Simulation of Houston-Galveston Airshed Ozone Episode with EPA’s CMAQ

Daewon Byun: PI

Soontae Kim, Beata Czader, Seungbum Kim

Emissions input

Chemical Mechanisms

Vertical Mixing

Benefits• Comparative evaluation of two models provides tremendous insights on the validity of model inputs, model configurations and results

• Help identify strengths/shortcomings of the many components in the system

• Can provide “weight of evidence” information for the present SIP modeling

Objectives – Evaluation of modeled HRVOC effects with an alternative modeling tool

• Air quality models based on first-principle description of nature are extremely complex and depends on various inputs and model assumptions

•TCEQ – utilizes Environ’s CAMx (Comprehensive Air quality Model– Extended)

•Model being compared: EPA’s CMAQ (Community Multiscale Air Quality) model

Emissions InventoryEmissions Inventory• Standard vs. imputed (base5b/psito2n2)

HRVOC emissions– CB-IV mechanism– SAPRC mechanism

• 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 StudiesInstitute for Multi-dimensional Air Quality StudiesInstitute for Multi-dimensional Air Quality StudiesInstitute for Multi-dimensional Air Quality Studies

Transport Algorithms Used in CMAQ and CAMx

 

Process UH CMAQ TCEQ CAMx

Horizontal advection PPM (Piecewise Parabolic Method)

PPM

Vertical advection PPM Semi-implicit (Crank-Nicholson)

Horizontal diffusion K-theory, constant K-theory, variable

Vertical diffusion K-theory with PBL similarity method for Kv calculation

K-theory with O'Brien (1970) scheme for Kz calculation

Mass adjustment Yes Yes

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: LaPortewith base Texas Emissions

Two models are quite comparable

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: LaPortewith Imputed HRVOC (ETH, OLE)

CAMx responds to the imputed data much more

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/Clintonwith Base Texas Emissions

Some missing peaks with base emissions

Not much bias

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/Clintonwith Imputed HRVOC emissions

Some improvement here Often overpredicted

Mostly overpredicted

Aug. 28th Comparison with NOAA Aircraft

CMAQ/CB-4 with imputed HRVOC

Good correlation with observation;(model prediction somewhat lower)

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 Aircraftwith Imputed HRVOC (ETH, OLE)

Still significant underpredictionIn ETH conc.

Is there any other way to predictHigh ozone productivity in the model?

• Problem in the vertical distribution of the imputed HRVOC emissions?

• Different vertical mixing?

• Different chemical mechanism?

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

0

10

20

30

40

50

60

Em

issi

ons

(ton

s/da

y) ISOP

ETH

ALD2

FORM

PAR

OLE

Speciated OSD emissions mapped into the CB-4species

Stack parameters

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

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

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

0.8

0.7

0.9

0.9

0.9

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

0.001

3.56

He = Hs + a * F**b / U

F = 0.25*g * ( Ts-T )/T * V * D**2

Vertical re-allocation

Ozone concentrations predicted

Surprisingly not much difference…..

But improves ETH

CMAQ Kz Sensitivity Experiments

With

• CMAQ Eddy scheme

• CAMx Kz scheme (Louis79 & OB70)

• Holtslag and Boville (1993)

CAMx Aug. 28th Comparison with NOAA Aircraft CMAQ

Peaks matches well Missing peaks in plumes

CO shows serious mixing problem CO compares quite well

Column (21, 34) in East Houston and just north of Ship Channel

CMAQ Eddy schemeCAMx Kz scheme (Louis79 & OB70)

Holtslag and Boville (1993)I=21 J=34 21UTC(15CST) 08/25/2000

Kz (m2s-1)

0 100 200 300 400 500

HEIG

HT (m

AG

L)

0

500

1000

1500

2000

2500

3000

CMAQ CAMx HB93

O3 against NOAA AL aircraft data

08/25

08/30

CMAQ CAMxKz HB93

CB-4 vs. SAPRC mechanismsCB-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

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CB-4 mechanism SAPRC mechanism

Effects of chemical mechanism

Still missing some plume peaks; but the correlations are quite good (ozone)

CMAQ/SAPRC

CMAQ/SAPRC shows promising results for NOy

Conclusive Remarks

Ozone reactivities in 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