Predictive Models For Urban Air Quality Management Using AI

Authors: Dr. Pankaj Malik, Drishti Patidar, Ayush Soni, Divyansh Deore, Pratham Thattey

Abstract: Urban air pollution has emerged as a critical environmental concern due to rapid industrialization, vehicular emissions, and population growth. Conventional monitoring methods often fail to provide timely or spatially comprehensive insights required for effective air quality management. This research presents a predictive modeling framework for urban air quality management using Artificial Intelligence (AI) to forecast pollutant levels and support proactive decision-making. Real-time environmental data—including temperature, humidity, wind speed, and pollutant concentrations (PM₂.₅, PM₁₀, NO₂, SO₂, CO, and O₃)—were collected from multiple urban monitoring stations. Machine learning algorithms such as Random Forest (RF), Gradient Boosting (GB), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks were implemented and compared for prediction accuracy. Experimental results show that the LSTM model achieved the highest performance, with an average R² value of 0.96 and RMSE of 4.2 µg/m³ for PM₂.₅ prediction, outperforming traditional statistical and tree-based models. The RF model achieved an R² of 0.91, demonstrating its robustness for multi-pollutant forecasting. The integration of spatial-temporal data further improved predictive precision by 18%, enabling fine-grained mapping of pollution hotspots. These findings highlight that AI-based predictive models can significantly enhance urban air quality monitoring, early warning systems, and policy formulation. The study concludes that the proposed AI framework provides an efficient, scalable, and data-driven approach for sustainable urban air quality management and environmental planning.