1     0.3     0.5     0.9     10.6    0.2     0.1     0.1-0.1
    Copper (µg/litre)            1.1     4002    20     4.5     9.7     18.7    22.9    28.7    444.2   11.6    9.5     9.3-9.7
    Copper (µg/g creatinine)             4002           3.5     6.7     13.1    17.7    28.5    420.7   8.9     6.9     6.8-7.1
    Mercury (µg/litre)           0.2     4002    785    <0.2    0.5     2.6     3.9     6.0     53.9    1.1     0.5     0.5-0.6
    Mercury (µg/g creatinine)            4002           0.1     0.4     1.6     2.2     3.2     73.5    0.7     0.4     0.4-0.4
    Nicotine (µg/litre)          5       3750    1566   <5      9.3     1438    2431    3567    10 984  422     24.9    23.0-27.1
    Nicotine (µg/g creatinine)           3748           1.3     7.0     1003    1636    2431    10 478  292     18.4    17.0-20.0
    Cotinine (µg/litre)          5       3800    1813   <5      5.6     2037    2681    3483    6573    537     26.6    24.3-29.1
    Cotinine (µg/g creatinine)           3798           1.3     4.9     1396    1940    2788    8111    388     19.6    17.9-21.4
    Creatinine (mg/100 ml)       0       4002           0.7     1.5     2.5     2.9     3.2     5.7     1.5     1.4     1.3-1.4

    Annotations: QL = quantification limit, N = sample size, n < QL = number of values below QL, 10, 50, 90, 95, 98 = percentiles, 
    MAX = maximum value, AM = arithmetic mean, GM = geometric mean.

    Source: UBA, WaBoLu, Environmental Survey 1990-1992, Federal Republic of Germany.

         Knowledge of human exposures to environmental contaminants is an
    important component of environmental epidemiology, risk assessment,
    risk management and status and trends analysis. Exposure information
    provides the critical link between sources of contaminants, their
    presence in the environment and potential human health effects. This
    information, if used in the context of environmental management
    predicated on human risk reduction, will facilitate selection and
    analysis of strategies other than the traditional "command and
    control" approach. Most of the environmental management structures
    around the world rely directly on the measured contaminants in various
    media to judge quality, infer risk and interpret compliance. Even in
    these cases, exposure information can evaluate the effectiveness of
    protecting segments of population more susceptible or at higher risk.

         It is this direct connection that makes exposure measures
    invaluable for evaluation of environmental health impacts on a local,
    regional and global scale.


    3.1  Introduction

         Accurate estimates of human exposure to environmental
    contaminants are necessary for a realistic appraisal of the risks
    these pollutants pose and for the design and implementation of
    strategies to control and limit those risks. Three aspects of exposure
    are important for determining related health consequences:

    *   Magnitude: What is the pollutant concentration?

    *   Duration: How long does the exposure last?

    *   Frequency: How often do exposures occur?

    The design of an exposure study specifies the procedures that will be
    used to answer these three questions.

         In this chapter, strategies and designs for exposure studies are
    discussed with emphasis on their relative advantages and
    disadvantages. The brief discussion of study design presented in
    Chapter 1 is expanded upon here in terms of fundamental types of
    generic study designs and approaches to assessing human exposure to
    chemicals in the environment. Statistical considerations for study
    design are presented in Chapter 4. The reader is referred to
    subsequent chapters for details on implementing exposure study designs
    through modelling (Chapter 6), monitoring of environmental media
    (Chapters 7, 8 and 9) and monitoring of biological tissue (Chapter

    3.2  Study design

         A good study design is the most important element of any exposure
    study. A flow chart that includes critical elements is shown in Fig.
    9. First the purpose of the study is defined: epidemiology, risk
    assessment, risk management or analyses of status and trends (see also
    Chapter 2). Within this context, specific study objectives are
    formulated. Often studies have several objectives, which must be
    prioritized to ensure that the primary objective is fulfilled. Study
    parameters must be selected that are consistent with the objective. A
    study design is formulated which links objectives to measurement
    parameters in a cost-effective manner. Two critical and often
    overlooked elements of the study design are development of a
    statistical analysis plan and quality assurance (QA) objectives. For
    general population studies, methods for measurement and analysis of
    contaminants in collected environmental or biological samples must be
    sufficiently sensitive to determine their concentration at typical
    ambient levels. For multimedia studies, method detection limits must
    be consistent across media. The study design is not complete until a
    pilot study has been conducted to evaluate sample and field study

    FIGURE 9

    3.3  Sampling and generalization

         Decisions on population sampling strategies involve consideration
    both of the populations that are available and of the types of
    measurements needed. Of prime consideration are the people, place and
    time (i.e., individuals, locations, sampling period or conditions)
    from which exposure samples are to be collected. Also, it is important
    to determine if the estimates to be derived from the proposed sample
    could be generalized to a wider population of interest. For example,
    consider an exposure assessment study from a sample population of a
    small town in southwestern Australia. The many potential populations
    of interest which this sample might generalize include: all people
    living in that town; people living in a small southwestern Australia
    town; people living in southwestern Australia; people living in
    Australia; people living in any small town; people in general. In this
    case, the sample population is not likely to provide a representative
    sample of the latter two populations.

         The appropriateness of the generalization is determined by
    considering if the sample is randomly selected in such a way as to be
    representative of the larger population of interest (Whitmore, 1988).
    This randomization is in terms of the distribution of the collected
    data. For continuous outcomes, the percentages of key attributes, such
    as demographic factors, should be similar between the sample and the
    population. However, when this is not possible, owing to limited
    funding for example, a descriptive study (described below) can provide
    credible data, although the extent to which these can be generalized
    is limited.

    3.4  Types of study design

         Once the population is defined, then the attention shifts to
    sampling strategies; in particular, comprehensive samples, probability
    samples, and other types of samples. A  comprehensive sample includes
    all members of the selected population. In a  probability sample each
    member has a known likelihood of being selected.  Simple random 
     sampling is a special case where each member of the population has
    an equal probability of being selected. Other types of study groups
    are selected on the basis of other characteristics, such as
    availability or convenience.

    3.4.1  Comprehensive samples

         Complete populations can be used to collect a full picture of the
    process being studied, especially when the total population is
    relatively small such as families in a neighbourhood. In these cases,
    an exhaustive collection of measurements is taken from every potential
    subject, and the completed data describe the situation exactly. There
    is no sample variability except through the methods and procedures
    used for measurement and monitoring. The main reasons for studies of
    this nature are either a small population size, a need for a complete
    evaluation of the problem, high potential risk, high variability among
    units or legal requirements. The advantages of this type of study are

    that a complete description of the exposure is given, and there is no
    need for generalization because all potential subjects are covered.
    The disadvantage of this approach, if the population is large, lies in
    the expense: all individuals in all locations must be monitored at all

    3.4.2  Probability samples

         Surveys consist of a random sampling of subjects from the
    population of interest. This approach aims to remove selection bias
    and is useful for generalizing results beyond the study sample. It is
    important to distinguish that "random" does not translate to
    "haphazard". A truly random sample is independent of human judgement.
    Every unit in the total population has a known above-zero likelihood
    of being included in the sample. Effective study design allows
    researchers to draw statistically valid inferences about the general
    population that the sample is designed to represent (Kish, 1965). For
    these studies, one needs to (Sexton & Ryan, 1988):

    *  choose a population for investigation

    *  choose an appropriate unit for sampling and analysis (e.g., person,
       household, neighbourhood, city, etc.)

    *  stratify as appropriate

    *  choose a sampling strategy (e.g., simple random sampling,
       multistage sampling).

         The results of a probability survey can be used to make general
    statements about the population under investigation. The advantages
    include having results that represent the population, taking into
    account the possible error due to sampling. The disadvantages of this
    scheme lie in the complicated sample selection, difficulty in
    maintaining compliance from participants and the potentially complex
    statistical analysis. In addition, randomized surveys of insufficient
    sample size may miss rare hazardous events or small populations with
    high exposure or risk.

         Sampling strategies for survey studies include randomization
    methods for choosing subjects to enroll in the study. Simple random
    sampling is a scheme in which all sampling units of the same size have
    equal probability of being selected. It can be difficult to implement
    but relatively easy to generalize. Simple random sampling presents
    logistic and fiscal constraints when considered for exposure surveys
    that are large in geographic scope. For example, a national survey of
    5000 personal exposures to respirable particulate matter that utilizes
    simple random sampling may result in individuals selected from 1000
    cities and towns. The travel and site preparation costs of such a
    design may not be feasible in many situations.

         A variety of alternatives to simple random sampling exist that
    may be used to provide practical and efficient samples of large
    populations (Callahan et al., 1995).  Stratified sampling may be used
    to obtain more precise survey results if exposures are more
    homogeneous within strata than between them. Possible strata include
    urban, suburban and rural populations, or occupationally exposed and
    non-occupationally exposed individuals.

          Oversampling of target populations or contaminants also may
    yield substantial increases in the precision of results. Because the
    individuals anticipated to have the highest exposures to a particular
    pollutant may be rare in the population being studied, oversampling
    can be considered to obtain more precise estimates of exposure. Before
    committing substantial resources to oversampling, special care must be
    taken to ensure that assumptions or data used to support a rationale
    for selecting the oversampled population are accurate; otherwise
    erroneous oversampling may decrease the precision of the study results
    (Callahan et al., 1995).

          Multistage sampling designs utilize clusters of sampling units
    thereby limiting sampling locations to manageable areas. Depending on
    the scope of the study, the stages of probability sampling necessary
    may include:

    *  selection of primary sampling unit (e.g., a city)

    *  selection of sample area segments (e.g., blocks within the city)

    *  selection of sample housing units within sample segments (e.g.,
       residences within the blocks)

    *  selection of sample individuals within sample housing units

    *  selection of sample time points within the monitoring period
       (Callahan et al., 1995).

    The optimal degree of clustering depends on the variability of the
    survey variables between and within the clusters and the costs of
    fieldwork relative to sample collection and analysis costs. Although
    details of this approach are beyond the scope of this chapter, it
    should be recognized that cluster sampling introduces correlation
    among the sample individuals that affects the validity of the survey
    estimates. Thus, tradeoffs between increased sample size achieved
    through clustering and loss of validity must be considered carefully.
    Details of multistage and cluster sampling may be found in Hansen et
    al. (1953), Kish (1965), Cochran (1977), Kalton (1983), Kollander
    (1993) and Callahan et al. (1995).

         One concern with survey studies is maintaining participation of
    subjects who did not initially volunteer. Another issue, which is more
    conceptual, is subject selection for the more complex sampling
    strategies. In particular, stratification factors need to be carefully
    chosen so that potential confounders can be determined and the

    adjustments can be made from the resultant effects. Important
    considerations for the design of population-based (e.g., national or
    regional) exposure surveys, including response rates and confounders,
    are discussed by Whitmore (1988) and Callahan et al. (1995).

    3.4.3  Other sample types

         Non-probability sample studies ("anecdotal studies") may consist
    of selecting a sample based on the self-reporting of conditions, such
    as complaint cases for "sick building" syndrome. Data collected in
    this manner are potentially subject to biased reporting. It is
    difficult to generalize results unless causal relationships are very
    strong or unless there is little reason to believe that a confounder
    or an unmeasured significant factor is relevant. In general, such
    studies are used for description or exploration of a given situation.
    In particular, they can be used to evaluate the variability of
    outcomes and explore unknown situations for further explanatory study.
    When choosing subjects, it is useful to focus on variability in the
    expected outcome and also on the likelihood of completing the study.
    It is also helpful to focus on a simple, preferably dichotomous,
    hypothesis. Extensive validation will be necessary before accepting or
    rejecting the hypothesis since the generalization of the results is

         The advantages of targeted anecdotal studies are the inexpensive
    and quick ways in which they aid in the design of future studies. For
    example, when exploring protocols, determining stratification
    variables, potential biases and confounders, and identifying the units
    of analysis, the use of cooperative volunteers can simplify field
    operations. The uncertainty of the results of these studies is due to
    potential biases from the non-random and possibly non-representative
    sample (i.e., responder bias). Since the population in such
    non-probability sample studies is often made up of volunteers, there
    is usually some factor present which distinguishes them from those who
    do not choose to participate. This factor could influence the results;
    in particular, those who participate may tend to consider themselves
    strongly affected or not affected by the pollutant being studied and
    may alter their responses or behaviours as a result. This phenomenon
    is a special case of responder bias, often termed  self-selection 
     bias. Also, a poorly designed study can fail to control for temporal
    and spatial variability, as well as meteorological, site and source
    bias. This bias is a result of a single, "random-day", or grab
    sampling and single-location sampling, which decreases the potential
    for generalization.

         Controlled experiments are useful to examine a few factors and to
    study their influence on the resulting exposure. The use of
    randomization and control ensures that the effects are real and not
    the result of confounding causes, incorrectly measured variables or
    missing variables. Examples include chamber studies and other
    situations where the investigator has control over most of the
    environmental factors.

    3.5  Exposure assessment approaches

         As discussed in Chapter 1, strategies for assessing environmental
    exposure can be categorized as one of two general approaches; direct
    or indirect.  Direct approaches include personal exposure monitoring
    and biological markers of exposure.  Indirect approaches include
    environmental sampling, combined with exposure factor information,
    modelling and questionnaires.

    3.5.1  Direct approaches to exposure assessment

         Direct measures of exposure include samples collected at the
    interface between an exposure medium and the human body, e.g., at the
    breathing zone in the case of air pollutant exposure, or samples of
    biological tissue in which concentrations of target pollutants can be
    quantitated. Measurements in food or drinking-water (duplicate
    portions) which are ingested could also be viewed as a direct way of
    assessing exposure through these media. Thus, direct approaches to
    exposure assessment include personal exposure monitoring and
    biological markers of exposure. Personal monitoring methods are
    discussed below, and the subject of biomarkers of exposure is
    presented in detail in Chapter 10.

         Personal monitoring of exposure to environmental contaminants
    refers to collection of samples at the interface between the exposure
    medium and the human receptor (e.g., the breathing zone). Personal
    monitoring approaches are summarized in Table 6. Personal monitors
    make it possible to measure exposures for an identified subset of the
    general population. Moreover, if study participants maintain records
    of their activities, then locations where highest exposure
    concentrations occur as well as the nature of emission sources can
    often be inferred. Personal monitoring can be done for all potential
    exposure media (e.g., air, water, soil, food) and pollutants of
    interest. Although available, personal monitoring methods may not be
    employed in a particular investigation due to study design, time or
    expense considerations. The principal limitation on the use of
    personal monitoring for exposure assessment is the availability of
    sample collection methods that are sensitive, easy to operate, able to
    provide sufficient time resolution, free from interferences and
    cost-effective. Consideration should be given to the likelihood that
    the inconvenience of complying with personal monitoring protocols may
    alter the normal behaviour of the study participants. For example,
    participants tend to wear personal air monitors on days that they do
    not go to work. In duplicate portion studies, participants may not
    provide equal portions of expensive or well-liked foods, leading to
    underestimation of intake. Approaches to personal monitoring of
    inhalation, dietary and dermal exposures are discussed below.

    Table 6.  Summary of personal monitoring approaches

    Exposure route   Media       Environmental sample   Biological sample

    Inhalation       air         personal monitor       breath

    Ingestion        water       tap water              blood

    Ingestion        food        duplicate portion      faeces
                                                        breast milk

    Dermal           soil/dust   dermal patch           others
                                                                         Personal monitoring of inhalation exposures

         Personal monitoring of human exposure to air pollutants requires
    that study participants transport their sample collection device with
    them at all times during the assessment period. Examples include a
    diffusion tube used for passive sampling of gases, such as VOCs, or a
    filter with a battery-operated pump for active sampling of aerosols
    and their components (ACGIH, 1995).

         Personal air monitors can be grouped into two general categories:
     integrated samplers that collect the pollutant over a specified time
    period and then are returned to the laboratory for analysis, and
     continuous samplers that use a self-contained analytical system to
    measure and record the pollutant concentration on the spot.
    Instruments in both categories can be either active or passive.
     Active monitors use a pump and a power source to move air past a
    collector or sensor.  Passive monitors depend on diffusion to bring
    the pollutants into contact with the collector or sensor. Additional
    information may be found in Chapter 7 and ACGIH (1995).

         As Wallace & Ott (1982) pointed out, the direct measurement of
    exposures using personal monitors raises several methodological
    issues. Personal monitoring studies are complex, expensive, time
    consuming and labour intensive. Other challenges include selection and
    recruitment of representative subjects; distribution, maintenance and
    retrieval of many monitors; laboratory analysis of many air samples
    returned from monitors in the field or calibration and validation of
    many real-time monitors; and the transcription and statistical
    analysis of data on pollutant concentrations and time-activity
    patterns.  Personal monitoring of dietary exposures

         Exposures to contaminants in food may be directly measured by
    collecting meals as prepared for consumption by members of the study
    population; such samples are often termed duplicate portion samples.
    Duplicate portion study designs provide food samples as actually
    consumed, rather than samples of unprepared, individual food items
    that are typical of surveillance approaches to characterizing dietary
    exposures (US NRC, 1993). This distinction is important because the
    method by which food is prepared for consumption (e.g., washed, washed
    and cooked, or commercially processed) can influence contaminant
    residues. In addition, some pollutants can be generated during
    cooking, for example, benzo [a]pyrene (Waldman et al., 1991a) and
    heterocyclic amines (Skog et al., 1998). Thus, residue levels measured
    in duplicate portion samples are likely to more accurately reflect
    personal dietary ingestion exposures than raw agricultural commodities
    and other foods collected at the producer, processor or distributor
    level. Depending on the objectives of the study, water may also be
    included as part of the duplicate portion sample.

         Duplicate portion study designs use either collection of
    individual servings or meals or composite samples. In studies of this
    type, participants are often monitored over one or more days, and the
    duplicate portion samples are collected daily over the monitoring
    period. The former affords a detailed examination of contaminant
    levels in specific commodities or foods comprised of several
    commodities; however, the analytical chemistry costs associated with
    this degree of temporal resolution may be prohibitive. Composite
    samples provide an integrated measure of dietary exposure and provide
    an efficient means for characterizing total dietary exposures. Both
    collection schemes require a high level of effort from study
    participants, and the complex food matrices may present analytical
    chemistry challenges.

         Duplicate portion studies require a high degree of participation
    by the study respondents, because they are primarily responsible for
    preparation and storage of an additional serving of every food or meal
    consumed over the monitoring period. This burden makes it difficult to
    collect representative samples of all foods consumed by the
    respondent, especially when food is relatively expensive or scarce or
    is consumed outside the home. Respondent burden also makes it
    difficult to conduct studies of chronic dietary exposures using the
    duplicate portion approach. Additional information on assessment of
    dietary exposure, including both direct and indirect approaches, may
    be found in Chapter 7.4 as well as WHO (1985a, 1995c); EC (1997a).  Personal monitoring of dermal absorption exposures

         Personal monitoring of dermal exposure is used for those
    situations where a pollutant comes in contact with the skin and intake
    occurs via absorption through the skin. Dermal patches and skin wipe
    samples are used to evaluate exposures for residues adhering to the
    surface of the skin (US EPA 1992b; Fenske, 1993; Geno et al., 1996;

    Shealy et al., 1997). These methods have typically been used for
    industrial hygiene assessments where very high exposures are expected.
    Dermal patches and skin wipe samples have been used to characterize
    transfer of pesticide residues from soil and grass to skin as well as
    spot concentrations of residues on skin (Fenske et al., 1991). Dermal
    absorption can also occur during bathing, showering or swimming. In
    this case, the contaminant is in the water and exposure occurs when
    the water contacts the skin. Dermal exposure in this situation is
    defined as the concentration of the contaminant in the water and the
    duration of contact.

    3.5.2  Indirect approaches to exposure assessment

         Indirect measures of exposure include estimates derived from
    environmental monitoring (i.e., measurements made in locations
    frequented by the study participants), models and questionnaires.  Environmental monitoring

         Indirect estimates of exposure may be made by combining
    measurements of pollutant concentrations at fixed sites with
    information on rates of contact with these media recorded in data logs
    and diaries or time-activity surveys. Examples include air pollutant
    concentrations in specific areas combined with time budget records
    (see Chapter 5), food contaminant data combined with information on
    dietary patterns (see Chapter 7.4 for details), and pollutant
    concentrations on skin combined with data on frequency and duration of
    hand-to-mouth contact. Although collection of environmental,
    time-activity and questionnaire data needed for this exposure
    assessment approach is simpler than for personal monitoring, it is
    still invasive and laborious, and may lead to selection bias.

         Microenvironmental monitoring is a special case of environmental
    monitoring in which the location where measurements are made is
    considered to be homogeneous with respect to concentrations of the
    target pollutants over the averaging time of interest. The concept of
    a microenvironment has been widely applied in air pollution exposure
    assessments. Examples of potentially important micro-environments used
    for air pollution exposure assessment are listed in Table 7. The
    general form of the equation used to calculate time-weighted
    integrated exposure from micro environmental monitoring data is


    where  E is the time-weighted integrated exposure (e.g., mg/m3),
     C is the concentration (e.g., mg/m3),  t is the unit time (e.g.,
    minute),  T is the total elapsed time (e.g., minutes). The subscripts
     i, j and  k denote the medium, the pathway and the microenvironment
    respectively (Duan, 1982). The most important assumptions inherent in
    this model are:

    *  The concentration  Cj in microenvironment  j is assumed to be
       constant during the time that person  i is there.

    *  The concentration  Cj within microenvironment  j and the time
       that person  i spends there are assumed to be independent events.

    *  The number of microenvironments necessary to characterize personal
       exposure adequately is assumed to be small.

         The concept of a time-weighted integrated exposure is illustrated
    in Fig. 10. A unit width is indicated on the  j axis for each of five
    microenvironments: indoors at home, indoors at work, indoors in other
    locations, in transit, and outdoors. The concentration of respirable
    particles (RSP) is displayed on the  y axis, and the fraction of time
    that person  i spends in each microenvironment over the 24-h period
    is plotted on the  t axis. Even though the RSP concentration was low
    inside the home, it contributed significantly to the time-weighted
    exposure because this person spent 18 out of 24 h there. Conversely,
    the RSP concentration outdoors made only a minor contribution because
    this person was outdoors less than half an hour during the 24-h

         Indirect monitoring of ingestion exposures via hand-to-mouth
    contact may be obtained by collection of dermal wipe samples. However
    as indicated above, the use of this method has been limited to date. A
    drawback of the dermal wipe approach is that the integration time may
    be highly variable among subjects owing to variations in frequency of
    hand and body-washing, making interpretation of the results difficult
    (Fenske, 1993). Information on rates of contact between the
    contaminated skin and mouth is also required to complete the exposure
    assessment. A discussion of these types of data may be found in
    Chapter 5.

         Given the diversity of microenvironments that people move through
    each day (see Table 7), application of the indirect approach to
    exposure assessment is not straightforward. Its utility depends on
    identification of and sampling in the microenvironments with the
    greatest potential to influence human exposure. The costs and
    practical difficulties of monitoring in all, or even most, of the
    locations where people are likely to spend their time limits the scope
    of indirect measurements.

        Table 7.  Potentially important microenvironments for air pollution exposure assessment

    Microenvironments      Comments

    Urban                  metropolitan areas where air pollution levels are high as a result of high density of mobile and 
                           stationary sources

    Suburban               small- to medium-sized cities where air pollution levels tend to be lower than in metropolitan 
                           areas, although transport of urban pollution can affect local air quality under certain conditions

    Rural                  agricultural communities and small towns with few major anthropogenic sources of air pollution. 
                           Air pollution levels tend to be low, although transport of urban and suburban pollution can affect 
                           local air quality under certain conditions

    Industrial             manufacturing and production processes, such as those in petrochemical plants, pulp mills, power 
                           plants, and smelters

    Non-industrial         primarily service industries where workers are not involved in manufacturing and production 
                           processes, such as insurance companies, law offices, and retail sales outlets

    Residential            single-family houses, apartments, mobile homes, condominiums

    Commercial             restaurants, retail stores, banks, supermarkets

    Public                 post offices, courthouses, sports arenas, convention halls

    Institutional          schools, hospitals, convalescent homes

    Private                automobiles, private aeroplanes

    Public                 buses, subways, trains, commercial aeroplanes

    FIGURE 10  Models as an indirect approach to assessing exposure

         The microenvironmental exposure equation describes a model
    commonly used for assessment of air pollutant exposure. More
    generally, models are useful tools for quantifying the relationship
    between pollutant exposure and important explanatory variables, as
    well as for expanding existing exposure information to estimation of
    exposures of new populations and subgroups, and future exposure
    scenarios. Validated exposure models reduce the need for expensive
    measurement programmes. The challenge is to develop exposure databases
    and models that allow maximum extrapolation from minimum measurements
    or costs. Such models need to reflect the structures of the physical
    environments and human activities of interest in exposure assessment.

         In addition to the essentially physical (deterministic) exposure
    models, physical-stochastic (probabilistic) and statistical
    (regression) models are used. The former type is particularly useful
    for population exposure distribution assessments, the latter requires
    less supporting information but cannot be used for extrapolation
    outside of the study population. Exposure models are discussed in
    detail in Chapter 6.  Questionnaires as an indirect approach to assessing exposure

         Questionnaires typically provide qualitative, often
    retrospective, information. They may be used to categorize respondents
    into two or more groups with respect to potential exposure (e.g.,
    exposed or unexposed, high exposure or low exposure) and are commonly
    used for this purpose in epidemiological studies. As noted earlier,
    questionnaires may also be used to aid in interpretation of personal
    and environmental monitoring results.  A priori knowledge of the
    determinants of the exposure of interest is required to develop
    effective questionnaires relevant to exposure assessment (e.g., high
    formaldehyde exposure for workers in a certain industry, or high
    carbon monoxide and lead exposure for traffic policemen, bus drivers
    and road toll collectors). Most often the information necessary to
    develop questionnaires is obtained from previous studies that utilized
    environmental measurements, models or biological monitoring to measure
    exposure. In many cases, basic socio-demographic questionnaire data
    may provide extremely valuable information as they might be strong
    surrogates of exposure. It has long been known that rates of disease
    differ in social strata. In addition, it is readily apparent in many
    countries that the physical characteristics of one's residential
    environment are linked to income level. For lead exposure, differences
    in exposure among groups defined by income and social status have been
    demonstrated. Phoon et al. (1990) have shown that diet and job
    category were the most important predictors of blood lead levels among
    men in Singapore. In the USA, elevated blood lead levels have been
    linked to children who live in older, inner-city housing, particularly
    properties in poor repair (MMWR, 1997). Homes in these areas are more
    likely to have been painted with leaded paints (pre-1950) and have
    higher concentrations of lead in soil owing to deposition of emissions
    from leaded gasoline prior to the 1970s. Haan et al. (1987) found an

    increased risk of death among people living in a poverty area in the
    USA as compared to an adjacent non-poverty area, even after adjusting
    for differences in smoking, race, baseline health status, access to
    medical care, employment status, marital status, depression, sleep
    patterns and body mass index. These results suggest that sociophysical
    aspects of the environment, such as increased exposure to contaminants
    from poorer housing, may be important contributors to the association
    between socio-economic status and excess death rates.

    3.6  Summary

         A good study design is the most important element of any exposure
    assessment. It includes the purpose and objectives of the
    investigation as well as relevant methods for sampling, measurements,
    statistical analyses, and quality assurance. Methods for
    characterizing the magnitude, duration and time patterns of human
    contact with environmental contaminants may follow a direct approach
    or an indirect approach. Direct approaches to exposure assessment
    include point-of-contact measurements and measures of biological
    markers of exposure. Indirect approaches include environmental
    monitoring, modelling and questionnaires. These approaches may be
    employed in various types of exposure studies that are typified by the
    manner in which the study population is selected; for example,
    comprehensive studies that include all members of the study
    population, descriptive studies consisting of a non-probability
    sample, or surveys based on a randomly selected, representative sample
    of individuals.


    4.1  Introduction

         Statistics is a necessary and critical tool in exposure
    assessment studies. Statistics can be employed at each stage of the
    exposure assessment study. At the planning stage, statistics aids in
    selection of study design and determination of the amount and form of
    data to collect. After the data are collected, statistical description
    of the results helps understanding of the basic characteristics of
    exposure and its determinants. Statistics is also essential during
    final analysis of the data for hypothesis testing, characterizing
    exposure through various routes and media, and exploring relationships
    between ideal measurements (e.g., exact lung uptake) and feasible
    measurements (e.g., ambient, indoor, or personal measures).
    Furthermore, statistical inference allows one to generalize the
    observations derived from a sample to a wider population from which
    the sample was drawn. Finally, as noted in Chapter 11, statistics play
    an important role in quality assurance (QA) programmes.

         Selected applications of descriptive and inferential statistics
    in exposure assessment studies are discussed in the following
    sections. This chapter is not a substitute for a course in statistical
    methods, but is intended to provide a brief review and useful
    references. Widely available statistical software for personal
    computers can be used to perform data processing and necessary
    calculations. One example of such packages is the statistical
    programme Epi Info developed for and distributed by WHO (Dean et al.,

         Throughout the chapter, data collected as part of a lead exposure
    study performed in Malta and Mexico (WHO, 1985b) (Table 8) will be
    used to illustrate some key statistical concepts and methods. The
    purpose of this study was to investigate the relative importance of
    lead exposure via different routes of exposure. Blood lead
    concentrations were considered to be an indicator of lead uptake from
    all exposure routes, whereas faeces lead concentrations were
    considered to represent exposure via ingestion. In the course of this
    study, blood lead and faeces lead measurements were obtained from 36
    and 19 individuals in Malta and Mexico, respectively.

    4.2  Descriptive statistics

         Descriptive statistics summarize data in a simple manner to
    discern key points about the collected information. We typically
    assume that the collected data are a sample from a larger population
    of possible measurements and that the sample is representative of the
    population. The sample consists of the individual observations from
    the study population, with multiple variables or covariates recorded
    for each observation.  Univariate methods examine the distribution of
    a single variable;  multivariate methods describe relationships among
    two or more variables. That is, if we consider a single observation
    and know the value of one variable, multivariate methods indicate what

    Table 8.  Blood lead (PbB) and faeces lead (PbF) data from sample 
              populations in Malta and Mexico. Source: WHO, 1985b

                                 Malta                       Mexico
                           PbB          PbF            PbF          PbB
                           (µg/litre)   (µg/g)         (µg/litre)   (µg/g)

    1                      171           2.9           239          6.3
    2                      270          30.5           263          4.2
    3                      198           5.6           198          5.7
    4                      122           3.8           163          5.3
    5                       96          16.6           217          4.3
    6                      385          35.5           188          4.7
    7                      359          49.6           190          3.3
    8                      267           6.8           248          5.2
    9                      261           8.1           225          4.5
    10                     301          25.6           152          3.4
    11                     202           7.7           177          5.9
    12                     222          32.3           157          3.8
    13                     339          10.9           297          5.3
    14                     156           5.7           144          3.6
    15                     262          18.7           257          9.8
    16                     290          16.5           131          4.8
    17                     158           4.9           187          5.1
    18                     343          37.8           168          3.2
    19                     228           9.1           112          2.8
    20                     256          14.1
    21                     270           9.9
    22                     245           4.9
    23                     337          14.3
    24                     362          19.2
    25                     155           4.9
    26                     194           9
    27                     206           6.7
    28                     276          12.4
    29                     222          11.2
    30                     214          21.3
    31                     248           7.8
    32                     283          17.8
    33                     215          10.9
    34                     279          14.9
    35                     229           8.6
    36                     127          17.3

    Table 8.  (continued)

                                 Malta                       Mexico
                           PbB          PbF            PbF          PbB
                           (µg/litre)   (µg/g)         (µg/litre)   (µg/g)

    Median               246.5          11.1           188          4.7
    Mean                   243          14.8         195.4          4.8
    Standard deviation    70.9          10.8          49.5          1.6
    Standard error        11.8           1.8          11.4          0.4
    Minimum                 96           2.9           112          2.8
    Maximum                385          49.6           297          9.8
    Range                  289          46.7           185          7

    we can infer about the other variables. Both numerical and graphical
    techniques may be used to characterize the sample and any relevant
    subsets, and to obtain preliminary results from the study.

    4.2.1  Numerical summaries

         Numerical approaches include calculating descriptive statistics
    that describe the distribution of a variable (e.g., blood lead
    concentrations) in terms of central tendency and dispersion as well as
    descriptions of associations between pairs of variables. Other
    numerical descriptive measures can be used to describe points in the
    distribution (e.g., percentiles). Each of these descriptive statistics
    is described below and where appropriate the formulas used to
    calculate them are provided in Table 9.

         Standard measures of central tendency include the  sample 
     median (i.e., midpoint observation) and  sample mean (i.e.,
    average). Referring to the lower half of Table 8, note that the median
    blood lead concentration for the Maltese study population was 246.5
    µg/litre, intermediate between the eighteenth and nineteenth
    observations. Thus, 50% of the individuals in this sample had a blood
    level less than 246.5 µg/litre and 50% had a greater blood lead
    concentration. The sample mean blood lead concentration in the Maltese
    population was 243 µg/litre compared to 195.4 µg/litre in Mexico,
    indicating that blood lead levels were higher in the Maltese
    population. Methods for assigning confidence levels to statements such
    as this are described in Section 4.4. The sample mean is more precise
    for estimating the average of the distribution, but it is sensitive to
    measurement imprecision, errors and extreme values. Although the
    sample median is less precise for estimating the average, it is more
    robust with respect to errors in the data. Therefore, when outliers or
    extreme values are present, or when possible errors and contamination
    in the observed data are suspected, the median is likely to be a
    better descriptor of central tendency than the mean.

    TABLE 9

         Standard measures of dispersion include the sample variance, the
    sample standard deviation and the sample range (formulas in Table 9).
    These measures describe the spread of the observations. Examination of
    Table 8 reveals that blood lead concentrations are more variable in
    the Maltese sample population (standard deviation = 70.9 µg/litre,
    range = 289 µg/litre) than that in Mexico (standard deviation = 49.5
    µg/litre, range = 185 µg/litre). Measures of dispersion are useful for
    characterizing the degree of variability of a given measure among the
    members of a study population. As we will see later in this chapter,
    dispersion is also a key component of some study design issues.

         The concept of sample percentile is an important aspect of
    exposure assessment. A sample percentile for a variable in a data set
    is the value of the data such that at least  p % are at or below this
    value, and (1 -  p)% are at or above this value. A percentile is
    determined by first ordering the sample (i.e., rank from lowest to

    highest) and then identifying the observation that corresponds with
    the desired fraction of the data set. In the case of blood lead
    concentrations measured in the Maltese sample population, 283 µg/litre
    is the 75th percentile since it is the 27th of 36th rank-ordered
    values in the data set. Graphical representation of percentiles is
    discussed in the next section.

         Multivariate summary statistics allow one to evaluate
    relationships between or among different variables. Most of these
    examine correlation (the strength of the linear relationship) between
    variables, where the direction and magnitude of the relationship, or
    association, is described by the correlation coefficient  (p). The
    correlation coefficient ranges from -1 to +1, where negative values
    indicate an inverse relationship between two variables, positive
    values indicate a direct relationship, and values near zero, whether
    negative or positive, indicate a weak relationship. In the example
    case, the correlation between blood lead and faeces lead in the
    Maltese study population is 0.57, indicating these biomarkers of lead
    exposure have a moderate to strong positive association.

    4.2.2  Graphical summaries

         Graphical summaries of data provide illustrative information
    about the distribution of the observed values and associations between
    variables. Graphical presentations of data can suggest the shape of
    the distribution and aid in exploring hypothesized relationships
    between factors included in the study. In many situations and for many
    exposure analysts, graphical summaries of data convey information more
    readily than numerical summaries. Fundamental graphical presentation
    methods are described here. A description of advanced visualization
    methods may be found in Cleveland (1993) and Tufte (1983, 1997).  Histograms

         Histograms are bar charts used to illustrate the relative
    frequency of values or ranges of values within an exposure metric.
    Observations are assigned to ranges of the data, and the height of the
    bar represents the frequency of observations in that range. It is
    important to note that the choice of ranges can be arbitrary,
    resulting in many possible different pictures of the results. A
    histogram of the Maltese blood lead data is shown in Fig. 11. Here,
    the data were grouped into bins with interval ranges of 25 µg/litre.
    Blood lead concentrations between 200-225 µg/litre and 275-300
    µg/litre were observed the most often. Histograms can be used to
    illustrate absolute or relative frequency.  Cumulative frequency diagrams

         Cumulative frequency or probability diagrams can be used to
    graphically express percentiles of a distribution. A cumulative
    probability chart for the Maltese blood lead data is shown in Fig. 12.
    The value associated with a given percentile, or vice versa, can
    easily be determined from such a figure.

    FIGURE 11

    FIGURE 12  Box plots

         A box plot is another approach for graphically describing the
    distribution of a measurements in an exposure study. Some details of
    box plots vary among users; however, all of them display the sample
    median, mean, 25th percentile and 75th percentile. Selected other
    values, such as 10th and 90th percentiles or 5th and 95th percentiles
    as well as the extremes (i.e., the minimum and maximum) of the
    distribution are displayed, too. Fig. 13 shows box plots of the blood
    lead measurements from the Maltese and Mexican sample populations. The
    bottom and top horizontal lines of each box denote the interquartile
    range (i.e., the 25th and 75th percentiles) and the solid horizontal
    line across the centre indicates the sample median. The dotted line
    across the box indicates the mean of the distribution. The whiskers on
    the boxes in Fig. 12 extend to the 10th and 90th percentiles of the
    distributions, and the open circles denote all observations beyond
    those percentiles. As illustrated here, box plots are a convenient
    method for displaying information on the central tendency, dispersion,
    symmetry and tails of an exposure measure.

    FIGURE 13  Quantile-quantile plots

         Quantile-quantile plots can be used to compare the distribution
    of a variable with a different sample or a known distribution.
    Exposure measures are commonly compared to the normal or lognormal
    distribution (see section 4.3) for purposes of evaluating whether the
    normality assumptions inherent in numerous statistical inference
    methods are met. While a discussion of probability distributions and
    statistical inference methods is reserved for later in the chapter, a
    quantile-quantile plot is shown in Fig. 14. Here, the Maltese blood
    lead data are plotted against the standard normal distribution (see
    section 4.3). This special form of quantile-quantile plot is known as
    a  normal probability chart. Data that form an approximately straight
    line on such a chart are approximately normally distributed. Data that
    do not form a straight line follow a non-normal probability
    distribution.  Scatter plots

         Scatter plots display the relationship between two exposure
    variables measured from the same unit of observation (e.g., a person
    or location). Scatter plots are useful for graphically illustrating
    associations that are summarized numerically by correlation
    coefficients. Possible results include noticeable linear or non-linear
    trends, the absence of trend (a big "cloud") or a general trend with
    some observations as outliers.  Outliers are observations that do not
    follow the trends of the rest of the data and can strongly affect
    estimates of associations by masking real effects. Outliers can be the
    result of measurement error, human error or a correct but abnormal
    observation. Regardless, all potential outliers should be checked for
    accuracy and corrected or removed if this is justifiable. Fig. 15
    contains a scatter plot of blood lead and faeces lead measurements
    made concurrently on the Maltese sample population. Note that the plot
    indicates a positive association between the two measurements, but
    that the relationship is not 1 : 1, i.e., a unit change in blood lead
    levels is not accompanied by a constant change in blood faeces
    concentrations. This observation is consistent with the correlation
    coefficient between these measures of 0.57 that was noted in the
    previous section.

    4.3  Probability distributions

         Most exposure measurements can be considered random variables;
    that is, the different values obtained for a measurement of a given
    type are a function of a set of causative variables that may or may
    not be known to the analyst (Ott, 1995). Statistics allows for
    analysis of random variables by incorporation of variation through
    probability. This addition of variation allows for the generalization
    of results to populations larger or different than the sample under

    FIGURE 14

    FIGURE 15

         Continuous probability distributions are described by their
    probability density function (PDF), which provides the probability of
    an outcome taking values in a small interval, and by their cumulative
    distribution function (CDF), which describes the probability of an
    outcome being less than a particular value. The PDF and CDF are
    directly analogous to the concepts of a histogram and cumulative
    probability distribution discussed in Section 4.2.

         Probability models are used to make statements such as, "The
    probability that the daily maximum ozone concentration will be greater
    than 120 ng/litre today is 0.08." Such estimates can be based upon
    empirical evidence (i.e., by looking at the number of observed
    concentrations greater than 120 ng/litre in comparison with the total
    number of observed concentrations) or by choosing a distribution and
    parameters that describe the observed data. An example of the latter
    would be to model the distribution of blood lead levels in Maltese
    subjects as normally distributed with a mean of 243 µg/litre and
    standard deviation of 70.9 µg/litre and to use the properties of the
    distribution to estimate the probability. The amount of confidence in
    the accuracy of the estimates is related to the amount of data
    available and the sampling scheme used to collect the data, as well as
    the degree to which the mathematical distribution fits the observed

         Two standard distributions commonly used in exposure assessment
    for modelling continuous outcomes are the  normal and the
     lognormal distributions. The  binomial and  Poisson distributions
    are often used in exposure studies as well. Many other probability
    distributions are available which have more flexibility (Johnson &
    Kotz, 1970a,b), but these four are frequently used and thus warrant
    attention here.

    4.3.1  Normal distribution

         The normal distribution, also known as the  Gaussian 
     distribution, is one of the most important statistical
    distributions. It is characterized by a symmetric, bell-shaped
    frequency distribution and is commonly used as a basis for analysis of
    environmental exposure data. Usually, a random variable  (X) that
    follows a normal distribution with mean µ and variance rho2 is
    denoted by  X ~ N(µ, rho2). The probability density function of the
    normal distribution with parameters µ and rho2 is given in Table 10.

         Since the cumulative distribution function cannot be integrated
    in a closed form, the best we can do is to numerically compute the
    integral. The values µ = 0 and rho = 1 specify the  standard normal 
     distribution. The values of the CDF for the standard normal
    distribution have been tabulated and are available from most
    statistical textbooks and computer packages. The capital letter  Z is

    usually reserved to denote a standard normal random variable, i.e.,
     Z ~ N(0,1). The normal distribution ranges from positive infinity to
    negative infinity and is symmetric. Equation 4.7 can be used to
    transform any normal random variable  X to a standard normal random
    variable (Table 10). Standardized normal random variables are useful
    for computing the probability of an event occurring, e.g., the
    likelihood that someone in Malta has a blood lead concentration
    greater than 384 µg/litre. Assuming the Maltese blood lead data
    presented earlier are representative of the general population and the
    blood lead concentrations are approximately normally distributed, the
    standard normal distribution can be used to calculate the desired

    4.3.2  Lognormal distribution

         Many exposure measurements are strictly positive and right skewed
    (i.e., asymmetric). Examples include the size distribution of
    suspended particulate matter, personal exposures to various air
    pollutants and human time-activity patterns. The lognormal
    distribution is one possible model for describing data with these
    characteristics. The natural log (ln) transform of a lognormally
    distributed random variable has the properties of a normally
    distributed random variable. In other words, the distribution defined
    by the mean (µln x) and standard deviation (rholn x) of the
    ln-transformed values is bell-shaped and symmetric and can be
    standardized according to the procedure outlined in the previous
    section. Exponentiation of µln x and rholn x gives values termed the
    geometric mean (GM) and geometric standard deviation (GSD),
    respectively. The GM and GSD can also be used to define a lognormally
    distributed exposure measure.

         A histogram of the blood faeces data from the Maltese sample
    population is presented in Fig. 16a. The data depart from normality as
    they are clearly right skewed. The histogram in Fig. 16b shows that
    the ln-transformed values are approximately symmetric and indicates
    that the data approximate a lognormal distribution rather than a
    normal distribution. In this data set, µln x = 2.5 and rholn x = 0.7
    with corresponding GM = e2.5 = 11.8 µg/litre and GSD = e0.7 = 2.0. The
    degree to which the lognormal distribution accurately describes the
    data can be evaluated by plotting the raw data on lognormal
    probability paper. This procedure is identical to that described in
    relation in to Fig. 13, except that the  y axis is expressed on a
    logarithmic scale. The Maltese faecal lead data are plotted on
    lognormal probability paper in Fig. 17. The nearly straight line
    formed by the faecal lead measurements displayed on a logarithmic
    scale versus  Z indicates that the data are approximately lognormally

    4.3.3  Binomial distribution

         In some situation, the analyst may be interested in
    characterizing the frequency of a binary exposure outcome (e.g.,
    yes/no; true/false). The binomial distribution is useful for modelling

    TABLE 10
    FIGURE 16a;V214EH21.BMP

    FIGURE 16b;V214EH22.BMP

    FIGURE 17

    binary responses. The possible responses can be generally labelled as
    success or failure. Often we are not interested in a single outcome,
    but rather in the number of successes  (k) and failures  (n - k) for
    a specific number  (n) of repeated independent trials for the
    outcome. The probability of exactly  k successes in n independent
    trials, given a probability of success  (p) in a single trial, is
    given by the binomial probability distribution ( Pk) in Table 10.

         For example, assume daily exceedances of an ozone air quality
    standard are independent events in a study of 1-year and 3-year time
    periods. Let  k be a random variable describing the total number of
    exceedances encountered in a 1-year period ( n = 365 days). Further
    assume from historical data that the expected number of exceedance
    days each year is 1, thus  p = 1/365 = 0.00274. The calculated
    probabilities of  k days of exceedance per year are shown in Table
    12. Examination of the resulting probabilities in this example reveals
    a right-skewed distribution with the greatest probability occurring
    between  k = 0 and  k = 4 days.

    4.3.4  Poisson distribution

         Some exposure-related measurements are expressed as a rate of
    discrete events, i.e., the number of times an event occurs per unit
    time, such as the frequency (times per week) that a person consumes an
    ocean fish containing a methylmercury concentration greater than
    5.0 ppm. The Poisson distribution is used for describing potentially
    unlimited counts or events that take place during a fixed period of
    time (i.e., a rate), where the individual events are independent of

    one and other. The Greek letter lambda is typically used to denote the
    average or expected number of counts per unit time. In a Poisson
    distribution, the parameter lambda also describes the variance of the
    random variable. We can think about this intuitively by noting that as
    the expected number of counts or events increases (i.e., the rate of
    events increases), the amount of variability should increase as well.
    For example, if we expect a count of 1 then it is not too difficult to
    imagine observing 0 or 2. Likewise, if we expect a count of 20 000
    then it is not difficult to imagine 20 100 or 19 900 as reasonable
    observations. However, the variance is definitely larger in the second
    case. The formula used to compute the probability of a specific number
    of counts being observed over a fixed time interval is listed in Eq.
    4.11 of Table 10.

         For example, the Poisson distribution can be used in an exposure
    model to characterize the frequency with which a person comes in
    contact with a contaminant; say, the number of times per day a person
    encounters benzo [a]pyrene associated with environmental tobacco
    smoke. Assume that based on existing data, the expected number of
    encounters is anticipated to be 2 per day. Using Eq. 4.11, with lambda
    = 2, there is a 9% chance that an individual will have 4 (i.e.,
     n =4) encounters with benzo [a]pyrene on a given day. Subject to
    limitations associated with the independence assumption noted above,
    the Poisson distribution can be a useful exposure modelling tool.

    4.4  Parametric inferential statistics

         Inferential statistics is the process of using the observed data
    and assumptions about the distribution and variation of the data to
    draw conclusions. The two complementary components of inference are
     parameter estimation (either point or interval estimation) and
     hypothesis testing. Only frequentist, or classical, inference will
    be discussed here. However, Bayesian statistical inference, as well as
    decision theory, can be valuable for incorporating other aspects such
    as prior belief and loss into a statistical analysis, and they are
    worth consideration. Further information on Bayesian statistics may be
    found in Carlin & Louis (1996).

    4.4.1  Estimation

         Exposure measurement data can be used to estimate the parameters
    of a model (e.g., a probability distribution), especially those that
    describe the mean and variance of the variable. The two types of
    common reported estimates are  point estimates and  interval 

         Point estimation for quantities is commonly performed using
    maximum likelihood, ordinary least squares or weighted least squares
    methods. All estimates are chosen because they optimize (i.e., find
    the maximum or minimum of) some objective function such as the
    likelihood function or squared error function. One example is the
    sample mean for the population mean when the data are normal, using
    maximum likelihood, or for any data, using least squares.

         Two different forms of interval estimation are used to
    characterize variability in point estimates. The first is based on
    error propagation and is the result of simulating data to see what
    distribution of results might be expected under the model; the second
    is the usual statistical notion of confidence intervals. This approach
    focuses more on the variability of a modelled outcome due to
    variability of the input, and is useful in designing studies and
    determining which factors will have the greatest effect on the
    variability of the exposures. These procedures are described more
    fully in Chapter 6.

         The second form of interval estimate, the statistical (1-alpha)%
    confidence interval, gives a range of estimates, for a parameter,
    which is generated in a manner such that it contains the true
    parameter value (1-alpha)% of the time. For a normally distributed
    random variable, a one-sided confidence interval for the estimate of
    the mean is derived from the standard error and  Z1-alpha, while
     Z1-alpha/2 is used for a two-sided confidence interval. The standard
    error (rho×) is an expression of uncertainty about the mean and is
    calculated as the standard deviation divided by the square root of the
    number of observations  (n) (Table 9). Continuing with the example
    from the Maltese study, the standard error of the blood lead sample
    data is 11.8 µg/litre (Table 8). For alpha = 0.05, the two-sided 95%
    confidence interval about the estimated mean is computed as 243
    µg/litre ± 23.1 µg/litre, where the latter is equal to  Z1-(alpha/2) ×
    rho× or 1.96 × 11.8 (Table 11). Details of this procedure and
    related considerations may be found in most introductory statistics
    textbooks, for example Kleinbaum et al. (1988).

    4.4.2  Measurement error and reliability

         The term measurement error refers to the accuracy and precision
    of a given sample collection and analysis methodology.  Accuracy 
    describes the degree to which a measurement is free of bias.  Bias is
    systematic deviation in a measurement from the true value of the
    process being measured.  Precision refers to the reproducibility of a
    particular measurement system. Measurement reliability is a closely
    related concept in that a measurement with a high degree of accuracy
    and precision can be considered to be more reliable than one with a
    low degree of accuracy and precision. Additional information on
    measurement error and reliability is contained in Chapter 11, where
    the topic is discussed in the context of QA in exposure studies.
    Methods for assessing the accuracy of an exposure measure are also
    discussed in Chapter 11. Here, an approach for quantitatively
    estimating the precision of an exposure measurement system is

         Statistical analysis of environmental samples collected
    simultaneously in space and time can be used to estimate the precision
    of a measurement method. Such samples are often referred to as
     duplicates and are often collected in pairs. The difference in the
    measurement parameter (e.g., concentration) between a duplicate pair

    Table 11.  Standard normal cumulative probabilities

    z              p(Z < z)                z              p(Z < z)

    -4.265         0.00001                 0              0.50
    -3.891         0.00005                 0.126          0.55
    -3.719         0.0001                  0.253          0.60
    -3.291         0.0005
    -3.090         0.001                   0.385          0.65
    -2.576         0.005                   0.524          0.70
    -2.326         0.01                    0.674          0.75
                                           0.842          0.80
    -2.054         0.02                    1.036          0.85
    -1.960         0.025
    -1.881         0.03                    1.282          0.90
    -1.751         0.04                    1.341          0.91
    -1.645         0.05                    1.405          0.92
                                           1.476          0.93
    -1.555         0.06                    1.555          0.94
    -1.476         0.07
    -1.405         0.08                    1.645          0.95
    -1.341         0.09                    1.751          0.96
    -1.282         0.10                    1.881          0.97
                                           1.960          0.975
    -1.036         0.15                    2.054          0.98
    -0.842         0.20
    -0.674         0.25                    2.326          0.99
    -0.524         0.30                    2.576          0.995
    -0.385         0.35                    3.090          0.999
                                           3.291          0.9995
    -0.253         0.40                    3.719          0.9999
    -0.126         0.45                    3.891          0.99995
     0             0.50                    4.265          0.99999

    is indicative of the precision of the collection and analysis
    methodology. Descriptive statistics generated from a set of
    differences between duplicate samples can be used to characterize the
    average degree of precision as well as variability in precision.

         Consider a hypothetical study of respirable particulate matter
    (RSP) in outdoor air where 20 duplicate pairs of 24-h average
    measurements were made. Assume the average 24-h average concentration
    among the 40 measurements was 50 µg/m3. Further assume that the
    distribution of differences between the 20 pairs of duplicate samples
    was normally distributed with a mean and standard deviation of 5 and 1
    µg/m3, respectively. On average, then, a single measurement can be
    expected to be within 5 µg/m3 of the actual concentration. Utilizing

        Table 12.  Probability distribution for the number of exceedances, using the binomial model 
               with expected number of exceedances of 1.0

                                 1-Year period                           3-Year period
    Number of            Probability      Cumulative            Probability       Cumulative
    exceedances                           probability                             probability
    k                    Pk {k}           Fk{k}                 PM{k}             FM {k}

    0                    0.36737          0.36737               0.04958           0.04958

    1                    0.36838          0.73576               0.14916           0.19874

    2                    0.18419          0.91995               0.22414           0.42288

    3                    0.06123          0.98118               0.22435           0.64723

    4                    0.01522          0.99640               0.16826           0.81549

    5                    0.00302          0.99942               0.10086           0.91635

    6                    0.000498         0.999920              0.05034           0.96670

    7                    0.000070         0.9999904             0.02152           0.98821

    8                    0.0000086        0.9999989             0.00804           0.99625

    9                    0.0000009        0.9999999             0.00267           0.99892

    10                   0.00000009       0.9999999             0.00080           0.99972

    11                   0.000000008      0.9999999             0.00022           0.99993

    12                   0.000000001      0.9999999             0.00005           0.99998

    concepts presented in Chapter 4.4.1, a single measurement can be
    expected to be within approximately 3-7 µg/m3 of the true
    concentration 95% of the time, i.e., within ±2 standard deviations of
    the average difference.

         For a probability distribution, the coefficient of variation is
    defined as the ratio of the standard deviation to the point estimate
    of the mean. In this way, the coefficient of variation error describes
    the degree of dispersion of a data set relative to a measure of its
    central tendency. The coefficient of variation provides a quantitative
    estimate of the relative degree of variability among the observations

    in a data set. Using data from the hypothetical example described
    above, the coefficient of variation among the pairs of duplicate
    samples is 0.2. Thus, on average, a single measurement can be expected
    to be within 20% of the actual concentration.

    4.4.3  Hypothesis testing and two-sample problems

         Exposure assessments are often performed to determine whether the
    level of exposure to a pollutant is different between two or more
    groups of people or locations or periods of time. Additional
    attributes typically considered to be determinants of exposure include
    any number of demographic factors (e.g., age, gender, ethnicity) and
    behaviour patterns. This section describes the statistical procedure
    used to address this type of study objective.

         Statistical hypothesis testing is a procedure where a choice is
    made between two hypotheses that are not weighed equally; the null and
    the alternative. The  null hypothesis typically reflects what can be
    stated with confidence about a particular phenomenon on the basis of
    existing information. In practice, concluding that the null hypothesis
    is false indicates that the data provide strong evidence for a
    departure from conventional wisdom or practice. Thus, hypothesis tests
    are generally constructed such that the conclusion will lie with the
    null unless the evidence strongly suggests otherwise.

         Two types of errors can arise from hypothesis testing:

    *  concluding that the alternative hypothesis is true when it is in
       fact false (false negative)

    *  concluding that the null hypothesis is true when in fact it is
       false (false positive).

    The first type of error is known as a  type I error and the second
    one is a  type II error. The probability of a type I error is denoted
    by alpha and the probability of a type II error by ß. Only alpha is
    considered in the construction of the hypothesis test. However, as
    described later, both type I and type II errors are considered in
    sample size determinations.

         The general procedure for common tests that try to determine if
    some factor has an effect on the exposure outcome is as follows: a
    test statistic is constructed whose value is known if the null
    hypothesis is true. For example, if the null hypothesis is that the
    population mean is 1 (H0: µ=1), then under the null hypothesis, × =
    0, where × is the sample mean. Next, adjustments are made so that
    the distribution of this test statistic is known. For example, with
     s denoting the sample standard deviation and  n the sample size,
    the test statistic  T defined by Eq. 4.12 in Table 13, where  T has
    a distribution which follows a  t-distribution with  n-1 degrees of
    freedom. Now, using the known distribution of the test statistic, we
    construct ranges of values for which we reject (rejection region) and

    fail to reject (acceptance region) the null hypothesis. The rejection
    region is any area which has probability alpha, usually chosen to
    correspond to likelihoods between 0.025 and 0.05.

    TABLE 13

         A large number of problems in exposure assessment involve the
    comparison of two groups, for example, control and treatment; old
    method and new method; or normal and abnormal. If we focus on the
    location problem, where the means or the medians are compared, then
    depending on the assumptions we make with regard to the data,
    different tests can be performed. Assumptions typically made include:

    *  The data consist of a random sample from population 1 ( X1,i,
       i = 1, ...,  n), and a random sample from population 2 ( X2,i =
       1, ...,  n2)

    *  The two samples are independent of each other.

    *  Observed variables are on a continuous scale.

    *  Measurement scale is at least ordinal.

    *  Population 1 ( X1) has approximately the same distribution as

         If we assume that the data follows a normal distribution and that
    the data are independent, with the first group distributed  N(µ,
    rho21) and the second group distributed  N(µ, rho22) so that the
    variances are possibly different, a test can be constructed to see if
    the difference (Delta) between the means for the groups is equal to a
    hypothesized value (Delta0), typically set to zero. This scenario
    would result in a two-sample  t-test, and the test statistic is
    presented in Eq. 4.13 in Table 13, where  t is compared with a

     t-distribution with  df = min( n1-1,  n2-1) degrees of freedom,
    and  si2 is computed as described in section 4.2.1. The possible
    alternatives are that Delta > Delta0, Delta < Delta0, or the
    general alternative that Delta not equal Delta0. If we are looking
    for differences, we reject the null hypothesis that the groups are the
    same for the respective alternative if  t >  Tdf,alpha,  t < -
     Tdf,alpha, or | t| >  Tdf, alpha/2, where alpha is the prespecified type I
    error for the decision to be made.

         Referring once again to the blood lead example presented earlier,
    the following null hypothesis may be tested: mean blood lead
    concentrations in the Maltese sample population are equal to those in
    the Mexican sample population. The corresponding alternative
    hypothesis is: mean blood lead concentrations are not equal in the two
    sample populations. As indicated in Fig. 13, the point estimates of
    the respective sample means are different. Completion of the
    two-sample  t-test will allow for determination of whether the
    difference is statistically significant with 1-alpha% confidence.
    Using Eq. 4.13, the  t-statistic is computed to be 3.30. Setting
    alpha = 0.05, the critical  t-value is 1.96. Thus, the Maltese and
    Mexican sample mean blood concentrations are significantly different
    at the 0.05 level.

