Modeling Anthropogenic and Natural Aerosol Sources within EMEP

1. Progress with modelling anthropogenic and natural aerosol sources within EMEP Presented by Svetlana Tsyro Title TFMM 6th meeting, Zagreb, April 4-7, 2005

2. Why to model both anthropogenic and natural PM? • PM10 and PM2.5 mass closure; chemical composition • contribution to PM in different areas from anthropogenic(regulation and control) and natural aerosol sources • contribution from natural sources to the exceedance of PM limits – the model can help in identifying the high PM level episodes caused by natural PM emissions Meteorologisk Instituttmet.no

3. Anthropogenic SIA: SO42-, NO3-, NH4+ PPM2.5: OC, EC, mineral dust PPM coarse: EC, mineral dust Natural Sea salt Mineral dust: wind blown dust African dust PM in the EMEP Unified Model PM2.5 =SO4 + NO3_fine + NH4 + SS_fine + DU_fine+ PPM2.5 +water PMco =NO3_coar + SS_coar + Dust_coar + PPMco + water PM10 =PM2.5 + PMco Meteorologisk Instituttmet.no

4. Progress in calculating anthropogenic PM - 1 Revision of emission data • national totals • emission sector allocation (IIASA in bi-lateral discussions with countries, CAFÉ-BASELINE) • emission spatial distribution MSC-W & CONCAWE & JRC, CITY-DELTA Initial analysis - EMEP Report 1/2004 and presented at TF EIP, Oct. 2004. Further studies are on-going (2005) Meteorologisk Instituttmet.no

5. First main conclusions • Emission totals drive the changes in model concentrations • Emission sector allocation effect – through spatial distribution, height, temporal variation • General improvement of spatial correlation of SIA (esp. NO3- and NH4+) • Also for PM10 and PM2.5, but less pronounced (location of measurement comp. to emission changes ) • Emission changes were more significant for PPM than gaseous species – but not obvious in PM10 and PM2.5 verification How to validate primary PM emission? Meteorologisk Instituttmet.no

6. Progress in calculating anthropogenic PM (2) • Chemical speciation of PM2.5 and PM10 emissions IIASAinventory for submicron EC and OC emissions + coarse EC preliminary estimate (Febr. 2005) PPM2.5 Coarse PPM EC Min.dust EC OC Min.dust Then, we can compare model calculated EC with measurements(OC and mineral components ?) Meteorologisk Instituttmet.no

7. EC: calculations vs. measurements (2002) Bias= - 37% R = 0.87 • Missing sources (Forest fires) • Too efficient wet removal • BIC Meteorologisk Instituttmet.no

8. Summary on anthropogenic PM  Revised PM national emissions improve EC bias  Revised PM sector allocation improve EC spatial and temporal correlation STILL…  More measurements of EC are needed for validation of PM emissions  Measurements of OC with speciation Meteorologisk Instituttmet.no

9. Progress in modelling natural PM • Revised calculation scheme for sea salt • Implementation of wind blown dust Meteorologisk Instituttmet.no

10. Calculation of sea salt production • Parameterisations: Monahan et al. (1986) – for aerosols d > ca. 1μm Mårtensson et al. (2003) – for aerosols d < ca. 1μm SS_flux ~ U103.41 f(d) • Motivation for revision: underestimation of SS • New: use of monthly Sea Surface Temperature data (ECMWF) more accurate calculation of 10m wind – larger winds wet scavenging efficiency - testing Meteorologisk Instituttmet.no

11. Modelled sea salt vs. measurements (2002) BEFORE: the model underestimated Na+ in air by a factor of 2 and Na+ in precipitation by a factor of 8 OLD: Bias=- 91% R = 0.65 OLD: Bias= - 66% R = 0.78 Na+ in precipitation Na+ in air Bias = 42% R = 0.50 (0.9) Bias=-74% (F3) R = 0.71 Meteorologisk Instituttmet.no

12. Modelled sea salt vs. measurements (2001) Na+ in air Na+ in precipitation Bias = -62% R = 0.48 Bias = 26% R = 0.68 Meteorologisk Instituttmet.no

13. Verification of sea salt (2002) effect of scavenging efficiency Na+ in air Na+ wet deposition Bias = 58% R = 0.65 Bias= -77% R = 0.57 W=1*106 Bias= 35% R = 0.59 W=2*106 Bias= -79% R = 0.67 Doubling of scavenging ratio decreases Na+ in air and does not affect (very slight decrease) Na+ in precipitation Meteorologisk Instituttmet.no

