Exposure assessment is an important part of any intregated environmental health impact assessment, and also often one of the most demanding.  Difficulties come from the need to estimate exposures of large numbers of people at a spatial and temporal resolution that does not mask or distort important variations in exposure  - and to do so in the context of a highly mobile and variable population and inadequate environonmental and demographic data.  In the face of these challenges, a wide range of methods of exposure assessment have been devised, and often several of these need to be used in combination in order to provide a reliable basis for impact assessment (Figure 1). 

Methods of exposure assessment

As Figure 1 shows, exposure assessment can be done via two main approaches: monitoring and modelling.  Each of these, however, may be applied in two different ways - either targetted at humans themselves (anthropic methods) or at the context within which exposure occurs (environmental methods). 

These methods are not independent of each other.  Monitoring data, for example, are generally required as inputs to models and to validate model results.  Modelling is often needed to extrapolate from sample measurements to the wider population - and, in the context of most health impact assessments, to predict exposures for scenarios that have not yet occurred.  Environmental methods are often required to provide context to biological (PBPK or dose) models, while anthropic models are used to help interpret results obtained from environmental approaches.

The different methods nevertheless differ considerably in their specific strengths and weaknesses, and in their adaptability to different situations.  In general, direct measurements of humans (e.g. biomonitoring and personal exposure monitoring) can be assumed to provide the most accurate estimates of the subject’s true exposure, and are often used as the standard against which other methods are validated.  Likewise, models targetted at biological and biochemical processes (e.g. PBPK and dose models) should provide greater accuracy than environmental models, which are inherantly further removed from the exposure experience.  Limitations of data and knowledge, however, often inhibit the use of these approaches, with the consequence that exposure assessment has to resort to less direct, environmentally-based methods.

Whatever approach is taken, it is important to remember that exposures do not just depend on the nature of the agent and its behaviour or distribution in the environment.  It depends fundamentally also on the behavioural and other characteristics of the people concerned, such as their time activity patterns, diet and physiology, all of which help to determine where, when, how  and for how long individuals become exposed.  For this reason, personal data derived from questionnaires or diaries (or in the absence of these, from popualtion level surveys such as national censuses) are usually important, and feed into most methods of exposure assesment at some stage.

As this also implies, exposure assessment often involves combining data and methods from different levels - e.g. at individual, micro-environmental, neighbourhood and national or population scales.  While using broader-scale data in the assessment may mean that the specificity of individual experience is lost, the analysis becomes more generally applicable, and enables exposure distributions for the whole population (or population grouups) to be assessed.  These distributions can then be used to create specific exposure scenarios withjin which to analyse the impacts of different policy actions (Ott et al. 2006). 

Figure 1.  Methods for exposure assessment

Key: Rectangular boxes = monitoring; bevelled boxes = modcelling; red  = biological methods; green = environmental methods

Exposure modelling

Exposure models (like other models) can loosely be classified into four categories, as listed in Table 1.  In principle, mechanistic models are likely to be more robust, in that they are based on some understanding of the underlying processes that affect exposure; they should therefore translate with reasonable reliability between different data sets or situations.  Often, however, they do not give the best possible level of prediction in specific contexts, because they have not been attuned to the specific data.  They are also, of course, dependent on the level of real understanding of the processes involved.  In many situations, therefore, empirical approaches (often based on regression analysis) are used instead.  These carry two crucial dangers: while they may fit the data well (and thus appear to give good estimation of exposures in terms of the β-coefficient or regression r² of the models), they may not be realistic; and while they may work well (and be valid) in the conditions under which they were initially developed, they may be unreliable when applied in different situations.  Empirical models therefore should always be revalidated (and may need to be recalibrated) when used outside the range of the original data. 

Deterministic methods are often preferred by users, because they are easier to apply and appear to give explicit answers.  They ignore, however, the uncertainties that are inherent in the modelling and the variability that occurs in the real world.  They can therefore be misleading and may suppress valuable debate about policy options and falsely restrict policy choices.   Probabilistic models, in contrast, are designed to reflect these uncertainties and thereby present results in terms not of fixed estimates but of exposure distributions.  While this may seem to leave decision-makers without a clear message, it is a more honest reflection both of the status of the science and of the way the real world operates.  For policy assessments, therefore, mechanistic-stochastic models are likely to be the most informative. 

Table 1. The categories of exposure models (WHO 2005)

Further information on methods of exposure and intake modelling are provided via the links in the panel to the left.  The Toolkit section of this Toolbox also contains further information on, and operating versions of, a number of specific exposure models (see link under See also, below).