In this paper, a case study of applying the CALPUFF modeling for regional haze analysis is conducted for an industrial source with potential impact on a nearby Class I area. Results for a base case are discussed and influences of several variables on modeling results are evaluated.

1. Modeling Software for EHS Professionals
Implication of Applying CALPUFF to Demonstrate
Compliance with the Regional Haze Rule
Paper #651
Prepared By:
Weiping Dai, Ph.D.
Hung-Ming (Sue) Sung, Ph.D., P.E.
Curtis V. DeVore
BREEZE SOFTWARE
12700 Park Central Drive,
Suite 2100
Dallas, TX 75251
+1 (972) 661-8881
breeze-software.com

2. 1
Implication of Applying CALPUFF to Demonstrate
Compliance with the Regional Haze Rule
651
Weiping Dai, Ph.D.
Trinity Consultants, 12801 North Central Expressway, Suite 1200, Dallas, TX 75243
Hung-Ming (Sue) Sung, Ph.D., P.E.
Trinity Consultants, 12801 North Central Expressway, Suite 1200, Dallas, TX 75243
Curtis V. DeVore
Trinity Consultants, 12801 North Central Expressway, Suite 1200, Dallas, TX 75243
ABSTRACT
The U.S. Environmental Protection Agency promulgated the final Regional Haze Rule in July
1999 to protect Class I areas from visibility impairment. Industrial sources may be required to
conduct modeling analysis to demonstrate their potential impact on nearby Class I areas during
air permit applications. Although the widely used Industrial Source Complex Short-Term model
(ISCST3) can be applied (and it may be recommended as the first step screening tool by state
agencies), its results may not pass the strict criteria, which are set to protect the Class I areas.
Moreover, regional haze is generally a long-range transport phenomenon. Theoretically, the
ISCST3 model is not appropriate for a long-range transport (e.g., greater than 50 kilometers from
the emission sources). Variations of the meteorological and geophysical conditions in such a
distance require sophisticated temporal and spatial treatments of meteorological data,
geophysical data, and plume dispersions. The CALPUFF modeling system is designed to handle
long-range transport of various pollutants with consideration of chemical transformation.
For regional haze analysis, different states may have different modeling approach and guidance.
Colorado proposes a 3-tier approach: Tier 1 – ISCST3 modeling analysis; Tier 2 – CALPUFF
Screen analysis; and Tier 3 - Full CALPUFF analysis. CALPUFF Screen analysis is a simplified
approach with flat terrain assumption and meteorological data from one station. In this paper, a
case study of applying the CALPUFF modeling for regional haze analysis is conducted for an
industrial source with potential impact on a nearby Class I area. Results for a base case are
discussed and influences of several variables (e.g., distance between the source and the Class I
area, emission rates, stack parameters, and background ozone and ammonia concentrations) on
modeling results are evaluated. Modeling results indicate that control of NOX and SO2 is most
effective in reducing extinction change while stack parameters have small effects on extinction
change. Extinction change may also be a strong function of distance for distances less than
certain values (e.g., 80 km). In addition, it seems typical background ozone concentrations affect
the extinction change more than typical background ammonia concentrations.

