EMISSION CHARACTERISTICS FOR HEAVY-DUTY DIESEL TRUCKS UNDER DIFFERENT LOADS BASED ON VEHICLE-SPECIFIC POWER

Transcription

1 Zhang,Yu and Song EMISSION CHARACTERISTICS FOR HEAVY-DUTY DIESEL TRUCKS UNDER DIFFERENT LOADS BASED ON VEHICLE-SPECIFIC POWER Shuanghong Zhang, Ph.D. Candidate MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University ShangyuanCun, Haidian District, Beijing, P. R. China Tel: --, Fax: --, Lei Yu, Ph.D., P.E. (Corresponding Author) Professor of Texas Southern University Yangtze River Scholar and Adjunct Professor of Xuchang University and Beijing Jiaotong University College of Science, Engineering and Technology, Texas Southern University Cleburne Avenue, Houston, Texas Tel: --, Fax: --, Guohua Song, Ph.D., Associate Professor MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University ShangyuanCun, Haidian District, Beijing, P.R. China Tel: --, Fax: --, Word Count:, (Text) + * (Tables) + * (Figures) =, Submitted for Presentation at the th Transportation Research Board Annual Meeting and Publication in the Transportation Research Record Washington D.C., January, Date of Submission: July, Date of Revision: November,

2 Zhang,Yu and Song ABSTRACT To understand the effect of vehicle loads on emissions, both operating modes and emission factors for heavy-duty diesel (HDD) trucks were analyzed under different loads. Second-by-second speed data for different loads for HDD trucks were collected first. Then, a method for calculating the Vehicle-Specific Power (VSP) and an emission model for heavy-duty vehicles using the VSP as presented to evaluate the effect of different vehicle loads. The VSP distributions and emission characteristics for fully loaded and unloaded trucks were analyzed and compared. The results illustrate that the fully-loaded vehicles spent more time driving in steady modes and the time percentage of VSPs in the bin of kw/ton was lower than the percentage for unloaded trucks. However, the time percentage at the positive VSP was significantly higher than that for the unloaded trucks. The emission factors of fully-loaded trucks were significantly higher than for unloaded trucks. Emission factors were affected the most at speed intervals of -km/h where the emission factors for CO, CO, NOx, HC and PM were.%,.%,.%,.% and.% higher, respectively, than the levels for unloaded vehicles. With an increase of travel speed, the impact of the load on emissions weakened. Vehicle loads had the greatest effect on the emissions of NOx, followed by CO. PM was the least affected by vehicle loads. The impact of vehicle loads on emissions was affected by different acceleration behaviors under different loads. Keywords: HDD Trucks; Load; VSP Distribution; Emission Characteristics

3 Zhang,Yu and Song INTRODUCTION As one of the most severe pollution sources in the urban living environment, t road transportation has increasingly attracted attention from transportation professionals and the general public. To understand and determine the impact of different traffic scenarios on emissions better, different emission models have been developed. Over the past decade, numerous emission models have been developed for light-duty vehicles (-). However, only a few have been developed for heavy-duty diesel (HDD) vehicles. Although HDD trucks currently make up only a small fraction of the total vehicle population, they are a major contributor to the emissions inventory in urban areas. The Particulate matter (PM) and oxides of nitrogen (NOx) from HDD trucks are much higher than those for light-duty vehicles in China and account for a significant fraction of the total NOx and PM emission inventories. Classified by fuel types, NOx emissions from diesel vehicles account for approximately % of the total emissions from passenger cars, and PM for more than % of passenger cars ().With the increase in goods transportation likely in the future, there will be more and more HDD truck activity, which will then become an important share in emissions inventories. Thus, an accurate estimation of emissions for HDD trucks will be essential for precise development of management and control measures for HDD trucks. To measure emissions from heavy-duty vehicles, some researchers have developed emission models based on the engine certification data from laboratory tests (); however, do not properly represent real-world, on-the road vehicle emissions. Other researchers also have developed tools for heavy-duty vehicles based on standard cycles on stationary or portable chassis dynamometers (, ). Still, HDD trucks have a wide range of weights. Studies (, ) show that these loads have a great effect on the emissions of trucks even in the same traffic conditions. To handle fluctuations in the power ( right word?) that may arise, such as changing vehicle loads, an HDD truck model base on a physical power-demand approach was developed () as part of larger more comprehensive modal emissions modeling (CMEM). However, this model (?) has not yet been applied to the evaluation of emissions for real-world networks. Huo et al.() measured the emission factors for diesel trucks of different sizes and technologies in five Chinese cities and generated emission factors on the basis of these measurements, but these did not consider the effect of loads on emission factors. Further, the binning technique on the vehicle specific power (VSP) was also used for modeling HDD vehicles (, ). Considering the fact that different loads have a significant impact on the emissions of HDD trucks, it is necessary to study the characteristics of these trucks under different loads to provide a better estimation of the emissions of heavy-duty trucks. Some researchers have studied the emission characteristics under different engine loads from point of vehicle engineering. However, these research results could hardly represent the effect of loads on truckemissions running on real-world roads. To overcome this deficiency, Frey et al. () collected emission data for HDD vehicles under different loads using the Portable Emission Measurement System (PEMS) and also studied the differences in emissions between unloaded

