1. Title Performance of the EMEP aerosol model: current results and further needs Presented by Svetlana Tsyro (EMEP/MSC-W) EMEP workshop on Particulate Matter Measurements & Modelling, New Orleans, April 20-23, 2004

2. Outline Meteorologisk Institutt met.no Short description of the aerosol model Model performance evaluation Comparison with observations of calculated : PM10 and PM2.5 masses PM chemical composition, particle-bound water particle numbers Identified needs for the model further improvement and validation Summary / conclusions

3. EMEP Aerosol model (UNI-AERO): Meteorologisk Institutt met.no Aerosol components: SO 4 2-, NO 3 -, NH 4 +, OC, EC, dust, sea salt + aerosol water (not yet included: SOA, primary biogenic OC, wind blown dust) Aerosol size distribution - 4 monodisperse size modes: nucleation, Aitken, accumulation, coarse Assumption: particles in the same mode have the same size and the same chemical composition (internally mixed) Accounts for aerosol dynamics (MM32) : nucleation, condensation, coagulation, ‘mode merging’ Output: size resolved aerosol mass and number concentrations Resolution : 50 x 50 km 2, 20 layers up to 100 hPa

4. SO 4, HNO 3 / NO 3 NH 4 / NH 3, Na, Cl PM emissions Gas emissions aerosolgases OC EC Dust Aitken, accum. Dust coarse N, M, D H 2 SO 4 SO x NO x Irreversible chemistry (gaseous and aqueous) EQSAM Gas/aerosol & aerosol water NH 3 Aerosol dynamics (MM32) Nucleation H 2 SO 4 -H 2 O  SO 4 Condensation H 2 SO 4  SO 4 Coagulation Mode merging Coarse PM PM 2.5 sea salt Dry deposition Wet scavenging PM-bound water Output: number and mass size distribution, chemical composition, PM 2.5, PM 10 (PM 1 ) Schematic computational structure of UNI-AERO Meteorologisk Institutt met.no

5. Annual mean concentrations of PM in 2001 Meteorologisk Institutt met.no PM2.5 PM10 Aerosol model EMEP obs  Systematic underestimation Measure- ments spatial coverage

6. Annual mean PM10 and PM2.5 (2001, EMEP) Meteorologisk Institutt met.no Spain: bias = - 67%, corr = 0.44 wind eroded & Saharan dust N=17 Bias=-46% Corr=0.61 N=25 Bias=-51% Corr=0.15 The model underestimates measured PM10 and PM2.5 PM10 – complex pollutant. To explain the discrepancies between calculated and measured PM10 verification of the individual components is needed. elevated C. Europe: bias = - 41% corr = 0.59

7. Annual mean SIA (2001, EMEP) Meteorologisk Institutt met.no Bias=-19% Corr=0.81 Bias= 15% Corr=0.89 Sites without PM10 measurements Does not help to explain the discrepancy between modelled and measured PM Sites with PM10 measurements For that, co-located and concurrent measurements of ‘all’ aerosol components is needed

8. SIA (2001): model vs. EMEP measurements Meteorologisk Institutt met.no Bias = 9% Corr = 0.71 Bias = 19% Corr = 0.91 Bias = 7% Corr = 0.84

9. Meteorologisk Institutt met.no What is needed: “component-wise” verification of modelled PM EXAMPLE for Birkenes, Norway (2001)

10. Meteorologisk Institutt met.no PM10 components

11. Meteorologisk Institutt met.no PM10 components PM emissions validation

12. One more example: Vienna Meteorologisk Institutt met.no Daily PM2.5 (June 1999 - June 2000) :

13. Daily series of SO 4, NO 3 and NH 4 in PM2.5 Meteorologisk Institutt met.no NO3 NH4 SO4 OC EC Na EC PM emissions validation

14. Chemical composition of PM2.5 and PM10 (1): Meteorologisk Institutt met.no  Non-C atoms in organic aerosol  Particle-bound water  Measurement artefacts Full chemical mass closure is rarely achieved. Unaccounted PM mass - up to 35-40% Gravimetric method (Reference, EU and EMEP) for determining PM mass requires 48-h conditioning of dust-loaded filters at T=20C and Rh=50% - does not remove all water! At Rh=50% particles can contain 10-30% water Gravimetrically measured PM mass does not represent dry PM mass!!!

