Physics-Informed Spatio-Temporal Attention Modeling for Urban Air Quality Forecasting: A Case Study of Hyderabad

Air pollution has emerged as a serious environmental and public health issue in many urban regions across the world. Increasing industrial activities, vehicle emissions, and urbanization have significantly contributed to the deterioration of air quality. Accurate prediction of air pollution levels is essential for planning effective control strategies and minimizing the harmful effects of polluted air on human health and the environment. However, traditional statistical approaches often struggle to capture the complex spatial and temporal dependencies present in air quality data.Recent developments in deep learning techniques provide more powerful tools for analyzing complex environmental datasets. An attentiondriven spatio-temporal modeling approach is introduced to improve the accuracy of air quality forecasting. The framework analyzes historical air pollution data collected from monitoring stations and learns both spatial relationships between different locations and temporal patterns over time. By incorporating an attention mechanism, the model is able to focus on the most influential features and time steps that contribute to air quality variations.Historical datasets containing pollutant measurements such as PM2.5, PM10, nitrogen dioxide (NO₂), carbon monoxide (CO), and other environmental variables are used for training and evaluation. Performance is assessed using common evaluation metrics including Mean Absolute Error (MAE) and others. Accurate air quality forecasting can support environmental monitoring agencies and decision makers by providing early warnings about potential pollution levels. Such predictive systems help authorities implement timely measures to reduce pollution exposure and improve environmental management.