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

Fanyuan Deng, Zhaofeng Lv, Lijuan Qi, Xiaotong Wang, Mengshuang Shi and Huan Liu ()
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Fanyuan Deng: Tsinghua University
Zhaofeng Lv: Tsinghua University
Lijuan Qi: Tsinghua University
Xiaotong Wang: Tsinghua University
Mengshuang Shi: Tsinghua University
Huan Liu: Tsinghua University

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-16579-w

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