laboratory of applied thermodynamics copert 45]ntziachristos... · croatia copert zeljko juric,...

LABORATORY OF APPLIED THERMODYNAMICS

ARISTOTLE UNIVERSITY THESSALONIKI

SCHOOL OF ENGINEERING

DEPT. OF MECHANICAL ENGINEERING

COPERT 4

ERMES, 2010-06-23





Background & 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

Methodological developments are currently mainly supported by the Joint Research Centre / Institute for Environment and Sustainability

Software development and users support is provided by the European Environment Agency through the ETC-ACC budget

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

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)





Current Status

Major Points

1. Calculates emissions from all major pollutants, incl. exhaust and diffuse sources (evaporation, tyre and break wear), based on mean speed emission factors

2. Covers all major vehicle categories (241 individual types/technologies)

3. Best applied for national-scale inventories

4. Is supported by user-friendly software that allows straightforward data treatment and interface for CRF (UNFCCC) and NFR (CLRTAP)

5. Thoroughly tested by hundreds of users worldwide

Major Sources of Information

ARTEMIS/HBEFA: Practically all CO, NMVOC, NOx hot emission factors and fuel consumption

PARTICULATES: PM and PN information

MEET: Cold-start methodology

JRC/EUROPIA project: Evaporation methodology

LAT studies: N2O, NH3, CH4 emission factors

AEIG (LAT/TRL collaboration): Non-exhaust PM

JRC: Uncertainty estimates

DG ENV FLEETS: National data for calculations

EEA/ETC Work: Bus B30 emission factors

Future Updates

HBEFA: Cold-start methodology

Literature/TNO/NERI: Heavy metals emissions

Literature: NMVOC speciation

JRC Work: CO2/FC for PCs, LDVs

New tests: Euro 5/V, 6/VI, new hybrids emission/consumption factors

New tests: New fuels (E5, E85, CNG)





Uses & Users

COPERT Usage

COPERT

STEERS(CONCAWE)

TREMOVE

AEIG(Tier 3)

TERM

FLEETS

National Inventories

Individual Use

GAINS/RAINS /

EC4MACS

Field of applications - National level

Country Model Contact Person Email

EU27

Austria GLOBEMI Barbara SCHODL [email protected]

Belgium COPERT Catherine SQUILBIN [email protected]

Bulgaria Tier 1 Ivan LERINSKI [email protected]

Cyprus COPERT Chrysanthos SAVVIDES [email protected]

Czech Republic COPERT Pavel MACHALEK, Jiri DUFEK [email protected], [email protected]

Denmark COPERT Morten WINTHER [email protected]

Estonia COPERT Helen HEINTALU [email protected]

Finland LIPASTO (LIISA) Kristina SAARINEN [email protected]

France COPERT Jean Pierre CHANG [email protected]

Germany TREMOD Gunnar GOHLISCH [email protected]

Greece COPERT Alexandros KARAVANAS [email protected]

Hungary COPERT Tamas MERETEI [email protected]

Ireland COPERT Eimer COTTER [email protected]

Italy COPERT Riccardo DE LAURETIS [email protected]

Latvia COPERT Sabine KRUMHOLDE [email protected]

Lithuania COPERT Jolanta KOTVICKAJA [email protected]

Luxembourg COPERT Marc SCHUMAN [email protected]

Malta Tier 1 (COPERT) Christopher CAMILLERI [email protected]

Netherlands AGG. VERSIT+ Winand SMEETS [email protected]

Poland COPERT Janina FUDALA [email protected]

Portugal COPERT Pedro TORRES [email protected]

Romania COPERT Vlad Ioan GHIUTA TARALUNGA [email protected]

Slovakia COPERT Jozef MACALA [email protected]

Slovenia COPERT Alenka FRITZEL [email protected]

Spain COPERT Ana Rodriguez SECO [email protected]

Sweden ARTEMIS Gabriella HAMMARSKJOLD [email protected]

UK COPERT Based Justin GOODWIN [email protected]

