| 摘要: |
| 以2023年6月江苏省苏州市为研究区域,开展道路交通流量模型搭建、机动车污染物排放量计算分析工作。本研究基于道路遥测点位数据、路网信息和本地机动车统计数据,建立50 m×50 m 的高空间分辨率小时道路移动源排放清单。结果表明2023年6月道路移动源SO2、NOx、CO2、CO、VOCs、PM10和PM2.5的排放量分别为520.3、6826.8、7947.9、21098.2、2099.3、201.6和187.9吨,其中机动车CO、VOC和PM2.5排放主要集中在城市中心及外围,而NOx排放量主要位于市中心。通过对中心城区的国控站点臭氧浓度与NOx排放量、交通流量分析表明,工作日与非工作日受机动车影响存在显著差别；对于远离工业区的监测站点,在工作日时间段受西北风风速较高情况下可以降低臭氧污染。O3的产生受交通源排放以及气象条件的影响。 |
| 关键词: 排放源清单 苏州市 交通流量模型 机动车 高分辨率 |
| DOI： |
| 分类号:X511 |
| 基金项目:江苏省碳达峰碳中和科技创新专项；江苏省创新平台建设 |
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| Research into the Influence of Motor Vehicle Pollutant Emissions Guided by Real-time Traffic Flow upon O?—Exemplified by Suzhou in June 2023 |
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Ding Jian1, Ma Xiya1, Zhang Qijie2, Zhong Sheng1
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1.Jiangsu Provincial Environmental Monitoring Center;2.Nanchang Kinton Technology Co., Ltd
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| Abstract: |
| In June 2023, a study was conducted in Suzhou City, Jiangsu Province, focusing on the development of a road traffic flow model and the calculation and analysis of motor vehicle pollutant emissions. Leveraging data from road remote sensing points, road network information, and local motor vehicle statistics, a high-spatial-resolution hourly road mobile source emission inventory with a resolution of 50 m × 50 m was established. The results indicate that the emissions of SO2, NOx, CO2, CO, VOCs, PM10, and PM2.5 from road mobile sources in June 2023 were 520.3, 6826.8, 7947.9, 21098.2, 2099.3, 201.6, and 187.9 tons, respectively. Notably, CO, VOC, and PM2.5 emissions from motor vehicles were predominantly concentrated in the city center and its periphery, while NOx emissions were mainly located in the city center. Analysis of ozone concentrations at national control stations in the central urban area, along with NOx emissions and traffic flow data, revealed significant differences in the impact of motor vehicles on ozone pollution between working days and non-working days. For monitoring stations distant from industrial areas, higher northwest wind speeds during working days can mitigate ozone pollution. The formation of O3 is influenced by both traffic source emissions and meteorological conditions. |
| Key words: Emission inventory Suzhou City , Traffic flow model, Motor vehicles, High-resolution |
