上海市机动车高时空分辨率污染物排放特征

赵子涵; 史旭荣; 胡磬遥; 薛文博

doi:10.13205/j.hjgc.202609014

上海市机动车高时空分辨率污染物排放特征

doi: 10.13205/j.hjgc.202609014

1. 中国环境科学研究院, 北京 100012;
2. 生态环境部环境规划院, 北京 100041;
3. 上海市环境科学研究院国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233

基金项目:

国家重点研发计划“典型城市和区域机动车污染智慧管控技术集成与示范实践”（2022YFC3703600）；重点实验室开放基金（大气）“减污降碳协同目标下上海市‘十四五’汽车电动化发展路径研究”（SEPAir-2024080212）

详细信息

作者简介:
赵子涵(2001—),男,硕士研究生,主要从事大气污染防治方向研究。zhzh0817@163.com

通讯作者:
史旭荣(1993—),女,助理研究员,主要研究方向为移动源减污降碳路径。shixr@caep.org.cn

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出版历程
- 收稿日期: 2025-06-18
- 网络出版日期: 2026-11-05

High spatiotemporal resolution characteristics of motor vehicle pollutant emissions in Shanghai

1. Chinese Research Academy of Environmental Sciences, Beijing 100012, China;
2. Chinese Academy of Environmental Planning, Beijing 100041, China;
3. Key Laboratory of Urban Ambient Air Pollution Cause and Prevention, Ministry of Ecology and Environment, Shanghai Academy of Environ mental Sciences, Shanghai 200233, China

摘要

摘要: 构建了上海市2023年机动车部门1 km分辨率的污染物排放清单，并对机动车大气污染物排放进行了精细化的车型来源分析及高分辨率时空尺度分析。2023年上海市机动车排放的一氧化碳（CO）、碳氢化合物（HC）、氮氧化物（NO_x）和细颗粒物（PM_2.5）分别为10.3 万，2.5 万，8.9 万，1191 t。CO和HC的排放主要来源于小型客车，其分担率分别为71.3%和75.4%，；而NO_x和PM_2.5的排放主要来自重型货车，其分担率分别为77.5%和57.6%。4种污染物的时间分布变化呈现出时序差异，在冬季普遍出现最高值，日排放的周内水平显著大于周末，小时尺度排放形成早晚双峰的排放趋势。上海市机动车污染物排放呈现明显的空间分布特征，与环线结构、道路类型及土地功能高度耦合。中心城区以小客车为主，CO和HC排放强度最高，随距离递减；外环至郊外区域因货运集中，NO_x和PM_2.5排放最重。主干道是主要排放源，汽油车主要贡献CO和HC，柴油货车则集中在快速路上排放NO_x和PM_2.5。高架立交、物流节点等区域形成排放热点，反映出交通结构与车辆类型对污染分布的决定性影响。
- 机动车污染 /
- 排放清单 /
- 排放特征 /
- 高时空分辨率 /
- 上海市
Abstract: This study developed a 1-km resolution emission inventory for Shanghai's vehicle sector in 2023， conducting detailed vehicle-type source analysis and high-resolution spatiotemporal analysis of vehicular air pollutant emissions. In 2023， Shanghai's vehicle emissions of carbon monoxide （CO）， hydrocarbons （HC）， nitrogen oxides （NO_x ）， and fine particulate matter （PM_2.5） were 103143 tons， 25301 tons， 89428 tons， and 1191 tons， respectively. CO and HC emissions were primarily from small passenger cars， contributing 71.3% and 75.4%， respectively， while NO_x and PM_2.5 emissions were mainly from heavy-duty trucks， contributing 77.5% and 57.6%， respectively. Temporal variations showed seasonal differences， with peak emissions in winter， significantly higher weekday emissions compared to weekends， and a bimodal daily emission pattern with morning and evening peaks. Spatially， vehicle pollutant emissions exhibited distinct patterns closely coupled with ring road structures， road types， and land functions. The central urban area， dominated by passenger cars， had the highest CO and HC emission intensities， decreasing with distance. In contrast， NO_x and PM_2.5 emissions were highest from the outer ring to suburban areas due to concentrated freight transport. Main roads were the primary emission sources， with gasoline vehicles contributing mainly to CO and HC， and diesel trucks contributing to NO_x and PM_2.5 on expressways. Emission hotspots were identified at road intersections and logistics nodes， reflecting the decisive impact of traffic structure and vehicle types on pollution distribution.
- motor vehicle pollution /
- emission inventory /
- emission characteristics /
- high spatiotemporal resolution /
- Shanghai

