Journal article
Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence
Environmental science & technology, Vol.56(14), pp.9988-9998
06/29/2022
DOI: 10.1021/acs.est.2c03834
PMCID: PMC9301922
PMID: 35767687
Appears in UI Libraries Support Open Access
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 root-mean-square error of 4.89 (9.95) μg/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 μg/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.
Details
- Title: Subtitle
- Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence
- Creators
- Jing Wei - University of IowaSong Liu - Southern University of Science and TechnologyZhanqing Li - Earth System Science Interdisciplinary CenterCheng Liu - University of Science and Technology of ChinaKai Qin - China University of Mining and TechnologyXiong Liu - Center for Astrophysics Harvard & SmithsonianRachel T Pinker - Earth System Science Interdisciplinary CenterRussell R Dickerson - Earth System Science Interdisciplinary CenterJintai Lin - Peking UniversityK F Boersma - Royal Netherlands Meteorological InstituteLin Sun - Shandong University of Science and TechnologyRunze Li - Irvine UniversityWenhao Xue - Qingdao UniversityYuanzheng Cui - Hohai UniversityChengxin Zhang - University of Science and Technology of ChinaJun Wang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Environmental science & technology, Vol.56(14), pp.9988-9998
- DOI
- 10.1021/acs.est.2c03834
- PMID
- 35767687
- PMCID
- PMC9301922
- NLM abbreviation
- Environ Sci Technol
- ISSN
- 0013-936X
- eISSN
- 1520-5851
- Publisher
- American Chemical Society
- Grant note
- DOI: 10.13039/501100007196, name: Ministerie van Infrastructuur en Milieu; DOI: 10.13039/100011203, name: European Commission, award: 607405; DOI: 10.13039/100000001, name: National Science Foundation; DOI: 10.13039/100000104, name: NASA, award: 80NSSC19K0950, 80NSSC21K1980; name: College of Engineernig, University of Iowa
- Language
- English
- Date published
- 06/29/2022
- Academic Unit
- Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
- Record Identifier
- 9984270192102771
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