Comparative Study of Exposure Assessment of Dust in Building Materials Enterprises Using ART and Monte Carlo
Dust generated during the processing of building materials enterprises can pose a serious health risk. The study aimed to compare and analyze the results of ART and the Monte Carlo model for the dust exposure assessment in building materials enterprises, to derive the application scope of the two mo...
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Veröffentlicht in: | Safety and health at work 2024, 15(1), , pp.33-41 |
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Sprache: | eng |
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Zusammenfassung: | Dust generated during the processing of building materials enterprises can pose a serious health risk. The study aimed to compare and analyze the results of ART and the Monte Carlo model for the dust exposure assessment in building materials enterprises, to derive the application scope of the two models.
First, ART and the Monte Carlo model were used to assess the exposure to dust in each of the 15 building materials enterprises. Then, a comparative analysis of the exposure assessment results was conducted. Finally, the model factors were analyzed using correlation analysis and the scope of application of the models was determined.
The results show that ART is mainly influenced by four factors, namely, localized controls, segregation, dispersion, surface contamination, and fugitive emissions, and applies to scenarios where the workplace information of the building materials enterprises is specific and the average dust concentration is greater than or equal to 1.5 mg/m3. The Monte Carlo model is mainly influenced by the dust concentration in the workplace of building materials enterprises and is suitable for scenarios where the dust concentration in the workplace of the building materials enterprises is relatively uniform and the average dust concentration is less than or equal to 6mg/m3.
ART is most accurate when workplace information is specific and average dust concentration is > 1.5 mg/m3; whereas, The Monte Carlo model is the best when dust concentration is homogeneous and average dust concentration is |
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ISSN: | 2093-7911 2093-7997 |
DOI: | 10.1016/j.shaw.2023.12.003 |