laboratory of applied thermodynamics aristotle university thessaloniki school of engineering dept....

LABORATORY OF APPLIED THERMODYNAMICS

ARISTOTLE UNIVERSITY THESSALONIKISCHOOL OF ENGINEERING

DEPT. OF MECHANICAL ENGINEERING

Leon Ntziachristos Dimitrios

Gkatzoflias Charis KouridisGiorgos Mellios

Savvas GeivanidisZissis Samaras

COPERT 4COPERT 4

Copenhagen, 2008-06-19Copenhagen, 2008-06-19

Contents

1. Background & History2. Users and Uses3. Methodology and Comparison with the Guidebook4. New Elements compared to Spring/Summer 20075. Activity data (results of the Fleets project)6. Important, less important data7. Exhaust PM and airborne particle emission factors8. Non-exhaust PM




Background & HistoryBackground & History

Status of COPERT – Administrative Info

The name stands for COmputer Programme to calculate Emissions from Road Transport

Now in its COPERT 4 Version (fourth update of the original COPERT 85)

It incorporates results of several research and policy assessment projects

It is basically funded by the European Environment Agency through the ETC budget

It is scientifically and technically supported by the Lab of Applied Thermodynamics

It has recently attracted much attention from the Joint Research Centre in Ispra who are willing to support its further development

Status of COPERT – Technical Info

Calculates emissions of all (important) pollutants from road transport

Covers all (important) vehicle classes

Can be applied in all European countries and in several Asian ones

Can be used to produce total emission estimates from 1970 to 2020 (up to 2030 in TREMOVE)

Provides a user-friendly (MS-Office like) GUI to introduce and view data

History - Early Generations

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

COPERT85 COPERT90 COPERTII COPERTIIIDGXI EEA Task Force EEA EEA

CORINAIR Group CORINAIR Group MEET Group MEET

COST319 Report MEET DeliverablesNational DGVII

COST319 participants MEET Partners

ForeMove 1.0 ForeMove 2.0 ForeMove Upgr.DGXI DGXI/Concawe ACEA

LAT/EnviCon Auto Oil I Auto Oil II

CASPERDGXI

IFARE/LAT/EnviCon

Legend:

Product/Tool TRENDSFunding Eurostat/DGVII

Working Group LAT/DTU/INFRAS/Kalivoda

History - COPERT II and III

COPERT II was the first one with a GUI, built on MS Access 2 (1996). It provided emission factors up to Euro 1

COPERT III was based on menus, similar to MS Office (2000) and it was built on VBA for MS Access 97. Compared to version II: New hot emission factors for Euro 1 passenger cars New reduction factors over Euro 1 according to AutoOil Impact on emissions from 2000, 2005 fuel qualities Cold-start methodology for post Euro 1 PCs Emission degradation due to mileage Effect of leaded fuel ban in Europe Alternative evaporation methodology Detailed NMVOC speciation (PAHs, POPs, Dioxins and Furans) Updated hot emission factors for non regulated pollutants

History - COPERT 4

COPERT 4 is the ‘official’ version since Nov. 2006. Main differences with Copert III include: Software-wise

• Possibility for time-series in one file • Possibility of more than one scenarios in one file • Enhanced import/export capabilities (mainly Excel) • Configuration of fleet (local/regional vehicle technologies) • Data can be changed at methodological level (emission functions)

Methodology-wise• Hot EFs for PCs and PTWs at post Euro 1 level• Hybrid vehicle fuel consumption and emission factors• N2O/NH3 Emission Factors for PCs and LDVs• Particulate Matter and airborne particle emission factors• Non-exhaust PM• New evaporation methodology • New corrections for emission degradation due to mileage• HDV methodology (emission factors, load factors, road-gradient




Uses & UsersUses & Users

COPERT Usage

Auto - Oil II: Forecast scenarios on behalf of ACEA to estimate emission evolution up to 2015

EMEP/CORINAIR: COPERT methodology is the road transport and off-road machinery emission chapter in the EMEP/CORINAIR Emission Inventory Guidebook

EEA Activities: National and Central Estimates for Air Emissions from Road Transport TERM : Transport and Environment Reporting Mechanism (EEA) TRENDS: Development of a Database system for the Calculation of Indicators of

Environmental Pressure Caused by Transport (DG TrEn study) supported by EEA

COPERTCOPERT

Auto Oil Auto Oil IIII

TREMOVTREMOVEE

ACEA

EMEP EMEP GuidebooGuideboo

kk

TERMTERM

TRENDSTRENDS

National National InventorieInventorie

ss

Individual Individual UseUse

STEERSSTEERS(CONCAWE)(CONCAWE)

Field of applications - National level

Country Model Contact Person

EU27

Austria GLOBEMI Barbara SCHODL

Belgium COPERTLaurent BODARWE (Brussels) Pascal THATE (Wallon) Ina DE VLIEGER (Flanders)

