1. Use of National PM2.5 and Speciation Network Measurements for Model Evaluation For presentation at PM Model Performance Workshop February 10-11, 2004: Research Triangle Park, NC Neil Frank OAQPS/USEPA

2. Scope Networks –FRM –EPA Urban Speciation (aka STN) –IMPROVE –CASTNET Measurements –PM2.5 mass –Major Chemical Components –Sulfur and Nitrogen Species Data interpretation related to Model Evaluation

3. National Speciation Networks IMPROVE filters + HNO3 denuder EPA spec. filters + HNO3 denuder CASTNET Simple Filter pack Main PurposeVisibilityPM NAAQSDeposition Time avg24-hr weekly Particle sizePM2.5 >=PM2.5 Frequency1 in 3 days1 in 3 / 1 in 6complete No. Sites110 + 54=16454 + 186 = 24087  ? Sampler types161 ReportingLocal conditions STP (&local) SulfatesAmbient Ammonium-<=ambient?<= ambient pNO3Ambient <=ambient HNO3-->=ambient TNO3--~ambient OC/ECDRI method (TOR)EPA/NIOSH (TOT)- CrustalEstimated from Al, Si, Ca, Fe, Ti same-

4. CASTNET Network Dense in Ohio Valley and Eastern US PM2.5 speciation sites (became IMPROVE in 2001 ) FilterPacks 8 Supplementary PM2.5 Speciation Sites (1993-2002)

5. IMPROVE Network, 2002 98 Sites with “complete” data, Mar 01-Feb 02

6. Sulfate Variation in Rural Areas Comparison of CASTNET and IMPROVE Measurements in 2001 IMPROVE March 01 – Feb 02 CASTNET 2001 Concentrations, STP

7. Nitrate Variation in Rural Areas March 01 – Feb 02 IMPROVE March 01 – Feb 02 Better resolution will come from new ( 2002-2003) data CASTNET 2001 Gradient is overstated Concentrations, STP CASTNET 2001

8. HNO3 exhibit different spatial pattern TNO3 also shows a MidWest to East gradient HNO3 Concentrations, STP NO3 2.7 + 2.1 = 4.8 ug/m3 NO3 HNO3 TNO3 0.8 + 2.0 = 2.8 ug/m3 NO3 HNO3 TNO3

9. CASTNET Comparisons Sulfates and Nitrates Ames RB, Malm WC (2001) Comparison of sulfate and nitrate particle mass concentrations measured by IMPROVE and the CDN. ATMOSPHERIC ENVIRONMENT 35 (5): 905-916. Western and Eastern Sites

10. Sulfate: Comparison of 4-week mean IMPROVE and CASTNET Great Agreement for Sulfates (after adjustment to LTP)

11. Particle Nitrate: Comparison of 4-week mean IMPROVE and CASTNET Relative Bias for particle Nitrates /DC

12. From: Rodger B. Ames and William C. Malm Comparison of sulfate and nitrate particle mass concentrations measured by IMPROVE and the CDN

13. IL KY OH NW PA CASTNET filterpak and PM2.5 nitrates in the Eastern US: better agreement in Midwest and during later years Hypothesis: Better NH4NO3 retention on teflon with free ambient NH3

14. Less NO3 Agreement between CASTNET filter pack and PM2.5 nitrate as the sites move to the East Hypothesis: Poorer NH4NO3 retention on teflon in NH3 limited environments Note: different than pNO3 loss with FRM measurements for PM2.5

15. Quarterly Average NO3: CASTNet vs PM2.5 Speciation Good agreement in rural IL…. Relative Bias at Western NYS CASTNET NO3, ug/m3 PM2.5 Speciation 3:2 1:1

16. Routine Estimates of Ambient Carbon More Uncertain than other measurements Carbon –Inter-network differences in Measured C IMPROVE and STN use different thermo-optical techniques to measure carbon Many studies suggest that IMPROVE EC~=2x STN EC More recent results reveal more agreement – Total Carbonaceous Mass is estimated as TCM = k* OC +EC – Where k can be 1.2 to > 2.5 (+/- 30% regional uncertainty) – IMPROVE uses 1.4 OC is blank corrected for artifacts using network-wide estimates ……but still sufficiently robust for model evaluation

17. Blank corrections vary by Sampler Can Represent Substantial Portion of Measured Values Derived from network average quartz filter field blanks to adjust annual averages Varies by 24-hr sampler volume –MetOne (SASS): 9.6 m 3 –Anderson (RASS): 10.4 m 3 –R&P: 14.4 m 3 –URG (MASS): 24 m 3 –IMPROVE: 32.8 m 3 Preliminary OC Blank Corrections Used by Rao, Frank, et al* * National Air Quality and Emission Trends Report 2003 Special Studies Edition http://www.epa.gov/oar/aqtrnd03/