    4.4.4  Statistical models

         Statistical models make explicit the potential sources of
    variability to be measured. The response, exposure, is dependent upon
    a combination of measured factors and background variation from
    unmeasured influences. For example, in examining pesticide exposures,
    one might consider methods and amounts of applications, climate
    conditions and duration of potential exposure. Unmeasured factors
    might include exact knowledge of individual behaviours and locations,
    which may cause different levels of exposure between two individuals
    who are equal with respect to other exposure characteristics. One must
    consider as many of the potential relationships between the responses
    as possible, as well as how the possible factors will affect each
    other, before finalizing a study design.

         Since no simple model will perfectly describe all relationships,
    the goal is to construct a parsimonious model that describes the major
    factors of the process resulting in exposure. For example, in studying
    the exposure of children to lead, the presence of lead in paint, in
    house dust or in water could be important factors, whereas gender and
    age might have an indirect effect on exposure by influencing the
    location and patterns of play. However, both types of data will be
    important in determining response, even though one is only an indirect
    cause. The average outcome described above could be the annual average
    exposure to lead or perhaps the maximum daily exposure, depending upon
    whether a cumulative or a threshold effect is the focus. 

         As noted in Chapter 3, by considering the statistical model
    before finalizing the study design one can help ensure that most
    influential factors would be accounted for, and more importantly, that
    the true effects of factors can be estimated from the study data. It
    is possible to design a study where some influential factors were not
    accounted for. Suppose there is interest in the effects of location
    and time of day on outdoor ultraviolet radiation exposures. If
    measures are only taken at one site at one time of day, and then at
    another site at a different time of day, then the effects of location
    and time of day are not distinguishable from the collected data.

         The mean, or average, outcome is the most common summary used for
    modelling and testing of situations of different conditions, but other
    parameters, such as the variance, the percentiles or the median, can
    be used for estimation and testing. Common models and statistical
    analyses, such as the multiple linear regression model, the  t-test
    and analysis of variance (ANOVA) use the mean for modelling and
    testing. The models can be as simple as taking the sample, dividing it
    into groups and comparing the means in the different groups. The
    models can also be as complex as trying to construct a physical model
    for the means with the addition of terms which incorporate randomness
    due to unmeasured factors or other sources of variation.  Analysis of variance and linear regression

         ANOVA is a technique for assessing how several nominal
    independent variables affect a continuous dependent variable, and is
    usually concerned with comparisons involving several group means.
    Regression and ANOVA models are closely related and can be analysed
    within the same framework. The major difference is that for ANOVA, all
    the independent variables are treated as being nominal; whereas for
    regression analysis, any mixture of measurement scales (nominal,
    ordinal, or interval) is allowable for the independent variables.
    Examples of ANOVA used in exposure assessment can be found in Liu et
    al. (1994a), who used ANOVA models to examine the effects of wind
    speed, ozone concentrations, human subject and interaction between
    wind speed and concentration on the performance of an ozone passive
    personal sampler.

         Estimation for both ANOVA and linear regression models consists
    of obtaining point estimates for the parameters that describe the mean
    exposure under a certain set of conditions. Part of the estimation
    procedure is to determine how well the model fits. The first
    diagnostic is to examine the residual error (residual). A residual is
    simply the difference between the exposure estimated by the model and
    the actual exposure. By examining the residuals, one can determine for
    what ranges of actual exposures or conditions the model does not fit
    well, and use this to decide how to adjust the model.

         The simplest design (and corresponding model) occurs when
    measurements are taken while varying only one possible factor over a
    finite,  k, number of levels. Consider PM2.5 exposure; let the factor
    be the time of day when the levels are measured. For simplicity,
    divide time into three categories -- morning, afternoon, or evening --
    so  k = 3. If there is no known or hypothesized functional form for
    the relationship, the resulting abstract model for exposure,  Y, 
    during a particular time period,  i, should be the sum of the mean
    (average) during the time period  i, denoted by gammai, and an
    error, epsilon, which will represent the natural variation of the
    measurement. It is common to assume that the variation of the outcome
    is the same among all levels of the factor; this assumption is known
    as  homoscedasticity. 

         This model is referred to as the one-way ANOVA. The resulting
    model for the observed data, with  Yij denoting the  jth PM2.5
    measurement collected during the  ith time period with  i ranging
    from 1 to 3, is  Yij = gammai+ epsilonij where  gammai represents
    the average outcome due to the  ith factor level (in this example
     i ranges from 1 to 3), and  epsilonij (the error term) represents
    independent random variation. One common assumption is that the error
    terms follow a normal distribution with variance rho2. The parameters
    which need to be estimated in this model from the data are the means
    of the subsamples, gammai, and the variance of the outcome, rho2.
    This type of model, which compares the means of distinct groups, is
    the basis for ANOVA.

         Increasing the level of complexity leads to multiway or
    multifactor ANOVA as well as the multiple linear regression model,
    which is a more specific model for the effects of independent
    variables on the dependent variable. Let  Y denote the exposure level
    for a particular person or location; this is the dependent variable.
    Let  X, ..., Xn denote  n independent variables (known as
    covariates) which potentially influence the exposure level  Y. If the
    assumption of the existence of a linear relationship between the
    independent and dependent variables is reasonable, then a model for
    the outcome,  Y, based on the covariates  Xi, can be written as


    where the information not conveyed by the covariates results in the
    error (epsilon), which is assumed to be normally distributed. theta0
    denotes the average exposure when all the  X values are zero, and
    thetai denotes the change in exposure for a unit change in the  ith
    variable. An example would be 24-h personal exposures to nitrogen
    dioxide. In this case, the factors may be distinct times and locations
    (or microenvironments) where nitrogen dioxide exposure may occur; for
    example, outdoors, indoors while home cooking on a gas range, and in
    an automobile.

         A regression model is used to evaluate the relationship of one or
    more independent variables  X1,  X2, ...,  Xk to a single,
    continuous dependent variable  Y. It is often used in exposure
    assessment to characterize the relationship between the dependent and
    independent variables (continuous and discrete) by determining the
    extent, direction and strength of the association. For example, in the
    particle total exposure assessment methodology (PTEAM) study, indoor
    PM2.5 concentration  (Y) was regressed against outdoor air
    concentrations ( X1), smoking rates ( X2), cooking durations
    ( X3), air exchange rates ( X4) and house volumes ( X5) to
    determine the major factors affecting indoor PM2.5 concentrations
    (Ozkaynak et al., 1996).

         Further information on ANOVA and linear regression may be found
    in Ott (1995), Kleinbaum et al. (1988) and most introductory
    statistics textbooks.  Logistic regression

         An approach which is different from the linear or additive
    relationship described above is to consider a categorical outcome for
    exposures, e.g., exposure measurements grouped into ordinal levels
    such as low, medium and high. When the response is binary, that is, if
    an exposure is either present or absent (i.e.,  a threshold effect), 
    then a linear relationship is not appropriate. In this case, we must
    use an alternative model, for example:

    logit  P(Y = 1) = alpha + ß1 X1 + ß2 X2 + ... + ßk Xk

    where the logit function is logit  (x) = ln ( x/(1- x)), the
    function  P(Y = 1) denotes the probability that the response variable
     Y will take on the value 1 (denoting "success"), and the role of
    epsilon from the previous model is taken by modelling the parameter
    representing the probability that  Y = 1, as opposed to  Y itself.
    This model is known as logistic regression. In this model, alpha
    denotes the baseline odds for exposure given that the associated
    factors,  X ..., Xk , are zero, and ßi denotes the change in the
    log-odds that the response is  Y = 1 given a 1-unit increase in
     Xi. This approach can be adjusted to allow for the analysis of
    other types of categorical outcomes (McCullagh & Nelder, 1990). One
    common parameter which describes logistic regression results is the
     odds ratio. For a particular set of covariates,  Xi, the odds of
    the event occurring ( Y = 1), is exp(ß Xi). To compare the odds
    ratio for two situations, compute the first set of odds and take its
    ratio over the second odds. Usually, the situations will be identical,
    except that the covariate of interest will be zero for one of the
    sets. For example, if the model for the linear combination of
    covariates is 1.3 +2.5 X, then the odds ratio for  Y = 1, comparing
     X = 1 versus  X = 0, is e(1.3+2.5)/e1.3. A similar computation can be
    done when  X is a continuous random variable, for two different
    values of  X. In exposure assessment, a logistic regression model

    could be used to evaluate the importance of demographic or temporal
    factors on the likelihood that an individual will engage in an
    activity such as applying pesticides.

    4.4.5  Sample size determination

         Hypothesis testing attempts to determine if the data reject or
    fail to reject a particular (null) hypothesis. The test is based upon
    statistical considerations, and hence just reports how likely or
    unlikely the null hypothesis is. If there is minimal information, it
    will be difficult to statistically reject the null hypothesis; hence,
    sample size calculations are done in order to ensure that there is
    sufficient information from which to make a decision. The decision is
    between two unequally weighted hypotheses; the first is the null
    hypothesis, H0, which is the safe hypothesis, and the second is H1,
    the alternative or sceptical hypothesis, which requires sufficiently
    large evidence to believe in. The specifications of the test, based
    upon sceptical scientific belief or common usage, are the type I error
    and the type II error (defined in section 4.4.3). Introductory
    information on sample size determination is presented here; the reader
    is referred to Lemeshow et al. (1990) for details.

         To determine the minimum sample size required to observe the
    desired outcome, one needs to determine the smallest effect that is
    scientifically worth detecting (i.e., based on measurement limit or
    scientific principles), and use that to collect a sample with enough
    information to detect such a difference. The effect is the minimum
    significant difference in exposure between two groups. The smaller the
    effect, the more information is required to distinguish it. This
    effect is related to the type I error of a hypothesis test. There are
    two components, the type II error and the sample size, which are
    unspecified. When the type II error is specified, the resulting sample
    size can be determined. Once the sample size is determined, the power
    (1-type II error) of a study can be computed. The smaller the
    difference to be detected, the larger the sample size needed for fixed
    type I and type II error probabilities.

         The following describes the formula for a two-group comparison.
    To compare the means of two groups with equal sample size, let Delta
    represent a scientifically significant difference that we would like
    to detect, if it exists. Suppose that the first group can be modelled
    by  Y1 = µ1+epsilon1, where µ1 is the mean and epsilon1 ~
     N(0,rho2) is the error term, and the second group can be modelled
    by  Y22 + epsilon2 with the error term epsilon2 ~  N(0,
    rho2). The only difference between the two groups is the mean
    response. The groups will be considered statistically different only
    if |µ21| > Delta. The minimum number of observations in each
    group is given by Eq. 4.14 in Table 14 where  zgamma is defined as
    the value satisfying  P(Z >  z) = gamma, where  Z follows the
    distribution of a standard normal random variable, alpha is the type I
    error, and ß is the type II error. This formula can also be used to
    approximate the sample size needed for a difference of proportions

    (e.g., for dose-response models comparing two groups), by letting
    Delta represent the difference in proportions (instead of a difference
    in means).

    TABLE 14

    4.5  Non-parametric inferential statistics

         Each of the statistical analysis methods described previously
    assumes that the data can be adequately described by a probability
    distribution with known parameters, and that distribution can be
    transformed, if necessary, to meet the assumptions of the statistical
    model (e.g., normally distributed, independence, etc.). Many
    exposure-related data sets do not fit this description, however. One
    reason for this is that the data may not be normally distributed or
    cannot be transformed so that they are approximately normal. A more
    common reason is that although the underlying distribution of the
    population from which the sample is drawn may be reasonably assumed to
    be approximately normal or lognormal, there are too few samples to
    allow the nature of the underlying distribution to become apparent. In
    exposure studies sample sizes are often small (e.g., 10 or less)
    because of logistic difficulties in collecting samples and the expense
    of collecting and analysing the samples. In this case, the point
    estimates of the standard deviation and standard error are considered
    to be highly unstable. Consequently, confidence intervals generated
    using the estimation methods described above are considered to be
    unreliable. Non-parametric statistical analysis methods can be used to
    analyse data with these characteristics.

         Non-parametric statistical methods rely on rank statistics, i.e.,
    the order of observations in a data set. Glantz (1987) provides a
    concise introduction to non-parametric statistical methods with regard
    to health statistics. The sign test and Mann-Whitney rank sum test are
    two non-parametric methods for evaluating the equivalence of the
    median from two sample populations. These methods are analogous to the
    two-sample  t-test described in Section 4.4.3. The Kruskal-Wallis
    test is analogous to the  k-sample ANOVA method described in Section
    4.4.4 and is used to test whether the medians of more than two sample
    populations are equal. For further information on this topic, the
    reader is referred to Mosteller & Rourke (1973), a classic text on
    non-parametric methods, and also to Gilbert (1987) and Ott (1995) for
    a discussion of statistics based on rank order in an environmental

    4.6  Other topics

         Many new developments in statistical theory can be applied to the
    analysis of exposure assessment data. These include the topics of
    measurement error, missing data, spatial statistics, non-linear
    models, mixed effects, generalized mixed effects models, simulation
    models (e.g., Monte Carlo analysis), as well as others. Modern
    computing methods such as re-sampling and the bootstrap have made
    possible estimation, evaluation, and testing of complex models. In
    addition, other inferential philosophies, such as Bayesian and
    decision-theoretic approaches, can be useful. Recommended references
    for further reading on these and related subjects are Sachs (1986),
    WHO (1986), Gilbert (1987), Glantz (1987), Kleinbaum et al. (1988) and
    Ott (1995).

    4.7  Summary

         Statistical methods are a critical tool in applied and
    research-oriented exposure assessment studies. It is recommended that
    a statistician be involved in all aspects of an exposure
    investigation, especially during the design and data analysis stages.
    Sample size determination is an important use of statistics during the
    planning of an exposure assessment study. Numerical and graphical
    descriptive statistics can be used to summarize exposure data and
    perform preliminary analyses of relationships between and among
    exposure variables. In many cases, exposure data are approximately
    normally or lognormally distributed and can thus be readily
    incorporated into standard parametric statistical inference methods
    such as estimation and hypothesis testing. In addition, other
    parametric statistical models such as ANOVA, linear regression and
    logistic regression can be used to quantify associations among
    exposure measures. In situations where the number of observations is
    small or the data cannot be transformed to an approximately normal
    distribution, non-parametric methods such as the sign, Mann-Whitney
    and Kruskal-Wallis tests can be used to test hypotheses.


    5.1  Introduction

         Methods for the collection and application of time-use data in
    exposure studies are critically reviewed in this chapter. All methods
    have their limitations. With appropriate quality assurance, however,
    information on time use and activity patterns collected by
    questionnaire, diary, interview, observation or technical means can be
    very valuable for interpreting and modelling exposures. Although the
    methodologies of time-activity data collection are universal, they
    need to be applied and their vocabularies selected keeping in mind the
    population and culture of concern and objectives of the study.
    Accurately and reliably documenting the time-activity patterns of the
    general and target populations are important components of
    understanding and mitigating human exposure (see Table 15).

         People's activity patterns, eating and drinking habits, and
    lifestyle characteristics must be superimposed over concentrations in
    environmental media before it is possible to derive realistic
    estimates of actual human exposure. Too often in the past, pollutant
    concentrations in a particular medium have been assumed to represent
    exposure, only for it to be found later that they did not provide an
    accurate picture owing to modifying factors such as the time people
    spend indoors rather than outdoors, food preparation and cooking, and
    use of bottled water instead of tap water. Experience has shown that
    exclusive reliance on central monitoring sites (e.g., urban air
    pollution monitoring sites, samples from drinking-water reservoirs)
    and bulk sampling procedures (e.g., spot checks for pesticides in
    food) for determining human exposures may be insufficient in many

         In an exposure context, data about human time use and activity
    patterns (often referred to as time-activity data) have four related

    1.   Knowledge of the activities performed while a study participant
    carried a personal monitor can aid in identifying the determinants of
    exposure, i.e., "What did this person do that led her/him to have such
    a high exposure?" and "To what extent can exposure be explained the
    amount of time spent in specific activities or locations?" For
    instance, several studies in which activity pattern data were
    collected in conjunction with monitoring data have shown that
    indicators such as commuter status, work status, cooking fuel type,
    season, residential location and day of week are important in
    differentiating exposure to carbon monoxide and nitrogen dioxide
    (Akland et al., 1985; Ryan et al., 1990; Schwab et al., 1990; Berglund
    et al., 1994a). Investigations of VOC exposure have found that people
    who reported engaging in auto-related activities (e.g., exposure to
    vehicle exhausts, pumping gasoline and visiting a service station)
    were associated with statistically significant increases in breath and
    personal exposure levels of several aromatic and aliphatic compounds;

        Table 15.  Features of time-activity studies aimed at exposure assessment

    Location        Pollutant      Participant characteristics   Survey characteristics        Spatial and source           Reference

    Cincinnati,     No pollutant   487 people under age 70;      March and August 1985;        28 microenvironments;        Johnson, 1989
    Ohio, USA                      representative; includes      diary; minute resolution;     location data; breathing 
                                   children; oversample          3-day sample; time of         rate; smoking status; 
                                   asthmatics; data on age,      year; day of week; time       pollutant-related activity 
                                   gender, race, income,         of day                        questionnaire
                                   work status, health status

    California,     No pollutant   1780 people over age 11;      October 1987-July 1988; 24-h  50 microenvironments;        Wiley et al., 
    USA                            representative of             recall and questionnaire;     stressed activities with     1991; Jenkins et 
                                   English-speaking households;  time of year; day of week;    respect to toxics exposure   al., 1992
                                   stratified by region; data    time of day                   and high breathing rates; 
                                   on demographics and                                         location/ region; housing 
                                   socio-economic status                                       unit characteristics

    California,     No pollutant   1200 children under age       April 1989-March 1990; 24-h   113 activities; 63           Wiley et al., 
    USA                            12; representative of         recall and questionnaire;     locations; proximity to      1991
                                   English-speaking households;  time of year; day of week;    sources; location/region; 
                                   stratified by region; data    time of day                   housing unit characteristics
                                   on demographic and 
                                   socio-economic status

    Kanawha         No pollutant   90 children aged 9-11;        July and September 1989;      Home/near home/far;          Schwab et al., 
    Valley, West                   longitudinal (4 weeks);       diaries; 30-min resolution    school; indoor vs. outdoor;  1991, 1992
    Virginia, USA                  stratified by gender,         except travel (15 min);       exertion level; housing unit
                                   respiratory health; data on   time of year; day of week;    characteristics
                                   demographic, socio-economic   time of day
                                   status and health variables

    Table 15.  (continued)

    Location        Pollutant      Participant characteristics   Survey characteristics        Spatial and source           Reference

    New York,       No pollutant   1000 children aged 5-12;      Mid-1990 to mid-1991;         Usual commuting; frequency   Silvers et al., 
    New Jersey;                    stratified by state,          24-h recall of child's        of bathing, hand washing;    1994
    Pennsylvania,                  weekday/weekend and season;   activities by adult           weather conditions; clothing 
    Oregon;                        data on demographic,          caregiver; 30-min resolution; type; play surface; dwelling 
    Washington;                    socio-economic status and     questionnaire; time of        type
    California,                    community type                year; day of week; time 
    USA                                                          of day

    Berkeley,       Ozone          168 college freshmen (aged    Test-retest reliability       Time spent outdoors, time    Künzli et al., 
    California,                    17-21) raised in California;  study to recall lifetime      spent in physical activity   1997a,b
    USA                            convenience sample;           residential history           (outdoors)
                                   long-term ozone exposure

    Athens;         PM25, CO,      450 adults (aged 25-55)       Mostly 1997; 48-h personal,   Time spent in                Jantunen et al., 
    Basel;          VOC, NO2       personal air sampling;        indoor, outdoor and at work   microenvironments (e.g.,     1998
    Grenoble;                      approximately 1200 adults     monitoring; time-activity     Indoors, outdoors, at 
    Helsinki;                      with time-activity diary;     diary (Fig. 19); specific     work); traffic categories
    Milano;                        random population sample;     tasks
    Praha                          demographic and 
                                   socio-economic status

    Washington,     CO             700 adults aged 18-65;        1982-1983 (winter); diary     8 locations; transport       Hartwell et al., 
    USA                            representative; oversample    and questionnaire; minute     mode use; activity index;    1984; Akland et 
                                   long commutes and gas         resolution; 1-day sample;     smokers present; range use;  al., 1985
                                   ranges; excluded smokers;     time of year; day of week     in gavage; census tracts 
                                   data on age, gender, work     and day                       for work, home, other; 
                                   status                                                      housing unit characteristics


    Table 15.  (continued)

    Location        Pollutant      Participant characteristics   Survey characteristics        Spatial and source           Reference

    Denver,         CO             452 adults aged 18-65;        1982-1983 (winter); diary;    8 locations; transport       Johnson, 1984
    Colorado,                      representative; oversample    questionnaire; minute         mode; activity index; 
    USA                            gas ranges and long commutes; resolution; 2-day sample;     smokers present; range use; 
                                   excluded smokers; data on     time of year; day of week;    in gavage; census tracts 
                                   age, gender, work status      time of day                   for work, home, other; 
                                                                                               housing unit characteristics

    Elizabeth/      VOCs           355 people; representative;   Fall 1981; follow-up: 157     Activities > 1 h; high       Wallace et al., 
    Bayonne,                       oversampled high-exposure     in summer 1982; follow-up:    exposure activities (e.g.,   1985, 1986
    New Jersey,                    occupations; data on age,     49 in early 1983; 24-h        smokers, occupations, 
    USA                            gender, race, socio-economic  recall diary; activity        travel); proximity to 
                                   status, and proximity to      questionnaire                 industry; housing unit 
                                   VOC sources                                                 characteristics

    Portage,        NO2, RSP       120 children; selected from   1987; retrospective, actual,  11 microenvironments; home   Adair & Spengler, 
    Wisconsin                      larger (600) cohort;          and prospective diary;        zip codes; school location;  1989a,b
                                   stratified by cooking fuel;   10-15-min resolution; time    housing unit characteristics
                                   data on gender, age,          of year; day of week; time 
                                   parental education            of day

    Steubenville,   NO2, RSP       150 winter, 250 summer;       1987; retrospective actual    11 microenvironments; home   Adair & Spengler 
    Ohio                           selected from cohort of 600   and prospective diary;        zip codes; school location;  1989a,b
                                   children; stratified by       10-15-min resolution; time    housing unit characteristics
                                   cooking fuel; data on gender, of year; day of week; 
                                   age, parental education       time of day.

    Topeka,         NO2, RSP       300 winter, 300 summer;       1988; retrospective, actual   11 microenvironment; home    Adair & Spengler, 
    Kansas,                        selected from cohort of       and prospective               zip codes; school location;  1989a,b
    USA                            600 children; stratified by   questionnaires; 10-15-min     housing unit characteristics
                                   cooking fuel; data on gender, resolution; time of year; 
                                   age, parental education       day of week; time of day


    Table 15.  (continued)

    Location        Pollutant      Participant characteristics   Survey characteristics        Spatial and source           Reference

    California,     VOCs           188 people; representative;   February-March 1984;          Activities >1 h;             Wallace et. al., 
    USA                            oversampled high-exposure     follow-up: 52 in May-June     high-exposure activities     1988, 1991a,b
                                   occupation; data on age,      1984 and 51 in Feb and        (e.g., smokers, occupations, 
                                   gender, race, socio-economic  March 1987; 24-h recall       travel); proximity to 
                                   status and proximity to VOC   diary; activity               industry; housing unit 
                                   sources                       questionnaire                 characteristics

    Boston,         NO2            325 (winter), 298 (summer)    1986; diary and               6 microenvironments; range   Ryan et al., 
    Massachusetts,                 ages 8 and above;             questionnaire;                on; near roads; combustion;  1990
    USA                            representative; stratified    15-30-min resolution; 2-day   outside home; home location; 
                                   by range type; no personal    sample; time of year; day     housing unit characteristics
                                   data                          of week; time of day

    Los Angeles     NO2            620 people ages 8 and above   May 1987-May 1988; diary,     17 microenvironments:        Spengler et al., 
    and Orange                     sampled two 24-h periods; 65  questionnaire; 15-min         including near roads; home   1994; Schwab et 
    Counties;                      sampled eight cycles;         resolution; two-day sample;   zip codes; work zip codes;   al., 1990
                                   representative; data on age,  time of year; day of week;    climate region; housing 
                                   gender, work status           time of day                   unit characteristics

    Albuquerque,    NO2            1000+ infants; stratified     January 1988-December 1991;   Room in house; outside       Samet et al., 
    New Mexico,                    by range type; data on        every 2 months for the        of house (including travel); 1992
    USA                            child's health and parents'   first 18 months of life;      range use; housing unit 
                                   socio-economic and            60 min; time of year;         characteristics
                                   demographic characteristics   day of week; time of day

    reporting a smoker present in the home was associated with increased
    indoor concentrations and personal exposures of aromatic compounds;
    visiting dry cleaners, self-reports of proximity to smokers, pesticide
    use, exposure to solvent, degreasing compounds, and odorous chemicals,
    and employment status in certain occupations (e.g., paint, chemical or
    plastics plants) were associated with increased personal exposure to
    several VOCs (Wallace et al., 1985, 1986, 1988). Occupational exposure
    may be an important component of total exposure for some individuals
    or sub-populations.

    2.   Time-activity data allow modelling of human exposure to
    pollutants for which personal monitors are not yet available or are
    very expensive, or for which exposure is a function of multiple
    pathways. Total exposure can be simulated from information on the time
    spent doing various activities and/or in specific locations, coupled
    with knowledge about the likely range of pollutant concentrations in
    each situation. For example, the models SHAPE (Ott et al., 1988), NEM
    (Johnson et al., 1990), SIMSYS (Sexton & Ryan, 1988), and REHEX (Hall
    et al., 1992) are currently being used to estimate exposure to carbon
    monoxide, ozone, particulates, sulfur dioxide and nitrogen dioxide.
    Techniques are also being developed to allow prediction of dermal and
    ingestion exposures based on assumptions about human activity patterns
    (e.g., Fenske, 1993). The usefulness of all of these models is
    dependent upon the accurate characterization of pollutant-relevant
    time-activity patterns.

    3.   From an epidemiological perspective, activity patterns can be
    used to assess the relationship between exposure and health status
    (e.g., Armstrong, 1985). For instance, "Do those who engage in
    potentially high-exposure activities experience more frequent or
    severe illnesses?" or "Do sensitive individuals avoid potentially
    high-exposure activities or limit them to certain times of day or
    locations?" In epidemiology, time-activity data may serve four

    *  They may be a surrogate of the exposure of interest. For example,
       people may be asked about the hours they spend indoors with smokers
       to assess health effects of environmental tobacco smoke
       (Leuenberger et al., 1994).

    *  They may be used to improve another imperfect measure of exposure.
       For example, estimates of long-term exposure to ozone may be
       derived from fixed site monitor data, weighted, however, for
       duration of time spent in outdoor activities (Künzli et al.,

    *  They may be used as a surrogate for a cofactor which might confound
       the association between health and some other exposure. For
       example, the effect of ambient air pollution on lung function may
       be thought to be confounded by environmental tobacco smoke exposure
       (ETS). Time spent with smokers could thus be used to control this
       potential confounding.

    *  The association of an exposure with some health outcome might not
       be the same in subgroups of different time-activity patterns
       (modified effect). In this case, time-activity data will allow the
       investigator to address such interactions.

    4.   Another purpose of time-activity data is to describe patterns of
    population behaviour. The proportion of time spent by the population
    in various microenvironments or frequency of use of various facilities
    (e.g., swimming pools) may provide an indication for the contribution
    of each of the microenvironments or activities on total population
    exposure. In such studies, the emphasis is on characteristics of
    groups, and not on individual data. Therefore the precision of the
    estimates may be improved by the increased sample size although the
    survey tools may remain relatively simple and inexpensive.

         An understanding of the frequency and duration of the activities
    in which the target population engages can be used to set priorities
    for public health strategies designed to reduce exposure by limiting
    contact with contaminated media. Comprehensive exposure factor data
    for the US population may be found in AIHC (1994) and US EPA (1996a).
    Although this information is focused on the USA it may serve as a
    useful model to follow in other countries.

    5.2  Methods

    5.2.1  Activity pattern concepts

         Activity pattern data that may be useful in assessing exposure
    can be divided into three categories:

    *  the distribution of time among activities, referred to in this
       document as  time allocation parameters 

    *  the factors that influence the degree of media contamination in the
       activities or locations of interest, referred to in this document
       as  microenvironmental parameters 

    *  the  intensity of contact while engaging in each activity.  Time allocation parameters

         Time allocation parameters include the amount of time spent in a
    given activity, the time of day, week and year of contact, and the
    expected frequency with which the person or population engages in the
    activity. The relevant spatial resolution for describing time-use
    patterns, thus grouping activities for exposure assessment, depends
    upon the characteristics of the pollutant, the media, the location and
    the emission source(s).

         The concept of microenvironment has been used to define an area
    across which the concentration of an air pollutant is assumed to be
    homogeneous (Duan, 1982). The most basic division of microenvironments
    is whether a person is indoors or outdoors, although more refinement
    is necessary for many exposure assessments. Time spent indoors is
    especially important with regard to pollutants which depend on indoor
    sources. Other typical microenvironments of interest in studying air
    pollution are home, work or school, and modes of transportation.

         Depending on the characteristics of the media and the pollutant,
    a description of the actual activity may also be required to
    understand exposure. General activity categories such as "socializing"
    and "recreation" are less important than knowing whether the
    participant is involved in specific activities that lead to contact
    with environmental media in addition to or other than air. For
    instance, swimming leads to water contact, and farming and gardening
    lead to soil contact.  Microenvironment parameters

         The distinction between people's activities and the pollutant
    concentration in a microenvironment is not always clear. The use of
    household appliances and consumer products that emit environmental
    contaminants and/or influence pollutant fate and transport affect
    microenvironmental concentrations. Thus, information on the
    microenvironmental parameters, i.e., the factors affecting the
    concentration in a given location, have also been included under the
    rubric of time-activity data. Important microenvironmental parameters
    for air pollution exposure assessment include building structure and
    household characteristics (e.g., the type of heating and cooking fuel
    used, the presence of parking garages and air conditioning units),
    information on proximity to specific sources (e.g., heavy traffic,
    cigarette smoking, cooking, solvent, pesticides), timing of emissions
    for each source, indoor/outdoor air exchange rates and meteorological
    and topographic factors.  Intensity of contact

         In addition to time allocation measures and microenvironmental
    parameters, information on the intensity of contact is needed to
    assess exposure. Here the focus is on micro-level activities that
    affect the rate of contact with the contaminated media while the
    person is in a certain microenvironment (e.g., outdoors at home) and
    performing a specific activity (e.g., cleaning). The potential for
    dermal contact depends upon the surface area of exposed skin, thus
    clothing type and fabric consistency as well as the size of the
    person, whether the individual is sitting, crawling, kneeling or using
    their hands on the contaminated surface, or otherwise handling the
    contaminant. In addition, exposure for the given event depends upon
    the duration and frequency of each contact between the exposed skin
    and the contaminated media; e.g., 50 1-min contacts between the
    person's hand and the floor while cleaning. As described in Chapter 7,
    dietary factors, including the type of foods that are consumed and the

    amount consumed per time period of interest, are the most obvious.
    Concern also has been raised about the potential for contamination of
    foods from contact with surfaces during storage, preparation and
    consumption (Berry, 1992, Freeman et al., 1997). Hand-mouth and
    object-mouth contact, although difficult to measure, may be one of the
    most important routes of exposure to contaminants such as pesticides
    and lead that reside in house dust, especially in children (Charney et
    al., 1980; Rabinowitz & Bellinger, 1988; Davies et al., 1990). For
    pollutants for which inhalation is the primary route of exposure, the
    intensity of contact is influenced by one's level of exertion (often
    referred to as "activity level"). Breathing rate or heart rate is
    needed to predict dose (the amount of contaminant that enters the
    body), thereby producing a more accurate estimate of the resulting
    health effects.

         Finally, depending on the purpose of the exposure assessment, the
    required temporal resolution of the time-activity data may vary
    substantially. Whereas short-term time-activity patterns may be
    important for acute exposures, long-term average time-activity
    patterns may be more relevant in other circumstances. If long-term
    exposure is of major interest, e.g., over years or lifetime,
    residential history is an important information to assign respective
    ambient monitor data for the entire period of interest (Künzli et al.,

    5.2.2  Surrogates of time-activity patterns

         For many exposures surrogates of time-activity patterns may be
    developed on the basis of generalizations about the activities of
    people at a particular time, who live in a specific geographic
    location or who share a specific set of living conditions. Usually the
    most important time-activity surrogate is age group. Some activities
    that are useful for predicting exposure to air pollutants, such as
    distance and timing of travel or duration of work and its locations,
    also show systematic differences in their frequency and duration by
    demographic characteristics. For instance, Schwab et al. (1990)
    documents how time in the kitchen, which influences exposure to
    combustion products, is greater among women in the USA than among men,
    even after adjusting for whether the woman works outside the home;
    likewise, men spend more time in transit, regardless of their age or
    employment status. It is likely that the frequency of contact with a
    wide variety of toxins differs across groups defined by gender and
    age, owing to traditional divisions of labour in many cultures.

         Similarly, information about an individual's health condition may
    be important in characterizing their time-activity pattern. For
    instance, the limited data available on asthmatics suggests they may
    spend more time indoors than the general population (Goldstein et al.,
    1986, 1988; Lichtenstein et al., 1989; Schwab et al., 1991). As
    asthmatics are particularly sensitive to air pollutants, this activity
    information is important.

         Socioeconomic status may influence time-activity patterns related
    to, for instance, time spent travelling to work or outdoors.
    Currently, however, the gap existing in time-activity databases with
    respect to the activity patterns of sensitive (e.g., elderly) and
    potentially high-risk (e.g., low socioeconomic status) subgroups, is a
    limitation for extension of exposure models to these groups. Further
    study is needed to determine the extent to which income, education and
    occupation are reliable surrogates for exposure-related factors (e.g.,
    housing unit size and condition).

    5.2.3  Data collection methods

         Sociologists pioneered studies of activity patterns (Szalai,
    1972; Chapin, 1974; Robinson, 1977). These "time budget"
    investigations, which have been conducted in several nations,
    emphasize the purpose of activities (cooking, eating, TV watching).
    Ott (1989) summarizes such studies in relation to their usefulness to
    exposure assessment; a basic drawback for exposure assessment
    applications is the lack of information on location, particularly
    distinguishing whether the participant was indoors or outdoors. In the
    1960s and 1970s, a series of time-activity studies was conducted by
    geographers interested in the influence of the economic and physical
    structure of cities on travel patterns, e.g., journey to work (Hanson
    & Hanson, 1981), access to facilities (Fox, 1983) or shopping
    behaviour (Douglas, 1973). As such, the emphasis was on collecting
    information on the geographic location of trip origins and
    destinations as well as timing and mode use. Finally, the US
    Department of Transportation, in conjunction with the Census Bureau,
    has been collecting information on the travel activities (durations
    and mode use) of a representative national sample approximately every
    7 years since 1969 (US Federal Highway Administration, 1986, 1992).

         A variety of methods are available for collecting data about
    time-activity patterns, including interviewer-administered recall
    questionnaires, self-administered real-time diaries, direct
    observation and video recording. The diary techniques used in the
    social sciences for eliciting time-activity data have been applied to
    studies of total human exposure to air pollutants (see methodological
    reviews by Robinson (1988), Ott (1989), Quackenboss & Lebowitz
    (1989)). Specifically, participants are asked to complete a diary or
    questionnaire regarding their activities during the designated period
    (usually 12-48 h). The survey instruments used in these exposure
    studies, however, depart from any single type used previously. Rather
    than focusing on activity purposes or transportation exclusively, the
    instruments used in exposure studies probe for changes in location or
    activity that might lead to changes in the level of pollution to which
    the person came into contact.

         Time allocation measures for assessing exposure to air pollutants
    frequently have been collected using self-completed real-time diaries.
    Because this approach requests that participants record all activities
    over one or more 12-h or 24-h periods, it has the potential to provide
    the most comprehensive information on time allocation, sequencing, and

    frequency. Real-time diaries are particularly useful when it is
    important to know the time of day during which each activity was
    performed (e.g., the amount and location of exercise in the morning
    versus the afternoon when ozone levels tend to be higher).

         Two diary formats are common for collecting time-activity data:
    the open-ended style requires participants to describe their exact
    activity (see, for example, the instruments described in Akland et al.
    (1985), Johnson (1989), and Jenkins et al. (1992)), whereas the
    close-ended format (Fig. 18) involves simply checking the appropriate
    microenvironment for the given time of day (see, for example, the
    instruments used in EXPOLIS (Fig. 18) (Jantunen et al., 1998) or those
    described by Schwab et al. (1990) and Samet et al. (1992). Several
    researchers are developing electronic monitors to supplement diaries
    by detecting whether a participant is indoors or outdoors, a key
    parameter for assessing exposure to air pollutants (e.g., Hinton,
    1990; Moschandreas & Relwani, 1991; Waldman et al., 1991b).

    FIGURE 18

         Interviewer-administered questionnaires that ask participants to
    recall frequency and duration of time spent in specific activities
    during either the previous or typical day, month, year or age-period
    (i.e., usual activity patterns) also have been used to collect time
    allocation measures, microenvironmental parameters and exposure
    surrogates. Juster et al. (1985a) points out that data collected in
    this fashion are most accurate when the survey focuses on activities
    that are done frequently or on a routine basis (e.g., the daily
    commute to work). Questionnaires that take the form of checklists are
    also particularly useful when the researcher is only interested in
    certain well-defined activities. Questions to recall activity patterns
    over a long period may refer to defined age groups and/or to each
    residential location lived in (see Fig. 19). In environmental exposure
    studies, information on the proximity of the study participant to
    local contaminant sources is typically collected via questionnaires
    that ask whether or not the participant engaged in a certain activity.
    For instance, studies of VOC exposure have asked about use of
    household cleaners, visits to petrol stations and storage of gasoline
    products indoors (Wallace et al., 1987a,b). Questionnaires are also
    used to solicit information on housing unit characteristics (e.g.,
    type of cooking equipment or house volume) that influence
    concentrations indoors (Lebowitz et al., 1989). Surveys may request
    information on a variety of parameters that affect the concentration
    of combustion products to which an individual is exposed during
    travel, including traffic speed, time of day, mode of transportation,
    age of vehicle, trip timing and roadway used.

         Researchers have experimented with a variety of methods for
    collecting information on the intensity of contact. As described in
    Chapter 7, a number of approaches are used for quantifying food
    consumption rates. An inexpensive technique that has been used to link
    breathing and activity patterns is to have participants record the
    level of activity (e.g., high, medium, low) associated with each
    activity entry in the diary. This method has been used in several
    population-based studies (Johnson, 1989; Lichtenstein et al., 1989;
    Schwab et al., 1990; Wiley et al., 1991). Others have used
    questionnaires that request information about specific high-exertion
    activities such as exercising and working outdoors (Goldstein et al.,
    1986; Lebowitz et al., 1989). Categorical exertion-level data is not
    useful for calibrating activity pattern data, however, without an
    increased understanding of (1) the range of reported activities
    associated with each exertion level and (2) the range of breathing
    rates associated with each exertion level. A compendium on energy
    expenditure, which closely relates to ventilation rate, has been
    published for a variety of physical activities (Ainsworth et al.,
    1993). These data may be used to categorize activity data depending on
    levels of exertion (Künzli et al., 1997a,b). Data are becoming
    available through the application of electronic methods of tracking
    exertion levels; heart-rate and breathing-rate monitors have been used
    in the field studies by Raizenne & Spengler (1989), Shamoo et al.
    (1991) and Terblanche et al. (1991).

    FIGURE 19

         Standardized methods are not available for collecting information
    on hand-mouth contact. Several researchers (Charney et al., 1980;
    Brunekreef et al., 1983; Bellinger et al., 1986) have administered
    questionnaires to parents of toddler-age participants in order to
    qualitatively characterize the frequency with which children suck
    their fingers (i.e., usually, sometimes, never). Direct observation
    may be better suited to capturing micro-level activities, but such
    approaches have rarely been used in large-scale field studies owing to
    the expense of following more than a few participants and because of
    concerns that the observation process will lead to bias or alterations
    in typical patterns. Video techniques have now made it possible to
    record participant activities with less interference. Davies et al.
    (1990), for instance, used video methods to obtain data on the number
    of times 2 year olds put their hands and objects in their mouth while
    in standardized play situations. Zartarian et al. (1995) used
    videotape data to collect micro-level data on four young farm children
    at play inside their homes to quantify dermal and ingestion exposure
    to pesticides. As Zartarian et al. point out, however, researcher
    presence may still have influenced the participants' behaviour.
    Observation of children's hand-mouth contact also has been performed
    in clinical settings (e.g., Madden et al., 1980). All of these
    methods, however, share the limitation that they cannot quantify the
    full variability in factors that influence hand-mouth contact. Indeed,
    capturing this variability may not even be possible, as is discussed
    in a subsequent section of this chapter. In the absence of information
    on hand-mouth contact, several researchers have measured mineral
    levels in children's faeces to estimate typical soil ingestion rates
    (Binder et al., 1986; Calabrese et al., 1989, 1990). Such estimated
    ingestion rates can then be used to model exposure in areas with
    measured soil contamination levels.

         For dermal exposure, questionnaires are most appropriate for
    collecting categorical-type information, such as whether a person
    performed a certain activity during a designated activity. The US
    Environmental Protection Agency 1992 report entitled  Dermal 
     Exposure: Applications and Principles, reviews the literature
    regarding methods for estimating soil and water contact (US EPA,
    1992b). Hawley (1985) has used data from previous studies and
    professional judgement to develop assumptions for use in estimating
    outdoor soil contact time, but these estimates do not account for
    indoor exposure such as soil tracked into the house or for exposure to
    contaminants that reside primarily in indoor dust (e.g., pesticides)
    (US EPA, 1992b). The US EPA report cites Tarshis (1981) and James &
    Knuiman (1987) as sources of data on the frequency with which people
    shower and bathe. Few data are available on swimming (US EPA, 1992b)
    which could be important because of elevated chloroform concentrations
    found within air just above the pool-water surface, or other
    contaminants which can be swallowed or dermally absorbed from lakes or
    river waters.

         Linking activities with measurements of dermal exposure,
    researchers are testing innovative approaches to assessing skin
    contact with contaminated surfaces. For instance, Fenske et al.
    (1986a,b) applied non-toxic fluorescent tracers to lawns in lieu of
    insecticides; after participants engaged in a standard set of play
    activities, video imaging was used to ascertain the amount of tracer
    on the exposed skin. The degree of soil adherence to skin is a closely
    related issue and has been examined by several researchers (Driver et
    al., 1989; Finley et al., 1994a; Kissel et al., 1996).

    5.3  Potential limitations

         Time-activity data can enhance an understanding of sources and
    behaviours important in assessing exposures. Inferences can be drawn
    from simulations, case studies or even studies using large randomized
    designs. However, all users of time-activity data should be aware of
    its limitations for assessing human exposure to environmental

         The feasibility of collecting time-activity data is often limited
    by the burden which such studies impose on participating individuals.
    The data collection requires constant, or regular, attention to the
    fact that the subjects are participating in the study, that they have
    to remember about all activities and to fill in the diaries. This is
    often inconvenient and takes respondent's time. Collection of the data
    by an observer, which often is a method of choice in studies involving
    children, may be of limited feasibility owing to the restricted access
    of the observer to the subject under study and because typical
    activities may possibly be modified by the fact of being under

    5.3.1  Activity representativeness

         One of the uses of time-activity data is to allow
    characterization of the distribution of exposure for a given
    geographic, demographic or socioeconomic segment of the population.
    However, the study protocol may call for certain types of days or
    individuals to be excluded (e.g., travel that takes the participant
    away from the home for more than the 24-h or 48-h sampling period may
    lead to disqualification). Although standard techniques such as
    weighting and imputation can be used to treat non-response, these
    methods assume that refusal to participate is random and there is
    information about the non-respondents (Kalton & Kasprzyk, 1986). In
    the case of time-activity studies, however, once contacted, people may
    participate or not because of the variables that the study is designed
    to predict. As shown in the European multi-city study EXPOLIS, the
    subjects in Basel ready to participate had lower traffic density
    around their homes than non-participants (Oglesby, 1998). The
    potential for misrepresenting the exposure distribution must,
    therefore, be considered because there is no method for quantifying
    the direction and/or extent of the bias with respect to high-exposure

         The representativeness of the activity data collected may also be
    influenced by the increased burden imposed upon participants by
    exposure assessment studies. Epidemiologists and social scientists
    have found that participation rates and compliance with instructions
    may decline with increasing study periods, longer questionnaires, more
    complicated questions and more complex tasks. Whitmore (1988)
    speculated that the higher than average refusal rates experienced in
    air pollution exposure studies are related to the burden associated
    with carrying monitors and completing activity diaries. This has been
    shown in the European multi-city EXPOLIS study in Grenoble where
    participants had different time activity patterns in days with
    personal exposure monitors compared to days when only time-activity
    data was collected (Boudet et al., 1997).

    5.3.2  Validity and reliability

         Survey researchers in a number of fields have raised questions
    about the validity of data collected via self-administered surveys:
    i.e., is the instrument measuring what is intended (Laporte et al.,
    1985). Data validity is of particular importance when trying to link
    measured exposure with a given day's activity diary. The error
    introduced by an inaccurate diary affects both efforts to explain the
    contribution of certain activities to personal exposure and efforts to
    estimate the distribution of personal exposure from time-weighted
    microenvironmental measurements. The relationship between the degree
    of error in the diary and the degree of error in the predictive model
    depends upon the concentration in the microenvironment and the total
    time spent there. Neglecting to report even short-duration activities
    in high-concentration microenvironments will have more effect than
    underestimating a similar amount of time in a low-concentration
    microenvironment in which a large portion of the day is spent.

         Scientists who use activity pattern data have raised a variety of
    concerns about the effects of inadvertent and/or deliberate errors in
    reporting. For instance, activity diary data may be compromised by
    participants' misunderstanding of the definitions of various locations
    (microenvironments). Discussions with participants have revealed the
    potential for confusion about: How far is "far from home?" Is a
    "parking garage" inside or outside? Is "walking" a light- or
    medium-exertion activity? (Schwab et al., 1991, 1992).

         To a certain extent, the quality of the data can be controlled
    during data collection. Detailed instructions can improve participant
    compliance. Field and laboratory pretesting of the survey instrument
    and instructions, important components of the survey design process,
    can yield improvements in protocol and clearer definitions of survey
    terminology such as distinctions between microenvironmental categories
    (Bercini, 1992). Extensive training of participants in keeping the
    diary can be expensive, but detailed reference sheets and one-on-one
    sessions can greatly improve data quality. One of the more
    time-consuming but necessary steps is reviewing the returned diaries
    for temporal completeness and clarity of responses. Ideally, this
    should be done in the presence of the participant, and within 24 h of

    completion of the monitoring period. Another quality assurance step
    involves the use of a uniform system to code information on individual
    activities into microenvironmental categories.

         The validity and reliability of the diary data may be increased
    by the use of study forms that are simple and easy to understand. The
    language of the questions and instructions must be simple and the
    method of selection of answers, or of filling in data, obvious to
    minimize coding errors. The number of items on the questionnaire
    should be kept to a necessary minimum. Only the information for which
    there is clear use in analysis and data interpretation and which
    serves directly the study objectives should be included in the diary

         Verifying the validity of time-activity data is extremely
    difficult, if not impossible, because an absolute standard does not
    exist. Several researchers have sought to assess the reliability of
    self-reported data through test-retest procedures and by comparing
    different methods of collecting the same type of information (Laporte
    et al., 1985). The University of California at Berkeley ozone study
    required college students to recall time spent in physical activities
    outdoors, over years. The information was used as a surrogate to
    improve long-term ozone exposure assignment in an epidemiological
    study (Künzli et al., 1997b). The test-retest study revealed rather
    high correlations for time spent in heavy ( r = 0.81) or moderate
    ( r = 0.61) activity (Künzli et al., 1997b). This level of
    concordance is similar to that observed in dietary intake validation
    studies where food-frequency questionnaires and diet records
    correlated in the order of  r = 0.6 for the intake of a variety of
    nutrients (Rimm et al., 1992). Robinson (1985) found that a variety of
    methods for collecting time-activity data, including 24-h recall
    surveys, same-day diaries, records of the activities during 40
    randomly selected moments throughout the day (signalled using a
    beeper), and recall of the activities during a randomly chosen hour
    yielded essentially similar sample distributions of time the sample
    spent in a variety of activities. Quackenboss et al. (1986) also found
    consistency between diaries and the responses to self-administered
    recall questionnaires. Juster (1985b) found reasonably strong
    agreement in the reports of spouses regarding whether their partner
    was present at any given time throughout the day. Other comparisons of
    methods show that when asked about the usual time spent in selected
    activities, respondents tend to over-report time in unscheduled
    activities (relative to that recorded on their diaries), but reports
    are consistent for habitual activities such as commuting to work
    (Robinson, 1985). Waldman et al. (1991b) showed similar results when
    comparing activities recorded in electronic diaries with next-day
    recall; concordance between the methods was highest for routine,
    long-duration activities. Additional research, however, is necessary
    to determine the extent and direction of bias for the activities and
    the time frames of most concern in an exposure context (e.g., the
    frequency with which a person uses household cleaning products rather
    than the total time spent cleaning).

    5.3.3  Inter- and intra-person variability

         To be of use in exposure assessment, time-activity data must
    describe the aspects of human behaviour that influence the variability
    in pollutant concentrations contacted. There is both between- and
    within-individual variability in people's activities, which has
    implications for the use of time-activity data in exposure assessment.

         At one end of the spectrum are aspects of human activity patterns
    that tend to be highly regular. For instance, many people tend to
    follow daily routines with respect to how long they sleep and the time
    they depart for work. In addition, because basic routines are fairly
    uniform across individuals, diary data from several studies has shown
    that the distribution of time reported in the microenvironments that
    comprise the majority of the day (i.e., inside at home and inside at
    work/school) exhibit relatively little variation from year to year
    within a given study population or from place to place within the USA
    (Robinson, 1985; Schwab et al., 1990).

         The only large time-activity study done in conjunction with a
    continuous monitoring device was the Denver/Washington, DC study of CO
    exposures (Akland et al., 1985); this study yielded time-weighted
    concentrations in specified microenvironments. Analyses of these
    results suggest that variations in activities or locational attributes
    (e.g., variations in source strength) that are finer than those
    captured by these simple microenvironments explain much of the
    variability in exposure. Although less variability in the
    concentrations of some other air pollutants may be expected, these
    results confirm the concerns raised above regarding the ability to
    predict variations in exposure from the time allocation measures
    typically collected in diary-type studies.

         At the other end of the spectrum with respect to consistency in
    activity patterns are aspects of human behaviour that influence the
    intensity of contact with contaminated media. By their nature, these
    activities are highly variable both across individuals and across time
    for a given person. First, physical and demographic characteristics
    influence the frequency and duration of activities. For instance, in
    the case of dermal exposure it may be hypothesized that contamination
    from lying on a surface (e.g., a lawn to which a weedkiller has
    recently been applied) will be greater for a heavy person than a
    lighter person. Similarly, a child's standing and sitting height, in
    addition to crawling activities, mean that its breathing zone is much
    closer to the floor than that of an adult, raising the possibility of
    dust inhalation. Children also choose play locations that typical
    monitoring studies might ignore, such as stairwells and corners.

    5.4  Summary

         Information on people's activity patterns can be used to identify
    the determinants of measured exposures, predict unmeasured or
    unmeasurable exposures, assess relationships between exposure and
    health status, and identify high risk exposure situations that may be

    amenable to public health actions. Some of the main activity patterns
    important for assessing exposures by various media that were discussed
    in this chapter are summarized in Table 16.

         The relative cost of field sampling and laboratory analysis for
    environmental and biological measurements highlights the potential
    value of time-activity data. Assessments of long-term activity
    patterns (e.g., lifetime) may only be feasible using time-activity
    questionnaires. Various methods are used to collect information about
    human activities, including diaries and questionnaires, mechanical
    devices, and observation. Methods have only recently begun to be
    developed for assessing the role of time-activity patterns on dietary
    and non-dietary ingestion and dermal exposure pathways. Concerns about
    the ability of data collection methods to ensure activity
    representativeness and data validity and about the implications of
    inter- and intra-person variability in behaviour place limits on the
    application of time-activity data for human exposure assessment.
    However, with appropriate quality assurance programmes, information on
    time use and activity patterns can be very valuable for interpreting
    and modelling exposures.

    Table 16.  Type of information obtained from time-activity data 
               relevant to specific exposure pathways


    Personal air
                        time and location spent outdoors
                        type of indoor location
                        use of sources
                     In the presence of sources:
                        ventilation and filtration of indoor location
                        quantity of water consumed direct and indirectly
                        accidental ingestion from swimming (pools, rivers, 
                        dermal contact, time in showering/bathing
                        hand/body washing frequency

                        amount and type of food products consumed
                        preparation methods including cleaning
                        preparation location (e.g., street vendors)
                        storage practices

                        amount of contact time and type of soil 
                        (e.g., farm, garden/possible pesticide 
                        skin surface contact
                        frequency and duration of washing since contact


    6.1  Introduction

         An exposure model is a logical or empirical construct which
    allows estimation of individual or population exposure parameters from
    available input data. Such data may be measured or collected for this
    purpose, or obtained from other sources. Technological, logistic and
    financial constraints can make it difficult to monitor the exposure of
    humans to the various environmental agents. It is, therefore, prudent
    in many situations to use models to assess contaminant exposures.
    Models provide an analytic structure for combining data of different
    types collected from disparate studies in a manner that may make more
    complete use of the existing information on a particular contaminant
    than is possible from direct study methods (EC, 1997b). Exposure
    models, if supported by adequate observations, can be used to estimate
    group exposures (e.g., a population mean) or individual exposures
    (e.g., the distribution of exposures among members of a population).
    Model results also can be used to evaluate exposures at various points
    of population distributions which cannot be measured directly because
    of limitations of methods or resources (e.g., the upper 5% of
    exposures for a population). This chapter introduces the principal
    aspects of exposure modelling, including those for single and multiple
    environmental media. In addition, the concepts of variability,
    uncertainty and model validation are discussed.

    6.2  General types of exposure model

         Exposure models can be divided into three broad categories;
    statistical, deterministic and practical or combinations of
    statistical and deterministic models (Fig. 20). Statistical (often
    regression) models are in their simplest form numerical best fits
    between collected exposure measurements and potentially related
    factors (e.g., demographics). In statistical models, the magnitude and
    direction of association between the variables are inferred from the
    observations themselves. Such models cannot be considered reliable for
    predicting exposures outside the original study population and
    environmental setting without first validating them for that specific
    purpose. Deterministic (or physical) models are based on a logical
    expression of the physical environment and human behaviour in it. Such
    models need to be validated by actual exposure data, and can in
    principle be used for exposure prediction of new populations and
    settings. Although deterministic models can be useful for estimating
    mean population exposure, input data to estimate the distribution of
    exposure within a population are often not available. Probabilistic
    exposure models (section 6.6.3) are normally based on deterministic
    models, but because they incorporate the measured or estimated
    distributions of the input variables, they produce more realistic
    population exposure distributions than deterministic models. Practical
    models can combine features from these different types, e.g., a
    statistical model may include parts of a logical construct. Several
    important types of statistical models are discussed in Chapter 4, and
    deterministic and practical models are discussed here.

    FIGURE 20

         Using a deterministic model for a given contaminant, exposure
    concentration is estimated as a concentration averaged over a given
    period of time (see Eq. 3.1, p. 46).

         For the inhalation and dermal exposure routes, concentrations in
    the different microenvironments occupied by a person are integrated
    over time. The integrated time period is usually 24 h, 1 year or a
    lifetime of 70 years, although any time period may be used. The
    concept of microenvironment is often unnecessary for the ingestion
    route. In this case, the concentration of contaminants in the food
    consumed and the amount of food and beverages consumed during a given
    period of time are sufficient to determine exposure.

    6.3  Environmental media and exposure media

         In exposure analysis, we use human exposure assessments to
    translate contaminant levels in environmental media into quantitative
    estimates of the amount of contaminant that comes in contact with the
    human-environment boundaries, that is, the lungs, the gastrointestinal
    tract and the skin surface of individuals within a specified
    population. Environmental media of principal relevance to human
    exposure include air, ground-surface soil, root-zone soil, plants,
    groundwater and surface water in the contaminated landscape. As
    described in Chapter 2, exposure pathways define a link between an
    environmental medium and an exposure medium. Important exposure media
    include outdoor air, indoor air, food (commercial and homegrown),
    exterior soil, interior soil or household dust, and drinking and
    cooking water. Exposure then occurs by contact with contaminants in
    these exposure media via inhalation, ingestion and dermal uptake. Fig.
    21 illustrates the types of exposure pathways we use to carry out a
    multiple-media, multiple-route, multiple-pathway exposure assessment.

         Exposure assessments often rely implicitly on the assumption that
    exposure can be linked by simple parameters to ambient concentrations
    in air, water and soil. However, total exposure assessments that
    include time-activity patterns and microenvironmental data reveal that
    an exposure assessment is most valuable when it provides a
    comprehensive view of exposure routes and pathways and identifies
    major sources of uncertainty. Listed in Table 17 are potential
    interactions among environmental media, exposure media and exposure
    pathways that are addressed in this chapter.

