1. WRAP Stationary Sources Joint Forum Meeting August 16, 2006 The CMAQ Visibility Model Applied To Rural Ozone In The Intermountain West Patrick Barickman Colleen Delaney Brock LeBaron Tyler Cruickshank (Perl programming)

2. General O 3 trend in the Intermountain West moving towards the 8-hour standard Does ozone outside of the metropolitan areas need to be studied? Linear Trend Line for 5 Monitors Rural Ozone Monitors, 4 th High, Daily Max 8-Hour Value

3. What Are The Possible Causes? Long range transport Oil and gas development Fire Emissions from long range transport (Asia), oil and gas development, and wildfire are all projected to increase. Are high values at particular monitors episode, and location dependent? What is the relationship of large NOx sources to areas immediately downwind?

4. Uintah Basin: Focus Of Natural Gas Development In Utah

5. Is the model providing good estimates of hourly ozone production and depletion? Compare observed ozone at the 6 monitors in the domain with model estimates Statistical metrics  Mean normalized bias  Mean normalized error Time series charts Are the CMAQ model results a useful guide for rural ozone analysis? “Model Performance Evaluation” (MPE) answers this question Detailed MPE during 5 years of continuous model improvement Chemistry mechanism Meteorology

6. Model Sub-domain 12 km CMAQ Domain

7. Bilinear interpolation 4-cell window around each monitor Weighted average of 4 cells based on distance of cell center to monitor location Establishing model value for comparison

8. Time Series Charts ( North to South ) June 1 – July 31, 2002

9. Time Series Charts ( North to South )

10. Goal<= 15%<= 25% MonitorMNBMNGE Rocky Mountain N.P.4%16% Mesa Verde3%14% Centennial, WY-4%10% Pinedale-2%13% Gothic, CO7%17% Canyonlands-3%12% Mean Normalized Bias (MNB) Mean Normalized Gross Error (MNGE) Minimum cutoff 50 ppb – only hours with observations > 50 used in bias and gross error calculations Mean Normalized Bias (MNB): A value of zero would indicate that the model over predictions and model under predictions exactly cancel each other out. Mean Normalized Gross Error (MNGE): A value of zero would indicate that the model exactly matches the observed values at all points in space/time. Previous guidance in the modeling community set a goal of: MNB <= 15% and MNGE of <= 25%. This was based on the experience of actual model performance over the years.

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12. July 12-14, 2002

13. Conclusions Model performs well for rural ozone predictions Good model performance increases confidence in the meteorology and emissions inputs

14. Next Steps A suggested analytical approach in 4 broad areas 1.Use the emissions inventory for a general understanding of source contribution NOx Point source, oil/gas, etc. VOC Biogenic, oil/gas, etc. 2.Create a set of model runs to test reduction strategies Does biogenic VOC overwhelm other sources of VOC? Is there an increase in ozone from fires? Adjust boundary conditions for sensitivity to global transport. Are nested model runs at higher resolution needed for specific geographic areas?

15. Next Steps ( continued ) 3.Develop ancillary approaches Approach similar to WRAP weight-of- evidence approach  HYSPLIT wind trajectories for downwind analysis of source regions  Emissions inventory to identify potential source regions  Monitoring trends 4.Develop a toolset and multi-state resource of data and analysis tools Build upon the WRAP TSS infrastructure Unlikely to be a point-and-click solution Will require technically savvy staff for individual state analyses Contact: pbarickman@utah.gov