Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
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. MEASURING HUMAN EXPOSURES TO CHEMICALS IN AIR, WATER AND FOOD
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