Robert Joumard

Juhani Laurikko

Tuan Le Han

Savas Geivanidis

Zissis Samaras

Tamás Merétei

Philippe Devaux

Jean-Marc André

Erwin Cornelis

Stéphanie Lacour

Maria Vittoria Prati

Robin Vermeulen

Michael Zallinger

ACCURACY OF EXHAUST EMISSION FACTOR MEASUREMENTS ON CHASSIS DYNAMOMETER

Keywords: emission factor, light vehicle, model, inventory, regulated pollutant, recommendations, measurement conditions, method. IMPLICATIONS

In order to improve the accuracy, reliability and representativeness of emission factors, 10

European laboratories worked together to study the influence of 20 parameters on the measurement of light vehicle emission factors on chassis dynamometer of 4 main categories: driving patterns, vehicle related parameters, vehicle sampling and laboratory related parameters. The results are based i) on literature synthesis, ii) on about 2700 specific tests with 183 vehicles and iii) on the reprocessing of more than 900 tests. These tests concern the regulated atmospheric pollutants and pre-Euro to Euro 4 vehicles. Of the 20 parameters analyzed, 7 seemed to have no effect, 7 were qualitatively influential, and 6 were highly influential: gearshift strategy, vehicle mileage, ambient temperature, humidity, dilution ratio and driving cycle. The first 4 of the 6 were able to have correction factors developed for them. The results allow the design of recommendations or guidelines for the emission factor measurement method.

INTRODUCTION

Calculation of emissions has therefore gained institutional importance in the European Community, particularly with the development of the CAFÉ [START_REF] Ec | CAFÉ programme -Clean Air for Europe[END_REF][START_REF]EC. European Climate Change Programme (ECCP)[END_REF] programs. Reliable and credible emission estimates are a central prerequisite, but comparisons of the results from emission models such as COPERT [START_REF] Samaras | Computer Programme to Calculate Emissions from Road Traffic[END_REF][START_REF] Ntziachristos | Copert III Methodology and Emission Factors (Version 2.1)[END_REF] , FOREMOVE [START_REF] Samaras | Forecast of Road Traffic Emissions in the European Community up to the Year[END_REF] , TREMOVE [START_REF] De Ceuster | TREMOVE 2.30 model and baseline description[END_REF] , RAINS [START_REF] Amann | The RAINS model: a tool for assessing regional emission control strategies in Europe[END_REF][START_REF] Cofala | Calculating emission control scenarios and their costs in the RAINS model: Recent experience and future needs[END_REF] , Handbook [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1); Software, Infras[END_REF] and national models have shown substantial differences [START_REF] Zachariadis | Comparative assessment of European tools to estimate traffic emissions[END_REF][START_REF] Kioutsioukis | Uncertainty and global sensitivity analysis of road transport emission estimates[END_REF][START_REF] Schopp | Uncertainty analysis of emission estimates in the RAINS integrated assessment model[END_REF][START_REF] Ntziachristos | European Database of Vehicle Stock for the Calculation and Forecast of Pollutant and Greenhouse Gases Emissions with TREMOVE and COPERT[END_REF] (See Figure 1). This causes doubts about the credibility of the underlying data and methodologies and might mislead the political discussions.

The European MEET (Methodologies for Estimating air pollutant Emissions from Transport) project [START_REF] Hickman | MEET -Methodology for calculating transport emissions and energy consumption[END_REF] , the COST 319 action [START_REF] Joumard | COST 319 -Estimation of pollutant emissions from transport: Scientific state-of-the-art and network of European scientists[END_REF] and other research programs raised a main question in relation to passenger car emissions, summarized as follows: large differences in measured emission levels occurred between the different laboratories in Europe; these differences appeared to be more pronounced for more recent (at this time) vehicle technologies (i.e. Euro 1), irrespective of the way the emissions modeling is conducted (i.e. average speed dependency approach, traffic situation approach).

In order to be able to produce accurate emission factors for current and near-future technology, taking into consideration the aforementioned observations for modern car categories, a two-fold strategy is proposed in the present study: i) investigating and reducing the measurement differences between laboratories, ii) investigating, understanding and modeling the emission differences among comparable vehicles. The first aim is to study the sensitivity of pollutant emissions to the key parameters. The second aim is to develop methods that allow the harmonization of any European emission measurements.

This study, detailed in [START_REF] Joumard | Accuracy of exhaust emissions measurements on vehicle bench -Artemis deliverable 2; Inrets report[END_REF] is a part of the ARTEMIS project "Assessment and Reliability of Transport Emission Models and Inventory Systems", whose purpose is to arrive at a harmonized methodology and to develop software that calculates emissions of any transport mode, at local, national and international level -described for the light vehicles in [START_REF] Joumard | Emission factor modelling and database for light vehicles -Artemis deliverable 3; Inrets report[END_REF] .

METHODOLOGY

The influence of all the potential parameters on the exhaust emission level and accuracy is studied first with a literature review and then by laboratory tests on vehicles. Four types of parameters of the measurement conditions are studied:

-Driving patterns: driving cycles, gear choice behavior, driver type, cycle following -Vehicle related parameters: technical characteristics of the vehicles, short term emission stability, long term emission degradation (mileage), fuel properties, vehicle cooling, vehicle preconditioning -Vehicle sampling method: method of vehicle sampling, number of vehicles -Laboratory related parameters: ambient temperature, ambient humidity, dynamometer setting, dilution ratio, heated line sampling temperature, PM filter preconditioning, response time, instantaneous versus bag value, dilution air conditions Parallel to the study of the impact of different parameters on emissions, 9 roller test bench laboratories were compared to each other by performing a round robin test with a Euro 3 petrol vehicle rented through a rental company and reference gases.

A specific test program was built-up for each parameter studied, except the vehicle running conditions and the method of vehicle sampling, where only literature review or inquiries were performed.

Emissions of CO, CO 2 , HC, NOx, and PM are considered. Although a wide variety of driving cycles were tested for the whole study (65 cycles), most of them have been used either to study specifically the influence of the driving patterns, or in the case of already existing data (case of the minimum size of a vehicle sample). For the influence of the vehicle and laboratory related parameters, the 3 Artemis driving cycles [START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF] (See Figure 2) have generally been tested with hot start, but in a few cases without the rural or motorway cycles. In many cases cold and/or hot NEDC have been tested in addition. All the tested driving cycles are analyzed in [START_REF] André | Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles[END_REF] . The tests shared between the different partner laboratories.

Globally 2753 tests were carried out (1 test = 1vehicle x 1 driving cycle: See Table 1

RESULTS

According to the outputs of the above studies and in test conditions, some parameters have no influence on the emission measurements, or have a qualitative or quantitative influence. The parameters which were not found to have an influence on the emission measurements are those without any statistically significant influence on the emissions, usually with a margin of error of 5 %, but the statistical tests depend on the parameter studied and can be more sophisticated: See 16.

Parameters found to have a qualitative or quantitative influence on the emission measurements have a statistically significant influence. The influence is a qualitative one when the parameters are qualitative; the only exception is the driver (human / robot) where the difference cannot be explained by the driving characteristics. The difference is quantitative when quantitative correction factors or functions are available.

Non-Influencing Parameters

No statistically significant influence on emission measurement was found for some vehicle and laboratory related parameters. This does not mean that these parameters have no influence on the emission measurements, but only that no influence can be proved, taking into account the small data sample or the contradictory results.

-Short term emission stability or driving cycle repetition -Inspection-maintenance -Fuel properties. The results confirm the influence of fuel on exhaust emissions, but in spite of observing significant differences, especially for PM emissions with diesel vehicle, it was not possible to propose an explanation based on the today knowledge of fuel effect

Parameters With Qualitative Influence

Some parameters of the 4 broad categories have a qualitative influence: There is an influence but that it is not quantifiable within the limits of the available data. Therefore recommendations are made concerning these parameters:

-The driver can be a human driver or a robot. Only the CO 2 emission was significantly higher by +4 % with human driver than with a robot driver, but the difference cannot be explained by the driving characteristics.

