A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data

Abstract

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 highthroughput, 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-meansquare error (RMSE) of 1.00 log 10 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 substanceby-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.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency

“…These data have been used as a basis for estimating median population human exposure ( Isaacs et al, 2014 ; Wambaugh et al, 2014 ; Ring et al, 2019 ). Future work is planned that will address two important data gaps: 1) the application of computational exposure strategies to access data poor chemical exposures occurring within the workplace ( Minucci et al, 2023 ); and 2) chemical exposures that result from consumer articles such as furnishings and building materials.…”