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Energy & Fuels 2007, 21, 2710-2718

Gaseous and Particle Emission Factors from the Selected On-Road Petrol/Gasoline, Diesel, and Liquefied Petroleum Gas Vehicles T. L. Chan,*,† Z. Ning,† J. S. Wang,†,‡ C. S. Cheung,† C. W. Leung,† and W. T. Hung§ Research Centre for Combustion and Pollution Control, Department of Mechanical Engineering, The Hong Kong Polytechnic UniVersity, Hung Hom, Kowloon, Hong Kong, Research Centre for Combustion and EnVironmental Technology, Shanghai Jiaotong UniVersity, Shanghai 200030, China, and Department of CiVil and Structural Engineering, The Hong Kong Polytechnic UniVersity, Hung Hom, Kowloon, Hong Kong ReceiVed April 5, 2007. ReVised Manuscript ReceiVed June 5, 2007

Motor vehicle emissions have been identified as the major source of air pollution in most urban cities. They have a serious impact on our urban air quality and public health. In the present study, the vehicle exhaust gaseous and particle emissions from different fuel types of on-road representative vehicles including petrol/ gasoline, diesel, and liquefied petroleum gas vehicles for urban driving conditions ranging from 10 to 70 km h-1 were investigated using the chassis dynamometer and remote sensing testing systems. Both testing systems have been widely used for dirty screening/clean screening credit utility programs, and for inspection and maintenance programs. The measured vehicle exhaust emission concentrations from both testing systems were further calculated into the average gaseous and particle (i.e., PM0.1, PM2.5, and PM10) emission factors (EFs) in milligrams or grams per kilometer for different urban driving conditions. The results show that the different fuel types of the selected on-road vehicles and their driving conditions have a direct effect on the characterization of vehicle exhaust gaseous and particle emission factors. The correlation equations of the calculated average vehicle emission factors of different size fractionated exhaust particles and hydrocarbons (HCs) in milligrams or grams per kilometer with a good regression coefficient, R2, value from different fuel types of on-road representative vehicles were also established. It is demonstrated clearly that the correlation between EFPM and EFHC is highly sensitive to the different fuel types, engine cylinder sizes and rated powers, and aftertreatment emission control and maintenance conditions of the selected on-road vehicles. The results of the present study can be used for predicting the gaseous and particle pollutants dispersion from on-road vehicles in urban roadway environments.

1. Introduction Motor vehicle emissions are the major source of air pollution in most urban cities.1-6 Many vehicle emissions measurements and control methods have been carried out to provide criteria * Corresponding author. Tel.: (852) 2766 6656. Fax: (852) 2365 4703. E-mail: [email protected]. † Department of Mechanical Engineering, The Hong Kong Polytechnic University. ‡ Shanghai Jiaotong University. § Department of Civil and Structural Engineering, The Hong Kong Polytechnic University. (1) Mayer, H. Air Pollution in Cities. Atmos. EnViron. 1999, 33, 40294037. (2) Chan, T. L.; Ning, Z.; Leung, C. W.; Cheung, C. S.; Hung, W. T.; Dong, G. On-road Remote Sensing of Petrol Vehicle Emissions Measurement and Emission Factors Estimation in Hong Kong. Atmos. EnViron. 2004, 38, 2055-2066. (3) Chan, T. L.; Ning, Z. On-road Remote Sensing of Diesel Vehicle Emissions Measurement and Emission Factors Estimation in Hong Kong. Atmos. EnViron. 2005, 39, 6843-6856. (4) Wang, J. S.; Chan, T. L.; Ning, Z.; Leung, C. W.; Cheung, C. S.; Hung, W. T. Roadside Measurement and Prediction of CO and PM2.5 Dispersion from On-road Vehicles in Hong Kong. Transp. Res. D 2006, 11, 242-249. (5) Hong Kong Environmental Protection Department (HKEPD). Environmental Protection Department of Hong Kong SAR. http://www.epd.gov.hk/epd/english/environmentinhk/air/air_maincontent.html (accessed Jun 2007). (6) Ning, Z.; Chan, T. L. On-road Remote Sensing of Liquefied Petroleum Gas (LPG) Vehicle Emissions Measurement and Emission Factors Estimation. Paper under preparation.

for determining emission reduction requirements and evaluating the effectiveness of emission control strategies and regulations in order to meet clean/better air quality goals. The chassis dynamometer testing system has been widely used to investigate the characteristics of vehicle exhaust emissions. McKain and Clark7 used the chassis dynamometer testing system to develop the regression of driving speed and power for the quality control of heavy-duty vehicles. Recently, Hawley et al.8 carried out time-alignment sensitivity studies to assess the accuracy of instantaneous NOX emissions on a mass basis using the chassis dynamometer testing system. Joumard et al.9 have tested 39 light-duty diesel vehicles with the chassis dynamometer system. Their results showed the influence of the average cycle speed, load, and vehicle category on vehicle emissions. Ning et al.10 have investigated the relationship among ultrafineparticle, fine-particle, and coarse-particle emission factors and hydrocarbon (HC) emission factors from different fuel types of on-road representative vehicles under the chassis dynamometer (7) McKain, D. L.; Clark, N. N. Speed and Power Regressions for Quality Control of HeaVy Duty Vehicle Chassis Dynamometer Research; SAE Technical Paper No. 1999-01-0614; SAE: Warrendale, PA, 1999. (8) Hawley, J. G.; Brace, C. J.; Cox, A.; Ketcher, D.; Stark, R. Influence of Time-alignment on the Calculation of Mass Emissions on a Chassis Rolls Dynamometer; SAE Technical Paper No. 2003-01-0395; SAE: Warrendale, PA, 2003. (9) Joumard, R.; Andre´, M.; Vidon, R.; Tassel, P. Characterizing Real Unit Emissions for Light Duty Goods Vehicles. Atmos. EnViron. 2003, 37, 5217-5225.

