Articles | Volume 22, issue 3
Atmos. Chem. Phys., 22, 1939–1950, 2022
https://doi.org/10.5194/acp-22-1939-2022
Atmos. Chem. Phys., 22, 1939–1950, 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 et al.

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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
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
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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.
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