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Volume 16, issue 5
Atmos. Chem. Phys., 16, 3171–3184, 2016
https://doi.org/10.5194/acp-16-3171-2016
© 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, 3171–3184, 2016
https://doi.org/10.5194/acp-16-3171-2016
© 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 2: Impact of vehicle emission on urban air quality

Jianjun He1, Lin Wu1, Hongjun Mao1, Hongli Liu2, Boyu Jing1, Ye Yu3, Peipei Ren1, Cheng Feng4, and Xuehao Liu4 Jianjun He et al.
  • 1The College of Environmental Science and Engineering, Nankai University, Tianjin, China
  • 2Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, China
  • 3Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China
  • 4Tianjin Vehicle Emission Control Center, Tianjin, China

Abstract. A companion paper developed a vehicle emission inventory with high temporal–spatial resolution (HTSVE) with a bottom-up methodology based on local emission factors, complemented with the widely used emission factors of COPERT model and near-real-time (NRT) traffic data on a specific road segment for 2013 in urban Beijing (Jing et al., 2016), which is used to investigate the impact of vehicle pollution on air pollution in this study. Based on the sensitivity analysis method of switching on/off pollutant emissions in the Chinese air quality forecasting model CUACE, a modelling study was carried out to evaluate the contributions of vehicle emission to the air pollution in Beijing's main urban areas in the periods of summer (July) and winter (December) 2013. Generally, the CUACE model had good performance of the concentration simulation of pollutants. The model simulation has been improved by using HTSVE. The vehicle emission contribution (VEC) to ambient pollutant concentrations not only changes with seasons but also changes with time. The mean VEC, affected by regional pollutant transports significantly, is 55.4 and 48.5 % for NO2 and 5.4 and 10.5 % for PM2.5 in July and December 2013 respectively. Regardless of regional transports, relative vehicle emission contribution (RVEC) to NO2 is 59.2 and 57.8 % in July and December 2013, while it is 8.7 and 13.9 % for PM2.5. The RVEC to PM2.5 is lower than the PM2.5 contribution rate for vehicle emission in total emission, which may be due to dry deposition of PM2.5 from vehicle emission in the near-surface layer occuring more easily than from elevated source emission.

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Based on a vehicle emission inventory with high temporal–spatial resolution established by bottom-up methodology, this paper evaluates the performance of the CUACE model and investigates the vehicle emission contribution (VEC) to ambient NO2 and PM2.5 with an emission source sensitivity method. With good performance of pollutant concentrations, numerical simulation revealed that the mean VEC is 55.4 and 48.5 % for NO2 and 5.4 and 10.5 % for PM2.5 in July and December 2013 respectively.
Based on a vehicle emission inventory with high temporal–spatial resolution established by...
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