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Improving air quality assessment using physics-inspired deep graph learning

文献类型：期刊论文

作者Li, Lianfa5,6; Wang, Jinfeng6; Franklin, Meredith4,5; Yin, Qian6; Wu, Jiajie6; Camps-Valls, Gustau3; Zhu, Zhiping6; Wang, Chengyi2; Ge, Yong6; Reichstein, Markus1
刊名NPJ CLIMATE AND ATMOSPHERIC SCIENCE
出版日期2023-09-27
卷号6期号:1页码:13
ISSN号2397-3722
DOI10.1038/s41612-023-00475-3
通讯作者Li, Lianfa(lspatial@gmail.com) ; Wang, Jinfeng(wangjf@lreis.ac.cn)
英文摘要Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inconsistent or implausible results. Here, we report a hybrid multilevel graph neural network that encodes fluid physics to capture spatial and temporal dynamic characteristics of air pollutants. On a multi-air pollutant test in China, our method consistently improved extrapolation accuracy by an average of 11-22% compared to several baseline machine learning methods, and generated physically consistent spatiotemporal trends of air pollutants at fine spatial and temporal scales.
WOS关键词OZONE POLLUTION ; CHINA ; REGRESSION ; CHEMISTRY ; NETWORKS ; FRAMEWORK ; PM2.5
资助项目This research was funded by the National Natural Science Foundation of China grant (42071369), Key Project of Innovation LREIS (KP1004, 05Z5006JYA), the National Key Research and Development Program of China grant (2021YFB3900501), and the Strategic Priori[42071369] ; National Natural Science Foundation of China[KP1004] ; National Natural Science Foundation of China[05Z5006JYA] ; Key Project of Innovation LREIS[2021YFB3900501] ; National Key Research and Development Program of China[XDA19040501] ; Strategic Priority Research Program of the Chinese Academy of Sciences ; NVIDIA Corporation
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
出版者NATURE PORTFOLIO
WOS记录号WOS:001072596500001
资助机构This research was funded by the National Natural Science Foundation of China grant (42071369), Key Project of Innovation LREIS (KP1004, 05Z5006JYA), the National Key Research and Development Program of China grant (2021YFB3900501), and the Strategic Priori ; National Natural Science Foundation of China ; Key Project of Innovation LREIS ; National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; NVIDIA Corporation
源URL[http://ir.igsnrr.ac.cn/handle/311030/198084]  
专题中国科学院地理科学与资源研究所
通讯作者Li, Lianfa; Wang, Jinfeng
作者单位1.Max Planck Inst Biogeochem, Jena, Germany
2.Chinese Acad Sci, Aerosp Informat Res Inst, Natl Engn Res Ctr Geomat, Beijing, Peoples R China
3.Univ Valencia, Image Proc Lab IPL, Valencia, Spain
4.Univ Toronto, Dept Stat Sci, Toronto, ON, Canada
5.Univ Southern Calif, Dept Prevent Med, Los Angeles, CA 90007 USA
6.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Lianfa,Wang, Jinfeng,Franklin, Meredith,et al. Improving air quality assessment using physics-inspired deep graph learning[J]. NPJ CLIMATE AND ATMOSPHERIC SCIENCE,2023,6(1):13.
APA Li, Lianfa.,Wang, Jinfeng.,Franklin, Meredith.,Yin, Qian.,Wu, Jiajie.,...&Reichstein, Markus.(2023).Improving air quality assessment using physics-inspired deep graph learning.NPJ CLIMATE AND ATMOSPHERIC SCIENCE,6(1),13.
MLA Li, Lianfa,et al."Improving air quality assessment using physics-inspired deep graph learning".NPJ CLIMATE AND ATMOSPHERIC SCIENCE 6.1(2023):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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