Zhou Zang
Papers
3
Total Citations
73
H-Index
3
About
Zhou Zang is a leading researcher in satellite-based aerosol remote sensing, with a primary focus on distinguishing natural from anthropogenic aerosols and improving global air quality assessments. His major contributions center on developing high-resolution, reliable datasets that quantify aerosol fine-mode fraction (FMF) and fine-mode aerosol optical depth (fAOD) over land—critical proxies for human-caused particulate pollution. Zang’s most cited work, “A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches” (2022, 32 citations), pioneered a novel synergy of physical models and deep learning to overcome longstanding satellite retrieval inaccuracies over land. This was preceded by an improved global anthropogenic aerosol product spanning 2008–2016 (22 citations) and a new FMF dataset derived from MODIS (2021, 19 citations). Together, these studies have provided the scientific community with unprecedented, long-term records for tracking anthropogenic aerosol trends, informing climate models, and supporting public health research. Zang’s work is notable for bridging traditional physical retrieval methods with modern machine learning, setting a new standard for satellite-derived aerosol characterization and enabling more precise discrimination of pollution sources worldwide.
Research Focus
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Top Papers
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