Exposure estimation approaches

The scope and purpose of the exposure assessment inform the formulation of one or more scenarios for which exposures are to be estimated. The exposure estimation approach should be capable of faithfully representing the key structural assumptions of the scenario, such as exposed population, agents to be considered, spatial and temporal scope, microenvironments and so on (see section 3.2.1 for a complete list). If the modelling approach omits any of the relevant elements, then the estimates could be biased. If the modelling approach includes irrelevant, unnecessary or superfluous elements, then, at best, the model is likely to be more cumbersome to deal with than it needs to be, or, at worst, biases may be introduced. 

Passive dosimetry, which proved useful for the pursuit of better workplace hygiene in agriculture during the past 40 years (Durham and Wolfe, 1962), yields unvalidated and excessive amounts of worker exposure (Krieger, 1996). Currently, our approach with respect to indoor and agricultural exposure assessments has been the evaluation of exposure estimates using well-known, studied chemicals to first understand the work task and at a later time develop chemical-specific studies as required in the regulatory arena. 

It is clear that these goals place widely differing requirements on both the resolution and the accuracy of exposure estimates thus, the approach selected should be optimized to fit the requirements. 

For a limited number of exposure pathways (primarily inhalation of air in the vicinity of sources), pollutant fate and distribution models have been adapted to estimate population exposure. Examples of such models include the SAI and SRI methodologies developed for EPA s Office of Air Quality Planning and Standards (1,2), the NAAQS Exposure Model (3), and the GEMS approach developed for EPA s Office of Toxic Substances (4). In most cases, however, fate model output will serve as an independent input to an exposure estimate. 

Use of the TTC approach is dependent on rather precise quantitative exposure estimates. Experience from the EU Risk Assessment Program for Existing Substances is that it is very difficult to get sufficient information on the different uses and related exposure to make precise exposure estimates. The Nordic group considered that for substances where only... 

The Nordic group considered that in order to be sure of protective TTC values, the values would be rather small. Using rather cmde or conservative exposure estimates (e.g., worst-case scenarios and modeling), as is the case for risk characterization of industrial chemicals, would usually be at a quantitative higher level and thus this combination would probably lead to limit the use of the TTC approach to a great extent within REACH. 

Eurthermore, uncertainties in the exposure assessment should also be taken into account. However, no generally, internationally accepted principles for addressing these uncertainties have been developed. For predicted exposure estimates, an uncertainty analysis involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters, can be performed. The usual approach for assessing this uncertainty is the Monte Carlo simulation. This method starts with an analysis of the probability distribution of each of the variables in the uncertainty analysis. In the simulation, one random value from each distribution curve is drawn to produce an output value. This process is repeated many times to produce a complete distribution curve for the output parameter. 

Life-cycle assessment (LCA) is part of a special section on the trends and challenges of the new environmental landscape. Another area seen as a priority is that of exposure estimations and the elucidation of the biological mechanisms of chemicals. Pollution prevention, which aims to deal with problems before they occur, is clearly becoming a salient approach to industrial ecology (Environmental Science Technology, 1996 Anastas and Lankey, 2000). 

Output data are usually the temporal and spatial distribution of PM concentration values and sophisticated modelling approaches are available, which allow assessing PM at high spatial and temporal resolution. These PM concentration results will be the basis, with time-activity profiles, to exposure estimation. 

The general approach for exposure estimation can be expressed by Hertel et al. [8] equation ... 

As shown along this chapter, a reliable air quality model is a valuable tool for human exposure studies, once modelled concentrations at different spatial scales and time resolutions allow to better characterising the air quality at the microenvironments visited by a target population, rather than monitoring values that are site and time specific. Moreover, air quality and exposure modelling approach considers the contribution of indoor environments, where people spend most of their time, to the exposure estimation. 

Expand modeling approaches and case examples in which nonsteady-state biomonitoring data are simulated to explore the exposure conditions responsible for biomonitoring results this may provide exposure estimates that can be used in risk assessment (for example, Bayesian inference techniques and population behavior-exposure models). 

The ability to use probabilistic approaches to assess dietary pesticide exposure has also changed much of the emphasis of pesticide risk assessment practices from assessing long-term (chronic) exposure to short-term (acute) exposure. Deterministic approaches worked well with chronic assessments since the day-to-day variability in food consumption patterns and the variability of pesticide residue levels tended to average out over the course of a 70-year exposure period. Deterministic approaches have also often been used in the assessment of acute dietary risk by assuming an upper percentile level of food consumption and the maximum detected or allowable level of residue. The point estimate determined in this manner is then compared with the RfD to determine the acceptability of exposure under the specified conditions. 

