1. A big data approach to improving the vehicle emission inventory in China.
- Author
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Deng, Fanyuan, Lv, Zhaofeng, Qi, Lijuan, Wang, Xiaotong, Shi, Mengshuang, and Liu, Huan
- Subjects
EMISSION inventories ,BIG data ,TIME management ,FREIGHT & freightage ,VEHICLES - 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. There lacks a method to measure the rapid changes of vehicle emissions. Here the authors proposed a big data approach 'TrackATruck', and their estimates using the new approach show that the heavy-duty trucks (HDT) emissions of primary cargo routes/terminals were underestimated by 2–10 times in proxy-based emission inventories. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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