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LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
COPERT 4
ERMES, 2010-06-23
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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)
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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)
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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 Barbara.schodl@umweltbundesamt.at
Belgium COPERT Catherine SQUILBIN csq@ibgebim.be
Bulgaria Tier 1 Ivan LERINSKI Ileriniski@mt.government.bg
Cyprus COPERT Chrysanthos SAVVIDES csavvides@dli.mlsi.gov.cy
Czech Republic COPERT Pavel MACHALEK, Jiri DUFEK machalek@chmi.cz, dufek@cdv.cz
Denmark COPERT Morten WINTHER MWI@dmu.dk
Estonia COPERT Helen HEINTALU Helen.Heintalu@ic.envir.ee
Finland LIPASTO (LIISA) Kristina SAARINEN kristina.saarinen@environment.fi
France COPERT Jean Pierre CHANG jean-pierre.chang@citepa.org
Germany TREMOD Gunnar GOHLISCH gunnar.gohlisch@uba.de
Greece COPERT Alexandros KARAVANAS akaravanas@ekpaa.gr
Hungary COPERT Tamas MERETEI meretei@kti.hu
Ireland COPERT Eimer COTTER e.cotter@epa.ie
Italy COPERT Riccardo DE LAURETIS riccardo.delauretis@anpa.it
Latvia COPERT Sabine KRUMHOLDE Sabine.Krumholde@lvgma.gov.lv
Lithuania COPERT Jolanta KOTVICKAJA j.kotvickaja@am.lt
Luxembourg COPERT Marc SCHUMAN Marc.Schuman@aev.etat.lu
Malta Tier 1 (COPERT) Christopher CAMILLERI Christopher.Camilleri@mepa.org.mt
Netherlands AGG. VERSIT+ Winand SMEETS winand.smeets@rivm.nl
Poland COPERT Janina FUDALA j.fudala@ietu.katowice.pl
Portugal COPERT Pedro TORRES pmt@fct.unl.pt
Romania COPERT Vlad Ioan GHIUTA TARALUNGA ghiuta.vlad@anpm.ro
Slovakia COPERT Jozef MACALA Jozef.Macala@tuke.sk
Slovenia COPERT Alenka FRITZEL alenka.fritzel@gov.si
Spain COPERT Ana Rodriguez SECO ana.rodriguez@cedex.es
Sweden ARTEMIS Gabriella HAMMARSKJOLD gabriella.hammarskjold@naturvardsverket.se
UK COPERT Based Justin GOODWIN justin.goodwin@aeat.co.uk
Other Countries
Belarus COPERT Hanna MALCHYCHINA anna_malchihina@tut.by
Bosnia COPERT Martin TAIS martin.tais@smartnet.ba
Croatia COPERT Zeljko JURIC, Vjeko BOLANCA zeljko.juric@ekonerg.hr, vjekoslav.bolanca@mppv.hr
FYROM COPERT Igor PAUNOVSKI I.paunovski@moepp.gov.mk
Norway COPERT Based Alice GAUSTAD alice.gaustad@sft.no
Switzerland National Model Giovanni D'URBANO giovanni.durbano@bafu.admin.ch
Turkey Tier 1Ayse YILDIRIM COSGUN,
Fatma Betül BAYGÜVEN aycosgun@cevreorman.gov.tr, ali.can@tuik.gov.tr
Ukraine COPERT Kateryna SAVCHENKO k.savchenko@ukrntec.com
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.
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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: John@Smith.eduOrganization: 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
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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)
Examples of application in EC4MACS (EU27 - PCs)
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
Examples of application in EC4MACS (EU27 - PCs)
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
Examples of application in EC4MACS (EU27 - PCs)
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
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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
Emission Factor Uncertainty
Fourteen speed classes distinguished from 0 km/h to 140 km/h
Emission Factor Uncertainty
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.
LABORATORY OF APPLIED THERMODYNAMICS
ARISTOTLE UNIVERSITY THESSALONIKI
SCHOOL OF ENGINEERING
DEPT. OF MECHANICAL ENGINEERING
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
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