Robert Joumard
email: joumard@inrets.fr

Jean-Marc Andre

Mario Rapone
email: m.rapone@im.cnr.it

Michael Zallinger
email: zallinger@vkmb.tzgraz.ac.at

Natascha Kljun

Michel Andre

Zisis Samaras

Stéphane Roujol

Juhani Laurikko
email: juhani.laurikko@vtt.fi

Martin Weilenmann
email: martin.weilenmann@empa.ch

Jean-Marc André

Michel André

Zissis Samaras

Karine Markewitz

Savas Geivanidis

Delia Ajtay

Laurent Paturel
email: laurent.paturel@univ-savoie.fr

Zissis Samaras

Livia Della Ragione

Nadine Adra

Catherine Fantozzi

Inrets Anna Kadenko

Infras Mario Keller
email: mario.keller@infras.ch

Fabrice Cazier
email: cazier@univ-littoral.fr

Maria Antonietta Costagliola

Peter Mattrel

Agnès Mercier

Tuan Le Anh

Nikolas Kyriakis

Jean-Claude Déchaux

US Olivier Devos

Inrets Mireille Chiron

Isabelle Caplain

Valérie Nollet
email: valerie.nollet@univ-lille1.fr

professor, Zissis Samaras

Zissis Samaras

Zissis Samaras

Emission factor modelling and database for light vehicles -Artemis deliverable 3

Keywords: emission factor, passenger car, light vehicle, model, inventory, Europe, regulated and unregulated pollutant, cold start, auxiliary, mileage, temperature, humidity, slope, load, traffic situation, kinematic, instantaneous émission unitaire, voiture particulière, véhicule léger, modèle, inventaire, Europe, polluant réglementé et non réglementé, départ à froid, auxiliaire, kilométrage, température, humidité, pente, charge, situation de trafic, cinématique, instantané 15 Diffusion

In the frame of the Artemis project, the emission models for atmospheric pollutants have been updated and strongly improved for the road light vehicles. This development is based on a wide and specific measurement campaign, with more than 150 vehicles and about 3500 tests for a large number of pollutants, regulated and non regulated ones. The results of these measurements carried out by several European laboratories are included in a database especially designed, the Artemis LVEM database, available and open to future European measurements data.

The Artemis model for light vehicles contains a set of complementary sub-models. The base model calculates the hot emissions for each vehicle category according to the driving behaviour. It contains 5 alternative models: The main model considers traffic situations (discrete model), with emission factors for each of them; A simplified model, built on the same data, takes into account the driving behaviour through the average speed (continuous model); A continuous model, socalled kinematic, considers a limited number of aggregated kinematic parameters; 2 instantaneous models consider some instantaneous parameters as instantaneous speed. These models need input kinematic data of variable complexity and are therefore adapted to different usages, for assessing national emissions, as far as for calculating the impact of a local traffic control. They are associated to models taking into account the influence of several parameters, as cold start, using of auxiliaries like air conditioning, vehicle mileage, ambient air temperature and humidity, road slope and vehicle load, as far as evaporation. The building methods of all these models and the data or models they are based on are presented, as far as the models themselves. Dans le cadre du projet européen Artemis, les modèles d'émissions de polluants atmosphériques ont été actualisés et très fortement améliorés pour les véhicules routiers légers. Ce développement est basé sur une très importante campagne de mesures spécifique, sur plus de 150 véhicules et environ 3500 tests, pour un grand nombre de polluants réglementés ou non. Les résultats de cette campagne de mesures menées par plusieurs laboratoires européens sont intégrés dans une base de données créée spécialement, la base de données Artemis LVEM, disponible et ayant vocation à accueillir de futures mesures européennes. Le modèle Artemis pour les véhicules légers comporte un ensemble de sous-modèles complémentaires. Le modèle de base calcule les émissions à chaud pour chaque catégorie de véhicule en fonction du comportement de conduite. Il comporte 5 modèles alternatifs. Le modèle principal considère des situations de trafic (modèle discret), avec des facteurs d'émission pour chacune d'entre elles ; un modèle simplifié, construit sur les mêmes données, tient compte du comportement de conduite à travers la vitesse moyenne (modèle continu) ; un modèle continu, dit cinématique, tient compte d'un nombre limité de paramètres cinématiques agrégés ; 2 modèles instantanés tiennent compte de divers paramètres instantanés comme la vitesse. Ces modèles demandent des données d'entrée cinématiques de complexité variable et sont donc adaptés à des utilisations différentes, aussi bien pour calculer des émissions nationales que pour calculer l'impact d'une gestion locale du trafic. Ils sont associés à des modèles tenant compte de l'influence de divers paramètres comme le départ à froid, l'usage d'auxiliaires tels l'air conditionné, le kilométrage des véhicules, les température et humidité ambiantes, la pente et la charge des véhicules, ainsi que les évaporations. Les méthodes de construction de ces différents modèles et les données ou modélisations dont ils sont issus sont présentées, ainsi que les modèles eux-mêmes. 2.1.3. Driving cycles used .......................................................................................................................................... 2.1.4. Test sequence.................................................................................................................................................... 2.2 2.4.1. Objectives ......................................................................................................................................................... 2.4.2. Building............................................................................................................................................................. 2.4.3. Data submission ............................................................................................................................................... 2.4.4. Data harmonisation ......................................................................................................................................... 2.4.5. Content.............................................................................................................................................................. 2.4.6. Public availability ............................................................................................................................................ .1.2. Pollutants and units ......................................................................................................................................... 3.1.3. Volatile organic compounds VOCs considered.............................................................................................. .1.4. Polyaromatics PAH considered according to their toxicity........................................................................ .2.3. Conclusion of instantaneous models............................................................................................................... 3.3 3.3.1. Data................................................................................................................................................................... 3.3.2. Method .............................................................................................................................................................. 3.3.3. Results ............................................................................................................................................................... 3.4 3.4.1. Traffic situation definition ............................................................................................................................... 3.4.2. Speed data representative of the traffic situations......................................................................................... 3.4.3. Emission data harmonization through Reference test patterns..................................................................... 3.4.4 3.4.5. Emission factors of Traffic situations ............................................................................................................. 3.4.6. Emission factors of macro traffic situations................................................................................................... 3.5 3.5.1. Design through speed range averages............................................................................................................ 3.5.2. Design through Reference test patterns.......................................................................................................... 3.5.3. Comparison of the two average speed models ............................................................................................... 3.5.4. Reduction factors for future technologies....................................................................................................... 3.6.1. Homogeneity of the VOC and PAH emission data......................................................................................... 3.6.2. VOC and PAH emission factors ...................................................................................................................... 3.6.3 3.7.4. Conclusion ........................................................................................................................................................ 3.8

3

3

3

Introduction

Emissions from transport are an important and often dominant source of air pollution with direct and indirect negative impacts in particular on human health. At the same time, transport also contributes significantly to greenhouse gases, which should be reduced as has been agreed, for example, in the Kyoto protocol. It is therefore a widely accepted and undisputed objective to reduce pollutant and greenhouse gas emissions by appropriate ways and means. 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. For evaluating different measures it is necessary to quantify the past, present and future status of pollution, but also the potentials and effects of different approaches. 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). It is therefore a basic requirement that these emission models produce accurate, reliable and consistent results. Calculation of emissions has therefore gained institutional importance in the European Community, particularly with the development of the CAFÉ (EC, 2005a) and ECCP (EC, 2005b) programmes.

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 addresses the Competitive and sustainable growth programme of the 5th framework programme of the European Commission, Key Action KA 2: Sustainable mobility and intermodality, Task 2.2: Infrastructures and their interfaces with transport means and systems, Sector 2.2.2: Environment, Sub-Task 2.2.2/2: Monitoring emissions from transport including particulates. The project develops a harmonised emission model for all transport modes, which aims to provide consistent emission estimates at the national, international and regional level. This requires first of all additional basic research and a better understanding of the causes of the differences mainly with respect to emission factors.

The Artemis project 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 (Joumard, 1999), focused in particular on the production of emission factors and functions using most of the available measured data in Europe. These research projects are the basis of the Copert 3 software, well known in many countries. -The German and Swiss emission model HBEFA [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF], mainly used in some countries.

The main difference between the Copert and HBEFA approaches is, beside the data base differences, the taking into account of the kinematics: through the trip average speed in a continuous model for Copert, but through discrete traffic situations in HBEFA based on instantaneous modelling. A new method for synthesising the emission measurements from different laboratories is necessary, and INRETS report n°LTE 0523 should integrate existing methods that are based either on instantaneous vehicle operation or average speed.

In order to account for new concerns about air pollution, we must have a much better understanding of types of emission that have not yet been studied extensively. This mainly concerns the nonregulated pollutants: speciation of the volatile organic compounds necessary to model photochemical pollution, greenhouse gases, polyaromatic hydrocarbons, and particulate sizing which is becoming increasingly important in the assessment of health effects.

Therefore it is necessary to produce emission factors for exhaust emissions of regulated and nonregulated pollutants, and especially for the latest vehicle concepts (Euro 2 to Euro 4 petrol and diesel-engined vehicles) for which very few data exist. Extensive emission measurements have to be conducted in order to provide a wide and representative base for the estimation of the emission factors.

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. This report concerns only the light vehicles, i.e. the passenger cars and light duty vehicles. 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 of course the 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 not treated in detail in this report.

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 MEET or Copert exercises 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 3000 tests (1 vehicle, 1 driving cycle) were carried out within the Artemis project to improve the quality of the emission database and then the quality of the emission models designed within Artemis. The pollutants considered differ from one test to another. They are presented together with the vehicles tested and the test conditions, including the driving cycles.

Pollutants considered

The regulated pollutants (CO, CO 2 , HC, NOx, and PM for diesel cars) are systematically measured for all the tests.

In addition a large number of unregulated pollutants, especially hydrocarbon species, are measured by five laboratories [START_REF] Aakko | Emission factors of unregulated atmospheric pollutants for passenger cars[END_REF]2006). The compounds quantified and characterised are given per laboratory and per pollutant group in The sampling procedures and the analysis methods for unregulated pollutants depend on the laboratory and of the pollutant group: they are detailed in Annex 2.

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. Different methods are used:

-gas chromatography with flame ionization detection (GC-FID) for about 110 VOC species by EMPA [START_REF] Heeb | Methane, Benzene and Alkyl Benzene Cold Start Data of gasoline-driven Passenger Cars representing the Vehicle Technology of the last two Decades[END_REF]2004;[START_REF] Saxer | Temperature-dependent Methane, Benzene and Alkyl Benzene Cold Start Emissions of 3 conventional Passenger Cars (Euro 0), Poster[END_REF]2003;Weilenmann et al., 2003b;2005), for 18 species by IM (Prati et al., 2003a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]2005), for C 2 -C 6 compounds by Inrets-ULCO (Caplain et al., 2004;2006;Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF], -gas chromatograph with a mass spectrometer (GC-MS) for PAHs by IM (Prati et al., 2003a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]2005) and KTI, for C 6 -C 15 compounds by Inrets-ULCO [START_REF] Caplain | Measurement of non regulated pollutants from gasoline and diesel passengers cars[END_REF]2006;Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF], -gas chromatography for 13 compounds up to C 8 by VTT.

-high performance liquid chromatography (HPLC) for aldehydes and ketones by EMPA (Saxer et al., 2002;2003;Weilenmann et al., 2003b;2005), IM (Prati et al., 2003a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]2005), Inrets-USTL [START_REF] Caplain | Measurement of non regulated pollutants from gasoline and diesel passengers cars[END_REF]2006;Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF], KTI and VTT, for PAHs by Inrets-US [START_REF] Paturel | Unit PAH emissions at exhausts of recenttechnology light vehicles in Europe[END_REF]2005;[START_REF] Devos | Exhaust emissions of PAHs of passenger cars[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF].

Vehicle sample

154 vehicles were tested specifically for designing the new emission models, i.e. 152 passenger cars and 2 light duty vehicles. The samples per fuel, emission standard and laboratory are described in Table 2. Two thirds of the vehicles are petrol fuelled. 39 and 42 % of the vehicles are resp. Euro 2 and 3 vehicles, and 6 % Euro 0, Euro 1 and Euro 4 vehicles. All these vehicles were tested for hot emissions, and some of them in addition for other tasks. The average characteristics of the vehicle samples per task are given in Annex 3, and the characteristics of each vehicle tested per task in Annex 4. The vehicle samples of each laboratory were chosen to be representative of the national fleets. G the cycles 'VP faible motorisation' and 'VP forte motorisation' are alternative H Methane, Benzene, Toluene, Xylenes, NH 3 I for 7 slopes: -10, -5, -2.5, 0, +2.5, +5, +10 % J with unloaded, half loaded and loaded situations. The "loaded" situation designates the measurement with the full payload for this car type, in average it is 450 kg, and the "half loaded" designates the situation in the middle of "unloaded" and "loaded".

Table 3: Number of driving cycles tested per vehicle and per parameter studied, and number of vehicles tested by parameter and laboratory. The driving cycles and families of them are defined in Annex 5.

2.1.3.

Driving cycles used 36 driving cycles were used, but some of them only with few cars. The 3 Artemis cycles were used by almost all the vehicles tested; Then the most used driving cycles are the Artemis low or high motorisation, the EMPA BAB 1000, the Handbook, and the Inrets urbain fluide court ones. The main parameters of the driving cycles used are in Annex 5, with their description in Annex 6.

The driving cycles tested per vehicle and the number of vehicles tested are given per parameter studied and per laboratory in Table 3 L 2 cycles at 1 temperature, 5 cycles at 3 ambient temperatures: -20, -7 and +23°C

M 1 cycle at 1 temperature, 3 cycles at 3 ambient temperatures: -20, -7 and +23°C

Test sequence

The tests carried out are briefly described Table 4 per parameter studied and per laboratory. The test sequence depends on each laboratory: see specific reports describing the results per parameter, as given in section 3, or laboratory reports (as Joumard et al., 2004a andStettler et al., 2004 resp. for INRETS andEMPA tests).

The vehicles were tested as received on a chassis dynamometer. The emissions are sampled usually with a bag or a trap, giving an physical average of emission along the sampling time, or sampled and analysed continuously. In the first case the unit of measurement is the so-called bag or vehicletest, corresponding to a driving cycle and the analysis of different pollutants. In the second case, either the instantaneous emissions are considered (to design instantaneous models, see section 3.2), or the continuous signal is averaged for the whole cycle or a sub-cycle, defining again a unit of measurement called also bag or vehicle-test. The numbers of bags are given in Table 4. All together about 3500 bags or vehicle-tests were produced specifically within Artemis for the light vehicles, to improve the design of the Artemis emission models developed in section 3. About 2400 tests were carried out to design the basic hot emission model for regulated pollutants, 1600 tests for the emission factors of unregulated pollutants, 1200 tests to design the instantaneous models, 500 tests for the cold start model, and 800 tests for the other sub-models (LDV, influence of mileage, ambient temperature and humidity, gradient, load), but some tests are common to different tasks.

Other Artemis measurements

In parallel to emission tests performed to design new emission models for light vehicles, more than 2000 tests were carried out to study the influence of 20 parameters of the tests on vehicle bench, in order to improve the accuracy, reliability and representativeness of emission factors: driving patterns, vehicle related parameters, vehicle sampling, and laboratory related parameters. These tests concern the regulated atmospheric pollutants and pre-Euro to Euro 4 vehicles. Some tests were common to the model design and the accuracy research. They are presented and discussed in Joumard et al. (2006a).

In addition specific measurements of evaporative emissions were performed in order to design the evaporative emission model [START_REF] Hausberger | Evaporative emissions of vehicles. Artemis deliverable[END_REF], and measurements of particle properties carried within the clustered project Particulates [START_REF] Samaras | Characterisation of exhaust particulate emissions from road vehicles (PARTICULATES)[END_REF].

External data

Beside specific tests, external emission data were used to derive emission models, coming either from the literature, or from measurement campaigns carried out by the different partners, or from the former MEET project, or from the Handbook data base. Such data were used especially in the design of the traffic situation model, the average speed model, the Light Duty Vehicle model, and the cold start model.

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 other European data.

Objectives

The Artemis project is aiming, among other, at improving the exhaust emission factors for the passenger cars and light duty vehicles, by enlarging the emission factor database especially for nonregulated pollutants, recent passenger cars and light duty vehicles.

In this frame, the Artemis database is aimed at collecting 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 time period, so-called driving cycle or sub-cycle. It allows the Artemis partners to use the same internal and external data for designing the different PC and LDV emission factors, according to different parameters, as presented in section 3.

In order to be usable by Artemis partners but also by any other research team in the field of the emissions from transport modes, the data must contain not only the measured emissions but also all the explanatory parameters of the emission, as far as they are available.

A last aim of the database is to be easily supplemented in the future by new emission measurements.

Figure 1:

Simplified design of the database, including the 3 main tables and the 2 most important secondary tables.

INRETS report n°LTE 0523

Building

The design of the database [START_REF] Kljun | Design of the A300 data base[END_REF] is linked to the way how an emission factor is measured: an emission factor comes from the exhaust emission of a vehicle which followed a driving cycle on a dynamometer bench or on the road. Therefore to explain an emission factor we have to know the vehicle characteristics and the test characteristics, i.e. the driving cycle characteristics and the roller bench characteristics. The design of the database is basically made by 3 main tables and 2 secondary tables, shown Figure 1. The 3 main tables are:

-a vehicle identification through 103 parameters, providing information on the tested vehicles, such as testing laboratory, make, model, year of registration, size of engine, fuel type, etc., -a test identification through the vehicle and driving cycle identifications and 47 other test parameters, providing information on the measured tests, such as test date, technical details on the test procedure, dynamometer settings, etc.

-finally an emission identification through the test and pollutant identifications, the emission factor itself and its unit.

In complement to the main tables, 41 tables denoted as "G_xxx" provide additional information: see a more detailed design in Annex 7. In the following, a small selection of the "G_xxx" tables is listed:

G_cycle: description of the cycles through 14 parameters, e.g., cycle names, lists of subcycles, G_cycle_family:

name of the cycle family, e.g., Artemis cycles or Legislative cycles, G_cycle_stat:

provides the statistics and kinematic parameters of each cycle, G_EU_emis_standard: lists the European emission standards each vehicle should be assigned to, G_fuel_veh:

lists the fuel types of the included vehicles (e.g., diesel, petrol, LPG). A more detailed description of the fuel used for the particular tests can be found in G_fuel_test, G_laboratory: l aboratory names, G_pollutant:

lists the pollutants (regulated and unregulated), described through 6 parameters, G_veh_sample:

categorises the measurement campaigns (e.g., national program, Artemis, Particulates).

In each table, some parameters are compulsory. Each table contains a numerical identifier for simpler and faster handling of the data. For instance each vehicle has its own unique identifier. The identifier code provides information on the laboratory conducting the test and is derived as follows: two first digits (10 to 99) denote the laboratory identifier, the following four digits (0001 to 9999) denote the chronological number of the vehicle as provided by each laboratory.

The tables VEHICLES and TESTS are connected on the basis of a 1:n-relationship. This allows one vehicle to be measured for several tests. A counter named CHRONOLOGICAL_TEST-NB allows distinguishing the data of one vehicle measured several times for the same driving cycle. The EMISSION_DATA finally are assigned to the corresponding vehicle and test conditions using the vehicle identifier, the test identifier and the CHRONOLOGICAL_TEST-NB.

The present version of Artemis LVEM database is formatted as an Access XP-Database. It contains raw data (tables) plus some few queries giving an overview of the available data. There are no forms or macros included.

The actual emission factors are formatted using scientific notation to allow for a useful accuracy independent of their magnitude. They should be given in g km -1 . Detailed information on other fields' format and contents are given in the description of the respective tables.

Data submission

A datasheet provides the format that should be used when submitting data for the Artemis LVEM database. For every car involved, a separate copy of this Excel file should be used. This datasheet, detailed in Annex 8, contains five sheets:

-README: provides additional information and helps on how to use the datasheet.

-car: summarises the characteristics of the tested vehicle.

-test xx: describes the test characteristics of the tested car. One copy of this sheet is needed for each cycle tested. -instantaneous data test xx: contains instantaneous data as a function of time. The use of this sheet for instantaneous data is recommended but not compulsory. -pollutant names: lists the name convention for unregulated pollutants.

Data harmonisation

The database includes functions allowing to harmonise the emission data, to obtain comparable data. Four parameters are taken into account: the gearshift strategy, the vehicle mileage, the ambient air temperature, and the ambient air humidity. They are standardised at the following values, respectively: Artemis strategy, 50 000 km, 23°C, 10.71 g H 2 O/kg dry air. These four test parameters were found to have a quantifiable influence on the emission level (Joumard et al., 2006a). The harmonisation is an option and in any case the raw data, non harmonised, remain in the database. These corrections are quite important, as shown In addition the total HC emission factor units can be harmonised in g eq. C 3 H 8 or in g eq. CH 4 (see section 3.1.2). The difference is about 9 %.

Content

The Artemis LVEM DB merges emission data measured within the Artemis project itself plus data derived from other European measurement campaigns such as the Particulates database, the MEET data (data from INRETS, TNO, TRL, and LAT), the Handbook data (mainly EMPA, TUEV), OSCAR data (TRL, TNO), and additional INRETS and TNO data.

The present version of the Artemis LVEM database contains data of 2847 passenger cars and light duty vehicles, measured from 1980 to 2004. Their laboratories of origin are given in Table 6 together with the umber of vehicle-tests.

Public availability

In a first step, the Artemis LVEM database was developed and used only by the Artemis partners.

After the completion of the project and with the authorisation of all providing laboratories, the main part of the database is now available for anybody. It is managed by INRETS, but could be managed in the near future by another partner laboratory.

The database is at the same time open for data submission, through the same laboratory.

Emission modelling

After a quick presentation of the shape of the emission model for the light vehicles, and of the pollutants considered, each sub-model is presented in detail, synthesizing a lot of reports.

General shape

The Artemis project 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 (Joumard, 1999). These research projects are the basis of the Copert 3 software, 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], mainly used in some countries.

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 detailed in [START_REF] Sturm | Instantaneous emission data and their use in estimating passenger car emissions[END_REF], but through discrete traffic situations in HBEFA based on instantaneous modelling.

A new method is presented here for synthesising the emission measurements from different laboratories, integrating former methods. In addition the pollutants taken into account and the emission units are presented.

Shape of the emission model

Some preliminary studies on the best way to take into account the explaining parameters of the emission data of passenger cars [START_REF] Kadenko | Statistical syntheses of the vehicles emissions factor measurements results[END_REF] compared three statistical methods such as "Linear parametrical identification", "Linear regression" and "Non-linear regression: the Box-Cox model". The last one was more suitable. This statistical work was completed by different authors, as presented in sections below.

Finally the Artemis model for light vehicles was improved a lot in comparison with MEET/Copert or HBEFA approaches, especially with different ways to take into account the driving behaviour for hot and cold start sub-models, beside the considerable amount of new emission data presented in section 2. The driving behaviour and more generally the traffic characteristics are presented in a parallel report (André et al., 2006a).

The emission model, whose scheme is presented in Figure 2, is the sum of 3 sub-models:

emission = hot emission + cold start emission + 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: See Samaras et al. (2005a) for a INRETS report n°LTE 0523 detailed analysis of the influencing characteristics.

Hot emission hot emission = f K (kinematics, vehicle load, road gradient) . f M (mileage) . f T (ambient temperature) . f H ( ambient humidity) . f A (auxiliaries)

For the passenger cars (PC), 4 parallel models are proposed to take into account the kinematics (and secondly the vehicle load and road gradient): (instantaneous kinematics, vehicle load, road gradient) or f K (kinematics, load, gradient) = f K A PC (average kinematics) . f L (vehicle load, road gradient) or f K (kinematics, load, gradient) = f K TS PC (traffic situation) . f L (vehicle load, road gradient) or f K (kinematics, load, gradient) = f K V PC (average speed) . f L (vehicle load, road gradient) For light duty vehicles (LDV) a unique model is proposed: (average speed, vehicle load) f K I PC (instantaneous kinematics, vehicle load, road gradient) is an instantaneous model, presented in section 3.2, with 2 parallel models: The EMPA model (section 3.2.1) and the PHEM model (section 3.2.2). f K A PC (average kinematics) is the so-called kinematic regression model presented in section 3.3.

f K (kinematics, load, gradient) = f K I PC
f K (kinematics, load, gradient) = f K LDV
The instantaneous and kinematic models are the best way to take into account the kinematics, but need quite complex kinematic data, either through the speed curve, or some average parameters. f K TS PC (traffic situation) is the so-called traffic situation model presented in the section 3.4. It is the main way to take into account the kinematics in an accurate but simple way. f K V PC (average speed) is the so-called average speed model presented in section 3.5. It is comparable to Copert, but less accurate than the traffic situation model. f L (vehicle load, road gradient) models the influence of the vehicle load and road gradient, and is presented in section 3.10. f K LDV (average speed, vehicle load) is the LDV model taking into account the kinematics and vehicle load, presented in section 3.7.

Cold start emission

A first model models the extra-emission per start (section 3.12.3). For a traffic, 2 models are proposed: (driving statistics, ambient temperature) or cold start emission = f COLD 3 (average speed, ambient temperature, season, hour) The cold start models are presented in section 3.12, and more specifically in section 3.12.4. f COLD 2 (driving statistics, ambient temperature) is the second cold start model taking into account the driving statistics in a very complex but accurate way. f COLD 3 (average speed, ambient temperature, season, hour) is the third cold start model, based on an aggregation of the second one and therefore much easier to use.

cold start emission = f COLD 2

Evaporation evaporation = running losses + hot soak emissions + real time diurnal emissions

The model is briefly presented in section 3.13.

Figure 2:

Shape of the emission model.

Pollutants and units

The name and the unit of each pollutant considered must be clear, not ambiguous and scientifically based. It is especially the case for some pollutants as CO 2 , NOx, THC, groups of pollutants like alkanes... We consider the emissions of pollutants themselves and not of the pollutants after their physico-chemical transformations, which is out of the scope of this report.

For most of the pollutants, they correspond to a clearly defined molecule. Therefore the pollutant and the unit considered are not ambiguous. The unit is usually the mass emitted per distance unit, except when specified differently (particle properties for instance). For some pollutants, it must be specified.

For instance CO 2 is the carbon dioxide emitted at the end of the tailpipe and not the ultimate CO 2 , whose calculation needs assumptions on the evolution of the pollutants, to be done by the users of the emission model. CO 2 emission factors are expressed in mass of CO 2 per distance or time unit. CO 2 emission could be also expressed in mass equivalent of carbon C, by a simple proportionality by the factor 12.0110 / 44.011.

An emission of carbon C can be calculated by carbon balance of all the pollutants emitted, expressed in mass of C per distance unit: it is proportional to the fuel consumption (see below), but with a specific equivalence unit.

NOx is the sum of NO and NO 2 , expressed in mass equivalent NO 2 per distance unit.

HC or THC is the sum of all hydrocarbons, to be expressed in mass equivalent of a specified hydrocarbon per distance unit. We use in this report the mass equivalent propane C 3 H 8 , but is could be another hydrocarbon like methane CH 4 . In this case, the following correction must be applied when to correct a mass expressed in eq. (HC) 1 into a mass expressed in eq. (HC) 2 : The fuel mass can be transformed into a fuel volume, taking into account the average density for petrol (740 g/l) and diesel (830 g/l) fuels.

Volatile organic compounds VOCs considered

The emissions of the volatile organic compounds (VOC) are necessary to assess two environmental impacts of the traffic: the impact on the human health and the formation of photochemical oxidants.

Classification of VOCs according to their direct toxicity

The 1990 amendment of the US Clean Air Act mentioned some pollutants as Toxic air pollutants, whose PAHs and 4 VOCs are emitted by the traffic: benzene, 1,3-butadiene, formaldehyde and acetaldehyde. These 4 VOCs have very different impacts levels on the health: after an emission measurement campaign on 25 passenger cars, and using the lung cancer risk factors from USEPA, [START_REF] Flandrin | Mesure des emissions automobiles de composes organiques volatils et de derives carbonyles[END_REF] showed that, for the French traffic of the year 2000, the most toxic compound in highly dense areas for the lung cancer is the 1,3-butadiene, then benzene, then formaldehyde and finally acetaldehyde. EPA, 2000;[START_REF] Flandrin | Mesure des emissions automobiles de composes organiques volatils et de derives carbonyles[END_REF][START_REF] Cassadou | Selection des agents dangereux à prendre en compte dans l'evaluation des risques sanitaires lies aux infrastructures routieres (selection of hazardeous compounds to take into account for the health risk assessment from road infrastructures)[END_REF].

Recently a working group of the French ministry of health selected the hazardeous compounds to take into account for the health risk assessment from road infrastructures, after considering a long list of atmospheric pollutants (see Annex 9) [START_REF] Cassadou | Selection des agents dangereux à prendre en compte dans l'evaluation des risques sanitaires lies aux infrastructures routieres (selection of hazardeous compounds to take into account for the health risk assessment from road infrastructures)[END_REF]. Emission factors were known for some compounds (Ntziachristos and Samaras, 2000a;[START_REF] Fontaine | Les Composés Organiques Volatils dans les gaz d'échappement des automobiles : Etablissement de profils d'émission representatifs de différentes conditions de conduite[END_REF][START_REF] Flandrin | Mesure des emissions automobiles de composes organiques volatils et de derives carbonyles[END_REF], and some compounds have reference toxicological values, but both lists do not correspond. By combining both lists, the working group calculated the score of each compound (emission factor x reference toxicological value). The 16 compounds with the highest score are selected, whom 6 are VOCs (Table 10).

In addition the group recommended research on the emissions of 3 compounds, because of the proximity of the reference toxicological values and the ambient concentrations and/or the high health hazard: monobromomethane, 1,2 dibromoethane, and manganese. The particles should be expressed according to different parameters [START_REF] Samaras | Characterisation of exhaust particulate emissions from road vehicles (PARTICULATES)[END_REF], as the integrated active surface of the total particle population, the total particle number, the particle size distribution, the number of solid particles in different size ranges (aerodynamic diameter of 7-50 nm, 50-100 nm and 100 nm -1 µm for instance).

Classification of VOCs according to their ozone forming potential

The second interest of the VOC species is for smog modellers to assess the formation of photochemical oxidants, which have themselves, as secondary pollutants, health impacts among other impacts. The different VOC species contribute very differently to the ozone and other oxidants formation. Carter and Atkinson developed in 1987 a scale of Maximum Incremental Reactivity (MIR) in order to assess the ozone forming potential of any emitted molecule, so-called OFP, which is defined by OFP = Σ (MIR x EF), according to the emission factors EF. Such method was used often to assess the ozone formation potential of the VOC emission from a traffic. [START_REF] Carter | The SAPRC 99 chemical mechanism and updated VOC reactivity scales[END_REF] updated the MIR factors: See Annex 10. This scale is developed for low VOC/NOx ratios, when the ozone formation is more sensible to VOC concentrations. As each VOC specy has a specific MIR, it is justified to present as possible VOC emission factors per VOC compound.

It should be noted that, at the moment, the Carter proposal is the best one, but has been obtained following a theoretical modelling exercise using US input data from South California field, and an analysis using a specific photochemical mechanism: It is not stiff and should not be considered universal. Consequently this choice is clearly submitted to evolution and progress in this field.

Moreover the choice of the VOC species in the Artemis model depends on the actual VOC molecules which have been sampled and titrated by the various Artemis teams, which differ from a laboratory to another (see below).

When we calculate the OFP per VOC for different vehicle types for the motorway driving (Annex 11), it can be concluded that alkenes (olefins) and monoaromatics are fully necessary to be measured, for diesel as for petrol cars. In addition, aldehydes+ketones (= carbonyl compounds) should not be omitted for the diesel cars (with or without oxydation catalyst) because they are at the head of the two tables (formaldehyde and acetaldehyde).

The family of volatile organic compounds groups a vast array of molecules, which are classically classified as shown in We can also divide these compounds into hydrocarbons of low molecular weight called "light" (C 2 to C 6 ), and hydrocarbons of high molecular weight called "heavy" (C 6 to C 15 and + ).

The models of ozone used in the past only groups of VOC; The new ones use the species themselves, differentiating the species inside each group, with different MIR as shown in Annex 10. Therefore we should express the emission factors per compound, and if possible per group. The advantage to express the emission factors per group is the possibility to extrapolate the emissions more easily.

VOCs reported as emission factors

Artemis produced data on a huge number of different unregulated compounds and especially VOCs [START_REF] Aakko | Emission factors of unregulated atmospheric pollutants for passenger cars[END_REF]2006; see section 3.6.1). The list of individual compounds analyzed, however, varied from laboratory to another. But VOCs analyzed at different laboratories build up an inharmonic set of data. Clear differences were seen in the emission levels obtained at different

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laboratories for some pollutants. In addition, some suspected outliers were found. Thus ozone forming potential could not be calculated collectively from the Artemis database, but is however reported by the laboratories as specific reporting to ensure correct and reliable conclusions.

On the other hand, many individual VOC compounds, like benzene and formaldehyde, were analyzed extensively at all five laboratories that participated in the program.

It was necessary to select the species, which can be regarded as most important, most informative and most representative when limitations of Artemis data are taken into account. These VOCs are listed Table 10: they are all the 6 first level VOCs and 4 among the second level additional VOCs.

But the Artemis database includes a number of pollutants that does not belong to this short list. These results, e.g. ozone forming potential, cold temperatures, and FTIR results, will be reported later on or in specific reporting by laboratories as shown in Table 12.

Thus further analysis on the unregulated emission database would be beneficial.

Specific reporting references

individual VOCs other than reported in this report [START_REF] Caplain | Measurement of non regulated pollutants from gasoline and diesel passengers cars[END_REF]2006;[START_REF] Heeb | Methane, Benzene and Alkyl Benzene Cold Start Data of gasoline-driven Passenger Cars representing the Vehicle Technology of the last two Decades[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF]Prati et al., 2003a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]2005;[START_REF] Weilenmann | Effect of air conditioning activity on fuel consumption and CO2 emissions in Europe[END_REF] ozone forming potential [START_REF] Caplain | Measurement of non regulated pollutants from gasoline and diesel passengers cars[END_REF]2006;Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF] the group of the 4 most volatile PAHs [START_REF] Devos | Exhaust emissions of PAHs of passenger cars[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF][START_REF] Paturel | Unit PAH emissions at exhausts of recenttechnology light vehicles in Europe[END_REF]2005 group of 12 least volatile PAHs [START_REF] Devos | Exhaust emissions of PAHs of passenger cars[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF][START_REF] Paturel | Unit PAH emissions at exhausts of recenttechnology light vehicles in Europe[END_REF]2005 individual PAHs other than benzo[a]pyrene [START_REF] Devos | Exhaust emissions of PAHs of passenger cars[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF][START_REF] Paturel | Unit PAH emissions at exhausts of recenttechnology light vehicles in Europe[END_REF]2005;Prati et al., 2003a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]2005 PAH results divided into gaseous and particulate phases [START_REF] Devos | Exhaust emissions of PAHs of passenger cars[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF][START_REF] Paturel | Unit PAH emissions at exhausts of recenttechnology light vehicles in Europe[END_REF]2005 nitrogen containing compounds (N 2 O, NH 3 )

Table 12: Specific reporting on unregulated pollutants by participating laboratories.

Polyaromatics PAH considered according to their toxicity

16 PAHs are recommended to be analysed by the US Environment Protection Agency according to their carcinogenic and mutagenic power: See Table 13 their list and their classification by the International Association for Research on Cancer (IARC, 1983;[START_REF]Overall Evaluations of Carcinogenicity: An updating of IARC Monographs[END_REF] 13), i.e. the 6 most carcinogenic PAHs and the BjF. BghiP is also an indicator of the petrol emissions, with IP.

In addition we could differentiate PAHs as gaseous and particulate phases. Both phases are present in the 3 groups with ratios from 20 to 80 % for Euro 2 vehicles (Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF].

The different groups of PAHs are given in Table 13.

3 X An benzo[a]anthracene BaA 2A 2B X X BaA BaA benzo[a]py r ene BaP 2A 1 X X BaP BaP benzo[b]fluoranthene BbF 2B 2B X X BbF BbF benzo[g,h,i]perylene BghiP 3 3 X BghiP benzo[j]fluoranthene BjF - 2B X benzo[k]fluoranthene BkF 2B 2B X X BkF BkF chrysene Chr 3 2B X Chr dibenzo[a,h]anthracene DBahA 2A 2A X X DBahA DBahA fluoranthene F 3 3 X F fluorene Flu 3 3 X Flu indeno[1,2,3- c,d]pyrene IP 2B 2B X X IP IP naphthalene N - 2B X N phenanthrene Phe 3 3 X Phe pyrene P 3 3 X P

PAHs reported as emission factors

As for VOCs, the PAH Artemis database is an inharmonic set of data, with large differences between laboratories. Therefore only the benzo[a]pyrene and the sum of the 6 most carcinogenic PAHs are considered (see Table 13) when providing emission factors in section 3.6.

In addition the participating laboratories did specific reporting as shown in Table 12, using only the data whose they are the source.

Nitrogen oxydes

Most of the NOx in vehicle exhaust is usually present as NO, whereas most of the NO 2 in the atmosphere is formed by the reaction of NO with ozone (O 3 ). In ambient roadside air, NO 2 levels are generally limited by the local concentration of O 3 rather than the emission of NO from vehicles.

The NO 2 which is emitted directly from vehicle exhaust is commonly referred to as 'primary NO 2 '. Even though NO 2 is an important pollutant there is surprisingly little information on direct

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emissions. It is generally assumed for air quality modelling purposes that the proportion of NOx in vehicle exhaust which is emitted as NO 2 is 5 % (volume fraction). The figure of 5 % was based on relatively old measurements, from vehicles without after-treatment system. However, laboratory work, remote sensing studies, tunnel studies and ambient air pollution measurements have indicated that the actual proportion varies according to factors such as vehicle type, operating condition, and the measurement method, and can be much higher than 5 %, especially for diesel vehicles [START_REF] Latham | Assessment of primary NO 2 emissions, hydrocarbon speciation and particulate sizing on a range of road vehicles[END_REF][START_REF] Jimenez | Remote sensing of NO and NO 2 emissions from heavy-duty diesel trucks using tunable diode lasers[END_REF][START_REF] Kurtenbach | Investigations of emissions and heterogeneous formation of HONO in a road traffic tunnel[END_REF][START_REF] Jenkin | Analysis of sources and partitioning of oxidant in the UK-Part 2: contributions of nitrogen dioxide emissions and background ozone at a kerbside location in London[END_REF][START_REF] Carslaw | Investigating the potential importance of primary NO 2 emissions in a street canyon[END_REF][START_REF] Carslaw | Evidence of an increasing NO 2 /NOx emissions ratio from road traffic emissions[END_REF]. It has also been suggested that recent increases in the NO 2 proportion in NOx from diesel vehicles are linked to exhaust after-treatment devices, such as oxidation catalysts and continuously regenerating traps (CRTs) (e.g. [START_REF] Carslaw | Investigating the potential importance of primary NO 2 emissions in a street canyon[END_REF]. A recent working group raised the same conclusion in the UK (AQEG, 2006).

These issues highlight the need for investigating the direct NO 2 emissions from the current vehicle fleet in order to be able to assess the process leading to stagnation in the downward trend in annual mean NO 2 concentrations (see section 3.6.3).

Particulates

Current vehicle type-approval legislation requires the filter-based measurement of total mass of particulate matter (i.e. g/kWh for heavy-duty engines and g/km for light-duty vehicles) and applies only to vehicles powered by diesel engines. However, there are a number of reasons why alternatives to a standard based on total mass alone are desired, and why the emphasis may change from particle mass to other metrics relating to particle size, number and surface area. For example, the mass concentration of particles in the exhaust of diesel engines has reduced steadily over the last 20 years following the development and application of new technologies. Current and future legislation is reducing particulate mass emissions, and diesel targets, towards the threshold of reliable measurement. Standards based solely on total particulate mass are not ideal in terms of minimising the risks to health, as the size of particles determines how deeply they penetrate into the human respiratory system and where they are deposited. Conventional filter methods for assessing total exhaust particulate matter do not provide meaningful information on the ultrafine particles (smaller than 0.1 µm), which contribute little to the total mass.

Instantaneous models

There are basically two types of emissions and fuel consumption models: one based on bag measurements and the other based on instantaneous measurements. Bag measurement procedure consists in drawing the entire content of the tailpipe exhaust into a constant volume sampling (CVS) system, where it is diluted with fresh air and, afterwards, a representative sample is put into bags. The analysis of the bags gives a single overall figure for each emission, representing the total mass of emission produced over the driving cycle.

In an instantaneous (modal) emission model, the emissions and other vehicle-related data (vehicle speed, engine speed, etc.) are collected on a high time resolution (one to ten samples per second).

When integrated over the driving cycle, the instantaneous emissions data should be equivalent to the bag results.

Emission models based on bag values give results for the driving pattern similar to the one used to fill the bag. If the driving behaviour changes, new measurements with comparable driving patterns have to be performed. To account for the additional effects as load, slope or gearshift strategies, bag based models include correction functions. However, these correction functions are based on a small number of measurements with few vehicles, which may not be representative for the emissions behaviour. Moreover, the combination of these correction factors (i.e. when a vehicle drives uphill with a full load) can be extremely misleading.

Instantaneous emission modelling maps the emissions at a given time to their generating "engine state", like vehicle speed, engine speed, torque, etc. This makes it possible to integrate new, unmeasured driving patterns over the model and calculate their emission factors without further measurements. Thus, emission factors for a large number of driving situations can be determined from a small number of measurements.

Examples of instantaneous emission models for light-duty vehicles can be found in Joumard et al. (1995a) and [START_REF] Barth | Modal emissions modeling: A physical approach[END_REF], but their accuracy was questionable [START_REF] Sturm | Instantaneous emission data and their use in estimating passenger car emissions[END_REF]. Two new models were built within Artemis: a first EMPA model and a second PHEM model (Zallinger et al., 2005a).

EMPA model

A first approach for characterizing light-duty vehicle modal events is to set-up an emission matrix based on engine speed n [rpm], brake mean effective pressure bmep [bar] and the derivative of manifold pressure p & as dynamic variable able to express the transient generation of emissions (Ajtay and Weilenmann, 2004a;[START_REF] Ajtay | Modal Pollutant Emissions Model of Diesel and Gasoline Engines[END_REF]. This matrix provides the instantaneous emissions and fuel consumption for different combinations of instantaneous n, bmep and p & . The brake mean effective pressure can be considered as "scaled" engine torque size since: maps are built for the fuel consumption and the emissions using the same time basis as for the input signals. The basic model outputs of this model are the instantaneous fuel consumption and emissions at their location of formation (catalyst-out or engine-out). For this purpose the emission signal after the catalyst is built from the emission signal measured after the CVS (Weilenmann et al., 2002b;2003a;[START_REF] Ajtay | Towards Accurate Instantaneous Emission Models, 12 th int. symp. Transport and Air Pollution[END_REF]2004;2005;Ajtay and Weilenmann, 2004b;Le Anh et al., 2005a and b;Joumard et al., 2006a), so that emissions at their location of formation could be properly related to the engine variables. The objective is not to have a good prediction quality at each time step, but only the integrated emission results of a cycle of several minutes duration to be reasonably accurate.

d e V T bmep ! 4 = where V d = displacement

Validation

The developed model has been validated in a two level procedure: Firstly by a cross validation method, secondly by comparing measured and calculated emissions for new tested cars.

For the first model verification, a cross validation method was used. Fifteen of the measured cycles were used to develop the vehicle emission maps and the sixteenth left cycle was used for the verification of the model. Thus, its emission factors were calculated from the model and compared afterwards to the measured values. This was done for two cars and choosing different cycles as verification cycles.

The numerical qualification of the model is performed by calculating R 2 and the normalised mean square error NMSE ( ) ( )

p m p m E E E E NMSE 2 ! =
Here E m and E p represents the measured and predicted emission factors for all the sixteen cycles.

These results indicate a very good agreement in both integrated results and the instantaneous comparison.

In order to assess the application of the model at fleet level, the prediction quality of the emission factors for each vehicle category is studied by averaging the results obtained for individual vehicles, in the case of the static model (according to bmep and n only).

Figure 3:

Normalised mean square error for Euro 3 petrol vehicles (blue) and for average Euro 3 petrol car (red).

For the further use of the instantaneous emission models not just at micro-scale level, but also at meso-scale level, the prediction quality of the emission factors for each vehicle category is studied by averaging again the results obtained for individual vehicles. Figure 3 shows this statistical measure for each individual vehicle and for the average vehicle at each vehicle class, in the case of Euro 3 petrol vehicles. For all vehicle classes, the error becomes smaller, in the sense of lower average error (smaller NMSE value) and higher correlation (bigger R 2 ), when compared to individual vehicles. Therefore, these errors in prediction can be considered as random and not systematic (Ajtay and Weilenmann, 2005b). Thus the instantaneous emission models could be used at meso-scale level, such as for city or regional level.

