Quantifying and modeling respirable dust and crystalline silica exposure in rice mills using the CART algorithm

Area of Science:

• Occupational Health
• Environmental Science
• Industrial Hygiene

Background:

• Rice milling generates significant airborne dust, posing potential respiratory health risks to workers.
• Quantifying exposure to respirable dust (RD) and respirable crystalline silica (RCS) is crucial for assessing occupational hazards in this sector.

Purpose of the Study:

• To quantify RD and RCS exposure levels in Western Indian rice mills.
• To evaluate the association between these exposures and different rice varieties and operational activities.
• To utilize the Classification and Regression Trees (CART) algorithm for handling missing data.

Main Methods:

• Collection of 50 RD samples from workers' breathing zones across mills processing various rice types.
• Quantification of corresponding RCS levels.
• Statistical analysis using Minitab and R, including CART imputation for missing data.

Main Results:

• Operational activities significantly influenced RD (22.55%) and RCS (10.59%) variability (p < 0.05).
• Dehusking operations showed the highest RD and RCS concentrations, while sieving had the lowest.
• Rice varieties had a minimal impact, though Kolam and Parimal showed higher RCS than IR-8, remaining within occupational limits.

Conclusions:

• Operational activities are key drivers of RD and RCS exposure in rice mills, with dehusking being a high-risk task.
• RD and RCS levels in this study were lower than reported in existing literature, potentially due to advanced technology and regional factors.
• Enhanced dust control measures are essential, particularly during high-exposure tasks, and further research with additional environmental data could refine predictive models.