-The vehicle classification, through the type approval category (Euro 1 to 4) and the fuel, has a clear influence on the emissions, together with the engine capacity in some cases. But no correlation between emission behavior and emission control technologies was found as long as the cars belong to the same type approval category. Therefore the additional introduction of technological characteristics wouldn't improve the accuracy of emission data bases of conventional cars up to Euro 4.

-The vehicle preconditioning conditions have an influence in some cases, but very few for modern close loop vehicles. A 10-minute cycle at a constant speed of 80 km/hr can be considered as the most suitable preconditioning cycle. It resulted in the lowest emission levels and the lowest standard deviation for the majority of the measurements.

-The sample characteristics influence the emission levels: the vehicle classes given above, but also the size and engine power at the maximum power of the vehicle, which strongly influence the CO 2 emission and fuel consumption.

-Minimum size of vehicle sample. Usually 10 to 15 vehicles are required for all the pollutants, in order to build-up an emission model which is representative of an average vehicle's behavior.

Below these prescribed numbers, the weight of the individual behavior of some vehicles is too significant to obtain a mean, which is representative of an average behavior.

-The dynamometer setting has a clear influence on all emissions, but significantly only on CO 2 and fuel consumption, and on NOx for diesel vehicles. It cannot be excluded, however, that altered settings might affect these other pollutants too.

-Response time including instantaneous versus bag value. The measured instantaneous emission level must be corrected using specific functions, before building an instantaneous emission model [START_REF] Ajtay | Compensation of the exhaust gas transport dynamics for accurate instantaneous emission measurements[END_REF][START_REF] Ajtay | Towards Accurate Instantaneous Emission Models[END_REF][START_REF] Geivanidis | Development of a dynamic model for the reconstruction of tailpipe emissions from measurements on a constant volume sampling dilution system[END_REF] .

Influencing Parameters

6 parameters have a clear and statistically significant influence on the emissions measured. The influence of 4 parameters can be quantified and quantitative correction factors were derived from the test data and are available in order to standardize emission measurements for the gearshift strategy, vehicle mileage, ambient air temperature and ambient air humidity parameters.

-Driving cycle. The variation induced by the driving type or cycle was more significant than the variation induced by the fuel type (for HC, CO 2 ), or by the emission standard (NOx, CO 2 ), or even between the vehicles (CO 2 ), with quite contrasted behavior between diesel (rather sensitive to speed and stop parameters) and petrol cars (rather sensitive to accelerations). However, it was not possible to design a satisfying model or correction function that would enable a systematic correction, but an harmonization approach was then developed, based on the similarities between cycles from a kinematic point of view [START_REF] André | Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles[END_REF] .

-Gearshift strategy. It is possible to classify the gearshift strategies according to their CO 2 emission (the only pollutant always influenced by the strategy). The most polluting strategy is the gear change at given engine speeds whatever the cycle. The least polluting strategy seems to be the gear change at given vehicle speeds (defined in the NEDC cycle). The ratio between these two strategies is around 15 %. For urban cycle, the strategy depending on the vehicle power-tomass ratio and on the 3 rd gear ratio (part of the Artemis cycles) pollutes as the gear change at given vehicle speeds. For rural cycle, the Artemis strategy pollutes less than given engine speed strategy (9 %) but more than the given vehicle speed strategy (6 %). The correction factor (CF = emission CO 2 (Artemis strategy) / emission CO 2 (other strategy)) equals 1, except in the case of CO 2 for the NEDC gearshift strategy, for motorway (CF = 1.03) and rural (CF = 1.08) driving behavior.

-The mileage has no influence on the CO 2 emission nor on the emissions of diesel vehicles, but increases CO, HC and NOx emissions of petrol cars: between 0 and 100 000 km, these emissions increase by a factor 3.6 on average for Euro 1 and 2 vehicles, and by 15 % for Euro 3 and 4 vehicles. Figure 3 shows that, all vehicles together, the mileage influence is important especially for engine capacity higher than 2 liters. The influence of the mileage M 1 or M 2 [km] is expressed by

€ emission M 1 ( ) emission M 2 ( ) = y M 1 ( ) y M 2 ( ) (1)
y is available for Euro 1 to Euro 4 petrol cars in Table 2, for urban and rural situations, i.e. resp.

for an average speed lower than 19 km/h and higher than 63 km/h. For an intermediate speed V:

€ y V ( ) = y urban ( ) + V - 19
( )⋅ y rural ( ) -y urban ( ) ( ) 44 (2)
-Ambient air temperature. The hot emissions decrease with increasing temperature for petrol cars but mainly for diesel ones. Between 10 and 20°C, the CO and HC emissions varies by 15-20 %, the NOx and CO 2 emissions by 2 %, and PM is constant. The influence of the ambient temperature is usually a linear function and sometimes an exponential one. The influence of the temperature T 1 or T 2 [°C] is expressed by:

€ emission T 1 ( ) emission T 2 ( ) = y T 1 ( ) y T 2 ( ) (3)
y is available for urban, rural and motorway driving behavior in Table 3.

-The influence of the ambient humidity exists only for NOx and for some vehicle classes. It is a linear function. An increase in ambient humidity lowers the NOx emissions, which is also the expected general trend according to the humidity correction established in legislative testing 23 .

Figure 4 shows that in urban test cycle the standard correction is nearly valid for diesel cars with less than 5 % deviation from the now-established model. However, both groups of petrol cars would need much stronger correction, as the relative change over the allowed humidity range is about 35 % for the Euro 2 to and over 55 % for the Euro 3 test fleet, and the normative factor corrects only by some 20 % within the same range of humidity. ). The influence of the humidity on NOx emission is expressed by:

€ emission H 1 ( ) emission H 2 ( ) = y H 1 ( ) y H 2 ( ) (4)
y is available for some vehicle classes and for urban and rural driving behavior in -Exhaust gas dilution ratio. A higher dilution ratio increases only the diesel PM emission measurement.

Round Robin Test

The best accuracy (i.e. lowest spread in results) was encountered for CO 2 , where the average coefficient of variation was around 5 %. This latter is around 40 % for CO, below 40 % for NOx, and around 60 % for HC. When comparing these variations to those values calculated on the basis of the repeated tests at the begin and at the end of the whole round robin test in a same laboratory, the overall variability recorded for CO in the round robin test was roughly at the same order of magnitude than the "basic" repeatability combining the repeatability of the laboratory and fluctuations in the car performance. However, with HC the overall spread of results over the whole round robin test was higher (the coefficient of variation is only 40 % for repeated tests), suggesting that some external factors, like the change in fuel quality, affected and lowered the repeatability. In terms of NOx, the overall round robin test variability was also somewhat higher than the basic value obtained from one laboratory alone, but no speculations were made over the probable reasons for this.

This result means that the accuracy of an emission measurement with one vehicle and one driving cycle, in given conditions, has an inaccuracy of 5, 40, 10-40, and 40-60 % for CO 2 , CO, NOx and HC resp. It does not mean that it is the inaccuracy of the emission factors of a given vehicle class, because such emission factors are always based on several individual measurements, often on many, highly reducing the inaccuracy. The ranges of accuracy for NOx and HC mean that some parameters of inaccuracy are not well-understood, especially for NOx.

RECOMMENDATIONS

The knowledge of the sensitivity of vehicle pollutant emissions to the key parameters identified above allows the design of better practice for measuring emissions of the European passenger car traffic. These recommendations can be shown in four directions: Which cars to measure? In which conditions to test the cars? How to sample and analyze the pollutants? How to manage the data?

Vehicle Sampling

It is recommended to choose as far as possible a vehicle sample with similar distributions as the inuse fleet of the fuels, emission standard, vehicle size, maximum engine power. At least the means or medians of the cubic capacity, maximum power and mileage should be similar.

