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The New SCIPUFF Air Dispersion Model, With Comparison Against CALPUFF

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Dr Jesse Thé, Lakes Environmental

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The New SCIPUFF Air Dispersion Model, With Comparison Against CALPUFF

  1. 1. © 2018 – Lakes Environmental Software Jesse Thé, Ph.D. DMUG  Apr/19/2018 Evaluation of  SCIPUFF versus  CALPUFF in Complex  Terrain
  2. 2. © Copyright 2018 – Lakes Environmental Software 2 Objectives 1. CALPUFF and SCIPUFF/SCICHEM resolve  the transport of pollutants with different  approaches 2. To simplify evaluations, only passive gases  were evaluated (SCICHEM without  chemistry) 3. Does SCIPUFF produces significant  different concentrations than CALPUFF?
  3. 3. © Copyright 2018 – Lakes Environmental Software 3 SCIPUFF / SCICHEM CALPUFF Outperforms AERMOD Outperforms AERMOD after 3km Accurately models to 2000km Accurately models to 300km Windfield calculations DO NOT require the 1960’s radius of interpolation CALMET requires RMAX1, RMAX2, R1, R2, and TERRAD. These “factors” may work well for only a portion of the modelling domain. Handles dense gas dispersion Does not handle dense gas Easier data input, similar to AERMOD Complex data input Complete CMAQ photo-chemistry Empirical chemistry Comparison
  4. 4. © Copyright 2018 – Lakes Environmental Software 4 Evaluation of the Comprehensive Air Quality Model with Extensions (CAMx) Against  Three Classical Mesoscale Tracer Experiments  by Bret A. Anderson, Kirk Baker, Chris Emery 9th CMAS Conference, Chapel Hill, NC, October 11, 2010 SCIPUFF outperforms CALPUFF by a significant margin 0 0.5 1 1.5 2 2.5 CALPUFF SCIPUFF HYSPLIT FLEXPART CAMx RANK RANK “SCIPUFF performed best over 46 ADM models”
  5. 5. © Copyright 2018 – Lakes Environmental Software 5 CTEX3 Tracer LRT Model Evaluation Higher values better
  6. 6. © 2018 – Lakes Environmental Software Jesse Thé, Ph.D. DMUG  Apr/19/2018 2nd Order Closure
  7. 7. © Copyright 2018 – Lakes Environmental Software 7 2nd Order Closure? 1. Turbulence  What mixes pollutants in the atmosphere  Not solved directly  Gaussian Plume + Dispersion curves = Simplest turbulence model 2. To represent mixing / dilution we must model turbulence
  8. 8. © Copyright 2018 – Lakes Environmental Software 8 2nd Order Closure? 1. Navier Stokes Equations ‐ NS 2. Reynolds Averaged NS (RANS)  Mean and fluctuating components 3. Produces more unknowns than Equations 4. Turbulence Closure: Creating “approximate”  equations to solve the unknowns 5. > 200 Closure schemes! uuu 
  9. 9. © Copyright 2018 – Lakes Environmental Software 9 Momentum Conservation gVP Dt VD     2 x xxxx z x y x x x g z V y V x V x P z V V y V V x V V t V                                    2 2 2 2 2 2 y yyyy z y y y x y g z V y V x V y P z V V y V V x V V t V                                       2 2 2 2 2 2 z zzzz z z y z x z g z V y V x V z P z V V y V V x V V t V                                    2 2 2 2 2 2
