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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

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  • S. Kalaiselvi,
  • V. Anitha,
  • V. Manimaran,
  • Thomas Samraj Lawrence

摘要

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.