Remote Sensing and Source Apportionment of Aerosol, Ozone and Its Precursors

Atmospheric pollution, characterized by complex aerosols and ozone, poses a significant global challenge to climate, environment, and public health. Traditional ground-based monitoring is limited in spatial coverage, while satellite remote sensing provides a powerful tool for large-scale, continuous observation of these pollutants and their precursors. However, accurately quantifying their concentrations, understanding their formation mechanisms, and especially identifying and apportioning emission sources remain complex scientific issues. The emergence of artificial intelligence (AI) technologies offers novel paradigms to address these challenges by enhancing the retrieval accuracy of remote sensing products and deciphering the intricate relationships between pollutants and sources.The primary goal of this Research Topic is to gather high-quality original research and review articles that advance the application of remote sensing technology, particularly integrated with AI methods, in monitoring aerosols, ozone, and their precursors, and in pioneering source apportionment methodologies. We aim to explore innovative approaches that leverage satellite data, model simulations, and ground measurements to improve the quantification of pollution levels, elucidate precursor transformation pathways, and accurately identify and allocate contributions from various natural and anthropogenic sources. This issue ultimately seeks to support effective air quality management and policy-making by providing more precise and efficient scientific tools.We welcome submissions on themes including, but not limited to:• Novel remote sensing retrieval algorithms for aerosols, ozone, and key precursors (e.g., NO₂, HCHO).• AI-enhanced data fusion of multi-source remote sensing products and ground observations.• Spatiotemporal analysis of pollution characteristics and long-range transport using remote sensing.• AI-driven models (e.g., machine/deep learning) for source identification and apportionment.• Synergistic use of models (chemical transport, receptor) and remote sensing for source contribution analysis.• Case studies demonstrating applications in pollution episode analysis and policy assessment.Authors are encouraged to submit their manuscripts through the journal's online system. Please consult the journal's official "Instructions for Authors" for detailed guidelines on manuscript preparation and formatting before submission. Topic editors: Haoran Liu, Xiangguang Ji, Qihua Li, Wei Tan, Aimon Tanvir Source: https://www.frontiersin.org/research-topics/78840/remote-sensing-and-source-apportionment-of-aerosol-ozone-and-its-precursors