Uncertainties in emission inventories

Wilfried Winiwarter

Joint TFEIP & TFMM workshop on uncertainties in emission inventories and atmospheric models

Dublin, October 22, 2007

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Why consider uncertainties?

Uncertainty assessment as a requirement

Scientists like it

Uncertainty assessment helps identify priorities in further work

Performance review of measures taken requires knowledge on method reliability

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Regulatory requirements

(Experience from Austria)

Uncertainty assessment embedded in QA/QC program

Methodological inventory development routinely coupled with uncertainty analysis

Inventory improvement (also) based on a-priori uncertainty information: priorities set to assess more uncertain parameters

Inventory uncertainty is not used to qualify inventory data (no posterior use)

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Method

Understand source of uncertainty:natural variability, unc. of measurement, inapplicability of model

Statistical vs. systematic uncertainty (& gross error)

Uncertainty sampling

Combination of uncertainty

Output as a function of one input parameter: Sensitivity analysis

Output as a function of all input parameters:Uncertainty analysis

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Uncertainty sampling

Measured variations Discrepancy in literature Expert elicitation

“reasonable” upper and lower limits, best estimate (equivalent with 95% criterion, removing outliers, will yield µ +/- 2s)

Proper distribution may affect resulting distribution, but will influence result only marginally

Feedback to QA/QC program

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Uncertainty sheet (NatAir project)

parameter Best estimate

high low quality

Further comments, annotations

Emission factor* 24 kg/km² 90 6 E Assuming previous figures to estimate spread (factor 3.75)

Activity EU25* 3.82 M km²

4.24 3.40 A Data variability as difference between PBAP area and total area

Activity NATAIR domain*

11.79 M km²

14.78 8.80 A See above

Other parameters

Fraction cellulose (debris)

25% 10% 50% E

Fraction fungal spores

75% 50% 90% E Seasonal pattern

Totals

Total emissions EU 25

92 Gg 350 25 E Considering EF uncertainty only

Total emissions NATAIR domain

283 Gg 1060 75 E Considering EF uncertainty only

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EF N2O nat.Boden

EF CO2 Lösemittel

EF CO2 Verbrennung

EF CH4 Verbrennung

EF CH4 Klärschlamm

Akt. Vieh

Akt. Lösemittel

Akt. Klärschlamm

Method.: Akt. Energie Öl

Method.: Akt. Energie Gas

Method.: Akt. Energie Kohle

EF CH4 Mülldeponie

Method.: EF CH4 lw. Böden

EF CH4 Vieh

Method.: Akt. Mülldeponie

Method.: EF N2O lw. Böden

-2,00E-01 0,00E+00 2,00E-01 4,00E-01 6,00E-01 8,00E-01 1,00E+00

Rangkorrelation: R²

Sensitivity analysis

Assess which parameters contribute to overall uncertainty

Important tool to prioritize improvement efforts

But: often highly uncertain parameters are simply not accessible Pedigree analysis (van der Sluijs, 2007): independent

data quality assessment to understand Discrepancy in literature

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Emission calculation is simple

Standard mathematical treatment

Monte-Carlo methods

Error propagation …

22BAAB sss (additive terms)

22BAAB RSDRSDRSD (multiplicative terms)

s ... standard deviation, RSD ... relative standard deviation s/x

)()( AEF sAsEFAEFE

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Error propagation …

Error propagation algorithms work as well as Monte-Carlo methods do …

… as long as correlation is adequately addressed.

Error propagation works for uncorrelated (independent) variables:

Note: additive terms allow for overall decrease of relative uncertainty

Implicit error reduction: slice a problem into small pieces

...332211 AEFAEFAEFE

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Error propagation for correlated input

Transformation required to remove correlated parameters from calculation:

E = EF1 * A1 + EF2 * A2 + EF2 * A3 + …

E = EF1 * A1 + EF2 ( A2 + A3) + …

Note: Uncertainty decrease diminishes (especially if – in the above example – the major uncertainty is with EF)

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Correlated parameters in practice

Methane emissions from combustion

E = 1 * EF1 * A1 + 1 * EF2 * A2 + 1 * EF3 * A3 + …

E = 1 * (EF1 * A1 + EF2 * A2 + EF3 * A3 + …)

Note: Despite of apparently different EF’s, the largest share of uncertainty (1 as fraction of HC measured considered methane) is maintained due to correlation

Typical also for VOC species in total HC

PM fractions in TSP

HM in TSP

Possibly also connected with systematic errors

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Reported uncertainty ranges

compound Uncertainty range(+/- 2 s; in %)

SO2 4 (-10)

NOx 12

NMVOC, NH3 20 (-30)

CO2 1-2

CH4 15-30

N2O 30-200

Traffic NOx, VOC 30-50

Biogenic VOC +/- factor 4

Sources: Rypdal, 2002; Schöpp et al., 2005; Keizer et al., 2006; Kühlwein&Friedrich, 2000; Leitao et al., 2007

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Spatial (temporal) assignment and uncertainties

Uncertainty due to quality and applicability of surrogate

Variations differ by grid cell

About two thirds of grid cells display differences not larger than those expected from “plain” uncertainty calculationapprox. doubling of uncertainty

Geostatistical methods applied allow to identify that differences are spatially correlated surrogate explains only part of spatial variability

Sources: Winiwarter et al., 2003; Horabik&Nahorski, 2007

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Comparison of data sets

123 Validation Performance review

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Results

Uncertainty is small when emission factor is well defined, activity statistics are reliable: CO2

uncertainty associated with GHG emissions is small

Uncertainty becomes large when “problem slicing” does not work: PM fractions, HM, POP’s, VOC split, N2O

Uncertainty becomes large when underlying processes are not understood well VOC from forests; NO and N2O from soils

Spatial (temporal) variability

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Achievements

Sectors most strongly contributing to uncertainty

Robustness of inventory results: fit-for-purpose?

Uncertainty must not compromise inventory consistency (i.e., remain with one “best estimate” result to allow reproduction of the inventory calculations)

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Recommendations

Let uncertainty analysis drive your QA/QC program

Let sensitivity analysis drive your improvement program

Use inventory uncertainty as a reason to focus on key sectors