1. Using field campaigns results to reduce uncertainties in inventories Wenche Aas, Knut Breivik and Karl Espen Yttri And material from: Eiko Nemitz (CEH, UK) Svetlana Tsyro and David Simpson (EMEP MSC-W)

2. Use ambient air measurements to improve emission inventories ? Carbonaceous matter o Very uncertain emissions from wood combustion and biomass burning o Regular EC/OC measurements don’t distinguish between natural / anthropogenic and primary /secondary Heavymetals o Emissions too low to give model results comparable to measurements Case study (to be discussed by MSC-E) POPs o Measurements not always available in time and space to directly assess present emissions. I.e. on historical emissions and sea /water exchange (diffusion processes)

3. OC bsoa OC from biogenic sec. org. aerosols OC asoa OC from anthropogenic sec. org. aerosols OC bb OC from residential wood burning EC bb EC from residential wood burning OC ff OC from combustion of fossil fuel EC ff EC from combustion of fossil fuel OC pbs OC from fungal spores OC pbc OC from plant debris Sources of carbonaceous matter

4. 9 participating sites, situated in C, E, S, NW Europe. EMEP intensives 2008/2009 Carbonaceous matter 14 C–analysis delayed, ready in a month or so EC/OC and levoglucosan analysis are ready To be published in ACP Special issue in a few months 17 Sep – 16 Oct 2008 25 Feb – 26 Mar 2009

5. Results OCp and Levoglucosan OCp = particulate OC. Front – backup filter, conservative OC estimate OC wood from levoglucosan analysis increasing concentrations along a Southern and Eastern transect.

6. 4 sites in Northern Europe subject to extended sampling and chemical analysis the summer 2009 (SONORA project) EMEP Intensives – cont. Analyis of following tracers: Levoglucosan: wood burning Sugars/ sugar alcohol:fungal spores (PBAP) Cellulose:Plant debris (PBAP) 14 C analysis:modern and fossil carbon + Pinic acid: Biogenic VOC Organosulphates/nitrates: Biogenic VOC (these are not used quantitatively but for identification of sources Results to be presented in ACP special issue in a few months

7. SORGA - Measurements sites Oslo (Urban background) Hurdal (Rural Background) Osl o Hurdal Measurement campaigns Summer period: 19 June - 5 July 2006 Winter period: 1 - 8 Mars 2007

8. Results from SORGA project (2006 and2007) Source apportionment of TC p in PM 10 in Hurdal (NO) TC p = 2.9 ± 1.2 µg C m -3 Natural: 72% Anthropogenic: 28% TC p = 1.2 ± 0.5 µg C m -3 summer Natural: 8% Anthropogenic: 92% winter

9. Improvements in modelling of SOA VBS (volatility basis set) approach used for the first time Ref: David Simpson, MSC-W Improved modeling may give better emission inventories

10. From EC/OC campaign 2002-2003 From S. Tsyro, Dublin TFMM/TFEIP 2007 In winter, indication of overestimation of wood burning in N. Europe and underestimation in C/S Europe

11. Uncertainties in OC measurements: Estimates of the positive artefact of OC in PM 10 and PM 2.5 /PM 1 -June 2006 (OBQ approach) Difficult to use OC data without assessing the artefacts (i.e OC vs OC particulate)

12. High resolution measurements from EUCAARI (EU FP 7 project) Hourly data using AMS instrument Part of EMEP intensive 2008/2009 From E. Nemitz, Paris, TFMM, 2009

13. Concentrations Sep/Oct 2008 From E. Nemitz, Paris, TFMM, 2009

14. Identification of Organic Aerosol Classes by Positive Matrix Factorisation (PMF) From E. Nemitz, Paris, TFMM, 2009

15. POP measurements to use for emission inventories Difficult to use measurement data alone to assess quality of emission Limited number of measurements, both spatially and temporally Large uncertainty in the measurements Difficult to seperate primary from secondary emissions Necessary to use a model/measurement combination

16. EMEP POP passive campaign (2006) Ref: Halse AK, Schlabach M, Eckhardt S, Sweetman A, Jones KC, Breivik K. (2010). Spatial variability of POPs at European background air monitoring sites. In prep. for ACP EMEP Special issue:

17. Comparability between passive and high volume measurements Bias depends on : Component (particulate or gaseous) Site (meteorological difference) Laboratory performance (NILU (campaign) vs national Ref: Halse AK, Schlabach M, Eckhardt S, Sweetman A, Jones KC, Breivik K. (2010). Spatial variability of POPs at European background air monitoring sites. In prep. for ACP EMEP Special issue:

18. Predicted (Flexpart model)versus observed (PAS) air concentrations for PCB-28 Systematic bias may indicate that emission data are too high for PCB-28 Ref: Halse AK, Schlabach M, Eckhardt S, Sweetman A, Jones KC, Breivik K. (2010). Spatial variability of POPs at European background air monitoring sites. In prep. for ACP EMEP Special issue:

19. The power of high resolution data to assess emission sources Ref: Eckhardt et al. (2007)PCB peaks in the Arctic, Atmos. Chem. Phys., 7, 4527-4536. PCB episodes at Zeppelin Svalbard:  Agricultural waste burning in Eastern Europe in spring 2006  Forest fire in North America in July 2004  Used for calculating emission factors for the most important PCB congeners

20. EMEP (intensive) data can be used for identification and quantification of sources, to some extent A necessity and much more powerful if model and measurements are used in combination to evaluate emission estimates The combined effort of 14 C, TOA, and organic tracer analysis is a powerful tool to explore various sources of carbonaceous matter Uncertainty in measurements methods etc may hamper the comparability of results o Need for reference methods and/or centralized laboratories for advanced measurements Summary