Improving the Accuracy of Vehicle Emissions Profiles for Urban Transportation Greenhouse Gas and Air Pollution Inventories

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• 作者：Janet L. Reyna ; Mikhail V. Chester ; Soyoung Ahn ; Andrew M. Fraser
• 刊名：Environmental Science & Technology
• 出版年：2015
• 出版时间：January 6, 2015
• 年：2015
• 卷：49
• 期：1
• 页码：369-376
• 全文大小：439K
• ISSN：1520-5851

文摘

Metropolitan greenhouse gas and air emissions inventories can better account for the variability in vehicle movement, fleet composition, and infrastructure that exists within and between regions, to develop more accurate information for environmental goals. With emerging access to high quality data, new methods are needed for informing transportation emissions assessment practitioners of the relevant vehicle and infrastructure characteristics that should be prioritized in modeling to improve the accuracy of inventories. The sensitivity of light and heavy-duty vehicle greenhouse gas (GHG) and conventional air pollutant (CAP) emissions to speed, weight, age, and roadway gradient are examined with second-by-second velocity profiles on freeway and arterial roads under free-flow and congestion scenarios. By creating upper and lower bounds for each factor, the potential variability which could exist in transportation emissions assessments is estimated. When comparing the effects of changes in these characteristics across U.S. cities against average characteristics of the U.S. fleet and infrastructure, significant variability in emissions is found to exist. GHGs from light-duty vehicles could vary by 鈭?%鈥?1% and CAP by 鈭?7%鈥?28% when compared to the baseline. For heavy-duty vehicles, the variability is 鈭?1%鈥?5% and 鈭?2%鈥?74%, respectively. The results show that cities should more aggressively pursue the integration of emerging big data into regional transportation emissions modeling, and the integration of these data is likely to impact GHG and CAP inventories and how aggressively policies should be implemented to meet reductions. A web-tool is developed to aide cities in improving emissions uncertainty.