statistical evaluation of model uncertainties in copert iii, by i. kioutsioukis & s. tarantola...
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Statistical evaluation of model uncertainties in Copert III,
by
I. Kioutsioukis & S. Tarantola (JRC, I)
Sensitivity analysis tests performed using COPERT III
Interpretation of the results
Objectives of the statistical analyses
Need to check robustness of emission estimates to poorly known parameters and model assumptions.Reflect our poor knowledge on input parameters by means probability distributions and apply Monte Carlo analysis to estimate probability distributions of emissions.
Representation of a Monte Carlo simulation
Objectives
Precision of emission estimates depends on the assumptions made in the definition of the various model input parameters.
Uncertainty should always accompany an estimate, as it is a measure of the quality of the estimate.
Representation of the Monte Carlo simulation
Objectives
Objective is to apply up-to-date sensitivity analysis to identify theparameters mainly responsible for uncertainty in the emissions
Help us improving the quality of emission estimates if we direct efforts to improve our knowledge of the important parameters
Estimates (and related uncertainties) can then be used 1. to adopt traffic policy measures 2. for inventory systems3. as input to air quality models
Objectives
Statistical analyses
• Description of sources of uncertainty (input):
• Description of the set up of the analyses
• Results (Figures and Tables)
- Uncertainty in traffic parameters (how to model them)
- Uncertainty in average speed
- Uncertainty in emission factors
Country-specific mileage data taken from MEET deliverable #22
All the categories of vehicles considered
FBM–INFRAS used for decomposition of fleet into sub-categories
Model uncertainty in traffic parameters
τ: steers the technology stage percentages;
τ = –1, 0 and +1 represent fleet with 'low/medium/high' amount of new technology vehicles, respectively.δ: steers the diesel share of PC and LDV;
δ = –1, 0 and +1 represent fleet with 'low/medium/high' amount of diesel vehicles, respectively.σ : steers the size (weight class) distribution of HDV;
σ = –1, 0 and +1 represent fleet with 'low/medium/high' amount of heavy-weight HDV's, respectively.
Uncertainty in traffic parameters
FBM (expensive) is only executed at selected points
τ τ
δ σ
We feed COPERT with a representative configuration of fleet breakdown at each Monte Carlo run i.
We sample a point τi, δi, σi over the square and interpolating the FBM runs we obtain the configuration of fleet breakdown f (τi, δi, σi )
Uncertainty in average speed
Currently described with rather rough statistical distributions
Exploratory analyses have shown that average speed is rather an important parameter. Perform more refined analyses…
0,000
0,020
0,040
0,060
0,080
0,100
0,120
0,140
40 60 80 100 120
velocità media [km/h]
f(x)
■ Average speed in rural road ● average speed in motorway
More reliable pdf’s using Goodness of fit tests based on driving cycles
Uncertainty in emission factors
Very low regression coefficients
1
1
2
1
i
i
i
ij N
sN
sS
)1(*)1,0(* 22 RNSRefEF jreg
)1,0(NSefEF jreg
regef
Not sufficient
Uncertainty in load factorsPdf=Normal; mean=50%, std = 10%(questionnaire - expert opinion)
Uncertainty in meteo conditions(statistical model - INFRAS)
Uncertainty in average trip length Pdf=Log-Normal; mean=12Km, std=3Km(questionnaire - expert opinion)
Variable Description Units Distribution (,)
PPC population of PC - Normal (34799160, 347992)
PLDV population of LDV - Normal (2142083, 21421)
PHDV population of HDV - Normal (1293357, 12934)
PUB population of UB - Normal (83851, 838)
P2W population of 2-wheel vehicles - Normal (2000000, 20000)
MPC Annual mileage of PC Km Normal (10059, 1006)
MLDV Annual mileage of LDV Km Normal (17706, 1771)
MHDV Annual mileage of HDV Km Normal (38741, 3874)
MUB Annual mileage of UB Km Normal (41800, 4180)