18. Values used by Rao et al. 0.56 0.93 1.28 1.4 Newest Blank Corrections Are Slightly Different Reference: Analysis of Speciation Network Carbon Blank Data DRAFT REPORT, Flanagan et al RTI International August 30, 2002 OC now corrected with Total Carbon value

19. PM2.5 Mass Concentration, ug/m3 OC concentration, ug/m3 Evaluation of OC blank correction for 12-month averages using measured OC vs PM2.5 mass

20. TCM Variation in Rural Areas March 01 – Feb 02 TCM=1.8*OC+EC Based on TCM= OC*1.8+EC

21. 13 Selected Urban Sites are Paired with Rural Sites for “Urban PM2.5 Excess” Calculations Fresno Indy S.L. Tulsa Missoula SLC Bronx Charlotte Baltimore Atlanta Cleveland Richmond Birmingham 16 rural IMPROVE sites 13 urban STN sites

22. Urban PM2.5 is Higher than Nearby Rural Concentrations Top: Urban Bottom: Rural 12-month average PM2.5 mass from speciation samplers - PM2.5 STN mass is affected by high filter blanks prior to ~August 2001

23. Dolly Sods, WV Rural IMPROVE site (background) Baltimore MD STN urban site Rural Concentrations Superimposed on Urban Rural Concentrations Adjusted for Elevation differential “Urban Excess” = Urban – Rural concentrations Estimated Annual “Urban Excess” for Baltimore, MD Top bars are urban concentrations Bottom bars are nearby rural concentration

24. Range of TCM based on “k”= 1.4 to 1.8 (k=1.4) Ambient Urban Excess Concentrations for 13 example areas “Urban Excess” = urban concentration – regional background

25. Carbon is > 50-80% of the Urban Excess Note: GM excess is higher in part, because of bias in STN mass thru June 2001, and because GM contains water.

26. With straight inter-network comparison, We see a large OC increment, but…. OC urban increment is potentially over stated * * * “k” for OCM in rural areas is likely > 1.4, further reducing urban increment as presented Note: Comparisons based on different thermo-optical techniques

27. With straight inter-network comparison, we don’t always see a large urban increment for EC Assuming IMPROVE EC > STN EC, urban increment is potentially understated Note: Comparisons based on different thermo-optical techniques

28. By accounting for potential relative bias in reported EC The Urban EC Increment Can Be Bounded Assuming IMPROVE EC > STN EC > ½ IMPROVE EC Note: Comparisons based on different thermo-optical techniques Upper Estimate EC urban excess

29. Other Issues Data reporting conventions –STP vs LTP –Using high elevation site data to represent regional concentrations Inter-annual variability PM2.5 mass vs. Component species

30. Elevation adjustment is a small technical correction to the “Urban Excess” calculation Urban excess after elevation adjustment Concentration, ug/m3 Estimated Annual “Urban Excess” for Baltimore, MD Concentration at 1158m (Dolly Sods) is 12% lower than a “sea level” estimate

31. Focus on Dolly Sods, WV Average Sulfate March 01 – Feb 02 Elevation adjustment increases average DOSO sulfate to 4.8 ug/m3

32. http://capita.wustl.edu/CAPITA/CapitaReports/LocalPM10/LocalP10.HTML#combpandt Local Condition Concentrations < High Elevation STP Concentrations

33. http://capita.wustl.edu/CAPITA/CapitaReports/LocalPM10/LocalP10.HTML#combpandt Local Condition Concentrations > Cold Area STP Concentrations

34. http://capita.wustl.edu/CAPITA/CapitaReports/LocalPM10/LocalP10.HTML#combpandt Local Condition Concentrations vs STP Concentrations

35. Large Inter-annual Variability in NO3, 2000-02 Northern MidWest/NE Southeast Trend sites CASTNET sites Expressed as Ammonium Nitrates, 1.29*NO3

36. Use of PM2.5 Measurements FRM Mass not = Ambient PM2.5 Ambient PM2.5 = [Sulfates] + [Nitrates] + [Carbon Mass] +[Crustal] +[Other] Approximation used by IMPROVE program: PM2.5 = [SULFATE] + [NITRATES] + [OCM] + [LAC] + [fine soil] NH4 2 SO 4 and NH 4 NO 3 estimated from 3*S and NO3 OCM=1.4*OC Fine soil estimated as 2.2[Al]+2.49[Si]+1.63[Ca]+2.42[Fe]+1.94[Ti] FRM mass –does not retain all particle nitrates –Includes particle bound water and “other” (e.g. passive PM2.5) –OCM probably different than 1.4*OC = [Ammoniated Sulfate Mass] + [Retained Nitrate Mass] + [Retained Carbonaceous Mass] + [Metallic Metal Oxides] + [Other Components]

37. Summary Many issues associated with Air Quality Measurement Uncertainties are relatively small for Model Evaluation Purposes