Articles | Volume 21, issue 10
https://doi.org/10.5194/acp-21-7863-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-7863-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2021
Research article |
| 25 May 2021
| 25 May 2021
Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Rachel T. Pinker
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
Lin Sun
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China
Wenhao Xue
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Maureen Cribb
Earth System Science Interdisciplinary Center, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
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- Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region N. Ding et al. 10.1016/j.atmosenv.2024.120483
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Latest update: 29 Jun 2024
Short summary
This study developed a space-time Light Gradient Boosting Machine (STLG) model to derive the high-temporal-resolution (1 h) and high-quality PM2.5 dataset in China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution from the Himawari-8 Advanced Himawari Imager aerosol products. Our model outperforms most previous related studies with a much lower computation burden in terms of speed and memory, making it most suitable for real-time air pollution monitoring in China.
This study developed a space-time Light Gradient Boosting Machine (STLG) model to derive the...
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