MPW Annual mileage of PW Km Normal (5000, 1000)
UPC driving share (urban) of PC % Normal (35, 10)
HPC driving share (highway) of PC % Normal (15, 2.5)
ULDV driving share (urban) of LDV % Normal (40, 12)
HLDV driving share (highway) of LDV % Normal (30, 4.5)
UHDV driving share (urban) of HDV % Normal (30, 9)
HHDV driving share (highway) of HDV % Normal (50, 7.5)
UUB driving share (urban) of UB % Normal (75, 10)
HUB driving share (highway) of UB % Normal (15, 2.25)
UPW driving share (urban) of PW % Normal (30, 9)
HPW driving share (highway) of PW % Normal (40, 6)
VU velocity profile (urban) Km/h Normal (20, 3)
VRPC velocity profile (rural) of PC Km/h Normal (65, 9.75)
VHPC velocity profile (highway) of PC Km/h Normal (100, 15)
VRLDV velocity profile (rural) of LDV Km/h Normal (60, 9)
VHLDV velocity profile (highway) of LDV Km/h Normal (90, 13.5)
VRHDV velocity profile (rural) of HDV Km/h Normal (50, 7.5)
VHHDV velocity profile (highway) of HDV Km/h Normal (80, 12)
VRUB velocity profile (rural) of UB Km/h Normal (50, 7.5)
VHUB velocity profile (highway) of UB Km/h Normal (85, 12.75)
VRPW velocity profile (rural) of PW Km/h Normal (65, 9.75)
VHPW velocity profile (highway) of PW Km/h Normal (100, 15)
Ltrip Average trip length Km Log-Normal (12, 3)
LP load factor % Normal (50, 10)
slope slope category - Normal (0, 1)
A lowest minimum temperature C Normal (3.4, 0.35)
H highest minimum - lowest minimum temperature C Normal (14.9, 0.51)
D highest maximum - (A+H) temperature C Normal (13, 0.48) traffic parameter - Uniform (-1, 1) traffic parameter - Uniform (-1, 1) traffic parameter - Uniform (-1, 1)
eEF amplitude Emission Factor - Normal (0, 1)
Variable Description Units Distribution (,)
PPC population of PC - Normal (34799160, 347992)
PLDV population of LDV - Normal (2142083, 21421)
PHDV population of HDV - Normal (1293357, 12934)
PUB population of UB - Normal (83851, 838)
P2W population of 2-wheel vehicles - Normal (2000000, 20000)
MPC Annual mileage of PC Km Normal (10059, 1006)
MLDV Annual mileage of LDV Km Normal (17706, 1771)
MHDV Annual mileage of HDV Km Normal (38741, 3874)
MUB Annual mileage of UB Km Normal (41800, 4180)
MPW Annual mileage of PW Km Normal (5000, 1000)
UPC driving share (urban) of PC % Normal (35, 10)
HPC driving share (highway) of PC % Normal (15, 2.5)
ULDV driving share (urban) of LDV % Normal (40, 12)
HLDV driving share (highway) of LDV % Normal (30, 4.5)
UHDV driving share (urban) of HDV % Normal (30, 9)
HHDV driving share (highway) of HDV % Normal (50, 7.5)
UUB driving share (urban) of UB % Normal (75, 10)
HUB driving share (highway) of UB % Normal (15, 2.25)
UPW driving share (urban) of PW % Normal (30, 9)
HPW driving share (highway) of PW % Normal (40, 6)
VU velocity profile (urban) Km/h Normal (20, 3)
VRPC velocity profile (rural) of PC Km/h Normal (65, 9.75)
VHPC velocity profile (highway) of PC Km/h Normal (100, 15)
VRLDV velocity profile (rural) of LDV Km/h Normal (60, 9)
VHLDV velocity profile (highway) of LDV Km/h Normal (90, 13.5)
VRHDV velocity profile (rural) of HDV Km/h Normal (50, 7.5)
VHHDV velocity profile (highway) of HDV Km/h Normal (80, 12)
VRUB velocity profile (rural) of UB Km/h Normal (50, 7.5)
VHUB velocity profile (highway) of UB Km/h Normal (85, 12.75)
VRPW velocity profile (rural) of PW Km/h Normal (65, 9.75)
VHPW velocity profile (highway) of PW Km/h Normal (100, 15)
Ltrip Average trip length Km Log-Normal (12, 3)
LP load factor % Normal (50, 10)
slope slope category - Normal (0, 1)
A lowest minimum temperature C Normal (3.4, 0.35)
H highest minimum - lowest minimum temperature C Normal (14.9, 0.51)
D highest maximum - (A+H) temperature C Normal (13, 0.48) traffic parameter - Uniform (-1, 1) traffic parameter - Uniform (-1, 1) traffic parameter - Uniform (-1, 1)
eEF amplitude Emission Factor - Normal (0, 1)
first stage: screening analyses (Morris and Standardised Regression Coefficients (SRC)) to identify the non-influential input parameters.
