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Prediction of Composition and Emission Characteristics of Articles in Support of Exposure Assessment
Citation:
Addington, C., K. Phillips, AND K. Isaacs. Prediction of Composition and Emission Characteristics of Articles in Support of Exposure Assessment. SETAC HT Screening Meeting, Durham, NC, April 16 - 18, 2018.
Impact/Purpose:
This presentation describes a modeling methodology to predict the likely composition of chemicals in polymer consumer products, and their resulting emission characteristics. The resulting models will allow for the parameterization and application of high-throughput human exposure models for articles.
Description:
The risk to humans from chemicals in consumer products is dependent on both hazard and exposure. The prediction and quantification of near-field (i.e., indoor) chemical exposure from household articles such as furniture and building materials is an ongoing effort. As opposed to (for example) cosmetic formulations which are regulated by the FDA, the chemical composition of articles, their chemical emission characteristics, and the resulting chemical exposures are less clear. We have developed a modeling methodology to predict the weight fraction of chemicals in a polymeric substrate and corresponding emission characteristics based on chemical and substrate structure. We constructed a database of reported and measured chemical concentrations in articles from publicly available sources including Health Product Declarations (HPDs). This database was then used to trained a random forest algorithm which predicts a chemical weight fraction bin based on chemical structure and properties. From the predicted weight fractions and chemical properties, we applied a group contribution method, UNIversal quasi-chemical Functional-group Activity Coefficients-Free Volume (UNIFAC-FV), to approximate steady-state gas phase concentrations (y0) at the substrate surface. The model was compared to published experimental y0 data from gas-chamber experiments. The resulting estimates of y0 can be used to parameterize existing high-throughput exposure models for substrate-chemicals combinations found in consumer articles. Thus from only the “first-principles” of chemical and substrate molecular structure, we can generate an estimate of chemical exposure, which carries information quantifying risk. This abstract does not reflect EPA policy.