4 Zhang,Yu and Song and fully-loaded vehicles. These results showed that emissions for HDD vehicles under a full-load increases by % on average from those under the empty-load at different speed intervals. Huang et al. () also conducted real-world vehicles emission tests on heavy-duty trucks under unloaded and loaded conditions using a PEMS, and analyzed the emission characteristics under different loads. In field tests, the methods based on the PEMS can be difficult for maintaining the same traffic flow scenario for vehicles under different loads and therefore cannot ensure a comparable and consistent analysis of HDD trucks emissions under different loads. The VSP has been used to reflect the engine power required by a vehicle to overcome aerodynamic force, rolling drag force, and rotational force to move the vehicle forward on actual roads (). Emission models based on the VSP can be used to depict the vehicle emission levels more accurately. Thus, more and more researchers began to focus on the relationship between the traffic operating parameters and VSP characteristics as well as estimating vehicle emissions based on the VSP. For example, Song et al. () studied the VSP distribution characteristics for light-duty vehicles on expressways and developed a relationship between the VSP distribution and the vehicle average speed. Lai et al. () and Xu et al. () developed the emission estimation methods using the VSP for transit buses and light-duty vehicles. Song et al. () proposed a method to use to estimate the fuel consumption of the road traffic by a characterization of the VSP and speed based on the Floating Car Data (FCD). The MOVES modeling approach () further developed emission rates based on VSP bins for different vehicle types. Zhai et al () estimated link-based emissions for diesel transit buses based on average speed, which were associated with the VSP modes. Based on the above discussions, it was found that most of the existing studies on heavy-duty vehicles were mainly based on data collected from the engine dynamometer or PEMS. However, the former approach that provides the emissions in units of g/bhp-hr can hardly reflect the effect of truck loads on emissions in real-world traffic conditions and the latter cannot ensure the emission analysis for different load levels under same traffic conditions. Most studies on vehicle emissions based on the VSP have mainly focused on light-duty vehicles or transit buses and seldom on HDD trucks. In this context, this study analyzes the effect of loads in HDD trucks on emissions under the same traffic flow conditions based on the VSP. First, the second-by-second HDD trucks driving speed data were collected on real-world roads using GPS devices. Then, a method for calculating the VSP under different loads was developed, and the VSP distribution characteristics under different loads were analyzed. Subsequently, an emission model representing the effect of vehicle loads was developed, and emission factors under different loads were gathered. Finally, the differences in emissions between unloaded and fully-loaded HDD trucks were closely studied.

