气候变化领域集成服务门户(CCPortal): Assessing vehicle fuel efficiency using a dense network of CO2 observations

CCPortal

DOI	10.5194/acp-22-3891-2022
	Assessing vehicle fuel efficiency using a dense network of CO2 observations
	Fitzmaurice, Helen L.; Turner, Alexander J.; Kim, Jinsol; Chan, Katherine; Delaria, Erin R.; Newman, Catherine; Wooldridge, Paul; Cohen, Ronald C.
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	3891
结束页码	3900
卷号	22 期号:6 页码:10
英文摘要	Transportation represents the largest sector of anthropogenic CO2 emissions in urban areas in the United States. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important; however, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at the abovementioned scale. The BErkeley Air Quality and CO2 Network (BEACO(2)N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a similar to 1300 km(2) multicity spatial domain Here, we focus on a 5 km, high-volume, stretch of highway in the San Francisco Bay Area. We show that inversion of the BEACO(2)N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO2 emission rate of the average vehicle (in grams per vehicle kilometer) is shown to vary by as much as 27 % at different times of a typical weekday because of changes in these two factors. The BEACO(2)N-derived emission estimates are consistent to within similar to 3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the EMission FACtor model (EMFAC) of vehicle fuel efficiency.
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:000774568800001
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/273502
作者单位	University of California System; University of California Berkeley; University of Washington; University of Washington Seattle; University of California System; University of California Berkeley
推荐引用方式 GB/T 7714	Fitzmaurice, Helen L.,Turner, Alexander J.,Kim, Jinsol,et al. Assessing vehicle fuel efficiency using a dense network of CO2 observations[J],2022,22(6):10.
APA	Fitzmaurice, Helen L..,Turner, Alexander J..,Kim, Jinsol.,Chan, Katherine.,Delaria, Erin R..,...&Cohen, Ronald C..(2022).Assessing vehicle fuel efficiency using a dense network of CO2 observations.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(6),10.
MLA	Fitzmaurice, Helen L.,et al."Assessing vehicle fuel efficiency using a dense network of CO2 observations".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.6(2022):10.