10.1021/ef070172i CCC: $37.00 © 2007 American Chemical Society Published on Web 07/20/2007

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Table 1. A Comparison between the Chassis Dynamometer and Remote Sensing Testing Systems from Kuhns et al.22 chassis dynamometer

remote sensing

cost per vehicle tested measurement type duration of test precision of repeated measurements on same vehicles accuracy of fleet average emission factor

high individual vehicle long (user-selected test cycle) excellent

availability of vehicles tested

poor, must be procured from owner all types

chemical species measured selectivity of vehicles measured driving modes tested

poor, low number of associated with high cost

excellent all (user-selected)

testing system. Weilenmann et al.11 have investigated the cold start emissions of Euro-3 gasoline cars, Euro-2 diesel cars, and old pre-Euro-1 gasoline cars for different ambient temperatures using the chassis dynamometer testing system. Previously, Greeves and Wang12 presented an experimental study of a diesel engine for different operating conditions and established a correlation between the particle and HC emissions on a mass basis. Williams et al.13 measured a correlation between particulate matter (PM) and HC emissions in the Federal Test Procedures (FTP) of the United States on a fleet of light-duty and high-duty diesel vehicles. The results showed that high particle emission rates were associated with high HC emission rates with good regression coefficient, R2, values. Ristovski et al.14 have studied the particle and carbon dioxide emissions from a fleet of six dedicated liquefied-petroleum-gas- (LPG-) powered vehicles and five unleaded-petrol-powered vehicles for different driving speeds using the chassis dynamometer testing system. Since the late 1980s, a new vehicle emission measurement technique called remote sensing technology has been developed to measure the instantaneous vehicle exhaust emission profiles from real-world vehicles.15 The remote sensing vehicle exhaust emissions testing system uses infrared and ultraviolet spectroscopy to measure the gaseous concentrations (i.e., CO, CO2, HC, and NO) from vehicle exhaust tailpipes on the roadway. An on-road vehicle exhaust emissions survey using remote sensing technology offers a quick and effective method of monitoring exhaust emissions (i.e., CO, CO2, HC, and NO) from different on-road vehicles under normal driving operations. A large amount of vehicle emission concentrations and driving speed and acceleration information and the impact of high-emitting (10) Ning, Z.; Chan, T. L.; Wang, J. S.; Cheung, C. S.; Leung, C. W.; Hung, W. T. Relationship Between Ultrafine Particle, Fine Particle, Coarse Particle and Hydrocarbon (HC) Emission Factors from the Selected Local Representative In-use Vehicles in Hong Kong. Paper No. MoVE04-42; Proceedings of the MoVE2004 Workshop, Hong Kong, December 11-14, 2004. (11) Weilenmann, M.; Soltic, P.; Saxer, C.; Forss, A. M.; Heeb, N. Regulated and Nonregulated Diesel and Gasoline Cold Start Emissions at Different Temperatures. Atmos. EnViron. 2005, 39, 2433-2441. (12) Greeves, G.; Wang, C. H. T. Origins of Diesel Particulate Mass Emission. SAE Technical Paper No. 810260; SAE: Warrendale, PA, 1981. (13) Williams, D. J.; Milne, J. W.; Quigley, S. M.; Roberts, D. B. Particulate-emissions from In-use Motor Vehicle. 2. Diesel Vehicles. Atmos. EnViron. 1989, 23, 2647-2661. (14) Ristovski, Z. D.; Jayaratne, E. R.; Morawska, L.; Ayoko, G.A.; Lim, M. Particle and Carbon Dioxide Emissions from Passenger Vehicles Operating on Unleaded Petrol and LPG Fuel. Sci. Total EnViron. 2005, 245, 93-98. (15) Bishop, G. A.; Stedman, D. H. On-road Carbon Monoxide Emission Measurement Comparisons for the 1988-1989 Colorado Oxy-fuel Program. EnViron. Sci. Technol. 1990, 24, 843-847.

low individual vehicle instantaneous (0.5 s) fair, requires consistent operating mode during second measurement excellent, data can be stratified to reproduce average emissions for registered fleet excellent, no owner consent required restricted, remote sensing measurements only excellent some, may stratify emissions by vehicle-specific power (VSP) to reconstruct cycle

vehicles can be obtained and identified within a short period of time from a single traffic lane. The application of a remote sensing vehicle exhaust emissions testing system has been used in polluted areas of the United States, Canada, Mexico, Australia, Taiwan, Hong Kong, and many other parts of the world to achieve different tasks such as traffic fleet and volume characterization for the low- and high-emitter profiling, dirty screening program; clean screening credit utility program; and on-road vehicle emission inventories/factors development.2,3,6,16-18 Pokharel et al.19,20 used the remote sensing device to measure the gaseous emissions from on-road vehicles in the Denver, Colorado, area. They showed that there was an excellent correlation between the fleet averaged on-road remote sensing data and the IM240 program data. Compared with the traditional emission inspection methods and research work on the chassis dynamometer testing system, the remote sensing testing system can measure a large amount of gaseous emission data from onroad vehicles under real-world driving conditions. It also provides reliable emission information for evaluating real-world on-road vehicle emission dispersion models.21 On the other hand, the chassis dynamometer testing system provides vehicle emissions information under controlled lab conditions. Kuhns et al.22 have recently compared these two testing systems and summarized their major features, as listed in Table 1. Recently, the on-road vehicle emissions from aggregate gasoline/petrol,2 diesel,3 and LPG6 vehicles have been studied using the remote sensing exhaust emissions testing system. A unique database of the correlation of gasoline/petrol, diesel, and (16) Cross, T. Remote Sensing Technology. In Proceedings of Better Air Quality-Motor Vehicle Control & Technology Workshop, Session 2In-use Vehicle Testing Technology; The Hong Kong Polytechnic University: Hong Kong, 2000. (17) Walsh, M. P. High Polluting Petrol Fuelled Vehicles- An Approach to Reducing Emissions. Report to the Environmental Protection Department of Hong Kong SAR: China, 2001. (18) Pokharel, S. S.; Bishop, G. A.; Stedman, D. H. An On-road Motor Vehicle Emissions Inventory for Denver: An Efficient Alternative to Modeling. Atmos. EnViron. 2002, 36, 5177-5184. (19) Pokharel, S. S.; Bishop G. A.; Stedman, D. H. On-road Remote Sensing of Automobile Emissions in the Denver Area: Year 2. CRC Project Report No. E-23-4.; Coordinating Research Council: Alpharetta, GA, 2001. (20) Pokharel, S. S.; Bishop, G. A.; Stedman, D. H. Fuel-based Onroad Motor Vehicle Emissions Inventory for the Denver Metropolitan Area. In Proceedings of International Emission InVentory Conference, Denver, CO, 2001. (21) Ekstrom, M.; Sjodin, A.; Andreasson, K. Evaluation of the COPERT III Emission Model with On-road Optical Remote Sensing Measurements. Atmos. EnViron. 2004, 38, 6631-6641. (22) Kuhns, H. D.; Mazzoleni, C.; Moosmuller, H.; Nikolic, D. Remote Sensing of PM, NO, CO and HC Emission Factors for On-road Gasoline and Diesel Engine Vehicles in Las Vegas, NV. Sci. Total EnViron. 2004, 322, 123-137.

