valuation of the CALPUFF Model

edicting Concentration, Visibility and 

position at Class I Areas in Wyoming

seph Scire, Zhong Wu and Gary Moore

Earth Tech, Inc.

Concord, MA

Presented at the 

AWMA Specialty Conference

ideline on Air Quality Models: The Path Forward

Mystic, Connecticut

October 22-24, 2003
Study Objectives

Wyoming Regional Air Quality Modeling 

ct (Feb, 2001)

luate degree of degradation of air quality, 

bility and other AQRVs due to local and distant 

ind anthropogenic sources

Bridger and Fitzpatrick Class I areas

luate the performance of the CALPUFF 

deling system
Study Objectives (Cont.)

sion of Modeling and Additional 

sis

luate the importance of inflow boundary 

ditions and the Ammonia Limiting Method 

M) on nitrate aerosol concentration predictions

sitivity testing with FLAG-recommended 

stant ammonia vs. time-varying ammonia
Components of the Study

ion inventory development

r point sources

 sources (urban areas, minor sources)

forest fires and prescribed burns

le sources (roads and railways)

leum/mining sources

enic emissions

meteorological modeling

 simulation, 60 km grid, 20 km nest

ET diagnostic meteorological modeling

 grid (116 x 100 cells)

n enhanced precipitation

 as initial guess field
Components of the Study

UFF modeling

t, area, line, and volume sources

 organic aerosol module

bility, acid deposition, ambient pollutant 

centrations of SO

2, SO

4, NO, NO

2, HNO

3, NO

3, 

, and PM

ation

bility, PM, wet deposition fluxes
CALPUFF Modeling System

orological 

data

geophysical 

data

program control 

data

CALMET meteorological model

emissions data chemical, ozone, 

deposition data

terrain, receptor, 

meteorological data

program control 

data

CALPUFF dispersion model

postprocessing & graphical 

display programs
PUFF MODELING

.

.

. .

.

..
Why Use a Puff Approach for 

Regulatory Modeling ?

an framework

eproduce plume result under steady-state 

tions

to implement many of the plume-based modules

rovide a measure of consistency with previous 

atory results under appropriate conditions

x flows – near-field sources

lex terrain (terrain channeling, slope flows)

mogeneity in surface conditions/dispersion rates, 

/rural, land/water, land use variations

sion breakup, plume fumigation

and low wind speed dispersion

ant build-up in valleys, stagnation, re-circulation
Why Use a Puff Approach for 

Regulatory Modeling ?(cont.)

nge transport

al variability in meteorological fields

ality effects

ical transformation, wet deposition, dry deposition

scale circulations

nd/lake breeze circulations

/down valley flows

 source impacts within a spatially-varying 

lative impact assessments

e contributions

teady-state releases
-600 -400 -200 0 200 400 600 800

East (km)

MM5 and CALMET Domains

MM5 60km

MM5 20km

CALMET 4km
-300 -250 -200 -150 -100 -50 0 50 100

X Coordinate (km)

Idaho Falls

catello

Rock Springs

Evanston

Provo

Salt Lake City

Riverton

Jackson

Pinedale

Kemmerer

Lander

Thermopolis

Rawlins

Baggs

Craig 1200

140016001800200022002400260028003000320034003600

Terrain

Elevation

(M MSL)

80

15

Wyoming

Colorado

Utah

Idaho

LPUFF Domain – Terrain Elevations

lls, 4km cells
-300 -250 -200 -150 -100 -50 0 50 100

East (km)

-250-200-150-100

-50

0

50

100

Idaho Falls

Pocatello

Rock Springs

Evanston

Provo

Salt Lake City

Riverton

Jackson

Pinedale

Kemmerer

Lander

Thermopolis

Rawlins

Baggs

Craig

1. Urban 2. Agricultural 3.Rangeland 4. Forest

5. Water 6. Wetland 7. Barren land 8. Tundra

CALPUFF Domain – Land Use
Meteorological Observations

y surface observations

stations

r air observations

ations

pitation observations

tations
23062

23066

24021

24127

1

2

3 4 5

6

7

8

9

10

11

3017

23066

24018

24021

24027

24089

24127

24156

72574

72576

-300 -200 -100 0 100 200 300 400

East (km)

Surface stations (22) Upper air stations (4)

12001400160018002000220024002600280030003200340036003800

Class 1 Wilderness Areas

Terrain

Elevation

(M MSL)

Surface and Upper Air Stations
-250 -200 -150 -100 -50 0 50 100 150 200 250

East (km)

1

2

3

4

5

6

7

8

9

10

11

13 12

14

15

16

17

18

19 20

21

22 23

24

25

26 27

28

29

30

31

32

3334 35

36

37

38

39

40

41

42

43

44

45

46 47

48

49

50

51

52

53

54 55

56 57

58

59

60

61

62

63

64

65

66 67

120014001600180020002200240026002800300032003400360038004000

Terrain

Elevation

(M MSL)

