4.8 Article

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

Journal

ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 57, Issue 14, Pages 5947-5956

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.est.2c08234

Keywords

occupational exposure; high-throughput; screening; hierarchical model; Bayesian; air monitoring

Ask authors/readers for more resources

A high-throughput, data-driven approach using a Bayesian hierarchical model has been developed to estimate occupational exposure by predicting the distribution of workplace air concentrations based on industry type and the physicochemical properties of a substance. This model outperforms a null model in predicting substance detection and concentration, achieving 75.9% classification accuracy and a RMSE of 1.00 log10 mg m-3. It can also be used to predict air concentration distributions for new substances and improve occupational exposure consideration in risk-based chemical prioritization efforts.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available