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Table 2. Specifications of the Typical Selected On-road Vehicles vehicle type fuel code year of manufacture engine cylinder capacity (cc) net weight (kg) rated power maximum torque

light bus

private car 1

private car 2

diesel DLB 1993 3661

diesel DLD 1998 2779

light duty

diesel DT 1999 2986

taxi

gasoline/petrol PPC1 1997 2156

gasoline/petrol PPC2 1998 1498

LPG LPGT 2001 1998

taxi

2920-2960 72 kW @3400 rpm 240 Nm @1800rpm

1645-1725 66 kW @4000 rpm 192 Nm @2400 rpm

1340-1400 65 kW @4000 rpm 180 Nm @2400 rpm

1500 119 kW @5500rpm 217 Nm @4500rpm

1060-1115 74 kW @5600 rpm 137 Nm @4400 rpm

1330-1390 58 kW @4400rpm 160 Nm @2400rpm

LPG vehicle emission factors for different model years and engine sizes for urban driving patterns has been established. Mazzoleni et al.23 have measured the CO, HC, and NO emissions from vehicles using a commercial vehicle emission remote sensing system (VERSS) and also have measured the particulate matter emission factors simultaneously using a newly developed PM-VERSS in Las Vegas, Nevada. They also correlated the various gaseous emissions and PM emissions. The present study intends to investigate the vehicle exhaust gaseous and particle emissions from different fuel types of onroad representative vehicles including petrol/gasoline, diesel, and LPG vehicles for urban driving conditions ranging from 10 to 70 km h-1 using the chassis dynamometer and remote sensing testing systems, and it intends to establish a correlation of gaseous and particle emission factors (i.e., CO, HC, NO, PM0.1, PM2.5, and PM10) from different fuel types of the selected on-road vehicles as a function of different urban driving speeds.

concentrations and temperature and flow rate data from the vehicle exhaust tailpipe, each chassis dynamometer test was first allowed to run at the required constant driving speed for several minutes until the steady-state values had been reached. Details of gaseous and particle emission measurement systems can be found in our previous studies.10,26-29 2.1.1. Calculation of AVerage Vehicle Gaseous Emission Rate and Factor from the CD Testing System. The different fuel types of the selected on-road vehicles were tested for different steadystate driving conditions,10,23,24,27 namely, a constant driving speed of 10 to 70 km h-1, and the gaseous emission concentrations from vehicle exhaust tailpipes were collected. The average vehicle gaseous emission rate, ERi,j25 is converted to the average vehicle gaseous emission factor, EFi,j as follows:

2. Experimental Methodology

2.1.2. Calculation of AVerage Vehicular Particle Emission Rate and Factor from the CD Testing System. The different fuel types of the selected on-road vehicles were tested for different steadystate driving conditions,10,23,24 namely, a constant driving speed of 10 to 70 km h-1, and the particle emission concentrations from vehicle exhaust tailpipes were collected. The average vehicle particle emission rate, ERij is converted to the average vehicle particle emission factor, EFij as follows:27

In the present study, the different fuel types of on-road representative vehicles, namely, a diesel light-duty vehicle (DLD), diesel taxi (DT), diesel light bus (DLB), petrol/gasoline passenger cars (PPC1 and PPC2), and liquefied petroleum gas taxi (LPGT), were selected, which covers the majority of the on-road vehicle fleet. The typical selected on-road vehicle specifications are listed in Table 2. The exhaust gaseous and particle emissions from these selected vehicles were measured for different steady-state driving conditions24 using the chassis dynamometer and on-road remote sensing vehicle exhaust emissions testing systems for driving speeds ranging from 10 to 70 km h-1. The driving speed range covers most urban driving speed profiles. 2.1. Chassis Dynamometer (CD) Testing System. The different fuel types of the selected on-road vehicles were tested for different steady-state driving conditions ranging from 10 to 70 km h-1,10,24 using the CD testing system (ESP Precision Dynamometer Model PD-250) from Environmental Systems Products Holdings Inc. (ESP), U.S.A.25 The exhaust gaseous emissions (i.e., CO, HC, and NO) from the tested vehicles were measured using a series of gas analyzers including a heated HC analyzer (California Analytical Instrument Model 300 flame ionization detector), heated NO/NOx analyzer (California Analytical Instrument Model 400 chemiluminescence detector), and CO/CO2 analyzer (California Analytical Instrument Model 300 nondispersive infrared (NDIR)), while the exhaust particle emissions were measured using a scanning mobility particle sizer (TSI Inc. Model 3934- SMPS, U.S.A.) and aerodynamic particle sizer (TSI Inc. Model 3310- APS, U.S.A.) coupled with an ejector diluter (Dekati Ltd., Finland). To ensure the reliability and repeatability of the measured gaseous and particle (23) Mazzoleni, C.; Moosmu¨ller, H.; Kuhns, H. D.; Keislar, R. E.; Barber, P. W.; Nikolic, D.; Nussbaum, N. J.; Watson, J. G. Correlation Between Automotive CO, HC, NO, and PM Emission Factors from On-road Remote Sensing: Implications for Inspection and Maintenance Programs. Transp. Res. D 2004, 9, 477-496. (24) Protection of EnVironment, 40 CFR Sec. 85.2230: Steady State Test Dynamometer; Environmental Protection Agency: Washington, DC, 2003. (25) SerVice and Maintenance Manual for Chassis Dynamometer, edition 1.2; Environmental Systems Products, Ltd.: East Granby, CT, 2002.

ERij (mg s-1) ) Ci (ppm) × Di (g L-1) × FRi (m3 s-1)

(1)

EFij (mg km-1) ) [ERij (mg s-1)/Vj (km h-1)] × 3600 (s h-1) (2)

ERi,j (mg s-1) ) Cmass,i (mg cm-3) × FRi (m3 s-1) × 106 cm3 m-3 (3) EFi,j (mg km-1) ) [ERi,j (mg s-1)/Vj (km h-1)] × 3600 (s h-1) (4) 2.2. Remote Sensing (RS) Testing System. Jayaratne et al.30 observed that the remote sensing technology was developed by Stedman, Bishop and co-workers in the late 1980s31,32 using a set of NDIR emitters and sensors strategically placed on either side of a road lane for the remote detection of exhaust emission gases from passing vehicles. The infrared beam was positioned horizontally (26) Wong, C. P.; Chan, T. L.; Leung, C. W. Characterisation of Diesel Exhaust Particle Number and Size Distributions Using Mini-dilution Tunnel and Ejector-diluter Measurement Techniques. Atmos. EnViron. 2003, 37, 4435-4446. (27) Ning, Z. Measurement and Dispersion Prediction of Gaseous and Particle Emissions from On-road Vehicles in Hong Kong. MPhil Thesis, Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, April 2005. (28) Leung, D. Y. C.; Luo, Y.; Chan, T. L. Optimization of Exhaust Emissions of a Diesel Engine Fueled with Biodiesel. Energy Fuels 2006, 20, 1015-1023. (29) Chan, T. L.; Cheng, X. B. Numerical Modeling and Experimental Study of Combustion and Soot Formation in a Direct Injection Diesel Engine. Energy Fuels 2007, 21, 1483-1492. (30) Jayaratne, E. R.; Morawska, L.; Johnson, G. R. The Use of Carbon Dioxide as a Tracer in the Determination of Particle Number Emissions from Heavy-duty Diesel Vehicles. Atmos. EnViron. 2005, 39, 6812-6821. (31) Bishop, G. A.; Starkey, J. R.; Ihlenfeldt, A.; Williams, W. J.; Stedman, D. H. IR Long-path Photometry- A Remote-sensing Tool for Automobile Emissions. Anal. Chem. 1989, 61, 671A-677A. (32) Bishop, G. A.; Stedman, D. H. Measuring the Emissions of a Passing Car. Acc. Chem. Res. 1996, 29, 489-495.

