Mr. John P. Glass, Jr. Page 2 

April 30, 2007 

with section 3.2 of Appendix W Part 51: Guideline on Air Quality Models and thus SC 

DHEC must obtain approval formal approval for its use from the EPA Regional 

Administrator. In response to these comments and for the reasons discussed below, the 

following information is provided to assist SC DHEC in requesting formal approval for 

using this methodology for BART modeling analyses in South Carolina. As you know, 

similar BART exemption modeling analyses have been performed and approved in North 

Carolina using the new IMPROVE equation and thus the information provided below is 

consistent with a the request for approval submitted to EPA Region 4 by the North 

Carolina Department of Air Quality on March 19, 2007. 

The new IMPROVE equation, which is a much better representation of the effects of 

particulate matter on light extinction than the old IMPROVE equation, takes into account 

the latest scientific understanding of several parameters. 

1. The new algorithm overcomes biases of the old algorithm on the haziest days and the clearest 

days as demonstrated by comparing the measured light extinction from nephelometers at 

Class I areas to light extinction calculated using each of the equations. 

2. The new algorithm recognizes spatial and temporal variation in light extinction as size 

distribution of the aerosol changes by increasing extinction efficiency as sulfate, nitrate and 

organics concentrations increase. 

3. The new algorithm incorporates a term to reflect the contribution of fine sea salt and its 

hygroscopic growth with increasing relative humidity recognizing research findings showing 

that fine sea salt can be an important contributor to light extinction in coastal areas. 

4. The new algorithm reflects research finding that the mass concentration of particulate organic 

matter in rural areas is greater than represented by the old equation. 

5. The new algorithm includes a NO2 term to represent times when light absorption by NO2 is a 

meaningful contributor to light extinction. 

6. The new algorithm incorporates site-specific Rayleigh scattering values to better represent 

sites close to sea level or with very hot or cold climates. 

With this combination of revisions to the IMPROVE equation, the resulting 

apportionment of extinction to various components is more accurate on the haziest and 

clearest days. This is important for development of future state-wide emission control 

strategies since the benefits of control of concentrations of each species will be 

represented more correctly with the new algorithm. Further discussion of these reasons is 

included in Enclosure 1. 

The IP Georgetown mill is located in the coastal area of South Carolina. The ability to 

appropriately represent the contribution of sea salt in the natural background is important 

in determining the contribution of the BART eligible emission units to haze at the Cape 

Appendix H.6 - 950
Mr. John P. Glass, Jr. Page 3 

April 30, 2007 

Romain Wildlife Refuge. EPA Region 4, National Park Service, and the US Fish & 

Wildlife Service determined that adding site-specific data to account for the impact of sea 

salt and Rayleigh scattering to the old IMPROVE equation is not appropriate, but rather 

the entire new IMPROVE equation should be used. The post-processor spreadsheet 

developed by Dr. Tombach to implement the entire new IMPROVE equation for use with 

CALPUFF contains a modification of CALPOST that calculates the light extinction from 

the CALPUFF output. In their review of the spreadsheet and supporting documentation, 

EPA Region 4 staff commented that the VISTAS methodology contained in the 

spreadsheet developed by Dr. Tombach appropriately implements the features of the new 

IMPROVE equation, with the exception of the nitrogen dioxide term. EPA Region 4 

further stated that with the addition of their recommendation for addressing the NO2 term, 

the VISTAS methodology to incorporate the new IMPROVE equation is acceptable on a 

case-by-case basis upon consultation with the EPA Regional Office. In addition to its use 

for the IP mill, use of the full new IMPROVE equation is also important for other sources 

in eastern South Carolina. 

A comparison of the old versus new IMPROVE equation and nephelometer data at 

several Class I areas is included in Enclosure 1. The old IMPROVE equation tends to 

show a low bias on high extinction days. The new equation shows a better match on high 

extinction days with a slightly high bias. The correlation coefficients are often slightly 

lower for the new equation than the old equation because there is slightly more scatter at 

the low end of the equation. Performance of the equations versus measured data for Cape 

Romain is included in Enclosure 1. 

Since the initial EPA review of the methodology, Dr. Tombach has modified the 

spreadsheet to address the NO2 contribution. A copy of the modified spreadsheet and 

instructions is provided as Enclosure 3 for your review. 

 

Sea Salt Calculation Methodology 

IP is requesting approval for the use of the sodium ion or strontium approach to estimate annual 

sea salt concentrations as discussed in Dr. Ivar Tombach’s March 5, 2007 white paper entitled, 

“Evaluation of Alternative Methods for Estimating Sea Salt Concentrations at Cape Romain” 

(Enclosure 4). The new IMPROVE approach using either sodium or strontium measurements 

provides a more reliable estimate of sea salt and still generally shows an underestimate of the 

mass extinction measurements at Cape Romain and other sites. 

The important and valuable sodium ion measurements taken by VISTAS were not 

available to the public until March 2007. Prior to that data availability, it was still 

evident that the use of the chloride ion concentrations for determining the sea salt 

concentration term of the new IMPROVE equation has serious drawbacks, as 

documented in Dr. Tombach’s white paper. The chloride ion from sea salt reacts with 

Appendix H.6 - 951
Mr. John P. Glass, Jr. Page 4 

April 30, 2007 

naturally-occurring and anthropogenic acids (primarily sulfuric and nitric) in the 

atmosphere. This reaction converts the chloride to several gaseous forms where it is no 

longer captured on the filter media. As such, technical/scientific experts (including 

VISTAS’ expert consultant) readily acknowledge that the chloride ion cannot be used to 

obtain a representative value for naturally occurring sea salt concentrations. 

In lieu of chloride ion data, the facility-specific BART submittals for certain coastal emission 

sources in South Carolina used the more reliable sodium and strontium data for the submittals 

provided so far. Enclosure 2 provides a detailed survey of methods for estimating sea salt 

concentrations using the available data at Cape Romain. The results of the survey found that the 

strontium approach generally estimates average concentrations very close to average 

concentrations using VISTAS' "preferred" methods for applying measurements of sodium ion and 

elemental sodium. A long history of reliable observations is available from Cape Romain, 

including a continuous record of strontium measurements from September 1994 through 

September 2004. This data corroborates Ivar Tombach's average value of 0.77 ug/m3

 for 2004 

based on the preferred sodium ion approach from the VISTAS measurements, and illustrates a 

long-term average sea salt concentration of 1.1 ug/m3

 based on continuous strontium 

measurements that correlate well with sea salt concentrations estimated from elemental sodium 

and sodium ion measurements throughout the period. 

Now that the newly-released VISTAS sodium ion data for the 2002-2004 period and the 

strontium data for the 1994 through 2004 period is available, the BART exemption modeling 

analysis for the IP Georgetown mill has been updated with better estimates of average sea salt 

concentrations in the natural background. 

Revised BART Exemption Modeling Results 

The revised CALPUFF modeling results using the new IMPROVE equation and sea salt 

contribution included in the natural background visibility are summarized in Tables 1 and 

2 below. The modeling results continue to indicate that the IP Georgetown BART 

eligible emission units do not cause or contribute to any visibility impairment in the Cape 

Romain or Wolf Island Class I areas. Based on the monitor information described above 

and the modeling information and results presented in this report, IP Georgetown 

requests that the SCDHEC evaluate the modeling analysis based on this additional 

information. 

Included with the email submittal are the attachments that include the full set postprocessor files used to generate the results, including the associated Cape Romain sodium 

ion data used to calculate the representative sea salt concentration using the sodium ion 

method. The CALPUFF input and output files originally submitted with the August 10, 

2006 submittal have not changed. The following presents the modeling results for Cape 

Romain using the new IMPROVE equation and the sodium ion sea salt calculation 

methodology and for Wolf Island using the original guideline IMPROVE equation 

currently incorporated into the CALMET processor. 

Appendix H.6 - 952
Mr. John P. Glass, Jr. Page 5 

April 30, 2007 

Table 1 

Summary of BART Exemption Modeling Results 

 Individual Year Approach 

IP Georgetown Mill 

Class I Area 2001 2002 2003

Delta-Deciview 

Rank 1-8 

Delta-Deciview 

Rank 1-8 

Delta-Deciview 

Rank 1-8 

0.742 0.706 0.638 

0.608 0.693 0.609 

0.568 0.564 0.552 

0.537 0.555 0.465 

0.524 0.519 0.456 

0.484 0.487 0.450 

0.448 0.479 0.430 

Cape Romain 

0.433 0.477 0.429 

0.036 0.040 0.044 

0.032 0.039 0.031 

0.030 0.036 0.030 

0.027 0.034 0.027 

0.026 0.034 0.023 

0.024 0.029 0.023 

0.023 0.025 0.022 

Wolf Island 

0.022 0.023 0.021 

Table 2 

Summary of BART Exemption Modeling Results 

 Three-Year Approach 

IP Georgetown Mill 

Class I Area 

Distance (km) 

From Source 

to Class I 

Area 

Boundary 

No. of days 

with impact 

>0.5 dv in 

Class I Area: 

2001 

No. of days 

with impact 

>0.5 dv in 

Class I Area: 

2002 

No. of days 

with impact 

>0.5 dv in 

Class I Area: 

2003 

No. of days 

with impact 

> 1.0 dv in 

Class I Area 

for 3-yr 

Period 

Max. 24-hr 

impact over 

3-yr period 

Cape Romain 29.5 0 0 0 0 0.456 

Wolf Island 290.0 0 0 0 0 0.022 

Units = Delta-Deciview

Conclusion 

We look forward to your response to this request for approval of use of the VISTAS 

methodology incorporating the new IMPROVE equation and the sodium ion sea salt 

calculation approach for the IP mill for BART modeling purposes. In addition, based on 

the modeling results provided above, the IP Georgetown Mill’s BART-eligible emission 

Appendix H.6 - 953
Mr. John P. Glass, Jr. Page 6 

April 30, 2007 

units do not cause or contribute to regional haze at any Class I area. Thus, we are 

requesting that the IP Georgetown Mill be exempt from the BART rule. Should you have 

any questions whatsoever about the completeness or technical content of this submittal, 

please call me at (843) 545-2291. 

Sincerely, 

INTERNATIONAL PAPER 

GEORGETOWN MILL 

Gary N. Weinreich, P.E. 

Environmental Manager 

Attachments: 

Enclosure 1 Why Use the New IMPROVE Algorithm? 

Enclosure 2 Aerosol Scattering vs. Nephelometer Scattering Using New or Old 

IMPROVE Algorithm and Daily f(RH), Shining Rock, NC 1995-1999 

Enclosure 3 Instructions: A Postprocessor for Recalculating CALPOOST Visibility 

Outputs with the New IMPROVE Algorithm 

Enclosure 4 Evaluation of Alternative Methods for Estimating Sea Salt Concentrations at 

Cape Romain, 05 March 07 

(Files containing calculations sent via email on 4-30-07) 

cc: John Grover – IP 

 Kirk Carlson – IP 

Jim Witkowski - IP 

Randy Taylor – URS 

Appendix H.6 - 954
Prepared for: 

ISG Georgetown, Inc. 

Georgetown, SC 

Revised Source-Specific BART 

Exemption and Determination Modeling 

Protocol: ISG Georgetown Inc., 

Georgetown, SC 

ENSR Corporation 

May 2006 

Document No.: 12084-002-0101 

Appendix H.6 - 955
Prepared for: 

ISG Georgetown Inc. 

Georgetown, SC 

Revised Source-Specific BART 

Exemption and Determination Modeling 

Protocol: ISG Georgetown Inc., 

Georgetown, SC 

_________________________________ 

Prepared By: Robert Paine 

_________________________________ 

Reviewed By: Anthony Colella 

ENSR Corporation 

May 2006 

Document No.: 12084-002-0101 

Appendix H.6 - 956
i May 2006

Contents 

1.0 Introduction............................................................................................................................................... 1-1

1.1 Objectives........................................................................................................................................ 1-1

1.2 Location of source vs. relevant Class I Areas ................................................................................ 1-1

1.3 Organization of protocol document................................................................................................. 1-2

2.0 Source description and emissions data ............................................................................................... 2-1

2.1 Unit-specific source data................................................................................................................. 2-1

3.0 Geophysical and meteorological input to CALMET............................................................................ 3-1

3.1 Modeling domain............................................................................................................................. 3-1

3.2 Terrain and land use ....................................................................................................................... 3-1

3.3 Meteorological database................................................................................................................. 3-1

3.3.1 12-km regional CALMET domain...................................................................................... 3-1

3.3.2 4-km sub-regional CALMET domain and finer grid resolutions ....................................... 3-2

3.4 Air quality database (background ozone and ammonia) ............................................................... 3-3

3.5 Natural conditions and monthly f(RH) at Class I Areas ................................................................. 3-3

4.0 Air quality modeling procedures ........................................................................................................... 4-1

4.1 Model selection and features.......................................................................................................... 4-1

4.2 Modeling domain and receptors ..................................................................................................... 4-1

4.3 Technical options used in the modeling ......................................................................................... 4-1

4.4 Light extinction and haze impact calculations ................................................................................ 4-2

5.0 Presentation of modeling results........................................................................................................... 5-1

Appendix A: VISTAS BART Modeling Protocol (March 9, 2006 update) ..............................................A-1

Appendix H.6 - 957
ii May 2006

List of Tables 

Table 1-1 ISG Georgetown Inc., Georgetown, SC Mill – BART-eligible Units........................................ 1-2

Table 2-1 ISG Georgetown Inc., Georgetown, SC Mill - Baseline BART Stack Parameters and Criteria 

Pollutant Emissions .................................................................................................................. 2-2

Table 2-2 ISG Georgetown Inc., Georgetown, SC Mill - Baseline BART Particulate Speciation.......... 2-3

List of Figures 

Figure 1-1 Location of PSD Class I Areas in relation to ISC Georgetown............................................... 1-3

Appendix H.6 - 958
1-1 May 2006

1.0 Introduction 

1.1 Objectives 

The Regional Haze Rule regulations require Best Available Retrofit Technology (BART) for any BART-eligible 

source that ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any 

impairment of visibility” in any mandatory Class I federal area. Pursuant to federal regulations, states have the 

option of exempting a BART-eligible source from the BART requirements based on dispersion modeling 

demonstrating that the source cannot reasonably be anticipated to cause or contribute to visibility impairment 

in a Class I area. 

The ISG Georgetown Inc. Steel mill in Georgetown, SC (ISG Georgetown) has been identified as a BART 

eligible source. As noted above, a modeling demonstration can be used to show that a source identified as 

being eligible for BART requirements can be exempt if modeling shows the source does not cause or 

contribute to a visibility impairment as defined by the BART guideline (40 CFR Part 51, Appendix Y). 

The purpose of this document is to summarize the procedures by which a modeling analysis will be conducted 

for the ISG Georgetown mill, at which the following units have been identified by the South Carolina 

Department of Health and Environmental Control (DHEC) as BART eligible. 

• Meltshop - arc furnaces and lime handling systems 

• DRI production - DRI reformer, reduction furnace, and day bin 

• DRI production – passivation and conveying system 

• DRI production - drimetallized briquetting 

• DRI production - ore conveying and screening. 

Additional information regarding the start-up dates and potential emissions for these BART-eligible units is 

provided in Table 1-1. 

This exemption modeling analysis will determine whether the source is subject to BART requirements, and if 

so, describe the modeling procedure to be used for the visibility impacts factor in the BART determination step. 

The modeling procedures are consistent with those outlined in the final VISTAS common BART modeling 

protocol (updated on March 9, 2006), available at the link below: 

http://www.vistas-sesarm.org/BART/BARTModelingProtocol_rev2_9Mar2006.pdf and also attached as 

Appendix A. A further update to the VISTAS BART protocol is anticipated over the next several weeks. 

This protocol references certain relevant portions of the common VISTAS protocol. 

1.2 Location of source vs. relevant Class I Areas 

DHEC, which is in charge of the state’s BART program, has determined that units listed in Table 1-1 are 

BART-eligible for NOx, SO2 and PM. Figure 1-1 shows a plot of the Georgetown mill relative to nearby PSD 

Class I Areas. There are two Class I areas within 300 km of the mill: Cape Romain and Wolf Island 

Wilderness Areas. As shown in the figure, the distance to Cape Romain is about 30 km, while the distance to 

Wolf Island is about 290 km. The BART exemption and, if necessary, the determination modeling will be 

conducted for each of these Class I areas in accordance with the referenced VISTAS common BART 

modeling protocol and the procedures described in this source-specific BART modeling protocol. 

Appendix H.6 - 959
1-2 May 2006

1.3 Organization of protocol document 

Section 2 of this source-specific protocol describes the source emissions that will be used as input to the 

BART exemption modeling and, if necessary, the BART determination modeling. Section 3 describes the 

input data to be used for the modeling. Section 4 describes the air quality modeling procedures and Section 5 

discusses the presentation of modeling results. Since all of the references cited are also included in the 

VISTAS common BART modeling protocol, no additional references section is required in this document. 

Table 1-1-1 ISG Georgetown Inc., Georgetown, SC Mill – BART-eligible Units 

Allowable Emissions 

Source Description Start-up Date 

SO2 NOX PM10

 TPY TPY TPY 

Unit 1 Meltshop – arc furnaces 

and lime handling systems 

1969/1970/ 

1974 12.32 88.21 120.49 

Unit 2a DRI reformer day bin 1970 N/A N/A 9.72 

Unit 2b DRI reformer and 

reduction furnace 1970 14.84 339 5.38 

Unit 3 DRI passivation and 

conveying system 1974 N/A N/A 1.58 

Unit 4 DRI metallized briquetting 1974 N/A N/A 2.99 

Unit 5 DRI ore conveying and 

screening 1970 N/A N/A 1.98 

Note: For Unit 1, EAF #1 installed in 1969, EAF #3 installed in 1970, lime handling system installed 1974. 

Appendix H.6 - 960
1-3 May 2006

Figure 1-1 Location of PSD Class I Areas in relation to ISC Georgetown 

Appendix H.6 - 961
2-1 May 2006

2.0 Source description and emissions data 

2.1 Unit-specific source data 

The emissions data used to assess the visibility impacts at the Class I areas within 300 km of the subject plant 

is discussed in this section. The SO2, NOx and PM10 emissions were provided by ISG Georgetown and are 

representative of the highest 24-hour average actual emission rate for the years 2001 to 2003. Since various 

components of PM10 emissions have different visibility extinction efficiencies, the PM10 emissions are divided, 

or “speciated” into several components. We note that AP-42 does not contain speciation data or guidance for 

the BART eligibility source types. Therefore, reasonably conservative engineering judgments were made to 

develop this information, as described below. 

• Filterable PM10 emissions are assumed to be the same as reported PM10 emissions. 

• Fine PM10 is conservatively assumed to equal filterable PM10 based on control by baghouses. 

• Elemental carbon (EC) is assumed to be 5% of filterable fine PM10 based on engineering judgment 

and data for combustion sources, Table 6 of “Catalog of Global Emissions Inventories and Emission 

Inventory Tools for Black Carbon”, William Battye and Kathy Boyer, EPA Contract No. 68-D-98-046, 

January 2002. 

• SO4 emissions are assumed to be 5% of SO2 emissions times the ratio 96/64 to account for molecular 

weight. 

• Organic PM10 is assumed to be equal to VOC emissions. VOC emissions for Units 1 and 2 are based 

on the ratio of AP-42 emission factors (Draft Section 12.5.1) for VOC and filterable PM and the 

reported PM10 emissions. VOC emissions for Units 3, 4, and 5 are expected to be virtually zero. 

If the BART exemption modeling indicates that a BART determination is required, then ISG Georgetown would 

proceed with the BART determination using the five steps set forth in the final BART rule dated July 6, 2005. 

Tables 2-1 and 2-2 provide a summary of the baseline modeling emission parameters to be used in the BART 

CALPUFF modeling. 

Appendix H.6 - 962
2-2 

Table 2-1 ISG Georgetown Inc., Georgetown, SC Mill - Baseline BART Stack Parameters and Criteria Pollutant Emission

Location UTM 

(Zone 17 NAD-83) 

Maximum 

Short-Term Emissions 

Case Source / Unit UTM 

East 

UTM 

North 

Stack 

Ht 

Base 

Elev. 

Diameter Gas Exit Vel. Stack Gas Exit Temp. SO2 NOX PM10

 km km M ft m m/s deg K lb/hr lb/hr lb/hr 

Baseline Unit 1 659055 3693488 24.4 10 232.8 0.01 355.4 28.1 20.1 16.9 Baseline Unit 2a 659107 3693357 9.1 10 0.5 27.1 293.2 0 0 1.23 Baseline Unit 2b 659103 3693372 28.3 10 3.6 13.7 417.0 3.39 77.4 0 

Baseline Unit 3 659139 3693290 9.4 10 0.6 10.5 293.2 0 0 0.4 Baseline Unit 4 659109 3693366 8.5 10 0.5 12.6 293.2 0 0 0.7 Baseline Unit 5 No Emissions – Ore is Wet in this Process 

Notes:

• Emission parameters are provided for the baseline conditions of the units listed below. Emission 

parameters are not currently available for control cases as this information will be developed as part of the BART determination if required. 

• Unit1 = Meltshop - arc furnaces and lime handling systems • Unit 2a = DRI production - DRI reformer and reduction furnace • Unit 2b = DRI production - DRI reformer and day bin • Unit 3 = DRI production – passivation and conveying system • Unit 4 = DRI production - drimetallized briquetting • Unit 5 = DRI production - ore conveying and screening • Diameter listed for Unit 1 is an equivalent diameter. • Gas exit velocity listed for Unit 1 represents a non-vertical release. 

• Emissions reported are the highest 24-hour average actual emission rate for the years 2001 to 2003. 
2-3 

Table 2-2 ISG Georgetown Inc., Georgetown, SC Mill - Baseline BART Particulate Speciation 

Particle Speciation 

Fine Filterable PM Condensible PM10

Case Unit 

Total 

Filterable 

PM10

Course 

PM10

Total Soil EC Total Inorganic 

(SO4) 

Organic 

All Values in lb/hr 

Baseline Unit 1 16.9 0 16.9 16.1 0.85 11.84 2.11 9.73 Baseline Unit 2a 1.23 0 1.2 1.17 0.06 0.71 0 0.71 Baseline Unit 2b 0 0 0 0 0 0.25 0.25 0 Baseline Unit 3 0.4 0 0.4 0.38 0.02 0 0 0 Baseline Unit 4 0.7 0 0.7 0.67 0.04 0 0 0 Baseline Unit 5 No Emissions – Ore is Wet in this Process Notes:

• Unit1 = Meltshop - arc furnaces and lime handling systems • Unit 2a = DRI production - DRI reformer and reduction furnace • Unit 2b = DRI production - DRI reformer and day bin • Unit 3 = DRI production – passivation and conveying system • Unit 4 = DRI production - metallized briquetting • Unit 5 = DRI production - ore conveying and screening • Emissions reported are the highest 24-hour average actual emission rate for the years 2001 to 2003. 

• Filterable PM10 emissions are assumed to be the same as reported PM10 emissions. 

• Fine PM10 is conservatively assumed to equal filterable PM10 based on control by baghouses. 

• Elemental Carbon (EC) is assumed to be 5% of filterable fine PM10 based on engineering judgment and data for 

combustion sources, Table 6 of “Catalog of Global Emissions Inventories and Emission Inventory Tools for Black Carbon”, William Battye and Kathy Boyer, EPA Contract No. 68-D-98-046, January 2002. 

• SO4 emissions are assumed to be 5% of SO2 emissions times the ratio 96/64 to account for molecular weight 

• Organic PM10 is assumed to be equal to VOC emissions. VOC emissions for Units 1 and 2 are based on the ratio of 

AP-42 emission factors (Draft Section 12.5.1) for VOC and filterable PM and the reported PM10 emissions. VOC 

emissions for Units 3, 4, and 5 are expected to be zero. 
3-1 May 2006

3.0 Geophysical and meteorological input to CALMET 

3.1 Modeling domain 

The regional 12-km CALMET meteorological data was developed for three years (2001-2003) to cover all 

BART eligible sources and PSD Class I areas within the VISTAS states. The extent of the modeling domain is 

shown in Figure 4-2 of the VISTAS common BART modeling protocol. 

Five sub-regional 4-km CALMET meteorological databases were also developed so that a more refined 

analysis could be conducted if warranted. The sub-regional modeling domains are strategically designed to 

cover all potential BART eligible sources within VISTAS states and all PSD Class I areas within 300 km of 

those sources. The extents of the 4-km sub-regional domains are shown in Figure 4-4 of the VISTAS common 

BART modeling protocol. The selection of the meteorological database and the associated domain is 

discussed further in Section 3.3. 

3.2 Terrain and land use 

USGS 90-meter Digital Elevation Model (DEM) files were used by VISTAS to generate the terrain data at 12-

km and 4-km resolutions for input to the 12-km regional and 4-km sub-regional CALMET runs, respectively. 

Likewise, USGS 90-meter Composite Theme Grid (CTG) files were used by VISTAS to generate the land use 

data at 12-km and 4-km resolutions for input to the 12-km regional and 4-km sub-regional CALMET runs, 

respectively. 

3.3 Meteorological database 

An initial 12-km regional meteorological grid was developed by VISTAS with CALMET such that any Class I 

areas within VISTAS states and nearby areas could be evaluated with a consistent database. The associated 

CALMET output files have been obtained from VISTAS so that the initial screening exemption modeling can 

be performed. 

Three years of MM5 data (2001-2003) were used by VISTAS to develop the 12-km regional meteorological 

data that is eventually to be the basis for the regional CALPUFF BART modeling studies. These same MM5 

datasets are also used to generate the 4-km sub-regional meteorological datasets. The three years of MM5 

data are as follows (as recommended in the VISTAS protocol): 

• 2001 MM5 dataset at 12 km and 36 km resolution (developed for EPA) 

• 2002 MM5 dataset at 12 km and 36 km resolution (developed by VISTAS) 

• 2003 MM5 dataset at 36 km resolution (developed by the Midwest Regional Planning Organization) 

Figures 4-2 and 4-4 of the VISTAS common BART modeling protocol show the extent of the 12-km and 36-km 

MM5 used to generate the VISTAS CALMET data. 

3.3.1 12-km regional CALMET domain 

The development of the regional 12-km CALMET grid used the MM5 data as the sole source meteorological 

input. In each given year, the highest possible resolution MM5 data was used as input to CALMET (i.e. 12-km 

for 2001 and 2002, and 36-km for 2003). CALMET was run in what in known as “No-Obs” mode, meaning that 

there were no additional meteorological observations used to derive the CALMET wind field beyond that which 

were used in the MM5 simulations. Typically, CALMET wind fields are derived using the MM5 data as the 

Appendix H.6 - 965
3-2 May 2006

initial (“Step 1”) wind field and then observations (i.e., National Weather Service Airport hourly surface data 

and 12-hourly upper air data) are introduced in a “Step 2” to adjust the initial wind field to be in agreement with 

actual observations. However, since these observations are already incorporated into the actual MM5 

simulations (although they are part of a model output), the MM5 data should arguably represent these 

localized wind flow patterns, especially at a 12-km grid resolution. The Step 2 data was omitted in this case to 

provide the database as soon as possible for initial VISTAS CALPUFF modeling. The development of the 12-

km regional CALMET databases is discussed in more detail in the Section 4 of the VISTAS protocol. 

However, due to the omission of the Step 2 process, VISTAS has determined that use of this database is a 

screening step, so that the peak predicted source impact is to be compared to the 0.5 deciview threshold for a 

contribution to visibility impairment at a PSD Class I area. 

3.3.2 4-km sub-regional CALMET domain and finer grid resolutions 

In many instances, the use of a grid resolution finer than 12-km is advisable for assessing impacts at a Class I 

area, especially for Cape Romain, which is about 30 km from the facility. VISTAS is providing five subregional CALMET databases at 4-km resolution. These sub-regional 4-km CALMET modeling domains were 

strategically developed to cover all potential BART eligible sources within VISTAS states and all PSD Class I 

areas within 300 km of those sources. Figure 4-4 of the VISTAS common BART modeling protocol shows the 

locations of these sub-regional CALMET domains with respect to all Class I areas within VISTAS states. 

The sub-regional CALMET databases were developed using the same MM5 databases as used in the 

development of the 12-km regional CALMET database. However, in this instance, due to the finer grid 

resolution, additional observations of hourly surface meteorological data, precipitation data, and twice daily 

upper air soundings were incorporated into the development of the Step 2 wind field. Additionally, for those 

domains that extended over water, over water data (from buoys and shoreline meteorological CMAN stations) 

were also included. At a grid resolution of 4-km and smaller, localized wind flow patterns from actual 

observations sites become much more important in defining the wind field. Domain-specific CALMET settings 

used for each sub-regional CALMET run are to be provided by VISTAS. 

The VISTAS protocol also indicates that even finer grid resolution runs may be appropriate in areas of complex 

terrain or other complex areas such as shoreline environments. If a source is located within 50-100 km from a 

Class I area, the intervening terrain often plays an important role in depicting the plumes’ transport from the 

source to the Class I area. In such cases, a grid resolution less than 4 km is sometimes needed to accurately 

resolve the intervening terrain. In such a case, the VISTAS protocol states that if a source elects to use an 

even finer grid resolution that it should be considered on a case-by-case basis by the reviewing state agency. 

In this case, the South Carolina DHEC indicates that the VISTAS modeling contractor will conduct BART 

modeling for ISG’s Georgetown, SC mill since its Q/d is less than 10. Since the modeling for Cape Romain will 

be done with a 4-km modeling grid and, for convenience, the same grid will likely be used by the VISTAS 

modeling contractor for Wolf Island. If the results indicate that peak concentrations could be better predicted 

with a CALPUFF run using a finer grid, due to terrain or other factors, then we will consider a refinement in the 

modeling procedures and ask the state agency to accept this refinement. 

In the event that ISG Georgetown elects to use a finer grid resolution, CALMET must be rerun. Other 

modifications to inputs of CALMET would include the extent of the modeling domain, the resolution of the 

terrain and land use data, and possibly certain settings that affect the weighting factors of the Step 1 and Step 

2 wind field. The same MM5 data and observations as used for the 4-km sub-regional CALMET simulations 

would be used. The extent of the modeling domain would need to be changed because of disk space 

restrictions, especially if only Cape Romain were the sole problematic Class I area. The size of the CALMET 

output is directly proportional to the grid resolution of the run. The domain would be limited to the source and 

the exclusive Class I area(s) being assessed with a higher grid resolution, including a 50-km buffer in all 

directions. 

Appendix H.6 - 966
3-3 May 2006

If CALMET needs to be run at even a finer grid resolution, then the appropriate model setting/files (specifically 

the GEO.DAT file) will be modified. A summary of these modifications will be provided to the reviewing state 

agency for review for approval. Section 4 of the VISTAS protocol discusses in more detail the rationale and 

process for using a finer grid resolution CALMET domain. 

3.4 Air quality database (background ozone and ammonia) 

Hourly measurements of ozone from all non-urban monitors, as generated by VISTAS and available on the 

VISTAS CALPUFF page on the Earth Tech web site (http://www.src.com/verio/download/sample_files.htm), 

will be used as input to CALPUFF. VISTAS intends to provide monthly averaged gaseous ammonia 

concentrations to be used for the ammonia limiting method (ALM) for the 4-km runs. For any 12-km runs, a 

constant gaseous ammonia concentration of 0.5 ppb would be used without application of the ALM technique. 

3.5 Natural conditions and monthly f(RH) at Class I Areas 

For each of the Class I areas shown in Figure 1-1, natural background conditions must be established in order 

to determine a change in natural conditions related to a source’s emissions. EPA’s “Guidance for Estimating 

Natural Visibility Conditions Under the Regional Haze Rule," September 2003, Appendix B, indicates the 

deciviews associated with the best 20% of days in each Class I area should be used for background. To 

determine the input to CALPUFF, it is first necessary to convert the deciviews to extinction using the equation: 

Extinction (Mm-1) = 10 exp(deciviews/10). 

The BART rule indicates in the preamble that the deciviews associated with the best 20% of day is to be used, 

while the text of the rule indicates that the average day’s deciviews should be used. The U.S. EPA has 

proposed a litigation settlement for this issue that allows each state (or BART source) to determine which 

background value to use. ISG Georgetown Inc. elects to use the average natural background value if the 

BART source is provided with the choice. 

Appendix H.6 - 967
4-1 May 2006

4.0 Air quality modeling procedures 

This section provides a summary of the modeling procedures outlined in the VISTAS protocol that will be used 

for the refined CALPUFF analysis to be conducted for ISG’s Georgetown, SC mill. 

4.1 Model selection and features 

As noted in the VISTAS protocol, VISTAS will use CALPUFF Version 5.754 and CALMET as provided on the 

Earth Tech web site, which can be obtained at 

http://www.src.com/verio/download/download.htm#VISTAS_VERSION. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were developed by 

Earth Tech, Inc. and they are maintained on Earth Tech’s Atmospheric Studies Group CALPUFF website for 

public access. This release includes CALMET, CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as 

CALVIEW. 

The major features of the CALPUFF modeling system, including those of CALMET and the post processors 

(CALPOST and POSTUTIL) are referenced in Section 3 of the VISTAS protocol. 

4.2 Modeling domain and receptors 

Due to the relatively small distance between the facility and the closest Class I areas, the initial BART runs 

conducted by the VISTAS contractor will use the subdomain 4-km CALMET data to be supplied by VISTAS, 

as discussed above. This domain includes all Class I areas within 300 km of the source, plus a 50-km buffer. 

If we see the need for a refined analysis with a finer grid, we will provide a supplement to this modeling 

protocol with the proposed procedures. 

The receptors used for each of the Class I areas are based on the NPS database of Class I receptors, as 

recommended by VISTAS. 

4.3 Technical options used in the modeling 

CALMET modeling for the VISTAS-provided 4-km subdomain has been completed by the VISTAS contractor, 

and, therefore, we assume that VISTAS approves of the manner in which CALMET has been run for the 

subdomain data that they provide. If Mittal Steel decides to conduct additional modeling with a finer grid than 

4 km, this modeling protocol will be updated to specify the technical options used in the CALMET run, and 

state agency approval of the modified protocol will be required. 

For CALPUFF model options, ISG Georgetown will follow the VISTAS common BART modeling protocol 

recommendations. The VISTAS protocol also notes that building downwash effects are not required to be 

included unless the state directs the source to include these effects. Since Mittal Steel is about 30 km from the 

nearest Class I area, South Carolina DHEC has determined that building downwash effects do not need to be 

included in the CALPUFF modeling. 

The POSTUTIL utility program will be used to repartition HNO3 and NO3 using VISTAS-provided ammonia 

concentrations derived from previous 2002 CMAQ modeling conducted by EPA. For any 12-km grid screening 

modeling, VISTAS would use a default ammonia background concentration of 0.5 ppb. For refined modeling 

with a 4-km grid, Class I-specific monthly ammonia concentration values will be used as supplied by VISTAS. 

Appendix H.6 - 968
4-2 May 2006

4.4 Light extinction and haze impact calculations 

The CALPOST postprocessor will be used as prescribed in the VISTAS protocol for the calculation of the 

impact from the modeled source’s primary and secondary particulate matter concentrations on light extinction. 

The formula that is used is the existing (not the November 2005 revised) IMPROVE/EPA formula, which is 

applied to determine a change in light extinction due to increases in the particulate matter component 

concentrations. Using the notation of CALPOST, the formula is the following: 

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] + 0.6[Coarse Mass] + 10[EC] + bRay

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh scattering term 

(bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for tracking reasonable progress. 

However, as recommended in the VISTAS protocol, for refined 4-km grid (or smaller) CALPUFF runs if 

necessary, the Rayleigh scattering term will be modified for the specific elevation of the Class I area receptors. 

This will result in a relative reduction in the source impact for sea level locations such as Georgetown, SC. 

The assessment of visibility impacts at the Class I areas will use CALPOST Method 6. The current VISTAS 

protocol indicates that the natural background is specified as that for the 20% best days for each Class I area, 

as described in the previous section. Due to an anticipated EPA settlement of litigation regarding this issue 

(as discussed above), the VISTAS protocol is likely to be revised to allow each state to select either the annual 

average or best 20% days’ natural background. Each hour’s source-caused extinction is calculated by first 

using the hygroscopic components of the source-caused concentrations, due to ammonium sulfate and nitrate, 

and monthly Class I area-specific f(RH) values. The contribution to the total source-caused extinction from 

ammonium sulfate and nitrate is then added to the other, non-hygroscopic components of the particulate 

concentration (from coarse and fine soil, secondary organic aerosols, and from elemental carbon) to yield the 

total hourly source-caused extinction. 

The BART rule significance threshold for the contribution to visibility impairment is 0.5 deciviews. The VISTAS 

protocol indicates that with the use of the 4-km sub-regional CALMET database, a source does not cause or 

contribute to visibility impairment if the 98th percentile (or 8th highest) day’s change in extinction from natural 

conditions does not exceed 0.5 deciviews for any of the modeled years. As an added check, the 22nd highest 

prediction over the three years modeled should also not exceed 0.5 deciviews for a source to be exempted 

from a BART determination. 

Figure 4-1 of the VISTAS common BART modeling protocol presents a flow chart showing the components of 

that modeling protocol for the analysis to determine whether a source is subject to BART. 

If the exemption modeling demonstrates that ISG’s Georgetown, SC mill does not cause or contribute to 

visibility impairment, then the source will not be subject to BART requirements, and no further analysis is 

needed. 

Appendix H.6 - 969
5-1 May 2006

5.0 Presentation of modeling results 

The BART exemption and, if necessary, the BART determination modeling results for ISG’s Georgetown, SC 

mill will be provided to the state agency in a manner as described in the VISTAS protocol. A VISTAS 

contractor is conducting the initial BART exemption modeling, most likely with a 4-km grid, and will provide a 

BART modeling report. If more refined modeling is required, Mittal Steel will have ENSR work with the South 

Carolina DHEC to provide the appropriate modeling submittals. 

The BART modeling report will include the following elements (as suggested in the VISTAS protocol): 

1. A map of the source location and Class I areas within 300 km of the source. 

2. For the CALPUFF modeling domain, a table listing all Class I areas in the VISTAS domain and those in 

neighboring states and impacts from the BART 4-km grid exemption modeling at those Class I areas 

within 300 km of the source, as illustrated in Table 4-3 of the VISTAS protocol. 

3. A discussion of the number of Class I areas with visibility impairment due to source emissions for the 98th

percentile days in each year (and the 98th percentile over all three years modeled) greater than 0.5 

deciviews. 

4. For the Class I area with the maximum impact, a discussion of the number of days beyond those 

excluded (e.g., the 98th percentile for refined analyses) that the impact of the source exceeds 0.5 

deciviews, the number of receptors in the Class I area where the impact exceeds 0.5 deciviews, and the 

maximum impact. 

5. For any finer grid CALPUFF exemption modeling, results for those Class I areas for which impacts of the 

source exceeded 0.5 deciviews in the 4-km initial modeling. We would report the same type of results as 

provided for 4-km exemption modeling. 

For BART determination modeling, if necessary, the objective is to compare the visibility results among control 

options modeled and also with the baseline case. The same statistics as those mentioned above in Steps 3 

and 4 would be provided, and a summary of the relative results among all emission scenarios run would be 

produced. 

Additionally, the appropriate electronic files used to conduct the CALPUFF modeling will be provided to the 

South Carolina DHEC by the VISTAS modeling contractor on CD-ROM or DVD media. 

Appendix H.6 - 970
A-1 May 2006

Appendix A: VISTAS BART Modeling Protocol 

(March 9, 2006 update) 

Appendix H.6 - 971
Protocol for the Application of the 

CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART) 

December 22, 2005

(Revision 2 – 3/9/06)

Visibility Improvement State and Tribal Association 

of the Southeast (VISTAS)

Appendix H.6 - 972
Table of Contents iv

TABLE OF CONTENTS

Page

SUMMARY S-1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 1

1.1 Background 1

1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING 4

2.1 Overview of the Regional Haze BART Process 4

2.2 Model Recommendations for the BART Analysis 6

2.3 Performance of a Cap and Trade Program 7

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM 8

3.1 Capabilities and features of CALPUFF 8

3.1.1 Major Features of CALMET 10

3.1.2 Major Features of CALPUFF 12

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 16

3.2 Discussion of CALPUFF Applicability and Limitations 17

3.2.1 Transport and Diffusion 17

3.2.2 Aerosol Constituents 20

3.2.3 Regional Haze 28

4. VISTAS’ COMMON MODELING PROTOCOL 32

4.1 Overview of Common Modeling Approach 32

4.1.1 BART Exemption Analysis 32

4.1.2 BART Control Evaluation 34

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM 34

4.2 Optional Source-Specific Modeling 35

4.3 Initial Procedure for BART Exemption 36

4.3.1 Overview of Initial Approach 36

4.3.2 Discussion of Regional Initial Modeling Approach 36

4.3.3 Model Configuration and Settings for Initial Analysis 39

4.4 Finer Grid Modeling Procedures 43

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach 43

4.4.2 Model Configuration and Settings for Finer Grid Modeling 44

4.5 Presentation of Modeling Results 46

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources 51

5. SOURCE-SPECIFIC MODELING PROTOCOL 52

Appendix H.6 - 973
Table of Contents iv

6. QUALITY ASSURANCE 55

6.1 Scope and Purpose of the QA program 55

6.2 QA Procedures for Common Protocol Modeling 56

6.2.1 Quality Control of Input Data 56

6.2.2 Quality Control of Application of CALMET 57

6.2.3 Quality Control of Application of CALPUFF 58

6.2.4 Quality Control of Application of CALPOST and POSTUTIL 59

6.3 Additional QA Issues for Alternative Source-Specific Modeling 60

6.4 Assessment of Uncertainty in Modeling Results 61

7. REFERENCES 62

Appendix H.6 - 974
Summary S-1

SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available 

Retrofit Technology (BART) for the VISTAS Regional Planning Organization (RPO) describes 

common procedures for carrying out air quality modeling to support BART determinations that 

are consistent with guidelines of the U.S. Environmental Protection Agency in 40 CFR Part 51 

Appendix W and Appendix Y. The Protocol is intended to serve as the basis for a common 

understanding among the organizations that will be performing BART analyses or reviewing the 

BART modeling results in the VISTAS region.

Background

Best Available Retrofit Technology is required for any BART-eligible source that ‘‘emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “You 

can use dispersion modeling to determine that an individual source cannot reasonably be 

anticipated to cause or contribute to visibility impairment in a Class I area and thus is not subject 

to BART.” In the “individual source attribution approach,” a BART-eligible source that is 

responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment. 

A BART-eligible source that is responsible for a 0.5 dv change or more is considered to 

“contribute” to visibility impairment in a Class I area. Any source determined to cause or 

contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol 

to guide them, their sources, and reviewers in the BART determination and review effort. The 

Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat 

Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The 

VISTAS state BART contacts, particularly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon, 

AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol 

was enhanced and completed with the assistance of Joseph Scire, Christelle Escoffier-Czaja and 

Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from 

the VISTAS federal partners: Federal Land Managers and USEPA. The VISTAS RPO held a 

meeting on September 21, 2005 in Research Triangle Park, NC to discuss the Protocol with 

participants before starting a public comment period. The Protocol underwent formal external 

review during the period between September 26, 2005 and October 31, 2005. Numerous 

comments were received. All comments were carefully considered and discussed with VISTAS 

participants and federal partners. VISTAS gratefully acknowledges the very useful contributions 

of those that provided comments. On November 1st, 2005 VISTAS held another meeting with its 

participants in Nashville, TN to present and discuss the comments being considered for inclusion 

in the Protocol. No formal document will be prepared to address all the comments received on 

the Protocol. 

Appendix H.6 - 975
Summary S-2

Objectives

The objectives of the Protocol (discussed in Chapter 1) are to provide: 

 A consistent approach to determine if a source is subject to BART

 A consistent model (CALPUFF) and modeling guidelines for BART determinations

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site-specific modeling

 Common expectations for reporting model results

The Protocol is not intended to define the engineering analyses required by the USEPA BART 

Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading. 

Chapter 2 is intended to provide summary background on EPA’s guidance for BART modeling. 

The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for 

applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the 

specific information that should be included in site-specific protocols. Chapter 6 describes the 

quality assurance requirements for BART analyses in the VISTAS RPO.

Recommendations

The major recommendations for VISTAS BART modeling included in this Protocol are:

I. Process

 Follow the BART process steps discussed in Chapter 2:

1. Identify BART eligible sources

2. Identify which pollutants have greater than de minimis emission levels

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasible controls and emission changes

6. Identify the change in visibility impact for each candidate BART control option

7. Compare the visibility improvement of BART control options to other statutory factors in 

the engineering analysis

Appendix H.6 - 976
Summary S-3

II. CALPUFF Model Configuration

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a 

single source is subject to BART. VISTAS will use CALPUFF Version 5.754 and CALMET 

Version 5.7. These versions contain enhancements funded by the Minerals Management Service 

(MMS) and VISTAS. They were developed by Earth Tech, Inc. and they are maintained on Earth 

Tech’s Atmospheric Studies Group CALPUFF website (www.src.com) for public access. 

VISTAS is making publicly available 12-km CALMET output files for the entire VISTAS 

modeling domain (eastern United States) and intends to also provide CALMET output files for 

five 4-km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the 

CALMET input files, Earth Tech used the MM5 databases developed by EPA for 2001, VISTAS 

for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire 

VISTAS region, Earth Tech used the No-Obs setting (i.e., did not include additional surface and 

upper air observations beyond those incorporated in the MM5 calculations). For finer resolution 

subdomains (4 km grid or less), available surface and upper air observations will be used in 

addition to MM5 meteorological model outputs. The specific model settings will be provided 

with the CALMET files and via the CALPUFF website so that users can review or replicate the 

work. 

For CALPUFF modeling, source emissions should be defined using the maximum 24-hour actual 

emission rate during normal operation for the most recent 3 or 5 years. If maximum 24-hr actual 

emissions are not available, continuous emissions data, permit allowable emissions, potential 

emissions, and emissions factors from AP-42 source profiles may be used as available. 

Key points from comments received on the specific CALPUFF, CALPOST, and POSTUTIL 

configurations are highlighted below. 

• Use CMAQ modeling data from 2001-2003 to determine background concentrations of 

SO4 and total NO3 (HNO3 + NO3). CMAQ data in CALPUFF-ready format will be 

provided for each Class I area by VISTAS. After running CALPUFF for an individual 

facility, repartition NO3 in POSTUTIL using the CMAQ background data, including that 

for NH3.

• Use ozone data from non-urban monitors as the background ozone input. 

• Use the Pasquill-Gifford dispersion method.1

 

1 The Final VISTAS BART Modeling Protocol (Dec. 22, 2005) recommended using turbulence-based AERMOD 

dispersion methods, citing EPA’s Revision to the Guideline on Air Quality Models: Adoption of a Preferred General 

Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 

November 2005. Subsequently, EPA Region IV notified the VISTAS states that using turbulence-based dispersion 

methods would be considered a non-guideline application of CALPUFF. Thus this Protocol has been revised to 

indicate Pasquill-Gifford dispersion coefficients should be used. 

Appendix H.6 - 977
Summary S-4

• In CALPOST, use Method 6 with monthly average RH for calculating extinction, as 

recommended by the EPA. 

• Use EPA default calculations of light extinction under current and natural background 

conditions. In addition to the default assumptions, a source may choose to also calculate 

visibility using the recently revised IMPROVE algorithm described by Pitchford, et al., 

(2005). 

Provide results in tables as illustrated in Chapter 4 that describe, for each source:

• Number of receptors within a single Class I area with impact > 0.5 dv

• Number of days at all receptors in the Class I area with impact > 0.5 dv

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART 

For determining if a BART-eligible source is subject to BART CALPUFF modeling, use a twotier approach. For the initial exemption modeling use CALPUFF with 12-km grid CALMET. For 

finer resolution of meteorological fields, use CALPUFF with CALMET of 4-km or smaller grid 

size. 

VISTAS States are accepting EPA guidance that the threshold value to establish that a source 

contributes to visibility impairment is 0.5 deciview. 

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I area 

boundary (Q/d) as a presumptive indicator that a BART-eligible source is subject to BART. If 

Q/d for SO2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the 

source is subject to BART and no exemption modeling will be performed using VISTAS funds. If 

the source agrees with this presumption, then the source can proceed to the BART determination 

using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a 

source disagrees, the source may perform fine grid modeling to determine if its impact is <0.5 dv. 

For sources with Q/d less than or equal to 10, VISTAS intends to fund Earth Tech to assist States 

with the initial CALPUFF exemption modeling. Each State will prioritize which sources will be 

offered modeling by VISTAS. Modeling of these sources will be conducted in priority order to 

first accommodate States with nearer term timing constraints in their SIP development process. 

To conserve VISTAS resources, modeling will begin with sources at lower Q/d values and 

continue with sources with higher Q/d values until a Q/d value that consistently results in a 

greater than 0.5 dv impact is identified. Chapter 4 addresses the number of VISTAS sources 

eligible for BART based on Q/d analysis. 

Appendix H.6 - 978
Summary S-5

Note that VISTAS does not propose to use Q/d to exempt BART-eligible sources, but only to 

prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance 

not to use Q/d for exemption purposes. 

For the 12-km initial modeling exemption test, compare the highest single 24-hour average value 

across all receptors in the Class I area to the threshold value of 0.5 dv. If the highest 24-hr 

average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. If the 

highest 24-hr average value is greater than 0.5 dv, then the source may choose to perform finer 

grid modeling for exemption purposes or may accept determination that the source is subject to 

BART and proceed to establish visibility impacts prior to and after BART controls. If using the 

single highest 24-hr average value proves, after initial 12-km grid CALPUFF modeling, to be too 

conservative a screening level, VISTAS may allow some exceedances of the threshold value for 

exemption purposes, up to no more than the 98th percentile value. 

The 12-km modeling results can be used to focus finer grid modeling for exemption purposes on 

only those Class I areas where impacts greater than 0.5 dv were projected in the 12-km modeling.

For finer grid (4 km or less) analyses, use the 98th percentile impact value for the 24-hr average. 

Use either the 8th highest day in each year or the 22nd highest day in the 3-year period, whichever 

is more conservative, for comparison to the exemption threshold.

Use the same model assumptions for pre-BART visibility impact and for BART control options 

modeling: establish baseline visibility from the pre-BART run; change one control at a time; and 

evaluate the change in visibility impact, i.e. the delta-deciview. Note that “no control” may 

constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under 

the USEPA BART guideline. If a source accepts to institute the most stringent control, the 

engineering analyses are not required.

This common VISTAS Protocol consistently recommends conservative assumptions. Individual 

States ultimately have responsibility to determine which, if any, BART controls are 

recommended in their State Implementation Plans (SIPs).

Appendix H.6 - 979
Introduction and Protocol Objectives 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 

1.1 Background

Under regional haze regulations, the Environmental Protection Agency (EPA) has issued final 

guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70 

FR 39104-39172). The regional haze rule includes a requirement for BART for certain large 

stationary sources. Sources are BART-eligible if they meet three criteria including potential 

emissions of at least 250 tons per year of a visibility-impairing pollutant, were put in place 

between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories 

in the guidance. A BART engineering evaluation using five statutory factors -- 1) existing 

controls; 2) cost; 3) energy and non-air environmental impacts; 4) remaining useful life of the 

source; 5) degree of visibility improvement expected from the application of controls -- is 

required for any BART-eligible source that can be reasonably expected to cause or contribute to 

impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas protected 

under the regional haze rule. (Note that, depending on the five factors, the evaluation may result 

in no control.) Air quality modeling is an important tool available to the States to determine 

whether a source can be reasonably expected to contribute to visibility impairment in a Class I 

area.

Throughout this document the term “BART-eligible emission unit” is defined as any single 

emission unit that meets the criteria described above. A “BART-eligible source” is defined as the 

total of all BART-eligible emission units at a single facility. If a source has several emission 

units, only those that meet the BART-eligible criteria are included in the definition “BARTeligible source”. 

One of the listed categories is steam electric plants of more than 250 million BTU/hr heat input. 

To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially 

subject to BART, the boiler capacities of all electric generating units (EGUs) should be added 

together regardless of construction date. In this category, electric generating sources greater than 

750 MW have presumptive SO2 and NOx emission limits. States may presume the same limits for 

EGU sources between 250-750 MW. However, units at those sources constructed after the 

BART-eligibility dates are not subject to a BART engineering evaluation. EPA, in the Clean Air 

Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program 

satisfies the BART requirements for SO2 and NOx. VISTAS states are tentatively accepting this 

guidance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM 

emissions are above de minimis values.

As illustrated in Table 1-1, as of December 5, 2005, VISTAS States had identified a total of 274 

BART-eligible sources that fall into 20 of the 26 BART source categories. Of the 274 sources 

with BART-eligible units, 84 sources are utility EGUs and 190 are non-EGU industrial sources. 

(Note that these numbers are not final and are subject to slight adjustments and refinements.) No 

BART sources are located on Tribal lands.

Appendix H.6 - 980
Introduction and Protocol Objectives 2

Table 1-1. VISTAS BART Eligible Sources 

State Total Number 

of Sources

EGU Sources Non-EGU 

Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 31 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19

Total 273 84 189

1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for 

air quality modeling to support BART determinations that are consistent with the EPA guidelines. 

The protocol will serve as the basis for establishing a common understanding among the 

organizations who will be performing the BART analyses or reviewing the BART modeling 

results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers 

(FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA 

final guidance and comments that were received on VISTAS’ draft protocol2 and provides 

additional description of modeling procedures. 

The VISTAS States have accepted EPA’s guidance to use the CALPUFF modeling system to 

comply with the BART modeling requirements of the regional haze rule. A BART-eligible 

source will be required to submit a site-specific modeling protocol to the State for review and 

approval prior to performing CALPUFF modeling. States will consult with FLMs and the EPA 

when evaluating the site-specific BART protocols. The site-specific protocol will include the 

source-specific data on source location, stack parameters, and emissions. The methods of the 

 

2 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit 

Technology (BART). VISTAS, March 22, 2005 and September 20, 2005.

Appendix H.6 - 981
Introduction and Protocol Objectives 3

VISTAS common modeling protocol will be followed in the site-specific protocol unless the 

source proposes to the State, and the State approves, alternative methods or assumptions. 

Each VISTAS State or Local agency retains responsibility for the specific procedures and 

processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA, 

and public to determine BART controls for sources in the State. Nothing in the VISTAS process 

replaces States’ responsibility to determine BART controls. 

The remainder of this document describes the CALPUFF modeling system and the application of 

CALPUFF to two situations:

• Air quality modeling to determine whether a BART-eligible source is “subject to BART” 

and therefore the BART analysis process must be applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to 

BART, to evaluate regional haze benefits of alternative control options and to document 

the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s 

guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent 

chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s 

guidance for regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal 

Register notice. The CALPUFF model is the preferred model recommended by the EPA for 

BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The 

specific steps to determine whether a BART-eligible source is subject to BART and to evaluate 

BART controls are described in Chapter 4. The procedures include initial modeling of BARTeligible sources using CALPUFF run in a conservative mode with regional meteorological 

datasets. For sources determined to be subject to BART based on these first modeling analyses, 

further finer grid CALPUFF analyses would be performed. The model configuration for the 

common modeling protocol is described in Chapter 4. Details of the source-specific protocol are 

described in Chapter 5. A quality assurance plan is outlined in Chapter 6. 

EPA’s guidance allows for the use of appropriate alternative models, however VISTAS will not 

develop a protocol for alternative models. This protocol focuses on guidance for the application 

of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in 

its BART demonstration, the source will need to submit a detailed written justification to the 

State for review and approval. The State will provide the documentation to the EPA and Federal 

Land Managers for their review. 

Also, this protocol does not address a preferred modeling approach to demonstrate the 

effectiveness of an optional emissions cap and trade program. Such a cap and trade program is 

not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTAS 

States are not pursuing a regional trading alternative under the proposed EPA trading guidance 

(70 FR 44154-44175) that is to be promulgated early in 2006.

Appendix H.6 - 982
Review of EPA’s Guidance 4

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING

The final guidance for regional haze BART determinations was published in the Federal Register 

on 6 July 2005 (70 FR 39104 to 39172). It prescribes the modeling approaches that are to be used 

for various stages of the BART analysis process. 

This chapter provides a summary of EPA’s guidance for BART modeling. It is not intended to 

provide a comprehensive review of the guidance. Nor does this chapter address specific 

recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations 

appear in Chapter 4. 

2.1 Overview of the Regional Haze BART Process

The process of establishing BART emission limitations consists of four steps:

1) Identify whether a source is “BART-eligible” based on its source category, when it was put in 

service, and the magnitude of its emissions of one or more “visibility-impairing” air pollutants. The 

BART guidelines list 26 source categories of stationary sources that are BART-eligible. Sources 

must have been put in service between August 7, 1962 and August 7, 1977 in order to be BARTeligible. Finally, a source is eligible for BART if potential emissions of visibility-impairing air 

pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate 

matter (PM10) and gaseous precursors to secondary fine particulate matter, such as SO2 and NOx. 

Whether ammonia or volatile organic compounds (VOCs) should be included as visibilityimpairing pollutants for BART eligibility is left for the States to determine on a case-by-case basis. 

The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor 

pressure should be considered as primary PM2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential 

emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not 

subject to BART if their potential emissions are less than the de minimis levels (40 TPY for SO2

and NOx and 15 TPY of PM10 or PM2.5. This assumes that the other BART-eligibility criteria are 

met.)

2) Determine whether a BART-eligible source can be excluded from BART controls by 

demonstrating that the source cannot be reasonably expected to cause or contribute to visibility 

impairment in a Class I area. The preferred approach is an assessment with an air quality model 

such as CALPUFF or other appropriate model followed by comparison of the estimated 24-hr 

visibility impacts against a threshold above estimated natural conditions to be determined by the 

States.3 The threshold to determine whether a single source “causes” visibility impairment is set at 

 

3 The level of the natural conditions baseline that is to be used is described differently in the BART guideline and in 

the preamble to the BART rule. The BART guideline text says “natural conditions” at 70 FR 39162, col. 3, while 

the preamble says “natural visibility baseline for the 20% best visibility days” at 70 FR 39125, col. 1. Clarification 

received from Todd Hawes, US EPA, is that the intent was the 20% best days.

Appendix H.6 - 983
Review of EPA’s Guidance 5

1.0 deciview change from natural conditions over a 24-hour averaging period in the final BART 

rule (70 FR 39118). The guidance also states that the proposed threshold at which a source may 

“contribute” to visibility impairment should not be higher than 0.5 deciviews although, depending 

on factors affecting a specific Class I area, it may be set lower than 0.5 deciviews. The test against 

the threshold is “driven” by the contribution level, since if a source “causes”, by definition it 

“contributes”. 

EPA recommends that the 98th percentile value from the modeling be compared to the contribution 

threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not 

contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98th

percentile value exceeds the threshold must be determined at each Class I area. Over an annual 

period, this implies the 8th highest 24-hr value at a particular Class I area is compared to the 

contribution threshold. Over a 3-year modeling period, the 98th percentile value may be interpreted 

as the highest of the three annual 98th percentile values at a particular Class I area or the 22nd highest 

value in the combined three year record, whichever is more conservative.

Alternatively, States have the option of considering that all BART-eligible sources within the 

State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be 

able to do exactly the opposite, and use regional modeling to conclude that all BART-eligible 

sources in the State do not cumulatively contribute to “measurable” visibility impairment in any 

Class I areas. Also, the States have an option to exempt individual sources based on model plant 

analysis conducted by EPA in finalizing the BART rule. Under this option, sources with 

potential emissions of SO2 plus NOx of less than 500 tons and a distance from any Class I area 

greater than 50 kilometers or sources with SO2 plus NOx potential emissions of less than 1000 

tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM 

emissions are not specifically addressed in the model plant analysis, but subsequent discussions 

with EPA staff indicate that PM may be considered along with SO2 and NOx, so that a plant could 

be exempted if the combined potential emissions of SO2, NOx, plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting 

the “best” alternative, taking into consideration:

a) Any pollution control equipment in use at the source (which affects the availability of 

options and their impacts),

b) The costs of compliance with control options,

c) The remaining useful life of the facility, 

d) The energy and non air-quality environmental impacts of compliance, and

e) The degree of improvement in visibility that may reasonably be anticipated to result 

from the use of such technology.

Appendix H.6 - 984
Review of EPA’s Guidance 6

Note that if a source agrees to apply the most stringent controls available to BART-eligible units, 

the BART analysis is essentially complete and no further analysis is necessary (70 FR 39165).

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze, 

which is due by December 2007.

Instead of applying BART on a source-by-source basis, a State (or a group of States) has the option 

of implementing an emissions trading program that is designed to achieve regional haze 

improvements that are greater than the visibility improvements that could be expected from BART. 

If the geographic distributions of emissions under the two approaches are similar, determining 

whether trading is “better than BART” may be possible by simply comparing emissions expected 

under the trading program against the emissions that could be expected if BART was applied to 

eligible sources. If the geographic distributions of emissions are likely to be different, however, air 

quality modeling comparing the expected improvements in visibility from the trading program and 

from BART would be required. (See the proposed BART Alternative rule, at 70 FR 44160.) EPA 

suggests that regional modeling using a photochemical grid model may be more appropriate than 

CALPUFF for this purpose.

Note that EPA has indicated in the BART rule (70 FR 39138-39139) that emissions reductions 

under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO2 and NOx control 

for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR 

and therefore a BART analysis may still be required for PM. 

2.2 Model Recommendations for the BART Analysis 

To evaluate the visibility impacts of a BART-eligible source at Class I areas beyond 50 km from the 

source, the EPA guidance recommends the use of the CALPUFF model as “the best regulatory 

modeling application currently available for predicting a single source’s contribution to visibility 

impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA 

prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to 

submit a written justification and source-specific modeling protocol to its State for review and 

approval. As part of the consultation process, the State will provide documentation to EPA and 

FLM. 

For modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance 

recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and 

other methods.” The PLUVUE-II plume visibility model is mentioned as a possible model to 

consider instead of CALPUFF for a source within less than 50 km of a Class I area. 

The EPA guidance notes that “regional scale photochemical grid models may have merit, but such 

models have been designed to assess cumulative impacts, not impacts from individual sources” and 

they are “very resource intensive and time consuming relative to CALPUFF”, but States may 

consider their use for SIP development in the future as they may be adapted and “demonstrated to 

be appropriate for single source applications” (70 FR 39123). Photochemical grid models may be 

Appendix H.6 - 985
Review of EPA’s Guidance 7

more appropriate for cumulative modeling options such as in the determination of the aggregate 

contribution of all-BART-eligible sources to visibility impairment, but such use should involve 

consultation with the appropriate EPA Regional Office (70 FR 39163).

According to the BART guidance, a modeling protocol should be submitted for all modeling 

demonstrations regardless of the distance from the BART-eligible source to the Class I area. EPA’s 

role in the development of the protocol is only advisory as the “States better understand the BARTeligible source configurations” and factors affecting their particular Class I areas (70 FR 39126). 

In the BART modeling analyses the EPA recommends that the State use the highest 24-hour 

average actual emission rate for the most recent three to five-year period of record. Emissions on 

days influenced by periods of start-up, shutdown and malfunction are not to be considered in 

determining the appropriate emission rates. (70 FR 39129).

If a source is found to be subject to BART, CALPUFF or another appropriate model should be used 

to evaluate the improvement in visibility resulting from the application of BART controls. 

Visibility improvements may be evaluated on a pollutant-specific basis in the BART determination 

(70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA 

guidelines state that States are “encouraged to account for the magnitude, frequency, and duration 

of the contributions to visibility impairment caused by the source based on the natural variability of 

meteorology” (70 FR 39129). 

2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to 

demonstrate greater “reasonable progress” in reducing haze than would result if BART were 

applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading 

program that achieves greater progress at reducing emissions will also achieve greater progress at 

reducing haze. Such would be the case if the likely geographic distribution of emissions under the 

trading program would not be greatly different from the distribution if BART was in place. 

If the expected distribution of emissions is different under the two approaches, then “dispersion 

modeling” of all sources must be used to determine the difference in visibility at each impacted 

Class I area, in order to establish that the optional trading program will result in visibility 

improvements aggregated over all Class I areas that are “better than BART” (70 FR 39137-39138). 

The BART guidance does not specify the method to be used for this modeling. From a technical 

perspective, either applying CALPUFF to every source or using a regional photochemical model 

would satisfy the need.

A rulemaking procedure is currently underway to establish final guidance for such alternatives to 

BART (70 FR 44154-44175). The rule is expected to be finalized early in 2006.

Appendix H.6 - 986
Overview of the CALPUFF Modeling System 8

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities 

and limitations. It does not include specific recommendations regarding the use of the model for 

BART analysis in the VISTAS region. These specific recommendations can be found in Chapter 

4. 

3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred 

modeling approach for use in the BART analyses. CALPUFF and its meteorological model, 

CALMET, are designed to handle the complexities posed by the complex terrain, the large 

source-receptor distances, chemical transformation and deposition, and other issues related to 

Class I visibility impacts. The CALPUFF modeling system has been adopted by the EPA as a 

Guideline Model for source-receptor distances greater than 50 km, and for use on a case-by-case 

basis in complex flow situations for shorter distances (68 FR 18440-18482). CALPUFF is 

recommended for Class I impact assessments by the Federal Land Managers Workgroup (FLAG 

2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The 

final BART guidance recommends CALPUFF as “the best modeling application available for 

predicting a singe source’s contribution to visibility impairment” (70 FR 39122). As a result of 

these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its 

BART determinations.

The main components of the CALPUFF modeling system are shown in Figure 3-1. CALMET is 

a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It 

produces three-dimensional wind and temperature fields and two-dimensional fields of mixing 

heights and other meteorological fields. It contains slope flow effects, terrain channeling, and 

kinematic effects of terrain. CALMET includes special algorithms for treating the overwater 

boundary layer and coastal interaction effects. CALMET can use meteorological observational 

data and/or three-dimensional output from prognostic numerical meteorological models such as 

MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine-scale 

meteorological fields.

CALPUFF is a non-steady-state Lagrangian puff transport and dispersion model that advects 

Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been 

designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers 

from a source. It includes algorithms for near-field effects such as building downwash, stack tip 

downwash and transitional plume rise as well as processes important in the far-field such as 

chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to 

allow puff splitting in the horizontal and vertical directions, which extends the distance range of 

the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition 

fluxes evaluated at user-specified receptor locations.

Appendix H.6 - 987
Overview of the CALPUFF Modeling System 9

Figure 3-1. CALPUFF modeling system components.

A set of postprocessing programs associated with CALPUFF computes visibility effects and 

allows cumulative source impacts to be assessed, including potential non-linear effects of 

ammonia limitation on nitrate formation. The CALPOST postprocessor contains several options 

for computing change in extinction and deciviews for visibility assessments. The POSTUTIL 

postprocessor includes options for summing contributions of individual sources or groups of 

sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid-nitrate 

chemical equilibrium module, which allows the cumulative effects of ammonia consumption by 

background sources to be assessed in the postprocessor. In addition, the combination of 

CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be 

incrementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF modeling 

components in more detail.

Appendix H.6 - 988
Overview of the CALPUFF Modeling System 10

3.1.1 Major Features of CALMET

The CALMET meteorological model consists of a diagnostic wind field module and 

micrometeorological modules for overwater and overland boundary layers. When modeling a 

large geographical area, as would be necessary for the regional VISTAS domain, the user has the 

option to use a Lambert Conformal Projection coordinate system to account for Earth’s curvature. 

The major features and options of the meteorological model are summarized in Table 3-1. The 

techniques used in the CALMET model are briefly described below. 

Table 3-1. Major Features of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland Boundary Layer - Energy Balance Method

- Overwater Boundary Layer - Profile Method

-- COARE algorithm

-- OCD-based method

- Produces Gridded Fields of:

-- Surface Friction Velocity

-- Convective Velocity Scale

-- Monin-Obukhov Length

-- Mixing Height

-- PGT Stability Class

-- Air Temperature (3-D)

-- Precipitation Rate

• Diagnostic Wind Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimization

- Produces Gridded Fields of U, V, W Wind Components

- Inputs Include Domain-Scale Winds, Observations, and

 (optionally) Coarse-Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and 

overwater grid cells.

Overland Boundary Layer Model: Over land surfaces, the energy balance method of Holtslag and 

van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surface 

Appendix H.6 - 989
Overview of the CALPUFF Modeling System 11

friction velocity, Monin-Obukhov length, and convective velocity scale. Mixing heights are 

determined from the computed hourly surface heat fluxes and observed temperature soundings 

using a modified Carson (1973) method based on Maul (1980). The model also determines 

gridded fields of Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates.

Overwater Boundary Layer Model: The aerodynamic and thermal properties of water surfaces 

suggest that a different method is best suited for calculating the boundary layer parameters in the 

marine environment. A profile technique, using air-sea temperature differences, is used in 

CALMET to compute the micro-meteorological parameters in the marine boundary layer. The 

version of CALMET being used by VISTAS contains improvements in the overwater boundary 

layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response 

Experiment (COARE) and enhancements in the calculation of overwater mixed layer heights 

(Batchvarova and Gryning, 1991, 1994). Further details and the results of an evaluation of the 

model containing these enhancements are described in Scire et al. (2005). An upwind-looking 

spatial averaging scheme is optionally applied to the mixing heights and three-dimensional 

temperature fields in order to account for important advective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two-step approach to the computation of the wind fields 

(Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted for kinematic 

effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded 

MM5 can be used to define the initial guess field. The second step consists of an objective 

analysis procedure to introduce observational data into the Step 1 wind field to produce a final 

wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field 

defined by the MM5 prognostic meteorological model. Normally, the CALMET computational 

domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial 

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used to 

develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 algorithms 

in CALMET described below apply terrain adjustments to the initial guess field on the fine-scale 

CALMET grid. Thus, the CALMET winds are adjusted to respond to fine-scale terrain features 

not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain: The approach of Liu and Yocke (1980) is used to evaluate the 

effects of the terrain on the wind field. The initial guess field winds are used to compute a terrainforced vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme is 

applied iteratively until the three-dimensional divergence is less than a threshold value. 

Slope Flows: The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 

1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

Appendix H.6 - 990
Overview of the CALPUFF Modeling System 12

advective-gravity and equilibrium flow regimes. At night, the slope flow model parameterizes the 

flow down the sides of the valley walls into the floor of the valley, and during the day, upslope 

flows are parameterized. The magnitude of the slope flow depends on the local surface sensible 

heat flux and local terrain gradients. The slope flow wind components are added to the wind field 

adjusted for kinematic effects.

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are 

parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude 

number at a particular grid point is less than a critical value and the wind has an uphill 

component, the wind direction is adjusted to be tangent to the terrain. 

Step 2 Wind Field. The wind field resulting from the above adjustments of the initial-guess wind 

is the Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An inversedistance squared interpolation scheme is used which weights observational data heavily in the 

vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind 

field in regions with no observational data. The resulting wind field is subject to smoothing, an 

optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence 

minimization to produce a final Step 2 wind field. 

The introduction of observational data in the Step 2 calculation is an option. It is also possible to 

run the model in “no observations” (No-Obs) mode, which involves the use only of MM5 gridded 

data for the initial guess field followed by fine-scale terrain adjustments by CALMET. In NoObs mode, observational data are not used in the Step 2 calculations. The No-Obs mode is 

appropriate when the MM5 simulations adequately characterize the regional wind patterns and 

when local observations, especially surface observations, reflect local conditions on a scale 

smaller than that of the CALMET domain and hence their spatial representativeness may be 

limited. Such situations are most likely to occur when the CALMET grid scale is relatively large 

i.e., coarser than the scale of variation of the true wind field, which is particularly likely to occur 

in complex terrain or along the seashore,

3.1.2 Major Features of CALPUFF

By its puff-based formulation and through the use of three-dimensional meteorological data 

developed by the CALMET meteorological model, CALPUFF can simulate the effects of timeand space-varying meteorological conditions on pollutant transport from sources in complex 

terrain. The major features and options of the CALPUFF model are summarized in Table 3-2. 

Some of the technical algorithms are briefly described below. 

Complex Terrain: The effects of complex terrain on puff transport are derived from the 

CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor locations 

are simulated using one of two algorithms that modify the puff-height (either that of ISCST3 or a 

general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical 

dispersion derived from the weakly-stratified flow and dispersion module of the Complex Terrain 

Appendix H.6 - 991
Overview of the CALPUFF Modeling System 13

Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely 

on the receptor elevation (relative to the elevation at the source) and the height of the puff above 

the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the 

direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG treats terrain 

features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry 

et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of 

receptors using a dividing streamline height (Hd) to determine which pollutant material is 

deflected around the sides of a hill (below Hd) and which material is advected over the hill (above 

Hd). The local flow (near the feature) used to define Hd is taken from the gridded CALMET 

fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Functions: A set of accurate and computationally efficient puff sampling routines 

is included in CALPUFF, which solve many of the computational difficulties encountered when 

applying a puff model to near-field releases. For near-field applications during rapidly-varying 

meteorological conditions, an elongated puff (slug) sampling function may be used. An 

integrated puff approach may be used during less demanding conditions. Both techniques 

reproduce continuous plume results under the appropriate steady state conditions.

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both 

incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the 

choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been 

implemented in such a way as to allow the use of wind direction specific building dimensions. 

The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as 

an option.

Dispersion Coefficients: Several options are provided in CALPUFF for the computation of 

dispersion coefficients, including the use of turbulence measurements (σv and σw), the use of 

similarity theory to estimate σv and σw from modeled surface heat and momentum fluxes, or the 

use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion 

equations based on the CTDM. Options are provided to apply an averaging time correction or 

surface roughness length adjustments to the PG coefficients. In version 5.754 of CALPUFF

being used by VISTAS, an option is provided to use the AERMOD turbulence profiles for 

determining dispersion rates, which is the most recent approach to dispersion in EPA-approved 

regulatory modeling. In addition, turbulence advection is included. For additional details on 

these features, see Scire et al. (2005).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model 

contains both overwater and overland boundary layer algorithms, the effects of water bodies on 

plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff 

formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion 

conditions, including the abrupt changes that occur at the coastline of a major body of water.

Appendix H.6 - 992
Overview of the CALPUFF Modeling System 14

Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry 

deposition rates of gases and particulate matter as a function of geophysical parameters, 

meteorological conditions, and pollutant species. For particles, source-specific mass distributions 

may be provided for use in the resistance model. Of particular interest for BART analyses is the 

ability to separately model the deposition of fine particulate matter (< 2.5 μm diameter) from 

coarse particulate matter (2.5-10 μm diameter).

Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical 

wind shear effects across individual puffs to be simulated. Differential rates of dispersion and 

transport among the “new” puffs generated from the original, well-mixed puff can substantially 

increase the effective rate of horizontal spread of the material. Puffs may also be split in the 

horizontal when the puff size becomes large relative to the grid size, to account for wind shear 

across the puffs. 

Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF to compute 

the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging 

coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. 

liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOx, HNO3, and NO3

-

) 

employed in the MESOPUFF II model or a set of user-specified, diurnally-varying transformation 

rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine 

particulate matter (sulfate and nitrate) from emissions of SO2 and NOx and thus allows analyses 

of visibility impacts. Ambient ozone concentrations are used in the parameterized chemical 

transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia 

concentrations are used together with a temperature and relative humidity-dependent equilibrium 

relationship to partition nitric acid and nitrate on an hour-by-hour and receptor-by-receptor basis.

Appendix H.6 - 993
Overview of the CALPUFF Modeling System 15

Table 3-2. Major Features of the CALPUFF Dispersion Model 

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant or variable emissions)

- Volume sources (constant or variable emissions)

- Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions

- Gridded 3-D fields of meteorological variables (winds, temperature)

- Spatially-variable fields of mixing height, friction velocity, convective velocity scale,

 Monin-Obukhov length, precipitation rate

- Vertically and horizontally-varying turbulence and dispersion rates

- Time-dependent source and emissions data for point, area, and volume sources

- Temporal or wind-dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time-varying heat flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient (σy, σz) options

- Direct measurements of σv and σw

- Estimated values of σv and σw based on similarity theory 

-- AERMOD turbulence profiles

-- Original turbulence profiles

- Pasquill-Gifford (PG) dispersion coefficients (rural areas)

- McElroy-Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- Huber-Snyder method

- Schulman-Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

- Optional puff height adjustment: ISC3 or "plume path coefficient"

- Optional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)

Appendix H.6 - 994
Overview of the CALPUFF Modeling System 16

Table 3-2. Major Features of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, Hd, as in CTDMPLUS:

- Above Hd, material flows over the hill and experiences altered diffusion rates

- Below Hd, material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of space and time variations of deposition with a resistance model

- User-specified diurnal cycles for each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD-based method)

- Abrupt change in meteorological conditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and NO3

(MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2, HNO3, and NO3 

(RIVAD/ARM3 method)

- User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function of precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and 

POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing 

tabulations that summarize the results of the simulations, identifying, for example, the highest and 

second-highest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentrations from CALPUFF to compute light extinction 

and related measures of visibility (haze index in deciviews), reporting these for a 24-hour 

averaging time. 

The CALPOST processor contains several options for evaluating visibility impacts, including the 

method described in the BART guidance, which uses monthly average relative humidity values. 

CALPOST contains implementations of the IWAQM-recommended and FLAG-recommended 

visibility techniques and additional options to evaluate the impact of natural weather events (fog, 

rain and snow) on background visibility and visibility impacts from modeled sources.

Appendix H.6 - 995
Overview of the CALPUFF Modeling System 17

The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources 

from different simulations to be summed, can compute the difference between two sets of 

predicted impacts (useful for evaluating the benefits of BART controls), and contains a chemistry 

module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This 

capability allows the potential non-linear effects of ammonia scavenging by sulfate and nitrate 

sources to be evaluated in the formation of nitrate from an individual source. CALPUFF makes 

the full ambient ammonia concentration available to each puff without regard for any scavenging 

by other puffs. POSTUTIL corrects for such scavenging when the puffs generated by the 

CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or 

when nitrate formation is to be attributed to each of several sources that are in a cluster and whose 

plumes overlap, 

POSTUTIL will also compute the impacts of individual sources or groups of sources on sulfur 

and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the 

changes in deposition fluxes resulting from changes in emissions to be quantified. For example 

the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity 

(ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).

3.2 Discussion of CALPUFF Applicability and Limitations

3.2.1 Transport and Diffusion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport 

distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km 

should be done cautiously with an awareness of the likely problems involved.”4

IWAQM’s 200-km limitation derives from the observation that, when compared to the data of the 

Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF 

overestimated inert tracer concentrations by factors of 3 to 4 at receptors that were 300 to 1000 

km from the source. The apparent reason was insufficient horizontal dispersion of the simulated 

plume, presumably because an actual large plume does not remain coherent in the presence of 

vertical wind shears that typically occur, especially during the night, and of horizontal wind 

shears over the large puffs that arise over long transport distances. 

To better represent such situations, an optional puff splitting algorithm has since been added to 

CALPUFF to simulate wind shear effects across a well-mixed individual puff by dividing the 

puff horizontally and vertically into two or more pieces. Differential rates of transport among the 

 

4 The IWAQM presentation at EPA’s 6th Modeling Conference provides the background for this recommendation: 

“The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration 

impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For 

larger transport times and distances, our experience thus far is that CALPUFF tends to underestimate the 

horizontal extent of the dispersion and hence tends to overestimate the surface-level concentration maxima. This 

does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such 

instances be used cautiously and with some understanding.” (From page D-12 of the IWAQM Phase 2 report.)

Appendix H.6 - 996
Overview of the CALPUFF Modeling System 18

new puffs thus generated can increase the horizontal spread of the material in the plume due to 

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is 

designed to allow large puffs that may grow to be several grid cells or more in size to split into 

smaller puffs that can then more accurately respond to variations in the local wind field across the 

original large puff. This will also tend to increase horizontal dispersion of the plume. Since the 

creation of additional puffs via puff splitting will increase the computational requirements of the 

model, possibly substantially, puff splitting is not enabled by default, but can be turned on at the 

option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km, 

or possibly over shorter distances in complex terrain.

Turning to the shorter distance end of the transport range, the CALPUFF section of Appendix A 

of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended 

for use on scales from tens of meters from a source to hundreds of kilometers.” This is supported 

by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were 

designed to be suitable for both short and long distances. In this regard, CALPUFF does contain 

algorithms for such near-field effects as plume rise, building downwash, and terrain impingement 

and includes routines that deal with the computational difficulties encountered when applying a 

puff model in the field near to a source.

The recommendations for regulatory use in Appendix A of the Guideline on Air Quality Models

state, “CALPUFF is appropriate for long range transport (source-receptor distance of 50 to 

several hundred kilometers)”, but provisions for using CALPUFF in the near-field in “complex 

flow” situations are also included in the regulatory guidance. Complex flow situations may 

include complex terrain, coastal areas, situations where plume fumigation is likely, and areas 

where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to 

changes in valley orientation) are important. 

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in 

the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, 

additional studies of model performance have been performed at shorter distances, including at a 

power plant in New York state in complex terrain (at source-receptor distances of 2 to 8.5 km) 

and a second power plant in Illinois in simple terrain (at source-receptor distances in arcs ranging 

from 0.5 km to 50 km from the stack) (Strimaitis et al., 1998). Other CALPUFF evaluation 

studies over short-distances include ones by Chang et al. (2001) and Morrison et al. (2003). 

These studies demonstrate good model performance over source-receptor distances from a few 

hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The 

EPA has defined the "regulatory default" option in CALPUFF to allow either Pasquill-Gifford 

(PG) or turbulence-based dispersion coefficients. CALPUFF has been evaluated and shown to 

perform better using turbulence-based dispersion for tall stacks (Strimaitis et al, 1998). 

CALPUFF with turbulence-based dispersion has also been evaluated for overwater transport and 

coastal situations (Scire et al., 2005). In many other studies, including AERMOD evaluation 

studies conducted by EPA, the use of PG-dispersion, or more specifically the lack of a convective 

Appendix H.6 - 997
Overview of the CALPUFF Modeling System 19

probability density function (pdf) module, has been demonstrated to result in underprediction of 

peak concentrations.

In November 2005, EPA approved the AERMOD model, which relies on turbulence-based 

dispersion, as a regulatory Guideline Model5. The ISCST3 model and its PG dispersion 

coefficients are being phased out as an acceptable regulatory approach. However, EPA Region 

IV has indicated that the application of turbulence-based dispersion coefficients in CALPUFF 

needs to be further demonstrated before they are approved for BART application. They will 

consider accepting the use of turbulence dispersion coefficients on a case-by-case basis for 

sources that are close to Class I areas. 

For regional haze light extinction calculations, use of a plume-simulating model such as 

CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually 

discernible as a plume per se, but nevertheless its presence could alter the visibility through the 

background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 

km from a source. In this light, the BART guidance strongly recommends using CALPUFF for 

source-receptor distances greater than 50 km but also presents CALPUFF as an option that can be 

considered for shorter transport distances.

As discussed above, there do not appear to be any scientific reasons why CALPUFF cannot be 

used for even shorter transport distances than 30 km, though, as long as the scale of the plume is 

larger than the scale of the output grid so that the maximum concentrations and the width of the 

plume are adequately represented and so that the sub-grid details of plume structure can be 

ignored when estimating effects on light extinction. The standard 1-km output grid that has been 

established for Class I area analyses should serve down to source-receptor distances somewhat 

under 30 km; how much closer than 30 km will depend on the topography and meteorology of the 

area and should be evaluated on a case-by-case basis. For extremely short transport distances, 

depiction of the concentration distribution will require a grid that is finer than 1 km. (For 

reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 km of travel distance, 

assuming Pasquill-Gifford dispersion rates under neutral conditions.) 

As an additional consideration, if the plume width is small compared to the visual range, the 

atmospheric extinction along a typical sight path of tens of kilometers through the plume will be 

inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor 

point will not be correct. However, the effect of averaging light extinction estimates for 24 hours, 

during which the plume location shifts over various receptor points, is likely to mitigate this 

problem to some degree and suggests that using CALPUFF at distances under 30 km will often be 

appropriate. For the narrow plumes that result from short transport distances, though, the modeled 

peak 24-hr average extinction at a receptor will tend to overstate the effect of the source on 

regional haze. 

 

5 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex 

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.

Appendix H.6 - 998
Overview of the CALPUFF Modeling System 20

The U.S. EPA has suggested that the plume visibility model, PLUVUE-II, could be used in lieu 

of CALPUFF for simulating visibility effects at such short distances.6 PLUVUE-II is a Gaussian 

model that simulates the dispersion, chemical conversion, and optical effects of emissions of 

particles, SO2, and NOx from a single source. Its outputs include the discoloration of the sky by 

the plume (so called “plume blight”) and the effect of the plume on visibility along user-selected 

sight paths that pass through the plume. The impacts of the plume on visibility depend not only 

on the plume composition, but also on the sight path chosen and its direction relative to the axis 

of the plume and the location of the sun. It isn’t clear how such sight-path dependent results could 

be compared to the 0.5 and 1.0 deciview thresholds in the BART guidance. Since CALPUFF is 

designed to be useful for short transport distances (with features such as the simulation of plume 

downwash caused by structures at the source), CALPUFF seems more appropriate than 

PLUVUE-II for evaluating source impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM2.5

Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that 

CALPUFF can treat primary pollutants such as PM10. In actuality, CALPUFF can simulate PM10

or PM2.5 or some other size range, because the assumed size distribution of the particles is a user 

input. The smaller the particles, the more they disperse like an inert gas. In most cases, the 

dispersion of inert PM2.5 particles will be only minutely different from that of an inert gas, but the 

behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface. 

PM2.5 particles, which have a mass median diameter around 0.5 μm, have an average net 

deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine 

particles is usually not significant except for ground-level emissions. On the other hand, coarse 

particles (those PM10 particles larger than PM2.5) have an average deposition velocity of more 

than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks. 

CALPUFF includes parametric representations of particle and gas deposition in terms of 

atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the 

gravitational settling speed. Gravitational settling, which is of particular importance for the coarse 

fraction of PM10, is accounted for in the calculation of the deposition velocity. Effects of inertial 

impaction (important for the upper part of the PM10 distribution) and Brownian motion (important 

for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance 

recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light 

extinction efficiency than coarse particulate matter (2.5-10 μm diameters), should be treated 

separately in the model. CALPUFF allows for user-specified size categories to be treated as 

 

6 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE-II for BART 

application

Appendix H.6 - 999
Overview of the CALPUFF Modeling System 21

separate species, which includes calculating size-specific dry deposition velocities for each size 

category.

A primary PM2.5 emission from coal-fired electric generating units (EGUs) that is of relevance to 

visibility calculations is that of primary sulfate. Although primary sulfate emissions account for 

only a small fraction of the total sulfur emissions from such sources, it may be important to 

simulate their effect with CALPUFF, especially at shorter distances before significant formation 

of secondary sulfate conversion from SO2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate 

The MESOPUFF-II chemistry algorithm used in CALPUFF7 simulates the gas phase oxidation 

of sulfur dioxide to sulfate by a linear transformation rate that was developed using regression 

relationships derived from the analysis of chemical conversion rates produced by a complex 

photochemical box model (see Scire et al., 1984, for a description of the development of the 

chemical module). As in all empirically-derived models, the relationships are based on easilycomputed or observed parameters that are used as surrogates for the factors that control SO2

oxidation. 

The surrogate factors included in the parameterized chemistry during the daytime hours include 

solar radiation intensity, ambient ozone concentration, and atmospheric stability class. For 

example, gas phase SO2 oxidation is a function of OH radical concentrations. Ozone 

concentrations are correlated with OH radical concentrations during daytime hours, and their use 

in the daytime SO2 conversion rate in CALPUFF is based on this correlation relationship. The 

philosophy is that OH radical measurements are not available and cannot easily be computed 

within a model like CALPUFF, but ozone is commonly measured throughout the country, so the 

use of the well-known surrogate variable (ozone) is more useful in the empirical relationship than 

factors that are unknown or have a high degree of uncertainty. The same logic applies to the 

other variables in the relationship. They are surrogates for factors that the regression analysis has 

shown to be important in SO2 oxidation rates. At night, the SO2 conversion is set to a constant 

low value (default is 0.2%/hr). Aqueous phase oxidation of SO2 is represented by an additive 

term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF 

represents the chemical conversion as a linear process because it requires linear independence 

between puffs, although as explained below, non-linear behavior in nitrate formation can be 

modeled.

 

7 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO2, SO4

=

, NOx, HNO3, 

and NO3

-

) MESOPUFF-II system and the 6 species RIVAD system (which treats NO and NO2 separately). 

IWAQM recommends using the MESOPUFF-II system with CALPUFF. The RIVAD system is believed to be 

more appropriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM- and FLM-recommended 

MESOPUFF-II chemistry is most appropriate. 

Appendix H.6 - 1000
Overview of the CALPUFF Modeling System 22

The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing 

the gas phase sulfate formation but that it does not adequately account for the aqueous phase 

oxidation of SO2. Actual aqueous phase oxidation in clouds or fog can proceed at rates much 

greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in 

such situations. However, aqueous phase oxidation depends on liquid water content, not relative 

humidity. In reality, liquid water does not exist in the atmosphere at relative humidity much 

below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative 

humidity. This can lead CALPUFF to overestimate sulfate concentrations when the humidity is 

high but the cloud water that enables aqueous conversion is not present. Therefore, the direction 

of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1) 

overestimation of sulfate formation when NOx concentrations in the plume are high and in 

actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO2; 

and (2) lack of direct consideration of the effect of temperature on the conversion rates, which 

may cause the model to overstate sulfate formation on cold days (below 10C or 50°F) (Morris et 

al., 2003). However, in CALPUFF, the effects of temperature are, to some degree, compensated 

for indirectly by the use of the solar radiation surrogate variable in the empirical conversion 

equations. 

Whether these potential errors are important will depend on the setting. For example, Figure 3-2 

shows a comparison of predicted and observed 24-hour sulfate concentrations, due to a large 

number of SO2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et 

al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether 

CALPUFF predictions would compare as well with measurements in the Southeast remains to be 

seen. 

CALPUFF does not identify the chemical form of the sulfate compound that results from its 

reactions, which will generally be some form of ammoniated sulfate whose degree of 

neutralization will depend on the availability of ammonia in the atmosphere. This consideration, 

which has been found to be relevant for calculating light extinction in the VISTAS region, is not 

addressed by CALPUFF or CALPOST.

In most applications, the ozone concentrations required for the sulfate formation calculations are 

derived from ambient measurements, although concentrations simulated by regional models can 

be used. 

Appendix H.6 - 1001
Overview of the CALPUFF Modeling System 23

Figure 3-2. Observed vs. CALPUFF-predicted 24-hour sulfate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995.

NOx and Secondary Ammonium Nitrate

The MESOPUFF-II chemistry algorithm used in CALPUFF simulates the oxidation of NOx to 

nitric acid and organic nitrates (both gases) by transformation rates that depend on NOx

concentration, ambient ozone concentration, and atmospheric stability class during the day. The 

conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature- and 

humidity-dependent equilibrium between nitric acid gas and ammonium nitrate particles is taken 

into account when estimating the ammonium nitrate particle concentration, an equilibrium that 

depends on the ambient concentration of ammonia. The user supplies the value of the ambient 

concentration of ammonia. CALPUFF assumes that the sulfate reacts preferentially with that 

ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium 

nitrate. 

The IWAQM Phase 2 report considers that this mechanism is adequate for representing nitrate 

chemistry. Potential situations where this assumption may not be correct, however, include (1) 

plumes with high concentrations of NOx that deplete the ambient ozone and thus limit the 

[SO4] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

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

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4

Appendix H.6 - 1002
Overview of the CALPUFF Modeling System 24

transformation of NOx to nitric acid in the plume; and (2) when ambient temperature is below 10 

C, and thus the transformation rate is much slower and the nitrate concentration may be lower 

than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may 

overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nitrate 

concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when 

temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations 

tend to be the least. CALPUFF may overstate the impacts of NOx emissions at those times, 

especially in the colder northern states. This potential overestimate of nitrate was not evident, 

however, in an evaluation of CALPUFF-modeled nitrate against actual observational data in the 

Wyoming study, as shown in Figure 3-3a (Scire et al., 2001), 

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the 

background concentration of ammonia available to each puff, and that amount is scavenged by 

the sulfate in the puff. If puffs overlap, then that approach could overstate the amount of 

ammonium nitrate that is formed in total if, in reality, the combined scavenging by the 

overlapping puffs at a location would deplete the available ammonia enough that the combined 

nitrate formation was limited by the availability of ammonia. This effect of such ammonia 

limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one 

modeling analysis found the maximum light extinction impact of the source to be 7.4% (roughly 

0.74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting 

and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia 

limiting was considered (Escoffier-Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system 

has included the optional POSTUTIL postprocessing program, which repartitions the ammonia 

and nitric acid concentrations estimated by CALPUFF to reflect potential ammonia-limiting 

effects on the development of nitrate. This allows non-linearity associated with ammonia limiting 

effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate 

concentrations from all sources (modeled sources plus inflow boundary conditions) and estimates 

the amount of ammonia available for total nitrate formation after the preferential scavenging of 

ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is 

added to an existing mix of pollutants, POSTUTIL will estimate both the nitrate formed from the 

new source and the change in background nitrate as a result of the incremental depletion of 

ammonia (due to the new sulfate and nitrate) or addition of ammonia (from a new source of 

ammonia).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and 

spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage 

of the increased accuracy provided by POSTUTIL. The processor requires estimated 

concentrations of ammonia throughout the modeling domain and period. Such estimates can be 

inferred from CASTNet measurements, which are integrated over a week, from 24-hr SEARCH 

measurements, or from the output of a regional photochemical model such as CMAQ or CAMx. 

The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large, 

Appendix H.6 - 1003
Overview of the CALPUFF Modeling System 25

so defining the ammonia concentration throughout the Southeast would require extensive 

interpolation or extrapolation from the measured values. The quality of the SEARCH 

measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS 

domain. Modeled concentrations have the advantage of being resolved in space and time, but 

their accuracy should be evaluated by comparison with measurements wherever possible. 

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to 

determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation 

associated with temperature and relative humidity effects. For example, results of the Wyoming 

study (see Figure 3-3a) show that POSTUTIL adjustments produced daily average nitrate 

concentrations well within the factor of two lines and with very little mean bias. On the other 

hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced 

consistent overpredictions of nitrate by factors of 2-3 and 3-4, respectively, as shown in Figure 3-

3b (Scire et al., 2003). 

Figure 3-3a. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al., 

2001)

[NO3] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

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

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3

Appendix H.6 - 1004
Overview of the CALPUFF Modeling System 26

Figure 3-3b. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant 

ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench, 

2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90 

to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g., 

that from the burning of vegetation and deriving from VOC emissions from vegetation) and only 

5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky 

Mountains National Park in August 2002 (Tanner, et al., 2005) found that an average of 83% of 

the fine carbon was modern carbon

According to IMPROVE measurements, organics account for roughly 10% of the particle-caused 

light extinction in Class I areas in the Southeast. We can thus conclude that, in general, secondary 

organic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to 

have a large impact (around 1%) on current visibility. (Man-caused burning of vegetation can 

have significant localized, short-term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of 

the 1.4 μg/m3 concentration assumed for natural conditions by the EPA, which means that current 

fossil fuel burning would contribute less than 2% to visibility in an atmosphere that represents 

natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART 

[NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE 

Observations 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

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

CALPUFF Summed Repartition (

CALPUFF Separated Repartition 

CALPUFF Separated Repartition 

Factor 1

Factor 2 Factor 3

Factor 4

Factor 2

Factor 3

Factor 4

0.4 0.6 0.8 1 1.2

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

CALPUFF Summed Repartition (Seasonal NH3)

CALPUFF Separated Repartition (NH3=0.5ppb)

CALPUFF Separated Repartition (NH3=1.0ppb)

Factor 1

Factor 2 

or 3

Factor 2

Factor 3

Factor 4

Appendix H.6 - 1005
Overview of the CALPUFF Modeling System 27

sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) localized impact 

from a particularly large VOC source cannot be dismissed out of hand. 

CALPUFF has only rudimentary capabilities for addressing formation of visibility-impairing 

organic particles from some forms of volatile organic carbon (VOC). The capabilities that do 

exist include the following. 

First, PM10 emissions (such as from power plants) are often divided into filterable and 

condensable components, with the condensable mass being 100-200% of the filterable mass. For 

purposes of visibility analyses with CALPUFF, a fraction of the condensable part is typically 

treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the 

PM10 emissions condense into organic PM2.5 particles. The size of this organic fraction varies 

with process and process equipment, and can range from 20 to 100% of the condensable mass. 

These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable 

material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of secondary organic particles from organic emissions 

was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This 

simplified secondary organic aerosol (SOA) module is a linear, parameterized representation that 

is currently considered best suited for biogenic organics. It relies on the conventional wisdom that 

only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and 

Seinfeld, 1989). For example, according to this rule, isoprene (C5H8) does not make SOA but 

terpenes do, making pine trees more important biogenic contributors to SOA than oak trees.8

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA 

module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101 

estimated 24-hr SOA concentrations were within 2% of the measured values (Scire et al., 2001). 

This performance seems promising, although the developers view the SOA module as needing 

more testing and evaluation.

Thus, CALPUFF includes approaches for dealing with condensable VOC emissions that are 

characterized as condensable PM10 and with biogenic VOCs, although the soundness of 

concentration estimates by these approaches when modeling a plume from a single source is 

largely untested.9 The CALPUFF simulation of VOC emissions from sources whose VOC 

emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used 

for the simplified biogenic SOA module may be extended to anthropogenic VOCs when 

speciated VOC emissions information is available. If only those VOCs with more than six carbon 

atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC 

emissions. For example, the fugitive emissions of butane and ethane during petroleum processing 

 

8 Recent research suggests that isoprene may be a SOA precursor, however.

9 Note that neither of these VOC-related simulation approaches is described in the current (Version 5) CALPUFF 

User’s Guide dated January 2001. See the Wyoming report referenced above for a description of this module.

Appendix H.6 - 1006
Overview of the CALPUFF Modeling System 28

are not important, while aromatic emissions (such as of toluene and xylene) are considered by the 

SOA module’s mechanism. Development, testing, and evaluation would be needed before one 

could rely on such a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART-eligible sources, 

means other than CALPUFF will be needed. A technical approach using a regional 

photochemical model to evaluate visibility impacts of VOC emissions is presented in Section 

4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction 

of PM10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component concentrations on 

light extinction is carried out in the CALPOST postprocessor. The formula used is the usual 

IMPROVE/EPA formula, which is applied to determine a change in light extinction due to 

changes in component concentrations. Using the notation of CALPOST, the formula is the 

following:

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] +

 + 0.6[Coarse Mass] + 10[EC] + bRay (3-1)

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh 

scattering term (bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for 

tracking reasonable progress (EPA, 2003a).

There are a few important differences in detail and in notation between the CALPOST formula 

for estimating light extinction (i.e., Equation 3-1) and that of IMPROVE and EPA. First, the OC

in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation), 

which is 1.4 times the OC (i.e., organic carbon alone) in the IMPROVE formula. The EC above is 

synonymous with LAC in the IMPROVE formula. CALPOST still uses the old IMPROVE f(RH)

curve, whose values are documented in the December 2000 FLAG report. That curve differs 

slightly from the f(RH) now used by IMPROVE and EPA (as documented in EPA’s regional haze 

guidance documents), mainly at high relative humidity. Also, CALPOST sets the maximum RH

at 98% by default (although the user can change it), while the EPA’s guidance now caps it at 

95%. 

The haze index (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (bext/10) (3-2)

where HI is in units of deciviews (dv) and bext is in Mm-1. The impact of a source is determined 

by comparing HI for estimated natural background conditions with the impact of the source and 

without the impact of the source. 

Appendix H.6 - 1007
Overview of the CALPUFF Modeling System 29

CALPOST Methods

CALPOST uses Equation 3-1 to calculate the extinction increment due to the source of interest 

and provides various methods for estimating the background extinction against which the 

increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the 

change in light extinction due to a source or group of sources (called Methods 1-7). These are 

usually reported as 24-hour average values, consistent with EPA and FLM guidance. In addition, 

there are two techniques for computing the 24-hour average change in extinction (i.e., as the ratio 

of 24-hour average extinctions, or as the average of 24-hour ratios). A brief summary of the 

techniques is provided below. Method 2 is the current default, recommended by both IWAQM 

(EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART 

guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current 

actual levels of extinction and thus are not consistent with the “natural conditions” the BART 

proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs 

of estimated (e.g., natural conditions) background extinction or component concentrations, and 

thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficient 

for each receptor, specify that a certain fraction of that coefficient is due to hygroscopic species, 

and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH)

curve or, optionally, the EPA regional haze f(RH) curve (EPA, 2003b). The RH is capped at 98% 

or a user-selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled 

sulfate and nitrate. 

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 

9.09 Mm-1 that results from EPA’s default natural conditions concentrations, together with an 

assumption that for natural conditions, say, 0.9 Mm–1 (or 10%) of this amount results from 

hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to this 10%. 

In Method 2, user-specified, speciated monthly concentration values are used to describe the 

background. When applied to natural conditions, for which EPA’s default natural conditions 

concentrations are annual averages, the same component concentrations would have to be used 

throughout the year (unless potential refinements to those default values resulted in 

concentrations that vary during the year). Hourly background extinction is then calculated using 

these concentrations and hourly, site-specific f(RH) from a 1993 IWAQM curve (a different one 

Appendix H.6 - 1008
Overview of the CALPUFF Modeling System 30

than that in Method 1) or, optionally, the EPA regional haze f(RH) curve.10 Again the RH is 

capped at either 98% (default) or a user-selected value (most commonly at 95%). 

Method 3 is the same as Method 2, except that any hour in which the RH exceeds 98% (or the 

selected maximum) is dropped from the analysis. When 24-hr extinction is computed, no fewer 

than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as 

“missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly 

climatologically representative values in EPA (2003a, b)) are used in place of hourly values for 

calculating both the extinction impact of the source emissions and the background conditions 

extinction. Hourly source impacts, with the effect on extinction due to sulfates and nitrates 

calculated using the monthly-average relative humidity in f(RH), are compared against the 

monthly default natural background concentrations. Thus the monthly-averaged relative humidity 

is applied to the hygroscopic components (i.e., sulfate and nitrate) of both the source impact and 

the background extinction with Method 6. 

Method 7 is a new variant of Method 2 that was developed as a result of a ruling by the Assistant 

Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review 

case in Montana, that “natural conditions” should reflect the visibility impairment caused by 

significant meteorological events such as fog, precipitation, or naturally occurring haze (DOI, 

2003).11 Under Method 7, during hours when visibility is obscured by meteorological conditions, 

the actual measured visibility is used to represent natural conditions instead of the value that is 

calculated from EPA’s default natural conditions concentrations under Method 2. A recent 

modification developed in response to FLM comments on Method 7, in which the daily average 

natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibility

Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations 

are evaluating whether refinements are warranted to the methods recommended in EPA’s 

guidance to calculate default estimates of natural background visibility. In addition, IMPROVE 

has recently approved an alternative to the formula (Eq. 3-1) it uses to estimate extinction from 

particle concentration measurements (Pitchford et al., 2005).

Refinements in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity 

 

10 Note that the hourly-varying natural background extinction in this method is not consistent with that prescribed 

by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically-representative” f(RH) 

that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

11 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural 

conditions is unknown.

Appendix H.6 - 1009
Overview of the CALPUFF Modeling System 31

- Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq. 

3-1) from organic carbon measurements

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revising the extinction efficiencies (the numerical constants in Equation 3-1) for 

sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated 

by IMPROVE for all Class I areas. 

For the purposes of calculating current, future, and natural background visibility at VISTAS Class 

I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality 

modeling results using both the current EPA recommended assumptions and the newly revised 

aerosol extinction formula. If a BART-eligible source chooses to consider its projected impacts 

using the newly revised formula as well as the current formula, then modifications would need to 

be made to CALPOST to carry out calculations with the new algorithm. 

 

Appendix H.6 - 1010
VISTAS’ Common Modeling Protocol 32

4. VISTAS’ COMMON MODELING PROTOCOL

4.1 Overview of Common Modeling Approach

In this section, guidance is provided on the use of the CALPUFF modeling system for two 

purposes:

1) Evaluating whether a BART-eligible source is exempt from BART controls because it 

is not reasonably expected to cause or contribute to impairment of visibility in Class I 

areas, and 

2) Quantifying the visibility benefits of BART control options. 

For purpose 1), States must determine whether a source emits any air pollutant (SO2, NOx, PM, 

and in certain cases VOC and NH3) that “may reasonably be anticipated to cause or contribute to 

any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART-eligible sources in State are subject to BART. 

B) Demonstrate that all BART-eligible sources in the State together do not cause or 

contribute to any visibility impairment 

C) Determine if the impact from each individual BART-eligible source is greater than a 

threshold value. 

VISTAS States intend to follow Option C (determine if the visibility impact from individual 

sources exceeds a contribution threshold) for SO2, NOx, and PM emissions. The methods for 

Option C are described in Section 4.1.1. Option B (demonstrate that all BART eligible sources in 

a State do not impact visibility) is being pursued for VOC and NH3 emissions, and potentially for 

PM emissions. Methods for Option B are described in Section 4.1.3. 

4.1.1 BART Exemption Analysis

As illustrated in Figure 4-1, three steps will evaluate whether a BART-eligible source of SO2, 

NOx, or PM is subject to BART: 

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d 

for SO2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to 

BART. If the source agrees with this presumption, then no exemption modeling is required 

and the source can proceed to the BART determination using CALPUFF to evaluate impacts 

of control options and can perform the engineering analyses. If a source disagrees, the source 

may perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5 

dv.

Appendix H.6 - 1011
VISTAS’ Common Modeling Protocol 33

Figure 4-1. Flow chart showing the components of the VISTAS common modeling protocol. 

Assessment should be made for each Class I Area. (If a source agrees to install the most stringent 

controls then the modeling steps indicated above and engineering analyses and visibility impact 

modeling would not be required.) 

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12-

km regional VISTAS domain can be used to answer questions whether (a) a particular source 

may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were 

to be undertaken, which Class I areas should be included. Assumptions for the initial 

modeling assessment are conservative so that a source that contributes to visibility impairment 

is not exempted in error. If a source is shown not to contribute to visibility impairment using 

the initial modeling assessment, the source would not be subject to BART and would be 

exempted from further BART analyses. If a source is shown to contribute to visibility 

impairment using the initial modeling assessment, the source has the option to undertake finer 

grid CALPUFF modeling to evaluate further whether it is subject to BART. 

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the 

definitive test as to whether a source is subject to BART.

For large sources that will clearly exceed the initial screening thresholds, this step can be skipped 

and the analysis may proceed directly to the finer grid modeling analysis, which is described in 

Section 4.4. 

Appendix H.6 - 1012
VISTAS’ Common Modeling Protocol 34

4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BART review process 

involves evaluating the visibility benefits of different BART control measures. These benefits 

will be determined by making additional CALPUFF simulations using the same CALMET and 

CALPUFF configuration as those used in the finer grid analysis of Step 2. The only exception is 

that the source and emissions data used in the CALPUFF control evaluation simulations will 

reflect the BART control measures being evaluated. Using the same model configuration will 

produce an “apples-to-apples” comparison, where differences in impacts are due to the 

effectiveness of the controls rather than model configuration differences. For example, a control 

scenario evaluation that uses more conservative assumptions than the base case simulation may 

produce results showing no or little improvement in visibility impacts. That control scenario run 

with the same model configuration as the base case may show significant visibility improvement. 

Therefore, in order to not obscure the response to predicted visibility improvements by 

differences in the modeling approach, the same model configuration should be used in the BART 

control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current 

emissions” scenario for which the finer grid Step 2 modeling was performed. The postprocessing 

steps and procedures are the same as in the BART eligibility simulation. Side-by-side 

comparison of the visibility impacts will be tabulated to quantify the effectiveness of each control 

scenario relative to the base case.

The modeling evaluation is a unit-by-unit evaluation and can be conducted on a pollutant specific 

basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to 

decide which control technology, if any, is appropriate. Finally, if a source decides to use the 

most stringent control technology available, the BART control analysis, including modeling, is 

not necessary.

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM

Volatile Organic Compounds 

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its 

capability to simulate secondary organic aerosol formation from VOC emissions is not adequately 

tested, especially for anthropogenic emissions. (Separately, condensable organic carbon can be 

calculated from PM10.) 

VISTAS is currently performing a weight of evidence analysis to demonstrate, using the CMAQ 

regional air quality model, that all VOC emissions from all point sources (BART-eligible and 

non-BART) in each State do not contribute to visibility impairment. Emissions sensitivity 

simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid 

and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no 

response of organic carbon levels and light extinction at Class I areas to changing VOC emissions 

Appendix H.6 - 1013
VISTAS’ Common Modeling Protocol 35

from all anthropogenic sources in the VISTAS 12-km modeling domain (eastern US). Georgia 

Tech is currently repeating the sensitivity analyses for VISTAS using the VISTAS 12-km domain 

and CMAQ v 4.4 with a refined SOA module for a summer (Jun 1-Jul 10) and winter (Nov 19-

Dec 19) period in 2002. VOC emissions from all anthropogenic point sources in each VISTAS 

State are being reduced. Given that the impact of eliminating all VOC emissions from all point 

sources in a State is less than 0.5 dv, then the impact of any one BART-eligible source would be 

less than 0.5 dv. Based on these analyses, the VISTAS States have concluded that VOC 

emissions should not be subject to BART. When similar analyses for NH3 and PM have been 

completed, a technical justification for treatment of all three of these pollutants will be presented 

to EPA and FLMs for their review. 

Ammonia 

EPA has given states the option to address ammonia (NH3) emissions from BART-eligible 

sources. VISTAS has also contracted with Georgia Tech to perform emissions sensitivities using 

CMAQ v 4.4 with a refined SOA module and the Jun-Jul and Nov-Dec periods in 2002. All NH3

emissions from point sources (BART-eligible and not-BART) in each State are reduced for these 

analyses. If the sensitivity evaluation shows that the collective impact of all point NH3 emissions 

is less than 0.5 dv, then the impact of a single BART eligible source would be less than 0.5 dv. In 

that case, the VISTAS States would recommend that NH3 emissions not be subject to BART.

 Primary Particulate Matter 

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to 

which primary PM from BART-eligible sources contributes to impairment at Class I areas in the 

southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of 

SO2 and NOx under the CAIR rule meet the BART requirements, but these EGUs may still be 

subject to BART for primary PM. To determine the potential impacts of PM from EGU and nonEGU sources in the VISTAS states, two CMAQ sensitivity runs are underway for the first and 

third quarters of 2002. In one run, all primary PM from EGUs is removed while in the other run 

all primary PM from non-EGU sources is removed. All other CMAQ modeling components are 

held constant. The results will help determine at which, if any, Class I areas in the VISTAS 

region primary PM emissions contribute to regional haze. If the sensitivity evaluation shows that 

the collective impact of all EGU or non-EGU point primary PM emissions is less than 0.5 dv, 

then the impact of primary PM from any single BART eligible source would necessarily be less 

than 0.5 dv. These results will be reported at the same time as results for VOC and NH3. 

4.2 Optional Source-Specific Modeling

In some circumstances, a source may want to apply techniques designed to evaluate the impacts 

in a more detailed way than the standard VISTAS common protocol. A source may propose 

source-specific modeling procedures to address special issues to the State for State review. For 

example, sources very close to Class I areas may be better treated by a finer grid resolution that 

the generic Step 2 “fine” grid resolution meteorological fields provided by VISTAS. In some 

Appendix H.6 - 1014
VISTAS’ Common Modeling Protocol 36

situations, higher resolution MM5 or other prognostic meteorological datasets may be available 

than the standard 12-km or 36-km MM5 datasets provided by VISTAS. Because it is not possible 

to anticipate all of the situations where there would be a benefit to conducting more detailed 

source-specific analyses, the option to pursue this option is left as an open issue, to be resolved 

and justified based on specific factors relevant for the source in question.

A source-specific modeling protocol is required for each source. This document should describe 

the data sources and model configuration, and provide rationale for any changes in the model 

approach from the common protocol. This source-specific protocol must be provided for review 

and approval by the State. The State will share the protocol with EPA and the Federal Land 

Managers for their review. Discussion of approaches to source-specific modeling and an outline 

of the typical contents of the source-specific protocol are presented in Chapter 5. Discussions 

with the regulatory authorities should be conducted prior to development of a source-specific 

protocol to ensure all of the relevant issues are included in the protocol.

4.3 Initial Procedure for BART Exemption 

4.3.1 Overview of Initial Approach

The first step in the common protocol, the initial assessment in Figure 4-1, is a simple procedure 

to evaluate whether a source can be exempted from BART controls using a consistent set of 

meteorological and dispersion options. A pre-computed set of meteorological files and a predefined CALPUFF input option configuration, based on guidance in the final BART rule (70 FR 

39104-39172) and other EPA and FLAG model guidance, will allow relatively simple initial 

simulations. The regional initial domain is designed to allow any Class I areas within the 

VISTAS area to be evaluated with a single meteorological database and consistent CALPUFF 

modeling options. The second important question that this first screening step will answer is, if 

initial modeling indicates a source may impact visibility significantly, what Class I areas should 

be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a 

source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the 

closest Class I area will produce the controlling visibility impacts) are not likely to be universally 

reliable. 

4.3.2 Discussion of 12-km Initial Exemption Modeling

Meteorological Fields

A regional initial domain and a set of pre-computed regional CALMET meteorological files will 

be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with 

a consistent meteorological database and consistent CALPUFF modeling options. 

The following three years of MM5 meteorological data have been assembled by VISTAS for use 

in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

Appendix H.6 - 1015
VISTAS’ Common Modeling Protocol 37

- 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning 

Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has 

produced annual CALMET meteorological files at 12-km grid resolution for the domain shown in 

Figure 4-2. The CALMET modeling output files in the form of CALPUFF-ready threedimensional meteorological files will be available on external hard drives to the States and other 

parties.

The initial procedure to determine if a BART-eligible source is subject to BART uses the precomputed CALMET meteorological fields for the years 2001-2003 on the 12-km CALMET 

domain in Figure 4-2 and simulates with CALPUFF any BART-eligible source to be screened. 

The CALMET simulations will be developed using the highest resolution MM5 data available for 

each year (i.e., 36-km MM5 data for 2003, 12-km MM5 data for 2001 and 2002). 

The development of the regional CALMET meteorological fields from MM5 data will be 

conducted in No-Observations (“No-Obs”) mode. The MM5 data already reflect assimilation of 

observational data and are likely to adequately characterize regional wind patterns that are 

consistent with the 12-km grid scale. Blending of MM5 data with local observations (which are 

mainly at the surface) could lead to wind structures that may not be realistic under some 

conditions and may result in poorer characterization of the regional winds. Thus, the effort 

required to prepare observational data sets for CALMET for the large regional domain involves 

considerable effort that may not provide corresponding improvement of the wind field. 

For 2003, the 36-km MM5 data will be used as CALMET’s initial guess field and then the 

CALMET diagnostic terrain adjustments (see Section 3.1.1) will be applied to reflect terrain on 

the scale of the CALMET grid (i.e., 12-km). When the 12-km MM5 (2001 and 2002) data are 

used, the diagnostic CALMET terrain adjustments will be turned off since the grid resolution of 

the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid 

scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be 

interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will 

compute mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. 

Appendix H.6 - 1016
VISTAS’ Common Modeling Protocol 38

Figure 4-2. VISTAS Regional 12-km Resolution CALMET Modeling Domain (color area with 

terrain contours). The locations of the 36-km resolution MM5 grid points are shown on the plot. 

Impact Threshold

The final BART guidance recommends that the threshold value to define whether a source 

“contributes” to visibility impairment is 0.5 dv change from natural conditions (although states 

may set a lower threshold). The 98th percentile (8th highest annual) 24-hr average predicted 

impact at the Class I area, as calculated using CALPOST Method 6 (monthly average relative 

humidity values), is to be compared to this contribution threshold value. For this comparison, the 

predicted impact at the Class I area on any day is taken to be the highest 24-hr average impact at 

any receptor in the Class I area on that day. (Note that the receptor where the highest impact 

Appendix H.6 - 1017
VISTAS’ Common Modeling Protocol 39

occurs can change from day to day.) According to clarification of the BART guidance received 

from EPA, for a three-year simulation the modeling values to be compared with the threshold are 

the greatest of the three annual 8th highest values or the 22nd highest value over all three years 

combined, whichever is greater. 

For the purposes of the initial analysis, however, the highest value over the three-year period (not 

the 98th percentile value) is to be compared to the contribution threshold. This ensures a 

significant measure of conservatism in the initial approach. VISTAS will evaluate the initial 

CALPUFF results to determine if using the single highest value provides too conservative a 

screen for exemption purposes. If so, VISTAS may increase the number of exceedances of the 

contribution threshold that would be allowed and still qualify to exempt a source. 

4.3.3 Model Configuration and Settings for Initial Analysis

VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were 

developed by Earth Tech, Inc. and they are maintained on Earth Tech’s Atmospheric Studies 

Group CALPUFF website (www.src.com) for public access. This version includes CALMET, 

CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as CALVIEW. 

The initial analysis uses a CALPUFF computational domain that includes all Class I areas within 

300 km of a source. These Class I areas are specified in the CALPUFF control file for analysis. 

States could decide to require a different value for the maximum distance threshold for the 

CALPUFF domain, depending on the locations of the Class I areas in their states and other 

factors such as meteorological conditions and the magnitudes of the emissions from BARTeligible sources. The regional CALMET domain will be unchanged by these adjustments. 

Also, the initial approach is designed to significantly reduce the CALPUFF simulation time by 

restricting the CALPUFF computational domain size to include only areas where significant 

impacts are feasible rather than the entire regional domain. CALPUFF allows its computational 

domain to be specified as a subset of the CALMET meteorological domain by settings within the 

CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in 

the regional CALPUFF simulations is that CALPUFF run time is proportional to the number and 

residence time of the puffs on the domain (and other factors such as the number of receptors and 

the internal time step computed by the model). A CALPUFF domain covering an area 300 km 

from a source in all directions would involve only 50 x 50 12-km grid cells, which will require 

modest computational resources.

CALMET output files for the VISTAS regional domain shown in Figure 4-2 will be provided to 

VISTAS by Earth Tech. These files will be in CALPUFF-ready format, and as such, no 

CALMET user inputs will be required. An option in CALMET allows finer grid CALMET input 

files to be calculated from the 12-km CALMET files. 

Appendix H.6 - 1018
VISTAS’ Common Modeling Protocol 40

The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the 

initial analyses are listed below.

CALMET Modeling Configuration (12-km initial exemption modeling)

The CALMET model configuration for the regional CALMET simulations will be defined by 

Earth Tech in collaboration with the VISTAS States. The basic model configuration will follow 

the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted 

below. 

The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)

- Meteorological inputs: MM5 data provide initial guess fields in CALMET 

- CALMET grid resolution: 12-km (same Lambert Conformal coordinate system and grid 

cells as the 12-km 2001/2002 MM5 simulations)

- CALMET vertical layers: 10 layers. Cell face heights (meters): 0, 20, 40, 80, 160, 320, 

640, 1200, 2000, 3000, 4000.

- CALMET mode: No-Observations mode including option to read overwater data directly 

from MM5.

- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain 

blocking and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but 

no diagnostic terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 

2003 simulations). Values of ~10-20 km will be tested, and an appropriate value 

determined.

- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature 

allows the temperature field over large bodies of water such as the Atlantic Ocean and the 

Great lakes to be properly characterized by buoy observations.

- Mixing height averaging parameter (MNMDAV) will be determined by Earth Tech for the 

regional simulations based on sensitivity tests. The purpose of the testing is to optimize the 

variable to allow spatial variability in the mixing height field, but without excessive noise.

- Geophysical data for regional runs: SRTM-GTOPO30 30-arcsec terrain data, Composite 

Theme Grid (CTG) USGS 200m land use dataset. References for these and other CALMET 

Appendix H.6 - 1019
VISTAS’ Common Modeling Protocol 41

datasets can be found on the CALPUFF data page of the official CALPUFF site 

(www.src.com). 

CALPUFF Modeling Configuration (Initial exemption modeling)

The CALPUFF model configuration for the regional CALPUFF initial simulations will follow the 

recommended IWAQM guidance (EPA, 1998; Pages B-1 through B-8), except as noted below: 

- CALPUFF domain configured to include the source and all Class I areas within 300km of 

the source plus 50km buffer zone in each direction. CALPUFF is recommended for all 

source-receptor distances to be considered in the BART analyses.

- Chemical mechanism: MESOPUFF II module

- Background concentrations of SO4 and TNO3 (HNO3 + NO3) from CMAQ 2001-2003 

annual runs

- Species modeled: SO2, SO4, NOx, HNO3, NO3 and particulate matter in size categories of 

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic 

diameters. As noted below, the particulate matter emissions by size category will be 

combined into the appropriate species for the visibility analysis (i.e., elemental carbon (EC), 

fine PM or “soil” (< 2.5 μm in diameter), coarse PM (between 2.5-10 μm in diameter) and 

organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).

- Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual 

emission rate with normal operations from the highest emitting day of the meteorological 

period modeled (excluding days where start-up, shutdown or malfunctions occurred 

sometime during the day.) Note that potential emissions are used to determine if a source is 

BART-eligible, but 24-hour average maximum emissions are used for modeling purposes 

(70 FR 39162). Pollutants considered include SO2, H2SO4, NOx and PM10. 

Condensable emissions are considered as primary fine particulate matter and allocated 

equally to the two submicrometer particle size classes. If actual source emissions data are 

not available, the modeling should be based on permit limits. If source-specific size 

categories are not available, then AP-42 factors may be used for sources where AP-42 

factors are available. For sources where AP-42 factors are not available, assumptions for 

partitioning will be investigated by VISTAS based on review of available source categories. 

Excluded from the modeling are pollutants with plant-wide emissions less than de minimis

levels (40 tons per year for SO2 and NOx and 15 tons per year for PM10). De minimis levels 

are plant wide for each visibility-impairing pollutant, so individual units may be modeled 

even if they have emissions below de minimis if the plant total is greater than de minimis.

- Particulate emissions speciation: Break down, as appropriate, filterable and condensable 

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine 

Appendix H.6 - 1020
VISTAS’ Common Modeling Protocol 42

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. The 

process is illustrated in Figure 4-3. If source-specific emissions factors are not available, AP42 factors can be used to estimate the PM speciation for those source sectors for which AP42 emissions factors have been developed. Otherwise assumptions will need to be proposed 

by the source, and reviewed and approved by the state.

Figure 4-3. Speciation of PM-10 Emissions. (PMC is coarse particulate matter -- 2.5 to 10 μm 

diameter.)

- Class I receptors: Use FLM Class I receptor list with receptor elevations provided 

(available from the NPS).

- CALPUFF model options: Use IWAQM (EPA, 1998) default guidance, including 

Pasquill-Gifford dispersion coefficients. 

