Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2425-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2425-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
22 May 2024
Data description paper |
| 22 May 2024
| 22 May 2024
LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Institute of Eco-Chongming, 20 Cuiniao Rd., Chongming, Shanghai 202162, China
Ke Li
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Liuqing Shao
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Xinran Li
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Chaoshun Liu
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhengqiang Li
State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Mingliang Ma
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
Di Han
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yibing Sun
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhe Zheng
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Ruijie Li
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Ni-Bin Chang
Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States of America
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
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Cited
8 citations as recorded by crossref.
- Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
- PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022 H. Hao et al. 10.5194/essd-16-4051-2024
- Construction and analysis of atmospheric visibility and fog-haze datasets in China (2001−2023) based on machine learning models H. Xu et al. 10.1016/j.atmosres.2025.108160
- SCAGAT: A scene-aware ensemble graph attention network for global PM2.5 pollution mapping via land–atmosphere interactions K. Bai et al. 10.1016/j.isprsjprs.2025.04.019
- Estimation of Solar Diffuse Radiation in Chongqing Based on Random Forest P. Wan et al. 10.3390/en18040836
- Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation X. Sun et al. 10.1016/j.jag.2025.104534
- Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. 10.1016/j.atmosenv.2024.120799
- A continuous 2011–2022 record of fine particulate matter (PM2.5) in East Asia at daily 2-km resolution from geostationary satellite observations: Population exposure and long-term trends D. Pendergrass et al. 10.1016/j.atmosenv.2025.121068
8 citations as recorded by crossref.
- Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd) S. Wang et al. 10.5194/essd-16-3565-2024
- PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022 H. Hao et al. 10.5194/essd-16-4051-2024
- Construction and analysis of atmospheric visibility and fog-haze datasets in China (2001−2023) based on machine learning models H. Xu et al. 10.1016/j.atmosres.2025.108160
- SCAGAT: A scene-aware ensemble graph attention network for global PM2.5 pollution mapping via land–atmosphere interactions K. Bai et al. 10.1016/j.isprsjprs.2025.04.019
- Estimation of Solar Diffuse Radiation in Chongqing Based on Random Forest P. Wan et al. 10.3390/en18040836
- Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation X. Sun et al. 10.1016/j.jag.2025.104534
- Accuracy assessment on eight public PM2.5 concentration datasets across China Y. Di et al. 10.1016/j.atmosenv.2024.120799
- A continuous 2011–2022 record of fine particulate matter (PM2.5) in East Asia at daily 2-km resolution from geostationary satellite observations: Population exposure and long-term trends D. Pendergrass et al. 10.1016/j.atmosenv.2025.121068
Latest update: 29 May 2025
Short summary
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide...
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