1. Uncertainties in emission inventories
Wilfried Winiwarter
Joint TFEIP & TFMM workshop on uncertainties in emission inventories and atmospheric models
Dublin, October 22, 2007

2. 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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3. 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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4. 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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5. 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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6. 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
See above
Other parameters
Fraction cellulose (debris)
25%
10%
50%
Fraction fungal spores
75%
90%
Seasonal pattern
Totals
Total emissions EU 25
92 Gg
350 25
Considering EF uncertainty only
Total emissions NATAIR domain
283 Gg
1060 75
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7. 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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8. Error propagation … Emission calculation is simple
Standard mathematical treatment
Monte-Carlo methods
(additive terms)
(multiplicative terms)
s ... standard deviation, RSD ... relative standard deviation s/x
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9. 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
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10. 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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11. Correlated parameters in practice
Methane emissions from combustion
E = g1 * EF1 * A1 + g1 * EF2 * A2 + g1 * EF3 * A3 + …
 E = g1 * (EF1 * A1 + EF2 * A2 + EF3 * A3 + …)
Note: Despite of apparently different EF’s, the largest share of uncertainty (g1 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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12. 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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13. 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 calculation approx. 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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14. Comparison of data sets
Validation
Performance review 3 1 2
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15. 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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16. 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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17. 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
Chapter authors:
8 - Joe Mangino
7 - Kristin Rypdal
A2 - Jos Olivier
A1- Ian Galbally
6 - Simon Eggleston
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