Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Jing Wei; Z. Li; Rachel T Pinker; Jun Wang; Lin Sun; Wenhao Xue; Runze Li; Maureen Cribb

doi:10.5194/acp-21-7863-2021

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Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

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Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Jing Wei, Z. Li, Rachel T Pinker, Jun Wang, Lin Sun, Wenhao Xue, Runze Li and Maureen Cribb

Atmospheric chemistry and physics, Vol.21(10), pp.7863-7880

05/01/2021

DOI: 10.5194/acp-21-7863-2021

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Abstract

Fine particulate matter with a diameter of less than 2.5 µm ( PM2.5 ) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5 . Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHigh PM2.5 ) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV- R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 µg m−3 , respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT + 8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

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Title: Subtitle: Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)
Creators: Jing Wei - Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
Z. Li - 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
Jun Wang - 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
Runze Li - 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
Resource Type: Journal article
Publication Details: Atmospheric chemistry and physics, Vol.21(10), pp.7863-7880
DOI: 10.5194/acp-21-7863-2021
ISSN: 1680-7316
eISSN: 1680-7324
Publisher: Copernicus Publications
Grant note: DOI: 10.13039/501100012166, name: National Key Research and Development Program of China, award: 2017YFC1501702; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 42030606
Language: English
Date published: 05/01/2021
Academic Unit: Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
Record Identifier: 9984104813302771

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