V:\corporate\marketing\overview.ppt CRGAQS: Revised CAMx Results Presentation to the Gorge Study Technical Team By ENVIRON International Corporation December 6, 2006

V:\corporate\marketing\overview.ppt Today’s Presentation Recap modeling performance issues Describe latest CAMx simulations –Model and emission changes Performance for PM and light scattering Next Steps

V:\corporate\marketing\overview.ppt Modeling Issues Episodes –August 10-22, 2004 –November 4-18, 2004 Identified issues from sensitivity runs –Primary fine/coarse PM over predicted in both episodes Dominating modeled light scattering Windblown dust vs. fires? Nope: construction + agricultural fugitive dust

V:\corporate\marketing\overview.ppt Modeling Issues –OC over predicted in both episodes SOA is dominant in Aug (mainly biogenic) POA (+EC) is dominant in Nov near Portland (wood smoke) –Why is modeled scattering nearly zero in mid-November in the Mt Zion area? Easterly winds accelerate through Gorge and send Portland emissions offshore

V:\corporate\marketing\overview.ppt Modeling Issues –Why is modeled scattering so low at the eastern sites in November? No speciated data at Gorge study sites IMPROVE data on Nov 11 indicates dominance of NO3 and OC –OC, EC, and SO4 performance is good in east –NO3 is under predicted in east We rely on modeled RH to generate the nitrate (complex process) We use observed RH to translate nitrate mass to nitrate scattering (simple process)

V:\corporate\marketing\overview.ppt Revised Model Configuration SOA –Historically under predicted by CMAQ and CAMx in the western U.S. (e.g., WRAP) Attributed to the biogenic component –We employed a chemical improvement in CAMx for biogenic SOA Same as put into CMAQ for the RPOs Terpene 2-product mechanism: higher yields, higher volatility Should reduce biogenic SOA

V:\corporate\marketing\overview.ppt Revised Model Configuration Fugitive dust –2 SMOKE problems: WRAP speciation profiles caused a double- counting of OR/WA dust estimates –This also impacted OR/WA woodsmoke emissions No county-level “canopy escape factor” was applied (as developed and applied in WRAP) –Both have been fixed and SMOKE re-run Significant coarse PM reductions –Note: WRAP has chosen to completely disregard primary coarse PM predictions

V:\corporate\marketing\overview.ppt Performance Evaluation

V:\corporate\marketing\overview.ppt August Performance Evaluation Gorge Site Bscat East Portland

V:\corporate\marketing\overview.ppt August Performance Evaluation Bonneville Mt. Zion Wishram

V:\corporate\marketing\overview.ppt August Performance Statistics

V:\corporate\marketing\overview.ppt August Performance Evaluation

V:\corporate\marketing\overview.ppt August Performance Evaluation

V:\corporate\marketing\overview.ppt Gorge Study vs. IMPROVE At Mt. Zion Organic and Elemental Carbon

V:\corporate\marketing\overview.ppt Gorge Study vs. IMPROVE At Mt. Zion Sulfate and Nitrate

V:\corporate\marketing\overview.ppt August Summary Overall good performance for –Total PM2.5 –Light scattering Low SO4/NH4 and primary fine –Questionable Gorge measurements –NO3 insufficient to worry about High carbon –SOA modification ineffective –What is underlying cause? Model over predicts diurnal variation

V:\corporate\marketing\overview.ppt November Performance Evaluation Gorge Site Bscat Portland Eastern Gorge

V:\corporate\marketing\overview.ppt November Performance Evaluation Bonneville Mt. Zion

V:\corporate\marketing\overview.ppt November Performance Statistics

V:\corporate\marketing\overview.ppt November Performance Evaluation

V:\corporate\marketing\overview.ppt November Performance Evaluation

V:\corporate\marketing\overview.ppt November Summary Generally high total PM2.5 –Dominated by carbon and primary fine Bifurcated performance for light scattering –Over predicted in Portland area: high carbon and primary fine –Under predicted in eastern Gorge: low SO4/NO3/NH4 Need more humidity/clouds: –Generate more SO4 –Condense more NO3

V:\corporate\marketing\overview.ppt Next Steps Recommendations: –Focus on August episode 2018 Case PSAT “What-if” scenarios Use model trends in relative sense to scale IMPROVE observations –Relegate November episode Revisit with possible follow-on funding?