Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace

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 (r_s = 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.