The variability between vehicles is also identified as a significant and preponderant factor, together with the emitter status (high/ or normal emitter). It is not possible to know a priori the emitter status before measuring, but the high variability between vehicles of a same category obliges the random choice of cars within a category and to sample a minimum number of vehicles. The minimum sample size per vehicle category, with the aim of calculating only an emission average per vehicle category, seems to be not less than 10 vehicles. It is recommended to carry out only a limited number of repetition tests on these cars instead of taking a smaller sample tested many times. The vehicles to test should be chosen as randomly as possible in a list created by an official body such as the government, because it will give results closest to the fleet representativeness. If an official list cannot be obtained, the list created in laboratories should be completed by vehicle owners, whose the profession is not linked with pollution, such as laboratory staff.

Usage Conditions Of The Vehicles

The vehicle conditions in the measuring laboratory should correspond to the range of traffic conditions observed in Europe: it concerns not only the driving patterns, but also the environmental conditions, the vehicle load, the fuel used...

Driving Cycle:

It is highly recommended to test the passenger cars with real-world driving cycles.

Many such driving cycles are available in Europe. The so-called Artemis driving cycles now widely used in Europe to measure passenger cars emissions [START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF] (See Figure 2) are recommended, or vehiclespecific driving cycles [START_REF] André | Real-world European driving cycles for measuring pollutant emissions from high and low powered cars[END_REF] to measure actual European pollutant emission factors.

Gearshift Strategy:

The gearshift strategy depending on the vehicle power-to-mass ratio and on the 3 rd gear ratio, i.e. foreseen in the Artemis and vehicle-adapted driving cycles, seems to be the most appropriate. But the strategy impact remains nevertheless relatively low as soon as realistic patterns are selected.

Vehicle Preconditioning: As preconditioning cycle for hot start emission measurement, a constant speed cycle is recommended, with a reasonable vehicle speed level, especially for petrol cars: a 10minute cycle at a constant speed of 80 km/hr for instance.

Driver: The robot does not give more stable emissions and some driving cycles are too aggressive for it. There is therefore no reason to prefer a robot to a human driver. A cycle following should be in the tolerance band (± 2 km/hr and ± 1 sec) for more than 99% of time and with a driven distance within 1 % to the reference distance. A test is accepted with remark if it fails these values due to insufficient power, wheel slip, difficult gear box, in NEDC if deceleration is steeper than reference or if the engine stalls or does not activate immediately at test start. In all other cases a test should be rejected.

Fuel Characteristics: Both diesel and petrol fuels strongly influence the emissions, but not CO 2 .

Therefore it is recommended to use common fuels rather than laboratory fuels.

Ambient Air Temperature And Humidity: It is recommended to measure the emissions close to the average ambient temperature and humidity rather than at the "standard" one when this one is far from the reality.

Vehicle Cooling: The use a high power cooling system is recommended, in order to reproduce as far as possible the real-world cooling.

Dynamometer Setting: Although only few effects were found significant, the chassis dynamometer settings should lead to a load applied to the driving wheels of a vehicle that is equivalent to the load experienced on the road at all speeds and accelerations. For the testing to be performed for the determination of real world emission factors, it is therefore primarily recommended to use road load information derived from the coast down method performed by the laboratory, and an inertia setting as close to the actual on-road inertia as possible, which is also determined by the laboratory.

CONCLUSION

The study was designed to study the influence of many of parameters of the measurement of light vehicle emission factors: driving patterns, vehicle related parameters, vehicle sampling method and laboratory related parameters.

In test conditions, some parameters were not found to have an influence. For some other parameters, a qualitative influence was found but not quantifiable. Finally some parameters have a clear and quantifiable influence and can be used to normalize emission measurements when the level of these parameters during the experiment is known, by using correction factors: gearshift strategy, vehicle mileage, ambient air temperature and humidity, exhaust gas dilution ratio. The results allow the design of recommendations or guidelines for the real-world emission factor measurement method. All these outputs have been used to design the ARTEMIS emission inventorying tools for light vehicles, on a better basis than the previous European models.

The outputs of this study are nevertheless not fully positive, mainly because of the low number of tests performed to study the influence of some parameters, which did not allow any significant influence to be found. Some parameters could therefore be studied again.

-

  Vehicle cooling. The open and closed bonnet, the height of a small blower have no influence on the emissions measured. The cooling power, i.e. the flow of the cooling air, does not have a clear influence on the measured emissions -Heated line temperature, because the observed emission change contradicts what is expected from the physico-chemical characteristics of the diluted emissions -PM filter conditions -Dilution air condition

Figure 1 .

 1 Figure 1. Comparison of emission calculations performed for several European countries and the European Union by using 4 emission models 13 .

Figure 2 .

 2 Figure 2. Artemis driving cycle compared with the New European Driving Cycle.

Figure 3 .

 3 Figure 3. NO x degradation in urban driving behavior for petrol vehicles.

Figure 4 .

 4 Figure 4. Linear models of (uncorrected) NOx emissions measured in Artemis urban driving cycle, fitted in average values for high, medium and low humidity, and correction factor according to legislative test protocol (as 1/kH).

  ), i.e. 537 tests to study the influence of the driving patterns (48 vehicles), 1334 tests to study the influence of the vehicle parameters (70 vehicles), 672 tests to study the influence of the laboratory related parameters (64 vehicles), and 210 tests are part of the round robin test conducted within 9 laboratories with a petrol passenger car. In addition at least 910 tests (81 vehicles) from the

	European ARTEMIS data base but not carried out within the project are processed in order to study
	the influence of the driver and mainly the vehicle sample size.
	In all, 183 vehicles were tested, resp. 119 and 64 petrol and diesel ones, 2 pre-Euro 1, 13 Euro 1, 77
	Euro 2, 75 Euro 3, and 9 Euro 4.

Table 4 .

 4 It is recommended to use the rural figures for motorway driving behavior, and to use the petrol Euro 2 figures for petrol Euro 0 and 1, petrol Euro 3 figures for petrol Euro 4, and diesel Euro 2 figures for the other diesel cases. For other pollutants, no correction factors are proposed.

Table 1 .

 1 Description of the tests carried out, per parameter and laboratory.

	Parameter	lab.	N. veh.	N. driving cycles	Tested cases	N. tests
			IM	1	14		14
	Driving patterns	Driving cycles Gear choice	Inrets KTI TNO Inrets KTI	24 6 1 1 9 4 2	8 18 14 14 3 2 2	Large range of driving conditions 5 strategies	192 108 14 14 195
		Driver	Empa	1	15	4 times 1 robot driver, 4 human drivers	120
			LAT,
	Vehicle parameters	Techn. char. veh. Emission stability Emis. degradation Fuel properties Vehicle cooling	Renault & TUG all LAT Renault VTT	43 12 a 2 2 6	6 2 6 6 2	Tests repeated twice Tests repeated 5 times Test every 20 000 km, before and after maintenance; 1, 2 or 3 repetitions 4 fuels, tests repeated twice A small fan at 2 heights, a large fan with 3 air speeds, with open air closed bonnet	516 120 174 96 108
		Vehicle precond.	IM & KTI	5	4	4 preconditioning cycles, driving cycle repeated 4 times	320
		Veh. sample size b	Inrets	80 c	29 c		790
	Laborat. related parameters	Ambient temp. Ambient humidity Dynamo. setting Dilution ratio Heated line temp. PM filter precond. Response time Dilution air cond.	Empa VTT VTT TNO KTI IM LAT KTI TNO Empa, TUG & LAT IM	18 13 11 5 2 3 3 1 1 5 2	3 2 2 5 2 5 5 2 4 30 3	3 ambient temperatures: -20, -7 and +23°C 3 ambient humidity levels, tests repeated 3 settings for road load and inertia 3 ratios for 1 veh., 5 ratios for the 2nd one 3 dilution ratios 2 dilution factors 2 temperatures 3 temperatures and 3 humidity levels Specific tests 3 levels of polluted dilution levels, tests repeated twice	240 131 75 91 4 20 75 36
		Round robin test	Most d	1	6	2 to 10 repetitions	210
	a TUG: 3, IM: 2, Empa, Inrets, KTI, LAT, Renault, TNO, VTT: 1 each
	b corresponds to existing data, not measured within the project
	c all vehicles have not been tested with all driving cycles
	d Empa, IM, Inrets, KTI, LAT, MTC, TNO, TUG, VTT

Table 2 .

