Seasonal Analysis of Air Pollutants and Ensemble Machine Learning–Based AQI Prediction

This research investigated the concentration of major air pollutants that vary seasonally and developed a machine learning approach to estimate the Air Quality Index (AQI). Monthly analysis revealed distinct temporal trends, with particulate matter (PM₁₀ and PM₂.₅) showing consistently higher concentrations compared to gaseous pollutants. Both nitrogen oxides and ammonia have moderate fluctuation based on seasons, and sulfur dioxide and carbon monoxide show less fluctuation. Ozone is also found to be significantly variable, indicating photochemical effects. For predicting AQI, four machine learning models, namely Random Forest Regressor, Gradient Boosting Regressor, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), were employed for AQI prediction. The Gradient Boosting approach was the best at predicting AQI (R2 = 0.9899, RMSE = 1.2328), followed by Random Forest (R2 = 0.9877, RMSE = 1.3647). Analysis of residual plots confirms that the ensemble models were robust and had very less errors, but that SVR had greater errors in predictions. A feature importance analysis using the Random Forest approach shows that particulates (PM10 and PM2.5) are the two most important features of AQI, together accounting for the majority of the predicted values of AQI. These results indicate the significance of particulate matter to the determination of air quality and illustrate the use of ensemble learning methods to accurately forecast AQI to support data-driven decision-making for air quality management.