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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Cited
13 citations as recorded by crossref.
- Impacts of Built-Environment on Carbon Dioxide Emissions from Traffic: A Systematic Literature Review Y. Huang et al. 10.3390/ijerph192416898
- Health impacts of spatiotemporal variation in PM2.5 concentrations from heavy-duty diesel trucks in Beijing B. Zhang et al. 10.1016/j.jclepro.2023.140025
- Assessment of On-Road High NOx Emitters by Using Machine Learning Algorithms for Heavy-Duty Vehicles F. Kazan et al. 10.1007/s40825-023-00232-1
- Operationalizing Digitainability: Encouraging Mindfulness to Harness the Power of Digitalization for Sustainable Development S. Gupta et al. 10.3390/su15086844
- Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace Y. Liang et al. 10.3390/su16229615
- Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data S. Wang et al. 10.1016/j.envpol.2024.123371
- The future air quality impact of electric vehicle promotion and coordinated charging in the Beijing-Tianjin-Hebei region Y. Jiang et al. 10.1016/j.envpol.2023.121928
- Characterization of roadside air pollutants: An artery road of Lanzhou city in northwest China Y. Zhang et al. 10.1051/e3sconf/202236001039
- Characteristics and prediction of traffic-related PMs and CO2 at the urban neighborhood scale Z. Liu et al. 10.1016/j.apr.2023.101985
- Multiscale spatiotemporal variations of NOx emissions from heavy duty diesel trucks in the Beijing-Tianjin-Hebei region S. Cheng et al. 10.1016/j.scitotenv.2022.158753
- Structural decomposition of heavy-duty diesel truck emission contribution based on trajectory mining S. Cheng et al. 10.1016/j.jclepro.2022.135172
- High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator L. Lv et al. 10.1016/j.envint.2024.108421
- Transportation carbon reduction technologies: A review of fundamentals, application, and performance X. Wang et al. 10.1016/j.jtte.2024.11.001
13 citations as recorded by crossref.
- Impacts of Built-Environment on Carbon Dioxide Emissions from Traffic: A Systematic Literature Review Y. Huang et al. 10.3390/ijerph192416898
- Health impacts of spatiotemporal variation in PM2.5 concentrations from heavy-duty diesel trucks in Beijing B. Zhang et al. 10.1016/j.jclepro.2023.140025
- Assessment of On-Road High NOx Emitters by Using Machine Learning Algorithms for Heavy-Duty Vehicles F. Kazan et al. 10.1007/s40825-023-00232-1
- Operationalizing Digitainability: Encouraging Mindfulness to Harness the Power of Digitalization for Sustainable Development S. Gupta et al. 10.3390/su15086844
- Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace Y. Liang et al. 10.3390/su16229615
- Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data S. Wang et al. 10.1016/j.envpol.2024.123371
- The future air quality impact of electric vehicle promotion and coordinated charging in the Beijing-Tianjin-Hebei region Y. Jiang et al. 10.1016/j.envpol.2023.121928
- Characterization of roadside air pollutants: An artery road of Lanzhou city in northwest China Y. Zhang et al. 10.1051/e3sconf/202236001039
- Characteristics and prediction of traffic-related PMs and CO2 at the urban neighborhood scale Z. Liu et al. 10.1016/j.apr.2023.101985
- Multiscale spatiotemporal variations of NOx emissions from heavy duty diesel trucks in the Beijing-Tianjin-Hebei region S. Cheng et al. 10.1016/j.scitotenv.2022.158753
- Structural decomposition of heavy-duty diesel truck emission contribution based on trajectory mining S. Cheng et al. 10.1016/j.jclepro.2022.135172
- High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator L. Lv et al. 10.1016/j.envint.2024.108421
- Transportation carbon reduction technologies: A review of fundamentals, application, and performance X. Wang et al. 10.1016/j.jtte.2024.11.001
Latest update: 11 Dec 2024
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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