Air Pollution Forecasting with Machine Learning: A Step Toward Proactive Environmental Management

Urban air pollution is still a critical environmental and public health issue, fueled by increasing levels of fine Particulate Matter (PM2.5), Ozone (O3), and Sulfur Dioxide (SO2). Current monitoring systems, though accurate in real-time measurement, are inadequate in predicting pollutant levels, which prevents proactive decision-making. This project suggests a data-driven solution based on Multitask Learning (MTL) to forecast hourly concentrations of major air pollutants up to 24 hours ahead. Building on past meteorological observations and pollution records, we compare the performance of a variety of algorithms—Random Forest, Extra Trees, Lasso Regression, and Support Vector Machines—in the MTL setting. To provide temporal consistency in the predictions, we add regularization methods like temporal smoothness penalties. Model training and model performance measurement are done on an open-source urban air quality dataset, and the metrics used are Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The MTL architecture facilitates the concurrent forecasting of different pollutants and, thus, reduces computational time and enhances inter-pollutant correlation capture. This study offers a replicable method for short-term air quality forecasting, with relevance to urban planning, emergency management, and environmental policy. The findings show the potential for the application of machine learning in air quality management as a scalable instrument for cities that seek to reduce pollution-related health hazards.