Air quality prediction using multi-source remote sensing data integration with hybrid deep learning framework

Abstract

Growing pollution levels and associated detrimental effects on human health have made air quality monitoring and forecasting crucial issues in urban environmental management. This paper introduces a novel approach to air quality prediction by using a hybrid deep learning framework that combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) with multi-source remote sensing data. We introduce a Multi-Modal Attention-based Spatio-Temporal Network (MAST-Net) designed to jointly analyse satellite imagery, meteorological variables and ground observations for predicting concentrations of PM2.5, PM10, NO₂, and O₃. The framework leverages data from Sentinel-5P, MODIS, and Landsat-8 satellites, integrates a dynamic feature selection strategy, and incorporates uncertainty quantification to enhance reliability. When compared to conventional approaches, experimental validation over metropolitan areas demonstrates higher performance with RMSE improvements of 23–31%, reaching correlation coefficients of 0.91–0.94 for various contaminants. With its strong prediction capabilities across a range of geographic and seasonal situations, the suggested architecture has great promise for real-time air quality control systems.