Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-19-8831-2019 |
High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets | |
Yang, Daoyuan1; Zhang, Shaojun1,2; Niu, Tianlin1,3; Wang, Yunjie1; Xu, Honglei5; Zhang, K. Max2; Wu, Ye1,4 | |
2019-07-11 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS
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ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2019 |
卷号 | 19期号:13页码:8831-8843 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA |
英文摘要 | On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet), based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km(2)). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that nonlocal HDTs could account for 29% and 38% of estimated total on-road emissions of NOx and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir (R), to simulate vehicular NOx concentrations at fine resolutions (10m x 10m in the entire municipality and 1m x 1m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 mu gm(-3) of NOx in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000474901700002 |
WOS关键词 | TIANJIN-HEBEI REGION ; DIESEL VEHICLES ; AIR-POLLUTION ; BLACK CARBON ; CHINA ; INVENTORY ; IMPACTS ; TRENDS |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/184942 |
专题 | 地球科学 |
作者单位 | 1.Tsinghua Univ, State Key Joint Lab Environm Simulat & Pollut Con, Sch Environm, Beijing 100084, Peoples R China; 2.Cornell Univ, Sibley Sch Mech & Aerosp Engn, Ithaca, NY 14853 USA; 3.Ricardo Energy & Environm, Beijing 100028, Peoples R China; 4.State Environm Protect Key Lab Sources & Control, Beijing 100084, Peoples R China; 5.Minist Transport, Transport Planning & Res Inst, Beijing 100028, Peoples R China |
推荐引用方式 GB/T 7714 | Yang, Daoyuan,Zhang, Shaojun,Niu, Tianlin,et al. High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2019,19(13):8831-8843. |
APA | Yang, Daoyuan.,Zhang, Shaojun.,Niu, Tianlin.,Wang, Yunjie.,Xu, Honglei.,...&Wu, Ye.(2019).High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets.ATMOSPHERIC CHEMISTRY AND PHYSICS,19(13),8831-8843. |
MLA | Yang, Daoyuan,et al."High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets".ATMOSPHERIC CHEMISTRY AND PHYSICS 19.13(2019):8831-8843. |
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