Bulgaria Tier 1 Tzvetina TZENOVACyprus COPERT Chrysanthos SAVVIDESCzech Republic COPERT Jiri DUFEKDenmark COPERT Morten WINTHEREstonia COPERT Helen HEINTALUFinland LIPASTO Kristina SAARINENFrance COPERT Jean Pierre CHANGGermany TREMOD Gunnar GOHLISCHGreece COPERT Dimitrios HADJIDAKISHungary COPERT Tamas MERETEIIreland COPERT Eimer COTTERItaly COPERT Riccardo DE LAURETISLatvia COPERT Intars CAKARLithuania COPERT Aurelija CICENAITELuxembourg COPERT Frank THEWES (to be replaced)Malta Tier 1 Christofer CAMILLERINetherlands Agg. VERSIT+ Anco HOENPoland COPERT Janina FUDALAPortugal COPERT Pedro TORRESRomania COPERT Vlad Ioan GHIUTA TARALUNGASlovakia COPERT Janka SZEMESOVASlovenia COPERT Martina LOGAR, Alenka FRITZELSpain COPERT Antonio FERREIROSweden EMV Magnus LINDGREN

UK National System Justin GOODWIN

Other Countries

Belarus COPERT Hanna MALCHYKHINABosnia Herzegovina COPERT Martin TAISCroatia COPERT Zeljko JURIC, Vjeko BOLANCAMoldova COPERT Victor AMBROCINorway COPERT Alice GAUSTADSwitzerland National System Felix REUTIMANN

Turkey Tier 1 Fatma Betül BAYGÜVEN

Field of Applications – Literature 1(2)

Evaluation of COPERT

Robin Smit, Muriel Poelman, Jeroen Schrijver, Improved road traffic emission inventories by adding mean speed distributions, Atmospheric Environment, Volume 42, Issue 5, February 2008, Pages 916-926.

Fabio Murena, Giuseppe Favale, Continuous monitoring of carbon monoxide in a deep street canyon, Atmospheric Environment, Volume 41, Issue 12, April 2007, Pages 2620-2629.

Spyros P. Karakitsios, Vasileios K. Delis, Pavlos A. Kassomenos, Georgios A. Pilidis, Contribution to ambient benzene concentrations in the vicinity of petrol stations: Estimation of the associated health risk, Atmospheric Environment, Volume 41, March 2007, Pages 1889-1902.

Ioannis Kioutsioukis, Stefano Tarantola, Andrea Saltelli, Debora Gatelli, Uncertainty and global sensitivity analysis of road transport emission estimates, Atmospheric Environment, Volume 38, Contains Special Issue section on Measuring the composition of Particulate Matter in the EU, December 2004, Pages 6609-6620.

M. Ekstrom, A. Sjodin, K. Andreasson, Evaluation of the COPERT III emission model with on-road optical remote sensing measurements, Atmospheric Environment, Volume 38, Contains Special Issue section on Measuring the composition of Particulate Matter in the EU, December 2004, Pages 6631-6641.

M. Pujadas, L. Nunez, J. Plaza, J. C. Bezares, J. M. Fernandez, Comparison between experimental and calculated vehicle idle emission factors for Madrid fleet, Science of The Total Environment, Volumes 334-335, Highway and Urban Pollution, December 2004, Pages 133-140.

R. Smit, A.L. Brown, Y.C. Chan, Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow?, Environmental Modelling & Software, Volume 23, October-November 2008, Pages 1262-1270.

Robert Joumard, Michel Andre, Robert Vidon, Patrick Tassel, Characterizing real unit emissions for light duty goods vehicles, Atmospheric Environment, Volume 37, Issue 37, 11th International Symposium, Transport and Air Pollution, December 2003, Pages 5217-5225.

Morten Winther, Petrol passenger car emissions calculated with different emission models, The Science of The Total Environment, Volume 224, Issues 1-3, 11 December 1998, Pages 149-160.

Field of Applications – Literature 2

Application

Leonidas Ntziachristos, Marina Kousoulidou, Giorgos Mellios, Zissis Samaras, Road-transport emission projections to 2020 in European Urban environments, Atmospheric Environment, October 2008, accepted.

Rajiv Ganguly, Brian M. Broderick, Performance evaluation and sensitivity analysis of the general finite line source model for CO concentrations adjacent to motorways: A note, Transportation Research Part D: Transport and Environment, Volume 13, May 2008, Pages 198-205.

Hao Cai, Shaodong Xie, Estimation of vehicular emission inventories in China from 1980 to 2005, Atmospheric Environment, Volume 41, December 2007, Pages 8963-8979.

B.M. Broderick, R.T. O'Donoghue, Spatial variation of roadside C2-C6 hydrocarbon concentrations during low wind speeds: Validation of CALINE4 and COPERT III modelling, Transportation Research Part D: Transport and Environment, Volume 12, December 2007, Pages 537-547.

Seref Soylu, Estimation of Turkish road transport emissions, Energy Policy, Volume 35, Issue 8, Pages 4088-4094.R. Bellasio, R. Bianconi, G. Corda, P. Cucca, Emission inventory for the road transport sector in Sardinia (Italy), Atmospheric

Environment, Volume 41, February 2007, Pages 677-691.Pavlos Kassomenos, Spyros Karakitsios, Costas Papaloukas, Estimation of daily traffic emissions in a South-European urban

agglomeration during a workday. Evaluation of several 'what if' scenarios, Science of The Total Environment, Volume 370, November 2006, Pages 480-490.

G. Lonati, M. Giugliano, S. Cernuschi, The role of traffic emissions from weekends' and weekdays' fine PM data in Milan, Atmospheric Environment, Volume 40, Issue 31, 13th International Symposium on Transport and Air Pollution (TAP-2004), October 2006, Pages 5998-6011.