- Ozone dataset – use observed ozone data for 2001-2003 from CASTNet and AIRS stations. 

Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average 

ozone (backup) background values are to be computed based on daytime average ozone 

concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month). 

- Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) values for 

ammonia. 

PM-10

Filterable Condensable

EC PM-2.5 PMC PM

(including 

H2SO4)

As Prim. SO4)

Organic PM

(SOA)

EC Soil PMC SO4

f(RH)

Non-SO4

Inorganic

OC

Appendix H.6 - 1021
VISTAS’ Common Modeling Protocol 43

- Puff representation: integrated puff sampling methodology.

- Building downwash: Ignore building downwash unless source is within 50-km of a Class I 

area and the State instructs the source to specifically consider building downwash. 

CALPOST and POSTUTIL Configuration (Initial exemption modeling)

- Use Visibility Method 6 (for initial modeling), with EPA (2003a, b) Class I area-specific 

(centroid) monthly relative humidity values

- Species considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic 

emissions), soil, coarse PM

- Natural background light extinction: Method 6 requires input of natural background 

concentrations while the EPA provides (EPA, 2003b) haze index value (in deciviews) for the 

20% best days. To produce the appropriate natural background in CALPOST, use Equation 

3-2 to calculate the extinction coefficient that corresponds to EPA’s haze index value for the 

Class I area, subtract the Rayleigh scattering value of 10 Mm-1, and enter a soil 

concentration (in μg/m3) into CALPOST that is numerically equal to this result. (Since the 

extinction efficiency of soil is 1 m2/g, Equation 3-1 shows that this produces a background 

extinction that equals the EPA’s value.)

- Light extinction efficiencies: Use EPA (2003a) values. If a source chooses, the new 

IMPROVE algorithm for calculating light extinction (see Section 3.2.3) may be used in 

addition to the default IMPROVE algorithm. (Calculations would need to be performed 

outside CALPOST or CALPOST would need to be modified to accommodate the new 

algorithm.)

- Ammonia Limiting Method: Do not use for the initial modeling. 

The initial run results will be based on the highest change in light extinction (deciviews) from 

natural conditions over the three-year modeling period for each Class I area considered. 

Predicted changes exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid 

CALPUFF modeling analysis.

4.4 Finer Grid Modeling Procedures

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach

There are two potential applications for finer grid CALPUFF modeling: 

BART Exclusion Modeling. First, finer grid CALPUFF modeling can be used to demonstrate 

that a source does not cause or contribute to visibility impairment in any Class I areas, and thus 

can be excluded from BART controls. As shown in Figure 4-1, if the initial regional modeling 

results are not below the threshold for visibility impacts, the next step is to conduct modeling 

using a finer grid resolution for the meteorological fields and the treatment of terrain effects and 

Appendix H.6 - 1022
VISTAS’ Common Modeling Protocol 44

land use variability. In the finer grid modeling the predicted visibility impairment that is 

compared to the threshold is based on the BART guidance of the 98th percentile change in 

deciviews value rather than the more conservative highest value used in the initial analysis.

The BART guidance indicates that the emissions rate to be used for such modeling is the highest 

24-hr rate during the modeling period. Depending on the availability of source data, the following 

emissions information (listed in order of priority) should be used with CALPUFF for BART 

exclusion modeling:

- 24 hr maximum value emissions for the period 2001-2003 (Continuous Emission Monitor, 

CEM data)

- 24 hr maximum value from continuous emissions monitoring data 

- facility stack test emissions

- potential to emit

- permit allowable emissions, if available

- emissions factors from AP-42 source profiles 

Quantify Benefits of BART. The second application of refined modeling is to quantify the 

visibility benefits from the BART control options. This is accomplished by running CALPUFF 

with the baseline emissions rates and again with emissions after BART controls. It is important 

that emission reductions be evaluated in the postprocessing step rather than by using “negative” 

emission rates in the CALPUFF model. The chemical scheme requires that emission rates always 

be positive. 

For any of these applications, a source-specific modeling protocol that defines source properties 

and the specific model configuration is required. As discussed in Section 5, the source specific 

protocol should include source-specific emissions data and can refer to this document for all 

methods and assumptions that follow this common protocol. 

4.4.2 Model Configuration and Settings for Finer Grid Modeling

Grid resolution substantially better than 12-km is needed for a finer grid CALPUFF assessment of 

visibility impacts in most cases involving Class I areas in complex terrain or coastal areas. Thus, 

the CALMET fine grid resolution in the subregional modeling domains used for finer grid 

modeling will depend on the terrain, land use (especially coastal boundaries), location of the 

source, distance of the source from Class I areas, and total size of the subregional modeling 

domain. 

VISTAS intends to provide 2001-2003 CALMET files for five 4-km sub-regional domains as 

illustrated in Figure 4-4. The subdomains are designed to address all BART eligible sources 

Appendix H.6 - 1023
VISTAS’ Common Modeling Protocol 45

within each VISTAS states and all Class I areas within 300 km of the BART-eligible sources. 

For application for a single source, a smaller domain of roughly 200-300 km by 200-300 km is 

recommended. 

Figure 4-4. The five subregional domains for 4-km CALMET modeling.

In some instances, as part of the source-specific protocol, a source may propose to the State to use 

an even finer grid simulation to properly characterize the flow fields and land use changes that 

affect dispersion. An application for source-receptor distances within about 50 km may require a 

grid resolution less than 1 km if complex terrain effects are likely to be important. This 

determination should be made on a case-by-case basis. There is not a single distance at which a 

particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the 

source-receptor distances involved, the location of the source relative to the terrain features, the 

physical stack parameters (e.g., a tall stack in complex terrain may be unaffected by the terrainAppendix H.6 - 1024
VISTAS’ Common Modeling Protocol 46

forced flow), proximity of the source and Class I area to a coastline, and other factors including 

availability of representative observational data.

The finer grid CALMET simulations will be run by Earth Tech in hybrid mode, using both MM5 

data to define the initial guess fields and meteorological observational data in the Step 2 

calculations. Overwater (buoy) data will be provided in addition to the hourly surface 

meteorological observations, precipitation observations and twice-daily upper air sounding data. 

A domain-specific set of modeling parameters will be defined for each subregional domain. The 

proper selection of the CALMET diagnostic wind field parameters that are used to blend 

observations with the Step 1 wind field depends on factors such as the locations of the 

meteorological stations relative to terrain and coastal features (which affects the 

representativeness of the observational data), the terrain length scale, and the quality (resolution) 

of the MM5 data used to define the initial guess field and its ability to properly resolve wind 

flows on the fine-scale CALMET domain. The definition of the proper CALMET parameters is 

done as part of sensitivity testing where model performance is evaluated against available 

observations and expected terrain effects, such as channeling of flows within a valley. 

In addition to the better grid resolution and the introduction of observational data in the finer grid 

simulations, several other modeling refinements can enhance the accuracy of the finer grid 

modeling. These include use of the higher resolution terrain DEM data (~3 arc sec USGS data) 

in defining the gridded terrain fields and application of the ammonia limiting method in the 

POSTUTIL post-processor (using Class I area specific monthly diurnal profiles of NH3

concentrations developed from the 2002 CMAQ regional modeling performed by VISTAS). 

Otherwise, the source configuration, emissions, pollutant speciation, Class I receptors, ozone 

datasets and CALPUFF model options will be the same as in the initial runs.

For the finer grid BART exclusion analysis, the test for evaluating whether a source is 

contributing to visibility impairment is based on the 98th percentile modeled value (rather than the 

highest predicted value used for the initial evaluation), which is consistent with EPA’s BART 

guidance. A coding change is required in the CALPOST postprocessor in order to allow the 98th

percentile change in extinction to be computed. 

4.5 Presentation of Modeling Results

The CALPOST processing computes the daily maximum change in deciviews. A sample of the 

summary table produced by CALPOST is shown in Table 4-1. For evaluating compliance with 

the VISTAS screening threshold, the highest change in extinction value, located at the bottom of 

the CALPOST list file is compared to the threshold value (e.g., 0.5 dv). For example, in the 

sample shown in Table 4-1, the summary at the bottom shows that the highest visibility impact is 

1.219 dv, with 9 days over the year showing values greater than 0.5 dv. Therefore this source 

would not pass the initial analysis, and finer grid modeling would be required. 

Appendix H.6 - 1025
VISTAS’ Common Modeling Protocol 47

Table 4-1. Example of CALPOST Output, Showing Maximum Daily Impacts of Source and Locations of Those Impacts.

YEAR DAY HR RECEPTOR COORDINATES (km) TYPE DV(Total) DV(BKG) DELTA DV F(RH) %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF 

2001 2 0 3 20.540 79.782 D 5.397 5.358 0.039 4.314 44.33 47.22 3.07 1.07 0.00 4.30 

2001 3 0 9 31.680 79.822 D 4.566 4.421 0.145 1.767 40.75 33.89 9.19 3.24 0.00 12.94 

2001 4 0 1 24.723 77.951 D 4.540 4.540 0.000 2.076 0.00 0.00 0.00 0.00 0.00 0.00 

2001 5 0 77 30.228 94.571 D 4.950 4.939 0.011 3.144 43.13 44.74 4.64 1.45 0.00 6.05 

2001 6 0 1 24.723 77.951 D 5.181 5.166 0.015 3.772 38.58 56.05 1.90 0.70 0.00 2.76 

2001 7 0 3 20.540 79.782 D 6.366 5.745 0.620 5.439 44.98 44.99 3.69 1.26 0.00 5.08 

 .

 .

 .

2001 363 0 113 27.414 103.782 D 5.725 5.652 0.073 5.164 53.49 35.51 4.03 1.39 0.00 5.58 

2001 364 0 113 27.414 103.782 D 6.554 6.521 0.033 7.826 48.12 47.09 1.67 0.64 0.00 2.48 

2001 365 0 1 24.723 77.951 D 6.499 6.499 0.000 7.757 0.00 0.00 0.00 0.00 0.00 0.00 

--- Number of days with Delta-Deciview => 0.50: 9

--- Number of days with Delta-Deciview => 1.00: 2

--- Largest Delta-Deciview = 1.219

Appendix H.6 - 1026
VISTAS’ Common Modeling Protocol 48

In addition to the highest change in deciview value on each day over all the receptors in a 

particular Class I area, the CALPOST summary table in Table 4-1 contains the coordinates of the 

receptor, receptor type (D indicates discrete receptors), the total haze level (background + source, 

in dv), the background haze in deciviews, the change in haziness (delta dv), the humidity term 

applied to hygroscopic aerosols (F(RH)), and the contribution of each species to light extinction 

(in percent of the total source contribution) for SO4, NO3, organics, elemental carbon, coarse and 

fine particulate matter.

For the finer grid analysis, the data in the table can be imported into a spreadsheet and sorted on 

the delta dv column. Table 4-2 shows an example of the ranked visibility impacts (change in dv) 

for each of three years at six different Class I areas. The 98th percentile (8th highest value) in the 

sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the example 

shown in this table, the source passes the finer grid analysis because the highest 98th percentile 

visibility impact is below the contribution threshold of 0.5 dv. 

The Results section of the CALPUFF modeling report should contain the following information:

1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all 

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source, as illustrated in Table 4-3. 

3. A discussion of the number of Class I areas with visibility impairment from the source on 

98th percentile days in each year greater than 0.5 dv (total visibility impairment minus 

impairment on 20% best days for natural background visibility equals delta-dv, the 

visibility impact attributed to the source). 

4. For the Class I area with the maximum impact, discussion of the number of days below 

the 98th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in 

the Class I area where the impact exceeds 0.5 dv, and the maximum impact. 

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which 

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report 

same results as provided for 12-km initial exemption modeling.

6. For control option modeling, each control option tested should be listed in tabular format. 

For each control option and for each Class I area where the impact of the source exceeded 

0.5 dv, report the change in pollutant emissions and the change in visibility impact from 

the source as a result of the control option. The effectiveness of candidate control options 

are to be compared to each other, not to a specific target improvement. 

Appendix H.6 - 1027
VISTAS’ Common Modeling Protocol 49

Table 4-2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

Great Smoky NP

0.99

0.88

0.62

0.59

0.55

0.52

0.48

0.47

0.95

0.63

0.51

0.50

0.46

0.42

0.37

0.36

1.20

0.90

0.73

0.72

0.59

0.47

0.45

0.42

Linville Gorge

0.67

0.45

0.43

0.33

0.29

0.27

0.25

0.23

0.81

0.69

0.65

0.50

0.45

0.33

0.31

0.29

0.76

0.47

0.37

0.35

0.31

0.30

0.28

0.28

Shining Rock

0.66

0.43

0.41

0.35

0.26

0.24

0.23

0.22

0.73

0.69

0.63

0.52

0.46

0.34

0.29

0.26

0.75

0.45

0.36

0.34

0.28

0.27

0.26

0.25

Cohutta

0.26

0.23

0.22

0.21

0.20

0.19

0.18

0.16

0.54

0.47

0.43

0.37

0.37

0.31

0.31

0.30

0.61

0.42

0.30

0.29

0.28

0.28

0.25

0.25

Joyce Kilmer-Slickrock

0.34

0.33

0.31

0.26

0.24

0.20

0.18

0.17

0.52

0.43

0.32

0.31

0.30

0.28

0.24

0.24

0.27

0.24

0.23

0.20

0.14

0.13

0.11

0.10

Mammoth Cave NP

0.56

0.44

0.38

0.29

0.25

0.24

0.22

0.21

0.57

0.56

0.53

0.35

0.33

0.33

0.30

0.29

0.50

0.37

0.36

0.35

0.31

0.24

0.21

0.19

Appendix H.6 - 1028
VISTAS’ Common Modeling Protocol 50

Table 4-3. Format of Summary of Results for CALPUFF Modeling in VISTAS’ 12-km Modeling 

Domain to Determine if a BART Eligible Source is Subject to BART. 

Class I area Distance 

(km) 

from 

source to 

Class I 

area 

boundary

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2001

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2002

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2003

# of days1 and 

# of receptors 

with impact 

> 1.0 dv in 

Class I area 

for 3-yr 

period

Max. 24-hr 

impact over 

3-yr period

Dolly Sods, WV

Shenandoah, VA

James River 

Face, VA

Mammoth Cave, 

KY

Sipsey, AL

Great Smoky 

Mtns, TN

Cohutta, GA

Shining Rock, 

NC

Linville Gorge, 

NC

Swanquarter, NC

Cape Romain, 

SC

Okefenokee, GA

Saint Marks, FL

Chassahowitzka, 

FL

Everglades, FL

Brigantine, NJ

Breton Island, 

LA

Caney Creek, 

AR

Upper Buffalo, 

AR

Mingo, MO

Hercules Glade, 

MO

1

Days below the 98th percentile of days in each year or the three-year modeling period, as appropriate

Appendix H.6 - 1029
VISTAS’ Common Modeling Protocol 51

States will provide further guidance on graphic presentation of results to simplify 

evaluation of effectiveness of control measures. For example, a temporal plot of the 

change in deciviews between the controlled and uncontrolled cases could be developed for 

the receptor with the maximum modeled impact in each Class I area. 

7. Copies of all input files and input data in electronic format for the CALMET, CALPUFF, 

CALPOST and POSTUTIL runs should be archived and provided to the State.

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources

VISTAS will provide updates and supporting information concerning the Common Modeling 

Protocol (this document) on the VISTAS website. In addition, VISTAS will make publicly 

available the following data bases developed by Earth Tech:

• VISTAS version of the CALPUFF modeling system, maintained on the Earth Tech website. 

Version 5.754 includes CALMET, CALPUFF, CALPOST, and POSTUTIL files, updated in 

December 2005. The last update in this VISTAS version is a CALMET update that addresses 

over water dispersion, which was developed for Mineral Management Services (MMS) in fall 

2005. When available in January 2006, this VISTAS version of CALPUFF will not be 

updated further unless errors are found in the code. BART-eligible sources in the VISTAS 

states will be able to use this VISTAS version throughout the BART modeling exercise. 

• 12-km CALMET output files for 2001, 2002, and 2003 produced as described in previous 

sections. Further detail on model configuration and settings will be provided with the output 

files and will be made available on the CALPUFF website.

• CALMET will include a software modification to allow the meteorological data inputs into 

CALMET to be used to generate finer grid CALMET files without having to go back to the 

original MM5 output files

• Five 4-km CALMET subdomains for 2001, 2002, and 2003, produced as described in 

previous sections. Further detail on model configuration and settings will be provided with 

the output files and will be made available on the website.

• File with CALPUFF model configuration and settings sufficient to replicate CALPUFF 

modeling done for VISTAS using 12 km CALMET, including

o Ozone data used to run CALPUFF

o Ammonia data used to run CALPUFF and to partition NO3 in POSTUTIL.

o Background concentrations files for use in POSTUTIL 

o All other set up files used in VISTAS 12-km CALPUFF run

Appendix H.6 - 1030
Source-Specific Modeling Protocol 52

5. SOURCE-SPECIFIC MODELING PROTOCOL

Sources are required to submit a source-specific protocol to the State for review and approval 

prior to source-specific modeling. States will provide the documentation to EPA and FLM for 

their review. An outline of the typical contents of the site-specific protocol is provided in Table 

5-1.

If a source-specific modeling approach is proposed that differs from the common approach in 

Chapter 4, a more-detailed modeling protocol than that required under the common procedures is 

required. This protocol must explain the data sources, model configuration, and rationale for 

changes in the model approach from the common protocol and must be approved by the State. 

Unit-specific source data include the following parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO2, H2SO4, NOx and PM10).

Additional building dimension information (building width, length, height and corner locations) 

is needed for short stacks that are less than Good Engineering Practice (GEP) height. This 

information is used in providing effective structure dimensions for building downwash 

calculations. (The requirement to conduct building downwash modeling may be waived by 

individual States or if the transport distance is greater than 50 km.)

The source coordinates must be expressed in the coordinate system used to define the CALMET 

and CALPUFF modeling domains. For the regional screening simulations, a Lambert Conformal 

Conic (LCC) coordinate system will be used. The required parameters to define an LCC 

coordinate include two matching parallels, latitude/longitude of the projection origin, coordinate 

datum, and false Easting and Northing (if used) of the projection origin. Subregional and sourcespecific domains may be using either an LCC or UTM projection. 

The Earth Tech Graphical User Interface (GUI) system provides software (called COORDS) to 

compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datums. In 

addition, the CALVIEW graphics feature allows the use of georeferenced satellite or aerial 

photographs to be used as base maps to confirm source locations. Links to sources of suitable 

base maps can be found on the CALPUFF data site (www.src.com) in the section on “Aerial 

Photos”.

Appendix H.6 - 1031
Source-Specific Modeling Protocol 53

Table 5-1. Sample Table of Contents of a Source-Specific Fine-Scale Modeling Protocol.

1. INTRODUCTION

1.1 Objectives

1.2 Location of Source vs. Relevant Class I Areas

1.3 Source Impact Evaluation Criteria

2. SOURCE DESCRIPTION

2.1 Unit-specific Source Data

2.2 Boundary Conditions

3. GEOPHYSICAL AND METEOROLOGICAL DATA

3.1 Modeling Domain and Terrain

3.2 Land Use

3.3 Meteorological Data Base

3.3.1 MM5 Simulations

3.3.2 Measurements and Observations

3.4 Air Quality Data Base

3.4.1 Ozone Concentrations – Measured or Modeled

3.4.2 Ammonia Concentrations – Measured or Modeled

3.4.3 Concentrations of Other Pollutants – Measured or Modeled

3.5 Natural Conditions at Class I Areas

4. AIR QUALITY MODELING METHODOLOGY

4.1 Plume Model Selection

4.1.1 Major Relevant Features of CALMET

4.2.2 Major Relevant Features of CALPUFF

4.2 Modeling Domain Configuration

4.3 CALMET Meteorological Modeling

4.4 CALPUFF Computational Domain and Receptors

4.5 CALPUFF Modeling Option Selections

4.6 Light Extinction and Haze Impact Calculations

4.7 Modeling Products

5. REVIEW PROCESS

6.1 CALMET Fields 

6.2 CALPUFF, CALPOST, and POSTUTIL Results 

6. REFERENCES

APPENDICES

A.1 VISTAS BART MODELING PROTOCOL 

A.2 ... other appendices as needed

Appendix H.6 - 1032
Source-Specific Modeling Protocol 54

An example of the data that need to be reported is provided in Table 5-2. More detail on the 

stack data, emissions species, and particulate size fractions to be reported will be made available 

on the VISTAS website, www.src.com, Check with your State for the more detailed format of 

Table 5-2 that is to be used. 

Discussions with the regulatory authorities should be conducted prior to development of a 

protocol to ensure all of the relevant issues are included in the protocol. 

Table 5-2. Example of Source Documentation for BART Eligible Source. 

Unit name 

and/or 

description

Start-up dates SO2 potential 

emissions (tpy)

NOx potential 

emissions (tpy)

Total PM 

potential 

emissions (tpy) 

Emissions source

name

...

Total emissions

Potential BARTeligible 

emissions

Appendix H.6 - 1033
Quality Assurance 55

6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol is an important tool for use in determining 

whether a BART-eligible source can be reasonable expected to cause or contribute to visibility 

impairment in a Class I area, and therefore whether this source should be subject to BART 

controls, and if so, to determine the relative benefits of various BART controls. The purpose of 

the quality assurance (QA) program is to establish procedures for ensuring that products produced 

by the application of the modeling techniques for BART studies satisfy the regulatory objectives 

of the BART program.

The scope of the QA program affects different users differently. Common features of most 

applications will be the setup and execution of the CALPUFF air quality model and processing of 

modeling results to determine if a source contributes to visibility impairment at a Class I area. In 

many cases, users will be provided meteorological datasets that have been developed with 

VISTAS funding under a suitable QA program for use in the BART modeling. Other users will 

be involved in site-specific or source-specific analyses that will use additional datasets and 

potentially different modeling options and/or tools. More extensive quality assurance will be 

required in these latter types of applications. It is the responsibility of the modeler to ensure that 

an adequate QA protocol is in place for a particular application.

The CALPUFF modeling system contains built-in features to facilitate quality assurance of the 

modeling results. These include the automatic production of “QA” files for various datasets, 

including geophysical fields, sources and receptors, and imbedded tracking of model options and 

switches within the output files from the major modeling units of the modeling system. The 

Graphical User Interface system (GUI) provided as part of the latest CALPUFF modeling system 

allows these QA files to be displayed graphically.

In addition, a detailed software management system is in place to track version and level numbers 

associated each program and utility within the CALPUFF modeling system. This information is 

carried forward in all of the output files to create an audit trail of software versions and major 

model options used that can be retrieved and displayed from the model output files.

Because the required QA procedures will depend heavily on the exact application, there will be 

differences among different users and different applications.

In addition, the BART modeling process involves multiple organizations. The States have overall 

responsibility for the process and may also execute some or all of the modeling. VISTAS is 

contributing general guidance via this protocol and is preparing meteorological fields and 

performing modeling under the guidance of the States. The sources that are BART-eligible need 

to provide process information and emissions data for use in the analyses. In addition, those 

sources that are involved in BART assessments will need to be actively involved in control

Appendix H.6 - 1034
Quality Assurance 56

technology decisions and assessments. Finally, some of the modeling steps may be carried out by 

contractors on behalf of VISTAS, a State, or a source.

Each of these organizations has a responsibility to ensure that it is providing correct information 

to others and to evaluate the quality of any analyses it is performing, whether with data of its own 

or from others. This chapter provides general guidance and information on those aspects of 

quality assurance that are specific to the CALPUFF modeling effort, irrespective of which 

organization is carrying out the effort. The focus is on the common protocol efforts described in 

Chapter 4. As described in Section 6.3, more comprehensive QA may be needed for the unique 

aspects of the source-specific modeling described in Chapter 5.

6.2 QA Procedures for Common Protocol Modeling

The VISTAS common protocol (Section 4) describes the methods and procedures for use in 

conducting regional scale screening modeling to determine the whether a particular source or 

group of sources is subject to BART controls. In the initial application, the regional CALPUFFready meteorological data files will be provided by VISTAS. The amount of effort for end-users 

performing QA of these pre-defined meteorological fields will be reduced from what is required 

in developing source-specific meteorological fields, as described below. Also, VISTAS is 

planning to provide five subregional CALMET meteorological datasets in a CALPUFF-ready 

format. The development of these CALMET datasets will be subject to a QA program as part of 

their development, so the necessary quality assurance activity of end-users is again reduced from 

what would be required in the development of the dataset. It is not expected that the quality 

assurance steps in the development will be repeated in each application. The VISTAS-provided 

regional and subregional meteorological fields will include a test case simulation for 

demonstrating that expected modeling results are obtained on the user’s computer platform. This 

test should be repeated by every user.

Although the CALPUFF modeling system is recommended by the U.S. Environmental Protection 

Agency for application to BART analyses, a considerable amount of expertise and modeling 

judgment is needed at certain stages of the analysis. The modeling is not a “cookbook” exercise, 

a fact that was recognized by the U.S. EPA in describing the expertise needed for CALMET 

modeling (EPA, 1998; pp. 9-10,). Current methods for performing refined chemistry calculation 

also require an understanding of the chemical and meteorological processing affecting 

ammonium nitrate formation. VISTAS has committed to provide appropriate CALPUFF training 

to assist States in obtaining the necessary expertise with the latest CALPUFF modeling tools and 

techniques. An appropriate level of knowledge of the model formulation, technical approach and 

assumptions is essential for successful BART modeling. 

6.2.1 Quality Control of Input Data

The input data required by the model depends on the application. At a minimum, source data is 

required by CALPUFF (see Section 6.2.3) along with a list of choices made about model options 

and switches. Most of the modeling option choices are specified or recommended by regulatory 

Appendix H.6 - 1035
Quality Assurance 57

guidance and default values (see references in Section 4.3.3). However, remodeling of the 

boundary conditions is not required for VISTAS-provided finer grid domains so the expertise 

level is not as high as it would be for development of the boundary conditions files from scratch.

To the extent that modeling applications are using pre-defined CALMET files and CALPUFF 

templates, the quality assurance will be straightforward. More detailed steps are needed for the 

setup of modeling files for source-specific applications of subregional domains finer than 4 km. 

The basic procedures that will apply to all CALPUFF model applications will include a 

confirmation of the source data, including units, verification of the correct source and receptor 

locations, including datum and projection, confirmation of the switch selections relative to 

modeling guidance, checks of the program switches and file names for the various processing 

steps, and confirmation of the use of the proper version and level of each model program. It is a 

common and recommended procedure for an independent modeler not involved in the setup of 

the modeling files to independently confirm the model switches and data entry in the actual 

model input files and to conduct an independent run of the worst case event as a confirmation 

check.

In addition, common practice requires that a model project CD (or DVD or set of DVDs) be 

created that contains all of the data and program files needed to reproduce the model results 

presented in a report. The model list files from each step are included on the project CD. This 

information allows independent checking and confirmation of the modeling process.

6.2.2 Quality Control of Application of CALMET

For users of the VISTAS CALPUFF-ready CALMET meteorological files, a number of large 

datafiles will be provided by VISTAS on external USB2 or Firewire hard drives in a format ready 

for use with the CALPUFF model. The QA steps associated with the development of the 

VISTAS common datasets will be provided separately as part of the modeling documentation. It 

is not expected that the QA steps conducted in the development of the meteorological datasets 

will be repeated in each application, although tests to confirm that the dataset is suitable for the 

application for which it is being used should be performed as part of the QA. This is discussed in 

more detail below.

The regional screening CALMET grid is defined in Chapter 4 on a 12-km Lambert Conformal 

Conic (LCC) grid system. The subregional and source-specific domains may be defined in either 

LCC or Universal Transverse Mercator (UTM) coordinates. In the case of the LCC projection, 

two matching parallels, latitude/longitude of the projection origin, coordinate datum, and false 

Easting and Northing (if used) of the projection origin must also be defined. For any domains in 

UTM coordinates, the UTM zone (see Appendix D of the CALMET User’s Guide) and datum 

must be defined. The appropriate projection and map factors are provided as part of the 

definition of the VISTAS regional grid system. For a source-specific domain, the grid parameters 

will be provided as part of the source-specific protocol.

Appendix H.6 - 1036
Quality Assurance 58

Appendix A of the IWAQM report (EPA, 1998) contains a list of recommended CALMET switch 

settings. Except as modified in Chapter 4 of this protocol or in a source-specific protocol, the 

IWAQM guidance should be used in setting up the CALMET simulations. The CALMET model 

obtains the switch settings from an ASCII “control file” with a default name of CALMET.INP. 

Whether the model is run using a GUI or from the control line in a DOS, Linux, or Unix window, 

it is essential that the control file be reviewed as part of the CALMET QA analysis. The 

CALMET GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. This includes the default value for each variable, a text description 

of the variable, the meaning of each variable option, the units of the variable and interrelationships among variables indicating if/when the variable is used. Some third-party 

commercial GUIs strip out this descriptive information, which makes the QA step more difficult, 

although it is essential for perform nonetheless using the variable names as references for the 

variables in the file.

Part of the CALPUFF modeling system’s built-in QA capabilities is a variable tracking system 

that retains the control file inputs for CALMET and CALPUFF in the output files create by the 

models. This information includes the Version and Level numbers of the processor codes and 

main model codes used in the simulations as well as the control files from the main models 

(CALMET and CALPUFF). The information from the preprocessing steps and the CALMET 

and CALPUFF model simulations is all carried forward and saved in the 

CALPUFF/postprocessor output files so that the final concentration/flux files contain a history of 

the model options and switch settings. This allows a user or reviewing agency to confirm the 

switch settings provided in a control file with that actually used in the model simulations. An 

optional switch in the CALPOST processor creates a complete listing of the QA data. This step 

requires access to the output CALPUFF concentration and/or flux files, which are normally 

practical to store on CDs or DVDs and to provide a part of the Project CD/DVD set.

6.2.3 Quality Control of Application of CALPUFF

The quality assurance of the source and emissions data is a major component of the CALPUFF 

modeling. Also, many errors are found in source coordinates and related projection/datum 

parameters, so confirmation of the source location is an important part of the modeling QA.

The locations of the Class I area receptors are another important CALPUFF input. The use of 

pre-defined receptors as provided by the National Park Service (NPS) receptor dataset is 

recommended in the VISTAS common protocol. However, although the latitude and longitude of 

each receptor point is provided, it is necessary to ensure that the proper UTM or LCC coordinates 

have been computed for computational domain selected. In particular, the datum of the NPS 

conversion software is not specified, so it is recommended that coordinates be checked using the 

CALPUFF GUI’s COORDS software or another comparable coordinate translation software 

package that recognizes various datums.

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of 

Appendix H.6 - 1037
Quality Assurance 59

this protocol or in a source-specific protocol, the IWAQM guidance should be used in setting up 

the CALPUFF simulations. The CALPUFF model obtains the switch settings from an ASCII 

“control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the 

CALPUFF GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. Some third-party commercial GUIs strip out this descriptive 

information, which makes the QA step more difficult, although it is essential for perform 

nonetheless using the variable names as references for the variables in the file.

6.2.4 Quality Control of Application of CALPOST and POSTUTIL

CALPOST is run separately for each Class I area in order to obtain the necessary visibility 

statistics for evaluating compliance with the BART screening and finer grid modeling thresholds. 

The inputs to CALPOST involve selection of the visibility method (Method 6 in the standard 

EPA BART guidance), entry of Class I area-specific data for computing background extinction 

and monthly relative humidity factors for hygroscopic aerosols. CALPOST contains a receptor 

screening that allow subsets of a receptor network modeling in CALPUFF to be selected for 

processing in a given CALPOST run. This is how receptors within a single Class I area are 

selected for processing from a CALPUFF output file that may contain receptors from several 

Class I areas. CALPOST contains options for creating plot files that will help in the confirmation 

that the proper receptor subset is extracted.

The CALPOST output file contains a listing of the highest visibility impact each day of the model 

simulation over all receptors included in CALPOST analysis. Receptors will normally be 

selected in each CALPOST run so that each CALPOST run represents the impacts at a single 

Class I area. The table includes the data shown in the example in Table 4-1. For a screening 

assessment, the peak value of the change in extinction is shown at the bottom of the visibility 

table (see Table 4-1). For a finer grid simulation, the 98th percentile value (8th highest day) is 

used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the 

results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily 

change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that need to be carefully checked as part of the CALPOST quality 

assurance are:

- Visibility technique (Method 6 in the common VISTAS protocol)

- Monthly Class I-specific relative humidity factors for Method 6

- Background light extinction values

- Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, 

organics, (as SOA), coarse and fine particulate matter and elemental carbon.

Appendix H.6 - 1038
Quality Assurance 60

- Appropriate species names for coarse PM used

- Extinction efficiencies for each species

- Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area 

specific value for finer grid modeling)

- Screen to select appropriate Class I receptors for each CALPOST simulation.

The CALPOST program produces plot files compatible with CALVIEW that allow confirmation 

of receptor locations that is useful in evaluating the receptor screening step.

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF 

simulations into a total concentration file. In addition, it contains options to scale the 

concentrations from different modeled species (e.g., different particle sizes) into speciesdependent size distributions for the particulate matter. For example, PM is often simulated with 

unit emission rates for each particle size category and, in the POSTUTIL stage, the contributions 

of each size category based on the species being considered (e.g., elemental carbon, coarse 

particulate matter, etc.) are combined to form the species concentrations for input into 

CALPOST. This process, although simple, requires a careful review of the weighting factors for 

each source. POSTUTIL also allows a repartitioning of nitric acid and nitrate to account for the 

effects of ammonia limiting conditions. 

If source-specific modeling is performed using different sources of data or different techniques, 

the source-specific modeling protocol should provide justification for deviations from the 

VISTAS common protocol, and a QA plan specific for the application provided to address the 

quality assurance of the data used.

6.3 Additional QA Issues for Alternative Source-Specific Modeling

The level of QA required for application of source-specific protocols will be substantially higher 

than for the use of datasets that have already been subject to a QA procedure. For example, 

source-specific protocols may include the use of on-site meteorological datasets, the use of higher 

resolution prognostic meteorological (e.g., MM5) datasets, alternative visibility calculations, 

different extinction coefficients, or other changes to the common protocol. In addition to 

providing a source-specific modeling protocol describing and justifying the changes to the 

modeling approach from the VISTAS common protocol, the site-specific applications should 

include the development of a QA plan to properly evaluate the data used in the site-specific 

modeling.

The critical CALMET input parameters depend on the mode in which the model is run 

(observations mode, hybrid mode or no-observations mode), and the location and spatial 

representativeness of any observational data. In a site specific protocol involving the 

development of a meteorological dataset, the elements of the QA process include preparation of 

wind rose (using observed, MM5 and CALMET-derived data), including examination of the data 

Appendix H.6 - 1039
Quality Assurance 61

as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, 

and presentation of 2-D vector plots illustrating terrain effects/sea breeze circulation or other 

features of the flow expected to occur within the domain. For example, 2-D vector plots 

produced during light wind speed stable conditions (e.g., early morning such as 4 am) are good 

for assessing the performance of the CALMET model configuration and switches in reproducing 

terrain effects because these conditions are likely to maximize the terrain impacts in the model. 

Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea 

breeze circulations that may be important for certain applications. Customization of the QA 

process for the individual site-specific domain based on the availability of data and the physical 

processes expected to be important at that location should be conducted as part of the site-specific 

QA plan development.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-specific modeling domain should be 

evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than 

derived ammonia based on aerosol measurements) will require an evaluation of the model 

performance relative to the techniques in the VISTAS common protocol.

In any site-specific protocol a site-specific QA plan should be prepared.

6.4 Assessment of Uncertainty in Modeling Results

Chapter 3 discussed the uncertainties and known limitations in CALPUFF. The source specific 

modeling report does not need to repeat the uncertainties listed in Chapter 3, but the reviewer 

should interpret results in light of these limitations. It is expected that the performance of the 

model will be better in predicting changes in visibility impacts due to BART controls than in 

predicting absolute visibility values. This is because uncertainties in meteorological conditions 

transport and dispersion are expected to be less important in evaluating a change in impact, since 

a comparable effect will be included in both the base and sensitivity simulations. 

Appendix H.6 - 1040
References 62

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and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. 

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast Cycle: 

the RUC, Mon. Wea. Rev., 132, 495-518.

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary layer. 

Quart. J. Roy. Meteor. Soc., 99, 450-467.

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC and 

VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466.

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of the 

Montana Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources 

Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, 

Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze Rule; 

EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions under 

the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research Triangle 

Park, NC.

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for Modeling 

Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R-98-019, U.S. Environmental Protection Agency, RTP, NC.

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Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol Formation 

and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on the Applications 

of Air Pollution Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: 

Bulk parameterization of air-sea fluxes: Updates and verification for

the COARE algorithm. Journal of Climate, 16, 571-591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis of 

PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory. 

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. 

U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR 

MM5, NCAR TN-398-STR, NCAR Technical Note.

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary Organic 

Aerosols. Atmos. Environ., 23, 1733-1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes 

from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with 

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E. 

Gryning and F.A. Schiermeier. Plenum Press, New York, NY.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating 

Bureau MID/SSD/80/0026/R. Nottingham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation Mechanism 

in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference – Guideline on 

Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 22-

26 June 2003, San Diego, CA.

O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary 

boundary layer. J. Atmos. Sci., 27, 1213-1215.

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Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE 

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering 

Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. 

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms 

for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User Instructions. EPA/600/8-

89/041, U.S. EPA, Research Triangle Park, North Carolina.

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME Plume 

Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390. 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting & 

Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET 

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental 

Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

Scire, J.S., Z.-X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, Visibility 

and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline 

on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF model for 

offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation 

within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17-20 

October 2005.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model with 

Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution Meteorology, 

Phoenix, Arizona. American Meteorological Society. 11-16 January 1998.

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Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic 

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the 

AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11 

February 2005.

Appendix H.6 - 1044
A Trusted Global Environmental, Health and Safety Partner 

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CA, Sacramento 

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CO, Ft. Collins 

(970) 493-8878 

Ft. Collins Tox Lab 

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CT, Stamford 

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CT, Willington 

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FL. St. Petersburg 

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FL, Tallahassee 

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GA, Norcross 

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IL, Chicago 

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IL, Collinsville 

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LA, Baton Rouge 

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MA, Air Laboratory 

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NY, Rochester 

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NY, Syracuse 

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NY, Syracuse Air Lab 

(315) 434-9834 

OH, Cincinnati 

(513) 772-7800 

PA, Langhorne 

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PA, Pittsburgh 

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RI, Providence 

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WI, Milwaukee 

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MA, Westford 

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Japan 

Malaysia 

Philippines 

Thailand 

Turkey 

United Kingdom 

Venezuela 

www.ensr.aecom.com 

Appendix H.6 - 1045
Prepared for: 

ISG Georgetown Inc. 

Georgetown, SC 

Source-Specific BART Exemption 

Modeling Results: 

ISG Georgetown Inc., Georgetown, SC 

ENSR Corporation 

September 2006 

Document No.: 12084-002-0102 

Appendix H.6 - 1046
Prepared for: 

ISG Georgetown Inc. 

Georgetown, SC 

Source-Specific BART Exemption 

Modeling Results: 

ISG Georgetown Inc., Georgetown, SC 

_________________________________ 

Prepared By 

_________________________________ 

Reviewed By 

ENSR Corporation 

September 2006 

Document No.: 12084-002-0102 

Appendix H.6 - 1047
ISG Georgetown SC – Source-Specific BART Exemption i September 2006

Modeling Results – 12084-002-0102

Contents 

1.0 Introduction ............................................................................................................................................ 1-1

1.1 Objectives ........................................................................................................................................ 1-1

1.2 Location of source vs. relevant Class I Areas ................................................................................ 1-1

1.3 Organization of protocol document................................................................................................. 1-2

2.0 Source Description and Emissions Data............................................................................................ 2-1

2.1 Unit-Specific Source Data ............................................................................................................... 2-1

3.0 Geophysical and Meteorological Input to CALMET.......................................................................... 3-1

3.1 Modeling Domain............................................................................................................................. 3-1

3.2 Terrain and Land Use...................................................................................................................... 3-1

3.3 Meteorological Database................................................................................................................. 3-1

3.4 Air Quality Database (Background Ozone and Ammonia) ............................................................ 3-2

3.5 Natural Conditions and Monthly f(RH) at Class I Areas................................................................. 3-2

4.0 Air Quality Modeling Procedures ........................................................................................................ 4-1

4.1 Model Selection and Features ........................................................................................................ 4-1

4.2 Modeling Domain and Receptors.................................................................................................... 4-1

4.3 Technical Options Used in the Modeling ........................................................................................ 4-1

4.4 Light Extinction and Haze Impact Calculations .............................................................................. 4-1

5.0 BART Exemption Modeling Results.................................................................................................... 5-1

Appendix H.6 - 1048
ISG Georgetown SC – Source-Specific BART Exemption ii September 2006

Modeling Results – 12084-002-0102

List of Tables 

Table 1-1: ISG Georgetown Inc., Georgetown, SC Mill – BART-eligible Units........................................ 1-2

Table 2-1: ISG Georgetown Inc., Georgetown, SC Mill – Baseline BART Stack Parameters and Criteria 

Pollutant Emissions .................................................................................................................. 2-2

Table 5-1: Summary of Results – ISG Georgetown Refined BART Exemption Modeling...................... 5-1

Table 5-2: Detailed Cape Romain Results – ISG Georgetown Refined BART Exemption Modeling..... 5-1

List of Figures 

Figure 1-1: Location of PSD Class I Areas in Relation to ISG Georgetown ............................................. 1-3

Appendix H.6 - 1049
ISG Georgetown SC – Source-Specific BART Exemption 1-1 September 2006

Modeling Results – 12084-002-0102

1.0 Introduction 

1.1 Objectives 

The Regional Haze Rule regulations require Best Available Retrofit Technology (BART) for any BART-eligible 

source that ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any 

impairment of visibility” in any mandatory Class I federal area. Pursuant to federal regulations, states have the 

option of exempting a BART-eligible source from the BART requirements based on dispersion modeling 

demonstrating that the source cannot reasonably be anticipated to cause or contribute to visibility impairment 

in a Class I area. 

The ISG Georgetown Inc. Steel mill in Georgetown, SC (ISG Georgetown) has been identified as a BART 

eligible source, due to the fact that the units were placed in service between 1962 and 1977 and that potential 

emissions of one pollutant, NOx, exceed 250 tons per year. As noted above, a modeling demonstration can be 

used to show that a source identified as being eligible for BART requirements can be exempt if modeling 

shows the source does not cause or contribute to a visibility impairment as defined by the BART guideline (40 

CFR Part 51, Appendix Y). 

The purpose of this document is to present the results of a BART exemption modeling analysis that was 

conducted by the VISTAS modeling contractor, TRC, for the ISG Georgetown mill. The following units were 

identified by the South Carolina Department of Health and Environmental Control (DHEC) as BART eligible: 

• Meltshop - arc furnaces and lime handling systems 

• DRI production - DRI reformer, reduction furnace, and day bin 

• DRI production – passivation and conveying system 

• DRI production - drimetallized briquetting 

• DRI production - ore conveying and screening. 

Additional information regarding the start-up dates and potential emissions for these BART-eligible units is 

provided in Table 1-1. 

The exemption modeling analysis was conducted to determine whether the source is subject to BART 

requirements. The modeling procedures are consistent with those outlined in the VISTAS common BART 

modeling protocol, available at the VISTAS web site: http://www.vistas-sesarm.org/BART/. 

This protocol references certain relevant portions of the common VISTAS protocol. 

1.2 Location of source vs. relevant Class I Areas 

DHEC, which is in charge of the state’s BART program, has determined that units listed in Table 1-1 are 

BART-eligible for NOx, SO2 and PM. Figure 1-1 shows a plot of the Georgetown mill relative to nearby PSD 

Class I Areas. There are two Class I areas within 300 km of the mill: Cape Romain and Wolf Island 

Wilderness Areas. As shown in the figure, the distance to Cape Romain is about 30 km, while the distance to 

Wolf Island is about 290 km. The BART exemption modeling was conducted for these two Class I areas by 

the VISTAS modeling contractor, TRC, in accordance with the referenced VISTAS common BART modeling 

protocol and the procedures described in this source-specific BART modeling protocol. 

Appendix H.6 - 1050
ISG Georgetown SC – Source-Specific BART Exemption 1-2 September 2006

Modeling Results – 12084-002-0102

1.3 Organization of protocol document 

Section 2 of this source-specific protocol describes the source emissions that were used as input to the BART 

exemption modeling. Section 3 describes the input data to be used for the modeling. Section 4 describes the 

air quality modeling procedures and Section 5 presents the modeling results. Since all of the references cited 

are also included in the VISTAS common BART modeling protocol, no additional references section is 

required in this document. 

Table 1-1: ISG Georgetown Inc., Georgetown, SC Mill – BART-eligible Units 

Allowable Emissions 

Source Description Start-up Date 

SO2 NOX PM10

 TPY TPY TPY 

Unit 1 Meltshop – arc furnaces 

and lime handling systems 

1969/1970/ 

1974 12.32 88.21 120.49 

Unit 2a DRI reformer day bin 1970 N/A N/A 9.72 

Unit 2b DRI reformer and 

reduction furnace 1970 14.84 339 5.38 

Unit 3 DRI passivation and 

conveying system 1974 N/A N/A 1.58 

Unit 4 DRI metallized briquetting 1974 N/A N/A 2.99 

Unit 5 DRI ore conveying and 

screening 1970 N/A N/A 1.98 

Note: For Unit 1, EAF #1 installed in 1969, EAF #3 installed in 1970, lime handling system installed 1974. 

Appendix H.6 - 1051
ISG Georgetown SC – Source-Specific BART Exemption 1-3 September 2006

Modeling Results – 12084-002-0102

Figure 1-1: Location of PSD Class I Areas in Relation to ISG Georgetown 

Appendix H.6 - 1052
ISG Georgetown SC – Source-Specific BART Exemption 2-1 September 2006

Modeling Results – 12084-002-0102

2.0 Source Description and Emissions Data 

2.1 Unit-Specific Source Data 

The emissions data used to assess the visibility impacts at the Class I areas within 300 km of the subject plant 

is discussed in this section. The SO2, NOx and PM10 emissions were provided by ISG Georgetown and are 

representative of the highest 24-hour average actual emission rate for the years 2001 to 2003. Since various 

components of PM10 emissions have different visibility extinction efficiencies, the PM10 emissions are divided, 

or “speciated”, into several components. We note that AP-42 does not contain speciation data or guidance for 

the BART eligibility source types. Therefore, the PM10 emissions were conservatively modeled as being all 

organic condensible PM. Table 2-1 provides a summary of the baseline modeling emission parameters that 

were used in the BART CALPUFF modeling. 

Appendix H.6 - 1053
ISG Georgetown SC – Source-Specific BART Exemption 2-2 

Modeling Results – 12084-002-0102

Table 2-1: ISG Georgetown Inc., Georgetown, SC Mill – Baseline BART Stack Parameters and Criteria Pollutant Emissio

Location UTM 

(Zone 17 NAD-83) 

Maximum 

Short-Term Emissio

Stack ID Source / Unit UTM 

East 

UTM 

North 

Stack 

Ht 

Base 

Elev. 

Diameter Gas Exit Vel. Stack Gas Exit Temp. SO2 NOX 

 km Km M ft m m/s deg K lb/hr lb/hr 

6 Unit 1 659055 3693488 24.384 10 232.751 0.010 355.37 28.13 20.14 

471 Unit 2a 659107 3693357 9.144 10 0.488 27.127 293.15 0 0 

PS Unit 2b 659103 3693372 28.346 10 3.569 13.679 417.04 3.39 77.40 

3 Unit 3 659139 3693290 9.449 10 0.610 10.516 293.15 0 0 4 Unit 4 659109 3693366 8.534 10 0.488 12.649 293.15 0 0 

 Unit 5 No Emissions – Ore is Wet in this Process 

Notes:

• Emission parameters were provided for the baseline conditions of the units listed below. 

• Unit1 = Meltshop - arc furnaces and lime handling systems (CALPUFF Source #1) • Unit 2a = DRI production - DRI reformer and reduction furnace (CALPUFF Source #3) • Unit 2b = DRI production - DRI reformer and day bin (CALPUFF Source #2) • Unit 3 = DRI production – passivation and conveying system (CALPUFF Source #4) • Unit 4 = DRI production - drimetallized briquetting (CALPUFF Source #5) 

• Unit 5 = DRI production - ore conveying and screening • Diameter listed for Unit 1 is an equivalent diameter. • Gas exit velocity listed for Unit 1 represents a non-vertical release for a volume source with lateral dimension = 0

dimension = 1.43 m 

• Emissions reported are the highest 24-hour average actual emission rate for the years 2001 to 2003 
ISG Georgetown SC – Source-Specific BART Exemption 3-1 September 2006

Modeling Results – 12084-002-0102

3.0 Geophysical and Meteorological Input to CALMET 

3.1 Modeling Domain 

The regional 12-km CALMET meteorological data was developed for three years (2001-2003) to cover all 

BART eligible sources and PSD Class I areas within the VISTAS states. The extent of the modeling domain is 

shown in Figure 4-2 of the VISTAS common BART modeling protocol. 

Five sub-regional 4-km CALMET meteorological databases were also developed so that a more refined 

analysis could be conducted. The sub-regional modeling domains are strategically designed to cover all 

potential BART eligible sources within VISTAS states and all PSD Class I areas within 300 km of those 

sources. The extents of the 4-km sub-regional domains are shown in Figure 4-4 of the VISTAS common 

BART modeling protocol. The selection of the meteorological database and the associated domain is 

discussed further in Section 3.3. 

3.2 Terrain and Land Use 

USGS 90-meter Digital Elevation Model (DEM) files were used by VISTAS to generate the terrain data at 4-km 

resolution for input to the 4-km sub-regional CALMET runs. Likewise, USGS 90-meter Composite Theme Grid 

(CTG) files were used by VISTAS to generate the land use data at a 4-km resolution for input to the 4-km subregional CALMET runs, respectively. 

3.3 Meteorological Database 

Three years of MM5 data (2001-2003) were used by VISTAS to develop the 4-km sub-regional meteorological 

datasets. The three years of MM5 data are as follows (as recommended in the VISTAS protocol): 

• 2001 MM5 dataset at 12 km and 36 km resolution (developed for EPA) 

• 2002 MM5 dataset at 12 km and 36 km resolution (developed by VISTAS) 

• 2003 MM5 dataset at 36 km resolution (developed by the Midwest Regional Planning Organization) 

Figures 4-2 and 4-4 of the VISTAS common BART modeling protocol show the extent of the 12-km and 36-km 

MM5 used to generate the VISTAS CALMET data. 

VISTAS provided five sub-regional CALMET databases at 4-km resolution. These sub-regional 4-km 

CALMET modeling domains were strategically developed to cover all potential BART eligible sources within 

VISTAS states and all PSD Class I areas within 300 km of those sources. Figure 4-4 of the VISTAS common 

BART modeling protocol shows the locations of these sub-regional CALMET domains with respect to all Class 

I areas within VISTAS states. Subdomain #4 is appropriate for use in this application. 

The sub-regional CALMET databases were developed using the same MM5 databases as used in the 

development of the 12-km regional CALMET database. However, in this instance, due to the finer grid 

resolution, additional observations of hourly surface meteorological data, precipitation data, and twice daily 

upper air soundings were incorporated into the development of the Step 2 wind field. Additionally, for those 

domains that extended over water, over water data (from buoys and shoreline meteorological CMAN stations) 

were also included. At a grid resolution of 4-km and smaller, localized wind flow patterns from actual 

observations sites become much more important in defining the wind field. Domain-specific CALMET settings 

used for each sub-regional CALMET run were provided and used by the VISTAS modeling contractor. 

Appendix H.6 - 1055
ISG Georgetown SC – Source-Specific BART Exemption 3-2 September 2006

Modeling Results – 12084-002-0102

3.4 Air Quality Database (Background Ozone and Ammonia) 

Hourly measurements of ozone from all non-urban monitors, as generated by VISTAS and available on the 

VISTAS CALPUFF page on the TRC web site (http://www.src.com/verio/download/sample_files.htm), were 

used as input to CALPUFF. A constant gaseous ammonia concentration of 0.5 ppb was used with application 

of the ALM technique for the 4-km subdomain CALPUFF runs. 

3.5 Natural Conditions and Monthly f(RH) at Class I Areas 

For each of the Class I areas shown in Figure 1-1, natural background conditions must be established in order 

to determine a change in natural conditions related to a source’s emissions. EPA’s “Guidance for Estimating 

Natural Visibility Conditions Under the Regional Haze Rule," September 2003, Appendix B, indicates the 

deciviews associated with the best 20% of days in each Class I area should be used for background. To 

determine the input to CALPUFF, it is first necessary to convert the deciviews to extinction using the equation: 

Extinction (Mm-1) = 10 exp(deciviews/10). 

The U.S. EPA has finalized a litigation settlement for this issue that allows each state (or BART source) to 

determine which background value to use (best 20% of days, or annual average value). ISG Georgetown Inc. 

elected to use the annual average natural background value. 

Appendix H.6 - 1056
ISG Georgetown SC – Source-Specific BART Exemption 4-1 September 2006

Modeling Results – 12084-002-0102

4.0 Air Quality Modeling Procedures 

This section provides a summary of the modeling procedures outlined in the VISTAS protocol that were used 

for the refined CALPUFF analysis that was conducted for ISG’s Georgetown, SC mill. 

4.1 Model Selection and Features 

As noted in the VISTAS protocol, VISTAS used CALPUFF Version 5.754 and CALMET as provided on the 

TRC web site, which can be obtained at 

http://www.src.com/verio/download/download.htm#VISTAS_VERSION. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They are maintained on 

TRC’s Atmospheric Studies Group CALPUFF website for public access. This release includes CALMET, 

CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as CALVIEW. 

The major features of the CALPUFF modeling system, including those of CALMET and the post processors 

(CALPOST and POSTUTIL) are referenced in Section 3 of the VISTAS protocol. 

4.2 Modeling Domain and Receptors 

Due to the relatively small distance between the facility and the closest Class I areas, the BART runs 

conducted by the VISTAS contractor used the subdomain #3 4-km CALMET data, as discussed above. This 

domain includes all Class I areas within 300 km of the source, plus a 50-km buffer. The receptors used for 

each of the Class I areas were based on the NPS database of Class I receptors, as recommended by VISTAS. 

4.3 Technical Options Used in the Modeling 

CALMET modeling for the VISTAS-provided 4-km subdomain was completed by the VISTAS contractor, and, 

therefore, we assume that VISTAS approves of the manner in which CALMET has been run for the subdomain 

data that they provide. For CALPUFF model options, ISG Georgetown proposed the use of the VISTAS 

common BART modeling protocol recommendations. The VISTAS protocol also notes that building 

downwash effects are not required to be included unless the state directs the source to include these effects. 

Since ISG Georgetown is about 30 km from the nearest Class I area, South Carolina DHEC has determined 

that building downwash effects do not need to be included in the CALPUFF modeling. 

VISTAS uses a default ammonia background concentration of 0.5 ppb. 

4.4 Light Extinction and Haze Impact Calculations 

The CALPOST postprocessor was used as prescribed in the VISTAS protocol for the calculation of the impact 

from the modeled source’s primary and secondary particulate matter concentrations on light extinction. The 

formula that is used was the existing (not the November 2005 revised) IMPROVE/EPA formula, which is 

applied to determine a change in light extinction due to increases in the particulate matter component 

concentrations. Using the notation of CALPOST, the formula is the following: 

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] + 0.6[Coarse Mass] + 10[EC] + bRay

The concentrations, in square brackets, are in μg/m3

 and bext is in units of Mm-1. The Rayleigh scattering term 

(bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for tracking reasonable progress. 

The assessment of visibility impacts at the Class I areas used CALPOST Method 6. The VISTAS protocol 

indicates that the natural background allows each state or source to select either the annual average or best 

20% days’ natural background. ISG Georgetown Inc. elected to use the annual average natural background 

Appendix H.6 - 1057
ISG Georgetown SC – Source-Specific BART Exemption 4-2 September 2006

Modeling Results – 12084-002-0102

value. Each hour’s source-caused extinction is calculated by first using the hygroscopic components of the 

source-caused concentrations, due to ammonium sulfate and nitrate, and monthly Class I area-specific f(RH) 

values. The contribution to the total source-caused extinction from ammonium sulfate and nitrate is then 

added to the other, non-hygroscopic components of the particulate concentration (from coarse and fine soil, 

secondary organic aerosols, and from elemental carbon) to yield the total hourly source-caused extinction. 

The BART rule significance threshold for the contribution to visibility impairment is 0.5 deciviews. The VISTAS 

protocol indicates that with the use of the 4-km sub-regional CALMET database, a source does not cause or 

contribute to visibility impairment if the 98th percentile (or 8th highest) day’s change in extinction from natural 

conditions does not exceed 0.5 deciviews for any of the modeled years. As an added check, the 22nd highest 

prediction over the three years modeled should also not exceed 0.5 deciviews for a source to be exempted 

from a BART determination. 

Figure 4-1 of the VISTAS common BART modeling protocol presents a flow chart showing the components of 

that modeling protocol for the analysis to determine whether a source is subject to BART. 

According to the VISTAS common BART modeling protocol, an exemption modeling result that demonstrates 

that ISG’s Georgetown, SC mill does not cause or contribute to visibility impairment would lead the conclusion 

that the source will not be subject to BART requirements, and no further analysis is needed. 

Appendix H.6 - 1058
ISG Georgetown SC – Source-Specific BART Exemption 5-1 September 2006

Modeling Results – 12084-002-0102

5.0 BART Exemption Modeling Results 

The exemption modeling results are summarized in Table 5-1, and they show that even the highest day’s 

prediction at both Cape Romain and Wolf Island do not exceed 0.5 delta-deciview. Cape Romain results are 

presented in more detail in Table 5-2, since this area had the higher impacts by far. The tabulated values 

indicate that among the three years modeled, the highest 98th percentile predicted impact is 0.279 deltadeciview. These results demonstrate that ISG Georgetown’s peak daily emissions are well below the 

threshold that would cause or contribute to visibility impairment. Therefore, the source is not subject to BART, 

and no further BART analysis is required. 

The VISTAS contractor has provided the appropriate electronic files used to conduct the CALPUFF modeling 

to the South Carolina DHEC. 

Table 5-1: Summary of Results – ISG Georgetown Refined BART Exemption Modeling 

Class I Area 2001 2002 2003 

Annual 

average 

background 

bext

Maximum delta-deciview, (# days>0.5 dv, # days >1 dv) (Mm-1) 

Cape Romain 0.473 (0 0) 0.499 (0 0) 0.397 (0 0) 21.22 

Wolf Island 0.025 (0 0) 0.034 (0 0) 0.027 (0 0) 21.33 

Table 5-2: Detailed Cape Romain Results – ISG Georgetown Refined BART Exemption Modeling 

2001 2002 2003 

Change in 

Deciview (dv) Day Change in 

Deciview (dv) Day Change in 

Deciview (dv) Day Rank 

Cape 

Romain 

0.473 

0.405 

0.369 

0.358 

0.326 

0.284 

0.241 

0.230 

Nov 22 

Dec 31 

Oct 30 

Oct 29 

Mar 8 

Nov 10 

Sep 18 

Dec 30 

0.499 

0.462 

0.420 

0.403 

0.307 

0.289 

0.282 

0.279 

Dec 30 

Dec 7 

Dec 8 

Feb 25 

Mar 23 

Feb 19 

Oct 19 

Nov 8 

0.397 

0.375 

0.336 

0.276 

0.275 

0.260 

0.247 

0.217 

Jan 28 

Dec 27 

Oct 20 

Jan 16 

Dec 8 

Jan 5 

Nov 21 

Dec 26 

1 

2 

3 

4 

5 

6 

7 

8 

Appendix H.6 - 1059
A Trusted Global Environmental, Health and Safety Partner 

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NY, Syracuse Air Lab. 

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SC, Columbia 

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Headquarters 

MA, Westford 

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www.ensr.aecom.com 

Appendix H.6 - 1060
1100 Johnson Ferry Road, Suite 685, Atlanta, Georgia 30342 U.S.A. (404) 256-1919  Fax (404) 256-1952

Offices nationwide  trinityconsultants. c o m

March 17, 2006 

Mr. John P. Glass 

Bureau of Air Quality – Modeling Section 

South Carolina Department of Health and Environmental Control 

2600 Bull Street 

Columbia, SC 29201 

RE: Best Available Retrofit Technology Applicability Analysis 

Air Quality Modeling Protocol 

MeadWestvaco Corporation – North Charleston, South Carolina 

Dear Mr. Glass: 

Please find enclosed three (3) copies of an air quality modeling protocol prepared by Trinity 

Consultants (Trinity) on behalf of MeadWestvaco Corporation’s paper mill located in 

North Charleston, South Carolina. The North Charleston Mill is considered eligible for 

regulation under the Best Available Retrofit Technology (BART) provisions of the Regional 

Haze Rule because certain sources are operated within the Kraft pulp mill source category, were 

built or reconstructed between August 7, 1962 and August 7, 1977, and collectively have the 

potential to emit more than 250 tons per year of sulfur dioxide, oxides of nitrogen, and particulate 

matter, which are considered visibility affecting pollutants. The modeling protocol is provided to 

the South Carolina Department of Health and Environmental Control (DHEC) to provide the 

opportunity for review and comment on the proposed modeling methods, data resources, and 

determination of MeadWestvaco’s BART-eligible source. 

The North Charleston Mill is located within 300 km of three federally protected Class I areas: the 

Cape Romain National Wildlife Refuge (NWR) (approximately 30 km distant), Wolf Island 

NWR (212 km), and Okefenokee NWR (297 km). The enclosed modeling protocol describes the 

air quality analyses Trinity will assist MeadWestvaco in conducting to determine whether the 

BART-eligible source at the North Charleston Mill contributes to visibility impairment at any of 

these Class I areas, and is therefore subject to BART requirements. The additional copies of the 

air quality modeling protocol are provided for your transmittal to the U.S. EPA Region 4 and the 

U.S. Fish & Wildlife Service, which is the Federal Land Manager for all three Class I areas 

potentially affected by MeadWestvaco’s operations. 

The air quality modeling methods and data resources described in MeadWestvaco’s sourcespecific BART modeling protocol generally conform to the common VISTAS BART Modeling 

Protocol utilized by South Carolina and other states in the VISTAS regional planning 

organization. As noted in the enclosed protocol, certain refined model processing options and 

data resources have not yet been specified or made available by VISTAS. Should refined 

modeling of the North Charleston Mill be necessary, Trinity and MeadWestvaco anticipate 

maintaining close communication with DHEC to ensure that refined analyses are conducted using 

mutually agreeable data resources and processing options. 

Appendix H.6 - 1061
Mr. John P. Glass - Page 2 

March 17, 2006 

Trinity and MeadWestvaco appreciate your timely review of the enclosed modeling protocol for 

the North Charleston Mill and your continued cooperation in working with BART-eligible 

sources throughout the BART evaluation process. If you have any questions or comments about 

the information presented in this letter, please do not hesitate to call Duane Mummert of 

MeadWestvaco at (843) 745-3139 or me at (404) 256-1919. 

Sincerely, 

TRINITY CONSULTANTS 

Ryan A. Gesser 

Managing Consultant 

Enclosures 

cc: Mr. Duane Mummert, MeadWestvaco (North Charleston) 

 Ms. Amy Artmeier, MeadWestvaco (North Charleston) 

Appendix H.6 - 1062
BEST AVAILABLE RETROFIT TECHNOLOGY APPLICABILITY ANALYSIS

AIR QUALITY MODELING PROTOCOL

MEADWESTVACO CORPORATION  NORTH CHARLESTON OPERATIONS

TRINITY CONSULTANTS

1100 Johnson Ferry Road 

Suite 685 

Atlanta, Georgia 30342 

(404) 256-1919 

March 2006 

Project 061101.0024 

 

Appendix H.6 - 1063
TABLE OF CONTENTS

1. INTRODUCTION........................................................................................................1

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS .........................2 

1.1.1 DETERMINATION OF BART-ELIGIBILITY............................................................... 2

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND BART 

APPLICABILITY ...................................................................................................... 2

1.2 SCHEDULE FOR BART IMPLEMENTATION IN SOUTH CAROLINA...............................8 

1.3 ORGANIZATION OF MODELING PROTOCOL................................................................9 

2. BART-ELIGIBLE SOURCE DESCRIPTION .............................................................10

2.1 BART-ELIGIBLE EMISSION UNITS..........................................................................10 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY .....................................11 

2.2.1 RECOVERY BOILER NO. 9 PARTICULATE SPECIATION ........................................ 14

2.2.2 LIME KILN PARTICULATE SPECIATION ................................................................ 17

2.2.3 SMELT DISSOLVING TANK PARTICULATE SPECIATION ....................................... 18

2.3 MODELED STACK PARAMETERS AND EMISSIONS....................................................21 

3. GEOPHYSICAL AND METEOROLOGICAL DATA ....................................................23

3.1 TERRAIN ELEVATIONS WITHIN THE MODELING DOMAIN ........................................23 

3.2 LAND USE AND COVER WITHIN THE MODELING DOMAIN.......................................25 

3.3 METEOROLOGICAL DATABASE ...............................................................................25 

3.3.1 MM5 SIMULATIONS............................................................................................. 26

3.3.2 MEASUREMENTS AND OBSERVATIONS ................................................................ 26

3.4 AIR QUALITY DATABASE........................................................................................28 

3.4.1 OZONE BACKGROUND CONCENTRATIONS........................................................... 28

3.4.2 AMMONIA BACKGROUND CONCENTRATIONS ..................................................... 28

3.4.3 OTHER POLLUTANT BACKGROUND AND BOUNDARY CONDITIONS .................... 29

3.5 NATURAL CONDITIONS AT CLASS I AREAS.............................................................29 

4. AIR QUALITY MODELING METHODOLOGY..........................................................32

4.1 PLUME MODEL SELECTION .....................................................................................32 

4.1.1 MAJOR RELEVANT FEATURES OF CALMET ....................................................... 33

4.1.2 MAJOR RELEVANT FEATURES OF CALPUFF ...................................................... 35

4.2 MODELING DOMAIN CONFIGURATION ....................................................................37 

4.3 CALMET METEOROLOGICAL MODELING..............................................................37 

4.4 CALPUFF COMPUTATIONAL DOMAIN AND RECEPTORS........................................38 

4.5 CALPUFF MODELING OPTION SELECTIONS ..........................................................39 

4.6 CALPOST PROCESSING OPTION SELECTIONS FOR LIGHT EXTINCTION AND HAZE 

IMPACT CALCULATIONS..........................................................................................40 

4.7 MODELING PRODUCTS ............................................................................................40 

MeadWestvaco North Charleston Operations. i Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1064
5. QUALITY ASSURANCE METHODS..........................................................................42

5.1 CALMET FIELDS...................................................................................................42 

5.2 CALPUFF, CALPOST, AND POSTUTIL RESULTS ..............................................43 

6. REFERENCES..........................................................................................................45

APPENDIX A VISTAS BART MODELING PROTOCOL

MeadWestvaco North Charleston Operations. ii Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1065
1. INTRODUCTION

MeadWestvaco Corporation (MeadWestvaco) operates an unbleached paper mill in North Charleston, 

South Carolina. The North Charleston facility is a major source of air emissions and operates under a 

Part 70 (Title V) major source operating permit (TV-0560-0008) issued by the South Carolina 

Department of Health and Environmental Control (DHEC). The facility is considered eligible to be 

regulated under the Best Available Retrofit Technology (BART) provisions of the Regional Haze 

Rule. Air quality modeling will be used to determine whether the emissions from MeadWestvaco’s 

BART-eligible sources cause or contribute to visibility impairment at any federally protected Class I 

area, and hence whether a BART determination is necessary. This modeling protocol is presented to 

describe the procedures, analytical techniques, and data resources MeadWestvaco proposes to use to 

make the applicability determination. MeadWestvaco’s evaluation of BART-eligibility and the 

proposed modeling methods to determine applicability of BART as described in this protocol are 

consistent with the following guidance documents: 

V U.S. EPA, “Regional Haze Regulations and Guideline for Best Available Retrofit 

Technology (BART) Determinations,” Federal Register Volume 70, Number 128, 

July 6, 2005. 

V U.S. EPA, Guidance for Tracking Progress under the Regional Haze Rule

(EPA-54/B-03-004), September 2003. 

V U.S. EPA, Guidance for Estimating Natural Visibility Conditions under the Regional Haze 

Rule (EPA-454/B-03-005), October 2003. 

V U.S. EPA, Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report (EPA-454/R-98-019), December 1998. 

V U.S. EPA, Guideline on Air Quality Models, 40 CFR Part 51, Appendix W (Revised, 

November 9, 2005). 

V VISTAS, Protocol for the Application of the CALPUFF Model for Analyses of Best Available 

Retrofit Technology (BART), December 22, 2005. 

V VISTAS and U.S. EPA, “Q&A for Source by Source BART Rule” (Draft), October 28, 2005. 

V South Carolina DHEC, electronic communication to BART-eligible sources, March 3, 2006. 

The VISTAS BART Modeling Protocol is incorporated by reference for MeadWestvaco’s sourcespecific modeling protocol, and is presented in Appendix A of this modeling protocol document. 

This BART applicability modeling protocol is submitted to the South Carolina DHEC to provide the 

opportunity for review and comment in conjunction with review by the U.S. EPA and Federal Land 

Managers (FLM) responsible for oversight of the federally-protected Class I areas potentially affected 

by MeadWestvaco’s North Charleston BART-eligible operations. As of the date of this protocol, 

certain modeling data resources have not yet been made available by VISTAS, as identified in this 

document. In addition to South Carolina DHEC’s review and comment on this protocol, 

MeadWestvaco anticipates additional communication with South Carolina DHEC throughout the 

BART implementation process as details become available during the time between this modeling 

protocol is submitted and MeadWestvaco’s BART Applicability Analysis is submitted to ensure that 

refined analyses are conducted using mutually agreeable data resources and processing options. 

MeadWestvaco North Charleston Operations 1 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1066
1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS

The Regional Haze Rule requires that major sources of visibility affecting pollutants belonging to one 

or more of 26 specific industrial source categories evaluate BART if the source was “in existence” 

(i.e., built or reconstructed) before August 7, 1977 and began operation after August 7, 1962. Such 

sources are termed “BART-eligible sources.” Major sources of visibility affecting pollutants have the 

potential to emit 250 tons per year (tpy) of one or more of oxides of nitrogen (NOX), sulfur dioxide 

(SO2), and particulate matter less than 10 micrometers in aerodynamic diameter (PM10). Kraft pulp 

mills are one of the listed source categories, and include pulp and paper manufacturing operations that 

are considered to be part of major Standard Industrial Classification 26 – Paper and Allied Products. 

Chemical plants are also one of the listed BART-eligible source categories, and include all chemical 

manufacturing process operations that are considered to be part of Standard Industrial Classification 

28 – Chemicals and Allied Products. Industrial boilers greater than 250 million British thermal units 

per hour (MMBtu/hr) in heat input capacity are also considered a listed BART-eligible source 

category. Hereafter, the “BART-eligible source” is taken to mean the collection of sources at a 

facility in existence during the relevant time period within one or more BART source categories that 

has potential emissions of one or more visibility affecting pollutants in excess of 250 tpy. The 

BART-eligible source may include multiple emission units, but need not include the entire facility. 

1.1.1 DETERMINATION OF BART-ELIGIBILITY

The U.S. EPA BART guidelines define the following three steps for determining which 

sources at a facility are BART-eligible: 

1. Identify the emission units in the BART source categories. 

2. Identify the start-up dates of those units. 

3. Compare potential emissions to the 250 tpy cutoff. 

MeadWestvaco’s North Charleston facility has determined that fourteen emission units 

comprise the BART-eligible source because the units operate at a Kraft pulp mill, were in 

existence on August 7, 1977, and began operation after August 7, 1962. This collection of 

emission units has potential emissions of 630 tpy NOX, 592 tpy PM10, and 2,143 tpy SO2. 

Accordingly, the BART-eligible emission units at MeadWestvaco’s North Charleston Mill 

will be evaluated to determine whether a BART determination is required or whether the 

facility can be considered exempt from BART. Specific information about these emission 

units is provided in Section 2 of this modeling protocol. Note that MeadWestvaco 

identified no BART-eligible emission units associated with the specialty chemicals 

manufacturing operation at the North Charleston facility. 

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND

BART APPLICABILITY

In its role as technical analysis coordinator, VISTAS developed a common modeling 

protocol and data resources for use by state regulatory agencies and BART-eligible 

sources. The final VISTAS BART Modeling Protocol was issued on December 22, 2005, 

MeadWestvaco North Charleston Operations 2 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1067
and prescribes modeling techniques and data resources to conduct screening and refined 

analyses to assess whether a BART-eligible source is subject to BART. 

A BART-eligible source is determined to be subject to BART if the source causes or 

contributes to visibility impairment at a federally protected Class I area. Causation is 

defined as a single-source impact of 1.0 deciviews (dv) or more; contribution is defined as 

a single-source impact of 0.5 dv or more (each evaluated on a 24-hour average basis). The 

deciview is a metric used to represent normalized light extinction attributable to visibility 

affecting pollutants. To determine whether a BART-eligible facility causes or contributes 

to visibility impairment, U.S. EPA guidance requires the use of an air quality model, 

specifically recommending the CALPUFF modeling system, to quantify the impacts 

attributable to a single BART-eligible source. Because contribution to visibility 

impairment is sufficient cause to require a BART determination, 0.5 dv is the critical 

threshold for assessment of BART applicability. 

Regional haze is measured using the light extinction coefficient (bext), which is expressed 

in terms of the haze index expressed in dv. The haze index (HI) is calculated as shown in 

the following equation. 







 





 = 10

ln10 bext HI

The impact of a BART-eligible source is determined by comparing HI for estimated 

natural background conditions with the impact of the source and without the impact of the 

source. The background extinction coefficient bext, background is affected by various chemical 

species and the Rayleigh scattering phenomenon and can be calculated as shown in the 

following equation. 

bext background SO NO OC soil coarse ++++++= bbbbbbbkm rayap

−

4 3

)(

1

,

where, 

[ ]

[ ]

[ ]

[ ]

[ ]

3

Ray

34

424

][ Concentrat μg/minion

f(RH) relative humidity adjustment factor

b Rayleigh Scattering

Elemental[01.0 Carbon]

0006.0 Coarse Mass

Soil001.0

OC004.0

)(NONH003.0

)(SO)(NH003.0

3

4

=

=

=

=

=

=

=

=

=

ap

Coarse

Soil

OC

NO

SO

b

b

b

b

b RHf

b RHf

MeadWestvaco North Charleston Operations 3 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1068
Values for the parameters listed above specific to the natural background conditions at the 

Class I areas considered in this analysis are provided on an annual average basis in the 

U.S. EPA’s Guidance for Estimating Natural Visibility Conditions Under the Regional 

Haze Rule.

1

 More detailed information about the natural background conditions particular 

to Class I areas potentially affected by MeadWestvaco’s operations at the North Charleston 

Mill are provided in Section 3.5 of this protocol. 

Particulate species that affect visibility are emitted from anthropogenic sources in various 

phases and include coarse particulate matter (PMC), fine particulate matter (PMF), 

secondary organic aerosols (SOA), and elemental carbon (EC), as well as precursors to fine 

particulate matter such as sulfates (SO4) and nitrates (NO3). MeadWestvaco’s calculation 

of speciated visibility affecting pollutant emissions is presented in Section 2 of this 

modeling protocol. The extinction coefficient due to emissions of visibility affecting 

pollutants from a single BART-eligible source bext,source is calculated using an air quality 

model. The extinction due to the BART-eligible will be calculated as shown in the 

following equation. 

( ) sourceext SO NO PMC PMF SOA EC +++++= bbbbbbkmb −

4 3

1

,

where, 

[ ]

[ ]

3

34

424

][ Concentrat μg/minion

relative)( humidity adjustment factor

][01.0

][004.0

][001.0

][0006.0

)(NONH003.0

)(SO)(NH003.0

3

4

=

=

=

=

=

=

=

=

RHf

b EC

b SOA

b PMF

b PMC

b RHf

b RHf

EC

soa

PMF

PMC

NO

SO

MeadWestvaco proposes to utilize screening modeling techniques, and if necessary, 

refined modeling techniques as described in the VISTAS BART Modeling Protocol to 

determine whether BART-eligible operations at the North Charleston Mill contribute to 

visibility impairment at any of these Class I areas. The CALPUFF modeling system will 

be used to compute the 24-hour average visibility impairment attributable to 

MeadWestvaco to assess whether the 0.5 dv contribution threshold is exceeded, and if so, 

the frequency, duration, and magnitude of any exceedance events. The U.S. EPA BART 

guidelines prescribe that the 98th percentile, 24-hour average, visibility impact computed in 

a modeling analysis that evaluates three years of meteorological data should be compared 

to the contribution threshold. However, VISTAS prescribes that the maximum computed 

visibility impact be used as the basis for comparison in the screening analysis and the 98th

percentile impact in the refined analysis. To assess whether BART-eligible operations 

 

1

 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, Table 2-1, 

Attachment A, September 2003, EPA-454/B-03-005. 

MeadWestvaco North Charleston Operations 4 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1069
contribute to visibility impairment, MeadWestvaco’s applicability modeling analysis will 

quantify the top eight 24-hour average visibility impacts of each year modeled to illustrate 

the distribution (i.e., frequency, duration, and magnitude) of peak visibility impairment 

episodes attributable to the North Charleston Mill. 

CALPUFF is a refined air quality modeling system that is capable of simulating the 

dispersion, chemical transformation, and long-range transport of multiple visibility 

affecting pollutant emissions from a single source and is therefore preferred for BART 

applicability and determination analyses. The CALPUFF modeling system is described in 

technical detail in the VISTAS BART Modeling Protocol and its use in screening and 

refined analyses for BART applicability assessment of MeadWestvaco’s North Charleston 

Mill is described in Sections 3, 4, and 5 of this modeling protocol. 

The VISTAS modeling protocol specifies that all Class I areas within 300 km of a BARTeligible source must be initially evaluated to determine whether the source contributes to 

visibility impairment. Figure 1-1 illustrates the location of the North Charleston Mill, and 

Figure 1-2 illustrates the location of MeadWestvaco’s North Charleston operations relative 

to the following three federally protected Class I areas that are located within 300 km of 

MeadWestvaco’s North Charleston operations: 

V Cape Romain National Wildlife Refuge (NWR), approximately 30 km to the 

northeast of the facility along South Carolina’s Coast 

V Wolf Island NWR, located approximately 212 km southwest of the facility along 

Georgia’s coast 

V Okefenokee NWR, located approximately 297 km southwest of the facility north 

of the Georgia-Florida border 

The locations of Class I areas and receptor locations evaluated in the modeling analysis 

were determined by, and obtained from, the U.S. Fish & Wildlife Service, which is the 

FLM for all three of these Class I areas.2

 

2

 National Park Service compilation of Class I area receptors, http://www2.nature.nps.gov/air/maps/receptors/. 

MeadWestvaco North Charleston Operations 5 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1070
FIGURE 1-1. LOCATION OF MEADWESTVACO’S NORTH CHARLESTON MILL

MeadWestvaco North Charleston Operations 6 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1071
FIGURE 1-2. LOCATION OF MEADWESTVACO’S NORTH CHARLESTON OPERATIONS 

RELATIVE TO CLASS I AREAS WITHIN 300 KM

The initial screening analysis results will be used to demonstrate whether refined analysis 

is necessary, and whether the scope of the refined analysis can be focused on fewer than 

three Class I areas within 300 km. For example, should the screening analysis demonstrate 

that the facility does not contribute to visibility impairment at the more distant Wolf Island 

and Okefenokee Class I areas, but does contribute at nearby Cape Romain, the refined 

analysis would be conducted so as to only assess visibility impairment at Cape Romain. It 

is worth noting that Cape Romain is relatively close (i.e., approximately 30 km) to 

MeadWestvaco’s North Charleston Mill, but that the same CALPUFF modeling analysis 

techniques have been determined to be suitable for regional haze modeling at Class I areas 

near and far. Specifically, Section 3.2.1 of the VISTAS BART Modeling Protocol offers the 

following recommendations on the use of CALPUFF for BART applicability assessment at 

relatively short distances. 

MeadWestvaco North Charleston Operations 7 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1072
The BART guidance strongly recommends using CALPUFF for sourcereceptor distances greater than 50 km but also presents CALPUFF as an 

option that can be considered for shorter transport distances...there do not 

appear to be any scientific reasons why CALPUFF cannot be used for even 

shorter transport distances than 30 km, though, as long as the scale of the 

plume is larger than the scale of the output grid so that the maximum 

concentrations and the width of the plume are adequately represented and so 

that the sub-grid details of plume structure can be ignored when estimating 

effects on light extinction. The standard 1-km output grid that has been 

established for Class I area analyses should serve down to source-receptor 

distances somewhat under 30 km; how much closer than 30 km will depend on 

the topography and meteorology of the area and should be evaluated on a 

case-by-case basis...using CALPUFF at distances under 30 km will often be 

appropriate. 

In light of this recommendation, MeadWestvaco will initially conduct screening modeling 

of visibility impacts at all Class I areas within 300 km, but also anticipates conducting 

refined modeling of visibility impacts at Cape Romain as described in this modeling 

protocol. Note that the VISTAS BART Modeling Protocol envisions the use of several 

refined modeling techniques (e.g., refined meteorological data sets, boundary condition 

emissions and CMAQ-modeled background ammonia concentrations) that are only 

generally described, but for which specific details were not provided. As of the date of this 

source-specific modeling protocol, many of these details (which are identified in this 

document) have still not been finalized by VISTAS. Therefore, MeadWestvaco has 

identified the refined techniques that may be used in the BART applicability analysis, and 

anticipates additional communication with South Carolina DHEC as details become 

available during the time between the modeling protocol submittal and applicability 

analysis deadline. 

1.2 SCHEDULE FOR BART IMPLEMENTATION IN SOUTH CAROLINA

Each state within the VISTAS region has established its own schedule for BART implementation, 

which includes separate deadlines for BART applicability assessment and determination protocols 

and final reports. South Carolina DHEC has established the following milestones for BART 

implementation, with which MeadWestvaco will conform throughout the implementation process. 

V BART Applicability Modeling Protocol due March 17, 2006 

V DHEC response on BART Applicability Modeling Protocol due April 10, 2006 

V BART Applicability Modeling Analysis due April 24, 2006 

V DHEC response on BART Applicability Modeling Analysis due June 19, 2006 

V BART Determination Modeling Protocol due July 17, 2006 

V DHEC response on BART Determination Modeling Protocol due August 15, 2006 

V BART Determination Modeling, Engineering Analyses, and Permit Application due 

September 15, 2006 

MeadWestvaco North Charleston Operations 8 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1073
1.3 ORGANIZATION OF MODELING PROTOCOL

The remainder of this modeling protocol is organized as follows. Section 2 describes the BARTeligible emission units at the North Charleston Mill and the emission rates to be modeled in the 

BART applicability analysis. Section 3 describes the procedural and technical guidance for 

conducting Class I area analyses. Section 4 describes the proposed approach for CALPUFF 

modeling, including the data resources and technical modeling options to be used in the CALMET, 

CALPUFF, and CALPOST analyses. The presentation of results from, and quality assurance 

techniques for, BART applicability modeling analyses are described in Section 5. The air quality 

modeling analysis methodology will generally conform to the VISTAS BART modeling protocol, 

which is provided in Appendix A of this report for reference. 

MeadWestvaco North Charleston Operations 9 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1074
2. BART-ELIGIBLE SOURCE DESCRIPTION

This section of the modeling protocol describes the emission units that comprise the BART-eligible 

source at MeadWestvaco’s North Charleston Mill. Emissions and exhaust characteristics of each 

source are quantified to demonstrate how each source will be represented in the modeling analysis. 

2.1 BART-ELIGIBLE EMISSION UNITS

MeadWestvaco reviewed the criteria for BART-eligibility and determined that the fourteen emission 

units described in Table 2-1 comprise the BART-eligible source at the North Charleston Mill. 

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS

 

Emission Unit 

Source Code/

Stack ID 

BART 

Source 

Category* 

Date†

Built or 

Modified 

Potential

SO2

Emissions

(tpy) 

Potential 

NOX

Emissions 

(tpy) 

Potential

PM10

Emissions

(tpy) 

Potential

VOC 

Emissions

(tpy) 

 

No. 4 Lime Kiln 001/CP001 3 1972 55 108 42.6 2.2 

No. 4 Primary Slaker 002/CP028 3 1966 -- -- -- 0.31 

No. 4 Lump Slaker 002/CP029 3 (1966) -- -- -- 0.31 

No. 4 Snubber Tanks Nos. 1 & 2 002/CP054 3 1972 -- -- -- 0.26 

No. 13 Storage Tank 003/PM013 3 1977 -- -- -- 3.8 

No. 9 Recovery Boiler 006/REC007 3, 22 1966 2,030 522 390.2 45.7 

No. 9 East Smelt Dissolving Tank 005/REC008 3 1966 29 -- 79.4 49.1 

No. 9 West Smelt Dissolving Tank 005/REC009 3 (1966) 29 -- 79.4 49.1 

Kamyr Continuous Digester 007/UPM036 3 1966 -- -- -- -- 

Pine & 2 Swing Digesters 007/UPM035 3 1962† -- -- -- -- 

Hardwood Digester 007/UPM035 3 1962† -- -- -- -- 

East Washer Line Vent 1 008/UPM014 3 1963 -- -- -- 43.5 

East Washer Line Vent 2 008/UPM015 3 (1963) -- -- -- 43.5 

Wastewater Treatment Plant 009/WTP001 3 1970 -- -- -- -- 

 

Total Potential Emissions (tpy) 2,143 630 592 238 

* Category 3 denotes Kraft pulp mills; category 22 denotes large fossil fuel boilers, which for the purposes of BART eligibility 

include boilers individually greater than 250 MMBtu/hr that burn any amount of fossil fuel. 

† Dates in parenthesis indicate uncertain, but assumed, dates of original construction or reconstruction. 

‡ These emission units were subsequently modified in 1995; however, modification after 1977 does not change the “in existence” 

date for the purposes of BART-eligibility. 

MeadWestvaco’s North Charleston operations are BART-eligible since potential emissions exceed 

250 tpy for at least one (in fact, each) of NOX, SO2, and PM10, which are considered visibilityaffecting pollutants. Note that many of the otherwise BART-eligible emission units have potential 

emissions only of volatile organic compounds (VOC), which VISTAS and South Carolina DHEC 

have determined are not visibility affecting pollutants. Section 4.1.3 of the VISTAS BART Modeling 

Protocol describes the regional modeling analyses showing that cumulative VOC emissions do not 

contribute to visibility impairment within the VISTAS region: 

VOC emissions from all anthropogenic point sources in each VISTAS State are being 

reduced. Given that the impact of eliminating all VOC emissions from all point 

MeadWestvaco North Charleston Operations 10 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1075
sources in a State is less than 0.5 dv, then the impact of any one BART-eligible 

source would be less than 0.5 dv. Based on these analyses, the VISTAS States have 

concluded that VOC emissions should not be subject to BART. 

As a result of this determination, ten of the otherwise BART-eligible emission units operated by 

MeadWestvaco’s North Charleston Mill are excluded from further evaluation since these units do not 

emit visibility-affecting pollutants. The remaining four emission units, the No. 4 Lime Kiln, the 

No. 9 Recovery Boiler, and the No. 9 East and West Smelt Dissolving Tanks, are considered the 

BART-eligible source at MeadWestvaco’s North Charleston Mill. 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY

Whereas the BART eligibility determination relies on potential emissions of visibility-affecting 

pollutants, the BART applicability modeling analysis utilizes maximum actual 24-hour average 

emission rates of NOX, SO2, and PM10. The VISTAS BART Modeling Protocol specifies the following 

hierarchy of information resources to establish the maximum actual 24-hour average emission rate for 

BART applicability modeling over the prior three-to-five year period: 

V 24-hour maximum emissions observed using a Continuous Emission Monitor (CEM) for the 

period 2001 through 2003 

V 24-hour maximum emissions observed using a CEM for any representative period 

V Facility stack test emissions 

V Potential to emit 

V Permit allowable emissions 

V Emissions factors from U.S. EPA AP-42 source profiles 

MeadWestvaco used a combination of representative stack test data, potential emissions based on 

enforceable emissions and operating limits, and industry-specific emission factors compiled by the 

National Council for Air and Stream Improvements (NCASI) to determine the 24-hour average 

maximum actual emission rates of visibility affecting pollutants. Table 2-2 summarizes these 

emission rates from each BART-eligible emissions unit. 

TABLE 2-2. SUMMARY OF 24-HOUR AVERAGE MAXIMUM ACTUAL EMISSION RATES

 

Emission Unit 

NOX

Emissions 

(lb/hr) 

SO2

Emissions 

(lb/hr) 

Total PM10

Emissions 

(lb/hr) 

Total PM2.5

Emissions 

(lb/hr) 

H2SO4

Emissions 

(lb/hr) 

No. 4 Lime Kiln 43.60 22.14 9.57 8.34 0.24 

No. 9 Recovery Boiler 157.95* 829.00* 49.15 45.47 0.75 

No. 9 East Smelt Dissolving Tank -- 6.63 25.19 21.59 -- 

No. 9 West Smelt Dissolving Tank -- 6.63 25.06 21.48 -- 

* The table presents maximum short-term emission rate when burning fuel oil, which is a permitted operating scenario. The No. 9 

Recovery Boiler typically fires black liquor solids, which results in lower emission rates of NOX and SO2. 

MeadWestvaco North Charleston Operations 11 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1076
Table 2-2 includes 24-hour maximum emission rates of primary sulfates (as sulfuric acid mist from 

combustion sources) and distinguishes the emission rates of Total PM10 (TPM10), which includes 

emissions of TPM2.5. Modeling of visibility impairment requires that the components of the exhaust 

stream be speciated because different types of particulate matter affect visibility to varying extents. 

The amount by which a mass of a certain species scatters or absorbs light is termed the extinction 

efficiency or extinction coefficient, and ranges from values of 0.6 m2

/g for coarse particulate matter to 

10 m2

/g for elemental carbon. Fine particulate matter (1 m2/g) and organic aerosols (4 m2/g) scatter 

light with intermediate efficiencies, and ammonium sulfate and ammonium nitrate (that forms from 

precursor SO2 and NOX emissions in the presence of ambient ammonia) are hygroscopic species that 

are particularly efficient light scatters in the presence of ambient water vapor (3f(RH) m2

/g, where 

f(RH) is a function of the relative humidity). The size distribution of particle species is also 

important, since smaller particles may be transported longer distances than larger particles and 

dispersed differently under prevailing ambient conditions. Figure 2-1 depicts the speciation of 

visibility-affecting pollutant emissions as represented in the VISTAS BART Modeling Protocol. 

FIGURE 2-1. PARTICULATE MATTER SPECIATION

(AFTER FIGURE 4-3 OF THE VISTAS BART MODELING PROTOCOL) 

Although the FLM have published speciation guidance for certain source types (e.g., gas-, oil-, and 

coal-fired combustion sources), MeadWestvaco is not aware of specific guidance for facilities 

engaged in Kraft pulp production. Default speciation profiles compiled by VISTAS did not include 

specific source types operated at MeadWestvaco’s North Charleston Mill. Therefore, MeadWestvaco 

developed the following emissions profiles based on engineering knowledge of Kraft pulp operations 

and available reference data (e.g., AP-42 and NCASI analyses). 

MeadWestvaco North Charleston Operations 12 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1077
While few data are available to estimate speciated emissions, MeadWestvaco has reviewed what 

data are available to arrive at a conservative, yet reasonable estimate of speciated emissions. 

However, it should be noted that the data quality on PM speciation is inadequate for setting 

regulatory emission limits and are provided here solely as the best estimated data for a 

scientific analysis of potential impacts on visibility impairment at Class I areas using CALPUFF 

modeling. The following analysis does not represent source test results for specific sources at 

the North Charleston Mill. 

NOX emissions from the North Charleston Mill’s BART-eligible sources result from combustion 

operations. Similarly, SO2 emissions emanate from combustion sources, and result from the 

incomplete oxidation of fuel-bound sulfur. Primary emissions of sulfuric acid mists or vapors, if any, 

are assumed to occur as only a small percentage of the primary SO2 emissions unless an emission 

factor has otherwise been published. Because of the condensable nature of such emissions and the 

distinct effect on visibility caused by sulfates, primary sulfate is evaluated as a distinct, speciated 

particulate fraction. Condensable particulate matter (CPM) emissions, unlike filterable emissions, 

have not historically been measured or regulated for the affected sources. 

PM emissions can be differentiated with respect to size, point of formation, and composition. 

Table 2-3 gives definitions for the nomenclature used herein. 

TABLE 2-3. NOMENCLATURE FOR EMISSIONS SPECIATION ANALYSIS

 

Nomenclature Description 

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μm 

PM10 Filterable particulate matter with an aerodynamic diameter < 10 μm 

PM6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μm 

PM2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μm 

PM2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μm 

PM1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μm 

PM1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μm 

PM0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

PM0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μm 

CPM Condensable particulate matter (organic and inorganic) 

TPM10 Filterable PM10 + CPM 

TPM2.5 Filterable PM2.5 + CPM 

These PM classifications are necessary in the Class I visibility analysis because each type of PM has a 

different effect on visibility as defined by the extinction efficiency. The emission rates of each of 

these particulate phases and size categories are modeled in CALPUFF and grouped according to 

visibility affecting characteristics as was illustrated in Figure 2-1. Elemental carbon (EC), if emitted, 

typically results from unburned carbonaceous fuel and is distinguished from other PM types because 

of its light extinction characteristics. Coarse PM (PMC) comprises PM2.5-6 and PM6-10. Fine PM 

(PMF) comprises PM0.5-0.625, PM0.625-1, PM1-1.25, and PM1.25-2.5. CPM comprises both organic and 

inorganic species. The organic fraction of CPM is represented in CALPUFF as secondary organic 

aerosols (SOA), which are direct primary emissions but are called SOA by convention and due to 

MeadWestvaco North Charleston Operations 13 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1078
their visibility affecting characteristics. Primary emissions of inorganic CPM may contain 

hygroscopic sulfates (SO4) and nitrates (NO3), as well as other salts (e.g., carbonates) that may be 

hygroscopic to a lesser degree, and hence are considered in a manner similar to PMF.3

 Therefore, it is 

important to distinguish inorganic CPM since certain hygroscopic species (i.e., sulfate and nitrate 

species) will have a greater extinction coefficient than non-hygroscopic (i.e., non-sulfate and nonnitrate) species. Even the distinction between primary sulfate and nitrate emissions is important since 

primary nitrate emissions will be affected by the partitioning of nitrate and nitric acid in the presence 

of ambient ammonia, which is modeled explicitly in CALPUFF and can be corrected when the 

ammonia limiting method (as described in Section 4 of this protocol) is applied. For the initial 

screening analysis, MeadWestvaco will conservatively represent all condensable, inorganic species as 

primary sulfate emissions, thereby assuming all emissions are hygroscopic and not affected by the 

partition of ambient nitrate, nitric acid, and ammonia. In the refined analyses, if necessary, 

MeadWestvaco would distinguish primary emissions of sulfates and nitrates, which would be 

assigned to the appropriate modeled PM type (i.e., SO4 and NO3, respectively), from nonhygroscopic species (e.g., carbonates), which would be assigned to the PMF modeled species. 

Table 2-4 summarizes the grouping of PM species and extinction coefficient of each component. 

A discussion of the PM speciation methodologies for each of the BART-eligible sources at the North 

Charleston Mill is presented in the following sections of this protocol. 

TABLE 2-4. ASSIGNMENT OF EMITTED PM SPECIES TO MODELED PM CATEGORIES

 

Modeled PM 

Category Components 

Extinction Coefficient

(m2

/g) 

PMC Filterable coarse particles (PM6-10, PM2.5-6) 0.6 

PMF Filterable fine particles (PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625) 

and non-hygroscopic primary condensable inorganic emissions* 1 

SO4 Primary condensable inorganic emissions* 3f(RH) 

NO3 Primary condensable inorganic emissions of nitrates* 3f(RH) 

SOA Organic, condensable inorganic primary emissions 4 

EC Uncombusted carbonaceous fuel 10 

* In the screening analyses, all condensable, inorganic emissions will be conservatively represented as primary sulfate emissions. 

The refined analysis, if necessary, would distinguish between primary sulfate, nitrate, and other emissions. 

2.2.1 RECOVERY BOILER NO. 9 PARTICULATE SPECIATION

MeadWestvaco operates a direct contact evaporator (DCE) recovery boiler (source code 

REC007) with an electrostatic precipitator (ESP). PM emissions from REC007 are due to 

the combustion of fuel oil and/or black liquor solids (BLS), which are composed primarily 

of sodium sulfate and sodium carbonate. 

 

3

 The U.S. EPA’s Guidance for Tracking Progress under the Regional Haze Rule identifies carbonates, 

magnesium oxides, and sodium oxides as components of the soil mass concentration when analyzed to assess natural 

background visibility (Malm 1994). 

MeadWestvaco North Charleston Operations 14 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1079
To speciate the PM emissions from the recovery boiler, data from the NCASI were used to 

determine PM size fractions and CPM emissions.4

 Additional particle size information 

was obtained from Section 10.2 of U.S. EPA’s AP-42 emission factor database.5

 No data 

were found to determine the percentage of elemental carbon in the PM emissions; 

therefore, elemental carbon was assumed to be 0% of TSP. Table 2-5 summarizes the 

relevant data for this source. 

TABLE 2-5. RECOVERY BOILER SPECIATION DATA

 

Speciation Data Value Reference 

PM10 as a % of TSP 75.0% 

PM2.5 as a % of TSP 56.7% 

CPM (lb/TBLS) 0.38 

Filterable portion of TPM10* 30.7% 

Condensable portion of TPM10* 69.3% 

Organic portion of CPM† 7.2% 

Inorganic portion of CPM† 92.8% 

NCASI Data, 

DCE Kraft Recovery Furnaces 

PM6-10 as a % of PM10 9.1% 

PM2.5-6 as a % of PM10 19.2% 

PM1.25-2.5 as a % of PM10 17.7% 

PM1-1.25 as a % of PM10 8.4% 

PM0.625-1 as a % of PM10 16.0% 

PM0.5-0.625 as a % of PM10 29.6% 

AP-42 Section 10.2, Table 10.2-2 

DCE Kraft Furnaces with ESPs (controlled)‡ 

 

* Values are calculated by dividing the either the PM10 emissions or the CPM emissions (CPM factor x hourly capacity 

of unit) by the sum of the PM10 emissions and the CPM emissions. 

† Values calculated based on the average amount of inorganic (water soluble) portion or organic (MeCl2 soluble) 

portion of CPM divided by the total CPM. 

‡ Values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % of 

TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. Remainder of PM10 that did not fall within any other category was assumed to be PM0.5-0.625. 

Using the information presented in Table 2-5, MeadWestvaco first calculated TPM10 and 

TPM2.5 emissions by multiplying the maximum hourly TSP emission rate of 20.14 lb/hr by 

75% and 56.7%, respectively, and then adding the CPM emissions of 34.05 lb/hr 

(calculated using the emission factor of 0.38 lb/TBLS by the capacity of 89.6 TBLS/hr) 

yielding 49.15 lb/hr TPM10 and 45.47 lb/hr TPM2.5. 

Next, the organic CPM emissions were determined by multiplying the calculated TPM10

emissions by the condensable percentage of TPM10 and the organic percentage of CPM. 

Organic CPM was assumed to be evenly split between the two size categories, 

0.625-1.0 μm and 0.5-0.625 μm. The organic CPM calculation is shown below. 

hour

category sizeeach for CPM organic lb 23.12/

CPM

CPM organic %2.7

TPM

CPM %3.69

hr

TPM lb 49.15 10

10  = 

 









 









 





 

4

 Ashok Jain (NCASI), Particulate Emissions Data for Pulp and Paper Industry Specific Sources, March 2006. 

5

 AP-42, Section 10.2, Chemical Wood Pulping, September 1990. 

MeadWestvaco North Charleston Operations 15 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1080
Inorganic CPM emissions were calculated using a similar methodology, yielding 

15.80 lb/hr inorganic CPM for each of the two size categories, 0.625-1.0 μm and 

0.5-0.625 μm. 

Filterable PM10 emissions were divided amongst six different size categories by 

multiplying TPM emissions by the filterable percentage and then by the percentage of 

PM10 for each size category, yielding hourly emissions of PM6-10, PM2.5-6, PM1.25-2.5, 

PM1-1.25, PM0.625-1, and PM0.5-0.625. A sample calculation for PM6-10 is shown below. 

hour

PM lb 37.1

PM

PM %1.9

TPM

PM %7.30

hr

TPM lb 49.15 10-6

10

10-6

10

10 10

=



















 









 





Table 2-6 presents a summary of the speciated PM emissions for the No. 9 Recovery 

Furnace and Figure 2-2 presents a graphical representation of the speciation of the total 

49.15 lb/hr of TPM10. 

TABLE 2-6. NO. 9 RECOVERY FURNACE PM SPECIATED EMISSIONS (LB/HR) 

 

Organic CPM (μm) Inorganic CPM (μm) Coarse Filterable PM (μm) Fine Filterable PM (μm)

0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 

 

1.23 1.23 15.80 15.80 1.37 2.90 2.68 1.27 2.42 4.47 

FIGURE 2-2. NO. 9 RECOVERY FURNACE TPM10 SPECIATION (LB/HR) 

2.46

31.59

1.37

2.90

2.68

1.27

2.42

4.47

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

MeadWestvaco North Charleston Operations 16 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1081
2.2.2 LIME KILN PARTICULATE SPECIATION

MeadWestvaco operates the No. 4 Lime Kiln (CP001) with a wet scrubber to control PM. 

PM emissions from lime kilns are primarily composed of sodium sulfate and sodium 

carbonate. To speciate the PM emissions from the lime kiln, data from NCASI were used 

to determine PM size fractions and CPM emissions.6

 Additional particle size information 

was obtained from Section 10.2 of U.S. EPA’s AP-42 emission factor database. No data 

was found to determine the percentage of elemental carbon in the PM emissions; therefore, 

elemental carbon was assumed to be 0% of TSP. Table 2-7 summarizes the relevant data 

for this source. 

TABLE 2-7. LIME KILN SPECIATION DATA

 

Speciation Data Value Reference 

TPM10 as a % of TSP 101% 

TPM2.5 as a % of TSP 88% 

Filterable portion of TPM10 97.9% 

Condensable portion of TPM10 2.1% 

Organic portion of CPM* 7.4% 

Inorganic portion of CPM* 92.6% 

NCASI Data, 

Lime Kilns with Wet Scrubbers 

PM6-10 as a % of PM10 0.1% 

PM2.5-6 as a % of PM10 2.2% 

PM1.25-2.5 as a % of PM10 11.2% 

PM1-1.25 as a % of PM10 6.2% 

PM0.625-1 as a % of PM10 25.0% 

PM0.5-0.625 as a % of PM10 55.2% 

AP-42 Section 10.2, Table 10.2-4 

Lime Kilns with a Venturi Scrubber (controlled) † 

 

* Values calculated based on the average amount of inorganic (water soluble) portion or organic (MeCl2 soluble) portion of 

CPM divided by the total CPM. 

† Values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % of 

TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. Remainder of PM10 that did not fall within any other category was assumed to be PM0.5-0.625. 

Using the information presented in Table 2-7, MeadWestvaco first calculated TPM10 and 

TPM2.5 emissions by multiplying the maximum hourly TSP emission rate of 9.48 lb/hr by 

101% and 88%, respectively, yielding 9.57 lb/hr TPM10 and 8.34 lb/hr TPM2.5. 

Next, the organic CPM emissions were determined by multiplying the calculated TPM10

emissions by the condensable percentage of TPM10 and the organic percentage of CPM. 

Organic CPM was assumed to be evenly split between the two size categories, 

0.625-1.0 μm and 0.5-0.625 μm. The organic CPM calculation is shown below. 

hour

category sizeeach for CPM organic lb 0075.02/

CPM

CPM organic %4.7

TPM

CPM %1.2

hr

TPM lb 57.9 10

10  = 

 









 









 





 

6

 Ashok Jain (NCASI), Particulate Emissions Data for Pulp and Paper Industry Specific Sources, March 2006. 

MeadWestvaco North Charleston Operations 17 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1082
Inorganic CPM emissions were calculated using a similar methodology, yielding 0.09 lb/hr 

inorganic CPM for each of the two size categories, 0.625-1.0 μm and 0.5-0.625 μm. 

Filterable PM10 emissions were divided amongst six different size categories by 

multiplying TPM10 emissions by the filterable % and then by the % of PM10 for each size 

category, yielding hourly emissions of PM6-10, PM2.5-6, PM1.25-2.5, PM1-1.25, PM0.625-1, and 

PM0.5-0.625. A sample calculation for PM6-10 is shown below. 

hour

PM lb 0095.0

PM

PM %1.0

TPM

PM %9.97

hr

TPM lb 9.57 10-6

10

10-6

10

10 10

=



















 









 





Table 2-8 presents a summary of the speciated PM emissions for the No. 4 Lime Kiln. 

Figure 2-3 presents a graphical representation of the of the total 9.57 lb/hr of TPM10. 

TABLE 2-8. NO. 4 LIME KILN PM SPECIATED EMISSIONS (LB/HR) 

 

Organic CPM (μm) Inorganic CPM (μm) Coarse Filterable PM (μm) Fine Filterable PM (μm)

0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 

 

0.0075 0.0075 0.094 0.094 0.0095 0.21 1.05 0.58 2.35 5.18 

FIGURE 2-3. NO. 4 LIME KILN TPM10 SPECIATION (LB/HR) 

0.02

0.19

0.01

0.21

1.05

0.58

2.35

5.18

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

2.2.3 SMELT DISSOLVING TANK PARTICULATE SPECIATION

MeadWestvaco operates two smelt dissolving tanks, the No. 9 East Smelt Dissolving Tank 

(REC008) and No. 9 West Smelt Dissolving Tank (REC009), with wet scrubbers to control 

PM. PM emissions from smelt dissolving tanks are primarily composed of inorganic 

components such as sodium sulfate and sodium carbonate. 

MeadWestvaco North Charleston Operations 18 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1083
To speciate the PM emissions from the smelt dissolving tanks, data from NCASI were used 

to determine PM size fractions and CPM emissions.7

 Additional particle size information 

was obtained from Section 10.2 of U.S. EPA’s AP-42 emission factor database. No data 

was found to determine the percentage of elemental carbon in the PM emissions; therefore, 

elemental carbon was assumed to be 0% of TSP. Table 2-9 summarizes the relevant data 

for these sources. 

TABLE 2-9. SMELT DISSOLVING TANK SPECIATION DATA

 

Speciation Data Value Reference 

TPM10 as a % of TSP 84.0% 

TPM2.5 as a % of TSP 72.0% 

Filterable portion of TPM10 98.1% 

Condensable portion of TPM10 1.9% 

Organic portion of CPM* 26.0% 

Inorganic portion of CPM* 74.0% 

NCASI Data, 

Smelt Dissolving Tanks 

PM6-10 as a % of PM10 1.2% 

PM2.5-6 as a % of PM10 7.9% 

PM1.25-2.5 as a % of PM10 19.9% 

PM1-1.25 as a % of PM10 9.8% 

PM0.625-1 as a % of PM10 17.9% 

PM0.5-0.625 as a % of PM10 43.2% 

AP-42 Section 10.2, Table 10.2-2 

Smelt Dissolving Tanks with Venturi Scrubbers 

(controlled)† 

 

* Values calculated based on the average amount of inorganic (water soluble) portion or organic (MeCl2 soluble) 

portion of CPM divided by the total CPM. 

† Values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % of 

TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. Remainder of PM10 that did not fall within any other category was assumed to be PM0.5-0.625. 

Using the information presented in Table 2-9, MeadWestvaco first calculated TPM10 and 

TPM2.5 emissions by multiplying the maximum hourly TSP emission rate of each unit by 

84% and 72%, respectively. For the No. 9 East Smelt Dissolving Tank, PM emissions of 

29.99 lb/hr result in 25.19 lb/hr TPM10 and 21.59 lb/hr TPM2.5. For the No. 9 West Smelt 

Dissolving Tank, PM emissions of 29.83 lb/hr result in 25.06 lb/hr TPM10 and 21.48 lb/hr 

TPM2.5. 

Next, the organic CPM emissions for each smelt dissolving tank were determined by 

multiplying the calculated TPM10 emissions by the condensable % of TPM10 and the 

organic % of CPM. Organic CPM was assumed to be evenly split between the two size 

categories, 0.625-1.0 μm and 0.5-0.625 μm. The organic CPM calculation for the 

No. 9 East Smelt Dissolving Tank is shown below; the same methodology was used for the 

No. 9 West Smelt Dissolving Tank organic CPM emissions calculation. 

hour

category sizeeach for CPM organic lb 063.02/

CPM

CPM organic %26

TPM

CPM %9.1

hr

TPM lb 19.25 10

10  = 

 









 









 





 

7

 Ashok Jain (NCASI), Particulate Emissions Data for Pulp and Paper Industry Specific Sources, March 2006. 

MeadWestvaco North Charleston Operations 19 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1084
Inorganic CPM emissions were calculated using a similar methodology, yielding 0.18 lb/hr 

inorganic CPM for each of the two size categories, 0.625-1.0 μm and 0.5-0.625 μm, for the 

No. 9 East Smelt Dissolving Tank and 0.18 lb/hr for each of the two size categories for the 

No. 9 West Smelt Dissolving Tank. 

Filterable PM10 emissions were divided amongst six different size categories by 

multiplying TPM10 emissions by the filterable percentage and then by the percentage of 

PM10 for each size category, yielding hourly emissions of PM6-10, PM2.5-6, PM1.25-2.5, 

PM1-1.25, PM0.625-1, and PM0.5-0.625. A sample calculation for PM6-10 for the No. 9 East Smelt 

Dissolving Tank is shown below. 

hour

PM lb 30.0

PM

PM %2.1

TPM

PM %1.98

hr

TPM lb 19.25 10-6

10

10-6

10

10 10

=



















 









 





Tables 2-10 and 2-11 present summaries of the speciated PM emissions for the No. 9 East 

and West Smelt Dissolving Tanks. Figures 2-4 and 2-5 present graphical representations 

of the speciated TPM10 emissions of 25.19 lb/hr and 25.06 lb/hr for the No. 9 East and 

West Smelt Dissolving Tanks, respectively. 

TABLE 2-10. NO. 9 EAST SMELT DISSOLVING TANKS PM SPECIATED EMISSIONS (LB/HR) 

 

Organic CPM (μm) Inorganic CPM (μm) Coarse Filterable PM (μm) Fine Filterable PM (μm)

0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 

 

0.063 0.063 0.18 0.18 0.30 1.96 4.91 2.43 4.42 10.68 

FIGURE 2-4. NO. 9 EAST SMELT DISSOLVING TANK TPM10 SPECIATION (LB/HR) 

0.13

0.36

0.30

1.96

4.91

2.43

4.42

10.68

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

MeadWestvaco North Charleston Operations 20 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1085
TABLE 2-11. NO. 9 WEST SMELT DISSOLVING TANKS PM SPECIATED EMISSIONS (LB/HR) 

 

Organic CPM (μm) Inorganic CPM (μm) Coarse Filterable PM (μm) Fine Filterable PM (μm)

0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 

 

0.063 0.063 0.18 0.18 0.30 1.95 4.89 2.42 4.39 10.63 

FIGURE 2-5. NO. 9 WEST SMELT DISSOLVING TANK TPM10 SPECIATION (LB/HR) 

0.13

0.36

0.30

1.95

4.89

2.42

4.39

10.63

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

2.3 MODELED STACK PARAMETERS AND EMISSIONS

Actual stack parameters will be input to the CALPUFF model to represent the point of visibility 

affecting pollutant emissions. The location of each point source will be represented consistently in 

the Lambert Conformal Coordinate system used for the screening and refined meteorological data 

analyses prepared by VISTAS. Each exhaust discharges vertically without obstruction. Since the 

nearest Class I area is located 30 km to the facility, effects of building downwash will not be 

considered unless specifically requested by South Carolina DHEC. Table 2-12 summarizes the stack 

parameters and Table 2-13 summarizes the modeled emission rates for BART-eligible emission units 

at MeadWestvaco’s North Charleston Mill. 

MeadWestvaco North Charleston Operations 21 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1086
TABLE 2-12. STACK PARAMETERS FOR BART-ELIGIBLE EMISSION UNITS

Emission Unit Stack ID 

LCC East 

(km) 

LCC North 

(km) 

Stack 

Height 

(feet) 

Stack 

Diameter 

(feet) 

Exha

Temper

(°F

No. 4 Lime Kiln CP001 1580.925 -637.769 113.5 5.83 152 No. 9 Recovery Boiler REC007 1581.068 -637.835 257.0 9.96 320 No. 9 East Smelt Dissolving Tank REC008 1581.032 -637.932 251.0 6.49 17No. 9 West Smelt Dissolving Tank REC009 1581.017 -637.924 251.0 6.49 17

TABLE 2-13. MODELED EMISSION RATES OF SPECIATED FOR BART-ELIGIBLE EMISSION UNIT

PMC (lb/hr) PMF (lb/hr) SO4 (lb/hr) 

Emission Unit Stack ID 6-10 μm 2.5-6 μm 1.25-2.5 μm 1.0-1.25 μm 0.625-1.0 μm 0.5-0.625 μm 0.625-1.0 μm 0.5-0.625 μm No. 4 Lime Kiln CP001 0.0095 0.21 1.05 0.58 2.35 5.18 0.094 0.094 No. 9 Recovery Boiler REC007 1.37 2.90 2.68 1.27 2.42 4.47 15.80 15.80 No. 9 East Smelt Dissolving Tank REC008 0.30 1.96 4.91 2.43 4.42 10.68 0.18 0.18 No. 9 West Smelt Dissolving Tank REC009 0.30 1.95 4.89 2.42 4.39 10.63 0.18 0.18 

MeadWestvaco North Charleston Operations 22 BART Applicability Modeling Protocol 
3. GEOPHYSICAL AND METEOROLOGICAL DATA

Section 3 of this BART applicability modeling protocol for MeadWestvaco’s North Charleston Mill 

describes the geophysical and meteorological data that will be used in the screening and refined 

analyses. The information in this Section 3 is largely adapted from the VISTAS BART Modeling 

Protocol, which is presented in Appendix A of this source-specific protocol for reference, and sample 

model files made available on the VISTAS technical contractor website.8

CALMET requires geophysical data about the domain to characterize the terrain and land use 

parameters that potentially affect dispersion. Terrain features affect flows and create turbulence in 

the atmosphere and are potentially subjected to higher concentrations of elevated puffs, and different 

land uses exhibit variable characteristics such as surface roughness, albedo, Bowen ratio, and leafarea index that also affect turbulence and dispersion. 

3.1 TERRAIN ELEVATIONS WITHIN THE MODELING DOMAIN

Terrain elevations within the modeling domain were processed from SRTM-GTOPO30 digital terrain 

data format with 30-arcsec resolution. SRTM30 is a digital elevation data set that spans the globe 

from 60° north latitude to 56° south latitude, approximately from the southern tip of Greenland to 

below the southern tip of South America. It has a horizontal grid spacing of 30 arc-seconds 

(approximately 1 kilometer). GTOPO30 is a global digital elevation model with a horizontal grid 

spacing of 30 arc-seconds (approximately 1 kilometer) that was derived from several raster and vector 

sources of topographic information that include U.S. Geological Survey digital elevation models. 

The VISTAS technical contractor used data preprocessors to format and assimilate these data into a 

single geophysical data file for processing by CALMET. The representation of terrain in the regional 

screening analysis resolves the terrain onto the 12-km regional grid depicted in Figure 3-1. 

Refined analyses may be necessary as part of the MeadWestvaco BART applicability analysis, in 

which case CALMET grid sizes of 4 km or less (e.g., 1 km) would be used to represent the modeling 

domain. As described in the VISTAS BART Modeling Protocol, VISTAS intends to provide 2001 

through 2003 CALMET files for five 4-km sub-regional domains, one of which appears to include 

MeadWestvaco’s North Charleston Mill and Cape Romain. The VISTAS BART Modeling Protocol

envisions the use of higher resolution terrain DEM data (e.g., 3 arc-second U.S. Geological Survey 

data) for this analysis. Because the North Charleston Mill is located approximately 30 km from the 

nearest Class I area (Cape Romain) near the South Carolina coastline, MeadWestvaco will evaluate 

whether higher resolution CALMET/CALPUFF processing at a grid size of, for example, 1 km is 

appropriate as is suggested by the VISTAS BART Modeling Protocol, which provides the following 

recommendation in Section 4.4.2: 

 

8

 http://src.com/verio/download/download.htm#VISTAS_VERSION. 

MeadWestvaco North Charleston Operations 23 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1088
An application for source-receptor distances within about 50 km may require a grid 

resolution less than 1 km if complex terrain effects are likely to be important. This 

determination should be made on a case-by-case basis. There is not a single distance 

at which a particular grid size is appropriate. It depends on factors such as the 

complexity of the terrain, the source-receptor distances involved, the location of the 

source relative to the terrain features, the physical stack parameters (e.g., a tall stack 

in complex terrain may be unaffected by the terrain-forced flow), proximity of the 

source and Class I area to a coastline, and other factors including availability of 

representative observational data.

FIGURE 3-1. TERRAIN REPRESENTATION IN THE 12-KM REGIONAL SCREENING GRID

(AFTER FIGURE 4-2 OF THE VISTAS BART MODELING PROTOCOL)

MeadWestvaco North Charleston Operations 24 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1089
3.2 LAND USE AND COVER WITHIN THE MODELING DOMAIN

Land use and land cover (LULC) within the modeling domain was assimilated by the VISTAS 

technical contractor into a single geophysical data file for processing by CALMET using Composite 

Theme Grid (CTG) data archived by the U.S. Geological Survey at a resolution of 200 meters. 

CALMET was used to calculate the fractional land use types within each cell of the 12-km size 

regional grid. LULC in each grid cell was used by CALMET to compute the micrometeorological 

parameters (i.e., surface roughness, Bowen ratio, albedo, soil heat flux) that affect turbulent 

dispersion in the boundary layer. 

Refined analyses may be necessary as part of the MeadWestvaco BART applicability analysis, in 

which case CALMET grid resolutions of 4 km or less (e.g., 1 km) would be used to represent the 

modeling domain. As described in the VISTAS BART Modeling Protocol, VISTAS intends to provide 

2001 through 2003 CALMET files for five 4-km sub-regional domains, one of which appears to 

include MeadWestvaco’s North Charleston Mill and Cape Romain. The VISTAS BART Modeling 

Protocol does not explicitly indicate that higher resolution LULC data would be used for a refined 

analysis; therefore the 200-meter CTG format will provide adequate resolution for CALMET grid 

spacing down to 1 km. Therefore, refined analyses performed by MeadWestvaco, if necessary, will 

utilize the same CTG LULC data. 

3.3 METEOROLOGICAL DATABASE

CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields 

from mesoscale model (MM) output, raw observations of surface and upper air conditions, 

precipitation measurements, and geophysical parameters into a single hourly, gridded data set for 

input to CALPUFF. The federal Guideline for CALPUFF processing provides the following 

recommendations for the meteorological data period at Section 9.3.1.2: 

Less than five, but at least three, years of meteorological data (need not be 

consecutive) may be used if mesoscale meteorological fields are available, as 

discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used 

in conjunction with available standard [National Weather Service] NWS or 

comparable meteorological observations within and near the modeling domain. 

The VISTAS BART Modeling Protocol describes a regional domain and a set of pre-computed 

regional CALMET meteorological files with 12 km grid size for the years 2001, 2002, and 2003, 

prepared by the VISTAS technical contractor to allow any Class I areas within the VISTAS area to be 

evaluated with a consistent meteorological database and consistent CALPUFF modeling options. The 

CALMET modeling output files in the form of CALPUFF-ready three-dimensional meteorological 

files were made available on external hard drives to the States and other parties. These data were 

obtained by MeadWestvaco’s technical contractor and will be utilized in the regional screening 

analyses. Should refined analyses be necessary, MeadWestvaco will initially utilize one of the 4-km 

refined CALMET grids provided by VISTAS, also for the 2001 through 2003 data period. Higher 

resolution analyses, for example with 1 km grid spacing, would also be prepared for this data period 

using the resources described in the following sections. 

MeadWestvaco North Charleston Operations 25 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1090
3.3.1 MM5 SIMULATIONS

MM data are used as “observed” or “first-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional 

grid. The following three years of MM5 meteorological data have been assembled by 

VISTAS for use in the regional CALPUFF modeling effort: 

V 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA) 

V 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS) 

V 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning 

Organization). 

These data sets were provided to the VISTAS technical contractor, which produced annual 

CALMET meteorological files at for the 12-km grid resolution regional domain. The 

development of the regional CALMET meteorological fields from MM5 data were 

conducted in No-Observations (“No-Obs”) mode since the MM5 data already reflect 

assimilation of observational data and are likely to adequately characterize regional wind 

patterns that are consistent with the 12-km grid scale. 

When the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain 

adjustments were turned off since the grid resolution of the MM5 data is the same as the 

CALMET grid and the terrain adjustments on the 12-km grid scale will already be 

reflected in the MM5 dataset. In this case, the MM5 winds will be interpolated by 

CALMET to the CALMET layers and CALMET’s boundary layer modules will compute 

mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. For 2003, the 36-km MM5 data were used as CALMET’s initial 

guess field and then the CALMET diagnostic terrain adjustments (see Section 3.1.1 of the 

VISTAS BART Modeling Protocol) were applied to reflect terrain on the scale of the 

CALMET grid (i.e., 12 km). 

3.3.2 MEASUREMENTS AND OBSERVATIONS

The finer grid (4 km) CALMET simulations, which MeadWestvaco anticipates utilizing as 

part of a refined analysis, will be run by VISTAS technical contractor in hybrid mode, 

using both MM5 data to define the initial guess fields and NWS meteorological 

observational data in the Step 2 calculations. In this manner, actual observations of threedimensional meteorological conditions can be used in the model to smooth the coarse 

MM5 resolution to better represent areas in which terrain features and coastlines may have 

an important effect on meteorological conditions, but not be well resolved in the mesoscale 

model. Surface, upper air, precipitation, and offshore buoy observation points are readily 

available for use in CALMET. As of the date of this modeling protocol, the VISTAS 

technical contractor has not completed the refined CALMET analyses; hence the specific 

observations used have not been identified. The following generally describes the use of 

NWS observations in Step 2 of the CALMET analyses. 

Parameters affecting turbulent dispersion that are observed hourly at surface stations 

include wind speed and direction, temperature, cloud cover and ceiling, relative humidity, 

MeadWestvaco North Charleston Operations 26 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1091
and precipitation type. Surface data would be selected from the available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations would be 

obtained from the National Climatic Data Center (NCDC), quality assured, and merged 

using the SMERGE pre-processor to create a single assimilated data file of surface 

observations for each year analyzed. 