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Energy & Fuels, Vol. 21, No. 5, 2007 2713

at the mean height of the typical vehicle exhaust tailpipe, and the sensor was programmed to trigger each time a moving vehicle drove past so that the gaseous sampling was at roughly the same position relative to each vehicle. In this way, they were able to determine ratios of CO and HC concentrations to the CO2 concentration. These two ratios were assumed constant for a given exhaust plume. Typical engine parameters were then used to calculate the mass emission factor of CO2 from its vehicular exhaust tailpipe, and it was assumed that they were from a fully stoichiometric gasoline vehicle, and hence the CO and HC emission factors were then estimated. It should be noted that this system only measured the ratios of the two gases to the CO2 concentration, and therefore it did not matter where the gaseous sampling was taken within the vehicular exhaust plume of a moving vehicle as each component gas was expected to be diluted by the same fraction at every point. Wang et al.33 also further investigated the influence of temperature and composition of the exhaust gas on the inferred CO concentration through both line strength and line width, and proposed a suitable approach to reduce these effects in the typical exhaust temperature ranges. In the present study, the different fuel types of the selected onroad vehicles were also tested for different driving speeds ranging from 10 to 70 km h-1 using the RS testing system (AccuScan RSD 4000, Environmental System Products Holding Inc., U.S.A.). A device for measuring the speed and acceleration/deceleration of moving vehicles driving past the remote sensor, which includes an emitter bar and a detector bar, was also used. The vehicle license plate can also be captured by the digital color video camera system and the vehicle information can be obtained at a later stage.2,3,6 Owing to the unavoidable change of local environmental conditions (i.e., surrounding vehicle emission concentrations, humidity, temperature, wind speed, etc.) from time to time and site to site, the measurement should be initiated right after the completion of system calibration at the site. The gaseous calibration of the RS testing system should be performed more frequently in order to minimize the effect of environmental conditions. In general, the RS testing system requires a minimal amount of exhaust gas concentration. If the vehicular plume concentration from the moving vehicle does not meet the minimal gas concentration requirements, the measurement record will be marked as “invalid” automatically from the RS testing system.2,3,6 To ensure the repeatability of the vehicle emission data, each selected vehicle was tested at least 70 times for obtaining enough and reliable samplings for further analysis in the present study. 2.2.1. Calculation of AVerage Vehicle Emission Factors from the RS Testing System. On the basis of the measured gaseous emission data from different fuel types of the selected on-road vehicles using the remote sensing testing system, the vehicle emission factors of CO, HC, and NO were calculated under realworld vehicle driving conditions in respect to instantaneous driving speed profiles. The measured ratios of CO, HC, and NO to CO2 concentrations (i.e., Q, Q′, and Q′′) were further analyzed to correlate the different fuel types of on-road vehicle emissions and the driving conditions using the regression approach. It should be noted that the ratio of volume emission concentration in Q, Q′, and Q′′ along the vehicular exhaust plume would not change during the remote sensing measurement.32 Two mathematical forms of the regression analysis were established to correlate the on-road vehicle emissions and the driving conditions on the road lane on the basis of the calculated regression coefficient, R2. They were similar to prior research work2,3,6,34 which had been incorporated into the volume emission concentrations as follows: Q ) c1 + c2V + c3V 2 + c4a + c5a2

(5)

Q′ ) c′1 + c′2V + c′3V 2 + c′4a + c′5a2

(6)

ln Q′′ ) c′′1 + c′′2 V + c′′3V 2 + c′′4a + c′′5a2

(7)

Figure 1. A comparison of average (a) EFCO, (b) EFHC, and (c) EFNO for different fuel types of the selected on-road vehicles and driving speeds using the CD testing system.

where the constant coefficients of c1, c2, c3, ..., c′′4, and c′′5 are determined by the regression analysis. The ratios were further converted into a mass-based emission rate, and the conversion equations in mass emission concentrations35,36 (33) Wang, J.; Maiorov, M.; Jeffries, J. B.; Garbuzov, D. Z.; Connolly, J. C.; Hanson, R. K. A Potential Remote Sensor of CO in Vehicle Exhausts Using 2.3 µm Diode Lasers. Meas. Sci. Technol. 2000, 11, 1576-1584. (34) Yu, L. Remote Vehicle Exhaust Emission Sensing for Traffic Simulation and Optimization Models. Transp. Res. D 1998, 3, 337-347. (35) Singer, B. C.; Harley, R. A.; Littlejohn, D.; Ho, J.; Vo, T. Scaling of Infrared Remote Sensor Hydrocarbon Measurements for Motor Vehicle Emission Inventory Calculations. EnViron. Sci. Technol. 1998, 32, 32413248.

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Table 3. The Regression Equations of EFCO, EFHC, and EFNO for Different Fuel Types of the Selected On-Road Vehicles in Terms of Instantaneous Driving Speed Profiles Using the RS Testing System constant coefficients vehicle type

regression equation

R2

samples

b

c

d

DLD DT DLB PPC1 PPC2 LPGT DLD DT DLB PPC1 PPC2 LPGT DLD DT DLB PPC1 PPC2 LPGT

EFCO) b + cV + + f/V EFCO ) b + c ln(V)/V + d/V + fe-V 2 EFCO ) b + cV + dV + fV2 ln(V) + g ln(V)/V EFCO ) becV EFCO ) be-V/c + d EFCO ) bV2 + cV + d EFHC ) be-V/c + d EFHC ) b + cV + d ln(V)/V2 + f/V2 + g-V EFHC ) be-V/c + d EFHC ) bV2 + cV + d EFHC ) b + cV + d/V0.5 + f ln(V)/V + ge-V EFHC ) bV-c EFNO ) b + cV + dV0.5 + f ln(V) EFNO ) b + cV + d/ln(V)+ f/V0.5 + g/V2 EFNO ) be-V/c + d EFNO ) be-V/c + d EFNO ) b + c ln(V) EFNO ) be-V/c + d

0.9715 0.9633 0.9925 0.7491 0.9532 0.8405 0.9957 0.9239 0.9936 0.7622 0.9796 0.9247 0.9913 0.9976 0.9911 0.9442 0.7710 0.8960

