1. Synergisms in the Development of the CMAQ and CAMx PM/Ozone Models Ralph E. Morris, Greg Yarwood Chris Emery, Bonyoung Koo ENVIRON International Corporation 101 Rowland Way Novato, CA Presented at CMAS Models-3 User’s Workshop October 27-29, 2003 Research Triangle Park, NC Presents:slides/

2. Introduction Numerous challenges in particulate matter modeling: > Multiple Components SO 4, NO 3, SOA, POC, EC, Crustal, Coarse, Other > Multiple Processes Gas-, Aqueous-. Heterogeneous-, Aerosol-Phase Chemistry Rainout/washout, dry deposition of Gases and Particles Advections and Diffusion Clouds, Canopy, Terrain, etc. > Numerous Uncertainties Chemistry (e.g., nitrate, SOA, aromatic, etc.), PM Size Distribution, Meteorology, Emissions, Measurements

3. Introduction > CMAS Workshop Good Forum to Discuss Challenges, Approaches and Potential Solutions for Improving PM Modeling > CMAS Workshop Theme Emphasizes the Common Challenges of PM Modeling One Atmosphere One Community One Model

4. One Atmosphere

5. One Community

6. One Model CMAQ

7. One Model? CMAQ MM5 RAMS WRF

8. One Model?? CMAQ MM5 RAMS WRF SMOKE EMS EPS OPEM

9. One Model??? CMAQ MM5 RAMS WRF SMOKE EMS EPS OPEM MOBILE NONROAD EDMS EMFAC AP42

10. One Model???? CMAQ MM5 RAMS WRF SMOKE EMS EPS OPEM MOBILE NONROAD EDMS EMFAC AP42 IMPROVE CASTNET STN AQS/AIRS NADP SuperSites

11. Multi-Model Intercomparisons > Intercomparing models and alternative formulations is an integral part of model development > Photochemical grid model development has taught us that much more can be learned from comparing different models with different formulations – this is even more true for PM models due to more uncertainties in processes Early 1980sUAM vs. CIT ~ 1990UAM vs. CALGRID Early 1990sUAM-V vs. UAM vs. SAQM Mid 1990sUAM-V vs. CAMx vs. MAQSIP Early2000sCMAQ vs. CAMx

12. Early CMAQ vs. CAMx Comparisons for Ozone 1991 Lake Michigan Ozone Study (LMOS) Databases > Tesche ands co-workers (2001) (available at www.crcao.com as CRC Project A-25)www.crcao.com > MM5 and RAMS Meteorology > No one model performing sufficiently better than another > CMAQ and CAMx using MM5 more similar than CAMx using RAMS > Similar ozone responses to VOC/NOx controls > CMAQ using QSSA and SMVGEAR chemistry solvers takes ~5 and ~8 times longer to run than CAMx  EPA implements faster Hertel/MEBI chemistry solver in CMAQ

13. Early CMAQ vs. CAMx Comparisons for Ozone July 1995 NARSTO-Northeast Ozone Episode > Morris and co-workers (available at www.crcao.com as CRC Project A-24)www.crcao.com > MM5 and RAMS Meteorology > Layer 1 K V mixing issues  EPA implements 1.0 m 2 /s minimum K V in MCIP, land use specific lower layers minimum K V used with CAMx > QSSA chemistry solver accuracy and stability issues  Hertel/MEBI solver implemented in CMAQ > Smolarkiewicz advection solver is overly diffusive.  Smolarkiewicz removed from CAMx (not in CMAQ)

14. Early CMAQ vs. CAMx Comparisons for Ozone July 1995 NARSTO-Northeast Ozone Episode > SAPRC97 chemistry more reactive than CB-IV  Both CMAQ and CAMx implement SAPRC99 chemistry > Different horizontal diffusion (K H ) formulations in CMAQ and CAMx CMAQ inversely and CAMx proportional to grid spacing  Area of future research and sensitivity tests (e.g., spawned BRAVO sensitivity test) > MM5 convective activity potentially can produce modeling artifacts  MM5 interface an area of continued research for CMAQ and CAMx

15. Emerging PM Model Development Issues Aqueous-Phase Chemistry > High pH dependency of aqueous-phase O 3 +SO 2 reaction > Coarse and fine droplets may have different buffering and different pH effects on aqueous-phase sulfate formation > Test this effect using PMCAMx sectional PM model that incorporates CMU VSRM aqueous-phase chemistry module October 17-19, 1995 Southern California PM episode Two aqueous-phase chemistry modules used – CMU 1-section bulk module – CMU 2-section VSRM module

16. Southern California Modeling Domain

17. VSRM (Multi-Section) vs. Bulk Aqueous Chemistry Percent Increase in Sulfate (%) By second day, VRSM estimates ~15-30% more sulfate across the SoCAB with > 50% increase offshore and around Long Beach

18. VSRM (Multi-Section) vs. Bulk Aqueous Chemistry VRSM can form significantly more sulfate than the bulk 1-section aqueous-phase chemistry module

19. Emerging PM Model Development Issues Conclusions on Bulk vs. Multi-Section Aqueous-Phase Chemistry Tests > Multi-section aqueous-phase chemistry module made significantly more sulfate in the Southern California test case > Due to low sulfate in Southern California, differences were not significant enough to appreciably affect sulfate model performance > Need further testing for eastern US where higher sulfate concentrations occur > Merging of CAMx4 and PMCAMx models provides platform for testing RADM and CMU 1-section bulk aqueous-phase chemistry modules against the CMU VSRM multi-section module > CMU VSR multi-section module requires ~5 times more CPU time than CMU 1-section module (Further optimization warranted)

20. Emerging PM Model Development Issues Aerosol Thermodynamics Gas/Particle Partitioning > Gas/Particle equilibrium usually assumed > ISORROPIA equilibrium scheme widely used Fast and reliable CMAQ, CAMx, URM, etc. > Equilibrium assumption may not always be correct, especially for coarse particles > PMCAMx sectional PM model includes three options for Gas/Particle partitioning: Equilibrium (ISORROPIA) Dynamic (MADM) Hybrid (equilibrium for fine/dynamic for coarse particles) > Testing using October 1995 Southern California Database

21. Equilibrium vs. Dynamic vs. Hybrid

23. Emerging PM Model Development Issues Conclusions on use of equilibrium approach for gas/particle partitioning > For Southern California application: dynamic and hybrid modules produce nearly identical results most of the time equilibrium approach produces results very close to dynamic and hybrid approaches, but differences as high as 30% did occur dynamic (MADM) approach requires approximately 10 times the CPU time as equilibrium approach > Further tests of equilibrium assumption warranted > Given sufficient accuracy, uncertainties and computational requirements, equilibrium approach appears adequate for annual modeling

24. Emerging PM Model Development Issues Particle Size Distribution > Different representations of particle size distribution in difference models CMAQ modal approach using 3 modes and assumes all secondary PM is fine CAMx4, REMSAD and MADRID1 assume fine and coarse PM (all secondary PM is fine) PMCAMx, CMAQ-AIM and MADRID2 are fully sectional models where PM10 is divided up into N sections (e.g., N=10)

25. Emerging PM Model Development Issues Particle Size Distribution > Testing of assumptions of particle size distribution using new merged CAMx4/PMCAMX code M4 = CAMx4 2 section plus RADM aqueous EQUI = N sections equilibrium + VRSM aqueous MADM = 10 sections dynamic + VRSM aqueous RADM/EQ = 10 sections equil. + RADM aqueous RADM/EQ4 = 4 sections equil. + RADM aqueous > October 17-18, 1995 Southern California Episode

26. M4 EQUI 24-Hour Sulfate (  g/m 3 ) October 18, 1995 M4 peak SO 4 39  g/m 3 EQUI peak SO 4 51  g/m 3 ~ Long Beach Area Differences due to more sulfate production in CMU VRSM than RADM aqueous-phase chemistry Further downwind (Riverside) M4 produces more sulfate than EQUI

27. 24-Hour Nitrate (  g/m 3 ) October 18, 1995 M4 peak NO 3 83  g/m 3 EQUI peak NO 3 54  g/m 3 Observed NO 3 peak at Riverside ~40  g/m 3 Differences partly due to assuming all nitrate is fine vs. PM nitrate represented by 10 size sections (EQUI) Differences in M4 RADM and EQU VSRM also contribute M4 EQUI

28. 24-Hour Nitrate (  g/m 3 ) October 18, 1995 M4 peak NO 3 83  g/m 3 EQUI peak NO 3 54  g/m 3 EQUI 10-Section grows PM NO 3 into coarser sections where it dry deposits faster than M4 NO 3 that is assumed to be fine Result is less NO 3 in downwind Riverside area that agrees better with observations M4 M4 - EQUI

29. Sensitivity to Number of Size Sections (10 vs. 4) @ (34,16)

30. Computational Efficiency Model Configurations CPU hours per simulation day (based on Athlon 1600 CPU) 0.42 0.52 1.2 5.8 63

31. Emerging PM Model Development Issues Nighttime Nitrate Chemistry > September 2003 CMAQ release Zero N 2 O 5 +H 2 O gas-phase reaction rate 0.02 and 0.002 probability for heterogeneous rate > April 2003 CAMx4 release Keep gas-phase N 2 O 5 +H 2 O reaction rate – German smog tests provide upper bound rate, but is real gas- phase reaction Current research suggests part of overestimation tendency may be due in part to assuming all nitrate is fine > More updates in future

32. Emerging PM Model Development Issues Interface with Meteorological Model (MM5/RAMS) > Mass Conservations and Mass Consistency > Clouds and Precipitation (resolved and unresolved) > Instantaneous meteorological data (convective down bursts) > MM5 PBL heights – what to do when collapsed from clouds/snow

33. Conclusions on Model Development Synergisms CMAQ and CAMx offer two completely different platforms to test alternative PM modules and formulations > provides an “independent” test of the assumptions > identifies potential for introducing compensatory errors Numerous common challenges in PM modeling, the more ways of looking at the problem the better > nitrate formation, size sections and deposition > aqueous-phase chemistry > PM size distribution > meteorology > computational efficiency

34. Toola to Facilitate Model Intercomparisons MM5 Interface Software > MCIP 2.2 > MM5CAMx + kvpatch CMAQ-to-CAMx conversion software > Emissions > IC/BC CAMx-to-CMAQ conversion software > Emissions > IC/BC

35. Current CMAQ/CAMx Comparisons 1996 Western USA > WRAP and CRC Jan 2002, July 2001, July 1991Eastern USA > VISTAS August – September 1997 Southern CalEfornia > CRC Midwest US/Supersites > MRPO