For the second level validation, extensive vehicle measurements of three Euro 3 petrol vehicles and of one Euro 3 diesel car are available. For each vehicle, the measurement program included 16 basic cycles which were used to develop the vehicle instantaneous emission model. Beside that, eighteen traffic situations with different vehicle loadings (medium or full load), different slopes of the road (uphill or downhill), different gear-shift strategies and combinations of them were also measured. Each of these traffic situations accounts for a different driving pattern like urban, rural, highway driving, etc. To verify the model quality, the emission factors for each cycle were simulated using the developed emission matrices and the so predicted emission factors were compared to the measured values (Ajtay and Weilenmann, 2005a). The results show excellent prediction quality for the engine-out emissions of the petrol vehicles (Figure 4). For the diesel vehicle, the quality of the simulation is very good for CO 2 and for NO x and satisfactory for HC and CO (Figure 61 in Annex 12). However, this result is predictable since the CO analyzer is calibrated at a range of 5% and the CO emissions of this vehicle are significantly lower.

No extrapolation of data points is possible in the map, which is the basis for the model due to the highly nonlinear character of the problem. Thus, measurements should be designed to add extreme points in the map, in order to be able to predict situations with different slopes of the road and loading of the vehicle.

PHEM model

The TUG approach involved the definition of an emission matrix based on engine speed n [rpm] and effective engine power P [kW] (Zallinger et al., 2005a and b).

To generate the emission matrixes the instantaneous emission measurements on the roller test beds in the Artemis cycles are used. In the first step, as in the previous section, the measured instantaneous emissions are corrected from the time delay of the analyzer and the variable transport time in the measurement system according to Le Anh et al. (2005) or Joumard et al. (2006). In the second step, the instantaneous emissions are allocated to the corresponding engine load and engine power value for each second in the test cycle. As a result 2638 values for the emission map are obtained from the Artemis cycles (i.e. one point per second). From the measured points in the engine map the emission values for a defined matrix are then interpolated using a modifies Shepard method. This method is preferred to simply rasterize the measured values into a grid of the engine map since the interpolation method does not leave cells blank and is in line with the calculation applied for simulating the vehicle emissions from a given driving cycle.

To simulate fuel consumption and emissions in any other cycle than in Artemis ones, the actual engine power and the engine speed are simulated in 1 Hz resolution and the corresponding emission value is interpolated from the emission matrix.

The engine power P is simulated second per second, based on the driving resistances and the transmission losses: The formulas used are described in the final report of the Heavy Duty Vehicle part of Artemis [START_REF] Rexeis | Heavy duty vehicle emission. Artemis deliverable[END_REF], since the simulation routine is similar for cars and HDV.

The actual engine speed is calculated from the transmission ratios, the wheel diameter and the gear shift rules from the actual test cycle. For the simulation of real world driving a driver gear shift model is included.

To improve the accuracy of the model, the interpolated emission values are corrected in a final step for the "dynamics" of the actual cycle. This transient correction function explains different emission levels at similar engine loads as a function of differences in the engine load course between one to 40 seconds before the emission happens (Zallinger et al., 2005a and b).

Cold start extra emissions are simulated by the model based on a simplified heat balance of the engine and the exhaust gas after treatment system. The temperature of the coolant and of the catalyst are calculated as function of the heat losses Q l . Q l is the difference of the energy flow delivered by the fuel and the actual engine power output. The cold start extra emissions are then interpolated from emission matrices as function of the actual temperatures and the engine power. The emission matrices are set up also from the measurements of cold starts on the roller test beds where the relevant temperatures have to be measured too.

At the end of 2006 the model PHEM includes input data for 32 single passenger cars (21 vehicles tested within the Artemis project, then 11 vehicles tested outside Artemis). From this data "average passenger cars" are generated for Euro 0 to Euro 4 for petrol and diesel. The user can either simulate single vehicles or average vehicle fleets. The model PHEM also offers an interface to micro traffic models where the total vehicle fleet (passenger cars and heavy duty vehicles) can be simulated in all relevant driving conditions.

Validation

The validation of the model for the simulation of different road gradients was done in [START_REF] Zallinger | WP300, Task 321: Road gradient and vehicle load[END_REF]. In the following, some validation results for different driving cycles are described.

For the validation, the average emissions measured in 12 Handbook driving cycles for 5 Euro 2 diesel and 6 Euro 3 petrol cars were compared with the simulation results for resp. the average Euro 2 diesel car (see Figure 62 in Annex 12) and the average Euro 3 petrol car (see Figure 5). The engine maps for the average cars were created using the instantaneous measurements resp. of eight diesel cars in the Artemis driving cycles and six Euro 3 petrol cars in the Handbook driving cycles. From the engine maps of the single cars, simply the average for each map point was calculated to establish the average engine map for a vehicle category. In the simulation the average vehicle characteristics (mass, drag coefficient etc) were used together with the average engine map. The results of the average diesel car already show a high accuracy for fuel consumption and NO x and adequate results for HC, CO and particulate mass even without transient correction functions. Similar results were gained for all single diesel cars. Since the engine maps were created from a completely different set of measurements (Artemis) than the simulated cycles (Handbook) in terms of gear shift rules and acceleration values, the model for diesel cars seems to be very reliable.

The results of the average petrol car show that for fuel consumption, CO and HC the accuracy of the simulation is already good. The reasons for NO x overestimation are still not clear. But the very low absolute values have to be taken into consideration, when looking at the deviation between measurement and simulation.

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Conclusion of instantaneous models

Emission models based on mapping the emissions onto engine speed and brake mean effective pressure (or engine torque) were 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 and 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 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.

For the model PHEM, already average engine maps and transient correction functions for Euro 0 to Euro 4 were elaborated, allowing the simulation of fleet emission factors for passenger cars. The model was used to assess the correction factors for road gradients and vehicle load (see section 3.10).

Kinematic regression model

The general objectives of this activity presented in detail in [START_REF] Ragione | The effect of different kinematic aspects on emission factors of Diesel cars, 6th International Conference on Engines for Automobile -ICE[END_REF]Rapone et al., 2003;Rapone et al.;2005a to e;2006a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF] were: -To analyse emissions data of different combination of vehicle type and driving behaviour in a large data base, -To develop a prediction model capable to evaluate emissions relative to a micro-trip as a function of kinematic parameters detectable by urban, rural or highway micro-trip speed profile, as obtained for example either by on-road records or by micro-simulation programs.

Data

We have considered the full data set of emission data available in the Artemis data base (see section 2.4). That means to consider besides data obtained within Artemis tasks (detected under operating conditions assumed as reference ones, without considering emission data obtained with specific treatment relative to the assessment of different factors, as humidity, temperature etc.), also data relative to different projects and delivered by all laboratories.

Emission data considered are relative to emission measurements performed utilizing complete driving cycles and sub-cycles. Driving cycles considered in the analyses are Artemis Motorway (1-4), Artemis Rural (1-5), Artemis Urban (1-5); Handbook (R1-R4); Oscar (C, D1, D2, E, F, G1, G2, H1, H2, H3); TRL Motorway, Sub-urban and Rural, Urban; TUG; Modem (urban5713, road_total); Napoli (10_23; 15_18_21; 6_17); LDV_PVU. They are reported in the Figure 6, ordered by mean speed.

Vehicles of data base have been grouped into classes making reference to homologation (Euro 1 to 4) and engine displacement. Three displacement classes have been considered (1200-1400, 1400-2000, > 2000 cm 3 ) when data were consistent, otherwise data were grouped in larger classes.

Method

Firstly, an analysis of variance was carried out on the whole set of data to examine the effects of driving cycle, emission standard and engine size (assumed as qualitative factors) on emissions, and to estimate the amount of emission variability contributed by each factor.

Then, for each case study, the effect of driving cycles on emission factors is estimated as a function of kinematic parameters, calculating regression models. To this end an analytical model has been developed using a consistent set of kinematic parameters and a multivariate regression method based on principal components [START_REF] Rapone | A Multiblock Regression Model For The Determination Of Emission Factors[END_REF].

The considered emission is the unit emission mass of CO, HC, NOx, CO 2 and PM (only for diesel) measured in a driving cycle, expressed in g/km. A log-transform of these emissions was applied in the regression because driving cycle emission quantities are close to zero with large coefficient of variation and because analysed emission data result generally distributed according to a lognormal distribution, moreover this transform better explains non linear relations of response with explicative variables.

The explicative variables characterize the kinematics of driving cycles: They were determined considering two complementary ways of explaining emission variation: the exhaust mass, function of total energy spent by vehicle in a driving cycle, and the frequency of acceleration events at different speeds. Hence, variables were divided into two conceptually meaningful blocks.

The regression models used were based on the following two blocks of variables:

-Block 1 of 7 variables, referring to variables defined from the dynamic vehicle equation, plus -Block 2 of 42 variables, summarizing kinematic acceleration events, which especially affect CO, HC and NOx emissions, proposed and used to analyse and determine Artemis driving cycles on the basis of a wide collection of real driving cycles sampled in on road tests [START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF]:

f va (v, a): Two-dimensional distribution of the instantaneous speed v and acceleration a with 6 speed classes limited by 0, 20, 40, 60, 80 and 100 km/h, and 7 acceleration classes limited by -1.4, -0.6, -0.2, 0.2, 0.6 and 1.0 ms -2 . These quantities are the log of a relative frequency divided by the geometric mean, thus they are dimensionless.

A logarithm transform was applied to the response Y i (i.e. the emission), thus quantities predicted by model fit to data as lnY are to be retransformed in original scale to get emission factors expressed in g/km. The following naïve estimate was used to calculate model expectations [START_REF] Duan | Smearing Estimate: A Nonparametric retransformation Method[END_REF]:

( ) ! " # $ % & + = = 2 / E RMSE Y ln exp Y ˆ [g/km] class) veh. e(p, 2 )
where Y ln is the quantity calculated by putting coefficients and E RMSE ˆ (i.e. the root mean square error, i.e. the standard deviation of model residuals) is calculated by the model fit for each case data set.

Because of the high number of variables and co-linearity problems (variables correlation) the Partial Least Squares (PLS) regression method has been utilized to calculate models [START_REF] Tenenhaus | La regression PLS -Théorie et Pratique[END_REF]Westerhuis et ali, 1998). According to the multi-block PLS approach, a regression model is fitted to any block of variables (1 or 2) separately; As a consequence a base model 1 was defined for the block 1, a model 2 was calculated on the block of variables 2. Finally an upper level model (model 3) was calculated on the pooled two blocks of variables 1 and 2.

Then emission factors can be calculated from of each of these three models (1 to 3), according to the best fit for each specific case study.

Preliminarily, for each case study, a model considering individual vehicle effect on emissions has been fitted to data, to outline individual emission trends and determine eventual outliers. The effect of individual vehicle has been estimated by building a further model so called model 1D, which is an extension of the model 1, by including beside the 7 quantitative variables of the block 1 a set of dummy variables having values (0 or 1), indicating respectively the absence or presence of a specific vehicle. By this model the percentage effects of individuals (vehicles) on the expected emission were calculated and vehicles have been divided into three sets: normal emitters, i.e. vehicles having a percentage effect less than 150 %, high emitters i.e. vehicles having a percentage greater than 150 % but less than 300 %, vehicles having a percentage effect greater than 300%. The last set of vehicles was considered as abnormal and excluded by the analysis.

Then emission factors are obtained for each set of vehicles defined in a case study considering vehicle effect as a random effect not explained by the model.

On the basis of relative vehicle effect on emissions detectable within each class of vehicle analysed in a case study, emission factors (mean and confidence intervals) were calculated for all vehicles in the class, for normal and for high emitters.

Results

The final kinematic model is made, for each vehicle class (fuel, capacity, emission standard) of: -a model for low emitters -a model for high emitters -a model for all vehicles -a model for all vehicles with dummy quantifying the relative effect of each vehicle on the overall mean.

For each of the The values of a 1 to a 7 , b ij , c 1 to c 7 , d ij relative to different pollutants for the case study of Euro 3 petrol 1.4-2.0 l passenger car class are reported in Annex 13. Coefficients c 1 to c 7 and d ij are not explicitly reported, but quantities from which they can be calculated are shown.

The Figure 7 illustrates for CO 2 and the same case data set above mentioned, the measured emissions and the emissions calculated with the three models, to illustrate models ability to follow the data trend and the comparison with a simple average speed regression model.

Goodness of model fit to emission data of same data set of above are reported in Table 14, where the R 2 determination coefficients are shown. Cases relative to normal + high emitters, normal and high emitters separately are considered in the three tables. R 2 are generally low for all pollutants except for CO 2 . This is mostly due to large variability of emission data relative to each driving cycle. In fact, the component of data variance contributed by driving cycles is comparable or less than the component contributed by vehicle model, as it is shown in Table 15, where variance components for the normal emitters data set relative to vehicle model, cycle and experimental error (considered as random effect factors) computed by analysis of variance are reported. 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

The estimation of the pollutant emissions from the road transport is needed at a low spatial scale (i.e. in one street, as a function of the traffic conditions), to enable detailed inventories or impact studies. As shown in sections 3.2 and 3.3, pollutant emissions are very sensitive to the driving conditions, but the existing emission estimation tools were however not always ready, nor designed for such a low scale usage. In the previous European approaches for estimating the pollutant emissions (Joumard, 1999), we had the trivial structure in urban, rural and motorway traffic situations. A more detailed structure was designed in the Handbook approach used in Switzerland, Germany and Austria [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF]) that considered traffic situations as a combination of road and traffic parameters.

In the frame of Artemis and of the COST 346 action [START_REF] Sturm | Emissions and Fuel Consumption from Heavy Duty Vehicles -Final report of the COST346 action[END_REF], it was considered that a low scale approach was necessary and requested by the users. Such an approach was called a "traffic situation approach". This approach is a non continuous or discrete model, in opposition to instantaneous, kinematic or average speed models (described resp. in sections 3.2, 3.3 and 3.5).

Compared to instantaneous or kinematic models, this approach could be less accurate, but:

-the kinematic input data are much simpler, as no speed profile neither complex kinematic parameters are necessary -the kinematic input data are replaced by user oriented parameters, usually known by the traffic engineers.

Works have then been conducted in these aims (André et al., 2006a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF][START_REF] Carslaw | Evidence of an increasing NO 2 /NOx emissions ratio from road traffic emissions[END_REF] to:

-Develop a pertinent structure of traffic situations -Describe these traffic situations in terms of driving behaviour -Estimate the pollutant emissions for each of these newly defined traffic situations

Traffic situation definition

The estimation of the pollutant emissions at a street level implies the definition of "traffic situations" which should be understandable across the different countries and users, and preferably close to the classifications usually implemented by traffic engineers [START_REF] Fantozzi | Development of a new approach for the estimation of the pollutant emissions from the road transport at the street level[END_REF][START_REF] André | Development of an approach for estimating the pollutant emissions from road transport at a street level[END_REF]. The definition concerns also the road characteristics (sinuosity, gradient, speed limit, etc.) and the traffic conditions.

The definition of traffic situations was elaborated after a large review of the European practices and of long discussion within the Artemis and COST 346 projects and also with traffic engineers (André, 2002a).

In the following of numerous international works and recommendations, a road classification should distinguish urban and rural according to a morphological point of view (i.e. continuity of the buildings around a centre and coherence) or to a functional point of view (Functional Urban Area), as mobility and traffic are to a large extent linked to these contexts, and as the traffic is mainly generated and managed at such a scale. It was proposed to adopt a road classification according to the function (access / distribution / through) and to the road network hierarchical organization. The distinction between motorway and normal road and the road characteristics were then considered according to the most usual practices in Europe to propose an agreed urban and rural road typology. The resulting traffic situation scheme is relatively complex but it relies on rigorous bases. The urban and rural typologies are presented in Table 16, and in a more detailed way in Table 30

Free-flow Heavy

Stop and Go

Quasi-Saturated

Figure 8: Traffic conditions as regards speed and traffic flow [START_REF] Lhuillier | Le projet routier, acte d'aménagement : de l'analyse des données à la prise de décision[END_REF] .

The road gradient and sinuosity should also be considered, especially because they influence the heavy duty vehicle emissions and fuel consumption. For large scale estimation, a qualitative approach was proposed (Flat -sinuous / non-sinuous, Hilly -ramps / sinuous, Mountainous). Indeed, a gradient value (i.e. 4 %) has a sense for a short road section but not for one entire trip or a road network. Because of the poor information about the sinuosity and gradient as regards driving, these parameters are only considered for rural roads.

For a good coverage of the actual traffic conditions, a structure in 4 levels was proposed (Figure 8), with free-flow traffic (average speed at 85-100 % of the free (or maximum) speed), heavy traffic (constraint speed at 65-85 % of the free speed), unsteady quite saturated traffic (variable speed with possible stops in the range of 30 to 60 % of the free speed) and the stop-and-go (speed in the range of 10 km/h).

The traffic situation scheme constitutes the basic structure for the elaboration of an emission estimation model at a local scale. The next steps should consist in acquiring the necessary data for that structure (i.e. speed data for each traffic situation) and in building-up a method for calculating the emissions at that level.

Speed data representative of the traffic situations

Apart from the traffic situation definition, the estimation approach requires speed data characterizing each of the traffic situations. A large collection of the existing European data recorded on-board vehicles was managed in that aim [START_REF] André | Development of an approach for estimating the pollutant emissions from road transport at a street level[END_REF].

In all, more than 1500 speed versus time curves were considered, but most often, the information on the traffic condition was not available. Few data was available for rural, hilly and mountainous situations. The available speed data were affected to the different traffic situations according to the background information. This affectation was validated considering the driving statistics (average speed, stop number, etc.) and by comparison between similar situations (coherency).

This process 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 (see the list of these 88 situations in Annex 19). For the other traffic situations, an affectation by similarity was done (i.e. congestion for two roads with close speed limits should be comparable, etc.), giving a simple correspondence between each remaining traffic situation and one of the 69 traffic situations well defined. 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, equivalent resp. to the Artemis urban cycle, the rural one, and a combination of the Artemis motorway and motorway_130 driving cycles.

In addition to all these traffic situations, we designed the most macroscopic traffic situation corresponding to the European situation, aggregating all other situations.

Emission data harmonization through Reference test patterns

The Artemis project has enabled the collection of a large number of car emission data (2 800 passenger cars, 27 700 vehicle x test cycle -including sub-cycles and transition cycles, hot emissions -see section 2.4), using more than 800 different driving cycles. In spite of its richness this heterogeneous dataset required a correction as regards the driving cycle. An approach was developed in that aim, which consists in the building-up of a typology of test patterns to aggregate similar test cycles and the calculation of reference emissions (André et al., 2006b;André and Rapone, 2006).

824 cycles/sub-cycles were analysable and 375 pertinent, i.e. after eliminating transition and preconditioning phases, artificial cycles such as constant speed, constant accelerations, cycles with a gradient, cycles without representativity, cycles for vans, etc. The most significant driving cycles, i.e. 98 cycles or sub-cycles representing the actual driving conditions and for which there are a significant number of emission data, were used to develop a typology of the test cycles. The other pertinent cycles do not contribute to the construction of the typology but are also classified according to this typology.

In this aim, we consider the 2-dimensional distribution of the instant speed and acceleration to describe the cycles. We apply then a Binary Correspondence Analysis (factorial or multidimensional analysis) and an automatic clustering. The typology into classes maximizes then

INRETS report n°LTE 0523
the cycles homogeneity within the classes and the contrast between classes. These 15 classes or Reference Test Patterns (RTP) include then a sub-set of homogeneous driving cycles (as regards kinematic conditions), which can be combined together at a later stage to compute emissions (Figure 9). For each Reference Test Pattern, one or several Reference Test Cycles are selected amongst the most significant (in term of representativeness and of number of associated emission data). They are given in Table 32 and the characteristics of the Reference Test Patterns in Table 33, both in Annex 16.

Emission factors of Reference test patterns

After designing the 15 Reference test patterns, it is necessary to process the emission data to assess the emission factors of each reference test pattern.

As a first step, the measured emission data were corrected according to the vehicle mileage (section 3.8), gearshift behaviour, ambient temperature and humidity (section 3.9), in order to process standardised data. A detailed description of the correction factors can be found in Joumard et al. (2006a to c). In average per fuel and pollutant, the correction factors tend to be between 0.99 and 1.12 for petrol vehicles, and between 0.96 and 1.00 for diesel vehicles (see Table 5 on page 19). However, for certain vehicle sub-classes or individual tests, the correction factors can be even higher. Applying these correction factors, the so-called "harmonised" data base was derived. The emission data, and later the emission models, were harmonised as follows:

-vehicle mileage = 50 000 km -ambient air temperature = 23°C -ambient air humidity = 10.71 g H 2 O/kg dry air As a second step two models for deriving the emission factors for the Reference test patterns were developed.

Model 1

Model 1 took into account a subset of the emission database [START_REF] Kljun | Emission factors PC, internal working paper[END_REF]. For each of 14 Reference test patterns out of 15, the emission data of 15 respective Reference test cycles (out of 21 -see Table 32 in Annex 16) were selected (14 Artemis sub-cycles plus one additional cycle); For the last Reference test pattern (urban stop and go), the emission data of another cycle were selected (Handbook StGoAB: see Figure 49 in Annex 6). The Reference test patterns considered here are therefore not rigorously those designed in section 3.4.3. The emission factor per Reference test pattern is then derived from the average of measured emissions of the vehicle sample.

Model 1 thus emphasises the importance of the sample of vehicles. The emission factors are computed based on a consistent sample of vehicles, i.e. the same vehicle sample for all Reference test patterns as far as possible. Nevertheless for 3 out of the 15 Reference test patterns, the sample of vehicles was only a subset of the sample for the 12 other ones. The objective was to avoid that the emissions behaviour was dominated by the vehicle choice rather than by engine specifications. With this restriction, the number of available measurements was significantly reduced to mostly Artemis subcycle measurements of Euro 2 and Euro 3 vehicles. However, the subset still consists of 1 500 vehicle.tests corresponding to 94 hours of measurements and 9 200 emission measurements.

For vehicle categories other than Euro 2 and Euro 3 where no coherent data (i.e. same vehicle sample for the selected driving cycles) were available, the emission factors were derived from those of Euro 2 and Euro 3 by applying conversion ratios. The conversion ratios for vehicles earlier than Euro 2 were computed from emission data of Copert 3 (Ntziachristos and Samaras, 2000a and b) and the Handbook [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF], included in the Artemis database, but using a different methodology. For vehicle categories later than Euro 3, the conversion ratios were computed from the assumptions for new (not yet measured) vehicle technologies provided in Section 3.5.4.

Model 2

The model 1 has however some drawbacks:

-Loss of representativity due to the low number of cars considered in comparison with the whole Artemis database, although a larger quantity of data doesn't mean necessarily a better quality of the model. -Weakness of the emission data measured with short sub-cycles (2-3 minutes) only, compared to entire cycles. -Loss of representativity of the driving cycles used compared to the whole set of Reference test cycles, statistically representative of the emission data set with respect to their kinematic content and of the large variability of the driving conditions. -The use of conversion ratios, for vehicle categories other than Euro 2 and Euro 3, implicitly supposes that the ratios do not depend on the traffic dynamic, but an attempt of characterizing the dynamic response for different car categories does not indicate such a similarity (see the example of NOx in Annex 18). The validity of the conversion ratios based on emissions functions established in different context, using different data set and approaches, is therefore questionable.

Model 2 considered the whole Artemis light vehicle emission measurement database (version 3 October 2005) described in section 2.4 (André et al., 2006a). In a first step the passenger car and 4x4 emission data were extracted. It was intended to set-up a definitive list of coherent cycles for each test / driving pattern, to compute then their reference emissions. This implied the analysis of the variability and coherency of the emission data within each class and for each vehicle category (the emission standard is considered) and fuel. The coherency throughout the vehicle categories was also examined. Out of the 25 000 data, about 19 000 were analysed. The average emission values observed for the Reference test pattern (i.e. the whole class) were considered and for the Reference

INRETS report n°LTE 0523
test cycles on one side, and the individual figures for each of the cycles belonging to the class on the other side. Some deviating cycles, generally far away from the Reference test cycles in term of kinematic, showed however quasi-systematic under-or over-estimation: When they did not represent a high quantity of tests, the corresponding data were cancelled. When the difference was not at all systematic or understandable, the cancellation of the related data was unavoidable. 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 (after exclusion of the non pertinent cycles). The number of emission measurements is given per pollutant and vehicle category in Annex 17.

It enabled the computation of the emission for diesel and petrol cars, from pre-Euro to Euro 4 passenger cars (see an example on Figure 10). 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. Several cases were however insufficiently covered. Mechanisms of interpolation were thus implemented to cover these cases as follows:

-Extrapolation of the rate Euro4/Euro3 (resp. Euro 3/Euro 2, etc.) observed on a similar test pattern (urban, rural or motorway) -Equivalence between close vehicle categories (i.e. Euro 4 and Euro 3, etc.) when they were too few data (case of the particulates and CO 2 per engine size)

We should note that, weighing factors -as initially envisaged and according to the quality of the cycles and to the number of data -were implicitly (but not rigorously) implemented through the above cycle selection process.

The emission factors for diesel Euro 4 vehicles must be taken with precaution, as they are based on few measurements.

The whole set of emission data is provided in Annex 17, including the extrapolations. This process (computation of the emission per driving pattern) is a robust approach as it relies on contrasted driving conditions and considers the cycles according to their quality. It seems then pertinent to build-up emissions functions while starting from this basis. However, at the same time, the larger vehicle sample introduces some heterogeneity since the different Reference test patterns are represented by different vehicle samples. Furthermore, the cartography of the test cycles constitutes a good mapping of the driving conditions as regards the average speed and acceleration, i.e. the dynamic of the traffic conditions. Indeed, we clearly identify for certain pollutants (NOx and CO 2 ) and vehicle categories, two classes of driving along the speed scale, i.e. the stable or normal driving with low acceleration and stop frequencies on one side, and the unsteady driving on the opposite, as shown in an example in Figure 11.

Emission factors of Traffic situations

The emission cartography developed through the Reference test patterns is particularly appropriated to compute emission for the different traffic situations defined in sections 3.4.1 and 3.4.2, as the structure enables already the analysis at a relatively microscopic scale. The idea was then to "link" a given traffic situation as a function of the different sub-cycles for which emissions are known [START_REF] André | Development of an approach for estimating the pollutant emissions from road transport at a street level[END_REF].

In that aim, the representative speed curves of the traffic situations designed in section 3.4.2 were analysed together with the test cycles described in section 3.4.3 as regards their speed and acceleration distribution. Binary Correspondences Analysis enabled to transform the time distribution into factorial coordinates (orthonormal axes system) and to compute thus and easily distances between a speed curve (i.e a traffic situation) and the test cycles. It should be noted that it was exactly the same method that was implemented to characterize the driving patterns, to build-up the Artemis driving cycles, and also to constitute the emission factors of the Reference test patterns in the second model, above.

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 Reference test patterns, proportional to the proximity -in term of kinematic -to these test patterns. We realised then a projection on the plan (when 3 reference points are selected), on the line (with 2 points), or on a hyper-plan (4 or 5 points) determined by the reference points (always an interpolation process, and never an extrapolation). A set of weighting coefficients for each traffic situation were determined according to the 15 Reference test patterns, given in Annex Figure 12: Traffic situation approach illustration: NOx and CO 2 emissions of cars have been estimated for an urban trunk road (speed limit: 50 km/h), at different traffic conditions, according to dedicated speed curves.

Emission factors of macro traffic situations

The four macro traffic situations (urban, rural, motorway, European) are based on the weight of the Artemis cycles in the traffic, part of the design of these driving cycles [START_REF] André | Real-world driving cycles for measuring cars pollutant emissions -Part A : The Artemis European driving cycles[END_REF]. They can be expressed according to the three Artemis cycles or according to all Artemis sub-cycles. As each of the Artemis cycles and sub-cycles are also specific traffic situations, the macro traffic situations can be expressed according to these traffic situations (see their weights according to the situations 1002 to 1024 in Annex 19). The macro traffic situations are called "composite" when they are expressed according to the Artemis sub-cycles.

As all the traffic situations are expressed according the Reference test patterns, the macro traffic situations, including the composite ones, can be expressed according to the reference test patterns (Annex 19).

The composite macro traffic situations are not useful to calculate the hot emission factors, where the normal macro traffic situations are simpler as they are expressed according to the main Artemis cycles. They are useful when emission factors are expressed according to the average speed and are not linear functions of the speed, as for instance for the cold start emissions (third model -see section 3.12.4), or for the LDVs (see section 3.7). The taking into account of the composite macro traffic situations rather than the non-composite macro situations should improve hardly the accuracy of the corresponding emission factors.

Average speed model

A fourth type of hot emission model was designed, similar to the Meet or Copert ones [START_REF] Eggleston | CORINAIR working group on emission factors for calculating 1990 emissions from road traffic -volume 1 : methodology and emission factors[END_REF]Joumard, 1999;Ntziachristos and Samaras, 2000a and b), 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 speed range averaging -And a second model designed from the 15 Reference test pattern emission factors.

Design through speed range averages

Model 1 was made from the emission data of the Artemis LVEM database -see section 2.4 (Samaras and Geivanidis, 2005), after averaging emission data per speed range.

All artificial driving cycles or cycles used in parametric studies were excluded in order to get data as close as possible to the real world performance. Cycles produced as sum of bags already contained in the database were excluded in order to avoid overweighting of certain data points.

Only the average emission sub-factor of each vehicle and cycle combination was taken into account as a measure to avoid overweighting of vehicles. In addition to passenger cars, all 4 wheel drive vehicles were also included as none of them resided in the N1 category due to their low vehicle weight.

All data were corrected and homogenized against the ambient temperature and humidity (see section 3.9), and gear choice strategy effects (Joumard et al., 2006), but not according to vehicle mileage. The correction had minimal or no effect on the level of emissions. The mileage correction should be applied as a post processing procedure after the estimation of the emission factor of a specific vehicle class, according to its average mileage.

Due to the low number of data available at certain speed levels and in order to avoid overweighting of specific speed points with high number of data, emission data were averaged per speed range of 10 km/h, i.e. for 0-10 km/h, 10-20 km/h, etc. 130 km/h and above. Each average emission is associated to an average speed from 5 up to 135 km/h. These average values were then evaluated taking into account the number of data which each average value consisted of. Average values that were a product of a low number of data were considered in some cases not reliable due to the high scatter of data in conjunction with the low number of data, and eliminated. Outliers were also eliminated in this aspect according to the relative average value quality. In the uniform case of all data points consisting of low number of data, no data were excluded and an emission factor was produced with reduced reliability though.

In all cases an equation of the following general form was used:

x f x d x b x e x c a y + ! + ! + ! + ! + = 2 2 1
where: y:

speed dependent emission factor of fuel consumption [g/km] x:

average speed [km/h] a to f: coefficients The characteristic of this equation is the ability to reproduce the high emissions that was observed in some cases at low and high speed, due to enrichment and low catalyst efficiency at high speed.

The choice of the split of emission factors into more detailed segmentation according to engine INRETS report n°LTE 0523 capacity was applied in the cases where there was an obvious effect of engine capacity on the emission factor. Restrictive parameter was the availability of data.

Finally, an example of hybrid petrol vehicle emission and fuel consumption factors is being presented. The data were derived from measurements on a specific vehicle (Toyota Prius) with can be considered as highly representative of this vehicle segment especially for the European market [START_REF] Fontaras | Development and evaluation of hybrid vehicle emission and fuel consumption factors based on measurements over real world cycles, 2 nd conf[END_REF].

The calculation was made for Euro 1 to Euro 3 petrol and diesel vehicles, and Euro 4 petrol ones. The whole set of emission functions is given in Annex 20 and examples are shown in Figure 13 for NOx. The equations lead sometimes to abrupt changes of behaviour out of the range of computation, and, therefore, it is not allowed to apply these equations out of their boundary limits, i.e. from 5 up to 135 km/h. Out of these limits, the model should use the figures at the limits. The choice of the regression (power or 2 nd to 5 th order polynomial) is made for each data set with the following objectives:

-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 -correspond to the apparent shape of the points according to the average speed INRETS report n°LTE 0523 -avoid important oscillations -never give negative figures -be as simple as possible This approach has the advantage to be fully coherent with the traffic situation model, based also on the Reference test pattern emission factors.

Although it is tried to avoid oscillations, the shape of some curves according to the average speed can show sometimes smooth oscillations, especially in the speed range 80-125 km/h, as a compromise between the different objectives. These smooth oscillations could not be representative of real behaviour. But in any case, the average speed model should not be used to compare close speeds.

The shape of the curves at the highest speeds could nevertheless give unexpected figures for out-ofrange speeds. It is not the case for speeds lower than the slowest traffic situation (7 km/h), but it is sometimes the case for speeds higher than the quickest traffic situation at 125 km/h. Therefore in this case the model uses the figures at 125 km/h.

Comparison of the two average speed models

The comparison of the second approach based on Reference test patterns with the first one based on speed range averages 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, and giving fully standardised emission factors.

-The way the emission data are clustered, by 10 km/h speed range in the first method, by a statistical multi-dimensional clustering in the second case. The clustered emission factors can be very different between the two methods, as shown in Figure 14.

-The choice of the equation type, made in the first method for its adaptability to the high slope at low and high speed, making often the extreme points (lowest, highest speeds) better adjusted than the other points. In the second method the equation is chosen mainly to avoid to go outside the envelope of the points.

The differences between the two models show that such model depends a lot on the methodological assumptions. It is the reason why the second approach was developed in order to be fully coherent with the main Artemis emission model, the so-called traffic situation model. Indeed in both approaches (traffic situations, average speed model based on RTP), the emission measurements are firstly aggregated into Reference test patterns emission factors and then into traffic situations factors or into an emission function according to speed.

The range of vehicle categories and pollutants covered by each of the two methods differs slightly:

The second model does not cover CO 2 for diesel Euro 4 vehicles > 2 l, and the first one does not cover the pre-Euro vehicles, the diesel Euro 4 vehicles and the PM of petrol cars. In addition the first model considers fuel consumption and not CO 2 , the second one considering CO 2 only.

A speed dependent emission model should nevertheless not be used to compare different driving patterns, as the taking into account of the driving behaviour only through the average speed is not accurate enough and too simplified: either the traffic situation model (section 3.4), or the kinematic regression model (section 3.3) or an instantaneous model (section 3.2) is necessary for such assessment. A speed dependent emission model could be used for a quick emission estimation or if information on the driving patterns is especially poor, without allowing the use of another model: but even in this case we advice to use the macro traffic situations defined in sections 3.4.2 and the corresponding emission factors defined in section 3.4.6.

Reduction factors for future technologies

Due to the lack of both measurement and literature data, it was decided to cover future vehicle technologies using reduction factors (Samaras and Geivanidis, 2005).

Petrol vehicles

Considering the fact that Euro 5 emission standards will remain the same as Euro 4 it is proposed to use the Euro 4 equations for Euro 5 petrol vehicles as well.

As regards direct ignition petrol vehicles (DISI), both literature and the limited available data lead to an estimation of about 10 % reduction of fuel consumption which is proposed to be used as a reduction factor against the respective technology emission factors. All other factors are considered not to be altered by Direct Injection technology.

Diesel vehicles

Table 17 presents the reduction of emissions expected in Euro 4 and 5 diesel vehicles using as basis the emissions of Euro 3 vehicles. These factors where derived from the ratios of the established Euro 4 or expected Euro 5 emission standards (Table 18) over the emission standards of Euro 3.

Table 19 presents the PMm reduction potential of the installation of a Diesel Particulate Filter (DPF) on a vehicle. The factors were derived under the assumption that the application of DPF leads to PMm levels comparable to the expected Euro 5 limit.

Unregulated pollutants of passenger cars

Unregulated pollutants concern VOCs, PAHs, NO 2 and particle properties.

Homogeneity of the VOC and PAH emission data

As shown by [START_REF] Aakko | Emission factors of unregulated atmospheric pollutants for passenger cars[END_REF]2006), clear differences were seen in the emission levels measured at different laboratories for some pollutants, sometimes with differences of several orders of magnitude, and some suspected outliers were found for instance. In some cases, a single VOC compound emission was even higher than total hydrocarbons THC (e.g. one test showed 1,4diethylbenzene emission, 5.3 g/km, which was 14 times higher value than the total HC result from this specific test). Individual cars varied from laboratory to another, and also, the test matrices at different laboratories varied as regards test cycles, emission class of cars etc (see section 2.1). Due to the different measurement methods and protocols, the set of compounds analyzed varied from laboratory to another, and thus the sums of groups, like VOCs or alkanes, are not comparable between laboratories.

More detailed discussion of uncertainty is needed as it is one of the key issues when emission factors are determined. In principle, the emission levels of cars tend to differentiate more than the uncertainties of the typical measurement methods. Thus emission factors of individual cars may be reliable, if a representative set of cars are measured. However, this does not necessarily apply to low-emission cars, especially Euro 3 or newer, due to their low emission level, near to the detection limits. Thus, representative set of cars in more than one laboratory is needed to define reliable emission factors for different pollutants. The most problematic emission categories, when number of samples is considered, were Euro 4 petrol cars (2 cars tested), pre-Euro 1 diesel cars (2 cars tested) and Euro 1 diesel cars (3 cars tested). The sample size in these emission categories was so low that the final conclusions on VOC and PAH emission factors should be taken very carefully.

A first way to analyse the data is the comparison of the results from different laboratories, even without a common basis for comparison, due to the absence of round-robin of unregulated components in the project. The major obstacle for comparability study is that typically major differences are found between individual cars, and secondly due to differences in the analytical methods. The emissions between individual cars can vary a lot even in the same emission class. This is pronounced for old cars (pre-Euro 1), as for newer cars. For example the publicly available type approval data from UK of present-day petrol cars show that, amongst some 1000 Euro 3 and 1500 Euro 4 cars, the spread in HC emissions varies by a factor 17 and 29 resp. Therefore, the spread for Euro 4 was even twice as large as with Euro 3.

The comparability between laboratories was studied by screening the results with cars representing the same emission class. A few test cycles were same at different laboratories. Especially at EMPA, IM, KTI and VTT several common test cycles were used.

For benzene, EMPA, IM, KTI and VTT figures seem coherent (in the same range), but INRETS results seem an order of magnitude higher, especially but not only for petrol cars. For formaldehyde, INRETS data seem also an order of magnitude higher than EMPA figures, IM results being intermediate.

In the case of polyaromatic hydrocarbons PAHs analyzed by INRETS, IM and KTI, INRETS figures for BaP are 2 or 3 orders of magnitude lower than IM results, KTI results being intermediate. These results seem to be in accordance with the results from literature. However, the differences in the level of PAH results obtained at IM and INRETS are significant, and has to be taken into account when the conclusions are drawn. The simple averages of emission factors may give misleading results when test matrices are not harmonized and emission levels vary from laboratory to another. However, evolution of the emission categories of cars can be monitored within laboratory. Only final emission factors of PAHs need careful consideration.

One interesting parameter to study is the share of VOC from THC, even though it should be noted that VOC and THC cannot be reliably compared with each other due to different measurement principles, e.g. FID used for measuring THC is sensitive for oxygen containing compounds (aldehydes). In addition, THC measurement does not take into account carbonyl compounds, which are included in the sum of VOCs in this report. Figure 15 shows that there are significant inconsistencies when total VOCs are compared to THCs, whereas comparison of sum of considered VOCs shows more reasonable trends. This phenomenon should be studied more closely before further analysis of other VOC compounds than the considered ones.

Comparability of the laboratories was fairly good as concerns benzene and formaldehyde emission, even though benzene level was somewhat higher at INRETS, and formaldehyde level lower at EMPA, than respective emissions at other laboratories. The most significant difference between laboratories was seen in PAH results. This is specifically important due to the fact that the test matrix on PAH emissions was not as extensive as e.g. matrix on benzene and formaldehyde. Thus PAH results have to be carefully considered when conclusions are drawn.

Thus the emission factors given below should be taken with caution as large inconsistencies between laboratories occur. Further analysis is needed.

VOC and PAH emission factors

Taking into account the limitation on the results pointed above, average emission factors were calculated for the so-called considered unregulated compounds listed in Table 10 on page 28 and Table 13 on page 31 [START_REF] Aakko | Emission factors of unregulated atmospheric pollutants for passenger cars[END_REF]2006). The average emission factors, deviation and number of measurements are summarized in Annex 22 per vehicle category.

Individual emission factors of benzene are shown in Figure 16, as an example.

The influence of the emission standard is illustrated for sums of VOCs and the sum of the 6 most carcinogenic PAHs in Figure 17. The VOC emission factors are drastically decreasing from pre-Euro 1 to Euro 1 petrol cars (in average by an order of magnitude). Thereafter the decrease is lower, by a factor 5 to 10 from Euro 1 to Euro 3. This evolution depends in fact on the species. For diesel cars, the decrease occurs also, but to a much lower extent: by 50 % only from Euro 0 to Euro 1 and from Euro 1 to Euro 2.

INRETS report n°LTE 0523 10 and Table 13 the definition of these compounds).

The PAHs of petrol cars are decreasing also by a order of magnitude from Euro 0 to Euro 1 and then only by 40 % from Euro 1 to Euro 3. Emission of benzo(a)pyrene is even increasing by a factor 3 between Euro 1 and Euro 2 (or 3).

When comparing with literature results, it is seen that generally the results from Artemis and other studies are in line.

Other results have been drawn by each partner laboratory for their data, as listed in Table 12, but they are not figures from the whole Artemis data. For instance, the separate measurement of particulate and semi-volatile phases showed that for petrol cars 35 % and for diesel cars 23 % of the 6 most carcinogenic PAHs were found from semi-volatile (gaseous) phase and the rest from particulate matter in the hot-start tests (Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF].

NO 2 emission factors

Gense et al. (2006) and AQEG ( 2006) present state-of-the-art reviews of the origins, measurement and impacts of primary NO 2 emissions in relation to modern road vehicles and specific emissioncontrol technologies. Data on direct NO 2 emissions, and the proportion of NO 2 in NOx, were gathered from measurement programmes carried out by [START_REF]Emission factors for cars[END_REF], [START_REF] Millbrook | The effects of emerging technologies on certain vehicle emissions not currently regulated[END_REF], LAT and mainly TNO Automotive and EMPA. These data were reviewed with respect to their accuracy and reliability.

The available data showed that the measurement method had a substantial influence on the measured direct NO 2 emission. The balance between NO and NO 2 was also found to be very sensitive to the measurement conditions. The authors defined a measurement procedure which is suitable for the assessment of NO 2 emissions from current vehicles. This procedure mainly focuses on diesel vehicles, for which primary NO 2 emissions represent a particular problem. The procedure involves the determination of the NO 2 mass emission by means of simultaneous analysis of the NO and NO x concentrations in the raw (undiluted) exhaust gas, sampled on-line at the exhaust pipe. For the gas analysis an instrument using the chemoluminescence principle was proposed. However, problems relating to interference from ammonia will need to be considered when testing near-future SCR-DeNO x systems (and also petrol-engined vehicles which known to emit substantial amounts of ammonia). The test procedure was used as the basis for a large-scale measurement programme at TNO Automotive and EMPA, in which a total of 63 passenger cars were tested, from pre Euro to Euro 4 petrol and diesel ones (see the number of vehicles tested [START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF]AQEG (2006).

The results from the emission measurement programme showed some clear trends (see emission factors and NO 2 fractions resp. in Figure 18 and Figure 19). The measured levels of NO 2 and the fraction of NO 2 in NOx were higher for diesel cars then for petrol cars. [START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF].

For petrol cars the measured absolute NO 2 emissions were low compared with those of modern diesel cars, as both the fraction of NO 2 and the absolute level of NOx were much lower. As the values were too low to determine reliable estimates, [START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF] considered that no accurate NO 2 fractions could be determined for petrol cars. However, it has been assumed here that typical average NO 2 fractions would be 3 % and 9 % for pre-Euro and Euro 3-4 petrol cars respectively. For petrol Euro 1-2 cars, an intermediate fraction of 6 % has been assumed.

The average speed seems to have a negative influence on the NO 2 fraction for pre Euro to Euro 2 vehicles (AQEG, 2006), but no influence for more recent Euro 3 and Euro 4 vehicles [START_REF]Emission factors for cars[END_REF][START_REF] Millbrook | The effects of emerging technologies on certain vehicle emissions not currently regulated[END_REF]. We propose in a first step not to take into account the average speed.

The NO 2 fractions for the different passenger car categories are given in Table 20.

Emission factors for particle properties

In the framework of the Particulates project [START_REF] Samaras | Characterisation of exhaust particulate emissions from road vehicles (PARTICULATES)[END_REF], a dedicated sampling and measurement system was employed in several laboratories in order to characterize the particle emissions of light duty vehicles of various technologies and using several fuels, under a number of test cycles [START_REF] Samaras | Characterisation of exhaust particulate emissions from road vehicles (PARTICULATES)[END_REF]. The results obtained from these measurements have been used for the development of emission factors for several particle properties of light duty vehicle exhaust (Samaras and Geivanidis, 2005).

In particular, 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). Hot-start cycles of real-world (transient) pattern were considered in this analysis. Specifically, separate emission factors were developed for urban, rural and motorway conditions, using the results obtained under the corresponding Artemis cycles.

The only significant fuel effect observed was that of sulphur on the total particle number and surface of diesel vehicles. Therefore, separate emission factors were derived for diesel fuels fulfilling different specifications of directive 2003/17/EC. The fuels have been distinguished into EN590:2000 speciations (<350 ppm wt. S) and EN590:2005-2009 speciations (<50 ppm wt. S). On the other hand, a single emission factor, irrespective of the fuel used, was produced for petrol vehicles.

