Comprehensive Chemical Exposure Framework
1.0 INTRODUCTIONThere is a growing awareness in recent years that a personís exposure to particular chemicals occurs via multiple routes from multiple sources. To adequately evaluate such exposures, the scientific community requires models that can predict the occurrence of exposures for each potential combination of pathways and sources, and then accumulate these exposures over time. Ideally, the models will account for variations in people's activity patterns that are influenced by age, gender, occupation, and other demographic factors. These activity patterns should realistically simulate the movements of representative people through zones defined by geographic location and micro-environment.
Recently, the Food Quality Protection Act mandated that the exposure assessment community address the limitations of existing models and provide improved models that can be used to estimate exposures to agricultural-related compounds. Although exposure modeling on agricultural pesticides is clearly moving forward, there is also a pressing need for improved models to assess exposures to non-agricultural compounds. To this end, the American Chemistry Council has requested the design of a Comprehensive Chemical Exposure Framework (CCEF), which is intended to inform and advise the American Chemistry Council in its effort to identify, facilitate, and communicate generic research that will characterize peopleís exposure to chemicals, especially non-agricultural chemicals, and raise the confidence and lower the uncertainty for quantitative estimates of exposure associated with potential human health effect to chemicals.
The American Chemistry Council's Human Health Exposure Assessment Technical Implementation Panel funded this research, and Battelle Pacific Northwest Division and Battelle Columbus Operations staff conducted the work. This technical report is a final product of this research and is property of the American Chemistry Council. This report documents the design of the CCEF, research gaps in exposure modeling, algorithms, and data, and sensitivity of exposure results to specific models, processes, algorithms, and data.
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