Articles | Volume 23, issue 17
Research article
14 Sep 2023
Research article |  | 14 Sep 2023

Spatiotemporal modeling of air pollutant concentrations in Germany using machine learning

Vigneshkumar Balamurugan, Jia Chen, Adrian Wenzel, and Frank N. Keutsch


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-463', Anonymous Referee #1, 01 Jun 2023
  • RC2: 'Comment on egusphere-2023-463', Anonymous Referee #2, 23 Jun 2023
  • AC1: 'Comment on egusphere-2023-463', Vigneshkumar Balamurugan, 28 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Vigneshkumar Balamurugan on behalf of the Authors (28 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (31 Jul 2023) by Harald Saathoff
AR by Vigneshkumar Balamurugan on behalf of the Authors (14 Aug 2023)  Manuscript 
Short summary
In this study, machine learning models are employed to model NO2 and O3 concentrations. We employed a wide range of sources of data, including meteorological and column satellite measurements, to model NO2 and O3 concentrations. The spatial and temporal variability, and their drivers, were investigated. Notably, the machine learning model established the relationship between NOx and O3. Despite the fact that metropolitan regions are NO2 hotspots, rural areas have high O3 concentrations.
Final-revised paper