15. Chemical composition of PM2.5 and PM10 (2): To what extend can particle-bound water explain the model underestimation of measured PM? Meteorologisk Institutt met.no Unaccounted PM mass in obs Aerosol water in model results ViennaStreithofen PM2.5 Austria,1-6/2000 (AUPHEP) PM10PM25

16. Meteorologisk Institutt met.no Modelled dry PM 2.5 vs. Identified PM 2.5 mass Modelled water in PM 2.5 vs. Unaccounted PM 2.5 mass

17. Meteorologisk Institutt met.no Model calculated dry PM2.5 (blue) and PM2.5 including aerosol water (black) vs. measured PM2.5 (red) Accounting for water in modelled PM2.5 gives better agreement with measurements BUT: verification of model calculated aerosol water with measurements is needed

18. Meteorologisk Institutt met.no Accounting for particle-bound water in PM2.5 Model calculations vs. gravimetric PM2.5 (EMEP, 2001) Dry PM 2.5 N=13 Bias=- 47% Corr=0.69 N=13 Bias=-28% Corr=0.68 Dry PM 2.5 + water Smaller negative bias

19. Meteorologisk Institutt met.no Dry PM10 N=13 Bias=- 64% Corr=0.26 N=13 Bias=-38% Corr=0.29 Dry PM10 + water Smaller negative bias Slightly improved correlation Accounting for particle-bound water in PM 10 Model calculations vs. gravimetric PM10 (EMEP, 2001)

20. Meteorologisk Institutt met.no Verification of daily PM2.5 with EMEP measurements SitesObs.Mean Bias dry PM 2.5 Bias PM 2.5 +water Correlation PM 2.5 +water AT02 Illmitz 19.54-51-350.56 DE02 Langenbrügge 12.46-14140.69 DE03 Schauinsland 7.9316550.15 DE04 Deuselbach 11.71-8250.59 CH02 Payerne 14.80-50-330.47 CH04 Chaumont 8.12-8220.41 IT04 Ispra 32.01-60-490.42 NO01 Birkenes 4.04-43-240.57 ES07 Viznar 12.46-68-570.37 ES08 Niembro 11.16-48-270.34 ES09 Campisabalos 9.02-48-270.21 ES10 Cabo de Creus 12.09-51-360.29 ES11 Barcarrota 11.36-58-400.39 ES12 Zarra 8.89-44-230.40 ES13 Penausende 9.70-43-230.46 ES14 Els Torms 12.41-53-360.50 ES15 Risco Llano 8.46-42-210.05

21. Meteorologisk Institutt met.no Daily PM2.5 vs. EMEP measurements Hourly PM2.5 Aspvreten, SE 2000

22. Aitken number (10 2 /cm 3 ) at Hyytiälä, Finland, 2000 Meteorologisk Institutt met.no Hourly Daily Hourly, october Hourly, december

23. Hourly Aitken number : nucleation effect Meteorologisk Institutt met.no Nucleation events (June 10-22, 2000) No nucleation (July 12-17, 2000)  Prediction of nucleation events  Number of nucleated particles  Growth of newly formed particles Hyytiälä, Finland BIOFOR

24. Accumulation (0.1 – 0.5 μm) particle number, 2000 (10 -2 /cm 3 ) Meteorologisk Institutt met.no Aspvreten, hourly Värriö, hourly Aspvreten, daily Värriö, daily

25. Daily total particle number, Austria (AUPHEP) Meteorologisk Institutt met.no Vienna urban Streithofen rural Emissions + meteorology

26. Summary on the model performance Meteorologisk Institutt met.no The EMEP aerosol model underestimates PM2.5 and PM10 ( SOA and natural dust not yet included) Accounting for particle-bound water improves the agreement between model calculated and gravimetrically determined PM mass Verification of model calculated aerosol water Largest discrepancy: OC, EC, mineral dust Implementation of SOA, wind blown dust. Emissions chemical speciation! Particle number – more difficult (esp. Aitken): Emissions size disaggregation! Aerosol dynamics

27. Measurement needs Meteorologisk Institutt met.no Information on PM chemical composition is essential for further improvement of PM mass calculations Co-located concurrent measurements are needed: (process understanding, source allocation) Particle-bound water Particle number concentration : size distribution Particle fluxes (dry deposition) over different land-use types, size resolved Wet scavenging Vertical profiles