MODEL ANALYSIS FOR EMISSIONS OF LIGHT-DUTY GASOLINE VEHICLES IN A TYPICAL CITY

YIN Zi-yuan; ZHANG Kai-shan

doi:10.13205/j.hjgc.202104011

Volume 39 Issue 4

Jul. 2021

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > ENVIRONMENTAL ENGINEERING > 2021 > 39(4): 64-71

YIN Zi-yuan, ZHANG Kai-shan. MODEL ANALYSIS FOR EMISSIONS OF LIGHT-DUTY GASOLINE VEHICLES IN A TYPICAL CITY[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(4): 64-71. doi: 10.13205/j.hjgc.202104011

Citation:

YIN Zi-yuan, ZHANG Kai-shan. MODEL ANALYSIS FOR EMISSIONS OF LIGHT-DUTY GASOLINE VEHICLES IN A TYPICAL CITY[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(4): 64-71. doi: 10.13205/j.hjgc.202104011

YIN Zi-yuan, ZHANG Kai-shan. MODEL ANALYSIS FOR EMISSIONS OF LIGHT-DUTY GASOLINE VEHICLES IN A TYPICAL CITY[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(4): 64-71. doi: 10.13205/j.hjgc.202104011

Citation:

YIN Zi-yuan, ZHANG Kai-shan. MODEL ANALYSIS FOR EMISSIONS OF LIGHT-DUTY GASOLINE VEHICLES IN A TYPICAL CITY[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(4): 64-71. doi: 10.13205/j.hjgc.202104011

PDF( 3821 KB)

MODEL ANALYSIS FOR EMISSIONS OF LIGHT-DUTY GASOLINE VEHICLES IN A TYPICAL CITY

doi: 10.13205/j.hjgc.202104011

YIN Zi-yuan,
ZHANG Kai-shan^,

College of Architecture & Environment, Sichuan University, Chengdu 610065, China

Received Date: 2020-06-02
Available Online: 2021-07-21

Abstract

Abstract

The objective of this work was to develop driving modes for a typical city in China and quantify its corresponding emissions. Using Chengdu as an example, twenty-six light-duty gasoline vehicles in compliance with the Phase V national emission standards (GB 18352.5-2013) were selected in this study. A portable emission measurement system (PEMS) was used for driving cycle and real-world emission measurements. The classification and regression tree (CART) was used to develop driving modes for emission modeling. Five driving modes, including acceleration, deceleration, cruise, idle, and stop and go (SNG) were defined and demonstrated to be able to characterize the real-world emissions and fuel consumption. In general, emissions and fuel consumption differ by driving modes with acceleration being the highest, followed by cruise, SNG, deceleration and idling. Depending on pollutant, the ratio of emissions between two different driving modes could be as high as a factor of 12. Furthermore, vehicle emissions from a majority of selected vehicles in this study exceeded its compliance emission standards and were episodic. This indicated that the developed driving modes could be used to characterize vehicle emissions at a high temporal resolution, and develop emission inventory for traffic management and emission control.

FullText(HTML)

References(29)