3. 2
INTRODUCTION
The U.S. Environmental Protection Agency (USEPA) promulgated the final Regional Haze Rule
in July of 1999 in order to protect the 156 Class I areas from any future visibility impairment and
remedy any existing visibility impairment due to manmade air pollution.1
Class I Areas are
defined as national parks over 6,000 acres and wilderness areas and memorial parks over 5,000
acres in the Clean Air Act. According to the Regional Haze Rule, visibility impairment means
any humanly perceptible change in visibility (e.g., light extinction, visual range, contrast, and
coloration) from that which would have existed under natural conditions. Regional haze means
visibility impairment that is caused by the emissions of air pollutants from numerous sources
located over a wide geographic area. Such sources include, but are not limited to, major and
minor stationary sources, mobile sources, and area sources.
Visual air quality in Class I areas has been significantly degraded due to various anthropogenic
sources. The current visual range is only 30 – 90 miles in the West and 14 – 24 miles in the East
while the natural visual range should be approximately 140 miles in the west and 90 miles in the
east.2
Visibility impairment is primarily caused by the scattering and absorption of light due to
airborne fine particulate matter such as sulfates, nitrates, organic carbon, elemental carbon, and
soil dust. Ammonium sulfate and ammonium nitrate typically are secondary pollutants formed
from the oxidation of sulfur dioxide (SO2) and nitrogen oxides (NOX) in the presence of
ammonia gas (NH3) during processes of transport and dispersion. Anthropogenic sources emit
significant amount of SO2 and NOX.
For new source permit applications, applicants may be required to perform regional haze
analysis to assess the impact of emissions from the proposed sources on nearby Class I areas.
Several dispersion models such as VISCREEN, PLUVUE II, ISCST3, AERMOD, and
CALPUFF can be utilized for this modeling analysis purpose. Different states may have different
modeling analysis guidance. For example, Colorado recommends a 3-tier approach for long-
range transport modeling analysis: Tier 1 – ISCST3 modeling analysis; Tier 2 – CALPUFF
Screen analysis; and Tier 3 - Full CALPUFF analysis.3
CALPUFF Screen analysis is a simplified
approach with flat terrain assumption and meteorological data from one station. Although the
widely used ISCST3 can be applied (and it may be recommended as the first step screening tool
by state agencies), its results may not pass the strict criteria, which are set to protect the Class I
areas. Moreover, ISCST3 is not appropriate for long-range transport (e.g., greater than 50
kilometers from the emission sources) because variations of the meteorological and geophysical
conditions in such a distance require sophisticated temporal and spatial treatments of
meteorological data, geophysical data, and plume dispersions. Regional haze is generally a long-
range transport phenomenon. The CALPUFF modeling system is designed to handle long-range
transport of various pollutants with consideration of chemical transformation.
The CALPUFF modeling system consists of three major components: CALMET, CALPUFF,
and CALPOST.4
CALMET is a meteorological model for generating hourly three-dimensional
wind and temperature fields and various two-dimensional fields (mixing height, surface
characteristics, and dispersion properties) on a gridded modeling domain. These meteorological
fields developed by CALMET are used by CALPUFF. CALPUFF is a non-steady state transport
and dispersion model for calculating hourly pollutant concentrations and deposition fluxes at
selected receptors due to various emission sources. CALPOST is used to process the CALPUFF

4. 3
results for performing visibility, deposition, or concentration analysis for selected averaging
periods. Typically, a 24-hour averaging period is used for visibility analysis.
This study focuses on the application of full CALPUFF modeling analysis for regional haze
analysis. A case study is conducted for assessing the impact of SO2, NOX, and PM10 emissions
from a hypothetical industrial source on the nearby Class I area. Results for a base case are
discussed and influences of several variables (distance between the source and the Class I area,
emission rates, stack parameters, and background ozone and ammonia concentrations) on
modeling results are evaluated. Results from CALPUFF Screen analysis are also compared and
discussed. Finally, implication of applying ISCST3 and AERMOD for regional haze analysis is
also briefly discussed.
VISIBILITY ANALYSIS AND ANALYSIS THRESHOLDS
Visibility can be characterized by the light-extinction coefficient, which represents the
attenuation of light per unit distance (e.g., million meters [Mm]) due to scattering and absorption
by various chemicals such as ammonium sulfate, ammonium nitrate, soil, and other particulate
matter in the atmosphere. Visibility analysis is used to evaluate the extent of change of the
extinction coefficient in the areas of interest due to the emissions of various pollutants such as
SO2, NOX, and PM10. SO2 and NOX can be transformed to sulfate and nitrate during plume
transport and dispersion and then sulfate and nitrate combine with available ammonia in the
atmosphere to form ammonium sulfate [(NH4)2SO4] and ammonium nitrate (NH4NO3). The
change of extinction due to the emission source can be calculated as follows:
100
,
,

