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Advancing Models and Data for Characterizing Exposures to Chemicals in Consumer Products
Citation:
Isaacs, K., K. Dionisio, K. Phillips, J. Wambaugh, P. Price, AND A. Guiseppi-Elie. Advancing Models and Data for Characterizing Exposures to Chemicals in Consumer Products. 2017 ISES Annual Meeting, Research Triangle Park, NC, October 15 - 19, 2017.
Impact/Purpose:
This presentation describes an overview of ORD research efforts related to consumer product exposures. The abstract is for submittal to an ISES Symposium titled: Consumer Exposure Assessment: Tools and Information to Support a Fit-for-Purpose Approach to Exposure and Risk Assessment.
Description:
EPA’s Office of Research and Development (ORD) is leading several efforts to develop data and methods for estimating population chemical exposures related to the use of consumer products. New curated chemical, ingredient, and product use information are being collected from public sources including 1) chemical weight fractions (WFs) collected from Material Safety Data Sheets 2) WFs predicted from ingredient lists, 3) product use patterns developed from purchasing data and 4) chemical function data. The functional use data is also being used to develop chemical structure and/or property-based models for function and product weight fraction, which can aid in filling gaps in existing ingredient data. In addition, under the Exposure Forecasting (ExpoCast) project, high throughput (HT) analytical methods are being evaluated for characterizing the composition of different products, a potentially rich source of new data for modeling and evaluation. These new data streams can parameterize mechanistic models for use in both high-throughput chemical screening and prioritization and higher-tier applications such as targeted exposure assessments or chemical life-cycle impact evaluations. For chemical prioritization, ORD has developed the High Throughput Stochastic Human Exposure and Dose Simulation (SHEDS-HT) model, which predicts aggregate population exposures based on chemical WFs and use patterns in over 300 consumer product categories. A higher-tier model, the Human Exposure Model (HEM), is being developed to consider additional factors impacting variability in exposures and internal chemical concentrations, including physiological variability and demographic and longitudinal patterns in product use. HEM will incorporate agent-based models of product use derived from considering a “needs-based” (e.g., personal hygiene, pest control) framework. These data and models will improve the prediction of chemical exposures in support of risk-based decision making.