In evaluating dietary exposure, the USEPA takes a tiered approach by first making a conservative, screening-level, worst-case estimate (Tier I). If the resulting data warrants, subsequent refinements are made to obtain more realistic exposure estimates (Tier II-IV). The tiered approach for assessing chronic dietary exposure is presented below. 

Knowledge of key sources of uncertainty in exposure estimates helps guide additional data collection to reduce uncertainty in order to improve the precision of the estimates. For example, the identification of key sources of uncertainty can be used to prioritize information-gathering efforts for the most important inputs. Because uncertainty results from lack of knowledge, an effective approach to its reduction is to obtain more knowledge, through additional measurements or the development of more precise and accurate measurement methods. 

Validation is the process by which the reliability and relevance of a particular approach, method or assessment are established for a defined purpose (IPCS, 2004). Uncertainty analysis can contribute to this, by indicating the reliability of the exposure estimate (how different the true exposure might be) and hence its usefulness for decision-making. 

The approach and elements of the characterization of uncertainty are described initially, followed by presentation of the overall outcome and discussion of relevant aspects of communication to various target audiences. A description of the underlying exposure estimation and more detailed information on each of the components are subsequently presented in Appendix 1 to this annex. 

Closer inspection of Equation A1.4 shows that substances with a high expected risk ratio (nE//iRfD) contribute most to the uncertainty (or variance) in the HI. If 1 or 2 components dominate the mixture, it seems sufficient to base the uncertainty assessment on these dominant components. However, mixtures are often dominated by more than 2 components. Furthermore, the covariance between the individual risk ratios should not be ignored, since exposure estimates (E,) of individual mixture components can be (positively) correlated, as well as their reference values IA>fDr). The uncertainty in the HI may be severely underestimated if these correlations are not accounted for, which is evident from the last part of Equation A1.4. The central limit theorem states that the final HI will approach a normal distribution when the number of substances in the mixture becomes large or if no single risk ratio dominates the sum (De Groot 1986). 

Although human exposure data are essential for accurate evaluation of an agent s risk potential, data of sufficient quality and quantity are frequently unavailable. Thus, there is uncertainty in the exposure component of the evaluative process, even as there is in hazard characterization. When toxicity data indicate the potential for an adverse effect, the need to estimate the nature of human exposure becomes imperative. In those instances, exposure estimates can be derived using modeling approaches based on data from other sources, and one or more default assumptions can be used. The greater the number of default assumptions employed, the greater the uncertainty about the accuracy of the expert judgment. 

Some aspects of degree of concern currently can be considered in a quantitative evaluation. For example, EPA considers human and animal data in the process of calculating the RfD, and these data are used as the critical effect when they indicate that developmental effects are the most sensitive endpoints. When a complete database is not available, a database UF is recommended to account for inadequate or missing data. The dose-response nature of the data is considered to an extent in the RfD process, especially when the BMD approach is used to model data and to estimate a low level of response however, there is no approach for including concerns about the slope of the dose-response curve. Because concerns about the slope of the dose-response curve are related to some extent to human exposure estimates, this issue must be considered in risk characterization. (If the MOE is small and the slope of the dose-response curve is very steep, there could be residual uncertainties that must be dealt with to account for the concern that even a small increase in exposure could result in a marked increase in response.) On the other hand, a very shallow slope could be a concern even with a large MOE, because definition of the true biological threshold will be more difficult and an additional factor might be needed to ensure that the RfD is below that threshold. 

In the past few years, there have been increasing efforts towards international harmonization of approaches to pesticide exposure assessment. Harmonization allows exposure assessors to share expertise and resources and develop better methods. Ongoing efforts towards international harmonization are discussed in Chapter 10. The development of generic databases has provided an impetus for harmonization of methodologies for generating the data. Further harmonization would increase the number of studies that could be included in databases, thus improving the exposure estimates derived from them. The use of harmonized factors for dermal absorption and clothing penetration, plus protective factors... 

The use of fluorescent compounds can be coupled with video-imaging analysis to produce exposure estimates over virtually the entire body (Fenske and Bim-baum, 1997). This approach requires pre- and post-exposure images of skin surfaces under long-wavelength ultraviolet illumination, development of a standard curve relating dermal fluorescence to skin-deposited tracer, and chemical residue sampling to quantify the relationship between the tracer and the chemical substance of interest as they are deposited on the skin. 

Despite the fact that few epidemiological studies with quantitative exposure assessment data are available for pesticide exposure, more insight is now present on how the optimal exposure assessment strategy might look. In particular, the use of determinants of exposure studies, as reviewed recently, and their application in health-based exposure estimation, seems a promising approach that can solve many of the problems associated with pesticide exposure assessment in agriculture. This approach will be of use in both occupational and domestic epidemiological studies on this topic. 

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