Articles | Volume 25, issue 21
https://doi.org/10.5194/acp-25-14643-2025
https://doi.org/10.5194/acp-25-14643-2025
Measurement report
 | 
04 Nov 2025
Measurement report |  | 04 Nov 2025

Measurement report: Unraveling PM10 sources and oxidative potential across Chinese regions based on CNN-LSTM data preprocessing and receptor model

Qinghe Cai, Dongqing Fang, Junli Jin, Xiaoyu Hu, Yuxuan Cao, Tianyi Zhao, Yang Bai, and Yang Zhang

Viewed

Total article views: 4,554 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,374 972 208 4,554 281 161 204
  • HTML: 3,374
  • PDF: 972
  • XML: 208
  • Total: 4,554
  • Supplement: 281
  • BibTeX: 161
  • EndNote: 204
Views and downloads (calculated since 20 May 2025)
Cumulative views and downloads (calculated since 20 May 2025)

Viewed (geographical distribution)

Total article views: 4,554 (including HTML, PDF, and XML) Thereof 4,487 with geography defined and 67 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 26 Jul 2026
Download
Short summary
This study analyzed PM10 and oxidative potential (OP) in 12 Chinese regions (Jun 2022-May 2023) via Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) and Positive Matrix Factorization (PMF) at 4 representative sites. PM10 was higher in the northwest, lower in the northeast; urban areas had higher OP. Most sites showed peak PM10 and OP in winter, lowest in summer. Traffic, biomass burning, and coal combustion were major OP contributors.
Share
Altmetrics
Final-revised paper
Preprint