116 157 74 125 130 100 116 157 74 125 130 100 116 157 74 125 130 100

1.2529 -0.9740 -11.0911 2.0471 3.8036 0.0007 1.9578 -0.0614 0.0236 0.0001 -5.5044 15.4695 2.2361 -26.3678 4.0696 0.2805 0.2960 0.3057

-0.0035 22.1021 0.4630 -0.0087 6.4958 -0.0676 17.9692 0.0013 2.6996 0.0129 0.0161 1.3162 -0.0458 0.0073 18.4841 16.9385 -0.0659 22.8876

0.0002 -24.8924 -0.0211

6.6809 2.28 × 103 0.0038

46.8421

1.1323 2.5607 0.0004 142.9499 14.9035 0.4038 78.9564

-2.5947 × 102

4.11 × 103

-81.4462

-3.66 × 103

-2.3102 -1.68 × 102

-4.62 × 102

dV2

can be expressed as follows: ECO(g L-1) )

Q 28 × Dfuel × 1 + Q + (3Q′/0.493) Mfuel

(8)

EHC(g L-1) )

Q′/0.493 44 × Dfuel × 1 + Q + (3Q′/0.493) Mfuel

(9)

ENO(g L-1) )

Q′′ 30 × Dfuel × 1 + Q + (3Q′/0.493) Mfuel

(10)

where Mfuel is the molar mass of the fuel type; Dfuel is the density of the fuel type. The average vehicle emission factor, EFi,j, of individual emission species i (i.e., CO, HC, and NO) for the selected fuel type of onroad vehicle j (i.e., gasoline/petrol, diesel, or liquefied petroleum gas) can be calculated as EFi,j (g km-1) ) Ei,j (g L-1) × Gj[L × 100 (km-1)]/100 (11) where Gj is the fuel consumption of the selected fuel type of onroad vehicle j.2,3,6, 10,37-40

3. Results and Discussion 3.1. Vehicle Gaseous Emission Factors (EF) Using the CD Testing System. On the basis of the measured gaseous emission data from different fuel types of the selected on-road vehicles using the CD testing system, the average vehicle gaseous emission factors of different fuel types of the selected on-road vehicles for different driving speeds were calculated. Figure (36) Holmen, B. A.; Niemeier, D. A. Characterizing the Effects of Driver Variability on Real-world Vehicle Emissions. Transp. Res. D 1998, 3, 117128. (37) Tong, H. Y.; Hung, W. T.; Cheung, C. S. On-road Motor Vehicle Emissions and Fuel Consumption in Urban Driving Conditions. J. Air Waste Manage. Assoc. 2000, 50, 543-554. (38) E-LPG, Autogas in Hong Kong. Available from http://www.e-lpg.com/autogas.asp?cname)hong%20kong&isreg)f (accessed Jun 2007). (39) Fuel Consumption Guide Database 1986-2003; Australian Greenhouse Office (AGO): Canberra, Australia. Available from http://www.greenhouse.gov.au/fuelguide/ (accessed Jun 2007). (40) Hung, W. T.; Tong, H. Y.; Cheung, C. S. A Modal Approach to Vehicular Emissions and Fuel Consumption Model Development. J. Air Waste Manage. Assoc. 2005, 55, 1431-1440.

1.3005 1.96 × 102 0.2687 0.0250

f

g

0.0604

1a-c show the calculated average vehicle EFCO, EFHC, and EFNO of different vehicles for the driving speed range of 10 to 70 km h-1. Generally, the average EFCO, EFHC, and EFNO of different fuel types of the selected on-road vehicles decrease with an increase in the driving speed, as shown in Figure 1a-c. This trend is similar to the emission factor data from the on-road remote sensing tests (Chan et al.,2 Chan and Ning,3 and Ning and Chan6). The decrease of EFCO from 10 to 70 km h-1 is 75%, 81%, 78%, 85%, 88%, and 85% for DLD, DT, DLB, PPC1, PPC2, and LPGT, respectively. The decrease of EFHC from 10 to 70 km h-1 is 70%, 76%, 74%, 82%, 92%, and 90% for DLD, DT, DLB, PPC1, PPC2, and LPGT, respectively. The decrease of EFNO from 10 to 70 km h-1 is 66%, 67%, 71%, 65%, 79%, and 83% for DLD, DT, DLB, PPC1, PPC2, and LPGT, respectively. For the tested diesel vehicles of DLD, DT, and DLB, the vehicle emission factors of CO, HC, and NO are generally higher than that of petrol and LPG vehicles, as shown in Figure 1a-c. The DLB vehicle has the highest emission factors of CO, HC, and NO among all different fuel types of the selected on-road vehicles due to its having the largest engine cylinder size and rated power, and a maintenance problem. The petrol vehicles and LPG vehicle share a close emission factor range for CO, HC, and NO. 3.2. Vehicle Gaseous Emission Factors Using the RS Testing System. On the basis of the measured gaseous emission data from different fuel types of the selected on-road vehicles using the remote sensing exhaust emissions testing system, the average EFCO, EFHC, and EFNO of different fuel types of the selected on-road vehicles and driving speeds were calculated. The regression equations of the average EFCO, EFHC, and EFNO of different fuel types of the selected on-road vehicles as a function of different driving speeds with a good regression coefficient, R2, value are listed in Table 3. Hence, a good correlation between the average vehicle emission factors of different vehicles and driving speeds are demonstrated. The overall regression equations of average EFCO, EFHC, and EFNO with a good R2 value for different fuel types of the selected on-road vehicles in terms of instantaneous driving speeds ranging from 10 to 70 km h-1 using the RS testing system are listed in Table 4. The results also indicate that on-road vehicle driving conditions have a direct effect on the characterization of vehicle emission factors.