Observations of meteorological conditions in the upper atmosphere provide a profile of 

turbulence from the surface through the depth of the boundary layer in which dispersion 

occurs. Upper air data are collected by balloons launched simultaneously across the 

observation network at 0000 Greenwich Mean Time (GMT) (7 o’clock PM in South 

Carolina) and 1200 GMT (7 o’clock AM in South Carolina). Sensors observe pressure, 

wind speed and direction, and temperature (among other parameters) as the balloon rises 

through the atmosphere. The upper air observation network is less dense than surface 

observation points since upper air conditions vary less and are generally not as affected by 

local effects (e.g., terrain or coastlines). Upper air data would be extracted from the 

NCDC’s available data inventory to optimize spatial coverage and representation of the 

domain, and utilization from year to year may vary due to availability and data quality. 

The effects of aqueous phase chemical transformations and wet deposition processes on 

ambient pollutant concentrations are an important part of the BART applicability analysis. 

Therefore, it is necessary to include observations of precipitation in the CALMET analysis. 

Precipitation data would be collected from selected surface meteorological data stations 

included in the analysis, plus Cooperative Observation Network (COOP) stations nearer to 

or within the domain. Precipitation data were extracted from among the NCDC’s available 

data inventory to optimize spatial coverage and representation of the domain. Raw 

observations from these stations would be quality assured and merged using the PMERGE 

pre-processor to create a single assimilated data file of precipitation observations. 

Because parts of the modeling domain encompass the open waters of the Atlantic Ocean 

and the Wolf Island and Cape Romain Class I areas are located along the Atlantic Coast, 

meteorological data from coastal and offshore buoys can be useful so that overwater 

meteorological processing algorithms in CALMET can be utilized. The critical differences 

in behavior of the inland and marine boundary layers, and atmospheric dispersion 

phenomena occurring within these distinct regimes, is well documented and recognized to 

play a vital role in the dispersion of pollutants originating in, or destined to affect, coastal 

areas. Key phenomena occurring in coastal environments that affect pollutant dispersion 

include land/sea-breezes that cause recirculation of pollutant mass, temperature moderation 

that results in sharp gradients and mixing height discontinuities at the land-sea interface, 

and thermal internal boundary layers that could cause severe fumigation under certain 

conditions. The CALMET processor is equipped to assimilate overwater data obtained 

from coastal, near-shore, and offshore observation platforms. CALMET uses a profile 

method to simulate boundary layer effects by computing the friction velocity, MoninObukhov length, surface roughness, and mixing height over the water surface. The details 

of the formulation of marine dispersion algorithms are provided in the documentation 

accompanying the CALPUFF modeling system. 

MeadWestvaco North Charleston Operations 27 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1092
To perform its simulation of the coastal environment, CALMET requires hourly 

observations of air temperature, air-sea temperature difference, wind speed and direction, 

relative humidity, overwater mixing height, and the overwater temperature gradients above 

and below the overwater mixing height. For practical applications of overwater boundary 

layer computations, these data can be obtained from the National Data Buoy Center 

(NBDC). The NBDC maintains an inventory of standard meteorological data observed by 

ships, buoys, and C-MAN stations in coastal, near-shore, and offshore locations. NDBC’s 

data sets provide direct wind and temperature measurements, and relative humidity can be 

inferred from pressure and dewpoint observations. The mixing height and temperature 

gradients and default values must be applied by CALMET when simulating the coastal 

atmosphere. 

In refined analyses for MeadWestvaco’s BART applicability modeling analysis (if 

necessary), overwater data would be provided in addition to the hourly surface 

meteorological observations, precipitation observations, and twice-daily upper air sounding 

data due to the location of the North Charelston Mill on the Atlantic Ocean coastline. 

3.4 AIR QUALITY DATABASE

The CALPUFF model is capable of simulating linear chemical transformation effects by using 

pseudo-first-order chemical reaction mechanisms for the conversions of SO2 to SO4, and NOX, which 

consists of nitrogen oxide (NO) and nitrogen dioxide (NO2), to nitrate (NO3) and nitric acid (HNO3). 

In this study, chemical transformations involving five species (SO2, SO4, NOX, HNO3, and NO3) will 

be modeled using the MESOPUFF II chemical transformation scheme. Ambient concentrations of 

ammonia and ozone concentrations as represented in the model affect the MESOPUFF II chemical 

transformation simulation. 

3.4.1 OZONE BACKGROUND CONCENTRATIONS

Both screening and refined analyses will utilize observed ozone data for 2001 through 

2003 from non-urban CASTNet and AIRS stations compiled by the VISTAS technical 

contractor for the regional domain. Monthly average ozone background values will be 

computed based on daytime average ozone concentrations from the OZONE.DAT file 

(6am-6pm average ozone concentrations computed by month) for substitution should all 

observations be missing for a particular hour of the dataset. 

3.4.2 AMMONIA BACKGROUND CONCENTRATIONS

In the screening and refined analyses, a constant background value (0.5 ppb) for ammonia 

will be utilized. The VISTAS BART Modeling Protocol envisions that CMAQ-modeled 

NH3 data for each Class I area would be used during postprocessing to repartition HNO3

and NO3 using the ammonia limiting method. However, as of the date of this modeling 

protocol, the specific values for the Cape Romain, Wolf Island, and Okefenokee Class I 

areas considered in MeadWestvaco’s modeling analysis have not been specified. 

Similarly, ammonia boundary conditions and modeled background levels that could be 

used in the refined CALPUFF analyses have not been provided. MeadWestvaco 

anticipates additional communication with South Carolina DHEC as details become 

MeadWestvaco North Charleston Operations 28 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1093
available during the time between the modeling protocol submittal and applicability 

analysis deadline to ensure a mutually agreeable approach for representing ammonia is 

used in the analyses. 

3.4.3 OTHER POLLUTANT BACKGROUND AND BOUNDARY CONDITIONS

The VISTAS BART Modeling Protocol envisions the use of modeled boundary conditions 

of ammonia and sulfates in the refined analysis of chemical transformations involving 

these species and nitrates. The VISTAS BART Modeling Protocol did not provide specific 

details about these boundary conditions, and as of the data of this source-specific modeling 

protocol for MeadWestvaco’s North Charleston Mill, VISTAS has not yet provided this 

information. Because MeadWestvaco anticipates completing refined analyses for the 

BART-eligible source at North Charleston, appropriate boundary conditions will be used in 

the refined analyses and appropriate documentation will be provided in the BART 

applicability modeling final report. 

3.5 NATURAL CONDITIONS AT CLASS I AREAS

The visibility goal of the Clean Air Act is both the remedying of existing visibility impairment, and 

prevention of future visibility impairment. In its BART Implementation Guidance, U.S. EPA affirms 

that it interprets the goal to mean return atmospheric conditions to “natural visibility conditions.” For 

the purposes of the Regional Haze Rule program, the U.S. EPA’s Guidance for Estimating Natural 

Visibility Conditions Under the Regional Haze Rule and Guidance for Tracking Progress under the 

Regional Haze Rule documents specify the 20% best days at each Class I area as the natural visibility 

goal. 

For the three Class I areas within 300 km of the North Charleston Mill and potentially affected by 

MeadWestvaco’s operations, Table 3-1 summarizes the default natural background conditions using 

average natural concentrations of sulfate, nitrate, and particulate species for areas in the Eastern U.S. 

as tabulated in Table 2-1 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under 

the Regional Haze Rule. 

TABLE 3-1. NATURAL BACKGROUND CONCENTRATIONS OF

VISIBILITY AFFECTING POLLUTANTS

Dry Extinction 

West East Error Efficiency 

Component (μg/m3

) (μg/m3

) Factor (m2

/g) 

Ammonium sulfate 0.12 0.23 2 3 

Ammonium nitrate 0.1 0.1 2 3 

Organic carbon mass 0.47 1.4 2 4 

Elemental carbon 0.02 0.02 2-3 10 

Soil 0.5 0.5 11⁄2 - 2 1 

Coarse Mass 3 3 11⁄2 - 2 0.6 

MeadWestvaco North Charleston Operations 29 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1094
As is described in Section 4 of this protocol, the effects of relative humidity to amplify the visibility 

impairment of hygroscopic sulfates and nitrates will be characterized using “Method 6,” which 

computes ∆bext using a monthly average relative humidity adjustment particular to each Class I area 

applied to background and modeled sulfate and nitrate. Table 3-2 summarizes the monthly average 

humidity values that will be applied for the three Class I areas considered in this analysis, as tabulated 

in Table A-2 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the 

Regional Haze Rule. 

TABLE 3-2. MONTHLY AVERAGE f(RH) FOR SELECTED CLASS I AREAS* 

 

Class I Area January February March April May June July August September October November December 

 

Cape Romain 3.2 2.9 2.8 2.7 2.9 3.3 3.3 3.6 3.5 3.4 3.4 3.1 

 

Wolf Island 3.3 3.0 3.2 3.0 3.2 3.8 3.4 3.6 3.6 3.4 3.3 3.4 

 

Okefenokee 3.3 3.0 3.2 3.0 3.2 3.8 3.4 3.6 3.6 3.4 3.3 3.4 

* As tabulated in Table A-2 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule (2003). 

Natural background conditions for each Class I area will be calculated using the data summarized in 

Tables 3-1 and 3-2, and the default IMPROVE light extinction formula, which is summarized in the 

following equation. 

bext background SO NO OC soil coarse ++++++= bbbbbbbkm rayap

−

4 3

)(

1

,

where, 

[ ]

[ ]

[ ]

[ ]

[ ]

3

Ray

34

424

][ Concentrat μg/minion

f(RH) relative humidity adjustment factor

b Rayleigh Scattering

Elemental[01.0 Carbon]

0006.0 Coarse Mass

Soil001.0

OC004.0

)(NONH003.0

)(SO)(NH003.0

3

4

=

=

=

=

=

=

=

=

=

ap

Coarse

Soil

OC

NO

SO

b

b

b

b

b RHf

b RHf

As noted in the VISTAS BART Modeling Protocol, the U.S. EPA and the Regional Planning 

Organizations (including VISTAS) are evaluating whether refinements are warranted to the methods 

recommended in U.S. EPA’s guidance to calculate default estimates of natural background visibility. 

In addition, the Interagency Monitoring of Protected Visual Environments (IMPROVE) work group 

has recently approved an alternative to the default formula used to estimate extinction from particle 

concentration measurements.9

 Refinements in the revised IMPROVE formula include the following: 

 

9

 Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005. Revised IMPROVE 

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering Committee, 

November 2005. 

MeadWestvaco North Charleston Operations 30 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1095
V Adding a sea salt term, including a growth factor due to relative humidity 

V Increasing the factor used to calculate the mass of particulate organic matter from organic 

carbon measurements 

V Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates 

V Revising the extinction efficiencies (the numerical constants in Equation 3-1) for sulfates, 

nitrates, and organic carbon so that they vary with concentration 

V Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated by 

IMPROVE for all Class I areas. 

For the purposes of calculating current, future, and natural background visibility at Class I areas as 

part of its reasonable progress analyses, VISTAS intends to present regional air quality modeling 

results using both the current U.S. EPA recommended assumptions and the newly revised IMPROVE 

light extinction formula. Accordingly, in any refined BART applicability analyses that may be 

required, MeadWestvaco may evaluate the results of the analysis using both formulas to assess the 

frequency, duration, and magnitude of visibility impairment events. 

MeadWestvaco North Charleston Operations 31 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1096
4. AIR QUALITY MODELING METHODOLOGY

Section 4 of this BART applicability modeling protocol for MeadWestvaco’s North Charleston Mill 

describes the air quality modeling methodology that will be used in the screening and refined 

analyses. The information in this Section 4 is largely adapted from the VISTAS BART Modeling 

Protocol, which is presented in Appendix A of this source-specific protocol for reference, and sample 

model files made available on the VISTAS technical contractor website.10

Section 2.2 of the VISTAS BART Modeling Protocol summarizes recommendations for the air quality 

modeling analyses required to assess applicability of BART by determining whether 

MeadWestvaco’s North Charleston Mill contributes to visibility impairment at Class I areas within 

300 km, which include Cape Romain NWR (30 km distant), Wolf Island NWR (212 km), and 

Okefenokee NWR (297 km). The CALPUFF modeling system is recommended as the preferred 

modeling approach for use in the BART analyses. 

4.1 PLUME MODEL SELECTION

CALPUFF and its meteorological model, CALMET, are designed to handle the complexities posed 

by the complex terrain, the large source-receptor distances, chemical transformation and deposition, 

and other issues related to Class I visibility impacts. The CALPUFF modeling system has been 

adopted by the U.S. EPA as a Guideline model for source-receptor distances greater than 50 km, and 

for use on a case-by-case basis in complex flow situations for shorter distances. CALPUFF is 

recommended for Class I impact assessments by FLAG and IWAQM. The final BART guidance 

recommends CALPUFF as “the best modeling application available for predicting a singe source’s 

contribution to visibility impairment.” As a result of these recommendations, the VISTAS BART 

Modeling Protocol is based on the use of CALPUFF for its BART determinations. Specifically, 

VISTAS CALMET Version 5.7 and CALPUFF Version 5.8 will be used in the CALPUFF analyses 

for BART applicability assessment, or other appropriate versions as specified by VISTAS and it 

technical contractor.11

This source-specific modeling protocol for MeadWestvaco’s North Charleston Mill incorporates by 

reference the VISTAS BART Modeling Protocol, which is provided in Appendix A of this document. 

The following sections present a brief summary of major features of the CALMET and CALPUFF 

models, and further detailed information should be obtained from the VISTAS BART Modeling 

Protocol and documentation referenced therein. 

 

10 http://src.com/verio/download/download.htm#VISTAS_VERSION. 

11 Although the VISTAS BART Modeling Protocol specifies CALMET Version 5.7 and CALPUFF Version 5.8, 

as of the date of this modeling protocol, the technical contractor website maintains CALPUFF Version 5.754 and CALMET 

Version 5.711. 

MeadWestvaco North Charleston Operations 32 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1097
4.1.1 MAJOR RELEVANT FEATURES OF CALMET 

The CALMET meteorological model consists of a diagnostic wind field module and 

boundary layer micrometeorological modules for overwater and overland boundary layers. 

Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used to 

compute hourly gridded fields of the sensible heat flux, surface friction velocity, MoninObukhov length, and convective velocity scale. Mixing heights are determined from the 

computed hourly surface heat fluxes and observed temperature soundings using a modified 

Carson (1973) method based on Maul (1980). The model also determines gridded fields of 

Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates. 

The aerodynamic and thermal properties of water surfaces suggest that a different method 

is best suited for calculating the boundary layer parameters in the marine environment. 

A profile technique, using air-sea temperature differences, is used in CALMET to compute 

the micro-meteorological parameters in the marine boundary layer. The version of 

CALMET being used by VISTAS contains improvements in the overwater boundary layer 

parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response 

Experiment (COARE) and enhancements in the calculation of overwater mixed layer 

heights (Batchvarova and Gryning, 1991, 1994). Further details and the results of an 

evaluation of the model containing these enhancements are described in Scire et al. (2005). 

An upwind-looking spatial averaging scheme is optionally applied to the mixing heights 

and three-dimensional temperature fields in order to account for important advective 

effects. 

The diagnostic wind field module uses a two-step approach to the computation of the wind 

fields (Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted 

for kinematic effects of terrain, slope flows, and terrain blocking effects to produce a 

Step 1 wind field. Gridded MM5 can be used to define the initial guess field. The second 

step consists of an objective analysis procedure to introduce observational data into the 

Step 1 wind field to produce a final wind field. 

Development of the Step 1 wind field begins with the initial guess field defined by the 

MM5 prognostic meteorological model. Normally, the CALMET computational domain is 

specified to be at finer grid resolution than the MM5 dataset used to initialize the initial 

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used 

to develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 

algorithms in CALMET described below apply terrain adjustments to the initial guess field 

on the fine-scale CALMET grid. Thus, the CALMET winds are adjusted to respond to 

fine-scale terrain features not necessarily seen by the coarser scale MM5 model. 

The approach of Liu and Yocke (1980) is used to evaluate the effects of the kinematic 

terrain on the wind field. The initial guess field winds are used to compute a terrain-forced 

vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme 

is applied iteratively until the three-dimensional divergence is less than a threshold value. 

MeadWestvaco North Charleston Operations 33 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1098
The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 1997) 

based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

advective-gravity and equilibrium flow regimes. At night, the slope flow model 

parameterizes the flow down the sides of the valley walls into the floor of the valley, and 

during the day, upslope flows are parameterized. The magnitude of the slope flow depends 

on the local surface sensible heat flux and local terrain gradients. The slope flow wind 

components are added to the wind field adjusted for kinematic effects. 

The thermodynamic blocking effects of terrain on the wind flow are parameterized in terms 

of the local Froude number (Allwine and Whiteman, 1985). If the Froude number at a 

particular grid point is less than a critical value and the wind has an uphill component, the 

wind direction is adjusted to be tangent to the terrain. 

The wind field resulting from the preceding adjustments of the initial-guess wind is the 

Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An 

inverse-distance squared interpolation scheme is used which weights observational data 

heavily in the vicinity of the observational station, while the Step 1 wind field dominates 

the interpolated wind field in regions with no observational data. The resulting wind field 

is subject to smoothing, an optional adjustment of vertical velocities based on the O’Brien 

(1970) method, and divergence minimization to produce a final Step 2 wind field. 

The introduction of observational data in the Step 2 calculation is an option. It is also 

possible to run the model in “no observations” (No-Obs) mode, which involves the use 

only of MM5 gridded data for the initial guess field followed by fine-scale terrain 

adjustments by CALMET. In No-Obs mode, observational data are not used in the Step 2 

calculations. The No-Obs mode is appropriate when the MM5 simulations adequately 

characterize the regional wind patterns and when local observations, especially surface 

observations, reflect local conditions on a scale smaller than that of the CALMET domain 

and hence their spatial representativeness may be limited. Such situations are most likely to 

occur when the CALMET grid scale is relatively large i.e., coarser than the scale of 

variation of the true wind field, which is particularly likely to occur in complex terrain or 

along the seashore. 

As was described in Section 3.1.1 of this protocol, when the 12-km MM5 (2001 and 2002) 

data are used, the diagnostic CALMET terrain adjustments were turned off since the grid 

resolution of the MM5 data is the same as the CALMET grid and the terrain adjustments 

on the 12-km grid scale will already be reflected in the MM5 dataset. In this case, the 

MM5 winds will be interpolated by CALMET to the CALMET layers and CALMET’s 

boundary layer modules will compute mixing heights, turbulence parameters and other 

meteorological parameters that are required by CALPUFF. For 2003, the 36-km MM5 

data were used as CALMET’s initial guess field and then the CALMET diagnostic terrain 

adjustments (see Section 3.1.1 of the VISTAS BART Modeling Protocol) were applied to 

reflect terrain on the scale of the CALMET grid (i.e., 12 km). Refined analyses, if 

MeadWestvaco North Charleston Operations 34 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1099
required, will utilize the MM5 data as the first-guess wind field, apply the diagnostic 

algorithms to create the Step 1 winds, and use NWS data for smoothing in Step 2. 

4.1.2 MAJOR RELEVANT FEATURES OF CALPUFF 

By its puff-based formulation and through the use of three-dimensional meteorological 

data developed by the CALMET meteorological model, CALPUFF can simulate the 

effects of time- and space-varying meteorological conditions on pollutant transport from 

sources in complex terrain. The major features and options of the CALPUFF model are 

summarized in Table 3-2 of the VISTAS BART Modeling Protocol. Some of the technical 

algorithms are briefly described as follows. 

V Complex Terrain: The effects of complex terrain on puff transport are derived from 

the CALMET winds. In addition, puff-terrain interactions at gridded and discrete 

receptor locations are simulated using one of two algorithms that modify the 

puff-height (either that of ISCST3 or a general “plume path coefficient” adjustment), 

or an algorithm that simulates enhanced vertical dispersion derived from the 

weakly-stratified flow and dispersion module of the Complex Terrain Dispersion 

Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms 

rely on the receptor elevation (relative to the elevation at the source) and the height of 

the puff above the surface. The enhanced dispersion adjustment relies on the slope of 

the gridded terrain in the direction of transport during the time step. 

V Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG 

treats terrain features that are not resolved by the gridded terrain field, and is based 

on the CTDMPLUS (Perry et al., 1989). Plume impingement on subgrid-scale hills 

is evaluated at the CTSG subgroup of receptors using a dividing streamline height 

(Hd) to determine which pollutant material is deflected around the sides of a hill 

(below Hd) and which material is advected over the hill (above Hd). The local flow 

(near the feature) used to define Hd is taken from the gridded CALMET fields. As in 

CTDMPLUS, each feature is modeled in isolation with its own set of receptors. 

V Puff Sampling Functions: A set of accurate and computationally efficient puff 

sampling routines is included in CALPUFF, which solve many of the computational 

difficulties encountered when applying a puff model to near-field releases. For 

near-field applications during rapidly-varying meteorological conditions, an 

elongated puff (slug) sampling function may be used. An integrated puff approach 

may be used during less demanding conditions. Both techniques reproduce 

continuous plume results under the appropriate steady state conditions. 

V Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are 

both incorporated into CALPUFF. An option is provided to use either model for all 

stacks, or make the choice on a stack-by-stack and wind sector-by-wind sector basis. 

Both algorithms have been implemented in such a way as to allow the use of wind 

direction specific building dimensions. The PRIME building downwash model 

(Schulman et al., 2000) is also included in CALPUFF as an option. 

V Dispersion Coefficients: Several options are provided in CALPUFF for the 

computation of dispersion coefficients, including the use of turbulence measurements 

(σv and σw), the use of similarity theory to estimate σv and σw from modeled surface 

MeadWestvaco North Charleston Operations 35 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1100
heat and momentum fluxes, or the use of Pasquill-Gifford (PG) or McElroy-Pooler 

(MP) dispersion coefficients, or dispersion equations based on the CTDM. Options 

are provided to apply an averaging time correction or surface roughness length 

adjustments to the PG coefficients. In Version 5.8 of CALPUFF being used by 

VISTAS, an option is provided to use the AERMOD turbulence profiles for 

determining dispersion rates, which is the most recent approach to dispersion in EPAapproved regulatory modeling. In addition, turbulence advection is included. For 

additional details on these features, see Scire et al. (2005). 

V Overwater and Coastal Interaction Effects: Because the CALMET meteorological 

model contains both overwater and overland boundary layer algorithms, the effects of 

water bodies on plume transport, dispersion, and deposition can be simulated with 

CALPUFF. The puff formulation of CALPUFF is designed to handle spatial changes 

in meteorological and dispersion conditions, including the abrupt changes that occur 

at the coastline of a major body of water. 

V Dry Deposition: A resistance model is provided in CALPUFF for the computation of 

dry deposition rates of gases and particulate matter as a function of geophysical 

parameters, meteorological conditions, and pollutant species. For particles, sourcespecific mass distributions may be provided for use in the resistance model. Of 

particular interest for BART analyses is the ability to separately model the deposition 

of fine particulate matter (< 2.5 μm diameter) from coarse particulate matter (2.5-10 

μm diameter). 

V Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that 

allows vertical wind shear effects across individual puffs to be simulated. 

Differential rates of dispersion and transport among the “new” puffs generated from 

the original, well-mixed puff can substantially increase the effective rate of 

horizontal spread of the material. Puffs may also be split in the horizontal when the 

puff size becomes large relative to the grid size, to account for wind shear across the 

puffs. 

V Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF 

to compute the depletion and wet deposition fluxes due to precipitation scavenging. 

The scavenging coefficients are specified as a function of the pollutant and 

precipitation type (i.e., frozen vs. liquid precipitation). 

V Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOX, HNO3, and 

NO3

-

) employed in the MESOPUFF II model or a set of user-specified, diurnallyvarying transformation rates. The MESOPUFF II scheme is recommended by 

IWAQM. It produces secondary fine particulate matter (sulfate and nitrate) from 

emissions of SO2 and NOX and thus allows analyses of visibility impacts. Ambient 

ozone concentrations are used in the parameterized chemical transformation module 

as a surrogate for OH radicals during daylight hours. Ambient ammonia 

concentrations are used together with a temperature and relative humidity-dependent 

equilibrium relationship to partition nitric acid and nitrate on an hour-by-hour and 

receptor-by-receptor basis. 

MeadWestvaco North Charleston Operations 36 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1101
4.2 MODELING DOMAIN CONFIGURATION

The VISTAS regional modeling domain was illustrated in Figure 3-1 in the preceding section of this 

protocol, and was designed to allow any Class I areas within the VISTAS area to be evaluated with a 

single meteorological database and consistent CALPUFF modeling options. The horizontal domain 

is comprised of grid cells, each containing a central grid point at which meteorological and 

computational parameters are calculated at each time step. For the initial regional analysis, a grid 

spacing interval of 12 km was selected. Given this interval, the domain consists of 160 by 172 grid 

cells. A Lambert Conformal Coordinate projection system is used to describe the horizontal grid, 

with origin at 40 degrees North latitude and 97 degrees West longitude. Standard parallels for the 

projection were set at 33 degrees North and 45 degrees North. 

Table 4-1 summarizes the vertical grid structure selected for this analysis, which comprises ten 

vertical layers. The cell face height of each layer indicates its vertical extent. The vertical domain is 

composed of terrain-following grid cells, the number and size of which are chosen so as to constrain 

the boundary layer in which dispersion and chemical transformations take place. The highest cell 

face was selected to be 4,000 meters to constrain the default maximum mixing height of 3,000 meters. 

TABLE 4-1. VERTICAL GRID STRUCTURE

 

Vertical Grid Cell 

Cell Face Height 

(meters) 

1 20 

2 40 

3 80 

4 160 

5 320 

6 640 

7 1,200 

8 2,000 

9 3,000 

10 4,000 

Refined analyses, if necessary, will be conducted using one or more of the subregional 4-km grids 

that the VISTAS technical contractor will provide. MeadWestvaco anticipates that refined analyses 

may be necessary for the Cape Romain NWR, in which case the appropriate subregional grid would 

be utilized. Additional runs at higher resolution (e.g., 1 km) may be performed and the modeling 

subdomain would be selected appropriately from the VISTAS regional grid. 

4.3 CALMET METEOROLOGICAL MODELING

CALMET meteorological modeling for the initial regional screening analysis was conducted over the 

entire VISTAS regional domain described in section 4.2. The major features of CALMET were 

described in Section 4.1.1 of this protocol, and the geophysical and meteorological databases were 

described in Section 3. CALMET processing was conducted generally in accordance with the 

recommendations of IWAQM Phase 2 Summary Report and Recommendations for Modeling Long 

Range Transport Impacts, with the following exceptions and/or specifications of non-default values. 

MeadWestvaco North Charleston Operations 37 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1102
V Modeling period: 2001 through 2003 

V Meteorological inputs: MM5 data provide initial guess fields in CALMET 

V CALMET mode: No-Observations mode including option to read overwater data directly 

from MM5 

V Diagnostic options: IWAQM default values, except as follows: diagnostic terrain blocking 

and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but no 

diagnostic terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

V CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

V TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 

2003 simulations). Values of ~10-20 km will be tested, and a value of 15 km was selected by 

the VISTAS technical contractor.

V Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature 

allows the temperature field over large bodies of water such as the Atlantic Ocean and the 

Great lakes to be properly characterized by buoy observations.

V Mixing height averaging parameter (MNMDAV) was determined by the VISTAS technical 

contractor to be 1 grid cell for regional simulations based on sensitivity tests. The purpose of 

the testing was to optimize the variable to allow spatial variability in the mixing height field, 

but without excessive noise. 

Refined analyses, if necessary, will be prepared using appropriate CALMET model settings as 

described by the VISTAS technical contractor for the 4 km subregional grids utilized. Should even 

higher resolution analyses be conducted, appropriate CALMET processing options will be evaluated 

and documented in the final applicability modeling report. 

4.4 CALPUFF COMPUTATIONAL DOMAIN AND RECEPTORS

CALPUFF analyses to assess the visibility impacts attributable to MeadWestvaco’s North Charleston 

Mill will be performed on a computational domain that is a subset of the VISTAS regional domain. 

The size of the domain will be selected to encompass the North Charleston Mill and the Wolf Island, 

Okefenokee, and Cape Romain areas, and to extend at least 50 km beyond in all directions. The size 

of the domain allows for the possible recirculation of puffs beyond the facility and areas being 

evaluated. 

Class I receptors: Use FLM Class I receptor list with receptor elevations provided (available from the 

NPS). Ambient impacts will be predicted at receptors specified by the FLM to represent the Wolf 

Island, Okefenokee, and Cape Romain areas as depicted in Figure 1-2 of this protocol.12

 

12 http://www2.nature.nps.gov/air/maps/Receptors/index.htm 

MeadWestvaco North Charleston Operations 38 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1103
4.5 CALPUFF MODELING OPTION SELECTIONS

The CALPUFF analysis will be conducted generally in accordance with the recommendations of 

IWAQM Phase 2 Summary Report and Recommendations for Modeling Long Range Transport 

Impacts, with the following exceptions and/or specifications of non-default values.

V Chemical mechanism: MESOPUFF II module 

V Background concentrations of SO4 and TNO3 (HNO3 + NO3) from CMAQ 2001-2003 annual 

runs. Note that as of the date of this protocol, the appropriate background levels for each 

Class I area have not yet been specified. MeadWestvaco requests that DHEC confirm the 

appropriate values in response to this modeling protocol. 

V Species modeled: SO2, SO4, NOX, HNO3, NO3 and particulate matter in size categories of 

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic 

diameters. 

V Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual 

emission rate with normal operations from the highest emitting day of the meteorological 

period modeled (excluding days where start-up, shutdown or malfunctions occurred 

sometime during the day). Note that potential emissions are used to determine if a source is 

BART-eligible, but 24-hour average maximum emissions are used for modeling purposes 

(70 FR 39162). Pollutants considered include SO2, H2SO4, NOX and PM10. The basis for 

modeled emission rates was described in Section 2 of this protocol. 

V Condensable emissions are considered as primary fine particulate matter and allocated 

equally to the two sub-micrometer particle size classes. Industry-specific emission factors 

were utilized to analyze the phase, size, and character of PM emissions as described in 

Section 2 of this protocol. 

V Excluded from the modeling are pollutants with plant-wide emissions less than de minimis

levels (40 tons per year for SO2 and NOX and 15 tons per year for PM10). De minimis levels 

are plant wide for each visibility-impairing pollutant, so individual units may be modeled 

even if they have emissions below de minimis if the plant total is greater than de minimis. 

V Particulate emissions speciation: Break down, as appropriate, filterable and condensable 

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine 

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. 

Industry-specific emission factors were utilized to analyze the phase, size, and character of 

PM emissions as described in Section 2 of this protocol. 

V CALPUFF model options: Use IWAQM guidance, except use turbulence-based dispersion 

coefficients and probability density function (pdf) dispersion, as used in the AERMOD 

model. 

V Ozone dataset: use observed ozone data for 2001 through 2003 from CASTNet and AIRS 

stations. Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly 

average ozone (backup) background values are to be computed based on daytime average 

ozone concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month). 

V Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) values for 

ammonia. CMAQ NH3 data will be used for each Class I area in POSTUTIL to repartition 

HNO3 and NO3. Note that as of the date of this protocol, the appropriate background levels 

MeadWestvaco North Charleston Operations 39 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1104
for each Class I area have not yet been specified. MeadWestvaco requests that DHEC 

confirm the appropriate values in response to this modeling protocol. 

V Puff representation: integrated puff sampling methodology. 

V Building downwash: Since the nearest Class I area is located 30 km to the facility, effects of 

building downwash will be evaluated in the initial regional modeling runs to determine 

whether downwash effects may affect visibility impacts at Cape Romain. 

4.6 CALPOST PROCESSING OPTION SELECTIONS FOR

LIGHT EXTINCTION AND HAZE IMPACT CALCULATIONS

The following postprocessing techniques will be used to compute the 24-hour average visibility 

impacts at each Class I area within 300 km of MeadWestvaco’s North Charleston Mill. 

V Species considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic 

emissions), soil, coarse PM 

V Visibility Method 6 will be used with Class I area-specific, monthly average, relative 

humidity values as described in Section 3.5 of this protocol. 

V Natural background light extinction will be represented at each Class I area within 300 km of 

MeadWestvaco’s North Charleston Mill as described in Section 3.5 of this protocol. 

V Light extinction efficiencies: Use EPA (2003a) values. MeadWestvaco will evaluate the use 

of the 2005 IMPROVE algorithm for calculating light extinction, which the VISTAS BART 

Modeling Protocol indicates may be used in addition to the default IMPROVE algorithm. 

Calculations would be performed outside CALPOST unless CALPOST is modified to 

accommodate the new algorithm. 

V Ammonia Limiting Method: Use ammonia from CMAQ to define NH3 for each Class I area. 

Choose ammonia from either the CMAQ grid cell where the IMPROVE monitor is located or 

the grid cell of the centroid of the Class I area (the latter in the case that the IMPROVE 

monitor is located outside the Class I area or there is no IMPROVE monitor.) 

The initial run results will be based on the highest change in light extinction (deciviews) from natural 

conditions over the three-year modeling period for each Class I area considered. Predicted changes 

exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid CALPUFF modeling 

analysis. To assess whether BART-eligible operations contribute to visibility impairment, 

MeadWestvaco’s applicability modeling analysis will demonstrate the top eight 24-hour average 

visibility impacts of each year modeled to illustrate the distribution (i.e., frequency, duration, and 

magnitude) of peak visibility impairment episodes attributable to the North Charleston Mill. 

Only the 98th percentile 24-hour average visibility impact would be evaluated in the refined analysis, 

if necessary. 

4.7 MODELING PRODUCTS

MeadWestvaco will prepare and submit a BART applicability analysis result describing the modeling 

procedures, data resources, and results of screening and refined modeling (if necessary) used to assess 

whether the North Charleston Mill is subject to BART. The presentation of modeling results will 

generally conform to the expectations described in the VISTAS BART Modeling Protocol and 

MeadWestvaco North Charleston Operations 40 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1105
described as follows. The Results section of the CALPUFF modeling report should contain the 

following information: 

1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all 

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source 

3. A discussion of the number of Class I areas with visibility impairment from the source on 98th

percentile days in each year greater than 0.5 dv (total visibility impairment minus impairment 

on 20% best days for natural background visibility equals delta-dv, the visibility impact 

attributed to the source). 

4. For the Class I area with the maximum impact, discussion of the number of days below the 

98th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in the 

Class I area where the impact exceeds 0.5 dv, and the maximum impact. 

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which 

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report same 

results as provided for 12-km initial exemption modeling. 

MeadWestvaco North Charleston Operations 41 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1106
5. QUALITY ASSURANCE METHODS

MeadWestvaco will conduct quality assurance of CALMET, CALPUFF, and CALPOST analyses in 

a manner the generally conforms to the VISTAS BART Modeling Protocol. A description of the 

quality assurance methods and products (e.g., test case simulations, graphic representations of model 

fields and performance) will be provided in MeadWestvaco’s BART Applicability Modeling Report. 

The following sections describe techniques that can be used to visualize and quality assure 

performance of each model component as described in the VISTAS BART Modeling Protocol. 

5.1 CALMET FIELDS 

Section 4 of the VISTAS BART Modeling Protocol describes the methods and procedures for use in 

conducting regional scale screening modeling to determine the whether a particular source or group 

of sources is subject to BART controls. In the initial application, the regional CALPUFF-ready 

meteorological data files will be provided by VISTAS. The amount of effort for end-users 

performing QA of these pre-defined meteorological fields will be reduced from what is required in 

developing source-specific meteorological fields, as described below. Also, VISTAS is planning to 

provide five subregional CALMET meteorological datasets in a CALPUFF-ready format. The 

development of these CALMET datasets will be subject to a QA program as part of their 

development, so the necessary quality assurance activity of end-users is again reduced from what 

would be required in the development of the dataset. It is not expected that the quality assurance 

steps in the development will be repeated in each application. The VISTAS-provided regional and 

subregional meteorological fields will include a test case simulation for demonstrating that expected 

modeling results are obtained on the user’s computer platform. MeadWestvaco will execute this test 

case simulation to demonstrate that the expected results can be reproduced. 

The critical CALMET input parameters depend on the mode in which the model is run (i.e., 

observations mode, hybrid mode, or no-observations mode), and the location and spatial 

representativeness of any observational data. In a site-specific protocol involving the development of 

a meteorological dataset, the elements of the QA process include preparation of wind rose (using 

observed, MM5 and CALMET-derived data), including examination of the data as a function of 

season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, and presentation of 

2-D vector plots illustrating terrain effects/sea breeze circulation or other features of the flow 

expected to occur within the domain. For example, 2-D vector plots produced during light wind 

speed stable conditions (e.g., early morning such as 4 am) are good for assessing the performance of 

the CALMET model configuration and switches in reproducing terrain effects because these 

conditions are likely to maximize the terrain impacts in the model. Season wind roses at 4 am, 10 am 

and 4 pm would be expected to show the development of sea breeze circulations that may be 

important for certain applications. Customization of the QA process for the individual site-specific 

domain based on the availability of data and the physical processes expected to be important at that 

location will be conducted as part of the site-specific QA plan development. 

MeadWestvaco North Charleston Operations 42 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1107
5.2 CALPUFF, CALPOST, AND POSTUTIL RESULTS

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of the 

VISTAS BART Modeling Protocol or in a source-specific protocol, the IWAQM guidance should be 

used in setting up the CALPUFF simulations. The CALPUFF model obtains the switch settings from 

an ASCII “control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the 

CALPUFF GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. 

CALPOST is run separately for each Class I area in order to obtain the necessary visibility statistics 

for evaluating compliance with the BART screening and finer grid modeling thresholds. The inputs 

to CALPOST involve selection of the visibility method (Method 6 in the standard EPA BART 

guidance), entry of Class I area-specific data for computing background extinction and monthly 

relative humidity factors for hygroscopic aerosols. CALPOST contains a receptor screening that 

allow subsets of a receptor network modeling in CALPUFF to be selected for processing in a given 

CALPOST run. This is how receptors within a single Class I area are selected for processing from a 

CALPUFF output file that may contain receptors from several Class I areas. CALPOST contains 

options for creating plot files that will help in the confirmation that the proper receptor subset is 

extracted. 

The CALPOST output file contains a listing of the highest visibility impact each day of the model 

simulation over all receptors included in CALPOST analysis. Receptors will normally be selected in 

each CALPOST run so that each CALPOST run represents the impacts at a single Class I area. For a 

screening assessment, the peak value of the change in extinction is shown at the bottom of the 

visibility table. For a finer grid simulation, the 98th percentile value (8th highest day) is used for 

comparison against the BART threshold of 0.5 deciviews. It is necessary to import the results of the 

CALPOST table into a sorting program such as a spreadsheet to rank the daily change in extinction 

values such as is presented in Table 4-2. 

The CALPOST inputs that will be carefully checked as part of the CALPOST quality assurance 

include the following: 

V Visibility technique (Method 6) 

V Monthly Class I-specific relative humidity factors for Method 6 

V Background light extinction values 

V Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, organics), 

coarse and fine particulate matter, and elemental carbon. 

V Appropriate species names for coarse PM used 

V Extinction efficiencies for each species 

V Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area 

specific value for finer grid modeling) 

V Screen to select appropriate Class I receptors for each CALPOST simulation. 

MeadWestvaco North Charleston Operations 43 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1108
POSTUTIL allows the user to sum the contributions of sources from different CALPUFF simulations 

into a total concentration file. In addition, it contains options to scale the concentrations from 

different modeled species (e.g., different particle sizes) into species-dependent size distributions for 

the particulate matter. For example, PM is often simulated with unit emission rates for each particle 

size category and, in the POSTUTIL stage, the contributions of each size category based on the 

species being considered (e.g., elemental carbon, coarse particulate matter, etc.) are combined to form 

the species concentrations for input into CALPOST. This process, although simple, requires a careful 

review of the weighting factors for each source. POSTUTIL also allows a repartitioning of nitric acid 

and nitrate to account for the effects of ammonia limiting conditions. 

 

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-specific modeling domain should be evaluated. 

The use of alternative ammonia concentration data (e.g., CMAQ output rather than derived ammonia 

based on aerosol measurements) will require an evaluation of the model performance relative to the 

techniques in the VISTAS common protocol. 

MeadWestvaco North Charleston Operations 44 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1109
6. REFERENCES

The following guidance documents, regulations, and technical publications were referenced in 

preparation of this BART Applicability Modeling Protocol for MeadWestvaco and the VISTAS BART 

Modeling Protocol. 

Allwine, K.J. and C.D. Whiteman, 1985: MELSAR: A Mesoscale Air Quality Model for Complex 

Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest 

Laboratory, Richland, Washington. 

Batchvarova, E. and S.-E. Gryning, 1991: Applied model for the growth of the daytime mixed layer. 

Boundary-Layer Meteorol., 56, 261-274. 

Batchvarova, E. and S.-E. Gryning, 1994: An applied model for the height of the daytime mixed 

layer and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323. 

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. 

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast 

Cycle: the RUC, Mon. Wea. Rev., 132, 495-518. 

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary 

layer. Quart. J. Roy. Meteor. Soc., 99, 450-467. 

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC 

and VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466. 

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of 

the Montana Department of Environmental Quality. 

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air 

Resources Board, Sacramento, California. 

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: 

Causes, Consequences and Controversies, Asheville, NC, October 2004. 

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze 

Rule; EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions 

under the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 

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BART Applicability Modeling Protocol 

Appendix H.6 - 1110
Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for 

Modeling Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling 

(IWAQM). EPA-454/R-98-019, U.S. Environmental Protection Agency, RTP, NC. 

Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol 

Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on 

the Applications of Air Pollution Meteorology, Norfolk, VA. May 2002. 

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: Bulk parameterization of 

air-sea fluxes: Updates and verification for the COARE algorithm. Journal of Climate, 16, 571-591. 

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis 

of PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory. FLAG, 2000. Federal 

Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. U.S. Forest 

Service, National Park Service, U.S. Fish and Wildlife Service. 

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn 

State/NCAR MM5, NCAR TN-398-STR, NCAR Technical Note. 

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary 

Organic Aerosols. Atmos. Environ., 23, 1733-1747. 

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface 

fluxes from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529. 

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with 

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E. 

Gryning and F.A. Schiermeier. Plenum Press, New York, NY. 

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16. 

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711. 

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity 

Generating Bureau MID/SSD/80/0026/R. Nottingham, England. 

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation 

Mechanism in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference 

– Guideline on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003. 

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 

22-26 June 2003, San Diego, CA. 

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BART Applicability Modeling Protocol 

Appendix H.6 - 1111
National Council for Air and Stream Improvement. Performance of EPA stack sampling methods for 

PM10, PM2.5 and condensible particulate matter on sources equipped with electrostatic precipitators 

(Technical Bulletin No. 852), September 2002. 

National Council for Air and Stream Improvement. Condensible particulate matter emissions from 

sources equipped with wet scrubbers (Technical Bulletin No. 898), March 2005. 

National Council for Air and Stream Improvement. Compilation of criteria air pollutant emissions 

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(Revised). 

O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary 

boundary layer. J. Atmos. Sci., 27, 1213-1215. 

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised 

IMPROVE Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to 

IMPROVE Steering Committee, November 2005 

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. 

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus 

Algorithms for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User 

Instructions. EPA/600/8-89/041, U.S. EPA, Research Triangle Park, North Carolina. 

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME 

Plume Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390. 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle 

Park, NC. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting 

& Exhibition, Toronto, Ontario, Canada. 8-13 June 1997. 

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts 

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET 

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts. 

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of 

Environmental Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA. 

MeadWestvaco North Charleston Operations 47 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1112
Scire, J.S., Z.-X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, 

Visibility and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty 

Conference, Guideline on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003. 

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF 

model for offshore and coastal applications. Proceedings of the Tenth International Conference on 

Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), 

Crete, Greece. 17-20 October 2005. 

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model 

with Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution 

Meteorology, Phoenix, Arizona. American Meteorological Society. 11-16 January 1998. 

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic 

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at 

the AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 

7-11 February 2005. 

MeadWestvaco North Charleston Operations 48 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1113
APPENDIX A 

VISTAS BART MODELING PROTOCOL

MeadWestvaco North Charleston Operations A - 1 Trinity Consultants 

BART Applicability Modeling Protocol 

Appendix H.6 - 1114
Protocol for the Application of the 

CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART) 

December 22, 2005

Visibility Improvement State and Tribal Association 

of the Southeast (VISTAS)

Appendix H.6 - 1115
Table of Contents iv

TABLE OF CONTENTS

Page

SUMMARY S-1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 1

1.1 Background 1

1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING 4

2.1 Overview of the Regional Haze BART Process 4

2.2 Model Recommendations for the BART Analysis 6

2.3 Performance of a Cap and Trade Program 7

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM 8

3.1 Capabilities and features of CALPUFF 8

3.1.1 Major Features of CALMET 10

3.1.2 Major Features of CALPUFF 12

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 16

3.2 Discussion of CALPUFF Applicability and Limitations 17

3.2.1 Transport and Diffusion 17

3.2.2 Aerosol Constituents 20

3.2.3 Regional Haze 28

4. VISTAS’ COMMON MODELING PROTOCOL 32

4.1 Overview of Common Modeling Approach 32

4.1.1 BART Exemption Analysis 32

4.1.2 BART Control Evaluation 34

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM 34

4.2 Optional Source-Specific Modeling 35

4.3 Initial Procedure for BART Exemption 36

4.3.1 Overview of Initial Approach 36

4.3.2 Discussion of Regional Initial Modeling Approach 36

4.3.3 Model Configuration and Settings for Initial Analysis 39

4.4 Finer Grid Modeling Procedures 43

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach 43

4.4.2 Model Configuration and Settings for Finer Grid Modeling 44

4.5 Presentation of Modeling Results 46

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources 51

5. SOURCE-SPECIFIC MODELING PROTOCOL 52

Appendix H.6 - 1116
Table of Contents iv

6. QUALITY ASSURANCE 55

6.1 Scope and Purpose of the QA program 55

6.2 QA Procedures for Common Protocol Modeling 56

6.2.1 Quality Control of Input Data 56

6.2.2 Quality Control of Application of CALMET 57

6.2.3 Quality Control of Application of CALPUFF 58

6.2.4 Quality Control of Application of CALPOST and POSTUTIL 59

6.3 Additional QA Issues for Alternative Source-Specific Modeling 60

6.4 Assessment of Uncertainty in Modeling Results 61

7. REFERENCES 62

Appendix H.6 - 1117
Summary S-1

SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available 

Retrofit Technology (BART) for the VISTAS Regional Planning Organization (RPO) describes 

common procedures for carrying out air quality modeling to support BART determinations that 

are consistent with guidelines of the U.S. Environmental Protection Agency in 40 CFR Part 51 

Appendix W and Appendix Y. The Protocol is intended to serve as the basis for a common 

understanding among the organizations that will be performing BART analyses or reviewing the 

BART modeling results in the VISTAS region.

Background

Best Available Retrofit Technology is required for any BART-eligible source that ‘‘emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “You 

can use dispersion modeling to determine that an individual source cannot reasonably be 

anticipated to cause or contribute to visibility impairment in a Class I area and thus is not subject 

to BART.” In the “individual source attribution approach,” a BART-eligible source that is 

responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment. 

A BART-eligible source that is responsible for a 0.5 dv change or more is considered to 

“contribute” to visibility impairment in a Class I area. Any source determined to cause or 

contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol 

to guide them, their sources, and reviewers in the BART determination and review effort. The 

Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat 

Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The 

VISTAS state BART contacts, particularly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon, 

AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol 

was enhanced and completed with the assistance of Joseph Scire, Christelle Escoffier-Czaja and 

Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from 

the VISTAS federal partners: Federal Land Managers and USEPA. The VISTAS RPO held a 

meeting on September 21, 2005 in Research Triangle Park, NC to discuss the Protocol with 

participants before starting a public comment period. The Protocol underwent formal external 

review during the period between September 26, 2005 and October 31, 2005. Numerous 

comments were received. All comments were carefully considered and discussed with VISTAS 

participants and federal partners. VISTAS gratefully acknowledges the very useful contributions 

of those that provided comments. On November 1st, 2005 VISTAS held another meeting with its 

participants in Nashville, TN to present and discuss the comments being considered for inclusion 

in the Protocol. No formal document will be prepared to address all the comments received on 

the Protocol. 

Appendix H.6 - 1118
Summary S-2

Objectives

The objectives of the Protocol (discussed in Chapter 1) are to provide: 

 A consistent approach to determine if a source is subject to BART

 A consistent model (CALPUFF) and modeling guidelines for BART determinations

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site-specific modeling

 Common expectations for reporting model results

The Protocol is not intended to define the engineering analyses required by the USEPA BART 

Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading. 

Chapter 2 is intended to provide summary background on EPA’s guidance for BART modeling. 

The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for 

applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the 

specific information that should be included in site-specific protocols. Chapter 6 describes the 

quality assurance requirements for BART analyses in the VISTAS RPO.

Recommendations

The major recommendations for VISTAS BART modeling included in this Protocol are:

I. Process

 Follow the BART process steps discussed in Chapter 2:

1. Identify BART eligible sources

2. Identify which pollutants have greater than de minimis emission levels

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasible controls and emission changes

6. Identify the change in visibility impact for each candidate BART control option

7. Compare the visibility improvement of BART control options to other statutory factors in 

the engineering analysis

Appendix H.6 - 1119
Summary S-3

II. CALPUFF Model Configuration

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a 

single source is subject to BART. VISTAS will use CALPUFF Version 5.8 and CALMET 

Version 5.7. These versions contain enhancements funded by the Minerals Management Service 

(MMS) and VISTAS. They were developed by Earth Tech, Inc. and they are maintained on Earth 

Tech’s Atmospheric Studies Group CALPUFF website (www.src.com) for public access. 

VISTAS is making publicly available 12-km CALMET output files for the entire VISTAS 

modeling domain (eastern United States) and intends to also provide CALMET output files for 

five 4-km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the 

CALMET input files, Earth Tech used the MM5 databases developed by EPA for 2001, VISTAS 

for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire 

VISTAS region, Earth Tech used the No-Obs setting (i.e., did not include additional surface and 

upper air observations beyond those incorporated in the MM5 calculations). For finer resolution 

subdomains (4 km grid or less), available surface and upper air observations will be used in 

addition to MM5 meteorological model outputs. The specific model settings will be provided 

with the CALMET files and via the CALPUFF website so that users can review or replicate the 

work. 

For CALPUFF modeling, source emissions should be defined using the maximum 24-hour actual 

emission rate during normal operation for the most recent 3 or 5 years. If maximum 24-hr actual 

emissions are not available, continuous emissions data, permit allowable emissions, potential 

emissions, and emissions factors from AP-42 source profiles may be used as available. 

Key points from comments received on the specific CALPUFF, CALPOST, and POSTUTIL 

configurations are highlighted below. 

• Use CMAQ modeling data from 2001-2003 to determine background concentrations of 

SO4 and total NO3 (HNO3 + NO3). CMAQ data in CALPUFF-ready format will be 

provided for each Class I area by VISTAS. After running CALPUFF for an individual 

facility, repartition NO3 in POSTUTIL using the CMAQ background data, including that 

for NH3.

• Use ozone data from non-urban monitors as the background ozone input. 

• Use the AERMOD dispersion method, which has been adopted by the EPA as an advance 

over the traditional Pasquill-Gifford method.1

• In CALPOST, use Method 6 with monthly average RH for calculating extinction, as 

recommended by the EPA. 

 

1 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex 

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.

Appendix H.6 - 1120
Summary S-4

• Use EPA default calculations of light extinction under current and natural background 

conditions. In addition to the default assumptions, a source may choose to also calculate 

visibility using the recently revised IMPROVE algorithm described by Pitchford, et al., 

(2005). 

Provide results in tables as illustrated in Chapter 4 that describe, for each source:

• Number of receptors within a single Class I area with impact > 0.5 dv

• Number of days at all receptors in the Class I area with impact > 0.5 dv

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART 

For determining if a BART-eligible source is subject to BART CALPUFF modeling, use a twotier approach. For the initial exemption modeling use CALPUFF with 12-km grid CALMET. For 

finer resolution of meteorological fields, use CALPUFF with CALMET of 4-km or smaller grid 

size. 

VISTAS States are accepting EPA guidance that the threshold value to establish that a source 

contributes to visibility impairment is 0.5 deciview. 

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I area 

boundary (Q/d) as a presumptive indicator that a BART-eligible source is subject to BART. If 

Q/d for SO2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the 

source is subject to BART and no exemption modeling will be performed using VISTAS funds. If 

the source agrees with this presumption, then the source can proceed to the BART determination 

using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a 

source disagrees, the source may perform fine grid modeling to determine if its impact is <0.5 dv. 

For sources with Q/d less than or equal to 10, VISTAS intends to fund Earth Tech to assist States 

with the initial CALPUFF exemption modeling. Each State will prioritize which sources will be 

offered modeling by VISTAS. Modeling of these sources will be conducted in priority order to 

first accommodate States with nearer term timing constraints in their SIP development process. 

To conserve VISTAS resources, modeling will begin with sources at lower Q/d values and 

continue with sources with higher Q/d values until a Q/d value that consistently results in a 

greater than 0.5 dv impact is identified. Chapter 4 addresses the number of VISTAS sources 

eligible for BART based on Q/d analysis. 

Note that VISTAS does not propose to use Q/d to exempt BART-eligible sources, but only to 

prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance 

not to use Q/d for exemption purposes. 

Appendix H.6 - 1121
Summary S-5

For the 12-km initial modeling exemption test, compare the highest single 24-hour average value 

across all receptors in the Class I area to the threshold value of 0.5 dv. If the highest 24-hr 

average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. If the 

highest 24-hr average value is greater than 0.5 dv, then the source may choose to perform finer 

grid modeling for exemption purposes or may accept determination that the source is subject to

BART and proceed to establish visibility impacts prior to and after BART controls. If using the 

single highest 24-hr average value proves, after initial 12-km grid CALPUFF modeling, to be too 

conservative a screening level, VISTAS may allow some exceedances of the threshold value for 

exemption purposes, up to no more than the 98th percentile value. 

The 12-km modeling results can be used to focus finer grid modeling for exemption purposes on 

only those Class I areas where impacts greater than 0.5 dv were projected in the 12-km modeling.

For finer grid (4 km or less) analyses, use the 98th percentile impact value for the 24-hr average. 

Use either the 8th highest day in each year or the 22nd highest day in the 3-year period, whichever 

is more conservative, for comparison to the exemption threshold.

Use the same model assumptions for pre-BART visibility impact and for BART control options 

modeling: establish baseline visibility from the pre-BART run; change one control at a time; and 

evaluate the change in visibility impact, i.e. the delta-deciview. Note that “no control” may 

constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under 

the USEPA BART guideline. If a source accepts to institute the most stringent control, the 

engineering analyses are not required.

This common VISTAS Protocol consistently recommends conservative assumptions. Individual 

States ultimately have responsibility to determine which, if any, BART controls are 

recommended in their State Implementation Plans (SIPs).

Appendix H.6 - 1122
Introduction and Protocol Objectives 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 

1.1 Background

Under regional haze regulations, the Environmental Protection Agency (EPA) has issued final 

guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70 

FR 39104-39172). The regional haze rule includes a requirement for BART for certain large 

stationary sources. Sources are BART-eligible if they meet three criteria including potential 

emissions of at least 250 tons per year of a visibility-impairing pollutant, were put in place 

between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories 

in the guidance. A BART engineering evaluation using five statutory factors -- 1) existing 

controls; 2) cost; 3) energy and non-air environmental impacts; 4) remaining useful life of the 

source; 5) degree of visibility improvement expected from the application of controls -- is 

required for any BART-eligible source that can be reasonably expected to cause or contribute to 

impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas protected 

under the regional haze rule. (Note that, depending on the five factors, the evaluation may result 

in no control.) Air quality modeling is an important tool available to the States to determine 

whether a source can be reasonably expected to contribute to visibility impairment in a Class I 

area.

Throughout this document the term “BART-eligible emission unit” is defined as any single 

emission unit that meets the criteria described above. A “BART-eligible source” is defined as the 

total of all BART-eligible emission units at a single facility. If a source has several emission 

units, only those that meet the BART-eligible criteria are included in the definition “BARTeligible source”. 

One of the listed categories is steam electric plants of more than 250 million BTU/hr heat input. 

To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially 

subject to BART, the boiler capacities of all electric generating units (EGUs) should be added 

together regardless of construction date. In this category, electric generating sources greater than 

750 MW have presumptive SO2 and NOx emission limits. States may presume the same limits for 

EGU sources between 250-750 MW. However, units at those sources constructed after the 

BART-eligibility dates are not subject to a BART engineering evaluation. EPA, in the Clean Air 

Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program 

satisfies the BART requirements for SO2 and NOx. VISTAS states are tentatively accepting this 

guidance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM 

emissions are above de minimis values.

As illustrated in Table 1-1, as of December 5, 2005, VISTAS States had identified a total of 274 

BART-eligible sources that fall into 20 of the 26 BART source categories. Of the 274 sources 

with BART-eligible units, 84 sources are utility EGUs and 190 are non-EGU industrial sources. 

(Note that these numbers are not final and are subject to slight adjustments and refinements.) No 

BART sources are located on Tribal lands.

Appendix H.6 - 1123
Introduction and Protocol Objectives 2

Table 1-1. VISTAS BART Eligible Sources 

State Total Number 

of Sources

EGU Sources Non-EGU 

Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 31 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19

Total 273 84 189

1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for 

air quality modeling to support BART determinations that are consistent with the EPA guidelines. 

The protocol will serve as the basis for establishing a common understanding among the 

organizations who will be performing the BART analyses or reviewing the BART modeling 

results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers 

(FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA 

final guidance and comments that were received on VISTAS’ draft protocol2 and provides 

additional description of modeling procedures. 

The VISTAS States have accepted EPA’s guidance to use the CALPUFF modeling system to 

comply with the BART modeling requirements of the regional haze rule. A BART-eligible 

source will be required to submit a site-specific modeling protocol to the State for review and 

approval prior to performing CALPUFF modeling. States will consult with FLMs and the EPA 

when evaluating the site-specific BART protocols. The site-specific protocol will include the 

source-specific data on source location, stack parameters, and emissions. The methods of the 

 

2 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit 

Technology (BART). VISTAS, March 22, 2005 and September 20, 2005.

Appendix H.6 - 1124
Introduction and Protocol Objectives 3

VISTAS common modeling protocol will be followed in the site-specific protocol unless the 

source proposes to the State, and the State approves, alternative methods or assumptions. 

Each VISTAS State or Local agency retains responsibility for the specific procedures and 

processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA, 

and public to determine BART controls for sources in the State. Nothing in the VISTAS process 

replaces States’ responsibility to determine BART controls. 

The remainder of this document describes the CALPUFF modeling system and the application of 

CALPUFF to two situations:

• Air quality modeling to determine whether a BART-eligible source is “subject to BART” 

and therefore the BART analysis process must be applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to 

BART, to evaluate regional haze benefits of alternative control options and to document 

the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s 

guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent 

chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s 

guidance for regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal 

Register notice. The CALPUFF model is the preferred model recommended by the EPA for 

BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The 

specific steps to determine whether a BART-eligible source is subject to BART and to evaluate 

BART controls are described in Chapter 4. The procedures include initial modeling of BARTeligible sources using CALPUFF run in a conservative mode with regional meteorological 

datasets. For sources determined to be subject to BART based on these first modeling analyses, 

further finer grid CALPUFF analyses would be performed. The model configuration for the 

common modeling protocol is described in Chapter 4. Details of the source-specific protocol are 

described in Chapter 5. A quality assurance plan is outlined in Chapter 6. 

EPA’s guidance allows for the use of appropriate alternative models, however VISTAS will not 

develop a protocol for alternative models. This protocol focuses on guidance for the application 

of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in 

its BART demonstration, the source will need to submit a detailed written justification to the 

State for review and approval. The State will provide the documentation to the EPA and Federal 

Land Managers for their review. 

Also, this protocol does not address a preferred modeling approach to demonstrate the 

effectiveness of an optional emissions cap and trade program. Such a cap and trade program is 

not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTAS 

States are not pursuing a regional trading alternative under the proposed EPA trading guidance 

(70 FR 44154-44175) that is to be promulgated early in 2006.

Appendix H.6 - 1125
Review of EPA’s Guidance 4

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING

The final guidance for regional haze BART determinations was published in the Federal Register 

on 6 July 2005 (70 FR 39104 to 39172). It prescribes the modeling approaches that are to be used 

for various stages of the BART analysis process. 

This chapter provides a summary of EPA’s guidance for BART modeling. It is not intended to 

provide a comprehensive review of the guidance. Nor does this chapter address specific 

recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations 

appear in Chapter 4. 

2.1 Overview of the Regional Haze BART Process

The process of establishing BART emission limitations consists of four steps:

1) Identify whether a source is “BART-eligible” based on its source category, when it was put in 

service, and the magnitude of its emissions of one or more “visibility-impairing” air pollutants. The 

BART guidelines list 26 source categories of stationary sources that are BART-eligible. Sources 

must have been put in service between August 7, 1962 and August 7, 1977 in order to be BARTeligible. Finally, a source is eligible for BART if potential emissions of visibility-impairing air 

pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate 

matter (PM10) and gaseous precursors to secondary fine particulate matter, such as SO2 and NOx. 

Whether ammonia or volatile organic compounds (VOCs) should be included as visibilityimpairing pollutants for BART eligibility is left for the States to determine on a case-by-case basis. 

The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor 

pressure should be considered as primary PM2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential 

emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not 

subject to BART if their potential emissions are less than the de minimis levels (40 TPY for SO2

and NOx and 15 TPY of PM10 or PM2.5. This assumes that the other BART-eligibility criteria are 

met.)

2) Determine whether a BART-eligible source can be excluded from BART controls by 

demonstrating that the source cannot be reasonably expected to cause or contribute to visibility 

impairment in a Class I area. The preferred approach is an assessment with an air quality model 

such as CALPUFF or other appropriate model followed by comparison of the estimated 24-hr 

visibility impacts against a threshold above estimated natural conditions to be determined by the 

States.3 The threshold to determine whether a single source “causes” visibility impairment is set at 

 

3 The level of the natural conditions baseline that is to be used is described differently in the BART guideline and in 

the preamble to the BART rule. The BART guideline text says “natural conditions” at 70 FR 39162, col. 3, while 

the preamble says “natural visibility baseline for the 20% best visibility days” at 70 FR 39125, col. 1. Clarification 

received from Todd Hawes, US EPA, is that the intent was the 20% best days.

Appendix H.6 - 1126
Review of EPA’s Guidance 5

1.0 deciview change from natural conditions over a 24-hour averaging period in the final BART 

rule (70 FR 39118). The guidance also states that the proposed threshold at which a source may 

“contribute” to visibility impairment should not be higher than 0.5 deciviews although, depending 

on factors affecting a specific Class I area, it may be set lower than 0.5 deciviews. The test against 

the threshold is “driven” by the contribution level, since if a source “causes”, by definition it 

“contributes”. 

EPA recommends that the 98th percentile value from the modeling be compared to the contribution 

threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not 

contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98th

percentile value exceeds the threshold must be determined at each Class I area. Over an annual 

period, this implies the 8th highest day at a particular Class I area is compared to the contribution 

threshold. Over a 3-year modeling period, the 98th percentile value may be interpreted as the 

highest of the three annual 98th percentile values at a particular receptor or the 22nd highest value in 

the combined three year record, whichever is more conservative.

Alternatively, States have the option of considering that all BART-eligible sources within the 

State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be 

able to do exactly the opposite, and use regional modeling to conclude that all BART-eligible 

sources in the State do not cumulatively contribute to “measurable” visibility impairment in any 

Class I areas. Also, the States have an option to exempt individual sources based on model plant 

analysis conducted by EPA in finalizing the BART rule. Under this option, sources with 

potential emissions of SO2 plus NOx of less than 500 tons and a distance from any Class I area 

greater than 50 kilometers or sources with SO2 plus NOx potential emissions of less than 1000 

tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM 

emissions are not specifically addressed in the model plant analysis, but subsequent discussions 

with EPA staff indicate that PM may be considered along with SO2 and NOx, so that a plant could 

be exempted if the combined potential emissions of SO2, NOx, plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting 

the “best” alternative, taking into consideration:

a) Any pollution control equipment in use at the source (which affects the availability of 

options and their impacts),

b) The costs of compliance with control options,

c) The remaining useful life of the facility, 

d) The energy and non air-quality environmental impacts of compliance, and

e) The degree of improvement in visibility that may reasonably be anticipated to result 

from the use of such technology.

Appendix H.6 - 1127
Review of EPA’s Guidance 6

Note that if a source agrees to apply the most stringent controls available to BART-eligible units, 

the BART analysis is essentially complete and no further analysis is necessary (70 FR 39165).

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze, 

which is due by December 2007.

Instead of applying BART on a source-by-source basis, a State (or a group of States) has the option 

of implementing an emissions trading program that is designed to achieve regional haze 

improvements that are greater than the visibility improvements that could be expected from BART. 

If the geographic distributions of emissions under the two approaches are similar, determining 

whether trading is “better than BART” may be possible by simply comparing emissions expected 

under the trading program against the emissions that could be expected if BART was applied to 

eligible sources. If the geographic distributions of emissions are likely to be different, however, air 

quality modeling comparing the expected improvements in visibility from the trading program and 

from BART would be required. (See the proposed BART Alternative rule, at 70 FR 44160.) EPA 

suggests that regional modeling using a photochemical grid model may be more appropriate than 

CALPUFF for this purpose.

Note that EPA has indicated in the BART rule (70 FR 39138-39139) that emissions reductions 

under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO2 and NOx control 

for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR 

and therefore a BART analysis may still be required for PM. 

2.2 Model Recommendations for the BART Analysis 

To evaluate the visibility impacts of a BART-eligible source at Class I areas beyond 50 km from the 

source, the EPA guidance recommends the use of the CALPUFF model as “the best regulatory 

modeling application currently available for predicting a single source’s contribution to visibility 

impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA 

prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to 

submit a written justification and source-specific modeling protocol to its State for review and 

approval. As part of the consultation process, the State will provide documentation to EPA and 

FLM. 

For modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance 

recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and 

other methods.” The PLUVUE-II plume visibility model is mentioned as a possible model to 

consider instead of CALPUFF for a source within less than 50 km of a Class I area. 

The EPA guidance notes that “regional scale photochemical grid models may have merit, but such 

models have been designed to assess cumulative impacts, not impacts from individual sources” and 

they are “very resource intensive and time consuming relative to CALPUFF”, but States may 

consider their use for SIP development in the future as they may be adapted and “demonstrated to 

be appropriate for single source applications” (70 FR 39123). Photochemical grid models may be 

Appendix H.6 - 1128
Review of EPA’s Guidance 7

more appropriate for cumulative modeling options such as in the determination of the aggregate 

contribution of all-BART-eligible sources to visibility impairment, but such use should involve 

consultation with the appropriate EPA Regional Office (70 FR 39163).

According to the BART guidance, a modeling protocol should be submitted for all modeling 

demonstrations regardless of the distance from the BART-eligible source to the Class I area. EPA’s 

role in the development of the protocol is only advisory as the “States better understand the BARTeligible source configurations” and factors affecting their particular Class I areas (70 FR 39126). 

In the BART modeling analyses the EPA recommends that the State use the highest 24-hour 

average actual emission rate for the most recent three to five-year period of record. Emissions on 

days influenced by periods of start-up, shutdown and malfunction are not to be considered in 

determining the appropriate emission rates. (70 FR 39129).

If a source is found to be subject to BART, CALPUFF or another appropriate model should be used 

to evaluate the improvement in visibility resulting from the application of BART controls. 

Visibility improvements may be evaluated on a pollutant-specific basis in the BART determination 

(70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA 

guidelines state that States are “encouraged to account for the magnitude, frequency, and duration 

of the contributions to visibility impairment caused by the source based on the natural variability of 

meteorology” (70 FR 39129). 

2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to 

demonstrate greater “reasonable progress” in reducing haze than would result if BART were 

applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading 

program that achieves greater progress at reducing emissions will also achieve greater progress at 

reducing haze. Such would be the case if the likely geographic distribution of emissions under the 

trading program would not be greatly different from the distribution if BART was in place. 

If the expected distribution of emissions is different under the two approaches, then “dispersion 

modeling” of all sources must be used to determine the difference in visibility at each impacted 

Class I area, in order to establish that the optional trading program will result in visibility 

improvements aggregated over all Class I areas that are “better than BART” (70 FR 39137-39138). 

The BART guidance does not specify the method to be used for this modeling. From a technical 

perspective, either applying CALPUFF to every source or using a regional photochemical model 

would satisfy the need.

A rulemaking procedure is currently underway to establish final guidance for such alternatives to 

BART (70 FR 44154-44175). The rule is expected to be finalized early in 2006.

Appendix H.6 - 1129
Overview of the CALPUFF Modeling System 8

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities 

and limitations. It does not include specific recommendations regarding the use of the model for 

BART analysis in the VISTAS region. These specific recommendations can be found in Chapter 

4. 

3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred 

modeling approach for use in the BART analyses. CALPUFF and its meteorological model, 

CALMET, are designed to handle the complexities posed by the complex terrain, the large 

source-receptor distances, chemical transformation and deposition, and other issues related to 

Class I visibility impacts. The CALPUFF modeling system has been adopted by the EPA as a 

Guideline Model for source-receptor distances greater than 50 km, and for use on a case-by-case 

basis in complex flow situations for shorter distances (68 FR 18440-18482). CALPUFF is 

recommended for Class I impact assessments by the Federal Land Managers Workgroup (FLAG 

2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The 

final BART guidance recommends CALPUFF as “the best modeling application available for 

predicting a singe source’s contribution to visibility impairment” (70 FR 39122). As a result of 

these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its 

BART determinations.

The main components of the CALPUFF modeling system are shown in Figure 3-1. CALMET is 

a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It 

produces three-dimensional wind and temperature fields and two-dimensional fields of mixing 

heights and other meteorological fields. It contains slope flow effects, terrain channeling, and 

kinematic effects of terrain. CALMET includes special algorithms for treating the overwater 

boundary layer and coastal interaction effects. CALMET can use meteorological observational 

data and/or three-dimensional output from prognostic numerical meteorological models such as 

MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine-scale 

meteorological fields.

CALPUFF is a non-steady-state Lagrangian puff transport and dispersion model that advects 

Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been 

designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers 

from a source. It includes algorithms for near-field effects such as building downwash, stack tip 

downwash and transitional plume rise as well as processes important in the far-field such as 

chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to 

allow puff splitting in the horizontal and vertical directions, which extends the distance range of 

the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition 

fluxes evaluated at user-specified receptor locations.

Appendix H.6 - 1130
Overview of the CALPUFF Modeling System 9

Figure 3-1. CALPUFF modeling system components.

A set of postprocessing programs associated with CALPUFF computes visibility effects and 

allows cumulative source impacts to be assessed, including potential non-linear effects of 

ammonia limitation on nitrate formation. The CALPOST postprocessor contains several options 

for computing change in extinction and deciviews for visibility assessments. The POSTUTIL 

postprocessor includes options for summing contributions of individual sources or groups of 

sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid-nitrate 

chemical equilibrium module, which allows the cumulative effects of ammonia consumption by 

background sources to be assessed in the postprocessor. In addition, the combination of 

CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be 

incrementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF modeling 

components in more detail.

Appendix H.6 - 1131
Overview of the CALPUFF Modeling System 10

3.1.1 Major Features of CALMET

The CALMET meteorological model consists of a diagnostic wind field module and 

micrometeorological modules for overwater and overland boundary layers. When modeling a 

large geographical area, as would be necessary for the regional VISTAS domain, the user has the 

option to use a Lambert Conformal Projection coordinate system to account for Earth’s curvature. 

The major features and options of the meteorological model are summarized in Table 3-1. The 

techniques used in the CALMET model are briefly described below. 

Table 3-1. Major Features of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland Boundary Layer - Energy Balance Method

- Overwater Boundary Layer - Profile Method

-- COARE algorithm

-- OCD-based method

- Produces Gridded Fields of:

-- Surface Friction Velocity

-- Convective Velocity Scale

-- Monin-Obukhov Length

-- Mixing Height

-- PGT Stability Class

-- Air Temperature (3-D)

-- Precipitation Rate

• Diagnostic Wind Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimization

- Produces Gridded Fields of U, V, W Wind Components

- Inputs Include Domain-Scale Winds, Observations, and

 (optionally) Coarse-Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and 

overwater grid cells.

Overland Boundary Layer Model: Over land surfaces, the energy balance method of Holtslag and 

van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surface 

Appendix H.6 - 1132
Overview of the CALPUFF Modeling System 11

friction velocity, Monin-Obukhov length, and convective velocity scale. Mixing heights are 

determined from the computed hourly surface heat fluxes and observed temperature soundings 

using a modified Carson (1973) method based on Maul (1980). The model also determines 

gridded fields of Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates.

Overwater Boundary Layer Model: The aerodynamic and thermal properties of water surfaces 

suggest that a different method is best suited for calculating the boundary layer parameters in the 

marine environment. A profile technique, using air-sea temperature differences, is used in 

CALMET to compute the micro-meteorological parameters in the marine boundary layer. The 

version of CALMET being used by VISTAS contains improvements in the overwater boundary 

layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response 

Experiment (COARE) and enhancements in the calculation of overwater mixed layer heights 

(Batchvarova and Gryning, 1991, 1994). Further details and the results of an evaluation of the 

model containing these enhancements are described in Scire et al. (2005). An upwind-looking 

spatial averaging scheme is optionally applied to the mixing heights and three-dimensional 

temperature fields in order to account for important advective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two-step approach to the computation of the wind fields 

(Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted for kinematic 

effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded 

MM5 can be used to define the initial guess field. The second step consists of an objective 

analysis procedure to introduce observational data into the Step 1 wind field to produce a final 

wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field 

defined by the MM5 prognostic meteorological model. Normally, the CALMET computational 

domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial 

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used to 

develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 algorithms 

in CALMET described below apply terrain adjustments to the initial guess field on the fine-scale 

CALMET grid. Thus, the CALMET winds are adjusted to respond to fine-scale terrain features 

not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain: The approach of Liu and Yocke (1980) is used to evaluate the 

effects of the terrain on the wind field. The initial guess field winds are used to compute a terrainforced vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme is 

applied iteratively until the three-dimensional divergence is less than a threshold value. 

Slope Flows: The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 

1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

Appendix H.6 - 1133
Overview of the CALPUFF Modeling System 12

advective-gravity and equilibrium flow regimes. At night, the slope flow model parameterizes the 

flow down the sides of the valley walls into the floor of the valley, and during the day, upslope 

flows are parameterized. The magnitude of the slope flow depends on the local surface sensible 

heat flux and local terrain gradients. The slope flow wind components are added to the wind field 

adjusted for kinematic effects.

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are 

parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude 

number at a particular grid point is less than a critical value and the wind has an uphill

component, the wind direction is adjusted to be tangent to the terrain. 

Step 2 Wind Field. The wind field resulting from the above adjustments of the initial-guess wind 

is the Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An inversedistance squared interpolation scheme is used which weights observational data heavily in the 

vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind 

field in regions with no observational data. The resulting wind field is subject to smoothing, an 

optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence 

minimization to produce a final Step 2 wind field. 

The introduction of observational data in the Step 2 calculation is an option. It is also possible to 

run the model in “no observations” (No-Obs) mode, which involves the use only of MM5 gridded 

data for the initial guess field followed by fine-scale terrain adjustments by CALMET. In NoObs mode, observational data are not used in the Step 2 calculations. The No-Obs mode is 

appropriate when the MM5 simulations adequately characterize the regional wind patterns and 

when local observations, especially surface observations, reflect local conditions on a scale 

smaller than that of the CALMET domain and hence their spatial representativeness may be 

limited. Such situations are most likely to occur when the CALMET grid scale is relatively large 

i.e., coarser than the scale of variation of the true wind field, which is particularly likely to occur 

in complex terrain or along the seashore,

3.1.2 Major Features of CALPUFF

By its puff-based formulation and through the use of three-dimensional meteorological data 

developed by the CALMET meteorological model, CALPUFF can simulate the effects of timeand space-varying meteorological conditions on pollutant transport from sources in complex 

terrain. The major features and options of the CALPUFF model are summarized in Table 3-2. 

Some of the technical algorithms are briefly described below. 

Complex Terrain: The effects of complex terrain on puff transport are derived from the 

CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor locations 

are simulated using one of two algorithms that modify the puff-height (either that of ISCST3 or a 

general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical 

dispersion derived from the weakly-stratified flow and dispersion module of the Complex Terrain 

Appendix H.6 - 1134
Overview of the CALPUFF Modeling System 13

Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely 

on the receptor elevation (relative to the elevation at the source) and the height of the puff above 

the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the 

direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG treats terrain 

features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry 

et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of 

receptors using a dividing streamline height (Hd) to determine which pollutant material is 

deflected around the sides of a hill (below Hd) and which material is advected over the hill (above 

Hd). The local flow (near the feature) used to define Hd is taken from the gridded CALMET 

fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Functions: A set of accurate and computationally efficient puff sampling routines 

is included in CALPUFF, which solve many of the computational difficulties encountered when 

applying a puff model to near-field releases. For near-field applications during rapidly-varying 

meteorological conditions, an elongated puff (slug) sampling function may be used. An 

integrated puff approach may be used during less demanding conditions. Both techniques 

reproduce continuous plume results under the appropriate steady state conditions.

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both 

incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the 

choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been 

implemented in such a way as to allow the use of wind direction specific building dimensions. 

The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as 

an option.

Dispersion Coefficients: Several options are provided in CALPUFF for the computation of 

dispersion coefficients, including the use of turbulence measurements (σv and σw), the use of 

similarity theory to estimate σv and σw from modeled surface heat and momentum fluxes, or the 

use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion 

equations based on the CTDM. Options are provided to apply an averaging time correction or 

surface roughness length adjustments to the PG coefficients. In Version 5.8 of CALPUFF being 

used by VISTAS, an option is provided to use the AERMOD turbulence profiles for determining 

dispersion rates, which is the most recent approach to dispersion in EPA-approved regulatory 

modeling. In addition, turbulence advection is included. For additional details on these features, 

see Scire et al. (2005).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model 

contains both overwater and overland boundary layer algorithms, the effects of water bodies on 

plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff 

formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion 

conditions, including the abrupt changes that occur at the coastline of a major body of water.

Appendix H.6 - 1135
Overview of the CALPUFF Modeling System 14

Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry 

deposition rates of gases and particulate matter as a function of geophysical parameters, 

meteorological conditions, and pollutant species. For particles, source-specific mass distributions 

may be provided for use in the resistance model. Of particular interest for BART analyses is the 

ability to separately model the deposition of fine particulate matter (< 2.5 μm diameter) from 

coarse particulate matter (2.5-10 μm diameter).

Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical 

wind shear effects across individual puffs to be simulated. Differential rates of dispersion and 

transport among the “new” puffs generated from the original, well-mixed puff can substantially 

increase the effective rate of horizontal spread of the material. Puffs may also be split in the 

horizontal when the puff size becomes large relative to the grid size, to account for wind shear 

across the puffs. 

Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF to compute 

the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging 

coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. 

liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOx, HNO3, and NO3

-

)

employed in the MESOPUFF II model or a set of user-specified, diurnally-varying transformation 

rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine 

particulate matter (sulfate and nitrate) from emissions of SO2 and NOx and thus allows analyses 

of visibility impacts. Ambient ozone concentrations are used in the parameterized chemical 

transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia 

concentrations are used together with a temperature and relative humidity-dependent equilibrium 

relationship to partition nitric acid and nitrate on an hour-by-hour and receptor-by-receptor basis.

Appendix H.6 - 1136
Overview of the CALPUFF Modeling System 15

Table 3-2. Major Features of the CALPUFF Dispersion Model 

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant or variable emissions)

- Volume sources (constant or variable emissions)

- Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions

- Gridded 3-D fields of meteorological variables (winds, temperature)

- Spatially-variable fields of mixing height, friction velocity, convective velocity scale,

 Monin-Obukhov length, precipitation rate

- Vertically and horizontally-varying turbulence and dispersion rates

- Time-dependent source and emissions data for point, area, and volume sources

- Temporal or wind-dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time-varying heat flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient (σy, σz) options

- Direct measurements of σv and σw

- Estimated values of σv and σw based on similarity theory 

-- AERMOD turbulence profiles

-- Original turbulence profiles

- Pasquill-Gifford (PG) dispersion coefficients (rural areas)

- McElroy-Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- Huber-Snyder method

- Schulman-Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

- Optional puff height adjustment: ISC3 or "plume path coefficient"

- Optional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)

Appendix H.6 - 1137
Overview of the CALPUFF Modeling System 16

Table 3-2. Major Features of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, Hd, as in CTDMPLUS:

- Above Hd, material flows over the hill and experiences altered diffusion rates

- Below Hd, material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of space and time variations of deposition with a resistance model

- User-specified diurnal cycles for each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD-based method)

- Abrupt change in meteorological conditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and NO3

(MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2, HNO3, and NO3 

(RIVAD/ARM3 method)

- User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function of precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and 

POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing 

tabulations that summarize the results of the simulations, identifying, for example, the highest and 

second-highest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentrations from CALPUFF to compute light extinction 

and related measures of visibility (haze index in deciviews), reporting these for a 24-hour 

averaging time. 

The CALPOST processor contains several options for evaluating visibility impacts, including the 

method described in the BART guidance, which uses monthly average relative humidity values. 

CALPOST contains implementations of the IWAQM-recommended and FLAG-recommended 

visibility techniques and additional options to evaluate the impact of natural weather events (fog, 

rain and snow) on background visibility and visibility impacts from modeled sources.

Appendix H.6 - 1138
Overview of the CALPUFF Modeling System 17

The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources 

from different simulations to be summed, can compute the difference between two sets of 

predicted impacts (useful for evaluating the benefits of BART controls), and contains a chemistry 

module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This

capability allows the potential non-linear effects of ammonia scavenging by sulfate and nitrate 

sources to be evaluated in the formation of nitrate from an individual source. CALPUFF makes 

the full ambient ammonia concentration available to each puff without regard for any scavenging 

by other puffs. POSTUTIL corrects for such scavenging when the puffs generated by the 

CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or 

when nitrate formation is to be attributed to each of several sources that are in a cluster and whose 

plumes overlap, 

POSTUTIL will also compute the impacts of individual sources or groups of sources on sulfur 

and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the 

changes in deposition fluxes resulting from changes in emissions to be quantified. For example 

the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity 

(ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).

3.2 Discussion of CALPUFF Applicability and Limitations

3.2.1 Transport and Diffusion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport 

distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km 

should be done cautiously with an awareness of the likely problems involved.”4

IWAQM’s 200-km limitation derives from the observation that, when compared to the data of the 

Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF 

overestimated inert tracer concentrations by factors of 3 to 4 at receptors that were 300 to 1000 

km from the source. The apparent reason was insufficient horizontal dispersion of the simulated 

plume, presumably because an actual large plume does not remain coherent in the presence of 

vertical wind shears that typically occur, especially during the night, and of horizontal wind 

shears over the large puffs that arise over long transport distances. 

To better represent such situations, an optional puff splitting algorithm has since been added to 

CALPUFF to simulate wind shear effects across a well-mixed individual puff by dividing the 

puff horizontally and vertically into two or more pieces. Differential rates of transport among the

 

4 The IWAQM presentation at EPA’s 6th Modeling Conference provides the background for this recommendation: 

“The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration 

impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For 

larger transport times and distances, our experience thus far is that CALPUFF tends to underestimate the 

horizontal extent of the dispersion and hence tends to overestimate the surface-level concentration maxima. This 

does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such 

instances be used cautiously and with some understanding.” (From page D-12 of the IWAQM Phase 2 report.)

Appendix H.6 - 1139
Overview of the CALPUFF Modeling System 18

new puffs thus generated can increase the horizontal spread of the material in the plume due to 

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is 

designed to allow large puffs that may grow to be several grid cells or more in size to split into 

smaller puffs that can then more accurately respond to variations in the local wind field across the 

original large puff. This will also tend to increase horizontal dispersion of the plume. Since the 

creation of additional puffs via puff splitting will increase the computational requirements of the 

model, possibly substantially, puff splitting is not enabled by default, but can be turned on at the 

option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km, 

or possibly over shorter distances in complex terrain.

Turning to the shorter distance end of the transport range, the CALPUFF section of Appendix A 

of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended 

for use on scales from tens of meters from a source to hundreds of kilometers.” This is supported 

by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were 

designed to be suitable for both short and long distances. In this regard, CALPUFF does contain 

algorithms for such near-field effects as plume rise, building downwash, and terrain impingement 

and includes routines that deal with the computational difficulties encountered when applying a 

puff model in the field near to a source.

The recommendations for regulatory use in Appendix A of the Guideline on Air Quality Models

state, “CALPUFF is appropriate for long range transport (source-receptor distance of 50 to 

several hundred kilometers)”, but provisions for using CALPUFF in the near-field in “complex 

flow” situations are also included in the regulatory guidance. Complex flow situations may 

include complex terrain, coastal areas, situations where plume fumigation is likely, and areas 

where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to 

changes in valley orientation) are important. 

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in 

the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, 

additional studies of model performance have been performed at shorter distances, including at a 

power plant in New York state in complex terrain (at source-receptor distances of 2 to 8.5 km) 

and a second power plant in Illinois in simple terrain (at source-receptor distances in arcs ranging 

from 0.5 km to 50 km from the stack) (Strimaitis et al., 1998). Other CALPUFF evaluation 

studies over short-distances include ones by Chang et al. (2001) and Morrison et al. (2003). 

These studies demonstrate good model performance over source-receptor distances from a few 

hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The 

EPA has defined the "regulatory default" option in CALPUFF to allow either Pasquill-Gifford 

(PG) or turbulence-based dispersion coefficients. CALPUFF has been evaluated and shown to 

perform better using turbulence-based dispersion for tall stacks (Strimaitis et al, 1998). 

CALPUFF with turbulence-based dispersion has also been evaluated for overwater transport and 

coastal situations (Scire et al., 2005). In many other studies, including AERMOD evaluation 

studies conducted by EPA, the use of PG-dispersion, or more specifically the lack of a convective 

Appendix H.6 - 1140
Overview of the CALPUFF Modeling System 19

probability density function (pdf) module, has been demonstrated to result in underprediction of 

peak concentrations.

In November 2005, EPA approved the AERMOD model, which relies on turbulence-based 

dispersion, as a regulatory Guideline Model5. The ISCST3 model and its PG dispersion 

coefficients are being phased out as an acceptable regulatory approach. The use of AERMOD 

dispersion in CALPUFF is consistent with EPA's goals to encourage the turbulence-based 

dispersion option.

For regional haze light extinction calculations, use of a plume-simulating model such as 

CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually 

discernible as a plume per se, but nevertheless its presence could alter the visibility through the 

background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 

km from a source. In this light, the BART guidance strongly recommends using CALPUFF for 

source-receptor distances greater than 50 km but also presents CALPUFF as an option that can be 

considered for shorter transport distances.

As discussed above, there do not appear to be any scientific reasons why CALPUFF cannot be 

used for even shorter transport distances than 30 km, though, as long as the scale of the plume is 

larger than the scale of the output grid so that the maximum concentrations and the width of the 

plume are adequately represented and so that the sub-grid details of plume structure can be 

ignored when estimating effects on light extinction. The standard 1-km output grid that has been 

established for Class I area analyses should serve down to source-receptor distances somewhat 

under 30 km; how much closer than 30 km will depend on the topography and meteorology of the 

area and should be evaluated on a case-by-case basis. For extremely short transport distances, 

depiction of the concentration distribution will require a grid that is finer than 1 km. (For 

reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 km of travel distance, 

assuming Pasquill-Gifford dispersion rates under neutral conditions.) 

As an additional consideration, if the plume width is small compared to the visual range, the 

atmospheric extinction along a typical sight path of tens of kilometers through the plume will be 

inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor 

point will not be correct. However, the effect of averaging light extinction estimates for 24 hours, 

during which the plume location shifts over various receptor points, is likely to mitigate this 

problem to some degree and suggests that using CALPUFF at distances under 30 km will often be 

appropriate. For the narrow plumes that result from short transport distances, though, the modeled 

peak 24-hr average extinction at a receptor will tend to overstate the effect of the source on 

regional haze. 

 

5 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex 

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.

Appendix H.6 - 1141
Overview of the CALPUFF Modeling System 20

The U.S. EPA has suggested that the plume visibility model, PLUVUE-II, could be used in lieu 

of CALPUFF for simulating visibility effects at such short distances.6 PLUVUE-II is a Gaussian 

model that simulates the dispersion, chemical conversion, and optical effects of emissions of 

particles, SO2, and NOx from a single source. Its outputs include the discoloration of the sky by 

the plume (so called “plume blight”) and the effect of the plume on visibility along user-selected 

sight paths that pass through the plume. The impacts of the plume on visibility depend not only 

on the plume composition, but also on the sight path chosen and its direction relative to the axis 

of the plume and the location of the sun. It isn’t clear how such sight-path dependent results could 

be compared to the 0.5 and 1.0 deciview thresholds in the BART guidance. Since CALPUFF is 

designed to be useful for short transport distances (with features such as the simulation of plume 

downwash caused by structures at the source), CALPUFF seems more appropriate than 

PLUVUE-II for evaluating source impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM2.5

Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that 

CALPUFF can treat primary pollutants such as PM10. In actuality, CALPUFF can simulate PM10

or PM2.5 or some other size range, because the assumed size distribution of the particles is a user 

input. The smaller the particles, the more they disperse like an inert gas. In most cases, the 

dispersion of inert PM2.5 particles will be only minutely different from that of an inert gas, but the 

behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface. 

PM2.5 particles, which have a mass median diameter around 0.5 μm, have an average net 

deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine 

particles is usually not significant except for ground-level emissions. On the other hand, coarse 

particles (those PM10 particles larger than PM2.5) have an average deposition velocity of more 

than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks. 

CALPUFF includes parametric representations of particle and gas deposition in terms of 

atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the 

gravitational settling speed. Gravitational settling, which is of particular importance for the coarse 

fraction of PM10, is accounted for in the calculation of the deposition velocity. Effects of inertial 

impaction (important for the upper part of the PM10 distribution) and Brownian motion (important 

for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance 

recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light 

extinction efficiency than coarse particulate matter (2.5-10 μm diameters), should be treated 

separately in the model. CALPUFF allows for user-specified size categories to be treated as 

 

6 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE-II for BART 

application

Appendix H.6 - 1142
Overview of the CALPUFF Modeling System 21

separate species, which includes calculating size-specific dry deposition velocities for each size 

category.

A primary PM2.5 emission from coal-fired electric generating units (EGUs) that is of relevance to 

visibility calculations is that of primary sulfate. Although primary sulfate emissions account for 

only a small fraction of the total sulfur emissions from such sources, it may be important to 

simulate their effect with CALPUFF, especially at shorter distances before significant formation 

of secondary sulfate conversion from SO2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate 

The MESOPUFF-II chemistry algorithm used in CALPUFF7 simulates the gas phase oxidation 

of sulfur dioxide to sulfate by a linear transformation rate that was developed using regression 

relationships derived from the analysis of chemical conversion rates produced by a complex 

photochemical box model (see Scire et al., 1984, for a description of the development of the 

chemical module). As in all empirically-derived models, the relationships are based on easilycomputed or observed parameters that are used as surrogates for the factors that control SO2

oxidation. 

The surrogate factors included in the parameterized chemistry during the daytime hours include 

solar radiation intensity, ambient ozone concentration, and atmospheric stability class. For 

example, gas phase SO2 oxidation is a function of OH radical concentrations. Ozone 

concentrations are correlated with OH radical concentrations during daytime hours, and their use 

in the daytime SO2 conversion rate in CALPUFF is based on this correlation relationship. The 

philosophy is that OH radical measurements are not available and cannot easily be computed 

within a model like CALPUFF, but ozone is commonly measured throughout the country, so the 

use of the well-known surrogate variable (ozone) is more useful in the empirical relationship than 

factors that are unknown or have a high degree of uncertainty. The same logic applies to the 

other variables in the relationship. They are surrogates for factors that the regression analysis has 

shown to be important in SO2 oxidation rates. At night, the SO2 conversion is set to a constant 

low value (default is 0.2%/hr). Aqueous phase oxidation of SO2 is represented by an additive 

term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF 

represents the chemical conversion as a linear process because it requires linear independence 

between puffs, although as explained below, non-linear behavior in nitrate formation can be 

modeled.

 

7 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO2, SO4

=

, NOx, HNO3, 

and NO3

-

) MESOPUFF-II system and the 6 species RIVAD system (which treats NO and NO2 separately). 

IWAQM recommends using the MESOPUFF-II system with CALPUFF. The RIVAD system is believed to be 

more appropriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM- and FLM-recommended 

MESOPUFF-II chemistry is most appropriate. 

Appendix H.6 - 1143
Overview of the CALPUFF Modeling System 22

The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing 

the gas phase sulfate formation but that it does not adequately account for the aqueous phase 

oxidation of SO2. Actual aqueous phase oxidation in clouds or fog can proceed at rates much 

greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in 

such situations. However, aqueous phase oxidation depends on liquid water content, not relative 

humidity. In reality, liquid water does not exist in the atmosphere at relative humidity much 

below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative 

humidity. This can lead CALPUFF to overestimate sulfate concentrations when the humidity is 

high but the cloud water that enables aqueous conversion is not present. Therefore, the direction 

of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1) 

overestimation of sulfate formation when NOx concentrations in the plume are high and in 

actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO2; 

and (2) lack of direct consideration of the effect of temperature on the conversion rates, which 

may cause the model to overstate sulfate formation on cold days (below 10C or 50°F) (Morris et 

al., 2003). However, in CALPUFF, the effects of temperature are, to some degree, compensated 

for indirectly by the use of the solar radiation surrogate variable in the empirical conversion 

equations. 

Whether these potential errors are important will depend on the setting. For example, Figure 3-2 

shows a comparison of predicted and observed 24-hour sulfate concentrations, due to a large 

number of SO2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et 

al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether 

CALPUFF predictions would compare as well with measurements in the Southeast remains to be 

seen. 

CALPUFF does not identify the chemical form of the sulfate compound that results from its 

reactions, which will generally be some form of ammoniated sulfate whose degree of 

neutralization will depend on the availability of ammonia in the atmosphere. This consideration, 

which has been found to be relevant for calculating light extinction in the VISTAS region, is not 

addressed by CALPUFF or CALPOST.

In most applications, the ozone concentrations required for the sulfate formation calculations are 

derived from ambient measurements, although concentrations simulated by regional models can 

be used. 

Appendix H.6 - 1144
Overview of the CALPUFF Modeling System 23

Figure 3-2. Observed vs. CALPUFF-predicted 24-hour sulfate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995.

NOx and Secondary Ammonium Nitrate

The MESOPUFF-II chemistry algorithm used in CALPUFF simulates the oxidation of NOx to 

nitric acid and organic nitrates (both gases) by transformation rates that depend on NOx

concentration, ambient ozone concentration, and atmospheric stability class during the day. The 

conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature- and 

humidity-dependent equilibrium between nitric acid gas and ammonium nitrate particles is taken 

into account when estimating the ammonium nitrate particle concentration, an equilibrium that 

depends on the ambient concentration of ammonia. The user supplies the value of the ambient 

concentration of ammonia. CALPUFF assumes that the sulfate reacts preferentially with that 

ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium 

nitrate. 

The IWAQM Phase 2 report considers that this mechanism is adequate for representing nitrate 

chemistry. Potential situations where this assumption may not be correct, however, include (1) 

plumes with high concentrations of NOx that deplete the ambient ozone and thus limit the 

[SO4] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

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

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4

Appendix H.6 - 1145
Overview of the CALPUFF Modeling System 24

transformation of NOx to nitric acid in the plume; and (2) when ambient temperature is below 10 

C, and thus the transformation rate is much slower and the nitrate concentration may be lower 

than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may 

overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nitrate 

concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when 

temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations 

tend to be the least. CALPUFF may overstate the impacts of NOx emissions at those times, 

especially in the colder northern states. This potential overestimate of nitrate was not evident, 

however, in an evaluation of CALPUFF-modeled nitrate against actual observational data in the 

Wyoming study, as shown in Figure 3-3a (Scire et al., 2001), 

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the 

background concentration of ammonia available to each puff, and that amount is scavenged by 

the sulfate in the puff. If puffs overlap, then that approach could overstate the amount of 

ammonium nitrate that is formed in total if, in reality, the combined scavenging by the 

overlapping puffs at a location would deplete the available ammonia enough that the combined 

nitrate formation was limited by the availability of ammonia. This effect of such ammonia 

limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one 

modeling analysis found the maximum light extinction impact of the source to be 7.4% (roughly 

0.74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting 

and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia 

limiting was considered (Escoffier-Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system 

has included the optional POSTUTIL postprocessing program, which repartitions the ammonia 

and nitric acid concentrations estimated by CALPUFF to reflect potential ammonia-limiting 

effects on the development of nitrate. This allows non-linearity associated with ammonia limiting 

effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate 

concentrations from all sources (modeled sources plus inflow boundary conditions) and estimates 

the amount of ammonia available for total nitrate formation after the preferential scavenging of 

ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is 

added to an existing mix of pollutants, POSTUTIL will estimate both the nitrate formed from the 

new source and the change in background nitrate as a result of the incremental depletion of 

ammonia (due to the new sulfate and nitrate) or addition of ammonia (from a new source of 

ammonia).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and 

spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage 

of the increased accuracy provided by POSTUTIL. The processor requires estimated 

concentrations of ammonia throughout the modeling domain and period. Such estimates can be 

inferred from CASTNet measurements, which are integrated over a week, from 24-hr SEARCH 

measurements, or from the output of a regional photochemical model such as CMAQ or CAMx. 

The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large, 

Appendix H.6 - 1146
Overview of the CALPUFF Modeling System 25

so defining the ammonia concentration throughout the Southeast would require extensive 

interpolation or extrapolation from the measured values. The quality of the SEARCH 

measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS 

domain. Modeled concentrations have the advantage of being resolved in space and time, but 

their accuracy should be evaluated by comparison with measurements wherever possible. 

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to 

determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation 

associated with temperature and relative humidity effects. For example, results of the Wyoming 

study (see Figure 3-3a) show that POSTUTIL adjustments produced daily average nitrate 

concentrations well within the factor of two lines and with very little mean bias. On the other 

hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced 

consistent overpredictions of nitrate by factors of 2-3 and 3-4, respectively, as shown in Figure 3-

3b (Scire et al., 2003). 

Figure 3-3a. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al., 

2001)

[NO3] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

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

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3

Appendix H.6 - 1147
Overview of the CALPUFF Modeling System 26

Figure 3-3b. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant 

ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench, 

2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90 

to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g., 

that from the burning of vegetation and deriving from VOC emissions from vegetation) and only 

5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky 

Mountains National Park in August 2002 (Tanner, et al., 2005) found that an average of 83% of 

the fine carbon was modern carbon

According to IMPROVE measurements, organics account for roughly 10% of the particle-caused 

light extinction in Class I areas in the Southeast. We can thus conclude that, in general, secondary 

organic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to 

have a large impact (around 1%) on current visibility. (Man-caused burning of vegetation can 

have significant localized, short-term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of 

the 1.4 μg/m3 concentration assumed for natural conditions by the EPA, which means that current 

fossil fuel burning would contribute less than 2% to visibility in an atmosphere that represents 

natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART 

[NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE 

Observations 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

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

CALPUFF Summed Repartition (

CALPUFF Separated Repartition 

CALPUFF Separated Repartition 

Factor 1

Factor 2 Factor 3

Factor 4

Factor 2

Factor 3

Factor 4

0.4 0.6 0.8 1 1.2

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

CALPUFF Summed Repartition (Seasonal NH3)

CALPUFF Separated Repartition (NH3=0.5ppb)

CALPUFF Separated Repartition (NH3=1.0ppb)

Factor 1

Factor 2 

or 3

Factor 2

Factor 3

Factor 4

Appendix H.6 - 1148
Overview of the CALPUFF Modeling System 27

sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) localized impact 

from a particularly large VOC source cannot be dismissed out of hand. 

CALPUFF has only rudimentary capabilities for addressing formation of visibility-impairing 

organic particles from some forms of volatile organic carbon (VOC). The capabilities that do 

exist include the following. 

First, PM10 emissions (such as from power plants) are often divided into filterable and 

condensable components, with the condensable mass being 100-200% of the filterable mass. For 

purposes of visibility analyses with CALPUFF, a fraction of the condensable part is typically 

treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the 

PM10 emissions condense into organic PM2.5 particles. The size of this organic fraction varies 

with process and process equipment, and can range from 20 to 100% of the condensable mass. 

These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable 

material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of secondary organic particles from organic emissions 

was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This 

simplified secondary organic aerosol (SOA) module is a linear, parameterized representation that 

is currently considered best suited for biogenic organics. It relies on the conventional wisdom that 

only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and 

Seinfeld, 1989). For example, according to this rule, isoprene (C5H8) does not make SOA but 

terpenes do, making pine trees more important biogenic contributors to SOA than oak trees.8

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA 

module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101 

estimated 24-hr SOA concentrations were within 2% of the measured values (Scire et al., 2001). 

This performance seems promising, although the developers view the SOA module as needing 

more testing and evaluation.

Thus, CALPUFF includes approaches for dealing with condensable VOC emissions that are 

characterized as condensable PM10 and with biogenic VOCs, although the soundness of 

concentration estimates by these approaches when modeling a plume from a single source is 

largely untested.9 The CALPUFF simulation of VOC emissions from sources whose VOC 

emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used 

for the simplified biogenic SOA module may be extended to anthropogenic VOCs when 

speciated VOC emissions information is available. If only those VOCs with more than six carbon 

atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC 

emissions. For example, the fugitive emissions of butane and ethane during petroleum processing 

 

8 Recent research suggests that isoprene may be a SOA precursor, however.

9 Note that neither of these VOC-related simulation approaches is described in the current (Version 5) CALPUFF 

User’s Guide dated January 2001. See the Wyoming report referenced above for a description of this module.

Appendix H.6 - 1149
Overview of the CALPUFF Modeling System 28

are not important, while aromatic emissions (such as of toluene and xylene) are considered by the 

SOA module’s mechanism. Development, testing, and evaluation would be needed before one 

could rely on such a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART-eligible sources, 

means other than CALPUFF will be needed. A technical approach using a regional 

photochemical model to evaluate visibility impacts of VOC emissions is presented in Section 

4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction 

of PM10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component concentrations on 

light extinction is carried out in the CALPOST postprocessor. The formula used is the usual 

IMPROVE/EPA formula, which is applied to determine a change in light extinction due to 

changes in component concentrations. Using the notation of CALPOST, the formula is the 

following:

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] +

 + 0.6[Coarse Mass] + 10[EC] + bRay (3-1)

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh 

scattering term (bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for 

tracking reasonable progress (EPA, 2003a).

There are a few important differences in detail and in notation between the CALPOST formula 

for estimating light extinction (i.e., Equation 3-1) and that of IMPROVE and EPA. First, the OC

in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation), 

which is 1.4 times the OC (i.e., organic carbon alone) in the IMPROVE formula. The EC above is 

synonymous with LAC in the IMPROVE formula. CALPOST still uses the old IMPROVE f(RH)

curve, whose values are documented in the December 2000 FLAG report. That curve differs 

slightly from the f(RH) now used by IMPROVE and EPA (as documented in EPA’s regional haze 

guidance documents), mainly at high relative humidity. Also, CALPOST sets the maximum RH

at 98% by default (although the user can change it), while the EPA’s guidance now caps it at 

95%. 

The haze index (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (bext/10) (3-2)

where HI is in units of deciviews (dv) and bext is in Mm-1. The impact of a source is determined 

by comparing HI for estimated natural background conditions with the impact of the source and 

without the impact of the source. 

Appendix H.6 - 1150
Overview of the CALPUFF Modeling System 29

CALPOST Methods

CALPOST uses Equation 3-1 to calculate the extinction increment due to the source of interest 

and provides various methods for estimating the background extinction against which the 

increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the 

change in light extinction due to a source or group of sources (called Methods 1-7). These are 

usually reported as 24-hour average values, consistent with EPA and FLM guidance. In addition, 

there are two techniques for computing the 24-hour average change in extinction (i.e., as the ratio 

of 24-hour average extinctions, or as the average of 24-hour ratios). A brief summary of the 

techniques is provided below. Method 2 is the current default, recommended by both IWAQM 

(EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART 

guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current 

actual levels of extinction and thus are not consistent with the “natural conditions” the BART 

proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs 

of estimated (e.g., natural conditions) background extinction or component concentrations, and 

thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficient 

for each receptor, specify that a certain fraction of that coefficient is due to hygroscopic species, 

and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH)

curve or, optionally, the EPA regional haze f(RH) curve (EPA, 2003b). The RH is capped at 98% 

or a user-selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled 

sulfate and nitrate. 

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 

9.09 Mm-1 that results from EPA’s default natural conditions concentrations, together with an 

assumption that for natural conditions, say, 0.9 Mm–1 (or 10%) of this amount results from 

hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to this 10%. 

In Method 2, user-specified, speciated monthly concentration values are used to describe the 

background. When applied to natural conditions, for which EPA’s default natural conditions 

concentrations are annual averages, the same component concentrations would have to be used 

throughout the year (unless potential refinements to those default values resulted in 

concentrations that vary during the year). Hourly background extinction is then calculated using 

these concentrations and hourly, site-specific f(RH) from a 1993 IWAQM curve (a different one 

Appendix H.6 - 1151
Overview of the CALPUFF Modeling System 30

than that in Method 1) or, optionally, the EPA regional haze f(RH) curve.10 Again the RH is 

capped at either 98% (default) or a user-selected value (most commonly at 95%). 

Method 3 is the same as Method 2, except that any hour in which the RH exceeds 98% (or the 

selected maximum) is dropped from the analysis. When 24-hr extinction is computed, no fewer 

than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as 

“missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly 

climatologically representative values in EPA (2003a, b)) are used in place of hourly values for 

calculating both the extinction impact of the source emissions and the background conditions 

extinction. Hourly source impacts, with the effect on extinction due to sulfates and nitrates 

calculated using the monthly-average relative humidity in f(RH), are compared against the 

monthly default natural background concentrations. Thus the monthly-averaged relative humidity 

is applied to the hygroscopic components (i.e., sulfate and nitrate) of both the source impact and 

the background extinction with Method 6. 

Method 7 is a new variant of Method 2 that was developed as a result of a ruling by the Assistant 

Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review 

case in Montana, that “natural conditions” should reflect the visibility impairment caused by 

significant meteorological events such as fog, precipitation, or naturally occurring haze (DOI, 

2003).11 Under Method 7, during hours when visibility is obscured by meteorological conditions, 

the actual measured visibility is used to represent natural conditions instead of the value that is 

calculated from EPA’s default natural conditions concentrations under Method 2. A recent 

modification developed in response to FLM comments on Method 7, in which the daily average 

natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibility

Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations 

are evaluating whether refinements are warranted to the methods recommended in EPA’s 

guidance to calculate default estimates of natural background visibility. In addition, IMPROVE 

has recently approved an alternative to the formula (Eq. 3-1) it uses to estimate extinction from 

particle concentration measurements (Pitchford et al., 2005).

Refinements in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity 

 

10 Note that the hourly-varying natural background extinction in this method is not consistent with that prescribed 

by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically-representative” f(RH) 

that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

11 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural 

conditions is unknown.

Appendix H.6 - 1152
Overview of the CALPUFF Modeling System 31

- Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq. 

3-1) from organic carbon measurements

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revising the extinction efficiencies (the numerical constants in Equation 3-1) for

sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated 

by IMPROVE for all Class I areas. 

For the purposes of calculating current, future, and natural background visibility at VISTAS Class 

I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality 

modeling results using both the current EPA recommended assumptions and the newly revised 

aerosol extinction formula. If a BART-eligible source chooses to consider its projected impacts 

using the newly revised formula as well as the current formula, then modifications would need to 

be made to CALPOST to carry out calculations with the new algorithm. 

 

Appendix H.6 - 1153
VISTAS’ Common Modeling Protocol 32

4. VISTAS’ COMMON MODELING PROTOCOL

4.1 Overview of Common Modeling Approach

In this section, guidance is provided on the use of the CALPUFF modeling system for two 

purposes:

1) Evaluating whether a BART-eligible source is exempt from BART controls because it 

is not reasonably expected to cause or contribute to impairment of visibility in Class I 

areas, and 

2) Quantifying the visibility benefits of BART control options. 

For purpose 1), States must determine whether a source emits any air pollutant (SO2, NOx, PM, 

and in certain cases VOC and NH3) that “may reasonably be anticipated to cause or contribute to 

any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART-eligible sources in State are subject to BART. 

B) Demonstrate that all BART-eligible sources in the State together do not cause or 

contribute to any visibility impairment 

C) Determine if the impact from each individual BART-eligible source is greater than a 

threshold value. 

VISTAS States intend to follow Option C (determine if the visibility impact from individual 

sources exceeds a contribution threshold) for SO2, NOx, and PM emissions. The methods for 

Option C are described in Section 4.1.1. Option B (demonstrate that all BART eligible sources in 

a State do not impact visibility) is being pursued for VOC and NH3 emissions, and potentially for 

PM emissions. Methods for Option B are described in Section 4.1.3. 

4.1.1 BART Exemption Analysis

As illustrated in Figure 4-1, three steps will evaluate whether a BART-eligible source of SO2, 

NOx, or PM is subject to BART: 

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d 

for SO2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to 

BART. If the source agrees with this presumption, then no exemption modeling is required 

and the source can proceed to the BART determination using CALPUFF to evaluate impacts 

of control options and can perform the engineering analyses. If a source disagrees, the source 

may perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5 

dv.

Appendix H.6 - 1154
VISTAS’ Common Modeling Protocol 33

Figure 4-1. Flow chart showing the components of the VISTAS common modeling protocol. 

Assessment should be made for each Class I Area. (If a source agrees to install the most stringent 

controls then the modeling steps indicated above and engineering analyses and visibility impact 

modeling would not be required.) 

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12-

km regional VISTAS domain can be used to answer questions whether (a) a particular source 

may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were 

to be undertaken, which Class I areas should be included. Assumptions for the initial 

modeling assessment are conservative so that a source that contributes to visibility impairment 

is not exempted in error. If a source is shown not to contribute to visibility impairment using 

the initial modeling assessment, the source would not be subject to BART and would be 

exempted from further BART analyses. If a source is shown to contribute to visibility 

impairment using the initial modeling assessment, the source has the option to undertake finer 

grid CALPUFF modeling to evaluate further whether it is subject to BART. 

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the 

definitive test as to whether a source is subject to BART.

For large sources that will clearly exceed the initial screening thresholds, this step can be skipped 

and the analysis may proceed directly to the finer grid modeling analysis, which is described in 

Section 4.4. 

Appendix H.6 - 1155
VISTAS’ Common Modeling Protocol 34

4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BART review process 

involves evaluating the visibility benefits of different BART control measures. These benefits 

will be determined by making additional CALPUFF simulations using the same CALMET and 

CALPUFF configuration as those used in the finer grid analysis of Step 2. The only exception is 

that the source and emissions data used in the CALPUFF control evaluation simulations will 

reflect the BART control measures being evaluated. Using the same model configuration will 

produce an “apples-to-apples” comparison, where differences in impacts are due to the 

effectiveness of the controls rather than model configuration differences. For example, a control 

scenario evaluation that uses more conservative assumptions than the base case simulation may 

produce results showing no or little improvement in visibility impacts. That control scenario run 

with the same model configuration as the base case may show significant visibility improvement. 

Therefore, in order to not obscure the response to predicted visibility improvements by 

differences in the modeling approach, the same model configuration should be used in the BART 

control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current 

emissions” scenario for which the finer grid Step 2 modeling was performed. The postprocessing 

steps and procedures are the same as in the BART eligibility simulation. Side-by-side 

comparison of the visibility impacts will be tabulated to quantify the effectiveness of each control 

scenario relative to the base case.

The modeling evaluation is a unit-by-unit evaluation and can be conducted on a pollutant specific 

basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to 

decide which control technology, if any, is appropriate. Finally, if a source decides to use the 

most stringent control technology available, the BART control analysis, including modeling, is 

not necessary.

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM

Volatile Organic Compounds 

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its 

capability to simulate secondary organic aerosol formation from VOC emissions is not adequately 

tested, especially for anthropogenic emissions. (Separately, condensable organic carbon can be 

calculated from PM10.) 

VISTAS is currently performing a weight of evidence analysis to demonstrate, using the CMAQ 

regional air quality model, that all VOC emissions from all point sources (BART-eligible and 

non-BART) in each State do not contribute to visibility impairment. Emissions sensitivity 

simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid 

and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no 

response of organic carbon levels and light extinction at Class I areas to changing VOC emissions 

Appendix H.6 - 1156
VISTAS’ Common Modeling Protocol 35

from all anthropogenic sources in the VISTAS 12-km modeling domain (eastern US). Georgia 

Tech is currently repeating the sensitivity analyses for VISTAS using the VISTAS 12-km domain 

and CMAQ v 4.4 with a refined SOA module for a summer (Jun 1-Jul 10) and winter (Nov 19-

Dec 19) period in 2002. VOC emissions from all anthropogenic point sources in each VISTAS 

State are being reduced. Given that the impact of eliminating all VOC emissions from all point 

sources in a State is less than 0.5 dv, then the impact of any one BART-eligible source would be 

less than 0.5 dv. Based on these analyses, the VISTAS States have concluded that VOC 

emissions should not be subject to BART. When similar analyses for NH3 and PM have been 

completed, a technical justification for treatment of all three of these pollutants will be presented 

to EPA and FLMs for their review. 

Ammonia 

EPA has given states the option to address ammonia (NH3) emissions from BART-eligible 

sources. VISTAS has also contracted with Georgia Tech to perform emissions sensitivities using 

CMAQ v 4.4 with a refined SOA module and the Jun-Jul and Nov-Dec periods in 2002. All NH3

emissions from point sources (BART-eligible and not-BART) in each State are reduced for these 

analyses. If the sensitivity evaluation shows that the collective impact of all point NH3 emissions 

is less than 0.5 dv, then the impact of a single BART eligible source would be less than 0.5 dv. In 

that case, the VISTAS States would recommend that NH3 emissions not be subject to BART.

 Primary Particulate Matter 

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to 

which primary PM from BART-eligible sources contributes to impairment at Class I areas in the 

southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of 

SO2 and NOx under the CAIR rule meet the BART requirements, but these EGUs may still be 

subject to BART for primary PM. To determine the potential impacts of PM from EGU and nonEGU sources in the VISTAS states, two CMAQ sensitivity runs are underway for the first and 

third quarters of 2002. In one run, all primary PM from EGUs is removed while in the other run 

all primary PM from non-EGU sources is removed. All other CMAQ modeling components are 

held constant. The results will help determine at which, if any, Class I areas in the VISTAS 

region primary PM emissions contribute to regional haze. If the sensitivity evaluation shows that 

the collective impact of all EGU or non-EGU point primary PM emissions is less than 0.5 dv, 

then the impact of primary PM from any single BART eligible source would necessarily be less 

than 0.5 dv. These results will be reported at the same time as results for VOC and NH3. 

4.2 Optional Source-Specific Modeling

In some circumstances, a source may want to apply techniques designed to evaluate the impacts 

in a more detailed way than the standard VISTAS common protocol. A source may propose 

source-specific modeling procedures to address special issues to the State for State review. For 

example, sources very close to Class I areas may be better treated by a finer grid resolution that 

the generic Step 2 “fine” grid resolution meteorological fields provided by VISTAS. In some 

Appendix H.6 - 1157
VISTAS’ Common Modeling Protocol 36

situations, higher resolution MM5 or other prognostic meteorological datasets may be available 

than the standard 12-km or 36-km MM5 datasets provided by VISTAS. Because it is not possible 

to anticipate all of the situations where there would be a benefit to conducting more detailed 

source-specific analyses, the option to pursue this option is left as an open issue, to be resolved 

and justified based on specific factors relevant for the source in question.

A source-specific modeling protocol is required for each source. This document should describe 

the data sources and model configuration, and provide rationale for any changes in the model 

approach from the common protocol. This source-specific protocol must be provided for review 

and approval by the State. The State will share the protocol with EPA and the Federal Land 

Managers for their review. Discussion of approaches to source-specific modeling and an outline 

of the typical contents of the source-specific protocol are presented in Chapter 5. Discussions 

with the regulatory authorities should be conducted prior to development of a source-specific 

protocol to ensure all of the relevant issues are included in the protocol.

4.3 Initial Procedure for BART Exemption 

4.3.1 Overview of Initial Approach

The first step in the common protocol, the initial assessment in Figure 4-1, is a simple procedure 

to evaluate whether a source can be exempted from BART controls using a consistent set of 

meteorological and dispersion options. A pre-computed set of meteorological files and a predefined CALPUFF input option configuration, based on guidance in the final BART rule (70 FR 

39104-39172) and other EPA and FLAG model guidance, will allow relatively simple initial 

simulations. The regional initial domain is designed to allow any Class I areas within the 

VISTAS area to be evaluated with a single meteorological database and consistent CALPUFF 

modeling options. The second important question that this first screening step will answer is, if 

initial modeling indicates a source may impact visibility significantly, what Class I areas should 

be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a 

source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the 

closest Class I area will produce the controlling visibility impacts) are not likely to be universally 

reliable. 

4.3.2 Discussion of Regional Initial Modeling Approach

Meteorological Fields

A regional initial domain and a set of pre-computed regional CALMET meteorological files will 

be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with 

a consistent meteorological database and consistent CALPUFF modeling options. 

The following three years of MM5 meteorological data have been assembled by VISTAS for use 

in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

Appendix H.6 - 1158
VISTAS’ Common Modeling Protocol 37

- 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning 

Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has 

produced annual CALMET meteorological files at 12-km grid resolution for the domain shown in 

Figure 4-2. The CALMET modeling output files in the form of CALPUFF-ready threedimensional meteorological files will be available on external hard drives to the States and other 

parties.

The initial procedure to determine if a BART-eligible source is subject to BART uses the precomputed CALMET meteorological fields for the years 2001-2003 on the 12-km CALMET 

domain in Figure 4-2 and simulates with CALPUFF any BART-eligible source to be screened. 

The CALMET simulations will be developed using the highest resolution MM5 data available for 

each year (i.e., 36-km MM5 data for 2003, 12-km MM5 data for 2001 and 2002). 

The development of the regional CALMET meteorological fields from MM5 data will be 

conducted in No-Observations (“No-Obs”) mode. The MM5 data already reflect assimilation of 

observational data and are likely to adequately characterize regional wind patterns that are 

consistent with the 12-km grid scale. Blending of MM5 data with local observations (which are 

mainly at the surface) could lead to wind structures that may not be realistic under some 

conditions and may result in poorer characterization of the regional winds. Thus, the effort 

required to prepare observational data sets for CALMET for the large regional domain involves 

considerable effort that may not provide corresponding improvement of the wind field. 

For 2003, the 36-km MM5 data will be used as CALMET’s initial guess field and then the 

CALMET diagnostic terrain adjustments (see Section 3.1.1) will be applied to reflect terrain on 

the scale of the CALMET grid (i.e., 12-km). When the 12-km MM5 (2001 and 2002) data are 

used, the diagnostic CALMET terrain adjustments will be turned off since the grid resolution of 

the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid 

scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be 

interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will 

compute mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. 

Appendix H.6 - 1159
VISTAS’ Common Modeling Protocol 38

Figure 4-2. VISTAS Regional 12-km Resolution CALMET Modeling Domain (color area with 

terrain contours). The locations of the 36-km resolution MM5 grid points are shown on the plot. 

Impact Threshold

The final BART guidance recommends that the threshold value to define whether a source 

“contributes” to visibility impairment is 0.5 dv change from natural conditions (although states 

may set a lower threshold). The 98th percentile (8th highest annual) 24-hr average predicted 

impact at the Class I area, as calculated using CALPOST Method 6 (monthly average relative 

humidity values), is to be compared to this contribution threshold value. For this comparison, the 

predicted impact at the Class I area on any day is taken to be the highest 24-hr average impact at 

any receptor in the Class I area on that day. (Note that the receptor where the highest impact 

Appendix H.6 - 1160
VISTAS’ Common Modeling Protocol 39

occurs can change from day to day.) According to clarification of the BART guidance received 

from EPA, for a three-year simulation the modeling values to be compared with the threshold are 

the greatest of the three annual 8th highest values or the 22nd highest value over all three years 

combined, whichever is greater. 

For the purposes of the initial analysis, however, the highest value over the three-year period (not 

the 98th percentile value) is to be compared to the contribution threshold. This ensures a 

significant measure of conservatism in the initial approach. VISTAS will evaluate the initial 

CALPUFF results to determine if using the single highest value provides too conservative a 

screen for exemption purposes. If so, VISTAS may increase the number of exceedances of the 

contribution threshold that would be allowed and still qualify to exempt a source. 

4.3.3 Model Configuration and Settings for Initial Analysis

VISTAS will use CALPUFF Version 5.8 and CALMET Version 5.7. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were 

developed by Earth Tech, Inc. and they are maintained on Earth Tech’s Atmospheric Studies 

Group CALPUFF website (www.src.com) for public access. This version includes CALMET, 

CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as CALVIEW. 

The initial analysis uses a CALPUFF computational domain that includes all Class I areas within 

300 km of a source. These Class I areas are specified in the CALPUFF control file for analysis. 

States could decide to require a different value for the maximum distance threshold for the 

CALPUFF domain, depending on the locations of the Class I areas in their states and other 

factors such as meteorological conditions and the magnitudes of the emissions from BARTeligible sources. The regional CALMET domain will be unchanged by these adjustments. 

Also, the initial approach is designed to significantly reduce the CALPUFF simulation time by 

restricting the CALPUFF computational domain size to include only areas where significant 

impacts are feasible rather than the entire regional domain. CALPUFF allows its computational 

domain to be specified as a subset of the CALMET meteorological domain by settings within the 

CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in 

the regional CALPUFF simulations is that CALPUFF run time is proportional to the number and 

residence time of the puffs on the domain (and other factors such as the number of receptors and 

the internal time step computed by the model). A CALPUFF domain covering an area 300 km 

from a source in all directions would involve only 50 x 50 12-km grid cells, which will require 

modest computational resources.

CALMET output files for the VISTAS regional domain shown in Figure 4-2 will be provided to 

VISTAS by Earth Tech. These files will be in CALPUFF-ready format, and as such, no 

CALMET user inputs will be required. An option in CALMET allows finer grid CALMET input 

files to be calculated from the 12-km CALMET files. 

Appendix H.6 - 1161
VISTAS’ Common Modeling Protocol 40

The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the 

initial analyses are listed below.