R. Berkowicz, M. Winther, M. Ketzel, Traffic pollution modelling and emission data, Environmental Modelling & Software, Volume 21, Issue 4.

Jose M. Buron, Francisco Aparicio, Oscar Izquierdo, Alvaro Gomez, Ignacio Lopez, Estimation of the input data for the prediction of road transportation emissions in Spain from 2000 to 2010 considering several scenarios, Atmospheric Environment, Volume 39, Pages 5585-5596.

Jose M. Buron, Jose M. Lopez, Francisco Aparicio, Miguel A. Martin, Alejandro Garcia, Estimation of road transportation emissions in Spain from 1988 to 1999 using COPERT III program, Atmospheric Environment Volume 38, February 2004, Pages 715-724.

Roberto M. Corvalan, David Vargas, Experimental analysis of emission deterioration factors for light duty catalytic vehicles Case study: Santiago, Chile, Transportation Research Part D: Transport and Environment Volume 8, July 2003, Pages 315-322.

Salvatore Saija, Daniela Romano, A methodology for the estimation of road transport air emissions in urban areas of Italy, Atmospheric Environment Volume 36, Issue 34, November 2002, Pages 5377-5383.

C. Mensink, I. De Vlieger, J. Nys, An urban transport emission model for the Antwerp area, Atmospheric Environment, Volume 34, Issue 27, 2000, Pages 4595-4602.




COPERT 4Statistics

Notes:

Information in this presentation collected from people that downloaded COPERT 4 in the period Jun 2006 – Nov 2007

In total, 1131 individual downloads (without doubles) were registered

The registration is only for people that have actually downloaded COPERT, not just visiting the site.

The following form needs to be filled by users every time COPERT 4 is downloaded (example with artificial data is given).

User's info:

Name: John SmithCountry: ItalyE-mail: [email protected]: University of EmissionsFound out from: EEA Usage: Calculate pollutants emissions

The following charts were produced by processing the information contained in these forms

Continent Distribution

Distribution of users from Europe

Distribution of users from Africa

Distribution of users from Asia

Distribution of users from America

Monthly Distribution of Downloads

Daily Distribution of Downloads

User Affiliation

Private sector includes consultants, construction companies, emission and transport research, etc.

International organizations include fuel, insurance and transport companies and authorities

Local authorities mainly include regional environmental offices

Applications

Academic use is for lectures, courses, theses Evaluation / research : General application not specified in more detail by

the users Emissions / emission factors: Application on particular studies

necessitating total estimates or just derivation of emission factors

Summary of Copert (III) application – 1(3)

There is a great interest for national inventories Requires simplicity in interface and limited input from

the user There is a great interest for GHGs emissions They require a link to higher-level software (i.e. IPCC

tables, CollectER, etc.)

Several new MSs and NIS countries still consider that input data are difficult to collect How to allocate technology classes How to estimate mileage and road shares Sometimes use “rule of thumb” methods of

questionable quality

Summary of Copert (III) application – 2(3)

Several “advanced” countries hesitate using a common methodology Have developed own tools and are familiar with Trust own methods provide more accurate results than

a generic model Politics and priorities may also play a role

As a result: Countries’ absolute contribution may be misjudged Time-series reporting is less uncertain Introduction of a new model will require re-estimation in

time series

Such a model is a very elegant tool for centralised emission estimates

Summary of Copert (III) application - 3

Number of specialised uses is rather infinite In South Africa, it has been applied to a road 550 km

between Durban and East London. Problem was level of maintenance

In Chile and Mexico, it is used for urban inventories in high altitude

Eurocontrol considers its use for estimating road transport contribution to local air quality in airport areas

Particular cases (Greek taxis, small vehicle categories in Italy < 800 cc, technology classes in Eastern Europe, etc.)




Methodology and Methodology and Comparison to the Comparison to the

GuidebookGuidebook

Pollutants – 1(2)

Group 1

Carbon monoxide (CO)

Nitrogen oxides (NOx: NO and NO2)

Volatile organic compounds (VOCs)

Methane (CH4)

Non-methane VOCs (NMVOCs)

Nitrous oxide (N2O)

Ammonia (NH3)

Particulate matter (PM)

PM number and surface area

Pollutants for which a detailed methodology exists, based on specific emission

factors

Pollutants which are estimated based on fuel

consumption

Group 2

Carbon dioxide (CO2)

Sulphur dioxide (SO2)

Lead (Pb)

Cadmium (Cd)

Chromium (Cr)

Copper (Cu)

Nickel (Ni)

Selenium (Se)

Zinc (Zn)

Pollutants - 2

Group 3

Polycyclic aromatic hydrocarbons (PAHs) and persistent organic pollutants (POPs)

Polychlorinated dibenzo dioxins (PCDDs) and polychlorinated dibenzo furans (PCDFs)

Group 4

Alkanes (CnH2n+2):

Alkenes (CnH2n):

Alkynes (CnH2n-2):

Aldehydes (CnH2nO)

Ketones (CnH2nO)

Cycloalkanes (CnH2n)

Aromatic compounds

Pollutants for which a simplified methodology is applied, mainly due to the absence of detailed data

Pollutants which are derived as a fraction of total NMVOC emissions.