5 Zhang,Yu and Song DATA PREPARATION Data Collection and Testing Vehicles In order to study the characteristics of the VSP Distribution and emissions for HDD trucks under different loads, four HDD refuse trucks of the same type were selected in the Haidian District of Beijing, China, to collect second-by-second on-road driving speed data. When considering the daily operations of refuse trucks, there were only two conditions-- unloaded and fully loaded. Thus, the data collected for this the paper only covers unloaded and fully-loaded vehicles and consequently the emissions under these two load conditions. The testing vehicle parameters are provided here (see Table ). The load was approximately. tons for fully-loaded vehicles, and the vehicle curb weight was. ton. On-board GPS devices were used to collect all speed data. The data parameters are shown here (see Table ). The load conditions, including unloaded or fully loaded, were manually recorded. TABLE Testing Vehicle Parameters Vehicl e ID Vehicle Type Model Year Odometer (km) Mass (kg) Power (kw. (r.min)-) Fuel Type Emission Standard SanLianYSYZXX kw Diesel National IV SanLianYSYZXX kw Diesel National IV SanLianYSYZXX kw Diesel National IV SanLianYSYZXX kw Diesel National IV TABLE Sample of Tested Driving Data Date Time Latitude N/S( ) Longitude E/W( ) Altitude (m) Speed (km/h) Azimuth ( ) // ::... // ::... // ::... // ::... // ::... // ::... // ::... // ::.. Testing Time and Testing Route A large amount of continuous speed data were collected for a month from April, to May,. The test period on each day was : A. M. to : P. M. and included both peak hours and non-peak hours. The testing routes covered all road classes in Beijing, including expressways, principle arterials, minor arterials, and local streets. The number of miles traveled

6 Zhang,Yu and Song by the testing vehicles was, km in total. Figure shows the testing routes. Considering the large sample size and the high quality of the data gathered on expressways, the study was able to focus on the effect of different loads on emissions for HDD trucks on expressways. FIGURE. Testing routes in Beijing, China. Data Preparation After the data were collected and a data quality control process was completed, a total of,valid second-by-second speed data were identified, including, records for fully-loaded trucks and, for unloaded trucks respectively. Time fractions for both speed and acceleration for unloaded and fully-loaded conditions were analyzed and are shown in Figure and Figure.

7 Percentage of time (%) Percentage of time (%) Zhang,Yu and Song Second-by-second speed (km/h) FIGURE Time fraction of instantaneous speed during each speed interval for empty vehicles and full load vehicles. Acceleration Bin (m/s ) FIGURE. Acceleration distributions for vehicles with a empty load and vehicles with full load. It can be seen in Figure that the speed data of vehicles with a full load were more concentrated than those with an empty load. Vehicles with full-load traveled mainly in the speed range of (-) km/h. The speed of vehicles with empty load was more scattered. All testing vehicles were city refuse vehicles. Thus, the driving direction of vehicles with a full-load was from the inner city to the outer city, but in an opposite direction for vehicles with empty-load. At the peak hour of the morning time, the traffic condition for fully-loaded vehicles operated well in the outer-city direction; thus, the fully-loaded vehicles travelled at a higher speed,

8 Zhang,Yu and Song resulting in a more concentrated pattern for speed. In contrast, the traffic congestion going from the outer city to the inner city had the unloaded vehicles travel at lower speeds, resulting in a more scattered speed pattern. Figure shows the effect of load no load (?) on the acceleration behaviors of the HDD trucks. It can be seen from Figure that the accelerations of vehicles with an empty load were more aggressive than those with a full load, and the time fraction travelling at a constant speed for vehicles with a full load was significantly higher than those with an empty load. As the absolute value of the acceleration increased, the time fraction of unloaded vehicles increased more rapidly than the time fraction for the fully-loaded vehicles. VSP DISTRIBUTION CHARACTERISTICS FOR HDD TRUCKS UNDER DIFFERENT LOADS Considering that VSP distribution is a critical parameter that determines both fuel consumption and emissions for vehicles (, ), this section strives to analyzes the differences between VSP distributions under different loads. To this end, instead of using the unique mass of a vehicle to calculate the VSP for heavy-duty vehicles, the VSP calculation was refined according to the load to represent the impacts of vehicle load on operating mode in this paper; then the VSP distributions at each speed interval and under different loads were derived. Finally, the differences in operating mode between fully-loaded and unloaded conditions were compared. The methodology divided this analysis of the influence of loads on vehicle s emission into two aspects: () The influence of loads on a vehicle s operating mode. () The influence of loads on a vehicle s emission rates (g/s). VSP Calculation for Different Loads To ensure the quality of the data for analyzing load effect on vehicle activities, a data quality control process was conducted by labeling the data on graded roads both uphill and downhill and identifying any missing and wrong data. These labeled data were excluded when calculating the VSP. The equation for calculating the VSP for HDD trucks was as follows (): VSP t = Av Bv Cv mv a t t t f scale t where, VSP t is the value of VSP at time t (kw/ton); v t is the instantaneous vehicle speed at time t (m/s); a t is the instantaneous acceleration of the vehicle at time t (m/s ); m is the mass of the individual test vehicle (ton); A, B, and C are the rolling resistance coefficient (kw-sec/m), rotational resistance coefficient (kw-sec /m ), and aerodynamic drag coefficient (kw-sec /m ), respectively; and f scale is a scaling factor, with a fixed value of. being suggested (). In Equation (), the coefficients of A, B, and C are fixed values for a fixed vehicle type. ()