 2 Emission degradation correction factor y = a x Mileage + b, for petrol vehicles. Mileage expressed in km, y normalized for the corresponding average mileage.

								Value at ≥
				Capacity class [l]	Average mileage [km]	a	b	120 000 km (Euro 1 & 2), or 160 000 km
								(Euro 3 & 4)
				≤1.4	29 057	1.523E-05	0.557	2.39
		y(urban)	CO	1.4-2.0	39 837	1.148E-05	0.543	1.92
		for		>2.0	47 028	9.243E-06	0.565	1.67
		V≤19 km/h		≤1.4	29 057	1.215E-05	0.647	2.10
	Petrol Euro 1 & 2	(urban situation) y(rural) for V≥63 km/h (rural situation)	HC NOx CO HC	1.4-2.0 >2.0 all ≤1.4 1.4-2.0 >2.0 ≤1.4 1.4-2.0 >2.0	39 837 47 028 44 931 29 057 39 837 47 028 29 057 39 837 47 028	1.232E-05 1.208E-05 1.598E-05 1.689E-05 9.607E-06 2.704E-06 6.570E-06 9.815E-06 6.224E-06	0.509 0.432 0.282 0.509 0.617 0.873 0.809 0.609 0.707	1.99 1.88 2.20 2.54 1.77 1.20 1.60 1.79 1.45
			NOx	all	47 186	1.220E-05	0.424	1.89
		y(urban)	CO	≤1.4 >1.4	32 407 16 993	7.129E-06 2.670E-06	0.769 0.955	1.91 1.38
	Petrol Euro 3 & 4	for V≤19 km/h (urban situation) y(rural) for	HC NOx CO	≤1.4 >1.4 ≤1.4 >1.4 ≤1.4 >1.4	31 972 17 913 31 313 16 993 30 123 26 150	3.419E-06 0 0 3.986E-06 1.502E-06 0	0.891 1 1 0.932 0.955 1	1.44 1 1 1.57 1.20 1
		V≥63 km/h	HC	all	28 042	0	1	1
		(rural situation) NOx	all	26 150	0	1	1

Table 3 .

 3 Correction factor y = a x Temperature + b, or y = a e b x Temperature when in italics, for urban, rural or motorway driving behavior. Temperature in °C. y normalized at 23°C.

				urban	rural		motorway
				a	b	a	b	a	b
			Euro 0	0.0021	0.95	0.003	0.93	0.0054	0.88
	CO	petrol	Euro 2 Euro 3	-0.0115 -0.0087	1.3 1.2	0.002 0.0053	0.95 0.88	--0.0008	-1.02
			Euro 4	No correction	0.017	0.61	-	-
		diesel	Euro 2	-0.034	1.784	-0.075	2.72	-0.024	1.56
			Euro 0	-0.001	1.02	-0.0027	1.066	No correction
			Euro 2	-0.016	1.37	No correction	-	-
	HC	petrol	Euro 3 Euro 4	-0.0525 3.4627	2.21 -0.0544	-0.025 0.0107	1.57 0.7442	-0.001 -	1.02 -
				y = a e bT			y = a e bT
		diesel	Euro 2	-0.027	1.62	-0.032	1.75	1.43	-0.015
			Euro 0	-0.0075	1.17	-0.0063	1.14	-0.0035	1.08
	NOx	petrol	Euro 2 Euro 3	-0.0091 -0.0084	1.21 1.19	0.0045 -0.0027	0.895 1.065	--0.002	-1.05
			Euro 4	-0.01	1.23	0.0013	0.97	-	-
		diesel	Euro 2	-0.0015	1.05	-0.0015	1.05	-0.0006	1.016
			Euro 0	-0.0038	1.09	-0.0038	1.09	-0.0033	1.08
	CO 2	petrol	Euro 2 Euro 3	-0.0013 -0.001	1.03 1.03	-0.0017 -0.0013	1.04 1.03	--0.0015	-1.0342
			Euro 4	-0.0028	1.0619	-0.0016	1.0334	-	-
		diesel	Euro 2	-0.0015	1.03	-0.0017	1.04	-0.0009	1.0205
	PM	diesel	Euro 2	0.005	0.88	No correction	-0.005	1.11

Table 4 .

 4 Correction factor y = a x Humidity + b, for NOx emissions corrected or not using the current method, and for urban or rural driving behavior. Humidity in g H 2 O/kg dry air, y normalized at 10.71 g H 2 O/kg dry air.

				urban	rural
				a	b	a	b
	Uncorrected emissions	petrol	Euro 2 Euro 3	-0.052 -0.081	1.5592 1.8669	-0.0293 -0.0284	1.31 1.3
		diesel	Euro 2	-0.0249	1.2668	-0.0307	1.325
	Corrected emissions	petrol	Euro 2 Euro 3	-0.0182 -0.0529	1.1944 1.5654	0.004 -0.0093	0.9571 1.0996
		diesel	Euro 2	0.0067	0.9281	0.0106	0.8869

ACKNOWLEDGMENTS

The authors wish to thank the European Commission for its financial support as part of the ARTEMIS research contract n°1999-RD.10429 "Assessment and reliability of transport emission models and inventory systems", workpackage 300 "Improved methodology for emission factor building and application to passenger cars and light duty vehicles" -Project funded under the Competitive and sustainable growth program of the 5th framework program, Pierre Rouveirolles at Renault, Lardy, France, and Zoltán Oláh at KTI, Budapest, Hungary, for their participation.


Robert Joumard
email: joumard@inrets.fr

Santiago Mancebo Quintana

Gerassimos Arapis

Tomasz Zacharz

Seminar COST 356 EST "Towards the definition of a measurable environmentally sustainable transport", 20 February 2008, Oslo, Norway Chains of causalities of environmental impacts

Keywords: environmental impact, classification, process, chain of causalities, transport. 1

In order to prepare an encompassing ex-ante assessment of the impacts of the transport system on the environment by building impact indicators, the pressure-state-impact concept is widen into the concept of chain of causalities or process. A process is defined by an output of the transport system, a homogeneous series of physical, chemical, biological, psychological relationships between elements, and a final target. All the known today impacts of the transport system on the environment are described as a list of 43 chains of causalities. The knowledge of the relationships will be used to design indicators or to know what does and not does measure a given indicator.

Introduction

To build tools for assessing the impacts on the environment of a transport system or sub-system asks for a definition of the impacts on the environment, defined by final targets and modifications of target. For instance, the final targets of the traffic safety are mainly the humans with death and injuries. To assess each impact, the best way should be to measure the impact itself, by counting or evaluating for instance the number of people injured or dead because the traffic system. But such counting can't be made only ex-post and does not give any indication on the causes of the impact, because the impact cannot be linked by a one-to-one relationship with the accidents: the accidents are not the only causality of human death and injuries: local air pollution, greenhouse effect, hazards, among others cause death and injuries. The account of death and injuries due to the accident needs to take into account the process of accident. It is especially easy in the field of traffic safety, much more complex for most of the impacts.

For an evaluation ex-ante or for looking for the causes of an ex-post evaluation, a clear and precise relationship has to be established with the transport system. Each process, each chain of causalities from the source to each final impact on the environment has to be described in detail: in terms of sources, intermediate and final targets, mechanisms between intermediate sources and intermediate targets. Such description allows us also to express clearly what a potential indicator measures and does not measure, and on which scientific mechanisms an indicator should be based. For instance the global warming potential evaluates the global temperature increase and not really the final impacts of greenhouse effect as sea level increase, the amount of fauna, flora and human habitat destruction, the food chain changes... The knowledge of the physical mechanism of the climate and temperature modifications as a function of greenhouse gas emissions allows to build the shape of the indicator 'global warming potential'.