Table 21 gives the vehicle categories tested in the Particulates project (and therefore considered here) and the sample size. Due to the relatively small vehicle sample no further categorization was applied with respect to engine capacity. The pooled average of the vehicle emissions in each category were used for the derivation of the emission factors. A significant fuel effect was only observed over the motorway tests for the conventional diesels and over the rural and motorway tests for the Diesel Particulate Filter vehicles.

In that respect separate emission factors (for each fuel type) were only derived for these driving conditions.

INRETS report n°LTE 0523

The emission factors for the total and solid particle population are summarized in Table 34 and

Light Duty Vehicles

In the most recent version of the European inventorying tool Copert (Ntziachristos and Samaras, 2000a and b), the Light Duty Vehicle emission factors have been built by extrapolating data obtained from passenger cars to light duty vehicles. Only pre-Euro 1 and Euro 1 vehicles were studied and emission factors expressed according to the average speed. To improve the accuracy of this model, we incorporated the results of a former Inrets project [START_REF] Joumard | Émissions unitaires de polluants des véhicules utilitaires légers[END_REF](Joumard et al., , 2003) ) and the tests carried out previously by other European laboratories (Tuev in Germany, TRL in UK, TNO in the Netherlands, Empa in Switzerland and KTI in Hungary). Specific cycles for light duty vehicles were developed for some of these programmes, taking into account the road type and loading rates [START_REF] André | Cycles de conduite pour la mesure des émissions de polluants des petits véhicules utilitaires[END_REF].

This study is described in detail in [START_REF] Markewitz | Atmospheric pollutant emission factors of light duty vehicles. Inrets report[END_REF]2006).

Data extraction and classification

The first step of the analysis consisted in exhaustively extracting all the light duty vehicles from the Artemis LVEM database [START_REF] Kljun | Design of the A300 data base[END_REF]Andre, 2005). The LDV database concerns light vans, vans and minivans, i.e. 150 vehicles and 2035 tests (1 test = 1 vehicle and 1 driving cycle). The vehicles were then grouped according to the European categorisation (N1-I to N1-III) based on the vehicle tare weight (cut points: 0, 1305, 1760 and 3859 kg), associated with the European emission standards (pre-Euro 1 to Euro 3) and the type of fuel used (diesel, petrol). 24 different groups were distinguished, but the different groups were not equal because 19 groups contained less than 4 vehicles while 5 groups contained more than 10 vehicles. In addition, whereas 6 laboratories tested LDVs, no group contained data of more than 4 laboratories and, in a third of cases, only one laboratory was represented per group. This may have had an artificial homogenising effect on the data since the production source of each group was not very diversified. However, as shown in Figure 21, the source of the vehicles was balanced for 5 laboratories out of 6, meaning that the representativeness of 5 out of 6 laboratories was satisfactory.

Emission as a function of average speed

In contrast to passenger cars, the emission measurements available did not allow to derive emission factors for traffic situations. Hence, the emission factors were developed based on the average speed approach only.

To build a model of emission factors, 4 hypotheses were applied:

-The group of vehicles extracted is representative of the global fleet of light duty vehicles and its conditions of use. -All the vehicles of a group are equivalent. The emission measured is independent of vehicle make and analysis laboratory. -All the cycles have the same weight as a function of representativeness.

-The number of tests carried out on a vehicle does not influence the weight of the emission.

The consequence of these hypotheses is that each data is considered with the same weight of representativeness.

For each vehicle, the emission data were analysed according to the average speed of the cycle. The best fit is chosen to minimise the standard deviation between the model and the measurement points. The emissions describe a polynomial curve of order 2 in the great majority of cases and a power curve in a few cases. The coefficients of determination were generally significant (> 0.7). However, for certain vehicles and pollutants, the values obtained were low (< 0.3) showing that other parameters than the average speed (e.g. acceleration) have a significant influence on the emissions.

For each of the 24 groups, the average equation was also calculated by polynomial equation of order 2 or power equation with an average coefficient of determination of 0.41 for diesel and 0.5 for petrol vehicles (see an example Figure 22). The validity of using an average equation was verified by Pearson's test with an error of 0.5 %. The aim of this statistical test was to verify the hypothesis that the two groups were identical, i.e. the data calculated by the average equation and either the raw data or the data obtained from the equations of each vehicle. The results were then analysed in several steps. The first step consisted in distinguishing the vehicles that satisfied Pearson's test or not. The vehicles that verified Pearson's test were separated into 3 groups as a function of the coefficient of determination obtained. If it was higher than 0.7, the emission was considered as depending only on the average speed. If the coefficient of determination was from 0.5 to 0.7, the equation for this group of vehicles was satisfactory, but other parameters could play a role. If the coefficient of determination was lower than 0.5 and the test validated, the equation was only accepted if the addition of the parameter did not permit any increase of the coefficient of determination. In this case, it was necessary to carry out additional searches.

The results are as follows: the petrol vehicles had 18 validated emission factors (confirmation of Pearson's test and a coefficient of correlation higher than 0.7) -i.e. 34 % of the equations -and the diesel vehicles had only 12 -i.e. 12 %. Furthermore, 8 diesel vehicle emission factors and 6 petrol vehicle factors were not validated by Pearson's test. The emission factors were therefore not only dependent on average speed despite the fact that a large number of emissions can be determined by using this single parameter.

Emission as a function of vehicle loading rate

The loading rate τ is expressed in % and is calculated according to the weight of the vehicle during the test M test and the vehicle tare weight M empty , using the following equation:

100 ! " = empty empty test M M M #
The loading rate, calculated for all the vehicles and all the cycles carried out, varied from 0 to 91 %. However, for 12 % of the tests, it was not possible to calculate the loading rate due to the lack of the vehicle weight during the test: the corresponding emission data won't be used for the calculation of emission factor as a function of loading rate.

The vehicle load is not the only parameter influencing emission variations since the emission as a function of the average speed of the groups with a low range of loads has not necessarily a high coefficient of determination nor even validate Pearson's test.

It appears that the entire emission curve is changed by the increase of the vehicle weight: For a given pollutant whose emission curve y has as equation according to the average speed v y=av 2 +bv+c (the most frequent case), the coefficients a, b and c are related to the load. To define the link between these coefficients and the loading rates, we defined speed zones so that the variety of the group in terms of load and vehicles was represented. The equation describing the pollutant emission as a function of load was calculated for each zone. It is a polynomial curve of order 2 at most (order 1 for groups in which fewer than 4 vehicles were studied). The equation in each speed zone was therefore y=a'τ 2 +b'τ +c'. The coefficients of determination between this curve and the emission data were 0.65 and 0.52 on average for diesel and petrol vehicles respectively. The equations for which the coefficient of determination was less than 0.2 were not used. Then the coefficients a', b' and c' were expressed according to the average speed of the speed zone (see an example Figure 23). If the coefficient of determination was lower than 0.5, the speed zones were revised until a better coefficient was obtained, otherwise this group of vehicles was withdrawn from the load study. The coefficients obtained were thus 0.75 and 0.76 on average for diesel and petrol vehicles respectively. The equation of each coefficient was then incorporated into the pollutant emission equation, which depends on the average speed and load: y=a'(v)τ 2 +b'(v)τ +c'(v).

Two series of verifications were applied to the equation obtained. The first verification consisted in verifying that the group of measured emission data and that of calculated emissions could be considered identical. To do this, Pearson's test was performed and compared with Student's law with (n-2) degrees of freedom for an error of less than 0.5 %. The second verification consisted in comparing the values calculated by the equation to the values of the emission curves. As before, the two groups were compared using Pearson's test for an error less than 0.5 %. The equations were also classified into 4 groups as a function of the validation of Pearson's test and of the coefficient of determination obtained. Figure 24 shows an example of correspondence between the emissions calculated as a function of speed and loading rate for the group of vehicles and the raw data or the emissions calculated as a function of speed for each vehicle.

INRETS report n°LTE 0523 For the other groups, an equation defining the emission as a function of average speed and load was established with an average coefficient of determination of 0.56 for diesel vehicles and 0.61 for petrol vehicles. This shows that the load parameter has a significant impact on the precision of the emission factor equation. In addition, load and speed permit defining a satisfactory emission factor (Pearson test validated and coefficient of determination higher than 0.7) for 26 % and 27 % of the groups of diesel and petrol vehicles respectively. A synthesis of the results is presented in Figure 25.

A result is considered as satisfactory when the Pearson test is validated and the coefficient of determinations is higher than 0.7. This is the case for 34 % and 39 % of the emission factors calculated for diesel and petrol vehicles respectively. For these groups it was possible to determine an equation highly representative of the group by using average speed and/or the loading rate as sole parameters. A result is considered as valid but needing further analysis for another impact parameter on the emission, when the result is validated by Pearson's test and the two coefficients of correlation are higher than 0.5 (and do not belong to the previous case). This is the case of 32 % and 25 % of the emission factors calculated for diesel and petrol vehicles respectively. Thus, in 66 % of groups containing diesel vehicles and 64 % of the groups containing petrol vehicles, an equation representing the group is determined and validated with a coefficient of determination higher than 0.5 and therefore higher than the average value of the equations used in Copert. Regarding the remaining vehicles, it was necessary to study another factor even though only 3 % and 4 % of the equations were not validated by the Pearson test. These are HC and particle emissions for the vehicles of group N1-II diesel Euro 2 and CO, HC and NOx of N1-III petrol Euro 2 where few data were obtained with only two vehicles.

For each of the equations obtained and in particular for those including the load factor, it was necessary to model the behaviour of the emission outside the load zone studied. For that, the lower and higher values of the emission at the limit test load were compared to the values calculated at 0 % and 100 % respectively. When the difference is greater than 30 %, the equation is not used outside the study zone and the value used outside is equal to that of the nearest bound. In other cases, the calculation is carried out on the basis of the equation for any load or speed.

Conclusion

After extracting the emission data of light duty vehicles from the European Artemis LVEM database, it was possible to formulate equations of emission factors for these vehicles as a function of average speed [km/h] and loading rate [%] as calculated in section 3.7.3: See Annex 24. This method was used to statistically validate 97 % and 96 % of the emission factors of diesel and petrol LDV respectively.

Furthermore, a considerable increase in precision of the quality of the equations was observed. In the Copert model, the average coefficient of determination was 0.39 for diesel LDV and 0.49 for petrol LDV. By updating the data in Artemis and calculating the emission factors by using only the average speed as parameter, a slight improvement of the coefficients of determination could be observed since it was resp. 0.41 and 0.5. However, by adding load as a parameter, the average coefficient of determination changed to 0.59 for diesel and 0.56 for petrol vehicles. In addition, whereas Copert only deals with pre-Euro 1 and Euro 1 vehicles, we propose emission factors for Euro 2 vehicles.

Testing of the emission factors obtained must, however, be continued and their equations improved, if necessary, by using additional vehicles added during updates of the Artemis LVEM database.

Influence of mileage

The influence of the vehicle mileage on hot emissions is presented in details in (Geivanidis and[START_REF] Geivanidis | Investigation of the emission degradation of petrol vehicles[END_REF]2005) and in the Artemis deliverable 2 (Joumard et al., 2006a). It is only synthesized hereafter.

As regards Euro 1 and Euro 2 vehicles, MEET data are proposed to be used as the majority of data covering these vehicle categories contained in the Artemis database (see section 2) originated from the same dataset used for the MEET estimations. In order to estimate the degradation of modern Euro 3 and Euro 4 vehicles, an analysis was performed on the data derived from the Artemis LVEM database (version 1/12/2004). The mileage effect on CO, HC and NO x emissions was examined as CO 2 emissions have been proven to be unaffected by mileage.

The analysis was performed in two driving mode regions: urban and rural. The effect of average speed on emission degradation is taken into account by combining the observed degradation lines over the two driving modes (urban, rural). It is assumed that for speeds outside the region defined by the average speed of urban driving (19 km/h) and rural driving (63 km/h), the degradation is independent of speed. Linear interpolation between the two values provides the emission degradation in the intermediate speed region.

The correction factor by which the basic emission factor should be multiplied in order to take into account the degradation of emissions due to mileage which is given by the equation:

y(M, p, V) = a(p, V) × M + b(p, V
) where: y: the mileage correction for a given mileage (M), a pollutant p and an average speed V M:

the fleet mileage of vehicles for which correction is applied p: pollutant V: average speed, in km/h a(p, V): the degradation of the emission performance per kilometre b(p, V): the emission level of a fleet of brand new vehicles Then, for mileages M 1 and M 2 :

emission M 1 ( ) emission M 2 ( ) = y M 1 ( ) y M 2 ( )
y is available in Annex 25, for Euro 1 and 2 petrol cars in Table 36, and for Euro 3 and 4 petrol cars in Table 37, in both cases 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, the following formulae has to be used:

y V ( ) = y urban ( ) + V "19 ( )# y rural ( ) " y urban ( ) ( ) 44
By lack of data, it is assumed that emissions do not further degrade above 120 000 km for Euro 1 and 2 vehicles and 160 000 km for Euro 3 and 4 vehicles. Globally, the mileage has no influence on the CO 2 emission neither on the emissions of diesel vehicles, but increases a lot CO, HC and NOx emissions of petrol cars: between 0 and 100 000 km, these emissions increase by a factor 3.6 in average for Euro 1 and 2 vehicles, but only by 15 % for Euro 3 and 4 vehicles (see an example Figure 26).

Influence of ambient air temperature and humidity

The influences of the ambient air temperature and humidity are presented in details in the Artemis deliverable 2 (Joumard et al., 2006a). They are only synthesized hereafter.

Influence of ambient air temperature

The methodology followed (31 passenger cars tested with hot Artemis driving cycles but for 3 ambient air temperatures: See section 2.1) shows that the lowering of the ambient temperature increases generally the emissions of CO, HC, NOx and CO 2 (Laurikko, 2005a). However, in some cases a decrease in CO was detected, most notably in case of CO for petrol-fuelled cars in rural and motorway driving.

On average over all tested driving cycles, the ratio between emissions at -10°C and at +20°C was for all tested petrol-fuelled cars (Euro 2, Euro 3 and Euro 4) 0.96, 1.54, 1.11 and 1.05 respectively for CO, HC, NOx and CO 2 , and for diesel Euro 2 cars the ratios were respectively 2.14, 1.73, 1.04, 1.04 and 1 for PM. Therefore in most of the cases, emission is a decreasing function of the ambient temperature.

On average, these ratios do not depend much on the emission standard of the vehicle, as almost equal responses were observed for each type approval level tested. However, in urban type of driving (i.e. low speed and low thermal load in the engine) the hydrocarbon emissions showed increasing sensitivity to low ambient temperature with the advance in Euro standards, i.e. Euro 4 cars were the most sensitive ones, and the Euro 0 cars were least affected. In terms of CO, the responses were most scattered regarding the influence of the driving type (urban, rural, motorway), whereas regarding CO 2 , the response was most uniform, i.e. less dependence on the road type.

The influence of the ambient temperature on the emissions was in most cases linear (see an example Figure 27), but in a few cases (urban HC for petrol Euro 4, and motorway HC for diesel Euro 2), exponential type of function gave better match. In a few cases we could not set any trend, as ambient temperature did not seem to have any effect.

The influence of the temperature T 1 or T 2 [°C] is expressed by the formulae

emission T 1 ( ) emission T 2 ( ) = y T 1 ( ) y T 2 ( )
y is available for urban, rural and motorway driving behaviour in Table 38 in Annex 26.

Globally 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.

Influence of ambient air humidity

The results of the measurements carried out (see section 2.1) show that overall an increase in ambient humidity lowers the NOx emissions [START_REF] Laurikko | The Effect of Ambient Humidity on Emissions[END_REF], which is also the expected general trend according to the humidity correction established in legislative testing (EEC, 1991). Figure 28 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. Therefore, the normalisation provided by the standard correction factor is not enough. However, the case is very different when rural driving cycle is employed. All linear correction models developed here lie almost on top of each other, and the necessary correction is less than 20 %, even somewhat less than provided by the standard method. So, using the standard correction factor here actually leads to a slight "overcorrection". For CO and HC, in case of diesel vehicles, CO correlates to the absolute humidity by 0.60 (rural) to 0.73 (urban), and HC to humidity by 0.28 (urban) and 0.41 (rural). The plotting of the relative influence of the humidity shows a clear influence of the humidity in the following cases:

-CO for diesel cars -CO for petrol Euro 2 vehicles in urban situation -HC for diesel cars and for petrol Euro 2 cars -HC for petrol Euro 3 cars in urban situation However, no correction factors have been proposed in these cases.

In the case of NOx emissions, the influence of the humidity is expressed by the formulae

emission H 1 ( ) emission H 2 ( ) = y H 1 ( ) y H 2 ( )
y is available for some vehicle classes and for urban and rural driving behaviour in

Influence of road gradient and vehicle load

Positive road gradient increases and negative road gradient decreases the driving resistance of a vehicle. Engine power demand is a decisive parameter for the vehicle emissions and fuel consumption. Nevertheless it is a fact, that additional emissions at positive road gradient will not be compensated by lower emissions at negative road gradient [START_REF] Hassel | Abgas-Emissionsfaktoren von Pkw in der Bundesrepublik Deutschland -Abgasemissionen von Fahrzeugen der Baujahre 1986 bis[END_REF].

Engine power demand is also increasing by increasing the vehicle loading. The increase of the power demand due to vehicle loading is less than the increase due to road gradient. It is therefore not obvious that emissions will also increase with increasing vehicle loading, because it may happen that the conversion efficiency of the exhaust after treatment system is better (it was the case for some light duty vehicle classes: see section 3.7). Thus the net emission may even decrease, even if the raw engine-out emissions increase.

Measurements of cycles with different road gradients exist, however they do not cover new vehicle technologies. For emission factors of current and near future vehicles these data had to be established. Therefore new measurements were carried out within ranges of statistical significance in Europe for road gradients and payloads (see section 2.1). The numbers of measurements are too small to obtain emission factors for all driving and gear shift situations, thus the results are only valid for the driving situations of the measurements carried out. Therefore after a comparison between measurement and simulation, all factors were simulated with the model PHEM. A more detailed report is available: See [START_REF] Zallinger | WP300, Task 321: Road gradient and vehicle load[END_REF].

INRETS report n°LTE 0523

Measurement results

Road gradient

Because of the small sample and the different vehicles (engine power and capacity) in this sample it is not reasonable to calculate the average emission for diesel and petrol for the different road gradients. It is better to calculate the ratio between measured and 0 % road gradient for every measurement and afterwards to calculate the average ratio for the varying road gradients for diesel and petrol vehicles. Examples of these road gradient factors are represented for NOx in Figure 29 for Euro 3 diesel vehicles. For NOx, PM and fuel consumption from diesel vehicles, a trend can be seen where as for CO and HC no describable shape was indicated. The level of these emissions (CO and HC) is quite low for diesel engines and therefore a small difference in the measured values for road gradients has a great influence on the factor. Fuel consumption, CO and HC factors of petrol vehicles show a progressive shape of the curve, where as for NOx no describable shape was indicated.

Vehicle load

The base for all driving cycles was the common measurement situation (vehicle plus driver; "unloaded"). The "loaded" situation designates the measurement with the full payload for this car type -in average it is 450 kg and the "half loaded" designates the situation in the middle of "unloaded" and "loaded". For the diesel vehicle relevant emissions (NOx, PM and FC) the influence of the loading situation can be seen and thus it is possible to generate loading factors for these emissions. For HC and CO the influence of vehicle loading is in the same range as the standard deviation of the repeatability tests (see section 3.2.2. of Joumard et al., 2006). In this case (small vehicle sample) it is not correct to produce loading factors for CO and HC.

Concerning petrol vehicles it is only possible to generate a loading factor for fuel consumption. Because of the small sample, the results for the emissions are again in the range of repeatability.

From these measurements the final factors (see section 3.10.4) for diesel (FC, NOx and PM) and petrol vehicles (FC) were calculated for an average loading situation by linear interpolation. This loading situation should describe the average payload of a car with approximately 1.5 persons.

Comparison with other sources

To increase the data base on road gradient and vehicle load a comparison with other researches is provided in this chapter.

Road gradient

For road gradient a comparison with two other sources was done. One of the sources is the Handbook of emission factors or HBEFA [START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF], whose road gradient factors are based on measurements of Euro 0 and Euro 1 vehicles [START_REF] Hassel | Abgas-Emissionsfaktoren von Pkw in der Bundesrepublik Deutschland -Abgasemissionen von Fahrzeugen der Baujahre 1986 bis[END_REF]. The other source was the simulation with our instantaneous emission model PHEM which was developed within Artemis (see section 3.2 and Rexeis et al., 2005).

Using the HBEFA, emission factors for road gradients from -6 % up to 6 % can be gained. For this comparison a rural cycle (AO_HVS3) was chosen, whose average cycle velocity is in the same range as the average velocity of the measured cycle. Figure 31 shows the comparison of the measured and the Handbook factors, in the case of NOx for diesel vehicles.

In the case of diesel vehicles, for fuel consumption and particulate mass the agreement between Handbook factors and measurement is very good, whereas for NOx, especially for positive gradient, it is worse. This disagreement can perhaps be explained in the different gearshift strategy of Handbook cycles and TUG-cycle measurement on one hand and in the difference between the cycle velocities on the other hand. A reasonable technical explanation may be the fact that Euro 3 cars have EGR (Exhaust Gas Recirculation, to lower the NOx emissions), which is not active at high engine loads and engine speeds, which occur frequently at high road gradients. The inactive EGR explains the strong NOx increase at 6 % and at 10 % road gradient, because the cars measured for the HBEFA did not have EGR. For CO and HC the measurement produces a higher factor than the Handbook. But for these emissions the measurement is quite sensible because of the high deviations. In the case of petrol vehicles, the agreement of the measurement and the Handbook factors is quite good for fuel consumption and CO. For HC and NOx emissions the congruence is not that good, however for HC the calculation of an average road gradient factor (±) is in the same range as the Handbook factor.

To validate whether our simulation tool PHEM is useful for the road gradient emission simulation, a comparison with the measurement was done, using the Artemis rural cycle for 0, ±2 and ±4 % road gradient. For ±6, ±8 and ±10 % road gradient the average velocity of this cycle was adapted to the average velocity of the Handbook cycles (decreased). By using a multiplicative adjustment (<1) the acceleration was also decreased, which seems to be logical for higher road gradients. The following calculation results are valid for average Euro 3 diesel and petrol vehicles (average engine emission maps of 8 petrol and 7 diesel Euro 3 vehicles were used as model input). Example of comparisons is shown on Figure 32 for Euro 3 diesel vehicles, in the case of NOx.

For the diesel relevant emissions (NOx and PM) and fuel consumption, the simulation results are in good congruence with the measurements. Simulation of road gradients higher than 8 % is quite sensible. This is due to the fact, that the engine map used for the simulation is not filled up with data at this engine map range and therefore the calculation in this area had to be extrapolated. The comparison for HC shows also good results, but for CO the result is worse. Nevertheless for CO (and for HC) the measured emission values for 0 % road gradient (basis) are almost near to zero and therefore the results, calculated with factor of ten or more for road gradients still produces low emissions.

For relevant petrol emissions (CO and HC) and fuel consumption the simulation results have a good agreement with the measurements. For road gradients which are higher than 8 % the result for CO and HC shows the same effect which was reflected for diesel vehicles for NOx.

The simulation reproduces the measurement in a wide range of road gradient with an excellent congruence for diesel as well as for petrol vehicles. Thus the simulation is a useful tool to generate emission factors for other traffic situations, which can be found in section 3.10.4.

Vehicle loading

Because of the small vehicle sample a comparison with other sources is necessary to evaluate the influence of loading rates on the emissions and fuel consumption. For this purpose we used INRETS investigation on LDV and a simulation with PHEM.

In the INRETS investigation, 27 diesel light duty vehicles were tested on a chassis dynamometer with different driving cycles and loading rates (Joumard et al., 2003). The vehicles comply with the European emission standards 88/436 (1 vehicle), Euro 1 (7 vehicles) and Euro 2 (19 vehicles). The mileage and age of the vehicles vary significantly from one category to another. For light vans the load generally leads to a decrease in emissions, slight for gaseous emissions (-2 to -7% depending on the pollutant), and more marked for particles (-20 %).

For vans the load has a contradictory influence depending on the pollutant. For 2.5 t vans the load has a very clear influence on CO and HC emissions (a decrease by one third on average, and even more in urban traffic) and only a slight influence on particles (-8 %). For NOx, CO 2 and fuel consumption, the increase in load systematically increases the emissions by 10 to 20 %, whatever the speed. For the 3.5 t vans, the load decreases the HC and the particle emissions by -10 to -15 %, has practically no influence on the CO emissions and considerably increases the CO 2 emissions (+14 % regardless of the average speed) and especially the NOx emissions (+44 % and in an even more marked fashion in extra urban areas).

The comparison between that study for light duty vehicles should be considered with caution if comparing with passenger cars, because the mass ratio between unloaded/loaded for passenger cars differs to the mass ratio for light duty vehicles (less payload for cars compared to the empty weight).

The results of the simulation with PHEM reproduce the emission ratio of a Euro 3 vehicle loaded/unloaded. For the calculation of the three different driving situations (urban, rural and motorway) the Artemis driving cycle was chosen. In the case of diesel vehicles, for NOx, PM and fuel consumption, the agreement of the simulation with the measurement is good (see Figure 33). The results for CO look worse at first view.

Nevertheless the basis emission (unloaded) is quite low and therefore the factor will be extremely high. And a further fact is the worse repeatability for CO (see section 3.2.2. of Joumard et al., 2006), which made this comparison not so good, as the measurement results are based only on two vehicles without cycle repetition.

For petrol Euro 3 vehicles, only the simulation of FC has a good congruence with the measurement, but for the emissions the agreement is not so good. The repeatability for petrol vehicle emissions shows a worse repeatability, so it is not possible to gain an accurate loading factor from that small sample of measurements without repetitions.

Combination of road gradient and vehicle loading

For reasons of economy no tests were performed with different loading situations and road gradients, but nevertheless it is important to know whether the influence of the vehicle weight will be stronger at higher road gradients. That is one of the fields of application for the emission simulation. To find out the influence of loading at different road gradients the same driving cycles as above (urban, rural and motorway) were simulated with varying vehicle payload (unloaded, half loaded and loaded) for both average diesel and petrol Euro 3 vehicles.

For the relevant diesel emissions (NOx and PM) and fuel consumption the simulation indicates a minor influence of loading as it was expected at higher road gradients (see Figure 34). CO and HC factors are too sensible to make a statement about the influence of the loading rate.

The influence of the loading rate on the emissions and fuel consumption of petrol vehicles is more or less the same at different road gradients. Only for NOx emissions the influence of loading at higher road gradients is increased, but that emission component is not relevant as emission level is quite low. Higher road gradients (>8 %) can not be simulated with the common engine map generated from the Artemis cycles.

Final correction factors

To extend the data pool of road gradient emission factors for other traffic situations and for all European emission standards the factors for the final Artemis data base were simulated with the model PHEM. The vehicle loading correction is proposed for fuel consumption, NOx and particulate mass for diesel vehicles and for petrol vehicles only for fuel consumption.

Road gradient

As it is described above some adjustments on the used driving cycles were made. The cycle for the motorway driving situation was recorded on a hilly highway in Austria on all road gradients (-6 up to 6 %). For the rural driving situation the Artemis rural cycle was used. The urban driving situation was simulated with one of the Handbook driving cycles for urban traffic situations, at different road gradients. The main focus for the simulation cycles was to adjust the average velocity to be in the same range as the average velocity of the Handbook traffic situations for urban, rural and motorway.

For all European emission regulations (Euro 0 to Euro 4), diesel and petrol car engine maps were obtained for the simulation from available measurements.

Conclusion and discussion

Road gradient

Because of the different vehicles (engine capacity, power, etc.) from every single measurement a factor was calculated which represents the ratio between measured emissions at x % road gradient to measured emissions at 0 % road gradient. Afterwards with this single measurement factor, average factors for all road gradients were generated separately for petrol and diesel vehicles. Due to the fact that there were measured Euro 3 vehicles only and in one cycle only, which represents a rural traffic situation, it was not possible to create factors for other traffic situations and different Euro categories from these measurements. Thus the absent factors were calculated with PHEM, an instantaneous emission model which was generated for passenger cars as well as for heavy duty vehicles [START_REF] Rexeis | Heavy duty vehicle emission. Artemis deliverable[END_REF].

Before the application of the emission model a comparison between simulation and measurement was done. For this comparison as well as for the subsequent simulation, cycles had to be defined.

For the urban driving situation the Handbook cycle LE6, for the rural traffic situation the Artemis rural cycle (adjusted for higher road gradients) and for the motorway situation a recorded cycle of a hilly highway in Austria were chosen. With these three cycles and the average engine maps the road gradient factors for all Euro categories for diesel and petrol vehicles were calculated.

Vehicle loading

Three different loading rates were measured with four different cycles which should stand for the three traffic situations: urban, rural and motorway. The loading rates at no payload and at the maximum vehicle payload were measured, as well as one setting between these two rates.

The simulation of the loading influence is quite sensible for vehicle payload of 100 kg, therefore the loading factors were generated from the measurements. These final factors represent an average loading situation in Europe (1.5 persons), but only for NO x and PM for diesel and fuel consumption for both diesel and petrol vehicles factors are calculated. For the other exhaust gas components, the load factors measured are within the range of the repeatability in emission tests.

Influence of auxiliaries of passenger cars

A European Climate Change Programme working group estimated that the usage of air conditioning (AC) systems under average European conditions causes an increase of fuel consumption between 4 and 8 % in 2020 [START_REF] Eccp | How to considerably reduce greenhouse gas emissions due to mobile air conditioners[END_REF]. A recent study valuated an increase of fuel consumption in 2025 below 1 % [START_REF] Hugrel | The contribution of passenger cars to the greenhouse effect : influence of air-conditioning and ACEA's commitment (JAMA and KAMA included) on the CO 2 emissions[END_REF]. That is why it is proposed to undertake a state-of-the-art review of this area, to include fleet characteristics and a collection of data on auxiliaries, in order to improve the exhaust emission factors for the passenger cars and light duty vehicles, by enlarging the emission factor database, especially for effects of auxiliaries [START_REF] Roujol | Influence of passenger car auxiliaries on pollutant emissions -Artemis 324 report[END_REF][START_REF] Roujol | Influence of passenger car auxiliaries on pollutant emission factors within the Artemis model[END_REF].

Studies about air conditioning have been done in Europe focussed on the evaluation of individual passenger car emission due to AC [START_REF] Barbusse | Climatisation automobile, énergie et environnement[END_REF][START_REF] Gense | Driving style, fuel consumption and tail pipe emissions -Final report[END_REF][START_REF] Pelkmans | Impact of air conditioning, ambient temperature and humidity on emissions of modern passenger cars[END_REF][START_REF] Weilenmann | Effect of air conditioning activity on fuel consumption and CO2 emissions in Europe[END_REF], or on the improvement of AC [START_REF] Benouali | Fuel consumption of Mobile air conditioning -Method of testing and results[END_REF]. A major study about AC impact has been carried out in the framework of Mobile 6 by the USEPA, focussed on the real use of AC in real conditions [START_REF] Koupal | Air Conditioning Activity Effects in MOBILE6[END_REF] and on the effect of air conditioning running at full load on regulated pollutants [START_REF] Koupal | Air Conditioning Corrections Factors in MOBILE6[END_REF].

Emission database and analysis of effects on fuel consumption and CO 2

Air conditioning database is made up of experimental data from and4). The climatic conditions are specific to each laboratory, but have been chosen in order to represent severe climatic conditions. The small size of the database allows us to perform a simple statistical analysis.

According to Mobile 6, emitter classes, vehicle type, driving cycle, emission AC off and mean speed have to be distinguished to estimate effect of AC. At this short list, we can add, as proposed by [START_REF] Benouali | Fuel consumption of Mobile air conditioning -Method of testing and results[END_REF], the regulation type and the compressor technology type.

The excess emission of pollutants due to air conditioning is the difference of emission with and without air conditioning running in the same condition. We have first to decide the type of unit to express the excess fuel consumption due to AC: in volume per distance unit or in volume per time unit. For physical reason (no strong relation between cooling demand and vehicle speed), it seems that volume per time (l/h for instance) is better.

Effects of mean vehicle speed and driving cycle

The mean speed has little impact on excess fuel consumption, but variance test indicates that the relation is statistically significant. The relationship is mainly influenced by the data at 90 and 120 km/h constant speed. It seems due to the engine efficiency, which varies with load and engine speed. The effect of AC on fuel consumption is partially hidden by the improvement of engine efficiency, but not at high speed or load. A similar conclusion is given in a recent experimental study on two vehicles in real driving conditions [START_REF] Roumégoux | Consommation de carburant et émission de CO 2 des auxiliaires : climatisation et alternateur, impact des optimisations[END_REF]. The effect of speed is explained by the fact that the engine load for EUDC cycle is particularly low. For real driving cycle, engine load is slightly higher, and fuel consumption due to AC should be quite independent of the speed or type of driving cycle.

Effects of technological parameters

Technological parameters analysed are parameters connected to the vehicle engine, to the AC system and to the body shape of the vehicle. The data are displayed according to the engine size, the fuel type, the vehicle size, the type of compressor and the type of regulation. Most of the test vehicles were equipped with a variable-displacement compressor. Only two small vehicles and one large vehicle were equipped with fixed-displacement compressors. All cars with an engine size > 2.0 litres and SUVs were equipped with automatic temperature regulation systems. Apart from one vehicle, all small cars and medium-size cars with an engine size < 2.0 litres were equipped with manual temperature regulation systems. In order to get enough data per class, only 4 types of vehicles are distinguished (see Table 23). The results show that the fuel consumptions are quite close with large standard deviations. Therefore we assume that the fuel consumption of AC does not depend on technical parameters.

Effects of climatic conditions

The climatic conditions and set temperature have certainly a huge influence on AC running, and then on pollutants emissions. No experimentation is performed according to the solar radiation, although, according to [START_REF] Barbusse | Climatisation automobile, énergie et environnement[END_REF], solar load represents 45 % of the total load of the air conditioning. According to Figure 35, the variation of excess fuel consumption with the outside temperature is lower than expected: although the uncertainty of the measurements, the outside temperature at which there is no cooling or heating, obtained by linear extrapolation, seems to be below 0°C. Theoretically, the relation between fuel consumption and outside temperature is quite linear because of convective heat gains linearly linked with the difference between outside and inside temperatures. That seems to demonstrate that AC is running quite close to full load for outside temperature higher than 28°C. An extrapolation of these data is therefore non applicable. As the experiments do not allow us to take into account temperature below 28°C and solar heat radiation, a physical model is therefore developed.

A physical model for air conditioning effects

The physical modelling approach needed to take into account each component involved in the system, including the cabin, the A/C system and the engine. The phenomena taken into account were heat exchanges between the cabin air and the outdoor air, heat exchanges between the evaporator and the A/C system -which allows a reduction in air flow temperature and leads to its dehumidification -and between the A/C system and the engine.

Passenger compartment

The passenger compartment modelling is based on a description of heat exchange as it is usually done in mono-zone thermal building modelling [START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]. Air temperature and humidity in the cabin is assumed to be uniform. Heat exchanges governing temperature of cabin are due to the global heat exchange coefficient, UA (W.m -2 .K -1 ), the untreated air flow rate due to permeability, m p (kg.s -1 ), the internal heat gains due to occupants and electrical equipments, A int (W), the solar gains, A sol (W), and the treated air flow, m t (kg.s -1 ).

The modelling of solar gains [START_REF] Fraisse | Modélisation numérique d'un bâtiment multizone sous TRNSYS. 4es Journées TRNSYS Francophones[END_REF] depends on the direct and diffuse solar radiation, the position of the sun in sky and the geometric and physical properties of the vehicle window. Temperature and flow rate of treated air flow are regulated in order to maintain cabin air temperature to set temperature.

The thermal mass of the vehicle's interior has an effect in dynamic behaviour, increasing cooling demands during cool down for instance, but has no effect during steady state cooling and is therefore neglected. [START_REF] Weilenmann | Effect of air conditioning activity on fuel consumption and CO2 emissions in Europe[END_REF] have studied initial cool down, by combining the effect of initial cool down of the overheated passenger compartment and the effect of cold start. Two counteracting effects occur: Because of thermal mass, AC running involves more power than at steady state, and AC running involves that engine compartment is heated much faster than without AC running. These two effects compensate each other, and excess emission due to initial cool down in comparison to steady state emission is in the same order of magnitude than the cold start excess emission in the same temperature conditions.

With the internal temperature T int , the temperature of treated air T t , and the outside temperature T ext , the conservative equation of energy is:

( ) ( ) ( ) sol ext ext p t t p t A T T UA A T m T m T m m + ! " + = " + " ! " + int int int
The internal temperature is chosen according to the thermal comfort theory [START_REF] Fanger | Thermal comfort[END_REF]. The conditions of thermal comfort are a combination of skin temperature and body's core temperature providing a sensation of thermal neutrality and the fulfilment of body's energy balance. From ASHRAE standard 55 (1992) and [START_REF] Charles | Fanger's Thermal comfort and draught models[END_REF], 23°C is chosen as default value. The sensible heat exchange P sens at evaporator to maintain internal temperature at the comfort temperature can be deduced, and, if air treated rate m t is known, air treated temperature T t can be calculated:

( ) ( ) ( ) int int A A T T UA m m T T m P sol ext p t t ext t sens + + ! " + + = ! " =

Evaporator and A/C regulator modelling

Heat exchange at the evaporator can cause dehumidification of air treated. . The total heat exchange at the evaporator is the sum of sensible heat exchange and dehumidification. The average surface temperature humidity of air treated across AC evaporator depends on the heat transfer coefficients of evaporator and the temperature of coolant. If the average surface temperature is known, the airside heat exchange efficiency can be used to calculate the average surface temperature and humidity of the outlet air. The value of this efficiency is usually between 60% and 80% [START_REF] Morisot | Simplified model for the operation of chilled water cooling coils under non-nominal conditions[END_REF].

In the model, the value of the air side efficiency was assumed to be 0.8. With the air side heat exchange efficiency, it allows us to calculate the average surface temperature and humidity of outlet air.

It was assumed that the user or A/C regulation tries to maintain a minimum air flow rate (in order to reduce thermal load). On the other hand, the temperature of the treated air must not to be too low because of comfort consideration and the risk of freezing condensed water in the evaporator. A minimum air flow rate of 300 m 3 /h and a minimum average surface temperature of 0°C were therefore assumed.

Efficiency energy ratio of A/C and energy efficiency of engine

It was assumed that the efficiencies of the A/C and the engine were constant. For energy efficiency of the engine, experimental data show that running conditions of the engine have a small effect on CO 2 emissions due to air conditioning. According to [START_REF] Park | Steady state and cycling performance of a typical R134a Mobile A/C System[END_REF], the main parameters on AC efficiency are the temperature conditions, but the effects of temperature on energy efficiency are lower than on cooling demands.

Validity of the model

The model is applied to all experimental conditions either presented in section 3.11.1, or by [START_REF] Weilenmann | Effect of air conditioning activity on fuel consumption and CO2 emissions in Europe[END_REF], with temperature range resp. of 28-40°C and 13-37°C. The results of the model are compared to the experimental results (see Figure 36). They are quite close for temperature higher than 30°C. From 20°C to 30°C, the model underestimates the fuel consumption; And below 20°C, hourly fuel consumption from model are null, but experimental excess fuel consumption can be linked to the electrical consumption of ventilation to avoid windscreen fogging. A second comparison is done with the Mobile 6 model of demand factor based on experimental measurements. Demand factor is defined by Mobile 6 as the fraction of running time of AC, but can be also defined as the ratio of part load power consumption to the full load power consumption, estimated at 0.85 l/h. The Mobile 6 model and the proposed model are applied with hourly weather data of Seville in Spain, which has the closest climate in Europe to the climate of Denver where vehicle were followed in order to determine demand factor in Mobile 6. In order to take into account the solar loads, Mobile 6 distinguishes daytime and night, and our model calculates the solar loads for each climatic condition described in the weather data. As shown Figure 37, demand factors obtained by Mobile 6 and our model are quite close for temperature higher than 20°C.

Below 20°C, demand factor from Mobile 6 model is null but slightly above 0 for our model because of solar loads heating.

We consider that the model satisfied our objective, which is to determine hourly fuel consumption in non-tested weather conditions. The differences between results from model and data from EMPA (Figure 36) at temperature below 20°C are not well understood and required additional experiments at these particular conditions.

Simplified model of excess fuel consumption and weather data

A physical model of excess fuel consumption due to AC seems to be too complex to be implemented in an inventory software as Artemis. Therefore we computed the physical model with weather data of 91 regions all over Europe defined in Annex 29, and looked for a relationship by statistical regressions between hourly fuel consumption and the following explicative variables: ambient temperature, humidity, position of sun in the sky, and solar radiation, replaced by the hour in the day. The general form of the simplified model is: the hour (between 1 and 24) a 1,…5 : coefficients depending on the location The coefficients a 1 to a 5 are available for each location in Annex 30. But in addition, two other sets of coefficient a are provided: The first set is given according to 6 modified Köppen climate classification (see Annex 29), based on the annual and monthly averages of temperature and precipitation (DOE, 2004), and the second set corresponds to an average. The excess fuel consumption and CO 2 emission for a fleet is calculated by summing hfc according to the number of vehicles with AC running for a given road segment, expressed in number of INRETS report n°LTE 0523 vehicles per hour. The general equation to calculate the excess fuel consumption fc f for a fleet f due to the use of air conditioning is (more details are given in Annex 31):

hfc = a 1,wf + a 2,
@ A !!!! " = loc ext T TS i loc T TS i AC f T T h hfc n fc int , , , , , ,
Excess CO 2 emission is:

( ) !!!! " " = loc ext T TS i i CO loc T TS i AC f T T h hfc c n eCO int , , , , , 2 ,

Excess pollutants emissions analysis

Data available for pollutant emissions (CO, HC, NOx, PM) due to AC are rare in comparison with data available for CO 2 emission, mainly because only 13 petrol and diesel vehicles are tested.

As it was shown in section 3.11.1, AC system is running quite close to the full load at the test conditions (outside temperature > 28°C), where pollutants emissions are assumed to be full load ones. An example of data is shown in Figure 38: NOx emission and effect of AC are larger during the urban driving cycle ECE15 than during the extra-urban cycle EUDC. For each pollutant a relationship is proposed between excess emission and hot emission without AC (Figure 38). Results of petrol vehicles are in accordance with the theoretical explanation proposed by [START_REF] Soltic | Influence of Electric Load on the Exhaust Gas Emissions of Passenger Cars[END_REF]: As long as the increased torque does not cause a air fuel mixture enrichment, an increase in the exhaust temperature, a slight reductions of HC and CO emissions, and an increase of NOx emission are expected. If an increased torque level causes an increase of enrichment, CO and HC emissions will also increase.

For the pollutants emissions modelling, we assume that pollutants emissions ef pollutant, AC at part load are a fraction of emissions at full load f(hot emission without AC), with the fraction being equal to the demand factor. The demand factor is the ratio of hourly fuel consumption at given condition hfc to hourly fuel consumption at full load (0.85 l/h).

ef pollutant, AC = f(hot emission without AC) . hfc / 0.85

Because of the lack of data, only a distinction between the petrol and diesel vehicles is proposed. The model does not explicitly distinguish the age of vehicle, because we consider it has no influence on excess CO 2 emission. The effect of emission standard on pollutant emission is taken into account through the hot emission, which depends on standard emission. The emission models (functions cf) are given in Annex 32.

For the future vehicles, some counteracting effects occur: Firstly, technological improvements of efficiency of AC system are expected:

-By reducing the thermal load of the vehicle [START_REF] Türler | Reducing vehicle auxiliary loads using advanced thermal insulation and window technologies[END_REF][START_REF] Farrington | Opportunities to reduce vehicle climate control loads[END_REF][START_REF] Farrington | Challenges and potentials solutions for reducing climate control loads in conventional and hybrid electric vehicles[END_REF] through the use of advanced glazing which reduces the transmission of infrared solar radiation.

The improvement of air cleaning allows reducing the amount of outside air, reducing by the way thermal load and power consumption of fan. Advanced regulation of ventilation allows ventilating parked vehicles reducing the peak cooling load. -By increasing energy efficiency ratio of AC system [START_REF] Benouali | External and internal control compressors for mobile air-conditioning systems[END_REF][START_REF] Barbusse | La climatisation automobile, impact énergétique et environnemental[END_REF]. The first improvement will be due to the improvement of AC components as the external control of compressor, the electrical compressor, a high efficiency heat exchanger. At long term, alternative technologies are investigated as magnetic cooling, desiccant cooling, and absorption.

Secondly, the evolution in the vehicle design and in the leakage refrigerant standard will certainly increase the CO 2 emission due to the use of AC. The constraint against refrigerant leakage drives to use alternative refrigerant with a lower Global Warming Potential as HFC 152a and CO 2 . These alternative refrigerants have the drawback to reduce the efficiency of AC system because their lower thermodynamic properties. The use of alternative refrigerant as the CO 2 allows using AC system as a heat pump in order to warm passenger compartment, made more and more difficult by the development of high efficiency engine which could reduce the possibility to use the engine heat to warm the passenger compartment and which justifies the development of reversible system.

