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This study aims to assess PM2.5 emissions from biomass combustion in rural India and the role of meteorological factors in pollutant dispersion.

Spatiotemporal assessment and machine learning-based prediction of PM2.5 Emissions from biomass combustion in Rural India

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Randomized trial analyzes PM2.5 emissions from biomass cooking, suggesting the importance of clean cooking interventions.

This study aims to assess PM2.5 emissions from biomass combustion in rural India and the role of meteorological factors in pollutant dispersion.
Monitored PM levels (PM₁, PM2.5, PM₄, PM₁₀) over one year in Jhunjhunu and Mahendragarh.
Employed machine learning techniques including Regression models, Random Forest, and XGBoost to analyze pollution patterns.
Collected data on meteorological factors like temperature, humidity, wind speed, and rainfall.
PM levels often exceeded WHO air quality standards and peaked during winter and evening hours.
Random Forest Regression exhibited the best performance with R² = 0.87 and RMSE = 18.3 µg/m³ in Jhunjhunu.
Classification accuracy reached 98%, identifying specific pollution regimes and high-pollution events.

Kumar et al. (2026) studied this question.

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