At the same time, the description of the chains of causalities allows us to define quite precisely the term 'environment': What are the impacts on the environment? What are their characteristics or typical features?

The most common presentation of the environment, especially by economists, considers it as a resource used by the humans for producing economic goods. This resource is an ecosystem, i.e. the association between a physicochemical and abiotic (the biotope) environment and a living community characteristic of the latter (the biocenosis), including fossil resources. This resource is destroyed but can be renewed at a given extend: the environmental issue is a question of resource flow and capacity of the biosphere to support the effects of the human activities (carrying capacity): It calls the 7th principle of the Rio declaration (UNCED, 1992): "...to conserve, protect and restore [...] the integrity of the Earth's ecosystem [...] the pressures their societies place on the global environment". The pressure-state-impact (PSI) system from OECD seems well applicable to this meaning with a pressure representing a flow.

In parallel, the environment is often understood as the quality of our physical environment or the quality of life: a calm area with pure air and pure water, a beautiful landscape... It calls the first principle of the Rio declaration: "Human beings [...] are entitled to a healthy and productive life in harmony with nature". It is here often difficult to consider only flows or pressures.

These both meanings of the environment correspond roughly to the external and internal territory sustainability by [START_REF] Wackernagel | Notre empreinte écologique[END_REF]: the internal sustainability consists in protecting its direct environment and living area, but the external sustainability consists in protecting the world. Correspondence between the environmental impacts as listed by 5 references, and the list of processes proposed in this paper.

Precise list of environmental impacts

Such definitions are much too global and rough to be useful for describing the environmental issue or the impact on the environment of a human activity as the transport system, and for designing environmental impact indicators. An exhaustive list of the chains of causalities is necessary to present a full picture. But the definition of the environmental or ecological impacts is neither clear nor precise in the literature. When lists of environmental impacts are given, they are often heterogeneous, merging impacts and sources: See some examples Table 1. For instance USEPA (1996) lists mainly the pressures or the first consequences of the transport system on the environment rather than environmental impacts (although designed as impacts). The use of natural resources, the hydrologic and hydraulic risks, the traffic safety and the final impacts as sensitive pollution are missing; A contrario rarely mentioned impacts are listed as introduction of non-native species, habitat disruption by wakes or anchors, direct waste from vehicles, roadkill and wildlife collisions. The other references examined merge impacts on the environment as climate change or visual effects, and intermediate states of the environment as local air quality, water quality. [START_REF] Goger | Un indicateur d'impact environnemental global des polluants atmosphériques émis par les transports[END_REF] and [START_REF] Goger | A method of building an aggregated indicator of air-pollution impacts. 3 rd int. conf. Sustainable development[END_REF] give the most precise list but only due to atmospheric pollutant emissions: In this field, impacts are distinguished when they are due to different chains of causalities, taking into account the fact that, as stated in [START_REF] Wäger | Life Cycle Assessment -a short overview[END_REF], the impact categories shall together enable an encompassing assessment of relevant impacts, which are known today (completeness), but at the same time should have the least overlap as possible (independence).

The concept of chain of causalities

It is the reason why we prefer to enlarge the PSI picture to the concept of process or chain of causalities between a cause and a final impact, with possibly a succession of couples cause-impact.

A good example is the greenhouse effect with the greenhouse gases (GHG) as a first cause, which by physical phenomenon increases the earth temperature, which modifies the global and local climates, with impacts on the agriculture, sea level, with impacts on all the biocenosis including the humans. If an initial pressure can be easily detected (GHG emissions), they are afterwards a lot of intermediate states and impacts. Another advantage of the concept of process or chain of causalities is to be much wider than a physical flow problem: any process can be taken into account, as cultural, psychological, psycho-physical, or biological effect.

A chain of causalities can be described through:

-The element(s) of the transport system, which is at the begin of the process, taking into account the life cycle approach, ie. considering all the activities involved. 3 main subsystems are involved (infrastructure, fuel, and vehicle), and for each of them 5 types of activities (production, existence, use, maintenance, and destruction). All together there are 13 subsystems-activities: See Table 2. The 13 subsystems can be simplified into 4, as coloured in Table 2 and used in Table 4, by considering the 3 main subsystems but extracting the traffic, i.e. the use of the infrastructure, final energy and vehicle.

building (1) final electricity production (5) production ( 9) existence (2) electricity distribution ( 6) existence ( 10 Table 2: Typology of the main transport subsystems involved in the environmental impacts.

Colours correspond to wider subsystems as used in Table 4.

-The final targets: [START_REF] Goger | Un indicateur d'impact environnemental global des polluants atmosphériques émis par les transports[END_REF] and [START_REF] Goger | A method of building an aggregated indicator of air-pollution impacts. 3 rd int. conf. Sustainable development[END_REF] consider 3 targets (nature, humans, man-made heritage) and a pseudo-target, the earth. In addition the Eco-indicator approach [START_REF] Brand | Weighting in Ecobalances with the ecoscarcity method -Ecofactors 1997[END_REF][START_REF] Goedkoop | The Eco-Indicator 99: A damage oriented method for Life Cycle Impact Assessment[END_REF] includes three types of endpoint damages: resources, ecosystem quality, and human health. The 2 first are subdivisions of the target nature. The (human) health is defined by World Health Organisation [START_REF] Who | Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference[END_REF] as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity". Therefore it is useful to distinguish health in a restricted meaning (absence of disease or infirmity) and the complement so-called human well-being, because the processes are often very different. Finally we get the target structure presented Table 3.

Targets Pseudo-target Resources

Nature Ecosystems: Nature understood as ecosystems, i.e. the association between a physicochemical and abiotic (the biotope) environment and a living community characteristic of the latter (the biocenosis)

Human health:

In a restricted meaning Humans:

Humankind which we extract from nature and focus on its health as defined by the WHO Human well-being

Man-made heritage:

With a distinction is made between common and historic buildings Earth:

Covers all the targets: the three previous targets (ecosystems, humans and man made heritage) and physical environments such as the atmosphere and the oceans

Table 3: Structure of the targets of the impacts on the environment.

-The in-between elements, i.e. the chain of causalities between the transport system and the final targets, to be described in detail. To design impact indicators, it is important to know the scientific milieu able to understand the process, and therefore to give the scientific disciplines involved. We propose a first and simple science structure: physics, chemistry, biology, psychology/sociology. It is important also to know if the process is linear or not, and if the transport system characteristics are major or minor explanation parameters, in order to know how these characteristics can be used for indicator building. Finally the reversibility is a major parameter from the sustainability point of view; The distance and time scales indicate who is concerned, if it is a local/global, shot/medium/long term impact.

It disaggregates the different impacts found in the literature in order to understand the complexity of the processes involved, to identify the related sciences and to estimate the order of magnitude of the impact in space and time.

Typology of chains of causalities

According to this structure, a typology of the chains of causalities of the environmental impacts of the transport system is proposed Table 4. 29 chains are distinguished, and 43 when taking into account differentiation in the last steps of the process corresponding to different final targets. The chains are independent and encompass all the relevant impacts found in the literature.

The description of the chains could be more detailed, by dividing a chain into two or more chains, if it is considered as not homogeneous in terms of process or targets. In addition some chains can be missing.

A contrario, the aggregation of impacts is possible when the knowledge necessary to build impact indicators is similar and if the main characteristics of the chain are similar. As, to be practical, the number of categories should amount to a not too high number, and considering the importance of each impact and the availability of indicators, some impacts could be merged, or minor chains be deleted. Because it is important to give the possibility to further users to perform such simplifications, the chain structure has to be as detailed as possible: It is easier to merge and delete than to add processes. Dispersion in the atmosphere (P), physical reactions (P) and sometimes chemical reactions (C), regional dimming, regional temperature decrease, global climate changes, destruction or modification of habitat for fauna, flora and humans (P), change in food chain (B), economic losses (PS)...

