气候变化领域集成服务门户(CCPortal): High-resolution mapping of regional traffic emissions using land-use machine learning models

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DOI	10.5194/acp-22-1939-2022
	High-resolution mapping of regional traffic emissions using land-use machine learning models
	Wu, Xiaomeng; Yang, Daoyuan; Wu, Ruoxi; Gu, Jiajun; Wen, Yifan; Zhang, Shaojun; Wu, Rui; Wang, Renjie; Xu, Honglei; Zhang, K. Max; Wu, Ye; Hao, Jiming
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	1939
结束页码	1950
卷号	22 期号:3 页码:12
英文摘要	On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on road traffic monitoring datasets in the Beijing-Tianjin-Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than other machine learning models on most occasions in this study. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. NOx, fine particulate matter (PM2.5), and black carbon (BC) emissions from heavy-duty trucks (HDTs) generally have a higher emission intensity on the highways connecting to regional ports. The model found a general reduction in light-duty passenger vehicles when traffic restrictions were implemented but a much more spatially heterogeneous impact on HDTs, with some road links experiencing up to 40 % increases in the HDT traffic volume. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, thereby providing a platform to realize the near-real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000759323100001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273565
作者单位	Tsinghua University; Tsinghua University; Cornell University; Tsinghua University
推荐引用方式 GB/T 7714	Wu, Xiaomeng,Yang, Daoyuan,Wu, Ruoxi,et al. High-resolution mapping of regional traffic emissions using land-use machine learning models[J],2022,22(3):12.
APA	Wu, Xiaomeng.,Yang, Daoyuan.,Wu, Ruoxi.,Gu, Jiajun.,Wen, Yifan.,...&Hao, Jiming.(2022).High-resolution mapping of regional traffic emissions using land-use machine learning models.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(3),12.
MLA	Wu, Xiaomeng,et al."High-resolution mapping of regional traffic emissions using land-use machine learning models".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.3(2022):12.