         An assessment of intake requires that we determine how much
    crosses these boundaries. Thus, we see the need to address many types
    of "multiples" in the quantification of human exposure, such as
    multiple media (air, water, soil); multiple exposure pathways (or
    scenarios); multiple routes (inhalation, ingestion, dermal); multiple
    chemicals; multiple population subgroups; and multiple health
    end-points. The matter is further complicated by the fact that
    pollutants may have both systemic and route specific health effects.
    For the compounds that have mainly systemic effects the total exposure
    -- sum of all routes -- is most relevant; for other agents such as
    pneumococci aerosols in the lung, dermal vs. ingestion absorption of

    FIGURE 21

        Table 17.  Interactions among environmental media, exposure media and exposure pathways

    Exposure routes                                              Media

                       Air                                Soil                                  Water
                       (gases and particles)              (ground-surface soil,                 (surface water and groundwater)
                                                          root-zone soil)

    Inhalation         gases and particles in             soil vapours that migrate to          contaminants transferred from 
                       outdoor air                        indoor air                            tap water

                       gases and particles                soil particles transferred to 
                       transferred from outdoor air       indoor air
                       to indoor air

    Ingestion          fruits, vegetables, and grains     soil                                  tap water
                       contaminated by transfer of 
                       atmospheric chemicals to plant     fruits, vegetables, and grains        irrigated fruits, vegetables, and 
                       tissues                            contaminated by transfer from soil    grains

                       meat, milk, and eggs               meat, milk, and eggs contaminated     meat, milk, and eggs from animals 
                       contaminated by transfer of        by transfer from soil to plants       consuming contaminated water
                       contaminants from air to plants    to animals
                       to animals

                       meat, milk and eggs contaminated   meat, milk, and eggs contaminated     fish and sea food
                       through inhalation by animals      through soil ingestion by animals

                       mother's milk                      mother's milk                         mother's milk

    Dermal contact     (not included)                     soil                                  baths and showers
                                                                                                swimming, etc.

    solvents which are rapidly metabolized in the liver, or fine
    particulate matter in the ambient air, the route of exposure is
    crucial, and total exposure as a sum of all exposure routes may be
    meaningless. Multiple media exposure models are discussed in section

    6.4  Single-medium models

         Most of the transport models that have been developed for
    describing the behaviour of contaminants in the environment have dealt
    with specific environmental media, such as indoor and outdoor air,
    surface water and sediments, groundwater and soils. These
    single-medium models operate at various levels of spatial and temporal
    detail, depending on the particular conditions being assessed. The
    following discussion will highlight some of the more commonly used
    methods for characterizing contaminant transport in environmental
    media. Additional information on transport modelling for use in
    exposure assessments can be found in Masters (1991).

    6.4.1  Outdoor and indoor air

         Substances in outdoor air are transported from sources to
    receptors by atmospheric advection and dispersion. In general,
    pollutant concentrations in outdoor air are directly proportional to
    emission strength and inversely proportional to dispersion. The
    physical relationship, e.g., lateral and vertical distance, between
    sources and receptors is also an important factor. Meteorological
    parameters have an overwhelming influence on the dispersion of
    contaminants in the lower atmosphere. Among them, wind parameters
    (direction, velocity, and turbulence) and thermal properties
    (stability) are the most important. A number of models are available
    for estimation of ambient concentrations of pollutants. Most of them
    are founded on the Gaussian air dispersion model, an introduction to
    which may be found in Wilson & Spengler (1996). Two of the seminal
    works in this field are Pasquill (1961) and Gifford & Hanna (1973).

         Another area of air quality models focuses on determining the
    sources of pollutants in outdoor air. As discussed in Chapter 2,
    information on sources of exposure is important for evaluating
    alternative strategies for managing risk. These models are commonly
    used for apportioning concentrations of airborne particulate matter
    among its various sources (e.g., coal-fired power plants,
    gasoline-powered vehicles and diesel-powered vehicles). In such source
    apportionment models, profiles of element concentrations in
    particulate matter emitted from different sources are combined with
    sophisticated statistical methods (e.g., principal component or factor
    analysis) to estimate the relative abundance of particles from each
    source type. Glover et al. (1991) and Daisey et al. (1986) provide a
    good introduction to source apportionment models for particulate
    matter, while Edgerton & Shah (1991) describe a source apportionment
    model for VOCs.

         Several approaches have been used to estimate expected indoor air
    pollution concentrations (for reviews see Cooke, 1991; WHO, 1997b).
    These include deterministic models based on a pollutant mass balance
    around a particular indoor air volume; a variety of empirical
    approaches based on statistical evaluation of test data and (usually)
    a least squares regression analysis; or a combination of both
    approaches, empirically fitting the parameters of a deterministic
    model with values statistically derived from experimental measurements
    (see Chapter 4). All three approaches have advantages and weaknesses.
    The deterministic model provides more generality in its application,
    but the results lack accuracy and precision. Deterministic models
    include single- and multiple-compartment models. The empirical models,
    when applied within the range of measured conditions for which they
    were fitted, provide more accurate information. An example of an
    empirical model for indoor concentrations of respirable particulate
    matter may be found in Chapter 12. Often the compartment of the indoor
    air mass balance models that is most difficult to represent is the
    role of indoor surfaces as sources or sinks for contaminants. This is
    an important field of inquiry with respect to inhalation exposures to
    ozone and VOCs (Reiss et al., 1995).

    6.4.2  Potable water

         Exposure to contaminants in water may occur via the ingestion,
    dermal absorption and inhalation routes. Ingestion of water primarily
    occurs via two pathways: direct ingestion via drinking or cooking and
    intrinsic water intake (i.e., the water intrinsic in foods prior to
    preparation). It is important to consider both routes. Drinking-water
    ingestion rates have also been shown to vary according to cultural
    differences and can be an important source of uncertainty about
    chemical exposure when extrapolating results of epidemiological
    studies from one culture to another (e.g., Mushak & Crocetti, 1995).
    Lognormal distributions of drinking-water ingestion rates for
    individuals comprising various age groups in the USA (Table 18) are
    available in the literature (Roseberry & Burmaster, 1992). Additional
    information on drinking and cooking water as exposure media may be
    found in Chapter 7.

         Dermal absorption of contaminants in residential water sources
    may occur during bathing and other forms of washing or cleaning. There
    are three principal mechanisms by which molecules can transverse the
    skin and enter the body: passive transfer or diffusion, facilitated
    diffusion and active transport. Passive diffusion is the mechanism
    most commonly expressed in dermal exposure models. The rate of passive
    diffusion is a function of the concentration gradient of the
    contaminant on the surface of the skin and in the tissue immediately
    below the skin and the ease with which a molecule of the contaminant
    can move through the lipophilic interior of the skin membrane. Ease of
    passage is a function of the partition coefficient of the contaminant
    (e.g., the octanol-water partition coefficient,  Kow), molecular
    size, the degree of ionization and the porosity of the skin. Porosity
    of the skin to VOCs present in drinking-water treated with chlorine
    has been shown to be temperature dependent (Gordon et al., 1998).

    Table 18.  Lognormal distributions of water intake by age group in 
               the USA. Source: Roseberry & Burmaster (1992)

    Age group      Geometric mean (ml/day)    Geometric standard deviation
    Drinking and cooking water intake
    < 1 year                267                         1.85
    2-11                    620                         1.65
    12-20                   786                         1.72
    21-65                  1122                         1.63
    > 65                   1198                         1.62

    Total water intake (direct + intrinsic)
    < 1 year               1074                         1.34
    2-11                   1316                         1.40
    12-20                  1790                         1.41
    21-65                  1926                         1.49
    > 65                   1965                         1.50

         Inhalation exposures to VOCs transferred from water to air could
    be as great as, or even greater than, exposures from ingestion.
    Inhalation pathways include contaminants transferred to the air from
    showers, baths, toilets, dishwashers, washing machines and cooking.
    Several models have been proposed to explain the mass-transfer
    process; in particular, a time-dependent, three-compartment model for
    residential exposure (McKone et al., 1987). The three compartments
    used in such a model are the shower/bath stall, the bathroom and the
    remaining residential volume. Factors that affect the projected
    exposure are chemical mass-transfer rates from water to air,
    compartment volumes, air-exchange rates and human occupancy factors.

    6.4.3  Surface waters

         The transport of contaminants in surface waters is determined by
    two factors: the rate of physical transport in the water system and
    the chemical reactivity. Physical transport processes are dependent to
    a large extent on the type of water body under consideration (e.g.,
    oceans, seas, estuaries, lakes, rivers or wetlands). Schnoor (1981)
    and Schnoor & MacAvoy (1981) have summarized important issues related
    to surface water transport. At low concentrations, contaminants in
    natural waters exist in both dissolved and sorbed phases. In rapidly
    moving water systems, advection controls mass transport and dissolved
    substances move at essentially the same velocity as the bulk of the
    water in the water system. Contaminants that are sorbed to colloidal
    materials and fine suspended solids can also be entrained in the
    current, but they may undergo additional transport processes that
    increase their effective residence time in surface waters. Such
    processes include sedimentation, deposition, scour and resuspension.
    Thus, determining the transport of contaminants in surface waters
    requires that we follow both water movement and sediment movement.

         A water balance is the first step in assessing surface water
    transport. A water balance is established by equating gains and losses
    in a water system with storage. Water can be stored within estuaries,
    lakes, rivers and wetlands by change in elevation or stage. Water
    gains include inflows (both runoff and stream input) and direct
    precipitation. Water losses include outflows and evaporation.

    6.4.4  Groundwater

         In groundwater, the dilution of contaminants occurs much more
    slowly than it does in surface water. After precipitation, water
    infiltrates the ground surface where it travels vertically down
    through the unsaturated zone until it contacts the water table, and
    then flows approximately horizontally. This horizontal movement is
    driven by the hydraulic gradient, which is the difference in hydraulic
    head at two points divided by the distance (along the flow path)
    between the points. Bear & Verruijt (1987) and Freeze & Cherry (1979)
    have compiled extensive reviews on the theory and modelling of
    groundwater flow and on transport of contaminants in groundwater. The
    movement of contaminants in groundwater is described by two principal
    mechanisms: gross fluid movement (advective flow), and dispersion.
    Dispersion depends on both fluid mixing and molecular diffusion. The
    transport of many chemical species in groundwater is often slowed or
    "retarded" relative to the flow of the bulk fluid by sorption of the
    contaminant material to soil particles or rock. As is pointed out by
    Bear & Verruijt (1987), many groundwater models are available for
    assessing the transport of contaminants in the subsurface environment,
    ranging from simple one-dimensional hand calculations to large
    three-dimensional computer programmes. The choice of an appropriate
    model for any situation depends to a large extent on the information
    available, the type of information needed to carry out an exposure
    assessment and the tolerance of the analyst for large, complex
    computer programmes.

    6.4.5  Soil

         Soil, the thin outer zone of the earth's crust that supports
    rooted plants, is the product of climate and living organisms acting
    on rock. A true soil is a mixture of air, water, mineral and organic
    components (Horne, 1978). The relative mix of these components
    determines to a large extent how a chemical will be transported and/or
    transformed within the soil. The movement of water and contaminants in
    soil is typically vertical as compared to horizontal transport in the
    groundwater (i.e., saturated) zone. A chemical contaminant in soil is
    partitioned between soil water, soil solids, and soil air. For
    example, the rate of volatilization of an organic compound from the
    soil solids or from soil water depends on the partitioning of the
    compound into the soil air and on the porosity and permeability of the

         Models developed for assessing the behaviour of contaminants in
    soil can be categorized in terms of the transport/transformation
    processes being modelled. Partition models such as the fugacity models
    of Mackay (1979) and Mackay & Paterson (1981, 1982) describe the
    distribution of a contaminant among the liquid, solid and water phases
    of soils. Jury et al. (1983) have developed an analytical screening
    model that can be used to calculate the extent to which contaminants
    buried in soil evaporate to the atmosphere. The multiple-media model
    GEOTOX (McKone & Layton, 1986) has been used to determine the
    inventory of chemical elements and organic compounds in soil layers
    following various contamination events. This model addresses
    volatilization to atmosphere, runoff to surface water, and leaching to
    groundwater and first-order chemical transformation processes.

    6.5  Multiple-media modelling

         Human beings come directly into contact with certain media via
    certain routes and are exposed to the chemicals therein as depicted in
    Table 19. Efforts to assess human exposure from multiple media date
    back to the 1950s when the need to assess human exposure to global
    fallout led rapidly to a framework that included transport both
    through and among air, soil, surface, water, vegetation and food
    chains (Whicker & Kirchner, 1987). Efforts to apply such a framework
    to non-radioactive organic and inorganic toxic chemicals have been
    more recent and have not as yet achieved such a high level of
    sophistication. In response to the need for multiple-media models in
    exposure assessment, a number of transport and transformation models
    have recently appeared. In an early book on multiple-media transport,
    Thibodeaux (1996) proposed the term "chemodynamics" to describe a set
    of integrated methods for assessing the cross-media transfers of
    organic chemicals. The first widely used multiple-media compartment
    models for organic chemicals were the fugacity models proposed by
    Mackay (1979, 1991) and Mackay & Paterson (1981, 1982). Cohen and his
    co-workers introduced the concept of the multiple-media compartment
    model and more recently the spatial multiple-media compartment model,
    which allows for non-uniformity in some compartments (Cohen & Ryan,
    1985, Cohen et al., 1990). Another multiple-media screening model,
    called GEOTOX (McKone & Layton, 1986; McKone et al., 1987), was one of
    the earliest to explicitly address human exposure.

         The preceding models deal with inter-media transfer of
    contaminants on a relatively large scale, but other models are scaled
    to the residence and exposures that may occur therein. Exposure to
    chemicals in consumer products such as cleaning agents and paint are
    the focus of a model called CONSEXPO (van Veen, 1996).

         All multiple-media exposure models have at least two features in
    common, regardless of the objective for which they were designed.
    First, movement of contaminants from one medium to another is
    characterized. Second, the rate and/or frequency of human contact with
    environmental media is modelled. The former may be referred to as
     inter-media transfer factors and the latter as  exposure factors. 

    Table 19.  Potential human exposure media and routes

    Environmental medium      Exposure routes

    Air                       dermal contact inhalation

    Tap water                 dermal contact ingestion

    Food and beverages        ingestion

    Surface soil              dermal contact ingestion

    Surface water             dermal contact ingestion

    6.5.1  Inter-media transfer factors

         Transfer of contaminants between media is commonly modelled as
    partitioning of a chemical between two or more media. Thus,
    multiple-pathway models require the measurement or estimation of
    partition coefficients of contaminants between several pairs of
    environmental media. There are two general classes of partitioning
    coefficients. The first class relies on basic physicochemical
    properties of the compounds of interest such as aqueous solubility,
    vapour pressure and dipole moment; they describe partitioning due to
    diffusive processes. Coefficients in the second class describe
    partitioning resulting from what may be considered advective
    processes, but also implicitly include diffusive partitioning.  Diffusive partition coefficients

         The class of diffusive partition coefficients includes those
    between soil and water in soil (e.g., groundwater), air and plants,
    soil and plants, animal intake and food, surface water and fish,
    mother's uptake and breast milk, residential water and indoor air,
    soil-gas and indoor air, human skin and soil, and human skin and
    water. In many cases, partition coefficients developed from
    laboratory-scale experiments are the basis for modelling partitioning
    of a compound between environmental media (Lyman et al., 1990). For
    example, the octanol-water partition coefficient is often used as a
    proxy for partitioning non-polar organic compounds (e.g.,
    organochlorine substances) between water and fish lipids. In this
    case,  n-octanol is considered a good model for fish lipids.
    Similarly, the organic carbon-water partition coefficient is used to
    characterize partitioning of non-polar substances between organic
    matter in soil and water. Finally, Henry's constant describes
    partitioning of volatile and non-volatile compounds between air and
    water. Connell et al. (1997) provide a comprehensive introduction to
    the use of this type of partition coefficient in environmental science
    and exposure assessment.  Advective partition coefficients

         The second class of partitioning coefficients jointly describe
    bulk transfer of compounds from one medium to another and diffusive
    partitioning. They are often used to model active uptake of
    contaminants by animals, principally livestock and game such as fish
    or fowl. Factors of this type are used to model transfer of
    semi-volatile compounds (SVOCs) such as dioxins from air to soil, soil
    to beef and soil to cow's milk (e.g., Nessel et al., 1991; Fries,
    1995). Bioaccumulation of lipophilic compounds and some forms of heavy
    metals (e.g., methylmercury) in fish from ingestion of contaminated
    prey and diffusive uptake through respiration is also modelled using
    partition coefficients such as these (e.g., MacIntosh et al., 1994).

    6.5.2  Exposure factors

         In constructing exposure models one needs to define the
    characteristics of individuals in various age and sex categories and
    the characteristics of the microenvironments in which they live or
    from which they obtain water and food. The types of data needed to
    carry out the exposure assessment include exposure duration and
    averaging time, time-activity patterns of individuals, food
    consumption patterns, household parameters, human factors such as body
    weight, surface area, soil ingestion and breast milk intake, and
    parameters associated with food crops and food-producing animals.

         Time-activity patterns provide information on how individuals
    distribute their time among a number of potential exposure media.
    Time-activity pattern data describe such things as the average number
    of hours spent indoors at home and in what rooms and the nature of
    activity. Time-activity data also includes information on time spent
    outdoors at home or spent in microenvironments, such as bathrooms
    (including shower and bathing time). Exposure times are activity data
    that involve the number of days per year and hours per day spent in
    contact with soil during recreation and home gardening and in contact
    with surface water during swimming or other water recreation.
    Household factors relate to drinking-water supply and use,
    room-ventilation rates, and soil and dust concentrations within homes.
    Soil ingestion rates and soil contact on skin are also needed. Methods
    for measuring time-activity patterns and related considerations are
    discussed in detail in Chapter 5.

         Input data of these types may be measured in the population under
    investigation, i.e., site specific, or may be drawn from standard
    references such as AIHC (1994), Finley et al. (1994b) and US EPA
    (1996a). Site-specific data are preferred, in case the population of
    interest may exhibit unique characteristics expected to influence
    exposure. If site-specific data are not available, values observed in
    other populations or estimates may be applied. Some model applications
    may rely solely on estimated inputs. For example, screening models are
    often used to assess exposure and health risks associated with new
    products such as pesticides designed for agricultural and residential
    use. In this case, model inputs may be determined in a manner such

    that the model result is unlikely to underestimate the true level of
    exposure experienced by the population of interest. Models such as
    these are often referred to as "worst-case" models. An exposure
    modelling system recently developed by the European Union contains a
    suite of screening models (EC, 1996).

    6.5.3  Multiple-media/multiple-pathway models

         Multiple-media or so-called "total" exposure models provide
    methods for integrating multiple exposure pathways from multiple
    environmental media into a model system that relates concentrations of
    toxic chemicals to potential total human dose at toxic substances
    release sites. This type of simulation matrix is used to generate the
    hypothetical histogram shown in Fig. 22. The scenarios used to develop
    this particular histogram are for a representative VOC incorporated in
    the top several metres of soil. Here we can see that, based on a
    multiple-media and multiple-pathway assessment, we get indications of
    where it is most valuable to focus our resources to more fully
    characterize distributions of population exposure. In this way, we
    characterize total potential dose using comprehensive, simple and
    possibly stochastic models to focus efforts on those exposure
    pathways, media and scenarios that require more realistic assessment
    of the distribution of dose within the population. This matrix allows
    us to make both pathway-to-pathway and medium-specific comparisons of
    total potential doses from multiple environmental media.

    6.6  Probabilistic exposure models

          Variability and  uncertainty are two important and related
    concepts in exposure modelling, but it is important to distinguish
    between them.  Variability arises from true heterogeneity across
    people, places or time; uncertainty represents a lack of knowledge
    about factors affecting exposure (or risk). Thus, variability can
    affect the precision of model estimates and the degree to which they
    can be generalized, whereas uncertainty can lead to inaccurate or
    biased estimates (Hoffman & Hammonds, 1994). It should be noted that
    variability and uncertainty can complement or confound one another.
    They may also have fundamentally different manifestations. For
    example, uncertainty may force decision-makers to judge how
    practicable it is that exposures have been over- or underestimated for
    every member of the exposed population, whereas variability forces
    them to cope with the certainty that different individuals are subject
    to exposures both above and below any of the exposure levels chosen as
    reference points (US NRC, 1994).

         Failing to distinguish between variability and uncertainty makes
    it difficult to accurately characterize the population distribution of
    exposure and to make informed decisions about priorities for future
    research objectives. Exposure models can allow for consideration of
    both variability and uncertainty.

    FIGURE 22

    6.6.1  Variability

         Diverse sources of environmental contaminants lead to various
    contaminated media (e.g., soil, dust, water, air, food), which in turn
    result in a multitude of routes and pathways of human exposure. For a
    given contaminant, the magnitude and relative contribution of each
    exposure route and pathway may vary among geographic regions and over
    time. In addition, differences in activities among individuals lead to
    disparate rates of contact with contaminated media. In aggregate,
    these factors result in varying levels of personal exposure to a
    particular contaminant among the members of a population, i.e., a
     distribution of exposures.

         Exposure model inputs expressed as distributions can be used to
    model inter-individual variability of exposures. Examples of
    probabilistic human exposure models that explicitly consider
    variability of exposure among individuals may be found in Finley et
    al. (1994a) and MacIntosh et al. (1995, 1996). Variable parameters are
    those that are stochastic with respect to the reference unit of the
    assessment question (IAEA, 1989) and are described by probability
    distributions that reflect their intrinsic randomness. Exposure
    concentrations may vary between individuals owing to the influence of
    personal activities (e.g., cigarette smoking contributions to indoor
    respirable particulate levels). Such differences represent true
    variability of factors that affect exposure among individuals and can
    determine the relative position of an individual or type of individual
    within the distribution of exposures for the population.

    6.6.2  Uncertainty

         Several publications have stressed the importance of
    distinguishing among different types of uncertainty (IAEA, 1989; US
    EPA, 1992c). Explicit consideration of uncertainty in exposure and
    risk assessments is important for understanding the range and
    likelihood of potential outcomes and the relative influence of
    different assumptions, decisions, knowledge gaps and stochastic
    variability in inputs on these outcomes (Bogen & Spear, 1987; Iman &
    Helton, 1988; IAEA, 1989; Morgan & Henrion, 1990; US EPA, 1992c). This
    understanding can help the analyst advise the decision-maker on an
    appropriate course of remedial action, decide whether it is worthwhile
    to collect additional information regarding model parameters, choose
    the appropriate model to use and evaluate which of these actions could
    be most effective in reducing uncertainty about the outcomes (IAEA,
    1989; Morgan & Henrion, 1990).

         Three types of uncertainty are commonly considered:  scenario 
     uncertainty, arising from a lack of knowledge required to fully
    specify the problem;  model uncertainty, arising from a lack of
    knowledge required to formulate the appropriate conceptual or
    computational models; and  parameter uncertainty, arising from a lack
    of knowledge about the true value or distribution of a model parameter
    (US EPA, 1992c). In practice, scenario and model uncertainty are
    commonly considered to be negligible relative to parameter

    uncertainty, although in many cases they may be the largest sources of
    true uncertainty.

         Uncertain parameters are those for which the true value is not
    known or cannot be measured. For example, the true annual mean
    concentration of respirable particles in Mexico City during 1996 is
    uncertain because it can only be estimated from existing data which do
    not cover every day of the year nor every location of the city.
    Another example, is the mean and variance of soil ingestion by
    children aged 6-10 years in Taipei. Presumably, a single distribution
    can be used to describe this behaviour; however, its parameters can
    only be estimated.

         The uncertainty about various parameters of an assessment can be
    formally incorporated into exposure models to estimate uncertainty
    about the prediction end-point, identify the components that influence
    prediction uncertainty and prioritize future research needs (Bogen &
    Spear, 1987; IAEA, 1989). Uncertainty about the true population
    distributions is characterized by propagating the estimated
    uncertainty about model inputs through to the distributions of the
    prediction end-points.

    6.6.3  Implementing probabilistic exposure models

         Although probabilistic exposure models are computationally more
    challenging to implement than deterministic (i.e., point estimate)
    models, the advantages of being able consider population distributions
    and sources and magnitude of uncertainty are often worth the
    additional effort. Several tools are available for propagating input
    parameter variability and uncertainty through to the assessment
    end-point. Classical error propagation techniques may be convenient
    for models with relatively few inputs and small coefficients of
    variation (Bevington, 1969; Seiler, 1987). For more complex models,
    computer-based simulation techniques are likely to be the method of

         Probabilistic exposure models may be run in one or two
    dimensions.  One-dimensional models estimate either variability among
    exposures to individuals or uncertainty about a single exposure
    metric; for example, the mean 8-h average carbon monoxide exposure for
    individuals in a specific area.  Two-dimensional simulation models 
    may be used to estimate both population distributions (i.e.,
    inter-individual variability) and uncertainty about the population
    distribution. The IAEA (1989) has suggested a Monte Carlo simulation
    approach for conducting two-dimensional simulations. In the first
    phase, a single realization is obtained from the distribution of each
    uncertain parameter. In the second phase, repeated realizations are
    obtained from the variable parameters. The entire process of a single
    sampling from the uncertain parameters, followed by repeated sampling
    from the variable parameters, is referred to as a  simulation. A
    single model run consists of generating  k simulations each composed
    of  i iterations, which produces a family of  k predicted
    distributions of population exposures. Prediction uncertainty is

    represented by the distribution of individual estimates for a specific
    percentile or summary statistic among the family of population
    distributions. In this way, the type of plot shown in Fig. 23 contains
    probabilistic information on estimates of both inter-individual
    variability in the prediction end-point, and uncertainty about any
    specific percentile of the population distribution.

    6.7  A generalized dose model

         The magnitude of exposure (dose) is the amount of agent available
    at human exchange boundaries (skin, lungs, gastrointestinal tract)
    where absorption takes place over a specified period of time.
    Depending upon boundary assumptions, a number of dose questions may be
    derived. The  average daily dose (ADD) is one of the most useful
    approaches, and is applied for exposure to non-carcinogenic compounds
    (for carcinogens,  lifetime average daily dose, LADD, is often
    employed). The ADD is calculated by averaging the potential dose
    ( Dpot) over body weight and the appropriate averaging exposure time:

         ADD = total potential dose/body weight × averaging time,

    where the potential dose is a product of contaminant concentration
     (C) in the exposure medium contacting the body, intake rate  (IR) 
    and exposure duration  (ED):

         total potential dose =  C ×  IR ×  ED.

    The intake rate refers the rates of inhalation, ingestion or dermal
    contact depending on the route of exposure.

         The concentrations in air, water and soil used for an exposure
    assessment are those measured or estimated to be available in these
    environmental media at the nearest receptor point to the source (e.g.,
    soil or groundwater at a hazardous waste site). When an environmental
    concentration is assumed constant over a long time period, the
    population-averaged potential dose (for ingestion or inhalation
    pathways) or absorbed dose (for dermal contact) is expressed as an
    average daily dose (ADD) in mg kg-1 day-1:


    where [ Ci/ Ck] is the intermedia-transfer factor, which expresses
    the ratio of contaminant concentration in the exposure medium  i 
    (i.e., personal air, tap water, milk, soil, etc.) to the concentration
    in an environmental medium  k (ambient air gases or particles,
    surface soil, root-zone soil, surface water and groundwater);
    [ IUi / BW] is the intake or uptake factor per unit body weight
    associated with the exposure medium  i. For exposure through the

    FIGURE 23

    inhalation or ingestion pathway [ IUi / BW] is the intake rate per
    unit body weight of the exposure medium such as m3(air) kg-1 day-1,
    litres(milk) kg-1 day-1, or kg(soil) kg-1 day-1. For exposure
    through the dermal pathway, [ IUi / BW] is replaced by  UFi, the
    uptake factor per unit body weight as a fraction of the initial
    concentration in the applied medium with nominal units [litres(water)
    kg-1 day-1 or kg(soil) kg-1 day-1];  EF is the exposure frequency
    for the exposed population in days per year;  ED is the exposure
    duration for the exposed population in years;  AT is the averaging
    time for the exposed population in days; and  Ck is the contaminant
    concentration in environmental medium  k. 

         The potential dose factor, PDF( k-> i), is defined as the
    ratio of dose to concentration, as expressed in the following


         The ADD is used to make route and route-to-route comparisons and
    allows one to consider the relative significance of several exposure
    routes. With the ADD, we compare inhalation, ingestion or dermal
    exposures to the same medium such as tap water and compare exposures
    through indirect pathways (e.g., food-chain transfers) to those from
    direct pathways (e.g., inhalation or ingestion). As an example, the
    ADD for the ingestion route for chloroform for a 70-kg individual
    ingesting 2 litres/day of tap water containing 1 µg/litre chloroform,
    365 days/year for a lifetime is 2 µg/day divided by 70 kg or 0.029
    µg/kg-1 day-1. This ADD can be used as the basis for determining the
    relative significance of dermal, inhalation, and other ingestion
    exposures attributable to tap water.

    6.8  Physiologically based pharmacokinetic models

         Human exposure to contaminants results in dose to the critical
    organs. A mass balance on the contaminants that enter the body
    accounts for the distribution in the various organs, transformation
    into by-products, and excretion via specific mechanisms. The three
    major exposure routes by which contaminants enter the human body are
    inhalation, dermal absorption and ingestion. The vehicle that moves
    contaminants between organs is blood. Transformations include the
    metabolism of specific contaminants in specific organs. Mechanisms of
    excretion include exhaled air, sweat, urine and faeces.

         The above processes that occur in the human body can be modelled
    by using physiologically based pharmacokinetic (PBPK) principles
    (Masters, 1991). These principles can be applied at differing levels
    of complexity. Simple models assume steady states and total absorption

    and estimate dose to critical organs in a gross manner. They can be
    solved by using linear algebraic relationships. Complex models include
    time dependency, assume the human body to consist of multiple
    homogeneous boxes, each representing an organ or a portion thereof,
    and determine the distribution of contaminants in the different boxes
    as a function of time. The relationships usually end up as non-linear
    ordinary differential equations that are solved by using numerical
    integration techniques. Examples of PBPK models may be found in Cox
    (1996) for inhalation of benzene, Bookout et al. (1997) for dermal
    absorption of chemicals and Rao & Ginsberg (1997) for multiple-route
    exposure to methyl  tert-butyl ether. A wide array of PBPK models
    have been developed for other chemicals and chemical classes and may
    be found in the relevant literature.

         Whatever the complexity of the model representing the human body,
    the difficulty is interpreting the dose results to characterize risk.
    Usually, these human models are extrapolated to parallel animal models
    for which toxicological data are available.

    6.9  Validation and generalization

         The modelling approaches described above are mathematical
    abstractions of physical reality that may or may not provide adequate
    estimates of exposure. The preferred way to be sure that a model is
    capable of providing useful and accurate information is by validation,
    i.e., comparing model predictions with measurements independent of
    these used to develop the model. Models can be validated in terms of
    prediction accuracy and precision by comparing predicted values to
    those measured in the field. Although measurements are preferable as
    the "gold standard" in validation of models, comparison of results
    from different assessment methods or modelling approaches can also be
    used to evaluate validity, or at least agreement. This may be the only
    option when measurements are not feasible; for example, in
    retrospective assessment of exposure. Model validation is a necessary
    precondition for the generalization of model results to a different or
    larger population (Ott et al., 1988).

         In the statistical modelling approach, data collection is an
    integral part of model construction. If the data are known to be from
    a statistically representative sample of the population, then there is
    no need for further validation. However, validation is necessary if
    the results are to be extrapolated beyond the range for which the
    existing database provides a statistical description. The physical and
    physical-stochastic modelling approaches must be validated with actual
    data from separately conducted field studies. Care must be taken that
    the data used to validate a model are not biased with respect to
    crucial model parameters. The validation step is useful only to the
    degree that the sample population is representative of the group to
    which results will be extrapolated.

         Finally, when modelling environmental-response-health processes,
    and when validating such models, it is important to realize that in
    principle perfect modelling is possible only for closed systems, and

    the systems described in this report are very open-ended. The
    practical implication of this fact is that even the best models need
    to be validated for each new population and environmental setting
    before application.

    6.10  Summary

         An exposure model is a logical or empirical construct which
    allows estimation of individual or population exposure parameters from
    available input data. Exposure models, if supported by adequate
    observations, can be used to estimate group exposures (e.g., a
    population mean) or individual exposures (e.g., the distribution of
    exposures among members of a population). Models may be used to
    estimate exposure via single or multiple media. The latter is
    particularly useful for comparing the magnitude of exposures likely to
    occur from different media and thus for priority-setting. Exposure
    models may be statistical or deterministic in nature or a combination
    of both. Probabilistic methods may be applied to all three types as a
    means to estimate population distributions of exposure, i.e.,
    variability of exposure among individuals. In addition, probabilistic
    methods may be used to characterize uncertainty in model input
    parameters and propagate that uncertainty through to the prediction
    end-point. Evaluation of the accuracy of model results is critical
    before relying on model output for decision-making.


    7.1  Introduction

         This chapter describes sampling methods used in environmental
    exposure assessment to analyse chemical concentrations in air, water
    and food. The information presented provides a general description of
    available sampling methods and guidance for their selection. It is not
    intended to be comprehensive and the reader should refer to the
    research literature for specific details.

         Assessment of human exposures to contaminants in environmental
    media requires establishing measurement strategies and selecting
    appropriate sampling instruments and analytical methods. Taken
    together, these three elements define a monitoring programme.
    Monitoring methods can be used to determine the magnitude, duration
    and frequency of exposure to an environmental contaminant. Magnitude
    of exposure is defined as the concentration of a specific pollutant
    averaged over a predetermined time interval, such as 1 h, 24 h or a
    lifetime. Different measurement methods have specific characteristics
    that determine the locations in which they are feasible for use. In
    the case of air, the method's sensitivity to pollutants determines the
    averaging times over which it will provide reliable responses.
    Therefore, a clear understanding of the concentration range
    anticipated, averaging time of interest, and expected frequency of
    exposure events is needed to identify appropriate field and laboratory
    methods. In the absence of any prior information, pilot studies may be
    performed to obtain the information needed to finalize the design of
    the monitoring programme.

         Selection of instruments will depend on the target population
    (e.g., children or adults) and study objectives. In some situations,
    understanding the distribution or the average population exposure to a
    contaminant may be sufficient. In fact, most environmental monitoring
    of contaminants in outdoor air, water at the point of distribution and
    "market basket" surveys implicitly assumes that indicators of
    population exposure are more relevant than information at the
    individual level. Studies assessing individual exposures using such
    surrogate measures should select sampling instruments and analysis
    methods based on sensitivity, selectivity, response rate, portability,
    durability and cost, among other factors. Table 20 summarizes these

    7.2  Air monitoring

         Air sampling methodologies should conform to the exposure
    assessment approach selected, either direct or indirect, as described
    in Chapter 3.

         Direct monitoring methods for exposure measurements include the
    use of personal air monitors and/or analysis of human tissue and/or
    biological fluids. Aspects of biomonitoring are described in
    Chapter 10. Indirect air monitoring methods can include

        Table 20.  Selection factors for instruments and methods

    Factor              Comment

    Sensitivity         The magnitude and duration of contaminant exposure define the sensitivity required. As a general 
                        guide, one order of magnitude below and above the concentration of interest is desired. 
                        Reproducibility (precision) as measured by percentage relative error should be below 5%. Sensitivity 
                        is usually inversely proportional to integration time or amount of sample collected

    Selectivity         Response to a specific compound or analyte without interferences. In some cases, non-selective 
                        instruments may be appropriate if exposure situation (e.g., sources, emissions) are understood. 
                        Specific or selective response may require more expensive equipment or more time-consuming 
                        analytical procedures

    Response rate       There are two aspects of response rate: (i) time required for instrument to respond to 90% of a 
                        step change in concentration; (ii) time required between sampling and final processing of data. The 
                        appropriate instrument response rate depends, in part, on the relationship between the contaminant 
                        and the health effect of interest. Acute effects may require instrument methods that can resolve 
                        exposures over intervals of minutes. If health effects from chronic exposures are of primary concern 
                        or the metabolic half-life is long, then rapid response is not necessary

    Portability         Instruments and sampling procedures should not modify behaviour of subjects. Portability includes 
                        size, weight, noise, power, and safety considerations. Portability will influence study design and 
                        usually involves a tradeoff with sensitivity and response rate (e.g., integrated samples rather than 

    Durability          Instruments used for air sampling are subjected to a broad range of conditions. Since temperature 
                        and humidity are potentially interferents and are not easily controlled, the performance of 
                        instruments/methods must be fully evaluated

    Cost                Instrument cost and analytical expenses will influence study design. It may be necessary to trade 
                        off sample cost for accuracy, precision, and response rate. Increasing the number of samples per 
                        subject and/or the number of subjects, or relaxing resolution requirements could compensate for the 
                        use of less expensive methods

    microenvironmental sampling in combination with questionnaires and
    time-activity logs. Ambient air monitors can also be used to estimate
    exposures when combined with information such as building
    characteristics, indoor/outdoor contaminant ratios and time-activity

         The direct approach depends largely on the availability of
    sensitive, small, quiet, lightweight and portable personal monitors.
    Personal air monitors can be used for microenvironmental monitoring as
    well. In addition, microenvironmental monitors with larger sampling
    flows are used for indoor/outdoor sampling. Ambient monitors are
    generally high-volume samplers and are not suitable for indoor use.
    Suitable air monitors must also fulfil several requirements, such as
    detection limits, interferences, time resolution, easy operation and
    of course, cost. There are several good references on air monitoring
    and analysis. The reader is referred to  Air Sampling Instruments 
     for Evaluation of Atmospheric Contamination (ACGIH, 1995).
    Additional general publications include US EPA (1994, 1996b), and
    Lodge (1988). It is important, however, to refer to the published
    scientific literature for the most appropriate and recent air
    monitoring methods.

         The following sections describe methods available for air
    sampling of gases and vapours, airborne particulate matter, SVOCs and
    reactive gases. The methods are classified into active and passive or
    continuous monitors. A detailed list of sampling methods, air
    pollutants for which they are used, sources and other pertinent
    information is presented in Table 21-24. An indicator of their
    suitability for personal, indoor or ambient monitoring is also

    7.2.1  Gases and vapours  Passive samplers

         Commercial passive samplers are available for a variety of air
    pollutants, including inorganic gases such as carbon monoxide,
    nitrogen dioxide, sulfur dioxide and ozone, and VOCs (e.g., benzene,
    toluene, xylene, etc.). Passive air samplers are probably the most
    convenient tool for conducting large-scale personal exposure
    assessments because they are small, inexpensive and easy to use.
    However, sampling rates are of the order of 10-50 ml/min and absorbing
    capacity is limited. Passive samplers operate on the principle of
    molecular diffusion. The rate of diffusion is related to the diffusion
    coefficient of the compound, the cross-sectional area of the absorbing
    surface and the length of diffusion path. Specific information on the
    calculation of sampling rates can be obtained from the manufacturers.
    The collection mechanism relies either on physico-chemical absorption
    or adsorption or chemical reactions. The samplers for inorganic gases
    rely on reaction of the contaminant with a chemical coating on the
    collection surface. The samplers for VOCs typically rely on absorption
    by a liquid or adsorption by a solid collection medium. Selection and
    use of passive samplers should take into consideration potential

    sources of error such as wind effects, temperature, humidity and
    interfering gases.

         In practical applications, personal monitoring is performed by
    mounting the passive sampler on a participant's collar to estimate air
    pollution concentrations in the breathing zone. After collection, the
    adsorbent is removed from the sampler and extracted with the
    recommended solvent. The extract is then analysed by a suitable method
    (e.g., spectrophotometry, gas chromatography with specific or
    unspecific detectors, HPLC, etc.). As with any monitoring procedure,
    measures should be taken to evaluate sample preservation and
    integrity. These procedures should be described as part of the quality
    assurance (QA) protocol and the standard operation procedures (SOPs)
    (see Chapter 11).  Active samplers

         There are many commercially available liquid-media samplers for
    reactive and soluble gases, such as liquid-containing bottles, and
    solid-sorbent tubes for insoluble and non-reactive gases and vapours,
    such as activated charcoal, silica gel, porous polymers or other
    materials. Pollutants are transported with the carrier gas (air), and
    are captured by collecting media. The most frequently applied
    mechanisms in the collection of air pollutants in these media are
    chemical reactions (e.g., acid-base and colour-forming), and
    absorption/adsorption of the pollutant molecules on collecting media.
    Solid sorbent collection efficiency depends on contacting surface
    area, air flow rate, temperature, humidity and presence of interfering

         The sampling rate, breakthrough volume and method limit of
    detection are important parameters which need to be considered for an
    accurate exposure assessment by active samplers. The identification
    and quantification of collected air pollutants are usually performed
    by analytical instruments, such as spectrophotometry, gas
    chromatography with specific or non-specific detectors, HPLC, etc.
    Although not yet used extensively, small, evacuated canister samplers
    have been developed for personal monitoring (Pleil & Lindstrom, 1995).
    These have the advantage of not using sorbents. Analysis is typically
    done by gas chromatography following thermal desorption.  Direct-reading instruments

         The concentration of gases and vapours (e.g., carbon monoxide,
    sulfur dioxide) in an individual's breathing zone can also be
    determined with the use of portable direct-reading instruments.
    Commercially available direct-reading instruments have data logging
    capabilities to store measurements at a rate of 1 s-1. Depending on
    the frequency of measurements, these instruments can operate up to
    2 weeks continuously. Instrument software allows for direct
    calculation of concentrations with different averaging times and
    statistical analysis of the data.

        Table 21.  Air sampling methods for inorganic gases

    Carbon monoxide           Manufacturer            Comments                                     Application

       Electrochemical        Energetic Sciences      0-50, 0-100 ppm; portable and personal;      environmental/personal
                                                      LOD ~ 2 ppm

                              Interscan               Various ranges; LOD ~ 1 ppm                  environmental

                              Bacharah                Based on the measurement of Hg vapour        environmental
                                                      from a pellet oxidized by CO. Range: 0-5, 
                                                      0-20 dl: 1 ppm Sample flowrate: 4.7 

       Photometers            Beckman Instruments     Based on dual-isotope fluorescence,          environmental
                                                      LOD = 0.1 ppm

       Diffusion detectors    Lab Safety Supply Co.   Changes colour; LOD ~ 50 ppm for 8 h         personal

                              Quantum Group Inc.      Simple colour change detector                personal

                              3M Corporation          Indicates presence of CO by colour           personal

                              Wilson Safety Products  Dosimeter badge. Colour change is            personal
                                                      proportional to CO concentration

    Active                    MSA                     Air is pumped through activated charcoal     personal
                                                      tubes that change colour when CO is present


    Table 21.  (continued)

    Carbon monoxide           Manufacturer            Comments                                     Application

       Infrared               GasTech                 300-5000 ppm                                 environmental

                              Rosemount Analytical    Measures CO, CO2, NO and hydrocarbons        environmental

                              SKC West                Sampling frequency: 8 s to 30 min            environmental

       Electrochemical        Devco Engineering       Based on conductivity in water due to        environmental
                                                      ionization of gas

    Nitrogen oxides
       Electrochemical        Trasducer Research      LOD > 2 ppb                                  environmental

                              Interscan               Various ranges; LOD > 20 ppm                 environmental

       Chemiluminescence      Beckman Instruments     Range: 0.1-1 ppm. Operates continuously      environmental
                                                      for 7 days. Analyses NO, NO2, NOx based 
                                                      on the excitation of molecules by light

                              Columbia Scientific     Uses the chemiluminescence reaction of O3    environmental
                                                      with NO. Sampling rate: 1.2 litre/min

                              Rosemount Analytical    Designed to monitor continuous emissions     environmental

       Colorimetric           Phillips Electronics    Set for a variety of chemicals, depending    environmental
                              Instruments             on the electrolyte. Measures concentration
                                                      based on a specific chemical reaction


    Table 21.  (continued)

    Carbon monoxide           Manufacturer            Comments                                     Application

       Diffusion              Env Sciences and        LOD ~ 500 ppb for a 1-h exposure             personal
       tubes/badges           Physiology

                              MDA Scientific          Palmes sampler is an acrylic tube with       personal
                                                      stainless steel grids coated with 
                                                      triethanolamine placed at the bottom

                              RS Landauer Jr. & Co    Pen-shaped badge for the collection of       personal
                                                      N2O on a molecular sieve. Analysis with 

    Electrochemical           MDA Scientific          2-3 ppm; measurement on a 15-min basis       personal

       Chemiluminescence      Beckman Instruments     Operates continuously for 7 days Based on    environmental
                                                      the reaction of ozone with ethylene to 
                                                      produce light  Range: 0-0.0025 ppm, 
                                                      DL: 0.01 ppm

                              Philips Electronics     Operates continuously for 7 days. Based      environmental
                              Instruments             on the reaction of ozone with ethylene 
                                                      to produce light

                              Columbia Scientific     Based on the reaction of ozone with          environmental
                                                      ethylene. Ranges: 0-0.1, 0-0.2, 0-0.5, 
                                                      0-1.0 ppm

       UV Vis photometer      Dasibi Environmental    Concentration is determined by detecting     environmental
                                                      the absorption level of UV within a volume 
                                                      of air


    Table 21.  (continued)

    Carbon monoxide           Manufacturer            Comments                                     Application

                              Mast Development        Portable. Sampling rate 2 litre/min,         environmental
                                                      measurement cycle 20 s

       Diffusion monitors     Ogawa                   Uses 2 multitube diffusion barriers          environmental/
                                                      with collection on glass fibre filters       personal
                                                      coated with nitrite

    LOD: Level of Detection

    Table 22. Air sampling methods for organic vapours

                                    Manufacturer                             Comments                                 Application

       Photo-ionization detector    Thermo Environmental Instruments         Based on UV light, photoionization       environmental
                                                                             detectors can detect a wide 
       Flame ionization detectors   Columbia Scientific                      variety of chemical compounds.

                                    Foxboro                                  Measures hydrocarbons as methane         environmental
                                                                             equivalents. Sample flowrate 
                                                                             20 ml/min

                                                                             Mainly used as a portable survey         environmental
                                                                             equipment. Based on hydrogen flame 
                                                                             ionization detection. Sample 
                                                                             flowrate 2 litre/min, LOD ~ 0.2 ppm

       Thermal ionization           Photovac International                   Semiquantitative response                environmental

       Infrared photometers         Foxboro                                  Miran portable air analyser. Owing       environmental
                                                                             to its tunable IR wavelength, can 
                                                                             detect several organic compounds. 
                                                                             Sampling rate 28 litre/min

                                    Infrared Industries                      2 models. LOD = 25 ppm                   environmental

       Portable gas                 Photovac International                   Portable. Can detect selected VOCs:      environmental
       chromatographs                                                        Benzene, C4-C8, halocarbons down 
                                                                             to ppb level

                                    H-Nu Systems                             Portable gas chromatographs with 5       environmental
                                                                             different detector options (FID, 
                                                                             PID, ECD, TCD, FPD)

    Table 22. (continued)

                                    Manufacturer                             Comments                                 Application

                                    Microsensor Systems                      Portable, isothermal gas                 environmental
                                                                             chromatograph. Samples are 
                                                                             concentrated in tubes, heated and 
                                                                             analysed. LOD = 2 ppb

       Charcoal badges              3M                                       Single charcoal strips (300 mg).         personal/
                                                                             Sampling rate depends on the number      environmental
                                    SKC                                      of windows (1 or 2): 35-70 cm3/min. 
                                                                             Minimum collectable sample: 
                                    Gilian Instrument                        0.2 ppm/h

                                    Perkin Elmer                             Require laboratory analysis


                                    3M                                       Two charcoal strips to avoid             personal/
                                                                             breakthrough and increase sample         environmental
                                    SKC                                      amount. Sampling rate depends on 
                                                                             the number of windows (1 or 2): 
                                    Gilian Instrument                        35-70 cm3/min. Minimum collectable 
                                                                             sample: 0.2 ppm/h.
                                    Perkin Elmer
                                                                             Desorption efficiency depends on 
                                    Pro-Tek                                  the amount and type of solvent used

                                                                             Require laboratory analysis


    Table 22. (continued)

                                    Manufacturer                             Comments                                 Application

       Charcoal tubes               Perkin Elmer                             The most commonly used adsorbent         personal/
                                                                             is activated charcoal.                   environmental
                                    National Draeger                         2 sizes of tubes are available : 
                                                                             100/50 mg or 200/100 mg.

    Passive                         GMD Systems                              LOD > 0.2 ppm for 15 min                 personal

                                    Interscan                                Various ranges; LOD > 20 ppm             personal/

                                    Air Quality Research                     LOD ~ 0.01 ppm for a 7-day exposure      personal

                                    DuPont                                   1.6-54 ppm up to 7 days                  environmental

                                    3M                                       LOD ~ 0.8 ppm for a 1-h exposure         personal
                                                                             Requires colorimetric analysis
                                    SKC                                                                               personal/

                                    AirScan Environmental Technologies       Based on crystal growth and              personal
                                                                             nucleation Length of stain is 
                                                                             proportional to concentration. 

                                    Environmental Science and Physiology     LOD ~ 500 ppb for a 1-h exposure         personal

                                    Envirometrics Products                   Based on electric reaction               personal
                                                                             with a lead-acid battery


    Table 22. (continued)

                                    Manufacturer                             Comments                                 Application

    Gases and Vapours
       Solid adsorbents             Barneby Cheney                           Large number of chemicals efficiently    personal/
                                                                             collected under a wide variety of        environmental
                                    Columbia Scientific Instruments          conditions.
                                                                             The choice of the adsorbent is 
                                    Draeger                                  designed to maximize collection 
                                                                             efficiency while retaining low 
                                    Fischer Scientific                       selectivity.
                                                                             Approximately 50 sorbent types 
                                    Perkin Elmer                             are available; some are chemically 

                                                                             treated to facilitate their 
                                    3M                                       collection properties.

                                    SKC                                      Most tubes contain a primary sorbent 
                                                                             section and a backup bed that is 
                                    Supelco                                  used to indicate breakthrough.

                                    Westvaco                                 Require laboratory analysis

       Polyurethane foam            Supelco                                  Collection of pesticides and PCBs        personal/

       Diffusion monitors           3M                                       In general, the sorbent used is          personal/
                                                                             activated charcoal protected by a        environmental
                                    Gilian Instrument                        screen.

                                    SKC                                      Some monitors have a backup layer 
                                                                             used to indicate breakthrough.
                                                                             Each compound has a particular 
                                                                             diffusion rate

    Table 22. (continued)

                                    Manufacturer                             Comments                                 Application

                                                                             Require laboratory analysis

                                                                             Desorption efficiency will vary 
                                                                             with the amount of material on 
                                                                             the charcoal and with the amount 
                                                                             and type of desorber used.

    Table 23.  Air sampling methods for particulate matter/aerosols

                                   Manufacturer              Comments                                         Application

      Light-scattering             PPM                       LDL ~ 10 µm/m3 Handheld monitor                  environmental
                                   Air Technique             Portable. Sample rate 28.3 litre/min             environmental
                                                             Suction: vacuum pump. Particles can be 
                                                             collected downstream of the filter 

                                   Virtis                    Near-forward sampling, Sample rate 28.3          environmental
                                                             litre/min. Suction: vacuum pump. 
                                                             Particles can be collected downstream 
                                                             of the filter 

                                   Hund                      Measures respirable aerosol mass                 personal or
                                                             concentration by IR scattering                   environmental
                                                             detection. Average of 8 h

                                                             Measures fine dust (0.2-10 µm) mass              environmental
                                                             concentration by IR scattering 
                                                             detection. Average of 8 h

                                   MIE                       Detects respirable dust. Portable.               fixed point/
                                                             Sample rate 2 litre/min                          environmental
                                                             Averages measurement over 8 h

                                                             Miniram dust monitor. Provides                   personal
                                                             instantaneous or 8 h average concentration

                                   Negretti                  Portable dust monitor, Range                     environmental
                                                             0.01-20/0.1-200 mg/m3. Particles can 
                                                             be collected on a filter


    Table 23.  (continued)

                                   Manufacturer              Comments                                         Application

                                   Casella                   Handheld, Range 0.01-20/0.1-200 mg/m3),          environmental
                                                             size >0.1 µm. Particles can be collected 
                                                             on a filter

                                   TSI                       Integrating nephelometer averages over           mostly used 
                                                             30-s periods. Has different wavelengths          for visibility
                                                             depending on the aerosol characteristics

                                                             Laser photometer for particles >0.1              environmental
                                                             µm diameter. Measures aerosol 
                                                             concentration in mg/m3

                                   Belfort Instruments       Integrating nephelometer. Flowrate               environmental
                                                             10 litre/min

    Instantaneous                  MIE                       0.01-10 mg/m3 or 0.1-100 mg/m3                   personal

      Condensation nucleus         Met One                   2 models. Sample flowrate 1.4 or 2.8             environmental
      counters                                               litre/min. Ultrafine particles are grown 
                                                             in alcohol vapour condensation

      Optical particle counters    Climet Instruments        6 models. Flowrate 0.3 -1.0 litre/min.           environmental
                                                             Size range: 0.3-20 µm, 5-16 size 
                                                             channels. Light source: white light or 

                                   Hiac/Royco                6 models. Flowrate 0.01-1.0 litre/min            environmental
                                                             Range: 0.3-10 µm, 6 size channels
                                                             Light source: white light or laser


    Table 23.  (continued)

                                   Manufacturer              Comments                                         Application

                                   Met One                   3 models. Flowrate 0.1-1.0 litre/min             environmental
                                                             Range: 0.1-1 µm, 6 size channels
                                                             Light source: white light or laser

                                   Particle Measuring        Flowrate 0.1-1.0 litre/min; range                environmental
                                   Systems                   0.05-5 µm, 4-16 size channels 
                                                             Illumination source: laser

                                   Faley International       4 models. Flowrate 0.017-1.0 litre/min           environmental
                                                             Range 0.3-5 µm, 2-5 size channels. 
                                                             Light source: white light

                                   TSI                       Flowrate: 0.1-1.0 litre/min; range               environmental
                                                             0.05-5 µm; 4-16 size channels

      Piezobalance                 TSI                       Less reliable for concentrations                 environmental
                                                             <10 µg/m3. Difficult to calibrate

      Beta gauge                   Wedding & Assoc           The particulate collected on the filter is       environmental
                                                             continuously measured by the attenuation of 

    Active (Total)
      Open cassette                SKC                       Air is pulled through a filter with no           personal
                                                             size selection device

      IOM inlet                    SKC                       Samples inhalable dust particles. Reusable       personal
                                                             filter cassettes. Sampling rate 2 litre/min. 
                                                             Cut point 100 µm

    Active (size selective)
      PM10 impactors               BGI                       Sampling rate 28.3 litre/min                     environmental
                                                             Suction: pump aerosol spectrometer


    Table 23.  (continued)

                                   Manufacturer              Comments                                         Application

                                   MSP                       Sampling rate 4 or 10 litre/min;                 personal
                                                             Suction: pump

                                                             10-2.5 µm virtual impactor.                      environmental
                                                             Sampling rate 1130 litre/min

                                   TSI                       Sampling rate 1 litre/min;                       personal
                                                             Suction: pump

                                   MIE                       Sampling rate 2 litre/min;                       personal
                                                             Suction: pump

                                   Air Diagnostics           Sampling rate 4 litre/min;                       fixed 
                                                             Suction: pump                                    location/
                                                             environmental                                    environmental

                                   Graseby Andersen          Virtual dichotomous. Cutpoints 10                environmental
                                                             and 2.5 µm; sampling rate 16.7 litre/min

                                                             High-volume sampler. Sampling rate               environmental
                                                             1100 litre/min

                                   SKC                       Personal impactor. Single stage. Suction:        personal/
                                                             personal pump. Sampling rate 2, 4 or 10          environmental

      PM2.5 impactors              URG                       Sampling rate 4 litre/min; part are              personal
                                                             collected in filters and organics in a 
                                                             polyurethane foam

                                   MSP                       Sampling rate 4 or 10 litre/min;                 personal
                                                             Suction: pump 


    Table 23.  (continued)

                                   Manufacturer              Comments                                         Application

                                   SKC                       Personal impactor. Single stage.                 personal/
                                                             Suction: personal pump.                          environmental
                                                             Sampling rate 2, 4 or 10 litre/min

      Cascade impactors            BGI                       7 stages. Sampling rate 5 litre/min;             environmental
                                                             Cut points: 32, 16, 8, 4, 2, 1 µm

                                   Graseby Andersen          13 stages; sampling rate: 3 litre/min;           environmental
                                                             Cut points: 13-0.08 µm

                                                             9 stages; sampling rate: 7 litre/min;            environmental
                                                             Cut points: 18, 11, 4.4, 2.65, 1.7, 0.95, 
                                                             0.53, 0.32, 0.16 µm

                                                             8 stages; sampling rate 28 litre/min;            environmental
                                                             Cut points 10-0.4 µm

                                                             7 stages; sampling rate 28 litre/min;            environmental
                                                             Cut points 6, 4.6, 3.3, 2.2, 1.1, 0.7, 0.4 µm

                                                             6 stages; sampling rate 0.3-20 litre/min;        personal
                                                             Cut points 0.5-20 µm

                                                             5 stages; sampling rate 1132 litre/min;          environmental
                                                             Cut points 7.2, 3, 1.5, 0.95, 0.49 µm

                                                             4, 6 or 8 stages; flowrate 2 litre/min           personal
                                                             Cut points 20-0.6 with 8 stages, 10-0.6 
                                                             µm with 6, and 20-3.5 with 4

                                                             Radial slot impactor. 6, 8 or 10 stages with     environmental
                                                             an optional cyclone


    Table 23.  (continued)

                                   Manufacturer              Comments                                         Application

                                   Hauke KG                  Sampling rate 30 litre/min                       environmental
                                                             Cut points below 0.1 µm

                                   in-Tox Products           4 models of 7 stages each.                       environmental
                                                             Cut points 3.1-0.33 µm @ 0.1 litre/min;
                                                             4.5-0.32 µm @ 1 litre/min; 5-0.25 µm @ 
                                                             2 litre/min; 5-0.0.5 µm @ 5 litre/min

                                   MSP                       8 stages, Sampling rate: 30 litre/min;           environmental
                                                             Cut points: 10, 5.62, 3.16, 1.78, 1, 
                                                             0.56, 0.316, 0.178, 0.1, 0.056 µm

      Virtual impactors            BGI                       3 virtual stages, flowrate: 30 litre/min;        environmental
                                                             Cut points 1.2, 4 and 14 µm

                                   Graseby Andersen          The dichotomous sampler fractions the            environmental
                                                             particles in 2 sizes: 10 and 2.5 µm. 
                                                             Sampling rate 17.6 litre/min

                                   MSP                       Sampling rate 30 litre/min;                      environmental
                                                             Cut points below 0.1 µm

                                                             High-volume operates at 1130 litre/min           environmental
                                                             Cut point 2.5 µm

      Cyclones                     Mine Safety Appliances    Measures respirable particles with a pre-cut     personal
                                                             diameter of 3.5 µm @ 2 litre/min

                                   Sensidyne                 Measures respirable particles in ambient         environmental
                                                             air @ 240 litre/min


    Table 23.  (continued)

                                   Manufacturer              Comments                                         Application

                                                             Measures respirable particles in ambient         environmental
                                                             air @ 9 litre/min

                                                             Measures respirable particles @                  personal
                                                             1.7 litre/min

                                   SKC                       Measures respirable particles with a cut         personal
                                                             point of 3.5 µm @ 1.9 litre/min

      Elutriators                  Casella                   Horizontal elutriator that retains particles     environmental
                                                             with a cut point of 3.5 µm at a flowrate 
                                                             of 50 litre/min

                                                             Horizontal elutriator that retains particles     personal
                                                             with a cut point of 3.5 µm at a flowrate of 
                                                             2.5 litre/min

    LOD: Level of detection

    Table 24.  Air sampling methods for reactive gases

                          Manufacturer                   Comments                                       Application

    Hydrogen sulfide/sulfur dioxide/ammonia/chloride

      Electrochemical     Devco Engineering

                          CEA Instruments
                                                         All based on conductivity change in            environmental
                          Sensidyne                      water due to ionization of gas



      Colorimetric        Phillips Electronics           Based on the reaction of the gas with          environmental
                          Instruments                    the reagent to produce a coloured 
                                                         Each compound has a specific reagent 
                          CEA Instruments                for its detection

      Potentiometric      AIM                            Conductivity of the reagent changes in         environmental
                                                         proportion to the concentration of the 
                          Calibrated Instruments         gas being sampled and is measured 
                                                         by an electrode
                          Eitel Manufacturing

      UV and visible      Barringer Research             Based on the correlation with the              environmental
      light photometers                                  absorption spectra of SO2 in the UV
                                                         Sensitivity 2 ppm

      UV and visible      Beckman Instruments            Based on the fluorescence of SO2               environmental
      light photometers                                  under UV light

    Table 24.  (continued)

                          Manufacturer                   Comments                                       Application

                          Rosemount Analytical           For SO2 uses a non-dispersive UV               environmental
                                                         "transflectance" analysis

                          Columbia Scientific            Uses a continuous UV source of high            environmental
                                                         intensity to detect SO2

      Solid adsorbents    Barneby Cheney                 Large number of chemicals are 
                                                         efficiently collected under a wide 
                                                         variety of conditions
                          Columbia Scientific            Choice of adsorbent is designed to 
                          Instruments                    maximize collection efficiency while 
                                                         retaining low selectivity
                                                         Approximately 50 sorbent types are 
                          Draeger                        available; some are chemically treated         personal
                                                         to facilitate their collection 
                          Fischer Scientific             properties
                                                         Most tubes contain a primary sorbent           personal
                                                         section and a backup bed that is 
                          Perkin Elmer                   used to indicate breakthrough

                          3M                             Require laboratory analysis                    





    Table 24.  (continued)

                          Manufacturer                   Comments                                       Application

      Annular denuders    URG                            Different models can be used to collect        personal/
                                                         only gases or gases and particles/aerosols.    environmental
                                                         Especially used for acid aerosols (SO2, 
                                                         H2SO4, HNO3, (NH4)2SO4, NH4HSO4, NH4NO3)

    7.2.2  Aerosols

         At present, active sampling is the only feasible way to perform
    exposure assessments on particulates directly. Active particle
    samplers operate by drawing aerosols into a sensor or on to a
    collection surface (e.g., a filter) by means of a pump (Hinds, 1982;
    Lehtimäki & Willeke 1993). Large stationary samplers that operate with
    a standard flow rate of approximately 1000 litre/min are available
    commercially and are useful for collecting large sample volumes. Small
    stationary samplers that operate with flow rates in the range of 1-10
    litre/min are also commercially available. Both sizes are available in
    configurations that allow for sampling of total suspended particulate
    matter (i.e., not size separated) or specific size fractions (e.g.,
    PM2.5 or PM10). Personal aerosol samplers that allow collection of
    total inhalable particulate matter of specific size fractions are also

         The cyclone and, particularly, the impactor are the two most
    commonly used size preselectors. Cyclones can collect suspended
    particulate matter of various sizes depending on the geometry of the
    cyclone and the flow rate. It operates on the principle of centrifugal
    forces that drive particles in the direction of the outer wall of the
    cyclone (Hinds, 1982). Particles with aerodynamic diameter greater
    than the cut-point of the cyclone impact upon the wall and/or the
    bottom of the cyclone. Particles with aerodynamic diameter less than
    the cut-point remain in the air stream and are collected on a filter

         Impactors rely on inertial forces to separate particles based on
    aerodynamic diameter. Air is accelerated through a nozzle or jet and
    then forced to make a 90° turn around an impaction plate before
    passing through a filter and exiting the sampler. Depending on their
    size, particles suspended in the air stream pass through the
    acceleration nozzle and then either remain entrained in the flow or
    collide and are retained on the impaction plate. The cut-point of an
    impactor is determined by the flow rate, jet size and shape (e.g., the
    distance between the jet and the impaction surface) (Pastuska, 1988;
    Lehtimäki & Willeke, 1993). The air flow rate must be calibrated
    carefully because correct size selection depends largely on precise
    flow rates.

