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A big data approach to improving the vehicle emission inventory in China

Author

Listed:
  • Fanyuan Deng

    (Tsinghua University)

  • Zhaofeng Lv

    (Tsinghua University)

  • Lijuan Qi

    (Tsinghua University)

  • Xiaotong Wang

    (Tsinghua University)

  • Mengshuang Shi

    (Tsinghua University)

  • Huan Liu

    (Tsinghua University)

Abstract

Estimating truck emissions accurately would benefit atmospheric research and public health protection. Here, we developed a full-sample enumeration approach TrackATruck to bridge low-frequency but full-size vehicles driving big data to high-resolution emission inventories. Based on 19 billion trajectories, we show how big the emission difference could be using different approaches: 99% variation coefficients on regional total (including 31% emissions from non-local trucks), and ± as large as 15 times on individual counties. Even if total amounts are set the same, the emissions on primary cargo routes were underestimated in the former by a multiple of 2–10 using aggregated approaches. Time allocation proxies are generated, indicating the importance of day-to-day estimation because the variation reached 26-fold. Low emission zone policy reduced emissions in the zone, but raised emissions in upwind areas in Beijing's case. Comprehensive measures should be considered, e.g. the demand-side optimization.

Suggested Citation

  • Fanyuan Deng & Zhaofeng Lv & Lijuan Qi & Xiaotong Wang & Mengshuang Shi & Huan Liu, 2020. "A big data approach to improving the vehicle emission inventory in China," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16579-w
    DOI: 10.1038/s41467-020-16579-w
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    Cited by:

    1. Mengru Shao & Chao Chen & Qingchang Lu & Xinyu Zuo & Xueling Liu & Xiaoning Gu, 2023. "The Impacts of Low-Carbon Incentives and Carbon-Reduction Awareness on Airport Ground Access Mode Choice under Travel Time Uncertainty: A Hybrid CPT-MNL Model," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    2. Yang, Zhiwei & Chen, Xiaohong & Deng, Jihao & Li, Tianhao & Yuan, Quan, 2023. "Footprints of goods movements: Spatial heterogeneity of heavy-duty truck activities and its influencing factors in the urban context," Journal of Transport Geography, Elsevier, vol. 113(C).
    3. Liu, Geng & Sun, Shida & Zou, Chao & Wang, Bo & Wu, Lin & Mao, Hongjun, 2022. "Air pollutant emissions from on-road vehicles and their control in Inner Mongolia, China," Energy, Elsevier, vol. 238(PB).
    4. 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.
    5. Xiaoyu Long & Luyao Wang & Weipeng Li, 2023. "Geographical Influences on Job–Housing Balance: A Study of Coastal Urban Areas in Boston," Sustainability, MDPI, vol. 15(22), pages 1-21, November.

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