References

[1]	中华人民共和国生态环境部.中国移动源环境管理年报2019[R].北京:中华人民共和国生态环境部, 2019.8.
[2]	张凯山,宋宁,第宝锋. 谈建立城市特征驾驶工况的必要性[C]//四川省环境科学学会二〇一一年学术年会论文集,2011.287-293.
[3]	USEPA. User's Guide to MOBILE6.1 and MOBILE6.2:Mobile Source Emission Factor Model[R]. EPA420-R-03-010, 2003.
[4]	California Air Resource Board. EMFAC User's Guide[M]. U.S.California:California Air Resource Board, 2002. 20-36.
[5]	NTZIACHRISTOS L, SAMARAS Z, et al. COPERT Ⅲ Computer program to calculate emissions from road transport, Methodology and emission factors (Version 2.1)[R]. Copenhagen:European Environmental Agency, 2000.
[6]	张兰怡,胡喜生,邱荣祖. 机动车尾气污染物排放模型研究综述[J]. 世界科技研究与发展, 2017,39(4):355-362.
[7]	陈琳,张凯山,张健,等. 城市特征驾驶工况建立及结果比较研究[J]. 环境科学与技术, 2014, 37(1):148-154.
[8]	WANG A J, GE Y S, TAN J W, et al. On-road pollutant emission and fuel consumption characteristics of buses in Beijing[J]. Journal of Environmental Sciences,2011, 23(3):419-426.
[9]	USEPA. User Guide for Motor Vehicle Emission Simulator MOVES2010(EPA-420-B-09-041)[R]. Washington D.C.:U.S. Environmental Protection Agency, 2012.
[10]	JIMENEZ-PALACIOS J L. Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing[D]. Cambridge:Massachusetts Institute of Technology, 1999. 54-56.
[11]	FREY H C, ROUPHAIL N M, UNAL A, et al. Emissions reduction through better traffic management:an empirical evaluation based upon on-road measurements[C]//North Carolina Department of Transportation. North Carolina:North Carolina State University, 2001.
[12]	UNAL A, FREY H C, ROUPHAIL N M. Quantification of highway vehicle emissions hot spots based upon on-board measurements[J]. Journal of the Air & Waste Management Association (1995), 2004, 54(2):130-140.
[13]	BECKWITH M, BATES E, GILLAH A, et al. NO₂ hotspots:are we measuring in the right places?[J]. Atmospheric Environment:X, 2019, 2:100025.
[14]	LEWIS R J. An introduction to classification and regression tree (CART) analysis[A]. In:the 2000 Annual Meeting of the Society for Academic Emergency Medicine. California:Harbor-UCLA Medical Center, 2000.
[15]	LI Z, ZHANG K S, PANG K L, et al. A fuel-based approach for emission factor development for highway paving construction equipment in China[J]. Journal of the Air & Waste Management Association, 2016, 66(12):1214-1223.
[16]	ODURO S D, HA Q P, DUC H. Vehicular emissions prediction with CART-BMARS hybrid models[J]. Transportation Research Part D:Transport and Environment, 2016, 49:188-202.
[17]	AMIN T, BRYAN C P. Modeling multiple land use changes using ANN, CART and MARS:comparing tradeoffs in goodness of fit and explanatory power of data mining tools[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 28:102-116.
[18]	ZHANG S C. Cost-sensitive KNN classification[J]. Neurocomputing, 2020, 391:234-242.
[19]	DAS L, SIVARAM A, VENKATASUBRAMAANIAN V. Hidden representations in deep neural networks:Part 2. Regression problems[J]. Computers & Chemical Engineering, 2020, 139:106895.
[20]	XU X D, ABDUL A H M, GUENSLER R. A modal-based approach for estimating electric vehicle energy consumption in transportation networks[J]. Transportation Research Part D:Transport and Environment, 2019, 75:249-264.
[21]	CHEN L F, LIANG Z R, ZHANG X, et al. Characterizing particulate matter emissions from GDI and PFI vehicles under transient and cold start conditions[J]. Fuel, 2017, 189:131-140.
[22]	YU S, DONG G,LI L. Transient characteristics of emissions during engine start/stop operation employing a conventional gasoline engine for HEV application[J]. International Journal of Automotive Technology, 2008, 9:543-549.
[23]	YANG W D, ZHANG Q Y, WANG J L, et al, Emission characteristics and ozone formation potentials of VOCs from gasoline passenger cars at different driving modes[J]. Atmospheric Pollution Research, 2018, 9(5):804-813.
[24]	WANG Y C, HAO C X, GE Y S, et al. Fuel consumption and emission performance from light-duty conventional/hybrid-electric vehicles over different cycles and real driving tests[J]. Fuel, 2020, 278:118340.
[25]	钱芳. CNG/汽油双燃料出租车排放特性研究[D].南京:东南大学,2015.
[26]	CHONG H S, PARK Y, KWON S, et al. Analysis of real driving gaseous emissions from light-duty diesel vehicles[J]. Transportation Research Part D:Transport and Environment, 2018, 65:485-499.
[27]	李加强,葛蕴珊,何超,等.发动机工况与行驶挡位对轻型汽油车道路排放的影响[J].环境工程,2018,36(12):130-134 ,154.
[28]	HE L Q, HU J N, YANG L H, et al. Real-world gaseous emissions of high-mileage taxi fleets in China[J]. Science of the Total Environment,2019,659:267-274.
[29]	付秉正,杨正军,尹航,等.轻型汽油车实际行驶污染物排放特性的研究[J].汽车工程,2017,39(4):376-380.

Relative Articles

[1]	LI Zhen, WANG Jun-zhang, SHEN Li-ming, ZHAO Jun-ji, SHI Peng-fei, WANG Jie, ZHU Tao. AN OVERVIEW OF COAL-TO-LIQUID TECHNOLOGY AND COMPREHENSIVE UTILIZATION OF COAL-TO-LIQUID RESIDUE[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(5): 135-141,149. doi: 10.13205/j.hjgc.202105019
[7]	Wu Gaoqiang, Shi Xiaoliang, Gu Hongbo, Feng Hong, Liu Zhengzheng, Sun Jianxi. CHARACTERISTICS IDENTIFICATION OF ASH GENERATED BY COAL-FIRED POWER PLANT BLENDING SEWAGE SLUDGE[J]. ENVIRONMENTAL ENGINEERING , 2015, 33(2): 113-116. doi: 10.13205/j.hjgc.201502025