Articles | Volume 16, issue 8
https://doi.org/10.5194/amt-16-2237-2023
https://doi.org/10.5194/amt-16-2237-2023
Research article
 | 
26 Apr 2023
Research article |  | 26 Apr 2023

Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique

Qiansi Tu, Frank Hase, Zihan Chen, Matthias Schneider, Omaira García, Farahnaz Khosrawi, Shuo Chen, Thomas Blumenstock, Fang Liu, Kai Qin, Jason Cohen, Qin He, Song Lin, Hongyan Jiang, and Dianjun Fang

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'review of  "Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and machine learning technique" by Tu et al', Anonymous Referee #1, 15 Jul 2022
    • AC1: 'Reply on RC1', Qiansi Tu, 15 Feb 2023
  • RC2: 'Comment on amt-2022-176', Anonymous Referee #2, 05 Aug 2022
    • AC2: 'Reply on RC2', Qiansi Tu, 15 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Qiansi Tu on behalf of the Authors (15 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (17 Feb 2023) by Jian Xu
RR by Anonymous Referee #1 (13 Mar 2023)
ED: Publish as is (17 Mar 2023) by Jian Xu
AR by Qiansi Tu on behalf of the Authors (31 Mar 2023)  Manuscript 
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Short summary
Four-year TROPOMI observations are used to derive tropospheric NO2 emissions in two mega(cities) with high anthropogenic activity. Wind-assigned anomalies are calculated, and the emission rates and spatial patterns are estimated based on a machine learning algorithm. The results are in reasonable agreement with previous studies and the inventory. Our method is quite robust and can be used as a simple method to estimate the emissions of NO2 as well as other gases in other regions.