General Concept for Exhaust Emissions/Consumption

EHOT [g/veh] =

M [km] x EFHOT [g/km]

ECOLD [g/veh] =

β x M [km] x EFHOT [g/km] x (eCOLD/eHOT-

1)

β = lCOLD/lTOTAL

What are exhaust emissions dependent on?

Activity Number of vehicles [veh.] Distance travelled [km/period of inventory]

Hot Emissions Technology / Emission Standard Mean travelling speed [km/h]

Cold Emissions Technology / Emission Standard Mean travelling speed [km/h] Ambient temperature [Celsius] Mean trip distance [km]

Non-exhaust emissions (evaporation)

Engine Fuel Tank

Fuel Line

Canister

Liquid

Vapour

Vent

Breathing Losses

Permeation / Leakages

Mechanisms causing evaporation emissions

• Diurnal emissions

• Hot soak emissions

• Running losses

Parked vehicle

Engine running

Only relevant for Gasoline!

What is evaporation dependant on

Vehicle technology

Tank (vehicle) size

Canister (vehicle) size

Vehicle mileage (adsorption potential)

Temperature variation

Fuel vapour pressure (kPa)

Fuel tank fill level

Parking time distribution

Trip duration

Non – Exhaust PM

Particulate Matter due to road transport is also produced by: Tyre abrasion Brake abrasion Road wear

Emission rates depend on: Vehicle category (car, truck, motorcycle) Number of axles/wheels (trucks) Vehicle load Vehicle speed

Vehicle Categories – Heavy Duty Vehicles

Vehicle Categories – Rigid Trucks (Lorries)

Vehicle Categories – Articulated Vehicles

==

++TractorTractor Semi-TrailerSemi-Trailer




New ElementsNew Elements(Compared to Spring/Summer (Compared to Spring/Summer

2007)2007)

COPERT 4 VX.YCOPERT 4 VX.Y

X… Methodology updateX… Methodology update

Y… Software updateY… Software update

COPERT 4 V4.0 – October 2007

Consistent with the following EMEP/CORINAIR Guidebook chapters: B710: Road Transport (Activities 070100 – 070500) Ver. 6.0 B760: Fuel Evaporation (Activity 070600) Ver 2.1

Methodology issues added/updated in this version: Emissions from CNG Buses Emissions with use of Biodiesel Distinction of primary NOx emissions to NO2 and NO Emission factors of Euro 4 Diesel Passenger Cars Reductions for future emission standards Euro 5, Euro 6 and

Euro V, Euro VI Revised CO2 calculation equations (biofuels and alternative

fuels) Revised CH4 emission factors Corrected N2O and NH3 emission factors Revised calculation algorithm for CH4, N2O and NH3 hot/cold

emissions

COPERT 4 Version 5.0 - December 2007

Consistent with the following EMEP/CORINAIR Guidebook chapters: B710: Road Transport (Activities 070100 – 070500) Ver. 6.0

with modified N2O emission factors for HDV B760: Fuel Evaporation (Activity 070600) Ver 3.0 B770: Road vehicle tyre and break wear (Activity 070700)

Ver 1.0

Methodology issues added/updated in this version: Determination of the fraction of Elemental and Organic

Carbon in exhaust PM New methodology to calculate evaporation emissions Inclusion of non-exhaust (tyre & break wear) PM Updated N2O emission factors for HDV

COPERT 4 Version 5.1 - February 2008

Mainly a software update (bug corrections and additions) Mileage used for N2O and NH3 emission degradation

changed (was annual - > became cumulative) Different RVP and Temperature values per year can be

imported from Excel Corrected mileage import from Excel Warning message on evaporation emissions removed Evaporation method now works also for negative

temperature values




Activity DataActivity Data

(Results of the Fleets (Results of the Fleets project)project)




Important, less Important, less important dataimportant data

Guide to national road-transport inventory compilation - 1

1. Obtain fuel consumption from national statistics2. Estimate effects of tank tourism, black market3. From 1 and 2 estimate true consumption of road transport

4. Collect data on total fleet in operation per category National registers (cars, light trucks, heavy trucks, busses,

motorcycles) Police (mopeds)

5. Collect data on vehicle distribution per fuel and sub-category National registers Data from countries with similar structure (data from the

Fleets project)

List open to suggestionsList open to suggestions


6. Use age distributions to allocate vehicles to emission standards• pre ECE vehicles up to 1971• ECE 15 00 & 01 1972 to 1977• ECE 15 02 1978 to 1980• ECE 15 03 1981 to 1985• ECE 15 04 1985 to 1992• Euro 1 1992 to 1996• Euro 2 1996 to 2000• Euro 3 2000 to 2004• Euro 4 2005 to 2010

Use information on sales/new registrations

Watch out for second-hand registrations

7. Obtain average min and max monthly temperatures for major cities and produce average. Data can be found on websites (e.g www.weatherbase.com) as well.

8. Estimate travelling speeds for urban areas (e.g. 25 km/h), rural areas (e.g. 60 km/h) and highways (e.g. 90 km/h). Estimation needs to be reasonable but not exact.


9. Estimate mileage shares in the three modes. The sum should make up 100%. Reasonable but not exact estimation is required.

