期刊
JOURNAL OF CLEANER PRODUCTION
卷 378, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2022.134458
关键词
Carbon emission; Vehicle emission standard; PEMS; Machine learning; Policy evaluation
资金
- National Natural Science Foundation of China [16ZDA048]
- National Planning Office of Philosophy and Social Science
- Cornell China Center
- SJTU Global Strategic Partnership Fund (2021 SJTU-CORNELL)
- [12072195]
This study used the PEMS to reveal real-world emissions from two light-duty gasoline vehicles, and found that engine load and engine speed are the most dominant factors affecting vehicular carbon emissions under both China V and China VI standards. Additionally, the study showed that carbon emissions were significantly higher in urban areas compared to suburban areas.
On-road vehicle has been a prominent emission source, hence a key target of control for environment, health, and climate concerns. While considerable efforts have been made to investigate criteria pollutants such as adverse gaseous and particulate emissions across the world, there is limited systematic research to characterize on-road carbon emissions, especially as countries strive to set and achieve various climate goals (i.e., carbon neutrality, carbon peaking by a certain year, etc.) through cleaner vehicle technologies such as the latest vehicle emission standard, China VI (2020). This study used the portable emission measurement system (PEMS) to reveal the real-world emissions from two light-duty gasoline vehicles, one meeting the China V standard and the other China VI. The PEMS measurements were performed secondly in urban and suburban areas respectively, and finally yielded 10 h of rich and finely resolved emissions data. In addition of single-factor analysis, this study employed two machine learning models, stepwise regression and light gradient boosting machine (LightGBM), to investigate the coupling effects among vehicle/traffic parameters and identify driving behavior, engine condi-tion, and external environment as key determining factors that affect vehicular carbon emissions. In particular, engine load percent and engine speed were confirmed to be the most dominant factors affecting vehicular carbon emissions under both China V and China VI standards, explaining similar to 60% of variability in carbon emissions. As a result, vehicle specific power (VSP) is derived from vehicle travel parameters and used as a surrogate for carbon emission estimation. The external facility and environmental factors turned out significant as well in affecting vehicle carbon emissions: carbon emissions from the same vehicle were significantly higher in an urban setting then in suburban areas, mostly due to the more aggressive driving patterns in congested urban traffic. Travelling under relatively better traffic conditions on expressways gave the lowest per-distance emission factors of CO2, CO, and NOx. Comparing between the China V and VI vehicle, our PEMS data show significant carbon emission reduction benefits (15.9% in CO2, 28.8% in CO) as we move to the new China VI standard, although such benefit was not observed in NOx emissions.
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