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

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Cited articles

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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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