Research On Dynamic Quantification Method Of Vehicle Pollutant Discharge In Urban Roads

Nowadays,automobile industry has been changing quickly accompanying with rapid economic development,especially in developing countries.As of June 2019,the number of motor vehicles in China had reached 340 million,among them 250 million were automobiles—almost 74.58%of the total,while the number of passenger vehicles was 198 million.As the provincial capital of Jiangsu Province in the southeast coastal region of China,the car ownership of Nanjing was also experiencing explosive growth and the number hit 2.58 million at the end of 2018,which made the city ranking 16th among 660 cities nationally.Among them,passenger cars constituted a major share mainly due to overburdened public transportation.According to statistics,the sheer number of additional small-sized passenger cars,over 241,000in 2017,accounted for 91.3%of the new registered automobiles that year(Nanjing Statistics Bureau,2018).Expanding car ownership has imposed heavy pressure on urban traffic and made vehicle emissions become the leading source of air pollution in major Chinese cities.According to the China Vehicle Environmental Management Annual Report(2019),the total emission of four gas contaminants from motor vehicles nationwide was preliminarily calculated as40,653million tons in 2018,including 30,894 million tons of carbon monoxide(CO),3,688million tons of hydrocarbons(HC),5,629 million tons of nitrogen oxides(NO_x),and 0.442million tons of particulate matter(PM).Among them,CO,NO_x,PM and HC exceeded 80%of the total air pollution emission.Hereby,controlling emissions of in-use vehicles is considered as one of the most effective means to reduce environmental pollution.In order to solve the problem of environmental pollution,the emissions of in-use vehicles should be quantified at first.Driving cycles,always expressed by velocity and time sequence,could represent the driving behavior of a certain area.As an important index in quantifying vehicle emissions,the main purpose of driving cycles is to assess vehicle polluting emissions and fuel consumption by simulating the real driving patterns.For decades,driving cycles have always been applied to accomplish the emission certification procedure for new vehicles.China has always adopted European emission certification standards test cycle,World Light Vehicle Test Procedure(WLTP)will replace New European Driving Cycle(NEDC)as the emission certification standards test cycle for new light-duty vehicles in July 2020.However,with the rapid increase of vehicles in China,the discrepancy of driving conditions between China and Europe appears to be larger in recent years.There is increasing concern about whether the driving cycles could adequately represent the real-world driving conditions of vehicles and provide more accurate estimates.Therefore,more and more local driving cycles,which are characterized by a series of speed vs time profiles representing the driving behavior of certain areas,have already aroused a growing concern from the researchers.In this study,LDC for small-sized passenger cars in Nanjing is developed using a statistical combination of micro-trips by principle component analysis and clustering analysis method.18driving parameters are picked out for identifying the 373 micro-trips,five principal components are extracted by principal component analysis and the correlation coefficient between the 5principal components and 18 driving parameters are rotated by the method of varimax,then373 micro-trips characterized by five principal components are classified into three types by cluster analysis method.The method for developing Nanjing local driving cycle in this paper is more appropriate and systematic than previous research,and the result is consistent with real-world data,with the relative error less than 10%.Furthermore,some driving parameters for LDC in Nanjing are compared with standard driving cycles and other LDCs in China cities(Beijing,Shanghai,Tianjin and Ningbo).The results show that even in different cities in the same country,significant differences exist in driving conditions,partly because of the different road infrastructure,traffic conditions,driving habits,and so on.Therefore,it is necessary to develop local driving cycles for different cities in order to provide more accurate assessment of vehicle emissions.Nanjing local driving cycles for passenger cars in 2009 and 2017respectively are developed using the same data acquisition and processing methods.The same study area is chosen and the same data collection system(SEMTECH-DS)is used in the two experiments.Through comparing the typical driving parameters between the Nanjing local driving cycles in 2009 and 2017,Significant differences in driving parameters between Nanjing local driving cycles in 2009 and 2017 show the necessity of this study from Time dimension analysis.As a non-standard driving cycle,local driving cycle used to estimate vehicle emissions should be updated every few years.The developed LDC is simulated on chassis dynamometer to analyze the impact of speed and acceleration on emission rates for different pollutants.Emission data are obtained by calculating the average value of the chassis dynamometer data and the on-road emission test data.The results indicate that for different pollutant gases,the variation rules of emission rates with speed and acceleration are different.The emission rates of CO₂ and NO_x are more greatly affected by speed and acceleration than CO and HC.Instead,CO and HC hasn't show distinct effect of speed on the emission rates.These findings could provide the theory basis for how to mitigate certain pollutants emitted by motor vehicles via traffic management methods.Furthermore,the emissions rates and factors of four pollutions(CO₂,CO,NO_x and HC)obtained on chassis dynamometer in Nanjing local driving cycle are compared with other standard driving cycles(ASM,VMAS,NEDC)in different driving patterns(acceleration,deceleration,cruising,idling).In acceleration mode,the emission rates and emission factors of CO₂ and HC are the smallest in LDC,Except for HC,there are not significantly different for the emission factors and rates among different driving cycles in deceleration mode.In cruising mode,the smallest emission of CO₂ appears in LDC,but the largest NO_x emission rate also appears in LDC.In idling mode,the CO₂ and NO_x emission rates are highest in LDC,while the emission rate of HC in NEDC is 25 times higher than that in LDC.The comparison with the LDC and other standard driving cycles suggests that emission rates and emission factors produced from the ASM,VMAS or NEDC cycles-based tests are significantly different from those from LDC in Nanjing.Thus it is important to develop and apply local driving cycles that can represent real-world driving behavior in specific regions to evaluate vehicular emissions.The RFID non-contact automatic identification technology is used for collecting traffic information,the readers installed on the roadside communicating with the vehicle electronic tags,can fully utilize the advantages of RFID collection technology to identify vehicle attributes and obtain corresponding vehicle emission factors.The RFID redundant data(including duplicate data and similar data)is cleaned and pre-processed,and the vehicle trajectory probability model is built to fill the missing data of the vehicle trajectory.By calculating the time lag that the vehicle is identified by different base stations,the vehicle can be obtained through the base station pair.The travel time obtains the vehicle travel distance according to the path information between the base stations in the road network and obtains the interval speed between the two base stations of the single vehicle.By arranging and analyzing the obtained data,the interval speed of the vehicle under different time and space conditions can be obtained.Finally,through the case analysis,the data processing method is implemented.The RFID data processing and analysis method proposed in this paper aims to improve the ability to extract road traffic parameters using RFID data in the context of intelligent traffic data,and to provide a more accurate and stable road traffic information collection method for traffic management and planning personnel,and provide technical support to optimize transportation decisions.A dynamic quantitative system for vehicle pollutant emissions in urban roads is finally established,it is analyzed from the overall system architecture and system function modules,and the system software architecture is planned and designed from the aspects of design principles,logical structure and functional configuration,and finally the operator role models and capability feature models in the system are constructed.