Dynamic feature-adaptive representation learning for spatiotemporal air pollution forecasting

Atmospheric pollution remains a major concern for public health and city sustainability, especially in densely populated metropolitan regions with complex environmental dynamics. Traditional prediction techniques often rely on static feature selection methods that disregard the spatial and temporal variations embedded in pollutant behaviour. In this work, we introduce FAST (Feature-Adaptive Selection and Transformation), a novel approach for dynamic feature selection for spatiotemporal air quality forecasting. FAST applies attention-based methods to dynamically update relevant features based on spatial and meteorological conditions at any given time while improving interpretability and predictive performance. We evaluate FAST over three of the largest Indian metropolitan areas, Delhi, Mumbai, and Bengaluru, based on real-world air quality measurements from 90 + monitoring stations. The system is benchmarked against conventional filter, wrapper, and embedded feature selection techniques and demonstrates consistent improvements in forecasting accuracy, with up to 18% lower Mean Absolute Error (MAE) and up to 12% improvement in R² values. FAST demonstrates consistent performance across the evaluated 6-hour and 48-hour forecasting horizons while also providing interpretable insights into seasonal and spatial patterns of pollution. By dynamically adapting to the evolving nature of urban air quality, FAST provides an approach for data-driven air quality forecasting in urban environments.