At short time, we assume that these two effects compensate each other. No correction is proposed for future vehicles.

Other auxiliaries

The effects of other auxiliary systems on emissions were determined based on the work done by [START_REF] Soltic | Influence of Electric Load on the Exhaust Gas Emissions of Passenger Cars[END_REF]. Excess fuel consumption due to other auxiliary systems hfc aux was expressed in litres per hour, as for A/C, and it was assumed that excess fuel consumption was proportional to electrical load. Table 24 lists auxiliary systems, and gives electrical power consumption. The group of auxiliary systems in the Table excludes some other important electrical power consumers, such as components linked to the engine or linked to security. According to these authors, we evaluated an average excess fuel consumption of 0.075 l/h for an electrical load of 160 W, corresponding to dipped headlights.

hfc aux (l/h) = 0.075 (l/h). Power of the auxiliaries (W) / 160 (W) . % of use time

In order to be in accordance with excess pollutant emission due to AC, we proposed to use a similar way for excess emission due to auxiliaries ef pollutant, aux . Excess pollutant emission due to AC at a given conditions is a fraction to excess pollutant emission at full load. This fraction is calculated as a ratio of excess fuel consumption at given condition hfc aux to excess fuel consumption at full load, estimated at 0.85 l/h (see section 3.11.2). We proposed to use the same model by replacing the excess fuel consumption of AC by the excess fuel consumption of auxiliaries.

ef pollutant, aux = f(hot emission without AC) . hfc aux / 0.85; with hfc aux / 0.85≤1

For instance, in the case when dipped headlights are used, the value of fraction is 0.075/0.85. Table 24: Power consumption of auxiliaries and estimation of the use of auxiliaries [START_REF] Soltic | Influence of Electric Load on the Exhaust Gas Emissions of Passenger Cars[END_REF].

Conclusion

The different analyses show that the excess fuel consumption expressed in l/h is quite independent to the speed or to the traffic situation. No significant technological parameters are found. That does not mean that no relation exists between excess fuel consumption and technological parameters, but that the number of data is not sufficient to extract this type of relation or that the technological solutions are too close each other.

The excess fuel consumption due to air conditioning is well know in warm conditions because of the large number of experiments. It is quite different in normal climatic conditions with lower solar radiation, because of the reduced number of experiments. To approach the behaviour of AC system at these conditions, a physical model is proposed and compared to experimental data. According to the objective of the model, the results show a good agreement in warm conditions. At normal conditions, the model underestimates the excess fuel consumption without understanding the reason. The effect of AC in normal conditions should be studied more, because of the high occurrence of these conditions in comparison to warm conditions. In the model, based on the usual comfort theory, we assume that the set temperature is 23°C for all the vehicles equipped with AC, but experiments on real world vehicles with air conditioning could improve the knowledge of user's behaviour.

Cold start emissions of passenger cars

As expressed by [START_REF] Duboudin | Analyse de la méthodologie COPERT III (Analysis of the Copert III methodology)[END_REF], as long as a vehicle does not reach its running temperature, the emissions of atmospheric pollutants are increased. In the case of cars not equipped with a 3-way catalyst, this excess of emission comes from a non-optimal engine running. Therefore the engine temperature is the main parameter. In the case of vehicles equipped with a 3-way catalyst, the catalyst temperature and fuel-to-air ratio determine the functioning of the catalytic converter and thus also the net emissions. In both cases we define the time needed for a vehicle to reach its normal running temperature, and an over-emission occurring before that. The concept of over-emission is defined below. The evolution of the instantaneous emission of a vehicle along the time, for a given pollutant, an engine speed and an initial engine temperature, can be split up into a first phase with a decreasing emission due to the progressive increase in the engine or catalyst temperature, followed by a quite stable phase when the normal engine temperature is reached (Figure 39). The first phase corresponds to the time t cold . This time t cold is linked to the distance d cold by the mean speed of the driving cycle during the cold period. The total emission E tot during a driving cycle of a vehicle which does not start in hot conditions can be calculated by the sum of the hot emission E hot and the cold start excess emission EE cold :

E tot =E hot +EE cold EE cold is the absolute cold start excess emission (in gram) defined as the additional emission value obtained under cold conditions compared to the emissions values that have been recorded for the same driving distance or time period (cycle) under hot conditions (Figure 40).

When we consider a driving cycle, composed of a succession of different vehicle speeds and therefore different engine speeds, the instantaneous emission is much more complex and unsteady. It depends on the different running phases and on the progressive temperature increase (the Figure 39 is not really an example, but rather an illustration of that, when the engine speed variations are much quicker than the temperature increase).

Three methods are till now available in Europe to model excess emission at start:

-The Handbook, applied mainly in Germany and Switzerland [START_REF] Keller | Émissions polluantes du trafic routier de 1950 à 2010[END_REF][START_REF] Keller | Handbook emission factors for road traffic (HBEFA, version 2.1). Software, Infras[END_REF] -The MEET approach, based on a synthesis of the available cold emission data in Europe (Joumard and Serié, 1999) -The Copert III approach (Ntziachristos and Samaras, 2000a), which is a mixture of the former Copert and MEET approaches.

Samaras et al. ( 2001) evaluated the values of excess emissions for various situations in Europe, by using the three approaches. They found that, due to the differences between the methodologies of Copert III and MEET, there are differences between the modelled cold excess emissions. These effects however are mostly exhibited at very low values of the speed and ambient temperature and become negligible when intermediate values of these parameters are approached. In general, the difference between the results obtained by Copert and those by MEET are reduced for temperatures between 15°C and 25°C and also for high vehicle speeds. The agreement between the results of Copert III and those of the model suggested in the Handbook is very good, especially in the case of Euro 1 vehicles, even though the two models exhibit several differences with respect to the methodology. All these calculations show that the excess emissions depend of course on the methodology used and on the emission data used.

The model developed here (André & Joumard, 2005;2006) should have a wide range of applications: Large-scale applications as national inventories, but also smaller scale applications as at street level for instance.

New method to calculate the absolute cold start excess emission

At the beginning of the work, two methods were available to calculate cold start excess emissions on the basis of repeated or successive driving cycles.

-The first method, so-called standard deviation method, (developed at INRETS by Joumard & Sérié, 1999 -see Figure 41) consists in calculating the standard deviation on the measurements working backwards from the end of the cycle, adding one measurement at a time. As long as the emissions are stable (i.e. hot), the variation occurs randomly around a mean (the hot emission), and the standard deviation is therefore a decreasing function of the number of points considered. However, the standard deviation increases rapidly as soon as cold-start part of the cycle is reached, and the cold-start distance d cold therefore equates to the minimum value of the standard deviation. The hot emission is calculated from the values beyond (forward in time) the minimum. The absolute cold-start emission is calculated over the entire cold-start distance, and the cold-start excess emission is calculated by subtracting the hot emission from the absolute cold-start emission. -The second method, so-called linear regression method, developed at EMPA by (Weillenmann, 2001;Weillenmann et al., 2002a) -see Figure 42 -consists in calculating the continuous cumulative emissions from the start. A linear regression model is then fitted to the cumulative emission data from the hot part of the cycle alone, and the regression value at zero distance gives the cold-start emission value. The hot/cold limit is firstly arbitrary chosen, and then by plotting two straight lines parallel to the linear regression during the rough hot part. They have the same slope but the constant of the first line is equal to 95 % of the emission while the second is equal to 105 %. The precise cold driving distance is determined by the last time the total emission falls between these two lines.

These two first methods show that there are quite distinct differences in cold start excess emission calculation and, above all, in cold start distance. In the first method, the cold distance is overestimated because the method looks for the minimum of the standard deviation, which appears during hot conditions. In the second method, the determination of the cold distance is based on calculation along the hot conditions which are not determined rigorously. So we decided to develop a new method based on the advantages of these two first methods.

In the new method developed, so-called Artemis method (see Figure 43 and Figure 44), we first calculate a rough cold start distance by using the first method. Then we calculate the hot emission, the standard deviation and the linear regression of the cumulative hot emission. The value of the regression at zero distance gives the cold start excess emission. The exact cold start distance is determined by looking at the distance where the emission falls entirely between two straight lines which are the hot emission ±2 standard deviations. Table 25 shows that, for the same emission data, the three methods give almost the same cold-start excess emission, but not the same cold-start distance.

The method is used for the regulated pollutants. For the unregulated pollutants, as the emissions were not measured on successive cycles, we apply the cold start distance calculated for total hydrocarbons (HC). The cold start excess emission is calculated by the difference of the value for a cycle beginning in cold conditions to the value of the same cycle beginning in hot conditions. Table 25: Comparison of the cold-start distance and the cold-start CO emission (Euro 1 diesel at 18°C) calculated using the different methods.

Data considered

The work is aiming at modelling the cold start impact on road vehicle emissions as functions of the pollutant and the vehicle type, using all the existing data in Europe. This model is developed empirically, considering the available data in Europe for passenger cars: Excess emissions indeed, but also ambient temperature, and driving behaviour statistics.

The cold start excess emission data come from the MEET project, from national programs and from measurements made within the Artemis study (see section 2.1). The external data were obtained through two inquiries made among 14 European laboratories, in January 1994 and then December 2002. After elimination of data without both hot and cold emission factors and selection of usable data, the data used to design the cold start models come from five laboratories: EMPA, INRETS, IM, TNO and VTT.

Concerning excess emission data as a function of the cycle, the total number of obtained data is 35 941, all categories and all pollutants merged, i.e. 28 337 and 8 604 data resp. for regulated and unregulated pollutants. These data were measured with 1 766 vehicles, i.e. 1 604 and 102 vehicles resp. for regulated and unregulated pollutants, over five different driving cycles (FTP-72, ECE-15, Inrets urbain fluide court (IUFC), Inrets route court, and Artemis urban -see Annex 5). All vehicle samples were selected by various laboratories, in order the vehicle distribution to be representative, to some extent, of the fleet corresponding to each country. The number of vehicles tested and the corresponding driving cycles are given for the regulated and unregulated pollutants in [START_REF] André | Modelling of cold start excess emissions for passenger cars[END_REF]2006;André et al., 2004), together with the number of measurements according to the mean temperature, and the minimal and maximal temperatures per driving cycle and per laboratory. The vehicles taken into account comply with the emission standards Euro 0 to Euro 4 for diesel and petrol fuel type.

We used data recorded with Inrets court and Artemis driving cycles [START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF][START_REF] André | Real-world driving cycles for measuring cars pollutant emissions -Part A : The Artemis European driving cycles[END_REF][START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]André and Joumard, 2004), and with legislative cycles. We know that these last cycles do not reflect the reality, but they represent the main part of the data.

A previous study [START_REF] Joumard | Changes in pollutant emissions from passenger cars under cold start conditions[END_REF] showed that ECE-15 cycle could not cover entirely the cold period due to the cold start. So, we introduced a correction coefficient for this cycle to transform the measured excess emission during standard cycles into a full cold excess emission. This coefficient is deduced from measurement data recorded using IUFC cycle (because the mean speed is near the ECE-15 mean speed), which covers the whole cold period. Using this "cold" distance, calculated with the Artemis method on the short Inrets cycle data, we calculate the correction coefficient to be applied to adjust the standardised cycles to the representative cycles.

When applying the whole methodology, the cold-start distance for the four regulated pollutants, two driving cycles (at 19.0 and 41.1 km/h) and a number of cases (vehicle type, ambient temperature) ranged from 2 km to 9 km, with an average of 5.2 km at 20°C.

Cold excess emission for a start

The collected data allow us to express the cold start excess emission (EE) for a start and a vehicle type (i.e. a emission standard and a fuel type) and a regulated pollutant as a function of the ambient temperature (T), the mean speed during the cold period (V), the distance (d) and the parking time duration (t) before starting. So EE could be expressed as: The cold distance d c , ω(T,V) and h(δ) are computed from the data by using the method described in section 3.12.1., and then modelled. The cold distance d c (T,V) is a 3D linear regression depending on T and V, whose function is given in Annex 33. f(T,V) is a 3D linear regression which depends of T and V with the condition that the function f must tend toward 0 when T increases. f(T,V) and then ω(T,V) are given in Annex 34.

EE(T,V,δ,t) = ω
h(δ) is an exponential function of δ which can be expressed as

"

where a is deduced from the data. The coefficient a is given in Annex 35.

To take into account the parking duration, which influences the initial engine temperature, we process the rare data available, from CARB [START_REF] Sabate | Methodology for calculating and redefining cold and hot start emissions, CARB report[END_REF], EMPA (Schweizer et al., 1997), TUG [START_REF] Hausberger | Globale Modellbildung für Emissions-und Verbrauchsszenarien im Verkehrssektor (Global Modelling of Scenarios Concerning Emission and Fuel Consumption in the Transport Sector)[END_REF] and VTI [START_REF] Hammarström | Betydelsen av korta motoravstängningar och körtid för avgasemissioner fran bensindrivna bilar med och utan katalysator[END_REF]

t g h V T d V T f Gasoline Euro CO e e V T t V T EE h km C ! ! + ! ! ! ! ! " " " # $ % % % & ' ( ( ! ! + ! ( ! = ( ! + ! ( ( °) * )
We also introduce in the above model, the possibility to compute excess emission for near future vehicles by using the reduction rates proposed by Samaras and Geivanidis (2005) for Euro 4 and Euro 5 vehicles in comparison with present vehicles (see section 3.5.4). These rates are deduced from the future evolution of the European emission standards and from some rare measurements: See Table 26 The rate α can be applied either to the cold start distance d c or to the cold start excess emission ω 20°C,20km/h of present vehicles, but not to both parameters (the total decrease would be in this case α 2 ). We propose to apply these rates to the cold start excess emission ω 20°C,20km/h .

The different cold start Artemis models

The final Artemis model is provided for different users. Each one has not the same information to compute emissions. So it was decided to give three different models depending on the available input data.

First model

The first model gives an excess emission per start (i.e. per trip) in mass unit for a vehicle type i and a given pollutant p as a function of the ambient temperature T, the mean speed V during the cold period, the travelled distance d and the parking time t. It is the equation described in the section above 3.12.3.

EE(i,p,T,V,d,t) = ω 20 °C,20 km/h (i,p) . f(i,p,T,V) . h(i,p,δ(i,p,T,V,d)) . g(i,p,t) ω 20°C, 20km/h (i,p) and f(i,p,T,V) are given in Annex 34 for each vehicle category i and for each pollutant p (regulated or unregulated one). h(i,p,δ(i,p,T,V,d)) and g(i,p,t), given resp. in Annex 35 and Annex 36, are not available for the unregulated hydrocarbons (URHC). For theses components, the functions h and g used are the specific ones for the total hydrocarbons (THC): h(i,URHC,δ(i,URHC,T,V,δ))= h(i,THC,δ(i,THC,T,V,δ)) and g(i,URHC,t)= g(i,THC,t)

Second model

In a number of cases, assessing cold-start-related excess emissions for a single trip (for some micro inventories) is sufficient, but most emission inventories require calculating cold-start-related excess emissions not for a single vehicle and over a single trip, but for the whole traffic characterised by a number of parameters such as vehicle flow, average speed and environment conditions (hour, ambient temperature...). It is the aim of the second model.

The first model, initially applied to a single trip, must be extended to the whole traffic by using the available statistical data relative to traffic parameters. The excess emission of a traffic due to cold starts is therefore the product of the unit excess emission for a trip EE (first model), by the number of trips cold starting N tcs : E c = N tcs . EE N tcs is expressed globally as the ratio of the total distance started with cold start L cold Total by the mean distance of the trips started in cold conditions L cold Mean: N tcs = L cold Total / L cold Mean L cold Total is the product of the traffic flow t fi expressed in veh.km by the percentage of mileage cm(s,i) started at cold start. This last parameter depends of the season s and the mean trip speed v i . L cold Total = tf i .cm(s,v i ) If we consider only the cold started trips of length d m , their number is expressed as:

N tcs (d m ) = L cold Total . p m / d m
Where p m is the share of total distance started with a cold start corresponding to trips of length d m .

In the same way, if we consider the cold started trips with an average speed v i , these trips correspond to a cold distance of average cold speed v j : N tcs (d m ,v i )= ! j L cold Total . p m,j . p i,j / d m Where p i,j is the distribution (%) of the cold started distance with an average trip speed v i among the different speeds v j during the cold distance. At the same time p m has to be related to the speed v j and expressed as p m,j . In the same way, if we consider a stop or a parking time t n , it corresponds to a distance share p n : N tcs (d m ,v i ,t n ) = ! j L cold Total.p m,j . p i,j . p n / d m As we must take into account all the cold started trips length d m and all parking time duration t n , the number of trips cold starting is therefore the summations over m and n of the above expression.

In addition we would like to take into account the influence of the hour of the day on the start number and on the parking time. Therefore the traffic flow t fi and the parking time share p n are functions of the hour and are transformed into tf i,h and p n,h . At the same time, the cold starts must be distributed along the day by introducing the relative number of cold starts ptf i,h of the hour h (relative to the average hourly cold start number) p h . Moreover, all the distributions depend hardly on the season s because the driving behaviour changes hardly between the seasons. So the equation of the model 2 could be expressed as:

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ! ! " # $ % & ' ( ) * * * * * + + , - * * * * = ... . . j m n n m j j m h n j m j i i h h i h h i i i c t p g d v T p p h T v p f d s p s p s p ptf s p tf p v s cm p E , , , , , , , 10 100 , 6 , , , , , / 0
This equation gives an excess emission of a traffic in g as a function of the traffic flow, the season, the average speed, the ambient temperature and the hour of the day. Among all the parameters of this equation, we can distinguish three types:

-Some ones are purely internal and should not be modified by the user: d c (p,v j ,T), ω i (p), f(p,v j ,T), h(p,δ) and g(p,t n ), coming from the first model (and given in Annex 33, Annex 34, Annex 35 and Annex 36) -Some ones are input parameters: i, s, v i , h, tf i,h , ptf i,h and T -Some ones are internal parametres but could be modified by an advanced user: cm(s,v i ), p h , p i,j , p m,j , p n,h , d m and v j

According to [START_REF] Duboudin | Analyse de la méthodologie COPERT III (Analysis of the Copert III methodology)[END_REF], the taking into account of the average speed in the above equation is problematic, because the difference between the average speed during the cold period and the average speed during the whole trip. A trip with an average trip speed v i is subdivided into a cold and a hot phase. The cold one can have an average speed v j different from the global speed v i . To calculate the global emission, we add a hot emission calculated with v i and a cold excess emission calculated with v j :

E total (trip) = EE cold (v j )+E hot (v i )
It is not really coherent: If the distance travelled during the cold phase d c corresponds to an average speed v j different from the speed of the whole traffic v i , the travelled distance in hot conditions cannot have an average speed v i , and the global emission should be calculated with the formulae:

E total (d c +d hot ) = EE cold (v j ,d c ) + E hot (v j ,d c ) + E hot (v hot ,d hot )
where v hot is the average speed of the hot distance d hot . Therefore, when we calculate the traffic emission, we should use the equation of the model 2, but add (E hot (v j ,d c )-E hot (v i ,d c )). As the difference should be quite small, we do not apply this correction.

Third model

Both models 1 and 2 are not at all easy to be used by a common user: The first model needs to be completed by a model giving the number and the characteristics of the starts, which is far from simple. The second model is the most comprehensive and accurate model, the most open to any user data, but is especially complex to use: As a lot of necessary statistics are really not common, to use the model can lead to misleading results.

Therefore, a simplified approach was developed, whereby the second model, with all its default values, was executed and the outputs were transformed to give excess cold-start emission factors in mass per unit distance, needing only few open input data: It is the third model.

This third model gives for a given vehicle type and an atmospheric pollutant an excess unit emission of a traffic in g/km, according to the season s, the ambient temperature T, the average speed v i and the hour h of the day. It is a combined table for 4 seasons (winter, summer, intermediate, whole year), 8 speed classes (5 to 75 km/h), 7 temperature classes (-25°C to 35°C) and 25 hours (24 hours and the whole day): See all these tables in the appendices 23 to 37 of [START_REF] André | Modelling of cold start excess emissions for passenger cars[END_REF] and only as Excel sheet for each of the 24 hours.

The third model allows us to take into account the distribution of the cold starts along the day. But the development of the third model needs a specific assumption on the relative traffic distribution along the day (ptf i,h ): We used the so-called base distribution presented in Annex 37. But when applying this third model, if the actual traffic distribution is very different from this base distribution, the overall emission calculated during the day can be wrong. For instance for average traffic distributions representative of USA, Belgium and Switzerland (Figure 70 in Annex 37), the using of the third model introduces an error for the whole day between 3 and 7 %. In this case, we recommend not to use the third model hour per hour, but:

-Either to use the second model: the calculation will be very precise, with a detailed distribution of the cold excess emission along the day and an accurate summation over the day, -Or to use the third model for the whole day (hour = whole day): the summation over the day of the hourly cold excess emissions will be accurate, but its distribution among the hours will not be accurate. In order to look at the relative influence of different parameters, the Figure 46 shows the influence of the average speed on the cold start emission. Other illustrations are given in Annex 38 for the ambient temperature, the vehicle category, the season and the hour. The influence of all these parameters depends on the pollutant considered. Nevertheless the ambient temperature, the mean speed and the hour in the day play the major role. The season, for a same temperature, plays a minor role.

Conclusion

This modelling of excess emission under cold start conditions for passenger cars was achieved using data provided by various European research organisations. The models take into account the average speed, ambient temperature, travelled distance and parking duration, among other parameters. The modelling counts in fact three models.

The models can be applied at different geographic scales: at a macroscopic scale (national inventories) using road traffic indicators and temperature statistics, or at a microscopic scale for a vehicle and a trip. Where a model user does not have access to the necessary statistics, it is recommended that the most aggregated model (i.e. the third model) is used, which is parallel to the hot emission modelling, with the same shape.

This study corresponds to the state-of-the-art at the present time. In the future, this model could be improved by different ways:

-By updating this model using new data when available, either for the most recent passenger cars, or the light duty vehicles, or the heavy duty vehicles. -It would be much more precise to have crossed distributions for different speeds and ambient temperatures. -The amount of supporting data has to be increased, especially for different speeds, lower and higher temperatures, and unregulated pollutants.

Evaporative emissions

Evaporative emissions mainly occur as a result of temperature changes of the vehicle fuel system, which occur due to the daily variation of the ambient temperature and during a normal driving procedure. Although this report focuses on exhaust emissions, we present shortly the work done within Artemis on the evaporative emissions of light vehicles, detailed in [START_REF] Hausberger | Evaporative emissions of vehicles. Artemis deliverable[END_REF].

The following reasons for evaporation are considered:

-Running losses -Hot soak emissions -Real time diurnal emissions (sum of diurnal emissions and resting losses)

From the literature review and the measurements carried out with three cars in SHED tests, it was possible to cover the following petrol driven vehicles:

-Cars pre Euro -Cars Euro 1 and 2 -Cars Euro 3 and 4 -Cars Euro 1 to Euro 4 with failures in the fuel system (leakages)

Evaporative emissions from diesel-fueled vehicles are considered negligible due to the extremely low volatility of diesel fuel. Data for light goods vehicles (< 3.5 t maximum gross vehicle weight) is not available, and thus we suggest using the formulas for passenger cars for this category.

The new model, based to a large extent on extensive work of the US EPA, shows that evaporative emissions of Euro 3 and 4 are substantially lower than for Euro 1 and 2. Reasons for this can mainly be found in the more stringent emission legislation and the advanced test procedure. This leads to the introduction of more sophisticated and durable technologies, which are monitored by on board diagnostic systems. The main remaining sources of evaporative emissions in road traffic are thus old cars without a carbon canister and newer cars with failures in the fuel system.

The introduction of failure rates for the vehicles (only based on assumptions) as well as the different model approaches lead to evaporative emission levels which are higher than those provided by the European Corinair model [START_REF] Eggleston | CORINAIR working group on emission factors for calculating 1990 emissions from road traffic -volume 1 : methodology and emission factors[END_REF]. For typical driving of a vehicle on a summer day, the new Artemis model gives approx. 145 % higher evaporative emissions for the average pre Euro car, +360 % for the Euro 1 and 2 cars and +80 % for Euro 3 and 4 cars than compared to the Corinair approach. Since Corinair does not include emissions measured within the last decade and Artemis is only based on 3 new European cars measured, it is obvious that the database is much too small to establish a really reliable model on evaporative emissions.

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.

For the hot emissions, an new discrete model approach based on traffic situations is provided. This approach is similar to the one used in the German-Swiss handbook, but the traffic situations used here are defined differently. In addition to a broad set of specific traffic situations, the model also provides emission factors for aggregate traffic situations for "urban", "rural" and "motorways" as well as an "overall European average" for simple or macroscopic assessments.

In addition to the traffic situation approach a model similar to Copert was developed taking into account the driving behaviour only through the average speed. For deriving the corresponding emission functions, the same database was used as for the traffic situation approach, in order to keep a basic consistency between the two approaches. Both models are based on the whole Artemis light vehicle emission measurement database through the definition of Reference test patterns and their corresponding emissions.

Beside these models, three additional models (instantaneous or kinematic models) were developed within the project. They take into account the driving behaviour very accurately, either through the instantaneous driving data for two of them, or through quite complex kinematic parameters for the third. These models are, for the first time, able to calculate the emissions of a vehicle or a traffic for any driving behaviour, and should be used for assessing the influence of local policies influencing the driving behaviour (traffic lights, traffic management, speed control...). The availability of these instantaneous and kinematic models is restricted to scientists, as they need a more profound expertise on the driving behaviour.

The improvement of the models is also the result of new modelling of the influence of many additional parameters, as cold start, auxiliaries like air conditioning, mileage, ambient air temperature, and road gradient (and evaporation detailed elsewhere). All these models are based on a large amount of specific measurements made within the project. Some of them are given in different versions, some in different levels of complexity, for simplified aggregate to very specific and complex applications.

Specific emission factors were derived for light commercial vehicles, and for non regulated pollutants, on the basis of specific measurements. These emission factors, however, are only provided as average speed functions and only for a subset of vehicle classes due to limited availability of emission measurements.

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.

INRETS report n°LTE 0523

The model for passenger cars and light commercial vehicles is implemented in the Artemis software (Boulter et al., 2007), together with the models for 2 wheelers and heavy duty vehicles. In addition, separate models are provided for the non-road 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 Light Vehicle Emission Measurement (LVEM) database includes almost all the measurements made in Europe until now on light vehicles, with all the necessary test conditions: about 2800 vehicles, 18 000 tests and 180 000 emission factors, including 25 000 for the unregulated pollutants. Its main part is publicly available and could be used by any user, for his or her own purposes. It should be updated and extended with the future European emission tests, if possible.

For information on the other tasks of Artemis, including the whole model for the different transport modes, the software and the Artemis LVEM database, or the future side developments, please look at the Artemis website www.trl.co.uk/artemis.

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.

Annex 1 : Unregulated pollutants measured per laboratory

Count means number of data in the database. More information on the pollutants is given in Annex 9, except for pollutants written in blue, which are not considered as toxic in Annex 9.

NAME

Annex 2: Analysis methods for unregulated pollutants

The sampling procedures, the measurements and other analysis methods depend on the laboratory [START_REF] Aakko | Emission factors of unregulated atmospheric pollutants for passenger cars[END_REF]2006). The list of compounds quantified and characterised is given in Annex 1 per laboratory.

A2.1. EMPA methods

The EMPA methods are presented in [START_REF] Saxer | Temperature-dependent Methane, Benzene and Alkyl Benzene Cold Start Emissions of 3 conventional Passenger Cars (Euro 0), Poster[END_REF]2003;Weilenmann et al., 2003b;2005;[START_REF] Heeb | Methane, Benzene and Alkyl Benzene Cold Start Data of gasoline-driven Passenger Cars representing the Vehicle Technology of the last two Decades[END_REF]2004).

On-line measurement by chemical ionization mass spectrometry

At a time-resolution of about 1 second, concentrations of individual hydrocarbons were investigated on-line by chemical ionization mass spectrometry (CI-MS) either from diluted exhaust (CVSsystem) or at the tail pipe from heated raw exhaust. Two different ionization modes were used.

Methane and benzene are ionized with xenon ions (Xe + , 12.2eV), benzene, toluene and the C 2benzene class of compounds (xylenes and ethyl benzene) were monitored using mercury ions (Hg + , 10.4 eV).

Prior to each test cycle both mass spectrometers were calibrated using gas standards. Validation of the analytical procedures was achieved with an independent method based on gas chromatography with flame ionization detection (GC-FID, see section below) revealing good comparability for the reported pollutants.

Off-line measurement by gas chromatography

The exhaust gas was diluted in the CVS-system of the chassis dynamometer. As for regulated measurements a bag was filled with a constant flow during the test. The hydrocarbons were analysed by gas chromatography with flame ionisation detection (GC-FID). Two different GC systems were used for the light end (C 1 -C 5 ) and the mid-range (C 6 -C 12 ) hydrocarbons. An aliquot of two milliliters of the diluted exhaust gas was injected directly into the gas chromatograph.

The GC-systems were calibrated by means of reference gases consisting of thirteen compounds (ethene, ethine, propane, propene, isobutane, isobutene, n-hexane, benzene, toluene, ethylbenzene, o-xylene, m-xylene, p-xylene). About 110 VOC species were specified. The compounds were identified by gas chromatography coupled with mass spectrometry (GC-MS). The samples were analysed within 8 hours to minimize the degradation of instable compounds. A sample of the dilution air was analysed simultaneously to every test and the VOC concentration of the exhaust samples corrected with the VOC concentration of the dilution air.

Aldehydes and ketones

The exhaust gas was sampled from the CVS-system into impingers containing a solution of 2,4dinitrophenylhydrazine in acetonitrile. The aldehydes and ketones were analysed as their 2,4dinitro-phenylhydrazone derivatives using high performance liquid chromatography (HPLC) with UV detection. The target list of the method includes 13 carbonyl compounds.

A2.2. IM -methods

VOC, or better HCs, have been sampled from the CVS Tedlar-bags immediately after the test phase through a non heated Teflon line (Prati et al., 2003a;[START_REF] Bolher | Méthode de calcul des consommations des bâtiments climatisés ConsoClim[END_REF]2005). Analysis has been performed by GC-FID (HP 5890). 18 hydrocarbons (from C1 to C8) have been calibrated individually with the corresponding compounds in pressurized calibration gas mixture. The calibration curves have shown a good linearity with a minimum correlation coefficient of 0.99. VOC's have been quantified without background correction.

PAHs sampling line is constituted by a particulate filter (Ø 47 mm Pallflex Fiberfilm T60A20) and a washed XAD-2 tube with a front amberlite load of 100 mg and a back amberlite load of 50 mg (Supelco ORBO 43). Both filters have been positioned upstream a pump and a volumetric counter. Diluted exhaust sample rate through this line was about 8 l/min. After sampling, sorbent tube was put in refrigerant (+2°C). The PAH compounds were Soxhlet extracted either from the filter and from the XAD tube with methylene chloride (CH 2 Cl 2 -DCM). In order to estimate sample losses because of hot extraction, a standard mix of 5 deuterated compounds (PAH -mix 31 containing naphtaleneD8, acenaphteneD10, phenantreneD10, criseneD12, perileneD12) diluted to 1 m g/ml in DCM is added to solution before Soxleth extraction. Then the extracted were concentrated by evaporation of the solvent in a rotary evaporator under vacuum to 1 cm 3 . The quantitative analysis of 16 and more PAHs have been done by GC-MS (gas chromatograph HP 5890 equipped with a mass spectrometer detector HP 5971) in SIM mode. Calibration method has been realised by using a standard mixture concentration (PAH mix 45) and diluting it with methylene chloride to obtain 5 points for calibration. The calibration curves have shown a good linearity with a minimum correlation coefficient of 0.98.

Carbonylic compounds sampling line is composed by a dynamic probe inserted in dilution tunnel, a filter holder to block particulate matter, a DNPH-cartridge, a pump and a volumetric counter. The flow rate in the cartridge was kept at about 1 liter/min. The carbonyl compounds, highly reactive, in the presence of 2,4-dinitrophenylhydrazine form the correspondent and much more stable hydrazones. The cartridges are Sep-Pak DNPH-silica cartridges short body (360 mg) by Waters. The cartridges after the test have been extracted by 5 cc of acetonitrile and stored in vials in a refrigerator at 2°C. The quantification of carbonylic compounds is performed by High Pressure Liquid Chromatography (HPLC) with UV detection. The chromatographic column was a Waters Nova-Pack C18 , 150 mm lenght and 3.9 mm as internal diameter. The evaluation of 15 carbonylic compounds (aldehydes/ketones) has been realized by using a standard mixture concentration containing 2,4-dinitrofenilidrazonici derivatives (Mix TO11/IP-6A DNPH Mix) and diluting it in acetonitrile to obtain five concentration points.

N2O analysis and quantification is carrier out by FT-IR (NICOLET).

A2.3. INRETS-US-ULCO-USTL methods

VOC measurement

The methods for VOC and carbonyl compounds are presented in [START_REF] Caplain | Measurement of non regulated pollutants from gasoline and diesel passengers cars[END_REF]2006;Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF]. VOC are sampled in the dilution tunnel using sorbent tubes: Carbotrap B and C, Carbosieve III for "light" hydrocarbons C 2 -C 6 and Tenax for semi-volatile hydrocarbons C 6 -C 12 . The tubes were transferred to CCM for analyses in a temperature controlled container at 0°C and were stored at -18°C before analysis. As soon as it's possible sorbent tubes are thermally desorbed before analysis by gas chromatography. The "light" compounds (C 1 -C 6 ) are separated on RT alumina Restek column and detected by FID Detector (Perkin Elmer).The "heavy" compounds (C 6 -C 15 ) are separated on a 5% diphenyl -95% dimethylpolysiloxane (DB5) column and detected by mass spectrometry (EM 640 Brucker), the identification is made by comparison of retention times and comparison of mass spectrum. The sampling was optimized by the use of two cartridges in series because during the first tests about 30% of light hydrocarbons are sampled on the second cartridge, by decreasing the sampling flow and by recording the background contribution of the air dilution (corrected, sum of quantities trapped on the two cartridges). By combining the two sets of speciation data we provided a profile of the gas phase hydrocarbons emissions from unleaded petrol and diesel fuel.

GC-FID (C2 to

Polycyclic Aromatic Hydrocarbons (PAH) measurement: US

The PAH were sampled at the end of the CVS dilution tunnel using two successive cartridges [START_REF] Paturel | Unit PAH emissions at exhausts of recenttechnology light vehicles in Europe[END_REF]2005;[START_REF] Devos | Exhaust emissions of PAHs of passenger cars[END_REF]Joumard et al., 2004a;[START_REF] André | The ARTEMIS European driving cycles for measuring car pollutant emissions[END_REF]. The packing, Teflon wool and Amberlite XAD2 resin were purified in the laboratory by two successive Soxhlet cycles of 8 hours with cyclohexane. The two sampling media were subjected to special treatment before the analysis as such, i.e. extraction of PAH from the media by an organic solvent, concentration of the extract and purification of the matrix obtained. After evaporation under nitrogen flow until the eluate was almost dry, the purified sample was retreated with 0.5ml acetonitrile.

The study was carried out on a Merck-Hitachi chromatograph equipped with a LiChroCart column, fed by an injection loop with a volume fixed at 20µl and coupled with adsorption and fluorescence spectrometers. Elution was performed using ACN/H2O in mobile phase at a flow of 1ml/mn.

A2.4. KTI methods

Ammonia

Sampling: at the end of the dilution tunnel of the CVS using diluted sulfuric acid absorbing agent in two series connected recipients being cooled by melting water Analysis: Gas Chromatography (HP 5890)

A2.5. VTT methods

Hydrocarbon speciation (13 compounds up to C8) from bag samples with GC (Hewlett-Packard)

Exhaust gas was diluted with CVS unit and a part of the diluted exhaust gas was collected to tedlar bags (the same as used for regulated emissions). The samples from tedlar bags were taken immediately after the test phase through direct lines to the gas chromatograph (GC: HP 5890 Series II, sample loop of 2 cm3). The gas was dried using an inline CaCl2 drying tube. Hydrocarbons from C1 to C8 were identified by retention times and quantitative analysis was done by external standard method. The standard gas mixtures were used including methane, ethene, propene, i-butene, ipentane, 1,3-butadiene, benzene, toluene, ethylbenzene, m-, p-and o-xylenes. Programmed heating starting from +60°C, 50 m x 0.53 mm ID x 10 µm df Al2O3/KCl PLOT fused silica column added with a particle trap (Chrompack Particle Trap 2.5 m x 0.53 mm ID x 10 µm df).

Aldehydes

Aldehyde samples were collected from the diluted exhaust gas (CVS) by using dinitrophenylhydrazine (DNPH) cartridges. The DNPH derivatives were extracted with acetonitrile/water mixture. Altogether 11 aldehydes (formaldehyde, acetaldehyde, acrolein, propionaldehyde, crotonaldehyde, methacrolein, butyraldehyde, benzaldehyde, valeraldehyde, mtolualdehyde, hexanal) were analyzed with the HPLC-technology (HP 1050, UV detector, Nova-Pak C18 column).

Analysis of unregulated components with SESAM/FTIR: N2O, NO/NO2, NH3 and formaldehyde.

The on-line multicomponent analysis was made using Siemens Sesam II Fourier transform infrared (FTIR) which monitors simultaneously 20 gaseous emission components at one second time interval. Hot, filtered raw exhaust was monitored. Each component is multipoint calibrated The primary result is vol-ppm which is converted into mg/km using the momentary dilution ratio which is obtained from tracers measuring raw and diluted CO 2 .

Annex 3: Average characteristics of the vehicle samples

Annex 8: Artemis LVEM datasheet description

The Artemis LVEM datasheet provides the format that should be used when submitting data for the Artemis LVEM database. For every car involved, a separate copy of this Excel file should be used. Dark grey or red cells denote cells that need to be filled. A light grey background marks cells which can be filled optionally. The Artemis LVEM datasheet contains five sheets:

-README: provides additional information and help on how to use the datasheet.

-car: summarises the characteristics of the tested vehicle. The sheet is divided into eight sections:

-laboratory name -vehicle data (e.g., make, model, fuel type) -official emission level data -chassis dynamometer and fan settings -engine data (e.g., capacity, number of cylinders) -gear box data (e.g., type, number of speeds) -aftertreatment system (information on catalysts) -emission units (for regulated and unregulated pollutants)

-test xx: describes the test characteristics of the tested car. One copy of this sheet is needed for each cycle tested. The sheet is divided into nine sections:

-lab and car identification (linked to car sheet except for sulphur and benzene content) -chassis dynamometer (linked to car sheet, exceptions of test xx need to be entered manually) -ambient conditions (ambient pressure ,temperature and humidity) -fan (linked to car sheet, exceptions of test xx need to be entered manually) -test data (e.g., test number, test date) -test results (e.g., actual distance and duration, bag values. Add a row for each pollutant.) -drift measurements -instantaneous data (file format, content of the file) -additional questions (space for further comments)

-instantaneous data test xx: contains instantaneous data as a function of time. The use of this sheet for instantaneous data is recommended but not compulsory.

-pollutant names: lists the name convention for unregulated pollutants.

Annex 9: Atmospheric exhaust pollutants according to their toxicity

List of atmospheric pollutants emitted by exhaust pipes [START_REF] Cassadou | Selection des agents dangereux à prendre en compte dans l'evaluation des risques sanitaires lies aux infrastructures routieres (selection of hazardeous compounds to take into account for the health risk assessment from road infrastructures)[END_REF], with their classification of the Academy of sciences (CAS) from http://chemfinder.cambridgesoft.com/. On the left columns the main and secondary level pollutants are indicated, together with the PAH belonging to the groups of the 6 most carcinogenic, 12 least volatile, and 4 most volatile PAHs. All these pollutants are in a green line. [START_REF] Carter | The SAPRC 99 chemical mechanism and updated VOC reactivity scales[END_REF]. [START_REF] Flandrin | Mesure des emissions automobiles de composes organiques volatils et de derives carbonyles[END_REF].

Annex 12: Comparison of emissions measured and modelled with instantaneous models

Annex 13: Kinematic models

The response considered by all models presented in this annexe is the unit emission mass of CO, HC, NO X and CO 2 expressed in (g/km), a log-transform of Y was applied. Log transform of Y's observed data are scaled to unit variance (dividing by standard deviation) before model calculation.

The basic idea and mathematics of models are illustrated into details in Rapone (1995).

Explicative variables characterize the kinematics of driving cycles, variables were divided into two conceptually meaningful blocks, then the following two basic regression equations were developed:

! + + + + + + + + = INVDIST a Tidle a Trunning a POS MVA a MV a MV a MV a a Y 7 6 5 4 3 2 1 0 _ 3 2 ln
(1)

! + + + + + = 7 101 _ ... 2 20 _ 1 20 _ ln 42 2 1 0 a V FS b a V FS b a V FS b b Y (2)
where random noise ε is assumed to be a random variable normally distributed ε ~ N(0,σ 2 ).

X's data are centred (subtracting mean) and scaled to unit variance (dividing by standard deviation).

Considering the number of X-variables, the most of which are correlated, it is convenient to utilize a regression method based on principal components (PC), which are latent variables function of original variables and orthogonal each other. In particular, the sparse matrix of data and the presence of missing values suggested to apply the Partial Least Square method and the NIPALS algorithm to estimate the regression model. Moreover, because response variables Y's may be correlated, a multivariate response Y (whose components are CO, CO 2 , HC, NO X ) was considered and a multivariate PLS method applied (Tenenhaus,1998). To consider both the contributes of the two blocks in one model, a Hierarchical Multi-block PLS method [START_REF] Westerhuis | Analysis of Multiblock and Hierarchical PCA and PLS Models[END_REF]) is adopted. Following this approach, a set (t 1 , t 2 ,…, t k ) of principal components (X-scores) is estimated separately for each block of variables, fitting a PLS base model to each block. Then, the superblock regression model (named top-model) is built, by applying the PLS regression of Y-variables on super-scores made by the union of scores of the two base models.

The regression equation for top model is the following:

! C C C C C C C C = " ' ' ' ' . 2 .. . 2 . 2 . 1 .. . 2 . 1 ln 2 2 1 1 2 2 1 1 k K k k k k T M C T M C T M C T M C T M C T M C Y (3)
where k' is the number of scores of the first block and k" is the number of scores of the second block, calculated by the fit of models to data as a function of X, and K= k'+k". Thus the top model estimates the coefficients C K and the predicted values of regression of lnY on the full set of Xvariables made of the two blocks, as it can be argued by the following equations:

! + + + + + = INVDIST w mv w mv w mv w T M i i i i i 7 3 2 1 ...... 3 2 . 1 i=1, 2,..k' (4)
! + + + + + = 7 101 ...... 3 20 2 20 1 20 1 . 2 42 3 2 1 a FSv w a FSv w a FSv w a FSv w T M i i i i i i=1, 2,…k'' (5)
A logarithm transform was applied to the response Y , thus quantities predicted by model fit to data as lnY are to be retransformed in original scale to get emission factors. The following naïve estimate was used to calculate model expectations:

e(p, veh. class) [g/km] = ˆ Y = exp ln ) Y + RMSE ˆ E ( ) 2 /2 " # $ % & '
where Y ln is the quantity calculated by putting coefficients calculated by model fit for each case model 2 Where M*.t* are the scores of the two model base. Moreover t* = Xw where w is the weight matrix of X as detailed shown in the equation ( 4) and ( 5).

