中文版 | English
Title

Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence

Author
Corresponding AuthorWei, Jing; Li, Zhanqing; Wang, Jun
Publication Years
2022-06-01
DOI
Source Title
ISSN
0013-936X
EISSN
1520-5851
Volume56Pages: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
SCI ; EI
Language
English
Important Publications
NI Journal Papers ; ESI Highly Cited Papers
SUSTech Authorship
Others
Funding Project
NASA[
WOS Research Area
Engineering ; Environmental Sciences & Ecology
WOS Subject
Engineering, Environmental ; Environmental Sciences
WOS Accession No
WOS:000823123200001
Publisher
EI Accession Number
20223012418117
EI Keywords
Air Pollution ; Artificial Intelligence ; Big Data ; Health Risks ; Mean Square Error ; Nitrogen Oxides ; Rural Areas ; Troposphere
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
ESI Research Field
ENVIRONMENT/ECOLOGY
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:40
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/355833
DepartmentSchool 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.
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.
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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