Prediction of PM_2.5 Using Machine Learning Algorithms: A Case Study for Bengaluru, India

Air quality degradation has emerged as a significant urban challenge in recent decades, with fine particulate matter (PM_2.5) recognized as one of the most hazardous pollutants. PM_2.5 arises from both primary sources, including vehicular emissions, biomass burning, and industrial activities, and from secondary particle formation via atmospheric reactions involving precursor gases such as SO₂, NOx, NH₃, and VOCs. Exposure to these pollutants is closely associated with elevated risks of respiratory and cardiovascular diseases. This research applies machine learning (ML) algorithms to forecast PM_2.5 concentrations using hourly data from Bengaluru, a major metropolitan area in southern India. Multiple models—Extra Trees, k-Nearest Neighbors, Random Forests, Support Vector Machines, CatBoost, and Decision Trees—were developed and evaluated using R², RMSE, MAE, and MSE as performance metrics. The ExtraTreesRegressor demonstrated superior predictive performance, achieving an R² value of 0.92 and the lowest error among the models tested. Hyperparameter optimization and Explainable AI techniques, including LIME and SHAP, were employed to improve model interpretability and robustness. The findings indicate that ensemble ML models can deliver reliable short-term forecasts of PM_2.5 concentrations, thereby supporting air quality professionals and policymakers in issuing timely health advisories and implementing effective emission control measures.