Other Countries

Belarus COPERT Hanna MALCHYCHINA [email protected]

Bosnia COPERT Martin TAIS [email protected]

Croatia COPERT Zeljko JURIC, Vjeko BOLANCA [email protected], [email protected]

FYROM COPERT Igor PAUNOVSKI [email protected]

Norway COPERT Based Alice GAUSTAD [email protected]

Switzerland National Model Giovanni D'URBANO [email protected]

Turkey Tier 1Ayse YILDIRIM COSGUN,

Fatma Betül BAYGÜVEN [email protected], [email protected]

Ukraine COPERT Kateryna SAVCHENKO [email protected]

Example of Applications

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 Spainfrom 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

0

100

200

300

400

500

600

Other EU15 members

Other new member states

Other Europe

Distribution of users from Africa

Distribution of users from Asia

Distribution of users from America

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





Centralised vs National Emission Calculations

Background: Data sources and usage

In 2008, EC (DG ENV) collected and produced streamlined road stock and activity data for all EU27 MSs + (CH, HR, NO, TR) to feed COPERT and TREMOVE (FLEETS project)

Base year: 2005

Historic years : Mostly back to 1995 (some countries already starting 1970)

EC (DG ENV) will base transport projections to 2030 on these data

TREMOVE for impact assessments

EC4MACS (GAINS, PRIMES) for integrated assessment

Methodology: National submissions

For main pollutants (CO, VOC, NOx, PM): emission data officially

submitted to CLRTAP

For CO2: emission data officially submitted to UNFCCC

Data collected for EEA30 countries (except Iceland and Liechtenstein),

years 2000 and 2005

Aggregated and sectoral (except CO2) data available for most countries

Methodology: Centralised calculations

Transport activity data from the FLEETS database (vehicle stock,

mileage, speeds, shares, etc.)

Data obtained from international sources (Eurostat, ACEA, …) and

national data (experts, projects)

Data collected for EEA30 countries (except Iceland and Liechtenstein)

and for years 2000 and 2005

Calculations performed with COPERT 4 (v6.1) to estimate pollutant and

CO2 emissions

Mileage adjusted to match national fuel use statistics (from

UNFCCC submissions) – NOT always the case in national

inventories

Bias introduced if UNFCCC and CLRTAP equivalent fuel

consumptions differ (e.g. AT, LU, IE, …)

Results: Total emissions (kt)

National data2000

Centralised2000

Excluded countries

National data2005

Centralised2005

Excluded countries

EU15

CO 14915 14520 DK, GR, LU 9608 9222 DK, LU

VOC 2407 2261 DK, GR, LU 1453 1329 DK, LU

NOx 4765 4863 GR, LU 3779 4039 LU

PM2.5 259 222 GR, LU 217 171 GR, LU

CO2 758743 760157 LU 786181 790687 LU

EU27

CO 15387 14898BG,CZ,DK,GR,HU,

LT,LU,MT,PL,RO 11860 11128DK, LU, MT

VOC 2477 2318BG,CZ,DK,GR,HU,

LT,LU,PL,RO 1821 1621 DK, LU

NOx 4862 4970BG,CZ,GR,HU,LT

LU,PL,RO 4547 4710 LU

PM2.5 264 226BG,CZ,GR,HU,LT

LU,PL,RO 253 195 BG, GR, LU, RO

CO2 836381 836377 CY,LU,MT 886457 885369 CY, LU, MT

EEA30

CO 15925 15631BG,CZ,DK,GR,HU,LTLU,

MT,PL,RO,TR 12243 11535 DK, LU, MT, TR

VOC 2548 2418BG,CZ,DK,GR,HU,LT

LU,PL,RO,TR 1868 1674 DK, LU, TR

NOx 4961 5127BG,CZ,GR,HU,LT

LU,PL,RO,TR 4625 4815 LU, TR

PM2.5 269 231BG,CZ,GR,HU,LT

LU,PL,RO,TR 257 199 BG,GR,LU,RO,TR

CO2 890892 889462 CY,LU,MT 945948 940660 CY, LU, MT

Results: CO Indicators

0

20

40

60

80

100

120

140

NL UK IE DE BE ES FR AT CH SE PT CZ CY LT PL IT SK NO SI EE FI DK LV BG RO HU GR

CO

(g

/kg

fu

el)

