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 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 ro

SUBMITTER: Minucci JM 

PROVIDER: S-EPMC10100548 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature