Alternative Model Simulations: CAMx vs. CMAQ and PSAT vs. TSSA

1. Alternative Model Simulations: CAMx vs. CMAQ and PSAT vs. TSSA Ralph Morris, Greg Yarwood, Bonyoung Koo, Steven Lau and Abby Hoats ENVIRON International Corporation, Novato, CA Gail Tonnesen, Chao-Jung Chien and Zion Wang University of California, Riverside WRAP Modeling Forum Meeting, San Francisco, CA March 8-9 18, 2005

2. Purpose Approach CAMx/CMAQ Model Performance Evaluation PM Source Apportionment Technology (PSAT) Formulation and Testing WRAP Application Comparisons with CMAQ TSSA Conclusions on Alternative Models and PM Source Apportionment Content

3. Compare CMAQ and CAMx model performance for February and July 2002 using latest 2002 databases Compared CMAQ Tagged Species Source Apportionment (TSSA) and CAMx PM Source Apportionment Technology (PSAT)  Should we run alternative models for key 2002 simulations in 2005-2006? Purpose

4. Develop CAMx modeling databases for February and July 2002 and the 36km Continental US Inter-RPO Domain 15 day spin-up period (45 day simulations) MM5CAMx to process latest 2002 36 km MM5 data Used CMAQ Kv vertical diffusivity option CMAQ-to-CAMx Processors IC/BC and Emissions Develop other CAMx inputs Photolysis rates (TUV), landuse and terrain, Albedo/Haze/Ozone column, etc. Approach (1)

5. Perform February and July 2002 36 km CAMx Base D (pre02d) Base Case simulations Model performance evaluation and comparison against CMAQ Base D (pre02d) Base Case Set up CAMx PSAT PM Source Apportionment using same source regions and categories as CMAQ TSSA Run for Sulfate and Nitrate source apportionment and compare with CMAQ TSSA Approach (2)

6. Extract PSAT SO4 and NO3 Source Apportionment results at Class I areas Generate 24-hour average Model performance evaluation and comparison against CMAQ Base D (pre02d) Base Case Set up CAMx PSAT PM Source Apportionment using same source regions and categories as CMAQ TSSA Run for Sulfate and Nitrate source apportionment and compare with CMAQ TSSA for 24-hour impacts at Class I areas Approach (3)

7. Continental US 36 km Inter-RPO Domain 6 Subregions: All US, WRAP, CENRAP, MRPO, VISTAS and MANE-VU States Three Networks: IMPROVE, CASTNet, STN PM Species Components SO4, NO3, EC, OC, Soil, CM and TCM CAMx V4.20beta Base D (pre02d) vs. CMAQ V4.4 Base D (pre03d) Model Evaluation – CAMx/CMAQ

8. SO4 July 2002 USA CMAQ vs. CAMx BaseD SO4 CASTNet SO4 IMPROVE IMPROVE

9. SO4 2002 USA CMAQ vs. CAMx BaseD Jan SO4 IMPROVE Jul SO4 STN

10. SO4 Jan 2002 USA CMAQ vs. CAMx BaseD SO4 Jan STN SO4 Jan CASTnet

11. NO3 July 2002 USA CMAQ vs. CAMx BaseD NO3 IMPROVE NO3 CASTNet

12. NO3 July 2002 USA CMAQ vs. CAMx BaseD NO3 STN HNO3 CASTNet

13. NO3 January 2002 USA CMAQ vs. CAMx BaseD NO3 IMPROVE NO3 CASTNet

14. NO3 Jan 2002 USA CMAQ vs. CAMx BaseD NO3 STN HNO3 CASTNet

15. Carbon July 2002 USA CMAQ vs. CAMx BaseD OC IMPROVE TCM STN

16. Carbon Jan 2002 USA CMAQ vs. CAMx BaseD OC IMPROVE TCM STN

17. EC IMPROVE USA CMAQ vs. CAMx BaseD July EC January EC

18. Hourly TCM July 2002 at SEARCH Yorkville Observed, CMAQ and CAMx

19. SOIL IMPROVE USA CMAQ vs. CAMx BaseD July SOIL January SOIL Note that Crustal emissions were not modeled separately as normally done in CAMx due to use of CMAQ2CAMx processor

20. CM IMPROVE USA CMAQ vs. CAMx BaseD July Coarse Mass January Coarse Mass

21. SO4 IMPROVE WRAP CMAQ vs. CAMx BaseD July SO4 WRAP January SO4 WRAP

22. NO3 IMPROVE WRAP CMAQ vs. CAMx BaseD July NO3 WRAP January NO3 WRAP

23. OC IMPROVE WRAP CMAQ vs. CAMx BaseD July OC WRAP January OC WRAP

24. EC IMPROVE WRAP CMAQ vs. CAMx BaseD July EC WRAP January EC WRAP

25. SOIL IMPROVE WRAP CMAQ vs. CAMx BaseD July SOIL WRAP January SOIL WRAP

26. CM IMPROVE WRAP CMAQ vs. CAMx BaseD July Coarse Mass WRAP January Coarse Mass WRAP

27. Both models exhibit very similar good model performance for SO4 in summer Slight SO4 overestimation in winter, CAMx overestimation greater than CMAQ Both models poor NO3 performance Summer underestimation (CMAQ worse than CAMx) Winter overestimation (CAMx worse than CMAQ) OC, EC, TCM, Soil and CM performance mixed Further analysis needed Conclusions: CMAQ vs. CAMx Performance

28. CALPUFF: “chemistry” highly simplified, incorrect and over 20 years old (1983) SCICHEM: needs 3-D concentrations fields, currently computationally demanding Photochemical Grid Models: Zero-Out Runs (actually sensitivity approach) Reactive Tracer PSAT/TSSA approaches shows promise for source apportionment modeling Source Apportionment Approaches

