Monitoring Air Pollution from Space using an Integrated Approach: Satellite Observations, Ground-Based Measurements, Reanalysis Data, and AI/ML Techniques

Author(s)	Ms. Siddhi Sagar Shah, Suchita Patil
Country	India
Abstract	Air pollution poses a severe global health risk, causing millions of premature deaths annually. This study presents an integrated framework for air quality monitoring that combines satellite observations (Sentinel-5P, MODIS, GEMS), reanalysis datasets (CAMS, MERRA-2), and ground-based measurements using advanced AI/ML models. The hybrid CNN-LSTM-Transformer approach effectively fuses multi-source data, enhancing spatial and temporal resolution of pollutant estimates. Results show up to 29% improvement in PM2.5 prediction accuracy compared to traditional models. The framework enables near-real-time forecasting, supporting data-driven policies and sustainable urban planning.
Keywords	Air pollution monitoring, satellite remote sensing, reanalysis data, AI, machine learning, deep learning, data fusion, PM2.5 forecasting
Field	Engineering
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-02-16

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