| Autor: |
Ta-Yuan Chang, Guan-Yu Chen, Jing-Jie Chen, Li-Hao Young, Li-Te Chang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Environment International, Vol 182, Iss , Pp 108317- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
0160-4120 |
| DOI: |
10.1016/j.envint.2023.108317 |
| Popis: |
The Internet of Things (IoT) and low-cost sensor technology have become common tools for environmental exposure monitoring; however, their application in measuring respirable dust (RD) in the workplace remains limited. This study aimed to develop a predictive model for RD using artificial intelligence (AI) algorithms and low-cost sensors and subsequently assess its validity using a standard sampling approach. Various low-cost sensors were combined into an RD sensor module and mounted on a portable aerosol monitor (GRIMM 11-D) for two weeks. AI algorithms were used to capture data per minute over 14 days to establish predictive RD models. The best-fitting model was validated using an aluminum cyclone equipped with an air pump and polytetrafluoroethylene filters to sample the 8-hour RD for 5 days at an aircraft manufacturing company. This module was continuously monitored for two weeks to evaluate its stability. The RD concentration measured by GRIMM 11-D in a general outdoor environment over two weeks was 28.1 ± 16.1 μg/m3 (range: 2.4–85.3 μg/m3). Among the various established models, random forest regression was observed to have the best prediction capacity (R2 = 0.97 and root mean square error = 2.82 μg/m3) in comparison to the other 19 methods. Field-based validation revealed that the predicted RD concentration (35.9 ± 4.1 μg/m3, range: 32.7–42.9 μg/m3) closely approximated the results obtained by the traditional method (38.1 ± 8.9 μg/m3, range: 28.1–52.5 μg/m3), and a strong positive Spearman correlation was observed between the two (rs = 0.70). The average bias was −2.2 μg/m3 and the precision was 5.8 μg/m3, resulting in an accuracy of 6.2 μg/m3 (94.2 %). Data completeness was 99.7 % during the continuous two-week monitoring period. The developed sensor module of RD exhibited excellent predictive performance and good data stability that can be applied to exposure assessments in occupational epidemiological studies. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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