9 Zhang,Yu and Song Thus, the vehicle speed, acceleration, and the total mass of the vehicle will represent the power required for vehicles moving forward. Equation () shows that for vehicles with the same driving condition, the increase of m results in an increase of the VSP value directly. Thus, after examining the loaded weight of the tested refuse trucks, the value of m in Equation () for calculating the VSP for vehicles with full-load and empty-load is set to. and., respectively. The method for obtaining VSP distributions under different loads was the following: Step: Calculating the second-by-second VSP values under full load and empty load using Equation () by setting different values for m. Step : Clustering the second-by-second VSP values to obtain the bins of the VSP, denoted as VSPBin here, as follows: : N. VSP N. t VSPBin N () N [,], N Z Step : Obtaining the VSP distributions by counting the time fraction in each VSPBin at a specific speed interval for fully loaded and unloaded vehicles, respectively. To ensure the same traffic flow condition for vehicles with different loads and thus have the VSP distributions under different loads well comparable, the second-by-second speed data were divided into trajectory pieces at a -second interval. The average speed of each trajectory was calculated. The differences in VSP distributions and emissions were based on the same specific speed interval. Finally, the average speed for each trajectory was divided into speed intervals in steps of km/h, as shown in Table : TABLE Trajectory Average Speed Interval and Second-by-Second Speed Speed Bin ID Average Speed (km/h) Speed Bin ID Average Speed (km/h) (-] (-] (-] (-] (-] (-] (-]. (-] Characteristics of VSP Distributions Under Different Loads The VSP distribution is a variable that represents the power needed for a specific operating mode condition (), which then determines the emission level of vehicles. To study the characteristics of VSP distributions under different operating mode conditions, this study grouped the operating modes into four levels: Driving at low speed (-) km/h, driving at medium low speed (-) km/h, driving at medium speed (-) km/h, and driving at high speed (-) km/h. VSP distributions during each operating mode condition were analyzed and compared, respectively, and the VSP distribution characteristics were further extracted. In analysis of the above four

10 Frequency (%) Frequency (%) Frequency (%) Frequency (%) Zhang,Yu and Song operating mode conditions, the VSP distributions and acceleration distributions at speed intervals of (-) km/h, (-) km/h, (-) km/h, and (-) km/h are illustrated in Figure and Figure. % % % % % % VSPBin ID % % % % % % VSPBin ID % % % % % % (a) (-) km/h % VSPBin ID (c) (-) km/h % % % % % (b) (-) km/h % VSPBin ID (d) (-) km/h FIGURE. VSP distribution for HDD trucks with empty and full loads at different bins of average travel speed.

11 Percentage (%) Percentage (%) Percentage (%) Percentage (%) Zhang,Yu and Song Acceleration (m/s ) Acceleration (m/s ) (a) (-) km/h (b) (-) km/h Acceleration (m/s ) Acceleration (m/s ) (c) (-) km/h (d) (-) km/h FIGURE. Acceleration distribution for HDD trucks with empty- and full loads at different bins of average travel speed. Figure shows that the VSP distributions within all the speed intervals were shaped like a pendulum, regardless of the load conditions. The bottom of the pendulum for vehicles in full-load was apparently wider than that for empty-load, which indicates that load does have a significant effect on trucks VSP distributions (see Figure ). After analyzing the VSP distributions and acceleration distributions within each speed interval in Figure, several interesting characteristics were observed: When the vehicles were in full-load, the time percentage of the VSP in the bin of kw/ton was lower than that for empty-load. Specifically, the percentages of VSP bins of kw/ton of fully-loaded vehicles at the speed interval of (-] km/h, (-] km/h, (-] km/h, and (-] km/h were %, %, %, and % lower than those for unloaded vehicles. This result means that the fully-loaded vehicles spent more time to driving in steady mode with less rapid acceleration and deceleration. The VSP bin with the highest percentage moved to the right as the average speed increased, while the one for the fully-loaded vehicles moved faster than that for the unloaded vehicles. The peak percentage of the VSP distribution for all the vehicles appeared as the bin of kw/ton when vehicles drove at a speed of (-) km/h. As the average speed increased to (-) km/h, the peak percentage of the VSP distribution for fully-loaded vehicles moved to the bin of kw/ton. However, the VSP distribution of unloaded vehicles remained the same.

