1. AoH Work Group Weight of Evidence Framework WRAP Meeting – Tucson, AZ January 10/11, 2006 Joe Adlhoch - Air Resource Specialists, Inc.

2. Overview Review of RHR visibility goals What do we mean by weight of evidence (WOE) approach? Review of model approach to determine reasonable progress Review of other data inputs

3. Review of RHR Visibility Goals Define current conditions at each Class I area using the 2000-04 baseline period Define “natural conditions” Improve visibility such that the average Haze Index for the 20% worst days in the baseline period reach “natural conditions” by 2064 Ensure that visibility on the 20% best days does not degrade Periodically assess the improvement in visibility between the baseline period and 2064 and show that “reasonable progress” is being achieved

4. Schematic of Glide Path From: Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, EPA 2003

5. WOE Definition Set of analyses supplemental to primary measurement/modeling efforts WRAP AoH working definition:  Review of all available analyses that bear on Class I area visibility Monitoring data Emissions data Model results Attribution results (combination of multiple methods) Review of trends (monitoring and emissions) Review of episodic (“natural” ?) events Back trajectory and other analyses  Assigning appropriate weight to each analysis (based on relevance and uncertainty)  Ultimately, this will take the form of a checklist of things to review and instructions on how to weigh each piece

6. Use of AQ Model to Estimate 2018 Visibility (simplified) Assumption: the AQ model is better at predicting relative changes in concentration than absolute concentrations Steps: 1.Determine the 20% worst days from the 2002 IMPROVE data 2.Model species concentrations for 2002 3.Model species concentrations for 2018 base and scenarios 4.Determine a species-specific relative reduction factor (RRF) for the average of the 20% worst days (based on step #1 above): RRF sulfate = 2018 sulfate / 2002 sulfate 5.Project 2018 concentrations by applying the RRFs to the IMPROVE data for the 20% worst days in each baseline year: Projected 2018 concentration ~ Avg. [RRF x Baseline concentration ] 6.Calculate projected 2018 visibility for 20% worst days and compare to the Glide Path

7. 2002 Model Performance: Agua Tibia, CA

8. 2018 -2002 Model Change: Agua Tibia, CA

9. 2002 Model Performance: Zion, UT

10. 2018 -2002 Model Change: Zion, UT

11. Is Model Prediction of Reasonable Progress… Reasonable? Determine if the major species causing visibility impairment are handled well by the model The variability in the 5-year baseline could be used as an “uncertainty range” to bound the projected 2018 visibility:  Which species most affect variability?  Meteorological dependencies?  Could this be tied to monitoring uncertainties? Are there episodic events that could justifiably be removed from the data set (e.g., large fire episodes during baseline period)? Review attribution source regions and their emissions:  How well do attribution methods agree?  If source regions can be identified with confidence, do the projected emissions reductions for 2018 support the model’s visibility reductions?

12. Median Uncertainty of IMPROVE Data Across WRAP Uncertainty based only on lab reported uncertainties for daily samples (2000 – 2004) OC, EC, Soil, and CM uncertainty determined from standard propagation of error analysis on individual component terms Uncertainty due to flow/size cut errors not included

13. Glide Path for Agua Tibia, CA

14. Baseline Variability (dv) Glide Path for Agua Tibia, CA Baseline Variability by Species

15. Glide Path for San Gabriel, CA

16. Baseline Variability (dv) Baseline Variability by Species

17. Glide Path for Goat Rocks, WA

18. Baseline Variability (dv) Baseline Variability by Species

19. Large Episodic Fire Impacts in 2002

20. SO2 Point and Area Emissions Reductions

21. NOx Point and Area Emissions Reductions

22. Expected Attribution Results The modeled attribution results (CAMx and PSAT method) will tell us how much species mass is likely due to specific source regions (states, Canada, Mexico, Pacific, etc.) The results can be displayed as:  Amount or percent of species mass attributed by a region  Amount or percent of extinction attributed by a region

23. Phase I Attribution Graphics

24. Phase 2 Attribution “Footprint” The following maps show mock ups for how attribution results might be displayed in Phase 2 (data shown is from Phase I) Helps to answer the questions:  Which states need to consult on visibility issues  What contributions to haze might be coming from outside the WRAP or the U.S.

25. Phase I Sulfate and Nitrate Extinction Attributed to Arizona (TSSA Analysis)

26. Phase I Sulfate and Nitrate Extinction Attributed to Oregon (TSSA Analysis)

27. Phase I Sulfate Extinction Attributed to WRAP States (excluding UT, WA, WY) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Phase I clustering based on SO4/NO3 attribution

28. Phase I Sulfate Extinction Attributed to non-WRAP Source Regions

29. Phase I Nitrate Extinction Attributed to non-WRAP Source Regions