This study is part of a larger project on the prevalence and control of silicosis in Rajasthan, focusing on the stone-carving industry due to the rising number of silicosis cases. The study measures workplace particulate matter (PM) emissions, including PM10, PM4, PM2.5, and PM1, in the winter and spring seasons. Monitoring conducted on-site reveals that PM concentrations in the working environment are alarmingly high, with average winter PM levels of 858.5 µg/m3 for PM10, 322.8 µg/m3 for PM4, 178.8 µg/m3 for PM2.5, and 94.6 µg/m3 for PM1. Particle size analysis shows a wide range of sizes, peaking in the 2.530–3.515 µm range, which poses significant health risks to the workers. Multiple linear regression analysis demonstrates a strong predictive model, with temperature positively affecting PM1 levels, while humidity has a negative impact. The study finds strong correlations between PM1 and larger PM fractions, indicating a shared source and suggesting that monitoring larger particulates could help infer PM1 levels. These findings underscore the urgent need for targeted interventions to reduce PM exposure and mitigate respiratory risks in stone-carving environments.

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Comprehensive Analysis of Particulate Matter Emissions and Meteorological Influences in the Stone-Carving Industry

  • Shubham Sharma,
  • Nivedita kaul,
  • Sumit khandelwal

摘要

This study is part of a larger project on the prevalence and control of silicosis in Rajasthan, focusing on the stone-carving industry due to the rising number of silicosis cases. The study measures workplace particulate matter (PM) emissions, including PM10, PM4, PM2.5, and PM1, in the winter and spring seasons. Monitoring conducted on-site reveals that PM concentrations in the working environment are alarmingly high, with average winter PM levels of 858.5 µg/m3 for PM10, 322.8 µg/m3 for PM4, 178.8 µg/m3 for PM2.5, and 94.6 µg/m3 for PM1. Particle size analysis shows a wide range of sizes, peaking in the 2.530–3.515 µm range, which poses significant health risks to the workers. Multiple linear regression analysis demonstrates a strong predictive model, with temperature positively affecting PM1 levels, while humidity has a negative impact. The study finds strong correlations between PM1 and larger PM fractions, indicating a shared source and suggesting that monitoring larger particulates could help infer PM1 levels. These findings underscore the urgent need for targeted interventions to reduce PM exposure and mitigate respiratory risks in stone-carving environments.