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

文献类型：期刊论文

作者Cheng, Shifen1,2; Zhang, Beibei3; Peng, Peng1,2; Yang, Zhenzhen5,6; Lu, Feng1,2,3,4
刊名JOURNAL OF CLEANER PRODUCTION
出版日期2020-01-20
卷号244页码:17
ISSN号0959-6526
关键词Heavy-duty diesel trucks Transport emission Trajectory mining Spatiotemporal cube Spatiotemporal evolution pattern
DOI10.1016/j.jclepro.2019.118654
通讯作者Lu, Feng(luf@lreis.ac.cn)
英文摘要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 spatio-temporal 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. (C) 2019 Elsevier Ltd. All rights reserved.
WOS关键词REAL-WORLD EMISSIONS ; HEBEI BTH REGION ; VEHICLE EMISSIONS ; NITROGEN-OXIDES ; BLACK CARBON ; TRAFFIC DATA ; INVENTORY ; TRENDS ; POLLUTION ; MODEL
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23010202] ; Regional Key Project under Science and Technology Service Network Initiative of Chinese Academy of Sciences[KFJ-STS-QYZD-xxx]
WOS研究方向Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000503172600158
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Regional Key Project under Science and Technology Service Network Initiative of Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/130845]  
专题中国科学院地理科学与资源研究所
通讯作者Lu, Feng
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Fuzhou Univ, Acad Digital China, Fuzhou, Fujian, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
5.Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China
6.Beijing PalmGo Infotech Co Ltd, Beijing 100085, Peoples R China
推荐引用方式
GB/T 7714		 Cheng, Shifen,Zhang, Beibei,Peng, Peng,et al. Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China[J]. JOURNAL OF CLEANER PRODUCTION,2020,244:17.
APA Cheng, Shifen,Zhang, Beibei,Peng, Peng,Yang, Zhenzhen,&Lu, Feng.(2020).Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China.JOURNAL OF CLEANER PRODUCTION,244,17.
MLA Cheng, Shifen,et al."Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China".JOURNAL OF CLEANER PRODUCTION 244(2020):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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