Study on Quadrature Experiment Design and Control Measures for on-road Particulate Matter Emissions
编号:1027 访问权限:仅限参会人 更新:2021-12-16 07:47:39 浏览:95次 张贴报告

报告开始:2021年12月17日 09:00(Asia/Shanghai)

报告时间:1min

所在会场:[P1] Poster2020 [P1T6] Track 6 Future Transportation and Modern Logistics

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摘要
Particulate matter (PM) poses a serious hazard to the health of dwellers. In order to investigate the relationship between PM concentration and its influencing factors, a quadrature experiment based on multi-factor sensitivity analysis was designed. The source strength of PM was estimated, and a discrete phase model was developed. The continuity, momentum, turbulence and component transport equations were addressed to assess PM concentration at the height of 1.5 m above the ground level. The Reynolds stress model combined with the discrete phase model was used to calculate the PM trajectory in the air flow field. The results indicated that, (1) PM concentration is the lowest under free flow, followed by slightly congested flow, moderately congested flow and severely congested flow. PM2.5 concentration on the leeward side under four traffic flow statuses is 3.73, 4.74, 5.68 and 7.00 ug/m3, respectively. In the same way, variation exists on the windward side of PM2.5 and PM10 concentration. (2) PM concentration will be a significant drop if taxis and cars are reduced by 50% and passengers are transferred to buses. And it will be the lowest if the passengers are transferred to electric vehicles. PM pollution caused by automobile exhaust will be less and less with the increasing use of electric vehicles and buses. (3) Within the range of observed vehicle speed, PM concentration is the highest under the speed of 35-40 km/h, followed by 40-45 km/h and 45-50 km/h. The increase in vehicle speed will bring a drop in the PM2.5 concentration.
关键词
CICTP
报告人
Xiaoxia Wang
Associate Professor Guangdong University of Technology

Dr. Wang is an Associate Professor at the School of Civil and Transportation Engineering, Guangdong University of Technology, China. She obtained her Ph.D. degree in Transportation Planning and Management at Chang'an University, China. She has long been engaged in research related to Green and low-carbon urban traffic management, Smart transportation system planning. Her current main research interests are related to Green transportation, Carbon emissions, Big Data, and machine learning.

Currently, her research has been supported by the Natural Science Foundation of China (NSFC), and other funding sources. And, she has participated in several Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and other research projects. She has published several papers on SCI journals, including Sustainable Cities and Society, Transportation Research Part D: Transport and environment, Journal of Cleaner Production, etc. She is also a reviewer for some SCI, SSCI journals.
 

稿件作者
Xiaoxia Wang Guangdong University of Technology
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重要日期
  • 会议日期

    12月17日

    2021

    12月20日

    2021

  • 12月16日 2021

    报告提交截止日期

  • 12月24日 2021

    注册截止日期

主办单位
Chinese Overseas Transportation Association
Chang'an University
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