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

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.