E

Table 4: Proposed list of the main chains of causalities of environmental impacts with some characteristics.

Conclusion

To describe the environmental impacts of an activity as transport through a complete list of independent chains of causalities allows us firstly to give a precise definition of the term 'environment'. In the literature, the differences in the impacts considered translate often the research area of the author, and, when the work is more global, the local perception of the environmental or ecological issue. For instance the loss of visibility above the cities, due to air pollution, is always cited in North America, but never in Europe, although the physical situations are similar. It is especially important to define the term environment, when today the environmental issue is taken into account by most of the transport specialists without precise knowledge of this field: In this case the environmental issue is very often reduced to greenhouse gases or to few well known impacts, or are reduced unconsciously to impacts for which simple to use assessment tools are available.

According to COST 356 [START_REF] Joumard | Definitions of indicator within the COST action 356 EST[END_REF], an indicator of environmentally sustainable transport is a variable, based on measurements, representing potential or actual impacts on the environment, or factors that may cause such impacts, due to transport systems, flows or policies, as accurately as possible and necessary. The precise description of the environmental processes constitutes then a powerful tool for indicator assessment, similar to that done by USEPA (1996). A priori, it can be stated that the more to the right the indicator is, the more precise the final impact is. It is mainly a tool to define what precisely an indicator does represent: Does it represent the final impact, or an intermediate one? How accurately is the process translated into the indicator function? Which relevant impacts are not taken into account by existing indicators? Isn't it possible double counting?

When the aim is to design new indicators of environmentally sustainable transport, the knowledge of the process indicates which scientists should be asked about the best way to represent the impact.

It is also a comprehensive basis to study the social perception of the environmental issue by survey, whom outputs can be used to balance the quality of local air, of regional air, noise, greenhouse effect… according to the focus placed on each of these impacts, as made for the Personal Security Index designed by the Canadian Council on Social Development: See [START_REF] Tsoukalas | Personal Security Index 2003: A reflection of how Canadians feel five years later[END_REF].

  -final energy -vehicle use(13)

Table 1 :

 1

	Table 4


Robert Joumard
email: joumard@inrets.fr

Jean-Marc Andre

Mario Rapone

Michael Zallinger

Natascha Kljun

Michel Andre

Zisis Samaras

Stéphane Roujol

Juhani Laurikko

Martin Weilenmann

Jean-Marc André

Michel André

Zissis Samaras

Karine Markewitz

Savas Geivanidis

Delia Ajtay

Laurent Paturel

Emission factor modelling for light vehicles within the European Artemis model

Keywords: Passenger car, inventory, Europe, regulated and unregulated pollutant, cold start, auxiliary, mileage, temperature, humidity, slope, load, traffic situation, kinematic

scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers,

EMISSION FACTOR MODELLING FOR LIGHT VEHICLES WITHIN THE EUROPEAN ARTEMIS MODEL 1. INTRODUCTION

In order to assess the present and future state of the emissions from transport and to evaluate different policies for reducing the emissions, it is necessary to have reliable knowledge about the sources and causes of the pollution, the technological and behavioural parameters of influence and the potentials of different strategies to reduce the pollution. The quantitative effects in general are calculated by emission models, which are also the basis for inventory systems at different levels of spatial resolution (local, regional, national, international). The Artemis project "Assessment and reliability of transport emission models and inventory systems" proposes to combine the experience from different emission calculation models and ongoing research in order to arrive at a harmonised methodology for emission estimates at the national and international level. It is the following step after two inventorying model developments in Europe: The European MEET (Methodologies for Estimating air pollutant Emissions from Transport) project [START_REF] Hickman | MEET -Methodology for calculating transport emissions and energy consumption[END_REF] and the COST 319 action [START_REF]COST 319 -Estimation of pollutant emissions from transport: Scientific state-of-the-art and network of European scientists[END_REF], which are the basis of the Copert 3 software [START_REF] Ntziachristos | Copert III, Computer Program to Calculate Emissions from Road Transport[END_REF] used in many countries; The German and Swiss emission model Handbook of emission factors HBEFA [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF]. The main shape difference between the MEET/Copert and HBEFA approaches is the taking into account of the kinematics: through the trip average speed in a continuous model for MEET/Copert, but through discrete traffic situations in HBEFA based on instantaneous modelling.

A rigorous statistical evaluation should be made of the whole sequence of operation of the inventory model, including the basic data (emission measurements, traffic statistics etc.), the parameters and assumptions of the models. For that purpose a wide European emission data base has been designed and most of the emission measurements available in Europe have been collected, including measurements carried out within the project itself. Then a deep and comprehensive analysis of the ways to take into account the driving behaviour has been made, allowing us to design several emission models adapted to different purposes, more applied or research oriented. Several sub-models are then built for taking into account the hot emission, cold start, auxiliaries, ambient air temperature and humidity, road gradient and vehicle load, but also evaporations treated in [START_REF] Hausberger | Evaporative emissions of vehicles. Artemis deliverable[END_REF]. The method and outputs of the study are detailed in [START_REF] Joumard | Emission factor modelling and database for light vehicles -Artemis deliverable 3. Inrets report[END_REF].

EMISSION DATA BASE

The database used to derive the Artemis light vehicle emission models includes the existing European emission data, either already collected within the European MEET project or Copert exercise, from the Handbook data base [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF] or later than them, and the results of the vehicle tests carried out specifically within the project by the different partners. Finally all these data were included in the so-called Artemis LVEM database, aiming at gathering all European emission measurements.

Specific measurements

About 3500 tests (1 vehicle, 1 driving cycle) were carried out within the project to improve the quality of the emission models. The pollutants considered differ from one test to another. The regulated pollutants are systematically measured for all the tests. In addition a large number of unregulated pollutants, especially hydrocarbon species, are measured by five laboratories. The compounds quantified and characterised depend on laboratory: See

Artemis light vehicle emission measurement database (Artemis LVEM DB)

Beside the emission measurement campaign carried out within the project, a database was developed to collect these data and all emission measurements made in Europe for passenger cars (PC) and light duty vehicles (LDV) for a driving cycle. Such data can be derived from measurements on a vehicle bench or on the road, but always after integration on a driving cycle or sub-cycle. It allowed the project partners to use the same internal and external data for designing the different PC and LDV emission factors, as presented in section 3.

The design of the database is linked to the emission parameters: the vehicle characteristics and the test characteristics, i.e. the driving cycle characteristics and the roller bench characteristics. The present version of Artemis LVEM database is formatted as an Access XP-Database. It database includes functions allowing to harmonise the emission data, to obtain comparable data. Four parameters found to have a quantifiable influence on the emission level [START_REF] Joumard | Accuracy of exhaust emissions measurements on vehicle bench -Artemis deliverable 2[END_REF] are taken into account: the gearshift strategy, the vehicle mileage, the ambient air temperature, and the ambient air humidity, standardised at the following values, resp.: Artemis strategy, 50 000 km, 23°C, 10.71 g H 2 O/kg dry air. The harmonisation is an option and in any case the raw data, non harmonised, remain in the database. These corrections are quite important: when averaged per fuel type, emission standard and pollutant, they range from 0.83 to 1.48, but can be much higher for vehicle sub-classes or individual tests.

The present version of the Artemis LVEM database contains data of 2847 passenger cars and light duty vehicles, measured from 1980 to 2004, originated from laboratories from 10 European countries (Austria, Finland, France, Germany, Greece, Hungary, Italy, Sweden, Switzerland, The Netherlands, and United Kingdom).

EMISSION MODELLING

Shape of the emission model

The emission model, whose scheme is presented in Figure 1, is the sum of 3 sub-models, for hot emission, cold start extra emission, and evaporation. All the models and sub-models consider as an input parameter the technical characteristics of the vehicles, and especially the fuel used and the emission standard, and the driving behaviour and more generally the traffic characteristics presented in [START_REF] André | Traffic characteristics for the estimation of pollutant emissions from road transport -Artemis deliverable 10[END_REF].