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参考文献(39)

[1]	WU Y L，YANG J，SHAO Z J，et al. A study of vehicle emission inventory and its characteristics in Suzhou，China［J］. Acta Scientiae Circumstantiae，2024，44（9）:129-139. 吴雨涟，杨洁，邵智娟，等. 2024. 苏州市机动车排放清单及特征研究［J］. 环境科学学报，44（9）:129-139.
[2]	CHEN X，SHAO S，TIAN Z，et al. Impacts of air pollution and its spatial spillover effect on public health based on China's big data sample［J］. Journal of Cleaner Production，2017，142:915-925.
[3]	LIN S，XIAO L，WANG X，et al. Does air pollution hinder technological innovation in China? A perspective of innovation value chain［J］. Journal of Cleaner Production，2021，278.
[4]	ZHANG Q J，WU L，YANG Z W，et al. Characteristics of gaseous and particulate pollutants exhaust from logistics transportation vehicle on real-world conditions［J］. Transportation Research. Part D:Transport and Environment，2016，43:40-48.
[5]	LIU Z，LEI Y，XUE W，et al. Mitigating China's ozone pollution with more balanced health benefits［J］. Environmental Science& Technology，2022，56（12）:7647-7656.
[6]	JACOBSON T A，KLER J S，HERNKE M T，et al. Direct human health risks of increased atmospheric carbon dioxide［J］. Nature Sustainability，2019，2（8）:691-701.
[7]	DENG F，LÜ Z，QI L，et al. A big data approach to improving the vehicle emission inventory in China［J］. Nature Communications，2020，11（1）:2801.
[8]	CCTP. 中国交通零排放转型蓝皮书［EB/OL］.2023-02-09. https://www.cctp.org.cn/uploads/20230607/CCTP%20report.pdf.
[9]	SUN S，SUN L，LIU G，et al. Developing a vehicle emission inv-entory with high temporal-spatial resolution in Tianjin，China［J］. Science of the Total Environment，2021，776:145873.
[10]	SUN S D，WANG B，SUN L N，et al. Development and characteristics of vehicle emission inventory with high spatiotemporal resolution in Jiangsu Province［J］. China Environmental Science，2023，43（9）:4490-4502. 孙世达，王博，孙露娜，等. 江苏省高时空分辨率机动车排放清单构建及特征［J］. 中国环境科学，2023，43（9）:4490-4502.
[11]	WANG W N，LIU G G，WEI H G，et al. Research of vehicle emission characteristics in Zhengzhou based on high-resolution dynamic emission inventory［J］. Environmental Engineering，2024，42（10）:92-101. 王雯楠，刘格格，魏红格，等. 基于高分辨率动态排放清单的郑州市机动车排放特征研究［J］. 环境工程，2024，42（10）:92-101
[12]	CHEN X. Investigations on vehicle emissions and common source areas of air pollution for fine controls［D］. Hangzhou:Zhejiang University，2022. 陈雪. 面向精细化管控的机动车尾气排放及大气污染共同来源区域研究［D］. 杭州:浙江大学，2022.
[13]	ZHANG X Q，HE C，LI J Q，et al. Non-exhaust pollutant emission inventory and emission characteristics of motor vehicles on Kunming expressways based on MOVES model［J］. Mechanical& Electrical Engineering Technology，2025，54（7）:65-69. 张晓青，何超，李加强，等. 基于MOVES模型的昆明市高速公路机动车非尾气污染物排放清单及排放特征［J］. 机电工程技术，2025，54（7）:65-69.
[14]	SHEN Y，WU T R，YAN J，et al. Investigation on air pollutants and carbon dioxide emissions from motor vehicles in Beijing based on COPERT model［J］. Journal of Environmental Engineering Technology，2021，11（6）:1075-1082. 沈岩，武彤冉，闫静，等. 基于COPERT模型北京市机动车大气污染物和二氧化碳排放研究［J］. 环境工程技术学报，2021，11（6）:1075-1082.
