Environmental Health Criteria 214

HUMAN EXPOSURE ASSESSMENT
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
choice.
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