Scenario analysis to vehicular emission reduction in Beijing-Tianjin-Hebei (BTH) region, China.
Motor vehicle emissions are increasingly becoming one of the important factors affecting the urban air quality in China. It is necessary and useful to policy makers to demonstrate the situation given the relevant pollutants reduction measures are taken. This paper predicted the reduction potentials of conventional pollutants (PM10, NOx, CO, HC) under different control strategies and policies in the Beijing-Tianjin-Hebei (BTH) region during 2011-2020. There are the baseline and 5 control scenarios designed, which presented the different current and future possible vehicular emissions control measures. Future population of different kinds of vehicles were predicted based on the Gompertz model, and vehicle kilometers travelled estimated as well. After that, the emissions reduction under the different scenarios during 2011-2020 could be estimated using emission factors and activity level data. The results showed that, the vehicle population in the BTH region would continue to grow up, especially in Tianjin and Hebei. Comparing the different scenarios, emission standards updating scenario would achieve a substantial reduction and keep rising up for all the pollutants, and the scenario of eliminating high-emission vehicles can reduce emissions more effectively in short-term than in long-term, especially in Beijing. Due to the constraints of existing economical and technical level, the reduction effect of promoting new energy vehicles would not be significant, especially given the consideration of their lifetime impact. The reduction effect of population regulation scenario in Beijing cannot be ignorable and would keep going up for PM10, CO and HC, excluding NOx. Under the integrated scenario considering all the control measures it would achieve the maximum reduction potential of emissions, which means to reduce emissions of PM10, NOx, CO, HC, by 56%, 59%, 48%, 52%, respectively, compared to BAU scenario for the whole BTH region in 2020.
College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
Xiurui
X
Guo
College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
Liwei
L
Fu
College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
Muse
M
Ji
College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
Jianlei
J
Lang
College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
Dongsheng
D
Chen
College of Environmental & Energy Engineering, Beijing University of Technology, Beijing, 100124, China; Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China.
Shuiyuan
S
Cheng
D000397:Air Pollution / Q000032:analysis*; D000068476:Beijing; D005544:Forecasting; D018986:Motor Vehicles; D001335:Vehicle Emissions / Q000032:analysis*
D001335:Vehicle Emissions
Beijing-Tianjin-Hebei (BTH) region; Scenario analysis; Vehicular emissions