Preprints
https://doi.org/10.5194/acp-2021-281
https://doi.org/10.5194/acp-2021-281

  18 May 2021

18 May 2021

Review status: this preprint is currently under review for the journal ACP.

High-resolution mapping of regional traffic emissions by using land-use machine learning models

Xiaomeng Wu1,, Daoyuan Yang2,, Jiajun Gu3, Yifan Wen1, Shaojun Zhang1,4,5, Rui Wu2, Renjie Wang2, Honglei Xu2, K. Max Zhang3, Ye Wu1,4,5, and Jiming Hao1,4,5 Xiaomeng Wu et al.
  • 1School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China, Tsinghua University, Beijing 100084, P. R. China
  • 2Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
  • 3Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, U.S.A.
  • 4State Environmental Protection Key Lab of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China
  • 5Beijing Laboratory of Environmental Frontier Technologies, Beijing 100084, P. R. China
  • These authors contributed equally to this work.

Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly-based, link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on the datasets of road traffic monitoring in the Beijing-Tianjin-Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than a Gaussian process regression (GPR) model. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. One notable finding is that NOX, fine particulate matter (PM2.5) and black carbon (BC) emissions from heavy-duty trucks (HDTs) in general have higher emission intensity on the highways connecting to regional ports. Even when traffic restrictions were implemented, a detour of the HDTs in Hebei was observed resulting in relatively lower emission reductions in Hebei than Beijing. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, which 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.

Xiaomeng Wu et al.

Status: open (until 13 Jul 2021)

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 reply
  • RC2: 'Comment on acp-2021-281', Anonymous Referee #2, 23 Jun 2021 reply

Xiaomeng Wu et al.

Xiaomeng Wu et al.

Viewed

Total article views: 278 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
199 73 6 278 21 2 3
  • HTML: 199
  • PDF: 73
  • XML: 6
  • Total: 278
  • Supplement: 21
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 18 May 2021)
Cumulative views and downloads (calculated since 18 May 2021)

Viewed (geographical distribution)

Total article views: 273 (including HTML, PDF, and XML) Thereof 273 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jun 2021
Download
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.
Altmetrics