Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
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.
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