12 Zhang,Yu and Song Compared to the VSP distributions at each speed interval, the percentages of the VSP in the bins of kw/ton, kw/ton, kw/ton, and kw/ton for fully-loaded vehicles were much higher than those for unloaded vehicles, which also likely resulted in significant differences in emissions. Figure shows that the time fraction of the acceleration of unloaded vehicles is much higher than that of fully-loaded vehicles at the speed interval of (-] km/h, (-] km/h, and (-] km/h, which will likely result in high emissions (see Figure ). Thus, the effect of load on emissions is not only determined by the high power required to travel under a high load, but also by the different acceleration behaviors that occur under different loads. The effect of the acceleration behaviors on emissions will trade-off partly with the effect of the loads on emissions. EMISSION FACTORS UNDER DIFFERENT LOADS VSP-Based Estimation of Emission Factors Under Different Loads From the preceding section, it can be seen that there are several differences in VSP distributions between fully loaded and unloaded vehicles, which may result in considerable differences in emissions under different loads. To compare and analyze the variations of emissions for HDD trucks under different loads further, emission factors for vehicles with different loads were calculated. The emission rates in each VSP bin (), associated with VSP distributions under different loads, were used to calculate the emission factors for unloaded and fully-loaded vehicles, as shown in Equations () to () below: Emission ( v ) AvgERate VSPDistribution ( unloaded ) () unloaded k i i i EF ( v ) Emission ( v ). / v () unloaded k unloaded k k Emission ( v ) AvgERate VSPDistribution ( loaded ) () loaded k i i i EF ( v ) Emission ( v ). / v () loaded k loaded k k where, Emission unloaded (v k ) and Emission loaded (v k ) are the emissions per second of unloaded and fully loaded vehicles at the speed interval of k (g); VSP Distribution i (unloaded) and VSP Distribution i (loaded) are the percentage of VSP Bin of i (%) for unloaded and fully-loaded vehicles; AvgRate i is the average emission rates of VSP bin of i (g/s), obtained by the vehicle emission test (); EF unloaded (v k ) and EF loaded (v k ) are vehicle emission factors at the speed of v k for unloaded and fully loaded vehicles. Emission Differences Under Different Loads To quantify the differences in emissions between fully loaded and unloaded vehicles, a variable a

13 EFof PM (mg/km) α (%) EFof THC (mg/km) α (%) EFof NOx (g/km) α (%) EF of CO (g/km) α (%) EF of CO (g/km) α (%) Zhang,Yu and Song (%) representing the relative difference of emission factors between loaded and unloaded trucks was defined as follows: EFloaded ( vk )- EFunloaded ( vk ) = EF ( v ) () unloaded k ) The curves of the emission factors and a under different loads are illustrated (see FIGURE. α Speed Bin ID (a) Effect of loads on CO emission α Speed Bin ID (c) Effect of loads on THC emission α Speed Bin ID (e) Effect of loads on PM emission The Effect of Loads on Pollution Emissions. α Figure In Figure, all emission factors decrease as speed increases. When the travelling speed is low at (-) km/h, as the speed decreases further, the emission factors increase much more Speed Bin ID (b) Effect of loads on CO emission α Speed Bin ID (d) Effect of loads on NOx emission

