| 辽宁省2000~2030年机动车排放清单及情景分析 |
| 摘要点击 1900 全文点击 719 投稿时间：2019-04-30 修订日期：2019-09-16 |
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| 中文关键词 机动车污染 排放清单 情景分析 COPERT模型 辽宁 |
| 英文关键词 vehicle pollution emission inventory scenario analysis COPERT Liaoning |
| 作者 | 单位 | E-mail | | 金嘉欣 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | jiaxinji_812@163.com | | 孙世达 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | | | 王芃 | 香港理工大学土木与环境工程系, 香港 999077 | | | 林应超 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | | | 王婷 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | | | 吴琳 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | | | 魏宁 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | | | 常俊雨 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | | | 毛洪钧 | 南开大学环境科学与工程学院, 城市交通污染防治研究中心, 天津 300071 | hongjun_mao@hotmail.com |
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| 中文摘要 |
| 机动车排放已经成为城市地区大气污染的主要来源.基于COPERT模型和ArcGIS技术，建立了2000~2030年辽宁省机动车排放清单，分析6类污染物（CO、NMVOC、NOx、PM10、SO2和CO2）排放的总体趋势与空间演变特征，同时以2016年为基准年，基于情景分析法设置8类控制措施情景并评估不同控制措施对污染物的减排效果.结果表明2000~2016年，机动车的CO、NMVOC、NOx和PM10排放量呈现先增后降的趋势，SO2排放量呈现波动变化，而CO2排放量则呈现持续增长态势.轻型载客车和摩托车是CO和NMVOC排放的主要贡献车型，重型载客车和重型载货车是NOx和PM10的主要排放源，SO2和CO2则主要是由轻型载客车排放.辽宁省中部及南部机动车排放量明显高于辽东和辽西.从城市层面来看，排放主要集中在沈阳市和大连市.情景分析表明，实施更加严格的排放标准可以增强减排效果，且升级排放标准的时间越提前减排效果越好.综合情景将实现减排最大化，强化综合情景对CO、NMVOC、NOx、PM10、CO2和SO2的削减率达到了30.7%、14.3%、81.7%、29.4%、12.3%和12.1%. |
| 英文摘要 |
| Vehicle emissions have become a major source of air pollution in urban cities. The vehicle emission inventory of the Liaoning province from 2000 to 2030 was established based on the COPERT model and ArcGIS, and the temporal and spatial distribution characteristics of six pollutants (CO, NMVOC, NOx, PM10, SO2, and CO2) were analyzed. Taking 2016 as the base year, eight scenarios of control measures were designed based on scenario analysis, and the effects of different scenarios on emission reduction were assessed. Results showed that during 2000-2016, CO, NMVOC, NOx, and PM10 emissions at first exhibited increasing trends, after which they decreased. Emissions of SO2 exhibited fluctuating trends, while the emissions of CO2 showed a continuous increase. Passenger cars and motorcycles were the main contributors of CO and NMVOC emissions. Heavy-duty trucks and buses were the main sources of NOx and PM10 emissions. Passenger cars were the major contributors to SO2 and CO2 emissions. Vehicle emissions were significantly higher in the central and southern in Liaoning Province. At the city level, vehicle emissions were mainly concentrated in Shenyang and Dalian. The scenario analysis showed that the implementation of stricter vehicle emission standards can enhance the emission reduction effect. Moreover, accelerating the implementation of new emission standards was beneficial to reduce emissions. The integrated scenario would achieve the maximum emission reduction, with reduction rates of CO, NMVOC, NOx, PM10, CO2, and SO2 at 30.7%, 14.3%, 81.7%, 29.4%, 12.3%, and 12.1%, respectively. |
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