CO [g/km] = exp [-2,69653 -0,0809634 • FS_V100xaccel1 -0,0372766 • FS_V100xaccel2 -0,0463132 • FS_V100xaccel3 -0,0196432 • FS_V100xaccel4 -0,0402573 • FS_V100xaccel + 50,040295 • FS_V100xaccel6 -0,0708533 • FS_V100xaccel7 -0,116826 • FS_V101xaccel + 10,0501926 • FS_V101xaccel2 + 0,101095 • FS_V101xaccel3 + 0,0771687 • FS_V101xaccel4 + 0,103904 • FS_V101xaccel5 + 0,0135185 • FS_V101xaccel6 -0,0924244 • FS_V101xaccel7 -0,0194603 • FS_V20xaccel1 -0,0148916 • FS_V20xaccel2 -0,0307388 • FS_V20xaccel3 -0,00532937 • FS_V20xaccel4 -0,038722 • FS_V20xaccel5 -0,0190686 • FS_V20xaccel6 + 0,00451553 • FS_V20xaccel7 + 0,00433718 • FS_V40xaccel1 -0,0104214 • FS_V40xaccel2 -0,0555255 • FS_V40xaccel3 -0,0657504 • FS_V40xaccel4 -0,0332443 • FS_V40xaccel5 -0,0323581 • FS_V40xaccel6 -0,00303055 • FS_V40xaccel7 + 0,00172708 • FS_V60xaccel1 + 0,0103377 • FS_V60xaccel2 -0,0258137 • FS_V60xaccel3 -0,00246483 • FS_V60xaccel4 + 0,0139397 • FS_V60xaccel5 + 0,00256516 • FS_V60xaccel6 + 0,0184218 • FS_V60xaccel7 -0,070793 • FS_V80xaccel1 + 0,0796336 • FS_V80xaccel2 -0,0365832 • FS_V80xaccel3 -0,0492469 • FS_V80xaccel4 -0,0190527 • FS_V80xaccel5 + 0,0157882 • FS_V80xaccel6 + 0,0659957 • FS_V80xaccel7 + (1,27928) 2 /2]; CO2 [g/km] = exp [ 5,33001 + 0,00320509 • FS_V100xaccel1 + 0,00274146 • FS_V100xaccel2 + - 0,0208135 • FS_V100xaccel3 + -0,0169727 • FS_V100xaccel4 + -0,0151693 • FS_V100xaccel5 + 0,0406771 • FS_V100xaccel6 + 0,0484331 • FS_V100xaccel7 + -0,00421207 • FS_V101xaccel1 + -0,00699693 • FS_V101xaccel2 + -0,00512029 • FS_V101xaccel3 + - 0,00942676 • FS_V101xaccel4 + 0,00528796 • FS_V101xaccel5 + 0,00925922 • FS_V101xaccel6 + -0,00393966 • FS_V101xaccel7 + 0,00134039 • FS_V20xaccel1 + 0,0149632 • FS_V20xaccel2 + 0,0285225 • FS_V20xaccel3 + 0,0274375 • FS_V20xaccel4 + 0,0324384 • FS_V20xaccel5 + 0,00247971 • FS_V20xaccel6 + 0,0193321 • FS_V20xaccel7 + 0,0453323 • FS_V40xaccel1 + 0,0148512 • FS_V40xaccel2 -0,0276028 • FS_V40xaccel3 - 0,0136647 • FS_V40xaccel4 -0,0155461 • FS_V40xaccel5 -0,00961286 • FS_V40xaccel6 + 0,0330918 • FS_V40xaccel7 -0,0884033 • FS_V60xaccel1 + 0,0112486 • FS_V60xaccel2 - 0,0449015 • FS_V60xaccel3 -0,0263389 • FS_V60xaccel4 -0,030274 • FS_V60xaccel5 + 0,0179473 • FS_V60xaccel6 -0,0781766 • FS_V60xaccel7 + 0,021587 • FS_V80xaccel1 + 0,0740122 • FS_V80xaccel2 -0,0160819 • FS_V80xaccel3 -0,0242275 • FS_V80xaccel4 + 0,00723459 • FS_V80xaccel5 + 0,0407075 • FS_V80xaccel6 -0,0685132 • FS_V80xaccel7 + (0,136814)2/2]; HC [g/km] = exp [-4,34117 -0,0409743 • FS_V100xaccel1 -0,0169436 • FS_V100xaccel2 -0,0588976 • FS_V100xaccel3 -0,0383996 • FS_V100xaccel4 -0,0475969 • FS_V100xaccel5 + 0,081687 • FS_V100xaccel6 + 0,0193362 • FS_V100xaccel7 -0,0735987 • FS_V101xaccel1 + 0,0184655 • FS_V101xaccel2 + 0,0484907 • FS_V101xaccel3 + 0,0291051 • FS_V101xaccel4 + 0,0649944 • FS_V101xaccel5 + 0,0208593 • FS_V101xaccel6 -0,0617134 • FS_V101xaccel7 + 0,000213868 • FS_V20xaccel1 + 0,0208999 • FS_V20xaccel2 + 0,0309904 • FS_V20xaccel3 + 0,0416199 • FS_V20xaccel4 + 0,032574 • FS_V20xaccel5 + 0,00119606 • FS_V20xaccel6 + 0,0389355 • FS_V20xaccel7 + 0,0717529 • FS_V40xaccel1 + 0,0226889 • FS_V40xaccel2 - 0,0650945 • FS_V40xaccel3 -0,0512134 • FS_V40xaccel4 -0,035332 • FS_V40xaccel5 - 0,0265652 • FS_V40xaccel6 + 0,0588915 • FS_V40xaccel7 -0,117034 • FS_V60xaccel1 + 0,0244123 • FS_V60xaccel2 -0,0851562 • FS_V60xaccel3 -0,0437449 • FS_V60xaccel4 - 0,0373423 • FS_V60xaccel5 + 0,0288276 • FS_V60xaccel6 -0,11324 • FS_V60xaccel7 - 0,00873983 • FS_V80xaccel1 + 0,138059 • FS_V80xaccel2 -0,0551315 • FS_V80xaccel3 - 0,0710085 • FS_V80xaccel4 -0,0134046 • FS_V80xaccel5 + 0,0603888 • FS_V80xaccel6 - 0,0764631 • FS_V80xaccel7 + (0,97709) 2 /2]; NOX [g/km] = exp [-3,84669 -0,0312936 • FS_V100xaccel1 -0,0172218 • FS_V100xaccel2 -0,0359896 • FS_V100xaccel3 -0,0248533 • FS_V100xaccel4 -0,031327 • FS_V100xaccel5 + 0,0344145 • FS_V100xaccel6 -0,0232601 • FS_V100xaccel7 -0,0522676 • FS_V101xaccel1 + 0,00499107 • FS_V101xaccel2 + 0,0200052 • FS_V101xaccel3 + 0,0117509 • FS_V101xaccel4 + 0,0281851 • FS_V101xaccel5 + 0,00223383 • FS_V101xaccel6 -0,0492098 • FS_V101xaccel7 + 0,00992933 • FS_V20xaccel1 + 0,0161572 • FS_V20xaccel2 + 0,017766 • FS_V20xaccel3 + 0,0236112 • FS_V20xaccel4 + 0,0176995 • FS_V20xaccel5 + 0,00824783 • FS_V20xaccel6 + 0,0273362 • FS_V20xaccel7 + 0,041514 • FS_V40xaccel1 + 0,0191147 • FS_V40xaccel2 - 0,0245991 • FS_V40xaccel3 -0,0137067 • FS_V40xaccel4 -0,00693182 • FS_V40xaccel5 - 0,00379126 • FS_V40xaccel6 + 0,0392298 • FS_V40xaccel7 -0,0334126 • FS_V60xaccel1 + 0,0291515 • FS_V60xaccel2 -0,0243012 • FS_V60xaccel3 -0,010852 • FS_V60xaccel4 - 0,00105535 • FS_V60xaccel5 + 0,0332561 • FS_V60xaccel6 -0,0620697 • FS_V60xaccel7 - 0,019655 • FS_V80xaccel1 + 0,0544703 • FS_V80xaccel2 -0,0344784 • FS_V80xaccel3 - 0,039486 • FS_V80xaccel4 -0,0155741 • FS_V80xaccel5 + 0,022913 • FS_V80xaccel6 - 0,0542544 • FS_V80xaccel7 + (0,952002) 2 /2]; model 3 CO [g/km] = exp [-2,5151 + -0,253973 • M1.t1 + 0,25643 • M1.t2 + 0,0192314 • M1.t3 + -0,0900483 • M2.t1 + 0,0727555 • M2.t2 + 0,147771 • M2.t3 + 0,0140601 • M2.t4 + -0,167815 • M2.t5 + 0,0426933 • M2.t6 + 0,142154 • M2.t7 + (1, 26823) 2 /2]

FS_V100xaccel1

-0,0601457 0,171516 -0,215116 -0,088999 0,183235 0,0642824 -0,241913

FS_V100xaccel2

-0,14579 0,111674 -0,00639852 0,00225961 0,152912 -0,227425 -0,0163727

FS_V100xaccel3

-0,17877 -0,0504258 -0,128454 -0,00634528 0,0600539 -0,309946 0,0997569

FS_V100xaccel4

-0,182547 -0,0591483 -0,126135 -0,000434923 0,0100756 -0,2538 0,181222

FS_V100xaccel5

-0,18419 -0,0182767 -0,12768 -0,0066852 0,0908313 -0,249793 0,0842607

FS_V100xaccel6

-0,132899 0,16862 0,210052 0,0879018 0,216464 -0,0993568 0,0422321

FS_V100xaccel7

-0,0265885 0,0705269 -0,357088 0,149617 0,112532 0,320831 -0,0402255

FS_V101xaccel1

-0,0583485 0,154504 -0,282579 -0,0604535 0,124475 0,0832292 -0,321722

FS_V101xaccel2

-0,078063 0,213259 -0,0659814 -0,160076 -0,0592438 0,168383 0,00980888

FS_V101xaccel3

-0,151354 0,211423 0,0828917 -0,0993271 -0,209744 0,152257 0,249583

FS_V101xaccel4

-0,1523 0,200459 0,061751 -0,132611 -0,262248 0,133923 0,243858

FS_V101xaccel5

-0,138176 0,238932 0,133972 -0,0598309 -0,166677 0,23636 0,218943

FS_V101xaccel6

-0,128729 0,204696 0,0194507 -0,0500324 0,098282 -0,00823522 -0,117535

FS_V101xaccel7

-0,0564239 0,119683 -0,322545 -0,0405908 0,0877616

FS_V60xaccel7

-0,0940662 -0,0404515 0,0799789 0,0886469 -0,449371 -0,0667802 -0,444941

FS_V80xaccel1

-0,136739 0,166535 -0,0128682 0,02164 0,193773 -0,151076 -0,163228

FS_V80xaccel2

-0,137073 -0,11237 0,263839 0,436268 0,0824722 0,195137 0,0649798

FS_V80xaccel3

-0,167882 -0,328256 -0,107333 0,134418 -0,0418911 0,0123108 0,000641246

FS_V80xaccel4

-0,167647 -0,346333 -0,135514 0,0720638 -0,0578164 -0,106407 -0,0500598

FS_V80xaccel5

-0,155134 -0,300871 -0,0391485 0,206091 0,0589178 0,161089 0,00723368

FS_V80xaccel6

-0,163674 -0,0261229 0,259459 0,227104 0,187194 0,0111906 -0,133109

FS_V80xaccel7

-0,108869 -0,0170015 0,0282064 0,133633 -0,459025 0,0075367 -0,340058 Urban 5a: National / regional motorway in an urban area (strategic network) The extrapolated figures are coloured:

-Blue: based on 1.4-2 l and on the global ratio 1.4-2 l / >2 l -Purple: based on 1.4 -2 l and RTP 15 -Green: based on 1.4 -2 l and RTP 1 and 7 -Brown: based on 1.4 -2 l and RTP 9 and 11 -Red: based on Euro 3 and ratio with 1.4 -2 l. Formulae of 2 nd order polynomial function is: emis. factor [g/km] = a 0 +a 1 V+a 2 V 2 Formulae of 3 rd order polynomial function is: emis. factor [g/km] = a 0 +a 1 V+a 2 V 2 +a 3 V 3 Formulae of 4 th order polynomial function is: emis. factor [g/km] = a 0 +a 1 V+a 2 V 2 +a 3 V 3 +a 4 V 4 Formulae of 5 th order polynomial function is: emis. factor [g/km] = a 0 +a 1 V+a 2 V 2 +a 3 V 3 +a 4 V 4 +a 5 V 5 with V [km/h] For V > 125 km/h, EF(V) = EF(125)

Annex 18: Conversion ratios between standards based on Reference Test Pattern emissions

Annex 22: Hot emission factors for unregulated VOCs and PAHs

Only ambient temperatures >18°C are considered.

HC E= 9,92x10 -5 v 2 -1,15x10 -2 v + 0,485 NOx E= 4,41x10 -4 v 2 -4,46x10 -2 v + 1,69 Pre-Euro-1 PM 1 E= 5.8x10 -5 v 2 -0.0086v+0.45 CO 0-25 E= (-3,83x10 -6 v 2 + 3,30x10 -4 v + 1,28x10 -2 )p+1,84x10 -4 v 2 -2,50x10 -2 v + 1,26
CO 2 0-25 E= (-0,0012v 2 + 0,0654v + 6,0995)p+(0,0249v 2 -2,3223v + 176,92) FC 7-25 E= (-5,86x10 -5 v 2 + 8,84x10 -3 v -4,91x10 -1 )p 2 +(0,0019v 2 -0,2989v + 18,565)p+ 0,4963v -32,605 HC 3 7-25 E= (2,42x10 -5 v -2,43x10 -3 )p 2 +( -8,44x10 -4 v + 8,41x10 -2 )p+ 0,0041v -0,3375; E≥0,04 NOx 7-25 E= (4,80x10 -5 v -6,80x10 -3 )p 2 +( -1,73x10 -3 v + 0,246)p+1,02x10 -2 v -0,955

Euro-1

PM 2 E= -1,71x10 -5 v 2 + 2,419x10 -3 v + 2,31x10 -2 CO E= 8,66x10 -5 v 2 -1,56x10 -2 v + 0,912 CO 2 E= 0,0245v 2 -3,4055v + 273,56 FC E= 8,35x10 -3 v 2 -1,20v + 84,3 HC E= 3,47x10 -5 v 2 -6,17x10 -3 v + 0,293 NOx E= 2,23x10 -4 v 2 -2,89x10 -2 v + 1,47 N1-I Euro-2 PM E= 1,50x10 -5 v 2 -2,19x10 -3 v + 0,113 CO 0-62 E= (-2,11x10 -7 v 2 + 3,76x10 -5 v -2,03x10 -3 )p 2 +(1,33x10 -5 v 2 -2,70x10 -3 v + 0,161)p+(1,46x10 -4 v 2 -1,14x10 -2 v + 0,398) CO 2 0-62 E= (6,21x10 -6 v 2 -2,35x10 -3 v + 7,58x10 -2 )p 2 +( 2,49x10 -4 v 2 + 7,25x10 -2 v -4,04 )p+0,0211v 2 -3,7377v + 357,35 FC 0-62 E= (2,45x10 -6 v 2 -6,32x10 -4 v + 2,72x10 -2 )p 2 +(2,25x10 -2 v -1,77 )p+(7,98x10 -3 v 2 -1,31 v + 125) HC 3 6-62 E= (-5,29x10 -8 v 2 + 1,09x10 -5 v -8,56x10 -4 )p 2 +(3,48x10 -6 v 2 -7,24x10 -4 v + 5,76x10 -2 )p -4,39x10 -5 *v 2 + 5,54x10 -3 v -0,138; E≥0,05 NOx 3 0-62 E= (-3,23x10 -5 v + 2,42x10 -3 )p 2 +(1,41x10 -3 v -6,83x10 -2 )p-1,17x10 -2 v + 1,61; E≥0,5 Diesel N1-II Pre-Euro-1 PMm 6-50 4 E= (-1,33x10 -8 v 2 + 9,48x10 -6 v -1,02x10 -3 )p 2 +(4,72x10 -6 v 2 -1,19x10 -3 v + 8,35x10 -2 )p+2,82x10 -6 v 2 + 5,36x10

!

: density of fuel (kg/l): 0.766 kg/l for petrol and 0.8414 kg/l for diesel.

f clim,i : fraction of vehicles equipped with air conditioning in segment i. The fraction of vehicles equipped with AC is calculated with the penetration rate (pr AC,i ). Value of pr AC,I are given for the France in Annex 3 [START_REF] Hugrel | The contribution of passenger cars to the greenhouse effect : influence of air-conditioning and ACEA's commitment (JAMA and KAMA included) on the CO 2 emissions[END_REF]. n i,TS,T,loc : number of vehicles belonging to segment i, at the situation of traffic TS, at time T, and at location loc: The formula below describes the cold distance d c (km) as a function of the average speed V (km/h) and the temperature T (°C). The results of this formula must be positive. Annex 34: Equation of the cold start excess emission ω(T,V) and correction coefficients f(V,T)

The equation describes the influence of the mean speed V [km/h] and the ambient temperature T [°C] on excess emission ω(T,V) [g] and the associated dimensionless correction coefficients f(V,T). This equation results in a 3D linear regression (best fitted plan). VOCs considered, the sum of the five 1 st level VOCs, all BaP exluded, and the sum of the 6 most carcinogenic PAHs (see Table 10 and Table 13 the definition of these compounds).58 Figure 18: NO and NO 2 emissions of various technologies and emission standards. The values are derived from a mixture of driving situations (urban with a cold start, rural and highway), as measured by TNO Automotive [START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF]

  . OTHER ARTEMIS MEASUREMENTS ........................................................................................................................... 2.3. EXTERNAL DATA ....................................................................................................................................................... 2.4. ARTEMIS LIGHT VEHICLE EMISSION MEASUREMENT DATABASE (ARTEMIS LVEM DB) ......................................

  GENERAL SHAPE........................................................................................................................................................ 3.1.1. Shape of the emission model............................................................................................................................ Hot emission................................................................................................................................................................................... Cold start emission......................................................................................................................................................................... Evaporation ....................................................................................................................................................................................

  Classification of VOCs according to their direct toxicity ........................................................................................................... Classification of VOCs according to their ozone forming potential .......................................................................................... VOCs reported as emission factors...............................................................................................................................................

  Validation .......................................................................................................................................................................................

  . KINEMATIC REGRESSION MODEL..............................................................................................................................

  . TRAFFIC SITUATIONS MODEL....................................................................................................................................

  . AVERAGE SPEED MODEL ...........................................................................................................................................

  Petrol vehicles ................................................................................................................................................................................ Diesel vehicles ............................................................................................................................................................................... 3.6. UNREGULATED POLLUTANTS OF PASSENGER CARS .................................................................................................

  urbain fluide court VP fa/fo mot. urbain & aut.

Figure 4 :

 4 Figure 4: Simulation quality for the engine-out emission factors of the three Euro 3 petrol vehicles with the EMPA instantaneous emission model. A point represents a driving pattern.

Figure 5 :

 5 Figure 5: Simulation quality for the emission factors of the average Euro 3 petrol car in the instantaneous model PHEM.

Figure 6 :

 6 Figure 6: Diagram of driving cycles used for developing the kinematic model, ordered by mean speed.

Figure 9 :

 9 Figure 9: Variability of the test driving cycles and 15 Reference Test Patterns as regards driving speed and acceleration.

Figure 10 :

 10 Figure 10: Variation of the pollutant emissions (NOx of Euro 3 Diesel) according to the 15 Reference test patterns.

Figure 11 :

 11 Figure 11: Dynamic influence on the NOx emissions for petrol cars. Stable and unstable Reference test patterns are defined in Annex 16.

  of hundreds traffic situations are computed by linear combination (Annex 19) of the reference emissions of the closest Reference test patterns, as defined in the model 2 (Annex 17). These emission factors are illustrated in Figure 12 according to some vehicle classes and for the four traffic conditions described in Figure 8. The use of the model 1 to calculate these emission factors is possible but needs a new computation of the combination factors (compared to those given in Annex 19).

Figure 13 :

 13 Figure 13: Petrol and diesel Euro 3 NOx emission functions according to average speed, as designed through speed range averages, with the number of data per average emission (clustered), and comparison with Copert 3 functions.

Figure 14 :

 14 Figure 14: Petrol and diesel Euro 3 NOx emission functions and data according to average speed, as designed through Reference Test Patterns or through speed range averages.

Figure 15 :

 15 Figure 15: Total VOCs and considered VOCs only, compared to total hydrocarbons.

Figure 16 :

 16 Figure 16: Benzene emission factors in the hot-start tests at temperature >18°C. Each marker represents a single test. "average" is the arithmetic mean for each emission class of cars; na=not available.

Figure 17 :

 17 Figure 17: Influence of the emission standard on the emission factors of the sum of the eightVOCs considered, the sum of the five 1 st level VOCs, all BaP exluded, and the sum of the 6 most carcinogenic PAHs (see Table10and Table13the definition of these compounds).

Figure 20 :

 20 Figure 20: Emission factors of the total particle population for conventional diesel Euro 1 to 3 vehicles and EN590:2000 diesel fuel. The average emissions of each individual vehicle are also plotted as dots. The error-bars correspond to the minimum and maximum result obtained for each vehicle category.

Figure 21 :

 21 Figure 21: Source laboratory of Light Duty Vehicle data.

Figure 22 :

 22 Figure 22: Average emission for the vehicle group N1-III diesel Euro 1, as a function of average speed only.

Figure 23 :

 23 Figure 23: Coefficient a' of the HC emission of group N1-III diesel Euro 1 as a function of average speed.

Figure 24 :Figure 25 :

 2425 Figure 24: Statistical validation of the emission as a function of loading rate and average speed for the HC emission of group N1-III diesel Euro-1.

Figure 26 :

 26 Figure 26: NO x degradation according to petrol vehicle mileage in urban driving behaviour.

Figure 27 :

 27 Figure 27: Influence of the ambient temperature on the NOx emissions of Euro 3 petrol cars over the Artemis urban driving cycle.

Figure 28 :

 28 Figure 28: 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).

Figure 29 :

 29 Figure 29: Measured road gradient NOx factors for Euro 3 diesel vehicles on different road gradients.

  Examples of results for Euro 3 vehicles and NOx are shown in Figure30.

Figure 30 :

 30 Figure 30: Measured NOx loading factors for Euro 3 diesel vehicles for different loading situations.

Figure 31 :

 31 Figure 31: Comparison of measured and HBEFA road gradient factors for diesel vehicles and NOx.

Figure 32 :

 32 Figure 32: Comparison of simulation and measured road gradient factors for Euro 3 diesel vehicles.

Figure 33 :

 33 Figure 33: Loading factors for different driving situations for diesel Euro 3 vehicle and NOx.

Figure 34 :

 34 Figure 34: Progression of the road gradient and loading factors for Euro 3 diesel vehicle and NOx.

Figure 35 :

 35 Figure 35: Excess fuel consumption (l/h) due to AC versus outside temperature (°C), with linear regression.

Figure 36 :

 36 Figure 36: Comparison of the results from model and from experiments as a function of outside temperature for two internal temperatures (20 and 23°C).

Figure 37 :

 37 Figure 37: Comparison of the Mobile 6 model (upper curve for daytime and lower curve for night) with the proposed model (set temperature at 23°C).

  i,TS,T,loc : number of vehicles with AC running for segment i, at the traffic situation TS (i.e. urban, rural, highway), at the time T, at the location loc, expressed in number of vehicle per hour. hfc: hourly fuel consumption depending on the hour of the day, external temperature and internal temperature (l/h).c CO2,i : transformation factor from fuel to CO 2 depending on vehicle segment i.

Figure 38 :

 38 Figure 38: NOx excess emission versus NOx emission AC off according to the fuel and driving cycle for Euro 1 vehicles, for urban ECE15 and extra-urban EUDC driving cycles, with the corresponding modelling.

Figure 39 :

 39 Figure 39: Evolution of the instantaneous emission of a vehicle according to travelled distance ingiven running conditions, together with the emission per cycle.

Figure 40 :

 40 Figure 40: Calculation of hot emission factor and cold excess emission from the emission measured using repeated cycles, for given running conditions.

Figure 41 :

 41 Figure 41: Standard deviation method for calculating the cold start excess emission: Example of cold start distance and emission calculation for Euro 1 diesel vehicle and CO at 18°C. The distance is in km and the emission in g per cycle.

Figure 42 :

 42 Figure 42: Linear regression method for calculating the cold start excess emission: Example of cold start distance and emission calculation.

Figure 43 :

 43 Figure 43: Artemis method for calculating the cold start excess emission: Example of calculation of the rough and exact cold start distances.

Figure 44 :

 44 Figure 44: Artemis method: Example of cold start excess emission calculation.

  20 °C,20 km/h .f(T,V).h(δ).g(t) with: EE (T, V, δ): excess emission in mass per start T: temperature (°C) V: average speed (km/h) δ = d/d c : dimensionless travelled distance d: travelled distance (km) d c : cold distance (km) t: parking time ω 20°C,20km/h : excess emission at 20 °C and 20 km/h f(T,V): cycle speed and the temperature influence dimensionless function, with f(T,V) = ω(T,V)/ ω 20°C,20km/h ω(T,V): cycle speed and the temperature influence function h(δ): distance influence function g(t): parking-time influence function

Figure 45 :

 45 Figure 45: Parking duration influence on the total excess CO emission for petrol cars with catalyst.

  from Euro 3 diesel **: computed from Euro 4 no DISI petrol

Figure 46 :

 46 Figure 46: CO 2 cold start unit excess emission according to vehicle type and average speed.

  C6): ULCO Thermodesorber: Cartridge desorption: 7,5 min at 250 °C; Cold trap desorption: 1min at 300 °C; Transfer line: 200 °C; GC/FID: Air pressure: 2 bars, Hydrogen pressure: 3,3 bar, Column : RT alumina Restek (50 m*0.53*1µm,Al 2 O 3 /KCl), Carrier gas: Nitrogen, Pressure: 8 Psis, T° détector : 250°C, Temperature programm : 35 °C during 5 min, 5°C/min until 110°C, 10°C/min until 200°C, 200°C during 40 min GC-MS (C6 to C15): ULCO Detection mass : 40 à 300 u.m.a., Column: JW Scientific DB5 (25 m x 0,32 mm x 0,25 µm), (5% diphényl et 95% diméthylpolysiloxane), Carrier gas : Nitrogen, Pressure : 0,3 bar, T° cap : 200°C, T° detector : 200°C, T° connector : 200°C, T° Interface : 200°C, T° thermodesorber: 220°C, Desorption time: 5 min, Injection time: 8 s, Injector purge : 10 min, Temperature: 35 °C during 5 min, 5°C/min until 220°C, 220°C during 20 minCarbonyl compounds measurement: USTLCarbonyl Compounds are sampled by the method of derivatisation using 2,4 dinitrophenylhydrazine (2,4DNPH) traps. Emissions were passed on cartridges filled with C18 phase impregnated with acidified 2,4 -DNPH. After elution with 2ml of acetonitrile the sample is analysed by High Performance Liquid Chromatography (SPECTRA PHYSICS P4000) with an UV detection (365nm) (Spectra Focus 3000). Column: ALLTIMA C18 50 (250mm * 4.6mm), Flow : 1,2 ml/min, Volume : 20µl

  Analysis:Giving Nessler -reagent to the sample, evaluation by change of its colour by photocolorimetry at 440 nm VOC Sampling: at the end of the dilution tunnel of the CVS using Anasarb CSC cartridge filled by active carbon INRETS report n°LTE 0523 Analysis: Gas Chromatography (HP 5890) Aldehydes Sampling: at the end of the dilution tunnel of the CVS using a a two zones cartridge filled by silica gel impregnated by 2,4 DPNH Analysis: Liquid Chromatography (HP 1090) PAH Sampling: at the end of the dilution tunnel of the CVS using filtering (teflon wool)

Figure 47 :

 47 Figure 47: Shape of the driving cycles Artemis urban, rural and motorway (and motorway 130).

Figure 48 :

 48 Figure 48: Shape of the driving cycle EMPA BAB 1000.

Figure 49 :

 49 Figure 49: Shape of the driving cycles Handbook R1, R2, R3, and R4.

Figure 50 :

 50 Figure 50: Shape of the driving cycles Inrets fluide court and Inrets route court.

Figure 51 :

 51 Figure 51: Shape of the legislative driving cycle NEDC (ECE 15 + EUDC).

Figure 52 :

 52 Figure 52: Shape of the legislative driving cycle FTP 75.

Figure 53 :

 53 Figure 53: Shape of the driving cycle modem urban 5+7+13.

Figure 54 :

 54 Figure 54: Shape of the driving cycle modem Hyzem pure road.

Figure 55 :

 55 Figure 55: Shape of the driving cycles Napoli 10-23, 15-18-21, and 6-17.

Figure 56 :

 56 Figure 56: Shape of the driving cycle PVU commerciale grand routier (or LDV commercial cars motorway).

Figure 57 :

 57 Figure 57: Shape of the driving cycles PVU fourgon 3.5 t urbain lent, urbain fluide, livraison, route, and autoroute (or LDV 3.5 tons van slow urban, free-flow urban, delivery, rural, and motorway).

Figure 58 :

 58 Figure 58: Shape of the driving cycle TUG Ries Road Gradient used for determining the influence of the slope.

Figure 59 :

 59 Figure 59: Shape of the driving cycle VP faible motorisation urbain dense, urbain, urbain fluide, route, and autoroute (or Artemis low motorisation urban dense, urban, free urban, rural, and motorway).

Figure 60 :

 60 Figure 60: Shape of the driving cycle VP forte motorisation urbain dense, urbain, urbain fluide, route, and autoroute (or Artemis high motorisation urban dense, urban, free urban, rural, and motorway).

Figure 61 :

 61 Figure 61: Simulation quality for the emission factors of the diesel vehicle with the EMPA instantaneous emission model. A point represents a driving pattern.

Figure 62 :

 62 Figure 62: Simulation quality for the emission factors of the average Euro 2 diesel car with PHEM (engine maps from the Artemis driving cycle used for simulation of the Handbook driving cycles).

  the root mean square error i.e. the standard deviation of model residuals. km] = exp[ -4,44416 + 0,00557634 • mv + 5,54E-05 • mv2 + 4,95E-07 • mv3 + 0,00165646 • tidle + 0,000397725 • trunning + 0,131082 • mva + 14,4737 1/d + (1,29265) 2 /2]; CO2 [g/km] = exp [ 4,85823 -0,00208098• mv + 3,74E-06• mv2 + 1,18E-07• mv3 + 0,00261577• tidle -6,25E-05• trunning + 0,0288376• mva + 415,9681/d + (0

  ; CO2 [g/km] = exp [5,32258 + 0,0552223 • M1.t1 + 0,160689 • M1.t2 + -0,0285125 • M1.t3 + 0,0264709 • M2.t1 + 0,0322054 • M2.t2 + 0,0176457 • M2.t3 + 0,0401403 • M2.t4 + 0,0573429 • M2.t5 + 0,0318563 • M2.t6 + 0,013705 • M2.t7 + (0,132317) 2 /2]; HC [g/km] = exp [-4,12042 + -0,0109236 • M1.t1 + 0,467298 • M1.t2 + -0,0175954 • M1.t3 + 0,0152887 • M2.t1 + 0,0941628 • M2.t2 + 0,130208 • M2.t3 + 0,0715776 • M2.t4 + 0,00547747 • M2.t5 + 0,0782761 • M2.t6 + 0,108391 • M2.t7 + (0,962523) 2 /2]; NOX [g/km] = exp [-3,34465 + 0,0785951 • M1.t1 + 0,299503 • M1.t2 + 0,065518 • M1.t3 + 0,0560129 • M2.t1 + 0,0261385 • M2.t2 + 0,107069 • M2.t3 + 0,00155128 • M2.t4 + -0,0200644 • M2.t5 + 0,0231157 • M2.t6 + 0,0626605 • M2.t7 + (0,944445) 2 /2];

Figure 66 :Figure 67 :Figure 68 :

 666768 Figure 66: CO excess emission due to auxiliaries at full load for diesel and petrol vehicles.

Figure 69 :

 69 Figure 69: PM excess emission due to auxiliaries for diesel vehicles.

Figure 71 :Figure 72 :Figure 73 :

 717273 Figure 71: CO 2 cold start unit excess emission according to the ambient temperature and average speed.

  2 -weight matrix of X. ......................................................................... 148 Table 30: Urban road classification. ................................................................................ 149 Table 31: Rural road classification. ................................................................................. 150

Figure 1 :

 1 Figure 1: Simplified design of the database, including the 3 main tables and the 2 most important secondary tables. .......................................................................................................... 17 Figure 2: Shape of the emission model. ............................................................................. 25 Figure 3: Normalised mean square error for Euro 3 petrol vehicles (blue) and for average Euro 3 petrol car (red).............................................................................................................. 34 Figure 4: Simulation quality for the engine-out emission factors of the three Euro 3 petrol vehicles with the EMPA instantaneous emission model. A point represents a driving pattern..... 35 Figure 5: Simulation quality for the emission factors of the average Euro 3 petrol car in the instantaneous model PHEM.......................................................................................... 37 Figure 6: Diagram of driving cycles used for developing the kinematic model, ordered by mean speed. 39 Figure 7: Comparison of measured and calculated emissions of CO 2 [g/km] for the kinematic regression model 1 (PLS MG, dark red), model 2 (PLS MVA, blue) and model 3 (PLS MT, red), and for a average speed regression model (GLM, black) with the mean measured emission (mean, green) versus the driving cycle mean speed. ....................................... 41 Figure 8: Traffic conditions as regards speed and traffic flow (Lhuillier, 2004) . ............... 44 Figure 9: Variability of the test driving cycles and 15 Reference Test Patterns as regards driving speed and acceleration.................................................................................................. 46 Figure 10: Variation of the pollutant emissions (NOx of Euro 3 Diesel) according to the 15 Reference test patterns.................................................................................................. 48 Figure 11: Dynamic influence on the NOx emissions for petrol cars. Stable and unstable Reference test patterns are defined in Annex 16............................................................ 49 Figure 12: Traffic situation approach illustration: NOx and CO 2 emissions of cars have been estimated for an urban trunk road (speed limit: 50 km/h), at different traffic conditions, according to dedicated speed curves. ............................................................................ 50 Figure 13: Petrol and diesel Euro 3 NOx emission functions according to average speed, as designed through speed range averages, with the number of data per average emission (clustered), and comparison with Copert 3 functions. ................................................... 52 Figure 14: Petrol and diesel Euro 3 NOx emission functions and data according to average speed, as designed through Reference Test Patterns or through speed range averages.............. 53 Figure 15: Total VOCs and considered VOCs only, compared to total hydrocarbons......... 57 Figure 16: Benzene emission factors in the hot-start tests at temperature >18°C. Each marker represents a single test. "average" is the arithmetic mean for each emission class of cars; na=not available. .......................................................................................................... 58 Figure 17: Influence of the emission standard on the emission factors of the sum of the eightVOCs considered, the sum of the five 1 st level VOCs, all BaP exluded, and the sum of the 6 most carcinogenic PAHs (see Table10and Table13the definition of these compounds).58 Figure18: NO and NO 2 emissions of various technologies and emission standards. The values are derived from a mixture of driving situations (urban with a cold start, rural and highway), as measured by TNO Automotive[START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF]...................................................... 60 Figure 19: NO 2 percentage of NOx for various technologies and emission standards, including the ranges (minimum and maximum values) as measured by TNO Automotive and EMPA (Gense et al., 2006). ................................................................................................................. 60

Figure 2 :

 2 Figure 1: Simplified design of the database, including the 3 main tables and the 2 most important secondary tables. .......................................................................................................... 17 Figure 2: Shape of the emission model. ............................................................................. 25 Figure 3: Normalised mean square error for Euro 3 petrol vehicles (blue) and for average Euro 3 petrol car (red).............................................................................................................. 34 Figure 4: Simulation quality for the engine-out emission factors of the three Euro 3 petrol vehicles with the EMPA instantaneous emission model. A point represents a driving pattern..... 35 Figure 5: Simulation quality for the emission factors of the average Euro 3 petrol car in the instantaneous model PHEM.......................................................................................... 37 Figure 6: Diagram of driving cycles used for developing the kinematic model, ordered by mean speed. 39 Figure 7: Comparison of measured and calculated emissions of CO 2 [g/km] for the kinematic regression model 1 (PLS MG, dark red), model 2 (PLS MVA, blue) and model 3 (PLS MT, red), and for a average speed regression model (GLM, black) with the mean measured emission (mean, green) versus the driving cycle mean speed. ....................................... 41 Figure 8: Traffic conditions as regards speed and traffic flow (Lhuillier, 2004) . ............... 44 Figure 9: Variability of the test driving cycles and 15 Reference Test Patterns as regards driving speed and acceleration.................................................................................................. 46 Figure 10: Variation of the pollutant emissions (NOx of Euro 3 Diesel) according to the 15 Reference test patterns.................................................................................................. 48 Figure 11: Dynamic influence on the NOx emissions for petrol cars. Stable and unstable Reference test patterns are defined in Annex 16............................................................ 49 Figure 12: Traffic situation approach illustration: NOx and CO 2 emissions of cars have been estimated for an urban trunk road (speed limit: 50 km/h), at different traffic conditions, according to dedicated speed curves. ............................................................................ 50 Figure 13: Petrol and diesel Euro 3 NOx emission functions according to average speed, as designed through speed range averages, with the number of data per average emission (clustered), and comparison with Copert 3 functions. ................................................... 52 Figure 14: Petrol and diesel Euro 3 NOx emission functions and data according to average speed, as designed through Reference Test Patterns or through speed range averages.............. 53 Figure 15: Total VOCs and considered VOCs only, compared to total hydrocarbons......... 57 Figure 16: Benzene emission factors in the hot-start tests at temperature >18°C. Each marker represents a single test. "average" is the arithmetic mean for each emission class of cars; na=not available. .......................................................................................................... 58 Figure 17: Influence of the emission standard on the emission factors of the sum of the eightVOCs considered, the sum of the five 1 st level VOCs, all BaP exluded, and the sum of the 6 most carcinogenic PAHs (see Table10and Table13the definition of these compounds).58 Figure18: NO and NO 2 emissions of various technologies and emission standards. The values are derived from a mixture of driving situations (urban with a cold start, rural and highway), as measured by TNO Automotive[START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF]...................................................... 60 Figure 19: NO 2 percentage of NOx for various technologies and emission standards, including the ranges (minimum and maximum values) as measured by TNO Automotive and EMPA (Gense et al., 2006). ................................................................................................................. 60

Figure 20 :

 20 Figure 1: Simplified design of the database, including the 3 main tables and the 2 most important secondary tables. .......................................................................................................... 17 Figure 2: Shape of the emission model. ............................................................................. 25 Figure 3: Normalised mean square error for Euro 3 petrol vehicles (blue) and for average Euro 3 petrol car (red).............................................................................................................. 34 Figure 4: Simulation quality for the engine-out emission factors of the three Euro 3 petrol vehicles with the EMPA instantaneous emission model. A point represents a driving pattern..... 35 Figure 5: Simulation quality for the emission factors of the average Euro 3 petrol car in the instantaneous model PHEM.......................................................................................... 37 Figure 6: Diagram of driving cycles used for developing the kinematic model, ordered by mean speed. 39 Figure 7: Comparison of measured and calculated emissions of CO 2 [g/km] for the kinematic regression model 1 (PLS MG, dark red), model 2 (PLS MVA, blue) and model 3 (PLS MT, red), and for a average speed regression model (GLM, black) with the mean measured emission (mean, green) versus the driving cycle mean speed. ....................................... 41 Figure 8: Traffic conditions as regards speed and traffic flow (Lhuillier, 2004) . ............... 44 Figure 9: Variability of the test driving cycles and 15 Reference Test Patterns as regards driving speed and acceleration.................................................................................................. 46 Figure 10: Variation of the pollutant emissions (NOx of Euro 3 Diesel) according to the 15 Reference test patterns.................................................................................................. 48 Figure 11: Dynamic influence on the NOx emissions for petrol cars. Stable and unstable Reference test patterns are defined in Annex 16............................................................ 49 Figure 12: Traffic situation approach illustration: NOx and CO 2 emissions of cars have been estimated for an urban trunk road (speed limit: 50 km/h), at different traffic conditions, according to dedicated speed curves. ............................................................................ 50 Figure 13: Petrol and diesel Euro 3 NOx emission functions according to average speed, as designed through speed range averages, with the number of data per average emission (clustered), and comparison with Copert 3 functions. ................................................... 52 Figure 14: Petrol and diesel Euro 3 NOx emission functions and data according to average speed, as designed through Reference Test Patterns or through speed range averages.............. 53 Figure 15: Total VOCs and considered VOCs only, compared to total hydrocarbons......... 57 Figure 16: Benzene emission factors in the hot-start tests at temperature >18°C. Each marker represents a single test. "average" is the arithmetic mean for each emission class of cars; na=not available. .......................................................................................................... 58 Figure 17: Influence of the emission standard on the emission factors of the sum of the eightVOCs considered, the sum of the five 1 st level VOCs, all BaP exluded, and the sum of the 6 most carcinogenic PAHs (see Table10and Table13the definition of these compounds).58 Figure18: NO and NO 2 emissions of various technologies and emission standards. The values are derived from a mixture of driving situations (urban with a cold start, rural and highway), as measured by TNO Automotive[START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF]...................................................... 60 Figure 19: NO 2 percentage of NOx for various technologies and emission standards, including the ranges (minimum and maximum values) as measured by TNO Automotive and EMPA (Gense et al., 2006). ................................................................................................................. 60

Figure 45 :

 45 Figure 45: Parking duration influence on the total excess CO emission for petrol cars with catalyst. ........................................................................................................................ 92 Figure 46: CO 2 cold start unit excess emission according to vehicle type and average speed.96 Figure 47: Shape of the driving cycles Artemis urban, rural and motorway (and motorway 130). 120 Figure 48: Shape of the driving cycle EMPA BAB 1000. ................................................ 120 Figure 49: Shape of the driving cycles Handbook R1, R2, R3, and R4............................. 122 Figure 50: Shape of the driving cycles Inrets fluide court and Inrets route court. ............. 122 Figure 51: Shape of the legislative driving cycle NEDC (ECE 15 + EUDC).................... 122 Figure 52: Shape of the legislative driving cycle FTP 75. ................................................ 123 Figure 53: Shape of the driving cycle modem urban 5+7+13. .......................................... 123 Figure 54: Shape of the driving cycle modem Hyzem pure road. ..................................... 123 Figure 55: Shape of the driving cycles Napoli 10-23, 15-18-21, and 6-17........................ 124 Figure 56: Shape of the driving cycle PVU commerciale grand routier (or LDV commercial cars motorway).................................................................................................................. 125 Figure 57: Shape of the driving cycles PVU fourgon 3.5 t urbain lent, urbain fluide, livraison, route, and autoroute (or LDV 3.5 tons van slow urban, free-flow urban, delivery, rural, and motorway).................................................................................................................. 126 Figure 58: Shape of the driving cycle TUG Ries Road Gradient used for determining the influence of the slope................................................................................................................. 126 Figure 59: Shape of the driving cycle VP faible motorisation urbain dense, urbain, urbain fluide, route, and autoroute (or Artemis low motorisation urban dense, urban, free urban, rural, and motorway).................................................................................................................. 128 Figure 60: Shape of the driving cycle VP forte motorisation urbain dense, urbain, urbain fluide, route, and autoroute (or Artemis high motorisation urban dense, urban, free urban, rural, and motorway).................................................................................................................. 130 Figure 61: Simulation quality for the emission factors of the diesel vehicle with the EMPA instantaneous emission model. A point represents a driving pattern. ........................... 143 Figure 62: Simulation quality for the emission factors of the average Euro 2 diesel car with PHEM (engine maps from the Artemis driving cycle used for simulation of the Handbook driving cycles)............................................................................................................ 144 Figure 63: Conversion ratios of NOx emission factors of Reference test patterns for petrol cars between pre Euro 1, Euro 1, Euro 4, and resp. Euro 2, Euro 2, Euro 3 categories. Emission factors are in Annex 17............................................................................................... 169 Figure 64: Conversion ratios of NOx emission factors of Reference test patterns for diesel cars between pre Euro 1, Euro 1, Euro 4, and resp. Euro 2, Euro 2, Euro 3 categories. Emission factors are in Annex 17............................................................................................... 169 Figure 65: Localization of the 91 European cities considered for auxiliary emission modelling. 194 Figure 66: CO excess emission due to auxiliaries at full load for diesel and petrol vehicles.206 Figure 67: HC excess emission due to auxiliaries at full load for diesel and petrol vehicles.207 Figure 68: NOx excess emission due to auxiliaries at full load for diesel and petrol vehicles.208 Figure 69: PM excess emission due to auxiliaries for diesel vehicles. .............................. 209 Figure 70: Average traffic distributions representative of 3 countries (relative to the hourly average), and relative base distribution (average) used in the third model design. Relative influence of the using of the different distributions on the daily emissions.................. 216 Figure 71: CO 2 cold start unit excess emission according to the ambient temperature and average speed. 217 Figure 72: CO 2 cold start unit excess emission according to the season and average speed.218 Figure 73: CO 2 cold start unit excess emission according to the hour and average speed. 218

  2.1. SPECIFIC MEASUREMENTS ........................................................................................................................................ 2.1.1. Pollutants considered....................................................................................................................................... 2.1.2. Vehicle sample..................................................................................................................................................

  .2. INSTANTANEOUS MODELS.........................................................................................................................................

.. PAHs reported as emission factors ...............................................................................................................................................

3

.1.5. Nitrogen oxydes................................................................................................................................................ 3.1.6

. Particulates....................................................................................................................................................... 33.2.1. EMPA model..................................................................................................................................................... Validation .......................................................................................................................................................................................

3

.2.2. PHEM model ......................................................................................................................................................

  Model 2...........................................................................................................................................................................................

. Emission factors of

Reference test patterns................................................................................................

.... Model 1...........................................................................................................................................................................................

  .NO 2 emission factors........................................................................................................................................ 3.6.4. Emission factors for particle properties ......................................................................................................... 3.7. LIGHT DUTY VEHICLES ............................................................................................................................................ 3.7.1. Data extraction and classification .................................................................................................................. 3.7.2. Emission as a function of average speed ........................................................................................................ 3.7.3. Emission as a function of vehicle loading rate...............................................................................................

  . INFLUENCE OF MILEAGE ........................................................................................................................................... 3.9. INFLUENCE OF AMBIENT AIR TEMPERATURE AND HUMIDITY .................................................................................. .10. INFLUENCE OF ROAD GRADIENT AND VEHICLE LOAD ............................................................................................

3.9.1. Influence of ambient air temperature ............................................................................................................. 3.9.2. Influence of ambient air humidity ................................................................................................................... 33.10.1. Measurement results ......................................................................................................................................

Table 1 :

 1 Table 1 and in detail in Annex 1. All together 169 unregulated pollutants are measured. Numbers of unregulated pollutants measured per laboratory.