bext
sext
b
b
)Change (%Extinction , (1)
where bext,s is the extinction coefficient due to the emission source(s) and bext,b is the background
extinction coefficient under natural conditions. Note that the scattering and absorption of light by
gases also contribute to the light extinction coefficient. In general, bext,b is affected by various
background chemical species and the Rayleigh scattering phenomenon and can be calculated as
follows:5, 6
rayapcoarsesoilOCNOSObext bbbbbbbMmb 
34
1
, )( , (2)
where   )RH(fSO)(NH3b 4244SO  ,   )RH(fNONH3b 343NO  ,  OC4bOC  ,  Soil1bsoil  ,
 MassCoarse6.0bcoarse  , ]CarbonElemental[10bap  , rayb Rayleigh scattering, )RH(f relative
humidity adjustment factor, and ][ concentration in g/m3
. Note that these chemical species
can be divided into two categories – hygroscopic chemicals [(NH4)2SO4 and NH4NO3] and non-
hygroscopic chemicals (organic carbon [OC], soil, coarse mass, and elemental carbon). Visibility
is normally much better in dry conditions than in wet conditions because fine particles absorb
moisture in the atmosphere and scatter light more efficiently due to the increased size.7
The
extinction due to hygroscopic chemicals is a function of the relative humidity factor, which has a
nonlinear relationship with the ambient relative humidity.

5. 4
The Draft Phase I Report of the Federal Land Managers’ Air Quality Related Values Workgroup
(FLAG) lists the site-specific background extinction under natural conditions for Class I areas.6
Note that many of the natural background extinction coefficients in Table 2.B-1 of the Draft
FLAG Report are in error. For example, all of the “Hygro” values for the eastern U.S. should be
0.9 Mm-1
, but some of them are incorrectly reported as 0.6 Mm-1
.
According to the guidance developed by FLAG, the following analysis thresholds of extinction
change due to sources located at least 50 km from the Class I areas will be applied in New
Source Review Visibility Analysis: (1) if the extinction change due to the proposed source is
greater than 10% for at least one modeled day, the federal land managers (FLM) will consider
the magnitude, frequency, duration, and other factors to assess the impact. However, the FLM is
likely to object to the issuance of the permit; (2) if the extinction change due to the proposed
source is less that 5% for all days, the FLM is not likely to object to the issuance of the permit;
(3) if the extinction change due to the proposed source is greater than 5% but less than 10%, a
cumulative analysis including the new source and all new source growth will be requested; (4) if
cumulative analysis results indicate that the effects from the combined sources are expected to
cause an extinction change less than 10% for all modeled 24-hour periods, the FLM is not likely
to object to the issuance of the permit; however, (5) if cumulative analysis results shows that the
cumulative extinction change is greater than 10% and the contribution from the proposed source
is not less than 0.4%, the FLM is likely to object to the issuance of the permit, even though the
magnitude, frequency, duration, and other factors will be considered.6
Note that the above
criteria are not yet final and may be revised in response to public comments.
In this study, three visibility-related pollutants (SO2, NOX, and PM10) are considered to be
emitted from a hypothetical industrial source. The SO2 and NOX are transformed to (NH4)2SO4
and NH4NO3 during transport and dispersion. Therefore, the chemicals with contribution to
visibility change are (NH4)2SO4, NH4NO3, and PM10. The extinction coefficient due to these
three chemicals (bext,s) can be expressed as follows:
PMNOSOsext bbbMmb 
34
1
, )( , (3)
where   )RH(fSO)(NH3b 4244SO  ,   )RH(fNONH3b 343NO  , and  10PM PM6.0b  .