[15]	Chinese Society For Environmental Science. Technical guideline on city-level air pollutant emission inventory development［R］． 2024. 中国环境科学学会. 城市大气污染源排放清单编制技术指南［R］. 2024.
[16]	SONG X W，HAO Y P，ZHU X D. Research on vehicular pollutants emission control in the Yangtze River Delta urban agglomeration based on scenario analysis［J］. Ecology and Environmental Sciences，2019，28（11）:2245-2256. 宋晓伟，郝永佩，朱晓东. 基于情景分析的长三角城市群机动车污染排放控制研究［J］. 生态环境学报，2019，28（11）:2245-2256.
[17]	ZHAO D D，ZHANG Y，SHI X R，et al. Vehicles emission inventory of air pollutants with 1 km resolution in Henan Province［J］. Environmental Pollution& Control，2022，44（4）:469-475. 赵大地，张宇，史旭荣，等. 河南省1 km分辨率机动车大气污染物排放清单［J］. 环境污染与防治，2022，44（4）:469-475.
[18]	Ministry of Ecology and Environmental of the People Republic of China. China mobile source environmental management annual report［R］. 2023. 生态环境部. 中国移动源环境管理年报［R］. 2023.
[19]	LUO X，DONG L，DOU Y，et al. Analysis on spatial-temporal features of taxis’ emissions from big data informed travel patterns:a case of Shanghai，China［J］. Journal of cleaner production，2017，142:926-935.
[20]	LIN X F，XU W J，LIU Y H，et al. Survival probability pattern and its influencing factors of vehicles in Guangdong Province［J］. Acta Scientiarum Naturalium Universitatis Sunyatseni，2019，58（1）:65-73. 林晓芳，徐伟嘉，刘永红，等. 广东省车辆存活概率曲线及规律［J］. 中山大学学报（自然科学版），2019，58（1）:65-73.
[21]	LIU S，ZHU X L，XU G Q. Research on survival probability model of passenger cars based on Weibull distribution［J］. Industry and Science Forum，2012，11（18）:122-124. 刘森，朱向雷，徐国强. 基于威布尔分布的轿车存活概率模型研究［J］. 产业与科技论坛，2012，11（18）:122-124.
[22]	ZHU X L，XU G Q，LIU S. Study of truck survival regular pattern［J］. Special Purpose Vehicle，2012（12）:83-84. 朱向雷，徐国强，刘森. 货车存活规律研究［J］. 专用汽车，2012（12）:83-84.
[23]	HAO H，WANG H W，OUYANG M G，et al. Vehicle survival patterns in China［J］. Sci China Tech Sci，2011，41（3）:301-305. 郝瀚，王贺武，欧阳明高，等. 我国汽车存活规律研究［J］. 中国科学:技术科学，2011，41（3）:301-305.
[24]	ZHENG J Y，CHE W W，WANG Z L. Traffic flow and road network-based spatial allocation of regional mobile source emission inventories［J］. Acta Scientiae Circumstantiae，2009，29（4）:815-821. 郑君瑜，车汶蔚，王兆礼. 2009. 基于交通流量和路网的区域机动车污染物排放量空间分配方法［J］. 环境科学学报，29（4）:815-821.
[25]	MAO H M，ZHANG K S，DI B F，et al. The high-resolution temporal and spatial allocation of emission inventory for Chengdu［J］. Acta Scientiae Circumstantiae，2017，37（1）:23-33. 毛红梅，张凯山，第宝锋，等．成都市大气污染物排放清单高分辨率的时空分配［J］．环境科学学报，2017，37（1）:23-33
[26]	Ministry of Transport. Statistics from the Ministry of Transport［EB/OL］. https://www.mot.gov.cn/tongjishuju/gonglu/. 交通运输部. 交通运输部统计数据［EB/OL］. https://www.mot.gov.cn/tongjishuju/gonglu/.