	Inrets +

Table 2 :

 2 Vehicle sample as regards laboratory, fuel and emission standard.

					petrol				CNG				diesel
			Euro 0	Euro 1	Euro 2	Euro 3	Euro 4	total	Euro 3	Euro 4	total	Euro 0	Euro 1	Euro 2	Euro 3	total	total
		Empa	6		1	15		22						6		6	28
		IM	1	1	3	4	1	10						1	2	3	13
	Passenger cars	Inrets KTI LAT TNO TUG		6	7 1 3 3	4 1 10 1 7	5	17 2 13 4 12	1	1	2	2	3	11 1 1 1	2 1 3 7	18 1 2 4 7	35 3 15 8 21
		VTT			15	5	2	22						5	2	7	29
		Total	7	7	33	47	8	102	1	1	2	2	3	26	17	48 152
	LDV KTI											1	1		2	2

  .

	Parameter			lab.	N. veh.	N. driving cycles	Tested cases	N. bags
				EMPA	20 16 B, C, K	all together 37 bags per vehicle
	Instantaneous emissions		TUG	21 D	23	gradients I, J Different vehicle loadings and road	1223
				EMPA	18	25	3 ambient temperatures (-20, -7 and +23°C) for the Artemis cycles
					10	19
				IM	13 7 or 18
	Hot regulated pollutants (PC)	INRETS KTI	29 6 3 9 or 20 14 24		32	2444
				LAT	15	5		75
				TNO	8 3 or 14		35
				TUG	21	21
				VTT	29	5	3 ambient temperatures (-20, -7 and +23°C) for 2 cycles
		7 URP H 190 HCs	EMPA	18	39 18
		VOC & PAH	IM	11	11	15 repetitions
				INRETS-
	Unregulated pollutants	VOC & PAH	US-ULCO-	30	4		1639
				USTL
		VOC & PAH	KTI	2	8		16
		VOC	VTT	6 13	7 L 7 L	by gas chromatography by FTIR
	Light Duty Vehicles			KTI	2	6	10 and 50 % load for 3 among 6 cycles	18	18
							Test every 20 000 km, before and
	Influence of vehicle mileage		LAT	2	6	after maintenance; 1, 2 or 3	174
							repetitions
	Influence of ambient temperature	Empa VTT	18 13	3 2	3 ambient temperatures: -20, -7 and +23°C	240
	Influence of ambient humidity	VTT	11	2	3 ambient humidity levels, tests repeated	131
	Influence of gradient			TUG	1 4	2 1	7 slopes: -10, -5, -2.5, 0, 2.5, 5, 10 %	14 28	42
	Influence of load			TUG	2 3	3 2	Unloaded, half loaded, loaded	18 18	36
	Influence of auxiliaries			TUG	3	53	with and without air conditioning, lighting, rear-window heater, radio	159
				IM	10	7		70
	Cold start emissions			Inrets VTT	30 13	9 10 M		470
		B, C, D, F, H, I, J: see Table 3
		K	with 2 gearshift strategies for 2 Handbook driving cycles.

Table 4 :

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

INRETS report n°LTE 0523

Table 5

 5

	diesel

, and can be much higher for vehicle subclasses or individual tests.

Table 5 :

 5 Influence

of the data harmonisation (ratio after / before) on the whole Artemis LVEM database, per pollutant and vehicle class.

  Regarding pollutants per vehicle and sub-cycle, 177 861 emission factors (g km -1 ) have been derived, for 404 pollutants, which are detailed in Table8. 25 430 among these emission factors concern unregulated pollutants.[START_REF] André | Vehicle emission measurement collection of the Artemis database -Artemis 3312 report[END_REF] gives a more detailed description of the content of the database in terms of vehicle characteristics, driving cycles and pollutants (but for the database dated December 1 st , 2004).

	Laboratory	Country	Number of Vehicles	Number of Tests
	ADAC	Germany	39	117
	CNR-IM	Italy	13	457
	EMPA	Switzerland	203	3838
	Ford	Germany	1	14
	IFP	France	4	98
	INRETS	France	180	2294
	KTI	Hungary	5	237
	LAT	Greece	73	1026
	MTC	Sweden	9	439
	RW TUEV	Germany	293	1867
	Shell	United Kingdom	4	643
	TNO-Automotive	The Netherlands	1629	4508
	TRL	United Kingdom	127	998
	TUEV Rheinland	Germany	217	1417
	TUG	Austria	21	290
	VTT	Finland	29	581
	Total (all laboratories)		2847	18824

Table 7 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 sub-cycle level, and 18 824 tests respectively when analysing the cycle-level.

Table 6 :

 6 Number

	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

of vehicles and tests measured by each laboratory in the Artemis LVEM database. The tests are summed at the level of cycles.

Table 7 :

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

Table 8 :

 8 Number of pollutants per group and per family in the Artemis LVEM database.

  More generally the USEPA (2000) gives 9 VOCs known as Mobile air toxics and Flandrin et al. give a list of 12 VOCs emitted by transport to be considered as toxic for the health: See Table9. Naphtalene belongs to the second list, but was already included in the group of the 4 most volatile PAHs.

	Compound	US EPA	Fland rin	Cass adou	Toxicity (IARC classification)
	acetaldehyde	X	X	XX	possibly carcinogenic (2B)
	acetone		X	x
	acrolein	X		XX
	benzene	X	X	XX	carcinogenic (1)
	benzo[a]pyrene (PAH)	X		XX
	bromomethane			x
	1,3-butadiene	X	X	XX	probably carcinogenic (2A)
	cumene		X
	1,2 dibromoethane			x
	ethylbenzene	X	X	x	possibly carcinogenic (2B)
	formaldehyde	X	X	XX	probably carcinogenic (2A)
	1,2,3,7,8,9 hexachlorodibenzodioxine			x	carcinogenic
	n-hexane	X	X	x	peripheric nervous system
	naphtalene (PAH)		X	x
	styrene		X	x	bone medulla, liver, possibly carcinogenic (2B)
	2,3,7,8 tetracholodibenzo-para-dioxine			x	teratogene
	toluene	X	X	x	teratogene (3)
	xylenes	X	X	x	not classifiable as to its carcinogenicity (3)

Table 9 :

 9 List

of VOC considered as toxic for the human health (US

  The ethylbenzene, n-hexane, naphtalene (PAH), styrene, toluene, xylenes were not selected because of too low emission factors. As these compounds have high reference toxicological values, it would be useful to provide emission factors more accurate than the existing ones. The 1,2,3,7,8,9 hexachlorodibenzodioxine (carcinogenic, one of the hexachlorodibenzodioxines) and 2,3,7,8 tetracholodibenzo-para-dioxine (teratogene, one of the tetracholodibenzodioxines) have also high reference toxicological values and should be taken into account. Therefore the compounds listed as additional ones in Table10should be considered in addition.

		1 st level compounds	2 nd level or additional compounds
		arsenic	manganese
		baryum
		cadmium
	Non VOC	chrome lead mercury nickel
		nitrogene dioxide
		par ticl es ( dies el)
		sulfur dioxide
		acetald eh yd e	acetone
		acrol ein	monobromomethane
		benzen e	1,2 dibromoethane
		benzo[a]py r ene (PAH)	eth ylb enzen e
	VOC	1,3-bu tadi en e formald eh yd e	1,2,3,7,8,9 hexachlorodibenzodioxine n-hexan e
			naphtalene (PAH)
			styrene
			2,3,7,8 tetrachlorodibenzo-para-dioxine
			toluen e

The acetone was not selected because of too low emission factors, but as Denox systems should emit it, it would be INRETS report n°LTE 0523 usefull to select this coumpound. xylenes (= m-xylene + p-xylene + o-xylene)

Table 10 :

 10

Compounds with the highest score (1 st level -

[START_REF] Cassadou | Selection des agents dangereux à prendre en compte dans l'evaluation des risques sanitaires lies aux infrastructures routieres (selection of hazardeous compounds to take into account for the health risk assessment from road infrastructures)[END_REF]

, and additional compounds to consider. Pollutants in green bold correspond to emission factors proposed in section 3.6: particles, five 1 st level VOCs (BaP excluded), 8 VOCs (BaP excluded).

Table 11

 11

	.
	VOC group	number in Annex 9
	-alkanes (saturated)	64
	-alkenes and alkynes (unsaturated)	46
	-monoaromatic hydrocarbons	37
	-polyaromatic hydrocarbons (light and heavy)	13 and 42
	-carbonyl compounds (aldehydes and ketones)	23
	-ethers	4
	-POP (persistant organic pollutants)	8
	-dioxines and furanes	5 and 5

Table 11 :

 11

Groups of VOC, as listed in Annex 9. The groups with the highest ozone forming potential OFP are in orange.

  according to their toxicity. The group, defined here, of the 6 most carcinogenic PAHs among the 16 PAHs contains all the PAHs classified in 1987 by IARC as probably (group 2A) or possibly (group 2B) carcinogenic. We should note that the IARC classification has changed recently(IARC, 2002; 2006): We have now 1 PAH (BaP) classified 1, 1 PAH classified 2A and 7 PAHs classified 2B: 3 PAHs (BjF, Chr, N) should now belong in addition to the group of the most carcinogenic PAHs. In parallel the group of the 4 most volatile PAHs, with the lightest molecular weight (N, Ace, Flu, Acy), are analysed with difficulty because the losses are important. Therefore the accuracy of their emission factors is low. Most of the authors do not give any result for them. The 12 other PAHs should therefore be considered as the group of the 12 least volatile PAHs, including the group of the 6 most recent European directive (2004/107/EC of 15 December 2004) asks the Member States to monitor at least 7 relevant PAHs at a limited number of measurement sites (see Table

carcinogenic PAHs. The benzo

[a]

pyrene (BaP) belongs to both groups of the 12 least volatile and 6 most carcinogenic PAH. It is the PAH measured the most often because it is very easy, and therefore the most known PAH. It is also the only PAH classified now as carcinogenic (class 1). Therefore specific emission factors should be provided for BaP. A

Table 13 :

 13

List of PAHs proposed by USEPA and the European directive 2004/107, the 12 least volatile PAHs and the 6 most carcinogenic, according to the IARC classifications for humans (group 1: carcinogenic, group 2A: probably carcinogenic, group 2B: possibly carcinogenic, group 3: not classifiable as to its carcinogenicity, group 4: probable non-carcinogenic). Pollutants in green bold correspond to emission factors proposed in section

3.6.

Table 14

 14

	: Model fit coefficients of correlation R 2 for the 3 data sets (normal + high emitters
	considered together, normal and high emitters considered separately), and for the 3
	kinematic regression models.
		CO	HC	NOx	CO 2
	cycle	44.4	29.2	41.5	88.6
	model	33.4	47.9	37.7	9.0
	error	22.3	22.8	20.8	2.4

Table 15 :

 15 Contribution of the variance components in percentage, for the normal emitter data set.

  and Table 31 in Annex 14. They are illustrated by pictures in Annex 15, to help model users in the understanding of the current definitions.

		Main function	Characteristics	Speed limit (km/h)
		National and regional network -Through-traffic	5a -Motorway 5b -Non-motorway	80 -130 70 -100
	urban	Agglomeration primary network -Primary distributor Districts distributor	4a -Motorway (ring, etc.) 60 -110 4b -Non-motorway 50 -90 3 -Road 50 -80
		Local distributor-Inner exchange, local traffic	2 -Road	50 -60
		Access road -Local traffic.	1 -Road, side road, etc.	30 -50
	rural	5 -Motorway National and regional network -Through and distribution 4 -Trunk road Distributor 3 -Road Local distributor -Inner exchange, local traffic 2 -Road	80 -150 60 -110 50 -100 50 -80
		Access road -Local traffic	1 -Road, side road, etc.	30 -50

Table 16 :

 16 Typologies of urban roads and of rural roads.

	Speed (km/h)
	120
	90
	60
	30
	2000	4000	6000	8000
			Traffic flow (Veh./h)

Petrol, Euro III, NO x

			0.3
								Artemis, All capacities
								COPERT, <1.4 l
		0.25					COPERT, 1.4-2.0 l
								COPERT, >2.0 l
								Artemis, All capacities
			0.2
	[g/km]	0.15												32
				226
			0.1	109	159	101	167	134	138	87			92	139	89
		0.05								81
			0
			0	15	30		45	60		75	90		105	120	135
								Average Speed [km/h] Diesel, Euro III, NO x
			2.5
								Artemis, All capacities					4
			2					COPERT, All capacities
								Artemis, All capacities
				28
	[g/km]	1 1.5	75	46	19	40	27	38	11	11	3	16	48	21
			0.5
			0
			0	15	30		45	60		75	90		105	120	135
								Average Speed [km/h]

Table 17 :

 17 Reduction factors for future diesel vehicle technologies.

		CO	HC	HC+NOx	NOx	PM
	Euro 3	0.64	0.06	0.56	0.5	0.05
	Euro 4	0.5	0.05	0.3	0.25	0.025
	Euro 5 (expected)				0.175	0.005

Table 18 :

 18 Emission standards of diesel passenger cars.

		PM
	Euro 3 + DPF	0.1	x Euro 3
	Euro 4 + DPF	0.1	x Euro 4

Table 19 :

 19 Reduction of PMm emissions due to the addition of a Diesel Particulate Filter.

  Table20). Some other vehicles were tested byRicardo, Millbrook and LAT.

			petrol		diesel
	vehicle type	pre Euro	Euro 1-2	Euro 3-4	pre Euro to Euro 2	Euro 3-4
	veh. tested by Gense et al.	7		17	21	18
	NO 2 fraction (%)	3	6	9	17	50

Table 20 :

 20 NO 2 as a percentage of NOx for different car categories, based on the results presented by

  , at around 50%. The absolute NO 2 emission increased sharply from Euro 2 to Euro 3, and remained at the same level for Euro 4. Measurements on four cars (three with a catalysed diesel particle filter and one with a D-kat) yielded NO 2 proportions which were higher than 50 %.

		0.80
		0.70
	NO and NO 2 [g/km]	0.20 0.30 0.40 0.50 0.60
		0.10
		0.00
		Pre Euro	Euro 3	Euro 4	Euro 4 DI Pre Euro	Euro 1	Euro 2	Euro 3	Euro 3	Euro 4	Euro 4	Euro 4 D-	Euro 4
		1		IDI					1				FBC	FBC	KAT	CDPF
													DPF	DPF
		Petrol	Petrol	Petrol	Petrol	Diesel	Diesel	Diesel	Diesel	Diesel	Diesel	Diesel	Diesel	Diesel
		90%
		80%
	NO 2 mass percentage [%]	20% 30% 40% 50% 60% 70%	MIN -MAX Average
		10%
		0%
		Pre	Euro 3 Euro 4	Euro 4	Euro 4	Pre	Euro 1 Euro 2 Euro 3 Euro 3	Euro 3	Euro 4 Euro 4	Euro 4	Euro 4
		Euro 1		IDI		DI	EMPA	Euro 1				EMPA	FBC	FBC	D-KAT	CDPF
														DPF	DPF
		Petrol Petrol Petrol Petrol Petrol Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel Diesel
	Figure 19: NO 2 percentage of NOx for various technologies and emission standards, including
		the ranges (minimum and maximum values) as measured by TNO Automotive and
		EMPA

For diesel cars the fraction ranged from about 5% to almost 80 %. A large step change was evident for diesel cars from Euro 2 to Euro 3. From pre Euro to Euro 2 the average NO 2 fraction did not vary much, and was about 15 % to 20% (an average of 17 % was assumed). For Euro 3 diesel cars the measured NO 2 fraction was considerably higher

NO NO2

Figure

18

: NO and NO 2 emissions of various technologies and emission standards. The values are derived from a mixture of driving situations (urban with a cold start, rural and highway), as measured by TNO Automotive

[START_REF] Gense | NO 2 emission from passenger cars. 2 nd conf[END_REF]

.

Table 21 :

 21 Passenger car categories considered in the Particulates project, and number of sample vehicles in each category.

  Table 35 respectively, both in Annex 23. A more detailed representation is given as an example in Figure 20.

			1.8E+15
	Total particle number emission rate	[#/km]	0.0E+00 2.0E+14 4.0E+14 6.0E+14 8.0E+14 1.0E+15 1.2E+15 1.4E+15 1.6E+15	Urban	Road	Motorway	PC diesel Euro-1 emission factor PC diesel Euro-2 emission factor PC diesel Euro-3 emission factors PC diesel Euro-2 vehicles PC diesel Euro-3 vehicles	Active surface area [cm 2 /km]

Table 39

 39

	in

a and b depend on the driving behaviour and also on the initial correction applied to NOx emission: either NOx is not corrected, or NOx is already corrected by using the standard (or legislative) correction factor.

It is recommended to use the rural figures for motorway driving behaviour, 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.

  Unfortunately, vehicles were not measured with the Artemis cycle for Euro 1 and Euro 2 petrol vehicles, which is necessary for the map building. Consequently the factors for petrol vehicles have the same values for Euro 1 and for Euro 0 and furthermore the Euro 2 factors are the same as the Euro 3. Likewise for diesel vehicles the Euro 4 factors are the same as the Euro 3 factors. With this adjustment the final road gradient factors for the Artemis database were calculated with the model PHEM (see the factors in Annex 28).

	urban rural motorway urban rural Vehicle FC 1.014 1.006 Diesel vehicle 0.993 1.026 1.010 Petrol vehicle motorway 1.039	NOx 1.112 1.086 1.016	PM 0.951 0.886 1.011

load

From the measurement with different loading rates the final factors for diesel cars (FC, NOx and PM) and petrol cars (FC) were calculated by linear interpolation. These final factors are representative for an average loading situation in Europe with approximately 1.5 persons in the INRETS report n°LTE 0523 vehicle: see Table

22

.

Table 22 :

 22 Loading factors for diesel and petrol vehicles for different traffic situations.

  3 European laboratories (Utac and Cenerg in France, Vito in Belgium), i.e. 27 vehicles and 146 tests. Driving cycle, number of vehicle tests, type of vehicle, experimental objectives vary with experimentation. The choice of vehicles covers the main types of vehicle (small and large vehicles), different propulsion systems (petrol and diesel) and the emission standards (mainly Euro 1, but also Euro 3

Table 23 :

 23 Average fuel consumption due to air conditioning (l/h) for the 4 vehicle types.

  wf " T ext,wf + a 3,wf " (T int # 23) + a 4,wf " h + a 5,wf " h 2

	with	hfc	!	0
	with:
	hfc: hourly excess fuel consumption (l/h)
	T ext,wf : external temperature provided by hourly, daily or monthly weather data (°C), which contain
	resp. 8760, 365 and 12 values
	T int : set temperature in the cabin; default value is 23°C
	h:

  : See an example Figure45. We calculate an average table of parking time influence for each pollutant and vehicle category and plot the best fit, after excluding the CARB data because they do not represent the European behaviour. It was thus possible to give a polynomial function for each case, equal to 1 for 12 h parking: See Annex 36.

	When applying the functions given in annexes, we obtain for instance the following model for the
	CO cold excess emission for a start, EE(T,V,δ,t), in the case of Euro 2 petrol cars:
	(	,	,	,	)	3 2 1 060 . 17 / 20 , 20	[ 1 4 4 4 4 4 2 4 4 4 4 4 3 ] ) , ( 003 . 0 043 . 0 927 . 1	4 4 4 4 3 2 4 4 4 4 1 007 . 9 024 . 0 002 . 0 409 . 4 . 007 . 9 1 1
	,	2	,						(	)
						( 1 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 3 ) 1 -2 4 -3 7 -t 10 4.315 t 10 1.941 -t 10 2.916 -
							(	)

Table 26 :

 26 . Reduction rates to apply to the cold excess emissions for petrol and diesel vehicles.

	Emission standard		CO	Petrol CO 2	HC	NOx	CO	Diesel CO 2 HC	NOx
	Euro 3							base = 1
	Euro 4			base = 1			0.781	1	0.833	0.5
	Euro 5	no DISI DISI	1 1	1 0.9	1 1	1 1	0.781	1	0.833	0.35
	For petrol vehicles, the direct ignition vehicles (DISI) should have a specific
	behaviour.

  Cycles in italics and yellow are summation of cycles.

	Sample size Petrol Diesel 7 13 14 7 95 48 44 30 0 2 2 0 22 9 9 2 2 2 1 2 43 30 D E2 1999 2835 92 2065 59 1 Cubic capacity (cm 3 ) total Petrol Diesel total 20 2095 1642 21 1785 1882 143 1609 1971 74 1602 1954 2 0 2340 2 1073 -31 1785 2001 11 1572 1947 4 1610 1688 3 1895 2047 73 1610 1954 D E2 1999 1995 60 1430 69 1 1 Petrol Diesel Power (kW) 78 76 85 81 76 72 74 68 0 59 46 -81 77 76 73 65 63 77 81 75 68 T D E2 1997 1905 66 1365 94 1 1 T D E2 1999 1896 81 1105 31 1 1 T D E2 2001 1896 81 1375 103 1 1 T P E0 1985 3430 160 1470 167 1 T D E2 1999 1995 60 1239 70 1 D E2 1999 1868 51 1009 0 1 D E2 2000 1997 66 1155 11 1 D E2 2000 1997 80 1410 26 1 D E2 2000 2188 83 1630 15 1 D E2 2000 1870 55 1115 30 1 P E3 2001 1360 55 1025 25 P E3 2002 1796 143 1232 19 P E3 2001 1298 64,2 948 23 D E2 1999 2497 96 1650 43 D E2 1999 1896 81 1306 46 D E3 2001 2926 142 1713 17 Astra Caravan 1.6 Lab. EMPA PHEM / TUG all all KTI LAT all VTT TUG TUG all Pajero Zafira A 20 TD 406 1.9 DT Ibiza GT TDI EM. Volkswagen Passat Parameter instant. emis. hot reg. poll. PC unregul. poll. PC Light Duty Veh. mileage ambient temp. amb. humidity gradient, load auxiliaries cold start EM. Mitsubishi EM. Opel EM. Peugeot EM. Seat EM. BMW 635CSI Inr. Opel Astra DTI 16V Inr. Peugeot 206D Inr. Peugeot 306 HDI Inr. Peugeot 406 HDI Inr. Renault Espace 2.2DT Inr. Renault Mégane 1.9D LAT Peugeot 206 LAT Toyota Corolla TS LAT Toyota Yaris TNO Opel Omega 2.5 TD TNO Volkswagen Golf 1.9 TDI TNO BMW 530D TOURING VTT Opel P E2 2001 1598 62 1235 13 1 T VTT Opel Corsa 1.2 P E2 1999 1190 48 950 41 VTT Peugeot 306 1.6I Break 5D P E2 2000 1587 65 1195 23 1 T VTT Peugeot 406 2.0I 4D Saloon P E2 1997 1998 97,4 1430 30 VTT Saab 95 Estate 2.0 P E2 2001 1985 110 1680 17 1 T VTT Toyota Avensis 1.6 P E2 1999 1598 81 1270 66 H NEDC = UDC + EUDC 11.007 1180 33.58 120.00 0.44 legislative EUDC 6.955 400 62.60 120.00 0.38 H ECE 15 (or UDC) 4.052 780 18.70 50.00 0.47 urbain fluide court 0.985 189 18.76 44.00 0.81 route court 1.439 126 41.11 74.90 0.90 Inrets EM. Ford Focus 1.8 TD D E2 2000 1753 66 1273 36 1 1 T Inr. Fiat Punto TD Cult D E2 1999 1698 46 1025 59 1 LAT Opel Corsa P E3 2001 1199 66 1073 14 VTT Nissan Almera Hatchb. 1.8 P E2 2000 1760 84 1300 26 R1 = AE1+AE2+AE3 41.157 1341 110.49 131.10 0.20 Annex 4: Characteristics of the tested vehicles All the vehicles, except LDVs, are PC tested for hot regulated pollutant emissions. In the column 'Emis. standard', 'E0' means 'pre Euro 1', and '04' means 'ECE 1504'. Lab. Make Model Petrol / CNG / Diesel Emis. standard Year Capacity (cm 3 ) Max. power (kW) Weight (kg) Mileage (Mm) instantaneous emis.. unreg. pollutants PC LD: Light Duty Veh. G: gradient, H: humidity L: load, T: temperature EM. Alfa Romeo 156 2.4 JTD D E2 1998 2387 100 1410 71 1 1 T IM Lancia Y Elefantino Rosso P E3 2000 1242 59 920 81 1 IM Lancia Y Elefantino Rosso P E3 1999 1242 59 930 15 1 IM Volkswagen Golf P E4 2002 1598 77 1259 4 Inr. Mercedes-B 190D 2.5I D 04 1988 2497 66 1175 220 1 Inr. Peugeot 309 GLD D 04 1990 1905 48 950 212 1 Inr. Fiat Brava 1.9LD D E1 1996 1929 48 1130 114 1 Inr. Ford Fiesta 1.8L D E1 1995 1753 44 925 135 1 Inr. Renault 19 1.9D D E1 1995 1870 48 1030 135 1 Inr. Citroen ZX TD Break D E2 1997 1905 66 1150 65 1 LAT Citroen Xsara P E2 1998 1587 67,1 1078 95 LAT Opel Astra P E2 1999 1389 66 1180 95 LAT Rover 200 P E2 1998 1396 76,1 1000 50 LAT Alfa Romeo 156 P E3 2003 1598 88 1265 13 LAT Daewoo Kalos P E3 2003 1150 53 982 11 LAT Daewoo Lanos P E3 2001 1349 55 1030 88 LAT Daewoo Matiz P E3 2001 796 37,5 835 6 LAT Fiat Punto P E3 2002 1242 44 875 17 LAT Ford Focus P E3 2002 1596 74 1208 6 VTT Peugeot 307 Hatc. 2.0 HDI-D E2 2001 1997 79 1354 13 H VTT Volkswagen Passat 1.9TDI Sal. D E2 1999 1896 85 1453 93 1 T VTT Volkswagen Passat Var. 1.9 TDI D E2 1999 1890 66 1461 88 VTT Opel Vectra 2.2DTI Sal. D E3 2001 2170 92 1450 3 VTT Volkswagen Polo Classic 1.9 SDI D E3 2001 1896 50 1197 3 1 T VTT Alfa Romeo 147 Hatchback 1.6 P E2 2001 1598 88 1295 46 1 T VTT Fiat Bravo Hatchb. 1.2 P E2 2000 1241 60 1085 40 H VTT Fiat Marea 1.6 Weekend P E2 1999 1581 76 1275 65 1 T VTT Ford Mondeo 2.5 P E2 1997 2540 125 1445 89 urban 4.472 921 17.48 57.70 0.79 rural 14.724 862 61.49 111.50 0.58 motorway 130 23.793 736 116.38 131.80 0.39 Artemis motorway 24.602 736 120.34 150.40 0.39 EMPA BAB 1000 32.646 1000 117.53 160.85 0.15 R4 = LE6+StGoAB+StGoIO 6.117 1340 16.43 60.90 0.40 R3 = LE2u+LE3+LE5 14.140 1080 47.13 79.20 0.46 R2 = A4+LE1+LE2s 22.342 1080 74.47 105.90 0.27 Handbook Lab. Make Model Petrol / CNG / Diesel Emis. standard Year Capacity (cm 3 ) Max. power (kW) Weight (kg) Mileage (Mm) instantaneous emis. Unreg. pollutants PC LD: Light Duty Veh. G: gradient, H: humidity L: load, T: temperature IM Alfa Romeo 156J TwinSp. 16v P E3 2001 1970 121 1335 0 1 IM Alfa Romeo 156 P E3 2000 1800 106 1265 21 1 Lab. Make Model Petrol / CNG / Diesel Emis. standard Year Capacity (cm 3 ) Max. power (kW) Weight (kg) Mileage (Mm) instantaneous emis. Unreg. pollutants PC LD: Light Duty Veh. G: gradient, H: humidity L: load, T: temperature LAT Volkswagen Golf D E2 1996 1896 66 1120 95 LAT Renault Laguna D E3 2001 1870 79 1310 30 Lab. Make Model Petrol / CNG / Diesel Emis. standard Year Capacity (cm 3 ) Max. power (kW) Weight (kg) Mileage (Mm) instantaneous emis. Unreg. pollutants PC LD: Light Duty Veh. G: gradient, H: humidity L: load, T: temperature VTT Alfa Romeo 156 2.4 TD D E2 1998 2387 100 1425 136 1 T VTT Audi A4TDI D E2 1996 1896 66 1395 38 H Dist-ance Dura-tion Aver. speed Max. speed St. dev. accel. accel. total 77 83 75 72 59 46 80 80 72 1 1 1 1 1 1 1 1 1 1 1 1.06 1 0.83 1.06 1 1 64 1 0.78 1 76 cold start 1 1 1 1 1 1 1 1 M M 1 1 1 1 2.86 2.36 1.28 1.28 0.32 1.39 1.00 1.86 A: auxiliaries, M: mileage A: auxiliaries, M: mileage cold start 1 1 A: auxiliaries, M: mileage cold start 1 Max. (km) (s) (km/h) (km/h) (m/s 2 ) (m/s 2 ) family Cycle name (within the family) Driving cycle A: auxiliaries, M: mileage cold start Annex 5: Characteristics of the driving cycles used Annex 6: Driving cycles used
	Weight (kg) P E0 1986 999 33 D E2 1999 1870 47 D E3 2000 1900 51 1195 11 Mileage (Mm) 795 110 1 995 47 P E2 1999 1595 74 1295 23 US FTP 75 1 st (or 3 rd ) part Parameter Uno 45 Clio 1.9d Corolla VTT Volkswagen Golf 1.6 4D Auto EM. Fiat Inr. Renault TNO Toyota 5.779 505 41.20 91.20 Lab. Petrol Diesel total Petrol Diesel EM. Honda Accord 2.0I Auto P E0 1985 1954 85 1155 117 1 Inr. Volkswagen Passat TDI D E2 2000 1896 85 1437 74 1 TNO Ford Mondeo P E2 1999 1796 85 1325 10 VTT Volkswagen Golf Variant 1.6 5D P E2 2000 1598 77 1396 30 1 US FTP 75 2 nd part 6.263 867 26.01 55.20 total H 0.65 T T T 0.61 EMPA 1432 1165 1258 66 56 instant. emis. PHEM / TUG 1270 1315 1285 10 16 12 hot reg. poll. PC all 1161 1281 1201 37 43 unregul. poll. PC all 1125 1244 1173 46 EM. Opel Kadett D 1.3 P E0 1984 1296 50 920 128 1 1 T EM. Peugeot 505 GTI Auto P E0 1984 2164 95,5 1235 58 1 1 T Inr. Volkswagen Sharan TDI D E2 1998 1896 81 1691 110 1 TNO Opel Omega Y22XE P E2 1999 2198 106 1655 22 VTT Volkswagen Polo Variant 1.4 P E2 1998 1390 44 1105 23 modem urban 5+7+13 9.193 1426 23.21 82.40 0.86 EM. Volkswagen Golf 19E P E0 1984 1595 55 910 164 1 1 T EM. Alfa Romeo 156 2.0 TwinS.16V P E2 1998 1970 114 1250 74 EM. BMW 323CI P E3 2000 2494 125 1370 28 1 T Inr. Peugeot 307 HDI D E3 2001 1997 66 1260 24 1 Inr. Renault Mégane Scénic DCI D E3 2001 1870 75 1290 5 1 Inr. Citroen AX 1.0 P E1 1995 954 37 706 33 1 Inr. Citroen ZX 1.4I P E1 1996 1361 55 895 103 TNO Volkswagen Lupo 1.0 P E2 1998 997 37 935 26 TNO Alfa Romeo 147 1.6 P E3 2001 1598 77 1234 19 TUG Alfa Romeo 156 Estate D E3 2001 1910 81 1355 0 1 TUG Audi A2 1.2 TDI bioD E3 2001 1191 45 940 25 1 G L VTT Volvo S60 Saloon 2.4 P E2 2001 2435 103 1548 59 1 T VTT Citroen C5 Break 2.0I P E3 2002 1997 100 1442 7 modem Hyzem pure road 10.682 743 51.75 103.40 0.75 H VTT Honda CIVIC Hatch. 1.6 4D P E3 2001 1590 81 1210 21 10-23 3.362 1081 11.20 49.96 0.52 H VTT Peugeot 307 Hatch. 1.6 I 4D P E3 2001 1587 80 1268 19 H 15-18-21 4.467 1070 15.03 52.00 0.57 Napoli 57 Light Duty Veh. KTI 0 1590 1590 0 3 EM. Citroën Xsara P E3 2001 1360 55 1191 21 1 Inr. Hyundai Pony 5 P E1 1995 1341 62 930 95 1 TUG BMW 320D Limous. E46 D E3 2003 1995 110 1415 0 1 VTT Renault Clio Hatchback 1.2 P E3 2002 1149 43 955 2 1 H T 6-17 16.469 1038 57.12 105.51 0.54 3 mileage LAT 933 -933 47 -EM. Fiat Punto HGT P E3 2000 1747 96 1095 22 1 Inr. Peugeot 406 SL P E1 1995 1762 81 1275 80 TUG Ford Mondeo T.TDCI 16V D E3 2002 1998 96 1505 3 1 VTT Renault Mégane Br. 1.4 16V P E3 2002 1390 70 1210 5 H PVU commerciale grand routier 18.755 828 81.54 128.60 0.61 47 ambient temp. all 1215 1337 1251 53 58 amb. humidity VTT 1241 1375 1265 24 24 gradient, load TUG 1233 1165 1199 1 20 auxiliaries TUG 1385 1373 1377 0 18 cold start all 1129 1244 1176 47 EM. Ford Focus 1.6 16V P E3 2000 1596 74 1151 16 1 T EM. Ford Mondeo P E3 2001 1999 107 1460 32 1 EM. Honda Accord P E3 2000 1997 108 1500 28 1 EM. Hyundai Accent 1.3 GS P E3 2000 1341 62 990 22 1 1 T EM. Mazda Demia P E3 2001 1498 55 1100 21 1 EM. Mitsubishi Galant 2.5 V6 Auto P E3 2000 2498 120 1445 33 1 T Inr. Renault Clio 1.2L P E1 1995 1171 43 845 112 1 Inr. Renault Laguna 1.8 RN P E1 1994 1783 69 1225 114 Inr. Audi A4 1.8 Turbo P E2 1998 1781 110 1283 24 1 Inr. Ford Fiesta 1.2 P E2 2000 1242 55 989 10 1 Inr. Renault Clio 1.4RXT P E2 2000 1390 70 980 24 1 Inr. Renault Laguna RXE P E2 1995 1783 66 1255 62 1 TUG Nissan Almera -N15 D E3 2000 2184 81 1390 53 1 G L A A TUG Peugeot 307 XS HDI 90 5T D E3 2001 1997 66 1280 16 1 TUG Volkswagen Golf 1.9 PD TDI VTT Skoda Octavia Hatchb. 2.0 P E4 2002 1984 85 1310 2 1 T VTT Toyota Corolla Saloon 1.4 P E4 2002 1398 71 1185 3 1 T urbain lent (or slow urban) a 2.190 649 12.15 57.90 0.71 PVU fourgon urbain fluide (or free-flow urban) 2.893 467 22.30 52.50 0.73 3.5 t D E3 2000 1896 85 1320 18 1 TUG Alfa Romeo 147 1.6 TS livraison (or delivery) 1.592 546 10.50 32.30 0.48 (or P E3 2001 1598 77 1190 13 1 TUG BMW 316I P E3 2000 1895 77 1385 0 1 route (or rural) a 9.646 544 63.83 86.20 0.37 LDV PVU G L A TUG Chrysler PT Cruiser P E3 2001 1598 85 1309 8 3.5 tons vans) autoroute (or motorway) a 30.736 1226 90.25 130.40 0.43 1 57 EM. Nissan Primera 2.0 CVT P E3 2000 1998 103 1325 30 1 T Inr. Renault Mégane Coupe 1.6 P E2 2000 1598 79 1060 4 TUG Daewoo Kalos 1.4 SE SOHC P E3 2003 1399 61 949 0 1 TUG Ries Road Gradient b 6.842 510 48.30 87.60 0.47	1 1.50 1 1 1 1.50 1 1 3.08 1 1 1 1 1 1 1 2.42 1.90 1.80 1 1 2.09 2.14 1 1 1 1 1 1 1 1 1 2.53 1 2.17 1.44 0.97 1.44 1 1.44
	EM. Peugeot EM. Renault Inr. Rover TUG Fiat VP faible EM. Renault Inr. Volkswagen Polo 1.4 306 Mégane 414I Multipla bipower urbain dense (or urban dense) P E3 2001 1761 81 1245 19 1 P E2 1997 1396 76 1100 51 CNG E3 2001 1581 76 1490 25 1 2.935 711 14.86 55.20 1 P E3 2001 1598 79 1195 20 1 Mégane Scénic P E2 1999 1390 44 967 15 1 TUG Hyundai Tiburon Coupe 2.7 P E3 2001 2656 123 1370 4 1 urbain (or urban) 4.799 945 18.28 55.70 motorisation P E3 2001 1998 100 1400 80 1 EM. Toyota Yaris 1.0 P E3 2000 998 50 900 37 1 1 IM Fiat Marea Wee. TD100 D E2 1997 1910 74 1255 187 1 IM Fiat Bravo 105 JTD SX D E3 2000 1910 77 1095 25 1 Inr. Peugeot 206 XS16S P E3 2001 1587 80 1013 3 1 Inr. Peugeot 206XR P E3 2001 1124 44 910 17 1 Inr. Renault Laguna II 1.6 16V P E3 2001 1598 79 1270 7 1 Inr. Renault Scenic 1.6 16S P E3 2001 1598 79 1250 4 1 TUG Mazda 323F 1.3I Evision P E3 2003 1324 53 1080 1 1 TUG Saab 95 4D 2,3T Auto urbain fluide (or free urban) 4.818 710 24.43 56.70 (or P E3 2000 2290 136 1485 20 1 TUG Audi A2 1.6 FSI P E4 2003 1599 81 995 0 route (or rural) 13.149 821 57.66 111.50 Artemis low 1 TUG Opel Vectra C P E4 2003 1796 90 1300 1 1 motorisation) autoroute (or motorway) 24.090 729 118.97 150.70	0.67 0.68 T G L 0.73 0.57 0.39	1 2.44 1 2.50 1 1 1 1 1 1 1 3.19 2.19 1.28
	IM KTI Mazda Fiat TUG Skoda VP forte	Punto JTD E2200 Fabia urbain dense (or urban dense) D E3 2001 1910 59 D E1 1993 2184 44 1335 3 965 1 P E4 2001 1390 74 1081 12 1 2.907 730 14.34 57.60 1 LD	0.64	1 2.67
	IM KTI Ford Fiat TUG Toyota motorisation	Regata Giardinetta Transit TD Yaris 5-T. 1.0 VVTI urbain (or urban)	P E0 1987 1585 74 1005 96 D E2 1996 2496 74 1845 3 P E4 2003 998 48 940 1 4.924 918 19.31 57.60 1 LD 1	0.71	1 2.39
	IM KTI Ford Fiat TUG Volvo (or	Uno 1.1 IE Mondeo 1.8TD Est. V70 2.4 urbain fluide (or free urban) P E1 1995 1108 36 D E2 1996 1753 65 1345 3 845 79 CNG E4 2002 2435 103 1606 30 1 4.780 710 24.23 61.30 1 1	0.76	1 1 2.14
	IM IM KTI Lada Alfa Romeo 146 J 1.4 Twin Sp. Fiat Marea bipower 2110 1.5 16V TUG Volvo V70 2.4 route (or rural) Artemis high IM Fiat Punto KTI Suzuki Swift 1.3 GLX motorisation) autoroute (or motorway) P E2 1998 1370 76 1160 107 P E2 2000 1499 69 1025 3 P E4 2002 2435 103 1606 30 1 14.224 844 60.67 110.50 1 P E2 1997 1581 76 1185 10 P E2 1997 1242 54 950 7 1 P E3 2001 1298 50 830 3 1 25.377 750 121.81 157.10	0.60 0.37	1 2.14 1 1 2.00
			INRETS report n°LTE 0523 INRETS report n°LTE 0523

