Title | Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence |
Author | |
Corresponding Author | Wei, Jing; Li, Zhanqing; Wang, Jun |
Publication Years | 2022-06-01
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DOI | |
Source Title | |
ISSN | 0013-936X
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EISSN | 1520-5851
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Volume | 56Pages:9988-9998 |
Abstract | ABSTRACT: Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed a novel, artificial intelligence approach by integrating spatiotemporally weighted information into the missing extra-trees and deep forest models to first fill the satellite data gaps and increase data availability by 49% and then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for the period 2019-2020 by combining surface NO2 measurements, satellite tropospheric NO2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO2 estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination of 0.93 (0.71) and rootmean-square error of 4.89 (9.95) mu g/m3. The daily seamless high-resolution and high-quality dataset "ChinaHighNO2" allows us to examine spatial patterns at fine scales such as the urban-rural contrast. We observed systematic large differences between urban and rural areas (28% on average) in surface NO2, especially in provincial capitals. Strong holiday effects were found, with average declines of 22 and 14% during the Spring Festival and the National Day in China, respectively. Unlike North America and Europe, there is little difference between weekdays and weekends (within +/- 1 mu g/m3). During the COVID-19 pandemic, surface NO2 concentrations decreased considerably and then gradually returned to normal levels around the 72nd day after the Lunar New Year in China, which is about 3 weeks longer than the tropospheric NO2 column, implying that the former can better represent the changes in NOx emissions. KEYWORDS: surface NO2, air pollution, big data, artificial intelligence, COVID-19 |
URL | [Source Record] |
Indexed By | |
Language | English
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Important Publications | NI Journal Papers
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SUSTech Authorship | Others
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Funding Project | NASA[
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WOS Research Area | Engineering
; Environmental Sciences & Ecology
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WOS Subject | Engineering, Environmental
; Environmental Sciences
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WOS Accession No | WOS:000823123200001
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Publisher | |
EI Accession Number | 20223012418117
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EI Keywords | Air Pollution
; Artificial Intelligence
; Big Data
; Health Risks
; Mean Square Error
; Nitrogen Oxides
; Rural Areas
; Troposphere
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ESI Classification Code | Atmospheric Properties:443.1
; Air Pollution:451
; Health Care:461.7
; Data Processing And Image Processing:723.2
; Artificial Intelligence:723.4
; Inorganic Compounds:804.2
; Mathematical Statistics:922.2
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ESI Research Field | ENVIRONMENT/ECOLOGY
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Data Source | Web of Science
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Citation statistics |
Cited Times [WOS]:29
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Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/355833 |
Department | School of Environmental Science and Engineering |
Affiliation | 1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China 2.Univ Sci & Technol China, Dept Precis Machinery & Precis Instrumentat, Hefei 230026, Peoples R China 3.China Univ Min & Technol, Sch Environm & Geoinformat, Xuzhou 221116, Jiangsu, Peoples R China 4.Ctr Astrophys Harvard & Smithsonian, Atom & Mol Phys Div, Cambridge, MA 02138 USA 5.Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA 6.Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Lab Climate & Ocean Atmosphere Studies, Beijing 100871, Peoples R China 7.Royal Netherlands Meteorol Inst, Satellite Observat Dept, NL-3731 GA De Bilt, Netherlands 8.Wageningen Univ, Meteorol & Air Qual Grp, NL-6708 PB Wageningen, Netherlands 9.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China 10.Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA 92697 USA 11.Qingdao Univ, Sch Econ, Qingdao 266071, Peoples R China 12.Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China 13.Univ Iowa, Dept Chem & Biochem Engn, Iowa Technol Inst, Ctr Global & Reg Environm Res, Iowa City, IA 52242 USA |
Recommended Citation GB/T 7714 |
Wei, Jing,Liu, Song,Li, Zhanqing,et al. Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2022,56:9988-9998.
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APA |
Wei, Jing.,Liu, Song.,Li, Zhanqing.,Liu, Cheng.,Qin, Kai.,...&Wang, Jun.(2022).Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.ENVIRONMENTAL SCIENCE & TECHNOLOGY,56,9988-9998.
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MLA |
Wei, Jing,et al."Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence".ENVIRONMENTAL SCIENCE & TECHNOLOGY 56(2022):9988-9998.
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