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Ground-Level NO2Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence
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High Resolution Image Download MS PowerPoint Slide Nitrogen dioxide (NO 2 ) 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 NO 2 concentrations over mainland China with full spatial coverage (100%) for the period 2019–2020 by combining surface NO 2 measurements, satellite tropospheric NO 2 columns derived from TROPOMI and OMI, atmospheric reanalysis, and model simulations. Our daily surface NO 2 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/m 3 . The daily seamless high-resolution and high-quality dataset “ChinaHighNO 2 ” 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 NO 2, 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/m 3 ). During the COVID-19 pandemic, surface NO 2 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 NO 2 column, implying that the former can better represent the changes in NO x emissions.
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Self-citation
Metadata
- Authors
- Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R. T., Dickerson, R. R., Lin, J., Boersma, K. F., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., & Wang, J.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-06-29
- Volume
- 56
- Issue
- 14
- Pages
- 9988-9998
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W4283702811
- Cited by
- 361
- References
- 49
- FWCI
- 19.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
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