CALMET Modeling Configuration (Regional 12-km runs)

The CALMET model configuration for the regional CALMET simulations will be defined by 

Earth Tech in collaboration with the VISTAS States. The basic model configuration will follow 

the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted 

below. 

The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)

- Meteorological inputs: MM5 data provide initial guess fields in CALMET 

- CALMET grid resolution: 12-km (same Lambert Conformal coordinate system and grid 

cells as the 12-km 2001/2002 MM5 simulations)

- CALMET vertical layers: 10 layers. Cell face heights (meters): 0, 20, 40, 80, 160, 320, 

640, 1200, 2000, 3000, 4000.

- CALMET mode: No-Observations mode including option to read overwater data directly 

from MM5.

- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain 

blocking and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but 

no diagnostic terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 

2003 simulations). Values of ~10-20 km will be tested, and an appropriate value 

determined.

- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature 

allows the temperature field over large bodies of water such as the Atlantic Ocean and the 

Great lakes to be properly characterized by buoy observations.

- Mixing height averaging parameter (MNMDAV) will be determined by Earth Tech for the 

regional simulations based on sensitivity tests. The purpose of the testing is to optimize the 

variable to allow spatial variability in the mixing height field, but without excessive noise.

- Geophysical data for regional runs: SRTM-GTOPO30 30-arcsec terrain data, Composite 

Theme Grid (CTG) USGS 200m land use dataset. References for these and other CALMET 

Appendix H.6 - 1162
VISTAS’ Common Modeling Protocol 41

datasets can be found on the CALPUFF data page of the official CALPUFF site 

(www.src.com). 

CALPUFF Modeling Configuration (Initial regional runs)

The CALPUFF model configuration for the regional CALPUFF initial simulations will follow the 

recommended IWAQM guidance (EPA, 1998; Pages B-1 through B-8), except as noted below: 

- CALPUFF domain configured to include the source and all Class I areas within 300km of 

the source plus 50km buffer zone in each direction. CALPUFF is recommended for all 

source-receptor distances to be considered in the BART analyses.

- Chemical mechanism: MESOPUFF II module

- Background concentrations of SO4 and TNO3 (HNO3 + NO3) from CMAQ 2001-2003 

annual runs

- Species modeled: SO2, SO4, NOx, HNO3, NO3 and particulate matter in size categories of 

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic 

diameters. As noted below, the particulate matter emissions by size category will be 

combined into the appropriate species for the visibility analysis (i.e., elemental carbon (EC), 

fine PM or “soil” (< 2.5 μm in diameter), coarse PM (between 2.5-10 μm in diameter) and 

organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).

- Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual 

emission rate with normal operations from the highest emitting day of the meteorological 

period modeled (excluding days where start-up, shutdown or malfunctions occurred 

sometime during the day.) Note that potential emissions are used to determine if a source is 

BART-eligible, but 24-hour average maximum emissions are used for modeling purposes 

(70 FR 39162). Pollutants considered include SO2, H2SO4, NOx and PM10. 

Condensable emissions are considered as primary fine particulate matter and allocated 

equally to the two submicrometer particle size classes. If actual source emissions data are 

not available, the modeling should be based on permit limits. If source-specific size 

categories are not available, then AP-42 factors may be used for sources where AP-42 

factors are available. For sources where AP-42 factors are not available, assumptions for 

partitioning will be investigated by VISTAS based on review of available source categories. 

Excluded from the modeling are pollutants with plant-wide emissions less than de minimis

levels (40 tons per year for SO2 and NOx and 15 tons per year for PM10). De minimis levels 

are plant wide for each visibility-impairing pollutant, so individual units may be modeled 

even if they have emissions below de minimis if the plant total is greater than de minimis.

- Particulate emissions speciation: Break down, as appropriate, filterable and condensable 

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine 

Appendix H.6 - 1163
VISTAS’ Common Modeling Protocol 42

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. The 

process is illustrated in Figure 4-3. If source-specific emissions factors are not available, AP42 factors can be used to estimate the PM speciation for those source sectors for which AP42 emissions factors have been developed. Otherwise assumptions will need to be proposed 

by the source, and reviewed and approved by the state.

Figure 4-3. Speciation of PM-10 Emissions. (PMC is coarse particulate matter -- 2.5 to 10 μm 

diameter.)

- Class I receptors: Use FLM Class I receptor list with receptor elevations provided 

(available from the NPS).

- CALPUFF model options: Use IWAQM (EPA, 1998) guidance, except use turbulencebased dispersion coefficients and probability density function (pdf) dispersion, as used in the 

AERMOD model. 

- Ozone dataset – use observed ozone data for 2001-2003 from CASTNet and AIRS stations. 

Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average 

ozone (backup) background values are to be computed based on daytime average ozone 

concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month). 

PM-10

Filterable Condensable

EC PM-2.5 PMC PM

(including 

H2SO4)

As Prim. SO4)

Organic PM

(SOA)

EC Soil PMC SO4

f(RH)

Non-SO4

Inorganic

OC

Appendix H.6 - 1164
VISTAS’ Common Modeling Protocol 43

- Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) values for 

ammonia. (But, as indicated below, use CMAQ NH3 data for each Class I area in 

POSTUTIL to repartition HNO3 and NO3.) 

- Puff representation: integrated puff sampling methodology.

- Building downwash: Ignore building downwash unless source is within 50-km of a Class I 

area and the State instructs the source to specifically consider building downwash. 

CALPOST and POSTUTIL Configuration (Regional initial and exemption runs)

- Use Visibility Method 6 (for initial modeling), with EPA (2003a, b) Class I area-specific 

(centroid) monthly relative humidity values

- Species considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic 

emissions), soil, coarse PM

- Natural background light extinction: Use EPA (2003b) values for 20% best days- Rayleigh 

scattering value: 10 Mm-1 (all Class I areas). Use EPA’s default f(RH) curves since the EPA 

has not provided f(RH) values specific to the 20% best days.

- Light extinction efficiencies: Use EPA (2003a) values. If a source chooses, the new 

IMPROVE algorithm for calculating light extinction (see Section 3.2.3) may be used in 

addition to the default IMPROVE algorithm. (Calculations would need to be performed 

outside CALPOST or CALPOST would need to be modified to accommodate the new 

algorithm.)

- Ammonia Limiting Method: Use ammonia from CMAQ to define NH3 for each Class I 

area. Choose ammonia from either the CMAQ grid cell where the IMPROVE monitor is 

located or the grid cell of the centroid of the Class I area (the latter in the case that the 

IMPROVE monitor is located outside the Class I area or there is no IMPROVE monitor.) 

The initial run results will be based on the highest change in light extinction (deciviews) from 

natural conditions over the three-year modeling period for each Class I area considered. 

Predicted changes exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid 

CALPUFF modeling analysis.

4.4 Finer Grid Modeling Procedures

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach

There are two potential applications for finer grid CALPUFF modeling: 

BART Exclusion Modeling. First, finer grid CALPUFF modeling can be used to demonstrate 

that a source does not cause or contribute to visibility impairment in any Class I areas, and thus 

can be excluded from BART controls. As shown in Figure 4-1, if the initial regional modeling 

Appendix H.6 - 1165
VISTAS’ Common Modeling Protocol 44

results are not below the threshold for visibility impacts, the next step is to conduct modeling 

using a finer grid resolution for the meteorological fields and the treatment of terrain effects and 

land use variability. In the finer grid modeling the predicted visibility impairment that is 

compared to the threshold is based on the BART guidance of the 98th percentile change in 

deciviews value rather than the more conservative highest value used in the initial analysis.

The BART guidance indicates that the emissions rate to be used for such modeling is the highest 

24-hr rate during the modeling period. Depending on the availability of source data, the following 

emissions information (listed in order of priority) should be used with CALPUFF for BART 

exclusion modeling:

- 24 hr maximum value emissions for the period 2001-2003 (Continuous Emission Monitor, 

CEM data)

- 24 hr maximum value from continuous emissions monitoring data 

- facility stack test emissions

- potential to emit

- permit allowable emissions, if available

- emissions factors from AP-42 source profiles 

Quantify Benefits of BART. The second application of refined modeling is to quantify the 

visibility benefits from the BART control options. This is accomplished by running CALPUFF 

with the baseline emissions rates and again with emissions after BART controls. It is important 

that emission reductions be evaluated in the postprocessing step rather than by using “negative” 

emission rates in the CALPUFF model. The chemical scheme requires that emission rates always 

be positive. 

For any of these applications, a source-specific modeling protocol that defines source properties 

and the specific model configuration is required. As discussed in Section 5, the source specific 

protocol should include source-specific emissions data and can refer to this document for all 

methods and assumptions that follow this common protocol. 

4.4.2 Model Configuration and Settings for Finer Grid Modeling

Grid resolution substantially better than 12-km is needed for a finer grid CALPUFF assessment of 

visibility impacts in most cases involving Class I areas in complex terrain or coastal areas. Thus, 

the CALMET fine grid resolution in the subregional modeling domains used for finer grid 

modeling will depend on the terrain, land use (especially coastal boundaries), location of the 

source, distance of the source from Class I areas, and total size of the subregional modeling 

domain. 

Appendix H.6 - 1166
VISTAS’ Common Modeling Protocol 45

VISTAS intends to provide 2001-2003 CALMET files for five 4-km sub-regional domains as 

illustrated in Figure 4-4. The subdomains are designed to address all BART eligible sources 

within each VISTAS states and all Class I areas within 300 km of the BART-eligible sources. 

For application for a single source, a smaller domain of roughly 200-300 km by 200-300 km is 

recommended. 

Figure 4-4. The five subregional domains for 4-km CALMET modeling.

In some instances, as part of the source-specific protocol, a source may propose to the State to use 

an even finer grid simulation to properly characterize the flow fields and land use changes that 

affect dispersion. An application for source-receptor distances within about 50 km may require a 

grid resolution less than 1 km if complex terrain effects are likely to be important. This 

determination should be made on a case-by-case basis. There is not a single distance at which a

particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the 

source-receptor distances involved, the location of the source relative to the terrain features, the 

Appendix H.6 - 1167
VISTAS’ Common Modeling Protocol 46

physical stack parameters (e.g., a tall stack in complex terrain may be unaffected by the terrainforced flow), proximity of the source and Class I area to a coastline, and other factors including 

availability of representative observational data.

The finer grid CALMET simulations will be run by Earth Tech in hybrid mode, using both MM5 

data to define the initial guess fields and meteorological observational data in the Step 2 

calculations. Overwater (buoy) data will be provided in addition to the hourly surface 

meteorological observations, precipitation observations and twice-daily upper air sounding data. 

A domain-specific set of modeling parameters will be defined for each subregional domain. The 

proper selection of the CALMET diagnostic wind field parameters that are used to blend 

observations with the Step 1 wind field depends on factors such as the locations of the 

meteorological stations relative to terrain and coastal features (which affects the 

representativeness of the observational data), the terrain length scale, and the quality (resolution) 

of the MM5 data used to define the initial guess field and its ability to properly resolve wind 

flows on the fine-scale CALMET domain. The definition of the proper CALMET parameters is 

done as part of sensitivity testing where model performance is evaluated against available 

observations and expected terrain effects, such as channeling of flows within a valley. 

In addition to the better grid resolution and the introduction of observational data in the finer grid 

simulations, several other modeling refinements can enhance the accuracy of the finer grid 

modeling. These include the use of the higher resolution terrain DEM data (~3 arcsec USGS 

data) in defining the gridded terrain fields. Otherwise, the source configuration, emissions, 

pollutant speciation, Class I receptors, ozone datasets and CALPUFF model options will be the 

same as in the initial runs.

For the finer grid BART exclusion analysis, the test for evaluating whether a source is 

contributing to visibility impairment is based on the 98th percentile modeled value (rather than the 

highest predicted value used for the initial evaluation), which is consistent with EPA’s BART 

guidance. A coding change is required in the CALPOST postprocessor in order to allow the 98th

percentile change in extinction to be computed. 

4.5 Presentation of Modeling Results

The CALPOST processing computes the daily maximum change in deciviews. A sample of the 

summary table produced by CALPOST is shown in Table 4-1. For evaluating compliance with 

the VISTAS screening threshold, the highest change in extinction value, located at the bottom of 

the CALPOST list file is compared to the threshold value (e.g., 0.5 dv). For example, in the 

sample shown in Table 4-1, the summary at the bottom shows that the highest visibility impact is 

1.219 dv, with 9 days over the year showing values greater than 0.5 dv. Therefore this source 

would not pass the initial analysis, and finer grid modeling would be required. 

Appendix H.6 - 1168
VISTAS’ Common Modeling Protocol 47

Table 4-1. Example of CALPOST Output, Showing Maximum Daily Impacts of Source and Locations of Those Impacts.

YEAR DAY HR RECEPTOR COORDINATES (km) TYPE DV(Total) DV(BKG) DELTA DV F(RH) %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF 

2001 2 0 3 20.540 79.782 D 5.397 5.358 0.039 4.314 44.33 47.22 3.07 1.07 0.00 4.30 

2001 3 0 9 31.680 79.822 D 4.566 4.421 0.145 1.767 40.75 33.89 9.19 3.24 0.00 12.94 

2001 4 0 1 24.723 77.951 D 4.540 4.540 0.000 2.076 0.00 0.00 0.00 0.00 0.00 0.00 

2001 5 0 77 30.228 94.571 D 4.950 4.939 0.011 3.144 43.13 44.74 4.64 1.45 0.00 6.05 

2001 6 0 1 24.723 77.951 D 5.181 5.166 0.015 3.772 38.58 56.05 1.90 0.70 0.00 2.76 

2001 7 0 3 20.540 79.782 D 6.366 5.745 0.620 5.439 44.98 44.99 3.69 1.26 0.00 5.08 

 .

 .

 .

2001 363 0 113 27.414 103.782 D 5.725 5.652 0.073 5.164 53.49 35.51 4.03 1.39 0.00 5.58 

2001 364 0 113 27.414 103.782 D 6.554 6.521 0.033 7.826 48.12 47.09 1.67 0.64 0.00 2.48 

2001 365 0 1 24.723 77.951 D 6.499 6.499 0.000 7.757 0.00 0.00 0.00 0.00 0.00 0.00 

--- Number of days with Delta-Deciview => 0.50: 9

--- Number of days with Delta-Deciview => 1.00: 2

--- Largest Delta-Deciview = 1.219

Appendix H.6 - 1169
VISTAS’ Common Modeling Protocol 48

In addition to the highest change in deciview value on each day over all the receptors in a 

particular Class I area, the CALPOST summary table in Table 4-1 contains the coordinates of the 

receptor, receptor type (D indicates discrete receptors), the total haze level (background + source, 

in dv), the background haze in deciviews, the change in haziness (delta dv), the humidity term 

applied to hygroscopic aerosols (F(RH)), and the contribution of each species to light extinction 

(in percent of the total source contribution) for SO4, NO3, organics, elemental carbon, coarse and 

fine particulate matter.

For the finer grid analysis, the data in the table can be imported into a spreadsheet and sorted on 

the delta dv column. Table 4-2 shows an example of the ranked visibility impacts (change in dv) 

for each of three years at six different Class I areas. The 98th percentile (8th highest value) in the 

sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the example 

shown in this table, the source passes the finer grid analysis because the highest 98th percentile 

visibility impact is below the contribution threshold of 0.5 dv. 

The Results section of the CALPUFF modeling report should contain the following information:

1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all 

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source, as illustrated in Table 4-3. 

3. A discussion of the number of Class I areas with visibility impairment from the source on 

98th percentile days in each year greater than 0.5 dv (total visibility impairment minus 

impairment on 20% best days for natural background visibility equals delta-dv, the 

visibility impact attributed to the source). 

4. For the Class I area with the maximum impact, discussion of the number of days below 

the 98th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in 

the Class I area where the impact exceeds 0.5 dv, and the maximum impact. 

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which 

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report 

same results as provided for 12-km initial exemption modeling.

6. For control option modeling, each control option tested should be listed in tabular format. 

For each control option and for each Class I area where the impact of the source exceeded 

0.5 dv, report the change in pollutant emissions and the change in visibility impact from 

the source as a result of the control option. The effectiveness of candidate control options 

are to be compared to each other, not to a specific target improvement. 

Appendix H.6 - 1170
VISTAS’ Common Modeling Protocol 49

Table 4-2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

Great Smoky NP

0.99

0.88

0.62

0.59

0.55

0.52

0.48

0.47

0.95

0.63

0.51

0.50

0.46

0.42

0.37

0.36

1.20

0.90

0.73

0.72

0.59

0.47

0.45

0.42

Linville Gorge

0.67

0.45

0.43

0.33

0.29

0.27

0.25

0.23

0.81

0.69

0.65

0.50

0.45

0.33

0.31

0.29

0.76

0.47

0.37

0.35

0.31

0.30

0.28

0.28

Shining Rock

0.66

0.43

0.41

0.35

0.26

0.24

0.23

0.22

0.73

0.69

0.63

0.52

0.46

0.34

0.29

0.26

0.75

0.45

0.36

0.34

0.28

0.27

0.26

0.25

Cohutta

0.26

0.23

0.22

0.21

0.20

0.19

0.18

0.16

0.54

0.47

0.43

0.37

0.37

0.31

0.31

0.30

0.61

0.42

0.30

0.29

0.28

0.28

0.25

0.25

Joyce Kilmer-Slickrock

0.34

0.33

0.31

0.26

0.24

0.20

0.18

0.17

0.52

0.43

0.32

0.31

0.30

0.28

0.24

0.24

0.27

0.24

0.23

0.20

0.14

0.13

0.11

0.10

Mammoth Cave NP

0.56

0.44

0.38

0.29

0.25

0.24

0.22

0.21

0.57

0.56

0.53

0.35

0.33

0.33

0.30

0.29

0.50

0.37

0.36

0.35

0.31

0.24

0.21

0.19

Appendix H.6 - 1171
VISTAS’ Common Modeling Protocol 50

Table 4-3. Format of Summary of Results for CALPUFF Modeling in VISTAS’ 12-km Modeling 

Domain to Determine if a BART Eligible Source is Subject to BART. 

Class I area Distance 

(km) 

from 

source to 

Class I 

area 

boundary

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2001

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2002

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2003

# of days1 and 

# of receptors 

with impact 

> 1.0 dv in 

Class I area 

for 3-yr 

period

Max. 24-hr 

impact over 

3-yr period

Dolly Sods, WV

Shenandoah, VA

James River 

Face, VA

Mammoth Cave, 

KY

Sipsey, AL

Great Smoky 

Mtns, TN

Cohutta, GA

Shining Rock, 

NC

Linville Gorge, 

NC

Swanquarter, NC

Cape Romain, 

SC

Okefenokee, GA

Saint Marks, FL

Chassahowitzka, 

FL

Everglades, FL

Brigantine, NJ

Breton Island, 

LA

Caney Creek, 

AR

Upper Buffalo, 

AR

Mingo, MO

Hercules Glade, 

MO

1

Days below the 98th percentile of days in each year or the three-year modeling period, as appropriate

Appendix H.6 - 1172
VISTAS’ Common Modeling Protocol 51

States will provide further guidance on graphic presentation of results to simplify 

evaluation of effectiveness of control measures. For example, a temporal plot of the 

change in deciviews between the controlled and uncontrolled cases could be developed for 

the receptor with the maximum modeled impact in each Class I area. 

7. Copies of all input files and input data in electronic format for the CALMET, CALPUFF, 

CALPOST and POSTUTIL runs should be archived and provided to the State.

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources

VISTAS will provide updates and supporting information concerning the Common Modeling 

Protocol (this document) on the VISTAS website. In addition, VISTAS will make publicly 

available the following data bases developed by Earth Tech:

• VISTAS version of the CALPUFF modeling system, maintained on the Earth Tech website. 

Version 5.8 includes CALMET, CALPUFF, CALPOST, and POSTUTIL files, updated in 

December 2005. The last update in this VISTAS version is a CALMET update that addresses 

over water dispersion, which was developed for Mineral Management Services (MMS) in fall 

2005. When available in January 2006, this VISTAS version of CALPUFF will not be 

updated further unless errors are found in the code. BART-eligible sources in the VISTAS 

states will be able to use this VISTAS version throughout the BART modeling exercise. 

• 12-km CALMET output files for 2001, 2002, and 2003 produced as described in previous 

sections. Further detail on model configuration and settings will be provided with the output 

files and will be made available on the CALPUFF website.

• CALMET will include a software modification to allow the meteorological data inputs into 

CALMET to be used to generate finer grid CALMET files without having to go back to the 

original MM5 output files

• Five 4-km CALMET subdomains for 2001, 2002, and 2003, produced as described in 

previous sections. Further detail on model configuration and settings will be provided with 

the output files and will be made available on the website.

• File with CALPUFF model configuration and settings sufficient to replicate CALPUFF 

modeling done for VISTAS using 12 km CALMET, including

o Ozone data used to run CALPUFF

o Ammonia data used to run CALPUFF and to partition NO3 in POSTUTIL.

o Background concentrations files for use in POSTUTIL 

o All other set up files used in VISTAS 12-km CALPUFF run

Appendix H.6 - 1173
Source-Specific Modeling Protocol 52

5. SOURCE-SPECIFIC MODELING PROTOCOL

Sources are required to submit a source-specific protocol to the State for review and approval 

prior to source-specific modeling. States will provide the documentation to EPA and FLM for 

their review. An outline of the typical contents of the site-specific protocol is provided in Table 

5-1.

If a source-specific modeling approach is proposed that differs from the common approach in 

Chapter 4, a more-detailed modeling protocol than that required under the common procedures is 

required. This protocol must explain the data sources, model configuration, and rationale for 

changes in the model approach from the common protocol and must be approved by the State. 

Unit-specific source data include the following parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO2, H2SO4, NOx and PM10).

Additional building dimension information (building width, length, height and corner locations) 

is needed for short stacks that are less than Good Engineering Practice (GEP) height. This 

information is used in providing effective structure dimensions for building downwash 

calculations. (The requirement to conduct building downwash modeling may be waived by 

individual States or if the transport distance is greater than 50 km.)

The source coordinates must be expressed in the coordinate system used to define the CALMET 

and CALPUFF modeling domains. For the regional screening simulations, a Lambert Conformal 

Conic (LCC) coordinate system will be used. The required parameters to define an LCC 

coordinate include two matching parallels, latitude/longitude of the projection origin, coordinate 

datum, and false Easting and Northing (if used) of the projection origin. Subregional and sourcespecific domains may be using either an LCC or UTM projection. 

The Earth Tech Graphical User Interface (GUI) system provides software (called COORDS) to 

compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datums. In 

addition, the CALVIEW graphics feature allows the use of georeferenced satellite or aerial 

photographs to be used as base maps to confirm source locations. Links to sources of suitable 

base maps can be found on the CALPUFF data site (www.src.com) in the section on “Aerial 

Photos”.

Appendix H.6 - 1174
Source-Specific Modeling Protocol 53

Table 5-1. Sample Table of Contents of a Source-Specific Fine-Scale Modeling Protocol.

1. INTRODUCTION

1.1 Objectives

1.2 Location of Source vs. Relevant Class I Areas

1.3 Source Impact Evaluation Criteria

2. SOURCE DESCRIPTION

2.1 Unit-specific Source Data

2.2 Boundary Conditions

3. GEOPHYSICAL AND METEOROLOGICAL DATA

3.1 Modeling Domain and Terrain

3.2 Land Use

3.3 Meteorological Data Base

3.3.1 MM5 Simulations

3.3.2 Measurements and Observations

3.4 Air Quality Data Base

3.4.1 Ozone Concentrations – Measured or Modeled

3.4.2 Ammonia Concentrations – Measured or Modeled

3.4.3 Concentrations of Other Pollutants – Measured or Modeled

3.5 Natural Conditions at Class I Areas

4. AIR QUALITY MODELING METHODOLOGY

4.1 Plume Model Selection

4.1.1 Major Relevant Features of CALMET

4.2.2 Major Relevant Features of CALPUFF

4.2 Modeling Domain Configuration

4.3 CALMET Meteorological Modeling

4.4 CALPUFF Computational Domain and Receptors

4.5 CALPUFF Modeling Option Selections

4.6 Light Extinction and Haze Impact Calculations

4.7 Modeling Products

5. REVIEW PROCESS

6.1 CALMET Fields 

6.2 CALPUFF, CALPOST, and POSTUTIL Results 

6. REFERENCES

APPENDICES

A.1 VISTAS BART MODELING PROTOCOL 

A.2 ... other appendices as needed

Appendix H.6 - 1175
Source-Specific Modeling Protocol 54

An example of the data that need to be reported is provided in Table 5-2. More detail on the 

stack data, emissions species, and particulate size fractions to be reported will be made available 

on the VISTAS website, www.src.com, Check with your State for the more detailed format of 

Table 5-2 that is to be used. 

Discussions with the regulatory authorities should be conducted prior to development of a 

protocol to ensure all of the relevant issues are included in the protocol. 

Table 5-2. Example of Source Documentation for BART Eligible Source. 

Unit name 

and/or 

description

Start-up dates SO2 potential 

emissions (tpy)

NOx potential 

emissions (tpy)

Total PM 

potential 

emissions (tpy) 

Emissions source

name

...

Total emissions

Potential BARTeligible 

emissions

Appendix H.6 - 1176
Quality Assurance 55

6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol is an important tool for use in determining 

whether a BART-eligible source can be reasonable expected to cause or contribute to visibility 

impairment in a Class I area, and therefore whether this source should be subject to BART 

controls, and if so, to determine the relative benefits of various BART controls. The purpose of 

the quality assurance (QA) program is to establish procedures for ensuring that products produced 

by the application of the modeling techniques for BART studies satisfy the regulatory objectives 

of the BART program.

The scope of the QA program affects different users differently. Common features of most 

applications will be the setup and execution of the CALPUFF air quality model and processing of 

modeling results to determine if a source contributes to visibility impairment at a Class I area. In 

many cases, users will be provided meteorological datasets that have been developed with 

VISTAS funding under a suitable QA program for use in the BART modeling. Other users will 

be involved in site-specific or source-specific analyses that will use additional datasets and 

potentially different modeling options and/or tools. More extensive quality assurance will be 

required in these latter types of applications. It is the responsibility of the modeler to ensure that 

an adequate QA protocol is in place for a particular application.

The CALPUFF modeling system contains built-in features to facilitate quality assurance of the 

modeling results. These include the automatic production of “QA” files for various datasets, 

including geophysical fields, sources and receptors, and imbedded tracking of model options and 

switches within the output files from the major modeling units of the modeling system. The 

Graphical User Interface system (GUI) provided as part of the latest CALPUFF modeling system 

allows these QA files to be displayed graphically.

In addition, a detailed software management system is in place to track version and level numbers 

associated each program and utility within the CALPUFF modeling system. This information is 

carried forward in all of the output files to create an audit trail of software versions and major 

model options used that can be retrieved and displayed from the model output files.

Because the required QA procedures will depend heavily on the exact application, there will be 

differences among different users and different applications.

In addition, the BART modeling process involves multiple organizations. The States have overall 

responsibility for the process and may also execute some or all of the modeling. VISTAS is 

contributing general guidance via this protocol and is preparing meteorological fields and 

performing modeling under the guidance of the States. The sources that are BART-eligible need 

to provide process information and emissions data for use in the analyses. In addition, those 

sources that are involved in BART assessments will need to be actively involved in control 

Appendix H.6 - 1177
Quality Assurance 56

technology decisions and assessments. Finally, some of the modeling steps may be carried out by 

contractors on behalf of VISTAS, a State, or a source.

Each of these organizations has a responsibility to ensure that it is providing correct information 

to others and to evaluate the quality of any analyses it is performing, whether with data of its own 

or from others. This chapter provides general guidance and information on those aspects of 

quality assurance that are specific to the CALPUFF modeling effort, irrespective of which 

organization is carrying out the effort. The focus is on the common protocol efforts described in 

Chapter 4. As described in Section 6.3, more comprehensive QA may be needed for the unique 

aspects of the source-specific modeling described in Chapter 5.

6.2 QA Procedures for Common Protocol Modeling

The VISTAS common protocol (Section 4) describes the methods and procedures for use in 

conducting regional scale screening modeling to determine the whether a particular source or 

group of sources is subject to BART controls. In the initial application, the regional CALPUFFready meteorological data files will be provided by VISTAS. The amount of effort for end-users 

performing QA of these pre-defined meteorological fields will be reduced from what is required 

in developing source-specific meteorological fields, as described below. Also, VISTAS is 

planning to provide five subregional CALMET meteorological datasets in a CALPUFF-ready 

format. The development of these CALMET datasets will be subject to a QA program as part of 

their development, so the necessary quality assurance activity of end-users is again reduced from 

what would be required in the development of the dataset. It is not expected that the quality 

assurance steps in the development will be repeated in each application. The VISTAS-provided 

regional and subregional meteorological fields will include a test case simulation for 

demonstrating that expected modeling results are obtained on the user’s computer platform. This 

test should be repeated by every user.

Although the CALPUFF modeling system is recommended by the U.S. Environmental Protection 

Agency for application to BART analyses, a considerable amount of expertise and modeling 

judgment is needed at certain stages of the analysis. The modeling is not a “cookbook” exercise, 

a fact that was recognized by the U.S. EPA in describing the expertise needed for CALMET 

modeling (EPA, 1998; pp. 9-10,). Current methods for performing refined chemistry calculation 

also require an understanding of the chemical and meteorological processing affecting 

ammonium nitrate formation. VISTAS has committed to provide appropriate CALPUFF training 

to assist States in obtaining the necessary expertise with the latest CALPUFF modeling tools and 

techniques. An appropriate level of knowledge of the model formulation, technical approach and 

assumptions is essential for successful BART modeling. 

6.2.1 Quality Control of Input Data

The input data required by the model depends on the application. At a minimum, source data is 

required by CALPUFF (see Section 6.2.3) along with a list of choices made about model options 

and switches. Most of the modeling option choices are specified or recommended by regulatory 

Appendix H.6 - 1178
Quality Assurance 57

guidance and default values (see references in Section 4.3.3). However, remodeling of the 

boundary conditions is not required for VISTAS-provided finer grid domains so the expertise 

level is not as high as it would be for development of the boundary conditions files from scratch.

To the extent that modeling applications are using pre-defined CALMET files and CALPUFF 

templates, the quality assurance will be straightforward. More detailed steps are needed for the 

setup of modeling files for source-specific applications of subregional domains finer than 4 km. 

The basic procedures that will apply to all CALPUFF model applications will include a 

confirmation of the source data, including units, verification of the correct source and receptor 

locations, including datum and projection, confirmation of the switch selections relative to 

modeling guidance, checks of the program switches and file names for the various processing 

steps, and confirmation of the use of the proper version and level of each model program. It is a 

common and recommended procedure for an independent modeler not involved in the setup of 

the modeling files to independently confirm the model switches and data entry in the actual 

model input files and to conduct an independent run of the worst case event as a confirmation 

check.

In addition, common practice requires that a model project CD (or DVD or set of DVDs) be 

created that contains all of the data and program files needed to reproduce the model results 

presented in a report. The model list files from each step are included on the project CD. This 

information allows independent checking and confirmation of the modeling process.

6.2.2 Quality Control of Application of CALMET

For users of the VISTAS CALPUFF-ready CALMET meteorological files, a number of large 

datafiles will be provided by VISTAS on external USB2 or Firewire hard drives in a format ready 

for use with the CALPUFF model. The QA steps associated with the development of the 

VISTAS common datasets will be provided separately as part of the modeling documentation. It 

is not expected that the QA steps conducted in the development of the meteorological datasets 

will be repeated in each application, although tests to confirm that the dataset is suitable for the 

application for which it is being used should be performed as part of the QA. This is discussed in 

more detail below.

The regional screening CALMET grid is defined in Chapter 4 on a 12-km Lambert Conformal 

Conic (LCC) grid system. The subregional and source-specific domains may be defined in either 

LCC or Universal Transverse Mercator (UTM) coordinates. In the case of the LCC projection, 

two matching parallels, latitude/longitude of the projection origin, coordinate datum, and false 

Easting and Northing (if used) of the projection origin must also be defined. For any domains in 

UTM coordinates, the UTM zone (see Appendix D of the CALMET User’s Guide) and datum 

must be defined. The appropriate projection and map factors are provided as part of the 

definition of the VISTAS regional grid system. For a source-specific domain, the grid parameters 

will be provided as part of the source-specific protocol.

Appendix H.6 - 1179
Quality Assurance 58

Appendix A of the IWAQM report (EPA, 1998) contains a list of recommended CALMET switch 

settings. Except as modified in Chapter 4 of this protocol or in a source-specific protocol, the 

IWAQM guidance should be used in setting up the CALMET simulations. The CALMET model

obtains the switch settings from an ASCII “control file” with a default name of CALMET.INP. 

Whether the model is run using a GUI or from the control line in a DOS, Linux, or Unix window, 

it is essential that the control file be reviewed as part of the CALMET QA analysis. The 

CALMET GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. This includes the default value for each variable, a text description 

of the variable, the meaning of each variable option, the units of the variable and interrelationships among variables indicating if/when the variable is used. Some third-party 

commercial GUIs strip out this descriptive information, which makes the QA step more difficult, 

although it is essential for perform nonetheless using the variable names as references for the 

variables in the file.

Part of the CALPUFF modeling system’s built-in QA capabilities is a variable tracking system 

that retains the control file inputs for CALMET and CALPUFF in the output files create by the 

models. This information includes the Version and Level numbers of the processor codes and 

main model codes used in the simulations as well as the control files from the main models 

(CALMET and CALPUFF). The information from the preprocessing steps and the CALMET 

and CALPUFF model simulations is all carried forward and saved in the 

CALPUFF/postprocessor output files so that the final concentration/flux files contain a history of 

the model options and switch settings. This allows a user or reviewing agency to confirm the 

switch settings provided in a control file with that actually used in the model simulations. An 

optional switch in the CALPOST processor creates a complete listing of the QA data. This step 

requires access to the output CALPUFF concentration and/or flux files, which are normally 

practical to store on CDs or DVDs and to provide a part of the Project CD/DVD set.

6.2.3 Quality Control of Application of CALPUFF

The quality assurance of the source and emissions data is a major component of the CALPUFF 

modeling. Also, many errors are found in source coordinates and related projection/datum 

parameters, so confirmation of the source location is an important part of the modeling QA.

The locations of the Class I area receptors are another important CALPUFF input. The use of 

pre-defined receptors as provided by the National Park Service (NPS) receptor dataset is 

recommended in the VISTAS common protocol. However, although the latitude and longitude of 

each receptor point is provided, it is necessary to ensure that the proper UTM or LCC coordinates 

have been computed for computational domain selected. In particular, the datum of the NPS 

conversion software is not specified, so it is recommended that coordinates be checked using the 

CALPUFF GUI’s COORDS software or another comparable coordinate translation software 

package that recognizes various datums.

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of 

Appendix H.6 - 1180
Quality Assurance 59

this protocol or in a source-specific protocol, the IWAQM guidance should be used in setting up 

the CALPUFF simulations. The CALPUFF model obtains the switch settings from an ASCII 

“control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the 

CALPUFF GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. Some third-party commercial GUIs strip out this descriptive 

information, which makes the QA step more difficult, although it is essential for perform 

nonetheless using the variable names as references for the variables in the file.

6.2.4 Quality Control of Application of CALPOST and POSTUTIL

CALPOST is run separately for each Class I area in order to obtain the necessary visibility 

statistics for evaluating compliance with the BART screening and finer grid modeling thresholds. 

The inputs to CALPOST involve selection of the visibility method (Method 6 in the standard 

EPA BART guidance), entry of Class I area-specific data for computing background extinction 

and monthly relative humidity factors for hygroscopic aerosols. CALPOST contains a receptor 

screening that allow subsets of a receptor network modeling in CALPUFF to be selected for 

processing in a given CALPOST run. This is how receptors within a single Class I area are 

selected for processing from a CALPUFF output file that may contain receptors from several 

Class I areas. CALPOST contains options for creating plot files that will help in the confirmation 

that the proper receptor subset is extracted.

The CALPOST output file contains a listing of the highest visibility impact each day of the model 

simulation over all receptors included in CALPOST analysis. Receptors will normally be 

selected in each CALPOST run so that each CALPOST run represents the impacts at a single 

Class I area. The table includes the data shown in the example in Table 4-1. For a screening 

assessment, the peak value of the change in extinction is shown at the bottom of the visibility 

table (see Table 4-1). For a finer grid simulation, the 98th percentile value (8th highest day) is 

used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the 

results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily 

change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that need to be carefully checked as part of the CALPOST quality 

assurance are:

- Visibility technique (Method 6 in the common VISTAS protocol)

- Monthly Class I-specific relative humidity factors for Method 6

- Background light extinction values

- Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, 

organics, (as SOA), coarse and fine particulate matter and elemental carbon.

Appendix H.6 - 1181
Quality Assurance 60

- Appropriate species names for coarse PM used

- Extinction efficiencies for each species

- Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area 

specific value for finer grid modeling)

- Screen to select appropriate Class I receptors for each CALPOST simulation.

The CALPOST program produces plot files compatible with CALVIEW that allow confirmation 

of receptor locations that is useful in evaluating the receptor screening step.

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF 

simulations into a total concentration file. In addition, it contains options to scale the 

concentrations from different modeled species (e.g., different particle sizes) into speciesdependent size distributions for the particulate matter. For example, PM is often simulated with 

unit emission rates for each particle size category and, in the POSTUTIL stage, the contributions 

of each size category based on the species being considered (e.g., elemental carbon, coarse 

particulate matter, etc.) are combined to form the species concentrations for input into 

CALPOST. This process, although simple, requires a careful review of the weighting factors for 

each source. POSTUTIL also allows a repartitioning of nitric acid and nitrate to account for the 

effects of ammonia limiting conditions. 

If source-specific modeling is performed using different sources of data or different techniques, 

the source-specific modeling protocol should provide justification for deviations from the 

VISTAS common protocol, and a QA plan specific for the application provided to address the 

quality assurance of the data used.

6.3 Additional QA Issues for Alternative Source-Specific Modeling

The level of QA required for application of source-specific protocols will be substantially higher 

than for the use of datasets that have already been subject to a QA procedure. For example, 

source-specific protocols may include the use of on-site meteorological datasets, the use of higher 

resolution prognostic meteorological (e.g., MM5) datasets, alternative visibility calculations, 

different extinction coefficients, or other changes to the common protocol. In addition to 

providing a source-specific modeling protocol describing and justifying the changes to the 

modeling approach from the VISTAS common protocol, the site-specific applications should 

include the development of a QA plan to properly evaluate the data used in the site-specific 

modeling.

The critical CALMET input parameters depend on the mode in which the model is run 

(observations mode, hybrid mode or no-observations mode), and the location and spatial 

representativeness of any observational data. In a site specific protocol involving the 

development of a meteorological dataset, the elements of the QA process include preparation of 

wind rose (using observed, MM5 and CALMET-derived data), including examination of the data 

Appendix H.6 - 1182
Quality Assurance 61

as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, 

and presentation of 2-D vector plots illustrating terrain effects/sea breeze circulation or other 

features of the flow expected to occur within the domain. For example, 2-D vector plots 

produced during light wind speed stable conditions (e.g., early morning such as 4 am) are good 

for assessing the performance of the CALMET model configuration and switches in reproducing 

terrain effects because these conditions are likely to maximize the terrain impacts in the model. 

Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea 

breeze circulations that may be important for certain applications. Customization of the QA 

process for the individual site-specific domain based on the availability of data and the physical 

processes expected to be important at that location should be conducted as part of the site-specific 

QA plan development.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-specific modeling domain should be 

evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than 

derived ammonia based on aerosol measurements) will require an evaluation of the model 

performance relative to the techniques in the VISTAS common protocol.

In any site-specific protocol a site-specific QA plan should be prepared.

6.4 Assessment of Uncertainty in Modeling Results

Chapter 3 discussed the uncertainties and known limitations in CALPUFF. The source specific 

modeling report does not need to repeat the uncertainties listed in Chapter 3, but the reviewer 

should interpret results in light of these limitations. It is expected that the performance of the 

model will be better in predicting changes in visibility impacts due to BART controls than in 

predicting absolute visibility values. This is because uncertainties in meteorological conditions 

transport and dispersion are expected to be less important in evaluating a change in impact, since 

a comparable effect will be included in both the base and sensitivity simulations. 

Appendix H.6 - 1183
References 62

7. REFERENCES

Allwine, K.J. and C.D. Whiteman, 1985: MELSAR: A Mesoscale Air Quality Model for Complex 

Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest Laboratory, 

Richland, Washington.

Batchvarova, E. and S.-E. Gryning, 1991: Applied model for the growth of the daytime mixed layer. 

Boundary-Layer Meteorol., 56, 261-274.

Batchvarova, E. and S.-E. Gryning, 1994: An applied model for the height of the daytime mixed layer 

and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. 

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast Cycle: 

the RUC, Mon. Wea. Rev., 132, 495-518.

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary layer. 

Quart. J. Roy. Meteor. Soc., 99, 450-467.

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC and 

VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466.

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of the 

Montana Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources 

Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, 

Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze Rule; 

EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions under 

the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research Triangle 

Park, NC.

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for Modeling 

Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R-98-019, U.S. Environmental Protection Agency, RTP, NC.

Appendix H.6 - 1184
References 63

Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol Formation 

and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on the Applications 

of Air Pollution Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: 

Bulk parameterization of air-sea fluxes: Updates and verification for

the COARE algorithm. Journal of Climate, 16, 571-591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis of 

PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory. 

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. 

U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR 

MM5, NCAR TN-398-STR, NCAR Technical Note.

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary Organic 

Aerosols. Atmos. Environ., 23, 1733-1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes 

from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with 

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E. 

Gryning and F.A. Schiermeier. Plenum Press, New York, NY.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating 

Bureau MID/SSD/80/0026/R. Nottingham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation Mechanism 

in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference – Guideline on 

Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 22-

26 June 2003, San Diego, CA.

O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary 

boundary layer. J. Atmos. Sci., 27, 1213-1215.

Appendix H.6 - 1185
References 64

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE 

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering 

Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. 

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms 

for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User Instructions. EPA/600/8-

89/041, U.S. EPA, Research Triangle Park, North Carolina.

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME Plume 

Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390. 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting & 

Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET 

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental 

Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

Scire, J.S., Z.-X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, Visibility 

and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline 

on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF model for 

offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation 

within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17-20 

October 2005.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model with 

Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution Meteorology, 

Phoenix, Arizona. American Meteorological Society. 11-16 January 1998.

Appendix H.6 - 1186
References 65

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic 

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the 

AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11 

February 2005.

Appendix H.6 - 1187
EPA Region 4 BART CALPUFF Modeling Protocol Comments 

Facility: MeadWestvaco Corporation, North Charleston, South Carolina 

Protocol Developer: Trinity Consultants 

Reviewers: Brenda Johnson and Rick Gillam 

Date: April 4, 2006 

1. Fine Grid Modeling for Capr Romain: The protocol states(at section 1.1.2, Page 8) 

that “...MeadWestvaco will initially conduct screening modeling of visibility at all Class 

I areas within 300 km, but also anticipates conducting refined modeling of visibility 

impacts at Camp Romain...” EPA Region 4 agrees that refined modeling should be 

conducted to assess the visibility impacts at Camp Romain because of its close proximity 

to the facility (30 km) and to better account for the complex meteorology of the coastline. 

2. In addition to the 12-km CALMET domain discussed in the VISTAS protocol for 

initial screening modeling, VISTAS has developed a number for finer-scale 4-km 

CALMET domains. One of these domains (Domain #4 on the CALMET Domains Map 

on http://www.vistas-sesarm.org/BART/CALMETSubdomains_21Dec2005.jpg ) 

encompasses MeadWestvaco’s proposed modeling domain. EPA recommends that this 

4-km CALMET domain be used for modeling the visibility impacts on Cape Romain. 

3. Dispersion coefficients: AERMOD dispersion parameters are proposed to be used in 

the CALPUFF modeling. EPA continues to recommend the use of the Pasquill-Gifford 

(P-G) dispersion parameters with CALPUFF. The March 16, 2006 EPA Model 

Clearinghouse memo, “Dispersion Coefficients for Regulatory Air Quality Modeling in 

CALPUFF,” from Tyler Fox to Kay Prince, discusses the Agency’s position on 

dispersion coefficients for CALPUFF. The memo also provides a list of the regulatory 

default modeling options for use in the CALPUFF modeling system. The memo is 

located on the EPA website at http://www.epa.gov/ttn/scram/guidance/mch/cfym109.pdf. 

The BART modeling protocol for MeadWestvaco should be revised to clearly indicate 

that P-G dispersion parameters will be used in the BART modeling. 

Appendix H.6 - 1188
 

BEST AVAILABLE RETROFIT TECHNOLOGY APPLICABILITY ANALYSIS

AIR QUALITY MODELING PROTOCOL (REVISED) 

SANTEE COOPER  GRAINGER, JEFFERIES, AND WINYAH STATIONS

TRINITY CONSULTANTS

1100 Johnson Ferry Road 

Suite 685 

Atlanta, GA 30342 

(404) 256-1919 

July 2007 

Project 061101.0117 

Appendix H.6 - 1189
Santee Cooper – Grainger, Jefferies, Winyah i Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE OF CONTENTS

1. INTRODUCTION....................................................................................................................1

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS .........................2 

1.1.1 DETERMINATION OF BART-ELIGIBILITY ........................................................ 3

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND BART

 APPLICABILITY................................................................................................ 3

1.1.3 POTENTIALLY AFFECTED CLASS I AREAS....................................................... 5

1.1.4 CALPUFF MODELING ANALYSES .................................................................. 7

1.2 ORGANIZATION OF BART APPLICABILITY ANALYSIS MODELING PROTOCOL ..........8 

2. BART-ELIGIBLE SOURCE DESCRIPTION ...........................................................................9

2.1 BART-ELIGIBLE EMISSION UNITS............................................................................9 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY .....................................12 

2.2.1 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP ONLY ..... 15

2.2.2 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP AND FGD18

2.3 MODELED STACK PARAMETERS AND EMISSIONS....................................................21 

3. AIR QUALITY MODELING ANALYSES ...............................................................................25

3.1 AIR QUALITY MODELING SYSTEM..........................................................................25 

3.2 SCREENING AND REFINED ANALYSIS TECHNIQUES.................................................25 

3.2.1 VISIBILITY ASSESSMENT METRIC ................................................................. 26

3.2.2 METEOROLOGICAL AND COMPUTATIONAL GRIDS........................................ 26

3.2.3 MODEL PROCESSING AND POSTPROCESSING METHODS ............................... 27

3.3 CALMET METEOROLOGICAL PROCESSING............................................................27 

3.3.1 CALMET METEOROLOGICAL DOMAINS ...................................................... 28

3.3.2 MM5 SIMULATIONS ...................................................................................... 29

3.3.3 GEOPHYSICAL DATA ..................................................................................... 30

3.3.4 12-KM SCREENING ANALYSIS CALMET PROCESSING................................. 30

3.3.5 4-KM REFINED GRID CALMET PROCESSING ............................................... 31

3.3.6 QUALITY ASSURANCE OF CALMET ANALYSES .......................................... 32

3.4 CALPUFF MODEL PROCESSING.............................................................................32 

3.4.1 MODELED EMISSIONS AND CHEMICAL TRANSFORMATIONS ........................ 32

3.4.2 CALPUFF DISPERSION ALGORITHMS .......................................................... 36

3.4.3 BUILDING DOWNWASH .................................................................................. 36

3.4.4 CALPUFF MODELING DOMAINS AND CLASS I AREA RECEPTORS .............. 36

3.4.5 BACKGROUND OZONE AND AMMONIA CONCENTRATIONS........................... 41

3.4.6 PUFF REPRESENTATION................................................................................. 43

3.4.7 QUALITY ASSURANCE OF CALPUFF ANALYSES ......................................... 44

3.5 CALPOST POSTPROCESSING AND NATURAL BACKGROUND CONDITIONS FOR 

LIGHT EXTINCTION AND HAZE INDEX CALCULATIONS ...........................................44 

3.5.1 POSTUTIL PROCESSING............................................................................... 44

3.5.2 CLASS I AREA-SPECIFIC NATURAL BACKGROUND CONDITIONS .................. 45

Appendix H.6 - 1190
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BART Applicability Modeling Protocol 

3.5.3 VISIBILITY IMPACT CALCULATION AND REVISED IMPROVE LIGHT 

EXTINCTION ALGORITHM.............................................................................. 47

3.5.3.1 Background NO2 Concentrations ......................................................... 50

3.5.3.2 Site-Specific Rayleigh Scattering......................................................... 50

3.5.3.3 Background Sea Salt Aerosol Concentrations...................................... 50

3.5.4 QUALITY ASSURANCE OF POSTPROCESSING ANALYSES............................... 55

APPENDIX A VISTAS BART Modeling Protocol (Revision 3.2, August 31, 2006) 

APPENDIX B CPM Factor Analysis

Appendix H.6 - 1191
Santee Cooper – Grainger, Jefferies, Winyah 1 Trinity Consultants 

BART Applicability Modeling Protocol 

1. INTRODUCTION

Santee Cooper operates several electrical generating stations in South Carolina, including the 

Grainger, Jefferies, and Winyah Generating Stations. Each of the three facilities operates under a 

Title V Air Quality Permit issued by the South Carolina Department of Health and Environmental 

Control (DHEC) and consists of coal-fired utility boilers and supporting operations (e.g., coal storage 

and handling operations). 

South Carolina DHEC considers the Santee Cooper Grainger, Jefferies, and Winyah Generating 

Stations eligible to be regulated under the Best Available Retrofit Technology (BART) provisions of 

the Regional Haze Rule. Air quality modeling will be used to determine whether the emissions from 

each facility’s BART-eligible units cause or contribute to visibility impairment at any federally 

protected Class I area, and hence whether each facility is subject to BART and a BART determination 

is necessary. This modeling protocol is presented to describe the procedures, analytical techniques, 

and data resources Santee Cooper proposes to use to make the applicability determinations for each of 

the facilities. Note that the BART applicability analyses will be conducted separately for each facility 

but a single protocol is being submitted due to the similarities in the sources and the methodologies 

proposed for conducting each demonstration. 

Santee Cooper’s evaluation of BART-eligibility and the proposed modeling methods to determine 

applicability of BART as described in this protocol are consistent with the following guidance 

documents: 

▲ U.S. EPA, “Regional Haze Regulations and Guideline for Best Available Retrofit 

Technology (BART) Determinations,” Federal Register Volume 70, Number 128, 

July 6, 2005. 

▲ U.S. EPA, Guidance for Tracking Progress under the Regional Haze Rule

(EPA-454/B-03-004), September 2003. 

▲ U.S. EPA, Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule (EPA-454/B-03-005), October 2003. 

▲ U.S. EPA, Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report (EPA-454/R-98-019), December 1998. 

▲ U.S. EPA, Guideline on Air Quality Models, 40 CFR Part 51, Appendix W (Revised, 

November 9, 2005). 

▲ VISTAS, Protocol for the Application of the CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART), Revision 3.2, August 31, 2006. 

▲ VISTAS and U.S. EPA, “Q&A for Source by Source BART Rule,” August 3, 2006. 

▲ D.C. Circuit Court Ruling No. 05-1353, Utility Air Regulatory Group v. U.S. EPA, 

December 12, 2006. 

▲ South Carolina DHEC communication with Santee Cooper, various dates. 

The VISTAS BART Modeling Protocol, as revised on August 31, 2006, is incorporated by reference 

for Santee Cooper’s source-specific modeling protocol, and is presented in Appendix A. The VISTAS 

Appendix H.6 - 1192
Santee Cooper – Grainger, Jefferies, Winyah 2 Trinity Consultants 

BART Applicability Modeling Protocol 

BART Modeling Protocol and related information (generally made available at the VISTAS BART 

website1

) establish common technical approaches for quantifying emissions from BART-eligible 

emission units and conducting screening and refined modeling analyses using the CALPUFF 

modeling system and common data resources. Santee Cooper’s proposed analyses will generally 

adhere to the recommendations made in the VISTAS BART Modeling Protocol as described in this 

protocol and adapted per South Carolina DHEC guidance to the source-specific analysis of visibility 

impacts attributable to the Grainger, Jefferies, and Winyah Generating Stations. 

Santee Cooper submits this revised BART applicability modeling protocol to South Carolina DHEC 

to provide the continued opportunity for review and comment in conjunction with review by the 

U.S. EPA and Federal Land Managers (FLM) responsible for oversight of the federally-protected 

Class I areas potentially affected by Santee Cooper’s BART-eligible facilities. Santee Cooper 

anticipates additional communication with South Carolina DHEC throughout the BART 

implementation process to ensure that analyses are conducted using mutually agreeable data resources 

and processing options and are submitted in accordance with a mutually agreeable schedule. 

To facilitate South Carolina DHEC’s review of this revised BART Applicability Modeling Protocol, 

the following log of revisions summarizes changes made to address DHEC’s initial comments and 

provide additional information. 

▲ The description of Santee Cooper’s BART-eligible emission units in Section 2.2 has been 

revised to include potential emissions of all visibility-impairing pollutants. A new 

Table 2-1 was added to summarize the BART eligibility of emission units at each of the 

facilities. Additional detail was added to Table 2-2 (formerly Table 2-1). Subsequent 

Section 2 table numbers were also renumbered. 

▲ The total particulate matter (PM) emissions speciations shown in the various Section 2 

tables and text were revised based on the usage of custom condensable PM (CPM) 

emission factors. 

▲ The discussion of the use of the new IMPROVE algorithm has been expanded to include 

a more robust description and justification. 

▲ The discussion of sea-salt estimation at Cape Romain has been enhanced to include more 

data, description and justification. 

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS

The Regional Haze Rule requires that major sources of visibility-affecting pollutants belonging to one 

or more of 26 specific industrial source categories evaluate BART if the source was “in existence” 

(i.e., built or reconstructed) before August 7, 1977 and began operation after August 7, 1962. Such 

sources are termed “BART-eligible sources.” Major sources of visibility-affecting pollutants have the 

potential to emit 250 tons per year (tpy) of one or more of oxides of nitrogen (NOX), sulfur dioxide 

(SO2), and particulate matter less than 10 micrometers in aerodynamic diameter (PM10). Fossil-fueled 

electric generating units (EGUs) are one of the listed source categories, and fall under SIC code 4911 

– Electric Services. 

 

1

 http://vistas-sesarm.org/BART/index.asp 

Appendix H.6 - 1193
Santee Cooper – Grainger, Jefferies, Winyah 3 Trinity Consultants 

BART Applicability Modeling Protocol 

Hereafter, the “BART-eligible source” is taken to mean each collection of emission units at each of 

Santee Cooper’s three plants in existence during the relevant time period. The BART-eligible source 

may include multiple emission units but need not include the entire facility. 

1.1.1 DETERMINATION OF BART-ELIGIBILITY

The U.S. EPA BART guidelines define the following three steps for determining which 

sources at a facility are BART-eligible: 

1. Identify the emission units in the BART source categories. 

2. Identify the start-up dates of those units. 

3. Compare potential emissions to the 250 tpy cutoff. 

It is important to note that the evaluation of BART at Santee Cooper’s facilities is different 

than BART-eligible sources at other facilities on the list of 26. The Santee Cooper BARTeligible units are EGUs that, in addition to being subject to the BART Rule, are subject to 

the Clean Air Interstate Rule (CAIR), which targets emission reductions for both SO2 and 

NOX (both of which are precursors to regional particulate matter concentrations). 

U.S. EPA conducted technical analyses as part of the CAIR rule development and 

concluded that for EGUs that are subject to both CAIR and the BART Rule, the 

requirements of CAIR will satisfy BART. The D.C. Circuit court recently ruled that this 

approach is acceptable.2

 South Carolina DHEC has indicated to Santee Cooper that it 

intends to adopt U.S. EPA’s position that CAIR satisfies BART for NOX and SO2

emissions; thus South Carolina DHEC has stated that the evaluation of BART for EGUs 

should be restricted to PM10 emissions only. Therefore, this BART-eligibility analysis 

only includes evaluations in terms of direct PM10 emissions from Santee Cooper’s BARTeligible EGU sources, and excludes all direct emissions from, and secondary reactions 

with, SO2 and NOX emissions. 

Santee Cooper and South Carolina DHEC have developed an inventory of six emission 

units that have been initially determined to meet the preceding criteria and are BARTeligible since they are fossil-fueled EGUs, were in existence on August 7, 1977, and began 

operation after August 7, 1962. Each source also has potential emissions of greater than 

250 tpy of total PM10. Thus, each of the BART-eligible emission units will be analyzed to 

evaluate whether any of the three facilities is exempt from BART. Specific information 

about these emission units is provided in Section 2 of this BART Applicability Modeling 

Protocol. 

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND

BART APPLICABILITY

In its role as technical analysis coordinator, VISTAS developed a common modeling 

protocol and data resources for use by state regulatory agencies and BART-eligible 

 

2

 D.C. Circuit Court Ruling No. 05-1353, Utility Air Regulatory Group v. U.S. EPA, December 12, 2006. 

Appendix H.6 - 1194
Santee Cooper – Grainger, Jefferies, Winyah 4 Trinity Consultants 

BART Applicability Modeling Protocol 

sources. The final VISTAS BART Modeling Protocol was issued on December 22, 2005, 

was revised most recently on August 31, 2006, and prescribes modeling techniques and 

data resources to conduct screening and refined analyses to assess whether a BARTeligible source is subject to BART. 

A BART-eligible source is determined to be subject to BART if the source causes or 

contributes to visibility impairment at a federally protected Class I area. Causation is 

defined as a single-source impact of 1.0 deciviews (dv) or more and contribution is defined 

as a single-source impact of 0.5 dv or more (each evaluated on a 24-hour average basis). 

The deciview is a metric used to represent normalized light extinction attributable to 

visibility-affecting pollutants. To determine whether a BART-eligible facility causes or 

contributes to visibility impairment, U.S. EPA guidance requires the use of an air quality 

model, specifically recommending the CALPUFF modeling system, to quantify the 

impacts attributable to a single BART-eligible source. Because contribution to visibility 

impairment is sufficient cause to require a BART determination, 0.5 dv is the critical 

threshold for assessment of BART applicability. 

Regional haze is quantified using the light extinction coefficient (bext), which is expressed 

in terms of the haze index expressed in dv. The haze index (HI) is calculated as shown in 

the following equation. 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = 10

ln10 ext b

HI

The impact of a BART-eligible source is determined by comparing the HI attributable to a 

source relative to estimated natural background conditions. The background extinction 

coefficient bext, background is affected by various chemical species and the Rayleigh scattering 

phenomenon and can be calculated as shown in the following equation. 

( ) ext background SO NO OC Soil Coarse ap Ray b ++++++= bbbbbbbMm−

4 3

1

,

where, 

[ ] ( ) ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

( )

[ ] 3

1

3 34

4 2 4 4

Concentrat μginion m

Humidity Relative Function

Rayleigh defaultby Mm 10 Scattering

EC10

Mass Coarse6.0

Soil1

OC4

NONH3

SONH3

=

=

=

=

=

=

=

=

=

−

RHf

b

b

b

b

b

b RHf

b RHf

Ray

ap

Coarse

Soil

OC

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh air todue scattering is Scattering molecules

concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes Mass Coarse dusts coarse ofion 

concentrat thedenotes Soil soils fine ofion 

concentrat thedenotes OC carbon organic ofion 

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

 

Values for the parameters listed above specific to the natural background conditions at the 

Class I areas to be considered in this analysis are provided on an annual average basis in 

Appendix H.6 - 1195
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BART Applicability Modeling Protocol 

the U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule.

3

 More detailed information about the natural background conditions particular 

to Class I areas potentially affected by Santee Cooper’s operations at the Grainger, 

Jefferies, and Winyah Generating Stations are provided in Section 3.5 of this protocol. 

Particulate species that affect visibility are emitted from anthropogenic sources and include 

coarse particulate matter (PMC), fine particulate matter (PMF), and elemental carbon (EC), 

as well as precursors to secondary organic aerosols (SOA) and fine particulate matter such 

as SO2 and NOX. Santee Cooper’s calculation of speciated visibility-affecting pollutant 

emissions is presented in Section 2 of this modeling protocol. The extinction coefficient 

due to emissions of visibility-affecting pollutants from a single BART-eligible source 

bext,source is calculated using an air quality model. The extinction due to the BART-eligible 

source will be calculated as shown in the following equation. 

( ) sourceext SO NO PMC PMF SOA +++++= bbbbbbMmb EC

−

4 3

1

,

where, 

[( ) ] ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

[ ] 3

34 3

4 2 4 4

Concentrat μginion m

Humidity Relative Function

EC10

PMC6.0

PMF1

SOA4

NONH3

SONH3

=

=

=

=

=

=

=

=

RHf

b

b

b

b

b RHf

b RHf

EC

PMC

PMF

SOA

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ] concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes PMC PM coarse ofion 

concentrat thedenotes PMF PM fine ofion 

concentrat thedenotes SOA secondary ofion aerosols organic

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

1.1.3 POTENTIALLY AFFECTED CLASS I AREAS

The VISTAS modeling protocol specifies that all Class I areas within 300 km of a BARTeligible source must be initially evaluated to determine whether the source contributes to 

visibility impairment. Table 1-1 summarizes the distances separating Santee Cooper’s 

three facilities from all Class I areas within the VISTAS region and adjacent states. 

Consistent with the VISTAS BART Modeling Protocol, only those Class I areas within 

300 km are considered further in the BART applicability modeling analysis. 

 

3

 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, Table 2-1, 

Attachment A, September 2003, EPA-454/B-03-005. 

Appendix H.6 - 1196
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BART Applicability Modeling Protocol 

TABLE 1-1. DISTANCES (KILOMETERS) SEPARATING VISTAS CLASS I AREAS AND

SANTEE COOPER’S JEFFERIES, GRAINGER, AND WINYAH GENERATING STATIONS

Class I Area Grainger Jefferies Winyah

Cape Romain (SC) 85 45 25

Wolf Island (GA) 345 242 284

Swanquarter (NC) 294 402 351

Linville Gorge (NC) 340 334 360

Okefenokee (GA) 430 324 371

Shining Rock (NC) 382 349 387

Great Smoky Mountains (NC/TN) 416 385 423

James River Face (VA) 416 481 469

Joyce Kilmer/Slickrock (NC) 476 431 475

Cohutta (GA) 514 456 505

Shenandoah (VA) 468 542 524

St. Marks (FL) 621 513 564

Chassahowitzka (FL) 660 560 599

Otter Creek (WV) 569 632 622

Dolly Sods (WV) 570 636 624

Mammoth Cave (KY) 730 699 738

Sipsey (AL) 765 690 745

Everglades (FL) 915 837 857

Figure 1-1 illustrates the location of the three Santee Cooper facilities to be evaluated and 

their location relative to Class I areas in the VISTAS region that are located within 300 km 

of the Santee Cooper facilities. 

The locations of Class I areas and receptor locations to be evaluated in the modeling 

analysis were determined by, and will be obtained from, the U.S. Fish and Wildlife 

Service, which is the FLM for the Cape Romain, Wolf Island, and Swanquarter National 

Wildlife Refuges.4

 

 

4

 National Park Service compilation of Class I area receptors, http://www2.nature.nps.gov/air/maps/receptors/. 

Appendix H.6 - 1197
Santee Cooper – Grainger, Jefferies, Winyah 7 Trinity Consultants 

BART Applicability Modeling Protocol 

FIGURE 1-1. LOCATION OF SANTEE COOPER’S BART-ELIGIBLE FACILITIES RELATIVE TO 

VISTAS CLASS I AREAS

1,300 1,400 1,500 1,600 1,700 1,800

Lambert Conformal Coordinate East (km)

-800

-700

-600

-500

-400

-300

-200

Lambert Conformal Coordinate North (

km)

Grainger

Jefferies

Winyah

Wolf Island

Cape

Romain

Swanquarter

1.1.4 CALPUFF MODELING ANALYSES

Santee Cooper proposes to utilize screening and refined modeling techniques as described 

in the VISTAS BART Modeling Protocol to determine whether BART-eligible operations at 

the Grainger, Jefferies, or Winyah Generating Stations contribute to visibility impairment 

at any Class I areas. The CALPUFF modeling system will be used to compute the 24-hour 

average visibility impairment attributable to each Santee Cooper facility to assess whether 

the 0.5 dv contribution threshold is exceeded, and if so, the frequency, duration, and 

magnitude of any exceedance events. CALPUFF is a refined air quality modeling system 

that is capable of simulating the dispersion, chemical transformation, and long-range 

transport of multiple visibility-affecting pollutant emissions from a single source and is 

therefore preferred for BART applicability and determination analyses. 

Appendix H.6 - 1198
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BART Applicability Modeling Protocol 

The U.S. EPA BART guidelines prescribe that the 98th percentile, 24-hour average, 

visibility impact computed in a modeling analysis that evaluates three years of 

meteorological data should be compared to the contribution threshold. However, VISTAS 

prescribes that the maximum computed visibility impact be used as the basis for 

comparison in the screening analysis and the 98th percentile impact in the refined analysis. 

To assess whether BART-eligible operations contribute to visibility impairment, the 

applicability modeling analyses will quantify the top eight 24-hour average visibility 

impacts of each year modeled to illustrate the distribution (i.e., frequency, duration, and 

magnitude) of peak visibility impairment episodes attributable to each of the facilities. 

It is worth noting that Cape Romain is relatively close (i.e., less than 50 km) to the Jeffries 

and Winyah Generating Stations, but that the same CALPUFF modeling analysis 

techniques have been determined to be suitable for regional haze modeling at Class I areas 

near and far. Specifically, Section 3.2.1 of the VISTAS BART Modeling Protocol

recommends the use of CALPUFF for BART applicability assessment at relatively short 

distances. 

The CALPUFF modeling system is described in technical detail in the VISTAS BART 

Modeling Protocol as revised and its use in screening and refined analyses for BART 

applicability assessment of Santee Cooper’s Grainger, Jefferies, and Winyah Generating 

Stations is described in Section 3 of this document. 

1.2 ORGANIZATION OF BART APPLICABILITY ANALYSIS MODELING 

PROTOCOL

The remainder of this BART applicability modeling protocol is organized as follows. 

▲ Section 2 describes the BART-eligible emission units at the three Santee Cooper facilities 

and the emission rates to be modeled in the BART applicability analyses. 

▲ Section 3 describes the procedural and technical guidance for conducting Class I area 

analyses using the CALPUFF modeling system, including the data resources, technical 

modeling options, and quality assurance methods used in the CALMET, CALPUFF, and 

CALPOST analyses. 

▲ Appendix A contains a copy of the VISTAS BART Modeling Protocol (Revision 3.2). 

▲ Appendix B contains tables presenting the CPM factor analysis. 

Appendix H.6 - 1199
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BART Applicability Modeling Protocol 

2. BART-ELIGIBLE SOURCE DESCRIPTION

This section of the BART Applicability Analysis modeling protocol describes the emission units that 

comprise the BART-eligible source at Santee Cooper’s Grainger, Jefferies, and Winyah Generating 

Stations. Emissions and exhaust characteristics of each source are quantified to demonstrate how 

each source will be represented in the modeling analysis. 

2.1 BART-ELIGIBLE EMISSION UNITS

Santee Cooper and South Carolina DHEC reviewed the criteria for BART-eligibility and determined 

that two EGUs at each of the three generating stations are BART-eligible sources. These units are 

pulverized coal-fired, dry-bottom boilers.5

 Each of the six BART-eligible boilers employs an 

electrostatic precipitator (ESP) to control particulate emissions, and the two BART-eligible boilers at 

the Winyah Generating Station also utilize flue gas desulfurization (FGD) systems to reduce sulfur 

emissions. Table 2-1 provides a summary of all boilers and ancillary units at each of the three sites. 

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS AND POTENTIAL EMISSIONS

 

Facility Emission Unit 

Source 

Code 

BART 

Source 

Category* 

Date 

Built or 

Modified 

SO2

Emissions 

(tpy) 

NOX

Emissions 

(tpy) 

PM10

Emissions

(tpy) 

Grainger Boiler 1 001 1 1966 13,504 3,640 155 

 Boiler 2 002 1 1966 13,504 3,640 155 

 Coal Unloading System 003 1 1966 -- -- < 10 

Jefferies Boiler 1 (Oil-fired) 001 1 Before 1962 † † † 

 Boiler 2 (Oil-fired) 002 1 Before 1962 † † † 

 Boiler 3 003 1 1966 23,501 6,337 269 

 Boiler 4 004 1 1966 23,501 6,337 269 

 Coal Unloading System 005 1 1966 -- -- < 5 

 Coal Handling System 006 1 1966 -- -- < 5 

Winyah Boiler 1 001 1 1971 33,204 10,592 327 

 Boiler 2 002 1 1974 13,586 8,156 327 

 Boiler 3 003 1 After 1977 † † † 

 Boiler 4 004 1 After 1977 † † † 

 Coal Unloading System 005 1 1971 -- -- < 15 

 Coal Handling System #1 006 1 1974 -- -- < 15 

 Coal Handling System #2 007 1 After 1977 † † † 

 Synfuel Processing 009 1 After 1977 † † † 

 Limestone Handling 010 1 After 1977 † † † 

 Waste Handling 011 1 After 1977 † † † 

Grainger BART-Eligible Emission Units Total Potential Emissions (tpy) 27,008 7,280 320 

Jefferies BART-Eligible Emission Units Total Potential Emissions (tpy) 47,002 12,674 548 

Winyah BART-Eligible Emission Units Total Potential Emissions (tpy) 46,790 18,748 684 

* Category 1 denotes fossil fuel-fired steam electric plants of more than 250 MMBtu/hr heat input. 

† Emissions not listed since emission unit was constructed prior to or after the BART-eligibility period. 

 

5

 While the Winyah boilers are also permitted to combust fuel oil, they primarily combust bituminous coal. 

Appendix H.6 - 1200
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BART Applicability Modeling Protocol 

Table 2-1 shows the potential annual emissions from the BART-eligible units at each of the three 

facilities exceed 250 tpy of each of NOX, SO2, and total PM10 (condensable and filterable). Therefore, 

all three of the Santee Cooper facilities are BART-eligible since potential emissions exceed 250 tpy 

for at least one (in fact, all) of NOX, SO2, and PM10, which are considered visibility-affecting 

pollutants, at each of the three facilities. 

Supporting operations at the three facilities were also evaluated to determine BART-eligibility and 

are shown in Table 2-1. All individual supporting operations, which are grouped into coal handling 

and coal unloading systems, at the Grainger, Jefferies, and Winyah facilities that came into 

“existence” during the time period covered by BART have potential PM10 emissions of less than 

5 tpy. Santee Cooper has assumed that individual sources with emission rates below the Title V 

insignificant emission unit thresholds (e.g., less than 5 tpy PM10) can be excluded from further 

evaluation since these units are generally fugitive in nature and would not be expected to impact 

visibility at the Class I areas to be evaluated in the BART Applicability Analysis. A list of the 

individual units and their collective source codes are included in Table 2-2. 

In addition to SO2, NOX, and PM10, there are also two other visibility affecting pollutants that should 

be mentioned: volatile organic compounds (VOC) and ammonia. VISTAS conducted regional VOC 

modeling analyses that show cumulative VOC emissions from VISTAS sources do not contribute to 

visibility impairment within the VISTAS region: 

...the VISTAS States have ...determined that anthropogenic VOC emissions [from 

BART sources] do not cause or contribute to visibility impairment at VISTAS Class I 

areas and that VOC emissions do not need to be considered in BART analyses.6

Since VOC emissions from all VISTAS sources were determined not to cause or contribute to 

visibility impairment, primary VOC emissions from otherwise BART-eligible sources at the Santee 

Cooper facilities will not be included in the BART applicability modeling analyses. 

VISTAS also conducted preliminary regional ammonia modeling analyses to determine whether 

ammonia emissions should be considered visibility-affecting for the purposes of source-specific 

BART applicability analyses. Section 4.1.3 of the VISTAS BART Modeling Protocol indicates that 

there are 13 facilities whose ammonia emissions contribute to visibility impacts, and other than these 

13 facilities, ammonia emissions from VISTAS sources do not contribute to visibility impairment 

within the VISTAS region: 

Based on these analyses, the VISTAS states recommend that, except for these 13 facilities, 

ammonia emissions not be included in the BART modeling. 

Since the Santee Cooper facilities are not included in this list of 13 sources, primary ammonia 

emissions from otherwise BART-eligible sources at the three Santee Cooper facilities will not be 

included in the BART applicability modeling analyses. 

 

6 VISTAS BART Modeling Protocol, revised on August 31, 2006, page 34. 

Appendix H.6 - 1201
Santee Cooper – Grainger, Jefferies, Winyah 11 Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE 2-2. TITLE V INSIGNIFICANT EMISSION UNITS EXCLUDED FROM BART ANALYSIS 

 

Facility 

Emission 

Unit Group 

Equipment 

ID(s) Individual Insignificant Activities* 

Potential 

Emissions† 

(tpy) 

Grainger Coal P01 Coal Rail Car Shaker < 0.1 

Unloading P02 Coal Rail Car Shaker Hoppers < 0.1 

System P03, P04, P06 Coal Unloading Conveyors D, E, & F < 0.1, each 

P05 Coal Crusher < 2 

P12 Coal Bunker < 0.1 

F01 Coal Pile Management (Bulldozer) < 2 

F02 Coal Pile Wind Erosion < 4 

Jefferies Coal V01 Coal Car Unloading < 0.2 

Unloading C09, C10 Coal Unloading Conveyors E & F < 0.2, each 

System P03 Coal Stock Out Chute < 0.2 

F02 Coal Pile Management (Bulldozer) < 1 

F01 Coal Pile Wind Erosion < 2 

Jefferies Coal P04 Coal Handling Reclaim Hoppers < 0.2 

Handling C01 – C08 Coal Handling Conveyors 3D, 4D, 3C, 4C, 3B, 4B, 3A, 4A < 0.1, each 

System P05 Coal Crushers A, B, & C < 4 

X01 Coal Transfer Building < 0.2 

X02 Coal Transfer Tower < 0.2 

P06 Coal Bunkers < 0.2 

Winyah Coal P01 Coal Rail Car Shaker < 1 

Unloading P02 Coal Rail Car Shaker Hoppers < 1 

System P03 Coal Rail Car Belt A < 1 

 P04 – P07 Coal Conveyors B-1, B-2, B-3, & B-4 < 1, each 

X01 Coal Transfer Tower 1 < 1 

F08 Coal Pile Management (Bulldozer) < 3 

F01 Coal Pile Wind Erosion < 5 

Winyah Coal P10, P11, P20 Coal Crusher Feed Conveyors C-1, C-2, & C-3 < 0.2, each 

 Handling P12/P13, P21 Coal Crushers (2) < 3, each 

System #1 P14, P15, P22 Coal Transfer Tower Feed Conveyors D-1, D-2, & D-3 < 0.2, each 

 X02, X03 Coal Transfer Towers 2 & 3 < 0.2, each 

 P16, P23 Coal Silo Feed Conveyors E-1 & E-3 < 0.2, each 

 P17, P24 Coal Silos (2) < 0.2, each 

 P18, P25 Coal Pulverizers (2) < 4, each 

P19 Coal Cross Conveyor E-3 < 0.2 

* Emission units listed are presumed to be constructed during the BART applicability period of August 7, 1962 – August 7, 1977. 

† Potential emissions are based on application of a safety factor to actual emissions. Values are rounded. 

Appendix H.6 - 1202
Santee Cooper – Grainger, Jefferies, Winyah 12 Trinity Consultants 

BART Applicability Modeling Protocol 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY

Whereas the BART eligibility determination relies on potential emissions of visibility-affecting 

pollutants, the BART applicability analysis utilizes maximum actual 24-hour average emission rates 

of NOX, SO2, and PM10. The VISTAS BART Modeling Protocol specifies the following hierarchy of 

resources to establish the maximum actual 24-hour average emission rate over the prior three-to-five 

year period for BART applicability modeling: 

▲ 24-hour maximum emissions observed using a Continuous Emission Monitor (CEM) for 

the period 2001 through 2003 

▲ 24-hour maximum emissions observed using a CEM for any representative period 

▲ Facility stack test emissions 

▲ Potential to emit 

▲ Permit allowable emissions 

▲ Emissions factors from U.S. EPA AP-42 source profiles 

As previously mentioned, PM10 emissions are the only emissions that will be included in the BART 

applicability modeling for the boilers since they are EGUs subject to CAIR. Santee Cooper is 

proposing to utilize the 24-hour controlled potential PM10 emissions in the BART applicability 

modeling. If the use of 24-hour controlled potential PM10 emissions results in overly conservative 

visibility impacts, Santee Cooper reserves the right to later model the maximum actual 24-hour 

average emissions. Table 2-3 summarizes the 24-hour controlled potential PM10 emission rates from 

each BART-eligible emission unit that will be used in the analyses. 

TABLE 2-3. SUMMARY OF 24-HOUR AVERAGE MAXIMUM POTENTIAL BART EMISSION RATES 

 

Facility Emission Unit 

NOX

Emissions* 

(lb/hr) 

SO2

Emissions* 

(lb/hr) 

Total PM10

Emissions† 

(lb/hr) 

Total PM2.5

Emissions† 

(lb/hr) 

Grainger Boiler 1 -- -- 35.29 25.26 

 Boiler 2 -- -- 35.29 25.26 

Jefferies Boiler 3 -- -- 61.47 44.00 

 Boiler 4 -- -- 61.47 44.00 

Winyah Boiler 1 -- -- 74.75 44.42 

 Boiler 2 -- -- 74.75 44.42 

* NOX and SO2 emissions for EGUs are not required to be included in the BART analysis. 

† Emissions include both filterable and condensable particulate matter. 

Table 2-3 includes 24-hour maximum emission rates and distinguishes the emission rates of Total 

PM10 (TPM10), which includes emissions of Total PM less than 2.5 microns in aerodynamic diameter 

(TPM2.5) and all filterable and condensable components. Modeling of visibility impairment requires 

that the components of the exhaust stream be speciated because different types of particulate matter 

affect visibility to varying extents. Figure 2-1 depicts the speciation of visibility-affecting pollutant 

Appendix H.6 - 1203
Santee Cooper – Grainger, Jefferies, Winyah 13 Trinity Consultants 

BART Applicability Modeling Protocol 

emissions as represented in the VISTAS BART Modeling Protocol. Using the default VISTAS 

speciation nomenclature as a starting point, Santee Cooper adapted the speciation presented in the 

following sections. 

Note that the organic fraction of condensable particulate matter (CPM) is represented as primary 

organic condensable (POC) emissions, which are direct emissions but are sometimes referred to as 

secondary organic aerosols (SOA) by convention. Also, note that primary emissions of inorganic 

CPM may contain hygroscopic sulfates (SO4) and nitrates (NO3), as well as non-hygroscopic (PIC) 

particles. Since hygroscopic species will have different extinction coefficients than non-hygroscopic 

species, it is important to distinguish inorganic CPM. The distinction of nitrate emissions is 

important since primary nitrate emissions will be affected by the partitioning of nitrate and nitric acid 

in the presence of ambient ammonia. In the preliminary analyses, all condensable inorganic 

emissions will be conservatively represented as sulfate emissions. Further analyses, if necessary, may 

distinguish between primary nitrate and other primary condensable inorganic emissions. 

Table 2-4 provides a list and description of the particulate species used in the remaining discussion 

about particulate speciation. 

FIGURE 2-1. PARTICULATE MATTER SPECIATION

(AFTER FIGURE 4-3 OF THE VISTAS BART MODELING PROTOCOL) 

Appendix H.6 - 1204
Santee Cooper – Grainger, Jefferies, Winyah 14 Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE 2-4. NOMENCLATURE FOR EMISSIONS SPECIATION ANALYSIS

 

Nomenclature Description 

 

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μm 

PM10 Filterable particulate matter with an aerodynamic diameter < 10 μm 

PM6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μm 

PM2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μm 

PM2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μm 

PM1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μm 

PM1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μm 

PM0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

PM0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μm 

PMC Filterable Coarse particulate matter = PM6-10 + PM2.5-6

PMF Filterable Fine particulate matter = PM6-10 + PM2.5-6

CPM Condensable particulate matter (organic and inorganic) 

POC Primary organic condensable emissions 

PIC Primary non-hygroscopic inorganic condensable emissions 

TPM10 Filterable PM10 + CPM 

TPM2.5 Filterable PM2.5 + CPM 

SO4 Inorganic Sulfate CPM 

NO3 Inorganic Nitrate CPM 

EC Elemental Carbon 

The FLM have published speciation guidance for certain source types (e.g., gas-, oil-, and coal-fired 

combustion sources) which can be used to create speciation profiles. The speciation profiles based on 

FLM guidance include the following particulate species: PMC, PMF, EC, POC, and primary 

inorganic CPM. These profiles can be augmented based on test data, engineering knowledge, and 

available reference data (e.g., AP-42 analyses) for each applicable emission unit. Santee Cooper 

evaluated the basis of the AP-42 CPM emission factors and determined that these factors, which are 

utilized in the NPS speciation workbooks, are based on test data from a very limited number of 

boilers, including boilers firing different coal types than the coal utilized in the Santee Cooper boilers. 

Thus, Santee Cooper elected to collect additional, more representative data and develop more 

appropriate CPM emission factors for their EGUs rather than rely upon in the factors in the FLM 

guidance; Appendix B includes the AP-42 factor analysis and cumulative CPM factor evaluations 

based on the CPM factor data Santee Cooper collected.7

 Santee Cooper will also supplement the 

FLM guidance for speciating primary inorganic CPM emissions for the six boilers as described in the 

following paragraph. 

Primary inorganic CPM is typically assumed to consist of two species: hygroscopic sulfate and 

hygroscopic nitrate inorganic condensable particulate matter, and the H2SO4 emission rate is often 

subtracted from the estimate of the total primary inorganic CPM emission rate (given by the FLM’s 

spreadsheets) to determine the emission rate of hygroscopic nitrate inorganic condensable particulate 

 

7

 Santee Cooper has collected readily-available CPM data from other coal-fired EGUs, including an extensive 

inventory from North Carolina Department of Environment and Natural Resources (NCDENR), and evaluated the mean and 

median factors from the larger data sets. 

Appendix H.6 - 1205
Santee Cooper – Grainger, Jefferies, Winyah 15 Trinity Consultants 

BART Applicability Modeling Protocol 

matter (NO3). However, Santee Cooper will be utilizing the more conservative assumption that all 

primary inorganic CPM is hygroscopic sulfate (this assumption results in worst-case visibility 

impacts). A more refined analysis, if necessary, may distinguish non-sulfate inorganic CPM between 

primary nitrate (NO3) and/or other non-hygroscopic primary inorganic CPM (PIC) emissions (such as 

carbonates). 

The individual particulate species will be modeled in CALPUFF and then grouped into categories 

according to visibility-affecting characteristics using POSTUTIL. The extinction coefficients that 

will be used for each species category are shown in Table 2-5. 

A discussion of the PM speciation assumptions for each of the BART-eligible sources at the three 

Santee Cooper facilities is presented in the following sections of this protocol. 

TABLE 2-5. ASSIGNMENT OF EMITTED PM SPECIES TO MODELED PM CATEGORIES

 

Modeled PM 

Category† Components 

Output 

Category‡

Extinction 

Coefficient 

(m2

/g) 

PMC Filterable coarse particles (PM6-10, PM2.5-6) PMC 0.6 

PMF Filterable fine particles (PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625) SOIL 1 

PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 

SO4 Primary inorganic condensable emissions of sulfates* SO4 3f(RH) 

NO3 Primary inorganic condensable emissions of nitrates* NO3 3f(RH) 

POC Primary organic condensable emissions SOA 4 

EC Uncombusted carbonaceous fuel EC 10 

* In the preliminary analyses, all condensable inorganic emissions will be represented as sulfate emissions. Further analyses, if 

necessary, may distinguish between primary nitrate and other primary condensable inorganic emissions. 

† Modeled PM Category denotes the input of emissions data into CALPUFF. 

‡ Output Category denotes the assignment of modeled emissions in POSTUTIL for the visibility calculations in CALPOST. 

2.2.1 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP ONLY

The Grainger and Jefferies Generating Stations each operate two BART-eligible coal-fired 

boilers. Each of the boilers is controlled by an ESP and exhausts through its own stack. 

PM emissions from these boilers are due to the combustion of bituminous coal. Each 

boiler is subject to a 0.03 lb/MMBtu filterable TSP emission limit.8

 To determine the 

TPM10 potential emissions and to speciate the PM emissions from the coal-fired boilers, 

site-specific data and data from the default speciation profile compiled by VISTAS were 

used. Additionally, Santee Cooper proposes to use a fixed CPM factor of 0.02 lb/MMBtu, 

which is greater than both the mean (0.018 lb/MMBtu) and median (0.014 lb/MMBtu) 

CPM factors for EGUs with PM-only controls and well above the mean (0.009 lb/MMBtu) 

and median (0.006 lb/MMBtu) CPM factors for the North Carolina EGUs, which are most 

representative of the Santee Cooper EGUs. Refer to Table B-3 in Appendix B for details 

 

8

 Consent decree between EPA and Santee Cooper, June 24, 2004. 

www.epa.gov/Compliance/resources/decrees/civil/caa/santee-cd.pdf 

Appendix H.6 - 1206
Santee Cooper – Grainger, Jefferies, Winyah 16 Trinity Consultants 

BART Applicability Modeling Protocol 

of the CPM factor development. The percentage of elemental carbon in the PM emissions 

was also based on the default speciation profile compiled by VISTAS. Table 2-6 

summarizes the relevant data for the boilers at the Grainger and Jefferies Generating 

Stations. 

TABLE 2-6. COAL BOILERS WITH ESP SPECIATION DATA

 

Speciation Data Value Reference 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 9.10% 

Maximum Coal Sulfur Content 1.37% 

Maximum Coal Heat Content (Btu/lb) 12,618.7 

Grainger Generating Station Emission Limit and 

Annual Average Coal Properties* 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 9.66% 

Maximum Coal Sulfur Content 1.39% 

Maximum Coal Heat Content (Btu/lb) 12,571.2 

Jefferies Generating Station Emission Limit and 

Annual Average Coal Properties* 

CPM Factor (lb/MMBtu) 0.02 Santee Cooper Custom CPM Factor 

PM10 as a % of PM 67.0% 

EC as a % of Fine PM10 3.7% 

EC as a % of PM10‡ 1.6% 

Organic portion of CPM 20.0% 

Inorganic portion of CPM 80.0% 

PM6-10 as a % of PM10 25.4% 

PM2.5-6 as a % of PM10 31.3% 

PM1.25-2.5 as a % of PM10 17.9% 

PM1-1.25 as a % of PM10 4.5% 

PM0.625-1 as a % of PM10 3.0% 

PM0.5-0.625 as a % of PM10 17.9% 

VISTAS Default Factors, Coal-Fired Boiler 

with Dry Bottom ESP (controlled), based on 

AP-42 Section 1.1, Tables 1.1-5 and 1.1-6† 

 

* Annual average coal values are the maximum or minimum value for the period of 2001, 2003-2005, whichever values 

result in the higher factors. Although 2002 data was not available, the variation between the existing coal data, 

calculated as the standard deviation divided by the mean value, was less than 8% for all parameters. Note that each 

facility utilizes only one coal pile for the subject boilers. 

† Size values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % 

of TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. 

‡ Value calculated based on the percentage of EC composing PM2.5 multiplied by the percentage of PM10 that is PM2.5. 

The site-specific information presented in Table 2-6 was input into the FLM speciation 

profile workbook, which uses the coal data and site-specific filterable and condensable PM 

factors to determine the TPM10 emissions on a pound per hour basis.9

 The TPM10

emissions are then speciated in the various filterable and condensable PM components. 

Table 2-7 presents the TPM10 percentages and site-specific PM10, CPM, and TPM10

emissions for the Grainger and Jefferies boilers. 

 

9

 Workbook is available at the NPS website: www2.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm. 

Appendix H.6 - 1207
Santee Cooper – Grainger, Jefferies, Winyah 17 Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE 2-7. DATA USED TO CALCULATE TPM10 EMISSIONS (LB/HR) FOR EACH COAL BOILER 

WITH ESP (GRAINGER AND JEFFRIES) 

 

 

% of TPM10

that is PM10* 

% of TPM10

that is CPM* 

Boiler Heat 

Input 

(MMBtu/hr) 

PM10

Emissions 

(lb/hr)† 

TPM10

Emissions 

(lb/hr)‡ 

CPM 

Emissions 

(lb/hr)** 

Grainger 50.1 49.9 880 17.69 35.29 17.60 

Jefferies 50.1 49.9 1,533 30.81 61.47 30.66 

* Percentages calculated using the FLM speciation workbooks. 

† Calculated as: (Boiler heat input, MMBtu/hr) × (PM factor, lb/MMBtu) × (PM10 as a % of PM). 

‡ Calculated as: (PM10 emissions, lb/hr) / (% of TPM10 that is PM10). 

** Calculated as: (Boiler heat input, MMBtu/hr) × (CPM factor, lb/MMBtu). 

Next, POC emissions were determined by multiplying the calculated CPM emissions by 

the organic condensable percentage of 20%. POC was assumed to be evenly split between 

the two size categories, 0.625-1.0 μm and 0.5-0.625 μm. PIC emissions were calculated 

using a similar methodology, using the inorganic condensable percentage of 80%. 

The filterable PM10 emissions were then divided amongst six different size categories by 

multiplying filterable PM10 emissions by the percentage of PM10 for each size category 

(refer to Table 2-5), yielding hourly emissions of PM6-10, PM2.5-6, PM1.25-2.5, PM1-1.25, 

PM0.625-1, and PM0.5-0.625. All elemental carbon was assumed to be in the smallest size 

category per the FLM speciation workbook. 

Table 2-8 presents a summary of the speciated PM emissions for the BART eligible coalfired boilers at the Grainger and Jefferies Generating Stations while Figures 2-2 and 2-3 

present graphical representations of the speciation. Note that the values presented in the 

table and figures are for each boiler at the facility. 

TABLE 2-8. TPM10 SPECIATED EMISSIONS (LB/HR) FOR EACH COAL BOILER WITH ESP

(GRAINGER AND JEFFRIES) 

 

 POC 

(μm)

PIC (sulfate) 

(μm)

PMC 

(μm)

PMF 

(μm) EC

Facility 0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 0.5-0.625 

Grainger 1.76 1.76 7.04 7.04 4.49 5.54 3.17 0.79 0.53 2.89 0.28 

Jefferies 3.07 3.07 12.26 12.26 7.82 9.66 5.52 1.38 0.92 5.00 0.52 

Appendix H.6 - 1208
Santee Cooper – Grainger, Jefferies, Winyah 18 Trinity Consultants 

BART Applicability Modeling Protocol 

FIGURE 2-2. TPM10 SPECIATION (LB/HR) FOR EACH GRAINGER BOILER

3.52

14.08

-

4.49 5.54

3.17

0.79

0.53

2.89

0.28

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

FIGURE 2-3. TPM10 SPECIATION (LB/HR) FOR EACH JEFFERIES BOILER

6.13

24.53

-

7.82 9.66

5.52

1.38

0.92

5.00

0.52

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

2.2.2 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP AND FGD 

The Winyah Generating Station operates two BART-eligible coal-fired boilers. Each of 

the boilers is controlled by an ESP and a FGD. PM emissions from these boilers are due 

primarily to the combustion of bituminous coal (the boilers are also authorized to combust 

Appendix H.6 - 1209
Santee Cooper – Grainger, Jefferies, Winyah 19 Trinity Consultants 

BART Applicability Modeling Protocol 

fuel oil). Each boiler is subject to a 0.03 lb/MMBtu filterable TSP emission limit.10 To 

determine the TPM10 potential emissions and to speciate the PM emissions from the coalfired boilers, site-specific data and data from the default speciation profile compiled by 

VISTAS were used. Additionally, Santee Cooper proposes to use a custom CPM factor of 

0.008 lb/MMBtu, which is greater than both the mean (0.0077 lb/MMBtu) and median 

(0.0052 lb/MMBtu) CPM factors for EGUs with PM and SO2 controls; refer to Table B-4 

in Appendix B for details on the CPM factors. Note this custom factor is also similar to the 

mean (0.009 lb/MMBtu) and median (0.006 lb/MMBtu) CPM factors for the North 

Carolina EGUs, which only have PM controls and will have greater sulfate (PIC) 

emissions and thus, greater CPM emissions than the Winyah EGUs. The percentage of 

elemental carbon in the PM emissions was also based on the default speciation profile 

compiled by VISTAS. Table 2-9 summarizes the relevant data for the two boilers at the 

Winyah Generating Station. 

TABLE 2-9. COAL BOILERS WITH ESP & FGD SPECIATION DATA

 

Speciation Data Value Reference 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 8.78% 

Maximum Coal Heat Content (Btu/lb) 12,662.8 

Winyah Generating Station Boiler 1 Emission 

Limit and Annual Average Coal Properties* 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 8.90% 

Maximum Coal Heat Content (Btu/lb) 12,727.3 

Winyah Generating Station Boiler 2 Emission 

Limit and Annual Average Coal Properties* 

CPM Factor (lb/MMBtu) 0.008 Santee Cooper Custom CPM Factor 

PM10 as a % of PM 67.0% 

EC as a % of Fine PM10 3.7% 

EC as a % of PM10‡ 1.6% 

Organic portion of CPM 20.0% 

Inorganic portion of CPM 80.0% 

PM6-10 as a % of PM10 25.4% 

PM2.5-6 as a % of PM10 31.3% 

PM1.25-2.5 as a % of PM10 17.9% 

PM1-1.25 as a % of PM10 4.5% 

PM0.625-1 as a % of PM10 3.0% 

PM0.5-0.625 as a % of PM10 17.9% 

VISTAS Default Factors, Coal-Fired Boiler 

with Dry Bottom ESP (controlled), based on 

AP-42 Section 1.1, Tables 1.1-5 and 1.1-6† 

 

* Annual average coal values are the maximum or minimum value for the period of 2001, 2003-2005, whichever values 

result in the higher emission factors. Although 2002 data was not available, the variation between the existing coal 

data, calculated as the standard deviation divided by the mean value, was less than 8% for all parameters. Note that 

each boiler utilizes its own coal pile. 

† Size values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % 

of TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. 

‡ Value calculated based on the percentage of EC composing PM2.5 multiplied by the percentage of PM10 that is PM2.5. 

 

10 Consent decree between EPA and Santee Cooper, June 24, 2004. 

www.epa.gov/Compliance/resources/decrees/civil/caa/santee-cd.pdf 

Appendix H.6 - 1210
Santee Cooper – Grainger, Jefferies, Winyah 20 Trinity Consultants 

BART Applicability Modeling Protocol 

The site-specific information presented in Table 2-9 was input into the FLM speciation 

profile workbook, which uses the coal data and site-specific filterable and condensable PM 

factors to determine the TPM10 emissions on a pound per hour basis.11 The TPM10

emissions are then speciated in the various filterable and condensable PM components. 

Table 2-10 presents the TPM10 percentages and site-specific PM10, CPM, and TPM10

emissions for the Winyah boilers. 

TABLE 2-10. DATA USED TO CALCULATE TPM10 EMISSIONS (LB/HR) FOR EACH COAL BOILER 

WITH ESP (WINYAH) 

 

% of TPM10

that is PM10* 

% of TPM10

that is CPM* 

Boiler Heat 

Input 

(MMBtu/hr) 

PM10

Emissions 

(lb/hr)† 

TPM10

Emissions 

(lb/hr)‡ 

CPM 

Emissions 

(lb/hr)** 

Boiler 1 71.5 28.5 2,660 53.47 74.75 21.28 

Boiler 2 71.5 28.5 2,660 53.47 74.75 21.28 

* Percentages calculated using the FLM speciation workbooks. 

† Calculated as: (Boiler heat input, MMBtu/hr) × (PM factor, lb/MMBtu) × (PM10 as a % of PM). 

‡ Calculated as: (PM10 emissions, lb/hr) / (% of TPM10 that is PM10). 

** Calculated as: (Boiler heat input, MMBtu/hr) × (CPM factor, lb/MMBtu). 

The POC, PIC, and various filterable PM10 size grouping emissions for the Winyah boilers 

were determined using the same methodology as for the Grainger and Jefferies boilers. 

Table 2-11 presents a summary of the speciated PM emissions for the BART eligible coalfired boilers at the Winyah Generating Station while Figures 2-4 and 2-5 present graphical 

representations of the speciation. 

TABLE 2-11. TPM10 SPECIATED EMISSIONS (LB/HR) FOR WINYAH COAL BOILERS WITH ESP & FGD 

 

 POC 

 (μm)

PIC (sulfate) 

 (μm)

PMC 

(μm)

PMF 

(μm) EC

Winyah 0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 0.5-0.625 

Boiler 1 2.13 2.13 8.51 8.51 13.57 16.76 9.58 2.39 1.60 8.76 0.82 

Boiler 2 2.13 2.13 8.51 8.51 13.57 16.76 9.58 2.39 1.60 8.75 0.83 

 

11 Workbook is available at the NPS website: www2.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm. 

Appendix H.6 - 1211
Santee Cooper – Grainger, Jefferies, Winyah 21 Trinity Consultants 

BART Applicability Modeling Protocol 

FIGURE 2-4. TPM10 SPECIATION (LB/HR) FOR WINYAH BOILER 1 

4.26

17.02 -

13.57

16.76

9.58 2.39

1.60

8.76

0.8

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

FIGURE 2-5. TPM10 SPECIATION (LB/HR) FOR WINYAH BOILER 2 

4.26

17.02 -

13.57

16.76

9.58 2.39

1.60

8.75

0.8

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

2.3 MODELED STACK PARAMETERS AND EMISSIONS

Actual stack parameters will be input to the CALPUFF model to represent the point of visibilityaffecting pollutant emissions. The location of each point source will be represented consistently in 

the Lambert Conformal Coordinate (LCC) system used for the screening and refined meteorological 

data analyses prepared by VISTAS. Each boiler exhaust discharges vertically without obstruction. 

Appendix H.6 - 1212
Santee Cooper – Grainger, Jefferies, Winyah 22 Trinity Consultants 

BART Applicability Modeling Protocol 

Per VISTAS guidance, the effects of building downwash will not be considered in the BART 

Applicability Analyses.12 

Table 2-12 summarizes the stack parameters and Table 2-13 summarizes the modeled emission rates 

of TPM10 species for BART-eligible emission units at each of Santee Cooper’s three BART-eligible 

facilities. 

 

12 VISTAS BART Modeling Protocol, revised on August 31, 2006, page 45 states: “Ignore building downwash 

unless source is within 50-km of a Class I area and the State instructs the source to specifically consider building 

downwash.” South Carolina DHEC has commented in other BART modeling protocols (e.g., MeadWestvaco’s North 

Charleston Mill BART modeling protocol) that downwash does not need to be considered for any sources, even those less 

than 50 km from a Class I area. 

Appendix H.6 - 1213
Santee Cooper – Grainger, Jefferies, Winyah 23 

BART Applicability Modeling Protocol 

TABLE 2-12. STACK PARAMETERS FOR BART-ELIGIBLE EMISSION UNITS

Facility 

Emission 

Unit Stack ID 

LCC East 

(km) 

LCC North

(km) 

Base 

Elevation

(feet) 

Stack 

Height 

(feet) 

Stack 

Diameter

(feet) 

Exhaust 

Temperature

(°F) 

Grainger Boiler 1 GS01 1,644.618 -520.823 40 300 10.0 309  Boiler 2 GS02 1,644.643 -520.818 40 300 10.0 315 Jefferies Boiler 3 JS03 1,572.042 -600.906 56 300 13.0 325  Boiler 4 JS04 1,572.073 -600.932 56 300 13.0 325 Winyah Boiler 1 WS01 1,627.285 -580.090 18 404 20.0 125  Boiler 2 WS02 1,627.285 -580.090 18 404 20.0 125 

TABLE 2-13. MODELED EMISSION RATES FOR BART-ELIGIBLE EMISSION UNITS

 Emission Stack PMC (lb/hr) PMF (lb/hr) PIC (as sulfate) (lb/hr) Facility Unit ID 6-10 μm 2.5-6 μm 1.25-2.5 μm 1.0-1.25 μm 0.625-1.0 μm 0.5-0.625 μm 0.625-1.0 μm 0.5-0.625 μm 0.6Grainger Boiler 1 GS01 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04  Boiler 2 GS02 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04 Jefferies Boiler 3 JS03 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26  Boiler 4 JS04 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26 Winyah Boiler 1 WS01 13.57 16.76 9.58 2.39 1.60 8.76 8.51 8.51  Boiler 2 WS02 13.57 16.76 9.58 2.39 1.60 8.75 8.51 8.51 
Santee Cooper – Grainger, Jefferies, Winyah 24 Trinity Consultants 

BART Applicability Modeling Protocol 

As indicated in Table 2-12, stack heights of all six of the boilers are greater than the nominal default 

Good Engineering Practice (GEP) stack height of 65 meters (213.25 feet). South Carolina DHEC Air 

Pollution Control Regulation 61-62.7 “Good Engineering Practice Stack Height” applies to “all stacks 

excluding flares which were not ‘in existence’ before December 31, 1970” and to “stack heights or 

dispersion techniques at sources which were reconstructed or under major modification after 

December 31, 1970.” The GEP stack height regulation requires that modeling of stack heights not 

exceed the greater of 65 meters or Hg = H + 1.5L, where Hg = good engineering practice stack height, 

measured from the ground-level elevation at the base of the stack, H = height of nearby structure(s) 

measured from the ground-level elevation at the base of the stack, and L = lesser dimension, height or 

projected width of nearby structure(s). 

Each of the boilers at the Grainger and Jefferies Generating Stations became “in existence” during 

1966 and 1967, respectively, before 1970. Therefore, the GEP stack height regulation does not apply 

to these four boilers. The GEP stack height regulation does apply to the two Winyah boilers, which 

became “in existence” in 1971 and 1974. Therefore, Santee Cooper has evaluated GEP stack height 

for each of the Winyah boiler stacks using the U.S. EPA’s Building Profile Input Program (BPIP), 

which computes the GEP stack height based on the dimensions of nearby building structures. Using 

the associated boiler houses, which each have a height of 66.14 meters and a projected width of equal 

to or greater than the height, the GEP for each of the Winyah boilers is: 

Hg = H + 1.5L = 66.14 m + 1.5(66.14 m) = 165.35 m 

This calculation demonstrates that the Winyah boilers can be modeled at actual stack height 

(123.14 m) since creditable GEP stack height is not exceeded. 

Appendix H.6 - 1215
Santee Cooper – Grainger, Jefferies, Winyah 25 Trinity Consultants 

BART Applicability Modeling Protocol 

3. AIR QUALITY MODELING ANALYSES

Section 3 of this BART applicability modeling protocol for Santee Cooper’s Grainger, Jefferies, and 

Winyah Generating Stations describes the modeling methods, data resources, and technical options 

that will be used to conduct screening and refined analyses to assess visibility impacts. Air quality 

modeling will be conducted generally following the methods described in the VISTAS BART 

Modeling Protocol as revised. 

3.1 AIR QUALITY MODELING SYSTEM

CALPUFF is a multi-layer, multi-species, non-steady-state Lagrangian puff model, which can 

simulate the effects of time- and space-varying meteorological conditions on pollutant transport, 

transformation, and removal. The modeling system is designed to handle the complexities posed by 

the complex terrain, the large source-receptor distances, chemical transformation and deposition, and 

other issues related to Class I visibility impacts. A complete description of the model formulation and 

capabilities is provided in the User’s Guides for the CALPUFF modeling system and the VISTAS 

BART Modeling Protocol. 

The CALPUFF modeling system has been adopted by the U.S. EPA as a recommended regulatory 

guideline model for source-receptor distances greater than 50 km, and for use on a case-by-case basis 

in complex flow situations for shorter distances. CALPUFF is recommended for Class I area impact 

assessments by the Interagency Workgroup on Air Quality Models (IWAQM). The U.S. EPA’s 

BART guidance recommends CALPUFF as “the best modeling application available for predicting a 

singe source’s contribution to visibility impairment.” 

As a result of these recommendations, the VISTAS BART Modeling Protocol was based on the use of 

CALPUFF and will be used by Santee Cooper for the source-specific analysis of visibility impacts 

attributable to the three facilities. Specifically, CALMET Version 5.726/060801, CALPUFF 

Version 5.756/060725, POSTUTIL Version 1.52/060412, and CALPOST Version 5.6393/06020213

are designated as the “VISTAS Recommended Version for BART Modeling” by the VISTAS 

Technical Contractor and will be used in the CALPUFF BART applicability analyses for Santee 

Cooper’s Grainger, Jefferies, and Winyah Generating Stations.14

3.2 SCREENING AND REFINED ANALYSIS TECHNIQUES

The VISTAS BART Modeling Protocol distinguishes between screening and refined applications of 

the CALPUFF model for the purposes of BART Applicability Analyses. As the names of these 

techniques imply, screening analyses are intended to provide a conservative estimate of visibility 

impacts using computationally efficient techniques. Refined analyses utilize less conservative and 

 

13 A version that incorporates many of the changes to the original EPA version of the program was approved as the 

new regulatory version by the U.S. EPA on June 29, 2007. 

14 http://src.com/verio/download/download.htm#VISTAS_VERSION 

Appendix H.6 - 1216
Santee Cooper – Grainger, Jefferies, Winyah 26 Trinity Consultants 

BART Applicability Modeling Protocol 

more representative data and modeling methods to compute visibility impacts following U.S. EPA’s 

BART guidelines. Screening analyses may be used to determine that a facility is not subject to 

BART using a conservative assessment, or to focus the scope of refined analyses by demonstrating 

that visibility impairment is not likely to occur at more distant Class I areas. 

Although certain elements of the modeling analyses are identical regardless of which approach is 

used (e.g., representation of emissions sources, chemical transformation algorithms, Class I area 

receptors), the VISTAS BART Modeling Protocol makes several important distinctions between 

screening and refined analyses. 

3.2.1 VISIBILITY ASSESSMENT METRIC

The U.S. EPA BART guidelines prescribe that the 98th percentile, 24-hour average 

visibility impact computed in a modeling analysis that evaluates three years of 

meteorological data should be compared to the contribution threshold of 0.5 dv to assess 

BART applicability. However, VISTAS prescribes that the maximum computed visibility 

impact be used as the basis for comparison in the screening analysis to ensure that the 

modeled result is a conservative representation of visibility impairment. The VISTAS 

refined approach prescribes that the 98th percentile impact be evaluated in refined analyses 

as the eighth-highest 24-hour average visibility impact of each of three years modeled, or 

the 22nd-highest 24-hour average visibility impact over the three years modeled, whichever 

is more conservative. 

To assess whether BART-eligible operations contribute to visibility impairment, Santee 

Cooper’s BART applicability analyses will quantify the top eight 24-hour average 

visibility impacts of each year modeled to illustrate the distribution (i.e., frequency, 

duration, and magnitude) of peak visibility impairment episodes attributable to each 

facility. The maximum 24-hour impact will be used for interpretation of the screening 

analyses and the eighth-highest 24-hour average visibility impact of each of three years 

modeled will be used for interpretation of the refined analyses. Each Santee Cooper 

facility will be modeled and evaluated separately in comparison to the 0.5 dv contribution 

threshold to assess BART applicability. 

3.2.2 METEOROLOGICAL AND COMPUTATIONAL GRIDS

The VISTAS BART Modeling Protocol prescribes that screening analyses be conducted on 

a 12-km meteorological and computational grid and that refined analyses be conducted on 

a 4-km meteorological and computational grid. The resolution of the computational 

analysis dictates the extent to which geophysical (i.e., terrain and land use) and 

meteorological conditions are represented in the CALMET meteorological model, hence 

the advection, dispersion, and chemical transformation of visibility-affecting pollutants in 

the CALPUFF meteorological model. The VISTAS Technical Contractor conducted 

meteorological modeling using both screening 12-km and refined 4-km grids to provide 

common data resources to eligible sources conducting BART Applicability Analyses, 

which will be used by Santee Cooper to assess visibility impacts attributable to each 

facility. The following section of this document describes the CALMET meteorological 

modeling in greater detail. 

Appendix H.6 - 1217
Santee Cooper – Grainger, Jefferies, Winyah 27 Trinity Consultants 

BART Applicability Modeling Protocol 

3.2.3 MODEL PROCESSING AND POSTPROCESSING METHODS

The IWAQM Phase 2 Report prescribes recommended default model processing options to 

be used in CALPUFF analyses, which are in most cases considered regulatory default 

options under U.S. EPA’s Guideline on Air Quality Models. With few exceptions, 

IWAQM default model options as described in the following section of this document were 

prescribed for use in both screening and refined analyses. One exception in the application 

of the CALPUFF model is the use of puff-splitting algorithms, which are disabled by 

default and as the prescribed setting in screening analyses. As described by the VISTAS 

BART Modeling Protocol, puff splitting is typically enabled in refined analyses to simulate 

wind shear effects across a well-mixed individual puff by dividing the puff horizontally 

and vertically into two or more pieces. Differential rates of transport among the new puffs 

thus generated can increase the horizontal spread of the material in the plume due to 

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm 

is designed to allow large puffs that may grow to be several grid cells or more in size to 

split into smaller puffs that can then more accurately respond to variations in the local wind 

field across the original large puff. Puff splitting may be appropriate for transport 

distances over 200 to 300 km, or possibly over shorter distances in complex terrain. 

In the postprocessing of CALPUFF-computed concentrations of visibility-affecting 

pollutants, the POSTUTIL postprocessing utility can be used to apply the ammonia 

limiting method (ALM) by re-partitioning the distribution of HNO3 and NO3

concentrations at each Class I area as a function of the temperature and relative humidity 

during each hour. ALM processing is not run by default, and therefore will not be used in 

the screening analyses. ALM re-partitioning using the default ammonia background level 

of 0.5 ppb will be conducted in refined analyses if nitrate species are modeled. 

3.3 CALMET METEOROLOGICAL PROCESSING

CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields 

from mesoscale model (MM) output, raw observations of surface and upper air conditions, 

precipitation measurements, and geophysical parameters into a single hourly, gridded data set for 

input to CALPUFF. The CALMET User’s Guide and VISTAS BART Modeling Protocol provide a 

detailed description of the model’s formulation and capabilities. 

The federal Guideline for CALPUFF processing provides the following recommendations for the 

meteorological data period at Section 9.3.1.2: 

Less than five, but at least three, years of meteorological data (need not be 

consecutive) may be used if mesoscale meteorological fields are available, as 

discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used 

in conjunction with available standard [National Weather Service] NWS or 

comparable meteorological observations within and near the modeling domain. 

The VISTAS BART Modeling Protocol prescribes the years 2001 through 2003 for BART 

applicability analyses. The primary basis for this data analysis period was the availability of quality 

Appendix H.6 - 1218
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BART Applicability Modeling Protocol 

assured MM5 data for the entire VISTAS area. For its BART applicability analyses, Santee Cooper 

will utilize 12-km screening and 4-km CALMET data prepared by the VISTAS Technical Contractor 

using the following data resources and CALMET modeling techniques. 

3.3.1 CALMET METEOROLOGICAL DOMAINS

12-km screening and 4-km refined CALMET meteorological data sets were prepared for 

the domains depicted in Figure 3-1. Santee Cooper’s BART applicability analyses will 

utilize the Regional Screening Domain 0 for 12-km screening analyses and depending on 

the predicted impacts at Wolf Island and Swanquarter, refined Sub-regional Domains 4 and 

5 for the 4-km refined analyses. The CALPUFF computational domains are selected as a 

subset of these domains as described in the following section of this document. 

FIGURE 3-1. 12-KM REGIONAL SCREENING ANALYSIS AND

4-KM SUB-REGIONAL REFINED ANALYSIS CALMET DOMAINS

The VISTAS regional modeling domain was designed to allow any Class I areas within the 

VISTAS area to be evaluated with a single meteorological database and consistent 

CALPUFF modeling options. The horizontal domain is comprised of grid cells, each 

containing a central grid point at which meteorological and computational parameters are 

Appendix H.6 - 1219
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BART Applicability Modeling Protocol 

calculated at each time step. For the initial regional analysis, a grid spacing interval of 

12 km was selected. The LCC projection system is used to describe the horizontal grid, 

with origin at 40 degrees North latitude and 97 degrees West longitude. Standard parallels 

for the projection were set at 33 degrees North and 45 degrees North. 

Table 3-1 summarizes the vertical grid structure selected for this analysis, which comprises 

ten vertical layers. The cell face height of each layer indicates its vertical extent. The 

vertical domain is composed of terrain-following grid cells, the number and size of which 

are chosen so as to constrain the boundary layer in which dispersion and chemical 

transformations take place. The highest cell face was selected to be 4,000 meters to 

constrain the default maximum mixing height of 3,000 meters. 

TABLE 3-1. VERTICAL GRID STRUCTURE

 

Vertical Grid Cell 

Cell Face Height 

(meters) 

1 20 

2 40 

3 80 

4 160 

5 320 

6 640 

7 1,200 

8 2,000 

9 3,000 

10 4,000 

Refined 4-km analyses were also conducted using this vertical grid structure. Sub-regional 

Domains 4 and 5, which Santee Cooper may utilize for its refined analyses, contain all 

portions of South Carolina, Georgia, and North Carolina in which the BART-eligible 

sources and potentially affected Class I areas are located. 

3.3.2 MM5 SIMULATIONS

MM data are used as “observed” or “first-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional 

grid. The following three years of MM5 meteorological data were assembled by VISTAS 

for use in the regional CALPUFF modeling effort: 

σ 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA) 

σ 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS) 

σ 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning 

Organization). 

These data sets were provided to the VISTAS Technical Contractor, which produced threedimensional, CALMET-ready meteorological files for the VISTAS domain, which were 

Appendix H.6 - 1220
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BART Applicability Modeling Protocol 

subsequently used to run CALMET for the 12-km screening and 4-km sub-regional refined 

analyses. 

3.3.3 GEOPHYSICAL DATA

According to the VISTAS BART Modeling Protocol, terrain elevations within the modeling 

domain were processed from SRTM-GTOPO30 digital terrain data format with 30-arcsec 

resolution. SRTM30 is a digital elevation data set that spans the globe from 60° north 

latitude to 56° south latitude, approximately from the southern tip of Greenland to below 

the southern tip of South America. It has a horizontal grid spacing of 30 arc-seconds 

(approximately 1 kilometer). GTOPO30 is a global digital elevation model with a 

horizontal grid spacing of 30 arc-seconds (approximately 1 kilometer) that was derived 

from several raster and vector sources of topographic information that include U.S. 

Geological Survey (USGS) digital elevation models. The VISTAS Technical Contractor 

used data preprocessors to format and assimilate these data into a single geophysical data 

file for processing by CALMET to generate the 12-km screening data set. According to 

the VISTAS BART Modeling Protocol, higher resolution 3 arc-second DEM data were used 

to simulate terrain elevations in the 4-km refined data set. 

Land use and land cover (LULC) within the modeling domain was assimilated by the 

VISTAS technical contractor into a single geophysical data file for processing by 

CALMET using Composite Theme Grid (CTG) data archived by the USGS at a resolution 

of 200 meters. CALMET was used to calculate the fractional land use types within each 

cell of the CALMET grid. LULC in each grid cell was used by CALMET to compute the 

micrometeorological parameters (i.e., surface roughness, Bowen ratio, albedo, soil heat 

flux) that affect turbulent dispersion in the boundary layer. According to the VISTAS 

BART Modeling Protocol, 200-meter CTG LULC data were utilized for both 12-km 

screening and 4-km refined analyses. 

3.3.4 12-KM SCREENING ANALYSIS CALMET PROCESSING

The development of the regional screening CALMET meteorological fields from MM5 

data were conducted in No-Observations (“No-Obs”) mode since the MM5 data already 

reflect assimilation of observational data and are likely to adequately characterize regional 

wind patterns that are consistent with the 12-km grid scale. 

When the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain 

adjustments were turned off since the grid resolution of the MM5 data is the same as the 

CALMET grid and the terrain adjustments on the 12-km grid scale will already be 

reflected in the MM5 dataset. In this case, the MM5 winds were interpolated by CALMET 

to the CALMET layers and CALMET’s boundary layer modules computed mixing heights, 

turbulence parameters, and other meteorological parameters that are required by 

CALPUFF. For 2003, the 36-km MM5 data were used as CALMET’s initial guess field 

and then the CALMET diagnostic terrain adjustments (see Section 3.1.1 of the VISTAS 

BART Modeling Protocol) were applied to reflect terrain on the scale of the CALMET grid 

(i.e., 12 km). 

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BART Applicability Modeling Protocol 

3.3.5 4-KM REFINED GRID CALMET PROCESSING

The finer grid (4 km) CALMET simulations were run by the VISTAS Technical 

Contractor in hybrid mode, using MM5 data to define the initial guess fields, diagnostic 

terrain adjustments to create the Step 1 wind fields, and NWS meteorological observational 

data in the Step 2 smoothing calculations. In this manner, actual observations of threedimensional meteorological conditions were used in the model to smooth the coarse MM5 

resolution to better represent areas in which terrain features and coastlines may have an 

important effect on meteorological conditions, but not be well resolved in the mesoscale 

model. Surface, upper air, precipitation, and offshore buoy observation points are readily 

available for use in CALMET. The following generally describes the use of NWS 

observations in Step 2 of the CALMET analyses. 

Parameters affecting turbulent dispersion that are observed hourly at surface stations 

include wind speed and direction, temperature, cloud cover and ceiling, relative humidity, 

and precipitation type. Surface data were selected from the available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations were 

obtained from the National Climatic Data Center (NCDC), quality assured, and merged 

using the SMERGE pre-processor to create a single assimilated data file of surface 

observations for each year analyzed. 

Observations of meteorological conditions in the upper atmosphere provide a profile of 

turbulence from the surface through the depth of the boundary layer in which dispersion 

occurs. Upper air data are collected by balloons launched simultaneously across the 

observation network at 0000 Greenwich Mean Time (GMT) (7 o’clock PM in South 

Carolina) and 1200 GMT (7 o’clock AM in South Carolina). Sensors observe pressure, 

wind speed and direction, and temperature (among other parameters) as the balloon rises 

through the atmosphere. The upper air observation network is less dense than surface 

observation points since upper air conditions vary less and are generally not as affected by 

local effects (e.g., terrain or coastlines). Upper air data were extracted from the NCDC’s 

available data inventory to optimize spatial coverage and representation of the domain, and 

utilization from year to year may vary due to availability and data quality. 

The effects of wet deposition processes on ambient pollutant concentrations are an 

important part of the BART applicability analysis. Therefore, it is necessary to include 

observations of precipitation in the CALMET analysis. Precipitation data were collected 

from selected surface meteorological data stations included in the analysis, plus 

Cooperative Observation Network (COOP) stations nearer to or within the domain. 

Precipitation data were extracted from among the NCDC’s available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations from these 

stations were quality assured and merged using the PMERGE pre-processor to create a 

single assimilated data file of precipitation observations. 

Because parts of the modeling domain encompass the open waters of the Atlantic Ocean 

and the Cape Romain, Wolf Island, and Swanquarter Class I areas are located along the 

Atlantic Coast, meteorological data from coastal and offshore buoys can be useful so that 

Appendix H.6 - 1222
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BART Applicability Modeling Protocol 

overwater meteorological processing algorithms in CALMET can be utilized. The critical 

differences in behavior of the inland and marine boundary layers, and atmospheric 

dispersion phenomena occurring within these distinct regimes, is well documented and 

recognized to play a vital role in the dispersion of pollutants originating in, or destined to 

affect, coastal areas. Key phenomena occurring in coastal environments that affect 

pollutant dispersion include land/sea-breezes that cause recirculation of pollutant mass, 

temperature moderation that results in sharp gradients and mixing height discontinuities at 

the land-sea interface, and thermal internal boundary layers that could cause severe 

fumigation under certain conditions. The CALMET processor is equipped to assimilate 

overwater data obtained from coastal, near-shore, and offshore observation platforms. 

CALMET uses a profile method to simulate boundary layer effects by computing the 

friction velocity, Monin-Obukhov length, surface roughness, and mixing height over the 

water surface. The details of the formulation of marine dispersion algorithms are provided 

in the documentation accompanying the CALPUFF modeling system. 

To perform its simulation of the coastal environment, CALMET requires hourly 

observations of air temperature, air-sea temperature difference, wind speed and direction, 

relative humidity, overwater mixing height, and the overwater temperature gradients above 

and below the overwater mixing height. For practical applications of overwater boundary 

layer computations, these data can be obtained from the National Data Buoy Center 

(NDBC). The NDBC maintains an inventory of standard meteorological data observed by 

ships, buoys, and C-MAN stations in coastal, near-shore, and offshore locations. NDBC’s 

data sets provide direct wind and temperature measurements, and relative humidity can be 

inferred from pressure and dewpoint observations. The mixing height and temperature 

gradients and default values must be applied by CALMET when simulating the coastal 

atmosphere. 

3.3.6 QUALITY ASSURANCE OF CALMET ANALYSES

Santee Cooper’s BART Applicability Analyses will be conducted using 12-km screening 

and 4-km refined CALMET data prepared by the VISTAS Technical Contractor. The 

VISTAS BART Modeling Protocol describes quality assurance techniques used by the 

contractor to validate the model’s performance. 

3.4 CALPUFF MODEL PROCESSING

CALPUFF analyses to assess the visibility impacts attributable to Santee Cooper’s Grainger, 

Jefferies, and Winyah Generating Stations will be conducted generally in accordance with the 

recommendations of IWAQM Phase 2 Summary Report and Recommendations for Modeling Long 

Range Transport Impacts, Appendix B of which provides recommended default CALPUFF 

parameters. 

3.4.1 MODELED EMISSIONS AND CHEMICAL TRANSFORMATIONS

Section 2 of this document described the BART-eligible emission units operated by Santee 

Cooper and the visibility-affecting pollutants considered in the CALPUFF analysis. 

Emission rates were calculated following U.S. EPA and South Carolina DHEC BART 

Appendix H.6 - 1223
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BART Applicability Modeling Protocol 

guidance for primary emissions of PM10. PM was further speciated into size categories of 

less than 0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm 

aerodynamic diameters. Site specific and AP-42 emission factors were utilized to analyze 

the phase, size, and character of PM emissions as described in Section 2 of this document. 

The MESOPUFF-II chemical transformation algorithms, which are considered the default 

for regulatory CALPUFF modeling, will be employed. However, preliminary analyses 

will not include pollutants that undergo chemical transformation. Background levels of 

ozone and ammonia, which drive the simulated chemical transformation of emitted 

pollutants into visibility affecting species, will be input to the model as described in 

subsequent sections of this document. 

Santee Cooper operates two BART-eligible EGUs at each of the three facilities to be 

evaluated in the BART applicability analysis, as described in Section 2 of this document as 

having distinct emission release points and characteristics. Sample model processing files 

provided by the VISTAS Technical Contractor demonstrate modeling of a single point 

source using CALPUFF, POSTUTIL, and CALPOST to assess visibility change.15 The 

sample approach simulates actual emissions of each of three gaseous pollutants (SO2, SO4, 

and NOX) and unit emissions (e.g., 1 g/s) of each of six generic particle categories 

distinguished and designated by size: PM800, PM425, PM187, PM112, PM081, and 

PM056, to represent PM6-10, PM2.5-6, PM1.25-2.5, PM1-1.25, PM0.625-1, and PM0.5-0.625, 

respectively. The size distribution is the only distinguishing feature of these particle 

categories. Table 3-2 summarizes the relevant model input parameters to be used for each 

size category. 

TABLE 3-2. REPRESENTATION OF PM SIZE CATEGORIES IN CALPUFF 

 

 Computed Geometric Mass Geometric Standard Precipitation Scavenging Coefficient

Model Species 

Deposition 

Mode 

Mean Diameter 

(microns) 

Deviation 

(microns)* 

Liquid 

(s-1) 

Frozen 

(s-1) 

PM800 Particle 8.00 0 1.0 x 10-4 3.0 x 10-5

PM425 Particle 4.25 0 1.0 x 10-4 3.0 x 10-5

PM187 Particle 1.87 0 1.0 x 10-4 3.0 x 10-5

PM112 Particle 1.12 0 1.0 x 10-4 3.0 x 10-5

PM081 Particle 0.81 0 1.0 x 10-4 3.0 x 10-5

PM056 Particle 0.56 0 1.0 x 10-4 3.0 x 10-5

 

* Zero geometric standard deviation indicates that CALPUFF utilizes the deposition velocity associated with the geometric mass mean diameter. 

Because unit emission rates were modeled from the single point source in the sample 

approach, actual emission rates were used in the POSTUTIL postprocessing step to 

combine PM types and sizes into light scattering groups by scaling the modeled 

concentrations up or down by the multiplicative factor of the actual emission rate of each 

PM size category, which includes multiple types of PM (e.g., coarse filterable PM, fine 

filterable PM, organic condensable PM, inorganic condensable PM, and elemental carbon). 

 

15 http://www.src.com/verio/download/sample_files.htm#EXAMPLE_BART 

Appendix H.6 - 1224
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BART Applicability Modeling Protocol 

The output concentration file from POSTUTIL was then input to CALPOST to calculate 

visibility change attributable to emissions from the point source in the sample analysis. 

The preceding approach is reasonable for modeling a single point source; however, for 

facilities with multiple emission point sources, the preceding approach is not appropriate. 

Specifically, for sources with multiple emission points that have different exhaust 

characteristics (e.g., stack height, diameter, velocity, and temperature) and/or different 

emissions profiles of speciated PM, the use of unit emission rates is not appropriate since 

the CALPUFF output concentrations at particular receptors will not distinguish which 

source(s) contribute to the PM concentrations. Therefore, the POSTUTIL source profile 

technique cannot be applied. One alternative to this approach for sources with multiple 

emission points is to model each emission point individually in multiple CALPUFF runs 

using unit emission rates, then run POSTUTIL to combine the PM concentrations at each 

receptor for each modeled emission point, finally running the CALSUM postprocessing 

utility to combine the impacts of all sources. This approach, though conceptually 

appropriate, is undesirable due to substantial additional computer runtime required to 

model and post-process each emission point individually. 

As a computationally efficient alternative to the preceding approaches, the BART 

applicability analysis for Santee Cooper’s facilities will be conducted by explicitly 

modeling in CALPUFF the actual emission rate of each of 14 particle species defined as 

described in Table 3-3. The nomenclature used in Table 3-3 is analogous to that used to 

describe the emissions from the BART-eligible emission units in Section 2. 

TABLE 3-3. EXPLICIT MODELING OF PM TYPES AND SIZE CATEGORIES

 

Modeled PM 

Category Components 

 

PMC800 

PMC425 

Filterable coarse particles divided between two size categories 

(PM6-10, PM2.5-6) 

PMF187 

PMF112 

PMF081 

PMF056 

Filterable fine particles divided among four size categories 

(PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625) 

POC081 

POC056 

Primary condensable organic emissions divided between two size categories (PM0.625-1

and PM0.5-0.625) 

PIC081 

PIC056 

Primary condensable inorganic emissions divided between two size categories 

(PM0.625-1 and PM0.5-0.625) 

EC187 

EC112 

EC081 

EC056 

Primary elemental carbon emissions divided among four size categories (PM1.25-2.5, 

PM1-1.25, PM0.625-1, PM0.5-0.625) 

 

So that explicit modeling of the 14 particle species and sizes can be conducted equivalently 

to the unit emissions approach, identical model processing options for each PM size 

category will be enabled as summarized in Table 3-4. 

Appendix H.6 - 1225
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BART Applicability Modeling Protocol 

TABLE 3-4. REPRESENTATION OF EXPLICITLY MODELED PM SIZE CATEGORIES IN CALPUFF 

 

 Computed Geometric Mass Geometric Standard Precipitation Scavenging Coefficient

Model Species 

Deposition 

Mode 

Mean Diameter 

(microns) 

Deviation 

(microns)* 

Liquid 

(s-1) 

Frozen 

(s-1) 

PMC800 Particle 8.0 0 1.0 x 10-4 3.0 x 10-5

PMC425 Particle 4.25 0 1.0 x 10-4 3.0 x 10-5

PMF187 

EC187 Particle 1.87 0 1.0 x 10-4 3.0 x 10-5

PMF112 

EC112 Particle 1.12 0 1.0 x 10-4 3.0 x 10-5

PMF081 

EC081 

POC081 

PIC081† 

Particle 0.81 0 1.0 x 10-4 3.0 x 10-5

PMF056 

EC056 

POC056 

PIC056† 

Particle 0.56 0 1.0 x 10-4 3.0 x 10-5

 

* Zero geometric standard deviation indicates that CALPUFF utilizes the deposition velocity associated with the geometric mass mean diameter. 

† Santee Cooper is proposing to preliminarily model all PIC as sulfate. Should Santee Cooper elect to segregate PIC into sulfate and non-sulfate 

species, the PIC species would be modeled as shown in this table. 

To post-process the CALPUFF output concentrations that result from explicitly modeled 

multiple emission points, POSTUTIL will be used only to group modeled PM into light 

extinction groups. Unit scaling factors will be used in POSTUTIL and there will be no 

adjustment to the explicitly modeled emission rate. Table 3-5 summarizes the POSTUTIL 

grouping of modeled PM species into light extinction groups, and the light extinction 

coefficient subsequently to be used in CALPOST to compute light extinction due to the 

multiple emission points at the source. 

Appendix H.6 - 1226
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BART Applicability Modeling Protocol 

TABLE 3-5. ASSIGNMENT OF MODELED PM SPECIES TO LIGHT EXTINCTION GROUPS

 

Modeled 

Components 

CALPOST Light 

Extinction Group 

Extinction Coefficient 

(m2

/g) 

PMC800 

PMC425 PMC 0.6 

PMF187 

PMF112 

PMF081 

PMF056 

PIC081* 

PIC056* 

SOIL 1 

POC081 

POC056 SOA 4 

EC187 

EC112 

EC081 

EC056 

EC 10 

 

* Santee Cooper is proposing to preliminarily model all PIC as sulfate. Should 

Santee Cooper elect to segregate PIC into sulfate and non-sulfate species, the PIC 

species would be modeled as shown in this table. 

3.4.2 CALPUFF DISPERSION ALGORITHMS

As specified in a March 16, 2006 U.S. EPA memorandum16 and the most recent revision to 

the VISTAS BART Modeling Protocol, the use of Pasquill-Gifford (ISC-like) dispersion 

coefficients will be enabled as the default option in screening and refined CALPUFF 

analyses. 

3.4.3 BUILDING DOWNWASH

Per VISTAS guidance, the effects of building downwash will not be considered in the 

Santee Cooper BART applicability analyses.17

3.4.4 CALPUFF MODELING DOMAINS AND CLASS I AREA RECEPTORS

CALPUFF modeling for 12-km screening and 4-km refined analyses will be performed on 

computational domains that are subsets of the VISTAS Regional Domain 0 and 

Sub-regional Domains 4 and 5. The size of each computational domain will be selected to 

 

16 U.S. EPA Memorandum “Dispersion Coefficients for Regulatory Air Quality Modeling in CALPUFF” from 

Mr. Dennis Atkinson and Mr. Tyler Fox (Air Quality Modeling Group) to Ms. Kay Prince (Regulatory Planning Branch) 

dated March 16, 2006. 

17 VISTAS BART Modeling Protocol, revised on August 31, 2006, page 45 states: “Ignore building downwash 

unless source is within 50-km of a Class I area and the State instructs the source to specifically consider building 

downwash.” South Carolina DHEC has commented in other BART modeling protocols (e.g., MeadWestvaco’s North 

Charleston Mill BART modeling protocol) that downwash does not need to be considered for any sources, even those less 

than 50 km from a Class I area. 

Appendix H.6 - 1227
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BART Applicability Modeling Protocol 

encompass the Santee Cooper facilities and the Class I areas located with 300 km of each 

facility, and to extend at least 50 km beyond in all directions. The size of the domains 

allows for the possible recirculation of puffs beyond the facility and areas being evaluated. 

Computational domains will use the same vertical grid structure as described in the 

CALMET model formulation in Section 3.3. Figure 3-2 shows the terrain in the 12-km 

regional screening grid. Figures 3-3 through 3-5 illustrate the proposed 12-km screening 

and 4-km refined computational domains to be used in Santee Cooper’s BART 

applicability analyses. Note that Jefferies and Winyah will utilize the same 4-km domain. 

FIGURE 3-2. TERRAIN REPRESENTATION IN THE 12-KM REGIONAL SCREENING GRID

(AFTER FIGURE 4-2 OF THE VISTAS BART MODELING PROTOCOL)

Appendix H.6 - 1228
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BART Applicability Modeling Protocol 

FIGURE 3-3. 12-KM SCREENING ANALYSIS COMPUTATIONAL DOMAIN

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200

Lambert Conformal Coordinate East (km)

-1,200

-1,100

-1,000

-900

-800

-700

-600

-500

-400

-300

-200

-100

0

Lambert Conformal Coordinate North (km)

Grainger

Jefferies

Winyah

Wolf Island

Cape

Romain

Swanquarter

12-km Domain

Appendix H.6 - 1229
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BART Applicability Modeling Protocol 

FIGURE 3-4. 4-KM REFINED ANALYSIS COMPUTATIONAL DOMAIN FOR JEFFERIES &

WINYAH GENERATING STATIONS

1,200 1,250 1,300 1,350 1,400 1,450 1,500 1,550 1,600 1,650 1,700 1,750 1,800

Lambert Conformal Coordinate East (km)

-1,100

-1,050

-1,000

-950

-900

-850

-800

-750

-700

-650

-600

-550

-500

-450

-400

-350

Lambert Conformal Coordinate North (km)

Winyah

Jefferies

Wolf Island

Cape

Romain

4-km Domain

Appendix H.6 - 1230
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BART Applicability Modeling Protocol 

FIGURE 3-5. 4-KM REFINED ANALYSIS COMPUTATIONAL DOMAIN FOR GRAINGER 

GENERATING STATION

1,350 1,400 1,450 1,500 1,550 1,600 1,650 1,700 1,750 1,800 1,850 1,900 1,950

Lambert Conformal Coordinate East (km)

-800

-750

-700

-650

-600

-550

-500

-450

-400

-350

-300

-250

-200

-150

-100

Lambert Conformal Coordinate North (km)

Grainger

Cape

Romain

Swanquarter

4-km Domain

Appendix H.6 - 1231
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BART Applicability Modeling Protocol 

3.4.5 BACKGROUND OZONE AND AMMONIA CONCENTRATIONS

The CALPUFF model is capable of simulating linear chemical transformation effects by 

using pseudo-first-order chemical reaction mechanisms for the conversions of SO2 to SO4, 

and NOX, which consists of nitrogen oxide (NO) and nitrogen dioxide (NO2), to NO3 and 

HNO3. For the BART applicability analyses, chemical transformations involving five 

species (SO2, SO4, NOX, HNO3, and NO3) may be modeled using the MESOPUFF II 

chemical transformation scheme. Ambient concentrations of ammonia and ozone 

concentrations as represented in the model drive the MESOPUFF II chemical 

transformation simulation. 

Both screening and refined analyses will utilize observed ozone data for 2001 through 

2003 from non-urban CASTNet and AIRS stations compiled by the VISTAS Technical 

Contractor for the regional domain. Santee Cooper will extract data from ozone 

observation stations in and near the modeling domains, as illustrated in Figure 3-6 for the 

12-km domain. 

Appendix H.6 - 1232
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BART Applicability Modeling Protocol 

FIGURE 3-6. OZONE DATA STATIONS TO BE UTILIZED IN

SANTEE COOPER’S BART APPLICABILITY ANALYSES

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200

Lambert Conformal Coordinate East (km)

-1,200

-1,100

-1,000

-900

-800

-700

-600

-500

-400

-300

-200

-100

0

Lambert Conformal Coordinate North (km)

Grainger

Jefferies

Winyah

Wolf Island

Cape

Romain

Swanquarter

12-km Domain

Appendix H.6 - 1233
Santee Cooper – Grainger, Jefferies, Winyah 43 Trinity Consultants 

BART Applicability Modeling Protocol 

Monthly average ozone background values will be computed based on daytime average 

ozone concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month) of each year for substitution should all observations be missing for a 

particular hour of the dataset. Table 3-6 summarizes the monthly average ozone values 

computed for this substitution in the screening analysis. 

TABLE 3-6. MONTHLY AVERAGE SUBSTITUTE OZONE VALUES FOR SCREENING ANALYSIS

Month

2001 Substitute 

Ozone Values 

(ppb)

2002 Substitute 

Ozone Values 

(ppb)

2003 Substitute 

Ozone Values 

(ppb)

January 27.26 25.76 27.45

Februrary 28.85 32.17 27.41

March 36.58 34.66 34.56

April 44.26 41.19 42.66

May 48.53 43.06 41.53

June 37.09 40.61 38.23

July 35.85 41.33 30.72

August 38.37 38.58 28.39

September 33.89 31.57 32.69

October 33.54 28.72 34.86

November 33.96 26.55 29.99

December 25.20 24.75 24.85

In both the screening and refined analyses, a constant ammonia background value of 

0.5 ppb will be input to the CALPUFF model. The initial VISTAS BART Modeling 

Protocol envisioned the use of modeled boundary conditions of ammonia and sulfates in 

the refined analysis of chemical transformations involving these species and nitrates. 

However, VISTAS’ Technical Analysis Workgroup has since concluded that modeled 

background and boundary conditions should not be utilized for BART modeling purposes 

because “EPA and FLM recommend that ALM approach using CMAQ concentration data 

(SOX, NOX, total NH3) be reviewed by EPA Modeling Clearinghouse before being used in 

a regulatory application.” Accordingly, such background data will not be utilized in Santee 

Cooper’s BART applicability modeling analyses. As described in the following section of 

this document, only refined analyses will utilize the POSTUTIL postprocessing tool to repartition the HNO3 and NO3 equilibrium distributions using only a constant 0.5 ppb 

ammonia background level. 

3.4.6 PUFF REPRESENTATION

As recommended by the VISTAS BART Modeling Protocol, the default integrated puff 

sampling methodology will be enabled for both screening and refined CALPUFF analyses. 

In the screening analyses, the default setting to disable puff splitting algorithms will be 

retained. In the refined analyses, puff splitting may be evaluated. 

Appendix H.6 - 1234
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BART Applicability Modeling Protocol 

3.4.7 QUALITY ASSURANCE OF CALPUFF ANALYSES

Sample CALPUFF input files for the screening and refined analyses will be presented in 

the applicability analysis modeling report and copies of all input and output files will be 

included on the electronic media enclosed with that report. Comparison of the CALPUFF 

model inputs will be conducted to ensure that default values, as prescribed by IWAQM 

(Appendix B) and in the VISTAS BART Modeling Protocol, are used with the exception of 

the source-specific model options proposed in this document. Source parameters input for 

each BART-eligible emissions unit will be verified with the information presented in 

Section 2. The location and elevations of Class I area receptors will also be verified by 

comparison to NPS maps. 

3.5 CALPOST POSTPROCESSING AND NATURAL BACKGROUND CONDITIONS 

FOR LIGHT EXTINCTION AND HAZE INDEX CALCULATIONS

Using the concentrations of visibility-affecting pollutants computed by CALPUFF, two 

postprocessors, POSTUTIL and CALPOST, will be used to compute light extinction attributable to 

each of Santee Cooper’s facilities and the relevant metrics for the BART applicability analysis 

determination. The computation of light extinction attributable to the natural background and source 

will occur as generally described in this protocol. 

3.5.1 POSTUTIL PROCESSING

The first postprocessing step will involves running POSTUTIL to calculate the 

concentrations of visibility-affecting species SO4, NO3, SOA, EC, PMF, and PMC, as 

described in Section 3.4.1. Specifically, POSTUTIL will be used to group modeled species 

into visibility-affecting pollutant groups as originally shown as Table 3-5, reproduced here 

as Table 3-7. 

Appendix H.6 - 1235
Santee Cooper – Grainger, Jefferies, Winyah 45 Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE 3-7. ASSIGNMENT OF MODELED PM SPECIES TO

LIGHT EXTINCTION GROUPS IN POSTUTIL 

 

Modeled 

Components 

CALPOST Light 

Extinction Group 

Extinction Coefficient 

(m2

/g) 

PMC800 

PMC425 PMC 0.6 

PMF187 

PMF112 

PMF081 

PMF056 

PIC081* 

PIC056* 

SOIL 1 

POC081 

POC056 SOA 4 

EC187 

EC112 

EC081 

EC056 

EC 10 

 

* Santee Cooper is proposing to preliminarily model all PIC as sulfate. Should 

Santee Cooper elect to segregate PIC into sulfate and non-sulfate species, the PIC 

species would be modeled as shown in this table. 

In screening analyses, POSTUTIL will be only run once to achieve this pollutant grouping. 

In refined analyses, POSTUTIL will be run a second time to apply the ammonia limiting 

method (ALM) to re-partition the distribution of HNO3 and NO3 concentrations at each 

Class I area as a function of the temperature, relative humidity, and free ammonia during 

each hour. ALM processing is not run by default, and therefore will not used in the 

screening analyses. If nitrate species are modeled, ALM re-partitioning using the default 

ammonia background level of 0.5 ppb will be conducted in refined analyses. 

3.5.2 CLASS I AREA-SPECIFIC NATURAL BACKGROUND CONDITIONS

The visibility goal of the Clean Air Act is both the remedying of existing visibility 

impairment and prevention of future visibility impairment. In its BART Implementation 

Guidance, U.S. EPA affirms that it interprets the goal to mean return atmospheric 

conditions to “natural visibility conditions.” For the purposes of BART analyses, the 

U.S. EPA has determined that it “did not intend to limit States to the use of the 20% best 

visibility days...States may use 20% best visibility days or annual average” to assess 

BART applicability.18 The August 31, 2006 revision to the VISTAS BART Modeling 

Protocol indicates that several options are available at the discretion of the state including 

(1) a single annual average natural background condition for each Class I area; (2), a single 

value representing the average haze index on the 20% estimated best visibility days at each 

Class I area; or, (3) monthly average natural background conditions computed from 

 

18 U.S. EPA Memorandum from Mr. Joseph Paisie to Ms. Kay Prince, as Attachment A to a proposed settlement 

agreement between the Utility Air Regulatory Group and U.S. EPA, published at 71 Federal Register No. 84, pp. 25,838-

25,840, May 2, 2006. 

Appendix H.6 - 1236
Santee Cooper – Grainger, Jefferies, Winyah 46 Trinity Consultants 

BART Applicability Modeling Protocol 

estimated components of visibility-affecting pollutants and monthly average relative 

humidity values specific to each Class I area. The Option (1) annual average natural 

background condition appears to be the preferred approach since analyses conducted by the 

VISTAS Technical Contractor for certain sources in the region used this method 

exclusively. Therefore, Santee Cooper proposes to use Option (1) in its analyses. 

For the three Class I areas within 300 km of each of the Santee Cooper BART-eligible 

sources and potentially affected by the Santee Cooper’s operations, Table 3-8 summarizes 

the annual average background visibility as tabulated in Appendix B of U.S. EPA’s 

Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. 

These values will be used to calculate natural background conditions. 

TABLE 3-8. ANNUAL AVERAGE BACKGROUND CONCENTRATIONS

FOR CLASS I AREAS POTENTIALLY AFFECTED BY THE SANTEE COOPER FACILITIES

Class I Area 

Annual Average 

Haze Index (dv) 

Cape Romain 7.52 

Wolf Island 7.58 

Swanquarter 7.38 

* As tabulated in Appendix B of U.S. EPA’s Guidance for Estimating 

Natural Visibility Conditions under the Regional Haze Rule (2003). 

To represent natural conditions in the absence of anthropogenic sources of sulfates and 

nitrates, the monthly background extinction coefficient is expressed in terms of Rayleigh 

scattering and scattering due to soils (i.e., fine particles) based on the annual average haze 

index, and is calculated using the following equations. 

⎟

⎠

⎞ ⎜

⎝

⎛ = 10 exp10 HI bback , 

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total bback for the 

best days at Cape Romain, Wolf Island, and Swanquarter, including the Rayleigh scattering 

coefficient is calculated according to the above equation. A sample calculation for Cape 

Romain is shown below for the annual average natural background. 

Mm 21.21 Mm 10 Romain Capefor Mm 21.11 10

52.7 exp10 −1 −1 −1 ⎟ = =+= =⇒+ ⎠

⎞ ⎜

⎝

⎛ bback = ray bb soil soil bb soil

The effects of relative humidity to amplify the visibility impairment of hygroscopic 

sulfates and nitrates are characterized using CALPOST “Method 6,” which computes Δbext

using a monthly average relative humidity adjustment particular to each Class I area 

applied to background and modeled sulfate and nitrate. Table 3-9 summarizes the monthly 

Appendix H.6 - 1237
Santee Cooper – Grainger, Jefferies, Winyah 47 Trinity Consultants 

BART Applicability Modeling Protocol 

average humidity values that will be applied for the three Class I areas considered in the 

analyses, as tabulated in Table A-3 of U.S. EPA’s Guidance for Estimating Natural 

Visibility Conditions under the Regional Haze Rule. 

TABLE 3-9. MONTHLY AVERAGE f(RH) FOR SELECTED CLASS I AREA* 

 

Class I Area January February March April May June July August September October November December 

Cape Romain 3.3 3.0 2.9 2.8 3.2 3.7 3.6 4.1 4.0 3.7 3.4 3.2 

Wolf Island 3.4 3.1 3.1 3.0 3.3 3.7 3.7 4.1 4.0 3.7 3.5 3.5 

Swanquarter 2.9 2.7 2.6 2.5 2.9 3.2 3.4 3.5 3.4 3.1 2.8 2.9 

* As tabulated in Table A-3 of U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule (2003). 

Natural background conditions for each Class I area will be calculated using the data 

summarized in Tables 3-7, 3-8, and 3-9 and the newly revised Interagency Monitoring of 

Protected Visual Environments (IMPROVE) light extinction formula, which is 

summarized in Section 3.5.3. 

3.5.3 VISIBILITY IMPACT CALCULATION AND REVISED IMPROVE LIGHT EXTINCTION 

ALGORITHM

CALPOST will be run separately for each Class I area to obtain the necessary visibility 

statistics for evaluating compliance with the BART visibility impairment thresholds. The 

inputs to CALPOST involve selection of the visibility method (i.e., Method 6) and entry of 

Class I area-specific data for computing background extinction and monthly relative 

humidity factors for hygroscopic aerosols as described in Section 3.5.2. CALPOST 

contains a receptor selection option that allow subsets of a receptor network modeling in 

CALPUFF to be selected for processing in a given CALPOST run. This selection specifies 

which receptors representing a single Class I area are selected for processing from a 

CALPUFF output file that may contain receptors from several Class I areas. 

Refined analyses will be analyzed by tabulating the 98th percentile 24-hour average 

visibility impact for each year and Class I area. The previously described Option (1) was 

used to quantify visibility impacts relative to annual average natural background 

conditions. As described in Section 3.2.1, the default interpretation of the 98th percentile 

(8th-highest day regardless of variability at each receptor) will be evaluated for each year. 

Santee Cooper will present visibility impacts calculated as a change in the “haze index” 

expressed in units of deciviews, as computed in the following equation. 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = 10

ln10 ext b

HI

Light extinction (bext) results from particulate species emitted from anthropogenic sources 

including coarse particulate matter (PMC), fine particulate matter (PMF), and elemental 

Appendix H.6 - 1238
Santee Cooper – Grainger, Jefferies, Winyah 48 Trinity Consultants 

BART Applicability Modeling Protocol 

carbon (EC), as well as precursors to secondary organic aerosols (SOA) and fine 

particulate matter such as SO2 and NOX that form sulfates (SO4) and nitrates (NO3) after 

chemical transformations take place. The extinction coefficient due to emissions of 

visibility-affecting pollutants from a single BART-eligible source bext,source is calculated 

using an air quality model such as CALPUFF. The extinction due to the BART-eligible 

source will be calculated as shown in the following equation. 

( ) sourceext SO NO PMC PMF SOA +++++= bbbbbbMmb EC

−

4 3

1

,

where, 

[( ) ] ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

[ ] 3

34 3

4 4 2 4

Concentrat μginion m

Humidity Relative Function

EC10

PMC6.0

PMF1

SOA4

NONH3

SONH3

=

=

=

=

=

=

=

=

RHf

b

b

b

b

b RHf

b RHf

EC

PMC

PMF

SOA

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ] concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes PMC PM coarse ofion 

concentrat thedenotes PMF PM fine ofion 

concentrat thedenotes SOA secondary ofion aerosols organic

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

The relative impact of a BART-eligible source is determined by comparing the HI

attributable to a source relative to estimated natural background conditions. The 

background extinction coefficient bext,background is affected by various chemical species and 

the Rayleigh scattering phenomenon and was initially calculated as shown in the following 

default equation. 

( ) ext background SO NO OC Soil Coarse ap Ray b ++++++= bbbbbbbMm−

4 3

1

, where, 

[ ] ( ) ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

( )

[ ] 3

1

3 34

4 2 4 4

Concentrat μginion m

Humidity Relative Function

Rayleigh defaultby Mm 10 Scattering

EC10

Mass Coarse6.0

Soil1

OC4

NONH3

SONH3

=

=

=

=

=

=

=

=

=

−

RHf

b

b

b

b

b

b RHf

b RHf

Ray

ap

Coarse

Soil

OC

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh air todue scattering is Scattering molecules

concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes Mass Coarse dusts coarse ofion 

concentrat thedenotes Soil soils fine ofion 

concentrat thedenotes OC carbon organic ofion 

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

Values for the parameters listed above specific to the natural background conditions at the 

Class I areas considered in this analysis are provided on an annual average basis in the 

U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule. More detailed information about the natural background conditions particular 

to Class I areas potentially affected by Santee Cooper’s operations were provided in 

Tables 3-8 and 3-9 to represent annual average natural background conditions at Cape 

Romain, Wolf Island and Swanquarter. 

Appendix H.6 - 1239
Santee Cooper – Grainger, Jefferies, Winyah 49 Trinity Consultants 

BART Applicability Modeling Protocol 

The preceding equation represents what has been termed the “default” light extinction 

calculation that has been utilized for computation of visibility impacts since it was initially 

developed by the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

in the mid-1990’s. As noted in the VISTAS BART Modeling Protocol, the U.S. EPA and 

the Regional Planning Organizations (including VISTAS) are evaluating whether 

refinements are warranted to the methods recommended in U.S. EPA’s guidance to 

calculate estimates of natural background visibility. For the purposes of calculating 

current, future, and natural background visibility at Class I areas as part of its reasonable 

progress analyses, VISTAS intends to present regional air quality modeling results using 

both the default and the newly revised IMPROVE light extinction formula. 

In December 2005, the IMPROVE workgroup issued a report describing an alternative to 

the default formula used to estimate extinction from particle concentration measurements 

(referred to as the “2005 IMPROVE Report” hereafter).19 A final report was issued in 

March 2006 to present a thorough survey of estimates of mass scattering efficiencies from 

recent peer-reviewed literature and provisional recommendations for refinements to the 

IMPROVE equation (referred to as the “2006 IMPROVE Report” hereafter).20 

Refinements in the revised IMPROVE formula include: 

▲ Distinguishing the size (i.e., age) of secondary pollutants and growth factors for 

different sizes of particle species 

▲ Adding a site-specific Rayleigh scattering term to the formula to represent the 

dependence of Rayleigh scattering on air density (i.e., elevation) 

▲ Adding a sea salt term, including a growth factor due to relative humidity 

▲ Adding a term for visibility impairment resulting from gaseous nitrogen dioxide 

(NO2). 

The revised IMPROVE light extinction algorithm takes the following form: 

bext ≈

( )[ ( ) ] ( )[ ( ) ]

( )[ ] ( )[ ]

[ ] [ ] [ ][ ] [ ]

( )[ ] [ ] Site Rayleigh specific Scattering 2

Small Large

34 Small 34 Large

44 2 Small 44 2 Large

4.1 Salt Sea NO33.0

PMC6.0PMF1EC10OC1.6OC8.2

4.2 1.5NONH NONH

2.2 SONH 8.4 SONH

+ +

+ +++ +

+ +

+ +

RHf b −

RHf RHf

RHf RHf

SS

S L

S L

To facilitate the use of the revised IMPROVE algorithm for assessing BART applicability, 

the VISTAS Technical Contractor developed and distributed a spreadsheet tool to 

implement the revised IMPROVE light extinction algorithm, designated as the CALPOSTIMPROVE Processor Version 2 (September 29, 2006). This processor calculates the 

 

19 Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand. Revised IMPROVE Algorithm for 

Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering Committee, November 2005. 

20 Hand, J. and W. Malm. Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients 

- Final Report, March 2006. 

Appendix H.6 - 1240
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BART Applicability Modeling Protocol 

reconstructed light extinction using the revised IMPROVE algorithm and output from the 

current version of the CALPOST postprocessor. To implement the CALPOST-IMPROVE 

tool, three additional data points are required: background concentration of gaseous NO2, 

Rayleigh scattering parameter corrected for site-specific elevation, and an estimate of the 

average background sea salt aerosol concentration. 

3.5.3.1 BACKGROUND NO2 CONCENTRATIONS

As no NO2 species are modeled as part of the EGU BART modeling, 

concentrations are not calculated relevant to the specific receptors. As such, a 

conservative value of zero is used in the CALPOST-IMPROVE tool. 

3.5.3.2 SITE-SPECIFIC RAYLEIGH SCATTERING

The 2005 IMPROVE report21 describes the justification for using a site-specific 

Rayleigh scattering parameter in the revised light extinction algorithm. 

Rayleigh scattering refers to the scattering of light from the molecules of the air 

and is therefore dependent on the air density, which varies with temperature and 

pressure. Site-specific Rayleigh scattering was calculated using the standard 

atmospheric pressure corresponding to the monitoring site elevation, and an 

estimated annual mean temperature. The temperature data were obtained from 

the nearest weather stations for time periods encompassing ten to thirty years 

and were interpolated to the monitoring site location. The 2005 IMPROVE 

report tabulated site-specific values of Rayleigh scattering for locations at 

which IMPROVE monitors visibility, including Cape Romain, Wolf Island, and 

Swanquarter, for which values of 11.5, 11.4 (based on neighboring Class I area 

Okefenokee NWR), and 11.6 Mm-1 were calculated. Compared to the default 

Rayleigh scattering value of 10 Mm-1, the corrected values reflect the sea level 

elevations of Cape Romain, Wolf Island, and Swanquarter. 

3.5.3.3 BACKGROUND SEA SALT AEROSOL CONCENTRATIONS

The 2005 IMPROVE Report identified sea salt aerosols as an important 

component of natural visibility conditions, particularly in humid coastal 

environments such as the Class I areas potentially affected by Santee Cooper’s 

BART-eligible facilities. The 2006 IMPROVE Report identifies sea salt as a 

statistically significant component of fine particle mass at Cape Romain (which 

is considered representative of Wolf Island). Further analyses have been 

suggested to quantify a representative natural background sea salt concentration 

for implementation in the CALPOST-IMPROVE processor for BART 

applicability analyses. 

Measurement and estimation techniques for naturally-occurring sea salt 

concentrations continue to be a focus within the scientific community in 

 

21http://vista.cira.colostate.edu/IMPROVE/Publications/GrayLit/019_RevisedIMPROVEeq/RevisedIMPROVEAl

gorithm3.doc 

Appendix H.6 - 1241
Santee Cooper – Grainger, Jefferies, Winyah 51 Trinity Consultants 

BART Applicability Modeling Protocol 

general and stakeholders in the VISTAS region in particular. The IMPROVE 

workgroup has developed a dry extinction efficiency and hygroscopic growth 

curve (fSS(RH)) for sea salt aerosols based on observations in remote marine 

environments and thermodynamic models. Although there is reasonable 

confidence in these components of the calculation of light extinction due to sea 

salt aerosols, there is greater uncertainty in the average sea salt mass 

concentration. Specific questions remain about how ambient sea salt 

concentrations are reliably inferred from measurements of elemental markers 

and the period of record for which reliable data are available to compute a 

representative natural background level. VISTAS states, including South 

Carolina, are using annual average natural background conditions for visibility 

impairment caused by the species (e.g., sulfates, nitrates, organic and inorganic 

PM) represented in the IMPROVE light extinction equation. The following 

analysis reviews available techniques for estimating the long-term average sea 

salt concentration for consistent application as a visibility-impairing component 

of the natural background. 

The 2006 IMPROVE Report describes challenges in measuring sea salt 

aerosols in ambient environments due to imprecise analysis techniques and 

various investigators have surveyed methods for estimating sea salt 

concentration to compute an average value for Cape Romain. Sodium (Na+

) 

and Chloride (Cl-

) ions are generally accepted as the best markers for the 

presence of sea salt. Difficulties in computing sea salt from data from the 

IMPROVE network arise because positive ions have historically not analyzed; 

therefore, sodium ion (the strongest indicator of sea salt) data are not readily 

available, except for a brief period between September 2002 and December 

2004 during which time VISTAS measured Na+

 at Cape Romain and other 

Southeastern Class I areas. This Na+

 approach computes the sea salt 

concentration as: 

 [Sea Salt] = 3.27[Na+

non-soil], 

where [Na+

non-soil] = [Na+

] – 0.57[Fe] and [Fe] is the measured iron 

concentration. 

Issues arise when using the chloride ion or elemental chlorine to estimate sea 

salt because reaction of gaseous nitric acid with sea salt produces sodium 

nitrate particles and the release of gaseous hydrochloric acid. The depletion of 

chlorine during this reaction results in an underestimation of sea salt when 

using chlorine as the marker. The sea salt concentration would be computed as: 

 [Sea Salt] = 1.8[Cl] or [Sea Salt] = 1.8[Cl-

], 

based on reliable measurements of [Cl-

] using ion chromatography or [Cl]. The 

VISTAS Technical Contractor originally computed an average sea salt 

concentration of 0.20 μg/m3

 using Cl- as the marker, which is recognized to 

Appendix H.6 - 1242
Santee Cooper – Grainger, Jefferies, Winyah 52 Trinity Consultants 

BART Applicability Modeling Protocol 

underestimate the sea salt aerosol concentration.22 Appendix 1 of the 2006 

IMPROVE Report lists an average sea salt concentration of 0.377 μg/m3

, which 

is also based on Cl-

 measurements that may underestimate sea salt 

concentrations. 

Elemental sodium (Na) data are available from X-ray Fluorescence (XRF) 

analyses; however, sensitivity issues regarding poor detection of Na result in 

large uncertainties corresponding to Na from XRF (White et al., 2004).23 Sea 

salt concentrations inferred from Na measured by Proton Induced X-ray 

Emission (PIXE) are considered reliable; however, such measurements have 

not been made at Cape Romain since 2001. Paine et al. (2006) computed an 

average sea salt concentration of 1.123 μg/m3

 at Cape Romain using Na as the 

marker from measurement collected between 1994 and 2001, during which 

time the more reliable PIXE analysis method for sodium was in use.24 This 

sodium approach uses ambient measurements of sodium and fine soil 

concentrations and a reference concentration for soil-derived sodium provided 

as: 

[ ] = [Soil0283.0Na ] Soil , 

where 0.0283 is the crustal reference composition of sodium. Using this value, 

the ambient sodium attributable to sea salt is computed as: 

[ ] [ ] [ ] Na Salt Sea = − NaNa Soil

from which the sea salt concentration is computed as: 

[ ] [ ]

0.306

Na Salt Sea Salt Sea = , 

where 0.306 is the average sodium content of fresh sea salt. An alternative 

estimate of sea salt concentration using [Na] can be constructed in a manner to 

the Na+

 approach as: 

 [Sea Salt] = 3.27[Nanon-soil], 

where [Nanon-soil] = [Na] – 0.57[Fe] and [Fe] is the measured iron concentration. 

 

22 Tombach, I. Estimating the Role of Sea Salt when Determining the Visibility Impact of a Source with 

CALPUFF, prepared for VISTAS, June 13, 2006. 

23 White, W. H., R. A. Eldred, P. J. Feeney, C. E. McDade, B. P. Perley, D. J. Shadoan, and P. H. Wakabayashi, 

Behavior of fine-particle elemental data near the detection limit, Paper # 24, A&WMA Specialty conference: Regional and 

Global Perspectives on Haze: Causes, Consequences and Controversies, Asheville, NC, October 25-29, 2004. 

24 Paine, R., J. Connors, and S. Heisler. Computation of Average Sea Salt Concentrations for Consideration in 

Determination of Natural background Levels at Eastern Class I Areas, August 2, 2006. 

Appendix H.6 - 1243
Santee Cooper – Grainger, Jefferies, Winyah 53 Trinity Consultants 

BART Applicability Modeling Protocol 

The VISTAS Technical Contractor25 summarized a preferred hierarchy for 

estimating sea salt concentrations as envisioned by Pitchford (2006)26: 

1. [Sea Salt] = 3.27[Na+

non-soil], where [Na+non-soil] = [Na+

] – 0.57[Fe] and 

Na+

 is collected on Nylon filters and analyzed using ion 

chromatography 

2. [Sea Salt] = 1.8[Cl-

], where Cl- is collected on Nylon filters and 

analyzed using ion chromatography 

3. [Sea Salt] = 1.8[Cl], where Cl is analyzed by XRF or PIXE, despite a 

known bias toward underestimates due to loss of HCl on the filter 

media 

4. [Sea Salt] = 3.27[Nanon-soil], where [Nanon-soil] = [Na] – 0.57[Fe] and Na 

is analyzed by PIXE, which presents an alternative method to that used 

by Paine et al. (2006) 

The VISTAS Technical Contractor evaluated available data at Cape Romain to 

assess the practical application of these methods given variations in 

measurement techniques over time and concluded that the Na+

 method is 

superior, but is limited by the brief period of time (September 2002 through 

December 2004) when such measurements were taken. The report identifies an 

annual average sea salt concentration in 2004 of 0.77 μg/m3

 using the Na+

method, but does not recommend a longer-term average value. The VISTAS 

Technical Contractor demonstrates that the 2004 annual average sea salt 

concentrations of 0.35 μg/m3

 estimated using [Cl-] and 0.11 μg/m3 estimated 

using [Cl] exhibit the expected underestimation bias due to the analysis 

technique. The elemental sodium (Na) approach could not be compared for this 

time period because Na measurements are available only from XRF, which is 

excluded as an unreliable method. 

To assess a long-term average natural background sea salt concentration, Santee 

Cooper applied reliable estimation techniques over the period of record when 

such measurements were taken. Additionally, a fifth estimation technique 

using strontium (Sr) as the elemental marker for sea salt was applied to 

corroborate sea salt estimates using other techniques. Lowenthal and Kumar 

(2006) suggest the following approach using strontium (Sr), which is measured 

accurately using XRF and for which measurements are available over the long-

 

25 Tombach, I. Evaluation of Alternative Methods for Estimating Sea Salt Concentrations at Cape Romain, 

prepared for VISTAS, March 5, 2007. 

26 Pitchford, M. Revised IMPROVE Algorithm for Estimating Light Extinction for Particle Speciation Data, 

November 2006. 

Appendix H.6 - 1244
Santee Cooper – Grainger, Jefferies, Winyah 54 Trinity Consultants 

BART Applicability Modeling Protocol 

term time period (1994 through 2004) of interest.27 This approach computes a 

non-crustal, marine-derived Sr concentration (xSr) as: 

[ ]= [SrxSr ]− [Si 0.0013528 ], 

where 0.0013528 is the crustal reference concentration of Sr and Si represents 

the silicon composition that is presumably entirely representative of entrained 

crustal material.28 The sea salt concentration is computed from this value using 

the average strontium composition of sea water, 0.000234. 

[ ] [ ]

0.000234

xSr Salt Sea =

The VISTAS Technical Contractor acknowledges Lowenthal and Kumar’s Sr 

approach as a newer method that has some uncertainty, but does not discount its 

application at Class I areas in the Southeastern U.S. The Sr approach is 

appealing to apply at Cape Romain because Sr measurements have been made 

consistently since September 1994 using the same analytical techniques that are 

considered reliable for this marker. 

Electronic files on the modeling DVD that will be included with the final report 

will contain an analysis of average ambient sea salt concentrations using the 

Paine et al. (2006) and Pitchford (2006) methods using elemental sodium 

derived from PIXE measurements for the available data period September 1994 

through November 2001, the VISTAS preferred sodium ion approach for the 

available data period September 2002 through December 2004, and the 

Lowenthal and Kumar (2006) method using strontium for the available data 

period September 1994 through December 2004. Table 3-10 summarizes data 

from each approach over the September 1994 through December 2001 time 

period during which reliable analytical techniques were in use for each species. 

 

27 Lowenthal, D. and N. Kumar. “Light Scattering from Sea-Salt Aerosols at Interagency Monitoring of Protected 

Visual Environments (IMPROVE) Sites.” J. Air & Waste Management Association, 56: 636-642, May 2006. 

28 Mason, B. Principles of Geochemistry, 4th ed. Wiley & Sons, Inc.: New York, 1982, p.344. 

Appendix H.6 - 1245
Santee Cooper – Grainger, Jefferies, Winyah 55 Trinity Consultants 

BART Applicability Modeling Protocol 

TABLE 3-10. SUMMARY OF SEA SALT CONCENTRATIONS COMPUTED USING VARIOUS METHODS,

SEPTEMBER 1994 – DECEMBER 2004 

 

[Sea Salt] (μg/m3)

Period Start Period End 

Paine et al. (2006)

[Na] Method 

Pitchford (2006) 

[Na] Method 

VISTAS (2007) 

[Na+

] Method 

Lowenthal & Kumar 

(2006) [Sr] Method 

September 1994 November 2001 1.1 1.1 -- 1.1 

December 2001 August 2002 -- -- -- 1.3 

September 2002 December 2004 -- -- 0.77 0.78 

Long-term Average (September 1994-December 2004) 1.1 

The summary results presented in Table 3-10 indicate that the Sr method for 

calculating sea salt concentrations replicates estimates of sea salt concentrations 

calculated using preferable methods that use Na or Na+

 as the marker. When 

applied for Sr measurements at Cape Romain over a continuous, long-term 

(1994 through 2004) time period, the average background concentration of sea 

salt is estimated to be 1.1 μg/m3

. Accordingly, Santee Cooper will evaluate 

visibility impacts from its BART-eligible sources at Cape Romain using both 

estimates of the annual average sea salt concentration (i.e., 0.77 μg/m3

 as 

estimated by the VISTAS Technical Contractor and 1.1 μg/m3

 as estimated by 

Santee Cooper) to represent that background condition in the CALPOSTIMPROVE tool as described in the following section of this report. 

3.5.4 QUALITY ASSURANCE OF POSTPROCESSING ANALYSES

Quality assurance of postprocessing analyses will be conducted by verifying the sequence 

of postprocessing (i.e., POSTUTIL, including ALM for refined analyses, followed by 

CALPOST) and that appropriate data files are referenced throughout the calculation 

sequence. The CALPOST inputs that will be checked include the following: 

▲ Visibility technique (Method 6) 

▲ Monthly Class I-specific relative humidity factors for Method 6 

▲ Background light extinction values calculated as appropriate using natural 

background options (1), (2), and (3) 

▲ Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, 

organics), coarse and fine particulate matter, and elemental carbon. 

▲ Appropriate species names for coarse PM used 

▲ Extinction efficiencies for each species 

▲ Screen to select appropriate Class I receptors for each CALPOST simulation. 

Appendix H.6 - 1246
Santee Cooper – Grainger, Jefferies, Winyah Trinity Consultants 

BART Applicability Modeling Protocol 

APPENDIX A 

VISTAS BART MODELING PROTOCOL (REVISION 3.2, AUGUST 31, 2006) 

Appendix H.6 - 1247
Protocol for the Application of the 

CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART) 

December 22, 2005

(Revision 3.2 – 8/31/06)

Visibility Improvement State and Tribal Association 

of the Southeast (VISTAS)

Appendix H.6 - 1248
Table of Contents iv

TABLE OF CONTENTS

Page

SUMMARY 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 1

1.1 Background 1

1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING 5

2.1 Overview of the Regional Haze BART Process 5

2.2 Model Recommendations for the BART Analysis 7

2.3 Performance of a Cap and Trade Program 8

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM 10

3.1 Capabilities and features of CALPUFF 10

3.1.1 Major Features of CALMET 12

3.1.2 Major Features of CALPUFF 14

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 18

3.2 Discussion of CALPUFF Applicability and Limitations 19

3.2.1 Transport and Diffusion 19

3.2.2 Aerosol Constituents 22

3.2.3 Regional Haze 30

4. VISTAS’ COMMON MODELING PROTOCOL 34

4.1 Overview of Common Modeling Approach 34

4.1.1 BART Exemption Analysis 34

4.1.2 BART Control Evaluation 36

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM 36

4.2 Optional Source-Specific Modeling 38

4.3 Initial Procedure for BART Exemption 38

4.3.1 Overview of Initial Approach 38

4.3.2 Discussion of 12-km Initial Exemption Modeling 39

4.3.3 Model Configuration and Settings for Initial Analysis 41

4.4 Finer Grid Modeling Procedures 46

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach 46

4.4.2 Model Configuration and Settings for Finer Grid Modeling 47

4.5 Presentation of Modeling Results 49

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources 54

5. SOURCE-SPECIFIC MODELING PROTOCOL 55

Appendix H.6 - 1249
Table of Contents iv

6. QUALITY ASSURANCE 58

6.1 Scope and Purpose of the QA program 58

6.2 QA Procedures for Common Protocol Modeling 59

6.2.1 Quality Control of Input Data 59

6.2.2 Quality Control of Application of CALMET 60

6.2.3 Quality Control of Application of CALPUFF 61

6.2.4 Quality Control of Application of CALPOST and POSTUTIL 62

6.3 Additional QA Issues for Alternative Source-Specific Modeling 63

6.4 Assessment of Uncertainty in Modeling Results 64

7. REFERENCES 65

Appendix H.6 - 1250
Summary S-1

SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available 

Retrofit Technology (BART) for the VISTAS Regional Planning Organization (RPO) describes 

common procedures for carrying out air quality modeling to support BART determinations that 

are consistent with guidelines of the U.S. Environmental Protection Agency in 40 CFR Part 51 

Appendix W and Appendix Y. The Protocol is intended to serve as the basis for a common 

understanding among the organizations that will be performing BART analyses or reviewing the 

BART modeling results in the VISTAS region.

Background

Best Available Retrofit Technology is required for any BART-eligible source that ‘‘emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “You 

can use dispersion modeling to determine that an individual source cannot reasonably be 

anticipated to cause or contribute to visibility impairment in a Class I area and thus is not subject 

to BART.” In the “individual source attribution approach,” a BART-eligible source that is 

responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment. 

A BART-eligible source that is responsible for a 0.5 dv change or more is considered to 

“contribute” to visibility impairment in a Class I area. Any source determined to cause or 

contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol 

to guide them, their sources, and reviewers in the BART determination and review effort. The 

Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat 

Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The 

VISTAS state BART contacts, particularly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon, 

AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol 

was enhanced and completed with the assistance of Joseph Scire, Christelle Escoffier-Czaja and 

Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from 

the VISTAS federal partners: Federal Land Managers and US EPA. The VISTAS RPO held a 

meeting on September 21, 2005 in Research Triangle Park, NC to discuss the Protocol with 

participants before starting a public comment period. The Protocol underwent formal external 

review during the period between September 26, 2005 and October 31, 2005. Numerous 

comments were received. All comments were carefully considered and discussed with VISTAS 

participants and federal partners. VISTAS gratefully acknowledges the very useful contributions 

of those that provided comments. On November 1st, 2005 VISTAS held another meeting with its 

participants in Nashville, TN to present and discuss the comments being considered for inclusion 

in the Protocol. No formal document will be prepared to address all the comments received on 

the Protocol. 

Appendix H.6 - 1251
Summary S-2

Objectives

The objectives of the Protocol (discussed in Chapter 1) are to provide: 

 A consistent approach to determine if a source is subject to BART

 A consistent model (CALPUFF) and modeling guidelines for BART determinations

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site-specific modeling

 Common expectations for reporting model results

The Protocol is not intended to define the engineering analyses required by the US EPA’s BART 

Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading. 

Chapter 2 is intended to provide summary background on EPA’s guidance for BART modeling. 

The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for 

applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the 

specific information that should be included in site-specific protocols. Chapter 6 describes the 

quality assurance requirements for BART analyses in the VISTAS RPO.

Recommendations

The major recommendations for VISTAS BART modeling included in this Protocol are:

I. Process

 Follow the BART process steps discussed in Chapter 2:

1. Identify BART eligible sources

2. Identify which pollutants have greater than de minimis emission levels

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasible controls and emission changes

6. Identify the change in visibility impact for each candidate BART control option

7. Compare the visibility improvement of BART control options to other statutory factors in 

the engineering analysis

Appendix H.6 - 1252
Summary S-3

II. CALPUFF Model Configuration

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a 

single source is subject to BART. VISTAS will use CALPUFF Version 5.754 and CALMET 

Version 5.7. These versions contain enhancements funded by the Minerals Management Service 

(MMS) and VISTAS. They were developed by Earth Tech, Inc. and are maintained on the 

CALPUFF website (www.src.com) for public access. 

VISTAS is making publicly available 12-km CALMET output files for the entire VISTAS 

modeling domain (eastern United States) and intends to also provide CALMET output files for 

five 4-km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the 

CALMET input files, Earth Tech used the MM5 databases developed by EPA for 2001, VISTAS 

for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire 

VISTAS region, Earth Tech used the No-Obs setting (i.e., did not include additional surface and 

upper air observations beyond those incorporated in the MM5 calculations). For finer resolution 

subdomains (4 km grid or less), available surface and upper air observations will be used in 

addition to MM5 meteorological model outputs. The specific model settings will be provided 

with the CALMET files and via the CALPUFF website so that users can review or replicate the 

work. 

For CALPUFF modeling, source emissions should be defined using the maximum 24-hour actual 

emission rate during normal operation for the most recent 3 or 5 years. If maximum 24-hr actual 

emissions are not available, continuous emissions data, permit allowable emissions, potential 

emissions, and emissions factors from AP-42 source profiles may be used as available. 

Key points from comments received on the specific CALPUFF, CALPOST, and POSTUTIL 

configurations are highlighted below. 

• After running CALPUFF for an individual facility, repartition NO3 in POSTUTIL.

1

• Use ozone data from non-urban monitors as the background ozone input.

• Use the Pasquill-Gifford dispersion method.2

 

1 The original intent, as expressed in the Final VISTAS BART Modeling Protocol (22 December 2005) was to use 

CMAQ-derived background data for SO2, NO3 and NH3 in POSTUTIL. After extensive discussion with the EPA 

and FLMs in early 2006, EPA did not approve the recommended approach so background gaseous 

concentrations from CMAQ 2002 modeling will not be provided by VISTAS for use in POSTUTIL. Rather the 

standard default NH3 concentrations specified on page 14 of the IWAQM Phase 2 report (IWAQM, 1998) will 

be used.

2 The Final VISTAS BART Modeling Protocol (Dec. 22, 2005) recommended using turbulence-based AERMOD 

dispersion methods, citing EPA’s Revision to the Guideline on Air Quality Models: Adoption of a Preferred 

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-

68261. 9 November 2005. Subsequently, EPA Region IV notified the VISTAS states that using turbulenceAppendix H.6 - 1253
Summary S-4

• In CALPOST, use Method 6 with monthly average RH for calculating extinction, as 

recommended by the EPA. 

• Use EPA default calculations of light extinction under current and natural background 

conditions. In addition to the default assumptions, a source may choose to also calculate 

visibility using the recently revised IMPROVE algorithm described by Pitchford, et al., 

(2005). 

Provide results in tables as illustrated in Chapter 4 that describe, for each source:

• Number of receptors within a single Class I area with impact > 0.5 dv

• Number of days at all receptors in the Class I area with impact > 0.5 dv

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART 

For determining if a BART-eligible source is subject to BART CALPUFF modeling, use a twotier approach. For the initial exemption modeling use CALPUFF with 12-km grid CALMET. For 

finer resolution of meteorological fields, use CALPUFF with CALMET of 4-km or smaller grid 

size. 

VISTAS States are accepting EPA guidance that the threshold value to establish that a source 

contributes to visibility impairment is 0.5 deciview. 

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I area 

boundary (Q/d) as a presumptive indicator that a BART-eligible source is subject to BART. If 

Q/d for SO2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the 

source is subject to BART and no exemption modeling will be performed using VISTAS funds. If 

the source agrees with this presumption, then the source can proceed to the BART determination 

using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a 

source disagrees, the source may perform fine grid modeling to determine if its impact is <0.5 dv. 

For sources with Q/d less than or equal to 10, VISTAS intends to fund TRC Environmental 

Corp.3 to assist States with the initial CALPUFF exemption modeling. Each State will prioritize 

which sources will be offered modeling by VISTAS. Modeling of these sources will be 

conducted in priority order to first accommodate States with nearer term timing constraints in 

their SIP development process. To conserve VISTAS resources, modeling will begin with 

sources at lower Q/d values and continue with sources with higher Q/d values until a Q/d value 

 

based dispersion methods would be considered a non-guideline application of CALPUFF. Thus this Protocol 

has been revised to indicate Pasquill-Gifford dispersion coefficients should be used. 

3 In April 2006, Earth Tech’s CALPUFF modeling staff became part of TRC Environmental Corporation. 

References to Earth Tech and to TRC in this protocol refer to the same technical staff, just at different times.

Appendix H.6 - 1254
Summary S-5

that consistently results in a greater than 0.5 dv impact is identified. Chapter 4 addresses the 

number of VISTAS sources eligible for BART based on Q/d analysis. 

Note that VISTAS does not propose to use Q/d to exempt BART-eligible sources, but only to 

prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance 

not to use Q/d for exemption purposes. 

For the 12-km initial modeling exemption test, compare the highest single 24-hour average value 

across all receptors in the Class I area to the threshold value of 0.5 dv. If the highest 24-hr 

average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. If the 

highest 24-hr average value is greater than 0.5 dv, then the source may choose to perform finer 

grid modeling for exemption purposes or may accept determination that the source is subject to 

BART and proceed to establish visibility impacts prior to and after BART controls. If using the 

single highest 24-hr average value proves, after initial 12-km grid CALPUFF modeling, to be too 

conservative a screening level, VISTAS may allow some exceedances of the threshold value for 

exemption purposes, up to no more than the 98th percentile value. 

The 12-km modeling results can be used to focus finer grid modeling for exemption purposes on 

only those Class I areas where impacts greater than 0.5 dv were projected in the 12-km modeling.

For finer grid (4 km or less) analyses, use the 98th percentile impact value for the 24-hr average. 

Use either the 8th highest day in each year or the 22nd highest day in the 3-year period, whichever 

is more conservative, for comparison to the exemption threshold.

Use the same model assumptions for pre-BART visibility impact and for BART control options 

modeling: establish baseline visibility from the pre-BART run; change one control at a time; and 

evaluate the change in visibility impact, i.e. the delta-deciview. Note that “no control” may 

constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under 

the USEPA BART guideline. If a source accepts to institute the most stringent control, the 

engineering analyses are not required.

This common VISTAS Protocol consistently recommends conservative assumptions. Individual 

States ultimately have responsibility to determine which, if any, BART controls are 

recommended in their State Implementation Plans (SIPs).

Appendix H.6 - 1255
Introduction and Protocol Objectives 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 

1.1 Background

Under regional haze regulations, the Environmental Protection Agency (EPA) has issued final 

guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70 

FR 39104-39172). The regional haze rule includes a requirement for BART for certain large 

stationary sources. Sources are BART-eligible if they meet three criteria including potential 

emissions of at least 250 tons per year of a visibility-impairing pollutant, were put in place 

between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories 

in the guidance. A BART engineering evaluation using five statutory factors -- 1) existing 

controls; 2) cost; 3) energy and non-air environmental impacts; 4) remaining useful life of the 

source; 5) degree of visibility improvement expected from the application of controls -- is 

required for any BART-eligible source that can be reasonably expected to cause or contribute to 

impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas protected 

under the regional haze rule. (Note that, depending on the five factors, the evaluation may result 

in no control.) Air quality modeling is an important tool available to the States to determine 

whether a source can be reasonably expected to contribute to visibility impairment in a Class I 

area.

Throughout this document the term “BART-eligible emission unit” is defined as any single 

emission unit that meets the criteria described above. A “BART-eligible source” is defined as the 

total of all BART-eligible emission units at a single facility. If a source has several emission 

units, only those that meet the BART-eligible criteria are included in the definition “BARTeligible source”. 

One of the listed categories is steam electric plants of more than 250 million BTU/hr heat input. 

To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially 

subject to BART, the boiler capacities of all electric generating units (EGUs) should be added 

together regardless of construction date. In this category, electric generating sources greater than 

750 MW have presumptive SO2 and NOx emission limits. States may presume the same limits for 

EGU sources between 250-750 MW. However, units at those sources constructed after the 

BART-eligibility dates are not subject to a BART engineering evaluation. EPA, in the Clean Air 

Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program 

satisfies the BART requirements for SO2 and NOx. VISTAS states are tentatively accepting this 

guidance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM 

emissions are above de minimis values.

As illustrated in Table 1-1, as of December 5, 2005, VISTAS States had identified a total of 274 

BART-eligible sources that fall into 20 of the 26 BART source categories. Of the 274 sources 

with BART-eligible units, 84 sources are utility EGUs and 190 are non-EGU industrial sources. 

(Note that these numbers are not final and are subject to slight adjustments and refinements.) No 

BART sources are located on Tribal lands.

Appendix H.6 - 1256
Introduction and Protocol Objectives 2

Table 1-1. VISTAS BART Eligible Sources (not updated since December 2005)

State Total Number 

of Sources

EGU Sources Non-EGU 

Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 31 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19

Total 273 84 189

1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for 

air quality modeling to support BART determinations that are consistent with the EPA guidelines. 

The protocol will serve as the basis for establishing a common understanding among the 

organizations who will be performing the BART analyses or reviewing the BART modeling 

results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers 

(FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA 

final guidance and comments that were received on VISTAS’ draft protocol4 and provides 

additional description of modeling procedures. The original final protocol of 22 December 2005 

has been revised since then to clarify items, resolve technical issues, and reflect decisions by the 

EPA and FLMs. This document is the third revision. 

The VISTAS States have accepted EPA’s guidance to use the CALPUFF modeling system to 

comply with the BART modeling requirements of the regional haze rule. A BART-eligible 

source will be required to submit a site-specific modeling protocol to the State for review and 

approval prior to performing CALPUFF modeling. States will consult with FLMs and the EPA 

when evaluating the site-specific BART protocols. The site-specific protocol will include the 

 

4 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology 

(BART). VISTAS, March 22, 2005 and September 20, 2005.

Appendix H.6 - 1257
Introduction and Protocol Objectives 3

source-specific data on source location, stack parameters, and emissions. The methods of the 

VISTAS common modeling protocol will be followed in the site-specific protocol unless the 

source proposes to the State, and the State approves, alternative methods or assumptions. 

Each VISTAS State or Local agency retains responsibility for the specific procedures and 

processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA, 

and public to determine BART controls for sources in the State. Nothing in the VISTAS process 

replaces States’ responsibility to determine BART controls. 

The remainder of this document describes the CALPUFF modeling system and the application of 

CALPUFF to two situations:

• Air quality modeling to determine whether a BART-eligible source is “subject to BART” 

and therefore the BART analysis process must be applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to 

BART, to evaluate regional haze benefits of alternative control options and to document 

the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s 

guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent 

chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s 

guidance for regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal 

Register notice. The CALPUFF model is the preferred model recommended by the EPA for 

BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The 

specific steps to determine whether a BART-eligible source is subject to BART and to evaluate 

BART controls are described in Chapter 4. The procedures include initial modeling of BARTeligible sources using CALPUFF run in a conservative mode with regional meteorological 

datasets. For sources determined to be subject to BART based on these first modeling analyses, 

further finer grid CALPUFF analyses would be performed. The model configuration for the 

common modeling protocol is described in Chapter 4. Details of the source-specific protocol are 

described in Chapter 5. A quality assurance plan is outlined in Chapter 6. 

EPA’s guidance allows for the use of appropriate alternative models, however VISTAS will not 

develop a protocol for alternative models. This protocol focuses on guidance for the application 

of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in 

its BART demonstration, the source will need to submit a detailed written justification to the 

State for review and approval. The State will provide the documentation to the EPA and Federal 

Land Managers for their review. 

Also, this protocol does not address a preferred modeling approach to demonstrate the 

effectiveness of an optional emissions cap and trade program. Such a cap and trade program is 

not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTAS 

Appendix H.6 - 1258
Introduction and Protocol Objectives 4

States are not pursuing a regional trading alternative under the proposed EPA trading guidance 

(70 FR 44154-44175) that is to be promulgated in 2006.

Appendix H.6 - 1259
Review of EPA’s Guidance 5

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING

The final guidance for regional haze BART determinations was published in the Federal Register 

on 6 July 2005 (70 FR 39104 to 39172). It prescribes the modeling approaches that are to be used 

for various stages of the BART analysis process. 

This chapter provides a summary of EPA’s guidance for BART modeling. It is not intended to 

provide a comprehensive review of the guidance. Nor does this chapter address specific 

recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations 

appear in Chapter 4. 

2.1 Overview of the Regional Haze BART Process

The process of establishing BART emission limitations consists of four steps:

1) Identify whether a source is “BART-eligible” based on its source category, when it was put in 

service, and the magnitude of its emissions of one or more “visibility-impairing” air pollutants. The 

BART guidelines list 26 source categories of stationary sources that are BART-eligible. Sources 

must have been put in service between August 7, 1962 and August 7, 1977 in order to be BARTeligible. Finally, a source is eligible for BART if potential emissions of visibility-impairing air 

pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate 

matter (PM10) and gaseous precursors to secondary fine particulate matter, such as SO2 and NOx. 

Whether ammonia or volatile organic compounds (VOCs) should be included as visibilityimpairing pollutants for BART eligibility is left for the States to determine on a case-by-case basis. 

The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor 

pressure should be considered as primary PM2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential 

emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not 

subject to BART if their potential emissions are less than the de minimis levels (40 TPY for SO2

and NOx and 15 TPY of PM10 or PM2.5. This assumes that the other BART-eligibility criteria are 

met.)

2) Determine whether a BART-eligible source can be excluded from BART controls by 

demonstrating that the source cannot be reasonably expected to cause or contribute to visibility 

impairment in a Class I area. The preferred approach is an assessment with an air quality model 

such as CALPUFF or other appropriate model followed by comparison of the estimated 24-hr 

visibility impacts against a threshold above estimated natural conditions to be determined by the 

States.5 The threshold to determine whether a single source “causes” visibility impairment is set at 

 

5 A recent draft settlement agreement with the EPA (to be published in the Federal Register for public comment) 

provides that a State has the discretion to decide whether annual average or 20% best natural conditions are to be 

used as the reference. This ruling resolves an ambiguity in EPA’s BART guidance, where the BART guideline 

Appendix H.6 - 1260
Review of EPA’s Guidance 6

1.0 deciview change from natural conditions over a 24-hour averaging period in the final BART 

rule (70 FR 39118). The guidance also states that the proposed threshold at which a source may 

“contribute” to visibility impairment should not be higher than 0.5 deciviews although, depending 

on factors affecting a specific Class I area, it may be set lower than 0.5 deciviews. The test against 

the threshold is “driven” by the contribution level, since if a source “causes”, by definition it 

“contributes”. 

EPA recommends that the 98th percentile value from the modeling be compared to the contribution 

threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not 

contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98th

percentile value exceeds the threshold must be determined at each Class I area. Over an annual 

period, this implies the 8th highest 24-hr value at a particular Class I area is compared to the 

contribution threshold. Over a 3-year modeling period, the 98th percentile value may be interpreted 

as the highest of the three annual 98th percentile values at a particular Class I area or the 22nd highest 

value in the combined three year record, whichever is more conservative.

Alternatively, States have the option of considering that all BART-eligible sources within the 

State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be 

able to do exactly the opposite, and use regional modeling to conclude that all BART-eligible 

sources in the State do not cumulatively contribute to “measurable” visibility impairment in any 

Class I areas. Also, the States have an option to exempt individual sources based on model plant 

analysis conducted by EPA in finalizing the BART rule. Under this option, sources with 

potential emissions of SO2 plus NOx of less than 500 tons and a distance from any Class I area 

greater than 50 kilometers or sources with SO2 plus NOx potential emissions of less than 1000 

tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM 

emissions are not specifically addressed in the model plant analysis, but subsequent discussions 

with EPA staff indicate that PM may be considered along with SO2 and NOx, so that a plant could 

be exempted if the combined potential emissions of SO2, NOx, plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting 

the “best” alternative, taking into consideration:

a) Any pollution control equipment in use at the source (which affects the availability of 

options and their impacts),

b) The costs of compliance with control options,

c) The remaining useful life of the facility, 

d) The energy and non air-quality environmental impacts of compliance, and

 

text says “natural conditions” at 70 FR 39162, col. 3, while the preamble to the BART rule says “natural visibility 

baseline for the 20% best visibility days” at 70 FR 39125, col. 1. 

Appendix H.6 - 1261
Review of EPA’s Guidance 7

e) The degree of improvement in visibility that may reasonably be anticipated to result 

from the use of such technology.

Note that if a source agrees to apply the most stringent controls available to BART-eligible units, 

the BART analysis is essentially complete and no further analysis is necessary (70 FR 39165).

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze, 

which is due by December 2007.

Instead of applying BART on a source-by-source basis, a State (or a group of States) has the option 

of implementing an emissions trading program that is designed to achieve regional haze 

improvements that are greater than the visibility improvements that could be expected from BART. 

If the geographic distributions of emissions under the two approaches are similar, determining 

whether trading is “better than BART” may be possible by simply comparing emissions expected 

under the trading program against the emissions that could be expected if BART was applied to 

eligible sources. If the geographic distributions of emissions are likely to be different, however, air 

quality modeling comparing the expected improvements in visibility from the trading program and 

from BART would be required. (See the proposed BART Alternative rule, at 70 FR 44160.) EPA 

suggests that regional modeling using a photochemical grid model may be more appropriate than 

CALPUFF for this purpose.

Note that EPA has indicated in the BART rule (70 FR 39138-39139) that emissions reductions 

under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO2 and NOx control 

for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR 

and therefore a BART analysis may still be required for PM. 

2.2 Model Recommendations for the BART Analysis 

To evaluate the visibility impacts of a BART-eligible source at Class I areas beyond 50 km from the 

source, the EPA guidance recommends the use of the CALPUFF model as “the best regulatory 

modeling application currently available for predicting a single source’s contribution to visibility 

impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA 

prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to 

submit a written justification and source-specific modeling protocol to its State for review and 

approval. As part of the consultation process, the State will provide documentation to EPA and 

FLM. 

For modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance 

recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and 

other methods.” The PLUVUE-II plume visibility model is mentioned as a possible model to 

consider instead of CALPUFF for a source within less than 50 km of a Class I area. 

The EPA guidance notes that “regional scale photochemical grid models may have merit, but such 

models have been designed to assess cumulative impacts, not impacts from individual sources” and 

Appendix H.6 - 1262
Review of EPA’s Guidance 8

they are “very resource intensive and time consuming relative to CALPUFF”, but States may 

consider their use for SIP development in the future as they may be adapted and “demonstrated to 

be appropriate for single source applications” (70 FR 39123). Photochemical grid models may be 

more appropriate for cumulative modeling options such as in the determination of the aggregate 

contribution of all-BART-eligible sources to visibility impairment, but such use should involve 

consultation with the appropriate EPA Regional Office (70 FR 39163).

According to the BART guidance, a modeling protocol should be submitted for all modeling 

demonstrations regardless of the distance from the BART-eligible source to the Class I area. EPA’s 

role in the development of the protocol is only advisory as the “States better understand the BARTeligible source configurations” and factors affecting their particular Class I areas (70 FR 39126). 

In the BART modeling analyses the EPA recommends that the State use the highest 24-hour 

average actual emission rate for the most recent three to five-year period of record. Emissions on 

days influenced by periods of start-up, shutdown and malfunction are not to be considered in 

determining the appropriate emission rates. (70 FR 39129).

If a source is found to be subject to BART, CALPUFF or another appropriate model should be used 

to evaluate the improvement in visibility resulting from the application of BART controls. 

Visibility improvements may be evaluated on a pollutant-specific basis in the BART determination 

(70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA 

guidelines state that States are “encouraged to account for the magnitude, frequency, and duration 

of the contributions to visibility impairment caused by the source based on the natural variability of 

meteorology” (70 FR 39129). 

2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to 

demonstrate greater “reasonable progress” in reducing haze than would result if BART were 

applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading 

program that achieves greater progress at reducing emissions will also achieve greater progress at 

reducing haze. Such would be the case if the likely geographic distribution of emissions under the 

trading program would not be greatly different from the distribution if BART was in place. 

If the expected distribution of emissions is different under the two approaches, then “dispersion 

modeling” of all sources must be used to determine the difference in visibility at each impacted 

Class I area, in order to establish that the optional trading program will result in visibility 

improvements aggregated over all Class I areas that are “better than BART” (70 FR 39137-39138). 

The BART guidance does not specify the method to be used for this modeling. From a technical 

perspective, either applying CALPUFF to every source or using a regional photochemical model 

would satisfy the need.

Appendix H.6 - 1263
Review of EPA’s Guidance 9

A rulemaking procedure is currently underway to establish final guidance for such alternatives to 

BART (70 FR 44154-44175). The rule is expected to be finalized in 2006.

Appendix H.6 - 1264
Overview of the CALPUFF Modeling System 10

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities 

and limitations. It does not include specific recommendations regarding the use of the model for 

BART analysis in the VISTAS region. These specific recommendations can be found in Chapter 

4. 

3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred 

modeling approach for use in the BART analyses. CALPUFF and its meteorological model, 

CALMET, are designed to handle the complexities posed by the complex terrain, the large 

source-receptor distances, chemical transformation and deposition, and other issues related to 

Class I visibility impacts. The CALPUFF modeling system has been adopted by the EPA as a 

Guideline Model for source-receptor distances greater than 50 km, and for use on a case-by-case 

basis in complex flow situations for shorter distances (68 FR 18440-18482). CALPUFF is 

recommended for Class I impact assessments by the Federal Land Managers Workgroup (FLAG 

2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The 

final BART guidance recommends CALPUFF as “the best modeling application available for 

predicting a singe source’s contribution to visibility impairment” (70 FR 39122). As a result of 

these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its 

BART determinations.

The main components of the CALPUFF modeling system are shown in Figure 3-1. CALMET is 

a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It 

produces three-dimensional wind and temperature fields and two-dimensional fields of mixing 

heights and other meteorological fields. It contains slope flow effects, terrain channeling, and 

kinematic effects of terrain. CALMET includes special algorithms for treating the overwater 

boundary layer and coastal interaction effects. CALMET can use meteorological observational 

data and/or three-dimensional output from prognostic numerical meteorological models such as 

MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine-scale 

meteorological fields.

CALPUFF is a non-steady-state Lagrangian puff transport and dispersion model that advects 

Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been 

designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers 

from a source. It includes algorithms for near-field effects such as building downwash, stack tip 

downwash and transitional plume rise as well as processes important in the far-field such as 

chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to 

allow puff splitting in the horizontal and vertical directions, which extends the distance range of 

the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition 

fluxes evaluated at user-specified receptor locations.

Appendix H.6 - 1265
Overview of the CALPUFF Modeling System 11

Figure 3-1. CALPUFF modeling system components.

A set of postprocessing programs associated with CALPUFF computes visibility effects and 

allows cumulative source impacts to be assessed, including potential non-linear effects of 

ammonia limitation on nitrate formation. The CALPOST postprocessor contains several options 

for computing change in extinction and deciviews for visibility assessments. The POSTUTIL 

postprocessor includes options for summing contributions of individual sources or groups of 

sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid-nitrate 

chemical equilibrium module, which allows the cumulative effects of ammonia consumption by 

background sources to be assessed in the postprocessor. In addition, the combination of 

CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be 

incrementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF modeling 

components in more detail.

Appendix H.6 - 1266
Overview of the CALPUFF Modeling System 12

3.1.1 Major Features of CALMET

The CALMET meteorological model consists of a diagnostic wind field module and 

micrometeorological modules for overwater and overland boundary layers. When modeling a 

large geographical area, as would be necessary for the regional VISTAS domain, the user has the 

option to use a Lambert Conformal Projection coordinate system to account for Earth’s curvature. 

The major features and options of the meteorological model are summarized in Table 3-1. The 

techniques used in the CALMET model are briefly described below. 

Table 3-1. Major Features of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland Boundary Layer - Energy Balance Method

- Overwater Boundary Layer - Profile Method

-- COARE algorithm

-- OCD-based method

- Produces Gridded Fields of:

-- Surface Friction Velocity

-- Convective Velocity Scale

-- Monin-Obukhov Length

-- Mixing Height

-- PGT Stability Class

-- Air Temperature (3-D)

-- Precipitation Rate

• Diagnostic Wind Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimization

- Produces Gridded Fields of U, V, W Wind Components

- Inputs Include Domain-Scale Winds, Observations, and

 (optionally) Coarse-Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and 

overwater grid cells.

Overland Boundary Layer Model: Over land surfaces, the energy balance method of Holtslag and 

van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surface 

Appendix H.6 - 1267
Overview of the CALPUFF Modeling System 13

friction velocity, Monin-Obukhov length, and convective velocity scale. Mixing heights are 

determined from the computed hourly surface heat fluxes and observed temperature soundings 

using a modified Carson (1973) method based on Maul (1980). The model also determines 

gridded fields of Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates.

Overwater Boundary Layer Model: The aerodynamic and thermal properties of water surfaces 

suggest that a different method is best suited for calculating the boundary layer parameters in the 

marine environment. A profile technique, using air-sea temperature differences, is used in 

CALMET to compute the micro-meteorological parameters in the marine boundary layer. The 

version of CALMET being used by VISTAS contains improvements in the overwater boundary 

layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response 

Experiment (COARE) and enhancements in the calculation of overwater mixed layer heights 

(Batchvarova and Gryning, 1991, 1994). Further details and the results of an evaluation of the 

model containing these enhancements are described in Scire et al. (2005). An upwind-looking 

spatial averaging scheme is optionally applied to the mixing heights and three-dimensional 

temperature fields in order to account for important advective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two-step approach to the computation of the wind fields 

(Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted for kinematic 

effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded 

MM5 can be used to define the initial guess field. The second step consists of an objective 

analysis procedure to introduce observational data into the Step 1 wind field to produce a final 

wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field 

defined by the MM5 prognostic meteorological model. Normally, the CALMET computational 

domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial 

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used to 

develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 algorithms 

in CALMET described below apply terrain adjustments to the initial guess field on the fine-scale 

CALMET grid. Thus, the CALMET winds are adjusted to respond to fine-scale terrain features 

not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain: The approach of Liu and Yocke (1980) is used to evaluate the 

effects of the terrain on the wind field. The initial guess field winds are used to compute a terrainforced vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme is 

applied iteratively until the three-dimensional divergence is less than a threshold value. 

Slope Flows: The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 

1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

Appendix H.6 - 1268
Overview of the CALPUFF Modeling System 14

advective-gravity and equilibrium flow regimes. At night, the slope flow model parameterizes the 

flow down the sides of the valley walls into the floor of the valley, and during the day, upslope 

flows are parameterized. The magnitude of the slope flow depends on the local surface sensible 

heat flux and local terrain gradients. The slope flow wind components are added to the wind field 

adjusted for kinematic effects.

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are 

parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude 

number at a particular grid point is less than a critical value and the wind has an uphill 

component, the wind direction is adjusted to be tangent to the terrain. 

Step 2 Wind Field. The wind field resulting from the above adjustments of the initial-guess wind 

is the Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An inversedistance squared interpolation scheme is used which weights observational data heavily in the 

vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind 

field in regions with no observational data. The resulting wind field is subject to smoothing, an 

optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence 

minimization to produce a final Step 2 wind field. 

The introduction of observational data in the Step 2 calculation is an option. It is also possible to 

run the model in “no observations” (No-Obs) mode, which involves the use only of MM5 gridded 

data for the initial guess field followed by fine-scale terrain adjustments by CALMET. In NoObs mode, observational data are not used in the Step 2 calculations. The No-Obs mode is 

appropriate when the MM5 simulations adequately characterize the regional wind patterns and 

when local observations, especially surface observations, reflect local conditions on a scale 

smaller than that of the CALMET domain and hence their spatial representativeness may be 

limited. Such situations are most likely to occur when the CALMET grid scale is relatively large 

i.e., coarser than the scale of variation of the true wind field, which is particularly likely to occur 

in complex terrain or along the seashore,

3.1.2 Major Features of CALPUFF

By its puff-based formulation and through the use of three-dimensional meteorological data 

developed by the CALMET meteorological model, CALPUFF can simulate the effects of timeand space-varying meteorological conditions on pollutant transport from sources in complex 

terrain. The major features and options of the CALPUFF model are summarized in Table 3-2 at 

the end of this subsection. Some of the technical algorithms are briefly described below. 

Complex Terrain: The effects of complex terrain on puff transport are derived from the 

CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor locations 

are simulated using one of two algorithms that modify the puff-height (either that of ISCST3 or a 

general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical 

dispersion derived from the weakly-stratified flow and dispersion module of the Complex Terrain

Appendix H.6 - 1269
Overview of the CALPUFF Modeling System 15

Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely 

on the receptor elevation (relative to the elevation at the source) and the height of the puff above 

the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the 

direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG treats terrain 

features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry 

et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of 

receptors using a dividing streamline height (Hd) to determine which pollutant material is 

deflected around the sides of a hill (below Hd) and which material is advected over the hill (above 

Hd). The local flow (near the feature) used to define Hd is taken from the gridded CALMET 

fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Functions: A set of accurate and computationally efficient puff sampling routines 

is included in CALPUFF, which solve many of the computational difficulties encountered when 

applying a puff model to near-field releases. For near-field applications during rapidly-varying 

meteorological conditions, an elongated puff (slug) sampling function may be used. An 

integrated puff approach may be used during less demanding conditions. Both techniques 

reproduce continuous plume results under the appropriate steady state conditions.

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both 

incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the 

choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been 

implemented in such a way as to allow the use of wind direction specific building dimensions. 

The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as 

an option.

Dispersion Coefficients: Several options are provided in CALPUFF for the computation of 

dispersion coefficients, including the use of turbulence measurements (σv and σw), the use of 

similarity theory to estimate σv and σw from modeled surface heat and momentum fluxes, or the 

use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion 

equations based on the CTDM. Options are provided to apply an averaging time correction or 

surface roughness length adjustments to the PG coefficients. In version 5.754 of CALPUFF 

being used by VISTAS, an option is provided to use the AERMOD turbulence profiles for 

determining dispersion rates, which is the most recent approach to dispersion in EPA-approved 

regulatory modeling. In addition, turbulence advection is included. For additional details on 

these features, see Scire et al. (2005).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model 

contains both overwater and overland boundary layer algorithms, the effects of water bodies on 

plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff 

formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion 

conditions, including the abrupt changes that occur at the coastline of a major body of water.

Appendix H.6 - 1270
Overview of the CALPUFF Modeling System 16

Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry 

deposition rates of gases and particulate matter as a function of geophysical parameters, 

meteorological conditions, and pollutant species. For particles, source-specific mass distributions 

may be provided for use in the resistance model. Of particular interest for BART analyses is the 

ability to separately model the deposition of fine particulate matter (< 2.5 μm diameter) from 

coarse particulate matter (2.5-10 μm diameter).

Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical 

wind shear effects across individual puffs to be simulated. Differential rates of dispersion and 

transport among the “new” puffs generated from the original, well-mixed puff can substantially 

increase the effective rate of horizontal spread of the material. Puffs may also be split in the 

horizontal when the puff size becomes large relative to the grid size, to account for wind shear 

across the puffs. 

Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF to compute 

the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging 

coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. 

liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOx, HNO3, and NO3

-

) 

employed in the MESOPUFF II model or a set of user-specified, diurnally-varying transformation 

rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine 

particulate matter (sulfate and nitrate) from emissions of SO2 and NOx and thus allows analyses 

of visibility impacts. Ambient ozone concentrations are used in the parameterized chemical 

transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia 

concentrations are used together with a temperature and relative humidity-dependent equilibrium 

relationship to partition nitric acid and nitrate on an hour-by-hour and receptor-by-receptor basis. 

Appendix H.6 - 1271
Overview of the CALPUFF Modeling System 17

Table 3-2. Major Features of the CALPUFF Dispersion Model 

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant or variable emissions)

- Volume sources (constant or variable emissions)

- Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions

- Gridded 3-D fields of meteorological variables (winds, temperature)

- Spatially-variable fields of mixing height, friction velocity, convective velocity scale,

 Monin-Obukhov length, precipitation rate

- Vertically and horizontally-varying turbulence and dispersion rates

- Time-dependent source and emissions data for point, area, and volume sources

- Temporal or wind-dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time-varying heat flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient (σy, σz) options

- Direct measurements of σv and σw

- Estimated values of σv and σw based on similarity theory 

-- AERMOD turbulence profiles

-- Original turbulence profiles

- Pasquill-Gifford (PG) dispersion coefficients (rural areas)

- McElroy-Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- Huber-Snyder method

- Schulman-Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

- Optional puff height adjustment: ISC3 or "plume path coefficient"

- Optional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)

Appendix H.6 - 1272
Overview of the CALPUFF Modeling System 18

Table 3-2. Major Features of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, Hd, as in CTDMPLUS:

- Above Hd, material flows over the hill and experiences altered diffusion rates

- Below Hd, material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of space and time variations of deposition with a resistance model

- User-specified diurnal cycles for each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD-based method)

- Abrupt change in meteorological conditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and NO3

(MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2, HNO3, and NO3 

(RIVAD/ARM3 method)

- User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function of precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and 

POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing 

tabulations that summarize the results of the simulations, identifying, for example, the highest and 

second-highest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentrations from CALPUFF to compute light extinction 

and related measures of visibility (haze index in deciviews), reporting these for a 24-hour 

averaging time. 

The CALPOST processor contains several options for evaluating visibility impacts, including the 

method described in the BART guidance, which uses monthly average relative humidity values. 

CALPOST contains implementations of the IWAQM-recommended and FLAG-recommended 

visibility techniques and additional options to evaluate the impact of natural weather events (fog, 

rain and snow) on background visibility and visibility impacts from modeled sources.

The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources 

from different simulations to be summed, can compute the difference between two sets of 

Appendix H.6 - 1273
Overview of the CALPUFF Modeling System 19

predicted impacts (useful for evaluating the benefits of BART controls), and contains a chemistry 

module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This 

capability allows the potential non-linear effects of ammonia scavenging by sulfate and nitrate 

sources to be evaluated in the formation of nitrate from an individual source. CALPUFF makes 

the full ambient ammonia concentration available to each puff without regard for any scavenging 

by other puffs. POSTUTIL corrects for such scavenging when the puffs generated by the 

CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or 

when nitrate formation is to be attributed to each of several sources that are in a cluster and whose 

plumes overlap, 

POSTUTIL will also compute the impacts of individual sources or groups of sources on sulfur 

and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the 

changes in deposition fluxes resulting from changes in emissions to be quantified. For example 

the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity 

(ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).

3.2 Discussion of CALPUFF Applicability and Limitations

3.2.1 Transport and Diffusion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport 

distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km 

should be done cautiously with an awareness of the likely problems involved.”6

IWAQM’s 200-km limitation derives from the observation that, when compared to the data of the 

Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF 

overestimated inert tracer concentrations by factors of 3 to 4 at receptors that were 300 to 1000 

km from the source. The apparent reason was insufficient horizontal dispersion of the simulated 

plume, presumably because an actual large plume does not remain coherent in the presence of 

vertical wind shears that typically occur, especially during the night, and of horizontal wind 

shears over the large puffs that arise over long transport distances. 

To better represent such situations, an optional puff splitting algorithm has since been added to 

CALPUFF to simulate wind shear effects across a well-mixed individual puff by dividing the 

puff horizontally and vertically into two or more pieces. Differential rates of transport among the 

new puffs thus generated can increase the horizontal spread of the material in the plume due to 

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is 

 

6 The IWAQM presentation at EPA’s 6th Modeling Conference provides the background for this recommendation: 

“The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration 

impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For 

larger transport times and distances, our experience thus far is that CALPUFF tends to underestimate the 

horizontal extent of the dispersion and hence tends to overestimate the surface-level concentration maxima. This 

does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such 

instances be used cautiously and with some understanding.” (From page D-12 of the IWAQM Phase 2 report.)

Appendix H.6 - 1274
Overview of the CALPUFF Modeling System 20

designed to allow large puffs that may grow to be several grid cells or more in size to split into 

smaller puffs that can then more accurately respond to variations in the local wind field across the 

original large puff. This will also tend to increase horizontal dispersion of the plume. Since the 

creation of additional puffs via puff splitting will increase the computational requirements of the 

model, possibly substantially, puff splitting is not enabled by default, but can be turned on at the 

option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km, 

or possibly over shorter distances in complex terrain.

Turning to the shorter distance end of the transport range, the CALPUFF section of Appendix A 

of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended 

for use on scales from tens of meters from a source to hundreds of kilometers.” This is supported 

by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were 

designed to be suitable for both short and long distances. In this regard, CALPUFF does contain 

algorithms for such near-field effects as plume rise, building downwash, and terrain impingement 

and includes routines that deal with the computational difficulties encountered when applying a 

puff model in the field near to a source.

The recommendations for regulatory use in Appendix A of the Guideline on Air Quality Models

state, “CALPUFF is appropriate for long range transport (source-receptor distance of 50 to 

several hundred kilometers)”, but provisions for using CALPUFF in the near-field in “complex 

flow” situations are also included in the regulatory guidance. Complex flow situations may 

include complex terrain, coastal areas, situations where plume fumigation is likely, and areas 

where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to 

changes in valley orientation) are important. 

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in 

the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, 

additional studies of model performance have been performed at shorter distances, including at a 

power plant in New York state in complex terrain (at source-receptor distances of 2 to 8.5 km) 

and a second power plant in Illinois in simple terrain (at source-receptor distances in arcs ranging 

from 0.5 km to 50 km from the stack) (Strimaitis et al., 1998). Other CALPUFF evaluation 

studies over short-distances include ones by Chang et al. (2001) and Morrison et al. (2003). 

These studies demonstrate good model performance over source-receptor distances from a few 

hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The 

EPA has defined the "regulatory default" option in CALPUFF to allow either Pasquill-Gifford 

(PG) or turbulence-based dispersion coefficients. CALPUFF has been evaluated and shown to 

perform better using turbulence-based dispersion for tall stacks (Strimaitis et al, 1998). 

CALPUFF with turbulence-based dispersion has also been evaluated for overwater transport and 

coastal situations (Scire et al., 2005). In many other studies, including AERMOD evaluation 

studies conducted by EPA, the use of PG-dispersion, or more specifically the lack of a convective 

probability density function (pdf) module, has been demonstrated to result in underprediction of 

peak concentrations.

Appendix H.6 - 1275
Overview of the CALPUFF Modeling System 21

In November 2005, EPA approved the AERMOD model, which relies on turbulence-based 

dispersion, as a regulatory Guideline Model7. The ISCST3 model and its PG dispersion 

coefficients are being phased out as an acceptable regulatory approach. However, EPA Region 

IV has indicated that the application of turbulence-based dispersion coefficients in CALPUFF 

needs to be further demonstrated before they are approved for BART application. They will 

consider accepting the use of turbulence dispersion coefficients on a case-by-case basis for 

sources that are close to Class I areas. 

For regional haze light extinction calculations, use of a plume-simulating model such as 

CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually 

discernible as a plume per se, but nevertheless its presence could alter the visibility through the 

background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 

km from a source. In this light, the BART guidance strongly recommends using CALPUFF for 

source-receptor distances greater than 50 km but also presents CALPUFF as an option that can be 

considered for shorter transport distances.

As discussed above, there do not appear to be any scientific reasons why CALPUFF cannot be 

used for even shorter transport distances than 30 km, though, as long as the scale of the plume is 

larger than the scale of the output grid so that the maximum concentrations and the width of the 

plume are adequately represented and so that the sub-grid details of plume structure can be 

ignored when estimating effects on light extinction. The standard 1-km output grid that has been 

established for Class I area analyses should serve down to source-receptor distances somewhat 

under 30 km; how much closer than 30 km will depend on the topography and meteorology of the 

area and should be evaluated on a case-by-case basis. For extremely short transport distances, 

depiction of the concentration distribution will require a grid that is finer than 1 km. (For 

reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 km of travel distance, 

assuming Pasquill-Gifford dispersion rates under neutral conditions.) 

As an additional consideration, if the plume width is small compared to the visual range, the 

atmospheric extinction along a typical sight path of tens of kilometers through the plume will be 

inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor 

point will not be correct. However, the effect of averaging light extinction estimates for 24 hours, 

during which the plume location shifts over various receptor points, is likely to mitigate this 

problem to some degree and suggests that using CALPUFF at distances under 30 km will often be 

appropriate. For the narrow plumes that result from short transport distances, though, the modeled 

peak 24-hr average extinction at a receptor will tend to overstate the effect of the source on 

regional haze. 

 

7 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex 

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.

Appendix H.6 - 1276
Overview of the CALPUFF Modeling System 22

The U.S. EPA has suggested that the plume visibility model, PLUVUE-II, could be used in lieu 

of CALPUFF for simulating visibility effects at such short distances.8 PLUVUE-II is a Gaussian 

model that simulates the dispersion, chemical conversion, and optical effects of emissions of 

particles, SO2, and NOx from a single source. Its outputs include the discoloration of the sky by 

the plume (so called “plume blight”) and the effect of the plume on visibility along user-selected 

sight paths that pass through the plume. The impacts of the plume on visibility depend not only 

on the plume composition, but also on the sight path chosen and its direction relative to the axis 

of the plume and the location of the sun. It isn’t clear how such sight-path dependent results could 

be compared to the 0.5 and 1.0 deciview thresholds in the BART guidance. Since CALPUFF is 

designed to be useful for short transport distances (with features such as the simulation of plume 

downwash caused by structures at the source), CALPUFF seems more appropriate than 

PLUVUE-II for evaluating source impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM2.5

Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that 

CALPUFF can treat primary pollutants such as PM10. In actuality, CALPUFF can simulate PM10

or PM2.5 or some other size range, because the assumed size distribution of the particles is a user 

input. The smaller the particles, the more they disperse like an inert gas. In most cases, the 

dispersion of inert PM2.5 particles will be only minutely different from that of an inert gas, but the 

behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface. 

PM2.5 particles, which have a mass median diameter around 0.5 μm, have an average net 

deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine 

particles is usually not significant except for ground-level emissions. On the other hand, coarse 

particles (those PM10 particles larger than PM2.5) have an average deposition velocity of more 

than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks. 

CALPUFF includes parametric representations of particle and gas deposition in terms of 

atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the 

gravitational settling speed. Gravitational settling, which is of particular importance for the coarse 

fraction of PM10, is accounted for in the calculation of the deposition velocity. Effects of inertial 

impaction (important for the upper part of the PM10 distribution) and Brownian motion (important 

for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance 

recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light 

extinction efficiency than coarse particulate matter (2.5-10 μm diameters), should be treated 

separately in the model. CALPUFF allows for user-specified size categories to be treated as 

 

8 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE-II for BART 

application

Appendix H.6 - 1277
Overview of the CALPUFF Modeling System 23

separate species, which includes calculating size-specific dry deposition velocities for each size 

category.

A primary PM2.5 emission from coal-fired electric generating units (EGUs) that is of relevance to 

visibility calculations is that of primary sulfate. Although primary sulfate emissions account for 

only a small fraction of the total sulfur emissions from such sources, it may be important to 

simulate their effect with CALPUFF, especially at shorter distances before significant formation 

of secondary sulfate conversion from SO2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate 

The MESOPUFF-II chemistry algorithm used in CALPUFF9 simulates the gas phase oxidation 

of sulfur dioxide to sulfate by a linear transformation rate that was developed using regression 

relationships derived from the analysis of chemical conversion rates produced by a complex 

photochemical box model (see Scire et al., 1984, for a description of the development of the 

chemical module). As in all empirically-derived models, the relationships are based on easilycomputed or observed parameters that are used as surrogates for the factors that control SO2

oxidation. 

The surrogate factors included in the parameterized chemistry during the daytime hours include 

solar radiation intensity, ambient ozone concentration, and atmospheric stability class. For 

example, gas phase SO2 oxidation is a function of OH radical concentrations. Ozone 

concentrations are correlated with OH radical concentrations during daytime hours, and their use 

in the daytime SO2 conversion rate in CALPUFF is based on this correlation relationship. The 

philosophy is that OH radical measurements are not available and cannot easily be computed 

within a model like CALPUFF, but ozone is commonly measured throughout the country, so the 

use of the well-known surrogate variable (ozone) is more useful in the empirical relationship than 

factors that are unknown or have a high degree of uncertainty. The same logic applies to the 

other variables in the relationship. They are surrogates for factors that the regression analysis has 

shown to be important in SO2 oxidation rates. At night, the SO2 conversion is set to a constant 

low value (default is 0.2%/hr). Aqueous phase oxidation of SO2 is represented by an additive 

term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF 

represents the chemical conversion as a linear process because it requires linear independence 

between puffs, although as explained below, non-linear behavior in nitrate formation can be 

modeled.

 

9 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO2, SO4

=

, NOx, HNO3, 

and NO3

-

) MESOPUFF-II system and the 6 species RIVAD system (which treats NO and NO2 separately). 

IWAQM recommends using the MESOPUFF-II system with CALPUFF. The RIVAD system is believed to be 

more appropriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM- and FLM-recommended 

MESOPUFF-II chemistry is most appropriate. 

Appendix H.6 - 1278
Overview of the CALPUFF Modeling System 24

The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing 

the gas phase sulfate formation but that it does not adequately account for the aqueous phase 

oxidation of SO2. Actual aqueous phase oxidation in clouds or fog can proceed at rates much 

greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in 

such situations. However, aqueous phase oxidation depends on liquid water content, not relative 

humidity. In reality, liquid water does not exist in the atmosphere at relative humidity much 

below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative 

humidity. This can lead CALPUFF to overestimate sulfate concentrations when the humidity is 

high but the cloud water that enables aqueous conversion is not present. Therefore, the direction 

of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1) 

overestimation of sulfate formation when NOx concentrations in the plume are high and in 

actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO2; 

and (2) lack of direct consideration of the effect of temperature on the conversion rates, which 

may cause the model to overstate sulfate formation on cold days (below 10C or 50°F) (Morris et 

al., 2003). However, in CALPUFF, the effects of temperature are, to some degree, compensated 

for indirectly by the use of the solar radiation surrogate variable in the empirical conversion 

equations. 

Whether these potential errors are important will depend on the setting. For example, Figure 3-2 

shows a comparison of predicted and observed 24-hour sulfate concentrations, due to a large 

number of SO2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et 

al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether 

CALPUFF predictions would compare as well with measurements in the Southeast remains to be 

seen. 

CALPUFF does not identify the chemical form of the sulfate compound that results from its 

reactions, which will generally be some form of ammoniated sulfate whose degree of 

neutralization will depend on the availability of ammonia in the atmosphere. This consideration, 

which has been found to be relevant for calculating light extinction in the VISTAS region, is not 

addressed by CALPUFF or CALPOST.

In most applications, the ozone concentrations required for the sulfate formation calculations are 

derived from ambient measurements, although concentrations simulated by regional models can 

be used. 

Appendix H.6 - 1279
Overview of the CALPUFF Modeling System 25

Figure 3-2. Observed vs. CALPUFF-predicted 24-hour sulfate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995.

NOx and Secondary Ammonium Nitrate

The MESOPUFF-II chemistry algorithm used in CALPUFF simulates the oxidation of NOx to 

nitric acid and organic nitrates (both gases) by transformation rates that depend on NOx

concentration, ambient ozone concentration, and atmospheric stability class during the day. The 

conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature- and 

humidity-dependent equilibrium between nitric acid gas and ammonium nitrate particles is taken 

into account when estimating the ammonium nitrate particle concentration, an equilibrium that 

depends on the ambient concentration of ammonia. The user supplies the value of the ambient 

concentration of ammonia. CALPUFF assumes that the sulfate reacts preferentially with that 

ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium 

nitrate. 

The IWAQM Phase 2 report considers that this mechanism is adequate for representing nitrate 

chemistry. Potential situations where this assumption may not be correct, however, include (1) 

plumes with high concentrations of NOx that deplete the ambient ozone and thus limit the 

[SO4] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

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

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4

Appendix H.6 - 1280
Overview of the CALPUFF Modeling System 26

transformation of NOx to nitric acid in the plume; and (2) when ambient temperature is below 10 

C, and thus the transformation rate is much slower and the nitrate concentration may be lower 

than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may 

overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nitrate 

concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when 

temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations 

tend to be the least. CALPUFF may overstate the impacts of NOx emissions at those times, 

especially in the colder northern states. This potential overestimate of nitrate was not evident, 

however, in an evaluation of CALPUFF-modeled nitrate against actual observational data in the 

Wyoming study, as shown in Figure 3-3a (Scire et al., 2001), 

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the 

background concentration of ammonia available to each puff, and that amount is scavenged by 

the sulfate in the puff. If puffs overlap, then that approach could overstate the amount of 

ammonium nitrate that is formed in total if, in reality, the combined scavenging by the 

overlapping puffs at a location would deplete the available ammonia enough that the combined 

nitrate formation was limited by the availability of ammonia. This effect of such ammonia 

limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one 

modeling analysis found the maximum light extinction impact of the source to be 7.4% (roughly 

0.74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting 

and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia 

limiting was considered (Escoffier-Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system 

has included the optional POSTUTIL postprocessing program, which repartitions the ammonia 

and nitric acid concentrations estimated by CALPUFF to reflect potential ammonia-limiting 

effects on the development of nitrate. This allows non-linearity associated with ammonia limiting 

effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate 

concentrations from all sources (modeled sources plus inflow boundary conditions) and estimates 

the amount of ammonia available for total nitrate formation after the preferential scavenging of 

ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is 

added to an existing mix of pollutants, POSTUTIL will estimate both the nitrate formed from the 

new source and the change in background nitrate as a result of the incremental depletion of 

ammonia (due to the new sulfate and nitrate) or addition of ammonia (from a new source of 

ammonia).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and 

spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage 

of the increased accuracy provided by POSTUTIL. The processor requires estimated 

concentrations of ammonia throughout the modeling domain and period. Such estimates can be 

inferred from CASTNet measurements, which are integrated over a week, from 24-hr SEARCH 

measurements, or from the output of a regional photochemical model such as CMAQ or CAMx. 

The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large, 

Appendix H.6 - 1281
Overview of the CALPUFF Modeling System 27

so defining the ammonia concentration throughout the Southeast would require extensive 

interpolation or extrapolation from the measured values. The quality of the SEARCH 

measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS 

domain. Modeled concentrations have the advantage of being resolved in space and time, but 

their accuracy should be evaluated by comparison with measurements wherever possible. 

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to 

determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation 

associated with temperature and relative humidity effects. For example, results of the Wyoming 

study (see Figure 3-3a) show that POSTUTIL adjustments produced daily average nitrate 

concentrations well within the factor of two lines and with very little mean bias. On the other 

hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced 

consistent overpredictions of nitrate by factors of 2-3 and 3-4, respectively, as shown in Figure 3-

3b (Scire et al., 2003). 

Figure 3-3a. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al., 

2001)

[NO3] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

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

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3

Appendix H.6 - 1282
Overview of the CALPUFF Modeling System 28

Figure 3-3b. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant 

ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench, 

2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90 

to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g., 

that from the burning of vegetation and deriving from VOC emissions from vegetation) and only 

5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky 

Mountains National Park in August 2002 (Tanner, et al., 2005) found that an average of 83% of 

the fine carbon was modern carbon

According to IMPROVE measurements, organics account for roughly 10% of the particle-caused 

light extinction in Class I areas in the Southeast. We can thus conclude that, in general, secondary 

organic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to 

have a large impact (around 1%) on current visibility. (Man-caused burning of vegetation can 

have significant localized, short-term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of 

the 1.4 μg/m3 concentration assumed for natural conditions by the EPA, which means that current 

fossil fuel burning would contribute less than 2% to visibility in an atmosphere that represents 

natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART 

[NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE 

Observations 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

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

CALPUFF Summed Repartition (

CALPUFF Separated Repartition 

CALPUFF Separated Repartition 

Factor 1

Factor 2 Factor 3

Factor 4

Factor 2

Factor 3

Factor 4

0.4 0.6 0.8 1 1.2

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

CALPUFF Summed Repartition (Seasonal NH3)

CALPUFF Separated Repartition (NH3=0.5ppb)

CALPUFF Separated Repartition (NH3=1.0ppb)

Factor 1

Factor 2 

or 3

Factor 2

Factor 3

Factor 4

Appendix H.6 - 1283
Overview of the CALPUFF Modeling System 29

sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) localized impact 

from a particularly large VOC source cannot be dismissed out of hand. 

CALPUFF has only rudimentary capabilities for addressing formation of visibility-impairing 

organic particles from some forms of volatile organic carbon (VOC). The capabilities that do 

exist include the following. 

First, PM10 emissions (such as from power plants) are often divided into filterable and 

condensable components, with the condensable mass being 100-200% of the filterable mass. For 

purposes of visibility analyses with CALPUFF, a fraction of the condensable part is typically 

treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the 

PM10 emissions condense into organic PM2.5 particles. The size of this organic fraction varies 

with process and process equipment, and can range from 20 to 100% of the condensable mass. 

These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable 

material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of secondary organic particles from organic emissions 

was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This 

simplified secondary organic aerosol (SOA) module is a linear, parameterized representation that 

is currently considered best suited for biogenic organics. It relies on the conventional wisdom that 

only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and 

Seinfeld, 1989). For example, according to this rule, isoprene (C5H8) does not make SOA but 

terpenes do, making pine trees more important biogenic contributors to SOA than oak trees.10

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA 

module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101 

estimated 24-hr SOA concentrations were within 2% of the measured values (Scire et al., 2001). 

This performance seems promising, although the developers view the SOA module as needing 

more testing and evaluation.

Thus, CALPUFF includes approaches for dealing with condensable VOC emissions that are 

characterized as condensable PM10 and with biogenic VOCs, although the soundness of 

concentration estimates by these approaches when modeling a plume from a single source is 

largely untested.11 The CALPUFF simulation of VOC emissions from sources whose VOC 

emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used 

for the simplified biogenic SOA module may be extended to anthropogenic VOCs when 

speciated VOC emissions information is available. If only those VOCs with more than six carbon 

atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC 

emissions. For example, the fugitive emissions of butane and ethane during petroleum processing 

 

10 Recent research suggests that isoprene may be a SOA precursor, however.

11 Note that neither of these VOC-related simulation approaches is described in the current (Version 5) CALPUFF

User’s Guide dated January 2001. See the Wyoming report referenced above for a description of this module.

Appendix H.6 - 1284
Overview of the CALPUFF Modeling System 30

are not important, while aromatic emissions (such as of toluene and xylene) are considered by the 

SOA module’s mechanism. Development, testing, and evaluation would be needed before one 

could rely on such a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART-eligible sources, 

means other than CALPUFF will be needed. A technical approach using a regional 

photochemical model to evaluate visibility impacts of VOC emissions is presented in Section 

4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction 

of PM10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component concentrations on 

light extinction is carried out in the CALPOST postprocessor. The formula used is the usual 

IMPROVE/EPA formula, which is applied to determine a change in light extinction due to 

changes in component concentrations. Using the notation of CALPOST, the formula is the 

following:

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] +

 + 0.6[Coarse Mass] + 10[EC] + bRay (3-1)

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh 

scattering term (bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for 

tracking reasonable progress (EPA, 2003a).

There are a few important differences in detail and in notation between the CALPOST formula 

for estimating light extinction (i.e., Equation 3-1) and that of IMPROVE and EPA. First, the OC

in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation), 

which is 1.4 times the OC (i.e., organic carbon alone) in the IMPROVE formula. The EC above is 

synonymous with LAC in the IMPROVE formula. CALPOST now offers the option of using the 

old IMPROVE f(RH) curve, whose values are documented in the December 2000 FLAG report, 

or the f(RH) now used by IMPROVE and EPA (as documented in EPA’s regional haze guidance 

documents). Also, CALPOST sets the maximum RH at 98% by default (although the user can 

change it), while the EPA’s guidance now caps it at 95%. 

The haze index (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (bext/10) (3-2)

where HI is in units of deciviews (dv) and bext is in Mm-1. The impact of a source is determined 

by comparing HI for estimated natural background conditions with the impact of the source and 

without the impact of the source. 

Appendix H.6 - 1285
Overview of the CALPUFF Modeling System 31

CALPOST Methods

CALPOST uses Equation 3-1 to calculate the extinction increment due to the source of interest 

and provides various methods for estimating the background extinction against which the 

increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the 

change in light extinction due to a source or group of sources (called Methods 1-7). These are 

usually reported as 24-hour average values, consistent with EPA and FLM guidance. In addition, 

there are two techniques for computing the 24-hour average change in extinction (i.e., as the ratio 

of 24-hour average extinctions, or as the average of 24-hour ratios). A brief summary of the 

techniques is provided below. Method 2 is the current default, recommended by both IWAQM 

(EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART 

guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current 

actual levels of extinction and thus are not consistent with the “natural conditions” the BART 

proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs 

of estimated (e.g., natural conditions) background extinction or component concentrations, and 

thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficient 

for each receptor, specify that a certain fraction of that coefficient is due to hygroscopic species, 

and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH)

curve or, optionally, the EPA regional haze f(RH) curve (EPA, 2003b). The RH is capped at 98% 

or a user-selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled 

sulfate and nitrate. 

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 

9.09 Mm-1 that results from EPA’s default natural conditions concentrations, together with an 

assumption that for natural conditions, say, 0.9 Mm–1 (or 10%) of this amount results from 

hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to this 10%. 

In Method 2, user-specified, speciated monthly concentration values are used to describe the 

background. When applied to natural conditions, for which EPA’s default natural conditions 

concentrations are annual averages, the same component concentrations would have to be used 

throughout the year (unless potential refinements to those default values resulted in 

concentrations that vary during the year). Hourly background extinction is then calculated using 

these concentrations and hourly, site-specific f(RH) from a 1993 IWAQM curve (a different one 

Appendix H.6 - 1286
Overview of the CALPUFF Modeling System 32

than that in Method 1) or, optionally, the EPA regional haze f(RH) curve.12 Again the RH is 

capped at either 98% (default) or a user-selected value (most commonly at 95%). 

Method 3 is the same as Method 2, except that any hour in which the RH exceeds 98% (or the 

selected maximum) is dropped from the analysis. When 24-hr extinction is computed, no fewer 

than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as 

“missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly 

climatologically representative values in EPA (2003a, b)) are used in place of hourly values for 

calculating both the extinction impact of the source emissions and the background conditions 

extinction. Hourly source impacts, with the effect on extinction due to sulfates and nitrates 

calculated using the monthly-average relative humidity in f(RH), are compared against the 

monthly default natural background concentrations. Thus the monthly-averaged relative humidity 

is applied to the hygroscopic components (i.e., sulfate and nitrate) of both the source impact and 

the background extinction with Method 6. 

Method 7 is a new variant of Method 2 that was developed as a result of a ruling by the Assistant 

Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review 

case in Montana, that “natural conditions” should reflect the visibility impairment caused by 

significant meteorological events such as fog, precipitation, or naturally occurring haze (DOI, 

2003).13 Under Method 7, during hours when visibility is obscured by meteorological conditions, 

the actual measured visibility is used to represent natural conditions instead of the value that is 

calculated from EPA’s default natural conditions concentrations under Method 2. A recent 

modification developed in response to FLM comments on Method 7, in which the daily average 

natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibility

Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations 

are evaluating whether refinements are warranted to the methods recommended in EPA’s 

guidance to calculate default estimates of natural background visibility. In particular, IMPROVE 

has recently approved an alternative to the formula (Eq. 3-1) it uses to estimate extinction from 

particle concentration measurements (Pitchford et al., 2005).

Refinements in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity 

 

12 Note that the hourly-varying natural background extinction in this method is not consistent with that prescribed 

by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically-representative” f(RH) 

that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

13 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural 

conditions is unknown.

Appendix H.6 - 1287
Overview of the CALPUFF Modeling System 33

- Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq. 

3-1) from organic carbon measurements

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revising the extinction efficiencies (the numerical constants in Equation 3-1) for 

sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated 

by IMPROVE for all Class I areas. 

For the purposes of calculating current, future, and natural background visibility at VISTAS Class 

I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality 

modeling results using both the current EPA recommended assumptions and the newly revised 

aerosol extinction formula. If a BART-eligible source chooses to consider its projected impacts 

using the newly revised formula as well as the current formula, then modifications would need to 

be made to CALPOST to carry out calculations with the new algorithm. 

 

Appendix H.6 - 1288
VISTAS’ Common Modeling Protocol 34

4. VISTAS’ COMMON MODELING PROTOCOL

4.1 Overview of Common Modeling Approach

In this section, guidance is provided on the use of the CALPUFF modeling system for two

purposes:

1) Evaluating whether a BART-eligible source is exempt from BART controls because it 

is not reasonably expected to cause or contribute to impairment of visibility in Class I 

areas, and 

2) Quantifying the visibility benefits of BART control options. 

For purpose 1), States must determine whether a source emits any air pollutant (SO2, NOx, PM, 

and in certain cases VOC and NH3) that “may reasonably be anticipated to cause or contribute to 

any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART-eligible sources in State are subject to BART. 

B) Demonstrate that all BART-eligible sources in the State together do not cause or 

contribute to any visibility impairment 

C) Determine if the impact from each individual BART-eligible source is greater than a 

threshold value. 

VISTAS States intend to follow Option C (determine if the visibility impact from individual 

sources exceeds a contribution threshold) for SO2 and NOx emissions. The methods for Option C 

are described in Section 4.1.1. In early 2006, VISTAS pursued Option B (demonstrate that all 

BART eligible sources in a State do not impact visibility) for VOC, NH3 and PM emissions. The 

approach and results for Option B are described in Section 4.1.3. As a result of this exercise, the 

VISTAS States have determined that the Option C exemption analyses should also include PM 

emissions and, for sources with large NH3 emissions, NH3. The States determined that 

anthropogenic VOC emissions do not cause or contribute to visibility impairment at VISTAS 

Class I areas and that VOC emissions do not need to be considered in BART analyses. 

4.1.1 BART Exemption Analysis

As illustrated in Figure 4-1, three steps will evaluate whether a BART-eligible source of SO2, 

NOx, or PM is subject to BART: 

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d 

for SO2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to 

BART. If the source agrees with this presumption, then no exemption modeling is required 

and the source can proceed to the BART determination using CALPUFF to evaluate impacts 

of control options and can perform the engineering analyses. If a source disagrees, the source 

Appendix H.6 - 1289
VISTAS’ Common Modeling Protocol 35

may perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5 

dv.

Figure 4-1. Flow chart showing the components of the VISTAS common modeling protocol. 

Assessment should be made for each Class I Area. (If a source agrees to install the most stringent 

controls then the modeling steps indicated above and engineering analyses and visibility impact 

modeling would not be required.) 

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12-

km regional VISTAS domain can be used to answer questions whether (a) a particular source 

may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were 

to be undertaken, which Class I areas should be included. Assumptions for the initial 

modeling assessment are conservative so that a source that contributes to visibility impairment 

is not exempted in error. If a source is shown not to contribute to visibility impairment using 

the initial modeling assessment, the source would not be subject to BART and would be 

exempted from further BART analyses. If a source is shown to contribute to visibility 

impairment using the initial modeling assessment, the source has the option to undertake finer 

grid CALPUFF modeling to evaluate further whether it is subject to BART. 

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the 

definitive test as to whether a source is subject to BART.

Appendix H.6 - 1290
VISTAS’ Common Modeling Protocol 36

For large sources that will clearly exceed the initial screening thresholds, this step can be skipped 

and the analysis may proceed directly to the finer grid modeling analysis, which is described in 

Section 4.4. 

4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BART review process 

involves evaluating the visibility benefits of different BART control measures. These benefits 

will be determined by making additional CALPUFF simulations using the same CALMET and 

CALPUFF configuration as those used in the finer grid analysis of Step 2. The only exception is 

that the source and emissions data used in the CALPUFF control evaluation simulations will 

reflect the BART control measures being evaluated. Using the same model configuration will 

produce an “apples-to-apples” comparison, where differences in impacts are due to the 

effectiveness of the controls rather than model configuration differences. For example, a control 

scenario evaluation that uses more conservative assumptions than the base case simulation may 

produce results showing no or little improvement in visibility impacts. That control scenario run 

with the same model configuration as the base case may show significant visibility improvement. 

Therefore, in order to not obscure the response to predicted visibility improvements by 

differences in the modeling approach, the same model configuration should be used in the BART 

control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current 

emissions” scenario for which the finer grid Step 2 modeling was performed. The postprocessing 

steps and procedures are the same as in the BART eligibility simulation. Side-by-side 

comparison of the visibility impacts will be tabulated to quantify the effectiveness of each control 

scenario relative to the base case.

The modeling evaluation is a unit-by-unit evaluation and can be conducted on a pollutant specific 

basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to 

decide which control technology, if any, is appropriate. Finally, if a source decides to use the 

most stringent control technology available, the BART control analysis, including modeling, is 

not necessary.

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM

Volatile Organic Compounds 

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its 

capability to simulate secondary organic aerosol formation from VOC emissions is not adequately 

tested, especially for anthropogenic emissions. (Separately, condensable organic carbon can be 

calculated from PM10.) 

VISTAS has performed a weight of evidence analysis to demonstrate, using the CMAQ regional 

air quality model, that the combined VOC emissions from all point sources (BART-eligible and 

non-BART) in each State do not contribute to visibility impairment. Emissions sensitivity 

Appendix H.6 - 1291
VISTAS’ Common Modeling Protocol 37

simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid 

and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no 

response of organic carbon levels and light extinction at Class I areas to changing VOC emissions 

from all anthropogenic sources in the VISTAS 12-km modeling domain (eastern US). Georgia 

Tech repeated the sensitivity analyses using the VISTAS 12-km domain and CMAQ v 4.4 with a 

refined SOA module for summer (Jun 1-Jul 10) and winter (Nov 19-Dec 19) periods in 2002. 

VOC emissions from all anthropogenic point sources in every VISTAS State were reduced by 

100% (i.e., eliminated). The maximum 24-hr impact of all VOC emissions from all point sources 

throughout the VISTAS domain was thus determined to be less than 0.5 dv (compared to annual 

average natural background) at every Class I area in the VISTAS domain and in adjacent States. 

It follows that the impact of any one BART-eligible source would be much less than 0.5 dv. 

Based on these analyses, the VISTAS States have concluded that VOC emissions from BART 

sources do not cause or contribute to visibility impairment and do not need to be included in 

BART analyses. 

Ammonia 

EPA has given states the option to address ammonia (NH3) emissions from BART-eligible 

sources. VISTAS also contracted with Georgia Tech to calculate NH3 emissions sensitivities 

using CMAQ v 4.4 with a refined SOA module and the same Jun-Jul and Nov-Dec periods in 

2002 that were used for the VOC sensitivity evaluation. The NH3 emissions from all point 

sources (BART-eligible and not-BART) in every State were reduced by 100% for these analyses. 

This sensitivity evaluation showed that the collective impact of all VISTAS region point NH3

emissions is greater than 0.5 dv (compared to annual average natural background) at several Class 

I areas. When the NH3 emissions were scaled to represent 100% reduction from only the BARTeligible sources in each State, then the maximum impact of those sources was under 0.5 dv at 

most, but not all Class I areas. The high values appear to result primarily from emissions from 13 

large NH3 sources. In the absence of those 13 facilities, the scaled NH3 emissions peak impacts at 

Class I areas were 0.3 dv or less. Based on these analyses, the VISTAS States recommended that, 

except for these 13 facilities, NH3 emissions not be included in BART modeling. States will 

provide instructions to those 13 sources as to how to evaluate contributions of their NH3

emissions to visibility impairment. For documentation purposes, in summer 2006 VISTAS is 

repeating the NH3 emissions sensitivity calculations, using CMAQ v4.5 with Base F emissions 

and reducing 100% of NH3 emissions from only the BART-eligible sources in the VISTAS states. 

Primary Particulate Matter 

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to 

which primary PM from BART-eligible sources contributes to impairment at Class I areas in the 

southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of 

SO2 and NOx under the CAIR rule meet the BART requirements, but these EGUs may still be 

subject to BART for primary PM. To determine the potential impacts of PM from EGU and nonEGU sources in the VISTAS states, two CMAQ sensitivity runs for the first and third quarters of 

2002 were carried out by VISTAS’ CMAQ modeling team of ENVIRON, UCR, and Alpine 

Appendix H.6 - 1292
VISTAS’ Common Modeling Protocol 38

Geophysics In one run, all primary PM from EGUs was removed while in the other run all 

primary PM from non-EGU sources was removed. All other CMAQ modeling components were 

held constant. At almost all Class I areas in the VISTAS region, primary PM emissions 

contribute to regional haze, with the collective impact of all EGU and non-EGU point primary 

PM emissions being greater than 0.5 dv compared to annual average natural background. In fact, 

the impacts of EGU PM emissions alone or of non-EGU PM emissions alone were each mostly 

greater than 0.5 dv. Although the impacts of BART sources alone would be smaller, the VISTAS 

States have concluded that all BART-eligible sources need to consider the impacts of their PM 

emissions.

4.2 Optional Source-Specific Modeling

In some circumstances, a source may want to apply techniques designed to evaluate the impacts 

in a more detailed way than the standard VISTAS common protocol. A source may propose 

source-specific modeling procedures to address special issues to the State for State review. For 

example, sources very close to Class I areas may be better treated by a finer grid resolution that 

the generic Step 2 “fine” grid resolution meteorological fields provided by VISTAS. In some 

situations, higher resolution MM5 or other prognostic meteorological datasets may be available 

than the standard 12-km or 36-km MM5 datasets provided by VISTAS. Because it is not possible 

to anticipate all of the situations where there would be a benefit to conducting more detailed 

source-specific analyses, the option to pursue this option is left as an open issue, to be resolved 

and justified based on specific factors relevant for the source in question.

A source-specific modeling protocol is required for each source. This document should describe 

the data sources and model configuration, and provide rationale for any changes in the model 

approach from the common protocol. This source-specific protocol must be provided for review 

and approval by the State. The State will share the protocol with EPA and the Federal Land 

Managers for their review. Discussion of approaches to source-specific modeling and an outline 

of the typical contents of the source-specific protocol are presented in Chapter 5. Discussions 

with the regulatory authorities should be conducted prior to development of a source-specific 

protocol to ensure all of the relevant issues are included in the protocol.

4.3 Initial Procedure for BART Exemption 

4.3.1 Overview of Initial Approach

The first step in the common protocol, the initial assessment in Figure 4-1, is a simple procedure 

to evaluate whether a source can be exempted from BART controls using a consistent set of 

meteorological and dispersion options. A pre-computed set of meteorological files and a predefined CALPUFF input option configuration, based on guidance in the final BART rule (70 FR 

39104-39172) and other EPA and FLAG model guidance, will allow relatively simple initial 

simulations. The regional initial domain is designed to allow any Class I areas within the 

VISTAS area to be evaluated with a single meteorological database and consistent CALPUFF 

modeling options. The second important question that this first screening step will answer is, if 

Appendix H.6 - 1293
VISTAS’ Common Modeling Protocol 39

initial modeling indicates a source may impact visibility significantly, what Class I areas should 

be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a 

source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the 

closest Class I area will produce the controlling visibility impacts) are not likely to be universally 

reliable. 

4.3.2 Discussion of 12-km Initial Exemption Modeling

Meteorological Fields

A regional initial domain and a set of pre-computed regional CALMET meteorological files will 

be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with 

a consistent meteorological database and consistent CALPUFF modeling options. 

The following three years of MM5 meteorological data have been assembled by VISTAS for use 

in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

- 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning

Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has 

produced annual CALMET meteorological files at 12-km grid resolution for the domain shown in 

Figure 4-2. The CALMET modeling output files in the form of CALPUFF-ready threedimensional meteorological files will be available on external hard drives to the States and other 

parties.

The initial procedure to determine if a BART-eligible source is subject to BART uses the precomputed CALMET meteorological fields for the years 2001-2003 on the 12-km CALMET 

domain in Figure 4-2 and simulates with CALPUFF any BART-eligible source to be screened. 

The CALMET simulations will be developed using the highest resolution MM5 data available for 

each year (i.e., 36-km MM5 data for 2003, 12-km MM5 data for 2001 and 2002). 

The development of the regional CALMET meteorological fields from MM5 data will be 

conducted in No-Observations (“No-Obs”) mode. The MM5 data already reflect assimilation of 

observational data and are likely to adequately characterize regional wind patterns that are 

consistent with the 12-km grid scale. Blending of MM5 data with local observations (which are 

mainly at the surface) could lead to wind structures that may not be realistic under some 

conditions and may result in poorer characterization of the regional winds. Thus, the effort 

required to prepare observational data sets for CALMET for the large regional domain involves 

considerable effort that may not provide corresponding improvement of the wind field. 

Appendix H.6 - 1294
VISTAS’ Common Modeling Protocol 40

Figure 4-2. VISTAS Regional 12-km Resolution CALMET Modeling Domain (color area with 

terrain contours). The locations of the 36-km resolution MM5 grid points are shown on the plot. 

For 2003, the 36-km MM5 data will be used as CALMET’s initial guess field and then the 

CALMET diagnostic terrain adjustments (see Section 3.1.1) will be applied to reflect terrain on 

the scale of the CALMET grid (i.e., 12-km). When the 12-km MM5 (2001 and 2002) data are 

used, the diagnostic CALMET terrain adjustments will be turned off since the grid resolution of 

the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid 

scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be 

interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will 

compute mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. 