6. 5
CASE STUDY: EMISSION SOURCE AND MODELING SETUP
Hypothetical Industrial Source
In this study, it is assumed that a new industrial source is proposed to be built east of a Class I
area. The facility will emit SO2, NOX, and PM10 from a stack. Parameter values for a base case
are listed in Table 1. For the base case, the facility is approximately 50 km away from the closest
edge of the Class I area. The distance of 50 km is chosen because it is a general criterion distance
to distinguish near-field modeling from long-range transport modeling, although CALPUFF is
not technically subject to this criterion.
Table 1. Parameter Values for the Base Case
Parameter Value
Source UTM Coordinates Easting: 506 km
Northing: 4180 km
Zone: 13
Emission Rate SO2 (tons per year) 500
NOX (tons per year) 500
PM10 (tons per year) 500
Stack Parameter Stack Height (m) 100
Stack Diameter (m) 3
Exit Velocity (m/s) 15
Exit Temperature (K) 400
Background
Concentration
Ozone (ppb) 40
Ammonia (ppb) 10
CALPUFF Modeling Setup
Figure 1 shows the Class I area location and its surrounding terrain characteristics. A mountain
range is between the source and the Class I area. The terrain is quite flat near the source and the
terrain elevation increases as approaching the Class I area. Figure 2 shows the modeling domain
setup, the location of the source relative to the Class I area, and the placement of receptors. The
gray color scale represents the terrain elevation. The modeling domain contains a 45  25 grid
with a spacing of 4 km. Eight vertical layers are defined with the cell face heights at 20, 40, 100,
250, 500, 1000, 2000, and 3500 m.
In this study, CALMET is used to develop one year of gridded meteorological data in the
modeling domain. The input data includes four surface stations, two upper air stations, five
precipitation stations, geophysical data, and the prognostic wind field developed by the
Mesoscale Model with four-dimensional data assimilation (MM4). MM4 data are used as the
initial guess wind field. MM4 gridded prognostic wind field data are extracted from the 1990
National Climatic Data Center (NCDC) MM4 CDs. Appropriate values are chosen for input

7. 6
parameters such as interpolation and radius of influence of surface data, upper air data,
precipitation data, and terrain by judging the terrain characteristics and following
recommendations from available guidance documents.4, 5, 6
For CALPUFF modeling setup, six species (i.e., SO2, SO4, NOX, nitric acid (HNO3), NO3, and
PM10) are modeled. The MESOPUFF II chemical transformation scheme is used. The
background concentration is 40 ppb for ozone and 10 ppb for ammonia for the base case. The
default nighttime conversion rates are used (i.e., 0.2 %/hr for SO2 loss, 2 %/hr for NOX loss, and
2 %/hr for HNO3 gain). Gas-phase dry deposition is modeled for SO2, NOX, and HNO3.
Particulate-phase dry deposition is modeled for SO4, NO3, and PM10. Wet deposition is modeled
for SO2, SO4, HNO3, NO3, and PM10. Default dry and wet deposition parameter values are used.
Stacktip downwash, vertical wind shear above stack top, and partial plume penetration is
modeled with inversion strength computed from temperature gradients. Building downwash is
not considered in this study. Plumes are modeled as puffs with Pasquill-Gifford (PG) coefficients
for rural areas. When the lateral size of a plume exceeds 550 m, Heffter Equation is used to
switch from distance-dependent to time-dependent lateral dispersion coefficients.4
Puff splitting
is also allowed in this study. The partial plume path adjustment method is used with default
plume path coefficients. In this study, CALPOST is used to perform the extinction change
calculation with the CALPUFF output of hourly concentration and relative humidity.
Background concentrations for both the hygroscopic chemicals and the non-hygroscopic
chemicals are 0.54 g/m3
as (NH4)2SO4 and 5.06 g/m3
as soil, respectively. The background
extinction derived from these concentrations match the reference level for Class I areas in the
western U.S.6
Figure 1. Class I Area and Terrain Characteristics (the source is out of the view of this picture
to the east of the Class I area)
Class I Area

