Machine Learning-Based Air Pollution Monitoring And Forecasting

Today, governments in developing countries are increasingly focused on managing air pollution, which results from vehicle fuel use, industrial operations, and the burning of waste. Poor air quality is a pressing health issue and is commonly assessed using PM2.5 levels among other variables. Accurate prediction and ongoing monitoring are crucial for pollution control. In this work, advanced machine learning and deep learning models—namely CatBoost, XGBoost, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) neural networks—are implemented and evaluated to forecast future air pollution levels and the Air Quality Index (AQI) using historical data on PM2.5, NH3, CO, NO, NOx, and NO2, and SO2. These novel techniques are compared with traditional models to assess their prediction accuracy and robustness. By leveraging daily atmospheric datasets from Indian cities, the study demonstrates that modern ensemble and deep learning approaches can provide improved and more reliable forecasts of air quality, supporting data-driven public health interventions and policy decisions.