Articles | Volume 22, issue 3
https://doi.org/10.5194/acp-22-1939-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-1939-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2022
Research article |
| 10 Feb 2022
| 10 Feb 2022
High-resolution mapping of regional traffic emissions using land-use machine learning models
Xiaomeng Wu
School of Environment and State Key Joint Laboratory of Environment
Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
Daoyuan Yang
Laboratory of Transport Pollution Control and Monitoring Technology,
Transport Planning and Research Institute, Ministry of Transport, Beijing
100028, PR China
Ruoxi Wu
School of Environment and State Key Joint Laboratory of Environment
Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
Jiajun Gu
Sibley School of Mechanical and Aerospace Engineering, Cornell
University, Ithaca, NY 14853, USA
Yifan Wen
School of Environment and State Key Joint Laboratory of Environment
Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
Shaojun Zhang
School of Environment and State Key Joint Laboratory of Environment
Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
Laboratory of Transport Pollution Control and Monitoring Technology,
Transport Planning and Research Institute, Ministry of Transport, Beijing
100028, PR China
State Environmental Protection Key Lab of Sources and Control of Air
Pollution Complex, Tsinghua University, Beijing 100084, PR China
Beijing Laboratory of Environmental Frontier Technologies, Beijing
100084, PR China
Rui Wu
Laboratory of Transport Pollution Control and Monitoring Technology,
Transport Planning and Research Institute, Ministry of Transport, Beijing
100028, PR China
Renjie Wang
Laboratory of Transport Pollution Control and Monitoring Technology,
Transport Planning and Research Institute, Ministry of Transport, Beijing
100028, PR China
Honglei Xu
Laboratory of Transport Pollution Control and Monitoring Technology,
Transport Planning and Research Institute, Ministry of Transport, Beijing
100028, PR China
K. Max Zhang
Sibley School of Mechanical and Aerospace Engineering, Cornell
University, Ithaca, NY 14853, USA
School of Environment and State Key Joint Laboratory of Environment
Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
Laboratory of Transport Pollution Control and Monitoring Technology,
Transport Planning and Research Institute, Ministry of Transport, Beijing
100028, PR China
State Environmental Protection Key Lab of Sources and Control of Air
Pollution Complex, Tsinghua University, Beijing 100084, PR China
Beijing Laboratory of Environmental Frontier Technologies, Beijing
100084, PR China
Jiming Hao
School of Environment and State Key Joint Laboratory of Environment
Simulation and Pollution Control, Tsinghua University, Beijing 100084, PR China
State Environmental Protection Key Lab of Sources and Control of Air
Pollution Complex, Tsinghua University, Beijing 100084, PR China
Beijing Laboratory of Environmental Frontier Technologies, Beijing
100084, PR China
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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.
Our work pioneered land-use machine learning methods for developing link-level emission...
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