National

Centralised

Increasing National CO

Results: Classification of CO deviations

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2

1,3

1,4

1,5

1,6

1,7

1,8

GR HU CY PL DK PT LV AT SI FR BE ES NO FI BG IT SK RO DE IE CH EE SE UK NL CZ LT

CO

In

dic

ato

r R

ati

o

Centralised/National

Results: ΝOx Indicators

0

5

10

15

20

25

30

35

CH CY NO NL IE IT SI MT DE SE UK FI BE PT EE ES CZ FR AT SK PL LV GR DK LT RO HU

NO

x (

g/k

g f

ue

l)

National

Centralised

Increasing National NOx

Results: Classification of NOx deviations

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

1,2

1,3

1,4

1,5

1,6

1,7

1,8

GR HU CY PL DK PT LV AT SI FR BE ES NO FI BG IT SK RO DE IE CH EE SE UK NL CZ LT

NO

x In

dic

ato

r R

ati

o

Centralised/National





Use of COPERT in EC4MACS

Modelling approach within EC4MACS

DEMAND (PRIMES 2009)

GJ/yVEHICLE

STOCK SIZE

VEHICLE TECHNOLOGY (Impl. Matrices)

Stock Module (FLEETS)

CONSUMPTION MODELFC

EQUALS PRIMES

NO

AGGREGATE/FORMAT

TO GAINS

PRIMES ASSUMPTIONS ON EFFICIENCY

NEW SALES & SURVIVING STOCK (TRENDS method)

COPERTYES

CALIBRATE MILEAGE

FIXED MILEAGE

Examples of application in EC4MACS (EU27 - PCs)

Total energy consumption (PJ)

0

1000

2000

3000

4000

5000

6000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG GAS H2

Total activity (Gvkm)

0

500

1 000

1 500

2 000

2 500

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG GAS H2

(Input)

(Calcs based on historical data)


Number of vehicles (x103)

0

50 000

100 000

150 000

200 000

250 000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG GAS H2

Hybrids IncludedNew vehicles over last 5 years (x103)

0

10 000

20 000

30 000

40 000

50 000

60 000

70 000

80 000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG GAS H2


Specific energy consumption (MJ/vkm)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG GAS H2

Annual mileage (km/veh.)

0

5 000

10 000

15 000

20 000

25 000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG


Total Emissions CO (kt)

10

100

1000

10000

100000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG

Total Emissions NMVOC (kt)

10

100

1000

10000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG

Total Emissions PM2.5 (kt)

1

10

100

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG

Total Emissions NOx (kt)

1

10

100

1000

10000

2000 2005 2010 2015 2020 2025 2030

GSL MD LPG





COPERT Uncertainty

Variance of the total stock

ITALYACEA ACEM ACI Eurostat

μ σ2005 2005 2005 2005

Passenger Cars 34 667 485 34 667 485 34 636 400 34 657 123 17 947

Light Duty Vehicles 3 257 525 3 633 900 3 445 713 266 137

Heavy Duty Vehicles 1 070 308 958 400 1 014 354 79 131

Buses 94 437 94 437 94 100 94 325 195

Mopeds 5 325 000 4 560 907 4 942 954 540 295

Motorcycles 4 938 359 4 938 359 4 933 600 4 936 773 2 748

POLANDACEA ACEM Poland Stat Eurostat

μ σ2005 2005 2005 2005

Passenger Cars 12 339 353 12 339 000 12 339 000 12 339 118 204

Light Duty Vehicles 1 717 435 2 304 500 2 178 000 2 066 645 308 968

Heavy Duty Vehicles 587 070 737 000 662 035 106 017

Buses 79 567 79 600 80 000 79 722 241

Mopeds 337 511 337 511 0

Motorcycles 753 648 754 000 753 824 249

Technology classification variance – 1(2)

