Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

Yufeng Cai, Xuelan Zeng (), Weichi Li, Song He, Zedong Feng and Zihang Tan
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Yufeng Cai: Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Collaborative Innovation Institute of Carbon Neutrality and Green Development, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Xuelan Zeng: Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Collaborative Innovation Institute of Carbon Neutrality and Green Development, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Weichi Li: School of Intelligent Engineering, Sun Yat-sen University, Guangzhou 510006, China
Song He: Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Collaborative Innovation Institute of Carbon Neutrality and Green Development, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Zedong Feng: Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Collaborative Innovation Institute of Carbon Neutrality and Green Development, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Zihang Tan: Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Collaborative Innovation Institute of Carbon Neutrality and Green Development, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 8, 1-16

Abstract: 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 CO 2 , 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.

Keywords: air pollution and climate change; urban road traffic emissions; uncertainty; fleet composition; sampling coverage (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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