10. Assume mileage values in the order of PCs: 11 – 15 Mm/year LDVs: 15 – 25 Mm/year HDVs: 50 – 80 Mm/year (national km only!) Busses: 50 – 70 Mm/year Mopeds: 2 – 5 Mm/year Motorycles: 4 – 8 Mm/year One could adjust mileage per age based on the ‘Fleets’ data

11. Perform COPERT run12. Compare statistical with calculated fuel consumption per year

Total fuel consumption Fuel consumption per fuel

13. Adjust mileage to equalize calculated with statistical values

Diesel, Euro II, NOx

25

70

38

53

335

36

686770

6094

139

75

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 15 30 45 60 75 90 105 120 135

Average Speed [km/h]

[g/k

m]

Artemis, All capacities

COPERT, All capacities


Hot Emission Factors of Regulated Pollutants from Conventional PCs – Example Comparison COPERT III and 4 –

Euro 2 Diesel NOx

Petrol, Euro III, CO

31

87136

88

79

84

13613216297142206104

0

1

2

3

4

5

6

7

0 15 30 45 60 75 90 105 120 135

Average Speed [km/h]

[g/k

m]


COPERT, <1.4 l

COPERT, 1.4-2.0 l

COPERT, >2.0 l


Hot Emission Factors of Regulated Pollutants from Conventional PCs – Example Comparison COPERT III and 4 –

Euro 3 Gas CO

Typical Variability of Measured Data - CO

Typical Variability of Measured Data – CO2

Performance of Individual Vehicles

0

0.2

0.4

0.6

0.8

1

1.2

0 20 40 60 80 100 120 140

average speed [km/h]

HC

[g

/km

]

Iveco 35/10 VW LT 35 Iveco 35-10 Turbo Daily Mercedes-Benz 210D

Mercedes-Benz 208D VW T4 Diesel VW LT 35 2 Ford Transit 120 2.5 TD

Importance of Input Variables

Parameter ImportanceAvailability of

statisticsNotes /Particular Issues

Total number of vehicles per class Question is the scooter and mopeds

registration availability

Distinction of vehicle to fuel used Question is the availability of records for

vehicles retrofitted for alternative fuel use

Distribution of cars/motorcycles to engine classes

Not important for conventional pollutants, more important for CO2 emission estimates

Distribution of heavy duty vehicles to weight classes Vehicle size important both for conventional

pollutant and CO2 emissions

Distinction of vehicles to technology level Imported, second-hand cars and scrappage

rates are an issue

Annual mileage driven Can be estimated from total fuel consumption. The effect of mileage with age requires attention.

Urban driving speed Affects the emission factors

Rural, highway driving speeds Little affect the emission factors, within their expected range of variation

Mileage share in different driving modes Little affect emissions, within their

expected range of variation




Exhaust Particulate Matter Exhaust Particulate Matter

and Airborne Particle and Airborne Particle Emission FactorsEmission Factors

Total Particle Number (7 nm – 1 μm)(negligible particle number above this range)

Integrated active surface concentration of total particle population (7 nm – 1 μm)

Number of solid particles of three different size ranges (value equivalent to PMP protocol)

7 – 50 nm

50 – 100 nm

10 nm – 1 μm

Distinguished according to: Vehicle category Technology Aftertreatment Fuel Sulphur content (for non solid particles)

Airborne Particle Information

Examples of emission factors of active surface

concentration and total particle number (solid and volatile particles)

Solid particle population

[#/km] ×10-13 Number of solid particles <50 nm Number of solid particles 50-100 nm Number of solid particles 100-1000 nm

Category Urban Road Motorway Urban Road Motorway Urban Road Motorway

PC diesel Euro-1 8.5 8.6 7.2 9.3 7.8 7.3 5.4 3.8 4.0

PC diesel Euro-2 7.6 7.6 6.1 8.8 7.7 7.2 5.1 3.6 4.0

PC diesel Euro-3 7.9 7.1 5.8 8.7 6.8 6.9 4.5 3.2 3.5

PC diesel Euro-3 DPF 0.0055 0.0040 0.023 0.0023 0.0016 0.0094 0.0016 0.0012 0.0028

PC petrol Euro-1 0.32 0.24 0.086 0.14 0.10 0.034 0.052 0.037 0.012

PC petrol Euro-3 0.0096 0.011 0.0055 0.0044 0.0054 0.0028 0.0026 0.0034 0.0051

PC petrol Euro-3 DISI 0.81 0.61 0.28 0.65 0.36 0.19 0.41 0.21 0.15

Emission factors for solid particle number in the size ranges 7-50 nm, 50-100 nm and 100 nm-1 μm (aerodynamic

diameter)

PM Speciation (OC/EC)

Definitions

Elemental Carbon (EC): It appears in PM samples mainly as graphitic particles formed in combustion. It is determined by thermal optical methods where carbon is converted to CO2.

Black Carbon (BC): It corresponds to the light attenuation elements of carbon and it is determined by aethalometers. Black carbon is mainly EC. However, it also includes highly refractory elements of organic carbon (such as OCX2). Also, EC from different sources may exhibit different light absorption efficiencies, hence there is no global equivalence between BC and EC.

