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河南省2016~2019年机动车大气污染物排放清单及特征
引用本文:高丹丹,尹沙沙,谷幸珂,卢轩,张欢,张瑞芹,王玲玲,齐艳杰.河南省2016~2019年机动车大气污染物排放清单及特征[J].环境科学,2021,42(8):3663-3675.
作者姓名:高丹丹  尹沙沙  谷幸珂  卢轩  张欢  张瑞芹  王玲玲  齐艳杰
作者单位:郑州大学化学学院, 郑州 450001;郑州大学生态与环境学院, 郑州 450001;河南省生态环境监测中心, 郑州 450004
基金项目:国家自然科学基金青年科学基金项目(41907187);国家重点研发计划项目(2017YFC0212401)
摘    要:基于城市机动车保有量和高速公路交通流量,结合行驶里程和VOCs源谱,采用排放因子法建立了河南省2016~2019年城市和2016年高速公路机动车高分辨率大气污染物排放清单.结果表明,2016年小型客车和普通摩托车等汽油车是CO、VOCs和NH3的主要贡献源,SO2、NOx和PM主要来自重型和轻型柴油货车,国1、国3和国4标准车对污染物排放贡献突出,郑州、周口和南阳的排放量较大;高速公路8~10月的车流量较高,11月最低,城市主干道周变化和日变化分别呈现出明显的周末效应和双峰特征;排放高值区集中在交通网密集、交通流量大的城市中心及市区附近向外辐射的道路上,连霍高速和京港澳高速是高排放道路;轻型汽油车对臭氧生成潜势(OFP)贡献最大,乙烯和丙烯等5个物种对VOCs排放量和OFP贡献均较大;2016~2019年机动车保有量年均增长率为5.7%;与2016年相比,2019年VOCs排放增加2.8%,SO2、PM2.5、PM10、NH3、CO和NOx的降幅分别为76.3%、51.7%、50.3%、43.1%、16.7%和5.9%;2019年各污染物在控制政策下的实际排放量相对基准情景的减排比例在15.6%~82.4%之间.

关 键 词:机动车  高速公路  时空分布  臭氧生成潜势(OFP)  趋势分析
收稿时间:2020/11/16 0:00:00
修稿时间:2021/2/21 0:00:00

Vehicle Air Pollutant Emission Inventory and Characterization in Henan Province from 2016 to 2019
GAO Dan-dan,YIN Sha-sh,GU Xing-ke,LU Xuan,ZHANG Huan,ZHANG Rui-qin,WANG Ling-ling,QI Yan-jie.Vehicle Air Pollutant Emission Inventory and Characterization in Henan Province from 2016 to 2019[J].Chinese Journal of Environmental Science,2021,42(8):3663-3675.
Authors:GAO Dan-dan  YIN Sha-sh  GU Xing-ke  LU Xuan  ZHANG Huan  ZHANG Rui-qin  WANG Ling-ling  QI Yan-jie
Institution:College of Chemistry, Zhengzhou University, Zhengzhou 450001, China;School of Ecology and Environment, Zhengzhou University, Zhengzhou 450001, China;Ecological Environment Monitoring Center of Henan Province, Zhengzhou 450004, China
Abstract:Based on the collected urban motor vehicle activity ownership and traffic flow of highways, combined with the mileage and source profiles of VOCs, using the emission factor method, we established high-resolution emission inventories from 2016 to 2019 for urban and 2016-based highway motor vehicles, respectively, in Henan Province, China. The results showed that gasoline vehicles, particularly minibuses and ordinary motorcycles, were the main contributors of CO, VOCs, and NH3, whereas heavy-duty and light-duty diesel trucks emitted SO2, NOx, and PM. Vehicles with China 1, China 3, and China 4 emission standards contributed significantly to pollutant emissions in the fleet. The temporal variation in traffic flow was consistent with the changes in freight and passenger traffic, with higher coefficients of variation for highways from August to October and the lowest in November. The weekly and daily changes in urban trunk roads showed distinct weekend effects and clear double-peak features, respectively. High-value emission areas were concentrated in urban centers with dense transport networks and high traffic volumes and on roads radiating outward from urban areas. The Lianhuo Expressway and the Beijing-Hong Kong-Macau Expressway were high-emission roads. Light-duty gasoline vehicles made the largest contribution to the ozone formation potential (OFP) of VOCs from motor vehicles. Five species, such as ethylene and propylene, contributed significantly to VOC emissions and OFP. The average annual growth rate of vehicle ownership from 2016 to 2019 was 5.7%. Compared with 2016, VOC emissions increased by 2.8% in 2019, whereas emissions of other pollutants showed decreasing trends of different degrees, with decreases of 76.3%, 51.7%, 50.3%, 43.1%, 16.7%, and 5.9% for SO2, PM2.5, PM10, NH3, CO, and NOx, respectively. The emission reduction percentage of each pollutant in 2019 under the control policies relative to the baseline scenario ranged from 15.6% to 82.4%.
Keywords:motor vehicles  expressway  spatial and temporal distribution  ozone formation potential (OFP)  trend analysis
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