Appendix H.6 - 1295
VISTAS’ Common Modeling Protocol 41

Impact Threshold

The final BART guidance recommends that the threshold value to define whether a source 

“contributes” to visibility impairment is 0.5 dv change from natural conditions14 (although States 

may set a lower threshold). The 98th percentile (8th highest annual) 24-hr average predicted 

impact at the Class I area, as calculated using CALPOST Method 6 (monthly average relative 

humidity values), is to be compared to this contribution threshold value. For this comparison, the 

predicted impact at the Class I area on any day is taken to be the highest 24-hr average impact at 

any receptor in the Class I area on that day. (Note that the receptor where the highest impact 

occurs can change from day to day.) According to clarification of the BART guidance received 

from EPA, for a three-year simulation the modeling values to be compared with the threshold are 

the greatest of the three annual 8th highest values or the 22nd highest value over all three years 

combined, whichever is greater. 

For the purposes of the initial analysis, however, the highest value over the three-year period (not 

the 98th percentile value) is to be compared to the contribution threshold. This ensures a 

significant measure of conservatism in the initial approach. VISTAS will evaluate the initial 

CALPUFF results to determine if using the single highest value provides too conservative a 

screen for exemption purposes. If so, VISTAS may increase the number of exceedances of the 

contribution threshold that would be allowed and still qualify to exempt a source. 

4.3.3 Model Configuration and Settings for Initial Analysis

VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were 

developed by Earth Tech, Inc. and they are maintained on the CALPUFF website (www.src.com) 

for public access. This version includes CALMET, CALPUFF, CALPOST, CALSUM, and 

POSTUTIL as well as CALVIEW. 

The initial analysis uses a CALPUFF computational domain that includes all Class I areas within 

300 km of a source. These Class I areas are specified in the CALPUFF control file for analysis. 

States could decide to require a different value for the maximum distance threshold for the 

CALPUFF domain, depending on the locations of the Class I areas in their states and other 

factors such as meteorological conditions and the magnitudes of the emissions from BARTeligible sources. The regional CALMET domain will be unchanged by these adjustments. 

Also, the initial approach is designed to significantly reduce the CALPUFF simulation time by 

restricting the CALPUFF computational domain size to include only areas where significant 

impacts are feasible rather than the entire regional domain. CALPUFF allows its computational 

domain to be specified as a subset of the CALMET meteorological domain by settings within the 

 

14 As described in Footnote 5 on page 6, States have the option of defining natural conditions as either the annual 

average default conditions or the average of the 20% best natural condition days.

Appendix H.6 - 1296
VISTAS’ Common Modeling Protocol 42

CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in 

the regional CALPUFF simulations is that CALPUFF run time is proportional to the number and 

residence time of the puffs on the domain (and other factors such as the number of receptors and 

the internal time step computed by the model). A CALPUFF domain covering an area 300 km 

from a source in all directions would involve only 50 x 50 12-km grid cells, which will require 

modest computational resources.

CALMET output files for the VISTAS regional domain shown in Figure 4-2 will be provided to 

VISTAS by Earth Tech. These files will be in CALPUFF-ready format, and as such, no 

CALMET user inputs will be required. An option in CALMET allows finer grid CALMET input 

files to be calculated from the 12-km CALMET files. 

The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the 

initial analyses are listed below.

CALMET Modeling Configuration (12-km initial exemption modeling)

The CALMET model configuration for the regional CALMET simulations will be defined by 

Earth Tech in collaboration with the VISTAS States. The basic model configuration will follow 

the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted 

below. 

The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)

- Meteorological inputs: MM5 data provide initial guess fields in CALMET 

- CALMET grid resolution: 12-km (same Lambert Conformal coordinate system and grid 

cells as the 12-km 2001/2002 MM5 simulations)

- CALMET vertical layers: 10 layers. Cell face heights (meters): 0, 20, 40, 80, 160, 320, 

640, 1200, 2000, 3000, 4000.

- CALMET mode: No-Observations mode including option to read overwater data directly 

from MM5.

- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain 

blocking and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but 

no diagnostic terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

Appendix H.6 - 1297
VISTAS’ Common Modeling Protocol 43

- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 

2003 simulations). Values of ~10-20 km will be tested, and an appropriate value 

determined.

- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature 

allows the temperature field over large bodies of water such as the Atlantic Ocean and the 

Great lakes to be properly characterized by buoy observations.

- Mixing height averaging parameter (MNMDAV) will be determined by Earth Tech for the 

regional simulations based on sensitivity tests. The purpose of the testing is to optimize the 

variable to allow spatial variability in the mixing height field, but without excessive noise.

- Geophysical data for regional runs: SRTM-GTOPO30 30-arcsec terrain data, Composite 

Theme Grid (CTG) USGS 200m land use dataset. References for these and other CALMET 

datasets can be found on the CALPUFF data page of the official CALPUFF site 

(www.src.com). 

CALPUFF Modeling Configuration (Initial exemption modeling)

The CALPUFF model configuration for the regional CALPUFF initial simulations will follow the 

recommended IWAQM guidance (EPA, 1998; Pages B-1 through B-8), except as noted below: 

- CALPUFF domain configured to include the source and all Class I areas within 300km of 

the source plus 50km buffer zone in each direction. CALPUFF is recommended for all 

source-receptor distances to be considered in the BART analyses.

- Chemical mechanism: MESOPUFF II module

- Species modeled: SO2, SO4, NOx, HNO3, NO3 and particulate matter in size categories of 

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic 

diameters. As noted below, the particulate matter emissions by size category will be 

combined into the appropriate species for the visibility analysis (i.e., elemental carbon (EC), 

fine PM or “soil” (< 2.5 μm in diameter), coarse PM (between 2.5-10 μm in diameter) and 

organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).

- Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual 

emission rate with normal operations from the highest emitting day of the meteorological 

period modeled (excluding days where start-up, shutdown or malfunctions occurred 

sometime during the day.) Note that potential emissions are used to determine if a source is 

BART-eligible, but 24-hour average maximum emissions are used for modeling purposes 

(70 FR 39162). Pollutants considered include SO2, H2SO4, NOx and PM10. 

Condensable emissions are considered as primary fine particulate matter and allocated 

equally to the two submicrometer-particle size classes. If actual source emissions data are 

not available, the modeling should be based on permit limits. If source-specific size 

Appendix H.6 - 1298
VISTAS’ Common Modeling Protocol 44

categories are not available, then AP-42 factors may be used for sources where AP-42 

factors are available. For sources where AP-42 factors are not available, alternative 

approaches to speciation are given below. 

Excluded from the modeling are pollutants with plant-wide emissions less than de minimis

levels (40 tons per year for SO2 and NOx and 15 tons per year for PM10). De minimis levels 

are plant wide for each visibility-impairing pollutant, so individual units may be modeled 

even if they have emissions below de minimis if the plant total is greater than de minimis.

- Particulate emissions speciation: Break down, as appropriate, filterable and condensable 

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine 

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. The 

process is illustrated in Figure 4-3. If source-specific speciated emissions factors are not 

available, AP-42 factors or speciation information developed by the National Park Service 

(http://www2.nature.nps.gov/air/permits/ect/index.cfm) can be used to estimate the PM 

speciation for many source sectors. 

Figure 4-3. Speciation of PM-10 Emissions. (PMC is coarse particulate matter -- 2.5 to 10 μm 

diameter.)

PM-10

Filterable Condensable

EC PM-2.5 PMC PM

(including 

H2SO4)

As Prim. SO4)

Organic PM

(SOA)

EC Soil PMC SO4

f(RH)

Non-SO4

Inorganic

OC

Appendix H.6 - 1299
VISTAS’ Common Modeling Protocol 45

Otherwise, assumptions will need to be proposed by the source, and reviewed and approved 

by the State. Possible acceptable alternative approaches to estimating speciation include the 

following:

 Speciation profiles developed by the SMOKE emissions model for use in 

VISTAS’ CMAQ regional air quality modeling (available at http://www.vistassesarm.org/BART/calpuff.asp).

 The approach described in a memo available at http://www.vistassesarm.org/BART/calpuff.asp, which provides reasonably conservative estimates 

in situations where data are incomplete.

- Class I receptors: Use FLM Class I receptor list with receptor elevations provided 

(available from the NPS).

- CALPUFF model options: Use IWAQM (EPA, 1998) default guidance, including 

Pasquill-Gifford dispersion coefficients. 

- Ozone dataset – use observed ozone data for 2001-2003 from CASTNet and AIRS stations. 

Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average 

ozone (backup) background values are to be computed based on daytime average ozone 

concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month). 

- Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) value for 

ammonia. 

- Puff representation: integrated puff sampling methodology.

- Building downwash: Ignore building downwash unless source is within 50-km of a Class I 

area and the State instructs the source to specifically consider building downwash. 

CALPOST and POSTUTIL Configuration (Initial exemption modeling)

- Use Visibility Method 6 in CALPOST

- Species considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic 

emissions), soil, coarse PM

- Natural background light extinction: Several options are acceptable at the discretion of the 

State: (1) A single annual average natural background extinction for each Class I area, as 

presented in Appendix B of EPA’s natural conditions guidance (EPA, 2003b); (2) A single 

value that represents the average haze index on the 20% best natural conditions days, again 

as presented in the same Appendix B; or (3) A monthly average natural background as 

calculated by CALPOST under Method 6, based on annual average default natural 

Appendix H.6 - 1300
VISTAS’ Common Modeling Protocol 46

conditions component concentrations and monthly average f(RH) values for the centroid of 

the Class I area, from Table A-3 in the natural conditions guidance document,. 

A special procedure is needed for options 1 and 2, since CALPOST requires input of natural 

background concentrations of PM components while the backgrounds for options 1 and 2 

are expressed in EPA’s guidance document as extinction coefficients or haze indices (in 

deciviews). In order to produce the appropriate natural background in CALPOST for these 

options, use Equation 3-2 to calculate the extinction coefficient that corresponds to EPA’s 

haze index value for the Class I area (if necessary), subtract the Rayleigh scattering value of 

10 Mm-1, and enter a soil concentration (in μg/m3) into CALPOST that is numerically equal 

to this result. (Since the extinction efficiency of soil is 1 m2/g, Equation 3-1 shows that this 

process produces a background extinction that equals the EPA’s value.) Leave the 

concentrations of all other species blank, since the number that is entered represents 

extinction by all components.

- Light extinction efficiencies: Use EPA (2003a) values. If a source chooses, the new 

IMPROVE algorithm for calculating light extinction (see Section 3.2.3) may be used in 

addition to the default IMPROVE algorithm. (Calculations would need to be performed 

outside CALPOST or CALPOST would need to be modified to accommodate the new 

algorithm.)

- Nitrate repartitioning in POSTUTIL: Do not use for the initial modeling. 

The initial run results will be based on the highest change in light extinction (deciviews) from 

natural conditions over the three-year modeling period for each Class I area considered. 

Predicted changes exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid 

CALPUFF modeling analysis.

4.4 Finer Grid Modeling Procedures

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach

There are two potential applications for finer grid CALPUFF modeling: 

BART Exclusion Modeling. First, finer grid CALPUFF modeling can be used to demonstrate 

that a source does not cause or contribute to visibility impairment in any Class I areas, and thus 

can be excluded from BART controls. As shown in Figure 4-1, if the initial regional modeling 

results are not below the threshold for visibility impacts, the next step is to conduct modeling 

using a finer grid resolution for the meteorological fields and the treatment of terrain effects and 

land use variability. In the finer grid modeling the predicted visibility impairment that is 

compared to the threshold is based on the BART guidance of the 98th percentile change in 

deciviews value rather than the more conservative highest value used in the initial analysis.

The BART guidance indicates that the emissions rate to be used for such modeling is the highest 

24-hr rate during the modeling period. Depending on the availability of source data, the following 

Appendix H.6 - 1301
VISTAS’ Common Modeling Protocol 47

emissions information (listed in order of priority) should be used with CALPUFF for BART 

exclusion modeling:

- 24 hr maximum value emissions for the period 2001-2003 (Continuous Emission Monitor, 

CEM data)

- 24 hr maximum value from continuous emissions monitoring data 

- facility stack test emissions

- potential to emit

- permit allowable emissions, if available

- emissions factors from AP-42 source profiles 

Quantify Benefits of BART. The second application of refined modeling is to quantify the 

visibility benefits from the BART control options. This is accomplished by running CALPUFF 

with the baseline emissions rates and again with emissions after BART controls. It is important 

that emission reductions be evaluated in the postprocessing step rather than by using “negative” 

emission rates in the CALPUFF model. The chemical scheme requires that emission rates always 

be positive. 

For any of these applications, a source-specific modeling protocol that defines source properties 

and the specific model configuration is required. As discussed in Section 5, the source specific 

protocol should include source-specific emissions data and can refer to this document for all 

methods and assumptions that follow this common protocol. 

4.4.2 Model Configuration and Settings for Finer Grid Modeling

Grid resolution substantially better than 12-km is needed for a finer grid CALPUFF assessment of 

visibility impacts in most cases involving Class I areas in complex terrain or coastal areas. Thus, 

the CALMET fine grid resolution in the subregional modeling domains used for finer grid 

modeling will depend on the terrain, land use (especially coastal boundaries), location of the 

source, distance of the source from Class I areas, and total size of the subregional modeling 

domain. 

VISTAS States have 2001-2003 CALMET files for five 4-km sub-regional domains as illustrated 

in Figure 4-4. The subdomains are designed to address all BART eligible sources within each 

VISTAS states and all Class I areas within 300 km of the BART-eligible sources. For application 

for a single source, a smaller domain of roughly 200-300 km by 200-300 km is recommended. 

Requests to obtain the 4-km CALMET files should be made to the State BART representatives. 

Appendix H.6 - 1302
VISTAS’ Common Modeling Protocol 48

Figure 4-4. The five subregional domains for 4-km CALMET modeling.

In some instances, as part of the source-specific protocol, a source may propose to the State to use 

an even finer grid simulation to properly characterize the flow fields and land use changes that 

affect dispersion. An application for source-receptor distances within about 50 km may require a 

grid resolution less than 1 km if complex terrain effects are likely to be important. This 

determination should be made on a case-by-case basis. There is not a single distance at which a 

particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the 

source-receptor distances involved, the location of the source relative to the terrain features, the 

physical stack parameters (e.g., a tall stack in complex terrain may be unaffected by the terrainforced flow), proximity of the source and Class I area to a coastline, and other factors including 

availability of representative observational data.

The finer grid CALMET simulations were run in hybrid mode, using both MM5 data to define 

the initial guess fields and meteorological observational data in the Step 2 calculations. 

Overwater (buoy) data will be provided in addition to the hourly surface meteorological 

observations, precipitation observations and twice-daily upper air sounding data. 

Appendix H.6 - 1303
VISTAS’ Common Modeling Protocol 49

A domain-specific set of modeling parameters will be defined for each subregional domain. The 

proper selection of the CALMET diagnostic wind field parameters that are used to blend 

observations with the Step 1 wind field depends on factors such as the locations of the 

meteorological stations relative to terrain and coastal features (which affects the 

representativeness of the observational data), the terrain length scale, and the quality (resolution) 

of the MM5 data used to define the initial guess field and its ability to properly resolve wind 

flows on the fine-scale CALMET domain. The definition of the proper CALMET parameters is 

done as part of sensitivity testing where model performance is evaluated against available 

observations and expected terrain effects, such as channeling of flows within a valley. 

In addition to the better grid resolution and the introduction of observational data in the finer grid 

simulations, several other modeling refinements can enhance the accuracy of the finer grid 

modeling. These include use of the higher resolution terrain DEM data (~3 arc sec USGS data) 

in defining the gridded terrain fields and application of the ammonia limiting method in the 

POSTUTIL post-processor. Otherwise, the source configuration, emissions, pollutant speciation, 

Class I receptors, ozone datasets and CALPUFF model options will be the same as in the initial 

runs. Similarly, CALPOST will be used in the same manner as for the initial analyses. However, 

POSTUTIL can be used to repartition nitrate in the finer grid modeling, using background 

ammonia concentrations according to the IWAQM Phase 2 report (IWAQM, 1998).

For the finer grid BART exclusion analysis, the test for evaluating whether a source is 

contributing to visibility impairment is based on the 98th percentile modeled value (rather than the 

highest predicted value used for the initial evaluation), which is consistent with EPA’s BART 

guidance.

4.5 Presentation of Modeling Results

The CALPOST processing computes the daily maximum change in deciviews. A sample of the 

summary table produced by CALPOST is shown in Table 4-1. For evaluating compliance with 

the VISTAS screening threshold, the highest change in extinction value, located at the bottom of 

the CALPOST list file is compared to the threshold value (e.g., 0.5 dv). For example, in the 

sample shown in Table 4-1, the summary at the bottom shows that the highest visibility impact is 

1.219 dv, with 9 days over the year showing values greater than 0.5 dv. Therefore this source 

would not pass the initial analysis, and finer grid modeling would be required. 

In addition to the highest change in deciview value on each day over all the receptors in a 

particular Class I area, the CALPOST summary table in Table 4-1 contains the coordinates of the 

receptor, receptor type (D indicates discrete receptors), the total haze level (background + source, 

in dv), the background haze in deciviews, the change in haziness (delta dv), the humidity term 

applied to hygroscopic aerosols (f(RH)), and the contribution of each species to light extinction 

(in percent of the total source contribution) for SO4, NO3, organics, elemental carbon, coarse and 

fine particulate matter.

Appendix H.6 - 1304
VISTAS’ Common Modeling Protocol 50

Table 4-1. Example of CALPOST Output, Showing Maximum Daily Impacts of Source and Locations of Those Impacts.

YEAR DAY HR RECEPTOR COORDINATES (km) TYPE DV(Total) DV(BKG) DELTA DV F(RH) %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF 

2001 2 0 3 20.540 79.782 D 5.397 5.358 0.039 4.314 44.33 47.22 3.07 1.07 0.00 4.30 

2001 3 0 9 31.680 79.822 D 4.566 4.421 0.145 1.767 40.75 33.89 9.19 3.24 0.00 12.94 

2001 4 0 1 24.723 77.951 D 4.540 4.540 0.000 2.076 0.00 0.00 0.00 0.00 0.00 0.00 

2001 5 0 77 30.228 94.571 D 4.950 4.939 0.011 3.144 43.13 44.74 4.64 1.45 0.00 6.05 

2001 6 0 1 24.723 77.951 D 5.181 5.166 0.015 3.772 38.58 56.05 1.90 0.70 0.00 2.76 

2001 7 0 3 20.540 79.782 D 6.366 5.745 0.620 5.439 44.98 44.99 3.69 1.26 0.00 5.08 

 .

 .

 .

2001 363 0 113 27.414 103.782 D 5.725 5.652 0.073 5.164 53.49 35.51 4.03 1.39 0.00 5.58 

2001 364 0 113 27.414 103.782 D 6.554 6.521 0.033 7.826 48.12 47.09 1.67 0.64 0.00 2.48 

2001 365 0 1 24.723 77.951 D 6.499 6.499 0.000 7.757 0.00 0.00 0.00 0.00 0.00 0.00 

--- Number of days with Delta-Deciview => 0.50: 9

--- Number of days with Delta-Deciview => 1.00: 2

--- Largest Delta-Deciview = 1.219

Appendix H.6 - 1305
VISTAS’ Common Modeling Protocol 51

For the finer grid analysis, the data in the table can be imported into a spreadsheet and sorted on 

the delta dv column. Table 4-2 shows an example of the ranked visibility impacts (change in dv) 

for each of three years at six different Class I areas. The 98th percentile (8th highest value) in the 

sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the example 

shown in this table, the source passes the finer grid analysis because the highest 98th percentile 

visibility impact is below the contribution threshold of 0.5 dv. 

The Results section of the CALPUFF modeling report should contain the following information:

1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all 

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source, as illustrated in Table 4-3. 

3. A discussion of the number of Class I areas with visibility impairment from the source on 

98th percentile days in each year greater than 0.5 dv (total visibility impairment minus 

impairment on 20% best days for natural background visibility equals delta-dv, the 

visibility impact attributed to the source). 

4. For the Class I area with the maximum impact, discussion of the number of days below 

the 98th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in 

the Class I area where the impact exceeds 0.5 dv, and the maximum impact. 

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which 

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report 

same results as provided for 12-km initial exemption modeling.

6. For control option modeling, each control option tested should be listed in tabular format. 

For each control option and for each Class I area where the impact of the source exceeded 

0.5 dv, report the change in pollutant emissions and the change in visibility impact from 

the source as a result of the control option. The effectiveness of candidate control options 

are to be compared to each other, not to a specific target improvement. 

States will provide further guidance on graphic presentation of results to simplify 

evaluation of effectiveness of control measures. For example, a temporal plot of the 

change in deciviews between the controlled and uncontrolled cases could be developed for 

the receptor with the maximum modeled impact in each Class I area. 

7. Copies of all input files and input data in electronic format for the CALMET, CALPUFF, 

CALPOST and POSTUTIL runs should be archived and provided to the State.

Appendix H.6 - 1306
VISTAS’ Common Modeling Protocol 52

Table 4-2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

Great Smoky NP

0.99

0.88

0.62

0.59

0.55

0.52

0.48

0.47

0.95

0.63

0.51

0.50

0.46

0.42

0.37

0.36

1.20

0.90

0.73

0.72

0.59

0.47

0.45

0.42

Linville Gorge

0.67

0.45

0.43

0.33

0.29

0.27

0.25

0.23

0.81

0.69

0.65

0.50

0.45

0.33

0.31

0.29

0.76

0.47

0.37

0.35

0.31

0.30

0.28

0.28

Shining Rock

0.66

0.43

0.41

0.35

0.26

0.24

0.23

0.22

0.73

0.69

0.63

0.52

0.46

0.34

0.29

0.26

0.75

0.45

0.36

0.34

0.28

0.27

0.26

0.25

Cohutta

0.26

0.23

0.22

0.21

0.20

0.19

0.18

0.16

0.54

0.47

0.43

0.37

0.37

0.31

0.31

0.30

0.61

0.42

0.30

0.29

0.28

0.28

0.25

0.25

Joyce Kilmer-Slickrock

0.34

0.33

0.31

0.26

0.24

0.20

0.18

0.17

0.52

0.43

0.32

0.31

0.30

0.28

0.24

0.24

0.27

0.24

0.23

0.20

0.14

0.13

0.11

0.10

Mammoth Cave NP

0.56

0.44

0.38

0.29

0.25

0.24

0.22

0.21

0.57

0.56

0.53

0.35

0.33

0.33

0.30

0.29

0.50

0.37

0.36

0.35

0.31

0.24

0.21

0.19

Appendix H.6 - 1307
VISTAS’ Common Modeling Protocol 53

Table 4-3. Format of Summary of Results for CALPUFF Modeling in VISTAS’ 12-km Modeling 

Domain to Determine if a BART Eligible Source is Subject to BART. 

Class I area Distance 

(km) 

from 

source to 

Class I 

area 

boundary

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2001

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2002

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2003

# of days1 and 

# of receptors 

with impact 

> 1.0 dv in 

Class I area 

for 3-yr 

period

Max. 24-hr 

impact over 

3-yr period

Dolly Sods, WV

Shenandoah, VA

James River 

Face, VA

Mammoth Cave, 

KY

Sipsey, AL

Great Smoky 

Mtns, TN

Cohutta, GA

Shining Rock, 

NC

Linville Gorge, 

NC

Swanquarter, NC

Cape Romain, 

SC

Okefenokee, GA

Saint Marks, FL

Chassahowitzka, 

FL

Everglades, FL

Brigantine, NJ

Breton Island, 

LA

Caney Creek, 

AR

Upper Buffalo, 

AR

Mingo, MO

Hercules Glade, 

MO

1

Days below the 98th percentile of days in each year or the three-year modeling period, as appropriate

Appendix H.6 - 1308
VISTAS’ Common Modeling Protocol 54

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources

VISTAS will provide updates and supporting information concerning the Common Modeling 

Protocol (this document) on the VISTAS website. In addition, VISTAS will make publicly 

available the following data bases developed by Earth Tech:

• VISTAS version of the CALPUFF modeling system, maintained on the CALPUFF website. 

Version 5.754 includes CALMET, CALPUFF, CALPOST, and POSTUTIL files, updated in 

December 2005. The last update in this VISTAS version is a CALMET update that addresses 

over water dispersion, which was developed for the Minerals Management Service (MMS) in 

fall 2005. This VISTAS version of CALPUFF will not be updated further unless errors are 

found in the code, except that a new one-step POSTUTIL procedure will be incorporated. 

BART-eligible sources in the VISTAS states will be able to use this VISTAS version 

throughout the BART modeling exercise. 

• 12-km CALMET output files for 2001, 2002, and 2003 produced as described in previous 

sections. Further detail on model configuration and settings will be provided with the output 

files and will be made available on the CALPUFF website.

• CALMET will include a software modification to allow the meteorological data inputs into 

CALMET to be used to generate finer grid CALMET files without having to go back to the 

original MM5 output files

• Five 4-km CALMET subdomains for 2001, 2002, and 2003, produced as described in 

previous sections. Further detail on model configuration and settings will be provided with 

the output files and will be made available on the website.

• File with CALPUFF model configuration and settings sufficient to replicate CALPUFF 

modeling done for VISTAS using 12 km CALMET, including

o Ozone data used to run CALPUFF

o Ammonia concentrations used to run CALPUFF.

o All other set up files used in VISTAS 12-km CALPUFF run

Samples of these data files and examples of their application with CALPUFF for BART 

screening analyses can be found on the CALPUFF web site at 

(http://www.src.com/verio/download/sample_files.htm).

Appendix H.6 - 1309
Source-Specific Modeling Protocol 55

5. SOURCE-SPECIFIC MODELING PROTOCOL

Sources are required to submit a source-specific protocol to the State for review and approval 

prior to source-specific modeling. States will provide the documentation to EPA and FLM for 

their review. An outline of the typical contents of the site-specific protocol is provided in Table 

5-1.

If a source-specific modeling approach is proposed that differs from the common approach in 

Chapter 4, a more-detailed modeling protocol than that required under the common procedures is 

required. This protocol must explain the data sources, model configuration, and rationale for 

changes in the model approach from the common protocol and must be approved by the State.

Unit-specific source data include the following parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO2, H2SO4, NOx and PM10).

Additional building dimension information (building width, length, height and corner locations) 

is needed for short stacks that are less than Good Engineering Practice (GEP) height. This 

information is used in providing effective structure dimensions for building downwash 

calculations. (The requirement to conduct building downwash modeling may be waived by 

individual States or if the transport distance is greater than 50 km.)

The source coordinates must be expressed in the coordinate system used to define the CALMET 

and CALPUFF modeling domains. For the regional screening simulations, a Lambert Conformal 

Conic (LCC) coordinate system will be used. The required parameters to define an LCC 

coordinate include two matching parallels, latitude/longitude of the projection origin, coordinate 

datum, and false Easting and Northing (if used) of the projection origin. Subregional and sourcespecific domains may be using either an LCC or UTM projection. 

The CALPUFF Graphical User Interface (GUI) system provides software (called COORDS) to 

compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datums. In 

addition, the CALVIEW graphics feature allows the use of georeferenced satellite or aerial 

photographs to be used as base maps to confirm source locations. Links to sources of suitable 

base maps can be found on the CALPUFF data site (www.src.com) in the section on “Aerial 

Photos”.

Appendix H.6 - 1310
Source-Specific Modeling Protocol 56

Table 5-1. Sample Table of Contents of a Source-Specific Fine-Scale Modeling Protocol.

1. INTRODUCTION

1.1 Objectives

1.2 Location of Source vs. Relevant Class I Areas

1.3 Source Impact Evaluation Criteria

2. SOURCE DESCRIPTION

2.1 Unit-specific Source Data

2.2 Boundary Conditions

3. GEOPHYSICAL AND METEOROLOGICAL DATA

3.1 Modeling Domain and Terrain

3.2 Land Use

3.3 Meteorological Data Base

3.3.1 MM5 Simulations

3.3.2 Measurements and Observations

3.4 Air Quality Data Base

3.4.1 Ozone Concentrations – Measured or Modeled

3.4.2 Ammonia Concentrations – Measured or Modeled

3.4.3 Concentrations of Other Pollutants – Measured or Modeled

3.5 Natural Conditions at Class I Areas

4. AIR QUALITY MODELING METHODOLOGY

4.1 Plume Model Selection

4.1.1 Major Relevant Features of CALMET

4.2.2 Major Relevant Features of CALPUFF

4.2 Modeling Domain Configuration

4.3 CALMET Meteorological Modeling

4.4 CALPUFF Computational Domain and Receptors

4.5 CALPUFF Modeling Option Selections

4.6 Light Extinction and Haze Impact Calculations

4.7 Modeling Products

5. REVIEW PROCESS

6.1 CALMET Fields 

6.2 CALPUFF, CALPOST, and POSTUTIL Results 

6. REFERENCES

APPENDICES

A.1 VISTAS BART MODELING PROTOCOL 

A.2 ... other appendices as needed

Appendix H.6 - 1311
Source-Specific Modeling Protocol 57

An example of the data that need to be reported is provided in Table 5-2. More detail on the 

stack data, emissions species, and particulate size fractions to be reported will be made available 

on the CALPUFF website, www.src.com, Check with your State for the more detailed format of 

Table 5-2 that is to be used. 

Discussions with the regulatory authorities should be conducted prior to development of a 

protocol to ensure all of the relevant issues are included in the protocol. 

Table 5-2. Example of Source Documentation for BART Eligible Source. 

Unit name 

and/or 

description

Start-up dates SO2 potential 

emissions (tpy)

NOx potential 

emissions (tpy)

Total PM 

potential 

emissions (tpy) 

Emissions source

name

...

Total emissions

Potential BARTeligible 

emissions

Appendix H.6 - 1312
Quality Assurance 58

6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol is an important tool for use in determining 

whether a BART-eligible source can be reasonable expected to cause or contribute to visibility 

impairment in a Class I area, and therefore whether this source should be subject to BART 

controls, and if so, to determine the relative benefits of various BART controls. The purpose of 

the quality assurance (QA) program is to establish procedures for ensuring that products produced 

by the application of the modeling techniques for BART studies satisfy the regulatory objectives 

of the BART program.

The scope of the QA program affects different users differently. Common features of most 

applications will be the setup and execution of the CALPUFF air quality model and processing of 

modeling results to determine if a source contributes to visibility impairment at a Class I area. In 

many cases, users will be provided meteorological datasets that have been developed with 

VISTAS funding under a suitable QA program for use in the BART modeling. Other users will 

be involved in site-specific or source-specific analyses that will use additional datasets and 

potentially different modeling options and/or tools. More extensive quality assurance will be 

required in these latter types of applications. It is the responsibility of the modeler to ensure that 

an adequate QA protocol is in place for a particular application.

The CALPUFF modeling system contains built-in features to facilitate quality assurance of the 

modeling results. These include the automatic production of “QA” files for various datasets, 

including geophysical fields, sources and receptors, and imbedded tracking of model options and 

switches within the output files from the major modeling units of the modeling system. The 

Graphical User Interface system (GUI) provided as part of the latest CALPUFF modeling system 

allows these QA files to be displayed graphically.

In addition, a detailed software management system is in place to track version and level numbers 

associated each program and utility within the CALPUFF modeling system. This information is 

carried forward in all of the output files to create an audit trail of software versions and major 

model options used that can be retrieved and displayed from the model output files.

Because the required QA procedures will depend heavily on the exact application, there will be 

differences among different users and different applications.

In addition, the BART modeling process involves multiple organizations. The States have overall 

responsibility for the process and may also execute some or all of the modeling. VISTAS is 

contributing general guidance via this protocol and is preparing meteorological fields and 

performing modeling under the guidance of the States. The sources that are BART-eligible need 

to provide process information and emissions data for use in the analyses. In addition, those 

sources that are involved in BART assessments will need to be actively involved in control 

Appendix H.6 - 1313
Quality Assurance 59

technology decisions and assessments. Finally, some of the modeling steps may be carried out by 

contractors on behalf of VISTAS, a State, or a source.

Each of these organizations has a responsibility to ensure that it is providing correct information 

to others and to evaluate the quality of any analyses it is performing, whether with data of its own 

or from others. This chapter provides general guidance and information on those aspects of 

quality assurance that are specific to the CALPUFF modeling effort, irrespective of which 

organization is carrying out the effort. The focus is on the common protocol efforts described in 

Chapter 4. As described in Section 6.3, more comprehensive QA may be needed for the unique 

aspects of the source-specific modeling described in Chapter 5.

6.2 QA Procedures for Common Protocol Modeling

The VISTAS common protocol (Section 4) describes the methods and procedures for use in 

conducting regional scale screening modeling to determine the whether a particular source or 

group of sources is subject to BART controls. In the initial application, the regional CALPUFFready meteorological data files will be provided by VISTAS. The amount of effort for end-users 

performing QA of these pre-defined meteorological fields will be reduced from what is required 

in developing source-specific meteorological fields, as described below. Also, VISTAS is 

planning to provide five subregional CALMET meteorological datasets in a CALPUFF-ready 

format. The development of these CALMET datasets will be subject to a QA program as part of 

their development, so the necessary quality assurance activity of end-users is again reduced from 

what would be required in the development of the dataset. It is not expected that the quality 

assurance steps in the development will be repeated in each application. The VISTAS-provided 

regional and subregional meteorological fields will include a test case simulation for 

demonstrating that expected modeling results are obtained on the user’s computer platform. This 

test should be repeated by every user.

Although the CALPUFF modeling system is recommended by the U.S. Environmental Protection 

Agency for application to BART analyses, a considerable amount of expertise and modeling 

judgment is needed at certain stages of the analysis. The modeling is not a “cookbook” exercise, 

a fact that was recognized by the U.S. EPA in describing the expertise needed for CALMET 

modeling (EPA, 1998; pp. 9-10,). Current methods for performing refined chemistry calculation 

also require an understanding of the chemical and meteorological processing affecting 

ammonium nitrate formation. VISTAS has committed to provide appropriate CALPUFF training 

to assist States in obtaining the necessary expertise with the latest CALPUFF modeling tools and 

techniques. An appropriate level of knowledge of the model formulation, technical approach and 

assumptions is essential for successful BART modeling. 

6.2.1 Quality Control of Input Data

The input data required by the model depends on the application. At a minimum, source data is 

required by CALPUFF (see Section 6.2.3) along with a list of choices made about model options 

and switches. Most of the modeling option choices are specified or recommended by regulatory

Appendix H.6 - 1314
Quality Assurance 60

guidance and default values (see references in Section 4.3.3). However, remodeling of the 

boundary conditions is not required for VISTAS-provided finer grid domains so the expertise 

level is not as high as it would be for development of the boundary conditions files from scratch.

To the extent that modeling applications are using pre-defined CALMET files and CALPUFF 

templates, the quality assurance will be straightforward. More detailed steps are needed for the 

setup of modeling files for source-specific applications of subregional domains finer than 4 km. 

The basic procedures that will apply to all CALPUFF model applications will include a 

confirmation of the source data, including units, verification of the correct source and receptor 

locations, including datum and projection, confirmation of the switch selections relative to 

modeling guidance, checks of the program switches and file names for the various processing 

steps, and confirmation of the use of the proper version and level of each model program. It is a 

common and recommended procedure for an independent modeler not involved in the setup of 

the modeling files to independently confirm the model switches and data entry in the actual 

model input files and to conduct an independent run of the worst case event as a confirmation 

check.

In addition, common practice requires that a model project CD (or DVD or set of DVDs) be 

created that contains all of the data and program files needed to reproduce the model results 

presented in a report. The model list files from each step are included on the project CD. This 

information allows independent checking and confirmation of the modeling process.

6.2.2 Quality Control of Application of CALMET

For users of the VISTAS CALPUFF-ready CALMET meteorological files, a number of large 

datafiles will be provided by VISTAS on external USB2 or Firewire hard drives in a format ready 

for use with the CALPUFF model. The QA steps associated with the development of the 

VISTAS common datasets will be provided separately as part of the modeling documentation. It 

is not expected that the QA steps conducted in the development of the meteorological datasets 

will be repeated in each application, although tests to confirm that the dataset is suitable for the 

application for which it is being used should be performed as part of the QA. This is discussed in 

more detail below.

The regional screening CALMET grid is defined in Chapter 4 on a 12-km Lambert Conformal 

Conic (LCC) grid system. The subregional and source-specific domains may be defined in either 

LCC or Universal Transverse Mercator (UTM) coordinates. In the case of the LCC projection, 

two matching parallels, latitude/longitude of the projection origin, coordinate datum, and false 

Easting and Northing (if used) of the projection origin must also be defined. For any domains in 

UTM coordinates, the UTM zone (see Appendix D of the CALMET User’s Guide) and datum 

must be defined. The appropriate projection and map factors are provided as part of the 

definition of the VISTAS regional grid system. For a source-specific domain, the grid parameters 

will be provided as part of the source-specific protocol.

Appendix H.6 - 1315
Quality Assurance 61

Appendix A of the IWAQM report (EPA, 1998) contains a list of recommended CALMET switch 

settings. Except as modified in Chapter 4 of this protocol or in a source-specific protocol, the 

IWAQM guidance should be used in setting up the CALMET simulations. The CALMET model 

obtains the switch settings from an ASCII “control file” with a default name of CALMET.INP. 

Whether the model is run using a GUI or from the control line in a DOS, Linux, or Unix window, 

it is essential that the control file be reviewed as part of the CALMET QA analysis. The 

CALMET GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. This includes the default value for each variable, a text description 

of the variable, the meaning of each variable option, the units of the variable and interrelationships among variables indicating if/when the variable is used. Some third-party 

commercial GUIs strip out this descriptive information, which makes the QA step more difficult, 

although it is essential for perform nonetheless using the variable names as references for the 

variables in the file.

Part of the CALPUFF modeling system’s built-in QA capabilities is a variable tracking system 

that retains the control file inputs for CALMET and CALPUFF in the output files create by the 

models. This information includes the Version and Level numbers of the processor codes and

main model codes used in the simulations as well as the control files from the main models 

(CALMET and CALPUFF). The information from the preprocessing steps and the CALMET 

and CALPUFF model simulations is all carried forward and saved in the 

CALPUFF/postprocessor output files so that the final concentration/flux files contain a history of 

the model options and switch settings. This allows a user or reviewing agency to confirm the 

switch settings provided in a control file with that actually used in the model simulations. An 

optional switch in the CALPOST processor creates a complete listing of the QA data. This step 

requires access to the output CALPUFF concentration and/or flux files, which are normally 

practical to store on CDs or DVDs and to provide a part of the Project CD/DVD set.

6.2.3 Quality Control of Application of CALPUFF

The quality assurance of the source and emissions data is a major component of the CALPUFF 

modeling. Also, many errors are found in source coordinates and related projection/datum 

parameters, so confirmation of the source location is an important part of the modeling QA.

The locations of the Class I area receptors are another important CALPUFF input. The use of 

pre-defined receptors as provided by the National Park Service (NPS) receptor dataset is 

recommended in the VISTAS common protocol. However, although the latitude and longitude of 

each receptor point is provided, it is necessary to ensure that the proper UTM or LCC coordinates 

have been computed for computational domain selected. In particular, the datum of the NPS 

conversion software is not specified, so it is recommended that coordinates be checked using the 

CALPUFF GUI’s COORDS software or another comparable coordinate translation software 

package that recognizes various datums.

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of 

Appendix H.6 - 1316
Quality Assurance 62

this protocol or in a source-specific protocol, the IWAQM guidance should be used in setting up 

the CALPUFF simulations. The CALPUFF model obtains the switch settings from an ASCII 

“control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the 

CALPUFF GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. Some third-party commercial GUIs strip out this descriptive 

information, which makes the QA step more difficult, although it is essential for perform 

nonetheless using the variable names as references for the variables in the file.

6.2.4 Quality Control of Application of CALPOST and POSTUTIL

CALPOST is run separately for each Class I area in order to obtain the necessary visibility 

statistics for evaluating compliance with the BART screening and finer grid modeling thresholds. 

The inputs to CALPOST involve selection of the visibility method (Method 6 in the standard 

EPA BART guidance), entry of Class I area-specific data for computing background extinction 

(either average or best 20% natural conditions, as prescribed by the State) and monthly relative 

humidity factors for hygroscopic aerosols. CALPOST contains a receptor screening that allow 

subsets of a receptor network modeling in CALPUFF to be selected for processing in a given 

CALPOST run. This is how receptors within a single Class I area are selected for processing 

from a CALPUFF output file that may contain receptors from several Class I areas. CALPOST 

contains options for creating plot files that will help in the confirmation that the proper receptor 

subset is extracted.

The CALPOST output file contains a listing of the highest visibility impact each day of the model 

simulation over all receptors included in CALPOST analysis. Receptors will normally be 

selected in each CALPOST run so that each CALPOST run represents the impacts at a single 

Class I area. The table includes the data shown in the example in Table 4-1. For a screening 

assessment, the peak value of the change in extinction is shown at the bottom of the visibility 

table (see Table 4-1). For a finer grid simulation, the 98th percentile value (8th highest day) is 

used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the 

results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily 

change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that need to be carefully checked as part of the CALPOST quality 

assurance are:

- Visibility technique (Method 6 in the common VISTAS protocol)

- Monthly Class I-specific relative humidity factors for Method 6

- Background light extinction values

Appendix H.6 - 1317
Quality Assurance 63

- Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, 

organics, (as SOA), coarse and fine particulate matter and elemental carbon.

- Appropriate species names for coarse PM used

- Extinction efficiencies for each species

- Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area 

specific value for finer grid modeling)

- Screen to select appropriate Class I receptors for each CALPOST simulation.

The CALPOST program produces plot files compatible with CALVIEW that allow confirmation 

of receptor locations that is useful in evaluating the receptor screening step.

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF 

simulations into a total concentration file. In addition, it contains options to scale the 

concentrations from different modeled species (e.g., different particle sizes) into speciesdependent size distributions for the particulate matter. For example, PM is often simulated with 

unit emission rates for each particle size category and, in the POSTUTIL stage, the contributions 

of each size category based on the species being considered (e.g., elemental carbon, coarse 

particulate matter, etc.) are combined to form the species concentrations for input into 

CALPOST. This process, although simple, requires a careful review of the weighting factors for 

each source. POSTUTIL also allows a repartitioning of nitric acid and nitrate to account for the 

effects of ammonia limiting conditions. 

If source-specific modeling is performed using different sources of data or different techniques, 

the source-specific modeling protocol should provide justification for deviations from the 

VISTAS common protocol, and a QA plan specific for the application provided to address the 

quality assurance of the data used.

6.3 Additional QA Issues for Alternative Source-Specific Modeling

The level of QA required for application of source-specific protocols will be substantially higher 

than for the use of datasets that have already been subject to a QA procedure. For example, 

source-specific protocols may include the use of on-site meteorological datasets, the use of higher 

resolution prognostic meteorological (e.g., MM5) datasets, alternative visibility calculations, 

different extinction coefficients, or other changes to the common protocol. In addition to 

providing a source-specific modeling protocol describing and justifying the changes to the 

modeling approach from the VISTAS common protocol, the site-specific applications should 

include the development of a QA plan to properly evaluate the data used in the site-specific 

modeling.

The critical CALMET input parameters depend on the mode in which the model is run 

(observations mode, hybrid mode or no-observations mode), and the location and spatial 

Appendix H.6 - 1318
Quality Assurance 64

representativeness of any observational data. In a site specific protocol involving the 

development of a meteorological dataset, the elements of the QA process include preparation of 

wind rose (using observed, MM5 and CALMET-derived data), including examination of the data 

as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, 

and presentation of 2-D vector plots illustrating terrain effects/sea breeze circulation or other 

features of the flow expected to occur within the domain. For example, 2-D vector plots 

produced during light wind speed stable conditions (e.g., early morning such as 4 am) are good 

for assessing the performance of the CALMET model configuration and switches in reproducing 

terrain effects because these conditions are likely to maximize the terrain impacts in the model. 

Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea 

breeze circulations that may be important for certain applications. Customization of the QA 

process for the individual site-specific domain based on the availability of data and the physical 

processes expected to be important at that location should be conducted as part of the site-specific 

QA plan development.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-specific modeling domain should be 

evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than 

derived ammonia based on aerosol measurements) will require an evaluation of the model 

performance relative to the techniques in the VISTAS common protocol. 

In any site-specific protocol a site-specific QA plan should be prepared.

6.4 Assessment of Uncertainty in Modeling Results

Chapter 3 discussed the uncertainties and known limitations in CALPUFF. The source specific 

modeling report does not need to repeat the uncertainties listed in Chapter 3, but the reviewer 

should interpret results in light of these limitations. It is expected that the performance of the 

model will be better in predicting changes in visibility impacts due to BART controls than in 

predicting absolute visibility values. This is because uncertainties in meteorological conditions 

transport and dispersion are expected to be less important in evaluating a change in impact, since 

a comparable effect will be included in both the base and sensitivity simulations. 

Appendix H.6 - 1319
References 65

7. REFERENCES

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Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest Laboratory, 

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and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. 

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast Cycle: 

the RUC, Mon. Wea. Rev., 132, 495-518.

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary layer. 

Quart. J. Roy. Meteor. Soc., 99, 450-467.

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC and 

VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466.

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of the 

Montana Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources 

Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, 

Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze Rule; 

EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions under 

the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research Triangle 

Park, NC.

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for Modeling 

Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R-98-019, U.S. Environmental Protection Agency, RTP, NC.

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Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol Formation 

and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on the Applications 

of Air Pollution Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: 

Bulk parameterization of air-sea fluxes: Updates and verification for

the COARE algorithm. Journal of Climate, 16, 571-591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis of 

PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory. 

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. 

U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR 

MM5, NCAR TN-398-STR, NCAR Technical Note.

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary Organic 

Aerosols. Atmos. Environ., 23, 1733-1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes 

from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with 

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E. 

Gryning and F.A. Schiermeier. Plenum Press, New York, NY.

IWAQM, 1998: Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report 

and Recommendations for Modeling Long Range Transport Impacts. EPA-454/R-98-019.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating 

Bureau MID/SSD/80/0026/R. Nottingham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation Mechanism 

in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference – Guideline on 

Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 22-

26 June 2003, San Diego, CA.

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O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary 

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Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE 

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering 

Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. 

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms 

for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User Instructions. EPA/600/8-

89/041, U.S. EPA, Research Triangle Park, North Carolina.

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Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390. 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

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Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting & 

Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET 

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental 

Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

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and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline 

on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

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offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation 

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October 2005.

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References 68

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic 

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the 

AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11 

February 2005.

Appendix H.6 - 1323
Santee Cooper – Grainger, Jefferies, Winyah Trinity Consultants 

BART Applicability Modeling Protocol 

APPENDIX B 

CPM FACTOR ANALYSIS

Appendix H.6 - 1324
Santee Cooper – Grainger, Jefferies, Winyah Trinity Consultants 

BART Applicability Modeling Protocol 

APPENDIX B 

CPM FACTOR ANALYSIS

Appendix H.6 - 1325
Table B-1. AP-42 Reference Tests Used to Develop CPM Factors

Santee Cooper - CPM Analysis

Ref. No. Cited Date(s) Company Name Facility Unit(s) Tested Location 

78 Nov. 1994 Public Service Electric and Gas Co. Mercer Generating Station Unit No. 2 Hamilton Township

79 Dec. 6, 1994 Madison Gas and Electric Co. Blount Street Station Units 7, 8, 9 Madison, WI 

80 Jan. 23-25, 1990 Marshfield Electric and Water Dept. Wildwood Station Unit 5 Marshfield, WI 

81 Jan. 6, 1995 Dairyland Power Cooperative J.P. Madgett Alma, WI 

82 Jan. 25, 1994 Portland General Electric Boardman Power Plant Boardman, OR 

83 Dec. 11, 1989 Marshfield Electric and Water Dept. Wildwood Station Unit 5 Marshfield, WI 

84 Aug. 24-27, 1995 Portland General Electric Boardman Power Plant Unit 1 Boardman, OR 

85 Sept. 19, 1996 Portland General Electric Boardman Power Plant Unit 1 Boardman, OR 

86 Oct. 17, 1990 Portland General Electric Boardman Power Plant Boardman, OR 

87 Jan. 29, 1991 Portland General Electric Boardman Power Plant Boardman, OR 

88 Jan. 14, 1991 Portland General Electric Boardman Power Plant Boardman, OR 

89 Apr. 3, 1991 Portland General Electric Boardman Power Plant Boardman, OR 

90 Jan. 21, 1992 Portland General Electric Boardman Power Plant Boardman, OR 

91 Apr. 4, 1992 Portland General Electric Boardman Power Plant Boardman, OR 

92 Sept. 9, 1992 Portland General Electric Boardman Power Plant Boardman, OR 

93 Nov. 6, 1992 Portland General Electric Boardman Power Plant Boardman, OR 

94 Jan. 26, 1993 Portland General Electric Boardman Power Plant Boardman, OR 

95 Nov. 1994 Keystone Cogeneration Facility Keystone Cogeneration Logan Township, 

96 Dec. 1992 San Antonio City Public Service Board J.K. Spruce Unit 1 Bexar, TX 

Trinity Consultants CPM Factor Analysis (20
Table B-2. CPM Factor Data for Coal-Fired EGUs

Santee Cooper - CPM Analysis

CPM Emissions (lb/MMBtu)

Ref. No. Date(s) Company Name Facility Control Type(s) Run 1 Run 2 Run 3 Avg Data Source

78 Oct. 13, 1994 Public Service Electric and Gas Co. Mercer Generating Station PM only 0.0558 0.0587 0.0666 0.0604 State Agency

79 Dec. 6, 1994 Madison Gas and Electric Co. Blount Street Station PM only * * * * N/A - not located

80 Jan. 23-25, 1990 Marshfield Electric and Water Dept. Wildwood Station PM only * * * * N/A - not located

81 Jan. 6, 1995 Dairyland Power Cooperative J.P. Madgett PM only * * * * N/A - not located

82 Jan. 25, 1994 Portland General Electric Boardman Power Plant PM only 0.0220 0.0060 0.0040 0.0107 State Agency

83 Dec. 11, 1989 Marshfield Electric and Water Dept. Wildwood Station PM only 0.0626 0.0573 0.0496 0.0565 State Agency

84 Aug. 24-27, 1995 Portland General Electric Boardman Power Plant PM only 0.0020 0.0041 0.0020 0.0027 Facility Contact

85 Sept. 19, 1996 Portland General Electric Boardman Power Plant PM only 0.0198 0.0224 0.0224 0.0215 Facility Contact

86 Oct. 17, 1990 Portland General Electric Boardman Power Plant PM only 0.0360 0.0300 0.0330 Facility Contact

87 Jan. 29, 1991 Portland General Electric Boardman Power Plant PM only 0.0395 0.0309 0.0275 0.0326 Facility Contact

88 Jan. 14, 1991 Portland General Electric Boardman Power Plant PM only * * * * N/A - not located

89 Apr. 3, 1991 Portland General Electric Boardman Power Plant PM only 0.0073 0.0012 0.0048 0.0024 Facility Contact

90 Jan. 21, 1992 Portland General Electric Boardman Power Plant PM only 0.0170 0.0200 0.0090 0.0150 Facility Contact

91 Apr. 4, 1992 Portland General Electric Boardman Power Plant PM only 0.0350 0.0300 0.0310 0.0320 Facility Contact

92 Sept. 9, 1992 Portland General Electric Boardman Power Plant PM only 0.0120 0.0280 0.0320 0.0240 Facility Contact

93 Nov. 6, 1992 Portland General Electric Boardman Power Plant PM only 0.0120 0.0110 0.0130 0.0120 Facility Contact

94 Jan. 26, 1993 Portland General Electric Boardman Power Plant PM only 0.0060 0.0120 0.0330 0.0170 Facility Contact

95 Nov. 1994 Keystone Cogeneration Facility Keystone Cogeneration PM and SO2 * * * * N/A - not located

96 Dec. 1992 San Antonio City Public Service Board J.K. Spruce PM and SO2 0.009 0.004 0.030 0.015 State Agency

BPP-1 Oct. 26-27, 1983 Portland General Electric Boardman Power Plant PM only 0.0317 0.0331 0.0280 0.0309 Facility Contact

BPP-2 Aug. 15-16, 1984 Portland General Electric Boardman Power Plant PM only 0.0391 0.0402 0.0085 0.0293 Facility Contact

BPP-3 July 17, 1985 Portland General Electric Boardman Power Plant PM only 0.0190 0.0190 0.0190 0.0190 Facility Contact

BPP-4 Oct. 2, 1990 Portland General Electric Boardman Power Plant PM only 0.0400 0.0300 0.0250 0.0317 Facility Contact

BPP-5 Dec. 18, 1990 Portland General Electric Boardman Power Plant PM only 0.0185 0.0377 0.0281 Facility Contact

BPP-6 Sept. 18, 1997 Portland General Electric Boardman Power Plant PM only 0.0100 0.0060 0.0080 0.0080 Facility Contact

BPP-7 Sept. 10, 1998 Portland General Electric Boardman Power Plant PM only 0.0167 0.0078 0.0026 0.0090 Facility Contact

BPP-8 Sept. 10, 1999 Portland General Electric Boardman Power Plant PM only 0.0220 0.0310 0.0210 0.0247 Facility Contact

BPP-9 Aug. 22, 2000 Portland General Electric Boardman Power Plant PM only 0.0232 0.0222 0.0251 0.0235 Facility Contact

BPP-10 Aug. 15, 2001 Portland General Electric Boardman Power Plant PM only 0.0123 0.0139 0.0145 0.0136 Facility Contact

BPP-11 Sept. 10, 2002 Portland General Electric Boardman Power Plant PM only 0.0117 0.0126 0.0166 0.0136 Facility Contact

BPP-12 Sept. 11, 2003 Portland General Electric Boardman Power Plant PM only 0.0160 0.0138 0.0110 0.0136 Facility Contact

BPP-13 Sept. 23, 2004 Portland General Electric Boardman Power Plant PM only 0.0136 0.0208 0.0169 0.0171 Facility Contact

BPP-14 Aug. 3, 2005 Portland General Electric Boardman Power Plant PM only 0.0095 0.0179 0.0103 0.0126 Facility Contact

DPC-1 June 1, 2006 Dairyland Power Cooperative Alma PM only 0.0070 0.0060 0.0070 0.0067 State Agency

DPC-2 Mar. 30, 2004 Dairyland Power Cooperative Alma PM only 0.0003 0.0070 0.0020 0.0031 State Agency

DPC-3 June 18, 2002 Dairyland Power Cooperative Alma PM only 0.0510 0.0240 0.0420 0.0390 State Agency

DPC-4 May 18, 2006 Dairyland Power Cooperative J.P. Madgett PM only 0.0100 0.0220 0.0120 0.0147 State Agency

DPC-5 May 18, 2004 Dairyland Power Cooperative J.P. Madgett PM only 0.0180 0.0040 0.0060 0.0093 State Agency

DPC-6 Apr. 9, 2002 Dairyland Power Cooperative J.P. Madgett PM only 0.0200 0.0070 0.0120 0.0130 State Agency

PSE-1 Oct. 18, 1994 Public Service Electric and Gas Co. Mercer Generating Station PM only 0.091 0.021 0.004 0.0387 State Agency

KCPL-1 Nov. 7, 2001 Kansas City Power & Light Hawthorn PM and SO2 0.0052 0.0052 0.0052 0.0052 PSD Permit Comments

KCPL-2 Aug. 15, 2002 Kansas City Power & Light Hawthorn PM and SO2 0.0026 0.0026 0.0026 0.0026 PSD Permit Comments

KCPL-3 Apr. 15, 2003 Kansas City Power & Light Hawthorn PM and SO2 0.0036 0.0036 0.0036 0.0036 PSD Permit Comments

KCPL-4 May 24-25, 2004 Kansas City Power & Light Hawthorn PM and SO2 0.0062 0.0062 0.0062 0.0062 PSD Permit Comments

DPC-7 May 11, 2006 Dairyland Power Cooperative Genoa PM and SO2 0.0030 0.0030 0.0030 0.0030 State Agency

DPC-8 Aug. 3, 2004 Dairyland Power Cooperative Genoa PM and SO2 0.0060 0.0070 0.0060 0.0063 State Agency

DPC-9 Mar. 7, 2002 Dairyland Power Cooperative Genoa PM and SO2 0.0210 0.0240 0.0170 0.0207 State Agency

PE-1 October 16, 2001 Progress Energy Roxboro U1 PM only 0.008 NCDENR Data

PE-2 November 2, 2001 Progress Energy Cape Fear U6 PM only 0.034 NCDENR Data

PE-3 February 13, 2002 Progress Energy Wspn. U1 PM only 0.003 NCDENR Data

PE-4 February 14, 2002 Progress Energy Wspn. U2 PM only 0.015 NCDENR Data

PE-5 March 6, 2002 Progress Energy Wspn. U3 PM only 0.020 NCDENR Data

PE-6 May 2, 2002 Progress Energy Lee Unit 1 PM only 0.005 NCDENR Data

PE-7 March 27, 2002 Progress Energy Lee Unit 2 PM only 0.015 NCDENR Data

PE-8 May 1, 2002 Progress Energy Lee Unit 3 PM only 0.006 NCDENR Data

PE-9 May 9, 2002 Progress Energy Mayo PM only 0.008 NCDENR Data

PE-10 May 22, 2002 Progress Energy Roxboro U2 PM only 0.005 NCDENR Data

PE-11 May 30, 2002 Progress Energy Roxboro U4 PM only 0.007 NCDENR Data

PE-12 June 15, 2002 Progress Energy Sutton U1 PM only 0.007 NCDENR Data

PE-13 June 16, 2002 Progress Energy Sutton U2 PM only 0.008 NCDENR Data

PE-14 June 13, 2002 Progress Energy Sutton U3 PM only 0.015 NCDENR Data

PE-15 July 23, 2002 Progress Energy Roxboro U1 PM only 0.006 NCDENR Data

PE-16 July 24, 2002 Progress Energy Roxboro U3 PM only 0.005 NCDENR Data

PE-17 September 24, 2002 Progress Energy Wspn. U3 PM only 0.006 NCDENR Data

PE-18 October 23, 2002 Progress Energy Cape Fear U5 PM only 0.004 NCDENR Data

DE-1 N/A Duke Energy Dan River 1 PM only 0.0180 NCDENR Data

DE-2 N/A Duke Energy Dan River 2 PM only 0.0270 NCDENR Data

DE-3 N/A Duke Energy Marshall 1 Low PM only 0.0094 NCDENR Data

DE-4 N/A Duke Energy Marshall 1 High PM only 0.0165 NCDENR Data

DE-5 N/A Duke Energy Marshall 2 PM only 0.0030 NCDENR Data

DE-6 N/A Duke Energy Marshall 3 PM only 0.0060 NCDENR Data

DE-7 N/A Duke Energy Cliffside 2 PM only 0.0050 NCDENR Data

DE-8 N/A Duke Energy Cliffside 3 PM only 0.0050 NCDENR Data

DE-9 N/A Duke Energy Cliffside 5 PM only 0.0080 NCDENR Data

DE-10 N/A Duke Energy Allen 1 PM only 0.0050 NCDENR Data

DE-11 N/A Duke Energy Allen 3 PM only 0.0050 NCDENR Data

DE-12 N/A Duke Energy Allen 4 PM only 0.0060 NCDENR Data

DE-13 N/A Duke Energy Buck, Boiler 5 PM only 0.0040 NCDENR Data

DE-14 N/A Duke Energy Buck, Boiler 6 PM only 0.0040 NCDENR Data

DE-15 N/A Duke Energy Buck, Boiler 8 PM only 0.0040 NCDENR Data

DE-16 N/A Duke Energy Riverbend 4 PM only 0.0050 NCDENR Data

DE-17 N/A Duke Energy Riverbend 5 PM only 0.0050 NCDENR Data

Trinity Consultants CPM Factor Analysis (2007-06-27).xls / Stack Test Data

Appendix H.6 - 1327
Table B-3. CPM Factor Analysis for EGUs with PM-only Control

Santee Cooper - CPM Analysis

Reference CPM Emissions (lb/MMBtu)

Number Company Name Facility Name Run 1 Run 2 Run 3

AP-42 No. 78 Public Service Electric and Gas Co. Mercer Generating Station 0.0558 0.0587 0.0666 

AP-42 No. 79 Madison Gas and Electric Co. Blount Street Station * * *

AP-42 No. 80 Marshfield Electric and Water Dept. Wildwood Station * * *

AP-42 No. 81 Dairyland Power Cooperative J.P. Madgett * * *

AP-42 No. 82 Portland General Electric Boardman Power Plant 0.0220 0.0060 0.0040 

AP-42 No. 83 Marshfield Electric and Water Dept. Wildwood Station 0.0626 0.0573 0.0496 

AP-42 No. 84 Portland General Electric Boardman Power Plant 0.0020 0.0041 0.0020 

AP-42 No. 85 Portland General Electric Boardman Power Plant 0.0198 0.0224 0.0224 

AP-42 No. 86 Portland General Electric Boardman Power Plant 0.0360 0.0300 

AP-42 No. 87 Portland General Electric Boardman Power Plant 0.0395 0.0309 0.0275 

AP-42 No. 88 Portland General Electric Boardman Power Plant * * *

AP-42 No. 89 Portland General Electric Boardman Power Plant 0.0073 0.0012 0.0048 

AP-42 No. 90 Portland General Electric Boardman Power Plant 0.0170 0.0200 0.0090 

AP-42 No. 91 Portland General Electric Boardman Power Plant 0.0350 0.0300 0.0310 

AP-42 No. 92 Portland General Electric Boardman Power Plant 0.0120 0.0280 0.0320 

AP-42 No. 93 Portland General Electric Boardman Power Plant 0.0120 0.0110 0.0130 

AP-42 No. 94 Portland General Electric Boardman Power Plant 0.0060 0.0120 0.0330 

BPP-1 Portland General Electric Boardman Power Plant 0.0317 0.0331 0.0280 

BPP-2 Portland General Electric Boardman Power Plant 0.0391 0.0402 0.0085 

BPP-3 Portland General Electric Boardman Power Plant 0.0190 0.0190 0.0190 

BPP-4 Portland General Electric Boardman Power Plant 0.0400 0.0300 0.0250 

BPP-5 Portland General Electric Boardman Power Plant 0.0185 0.0377 

BPP-6 Portland General Electric Boardman Power Plant 0.0100 0.0060 0.0080 

BPP-7 Portland General Electric Boardman Power Plant 0.0167 0.0078 0.0026 

BPP-8 Portland General Electric Boardman Power Plant 0.0220 0.0310 0.0210 

BPP-9 Portland General Electric Boardman Power Plant 0.0232 0.0222 0.0251 

BPP-10 Portland General Electric Boardman Power Plant 0.0123 0.0139 0.0145 

BPP-11 Portland General Electric Boardman Power Plant 0.0117 0.0126 0.0166 

BPP-12 Portland General Electric Boardman Power Plant 0.0160 0.0138 0.0110

BPP-13 Portland General Electric Boardman Power Plant 0.0136 0.0208 0.0169 

BPP-14 Portland General Electric Boardman Power Plant 0.0095 0.0179 0.0103 

DPC-1 Dairyland Power Cooperative Alma 0.0070 0.0060 0.0070 

DPC-2 Dairyland Power Cooperative Alma 0.0003 0.0070 0.0020 

DPC-3 Dairyland Power Cooperative Alma 0.0510 0.0240 0.0420 

DPC-4 Dairyland Power Cooperative J.P. Madgett 0.0100 0.0220 0.0120 

DPC-5 Dairyland Power Cooperative J.P. Madgett 0.0180 0.0040 0.0060 

DPC-6 Dairyland Power Cooperative J.P. Madgett 0.0200 0.0070 0.0120 

PSE-1 Public Service Electric and Gas Co. Mercer Generating Station 0.0910 0.0210 0.0040 

PE-1 Progress Energy Roxboro U1 0.0080 

PE-2 Progress Energy Cape Fear U6 0.0340 

PE-3 Progress Energy Wspn. U1 0.0030 

PE-4 Progress Energy Wspn. U2 0.0150 

PE-5 Progress Energy Wspn. U3 0.0200 

PE-6 Progress Energy Lee Unit 1 0.0050 

PE-7 Progress Energy Lee Unit 2 0.0150 

PE-8 Progress Energy Lee Unit 3 0.0060 

PE-9 Progress Energy Mayo 0.0080 

PE-10 Progress Energy Roxboro U2 0.0050 

PE-11 Progress Energy Roxboro U4 0.0070 

PE-12 Progress Energy Sutton U1 0.0070 

PE-13 Progress Energy Sutton U2 0.0080 

PE-14 Progress Energy Sutton U3 0.0150 

PE-15 Progress Energy Roxboro U1 0.0060 

PE-16 Progress Energy Roxboro U3 0.0050 

PE-17 Progress Energy Wspn. U3 0.0060 

PE-18 Progress Energy Cape Fear U5 0.0040 

DE-1 Duke Energy Dan River 1 0.0180 

DE-2 Duke Energy Dan River 2 0.0270 

DE-3 Duke Energy Marshall 1 Low 0.0094 

DE-4 Duke Energy Marshall 1 High 0.0165 

DE-5 Duke Energy Marshall 2 0.0030 

DE-6 Duke Energy Marshall 3 0.0060 

DE-7 Duke Energy Cliffside 2 0.0050 

DE-8 Duke Energy Cliffside 3 0.0050 

DE-9 Duke Energy Cliffside 5 0.0080 

DE-10 Duke Energy Allen 1 0.0050 

DE-11 Duke Energy Allen 3 0.0050 

DE-12 Duke Energy Allen 4 0.0060 

DE-13 Duke Energy Buck, Boiler 5 0.0040 

DE-14 Duke Energy Buck, Boiler 6 0.0040

DE-15 Duke Energy Buck, Boiler 8 0.0040

DE-16 Duke Energy Riverbend 4 0.0050 

DE-17 Duke Energy Riverbend 5 0.0050 

AP-42 Data

Minimum 0.0012 

Maximum 0.0666 

Range 0.0654 

Mean 0.0246 

Median 0.0222 

Std. Deviation 0.0186 

Mean - 1 Std. Deviation 0.0060 

Mean + 1 Std. Deviation 0.0431 

Variation* 75.5%

NCDENR Data

Minimum 0.0030 

Maximum 0.0340 

Range 0.0310 

Mean 0.0089 

Median 0.0060 

Std. Deviation 0.0070 

Mean - 1 Std. Deviation 0.0019 

Mean + 1 Std. Deviation 0.0160 

Variation* 78.8%

All Data

Minimum 0.0003 

Maximum 0.0910 

Range 0.0907 

Mean 0.0179 

Median 0.0136 

Std. Deviation 0.0154 

Mean - 1 Std. Deviation 0.0025 

Mean + 1 Std. Deviation 0.0333 

Variation* 86.1%

* Calculated as the Std. Deviation / Mean.

Trinity Consultants CPM Factor Analysis (2007-06-27).xls / Statistics (PM control only)

Appendix H.6 - 1328
Table B-4. CPM Factor Analysis for EGUs with PM and SO2 Control

Santee Cooper - CPM Analysis

Reference CPM Emissions (lb/MMBtu)

Number Company Name Facility Name Run 1 Run 2 Run 3

AP-42 No. 95 Keystone Cogeneration Facility Keystone Cogeneration * * *

AP-42 No. 96 San Antonio City Public Service Board J.K. Spruce 0.0090 0.0040 0.0300 

KCPL-1 Kansas City Power & Light Hawthorn 0.0052 0.0052 0.0052 

KCPL-2 Kansas City Power & Light Hawthorn 0.0026 0.0026 0.0026 

KCPL-3 Kansas City Power & Light Hawthorn 0.0036 0.0036 0.0036 

KCPL-4 Kansas City Power & Light Hawthorn 0.0062 0.0062 0.0062 

DPC-7 Dairyland Power Cooperative Genoa 0.0030 0.0030 0.0030 

DPC-8 Dairyland Power Cooperative Genoa 0.0060 0.0070 0.0060 

DPC-9 Dairyland Power Cooperative Genoa 0.0210 0.0240 0.0170 

AP-42 Data

Minimum 0.0040 

Maximum 0.0300 

Range 0.0260 

Mean 0.0143 

Median 0.0090 

Std. Deviation 0.0138 

Mean - 1 Std. Deviation 0.0005 

Mean + 1 Std. Deviation 0.0281 

Variation* 96.3%

All Data

Minimum 0.0026 

Maximum 0.0300 

Range 0.0274 

Mean 0.0077 

Median 0.0052 

Std. Deviation 0.0074 

Mean - 1 Std. Deviation 0.0003 

Mean + 1 Std. Deviation 0.0152 

Variation* 95.9%

* Calculated as the Std. Deviation / Mean.

Trinity Consultants CPM Factor Analysis (2007-06-27).xls / Statistics (PM & SO2 Control)

Appendix H.6 - 1329
July 3, 2007 

Mr. Kevin J. Clark, P.E. 

Santee Cooper 

1 Riverwood Drive 

Moncks Corner, SC 29461

Re: Revised Santee Cooper BART Determination Modeling Protocol 

Dear Mr. Clark: 

SC Department of Health and Environmental Control (SC DHEC) Bureau of Air Quality (BAQ) has 

reviewed the revised Best Available Retrofit Technology (BART) determination modeling protocol 

submitted by Santee Cooper for the Grainger, Jefferies, and Winyah Generating Stations on July 3, 2007. 

 This revised protocol addresses all of the issues that were identified in my response dated June 8, 2007, 

with the exception of the calculation of results for the sea salt term to be used in the new IMPROVE 

equation. The revised protocol indicates that Santee Cooper will calculate results using two alternative 

sea salt estimations based on non-default sodium and strontium measurements. As indicated in response 

to the original protocol submitted, we will require that results be calculated based on the IMPROVE 

default chloride estimated sea salt concentration at each Class I area, which can be found on the tab 

labeled “Rayleigh & Sea Salt” on the VISTAS CALPOST-newIMPROVE calculation spreadsheet, in 

addition to any calculations performed using alternative methods. Also, Santee Cooper should use the 

Rayleigh values found at the same location rather than the values indicated in the revised protocol. 

The current Santee Cooper protocol also revises the particulate matter (PM) emission speciation 

calculations based on the usage of custom condensable PM (CPM) emission factors. We will 

provisionally accept the use of these new factors, but will reserve the right to revise these estimates at a 

later time. 

Please proceed with the submittal of your BART determination analysis for the Grainger, Jefferies, 

and Winyah Generating Stations by July 9, 2007. SC DHEC will review your modeling and consider the 

information you provide in making a determination on the appropriateness of BART controls for the 

Santee Cooper BART-eligible sources. As required in the BART rule, SC DHEC will evaluate this data 

in consultation with the EPA and FLMs and, after that consultation, will make a decision on whether or 

not the Santee Cooper BART-eligible sources will be required to apply BART controls. 

If you have any questions concerning BART modeling or the BART process, please contact me. 

Sincerely, 

 John P. Glass, Jr. 

 Modeling Section Manager 

 Bureau of Air Quality 

glassjp@dhec.sc.gov 

803-898-4074 

cc: Tim Allen, US Fish and Wildlife Service 

 Brenda Johnson, EPA Region 4 

Appendix H.6 - 1330
 

Henry Porter, Bureau of Air Quality 

Appendix H.6 - 1331
 

BEST AVAILABLE RETROFIT TECHNOLOGY APPLICABILITY ANALYSIS

SANTEE COOPER  GRAINGER, JEFFERIES, AND WINYAH STATIONS

TRINITY CONSULTANTS

1100 Johnson Ferry Road 

Suite 685 

Atlanta, GA 30342 

(404) 256-1919 

July 2007 

Project 061101.0117 

Appendix H.6 - 1332
Santee Cooper – Grainger, Jefferies, Winyah i Trinity Consultants 

BART Applicability Analysis 

TABLE OF CONTENTS

1. INTRODUCTION....................................................................................................................1

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS .........................2 

1.1.1 DETERMINATION OF BART-ELIGIBILITY ........................................................ 2

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND BART

APPLICABILITY................................................................................................ 3

1.1.3 POTENTIALLY AFFECTED CLASS I AREAS....................................................... 6

1.1.4 CALPUFF MODELING ANALYSES .................................................................. 7

1.2 ORGANIZATION OF BART APPLICABILITY ANALYSIS REPORT .................................8 

2. BART-ELIGIBLE SOURCE DESCRIPTION ...........................................................................9

2.1 BART-ELIGIBLE EMISSION UNITS............................................................................9 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY .....................................12 

2.2.1 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP ONLY ..... 15

2.2.2 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP AND FGD18

2.3 MODELED STACK PARAMETERS AND EMISSIONS....................................................22 

3. AIR QUALITY MODELING ANALYSES ...............................................................................25

3.1 AIR QUALITY MODELING SYSTEM..........................................................................25 

3.2 SCREENING AND REFINED ANALYSIS TECHNIQUES.................................................25 

3.2.1 VISIBILITY ASSESSMENT METRIC ................................................................. 26

3.2.2 METEOROLOGICAL AND COMPUTATIONAL GRIDS........................................ 26

3.2.3 MODEL PROCESSING AND POSTPROCESSING METHODS ............................... 27

3.3 CALMET METEOROLOGICAL PROCESSING............................................................27 

3.3.1 CALMET METEOROLOGICAL DOMAINS ...................................................... 27

3.3.2 MM5 SIMULATIONS ...................................................................................... 29

3.3.3 GEOPHYSICAL DATA ..................................................................................... 29

3.3.4 12-KM SCREENING ANALYSIS CALMET PROCESSING................................. 30

3.3.5 4-KM REFINED GRID CALMET PROCESSING ............................................... 30

3.3.6 QUALITY ASSURANCE OF CALMET ANALYSES .......................................... 32

3.4 CALPUFF MODEL PROCESSING.............................................................................32 

3.4.1 MODELED EMISSIONS AND CHEMICAL TRANSFORMATIONS ........................ 32

3.4.2 CALPUFF DISPERSION ALGORITHMS .......................................................... 36

3.4.3 BUILDING DOWNWASH .................................................................................. 36

3.4.4 CALPUFF MODELING DOMAINS AND CLASS I AREA RECEPTORS .............. 36

3.4.5 BACKGROUND OZONE AND AMMONIA CONCENTRATIONS........................... 40

3.4.6 PUFF REPRESENTATION................................................................................. 44

3.4.7 QUALITY ASSURANCE OF CALPUFF ANALYSES ......................................... 44

3.5 CALPOST POSTPROCESSING AND NATURAL BACKGROUND CONDITIONS FOR 

LIGHT EXTINCTION AND HAZE INDEX CALCULATIONS ...........................................44 

3.5.1 POSTUTIL PROCESSING............................................................................... 45

3.5.2 CLASS I AREA-SPECIFIC NATURAL BACKGROUND CONDITIONS .................. 45

Appendix H.6 - 1333
Santee Cooper – Grainger, Jefferies, Winyah ii Trinity Consultants 

BART Applicability Analysis 

3.5.3 VISIBILITY IMPACT CALCULATION AND REVISED IMPROVE LIGHT 

EXTINCTION ALGORITHM.............................................................................. 47

3.5.3.1 Site-Specific Rayleigh Scattering......................................................... 50

3.5.3.2 Background Sea Salt Aerosol Concentrations...................................... 50

3.5.4 QUALITY ASSURANCE OF POSTPROCESSING ANALYSES............................... 55

4. SCREENING ANALYSIS RESULTS .......................................................................................56

4.1 GRAINGER GENERATING STATION..........................................................................56 

4.1.1 SCREENING ANALYSIS IMPACTS AT CAPE ROMAIN ...................................... 56

4.1.2 SCREENING ANALYSIS IMPACTS AT SWANQUARTER.................................... 56

4.2 JEFFERIES GENERATING STATION ...........................................................................57 

4.2.1 SCREENING ANALYSIS IMPACTS AT CAPE ROMAIN ...................................... 57

4.2.2 SCREENING ANALYSIS IMPACTS AT WOLF ISLAND ...................................... 57

4.3 WINYAH GENERATING STATION.............................................................................58 

4.3.1 SCREENING ANALYSIS IMPACTS AT CAPE ROMAIN ...................................... 58

4.3.2 SCREENING ANALYSIS IMPACTS AT WOLF ISLAND ...................................... 58

4.4 SUMMARY OF SCREENING ANALYSES.....................................................................59 

5. REFINED ANALYSIS RESULTS ...........................................................................................60

5.1 JEFFERIES GENERATING STATION ...........................................................................60 

5.2 WINYAH GENERATING STATION.............................................................................62 

6. CONCLUSIONS....................................................................................................................64

APPENDIX A Sample CALPUFF/POSTUTIL/CALPOST Input Files 

APPENDIX B CPM Factor Analysis 

APPENDIX C Electronic Media File Index

Appendix H.6 - 1334
Santee Cooper – Grainger, Jefferies, Winyah 1 Trinity Consultants 

BART Applicability Modeling Analysis 

1. INTRODUCTION

Santee Cooper operates several electrical generating stations in South Carolina, including the 

Grainger, Jefferies, and Winyah Generating Stations. Each of the three facilities operates under a 

Title V Air Quality Permit issued by the South Carolina Department of Health and Environmental 

Control (DHEC) and consists of coal-fired utility boilers and supporting operations (e.g., coal storage 

and handling operations). South Carolina DHEC considers the Santee Cooper Grainger, Jefferies, 

and Winyah Generating Stations eligible to be regulated under the Best Available Retrofit 

Technology (BART) provisions of the Regional Haze Rule. 

Santee Cooper submitted a source-specific BART Applicability Modeling Protocol on 

February 15, 2007. A revised protocol was submitted on July 3, 2007 to address South Carolina 

DHEC’s and U.S. EPA’s initial comments (received from South Carolina DHEC on June 8, 2007) 

and to provide more detail and additional information about certain aspects of the analysis. This 

BART Applicability Analysis Report was prepared to demonstrate that the three Santee Cooper 

generating stations with BART-eligible units should not be considered subject to BART because the 

analyses described in this report demonstrate that each of the three facilities neither causes nor 

contributes to visibility impairment at any Class I area. 

Santee Cooper’s evaluation of BART-eligibility and the modeling methods used to determine 

applicability of BART as described in this report are consistent with the following guidance 

documents: 

▲ U.S. EPA, “Regional Haze Regulations and Guideline for Best Available Retrofit 

Technology (BART) Determinations,” Federal Register Volume 70, Number 128, 

July 6, 2005. 

▲ U.S. EPA, Guidance for Tracking Progress under the Regional Haze Rule

(EPA-454/B-03-004), September 2003. 

▲ U.S. EPA, Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule (EPA-454/B-03-005), October 2003. 

▲ U.S. EPA, Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report (EPA-454/R-98-019), December 1998. 

▲ U.S. EPA, Guideline on Air Quality Models, 40 CFR Part 51, Appendix W (Revised, 

November 9, 2005). 

▲ VISTAS, Protocol for the Application of the CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART), Revision 3.2, August 31, 2006. 

▲ VISTAS and U.S. EPA, “Q&A for Source by Source BART Rule,” August 3, 2006. 

▲ D.C. Circuit Court Ruling No. 05-1353, Utility Air Regulatory Group v. U.S. EPA, 

December 12, 2006. 

▲ South Carolina DHEC communication with Santee Cooper, various dates. 

The VISTAS BART Modeling Protocol, as revised on August 31, 2006, is incorporated by reference 

for Santee Cooper’s source-specific modeling analyses. The VISTAS BART Modeling Protocol and 

Appendix H.6 - 1335
Santee Cooper – Grainger, Jefferies, Winyah 2 Trinity Consultants 

BART Applicability Modeling Analysis 

related information (generally made available at the VISTAS BART website1

) establish common 

technical approaches for quantifying emissions from BART-eligible emission units and conducting 

screening and refined modeling analyses using the CALPUFF modeling system and common data 

resources. Santee Cooper’s analyses generally adhere to the recommendations made in the VISTAS 

BART Modeling Protocol as described in this report and adapted per South Carolina DHEC guidance 

to the source-specific analysis of visibility impacts attributable to the Grainger, Jefferies, and Winyah 

Generating Stations. 

1.1 OVERVIEW OF REGIONAL HAZE RULE AND BART REQUIREMENTS

The Regional Haze Rule requires that major sources of visibility-affecting pollutants belonging to one 

or more of 26 specific industrial source categories evaluate BART if the source was “in existence” 

(i.e., built or reconstructed) before August 7, 1977 and began operation after August 7, 1962. Such 

sources are termed “BART-eligible sources.” Major sources of visibility-affecting pollutants have the 

potential to emit 250 tons per year (tpy) of one or more of oxides of nitrogen (NOX), sulfur dioxide 

(SO2), and particulate matter less than 10 micrometers in aerodynamic diameter (PM10). Fossil-fueled 

electric generating units (EGUs) are one of the listed source categories, and fall under SIC code 4911 

– Electric Services. 

Hereafter, the “BART-eligible source” is taken to mean each collection of emission units at each of 

Santee Cooper’s three plants in existence during the relevant time period. The BART-eligible source 

may include multiple emission units but need not include the entire facility. 

1.1.1 DETERMINATION OF BART-ELIGIBILITY

The U.S. EPA BART guidelines define the following three steps for determining which 

sources at a facility are BART-eligible: 

1. Identify the emission units in the BART source categories. 

2. Identify the start-up dates of those units. 

3. Compare potential emissions to the 250 tpy cutoff. 

It is important to note that the evaluation of BART at Santee Cooper’s facilities is different 

than BART-eligible sources at other facilities on the list of 26. The Santee Cooper BARTeligible units are EGUs that, in addition to being subject to the BART Rule, are subject to 

the Clean Air Interstate Rule (CAIR), which targets emission reductions for both SO2 and 

NOX (both of which are precursors to regional particulate matter concentrations). 

U.S. EPA conducted technical analyses as part of the CAIR rule development and 

concluded that for EGUs that are subject to both CAIR and the BART Rule, the 

requirements of CAIR will satisfy BART. The D.C. Circuit court recently ruled that this 

approach is acceptable.2

 South Carolina DHEC has indicated to Santee Cooper that it 

intends to adopt U.S. EPA’s position that CAIR satisfies BART for NOX and SO2

 

1

 http://vistas-sesarm.org/BART/index.asp 

2

 D.C. Circuit Court Ruling No. 05-1353, Utility Air Regulatory Group v. U.S. EPA, December 12, 2006. 

Appendix H.6 - 1336
Santee Cooper – Grainger, Jefferies, Winyah 3 Trinity Consultants 

BART Applicability Modeling Analysis 

emissions; thus South Carolina DHEC has stated that the evaluation of BART for EGUs 

should be restricted to PM10 emissions only. Therefore, this BART-eligibility analysis 

only includes evaluations in terms of direct PM10 emissions from Santee Cooper’s BARTeligible EGU sources, and excludes all direct emissions from, and secondary reactions 

with, SO2 and NOX emissions. 

Santee Cooper has developed an inventory of six EGUs that meet the preceding criteria and 

are BART-eligible since they are fossil-fueled EGUs, were in existence on August 7, 1977, 

and began operation after August 7, 1962. Each source also has potential emissions of 

greater than 250 tpy of total PM10. Thus, each of the BART-eligible EGUs was analyzed 

to evaluate whether any of the three facilities are exempt from BART. Specific 

information about these emission units is provided in Section 2 of this BART Applicability 

Analysis Report. 

1.1.2 ASSESSMENT OF CONTRIBUTION TO VISIBILITY IMPAIRMENT AND

BART APPLICABILITY

In its role as technical analysis coordinator, VISTAS developed a common modeling 

protocol and data resources for use by state regulatory agencies and BART-eligible 

sources. The final VISTAS BART Modeling Protocol was issued on December 22, 2005, 

was revised most recently on August 31, 2006, and prescribes modeling techniques and 

data resources to conduct screening and refined analyses to assess whether a BARTeligible source is subject to BART. 

A BART-eligible source is determined to be subject to BART if the source causes or 

contributes to visibility impairment at a federally protected Class I area. Causation is 

defined as a single-source impact of 1.0 deciviews (dv) or more and contribution is defined 

as a single-source impact of 0.5 dv or more (each evaluated on a 24-hour average basis). 

The deciview is a metric used to represent normalized light extinction attributable to 

visibility-affecting pollutants. To determine whether a BART-eligible facility causes or 

contributes to visibility impairment, U.S. EPA guidance requires the use of an air quality 

model, specifically recommending the CALPUFF modeling system, to quantify the 

impacts attributable to a single BART-eligible source. Because contribution to visibility 

impairment is sufficient cause to require a BART determination, 0.5 dv is the critical 

threshold for assessment of BART applicability. 

Regional haze is quantified using the light extinction coefficient (bext), which is expressed 

in terms of the haze index expressed in dv. The haze index (HI) is calculated as shown in 

the following equation. 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = 10

ln10 bext HI

The impact of a BART-eligible source is determined by comparing the HI attributable to a 

source relative to estimated natural background conditions. The background extinction 

coefficient bext, background is affected by various chemical species and the Rayleigh scattering 

phenomenon and can be calculated as shown in the following equation. 

Appendix H.6 - 1337
Santee Cooper – Grainger, Jefferies, Winyah 4 Trinity Consultants 

BART Applicability Modeling Analysis 

( ) ext background SO NO OC Soil Coarse ap Ray b ++++++= bbbbbbbMm−

4 3

1

,

where, 

[ ] ( ) ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

( )

[ ] 3

1

3 34

4 2 4 4

Concentrat μginion m

Humidity Relative Function

Rayleigh defaultby Mm 10 Scattering

EC10

Mass Coarse6.0

Soil1

OC4

NONH3

SONH3

=

=

=

=

=

=

=

=

=

−

RHf

b

b

b

b

b

b RHf

b RHf

Ray

ap

Coarse

Soil

OC

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh air todue scattering is Scattering molecules

concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes Mass Coarse dusts coarse ofion 

concentrat thedenotes Soil soils fine ofion 

concentrat thedenotes OC carbon organic ofion 

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

 

Values for the parameters listed above specific to the natural background conditions at the 

Class I areas to be considered in this analysis are provided on an annual average basis in 

the U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule.

3

 More detailed information about the natural background conditions particular 

to Class I areas potentially affected by Santee Cooper’s operations at the Grainger, 

Jefferies, and Winyah Generating Stations are provided in Section 3.5 of this document. 

More recently, the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

workgroup has proposed a more robust equation for calculating background visibility, as 

described in detail in a report entitled “Revised IMPROVE Algorithm for Estimating Light 

Extinction from Particle Speciation Data.”4

 The updated algorithm, which has been 

approved by the IMPROVE Steering Committee and is currently undergoing peer review, 

provides a more refined calculation by including visibility impairment due to the following 

processes: 

▲ Visibility impairment due to different sizes of sulfates, nitrates, and organic 

carbon 

▲ Visibility impairment due to sea salt particles 

▲ Distinct water growth curves (i.e., f(RH)) for small sulfates and nitrates, large 

sulfates and nitrates, and sea salt 

▲ Elevation-dependent (hence Class I area dependent) Rayleigh scattering 

coefficient 

▲ Visibility impairment due to gaseous nitrogen dioxide (NO2) 

 

3

 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, Table 2-1, 

Attachment A, September 2003, EPA-454/B-03-005. 

4

 http://vista.cira.colostate.edu/IMPROVE/Publications/GrayLit/gray_literature.htm. 

Appendix H.6 - 1338
Santee Cooper – Grainger, Jefferies, Winyah 5 Trinity Consultants 

BART Applicability Modeling Analysis 

The revised IMPROVE light extinction algorithm takes the following form: 

bext ≈

( )[ ( ) ] ( )[ ( ) ]

( )[ ] ( )[ ]

[ ] [ ] [ ][ ] [ ]

( )[ ] [ ] Site Rayleigh specific Scattering 2

Small Large

34 Small 34 Large

44 2 Small 44 2 Large

4.1 Salt Sea NO33.0

PMC6.0PMF1EC10OC1.6OC8.2

4.2 1.5NONH NONH

2.2 SONH 8.4 SONH

+ +

+ +++ +

+ +

+ +

RHf b −

RHf RHf

RHf RHf

SS

S L

S L

For the purposes of calculating current, future, and natural background visibility at Class I 

areas as part of its reasonable progress analyses, VISTAS intends to present regional air 

quality modeling results using both the default and the newly revised IMPROVE light 

extinction formula. Values for the parameters listed above specific to the natural 

background conditions at the Class I areas considered in this analysis are provided on an 

annual average basis in the U.S. EPA’s Guidance for Estimating Natural Visibility 

Conditions under the Regional Haze Rule.

5

 More detailed information about the natural 

background conditions particular to Class I areas potentially affected by Santee Cooper’s 

BART-eligible operations and implementation of the revised IMPROVE equation are 

provided in Section 3.5 of this report. 

Particulate species that affect visibility are emitted from anthropogenic sources and include 

coarse particulate matter (PMC), fine particulate matter (PMF), and elemental carbon (EC), 

as well as precursors to secondary organic aerosols (SOA) and fine particulate matter such 

as SO2 and NOX. Santee Cooper’s calculation of speciated visibility-affecting pollutant 

emissions is presented in Section 2 of this report. The extinction coefficient due to 

emissions of visibility-affecting pollutants from a single BART-eligible source bext,source is 

calculated using an air quality model. The extinction due to the BART-eligible source was 

calculated as shown in the following equation. 

( ) sourceext SO NO PMC PMF SOA +++++= bbbbbbMmb EC

−

4 3

1

,

where, 

[( ) ] ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

[ ] 3

34 3

4 2 4 4

Concentrat μginion m

Humidity Relative Function

EC10

PMC6.0

PMF1

SOA4

NONH3

SONH3

=

=

=

=

=

=

=

=

RHf

b

b

b

b

b RHf

b RHf

EC

PMC

PMF

SOA

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ] concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes PMC PM coarse ofion 

concentrat thedenotes PMF PM fine ofion 

concentrat thedenotes SOA secondary ofion aerosols organic

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

 

5

 U.S. EPA, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, Table 2-1, 

Attachment A, September 2003, EPA-454/B-03-005. 

Appendix H.6 - 1339
Santee Cooper – Grainger, Jefferies, Winyah 6 Trinity Consultants 

BART Applicability Modeling Analysis 

1.1.3 POTENTIALLY AFFECTED CLASS I AREAS

Santee Cooper used screening and refined modeling techniques as described in the VISTAS 

BART Modeling Protocol to determine whether BART-eligible operations at the Grainger, 

Jefferies, or Winyah Generating Stations contribute to visibility impairment at any Class I 

areas. The VISTAS BART Modeling Protocol specifies that all Class I areas within 300 km 

of a BART-eligible source must be initially evaluated to determine whether the source 

contributes to visibility impairment. Table 1-1 summarizes the distances separating Santee 

Cooper’s three facilities from all Class I areas within the VISTAS region and adjacent 

states. Consistent with the VISTAS BART Modeling Protocol, only those Class I areas 

within 300 km were considered further in the BART applicability modeling analysis. 

TABLE 1-1. DISTANCES (KILOMETERS) SEPARATING VISTAS CLASS I AREAS AND

SANTEE COOPER’S JEFFERIES, GRAINGER, AND WINYAH GENERATING STATIONS

Class I Area Grainger Jefferies Winyah

Cape Romain (SC) 85 45 25

Wolf Island (GA) 345 242 284

Swanquarter (NC) 294 402 351

Linville Gorge (NC) 340 334 360

Okefenokee (GA) 430 324 371

Shining Rock (NC) 382 349 387

Great Smoky Mountains (NC/TN) 416 385 423

James River Face (VA) 416 481 469

Joyce Kilmer/Slickrock (NC) 476 431 475

Cohutta (GA) 514 456 505

Shenandoah (VA) 468 542 524

St. Marks (FL) 621 513 564

Chassahowitzka (FL) 660 560 599

Otter Creek (WV) 569 632 622

Dolly Sods (WV) 570 636 624

Mammoth Cave (KY) 730 699 738

Sipsey (AL) 765 690 745

Everglades (FL) 915 837 857

Figure 1-1 illustrates the location of the three Santee Cooper facilities evaluated and their 

location relative to Class I areas in the VISTAS region that are located within 300 km of 

the Santee Cooper facilities. 

The locations of Class I areas and receptor locations evaluated in the modeling analysis 

were determined by, and were obtained from, the U.S. Fish and Wildlife Service, which is 

the FLM for the Cape Romain, Wolf Island, and Swanquarter National Wildlife Refuges.6

 

 

6

 National Park Service compilation of Class I area receptors, http://www2.nature.nps.gov/air/maps/receptors/. 

Appendix H.6 - 1340
Santee Cooper – Grainger, Jefferies, Winyah 7 Trinity Consultants 

BART Applicability Modeling Analysis 

FIGURE 1-1. LOCATION OF SANTEE COOPER’S BART-ELIGIBLE FACILITIES RELATIVE TO 

VISTAS CLASS I AREAS

1,300 1,400 1,500 1,600 1,700 1,800

Lambert Conformal Coordinate East (km)

-800

-700

-600

-500

-400

-300

-200

Lambert Conformal Coordinate North (

km)

Grainger

Jefferies

Winyah

Wolf Island

Cape

Romain

Swanquarter

1.1.4 CALPUFF MODELING ANALYSES

Santee Cooper utilized screening and refined modeling techniques as described in the 

VISTAS BART Modeling Protocol to determine whether BART-eligible operations at the 

Grainger, Jefferies, or Winyah Generating Stations contribute to visibility impairment at 

any Class I areas. The CALPUFF modeling system was used to compute the 24-hour 

average visibility impairment attributable to each Santee Cooper facility to assess whether 

the 0.5 dv contribution threshold is exceeded, and if so, the frequency, duration, and 

magnitude of any exceedance events. CALPUFF is a refined air quality modeling system 

that is capable of simulating the dispersion, chemical transformation, and long-range 

transport of multiple visibility-affecting pollutant emissions from a single source and is 

therefore preferred for BART applicability and determination analyses. 

Appendix H.6 - 1341
Santee Cooper – Grainger, Jefferies, Winyah 8 Trinity Consultants 

BART Applicability Modeling Analysis 

The U.S. EPA BART guidelines prescribe that the 98th percentile, 24-hour average, 

visibility impact computed in a modeling analysis that evaluates three years of 

meteorological data should be compared to the contribution threshold. However, VISTAS 

prescribes that the maximum computed visibility impact be used as the basis for 

comparison in the screening analysis and the 98th percentile impact in the refined analysis. 

To assess whether BART-eligible operations contribute to visibility impairment, the 

applicability modeling analyses quantified the top eight 24-hour average visibility impacts 

of each year modeled to illustrate the distribution (i.e., frequency, duration, and magnitude) 

of peak visibility impairment episodes attributable to each of the facilities. 

It is worth noting that Cape Romain is relatively close (i.e., less than 50 km) to the Jeffries 

and Winyah Generating Stations, but that the same CALPUFF modeling analysis 

techniques have been determined to be suitable for regional haze modeling at Class I areas 

near and far. Specifically, Section 3.2.1 of the VISTAS BART Modeling Protocol

recommends the use of CALPUFF for BART applicability assessment at relatively short 

distances. 

The CALPUFF modeling system is described in technical detail in the VISTAS BART 

Modeling Protocol as revised and its use in screening and refined analyses for BART 

applicability assessment of Santee Cooper’s Grainger, Jefferies, and Winyah Generating 

Stations is described in Section 3 of this document. 

1.2 ORGANIZATION OF BART APPLICABILITY ANALYSIS REPORT

The remainder of this BART Applicability Analysis Report is organized as follows. 

▲ Section 2 describes the BART-eligible emission units at the three Santee Cooper facilities 

and the emission rates modeled in the BART applicability analyses. 

▲ Section 3 describes the procedural and technical guidance for conducting Class I area 

analyses using the CALPUFF modeling system, including the data resources, technical 

modeling options, and quality assurance methods used in the CALMET, CALPUFF, and 

CALPOST analyses. 

▲ Section 4 describes the results of the screening modeling analyses. 

▲ Section 5 describes the results of the refined modeling analyses. 

▲ Section 6 presents a summary of the BART applicability analyses and conclusion that the 

Grainger, Jefferies, and Winyah Generating Stations are not subject to BART. 

Supplemental information is provided in several appendices to this report. Appendix A contains 

sample model and post-processing input and output files used for Santee Cooper’s analyses of the 

three BART-eligible facilities. Appendix B contains Santee Cooper’s CPM emission factor analysis. 

Electronic copies of model input and output files are provided on electronic media accompanying this 

BART Applicability Analysis Report, a file index for which is presented in Appendix C. 

Appendix H.6 - 1342
Santee Cooper – Grainger, Jefferies, Winyah 9 Trinity Consultants 

BART Applicability Modeling Analysis 

2. BART-ELIGIBLE SOURCE DESCRIPTION

This section of the BART Applicability Analysis Report describes the emission units that comprise 

the BART-eligible sources at Santee Cooper’s Grainger, Jefferies, and Winyah Generating Stations. 

Emissions and exhaust characteristics of each source are quantified to demonstrate how each source 

was represented in the modeling analysis. 

2.1 BART-ELIGIBLE EMISSION UNITS

Santee Cooper reviewed the criteria for BART-eligibility and determined that two EGUs at each of 

the three generating stations, shown in Table 2-1, are BART-eligible sources. These units are 

pulverized coal-fired, dry-bottom boilers.7

 Each of the six BART-eligible boilers employs an 

electrostatic precipitator (ESP) to control particulate emissions, and the two BART-eligible boilers at 

the Winyah Generating Station also utilize flue gas desulfurization (FGD) systems to reduce sulfur 

emissions. Table 2-1 provides a summary of all boilers and ancillary units at each of the three sites. 

TABLE 2-1. SUMMARY OF BART-ELIGIBLE EMISSION UNITS AND POTENTIAL EMISSIONS

 

Facility Emission Unit 

Source 

Code 

BART 

Source 

Category* 

Date 

Built or 

Modified 

SO2

Emissions 

(tpy) 

NOX

Emissions 

(tpy) 

PM10

Emissions

(tpy) 

Grainger Boiler 1 001 1 1966 13,504 3,640 155 

 Boiler 2 002 1 1966 13,504 3,640 155 

 Coal Unloading System 003 1 1966 -- -- < 10 

Jefferies Boiler 1 (Oil-fired) 001 1 Before 1962 † † † 

 Boiler 2 (Oil-fired) 002 1 Before 1962 † † † 

 Boiler 3 003 1 1966 23,501 6,337 269 

 Boiler 4 004 1 1966 23,501 6,337 269 

 Coal Unloading System 005 1 1966 -- -- < 5 

 Coal Handling System 006 1 1966 -- -- < 5 

Winyah Boiler 1 001 1 1971 33,204 10,592 327 

 Boiler 2 002 1 1974 13,586 8,156 327 

 Boiler 3 003 1 After 1977 † † † 

 Boiler 4 004 1 After 1977 † † † 

 Coal Unloading System 005 1 1971 -- -- < 15 

 Coal Handling System #1 006 1 1974 -- -- < 15 

 Coal Handling System #2 007 1 After 1977 † † † 

 Synfuel Processing 009 1 After 1977 † † † 

 Limestone Handling 010 1 After 1977 † † † 

 Waste Handling 011 1 After 1977 † † † 

Grainger BART-Eligible Emission Units Total Potential Emissions (tpy) 27,008 7,280 320 

Jefferies BART-Eligible Emission Units Total Potential Emissions (tpy) 47,002 12,674 548 

Winyah BART-Eligible Emission Units Total Potential Emissions (tpy) 46,790 18,748 684 

* Category 1 denotes fossil fuel-fired steam electric plants of more than 250 MMBtu/hr heat input. 

† Emissions not listed since emission unit was constructed prior to or after the BART-eligibility period. 

 

7

 While the Winyah boilers are also permitted to combust fuel oil, they primarily combust bituminous coal. 

Appendix H.6 - 1343
Santee Cooper – Grainger, Jefferies, Winyah 10 Trinity Consultants 

BART Applicability Modeling Analysis 

Table 2-1 shows the potential annual emissions from the BART-eligible units at each of the three 

facilities exceed 250 tpy of each of NOX, SO2, and total PM10 (condensable and filterable). Therefore, 

all three of the Santee Cooper facilities are BART-eligible since potential emissions exceed 250 tpy 

for at least one (in fact, all) of NOX, SO2, and PM10, which are considered visibility-affecting 

pollutants, at each of the three facilities. 

Supporting operations at the three facilities were also evaluated to determine BART-eligibility and 

are shown in Table 2-1. All individual supporting operations, which are grouped into coal handling 

and coal unloading systems, at the Grainger, Jefferies, and Winyah facilities that came into 

“existence” during the time period covered by BART have potential PM10 emissions of less than 

5 tpy. Santee Cooper assumed that individual sources with emission rates below the Title V 

insignificant emission unit thresholds (e.g., less than 5 tpy PM10) could be excluded from further 

evaluation since these units are generally fugitive in nature and would not be expected to impact 

visibility at the Class I areas evaluated in the BART Applicability Analysis. A list of the individual 

units and their collective source codes are included in Table 2-2. These units were not included in the 

modeling analyses. 

In addition to SO2, NOX, and PM10, there are also two other visibility affecting pollutants that should 

be mentioned: volatile organic compounds (VOC) and ammonia. VISTAS conducted regional VOC 

modeling analyses that show cumulative VOC emissions from VISTAS sources do not contribute to 

visibility impairment within the VISTAS region: 

...the VISTAS States have ...determined that anthropogenic VOC emissions [from 

BART sources] do not cause or contribute to visibility impairment at VISTAS Class I 

areas and that VOC emissions do not need to be considered in BART analyses.8

Since VOC emissions from all VISTAS sources were determined not to cause or contribute to 

visibility impairment, primary VOC emissions from otherwise BART-eligible sources at the Santee 

Cooper facilities were not included in the BART applicability modeling analyses. 

VISTAS also conducted preliminary regional ammonia modeling analyses to determine whether 

ammonia emissions should be considered visibility-affecting for the purposes of source-specific 

BART applicability analyses. Section 4.1.3 of the VISTAS BART Modeling Protocol indicates that 

there are 13 facilities whose ammonia emissions contribute to visibility impacts, and other than these 

13 facilities, ammonia emissions from VISTAS sources do not contribute to visibility impairment 

within the VISTAS region: 

Based on these analyses, the VISTAS states recommend that, except for these 13 facilities, 

ammonia emissions not be included in the BART modeling. 

Since the Santee Cooper facilities are not included in this list of 13 sources, primary ammonia 

emissions from otherwise BART-eligible sources at the three Santee Cooper facilities were not 

included in the BART applicability modeling analyses. 

 

8 VISTAS BART Modeling Protocol, revised on August 31, 2006, page 34. 

Appendix H.6 - 1344
Santee Cooper – Grainger, Jefferies, Winyah 11 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 2-2. TITLE V INSIGNIFICANT EMISSION UNITS EXCLUDED FROM BART ANALYSIS 

 

Facility 

Emission 

Unit Group 

Equipment 

ID(s) Individual Insignificant Activities* 

Potential 

Emissions† 

(tpy) 

Grainger Coal P01 Coal Rail Car Shaker < 0.1 

Unloading P02 Coal Rail Car Shaker Hoppers < 0.1 

System P03, P04, P06 Coal Unloading Conveyors D, E, & F < 0.1, each 

P05 Coal Crusher < 2 

P12 Coal Bunker < 0.1 

F01 Coal Pile Management (Bulldozer) < 2 

F02 Coal Pile Wind Erosion < 4 

Jefferies Coal V01 Coal Car Unloading < 0.2 

Unloading C09, C10 Coal Unloading Conveyors E & F < 0.2, each 

System P03 Coal Stock Out Chute < 0.2 

F02 Coal Pile Management (Bulldozer) < 1 

F01 Coal Pile Wind Erosion < 2 

Jefferies Coal P04 Coal Handling Reclaim Hoppers < 0.2 

Handling C01 – C08 Coal Handling Conveyors 3D, 4D, 3C, 4C, 3B, 4B, 3A, 4A < 0.1, each 

System P05 Coal Crushers A, B, & C < 4 

X01 Coal Transfer Building < 0.2 

X02 Coal Transfer Tower < 0.2 

P06 Coal Bunkers < 0.2 

Winyah Coal P01 Coal Rail Car Shaker < 1 

Unloading P02 Coal Rail Car Shaker Hoppers < 1 

System P03 Coal Rail Car Belt A < 1 

 P04 – P07 Coal Conveyors B-1, B-2, B-3, & B-4 < 1, each 

X01 Coal Transfer Tower 1 < 1 

F08 Coal Pile Management (Bulldozer) < 3 

F01 Coal Pile Wind Erosion < 5 

Winyah Coal P10, P11, P20 Coal Crusher Feed Conveyors C-1, C-2, & C-3 < 0.2, each 

 Handling P12/P13, P21 Coal Crushers (2) < 3, each 

System #1 P14, P15, P22 Coal Transfer Tower Feed Conveyors D-1, D-2, & D-3 < 0.2, each 

 X02, X03 Coal Transfer Towers 2 & 3 < 0.2, each 

 P16, P23 Coal Silo Feed Conveyors E-1 & E-3 < 0.2, each 

 P17, P24 Coal Silos (2) < 0.2, each 

 P18, P25 Coal Pulverizers (2) < 4, each 

P19 Coal Cross Conveyor E-3 < 0.2 

* Emission units listed are presumed to be constructed during the BART applicability period of August 7, 1962 – August 7, 1977. 

† Potential emissions are based on application of a safety factor to actual emissions. Values are rounded. 

Appendix H.6 - 1345
Santee Cooper – Grainger, Jefferies, Winyah 12 Trinity Consultants 

BART Applicability Modeling Analysis 

2.2 BART-ELIGIBLE SOURCE MODEL EMISSIONS INVENTORY

Whereas the BART eligibility determination relies on potential emissions of visibility-affecting 

pollutants, the BART applicability analysis utilizes maximum actual 24-hour average emission rates 

of NOX, SO2, and PM10. The VISTAS BART Modeling Protocol specifies the following hierarchy of 

resources to establish the maximum actual 24-hour average emission rate over the prior three-to-five 

year period for BART applicability modeling: 

▲ 24-hour maximum emissions observed using a Continuous Emission Monitor (CEM) for 

the period 2001 through 2003 

▲ 24-hour maximum emissions observed using a CEM for any representative period 

▲ Facility stack test emissions 

▲ Potential to emit 

▲ Permit allowable emissions 

▲ Emissions factors from U.S. EPA AP-42 source profiles 

As previously mentioned, PM10 emissions are the only emissions that were included in the BART 

applicability modeling for the boilers since they are EGUs subject to CAIR. Santee Cooper utilized 

the 24-hour controlled potential PM10 emissions in the BART applicability modeling. Usage of the 

24-hour controlled potential PM10 emissions results in overly conservative visibility impacts; that is, 

modeling of 24-hour actual average emissions would result in smaller visibility impacts. Thus 

modeled impacts are conservatively high. Table 2-3 summarizes the 24-hour controlled potential 

PM10 emission rates used in the analyses for each modeled BART-eligible emission unit. 

TABLE 2-3. SUMMARY OF 24-HOUR AVERAGE MAXIMUM POTENTIAL BART EMISSION RATES 

 

Facility Emission Unit 

NOX

Emissions* 

(lb/hr) 

SO2

Emissions* 

(lb/hr) 

Total PM10

Emissions† 

(lb/hr) 

Total PM2.5

Emissions† 

(lb/hr) 

Grainger Boiler 1 -- -- 35.29 25.26 

 Boiler 2 -- -- 35.29 25.26 

Jefferies Boiler 3 -- -- 61.47 44.00 

 Boiler 4 -- -- 61.47 44.00 

Winyah Boiler 1 -- -- 74.75 44.42 

 Boiler 2 -- -- 74.75 44.42 

* NOX and SO2 emissions for EGUs are not required to be included in the BART analysis. 

† Emissions include both filterable and condensable particulate matter. 

Table 2-3 includes 24-hour maximum emission rates and distinguishes the emission rates of Total 

PM10 (TPM10), which includes emissions of Total PM less than 2.5 microns in aerodynamic diameter 

(TPM2.5) and all filterable and condensable components. Modeling of visibility impairment requires 

that the components of the exhaust stream be speciated because different types of particulate matter 

affect visibility to varying extents. Figure 2-1 depicts the speciation of visibility-affecting pollutant 

Appendix H.6 - 1346
Santee Cooper – Grainger, Jefferies, Winyah 13 Trinity Consultants 

BART Applicability Modeling Analysis 

emissions as represented in the VISTAS BART Modeling Protocol. Using the default VISTAS 

speciation nomenclature as a starting point, Santee Cooper adapted the speciation presented in the 

following sections. 

Note that the organic fraction of condensable particulate matter (CPM) is represented as primary 

organic condensable (POC) emissions, which are direct emissions but are sometimes referred to as 

secondary organic aerosols (SOA) by convention. Also, note that primary emissions of inorganic 

CPM may contain hygroscopic sulfates (SO4) and nitrates (NO3), as well as non-hygroscopic (PIC) 

particles. Since hygroscopic species have different extinction coefficients than non-hygroscopic 

species, it is important to distinguish inorganic CPM. The distinction of nitrate emissions is 

important since primary nitrate emissions will be affected by the partitioning of nitrate and nitric acid 

in the presence of ambient ammonia. In the modeling analyses, all condensable inorganic emissions 

were conservatively represented as sulfate emissions; smaller impacts would be anticipated if Santee 

Cooper had elected to distinguish between primary nitrate and other primary condensable inorganic 

emissions. Thus, modeled impacts are conservatively high. 

Table 2-4 provides a list and description of the particulate species used in the remaining discussion 

about particulate speciation. 

FIGURE 2-1. PARTICULATE MATTER SPECIATION

(AFTER FIGURE 4-3 OF THE VISTAS BART MODELING PROTOCOL) 

Appendix H.6 - 1347
Santee Cooper – Grainger, Jefferies, Winyah 14 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 2-4. NOMENCLATURE FOR EMISSIONS SPECIATION ANALYSIS

 

Nomenclature Description 

 

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μm 

PM10 Filterable particulate matter with an aerodynamic diameter < 10 μm 

PM6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μm 

PM2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μm 

PM2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μm 

PM1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μm 

PM1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μm 

PM0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μm 

PM0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μm 

PMC Filterable Coarse particulate matter = PM6-10 + PM2.5-6

PMF Filterable Fine particulate matter = PM6-10 + PM2.5-6

CPM Condensable particulate matter (organic and inorganic) 

POC Primary organic condensable emissions 

PIC Primary non-hygroscopic inorganic condensable emissions 

TPM10 Filterable PM10 + CPM 

TPM2.5 Filterable PM2.5 + CPM 

SO4 Inorganic Sulfate CPM 

NO3 Inorganic Nitrate CPM 

EC Elemental Carbon 

The FLM have published speciation guidance for certain source types (e.g., gas-, oil-, and coal-fired 

combustion sources) which can be used to create speciation profiles. The speciation profiles based on 

FLM guidance include the following particulate species: PMC, PMF, EC, POC, and primary 

inorganic CPM. These profiles can be augmented based on test data, engineering knowledge, and 

available reference data (e.g., AP-42 analyses) for each applicable emission unit. Santee Cooper 

evaluated the basis of the AP-42 CPM emission factors and determined that these factors, which are 

utilized in the NPS speciation workbooks, are based on test data from a very limited number of 

boilers, including boilers firing different coal types than the coal utilized in the Santee Cooper boilers. 

Thus, Santee Cooper elected to collect additional, more representative data and develop more 

appropriate CPM emission factors for their EGUs rather than rely upon in the factors in the FLM 

guidance; Appendix B includes the AP-42 factor analysis and cumulative CPM factor evaluations 

based on the CPM factor data Santee Cooper collected (same information as presented in the revised 

modeling protocol).9

 Santee Cooper also supplemented the FLM guidance for speciating primary 

inorganic CPM emissions for the six boilers as described in the following paragraph. 

Primary inorganic CPM is typically assumed to consist of two species: hygroscopic sulfate and 

hygroscopic nitrate inorganic condensable particulate matter, and the H2SO4 emission rate is often 

subtracted from the estimate of the total primary inorganic CPM emission rate (given by the FLM’s 

spreadsheets) to determine the emission rate of hygroscopic nitrate inorganic condensable particulate 

 

9

 Santee Cooper has collected readily-available CPM data from other coal-fired EGUs, including an extensive 

inventory from North Carolina Department of Environment and Natural Resources (NCDENR), and evaluated the mean and 

median factors from the larger data sets. 

Appendix H.6 - 1348
Santee Cooper – Grainger, Jefferies, Winyah 15 Trinity Consultants 

BART Applicability Modeling Analysis 

matter (NO3). However, Santee Cooper utilized the more conservative assumption that all primary 

inorganic CPM is hygroscopic sulfate (this assumption results in worst-case visibility impacts). That 

is, no distinction of non-sulfate inorganic CPM between primary nitrate (NO3) and/or other nonhygroscopic primary inorganic CPM (PIC) emissions (such as carbonates) was made. 

The individual particulate species were modeled in CALPUFF and then grouped into categories 

according to visibility-affecting characteristics using POSTUTIL. The extinction coefficients that 

were used for each species category are shown in Table 2-5. 

A discussion of the PM speciation assumptions for each of the BART-eligible sources at the three 

Santee Cooper facilities is presented in the following sections of this document. 

TABLE 2-5. ASSIGNMENT OF EMITTED PM SPECIES TO MODELED PM CATEGORIES

 

Modeled PM 

Category† Components 

Output 

Category‡

Extinction 

Coefficient 

(m2

/g) 

PMC Filterable coarse particles (PM6-10, PM2.5-6) PMC 0.6 

PMF Filterable fine particles (PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625) SOIL 1 

PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 

SO4 Primary inorganic condensable emissions of sulfates* SO4 3f(RH) 

NO3 Primary inorganic condensable emissions of nitrates* NO3 3f(RH) 

POC Primary organic condensable emissions SOA 4 

EC Uncombusted carbonaceous fuel EC 10 

* All condensable inorganic emissions were conservatively represented as sulfate emissions. 

† Modeled PM Category denotes the input of emissions data into CALPUFF. 

‡ Output Category denotes the assignment of modeled emissions in POSTUTIL for the visibility calculations in CALPOST. 

2.2.1 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP ONLY

The Grainger and Jefferies Generating Stations each operate two BART-eligible coal-fired 

boilers. Each of the boilers is controlled by an ESP and exhausts through its own stack. 

PM emissions from these boilers are due to the combustion of bituminous coal. Each 

boiler is subject to a 0.03 lb/MMBtu filterable TSP emission limit.10 To determine the 

TPM10 potential emissions and to speciate the PM emissions from the coal-fired boilers, 

site-specific data and data from the default speciation profile compiled by VISTAS were 

used. Additionally, Santee Cooper elected to use a fixed CPM factor of 0.02 lb/MMBtu, 

which is greater than both the mean (0.018 lb/MMBtu) and median (0.014 lb/MMBtu) 

CPM factors for EGUs with PM-only controls and well above the mean (0.009 lb/MMBtu) 

and median (0.006 lb/MMBtu) CPM factors for the North Carolina EGUs, which are most 

representative of the Santee Cooper EGUs. Refer to Table B-3 in Appendix B for details 

of the CPM factor development. The percentage of elemental carbon in the PM emissions 

was also based on the default speciation profile compiled by VISTAS. Table 2-6 

 

10 Consent decree between EPA and Santee Cooper, June 24, 2004. 

www.epa.gov/Compliance/resources/decrees/civil/caa/santee-cd.pdf 

Appendix H.6 - 1349
Santee Cooper – Grainger, Jefferies, Winyah 16 Trinity Consultants 

BART Applicability Modeling Analysis 

summarizes the relevant data for the boilers at the Grainger and Jefferies Generating 

Stations. 

TABLE 2-6. COAL BOILERS WITH ESP SPECIATION DATA

 

Speciation Data Value Reference 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 9.10% 

Maximum Coal Sulfur Content 1.37% 

Maximum Coal Heat Content (Btu/lb) 12,618.7 

Grainger Generating Station Emission Limit and 

Annual Average Coal Properties* 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 9.66% 

Maximum Coal Sulfur Content 1.39% 

Maximum Coal Heat Content (Btu/lb) 12,571.2 

Jefferies Generating Station Emission Limit and 

Annual Average Coal Properties* 

CPM Factor (lb/MMBtu) 0.02 Santee Cooper Custom CPM Factor 

PM10 as a % of PM 67.0% 

EC as a % of Fine PM10 3.7% 

EC as a % of PM10‡ 1.6% 

Organic portion of CPM 20.0% 

Inorganic portion of CPM 80.0% 

PM6-10 as a % of PM10 25.4% 

PM2.5-6 as a % of PM10 31.3% 

PM1.25-2.5 as a % of PM10 17.9% 

PM1-1.25 as a % of PM10 4.5% 

PM0.625-1 as a % of PM10 3.0% 

PM0.5-0.625 as a % of PM10 17.9% 

VISTAS Default Factors, Coal-Fired Boiler 

with Dry Bottom ESP (controlled), based on 

AP-42 Section 1.1, Tables 1.1-5 and 1.1-6† 

 

* Annual average coal values are the maximum or minimum value for the period of 2001, 2003-2005, whichever values 

result in the higher factors. Although 2002 data was not available, the variation between the existing coal data, 

calculated as the standard deviation divided by the mean value, was less than 8% for all parameters. Note that each 

facility utilizes only one coal pile for the subject boilers. 

† Size values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % 

of TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. 

‡ Value calculated based on the percentage of EC composing PM2.5 multiplied by the percentage of PM10 that is PM2.5. 

The site-specific information presented in Table 2-6 was input into the FLM speciation 

profile workbook, which uses the coal data and site-specific filterable and condensable PM 

factors to determine the TPM10 emissions on a pound per hour basis.11 The TPM10

emissions were then speciated in the various filterable and condensable PM components. 

Table 2-7 presents the TPM10 percentages and site-specific PM10, CPM, and TPM10

emissions for the Grainger and Jefferies boilers. 

 

11 Workbook is available at the NPS website: www2.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm. 

Appendix H.6 - 1350
Santee Cooper – Grainger, Jefferies, Winyah 17 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 2-7. DATA USED TO CALCULATE TPM10 EMISSIONS (LB/HR) FOR EACH COAL BOILER 

WITH ESP (GRAINGER AND JEFFRIES) 

 

 

% of TPM10

that is PM10* 

% of TPM10

that is CPM* 

Boiler Heat 

Input 

(MMBtu/hr) 

PM10

Emissions 

(lb/hr)† 

TPM10

Emissions 

(lb/hr)‡ 

CPM 

Emissions 

(lb/hr)** 

Grainger 50.1 49.9 880 17.69 35.29 17.60 

Jefferies 50.1 49.9 1,533 30.81 61.47 30.66 

* Percentages calculated using the FLM speciation workbooks. 

† Calculated as: (Boiler heat input, MMBtu/hr) × (PM factor, lb/MMBtu) × (PM10 as a % of PM). 

‡ Calculated as: (PM10 emissions, lb/hr) / (% of TPM10 that is PM10). 

** Calculated as: (Boiler heat input, MMBtu/hr) × (CPM factor, lb/MMBtu). 

Next, POC emissions were determined by multiplying the calculated CPM emissions by 

the organic condensable percentage of 20%. POC was assumed to be evenly split between 

the two size categories, 0.625-1.0 μm and 0.5-0.625 μm. PIC emissions were calculated 

using a similar methodology, using the inorganic condensable percentage of 80%. 

The filterable PM10 emissions were then divided amongst six different size categories by 

multiplying filterable PM10 emissions by the percentage of PM10 for each size category 

(refer to Table 2-5), yielding hourly emissions of PM6-10, PM2.5-6, PM1.25-2.5, PM1-1.25, 

PM0.625-1, and PM0.5-0.625. All elemental carbon was assumed to be in the smallest size 

category per the FLM speciation workbook. 

Table 2-8 presents a summary of the speciated PM emissions for the BART-eligible coalfired boilers at the Grainger and Jefferies Generating Stations while Figures 2-2 and 2-3 

present graphical representations of the speciation. Note that the values presented in the 

table and figures are for each boiler at the facility. 

TABLE 2-8. TPM10 SPECIATED EMISSIONS (LB/HR) FOR EACH COAL BOILER WITH ESP

(GRAINGER AND JEFFRIES) 

 

 POC 

(μm)

PIC (sulfate) 

(μm)

PMC 

(μm)

PMF 

(μm) EC

Facility 0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 0.5-0.625 

Grainger 1.76 1.76 7.04 7.04 4.49 5.54 3.17 0.79 0.53 2.89 0.28 

Jefferies 3.07 3.07 12.26 12.26 7.82 9.66 5.52 1.38 0.92 5.00 0.52 

Appendix H.6 - 1351
Santee Cooper – Grainger, Jefferies, Winyah 18 Trinity Consultants 

BART Applicability Modeling Analysis 

FIGURE 2-2. TPM10 SPECIATION (LB/HR) FOR EACH GRAINGER BOILER

3.52

14.08

-

4.49 5.54

3.17

0.79

0.53

2.89

0.28

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

FIGURE 2-3. TPM10 SPECIATION (LB/HR) FOR EACH JEFFERIES BOILER

6.13

24.53

-

7.82 9.66

5.52

1.38

0.92

5.00

0.52

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

2.2.2 PARTICULATE SPECIATION FOR COAL-FIRED BOILERS WITH ESP AND FGD 

The Winyah Generating Station operates two BART-eligible coal-fired boilers. Each of 

the boilers is controlled by an ESP and a FGD. PM emissions from these boilers are due 

primarily to the combustion of bituminous coal (the boilers are also authorized to combust 

Appendix H.6 - 1352
Santee Cooper – Grainger, Jefferies, Winyah 19 Trinity Consultants 

BART Applicability Modeling Analysis 

fuel oil). Each boiler is subject to a 0.03 lb/MMBtu filterable TSP emission limit.12 To 

determine the TPM10 potential emissions and to speciate the PM emissions from the coalfired boilers, site-specific data and data from the default speciation profile compiled by 

VISTAS were used. Additionally, Santee Cooper elected to use a custom CPM factor of 

0.008 lb/MMBtu, which is greater than both the mean (0.0077 lb/MMBtu) and median 

(0.0052 lb/MMBtu) CPM factors for EGUs with PM and SO2 controls; refer to Table B-4 

in Appendix B for details. Note this custom factor is also similar to the mean (0.009 

lb/MMBtu) and median (0.006 lb/MMBtu) CPM factors for the North Carolina EGUs, 

which only have PM controls and will have greater sulfate (PIC) emissions and thus, 

greater CPM emissions than the Winyah EGUs. The percentage of elemental carbon in the 

PM emissions was also based on the default speciation profile compiled by VISTAS. 

Table 2-9 summarizes the relevant data for the two boilers at the Winyah Generating 

Station. 

