1. Svetlana Tsyro, David Simpson, Leonor Tarrasón
Title
Evaluation of uncertainties in Primary PM emissions within the EMEP model
Svetlana Tsyro, David Simpson, Leonor Tarrasón
TFMM 8-th meeting, April 2007

2. Top-down validation of PM emissions
Scope:
Top-down validation of PM emissions
through validation of PPM individual components:
EC (combustion), POC - levoglucosan (wood burning)
seasonal analyses of model performance vs observations
integrated source apportionment analysis for TC
looking at different emission estimates
Detailed results and discussion are given in accepted for publication in JGR articles Simpson et al., 2007 and Tsyro et al., 2007.
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3. In model calculations we have used:
IIASA CAFÉ baseline emissions of PM2.5 and PM (papers to JGR)
EMEP emissions of PM2.5 and PM10 (EMEP report 4/2006)
EC/OC emission inventory (Kupiainen & Klimont, 2006)
and accounted for:
EC emissions from wildfires (GFED)
EC ageing
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4. Emission sources of primary fine:
IIASA emission estimates - Kuppiainen & Klimont, 2007
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5. Contributions of main sources to EC emissions (%)NO IE SE FI GB NL DE PT SK CZ AT HU IT
Road traffic 20 55 22 31 51 49 41 21 36 28 53
Off-road mob. 19 11 25 16 9 23 10 29
Resid. Total 43 65 44 14 7 30 47 27
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Seasonal variations (GENEMIS):
Mobile: ca. even distribution (0.9 – 1.05),
Residential: f = in winter;
f= – 0.2 in summer
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6. There are a large number of uncertainties in estimates for PM (EC/OC) emissions
Domestic sources (esp. wood burning) are more variable in space and time than e.g. industrial sources
Domestic EF and activity statistics are highly uncertain: EF are technology dependent; also operation practice, fuel, combustion installations; not all fuel wood is probably recorded in official statistics
Temporal variation of these emissions are modelled only crudely (should depend on temperature – greater day to day variations)
Uncertainties in traffic emissions as well: super-emitters can emit up to 10 times more PM than well-maintained vehicles (5% of those increase emissions by 45%) – Bond, 2004
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7. Observation data: EMEP OC/EC campaign 14 sites, 1 day a week weekly
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
(July 2002-June 2003)
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8. Variation of EC from forest fires from year to year
Model calculated EC for 2004
Contribution from forest fires
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
2002
2003
2004
Variation of EC from forest fires from year to year
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9. EC (IIASA’s PPM)
Bias
Correlation
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10. Flattened out N-S gradient
Main findings
Northern sites – EC overestimation:
More southerly sites – EC underestimation
Average spatial correlation: Rs = 0.80
Average bias = -20%
Average temporal correlation Rt = 0.53
Varies from (Penicuik), (Aspvreten)
to (Birkenes), (Mace Head)
Flattened out N-S gradient
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11. Seasonal analysis: winter
Model overestimation of EC for northern sites and underestimation of EC for southern sites is even more pronounced
Main source of EC:
residential/commercial combustion with a singnificant contribution from wood burning
Levoglucosan (low vapour pressure organic compound) – tracer for wood burning emissions (10-20% of OC).
Simpson et al.: levo / OC = 13% (6.5 – 26%)
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12. Model bias for winter for EC and levoglucosan
Levoglucosan – tracer for wood burning emissions
Pink – countries with considerable contribution from wood burning emissions
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13. Seasonal analysis: winter
These results suggest:
overestimation of wood burning emissions in northern Europe
underestimation of wood burning emissions in central/southern Europe
Emissions spatial distribution … ?
Unaccounted local sources … ?
To illustrate the possible significance of wood burning over/under-estimates for EC results wood emissions were re-scaled by Obs/Mod for levoglucosan.
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14. Scaling of wood burning emissions by Obs/Mod for Levo in winter
Aveiro
F = 5.0
R=0 .36
R=0.51
Mineral dust
Aspvreten, SE
F = 0.2
R=0.37
R=0.60
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
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15. EC bias (IIASA’s PPM) Looking at summer EC (red)
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16. Seasonal analysis: summer
EC underestimation by 30-60% at 7 sites:
(central and southern Europe)
Main source: on road traffic and other mobile sources
Our results indicate that these emissions may be underestimated
Problems with dispersion?.. (similar to NO2)
Other sources?.... Forest fires
Burning agricultural residiences
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17. Forest fires Aspvreten, SE12 Virolahti, FI17 Birkenes, NO01 Aveiro, PT
K-Puszta, HU02
Kosetice, CZ03
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
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18. Forest fires Stara Lesna, SK04 Waldhof, DE02 Illmitz, AT02 Ispra, IT04
Mace Head, IE31
Kollumerwaard, NL09
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
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19. Seasonal analysis and sensitivity tests suggest:
Conclusions -
using IIASA PM emissions
Seasonal analysis and sensitivity tests suggest:
EC (PM) emissions from residential combustion, in particular wood burning, are likely to be
overestimated in Nordic countries
underestimated in central and southern Europe
EC (PM) emissions from mobile sources, may be underestimated in several countries
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20. Source apportionment of Total C (Monte-Carlo uncertainty analysis)
Levo
7.35
POCbb
0.166
ECbb
ECff = EC- ECbb
0.58
POCff
“Confirms” underestimation of wood burning emissions, but results within uncertainty-range for fossil fuel and for EC
NB! Compared not with Obs, but with derived values; different periods
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21. IIASA emissions larger
Differences in PM emissions: IIASA CAFÉ Baseline (2000) and EMEP 2003 (2006 update)
Domestic
IIASA emissions larger
EMEP emissions larger
Traffic
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22. PM emissions: IIASA CAFÉ baseline
EC bias (%)
PM emissions: IIASA CAFÉ baseline
PM emissions: EMEP 2006
Less overestimation at N sites (domestic)
Larger underestimation at C/S sites (traffic)
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23. EC temporal correlation: model vs. measurements
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24. using EMEP and IIASA PM emissions
Main results -
using EMEP and IIASA PM emissions
the model gives better EC results with EMEP emissions for Northern sites - better estimate of residential/commercial combustion
while greater EC underestimation for more southern sites – even lower emission estimate from mobile sources
both emission estimates give quite similar EC results despite their differences
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25. Summarizing,
Our results consistently indicate possible inaccuracies in EC/OC emission estimates from wood burning:
overestimation for northern countries
underestimation for southern countries
The results are not so conclusive with regard to EC (PM) emissions from road traffic (and other mobile sources) however,
as we do not presently have enough information to draw strong conclusions from…
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