Dynamic feature-adaptive representation learning for spatiotemporal air pollution forecasting

Abstract

ABSTRACT 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. GRAPHICAL ABSTRACTAn infographic summarizing a dynamic feature adaptive learning framework for spatiotemporal urban air pollution forecasting in India.The figure shows an infographic describing a dynamic feature adaptive representation learning framework for spatiotemporal air pollution forecasting. At the top, the title reads Dynamic Feature Adaptive Representation Learning for Spatiotemporal Air Pollution Forecasting. On the left, a rounded box labeled Background lists bullet points about urban air pollution as a public health concern, limitations of traditional static feature models, the design of a new attention based deep learning framework for dynamic feature selection in space and time, and recalibration of input relevance based on context. Below this, two small outline maps depict India and a highlighted region, and a larger map titled Air Pollution Monitoring Stations shows dense station locations across the highlighted area. In the center, an illustration shows an urban neighborhood with buildings, factories emitting smoke, trees, scattered trash, and people walking, representing polluted city conditions. Beneath this illustration, a block labeled FAST Framework connects four colored rectangles: one for pollutant, geospatial, and meteorological data; one for FAST feature engineering; one for dynamic feature importance estimation; and one for a deep forecasting block. On the right, a rounded box labeled Key Findings contains bullet points about achieving up to 18 percent lower mean absolute error and 12 percent higher coefficient of determination compared with traditional techniques, reporting lowest total loss values of 0.13 for Delhi, 0.17 for Mumbai, and 0.19 for Bengaluru, generalizing across diverse urban environments, and reducing forecasting error while improving interpretability. Farther right, a section labeled Forecasting Results contains several small line graphs with curves over time and several three dimensional surface plots that resemble low hills and peaks, representing spatial and temporal pollution forecasts; axes and numeric scales are too small to read. At the bottom, a full width text bar labeled Conclusion states that the FAST framework improves forecasting accuracy and interpretability, outperforms traditional methods across major Indian cities and forecasting horizons, and offers scalable support for data driven environmental policy making.