  10. 10. © Copyright 2018 – Lakes Environmental Software 10 Momentum Conservation gVP Dt VD     2 zji j i ii guu x V xx P Dt VD                    ''  '' jiuu '' uu '' vu '' wu '' vv '' wv '' ww
  11. 11. © Copyright 2018 – Lakes Environmental Software 11 Example of Closure 1st order ‐ Gradient on mean flow            i j eddyji x u uu  '' 2nd order ‐ Variance of fields – velocity, etc. u u uuu  Instantaneous = Mean + Fluctuating
  12. 12. © Copyright 2018 – Lakes Environmental Software 12 Closure Types Closure Approximation Applied 0.5 Order Bulk Method 1st Order • Gradient Transport • Mixing Length CALPUFF, AERMOD 2nd Oder Variances of Wind,  Temp, Concentration SCIPUFF Non‐Local Schemes Large Eddy Simulation LES Models
  13. 13. © Copyright 2018 – Lakes Environmental Software 13 Puff vs. Plume CALPUFF AERMOD
  14. 14. © Copyright 2018 – Lakes Environmental Software 14 SCIPUFF Lagrangian transport of  Gaussian puffs Concentration field represented by collection of 3-D puffs Q + Puffs characterized by 3-moments of the puff concentration – 0th Mass – 1st Centroid – 2nd Spread Puff concentration
  15. 15. © 2018 – Lakes Environmental Software Jesse Thé, Ph.D. DMUG  Apr/19/2018 Case Studies
  16. 16. © Copyright 2018 – Lakes Environmental Software 16 Project Parameters  July 15‐19, 2015 (5 days)  POINT Source – SO2 – 100g/s  Met Grid: 1km  Receptor Spacing: 1km
  17. 17. © Copyright 2018 – Lakes Environmental Software 17 1700m to 3000m Hayden, Colorado
  18. 18. © Copyright 2018 – Lakes Environmental Software 18 SCICHEM ‐ Hayden, CO SCICHEM with MEDOC WRF 1‐km ‐ Max:  506 μg/m3 July 17, hour 23 at ‐1.0, ‐2.0 WRF 4‐km ‐ Max:  792 μg/m3  July 15 , hour 9 at 0.0, 0.0
  19. 19. © Copyright 2018 – Lakes Environmental Software 19 CALPUFF ‐ Hayden, CO WRF 4‐km ‐ Max: 296 μg/m3 July 18, hour 21 at 1.5, 5.5 CALPUFF with Prognostic Data WRF 1‐km ‐ Max: 128 μg/m3 July 15, hour 21 at ‐5.5, 3.5
  20. 20. © Copyright 2018 – Lakes Environmental Software 20 CALPUFF ‐ Hayden, CO WRF 1‐km ‐ MMIF CALPUFF‐Ready Max: 159 μg/m3 – July 18, hour 22 at ‐4.5, 7.5 By-passing CALMET
  21. 21. © Copyright 2018 – Lakes Environmental Software 21 Comparisons ‐ Hayden, CO SCICHEM Vs CALPUFF Max 1-Hour SO2 Concentration SCICHEM – 4-km WRF (MMIF MEDOC) 792 μg/m3 SCICHEM – 1-km WRF (MMIF MEDOC) 506 μg/m3 CALPUFF – 4-km WRF (3D.DAT) 296 μg/m3 CALPUFF – 1-km WRF (3D.DAT) 128 μg/m3 CALPUFF - 1-km WRF (MMIF CALMET.DAT) 159 μg/m3
  22. 22. © Copyright 2018 – Lakes Environmental Software 22 Comparisons ‐ Hayden, CO
  23. 23. © Copyright 2018 – Lakes Environmental Software 23 SCICHEM vs CALPUFF (1‐km) SCICHEM 1‐KM WRF Max:  506 μg/m3 CALPUFF 1‐KM WRF Max:  128 μg/m3 Hayden, Colorado
  24. 24. © Copyright 2018 – Lakes Environmental Software 24 SCICHEM vs CALPUFF (4‐km) Hayden, Colorado SCICHEM 4‐KM WRF Max:  792 μg/m3 CALPUFF 4‐KM WRF Max:  296 μg/m3
  25. 25. © Copyright 2018 – Lakes Environmental Software 25 SCICHEM vs CALPUFF Wind Field SCICHEM 4‐KM WRF CALPUFF 4‐KM WRF Hayden, Colorado
  26. 26. © Copyright 2018 – Lakes Environmental Software 26 Sea level to 1500m Rio, Brazil
