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EPA perspective - exposure and effects prediction and monitoring
Citation:
Sobus, J. EPA perspective - exposure and effects prediction and monitoring. Presented at Society of Toxicologic Pathology Annual Meeting, Washington, DC, June 22 - 26, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Risk-based decisions for environmental chemicals often rely on estimates of human exposure and biological response. Biomarkers have proven a useful empirical tool for evaluating exposure and hazard predictions. In the United States, the Centers for Disease Control and Prevention’s National Health and Nutrition Examination Survey represents the largest publically available source of biomonitoring data. These data reflect human exposures to hundreds of environmental chemicals, as well as biological responses that may be linked to adverse health outcomes. While NHANES biomonitoring data are intended to track national trends and set research priorities, they are increasingly used to evaluate exposure and effects predictions in support of risk-based decisions. This represents a repurposing of the NHANES biomarker data, and highlights a need for rigorous and standardized computational protocols. In response to this need, a team of EPA scientists under the Chemical Safety for Sustainability Research Program performed a series of computational case studies with the goal of clearly delineating best-practices for examining publically-available biomonitoring data. This presentation will highlight the novel methodologies used for the case-studies, key findings, and possible implications for decision making.