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Statistical evaluation of model uncertainties in Copert III, by I. Kioutsioukis & S. Tarantola (JRC, I)

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Presentation on theme: "Statistical evaluation of model uncertainties in Copert III, by I. Kioutsioukis & S. Tarantola (JRC, I)"— Presentation transcript:

1 Statistical evaluation of model uncertainties in Copert III, by I. Kioutsioukis & S. Tarantola (JRC, I)

2 Sensitivity analysis tests performed using COPERT III Interpretation of the results Objectives of the statistical analyses

3 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.

4 Uncertainty should always accompany an estimate, as it is a measure of the quality of the estimate. Representation of the Monte Carlo simulation Objectives

5 Objective is to apply up-to-date sensitivity analysis to identify the parameters 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 systems 3. as input to air quality models Objectives

6 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

7 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

8 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

9 FBM (expensive) is only executed at selected points ττ δ σ We feed COPERT with a representative configuration of fleet breakdown at each Monte Carlo run i. We sample a point τ i, δ i, σ i over the square and interpolating the FBM runs we obtain the configuration of fleet breakdown f ( τ i, δ i, σ i )

10 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…

11 Average speed in rural road average speed in motorway More reliable pdfs using Goodness of fit tests based on driving cycles

12 Uncertainty in emission factors Very low regression coefficients Not sufficient

13 Uncertainty in load factors Pdf=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)

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16 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 parameters 15 parameters Identified 25 parameters that do not influence the variability of the emission estimates (eg meteo variables)

17 Results of the screening technique – yr 2000 Region of the non- influential parameters

18 Results of the screening technique – yr 2010 Region of the non- influential parameters

19 LAT data Uncertainty analysis on 15 parameters

20 Uncertainty analysis over-estimation of VOC: probably l-trip is overestimated LAT value

21 LAT data

22 Uncertainty analysis LAT value

23 LAT data

24 Uncertainty analysis LAT value

25 LAT data

26 Uncertainty analysis LAT value

27 Summary of Uncertainty Analysis

28 second phase: quantitative sensitivity analysis technique (extended-FAST) to apportion variance of emission estimates back to input parameters % of VOC variance explained by the top- three parameters increase of ltrip and decrease of VU becomes important in 2010

29 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.

30 important variables are e EF and MPC becomes important

31 CO 2 emissions are mostly influenced by MPC (S MPC =37%) and ltrip. Situation remains unchanged in 2010 VU becomes important in 2010

32 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 ( ). 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

33 Importance of emission factors, with the current statistical model, increases Uncertainty in emission factors should be explained by a set of kinetic parameters (not only average speeds). Acknowledge uncertainty in the emission factors at the level of driving cycles When driving cycles are combined to build TS, it is straightforward to calculate uncertainty bounds for TS.


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