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Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data

Author

Listed:
  • Wen, Yifan
  • Zhang, Shaojun
  • Zhang, Jingran
  • Bao, Shuanghui
  • Wu, Xiaomeng
  • Yang, Daoyuan
  • Wu, Ye

Abstract

The high populations of vehicles in global megacities have raised serious mobility and sustainability challenges, such as traffic congestion, air pollution deterioration and energy consumption issues. Detailed emission inventories at the link level are a prerequisite to accurately quantifying ambient pollution concentrations and identifying hotspots of human exposure within urban areas. The increasing adoption of intelligent transportation system data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. Based on the open-access traffic congestion index, we construct a high-resolution emission inventory of hourly fluxes of air pollutants and carbon dioxide from on-road vehicles over the whole road network in Shenzhen, China. Fine-grained quantification of ‘excess’ emissions from rush-hour traffic is explored, finding varied emission enhancement (14.3–30.4%) for different pollutants, as well as 24.3–26.8% and 19.6–22.0% ‘excess’ consumption for gasoline and diesel, respectively, in a central business district of Shenzhen during the rush hour periods. Also, we quantified the impacts of freight activities on pollutant emissions that freight activities can largely increase the street-level emission rates of nitrogen oxides, fine particulate matter and carbon dioxide, in which heavy-duty trucks share up to 50% of the total emissions of these species. This study provides a novel approach of high-resolution traffic emission inventory construction that can be potentially utilized in many types of cities, particularly for cities suffering from data-sparse situations, and further strengthen the intelligence and accuracy of vehicle emission management.

Suggested Citation

  • Wen, Yifan & Zhang, Shaojun & Zhang, Jingran & Bao, Shuanghui & Wu, Xiaomeng & Yang, Daoyuan & Wu, Ye, 2020. "Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919320446
    DOI: 10.1016/j.apenergy.2019.114357
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    Cited by:

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    2. Samuele Marinello & Francesco Lolli & Rita Gamberini, 2020. "The Impact of the COVID-19 Emergency on Local Vehicular Traffic and Its Consequences for the Environment: The Case of the City of Reggio Emilia (Italy)," Sustainability, MDPI, vol. 13(1), pages 1-22, December.
    3. Rosero, Fredy & Fonseca, Natalia & López, José-María & Casanova, Jesús, 2021. "Effects of passenger load, road grade, and congestion level on real-world fuel consumption and emissions from compressed natural gas and diesel urban buses," Applied Energy, Elsevier, vol. 282(PB).
    4. Hyoung-Yong Choi, 2022. "Working in the Metaverse: Does Telework in a Metaverse Office Have the Potential to Reduce Population Pressure in Megacities? Evidence from Young Adults in Seoul, South Korea," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    5. Lu Wang & Xue Chen & Yan Xia & Linhui Jiang & Jianjie Ye & Tangyan Hou & Liqiang Wang & Yibo Zhang & Mengying Li & Zhen Li & Zhe Song & Yaping Jiang & Weiping Liu & Pengfei Li & Xiaoye Zhang & Shaocai, 2022. "Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    6. Ma, Jie & Xu, Mengmeng & Jiang, Jiehui, 2023. "Mapping high-resolution urban road carbon and pollutant emissions using travel demand data," Energy, Elsevier, vol. 263(PE).
    7. Wen, Yifan & Wu, Ruoxi & Zhou, Zihang & Zhang, Shaojun & Yang, Shengge & Wallington, Timothy J. & Shen, Wei & Tan, Qinwen & Deng, Ye & Wu, Ye, 2022. "A data-driven method of traffic emissions mapping with land use random forest models," Applied Energy, Elsevier, vol. 305(C).
    8. Ke Wang & Yafei Zhao & Rajan Kumar Gangadhari & Zhixing Li, 2021. "Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China," Sustainability, MDPI, vol. 13(19), pages 1-35, October.

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