Advances in Satellite Remote Sensing for Air Quality Assessment: A Comprehensive Review

Satellite remote sensing has become an important tool for air pollution monitoring, especially in regions where ground stations are limited. This review explains how different satellite instruments, such as MODIS, MISR, OMI, VIIRS, and Sentinel-5P/TROPOMI, help measure key pollutants like aerosols (AOD), PM2.5 (indirect), NO₂, SO₂, CO, O₃, and CH₄. We describe how satellite data are processed, how column measurements are converted to ground-level concentrations, and how statistical models, chemical transport models, and machine learning improve the accuracy of these estimates. The review also highlights major applications, including mapping pollution hotspots, studying long-term trends, supporting health research, and monitoring special events like wildfires and dust storms. Although satellite data offer wide coverage, challenges such as cloud interference, limited resolution, and difficulties in linking column data to surface values remain. Future advances such as high-resolution sensors, geostationary satellites, improved retrieval algorithms, and AI-based data fusion are expected to further strengthen satellite-based air quality monitoring. Overall, this review emphasises the growing role of multi-satellite data, when combined with ground measurements and models, in building better air pollution assessments and supporting public-health decisions.