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Energy & Fuels, Vol. 21, No. 5, 2007 2715

Table 4. The Regression Equations of Average EFCO, EFHC, and EFNO from the Overall Different Fuel Types of the Selected On-Road Vehicles in Terms of Instantaneous Driving Speeds Ranging from 10 to 70 km h-1 Using the RS Testing System vehicle type

regression equation

EFCO ) 3.3146e-0.049V EFHC ) 1.3464e-0.0425V EFNO ) 23.3530V-1.2674 petrol vehicles EFCO ) 7.2151V-0.4415 EFHC ) 6.3260V-1.1644 EFNO ) 1.7327V-0.9894 LPG vehicles EFCO ) 0.0007V2 - 0.0676V + 2.5607 EFHC ) 15.4695V-1.3162 EFNO ) 0.3057e-V/22.8876 + 0.0604

diesel vehicles

R2

samples

0.8754 0.9055 0.8473 0.9583 0.8486 0.9211 0.8405 0.9247 0.8960

347

255

100

Table 5. The Correlation Equations of Average EFCO, EFHC, and EFNO from the Overall Different Fuel Types of the Selected On-Road Vehicles between the CD and RS Testing Systems vehicle type

regression equation

R2

diesel vehicles

EFCO(CD) ) 0.9405EFCO(RS) + 0.2453 EFHC(CD) ) 0.2030EFHC(RS) + 0.0778 EFNO(CD) ) 0.2582EFNO(RS) + 0.0823 EFCO(CD) ) 0.6839EFCO(RS) - 0.6211 EFHC(CD) ) 0.2047EFHC(RS) + 0.0151 EFNO(CD) ) 0.6987EFNO(RS) + 0.0366 EFCO(CD) ) 0.7520EFCO(RS) - 0.4861 EFHC(CD) ) 0.3851EFHC(RS) + 0.0016 EFNO(CD) ) 0.5261EFNO(RS) + 0.0011

0.9723 0.9605 0.9765 0.9651 0.9288 0.9271 0.9854 0.9897 0.9485

petrol vehicles LPG vehicles

3.3. Correlation of Average Vehicle Gaseous Emission Factors Using the CD and RS Testing Systems. On the basis of the obtained regression equations of the average EFCO, EFHC, and EFNO of the overall different fuel types of the selected on-road vehicles (i.e., diesel, petrol, and LPG vehicles) as a function of different driving speeds using the CD and RS testing systems, the average EFCO, EFHC, and EFNO for the corresponding driving speeds (i.e., 10, 30, 50, and 70 km h-1) were calculated. Finally, the highly correlated equations with a good R2 value of average EFCO, EFHC, and EFNO from the overall different fuel types of the selected on-road vehicles using the CD and RS testing systems for the corresponding driving speeds ranging from 10 to 70 km h-1 were then established and are listed in Table 5. The resultant good R2 demonstrates that the calculated average EFCO, EFHC, and EFNO obtained from the regression equations of the RS testing system are highly correlated to those from the regression equations of the CD testing system. 3.4. Average Vehicle Particle Emission Factors (EFPM) Using the CD Testing System. Average vehicle EFPM values of different fuel types of the selected on-road vehicles for different particle size ranges were calculated using eqs 3 and 4. Figure 2a-c show the comparison of average particle emission factors of Dp< 0.1 µm (PM0.1), Dp< 2.5 µm (PM2.5), and Dp< 10 µm (PM10) from different fuel types of the selected on-road vehicles for different driving speeds ranging from 10 to 70 km h-1. In general, the particle emission factors decrease with an increase in the driving speeds for the studied particle sizes ranging from 0.1 to 10 µm in the present study. A similar changing trend of particle emission factors can also be found in the National Atmospheric Emission Inventory (NAEI) of the United Kingdom.41 Wang et al.42 concluded that the increasing of the engine speeds would lead to a decrease in the engine exhaust concentrations of particulate matter due to the increase (41) National Atmospheric Emission Inventory (NAEI). Exhaust Emission Factors 2003: Database of Emission Factors. National Atmospheric Emission Inventory (NAEI) of United Kingdom. Available from: http:// www.naei.org.uk/emissions/index.php (accessed Jun 2007).

Figure 2. Comparison of (a) EFPM0.1, (b) EFPM2.5, and (c) EFPM10 for different fuel types of the selected on-road vehicles and driving speeds using the CD testing system.

in engine combustion efficiency. Recent studies have shown that the fuel and lube oil composition and engine duty cycle and operating conditions have a direct effect on the particle emissions.43,44 Chan and Cheng29 have also investigated the effects of engine load and fuel injection conditions on diesel engine combustion and gaseous and particulate emissions formation and concentration. The average particle emission factors from the selected diesel vehicles are typically much higher than those of the selected petrol and LPG vehicles for different driving conditions. It also indicates that the diesel vehicles are the major particle pollutant sources of on-road vehicles in most urban areas. Figure 2a shows that the average ultrafine particle emission factors (EFPM0.1) from different fuel types of the selected on(42) Wang, Y. F.; Huang, K. L.; Li, C. T.; Mi, H. H.; Luo, J. H.; Tsai, P. J. Emissions of fuel metals content from a diesel vehicle engine. Atmos. EnViron. 2003, 37, 4637-4643. (43) Kittelson, D. B.; Watts, W. F.; Johnson, J. P. On-road and laboratory evaluation of combustion aerosols-Part 1: Summary of diesel engine results. J. Aerosol Sci. 2006, 37, 913-930. (44) Kittelson, D. B.; Watts, W. F.; Johnson, J. P.; Schauer, J. J.; Lawson, D. R. On-road and laboratory evaluation of combustion aerosols-Part 2: Summary of spark ignition engine results. J. Aerosol Sci. 2006, 37, 931949.

2716 Energy & Fuels, Vol. 21, No. 5, 2007

Chan et al.

Figure 3. The correlation plots of (a) EFPM0.1 and EFHC, (b) EFPM2.5 and EFHC, and (c) EFPM10 and EFHC from the selected on-road diesel vehicles using the CD testing systems. Table 6. Regression Equations of EFPM0.1, EFPM2.5, EFPM10, and EFHC from the Selected On-Road Diesel Vehicles Using the CD Testing Systems vehicle type

regression equations

R2

samples

DLD DT DLB diesel vehicles DLD DT DLB diesel vehicles DLD DT DLB diesel vehicles

EFPM0.1 (mg km-1) ) 1.3198 + 15.2393EFHC (g km-1) EFPM0.1 (mg km-1) ) 3.3775 + 17.6305EFHC (g km-1) EFPM0.1 (mg km-1) ) 4.3095 + 18.0186EFHC (g km-1) EFPM0.1 (mg km-1) ) 2.5356 + 19.9360EFHC (g km-1) EFPM2.5 (g km-1) ) 0.1163 + 1.5314EFHC (g km-1) EFPM2.5 (g km-1) ) 0.1258 + 0.9950EFHC (g km-1) EFPM2.5 (g km-1) ) 0.8472 + 0.2236EFHC (g km-1) EFPM2.5 (g km-1) ) 0.1541 + 1.0958EFH (g km-1) EFPM10 (g km-1) ) 0.4583 + 4.5249EFHC (g km-1) EFPM10 (g km-1) ) 1.0064 + 4.2351EFHC (g km-1) EFPM10 (g km-1) ) 1.1201 + 4.6186EFHC (g km-1) EFPM10 (g km-1) ) 0.7791 + 4.9920EFHC (g km-1)

0.9579 0.9523 0.9851 0.6073 0.9906 0.9555 0.9120 0.8925 0.9629 0.9851 0.9956 0.7088