         Filters are made either from fibre mats of glass, cellulose or
    quartz or from synthetic membranes (e.g., Teflon). The selection of
    appropriate filters depends on the pump, filter static pressure,
    collection efficiency, extraction and analytical requirements, and the
    potential for sampling artefacts. Filter mass is determined by
    weighing the filter under controlled temperature and humidity
    conditions before and after use following a conditioning period of at
    least 24 h at those same conditions. The collected mass can be
    extracted and analysed for chemical composition. The extraction and
    analysis procedures used depend on the analytes of interest. A recent
    summary of methods for extraction and analysis of components of
    particulate matter may be found in Koutrakis & Sioutas (1996).

    7.2.3  Semivolatile compounds

         For airborne contaminants that are present in both the particle
    and the vapour phase at typical environmental conditions, it is
    necessary to use a combination of sampling methods. The most common
    approach consists of an aerosol sampling inlet (with or without size
    preselector) followed by a sorbent cartridge or tube. Examples of such
    contaminants include airborne PAHs, pesticides, polychlorinated
    biphenyls (PCBs), dioxins and furans. Semivolatile sampling systems
    are commercially available for personal air monitoring. Extraction and
    analysis of these samples are done separately for the particle and
    vapour phase and then the results are combined to provide a total
    concentration. An introduction to sampling and analysis methods for
    VOCs in air may be found in Binkova et al. (1995), Wallace & Hites
    (1996), Wallace et al. (1996) and Simonich & Hites (1997).

    7.2.4  Reactive gas monitoring

         Certain gases present in air may react with chemicals present in
    particles. For example, sulfuric acid particles collected on filters
    can be neutralized by the ammonia gas present in the sample or air
    stream. The preferred sampling approach to avoid this is to use a
    denuder to remove the reactive gas before it reaches the downstream
    filter. In the case of sulfuric acid monitoring, a citric-acid-coated
    denuder is used to remove the ammonia gas. Small denuder systems are
    commercially available for personal monitors. Denuder technologies are
    described in Lodge (1988) and Koutrakis & Sioutas (1996).

    7.3  Water

         The sampling and analysis of drinking-water characterizes the
    extent to which this carrier medium represents a source of specific
    chemical exposure. Contaminated drinking-water supplies contribute to
    the human intake of numerous chemical contaminants, including heavy
    metals, fertilizers, pesticides, aromatic hydrocarbons and
    organohalogens, among others. In some cases, drinking-water may be the
    primary source of human exposure. Chemical pollutants in water may
    originate from one or more of a myriad of sources, as summarized in
    Table 25. In the selection of measurement and sampling methods, it is
    important to consider raw water sources, water treatment processes,
    and distribution and service systems, all of which can either reduce
    or increase the contaminant concentrations in drinking-water.

         Samples collected at the end of the distribution system provide a
    better measure of potential exposure to individuals than samples
    collected at the source prior to any removal or treatment that might
    take place. Numerous texts detail sampling and analytical techniques
    specific to drinking-water, and these methods can be used to develop
    comprehensive exposure assessment protocols (UNEP/WHO, 1986; WHO,

        Table 25. Origins of chemicals commonly occurring in drinking-water (Hickman et al., 1982)


    Substances affecting the source (raw water)
    "Naturally occurring"                            Leached from geological formation (e.g., calcium, heavy metals)
                                                     Derived from soil and sediments

    Pollutants derived from point sources            Domestic sewage treatment (e.g., nitriloacetic acid)
                                                     Industrial effluents (e.g., synthetic organics, metals, cyanide)
                                                     Landfill waste disposal (e.g., metals, synthetic organics)

    Pollutants derived from non-point sources        Agricultural run-off (e.g., fertilizers, pesticides)
                                                     Urban run-off (e.g., salt, PAHs)
                                                     Atmospheric fall-out (e.g., PAHs, chlorinated organics, heavy metals)

    Substances resulting from treatment
    Substances formed during disinfection            Trihalomethanes, chlorophenols

    Treatment chemicals                              Chloramines, fluorides

    Treatment chemical impurities                    Acrylamide monomer, carbon tetrachloride

    Substances arising from the distribution 
    and service systems
    Contaminants arising from contact with           Lead, vinyl chloride monomer and asbestos fibres from piping, 
    construction material and protective coatings    cadmium from fittings, PAHs from coal tar linings

    Substances arising from point-of-use devices     Sodium, silver

         In developing countries it is quite common for individuals not to
    have access to treated water from distribution systems, so analysing
    water quality solely from distribution systems may not provide a true
    reflection of exposure. Even if drinking-water is obtained from piped
    supplies, it may not provide an adequate indication of exposure as
    many individuals are forced to store water after collection, when
    gross contamination may occur. In some areas of the world, run-off
    water is routinely collected from roofs for drinking and cooking
    needs. Dustfall attributable to traffic, industry, or construction may
    contribute to variable (potentially high) pollutant concentrations in
    this source.

         Exposure to contaminants in water is not limited to oral routes.
    For instance, disinfection by-products and radon gas dissolved in
    groundwater may be released into an indoor atmosphere providing an
    inhalation route. Heating water also releases dissolved VOCs. Exposure
    to contaminants may also occur through inhalation of aerosols from
    irrigation sprays. During other water-based activities (e.g.,
    swimming, showering and bathing), other contaminants may be absorbed
    via a dermal (percutaneous) route. Although the contribution of
    non-oral routes is usually much less than that of oral routes, these
    pathways should not be overlooked in the selection of measurement
    methods to assess exposure. Methods for modelling exposure through
    these pathways are discussed in Chapter 6.

    7.3.1  Factors influencing water quality

         In order to select appropriate measurement and monitoring
    methods, it is important to understand the following factors that
    influence the quality of the water being sampled, and the resultant

    *  treatment systems

    *  distribution networks

    *  storage practices

    *  spatial and temporal variations

    *  climatic and seasonal changes.

         Water treatment encompasses a variety of processes, ranging from
    simple screening and filtration to multi-step purification. The latter
    includes methods for coagulation, aeration, de-aeration, colour
    removal, softening, disinfection, fluoridation, stabilization and
    demineralization. Some of these steps constitute "removal", and others
    involve the "addition" of treatment chemicals to mitigate the hazards
    of contaminants in water. A list of chemical additives typically used
    in water treatment systems is shown in Table 26. The reaction of
    treatment chemicals with other substances present in raw (untreated)
    water often results in the generation of intermediate reaction
    products with adverse health significance. For instance, chlorine,

    accepted worldwide for disinfection and oxidation, results in the
    formation of disinfection by-products such as trihalomethanes (e.g.,

    Table 26.  Water treatment chemicals

    Activated alumina             Sodium bicarbonate
    Aluminum sulfate              Sodium calcium magnesium polyphosphate
    Ammonia                            (glassy)
    Ammonium hydroxide            Sodium carbonate
    Bentonite clay                Sodium chlorite
    Calcium hydroxide             Sodium fluoride
    Calcium hypochlorite          Sodium hydroxide
    Calcium oxide                 Sodium metabisulfite
    Carbon (activated, granular,  Sodium polyphosphate (glassy)
      and powder)                 Sodium silicate
    Carbon dioxide                Sodium siliconfluoride
    Chlorine                      Sodium tripolyphosphate
    Ferric chloride               Sodium zinc polyphosphate (glassy)
    Ferric sulfate                Sodium zinc potassium polyphosphate
    Ferrous sulfate                 (glassy)
    Fluosilicic acid              Sulfur dioxide
    Potassium permanganate        Sulfuric acid
    Sodium aluminate              Tetrasodium pyrophosphate

         Distribution networks constitute another potential source of
    chemical contaminants in drinking-water. The materials used in
    distribution networks may serve as a pollutant source by leaching into
    the water over time. Some examples include lead from lead-containing
    solders and pipes, asbestos fibres from the surface of asbestos-cement
    pipes and cadmium from metallic fittings. Other contaminants include
    PAHs from coal-tar-based sealants, plasticizers, stabilizers and
    solvents used in the manufacture of plastic pipes.

         Water sources experience considerable variations in quality over
    time and geographic location. The quality of river water may change
    rapidly during heavy storms, melting snows and droughts. The quality
    of water in lakes may be affected by climate, season, location or some
    combination thereof. Groundwater historically has enjoyed the most
    consistent quality, with relatively constant composition. However, the
    vulnerability of groundwater to contamination is gaining widespread
    attention, with particular emphasis on synthetic organic substances,
    surface impoundments, landfills, agriculture, leaks and spills, land
    disposal of wastewater, septic tanks and the petroleum/mining
    production industries.

    7.3.2  Water quality monitoring strategies

         There are numerous considerations in the design of a monitoring
    and measurement strategy for water quality assessment. The
    International Organization for Standardization (ISO) has provided
    guidance on a number of issues related to sampling strategies for
    water quality assessment (Table 27). A sound monitoring methodology
    must be followed by the appropriate sample storage and transportation,
    to minimize changes in sample composition. Losses can occur due to
    several physical, chemical and biological changes, such as ion
    exchange, adsorption with the container material, oxidation to
    precipitated forms, loss of volatiles to the vapour space and
    biochemical conversions. For contaminants at low source
    concentrations, these changes can introduce significant errors in the
    analytical results.

    Table 27.  ISO standards of water quality giving guidance on sampling

    ISO standard     Title (water quality)

    5667-1: 1980     Sampling - Part 1: Guidance on the design of sampling 
    5667-2: 1982     Sampling - Part 2: Guidance on sampling techniques
    5667-3: 1985     Sampling - Part 3: Guidance on the preservation and 
                     handling of samples
    5667-4: 1987     Sampling - Part 4: Guidance on sampling from lakes, 
                     natural and man-made
    5667-5: 1985     Sampling - Part 5: Guidance on sampling of 
                     drinking-water and water used for food and beverage 
    5667-6: 1985     Sampling - Part 6: Guidance on sampling of rivers and 

         The design of a water monitoring programme would be incomplete
    without consideration of the demographic and socioeconomic
    characteristics, and also an understanding of the historical
    development, of the potentially exposed community. The evolution of
    materials used in distribution systems changes the profiles of
    pollutants requiring measurement. Cultural and socio-economic factors
    affect usage patterns, which in turn influence the extent of exposure
    to contaminants in drinking-water.

         In order to ensure the representativeness and validity of water
    samples, sampling techniques must be carefully selected (WHO, 1992,
    1993). The first step in the design of a sampling programme is to
    develop concise objectives, accounting for

    *  the nature of the substance to be measured

    *  point of exposure

    *  the duration of time over which measurements will be taken.

         The type and magnitude of spatial and temporal variations in the
    concentration of water constituents will depend upon both their
    sources and their behaviour in the distribution and service systems.

    Substances can be classified into two main types: 

    *   Type 1. Substances whose concentration is unlikely to vary during
       distribution. The concentration of these substances in the
       distribution system is largely governed by the concentration in the
       water going into the supply, and the substances do not undergo any
       reaction in the distribution system. Examples of such substances
       are arsenic, chloride, fluoride, hardness, pesticides, sodium and
       total dissolved solids.

    *   Type 2. Substances whose concentrations may vary during
       distribution. These include

       -  substances whose concentration during distribution is dependent
          mainly on the concentration in the water going into the supply,
          but which may participate in reactions (which change the
          concentration) within the distribution system. Examples are
          aluminium, chloroform, iron, manganese and hydrogen ion (pH).

       -  substances for which the distribution system provides the main
          source, such as benzo [a]pyrene, copper, lead and zinc.

         This classification applies only to piped water supplies. In all
    other types of supply, water constituents should be regarded as type 1
    substances. The same substance may belong to different classes in
    different distribution systems.

    7.3.3  Sample collection

         The location, frequency and time of sampling is strongly
    dependent on the spatial and temporal variations for the particular
    pollutant of interest. There are many different methods to collect
    water samples and measure contaminant concentrations. The choice of a
    particular technique can have a profound effect on the analytical
    results. Some conventional measurement methods are briefly described

    *   Grab samples represent a "snapshot" of a situation at a
       particular time and place. Using samples taken at intervals and
       analysed individually, this method can characterize variations in
       source composition.

    *   First-draw (static) samples are collections immediately following
       a stagnation period (e.g., overnight). This reflects the influence
       of domestic plumbing on the inorganic content of water quality.

    *   Flushed samples are taken after taps have been run for a
       sufficient length of time to eliminate stagnant water.

    *   Composite samples involve regular sampling, usually over a 24-h
       period, followed by pooling of samples and analysis of the
       composite. This integrated method overcomes the disadvantages
       inherent in first-draw sampling. Time-composite samples approximate
       the potential exposure to drinking-water contaminants.

    7.4  Assessing exposures through food

         Exposure to chemical compounds in food can be measured directly
    by analysing duplicate diets or indirectly by analysing foods or total
    diets, matching food consumption data with information of chemical
    concentration in the foods or, for certain chemicals, estimating the
    total amount of the chemical available divided by the population of
    concern (called  per capita estimates). The consumption of water and
    the resulting exposure should also be determined if appropriate
    (FAO/WHO, 1997). The estimation of exposure to food chemicals is a
    complex activity and no single approach is suited to all
    circumstances. The method chosen depends on the information available,
    the population group of concern, whether acute or chronic effects of
    the chemical are being assessed, and the intended use of the result
    (Rees & Tennant, 1993). The Intake Assessment Group which has been
    added to the Joint FAO/WHO Expert Committee on Food Additives is also
    examining other means of evaluating dietary exposure assessments for
    food additives and contaminants.

         Direct approaches tend to consider samples of food as actually
    consumed because the method by which food is prepared for consumption
    (e.g., washing, peeling, cooking and commercial processing) can
    influence contaminant residue levels. For example, malathion
    concentrations were found to decrease by 99% when raw tomatoes were
    processed into canned tomatoes (Elkins, 1989). In contrast,
    concentrations of ethylenethiourea, a carcinogenic degradation product
    of maneb (manganese ethylene bisdithiocarbamate), rose 94% when turnip
    greens were washed, blanched, frozen and subsequently sautéed (Elkins,
    1989; Houeto et al., 1995). Although cooking may lead to a reduction
    in the lead content of vegetables, in areas where the lead
    concentrations in drinking-water are higher than average (e.g., due to
    lead pipes), cooking water can be a significant source of lead intake
    (UK MAFF, 1989).Therefore, preparation and processing can alter
    contaminant levels present in foods, or introduce new contaminants.
    For these reasons, the concentration of the target analyte in
     ready-to-eat foods is the most useful measure for purposes of
    dietary exposure assessment.

    7.4.1  Duplicate diet surveys

         Duplicate diet surveys are particularly useful because they
    reflect the range of preparation habits of the study population. These
    studies require that respondents save a serving of each meal or
    components of each meal and store them until collection by the
    research team. Following collection, the food is composited over
    predetermined time intervals (e.g., by meal or by day) and analysed
    for the target analytes. In duplicate diet studies, logistic and cost
    constraints typically require that foods be composited. The principal
    disadvantage of composite samples is that they do not allow for
    identification of the contribution of individual foods to total
    dietary exposure. A high degree of respondent burden is associated
    with duplicate diet studies, so they are not conducive to assessing
    chronic dietary exposures and may underestimate intake. Such
    approaches are only suitable for chemicals that can be analysed
    accurately, so direct diet methods are not traditionally used for
    assessing food additives exposure, for example. A summary of dietary
    exposure assessments for chemical contaminants in food using the
    duplicate diet performed worldwide may be found in Thomas et al.

         There are many indirect methods for estimating exposure to food
    chemicals because there are a variety of ways to collect consumption
    data, to express residue levels in the foods concerned (for example,
    legislative levels, manufacturer or industry use levels, predicted,
    proposed or analysed levels or any combination of these) and there are
    several approaches which can be used to combine the information to
    assess exposure (Rees & Tennant, 1994). Some methods are better than
    others, depending on the chemical; for example several countries have
    found it useful to assess food additive exposure by using  per 
     capita methods (Ito, 1993). More information on these indirect
    methods is given below, but the reader is strongly advised to refer to
    more comprehensive documents on dietary survey methodology and dietary
    exposure assessment approaches (WHO, 1985a, 1997c; FAO/WHO, 1995a,b,
    1996, 1997).

    7.4.2  Market basket or total diet surveys

         Market basket or total diet surveys utilize food chemical
    concentrations measured in ready-to-eat foods prepared in the
    laboratory linked to model diets derived from food consumption data
    and standard recipe preparation for large populations, households or
    individuals. Food products or food groups selected for sampling and
    analysis are generally intended to be representative of those most
    commonly consumed by the population of interest. Total diet studies
    have been carried out since the 1960s in many countries. Market basket
    surveys are often employed by regulatory agencies charged with
    ensuring and monitoring the safety of a national food supply (FAO/WHO,
    1995b). Initially this purpose was to estimate background exposures of
    the population to pesticides residues and radioactive contaminants.
    The emphasis has shifted from pesticides to toxic metals and more

    recently has included a variety of trace elements and organic

         For example, the US FDA Total Diet Study (US TDS) is a market
    basket survey based on heavy metal and pesticide data measured in
    samples of 234 different ready-to-eat food products selected to be
    representative of over 4000 foods common in the diet of residents in
    the USA, and the results of national food consumption surveys
    (Pennington, 1992). However, more commonly total diet (market basket)
    studies consider smaller food groups rather than individual foods (UK
    MAFF, 1985). The main advantage of the total diet (market basket)
    approach for estimating exposure is the ability to monitor trends
    without burdening study participants. The total diet approach allows
    data from separate studies of food consumption and contaminant
    residues to be combined (e.g., Tomerlin et al., 1996). Moreover, this
    approach allows analytical chemistry resources to be directed to the
    foods that are most likely to yield the greatest exposure (e.g., the
    foods consumed in greatest amounts and foods that are likely to
    contain the highest residue concentrations). Such foods may be
    indicated by information available from existing data such as the
    GEMS/Food (WHO, 1978, 1997c) and the US TDS (Pennington & Gunderson,

         However, this method cannot be used for all contaminants. This is
    because the analysis of food groups may be too expensive for some
    contaminants and may not be feasible for others. Analytical methods
    may not be sufficiently reliable, the limit of detection may be too
    high or the grouping of the foods (compositing) may decrease the
    likelihood of finding the source of the contaminant. Analysis of
    individual food products affords a detailed examination of contaminant
    levels in specific commodities -- either raw, processed or prepared.
    Sampling may be designed to characterize geographic and temporal
    variability of contaminant levels that may be a result of varying
    application rates of pesticides, natural levels of elements (e.g.,
    heavy metals), climate and other factors. In addition, samples can be
    collected at all steps in the process from field to consumer thereby
    providing insight into the sources and fate of contaminants in food.

         Further information on the strengthens and limitations of each of
    the approaches described above have been published in the
    comprehensive  Guidelines for the Study of Dietary Intake of 
     Chemical Contaminants (WHO, 1985a).

    7.4.3  Food consumption

         The FAO/WHO Consultation on Food Consumption and Exposure
    Assessment of Chemicals (called Exposure Consultation) reviewed
    current methodology for food additives, contaminants, pesticides,
    veterinary drugs and nutrients. The Exposure Consultation agreed to
    expand and revise the regional diets presently used by the GEMS/Food
    for pesticides and recommended that this consumption data can be used
    for estimate dietary exposure to certain other chemicals. The regional
    diets will be based on 1990-1994 FAO Food Balance Sheets which reflect

    a country's amount of raw commodities for consumption, and may not
    necessarily refer to foods in the forms people consume them. Waste at
    the household or individual level is not usually considered.

         Major methods for determining food consumption at the national
    levels were identified as population-based, household-based and
    individual-based. The report from a FAO/WHO consultation on the
    preparation and use of food-based guidelines (FAO/WHO, 1996) gives
    more information on food consumption study designs. The Exposure
    Consultation supported the concept that an improvement in dietary
    exposure assessments can be achieved by refining any combination of
    the contributing elements: food consumption data, food chemical
    concentration data or the method used to combine the two. This allows
    the risk assessor a greater flexibility in selecting cost-effective
    approaches to refine dietary exposure assessments using the resources
    available (WHO, 1997c).

         The five basic approaches discussed by the Exposure consultation
    for describing the diet of individual people are:

    *  food record/diary survey

    *  24-h recall

    *  food frequency questionnaire

    *  meal-based diet history

    *  food habit questionnaire (WHO, 1997c).

         The 24-h recall is a widely used dietary assessment method and is
    utilized in many exposure-related studies including the National
    Health and Nutrition Examination Survey conducted by the US Centers
    for Disease Control and Prevention (Witschi, 1990).  Food diaries

         Food diaries are detailed descriptions of types and amounts of
    foods and beverages consumed, meal by meal, over a prescribed period,
    usually 3-7 days. Food diaries and recalls may be presented in
    numerous formats or combined with food models and weighing procedures
    to characterize serving size more accurately; however, regardless of
    the specific details, dietary recording places a substantial burden on
    the subject (Witschi, 1990).  24-h recall

         The short-term nature of the 24-h recall and the facility to
    consider meal occasions or daily consumption from diary surveys make
    this method ideal for assessing exposure to substances that can give
    rise to acute health effects, such as the cholinesterase-inhibiting
    organophosphate and carbamate pesticides. Diary methods may be used
    for assessment of long-term exposure but the underlying assumption is

    that the food consumption is representative of usual habits.
    Probabilistic approaches can be useful to predict consumption and
    resulting exposure over longer periods of time.  Food frequency questionnaires

         Food frequency questionnaires (FFQs) are a standard tool for
    characterizing food intake over extended periods of time. A food
    frequency questionnaire consists of two basic components: a list of
    foods and a frequency response section for respondents to indicate how
    often a specific serving size of each food is consumed (Table 28). The
    underlying principle of the food frequency approach is that average
    long-term diet, for example, intake over weeks, months or years, is
    important rather than intake on a few specific days. This may not be
    true for all contaminant-health effect combinations (e.g., acute and
    reversible effects such as cholinesterase inhibition); however, it is
    reasonable in the context of assessing health effects that may be
    caused by cumulative exposure, such as cancer, or reproductive and
    developmental effects that may follow a threshold dose-response curve.
    Some FFQs include questions on usual food preparation methods,
    trimming of meats, use of dietary supplements and identification of
    the most common type or brand consumed. FFQs can be used to rank
    individuals by exposure to selected chemicals. Although FFQs are not
    designed to measure absolute exposure, the method may be more accurate
    than other methods for estimating average exposure to chemicals having
    large day-to-day variability and for which there are relatively few
    food sources. FFQs have several disadvantages too: specifically, they
    are less reliable in estimating consumption of rarely consumed foods
    and the food lists are often designed to assess nutrients and may
    require substantial revision to assess chemical exposures.  Meal-based diet history

         Meal-based diet history methods are designed to assess usual
    individual food consumption. It consists of a detailed listing of the
    types of foods and beverages commonly consumed at each meal over a
    defined time period which is often a "typical week".  Food habit questionnaires

         Food habit questionnaires are designed to collect either general
    or specific types of information, such as food perception and belief,
    food likes and dislikes, methods of preparing foods, use of dietary
    supplements and social setting surrounding eating occasions. This type
    of information is frequently considered with other methods but may be
    used on its own.

         Although the last two methods are seldom used in dietary exposure
    assessments they can contribute very useful background information and
    may be the only information for specific population group issues
    (e.g., organic food consumption by vegetarians). They can be targeted
    to answer specific questions or prioritize issues of concern and
    provide a cost-effective tool for the risk assessor.

        Table 28.  An example of food listing and frequency response options of an FFQ

    For each food listed, fill in the          Average use of the last 3 months
    circle indicating how often, on                                                                                            
    average, you have used the amount               Per month                Per week                    Per day
    specified, during the past 3 months                                                                                        
                                               Never or    1-3      1        2-4      5-6      1        2-3      4-5      6+
                                               less than 

    DAIRY FOODS    Skim or low-fat milk 
                   (8 oz glass)                0           0        0        0        0        0        0        0        0

                   Whole milk (8 oz glass)     0           0        0        0        0        0        0        0        0

                   Sherbet or ice milk 
                   (1/2 cup)                   0           0        0        0        0        0        0        0        0

                   Ice cream (1/2 cup)         0           0        0        0        0        0        0        0        0

         Food consumption data is often collected for nutritional or
    economic purposes, and foods may not be described in the detail
    required for exposure assessment (e.g., fish consumption may be
    recorded but the contaminant of interest may be found primarily in
    fatty fish or fish caught in a particular location). There are number
    of difficulties using the different types of consumption data. A
    report from the European Commission provides a good summary of the
    practical problems in using consumption data to estimate dietary
    exposure (EC, 1997a).

    7.4.4  Contaminants in food

         The vast majority of food that is actually consumed has undergone
    some form of processing, ranging from simple washing to complete
    reconstitution, as it progresses from the producer to ultimately being
    ingested by a consumer (FAO/WHO, 1995b). Several factors can influence
    contaminant concentrations in foods that are ready to eat. These
    factors include those that may vary by season and/or geographic
    region, such as food source (e.g., homegrown, locally grown by a small
    producer, domestically grown by a mass producer and imported), and
    former or current application of pesticides (US NRC, 1993). The form
    in which food is consumed (e.g., raw apple, apple sauce, apple juice)
    can be very different in different subpopulations (e.g., adults,
    elderly or young children).

         Residue levels measured in raw agricultural commodities collected
    at the producer, processor or distribution level are unlikely to be an
    accurate reflection of contaminant concentration in food as actually
    consumed. With the exception of the GEMS/Food, which collects
    contaminant and pesticide residue data from member countries, there
    are no centrally coordinated reference databases for other food
    chemicals in foods. Potential data sources at the national level may
    include supervised trial data, government monitoring and surveillance
    data (Pennington & Gunderson, 1987), national food composition
    databases (nutrients) and industry funded surveys. A number of
    analytical methods for contaminants in food have been published by the
    US FDA, EOAC and US EPA (e.g., US FDA 1997a,b). Different approaches
    have been used for calculating exposure when the contaminant
    concentrations fall below the detection limit (e.g., assuming the
    concentration is zero or some percent of the detection limit).

    7.5  Summary

         This chapter has introduced available sampling methodology for
    chemicals in air, water, and food. Common to the selection of these
    methods are considerations of detection limits, interferences, ease of
    operation and cost. Personal, microenvironmental and ambient air
    sampling methods are available for monitoring gases and vapours, both
    passively and actively, aerosols, SVOCs and reactive gases.

         Sampling considerations for assessing water quality are numerous.
    An important consideration is that exposure to contaminants is not
    limited to oral routes and that not all individuals have access to
    treated water from distribution systems. Guidance for sampling and
    monitoring programmes is provided.

         There are a number of methods for measuring estimating food
    consumption and contamination. The method chosen will depend on the
    information available, the population group of concern, whether acute
    or chronic effects of the chemical are being expressed, the intended
    use of the results and available resources. The reader is strongly
    advised to refer to more comprehensive documents on dietary survey
    methodology and dietary exposure assessment approaches.


    8.1  Introduction

          This chapter is intended to provide the reader with important
    concepts and a basic understanding of soil and settled dust sampling
    so that effective sampling strategies can be designed to meet specific
    research needs. Choices in sampling methods, sampling locations,
    sampling areas and the sampling time of the sample collection may be
    particularly important when the results are used for exposure
    assessment purposes. For these methods to be used successfully, it is
    important that the investigators understand the basic concepts behind
    collecting soil and settled dust and the limitations of different
    methods and strategies. Because this field of research is currently
    evolving rapidly, it is recommended that researchers consult the
    literature for new and complete information before designing a study
    to measure toxic metals, pesticides, PAHs, other products of
    incomplete combustion, fibres and biological matter. The most
    appropriate method for sampling soil and settled dust depends on the
    living conditions of the study population and the target contaminants.
    The information in this chapter is therefore intended to provide
    general guidance on approaches that might be taken.

          Soil is a mixture of air, water, mineral and organic components
    (Horne, 1978). The relative mix of these components determines to a
    large extent the capacity of a soil for containing chemical
    contaminants and the potential for it to be an important source of
    exposure. Settled dust, which may be found outdoors or indoors, is
    often a complex mixture of material from several sources. Outdoor
    settled dust is material deposited on roadways, streets and other
    paved surfaces. Indoor settled dust (house dust) is material deposited
    on indoor surfaces such as floors, carpets and furniture. Chemical
    contaminants present in indoor dust can originate from activities in
    the home or can be tracked into the home from road dust, soil or work
    sites (US EPA, 1991). Material present in soil, outdoor dust and
    indoor dust may include clay, sand, bacteria, viruses, allergens,
    products of incomplete combustion, environmental tobacco smoke, heavy
    metals, pesticides, asbestos fibres, paint fragments, solvents, flame
    retardants, cleaners, and residues from synthetic fibres, building
    products and many other materials and pollutants (Robert & Dickey,

          Unintentional ingestion of house dust, particularly for children,
    may be a significant contributor to the total human exposure to many
    potentially toxic substances, depending on personal living conditions
    and frequency of contact with this media. Because children spend more
    time in contact with soil and indoor surfaces than adults and have a
    greater dose given the same exposure, these exposure pathways are
    particularly relevant to children. For example, it is likely that
    children's lead exposure from settled dust is an important
    contribution to total lead exposure because of the past and present
    use of gasoline, lead-based paint on housing and steel structures, and

    airborne emissions from industrial point sources that settle in
    residential environments. In the USA, house dust is considered a major
    source of lead to most children (CDC, 1991; Lanphear & Roghmann,
    1997). Older homes are especially susceptible to lead dust exposure if
    paint is peeling or renovations are being done (Roberts et al., 1992).

          Soil and settled dust can be a significant source of exposure to
    numerous other toxicants in addition to lead, including pesticides and
    PAHs. Pesticides, although designed to degrade to different extents
    through natural environmental processes such as sun, rain and soil
    microbial activity, may accumulate in soil and dust and persist for
    long periods of time. Because of the lack of these external
    degradation processes, pesticides may be particularly persistent in
    indoor settled dust (Simcox et al., 1995). Studies have shown that in
    the general population in the USA the highest concentrations and
    largest number of pesticides are found in house dust as compared to
    soil, air and food (Whitmore et al., 1993; Lewis et al., 1994).
    Although many pesticides in house dust come from outdoor sources, many
    households use pesticides indoors. Because little or no training is
    provided for users of household pesticides, unnecessary exposures
    often occur. Pesticides often found in house dust include those used
    for control of insects; e.g., chlordane and heptachlor in homes
    treated for termites, pentachlorophenol and lindane in homes where
    wood preservatives had been used, and other harmful pesticides
    contained in flea and garden treatment (Roberts et al., 1992).

          Hazardous substances that originate at the worksite may also find
    their way (e.g., via clothes) into the homes of workers. The US
    National Institute for Occupational Safety and Health compiled a
    bibliography of more than 350 published and unpublished accounts of
    take-home, or "para-occupational" contamination worldwide (NIOSH,
    1994). The reports identified by NIOSH document the spread from
    workplace to home of toxic metals (lead, beryllium, cadmium and
    mercury), asbestos, and various other potentially hazardous
    substances. Settled dust was a major source of familial exposure in
    most of these studies.

    8.2  Selected sampling methods

    8.2.1  Soil

          Soil constitutes a potential exposure pathway through direct
    contact and ingestion or inhalation of resuspended soil particles.
    Children's activities make them more likely to be affected by such
    exposures. In addition, contaminated soil can be tracked inside homes,
    or may infiltrate indoors when resuspended. In either case, soil may
    become a component of settled indoor dust. There are no standard
    collection methods for soil sampling, as discussed later for settled
    dust (section 8.2.2). This limitation affects the ability to make
    comparisons of results from soil sampling across studies. However,
    information on soil contamination can provide insights into the
    relative importance of multimedia contaminants as they may affect
    exposure.  Surface soil collection

          The most commonly used approaches make use of an auger or similar
    sampler such that a sample is defined by cross-sectional area and
    predesigned depth of the auger. Alternatively, a predetermined amount
    of surface soil may be scooped with a small trowel, with less precise
    definition of sampler depth. In either case, the sample is stored in a
    clean, inert container and transferred to the laboratory for analysis.  Soil contact and intake measurements

          Skin contact has been measured by methods similar to those used
    for settled dust (e.g., self-adhesive labels, hand wipes), and
    controlled application followed by recovery of the fraction of
    deposited soil on the skin (Lepow et al., 1975; Roels et al., 1980;
    Que Hee et al., 1985). The amount of soil that adheres to the skin
    depends on a number of variables including soil properties (e.g.,
    water content, particle size, carbon content), region of the body and
    activity (Kissel et al., 1996). A number of studies have attempted to
    estimate soil ingestion based on hand adherence estimates and
    scenarios of activities, as well as analyses of soil tracers (e.g.,
    concentrations of aluminium, silicon or titanium) (e.g., Calabrese et
    al., 1989, 1990).

    8.2.2  Settled dust

          Although indoor dust is becoming recognized as a reservoir for
    many toxic substances and a potentially significant source of human
    exposure, there is no uniform standard for sampling settled dust. More
    than 15 methods have been described in the literature to date.
    Scientists do not yet agree either on the definition of settled dust
    or on the methods to measure it. This issue is further complicated by
    the fact that results from one settled dust sampling method may not be
    directly comparable to results from others. Even with these
    limitations, settled dust sampling methods have been used effectively
    and provided valuable insights into the total human exposure paradigm.

          Selected sampling methods are described below to give the reader
    an indication of the diversity of techniques available. The list is by
    no means exhaustive. Several of the methods described are simple to
    use and readily available to researchers worldwide. Brief descriptions
    of how to use the simpler methods are provided. Other methods require
    specialized equipment that is relatively expensive and may be
    difficult to obtain in some regions of the world. The methods are
    distinct from one another, but most fall into three categories: wipe,
    vacuum sampling and sedimentation methods. These methods are widely
    used for sampling settled dust indoors; however, in principle they may
    be applicable for outdoor settled dust as well. Bulk sample collection
    methods, such as sweeping, are not covered here. Key features of the
    various methods for collecting settled dust samples described in this
    chapter are summarized in Table 29.

        Table 29.  Comparison of features of different methods for collected settled dust samples

    Feature               Common    HUD     Preweighed   Commercial   DVM       Rotary   HVS3       Sirchee    Sedimentation
                          wipe      wipe    sample       vacuum       vacuum    vacuum   vacuum     -

    Widely available      Yes       Yes     Yes          Yes          No        No       No         No         Yes

    Cost                  Low       Low     Low          Medium       High      High     High       Medium     Medium

    Simple method         Yes       Yes     Yes          Yes          Yes       Yes      No         No         Yes

    Loading               Yes       Yes     Yes          No           Yes       Yes      Yes        Yes        Yes

    Concentration         No        No      Yes          Yes          Yes       Yes      Yes        Yes        Yes

    Sieving possible      No        No      No           Yes          No        No       Yes        Yes        Yes

    Portable              Yes       Yes     Yes          No           Yes       Yes      No         Yes        Yes

    Samples small areas   Yes       Yes     Yes          Yes          Yes       Yes      No         Yes        Yes

    AC powered            No        No      No           Yes          Yes       Yes      Yes        Yes        No

    Size selective        No        No      No           No           Yes       No       Yes        No         No
  Wipe sampling methods

          A common wipe sampling method uses  premoistened towelettes to
    wipe a measured area defined inside a template placed on the sampling
    surface (Vostal et al., 1974; US HUD, 1995). Typical sampling areas
    are in the range of 0.1 m2 and masking tape is commonly used as a
    template. The actual surface area inside the template is not critical
    as long as it is measured and recorded. However, sampling areas
    greater than 0.2 m2 are not recommended because larger areas cannot
    be wiped effectively with one towelette. This method has been used
    extensively in the USA to measure lead amounts in settled dust, but
    has also been used to ascertain levels of cadmium, chromium and
    arsenic, as well as many other metals and organic compounds.

          With the  HUD method, the person collecting the sample should
    wear a clean disposable glove on the hand that will come in contact
    with the towelette. To collect a sample, the surface inside the
    template is wiped with a towelette back and forth in vertical
    S-strokes. The exposed side of the towelette is then folded inward,
    exposing a clean portion, and the same area is wiped with horizontal
    S-strokes. The towelette is folded once more, again exposing a clean
    portion, and the area is wiped a final time with additional vertical
    S-strokes. The towelette is then folded, exposed side in, placed into
    a clean sealable plastic bag or container, and sent to a laboratory
    for analysis.

          Several researchers have used  preweighed wipe material, such as
    cotton gauze or filter paper, in order to determine the quantity of
    settled dust collected (Lepow et al., 1974; Stark et al., 1982;
    Rabinowitz et al., 1985; Levallois et al., 1991). The sampling
    material is then reweighed in a laboratory after sample collection.
    Theoretically, the weight of total dust collected can be calculated by
    subtraction, and toxicant concentration could be determined after
    analysis on a mass basis.

          An important issue that needs to be addressed when using the
    preweighed wipe methods is the potential loss of sampling material or
    dust during handling in the field or laboratory. Furthermore,
    Chavalitnitikul & Levin (1984) noted that filter paper tends to fall
    apart when rough surfaces are wiped. Loss of sampling material in the
    field would underestimate the amount of total dust collected when
    final weights are obtained, which would in turn overestimate the
    calculated mass concentration results. Because of water loss or gain,
    changes in humidity may also significantly affect the before and after
    weights of the samples. These potential sources of error must be
    carefully controlled to make the results from preweighed wipe methods

          A specially designed preweighed wipe sampling method has been
    developed to minimize the potential sources of error mentioned above.
    This method, known as the  Lioy-Weisel-Wainman (LWW) method, was
    developed to quantitatively measure the toxicant concentration (mg/g)

    and surface loading (mg/m2) of dust on flat surfaces (Lioy et al.,
    1993). The sampling device is not made from common materials and is at
    this time only available from the research group that developed it.  Vacuum methods

          Many researchers have collected samples from commercial household
    vacuum cleaners, which are often referred to in the refereed
    literature as research dust samplers. Some researchers state that they
    sampled only the fine dust that settled to the bottom of the bag.
    (Kaye et al., 1987; Moffat, 1989; Davies et al., 1990; Thornton et
    al., 1990; Jensen, 1992). Other researchers modified their vacuum
    cleaners to hold filters (Diemel et al., 1981; Watt et al., 1983).

          A settled  dust vacuum method, commonly called the DVM, is
    constructed from conventional industrial hygiene sampling materials
    that are likely to be available to researchers worldwide (Que Hee et
    al., 1985). The sampler consists of a common personal air-monitoring
    pump, usually operated at 2.5-3.0 litres/min. Sampling areas with this
    method are typically 25 cm × 25 cm, and often take more than 5 min to
    sample completely. A three-sided template is sometimes used on bare
    floors to vacuum dust that has migrated to the walls. Sampling areas
    are covered three times with overlapping passes in the horizontal and
    vertical directions. Que Hee et al. (1985) state that the sampler was
    designed to collect only small dust particles that would most likely
    stick to a child's hands, not total lead on a surface. Therefore, the
    amount of dust collected by this method from a given surface is
    usually less than collected by other methods. This sampler has been
    used in numerous studies in the USA and elsewhere, and its use has
    provided considerable information linking lead in settled dust to lead
    in children (e.g., Bornschein et al., 1985).

          Researchers have also used laboratory  rotary vane vacuum pumps 
    connected to the same three-piece filter cassettes as used with the
    DVM described above, but with a much higher flow rate. The filter
    cassette is often used openface or with a wide diameter nozzle so
    sampling areas can be covered in fewer passes than required for the
    DVM, thus reducing the time spent collecting samples (Solomon &
    Hartford, 1976).

          Prpic-Majic et al. (1992) described another vacuum pump sampling
    method that used a prescreen at its nozzle entrance to prevent coarse
    particles and small objects from being collected on the membrane
    filter that served as the sampling surface. Total dust measurement was
    obtained from the dust particles that reached the membrane filter.
    There was no mention of potential loss of fine dust trapped in the
    prescreen, especially after it was loaded with fibres and debris.

          A sophisticated vacuum sampling device, called the  HVS3, was
    designed to make dust collection efficiency from different surface
    types as consistent as possible (ASTM, 1993). The HVS3 is a
    high-powered vacuum cleaner equipped with a nozzle that can be
    adjusted to a specific static pressure and air flow rate to allow for
    consistent dust collection. The sampler uses a cyclone to separate
    particles greater than about 5 mm from the air stream and collects
    them in a 250 ml sample bottle screwed into the bottom of the cyclone.
    Smaller particles are not collected. The HVS3 can collect large,
    representative samples of settled dust from indoor surfaces, such as
    rugs and bare floors, and dust from outdoor surfaces, such as streets,
    sidewalks, lawns and bare, packed dirt. However, it cannot be used to
    sample from small or uneven areas because of the large size of the
    device. The HVS3 has been used in numerous exposure assessment studies
    to measure toxic metals and pesticides in settled dust. The sampler is
    not made from standard materials and is therefore relatively expensive
    to buy. Interested readers should consult the ASTM standard method
    (D5438-93) for more information on the specifications and availability
    of the HVS3 sampling device (ASTM, 1993).

          Farfel et al. (1994) modified the HVS3 by using the same cyclone
    as in the HVS3 but with a commercially available handheld vacuum to
    make the device smaller and more portable. These authors also used
    flexible tubing as the pickup nozzle to allow small surfaces, such as
    windowsills, to be sampled. This modification, called the  BRM 
     method, does not allow control of either the sampling flow rate or
    the static pressure at the pickup nozzle. The ASTM standard method for
    the HVS3 does not apply to the BRM, except for its description of the

          Another settled dust vacuum sampling method that has been used in
    several research studies, the  Sirchee-Spittler method, is a
    hand-held, battery-powered vacuum unit (Rinehart & Yanagisawa, 1993;
    Weitzman et al., 1993; Aschengrau et al., 1994). The sampler is simple
    to use, highly portable and can cover large areas in a short period of
    time. Unfortunately, there are not many Sirchee-Spittler sampling
    devices in service and its availability to researchers worldwide is
    therefore limited.  Sedimentation methods

          Sedimentation methods involve measuring the amount of dust which
    settles on a clean, preweighed surface over a given period of time.
    Such procedures can make use of a simple collecting cup (Aurand et
    al., 1983) or a flat plate (Pellizzari et al., 1995). After a
    specified period of time, the sample is collected and measured, and
    the dust is then analysed in a laboratory. Data from the German
    Environmental Survey (Schulz et al., 1995) on domestic dust
    precipitation is given in Table 30. Sedimentation methods are useful
    for collecting samples over a specific period of interest (e.g., a
    day, week or month). In contrast, the integration times of settled
    dust samples collected using the wipe or vacuum methods described
    above are not well characterized.

        Table 30.  Sedimentation of elements in indoor dust, Germany 1990-1992 (Schulz et al., 1995)

    Element        No. of     No. of              Percentiles                  Maximal        Confidence 
                   samples    values                                           value        interval 
                              >LOQ       10          50           95           (GM)           (GM)

    Dustfalla      3282       -          1.4         21.0         579          4.52           4.36-4.68

    Arsenicb       3279       965        < 4         33           1313         5.4            5.2-5.6

    Boron          2896       511        < 0.06      0.64         47.1         0.13           0.13-0.14

    Cadmiumb       3282       0          5           44           833          11.7           11.4-12.0

    Calcium        3277       25         17          273          2679         51.2           49.5-52.9

    Chromium       3282       14         0.02        0.28         3.92         0.07           0.06-0.07

    Copper         3277       1167       < 0.3       1.5          48.8         0.33           0.32-0.34

    Iron           3277       74         2           41           765          7.7            7.4-8.0

    Lead           3282       0          0.11        1.17         86.6         0.29           0.28-0.29

    Magnesium      3277       26         2           25           342          5.2            5.0-5.3

    Phosphorus     3277       1063       < 1.8       17           542          2.8            2.7-2.9

    Zinc           3277       15         0.9         8.6          108          2.2            2.1-2.3

    Units are µg m-2d-1 unless otherwise indicated.
    a  mg m-2d-1.
    b  ng m-2d-1.

    8.3  Sampling design considerations

          Section 8.2.2 describes numerous innovative methods that have
    been developed and used by researchers to collect settled dust from
    surfaces. Many more examples can be found in the literature. However,
    there has been little standardization among the methods. Differences
    in vacuum pump flow rates, nozzle shapes and sizes, and sampling
    technique will affect dust-pickup characteristics of vacuum sampling
    methods and will, therefore, affect the results. Differences in wipe
    sampling material and technique will also affect the results from wipe
    samples. Different recovery rates of dust from alternative
    sedimentation methods can also have a large effect on analytical
    results. These differences among methods, which are not well
    documented in the literature, can make interpretations and comparisons
    between studies difficult. It is important that sampling methods are
    well described when results from settled dust sampling are reported.

          Sampling design considerations for soil should follow the
    objectives of the study and consider the particular conditions of the
    site being monitored. For example, multiple soil samples can be
    obtained around the perimeter of a house at a sufficient distance so
    that the soil is representative of material that might be tracked into
    the home. In this case, the samples might be composited. Backyard soil
    might vary in the number and amounts of contaminants present, as well
    as usage and specific activities by residents. The number and location
    of samples to be obtained should be based on these considerations.

    8.3.1  Concentration and loading

          Almost all settled dust contains measurable levels of common
    environmental contaminants such as heavy metals and pesticides, and
    most residential surfaces, such as floors and windowsills, contain
    settled dust (CDC, 1991). The actual concentration of a target analyte
    in a sample of settled dust depends on the amount of dust collected
    that does not contain the analyte and the amount of dust collected
    that does contain the analyte.

          The analyte concentration, sometimes called a  mass 
     concentration, is usually expressed as micrograms of analyte per
    gram of dust (µg/g). The amount of dust on a surface can be expressed
    as grams of dust per unit area, such as per square metre, and is
    usually called  dust loading (g/m2). The analyte concentration,
    multiplied by the dust loading on a surface, gives a  analyte 
     loading value and is commonly expressed as micrograms of analyte per
    unit area (µg/m2). The dust loading and analyte loading measurements
    are both  area concentrations, that is, the concentration of dust or
    contaminant per unit area. In this report, "concentration" refers to
    mass concentration and "loading" refers to area concentration.

          The example of residential sampling for lead is used to simplify
    the discussion. Common wipe sampling methods, such as the HUD method,
    measure lead loading directly, without measuring lead concentration

    and dust loading. Fig. 24 illustrates what common wipe samples can
    measure, using realistic results collected from floors in a
    hypothetical residence. Assume that each diagonal line in the figure
    represents the lead loading results from one wipe sample. The diagonal
    lead loading lines show the infinite number of lead concentration
    ( y axis) and dust loading ( x axis) combinations that might result
    in the measured lead loading. As mentioned earlier, the product of the
    two parameters is the lead loading (µg/g × g/m2 = µg/m2). Using a
    log scale on the  x and  y axes ensures that the infinite number of
    combinations that result in the same lead loading value fall on a
    straight line. As noted in Chapter 4, the distribution of many
    measures of environmental exposure is skewed right and may often be
    approximated by a lognormal distribution. For lognormal distributions,
    geometric relationships (e.g., factorial) exist among quantiles of the
    distribution, in contrast to the linear relationships present in
    measures that follow a normal distribution. As described in Chapter 4,
    lognormal distributions can be "normalized" in a numerical sense by
    expressing the data as the log-transformed values or in a graphical
    sense by plotting data on log scales. This example assumes that lead
    concentration and dust loading are lognormally distributed and
    perfectly correlated with each other, i.e., lead loading in µg
    lead/m2 is assumed to be constant. A scatter plot of two perfectly
    correlated and lognormally distributed measures depicted on a normal
    scale would exhibit a curved relationship, but appears as a straight
    line when depicted on a log scale.

          Because common wipe sampling measures lead loading directly, but
    does not measure lead concentration and dust loading, the results from
    wipe sampling cannot be used to determine which combination of lead
    concentration and dust loading is present. Similarly, Davies et al.
    (1990) states that for a given contaminant loading value, the
    contaminant concentration can range from high where there is little
    dust to, conversely, low where there is a large volume of dust. The
    only way to measure both lead concentration and dust loading is to
    collect a house dust sample with one of the vacuum sampling methods,
    or with one of the preweighed wipe sampling methods. Common wipe
    sampling methods do not measure lead concentration.

          Although research studies have shown that estimates of both lead
    concentration and lead loading (area concentration) correlate
    significantly with children's blood lead levels, it is unclear which
    measure is better at predicting the true, long-term, lead dust
    exposures to children. Results from Davies et al. (1990) suggest that
    the average lead loading (lead area concentration) measured in a
    child's environment expressed more realistically the exposure of
    children to lead than did lead concentration (lead mass concentration)
    measurements. Results from the Lanphear et al. (1995) study also
    suggest that lead loading measurements correlate better with
    children's blood lead levels than does lead concentration. However,
    Bornschein et al. (1985) showed that, for their conditions, lead
    concentration and lead loading have very similar correlations with

    FIGURE 24

    children's blood lead levels. Laxen et al. (1987) found that blood
    lead levels did not correlate better with lead dust loading than with

    8.3.2  Collection efficiency

          Another important concept to understand is that the type of
    surface from which the dust is sampled directly affects the efficiency
    of dust collection from the surface. Furthermore, different sampling
    methods recover different amounts of total dust from the same sampled
    surface. These differences are due to different collection
    efficiencies of the methods. Differences in collection efficiency on
    different surface types and among sampling devices may influence
    measurements of toxicant levels in settled dust.

          Roberts et al. (1991) documented total dust recoveries that
    ranged from greater than 90% by weight on a smooth painted surface to
    about 30% on a carpet. Chavalitnitikul & Levin (1984) compared several
    types of wipe sampling methods. They conducted a laboratory wipe
    sampling experiment with wipe materials on a smooth surface (Formica)
    and a rough surface (plywood). The study examined different wipe
    materials, such as Whatman filters, paper towels and adhesives --
    paper labels, adhesive cloth and dermal adhesive. The researchers
    determined that, on smooth surfaces, all techniques were comparable,
    with about 85-90% recovery with carefully prescribed protocols. On
    plywood, however, recoveries dropped to less than 43%. They also noted
    that the Whatman filters fell apart on the rough surface. Other
    sampling method characterization studies document similar differences
    (US EPA, 1995a,b).

          Three commonly cited methods used to sample lead in settled dust
    (the DVM, BRM, and HUD methods) may collect very different amounts of
    total dust from the same surface (Lanphear et al., 1995). Assuming
    that a smooth hard surface is sampled, the difference in collection
    efficiency between the DVM and the other two methods may be greater
    than a factor of 10, with the DVM consistently collecting less dust
    than the BRM and HUD methods. The latter two methods would probably
    collect similar amounts of dust on a smooth hard surface.

          Since contaminant loading is directly related to total dust
    collected from the sampled surface, the DVM sampler will consistently
    measure lower contaminant loading values on hard surfaces than the BRM
    or HUD methods. This does not imply that a high collection efficiency
    is better than a low efficiency. An argument in favour of the DVM's
    low collection efficiency is that it measures the more biologically
    active fraction of leaded dust available to a child (Que Hee et al.,
    1985). However, results from the only study to use all three methods
    side by side in children's homes suggest that the BRM and HUD methods
    correlate slightly better with children's blood lead levels than the
    DVM method (Lanphear et al., 1995). The same study showed that the BRM
    collects much more dust from carpeted surfaces than the DVM or HUD
    methods. The point to note is that lead loading measurements on the
    same surface differ among sampling methods. Further research is needed

    to determine the importance of collection efficiency for exposure
    assessment studies.

          As with contaminant loading, differences in collection efficiency
    on different surface types and among sampling methods may affect
    measurements of contaminant concentration. Differences in the relative
    recovery of contaminant-containing dust and non-contaminant-containing
    dust can result in different contaminant concentration measurements.
    Theoretically, however, concentration measurements are likely to vary
    less among methods than are loading measurements. Results from the
    Lanphear study, which collected hundreds of side-by-side lead dust
    samples with the DVM and BRM methods, are consistent with this theory.
    Geometric mean lead levels and the corresponding standard deviations
    suggest that, on average, side-by-side lead loading measurements
    differ more between the two sampling methods than do the lead
    concentration measurements (Lanphear et al., 1995).

    8.4  Sampling strategies

          Choosing an appropriate sampling method is an important part of
    designing a study to measure toxicants in house dust. However, it is
    only part of designing a sampling strategy. The sampling method
    specifies how to collect settled dust, whereas the sampling strategy
    specifies the process of sampling. Several of the questions that need
    to be answered when developing a sampling strategy are:

    *   What age group is targeted by the study?

    *   Which surfaces and substrates should be sampled?

    *   When and how should sampling take place?

    *   Should a composite sample be created?

    *   How will the samples be analysed?

          As noted in the first section of this chapter, young children who
    play on floors are likely to have higher exposure to settled dust than
    adults. Children may be also routinely exposed to dust in areas of a
    residence that adults do not contact. Different sampling strategies
    may be appropriate for different age groups.

          The potential effect of the surface type and substrate on dust
    collection should be factored into the strategy because dust
    collection efficiencies from different surface types can vary greatly.
    For example, toxicant loading or concentration measurements may
    correlate relatively well with biological measurements when dust is
    collected on hard floors or on carpets. However, if the person's
    relative exposure to dust from floors versus carpets differs from the
    sampling method's relative collection efficiency on these surfaces,
    the relationship between biological and settled dust measurements will
    be different for each surface. Similar differences between a human's
    exposure and a sampling method's collection efficiency may be found

    between components within a room, such as between a windowsill and a

          Another issue to note is that the sources of dust, its temporal
    and spatial variability, and accessibility to humans, especially to
    young children, may vary greatly from person to person, room to room
    and house to house. However, little research has been done to examine
    this variability across space and time. Interpretations of house dust
    sample results may, therefore, be affected by this variation in
    addition to the variation introduced by the choice of sampling method.
    Short-term changes in a person's environment before sampling, possibly
    influenced by sporadic house cleaning practices or by a person who has
    just returned home from vacation, may offset the dust/biological
    relationships owing to the timing of sample collection.

          The toxicant levels in settled dust to which a person is exposed
    may be thought of as a weighted average across the areas where the
    person has dust contact, with weights roughly proportional to the time
    a person spends in different areas. From a sampling perspective, the
    average toxicant level to which a person is potentially exposed may be
    estimated by collecting many individual samples of settled dust for
    separate analysis and combining the results by calculating a weighted
    average after analysis. Or, field composite samples can be collected
    before laboratory analysis by collecting and physically combining two
    or more settled dust samples from each of several areas in a dwelling.
    Researchers have used both strategies for collecting dust samples
    (Farfel & Rhode, 1995).

