A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data.
Bayesian
air monitoring
hierarchical model
high-throughput
occupational exposure
screening
Journal
Environmental science & technology
ISSN: 1520-5851
Titre abrégé: Environ Sci Technol
Pays: United States
ID NLM: 0213155
Informations de publication
Date de publication:
11 04 2023
11 04 2023
Historique:
medline:
12
4
2023
pubmed:
31
3
2023
entrez:
30
3
2023
Statut:
ppublish
Résumé
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 log
Identifiants
pubmed: 36995295
doi: 10.1021/acs.est.2c08234
pmc: PMC10100548
doi:
Substances chimiques
Air Pollutants, Occupational
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5947-5956Références
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