Precipitation Stations
-180 -160 -140 -120 -100 -80 -60 -40 -20 0

East (km)

1 Wilderness Areas State Boundaries

12001400160018002000220024002600280030003200340036003800

Terrain

(M MSL)

10 m/s

MET Winds - 7/12/95, Hour: 3, 10m
-180 -160 -140 -120 -100 -80 -60 -40 -20 0

East (km)

 Wilderness Areas State Boundaries

11112222233333

Terrain

(M MSL)

6 m/s

T Winds - 7/17/95, Hour: 13, 10m
limatological Annual Precipitation 

(PRISM)
-300 -250 -200 -150 -100 -50 0 50 100 150 200 250

East (km)

00000

0

0

000

52

72

29

48

47 60

34

39

57

37

46

58

118

42

69

44 21 49

49

53

58 63

65

43 18

43

44

37

30

35

48

43

43

36

45 21

58

45

50

42

41

84

17 26

31 20

33

35

22 41

53

40

40

38 38

Class 1 Wilderness Areas

Units:

cm

1030507090110130150170190

XX = Annual precipitation (cm)

= Location of precipitation station

state boundaries

995 Annual Precipitation - Nearest 

Neighbor Technique – 4km Cells
00 -200 -100 0 100 200

East (km)

Units:

cm

1030507090110130150170190

995 Annual Precipitation – MM5 

Model – 20km Cells
n Enhanced Precipitation Algorithm

three sources of data

matological data to determine spatial pattern of 

cipitation distribution

5 precipitation data to determine the timing of 

cipitation where observations are not available

ervational precipitation data used where 

lable
0 -200 -100 0 100 200

East (km)

29 52

48

47

60

34

39

37

46

58

118

42

69

44 21 49

49

53

58 63

65

43 18

43

44

37

30

35

48

43

43

36

45 21

58

45

50

42

41

84

17 26

31 20

33

35

22

41

53

40

40

38 38

Units:

cm

1030507090110130150170190

Annual Precipitation – CALMET Terrain 

Adjusted Precipitation – 4 km cells
Emission Inventory

rehensive 1995 inventory developed 

top-down” and “bottom-up” approaches

GCVTC and 1990 EPA Interim Emissions 

ntory

 state records, population stats, industrial data, 

M data

ions components

stant point sourcesrly varying point sources

stant area sourcesrly-varying area sources
Emission Inventory

ions components (continued)

MS

nthly-varying area sources

nthly petroleum field emissions

bile sources

rly-varying on-road sources

rly-varying off-road sources

stant railroad sources

rly-varying biomass fire emissionsrly-varying biogenic emissions

nthly-varying ammonia emissions

Sources
-300 -250 -200 -150 -100 -50 0 50 100

East (km)

Class 1 Wilderness Areas State boundaries

12001400160018002000220024002600280030003200340036003800

Constant point sourcesCEM point sources

Excess point sources

TerrainElevation(M MSL)

ant and Varying Point Sources
-300 -250 -200 -150 -100 -50 0 50 100

East (km)

-250-200-150-100

-50

0

50

100

North (km)

Idaho Falls

Pocatello

Rock Springs

Evanston

Provo

Salt Lake City

Riverton

Jackson

Pinedale

Kemmerer

Lander

Thermopolis

Rawlins

Baggs

Craig

Class 1 Wilderness Areas State boundaries

12001400160018002000220024002600280030003200340036003800

County area sources City sources

Terrain

Elevation

(M MSL)

City and County Area Sources
Class 1 Wilderness Areas State boundaries

12001400160018002000220024002600280030003200340036003800

PAW sources

Terrain

Elevation

(M MSL)

-300 -250 -200 -150 -100 -50 0 50 100

East (km)

Idaho Falls

Pocatello

Rock Springs

Evanston

Provo

Salt Lake City

Riverton

Jackson

Pinedale

Kemmerer

Lander

Thermopolis

Rawlins

Baggs

Craig

Petroleum Sources
-300 -250 -200 -150 -100 -50 0 50 100

East (km)

50005000

50

0

50

00

Idaho Falls

Pocatello

Rock Springs

Evanston

Provo

Salt Lake City

Riverton

Jackson

Pinedale

Kemmerer

Lander

Thermopolis

Rawlins

Baggs

Craig

Class 1 Wilderness Areas State boundaries

12001400160018002000220024002600280030003200340036003800

Railroad Highway Unpaved road

Terrain

Elevation

(M MSL)

e Sources – Roads and Railroads
-120 -110 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10

X(km)

