Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.668 IF 5.668
  • IF 5-year value: 6.201 IF 5-year
    6.201
  • CiteScore value: 6.13 CiteScore
    6.13
  • SNIP value: 1.633 SNIP 1.633
  • IPP value: 5.91 IPP 5.91
  • SJR value: 2.938 SJR 2.938
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 174 Scimago H
    index 174
  • h5-index value: 87 h5-index 87
Volume 16, issue 5
Atmos. Chem. Phys., 16, 3161–3170, 2016
https://doi.org/10.5194/acp-16-3161-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Haze-fog forecasts and near real time (NRT) data application...

Atmos. Chem. Phys., 16, 3161–3170, 2016
https://doi.org/10.5194/acp-16-3161-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Mar 2016

Research article | 10 Mar 2016

Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

Boyu Jing1, Lin Wu1, Hongjun Mao1, Sunning Gong2, Jianjun He1, Chao Zou1, Guohua Song3, Xiaoyu Li1, and Zhong Wu4 Boyu Jing et al.
  • 1The College of Environmental Science & Engineering, Nankai University, Tianjin, China
  • 2Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, China
  • 3MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing, China
  • 4College of Civil and Transportation Engineering, Hohai University, Suzhou, China

Abstract. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission inventory with high temporal–spatial resolution (HTSVE) for the Beijing urban area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NOx, CO, HC and PM were 10.54  ×  104, 42.51  ×  104 and 2.13  ×  104 and 0.41  ×  104 Mg respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The grid-based emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Based on the results of this study, improved air quality simulation and the contribution of vehicle emissions to ambient pollutant concentration in Beijing have been investigated in a companion paper (He et al., 2016).

Publications Copernicus
Short summary
This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on road segments to develop a high temporal/spatial resolution vehicle emission inventory (HTSVE) for the urban Beijing area. The results show that the vehicle emissions of NOx, CO, HC and PM were 10.54 × 104, 42.51 × 104 and 2.13 × 104 and 0.41 × 104 Mg respectively.
This paper presents a bottom-up methodology based on the local emission factors, complemented...
Citation