Articles | Volume 23, issue 2
Research article
26 Jan 2023
Research article |  | 26 Jan 2023

Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations

Jing Wei, Zhanqing Li, Jun Wang, Can Li, Pawan Gupta, and Maureen Cribb


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
Short summary
This study estimated the daily seamless 10 km 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.
Final-revised paper