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, 25-27 April 2007

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. Meteorologisk Institutt met.no

In model calculations we have used: IIASA CAFÉ baseline emissions of PM2.5 and PM10 (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 Meteorologisk Institutt met.no

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

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 = 1.9 - 2.4 in winter; f= 0.1 – 0.2 in summer Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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) Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

EC (IIASA’s PPM) Bias Correlation Meteorologisk Institutt met.no

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 0.26 (Penicuik), 0.36 (Aspvreten) to 0.77 (Birkenes), 0.79 (Mace Head) Flattened out N-S gradient 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 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%) Meteorologisk Institutt met.no

Model bias for winter for EC and levoglucosan Levoglucosan – tracer for wood burning emissions Pink – countries with considerable contribution from wood burning emissions Meteorologisk Institutt met.no

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. Meteorologisk Institutt met.no

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. Meteorologisk Institutt met.no

EC bias (IIASA’s PPM) Looking at summer EC (red) Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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. Meteorologisk Institutt met.no

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. Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

IIASA emissions larger Differences in PM emissions: IIASA CAFÉ Baseline (2000) and EMEP 2003 (2006 update) Domestic IIASA emissions larger EMEP emissions larger Traffic Meteorologisk Institutt met.no

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) Meteorologisk Institutt met.no

EC temporal correlation: model vs. measurements Meteorologisk Institutt met.no

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 Meteorologisk Institutt met.no

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… Meteorologisk Institutt met.no