          A common criticism of composite sampling is that toxicant
    variation across a floor or throughout a residence cannot be
    determined; toxicant "hot spots" may be missed. It must be
    acknowledged, however, that any settled dust sampling strategy may
    miss hot spots. The important issue is how much these hot spots
    contribute to the total exposure of the average person. This question
    has not been answered by scientific studies. In any case, the
    statistical relationship between biological toxicant levels and
    average toxicant levels in settled dust levels across large areas in
    which a person may be exposed are likely to be better than the
    relationship between biological levels and a potential high-dose
    source of toxicant exposure for a short period of time. Davies et al.
    (1990) used this assumption to design a sampling strategy that
    collected settled dust "taken over all the exposed floor surface in
    the rooms concerned" (thus, the average level was measured in a room)
    rather than from small areas in the room, and found a relatively high
    statistical relationship with children's blood lead levels
    ( r = 0.46).

          Possibly the best measures of toxicants in settled dust for
    exposure assessment purposes are averages of dust measurements taken
    repeatedly over time. If one were to repeat sampling over time,
    averages across space and time could be obtained. However, most
    sampling strategies used in previous studies collected settled dust at
    only one point in time. An obvious advantage to cross-sectional (one

    time) studies is that they are less expensive than longitudinal
    (repeated measures) studies, which require repeated visits to a
    dwelling, greater occupant burden, and higher laboratory analysis

          One possible, but untested, approach to strengthening estimates
    of time-weighted average dust levels in cross-sectional studies may be
    to measure exposure-weighted average levels based on the activity of
    the person. This may be done by listing indoor locations where the
    person spends time, then roughly estimating the percent of time spent
    actively in each location, rounded to a convenient percentage. Samples
    can then be composited from the specific areas by adjusting the
    subsample areas to be proportional to the percent of time spent in
    each area. An exposure-weighted average toxicant dust level could then
    be estimated from the result.

          Finally, laboratories performing the chemical analysis should be
    consulted before settled dust samples are collected. This is
    particularly true when collecting composite wipe samples. An excess of
    towelette material may present problems during the laboratory
    digestion phase of analysis, requiring more reagents and larger
    beakers than normally used, and potentially reducing the toxicant
    recoveries owing to matrix effects. Similarly, vacuum sampling may
    collect more dust than is required for analysis. If this is the case,
    techniques need to be employed by the laboratory to ensure that the
    fraction of dust analysed represents the whole. Another potential
    source of error in the results lies in how the dust is handled after
    sampling and prior to analysis. If measurements of lead concentration
    in dust are important for the objectives of the study, sampling
    methods that present the dust to the laboratory in an easy-to-handle
    form should be considered over alternate methods. These issues and
    others should be well thought out before the commencement of a settled
    dust sampling effort.

    8.5  Summary

          Human contact with soil and settled dust can be an important
    source of exposure to chemical contaminants, especially for children.
    Although many sampling methods have been developed, no single approach
    has been demonstrated to be superior to the others. As a consequence,
    it is difficult to compare results from studies that utilize different
    sampling methods. Important factors to consider when selecting a
    sampling method include collection efficiency, differences in human
    activity patterns, physical variability of soil and dust levels over
    space and time, surface and substrate sampled, timing of sample
    collection and analytical methods used to measure toxicants in the


    9.1  Introduction

          Microbiological organisms have long played an important role in
    human ecology. Fungi are critical to the production of cheese and the
    fermentation of beer, and in some cases are a direct source of
    nourishment. In the first half of the 20th century,  Penicillium 
     chrysogenum colonies were discovered to inhibit growth of other
    organisms. Today pharmaceutical companies, among others, are exploring
    fungal enzymes for a variety of reasons including new drugs,
    non-chemical pesticides, biodegradation of waste and possible
    catalysis of chemical reactions.

          However, natural does not mean benign. Human exposures to
    microorganisms have resulted in allergic, toxic and infectious
    disease. As humans have modified the environment through cultivation,
    landscaping and building structures, ecological balances have been
    disturbed. The distribution of moisture and nutrients has been altered
    to a point where it is quite common to encounter reservoirs of fungi,
    bacteria and algae, and infestations of mites and cockroaches.

          Through airborne dispersion, ingestion or direct contact, humans
    confront components of microorganisms continuously. We may be affected
    through an immune reaction requiring sensitization. Predisposed
    individuals may not experience a reaction for some time after they
    have been exposed. Once an individual is sensitized, a reaction such
    as an asthmatic attack might be delayed hours following the exposure
    event. However, there are many infectious diseases induced by fungi
    and bacteria that require no period of sensitization before illness
    develops. There is yet another route whereby microorganisms can evoke
    irritation and health effects: some metabolites from moulds are
    carcinogenic (e.g., aflatoxin B) or immunosuppressors; some cause
    dermatoxic effects; others cause annoyance and irritation by the VOCs
    they release.

          Table 31 provides basic categories for the microorganisms of
    primary interest and some possible sources. Assessing exposures to
    microorganisms is very different in some aspects from assessing
    exposures to physical or chemical agents. For virtually all
    microorganisms, exposure-response or dose-response information is
    currently limited. Nevertheless, exposures to allergens, fungal
    spores,  Legionella, and tuberculosis, among many others, are being
    inferred from sampling. And, particularly for assayable antigens and
    endotoxin, dose-response data are accumulating rapidly. Observed
    increases in tuberculosis and asthma as well as atopy have brought a
    resurgence of epidemiology and expanded interest in exposure

        Table 31. Common bioaerosols, related diseases and typical sources

    Bioaerosol                Examples of diseases                 Common sources

    Pollens                   hay fever                            plants, trees, grasses, ferns harvesting, cutting, 
    Spores                    allergic rhinoconjunctivitis         shiploading
    Plant parts               asthma
                              upper airway irritation

    Fungi                     asthma, allergic diseases            plant material, skin, leather, oils; bird, bat and 
                              infection                            animal droppings; feathers, soil nutrients, 
                              toxicosis                            glues, wool

    Bacteria                  endotoxicosis                        humans, birds and animals (e.g., saliva, blood, 
                              tuberculosis                         dental secretions, skin, vomit, urine, faeces)
                              pneumonia, respiratory and wound     water sprays and surf, humidifiers, hot tubes, 
                              infections, legionellosis, Q and     pools, drinking water, cooling towers
                              pontiac fever

    Other allergen sources    asthma                               mite excreta, insect parts (cockroach, spiders, 
    Arthropods                dermatitis                           moths, midge)
    Vertebrates               hypersensitivity                     dander and saliva from cats, dogs, rabbits, 
                              pneumonitic                          mice and rats, bird serum, farm animal dander

    Virusesa                  respiratory infections, colds,       infected humans, animal excreta, 
                              measles, mumps, hepatitis A,         insect vectors, protozoab
                              influenza, chicken pox, Hanta virus

    a  Viruses are included in table for completeness but are not covered in this chapter.
    b  Protozoa in the form of free-living amoebae can be direct acting pathogens or allergens; they can also 
       interact with bacteria (e.g.,  Legionella growth within amoebae).
       Source: developed from Burge (1995).

          This chapter discusses the strategy and methodology for exposure
    assessment of five major categories of biological particles:

    *   house dust mites and their faeces

    *   allergens from pets and cockroaches

    *   allergens and/or toxins derived from

        -   fungi
        -   bacteria

        -   pollen

          For each category information will be presented regarding
    sampling methods, methods of analysis, and advantages and drawbacks of
    the different methods. Seasonal variations in mite allergen and fungi
    are illustrated by showing the summary results of an extensive survey
    conducted in Australia. Mite and pollen antigen as well as fungal
    organisms can vary substantially within homes and buildings, as
    illustrated in the figures in this chapter. The reader is referred to
    texts such as ACGIH (1989) and Burge (1995) for details on
    instrumentations, specific information relevant to the allergenic,
    infectious and toxigenic properties of many microorganisms and their
    constituents and metabolic by-products.

          There are three different basic approaches for the exposure
    assessment of biological particles: observational sampling, reservoir
    sampling (dust, surfaces, water) and air sampling.

    *    Observational sampling means that one uses sensory perception to
        collect data about potential sources of exposure to biological
        particles (e.g., visible fungal growth).

    *    Reservoir sampling refers to the collection of bulk material
        (e.g., surface contact, bulk material, water sample or dust sample)
        to estimate the potential exposure.

    *    Air sampling is the most likely to be representative of human

          This chapter will emphasize reservoir (primarily indoor dust) and
    air sampling of bioaerosols and not gaseous metabolic products.

          Designing a specific sampling programme requires consideration of
    the aim of the sampling, the nature of the biological particles
    (including size and expected concentrations) and parameters that
    influence the actual exposure to these particles. These parameters
    determine the choice of the sampling and quantification method, the
    sampling strategy (e.g., location, season, duration and frequency),
    and approaches for statistical analysis and interpretation of the
    data. For most situations, the exposure route of interest is
    inhalation. Therefore, ideally, the exposure should be assessed by

    personal air monitoring. As will become clear from the remainder of
    this chapter, however, no single sampler fulfils the characteristics
    of the ideal sampler to measure the total exposure to biological
    particles. Many of the methods used for estimating environmental
    concentrations of biological particles are not truly representative of
    an individual's exposure to these particles. As stated earlier, this
    is, in part, because the exposure measure of biological importance is
    not well understood. In addition, the field of environmental
    aeromicrobiology developed from a laboratory biology base that
    borrowed sampling techniques and equipment from other fields. Until
    recently there had been little convention or need for uniformity of
    methods. It is not surprising, therefore, to find a general lack of
    data regarding the validity of the methods used to estimate the
    exposure to biological particles. This situation has certainly changed
    as those investigating exposure assessment aspects of aerobiology have
    cooperated with environmental epidemiologists.

          Useful reference texts with regard to sampling and analysis of
    biological particles include those by the American Conference of
    Governmental Industrial Hygienists (ACGIH, 1995), the European
    Commission (EC, 1993), Hamilton et al. (1992), Pope et al. (1993),
    Burge (1990, 1995), and Burge & Solomon (1987), Reponen (1994), and
    Verhoeff (1994a,b).

    9.2  House dust mites

          House dust mites are members of the arachnid family having eight
    legs and an exoskeleton. They can be up to 300 µm in length and live
    off organic debris found in house dust (e.g., skin flakes, hair
    follicles and fungi) (Colloff, 1991). Because mites absorb water
    vapour they are critically dependent on the absolute humidity.
    Survival in the adult stage requires environmental moisture conditions
    be sustained not lower than 7-8 g/m3 (Korsgaard & Iversen, 1991;
    Fernandes-Caldas et al., 1994). This is equivalent to a relative
    humidity of about 50% at 20°C.

          Mite antigen is mainly found in the faecal pellets which may be
    10-20 µm in diameter and will not remain suspended for very long.
    Feather et al. (1993) identified enzymes derived from the mite gut as
    the source of allergens. These enzymes might remain as potent
    allergenic material in bedding, mattresses, carpets and furnishings
    long after the mite population has diminished, further complicating
    exposure determination. 

          Two different approaches, the sampling of air and of settled
    dust, are available to measure the presence of house dust mites and
    their allergens as indicators of environmental exposure. The latter is
    the most commonly used approach. 

    9.2.1  Air sampling for house dust mites

          Several techniques exist for volumetric sampling of airborne mite
    allergens, using cascade impactors or high- and low-volume samplers in
    combination with membrane filters (Swanson et al., 1985; Price et al.,
    1990; Sakaguchi et al., 1993; Oliver et al., 1995). These techniques
    have the advantage that they sample airborne allergens and might
    therefore be more representative of the true exposure than assays of
    settled dust. The literature is limited, however, on the validity of
    air sampling as measure of exposure to house dust mite allergens
    (Swanson et al., 1985; Price et al., 1990; Sakaguchi et al., 1993),
    and further research is needed.

          Mites themselves are not seen in air samples. Furthermore, in
    undisturbed rooms amounts of airborne mite allergens are small and
    difficult to detect, even after prolonged sampling. Most of the mite
    allergens bind to faecal pellets, which become airborne only as a
    result of disturbance, and little allergen is associated with
    particles that remain airborne for more than a few minutes. Therefore,
    practical disadvantages of airborne sampling of mite allergen are the
    requirements for long sampling periods (2-24 h) and very sensitive
    assays (Thien et al., 1994). Price et al. (1990) used a low-volume air
    sampler (2 litre/min) for 3 h to sample suspended dust mite allergen
    in homes. They reported that the airborne allergen levels correlated
    better with sensitization to mites among children than the levels in
    dust. Further, the air and dust antigen levels were not correlated.
    Although this is the only study linking atopy to airborne mite
    allergens, it does suggest potential limitations of using dust
    sampling as a surrogate exposure measure. In a small number of
    studies, air sampling and dust sampling were carried out in parallel
    (Price et al., 1990; Sakaguchi et al., 1993; Oliver et al., 1995). In
    only one study were significant correlations found between the levels
    of house dust mite allergens in air and dust (Oliver et al., 1995).
    Allergenic responses to dust mite allergens may be induced by
    short-duration high-concentration exposure events. Therefore, the
    clinical importance of integrated air samples may be more relevant in
    predicting prevalence of atopy to mites rather than predictive of
    acute health effects.

          At present no reliable information is available that will support
    adoption of a standardized method for air sampling of house dust mite
    allergens. According to an international workshop held in 1987
    (Platts-Mills & De Weck, 1989) airborne sampling has not been shown to
    be better than dust sampling to measure the level of mite infestation
    in homes or schools. This was confirmed by a second international
    workshop in 1990 (Platts-Mills et al., 1992). It was also stated that
    there are few or no data showing a relationship between airborne
    measurements and sensitization to house dust mites or symptoms. In
    contrast, a relationship is apparent between the concentrations of
    mite allergens in settled house dust and sensitization or symptoms.
    Therefore, air sampling was not recommended (Platts-Mills et al.,

    9.2.2  Dust sampling for house dust mites

          Dust sampling for measurement of the level of mite infestation is
    accepted and recommended as the best-validated "index of exposure" to
    house dust mite allergens. The approach assumes that the quantity of
    allergens released into the air is a function of what is present in
    settled dust, or, conversely, that the measurement of allergen in
    settled dust is related to both the long-term dose a person receives
    and to the short-term airborne levels experienced during events that
    raise dust.

          Standardized sampling procedures to measure house dust mites and
    their allergens in house dust have been proposed (Platts-Mills & De
    Weck, 1989; Platts-Mills et al., 1992; EC, 1993; Dreborg et al.,
    1995). Sampling sites should be consistent throughout the study and
    preferably include the upper mattress surface and the floors of the
    living room and bedroom. Sampling can be conducted with vacuum
    cleaners equipped with a special attachment to collect dust on a paper
    filter. Vacuuming 1 m2 of surface in 2 min is a commonly used
    sampling method. Depending on experiences with the amount of dust
    recovered in specific situations, investigators may have to modify the
    sampling procedures. Samples can also be obtained from upholstered
    furniture, soft toys and clothing. Alternative techniques for
    collecting dust samples include shaking blankets in a plastic bag and
    scraping flat surfaces higher than floor level with a piece of firm
    card. However, these techniques are less effective than collection by
    vacuum cleaner and not standardized. The dust samples may be sieved
    before analysis to obtain a sample of fine dust that can be weighed
    accurately. Unfortunately, dust samples may still vary in density
    after sieving. An alternate method for sampling airborne mite
    allergens is to collect settling dust on large Petri dishes over a
    period of 14 days (Tovey et al., 1992; Oliver et al., 1995). Brown
    (1994) developed a variation on the integrated settling method. A
    100-cm2 piece of sticky tape is placed in contact with the surface
    for 24 h. Under low-power magnification (36×), the trapped mites are
    counted. Using an empirically derived collection efficiency of 30%,
    the number of live mites per area is estimated. However, this does not
    reflect the true extent of exposure to mite allergens (see section

    9.2.3  Available methods of analysis for house dust mites

          There are three types of method for estimating the concentrations
    of house dust mites or their allergens in (airborne) dust samples:
    mite counts, immunochemical assays of mite allergen and guanine
    determinations. The choice of a particular method depends on the
    specific purpose of a study.  Mite counts

          The prevalence of mites in settled house dust can be determined
    by counting under a microscope after separation from the dust sample
    by flotation or suspension. This technique permits the identification

    of the predominant species and the recognition of live, dead, larval
    or adult types. The disadvantages of this method include:

    *   the need for training and development of skill in determining
        different mite species

    *   the failure to quantify faecal pellets and disintegrated mite
        bodies and therefore to reflect the true extent of exposure to mite
        allergen levels

    *   the unsuitability for large-scale (epidemiological) studies owing
        to the time-consuming nature of the work (Platts-Mills & De Weck,
        1989; EC, 1993).

          A further limitation of this method is variation among the actual
    extraction techniques. Bischoff et al. (1992) estimates that less than
    10% of the mites are removed from the carpet by typical vacuuming
    techniques, but this number varies with the type of surface, the type
    of vacuum used and the vacuuming technique.  Immunochemical assays of dust mite allergens

          Immunochemical assays are widely used to measure the
    concentrations of house dust-mite allergens. The dust mite germ is
     Dermatophogoides and allergens have been identified for three
    species. The conventional labelling of these allergens are denoted by
    the prefix "Der" followed by a letter indicating the species. These
    assays are possible because the major allergens produced by house dust
    mites, i.e., the group 1 allergens (Der p I, Der f I, Der m I) and the
    group 2 allergens (Der p II, Der f II, Der m II) are well
    characterized and purified. For immunochemical analysis, the dust
    sample is extracted (e.g., in a buffered saline solution), and then
    stored frozen until analysis.

          Total mite allergen content can be assessed by
    radioallergosorbent tests (RAST). This method provides a good estimate
    of the relative potency of different allergen extracts, but cannot be
    used for absolute quantification of mite allergen levels. An advantage
    of the method is that it measures "relevant" antigenic determinants
    that have elicited a response in allergic subjects, since human IgE is
    used. Results vary with the composition of the extract used on the
    solid phase and with the composition of the serum pool used for
    detecting bound allergen. However, RAST inhibition results are
    difficult to reproduce over an extended period of time.

          Individual mite allergens can be measured with enzyme-linked
    immunosorbent assays (ELISA) or radioimmunoassays (RIA). Sandwich
    radio- or enzyme immunoassays employ either rabbit polyclonal or mouse
    monoclonal antibody for capture, and a second monoclonal antibody for
    detection (see Fig. 25). These assays are more sensitive than RAST.
    Those using monoclonal antibodies in particular have also the great
    potential advantage of long-term reproducibility. Furthermore, ELISA
    assays have been shown to be highly reproducible (e.g., Munir et al.,

    FIGURE 25

    1993; Van Strien et al., 1994) and can quantify antigen levels to less
    than 1 ng/mg dust.

          Immunochemical assays are highly specific and the results
    obtained with these assays can be expressed in absolute units of a
    defined protein by unit weight of dust or by unit area sampled. They
    are suitable for large-scale surveys because they can be automated.
    However, a sophisticated laboratory is required.  Guanine determination

          The third possibility is the measurement of guanine, which is a
    nitrogenous excretory product of arachnids, found in house dust. Since
    mites are predominant among arachnids in house dust, determination of
    guanine content in the dust is an indirect method for assessing mite
    allergen levels. Analysis of guanine content is based on a colour
    reaction between guanine and an azo compound (Le Mao et al., 1989;
    Hoyet et al., 1991). The amounts of guanine can be measured
    quantitatively on a weight/weight basis using a spectrophotometer, or
    semiquantitatively using a commercially available test kit (Pauli et
    al., 1995). The quantitative assay has been reported to demonstrate a
    good correlation with the assay of Group 1 allergens (Platts-Mills et
    al., 1992), whereas the semiquantitative test was found to be less
    sensitive (Lau et al., 1990).

    9.2.4  Mite allergens

          Sampling strategies may vary depending on objectives but most
    studies collect vacuum samples using a protocol that, at least
    internally, standardizes equipment, area, duration and location. Mites
    are typically found in higher concentration in bedding. Typical areas

    would include mattresses, pillows, blankets and bedroom floors.
    Because of spatial variability, mixed floor samples can be used. Other
    areas of high use include living room, upholstered chairs and couches,
    and covered floors. Bischoff et al. (1992) describes an approach used
    to avoid depletion of the dust reservoir during routine and repeated

          Mite-antigen levels have been shown to vary with season,
    reflecting the moisture and temperature dependency controlling mite
    development stages. Garrett (1996) conducted a yearlong study in 80
    homes in eastern Australia. Fig. 26 reveals the temporal variation in
    Der p I, the prominent allergen. The allergen levels in dust collected
    from the bedroom and living room are higher during the warmer and more
    humid months of the year. Garrett (1996) has shown that the allergen
    level for Der p I is consistently higher in dust collected directly
    from the bedding. The between-home variation is quite apparent,
    ranging over almost two orders of magnitude. Examining Fig. 27 offers
    an explanation for the higher levels of greater variability in the
    allergen levels recovered from the bedding dust. Mites survive better
    in mattresses with spring cones than in foam rubber. Presumably, less
    moisture is retained in the hydrophobic foam material. Also, wool
    sheets and blankets favour the growth and retention of mite antigens
    more than alternative bedding material. Other studies on mites in wool
    rugs suggest that the thermal properties of wool help mites to survive
    fluctuations in temperature and moisture and, perhaps, inhibit their

    9.3  Allergens from pets and cockroaches

          For estimating the exposure to allergens derived from pets (e.g.,
    cats and dogs), and cockroaches, the same approaches are available as
    for house dust mites and their allergens (i.e., the sampling of air
    and dust). The major allergens of the cat (Fel d I), dog (Can f I),
    the German cockroach (Bla g I, Bla g II), and the American cockroach
    (Per a I), have been characterized and purified (Chapman et al., 1988;
    Pollart et al., 1991a; Schou et al., 1991, 1992). Research is still in
    progress to further unravel the structure of the allergens derived
    from pets and roaches (and house dust mites) using techniques for
    allergen cloning and sequencing.

    9.3.1  Air sampling for allergens from pets and cockroaches

          Cockroaches are year-round inhabitants of homes. They need access
    to both food and water, so they are often found in kitchens and
    bathrooms. Unlike mites, where the antigen source is in faecal matter,
    cockroaches are thought to secrete their allergen on to their bodies
    and on to surfaces (Vailes et al., 1990). This means that body parts,
    egg shells, faecal particles and saliva might contain allergens
    (Lehrer et al., 1991).

          Similarly, a wide range of materials derived from mammals contain
    potentially allergenic material, including hair, dander, serum,
    saliva, urine and faecal matter. Direct contact as well as inhalation

    and ingestion can cause allergic reactions (Burge, 1995). Because of
    the popularity of cats and dogs as domestic pets, they have been the
    subject of much of the work on mammalian allergenic reactions. Cat
    allergens from saliva and sebaceous gland secretions reside on
    particles less than 2.5 µm in size. Much of the dog allergen is
    believed to be associated with dander and hair, but saliva and serum
    are also important sources.

    FIGURE 26

    FIGURE 27

          There are only limited data on the size ranges for airborne
    allergen particles from dogs, rabbits, rats and other animals. In
    general, however, saliva sources tend to be small (<2 µm) whereas
    dander and urine particles are larger (10 µm).

          For the sampling of airborne allergens derived from cats, dogs
    and cockroaches, the same methods can be used as for the sampling of
    airborne mite allergens (see section 9.2.1). These allergens have been
    sampled using cascade impactors (Luczynska et al., 1990; De Blay et
    al., 1991), high-volume samplers in combination with fibreglass
    filters (Swanson et al., 1985; Sakaguchi et al., 1993) and liquid
    impingers (Luczynska et al., 1990).

          As is the case for house dust mite allergens, only limited data
    have been published on the validity of air sampling as a measure of
    exposure to allergens derived from pets and cockroaches. At present
    there is no reliable information to support adoption of a standard
    method for air sampling of these allergens. Airborne sampling has not
    yet been shown to be a better estimation of the exposure to these
    allergens than dust sampling. Therefore, further research to compare
    the usefulness of air and dust sampling is needed.

    9.3.2  Dust sampling for allergens from pets and cockroaches

          The sampling of house dust to investigate the presence of
    allergens derived from pets and cockroaches can be conducted exactly
    as for house dust mites and their allergens (see section 9.2.2).

    9.3.3  Available methods of analysis

          Immunochemical assays (ELISA) are available for detection of the
    allergens derived from cats (Chapman et al., 1988), dogs (Schou et
    al., 1992) and cockroaches (Pollart et al., 1991b; Schou et al., 1991)
    in (airborne) dust samples. The allergens of the American and German
    cockroach (i.e., Per a I and Bla g I) were demonstrated to be
    immunologically cross-reactive proteins and can be measured in the
    same assay (Schou et al., 1991). For immunochemical analysis, the dust
    sample is extracted (e.g., in a buffered saline solution), and then
    stored frozen until analysis.

          The ELISA assays for Fel d I and Can f I were found to be highly
    reproducible (Chapman et al., 1988; Schou et al., 1992). For the
    Bla g I and Bla g II ELISA assays the intra- and interassay
    variability were also found to be small (Pollart et al., 1991b).

    9.3.4  Typical allergen concentrations

          Cat and dog allergens have been reported more often than
    allergens from other mammals. Homes with cats have dust levels of Fel
    d I exceeding 10 µg/g, whereas homes without cats have typically less
    than 1 µg/g. A provisional value of 8 µg/g of dust has been proposed
    as indicating significant exposure. Cat antigen has been found in dust
    samples collected in theatres, offices, aeroplanes, schools and homes
    without a cat. Because of its small particle size, cat antigen can
    stick to clothing and be transported to other locations. Dog allergens
    have not been as extensively examined for non-residential sites.
    Dybendal et al. (1989) has reported that dog allergen was present in
    homes and schools where dogs were not kept.

    9.4  Fungi

          Fungi are a large and diverse class of microorganisms. They live
    on organic nutrients and have no chlorophyll or internal organs. The
    cells that make up fungal colonies contain complex carbohydrate
    macromolecules. Fungi must produce spores or conidia for their
    reproduction. Spores are usually 2-20 µm in size and oblong in shape.
    In the appropriate setting, spores reproduce new organisms.

          The two different approaches to assess the exposure to fungal
    particles are air sampling and dust sampling. For completeness, other
    approaches to "dust" sampling include lifting spores from a surface
    with sticky tape or direct contact with culture agar. The most
    commonly used approach is air sampling of culturable (viable) fungal

    9.4.1  Air sampling for fungi

          Several techniques have been described for volumetric sampling of
    fungi in outdoor and indoor environments. Table 32 presents an
    overview of the techniques most commonly used for the sampling of
    fungal particles. Detailed information on the different sampling

    devices can be found in ACGIH (1995). Some of the techniques give
    total counts of all airborne particles, viable and non-viable, whereas
    others only give counts of viable fungal particles (e.g., propagules
    or colony forming units (CFU)). A few methods are discussed that
    provide not only total counts, but also viable counts (e.g., filter
    samplers). The sampling efficacy of a bioaerosol sampler is both a
    physical and a biological problem. For air sampling of fungal
    particles the following physical sampling principles may be
    distinguished: impaction on to a solid or semi-solid surface (e.g., a
    culture medium or an adhesive), centrifugal impaction, filtration and
    liquid impingement.

          Impaction on to a culture medium (e.g., for culturable fungi) is
    the most widely used technique, particularly in non-industrial indoor
    environments. This process depends on the inertial properties of the
    particles, such as size, density and velocity, and on the physical
    parameters of the impactor, such as inlet-nozzle dimensions and
    airflow paths. Because of differences in characteristics, samplers
    differ in cut-off size ( d50) (e.g., the particle size above which
    50% or more of the particles are collected). As most impactors have
    very sharp cut-off characteristics, almost all particles larger than
    the  d50 are collected and  d50 is therefore assumed to be the size
    above which all particles larger than that size are collected
    (Nevalainen et al., 1992). No sampler collects all particles with
    equal efficiency, and it is therefore not surprising that different
    quantitative and qualitative results are obtained using different
    sampling devices for culturable fungi (Verhoeff et al., 1990). The
    choice of the collection (culture) medium also affects the kinds and
    levels of fungi recovered (Verhoeff et al., 1990). No single
    collection medium will enable the entire range of viable fungi in the
    air to be isolated. Media which are generally accepted for
    aerobiological studies include malt extract agar (MEA), V8 juice agar
    and dichloran 18% glycerol agar (DG18) (EC, 1993; ACGIH, 1995). MEA
    and V8 agar are broad spectrum media, whereas DG18 is intended to be a
    selective medium for xerophilic fungi, but many of the common fungal
    species in air can also be isolated (Verhoeff et al., 1990).

          Few published data are available on the validity (accuracy and
    precision) of the measurement of fungi in air as estimate of exposure.
    All commonly used cultural air samplers use short sampling periods,
    typically 30 seconds to several minutes (Table 32). The
    reproducibility of parallel duplicate samples and sequential duplicate
    samples is only moderate, both in terms of CFU/m3 and in terms of
    species isolated (Verhoeff et al., 1990). More importantly, repeated
    sampling within weeks has demonstrated that variation in time within
    homes is much higher than the variation between homes (Verhoeff et
    al., 1992). This means that a single air sample has only a low
    predictive value for exposure over time. Furthermore, the use of
    cultures for quantifying fungal particle concentrations in air samples
    will give an underestimate of the actual particle concentrations, and
    may cause significant fungal contamination to be missed altogether.
    The culturable fungal particles may comprise only a few percent of the

        Table 32.  Overview of sampling techniques for airborne fungal particlesa

    Method with examples                    Sampling rate and time               Remarks

    Non-viable, non-volumetric
    - settling surface, adhesive-coated     undefined, minutes to days           semi-quantitative, over-representation of 
                                                                                 larger particles, microscopic identification

    Non-viable, volumetric
    - rotating tape/slide impactors
      Burkard trap                          10 litre/min, 7 days                 cut-off 2.5 or 5.2 µm, depending on slot

    - rotating arm impactors
      Rotorod sampler                       47 litre/min, intermittent           cut-off unknown

    - filter methods
      cassette filters                      1-4 litre/min, hours                 viable counts possible by plating washings 
      high-volume filters                   150-2000 litre/min, hours            from the filters

    Viable, non-volumetric
    - settlement plates                     undefined, hours                     semi-quantitative, over-representation of 
                                                                                 larger particles

    Viable, volumetric
    - multiple hole impactors
      Andersen 6-stage sampler              28.3 litre/min, 1-30 min             cut-off 0.65-0.70 µm, size separation
      Andersen 2-stage sampler              28.3 litre/min, 1-30 min             cut-off 0.65-0.70 µm, size separation
      Andersen 1-stage (N6)                 28.3 litre/min, 1-30 min             cut-off 0.65-0.70 µm
      Surface Air System sampler            90/180 litre/min, 20 sec-6 min       cut-off depends on number of holes and flow
      Eight-stage personal impactor         2 litre/min, 5-30 min                cut-off 5.2 µm, size separation
      Burkard portable sampler              10/20 litre/min, 1-9 min             cut-off 4.1/2.9 µm (10/20 litre/min)

    - centrifugal impactors
      Reuter Centrifugal sampler (RCS)      ca. 40 litre/min, 20 sec-8 min       cut-off 3.8 µm
      Reuter Centrifugal Plus (RCS-Plus)    ca. 50 litre/min, 30 sec-8 min       cut-off unknown

    Table 32.  (continued)

    Method with examples                    Sampling rate and time               Remarks

    - rotating slit-to-agar impactors
      Mattson-Garvin air sampler            28 litre/min, 5-60 min               cut-off 0.5 µm

    - liquid impingers
      single-stage all glass impingers      12.5 litre/min                       cut-off 0.3 µm
      three-stage impingers                 20 litre/min                         cut-off <4 µm, size separation

    a For detailed information see ACGIH (1995).

    total number of fungal particles (Horner et al., 1994). Thus, in order
    to optimize the information available from air sampling, both types of
    particle should be sampled. However, even using the best available
    method, a large number of airborne spores will not grow in culture and
    cannot be visually identified with available methods.

          At present, there is no standardized method for the sampling of
    airborne fungi, although the American Conference of Governmental
    Industrial Hygienists (ACGIH, 1989) and the European Commission (EC,
    1993) have given recommendations. An outline for selecting a
    bioaerosol sampler is presented by the American Conference of
    Governmental Industrial Hygienists (ACGIH, 1995). Selection criteria
    include sampling location, form of recovered particles (intact or
    dispersed), the need for size separation and the expected
    concentrations of the particles.

    9.4.2  Settled dust for fungi

          Settled house dust can be sampled for viable fungi in exactly the
    same way as for house dust mites and their allergens (see section
    9.2.2). The dust samples can be stored at room temperature but the
    analysis should be performed within a few days.

          Few published data are available on the validity of the
    measurement of culturable fungi in settled dust as estimate of
    exposure. The results, both quantitatively and qualitatively, depend
    on the method of inoculation of the dust and on the culture medium
    used (Verhoeff et al., 1994a). The reproducibility of duplicate
    analyses in terms of CFU/g dust is acceptable, but in terms of species
    isolated only moderate. However, as is the case for air sampling, a
    single dust sample is a poor estimate of exposure to fungi over time
    (Verhoeff et al., 1994a).

    9.4.3  Available methods of analysis for fungi in air

          Air samples obtained with sampling devices collecting total
    fungal particles can be analysed by direct examination to obtain total
    counts of fungal particles. Samples collected on culture media have to
    be incubated to obtain counts of viable fungal particles. Dust can be
    plated either directly on to a culture medium or suspended and diluted
    prior to plating. Total counts of fungal particles in dust can also be
    obtained by partitioning into an aqueous two-phase system followed by
    epifluorescence microscopy (Strom et al., 1987).

          Samples are incubated for at least 4 days; up to 7 days is the
    typical time needed for spores to generate identifiable colonies. The
    temperature at which samples are incubated affects the recovery of
    culturable fungi. Since most environmental fungi grow well between
    20°C and 30°C, the incubation temperature is generally 25°C (EC,

          Sporulating colonies are identified by colour and texture, by the
    naked eye or microscopically. Non-sporulating spores might be
    transferred to different agar and exposed to different lighting in an
    attempt to colonize them. Fungal genera are sometimes reported and
    provide important insight into sources and possible health effects.
    Common outdoor fungi are  Cladosporium, Alternaria, Botrytis and
     Epicoccum. Penicillium, Aspergillus and  Stachybotrys can be found
    in higher concentrations indoors. It is difficult to generalize and
    there is considerable variability over time so it is important to
    simultaneously collect outdoor samples. Fig. 28 shows the distribution
    of viable mould spores collected inside and outside homes in Australia
    (Garrett, 1996). Outdoor viable spore counts decrease in the winter
    and so do the indoor levels for homes without substantial sources of
    sporulating fungi.

    FIGURE 28

          Immunochemical assays for fungal allergens are available for only
    a few fungi, primarily because fungal allergens are poorly
    characterized and purified. Alternative indicators of exposure to
    fungi, to be measured in (airborne) dust, may also be considered. For
    example, one can assess the levels of cell wall components such as
    ß-1,3-glucan (Rylander et al., 1992), or ergosterol, a membrane
    steroid (Horner et al., 1994), or extracellular polysaccharides (EPS)
    (Kamphuis et al., 1991). Immunochemical assays (ELISA) are presently
    being developed to measure these components in (airborne) dust.
    However, in one study by Miller et al. (1988), ergosterol in house
    dust correlated with CFU/m3 in the air and Saraf et al. (1997) have
    shown ergosterolin in house dust correlated with fungal CFU/g in the
    dust samples. In addition, Abramson et al. (1996) showed an
    association between ergosterol and atopy in adults.  Total counts of viable and non-viable fungal particles

          Total counts of fungal particles can be obtained by counting with
    a light microscope. If more detail is required, the samples can also
    be viewed with a scanning electron microscope (SEM) or a direct
    epifluorescence microscope. These techniques cannot be recommended as
    giving a good assessment of the composition of air spora because only
    fungi with distinctive spores can be identified. It is often difficult
    to identify the fungal spores to species or even genus. As indicated
    above, filter methods may be used not only to give total counts (e.g.,
    by direct epifluorescence microscopy), but also to obtain counts of
    viable fungal particles by plating washings from the filter.
    Furthermore, filter samples may be analysed for mycotoxins, EPS and
    glucans or tested for toxicity. The same applies for samples of
    settled dust.

    9.4.4  General considerations for fungi

          Since fungi vary so widely it is difficult to generalize about
    the presence of fungi in outdoor and indoor air. In temperate
    climates, outdoor spore counts are highest during and just following
    the growing season. Tropical climates show less variation by season.
    Garrett's (1996) doctoral thesis provides a useful comparison of
    viable mould spores from studies conducted in different climates.

          Spore counts can vary greatly indoors for several reasons
    including the presence of colonizing fungi. Fig. 29 illustrates this
    point. The figure shows the mean values of CFU per culture plate for
    four fungal taxa and all others collected during the summer in nine
    Portage, Wisconsin (USA) homes. Living room and bedroom samples
    reflect the taxa found outdoors. The basement samples show
     Penicillium and  Aspergillus fungi which grow favourably in damp
    areas (Burge, 1990).

    FIGURE 29

          In addition physical activity, such as vacuuming, starting an
    air-conditioning fan, children playing on a carpet or changing a
    filter, might raise spore concentrations by a factor of 10 or more.
    Even without mechanical disruption, periodic shedding or ejection of
    spores from growing fungi might similarly elevate concentrations.

    9.5  Bacteria (including actinomycetes)

          Bacteria are prokaryotic cells. Certain bacteria are infectious
    and can be transmitted by air and contact, including ingestion. Common
    contagious airborne diseases include tuberculosis and some forms of
    pneumonia. Other diseases, such as legionellosis from water,
    respiratory infections from  Pseudomonas in humidifiers and several
    others from handling animals are not transmitted from person to

          Sampling and identifying specific bacteria is very important in
    many settings, especially in hospitals with immune-compromised and
    immune-suppressed patients. There are numerous other situations where
    infectious diseases have been transmitted by airborne bacteria. These
    include tuberculosis transmission in aircraft, and  Legionella 
    transmission in hospitals, in hotels, in supermarkets and on cruise
    ships or even in buildings in locations affected by cooling tower
    mist. The two approaches available to measure the presence of bacteria
    as indicators of exposure are sampling of air and sampling of soil,
    dust or water.

    9.5.1  Air sampling for bacteria

          Most of the air sampling devices listed in Table 32 can also be
    used for bacteria. The most widely used devices for bacteria include
    multiple-hole impactors, centrifugal impactors and slit-to-agar
    impactors, provided with collection media suitable for viable
    bacteria. For sampling airborne viable bacteria, the same limitations
    apply as for viable fungal particles. Thus, the results, both
    quantitatively and qualitatively, will depend on the sampling device
    and collection medium used. A single air sample has only a low
    predictive value for exposure over time (EC, 1993). Media that collect
    viable bacteria include tryptone soya agar, tryptone yeast glucose
    agar, soybean casein digest agar, and nutrient agar (EC, 1993; ACGIH,
    1995). To prevent fungal growth, a suitable antimycotic may be used
    (e.g., cycloheximide). For specific groups of bacteria, selective
    media could be employed, such as half-strength nutrient agar for
    thermophilic actinomycetes.

    9.5.2  Dust sampling for bacteria

          The sampling of settled house dust for bacteria can be conducted
    in exactly the same way as for house dust mites and their allergens
    (see section 9.2.2).

    9.5.3  Available methods of analysis for bacteria

          Air samples obtained with sampling devices collecting total
    bacteria can be analysed by direct examination. Samples collected on
    culture media have to be inoculated to obtain counts of viable
    bacteria. Dust can be plated either directly on to a culture medium or
    suspended and diluted prior to plating. Dust can also be analysed for
    total bacteria counts. Furthermore, an assay is available to measure
    endotoxin content of (airborne) dust. Gram-negative bacteria contain
    endotoxins as integral components of their outer membrane. Endotoxins
    are potent biological agents.  Total count of viable and non-viable bacteria

          Total counts of bacteria, with some information on shape, can be
    obtained from some samples, for example, water and air, using
    epifluorescence microscopy (most commonly, with acridine orange). This
    method becomes less reliable as the amount of debris in the sample,
    both organic and inorganic, increases. More detail on bacterial shape
    is obtained using a scanning electron microscope but quantitative
    results are less reliable. Filter samples can also be used to obtain
    counts of viable bacteria, with subsequent taxonomic differentiation
    if desirable (using Gram staining and other biochemical tests). Filter
    samples can be analysed for endotoxin as well (see below).  Viable bacteria

          Environmental samples are usually incubated for 2-7 days at 25°C
    or 37°C. For bacteria, as with fungi, the incubation temperature
    affects the recovery. Most environmental bacteria grow well between
    20°C and 30°C, and more species were recovered with incubation at 20°C
    than at 37°C (Hyvarinen et al., 1991). Therefore, it is recommended
    that plates be incubated at room temperature (20-25°C) and examined
    daily for several days (EC, 1993). For isolation of human pathogenic
    organisms, plates can be incubated at 37°C, and for thermophilic
    actinomycetes at 55°C. After incubation the number of colonies is
    counted and expressed as CFU/m3.  Endotoxins

          Endotoxins are a group of lipopolysaccharide (LPS) molecules
    making up the outer membrane of Gram-negative bacteria. Specific LPS
    macromolecules that exist in the cells or as fragments of cell
    structures are known to cause fever, malaise, respiratory distress and
    a variety of biochemical changes in humans. Endotoxins are ubiquitous
    in nature but occur in high concentrations in particular industrial
    and agricultural settings. Cotton mills and industrial processes using
    recirculating water, waste-water collectors, humidifiers and swine
    barns are some locations where endotoxin contamination has been
    associated with respiratory disease (Milton, 1995).

          Airborne and settled dust samples can be examined for the
    presence of endotoxins. The  Limulus amoebocyte lysate (LAL) assay is
    commonly used to quantify environmental endotoxin (Walters et al.,
    1994; Douwes et al., 1995). The assessment of endotoxin exposure
    depends strongly on sampling, extraction and storage procedures
    (Douwes et al., 1995) and further validation studies are needed to
    adopt standard methods for sampling and analysis. Variation in the LAL
    reagent from lot to lot and between manufacturers may be a major cause
    of variation in results within and between laboratories (Saraf et al.,

          In airborne endotoxin, filter material and type of aerosol (e.g.,
    cotton dust, machine oil or saline mist) will affect the binding of
    endotoxin to the filter. Walters et al. (1994) showed that
    polycarbonate capillary pore membrane filters were optimal. Any
    conventional air sampler can be used with filter cassettes. Glass
    impingers have been used as alternatives to filters. Fluids and bulk
    samples can be collected directly if care is taken to ensure the use
    of endotoxin-free glass or plastic ware with low binding affinity.
    Samples should be analysed promptly and preferably not frozen.
    Endotoxin (LPS) is removed by solubilizing in a buffer solution (after
    Milton, 1992). Sonication might be necessary to dislodge particles.
    Concentrations, as measured by the  Limulus assay, are expressed as
    standard endotoxin units (EUs) which are defined as the potency of
    0.10 ng of a reference standard endotoxin (EC6, US Pharmacopoeia).

    9.6  Pollen

          Most people associate pollen with the common experience of hay
    fever and seasonal allergic rhinitis. Although only 10% of flowering
    plants shed wind-borne pollen, there are locations and times when the
    ambient concentrations exceed 1000 pollen grains/m3. Pollen grains
    generally are spheroidal or somewhat elongated and have a very durable
    outer wall. Most airborne pollen is between 10 and 70 µm in diameter.
    Larger pollen grains (>200 µm) are more likely to be transported by
    insects. Weather conditions such as higher temperature and lower
    relative humidity and wind promote pollen emissions (Ogden et al.,
    1969; Hart et al., 1994; Burge, 1995). Plant lifecycle stage, daylight
    and moisture affect the time and rate of shedding. Flowering of most
    plants, trees and grasses is seasonal and therefore cyclic.

          Although hay fever in association with plant pollen has been
    known for 175 years, the association of natural pollen with airway
    reactions and asthma has not been adequately studied. Most pollen
    measurements have been conducted by independent observers using a
    variety of samplers.

    9.6.1  Air sampling for pollen

          Several techniques exist for volumetric sampling of pollen grains
    in indoor or outdoor air. Table 33 provides an overview of the devices
    most commonly used for the sampling of pollen. Detailed information on

    the different sampling devices has been published by the American
    Conference of Governmental Industrial Hygienists (ACGIH, 1995). In all
    cases, the pollen impacts a semi-solid surface (tape strip or glass
    slide mounted with an adhesive). The main difference between the
    devices is that the moving tape/slide impactors provide the
    possibility of obtaining time-discriminated data, as opposed to the
    stationary and rotating rod impactors. The sampling devices listed in
    Table 33 may also be used to sample large fungal spores. Published
    data on the validity of air sampling to estimate the exposure to
    pollen are lacking.

    9.6.2  Dust sampling for pollen

          The sampling of settled dust to investigate the presence of
    pollen grains or allergens derived from pollen can be conducted
    exactly as for house dust mites and their allergens (see section
    9.2.2) Dybendal et al. (1989) and Yli-Panula & Rantio-Lehtimäki (1995)
    describe dust sampling techniques for pollens.

    9.6.3  Available methods of analysis for pollen in air

          Analysing air samples for pollen is commonly done by light
    microscopy, but scanning electron microscopy is also used.
    Immunochemical assays are not routinely used to assess the presence of
    pollen allergens in air or dust samples. It should be recognized that
    optically counted pollen grains may not relate to the pollen antigenic
    activity because empty grains do not contain allergenic protein

          For light microscopic counting and identification of pollen,
    staining of the sample is recommended. Staining can be done with basic
    fuchsin and phenosafranin, which stain the exine of the pollen (i.e.,
    the outermost portion of a pollen grain) red and dark pink
    respectively. The choice of the dye depends on the type of sample to
    be analysed. Pollen identification rests on the microscopic
    appearance, using published keys for identification. (Faegri &
    Iversen, 1989; Nilsson & Praglowski, 1992).

          Immunochemical assays have been used to analyse samples of
    settled house dust for the content of grass-pollen allergens
    (ryegrass, Lol p I) by direct RAST (Platts-Mills et al., 1987). Birch
    (Bet v I) and alder (Aln i I) pollen allergens have been analysed by
    means of RAST-inhibition (Dybendal et al., 1989). Jensen et al. (1989)
    have analysed outdoor air samples taken with a high volume sampler for
    timothy and birch pollen allergens by means of a RAST-inhibition
    assay. They found a strong correlation between the amounts of
    allergens and pollen counts obtained with a Burkard trap. As indicated
    above, immunochemical assays are not routinely used to measure pollen

        Table 33. Overview of sampling techniques for airborne pollen grainsa

    Method                           Examples                   Sampling rate and time        Remarks

    Rotating tape/slide impactors    Burkard trap               10 litre/min, 7 days          cut-off 2.5 or 5.2 µm, 
                                                                                              depending on slot
                                     Lanzoni sampler            10 litre/min, 7 days          cut-off 10 µm
    Moving slide impactors           Allergenco air sampler     15 litre/min, intermittent    cut-off 2.0 µm
    Stationary slide impactors       Burkard portable sampler   10 litre/min, 1-9 min         cut-off 5.2 µm
    Rotating arm impactors           Rotorod sampler            47 litre/min, intermittent    cut-off unknown

    a  for detailed information see ACGIH (1995).

    9.6.4  General considerations for pollen sampling

          The source of indoor pollens can be considered as entirely
    outdoors. Bringing flowers indoors might be a transient source, but
    only about 10% of flowering plants and trees spread their pollen
    through the air. Ventilation, footwear and clothing bring outdoor
    pollens indoors. Yli-Panula & Rantio-Lehtimäki (1995) demonstrated
    that antigenic activity indoors was lower, and peaked 3 weeks later
    than outdoors. They inferred that transport by occupants and pets is a
    more important vector for indoor pollen levels in the Finnish homes
    they sampled. O'Rourke & Lebowitz (1984) sampled pollen in dust from
    homes in the dry southwest of the USA (Tucson, Arizona). Higher
    loadings were found in the dust samples closer to entrances. Both of
    these studies, as well as others, indicate that indoor pollen
    allergens may be a major cause of asthma, especially since significant
    antigenicity can persist more than 2 months after pollen searches at
    peak outdoors. Once indoors, if not physically removed, pollen grains
    are protected from "weathering" which will denature the antigenic

    9.7  Summary

          Bioaerosols includes a variety of microorganisms or their
    components that can become airborne and inhaled. These include
    viruses, bacteria, pollens, fungi, protozoa and algae as viable
    organisms that can cause illness. Fragments or metabolic components of
    bacteria and fungi along with protein structures contained in these
    organisms as well as in the excreta and parts of insects, animals and
    arachnids can cause allergenic reactions. This chapter focused on the
    most ubiquitous bioaerosols commonly found indoors and contributing to
    allergenic reactions: mites, fungi, bacteria and pollen. Methods and
    strategies for sampling and analysing these agents in air and settled
    dust are presented. Examples of how some of these parameters vary
    indoors and outdoors across time and location are offered.

          Exposure assessment for microbiologicals is as advanced, at this
    time, as it is for many air contaminants. Personal samplers have not
    been developed. In fact, many of the techniques for sampling
    aerobiological agents have been adapted from instruments designed for
    other purposes. The field is maturing as professional organizations
    attempt to improve and standardize measurement methods, culturing and
    analysis protocols and data reporting. These aspects are of critical
    importance in comparing results reported by different investigators.

          By their very nature, bioaerosols have compositions and
    concentrations that are highly variable. The conditions favourable for
    growth, reproduction and dispersion vary within a wide range of
    temperature, moisture and nutrient conditions. These same factors
    influence by interactions with human and animal activities. Mechanical
    systems and machines can cause amplification and distribution of
    biological aerosols. As a result personal exposures are quite
    variable; this, in turn, has led many investigators to rely on area
    air sampling and/or bulk sampling of materials. For example, it is

    recommended that a surrogate measure of mite allergen exposure is
    derived from bedding and floor dust samples. Home samples, whether air
    or dust is sampled, are often the "exposure" value used in
    epidemiological investigations.

          Finally, the advancement of aerobiological exposure assessment to
    eventual use in quantitative risk assessment will require better
    understanding of relevant dose to cause sensitization and reactivity
    for many different organisms and/or agents.


    10.1  Introduction

          This chapter presents a summary of the major concepts,
    definitions, advantages, limitations, sampling, media and uses of
    biological markers as applied for exposure assessment to environmental
    contaminants. These exposures are frequently characterized by low
    contaminant concentrations, multiple sources and multiple types of
    chemicals. Use of biological markers has been extensively reviewed
    from both epidemiological and occupational monitoring perspectives as
    well as their use in risk assessment (Bernard & Lauwerys, 1986; Harris
    et al., 1987; US NRC, 1987; Hulka & Wilcosky, 1988; Cullen, 1989;
    Griffith et al., 1989; Henderson et al., 1989; Monster & Zielhuis,
    1991a,b; Schulte, 1991; Hulka & Margolin, 1992; IPCS, 1993; Aitio,
    1994; Grandjean et al., 1994; Silbergeld & Davis, 1994). The reader
    should refer to the research literature for a comprehensive and
    detailed understanding of the complex issues relevant to the
    development, validation, and application of biomarkers in studies of
    human exposure. The specific issue of using biological markers in
    occupational settings is beyond the scope of this chapter.

          It is important to indicate that, to date, very few biomarkers
    can be effectively used for quantitative estimation of prior
    environmental exposure to contaminants (e.g., carbon monoxide or
    lead). In most cases, there are qualitative or semi-quantitative
    indicators for monitoring such exposures. However, as the field
    evolves, and knowledge of the pharmacokinetics and pharmacodynamics of
    xenobiotics develops, the number of quantitatively validated
    biomarkers of exposure will also increase.

          Biological markers present unique advantages as tools for
    multimedia exposure assessment. They are highly sensitive indices of
    an individual's exposure to chemicals, since they provide a measure of
    the internal dose, account for all routes of exposure and integrate
    over a variety of sources of exposure (Friberg, 1985; Baselt, 1988;
    Sim & McNeil, 1992). Therefore, they can represent past exposure
    (e.g., the presence of lead in shed teeth), recent exposure to an
    external source (e.g., VOCs in exhaled breath) and even future
    internal exposure sources (e.g., pesticides in adipose tissue).
    Furthermore, their use may result in improved monitoring of total
    population exposure, characterization of individual and population
    exposures and evaluation of internal sources of exposure. These
    markers are also useful surveillance tools for monitoring chemical
    exposure in both individuals and populations over time.

          Use of biological markers of exposure can improve the risk
    assessment process by providing a critical link between chemical
    exposure, internal dose and health impairment (IPCS, 1993). Biological
    markers of exposure can improve clinical diagnoses where there is a
    well-established relationship between biological marker and outcome.

    10.2  General characteristics

          Biological markers represent events or changes in human
    biological systems as a result of exposure or disease (US NRC, 1991b).
    They are classified as markers of exposure, effect, and susceptibility
    and are considered to represent events along a theoretical continuum
    from causal exposure to resulting health outcome (US NRC, 1987;
    Schulte, 1989). A biological marker of exposure is defined as a
    xenobiotic substance or its metabolite(s) or the product of an
    interaction between a xenobiotic agent and some target molecule(s) or
    cell(s) that is measured within a compartment of an organism (US NRC,
    1989; IPCS, 1993). Biological markers of effect are measurable
    biochemical, physiological, behavioural or other alterations within an
    organism that, depending upon the magnitude, can be recognized as
    associated with an established or possible health impairment or
    disease (IPCS, 1993). Biological markers of susceptibility are
    indicators of inherent or acquired abilities of an organism to respond
    to the challenge of exposure to a specific xenobiotic substance (IPCS,
    1993). Although the distinction between some biological markers of
    exposure and effect may be overlapping, this chapter will focus on
    those biological markers which can be applied to environmental
    exposure assessment.

    10.3  Considerations for use in environmental exposure assessment

          The use of biological markers for exposure assessment represents
    a different perspective for evaluation of human exposure to a
    contaminant than traditional exposure assessment. Biological markers
    of exposure are considered measures of internal dose, whereas exposure
    is frequently defined as the concentration of an agent at the boundary
    between an individual and the environment multiplied by time (US NRC,
    1991b; IPCS, 1993). Owing to long-term storage of specific
    contaminants in biological tissues (e.g., bone and fat), some
    biological markers are markers of both cumulative dose and future
    internal exposure.

          Biological markers of exposure have been used most frequently in
    industrial hygiene and occupational medicine (Elkins, 1954, 1967;
    Lauwerys, 1983; Schulte, 1991). Before widespread application of
    biological markers for exposure assessment of the general population
    can occur, it is important to consider the situations which are best
    suited for biological monitoring. Ideally, a biological marker of
    exposure should be chemical-specific, detectable in trace quantities,
    available by non-invasive techniques and inexpensive to assay. Also,
    it must relate consistently and quantitatively to the extent of
    exposure and ideally also integrate the exposure over time (Bond et
    al., 1992). Currently there are very few biological markers that
    possess all these characteristics. However, the use of biological
    markers for exposure assessment is increasing.

          Biological markers represent one type of monitoring approach
    available for environmental exposure assessment. Validation of
    biomarkers is a complex process that should include determination of:

    specificity of the available biological marker, exposure-related
    toxicokinetics and toxicodynamics, dose-response relationship,
    biological variation associated with the marker, route of exposure and
    type of health effect associated with exposure. In addition,
    validation should consider behavioural factors that influence
    exposure, participant acceptance, feasibility and cost-effectiveness
    (Verberk, 1995), as well as biological variability and specificity
    within a human population of interest, and generation of baseline or
    normative data for the biological marker. These issues are addressed
    later in the chapter.

          Collection of samples from humans involves important ethical
    issues. Ethical concerns may limit the extent of investigations of
    chemically exposed individuals and populations (IPCS, 1993). Ensuring
    confidentiality both for subjects and for the obtained individual
    results is imperative (Schulte, 1992). Subjects have the right to know
    the implications of their participation, the analyses to be performed,
    the nature of the sampling procedure, the use of the data collected
    and the possible ramifications of positive findings. Knowledge of
    previous exposure or genetic predisposition may have adverse
    implications for an individual; for example, individuals may be denied
    health insurance on the basis of presumed future risk. Since
    biological markers are a relatively new tool, interpretation of
    results and subsequent health implications is difficult. For many
    biological markers, little information is available to interpret the
    result for the subject; this may lead to concern on the part of the
    individual. For example, knowledge of the presence of pesticides in
    breast milk may lead an individual to avoid breast-feeding without
    consideration of its advantages (Vandenberg, 1991; Sim & McNeil,

    10.3.1  Toxicokinetics and toxicodynamics

          Toxicokinetics describes the absorption, distribution, metabolism
    and excretion of a contaminant. Understanding the toxicokinetics and
    toxicodynamics of the agent is critical for development and use of a
    biological marker of exposure (Sampson et al., 1994). This information
    predicts the location and form of the chemical or its metabolite and
    identifies sources of biological variability in the population (Droz &
    Wu, 1991; Droz, 1992, 1993). Toxicokinetic modelling may be used to
    estimate the optimal time for sampling (Saltzman, 1988; Droz & Wu,
    1991; Droz, 1993). Differing kinetics determine whether the biological
    marker reflects recent exposure, historic exposure, or an integrated
    measure of exposure over time (Bernard, 1995).

          The utility of biological markers for assessing exposure can be
    evaluated on the basis of timing of sampling relative to the exposure
    and the biological half-life of the chemical. The parameter which best
    describes the toxicokinetic behaviour of a chemical in biological
    systems is the  elimination half-life, which reflects both the
    affinity of the chemical for the biological medium and the efficiency
    of the processes of elimination (Bernard, 1995). For samples taken

    immediately following exposure (e.g., solvents in exhaled air, blood
    and urine), the half-life reflects elimination from the central
    compartment of blood and vessel-rich tissues. For samples taken days,
    weeks or even years after exposure, the half-life corresponds to the
    elimination from those compartments from which chemical clearance is
    much slower; for example, lead from bone or lipophilic organic
    chemicals from adipose tissue (Bernard, 1995).

          Toxicokinetic data from animals and humans can aid in the
    determination of the utility of biological markers in assessing
    individual or population exposures. Fig. 30 illustrates how
    toxicokinetic data could be utilized to select biological markers of
    exposure based on the timing and concentration of exposure (IPCS,
    1993). In order to interpret the extent of exposure, it is critical to
    collect the biological sample after the exposure has reached an
    equilibrium state. This can be especially important for chemicals with
    short half-lives, such as benzene in exhaled air with a half-life of
    22 h (Bernard, 1995). For example, in occupational settings,
    biological monitoring has no advantages over environmental monitoring
    for airborne chemicals with biological half-lives less than 10 h,
    since the integration time for the marker is too short to allow
    accurate representation of exposure (Droz, 1993).