[27]	JIN Y，LIU H B. Carbon emissions calculation and spatio-temporal distribution of heavy duty trucks based on GPS data［J］. Transport Research，2022，8（6）:90-97. 金昱，刘皓冰. 基于GPS数据的重型货车碳排放计算及时空分布研究［J］. 交通运输研究，2022，8（6）:90-97.
[28]	ZHANG C，LIAN J，MIN H，et al. Shanghai as a model:research on the journey of transportation electrification and charging infrastructure development［J］. Sustainability，2024，17（1）:91.
[29]	WINE O，OSORNIO V A，CAMPBELL S M，et al. Cold climate impact on air-pollution-related health outcomes:A scoping review［J］. International Journal of Environmental Research and Public Health，2022，19（3）:1473.
[30]	LUAN X，PARYZAT H，CHU J，et al. Different roads take me home:the nonlinear relationship between distance and flows during China's Spring Festival［J］. Humanities and Social Sciences Communications，2024，11（1）:1-14.
[31]	Tsinghua Institute of Economics. China highway freight transport big data carbon emission report［R］.［2024-5-9］. https://www.tioe.tsinghua.edu.cn/__local/4/5F/85/1CD5DEFEFD33437221BA37F5149_9198476D_376E68.pdf. 清华大学经济学研究所. 中国公路货运排放大数据碳排放报告［R］.［2024-5-9］. https://www.tioe.tsinghua.edu.cn/__local/4/5F/85/1CD5DEFEFD33437221BA37F5149_9198476D_376E68.pdf.
[32]	LU T，CUI C，CAI Y，et al. Homeownership-based segregation and urban amenity differentiation in Shanghai［J］. Applied Spatial Analysis and Policy，2023，16（4）:1417-1441.
[33]	TIAN J L，WANG D，XIE D C，et al. Identifying the commuting features and patterns of typical employment areas in Shanghai using cellphone signaling data:A case study in Zhangjiang，Jinqiao and Lujiazui［J］. Geographical Research，2017，36（1）:134-148. 田金玲，王德，谢栋灿，等. 上海市典型就业区的通勤特征分析与模式总结:张江、金桥和陆家嘴的案例比较［J］. 地理研究，2017，36（1）:134-148.
[34]	WEI X，REN Y，SHEN L，et al. Exploring the spatiotemporal pattern of traffic congestion performance of large cities in China:A real-time data based investigation［J］. Environmental Impact Assessment Review，2022，95:106808.
[35]	SHARMA N，KUMAR P V P，DHYANI R，et al. Idling fuel consumption and emissions of air pollutants at selected signalized intersections in Delhi［J］. Journal of Cleaner Production，2019，212:8-21.
[36]	WANG J L. Study and evaluation on pollution characteristics and control policies of motor vehicles in the Yangtze River Delta region［D］. Hangzhou:Zhejiang University，2018. 王荆玲. 长三角机动车污染排放特征及控制政策评价研究［D］. 杭州:浙江大学，2018.
[37]	ZHENG B，HUO H，ZHANG Q，et al. High-resolution mapping of vehicle emissions in China in 2008［J］. Atmospheric Chemistry and Physics，2014，14（18）:9787-9805.
[38]	LIU Q Z，CHAO Y，YI M，et al. Study on vehicle emission inventory based on real-time traffic flow［J］. Shanghai Measurement and Testing，2024，51（2）:59-62. 刘启贞，巢渊，易敏，等. 基于实时交通流的上海市机动车碳污同源排放清单［J］. 上海计量测试，2024，51（2）:59-62.
[39]	WU Y Q，XIE Q Y，ZHANG R. Optimization of subsidies for container sea-rail intermodal transportation［J］. Journal of Transportation Systems Engineering and Information Technology，2024，24（1）:35-45. 吴云强，解乔雅，张戎. 集装箱海铁联运补贴优化研究［J］. 交通运输系统工程与信息，2024，24（1）:35-45.