Italy: Exact technology classification

Poland: Technology classification varying, depended on variable scrappage rate

Gasoline PC <1,4 l

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

0 5 10 15 20

age

φ(age)

G<1,4Alt1

Alt2Alt3

Boundaries Introduced:

Age of five years: ±5 perc.units

Age of fifteen years: ±10 perc.units

All scrappage rates respecting

boundaries are accepted → these

induce uncertainty

100 pairs finally selected by

selecting percentiles

Example of technology classification variance

Example for GPC<1.4 l Poland

Standard deviation: 3.7%, i.e. 95% confidence interval is 11%

PC Gasoline<1,4 EURO 1

0 16

323

1 099

1 706 1 708

1 073

320

28 00

200

400

600

800

1 000

1 200

1 400

1 600

1 800

2 000

755 - 78

0

780 - 80

5

805 - 83

0

830 - 85

5

855 - 88

0

880 - 90

5

905 - 93

0

930 - 95

5

955 - 98

0

980 - 1,05

0

Number of vehicles (thousands)

Frequency

Emission Factor Uncertainty

Emission factor functions are derived from several experimental measurements over speed


Fourteen speed classes distinguished from 0 km/h to 140 km/h


A lognormal distribution is fit per speed class, derived by the experimental data. Parameters for the lognormal distribution are given for all pollutants and all vehicle technologies in the Annex A of the report.

Results – Screening test Italy

Results – Influential Variables

Variable Significant for Italy Significant for Poland

Hot emission factor

Cold overemission

Mean trip distance

Oxygen to carbon ratio in the fuel

Population of passenger cars -

Population of light duty vehicles

Population of heavy duty vehicles

Population of mopeds -

Annual mileage of passenger cars

Annual mileage of light duty vehicles

Annual mileage of heavy duty vehicles

Annual mileage of urban busses -

Annual mileage of mopeds/motorcycles -

Urban passenger car speed

Highway passenger car speed -

Rural passenger car speed -

Urban speed of light duty vehicles -

Urban share of passenger cars -

Urban speed of light duty vehicles -

Urban speed of busses -

Annual mileage of vehicles at the year of their registration

-

The split between diesel and gasoline cars -

Split of vehicles to capacity and weight classes -

Allocation to different technology classes -

Results – total uncertainty Italy w/o fuel correction

Results – Necessary fuel correction for Italy

Unfiltered dataset: Std Dev = 7% of

mean

Filtered dataset: 3 Std Dev = 7% of

mean

Results – Descriptive statistics of Italy with corrected sample

CO VOC CH4 NOx N2O PM2.5 PM10 PMexh FC CO2 CO2e

Mean 1,134 325 19 614 3.1 32 37 27 36,945 110,735 112,094

Median 1,118 324 18 608 2.9 32 36 27 36,901 110,622 111,941

St. Dev. 218 38 7 59 0.8 3 3 3 1,241 4,079 4,203

Variation(%)

19 12 34 10 26 9 8 9 3 4 4

Italy – Contribution of items to total uncertainty 1(2)

VOC SI STI NOX SI STI PM2.5 SI STI PM10 SI STI PMexh SI STI

eEF 0.63 0.78 eEF 0.76 0.85 eEF 0.72 0.86 eEF 0.72 0.84 eEF 0.72 0.86

ltrip 0.08 0.22 milHDV 0.12 0.22 milHDV 0.08 0.22 milHDV 0.08 0.21 milHDV 0.09 0.23

eEFratio 0.05 0.15 HDV 0.01 0.08 ltrip 0.01 0.13 ltrip 0.01 0.13 ltrip 0.01 0.14

milMO 0.05 0.17 PC 0 0.08 HDV 0.01 0.12 HDV 0.01 0.11 HDV 0.01 0.14

VUPC 0.02 0.16 ltrip 0 0.08 eEFratio 0.01 0.13 milPC 0.01 0.12 eEFratio 0.01 0.14

