Using field campaigns results to reduce uncertainties in inventories

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 • Very uncertain emissions from wood combustion and biomass burning • Regular EC/OC measurements don’t distinguish between natural / anthropogenic and primary /secondary • Heavymetals • Emissions too low to give model results comparable to measurements Case study (to be discussed by MSC-E) • POPs • 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. Sources of carbonaceous matter OCbbOC from residential wood burning ECbbEC from residential wood burning OCffOC from combustion of fossil fuel ECffEC from combustion of fossil fuel OCpbs OC from fungal spores OCpbcOC from plant debris OCbsoaOC from biogenic sec. org. aerosols OCasoaOC from anthropogenic sec. org. aerosols

4. EMEP intensives 2008/2009 Carbonaceous matter 9 participating sites, situated in C, E, S, NW Europe. • 14C–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. EMEP Intensives – cont. 4 sites in Northern Europe subject to extended sampling and chemical analysis the summer 2009 (SONORA project) • Analyis of following tracers: • Levoglucosan: wood burning • Sugars/ sugar alcohol: fungal spores (PBAP) • Cellulose: Plant debris (PBAP) • 14C 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. Hurdal Oslo Oslo (Urban background) Hurdal (Rural Background) SORGA - Measurements sites Measurement campaigns Summer period: 19 June - 5 July 2006Winter period: 1 - 8Mars 2007

8. Results from SORGA project (2006 and2007) Source apportionment of TCp in PM10 in Hurdal (NO) TCp = 2.9 ± 1.2 µg C m-3 TCp = 1.2 ± 0.5 µg C m-3 Natural: 72% Anthropogenic: 28% Natural: 8% Anthropogenic: 92% summer winter

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

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

11. Uncertainties in OC measurements: Estimates of the positive artefact of OC in PM10 and PM2.5/PM1 -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 • 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 Ref: Eckhardt et al. (2007)PCB peaks in the Arctic, Atmos. Chem. Phys., 7, 4527-4536.

20. Summary • 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 14C, 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 • Need for reference methods and/or centralized laboratories for advanced measurements