Dataset Information

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

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 NO_x emissions.

SUBMITTER: Wei J

PROVIDER: S-EPMC9301922 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Wei Jing J Liu Song S Li Zhanqing Z Liu Cheng C Qin Kai K Liu Xiong X Pinker Rachel T RT Dickerson Russell R RR Lin Jintai J Boersma K F KF Sun Lin L Li Runze R Xue Wenhao W Cui Yuanzheng Y Zhang Chengxin C Wang Jun J

Environmental science & technology 20220629 14

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 2 ...[more]

PMID: 35767687

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Dataset Information

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

Publications

Ground-Level NO<sub>2</sub> Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence.

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