Integration of AI in Air Quality Monitoring Systems for Enhancing Environmental Health and Public Awareness through Predictive Analytics and Real-Time Sensing Networks

Air pollution remains a critical global challenge, severely impacting environmental health and public well-being in urban areas. This article presents an integrated framework combining artificial intelligence (AI) with real-time IoT sensing networks for advanced air quality monitoring, predictive analytics, and enhanced public awareness. Leveraging machine learning models such as LSTM and Random Forest on datasets from urban sensor deployments, the system forecasts key pollutants (PM2.5, PM10, NO2, CO) with up to 98% accuracy and RMSE values as low as 5.2 μg/m³, outperforming traditional methods by 25-30% in temporal forecasting.​The framework incorporates edge computing for low-latency data processing, anomaly detection for health risk alerts, and interactive dashboards for real-time public engagement, demonstrated through case studies in high-density cities showing a 40% increase in citizen-reported compliance with air quality advisories. Results validate the system's scalability, enabling proactive policy interventions and reduced healthcare burdens from pollution-related illnesses.