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Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China

Cheng, Shifen ; Zhang, Beibei ; Peng, Peng ; Yang, Zhenzhen ; Lu, Feng

Journal of cleaner production, 2020-01, Vol.244, p.118654, Article 118654 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China
  • Autor: Cheng, Shifen ; Zhang, Beibei ; Peng, Peng ; Yang, Zhenzhen ; Lu, Feng
  • Assuntos: Heavy-duty diesel trucks ; Spatiotemporal cube ; Spatiotemporal evolution pattern ; Trajectory mining ; Transport emission
  • É parte de: Journal of cleaner production, 2020-01, Vol.244, p.118654, Article 118654
  • Descrição: Emissions from heavy-duty diesel trucks (HDDTs) pose a major threat to environment and human health. Understanding the behavior of pollutant emissions from HDDTs facilitate the formulation of traffic-related policy measures to mitigate the adverse effects. This study proposes a new method to estimate the emission inventory of HDDTs and analyze their spatiotemporal evolution characteristics. Multi-source data were fused to provide a complete picture of the transport environment. With the idea of modeling pollutant emissions by “single vehicle” and “road segment,” emission inventories were constructed with different spatiotemporal scales using localized emission factors. A spatiotemporal cube model was introduced to represent the high-resolution emission inventory. A hot-spot and local-outlier analysis were conducted to explore the spatiotemporal evolution mechanism of pollutant emissions. The megacity of Tianjin in China was taken as the area for the case study. The average daily emissions of CO, NOx, PM, and VOC are 12,978.18, 48,675.22, 712.6, and 1217.72 kg d−1, respectively. Temporally, the pollutant emission had a significant peak at 06:00, 11:00, and 18:00 and was affected by major festivals. Spatially, the distribution pattern of emission was policy-driven and closely related to its spatial location. It increases radially outward from the outer-ring road to the periphery. The hot-spot analysis identified 16 pollutant emission patterns. The road segment with persistent cold spots accounted for 48.27% of the roads, mainly distributed within the outer ring road. The road segments with intensifying and persistent hot spots accounted for 19.04% of roads, mainly distributed on the intercity highway. The locations with outlier values reached 12,027, accounting for 31.90%. The key time intervals of the occurrence of the outlier pattern are 11:00–12:00 and 01:00–02:00. Road segments showing only the low–low cluster pattern is mainly located within the outer ring roads. Those with only low–high outlier pattern exhibits a relatively scattered spatial distribution, which is affected by the heterogeneous distribution of HDDTs. [Display omitted] •A high-resolution emission inventory was established with trajectory mining.•A cube model was proposed to visualize the variations of pollutant emissions.•The distribution characteristics and evolution mechanisms of emissions were mined.•The 16 pollutant emission patterns were identified by hot-spot analysis.•The key time intervals for the occurrence of outlier patterns are 11:00–12:00.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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