Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 6
Articles | Volume 20, issue 6
https://doi.org/10.5194/acp-20-3273-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-3273-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 6
Research article
 | 
19 Mar 2020
Research article |  | 19 Mar 2020

Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees

Jing Wei, Zhanqing Li, Maureen Cribb, Wei Huang, Wenhao Xue, Lin Sun, Jianping Guo, Yiran Peng, Jing Li, Alexei Lyapustin, Lei Liu, Hao Wu, and Yimeng Song

Viewed

Total article views: 13,699 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
10,923 2,637 139 13,699 183 372
  • HTML: 10,923
  • PDF: 2,637
  • XML: 139
  • Total: 13,699
  • BibTeX: 183
  • EndNote: 372
Views and downloads (calculated since 23 Oct 2019)
Cumulative views and downloads (calculated since 23 Oct 2019)

Viewed (geographical distribution)

Total article views: 13,699 (including HTML, PDF, and XML) Thereof 13,435 with geography defined and 264 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2025
Download
Short summary
This study introduced an enhanced space–time extremely randomized trees (STET) approach to improve the 1 km resolution ground-level PM2.5 estimates across China using the remote sensing technology. The STET model shows high accuracy and strong predictive power and appears to outperform most models reported by previous studies. Thus, it is of great importance for future air pollution studies at medium- or small-scale areas and will be applied to generate the historical PM2.5 dataset across China.
Read more
Share
Altmetrics
Final-revised paper
Preprint