TABLE 2-9. COAL BOILERS WITH ESP & FGD SPECIATION DATA

 

Speciation Data Value Reference 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 8.78% 

Maximum Coal Heat Content (Btu/lb) 12,662.8 

Winyah Generating Station Boiler 1 Emission 

Limit and Annual Average Coal Properties* 

Filterable PM Limit (lb/MMBtu) 0.03 

Minimum Coal Ash Content 8.90% 

Maximum Coal Heat Content (Btu/lb) 12,727.3 

Winyah Generating Station Boiler 2 Emission 

Limit and Annual Average Coal Properties* 

CPM Factor (lb/MMBtu) 0.008 Santee Cooper Custom CPM Factor 

PM10 as a % of PM 67.0% 

EC as a % of Fine PM10 3.7% 

EC as a % of PM10‡ 1.6% 

Organic portion of CPM 20.0% 

Inorganic portion of CPM 80.0% 

PM6-10 as a % of PM10 25.4% 

PM2.5-6 as a % of PM10 31.3% 

PM1.25-2.5 as a % of PM10 17.9% 

PM1-1.25 as a % of PM10 4.5% 

PM0.625-1 as a % of PM10 3.0% 

PM0.5-0.625 as a % of PM10 17.9% 

VISTAS Default Factors, Coal-Fired Boiler 

with Dry Bottom ESP (controlled), based on 

AP-42 Section 1.1, Tables 1.1-5 and 1.1-6† 

 

* Annual average coal values are the maximum or minimum value for the period of 2001, 2003-2005, whichever values 

result in the higher emission factors. Although 2002 data was not available, the variation between the existing coal 

data, calculated as the standard deviation divided by the mean value, was less than 8% for all parameters. Note that 

each boiler utilizes its own coal pile. 

† Size values provided in AP-42 were expressed as a % of TSP and were converted to % as PM10 by dividing by the % 

of TSP that is PM10 (as shown in the AP-42 table). For example, PM6-10 is calculated as (PM10% - PM6%)/(PM10%) = 

PM6-10 as % of PM10. 

‡ Value calculated based on the percentage of EC composing PM2.5 multiplied by the percentage of PM10 that is PM2.5. 

 

12 Consent decree between EPA and Santee Cooper, June 24, 2004. 

www.epa.gov/Compliance/resources/decrees/civil/caa/santee-cd.pdf 

Appendix H.6 - 1353
Santee Cooper – Grainger, Jefferies, Winyah 20 Trinity Consultants 

BART Applicability Modeling Analysis 

The site-specific information presented in Table 2-9 was input into the FLM speciation 

profile workbook, which uses the coal data and site-specific filterable and condensable PM 

factors to determine the TPM10 emissions on a pound per hour basis.13 The TPM10

emissions were then speciated in the various filterable and condensable PM components. 

Table 2-10 presents the TPM10 percentages and site-specific PM10, CPM, and TPM10

emissions for the Winyah boilers. 

TABLE 2-10. DATA USED TO CALCULATE TPM10 EMISSIONS (LB/HR) FOR EACH COAL BOILER 

WITH ESP (WINYAH) 

 

% of TPM10

that is PM10* 

% of TPM10

that is CPM* 

Boiler Heat 

Input 

(MMBtu/hr) 

PM10

Emissions 

(lb/hr)† 

TPM10

Emissions 

(lb/hr)‡ 

CPM 

Emissions 

(lb/hr)** 

Boiler 1 71.5 28.5 2,660 53.47 74.75 21.28 

Boiler 2 71.5 28.5 2,660 53.47 74.75 21.28 

* Percentages calculated using the FLM speciation workbooks. 

† Calculated as: (Boiler heat input, MMBtu/hr) × (PM factor, lb/MMBtu) × (PM10 as a % of PM). 

‡ Calculated as: (PM10 emissions, lb/hr) / (% of TPM10 that is PM10). 

** Calculated as: (Boiler heat input, MMBtu/hr) × (CPM factor, lb/MMBtu). 

The POC, PIC, and various filterable PM10 size grouping emissions for the Winyah boilers 

were determined using the same methodology as for the Grainger and Jefferies boilers. 

Table 2-11 presents a summary of the speciated PM emissions for the BART eligible coalfired boilers at the Winyah Generating Station while Figures 2-4 and 2-5 present graphical 

representations of the speciation. 

TABLE 2-11. TPM10 SPECIATED EMISSIONS (LB/HR) FOR WINYAH COAL BOILERS WITH ESP & FGD 

 

 POC 

 (μm)

PIC (sulfate) 

 (μm)

PMC 

(μm)

PMF 

(μm) EC

Winyah 0.625-1.0 0.5-0.625 0.625-1.0 0.5-0.625 6-10 2.5-6 1.25-2.5 1.0-1.25 0.625-1.0 0.5-0.625 0.5-0.625 

Boiler 1 2.13 2.13 8.51 8.51 13.57 16.76 9.58 2.39 1.60 8.76 0.82 

Boiler 2 2.13 2.13 8.51 8.51 13.57 16.76 9.58 2.39 1.60 8.75 0.83 

 

13 Workbook is available at the NPS website: www2.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm. 

Appendix H.6 - 1354
Santee Cooper – Grainger, Jefferies, Winyah 21 Trinity Consultants 

BART Applicability Modeling Analysis 

FIGURE 2-4. TPM10 SPECIATION (LB/HR) FOR WINYAH BOILER 1 

4.26

17.02 -

13.57

16.76

9.58 2.39

1.60

8.76

0.8

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

FIGURE 2-5. TPM10 SPECIATION (LB/HR) FOR WINYAH BOILER 2 

4.26

17.02 -

13.57

16.76

9.58 2.39

1.60

8.75

0.8

Condensable Organic (0.5-1.0 microns)

Condensable Inorganic (0.5-1.0 microns)

Sulfates

Coarse Filterable (6-10 microns)

Coarse Filterable (2.5-6 microns)

Fine Filterable (1.25-2.5 microns)

Fine Filterable (1.0-1.25 microns)

Fine Filterable (0.625-1.0 microns)

Fine Filterable (0.5-0.625 microns)

Elemental Carbon

Appendix H.6 - 1355
Santee Cooper – Grainger, Jefferies, Winyah 22 Trinity Consultants 

BART Applicability Modeling Analysis 

2.3 MODELED STACK PARAMETERS AND EMISSIONS

Actual stack parameters were input to the CALPUFF model to represent the point of visibilityaffecting pollutant emissions. The location of each point source was represented consistently in the 

Lambert Conformal Coordinate (LCC) system used for the screening and refined meteorological data 

analyses prepared by VISTAS. Each boiler exhaust discharges vertically without obstruction. Per 

VISTAS guidance and South Carolina concurrence with Santee Cooper’s BART Modeling Protocol, 

the effects of building downwash were not considered in the BART Applicability Analyses.14

Table 2-12 summarizes the stack parameters and Table 2-13 summarizes the modeled emission rates 

of TPM10 species for BART-eligible emission units at each of Santee Cooper’s three BART-eligible 

facilities. 

 

14 VISTAS BART Modeling Protocol, revised on August 31, 2006, page 45 states: “Ignore building downwash 

unless source is within 50-km of a Class I area and the State instructs the source to specifically consider building 

downwash.” 

Appendix H.6 - 1356
Santee Cooper – Grainger, Jefferies, Winyah 23 

BART Applicability Modeling Analysis 

TABLE 2-12. STACK PARAMETERS FOR BART-ELIGIBLE EMISSION UNITS

Facility 

Emission 

Unit Stack ID 

LCC East 

(km) 

LCC North

(km) 

Base 

Elevation

(feet) 

Stack 

Height 

(feet) 

Stack 

Diameter

(feet) 

Exhaust 

Temperature

(°F) 

Grainger Boiler 1 GS01 1,644.618 -520.823 40 300 10.0 309  Boiler 2 GS02 1,644.643 -520.818 40 300 10.0 315 Jefferies Boiler 3 JS03 1,572.042 -600.906 56 300 13.0 325  Boiler 4 JS04 1,572.073 -600.932 56 300 13.0 325 Winyah Boiler 1 WS01 1,627.285 -580.090 18 404 20.0 125  Boiler 2 WS02 1,627.285 -580.090 18 404 20.0 125 

TABLE 2-13. MODELED EMISSION RATES FOR BART-ELIGIBLE EMISSION UNITS

 Emission Stack PMC (lb/hr) PMF (lb/hr) PIC (as sulfate) (lb/hr) Facility Unit ID 6-10 μm 2.5-6 μm 1.25-2.5 μm 1.0-1.25 μm 0.625-1.0 μm 0.5-0.625 μm 0.625-1.0 μm 0.5-0.625 μm 0.6Grainger Boiler 1 GS01 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04  Boiler 2 GS02 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04 Jefferies Boiler 3 JS03 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26  Boiler 4 JS04 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26 Winyah Boiler 1 WS01 13.57 16.76 9.58 2.39 1.60 8.76 8.51 8.51  Boiler 2 WS02 13.57 16.76 9.58 2.39 1.60 8.75 8.51 8.51 
Santee Cooper – Grainger, Jefferies, Winyah 24 Trinity Consultants 

BART Applicability Modeling Analysis 

As indicated in Table 2-12, stack heights of all six of the boilers are greater than the nominal default 

Good Engineering Practice (GEP) stack height of 65 meters (213.25 feet). South Carolina DHEC Air 

Pollution Control Regulation 61-62.7 “Good Engineering Practice Stack Height” applies to “all stacks 

excluding flares which were not ‘in existence’ before December 31, 1970” and to “stack heights or 

dispersion techniques at sources which were reconstructed or under major modification after 

December 31, 1970.” The GEP stack height regulation requires that modeling of stack heights not 

exceed the greater of 65 meters or Hg = H + 1.5L, where Hg = good engineering practice stack height, 

measured from the ground-level elevation at the base of the stack, H = height of nearby structure(s) 

measured from the ground-level elevation at the base of the stack, and L = lesser dimension, height or 

projected width of nearby structure(s). 

Each of the boilers at the Grainger and Jefferies Generating Stations became “in existence” during 

1966 and 1967, respectively, before 1970. Therefore, the GEP stack height regulation does not apply 

to these four boilers. The GEP stack height regulation does apply to the two Winyah boilers, which 

became “in existence” in 1971 and 1974. Therefore, Santee Cooper evaluated GEP stack height for 

each of the Winyah boiler stacks using the U.S. EPA’s Building Profile Input Program (BPIP), which 

computes the GEP stack height based on the dimensions of nearby building structures. Using the 

associated boiler houses, which each have a height of 66.14 meters and a projected width of equal to 

or greater than the height, the GEP for each of the Winyah boilers is: 

Hg = H + 1.5L = 66.14 m + 1.5(66.14 m) = 165.35 m 

This calculation demonstrates that the Winyah boilers can be modeled at actual stack height 

(123.14 m) since creditable GEP stack height is not exceeded. Thus, all modeling was conducted 

using the actual stack heights shown in Table 2-12. 

Appendix H.6 - 1358
Santee Cooper – Grainger, Jefferies, Winyah 25 Trinity Consultants 

BART Applicability Modeling Analysis 

3. AIR QUALITY MODELING ANALYSES

Section 3 of this BART Applicability Analysis Report for Santee Cooper’s Grainger, Jefferies, and 

Winyah Generating Stations describes the modeling methods, data resources, and technical options 

that were used to conduct screening and refined analyses to assess visibility impacts. Air quality 

modeling was conducted generally following the methods described in the VISTAS BART Modeling 

Protocol as revised. 

3.1 AIR QUALITY MODELING SYSTEM

CALPUFF is a multi-layer, multi-species, non-steady-state Lagrangian puff model, which can 

simulate the effects of time- and space-varying meteorological conditions on pollutant transport, 

transformation, and removal. The modeling system is designed to handle the complexities posed by 

the complex terrain, the large source-receptor distances, chemical transformation and deposition, and 

other issues related to Class I visibility impacts. A complete description of the model formulation and 

capabilities is provided in the User’s Guides for the CALPUFF modeling system and the VISTAS 

BART Modeling Protocol. 

The CALPUFF modeling system has been adopted by the U.S. EPA as a recommended regulatory 

guideline model for source-receptor distances greater than 50 km, and for use on a case-by-case basis 

in complex flow situations for shorter distances. CALPUFF is recommended for Class I area impact 

assessments by the Interagency Workgroup on Air Quality Models (IWAQM). The U.S. EPA’s 

BART guidance recommends CALPUFF as “the best modeling application available for predicting a 

singe source’s contribution to visibility impairment.” 

As a result of these recommendations, the VISTAS BART Modeling Protocol was based on the use of 

CALPUFF and was used by Santee Cooper for the source-specific analysis of visibility impacts 

attributable to the three facilities. Specifically, CALMET Version 5.726/060801, CALPUFF 

Version 5.756/060725, POSTUTIL Version 1.52/060412, and CALPOST Version 5.6393/06020215

are designated as the “VISTAS Recommended Version for BART Modeling” by the VISTAS 

Technical Contractor and were used in the CALPUFF BART applicability analyses for Santee 

Cooper’s Grainger, Jefferies, and Winyah Generating Stations.16

3.2 SCREENING AND REFINED ANALYSIS TECHNIQUES

The VISTAS BART Modeling Protocol distinguishes between screening and refined applications of 

the CALPUFF model for the purposes of BART Applicability Analyses. As the names of these 

techniques imply, screening analyses are intended to provide a conservative estimate of visibility 

impacts using computationally efficient techniques. Refined analyses utilize less conservative and 

 

15 A version that incorporates many of the changes to the original EPA version of the program was approved as the 

new regulatory version by the U.S. EPA on June 29, 2007. 

16 http://src.com/verio/download/download.htm#VISTAS_VERSION 

Appendix H.6 - 1359
Santee Cooper – Grainger, Jefferies, Winyah 26 Trinity Consultants 

BART Applicability Modeling Analysis 

more representative data and modeling methods to compute visibility impacts following U.S. EPA’s 

BART guidelines. Screening analyses may be used to determine that a facility is not subject to 

BART using a conservative assessment, or to focus the scope of refined analyses by demonstrating 

that visibility impairment is not likely to occur at more distant Class I areas. 

Although certain elements of the modeling analyses are identical regardless of which approach is 

used (e.g., representation of emissions sources, chemical transformation algorithms, Class I area 

receptors), the VISTAS BART Modeling Protocol makes several important distinctions between 

screening and refined analyses. 

3.2.1 VISIBILITY ASSESSMENT METRIC

The U.S. EPA BART guidelines prescribe that the 98th percentile, 24-hour average 

visibility impact computed in a modeling analysis that evaluates three years of 

meteorological data should be compared to the contribution threshold of 0.5 dv to assess 

BART applicability. However, VISTAS prescribes that the maximum computed visibility 

impact be used as the basis for comparison in the screening analysis to ensure that the 

modeled result is a conservative representation of visibility impairment. The VISTAS 

refined approach prescribes that the 98th percentile impact be evaluated in refined analyses 

as the eighth-highest 24-hour average visibility impact of each of three years modeled, or 

the 22nd-highest 24-hour average visibility impact over the three years modeled, whichever 

is more conservative. 

To assess whether BART-eligible operations contribute to visibility impairment, Santee 

Cooper’s BART Applicability Analyses quantified the top eight 24-hour average visibility 

impacts of each year modeled to illustrate the distribution (i.e., frequency, duration, and 

magnitude) of peak visibility impairment episodes attributable to each facility. The 

maximum 24-hour impact is used for interpretation of the screening analyses and the 

eighth-highest 24-hour average visibility impact of each of three years modeled is used for 

interpretation of the refined analyses. Each Santee Cooper facility was modeled and 

evaluated separately in comparison to the 0.5 dv contribution threshold to assess BART 

applicability. 

3.2.2 METEOROLOGICAL AND COMPUTATIONAL GRIDS

The VISTAS BART Modeling Protocol prescribes that screening analyses be conducted on 

a 12-km meteorological and computational grid and that refined analyses be conducted on 

a 4-km meteorological and computational grid. The resolution of the computational 

analysis dictates the extent to which geophysical (i.e., terrain and land use) and 

meteorological conditions are represented in the CALMET meteorological model, hence 

the advection, dispersion, and chemical transformation of visibility-affecting pollutants in 

the CALPUFF meteorological model. The VISTAS Technical Contractor conducted 

meteorological modeling using both screening 12-km and refined 4-km grids to provide 

common data resources to eligible sources conducting BART Applicability Analyses, 

which were used by Santee Cooper to assess visibility impacts attributable to each facility. 

The following section of this document describes the CALMET meteorological modeling 

in greater detail. 

Appendix H.6 - 1360
Santee Cooper – Grainger, Jefferies, Winyah 27 Trinity Consultants 

BART Applicability Modeling Analysis 

3.2.3 MODEL PROCESSING AND POSTPROCESSING METHODS

The IWAQM Phase 2 Report prescribes recommended default model processing options to 

be used in CALPUFF analyses, which are in most cases considered regulatory default 

options under U.S. EPA’s Guideline on Air Quality Models. With few exceptions, 

IWAQM default model options as described in the following section of this document were 

prescribed for use in both screening and refined analyses. One exception in the application 

of the CALPUFF model is the use of puff-splitting algorithms, which are disabled by 

default and are the prescribed setting in screening analyses. As described by the VISTAS 

BART Modeling Protocol, puff splitting is typically enabled in refined analyses to simulate 

wind shear effects across a well-mixed individual puff by dividing the puff horizontally 

and vertically into two or more pieces. Differential rates of transport among the new puffs 

thus generated can increase the horizontal spread of the material in the plume due to 

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm 

is designed to allow large puffs that may grow to be several grid cells or more in size to 

split into smaller puffs that can then more accurately respond to variations in the local wind 

field across the original large puff. Puff splitting may be appropriate for transport 

distances over 200 to 300 km, or possibly over shorter distances in complex terrain. 

3.3 CALMET METEOROLOGICAL PROCESSING

CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields 

from mesoscale model (MM) output, raw observations of surface and upper air conditions, 

precipitation measurements, and geophysical parameters into a single hourly, gridded data set for 

input to CALPUFF. The CALMET User’s Guide and VISTAS BART Modeling Protocol provide a 

detailed description of the model’s formulation and capabilities. 

The federal Guideline for CALPUFF processing provides the following recommendations for the 

meteorological data period at Section 9.3.1.2: 

Less than five, but at least three, years of meteorological data (need not be 

consecutive) may be used if mesoscale meteorological fields are available, as 

discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used 

in conjunction with available standard [National Weather Service] NWS or 

comparable meteorological observations within and near the modeling domain. 

The VISTAS BART Modeling Protocol prescribes the years 2001 through 2003 for BART 

applicability analyses. The primary basis for this data analysis period was the availability of quality 

assured MM5 data for the entire VISTAS area. For its BART applicability analyses, Santee Cooper 

utilized 12-km screening and 4-km CALMET data prepared by the VISTAS Technical Contractor 

using the following data resources and CALMET modeling techniques. 

3.3.1 CALMET METEOROLOGICAL DOMAINS

12-km screening and 4-km refined CALMET meteorological data sets were prepared for 

the domains depicted in Figure 3-1. Santee Cooper’s BART applicability analyses utilized 

the Regional Screening Domain 0 for 12-km screening analyses and the refined 

Appendix H.6 - 1361
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BART Applicability Modeling Analysis 

Sub-regional Domain 4 for the 4-km refined analyses. The CALPUFF computational 

domains were selected as a subset of these domains as described in the following section of 

this document. 

FIGURE 3-1. 12-KM REGIONAL SCREENING ANALYSIS AND

4-KM SUB-REGIONAL REFINED ANALYSIS CALMET DOMAINS

The VISTAS regional modeling domain was designed to allow any Class I areas within the 

VISTAS area to be evaluated with a single meteorological database and consistent 

CALPUFF modeling options. The horizontal domain is comprised of grid cells, each 

containing a central grid point at which meteorological and computational parameters are 

calculated at each time step. For the initial regional analysis, a grid spacing interval of 

12 km was selected. The LCC projection system is used to describe the horizontal grid, 

with origin at 40 degrees North latitude and 97 degrees West longitude. Standard parallels 

for the projection were set at 33 degrees North and 45 degrees North. 

Table 3-1 summarizes the vertical grid structure selected for this analysis, which comprises 

ten vertical layers. The cell face height of each layer indicates its vertical extent. The 

vertical domain is composed of terrain-following grid cells, the number and size of which 

Appendix H.6 - 1362
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BART Applicability Modeling Analysis 

are chosen so as to constrain the boundary layer in which dispersion and chemical 

transformations take place. The highest cell face was selected to be 4,000 meters to 

constrain the default maximum mixing height of 3,000 meters. 

TABLE 3-1. VERTICAL GRID STRUCTURE

 

Vertical Grid Cell 

Cell Face Height 

(meters) 

1 20 

2 40 

3 80 

4 160 

5 320 

6 640 

7 1,200 

8 2,000 

9 3,000 

10 4,000 

Refined 4-km analyses were also conducted using this vertical grid structure. Sub-regional 

Domain 4, which Santee Cooper utilized for its refined analyses for Jefferies and Winyah 

facilities, contains all portions of South Carolina and Georgia in which the BART-eligible 

sources and potentially affected Class I areas are located. 

3.3.2 MM5 SIMULATIONS

MM data are used as “observed” or “first-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional 

grid. The following three years of MM5 meteorological data were assembled by VISTAS 

for use in the regional CALPUFF modeling effort: 

▲ 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA) 

▲ 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS) 

▲ 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning 

Organization). 

These data sets were provided to the VISTAS Technical Contractor, which produced threedimensional, CALMET-ready meteorological files for the VISTAS domain, which were 

subsequently used to run CALMET for the 12-km screening and 4-km sub-regional refined 

analyses. 

3.3.3 GEOPHYSICAL DATA

According to the VISTAS BART Modeling Protocol, terrain elevations within the modeling 

domain were processed from SRTM-GTOPO30 digital terrain data format with 30-arcsec 

resolution. SRTM30 is a digital elevation data set that spans the globe from 60° north 

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BART Applicability Modeling Analysis 

latitude to 56° south latitude, approximately from the southern tip of Greenland to below 

the southern tip of South America. It has a horizontal grid spacing of 30 arc-seconds 

(approximately 1 kilometer). GTOPO30 is a global digital elevation model with a 

horizontal grid spacing of 30 arc-seconds (approximately 1 kilometer) that was derived 

from several raster and vector sources of topographic information that include U.S. 

Geological Survey (USGS) digital elevation models. The VISTAS Technical Contractor 

used data preprocessors to format and assimilate these data into a single geophysical data 

file for processing by CALMET to generate the 12-km screening data set. According to 

the VISTAS BART Modeling Protocol, higher resolution 3 arc-second DEM data were used 

to simulate terrain elevations in the 4-km refined data set. 

Land use and land cover (LULC) within the modeling domain was assimilated by the 

VISTAS technical contractor into a single geophysical data file for processing by 

CALMET using Composite Theme Grid (CTG) data archived by the USGS at a resolution 

of 200 meters. CALMET was used to calculate the fractional land use types within each 

cell of the CALMET grid. LULC in each grid cell was used by CALMET to compute the 

micrometeorological parameters (i.e., surface roughness, Bowen ratio, albedo, soil heat 

flux) that affect turbulent dispersion in the boundary layer. According to the VISTAS 

BART Modeling Protocol, 200-meter CTG LULC data were utilized for both 12-km 

screening and 4-km refined analyses. 

3.3.4 12-KM SCREENING ANALYSIS CALMET PROCESSING

The development of the regional screening CALMET meteorological fields from MM5 

data were conducted in No-Observations (“No-Obs”) mode since the MM5 data already 

reflect assimilation of observational data and are likely to adequately characterize regional 

wind patterns that are consistent with the 12-km grid scale. 

When the 12-km MM5 (2001 and 2002) data are used, the diagnostic CALMET terrain 

adjustments were turned off since the grid resolution of the MM5 data is the same as the 

CALMET grid and the terrain adjustments on the 12-km grid scale are already be reflected 

in the MM5 dataset. In this case, the MM5 winds were interpolated by CALMET to the 

CALMET layers and CALMET’s boundary layer modules computed mixing heights, 

turbulence parameters, and other meteorological parameters that are required by 

CALPUFF. For 2003, the 36-km MM5 data were used as CALMET’s initial guess field 

and then the CALMET diagnostic terrain adjustments (see Section 3.1.1 of the VISTAS 

BART Modeling Protocol) were applied to reflect terrain on the scale of the CALMET grid 

(i.e., 12 km). 

3.3.5 4-KM REFINED GRID CALMET PROCESSING

The finer grid (4 km) CALMET simulations were run by the VISTAS Technical 

Contractor in hybrid mode, using MM5 data to define the initial guess fields, diagnostic 

terrain adjustments to create the Step 1 wind fields, and NWS meteorological observational 

data in the Step 2 smoothing calculations. In this manner, actual observations of threedimensional meteorological conditions were used in the model to smooth the coarse MM5 

resolution to better represent areas in which terrain features and coastlines may have an 

Appendix H.6 - 1364
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BART Applicability Modeling Analysis 

important effect on meteorological conditions, but not be well resolved in the mesoscale 

model. Surface, upper air, precipitation, and offshore buoy observation points are readily 

available for use in CALMET. The following generally describes the use of NWS 

observations in Step 2 of the CALMET analyses. 

Parameters affecting turbulent dispersion that are observed hourly at surface stations 

include wind speed and direction, temperature, cloud cover and ceiling, relative humidity, 

and precipitation type. Surface data were selected from the available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations were 

obtained from the National Climatic Data Center (NCDC), quality assured, and merged 

using the SMERGE pre-processor to create a single assimilated data file of surface 

observations for each year analyzed. 

Observations of meteorological conditions in the upper atmosphere provide a profile of 

turbulence from the surface through the depth of the boundary layer in which dispersion 

occurs. Upper air data are collected by balloons launched simultaneously across the 

observation network at 0000 Greenwich Mean Time (GMT) (7 o’clock PM in South 

Carolina) and 1200 GMT (7 o’clock AM in South Carolina). Sensors observe pressure, 

wind speed and direction, and temperature (among other parameters) as the balloon rises 

through the atmosphere. The upper air observation network is less dense than surface 

observation points since upper air conditions vary less and are generally not as affected by 

local effects (e.g., terrain or coastlines). Upper air data were extracted from the NCDC’s 

available data inventory to optimize spatial coverage and representation of the domain, and 

utilization from year to year may vary due to availability and data quality. 

The effects of wet deposition processes on ambient pollutant concentrations are an 

important part of the BART applicability analysis. Therefore, it is necessary to include 

observations of precipitation in the CALMET analysis. Precipitation data were collected 

from selected surface meteorological data stations included in the analysis, plus 

Cooperative Observation Network (COOP) stations nearer to or within the domain. 

Precipitation data were extracted from among the NCDC’s available data inventory to 

optimize spatial coverage and representation of the domain. Raw observations from these 

stations were quality assured and merged using the PMERGE pre-processor to create a 

single assimilated data file of precipitation observations. 

Because parts of the modeling domain encompass the open waters of the Atlantic Ocean 

and the Cape Romain, Wolf Island, and Swanquarter Class I areas are located along the 

Atlantic Coast, meteorological data from coastal and offshore buoys can be useful so that 

overwater meteorological processing algorithms in CALMET can be utilized. The critical 

differences in behavior of the inland and marine boundary layers, and atmospheric 

dispersion phenomena occurring within these distinct regimes, is well documented and 

recognized to play a vital role in the dispersion of pollutants originating in, or destined to 

affect, coastal areas. Key phenomena occurring in coastal environments that affect 

pollutant dispersion include land/sea-breezes that cause recirculation of pollutant mass, 

temperature moderation that results in sharp gradients and mixing height discontinuities at 

the land-sea interface, and thermal internal boundary layers that could cause severe 

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BART Applicability Modeling Analysis 

fumigation under certain conditions. The CALMET processor is equipped to assimilate 

overwater data obtained from coastal, near-shore, and offshore observation platforms. 

CALMET uses a profile method to simulate boundary layer effects by computing the 

friction velocity, Monin-Obukhov length, surface roughness, and mixing height over the 

water surface. The details of the formulation of marine dispersion algorithms are provided 

in the documentation accompanying the CALPUFF modeling system. 

To perform its simulation of the coastal environment, CALMET requires hourly 

observations of air temperature, air-sea temperature difference, wind speed and direction, 

relative humidity, overwater mixing height, and the overwater temperature gradients above 

and below the overwater mixing height. For practical applications of overwater boundary 

layer computations, these data can be obtained from the National Data Buoy Center 

(NDBC). The NDBC maintains an inventory of standard meteorological data observed by 

ships, buoys, and C-MAN stations in coastal, near-shore, and offshore locations. NDBC’s 

data sets provide direct wind and temperature measurements, and relative humidity can be 

inferred from pressure and dewpoint observations. The mixing height and temperature 

gradients and default values must be applied by CALMET when simulating the coastal 

atmosphere. 

3.3.6 QUALITY ASSURANCE OF CALMET ANALYSES

Santee Cooper’s BART Applicability Analyses were conducted using 12-km screening and 

4-km refined CALMET data prepared by the VISTAS Technical Contractor. The VISTAS 

BART Modeling Protocol describes quality assurance techniques used by the contractor to 

validate the model’s performance. 

3.4 CALPUFF MODEL PROCESSING

CALPUFF analyses to assess the visibility impacts attributable to Santee Cooper’s Grainger, 

Jefferies, and Winyah Generating Stations were conducted generally in accordance with the 

recommendations of IWAQM Phase 2 Summary Report and Recommendations for Modeling Long 

Range Transport Impacts, Appendix B of which provides recommended default CALPUFF 

parameters. Appendix A of this BART Applicability Analysis Report contains sample 12-km 

screening and 4-km refined CALPUFF input files that can be compared to the IWAQM 

recommendations to demonstrate that default options were used, including the following important 

model options. 

3.4.1 MODELED EMISSIONS AND CHEMICAL TRANSFORMATIONS

Section 2 of this document described the BART-eligible emission units operated by Santee 

Cooper and the visibility-affecting pollutants considered in the CALPUFF analysis. 

Emission rates were calculated following U.S. EPA and South Carolina DHEC BART 

guidance for primary emissions of PM10. PM was further speciated into size categories of 

less than 0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm 

aerodynamic diameters. Site specific and AP-42 emission factors were utilized to analyze 

the phase, size, and character of PM emissions as described in Section 2 of this document. 

The MESOPUFF-II chemical transformation algorithms, which are considered the default 

Appendix H.6 - 1366
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BART Applicability Modeling Analysis 

for regulatory CALPUFF modeling, were employed. However, analyses did not include 

pollutants that undergo chemical transformation. Background levels of ozone and 

ammonia, which drive the simulated chemical transformation of emitted pollutants into 

visibility affecting species, were input to the model as described in subsequent sections of 

this document. 

Santee Cooper operates two BART-eligible EGUs at each of the three facilities evaluated 

in the BART applicability analysis, as described in Section 2 of this document as having 

distinct emission release points and characteristics. Sample model processing files 

provided by the VISTAS Technical Contractor demonstrate modeling of a single point 

source using CALPUFF, POSTUTIL, and CALPOST to assess visibility change.17 The 

sample approach simulates actual emissions of each of three gaseous pollutants (SO2, SO4, 

and NOX) and unit emissions (e.g., 1 g/s) of each of six generic particle categories 

distinguished and designated by size: PM800, PM425, PM187, PM112, PM081, and 

PM056, to represent PM6-10, PM2.5-6, PM1.25-2.5, PM1-1.25, PM0.625-1, and PM0.5-0.625, 

respectively. The size distribution is the only distinguishing feature of these particle 

categories. Table 3-2 summarizes the relevant model input parameters to be used for each 

size category. 

TABLE 3-2. REPRESENTATION OF PM SIZE CATEGORIES IN CALPUFF 

 

 Computed Geometric Mass Geometric Standard Precipitation Scavenging Coefficient

Model Species 

Deposition 

Mode 

Mean Diameter

(microns) 

Deviation 

(microns)* 

Liquid 

(s-1) 

Frozen 

(s-1) 

PM800 Particle 8.00 0 1.0 x 10-4 3.0 x 10-5

PM425 Particle 4.25 0 1.0 x 10-4 3.0 x 10-5

PM187 Particle 1.87 0 1.0 x 10-4 3.0 x 10-5

PM112 Particle 1.12 0 1.0 x 10-4 3.0 x 10-5

PM081 Particle 0.81 0 1.0 x 10-4 3.0 x 10-5

PM056 Particle 0.56 0 1.0 x 10-4 3.0 x 10-5

 

* Zero geometric standard deviation indicates that CALPUFF utilizes the deposition velocity associated with the geometric mass mean diameter. 

Because unit emission rates were modeled from the single point source in the sample 

approach, actual emission rates were used in the POSTUTIL postprocessing step to 

combine PM types and sizes into light scattering groups by scaling the modeled 

concentrations up or down by the multiplicative factor of the actual emission rate of each 

PM size category, which includes multiple types of PM (e.g., coarse filterable PM, fine 

filterable PM, organic condensable PM, inorganic condensable PM, and elemental carbon). 

The output concentration file from POSTUTIL was then input to CALPOST to calculate 

visibility change attributable to emissions from the point source in the sample analysis. 

The preceding approach is reasonable for modeling a single point source; however, for 

facilities with multiple emission point sources, the preceding approach is not appropriate. 

 

17 http://www.src.com/verio/download/sample_files.htm#EXAMPLE_BART 

Appendix H.6 - 1367
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BART Applicability Modeling Analysis 

Specifically, for sources with multiple emission points that have different exhaust 

characteristics (e.g., stack height, diameter, velocity, and temperature) and/or different 

emissions profiles of speciated PM, the use of unit emission rates is not appropriate since 

the CALPUFF output concentrations at particular receptors do not distinguish which 

source(s) contribute to the PM concentrations. Therefore, the POSTUTIL source profile 

technique cannot be applied. One alternative to this approach for sources with multiple 

emission points is to model each emission point individually in multiple CALPUFF runs 

using unit emission rates, then run POSTUTIL to combine the PM concentrations at each 

receptor for each modeled emission point, finally running the CALSUM postprocessing 

utility to combine the impacts of all sources. This approach, though conceptually 

appropriate, is undesirable due to substantial additional computer runtime required to 

model and post-process each emission point individually. 

As a computationally efficient alternative to the preceding approaches, the BART 

applicability analyses for Santee Cooper’s facilities were conducted by explicitly modeling 

in CALPUFF the actual emission rate of each of 14 particle species defined as described in 

Table 3-3. The nomenclature used in Table 3-3 is analogous to that used to describe the 

emissions from the BART-eligible emission units in Section 2. 

TABLE 3-3. EXPLICIT MODELING OF PM TYPES AND SIZE CATEGORIES

 

Modeled PM 

Category Components 

 

PMC800 

PMC425 

Filterable coarse particles divided between two size categories 

(PM6-10, PM2.5-6) 

PMF187 

PMF112 

PMF081 

PMF056 

Filterable fine particles divided among four size categories 

(PM1.25-2.5, PM1-1.25, PM0.625-1, PM0.5-0.625) 

POC081 

POC056 

Primary condensable organic emissions divided between two size categories (PM0.625-1

and PM0.5-0.625) 

PIC081 

PIC056 

Primary condensable inorganic emissions divided between two size categories 

(PM0.625-1 and PM0.5-0.625) 

EC187 

EC112 

EC081 

EC056 

Primary elemental carbon emissions divided among four size categories (PM1.25-2.5, 

PM1-1.25, PM0.625-1, PM0.5-0.625) 

 

So that explicit modeling of the 14 particle species and sizes could be conducted 

equivalently to the unit emissions approach, identical model processing options for each 

PM size category were enabled as summarized in Table 3-4. 

Appendix H.6 - 1368
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BART Applicability Modeling Analysis 

TABLE 3-4. REPRESENTATION OF EXPLICITLY MODELED PM SIZE CATEGORIES IN CALPUFF 

 

 Computed Geometric Mass Geometric Standard Precipitation Scavenging Coefficient

Model Species 

Deposition 

Mode 

Mean Diameter

(microns) 

Deviation 

(microns)* 

Liquid 

(s-1) 

Frozen 

(s-1) 

PMC800 Particle 8.0 0 1.0 x 10-4 3.0 x 10-5

PMC425 Particle 4.25 0 1.0 x 10-4 3.0 x 10-5

PMF187 

EC187 Particle 1.87 0 1.0 x 10-4 3.0 x 10-5

PMF112 

EC112 Particle 1.12 0 1.0 x 10-4 3.0 x 10-5

PMF081 

EC081 

POC081 

PIC081† 

Particle 0.81 0 1.0 x 10-4 3.0 x 10-5

PMF056 

EC056 

POC056 

PIC056† 

Particle 0.56 0 1.0 x 10-4 3.0 x 10-5

 

* Zero geometric standard deviation indicates that CALPUFF utilizes the deposition velocity associated with the geometric mass mean diameter. 

† Santee Cooper is proposing to preliminarily model all PIC as sulfate. Should Santee Cooper elect to segregate PIC into sulfate and non-sulfate 

species, the PIC species would be modeled as shown in this table. 

To post-process the CALPUFF output concentrations that result from explicitly modeled 

multiple emission points, POSTUTIL was used only to group modeled PM into light 

extinction groups. Unit scaling factors were used in POSTUTIL and there was no 

adjustment to the explicitly modeled emission rate. Table 3-5 summarizes the POSTUTIL 

grouping of modeled PM species into light extinction groups, and the light extinction 

coefficient subsequently used in CALPOST to compute light extinction due to the multiple 

emission points at the source. 

Appendix H.6 - 1369
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BART Applicability Modeling Analysis 

TABLE 3-5. ASSIGNMENT OF MODELED PM SPECIES TO LIGHT EXTINCTION GROUPS

 

Modeled 

Components 

CALPOST Light 

Extinction Group 

Extinction Coefficient 

(m2

/g) 

PMC800 

PMC425 PMC 0.6 

PMF187 

PMF112 

PMF081 

PMF056 

PIC081* 

PIC056* 

SOIL 1 

POC081 

POC056 SOA 4 

EC187 

EC112 

EC081 

EC056 

EC 10 

 

* Santee Cooper modeled all PIC as sulfate. 

3.4.2 CALPUFF DISPERSION ALGORITHMS

As specified in a March 16, 2006 U.S. EPA memorandum18 and the most recent revision to 

the VISTAS BART Modeling Protocol, the use of Pasquill-Gifford (ISC-like) dispersion 

coefficients was enabled as the default option in screening and refined CALPUFF analyses. 

3.4.3 BUILDING DOWNWASH

Per VISTAS guidance and South Carolina DHEC’s concurrence with Santee Cooper’s sitespecific BART Modeling Protocol, the effects of building downwash were not considered 

in the Santee Cooper BART applicability analyses.19

3.4.4 CALPUFF MODELING DOMAINS AND CLASS I AREA RECEPTORS

CALPUFF modeling for 12-km screening and 4-km refined analyses was performed on 

computational domains that are subsets of the VISTAS Regional Domain 0 and 

Sub-regional Domain 4. The size of each computational domain was selected to 

encompass the Santee Cooper facilities and the Class I areas located with 300 km of each 

facility, and to extend at least 50 km beyond in all directions. The size of the domains 

allows for the possible recirculation of puffs beyond the facility and areas being evaluated. 

 

18 U.S. EPA Memorandum “Dispersion Coefficients for Regulatory Air Quality Modeling in CALPUFF” from 

Mr. Dennis Atkinson and Mr. Tyler Fox (Air Quality Modeling Group) to Ms. Kay Prince (Regulatory Planning Branch) 

dated March 16, 2006. 

19 VISTAS BART Modeling Protocol, revised on August 31, 2006, page 45 states: “Ignore building downwash 

unless source is within 50-km of a Class I area and the State instructs the source to specifically consider building 

downwash.” 

Appendix H.6 - 1370
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BART Applicability Modeling Analysis 

Computational domains used the same vertical grid structure as described in the CALMET 

model formulation in Section 3.3. Figure 3-2 shows the terrain in the 12-km regional 

screening grid. Figures 3-3 and 3-4 illustrate the 12-km screening and 4-km refined 

computational domains used in Santee Cooper’s BART applicability analyses. Note that 

Jefferies and Winyah utilize the same 4-km domain while no 4-km domain was required 

for Grainger since no refined analyses were required for that facility. 

FIGURE 3-2. TERRAIN REPRESENTATION IN THE 12-KM REGIONAL SCREENING GRID

(AFTER FIGURE 4-2 OF THE VISTAS BART MODELING PROTOCOL)

Appendix H.6 - 1371
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BART Applicability Modeling Analysis 

FIGURE 3-3. 12-KM SCREENING ANALYSIS COMPUTATIONAL DOMAIN

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200

Lambert Conformal Coordinate East (km)

-1,200

-1,100

-1,000

-900

-800

-700

-600

-500

-400

-300

-200

-100

0

Lambert Conformal Coordinate North (km)

Grainger

Jefferies

Winyah

Wolf Island

Cape

Romain

Swanquarter

12-km Domain

Appendix H.6 - 1372
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BART Applicability Modeling Analysis 

FIGURE 3-4. 4-KM REFINED ANALYSIS COMPUTATIONAL DOMAIN FOR JEFFERIES &

WINYAH GENERATING STATIONS

1,200 1,250 1,300 1,350 1,400 1,450 1,500 1,550 1,600 1,650 1,700 1,750 1,800

Lambert Conformal Coordinate East (km)

-1,100

-1,050

-1,000

-950

-900

-850

-800

-750

-700

-650

-600

-550

-500

-450

-400

-350

Lambert Conformal Coordinate North (km)

Winyah

Jefferies

Wolf Island

Cape

Romain

4-km Domain

Appendix H.6 - 1373
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BART Applicability Modeling Analysis 

3.4.5 BACKGROUND OZONE AND AMMONIA CONCENTRATIONS

The CALPUFF model is capable of simulating linear chemical transformation effects by 

using pseudo-first-order chemical reaction mechanisms for the conversions of SO2 to SO4, 

and NOX, which consists of nitrogen oxide (NO) and nitrogen dioxide (NO2), to NO3 and 

HNO3. For the BART applicability analyses, chemical transformations involving five 

species (SO2, SO4, NOX, HNO3, and NO3) were modeled using the MESOPUFF II 

chemical transformation scheme. Ambient concentrations of ammonia and ozone 

concentrations as represented in the model drive the MESOPUFF II chemical 

transformation simulation. 

Both screening and refined analyses utilized observed ozone data for 2001 through 2003 

from non-urban CASTNet and AIRS stations compiled by the VISTAS Technical 

Contractor for the regional domain. Santee Cooper extracted data from ozone observation 

stations in and near the modeling domains, as illustrated in Figure 3-5 for the 12-km 

domain and Figure 3-6 for the 4-km domain for Jefferies and Winyah. 

Appendix H.6 - 1374
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BART Applicability Modeling Analysis 

FIGURE 3-5. OZONE DATA STATIONS UTILIZED IN

SANTEE COOPER’S BART SCREENING APPLICABILITY ANALYSES

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200

Lambert Conformal Coordinate East (km)

-1,200

-1,100

-1,000

-900

-800

-700

-600

-500

-400

-300

-200

-100

0

Lambert Conformal Coordinate North (km)

Grainger

Jefferies

Winyah

Wolf Island

Cape

Romain

Swanquarter

12-km Domain

Appendix H.6 - 1375
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BART Applicability Modeling Analysis 

FIGURE 3-6. OZONE DATA STATIONS UTILIZED IN

SANTEE COOPER’S BART REFINED APPLICABILITY ANALYSES

1,200 1,250 1,300 1,350 1,400 1,450 1,500 1,550 1,600 1,650 1,700 1,750 1,800

Lambert Conformal Coordinate East (km)

-1,100

-1,050

-1,000

-950

-900

-850

-800

-750

-700

-650

-600

-550

-500

-450

-400

-350

Lambert Conformal Coordinate North (km)

Winyah

Jefferies

Appendix H.6 - 1376
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BART Applicability Modeling Analysis 

Monthly average ozone background values were computed based on daytime average 

ozone concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month) of each year for substitution should all observations be missing for a 

particular hour of the dataset. Table 3-6 summarizes the monthly average ozone values 

computed for this substitution in the screening analysis, while Table 3-7 summarizes the 

values computed for substitution in the refined analysis for Jefferies and Winyah. 

TABLE 3-6. MONTHLY AVERAGE SUBSTITUTE OZONE VALUES FOR SCREENING ANALYSIS

Month

2001 Substitute 

Ozone Values 

(ppb)

2002 Substitute 

Ozone Values 

(ppb)

2003 Substitute 

Ozone Values 

(ppb)

January 27.26 25.76 27.45

Februrary 28.85 32.17 27.41

March 36.58 34.66 34.56

April 44.26 41.19 42.66

May 48.53 43.06 41.53

June 37.09 40.61 38.23

July 35.85 41.33 30.72

August 38.37 38.58 28.39

September 33.89 31.57 32.69

October 33.54 28.72 34.86

November 33.96 26.55 29.99

December 25.20 24.75 24.85

TABLE 3-7. MONTHLY AVERAGE SUBSTITUTE OZONE VALUES FOR REFINED ANALYSIS

2001 Substitute 2002 Substitute 2003 Substitute

Ozone Values Ozone Values Ozone Values

Month (ppb) (ppb) (ppb)

January 25.65 23.14 27.92

February 27.40 30.25 26.20

March 35.34 33.20 32.75

April 43.48 39.91 38.83

May 49.60 41.35 38.96

June 39.37 43.51 39.32

July 39.35 47.76 30.44

August 40.75 41.53 29.02

September 35.55 31.99 34.49

October 32.79 27.72 33.02

November 33.37 24.88 27.35

December 24.50 23.37 21.81

Appendix H.6 - 1377
Santee Cooper – Grainger, Jefferies, Winyah 44 Trinity Consultants 

BART Applicability Modeling Analysis 

In both the screening and refined analyses, a constant ammonia background value of 

0.5 ppb was input to the CALPUFF model. The initial VISTAS BART Modeling Protocol

envisioned the use of modeled boundary conditions of ammonia and sulfates in the refined 

analysis of chemical transformations involving these species and nitrates. However, 

VISTAS’ Technical Analysis Workgroup has since concluded that modeled background 

and boundary conditions should not be utilized for BART modeling purposes because 

“EPA and FLM recommend that ALM approach using CMAQ concentration data (SOX, 

NOX, total NH3) be reviewed by EPA Modeling Clearinghouse before being used in a 

regulatory application.” Accordingly, such background data were not utilized in Santee 

Cooper’s BART applicability modeling analyses. As described in the following section of 

this document, only refined analyses utilized the POSTUTIL postprocessing tool to repartition the HNO3 and NO3 equilibrium distributions using only a constant 0.5 ppb 

ammonia background level. 

3.4.6 PUFF REPRESENTATION

As recommended by the VISTAS BART Modeling Protocol, the default integrated puff 

sampling methodology was enabled for both screening and refined CALPUFF analyses. In 

the screening analyses, the default setting to disable puff splitting algorithms was retained. 

In the refined analyses, puff splitting was evaluated and found to have little impact due to 

the close proximity of Cape Romain to the Jefferies and Winyah Generation Stations. 

Therefore, the refined analyses also utilized the default setting to disable puff splitting 

algorithms. 

3.4.7 QUALITY ASSURANCE OF CALPUFF ANALYSES

Sample CALPUFF input files for the screening and refined analyses are presented in 

Appendix A of this report and copies of all input and output files are included on the 

electronic media enclosed with this report. Comparison of the CALPUFF model inputs 

was conducted to ensure that default values, as prescribed by IWAQM (Appendix B) and 

in the VISTAS BART Modeling Protocol, were used with the exception of the sourcespecific model options described in this document. Source parameters input for each 

BART-eligible emissions unit were verified with the information presented in Section 2. 

The location and elevations of Class I area receptors were also verified by comparison to 

NPS maps. 

3.5 CALPOST POSTPROCESSING AND NATURAL BACKGROUND CONDITIONS 

FOR LIGHT EXTINCTION AND HAZE INDEX CALCULATIONS

Using the concentrations of visibility-affecting pollutants computed by CALPUFF, two 

postprocessors, POSTUTIL and CALPOST, were used to compute light extinction attributable to 

each of Santee Cooper’s facilities and the relevant metrics for the BART applicability analysis 

determination. The computation of light extinction attributable to the natural background and source 

occurred as generally described in this document. 

Appendix H.6 - 1378
Santee Cooper – Grainger, Jefferies, Winyah 45 Trinity Consultants 

BART Applicability Modeling Analysis 

3.5.1 POSTUTIL PROCESSING

The first postprocessing step involved running POSTUTIL to calculate the concentrations 

of visibility-affecting species SO4, NO3, SOA, EC, PMF, and PMC, as described in 

Section 3.4.1. Specifically, POSTUTIL was used to group modeled species into visibilityaffecting pollutant groups as originally shown as Table 3-5, reproduced here as Table 3-8. 

TABLE 3-8. ASSIGNMENT OF MODELED PM SPECIES TO

LIGHT EXTINCTION GROUPS IN POSTUTIL 

 

Modeled 

Components 

CALPOST Light 

Extinction Group 

Extinction Coefficient 

(m2

/g) 

PMC800 

PMC425 PMC 0.6 

PMF187 

PMF112 

PMF081 

PMF056 

PIC081* 

PIC056* 

SOIL 1 

POC081 

POC056 SOA 4 

EC187 

EC112 

EC081 

EC056 

EC 10 

 

* Santee Cooper modeled all PIC as sulfate. 

In screening and refined analyses, POSTUTIL was only run once to achieve this pollutant 

grouping. The ammonia limiting method (ALM), which is used to re-partition the 

distribution of HNO3 and NO3 concentrations at each Class I area as a function of the 

temperature, relative humidity, and free ammonia during each hour, was not used in either 

analysis since ALM processing is not run by default, and nitrate species were not modeled. 

3.5.2 CLASS I AREA-SPECIFIC NATURAL BACKGROUND CONDITIONS

The visibility goal of the Clean Air Act is both the remedying of existing visibility 

impairment and prevention of future visibility impairment. In its BART Implementation 

Guidance, U.S. EPA affirms that it interprets the goal to mean return atmospheric 

conditions to “natural visibility conditions.” For the purposes of BART analyses, the 

U.S. EPA has determined that it “did not intend to limit States to the use of the 20% best 

visibility days...States may use 20% best visibility days or annual average” to assess 

BART applicability.20 The August 31, 2006 revision to the VISTAS BART Modeling 

 

20 U.S. EPA Memorandum from Mr. Joseph Paisie to Ms. Kay Prince, as Attachment A to a proposed settlement 

agreement between the Utility Air Regulatory Group and U.S. EPA, published at 71 Federal Register No. 84, pp. 25,838-

25,840, May 2, 2006. 

Appendix H.6 - 1379
Santee Cooper – Grainger, Jefferies, Winyah 46 Trinity Consultants 

BART Applicability Modeling Analysis 

Protocol indicates that several options are available at the discretion of the state including 

(1) a single annual average natural background condition for each Class I area; (2), a single 

value representing the average haze index on the 20% estimated best visibility days at each 

Class I area; or, (3) monthly average natural background conditions computed from 

estimated components of visibility-affecting pollutants and monthly average relative 

humidity values specific to each Class I area. The Option (1) annual average natural 

background condition appears to be the preferred approach since analyses conducted by the 

VISTAS Technical Contractor for certain sources in the region used this method 

exclusively. Therefore, Santee Cooper used Option (1) in its analyses. 

For the three Class I areas within 300 km of each of the Santee Cooper BART-eligible 

sources and potentially affected by the Santee Cooper’s operations, Table 3-9 summarizes 

the annual average background visibility as tabulated in Appendix B of U.S. EPA’s 

Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. 

These values were used to calculate natural background conditions. 

TABLE 3-9. ANNUAL AVERAGE BACKGROUND CONCENTRATIONS

FOR CLASS I AREAS POTENTIALLY AFFECTED BY THE SANTEE COOPER FACILITIES

Class I Area 

Annual Average 

Haze Index (dv) 

Cape Romain 7.52 

Wolf Island 7.58 

Swanquarter 7.38 

To represent natural conditions in the absence of anthropogenic sources of sulfates and 

nitrates, the monthly background extinction coefficient is expressed in terms of Rayleigh 

scattering and scattering due to soils (i.e., fine particles) based on the annual average haze 

index, and is calculated using the following equations. 

⎟

⎠

⎞ ⎜

⎝

⎛ = 10 exp10 HI bback , 

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total bback for the 

best days at Cape Romain, Wolf Island, and Swanquarter, including the Rayleigh scattering 

coefficient was calculated according to the above equation. A sample calculation for Cape 

Romain is shown below for the annual average natural background. 

Mm 21.21 Mm 10 Romain Capefor Mm 21.11 10

52.7 exp10 −1 −1 −1 ⎟ = =+= =⇒+ ⎠

⎞ ⎜

⎝

⎛ bback = ray bb soil soil bb soil

The effects of relative humidity to amplify the visibility impairment of hygroscopic 

sulfates and nitrates are characterized using CALPOST “Method 6,” which computes Δbext

using a monthly average relative humidity adjustment particular to each Class I area 

Appendix H.6 - 1380
Santee Cooper – Grainger, Jefferies, Winyah 47 Trinity Consultants 

BART Applicability Modeling Analysis 

applied to background and modeled sulfate and nitrate. Table 3-10 summarizes the 

monthly average humidity values that were applied for the three Class I areas considered in 

the analyses, as tabulated in Table A-3 of U.S. EPA’s Guidance for Estimating Natural 

Visibility Conditions under the Regional Haze Rule. 

TABLE 3-10. MONTHLY AVERAGE f(RH) FOR SELECTED CLASS I AREA

 

Class I Area January February March April May June July August September October November December 

Cape Romain 3.3 3.0 2.9 2.8 3.2 3.7 3.6 4.1 4.0 3.7 3.4 3.2 

Wolf Island 3.4 3.1 3.1 3.0 3.3 3.7 3.7 4.1 4.0 3.7 3.5 3.5 

Swanquarter 2.9 2.7 2.6 2.5 2.9 3.2 3.4 3.5 3.4 3.1 2.8 2.9 

Natural background conditions for each Class I area were calculated using the data 

summarized in Tables 3-8, 3-9, and 3-10 and the newly revised Interagency Monitoring of 

Protected Visual Environments (IMPROVE) light extinction formula, which is 

summarized in Section 3.5.3. 

3.5.3 VISIBILITY IMPACT CALCULATION AND REVISED IMPROVE LIGHT EXTINCTION 

ALGORITHM

CALPOST was run separately for each Class I area to obtain the necessary visibility 

statistics for evaluating compliance with the BART visibility impairment thresholds. The 

inputs to CALPOST involve selection of the visibility method (i.e., Method 6) and entry of 

Class I area-specific data for computing background extinction and monthly relative 

humidity factors for hygroscopic aerosols as described in Section 3.5.2. CALPOST 

contains a receptor selection option that allow subsets of a receptor network modeling in 

CALPUFF to be selected for processing in a given CALPOST run. This selection specifies 

which receptors representing a single Class I area are selected for processing from a 

CALPUFF output file that may contain receptors from several Class I areas. 

Refined analyses were analyzed by tabulating the 98th percentile 24-hour average visibility 

impact for each year and Class I area. The previously described Option (1) was used to 

quantify visibility impacts relative to annual average natural background conditions. As 

described in Section 3.2.1, the default interpretation of the 98th percentile (8th-highest day 

regardless of variability at each receptor) was evaluated for each year. 

Santee Cooper is presenting visibility impacts calculated as a change in the “haze index” 

expressed in units of deciviews, as computed in the following equation. 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ = 10

ln10 ext b

HI

Light extinction (bext) results from particulate species emitted from anthropogenic sources 

including coarse particulate matter (PMC), fine particulate matter (PMF), and elemental 

carbon (EC), as well as precursors to secondary organic aerosols (SOA) and fine 

Appendix H.6 - 1381
Santee Cooper – Grainger, Jefferies, Winyah 48 Trinity Consultants 

BART Applicability Modeling Analysis 

particulate matter such as SO2 and NOX that form sulfates (SO4) and nitrates (NO3) after 

chemical transformations take place. The extinction coefficient due to emissions of 

visibility-affecting pollutants from a single BART-eligible source bext,source is calculated 

using an air quality model such as CALPUFF. The extinction due to the BART-eligible 

source was calculated as shown in the following equation. 

( ) sourceext SO NO PMC PMF SOA +++++= bbbbbbMmb EC

−

4 3

1

,

where, 

[( ) ] ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

[ ] 3

34 3

4 4 2 4

Concentrat μginion m

Humidity Relative Function

EC10

PMC6.0

PMF1

SOA4

NONH3

SONH3

=

=

=

=

=

=

=

=

RHf

b

b

b

b

b RHf

b RHf

EC

PMC

PMF

SOA

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ] concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes PMC PM coarse ofion 

concentrat thedenotes PMF PM fine ofion 

concentrat thedenotes SOA secondary ofion aerosols organic

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

The relative impact of a BART-eligible source is determined by comparing the HI

attributable to a source relative to estimated natural background conditions. The 

background extinction coefficient bext,background is affected by various chemical species and 

the Rayleigh scattering phenomenon and was initially calculated as shown in the following 

default equation. 

( ) ext background SO NO OC Soil Coarse ap Ray b ++++++= bbbbbbbMm−

4 3

1

, where, 

[ ] ( ) ( )

[ ] ( )

[ ]

[ ]

[ ]

[ ]

( )

( )

[ ] 3

1

34 3

4 2 4 4

Concentrat μginion m

Humidity Relative Function

Rayleigh defaultby Mm 10 Scattering

EC10

Mass Coarse6.0

Soil1

OC4

NONH3

SONH3

=

=

=

=

=

=

=

=

=

−

RHf

b

b

b

b

b

b RHf

b RHf

Ray

ap

Coarse

Soil

OC

NO

SO [( ) ]

[ ]

[ ]

[ ]

[ ]

[ ]

Rayleigh air todue scattering is Scattering molecules

concentrat thedenotes EC carbon elemental ofion 

concentrat thedenotes Mass Coarse dusts coarse ofion 

concentrat thedenotes Soil soils fine ofion 

concentrat thedenotes OC carbon organic ofion 

concentrat nitrate ammonium thedenotes NONH ion

concentrat sulfate ammonium thedenotes SONH ion

34

4 2 4

Values for the parameters listed above specific to the natural background conditions at the 

Class I areas considered in this analysis are provided on an annual average basis in the 

U.S. EPA’s Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Rule. More detailed information about the natural background conditions particular 

to Class I areas potentially affected by Santee Cooper’s operations were provided in 

Tables 3-9 and 3-10 to represent annual average natural background conditions at Cape 

Romain, Wolf Island, and Swanquarter. 

Appendix H.6 - 1382
Santee Cooper – Grainger, Jefferies, Winyah 49 Trinity Consultants 

BART Applicability Modeling Analysis 

The preceding equation represents what has been termed the “default” light extinction 

calculation that has been utilized for computation of visibility impacts since it was initially 

developed by the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

in the mid-1990’s. As noted in the VISTAS BART Modeling Protocol, the U.S. EPA and 

the Regional Planning Organizations (including VISTAS) are evaluating whether 

refinements are warranted to the methods recommended in U.S. EPA’s guidance to 

calculate estimates of natural background visibility. For the purposes of calculating 

current, future, and natural background visibility at Class I areas as part of its reasonable 

progress analyses, VISTAS intends to present regional air quality modeling results using 

both the default and the newly revised IMPROVE light extinction formula. 

In December 2005, the IMPROVE workgroup issued a report describing an alternative to 

the default formula used to estimate extinction from particle concentration measurements 

(referred to as the “2005 IMPROVE Report” hereafter).21 A final report was issued in 

March 2006 to present a thorough survey of estimates of mass scattering efficiencies from 

recent peer-reviewed literature and provisional recommendations for refinements to the 

IMPROVE equation (referred to as the “2006 IMPROVE Report” hereafter).22 

Refinements in the revised IMPROVE formula include: 

▲ Distinguishing the size (i.e., age) of secondary pollutants and growth factors for 

different sizes of particle species 

▲ Adding a site-specific Rayleigh scattering term to the formula to represent the 

dependence of Rayleigh scattering on air density (i.e., elevation) 

▲ Adding a sea salt term, including a growth factor due to relative humidity 

▲ Adding a term for visibility impairment resulting from gaseous nitrogen dioxide 

(NO2). 

The revised IMPROVE light extinction algorithm takes the following form: 

bext ≈

( )[ ( ) ] ( )[ ( ) ]

( )[ ] ( )[ ]

[ ] [ ] [ ][ ] [ ]

( )[ ] [ ] Site Rayleigh specific Scattering 2

Small Large

34 Small 34 Large

44 2 Small 44 2 Large

4.1 Salt Sea NO33.0

PMC6.0PMF1EC10OC1.6OC8.2

4.2 1.5NONH NONH

2.2 SONH 8.4 SONH

+ +

+ +++ +

+ +

+ +

RHf b −

RHf RHf

RHf RHf

SS

S L

S L

To facilitate the use of the revised IMPROVE algorithm for assessing BART applicability, 

the VISTAS Technical Contractor developed and distributed a spreadsheet tool to 

implement the revised IMPROVE light extinction algorithm, designated as the CALPOSTIMPROVE Processor Version 2 (September 29, 2006). This processor calculates the 

reconstructed light extinction using the revised IMPROVE algorithm and output from the 

 

21 Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand. Revised IMPROVE Algorithm for 

Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering Committee, November 2005. 

22 Hand, J. and W. Malm. Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients 

- Final Report, March 2006. 

Appendix H.6 - 1383
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BART Applicability Modeling Analysis 

current version of the CALPOST postprocessor. To implement the CALPOST-IMPROVE 

tool, two additional data points are required: Rayleigh scattering parameter corrected for 

site-specific elevation, and an estimate of the average background sea salt aerosol 

concentration. In addition, background concentration of gaseous NO2 may be 

incorporated. However, since NOX and NO3 were not explicitly modeled in the Santee 

Cooper BART Applicability Analyses, the background NO2 concentrations were not 

utilized. 

3.5.3.1 SITE-SPECIFIC RAYLEIGH SCATTERING

The 2005 IMPROVE report23 describes the justification for using a site-specific 

Rayleigh scattering parameter in the revised light extinction algorithm. 

Rayleigh scattering refers to the scattering of light from the molecules of the air 

and is therefore dependent on the air density, which varies with temperature and 

pressure. Site-specific Rayleigh scattering was calculated using the standard 

atmospheric pressure corresponding to the monitoring site elevation, and an 

estimated annual mean temperature. The temperature data were obtained from 

the nearest weather stations for time periods encompassing ten to thirty years 

and were interpolated to the monitoring site location. The 2005 IMPROVE 

report tabulated site-specific values of Rayleigh scattering for locations at 

which IMPROVE monitors visibility, including Cape Romain, Wolf Island, and 

Swanquarter, for which values of 12, 11 (based on neighboring Class I area 

Okefenokee NWR), and 12 Mm-1 were calculated. Compared to the default 

Rayleigh scattering value of 10 Mm-1, the corrected values reflect the sea level 

elevations of Cape Romain, Wolf Island, and Swanquarter. 

3.5.3.2 BACKGROUND SEA SALT AEROSOL CONCENTRATIONS

The 2005 IMPROVE Report identified sea salt aerosols as an important 

component of natural visibility conditions, particularly in humid coastal 

environments such as the Class I areas potentially affected by Santee Cooper’s 

BART-eligible facilities. The 2006 IMPROVE Report identifies sea salt as a 

statistically significant component of fine particle mass at Cape Romain (which 

is considered representative of Wolf Island). Further analyses have been 

suggested to quantify a representative natural background sea salt concentration 

for implementation in the CALPOST-IMPROVE processor for BART 

applicability analyses. 

Measurement and estimation techniques for naturally-occurring sea salt 

concentrations continue to be a focus within the scientific community in 

general and stakeholders in the VISTAS region in particular. The IMPROVE 

workgroup has developed a dry extinction efficiency and hygroscopic growth 

curve (fSS(RH)) for sea salt aerosols based on observations in remote marine 

 

23http://vista.cira.colostate.edu/IMPROVE/Publications/GrayLit/019_RevisedIMPROVEeq/RevisedIMPROVEAl

gorithm3.doc 

Appendix H.6 - 1384
Santee Cooper – Grainger, Jefferies, Winyah 51 Trinity Consultants 

BART Applicability Modeling Analysis 

environments and thermodynamic models. Although there is reasonable 

confidence in these components of the calculation of light extinction due to sea 

salt aerosols, there is greater uncertainty in the average sea salt mass 

concentration. Specific questions remain about how ambient sea salt 

concentrations are reliably inferred from measurements of elemental markers 

and the period of record for which reliable data are available to compute a 

representative natural background level. VISTAS states, including South 

Carolina, are using annual average natural background conditions for visibility 

impairment caused by the species (e.g., sulfates, nitrates, organic and inorganic 

PM) represented in the IMPROVE light extinction equation. The following 

analysis reviews available techniques for estimating the long-term average sea 

salt concentration for consistent application as a visibility-impairing component 

of the natural background. 

The 2006 IMPROVE Report describes challenges in measuring sea salt 

aerosols in ambient environments due to imprecise analysis techniques and 

various investigators have surveyed methods for estimating sea salt 

concentration to compute an average value for Cape Romain. Sodium (Na+

) 

and Chloride (Cl-

) ions are generally accepted as the best markers for the 

presence of sea salt. Difficulties in computing sea salt from data from the 

IMPROVE network arise because positive ions have historically not analyzed; 

therefore, sodium ion (the strongest indicator of sea salt) data are not readily 

available, except for a brief period between September 2002 and 

December 2004 during which time VISTAS measured Na+

 at Cape Romain and 

other Southeastern Class I areas. This Na+

 approach computes the sea salt 

concentration as: 

 [Sea Salt] = 3.27[Na+

non-soil], 

where [Na+

non-soil] = [Na+

] – 0.57[Fe] and [Fe] is the measured iron 

concentration. 

Issues arise when using the chloride ion or elemental chlorine to estimate sea 

salt because reaction of gaseous nitric acid with sea salt produces sodium 

nitrate particles and the release of gaseous hydrochloric acid. The depletion of 

chlorine during this reaction results in an underestimation of sea salt when 

using chlorine as the marker. The sea salt concentration would be computed as: 

 [Sea Salt] = 1.8[Cl] or [Sea Salt] = 1.8[Cl-

], 

based on reliable measurements of [Cl-

] using ion chromatography or [Cl]. The 

VISTAS Technical Contractor originally computed an average sea salt 

concentration of 0.20 μg/m3

 using Cl- as the marker, which is recognized to 

Appendix H.6 - 1385
Santee Cooper – Grainger, Jefferies, Winyah 52 Trinity Consultants 

BART Applicability Modeling Analysis 

underestimate the sea salt aerosol concentration.24 Appendix 1 of the 2006 

IMPROVE Report lists an average sea salt concentration of 0.377 μg/m3

, which 

is also based on Cl-

 measurements that may underestimate sea salt 

concentrations. 

Elemental sodium (Na) data are available from X-ray Fluorescence (XRF) 

analyses; however, sensitivity issues regarding poor detection of Na result in 

large uncertainties corresponding to Na from XRF (White et al., 2004).25 Sea 

salt concentrations inferred from Na measured by Proton Induced X-ray 

Emission (PIXE) are considered reliable; however, such measurements have 

not been made at Cape Romain since 2001. Paine et al. (2006) computed an 

average sea salt concentration of 1.123 μg/m3

 at Cape Romain using Na as the 

marker from measurement collected between 1994 and 2001, during which 

time the more reliable PIXE analysis method was in use.26 This sodium 

approach uses ambient measurements of sodium and fine soil concentrations 

and a reference concentration for soil-derived sodium provided as: 

[ ] = [Soil0283.0Na ] Soil , 

where 0.0283 is the crustal reference composition of sodium. Using this value, 

the ambient sodium attributable to sea salt is computed as: 

[ ] [ ] [ ] Na Salt Sea = − NaNa Soil

from which the sea salt concentration is computed as: 

[ ] [ ]

0.306

Na Salt Sea Salt Sea = , 

where 0.306 is the average sodium content of fresh sea salt. An alternative 

estimate of sea salt concentration using [Na] can be constructed in a manner to 

the Na+

 approach as: 

 [Sea Salt] = 3.27[Nanon-soil], 

where [Nanon-soil] = [Na] – 0.57[Fe] and [Fe] is the measured iron concentration. 

 

24 Tombach, I. Estimating the Role of Sea Salt when Determining the Visibility Impact of a Source with 

CALPUFF, prepared for VISTAS, June 13, 2006. 

25 White, W. H., R. A. Eldred, P. J. Feeney, C. E. McDade, B. P. Perley, D. J. Shadoan, and P. H. Wakabayashi, 

Behavior of fine-particle elemental data near the detection limit, Paper # 24, A&WMA Specialty conference: Regional and 

Global Perspectives on Haze: Causes, Consequences and Controversies, Asheville, NC, October 25-29, 2004. 

26 Paine, R., J. Connors, and S. Heisler. Computation of Average Sea Salt Concentrations for Consideration in 

Determination of Natural background Levels at Eastern Class I Areas, August 2, 2006. 

Appendix H.6 - 1386
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BART Applicability Modeling Analysis 

The VISTAS Technical Contractor27 summarized a preferred hierarchy for 

estimating sea salt concentrations as envisioned by Pitchford (2006)28: 

1. [Sea Salt] = 3.27[Na+

non-soil], where [Na+non-soil] = [Na+

] – 0.57[Fe] and 

Na+

 is collected on Nylon filters and analyzed using ion 

chromatography 

2. [Sea Salt] = 1.8[Cl-

], where Cl- is collected on Nylon filters and 

analyzed using ion chromatography 

3. [Sea Salt] = 1.8[Cl], where Cl is analyzed by XRF or PIXE, despite a 

known bias toward underestimates due to loss of HCl on the filter 

media 

4. [Sea Salt] = 3.27[Nanon-soil], where [Nanon-soil] = [Na] – 0.57[Fe] and Na 

is analyzed by PIXE, which presents an alternative method to that used 

by Paine et al. (2006) 

The VISTAS Technical Contractor evaluated available data at Cape Romain to 

assess the practical application of these methods given variations in 

measurement techniques over time and concluded that the Na+

 method is 

superior, but is limited by the brief period of time (September 2002 through 

December 2004) when such measurements were taken. The report identifies an 

annual average sea salt concentration in 2004 of 0.77 μg/m3

 using the Na+

method, but does not recommend a longer-term average value. The VISTAS 

Technical Contractor demonstrates that the 2004 annual average sea salt 

concentrations of 0.35 μg/m3

 estimated using [Cl-] and 0.11 μg/m3 estimated 

using [Cl] exhibit the expected underestimation bias due to the analysis 

technique. The elemental sodium (Na) approach could not be compared for this 

time period because Na measurements are available only from XRF, which is 

excluded as an unreliable method. 

To assess a long-term average natural background sea salt concentration, Santee 

Cooper applied reliable estimation techniques over the period of record when 

such measurements were taken. Additionally, a fifth estimation technique 

using strontium (Sr) as the elemental marker for sea salt was applied to 

corroborate sea salt estimates using other techniques. Lowenthal and Kumar 

(2006) suggest the following approach using strontium (Sr), which is measured 

accurately using XRF and for which measurements are available over the longterm time period (1994 through 2004) of interest.29 This approach computes a 

non-crustal, marine-derived Sr concentration (xSr) as: 

 

27 Tombach, I. Evaluation of Alternative Methods for Estimating Sea Salt Concentrations at Cape Romain, 

prepared for VISTAS, March 5, 2007. 

28 Pitchford, M. Revised IMPROVE Algorithm for Estimating Light Extinction for Particle Speciation Data, 

November 2006. 

29 Lowenthal, D. and N. Kumar. “Light Scattering from Sea-Salt Aerosols at Interagency Monitoring of Protected 

Visual Environments (IMPROVE) Sites.” J. Air & Waste Management Association, 56: 636-642, May 2006. 

Appendix H.6 - 1387
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BART Applicability Modeling Analysis 

[ ]= [SrxSr ]− [Si 0.0013528 ], 

where 0.0013528 is the crustal reference concentration of Sr and Si represents 

the silicon composition that is presumably entirely representative of entrained 

crustal material.30 The sea salt concentration is computed from this value using 

the average strontium composition of sea water, 0.000234. 

[ ] [ ]

0.000234

xSr Salt Sea =

The VISTAS Technical Contractor acknowledges Lowenthal and Kumar’s Sr 

approach as a newer method that has some uncertainty, but does not discount its 

application at Class I areas in the Southeastern U.S. The Sr approach is 

appealing to apply at Cape Romain because Sr measurements have been made 

consistently since September 1994 using the same analytical techniques that are 

considered reliable for this marker. 

Electronic files on the modeling DVD enclosed with this report contain an 

analysis of average ambient sea salt concentrations using the Paine et al. (2006) 

and Pitchford (2006) methods using elemental sodium derived from PIXE 

measurements for the available data period September 1994 through November 

2001, the VISTAS preferred sodium ion approach for the available data period 

September 2002 through December 2004, and the Lowenthal and Kumar 

(2006) method using strontium for the available data period September 1994 

through December 2004. Table 3-11 summarizes data from each approach over 

the September 1994 through December 2001 time period during which reliable 

analytical techniques were in use for each species. 

TABLE 3-11. SUMMARY OF SEA SALT CONCENTRATIONS COMPUTED USING VARIOUS METHODS,

SEPTEMBER 1994 – DECEMBER 2004 

 

[Sea Salt] (μg/m3)

Period Start Period End 

Paine et al. (2006)

[Na] Method 

Pitchford (2006)

[Na] Method 

VISTAS (2007) 

[Na+] Method 

Lowenthal & Kumar 

(2006) [Sr] Method 

September 1994 November 2001 1.1 1.1 -- 1.1 

December 2001 August 2002 -- -- -- 1.3 

September 2002 December 2004 -- -- 0.77 0.78 

Long-term Average (September 1994-December 2004) 1.1 

 

30 Mason, B. Principles of Geochemistry, 4th ed. Wiley & Sons, Inc.: New York, 1982, p.344. 

Appendix H.6 - 1388
Santee Cooper – Grainger, Jefferies, Winyah 55 Trinity Consultants 

BART Applicability Modeling Analysis 

The summary results presented in Table 3-11 indicate that the Sr method for 

calculating sea salt concentrations replicates estimates of sea salt concentrations 

calculated using preferable methods that use Na or Na+

 as the marker. When 

applied for Sr measurements at Cape Romain over a continuous, long-term 

(1994 through 2004) time period, the average background concentration of sea 

salt is estimated to be 1.1 μg/m3

. Accordingly, Santee Cooper evaluated 

visibility impacts from its BART-eligible sources at Cape Romain using both 

estimates of the annual average sea salt concentration (i.e., 0.77 μg/m3

 as 

estimated by the VISTAS Technical Contractor and 1.1 μg/m3

 as estimated by 

Santee Cooper) to represent that background condition in the CALPOSTIMPROVE tool as described in the following section of this report. In addition, 

for reference only, results are presented using the default IMPROVE value of 

0.2 μg/m3

. 

3.5.4 QUALITY ASSURANCE OF POSTPROCESSING ANALYSES

Quality assurance of postprocessing analyses was conducted by verifying the sequence of 

postprocessing (i.e., POSTUTIL, including ALM for refined analyses, followed by 

CALPOST) and that appropriate data files are referenced throughout the calculation 

sequence. The CALPOST inputs that were checked include the following: 

▲ Visibility technique (Method 6) 

▲ Monthly Class I-specific relative humidity factors for Method 6 

▲ Background light extinction values calculated to estimate annual average natural 

background 

▲ Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, 

organics), coarse and fine particulate matter, and elemental carbon. 

▲ Appropriate species names for coarse PM used 

▲ Extinction efficiencies for each species 

▲ Screen to select appropriate Class I receptors for each CALPOST simulation 

▲ Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling and 

Class I area specific value for refined modeling) 

▲ Estimates of annual average sea salt concentrations for updated IMPROVE 

natural background concentrations and light extinction 

▲ Application of the CALPOST-IMPROVE spreadsheet tool for the updated 

IMPROVE natural background concentrations and light extinction and relative 

source impact

Appendix H.6 - 1389
Santee Cooper – Grainger, Jefferies, Winyah 56 Trinity Consultants 

BART Applicability Modeling Analysis 

4. SCREENING ANALYSIS RESULTS

Screening analyses of visibility impacts attributable to Santee Cooper’s Grainger, Jefferies, and 

Winyah Generating Stations were conducted to determine whether any of the facilities are subject to 

BART using a conservative assessment, and to focus the scope of refined analyses by demonstrating 

that visibility impairment is not likely to occur at more distant Class I areas. The screening analyses 

were conducted using the emissions data represented in Section 2 of this report, the screening 

CALMET and CALPUFF methods described in Sections 3.2, 3.3, and 3.4 of this report, and the 

default postprocessing methods described in Section 3.5. 

4.1 GRAINGER GENERATING STATION

4.1.1 SCREENING ANALYSIS IMPACTS AT CAPE ROMAIN

Table 4-1 summarizes the modeled peak, 24-hour average visibility impacts at Cape 

Romain NWR attributable to Santee Cooper’s Grainger Generating Station. This table 

summarizes the results calculated using the default annual average natural background 

described in Section 3.5 for representing natural background conditions. Results are 

presented in terms of visibility impact and the total number of days exceeding the 1.0 dv 

causation and 0.5 dv contribution thresholds, respectively. 

TABLE 4-1. GRAINGER’S PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN

 

Visibility Impact 

2001 

Δdv (> 1.0 dv, > 0.5 dv) 

2002 

Δdv (> 1.0 dv, > 0.5 dv) 

2003 

Δdv (> 1.0 dv, > 0.5 dv) 

Peak 24-hour Average 0.207 (0, 0) 0.216 (0, 0) 0.235 (0, 0) 

The results presented in Table 4-1 indicate that refined analyses were not required to 

evaluate Grainger’s visibility impacts at Cape Romain since the peak visibility impact 

estimated using the default screening approach is less than the 0.5 dv contribution 

threshold. 

4.1.2 SCREENING ANALYSIS IMPACTS AT SWANQUARTER

Table 4-2 summarizes the modeled peak, 24-hour average visibility impacts at 

Swanquarter NWR attributable to Santee Cooper’s Grainger Generating Station. This table 

summarizes the results calculated using the default annual average natural background 

described in Section 3.5 for representing natural background conditions. Results are 

presented in terms of visibility impact and the total number of days exceeding the 1.0 dv 

causation and 0.5 dv contribution thresholds, respectively. 

Appendix H.6 - 1390
Santee Cooper – Grainger, Jefferies, Winyah 57 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 4-2. GRAINGER’S PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT SWANQUARTER

 

Visibility Impact 

2001 

Δdv (> 1.0 dv, > 0.5 dv) 

2002 

Δdv (> 1.0 dv, > 0.5 dv) 

2003 

Δdv (> 1.0 dv, > 0.5 dv) 

Peak 24-hour Average 0.057 (0, 0) 0.050 (0, 0) 0.096 (0, 0) 

The results presented in Table 4-2 indicate that refined analyses were not required to 

evaluate Grainger’s visibility impacts at Swanquarter since the peak visibility impact 

estimated using the default screening approach is less than the 0.5 dv contribution 

threshold. 

4.2 JEFFERIES GENERATING STATION

4.2.1 SCREENING ANALYSIS IMPACTS AT CAPE ROMAIN

Table 4-3 summarizes the modeled peak, 24-hour average visibility impacts at Cape 

Romain NWR attributable to Santee Cooper’s Jefferies Generating Station. This table 

summarizes the results calculated using the default annual average natural background 

described in Section 3.5 for representing natural background conditions. Results are 

presented in terms of visibility impact and the total number of days exceeding the 1.0 dv 

causation and 0.5 dv contribution thresholds, respectively. 

TABLE 4-3. JEFFERIES’ PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN

 

Visibility Impact 

2001 

Δdv (> 1.0 dv, > 0.5 dv) 

2002 

Δdv (> 1.0 dv, > 0.5 dv) 

2003 

Δdv (> 1.0 dv, > 0.5 dv) 

Peak 24-hour Average 0.774 (0, 4) 0.615 (0, 5) 0.646 (0, 8) 

The results presented in Table 4-3 indicate that refined analyses were necessary to evaluate 

Jefferies’ visibility impacts at Cape Romain. 

4.2.2 SCREENING ANALYSIS IMPACTS AT WOLF ISLAND

Table 4-4 summarizes the modeled peak, 24-hour average visibility impacts at Wolf Island 

NWR attributable to Santee Cooper’s Jefferies Generating Station. This table summarizes 

the results calculated using the default annual average natural background described in 

Section 3.5 for representing natural background conditions. Results are presented in terms 

of visibility impact and the total number of days exceeding the 1.0 dv causation and 0.5 dv 

contribution thresholds, respectively. 

Appendix H.6 - 1391
Santee Cooper – Grainger, Jefferies, Winyah 58 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 4-4. JEFFERIES’ PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT WOLF ISLAND

 

Visibility Impact 

2001 

Δdv (> 1.0 dv, > 0.5 dv) 

2002 

Δdv (> 1.0 dv, > 0.5 dv) 

2003 

Δdv (> 1.0 dv, > 0.5 dv) 

Peak 24-hour Average 0.133 (0, 0) 0.145 (0, 0) 0.194 (0, 0) 

The results presented in Table 4-4 indicate that refined analyses were not required to 

evaluate Jefferies’ visibility impacts at Wolf Island since the peak visibility impact 

estimated using the default screening approach is less than the 0.5 dv contribution 

threshold. 

4.3 WINYAH GENERATING STATION

4.3.1 SCREENING ANALYSIS IMPACTS AT CAPE ROMAIN

Table 4-5 summarizes the modeled peak, 24-hour average visibility impacts at Cape 

Romain NWR attributable to Santee Cooper’s Winyah Generating Station. This table 

summarizes the results calculated using the default annual average natural background 

described in Section 3.5 for representing natural background conditions. Results are 

presented in terms of visibility impact and the total number of days exceeding the 1.0 dv 

causation and 0.5 dv contribution thresholds, respectively. 

TABLE 4-5. WINYAH’S PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN

 

Visibility Impact 

2001 

Δdv (> 1.0 dv, > 0.5 dv) 

2002 

Δdv (> 1.0 dv, > 0.5 dv) 

2003 

Δdv (> 1.0 dv, > 0.5 dv) 

Peak 24-hour Average 1.118 (1, 22) 0.891 (0, 17) 1.566 (1, 21) 

The results presented in Table 4-5 indicate that refined analyses were necessary to evaluate 

Winyah’s visibility impacts at Cape Romain. 

4.3.2 SCREENING ANALYSIS IMPACTS AT WOLF ISLAND

Table 4-6 summarizes the modeled peak, 24-hour average visibility impacts at Wolf Island 

NWR attributable to Santee Cooper’s Winyah Generating Station. This table summarizes 

the results calculated using the default annual average natural background described in 

Section 3.5 for representing natural background conditions. Results are presented in terms 

of visibility impact and the total number of days exceeding the 1.0 dv causation and 0.5 dv 

contribution thresholds, respectively. 

Appendix H.6 - 1392
Santee Cooper – Grainger, Jefferies, Winyah 59 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 4-6. WINYAH’S PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT WOLF ISLAND

 

Visibility Impact 

2001 

Δdv (> 1.0 dv, > 0.5 dv) 

2002 

Δdv (> 1.0 dv, > 0.5 dv) 

2003 

Δdv (> 1.0 dv, > 0.5 dv) 

Peak 24-hour Average 0.119 (0, 0) 0.098 (0, 0) 0.100 (0, 0) 

The results presented in Table 4-6 indicate that refined analyses were not required to 

evaluate Winyah’s visibility impacts at Wolf Island since the peak visibility impact 

estimated using the default screening approach is less than the 0.5 dv contribution 

threshold. 

4.4 SUMMARY OF SCREENING ANALYSES

Because screening analyses of visibility impacts from Santee Cooper’s Jefferies and Winyah 

Generating Stations indicated peak 24-hour average impacts above the 0.5 dv contribution threshold 

at the Cape Romain NWR from both the Jefferies and Winyah Generating Stations, refined modeling 

was required to evaluate Santee Cooper’s contributions using refined modeling methods at the Cape 

Romain NWR for these two facilities. 

Default screening analyses demonstrate the Santee Cooper’s Grainger Generating Station does not 

contribute to visibility impairment at either the Cape Romain NWR or Swanquarter NWR; thus, 

refined analyses were not required for this facility for either Class I area. Default screening analyses 

also demonstrate the Santee Cooper’s Jefferies and Winyah Generating Stations do not contribute to 

visibility impairment at Wolf Island NWR, and refined analyses were not required for this Class I 

area. 

Appendix H.6 - 1393
Santee Cooper – Grainger, Jefferies, Winyah 60 Trinity Consultants 

BART Applicability Modeling Analysis 

5. REFINED ANALYSIS RESULTS

Screening analysis results described in Section 4 of this BART Applicability Analysis Report 

indicated that the 0.5 dv contribution threshold was exceeded only at the Cape Romain NWR for both 

the Jefferies and Winyah Generating Stations. Therefore, refined analyses were conducted for both 

facilities for Cape Romain using the refined postprocessing methods described in Section 3.5 of this 

report to assess the 98th percentile, 24-hour average impacts in comparison to the 0.5 dv contribution 

threshold. 

5.1 JEFFERIES GENERATING STATION

Table 5-1 summarizes the top eight modeled 24-hour average visibility impacts of each data analysis 

year at Cape Romain NWR attributable to Santee Cooper’s Jefferies Generating Station using refined 

modeling methods as described in this report. The 98th percentile impact is represented as the 

maximum 8th-high 24-hour average impact among the three years of analysis. Impacts are presented 

relative to annual average natural background conditions described in Section 3.5.3 of this report as 

calculated using the revised IMPROVE light extinction algorithm, including corrected Rayleigh 

scattering and naturally-occurring sea salt aerosols.31 Results are tabulated for analyses using both 

estimates of the annual average sea salt concentration, i.e., 0.77 μg/m3

 as estimated by the VISTAS 

Technical Contractor, 1.1 μg/m3

 as estimated by Santee Cooper In addition, for reference only, results 

are presented using the default IMPROVE value of 0.2 μg/m3

. 

. 

TABLE 5-1. PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN

 

Ranked 2001 Δdv Ranked 2002 Δdv Ranked 2003 Δdv Maximum 8th-High Δdv 

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

 

0.742 

0.383 

0.369 

0.328 

0.303 

0.300 

0.267 

0.245 

0.681 

0.355 

0.338 

0.301 

0.281 

0.273 

0.246 

0.225 

0.868 

0.439 

0.433 

0.385 

0.355 

0.347 

0.310 

0.285 

0.417 

0.369 

0.358 

0.320 

0.320 

0.309 

0.287 

0.257 

0.382 

0.342 

0.330 

0.295 

0.295 

0.286 

0.265 

0.237 

0.489 

0.424 

0.415 

0.371 

0.370 

0.354 

0.334 

0.299 

0.378 

0.347 

0.340 

0.338 

0.315 

0.311 

0.306 

0.273 

0.347 

0.319 

0.312 

0.311 

0.290 

0.287 

0.282 

0.252 

0.444 

0.408 

0.399 

0.391 

0.366 

0.360 

0.354 

0.318 

0.273 0.252 0.318 

 

As an alternative representation of the 98th percentile, Table 5-2 summarizes a ranking of the top 

25 daily average impacts over the three-year period modeled to quantify the 22nd-high impact over 

three years when the revised IMPROVE algorithm is applied. Results are tabulated for analyses 

 

31 Note that the impacts calculated using the default IMPROVE equation also result in a change of less than 

0.5 dv. As the .lst files show, the maximum 8th highest change is 0.450 dv (in 2003), while the 22nd highest ranked change is 

0.448 dv. 

Appendix H.6 - 1394
Santee Cooper – Grainger, Jefferies, Winyah 61 Trinity Consultants 

BART Applicability Modeling Analysis 

using various estimates of the annual average sea salt concentration, i.e., 0.77 μg/m3

 as estimated by 

the VISTAS Technical Contractor, 1.1 μg/m3

 as estimated by Santee Cooper, and 0.2 μg/m3 as 

estimated to be the default revised IMPROVE concentration for Cape Romain NWR. 

TABLE 5-2. RANKED 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN OVER

2001 THROUGH 2003 DATA ANALYSIS PERIOD

Overall Rank Δdv (0.77 μg/m3

) Δdv (1.1 μg/m3) Δdv (0.2 μg/m3)

1 0.742 0.681 0.868

2 0.417 0.382 0.489

3 0.383 0.355 0.444

4 0.378 0.347 0.439

5 0.369 0.342 0.433

6 0.369 0.338 0.424

7 0.358 0.330 0.415

8 0.347 0.319 0.408

9 0.340 0.312 0.399

10 0.338 0.311 0.391

11 0.328 0.301 0.385

12 0.320 0.295 0.371

13 0.320 0.295 0.370

14 0.315 0.290 0.366

15 0.311 0.287 0.360

16 0.309 0.286 0.355

17 0.306 0.282 0.354

18 0.303 0.281 0.354

19 0.300 0.273 0.347

20 0.287 0.265 0.334

21 0.273 0.252 0.318

22 0.273 0.252 0.316

23 0.267 0.246 0.310

24 0.257 0.237 0.299

25 0.255 0.235 0.296

Table 5-3 summarizes the results of the refined analyses for Cape Romain. 

TABLE 5-3. REFINED VISIBILITY ANALYSIS SUMMARY FOR CAPE ROMAIN

Sea Salt 2001 8th High 2001 8th High 2001 8th High 22nd High

Concentration Δdv Δdv Δdv Δdv

0.77 μg/m3 0.245 0.257 0.273 0.273

1.1 μg/m3 0.225 0.237 0.252 0.252

0.2 μg/m3 0.285 0.299 0.318 0.316

Appendix H.6 - 1395
Santee Cooper – Grainger, Jefferies, Winyah 62 Trinity Consultants 

BART Applicability Modeling Analysis 

The results summarized in Table 5-3 demonstrate that Santee Cooper’s Jefferies Generating Station is 

not subject to BART because the facility does not contribute to visibility impairment at Cape Romain 

as estimated using refined modeling techniques for any of the sea salt concentrations evaluated. 

5.2 WINYAH GENERATING STATION

Table 5-4 summarizes the top eight modeled 24-hour average visibility impacts of each data analysis 

year at Cape Romain NWR attributable to Santee Cooper’s Winyah Generating Station using refined 

modeling methods as described in this report. The 98th percentile impact is represented as the 

maximum 8th-high 24-hour average impact among the three years of analysis. Impacts are presented 

relative to annual average natural background conditions described in Section 3.5.3 of this report as 

calculated using the revised IMPROVE light extinction algorithm, including corrected Rayleigh 

scattering and naturally-occurring sea salt aerosols. Results are tabulated for analyses using both 

estimates of the annual average sea salt concentration, i.e., 0.77 μg/m3

 as estimated by the VISTAS 

Technical Contractor, 1.1 μg/m3

 as estimated by Santee Cooper. In addition, for reference only, 

results are presented using the default IMPROVE value of 0.2 μg/m3

. 

. 

TABLE 5-4. PEAK 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN

 

Ranked 2001 Δdv Ranked 2002 Δdv Ranked 2003 Δdv Maximum 8th-High Δdv 

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

0.77 

μg/m3

1.1 

μg/m3

0.2 

μg/m3

 

0.610 

0.495 

0.450 

0.449 

0.449 

0.426 

0.408 

0.401 

0.558 

0.452 

0.412 

0.411 

0.410 

0.391 

0.373 

0.371 

0.721 

0.585 

0.532 

0.531 

0.526 

0.499 

0.483 

0.461 

0.586 

0.465 

0.463 

0.457 

0.449 

0.393 

0.382 

0.377 

0.542 

0.429 

0.427 

0.421 

0.415 

0.361 

0.354 

0.347 

0.677 

0.538 

0.538 

0.529 

0.519 

0.461 

0.438 

0.438 

0.753 

0.570 

0.565 

0.528 

0.497 

0.479 

0.478 

0.476 

0.695 

0.521 

0.518 

0.482 

0.454 

0.439 

0.437 

0.435 

0.870 

0.673 

0.661 

0.624 

0.587 

0.565 

0.562 

0.561 

0.476 0.435 0.561 

 

As an alternative representation of the 98th percentile, Table 5-5 summarizes a ranking of the top 

25 daily average impacts over the three-year period modeled to quantify the 22nd-high impact over 

three years when the revised IMPROVE algorithm is applied. Results are tabulated for analyses 

using various estimates of the annual average sea salt concentration, i.e., 0.77 μg/m3

 as estimated by 

the VISTAS Technical Contractor, 1.1 μg/m3

 as estimated by Santee Cooper, and 0.2 μg/m3 as 

estimated to be the default revised IMPROVE concentration for Cape Romain NWR. 

Appendix H.6 - 1396
Santee Cooper – Grainger, Jefferies, Winyah 63 Trinity Consultants 

BART Applicability Modeling Analysis 

TABLE 5-5. RANKED 24-HOUR AVERAGE VISIBILITY IMPACTS AT CAPE ROMAIN OVER

2001 THROUGH 2003 DATA ANALYSIS PERIOD

Overall Rank Δdv (0.77 μg/m3

) Δdv (1.1 μg/m3) Δdv (0.2 μg/m3)

1 0.753 0.695 0.870

2 0.610 0.558 0.721

3 0.586 0.542 0.677

4 0.570 0.521 0.673

5 0.565 0.518 0.661

6 0.528 0.482 0.624

7 0.497 0.454 0.587

8 0.495 0.452 0.585

9 0.479 0.439 0.565

10 0.478 0.437 0.562

11 0.476 0.435 0.561

12 0.471 0.434 0.545

13 0.465 0.429 0.538

14 0.463 0.427 0.538

15 0.462 0.426 0.538

16 0.457 0.421 0.536

17 0.455 0.418 0.532

18 0.453 0.416 0.531

19 0.450 0.415 0.529

20 0.449 0.412 0.526

21 0.449 0.411 0.523

22 0.449 0.410 0.519

23 0.426 0.391 0.499

24 0.414 0.381 0.483

25 0.408 0.373 0.480

Table 5-6 summarizes the results of the refined analyses for Cape Romain. 

TABLE 5-6. REFINED VISIBILITY ANALYSIS SUMMARY FOR CAPE ROMAIN

Sea Salt 2001 8th High 2001 8th High 2001 8th High 22nd High

Concentration Δdv Δdv Δdv Δdv

0.77 μg/m3 0.401 0.377 0.476 0.449

1.1 μg/m3 0.371 0.347 0.435 0.410

0.2 μg/m3 0.461 0.438 0.561 0.519

The results summarized in Table 5-6 demonstrate that Santee Cooper’s Winyah Generating Station is 

not subject to BART when the more representative sea salt concentrations of 0.77 μg/m3

 as estimated 

by the VISTAS Technical Contractor and 1.1 μg/m3

 as estimated by Santee Cooper are utilized 

because the facility does not contribute to visibility impairment at Cape Romain as estimated using 

refined modeling techniques. 

Appendix H.6 - 1397
Santee Cooper – Grainger, Jefferies, Winyah 64 Trinity Consultants 

BART Applicability Modeling Analysis 

6. CONCLUSIONS

Using analysis methods prescribed by the U.S. EPA, VISTAS, and South Carolina DHEC, Santee 

Cooper conducted BART Applicability Analyses of emissions of visibility-affecting pollutants from 

the BART-eligible emission units at the Grainger, Jefferies, and Winyah Generating Stations. The 

analyses described in this BART Applicability Analysis Report include the most updated emissions 

inventory based on updated emission factor guidance and enforceable permit conditions, most 

updated versions of the CALPUFF modeling system, as well as an updated representation and 

innovative assessment of natural background conditions using the revised IMPROVE light extinction 

algorithm. 

The results of these analyses indicate Santee Cooper’s Grainger, Jefferies, and Winyah Generating 

Stations should not be considered subject to BART. The BART-eligible sources do not contribute to 

visibility impairment at the Wolf Island or Swanquarter NWR for any of the Santee Cooper BARTeligible Generation Stations located with 300 km of these Class I areas, as demonstrated by default 

screening analyses. The BART-eligible sources at the Jefferies and Winyah Generation Stations do 

not contribute to visibility impairment at the Cape Romain NWR as demonstrated by the refined 

modeling analyses that quantified the 98th percentile 24-hour average visibility impact as being less 

than the 0.5 dv contribution threshold using refined estimates of light extinction based on the revised 

IMPROVE algorithm and analyses of the most representative background sea salt concentrations.

Appendix H.6 - 1398
Santee Cooper – Grainger, Jefferies, Winyah A - 1 Trinity Consultants 

BART Applicability Modeling Analysis 

APPENDIX A 

SAMPLE CALPUFF, POSTUTIL, AND CALPOST INPUT FILES

CALPOST-IMPROVE PROCESSOR WORKSHEETS

Appendix H.6 - 1399
Santee Cooper – Grainger, Jefferies, Winyah A - 2 Trinity Consultants 

BART Applicability Modeling Analysis 

SAMPLE CALPUFF INPUT FILE

SCPWIN12KM02.INP 

WINYAH 12-KM SCREENING ANALYSIS YEAR 2002 

Appendix H.6 - 1400
SCPWIN12KM02.inp 6/27/2007

Santee Cooper Winyah,SC Facility

BART APPLICABILITY ANALYSIS 2002

Cape Romain/Wolf Island/Swan Quarter Analyses

---------------- Run title (3 lines) ------------------------------------------

CALPUFF MODEL CONTROL FILE

--------------------------

------------------------------------------------------------------------------

-

INPUT GROUP: 0 -- Input and Output File Names

--------------

Default Name Type File Name

------------ ---- --------- CALMET.DAT input * METDAT = *

or

ISCMET.DAT input * ISCDAT = *

or

PLMMET.DAT input * PLMDAT = *

or

PROFILE.DAT input * PRFDAT = *

SURFACE.DAT input * SFCDAT = * RESTARTB.DAT input * RSTARTB= * ------------------------------------------------------------------------------

--

CALPUFF.LST output ! PUFLST =T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KM02.LST !

CONC.DAT output ! CONDAT =T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KM02.CON

!

DFLX.DAT output * DFDAT = *

WFLX.DAT output * WFDAT = *

VISB.DAT output * VISDAT = * RESTARTE.DAT output * RSTARTE= * ------------------------------------------------------------------------------

--

Emission Files

--------------

PTEMARB.DAT input * PTDAT = * VOLEMARB.DAT input * VOLDAT = *

BAEMARB.DAT input * ARDAT = * LNEMARB.DAT input * LNDAT = *

------------------------------------------------------------------------------

--

Other Files

-----------

OZONE.DAT input ! OZDAT =T:\BART\SNTCPRSC\OZONE\OZONE02.DAT !

VD.DAT input * VDDAT = *

CHEM.DAT input * CHEMDAT= * H2O2.DAT input * H2O2DAT= *

HILL.DAT input * HILDAT= *

HILLRCT.DAT input * RCTDAT= *

COASTLN.DAT input * CSTDAT= * FLUXBDY.DAT input * BDYDAT= *

BCON.DAT input * BCNDAT= *

DEBUG.DAT output * DEBUG = *

MASSFLX.DAT output * FLXDAT= * MASSBAL.DAT output * BALDAT= *

FOG.DAT output * FOGDAT= * ------------------------------------------------------------------------------

--

All file names will be converted to lower case if LCFILES = T

Otherwise, if LCFILES = F, file names will be converted to UPPER CASE

T = lower case ! LCFILES = T !

F = UPPER CASE

1

Appendix H.6 - 1401
SCPWIN12KM02.inp 6/27/2007

NOTE: (1) file/path names can be up to 70 characters in length

Provision for multiple input files ----------------------------------

Number of CALMET.DAT files for run (NMETDAT)

Default: 1 ! NMETDAT = 12 !

Number of PTEMARB.DAT files for run (NPTDAT)

Default: 0 ! NPTDAT = 0 !

Number of BAEMARB.DAT files for run (NARDAT)

Default: 0 ! NARDAT = 0 !

Number of VOLEMARB.DAT files for run (NVOLDAT)

Default: 0 ! NVOLDAT = 0 !

!END!

-------------

Subgroup (0a)

-------------

The following CALMET.DAT filenames are processed in sequence if NMETDAT>1

Default Name Type File Name

------------ ---- --------- none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-01R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-02R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-03R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-04R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-05R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-06R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-07R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-08R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-09R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-10R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-11R.DAT !

!END!

none input ! METDAT=T:\VISTAS\DOMAIN0-2002\MET2002-12R.DAT !

!END!

------------------------------------------------------------------------------

--

INPUT GROUP: 1 -- General run control parameters

--------------

Option to run all periods found

in the met. file (METRUN) Default: 0 ! METRUN = 0 !

METRUN = 0 - Run period explicitly defined below

METRUN = 1 - Run all periods in met. file

Starting date: Year (IBYR) -- No default ! IBYR = 2002 !

2

Appendix H.6 - 1402
SCPWIN12KM02.inp 6/27/2007

(used only if Month (IBMO) -- No default ! IBMO = 1 !

METRUN = 0) Day (IBDY) -- No default ! IBDY = 1 !

Hour (IBHR) -- No default ! IBHR = 1 !

Base time zone (XBTZ) -- No default ! XBTZ = 5.0 !

PST = 8., MST = 7.

CST = 6., EST = 5.

Length of run (hours) (IRLG) -- No default ! IRLG = 8760 !

Number of chemical species (NSPEC)

Default: 5 ! NSPEC = 16 !

Number of chemical species

to be emitted (NSE) Default: 3 ! NSE = 15 !

Flag to stop run after

SETUP phase (ITEST) Default: 2 ! ITEST = 2 !

(Used to allow checking

of the model inputs, files, etc.)

ITEST = 1 - STOPS program after SETUP phase

ITEST = 2 - Continues with execution of program

after SETUP

Restart Configuration:

Control flag (MRESTART) Default: 0 ! MRESTART = 0 !

0 = Do not read or write a restart file

1 = Read a restart file at the beginning of

the run

2 = Write a restart file during run

3 = Read a restart file at beginning of run

and write a restart file during run

Number of periods in Restart

output cycle (NRESPD) Default: 0 ! NRESPD = 0 !

0 = File written only at last period

>0 = File updated every NRESPD periods

Meteorological Data Format (METFM)

Default: 1 ! METFM = 1 !

METFM = 1 - CALMET binary file (CALMET.MET)

METFM = 2 - ISC ASCII file (ISCMET.MET)

METFM = 3 - AUSPLUME ASCII file (PLMMET.MET)

METFM = 4 - CTDM plus tower file (PROFILE.DAT) and

surface parameters file (SURFACE.DAT)

METFM = 5 - AERMET tower file (PROFILE.DAT) and

surface parameters file (SURFACE.DAT)

Meteorological Profile Data Format (MPRFFM)

(used only for METFM = 1, 2, 3)

Default: 1 ! MPRFFM = 1 !

MPRFFM = 1 - CTDM plus tower file (PROFILE.DAT)

MPRFFM = 2 - AERMET tower file (PROFILE.DAT)

PG sigma-y is adjusted by the factor (AVET/PGTIME)**0.2

Averaging Time (minutes) (AVET)

Default: 60.0 ! AVET = 60. !

PG Averaging Time (minutes) (PGTIME)

Default: 60.0 ! PGTIME = 60. !

!END!

3

Appendix H.6 - 1403
SCPWIN12KM02.inp 6/27/2007

------------------------------------------------------------------------------

-

INPUT GROUP: 2 -- Technical options --------------

Vertical distribution used in the

near field (MGAUSS) Default: 1 ! MGAUSS = 1 !

0 = uniform

1 = Gaussian

Terrain adjustment method

(MCTADJ) Default: 3 ! MCTADJ = 3 !

0 = no adjustment

1 = ISC-type of terrain adjustment

2 = simple, CALPUFF-type of terrain

adjustment

3 = partial plume path adjustment

Subgrid-scale complex terrain

flag (MCTSG) Default: 0 ! MCTSG = 0 !

0 = not modeled

1 = modeled

Near-field puffs modeled as

elongated slugs? (MSLUG) Default: 0 ! MSLUG = 0 !

0 = no

1 = yes (slug model used)

Transitional plume rise modeled?

(MTRANS) Default: 1 ! MTRANS = 1 !

0 = no (i.e., final rise only)

1 = yes (i.e., transitional rise computed)

Stack tip downwash? (MTIP) Default: 1 ! MTIP = 1 !

0 = no (i.e., no stack tip downwash)

1 = yes (i.e., use stack tip downwash)

Method used to simulate building

downwash? (MBDW) Default: 1 ! MBDW = 1 !

1 = ISC method

2 = PRIME method

Vertical wind shear modeled above

stack top? (MSHEAR) Default: 0 ! MSHEAR = 0 !

0 = no (i.e., vertical wind shear not modeled)

1 = yes (i.e., vertical wind shear modeled)

Puff splitting allowed? (MSPLIT) Default: 0 ! MSPLIT = 0 !

0 = no (i.e., puffs not split)

1 = yes (i.e., puffs are split)

Chemical mechanism flag (MCHEM) Default: 1 ! MCHEM = 1 !

0 = chemical transformation not

modeled

1 = transformation rates computed

internally (MESOPUFF II scheme)

2 = user-specified transformation

rates used

3 = transformation rates computed

internally (RIVAD/ARM3 scheme)

4 = secondary organic aerosol formation

computed (MESOPUFF II scheme for OH)

4

Appendix H.6 - 1404
SCPWIN12KM02.inp 6/27/2007

Aqueous phase transformation flag (MAQCHEM)

(Used only if MCHEM = 1, or 3) Default: 0 ! MAQCHEM = 0 !

0 = aqueous phase transformation

not modeled

1 = transformation rates adjusted

for aqueous phase reactions

Wet removal modeled ? (MWET) Default: 1 ! MWET = 1 !

0 = no

1 = yes

Dry deposition modeled ? (MDRY) Default: 1 ! MDRY = 1 !

0 = no

1 = yes

(dry deposition method specified

for each species in Input Group 3)

Gravitational settling (plume tilt)

modeled ? (MTILT) Default: 0 ! MTILT = 0 !

0 = no

1 = yes

(puff center falls at the gravitational

settling velocity for 1 particle species)

Restrictions: - MDRY = 1

- NSPEC = 1 (must be particle species as well) - sg = 0 GEOMETRIC STANDARD DEVIATION in Group 8 is

set to zero for a single particle diameter

Method used to compute dispersion

coefficients (MDISP) Default: 3 ! MDISP = 3 !

1 = dispersion coefficients computed from measured values

of turbulence, sigma v, sigma w 2 = dispersion coefficients from internally calculated

sigma v, sigma w using micrometeorological variables

(u*, w*, L, etc.)

3 = PG dispersion coefficients for RURAL areas (computed using

the ISCST multi-segment approximation) and MP coefficients in

urban areas

4 = same as 3 except PG coefficients computed using

the MESOPUFF II eqns.

5 = CTDM sigmas used for stable and neutral conditions.

For unstable conditions, sigmas are computed as in

MDISP = 3, described above. MDISP = 5 assumes that

measured values are read

Sigma-v/sigma-theta, sigma-w measurements used? (MTURBVW)

(Used only if MDISP = 1 or 5) Default: 3 ! MTURBVW = 3 !

1 = use sigma-v or sigma-theta measurements

from PROFILE.DAT to compute sigma-y

(valid for METFM = 1, 2, 3, 4, 5)

2 = use sigma-w measurements

from PROFILE.DAT to compute sigma-z

(valid for METFM = 1, 2, 3, 4, 5)

3 = use both sigma-(v/theta) and sigma-w

from PROFILE.DAT to compute sigma-y and sigma-z

(valid for METFM = 1, 2, 3, 4, 5)

4 = use sigma-theta measurements

from PLMMET.DAT to compute sigma-y

(valid only if METFM = 3)

Back-up method used to compute dispersion

when measured turbulence data are

missing (MDISP2) Default: 3 ! MDISP2 = 3 !

5

Appendix H.6 - 1405
SCPWIN12KM02.inp 6/27/2007

(used only if MDISP = 1 or 5)

2 = dispersion coefficients from internally calculated

sigma v, sigma w using micrometeorological variables

(u*, w*, L, etc.)

3 = PG dispersion coefficients for RURAL areas (computed using

the ISCST multi-segment approximation) and MP coefficients in

urban areas

4 = same as 3 except PG coefficients computed using

the MESOPUFF II eqns.

[DIAGNOSTIC FEATURE]

Method used for Lagrangian timescale for Sigma-y

(used only if MDISP=1,2 or MDISP2=1,2)

(MTAULY) Default: 0 ! MTAULY = 0 !

0 = Draxler default 617.284 (s)

1 = Computed as Lag. Length / (.75 q) -- after SCIPUFF

10 < Direct user input (s) -- e.g., 306.9

[DIAGNOSTIC FEATURE]

Method used for Advective-Decay timescale for Turbulence

(used only if MDISP=2 or MDISP2=2)

(MTAUADV) Default: 0 ! MTAUADV = 0 !

0 = No turbulence advection

1 = Computed (OPTION NOT IMPLEMENTED)

10 < Direct user input (s) -- e.g., 300

Method used to compute turbulence sigma-v &

sigma-w using micrometeorological variables

(Used only if MDISP = 2 or MDISP2 = 2)

(MCTURB) Default: 1 ! MCTURB = 1 !

1 = Standard CALPUFF subroutines

2 = AERMOD subroutines

PG sigma-y,z adj. for roughness? Default: 0 ! MROUGH = 0 !

(MROUGH)

0 = no

1 = yes

Partial plume penetration of Default: 1 ! MPARTL = 1 !

elevated inversion?

(MPARTL)

0 = no

1 = yes

Strength of temperature inversion Default: 0 ! MTINV = 0 !

provided in PROFILE.DAT extended records?

(MTINV)

0 = no (computed from measured/default gradients)

1 = yes

PDF used for dispersion under convective conditions?

Default: 0 ! MPDF = 0 !

(MPDF)

0 = no

1 = yes

Sub-Grid TIBL module used for shore line?

Default: 0 ! MSGTIBL = 0 !

(MSGTIBL)

0 = no

1 = yes

Boundary conditions (concentration) modeled?

Default: 0 ! MBCON = 0 !

(MBCON)

6

Appendix H.6 - 1406
SCPWIN12KM02.inp 6/27/2007

0 = no

1 = yes, using formatted BCON.DAT file

2 = yes, using unformatted CONC.DAT file

Analyses of fogging and icing impacts due to emissions from

arrays of mechanically-forced cooling towers can be performed

using CALPUFF in conjunction with a cooling tower emissions

processor (CTEMISS) and its associated postprocessors. Hourly

emissions of water vapor and temperature from each cooling tower

cell are computed for the current cell configuration and ambient

conditions by CTEMISS. CALPUFF models the dispersion of these

emissions and provides cloud information in a specialized format

for further analysis. Output to FOG.DAT is provided in either

'plume mode' or 'receptor mode' format.

Configure for FOG Model output?

Default: 0 ! MFOG = 0 !

(MFOG)

0 = no

1 = yes - report results in PLUME Mode format

2 = yes - report results in RECEPTOR Mode format

Test options specified to see if

they conform to regulatory

values? (MREG) Default: 1 ! MREG = 0 !

0 = NO checks are made

1 = Technical options must conform to USEPA

Long Range Transport (LRT) guidance

METFM 1 or 2

AVET 60. (min)

PGTIME 60. (min)

MGAUSS 1

MCTADJ 3

MTRANS 1

MTIP 1

MCHEM 1 or 3 (if modeling SOx, NOx)

MWET 1

MDRY 1

MDISP 2 or 3

MPDF 0 if MDISP=3

1 if MDISP=2

MROUGH 0

MPARTL 1

SYTDEP 550. (m)

MHFTSZ 0

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 3a, 3b -- Species list

-------------------

------------

Subgroup (3a) ------------

The following species are modeled:

! CSPEC = SO2 ! !END!

! CSPEC = SO4 ! !END!

7

Appendix H.6 - 1407
SCPWIN12KM02.inp 6/27/2007

! CSPEC = NOX ! !END!

! CSPEC = HNO3 ! !END!

! CSPEC = NO3 ! !END!

! CSPEC = POC081 ! !END!

! CSPEC = POC056 ! !END!

! CSPEC = PIC081 ! !END!

! CSPEC = PIC056 ! !END!

! CSPEC = PMC800 ! !END!

! CSPEC = PMC425 ! !END!

! CSPEC = PMF187 ! !END!

! CSPEC = PMF112 ! !END!

! CSPEC = PMF081 ! !END!

! CSPEC = PMF056 ! !END!

! CSPEC = EC056 ! !END!

Dry

OUTPUT GROUP

SPECIES MODELED EMITTED DEPOSITED

NUMBER

NAME (0=NO, 1=YES) (0=NO, 1=YES) (0=NO, (0=

NONE,

(Limit: 12 1=COMPUTED-GAS 1=

1st CGRUP,

Characters 2=COMPUTED-PARTICLE 2=

2nd CGRUP,

in length) 3=USER-SPECIFIED) 3=

etc.)

! SO2 = 1, 1, 1, 0

!

! SO4 = 1, 1, 2, 0

!

! NOX = 1, 1, 1, 0

!

! HNO3 = 1, 0, 1, 0

!

! NO3 = 1, 1, 2, 0

!

! POC081 = 1, 1, 2, 0

!

! POC056 = 1, 1, 2, 0

!

! PIC081 = 1, 1, 2, 0

!

! PIC056 = 1, 1, 2, 0

!

! PMC800 = 1, 1, 2, 0

!

! PMC425 = 1, 1, 2, 0

!

! PMF187 = 1, 1, 2, 0

!

! PMF112 = 1, 1, 2, 0

!

! PMF081 = 1, 1, 2, 0

!

! PMF056 = 1, 1, 2, 0

!

! EC056 = 1, 1, 2, 0

!

!END!

-------------

Subgroup (3b) -------------

The following names are used for Species-Groups in which results

8

Appendix H.6 - 1408
SCPWIN12KM02.inp 6/27/2007

for certain species are combined (added) prior to output. The

CGRUP name will be used as the species name in output files.

Use this feature to model specific particle-size distributions

by treating each size-range as a separate species.

Order must be consistent with 3(a) above.

------------------------------------------------------------------------------

-

INPUT GROUP: 4 -- Map Projection and Grid control parameters

--------------

Projection for all (X,Y):

-------------------------

Map projection

(PMAP) Default: UTM ! PMAP = LCC !

UTM : Universal Transverse Mercator

TTM : Tangential Transverse Mercator

LCC : Lambert Conformal Conic

PS : Polar Stereographic

EM : Equatorial Mercator

LAZA : Lambert Azimuthal Equal Area

False Easting and Northing (km) at the projection origin

(Used only if PMAP= TTM, LCC, or LAZA)

(FEAST) Default=0.0 ! FEAST = 0.000 !

(FNORTH) Default=0.0 ! FNORTH = 0.000 !

UTM zone (1 to 60)

(Used only if PMAP=UTM)

(IUTMZN) No Default ! IUTMZN = 0 !

Hemisphere for UTM projection?

(Used only if PMAP=UTM)

(UTMHEM) Default: N ! UTMHEM = N !

N : Northern hemisphere projection

S : Southern hemisphere projection

Latitude and Longitude (decimal degrees) of projection origin

(Used only if PMAP= TTM, LCC, PS, EM, or LAZA)

(RLAT0) No Default ! RLAT0 = 40N !

(RLON0) No Default ! RLON0 = 97W !

TTM : RLON0 identifies central (true N/S) meridian of projection

RLAT0 selected for convenience

LCC : RLON0 identifies central (true N/S) meridian of projection

RLAT0 selected for convenience

PS : RLON0 identifies central (grid N/S) meridian of projection

RLAT0 selected for convenience

EM : RLON0 identifies central meridian of projection

RLAT0 is REPLACED by 0.0N (Equator)

LAZA: RLON0 identifies longitude of tangent-point of mapping plane

RLAT0 identifies latitude of tangent-point of mapping plane

Matching parallel(s) of latitude (decimal degrees) for projection

(Used only if PMAP= LCC or PS)

(XLAT1) No Default ! XLAT1 = 33N !

(XLAT2) No Default ! XLAT2 = 45N !

LCC : Projection cone slices through Earth's surface at XLAT1 and

XLAT2

PS : Projection plane slices through Earth at XLAT1

9

Appendix H.6 - 1409
SCPWIN12KM02.inp 6/27/2007

(XLAT2 is not used)

----------

Note: Latitudes and longitudes should be positive, and include a

letter N,S,E, or W indicating north or south latitude, and

east or west longitude. For example,

35.9 N Latitude = 35.9N

118.7 E Longitude = 118.7E

Datum-region

------------

The Datum-Region for the coordinates is identified by a character

string. Many mapping products currently available use the model of the

Earth known as the World Geodetic System 1984 (WGS-84). Other local

models may be in use, and their selection in CALMET will make its output

consistent with local mapping products. The list of Datum-Regions with

official transformation parameters is provided by the National Imagery

and

Mapping Agency (NIMA).

NIMA Datum - Regions(Examples)

-------------------------------------------------------------------------

-----

WGS-84 WGS-84 Reference Ellipsoid and Geoid, Global coverage (WGS84)

NAS-C NORTH AMERICAN 1927 Clarke 1866 Spheroid, MEAN FOR CONUS

(NAD27)

NAR-C NORTH AMERICAN 1983 GRS 80 Spheroid, MEAN FOR CONUS (NAD83)

NWS-84 NWS 6370KM Radius, Sphere

ESR-S ESRI REFERENCE 6371KM Radius, Sphere

Datum-region for output coordinates

(DATUM) Default: WGS-84 ! DATUM = NWS-84 !

METEOROLOGICAL Grid:

Rectangular grid defined for projection PMAP,

with X the Easting and Y the Northing coordinate

No. X grid cells (NX) No default ! NX = 160 !

No. Y grid cells (NY) No default ! NY = 172 !

No. vertical layers (NZ) No default ! NZ = 10 !

Grid spacing (DGRIDKM) No default ! DGRIDKM = 12. !

Units: km

Cell face heights

(ZFACE(nz+1)) No defaults

Units: m

! ZFACE = 0., 20., 40., 80., 160., 320., 640., 1200., 2000., 3000.,

4000. !

Reference Coordinates

of SOUTHWEST corner of

grid cell(1, 1):

X coordinate (XORIGKM) No default ! XORIGKM = 137.973 !

Y coordinate (YORIGKM) No default ! YORIGKM = -1625.974 !

Units: km

COMPUTATIONAL Grid:

The computational grid is identical to or a subset of the MET. grid.

10

Appendix H.6 - 1410
SCPWIN12KM02.inp 6/27/2007

The lower left (LL) corner of the computational grid is at grid point

(IBCOMP, JBCOMP) of the MET. grid. The upper right (UR) corner of the

computational grid is at grid point (IECOMP, JECOMP) of the MET. grid.

The grid spacing of the computational grid is the same as the MET. grid.

X index of LL corner (IBCOMP) No default ! IBCOMP = 101 !

(1 <= IBCOMP <= NX)

Y index of LL corner (JBCOMP) No default ! JBCOMP = 60 !

(1 <= JBCOMP <= NY)

X index of UR corner (IECOMP) No default ! IECOMP = 156 !

(1 <= IECOMP <= NX)

Y index of UR corner (JECOMP) No default ! JECOMP = 119 !

(1 <= JECOMP <= NY)

SAMPLING Grid (GRIDDED RECEPTORS):

The lower left (LL) corner of the sampling grid is at grid point

(IBSAMP, JBSAMP) of the MET. grid. The upper right (UR) corner of the

sampling grid is at grid point (IESAMP, JESAMP) of the MET. grid.

The sampling grid must be identical to or a subset of the computational

grid. It may be a nested grid inside the computational grid.

The grid spacing of the sampling grid is DGRIDKM/MESHDN.

Logical flag indicating if gridded

receptors are used (LSAMP) Default: T ! LSAMP = F !

(T=yes, F=no)

X index of LL corner (IBSAMP) No default ! IBSAMP = 1 !

(IBCOMP <= IBSAMP <= IECOMP)

Y index of LL corner (JBSAMP) No default ! JBSAMP = 1 !

(JBCOMP <= JBSAMP <= JECOMP)

X index of UR corner (IESAMP) No default ! IESAMP = 1 !

(IBCOMP <= IESAMP <= IECOMP)

Y index of UR corner (JESAMP) No default ! JESAMP = 1 !

(JBCOMP <= JESAMP <= JECOMP)

Nesting factor of the sampling

grid (MESHDN) Default: 1 ! MESHDN = 1 !

(MESHDN is an integer >= 1)

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 5 -- Output Options --------------

* * FILE DEFAULT VALUE VALUE THIS RUN ---- ------------- --------------

Concentrations (ICON) 1 ! ICON = 1 !

Dry Fluxes (IDRY) 1 ! IDRY = 0 !

Wet Fluxes (IWET) 1 ! IWET = 0 !

11

Appendix H.6 - 1411
SCPWIN12KM02.inp 6/27/2007

Relative Humidity (IVIS) 1 ! IVIS = 0 !

(relative humidity file is

required for visibility

analysis)

Use data compression option in output file?

(LCOMPRS) Default: T ! LCOMPRS = T !

*

0 = Do not create file, 1 = create file

DIAGNOSTIC MASS FLUX OUTPUT OPTIONS:

Mass flux across specified boundaries

for selected species reported hourly?

(IMFLX) Default: 0 ! IMFLX = 0 !

0 = no

1 = yes (FLUXBDY.DAT and MASSFLX.DAT filenames

are specified in Input Group 0)

Mass balance for each species

reported hourly?

(IMBAL) Default: 0 ! IMBAL = 0 !

0 = no

1 = yes (MASSBAL.DAT filename is

specified in Input Group 0)

LINE PRINTER OUTPUT OPTIONS:

Print concentrations (ICPRT) Default: 0 ! ICPRT = 0 !

Print dry fluxes (IDPRT) Default: 0 ! IDPRT = 0 !

Print wet fluxes (IWPRT) Default: 0 ! IWPRT = 0 !

(0 = Do not print, 1 = Print)

Concentration print interval

(ICFRQ) in hours Default: 1 ! ICFRQ = 1 !

Dry flux print interval

(IDFRQ) in hours Default: 1 ! IDFRQ = 1 !

Wet flux print interval

(IWFRQ) in hours Default: 1 ! IWFRQ = 1 !

Units for Line Printer Output

(IPRTU) Default: 1 ! IPRTU = 3 !

for for

Concentration Deposition

1 = g/m**3 g/m**2/s

2 = mg/m**3 mg/m**2/s

3 = ug/m**3 ug/m**2/s

4 = ng/m**3 ng/m**2/s

5 = Odour Units

Messages tracking progress of run

written to the screen ?

(IMESG) Default: 2 ! IMESG = 2 !

0 = no

1 = yes (advection step, puff ID)

2 = yes (YYYYJJJHH, # old puffs, # emitted puffs)

SPECIES (or GROUP for combined species) LIST FOR OUTPUT OPTIONS

---- CONCENTRATIONS ---- ------ DRY FLUXES ------ ------

WET FLUXES ------ -- MASS FLUX --

SPECIES

/GROUP PRINTED? SAVED ON DISK? PRINTED? SAVED ON DISK?

PRINTED? SAVED ON DISK? SAVED ON DISK?