10

0

102030405060708090

00

Gysum Creek

Pinedale

South Pass

Sinks Canyon

Ross Lake

Hobbs Lake

Black Joe Lake

Deep Lake

Gannett Peak

Mt. Bonneville

Wind River Peak

West Atlantic Peak

Green River Lake

W of New Fork Lake

Near V Lake

Near Soda Lake (Boulder)

Cottonwood Creek

Sweetwater Needles

Bridger IMPROVE

Lower Saddlebag Lake

Receptor Locations
Modeling Results

Base Simulation
0

.2.4.6.8

1

.2.4.6.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Bridger IMPROVE [SO4]. Units: ug/m**3 of SO4

ALPUFF Performance Predicting SO4

BCs and ALM (Bridger IMPROVE site)
0 0.1 0.2 0.3 0.4 0.5 0.6

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3

ALPUFF Performance Predicting NO

3

BCs and ALM (Bridger IMPROVE site

)
0

0.20.40.60.8

1

1.21.41.61.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Pinedale IMPROVE [SO2]. Units: ug/m**3 of SO2

CALPUFF Predicted [SO2]. Units: ug/m3 of SO2

CALPUFF Performance Predicting SO2

/ BCs and ALM (CASTNet Pinedale site)
61 91 121 151 181 211 241 271 301 331 361

Julian Day in 1995

CALPUFF RPT 247 (Method 2 - MM5 RH)Bridger Valid Transmis.

r CALPUFF Bext (Method 2) vs. 

r IMPROVE Transmissometer Data
61 91 121 151 181 211 241 271 301 331 361

Julian Day in 1995

CALPUFF RPT247 (Method 6 - FLAG RH)Bridger Valid Transmis.

r CALPUFF Bext (Method 6) vs. 

r IMPROVE Transmissometer Data
served and Predicted SO4, NO

3

and SO

2 (ug/m

3

)

366 .585 .172 .207 90%0978 .0922 .082 .081 78%453 .492 .286 .248 94%

Within 2x

PredSD

ObsSD

PredMean

ObsMean
served and Predicted Bext (Mm-1) 

Bridger Transmissometer Site

.6 5.5 7.2

-

-

3.2 22.5 24.7 -0.017 -0.030

Fractional BiasMethod 6

FractionalBiasMethod 2

CALPUFF ObsMethod 6

ALPUFF

Method 2
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

IMPROVE Observation (ug/m**3)

Plot of 24-hr SO

4 Concentrations at the 

er IMPROVE Site w/o Chemistry and 

Deposition Applied to BCs
0

0.10.20.30.40.50.6

0 0.1 0.2 0.3 0.4 0.5 0.6

IMPROVE Observation (ug/m**3)

BC Source Used in CALPUFF (ug/m**3)

Plot of 24-hr NO3 Concentrations at the 

er IMPROVE Site w/o Chemistry and 

Deposition Applied to BCs
Comparison of CALPUFF and NADP Wet Deposition at Sinks Canyon

2 3 4 5 6 7 8 9 10 11 12

Month

CALPUFF SulfurNADP Sulfur
Comparison of CALPUFF and NADP Wet Deposition at Sinks Canyon

2 3 4 5 6 7 8 9 10 11 12

Month

CALPUFF NitrogenNADP Nitrogen
Comparison of CALPUFF and NADP Wet Deposition at South Pass

2 3 4 5 6 7 8 9 10 11 12

Month

CALPUFF SulfurNADP Sulfur
Comparison of CALPUFF and NADP Wet Deposition at South Pass

1 2 3 4 5 6 7 8 9 10 11 12

Month

CALPUFF NitrogenNADP Nitrogen
Comparison of CALPUFF and NADP Wet Deposition at Gypsum Creek

1 2 3 4 5 6 7 8 9 10 11 12

Month

CALPUFF SulfurNADP Sulfur
Comparison of CALPUFF and NADP Wet Deposition at Gypsum Creek

1 2 3 4 5 6 7 8 9 10 11 12

Month

CALPUFF NitrogenNADP Nitrogen
ed and Predicted Annual Wet S and 

position (kg/ha/yr) at NADP Sites

Gypsum 0.73 0.72

Gypsum 1.18 1.24

South Pass 0.97 0.79

South Pass 1.43 1.28

Sinks Canyon 1.11 0.83

Sinks Canyon 1.34 1.36

Pinedale 0.75 0.69

Pinedale 0.99 1.17

Predicted

Wet Flux

Observed

Wet Flux

Site
Modeling Results

ting of ALM/BC vs FLAG 

Constant Ammonia

Assumption
ALPUFF Chemistry - ALM

/NO

3 equilibrium based on T and RH

ation of NH

4NO

3 may be limited by the 

bility of NH

3

miting condition shifts split of TNO

3

d HNO

3

e preferentially scavenges NH

3 over 

ground SO

4 and NO

3/HNO3, affect 

bility of NH

3 and the formation of NO

3
PUFF MODELING

.

