中文摘要：

机动车排放控制措施的有效实施对改善城市大气环境质量具有重要意义. 以北京市为例,利用情景预测法评估2011—2020年各项控制措施对城市机动车常规污染物（CO、NOx、HC、PM10）的削减效果.建立Gompertz模型并估算动态车龄分布以预测机动车保有量,运用排放因子法估算6种机动车排放控制情景的污染物削减量. 结果表明：与基准情景相比,轻型客车保有量调控情景对CO、HC和PM10的削减效果较显著,在2020年可分别削减7.81%、9.88%和5.78%；排放标准更新情景对4种污染物均能有效削减,尤其是对NOx和PM10,可分别削减21.19%和24.67%；而淘汰高排放机动车情景的短期削减效果显著,但中、长期效果较差；新能源车推广情景因受到经济、技术条件的限制,削减效果较弱；综合情景考虑了以上所有的削减控制措施并达到最大的削减效果,2020年对CO、NOx、HC和PM10的削减率分别达到29.45%、42.54%、28.04%和41.30%,与基准年（2010年）相比,分别削减约2.81×105、0.63×104、3.77×104和0.17×104t.

英文摘要：

Motor vehicle emission has become one of the important factors affecting air quality in China＇＇s megacities, and many cities have taken various control measures to reduce the vehicular emissions. Taken Beijing as a case, we assessed the reduction potential of different control strategies and policies using the scenario analysis. First, the future vehicle population was predicted based on the Gompertz model and dynamic vehicle age distribution. The designed scenarios included the Business-as-usual （BAU）, the regulations of light-duty passenger vehicles （LDVs） population, emission standards updating, elimination of high-emission vehicles and promotion of new energy vehicles. The emission reduction of CO, NOx, HC, PM10on the six scenarios in future years （2011-2020） were estimated using the emission factor and activity level. The results showed that, the scenario of LDVs population regulation can significantly reduce CO, HC, PM10, by 7.81%, 9.88% and 5.78% respectively in 2020compared with 2010. Emission standards updating scenario would achieve a substantial reduction for all the pollutants, especially for NOx and PM10.The reduction proportion amounted to 21.19% and 24.67%. The elimination of high-emission vehicles can more effectively reduce emissions in a short-term than in a long-term. Due to the limitations of economic and technical levels, the reduction effect of promotion of new energy vehicles would not be significant. The integrated scenario considering all the control measures would achieve the maximum emission reduction, and it can reduce emissions of CO, NOx, HC, PM10 by 29.45%, 42.54%, 28.04%, 41.30% respectively compared with BAU scenario in 2020. The quantity amounts to 2.81×105,0.63×104,3.77×104,0.17×104t of four pollutants in 2020compared with the base year 2010. The results can provide a useful scientific support for decision-making of local vehicle pollution control measures.