8. 7
Figure 2. CALPUFF Modeling Domain and Receptor Setup (Elevation increases as the shading
becomes darker.)
CASE STUDY: RESULTS AND DISCUSSION
Base Case Results
The base case has an emission rate of 500 tons per year (tpy) for each pollutant (SO2, NOX, and
PM10). Daily maximum extinction change (averaged over 24 hours) for the base case is shown in
Figure 3. All daily maximum extinction changes are below 3% except for four days. The highest
extinction change is 6.2% and there are two days have extinction change above 5%. In air permit
application, these results may result in request for accumulative analysis.
Three observations are made from the results. First, for the worst day, NOX contributes 58% to
the extinction while SO2 and PM10 contributes 35% and 7%, respectively. This implies that for
the same amount of emission, NOX contribute more than 1.5 times to the extinction change than
SO2 while SO2 contributes more than 8 times than PM10 under the modeling conditions. This is
reasonable because nitrate and sulfate oxidized from SO2 and NOX absorb moisture in the air and
form NH4NO3 and (NH4)2SO4 with available ammonia in the air and the oxidation of NOX is
typically faster than the oxidation of SO2. The NH4NO3 and (NH4)2SO4 aerosols scatter light
more effectively than PM10. Second, high extinction change typically occurs on the days with
high relative humidity (e.g., greater than 90%). This is reasonable because the relative humidity
factor increases dramatically for relative humidity higher than about 90%. Third, the visibility-
related pollutants can accumulate in the Class I area for more than one days and thus cause high
extinction change with the aid of relatively high relative humidity. For example, the highest
extinction change for the base case occurs at the second day of the accumulation with a relative
humidity of about 89%. Of course, the pollutant accumulation strongly depends on terrain
400 420 440 460 480 500 520 540 560 580
UTM Easting (km)
4,130
4,150
4,170
4,190
4,210
4,230
UTMNorthing(km)

9. 8
characteristics and wind patterns.
Figure 3. Daily Maximum Extinction Change for the Base Case
Sensitivity Analysis of Distance between Source and Class I Area
A sensitivity analysis is performed for distances of 30, 40, 50, 60, 70, 80, 90, and 100 km
between the source and Class I area. Note that the base elevation of the stack does not change for
these cases. Other parameter values are kept the same as those for the base case. The results are
presented in Figure 4.
The results show that as distance between the source and Class I area increases, the maximum
extinction change decreases. The rate of change increases when the source gets closer to the
Class I area and levels off for distances greater than 80 km. The maximum extinction change is
almost 11% for a distance of 30 km while the maximum extinction change is about 4% for
distances greater than 80 km. It seems distances less than 50 km a have significant impact on the
maximum extinction change. Moderate effects are expected for distances between 50 km and 80
km while the maximum extinction change becomes insensitive for distances greater than 80 km.
Note that the observations here may not be true for other cases due to the complicated nature of
many factors such as terrain effects.
0
1
2
3
4
5
6
7
1 30 59 88 117 146 175 204 233 262 291 320 349
Julian Day of 1990
ExtinctionChange(%)

10. 9
Figure 4. Effects of Distance on Maximum Extinction Change
Sensitivity Analysis of Emission Rate
A sensitivity analysis is performed by changing the emission rates of SO2, NOX, and PM10
separately from 100 tpy to 1500 tpy. Other parameter values are kept the same as the base case.
The results are presented in Figure 5.
The results show that the rate of maximum extinction change is approximately linear to the
change of the emission rate. Extinction change increases approximately 0.75% (note that this is
an absolute change of the extinction change) for an increase of 100 tpy for NOX, 0.44% for SO2,
and 0.09% for PM10. These results are quite consistent with the contributions of NOX, SO2, and
PM10 to the maximum extinction change shown in the base case. The results imply that reduction
in NOX emissions may be most effective for improving visibility quality, followed by reduction
in SO2 emissions under certain modeling conditions.
0
2
4
6
8
10
12
30 40 50 60 70 80 90 100
Distance (km)
ExtinctionChange(%)
Cases
5% Threshold
10% Threshold

