Predictive Analysis of PM 2.5 Concentrations for Enhanced Air Quality Monitoring

Global health remains at serious risk because of fine particulate matter (PM2.5) pollution which continues as a major threat throughout the world. Extremely small PM2.5 particles enter the body by evading natural defence mechanisms to reach deep locations within the lungs and bloodstream, where they contribute to respiratory and cardiovascular illnesses, and even premature death. In response to these growing concerns, this research presents a predictive air quality monitoring system that combines real-time data from environmental sensors with machine learning techniques to forecast PM2.5 concentrations. An evaluation process included five machine learning models namely Linear Regression, Random Forest, Gradient Boosting and Support Vector Regression (SVR) as well as XGBoost to identify which models performed best for prediction purposes. The best performance came from XGBoost model which produced an RMSE of 0.019315 alongside an R2 value of 0.999098. This study demonstrates that integrating low-cost sensor networks with advanced machine learning can offer an efficient, scalable, and accurate solution for real-time air quality monitoring. The proposed system not only supports early health risk detection but also has the potential to inform environmental policies and community-level interventions aimed at reducing the harmful effects of air pollution.