Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 1
Articles | Volume 23, issue 1
https://doi.org/10.5194/acp-23-375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 1
Research article
 | 
10 Jan 2023
Research article |  | 10 Jan 2023

Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

Jin Feng, Yanjie Li, Yulu Qiu, and Fuxin Zhu

Viewed

Total article views: 2,007 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,655 320 32 2,007 84 22 30
  • HTML: 1,655
  • PDF: 320
  • XML: 32
  • Total: 2,007
  • Supplement: 84
  • BibTeX: 22
  • EndNote: 30
Views and downloads (calculated since 19 Aug 2022)
Cumulative views and downloads (calculated since 19 Aug 2022)

Viewed (geographical distribution)

Total article views: 2,007 (including HTML, PDF, and XML) Thereof 1,998 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
Short summary
It is important to use weather data to estimate aerosol concentrations. Here, a weather index for aerosol concentration based on deep learning was developed, linking weather and short-term variations in aerosol concentrations over China. The index provides better performance than chemical transport model simulation and other data-based estimation approaches. It can be used as a robust tool for estimating daily variations in aerosol concentrations.
Read more
Altmetrics
Final-revised paper
Preprint