    FIGURE 30

    10.3.2  Biological variability

          The use of biological markers can also be affected by biological
    parameters. Variation can depict true differences in individual
    exposures, as represented for example by differing microenvironments
    or differing behaviour. It may also represent inherent
    inter-individual differences which affect the biological response to
    exposure (Droz, 1992). Sources of biological variability include
    demographic factors (e.g., age and sex), anthropometric
    characteristics (e.g., body size and fat distribution), behaviour
    (e.g., activity level or contaminant avoidance behaviours),
    biological/circadian rhythms and toxicokinetic differences due to
    genotype (Kompaore & Tsuruta, 1993), previous exposure, lifestyle
    factors (e.g., smoking) or dietary habits (Perera & Weinstein, 1982;
    Droz, 1992). Biological variability can complicate assessment of an
    individual's exposure using biological markers. However, the factors
    contributing to biological variability influence the internal dose and
    may ultimately be informative about potential health effects relating
    to exposure. For chemicals with large inter-individual variability in
    biological response to exposure, biological monitoring techniques are
    more useful for groups than for individuals.

    10.3.3  Validation of biological markers

          Validation of biological markers of exposure occurs at three

    *   The first validation level involves sample collection and
        determination of sample stability following collection and during
        transport and storage. Sample collection and handling can influence
        external contamination both during the sample collection and from
        the sample collection material. Stability of the sample prior to
        analysis can be affected by chemical degradation, evaporation,
        biological activity and interaction between the sample and its
        container or with other compounds (Bernard, 1995).

    *   The next level pertains to the analytical method itself and the
        ability to measure the marker accurately and reproducibly at levels
        relevant to environmental exposure. Low limits of detection and
        high analytical sensitivity (i.e., instrument response) are
        critical for evaluation of environmental exposures.

    *   The third validation level stems from the sensitivity and
        specificity of the biological marker itself. The biological marker
        must demonstrate that an exposure is occurring or has occurred, and
        separate individuals on the basis of their level of exposure
        (Hulka, 1991). Components of this level of validation include
        understanding the temporal relevance of the marker, identifying
        background variability and determining potential confounding
        factors (Perera, 1987; Sato, 1993). Temporal relevance is critical
        since it relates the timing of exposure to the appearance of a
        measurable biological marker and to the duration of time that the

        biological marker is measurable following the cessation of
        exposure. This includes both toxicokinetic and toxicodynamic

          Validation occurs in the laboratory, in pilot studies and in
    populations (Schulte & Talaska, 1995).

          For environmental exposure assessment, chemical-specific markers
    of exposure such as blood lead concentrations are preferred. However,
    for some chemicals, compound-class specific markers (e.g., PAH-DNA
    adducts) or non-specific markers (e.g., acetylcholinesterase
    inhibition) may be available. Non-specific markers of contaminants
    (i.e., some metabolites or non-chemical specific biological changes)
    as indicators of exposure may be more sensitive than external measures
    of exposure to predict an individual's total dose (van Welie et al.,
    1991; Smith & Suk, 1994). However, since non-specific markers are
    neither source- nor chemical-specific, characterization of the
    variability in these markers is an important validation component
    prior to use in exposure assessment. To date, few biomarkers of
    environmental exposure can be utilized to estimate such exposure
    quantitatively. In most cases, biomarkers of exposure provide a
    semi-quantitative or qualitative indication of exposure.

    10.3.4  Normative data

          Currently many biological markers are being developed for
    research purposes (Schulte, 1987; Kelsey, 1990; Bond et al., 1992;
    Fowle & Sexton, 1992). However, collecting population baseline data on
    these markers is necessary before they can be useful for monitoring
    and surveillance purposes. Longitudinal and cross-sectional
    information on normal ranges and correlation with environmental
    exposures and demographic characteristics are required. Reference
    ranges are available for some biological markers. For example, blood
    concentration data for a number of pesticides and metals were
    collected in a representative sample of the US population in the
    National Health and Nutrition Evaluation Survey (NHANES IV) and the
    German Environmental Survey (Krause et al., 1992). The ranges may vary
    in different exposed populations owing to differing residual levels in
    the environment (Grandjean, 1986; Alessio, 1993). Data from specimen
    banks are beginning to provide some exposure information on geographic
    and demographic parameters (Kemper, 1993).

    10.4  Advantages of biological markers for exposure assessment

          Biological markers integrate over all sources of exposure, which
    allows for efficient characterization of exposure to multiple sources
    and evaluation of past exposures (US NRC, 1991a; Sim & McNeil, 1992).
    When contaminants are found in many environmental media, biological
    monitoring can be the most effective way to determine an individual's
    total exposure. Biological markers may also indicate the presence of
    additional exposures; for example, biological markers used in
    occupational settings have been applied to determine if
    non-occupational exposures to similar agents are occurring (Lauwerys,

    1983). Some biological markers (e.g., PCB concentrations in breast
    milk) represent cumulative exposure to environmental contaminants.
    Markers which integrate over long periods of time can be used to
    evaluate past exposure in a much more accurate manner than historical
    record review and exposure reconstruction (DeRosa et al., 1993;
    Sampson et al., 1994). Biological markers can also be the best way to
    measure recent exposures, especially those where dermal contact is the
    primary route of entry. Table 34 summarizes the advantages and
    limitations of using biological markers in exposure assessment.

        Table 34.  Advantages and limitations of biological markers for environmental 
               exposure assessment

    Advantages                               Limitations

    Demonstrate exposure has occurred        source and route identification

    Integration over all exposure routes     kinetics and timing of exposure

    Characterization of individual           biological variability and confounding

    Inclusion of internal sources            altered response as a result of 
                                             multiple exposure

    Improved health effects investigations   invasive sampling

    Improved population risk                 availability of human samples
    assessment/risk management

    Validation of exposure models            lack of normative values for comparison

    10.4.1  Characterizing inter-individual variability

          A fundamental issue in the quantitative aspect of exposure
    assessment is the characterization of inter-individual variability in
    exposure. The pattern of exposure may differ within individuals,
    groups or populations. For example, workers in the same factory may
    have different exposures as a result of differing work habits
    (Rappaport et al., 1993); families living in very airtight houses may
    have greater exposure to indoor contaminants than families living in
    draughty homes. Biological markers represent one strategy to assess
    inter-individual variation in exposures, when measured environmental
    concentrations do not differ between individuals. Genotype, dietary
    habits, body size, state of health, lifestyle habits (e.g., smoking)
    and behaviour may all play a role in determining an individual's

    exposure (Bernard & Lauwerys, 1986). Biological markers of
    susceptibility may also be used to explore biological variation in
    response to exposure. For example, phenotypic differences in
    Delta-aminolaevulinic acid dehydratase (ALAD) may influence both blood
    lead levels and the health effects of lead (Wetmur, 1994;
    Milkovic-Kraus et al., 1997). Incorporation of biological markers of
    both exposure and susceptibility into biological monitoring studies
    may result in further insight into inter-individual variability.

    10.4.2  Efficacy of use

          In some situations, biological markers can be more efficacious
    tools than external exposure measurements for monitoring human
    exposure in population studies. For example, participant burden may be
    lower than in traditional monitoring schemes for some activities
    (e.g., motor vehicle repair) and for some subjects (e.g., children and
    the elderly) wearing a personal monitor may not be a practical
    strategy to monitor a subject's exposure. For some exposure routes
    such as dermal exposure, there is no adequate way to determine the
    extent of exposure using non-biological methods (Ward et al., 1986;
    Fiserova-Bergerova, 1987; Hemminki, 1992; Levesque et al., 1994). For
    example, chloroform in exhaled breath has been used as a biological
    marker to evaluate dermal exposure while swimming and showering
    (Levesque et al., 1994). Under certain conditions, wearing a sampling
    device alters behaviours that may, in turn, affect exposures as
    measured by a sampling device. Wearing a sampling device may be unduly
    burdensome in certain populations. Finally, monitoring devices may not
    exist to evaluate these exposures. In these cases, biological markers
    could be a preferred method to evaluate exposure.

          Biological markers can improve the evaluation of human health
    effects associated with environmental exposure to contaminants
    (Schulte, 1987; US NRC, 1987; Hulka, 1991; Hulka & Margolin, 1992).
    These markers have been advocated as a means to reduce measurement
    error in environmental epidemiology (Schulte, 1987; Hulka & Wilcosky,
    1988; Hatch & Thomas, 1993). Since they represent internal dose, they
    are anticipated to be more predictive of health effects than external
    measures of exposure (US NRC, 1987; Hulka, 1991) and they can be used
    to validate population exposure models (Georgopoulos & Lioy, 1994).
    For risk assessment purposes, biological markers can be useful tools
    in evaluation of intermediate end-points and improving the transition
    from environmental exposure assessment and animal dose-response models
    to actual human health outcomes (Hattis, 1986; IPCS, 1993; Mercier &
    Robinson, 1993).

    10.4.3  Internal exposure sources

          Contaminants can be stored long-term in the body and may be
    produced endogenously. These sources of exposure cannot be
    characterized without biological markers. Breast milk, bone mineral
    and adipose tissue as well as blood represent biological media
    available to assess body burden of contaminants, especially those that
    concentrate in biological tissue. Mobilization of contaminants from

    internal storage can be assessed using biological markers; for
    example, pesticide mobilization from adipose tissue can be measured in
    blood following fasting.

    10.5  Limitations of biological markers for exposure assessment

          Although the use of biological markers may result in improved
    exposure assessment, their use is not without limitations, since few
    validated markers are currently available. Challenges associated with
    the use of biological markers include source identification,
    pharmacokinetics, timing of exposure, biological variability, altered
    response as a result of exposure, potentially invasive sampling
    procedures and ethical concerns.

    10.5.1  Source identification

          Although the ability to integrate over all exposure sources is an
    advantage for total human exposure assessment, it limits the ability
    to identify the sources that contribute to exposure. Considerations
    for source identification include multiple exposure sources, multiple
    exposure regimes, non-specific biological response to exposure and
    endogenous production. Multiple sources of exposure can result in
    multimodal excretion rates because some exposures may be constant
    whereas additional exposures may be intermittent. Therefore,
    performing cross-sectional studies may miss multiple exposures (Que
    Hee, 1993) or changes in exposure patterns and/or concentrations. Some
    organic chemicals can be produced endogenously; this may result in an
    overestimate of the impact of external sources of exposure.

    10.5.2  Biological variability and altered exposure response

          Variability of biological response is an inherent characteristic
    of biological markers. Although variability may be associated with
    exposure levels, genetic differences in toxicokinetics and lifestyle
    factors such as diet, smoking and alcohol consumption may also result
    in biological variability. The impact of the inherent biological
    variability can be reduced by stratified sampling and the use of large
    sample sizes. Quantification of biological variability is difficult,
    and without adequate information on sources of variation the
    interpretation of results for exposure assessment purposes may be
    limited. Of particular concern is uncharacterized biological
    variability which may influence uptake, distribution, metabolism or
    excretion of the contaminant of interest and, therefore, the internal
    dose. Some authors consider that the influence of genetic variation is
    limited in comparison to the impact of other susceptibility factors
    (Perera & Weinstein, 1982). During validation studies, it is necessary
    to gather information on potential confounding factors. Baseline data
    on pre-exposure biological marker concentrations and demographic
    variables should be obtained to assess inter-individual biological
    variability more accurately.

          Previous exposure and pathophysiological states can change the
    biological response to current exposure. This is of particular concern
    in cross-sectional investigations or in studies of individuals with
    previous or long-term exposure. Previous or concurrent exposure to
    agents that elicit similar responses to the contaminant of interest
    may alter metabolic activity in response to exposure. These
    alterations may lead to an increase or decrease in the rate of
    metabolism or excretion, or a change in the type or amount of
    metabolites formed, which thereby alters the internal dose and
    potentially, the biologically effective dose. Interactions between the
    exposure of interest and other environmental exposures, diet,
    medication use, cigarette smoking and alcohol consumption may affect
    the outcome of the exposure and the interpretation of the biological
    monitoring results.

          In some cases, exposure to chemicals can cause maladaptive
    changes and the biological markers of exposure may represent
    subclinical disease. For example, long-term exposure to metals at high
    concentrations can result in kidney damage and thereby alter the
    processing of metals in the kidney. This results in potentially low
    excretion of metals in urine and subsequent underestimation of
    exposure (Lauwerys, 1983). In some situations, clinical tests can be
    conducted at the same time to address issues such as reduced kidney
    function. Conversely, individuals with long-term exposure may
    represent those individuals who are least susceptible to the agent's
    effects; this may be problematic when using workers to validate
    biological markers at high levels of exposure (Hauser et al., 1995). 

    10.5.3  Participant burden

          Collection of biological media presents challenges different from
    collection of environmental samples. At the minimum, it requires
    active participation on the part of the study subject. In designing
    studies, it is critical to recognize that collection of biological
    samples can be invasive. Some routine medical procedures, such as
    collection of a small vial of blood, may be more acceptable to
    participants than more involved, yet non-invasive, sampling methods
    such as collection of a 24-h urine specimen or wearing an air sampling
    pump for a day. Other sampling strategies, such as fat biopsies or
    collection of large volumes of blood, may be impractical for
    environmental exposure assessments because of excessive participant
    burden. Ethical considerations also include how participants should be
    informed about their results.

    10.5.4  Biosafety

          Biological media can serve as vectors of infectious disease, such
    as AIDS and hepatitis. Because of this hazard, special procedures must
    be used when collecting, transporting, storing, analysing and
    disposing of any human biological samples. All personnel must be
    trained for the proper handling of biological samples and protocols
    must include instructions for this.

    10.6  Media available for use

          Numerous biological media are available for use in environmental
    exposure assessment. Selection of sampling media depends on the
    contaminant of interest, the pattern of exposure, the timing of
    exposure, the population studied, ease of collection and storage and
    participant burden. Biological monitoring is frequently considered
    invasive; however, several media are available for exposure assessment
    that can be collected in a non-invasive manner. For occupationally
    used chemicals, biological exposure indices and monitoring protocols
    are available (Lauwerys, 1983; ACGIH, 1991; Que Hee, 1993); these may
    be regarded as starting points for biological sampling in
    environmental studies.

          Historically, blood and urine have been the primary media for
    biological exposure markers. Blood and urine, as well as exhaled
    breath and saliva, can be used to document recent exposures; past
    exposure can be evaluated using blood and urine as well as keratinized
    tissues (hair and nails), ossified tissue (teeth and bone), adipose
    tissue and breast milk. Adipose tissue and bone can also represent
    future sources of internal exposure. Other media available for
    biomarker studies include faeces, nasal lavage, tears, sputum, semen,
    cord blood and buccal cells, which can be feasible means for
    population exposure monitoring. Other media from cadaver or biopsy
    specimens (e.g., liver and kidney samples) have been collected in
    select populations but these cannot be used for exposure assessment of
    healthy individuals. Table 35 summarizes the media available for use
    in biological monitoring in environmental settings.

    10.6.1  Blood

          Blood has been frequently used for biological monitoring,
    especially in clinical settings such as occupational medicine. Blood
    can integrate all sources of exposure, including internal sources, and
    provide an indication of current internal dose. Since blood transports
    all agents throughout the organism, it represents an opportunity to
    sample all types of contaminants, such as gases, solvents, metals and
    fat-soluble compounds. Both specific (e.g., blood lead) and
    non-specific (e.g., sister chromatid exchange) analyses can be
    performed. Components of blood available for sampling are whole blood,
    red blood cells, white blood cells, plasma, serum and blood proteins,
    primarily haemoglobin and albumin.

    *    Whole blood consists of all the blood components and is
        preferable when the distribution of the analyte between plasma and
        cellular elements is unknown (Que Hee, 1993).

    *    Red blood cells make up a large portion of blood and their
        primary role is to transport oxygen via haemoglobin throughout the
        body. Mature red blood cells contain no nucleus and therefore no
        DNA, and have a 120-day lifetime. Chemicals that interact with
        haemoglobin, such as carbon monoxide, are found in red blood cells.

        Table 35.  Biological media available for sampling

    Media     Chemicals          Type of samples        Collection            Storage                 Issues                   References

    Adipose   lipophilic         surgical specimens,    invasive, minor       fat samples can be      good for chemicals       Patterson et
    tissue    chemicals,         fat biopsies           surgical procedure    archived                that bioconcentrate      al., 1986;
              pesticides, PCBs                                                                        or persist               Kohlmeier &
                                                                                                                               Kohlmeier, 1995

    Blood     gases, metals,     whole blood, red       invasive, quick,      for use of cells,       plasma can be analysed   Chase & Shields,
              pesticides,        blood cells, white     requires trained      sample must not be      for lipophilic           1990; Wolff
              semivolatile       blood cells, plasma,   phlebotomist          frozen, plasma          substances               et al., 1991;
              organics, VOCs     protein                                      samples can be frozen                            Bond et al.,
                                                                                                                               1992; Que Hee,

    Bone      metals (lead)      cortical bone,         non-invasive,         not applicable          minimal radiation        Rabinowitz, 1991;
                                 trabecular bone        30-60 min collection                          exposure to subject      Hoppin et al.,
                                 (X-ray fluorescent     time                                                                   1995; Hu et
                                 spectra)                                                                                      al., 1995

    Breast    lipophilic         fat and aqueous        non-invasive          samples easily          fat content of milk      Rogan et al.,
    milk      compounds          phases                                       stored in               varies, good for         1980; Sim &
                                                                              refrigerators           chemicals that are       McNeil, 1992

    Breath    gases,             mixed air, alveolar    non-invasive, quick,  samples should be       generally represent      Wallace, 1987;
              semivolatile       air                    requires air          analysed soon after     recent exposure; for     Que Hee, 1993;
              organics,                                 sampling apparatus    collection, not         airborne contaminants    Levesque et al.,
              volatile organics                                               easily available        should use alveolar      1994
                                                                                                      air samples

    Table 35.  (continued)

    Media     Chemicals          Type of samples        Collection            Storage                 Issues                   References

    Faeces    high molecular     faeces                 non-invasive, poor                            may provide              Doi et al.,
              weight organic                            compliance by                                 information on           1988; Wilhelm et
              compounds,                                subjects                                      chemicals that were      al., 1990;
              lipophilic                                                                              not absorbed by          Kemper, 1993
              substances,                                                                             through the GI tract

    Hair      cotinine,          scalp, pubic hair      non-invasive,         samples easily stored   high potential for       Vahter et al.,
              metals, PCBs                              important to          and archived            external contamination   1991; Bihl et
                                                        standardize                                                            al., 1993
                                                        collection location

    Saliva    metals,            mixed saliva           non-invasive, easy    samples can be          potential for            Tomita & Nishimura,
              pesticides,                               for participant       stored at room          contamination from       1982; Dabbs, 1991;
              semivolatile                                                    temperature             materials in mouth       Nigg & Wade; 1992;
              organics                                                                                                         Silbergeld, 1993

    Teeth     metals             shed deciduous teeth   non-invasive          samples easily stored   useful only in           Rabinowitz et
                                                                              and archived            children                 al., 1989

    Toenails  metals             single toenail or      non-invasive,         sample easily stored    potential for external   Garland et al.,
                                 composites             requires participant  and archived            contamination            1993
                                                        to collect sample

    Urine     low molecular      spot or grab samples,  non-invasive, 24-h    samples can be frozen   standardised to          Lauwerys, 1983;
              weight             first morning void,    urine sample requires                         creatinine               Baselt, 1988;
              metabolites,       24-h urine samples     motivated participant                         concentration, kidney    Que Hee, 1993
              metals, mutagens,                         for accurate sample                           damage can influence
              pesticides,                               collection                                    excretion rate

    *     Numerous types of  white blood cells are present in blood. These
          cells have a half-life ranging from 18-20 days to decades
          (Carrano & Natarajan, 1988). Since these circulating cells have
          DNA which can itself be altered or the expression of which can be
          changed as a result of exposure to a genotoxic agent, they may be
          used for biomarkers of exposure to genotoxic agents (Carrano &
          Natarajan, 1988; Kelsey, 1990). Interpretation of genotoxic
          response is complicated because DNA damage can result in either
          cell death or removal of the marker by DNA repair, or may alter
          cell functions (Perera, 1987). Regardless of this, correlations
          have been seen between environmental exposures and DNA adducts
          and other cytogenetic responses (Perera et al., 1988; Heddle et
          al., 1991; Santella et al., 1993; Yager et al., 1993).

    *      Plasma and  serum represent the non-cellular component of
          blood. Plasma is a straw-coloured aqueous solution of
          electrolytes, non-electrolytes and macromolecules (including
          clotting factors); serum is plasma without the clotting factors
          (Que Hee, 1993). Plasma represents a component of whole blood
          (approximately 60%), and it may contain the most biologically
          active fraction of blood borne contaminants, since plasma is in
          more immediate contact with tissues (Silbergeld, 1993). Plasma
          can be used for analysis of lipophilic chemicals, thereby
          avoiding the need for fat sampling.

    *      Blood proteins can be sensitive monitoring tools for chemicals
          that bind to macromolecules including DNA (Osterman-Golkar et
          al., 1976; Bond et al., 1992). Protein adducts, unlike DNA
          adducts, are not repaired and may prove to be a useful dosimeter
          of mutagen exposure (Grassman & Haas, 1993; Que Hee, 1993).
          Haemoglobin and albumin are two proteins available for use in
          exposure assessment. Haemoglobin is located in red blood cells in
          high concentration and has the half-life of red blood cells (120
          days); albumin is present in serum and has a half-life of 21
          days. Because of their differing biological half-lives, these
          proteins can be used to investigate the timing of exposure.

          Collection, storage, and shipping of blood samples can be
    resource-intensive. Blood sampling is invasive to the subject and
    requires a trained phlebotomist. Two primary methods of blood
    collection are venipuncture and finger stick. Blood from finger sticks
    is more subject to surficial contamination, has a higher percentage of
    red blood cells and is a smaller volume than that collected using
    venipuncture (Graziano, 1994). However in some populations, such as
    small children who have veins that are hard to find or who have fears
    of venipuncture, finger sticks can be the collection method of choice.

          The analytes of interest influence the sample collection regimen.
    Concerns about external contamination, interaction between the analyte
    and the sample collection vessel, and degradation, bacterial
    contamination and time until processing should be considered when
    designing the sampling protocol (Aitio & Järvisalo, 1984). Test tubes,
    additive agents such as EDTA and sample stoppers all represent

    potential contamination sources. Processing of serum samples should
    occur in a timely fashion to prevent degradation of the analytes of
    interest. For example, collection of serum samples for organochlorine
    pesticide analysis requires that:

    *   all equipment be solvent-rinsed before collection and processing

    *   care be taken to avoid contact between the blood and the stopper of
        the test tube

    *   the sample be centrifuged within 24 h of collection

    *   the serum be kept frozen prior to analysis.

          Shipment of frozen samples requires special precautions to
    prevent thawing during transport. For results to be valid, the
    biological sample should be analysed within holding time, as required
    by the specific medium and analyte.

    10.6.2  Urine

          Urine is another frequently collected biological medium. The
    concentrations of compounds found in urine usually reflect
    time-weighted averages in plasma during collection and storage in the
    bladder (Que Hee, 1993). The presence of a contaminant or its
    metabolite in urine generally represents recent exposure, though in
    some cases it may represent release from storage within the body
    (Lauwerys, 1983). Urine can be analysed for metabolites of organic
    chemicals (e.g., benzene and styrene), metals (e.g., arsenic and
    mercury) and pesticides as well as for mutagenic potential (Lauwerys,
    1983; Baselt, 1988; Que Hee, 1993). Since collection of urine samples
    is non-invasive, some investigators feel that, when validated, urine
    may be a better sampling medium than blood for monitoring (Smith &
    Suk, 1994).

          Three types of urine samples are used for biological monitoring:
    spot urine specimens, first morning voids and 24-h urine specimens
    (Baselt, 1988).

    *    Spot urine samples are relatively easy to collect but there may
        be significant variability with respect to exposure prediction as a
        result of metabolism, liquid consumption and kidney function.

    *    First morning void samples have less variability since they are
        more concentrated than spot samples, but require motivated subjects
        to collect the samples.

    *    Twenty-four hour urine samples control much of the
        intraindividual variability but require highly motivated subjects
        in order to collect useful samples (Baselt, 1988).

          The choice of sampling type is dependent upon the analyte of
    interest, the pattern of exposure, the anticipated concentration and
    the population. Urine samples should be refrigerated before analysis
    to reduce biological degradation and bacterial growth. Sample
    processing and storage should occur as soon as possible after
    collection in the manner required for the analyte of interest.

          To make the results of urine monitoring comparable between
    individuals, analytical results are frequently standardized to
    creatinine concentration or specific weight. Standardization reduces
    some of the variability of body size and urinary output (Lauwerys,
    1983; Sato, 1993). However, kidney damage can alter the creatinine
    excretion and therefore standardization may not be appropriate in all
    cases. Clinical data can be used to evaluate kidney function.

    10.6.3  Exhaled breath

          Exhaled breath analysis has been used in both occupational and
    environmental settings. Breath analysis is useful for assessing recent
    exposure to gases (e.g., carbon monoxide) and organic vapours and
    solvents (e.g., acetone and toluene). Limited studies have been made
    on the use of breath analysis for airborne particles and associated
    PAHs (Sugita et al., 1997). To be useful, breath measurements must
    relate both to exposure and to blood concentration (Bond et al.,
    1992). Breath sample concentrations of contaminants can vary as a
    function of body build, metabolism, sex, physical activity and
    ventilation rate as well as the exposure route (Que Hee, 1993). Two
    types of samples are available for collection: mixed exhaled breath
    and alveolar air, or end-expired air (Wallace, 1987; Wallace et al.,
    1991a,b; Que Hee, 1993).

    *    Exhaled breath can be a mixture of inhaled and exhaled air. If
        the exhaled biological marker is not present in inhaled air, then
        exhaled breath analysis is an effective means to measure internal
        exposure. For example, when alcohol has an internal source only
        (i.e., ingestion) a mixed breath sample is appropriate.

    *    Alveolar air provides a measure of the air that is in equilibrium
        with the blood in the deep lung (Bond et al., 1992). For analytes
        present in inhaled air, it is necessary to collect an alveolar air

          Breath samples can be used to assess microenvironmental exposure
    as well as exposure to chemicals with short biological half-lives that
    enter the body through non-inhalation routes (Wallace, 1987; Levesque
    et al., 1994). A number of methods are available to collect exhaled
    breath samples for organic and other gaseous contaminants (Wallace,
    1987a; Pellizzari et al., 1988; Periago et al., 1992). Exhaled breath
    collection is quick and easy for the participant, but the actual
    sample collection can involve complex air collection apparatus to
    gather a sufficient sample. The sampling tools are similar to those
    currently employed for sampling of air contaminants. Methods for
    airborne particles and components thereof have been used to a limited

    extent (Goto et al., 1997, Sugita et al., 1997). Potential sample
    collection error concerns in sample collection include loss of sample
    volume, sample storage prior to analysis and cross-reaction among

    10.6.4  Saliva

          Glands at four locations in the mouth produce saliva; the
    secretion rate varies at each location. Chemicals enter saliva via
    passive diffusion from plasma. Therefore, saliva may become a useful
    tool to non-invasively characterize plasma levels of contaminants
    (Silbergeld, 1993). Social science research has used saliva sampling
    because of its ease of collection and storage (Dabbs, 1991, 1993).
    Contaminants found in saliva include cotinine, drugs, metals, organic
    solvents, pesticides and steroid hormones (Tomita & Nishimura, 1982;
    Nigg & Wade, 1992; Silbergeld, 1993).

          Collection of saliva is relatively easy. One approach consists of
    having the subject rinse the mouth and chew on a stimulant, typically
    a piece of clingfilm or sugar-free gum. The subject then spits into
    the sampling container. Another method uses pumps to sample from the
    salivary glands. In either case, care should be taken to minimize
    contamination from the mouth. Because the concentrations of chemicals
    in saliva can fluctuate with circadian rhythm, the collection of
    saliva samples should occur at the same time each day (Dabbs, 1991).
    Saliva samples are quite stable and can be stored at room temperature
    for several days (Dabbs, 1991, 1993). However, in order to reduce
    viscosity from proteins, saliva samples are typically frozen prior to
    chemical analysis.

    10.6.5  Keratinized tissue (hair and nails)

          Keratinized tissues, primarily hair and toenails, are practical
    sampling media for evaluation of past exposure to metals (Bencko et
    al., 1986; Bencko, 1991; Subramanian, 1991; Kemper, 1993; Bencko,
    1995). Toenails are usually the medium of choice: see below. These
    media integrate exposures over a period of months, contain relatively
    larger concentrations of trace elements than blood or urine and are
    easy to collect, store and transport (Garland et al., 1993; Kemper,
    1993). Therefore, archiving specimens for future analyses is a viable
    option. Since hair and toenails are no longer in contact with the
    blood supply, they can provide a picture of past exposure. Because of
    the ease of collection, hair and toenails have been collected in
    numerous environmental studies, especially of children (DiVincenzo et
    al., 1985; Bencko et al., 1986; Wilhelm et al., 1989, 1991; Sukumar &
    Subramanian, 1992; Bustueva et al., 1994; Santos et al., 1994). Hair
    and toenails have been collected for elemental analyses in both
    environmental exposure assessment and nutritional evaluation studies
    (Garland et al., 1993; MacIntosh et al., 1997).

          Hair might be a useful medium to study exposure to environmental
    tobacco smoke (ETS). For example, in the framework of the German
    Environmental Survey (Krause et al., 1992) it was concluded that in
    large population studies nicotine and continine in urine as well as
    nicotine in hair are useful indicators of exposure for different
    levels of active and passive smoking. Continine and nicotine
    concentrations in hair have also been used to study fetal exposure by
    maternal smoking (Klein et al., 1993). Hair has also successfully been
    used in studies evaluating exposure to organic mercury (Suzuki et al.,
    1989) or PCB (Que Hee, 1993).

          One of the major concerns in the use of hair as a sampling medium
    is the potential for surficial contamination, in part due to the large
    surface area and the oily nature of hair. Sources of contamination
    include sweat, cosmetics, dirt and clothes (Doi et al., 1988;
    Raghupathy et al., 1988). Other potential disadvantages of sampling
    hair include sample preparation, differing growth rates at different
    locations on the body, unknown correlation with biological effects and
    biological variability (Wilhelm et al., 1990; Kemper, 1993). Hair
    grows approximately 1 cm/30 days (Que Hee, 1993) and can be evaluated
    along the shaft to provide a profile of exposure over time. Since
    growth rates of hair differ based on body location, standardization of
    sampling location is crucial.

          Because of concerns about contamination, exposure assessors
    usually prefer toenails to hair; however, some authors prefer hair for
    exposure assessment based on correlations with concentrations measured
    in environmental media and ease of collection (Wilhelm et al., 1991).
    When sampling nails, toenails are usually the medium of choice, since
    they are thicker and less subject to contamination than fingernails.
    Toenails can be collected by participants, shipped to distant research
    sites and stored in paper envelopes prior to analysis. To reduce
    problems associated with surficial contamination, the outer layer of
    the toenail can be removed. Each toenail grows at a different rate and
    therefore each nail may represent a different time period of exposure;
    long-term average exposure can be evaluated using a composite of all
    toenails (Garland et al., 1993).

    10.6.6  Ossified tissue  Teeth

          Teeth constitute a unique medium for assessment of past exposure.
    Depending on the tooth type and part of the tooth, one can reconstruct
    early childhood exposures to bone-seeking elements, such as lead
    (Rabinowitz et al., 1989). However, sample collection opportunities
    are limited to shed deciduous teeth in young children. Sample
    collection and storage are easy; removing the outer layer of the tooth
    minimizes external contamination.  Bone

          Bone represents both past exposure to bone-seeking elements and
    is a source for future internal exposure to these elements. The
    concentrations of elements in bone represent long-term exposure and
    storage of contaminants. For example, the half-life of lead in bone is
    approximately 10-40 years (Rabinowitz, 1991). Although numerous
    elements can be detected in bone tissue using destructive analyses
    such as atomic absorption spectroscopy (AAS),  in vivo measurement of
    environmental contaminants in bone has been limited to lead (e.g.,
    Somervaille et al., 1988; Hoppin et al., 1995). Lead concentration in
    bone can be analysed non-invasively using a technique known as X-ray
    fluorescence (XRF) (Hu et al., 1995). Bone lead concentrations in
    adults correlate well with integrated measurements of blood lead
    concentrations (Somervaille et al., 1988; Hu et al., 1995). Sources of
    variation in bone lead measurement include the degree of bone
    mineralization, the concentration of lead in bone, the overlying skin
    thickness and subject movement during the measurement. Lead and other
    elements can be mobilized from bone during physiological events such
    as pregnancy, lactation and osteoporosis (Silbergeld, 1991).

    10.6.7  Breast milk

          Environmental studies have used breast milk to evaluate past
    exposure to lipophilic chemicals (e.g., pesticides and PCBs) and
    metals (WHO, 1996b) and to examine potential exposures for
    breast-feeding infants (Niessen et al., 1984; Davies & Mes, 1987;
    Sikorski et al., 1990; Sim & McNeil, 1992). Organic chemicals found in
    breast milk have high lipid solubility, resistance to physical
    degradation or biological metabolism and slow or absent excretion
    rates (Rogan et al., 1980). Breast milk represents a major route of
    excretion of lipophilic chemicals for lactating women (Rogan et al.,
    1980; Sim & McNeil, 1992). Concentrations of chemicals in breast milk
    are a function of parity, age, body mass, time of sampling,
    nutritional status, lactation period and fat content of milk (Rogan et
    al., 1986; Sim & McNeil, 1992). Breast milk results are generally
    standardized to milk fat levels.

          Breast milk sampling represents a non-invasive method to estimate
    body burden of contaminants in adipose tissue. The correlation between
    contaminant concentrations in the lipid phase of milk and adipose
    tissue is good (Sim & McNeil, 1992). External contamination is a
    concern for breast milk samples; all sampling equipment should be
    cleaned in a manner that will prevent contamination. Although breast
    milk sampling is an applicable way to estimate population exposure to
    chemicals that bioconcentrate, sampling is limited to lactating women,
    who may or may not be representative of the population as a whole.

    10.6.8  Adipose tissue

          Exposure assessment studies using adipose tissue have been
    limited primarily to ecological studies comparing fat from cadavers or
    surgical specimens to general pollution levels. Adipose tissue
    represents a long-term reservoir of lipophilic compounds that the body
    slowly metabolizes and may release into the bloodstream. Unfortunately
    there is no non-invasive manner to sample fat stores directly, and
    many subjects see fat sampling as exceedingly invasive. The analytical
    method and detection limit requirements determine the quantity of
    adipose tissue necessary. In studies measuring body burden of dioxins
    in fat, a minor surgical procedure was necessary to collect a 20 g
    sample from healthy potentially exposed subjects (Patterson et al.,
    1986). When a smaller sample size is sufficient (200 mg or less),
    needle biopsies of fat stores in the buttocks can be used (Que Hee,
    1993; Kohlmeier & Kohlmeier, 1995).

    10.6.9  Faeces

          Faeces are a highly fat-soluble medium that provides information
    on compounds of high-molecular weight that exit the body via biliary
    excretion and on unabsorbed chemicals that enter the body via
    ingestion. Metals may also be monitored in faeces; however, it is
    unclear whether the metals found in faeces represent absorbed or
    unabsorbed elements (DiVincenzo et al., 1985; Vahter et al., 1991).
    Although collection of faeces is regarded as non-invasive, very few
    subjects are sufficiently motivated or interested in their collection
    (Bihl et al., 1993).

    10.6.10  Other media

          Several additional biological media have been used for
    determination of biomarkers of exposure including tears (e.g.,
    Ellegard, 1997), nasal lavage and nasal plugs (Steerenberg et al.,
    1997), sputum (e.g., Pizzichini et al., 1997) and semen (e.g., Sram et
    al., 1996). In most cases, these studies have focused on biomarkers of
    inflammation as indicators of exposure to airborne oxidants.

    10.7  Summary

          Biological markers represent a means to monitor environmental
    exposure by characterizing an individual's total dose of a contaminant
    from all sources of exposure. The main advantage of this strategy is
    in evaluation of an individual's total exposure using a measure which
    integrates over all exposure sources and is influenced by human
    behaviour. Biological markers are also believed to be more predictive
    of health effects than external measures of exposure. They address
    several exposure assessment needs:

    *   characterizing an individual's or a population's exposure

    *   generating population distributions of dose

    *   identifying the environmental and demographic determinants of

          The main disadvantage of biological markers is the difficulty in
    characterizing the individual sources which contribute to the
    subject's total exposure. When developing and utilizing biological
    markers, understanding the toxicokinetics of the contaminant in the
    system is crucial to characterize the biological variability and to
    determine whether the biological marker is valid for exposure
    assessment purposes at the concentration of interest. Biological
    markers have been crucial in improving our understanding of human
    exposure to certain contaminants, such as lead. Numerous non-invasive
    methods are available for biological monitoring, and exposure
    assessors should try to include these when developing environmental
    monitoring protocols.


    11.1  Introduction

          Human exposure studies are complex and often utilize specialized
    instrumentation and management of large amounts of data. Consequently,
    quality assurance (QA) should be applied to all aspects of an exposure
    study, including its design, implementation and reporting, to ensure
    the reliability and reproducibility of the results. A successful QA
    programme will monitor occurrence of potential errors in various
    components of the study and establish protocols for remedying such
    errors. In this chapter, important types of potential error in
    exposure results are described, and tools for identifying them are

          Exposure studies involve evaluations and comparison of exposures
    over time, geographical locations and populations. It is important
    that the results of the study accurately represent the exposures,
    rather than reflecting bias or error introduced by the study design or
    method. The reader is referred to the previous chapters on strategies
    and designs for exposure studies (Chapter 3) and related statistical
    concepts (Chapter 4) with respect to the principles of designing a
    high quality exposure study. 

          The reader is also referred to  Environmental Health Criteria 
     141: Quality Management for Chemical Safety Testing (IPCS, 1992) a
    monograph which deals explicitly with the organization of a QA
    programme. A prerequisite for producing data of good quality in
    exposure studies is the availability of adequate facilities,
    equipment, and personnel who are both well educated and trained.
    Studies need to be adequately designed and planned. Field and
    laboratory procedures need to be well defined, so that they can be
    carried out in the most appropriate way and in a consistent and
    reproducible manner. Other key elements in performing and reporting
    exposure studies include the final report and, lastly, the archiving
    and retention of data (IPCS, 1992)

    11.2  Quality assurance and quality control

          It is important to distinguish between the related concepts of QA
    and quality control (QC). QA refers to the overall management and
    organizational systems instituted to assess and maintain the integrity
    of the study. It includes independent monitoring that assures end
    users of the data that facilities, equipment, personnel, methods,
    practices, records and controls conform to accepted quality management
    principles. An effective QA programme provides confidence that the
    overall study meets the pre-established quality standards of accuracy,
    precision, completeness and clarity. QA should be integrated within
    the entire study so that the results are valid and that the final
    report accurately reflects these results (IPCS, 1992). Assessing data
    periodically is an essential aspect of QA.

          QC is a valuable QA tool that is applied to individual components
    of the study. Examples of such components are selection of study
    participants, collection of environmental samples, chemical analysis
    and analysis of data. The quality of an analytical measurement may be
    evaluated, for example, by comparing analytical results against a
    known standard, determining the sensitivity, accuracy, and precision
    of the analysis and ensuring that the analytical equipment has been
    properly maintained. These measurements would be part of a QC system
    (IPCS, 1992).

          Auditing procedures, on the other hand, are used to assess the
    quality of other aspects of a monitoring operation such as sampling
    procedure and transport of samples, as well as recording and reporting
    data. These procedures, although not necessarily quantitative, will
    generally promote vigilance by the operator against possible errors
    (WHO, 1986).

    11.3  Elements of a quality assurance programme

          All exposure studies must have a QA programme with a
    corresponding quality assurance plan that describes the implementation
    of the programme. The US EPA, for example, requires the development of
    detailed QA project plans that contain a complete description of all
    elements of the QA programme associated with the collection,
    measurement, validation and reporting of data. Common elements in QA
    plans are given in Table 36.

    Table 36. Common elements in quality assurance plans

          Organization and personnel

          Record keeping and data report

          Standard operating procedures

          Equipment maintenance and calibration

          Internal audit and corrective action

          Sample handle management

          A fundamental step in the QA process is the delineation of the
    study objective and a description of the study design which describes
    the study population, the data to be collected and the statistical
    analysis to be performed to meet the objectives. The programme
    management must be described (which identifies individuals in the
    programme and defines their responsibility from management, field
    monitoring, sample handling, sample analysis, data reduction and
    reporting). All programmes must have a QA officer who is independent
    of the programme and organization and who has oversight of all QA

    activities. The QA plans must define data quality objectives
    (accuracy, precision, detection limit, representativeness and
    completeness) and demonstrate that they are adequate to meet the study
    objectives. Specific procedures for generating and validating study
    data are given. An important component of any exposure study is
    demonstrating the feasibility of all study procedures through a pilot
    study which should be included in the QA plan. For complex field
    studies where methods of procedures may require modification in the
    field, it is important to clearly delineate corrective action
    procedures. Standard operating procedure (SOPs), detailed protocols
    for all components of the study, are appended to the QA plan. A
    description of these are given in section 11.4.3.

    11.4  Quality assurance programme

          Important elements in assuring that the outcome and data
    generated from the laboratory is accurate and reliable depends on the
    QA programme implemented by the laboratory. Key elements are given in
    the following.

    11.4.1  Organization and personnel

          It is important for the competent laboratory to establish an
    organization that can function to best serve the need of the testing
    facilities, regardless of the specific organization structure,
    personnel include management, study directors, QA coordinator and
    support staff. Properly trained personnel are crucial to the conduct
    of a quality study. The training requirements and procedures should
    cover professional, technical and support staff, to assure that they
    have adequate knowledge and skills to carry out their duties.

    11.4.2  Record-keeping and data recording

          Detailed records, with dates, should be kept on the introduction
    of new batches of so-called useable supplies (e.g., filters, reagents,
    sample containers, plasticware, pipettes, etc). Likewise, any changes
    in instrumentation or personnel have to be recorded. Afterwards, when
    identifying the reasons for changed analytical performance, this kind
    of thorough record-keeping is of utmost importance.

          The risk of introducing errors while transcribing data into
    record books or entering them into computer files is quite large.
    These are so-called clerical or human errors which may involve not
    only numerical errors but also formatting errors, identification
    errors, etc., which can lead to a serious bias in the analysis of
    data. There are several procedures for spotting such errors, from
    simply reviewing the data visually to computerized procedures which
    flag questionable data.

          Finally, data sets should be validated, that is approved for
    analysis. Data validation should form part of QA, and its procedures
    should be clearly defined. Data validation usually consists of two
    parts: data entry procedures to identify formatting errors at the time

    of entry, and acceptance procedures which are designed to compare the
    reported data against specified criteria in order to judge the
    reasonableness of reported values. Acceptance procedures identify
    various types of anomalies in the data including impossible values,
    individual and multiple outliers and entire subsets of incorrect data.

    11.4.3  Study plan and standard operating procedures

          QA involves the development and use of study plans and SOPs. The
    study plan (which should be very explicit and detailed) is the most
    important document for providing information to all participants on
    all aspects of the study, including information on responsible
    personnel, sample collection, sample storage and pre-analytical
    treatment, analytical procedures and data analysis. Deviations from
    the study plan in, for example, sampling frequency, sampling media or
    sampled population, necessitate a detailed amendment to the study
    plan. This is very important since in many monitoring studies sampling
    occurs on a regular basis, and the data obtained may be worthless if
    the present sampling scheme is not followed.

          SOPs are written instructions which describe how to perform all
    field and laboratory activities. These should be available to all
    personnel involved in the field and laboratory work and be a component
    of their training programme. SOPs ensure that all personnel associated
    with study operations will be familiar with and use the same
    procedures. If different individuals perform important study
    functions, such as storing samples, preparing solutions, running
    analytical equipment, or archiving documents, these operations should
    be performed in the same manner. By standardizing procedures for the
    conduct of studies, SOPs have a valuable QA function. They prevent the
    introduction of many potential errors in the generation, collection
    and reporting of data.

          The development of SOPs includes the following aspects: who
    should prepare, review and authorize SOPs; which field and laboratory
    operations require SOPs; and what information the SOPs should contain.
    The nature of the laboratory work being done and the training and
    experience of the laboratory personnel at a particular facility should
    determine exactly how extensive the content needs to be (IPCS, 1992).

    11.4.4  Collection of samples

          The potential for contamination is a major issue in environmental
    sampling. The highly sensitive analytical instrumentation currently
    available permits analysis of very small analyte quantities where the
    amounts of pollutants or chemicals present in environmental media are
    often quite low (e.g., parts per billion). The risk of significant
    contamination is particularly great when monitoring low concentrations
    of substances ubiquitous in the environment or present in the
    materials and tools coming into contact with the samples.

          Samples must be handled and stored in such a way that the level
    of the substance to be analysed remains stable. Processes that are
    most likely to decrease sample stability include precipitation,
    chemical deterioration (e.g., photolysis), surface absorption and
    evaporation of the analyte. Another factor that could be important is
    changes in the matrix, which may affect recovery of the analyte.

    11.4.5  Equipment maintenance and calibration

          Adequate equipment should be available for the receipt, handling,
    storage and analysis of samples. Its maintenance and calibration is
    essential to QA practise. Equipment used for analysis must be tested,
    calibrated and standardized.

    11.4.6  Internal audit and corrective action

          Internal audit and corrective action are critical activities in
    QA management. Audit procedures are to verify that the procedures are
    conducted the way they were planned and described in the study plan.
    Non-conformance must to be corrected to attain the study objectives.

    11.5  Quality control/quality assurance for sample measurement

    11.5.1  Method selection and validation

          A chemical compound can usually be analysed by a variety of
    different methods. Some methods emphasize quality of analysis, i.e.,
    accuracy and precision, whereas others are directed mainly towards
    practicality and low cost. However, even the best method may give
    incorrect results if improperly used. Methods are customarily
    classified according to their main use in the analytical field into
    definitive, reference and field routine methods. The best
    approximation of the true value obtained by analysis is the
     definitive value, i.e., the result obtained by the definitive
    method. The definitive method is, however, generally not considered
    practicable for daily laboratory use. Therefore, the purpose of a
     reference method is often to serve as a basis for the determination
    of the accuracy of the field methods.  Field methods (or  routine 
     methods) are those in everyday use in the laboratory. They are not
    necessarily as precise and accurate as reference and definitive
    methods. Their use is justified, however, because the definitive and
    reference methods are often too cumbersome and expensive and may not
    be available. The precision and bias relating to the method should be
    known for every routine method used.

          The following data quality specifications have to be validated in
    method selection to meet the requirements and objectives of the
    exposure assessment project.  Accuracy

          Accuracy is defined as the closeness of agreement between a test
    result and the accepted true value. Accuracy or validity of an
    analysis is primarily determined by the specificity of the method and
    the analytical recovery.

          Fig. 31A illustrates the ideal case where the reported values
    correspond exactly to the expected values. However, all analytical
    procedures are subject to a variety of analytical inaccuracies or
    biases. Fig. 31B shows the effects of a  proportional bias in which
    the reported values are higher than the expected values. This bias is
    called proportional because the amount of bias increases in direct
    proportion to the concentration of analyte in the specimen. Fig. 31C
    illustrates the effect of a  constant bias, in which the reported
    values are higher than the expected values by a constant amount, at
    all concentrations of analyte. The biases shown in Figs. 31B and 31C
    are positive biases, because the reported values are greater than the
    expected values. Of course, negative biases may also occur;
    furthermore, the reported values may fall along a curve rather than a
    straight line. Fig. 31D illustrates combined positive and negative
    proportion biases. When combined biases are present, there is
    frequently one concentration at which the reported value corresponds
    to the expected value. This phenomenon -- that the reported value at
    some analyte concentration is the same as the expected value whereas
    there is disagreement at all other concentrations -- is commonly
    observed and must be considered when interpreting quality control
    data. Because of this, it is recommended that reference samples should
    cover the range of expected measurement values.  Precision

          Precision or reliability of an analysis refers to the uniformity
    of the results of replicate analyses irrespective of the true
    concentration of the analyte. The precision of an analysis may vary
    depending on many factors, such as the skill and experience of the
    analyst, the purity of the chemicals, the quality of measuring devices
    and the time interval between replicate analyses. The precision is
    determined by calculating the percentage relative standard deviation
    among replicate analyses. In this manner precision can be defined as
    the closeness of agreement between independent test results obtained
    under prescribed conditions.

          The effects of random variation on analyses are illustrated in
    Figs. 32A and 32B, which show how laboratory's results may, on the
    average, fall along some operational line even though the individual
    results are distributed about the line, within certain limits of
    variability. Fig. 32A illustrates limits of variability that increases
    in proportion to the mean analyte concentration.

    FIGURE 31

    FIGURE 32  Sensitivity

          Sensitivity of a test method can be defined and measured as the
     limit of detection (LOD) and the  limit of quantification (LOQ).  Detection limits

          Appropriate determinations of detection limits is crucial to
    exposure data analysis and interpretations. Since there are several
    types of detection limits, each with its specific applicability, it is
    important that the methods and procedures to determine such limits be
    clearly presented as part of the QA/QC procedures. The term "lower
    limit of detection" (LLD) may refer to very different concepts. For
    example, the instrumental detection limit is defined as a multiple of
    the noise level of the analytical instrument, usually a factor of 3.
    However, when sampling and analysing environmental media, the
    instrumental detection limit constitutes only a fraction of the true
    limit of detection. The LOD takes into consideration the response of
    the analytical instrument to the specific analyte in standards of
    known concentration or, preferably, a matrix similar to that of the
    sample. Since the response of the instrument to repeated analysis of
    the same analyte concentration varies, the LOD is estimated as a
    function of the standard deviation (SD) of the repeated analysis,
    typically as  tn-0.01 × SD, where  t is the value of the Student
     t distribution with  n-1 degrees of freedom, alpha = 0.01 is the
    type I error and  n the number of repeated analyses, usually 7.

          However, sampling methods can have a background of one or more of
    the target analytes. For example, when using solid sorbent for air
    sampling, the sorbent may contain varying levels of background
    contamination across samples (e.g., varying benzene concentrations in
    Tenax). The  method detection limit (MDL) includes the background
    contamination in the sampler. It is determined using a similar
    statistical approach as the LOD, but using field and/or laboratory
    blanks instead of the known analyte concentrations. The MDL can be
    defined as the analyte concentration at which we have a given
    certainty (e.g., 1-alpha = 0.99) that the sample concentration differ
    from background. Finally, the lower quantification limit (LOQ) is
    defined as 10 SD, where SD is derived from the LOD determination. LOD,
    MDL and LOQ differ across laboratories using the same methods. More
    importantly, they vary over time for the same laboratory.

          The treatment of values below the MDL in statistical analysis of
    the data can have a strong impact on estimates of exposure
    distribution parameters. The reader may refer to Gilbert (1987), for
    more specific information. There also are upper limits of detection,
    defined by the range of linear response of the analytical method. In
    that case the detector may be overloaded with the result that it is
    not working in its linear detection range.

    11.5.2  Internal quality control

          The purpose of internal QC is to document that the method is in
    statistical control and without systematic errors so that the observed
    sampling results consistently fall within established control limits.
    Internal QC procedures are used primarily for the control of
    analytical precision (Taylor, 1988).  Control charts

          Sometimes changes in the analytical performance are not abrupt
    but take place gradually ("drift"). Such gradual changes are difficult
    to perceive from a single central result, but many become evident with
    time if the results of control samples are graphically displayed with
    respect to time or sequence of measurements.

          Control limits are used as criteria for signalling the need for
    action, or for judging whether a set of data does or does not indicate
    a state of statistical control. Control limits can be based entirely
    on the data from the samples, in which case the chart illustrates
    whether the method or procedure is repeatable. If the control chart
    contains limits that are based on analytical standards, then the chart
    is useful for discovering whether the observed measures for a sample
    value differ from the adopted standard values by an amount greater
    than should be expected by chance alone. Generally, control charts
    consist of warning limits and action limits. Warning limits often
    correspond to ± 2 SD from the mean, whereas action limits are set at
    ± 3 SD from the mean. Such thresholds mean that as long as the process
    is in control at the centre value there is a 5% chance that a result
    will exceed the warning limits and a 0.3% chance that a result will
    exceed the action limit, thus erroneously signalling an "out of
    control" message (WHO, 1986; ISO, 1993).

          To set up a Shewhard control chart (Fig. 33), measurements of,
    for instance, standard solutions, duplicates or spiked samples must be
    gathered while the analytical procedure is in control. The control
    parameters most frequently evaluated in Shewhard control charts
    include the average or median of control measures (average or median
    charts) and the range (difference) between duplicate analyses on the
    same test sample (range charts) (UNEP/WHO, 1986; AOAC, 1991; ISO,
    1991; Christensen et al., 1994). In a cumulative sum (cusum) chart
    (Fig. 34) a reference value, usually the intended or expected value,
    is subtracted from each observation. The cumulative sums of the
    deviations from the reference value are formed, and these cusums are
    plotted against the serial numbers of observations. The cusum chart is
    usually more sensitive to small shifts in level than the Shewhard
    chart and highlights persistent and recurring differences (Christensen
    et al., 1994; ISO, 1997).

    FIGURE 33

    FIGURE 34

          The Shewhard chart has six control limit lines corresponding to
    ±rhop, ±2rhop, and ±3rhop (rhop = rhoy/ n1/2) (rhop, appointed
    standard deviation in the control chart; rhoy, estimated standard
    deviation of the distribution of results;  n, number of control
    results) (Christensen et al., 1994).

    11.5.3  External quality control

          The aim of external QC is to demonstrate that analytical results
    obtained are accurate and comparable with the results ascertained by
    other laboratories. Usually, external QC measurements form part of
    external QA schemes or proficiency testing programmes. In such a
    scheme or programme, the coordinating laboratory prepares a
    homogeneous reference sample and distributes portions to the
    participating laboratories for analysis. Participating laboratories
    are required to examine the reference samples within a specified time,
    preferably together with collected samples, and submit the results of
    the reference samples to the coordinating laboratory which collates
    the data, performs a statistical analysis and sends an evaluation
    report back to the participating laboratories. In cases of poor
    performance, the laboratories may be contacted, and suggestions may
    made for improving performance.

          External QC schemes offer participating laboratories many
    advantages. They demonstrate the reliability of laboratory results and
    provide independent evidence of the quality of laboratory performance
    and individual analyst proficiency. In addition, external quality
    schemes allow participating laboratories to compare their own
    performance with that of other laboratories. Participation in an
    external QC scheme can encourage self-appraisal and minimization of
    laboratory errors. It can also be used to reduce the frequency of
    internal QC efforts when consistently favourable results are achieved
    on external QC test samples. Furthermore, it can assist in identifying
    needs for training and changes in laboratory procedures (AOAC, 1991).

          Examples of existing external QC schemes are given in Table 37
    (see also Christensen et al. (1994)).

          A specific example for an approach used in external QC is the
    UNEP/WHO regression method. The regression method is based on the QC
    programmes developed in the UNEP/WHO pilot project on assessment of
    human exposure to pollutants through biological monitoring for the
    determination of lead in blood and cadmium in kidney cortex (Vahter,
    1982). This external QC method was also applied in the UNEP/WHO Human
    Exposure Assessment Location (HEAL) study. For the studies on lead and
    cadmium, reference external QC samples of blood, air filter material,
    dust and diet were sent to participating laboratories (Vahter &
    Slorach, 1990). The same procedure was also adopted for the HEAL
    nitrogen dioxide study, using standard sodium nitrite solutions and
    nitrogen dioxide-exposed sample badges as QC samples (Matsushita &
    Tanabe, 1991).

    Table 37.  External QC schemes

    Scheme                                Country        Reference

    Centre de Toxicologie du Quebec       Canada         Weber, 1988

    Danish External Quality Assessment    Denmark        Anglov et al., 
    Scheme (DEQAS)                                       1993

    External quality control in the       Germany        Schaller, 1991
    toxicological analyses of biological
    materials, German Society of 
    Occupational Medicine

    Japanese External Quality             Japan          Sugita et al., 
    Assessment Scheme                                    1990

    NIOSH Proficiency Analytical          USA            NIOSH, 1979
    Testing Programme

    United Kingdom National External      United         UKNEQAS, 1993; 
    Quality Assessment Schemes            Kingdom        Bullock, 1986

          The accuracy and precision of the analytical results were
    evaluated by using a statistical technique known as the maximum
    allowable deviations (MAD) method (Vahter, 1982; WHO, 1986; UNEP/WHO,
    1991). This method is based on the linear regression line of reported
    versus "true" values and established acceptance criterion on how much
    the line may deviate from the ideal line where the measured equals the
    expected value. Acceptance criteria or the MADs have to be decided on
    separately for each pollutant or for each medium. The stringency of
    the MAD is primarily based on the required quality of the actual
    monitoring data and the sensitivity, accuracy and precision of the
    analytical methods. As in the use of control charts, decision
    concerning acceptance and rejection of results is based on statistical
    criteria, i.e., on the probability of making right or wrong decisions.
    A laboratory may be erroneously rejected when in fact its methodology
    is satisfactory, or it may be erroneously accepted when its
    methodology is not satisfactory. Power analysis is done to determine
    the probability of including an unsatisfactory laboratory. The concept
    of statistical power is described more fully in Chapter 4.

          On the basis of the power analysis, acceptance intervals (AI) can
    be calculated for a laboratory. The AI will be narrower than the
    interval between the MAD lines and the AI lines will be closer to the
    MAD lines with increasing number of QC samples. Also, methods with
    inherently smaller errors will decrease the difference between the MAD

    lines and the AI. Fig. 35 shows the regression line for the reported
    results of analysis of six QC samples versus the reference values. The
    MAD interval is indicated by solid lines and the acceptance interval
    by broken lines (Vahter, 1982). Fig. 36 shows the results of lead in
    blood from one laboratory during the training phase in the global
    UNEP/WHO Project on Assessment of Exposure to Lead and Cadmium through
    Biological Monitoring. The results of QC 2 and QC 3 were rejected,
    i.e., the regression lines fall outside the acceptance lines when
    evaluated between the reference values 100 and 400 µg/litre (Lind et
    al., 1987).

          The experience from the QA programme of the WHO/UNEP HEAL
    Programme shows that detailed protocols, instructions, and control
    activities covering the whole process, from sampling to analysis and
    data reporting, are essential if reliable data are to be obtained. The
    results clearly demonstrated that good analytical performance for a
    pollutant in one medium (e.g, lead in blood) is no guarantee of good
    performance for the same pollutant in another medium (e.g., lead in
    air filters). Similarly, good analytical performance at one
    concentration is no guarantee of good performance at higher or lower
    concentrations. It is, therefore, important that the QC samples have a
    matrix similar to that of the monitoring samples and that the
    concentrations of the QC samples cover the expected range of
    concentrations. One, or a few, reference samples are not sufficient
    for the evaluation of the evaluation of analytical performance using
    rigorous MAD criteria (Vahter & Slorach, 1990).