Results: total emissions in Italy for years 2000 and 2010
40 parameters 15 parameters
Identified 25 parameters that do not influence the variability of the emission estimates (eg meteo variables)
0 0.5 1 1.5 2 2.5 3
x 105
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2x 10
5
MU
SIG
MA
HDV
VU
LDVPCLP
ltrip
AHdUBVH126
VR126
VR34VH34
MPC
MUBMLDVMHDVMPW
UPC
UHDVULDVUUBHPCHLDVHHDVHUBUPWHPW
delta
tau
sigma
eEF
PW
VOC
Results of the screening technique – yr 2000
Region of the non-
influential parameters
0 1 2 3 4 5 6
x 104
0
2000
4000
6000
8000
10000
12000
14000
16000
MU
SIG
MA
HDV
VU
LDVPC
LP
ltrip
AHdUBVH126
VR126
VR34VH34
MPC
MUBMLDV MHDVMPW
UPC
UHDV
ULDVUUBHPCHLDV
HHDV
HUBUPWHPW
delta
tau
sigma
eEF
PW
VOC
Results of the screening technique – yr 2010
Region of the non-
influential parameters
VOC Emissions - Italy
0
100,000
200,000
300,000
400,000
500,000
600,000
700,00019
81
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
2000
2005
2010
2015
2020
[t]
Passenger Cars Light Duty Vehicles Heavy Duty Vehicles Buses
LAT dataUncertainty analysis on 15 parameters
Uncertainty analysis - 2010
VOC
0
20
40
60
80
100
120
140
160
180
8.1E
+04
1.0E
+05
1.3E
+05
1.5E
+05
1.7E
+05
1.9E
+05
2.1E
+05
2.4E
+05
2.6E
+05
2.8E
+05
3.0E
+05
3.2E
+05
3.5E
+05
3.7E
+05
3.9E
+05
Annual Emissions (tonnes)
Fre
qu
ency over-estimation of VOC:
probably l-trip is overestimated
LAT value
NOx Emissions - Italy
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
2000
2005
2010
2015
2020
[t]
Passenger Cars Light Duty Vehicles Heavy Duty Vehicles Buses
LAT data
NOX
0
20
40
60
80
100
120
140
160
180
200
2.1E
+05
2.4E
+05
2.6E
+05
2.9E
+05
3.1E
+05
3.4E
+05
3.7E
+05
3.9E
+05
4.2E
+05
4.4E
+05
4.7E
+05
5.0E
+05
5.2E
+05
5.5E
+05
5.8E
+05
Annual Emissions (tonnes)
Fre
qu
ency
Uncertainty analysis - 2010
LAT value
PM Emissions - Italy
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,00019
81
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
2000
2005
2010
2015
2020
[t]
Passenger Cars Light Duty Vehicles Heavy Duty Vehicles Buses
LAT data
PM
0
50
100
150
200
250
1.1E
+04
1.3E
+04
1.6E
+04
1.9E
+04
2.1E
+04
2.4E
+04
2.6E
+04
2.9E
+04
3.1E
+04
3.4E
+04
3.6E
+04
3.9E
+04
4.1E
+04
4.4E
+04
4.6E
+04
Annual Emissions (tonnes)
Fre
qu
ency
Uncertainty analysis - 2010
LAT value
CO2 Emissions - Italy
0
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
120,000,000
140,000,000
160,000,000
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
2000
2005
2010
2015
2020
[t]
Passenger Cars Light Duty Vehicles Heavy Duty Vehicles Buses
LAT data
CO2
0
20
40
60
80
100
120
140
160
180
200
9.1E
+07
9.8E
+07
1.1E
+08
1.1E
+08
1.2E
+08
1.2E
+08
1.3E
+08
1.4E
+08
1.4E
+08
1.5E
+08
1.6E
+08
1.6E
+08
1.7E
+08
1.8E
+08
1.8E
+08
Annual Emissions (tonnes)
Fre
qu
ency
Uncertainty analysis - 2010
LAT value
METHOD Morris FAST
No RUNS 350 5499
YEAR 2000 2010 2000 2010
Mean VC (%) Mean VC (%) Mean VC (%) Mean VC (%)
VOC 639 21 213 13 641 31 213 22
NOX 740 11 377 12 740 13 379 15
PM 39 22 21 21 40 25 21 26
CO2 117,621 7 136,406 6 113,852 9 133,350 9
Summary of Uncertainty Analysis
second phase: quantitative sensitivity analysis technique (extended-FAST) to apportion variance of emission estimates back to input parameters.
VOC
delta
ltrip
eEF
VU
MPC
1-SUM
2000 2010
68% of VOC variance explained by the top-three parameters
increase of ltrip and decrease of VU becomes important in 2010
PM
1-SUM
VU
MHDV
ltrip
eEF
delta
2000 2010
Uncertainty in diesel share of PC and LDV is important
The differences with the run conducted for 2000 are in the vehicle Populations, fleet breakdown and in the use of new fuel.
NOX
1-SUM
VU
ltrip
MPC
MHDV
delta
eEF
2000 2010
important variables are eEF and MPC becomes important
CO2
1-SUM
MHDV
UPC
VUltrip
MPC
2000 2010
CO2 emissions are mostly influenced by MPC (SMPC=37%)
and ltrip. Situation remains unchanged in 2010 VU becomes important in 2010
Output variability for each pollutant IS described by three most influential input parameters.ltrip, eEF, VU and are common to almost all the pollutants.
Technological and fuel improvements will result in reduced emissions for VOC, PM and NOX (2000 2010).
Interpretation and conclusions
Quality of emission estimates can be enhanced if we direct efforts to improve our knowledge on average trip length, emission factors, diesel share between PC and LDV and the annual mileage of passenger cars
Importance of emission factors , with the current statistical model, increases 2000 2010.
Uncertainty in emission factors should be explained by a set of kinetic parameters (not only average speeds).
Acknowledge uncertainty in the emission factorsat the level of driving cycles
When driving cycles are combined to build TS,it is straightforward to calculate uncertainty bounds for TS.