The pollutants reported as emission factors are all the regulated pollutants, NO 2 and some non regulated ones. Although we produced data on a huge number of different unregulated compounds, VOCs and PAHs analyzed at different laboratories build up an inharmonic set of data. Clear differences were seen in the emission levels obtained at different laboratories for some pollutants. In addition, some suspected outliers were found. We select the species, which can be regarded as most important, most informative and most representative when limitations of our data are taken into account. These unregulated pollutants are as VOCs, acetaldehyde, acrolein, benzene, 1,3-butadiene, formaldehyde, ethylbenzene, n-hexane, and toluene, and as PAHs, benzo[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene, benzo[k]fluoranthene, dibenzo[a,h]anthracene, and indeno[1,2,3-c,d]pyrene. For particle properties, outputs of the Particulates project [START_REF] Samaras | Characterisation of exhaust particulate emissions from road vehicles (Particulates)[END_REF] were taken into account: Emission factors were developed for the particle number (size range >7 nm) and the integrated active surface area (7 nm -1 µm) of the total particle population as well as the number of solid particles of three different size ranges: 7-50 nm, 50-100 nm and 100 nm-1 µm (aerodynamic diameter).

Instantaneous models

Instantaneous emission models for light-duty vehicles were already available [3, 7 for instance], but their accuracy was questionable [START_REF] Sturm | Instantaneous emission data and their use in estimating passenger car emissions[END_REF]. Emission models based on mapping the emissions onto engine speed and brake mean effective pressure (or engine torque) were then developed at EMPA and TU-Graz. Both models give accurate results for the pre Euro 1 petrol and for diesel vehicles. However, the prediction quality using this static map is not satisfactory for three-ways catalysts vehicles. Since emissions of modern catalyst cars are very low in regular hot conditions, some short peaks, which mainly occur during transient loads, dominate the overall emission factor.

To predict such emission peaks, the models were extended by adding transient corrections.

The EMPA model uses as dynamic variable the derivative of the manifold pressure. Using this dynamic map the engine-out emissions are very well predicted. A catalyst model is being furthermore considered which has as basic approach the modelling of the oxygen storage and release phenomena. The PHEM model developped at TU-Graz uses empirical transient correction functions based on several transient parameters, such as derivates of the engine power and engine speed over different time spans.

Considering fleets (groups) of vehicles, the quality of the models improves compared to the individual vehicle, even with a small number of vehicles. This proves that the errors in the individual vehicle models are random and not systematic. Thus, the two instantaneous emission models elaborated, although rather complex to develop, are able to predict contributory aspects like load, slope or different gear-shift scenarios, without introducing any ambiguous correction functions as it is usual for the bag based models.

Kinematic regression model

A second type of analytical model has been developed using emissions measured with 42 complete driving cycles and sub-cycles, a consistent set of kinematic parameters and a multivariate regression method based on principal components. The kinematic parameters belong to 2 blocks of variables: A first block of 7 variables (average running speed, average of the square speed, average of the cube speed, idling duration, duration at running speed, average product of instantaneous speed and acceleration, reciprocal of the trip length), and a second block of the two-dimensional distribution of the instantaneous speed (6 classes) and acceleration (7 classes). The final kinematic model is made, for each vehicle class, of a model for low emitters, a model for high emitters, and a model for all vehicles, and for each of the 3 cases, with a model 1 according to 7 kinematic parameters, a model 2 for 42 parameters, and a model 3 for 7+42 parameters.

The model 1 is the most understandable because the input parameters are average parameters, but it has the worst efficiency in terms of goodness of fit (R-square determination coefficient) as on overall for all emissions. The model 2 performs better and is in the most cases very close to the model 3, which is the most representative.

Traffic situations model

Following the Handbook approach [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF], a "traffic situation approach" was developped.

Compared to instantaneous or kinematic models, it is a non continuous or discrete model, which could be less accurate, but i) the kinematic input data are much simpler, as no speed profile neither kinematic parameters are necessary, ii) the kinematic input data are replaced by user oriented parameters, usually known by the traffic engineers.

The definition of traffic situations was elaborated after a large review of the European practices in order to be understandable across the different countries and users, and preferably close to the classifications usually implemented by traffic engineers. We adopted a road classification according to the function (access / distribution / through) and to the road network hierarchical organization, with 12 types of urban and rural roads. In addition, the road gradient and sinuosity were considered, with a qualitative approach (flat -sinuous / nonsinuous, hilly -ramps / sinuous, mountainous). Finally 4 levels of actual traffic conditions were also taken into account: free-flow traffic, heavy traffic, unsteady quite saturated traffic and the stop-and-go.

To characterize the speed characteristics of each traffic situation, a large collection of the existing European data recorded on-board vehicles, with 1500 speed -time curves, was compared with the traffic condition when available. It enabled a direct affectation of representative speed data for 69 traffic situations amongst more than 400. In addition 19 traffic situations correspond to each of the Artemis driving cycles or sub-cycles. For the other traffic situations, an affectation by similarity was done. However, this lack of data remains the main weakness of the approach and complementary data collection should be envisaged to improve it. 3 among the 19 traffic situations are macro situations, corresponding to urban, rural and motorway situations. In addition to all these traffic situations, we designed the most macroscopic traffic situation corresponding to the European situation, aggregating all other situations.

For the design of the corresponding emission factors, a large number of car emission data were considered, using more than 800 different driving cycles. In spite of its richness this heterogeneous dataset required a correction as regards the driving cycle. In that aim, we builtup a typology of test patterns to aggregate similar test cycles. Through Binary Correspondence Analysis and an automatic clustering, 15 classes or Reference Test Patterns (RTP) are defined, which can be combined together to compute emissions. For each RTP, one or several Reference Test Cycles are selected amongst the most significant in term of representativeness and number of associated emission data. To assess the emission factors of each RTP, the measured emission data were firstly corrected in order to process standardised data (see section 2.2); Then 2 models for deriving the emission factors for the RTP were developed.

For the model 1, the emission factors are computed on the base of a consistent sample of vehicles, i.e. the same vehicle sample for all RTPs as far as possible. For each of 14 RTPs out of 15, the emission data of 15 respective Reference test cycles (out of 21) were selected (14 Artemis sub-cycles plus one additional cycle); For the last RTP (urban stop and go), the emission data of another cycle were selected. With this restriction, the number of available measurements was significantly reduced to mostly Artemis subcycle measurements of Euro 2 and Euro 3 vehicles: The subset still consists of 1 500 vehicle.tests corresponding to 94 hours of measurements and 9 200 emission measurements. The emission factor per RTP is then derived from the average of measured emissions of the vehicle sample.

Model 2 considered the whole Artemis light vehicle emission measurement database (version 3 October 2005) described in section 2.2. The analyse of the variability and coherency of the emission data within each RTP and for each vehicle category shows some deviating cycles, generally far away from the Reference test cycles in term of kinematic, with however quasisystematic under-or over-estimation: When they did not represent a high quantity of tests, the corresponding data were cancelled. From the 19 000 initial data, 10 000 coherent data (2672 diesel and 7381 petrol cars), corresponding to 1280 hours of emission measurements (of which 7350 vehicle.tests and 940 hours for Euro 2 and 3 vehicles), were retained. It enabled the computation of the emission for pre-Euro to Euro 4 passenger cars (see an examples on Figure 2). For Euro 2 -Euro 3 vehicles, the amount of data processed was thus 5 and 10 times larger than in for model 1 resp. when considering the number of data and the hours of measurements. The emission cartography developed through the RTPs is particularly appropriated to compute emission for the different traffic situations. The distances between a traffic situation (represented by its speed curve) and the test cycles enabled thus identifying the closest test patterns and to consider each traffic situation as a linear combination of the RTPs, proportional to the proximity -in term of kinematic -to these test patterns. Therefore the emissions of hundreds traffic situations, including the 4 macro traffic situations, are computed by linear combination of the reference emissions of the closest RTPs, as defined in the model 2. These emission factors are illustrated in Figure 3 for the 4 traffic conditions.