a slope: 10 or 50 % of full load b average slope: 3 %, maximal slope: 14 %

  Scale of MIR reactivity expressed in gram of formed ozone per gram of added VOC

					methane nonane 2-methyl,1-pentene 1,2-diethylbenzene benzo[ghi]fluoranthene (circ) isovaleraldehyde Metals	CH 4 C 9 H 20 C 6 H 12 C 10 H 14 C 18 H 10 C 5 H 10 O	74-82-8 111-84-2 763-29-1 135-01-3 203-12-3 590-86-3 (12 compounds)
	1				ethane decane 2-methyl,1,4-pentadiene 1,3-diethylbenzene benzo[e]pyrene (circ) valeraldehyde Lead (circ)	C 2 H 6 C 10 H 22 C 6 H 10 C 10 H 14 C 20 H 12 C 5 H 10 O Pb	74-84-0 124-18-5 763-30-4 141-93-5 192-97-2 110-62-3 7439-92-1
	2 Annex 10: 12 1 6 12 1 1		propane isobutane undecane dodecane trans-2-methyl-1,3-pentadiene 2,3,3-trimethyl,1-butene methylindane 1,4-diethylbenzene benzo[ghi]perylene (circ) benzo[a]pyrene (circ) o-tolualdehyde m-tolualdehyde Cadmium Chrome MIR reactivity of VOC	C 3 H 8 C 4 H 10 C 11 H 24 C 12 H 26 C 6 H 10 C 7 H 14 C 10 H 12 C 10 H 14 C 22 H 12 C 20 H 12 C 8 H 8 O C 8 H 8 O Cd Cr	74-98-6 75-28-5 1120-21-4 112-40-3 926-54-5 594-56-9 27133-93-3 105-05-5 191-24-2 50-32-8 529-20-4 620-23-5 7440-43-9 7440-47-3
					butane tridecane cyclohexene n-butylbenzene benzo[c]phenanthrene (circ) p-tolualdehyde Copper	C 4 H 10 C 13 H 28 C 6 H 10 C 10 H 14 C 18 H 12 C 8 H 8 O Cu	106-97-8 629-50-5 110-83-8 104-51-8 195-19-7 104-87-0 7440-50-8
	1				dimethylpropane tetradecane 2-methyl,1-hexene 1-methyl-3-propylbenzene cyclopenta[cd]pyrene (circ) hexaldehyde Nickel	C 5 H 12 C 14 H 30 C 7 H 14 C 10 H 14 C 18 H 10 C 6 H 12 O Ni	463-82-1 629-59-4 6094-02-6 1074-43-7 27208-37-3 66-25-1 7440-02-0
		2 6 12		isopentane pentadecane trans-2-heptene 1-methyl-3-isopropylbenzene dibenzo[a,h]anthracene (circ) 2,5-dimethylbenzaldehyde Selenium	C 5 H 12 C 15 H 32 C 7 H 14 C 10 H 14 C 22 H 14 C 9 H 10 O Se	78-78-4 629-62-9 14686-13-6 535-77-3 53-70-3 5779-94-2 7782-49-2
					pentane hexadecane cycloheptene 1-methyl-4-propylbenzene dibenzo[a,j]anthracene 1,2-ethanedione Zinc	C 5 H 12 C 16 H 34 C 7 H 12 C 10 H 14 C 22 H 14 C 2 H 2 O 2 Zn	109-66-0 544-76-3 628-92-2 1074-55-1 224-41-9 107-22-2 7440-66-6
					cyclopentane heptadecane 1-octene (+1,1-dimethylcyclohexane) 1,4-dimethyl-2-ethylbenzene dibenzo[a,e]pyrene (circ) propanedione Manganese	C 5 H 10 C 17 H 36 C 8 H 16 C 10 H 14 C 3 H 4 O 2 Mn	287-92-3 629-78-7 111-66-0 1758-88-9 78-98-8 7439-96-5
					methylcyclopentane octadecane trans-2-octene 1,3-dimethyl-4-ethylbenzene dibenzo[a,h]pyrene (circ) methylvinylcetone Platine,	C 6 H 12 C 18 H 38 C 8 H 16 C 10 H 14 C 4 H 6 O Pt,	96-37-7 593-45-3 13389-42-9 874-41-9 78-94-4 7440-06-4,
					2,2-dimethylbutane nonadecane cis-2-octene 1,2-dimethyl-4-ethylbenzene dibenzo[a,l]pyrene Palladium,	C 6 H 14 C 19 H 40 C 8 H 16 C 10 H 14 C 24 H 14 Pd,	75-83-2 629-92-5 7642-04-8 934-80-5 191-30-0 7440-05-3,
					2,3-dimethylbutane icosane 1-nonene 1,3-dimethyl-2-ethylbenzene 1,4-dimethylphenantrene (circ) acide formique Rhodium	C 6 H 14 C 20 H 42 C 9 H 18 C 10 H 14 CH 2 O 2 Rh	79-29-8 112-95-8 124-11-8 2870-04-4 64-18-6 7440-16-6
	level of interest 1	PAH group	NAME 3-methylpentane docosane trans-4-nonene + trans-3-nonene 1,2,4,5-tetramethylbenzene 2-methylchrysene (circ) 2-methylpentane henicosane cis-4-nonene 1,2-dimethyl-3-ethylbenzene 3,6-dimethylphenantrene acide acetique Baryum	Formulae CAS C 6 H 14 96-14-0 C 22 H 46 629-97-0 C 9 H 18 10405-85-3 + 20063-92-7 C 10 H 14 95-93-2 C 6 H 14 107-83-5 C 21 H 44 629-94-7 C 9 H 18 10405-84-2 C 10 H 14 933-98-2 C 16 H 14 1576-67-6 C 2 H 4 O 2 64-19-7 Ba 7440-39-3
	1 2 6 12 4 2	hexane (n-hexane) tricosane 1-undecene 1,2,3,5-tetramethylbenzene 3-methylchrysene (circ) COV -Ethers	C 6 H 14 C 23 H 48 C 11 H 22 C 10 H 14	110-54-3 638-67-5 821-95-4 527-53-7 (4 compounds)
		2			cyclohexane 1,2-dibromoethane (circ) 1-dodecene 1,2,3,4-tetramethylbenzene 4-methylchrysene (circ) Methyl-Tertiobuthyl-Ether (MTBE)	C 6 H 12 C 2 H 4 Br 2 C 12 H 24 C 10 H 14 C 5 H 12 O	110-82-7 106-93-4 112-41-4 488-23-3 1634-04-4
		2			Carbon oxides 2,4-dimethylpentane monobromomethane acetylene 5-methylchrysene (circ) Ethyl-Tertiobuthyl-Ether (ETBE)	C 7 H 16 CH 3 Br C 2 H 2 C 6 H 14 O	(2 compounds) 108-08-7 74-83-9 74-86-2 637-92-3
	1 1 1 1 1 1 1 1 1 1 1	2 2 2 2 2 6 12 12 12 2 2 12 12 2 12 2 6 12 2 6 12 2 6 12 2	Carbon monoxide Carbon dioxide Nitrogen oxides Nitrogen monoxide Nitrogen dioxide [2] monoxyde de diazote peroxyde d'azote acide nitrique [2] acide nitreux[2] nitrate de peroxyacyle [2] Ammonia Sulfur oxides Sulfur dioxide Sulfur trioxide [2] acide sulfurique [2] sulfate d'ammonium acide [2] sulfate d'ammonium neutre [2] Particles PTS PM10 PM2,5 PM0,1 VOC -Alkanes 2-methylhexane 2,3-dimethylpentane 2,2dimethylpentane 2,2,3-trimethylbutane 3,3-dimethylpentane trimethylpentane 3-methylhexane 3-ethylpentane 2,3-Dimethylhexane 2,2-dimethylhexane 2,4-dimethylhexane 2,5-dimethylhexane 3,4-dimethylhexane methyloctane isooctane heptane 2-methylheptane 3-methylheptane 4-methylheptane methylcyclohexane ethylcyclopentane 1,3-dimethylcyclohexane 1,4-dimethylcyclohexane 1,2-dimethylcyclohexane cycloheptane butylcyclohexane methyl heptane methylnonane octane 1,2-dichloroethane (circ) VOC -Alkenes and alkynes ethylene (circ) propene propadiene 1-butene isobutene 1,3-butadiene (circ) cis-2-butene 1-butyne 2-butyne trans-2-butene cyclopentadiene isopentene 1-pentene trans-2-pentene cis-2-pentene 2-methyl-1-butene 1-hexene cis-2-hexene (+1-hexyne) trans-2-hexene trans-3-hexene 2-methyl,2-Pentene 3-methyl-1-Pentene cis-4-methyl-2-pentene cis-3-methyl,2-pentene trans-3-methyl,2-pentene propyne isoprene 2-methyl-2-butene dimethylhexene VOC -Monocyclic Aromatic Hydrocarbons benzene (circ) toluene ethylbenzene m-xylene p-xylene o-xylene styrene isopropylbenzene Polycyclic Aromatic Hydrocarbons Methyl-Tertio-Amyl-Ether (TAME) 6-methylchrysene (circ) "light" 4 naphtalene 1-methylphenanthrene (circ) thiofene 2-methylnaphtalene perylene (circ) 1-methylnaphtalene triphenylene (circ) Persistent Organic Pollutants 4 acenaphthylene propylene (circ) chlordane 4 fluorene (circ) indeno[1,2,3-cd]pyrene (circ) heptachlore aromatique C 13 H 12 coronene (circ) hexachlorobenzene phenanthrene (circ) toxaphene 3,7-dinitrofluoranthene (circ) anthracene (circ) aldrine 3,9-dinitrofluoranthene (circ) 4 acenaphtene dieldrine 1-nitropyrene (circ) 1-nitronaphtalene (circ) endrine 3-nitrofluoranthene (circ) 2-nitronaphtalene (circ) mirex 1,3-dinitropyrene (circ) 2-nitrofluorene (circ) 1,6-dinitropyrene (circ) 9-nitroanthracene (circ) Dioxines and furanes 1,8-dinitropyrene (circ) Dioxines 6-nitrobenzo[a]pyrene (circ) propylbenzene 3-ethyltoluene 4-ethyltoluene tetrachlorodibenzodioxines Polycyclic Aromatic Hydrocarbons "heavy" COV -Aldehydes and ketones (Carbonyl compounds) pentachlorodibenzodioxines hexachlorodibenzodioxines 1,3,5-trimethylbenzene 2-ethyltoluene 1,2,4-trimethylbenzene tert-butylbenzene isobutylbenzene sec-butylbenzene butylbenzene 1,2,3-trimethylbenzene cymene indane anthanthrene (circ) formaldehyde (circ) heptachlorodibenzodioxines fluoranthene (circ) acetaldehyde (circ) octachlorodibenzodioxines pyrene (circ) acetone chrysene (circ) acroleine (circ) Furanes propionaldehyde tetrachlorodibenzofuranes benzo[a]fluorene (circ) benzo[b]fluorene (circ) crotonaldehyde pentachlorodibenzofuranes benzo[a]anthracene (circ) 2-butanone (methyl ethyle cketone) hexachlorodibenzofuranes benzo[b]fluoranthene (circ) methacroleine heptachlorodibenzofuranes benzo[k]fluoranthene (circ) benzo[j]fluoranthene (circ) benzaldehyde isobutanaldehyde butyraldehyde octachlorodibenzofuranes	CO CO2 NO NO2 N2O N2O4 HNO3 HNO2 C 15 H 11 N 3 O 85-85-8 630-08-0 37210-16-5 (7 compounds) 10102-43-9 10102-44-0 10024-97-2 10544-72-6 7697-37-2 7782-77-6 (1 compound) NH3 7664-41-7 (5 compounds) SO2 7446-09-5 SO3 7446-11-9 H2SO4 7664-93-9 NH4HSO4 7803-63-6 (NH4)2SO 4 7783-20-2 (4 compounds) (64 compounds) C 7 H 16 591-76-4 C 7 H 16 565-59-3 C 7 H 16 590-35-2 C 7 H 16 464-06-2 C 7 H 16 562-49-2 C 8 H 18 29222-48-8 C 7 H 16 589-34-4 C 7 H 16 617-78-7 C 8 H 18 584-94-1 C 8 H 18 590-73-8 C 8 H 18 589-43-5 C 8 H 18 592-13-2 C 8 H 18 583-48-2 C 9 H 20 61193-19-9 C 8 H 18 540-84-1 C 7 H 16 142-82-5 C 8 H 18 592-27-8 C 8 H 18 589-81-1 C 8 H 18 589-53-7 C 7 H 14 108-87-2 C 7 H 14 1640-89-7 C 8 H 16 591-21-9 C 8 H 16 589-90-2 C 8 H 16 583-57-3 C 7 H 14 291-64-5 C 10 H 20 1678-93-9 C 8 H 18 50985-84-7 C 10 H 22 63335-87-5 C 8 H 18 111-65-9 C 2 H 4 Cl 2 1300-21-6 (46 compounds) C 2 H 4 74-85-1 C 3 H 6 115-07-1 C 3 H 4 463-49-0 C 4 H 8 106-98-9 C 4 H 8 115-11-7 C 4 H 6 106-99-0 C 4 H 8 590-18-1 C 4 H 6 107-00-6 C 4 H 6 503-17-3 C 4 H 8 624-64-6 C 5 H 6 542-92-7 C 5 H 10 563-45-1 C 5 H 10 109-67-1 C 5 H 10 646-04-8 C 5 H 10 627-20-3 C 5 H 10 563-46-2 C 6 H 12 592-41-6 C 6 H 12 7688-21-3 C 6 H 12 4050-45-7 C 6 H 12 13269-52-8 C 6 H 12 625-27-4 C 6 H 12 760-20-3 C 6 H 12 691-38-3 C 6 H 12 922-62-3 C 6 H 12 616-12-6 C 3 H 4 74-99-7 C 5 H 8 78-79-5 C 5 H 10 513-35-9 C 8 H 16 78820-82-3 (37 compounds) C 6 H 6 71-43-2 C 7 H 8 108-88-3 C 8 H 10 100-41-4 C 8 H 10 108-38-3 C 8 H 10 106-42-3 C 8 H 10 95-47-6 C 8 H 8 100-42-5 C 9 H 12 (13 compounds) C 6 H 14 O 994-05-8 C 10 H 8 C 4 H 4 S 110-02-1 91-20-3 C 11 H 10 C 20 H 12 198-55-0 91-57-6 C 11 H 10 C 18 H 12 217-59-4 (8 compounds) 90-12-0 C 12 H 8 C 3 H 6 115-07-1 12789-03-6 208-96-8 C 13 H 10 C 22 H 12 193-39-5 76-44-8 86-73-7 C 24 H 12 191-07-1 118-74-1 C 13 H 12 C 14 H 10 8001-35-2 85-01-08 C 14 H 10 309-00-02 120-12-7 C 12 H 10 60-57-1 83-32-9 72-20-8 C 10 H 7 NO 2 86-57-7 2385-85-5 C 10 H 7 NO 2 581-89-5 C 13 H 9 NO 2 607-57-8 (5 + 5 compounds) C 14 H 9 NO 2 602-60-8 98-82-8 C 9 H 12 103-65-1 C 9 H 12 620-14-4 C 9 H 12 (42 compounds) (23 compounds) 622-96-8 C 9 H 12 108-67-8 C 9 H 12 611-14-3 C 9 H 12 95-63-6 C 10 H 14 98-06-6 C 10 H 14 538-93-2 C 10 H 14 135-98-8 C 10 H 14 104-51-8 C 9 H 12 526-73-8 C 10 H 14 99-87-6 C 9 H 10 496-11-7 C 22 H 12 CH 2 O 50-00-0 191-26-4 C 16 H 10 C 2 H 4 O 75-07-0 206-44-0 C 16 H 10 C 3 H 6 O 67-64-1 129-00-0 218-01-9 C 3 H 4 O 107-02-8 C 18 H 12 C 3 H 6 O 123-38-6 C 17 H 12 C 4 H 6 O 4170-30-3 243-17-4 C 18 H 12 C 4 H 8 O 78-93-3 56-55-3 C 20 H 12 C 4 H 6 O 78-85-3 205-99-2 C 20 H 12 C 20 H 12 C 7 H 6 O 100-52-7 205-82-3 C 4 H 8 O 78-84-2 207-08-09 C 4 H 8 O 123-72-8
					INRETS report n°LTE 0523 INRETS report n°LTE 0523 INRETS report n°LTE 0523 INRETS report n°LTE 0523

Annex 11: Ozone forming potential for different vehicle types

	petrol cars Euro 0	decreasing OFP	petrol cars Euro 1 & 2	decreasing OFP
	M+p xylene	.562	ethylene	.0442
	ethylene	.453	M+p xylene	.0441
	toluene	.438	toluene	.0338
	3+4 ethyltoluene	.389	3+4 ethyltoluene	.0162
	orthoxylene	.223	ethylbenzene	.0050
	trimethylbenzene	.100	orthoxylene	.0035
	1,3 butadiene	.059	trimethylbenzene	.0029
	ethylbenzene	.053	1-3 butadiene	.0027
	acetaldehyde	.048	acetaldehyde	.0014
	propadiene	.035	isopentane	.0013
	2-methylpentene	.035	acetylene	.0012
	Trans-2-pentene	.037	n-hexane	.0012
	diesel cars Euro 0 & 1	decreasing OFP	diesel cars Euro 2	decreasing OFP
	ethylene	.0662	ethylene	.0445
	acetaldehyde	.0212	formaldehyde	.0183
	formaldehyde	.0172	acetaldehyde	.0121
	1,3-butadiene	.0099	1,3-butadiene	.0067
	acroleine	.0056	acetylene	.0062
	1-pentene	.0049	1-pentene	.0034
	acetylene	.0044	propionaldehyde	.0032
	crotonaldehyde	.0037	toluene.00	.0033
	butyraldehyde	.0037	butyraldehyde	.0016
	toluene	.0034	acroleine	.0015
	naphtalene	.0031	crotonaldehyde	.0013
	methacroleine	.0016	naphtalene	.00098

Table 27 :

 27 Calculated ozone forming potential OFP, in decreasing order, for different vehicle types on a Motorway driving cycle

Table 28 :

 28 Model 1 -weight matrix of X.

	INRETS report n°LTE 0523

Table 29 :

 29

	Annex 14:	Traffic situation description
	Main function	Comments		Le-vel	Characterist ics	Speed limit (km/h)	Number of lanes and geometry	Junction type & density: order of magnitude of dist. between junctions	Parking	Pedestrians, cycles, mopeds	Bus circulation and stops
	Through-traffic -Primary distributor -National and regional network	High-speed or major road through an urban area. Concerns Regional or national traffic	5a Motorway 5b Not motorway (trunk road)	110-130 80-90/97*	At least 2*2, separate road ways At least 2*1, separate road ways or no	Wide Wide	Always grade separated Grade separated	Low (10 km) Low	Specific area Specific area	Always prohibited Prohibited	Authorized, no bus stops Authorized, no bus stops
	Primary distributor -	High-speed or major roads through the urban	4a	Motorway (ring road)	80-113*	At least 2*2, separate road ways or no	Wide	Always grade separated	High (1 km)	Specific area	Always prohibited	Authorized, no bus stops
	Agglomeration network, city primary roads	area, major arterials. Quick exchanges at the city scale	4b	Not motorway (trunk road)	48*/50-90/97*	At least 2*1, separate road ways or no	Wide	Grade separated, roundabout, traffic lights	High	Specific area or on road side	Prohibited or on pavement, and cycle lane	Authorized, separate stops or on road side
	Districts distributors	Connection between districts or poles and access to/from primary distributors	3	road	48*/50 -70	At least 2*1 or 1*2, separate road ways or no, (perhaps 1*1 when one way road)	Lanes can be narrowed to limit the speed	Traffic lights, roundabout or grade separated	High	On road side	Pavement, cycles and mopeds on road or on a specific lane	Authorized, possibly on a specific lane, separate stops or on road side
	Local distributors -Inner exchange and local traffic	Connection between communities and within districts. Access to/from district distributors -Neighbourhood traffic	2	road	50/48*	At least 2*1 or 1*1 lane if one way road	Narrow lanes	Traffic lights, priority rule or roundabout	High	On road side	Pavement, cycles and mopeds on road or on a specific lane	Authorized, possibly on a specific lane, separate stops or on road side
	Access roads -Local traffic.	Access to housing and business places.	1	road, cul-de-sac, side road	5-10-30-50/48*	At best 2*1 or 1*2 lanes	Narrow lanes	Mostly priority rule, traffic lights and small roundabout	High (100 m)	On road side or on the street	Pavement, cycles and mopeds on road	Authorized, stops on road side

Table 30 :

 30 Urban road classification.

	150

Table 31 :

 31 Rural road classification.

Urban 5b: National/regional trunk road (strategic network) N74, Belgium Urban 4a: Urban motorway (city scale network)

	Speed limit: Speed limit: 90 to 110 km/h 110 to 130 km/h 2*5 lanes (left) 2*3 lanes 2*2 lanes (right) Grade separated Grade separated junctions, junctions, Grade separated Grade separated road ways , road ways with Street lighting, trees Capacity around Street lighting 2000 vehicles per Capacity around lane per hour. 1800 vehicles per
	lane per hour
	Speed limit:
	110 to 130 km/h
	2*2 lanes
	Grade separated 4a (could be also
	junctions, 5a National /
	Grade separated regional motorway
	road ways through an urban
	No lighting area)
	Capacity around
	1800 vehicles per
	lane per hour
	(Rural/urban)
	motorway (2x2
	with bus lane) -It Motorway (2x4) is a motorway M6 (can be also Rural in the approach of 5:National / regional motorway London
	in an urban area,
	strategic network)

Urban 4b: City primary road, major arterial (city scale network)

	Urban 3:
	Speed limit:
	90 to 110 km/h
	2*2 lanes
	Junction not
	grade-separated,
	Grade-separated
	road ways
	Street lighting
	Capacity around
	1500 vehicles per
	lane per hour
	4b -trunk road
	4b urban trunk
	road, major arterial
	(Left: could be
	also an Urban 3
	road category)
	Other illustrations

District distributor, inter-district road

  Urban 3: District distributor,

	Urban 2: Local distributor Urban 2: Local distributor (cont.) Urban 1: Access road Urban 1: Access road (cont.)
	Annex 16:	Description of reference test patterns and
	cycles Reference Test Pattern number Reference test cycle and characteristics 7 Urban Stop&go OSCAR.H1, OSCAR.H2, OSCAR.H3, TRL.WSL_CongestedTraffic 3 Urban Congested, stops Artemis.urban_3 2 Urban Congested, low speeds Artemis.urban_4	Average speed (km/h) 7 9 12	Average positive acceleration (m/s 2 ) 0,70 0,98 0,83	Speed limit: 50 km/h 2*2 lanes (left) 2*1 lanes (right) Grade separated road ways Sidewalks Parking area on road side Capacity around 800 vehicles per Speed limit: Speed limit: 30 to 50 km/h Capacity maximum 600 vehicles per hour One way road Parking area on road side Sidewalks in fact a trunk 30 km/h Capacity around 300 vehicles per Others illustrations Stop duration Stop/km (%) road (A-road) hour One way road 35 16,3 Parking on road side 58 10,2 Sidewalks 19 16,7
	1 Urban	Dense	Artemis.urban, Artemis.urban_1	17	0,82	lane per hour Other illustrations 29 5,2
	4 Urban	Free-flowing	Artemis.urban_5	22	0,80	10	4,3
	5 Urban	Free-flow, unsteady Artemis.urban_2	32	0,84	9	2,3
	6 Rural	Low speed	Artemis.rural_3	43	0,62	Speed limit 50 3 0,5
	11 Rural	Unsteady	Artemis.rural, Artemis.rural_1	58	0,71	km/h 3	0,3
	9 Rural	Steady	Artemis.rural_2	66	0,69	Speed limit: Capacity around 0 0,0
	10 Rural	Main roads, unsteady	Artemis.rural_4	79	0,58	Speed limit: 50 km/h Capacity maximum 700 vehicles per hour 0 0,0
	8 Rural	Main roads	Artemis.rural_5	88	0,38	50 to 70 km/h in vehicles per 2*1 lanes 0 0,0
	14 Motorway Unsteady 15 Motorway Stable 13 Motorway 12 Motorway High speed	Artemis.motorway_150_2 EMPA.BAB, modemHyzem.motorway, TRL.MotorwayM113 Artemis.motorway_130, Artemis.motorway_150_1 Artemis.motorway_150, Artemis.motorway_150_4 Artemis.motorway_150_3,	104 115 119 125	0,63 0,32 0,53 0,48	2*2 lanes Sidewalk Grade separated road ways (right) No parking Capacity around 1000 vehicles per lane per hour hour: -Left: No parking 0 0,0 -Left: 800 -Right: 2000 area, sidewalks, cycles on a 0 0,0 2*1 lanes Parking area on road side Sidewalks specific lane -Middle: Parking on 0 0,0 Other illustrations road side,, sidewalks parking on road -Right: No marks, 0 0,0
						side
						Speed limit:
						Speed limit: 50 km/h
						50 km/h Capacity maximum
						1*2 lanes in one 1600 vehicles per
						way hour
						Sidewalks 2*1 lanes (road
						Parking area on ways separated
						road side with a mark) Residential roads
						Specific cycles Parking on road (2x1)
						lane side
						Bus lane Sidewalks
						Capacity around
						800 vehicles per
						lane per hour
						With traffic calming
						With traffic
						calming
					INRETS report n°LTE 0523 INRETS report n°LTE 0523

Table 32 :

 32 Definition and average characteristics of the reference test patterns and corresponding reference test cycles, sorted by the average speed. Stable cycles are in blue, unstable ones in red.

	Reference Test Pattern number	Speed (km/h)		Stops	Accelerations
	and characteristics	Average	Running speed	Max. Speed	duration (%)	Frequency / km	Average positive acc.	Acc. /km	Strong acc./km
	Urban	Stop&go	7,1	11,1	41	35,4	16,3	0,70	10,6	1,7
	Urban	Congested, stops	8,7	20,8	46	58,2	10,2	0,98	6,8	5,1
	Urban	Congested, low speeds	11,7	14,4	40	18,6	16,7	0,83	16,7	4,8
	Urban	Dense	16,9	23,7	55	28,7	5,2	0,82	8,0	2,2
	Urban	Free-flowing	21,5	23,9	44	10,3	4,3	0,80	11,5	4,3
	Urban	Free-flow, unsteady	31,6	34,6	58	8,5	2,3	0,84	5,2	1,7
	Rural	Low speed	43,1	44,3	69	2,7	0,5	0,62	3,6	0,5
	Rural	Unsteady	58,0	60,0	101	3,4	0,3	0,71	3,1	0,5
	Rural	Steady	65,9	65,9	84	0,0	0,0	0,69	0,6	0,0
	Rural	Main roads, unsteady	78,5	78,5	112	0,0	0,0	0,58	1,3	0,0
	Rural	Main roads	87,6	87,6	104	0,0	0,0	0,38	0,5	0,0
	Motorway Unsteady	103,5	103,5	128	0,0	0,0	0,63	1,8	0,2
	Motorway Stable	115,3	115,3	146	0,1	0,02	0,13	0,06	0,0
	Motorway		118,8	118,8	132	0,0	0,0	0,53	0,4	0,02
	Motorway High speed	124,6	124,6	150	0,0	0,0	0,48	0,5	0,02

Table 33 :

 33 Detailed characteristics of the references test cycles (combination of one or several cycles), sorted by the average speed.

	Annex 17:	Reference emissions according to the
		Reference Test Patterns
		diesel	petrol

  Description and then weighting coefficients of the Reference Test Patterns of each of the 69 traffic situation for which speed data are available, and for the additional traffic situations corresponding to each of the Artemis driving cycles or sub-cycles, for the emission computation.The traffic situations 1002, 1009 and 1016 are macro traffic situations (in red) resp. for urban, rural and motorway situations. They are composed of one or two Reference test patterns. A last traffic situation is the most macroscopic situation corresponding to the European traffic situation. Alternative macro traffic situations are composite ones.Description of the traffic situation corresponding to the Artemis driving cycles or sub-cycles, and weighting factors of macro traffic situations according to these cycles.

	INRETS report n°LTE 0523 Speed limit (km/h) Gradient, sinuosity 110 Flat, non-sinuous Speed limit (km/h) Gradient, sinuosity 30 Flat, non-sinuous 30 Flat, non-sinuous b c d Traffic Situations according to Reference Test Patterns Area Road Category Traffic condition Identification Urban national/regional motorway Free-flow Area Road Category Traffic condition Identification Urban Local access Free-flow 250031 Urban Local access Saturated 250033 Eng. Equation a e f cap. Area Road Category Speed limit (km/h) Gradient, sinuosity Traffic condition Identification Rural national/regional motorway 130 Flat, non-sinuous Free-flow 110131 Rural national/regional motorway 150 Flat, non-sinuous Free-flow 110141 Rural national/regional motorway 150 Flat, non-sinuous Free-flow 110141 Rural national/regional motorway 150 Flat, non-sinuous Free-flow 110141 Rural Semi-motorway 90 Flat, non-sinuous Free-flow 112091 Rural Semi-motorway 90 Flat, non-sinuous Heavy traffic 112092 Rural Semi-motorway 90 Flat, non-sinuous Saturated 112093 Rural Semi-motorway 110 Flat, non-sinuous Free-flow 112111 Rural Semi-motorway 110 Flat, non-sinuous Heavy traffic 112112 Rural Semi-motorway 110 Flat, non-sinuous Saturated 112113 Rural national/regional trunk road 70 Flat, non-sinuous Free-flow 120071 Rural national/regional trunk road 90 Flat, non-sinuous Free-flow 120091 Rural national/regional trunk road 90 Hilly, sinuous Free-flow 120091 Rural national/regional trunk road 90 Hilly, sinuous Free-flow 120091 Rural national/regional trunk road 90 Hilly, sinuous Free-flow Urban national/regional motorway 110 Flat, non-sinuous Heavy traffic Urban national/regional motorway 110 Flat, non-sinuous Stop and go Urban national/regional motorway 130 Flat, non-sinuous Free-flow Urban City or urban motorway 80 Flat, non-sinuous Heavy traffic Urban City or urban motorway 90 Flat, non-sinuous Free-flow Urban City or urban motorway 100 Flat, non-sinuous Free-flow Urban City or urban motorway 100 Flat, non-sinuous Heavy traffic Urban City primary road, major arterial 50 Flat, non-sinuous Free-flow Urban City primary road, major arterial 50 Flat, non-sinuous Heavy traffic Urban City primary road, major arterial 50 Flat, non-sinuous Saturated Urban City primary road, major arterial 50 Flat, non-sinuous Stop and go Urban City primary road, major arterial 60 Flat, non-sinuous Free-flow Urban City primary road, major arterial 60 Flat, non-sinuous Heavy traffic Urban City primary road, major arterial 60 Flat, non-sinuous Saturated Urban City primary road, major arterial 60 Flat, non-sinuous Stop and go Urban City primary road, major arterial 80 Flat, non-sinuous Free-flow Urban City primary road, major arterial 80 Flat, non-sinuous Heavy traffic Urban City primary road, major arterial 80 Flat, non-sinuous Stop and go Urban Districts distributor, inter district road 50 Flat, non-sinuous Free-flow Urban Districts distributor, inter district road 50 Flat, non-sinuous Heavy traffic Urban Districts distributor, inter district road 50 Flat, non-sinuous Urban Local access 30 Flat, non-sinuous Stop and go 250034 Urban Local access 40 Flat, non-sinuous Free-flow 250041 Urban Local access 40 Flat, non-sinuous Saturated Euro All a+cx+ex^2 Annex 19: ID speed curve 2 ID speed curve 38 50 Fuel Pol. Em. Std. ID speed curve 3 4 1 8 7 5 6 16 17 18 19 12 13 14 15 9 10 11 28 29 30 39 36 49 0.995787107 -0.018772272 0.000108897 Euro All a+cx+ex^2 0.899711748 -0.017417942 8.77264E-05 Annex 21: Emission functions acc. to speed based on RTP CO 250043 37 Urban Local access 40 Flat, non-sinuous Stop and go 250044 34 Urban Local access 50 Flat, non-sinuous Free-flow 250051 48 Urban Local access 50 Flat, non-sinuous Saturated 250053 35 Urban Local access 50 Flat, non-sinuous Stop and go 250054 absolute weight (André, 2004a) ID spee d curve Name of the cyle or sub-cycle Area Road Category Speed limit (km/h) Gradient, sinuosity Traffic condition European European composite European 130 European 130 composite TS_Artemis.urban Urban Districts distributor, inter district road 50 Flat, non-sinuous Heavy traffic 0.292 0.292 TS_Artemis.urban_1 Urban Districts distributor, inter district road 50 Flat, non-sinuous Saturated 0.059 Euro All a+cx+ex^2+f/x 0.168637914 -0.002924642 1.24692E-05 1.095523771 <2.0 (a+cx+ex^2)/(1+bx+dx^2) 0.142282293 0.013776793 -0.002007015 -1.89805E-05 1.14818E-05 Euro >2.0 a+cx+ex^2 0.159093324 -0.002460623 1.2138E-05 <2.0 (a+cx+ex^2)/(1+bx+dx^2) 0.161234564 0.074607063 -0.001206231 -0.000335154 3.6292E-06 Euro Poll. fuel emis. st. shape order a 0 a 1 (x 100) but a 2 (x 10 4 ) a 3 (x 10 6 ) a 4 (x 10 8 ) a 5 (x 10 10 ) poll. fuel emis. st. shape order a 0 a 1 (x 100) a 2 (x 10 4 ) a 3 (x 10 6 ) a 4 (x 10 8 ) a 5 (x 10 10 ) eng. size HC power: a 1 >2.0 (a+cx+ex^2)/(1+bx+dx^2) 50057.71156 38026.82833 8033.150994 1150.215685 -26.61240156 <2.0 (a+cx+ex^2)/(1+bx+dx^2) 0.096521338 0.103000188 -0.000238314 -7.23554E-05 1.93331E-06 Euro >2.0 a+cx+ex^2 0.09124181 -0.001682045 8.93739E-06 Euro All (a+cx+ex^2)/(1+bx+dx^2) 3.095607924 0.141192269 -0.006175676 -0.000503115 0.000421523 Euro All (a+cx+ex^2)/(1+bx+dx^2) 2.398097386 0.076699891 -0.011576236 -0.000499938 0.000119971 NOx Euro All (a+cx+ex^2)/(1+bx+dx^2) 2.816405992 0.198187904 0.066873591 -0.001431755 -0.000463021 Euro All a+cx+ex^2 0.113797282 -0.00232673 pre Euro Polyn. 4 58.406 -285.959 596.918 -536.230 pre Euro Polyn. 5 675.3 -3704.9 10309.8 -13928.8 9050.0 -2240.0 176.000 Euro 1 Polyn. 4 8.745 -41.392 83.669 -73.635 24.200 Euro 2 Polyn. 3 1.881 -0.626 -5.965 5.793 Euro 3 Polyn. 2 1.290 -4.327 4.914 petrol Euro 1 Polyn. 5 571.3 -2888.2 7817.7 -10321.3 6574.8 -1600.8 Euro 2 Polyn. 5 666.4 -3654.8 10057.5 -13086.5 8002.0 -1835.4 Euro 3 Polyn. 4 521.9 -1982.3 3928.5 -3390.2 1080.3 all petrol diesel Euro 4 Polyn. 2 0.635 -2.808 3.343 pre Euro Power Euro 4 Polyn. 4 523.7 -1654.4 2635.4 -1771.5 442.9 CO pre Euro Polyn. 5 541.5 -2914.5 8401.0 -11717.0 7895.2 -2032.0 3.96601 -0.51532 2.2605E-05 Euro All a+cx+ex^2 0.086648957 -0.001421038 1.05592E-05 PM Euro All a+cx+ex^2 0.051499481 -0.000880012 8.11743E-06 Euro 1 Power 1.19086 -0.27682 Euro 2 Power 7.40303 -0.98045 Euro 3 Polyn. 3 0.43407 -1.03732 0.87366 -0.24979 diesel Euro 1 Polyn. 4 389.9 -1337.6 2561.0 -2081.5 633.9 Euro 2 Polyn. 4 444.3 -1623.8 3124.2 -2562.1 779.4 Euro 3 Polyn. 4 429.7 -1516.1 2930.1 -2480.6 all diesel 780.2 0.059 TS_Artemis.urban_2 Urban Districts distributor, inter district road 50 Flat, non-sinuous Saturated 0.122 0.122 TS_Artemis.urban_3 Urban Districts distributor, inter district road 50 Flat, non-sinuous Stop and go 0.037 0.037 <2.0 (a+cx+ex^2)/(1+bx+dx^2) 144.6558266 0.067270936 -0.187518726 -0.000316808 0.009469874 Euro 4 Power 2.77591 -1.29524 Euro 4 Polyn. 4 313.2 -959.4 1756.5 -1407.2 439.8 Euro >2.0 (a+cx+ex^2)/(1+bx+dx^2) 194.8899162 0.071928682 0.18722555 -0.000332188 0.009988524 pre Euro Polyn. 4 5.57576 -19.06828 29.83329 -21.15120 5.66600 pre Euro Polyn. 5 497.2 -2768.1 8112.8 -11624.8 8031.3 -2117.7 Saturated 31 Urban Districts distributor, inter district road 50 Flat, non-sinuous Stop and go 24 Urban Districts distributor, inter district road 60 Flat, non-sinuous Free-flow 25 Urban Districts distributor, inter district road 60 Flat, non-sinuous Heavy traffic 26 Urban Districts distributor, inter district road 60 Flat, non-sinuous Saturated 27 Urban Districts distributor, inter district road 60 Flat, non-sinuous Stop and go TS_Artemis.urban_4 Urban Districts distributor, inter district road 50 Flat, non-sinuous Stop and go 0.024 0.024 TS_Artemis.urban_5 Urban Districts distributor, inter district road 50 Flat, non-sinuous Heavy traffic 0.051 0.051 TS_Artemis.road Rural distributor, inter village road 90 Flat, non-sinuous Heavy traffic 0.449 0.449 TS_Artemis.road_1 Rural distributor, inter village road 90 Flat, non-sinuous Heavy traffic 0.108 0.108 TS_Artemis.road_2 Rural distributor, inter village road 90 Flat, non-sinuous Heavy traffic 0.072 0.072 <2.0 (a+cx+ex^2)/(1+bx+dx^2) 142.2433943 0.049847679 -0.651010459 -0.000169078 0.013231348 Euro >2.0 (a+cx+ex^2)/(1+bx+dx^2) 194.8899162 0.071928682 0.18722555 -0.000332188 0.009988524 FC Euro 1 Polyn. 4 0.86030 -3.33338 5.62091 -4.33858 1.28345 Euro 2 Polyn. 3 0.26336 -0.80892 0.93837 -0.34909 Euro 3 Polyn. 3 0.04445 -0.14022 0.19680 -0.06000 petrol Euro 1 Polyn. 4 476.9 -2085.1 4515.6 -4151.0 1377.7 Euro 2 Polyn. 4 505.9 -2304.4 5188.9 -4898.6 1645.6 Euro 3 Polyn. 4 434.0 -1508.0 2919.6 -2507.4 <1.4 l 804.4 <2.0 (a+cx+ex^2)/(1+bx+dx^2) 161.9413328 0.122981533 2.183647639 -0.000775895 -0.012779891 Euro >2.0 (a+cx+ex^2)/(1+bx+dx^2) 194.8899162 0.071928682 0.18722555 -0.000332188 0.009988524 Euro 4 Polyn. 2 0.00531 -0.00402 0.00697 pre Euro Power 1.25580 -0.70775 Euro 4 Polyn. 4 395.0 -1136.0 1731.8 -1086.0 248.5 HC pre Euro Polyn. 5 651.7 -3857.4 11781.8 -17414.1 12247.4 -3265.0 120091 Rural distributor, inter village road 50 Flat, non-sinuous Free-flow 130051 Rural distributor, inter village road 70 Mountainous, sinuous Free-flow 130071 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 Rural distributor, inter village road 90 Mountainous, sinuous Free-flow 130091 20 Urban Districts distributor, inter district road 70 Flat, non-sinuous Free-flow 21 Urban Districts distributor, inter district road 70 Flat, non-sinuous Heavy traffic 22 Urban Districts distributor, inter district road 70 Flat, non-sinuous Saturated 23 Urban Districts distributor, inter district road 70 Flat, non-sinuous Stop and go 32 Urban Local distributor 50 Flat, non-sinuous Free-flow 47 Urban Local distributor 50 Flat, non-sinuous Saturated 33 Urban Local distributor 50 Flat, non-sinuous Stop and go INRETS report n°LTE 0523 TS_Artemis.road_3 Rural distributor, inter village road 90 Flat, non-sinuous Heavy traffic 0.088 0.088 TS_Artemis.road_4 Rural distributor, inter village road 90 Flat, non-sinuous Heavy traffic 0.118 0.118 TS_Artemis.road_5 Rural national/regional trunk road 90 Flat, non-sinuous Heavy traffic 0.062 0.062 TS_Artemis.motorway Rural national/regional motorway 130 Fuel Pol. Em. Std. Eng. cap. Equation a b c d e f Euro 1 Polyn. 4 0.13189 -0.52432 0.95521 -0.75054 0.21400 Euro 2 Power 0.77744 -0.79025 Euro 3 Power 0.49531 -0.84573 diesel Euro 1 Polyn. 5 670.9 -3566.4 9913.8 -13327.0 8578.9 -2105.0 Euro 2 Polyn. 5 709.2 -3739.9 9864.6 -12420.5 7413.7 -1667.0 Euro 3 Polyn. 4 561.3 -2131.9 4130.1 -3447.4 1.4-2 l CO 2 petrol 1060.1 Flat, non-sinuous Heavy traffic 0.259 TS_Artemis.motorway_1 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic 0.093 0.095 TS_Artemis.motorway_2 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic 0.060 0.063 TS_Artemis.motorway_3 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic 0.062 TS_Artemis.motorway_4 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic 0.044 TS_Artemis.motorway130 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic CO Euro All a+cx+ex^2 0.000195394 3.80498E-05 Euro 4 Power 0.07700 -0.46350 Euro 4 Polyn. 4 497.8 -1728.7 3078.9 -2307.1 634.8 -2.639E-07 HC Euro All a+cx+ex^2 0.000550112 -8.5364E-06 4.94E-08 NOx Euro All a+cx+ex^2 0.014777397 -0.000419555 4.2874E-06 pre Euro Polyn. 3 1.53321 -1.47482 0.82264 0.98000 pre Euro Polyn. 5 1052.7 -6376.0 18827.6 -26956.0 18485.8 -4829.1 hybrid FC Euro All a+cx+ex^2 19.36416487 0.060648361 0.000753966 Euro 1 Polyn. 4 0.75701 -3.02899 6.51466 -5.75000 1.90000 Euro 2 Polyn. 3 0.55040 -1.05604 1.20798 -0.44300 Euro 3 Polyn. 2 0.12700 -0.16341 0.09709 Euro 1 Polyn. 4 644.8 -2708.0 5396.3 -4487.5 1374.7 Euro 2 Polyn. 4 931.8 -4584.3 9521.8 -8018.4 2382.1 >2 l petrol Euro 3 Polyn. 4 649.8 -2267.5 4049.3 -3182.1 935.8 0.259 TS_Artemis.motorway130_3 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic 0.061 TS_Artemis.motorway130_4 Rural national/regional motorway 130 Flat, non-sinuous Heavy traffic Euro 4 Polyn. 3 0.04399 0.12464 -0.39284 0.24000 pre Euro Polyn. 5 2.09128 -9.00145 22.72120 -29.65980 19.29000 -5.00000 Euro 4 Polyn. 3 672.0 -1723.5 2025.0 -755.0 0.0 NOx pre Euro Polyn. 4 456.0 -1942.3 4084.8 -3634.5 1181.3 0.040 Euro 1 Polyn. 5 2.02150 -9.98383 27.72528 -38.90979 26.93542 -7.14000 Euro 2 Polyn. 4 2.12341 -8.88250 18.62007 -16.94811 5.77800 Euro 3 Polyn. 4 2.25413 -9.34458 19.32741 -16.37522 5.03116 Euro 1 Polyn. 4 393.5 -1404.4 2833.6 -2476.7 808.3 Euro 2 Polyn. 4 433.5 -1642.2 3290.2 -2819.0 890.1 <2 l diesel Euro 4 Polyn. 3 0.78737 -1.91047 2.56467 -0.70976 pre Euro Polyn. 2 0.01217 -0.03603 0.03265 Euro 3 Polyn. 4 412.0 -1449.1 2818.8 -2388.6 750.2 Euro 4 Polyn. 4 313.2 -959.4 1756.5 -1407.2 439.8 pre Euro Polyn. 5 549.9 -2691.2 7720.0 -10806.3 7315.9 -1892.0 diesel
	petrol	Euro 1 Euro 2 Euro 3 Euro 3 Polyn. Polyn. Power Polyn. 2 3 Euro 1 Polyn. Euro 2 Polyn. >2 l	0.01217 -0.03603 0.00733 -0.02552 0.00823 -0.22603 5 534.9 -2403.7 0.03265 0.03021 5923.5 4 539.9 -1888.4 3346.0 4 542.1 -2003.9 3796.4	-0.00589 -6819.5 -2344.2 -2993.4	3685.9 584.6 863.3	-752.0
	PM	Euro 4 pre Euro Polyn. Power Euro 4	4	0.00223 -0.04700 0.40498 -1.87681	3.91769	-3.57870	1.23000
	diesel	Euro 1 Euro 2 Euro 3	Polyn. Polyn. Polyn.	3 2 2	0.08428 0.07940 -0.08685 0.01414 -0.30544 0.07496 0.05995 -0.12415 0.10291	0.27900
		Euro 4	Polyn.	4	0.05030 -0.11472	0.28151	-0.40400	0.24000
	Formulae polynomial functions: See next page
	Formulae of power function is: emis. factor [g/km] = a 0 . V a1
	with V [km/h]
	For V > 125 km/h, EF(V) = EF(125)

Figure 63: Conversion ratios of NOx emission factors of Reference test patterns for petrol cars between pre Euro 1, Euro 1, Euro 4, and resp. Euro 2, Euro 2, Euro 3 categories. Emission factors are in Annex 17.

Figure 64: Conversion ratios of NOx emission factors of Reference test patterns for diesel cars between pre Euro 1, Euro 1, Euro 4, and resp. Euro 2, Euro 2, Euro 3 categories. Emission factors are in Annex 17.

Table 34 :

 34 Emission factors for active surface area and particle number of the total particle population.

	Solid particle population [#/km] ×10 -13	Number of solid particles <50 nm		Number of solid particles 50-100 nm
	Category	Urban	Rural	Motorway	Urban	Rural	Motorway
	PC diesel Euro-1	8.5	8.6		7.2		9.3	7.8	7.3
	PC diesel Euro-2	7.6	7.6		6.1		8.8	7.7	7.2
	PC diesel Euro-3	7.9	7.1		5.8		8.7	6.8	6.9
	PC diesel Euro-3 DPF	0.0055	0.0040	0.023		0.0023	0.0016	0.0094
	PC petrol Euro-1	0.32	0.24	0.086		0.14	0.10	0.034
	PC petrol Euro-3	0.0096	0.011	0.0055	0.0044	0.0054	0.0028
	PC petrol Euro-3 DISI	0.81	0.61	0.28		0.65	0.36	0.19
	Solid particle population [#/km] ×10 -13	Number of solid particles 100-1000 nm
		Category		Urban	Rural	Motorway
		PC diesel Euro-1		5.4		3.8		4.0
		PC diesel Euro-2		5.1		3.6		4.0
		PC diesel Euro-3		4.5		3.2		3.5
	PC diesel Euro-3 DPF		0.0016	0.0012	0.0028
		PC petrol Euro-1		0.052	0.037		0.012
		PC petrol Euro-3		0.0026	0.0034	0.0051
	PC petrol Euro-3 DISI		0.41		0.21		0.15

Table 35 :

 35

	Annex 24:	Equation of emission factors for LDVs
	Fuel	Weight cat.	Standard Cat.	Poll.	Load (%) range	Equation factor E [g/km]; v = average speed [km/h]; p = load [%]
				CO		E= 5,83x10 -4 v 2 -6,99x10 -2 v + 2,53
				CO 2		E= 0,146v 2 -15,6v + 590
				FC		E= 4,75x10 -2 v 2 -5,05v + 191

Emission factors for solid particle number in the size ranges 7-50 nm, 50-100 nm and 100 nm -1 μm (aerodynamic diameter) -Fuel specifications later or equal to EN590:2000.

Table 36 :

 36 Emission degradation correction factor y = a x Mileage + b, for Euro 1 and Euro 2 petrol vehicles. Mileage expressed in km, y normalised for the corresponding average mileage.

	Petrol Euro 3 & 4	Capacity class [l]	Average mileage [km]	a	b	Value at ≥ 160 000 km
	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
	for V≤19 km/h	HC	≤1.4 >1.4	31 972 17 913	3.419E-06 0	0.891 1	1.44 1
	(urban situation) NOx	≤1.4 >1.4	31 313 16 993	0 3.986E-06	1 0.932	1 1.57
	y(rural) for	CO	≤1.4 >1.4	30 123 26 150	1.502E-06 0	0.955 1	1.20 1
	V≥63 km/h	HC	all	28 042	0	1	1
	(rural situation)	NOx	all	26 150	0	1	1

Table 37 :

 37 Emission degradation correction factor y = a x Mileage + b, for Euro 3 and Euro 4 petrol vehicles. Mileage expressed in km, y normalised for the corresponding average mileage.

	Annex 26:	Equations of the influence of air temperature
				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	-0.0525	2.21	-0.025	1.57	-0.001	1.02
			Euro 4	3.4627	-0.0544	0.0107	0.7442	-	-
				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 38 :

 38 Correction factor y = a x Temperature + b, or y = a e b x Temperature when in blue italics bold, for urban, rural or motorway driving behaviour. Temperature in °C. y normalised at 23°C.

	Annex 27:	Equations of the influence of air humidity on
		NOx emissions
					urban	rural
					a	b	a	b
	Uncorrected emissions	NOx	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
	Already corrected emissions	NOx	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

Table 39 :

 39 Correction factor y = a x Humidity + b, for NOx emissions already corrected or not by using the standard (or legislative) method, and for urban or rural driving behaviour. Humidity in g H 2 O/kg dry air, y normalised at 10.71 g H 2 O/kg dry air.It is recommended to use the rural figures for motorway driving behaviour, 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.