14 17 16 47 14 17 16 47 14 17 16 47

road vehicles decrease with an increase in the driving speeds. On the other hand, the EFPM0.1 from the selected diesel vehicles are much higher than those for the selected petrol and LPG vehicles. The EFPM0.1 values of petrol and LPG vehicles share similar ranges. The EFPM0.1 values of the petrol and LPG vehicles range from 1.40 to 9.04 × 10-2 mg km-1, and the EFPM0.1 values of diesel vehicles range from 2.71 to 11.1 mg km-1. The ratios of the average EFPM0.1 emitted from the diesel vehicles to those from the petrol and LPG vehicles are 149, 154, 163, and 164 at driving speeds of 10, 30, 50, and 70 km h-1, respectively. Figure 2b and c show the comparison of the average fine particle emission factors (EFPM2.5) and coarse particle emission factors (EFPM10) as a function of driving speed for different fuel types of the selected on-road vehicles. The results show that the EFPM2.5 and EFPM10 from different fuel types of the selected on-road vehicles decrease with an increase in driving speeds. The average particle emission factors of diesel vehicles for different particle size ranges are much higher than those of petrol and LPG vehicles. The EFPM2.5 ranges from 0.72 to 2.41 g km-1 for diesel vehicles and from 0.02 to 0.11 g km-1 for petrol and LPG vehicles. The ratios of the average EFPM2.5 emitted from the diesel vehicles to those from petrol and LPG vehicles are 30.2, 25.3, 24.3, and 23.3 at speeds of 10, 30, 50, and 70 km h-1, respectively. The particle emission factors of PM10 range

from 0.87 to 2.89 g km-1 for diesel vehicles and from 0.05 to 0.21 g km-1 for petrol and LPG vehicles. The ratios of the average EFPM10 emitted from the diesel vehicles to those from the petrol and LPG vehicles are 17.3, 14.2, 12.8, and 12.3 at speeds of 10, 30, 50, and 70 km h-1, respectively. In general, the average particle emission factor of the selected on-road diesel vehicles in the particle size range of Dp< 2.5 µm is 86% of the particle size range of Dp< 10 µm, as shown in Figure 2b and c. 3.5. Correlation between the Average Vehicle HC and Particle Emission Factors from the Selected On-Road Diesel Vehicles. The particle and HC emissions usually have similar sources, namely, liquid fuel in the combustion chamber, incomplete combustion, partial postflame oxidation, and so forth.45 Greeves and Wang12 established a correlation between PM and HC on a mass basis for a diesel engine. Williams et al.13 showed that high particle emission rates were associated with high HC emission rates with good R2 values from a fleet of light-duty and high-duty diesel vehicles from the FTP of the United States. (45) Kayes, D.; Hochgreb, S. Mechanisms of Particulate Matter Formation in Sspark-Ignition Engines. 1. Effect of Engine Operating Conditions. EnViron. Sci. Technol. 1999, 33, 3957-3967.

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Energy & Fuels, Vol. 21, No. 5, 2007 2717

Figure 4. The correlation plots of (a) EFPM0.1 and EFHC, (b) EFPM2.5 and EFHC, and (c) EFPM10 and EFHC from the selected on-road petrol and LPG vehicles using the CD testing systems. Table 7. Regression Equations of EFPM0.1, EFPM2.5, EFPM10, and EFHC from the Selected On-Road Petrol and LPG Vehicles using the CD Testing Systems vehicle type PPC1 PPC2 LPGT petrol petrol & LPG PPC1 PPC2 LPGT petrol petrol & LPG PPC1 PPC2 LPGT petrol petrol & LPG

regression equations km-1)

km-1)

EFPM0.1 (mg ) 0.0312 + 0.5840EFHC (g EFPM0.1 (mg km-1) ) 0.0122 + 0.0840EFHC (g km-1) EFPM0.1 (mg km-1) ) 0.0244 + 0.2045EFHC (g km-1) EFPM0.1 (mg km-1) ) 0.0309 + 0.1299EFHC (g km-1) EFPM0.1 (mg km-1) ) 0.0285 + 0.1590EFHC (g km-1) EFPM2.5 (mg km-1) ) 0.0758 + 0.3061EFHC (g km-1) EFPM2.5 (mg km-1) ) 0.0197 + 0.0517EFHC (g km-1) EFPM2.5 (mg km-1) ) 0.0383 + 0.0139EFHC (g km-1) EFPM2.5 (mg km-1) ) 0.0571 - 0.0108EFHC (g km-1) EFPM2.5 (mg km-1) ) 0.0502 + 0.0499EFHC (g km-1) EFPM10 (mg km-1) ) 0.1399 + 0.6601EFHC (g km-1) EFPM10 (mg km-1) ) 0.0527 + 0.0452EFHC (g km-1) EFPM10 (mg km-1) ) 0.0957 + 0.1892EFHC (g km-1) EFPM10 (mg km-1) ) 0.1141 - 0.0165EFHC (g km-1) EFPM10 (mg km-1) ) 0.1072 + 0.0640EFHC (g km-1)

According to the present study, the particle mass emission factors of selected diesel vehicles are much higher than those of selected petrol and LPG vehicles for different driving speeds. It indicates that the diesel vehicles are the major particle pollutant sources of on-road vehicles. In the present study, a unique correlation between the calculated average particle and HC emission factors from the selected diesel vehicles was established. The correlation plots of average particle emission factors (EFPM0.1, EFPM2.5, and EFPM10) and EFHC for the selected diesel vehicles are shown in Figure 3. They show that higher average particle emission factors correspond to higher average HC emission factors and vice versa. The regression equations of average particle and HC emission factors with high R2 values for the selected diesel vehicles are listed in Table 6. The high R2 values indicate that the highly correlated relationship between the average particle and HC emission factors for individual selected diesel vehicles was established. However, when all EFPM0.1, EFPM2.5, EFPM10, and EFHC values from the selected diesel vehicles’ data were combined together and correlated, the regression coefficient, R2, values for PM0.1, PM2.5, and PM10 were decreased from higher than 0.9 to 0.6073, 0.8925, and 0.7088, respectively. It indicates that the correlation between EFHC and EFPM is sensitive

R2

samples

0.9903 0.9376 0.9986 0.1252 0.0568 0.9860 0.9805 0.9772 0.0066 0.0002 0.9892 0.9052 0.9981 0.0037 0.0002