Organic Carbon (OC): It is the carbon desorbed when PM is heated at high temperature (i.e. 600-900°C) in inert atmosphere. Some of the organic species present in PM pyrolyse, instead of desorbing, and this falsely allocates them to EC instead of OC.

Organic Material (OM): It is the total mass of organic material (including the mass of hydrogen) that corresponds to the organic carbon. The organic mass corresponding to the organic carbon depends on the species profile. Usually, an empirical correction of ~1,2-1,4 is applied to OC to derive OM.

Results from tunnel measurements

Study Tunnel Temperature (oC) ConditionDHDV

Fraction (%)OC/EC PM10 EC/PM10 OC/EC PM2.5 EC/PM2.5 Notes

Caldecott, CA 23 4,2% upgrade 3,2-4,9% 34-60% 60-81%Caldecott, CA 23 4,2% upgrade 0,25-0,54% 170-490% 32-55%

Squirell Hill, PA 9 High Speed 6 58% 70%

Squirell Hill, PA 9 Low Speed 3,4 82% 39%

Squirell Hill, PA 9 High Truck Share 14,5 75% 35%

Squirell Hill, PA 26 Low Speed 3,4 29% 52%Gillies et al.(2001) Sepulveda, Los Angeles 19-28 1,7-4,3% 81% 45% 132% 37%

Caldecott, CA 17 4,2% upgrade 6-7,3% 52% 57% 60% 57%Caldecott, CA 17 4,2% upgrade 0,24% 86% 58% 182% 23%

Laschober et al. (2004) Kaisermühlen, Austria 14-30 Flat ~12% 66% 39%

Geller et al. (2006)

Grieshop et al. (2006)

Allen et al. (2002)

Emission Factors from tunnel measurements

Study Vehicle Class EC OC PM2.5 Units /Notes OC/EC (%) EC/PM2.5

LDV 26,6+/- 29,8 31,2+/-32,4 30,6+/-43,8 117% 87%

HDV 439+/- 109 269+/- 118 1060+/-160 61% 41%

LDV 15+/-71 39+/-22 73+/-51 260% 21%

HDV 788+/-332 495+/- 105 1285+/-237 63% 61%

LDV 29,4+/-4,3 67,1+/-11,2 44%

HDV 709+/-76 1015+/-127 70%

LDV 35+/-3 53+/-8 110+/-10 151% 32%

HDV 1300+/-300 500+/-40 2500+/-200 38% 52%

LDV 1,6+/-0,21 8,53+/-0,47 19%

HDV 122,7+/5,7 383,5+/-10,7 32%

mgC/ kg fuel

mgC/ km (LDV contain 5% diesel)

Grieshop et al. (2006)

Allen et al. (2002)


Kirchstetter et al. (1999)

Weingartner et al. (1997)

Emission factors from dynamometer measurements (excerpt)

Study Vehicle/Engine Operating Condition OC/EC PM2.5 EC/PM2.5

NEDC 37% 79%

Urban 88% 53%

Rural 136% 43%

Motorway 22% 75%

Euro 4+DPF LDV Average Driving 42% 45%

Rural 48% 53%

Rural 24% 38%

Rural 43% 83%

90 km/h 81% 50%

Rural 196% 10%

Rural 99% 6%

Road 243% 18%

90 km/h 300% 30%

Road 583% 10%

90 km/h 449% 7%

1200 rpm, 25% 300% 20%

1200 rpm, 50% 43% 70%

1200 rpm, 75% 6% 85%

1200 rpm, 100% 2% 95%

1800 rpm, 50% 124% 42%

1800 rpm, 75% 24% 82%

1800 rpm, 100% 3% 95%


Vouitsis et al. (2007)

Euro III Engine Several Low Sulfur Fuels

Research Single Cylinder Engine, 350 ppm S

Euro 3 DPF LDV

Euro 2 Gasoline LDV

Euro 3 Diesel LDV

Euro 4 Diesel LDV

Particulates Project

Kweon et al. (2002)

Conclusions from dyno studies

In Diesel heavy duty engines, the EC fraction increases and the OC/EC ratio decreases with engine load. At full load over 80% of total PM is EC

In Diesel light duty vehicles, EC is over 80% regardless of operation condition, due to the oxidation catalyst which significantly reduces OC.

In Gasoline light duty vehicles (non GDI), EC is a less than 30% fraction of total PM. The OC/EC ratio exceeds 100% (can reach up to 500% or higher)

Values proposed in the software (excerpt)

In cases where advanced aftertreatment is used (such as catalysed DPFs) then the EC and OM does not sum up to 100%. The remaining fraction is assumed to be ash, nitrates, sulphates, water and ammonium, that can be a significant fraction of total PM.

