6 th Annual CMAS Conference Chapel Hill, NC 1-3 October 2007 Presented by Jay Haney

1. COMPARATIVE MODEL PERFORMANCE EVALUATION OF CMAQ-VISTAS, CMAQ-MADRID, AND CMAQ-MADRID-APT FOR A NITROGEN DEPOSITION ASSESSMENT OF THE ESCAMBIA BAY, FLORIDA WATERSHED 6th Annual CMAS Conference Chapel Hill, NC 1-3 October 2007 Presented by Jay Haney ICF International, San Rafael, CA

2. Co-Authors: • Sharon Douglas • Tom Myers • Justin Walters • John Jansen • Krish Vijayaraghavan AER Project sponsored by Southern Co. ICF Southern Company

3. Background/Objectives • Atmospheric deposition of nitrogen is a source of contamination in Escambia Watershed • Air quality modeling performed to estimate change in nitrogen deposition in watershed due to controls at a local EGU as part of larger combined air/water quality modeling analysis • Objective for this part of study: Assess the ability of air quality models to replicate observed gaseous and particulate concentrations and wet and dry deposition

4. Air Quality Models Used:Based on CMAQ 4.5.1 • CMAQ-VISTAS: CB-IV, AERO4, modified SOA by VISTAS • CMAQ-MADRID: Sectional representation of particle size distribution as opposed to modal for CMAQ • CMAQ-MADRID-APT: “Advanced plume treatment” based on SCIPUFF with CHEMistry – SCICHEM

5. Air Quality Modeling Databases • Meteorological inputs: VISTAS 2002 inputs from RPO modeling analysis • Emissions: • CMAQ-VISTAS: Base_G1 • MADRID & APT: Base_F • Domain: 12-km ALGA, subset of VISTAS domain centered on Alabama & Georgia • Annual simulations for 2002

6. CMAQ ALGA Subdomain/Escambia Watershed Escambia Watershed CMAQ ALGA Subdomain Plant Crist

7. Air Quality Data Used in Evaluation • SEARCH: Hourly gaseous and 3-day speciated PM2.5 concentrations • IMPROVE: 3-day speciated PM2.5 concentrations • CASTNET: Weekly particulate concentrations and derived dry deposition based on concentration/ambient conditions • NADP: Weekly particulate concentrations and wet deposition

8. Model Performance Measures • Mean bias, normalized bias, fractional bias, mean error, normalized gross error, and fractional gross error • Paired for appropriate time interval • Statistics calculated using daily averages, except for CASTNET and NADP weekly measurements • Statistics calculated for all sites/species in ALGA domain with focus on sites near Escambia watershed

9. Location of SEARCH and CASTNET Sites in CMAQ Subdomain SEARCH Sites CASTNET Sites

10. Location of IMPROVE and NADP Sites in CMAQ Subdomain IMPROVE Sites NADP Sites

11. Results for Gaseous Species: SO2 for SEARCH Sites

12. Results for Gaseous Species: SO2 for SEARCH Sites

13. Results for Gaseous Species: HNO3 for SEARCH Sites

14. Results for Gaseous Species: HNO3 for SEARCH Sites

15. Gaseous Species Summary • For SO2, all models slightly underestimate concs nearby (evidence of differences between MADRID and APT in Atlanta area) • For HNO3, all models consistently overestimate at nearby sites • For NO2, all models do well and for NO, all models underestimate, but these are typically not major contributors to nitrogen deposition

16. Results for Particulate Species: NO3 for SEARCH Sites

17. Results for Particulate Species: NO3 for SEARCH Sites

18. Results for Particulate Species: NH4 for SEARCH Sites

19. Results for Particulate Species: NH4 for SEARCH Sites

20. Particulate Species Summary • For nitrate, CMAQ better simulates mean conc. but fractional bias and error are lower for MADRID and APT at nearby sites • For ammonium, all models show consistent underestimation at nearby sites, and overestimation at BHM and ATL

21. Results for Dry Deposition: NO3 for CASTNET Sites

22. Results for Dry Deposition: NH4 for CASTNET Sites

23. Results for Dry Deposition: HNO3 for CASTNET Sites

24. Results for Wet Deposition: NO3 for NADP Sites

25. Results for Wet Deposition: NH4 for NADP Sites

26. Dry Deposition Summary • For nitrate and ammonium dry deposition, all models show consistent gross underestimation • For HNO3 dry deposition, all models show consistent overestimation, with MADRID and APT showing more overestimation than CMAQ • With HNO3 higher than NO3 (simulated and observed), net result is that all models overestimate dry deposition of nitrates • Dry deposition estimates complicated by potential differences in meteorology used for data vs. model

27. Wet Deposition Summary • Models do better in simulating wet deposition and are consistent in underestimating wet deposition at nearby sites • Larger differences seen between models: effects of plume-in-grid treatment for APT?

28. Summary and Key Findings • Results are mixed: none of the models stand out as better performing • Greatest contributor to nitrogen deposition is dry deposition of HNO3, followed by wet deposition of nitrate (all forms) • Simulated net wet deposition of nitrogen is lower than observed while simulated net dry deposition is higher, so total loading of nitrogen in domain may be adequately simulated

29. Summary and Key Findings • Dry deposition monitoring not available in Escambia watershed, so performance may not be representative • Deposition output from all three models was used in water quality modeling assessment