Papers

2

Total Citations

170

H-Index

2

About

Mingliang Ma is a leading environmental data scientist whose work sits at the intersection of big Earth data, atmospheric remote sensing, and public health. His primary research focuses on developing advanced data-fusion frameworks to generate high-resolution, gap-free air quality datasets, addressing a critical need for consistent environmental monitoring. Ma’s most significant contribution is the creation of the Long-term Gap-free High-resolution Air Pollutant concentration (LGHAP) dataset. The foundational paper, “LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset,” has garnered 143 citations, showcasing its pivotal role in enabling robust environmental and epidemiological analyses. Building on this, his recent work, “LGHAP v2,” extends the dataset to a global scale, providing continuous aerosol optical depth and PM2.5 data since 2000, already accumulating 27 citations. By synergistically integrating multimodal satellite and ground-based observations using tensor-flow-based analytics, Ma has empowered researchers to study long-term pollution trends, health impacts, and climate interactions with unprecedented spatial and temporal detail. His achievements represent a transformative leap in Earth system science, offering an invaluable resource for policymakers and scientists worldwide.

Research Focus

Key Achievements

2
H-Index
2
Papers
170
Total Citations
85
Avg Citations/Paper
🏆 Most Cited Paper
LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion
143 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 15
🏛 Institutions: Chinese Academy of Sciences, Shandong Jianzhu University

Top Papers

  1. 1
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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago