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How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool

The Advanced REACH Tool (ART) is the most detailed exposure model currently available for estimating inhalation exposures to dusts, vapours, and aerosols under a broad range of exposure scenarios. The ART follows a Bayesian approach, making use of a calibrated source–receptor model to provide centra...

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Autor principal: McNally, Kevin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094536/
https://www.ncbi.nlm.nih.gov/pubmed/37048000
http://dx.doi.org/10.3390/ijerph20075386
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author McNally, Kevin
author_facet McNally, Kevin
author_sort McNally, Kevin
collection PubMed
description The Advanced REACH Tool (ART) is the most detailed exposure model currently available for estimating inhalation exposures to dusts, vapours, and aerosols under a broad range of exposure scenarios. The ART follows a Bayesian approach, making use of a calibrated source–receptor model to provide central estimates of exposures and information on exposure variability from meta-analyses in the literature. Uniquely amongst exposure models, the ART provides a facility to update the baseline estimates from the mechanistic model and variance components using measurement data collected on the exposure scenario; however, in practical use, this facility is little used. In this paper, the full capability of the ART tool is demonstrated using a small number of carefully chosen case studies that each had a sufficient breadth of personal exposure measurement data to support a measurement-led exposure assessment. In total, six cases studies are documented, three where the estimate from the source–receptor model of the ART was consistent with measurement data, and a further three case studies where the source–receptor model of the ART was inconsistent with measurement data, resulting in a prior-data conflict. A simulation study was designed that involved drawing subsets of between two and ten measurements from the available measurement dataset, with estimates of the geometric mean (GM) and 90th percentile of exposures from the posterior distribution of ART compared against measurement-based estimates of these summaries. Results from this work indicate that very substantial reductions in the uncertainty associated with estimates of the GM and 90th percentile could be achieved with as few as two measurements, with results in detail sensitive to both the measurements themselves and worker and company labels associated with the measurements. For case studies involving prior-data conflicts, the estimates of the GM and 90th percentile rapidly changed as measurement data were used to update the prior. However, results suggest that the current statistical model of the ART does not allow a complete resolution of a prior-data conflict.
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spelling pubmed-100945362023-04-13 How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool McNally, Kevin Int J Environ Res Public Health Article The Advanced REACH Tool (ART) is the most detailed exposure model currently available for estimating inhalation exposures to dusts, vapours, and aerosols under a broad range of exposure scenarios. The ART follows a Bayesian approach, making use of a calibrated source–receptor model to provide central estimates of exposures and information on exposure variability from meta-analyses in the literature. Uniquely amongst exposure models, the ART provides a facility to update the baseline estimates from the mechanistic model and variance components using measurement data collected on the exposure scenario; however, in practical use, this facility is little used. In this paper, the full capability of the ART tool is demonstrated using a small number of carefully chosen case studies that each had a sufficient breadth of personal exposure measurement data to support a measurement-led exposure assessment. In total, six cases studies are documented, three where the estimate from the source–receptor model of the ART was consistent with measurement data, and a further three case studies where the source–receptor model of the ART was inconsistent with measurement data, resulting in a prior-data conflict. A simulation study was designed that involved drawing subsets of between two and ten measurements from the available measurement dataset, with estimates of the geometric mean (GM) and 90th percentile of exposures from the posterior distribution of ART compared against measurement-based estimates of these summaries. Results from this work indicate that very substantial reductions in the uncertainty associated with estimates of the GM and 90th percentile could be achieved with as few as two measurements, with results in detail sensitive to both the measurements themselves and worker and company labels associated with the measurements. For case studies involving prior-data conflicts, the estimates of the GM and 90th percentile rapidly changed as measurement data were used to update the prior. However, results suggest that the current statistical model of the ART does not allow a complete resolution of a prior-data conflict. MDPI 2023-04-04 /pmc/articles/PMC10094536/ /pubmed/37048000 http://dx.doi.org/10.3390/ijerph20075386 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
McNally, Kevin
How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool
title How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool
title_full How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool
title_fullStr How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool
title_full_unstemmed How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool
title_short How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool
title_sort how valuable are small measurement datasets in supplementing occupational exposure models? a numerical study using the advanced reach tool
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094536/
https://www.ncbi.nlm.nih.gov/pubmed/37048000
http://dx.doi.org/10.3390/ijerph20075386
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