Citation:

Minucci, J., Tom Purucker, K. Isaacs, J. Wambaugh, AND K. Phillips. A data-driven approach to estimating occupational exposure using workplace compliance data. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 57(14):5947-5956, (2023). https://doi.org/10.1021/acs.est.2c08234

Impact/Purpose:

High-throughput computational methods offer the ability to rapidly screen substances based on exposure potential for the general population, from pathways such as ambient air, consumer products and drinking water. However, workers are a population whose chemical exposure remains relatively unexplored due to the immense variability in chemical use and production between and within workplaces. Here, we present a Bayesian hierarchical model that provides a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. The model uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration. We also found that workplace air concentration patterns varied strongly across industry types and physicochemical properties. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5,587 new substance-by-workplace type pairs reported in the US EPA’s Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for an improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.

Description:

A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m–3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA’s Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.

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