  27. 27. © Copyright 2018 – Lakes Environmental Software 27 SCICHEM ‐ Rio, Brazil SCICHEM with MEDOC WRF 1‐km ‐ Max: 558 μg/m3 July 16, hour 9 at 0.0, 0.0 WRF 4‐km ‐ Max: 969 μg/m3  July 15, hour 9 at 0.0, 0.0
  28. 28. © Copyright 2018 – Lakes Environmental Software 28 CALPUFF – Rio, Brazil WRF 4‐km ‐ Max: 327 μg/m3 July 16, hour 2 at ‐2.5, ‐3.5 CALPUFF with Prognostic Data WRF 1‐km ‐ Max: 263 μg/m3 July 16, hour 3 at ‐1.5, ‐4.5 
  29. 29. © Copyright 2018 – Lakes Environmental Software 29 CALPUFF – Rio, Brazil WRF 1‐KM ‐ MMIF CALPUFF‐Ready  Max: 206 μg/m3 – July 17, hour 9 at ‐0.5, 0.5 By-passing CALMET
  30. 30. © Copyright 2018 – Lakes Environmental Software 30 Comparisons – Rio, Brazil SCICHEM Vs CALPUFF Max 1-Hour SO2 Concentration SCICHEM – 4-km WRF (MMIF MEDOC) 969 μg/m3 SCICHEM – 1-km WRF (MMIF MEDOC) 558 μg/m3 CALPUFF – 4-km WRF (3D.DAT) 327 μg/m3 CALPUFF – 1-km WRF (3D.DAT) 263 μg/m3 CALPUFF - 1-km WRF (MMIF CALMET.DAT) 206 μg/m3
  31. 31. © Copyright 2018 – Lakes Environmental Software 31 Comparisons – Rio, Brazil
  32. 32. © Copyright 2018 – Lakes Environmental Software 32 SCICHEM vs CALPUFF (1‐km) SCICHEM 1‐KM WRF Max:  558 μg/m3 CALPUFF 1‐KM WRF Max:  263 μg/m3 Rio, Brazil
  33. 33. © Copyright 2018 – Lakes Environmental Software 33 SCICHEM vs CALPUFF (4‐km) Rio, Brazil SCICHEM 4‐KM WRF Max:  969 μg/m3 CALPUFF 4‐KM WRF Max:  327 μg/m3
  34. 34. © Copyright 2018 – Lakes Environmental Software 34 SCICHEM vs CALPUFF Wind Field CALPUFF 4‐KM WRFSCICHEM 4‐KM WRF Rio, Brazil
  35. 35. © 2018 – Lakes Environmental Software Jesse Thé, Ph.D. DMUG  Apr/19/2018 Conclusions
  36. 36. © Copyright 2018 – Lakes Environmental Software 36 Conclusions 1. CALPUFF and SCICHEM (SCIPUFF) generate  similar windfields when using WRF. 2. CALPUFF and SCIPUFF respond with  substantial concentration differences when  using WRF 4‐km and WRF 1‐km resolutions. 3. Higher resolution grids, in this case WRF 1‐km,  consistently produced smaller 1‐hour  maximum concentrations.
  37. 37. © Copyright 2018 – Lakes Environmental Software 37 Future Research 1. Evaluation supports the Hypothesis that SCIPUFF produces substantially different results from CALPUFF for “non‐reacting” pollutants. 2. Next step is to evaluate SCIPUFF against CALPUFF at multiple sites over extended periods.
  38. 38. © Copyright 2018 – Lakes Environmental Software 38 Questions ?
  39. 39. © Copyright 2018 – Lakes Environmental Software 39 uuu  u u Instantaneous = Mean + Fluctuating Turbulence
  40. 40. © Copyright 2018 – Lakes Environmental Software 40 IHOP Experiment – Windfield SCIPUFF Observed MM5 WRF
  41. 41. © Copyright 2018 – Lakes Environmental Software 41 ETEX ‐ SCIPUFF SCIPUFF performed best over 46 ADM models
  42. 42. © Copyright 2018 – Lakes Environmental Software 42 1. Develop prognostic  equations for each of the  moments 2. Assume that puff centroid is representative of the  whole puff 3. Splitting and merging of  puffs SPLIT MERGE Boundary SCIPUFF

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