23 25 21 48 69 23 25 21 48 69 23 25 21 48 69

to different fuel types, particle sizes, engine cylinder sizes and rated power, aftertreatment emission control and maintenance conditions, and so forth. The correlation equations with high R2 values provide a convenient and reliable method for estimating the particle mass emission factors from different engine cylinder sizes and rated powers of diesel vehicles. 3.6. Correlation between the Average HC and Particle Emission Factors from the Selected Petrol/Gasoline and LPG Vehicles. In the present study, a correlation between the calculated average particle and HC emission factors from the selected petrol and LPG vehicles was established. The correlation plots of calculated average particle emission factors (EFPM0.1, EFPM2.5, and EFPM10) and HC emission factors for the selected petrol/gasoline and LPG vehicle are shown in Figure 4. They also show that the higher average particle emission factors correspond to the higher average HC emission factors and vice versa. The larger deviation of calculated average particle emission factors and HC emission factors for the petrol vehicles (i.e., PPC1 and PPC2) is highly related to the larger engine cylinder size and rated power, as listed in Table 2. The regression equations of average particle and HC emission factors from the selected petrol and LPG vehicles are listed in Table 7. The high R2 values above 0.9 indicate the highly correlated

2718 Energy & Fuels, Vol. 21, No. 5, 2007

relationship between the average particle and HC emission factors for individual selected petrol and LPG vehicles, respectively. However, when all EFPM0.1, EFPM2.5, EFPM10, and EFHC values from the selected petrol vehicles were combined together and correlated, R2 values for PM0.1, PM2.5, and PM10 were decreased from higher than 0.9 to 0.1252, 0.0066, and 0.0037, respectively. On the other hand, when the petrol and LPG vehicles were combined together and correlated, the R2 values for PM0.1, PM2.5, and PM10 decreased from higher than 0.9 to only 0.0568, 0.0002, and 0.0002, respectively. The very low R2 demonstrates clearly that the correlation between EFHC and EFPM is highly sensitive to the different fuel types, particle sizes, engine cylinder sizes and rated powers, aftertreatment emission control and maintenance conditions, and so forth. The correlation equations of gaseous and particle emission factors from the selected on-road vehicles in Tables 6 and 7 have recently been used for predicting the characteristics of gaseous and particle pollutants dispersion from on-road vehicles in urban roadway environments.4 4. Conclusions In the present study, the on-road vehicle emissions from different fuel types of the selected on-road vehicles, namely, petrol/gasoline, diesel, and LPG vehicles, for urban driving conditions ranging from 10 to 70 km h-1 were investigated using the CD and RS testing systems. The measured vehicle exhaust gaseous and particle emission concentrations from both testing systems were further calculated into the average gaseous vehicle emission factors for different driving conditions. A unique correlation of the calculated average gaseous and particle emission factors from different fuel types of the selected onroad vehicles as a function of different driving speeds with a good regression coefficient, R2, value using both testing systems was established. Furthermore, the measured vehicle exhaust particle and HC emission concentrations were further calculated into the ultrafine particle (PM0.1), fine particle (PM2.5), coarse particle (PM10), and HC emission factors for different driving conditions. The results show that the different fuel types of the selected on-road vehicles and their driving conditions have a direct effect on the characterization of vehicle exhaust gaseous and particle emission factors. The correlation equations of the calculated average vehicle emission factors of different-sized fractionated exhaust particles and HCs in milligrams per kilometer or grams per kilometer with a good R2 value from different fuel types of on-road representative vehicles were also established. It is demonstrated clearly that the correlation between EFPM and EFHC is highly sensitive to the different fuel types, particle sizes, engine cylinder sizes and rated powers, and aftertreatment emission control and maintenance conditions of the selected on-road vehicles. The selected correlation equations of gaseous and particle emission factors have recently been used for predicting the gaseous and particle pollutants’ dispersion from on-road vehicles in urban roadway environments (i.e., Hong Kong).4 Acknowledgment. This work was supported by grants from the Research Grants Council of the Hong Kong Special Administra-

Chan et al. tive Region, China (RGC Project No. PolyU 5292/03E), and the Central Research Grants of The Hong Kong Polytechnic University (Project Nos. B-Q738 and 143-B1-9719). The chassis dynamometer was consigned by Pioneer Environmental Systems Ltd. (PES), Hong Kong, and Environmental Systems Products Holdings Inc. (ESP), U.S.A.

Nomenclature a ) instantaneous acceleration/deceleration rate of diesel vehicle in km h-1s-1. b, c, d, f, g ) constants of regression equations as defined in Table 3 c1, c2, c3,... c′′4, c′′5 ) constants of regression equations as defined in eqs 5-7 Ci ) the measured concentration of individual gaseous emission species, i (i.e., CO, HC, or NO), in ppm Cmass,i ) the measured mass concentration of individual particle emission species, i (i.e., Dp), in mg cm-3 Di ) the density of individual gaseous emission species, i (i.e., CO, HC or NO), under the specific driving speed mode, which is a function of exhaust gas temperature in g L-1 Dp ) the particle diameter (i.e., PM0.1, PM2.5, or PM10) in µm Dfuel ) the density of fuel type (i.e., gasoline/petrol, diesel, or LPG) in kg L-1 Ei ) the mass emission concentration of individual gaseous emission species, i (i.e., CO, HC, or NO), in g L-1, fuel burned as defined in eqs 8-10 Ei,j ) the mass emission concentration of individual gaseous emission species, i (i.e., CO, HC, or NO), and the selected fuel type of vehicle j (i.e., gasoline/petrol, diesel or LPG) in g L-1, fuel burned as defined in eq 11 EFi,j ) the vehicle emission factor of individual gaseous or particle emission species, i (i.e., CO, HC, NO, or particle), and the selected fuel type of vehicle j (i.e., petrol/gasoline, diesel, or LPG) under the specific driving speed mode in g km-1 or mg km-1 as defined in eq 2 or 4 or 11 ERi,j ) the vehicle emission rate of individual gaseous or particle emission species, i (i.e., CO, HC, NO, or particle), and the fuel type of vehicle j (i.e., petrol/gasoline, diesel, or LPG) in mg s-1 as defined in eq 1 or 3 FRi ) the vehicular exhaust flow rate of individual gaseous or particle emission species, i (i.e., CO, HC, NO or particle) in m3 s-1 as defined in eq 1 or 3 Gj ) the fuel consumption in L × 100 km-1 of the selected fuel type of vehicle, j (i.e., gasoline/petrol, diesel or LPG), as defined in eq 11 Mfuel ) the molar mass of the selected fuel type (i.e., gasoline/ petrol, diesel, or LPG) in kg mol-1 Q ) the ratio of CO to CO2 on a volume concentration basis after the regression analysis as defined in eq 5 Q′ ) the ratio of HC to CO2 on a volume concentration basis after the regression analysis as defined in eq 6 Q′′) the ratio of NO to CO2 on a volume concentration basis after the regression analysis as defined in eq 7 V ) an instantaneous driving speed in km h-1 as defined in eqs 5-7 Vj ) the specific driving speed mode of the selected fuel type of vehicle, j (i.e., petrol/gasoline, diesel, or LPG), in km h-1 as defined in eq 2 or 4 EF070172I