Average speed model

A fourth type of hot emission model was designed, similar to the MEET or Copert ones, i.e. taking into account kinematics through the average speed. Only hot emissions were used, but data were processed following two different statistical approaches, with different data clustering, leading to two alternative sets of speed dependent emission equations:

-A first model based on emission data clustering through 10 km h -1 speed range averaging in order to avoid overweighting of specific speed points with high number of data -And a second model designed from the 15 RTP emission factors.

For the model 1, the emission data were corrected and homogenized against the ambient temperature and humidity, and gear choice strategy effects, but not according to vehicle mileage. An equation of the following general form was used for the emission factor ef, according to the average speed v (a to f being coefficients):

! ef = a + c " v + e " v 2 1+ b " v + d " v 2 + f v
The second model is derived from the RTP emission factors, as calculated within the development of the traffic situation model 2: The emission function is calculated by regression between these 15 RTP emission factors, expressed according to the average speed.

The choice of the regression (power or 2 nd to 5 th order polynomial) is made for each data set with the following objectives: i)not to go outside the envelope of the measured points as far as possible, in order to avoid systematic over-or underestimation of emission for some speed ranges; ii)correspond to the apparent shape of the points according to the average speed; iii) avoid important oscillations; iv) never give negative figures; and v) be as simple as possible. This approach has the advantage to be fully coherent with the traffic situation model, based also on the RTP emission factors. The comparison of the second approach based on RTPs with the first one based on speed range averages (see Figure 4) shows some differences in terms of curve shape and emission level, sometimes up to a factor 2. These differences may be attributed to the homogenization of the data as regard the vehicle mileage, done only in the second method, the way the emission data are clustered, the choice of the equation type. They show that such model depends a lot on the methodological assumptions.

Due to the lack of both measurement and literature data, it was decided to cover future vehicle technologies using reduction factors based on literature, limited available data and emission standard ratios.

Other sub-models

Due to the relative lack of data for Light Duty Vehicles, the LDV model is built as a function of the average speed and the vehicle loading rate. The influence of the mileage on hot emissions is a linear function of the mileage, with a maximum above 120 000 km for Euro 1 and 2 vehicles and 160 000 km for Euro 3 and 4 vehicles. The influence of the ambient temperature and humidity on the emissions are in most cases a linear function [START_REF] Joumard | Accuracy of exhaust emissions measurements on vehicle bench -Artemis deliverable 2[END_REF]. Road gradient and vehicle load are taking into account through a specific sub-model, together with auxiliaries. The modelling of cold start influence is based on the modelling of the cold excess emission in mass per start, depending on the ambient temperature, average speed and parking time. Then this extra emission is attributed to the trips, using statistics of trip length, start temperature and ambient temperature: Finally the cold extra emission model is a function of the season, the ambient temperature, the average speed and the hour of the day. It has to be added in parallel to the hot emission.

CONCLUSION

The aim of Artemis was to improve and update the European emission inventorying tools, but also to develop an harmonised approach, common to all European countries, by avoiding the former situation where several models were concurrent and gave different outputs for the same situation. This objective was only partially achieved. However, the Artemis model provides a series of models for distinct situations.

The model has been tested to calculate the road emissions for the period 1990-2004 in Sweden [START_REF] Sjödin | Implementation and Evaluation of the ARTEMIS Road Model for Sweden's International Reporting Obligations on Air Emissions[END_REF] for international reporting obligations on air emissions. There was in general a fairly good agreement with on-road emission data. The model for passenger cars and light commercial vehicles is implemented in the Artemis software [2], together with the models for 2 wheelers and heavy duty vehicles. In addition, separate models are provided for the nonroad transport modes. All the light vehicle Artemis models (except the instantaneous and kinematic ones) and the software are publicly available and distributed free of charge.

The new vehicles, not tested in depth within the project, as Euro 4 and future Euro 5 ones, should be integrated on the basis of more extensive measurement campaign, including new or quite new concepts as for instance hybrid vehicles. For that the Artemis LVEM database could be used as far as this database includes all new emission measurements carried out in Europe on light vehicles.

The Artemis models were developed mainly for European users, although the model will be used, as Copert, by many users outside Europe. In some cases and especially for developing countries, the driving behaviour, the vehicles and the emission factors could be quite far from the European ones. The user's demand could also be different. It would be therefore very useful in the future to adapt the models to all the users, European or not.

Figure 1 :

 1 Figure 1: Shape of the Artemis emission model for light vehicles.

Figure 2 :

 2 Figure 2: Variation of the NOx emission for the Traffic Situation model 2, for Euro 3 Diesel according to 15 RTPs (left), and for stable / unstable RTPs for petrol cars (right).

Figure 3 :

 3 Figure 3: Traffic situation approach illustration: NOx emissions of cars estimated for an urban trunk road (speed limit: 50 km/h), at different traffic conditions.

Figure 4 :

 4 Figure 4: Diesel Euro 3 NOx emission functions and data according to average speed, as designed through speed range averages or through RTPs.

Table

Table 1 :

 1 Numbers of unregulated pollutants measured per laboratory and per pollutant group.

	ULCO

. On-line measurements are performed by EMPA by chemical ionization mass spectrometry (CI-MS) for methane, benzene, toluene, xylenes and ethyl benzene, and by VTT by Fourier transform infrared (FTIR) for N 2 O, NO/NO 2 , NH 3 and formaldehyde. The other measurements are off-line, with different methods used: -gas chromatography with flame ionization detection (GC-FID) for about 110 VOC species by EMPA, for 18 species by IM, for C 2 -C 6 compounds by Inrets-

Table 2

 2 shows how the tested vehicles are distributed between different European emission standards and fuel types. With these vehicles, 12 685 tests were conducted when splitting up into the cycle level, and 18 824 tests In a first step, the Artemis LVEM database was developed and used only by the Artemis partners. The main part of the database is now available for anybody. It is at the same time open for data submission from new emission measurements.

respectively when analysing the sub-cycle level. Regarding pollutants per vehicle and subcycle, 177 861 emission factors (g km

-1

) have been derived, for 404 pollutants. 25 430 among these emission factors concern unregulated pollutants.

Table 2 :

 2 Number of vehicles per emission standard and per fuel type in the Artemis LVEM database.

	emis. standard	petrol	LPG	CNG	diesel	biodiesel	Total
	pre-Euro 1	901			231		1132
	Euro 1	1227	7		68		1302
	Euro 2	169	3		64		236
	Euro 3	100	2	1	54	2	159
	Euro 4	15		1	2		18
	total	2412	12	2	419	2	2847

th International Conference 'Transport and Air Pollution' 2008, Graz

ACKNOWLEDGEMENTS

We wish to thank the European Commission for its financial support as part of the contract n°1999-RD.10429 under the Competitive and sustainable growth programme of the 5th framework programme.


Francisco F A T De Carvalho

Yves Lechevallier

Yves Lechevallier Partitioning

Partitioning Methods On Dissimilarity Matrices Set

Keywords:

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Partitioning Methods On Dissimilarity Matrices Set

Francisco de A. T. de Carvalho 1 & Yves Lechevallier 2 1 CIn/UFPE, Recife-PE, Brésil fatc@cin.ufpe.br 2 INRIA, Paris-Rocquencourt -78153, France Yves.Lechevallier@inria.fr Abstract. Cluster analysis have been widely used in numerous fields including pattern recognition, data mining and image processing. Their aim is to organize a set of items into clusters such that items within a given cluster have a high degree of similarity. In particular, partitioning clustering models aims to organize a set of items into a pre-defined number of clusters. Our reference clustering model is the k-means like algorithms .

In this presentation, we introduce partitioning clustering models and algorithms that are able to partitioning objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices have been generated using different sets of variables and dissimilarity functions. These methods are designed to furnish a partition and a prototype for each cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives.

Experiments with data sets (synthetic and from UCI machine learning repository) show the usefulness of the proposed methods.