11. 10
Figure 5. Effects of Emission Rates on Maximum Extinction Change
Sensitivity Analysis of Stack Parameters
A sensitivity analysis is performed by changing the stack height, stack exit temperature, and
stack exit velocity while other parameter values are kept the same as the base case. The results
are shown in Figures 6, 7, and 8. Stack height is changed from 40 to 120 m. Stack exit
temperature is changed from 300 to 500 K and stack exit velocity is changed from 5 to 25 m/s.
In general, individually increasing stack height, stack exit temperature, or stack exit velocity
decreases the maximum extinction change. However, the rate of decrease is small. On average,
the extinction change decreases about 0.06% for a 10-m increase of stack height and 0.14% for a
5 m/s increase of stack exit velocity. Changes in stack exit temperature seem to have a greater
impact on visibility change at the lower end of the investigated temperature range.
0
2
4
6
8
10
12
14
16
18
0 200 400 600 800 1000 1200 1400 1600
Emission Rates (tons per year)
ExtinctionChange(%)
SO2 Cases
NOX Cases
PM10 Cases
5% Threshold
10% Threshold

12. 11
Figure 6. Effects of Stack Height on Maximum Extinction Change
Figure 7. Effects of Stack Temperature on Maximum Extinction Change
4
6
8
10
12
40 60 80 100 120
Stack Height (m)
ExtinctionChange(%)
Cases
5% Threshold
10% Threshold
4
6
8
10
12
300 350 400 450 500
Stack Temperature (K)
ExtinctionChange(%)
Cases
5% Threshold
10% Threshold

13. 12
Figure 8. Effects of Stack Exit Velocity on Maximum Extinction Change
Sensitivity Analysis of Background Ozone and Ammonia Concentration
Background ozone and ammonia concentrations contribute to the visibility quality because these
two chemicals are involved in the chemical transformation of SO2 and NOX during transport and
dispersion. A sensitivity analysis is conducted by changing ozone concentration from 10 ppb to
100 ppb and changing ammonia concentration from 0.1 ppb to 20 ppb. Other parameter values
are kept the same as the base case. The results are presented in Figures 9 and 10. It seems that
change in background ozone concentration has noticeable impact on maximum extinction
change. On average, the maximum extinction change increases 0.26% for each 10-ppb increase
of ozone concentration. Results indicate that the maximum extinction change is relatively
insensitive to ammonia background concentration unless it is very low (e.g., 0.1-1 ppb).
4
6
8
10
12
5 10 15 20 25
Stack Exit Velocity (m/s)
ExtinctionChange(%)
Cases
5% Threshold
10% Threshold

14. 13
Figure 9. Effects of Background Ozone Concentration on Maximum Extinction Change
Figure 10. Effects of Background Ammonia Concentration on Maximum Extinction Change
4
6
8
10
12
0 20 40 60 80 100 120
Background Ozone Concentration (ppb)
ExtinctionChange(%)
Cases
5% Threshold
10% Threshold
4
6
8
10
12
0 5 10 15 20
Background Ammonia Concentration (ppb)
ExtinctionChange(%)
Cases
5% Threshold
10% Threshold

