Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 9
Articles | Volume 26, issue 9
https://doi.org/10.5194/acp-26-6507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-6507-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 9
Research article
 | 
15 May 2026
Research article |  | 15 May 2026

Machine-learning-based identification of influencing factors and synoptic patterns of foehn on the eastern foothills of the Taihang Mountains, China

Xinpeng Xu, Shoujuan Shu, Guochen Wang, and Weijun Li

Viewed

Total article views: 1,380 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
936 321 123 1,380 222 74 109
  • HTML: 936
  • PDF: 321
  • XML: 123
  • Total: 1,380
  • Supplement: 222
  • BibTeX: 74
  • EndNote: 109
Views and downloads (calculated since 29 Jan 2026)
Cumulative views and downloads (calculated since 29 Jan 2026)

Viewed (geographical distribution)

Total article views: 1,380 (including HTML, PDF, and XML) Thereof 1,365 with geography defined and 15 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 May 2026
Download
Short summary
We studied warm, dry winds called foehn on the east foothills of China’s Taihang Mountains, where over 30 million people live. Using synopic analysis and machine learning methods, we found these winds are driven mainly by surface conditions and the influence of them varies seasonally. These winds worsen pollution, heatwaves, and fire risk. Our findings help predict these events earlier, protecting health, crops, and air quality in a warming climate.
Read more
Share
Altmetrics
Final-revised paper
Preprint