Category Technology Euro StandardEC/PM2.5

(%)OM/EC

(%)Uncertainty Range (%)

No aftertreatment, carburettor PRE-ECE 2 4900 50Carburettor, no aftertreatment or oxidation catalyst

ECE 15 00/01 5 1900 50

Carburettor, no aftertreatment or oxidation catalyst

ECE 15 02/03 5 1900 50

Carburettor or SPI (few models) ECE 15 04 20 400 50Carburettor or SPI and TWC Open Loop 30 233 30MPI + closed loop TWC Euro 1 25 250 30MPI + closed loop TWC Euro 2 25 250 30MPI + closed loop TWC +twin lambda

Euro 3 15 300 30

MPI + closed loop TWC +twin lambda

Euro 4 15 300 30

No aftertreatment, Low Pressure Injection

Conventional 55 70 10

High-Pressure Injection (HPI) Euro 1 70 40 10HPI +Ox Cat Euro 2 80 23 10HPI +OxCat+EGR Euro 3 85 15 5HPI+multi OxCat+EGR Euro 4 87 13 5HPI +OxCat+DPF Euro 3, Euro 4, Euro 5 10 500 50HPI+Oxcat+CDPF Euro 3, Euro 4, Euro 5 20 200 50

No aftertreatment, IDI Conventional 50 80 20

Gasoline PC and LDT

Diesel PC and LDT

Diesel HDV




Non-Exhaust PMNon-Exhaust PM

Sources Tyre wear Brake wearRoad surface wear

TEi,j = Nj ∙ Mj ∙ (EF)j ∙ fi ∙ S(V)

TE... Total Emissions [g]N… Number of vehicles [veh.] M… Mileage driven by “average” vehicle [km/veh.]EF… TSP mass emission factor [g/km]fi… Mass fraction attributed to particle size class iS(V)… Correction factor for speed V (for road wear S(V)=1)

and indices,

i… TSP, PM10, PM2.5, PM1 and PM0.1 size classes,

j… Vehicle category

General Methodology

Example PM10 Non-Exhaust Emission Factors from different sources and comparison with exhaust PM

Tyre wear vs tyre PM emissions

Not all wear becomes airborne!

Particle size class (i)

Mass fraction (fT,i) of TSP

TSP 1.000

PM10 0.600

PM2.5 0.420

PM1 0.060

PM0.1 0.048

Wish List - 1

User Request

Ferreiro Antonio IPCC Uncertainty Calculations

Ricardo de Lauretis Mopeds

Martin Adams Correction for CO2 (based on weight classes or more detailed capacity classes)

Antonella Bernetii Include slope correction as a geographical parameter and not a vehicle specific parameter

Helen Heintalu Send information on updates / new versions to national experts

Martin Adams Provide export files to communicate to new CollectER and XML formats

Wish List - 2

User Request

Antonella Bernetti Make possible importing different fuel specifications per year from the Excel spreadsheets

Andrei Pilipchuk Include the effect of idling (in particular cold idling) on road transport emissions




N2O/NH3 Emission N2O/NH3 Emission FactorsFactors

LAT Database on N2O/NH3

Database with 3500 measurements from 40 literature sources (mainly US input)

Data organised according to: Mileage (vehicle/aftertreatment)

• Emission factor as a function of mileage

Aftertreatment temperature (driving profile)• Cold urban, hot urban, rural, highway

Vehicle category• PCs, LDVs, no info on HDVs & PTWs

Vehicle technology• Pre Euro, Euro 1 to Euro 4

Fuel• Gasoline, Diesel, CNG, LPG, Methanol Blends

Fuel sulphur content• 5 ppm, 30 ppm, 90-150 ppm, >150 ppm

N2O/NH3

Example of N2OEmission Factors

PCs - URBAN COLD - EURO 1

0

20

40

60

80

100

0 40000 80000 120000 160000Διανυθείσα απόσταση [km]

Επ

ίπεδ

ο ε

κπ

ομ

πή

ς

[mg

/km

]

S<30

90<=S<=150

S>=350

PCs - EURO 2 - 90<=S<=150

0

10

20

30

40

0 40000 80000 120000 160000Διανυθείσα απόσταση [km]

Επ

ίπεδ

ο ε

κπ

ομ

πή

ς

[mg

/km

]

UCUHRURHIGH

N2O/NH3

Example of NH3Emission Factors

LDVs - EURO 3-4 - S=30

0

20

40

60

80

100

120

140

0 40000 80000 120000 160000

Διανυθείσα απόσταση [km]

Επ

ίπεδ

ο εκ

πομ

πής

[mg/

km]

URBAN COLDURBAN HOTRURAL HIGHWAY




Emission DegradationEmission Degradation

New Emission degradation correction

factor parameters

for EURO 1 & EURO 2

New Emission degradation correction factor parameters for EURO 3 & EURO 4

Emission degradation correction factor as a function of speed




Heavy Duty Vehicle Heavy Duty Vehicle

MethodologyMethodology

Average Speed Model – Artemis Fuel consumption CO HC NOx PM

Categories Heavy Goods Vehicles

(Copert weight categories) Buses Coaches

Effects Vehicle Load Road Gradient

Heavy Duty Vehicle

Coverage

Heavy Duty Vehicles –

Example of Emission Factor –

Rigid Truck <=7,5t Euro 3

Vehicle Technologies 1980’s Euro 1 to 5 Taking into account the unexcected

behaviour of Euro 2 NOx


Example of effect of vehicle load on emissions –

Urban bus midi <15t

Euro 3


Example of road gradient on emissions –

Truck-trailerartic. truck 50-60t

Euro 2

The influence of fuel quality The influence of engine deterioration with time The effect of maintenance The effect of particle traps (diesel particulate filters – DPFs) Alternative engine concepts

Compressed natural gas (CNG) Bio-diesel

Heavy Duty Vehicles

– Remaining

Issues

CO Euro 4 and 5 lower than COPERT

HC Euro 4 and 5 lower than COPERT

NOx Euro 2-5 higher than COPERT

PM generally similar with small differences

Heavy Duty Vehicles

– Comparison with Copert

3




Cold StartCold Start

Cold Start Approach in Artemis

Level 1: Excess Emission per Start – Vehicle Level ),()),,,(,(,,)(,,, 20,20 tpgTVpdphTVpfpTVpEE

EE: Excess emission of pollutant p for a trip (g/veh.)

