Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/amt-16-2237-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-2237-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
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

Viewed

Total article views: 2,816 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,959 771 86 2,816 91 132
  • HTML: 1,959
  • PDF: 771
  • XML: 86
  • Total: 2,816
  • BibTeX: 91
  • EndNote: 132
Views and downloads (calculated since 09 Jun 2022)
Cumulative views and downloads (calculated since 09 Jun 2022)

Viewed (geographical distribution)

Total article views: 2,816 (including HTML, PDF, and XML) Thereof 2,763 with geography defined and 53 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Nov 2025
Download
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.
Read more
Share