15. 14
Other Considerations in Visibility Analysis
Quality Control of Modeled Wind Fields
CALMET output is the gridded meteorological data in an unformatted data file. In order to
confirm the reasonableness of the final wind fields developed by CALMET, hourly wind vector
plots are developed and visually evaluated for wind field characteristics such as downslope flow
during nighttime and upslope flow during daytime. Figure 11 presents an hourly wind vector plot
for demonstration purpose. The figure clearly shows the downslope flow and the wind vector
follows the terrain reasonably well.
Figure 11. Wind Vector Plots for Checking Final Modeled Wind Fields
Comparison of CALPUFF Screen Analysis and Full CALPUFF Analysis
The major difference between CALPUFF Screen analysis and full CALPUFF analysis is the way
to generate meteorological data and place receptors. Full CALPUFF analysis utilizes CALMET
to develop gridded meteorological data by using multiple meteorological (surface, upper air, and
precipitation) stations and accounting for terrain effects. CALMET can even make use of
sophisticated prognostic wind fields developed by other mesoscale models (e.g., MM4 and
MM5). In addition, receptors are placed only on the Class I Area of concern. However,
performing full CALPUFF analysis requires tremendous efforts and computer resources.
CALPUFF Screen analysis uses an ISC-type meteorological data from a single meteorological
data. It also assumes flat terrain. The conservatism in CALPUFF Screen analysis comes from the
placement of receptors. Instead of looking at receptors on the Class I area, rings of discrete
receptors (1-degree apart) are placed between the closest and farthest distance from the source to
the Class I area. Extinction change at each receptor is considered. This conservatism is based on
the fact that no terrain effects are taken into account and therefore no directional distinction
should be considered.

16. 15
Table 2 compares results from full CALPUFF analysis with those from CALPUFF Screen
analysis for the base case. The maximum extinction change from the Screen analysis is almost
three times higher than that from the full analysis. Moreover, the frequency exceeding the
threshold values (5% and 10%) significantly increases.
Table 2. Comparison of Results from CALPUFF Screen Analysis and Full CALPUFF Analysis
Parameter Full Analysis Screen Analysis
Maximum Extinction Change (%) 6.2 17.5
Days with Extinction Change > 5 % 2 50
Days with Extinction Change >10% 0 8
Application of ISCST3 or AERMOD for Visibility Analysis
Both ISCST3 and AERMOD are steady-state dispersion models. Theoretically, both ISCST3 and
AERMOD are not suitable for long-range transport modeling analysis. However, according to
Colorado modeling guidance, ISCST3 is acceptable as a screening step for long-range transport
modeling analysis even though it is also mentioned that the ISCST3 Screen Analysis may
eventually be replaced with the CALPUFF Screen.
Nevertheless, ISCST3 is the regulatory model for near-field (less than 50 km) analysis and
AERMOD is expected to be the replacement of ISCST3 in the future. Therefore, if it is desirable
to conduct visibility analysis for near-field sources, ISCST3 and AERMOD may be the options.
SUMMARY
This study has focused on performing full CALPUFF analysis for long-range transport regional
haze analysis. Extinction change can be affected by many factors (e.g., relative humidity,
distance between the emission source and Class I area, emission rates, stack parameters, and
background ozone and ammonia concentrations). Modeling results indicate that control of NOX
and SO2 is most effective in reducing extinction change while stack parameters have small
effects on extinction change. Extinction change may also be a strong function of distance for
distances less than certain values (e.g., 80 km). In addition, it seems typical background ozone
concentrations affect the extinction change more than typical background ammonia
concentrations.

17. 16
REFERENCES
1. U.S. Environmental Protection Agency, Regional Haze Regulations (Final Rule), Federal
Register, Vol. 63, No. 126, July 1, 1999, pp.35714-35774.
2. U.S. Environmental Protection Agency, Fact Sheet: Final Regional Haze Regulations for
Protection of Visibility in National Parks and Wilderness Areas, June 2, 1999.
3. Colorado Department of Public Health and Environment, Long-Range Transport Model
Selection and Application, Air Pollution Control Division/Technical Services Program, May
21, 1999.
4. Earth Tech Inc., A User’s Guide for the CALPUFF Dispersion Model, Concord, MA, May
1999.
5. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and
Recommendations for Modeling Long Range Transport, U.S. EPA, Office of Air Quality
Planning and Standards, EPA-454/R-98-019, December 1998.
6. U.S. Forest Service – Air Quality Program, National Park Service – Air Resources Division,
U.S. Fish and Wildlife Service – Air Quality Branch, Draft Phase I Report of the Federal
Land Managers’ Air Quality Related Values Workgroup (FLAG), October 1999.
7. N.D. Nevers, Air Pollution Control Engineering, McGraw-Hill, 1995, p506.