	Annex 28:	Road gradient factors
				Diesel vehicle			Petrol vehicle
		gradient	FC	NOx	HC	CO	PM	FC	NOx	HC	CO
		0%	1	1	1	1	1	1	1	1	1
		2%	1.241	1.345	0.981	1.037	1.083	1.129	1.359	1.048	1.099
	urban	-2% 4% -4%	0.773 1.534 0.578	0.692 1.783 0.453	1.006 0.995 1.035	0.971 1.067 0.953	0.921 1.263 0.876	0.869 1.291 0.767	0.689 1.883 0.490	0.968 1.128 1.013	0.910 1.203 0.842
		6%	1.902	2.382	1.045	1.655	1.550	1.460	2.459	1.176	1.277
		-6%	0.386	0.265	0.918	0.814	0.699	0.680	0.332	1.015	0.775
		0%	1	1	1	1	1	1	1	1	1
		2%	1.308	1.424	0.975	1.109	1.156	1.183	1.335	1.111	1.281
		-2%	0.818	0.735	1.292	1.153	1.165	0.868	0.759	0.951	0.793
		4%	1.656	1.897	0.958	1.129	1.384	1.381	1.699	1.263	1.656
	rural	-4% 6%	0.577 2.065	0.451 2.448	1.293 1.112	1.105 1.097	1.043 1.819	0.699 1.576	0.494 2.001	0.962 1.424	0.675 1.956
		-6%	0.321	0.214	1.043	0.836	0.686	0.582	0.302	1.046	0.595
		8%	2.437	2.888	1.280	1.316	2.102	1.827	2.475	1.656	1.935
		-8%	0.174	0.085	1.109	0.839	0.610	0.529	0.147	1.191	0.576
		10%	2.905	3.325	1.821	1.897	2.609	2.070	2.569	1.981	1.755
		-10%	0.109	0.046	0.994	0.726	0.471	0.481	0.097	1.132	0.528
		0%	1	1	1	1	1	1	1	1	1
		2%	1.354	1.485	0.885	0.874	1.180	1.248	1.148	1.323	1.791
	motorway	-2% 4% -4%	0.663 1.667 0.339	0.584 1.946 0.218	1.127 0.824 1.130	1.206 0.974 1.131	0.790 1.278 0.588	0.775 1.432 0.564	0.748 1.278 0.394	0.754 1.571 0.620	0.554 2.192 0.360
		6%	2.057	2.620	0.810	0.946	1.578	1.703	1.546	1.856	2.887
		-6%	0.134	0.047	1.074	0.975	0.474	0.416	0.194	0.587	0.282

Table 40 :

 40 Road gradient factors for Euro 0 diesel and petrol vehicles

	INRETS report n°LTE 0523

Table 41 :

 41 Road gradient factors for Euro 1 diesel and petrol vehicles

				Diesel vehicle			Petrol vehicle
		gradient	FC	NOx	HC	CO	PM	FC	NOx	HC	CO
		0%	1	1	1	1	1	1	1	1	1
		2%	1.245	1.352	0.945	0.987	1.235	1.190	1.125	1.312	1.354
	urban	-2% 4% -4%	0.770 1.540 0.572	0.687 1.797 0.444	1.047 0.941 1.125	1.019 1.013 1.066	0.764 1.577 0.575	0.807 1.438 0.637	0.819 1.231 0.623	0.728 1.918 0.564	0.716 2.118 0.524
		6%	1.909	2.403	0.979	1.568	2.003	1.722	1.414	2.798	3.423
		-6%	0.380	0.257	1.036	0.947	0.338	0.511	0.512	0.438	0.376
		0%	1	1	1	1	1	1	1	1	1
		2%	1.311	1.430	0.890	1.011	1.313	1.229	1.236	1.445	1.488
		-2%	0.813	0.727	1.491	1.464	0.922	0.822	0.815	0.708	0.709
		4%	1.661	1.908	0.814	0.979	1.675	1.469	1.469	2.097	2.276
	rural	-4% 6%	0.570 2.070	0.442 2.460	1.573 0.995	1.493 1.030	0.649 2.154	0.601 1.702	0.536 1.654	0.463 2.663	0.431 2.920
		-6%	0.316	0.209	1.326	1.141	0.283	0.448	0.397	0.299	0.258
		8%	2.443	2.904	1.147	1.265	2.503	2.110	2.537	2.988	3.396
		-8%	0.169	0.082	1.505	1.251	0.127	0.337	0.311	0.189	0.138
		10%	2.911	3.340	1.740	1.929	2.938	2.471	2.699	2.532	2.461
		-10%	0.106	0.044	1.408	1.119	0.067	0.272	0.256	0.152	0.092
		0%	1	1	1	1	1	1	1	1	1
		2%	1.356	1.489	0.745	0.666	1.331	1.271	1.735	1.786	2.048
	motorway	-2% 4% -4%	0.661 1.672 0.335	0.581 1.954 0.213	1.253 0.524 1.406	1.381 0.519 1.488	0.643 1.579 0.276	0.732 1.474 0.461	0.540 1.701 0.361	0.537 2.593 0.244	0.493 3.022 0.220
		6%	2.065	2.632	0.432	0.451	2.006	1.665	1.972	3.882	4.465
		-6%	0.129	0.044	1.474	1.453	0.081	0.265	0.227	0.130	0.120

Table 42 :

 42 Road gradient factors for Euro 2 diesel and petrol vehicles

	INRETS report n°LTE 0523

Table 43 :

 43 Road gradient factors for Euro 3 diesel and petrol vehicles

				Diesel vehicle			Petrol vehicle
		gradient	FC	NOx	HC	CO	PM	FC	NOx	HC	CO
		0%	1	1	1	1	1	1	1	1	1
		2%	1.197	1.404	1.075	1.650	1.207	1.180	1.127	1.100	1.296
	urban	-2% 4% -4%	0.827 1.440 0.686	0.673 1.954 0.411	0.905 1.192 0.794	0.655 2.226 0.504	0.855 1.426 0.772	0.831 1.400 0.662	0.842 1.120 0.662	0.930 1.207 0.896	0.781 1.820 0.614
		6%	1.715	2.686	1.315	2.642	1.564	1.659	1.203	1.396	2.693
		-6%	0.544	0.253	0.591	0.424	0.635	0.537	0.535	0.893	0.411
		0%	1	1	1	1	1	1	1	1	1
		2%	1.271	1.481	1.114	1.622	1.503	1.229	1.061	1.214	1.278
		-2%	0.766	0.642	0.880	0.560	0.681	0.804	0.852	0.826	0.837
		4%	1.573	2.030	1.235	1.873	1.892	1.474	1.116	1.484	1.553
	rural	-4% 6%	0.587 1.915	0.387 2.468	0.721 1.515	0.332 1.552	0.489 2.102	0.624 1.709	0.699 1.281	0.687 1.698	0.665 1.659
		-6%	0.471	0.207	0.570	0.210	0.392	0.486	0.579	0.593	0.502
		8%	2.244	2.855	1.784	1.862	2.586	2.035	1.362	2.060	2.692
		-8%	0.383	0.074	0.471	0.139	0.369	0.384	0.482	0.648	0.294
		10%	2.691	3.274	2.390	2.501	3.550	2.399	2.100	2.190	2.935
		-10%	0.313	0.040	0.383	0.104	0.438	0.312	0.413	0.678	0.178
		0%	1	1	1	1	1	1	1	1	1
		2%	1.315	1.715	1.124	3.223	1.287	1.281	0.922	1.415	1.235
	motorway	-2% 4% -4%	0.706 1.602 0.434	0.527 2.428 0.192	0.922 1.232 0.736	0.104 5.862 0.040	0.762 1.548 0.554	0.740 1.530 0.483	0.989 1.030 0.898	0.737 1.902 0.543	0.722 1.364 0.435
		6%	1.919	3.335	1.458	6.940	1.905	1.923	1.006	2.763	1.785
		-6%	0.277	0.045	0.381	0.013	0.429	0.299	0.584	0.376	0.325

Table 44 :

 44 Road gradient factors for Euro 4 diesel and petrol vehicles

Annex 29: Cities considered for auxiliary emission modelling and Köppen classes

		DNK	COPENHAGEN	12.67	55.63	Cfb	8.3
		ESP ITA	BARCELONA TORINO	2.07 7.65	41.28 45.22	Cfa Cfa	15.7 12.2
		ESP ITA	MADRID VENICE	-3.55 12.33	40.45 45.5	Cfa Cfa	14.3 13.2
		ESP NLD	PALMA AMSTERDAM	2.73 4.77	39.55 52.3	Cfa Cfb	16.7 10.0
		ESP NLD	SANTANDER BEEK	-3.82 5.78	43.47 50.92	Cfb Cfb	14.8 10.1
		ESP NLD	SEVILLA GRONINGEN	-5.9 6.58	37.42 53.13	Csa Cfb	18.4 9.1
		ESP POL	VALENCIA KOLOBRZEG	-0.47 15.58	39.5 54.18	Cfa Dfb	17.3 8.5
		FIN POL	HELSINKI KRAKOW	24.97 19.8	60.32 50.08	Dfb Dfb	5.2 8.2
		FIN POL	TAMPERE POZNAN	23.58 16.83	61.42 52.42	Dfb Dfb	4.3 8.6
		FRA POL	BORDEAUX WARSAW	-0.7 20.97	44.83 52.17	Cfb Dfb	13.2 8.4
		FRA PRT	BREST BRAGANCA	-4.42 -6.73	48.45 41.8	Cfb Cfb	11.2 12.4
		FRA PRT	CLERMONT-FERRAND COIMBRA	3.17 -8.42	45.78 40.2	Cfb Csb	11.4 15.3
		FRA PRT	DIJON EVORA	5.08 -7.9	47.27 38.57	Cfb Cfa	10.7 15.8
		FRA PRT	LYON FARO	5.08 -7.97	45.73 37.02	Cfb Cfa	11.9 17.8
		FRA PRT	MARSEILLE LAJES	5.23 -27.1	43.45 38.77	Cfa Cfa	14.8 17.5
		FRA PRT	MONTPELLIER PORTO	3.97 -8.68	43.58 41.23	Cfa Csb	14.8 14.3
		FRA SVK	NANCY BRATISLAVA	6.22 17.2	48.68 48.2	Cfb Dfb	10.2 10.4
		FRA SVK	NANTES KOSICE	-1.6 21.27	47.17 48.7	Cfb Dfb	12.2 9.1
		FRA SWE	NICE GOTEBORG_ LANDVETTER	7.2 12.3	43.65 57.67	Cfa Dfb	15.5 6.5
		FRA SWE	PARIS_ ORLY KARLSTAD	2.4 13.47	48.73 59.37	Cfb Dfb	11.1 5.9
		FRA SWE	STRASBOURG KIRUNA	7.63 20.33	48.55 67.82	Cfb Dfc	10.3 -1.1
		GBR SWE	ABERDEEN/DYCE OSTERSUND/FROSON	-2.22 14.5	57.2 63.18	Cfb Dfc	8.4 3.1
		GBR SWE	AUGHTON STOCKHOLM_ ARLANDA	-2.92 17.95	53.55 59.65	Cfb Dfb	9.5 6.5
		GBR	BELFAST	-6.22	54.65	Cfb	9.1
		GBR	BIRMINGHAM	-1.73	52.45	Cfb	9.7
		GBR	FINNINGLEY	-1	53.48	Cfb	9.5
		GBR	HEMSBY	1.68	52.68	Cfb	9.9
	Id country GBR	city JERSEY/CHANNEL ISLANDS	longitude latitude Köppen class average temperature -2.2 49.22 Cfb 11.2
	1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20	AUT GBR AUT GBR AUT GBR AUT GRC AUT GRC BEL GRC BEL IRL BEL IRL CHE IRL CZE IRL CZE IRL DEU IRL DEU IRL DEU ITA DEU ITA DEU ITA DEU ITA DEU ITA DEU ITA DEU ITA ITA	GRAZ LEUCHARS INNSBRUCK LONDON/GATWICK LINZ OBAN SALZBURG ANDRAVIDA VIENNA_ SCHWECHAT ATHENS BRUSSELS THESSALONIKI OOSTENDE BELMULLET SAINT HUBERT BIRR GENEVA CLONES OSTRAVA DUBLIN PRAGUE KILKENNY BERLIN MALIN BREMEN VALENTIA OBSERVATORY DUSSELDORF BRINDISI FRANKFURT AM MAIN GENOVA HAMBURG MESSINA KOLN MILAN MANNHEIM NAPLES MUNICH PALERMO STUTTGART PISA ROME	15.43 -2.87 11.35 -0.18 14.2 -5.47 13 21.28 16.57 23.73 4.53 22.97 2.87 -10 5.4 -7.88 6.13 -7.23 18.18 -6.25 14.28 -7.27 13.4 -7.33 8.8 -10.25 6.78 17.95 8.6 8.85 10 15.55 7.17 8.73 8.55 14.3 11.7 13.1 9.22 10.38 12.23	47 56.38 47.27 51.15 48.23 56.42 47.8 37.92 48.12 37.9 50.9 40.52 51.2 54.23 50.03 53.08 46.25 54.18 49.72 53.43 50.1 52.67 52.47 55.37 53.05 51.93 51.28 40.65 50.05 44.42 53.63 38.2 50.87 45.62 49.52 40.85 48.13 38.18 48.68 43.68 41.8	Dfb Cfb Dfb Cfb Dfb Cfb Dfb Csa Dfb Cfa Cfb Cfa Cfb Cfb Dfb Cfb Cfb Cfb Dfb Cfb Dfb Cfb Cfb Cfb Cfb Cfb Cfb Cfa Cfb Cfa Cfb Cfa Cfb Cfa Cfb Cfa Dfb Cfa Dfb Cfa Cfa	8.7 9.5 10.2 9.0 9.3 9.2 16.7 9.3 17.9 10.0 15.4 10.3 10.3 10.3 9.6 7.5 9.1 10.4 9.8 8.5 9.7 8.1 9.7 9.8 11.0 8.9 17.1 10.5 16.1 10.1 18.9 9.0 11.8 9.9 16.3 11.1 18.8 8.0 14.6 9.1 15.8
							INRETS report n°LTE 0523

Figure 65: Localization of the 91 European cities considered for auxiliary emission modelling.

Table 45 :

 45 Characteristics of the 91 European locations considered for auxiliary emission modelling, in terms on longitude, latitude, temperature and Köppen class.

	Cfa mild mid-latitude, moist with an average temperature of the warmest month above 22°C
	Cfb similar to Cfa with a cooler warmest month
	Csa Mediterranean climate, with an average temperature of the warmest month above 22°C
	Csb similar to Csa with a cooler warmest month
		moist continental mid-latitude climates, wet at all seasons with an average temperature of
	Dfb	warmest month below 22°C and an average temperature of the 4 warmest months above
		10°C
	Dfc close to Dfb, with an average temperature of 1 to 3 warmest months above 10°C

Table 46 :

 46 Modified Köppen climate classes of European locations.

	Annex 30:	Values of hourly fuel consumption of the
		auxiliaries simplified model
	Id (see Annex 29)	a 1	a 2	a 3	a 4	a 5
	1	-0.863	0.0402	-0.0376	0.0334	-0.00164
	2	-0.722	0.0312	-0.0294	0.0355	-0.00160
	3	-0.731	0.0332	-0.0326	0.0348	-0.00168
	4	-0.834	0.0362	-0.0339	0.0383	-0.00176
	5	-0.808	0.0355	-0.0329	0.0360	-0.00164
	6	-0.890	0.0386	-0.0349	0.0391	-0.00171
	7	-0.946	0.0370	-0.0330	0.0556	-0.00226
	8	-0.818	0.0302	-0.0281	0.0536	-0.00217
	9	-0.799	0.0346	-0.0333	0.0386	-0.00172
		-0.831	0.0364	-0.0336	0.0377	-0.00176
		-0.799	0.0345	-0.0323	0.0385	-0.00176
		-0.732	0.0319	-0.0300	0.0344	-0.00155
		-0.842	0.0363	-0.0355	0.0406	-0.00182
		-0.761	0.0322	-0.0306	0.0356	-0.00154
		-0.797	0.0345	-0.0322	0.0377	-0.00170
		-0.829	0.0353	-0.0324	0.0399	-0.00174
		-0.755	0.0326	-0.0315	0.0367	-0.00166
		-0.786	0.0342	-0.0335	0.0359	-0.00163
		-0.799	0.0334	-0.0319	0.0440	-0.00197
		-0.763	0.0328	-0.0319	0.0396	-0.00181
		-0.794	0.0307	-0.0284	0.0484	-0.00201
		-1.110	0.0461	-0.0416	0.0481	-0.00192
		-0.822	0.0331	-0.0338	0.0437	-0.00185
		-1.182	0.0504	-0.0450	0.0471	-0.00192
		-0.968	0.0393	-0.0344	0.0484	-0.00189
		-0.924	0.0383	-0.0391	0.0407	-0.00176
		-1.060	0.0454	-0.0408	0.0408	-0.00169
		-0.793	0.0291	-0.0292	0.0517	-0.00211
		-0.729	0.0277	-0.0283	0.0461	-0.00190
		-0.877	0.0355	-0.0354	0.0458	-0.00192
		-1.192	0.0370	-0.0328	0.0888	-0.00332
		-0.802	0.0336	-0.0332	0.0408	-0.00178
		-0.927	0.0400	-0.0377	0.0440	-0.00196
		-0.898	0.0375	-0.0373	0.0442	-0.00191
		-0.989	0.0408	-0.0384	0.0469	-0.00195
		-0.924	0.0390	-0.0375	0.0413	-0.00172
		-0.871	0.0377	-0.0342	0.0406	-0.00182
		-0.855	0.0343	-0.0345	0.0477	-0.00200
		-1.143	0.0468	-0.0413	0.0536	-0.00215
		-0.861	0.0367	-0.0346	0.0416	-0.00183
		-0.923	0.0416	-0.0365	0.0389	-0.00181
		-1.062	0.0394	-0.0327	0.0662	-0.00274
		-0.791	0.0308	-0.0264	0.0491	-0.00203
		-0.786	0.0313	-0.0271	0.0462	-0.00192
		-0.804	0.0305	-0.0289	0.0523	-0.00223

Table 47 :

 47 Values of hourly fuel consumption simplified model for hourly weather format for each location, as described in Annex 29.

	Id
	(see Annex 29)	a 1	a 2	a 3	a 4	a 5
	1	-1.286	0.0342	-0.0321	0.1281	-0.00477
	2	-0.869	0.0256	-0.0224	0.0825	-0.00300
	3	-1.119	0.0311	-0.0272	0.1071	-0.00394
	4	-1.189	0.0326	-0.0279	0.1157	-0.00429
	5	-1.099	0.0333	-0.0290	0.0971	-0.00364
	6	-1.245	0.0385	-0.0282	0.1039	-0.00375
	7	-1.071	0.0356	-0.0259	0.0894	-0.00331
	8	-0.873	0.0244	-0.0223	0.0836	-0.00302
	9	-1.118	0.0313	-0.0278	0.1043	-0.00375
		-1.125	0.0324	-0.0265	0.1141	-0.00440
		-1.296	0.0332	-0.0270	0.1311	-0.00488
		-0.952	0.0298	-0.0252	0.0817	-0.00303
		-1.390	0.0359	-0.0299	0.1353	-0.00490
		-0.891	0.0273	-0.0237	0.0785	-0.00287
		-1.039	0.0304	-0.0253	0.0945	-0.00341
		-1.079	0.0310	-0.0258	0.0996	-0.00363
		-1.115	0.0301	-0.0244	0.1073	-0.00386
		-1.135	0.0325	-0.0277	0.1057	-0.00389
		-1.194	0.0308	-0.0263	0.1244	-0.00459
		-1.318	0.0318	-0.0264	0.1340	-0.00481
		-0.936	0.0273	-0.0230	0.0892	-0.00338
		-1.069	0.0355	-0.0345	0.0887	-0.00331
		-1.040	0.0288	-0.0282	0.0982	-0.00346
		-1.266	0.0399	-0.0374	0.1188	-0.00449
		-0.945	0.0333	-0.0279	0.0709	-0.00265
		-1.301	0.0358	-0.0326	0.1179	-0.00418
		-1.110	0.0361	-0.0333	0.0934	-0.00344
		-1.118	0.0287	-0.0247	0.1105	-0.00401
		-0.993	0.0251	-0.0236	0.0992	-0.00355
		-1.150	0.0325	-0.0292	0.1063	-0.00381
		-1.559	0.0330	-0.0244	0.1633	-0.00576
		-1.136	0.0306	-0.0277	0.1104	-0.00399
		-1.319	0.0366	-0.0315	0.1235	-0.00446
		-1.207	0.0348	-0.0317	0.1100	-0.00401
		-1.133	0.0344	-0.0328	0.1032	-0.00386
		-1.128	0.0349	-0.0325	0.1009	-0.00378
		-1.146	0.0327	-0.0269	0.1089	-0.00394
		-1.195	0.0325	-0.0294	0.1118	-0.00399
		-1.169	0.0386	-0.0366	0.0958	-0.00362
		-1.129	0.0333	-0.0280	0.0990	-0.00355
		-1.377	0.0392	-0.0296	0.1269	-0.00460
		-1.458	0.0378	-0.0243	0.1503	-0.00576
		-0.873	0.0265	-0.0188	0.0794	-0.00296
		-0.768	0.0197	-0.0171	0.0776	-0.00283
		-1.317	0.0305	-0.0239	0.1376	-0.00504
		-1.135	0.0275	-0.0204	0.1190	-0.00436

Table 48 :

 48 Values of hourly fuel consumption simplified model for daily weather format for each location, as described in Annex 29.

	Id
	(see Annex 29)	a 1	a 2	a 3	a 4	a 5
	1	-0.403	0.0138	-0.0067	0.0493	-0.00186
	2	-0.219	0.0090	-0.0043	0.0259	-0.00096
	3	-0.345	0.0132	-0.0058	0.0381	-0.00142
	4	-0.301	0.0106	-0.0047	0.0381	-0.00142
	5	-0.371	0.0148	-0.0060	0.0392	-0.00148
	6	-0.258	0.0114	-0.0041	0.0252	-0.00091
	7	-0.293	0.0126	-0.0049	0.0267	-0.00098
	8	-0.149	0.0057	-0.0034	0.0182	-0.00068
	9	-0.425	0.0160	-0.0063	0.0443	-0.00163
		-0.322	0.0129	-0.0051	0.0375	-0.00144
		-0.289	0.0107	-0.0050	0.0352	-0.00134
		-0.279	0.0118	-0.0051	0.0286	-0.00107
		-0.254	0.0107	-0.0045	0.0284	-0.00106
		-0.235	0.0101	-0.0043	0.0248	-0.00093
		-0.307	0.0123	-0.0051	0.0331	-0.00122
		-0.216	0.0092	-0.0044	0.0243	-0.00091
		-0.247	0.0100	-0.0048	0.0275	-0.00101
		-0.386	0.0150	-0.0060	0.0409	-0.00152
		-0.336	0.0120	-0.0054	0.0416	-0.00157
		-0.364	0.0132	-0.0055	0.0407	-0.00149
		-0.246	0.0106	-0.0044	0.0246	-0.00096
		-0.705	0.0260	-0.0110	0.0655	-0.00239
		-0.629	0.0201	-0.0084	0.0648	-0.00222
		-0.796	0.0304	-0.0120	0.0764	-0.00285
		-0.394	0.0171	-0.0061	0.0322	-0.00119
		-0.833	0.0273	-0.0113	0.0779	-0.00271
		-0.803	0.0290	-0.0115	0.0718	-0.00256
		-0.220	0.0088	-0.0043	0.0253	-0.00095
		-0.213	0.0084	-0.0042	0.0243	-0.00090
		-0.495	0.0177	-0.0070	0.0520	-0.00189
		-0.368	0.0134	-0.0050	0.0355	-0.00128
		-0.383	0.0138	-0.0057	0.0445	-0.00163
		-0.485	0.0176	-0.0070	0.0523	-0.00192
		-0.444	0.0168	-0.0067	0.0472	-0.00173
		-0.641	0.0232	-0.0094	0.0656	-0.00245
		-0.623	0.0227	-0.0089	0.0639	-0.00239
		-0.372	0.0154	-0.0057	0.0384	-0.00143
		-0.414	0.0157	-0.0063	0.0420	-0.00153
		-0.702	0.0271	-0.0105	0.0641	-0.00241
		-0.425	0.0160	-0.0062	0.0417	-0.00151
		-0.503	0.0189	-0.0067	0.0515	-0.00190
		-0.278	0.0069	-0.0029	0.0363	-0.00139
		-0.123	0.0061	-0.0024	0.0110	-0.00042
		-0.122	0.0055	-0.0026	0.0115	-0.00043
		-0.329	0.0119	-0.0045	0.0346	-0.00128
		-0.272	0.0110	-0.0040	0.0280	-0.00106

Table 49 :

 49 Values of hourly fuel consumption simplified model for monthly weather format for each location, as described in Annex 29.

	Köppen classes	a1	a2	a3	a4	a5
	Cfa	-1.0368	0.0436	-0.0404	0.0455	-0.00189
	Cfb	-0.8575	0.0343	-0.0315	0.0480	-0.00202
	Csa/Csb	-0.9618	0.0393	-0.0380	0.0482	-0.00203
	Dfb	-0.7937	0.0333	-0.0319	0.0417	-0.00185
	Dfc	-0.6450	0.0242	-0.0250	0.0431	-0.00181

Table 50 :

 50 Values of hourly fuel consumption simplified model for hourly weather format for Köppen classes

	Köppen classes	a1	a2	a3	a4	a5
	Cfa	-1.1486	0.0372	-0.0352	0.0951	-0.00356
	Cfb	-1.0914	0.0305	-0.0243	0.1032	-0.00378
	Csa/Csb	-1.2800	0.0362	-0.0323	0.1205	-0.00443
	Dfb	-1.1300	0.0309	-0.0267	0.1110	-0.00414
	Dfc	-0.8225	0.0203	-0.0203	0.0857	-0.00313

Table 51 :

 51 Values of hourly fuel consumption simplified model for daily weather format for Köppen classes

	Köppen classes	a1	a2	a3	a4	a5
	Cfa	-0.6914	0.0264	-0.0106	0.0629	-0.00233
	Cfb	-0.3029	0.0117	-0.0046	0.0313	-0.00116
	Csa/Csb	-0.6573	0.0244	-0.0088	0.0616	-0.00226
	Dfb	-0.2979	0.0111	-0.0051	0.0349	-0.00132
	Dfc	-0.1095	0.0041	-0.0020	0.0135	-0.00050

Table 52 :

 52 Values of hourly fuel consumption simplified model for monthly weather format for Köppen classes

	a1	a2	a3	a4	a5
	-0.886	0.0363	-0.0339	0.0458	-0.00195

Table 53 :

 53 Average values of hourly fuel consumption simplified model for hourly weather format

	a1	a2	a3	a4	a5
	-1.116	0.0322	-0.0278	0.1034	-0.00382

Table 54 :

 54 Average values of hourly fuel consumption simplified model for daily weather format

	a1	a2	a3	a4	a5
	-0.407	0.0155	-0.0063	0.0407	-0.00151

Table 55 :

 55 Average values of hourly fuel consumption simplified model for monthly weather format

Annex 31: Detailed model of excess fuel consumption for a fleet due to AC The

  general equation to calculate the excess fuel consumption fc f for a fleet f due to the use of air conditioning is: ac,i,TS,T,loc : number of vehicles with AC running for segment i, at the traffic situation TS (i.e. urban, rural, highway), at the time T, at the location loc, expressed in number of vehicle per hour. depending on the hour of the day, external temperature and internal temperature (l/h). c CO2,i : transformation factor from fuel to CO 2 depending on vehicle segment i. The transformation factor is deduced from carbon balance equation and density of fuel. To calculate this factor, we neglected the mass of non-CO 2 pollutants in comparison with the mass of CO 2 . Hydrogen Carbon ratio depending of the type of fuel: 1.8 for petrol and 2 for diesel.

					fc	f	=	!!!!	n	AC	,	i	TS ,	T ,	,	loc	"	hfc	( h	,	ext T	,	T int	)
									loc	T TS i
	Excess CO 2 emission is:
				eCO	2	f	=	!!!!	n	AC	,	i	TS ,	T ,	,	loc	"	c	CO 2	,	i	"	hfc	( h ,	ext T	,	T int	)
								loc	T TS i
	with:
											n	AC,i,TS,T,	loc	=	i,TS,T,loc n	!	f	clim,i
	hfc:		hourly fuel consumption i fuel i C H fuel CO i CO r v m c , , , 008 . 1 011 . 12 011 . 44 2 2 ! " " + = =
	with:
	H r	C	i
		fuel,	i

n , :

Base traffic distribution used in the design of the third cold start model

  Here is the relative traffic distribution ptf i,h along the day used in the design of the third model, the so-called base distribution, showed in Figure70.

	Annex 35: Annex 37: Annex 38:	Coefficient a in the equation of the
							dimensionless cold start excess emission h(δ)
	h	(	)	=	a a e e ! ! 1 1 ."
	Pollutant	Emission Standard	Fuel Type	# of points hour 1	ptf i,h Excess Emission Equation ω(T,V) 0.12	Correction Coefficient f(T,V)
						Euro 0 w/o cat. Euro 0 cat.	Diesel Petrol Petrol	77 655 1561 2 3	5.102 -0.044*T -0.074*V 129.521 -5.361*T + 1.285*V 0.08 128.022 -5.731*T + 0.126*V 0.05	1.851 -0.016*T -0.027*V 2.698 -0.112*T + 0.027*V 8.044 -0.36*T + 0.008*V
			CO		Euro 1 Euro 2 Euro 3	Diesel Petrol Diesel Petrol Diesel Petrol	13 51 481 110 20 731	4 5 6 7 8	4.662 -0.067*T -0.061*V 0.08 30.369 -1.221*T + 0.437*V 7.711 -0.199*T -0.05*V 0.13 32.873 -0.74*T -0.051*V 0.32 2.455 -0.02*V 1.29 35.45 -1.455*T + 0.096*V 1.78	2.198 -0.031*T -0.029*V 2.068 -0.083*T + 0.03*V 2.824 -0.073*T -0.018*V 1.927 -0.043*T -0.003*V 1.194 -0.01*V 4.291 -0.176*T + 0.012*V
						Euro 4 Euro 0 w/o cat.	Petrol Diesel Petrol	14 76 617 10 9	31.627 -1.338*T 206.96 -1.934*V 1.16 214.922 -6.528*T -0.088*V 1.33	6.488 -0.274*T 1.23 -0.011*V 2.602 -0.079*T -0.001*V
						Euro 0 cat.	Petrol	1568 11	133.024 -0.306*V 1.50	1.048 -0.002*V
			CO 2		Euro 1 Euro 2	Diesel Petrol Diesel Petrol	24 57 633 12 13 173 14	206.07 -2.606*V 162.937 -5.435*T + 0.358*V 1.71 362.34 -10.921*T -0.14*V 1.40 194.662 -3.546*T + 0.504*V 1.60	1.338 -0.017*V 2.654 -0.089*T + 0.006*V 2.567 -0.077*T -0.001*V 1.454 -0.026*T + 0.004*V
						Euro 3 Euro 4	Diesel Petrol Petrol	34 791 15 14 16	171.52-0.381*V 186.055 -5.365*T + 2.283*V 1.93 168.005 -5.165*T 2.17	1.047-0.002*V 1.496 -0.043*T + 0.018*V 2.597 -0.08*T
						Euro 0 w/o cat.	Diesel Petrol	77 645 17 18	1.607-0.028*V 1.99 27.712 -1.278*T + 0.233*V 1.76	1.538-0.027*V 4.068 -0.188*T + 0.034*V
			HC		Euro 0 cat. Euro 1 Euro 2 Euro 3	Petrol Diesel Petrol Diesel Petrol Diesel Petrol	1557 24 19 53 632 20 21 93 34 22 643 23	10.853 -0.439*T + 0.035*V 0.75 -0.007*T -0.011*V 1.28 8.653 -0.114*V 2.38 -0.094*T -0.006*V 0.86 0.58 6.997 -0.059*T -0.071*V 0.129 + 0.001*V 0.39 8.229 -0.415*T + 0.049*V 0.31	3.893 -0.157*T + 0.013*V 1.835 -0.016*T -0.026*V 1.357 -0.018*V 6.247 -0.247*T -0.015*V 1.597 -0.014*T -0.016*V 0.863 + 0.007*V 9.093 -0.459*T + 0.054*V
						Euro 4	Petrol	14	24	5.184 -0.247*T 0.18	21.246 -1.012*T
						Euro 0 w/o cat.	Diesel Petrol	77 656 average	-0.489 + 0.015*V 0.547 -0.022*V 1.00	2.472 -0.074*V 5.523 -0.226*V
						Euro 0 cat.	Petrol	1568	2.159 -0.094*T + 0.023*V	2.894 -0.126*T + 0.031*V
		NOx		Euro 1 Euro 2	Diesel Petrol Diesel Petrol	19 57 633 136	2.672 -0.074*V 0.053 + 0.04*V 1.686 -0.082*T + 0.002*V 0.287 + 0.021*V	2.244 -0.062*V 0.063 + 0.047*V 0.406 + 0.03*V 20.076 -0.978*T + 0.024*V
						Euro 3	Diesel Petrol	34 718	-0.909 + 0.04*V 0.282 -0.002*T + 0.005*V	8.335 -0.367*V 0.808 -0.005*T + 0.015*V
						Euro 4	Petrol	14	0.186	1

Examples of influence of the temperature, season and hour on the cold start emission (3 rd model)

CO2 ; year ; day ; Euro 2 D.

Table 26 :

 26 Reduction rates to apply to the cold excess emissions for petrol and diesel vehicles. For petrol vehicles, the direct ignition vehicles (DISI) should have a specific behaviour..... 93 Table27: Calculated ozone forming potential OFP, in decreasing order, for different vehicle types on a Motorway driving cycle[START_REF] Flandrin | Mesure des emissions automobiles de composes organiques volatils et de derives carbonyles[END_REF]. .................................................. 142 Table 28: Model 1 -weight matrix of X. ......................................................................... 147 Table 29: Model

Table 32 :

 32 Definition and average characteristics of the reference test patterns and corresponding reference test cycles, sorted by the average speed. Stable cycles are in blue, unstable ones in red. 164 Table 33: Detailed characteristics of the references test cycles (combination of one or several cycles), sorted by the average speed. .......................................................................... 164 Table 34: Emission factors for active surface area and particle number of the total particle population. ................................................................................................................. 182 Table 35: Emission factors for solid particle number in the size ranges 7-50 nm, 50-100 nm and 100 nm -1 μm (aerodynamic diameter) -Fuel specifications later or equal to EN590:2000. 182 Table 36: Emission degradation correction factor y = a x Mileage + b, for Euro 1 and Euro 2 petrol vehicles. Mileage expressed in km, y normalised for the corresponding average mileage. 186

Table 37 :

 37 Emission degradation correction factor y = a x Mileage + b, for Euro 3 and Euro 4 petrol vehicles. Mileage expressed in km, y normalised for the corresponding average mileage. 186

Table 38 :

 38 Correction factor y = a x Temperature + b, or y = a e b x Temperature when in blue italics bold, for urban, rural or motorway driving behaviour. Temperature in °C. y normalised at 23°C. 187 Table39: Correction factor y = a x Humidity + b, for NOx emissions already corrected or not by using the standard (or legislative) method, and for urban or rural driving behaviour. Humidity in g H 2 O/kg dry air, y normalised at 10.71 g H 2 O/kg dry air. ...................................... 188 Table 40: Road gradient factors for Euro 0 diesel and petrol vehicles .............................. 189 Table 41: Road gradient factors for Euro 1 diesel and petrol vehicles .............................. 190 Table 42: Road gradient factors for Euro 2 diesel and petrol vehicles .............................. 191 Table 43: Road gradient factors for Euro 3 diesel and petrol vehicles .............................. 192 Table 44: Road gradient factors for Euro 4 diesel and petrol vehicles .............................. 193 Table 45: Characteristics of the 91 European locations considered for auxiliary emission modelling, in terms on longitude, latitude, temperature and Köppen class................... 196 Table 46: Modified Köppen climate classes of European locations.................................. 196 Table 47: Values of hourly fuel consumption simplified model for hourly weather format for each location, as described in Annex 29. ............................................................................ 198 Table 48: Values of hourly fuel consumption simplified model for daily weather format for each location, as described in Annex 29. ............................................................................ 200 Table 49: Values of hourly fuel consumption simplified model for monthly weather format for each location, as described in Annex 29. .................................................................... 202 Table 50: Values of hourly fuel consumption simplified model for hourly weather format for Köppen classes........................................................................................................... 203 Table 51: Values of hourly fuel consumption simplified model for daily weather format for Köppen classes........................................................................................................... 203 Table 52: Values of hourly fuel consumption simplified model for monthly weather format for Köppen classes........................................................................................................... 203

Table 53 :

 53 Average values of hourly fuel consumption simplified model for hourly weather format 203 Table 54: Average values of hourly fuel consumption simplified model for daily weather format 203 Table 55: Average values of hourly fuel consumption simplified model for monthly weather format 203

INRETS report n°LTE 0523

Acknowledgements

We 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 by

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 87 110141 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 88 110141 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 89 110141 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 84 112091 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 75 112092 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.84 0.00 0.00 0.16 0.00 0.00 0.00 0.00 77 112093 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.68 0.00 0.00 0.32 0.00 0.00 0.00 0.00 74 112111 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 67 112112 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11 0.11 0.00 0.00 0.00 0.79 68 112113 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.19 0.00 0.00 0.00 0.27 0.37 43 120071 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 41 120091 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.75 0.00 0.00 0.25 0.00 0.00 0.00 0.00 51 120091 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.34 0.66 0.00 0.00 0.00 0.00 52 120091 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.00 0.75 0.00 0.00 0.00 0.00 42 120091 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.40 0.00 0.00 0.00 0.00 46 130051 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 45 130071 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.80 0.00 0.00 0.292 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.449 0.171 0.088 0.000 0.000 (urban composite) 0.167 0.118 0.127 0.175 0.418 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 (rural composite) 0.000 0.000 0.000 0.000 0.000 0.196 0.000 0.138 0.160 0.263 0.241 0.000 0.000 0.000 0.000 (motorway comp.) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. E=(4,998x10 -8 v 2 + 1,830x10 -5 v -2,301x10 -3 )p 2 +(5,086x10 -6 v 2 -1,822x10 -3 v + 0,1435)p+9,10x10 -5 v 2 -1,48x10 -2 v + 0,499

E= 0,0343v 2 -5,1159v + 367,86 FC E= 0,0105v 2 -1,513v + 107,74

CO 2 0-100 E=(-4,06x10 -6 v 2 + 7,71x10 -4 v + 2,86x10 -2 )p 2 +( 3,84x10 -4 v 2 -6,11x10 -2 v -4,07)p+ 0,0444v 2 -6,1129v + 509,84 FC 0-100 E=( -2,74x10 -6 v 2 + 3,79x10 -4 v + 7,49x10 -3 )p 2 +(2,52x10 -4 v 2 -3,25x10 -2 v -1,11)p+ 0,0115v 2 -1,6528v + 161,8 HC 0-100 E=(6,67x10 -9 v 2 -6,78x10 -7 v + 5,95x10 -5 )p 2 +( -6,16x10 -7 v 2 + 9,76x10 -5 v -8,61x10 -3 )p+3,77x10 -5 v 2 -6,82x10 -3 v + 0,436 NOx 0-100 E=( 2,66x10 -7 v 2 -4,83x10 -5 v + 2,66x10 -3 )p 2 +( -2,62x10 -5 v 2 + 4,79x10 -3 v -0,262)p+( 0,0009v 2 -0,1511v + 8,3427)

Pre-Euro-1 PM 0-100 E=(1,34x10 -6 v + 2,89x10 -5 )p 2 +(-1,36x10 -4 v -3,88x10 -3 )p+1,49x10 -5 v 2 + 6,51x10 -4 v + 0,352 CO 7-50

E=(1,79x10 -7 p 2 -1,87x10 -5 p+4,3x10 -4 )v 2 +(-2,1x10 -5 p 2 +2,66x10 -3 p-6,94x10 -2 )v+(2,88x10 -3 p 2 -0,195p+4,05) CO 2 7-50 E=(2,56x10 -5 p 2 -1,89x10 -3 p+0,0654)v 2 +(7,4x10 -4 p 2 +0,108p-7,2126)v+(0,304p 2 -19,8p+662,1) FC 7-50 E=(2,37x10 -5 p 2 -1,3x10 -3 p+0,0279)v 2 +(-2,73x10 -3 p 2 +0,176p-3,8935)v+(0,193p 2 -11,1p+262,46) HC 3 7-50

Euro-1 PM 3 0-50 E=(-2,176x10 -6 v 2 + 3,695x10 -4 v -1,760x10 -2 )p+(8,98x10 -5 v 2 -1,33x10 -2 v + 0,642); E≥0,03 Pre-Euro-1 NOx 0-100

Euro-1 NOx E= 9,43x10 -5 v 2 -6,58x10 -3 v + 0,484 CO 4-56 E=( 8,37x10 -7 v 2 -1,43x10 -4 v + 6,56x10 -3 )p 2 +(-1,19x10 -5 v 2 + 5,52x10 -3 v -0,255)p+6,86x10 -4 v 2 -0,112v + 4,63 CO 2 E= 8,48x10 -2 v 2 -12,8v + 635 FC E= 0,0248v 2 -3,719v + 191,22 HC 4-56

E=(1,69x10 -7 v 2 -3,32x10 -5 v + 1,83x10 -3 )p 2 +(-5,85x10 -6 v 2 + 1,24x10 -3 v -6,60x10 -2 )p+7,10x10 -5 v -1,33x10 -2 v +0,641

Annex 36: Equations describing the parking time influence on the cold start excess emission

The parking time t is in min. g(720) = 1.

Pollutant Equation CO• g(t) = 4.614*10 -3 *t-2.302*10 -6 *t 2 -2.966*10 -9 *t 3 (t≤720)

• g(t) = 1 (t>720 min) CO 2

• g(t) =0.1349*10 -2 *t-2.915*10 -4 *t 2 (t≤20)

• g(t) = 0.136+0.0012*t (21≤t≤720)

• g(t) = 1 (t≥720) HC

• g(t) = 7.641*10 -3 *t-2.639*10 -5 *t 2 +3.128*10 -8 *t 3 (t≤240)

• g(t) = 0.625+5.208*10 -4 *t (241≤t≤720)

• g(t) = 1 (t≥720) Catalyst petrol cars NOx • g(t) =7.141*10 -3 *t+1.568*10 -3 *t 2 -3.204*10 -5 *t 3 +1.594*10 -7 *t 4 (t≤50 min) • g(t) = 1.290-4.030*10 -4 *t (51≤t≤720) • g(t) = 1 (t≥720) CO

• g(t) = -1.504*10 -2 *t+1.406*10 -4 *t 2 -2.547*10 -7 *t 3 (t≤240)

• g(t) = 1 (t>240 min) CO 2

• g(t) =5.287*10 -9 *t 3 -8.864*10 -6 *t 2 +5.035*10 -3 *t (t<720 min)

• g(t) = 1 (t>720 min) HC

• g(t) = 1.039*10 -3 *t-7.918*10 -6 *t 2 +4.211*10 -8 *t 3 -6.856*10 -11 *t 4 +3.650*10 - 14 *t 5 (t≤720)

• g(t) = 1 (t≥720) Petrol cars without catalyst NOx

• g(t) = 3.52*10 -2 **t-3.705*10 -4 *t 2 (t≤50)

• g(t) = 0.8170+2.537*10 -4 *t (51≤t≤720)

• g(t) = 1 (t≥720) CO • g(t) = 4.167*10 -3 *t (t≤240 min)

• g(t) = 1 (t≥240 min) CO 2

• g(t) = 4.339*10 -3 *t-4.747*10 -6 *t 2 (t≤460)

• g(t) = 0.978+3.077*10 -5 *t (461≤t≤715)

• g(t) = 1 (t≥715 min) HC

• g(t) = 3.070*10 -4 *t+4.402*10 -6 *t 2 -4.030*10 -9 *t 3 (t≤720)

• g(t) = 1 (t>720 min) NOx

• g(t) = 0 (t≤300 min)

• g(t) = -1.11+3.703*10 -3 *t (300 min<t<570 min)

• g(t) = 1 (t≥570 min) Diesel cars PM • g(t) = 0 (t≤60 min) • g(t) = -0.323+6.488*10 -3 *t-1.116*10 -5 *t 2 +6.545*10 -9 *t 3 (60 min<t<420 min) • g(t) = 0.808+2.667*10 -4 *t (t≥420 min)

INRETS report n°LTE 0523 Annex 39: List of tables and figures 23: Average fuel consumption due to air conditioning (l/h) for the 4 vehicle types... Table 24: Power consumption of auxiliaries and estimation of the use of auxiliaries [START_REF] Soltic | Influence of Electric Load on the Exhaust Gas Emissions of Passenger Cars[END_REF]

Literature

References in bold are Artemis deliverables.

References in dark green are specific Artemis reports presenting parts of the research synthetised in this report.