Dataset Information

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 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.

SUBMITTER: Minucci JM

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

REPOSITORIES: biostudies-literature

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

Minucci Jeffrey M JM Purucker S Thomas ST Isaacs Kristin K KK Wambaugh John F JF Phillips Katherine A KA

Environmental science & technology 20230330 14

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 ...[more]

PMID: 36995295

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Project description:In this study, 129 exposure situations (ESs) with six or more measured inhalation exposures to dust from solids or vapour from liquids in occupational settings were compared with modelled European Centre of Ecotoxicology and Toxicology of Chemicals (ECETOC) targeted risk assessment tool, version 3 (TRAv3) estimates. The measurement data were extracted from previously published studies examining TRAv3 performance and pooled into a curated database. The comparison exercise focussed on the vapour exposure scenarios, as there were too few dust scenarios for a meaningful analysis of any required model corrections. A group of experts in the exposure modelling field retrieved and reviewed the input parameters used in these ESs. Where considered appropriate, modifications were applied to better match the input parameter definitions and the scope of applicability of the TRAv3. Differences and mean absolute error (MAE) were calculated between the log-transformed modelled exposure value and the 75th percentile of each measured data set and regression analysis was performed. The results indicated that the TRAv3 overestimated 80% of the measured data sets. Both over- and underestimations were mostly by factors 1-5. The calculated MAE for liquids was 0.7, indicating that on average the difference between the 75th percentile and the TRAv3 estimate was less than one order of magnitude. A multiple linear regression showed that some input parameters such as medium volatility, certain process categories (PROC), industrial setting, and the presence of local exhaust ventilation are associated with underestimations. The results of the regression analysis can be used by TRAv3 users to review the degree of over- or underestimation in their current exposure assessments, compared to the curated database. Although multiple linear regression is an appropriate methodology to characterize the TRAv3's performance, more data sets are still needed in view of some remaining data gaps. Nevertheless, the results of the current analysis are being used by ECETOC to further develop the tool as a suitably conservative screening tool for use in REACH chemical safety assessment of occupational exposure to chemicals.

Dataset Information

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

Publications

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

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