气候变化领域集成服务门户(CCPortal): Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

CCPortal

DOI	10.3390/su16083504
	Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage
	Cai, Yufeng; Zeng, Xuelan; Li, Weichi; He, Song; Feng, Zedong; Tan, Zihang
发表日期	2024
EISSN	2071-1050
起始页码	16
结束页码	8
卷号	16 期号:8
英文摘要	With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.
英文关键词	air pollution and climate change; urban road traffic emissions; uncertainty; fleet composition; sampling coverage
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS记录号	WOS:001210839700001
来源期刊	SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299837
作者单位	Guangdong University of Technology; Sun Yat Sen University
推荐引用方式 GB/T 7714	Cai, Yufeng,Zeng, Xuelan,Li, Weichi,et al. Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage[J],2024,16(8).
APA	Cai, Yufeng,Zeng, Xuelan,Li, Weichi,He, Song,Feng, Zedong,&Tan, Zihang.(2024).Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage.SUSTAINABILITY,16(8).
MLA	Cai, Yufeng,et al."Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage".SUSTAINABILITY 16.8(2024).