Development and characteristics of vehicle emission inventory with high spatiotemporal resolution in Jiangsu Province

被引：0
|
作者：
Sun, Shi-Da [1 ]
Wang, Bo [2 ]
Sun, Lu-Na [3 ]
Huang, Xu [4 ]
Wang, Xing-Xing [5 ]
Zhang, Shi-Da [5 ]
Bo, Yu [1 ,6 ]
机构：
[1] Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing,100084, China
[2] Baotou Ecological Environment Bureau, Baotou,014060, China
[3] Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin,300071, China
[4] School of Geography, Nanjing Normal University, Nanjing,210023, China
[5] Nanjing Tiandi Environment Research Institute, Nanjing,210003, China
[6] Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing,100029, China
来源：
Zhongguo Huanjing Kexue/China Environmental Science | 2023年 / 43卷 / 09期
关键词：
In this study; the traditional methodological framework was systematically optimized by considering month-by-month emission calculation; mileage weighting; and traffic data integration to address the low spatiotemporal resolution associated with the top-down method of establishing vehicle emission inventory. Based on this; the vehicle emission inventory in Jiangsu Province in 2018 was developed; and the structural level characteristics; socio-economic correlation; and spatiotemporal distribution of emissions were analyzed. Vehicle emissions of CO; VOCs; NOx; and PM2.5 in Jiangsu Province were 839.97; 166.80; 617.16; and; 21.15kt; respectively; in 2018. CO and VOCs were mainly contributed by gasoline cars; while NOx and PM2.5 were mainly contributed by heavy-duty diesel trucks. Higher emission contribution was exhibited by vehicles that met China 3 (or below) standard than their vehicle population shares; and vehicle emissions in winter were higher than in other seasons. At the city level; vehicle emissions were concentrated in Suzhou; Wuxi; Nanjing; Xuzhou; Nantong; Lianyungang; and Changzhou; and the correlation between CO and VOC emissions per capita and road emissions intensity was higher than that of NOx and PM2.5. Vehicle emissions were highly correlated with GDP and the size of the built-up area. Vehicle CO and VOC emissions were concentrated in the urban area; while NOx and PM2.5 emissions were distributed in strips. Additionally; the two peaks and one valley characteristic was demonstrated by the temporal profile of daily emissions. © 2023 Chinese Society for Environmental Sciences. All rights reserved;
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页码：4490 / 4502
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