Articles | Volume 22, issue 3
https://doi.org/10.5194/acp-22-1939-2022
https://doi.org/10.5194/acp-22-1939-2022
Research article
 | 
10 Feb 2022
Research article |  | 10 Feb 2022

High-resolution mapping of regional traffic emissions using land-use machine learning models

Xiaomeng Wu, Daoyuan Yang, Ruoxi Wu, Jiajun Gu, Yifan Wen, Shaojun Zhang, Rui Wu, Renjie Wang, Honglei Xu, K. Max Zhang, Ye Wu, and Jiming Hao

Viewed

Total article views: 3,636 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,703 876 57 3,636 288 41 54
  • HTML: 2,703
  • PDF: 876
  • XML: 57
  • Total: 3,636
  • Supplement: 288
  • BibTeX: 41
  • EndNote: 54
Views and downloads (calculated since 18 May 2021)
Cumulative views and downloads (calculated since 18 May 2021)

Viewed (geographical distribution)

Total article views: 3,636 (including HTML, PDF, and XML) Thereof 3,676 with geography defined and -40 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Jul 2024
Download
Short summary
Our work pioneered land-use machine learning methods for developing link-level emission inventories, utilizing hourly traffic profiles, including volume, speed, and fleet mix, obtained from the governmental intercity highway monitoring network in the "capital circles" of China. This research provides a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
Altmetrics
Final-revised paper
Preprint