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, 22-23 April 2007

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 Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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, 2007 and Tsyro et al., JGR 2007. Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

Emission sources of primary fine: IIASA emission estimates - Kuppiainen & Klimont, 2007 Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

EC: model vs. measurements Bias Correlation Meteorologisk Institutt met.no

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%) Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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. Meteorologisk Institutt met.no

Same for Total Carbon Meteorologisk Institutt met.no

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? Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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… Meteorologisk Institutt met.no

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: SO4 -17% 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 Meteorologisk Institutt met.no

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. Meteorologisk Institutt met.no

Contributions to TC Gelenscer et al., JRC, 2007 Meteorologisk Institutt met.no

Forest fires Aspvreten, SE Virolahti, FI Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates. Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

THANK YOU! Meteorologisk Institutt met.no