1. WRAP CAMx-PSAT Source Apportionment Modeling Results Implementation Workgroup Meeting August 29, 2006

2. Source Apportionment Technique Regional gridded photochemical CAMx air quality aerosol model with PM Source Apportionment Technology (PSAT) Similar to CMAQ TSSA method used in AoH Phase I report, best available modeling analysis technique for source apportionment PSAT completed for 2 cases: –Plan02c 2000-04 average fire emissions, same time & place 2000-03 CAMD sources’ monthly average SOx/NOx profiles by state –Base18b – rules on the books controls and growth Tracked sources of sulfate and nitrate Tracking organic carbon too computationally intensive

3. Organic Carbon Source Apportionment PSAT work does not include treatment of primary & secondary organic aerosols: –CMAQ already completed includes 3 OC species primary organic aerosols anthropogenic secondary organic aerosols biogenic secondary organic aerosols –Analysis of these species for the various existing model runs will provide additional information on OC apportionment

4. Source Apportionment Modeling White paper describing CAMx PSAT modeling: www.cert.ucr.edu/aqm/308/docs www.cert.ucr.edu/aqm/308/docs

5. PSAT: 18 Source Regions on a 36 km Grid plus Initial and Boundary Conditions

6. PSAT: 6 Source Categories Examples of PSAT results: “source by region”:Examples of PSAT results: “source by region”: –MV_CO = mobile sources in Colorado –PT_CE = point sources in CENRAP PTPoint sources MVMobile sources ANFWRAP anthropogenic fires NaturalWRAP natural fires and biogenics NWFElevated fires in other RPOs ARAll other sources (non-elevated fires in other RPOs, area sources, offshore, oil & gas area sources, etc.)

7. Status of PSAT Modeling Plan02c & Base18b just completed last week Results presented here are examples Direct access to all results & custom displays available through TSS - later in this talk Results on RMC web page include: –Preconfigured bar charts & maps of monthly average source contributions by region –Contributing sources & regions on 20% best & 20% worst visibility days at each Class I area http://pah.cert.ucr.edu/aqm/308/cmaq.shtml –See: Table 2. CAMx Visibility Modeling and Source Apportionment Results.

8. Example Contributing Source Categories on 20% Worst Days Agua Tibia, CA Salt Creek, NM

9. Example Contributing Source Categories on 20% Worst Days Badlands, SD Sawtooth, ID

10. Example Top 10 Contributing Source Regions on 20% Worst Days Agua Tibia, CA Salt Creek, NM

11. Example Top 10 Contributing Source Regions on 20% Worst Days Badlands, SD Sawtooth, ID

12. Example Contributing Source Regions on 20% Worst Days Agua Tibia, CA Salt Creek, NM

13. Example Contributing Source Regions on 20% Worst Days Badlands, SD Sawtooth, ID

14. Example Contributing Source Controllability on 20% Worst Days Agua Tibia, CA Salt Creek, NM

15. Example Contributing Source Controllability on 20% Worst Days Badlands, SD Sawtooth, ID

16. Example Monthly Average Source Contribution Maps

20. PSAT: Errors/Uncertainties Additional errors have been discovered in the emissions inventories: –Base18b non-WRAP EGU emissions were underestimated. –PM2.5 emissions in the WRAP road dust inventory. –RNC is evaluating the effects of these errors.

21. Effects of non-WRAP EGU error July August September

22. Effect on PSAT results of non-WRAP EGU error Air quality prediction effects on WRAP states are relatively small. Small overestimate of base case progress in 2018 for eastern tier of WRAP region states. Base18b sufficient as starting point for development of regional haze control strategies Will correct non-WRAP region EGU error in “Control18” series of modeling simulations in 2007

23. WRAP TSS PSAT Display Tool http://vista.cira.colostate.edu/tss/Tools/SA.aspx