基于路网车流量的北京城市副中心机动车污染控制情景

应用基于路网车流信息的情景分析方法,对北京城市副中心地区依据不同控制情景,以2015年为基准年建立机动车尾气排放清单.通过计算未来年路网车流信息和各情景下实际路网机动车污染物的排放清单,预测2020年和2025年的污染物排放变化.结果表明,未来10年北京城市副中心路网密度和机动车行驶里程持续增长,与基准情景相比,各控制情景对污染物排放量均有削减,新能源车推广情景对各污染物减排效果显著,且对NOx和PM的减排效果更好.外埠车限行情景对各污染物减排效果均较为显著,淘汰高排放车措施在短时间内削减效果显著,但长期削减效果较弱.综合情景对污染物的削减率达到最佳,机动车污染物CO、NOx、HC和PM排放量分别下降39.0%、58.7%、49.2%和55.5%.

Motor Vehicle Pollution Control Scenarios of Beijing Subsidiary Administrative Center Based on Road Traffic Flow

In this study, the scenario analysis method was used to establish motor vehicle exhaust emission inventories based on road-traffic-flow information of the Beijing subsidiary administrative center under different scenarios, with a base year of 2015. The pollutant emissions in 2020 and 2025 were forecast by calculating the motor vehicle pollutant emission inventories of future road-traffic-flow information and various scenarios. The results showed that the kilometers travelled via motor vehicles and the road network density of the Beijing subsidiary administrative center both will increase continuously over the next 10 years. Compared with the baseline scenario, each control scenario had a certain degree of reduction in pollutant emissions, and the reduction rates increased with the strengthening of the measures. The emission reductions of pollutants were significant in the new energy vehicle promotion scenario, especially for NO_x and PM. The effect of emission reduction of every pollutant was significant in the outgoing vehicle restriction scenario. Elimination of highly polluting vehicles had a significant effect in the short term, but the effect of long-term reduction was weak. The combined scenario achieved the best reduction rate of pollutant emissions, with CO, NO_x, HC, and PM decreasing by 39.0%, 58.7%, 49.2%, and 55.5%, respectively.