14 Zhang,Yu and Song rapidly. There are also certain significant differences between full -loaded and unloaded vehicles:. Compared to unloaded vehicles, emission factors for all the pollutants for fully-loaded vehicles were significantly higher.. The effect of loads on emissions, the relative difference a (%), is correlated with speed, as shown in Table. When the travelling speed is medium low, (-) km/h, the load affects the emission factors the most, with an a (%) value in the range of %-%. The results by Frey et al. () showed that the emission factors of CO, CO, NOx, THC, and PM for fully-loaded vehicles were -% higher than those for unloaded vehicles in the same speed interval and also slightly higher than the results found in this study. When the travelling speed is medium, (-) km/h, the load affects the emission factors the next highest, with an a (%) value in the range of %-% (Table ).. As speed increases, the effect of the load on emissions decreases. Because the power required for a vehicle to overcome aerodynamic resistance increases cubically with speed, the effect of loads on the vehicle s power weakens relatively. When the speed is about km/h, the emissions of CO, CO, NOx, THC, and PM are affected by loads the most, with a (%) values of.%,.%,.%,.%, and.%, respectively (see Figure ).. The effect of loads on emissions differs for different pollutants, as shown in Table. Compared to unloaded vehicles, NOx is affected by the load the most, over % for the whole speed range. Except for the low speed interval, the emission factor for CO is affected the most next by % on average. PM is affected the least (see Table ). TABLE. Effect of Load on Emission Factors Difference in Emission Factors: a (%) Driving State/Range CO CO NOx THC PM Low speed Driving(-)km/h..... Medium low speed driving(-)km/h..... Medium speed driving(-)km/h..... High speed driving(>)km/h..... CONCLUSIONS AND RECOMMENDATIONS Considering the deficiencies of the current existing studies, based on data collected from an engine dynamometer or PEMS test, this paper designed a new model-based methodology to estimate the effect of truck load on emissions by collecting driving speed data for on-road trucks. To understand the influence of loads on emissions, the impact of loads on vehicle operating modes and emission rates were analyzed, respectively. First, second-by-second driving data for on-road HDD trucks under empty load and full load were collected. Based on these data, instead of using the unique mass of the vehicle to calculate the VSP for heavy-duty vehicles, the VSP

15 Zhang,Yu and Song calculation was refined according to the load to represent the impacts of vehicle loads, making it now feasible to analyze the emissions for on-road vehicles with different loads in the same traffic conditions. The differences in VSP distributions and emissions for HDD trucks under different loads were also analyzed. The major findings of this study can be thus summarized as follows:. Compared t unloaded trucks, there are significant differences in VSP distributions for fully loaded trucks. The bottom of the VSP distribution curve for fully-loaded vehicles is wider than that for unloaded vehicles at each speed interval. When trucks are in full-load mode, the percentages of low VSP range, i.e. <= kw/ton, are much lower than those for unloaded vehicles, while the percentages of high VSP ranges, i.e. > kw/ton, are significantly higher than those for unloaded vehicles. The acceleration and deceleration for unloaded vehicles are thus much more aggressive than those for fully-loaded vehicles.. When a heavy duty truck becomes fully loaded from being unloaded, its emissions increase noticeably. The emission factors of CO, CO, NOx, THC, and PM for fully loaded trucks increase the most at medium low speed, -km/h, which increases by.%,.%,.%,.%, and.% respectively. Following the medium low speed is medium speed driving, (-) km/h. With an increase in travel speed, the impact of load on emissions weakens. Vehicle loads have the greatest effect on the emissions of NOx, followed by their effects on CO. PM was affected the least by vehicle loads.. The impact of truck load on emissions is also affected by different acceleration behaviors under different loads. The aggressive behaviors of acceleration and deceleration under unloaded conditions tend to increase emissions, which trade off partially with the effect of loads on emissions. This paper was limited to a study of the effect of loads on the emissions from HDD trucks on expressways only. And only two levels of loads, full-load and empty-load, were included in the study. Thus, it becomes absolutely necessary to conduct a more in-depth study on more fine-grained load conditions, and a study on other road types as well in future studies. ACKNOWLEDGEMENT This research was supported by the Natural Science Foundation of China (NSFC) # and. REFERENCES. Smit, R., R. Smokers, and E. Rabé. A New Modelling Approach for Road Traffic Emissions: VERSIT+. Transportation Research Part D: Transport and Environment, Vol., No.,, pp. -.. Rakha, H., K. Ahn, and A. Trani. Development of VT-Micro Model for Estimating Hot Stabilized Light Duty Vehicle and Truck Emissions. Transportation Research Part D: Transport and Environment, Vol., No.,, pp. -.

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18 Zhang,Yu and Song difficulty research for study of transportation emission distribution laws in Beijing. Beijing Jiaotong Universty,