Articles | Volume 22, issue 3
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-281', Anonymous Referee #1, 11 Jun 2021
  • RC2: 'Comment on acp-2021-281', Anonymous Referee #2, 23 Jun 2021
  • AC1: 'Reply to comments on acp-2021-281', Ye Wu, 20 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ye Wu on behalf of the Authors (26 Sep 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (14 Dec 2021) by Rob MacKenzie
AR by Ye Wu on behalf of the Authors (20 Dec 2021)  Manuscript 
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.
Final-revised paper