Articles | Volume 22, issue 21
Research article
03 Nov 2022
Research article |  | 03 Nov 2022

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-251', Anonymous Referee #1, 03 Jun 2022
  • RC2: 'Comment on acp-2022-251', Anonymous Referee #2, 01 Jul 2022
  • AC1: 'Authors' response to referee comments on acp-2022-251', Tai-Long He, 29 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Tai-Long He on behalf of the Authors (29 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (02 Oct 2022) by Jerome Brioude
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
Final-revised paper