Co-control effects of motor vehicle pollutant emission in Shanghai
WANG Hui-hui1, ZENG Wei-hua1, WU Kai-ya2
1. School of Environment, Beijing Normal University, Beijing 100875, China;
2. National Innovative Institute for Public Management and Public Policy, Fudan University, Shanghai 200433, China
Based on the information for the vehicle traffic during 2007-2012 in Shanghai, the pollutant emissions of motor vehicles were calculated. Three scenarios for vehicle pollutant reduction control were designed by applying the co-control evaluation method, which are single measurement, structural measurement and integrated measurement, respectively. The results indicated that the vehicle pollutant emissions from 2007 to 2012 in Shanghai presented a decrease trend. Motorcycle (MC), light-duty gasoline vehicle (LDGV), heavy-duty diesel trucks (HDDT) and heavy-duty diesel vehicle (HDDV) were the major pollution sources, which accounted for more than 90% of the total vehicle emission. According to the growth rate of ownership of motor vehicles in Shanghai currently, the inhalable particulate matter (PM10) emission from vehicle will increase by 7%, and the growth rate of greenhouse gas emission is between 15% and 108%, of which the carbon dioxide (CO2) will increase by 45% in 2018. The pollutants and greenhouse gases would be reduced under three control scenarios, but the effectiveness of emission reductions had obvious differences. Under the single control measurement scenario, eliminating yellow label cars and releasing stringent emission standards would be more effective to reduce vehicle pollutants and greenhouse gases. The reduction rate could be more than 20%. Moreover, under the structural control measurement scenario, vehicle pollutants and greenhouse gases would be more effectively reduced the reduction rate being more than 40%, and the co-control effect would be obviously positive.
Ntziachristos L, Samaras Z. COPERTIII Computer programme to calculate emissions from road transport, Methodology and emission factor (Version 2.1) [R]. Technical Report No 49. Copenhagen: European Environmental Agency, 2000.
[4]
Ekstrom M, Sjodin A, Andreasson K. Evaluation of the COPERT III emission model with on road optical remote sensing measurements [J]. Atmospheric Environment, 2004,38:6631-6641.
[5]
Borge R, De Miguel I, De la Paz D, et al. Comparison of road traffic emission models in Madrid (Spain) [J]. Atmosp-heric Environment. 2012,62:461-471.
[6]
Kassomenos P, Karakitsios S, Pilidis G. Daily variation of traffic emissions in Athens, Greece [J]. Environment and Pollution, 2009,36:324-335.
[7]
Baltrenas P, Vaitiekūnas P, Vasarevi?ius S, et al. Modelling of motor transport exhaust gas influence on the atmosphere [J]. Journal of Environmental Engineering and Landscape Management, 2008,16(2):65-75.
[8]
Soylu S. Estimation of Turkish road transport emissions [J]. Energy Policy, 2007,35:4088-4094.
[9]
Wang H K, Chen C H, Huang C, et al. On-road vehicle emission inventory and its uncertainty analysis for Shanghai, China [J]. Science of the Total Environment, 2008,398(1/2/3):60-67.
[10]
Wang H K, Fu L X, Lin X, et al. A bottom-up methodology to estimate vehicle emissions for the Beijing urban area [J]. Science of the Total Environment, 2009, 407(6):1947-1953.
Che W W, Zheng J Y, Wang S S, et al. Assement of motor vehicle emission control policies using Model-3/CMAQ model for the Pearl River Delta region, China [J]. Atmospheric Environment, 2011,45:1470-1475.
[33]
Lumbreras J, Valdés M, Borge R, et al. Assement of vehicle emissions projections in Madrid (Spain) from 2004 to 2012 considering several control strategies [J]. Transportation Research Part A, 2008,42:646-658.
