1. Svetlana Tsyro, David Simpson, Leonor Tarrason
Title
Evaluation of uncertainties in primary PM emissions within the EMEP model
Svetlana Tsyro, David Simpson, Leonor Tarrason
TFMM workshop, Dublin, Ireland, April 2007

2. Layout Introduction (what is PM made of?) Main findings/conclusions
from top-down validation of primary PM emissions
Summary of underlying results:
EMEP model vs. observations
Concluding remarks on model PM underestimation
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3. Anthropogenic emissions
Atmospheric particle
Anthropogenic emissions
Natural sources
Anthr. SOA
SO2
NOx
NH3
SIA
SO4, NO3, NH4
biogenic SOA
Primary PM
(EC, POC, dust)
water
PM2.5
PM10
Mineral dust
bioaerosols
Sea
salt
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4. Results of the EMEP model calculations suggest:
Main findings / conclusions
Results of the EMEP model calculations suggest:
 underestimation of wood burning EC/PM emissions in central/southern Europe
 some overestimation of wood burning EC/PM emissions in northern Europe
 possible underestimation of emissions from road traffic and other mobile sources in central and southern Europe
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5. Top-down validation of PM emissions with the EMEP model
through validation of PPM components:
EC (combustion), POC - levoglucosan (wood burning)
seasonal analyses of model performance vs. observations
integrated source apportionment analysis for TC
Detailed results and discussion are given in Simpson et al., JGR, and Tsyro et al., JGR 2007.
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6. In calculations with the EMEP model we used
Anthropogenic PM emissions:
PM2.5 and PM10 emissions
EMEP 2005 and IIASA CAFÉ baseline 2000
EC/OC emission inventory (Kupiainen & Klimont, 2006)
“Natural” biomass burning:
EC emissions from forest fires (GFED):
< 2% (up to 5-10%) in EC concentrations on annual average
and accounted for EC ageing 1-6% increase of EC concentrations
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7. Emission sources of primary fine:
IIASA emission estimates - Kuppiainen & Klimont, 2007
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8. 1-week filters (PM2) analysed for:
Observation data:
EMEP OC/EC campaign
(July 2002-June 2003)
(July 2002-October 2004)
14 sites, 1 day a week
16 papers:
Present and retrospective State of Organic Aerosol Over Europe, J. Gephys. Res., 112, D23, 2007
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
1-week filters (PM2) analysed for: OC
EC  primary combustion PM
cellulose  bio-aerosols
levoglucosan  biomass burning
14C  modern/fossil carbon
Large uncertainties around EC/OC measurements,
Total carbon – more robust
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9. EC: EMEP model vs. measurements
IIASA PM emissions for 2000
EMEP PM emissions (reporting 2005)
Bias = -19%
R = 0.80
Bias = -34%
R = 0.85
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Southern sites model EC underestimation
Northern sites – tendency to EC overestimation
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10. EC: model vs. measurements
Bias
Correlation
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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 for winter than on average
Main source of EC:
residential/commercial combustion with a significant 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 EC levoglucosan
These results suggest that the contribution from wood burning emissions:
overestimated in northern Europe
underestimated in central/southern Europe
levoglucosan
Pink – countries with considerable contribution from wood burning emissions
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13. Scaling of wood burning emissions by ratio Obs_levo / Mod_levo
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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14. Same for Total Carbon
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15. Seasonal analysis: summer
EC underestimation by 30-70% for 7 sites in central and southern Europe
Main EC source: on road traffic and other mobile sources
Our results indicate that these emissions may be underestimated
Missing sources? agricultural waste burning
Problems with dispersion?
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16. NB! Compared not with Obs, but with derived values; different periods
Source apportionment of Total Carbon (Monte-Carlo uncertainty analysis)
Underestimated contribution from biogenic sources
“Confirms” underestimation of wood burning emissions, for fossil fuel and for EC the results are within uncertainty-range
NB! Compared not with Obs, but with derived values; different periods
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17. Summarizing,
Our results consistently indicate possible inaccuracies in EC/OC emission estimates from wood burning:
underestimation for southern countries
overestimation for northern countries
There are some indications of underestimation of PM emissions from road traffic and other mobile sources in some central/southern countries However, the results are not so conclusive, as we do not presently have enough information to draw strong conclusions from…
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18. Natural Large uncertainties
Concluding remarks
May we conclude that the model underestimation of PM10 and PM2.5 are resulting from uncertainties in the emission inventories?
Bias for 2005:
PM10: - 28%
PM2.5: - 23%
SO2
NOx
NH3
Anthr. SOA
SIA:
SO % NO3 -8% NH4 +3%
biogenic SOA
Primary PM
EC -34% ,
POC, dust
Natural Large uncertainties
PM2.5
PM10
water
bioaerosols
Mineral dust
Sea
salt
Anthropogenic
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19. Measured and modelled chemical composition of PM10 and PM2
Measured and modelled chemical composition of PM10 and PM2.5 in June 2006.
Birkenes
Melpitz
Montelibretti
Note:
only primary anth. OC in model results is compared with measurements
OM=1.7xOC;
OC measurements were corrected for artefacts at IT01, but not at NO01 and DE44.
ND means non-determined PM mass in measurements and particle water in model results.
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20. Contributions to TC Gelenscer et al., JRC, 2007
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21. Forest fires Aspvreten, SE Virolahti, FI
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
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22. Recommendations to the workshop
Primary wood burning emission estimates need revision
Further work is necessary to compile relevant information on natural primary PM sources:
primary biogenic
windblown dust
biomass burning and forest fires
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23. THANK YOU!
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