O2C 0.02 0.15 LDV 0 0.08 LDV 0.01 0.12 LDV 0.01 0.11 LDV 0.01 0.12

HDV 0.01 0.13 VHPC 0 0.1 milPC 0.01 0.13 eEFratio 0.01 0.13 milPC 0.01 0.14

MOP 0.01 0.14 VUPC 0 0.08 PC 0 0.13 PC 0.01 0.13 milMO 0.01 0.12

milHDV 0.01 0.14 O2C 0 0.08 milMO 0 0.11 milMO 0.00 0.10 PC 0.00 0.14

LDV 0.01 0.12 milPC 0 0.08 milLDV 0 0.12 milLDV 0.00 0.12 milLDV 0.00 0.13

PC 0 0.15 UPC 0 0.08 VHPC 0 0.13 VHPC 0.00 0.12 VHPC 0.00 0.14

VRPC 0 0.15 MOP 0 0.09 MOP 0.00 0.13 MOP 0.00 0.12 MOP 0.00 0.15

milPC 0 0.14 eEFratio 0 0.1 O2C 0 0.11 O2C 0 0.1 O2C 0 0.12

VHPC 0 0.13 milMO 0 0.07 UPC 0 0.12 UPC 0 0.11 UPC 0 0.13

milLDV 0 0.14 VRPC 0 0.1 VUPC 0 0.12 VRPC 0 0.12 VRPC 0 0.13

UPC 0 0.14 milLDV 0 0.08 VRPC 0 0.12 VUPC 0 0.11 VUPC 0 0.13

ΣSi 0.91 3.03 0.91 2.27 0.87 2.78 0.87 2.69 0.88 2.96

Conclusions – 1(3)

The most uncertain emissions calculations are for CH4 and N2O followed by CO. The hot or the cold emission factor variance which explains most of the uncertainty. In all cases, the initial mileage value is a significant user-defined parameter.

CO2 is calculated with the least uncertainty, as it directly depends on fuel consumption. It is followed by NOx and PM2.5 because diesel are less variable than gasoline emissions.

The correction for fuel consumption within plus/minus one standard deviation is very critical as it significantly reduces the uncertainty of the calculation in all pollutants.

The relative level of variance in Poland appears lower than Italy in some pollutants (CO, N2O). This is for three reasons, (a) Poland has an older stock and the variance of older technologies is smaller than new ones, (b) the colder conditions in Poland make the cold-start to be dominant, (c) artefact of the method as the uncertainty was not possible to quantify for some older technologies. Also, the contribution from PTWs much smaller than in Italy.

Despite the relatively larger uncertainty in CH4 and N2O emissions, the uncertainty in total Greenhouse Gas emissions is dominated by CO2

Conclusions – 2(3)

The Italian inventory uncertainty is affected by:

hot emission factors [eEF]: NOx (76%), PM (72%), VOC (63%), CO (44%), FC (43%), CO2 (40%), CH4 (13%)

cold emission factors [eEFratio]: CH4 (61%), N2O (59%), CO (19%), FC (11%), CO2 (10%), VOC (5%)

mileage of HDV [milHDV]: NOx (12%), PM (8-9%), FC (9%), CO2 (9%).

mean trip length [ltrip]: VOC (8%), N2O (6%), CO (5%)

Recommendations

There is little the Italian expert can do to reduce uncertainty. Most of it comes from emission factors

Better stock and mileage description is required for Poland to improve the emission inventory.





Outlook

Outlook

Improve methodology (main elements): CO2 calculation Inclusion of more vehicle technologies Improvement of Euro 5/V, 6/VI emission factors

Improve availability of activity data Identify and filter national and international data Link to 443/2009 database

Support users Improve software / add functionalities Train users to improve reliability of inventories

COPERT Usage Possibly extend use to other countries in Europe (e.g. EECAA) Extend use outside Europe in countries adopting ‘Euro’-standards

laboratory of applied thermodynamics copert 45]ntziachristos... · croatia copert zeljko juric,...

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