V: Mean speed during the cold period (km/h)

T: Ambient temperature (°C)

t = Parking time (h)

d: Actual travelled distance (km)

ω20,20: Reference excess emission (20 °C & 20 km/h) for a

trip distance longer or equal to the cold distance

dc: Cold distance (km)

δ: Dimensionless distance = d/dc(p,V,T)

f: ω correction for speed V and the temperature T

h: Fraction of cold overemission over distance δ

g: Percentage of reference excess emission for parking time less than 12 h


Level 2: Extension of Level 1 into fleet level

i h j m n

nj

m

nhjmji

hi

hhii

ic tpgphTVpf

d

ppp

ptf

ptfp

s, vcm=pE ,.,.,,.

.

....).(

100)(

106

,,,

,,

Ec = traffic excess emissions corresponding to a traffic tfi,h (g/time unit)

t = parking time (h) cm(s, vi) = % of mileage recorded under cold start or intermediate temperature conditions for

season s and speed vi of vehicle type i i = vehicle type s = season (winter, summer, middle, year) vi = traffic overall average speed for the vehicle type i (km/h) h = hour (1 to 24, day) tfi,h = traffic flow for the studied vehicle type i and the hour h(km.veh) ph = relative cold start number for the hour h (average=1) ptfi,h = relative traffic for the studied vehicle type i and the hour h (average=1) j = speed class with a cold engine m = trip length class n = class of stops (0 -1, 1 - 2, ... , >12h) pi,j = % of the distance travelled at speed j with a cold engine, for the average speed

considered, and for the studied vehicle type i (%) pm,j = % of the distance started with a cold engine and distance dm, for speed Vj with a cold

engine ph,n= % of the distance travelled after a stop with a duration of tn, for hour h dm = average distance of the trips under cold start conditions of class m (km) Vj = average speed with a cold engine corresponding to class j (km/h) T = ambient temperature (°C)

wi(p) = reference excess emission for the vehicle type i (g) f(p,Vj,T) = plane function of the speed Vj and the temperature T, for the pollutant p h(p,d) = (1-ea(p,T).d)/ (1-ea(p,T)) d = dimensionless distance = dm/dc(p,Vj,T) dc(p,Vj,T) = cold distance for the pollutant p (km) g(p,tn) = % of excess emission at 12h of parking as a function of the parking time tn for

the pollutant p


Level 3: Level 2 with assumptions/estimations for most parameters

Excess Emission [g/km]=f(pollutant, vehicle technology/category, season, ambient temperature, mean speed, [hour of day])

Notes:

Emissions in g/km difficult to perceive for a cold-start model Are directly multiplied with total mileage No differentiation for few starts/long trips

No effect of different trip distributions (model assumes default ones)

Model based on default parking-time profiles. This may be different for different applications/countries

Additional work will be required to transform this model into Copert approach.




Evaporation LossesEvaporation Losses

Diesel Vehicles CO Euro 2 and 3 lower than COPERT HC Euro 3 lower than COPERT NOx similar results PM Euro 2 and 3 lower than COPERT Fuel Consumption higher than COPERT ay high engine

capacities

Gasoline Vehicles CO lower than COPERT at low speeds ranges HC similar results NOx Euro 3 and Euro 4 lower than COPERT at high speed

ranges Fuel Consumption similar results

Hot Emission Factors of Regulated Pollutants from Conventional PCs – Summary of Comparison COPERT and

ARTEMIS




Hybrid VehiclesHybrid Vehicles

Hybrid Vehicles –

Measurements 1/2

The measurements were conducted on the LAT chassis dynamometer between 26-31/05/2005

Scope of the measurements was to obtain experience on hybrid technology and to study the vehicle performance in “real world” driving conditions

We followed the specifications of the relevant European Directives

Specific setup and conditioning guidelines given to us by Toyota Belgium on the Prius II vehicle tested

Hybrid Vehicles –

Measurements 2/2

Emissions measurements

The measurement protocol included the standard type-approval NEDC test and real world driving cycles (ARTEMIS cycles)

2 repetitions were conducted, during which all legislated gaseous pollutants and fuel consumption were measured

ΔSOC was measured using the vehicle SOC indicator

Fuel consumption and state-of-charge

ΔSOC was again measured using the vehicle SOC indicator

Repetitions of UDC, EUDC, Artemis urban and Artemis road in order to study the effect of ΔSOC and evaluate the measurements

Hybrid Vehicles –

How do measurements compare against conventional PCs

[email protected] Leon Ntziachristos +30 2310 996031

[email protected] Dimitris Gkatzoflias +30 2310 996051

laboratory of applied thermodynamics aristotle university thessaloniki school of engineering dept....

Documents

history copert

new hot emission factors

original copert

pcs emission degradation

load factors

development slide

total emission estimates

view data slide