Preprints
https://doi.org/10.5194/acp-2022-627
https://doi.org/10.5194/acp-2022-627
 
19 Sep 2022
19 Sep 2022

Ground-level gaseous pollutants across China: daily seamless mapping and long-term spatiotemporal variations

Jing Wei1, Zhanqing Li1, Jun Wang2, Can Li1, Pawan Gupta3,4, and Maureen Cribb1 Jing Wei et al.
  • 1Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 2Department of Chemical and Biochemical Engineering, Iowa Technology Institute, Center for Global and Regional Environmental Research, University of Iowa, USA
  • 3STI, Universities Space Research Association (USRA), Huntsville, AL, USA
  • 4NASA Marshall Space Flight Center, Huntsville, AL, USA

Abstract. Gaseous pollutants at the ground level seriously threaten the urban air quality environment and public health. There are few estimates of gaseous pollutants that are spatially and temporally resolved and continuous over long periods in China. This study takes advantage of big data and artificial intelligence technologies to generate seamless daily maps of three major pollutant gases, i.e., NO2, SO2, and CO, across China from 2013 to 2020 at a uniform spatial resolution of 10 km. Cross-validation illustrated a high data quality on a daily basis for NO2, SO2, and CO, with mean out-of-bag coefficients of determination (root-mean-square errors) of 0.84 (7.99 μg/m3), 0.84 (10.7 μg/m3), and 0.80 (0.29 mg/m3), respectively. They have experienced significant declines and then recoveries during and after the COVID-19 lockdown associated with changes in anthropogenic emissions in eastern China, while surface CO recovered faster than SO2 and NO2. All gaseous pollutants decreased significantly by 0.23, 2.01, and 49 μg/m3 per year (p < 0.001) across China during 2013–2020, especially in three urban agglomerations. The declining rates were larger during 2013–2017 but slowed down in recent years. Both the areas and occurrence probabilities of days exceeding air quality standards also gradually shrank and weakened over time, especially for SO2 and CO, which almost disappeared during 2018–2020, suggesting significant improvements in air quality in China. This reconstructed dataset of surface gaseous pollutants, i.e., ChinaHighNO2, ChinaHighSO2, and ChinaHighCO, will benefit future (especially short-term) air pollution and environmental health-related studies.

Journal article(s) based on this preprint

Jing Wei et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-627', Anonymous Referee #1, 26 Sep 2022
  • RC2: 'Comment on acp-2022-627', Anonymous Referee #2, 04 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jing Wei on behalf of the Authors (09 Dec 2022)  Author's response
ED: Referee Nomination & Report Request started (11 Dec 2022) by Hailong Wang
RR by Anonymous Referee #1 (20 Dec 2022)
RR by Anonymous Referee #3 (13 Jan 2023)
ED: Publish subject to minor revisions (review by editor) (13 Jan 2023) by Hailong Wang
AR by Jing Wei on behalf of the Authors (14 Jan 2023)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (15 Jan 2023) by Hailong Wang

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-627', Anonymous Referee #1, 26 Sep 2022
  • RC2: 'Comment on acp-2022-627', Anonymous Referee #2, 04 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jing Wei on behalf of the Authors (09 Dec 2022)  Author's response
ED: Referee Nomination & Report Request started (11 Dec 2022) by Hailong Wang
RR by Anonymous Referee #1 (20 Dec 2022)
RR by Anonymous Referee #3 (13 Jan 2023)
ED: Publish subject to minor revisions (review by editor) (13 Jan 2023) by Hailong Wang
AR by Jing Wei on behalf of the Authors (14 Jan 2023)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (15 Jan 2023) by Hailong Wang

Journal article(s) based on this preprint

Jing Wei et al.

Jing Wei et al.

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Short summary
This study estimated the long-term daily seamless ambient gaseous pollutants (NO2, SO2, and CO) across China using machine learning with extensive input variables measured on monitors, satellites, and models. Our dataset yields a high data quality via cross-validation at varying spatiotemporal scales and outperforms most previous related studies, making it most helpful to future (especially short-term) air pollution and environmental health-related studies.
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