1. Simulation of a High Ozone Episode in the Houston-Galveston-Brazoria Metropolitan Area: Evaluation of CMAQ Daewon Byun: Prof., IMAQS Director Contributed by: Soontae Kim, Seungbum Kim, Beata Czader Institute for Multidimensional Air Quality Studies (IMAQS) University of Houston

2. Benefits Comparative evaluation of two models can provide tremendous insights on the validity of model inputs, model configurations and results Help identify strengths/shortcomings of the many components in the systems Can provide “weight of evidence” information for the SIP modeling Objectives – Evaluation of modeled HRVOC effects with an alternative modeling tool Air quality models (AQMs) that are based on first-principle description of the nature is extremely complex. AQMs depend on various inputs and model assumptions TCEQ – utilizes Environ’s CAMx (Comprehensive Air quality Model– Extended) The model being compared: EPA’s CMAQ (Community Multiscale Air Quality) model

3. Emissions Inventories Used Standard vs. imputed (base5b/psito2n2) HRVOC emissions –CB-IV mechanism –SAPRC mechanism –Imputed = selected increase of ETH/OLE emissions by 3-13 times in the Houston Ship Channel CAMx and CMAQ both use the same emissions EI but some minor differences –Different plume rise methods cause different vertical distributions of elevated source emissions. –Some chemical species for CAMx are not used in CMAQ. Ex) MEOH, ETOH Institute for Multi-dimensional Air Quality Studies Texas AQS period studied: August 22-Aug 31, 2000

4. Transport Algorithms Used in CMAQ and CAMx ProcessUH CMAQTCEQ CAMx Horizontal advectionPPM (Piecewise Parabolic Method) PPM Vertical advectionPPM (updated)Semi-implicit (Crank- Nicholson) Horizontal diffusionK-theory, resolution & shear dependent K-theory, variable Vertical diffusionK-theory with PBL similarity method for Kz calculation K-theory with O'Brien (1970) scheme for Kz calculation Mass adjustmentByun algorithm (CMAQ default) Implicit vertical transport 4-km resolution simulation results

5. X22: CAMX CAMx 4.03 TAMU&ATMET Base5b regular C_a01, TCEQ Q22: CMAQ CMAQ4.2.2 TAMU (M_a02) Base5b regular C_a01, TCEQ Supersite: LaPorte with base Texas Emissions Two models are quite comparable

6. X20: CAMX CAMx 4.03 TAMU&ATMET Base5b psito2n2 C_a01, TCEQ Q20: CMAQ CMAQ4.2.2 TAMU (M_a02) Base5b psito2n2 C_a01, TCEQ Supersite: LaPorte with Imputed HRVOC (ETH, OLE) CAMx responds to the imputed data much more

7. X22: CAMX CAMx 4.03 TAMU&ATMET Base5b regular C_a01, TCEQ Q22: CMAQ CMAQ4.2.2 TAMU (M_a02) Base5b regular C_a01, TCEQ Supersites: LaPorte/Clinton with Base Texas Emissions Some missing peaks with base emissions Not much bias

8. X20: CAMX CAMx 4.03 TAMU&ATMET Base5b psito2n2 C_a01, TCEQ Q20: CMAQ CMAQ4.2.2 TAMU (M_a02) Base5b psito2n2 C_a01, TCEQ Supersites: LaPorte/Clinton with Imputed HRVOC emissions Some improvement here Often overpredicted Mostly overpredicted

9. Aug. 28 th Comparison with NOAA Aircraft CMAQ/CB-4 with imputed HRVOC Good correlation with observation; (model predictions somewhat lower than obs.)

10. X20: CAMX CAMx 4.03 TAMU&ATMET Base5b psito2n2 C_a01, TCEQ Q22: CMAQ CMAQ4.2.2 TAMU (M_a02) Base5b psito2n2 C_a01, TCEQ Comparison with NCAR Aircraft with Imputed HRVOC (ETH, OLE) Still significant underprediction in ETH conc. Is there any other way to predict High ozone productivity in the model? Problem in the vertical distribution of the imputed HRVOC emissions? Different vertical mixing? Different chemical mechanism?