14. Verification of sea salt (2002) Na+ in air Na+ wet deposition Meteorologisk Instituttmet.no

15. Temporal correlation for sea salt (2002) Meteorologisk Instituttmet.no

16. Summary for sea salt • Improved model calculation of sea salt for both air and precipitation concentrations • 25-40% overestimation of Na+ in air • 60- 75% underestimation of Na+ in precipitation • Spatial and temporal correlations are reasonable compared with other components • Too steep gradients? Too fast SS removal? Meteorologisk Instituttmet.no

17. Calculation of mineral dust • African dust (from outside EMEP area): Boundary conditions – monthly dust concentrations from Oslo CTM model • Wind blown dust production: parameterisation is developed based on works of Marticorena & Bergametti, Zender et al., Alfaro & Gomes et al., Woodward Meteorologisk Instituttmet.no

18. Calculation of mineral dust Dust mobilisation onset:U* > U*th Erosion threshold U*this calculated based on: • Drag partitioning (erodible/non-erodible elements) • Size of erodible aggregates • Soil moisture (inhibition of saltation) Sandblasting efficiency Horizontal (saltation) dust flux Supply of erodible material Meteorologisk Instituttmet.no

19. Calculation of mineral dust:implementation and uncertainties Where:desert (bare land), agricultural lands (crops outside growth season) Constrains:snow cover / frozen soil, soil moisture, precipitation / RH Dust emissions are sporadic and spatially heterogeneous Wind soil erosion depends on the local meteorology, aerodynamics and soil characteristics  Roughness length parameters (meso, micro) Soil characteristics (texture, crusting, morphology) Supply of erodible elements Calculated dust flux very sensitive to those input parameters Meteorologisk Instituttmet.no

20. Model calculated mineral dust in 2002 African Anthropogenic Wind blown Meteorologisk Instituttmet.no

21. Mineral dust: model vs. measurements Meteorologisk Instituttmet.no

22. Mineral dust: model vs. measurements Meteorologisk Instituttmet.no

23. Model calculated PMin 2002 PM10 PM2.5 PM10+ water Meteorologisk Instituttmet.no

24. PM10 in 2002 PM dry, no nat. dust PM dry, + nat. dust Bias=-48% Corr=0.56 Bias=-34% Corr=0.67 PM + nat.dust + water Bias=-22% Corr=0.70 Meteorologisk Instituttmet.no

25. PM2.5, 2002 PM dry, no nat. dust PM dry + nat. dust Bias=-45% Corr=0.79 Bias=-28% Corr=0.78 PM + nat. dust + water Bias=-12% Corr=0.79 Meteorologisk Instituttmet.no

26. PM2.5 and PM10 in 2001 PM10 + nat.dust + water PM2.5 + nat.dust + water Bias=-23% R = 0.68 Bias=-16% R = 0.82 OLD: Bias=-43% R = 0.78 OLD: Bias=-51% R = 0.39 Meteorologisk Instituttmet.no

27. Temporal correlation for PM10 (2002) ------- Dry PM, no Dust - - - - - PM with nat. dust ------- PM + nat.dust + water Meteorologisk Instituttmet.no

28. Temporal correlation for PM10 (2002) ------- Dry PM, no Dust ------- PM + nat.dust + water Meteorologisk Instituttmet.no

29. Chemical composition of PM10 and PM2.5 PM10 PM2.5 Monagrega – 1999-2000 Bemantes – 2001 Montseny - 2002 Meteorologisk Instituttmet.no

30. Relative contribution of natural sources in PM10 Sea salt Mineral dust Meteorologisk Instituttmet.no

31. Relative contribution of natural sources in PM2.5 Sea salt Mineral dust Meteorologisk Instituttmet.no

32. Mineral dust in PM10 Wind blown dust African dust (beyond EMEP) Meteorologisk Instituttmet.no

33. Summary for mineral dust • Natural sources of mineral dust have been implemented in the EMEP Unified model(several parameterisations have been tested) • First calculations give encouraging results • Inclusion of natural dust in the model improves comparison of PM2.5 and PM10 with measurement • Still large uncertainties (large sensitivity of parameterisation schemes to rather uncertain input) Appropriate input information, more tests, more measurements of dust components are needed Meteorologisk Instituttmet.no