.

. .

.

..
undary Condition Module

UFF BC module allows user to specify 

varying inflow boundary concentrations 

deled pollutants

asonal values of BC defined based on 

servational data

ent boundary types defined by the user 

east, west, north, south)

ground concentrations based on best

ble measurements in region 

ROVE, CASTNet and AIRS data)
undary Condition Module

UFF chemistry and deposition applied 

 air parcels

O

3/NO

3 repartitioned based on spatially

ying T and RH derived from CALMET 

teorological fields

emical transformation of BC air 

emically ages)

t deposition and dry deposition modeled 

BC air
00 -250 -200 -150 -100 -50 0 50 100

E AS T (k m )

[0 .4 5 ]

[0.87 ]

[1 .0 9 ]

0 .0

0

0 .0 10 .4

0

0 .9

0

1 .3

0

1 .7

0

Julian D ay: 15 7H o ur: 00

SO4

( ug / m 3 )

ndary Condition – SO4,Hr: 0
ndary Condition – SO4,Hr: 5

300 -250 -200 -150 -100 -50 0 50 100

EAST (k m )

[0 .4 5 ]

] [0 .8 7 ]

[1 .0 9 ]

0 .0

0

0 .0 10 .4

0

0 .9

0

1 .3

0

1 .7

0

Ju lian D ay: 1 5 7H o u r: 05

SO4

( u g / m 3 )
ndary Condition – SO4,Hr: 17

300 -250 -200 -150 -100 -50 0 50 100

EAST (km)

[0 .4 5 ]

] [0 .8 7 ]

[1 .0 9 ]

0.000.010.400.901.301.70

Ju lian D ay : 1 5 7H o u r: 1 7 SO 4

( ug/m 3 )
PUFF Model Performance – NO

3

s/ALM - Bridger IMPROVE Site

0.1 0.2 0.3 0.4 0.5 0.6

B r i dg e r IMP ROVE [ NO3 ] . Uni t s : ug / m**3 o f NO3
w/ Constant 0.5 ppb NH

3 - FLAG 

00) - Bridger IMPROVE Site 

( p p y)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

B r i d g e r IMP R OVE [ N O3 ] . U n i t s : u g / m **3 o f N O3
 Constant 0.5, 1.0 ppb NH

3 and 

rying NH

3 -Bridger IMPROVE 

Site

0.2 0.4 0.6 0.8 1 1.2

B ri d g e r IMP R OVE [ N O3 ] . U n i t s : u g / m **3 o f N O3

Fact or 1

Factor 2 Fact or 3

t or 4

Fact or 2

Fact or 3

Fact or 4

CALP UFF S ummed Re part it ion (S e asona l NH3)CALP UFF S e pa rat e d Repa rt it ion (NH3=0.5ppb)CALP UFF S e pa rat e d Repa rt it ion (NH3=1.0ppb)
clusions – Model Enhancements

developments improved model 

rmance

undary condition module

rain-enhanced precipitation

M (POSTUTIL processor)
sions – Distant vs. Local Sources

tantial fraction of sulfate and nitrate 

rved at Bridger is predicted to 

e from distant sources outside the 

eling domain (~90%)

ary pollutant impacts (e.g., SO

2) 

rimarily from local sources (~70%)

oximately 80-90% of wet S and N 

sition is due to distant sources
usions – Model Performance

el performance is excellent when 

 BC module and ALM

hr SO

4 and NO

3 distributions at Bridger 

PROVE site are mostly with ~10% of 

ervations

2 conservatively predicted at Pinedale 

STNet site (within 1.6 factor)

t annual N and S fluxes averages are 

hin 9% and 13% of observations at 

DP sites
Conclusions – Visibility

nty-four hour Bext seasonal and 

e of magnitudes relate well to 

missometer results

age predicted Bext within 10% of 

rvations

od 2 (hourly RH) and Method 6 

thly averaged RH) produces 

ts that are similar, but Method 2 

erforms Method 6 somewhat
usions – Model Performance

PUFF model shows substantial skill 

edicting parameters required in 

s I impact assessments

condary particulate matter 

centrations

mary pollutant concentrations

nd N deposition

bility - (Bext

)
Conclusions – Factors in 

Performance

quality emissions inventory

d resolution prognostic 

orological dataset to supplement 

rvations

ment of BCs and ammonia limiting 

ts
nclusions – Constant NH

3

f constant NH

3, even at the lowest 

s allowed by FLAG (0.5 and 1.0 ppb) 

s in substantial overprediction of NO

3

rs of 2-4 times)

deration of distant sources (BCs) and 

space-varying ammonia is essential to 

model performance for NO

3

commended that use of ALM/BCs be 

ed for refined AQRV analysis