11. Regular EI: includes Area/Nonroad, Mobile, Point and Biogenic emissions Imputed EI: Regular + Additional VOC emissions OSD (Ozone Season Day) emissions: ~ 130 tons/day Hourly emissions: 30 ~ 70 tons/day Most of the imputed HRVOC emissions are treated as fugitives and thus ends up in the lower model layers Vertical re-distribution of imputed HRVOC emissions (on-going study – preliminary results)

12. Speciated OSD emissions (before imputation) mapped into the CB-4 species

13. Stack parameters of point sources in HGA Species # of Stacks Emissions (tons/day) Mean Ht. (H>0.5m) Mean Dia. (D>0.01m) Mean Temp (T>293K) Mean Velo. (V>0.0001m/s) ETHYLENE PENTENE (1) BUTENE PROPYLENE BUTENE (1) BUTADIENE BUTENE (3-METHYL-1) BUTENE (2-METHYL-1) ISOPRENE HEXENE DECENE,1- PENTADIENE (E-1,3) BUTADIENE, 1,2- PROPADIENE Average 3131 1343 76 3623 1266 338 415 538 463 522 1 10 1 51.881 3.343 3.120 52.831 7.213 6.433 0.321 1.107 2.018 2.733 0.000 0.315 0.014 0.018 12.4 10.5 20.7 14.2 13.6 11.9 11.1 11.3 12.0 15.2 12.6 68.6 1.5 12.9 0.7 0.9 0.1 0.8 0.7 0.9 1.8 1.5 0.1 - 0.8 449.7 342.5 572.2 430.2 417.9 481.0 311.8 313.5 371.7 346.6 417.0 486.2 1089.0 294.0 411.0 4.992 1.222 0.005 4.957 2.464 6.659 0.076 0.080 1.140 0.967 8.232 1.251 0.001 3.56

14. He = Hs + a * F**b / U F = 0.25*g * ( Ts-T )/T * V * D**2 Effective Plume Height Emissions

15. Cut-off used (Vx2) (Vx4) Cut-off used

16. Vertical re-allocation of imputed emissions

17. Ozone concentrations predicted Surprisingly not much difference….. Before changeAfter mod. Vertical distribution

18. But improves ETH Before

19. CMAQ Kz Sensitivity Experiments With CMAQ Eddy scheme CAMx Kz scheme (Louis79 & OB70) Kzmin (extreme case) Holtslag and Boville (1993) (code provided by Jon Pleim)

20. Column (21, 34) in East Houston and just north of Ship Channel Kzmin OZONE HCHO NOx

21. CMAQ O3 against NOAA AL aircraft data 08/25 08/30 CMAQ-Kz Kz from CAMx

22. O3 against NOAA AL aircraft data 08/25 08/30 Kzmin HB93-Kz

23. CAMx Aug. 28 th Comparison with NOAA Aircraft CMAQ Peaks matches wellMissing peaks in plumes CO shows serious mixing problemCO compares quite well

24. CB-4 vs. SAPRC mechanisms Lumped VOC emissions in raw EI need to be speciated into individual model species prior to input to AQMs. Ex) CB4, SAPRC99, RADM2 As an alternative chemical mechanism, SAPRC99 includes more explicit chemical species than CB4, but still cooperates with grouped VOC species. It may not be enough to explain the roles of a variety of VOC species in petrochemical plant plumes over the HGA during the high ozone formations.  Extended SAPRC Institute for Multi-dimensional Air Quality Studies

25. CB-4 mechanism SAPRC mechanism Effects of chemical mechanism

26. Still missing some plume peaks; but the correlations are quite good (ozone) CMAQ/SAPRC

27. CMAQ/SAPRC shows promising results for NOy

28. Conclusive Remarks Ozone reactivities predicted by the air quality models are significantly affected by –HRVOC emissions –Vertical mixing –Chemical representation –Meteorological inputs (not shown today) –Model configuration (not shown today) Uncertainties in the HRVOC emissions data must be evaluated in conjunction with all other key modeling factors