    11.5.4  Reference materials

          Reference materials are important means of ensuring comparability
    of analytical results across laboratories, over time, among
    populations and among studies. Reference samples for internal as well
    as external QA have been used in the analysis of environmental media,
    such as air, water and food, as well as for biological tissues and
    body fluids, such as internal organs, blood and urine. It is important
    that reference materials have a matrix which is the same or very
    similar to the matrix the "real" monitoring samples are comprised of.
    Matrix effects may seriously invalidate analytical results. The
    concentrations of a substance to be measured should also cover the
    same range as is expected in the monitoring samples. For several
    substances, the chemical forms in which they may exist must be taken
    into consideration. For example, arsenic, mercury, tin and several
    other metals occur in a number of different valence states and in
    organic and inorganic forms which vary in stability.

          Various types of reference samples are commercially available.
    The International Atomic Energy Agency (IAEA) conducted a survey on
    analytical reference materials of biological and environmental origin.
    The IAEA database presently contains over 10 000 analyte values for
    455 substances in 650 reference materials produced by 30 different
    suppliers (IAEA, 1995, 1996). It is expected that this survey will
    help analysts to select reference materials for QA purposes that match

    FIGURE 35

    FIGURE 36

    as closely as possible, with respect to matrix type and concentrations
    of the samples and analytes of interest.

          The availability of certified reference materials is still
    limited, however. In addition, owing to their relatively high cost,
    these materials can only occasionally be used for QC. Alternatively, a
    laboratory may prepare control materials calibrated against and,
    therefore, traceable to certified reference materials, or a
    conventional true value may be established by one or more reference
    laboratories, preferably using a validated definitive method (ISO,

    11.6  Quality assurance and control issues in population-based 

          Population-based studies present specific challenges to QA/QC
    programmes that are not common to more conventional exposure
    investigations. A description of the potential problems and
    alternative approaches to address them, avoid them and estimate the
    biases that they may introduce in the results should therefore be part
    of the study plan. Study design, including QA/QC procedures, should
    consider methodologies that address configurations as they occur in
    the field, and provide for modifications that will not compromise the
    stated objectives.

          Among the most important considerations in human population
    studies are the potential differences in cultural, geographical or
    social class framework between the exposure assessment study team and
    the target population. The investigators should be cognizant of local
    culture and conditions and include those considerations in the study
    design. For example, perceptions and customs with regard to the
    importance of time in daily life varies across cultures and even among
    subpopulations within the same country. As a result, appointments for
    household visits and interviews may not be kept as the field personnel

          A good approach for identifying problems and alternatives is the
    use of pilot studies before full implementations of the field
    component of a study. In pilot studies, trained staff perform the
    complete set of SOPs established for the full investigation on a small
    subset of the sample population. Thus, pilot studies also have the
    purpose of effectively providing final training of all field
    personnel. The information collected during these studies is very
    useful for adjusting the study plan and SOPs for local conditions. It
    is important that all personnel participating in a field study, i.e.,
    study design personnel, principal and co-investigators, laboratory
    staff and field personnel require practical experience with the local
    field conditions. Pilot studies provide the means to obtain such

          Another potential confounder in population studies is the
     Hawthorne effect; that is, the alteration of behaviour patterns that

    might affect exposures as a result of participation in the study. For
    example, many population-based field studies provide compensation to
    individuals who agree to participate. Monetary compensation could
    alter behaviour patterns that may affect exposure: for example, the
    participant may decide not to go to work (a typical activity) the day
    he receives the payment and instead spend the time performing an
    activity that is not customary. This problem can be resolved by
    offering the payment a short time after the participants have
    completed the monitoring period. The presence of an environmental
    monitor (e.g., an air sampling inlet and pump) may cause participants
    to avoid situations in which they may feel conspicuous, such as their
    place of employment. An innovative approach for assessing the
    influence of this effect is to expand the study to include a subsample
    of participants who engage in only a portion of the study (e.g., a
    time-activity questionnaire) and to compare the results for this group
    to those for the remainder of the population that completed the full
    monitoring programme.

    11.7  Summary

          QA includes independent study monitoring that assures laboratory
    management and users of data that facilities, equipment, personnel,
    methods, practices, records and controls confirm to accepted quality
    management principles. Errors in exposure data may be due to
    analytical variation as well as changes that may take place during
    sample collection and handling, preparation and storage of samples,
    and data keeping and data recording. Analytical variation can be
    divided into two major categories: accuracy, which refers to the
    agreement between the amount of analyte measured and the amount
    actually present, and precision, which refers to the random
    variability or reproducibility of the method.

          The study plan is the most important document for providing
    information on the critical components of an investigation, e.g.,
    responsible personnel, sample collection, sample storage and
    pre-analytical treatment, analytical procedures and data analysis.
    SOPs are appended to the study plan and contain written detailed
    instructions on how to perform certain routine field and laboratory
    activities. The study plan and SOPs can be seen as management
    directives designed to ensure that all personnel associated with study
    operations will be familiar with and use the same procedures.

          QC refers specifically to the quality of the laboratory results.
    It has two components. Internal QC is a set of procedures used by
    staff of a laboratory for continuously assessing results as they are
    produced. External QC is a system for objective checking of laboratory
    performance by an independent agency. Internal QC includes displaying
    results of control samples in control charts (e.g., Shewhard and cusum
    charts), and use of control limits as criteria for signalling the need
    for action, or for judging whether a set of data does or does not
    include a state of statistical control. External QC, on the other
    hand, provides independent evidence of the quality of laboratory
    performance and individual analyst proficiency. Usually, a

    coordinating laboratory distributes samples of known concentration to
    the participating laboratories. Participating laboratories examine the
    reference samples and submit the results to the coordinating
    laboratory for performance evaluation.

          Reference samples used in internal and external QC should have a
    matrix and pollutant concentration which is similar to the real
    sample. In addition, for several substances it is necessary to take
    into consideration the chemical form in which they may exist.

          Finally, interactions with human populations present a unique set
    of study design and QA considerations that should be carefully
    evaluated together with the conventional issues of sampling analysis
    and procedures.


    12.1  Introduction

          Exposure studies have helped to establish a subdiscipline of
    environmental science. Exposure analysis is now essential to
    environmental epidemiology and quantitative risk assessment. Exposure
    studies have called attention to practices, locations and populations
    subjected to higher risk owing to environmental contamination.
    However, the full promise of exposure analysis has not been realized.
    Exposure information can influence public opinion and policy and offer
    cost-effective innovative strategies for those looking for
    alternatives to current regulatory approaches. Our current practice is
    to partition problems into specific media (air, water, soil and food)
    and structure our management as restrictive commands limiting
    discharge. Exposure analysis is an integrated and more comprehensive
    approach for dealing with risk management.

          Kirk Smith discusses the potential of human exposure assessment
    for air pollution regulation (in WHO, 1995a). He argues that for
    developing countries, without the investment in monitoring, regulation
    and control infrastructures, exposure reduction strategies "may
    provide a much more efficient pathway of control over time than the
    path followed by the currently developed countries". Structuring a
    regulatory control programme that bases performance on the reduction
    of human exposure while also considering cost should appeal to market
    economies in both developed and developing countries. In order to
    implement such strategies effectively one must have the ability to
    quantitatively assess the relative contribution of various sources to
    human exposure and risk by measurement or modelling. With the
    development of exposure science, trading exposures among sources and
    pathways results in the possibility of integrating the concept of
     product stewardship, which would include extraction, manufacturing,
    consumer use and disposal. If society sets the performance goal of
    exposure minimization both for humans and for the ecosystem, the
    commercial and governmental institutions can devise more
    cost-effective responses. For example, a company might receive credit
    for reformulating a product that reduces exposures to VOCs in the
    home. The cost to the company might be far less in terms of exposure
    reduction  per capita than equipping the manufacturing site with
    emissions control equipment that reduces local concentrations.

          Before exposure trading across media, across pollutants or
    between employees and the general public can seriously be considered,
    the science of exposure assessment must mature. A great deal more must
    be learned about risk-producing behaviours and activities, about
    variation in individual susceptibility, and about the chronic as well
    as acute implications of mixtures of pollutants. Progress has been
    made in these areas, and studies are continuing. Perhaps the
    contribution of exposure assessment to environmental epidemiology best
    exemplifies its advancements in recent years. As environmental
    epidemiology is practised today, quantification of exposure measures
    play a significant role vital to study design and to interpretation.

    Linet et al.'s article (1997) on residential exposures to magnetic
    fields and acute lymphoblastic leukemia in children is an excellent
    case in point. They measured magnetic field strength in the current
    and former housing in over 1200 cases and controls. No associations
    were reported for direct measures of magnetic field strength exposures
    in this study, although some previous studies had found association
    between childhood leukemia and surrogate indicators of exposures to
    magnetic fields (power line classification schemes).

          Exposure information has influenced risk management and public
    policy. A well-known example involves materials containing asbestos.
    In the USA, as a result of US EPA's response to actions regarding the
    handling of asbestos-containing material in schools as well as
    numerous legal suits on behalf of asbestos industry workers, there was
    a costly and widespread effort to remove all asbestos from buildings.
    Eventually the review and public dissemination of studies showing
    insignificant exposures to asbestos to building occupants in the vast
    majority of situations resulted in a clarification of US EPA's policy
    and an end to the indiscriminate removal of all asbestos-containing
    material regardless of its condition (HEI, 1991; Camus et al., 1998).

          We now see the concern for protecting the public from
    environmental risk requiring more information about exposures.
    Recently, the US Congress passed the Food Quality and Protection Act.
    For the first time legislation has explicitly recognized the problem
    of multiple source exposure. The Act requires that EPA and other
    agencies account for aggregate or multiple sources of exposure when
    setting maximum allowable levels (i.e., tolerances) for pesticides in
    food. In this case, regulations are ahead of the science as current
    knowledge on the numerous pathways of pesticide exposure is not
    sufficient to establish a standardized methodology.

          So in recent years, we have seen exposure analysis gain status.
    In 1998 the International Society of Exposure Analysis (ISEA) held its
    8th annual conference in conjunction with the International Society
    for Environmental Epidemiology. ISEA has a journal with more than 500
    subscribers worldwide. The European Commission in 1995 published
    reports on  Exposure Assessment (EUR 14356EN) and  Time-activity 
     Patterns in Exposure Assessment (EUR 15892EN) along with a report on
     Study Design (EUR 15095EN) for air pollution epidemiology in which
    exposure assessment was featured.

          The US EPA (1996a) updated its 1989  Exposure Factors Handbook 
    (EPA/600/8-89/043) with a comprehensive three-volume compilation of
    statistical data on various factors used in assessing exposure. The
    updated document incorporates new information available from the late
    1980s through the first half of the 1990s. Used by risk assessors in
    conjunction with the revised  Guidelines for Exposure Assessment (US
    EPA, 1992a), the new  Exposure Factors Handbook gives point estimates
    for many parameters along with distributional information. The median
    as well as the high end of individual and population risk can be
    calculated using these inputs and appropriate concentration data.

          As described earlier in the book, the WHO has been promoting
    exposure assessment methodology and investigations for almost
    20 years. The HEAL project has provided training, documents and
    assistance to investigators worldwide. Notable successes of HEAL have
    been the establishment of high-quality measurements for metals such as
    lead and cadmium (see Foreword and Chapter 11).

          A number of professional organizations have contributed to the
    promotion of exposure sciences through conferences, workshops and
    publications. Although a comprehensive description of all these
    efforts is beyond the scope of this chapter, readers may find some of
    the publications listed in Table 38 useful.

    12.2  Exposure studies

          Exposure studies described in this chapter serve as examples of
    the variety of approaches and purposes such investigations have taken.
    Exposure studies are conducted for different reasons. Some were
    designed for the simple purpose of demonstrating methodology for
    generating hypotheses. Other studies were components of
    epidemiological studies. Still others were designed for regulatory
    purposes to determine possible exposure routes and dose rates for
    specific products or applications. Such studies may have participants
    adhere to a certain regime. There are also examples of large and
    expensive studies recruiting representative populations to provide
    generalizable exposure and risk estimates. Brief descriptions of
    different types of studies are presented in this chapter. Those
    included illustrate a variety of design strategies. The following
    discussion is not intended as a comprehensive review but may give the
    reader the sense of how exposure assessment can serve a variety of

    12.3  Air pollution exposure studies

          Perhaps the most numerous examples of exposure studies are in the
    field of air pollution. This section briefly presents examples of air
    pollution studies performed for a variety of purposes.

    12.3.1  Particle studies

          Particle exposures have taken a new importance in light of a
    substantial and growing literature on morbidity and mortality effects
    of ambient particulate matter (Wilson & Spengler, 1996). In 1986, the
    US Congress mandated that the US EPA Office of Research and
    Development "carry out a TEAM study of human exposure to particles."
    The EPA Atmospheric Research and Exposure Assessment Laboratory joined
    with California's Air Resources Board to sponsor a Particle Total
    Exposure Assessment Methodology (PTEAM) study in the Los Angeles
    Basin. The study was carried out primarily by the Research Triangle
    Institute and the Harvard University School of Public Health. The main
    goal of the study was to estimate the frequency distribution of
    exposures to particles for residents of Riverside, California, a city
    of approximately 250 000 inhabitants located 75 km east of downtown

    Table 38.  Guide to documents on exposure assessment


    The Potential of Human Exposure Assessment for Air Pollution Regulation
      (WHO, 1995a)
    Human Exposure Assessment for Airborne Pollutants: Advances and
      Opportunities (US NRC, 1991b)
    Methods for Assessing Exposure of Human and Non-human Biota
      (Tardiff & Goldstein, 1991)
    Biological Monitoring of Metals (IPCS, 1992)
    Guidelines for Exposure Assessment (US EPA, 1992a)
    Dermal Exposure Assessment: Principles and Applications (US EPA, 1992b)
    Methodology for Assessing Health Risks Associated with Indirect Exposure
      to Combustion Emissions (US EPA, 1990)
    Estimating Exposures to Dioxin-Like Compounds (US EPA, 1994)
    Superfund Exposure Assessment Manual (US EPA, 1988a)
    Selection Criteria for Models Used in Exposure Assessments
      (US EPA, 1987, 1988b)
    Standard Scenarios for Estimating Exposure to Chemical Substances During
      Use of Consumer Products (US EPA, 1986a)
    Pesticide Assessment Guidelines, Subdivisions U and K
      (US EPA, 1984, 1986b)
    Methods for Assessing Exposure to Chemical Substances, Volumes 1-13
      (US EPA, 1983-1989)
    Assessment of Human Exposure to Lead: Comparison between Belgium,
      Malta, Mexico and Sweden (Bruaux & Svartengren, 1985)
    Guidance on Survey Design for Human Exposure Assessment Locations
      (HEAL) Studies (Kollander, 1993)
    Air pollution in African villages and cities (Koning de, 1988)
    Exposure Monitoring of Nitrogen Dioxide. An international pilot study 
      within the WHO/UNEP Human Exposure Assessment Location (HEAL) 
      Programme (Matsushita & Tanabe, 1991)
    Assessment of Human Exposure to Selected Organochlorine Compounds
      Through Biological Monitoring (Slorach & Vaz, 1983)
    Assessment of Human Exposure to Lead and Cadmium Through Biological
      Monitoring (Vahter, 1982)
    Global WHO/UNEP: Environment Monitoring System. Exposure Monitoring
      of Lead and Cadmium (Vahter & Slorach, 1990)
    Human Exposure to Carbon Monoxide and Suspended Particulate Matter in
      Zagreb, Yugoslavia (WHO, 1982a)
    Human Exposure to SO2, NO2 and Suspended Particulate Matter in Toronto
      (WHO, 1982b)
    Human Exposure to Suspended Particulate Matter and Sulphate in Bombay
      (WHO, 1984)
    Human Exposure to Carbon Monoxide and Suspended Particulate Matter in
      Beijing, People's Republic of China (WHO, 1985c).
    Guidelines for integrated air, water, food and biological exposure 
      monitoring (WHO/UNEP, 1986)
    Indoor Air Quality Study Maragua Area, Kenya (WHO/UNEP, 1987)
    Indoor Air Quality in the Basse Area, The Gambia (WHO/UNEP, 1988)

    Table 38.  (continued)


    An introductory guide to human exposure field studies: survey methods and
      statistical sampling (WHO/UNEP, 1992a)
    Endemic fluorosis: a global health issue (WHO/UNEP, 1992b)
    Human Exposure to Pollutants. Report on the pilot phase of the Human
      Exposure Assessment Locations Programme (UNEP/WHO, 1991)

    Los Angeles. Another goal was to determine particle concentrations in
    the participants' homes and immediately outside the homes.

          The study had a three-stage probability sampling procedure
    (Ozkaynak et al., 1996). Ultimately 178 residents of Riverside, took
    part in the study in the fall of 1990. Respondents represented
    139 000 ± 16 000 (SE) non-smoking Riverside residents aged 10 and
    above. Their homes represented about 60 000 Riverside homes.

          Each participant wore a personal exposure monitor (PEM) for two
    consecutive 12-h periods. Concurrent PM10 and PM2.5 samples were
    collected by a stationary indoor monitor (SIM) and stationary outdoor
    monitor (SAM) at each home. The SIM and SAM were essentially identical
    to the PEM. A total of 10 particle samples were collected for each
    household (day and night samples from the PEM10, SIM2.5, SIM10,
    SAM2.5, SAM10). Air exchange rates were also determined for each 12-h

          Up to 4 participants per day could be monitored, requiring
    48 days to conduct the study. A central outdoor site was maintained
    over the entire period (22 September-9 November 1990). The site had 2
    high-volume samples with 10 µm inlets (actual cut-point about 9.0 µm),
    2 dichotomous PM10 and PM2.5 samples (actual cut-point about 9.5 µm),
    1 PEM10, 1 SAM10 and 1 SAM2.5.

          More than 2750 particle samples were collected, about 96% of
    those attempted. All filters were analysed by XRF for a suite of
    40 metals. More than 1000 12-h average air exchange rate measurements
    were made. A complete discussion of the quality of the data is found
    in Thomas et al. (1993a). LODs, based on 3 times the standard
    deviation of the blanks, were of the order of 7-10 µg/m3. All field
    samples exceeded the LOD. Duplicate samples ( n = 363) showed
    excellent precision for all types of particle samplers at all
    locations, with median relative standard deviations ranging from 2 to

          Daytime mean personal PM10 concentrations (150 µg/m3) were more
    than 50% higher than either indoor or outdoor levels (95 µg/m3).
    Overnight mean personal PM10 concentrations (77 µg/m3) were similar

    to the indoor (63 µg/m3) and outdoor (86 µg/m3) levels. The higher
    personal concentrations do not appear to be due to skin flakes or
    clothing fibres; many skin flakes were found on filters (up to an
    estimated 150 000 per filter) in subsequent scanning electron
    microscope (SEM) analyses, but their mass does not appear to account
    for more than 10% of the excess personal exposure (Mamane, 1992).

          Mean PM2.5 daytime concentrations were similar indoors
    (48 µg/m3) and outdoors (49 µg/m3), but indoor concentrations fell
    off during the sleeping period (36 µg/m3) compared to 50 µg/m3
    outdoors. Thus the fine particle contribution of PM10 concentrations
    averaged about 51% during the day and 58% at night both indoors and
    outdoors. Unweighted distributions are displayed in Fig. 37 for 24-h
    average PM10 personal, indoor and outdoor concentrations. About 25%
    of the population of Riverside was estimated to have 24-h personal
    PM10 exposures exceeding the 150 µg/m3 24-h US National Ambient Air
    Quality Standard (NAAQS) for ambient air. Central-site PM2.5 and PM10
    concentrations agreed well with backyard concentrations. Overall, the
    data strongly suggest that a single central-site monitor can represent
    well the PM2.5 and PM10 concentrations throughout a wider area such
    as a town or small city, at least in the Los Angeles basin.

          Stepwise regressions resulted in smoking, cooking, and either air
    exchange rates or house volumes being added to outdoor concentrations
    as significant predictors of personal exposure. Smoking added about 30
    µg/m3 to the total PM2.5 concentrations. Cooking added 13 µg/m3 to
    the daytime PM2.5 concentration, but was not significant during the
    overnight period. At night, an increase in air exchange of one air
    change per hour resulted in a small increase of about 4.5 µg/m3 to
    the PM2.5 concentration, but was not significant during the day. The
    house volume was not significant at night, but was significant during
    the day, with larger homes resulting in smaller PM2.5 concentrations.
    Since air exchange and house volume were weakly correlated
    (negatively), they were not included together in the same regression.

          Following Koutrakis et al. (1992), a non-linear least squares
    regression equation was used to estimate penetration factors, decay
    rates and source strengths for particles and elements from both size
    fractions (Ozkaynak et al.1996). Penetration factors were very close
    to unity for nearly all particles and elements. The calculated decay
    rate for fine particles (< PM2.5) was 0.39 ± 0.16 h-1, and for PM10
    was 0.65 ± 0.28 h-1. Since PM10 contains the PM2.5 fraction, a
    separate calculation was made for the coarse particles (PM10 - PM2.5)
    with a resulting decay rate of 1.01 ± 0.43 h-1. Decay rates for
    elements associated with the fine fraction were generally lower than
    for elements associated with the coarse fraction, as would be
    expected, due to their lower settling velocities. For example, sulfur,
    which is associated with the fine fraction of aerosols in the form of
    sulfate, had calculated decay rates of 0.16 ± 0.04 and 0.21 ± 0.04 h-1
    for PM2.5 and PM10 fractions, respectively. The crustal elements

    FIGURE 37

    (calcium, aluminium, manganese, iron) on the other hand, had decay
    rates ranging from 0.6 to 0.8 h-1. Each cigarette emitted 22 ± 8 mg of
    PM10 on average, about two-thirds of which (14 ± 4 mg) was in the
    fine fraction. Cooking emitted 4.1 ± 1.6 mg/min of PM10 particles, of
    which about 40% (1.7 ± 0.6 mg/min) was in the fine fraction. All
    elements emitted by cooking were limited almost completely to the
    coarse fraction; presumably carbon or other elements not measured by
    XRF were contained in the fine fraction. Sources other than cooking
    and smoking emitted about 5.6 ± 3.1 mg/h of PM10, of which only about
    1.1 mg/h ± 1.0 (20%) was in the fine fraction (see Figs. 38 and 39).

          Based on the mass-balance model, outdoor air was the major source
    of indoor particles in Riverside, providing about 75% of fine
    particles and 65% of inhalable particles in the typical home. It was
    also the major source for most elements, providing 70-100% of the
    observed indoor concentrations for 12 of the 15 elements. Unidentified
    indoor sources accounted for most of the remaining particle and
    elemental mass collected on the indoor monitors. The nature of these
    sources is not yet understood. They do not include smoking, other
    combustion sources, cooking, dusting, vacuuming, spraying or cleaning,
    since all these sources together account for less than the
    unidentified sources. For example, the unidentified sources accounted
    for 26% of the average indoor PM10 particles, whereas smoking
    accounted for 4%, and cooking for 5%. Of the identified indoor
    sources, the two most important were smoking and cooking. Smoking was
    estimated to increase 12-h average indoor concentrations of PM10 and
    PM2.5 by 2 and 1.5 µg/m3 per cigarette, respectively. Most of this
    increase was in the fine fraction. Cooking increased indoor
    concentrations of PM10 by about 6 µg/m3 per min of cooking, with
    most of the increase in the coarse particles. Other household
    activities such as vacuuming and dusting appeared to make smaller
    contributions to indoor particle levels. An interesting finding was
    that commuting and working outside the home resulted in lower daytime
    particle exposures than for persons staying at home.

          Multivariate calculations in two separate studies resulted in
    rather similar estimates of the effect of smoking on indoor fine
    particle concentrations. Spengler et al. (1981) estimated an increase
    of about 20 µg/m3 per smoker, or 25 µg/m3 per smoking home, based on
    55 residences monitored over a year in 6 US cities. In another study,
    a smoking effect of about 32 µg/m3 was estimated for smoking homes
    based on data collected in Tennessee, USA (Spengler et al., 1985).

    12.3.2  Carbon monoxide

          The largest personal monitoring study of carbon monoxide
    exposures was carried out by US EPA in Washington, DC and Denver,
    Colorado in the winter of 1982-1983 (Johnson, 1983; Hartwell et al.,

    FIGURE 38

    FIGURE 39

    1984; Akland et al., 1985; Johnson et al., 1986). About 800 people in
    DC and 450 in Denver were monitored for 24 h (48 h in Denver) using
    electrochemical carbon monoxide monitors with specially designed data
    loggers. The data loggers were capable of sampling the current from
    the monitor about 4 times a second. They were equipped with buttons
    that the subject could press when one activity ended and the next
    began; at that point, the logger would average all preceding values
    from the time the activity began. (There was also an automatic
    averaging every hour.) The result was an extraordinarily rich
    database, with approximately 1200 people averaging 40 activities per
    day, each with an associated average carbon monoxide level. At the end
    of the monitoring period, each subject provided a breath sample. Major
    findings of the study included the following:

    *   Commuters had the highest exposures to carbon monoxide in general,
        averaging up to 13 µg/g. Parking garages had the highest carbon
        monoxide levels of any microenvironment, with churches and schools
        among the lowest.

    *   The main indoor sources of carbon monoxide were gas stoves and
        cigarettes. Gas stoves increased levels by about 2.5 µg/g when
        being used; homes with smokers had increases of about 1.5 µg/g on

    *   Personal exposures were higher than would be predicted by
        measurements at fixed monitoring stations. About 10% of DC
        residents appeared to exceed the 8-h standard of 9 µg/g, as
        determined by their breath concentrations, although only 1 of the
        11 fixed stations exceeded the standard during the monitoring

          A study of California homes (Wilson et al., 1993a,b, 1995; Colome
    et al., 1994), each monitored for 48 h, indicated that 13 of 277 homes
    (about 5%) had indoor 8-h averages exceeding 9 µg/g (the outdoor
    standard). Since the outdoor standard is to be exceeded only once per
    year, it is clear that the fraction of homes with 8-h indoor averages
    exceeding 9 µg/g more than once per year would be larger than the 5%
    observed in the single 48-h monitoring period. Homes with gas stoves
    and gas furnaces had indoor source levels for carbon monoxide that
    were about 3 times higher than homes without such sources. Homes with
    wall furnaces had higher levels of carbon monoxide than homes with
    forced-air gas furnaces. Homes with smokers ( n = 85) had levels of
    carbon monoxide about 0.5 µg/g higher than homes without smokers
    ( n = 190). Malfunctioning gas furnaces were a major cause of
    elevated concentrations of carbon monoxide. However, the homes with
    the highest carbon monoxide levels also included some with electric
    cooking stoves and electric heat, suggesting that other sources of
    carbon monoxide were present in these homes. Such sources could
    include cars idling in attached garages or unvented gas or kerosene
    space heaters.

    12.3.3  Nitrogen dioxide

          Nitrogen dioxide is a ubiquitous respiratory irritant for which
    air quality standards have been established in many countries (WHO,
    1997d). It is emitted by industrial processes and mobile sources, but
    also by indoor combustion appliances such as gas cooking stoves and
    furnaces. Several studies in the 1970s suggested that children in
    homes with gas stoves suffered more infectious disease than children
    in homes with electric stoves; a possible connection with nitrogen
    dioxide (in lowering resistance) was postulated (Samet & Spengler,
    1991). Also, exposure is likely to be higher for those living closer
    to roadways. 

          A study in Helsinki, Finland, explored weekly nitrogen dioxide
    exposure of preschool children as well as between- and within-children
    variances of repeated personal exposure measurements. The study tested
    the hypothesis that exposure to the low levels of nitrogen dioxide in
    Helsinki increases the risk of respiratory symptoms in preschool
    children (Mukala et al., 1996).

          The parents of 246 children, aged 3-6 years, returned a letter of
    consent to participate in a personal nitrogen dioxide exposure study.
    The children spent their days at one of three daycare centres, two
    located in the downtown area and one in a suburban area. All children
    carried personal Palmes tubes on outdoor clothing one week at a time
    during six consecutive weeks in winter (14 January-4 March 1991) and
    seven consecutive weeks in spring (8 April-27 May 1991). Weekly
    concentrations of nitrogen dioxide were also measured inside and
    outside each daycare centre to assess the usefulness of the stationary
    measurements in estimating the variation of exposures. Ambient
    concentrations of nitrogen dioxide were monitored at three fixed sites
    of the Helsinki Metropolitan Area council network with
    chemiluminescence monitors. The distance from each daycare centre to
    the nearest monitoring site varied from 0.5 to 11 km.

          The geometric mean of personal nitrogen dioxide exposure levels
    of in the total 13-week period was 26.5 µg/m3 in the downtown area
    and 17.5 µg/m3 in the suburban area. These exposure levels were
    significantly lower than ambient air levels of nitrogen dioxide in the
    same areas. Gas cooking stove or/and smoking at home significantly
    increased personal exposure to nitrogen dioxide. The weekly exposures
    averaged over all children in each daycare centre correlated poorly
    with the fixed site ambient air levels ( r2 = 0.37), but much better
    with the nitrogen dioxide levels inside and outside the daycare
    centres ( r2 = 0.88 and 0.86, respectively). In the suburban and
    downtown groups the between-child variances in nitrogen dioxide
    exposures were only 14% and 28% of the total variances, which were
    dominated by the within-child variances.

          Stationary measurements at the ambient air fixed sites and inside
    and outside the daycare centres explained the variations in personal
    nitrogen dioxide exposures of the children well during the spring, but

    not during the winter. A statistical model, where data from outside
    daycare centre measurements, fixed ambient air monitors, residential
    area and home characteristics (i.e., gas cooking stove, smoking inside
    at home, type of dwelling) were included, explained only 32% of the
    personal exposure variations in winter, but 67% in spring.

          There were significantly more days with stuffed nose (26% versus
    20%) and cough (18% versus 15%) in the downtown area than in the
    suburban area. The observed risk of cough was highest and
    statistically significantly increased compared to the levels of
    personal nitrogen dioxide. Also, when using daycare centre
    measurements or fixed site ambient air data for exposure assessment,
    there was a positive trend between nitrogen dioxide exposure and cough
    in winter these associations were, however, weaker and

          According to the result of the study, exposure to nitrogen
    dioxide should be measured using personal exposure measurements when
    studying health effects of the gas in non-symptomatic children in
    areas with low nitrogen dioxide levels. Even personal exposure
    measurements using weekly averages, however, may not adequately
    reflect all biologically relevant exposure, e.g., short-term peak
    concentrations. The most significant determinants of the personal
    nitrogen dioxide exposures of the children in Helsinki are living in
    downtown rather than in a suburban area, gas versus electric cooking
    stove and smoking in the home. However, even all risk factors together
    did not increase the personal exposures of downtown children up to
    suburban outdoor air levels.

    12.3.4  Ozone

          The UC Berkeley Ozone project (USA) is an example of an
    epidemiological study addressing long-term effects of lifetime ambient
    oxidant pollution on pulmonary function (Tager et al., 1998a,b). A
    major purpose was to address the feasibility of improving ozone
    exposure assignment by means of collecting lifetime information on
    relevant time-activity patterns to exposure, in combination with fixed
    site ambient air monitoring data. Individual factors considered to be
    relevant for exposure were:

    *   lifetime residential history

    *   time spent outdoors in different age periods

    *   time spent in outdoor physical activities in each lifetime

          To test the reliability of the retrospective assessment, a
    test-retest design was chosen. The study included a convenience sample
    of 168 non-smoking UC Berkeley college freshmen who had to be lifetime
    California (USA) residents (San Francisco Bay Area or Los Angeles
    Basin). It was shown that retrospective lifetime residential history
    is highly reliable (Künzli et al., 1996). Using pollution monitor

    data, averaged over lifetime across all respective residential
    locations, may in fact improve the health effects assessment.,
    compared with mere reliance on the ambient monitor data from the last
    or actual residence only (Künzli et al., 1997a). Three retrospective
    approaches to assess outdoor physical activity patterns have been
    tested and two methods gave rather reliable overall estimates for time
    spent in outdoor heavy or moderate activities, during summer. For the
    activity table format (see Fig. 19), only 13% of the total variance
    was attributed to reporting variability (test-retest). The
    categorization into heavy and moderate activities based on published
    data regarding energy expenditure (Ainsworth et al., 1993). Ambient
    long-term mean daytime concentrations of ozone were weighted by the
    duration and exertion level of the reported long-term average outdoor
    physical activity. This "effective exposure", therefore, may be
    considered a surrogate measure of ozone dose. Although the study had
    some promising results regarding the feasibility of retrospectively
    assessing exposure relevant surrogates over long periods of time,
    validity of the time-activity data cannot be directly assessed.
    Neither could the study answer the open question of whether
    time-activity data may be needed in the assessment of long-term
    effects of air pollution (Künzli et al., 1997a).

    12.3.5  Combined exposure studies

          The WHO, in a number of studies termed the Global Environmental
    Monitoring System (GEMS), sponsored several studies of combustion
    related air pollutants. GEMS, now renamed as the Air Management
    Information System (AMIS), continues to coordinate the gathering of
    data on levels of ambient air pollution in cities around the world.
    GEMS also conducted a series of exposure studies to examine the
    assumptions that fixed ambient monitoring accurately reflected
    personal exposures. Studies were carried out in Toronto, Canada (WHO,
    1982b); Beijing, China (WHO, 1985c); Zagreb, Croatia (WHO, 1982a); and
    Bombay, India (WHO, 1984).

          Through the GEMS, WHO and UNEP later formed the HEAL project. The
    goal was to improve exposure monitoring internationally, using direct
    measurements of human exposure with activity information, and provide
    guidelines for techniques that could be used uniformly around the
    world. Another goal was to increase the accuracy of risk assessment
    studies with the goal of better protection of human health.

          A study in Kenya was carried out to attempt to determine the risk
    of acute respiratory infection by quantifying indoor air pollution
    levels caused by home combustion sources (WHO, 1987). The study was
    carried out in 36 randomly chosen households that used biomass as
    fuel. It compared concentrations of particles, carbon monoxide,
    nitrogen dioxide and formaldehyde inside the home with outdoor
    pollution levels.

          The mean 24-h concentration of respirable particles was
    1400 µg/m3, and the estimated levels in areas where cooking and
    heating fires were used was 3000-4000 µg/m3. Although elevated levels
    of carbon dioxide and nitrogen dioxide were found in the homes, the
    levels were below those found in previous studies of indoor air
    pollution in developing countries. Little correlation was seen between
    indoor and outdoor levels, confirming that the source of the excess
    levels was combustion.

          The excessive levels of particulate, which included a large
    concentration of PAHs, suggested that individuals spending a
    significant amount of time indoors (e.g., women and small children)
    were at greater risk owing to exposure to smoke. Because the levels
    observed were homogeneous across the samples, leaving no unexposed
    control group, the researchers were unable to reach conclusions about
    the effect of the levels on occurrence of acute respiratory

    12.3.6  Assessing ambient pollution impacts indoors

          Santiago, Chile, a city of 5.2 million inhabitants in 1997 (40%
    of the Chilean population), has chronic high concentrations of certain
    air pollutants. For example, in 1995 the Chilean air quality standards
    for PM10, carbon monoxide and ozone were exceeded on more than
    200 days. Annual PM10 levels surpassed 100 µg/m3 in the 1990s and
    levels of 300 µg/m3 are common during the winter, especially when an
    inversion layer is formed.

          In many countries when pollutants in the outdoor air exceed
    standards, the population is advised to remain indoors, but if
    infiltration occurs and indoor sources are generating pollutants,
    indoor air quality (IAQ) might be even worse than outdoors. A study
    was designed to evaluate the contribution of outdoor pollution to IAQ
    in Santiago and in a small rural town (Curocori). Carbon monoxide,
    PM5 and PAHs were monitored simultaneously indoors and outdoors along
    heavy traffic roadways in Curocori and in Santiago. The methodology
    used is described in Gil & Adonis (1997).

          In downtown Santiago, carbon monoxide concentrations ranged from
    1.9 to 73 µg/g outdoors; indoor levels were 0.5-93 µg/g. Although
    levels were slightly higher indoors (but not significant,  p > 0.05)
    than outdoors, changes in outdoor levels (which were always related to
    vehicular traffic) simultaneously produced changes in indoor levels. A
    typical carbon monoxide profile is shown in Fig. 40 for outdoor and
    indoor levels in an office. Outdoor and indoor levels showed a high
    correlation ( r = 0.89) with the higher values occurring during the
    rush hour.

    FIGURE 40

          Levels of total PAHs, carcinogenic PAHs and PM5 were very high
    and showed no statistically significant differences indoors and
    outdoors ( p > 0.05). Highest PM5 levels were 260 and 280 µg/m3
    for indoors and outdoors respectively. Levels of benzo [a]pyrene
    indoors and outdoors were highly correlated ( r = 0.869). For
    restaurants which usually work with open doors, the correlation
    between indoor and outdoor levels was even higher ( r = 0.99). In
    Curocori, levels (a town with little vehicular traffic) were almost
    identical indoors and outdoors. Levels of carbon monoxide PM5, total
    PAHs and carcinogenic PAHs were considerably lower in this rural town
    and indoor/outdoor correlations were also much lower than those
    obtained in Santiago.

          These results confirm the importance of ambient pollution to
    population exposures when outdoor pollution levels are high. Only when
    the contribution of penetrating ambient pollution is lowered can the
    indoor contributions be more readily assessed.

    12.3.7  Volatile organic compounds

          Human exposure to VOCs occurs mainly through inhalation, although
    some VOCs are ingested as contaminants in drinking-water, food and
    beverages. Some hydrocarbons are known carcinogens or mutagens (e.g.,
    benzene). Almost all cause eye irritation, coughing, drowsiness,
    clumsiness and loss of alertness. Acute effects from industrial
    exposures at the parts per million (µg/g) level include skin
    reactions, dizziness and fainting. Sick building syndrome (SBS) and
    multiple chemical sensitivity (MCS) have been associated with
    relatively low (ng/g, parts per billion) concentrations of VOCs.

          Between 1979 and 1987, the US EPA carried out the TEAM studies to
    measure personal exposures of the general public to VOCs in several
    geographic areas in the USA (Pellizzari et al., 1987; Wallace et al.,
    1987a). About 20 target VOCs were included in the studies, which
    involved about 750 people, representing 750 000 residents of the
    areas. Each participant carried a personal air quality monitor
    containing 1.5 g Tenax. A small battery-powered pump pulled about
    20 litres of air across the sorbent over a 12-h period. Two
    consecutive 12-h personal air samples were collected for each person.
    Concurrent outdoor air samples were also collected in the
    participants' backyards. In the 1987 studies, fixed indoor air
    samplers were also installed in the living room of their homes.

          The initial TEAM pilot study (Wallace et al., 1982) in Beaumont,
    Texas and Chapel Hill, North Carolina indicated that personal
    exposures to about a dozen VOCs exceeded outdoor air levels, even
    though Beaumont has major oil producing, refining and storage
    facilities. These findings were supported by a second pilot study in
    Bayonne-Elizabeth, New Jersey (another major chemical manufacturing
    and petroleum refining area) and Research Triangle Park, North
    Carolina (Wallace et al., 1984a). A succeeding major study of 350
    people in Bayonne-Elizabeth (Wallace et al., 1984b) and an additional

    50 people in a non-industrial city and a rural area (Wallace et al.,
    1987a) reinforced these findings (Table 39). A second major study in
    Los Angeles and in Antioch-Pittsburgh, California (Wallace et al.,
    1988), with a follow-up study in Los Angeles in 1987 (Wallace et al.,
    1991a,b) added a number of VOCs to the list of target chemicals with
    similar results (Table 40). Major findings of these TEAM studies
    included the following:

    *   Personal exposures exceeded median outdoor air concentrations by
        factors of 2-5 for nearly all of the 11 prevalent VOCs (Fig. 41).
        The difference was even larger (factors of 10 or 29) when the
        maximum values were compared. This is so despite the fact that most
        of the outdoor samples were collected in areas with heavy industry
        (New Jersey) or heavy traffic (Los Angeles).

    *   Major sources are consumer products (bathroom deodorizers, moth
        repellents); personal activities (smoking, driving); and building
        materials (paints and adhesives). In the USA, one chemical (carbon
        tetrachloride) has been banned from consumer products and exposure
        is thus limited to the global background of about 0.7 µg/m3.

    *   Traditional sources (automobiles, industry, petrochemical plants)
        contributed only 20-25% of total exposure to most of the target
        VOCs (Wallace, 1991a,b). No difference in exposure was noted for
        persons living close to chemical manufacturing plants or petroleum

          The results of the VOC TEAM study encouraged investigators to
    explore the causes for higher personal exposures. In a study designed
    to better understand the VOC contributions of specific sources,
    Wallace et al. (1989) had 7 volunteers undertake about 25 activities.
    A number of these activities (using bathroom deodorizers, washing
    dishes, cleaning an automobile carburettor) resulted in 10-1000-fold
    increases in 8-h exposures to certain VOCs.

          A recent study of benzene and toluene in 293 California homes
    (Wilson et al., 1993a, b; 1995; Colome et al., 1994) resulted in some
    interesting differences between the two agents. For benzene, 48-h
    average indoor concentrations correlated fairly well with outdoor
    levels, but for toluene almost no correlation was observed. This is
    probably due to the much wider use of toluene in consumer products.
    Major variables associated with higher net indoor benzene levels were
    presence of a gas heating furnace and having two cars parked in an
    attached garage. For toluene, a particular brand of furnace had the
    highest partial correlation with net indoor toluene concentrations;
    apartments also had higher concentrations.

          A study of 170 homes in Avon, England found mean indoor levels of
    benzene to be 8 µg/m3, compared to outdoor concentrations of 5 µg/m3
    (Brown & Crump, 1996). The study employed passive Tenax tubes to
    collect 28-day indoor and outdoor samples. These results were in good
    agreement with the levels of 10 µg/m3 indoors and 6 µg/m3 outdoors
    at 50 homes in Los Angeles measured over two seasons in 1987 (Wallace
    et al., 1991).

        Table 39.  Weighted estimates of air and breath concentrations of 11 prevalent compounds for 130 000 Elizabeth-Bayonne 
               residents (fall 1981); 110 000 residents (summer 1982); and 49 000 residents (winter 1983)

    Compound                 Personal    Fall       Breath     Personal   Summer     Breath     Personal   Winter     Breath 
                             air         outdoor    (300)      air        outdoor    (110)      air        outdoor    (49)
                             (n = 340)   air                   (150)      air                   (49)       air
                                         (86)                             (60)                             (9)

    1,1,1-Trichloroethane    94a         7.0a       15b        67         12         15         45         1.7        4.0

    m,p-Dichlorobenzene      45          1.7        8.1        50         1.3        6.3        71         1.2        6.2

    m,p-Xylene               52          11         9.0        37         10         10         36         9.4        4.7

    Tetrachloroethylene      45          6.0        13         11         6.2        10         28         4.2        11

    Benzene                  28          9.1        19         NCc        NC         NC         NC         NC         NC

    Ethylbenxene             19          4.0        4.6        9.2        3.2        4.7        12         3.8        2.1

    o-Xylene                 16          4.0        3.4        12         3.6        5.4        13         3.6        1.6

    Trichloroethylene        13          2.2        1.8        6.3        7.8        5.9        4.6        0.4        0.6

    Chloroform               8.0         1.4        3.1        4.3        13         6.3        4.0        0.3        0.3

    Styrene                  8.9         0.9        1.2        2.1        0.7        1.6        2.4        0.7        0.7

    Carbon tetrachloride     9.3         1.1        1.34       1.0        1.0        0.4        NDd        ND         ND

    Total 11 (compounds)     338         48         80         200        59         66         216        25         31

    a  Average of arithmetic means of day and night 12-h samples (µg/m3).
    b  Arithmetic mean.
    c  Not calculated-high background contamination.
    d  Not detected in most samples.

    FIGURE 41

          Another study of benzene in four New Jersey homes was focused on
    the extent of contamination from attached garages (Thomas et al.,
    1993b). Each home was monitored for either 6 or 10 consecutive 12-h
    periods. At all four homes, garage levels of benzene were higher than
    outdoors, and at three of the four homes the garage levels were higher
    than in the living area. Air exchange measurements made it possible to
    calculate the amount of benzene transferred from the garage to the
    living area in the four homes; in the home without elevated benzene
    levels in the garage, the total air flow between the garage and the
    living area was extremely small. Benzene concentrations in the garages
    ranged from 5 to 200 µg/m3, and the 12-h average source strength
    estimates ranged from 730 to 26 000 µg/h. The mere presence of an
    attached garage was not a significant factor in affecting benzene
    concentrations in the living area. However, the total number of hours
    the car was parked in the garage had a significant effect on
    living-area benzene concentrations, as did the mass flow rate of
    benzene from the garage to the home.

          In 1991 a subsample of the German Environmental Survey (see
    Chapter 2.6) of 113 people took part in a study to assess exposure to
    VOCs by personal sampling. The subjects wore passive samplers
    (OVM-3500, 3M Company) for 7 consecutive days and simultaneously

    documented the length of time spent indoors, the room characteristics
    and any specific exposure such as that caused by renovation
    activities. Seventy-four VOCs were analysed by gas chromatography
    (Ullrich, 1992).

          The results of personal sampling showed, for example, that from
    the various types of environments the workplace has the highest impact
    on exposure to C8- and C9-aromatic hydrocarbons (Figs. 42 and 43).
    Other important factors that need to be considered are renovation
    activities, use of paints and lacquers and the frequent reading of
    newspapers and journals (printing inks contain many VOCs). Smoking
    contributes significantly to human VOC exposure. In the case of
    benzene, the multivariate model contained five variables: two related
    to smoking exposure indoors, two related to vehicle traffic and the
    residential density (Fig. 44). The two smoking variables alone
    accounted for 20% out of a total variance of 40% that could be
    explained (Hoffmann et al., 1996; Ullrich et al., 1996).

          Three large studies of VOCs, involving 300-800 homes, have been
    carried out in the Netherlands (Lebret et al., 1986), Germany (Krause
    et al., 1987) and the USA (Wallace, 1987). A small study of 15 homes
    was carried out in Northern Italy (De Bortoli et al., 1986). Observed
    concentrations were remarkable similar for most chemicals, indicating
    similar sources in these countries. One exception is chloroform,
    present at typical levels of 1-4 µg/m3 in the USA but not found in
    European homes. This is to be expected, since the likely source is
    volatilization from chlorinated water (Wallace et al., 1982; Andelman,
    1985a,b); Germany and the Netherlands do not chlorinate their water.

    12.3.8  Commuter exposures

          In crowded urban areas it is not uncommon to find substantial
    populations living near busy roads. Still others make their living
    working among cars or vending goods along busy streets. Around the
    world the routine of commuting between home and workplace exposes most
    of the urban population to motor vehicle exhaust (carbon monoxide,
    oxides of nitrogen, PAHs, VOCs and lead, in many cases) on a daily
    basis. There have been several studies designed to assess exposures to
    vehicle exhaust.

          In a study conducted in Stockholm, Sweden, Bostrom et al. (1991)
    demonstrated that nitrogen oxides can be used as tracers for VOCs
    originating from vehicular traffic. The most important sources of VOCs
    in Swedish cities are motor vehicles. Also, some 80-90% of NOx
    (nitric oxide and nitrogen dioxide) in large Swedish cities originates
    from motor vehicle traffic. Quantitative relationships were developed
    between NOx and individual hydrocarbons, independent of traffic
    intensity and time of year. For instance, a PAH/NOx ratio of 2.0 ×
    10-2 was reported for Gothenburg, Sweden, and a benzene/NOx ratio of
    0.16 was reported for Stockholm.

          Chan et al. (1991) assessed in-vehicle levels of carbon monoxide
    in Raleigh, North Carolina, USA during the summer of 1988. The ratio

    FIGURE 42

    FIGURE 43

    FIGURE 44

    of mean concentrations of carbon monoxide inside and outside the
    vehicle was 1 : 1, and the ratio of mean concentrations inside the
    vehicle to a fixed-site location was about 5. The ratio of in-vehicle
    concentrations under three different driving conditions, urban/
    interstate/rural, was roughly 3.3 : 2.8 : 1. An investigation of
    carbon monoxide concentrations inside private and public transport
    vehicles in Mexico City in 1993 (Fernandez-Bremauntz & Ashmore,
    1995a,b) found an average ratio of in-vehicle : ambient concentrations
    of 2 : 1 for the metro and 5 : 2 for cars.

          Liu et al. (1994b) conducted a study of carbon monoxide exposure
    among Taipei commuters (adults and students) in 1990. Roadside and
    in-vehicle measurements were made at the same time that commuters'
    personal exposure was assessed. Concentrations of carbon monoxide were
    measured for three transportation modes (bus, private car and
    motorcycle) and three times of day (morning rush hour, midday and
    evening rush hour). The ratio of in-vehicle to ambient concentrations
    of carbon monoxide was roughly 6 : 5, overall.

          As part of their study of carbon monoxide exposure, Liu et al.
    performed a survey of commuting patterns in Taipei, for students and
    adults. Adults had a significantly longer average commuting time than
    students (1.4 h versus 0.8 h). Students commuted typically by walking
    (58%) or by riding on public buses (29%). Adults commuted to work by
    motorcycle (28%), public bus (26%), or in private cars (26%).
    Commuters using public buses had the longest commuting times (1.8 h
    for adult workers, and 1.2 h for students).

          WHO recommended guidelines for carbon monoxide are 30 mg/m3 as a
    1-h mean, 60 mg/m3 for a 30-min mean, and 100 mg/m3 as a 15-min
    mean. These guidelines are designed to prevent carboxy-haemoglobin
    levels in the bloodstream from surpassing 2.5-3.0% in the non-smoking
    population, and to protect people who are prone to heart problems.
    According to the 1992 UNEP report of air pollution in megacities of
    the world, the 1-h WHO guideline is routinely exceeded by a factor of
    2-3 times in several cities in Asia (Amman, Bangkok, Jakarta,
    Peshawar, Shanghai) and Latin America (Mexico City, Santiago, Lima)
    (UNEP/WHO, 1992). Considering the exposure studies conducted in Mexico
    City and Taipei, the stationary monitors are an underestimate of the
    population at risk of elevated carbon monoxide levels.

    12.4  Exposures and biomarkers

    12.4.1  Exposure to lead and cadmium

          Dose-response relationships exist for lead toxicity in children
    and adults, and demonstrate that subtle effects begin at levels as low
    as 1 µg/dl of lead in blood. Severe toxicity is associated with
    blood-lead levels of 70 µg/dl or higher in children, and 100 µg/dl or
    higher in adults. Toxicity symptoms include poisoning of the central
    nervous system, causing convulsions, coma, and deep, irreversible
    mental retardation. Functional changes in the peripheral nervous
    system and anaemia can also occur at levels below 40 µg/dl.

          Particulate lead present in gasoline (from the octane enhancer
    tetraethyl lead) and bromine (from the lead scavenger ethylene
    dibromide) have traditionally been used as tracers for mobile sources.
    The WHO recommended ambient air quality guideline for lead is
    1 µg/m3, a level routinely exceeded in many large Asian cities today
    where lead is still permitted in gasoline. This guideline value is
    based on the assumption that 98% of the general population will be
    maintained below a blood level of 20 µg/litre, which is considered the
    maximum acceptable concentration in blood.

          Jimenez & Velasquez (1989) conducted a study in Manila,
    Philippines to measure blood lead concentrations in children. In a
    sample of 544 children, the average blood lead level was 22.8 µg/dl,
    with approximately 8% of the children having levels greater than
    30 µg/dl. The study also found a significant correlation between high
    blood lead levels and proximity of the household to dense traffic. In
    a 1990-1991 study of exposure to lead among schoolchildren in Manila,
    Subida & Torres (1991) found that mean blood lead concentrations were
    14 µg/dl ( n = 387), with 10% having levels over 10 µg/dl. The same
    study measured blood lead levels among child street vendors. Mean
    blood lead level for a sample of 101 vendors was 17.8 µg/dl, with 33%
    having levels over 20 µg/dl.

          Muangnoicharon (WHO, 1995b) reports on a lead exposure study of
    bus drivers in Bangkok. The study was a cooperation between the WHO
    HEAL Project and the Department of Medical Science, Ministry of Public
    Health of Thailand and was designed to assess exposure in a high risk
    group. Subjects were bus drivers assigned to traffic routes in Bangkok
    where ambient lead levels exceeded 1 µg/m3. Lead was analysed in 24-h
    air samples, duplicate food for each meal for 6 days and faeces for 5
    days, as well as blood collected on the day 7.

          Average personal air exposures were 0.117 µg/m3, which yielded
    an estimated 0.936 µg/day absorption by inhalation at an estimated
    absorption rate of lead via inhalation of 40% and 20 m3/day
    respiratory air. Intake by food was 87.92 µg/day (27.32 SD). Thus, the
    estimated average lead absorption from air, food and water was 13.325
    µg/day at an estimated absorption rate via ingestion of 10%.
    Researchers suggested that meals consumed from roadside restaurants
    and food stands resulted in higher than expected lead levels in food.
    Blood lead levels for bus drivers ranged between 5 and 12 µg/dl.

          The results for the Thai study were compared to other HEAL
    sponsored studies in China, Sweden and Yugoslavia (Table 41).

          One of the HEAL projects investigated lead and cadmium exposure
    among small groups of non-smoking women (Vahter & Slorach, 1990).
    Subjects kept activity diaries. Duplicate portions of food and
    corresponding faeces samples were collected along with personal air
    samples. Food was found to be the main source of both lead and
    cadmium. Faecal concentrations could be used for validation of the
    duplicate portion samples because of the low uptake in the

        Table 40.  Weighted estimates of air and breath concentrations of 19 prevalent compounds for 360 000 Los Angeles 
               residents (February 1984); 330 000 Los Angeles residents (May 1984); and 91 000 Contra Costa residents 
               (June 1984)

    Compound                         LA1 (February)                    LA2 (May)                        CC (June)
                            Personal    Outdoor   Breath     Personal   Outdoor    Breath     Personal   Outdoor    Breath 
                            air         air                  air        air                   air        air 
                            (n = 110)   (24)      (110)      (50)       (23)       (50)       (76)       (10)       (67)

    1,1,1-Trichloroethane   96a         34a       39b        44         5.9        23         16         2.8        16b
    m,p-Xylene              28          24        3.5        24         9.4        2.8        11         2.2        2.5
    m,p-Dichlorobenzene     18          2.2       5.0        12         0.8        2.9        5.5        0.3        3.7
    Benzene                 18          16        8.0        9.2        3.6        8.8        7.5        1.9        7.0
    Tetrachloroethylene     16          10        12         15         2.0        9.1        5.6        0.6        8.6b
    o-Xylene                13          11        1.0        7.2        2.7        0.7        4.4        0.7        0.6
    Ethylbenxene            11          9.7       1.5        7.4        3.0        1.1        3.7        0.9        1.2
    Trichloroethylene       7.8         0.8       1.5        6.4        0.1        1.0        3.8        0.1        0.6
    n-Octane                5.8         3.9       1.0        4.3        0.7        1.2        2.3        0.5        0.6
    n-Decane                5.8         3.0       0.8        3.5        0.7        0.5        2.0        3.8        1.3
    n-Undecane              5.2         2.2       0.6        4.2        1.0        0.7        2.7        0.4        1.2
    n-Dodecane              2.5         0.7       0.2        2.1        0.7        0.4        2.1        0.2        0.4
    alpha-Pinene            4.1         0.8       1.5        6.5        0.5        1.7        2.1        0.1        1.3
    Styrene                 3.6         3.8       0.9        1.8        no data    no data    1.0        0.4        0.7
    Chloroform              1.9         0.7       0.6        1.1        0.3        0.8        0.6        0.3        0.4

    a  Average of arithmetic means of day and night 12-h samples (µg/m3).
    b  Arithmetic mean.

    Table 41.  Results of lead HEAL exposure pilot studies in Bangkok, 
               Thailand; Beijing, China; Stockholm, Sweden and Zagreb, 

    Sample            Unit      Thailand    China      Sweden     Croatiaa
                                (n = 24)    (n = 10)   (n = 15)   (n = 17)

    Breathing zone    µg/m3     0.117       0.116      0.064      0.412
    Food              µg/day    89.72       46.0       26.0       15.0
    Faeces            µg/day    60.57       42.0       23.0       49.0
    Blood             µg/dl     8.83        7.3        2.9        5.0

    gastrointestinal tract. Inhalation was found to account for a few
    percent of the total exposures of cadmium. Inhalation contributed more
    than 70% of total lead exposure. On the other hand, where air lead
    levels were high, as in Zagreb, Croatia, inhalation contributed twice
    as much to total exposure as the ingestion route (see Fig. 45).

          These pilot studies illustrated that without thorough analytical
    QC it was not possible to compare results between countries. The pilot
    study, although expensive, identified problems in collection and
    analysis. The need to exchange standards in the various media and
    training in analytical methods and procedures were important
    components for the success of the studies.

          In a study conducted on Swedish women the bioavailability of
    dietary cadmium was contrasted for different diets. Dietary intake and
    uptake of cadmium were studied in non-smoking women, 20-50 years of
    age, consuming a mixed diet low in shellfish ( n = 34), or with
    shellfish once a week or more ( n = 17), or a vegetarian diet rich in
    fibre ( n = 23) (Berglund et al., 1994b; Vahter et al., 1996). The
    objectives were to identify important factors, dietary and other,
    influencing cadmium exposure and dose.

          Duplicate portions and corresponding faeces (using a coloured
    marker to indicate start and end of duplicate portion collection) were
    collected for four consecutive days (including weekdays and weekends),
    for the determination of cadmium intake. Blood and 24-h urine samples
    were collected for determination of total cadmium exposure. The women
    kept detailed dietary records for identification of significant

          The shellfish diets contained twice as much cadmium (22 µg/day)
    as the mixed diets (10 µg/day). The high fibre diets were intermediate
    (13 µg/day). The content in faeces were on the average 100%, 99% and
    98% of intake in the shellfish group, the mixed diet group and the
    fibre group, receptively, indicating a low average absorption of
    dietary cadmium. Despite the differences in cadmium intake there were

    FIGURE 45

    no significant differences in blood cadmium (about 0.25 µg/litre) or
    urine (0.1 µg/litre), indicating a lower absorption of cadmium in
    shellfish and in high fibre foods compared to the mixed diet (low in
    shellfish and cereals) or a difference in the kinetics. A higher
    cadmium absorption in the mixed diet and the fibre diet group compared
    to the shellfish group could partly be explained by lower body iron
    stores (measured as serum ferritin). Low body iron stores in women of
    reproductive age are very common. Serum ferritin levels were
    negatively correlated with blood cadmium concentration, indicating an
    increased absorption of cadmium at reduced body iron stores (defined
    as serum ferritin below 30 µg/litre).