29. Reactive tracer approach that operates in parallel to the host model to track PM precursor emissions and formation Set up to operate with families of tracers that can operate separately or together for: Sulfate, Nitrate, Ammonium, Mercury, Primary PM (EC, POA, crustal and other) PM Source Apportionment Technology (PSAT)

30. Modify CAMx to include families of tracers (tagged species) for user selected source “groups” Source group = source category and/or geographic area Build on CAMx ozone apportionment schemes (OSAT, APCA) Tag primary species as they enter the model SO2i , NOi , VOCi , primary PM (crustal, EC, etc.) When secondary species form, tag them according to their parent primary species SO4i , NO3i , SOAi PSAT Conceptual Approach

31. Zero-Out Comparisons for Sulfate • Use Eastern US/Canada modeling domain • Add four hypothetical point sources to base emissions • Test large and small emission rates to investigate signal/noise Large: SOx = 850 TPD Small: SOx = 0.85 TPD X X X X

32. Difference due to oxidant limitation MRPO Large Source: Episode Maximum SO4 PSAT versus “Zero Out” PSAT Zero-Out

33. MRPO Large Source: Episode Average SO4 PSAT versus “Zero Out” PSAT Zero-Out

34. Oxidant Limiting Sulfate Example PSAT Zero-Out • PSAT attributes 50% of SO4 to source A (and 50% to B) • Zero-out attributes zero SO4 to source A (no source is culpable) • Zero-out result (sensitivity) is not a reasonable apportionment for this example

35. Good agreement for extent and magnitude of sulfate impacts between PSAT and zero-out Comparing the outer plume edge is a stringent test Zero-out impacts can be smaller or larger due to oxidant limited sulfate formation and changes in oxidant levels. Run times look very good PSAT obtains 50+ SO4 source contributions in time needed for 1 zero-out assessment PSAT Sulfate Evaluation

36. PSAT Chemical Scheme for NOy Gasses • PSAT tracks 4 groups of NOy gasses • RGN • TPN • HN3 • NTR • Conversion of RGN to HN3 and NTR is slowly reversible • Conversion of RGN to TPN is reversible – rapidly or slowly

37. PSAT Partitioning of NOy Gasses CAMx box model run with 20 ppb initial NO and 100 ppb NO emissions at a constant rate. Looks reasonable, is it correct?

38. SOEM: Source Oriented External Mixture We only use part of the SOEM concept here Duplicate all NOy reactions in the chemical mechanism “blue NOy” and “red NOy” affects NO, NO2, PAN, HNO3, etc. difficulty for self-reactions, e.g., NO + NO --> 2 NO2 forms “red,” “blue” and “purple” NO2 SOEM may change the base result Model initial conditions (ICs) as “blue NOy” Model emissions as “red NOy” Implemented in CAMx, run for 1-D case (box model) Independent Check for NOy: SOEM

39. Comparing SOEM and PSAT for NOy • The independent SOEM method agrees well with PSAT

40. CAMx SOA scheme VOC -- OH, O3, NO3 --> Condensable Gas (CG) <==> SOA CGs partition to an SOA solution phase PSAT implementation straightforward, but many terms Three types of VOC precursor alkanes, aromatics, terpenes Five pairs of CG/SOA four anthropogenic, one biogenic low/high volatility products PSAT tracers for VOC, CG and SOA species Test implementation using another SOEM method duplicate “red/blue” reactions and species, similar to NOy testing Testing Secondary Organics (SOA)

41. PSAT apportionment of SOA to ICs and Emissions Biogenic emissions Biogenic ICs

42. PSAT SOA Apportionment for Emissions • Excellent 1:1 correspondence between SOEM and PSAT results

43. PSAT SOA Apportionment for Ics • 1:1 correspondence for ICs as well as for Emissions (last slide) • Conclusion: PSAT implementation for SOA is accurate

44. Full-Scale Application Testing by MRPO • 13 Source Regions • 6 Emission Categories • Boundary Conditions • Initial Conditions • Source apportionment to 90 groups for SO4, NO3, NH4, SOA and 6 primary species • Results courtesy of Kirk Baker, LADCO/MRPO Canada WRAP MANE-VU MRPO CENRAP VISTAS

45. Episode Average On-Road PNO3 for January 2002

46. Episode Average Point Source PSO4 for June 2002

47. Episode Average Biogenic SOA for June 2002

48. 15 Source Regions 5 Source Categories Biogenic On-Road Mobile Points Fires Area+Non-Road Initial Concentrations Boundary Conditions 77 Source Groups (77=15 x 5 + 2) Sulfate Family (2) SO2 (SO2) PS4 (SO4) Nitrate Family (5) RGN (NOx+NO3+HONO+N2O5) TPN (PAN+PNA) NTR (RNO3) HN3 (HNO3) PN3 (PM NO3 ) Ammonium Family (2) NH3 (NH3) PN4 (NH4) SOA (14), Hg (3) and Primary PM (6) Not Run WRAP PSAT Source Categories

49. PSAT/TSSA Source Region Map CA, NV, OR, WA, ID, UT, AZ, NM, CO, WY, MT, ND, SD, Eastern States and Mex/Can/Ocean

50. 24-hour Sulfate contributions ay Class I areas in the WRAP States February and July 2002 Bar charts of Sulfate contributions by source group = Category_Area Category = Bio, Mob, Pts, Fir, ANR Area = CA, NV, OR, WA, …, SD, EST, Mex Pts_NM = Point sources from New Mexico ANR_AZ = Area+Non-Road sources from Arizona Some differences in TSSA/PSAT Categories TSSA mv = on-road + non-road; fires???; BCs??? PSAT vs. TSSA