    12.4.2  Exposure to furans, dioxins and polychlorinated biphenyls

          Dioxins, furans and PCBs are persistent compounds found in
    industrial discharges and incinerator air emissions, and as trace
    contaminants in many products. These compounds accumulate in fat and
    undergo amplification in marine and terrestrial food chains. People
    consuming large amounts of contaminated seafood may have higher
    concentrations of organochlorine compounds in their tissues than the
    general population.

          For 35 years a magnesium-producing factory in the inner part of a
    fjord in southern Norway had discharged 50-100 kg TCDD toxic
    equivalents (TEQ) to the fjord area. PCDDs/Fs and PCBs were monitored
    in sediments and marine organisms in 1986, 1989-1990 and 1992. In
    spite of a reduction by >98% in the discharge from 1990 to 1992,

    levels were still very high. Restrictions in commercial fishing and
    advice to the general public regarding consumption were established.
    Some residents still catch and consume considerable amounts of local
    fish and shellfish, particularly crabs, during summer and autumn. The
    crabs contain high concentrations of PCDDs/Fs with a predominance of
    tetra- and hexa-CDFs and PCB-209.

          In the study by Johansen et al. (1996), 24 male crab consumers
    were recruited non-randomly from news announcements and 10 controls
    were drawn randomly from the population register. PCDDs/Fs and PCBs
    were measured in blood samples. Information on crab and fish
    consumption and intake of fatty food items were collected and the
    fishing site reported. The study was designed to determine if
    consumption of crabs from the contaminated fjord area led to increased
    body burden of PCDDs/Fs and PCBs. From the patterns of PCDDs/Fs and
    PCBs congeners in crabs, can congeners in blood be inferred sources?
    Finally, can exposure estimates based on blood levels be predicted by
    reported crab intake of location? A considerable increase of PCDDs/Fs
    in blood was found upon consumption of contaminated crabs. A direct
    relationship was found between blood level and the number of crabs
    times contamination level. See Fig. 46 from Johansen et al. (1996).
    The PCDD/F profile in the high intake group clearly reflected the
    profile found in the crab hepatopancreas. PCB-209 does not appear to
    be absorbed since it did not increase after crab consumption.

          Using a simplified toxicokinetic calculation, good correlation
    ( r = 0.61) was reported between estimated yearly intake based on
    blood values and intake based on reported intake and the fishing site.
    The intake calculated for the controls was 9.7 pg TEQ/kg body weight
    per week, in good agreement with estimated intake from food in Norway
    (8-10 pg TEQ/kg body weight per week). The average exposures of the
    moderate and high-intake groups were 31 (10-61) pg/kg body weight per
    week and 62 (24-114) pg/kg body weight per week. Most individuals in
    the high-intake group exceeded the recommended Nordic tolerable weekly
    intake (TWI) of 35 pg/kg body weight per week 

    12.4.3  Exposure to volatile organic compounds and urinary metabolites

          In Tokyo, Nakahama et al. (1997) measured personal VOC exposures
    over 12 h and the metabolites in urine. Thirteen men and 17 women
    participated. The VOCs 1,1,1-trichloroethane, trichloroethylene and
    tetrachloroethylene were sampled with passive absorbent badges.
    Trichloroethanol and trichloroacetic acid corrected for creatinine
    were measured in urine. Personal exposures were well correlated
    ( r = 0.80) with urinary metabolites. Interestingly, women inhaled
    twice as much VOCs as men, perhaps because of increased exposure to
    household chemicals and cosmetics.

    FIGURE 46

    FIGURE 47

    12.5  Exposure to contaminants in drinking-water

          The health effects of exposure to natural background radiation,
    specifically radon, through inhalation have been explored, but
    drinking-water is another source of natural background radiation that
    may contribute to cancer incidence. Elevated rates of bone cancer have
    been hypothesized as potential outcomes of exposure to radium-226 and
    radium-228 because of their accumulation in the bone (Bean et al.,
    1982). These two isotopes are found in some deep aquifers. Previous to
    the study 300 000 residents of Iowa were found to have levels of
    radium-226 in their municipal drinking water that exceeded the US
    Public Health Service's 1962 drinking water standard of 3 pCi/litre.
    The time periods 1969-1971 and 1973-1978 were studied for all 28
    municipalities in Iowa. After testing each town for radium-226 levels,
    the towns were divided into three groups, with respectively 0-2, 2-5
    and >5 pCi/litre of radium-226 in the water supply. In towns level
    > 5.0 pCi/litre, the incidence of lung and bladder cancer in men and
    lung and breast cancers among women was higher (Fig. 47). Although 77%
    of the individuals in the study had been on the same water supply for
    at least 10 years, misclassification due to uncertainties about past
    concentrations and past residential histories create problems for the

          A Taiwanese study of a population that used artesian wells
    suggests that there may be a link between high arsenic levels in
    drinking water and the incidence of internal cancers, particularly
    bladder cancer (Chiou et al., 1995). Levels above the maximal
    permitted level of 50 µg/litre occur in some locations in the western
    USA. In 1978 a study was done in Utah of individuals between the ages
    of 21 and 84. Concentrations of arsenic in the water ranged from 0.5
    to 160 µg/litre (mean 5.0 µg/litre). Two indexes of exposure were
    used, both of them assuming constant past levels in the water supply:

    *   total cumulative exposure was calculated using the duration of time
        spent in the town, the rate of water consumption, and the 1978
        levels of arsenic in the drinking-water

    *   intake concentration was calculated using the above measurements,
        as well as the total fluid intake, to approximate the arsenic
        concentration in the urine to which the bladder is exposed (Bates
        et al., 1995).

          Overall, no association between arsenic exposure and bladder
    cancer was seen with either index (Table 42). The only odds ratio
    (3.32) significantly different from 1 was for smokers with a
    cumulative dose greater than 53 mg. This suggests that smoking
    potentiates the relationship between arsenic and bladder cancer.

          That drinking-water can be a main source of exposure could be
    shown in the framework of German Environmental Survey (see Chapter
    2.6). Drinking-water (first draw and grab samples, see Chapter 7.3.2)
    was analysed in approximately 4000 German households. A significant
    correlation was observed between the lead content in drinking water
    and the lead content in the blood of the population (Nöllke et al.,
    1995; Becker et al., 1997).

    12.6  Exposure to microbes

          Examination of biological contamination involves a different
    approach, as discussed in Chapter 9. Bioaerosol samples are widely
    used and rely on impaction on to culture medium. The cut-off size of
    the samplers limits the ability to capture all bioaerosols, and no one
    culture medium and growth temperature is appropriate for all viable
    bacteria in the air. Therefore, the numbers from the impactor will be
    less than those actually present in the air because of limited power
    of detection. Chemical assay for endotoxin is independent of the
    ability to grow the bacteria, but it is sensitive to sampling and
    storage procedures.

          Previous studies of bioaerosols in the occupational setting have
    examined levels of airborne bacteria. A study of bioaerosols at water
    treatment plants was planned to go beyond previous studies and examine
    concentrations of both bacteria and endotoxin (Laitinen et al., 1994).
    Endotoxin (bacterial toxin not freely liberated into the surrounding
    medium) can cause fever, eye inflammation, fatigue and/or respiratory
    difficulties if inhaled, and may be a more reliable measure of

        Table 42.  Adjusted odds ratios (OR) and 90% confidence intervals (CI) for bladder cancer and arsenic exposure, restricted to subjects 
               enrolled into the National Bladder Cancer Study in Utah in 1978 who had spent at least 50% of their lifetimes until diagnosis 
               living in study towns (Bates et al., 1995)

    Exposurea     All subjects                            Never smoked                            Ever smoked
                  Cases   Controls  ORb     90% CI        Cases   Controls  ORb     90% CI        Cases   Controls  OR      90% CI

    Exposure index (cumulative dose (mg)

    <19           14      47        1.00                  10      25        1.00                  4       22        1.00

    19-< 33       21      36        1.56    0.8-3.2       10      19        1.09    0.4-3.1       11      17        3.33    1.0-10.8

    33-< 53       17      39        0.95    0.4-2.0       7       20        0.68    0.2-2.3       10      19        1.93    0.6-6.2

    „53           19      38        1.41    0.7-2.9       4       17        0.53    0.1-1.9       15      21        3.32    1.1-10.3

    Exposure index (mg/litre × year)

    <33           18      42        1.00                  11      19        1.00                  7       23        1.00

    33-< 53       16      42        0.69    0.3-1.5       3       19        0.21    0.1-0.8       13      23        1.95    0.7-5.6

    53-< 74       16      40        0.54    0.3-1.2       6       24        0.25    0.1-0.9       10      16        1.21    0.4-3.7

    >74           21      36        1.00    0.5-2.1       11      19        0.91    0.3-3.2       10      17        1.41    0.5-4.3

    a  Cut-points based on exposure quartiles of all subjects in the table.
    b  Adjusted for sex, age, smoking, (all subjects and smokers only), years of exposure to chlorinated surface water, history 
       of bladder infection, educational level, urbanization of the place of longest lifetime residence, and ever employed in a 
       high-risk occupation.

    biological exposure because it is independent of the ability to
    culture the microorganism.

          The treatment area, control room and outside were sampled at nine
    industrial waste water treatment plants. Bacteria were collected using
    an impactor which sampled on to agar plates. After incubation for 2
    days, the number of colonies of each plate were counted, and
    translated into numbers of CFU/m3 of air. Endotoxin was sampled on to
    sterile filters using a suction pump, and was reported as ng/m3.
    Levels of bacteria and endotoxin were correlated. Concentrations of
    endotoxins in the work areas of the treatment plant were assessed by
    collection on to glass fibres that were than tested using the
     Limulus endotoxin assay. The measured levels of endotoxin ranged
    between 0.1 and 350 ng/m3, and the 8-h time-weighted average
    concentration was greater than the exposure limit of 30 ng/m3 at some
    of the plants. Bacterial levels were between 10 and 105 CFU/m3
    (Table 43).

          Sixteen male workers with a mean exposure duration of 10 years
    were examined for symptoms related to exposure to bacteria and
    endotoxin. The workers' occupational history and symptoms were
    evaluated using written questionnaires. Symptoms were found in
    6 workers; 4 reported fever, shivering and eye irritation, and 3 a
    cough. Although the number of subjects was too low for epidemiological
    conclusions based on the symptoms, the levels in some locations were
    high. Excessive levels of contamination were restricted to certain
    areas of the plant, suggesting that exposure levels should be
    minimized by changing the physical layout of those work areas and
    improving hygienic practices by individuals after working in those

          Previous work has established that both settled and airborne
    house dust contains allergens such as dust mites, animal dander, and
    fungi. The connection between home dampness, fungus levels and
    respiratory symptoms was investigated in 60 homes as part of a
    Netherlands case-control study (Verhoeff et al., 1994b). A
    relationship between dust levels and exposure to moulds was
    hypothesized, as was a relationship between characteristics of homes
    (e.g., dampness and type of flooring).

          The level of viable fungal propagules present in settled dust was
    chosen as the sampling parameter; dust was collected from the floor
    and mattresses using a vacuum cleaner with a cellulose fibre filter.
    The presence and number of fungal propagules was determined by plating
    the dust on to agar growth medium. A checklist and a questionnaire on
    the residence type and occupant behaviour were filled out for each

          The geometric mean of the number of dust sampled from the floor
    was 8990 CFU/g and it was significantly higher in samples from
    carpeted floors (12 880 CFU/g) than from smooth floors (3550 CFU/g).
    The level from mattresses was 7660 CFU/g. In rooms where damp spots

        Table 43.  Concentrations of endotoxin and bacteria (Laitinen et al., 1994)

    Phase                            Endotoxin                Gram-negative bacteria        Total culturable bacteria     Number 
                                      (ng/m3)                     (103 CFU/m3)                   (103 CFU/m3)             of 
                              AM      MD      Range         AM      MD      Range           AM       MD      Range

    Wastewater pumping        16      18      <0.04-30      0.56    0.08    0.01-1.6        4.5      0.5     0.4-13       3

    Screening                 30      31      3.6-55        5.0     6.3     0.63-7.9        34       32      6.3-63       3

    Wastewater entering
      sedimentation basin     130     140     100-140       7.5     6.3     6.3-10          27       25      16-40        3

    Sedimentation basin
      (indoor)                7.1     1.6     1.0-19        0.5     0.2     0.05-1.3        1.9      0.5     0.32-5       3

    Aeration basin (indoor)   73      83      17-110        11      5.6     1.0-32          19       16      16-25        4

    Aeration basin (outdoor)  8.7     1.7     0.8-36        1.8     0.5     0.1-6.3         6.5      4.0     2.5-16       5

    Biofilter tower           38      38      4.8-71        13      13      10-16           180      180     130-250      2

    Sludge treatment          140     97      9.2-350       25      13      1.3-63          77       79      13-200       9

    Control room              3.5     1.4     <0.04-13      0.17    0.2     <0.002-0.32     3.8      3.2     0.2-13       7

    Outdoors                  0.6     0.3     <0.04-3.0     0.02    0.01    <0.002-0.06     0.15     0.12    0.01-0.4     8

    were observed the levels were higher, but not significantly, and the
    number was not related to the average relative indoor humidity. The
    hypothesized association between the presence of fungi in the dust and
    respiratory symptoms was not observed. Home characteristic and
    occupant behaviour were therefore seen as poor predictors of fungal
    levels, with the exception of floor type.

    12.7  Exposure studies and risk assessment

    12.7.1  The German Environmental Survey

          Much of the research into better exposure assessment strategies
    has come from the desire to more accurately estimate risks associated
    with environmental exposure in order to better protect human health:
    for instance, the HEAL studies, including the development of personal
    monitors and the use of biomarkers. The German Environmental Survey is
    a unique study providing a database of exposures to pollutants on a
    representative basis for the general population in Germany. The
    results of human biomonitoring provide important reference data for
    evaluating results of smaller studies addressing specific problems.
    The data of the study were also used for a number of risk assessments,
    for example in the case of liver cirrhosis in early childhood caused
    by copper in drinking water (Becker et al., 1997).

          Using the German Environmental Survey data and multiple
    regression analysis it was possible to identify relevant factors that
    influence the body burden of pollutants of the general population.

    12.7.2  The National Human Exposure Assessment Survey

          The National Human Exposure Assessment Survey (NHEXAS) was
    created in order to design an exposure surveillance programme covering
    the population of the USA (Sexton et al., 1995c). NHEXAS is concerned
    with policy issues that include differentiation between high and low
    risk exposure groups and individuals in society. Understanding
    expected values in the "normal" population is essential for use in
    comparing to contaminant levels for those who live in a polluted area.

          NHEXAS is conducted by researchers in the academic, private and
    governmental areas of science working in cooperation. The studies are
    coordinated; they share a common questionnaire on activity and
    sociodemographics, examine the same exposure sources and send samples
    for analysis to the same laboratory. The studies are unique in the
    degree of characterization of exposures of individuals. Multiple
    chemicals, chemical classes, exposure pathways and routes will be
    examined for each individual for each study. NHEXAS is conducted as
    four projects:

    *   Population study in Arizona of exposures to metals, VOCs and
        pesticides carried out by the University of Arizona, Batelle
        Columbus and the Illinois Institute of Technology.

    *   A study of population exposure measurements of metals, pesticides,
        PAHs and VOCs in two random populations drawn from the EPA's region
        5, which includes the industrial northern states of the USA.

    *   The relationship between long-term and short-term exposures of
        individuals to metals, PAHs, pesticides and VOCs is being studied
        on a population of 50 urban and suburban residents in the
        Baltimore, Maryland area.

    *   Parallel to the field studies is a modelling exercise, where
        existing information is used in Monte Carlo simulation routines to
        estimate exposure distributions. A preliminary study of the regions
        in the first two projects for exposure to pesticides, metals and
        VOCs is being carried out by the Harvard School of Public Health.

    12.7.3  Windsor, Canada exposure and risk study

          The Ontario Ministry of Environment in Canada conducted a
    pioneering study to assess air pollution exposure risks to populations
    living in Southern Ontario downwind of Detroit, Michigan, a large city
    in the USA.

          Windsor, Canada has a long history of air pollution monitoring
    dating back to the 1940s. The city's environmental concerns increased
    when an incinerator was built in Detroit, Michigan. At that time,
    Detroit had several steel mills in operation. Concern for
    transboundary transport of pollutants continues today.

          The Ontario government performed a study between 1991 and 1993 in
    order to determine the level of risk associated with air pollution and
    to limit the exposure of residents of Windsor to airborne toxics (Bell
    et al., 1994). After examination of concentrations of local airborne
    toxic pollutants, 10 compounds were chosen for study due to their
    persistence, bioaccumulation, and toxicity in the environment:

    *   metals: cadmium, chromium (VI), mercury

    *   VOCs: benzene, 1,3-butadiene, carbon tetrachloride,
        1,4-dichlorobenzene, formaldehyde

    *   PAHs: benzo [a]pyrene

    *   SVOCs: dioxins, furans.

          The study first identified the exposure levels by emissions
    monitoring, fixed site ambient air monitoring, mobile air monitoring,
    personal exposure measurements, and soil and garden produce survey.
    Data on emissions from fixed, area, and mobile sources were placed on
    to a grid with 1 km × 1 km squares, including both the Windsor and the
    Detroit areas. Ambient monitors in the Windsor area showed levels for
    most of the toxics to be lower than federal guidelines, with the
    exception of benzo [a]pyrene. Dioxin and furan levels were below
    regulatory levels, but were higher than in other Canadian cities. A

    mobile monitoring system travelled to find potential hot spots
    downwind of potential sources of toxins. The toxins were found to be
    below regulatory levels, with the exception of hydrogen fluoride and
    hydrogen chloride.

          Fifty-six VOCs and 8 airborne trace metals were sampled at
    residences, office, cars and recreation areas. Each personal exposure
    study had a 24-h exposure profile prepared using the concentrations in
    each environment and the time spent in that environment. It was found
    that the period of highest exposure to VOCs was during the commute,
    with outdoors the lowest. The highest exposure to heavy metals was
    inside the home.

          Heavy metal concentrations in garden produce and soil were below
    regulatory limits, but cadmium and mercury levels were higher in the
    Windsor area than in rural areas. Indoor air exposure had
    disproportionately higher health risk than did outside air. Inhalation
    was found to be more important than dermal absorption and ingestion
    for these compounds.

          The exposures to dioxins and furans were each less than one
    quarter of the tolerable daily dose. Mercury was 60% of the tolerable
    daily dose, and could be a health risk because of its persistence in
    the environment and bioaccumulation. The additive risks for all of the
    studied air pollutants were approximately 1 × 10-5.

    12.7.4  Pesticide exposure study

          The Non-Occupational Pesticide Exposure Study (NOPES) was
    designed to examine human exposure to 32 pesticides and pesticide
    degradation products in two cities in the USA: Springfield,
    Massachusetts and Jacksonville, Florida (Whitemore et al., 1994). The
    two goals of the project were to develop instrumentation, laboratory
    procedures and surveys needed for a study of non-occupational exposure
    to pesticides and to determine non-occupational exposure to

          The NOPES study used TEAM in determining exposure levels. A
    probability sampling design was used to make statistical conclusions
    on the health risks of the pesticides. More participants were chosen
    from high-exposure groups in order to facilitate estimation of risk
    levels. Jacksonville was chosen as the high-use region and Springfield
    as the low-use area. The two sites were studied during the summer of
    1986 (Jacksonville only), the spring of 1987 and the winter of 1988.
    The study population varied between 49 and 72 people. The study
    examined skin, food and water as routes of exposure, but focused on
    air as the primary route of exposure. Twenty-four-hour personal,
    indoor and outdoor samples were collected on polyurethane foam and
    analysed by gas chromatography/mass spectrometry and gas
    chromatography/electron capture detection. Personal samples were used
    as well as fixed monitors inside and outside the home. A questionnaire
    was administered after the end of the 24-h period to determine the
    activities of the subjects.

          The lowest concentrations were found in the winter and the
    highest concentrations in the summer, with the spring levels
    intermediate. Readings from the indoor monitors were correlated with
    personal monitors, but neither was comparable to the lower
    measurements from the outdoor monitors. The relative importance of
    dietary and respiratory routes of exposure varied between pesticides:
    most of the chemicals had the diet as the main routes of uptake, but
    pesticides used indoors were mainly taken via inhalation.

          The chlorinated hydrocarbons chlordane, heptachlor, aldrin and
    dieldrin were calculated to have the largest risk for health effects,
    although all but heptachlor and aldrin had negligible risks.
    Heptachlor and aldrin had excess lifetime cancer risks of 2 × 10-4
    and 1 × 10-4 respectively in Jacksonville, despite having been banned
    for many years.

    12.7.5  Czech study of air pollution impact on human health

          The aim of the Teplice programme has been to conduct a
    multi-end-point air monitoring and human biomonitoring study to assess
    the impact of air pollution on population health in the district of
    Teplice, Czech Republic (Sram et al., 1996). Particulate and gaseous
    air pollutants were measured in Teplice and in the control region
    Prachatice. PM2.5 and PM10 composition and toxic metals as well as
    concentrations of PAHs were measured daily in winter and periodically
    during the spring/summer season. The concentrations of all pollutants
    measured were significantly higher in winter compared to spring and
    summer. Average fine particle mass in Teplice was 122 µg/m3 compared
    to 44 µg/m3 in Prachatice during the winter of 1993, and 28.7 µg/m3
    and 17 µg/m3 respectively, in spring/summer. Total PAH concentrations
    in Teplice in winter were approximately twice as high as in Prachatice
    (278 versus 163 ng/m3). Evaluation of the benzo [a]pyrene to lead
    ratio in Teplice over time indicated the presence of at least two
    sources of PAHs. During the summer when mobile sources are the major
    contributor to benzo [a]pyrene, the ratio was about 0.01. During
    winter, when the ratio was 0.05 to 0.15, emission from inefficient
    combustion of brown coal in domestic heating systems is considered to
    be the most likely source of PAHs.

          Personal exposure and biomarkers were measured with the objective
    of simultaneously evaluating personal exposure to air pollution and
    internal measures of dose and genetic effects and susceptibility using
    a series of biomarkers. PAHs were selected as the pollutant marker for
    monitoring personal exposure. A group of 30 women working outdoors in
    Teplice district was compared with a group of 30 women from the
    Prachatice district. Personal exposure monitoring (PM2.5) was
    conducted for the 24-h period prior to collection of blood and urine.
    High correlation were observed between the mass of fine particles and
    personal exposure to total carcinogenic PAHs and benzo [a]pyrene.
    Significant correlations were observed between the personal exposures
    to PM2.5 or carcinogenic PAHs and blood lead and blood selenium. The
    urinary PAH metabolites, adjusted for creatinine content, were also

    significantly correlated with PM2.5 or PAHs. Significant correlations
    were found between personal exposure to carcinogenic PAHs and white
    blood cell DNA adduct level.

          The results consistently suggested that elevated levels of
    airborne fine particle pollution could result in measurable uptake,
    metabolism and cellular DNA damage in a population exposed to high
    concentrations, even for a short-term winter inversion period.


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    1.  Définition de l'exposition

          Pour qu'il y ait exposition, il faut qu'une substance toxique se
    trouve à l'instant t en un point donné de l'environnement et qu'une ou
    plusieurs personnes se trouvent également en ce point au même moment.
    En outre, la quantité de substance en contact avec le tégument entre
    également en ligne de compte.

          Lorsqu'on a pris conscience de l'intérêt intrinsèque de cette
    notion, "l'analyse de l'exposition" s'est imposée comme un aspect
    important de l'investigation scientifique en santé publique, qui vient
    compléter les disciplines traditionnelles que sont la toxicologie et
    l'épidémiologie et constitue désormais un élément essentiel de toute
    décision éclairée dans le domaine de l'hygiène de l'environnement.

    2.  Usage des données relatives à l'exposition humaine

          Toute analyse de l'exposition se caractérise par le ou les usages
    qu'on a l'intention d'en faire. Ainsi, les aspects de l'exposition qui
    sont à prendre en compte, la nature de l'information nécessaire, de
    même que la quantité et le niveau de qualité des données à réunir
    pourront varier selon que l'analyse est effectuée dans le cadre d'une
    enquête épidémiologique, d'une évaluation du risque, d'une analyse de
    tendance ou encore selon que l'on cherche à caractériser le risque ou
    à le maîtriser.

          La connaissance de l'exposition humaine aux polluants présents
    dans l'environnement est importante dans ce type de démarche ou
    d'action. Elle permet d'établir le lien déterminant qui existe entre
    les sources de contamination, leur présence dans l'environnement et
    les effets qui peuvent en découler pour la santé humaine. Les
    informations de ce genre, si elles sont utilisées en vue d'une gestion
    de l'environnement visant à réduire le risque pour la santé humaine,
    vont faciliter le choix et l'analyse de stratégies qui se distinguent
    de l'approche traditionnelle, basée sur "le commandement et la
    conduite des opérations". La plupart des structures de gestion de
    l'environnement se fondent sur une exploitation directe des données
    relatives à la concentration des polluants dans les divers milieux
    pour juger de la qualité environnementale d'un site, évaluer le risque
    et voir dans quelle mesure les normes sont respectées. Même en pareil
    cas, la connaissance de l'exposition peut permettre de déterminer dans
    quelle mesure on peut protéger efficacement tel ou tel groupe de
    population plus sensible ou plus exposé au risque.

          C'est ce lien direct qui fait que la mesure de l'exposition est
    d'une aide précieuse dans les études d'impact au niveau local,
    régional ou mondial.

    3.  Stratégies et protocoles pour l'évaluation de l'exposition

          L'élément le plus important d'une évaluation de l'exposition est
    constitué par le protocole de l'étude. Ce protocole doit préciser la
    finalité et les objectifs de l'étude ainsi que les méthodes
    d'échantillonnage, de mesure et d'analyse statistique qui seront
    utilisées, sans oublier l'assurance de la qualité. La caractérisation
    de l'ampleur, de la durée et de la chronologie des contacts entre
    sujets humains et polluants de l'environnement peut se faire de
    manière directe ou indirecte. Si l'on procède directement, on pourra
    par exemple effectuer des mesures aux points de contact et évaluer les
    marqueurs biologiques de l'exposition. Si l'on a recours à une méthode
    indirecte, elle pourra se fonder sur la surveillance de
    l'environnement, la modélisation ou l'usage de questionnaires. Toutes
    ces méthodes sont susceptibles d'être utilisées dans divers types
    d'études d'exposition caractérisées par la manière dont s'opère le
    choix de la population à étudier. Par exemple, il pourra s'agir
    d'études exhaustives portant sur la totalité de la population en
    cause, d'études descriptives sur un échantillon non aléatoire ou
    encore d'enquêtes basées sur un échantillons représentatif constitué
    d'individus tirés au sort.

    4.  Les méthodes statistiques utilisées dans l'évaluation de 

          Ces méthodes constituent un outil essentiel pour les études
    d'exposition à vocation heuristique ou à caractère appliqué. Il est
    souhaitable qu'un statisticien intervienne à tous les stades de
    l'étude et notamment lors de la mise au point du protocole et de
    l'analyse des données. L'un des aspects importants de l'utilisation
    des méthodes statistiques est la détermination de la taille de
    l'échantillon à laquelle on procède lors du stade de planification de
    l'étude. On peut également récapituler les données d'exposition sous
    la forme de statistiques descriptives numériques ou graphiques et
    effectuer une analyse préliminaire des relations entre les diverses
    variables. Les données d'exposition ont souvent une distribution
    normale ou log-normale et on peut donc les soumettre immédiatement à
    une analyse statistique paramétrique avec estimation et tests
    d'hypothèses. On peut également avoir recours à d'autres modèles
    statistiques paramétriques tels que l'analyse de la variance, la
    régression linéaire et la régression logistique afin de quantifier les
    associations entre les diverses mesures de l'exposition. Lorsque le
    nombre d'observations est faible ou que les données ne peuvent pas
    être transformées pour leur faire adopter une distribution
    sensiblement normale, on peut utiliser des méthodes non paramétriques
    comme le test des signes, le test de Mann-Whitney ou celui de
    Kruskal-Wallis pour vérifier les hypothèses.

    5.  Profils temps-activités des sujets humains et évaluation de 

          La connaissance du type d'activité des individus permet de mettre
    en évidence les déterminants de l'exposition mesurée, de déterminer
     a priori une exposition non mesurée ou non mesurable, d'établir les
    relations susceptibles d'exister entre exposition et état de santé ou
    encore de reconnaître les situations à haut risque justiciables d'une
    intervention par les autorités en charge de la santé publique.

          Le coût relatif des prélèvements sur le terrain et des analyses
    en laboratoire dans le cas d'échantillons environnementaux ou
    biologiques fait ressortir l'intérêt potentiel des données
    temps-activités. Pour une évaluation à très long terme (par ex. sur
    toute la durée de la vie), on n'a parfois pas d'autre possibilité que
    l'utilisation de questionnaires temps-activités. Il existe diverses
    méthodes pour recueillir des informations sur les activités humaines:
    agendas, questionnaires, dispositifs mécaniques ou observation. Plus
    récemment, on a commencé à élaborer des méthodes permettant d'évaluer
    le rôle du profil temps-activités sur les voies d'exposition
    alimentaire, non alimentaire ou cutanée. On s'interroge cependant sur
    le point de savoir si les méthodes de collecte des données
    garantissent la représentativité de l'activité effective et permettent
    de recueillir des informations valables, sans parler du problème des
    variations inter- et intraindividuelles, ce qui fait entrevoir les
    limites de l'utilisation des données temps-activités pour l'évaluation
    de l'exposition humaine. Cependant, au prix d'un bon programme
    d'assurance de la qualité, les profils temps-activités peuvent se
    révéler très précieux pour l'interprétation et la modélisation de

    6.  Exposition humaine et modélisation de la dose

          Un modèle d'exposition est une représentation logique ou
    expérimentale qui permet l'estimation des paramètres d'exposition d'un
    individu ou d'une population à partir d'un certain nombre de données
    initiales. Les modèles d'exposition qui s'appuient sur de bonnes
    observations peuvent être utilisés pour évaluer l'exposition
    collective (moyenne pour une population par ex.) ou individuelle (par
    ex. la distribution de l'exposition parmi les membres d'une
    population). Ces modèles peuvent également servir à estimer
    l'exposition par le canal d'un seul ou de plusieurs milieux. Cette
    dernière possibilité est particulièrement utile pour comparer
    l'intensité de l'exposition imputable à divers milieux et fixer des
    priorités. Les modèles d'exposition peuvent être de nature
    déterministe ou statistique ou tenir des deux. Des méthodes
    probabilistes sont applicables aux trois types de modèles pour obtenir
    la distribution estimative de l'exposition dans une population,
    c'est-à-dire pour se faire une idée de sa variabilité selon les
    individus. En outre, ces méthodes peuvent être utilisées pour
    caractériser l'incertitude des paramètres initiaux d'un modèle en
    conservant ce caractère incertain jusqu'au point d'aboutissement

    prévisible. Il est capital d'évaluer le degré d'exactitude d'un modèle
    avant de prendre une décision sur la base de ses résultats.

    7.  Mesure de l'exposition humaine aux substances chimiques présentes 
        dans l'air, l'eau ou les aliments

          Qu'il s'agisse de l'air, de l'eau ou des aliments, les mêmes
    considérations relatives aux limites de détection, aux interférences,
    à la commodité de mise en oeuvre et au coût se retrouvent lors du
    choix d'une méthode d'échantillonnage. Pour la surveillance passive ou
    active des gaz, des vapeurs, des aérosols, des produits organiques
    semi-volatils ou des gaz réactifs, on peut faire des prélèvements
    individuels, microenvironnementaux ou dans l'air ambiant.

          Lorsqu'on se propose de déterminer la qualité d'une eau,
    plusieurs aspects sont à prendre en considération. Il y en a un
    notamment, qui a son importance - à savoir le fait que l'exposition à
    d'éventuels contaminants ne se limite pas à la voie digestive et que
    tout le monde n'a pas accès à un réseau de distribution délivrant de
    l'eau traitée. Le document fournit des indications relatives aux
    programmes d'échantillonnage et de surveillance.

          Il existe un certain nombre de méthodes pour évaluer la
    consommation de nourriture et le degré de contamination des aliments.
    La méthode à adopter sera fonction des données disponibles, du groupe
    de population en cause, des effets - aigus ou chroniques- qui se
    manifestent, de l'utilisation que l'on se propose de faire des
    résultats et des ressources disponibles.

    8.  Mesure de l'exposition humaine à des contaminants chimiques 
        présents dans le sol et la poussière déposée

          Les contacts avec le sol et la poussière déposée peuvent
    constituer une source importante d'exposition humaine à des
    contaminants chimiques, notamment chez l'enfant. On a mis au point de
    nombreuses méthodes d'échantillonnage mais aucune d'entre elles ne
    s'est révélée supérieure aux autres. Il est donc difficile, dans ces
    conditions, de comparer les résultats d'études qui font appel à des
    techniques d'échantillonnage différentes. Lorsqu'on choisit une
    méthode d'échantillonnage, un certain nombre de facteurs importants
    sont à prendre en considération: l'efficacité de la collecte, les
    différences dans la nature des activités humaines, la variabilité des
    caractéristiques physiques du sol, l'évolution du taux de poussière
    dans le temps et l'espace, la surface et le substrat sur lesquels on
    effectue les prélèvements, le moment de la collecte et les méthodes
    d'analyse qui seront utilisées pour le dosage des produits toxiques en

    9.  Dosage des agents biologiques présents dans l'air et l'eau et 
        auxquels l'Homme est exposé

          Les aérosols biologiques contiennent divers microorganismes ou
    constituants de microorganismes aéroportés qui sont susceptibles
    d'être inhalés. Il s'agit notamment de virus, de bactéries, de
    pollens, de champignons, de protozoaires ou d'algues qui, lorsqu'ils
    sont viables, peuvent être la cause de maladies. Des fragments de
    bactéries ou de champignons, de même que les produits de leur
    métabolisme ou les structures protéiques que ces microorganismes
    contiennent ou encore des déjections ou des parties du corps d'animaux
    divers, comme des insectes ou des arachnides, sont capables de
    déclencher des réactions allergiques. Les aérosols biologiques les
    plus répandus que l'on trouve communément à l'intérieur des
    habitations contiennent des acariens, des champignons, des bactéries
    et des pollens.

          L'évaluation de l'exposition microbiologique en est actuellement
    au même stade que celle de nombreux autres polluants atmosphériques.
    On n'a pas encore mis au point d'échantillonneurs individuels. De
    fait, nombre de techniques d'échantillonnage utilisées pour
    l'évaluation de l'exposition aux agents biologiques aéroportés sont
    des adaptations de techniques employées à d'autres fins. C'est un
    domaine qui progresse, maintenant que des organismes professionnels
    s'efforcent d'améliorer et de normaliser les méthodes de mesure et de
    culture, les protocoles d'analyse et la présentation des comptes
    rendus. Tous ces points sont d'une importance capitale pour la
    comparaison des résultats obtenus par les différents chercheurs.

          De par leur nature même, les aérosols biologiques sont de
    composition et de concentration très variables. La croissance, la
    reproduction et la dispersion des microorganismes varient largement en
    fonction de la température, du degré d'humidité et de la présence de
    nutriments. Leurs interactions avec les activités humaines ou animales
    dépendent d'ailleurs de ces mêmes facteurs. Certains dispositifs
    mécaniques ou machines peuvent amplifier et disperser les aérosols
    biologiques. Il en résulte que l'exposition individuelle est
    passablement variable; dans ces conditions, de nombreux chercheurs ont
    été amenés à s'en remettre à des prélèvements d'air dans un secteur
    donné ou à un échantillonnage général. Par exemple, plutôt que de
    chercher à mesurer l'exposition individuelle aux allergènes des
    acariens, il est recommandé de s'en tenir à des prélèvements sur la
    literie ou dans la poussière du sol. La "mesure" de l'exposition
    utilisée dans les enquêtes épidémiologiques est souvent effectuée sur
    des échantillons intradomiciliaires constitués de prélèvements d'air
    ou de poussière.

          En définitive, si l'on veut que l'évaluation de l'exposition
    aérobiologique progresse suffisamment pour qu'on puisse l'utiliser en
    vue d'une estimation quantitative du risque, il va falloir déterminer
    avec une plus grande précision et pour de nombreux agents ou
    microorganismes, la dose nécessaire à la sensibilisation et à
    l'apparition de réactions.

    10.  Evaluation de l'exposition à partir de marqueurs biologiques

          Les marqueurs biologiques constituent un moyen de mesurer
    l'exposition environnementale en caractérisant la dose totale de
    contaminant reçue par un sujet à partir de l'ensemble des sources
    d'exposition. Le principal avantage de cette stratégie réside dans le
    fait que l'on obtient une évaluation de l'exposition individuelle
    totale par une mesure qui intègre la contribution de toutes les
    sources d'exposition et qui dépend du comportement humain. On estime
    également que les marqueurs biologiques permettent une meilleure
    prévision des effets sur la santé que les mesures externes de
    l'exposition. Ils permettent aussi de répondre à plusieurs des
    exigences de l'évaluation:

    *   caractérisation de l'exposition d'un individu ou d'une population

    *   obtention de la distribution de la dose à l'intérieur de la

    *   mise en évidence des déterminants environnementaux et
        démographiques de l'exposition

          Le principal inconvénient des marqueurs biologiques tient à la
    difficulté de caractériser individuellement les sources de pollution
    qui contribuent à l'exposition totale du sujet. Lorsqu'on met au point
    et que l'on utilise de tels marqueurs, il est capital de connaître la
    toxicocinétique du contaminant dans le système pour caractériser la
    variabilité biologique et déterminer si tel ou tel marqueur permet de
    déterminer valablement l'exposition à la substance en cause pour la
    concentration toxicologiquement intéressante. Les marqueurs
    biologiques ont joué un rôle capital dans la détermination de
    l'exposition humaine à certains polluants, comme le plomb. Il existe
    un grand nombre de méthodes non effractives pour la surveillance
    biologique et il faut s'efforcer, lorsque l'on se propose d'évaluer
    l'exposition à telle ou telle substance, de les inscrire dans les
    protocoles de surveillance de l'environnement élaborés à cet effet.

    11.  Assurance de la qualité des études d'exposition

          L'assurance de la qualité consiste en un suivi indépendant du
    déroulement de l'étude destiné à faire en sorte que les responsables
    du laboratoire et les utilisateurs des données aient l'assurance que
    les installations, le matériel, le personnel, les méthodes, les
    pratiques, les dossiers et les contrôles sont bien conformes aux
    critères de qualité en la matière. Certaines erreurs dans l'évaluation
    de l'exposition s'expliquent par des variations dans les résultats des
    analyses ou par des changements qui se produisent lors du prélèvement
    et de la manipulation des échantillons, de leur préparation et de leur
    conservation ou encore dans la tenue et l'enregistrement des données.
    Les variations dans les résultats des analyses peuvent se répartir en
    deux grandes catégories: celles qui concernent l'exactitude,
    c'est-à-dire la concordance entre le résultat du dosage et la quantité
    de substance effectivement présente dans la prise d'essai et celles

    qui concernent la précision, c'est-à-dire la variabilité aléatoire ou
    la reproductibilité de la méthode.

          Le plan de l'étude est le document le plus important à consulter
    pour obtenir des informations sur les éléments essentiels d'une
    enquête, c'est-à-dire le personnel responsable, le mode de collecte
    des données, la conservation des échantillons et leur traitement
    préliminaire, les méthodes d'analyse et l'analyse des résultats. Un
    mode opératoire normalisé est annexé au plan de l'étude. Il comporte
    des instructions détaillées sur la manière d'effectuer certaines
    tâches sur le terrain ou au laboratoire. On peut considérer le plan de
    l'étude et le mode opératoire normalisé comme des directives de
    gestion visant à faire en sorte que l'ensemble du personnel qui
    participe à l'étude se familiarise avec les diverses procédures et
    n'en utilise pas d'autres.

          Le contrôle de qualité vise expressément la qualité des résultats
    de laboratoire. Il comporte deux volets. Le contrôle interne consiste
    en un ensemble de procédures utilisées par le personnel du laboratoire
    pour évaluer en continu les résultats qu'il obtient. Le contrôle de
    qualité externe est un système de vérification objective de la bonne
    exécution des analyses par un organisme indépendant. Dans le contrôle
    interne, on affiche les résultats sur des cartes de contrôle (par ex.
    cartes de contrôle de Sheward ou à sommes cumulées) et on se base sur
    les limites de contrôle pour prendre des mesures le cas échéant ou
    juger si l'ensemble de données comporte ou non un contrôle
    statistique. Le contrôle externe, par contre, donne des indications
    indépendantes sur la qualité du travail effectué par le laboratoire et
    sur la compétence de tel ou tel opérateur. Généralement, un
    laboratoire coordinateur distribue aux laboratoires participants des
    échantillons contenant le produit à doser à une concentration connue.
    Les laboratoires participants effectuent leurs dosages sur ces
    échantillons et soumettent leurs résultats au laboratoire coordinateur
    qui vérifie alors la bonne exécution des analyses. Dans les
    échantillons de référence utilisés pour les contrôles de qualité
    interne et externe la matrice et le polluant doivent être à la même
    concentration que dans l'échantillon réel. En outre, pour certaines
    substances, il peut être nécessaire de prendre en considération la
    forme chimique sous laquelle elles sont susceptibles de se trouver.

          Enfin, les interactions avec la population humaine constituent un
    ensemble spécifique d'éléments à prendre en considération au niveau de
    la conception de l'étude et de l'assurance de la qualité, éléments qui
    doivent être soigneusement évalués en même temps que les questions
    plus traditionnelles relatives à l'échantillonnage, à l'analyse et aux
    modes opératoires.


    1.  Definición de la exposición

          El concepto de exposición implica la presencia de una sustancia
    tóxica ambiental en un determinado punto del espacio y el tiempo, y la
    presencia simultánea de una persona o personas en el mismo lugar.
    Además, es necesario precisar la cantidad de la sustancia que entra en
    contacto con la superficie externa del cuerpo humano.

          Tras reconocerse el valor intrínseco de la información
    relacionada con la exposición, el "análisis de la exposición" se ha
    convertido en un importante campo de investigación científica, que
    complementa las disciplinas tradicionales de la salud pública, como
    son la epidemiología y la toxicología, y constituye un componente
    esencial de la adopción de decisiones fundamentadas en materia de
    higiene del medio.

    2.  Usos de la información sobre la exposición humana

          Las particularidades de un determinado análisis de la exposición
    dependerán del uso o usos previstos. Por ejemplo, los aspectos de la
    exposición que interese examinar, la naturaleza de la información
    requerida y la cantidad y calidad de los datos dependerán de si la
    evaluación de la exposición se inscribe en el contexto de una
    investigación epidemiológica, de una evaluación del riesgo, de la
    gestión de los riesgos o de un análisis de la situación y las

          El conocimiento del nivel de exposición humana a los
    contaminantes ambientales es un componente importante de la
    epidemiología ambiental, la evaluación de los riesgos, la gestión de
    los riesgos y el análisis de situaciones y tendencias. La información
    sobre la exposición proporciona el nexo decisivo entre las fuentes de
    contaminantes, su presencia en el medio y los posibles efectos para la
    salud humana. Esta información, empleada en el contexto de una gestión
    del medio basada en la reducción de los riesgos para el ser humano,
    facilitará la selección y el análisis de otras estrategias distintas
    del tradicional enfoque de "dirección y control". En todo el mundo, la
    mayoría de las estructuras de gestión del medio ambiente dependen
    directamente de las mediciones de los contaminantes presentes en
    diversos medios para estimar la calidad del entorno, inferir los
    riesgos e interpretar el grado de observancia de las normas. Incluso
    en estos casos, la información sobre la exposición permite evaluar la
    eficacia de las medidas de protección de los sectores de la población
    más vulnerables o en situación de mayor riesgo.

          Es esa relación directa lo que hace de las mediciones de la
    exposición un arma inestimable a efectos de evaluación de las
    repercusiones para la higiene del medio a escala local, regional y

    3.  Estrategias y diseño de las evaluaciones de la exposición

          El correcto diseño del estudio es el elemento más importante de
    cualquier evaluación de la exposición. Se deben especificar los fines
    y objetivos de la investigación, así como los métodos idóneos de
    muestreo, medición, análisis estadístico y aseguramiento de la
    calidad. Para caracterizar la magnitud, duración y distribución
    temporal del contacto humano con los contaminantes del medio se pueden
    emplear métodos directos o indirectos. Entre los primeros cabe citar
    las mediciones en el punto de contacto y las mediciones de marcadores
    biológicos de la exposición. Los métodos indirectos incluyen la
    vigilancia ambiental, las modelizaciones y los cuestionarios. Estos
    métodos pueden emplearse en estudios de la exposición basados en
    distintas maneras de seleccionar a la población estudiada; se
    distinguen así, por ejemplo, los estudios amplios que abarcan a todos
    los miembros de la población analizada, los estudios descriptivos de
    una muestra no probabilística, o los estudios basados en muestras
    representativas de individuos, seleccionados al azar.

    4.  Métodos estadísticos de evaluación de la exposición

          Los métodos estadísticos son un instrumento crítico de los
    estudios -- aplicados o de investigación -- de evaluación de la
    exposición. Se recomienda que participe siempre un estadístico en
    todos los aspectos de la investigación de la exposición, en especial
    durante las fases de diseño y de análisis de los datos. La
    determinación del tamaño de la muestra es una aplicación importante de
    la estadística durante la planificación de esos estudios. La
    estadística descriptiva numérica y gráfica permite resumir los datos
    de exposición y llevar a cabo análisis preliminares de las relaciones
    entre las variables determinantes de la exposición. En muchos casos
    los datos sobre ésta adoptan una distribución aproximadamente normal o
    log-normal, y se prestan pues fácilmente a ser analizados mediante los
    métodos paramétricos habituales de inferencia estadística, como las
    estimaciones y la verificación de hipótesis. Se pueden emplear además
    otros modelos estadísticos paramétricos, como el análisis de la
    varianza (ANOVA), la regresión lineal y la regresión logística, para
    cuantificar la relación entre los niveles de exposición medidos.
    Cuando el número de observaciones es pequeño o no es posible
    transformar los datos en una distribución aproximadamente normal, se
    pueden utilizar métodos no paramétricos, como las pruebas de signo de
    Mann-Whitney y de Kruskal-Wallis, para verificar las hipótesis.

    5.  Patrones temporales de la actividad humana y evaluación de la 

          Se puede utilizar información sobre el perfil de actividades de
    la gente para identificar los determinantes de los niveles de
    exposición detectados, predecir exposiciones no medidas o no medibles,
    evaluar la relación entre la exposición y el estado de salud, e
    identificar situaciones de exposición de alto riesgo que puedan
    afrontarse con medidas de salud pública.

          El costo relativo del muestreo sobre el terreno y de los análisis
    de laboratorio en las mediciones ambientales y biológicas subraya el
    valor potencial de los datos tiempo-actividad. La evaluación de los
    perfiles de actividad a largo plazo (p. ej., toda la vida) sólo puede
    hacerse a veces empleando cuestionarios de análisis de la relación
    tiempo-actividad. Para reunir información sobre las actividades
    humanas se emplean diversos métodos, entre ellos diarios y
    cuestionarios, dispositivos mecánicos y medidas de observación. Sólo
    recientemente se han empezado a desarrollar métodos para evaluar la
    influencia de los perfiles de tiempo-actividad en la ingestión
    alimentaria y no alimentaria y en la exposición cutánea. Las dudas
    albergadas respecto a la capacidad de los métodos de acopio de datos
    para reflejar fielmente las actividades y garantizar la validez de la
    información, así como respecto a las repercusiones de la variabilidad
    ínter e intrapersonal del comportamiento, imponen límites a la
    aplicación de los datos de tiempo-actividad a la evaluación de la
    exposición humana. Sin embargo, con unos programas adecuados de
    aseguramiento de la calidad, la información sobre los patrones
    temporales de la actividad puede ser de enorme utilidad para
    interpretar y modelizar la exposición.

    6.  Modelización de la exposición humana y de las dosis

          Un modelo de exposición es un esquema lógico o empírico que
    permite estimar los parámetros de la exposición individual o
    poblacional a partir de una serie de datos. Los modelos de la
    exposición, cuando están basados en observaciones adecuadas, pueden
    emplearse para estimar exposiciones colectivas (p. ej., la media de
    una población) o individuales (p. ej., la distribución de la
    exposición entre los miembros de una población). Mediante esos modelos
    se puede estimar la exposición sufrida a través de uno o de varios
    medios. Esto último es especialmente útil para poder comparar la
    magnitud de las exposiciones previsibles a través de distintos medios,
    y por tanto para establecer las prioridades. Los modelos pueden ser
    estadísticos, deterministas, o una combinación de ambos. En los tres
    casos se pueden aplicar métodos probabilísticos para estimar la
    distribución de la exposición en la población, esto es, la
    variabilidad de la exposición entre los individuos. Además, los
    métodos probabilísticos pueden utilizarse para definir la
    incertidumbre de los parámetros introducidos en el modelo y propagar
    esa incertidumbre hasta la variable de evaluación sometida a
    predicción. La evaluación de la exactitud de los resultados del modelo
    es una condición fundamental para pasar a utilizarlos con fines de
    adopción de decisiones.

    7.  Medición de la exposición humana a productos químicos presentes en 
        el aire, el agua y los alimentos

          En la selección de los métodos de muestreo de productos químicos
    presentes en el aire, el agua y los alimentos intervienen
    consideraciones comunes relacionadas con los límites de detección, los
    factores de interferencia, la facilidad de manejo y los costos. Se

    dispone de métodos de muestreo para personas, aire de microambientes y
    aire ambiente, para la vigilancia de gases y vapores -- tanto pasiva
    como activamente--, aerosoles, compuestos orgánicos semivolátiles y
    gases reactantes.

          La evaluación de la calidad del agua obliga a considerar
    numerosos factores en relación con el muestreo. Una consideración
    importante es que la exposición a los contaminantes no se limita a las
    vías orales y que no todos los individuos tienen acceso al agua
    tratada que circula por los sistemas de distribución. Se proporciona
    orientación respecto al muestreo y los programas de vigilancia.

          Existen varios métodos para estimar el consumo de alimentos y la
    contaminación de éstos. El método elegido dependerá de la información
    disponible, del grupo de población de interés, de que lo expresado
    sean los efectos agudos o crónicos del producto químico, del uso
    previsto de los resultados y de los recursos disponibles.

    8.  Medición de la exposición humana a los contaminantes químicos 
        presentes en el suelo y en el polvo depositado

          El contacto humano con el suelo y el polvo depositado puede ser
    una importante fuente de exposición a contaminantes químicos, sobre
    todo en los niños. Aunque se han desarrollado muchos métodos de
    muestreo, no se ha demostrado que ninguno de ellos sea superior a los
    otros, por lo que resulta difícil comparar los resultados de estudios
    realizados con diferentes métodos. Entre los factores que deben
    tenerse en cuenta para seleccionar un método de muestreo cabe citar la
    eficiencia de la recogida de muestras, las diferencias entre los
    perfiles de actividad de las personas, la variabilidad física del
    suelo y de los niveles de polvo en el espacio y en el tiempo, las
    superficies y sustratos de muestra, el momento elegido para obtener
    las muestras y los métodos analíticos utilizados para medir las
    sustancias tóxicas en el laboratorio.

    9.  Medición de la exposición humana a agentes biológicos presentes en 
        el aire y el polvo

          Se consideran bioaerosoles diversos microorganismos, o
    componentes de los mismos, que pueden ser transportados por el aire e
    inhalados. Comprenden virus, bacterias, pólenes, hongos, protozoos y
    algas en forma de microorganismos viables potencialmente causantes de
    enfermedades. Diversos fragmentos o componentes metabólicos de las
    bacterias y los hongos, al igual que las estructuras proteicas
    presentes en esos microorganismos, así como en los excrementos y en
    distintas partes de los insectos, arácnidos y otros animales, pueden
    provocar reacciones alérgicas. Los bioaerosoles más extendidos en
    interiores y más implicados en las reacciones alérgicas son los
    ácaros, los hongos, las bacterias y el polen.

          La evaluación de la exposición a agentes microbiológicos está tan
    avanzada en estos momentos como la de muchos contaminantes del aire.
    No se han desarrollado muestreadores de personas. De hecho, muchas de

    las técnicas disponibles para obtener muestras de agentes
    aerobiológicos son adaptaciones de instrumentos diseñados con otros
    fines. Sin embargo, se observan progresos en el sector, a medida que
    diversas organizaciones profesionales intentan mejorar y normalizar
    sus métodos de medición, sus protocolos de cultivo y análisis y sus
    sistemas de notificación de datos, aspectos todos ellos decisivos para
    poder comparar los resultados de diferentes investigadores.

          Por su propia naturaleza, los bioaerosoles varían mucho en lo que
    respecta a su composición y concentración. Las condiciones favorables
    para su crecimiento, reproducción y dispersión varían dentro de un
    amplio margen de condiciones de temperatura, humedad y presencia de
    nutrientes. Esos mismos factores se ven alterados por las actividades
    del ser humano y de los animales. Los aerosoles biológicos pueden
    verse difundidos y amplificados por sistemas mecánicos y máquinas de
    diverso tipo. Como resultado de ello, las exposiciones personales son
    bastante variables; esto, a su vez, ha llevado a muchos investigadores
    a obtener muestras del aire de la zona y/o de grandes cantidades de
    material. Por ejemplo, se recomienda medir de forma indirecta la
    exposición a los alergenos de los ácaros a partir de muestras de polvo
    de la ropa de cama y el suelo. Esas muestras domésticas, ya sea de
    aire o de polvo, determinan a menudo el nivel de "exposición" empleado
    en las investigaciones epidemiológicas.

          Por último, para que los progresos de los métodos de evaluación
    de la exposición a agentes aerobiológicos permitan aplicarlos a la
    evaluación cuantitativa de los riesgos, habrá que determinar mejor las
    dosis causantes de sensibilización y reactividad para muy diversos
    microorganismos y/o agentes.

    10.  Evaluación de la exposición mediante marcadores biológicos

          Los marcadores biológicos permiten vigilar la exposición
    ambiental determinando la dosis total de un contaminante recibida por
    un individuo a partir de todas las fuentes de exposición. La principal
    ventaja de esta estrategia es que permite evaluar la exposición total
    usando una medida que integra todas las fuentes de exposición y está
    influida por el comportamiento humano. Se considera además que los
    marcadores biológicos tienen un mayor valor predictivo de los efectos
    sanitarios que las medidas externas de la exposición. Con ellos se
    responde a varias exigencias de la evaluación de la exposición, como
    son las siguientes:

    *   caracterización de la exposición de un individuo o población

    *   determinación de la distribución de la dosis en la población

    *   identificación de los determinantes ambientales y demográficos de
        la exposición

          El principal inconveniente de los marcadores biológicos estriba
    en la dificultad para caracterizar las fuentes individuales que
    contribuyen a la exposición total del sujeto. Al poner a punto y
    emplear marcadores biológicos, el conocimiento de la toxicocinética
    del contaminante en el sistema es fundamental para caracterizar la
    variabilidad biológica y determinar si el marcador biológico es válido
    para poder evaluar la exposición a la concentración de interés. Los
    marcadores biológicos han sido cruciales para profundizar en el
    conocimiento de la exposición humana a ciertos contaminantes, como el
    plomo. Se dispone de numerosos métodos no invasivos para la vigilancia
    biológica, métodos que los asesores en materia de exposición deberían
    intentar incorporar al desarrollar protocolos de vigilancia del medio

    11.  Aseguramiento de la calidad de los estudios de la exposición

          El aseguramiento de la calidad (AC) implica una vigilancia
    independiente del estudio que garantice a los responsables de la
    gestión del laboratorio y a los usuarios de los datos que las
    instalaciones, el equipo, el personal, los métodos, las prácticas, los
    registros y los controles se atienen a principios aceptados de gestión
    de la calidad. Los errores en los datos de exposición se deben tanto a
    variaciones analíticas como a los cambios que pueden afectar a la
    obtención y el manejo de las muestras, la preparación y el
    almacenamiento de las mismas y el registro y conservación de los
    datos. La variación analítica depende de dos factores principales: la
    exactitud, que refleja la concordancia entre la cantidad de analito
    medido y la cantidad realmente presente, y la precisión, que refleja
    la variabilidad aleatoria o la reproducibilidad del método.

          El protocolo es el documento más importante para detallar los
    componentes críticos de una investigación, como por ejemplo el
    personal encargado, el acopio de muestras, el almacenamiento de las
    muestras y el tratamiento preanalítico, los procedimientos analíticos
    y el análisis de los datos. Los procedimientos normalizados de
    utilización (PNU) que se añaden al protocolo contienen instrucciones
    escritas detalladas sobre la manera de llevar a cabo algunas
    actividades rutinarias sobre el terreno y en el laboratorio. El
    protocolo y los PNU pueden considerarse como directrices de gestión
    concebidas para asegurar que todo el personal que participe en las
    operaciones del estudio conozca y emplee correctamente los mismos

          El control de la calidad (CC) atañe específicamente a la calidad
    de los resultados de laboratorio, y presenta dos componentes. El CC
    interno es el conjunto de procedimientos que emplea el personal de un
    laboratorio para evaluar continuamente los resultados a medida que se
    obtienen. El CC externo es un sistema de control objetivo del
    desempeño de un laboratorio por un organismo independiente. El CC
    interno comprende la presentación de los resultados correspondientes a
    muestras de control en gráficos especiales (por ejemplo los gráficos
    de Shewhard y las sumas acumuladas) y el uso de límites de control
    como criterio para intervenir, o para juzgar si un conjunto de datos

    incluye o no algún tipo de control estadístico. El CC externo, por
    otra parte, aporta pruebas independientes de la calidad del desempeño
    de los laboratorios y de la competencia de los analistas. Por lo
    general, un laboratorio coordinador distribuye entre los laboratorios
    participantes muestras de una concentración conocida. Los laboratorios
    analizan las muestras de referencia y envían los resultados al
    laboratorio coordinador para que evalúe el desempeño.

          Las muestras de referencia de los CC interno y externo deben
    poseer una matriz y una concentración de contaminante similares a las
    de la muestra real. Además, a veces es necesario tener en cuenta las
    formas químicas que puede adoptar la sustancia.

          Por último, las interacciones con las poblaciones humanas
    plantean una serie de consideraciones peculiares respecto al diseño y
    el CC del estudio, que deben ser detenidamente evaluadas junto con los
    aspectos tradicionales concernientes al muestreo, los análisis y los

    See Also:
       Toxicological Abbreviations