12

Appendix H.6 - 1412
SCPWIN12KM02.inp 6/27/2007

------- ------------------------ ------------------------

------------------------ ---------------

! SO2 = 0, 1, 0, 0, 0,

0, 0 !

! SO4 = 0, 1, 0, 0, 0,

0, 0 !

! NOX = 0, 1, 0, 0, 0,

0, 0 !

! HNO3 = 0, 1, 0, 0, 0,

0, 0 !

! NO3 = 0, 1, 0, 0, 0,

0, 0 !

! POC081 = 0, 1, 0, 0, 0,

0, 0 !

! POC056 = 0, 1, 0, 0, 0,

0, 0 !

! PIC081 = 0, 1, 0, 0, 0,

0, 0 !

! PIC056 = 0, 1, 0, 0, 0,

0, 0 !

! PMC800 = 0, 1, 0, 0, 0,

0, 0 !

! PMC425 = 0, 1, 0, 0, 0,

0, 0 !

! PMF187 = 0, 1, 0, 0, 0,

0, 0 !

! PMF112 = 0, 1, 0, 0, 0,

0, 0 !

! PMF081 = 0, 1, 0, 0, 0,

0, 0 !

! PMF056 = 0, 1, 0, 0, 0,

0, 0 !

! EC056 = 0, 1, 0, 0, 0,

0, 0 !

OPTIONS FOR PRINTING "DEBUG" QUANTITIES (much output)

Logical for debug output

(LDEBUG) Default: F ! LDEBUG = F !

First puff to track

(IPFDEB) Default: 1 ! IPFDEB = 1

!

Number of puffs to track

(NPFDEB) Default: 1 ! NPFDEB = 10

!

Met. period to start output

(NN1) Default: 1 ! NN1 = 10 !

Met. period to end output

(NN2) Default: 10 ! NN2 = 10 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 6a, 6b, & 6c -- Subgrid scale complex terrain inputs -------------------------

---------------

Subgroup (6a)

---------------

13

Appendix H.6 - 1413
SCPWIN12KM02.inp 6/27/2007

Number of terrain features (NHILL) Default: 0 ! NHILL = 0

!

Number of special complex terrain

receptors (NCTREC) Default: 0 ! NCTREC = 0

!

Terrain and CTSG Receptor data for

CTSG hills input in CTDM format ?

(MHILL) No Default ! MHILL = 0

!

1 = Hill and Receptor data created

by CTDM processors & read from

HILL.DAT and HILLRCT.DAT files

2 = Hill data created by OPTHILL &

input below in Subgroup (6b);

Receptor data in Subgroup (6c)

Factor to convert horizontal dimensions Default: 1.0 ! XHILL2M = 0.

!

to meters (MHILL=1)

Factor to convert vertical dimensions Default: 1.0 ! ZHILL2M = 0.

!

to meters (MHILL=1)

X-origin of CTDM system relative to No Default ! XCTDMKM =

0.0E00 !

CALPUFF coordinate system, in Kilometers (MHILL=1)

Y-origin of CTDM system relative to No Default ! YCTDMKM =

0.0E00 !

CALPUFF coordinate system, in Kilometers (MHILL=1)

! END !

---------------

Subgroup (6b) ---------------

1 ** HILL information

HILL XC YC THETAH ZGRID RELIEF EXPO 1 EXPO 2

SCALE 1 SCALE 2 AMAX1 AMAX2

NO. (km) (km) (deg.) (m) (m) (m) (m)

(m) (m) (m) (m) ---- ---- ---- ------ ----- ------ ------ ------

------- ------- ----- -----

---------------

Subgroup (6c)

---------------

COMPLEX TERRAIN RECEPTOR INFORMATION

XRCT YRCT ZRCT XHH

(km) (km) (m) ------ ----- ------ ----

-------------------

1

Description of Complex Terrain Variables:

XC, YC = Coordinates of center of hill

THETAH = Orientation of major axis of hill (clockwise from

14

Appendix H.6 - 1414
SCPWIN12KM02.inp 6/27/2007

North)

ZGRID = Height of the 0 of the grid above mean sea

level

RELIEF = Height of the crest of the hill above the grid elevation

EXPO 1 = Hill-shape exponent for the major axis

EXPO 2 = Hill-shape exponent for the major axis

SCALE 1 = Horizontal length scale along the major axis

SCALE 2 = Horizontal length scale along the minor axis

AMAX = Maximum allowed axis length for the major axis

BMAX = Maximum allowed axis length for the major axis

XRCT, YRCT = Coordinates of the complex terrain receptors

ZRCT = Height of the ground (MSL) at the complex terrain

Receptor

XHH = Hill number associated with each complex terrain receptor

(NOTE: MUST BE ENTERED AS A REAL NUMBER)

**

NOTE: DATA for each hill and CTSG receptor are treated as a separate

input subgroup and therefore must end with an input group

terminator.

------------------------------------------------------------------------------

-

INPUT GROUP: 7 -- Chemical parameters for dry deposition of gases

--------------

SPECIES DIFFUSIVITY ALPHA STAR REACTIVITY MESOPHYLL

RESISTANCE HENRY'S LAW COEFFICIENT

NAME (cm**2/s) (s/cm)

(dimensionless)

------- ----------- ---------- ----------

-------------------- -----------------------

! SO2 = 0.1509, 1000., 8., 0.,

0.04 !

! NOX = 0.1656, 1., 8., 5.,

3.5 !

! HNO3 = 0.1628, 1., 18., 0.,

0.00000008 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 8 -- Size parameters for dry deposition of particles --------------

For SINGLE SPECIES, the mean and standard deviation are used to

compute a deposition velocity for NINT (see group 9) size-ranges,

and these are then averaged to obtain a mean deposition velocity.

For GROUPED SPECIES, the size distribution should be explicitly

specified (by the 'species' in the group), and the standard deviation

for each should be entered as 0. The model will then use the

deposition velocity for the stated mean diameter.

SPECIES GEOMETRIC MASS MEAN GEOMETRIC STANDARD

NAME DIAMETER DEVIATION

(microns) (microns) ------- ------------------- ------------------

! SO4 = 0.48, 2. !

15

Appendix H.6 - 1415
SCPWIN12KM02.inp 6/27/2007

! NO3 = 0.48, 2. !

! POC081 = 0.8125, 0. !

! POC056 = 0.5625, 0. !

! PIC081 = 0.8125, 0. !

! PIC056 = 0.5625, 0. !

! PMC800 = 8., 0. !

! PMC425 = 4.25, 0. !

! PMF187 = 1.875, 0. !

! PMF112 = 1.125, 0. !

! PMF081 = 0.8125, 0. !

! PMF056 = 0.5625, 0. !

! EC056 = 0.5625, 0. !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 9 -- Miscellaneous dry deposition parameters --------------

Reference cuticle resistance (s/cm)

(RCUTR) Default: 30 ! RCUTR = 30.0 !

Reference ground resistance (s/cm)

(RGR) Default: 10 ! RGR = 10.0 !

Reference pollutant reactivity

(REACTR) Default: 8 ! REACTR = 8.0 !

Number of particle-size intervals used to

evaluate effective particle deposition velocity

(NINT) Default: 9 ! NINT = 9 !

Vegetation state in unirrigated areas

(IVEG) Default: 1 ! IVEG = 1 !

IVEG=1 for active and unstressed vegetation

IVEG=2 for active and stressed vegetation

IVEG=3 for inactive vegetation

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 10 -- Wet Deposition Parameters ---------------

Scavenging Coefficient -- Units: (sec)**(-1)

Pollutant Liquid Precip. Frozen Precip.

--------- -------------- -------------- ! SO2 = 3.0E-05, 0.0E00 !

! SO4 = 1.0E-04, 3.0E-05 !

! HNO3 = 6.0E-05, 0.0E00 !

! NO3 = 1.0E-04, 3.0E-05 !

! POC081 = 1.0E-04, 3.0E-05 !

! POC056 = 1.0E-04, 3.0E-05 !

! PIC081 = 1.0E-04, 3.0E-05 !

! PIC056 = 1.0E-04, 3.0E-05 !

! PMC800 = 1.0E-04, 3.0E-05 !

! PMC425 = 1.0E-04, 3.0E-05 !

! PMF187 = 1.0E-04, 3.0E-05 !

! PMF112 = 1.0E-04, 3.0E-05 !

16

Appendix H.6 - 1416
SCPWIN12KM02.inp 6/27/2007

! PMF081 = 1.0E-04, 3.0E-05 !

! PMF056 = 1.0E-04, 3.0E-05 !

! EC056 = 1.0E-04, 3.0E-05 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 11 -- Chemistry Parameters ---------------

Ozone data input option (MOZ) Default: 1 ! MOZ = 1 !

(Used only if MCHEM = 1, 3, or 4)

0 = use a monthly background ozone value

1 = read hourly ozone concentrations from

the OZONE.DAT data file

Monthly ozone concentrations

(Used only if MCHEM = 1, 3, or 4 and

MOZ = 0 or MOZ = 1 and all hourly O3 data missing)

(BCKO3) in ppb Default: 12*80.

! BCKO3 = 25.76, 32.17, 34.66, 41.19, 43.06, 40.61, 41.33, 38.58,

31.57,

28.72, 26.55, 24.75 !

Monthly ammonia concentrations

(Used only if MCHEM = 1, or 3)

(BCKNH3) in ppb Default: 12*10.

! BCKNH3 = 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50,

0.50, 0.50 !

Nighttime SO2 loss rate (RNITE1)

in percent/hour Default: 0.2 ! RNITE1 = .2 !

Nighttime NOx loss rate (RNITE2)

in percent/hour Default: 2.0 ! RNITE2 = 2.0 !

Nighttime HNO3 formation rate (RNITE3)

in percent/hour Default: 2.0 ! RNITE3 = 2.0 !

H2O2 data input option (MH2O2) Default: 1 ! MH2O2 = 1 !

(Used only if MAQCHEM = 1)

0 = use a monthly background H2O2 value

1 = read hourly H2O2 concentrations from

the H2O2.DAT data file

Monthly H2O2 concentrations

(Used only if MQACHEM = 1 and

MH2O2 = 0 or MH2O2 = 1 and all hourly H2O2 data missing)

(BCKH2O2) in ppb Default: 12*1.

! BCKH2O2 = 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00,

1.00, 1.00 !

--- Data for SECONDARY ORGANIC AEROSOL (SOA) Option

(used only if MCHEM = 4)

The SOA module uses monthly values of:

Fine particulate concentration in ug/m^3 (BCKPMF)

Organic fraction of fine particulate (OFRAC)

VOC / NOX ratio (after reaction) (VCNX)

to characterize the air mass when computing

the formation of SOA from VOC emissions.

Typical values for several distinct air mass types are:

17

Appendix H.6 - 1417
SCPWIN12KM02.inp 6/27/2007

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

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Clean Continental

BCKPMF 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.

OFRAC .15 .15 .20 .20 .20 .20 .20 .20 .20 .20 .20 .15

VCNX 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50.

Clean Marine (surface)

BCKPMF .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5

OFRAC .25 .25 .30 .30 .30 .30 .30 .30 .30 .30 .30 .25

VCNX 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50.

Urban - low biogenic (controls present)

BCKPMF 30. 30. 30. 30. 30. 30. 30. 30. 30. 30. 30. 30.

OFRAC .20 .20 .25 .25 .25 .25 .25 .25 .20 .20 .20 .20

VCNX 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.

Urban - high biogenic (controls present)

BCKPMF 60. 60. 60. 60. 60. 60. 60. 60. 60. 60. 60. 60.

OFRAC .25 .25 .30 .30 .30 .55 .55 .55 .35 .35 .35 .25

VCNX 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15.

Regional Plume

BCKPMF 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20.

OFRAC .20 .20 .25 .35 .25 .40 .40 .40 .30 .30 .30 .20

VCNX 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15.

Urban - no controls present

BCKPMF 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100.

OFRAC .30 .30 .35 .35 .35 .55 .55 .55 .35 .35 .35 .30

VCNX 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.

Default: Clean Continental

! BCKPMF = 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00,

1.00, 1.00 !

! OFRAC = 0.15, 0.15, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20,

0.20, 0.15 !

! VCNX = 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00,

50.00, 50.00, 50.00, 50.00 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 12 -- Misc. Dispersion and Computational Parameters ---------------

Horizontal size of puff (m) beyond which

time-dependent dispersion equations (Heffter)

are used to determine sigma-y and

sigma-z (SYTDEP) Default: 550. ! SYTDEP =

5.5E02 !

Switch for using Heffter equation for sigma z

as above (0 = Not use Heffter; 1 = use Heffter

(MHFTSZ) Default: 0 ! MHFTSZ = 0

!

Stability class used to determine plume

growth rates for puffs above the boundary

layer (JSUP) Default: 5 ! JSUP = 5

18

Appendix H.6 - 1418
SCPWIN12KM02.inp 6/27/2007

!

Vertical dispersion constant for stable

conditions (k1 in Eqn. 2.7-3) (CONK1) Default: 0.01 ! CONK1 = .01

!

Vertical dispersion constant for neutral/

unstable conditions (k2 in Eqn. 2.7-4)

(CONK2) Default: 0.1 ! CONK2 = .1 !

Factor for determining Transition-point from

Schulman-Scire to Huber-Snyder Building Downwash

scheme (SS used for Hs < Hb + TBD * HL)

(TBD) Default: 0.5 ! TBD = .5 !

TBD < 0 ==> always use Huber-Snyder

TBD = 1.5 ==> always use Schulman-Scire

TBD = 0.5 ==> ISC Transition-point

Range of land use categories for which

urban dispersion is assumed

(IURB1, IURB2) Default: 10 ! IURB1 = 10

!

19 ! IURB2 = 19

!

Site characterization parameters for single-point Met data files ---------

(needed for METFM = 2,3,4,5)

Land use category for modeling domain

(ILANDUIN) Default: 20 ! ILANDUIN =

20 !

Roughness length (m) for modeling domain

(Z0IN) Default: 0.25 ! Z0IN = .25 !

Leaf area index for modeling domain

(XLAIIN) Default: 3.0 ! XLAIIN = 3.0

!

Elevation above sea level (m)

(ELEVIN) Default: 0.0 ! ELEVIN = .0

!

Latitude (degrees) for met location

(XLATIN) Default: -999. ! XLATIN = -999.0 !

Longitude (degrees) for met location

(XLONIN) Default: -999. ! XLONIN =

-999.0 !

Specialized information for interpreting single-point Met data files

-----

Anemometer height (m) (Used only if METFM = 2,3)

(ANEMHT) Default: 10. ! ANEMHT =

10.0 !

Form of lateral turbulance data in PROFILE.DAT file

(Used only if METFM = 4,5 or MTURBVW = 1 or 3)

(ISIGMAV) Default: 1 ! ISIGMAV = 1

!

0 = read sigma-theta

1 = read sigma-v

Choice of mixing heights (Used only if METFM = 4)

19

Appendix H.6 - 1419
SCPWIN12KM02.inp 6/27/2007

(IMIXCTDM) Default: 0 ! IMIXCTDM =

0 !

0 = read PREDICTED mixing heights

1 = read OBSERVED mixing heights

Maximum length of a slug (met. grid units)

(XMXLEN) Default: 1.0 ! XMXLEN = 1.0

!

Maximum travel distance of a puff/slug (in

grid units) during one sampling step

(XSAMLEN) Default: 1.0 ! XSAMLEN = 1.0 !

Maximum Number of slugs/puffs release from

one source during one time step

(MXNEW) Default: 99 ! MXNEW = 99

!

Maximum Number of sampling steps for

one puff/slug during one time step

(MXSAM) Default: 99 ! MXSAM = 99

!

Number of iterations used when computing

the transport wind for a sampling step

that includes gradual rise (for CALMET

and PROFILE winds)

(NCOUNT) Default: 2 ! NCOUNT = 2

!

Minimum sigma y for a new puff/slug (m)

(SYMIN) Default: 1.0 ! SYMIN = 1.0

!

Minimum sigma z for a new puff/slug (m)

(SZMIN) Default: 1.0 ! SZMIN = 1.0

!

Default minimum turbulence velocities sigma-v and sigma-w

for each stability class over land and over water (m/s)

(SVMIN(12) and SWMIN(12))

---------- LAND ---------- --------- WATER

---------- Stab Class : A B C D E F A B C D E

F

--- --- --- --- --- --- --- --- --- --- --- ---

Default SVMIN : .50, .50, .50, .50, .50, .50, .37, .37, .37, .37,

.37, .37

Default SWMIN : .20, .12, .08, .06, .03, .016, .20, .12, .08, .06,

.03, .016

! SVMIN = 0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 0.370, 0.370,

0.370, 0.370, 0.370, 0.370!

! SWMIN = 0.200, 0.120, 0.080, 0.060, 0.030, 0.016, 0.200, 0.120,

0.080, 0.060, 0.030, 0.016!

Divergence criterion for dw/dz across puff

used to initiate adjustment for horizontal

convergence (1/s)

Partial adjustment starts at CDIV(1), and

full adjustment is reached at CDIV(2)

(CDIV(2)) Default: 0.0,0.0 ! CDIV = .0,

.0 !

20

Appendix H.6 - 1420
SCPWIN12KM02.inp 6/27/2007

Minimum wind speed (m/s) allowed for

non-calm conditions. Also used as minimum

speed returned when using power-law

extrapolation toward surface

(WSCALM) Default: 0.5 ! WSCALM = .5

!

Maximum mixing height (m)

(XMAXZI) Default: 3000. ! XMAXZI =

3000.0 !

Minimum mixing height (m)

(XMINZI) Default: 50. ! XMINZI =

50.0 !

Default wind speed classes --

5 upper bounds (m/s) are entered;

the 6th class has no upper limit

(WSCAT(5)) Default : ISC RURAL : 1.54, 3.09, 5.14, 8.23, 10.8

(10.8+)

Wind Speed Class : 1 2 3 4 5 --- --- --- --- ---

! WSCAT = 1.54, 3.09, 5.14, 8.23, 10.80

!

Default wind speed profile power-law

exponents for stabilities 1-6

(PLX0(6)) Default : ISC RURAL values

ISC RURAL : .07, .07, .10, .15, .35, .55

ISC URBAN : .15, .15, .20, .25, .30, .30

Stability Class : A B C D E

F --- --- --- --- --- ---

! PLX0 = 0.07, 0.07, 0.10, 0.15, 0.35,

0.55 !

Default potential temperature gradient

for stable classes E, F (degK/m)

(PTG0(2)) Default: 0.020, 0.035

! PTG0 = 0.020, 0.035 !

Default plume path coefficients for

each stability class (used when option

for partial plume height terrain adjustment

is selected -- MCTADJ=3)

(PPC(6)) Stability Class : A B C D E

F

Default PPC : .50, .50, .50, .50, .35,

.35

--- --- --- --- ---

--- ! PPC = 0.50, 0.50, 0.50, 0.50, 0.35,

0.35 !

Slug-to-puff transition criterion factor

equal to sigma-y/length of slug

(SL2PF) Default: 10. ! SL2PF = 5.0 !

Puff-splitting control variables ------------------------

VERTICAL SPLIT --------------

21

Appendix H.6 - 1421
SCPWIN12KM02.inp 6/27/2007

Number of puffs that result every time a puff

is split - nsplit=2 means that 1 puff splits

into 2

(NSPLIT) Default: 3 ! NSPLIT = 3

!

Time(s) of a day when split puffs are eligible to

be split once again; this is typically set once

per day, around sunset before nocturnal shear develops.

24 values: 0 is midnight (00:00) and 23 is 11 PM (23:00)

0=do not re-split 1=eligible for re-split

(IRESPLIT(24)) Default: Hour 17 = 1

! IRESPLIT = 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 !

Split is allowed only if last hour's mixing

height (m) exceeds a minimum value

(ZISPLIT) Default: 100. ! ZISPLIT = 100.0 !

Split is allowed only if ratio of last hour's

mixing ht to the maximum mixing ht experienced

by the puff is less than a maximum value (this

postpones a split until a nocturnal layer develops)

(ROLDMAX) Default: 0.25 ! ROLDMAX =

0.25 !

HORIZONTAL SPLIT ----------------

Number of puffs that result every time a puff

is split - nsplith=5 means that 1 puff splits

into 5

(NSPLITH) Default: 5 ! NSPLITH = 5

!

Minimum sigma-y (Grid Cells Units) of puff

before it may be split

(SYSPLITH) Default: 1.0 ! SYSPLITH =

1.0 !

Minimum puff elongation rate (SYSPLITH/hr) due to

wind shear, before it may be split

(SHSPLITH) Default: 2. ! SHSPLITH =

2.0 !

Minimum concentration (g/m^3) of each

species in puff before it may be split

Enter array of NSPEC values; if a single value is

entered, it will be used for ALL species

(CNSPLITH) Default: 1.0E-07 ! CNSPLITH = 1.0E-07 !

Integration control variables ------------------------

Fractional convergence criterion for numerical SLUG

sampling integration

(EPSSLUG) Default: 1.0e-04 ! EPSSLUG =

1.0E-04 !

Fractional convergence criterion for numerical AREA

source integration

(EPSAREA) Default: 1.0e-06 ! EPSAREA =

1.0E-06 !

Trajectory step-length (m) used for numerical rise

integration

22

Appendix H.6 - 1422
SCPWIN12KM02.inp 6/27/2007

(DSRISE) Default: 1.0 ! DSRISE = 1.0

!

Boundary Condition (BC) Puff control variables ------------------------

Minimum height (m) to which BC puffs are mixed as they are emitted

(MBCON=2 ONLY). Actual height is reset to the current mixing height

at the release point if greater than this minimum.

(HTMINBC) Default: 500. ! HTMINBC =

500.0 !

Search radius (km) about a receptor for sampling nearest BC puff.

BC puffs are typically emitted with a spacing of one grid cell

length, so the search radius should be greater than DGRIDKM.

(RSAMPBC) Default: 10. ! RSAMPBC = 15.0 !

Near-Surface depletion adjustment to concentration profile used when

sampling BC puffs?

(MDEPBC) Default: 1 ! MDEPBC = 0

!

0 = Concentration is NOT adjusted for depletion

1 = Adjust Concentration for depletion

!END!

------------------------------------------------------------------------------

-

INPUT GROUPS: 13a, 13b, 13c, 13d -- Point source parameters

--------------------------------

---------------

Subgroup (13a) ---------------

Number of point sources with

parameters provided below (NPT1) No default ! NPT1 = 2 !

Units used for point source

emissions below (IPTU) Default: 1 ! IPTU = 1 !

1 = g/s

2 = kg/hr

3 = lb/hr

4 = tons/yr

5 = Odour Unit * m**3/s (vol. flux of odour compound)

6 = Odour Unit * m**3/min

7 = metric tons/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (13d) (NSPT1) Default: 0 ! NSPT1 = 0 !

Number of point sources with

variable emission parameters

provided in external file (NPT2) No default ! NPT2 = 0 !

(If NPT2 > 0, these point

source emissions are read from

the file: PTEMARB.DAT)

!END!

---------------

23

Appendix H.6 - 1423
SCPWIN12KM02.inp 6/27/2007

Subgroup (13b)

---------------

a

POINT SOURCE: CONSTANT DATA -----------------------------

b

c

Source X Y Stack Base Stack Exit Exit

Bldg. Emission

No. Coordinate Coordinate Height Elevation Diameter Vel. Temp.

Dwash Rates

(km) (km) (m) (m) (m) (m/s) (deg. K)

------ ---------- ---------- ------ ------ -------- ----- -------- ----- -------- 1 ! SRCNAM = WS01 !

1 ! X = 1627.285, -580.090, 123.14, 5.5, 6.10, 15.85, 324.8, .0,

0.00E+00, 2.15E+00, 0.00E+00, 0.00E+00, 0.00E+00,

2.68E-01, 2.68E-01, 0.00E+00, 0.00E+00,

1.71E+00, 2.11E+00, 1.21E+00, 3.02E-01, 2.01E-01, 1.10E+00,

1.03E-01 !

1 ! ZPLTFM = .0 !

1 ! FMFAC = 1.0 ! !END!

2 ! SRCNAM = WS02 !

2 ! X = 1627.285, -580.090, 123.14, 5.5, 6.10, 15.85, 324.8, .0,

0.00E+00, 2.15E+00, 0.00E+00, 0.00E+00, 0.00E+00,

2.68E-01, 2.68E-01, 0.00E+00, 0.00E+00,

1.71E+00, 2.11E+00, 1.21E+00, 3.02E-01, 2.01E-01, 1.10E+00,

1.04E-01 !

2 ! ZPLTFM = .0 !

2 ! FMFAC = 1.0 ! !END!

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

SRCNAM is a 12-character name for a source

(No default)

X is an array holding the source data listed by the column headings

(No default)

SIGYZI is an array holding the initial sigma-y and sigma-z (m)

(Default: 0.,0.)

ZPLTFM is the platform height (m) for sources influenced by an isolated

structure that has a significant open area between the surface

and the bulk of the structure, such as an offshore oil platform.

The Base Elevation is that of the surface (ground or ocean),

and the Stack Height is the release height above the Base (not

above the platform). Building heights entered in Subgroup 13c

must be those of the buildings on the platform, measured from

the platform deck. ZPLTFM is used only with MBDW=1 (ISC

downwash method) for sources with building downwash.

(Default: 0.0)

FMFAC is a vertical momentum flux factor (0. or 1.0) used to represent

the effect of rain-caps or other physical configurations that

reduce momentum rise associated with the actual exit velocity.

(Default: 1.0 -- full momentum used)

b

0. = No building downwash modeled, 1. = downwash modeled

NOTE: must be entered as a REAL number (i.e., with decimal point)

c

24

Appendix H.6 - 1424
SCPWIN12KM02.inp 6/27/2007

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by IPTU

(e.g. 1 for g/s).

---------------

Subgroup (13c)

---------------

BUILDING DIMENSION DATA FOR SOURCES SUBJECT TO DOWNWASH

-------------------------------------------------------

Source a

No. Effective building height, width, length and X/Y offset (in meters)

every 10 degrees. LENGTH, XBADJ, and YBADJ are only needed for

MBDW=2 (PRIME downwash option) ------

--------------------------------------------------------------------

--------

a

Building height, width, length, and X/Y offset from the source are

treated

as a separate input subgroup for each source and therefore must end with

an input group terminator. The X/Y offset is the position, relative to

the

stack, of the center of the upwind face of the projected building, with

the

x-axis pointing along the flow direction.

---------------

Subgroup (13d) ---------------

a

POINT SOURCE: VARIABLE EMISSIONS DATA ---------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 13b. Factors entered multiply the rates in 13b.

Skip sources here that have constant emissions. For more elaborate

variation in source parameters, use PTEMARB.DAT and NPT2 > 0.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

-------- a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

25

Appendix H.6 - 1425
SCPWIN12KM02.inp 6/27/2007

------------------------------------------------------------------------------

-

INPUT GROUPS: 14a, 14b, 14c, 14d -- Area source parameters --------------------------------

---------------

Subgroup (14a)

---------------

Number of polygon area sources with

parameters specified below (NAR1) No default ! NAR1 = 0 !

Units used for area source

emissions below (IARU) Default: 1 ! IARU = 1 !

1 = g/m**2/s

2 = kg/m**2/hr

3 = lb/m**2/hr

4 = tons/m**2/yr

5 = Odour Unit * m/s (vol. flux/m**2 of odour compound)

6 = Odour Unit * m/min

7 = metric tons/m**2/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (14d) (NSAR1) Default: 0 ! NSAR1 = 0 !

Number of buoyant polygon area sources

with variable location and emission

parameters (NAR2) No default ! NAR2 = 0 !

(If NAR2 > 0, ALL parameter data for

these sources are read from the file: BAEMARB.DAT)

!END!

---------------

Subgroup (14b)

--------------- a

AREA SOURCE: CONSTANT DATA

---------------------------- b

Source Effect. Base Initial Emission

No. Height Elevation Sigma z Rates

(m) (m) (m)

------- ------ ------ -------- ---------

-------- a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by IARU

(e.g. 1 for g/m**2/s).

---------------

Subgroup (14c) ---------------

COORDINATES (km) FOR EACH VERTEX(4) OF EACH POLYGON --------------------------------------------------------

Source a

26

Appendix H.6 - 1426
SCPWIN12KM02.inp 6/27/2007

No. Ordered list of X followed by list of Y, grouped by source

------ ------------------------------------------------------------

-------- a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

---------------

Subgroup (14d) ---------------

a

AREA SOURCE: VARIABLE EMISSIONS DATA --------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 14b. Factors entered multiply the rates in 14b.

Skip sources here that have constant emissions. For more elaborate

variation in source parameters, use BAEMARB.DAT and NAR2 > 0.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

-------- a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 15a, 15b, 15c -- Line source parameters

---------------------------

---------------

Subgroup (15a)

---------------

Number of buoyant line sources

with variable location and emission

parameters (NLN2) No default ! NLN2 = 0

!

(If NLN2 > 0, ALL parameter data for

these sources are read from the file: LNEMARB.DAT)

Number of buoyant line sources (NLINES) No default ! NLINES = 0

!

27

Appendix H.6 - 1427
SCPWIN12KM02.inp 6/27/2007

Units used for line source

emissions below (ILNU) Default: 1 ! ILNU = 1

!

1 = g/s

2 = kg/hr

3 = lb/hr

4 = tons/yr

5 = Odour Unit * m**3/s (vol. flux of odour compound)

6 = Odour Unit * m**3/min

7 = metric tons/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (15c) (NSLN1) Default: 0 ! NSLN1 = 0 !

Maximum number of segments used to model

each line (MXNSEG) Default: 7 ! MXNSEG = 7

!

The following variables are required only if NLINES > 0. They are

used in the buoyant line source plume rise calculations.

Number of distances at which Default: 6 ! NLRISE = 6

!

transitional rise is computed

Average building length (XL) No default ! XL = .0 !

(in meters)

Average building height (HBL) No default ! HBL = .0 !

(in meters)

Average building width (WBL) No default ! WBL = .0 !

(in meters)

Average line source width (WML) No default ! WML = .0 !

(in meters)

Average separation between buildings (DXL) No default ! DXL = .0 !

(in meters)

Average buoyancy parameter (FPRIMEL) No default ! FPRIMEL = .0 !

(in m**4/s**3)

!END!

---------------

Subgroup (15b)

---------------

BUOYANT LINE SOURCE: CONSTANT DATA

----------------------------------

a

Source Beg. X Beg. Y End. X End. Y Release Base

Emission

No. Coordinate Coordinate Coordinate Coordinate Height Elevation

Rates

(km) (km) (km) (km) (m) (m) ------ ---------- ---------- --------- ---------- ------- --------- ---------

--------

a

28

Appendix H.6 - 1428
SCPWIN12KM02.inp 6/27/2007

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by ILNTU

(e.g. 1 for g/s).

---------------

Subgroup (15c)

--------------- a

BUOYANT LINE SOURCE: VARIABLE EMISSIONS DATA ----------------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 15b. Factors entered multiply the rates in 15b.

Skip sources here that have constant emissions.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

--------

a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 16a, 16b, 16c -- Volume source parameters

---------------------------

---------------

Subgroup (16a)

---------------

Number of volume sources with

parameters provided in 16b,c (NVL1) No default ! NVL1 = 0 !

Units used for volume source

emissions below in 16b (IVLU) Default: 1 ! IVLU = 1 !

1 = g/s

2 = kg/hr

3 = lb/hr

4 = tons/yr

5 = Odour Unit * m**3/s (vol. flux of odour compound)

6 = Odour Unit * m**3/min

7 = metric tons/yr

29

Appendix H.6 - 1429
SCPWIN12KM02.inp 6/27/2007

Number of source-species

combinations with variable

emissions scaling factors

provided below in (16c) (NSVL1) Default: 0 ! NSVL1 = 0 !

Number of volume sources with

variable location and emission

parameters (NVL2) No default ! NVL2 = 0 !

(If NVL2 > 0, ALL parameter data for

these sources are read from the VOLEMARB.DAT file(s) )

!END!

---------------

Subgroup (16b) ---------------

a

VOLUME SOURCE: CONSTANT DATA

------------------------------

b

X Y Effect. Base Initial Initial

Emission

Coordinate Coordinate Height Elevation Sigma y Sigma z Rates

(km) (km) (m) (m) (m) (m)

---------- ---------- ------ ------ -------- -------- --------

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by IVLU

(e.g. 1 for g/s).

---------------

Subgroup (16c)

--------------- a

VOLUME SOURCE: VARIABLE EMISSIONS DATA

----------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 16b. Factors entered multiply the rates in 16b.

Skip sources here that have constant emissions. For more elaborate

variation in source parameters, use VOLEMARB.DAT and NVL2 > 0.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

30

Appendix H.6 - 1430
SCPWIN12KM02.inp 6/27/2007

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

-------- a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 17a & 17b -- Non-gridded (discrete) receptor information -----------------------

---------------

Subgroup (17a) ---------------

Number of non-gridded receptors (NREC) No default ! NREC = 246 !

!END!

---------------

Subgroup (17b)

--------------- a

NON-GRIDDED (DISCRETE) RECEPTOR DATA

------------------------------------

X Y Ground Height b

Receptor Coordinate Coordinate Elevation Above Ground

No. (km) (km) (m) (m) -------- ---------- ---------- --------- ------------ 1 ! X = 1609.918, -633.619, 0.0, 0.00, ! !END!

2 ! X = 1610.682, -633.471, 1.0, 0.00, ! !END!

3 ! X = 1610.506, -632.561, 1.0, 0.00, ! !END!

4 ! X = 1611.270, -632.414, 1.0, 0.00, ! !END!

5 ! X = 1611.094, -631.504, 1.0, 0.00, ! !END!

6 ! X = 1611.858, -631.356, 1.0, 0.00, ! !END!

7 ! X = 1610.154, -630.742, 1.0, 0.00, ! !END!

8 ! X = 1610.918, -630.594, 1.0, 0.00, ! !END!

9 ! X = 1611.682, -630.446, 1.0, 0.00, ! !END!

10 ! X = 1612.446, -630.298, 1.0, 0.00, ! !END!

11 ! X = 1610.742, -629.684, 1.0, 0.00, ! !END!

12 ! X = 1611.506, -629.536, 1.0, 0.00, ! !END!

13 ! X = 1612.269, -629.389, 1.0, 0.00, ! !END!

14 ! X = 1610.566, -628.774, 0.0, 0.00, ! !END!

15 ! X = 1611.330, -628.627, 1.0, 0.00, ! !END!

16 ! X = 1612.093, -628.479, 1.0, 0.00, ! !END!

17 ! X = 1612.857, -628.331, 1.0, 0.00, ! !END!

18 ! X = 1610.390, -627.865, 1.0, 0.00, ! !END!

19 ! X = 1611.154, -627.717, 1.0, 0.00, ! !END!

20 ! X = 1610.214, -626.955, 1.0, 0.00, ! !END!

21 ! X = 1610.978, -626.807, 1.0, 0.00, ! !END!

22 ! X = 1611.741, -626.660, 1.0, 0.00, ! !END!

23 ! X = 1610.802, -625.898, 1.0, 0.00, ! !END!

24 ! X = 1611.565, -625.750, 1.0, 0.00, ! !END!

25 ! X = 1610.626, -624.988, 1.0, 0.00, ! !END!

26 ! X = 1611.389, -624.840, 2.0, 0.00, ! !END!

27 ! X = 1612.152, -624.692, 1.0, 0.00, ! !END!

28 ! X = 1615.968, -623.952, 0.0, 0.00, ! !END!

29 ! X = 1611.213, -623.931, 1.0, 0.00, ! !END!

30 ! X = 1611.976, -623.783, 1.0, 0.00, ! !END!

31 ! X = 1616.554, -622.895, 0.0, 0.00, ! !END!

31

Appendix H.6 - 1431
SCPWIN12KM02.inp 6/27/2007

32!X= 1611.037, -623.021, 1.0, 0.00,! !END!

33!X= 1611.800, -622.873, 1.0, 0.00,! !END!

34!X= 1612.386, -621.816, 1.0, 0.00,! !END!

35!X= 1613.149, -621.668, 1.0, 0.00,! !END!

36!X= 1617.727, -620.779, 0.0, 0.00,! !END!

37!X= 1612.973, -620.758, 1.0, 0.00,! !END!

38!X= 1613.736, -620.610, 1.0, 0.00,! !END!

39!X= 1621.949, -618.069, 0.0, 0.00,! !END!

40!X= 1622.711, -617.920, 1.0, 0.00,! !END!

41!X= 1623.474, -617.771, 1.0, 0.00,! !END!

42!X= 1614.146, -618.643, 1.0, 0.00,! !END!

43!X= 1623.297, -616.862, 1.0, 0.00,! !END!

44!X= 1624.059, -616.713, 1.0, 0.00,! !END!

45!X= 1624.821, -616.564, 1.0, 0.00,! !END!

46!X= 1625.584, -616.415, 1.0, 0.00,! !END!

47!X= 1633.208, -614.921, 1.0, 0.00,! !END!

48!X= 1633.970, -614.771, 0.0, 0.00,! !END!

49!X= 1614.732, -617.586, 1.0, 0.00,! !END!

50!X= 1621.594, -616.250, 0.0, 0.00,! !END!

51!X= 1622.357, -616.102, 1.0, 0.00,! !END!

52!X= 1623.119, -615.953, 1.0, 0.00,! !END!

53!X= 1623.881, -615.804, 1.0, 0.00,! !END!

54!X= 1624.644, -615.655, 1.0, 0.00,! !END!

55!X= 1625.406, -615.506, 1.0, 0.00,! !END!

56!X= 1626.169, -615.357, 1.0, 0.00,! !END!

57!X= 1626.931, -615.208, 1.0, 0.00,! !END!

58!X= 1627.693, -615.059, 1.0, 0.00,! !END!

59!X= 1628.456, -614.909, 2.0, 0.00,! !END!

60!X= 1629.218, -614.760, 2.0, 0.00,! !END!

61!X= 1629.980, -614.611, 1.0, 0.00,! !END!

62!X= 1630.742, -614.461, 2.0, 0.00,! !END!

63!X= 1631.505, -614.311, 1.0, 0.00,! !END!

64!X= 1633.791, -613.862, 0.0, 0.00,! !END!

65!X= 1615.318, -616.528, 1.0, 0.00,! !END!

66!X= 1616.080, -616.380, 1.0, 0.00,! !END!

67!X= 1616.843, -616.232, 1.0, 0.00,! !END!

68!X= 1620.654, -615.490, 0.0, 0.00,! !END!

69!X= 1621.417, -615.341, 1.0, 0.00,! !END!

70!X= 1622.179, -615.192, 1.0, 0.00,! !END!

71!X= 1622.942, -615.044, 1.0, 0.00,! !END!

72!X= 1623.704, -614.895, 1.0, 0.00,! !END!

73!X= 1624.466, -614.746, 1.0, 0.00,! !END!

74!X= 1625.228, -614.597, 1.0, 0.00,! !END!

75!X= 1628.277, -614.000, 1.0, 0.00,! !END!

76!X= 1629.040, -613.851, 1.0, 0.00,! !END!

77!X= 1632.088, -613.253, 1.0, 0.00,! !END!

78!X= 1634.375, -612.803, 0.0, 0.00,! !END!

79!X= 1619.715, -614.729, 0.0, 0.00,! !END!

80!X= 1620.477, -614.580, 1.0, 0.00,! !END!

81!X= 1621.240, -614.432, 1.0, 0.00,! !END!

82!X= 1622.002, -614.283, 1.0, 0.00,! !END!

83!X= 1622.764, -614.134, 1.0, 0.00,! !END!

84!X= 1623.526, -613.986, 1.0, 0.00,! !END!

85!X= 1624.288, -613.837, 1.0, 0.00,! !END!

86!X= 1625.051, -613.688, 1.0, 0.00,! !END!

87!X= 1625.813, -613.539, 1.0, 0.00,! !END!

88!X= 1626.575, -613.389, 1.0, 0.00,! !END!

89!X= 1627.337, -613.240, 1.0, 0.00,! !END!

90!X= 1629.624, -612.792, 1.0, 0.00,! !END!

91!X= 1630.386, -612.643, 1.0, 0.00,! !END!

92!X= 1631.148, -612.493, 1.0, 0.00,! !END!

93!X= 1631.910, -612.344, 1.0, 0.00,! !END!

94!X= 1634.196, -611.894, 0.0, 0.00,! !END!

95!X= 1618.776, -613.968, 1.0, 0.00,! !END!

96!X= 1619.538, -613.820, 1.0, 0.00,! !END!

97!X= 1620.300, -613.671, 1.0, 0.00,! !END!

98!X= 1621.062, -613.523, 1.0, 0.00,! !END!

32

Appendix H.6 - 1432
SCPWIN12KM02.inp 6/27/2007

99 ! X = 1621.824, -613.374, 1.0, 0.00, ! !END!

100 ! X = 1622.587, -613.225, 1.0, 0.00, ! !END!

101 ! X = 1623.349, -613.076, 1.0, 0.00, ! !END!

102 ! X = 1624.111, -612.927, 1.0, 0.00, ! !END!

103 ! X = 1624.873, -612.778, 1.0, 0.00, ! !END!

104 ! X = 1625.635, -612.629, 1.0, 0.00, ! !END!

105 ! X = 1626.397, -612.480, 1.0, 0.00, ! !END!

106 ! X = 1627.159, -612.331, 1.0, 0.00, ! !END!

107 ! X = 1627.921, -612.182, 1.0, 0.00, ! !END!

108 ! X = 1628.683, -612.033, 1.0, 0.00, ! !END!

109 ! X = 1629.445, -611.883, 1.0, 0.00, ! !END!

110 ! X = 1630.207, -611.734, 1.0, 0.00, ! !END!

111 ! X = 1630.969, -611.584, 1.0, 0.00, ! !END!

112 ! X = 1634.017, -610.985, 0.0, 0.00, ! !END!

113 ! X = 1620.123, -612.762, 1.0, 0.00, ! !END!

114 ! X = 1620.885, -612.613, 1.0, 0.00, ! !END!

115 ! X = 1621.647, -612.465, 1.0, 0.00, ! !END!

116 ! X = 1622.409, -612.316, 1.0, 0.00, ! !END!

117 ! X = 1623.171, -612.167, 1.0, 0.00, ! !END!

118 ! X = 1623.933, -612.018, 1.0, 0.00, ! !END!

119 ! X = 1624.695, -611.869, 1.0, 0.00, ! !END!

120 ! X = 1625.457, -611.720, 1.0, 0.00, ! !END!

121 ! X = 1626.219, -611.571, 1.0, 0.00, ! !END!

122 ! X = 1626.981, -611.422, 1.0, 0.00, ! !END!

123 ! X = 1627.743, -611.273, 1.0, 0.00, ! !END!

124 ! X = 1628.505, -611.123, 1.0, 0.00, ! !END!

125 ! X = 1633.077, -610.226, 0.0, 0.00, ! !END!

126 ! X = 1633.839, -610.076, 1.0, 0.00, ! !END!

127 ! X = 1634.600, -609.927, 1.0, 0.00, ! !END!

128 ! X = 1621.470, -611.555, 1.0, 0.00, ! !END!

129 ! X = 1622.232, -611.407, 1.0, 0.00, ! !END!

130 ! X = 1622.993, -611.258, 1.0, 0.00, ! !END!

131 ! X = 1623.755, -611.109, 1.0, 0.00, ! !END!

132 ! X = 1624.517, -610.960, 1.0, 0.00, ! !END!

133 ! X = 1625.279, -610.811, 1.0, 0.00, ! !END!

134 ! X = 1626.041, -610.662, 1.0, 0.00, ! !END!

135 ! X = 1626.803, -610.513, 1.0, 0.00, ! !END!

136 ! X = 1627.565, -610.364, 1.0, 0.00, ! !END!

137 ! X = 1628.327, -610.214, 1.0, 0.00, ! !END!

138 ! X = 1629.089, -610.065, 1.0, 0.00, ! !END!

139 ! X = 1629.851, -609.916, 1.0, 0.00, ! !END!

140 ! X = 1630.613, -609.766, 1.0, 0.00, ! !END!

141 ! X = 1634.422, -609.018, 0.0, 0.00, ! !END!

142 ! X = 1624.340, -610.051, 1.0, 0.00, ! !END!

143 ! X = 1625.102, -609.902, 1.0, 0.00, ! !END!

144 ! X = 1625.863, -609.753, 1.0, 0.00, ! !END!

145 ! X = 1626.625, -609.604, 1.0, 0.00, ! !END!

146 ! X = 1627.387, -609.455, 1.0, 0.00, ! !END!

147 ! X = 1628.149, -609.305, 1.0, 0.00, ! !END!

148 ! X = 1628.911, -609.156, 1.0, 0.00, ! !END!

149 ! X = 1629.672, -609.007, 1.0, 0.00, ! !END!

150 ! X = 1630.434, -608.857, 1.0, 0.00, ! !END!

151 ! X = 1634.243, -608.109, 0.0, 0.00, ! !END!

152 ! X = 1628.732, -608.247, 1.0, 0.00, ! !END!

153 ! X = 1629.494, -608.098, 1.0, 0.00, ! !END!

154 ! X = 1630.256, -607.948, 1.0, 0.00, ! !END!

155 ! X = 1631.017, -607.799, 1.0, 0.00, ! !END!

156 ! X = 1629.316, -607.189, 1.0, 0.00, ! !END!

157 ! X = 1630.077, -607.039, 1.0, 0.00, ! !END!

158 ! X = 1630.839, -606.890, 1.0, 0.00, ! !END!

159 ! X = 1631.600, -606.740, 1.0, 0.00, ! !END!

160 ! X = 1629.899, -606.130, 1.0, 0.00, ! !END!

161 ! X = 1630.660, -605.981, 1.0, 0.00, ! !END!

162 ! X = 1631.422, -605.831, 1.0, 0.00, ! !END!

163 ! X = 1629.721, -605.221, 1.0, 0.00, ! !END!

164 ! X = 1630.482, -605.072, 1.0, 0.00, ! !END! * End CR

165 ! X = 1488.690, -835.192, 1.0, 0.00, ! !END! * Begin WI

33

Appendix H.6 - 1433
SCPWIN12KM02.inp 6/27/2007

166 ! X = 1489.472, -835.055, 1.0, 0.00, ! !END!

167 ! X = 1488.530, -834.276, 1.0, 0.00, ! !END!

168 ! X = 1487.428, -832.581, 3.0, 0.00, ! !END!

169 ! X = 1488.210, -832.444, 2.0, 0.00, ! !END!

170 ! X = 1488.993, -832.308, 1.0, 0.00, ! !END!

171 ! X = 1489.775, -832.171, 1.0, 0.00, ! !END!

172 ! X = 1486.486, -831.801, 1.0, 0.00, ! !END!

173 ! X = 1487.268, -831.665, 1.0, 0.00, ! !END!

174 ! X = 1488.051, -831.529, 1.0, 0.00, ! !END!

175 ! X = 1488.833, -831.392, 1.0, 0.00, ! !END!

176 ! X = 1489.615, -831.256, 1.0, 0.00, ! !END!

177 ! X = 1486.327, -830.886, 1.0, 0.00, ! !END!

178 ! X = 1487.109, -830.749, 1.0, 0.00, ! !END!

179 ! X = 1487.891, -830.613, 1.0, 0.00, ! !END!

180 ! X = 1488.673, -830.476, 1.0, 0.00, ! !END!

181 ! X = 1489.455, -830.340, 1.0, 0.00, ! !END!

182 ! X = 1485.385, -830.106, 1.0, 0.00, ! !END!

183 ! X = 1486.167, -829.970, 1.0, 0.00, ! !END!

184 ! X = 1486.949, -829.834, 1.0, 0.00, ! !END!

185 ! X = 1487.731, -829.697, 1.0, 0.00, ! !END!

186 ! X = 1488.513, -829.561, 1.0, 0.00, ! !END!

187 ! X = 1489.295, -829.424, 1.0, 0.00, ! !END!

188 ! X = 1485.225, -829.190, 1.0, 0.00, ! !END!

189 ! X = 1486.007, -829.054, 1.0, 0.00, ! !END!

190 ! X = 1486.789, -828.918, 1.0, 0.00, ! !END!

191 ! X = 1487.571, -828.781, 1.0, 0.00, ! !END!

192 ! X = 1488.353, -828.645, 1.0, 0.00, ! !END!

193 ! X = 1489.135, -828.508, 1.0, 0.00, ! !END!

194 ! X = 1485.848, -828.138, 1.0, 0.00, ! !END! * End WI

195 ! X = 1857.33, -305.922, 0, 0.00, ! !END! * Begin SQ

196 ! X = 1858.064, -305.752, 1, 0.00, ! !END!

197 ! X = 1857.122, -305.023, 1, 0.00, ! !END!

198 ! X = 1857.856, -304.852, 1, 0.00, ! !END!

199 ! X = 1856.913, -304.123, 0, 0.00, ! !END!

200 ! X = 1848.632, -305.092, 0, 0.00, ! !END!

201 ! X = 1844.755, -305.04, 0, 0.00, ! !END!

202 ! X = 1856.496, -302.324, 0, 0.00, ! !END!

203 ! X = 1857.229, -302.154, 1, 0.00, ! !END!

204 ! X = 1857.963, -301.984, 1, 0.00, ! !END!

205 ! X = 1861.631, -301.131, 1, 0.00, ! !END!

206 ! X = 1862.364, -300.96, 1, 0.00, ! !END!

207 ! X = 1863.831, -300.619, 1, 0.00, ! !END!

208 ! X = 1864.565, -300.448, 1, 0.00, ! !END!

209 ! X = 1846.015, -303.801, 1, 0.00, ! !END!

210 ! X = 1850.418, -302.784, 1, 0.00, ! !END!

211 ! X = 1854.086, -301.935, 0, 0.00, ! !END!

212 ! X = 1854.82, -301.765, 0, 0.00, ! !END!

213 ! X = 1855.553, -301.595, 1, 0.00, ! !END!

214 ! X = 1856.287, -301.425, 1, 0.00, ! !END!

215 ! X = 1857.02, -301.255, 1, 0.00, ! !END!

216 ! X = 1857.754, -301.084, 1, 0.00, ! !END!

217 ! X = 1860.688, -300.402, 0, 0.00, ! !END!

218 ! X = 1861.421, -300.232, 1, 0.00, ! !END!

219 ! X = 1862.155, -300.061, 1, 0.00, ! !END!

220 ! X = 1862.888, -299.89, 1, 0.00, ! !END!

221 ! X = 1863.622, -299.719, 1, 0.00, ! !END!

222 ! X = 1845.074, -303.071, 0, 0.00, ! !END!

223 ! X = 1845.808, -302.902, 1, 0.00, ! !END!

224 ! X = 1847.275, -302.563, 1, 0.00, ! !END!

225 ! X = 1848.743, -302.224, 1, 0.00, ! !END!

226 ! X = 1856.078, -300.525, 1, 0.00, ! !END!

227 ! X = 1856.812, -300.355, 1, 0.00, ! !END!

228 ! X = 1857.545, -300.185, 1, 0.00, ! !END!

229 ! X = 1859.745, -299.674, 0, 0.00, ! !END!

230 ! X = 1860.479, -299.503, 1, 0.00, ! !END!

231 ! X = 1861.212, -299.332, 1, 0.00, ! !END!

232 ! X = 1843.399, -302.509, 0, 0.00, ! !END!

34

Appendix H.6 - 1434
SCPWIN12KM02.inp 6/27/2007

233 ! X = 1844.133, -302.34, 1, 0.00, ! !END!

234 ! X = 1844.867, -302.171, 1, 0.00, ! !END!

235 ! X = 1848.535, -301.324, 0, 0.00, ! !END!

236 ! X = 1849.268, -301.155, 1, 0.00, ! !END!

237 ! X = 1856.603, -299.456, 1, 0.00, ! !END!

238 ! X = 1857.336, -299.286, 1, 0.00, ! !END!

239 ! X = 1858.803, -298.945, 1, 0.00, ! !END!

240 ! X = 1859.536, -298.774, 1, 0.00, ! !END!

241 ! X = 1848.327, -300.425, 1, 0.00, ! !END!

242 ! X = 1856.394, -298.556, 1, 0.00, ! !END!

243 ! X = 1858.594, -298.045, 1, 0.00, ! !END!

244 ! X = 1859.327, -297.875, 1, 0.00, ! !END!

245 ! X = 1859.118, -296.976, 1, 0.00, ! !END!

246 ! X = 1859.851, -296.805, 1, 0.00, ! !END!

-------------

a

Data for each receptor are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

Receptor height above ground is optional. If no value is entered,

the receptor is placed on the ground.

35

Appendix H.6 - 1435
Santee Cooper – Grainger, Jefferies, Winyah A - 3 Trinity Consultants 

BART Applicability Modeling Analysis 

SAMPLE POSTUTIL INPUT FILE

SCPWIN12KMUT02.INP 

WINYAH 12-KM SCREENING ANALYSIS YEAR 2002 

Appendix H.6 - 1436
SCPWIN12KMUT02.inp 3/19/2007

SANTEE COOPER WINYAH, SC

BART APPLICABILITY ANALYSIS 2002

CAPE ROMAIN/WOLF ISLAND/SWAN QUARTER

---------------- Run title (3 lines) ------------------------------------------

POSTUTIL MODEL CONTROL FILE

---------------------------

------------------------------------------------------------------------------

-

INPUT GROUP: 0 -- Input and Output File Names

--------------

-------------

Subgroup (0a) -------------

Output Files

------------

File Default File Name ---- -----------------

List File POSTUTIL.LST ! UTLLST = T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KMUT02.LST !

Data File MODEL.DAT ! UTLDAT =

T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KMUT02.CON !

Input Files

-----------

A time-varying file of "background" concentrations can be included

when the ammonia-limiting method (ALM) for setting the HNO3/NO3

concentration partition is accomplished in 1 step. This option

is selected by setting MNITRATE=3 in Input Group 1.

Species required in the "background" concentration file are:

SO4, NO3, HNO3 and TNH3 (total NH3 = NH3gaseous + NH3particulate).

File Default File Name

---- ----------------- BCKG File BCKGALM.DAT * BCKGALM =BCKGALM.DAT *

A number of CALPUFF data files may be processed in this application.

The files may represent individual CALPUFF simulations that were made

for a specific set of species and/or sources. Specify the total number

of CALPUFF runs you wish to combine, and provide the filename for each

in subgroup 0b.

Number of CALPUFF data files (NFILES)

Default: 1 ! NFILES = 1 !

Meteorological data files are needed for the HNO3/NO3 partition option.

Three types of meteorological data files can be used:

METFM= 0 - CALMET.DAT

METFM= 1 - 1-D file with RH, Temp and Rhoair timeseries

METFM= 2 - 2-D files with either Rh, Temp or Rhoair in each

(3 2_D files are needed)

The default is to use CALMET.DAT files.

Default: 0 ! METFM = 0 !

1

Appendix H.6 - 1437
SCPWIN12KMUT02.inp 3/19/2007

Multiple meteorological data files may be used in sequence to span the

processing period. Specify the number of time-period files (NMET) that

you need to use, and provide a filename for each in subgroup 0b.

- NMET is 0 if no meteorological files are provided - NMET is 1 if METFM=1 (multiple file feature is not available) - NMET is 1 or more if METFM=0 or 2 (multiple CALMET files or 2DMET files)

Number of meteorological data file time-periods (NMET)

Default: 0 ! NMET = 0 !

All filenames will be converted to lower case if LCFILES = T

Otherwise, if LCFILES = F, filenames will be converted to UPPER CASE

Convert filenames to lower case? Default: T ! LCFILES = T !

T = lower case

F = UPPER CASE

!END!

-----------------------------------------------------------

NOTE: file/path names can be up to 70 characters in length -----------------------------------------------------------

-------------

Subgroup (0b)

-------------

NMET CALMET Data Files (METFM=0):

Input File Default File Name

---------- ----------------- 1 MET.DAT * UTLMET =CALMET.DAT *

NMET 1-D Data Files (METFM=1):

Input File Default File Name

---------- ----------------- 1 MET_1D.DAT * MET1D = MET_1D.DAT * *END*

NMET 2-D Data Files of Each Type (METFM=2):

Input File Default File Name ---------- -----------------

1 RHUMD.DAT * M2DRHU = RELHUM.DAT * *END*

1 TEMP.DAT * M2DTMP = TEMP.DAT * *END*

1 RHOAIR.DAT * M2DRHO = RHOAIR.DAT * *END*

NFILES CALPUFF Data Files:

Input File Default File Name ---------- -----------------

1 CALPUFF.DAT ! MODDAT =

T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KM02.con ! !END!

-------------

Note: provide NMET lines of the form * UTLMET = name * *END*

or * MET1D = name * *END*

or * M2DRHU = name * *END*

(and) * M2DTMP = name * *END*

2

Appendix H.6 - 1438
SCPWIN12KMUT02.inp 3/19/2007

(and) * M2DRHO = name * *END*

and NFILES lines of the form * MODDAT = name * *END*

where the * should be replaced with an exclamation point,

the special delimiter character.

------------------------------------------------------------------------------

--

INPUT GROUP: 1 -- General run control parameters --------------

Starting date: Year (ISYR) -- No default ! ISYR = 2002 !

Month (ISMO) -- No default ! ISMO = 1 !

Day (ISDY) -- No default ! ISDY = 1 !

Hour (ISHR) -- No default ! ISHR = 1 !

Number of periods to process

(NPER) -- No default ! NPER = 8760 !

Number of species to process from CALPUFF runs

(NSPECINP) -- No default ! NSPECINP = 16 !

Number of species to write to output file

(NSPECOUT) -- No default ! NSPECOUT = 9 !

Number of species to compute from those modeled

(must be no greater than NSPECOUT)

(NSPECCMP) -- No default ! NSPECCMP = 4 !

When multiple files are used, a species name may appear in more than

one file. Data for this species will be summed (appropriate if the

CALPUFF runs use different source groups). If this summing is not

appropriate, remove duplicate species from the file(s).

Stop run if duplicate species names

are found? (MDUPLCT) Default: 0 ! MDUPLCT = 0 !

0 = no (i.e., duplicate species are summed)

1 = yes (i.e., run is halted)

Data for each species in a CALPUFF data file may also be scaled as

they are read. This can be done to alter the emission rate of all

sources that were modeled in a particular CALPUFF application.

The scaling factor for each species is entered in Subgroup (2d), for

each file for which scaling is requested.

Number of CALPUFF data files that will be scaled

(must be no greater than NFILES)

(NSCALED) Default: 0 ! NSCALED = 0 !

Ammonia-Limiting Method Option to recompute the HNO3/NO3 concentration

partition prior to performing other actions is controlled by MNITRATE.

This option will NOT alter any deposition fluxes contained in the CALPUFF

file(s). Three partition selections are provided. The first two are

typically used in sequence (POSTUTIL is run more than once). The first

selection (MNITRATE=1) computes the partition for the TOTAL (all sources)

concentration fields (SO4, NO3, HNO3; NH3), and the second (MNITRATE=2)

uses this partition (from the previous application of POSTUTIL) to

compute the partition for individual source groups. The third selection

(MNITRATE=3) can be used instead in a single POSTUTIL application if a

file of background concentrations is provided (BCKGALM in Input Group 0).

Required information for MNITRATE=1 includes:

3

Appendix H.6 - 1439
SCPWIN12KMUT02.inp 3/19/2007

species NO3, HNO3, and SO4

NH3 concentration(s)

met. data file for RH and T

Required information for MNITRATE=2 includes:

species NO3 and HNO3 for a source group

species NO3ALL and HNO3ALL for all source groups, properly

partitioned

Required information for MNITRATE=3 includes:

species NO3, HNO3, and SO4 for a source group

species NO3, HNO3, SO4 and TNH3 from the background BCKGALM file

If TNH3 is not in the background BCKGALM file, monthly TNH3

concentrations are used (BCKTNH3)

TNH3= total NH3 = NH3gaseous+NH3particulate

Recompute the HNO3/NO3 partition for concentrations?

(MNITRATE) Default: 0 ! MNITRATE = 0 !

0 = no

1 = yes, for all sources combined

2 = yes, for a source group

3 = yes, ALM application in one step

SOURCE OF AMMONIA:

Ammonia may be available as a modeled species in the CALPUFF files,

and it may or may not be appropriate to use it for repartitioning NO3/HNO3

(in option MNITRATE=1 or MNITRATE=3). Its use is contolled by NH3TYP.

When NH3 is listed as a processed species in Subgroup (2a), as one of

the NSPECINP ASPECI entries, and the right option is chosen for NH3TYP,

the NH3 modeled values from the CALPUFF concentration files will be used

in the chemical equilibrium calculation.

NH3TYP also controls when monthly background ammonia values are used. Both

gaseous (NH3) and total (TNH3=NH3gaseous+NH3particulate) ammonia can be

provided

monthly as BCKNH3/BCKTNH3.

What is the input source of Ammonia?

(NH3TYP) No Default ! NH3TYP = 0 !

0 = No background will be used.

ONLY NH3 or TNH3 from the concentration

files listed in Subgroup (2a&2b) as

a processed species will be used.

(Cannot be used with MNITRATE=3)

1 = NH3 Monthly averaged background (BCKNH3)

listed below will be added to NH3 from

concentration files listed in Subgroup (2a)

2 = NH3 from background concentration file BCKGALM

will be added to NH3 from concentration files

listed in Subgroup (2a&2b)

(ONLY possible for MNITRATE=3)

3 = NH3 Monthly averaged background (BCKNH3)

listed below will be used alone.

4 = NH3 from background concentration file BCKGALM

will be used alone

(ONLY possible for MNITRATE=3)

| OPTION | NH3 or TNH3 CONC | BCKNH3 or BCKTNH3 | TNH3/BCKGALM or BCKTNH3 |

|--------|------------------|-------------------|-------------------------|

| 0 | X | 0 | 0 |

|--------|------------------|-------------------|-------------------------|

4

Appendix H.6 - 1440
SCPWIN12KMUT02.inp 3/19/2007

| 1 | X | X | 0 |

|--------|------------------|-------------------|-------------------------|

| 2 | X | 0 | X |

|--------|------------------|-------------------|-------------------------|

| 3 | 0 | X | 0 |

|--------|------------------|-------------------|-------------------------|

| 4 | 0 | 0 | X |

|--------|------------------|-------------------|-------------------------|

Default monthly (12 values) background ammonia concentration (ppb)

used for HNO3/NO3 partition (need to choose one or the other):

Gaseous NH3 (BCKNH3) Default: -999

! BCKNH3 = 12*0.5 !

Total TNH3 (BCKTNH3) Default: -999 * BCKTNH3 = 1., 1., 1., 1.1, 1.4, 1.3, 1.3, 1.2, 4*1. *

If a single value is entered, this is used for all 12 months.

Month 1 is JANUARY, Month 12 is DECEMBER.

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 2 -- Species Processing Information

--------------

-------------

Subgroup (2a)

-------------

The following NSPECINP species will be processed:

! ASPECI = SO2 ! !END!

! ASPECI = SO4 ! !END!

! ASPECI = NOX ! !END!

! ASPECI = HNO3 ! !END!

! ASPECI = NO3 ! !END!

! ASPECI = POC081 ! !END!

! ASPECI = POC056 ! !END!

! ASPECI = PIC081 ! !END!

! ASPECI = PIC056 ! !END!

! ASPECI = PMC800 ! !END!

! ASPECI = PMC425 ! !END!

! ASPECI = PMF187 ! !END!

! ASPECI = PMF112 ! !END!

! ASPECI = PMF081 ! !END!

! ASPECI = PMF056 ! !END!

! ASPECI = EC056 ! !END!

-------------

Subgroup (2b) -------------

The following NSPECOUT species will be written:

! ASPECO = SO2 ! !END!

! ASPECO = SO4 ! !END!

! ASPECO = NOX ! !END!

! ASPECO = HNO3 ! !END!

! ASPECO = NO3 ! !END!

! ASPECO = EC ! !END!

! ASPECO = SOIL ! !END!

! ASPECO = SOA ! !END!

5

Appendix H.6 - 1441
SCPWIN12KMUT02.inp 3/19/2007

! ASPECO = PMC ! !END!

-------------

Subgroup (2c) -------------

The following NSPECCMP species will be computed by scaling and summing

one or more of the processed input species. Identify the name(s) of

the computed species and provide the scaling factors for each of the

NSPECINP input species (NSPECCMP groups of NSPECINP+1 lines each):

! CSPECCMP = EC !

! EC056 = 1.0000 !

!END!

! CSPECCMP = SOIL !

! PMF187 = 1.0000 !

! PMF112 = 1.0000 !

! PMF081 = 1.0000 !

! PMF056 = 1.0000 !

! PIC081 = 1.0000 !

! PIC056 = 1.0000 !

!END!

! CSPECCMP = SOA !

! POC081 = 1.0000 !

! POC056 = 1.0000 !

!END!

! CSPECCMP = PMC !

! PMC800 = 1.0000 !

! PMC425 = 1.0000 !

!END!

-------------

Subgroup (2d) -------------

Each species in NSCALED CALPUFF data files may be scaled before being

processed (e.g., to change the emission rate for all sources modeled

in the run that produced a data file). For each file, identify the

file name and then provide the name(s) of the scaled species and the

corresponding scaling factors (A,B where x' = Ax+B).

A(Default=1.0) B(Default=0.0)

-------------- --------------

6

Appendix H.6 - 1442
Santee Cooper – Grainger, Jefferies, Winyah A - 4 Trinity Consultants 

BART Applicability Modeling Analysis 

SAMPLE CALPOST INPUT FILE

SCPWIN12KMVANCR02.INP 

WINYAH CAPE ROMAIN 12-KM SCREENING ANALYSIS YEAR 2002 

ANNUAL AVERAGE NATURAL BACKGROUND 

Appendix H.6 - 1443
SCPWIN12KMVANCR02.inp 3/19/2007

SANTEE COOPER WINYAH, SC

BART APPLICABILITY ANALYSIS

CAPE ROMAIN VISTAS ANNUAL AVERAGE NATURAL BACKGROUND

---------------- Run title (3 lines) ------------------------------------------

CALPOST MODEL CONTROL FILE

--------------------------

------------------------------------------------------------------------------

-

INPUT GROUP: 0 -- Input and Output File Names

--------------

Input Files

-----------

File Default File Name ---- -----------------

Conc/Dep Flux File MODEL.DAT ! MODDAT = T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KMUT02.con !

Relative Humidity File VISB.DAT * VISDAT = * Background Data File BACK.DAT * BACKDAT = *

Transmissometer or VSRN.DAT * VSRDAT = * Nephelometer Data File or

DATSAV Weather Data File or

Prognostic Weather File

Output Files

------------

File Default File Name ---- -----------------

List File CALPOST.LST ! PSTLST = T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KMVANCR02.lst !

Pathname for Timeseries Files (blank) * TSPATH = * (activate with exclamation points only if

providing NON-BLANK character string)

Pathname for Plot Files (blank) * PLPATH = *

(activate with exclamation points only if

providing NON-BLANK character string)

User Character String (U) to augment default filenames

(activate with exclamation points only if

providing NON-BLANK character string)

Timeseries TSERIES_ASPEC_ttHR_CONC_TSUNAM.DAT

Peak Value PEAKVAL_ASPEC_ttHR_CONC_TSUNAM.DAT

* TSUNAM = *

Top Nth Rank Plot RANK(ALL)_ASPEC_ttHR_CONC_TUNAM.DAT

or RANK(ii)_ASPEC_ttHR_CONC_TUNAM.GRD

* TUNAM = *

Exceedance Plot EXCEED_ASPEC_ttHR_CONC_XUNAM.DAT

or EXCEED_ASPEC_ttHR_CONC_XUNAM.GRD

* XUNAM = *

Echo Plot

(Specific Days)

yyyy_Mmm_Ddd_hh00(UTCszzzz)_L00_ASPEC_ttHR_CONC.DAT

1

Appendix H.6 - 1444
SCPWIN12KMVANCR02.inp 3/19/2007

or yyyy_Mmm_Ddd_hh00(UTCszzzz)_L00_ASPEC_ttHR_CONC.GRD

Visibility Plot DAILY_VISIB_VUNAM.DAT * VUNAM = * (Daily Peak Summary)

Auxiliary Output Files ----------------------

File Default File Name

---- ----------------- Visibility Change DELVIS.DAT ! DVISDAT =

T:\BART\SNTCPRSC\12KM_CPM\SCPWIN12KMVANCR02.del !

------------------------------------------------------------------------------

--

All file names will be converted to lower case if LCFILES = T

Otherwise, if LCFILES = F, file names will be converted to UPPER CASE

T = lower case ! LCFILES = T !

F = UPPER CASE

NOTE: (1) file/path names can be up to 132 characters in length

NOTE: (2) Filenames for ALL PLOT and TIMESERIES FILES are constructed

using a template that includes a pathname, user-supplied

character(s), and context-specific strings, where

ASPEC = Species Name

CONC = CONC Or WFLX Or DFLX Or TFLX

tt = Averaging Period (e.g. 03)

ii = Rank (e.g. 02)

hh = Hour(ending) in LST

szzzz = Base time zone shift from UTC (EST is -0500)

yyyy = Year(LST) mm = Month(LST)

dd = day of month (LST)

are determined internally based on selections made below.

If a path or user-supplied character(s) are supplied, each

must contain at least 1 non-blank character.

!END!

------------------------------------------------------------------------------

--

INPUT GROUP: 1 -- General run control parameters

--------------

Option to run all periods found

in the met. file(s) (METRUN) Default: 0 ! METRUN = 1 !

METRUN = 0 - Run period explicitly defined below

METRUN = 1 - Run all periods in CALPUFF data file(s)

Starting date: Year (ISYR) -- No default ! ISYR = 2001 !

(used only if Month (ISMO) -- No default ! ISMO = 1 !

METRUN = 0) Day (ISDY) -- No default ! ISDY = 1 !

Hour (ISHR) -- No default ! ISHR = 1 !

Number of hours to process (NHRS) -- No default ! NHRS = 8760 !

Process every hour of data?(NREP) -- Default: 1 ! NREP = 1 !

(1 = every hour processed,

2 = every 2nd hour processed,

5 = every 5th hour processed, etc.)

Species & Concentration/Deposition Information ----------------------------------------------

2

Appendix H.6 - 1445
SCPWIN12KMVANCR02.inp 3/19/2007

Species to process (ASPEC) -- No default ! ASPEC = VISIB !

(ASPEC = VISIB for visibility processing)

Layer/deposition code (ILAYER) -- Default: 1 ! ILAYER = 1 !

'1' for CALPUFF concentrations,

'-1' for dry deposition fluxes,

'-2' for wet deposition fluxes,

'-3' for wet+dry deposition fluxes.

Scaling factors of the form: -- Defaults: ! A = 0.0 !

X(new) = X(old) * A + B A = 0.0 ! B = 0.0 !

(NOT applied if A = B = 0.0) B = 0.0

Add Hourly Background Concentrations/Fluxes?

(LBACK) -- Default: F ! LBACK = F !

Source information ------------------

Option to process source contributions:

0 = Process only total reported contributions

1 = Sum all individual source contributions and process

2 = Run in TRACEBACK mode to identify source

contributions at a SINGLE receptor

(MSOURCE) -- Default: 0 ! MSOURCE = 0 !

Receptor information

--------------------

Gridded receptors processed? (LG) -- Default: F ! LG = F !

Discrete receptors processed? (LD) -- Default: F ! LD = T !

CTSG Complex terrain receptors processed?

(LCT) -- Default: F ! LCT = F !

--Report results by DISCRETE receptor RING?

(only used when LD = T) (LDRING) -- Default: F ! LDRING = F !

--Select range of DISCRETE receptors (only used when LD = T):

Select ALL DISCRETE receptors by setting NDRECP flag to -1;

OR

Select SPECIFIC DISCRETE receptors by entering a flag (0,1) for each

0 = discrete receptor not processed

1 = discrete receptor processed

using repeated value notation to select blocks of receptors:

23*1, 15*0, 12*1

Flag for all receptors after the last one assigned is set to 0

(NDRECP) -- Default: -1

! NDRECP = 164*1, 30*0, 52*0 !

--Select range of GRIDDED receptors (only used when LG = T):

X index of LL corner (IBGRID) -- Default: -1 ! IBGRID = -1 !

(-1 OR 1 <= IBGRID <= NX)

Y index of LL corner (JBGRID) -- Default: -1 ! JBGRID = -1 !

(-1 OR 1 <= JBGRID <= NY)

X index of UR corner (IEGRID) -- Default: -1 ! IEGRID = -1 !

(-1 OR 1 <= IEGRID <= NX)

Y index of UR corner (JEGRID) -- Default: -1 ! JEGRID = -1 !

(-1 OR 1 <= JEGRID <= NY)

Note: Entire grid is processed if IBGRID=JBGRID=IEGRID=JEGRID=-1

3

Appendix H.6 - 1446
SCPWIN12KMVANCR02.inp 3/19/2007

--Specific gridded receptors can also be excluded from CALPOST

processing by filling a processing grid array with 0s and 1s. If the

processing flag for receptor index (i,j) is 1 (ON), that receptor

will be processed if it lies within the range delineated by IBGRID,

JBGRID,IEGRID,JEGRID and if LG=T. If it is 0 (OFF), it will not be

processed in the run. By default, all array values are set to 1 (ON).

Number of gridded receptor rows provided in Subgroup (1a) to

identify specific gridded receptors to process

(NGONOFF) -- Default: 0 ! NGONOFF = 0 !

!END!

--------------

Subgroup (1a) -- Specific gridded receptors included/excluded --------------

Specific gridded receptors are excluded from CALPOST processing

by filling a processing grid array with 0s and 1s. A total of

NGONOFF lines are read here. Each line corresponds to one 'row'

in the sampling grid, starting with the NORTHERNMOST row that

contains receptors that you wish to exclude, and finishing with

row 1 to the SOUTH (no intervening rows may be skipped). Within

a row, each receptor position is assigned either a 0 or 1,

starting with the westernmost receptor.

0 = gridded receptor not processed

1 = gridded receptor processed

Repeated value notation may be used to select blocks of receptors:

23*1, 15*0, 12*1

Because all values are initially set to 1, any receptors north of

the first row entered, or east of the last value provided in a row,

remain ON.

(NGXRECP) -- Default: 1

------------------------------------------------------------------------------

-

INPUT GROUP: 2 -- Visibility Parameters (ASPEC = VISIB)

--------------

Identify the Base Time Zone for the CALPUFF simulation

(BTZONE) -- No default ! BTZONE = 5.0 !

Particle growth curve f(RH) for hygroscopic species

(MFRH) -- Default: 2 ! MFRH = 2 !

1 = IWAQM (1998) f(RH) curve (originally used with MVISBK=1)

2 = FLAG (2000) f(RH) tabulation

3 = EPA (2003) f(RH) tabulation

Maximum relative humidity (%) used in particle growth curve

(RHMAX) -- Default: 98 ! RHMAX = 98.0 !

Modeled species to be included in computing the light extinction

Include SULFATE? (LVSO4) -- Default: T ! LVSO4 = T !

Include NITRATE? (LVNO3) -- Default: T ! LVNO3 = T !

Include ORGANIC CARBON? (LVOC) -- Default: T ! LVOC = T !

Include COARSE PARTICLES? (LVPMC) -- Default: T ! LVPMC = T !

Include FINE PARTICLES? (LVPMF) -- Default: T ! LVPMF = T !

Include ELEMENTAL CARBON? (LVEC) -- Default: T ! LVEC = T !

4

Appendix H.6 - 1447
SCPWIN12KMVANCR02.inp 3/19/2007

And, when ranking for TOP-N, TOP-50, and Exceedance tables,

Include BACKGROUND? (LVBK) -- Default: T ! LVBK = T !

Species name used for particulates in MODEL.DAT file

COARSE (SPECPMC) -- Default: PMC ! SPECPMC = PMC !

FINE (SPECPMF) -- Default: PMF ! SPECPMF = SOIL !

Extinction Efficiency (1/Mm per ug/m**3) ----------------------------------------

MODELED particulate species:

PM COARSE (EEPMC) -- Default: 0.6 ! EEPMC = 0.6 !

PM FINE (EEPMF) -- Default: 1.0 ! EEPMF = 1.0 !

BACKGROUND particulate species:

PM COARSE (EEPMCBK) -- Default: 0.6 ! EEPMCBK = 0.6 !

Other species:

AMMONIUM SULFATE (EESO4) -- Default: 3.0 ! EESO4 = 3.0 !

AMMONIUM NITRATE (EENO3) -- Default: 3.0 ! EENO3 = 3.0 !

ORGANIC CARBON (EEOC) -- Default: 4.0 ! EEOC = 4.0 !

SOIL (EESOIL)-- Default: 1.0 ! EESOIL = 1.0 !

ELEMENTAL CARBON (EEEC) -- Default: 10. ! EEEC = 10.0 !

Background Extinction Computation

---------------------------------

Method used for the 24h-average of percent change of light extinction:

Hourly ratio of source light extinction / background light extinction

is averaged? (LAVER) -- Default: F ! LAVER = F !

Method used for background light extinction

(MVISBK) -- Default: 2 ! MVISBK = 6 !

1 = Supply single light extinction and hygroscopic fraction - Hourly F(RH) adjustment applied to hygroscopic background

and modeled sulfate and nitrate

2 = Compute extinction from speciated PM measurements (A) - Hourly F(RH) adjustment applied to observed and modeled

sulfate

and nitrate

- F(RH) factor is capped at F(RHMAX)

3 = Compute extinction from speciated PM measurements (B) - Hourly F(RH) adjustment applied to observed and modeled

sulfate

and nitrate

- Receptor-hour excluded if RH>RHMAX - Receptor-day excluded if fewer than 6 valid receptor-hours

4 = Read hourly transmissometer background extinction measurements

- Hourly F(RH) adjustment applied to modeled sulfate and nitrate - Hour excluded if measurement invalid (missing, interference,

or large RH)

- Receptor-hour excluded if RH>RHMAX - Receptor-day excluded if fewer than 6 valid receptor-hours

5 = Read hourly nephelometer background extinction measurements

- Rayleigh extinction value (BEXTRAY) added to measurement

- Hourly F(RH) adjustment applied to modeled sulfate and nitrate - Hour excluded if measurement invalid (missing, interference,

or large RH)

- Receptor-hour excluded if RH>RHMAX

- Receptor-day excluded if fewer than 6 valid receptor-hours

6 = Compute extinction from speciated PM measurements

- FLAG monthly RH adjustment factor applied to observed and

modeled sulfate and nitrate

7 = Use observed weather or prognostic weather information for

background extinction during weather events; otherwise, use

Method 2 - Hourly F(RH) adjustment applied to modeled sulfate and nitrate

- F(RH) factor is capped at F(RHMAX)

5

Appendix H.6 - 1448
SCPWIN12KMVANCR02.inp 3/19/2007

- During observed weather events, compute Bext from visual range

if using an observed weather data file, or

- During prognostic weather events, use Bext from the prognostic

weather file - Use Method 2 for hours without a weather event

Additional inputs used for MVISBK = 1:

--------------------------------------

Background light extinction (1/Mm)

(BEXTBK) -- No default ! BEXTBK = 0.0 !

Percentage of particles affected by relative humidity

(RHFRAC) -- No default ! RHFRAC = 0.0 !

Additional inputs used for MVISBK = 6: --------------------------------------

Extinction coefficients for hygroscopic species (modeled and

background) are computed using a monthly RH adjustment factor

in place of an hourly RH factor (VISB.DAT file is NOT needed).

Enter the 12 monthly factors here (RHFAC). Month 1 is January.

(RHFAC) -- No default ! RHFAC = 3.3, 3.0, 2.9, 2.8,

3.2, 3.7, 3.6, 4.1,

4.0, 3.7, 3.4, 3.2 !

Additional inputs used for MVISBK = 7: --------------------------------------

The weather data file (DATSAV abbreviated space-delimited) that

is identified as VSRN.DAT may contain data for more than one

station. Identify the stations that are needed in the order in

which they will be used to obtain valid weather and visual range.

The first station that contains valid data for an hour will be

used. Enter up to MXWSTA (set in PARAMS file) integer station IDs

of up to 6 digits each as variable IDWSTA, and enter the corresponding

time zone for each, as variable TZONE (= UTC-LST).

A prognostic weather data file with Bext for weather events may be used

in place of the observed weather file. Identify this as the VSRN.DAT

file and use a station ID of IDWSTA = 999999, and TZONE = 0.

NOTE: TZONE identifies the time zone used in the dataset. The

DATSAV abbreviated space-delimited data usually are prepared

with UTC time rather than local time, so TZONE is typically

set to zero.

(IDWSTA) -- No default

! IDWSTA = 999999 !

(TZONE) -- No default

! TZONE = 0.0 !

Additional inputs used for MVISBK = 2,3,6,7:

--------------------------------------------

Background extinction coefficients are computed from monthly

CONCENTRATIONS of ammonium sulfate (BKSO4), ammonium nitrate (BKNO3),

coarse particulates (BKPMC), organic carbon (BKOC), soil (BKSOIL), and

elemental carbon (BKEC). Month 1 is January.

(ug/m**3)

(BKSO4) -- No default ! BKSO4 = 0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0 !

(BKNO3) -- No default ! BKNO3 = 0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0 !

(BKPMC) -- No default ! BKPMC = 0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0 !

(BKOC) -- No default ! BKOC = 0.0, 0.0, 0.0, 0.0,

6

Appendix H.6 - 1449
SCPWIN12KMVANCR02.inp 3/19/2007

0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0 !

(BKSOIL) -- No default ! BKSOIL= 11.21, 11.21, 11.21, 11.21,

11.21, 11.21, 11.21, 11.21,

11.21, 11.21, 11.21, 11.21 !

(BKEC) -- No default ! BKEC = 0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0,

0.0, 0.0, 0.0, 0.0 !

Additional inputs used for MVISBK = 2,3,5,6,7:

----------------------------------------------

Extinction due to Rayleigh scattering is added (1/Mm)

(BEXTRAY) -- Default: 10.0 ! BEXTRAY = 10.0 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 3 -- Output options --------------

Documentation

-------------

Documentation records contained in the header of the

CALPUFF output file may be written to the list file.

Print documentation image?

(LDOC) -- Default: F ! LDOC = F !

Output Units

------------

Units for All Output (IPRTU) -- Default: 1 ! IPRTU = 3 !

for for

Concentration Deposition

1 = g/m**3 g/m**2/s

2 = mg/m**3 mg/m**2/s

3 = ug/m**3 ug/m**2/s

4 = ng/m**3 ng/m**2/s

5 = Odour Units

Visibility: extinction expressed in 1/Mega-meters (IPRTU is ignored)

Averaging time(s) reported

--------------------------

1-hr averages (L1HR) -- Default: T ! L1HR = F !

3-hr averages (L3HR) -- Default: T ! L3HR = F !

24-hr averages (L24HR) -- Default: T ! L24HR = T !

Run-length averages (LRUNL) -- Default: T ! LRUNL = F !

User-specified averaging time in hours - results for

an averaging time of NAVG hours are reported for

NAVG greater than 0:

(NAVG) -- Default: 0 ! NAVG = 0 !

Types of tabulations reported ------------------------------

1) Visibility: daily visibility tabulations are always reported

for the selected receptors when ASPEC = VISIB.

In addition, any of the other tabulations listed

below may be chosen to characterize the light

7

Appendix H.6 - 1450
SCPWIN12KMVANCR02.inp 3/19/2007

extinction coefficients.

[List file or Plot/Analysis File]

2) Top 50 table for each averaging time selected

[List file only]

(LT50) -- Default: T ! LT50 = F !

3) Top 'N' table for each averaging time selected

[List file or Plot file]

(LTOPN) -- Default: F ! LTOPN = T !

-- Number of 'Top-N' values at each receptor

selected (NTOP must be <= 4)

(NTOP) -- Default: 4 ! NTOP = 1 !

-- Specific ranks of 'Top-N' values reported

(NTOP values must be entered)

(ITOP(4) array) -- Default: ! ITOP = 1 !

1,2,3,4

4) Threshold exceedance counts for each receptor and each averaging

time selected

[List file or Plot file]

(LEXCD) -- Default: F ! LEXCD = F !

-- Identify the threshold for each averaging time by assigning a

non-negative value (output units).

-- Default: -1.0

Threshold for 1-hr averages (THRESH1) ! THRESH1 = -1.0 !

Threshold for 3-hr averages (THRESH3) ! THRESH3 = -1.0 !

Threshold for 24-hr averages (THRESH24) ! THRESH24 = -1.0 !

Threshold for NAVG-hr averages (THRESHN) ! THRESHN = -1.0 !

-- Counts for the shortest averaging period selected can be

tallied daily, and receptors that experience more than NCOUNT

counts over any NDAY period will be reported. This type of

exceedance violation output is triggered only if NDAY > 0.

Accumulation period(Days)

(NDAY) -- Default: 0 ! NDAY = 0 !

Number of exceedances allowed

(NCOUNT) -- Default: 1 ! NCOUNT = 1 !

5) Selected day table(s)

Echo Option -- Many records are written each averaging period

selected and output is grouped by day

[List file or Plot file]

(LECHO) -- Default: F ! LECHO = F !

Timeseries Option -- Averages at all selected receptors for

each selected averaging period are written to timeseries files.

Each file contains one averaging period, and all receptors are

written to a single record each averaging time.

[TSERIES_ASPEC_ttHR_CONC_TSUNAM.DAT files]

(LTIME) -- Default: F ! LTIME = F !

Peak Value Option -- Averages at all selected receptors for

each selected averaging period are screened and the peak value

each period is written to timeseries files.

Each file contains one averaging period.

[PEAKVAL_ASPEC_ttHR_CONC_TSUNAM.DAT files]

8

Appendix H.6 - 1451
SCPWIN12KMVANCR02.inp 3/19/2007

(LPEAK) -- Default: F ! LPEAK = F !

-- Days selected for output

(IECHO(366)) -- Default: 366*0

! IECHO = 366*0 !

(366 values must be entered)

Plot output options -------------------

Plot files can be created for the Top-N, Exceedance, and Echo

tables selected above. Two formats for these files are available,

DATA and GRID. In the DATA format, results at all receptors are

listed along with the receptor location [x,y,val1,val2,...].

In the GRID format, results at only gridded receptors are written,

using a compact representation. The gridded values are written in

rows (x varies), starting with the most southern row of the grid.

The GRID format is given the .GRD extension, and includes headers

compatible with the SURFER(R) plotting software.

A plotting and analysis file can also be created for the daily

peak visibility summary output, in DATA format only.

Generate Plot file output in addition to writing tables

to List file?

(LPLT) -- Default: F ! LPLT = F !

Use GRID format rather than DATA format,

when available?

(LGRD) -- Default: F ! LGRD = F !

Auxiliary Output Files (for subsequent analyses) ------------------------------------------------

Visibility

A separate output file may be requested that contains the change

in visibility at each selected receptor when ASPEC = VISIB. This

file can be processed to construct visibility measures that are

not available in CALPOST.

Output file with the visibility change at each receptor?

(MDVIS) -- Default: 0 ! MDVIS = 1 !

0 = Do Not create file

1 = Create file of DAILY (24 hour) Delta-Deciview

2 = Create file of DAILY (24 hour) Extinction Change (%)

3 = Create file of HOURLY Delta-Deciview

4 = Create file of HOURLY Extinction Change (%)

Additional Debug Output

-----------------------

Output selected information to List file

for debugging?

(LDEBUG) -- Default: F ! LDEBUG = F !

Output hourly extinction information to REPORT.HRV?

(Visibility Method 7)

(LVEXTHR) -- Default: F ! LVEXTHR = F !

!END!

9

Appendix H.6 - 1452
Santee Cooper – Grainger, Jefferies, Winyah A - 5 Trinity Consultants 

BART Applicability Modeling Analysis 

SAMPLE CALPUFF INPUT FILE

SCPWIN4KM02.INP 

WINYAH 4-KM REFINED ANALYSIS YEAR 2002 

Appendix H.6 - 1453
SCPWIN4KM02.inp 7/2/2007

Santee Cooper Winyah,SC Facility

BART APPLICABILITY ANALYSIS 2002 - 4KM GRID

Cape Romain/Wolf Island Analyses

---------------- Run title (3 lines) ------------------------------------------

CALPUFF MODEL CONTROL FILE

--------------------------

------------------------------------------------------------------------------

-

INPUT GROUP: 0 -- Input and Output File Names

--------------

Default Name Type File Name

------------ ---- --------- CALMET.DAT input * METDAT = *

or

ISCMET.DAT input * ISCDAT = *

or

PLMMET.DAT input * PLMDAT = *

or

PROFILE.DAT input * PRFDAT = *

SURFACE.DAT input * SFCDAT = * RESTARTB.DAT input * RSTARTB= * ------------------------------------------------------------------------------

--

CALPUFF.LST output ! PUFLST =T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KM02.LST !

CONC.DAT output ! CONDAT =T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KM02.CON

!

DFLX.DAT output * DFDAT = *

WFLX.DAT output * WFDAT = *

VISB.DAT output * VISDAT = * RESTARTE.DAT output * RSTARTE= * ------------------------------------------------------------------------------

--

Emission Files

--------------

PTEMARB.DAT input * PTDAT = * VOLEMARB.DAT input * VOLDAT = *

BAEMARB.DAT input * ARDAT = * LNEMARB.DAT input * LNDAT = *

------------------------------------------------------------------------------

--

Other Files

-----------

OZONE.DAT input ! OZDAT =T:\BART\SNTCPRSC\OZONE\SCWINOZONE02.DAT !

VD.DAT input * VDDAT = *

CHEM.DAT input * CHEMDAT= * H2O2.DAT input * H2O2DAT= *

HILL.DAT input * HILDAT= *

HILLRCT.DAT input * RCTDAT= *

COASTLN.DAT input * CSTDAT= * FLUXBDY.DAT input * BDYDAT= *

BCON.DAT input * BCNDAT= *

DEBUG.DAT output * DEBUG = *

MASSFLX.DAT output * FLXDAT= * MASSBAL.DAT output * BALDAT= *

FOG.DAT output * FOGDAT= * ------------------------------------------------------------------------------

--

All file names will be converted to lower case if LCFILES = T

Otherwise, if LCFILES = F, file names will be converted to UPPER CASE

T = lower case ! LCFILES = T !

F = UPPER CASE

1

Appendix H.6 - 1454
SCPWIN4KM02.inp 7/2/2007

NOTE: (1) file/path names can be up to 70 characters in length

Provision for multiple input files ----------------------------------

Number of CALMET.DAT files for run (NMETDAT)

Default: 1 ! NMETDAT = 36 !

Number of PTEMARB.DAT files for run (NPTDAT)

Default: 0 ! NPTDAT = 0 !

Number of BAEMARB.DAT files for run (NARDAT)

Default: 0 ! NARDAT = 0 !

Number of VOLEMARB.DAT files for run (NVOLDAT)

Default: 0 ! NVOLDAT = 0 !

!END!

-------------

Subgroup (0a)

-------------

The following CALMET.DAT filenames are processed in sequence if NMETDAT>1

Default Name Type File Name

------------ ---- --------- none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-01a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-01b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-01c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-02a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-02b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-02c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-03a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-03b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-03c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-04a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-04b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-04c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-05a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-05b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-05c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-06a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-06b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-06c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-07a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-07b.DAT

2

Appendix H.6 - 1455
SCPWIN4KM02.inp 7/2/2007

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-07c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-08a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-08b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-08c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-09a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-09b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-09c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-10a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-10b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-10c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-11a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-11b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-11c.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-12a.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-12b.DAT

! !END!

none input ! METDAT=T:\VISTAS\Domain4-2002\met2002-dom4-12c.DAT

! !END!

------------------------------------------------------------------------------

--

INPUT GROUP: 1 -- General run control parameters

--------------

Option to run all periods found

in the met. file (METRUN) Default: 0 ! METRUN = 0 !

METRUN = 0 - Run period explicitly defined below

METRUN = 1 - Run all periods in met. file

Starting date: Year (IBYR) -- No default ! IBYR = 2002 !

(used only if Month (IBMO) -- No default ! IBMO = 1 !

METRUN = 0) Day (IBDY) -- No default ! IBDY = 1 !

Hour (IBHR) -- No default ! IBHR = 1 !

Base time zone (XBTZ) -- No default ! XBTZ = 5.0 !

PST = 8., MST = 7.

CST = 6., EST = 5.

Length of run (hours) (IRLG) -- No default ! IRLG = 8760 !

Number of chemical species (NSPEC)

Default: 5 ! NSPEC = 16 !

Number of chemical species

to be emitted (NSE) Default: 3 ! NSE = 15 !

Flag to stop run after

SETUP phase (ITEST) Default: 2 ! ITEST = 2 !

(Used to allow checking

3

Appendix H.6 - 1456
SCPWIN4KM02.inp 7/2/2007

of the model inputs, files, etc.)

ITEST = 1 - STOPS program after SETUP phase

ITEST = 2 - Continues with execution of program

after SETUP

Restart Configuration:

Control flag (MRESTART) Default: 0 ! MRESTART = 0 !

0 = Do not read or write a restart file

1 = Read a restart file at the beginning of

the run

2 = Write a restart file during run

3 = Read a restart file at beginning of run

and write a restart file during run

Number of periods in Restart

output cycle (NRESPD) Default: 0 ! NRESPD = 0 !

0 = File written only at last period

>0 = File updated every NRESPD periods

Meteorological Data Format (METFM)

Default: 1 ! METFM = 1 !

METFM = 1 - CALMET binary file (CALMET.MET)

METFM = 2 - ISC ASCII file (ISCMET.MET)

METFM = 3 - AUSPLUME ASCII file (PLMMET.MET)

METFM = 4 - CTDM plus tower file (PROFILE.DAT) and

surface parameters file (SURFACE.DAT)

METFM = 5 - AERMET tower file (PROFILE.DAT) and

surface parameters file (SURFACE.DAT)

Meteorological Profile Data Format (MPRFFM)

(used only for METFM = 1, 2, 3)

Default: 1 ! MPRFFM = 1 !

MPRFFM = 1 - CTDM plus tower file (PROFILE.DAT)

MPRFFM = 2 - AERMET tower file (PROFILE.DAT)

PG sigma-y is adjusted by the factor (AVET/PGTIME)**0.2

Averaging Time (minutes) (AVET)

Default: 60.0 ! AVET = 60. !

PG Averaging Time (minutes) (PGTIME)

Default: 60.0 ! PGTIME = 60. !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 2 -- Technical options

--------------

Vertical distribution used in the

near field (MGAUSS) Default: 1 ! MGAUSS = 1 !

0 = uniform

1 = Gaussian

Terrain adjustment method

(MCTADJ) Default: 3 ! MCTADJ = 3 !

0 = no adjustment

1 = ISC-type of terrain adjustment

2 = simple, CALPUFF-type of terrain

4

Appendix H.6 - 1457
SCPWIN4KM02.inp 7/2/2007

adjustment

3 = partial plume path adjustment

Subgrid-scale complex terrain

flag (MCTSG) Default: 0 ! MCTSG = 0 !

0 = not modeled

1 = modeled

Near-field puffs modeled as

elongated slugs? (MSLUG) Default: 0 ! MSLUG = 0 !

0 = no

1 = yes (slug model used)

Transitional plume rise modeled?

(MTRANS) Default: 1 ! MTRANS = 1 !

0 = no (i.e., final rise only)

1 = yes (i.e., transitional rise computed)

Stack tip downwash? (MTIP) Default: 1 ! MTIP = 1 !

0 = no (i.e., no stack tip downwash)

1 = yes (i.e., use stack tip downwash)

Method used to simulate building

downwash? (MBDW) Default: 1 ! MBDW = 1 !

1 = ISC method

2 = PRIME method

Vertical wind shear modeled above

stack top? (MSHEAR) Default: 0 ! MSHEAR = 0 !

0 = no (i.e., vertical wind shear not modeled)

1 = yes (i.e., vertical wind shear modeled)

Puff splitting allowed? (MSPLIT) Default: 0 ! MSPLIT = 0 !

0 = no (i.e., puffs not split)

1 = yes (i.e., puffs are split)

Chemical mechanism flag (MCHEM) Default: 1 ! MCHEM = 1 !

0 = chemical transformation not

modeled

1 = transformation rates computed

internally (MESOPUFF II scheme)

2 = user-specified transformation

rates used

3 = transformation rates computed

internally (RIVAD/ARM3 scheme)

4 = secondary organic aerosol formation

computed (MESOPUFF II scheme for OH)

Aqueous phase transformation flag (MAQCHEM)

(Used only if MCHEM = 1, or 3) Default: 0 ! MAQCHEM = 0 !

0 = aqueous phase transformation

not modeled

1 = transformation rates adjusted

for aqueous phase reactions

Wet removal modeled ? (MWET) Default: 1 ! MWET = 1 !

0 = no

1 = yes

Dry deposition modeled ? (MDRY) Default: 1 ! MDRY = 1 !

0 = no

1 = yes

(dry deposition method specified

for each species in Input Group 3)

Gravitational settling (plume tilt)

5

Appendix H.6 - 1458
SCPWIN4KM02.inp 7/2/2007

modeled ? (MTILT) Default: 0 ! MTILT = 0 !

0 = no

1 = yes

(puff center falls at the gravitational

settling velocity for 1 particle species)

Restrictions:

- MDRY = 1 - NSPEC = 1 (must be particle species as well)

- sg = 0 GEOMETRIC STANDARD DEVIATION in Group 8 is

set to zero for a single particle diameter

Method used to compute dispersion

coefficients (MDISP) Default: 3 ! MDISP = 3 !

1 = dispersion coefficients computed from measured values

of turbulence, sigma v, sigma w 2 = dispersion coefficients from internally calculated

sigma v, sigma w using micrometeorological variables

(u*, w*, L, etc.)

3 = PG dispersion coefficients for RURAL areas (computed using

the ISCST multi-segment approximation) and MP coefficients in

urban areas

4 = same as 3 except PG coefficients computed using

the MESOPUFF II eqns.

5 = CTDM sigmas used for stable and neutral conditions.

For unstable conditions, sigmas are computed as in

MDISP = 3, described above. MDISP = 5 assumes that

measured values are read

Sigma-v/sigma-theta, sigma-w measurements used? (MTURBVW)

(Used only if MDISP = 1 or 5) Default: 3 ! MTURBVW = 3 !

1 = use sigma-v or sigma-theta measurements

from PROFILE.DAT to compute sigma-y

(valid for METFM = 1, 2, 3, 4, 5)

2 = use sigma-w measurements

from PROFILE.DAT to compute sigma-z

(valid for METFM = 1, 2, 3, 4, 5)

3 = use both sigma-(v/theta) and sigma-w

from PROFILE.DAT to compute sigma-y and sigma-z

(valid for METFM = 1, 2, 3, 4, 5)

4 = use sigma-theta measurements

from PLMMET.DAT to compute sigma-y

(valid only if METFM = 3)

Back-up method used to compute dispersion

when measured turbulence data are

missing (MDISP2) Default: 3 ! MDISP2 = 3 !

(used only if MDISP = 1 or 5)

2 = dispersion coefficients from internally calculated

sigma v, sigma w using micrometeorological variables

(u*, w*, L, etc.)

3 = PG dispersion coefficients for RURAL areas (computed using

the ISCST multi-segment approximation) and MP coefficients in

urban areas

4 = same as 3 except PG coefficients computed using

the MESOPUFF II eqns.

[DIAGNOSTIC FEATURE]

Method used for Lagrangian timescale for Sigma-y

(used only if MDISP=1,2 or MDISP2=1,2)

(MTAULY) Default: 0 ! MTAULY = 0 !

0 = Draxler default 617.284 (s)

1 = Computed as Lag. Length / (.75 q) -- after SCIPUFF

10 < Direct user input (s) -- e.g., 306.9

6

Appendix H.6 - 1459
SCPWIN4KM02.inp 7/2/2007

[DIAGNOSTIC FEATURE]

Method used for Advective-Decay timescale for Turbulence

(used only if MDISP=2 or MDISP2=2)

(MTAUADV) Default: 0 ! MTAUADV = 0 !

0 = No turbulence advection

1 = Computed (OPTION NOT IMPLEMENTED)

10 < Direct user input (s) -- e.g., 300

Method used to compute turbulence sigma-v &

sigma-w using micrometeorological variables

(Used only if MDISP = 2 or MDISP2 = 2)

(MCTURB) Default: 1 ! MCTURB = 1 !

1 = Standard CALPUFF subroutines

2 = AERMOD subroutines

PG sigma-y,z adj. for roughness? Default: 0 ! MROUGH = 0 !

(MROUGH)

0 = no

1 = yes

Partial plume penetration of Default: 1 ! MPARTL = 1 !

elevated inversion?

(MPARTL)

0 = no

1 = yes

Strength of temperature inversion Default: 0 ! MTINV = 0 !

provided in PROFILE.DAT extended records?

(MTINV)

0 = no (computed from measured/default gradients)

1 = yes

PDF used for dispersion under convective conditions?

Default: 0 ! MPDF = 0 !

(MPDF)

0 = no

1 = yes

Sub-Grid TIBL module used for shore line?

Default: 0 ! MSGTIBL = 0 !

(MSGTIBL)

0 = no

1 = yes

Boundary conditions (concentration) modeled?

Default: 0 ! MBCON = 0 !

(MBCON)

0 = no

1 = yes, using formatted BCON.DAT file

2 = yes, using unformatted CONC.DAT file

Analyses of fogging and icing impacts due to emissions from

arrays of mechanically-forced cooling towers can be performed

using CALPUFF in conjunction with a cooling tower emissions

processor (CTEMISS) and its associated postprocessors. Hourly

emissions of water vapor and temperature from each cooling tower

cell are computed for the current cell configuration and ambient

conditions by CTEMISS. CALPUFF models the dispersion of these

emissions and provides cloud information in a specialized format

for further analysis. Output to FOG.DAT is provided in either

'plume mode' or 'receptor mode' format.

Configure for FOG Model output?

Default: 0 ! MFOG = 0 !

(MFOG)

7

Appendix H.6 - 1460
SCPWIN4KM02.inp 7/2/2007

0 = no

1 = yes - report results in PLUME Mode format

2 = yes - report results in RECEPTOR Mode format

Test options specified to see if

they conform to regulatory

values? (MREG) Default: 1 ! MREG = 0 !

0 = NO checks are made

1 = Technical options must conform to USEPA

Long Range Transport (LRT) guidance

METFM 1 or 2

AVET 60. (min)

PGTIME 60. (min)

MGAUSS 1

MCTADJ 3

MTRANS 1

MTIP 1

MCHEM 1 or 3 (if modeling SOx, NOx)

MWET 1

MDRY 1

MDISP 2 or 3

MPDF 0 if MDISP=3

1 if MDISP=2

MROUGH 0

MPARTL 1

SYTDEP 550. (m)

MHFTSZ 0

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 3a, 3b -- Species list -------------------

------------

Subgroup (3a)

------------

The following species are modeled:

! CSPEC = SO2 ! !END!

! CSPEC = SO4 ! !END!

! CSPEC = NOX ! !END!

! CSPEC = HNO3 ! !END!

! CSPEC = NO3 ! !END!

! CSPEC = POC081 ! !END!

! CSPEC = POC056 ! !END!

! CSPEC = PIC081 ! !END!

! CSPEC = PIC056 ! !END!

! CSPEC = PMC800 ! !END!

! CSPEC = PMC425 ! !END!

! CSPEC = PMF187 ! !END!

! CSPEC = PMF112 ! !END!

! CSPEC = PMF081 ! !END!

! CSPEC = PMF056 ! !END!

! CSPEC = EC056 ! !END!

Dry

OUTPUT GROUP

SPECIES MODELED EMITTED DEPOSITED

NUMBER

8

Appendix H.6 - 1461
SCPWIN4KM02.inp 7/2/2007

NAME (0=NO, 1=YES) (0=NO, 1=YES) (0=NO, (0=

NONE,

(Limit: 12 1=COMPUTED-GAS 1=

1st CGRUP,

Characters 2=COMPUTED-PARTICLE 2=

2nd CGRUP,

in length) 3=USER-SPECIFIED) 3=

etc.)

! SO2 = 1, 1, 1, 0

!

! SO4 = 1, 1, 2, 0

!

! NOX = 1, 1, 1, 0

!

! HNO3 = 1, 0, 1, 0

!

! NO3 = 1, 1, 2, 0

!

! POC081 = 1, 1, 2, 0

!

! POC056 = 1, 1, 2, 0

!

! PIC081 = 1, 1, 2, 0

!

! PIC056 = 1, 1, 2, 0

!

! PMC800 = 1, 1, 2, 0

!

! PMC425 = 1, 1, 2, 0

!

! PMF187 = 1, 1, 2, 0

!

! PMF112 = 1, 1, 2, 0

!

! PMF081 = 1, 1, 2, 0

!

! PMF056 = 1, 1, 2, 0

!

! EC056 = 1, 1, 2, 0

!

!END!

-------------

Subgroup (3b) -------------

The following names are used for Species-Groups in which results

for certain species are combined (added) prior to output. The

CGRUP name will be used as the species name in output files.

Use this feature to model specific particle-size distributions

by treating each size-range as a separate species.

Order must be consistent with 3(a) above.

------------------------------------------------------------------------------

-

INPUT GROUP: 4 -- Map Projection and Grid control parameters --------------

Projection for all (X,Y):

-------------------------

Map projection

9

Appendix H.6 - 1462
SCPWIN4KM02.inp 7/2/2007

(PMAP) Default: UTM ! PMAP = LCC !

UTM : Universal Transverse Mercator

TTM : Tangential Transverse Mercator

LCC : Lambert Conformal Conic

PS : Polar Stereographic

EM : Equatorial Mercator

LAZA : Lambert Azimuthal Equal Area

False Easting and Northing (km) at the projection origin

(Used only if PMAP= TTM, LCC, or LAZA)

(FEAST) Default=0.0 ! FEAST = 0.000 !

(FNORTH) Default=0.0 ! FNORTH = 0.000 !

UTM zone (1 to 60)

(Used only if PMAP=UTM)

(IUTMZN) No Default ! IUTMZN = 0 !

Hemisphere for UTM projection?

(Used only if PMAP=UTM)

(UTMHEM) Default: N ! UTMHEM = N !

N : Northern hemisphere projection

S : Southern hemisphere projection

Latitude and Longitude (decimal degrees) of projection origin

(Used only if PMAP= TTM, LCC, PS, EM, or LAZA)

(RLAT0) No Default ! RLAT0 = 40N !

(RLON0) No Default ! RLON0 = 97W !

TTM : RLON0 identifies central (true N/S) meridian of projection

RLAT0 selected for convenience

LCC : RLON0 identifies central (true N/S) meridian of projection

RLAT0 selected for convenience

PS : RLON0 identifies central (grid N/S) meridian of projection

RLAT0 selected for convenience

EM : RLON0 identifies central meridian of projection

RLAT0 is REPLACED by 0.0N (Equator)

LAZA: RLON0 identifies longitude of tangent-point of mapping plane

RLAT0 identifies latitude of tangent-point of mapping plane

Matching parallel(s) of latitude (decimal degrees) for projection

(Used only if PMAP= LCC or PS)

(XLAT1) No Default ! XLAT1 = 33N !

(XLAT2) No Default ! XLAT2 = 45N !

LCC : Projection cone slices through Earth's surface at XLAT1 and

XLAT2

PS : Projection plane slices through Earth at XLAT1

(XLAT2 is not used)

----------

Note: Latitudes and longitudes should be positive, and include a

letter N,S,E, or W indicating north or south latitude, and

east or west longitude. For example,

35.9 N Latitude = 35.9N

118.7 E Longitude = 118.7E

Datum-region

------------

The Datum-Region for the coordinates is identified by a character

string. Many mapping products currently available use the model of the

Earth known as the World Geodetic System 1984 (WGS-84). Other local

models may be in use, and their selection in CALMET will make its output

consistent with local mapping products. The list of Datum-Regions with

official transformation parameters is provided by the National Imagery

10

Appendix H.6 - 1463
SCPWIN4KM02.inp 7/2/2007

and

Mapping Agency (NIMA).

NIMA Datum - Regions(Examples)

-------------------------------------------------------------------------

-----

WGS-84 WGS-84 Reference Ellipsoid and Geoid, Global coverage (WGS84)

NAS-C NORTH AMERICAN 1927 Clarke 1866 Spheroid, MEAN FOR CONUS

(NAD27)

NAR-C NORTH AMERICAN 1983 GRS 80 Spheroid, MEAN FOR CONUS (NAD83)

NWS-84 NWS 6370KM Radius, Sphere

ESR-S ESRI REFERENCE 6371KM Radius, Sphere

Datum-region for output coordinates

(DATUM) Default: WGS-84 ! DATUM = NWS-84 !

METEOROLOGICAL Grid:

Rectangular grid defined for projection PMAP,

with X the Easting and Y the Northing coordinate

No. X grid cells (NX) No default ! NX = 248 !

No. Y grid cells (NY) No default ! NY = 257 !

No. vertical layers (NZ) No default ! NZ = 10 !

Grid spacing (DGRIDKM) No default ! DGRIDKM = 4. !

Units: km

Cell face heights

(ZFACE(nz+1)) No defaults

Units: m ! ZFACE = 0., 20., 40., 80., 160., 320., 640., 1200., 2000., 3000.,

4000. !

Reference Coordinates

of SOUTHWEST corner of

grid cell(1, 1):

X coordinate (XORIGKM) No default ! XORIGKM = 718.005 !

Y coordinate (YORIGKM) No default ! YORIGKM = -1214.003 !

Units: km

COMPUTATIONAL Grid:

The computational grid is identical to or a subset of the MET. grid.

The lower left (LL) corner of the computational grid is at grid point

(IBCOMP, JBCOMP) of the MET. grid. The upper right (UR) corner of the

computational grid is at grid point (IECOMP, JECOMP) of the MET. grid.

The grid spacing of the computational grid is the same as the MET. grid.

X index of LL corner (IBCOMP) No default ! IBCOMP = 179 !

(1 <= IBCOMP <= NX)

Y index of LL corner (JBCOMP) No default ! JBCOMP = 82 !

(1 <= JBCOMP <= NY)

X index of UR corner (IECOMP) No default ! IECOMP = 242 !

(1 <= IECOMP <= NX)

Y index of UR corner (JECOMP) No default ! JECOMP = 172 !

(1 <= JECOMP <= NY)

11

Appendix H.6 - 1464
SCPWIN4KM02.inp 7/2/2007

SAMPLING Grid (GRIDDED RECEPTORS):

The lower left (LL) corner of the sampling grid is at grid point

(IBSAMP, JBSAMP) of the MET. grid. The upper right (UR) corner of the

sampling grid is at grid point (IESAMP, JESAMP) of the MET. grid.

The sampling grid must be identical to or a subset of the computational

grid. It may be a nested grid inside the computational grid.

The grid spacing of the sampling grid is DGRIDKM/MESHDN.

Logical flag indicating if gridded

receptors are used (LSAMP) Default: T ! LSAMP = F !

(T=yes, F=no)

X index of LL corner (IBSAMP) No default ! IBSAMP = 1 !

(IBCOMP <= IBSAMP <= IECOMP)

Y index of LL corner (JBSAMP) No default ! JBSAMP = 1 !

(JBCOMP <= JBSAMP <= JECOMP)

X index of UR corner (IESAMP) No default ! IESAMP = 1 !

(IBCOMP <= IESAMP <= IECOMP)

Y index of UR corner (JESAMP) No default ! JESAMP = 1 !

(JBCOMP <= JESAMP <= JECOMP)

Nesting factor of the sampling

grid (MESHDN) Default: 1 ! MESHDN = 1 !

(MESHDN is an integer >= 1)

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 5 -- Output Options -------------- * *

FILE DEFAULT VALUE VALUE THIS RUN ---- ------------- --------------

Concentrations (ICON) 1 ! ICON = 1 !

Dry Fluxes (IDRY) 1 ! IDRY = 0 !

Wet Fluxes (IWET) 1 ! IWET = 0 !

Relative Humidity (IVIS) 1 ! IVIS = 0 !

(relative humidity file is

required for visibility

analysis)

Use data compression option in output file?

(LCOMPRS) Default: T ! LCOMPRS = T !

*

0 = Do not create file, 1 = create file

DIAGNOSTIC MASS FLUX OUTPUT OPTIONS:

Mass flux across specified boundaries

for selected species reported hourly?

(IMFLX) Default: 0 ! IMFLX = 0 !

0 = no

1 = yes (FLUXBDY.DAT and MASSFLX.DAT filenames

are specified in Input Group 0)

12

Appendix H.6 - 1465
SCPWIN4KM02.inp 7/2/2007

Mass balance for each species

reported hourly?

(IMBAL) Default: 0 ! IMBAL = 0 !

0 = no

1 = yes (MASSBAL.DAT filename is

specified in Input Group 0)

LINE PRINTER OUTPUT OPTIONS:

Print concentrations (ICPRT) Default: 0 ! ICPRT = 0 !

Print dry fluxes (IDPRT) Default: 0 ! IDPRT = 0 !

Print wet fluxes (IWPRT) Default: 0 ! IWPRT = 0 !

(0 = Do not print, 1 = Print)

Concentration print interval

(ICFRQ) in hours Default: 1 ! ICFRQ = 1 !

Dry flux print interval

(IDFRQ) in hours Default: 1 ! IDFRQ = 1 !

Wet flux print interval

(IWFRQ) in hours Default: 1 ! IWFRQ = 1 !

Units for Line Printer Output

(IPRTU) Default: 1 ! IPRTU = 3 !

for for

Concentration Deposition

1 = g/m**3 g/m**2/s

2 = mg/m**3 mg/m**2/s

3 = ug/m**3 ug/m**2/s

4 = ng/m**3 ng/m**2/s

5 = Odour Units

Messages tracking progress of run

written to the screen ?

(IMESG) Default: 2 ! IMESG = 2 !

0 = no

1 = yes (advection step, puff ID)

2 = yes (YYYYJJJHH, # old puffs, # emitted puffs)

SPECIES (or GROUP for combined species) LIST FOR OUTPUT OPTIONS

---- CONCENTRATIONS ---- ------ DRY FLUXES ------ ------

WET FLUXES ------ -- MASS FLUX -- SPECIES

/GROUP PRINTED? SAVED ON DISK? PRINTED? SAVED ON DISK?

PRINTED? SAVED ON DISK? SAVED ON DISK? ------- ------------------------ ------------------------

------------------------ ---------------

! SO2 = 0, 1, 0, 0, 0,

0, 0 !

! SO4 = 0, 1, 0, 0, 0,

0, 0 !

! NOX = 0, 1, 0, 0, 0,

0, 0 !

! HNO3 = 0, 1, 0, 0, 0,

0, 0 !

! NO3 = 0, 1, 0, 0, 0,

0, 0 !

! POC081 = 0, 1, 0, 0, 0,

0, 0 !

! POC056 = 0, 1, 0, 0, 0,

0, 0 !

! PIC081 = 0, 1, 0, 0, 0,

0, 0 !

! PIC056 = 0, 1, 0, 0, 0,

13

Appendix H.6 - 1466
SCPWIN4KM02.inp 7/2/2007

0, 0 !

! PMC800 = 0, 1, 0, 0, 0,

0, 0 !

! PMC425 = 0, 1, 0, 0, 0,

0, 0 !

! PMF187 = 0, 1, 0, 0, 0,

0, 0 !

! PMF112 = 0, 1, 0, 0, 0,

0, 0 !

! PMF081 = 0, 1, 0, 0, 0,

0, 0 !

! PMF056 = 0, 1, 0, 0, 0,

0, 0 !

! EC056 = 0, 1, 0, 0, 0,

0, 0 !

OPTIONS FOR PRINTING "DEBUG" QUANTITIES (much output)

Logical for debug output

(LDEBUG) Default: F ! LDEBUG = F !

First puff to track

(IPFDEB) Default: 1 ! IPFDEB = 1

!

Number of puffs to track

(NPFDEB) Default: 1 ! NPFDEB = 10

!

Met. period to start output

(NN1) Default: 1 ! NN1 = 10 !

Met. period to end output

(NN2) Default: 10 ! NN2 = 10 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 6a, 6b, & 6c -- Subgrid scale complex terrain inputs -------------------------

---------------

Subgroup (6a)

--------------- Number of terrain features (NHILL) Default: 0 ! NHILL = 0

!

Number of special complex terrain

receptors (NCTREC) Default: 0 ! NCTREC = 0

!

Terrain and CTSG Receptor data for

CTSG hills input in CTDM format ?

(MHILL) No Default ! MHILL = 0

!

1 = Hill and Receptor data created

by CTDM processors & read from

HILL.DAT and HILLRCT.DAT files

2 = Hill data created by OPTHILL &

input below in Subgroup (6b);

Receptor data in Subgroup (6c)

Factor to convert horizontal dimensions Default: 1.0 ! XHILL2M = 0.

14

Appendix H.6 - 1467
SCPWIN4KM02.inp 7/2/2007

!

to meters (MHILL=1)

Factor to convert vertical dimensions Default: 1.0 ! ZHILL2M = 0.

!

to meters (MHILL=1)

X-origin of CTDM system relative to No Default ! XCTDMKM = 0.0E00 !

CALPUFF coordinate system, in Kilometers (MHILL=1)

Y-origin of CTDM system relative to No Default ! YCTDMKM = 0.0E00 !

CALPUFF coordinate system, in Kilometers (MHILL=1)

! END !

---------------

Subgroup (6b) ---------------

1 **

HILL information

HILL XC YC THETAH ZGRID RELIEF EXPO 1 EXPO 2

SCALE 1 SCALE 2 AMAX1 AMAX2

NO. (km) (km) (deg.) (m) (m) (m) (m)

(m) (m) (m) (m)

---- ---- ---- ------ ----- ------ ------ ------

------- ------- ----- -----

---------------

Subgroup (6c)

---------------

COMPLEX TERRAIN RECEPTOR INFORMATION

XRCT YRCT ZRCT XHH

(km) (km) (m) ------ ----- ------ ----

-------------------

1

Description of Complex Terrain Variables:

XC, YC = Coordinates of center of hill

THETAH = Orientation of major axis of hill (clockwise from

North)

ZGRID = Height of the 0 of the grid above mean sea

level

RELIEF = Height of the crest of the hill above the grid elevation

EXPO 1 = Hill-shape exponent for the major axis

EXPO 2 = Hill-shape exponent for the major axis

SCALE 1 = Horizontal length scale along the major axis

SCALE 2 = Horizontal length scale along the minor axis

AMAX = Maximum allowed axis length for the major axis

BMAX = Maximum allowed axis length for the major axis

XRCT, YRCT = Coordinates of the complex terrain receptors

ZRCT = Height of the ground (MSL) at the complex terrain

Receptor

XHH = Hill number associated with each complex terrain receptor

(NOTE: MUST BE ENTERED AS A REAL NUMBER)

**

NOTE: DATA for each hill and CTSG receptor are treated as a separate

15

Appendix H.6 - 1468
SCPWIN4KM02.inp 7/2/2007

input subgroup and therefore must end with an input group

terminator.

------------------------------------------------------------------------------

-

INPUT GROUP: 7 -- Chemical parameters for dry deposition of gases --------------

SPECIES DIFFUSIVITY ALPHA STAR REACTIVITY MESOPHYLL

RESISTANCE HENRY'S LAW COEFFICIENT

NAME (cm**2/s) (s/cm)

(dimensionless)

------- ----------- ---------- ---------- -------------------- -----------------------

! SO2 = 0.1509, 1000., 8., 0.,

0.04 !

! NOX = 0.1656, 1., 8., 5.,

3.5 !

! HNO3 = 0.1628, 1., 18., 0.,

0.00000008 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 8 -- Size parameters for dry deposition of particles

--------------

For SINGLE SPECIES, the mean and standard deviation are used to

compute a deposition velocity for NINT (see group 9) size-ranges,

and these are then averaged to obtain a mean deposition velocity.

For GROUPED SPECIES, the size distribution should be explicitly

specified (by the 'species' in the group), and the standard deviation

for each should be entered as 0. The model will then use the

deposition velocity for the stated mean diameter.

SPECIES GEOMETRIC MASS MEAN GEOMETRIC STANDARD

NAME DIAMETER DEVIATION

(microns) (microns) ------- ------------------- ------------------

! SO4 = 0.48, 2. !

! NO3 = 0.48, 2. !

! POC081 = 0.8125, 0. !

! POC056 = 0.5625, 0. !

! PIC081 = 0.8125, 0. !

! PIC056 = 0.5625, 0. !

! PMC800 = 8., 0. !

! PMC425 = 4.25, 0. !

! PMF187 = 1.875, 0. !

! PMF112 = 1.125, 0. !

! PMF081 = 0.8125, 0. !

! PMF056 = 0.5625, 0. !

! EC056 = 0.5625, 0. !

!END!

------------------------------------------------------------------------------

-

16

Appendix H.6 - 1469
SCPWIN4KM02.inp 7/2/2007

INPUT GROUP: 9 -- Miscellaneous dry deposition parameters

--------------

Reference cuticle resistance (s/cm)

(RCUTR) Default: 30 ! RCUTR = 30.0 !

Reference ground resistance (s/cm)

(RGR) Default: 10 ! RGR = 10.0 !

Reference pollutant reactivity

(REACTR) Default: 8 ! REACTR = 8.0 !

Number of particle-size intervals used to

evaluate effective particle deposition velocity

(NINT) Default: 9 ! NINT = 9 !

Vegetation state in unirrigated areas

(IVEG) Default: 1 ! IVEG = 1 !

IVEG=1 for active and unstressed vegetation

IVEG=2 for active and stressed vegetation

IVEG=3 for inactive vegetation

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 10 -- Wet Deposition Parameters

---------------

Scavenging Coefficient -- Units: (sec)**(-1)

Pollutant Liquid Precip. Frozen Precip. --------- -------------- -------------- ! SO2 = 3.0E-05, 0.0E00 !

! SO4 = 1.0E-04, 3.0E-05 !

! HNO3 = 6.0E-05, 0.0E00 !

! NO3 = 1.0E-04, 3.0E-05 !

! POC081 = 1.0E-04, 3.0E-05 !

! POC056 = 1.0E-04, 3.0E-05 !

! PIC081 = 1.0E-04, 3.0E-05 !

! PIC056 = 1.0E-04, 3.0E-05 !

! PMC800 = 1.0E-04, 3.0E-05 !

! PMC425 = 1.0E-04, 3.0E-05 !

! PMF187 = 1.0E-04, 3.0E-05 !

! PMF112 = 1.0E-04, 3.0E-05 !

! PMF081 = 1.0E-04, 3.0E-05 !

! PMF056 = 1.0E-04, 3.0E-05 !

! EC056 = 1.0E-04, 3.0E-05 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 11 -- Chemistry Parameters

---------------

Ozone data input option (MOZ) Default: 1 ! MOZ = 1 !

(Used only if MCHEM = 1, 3, or 4)

0 = use a monthly background ozone value

1 = read hourly ozone concentrations from

the OZONE.DAT data file

17

Appendix H.6 - 1470
SCPWIN4KM02.inp 7/2/2007

Monthly ozone concentrations

(Used only if MCHEM = 1, 3, or 4 and

MOZ = 0 or MOZ = 1 and all hourly O3 data missing)

(BCKO3) in ppb Default: 12*80.

! BCKO3 = 23.14, 30.25, 33.20, 39.91, 41.35, 43.51, 47.76, 41.53,

31.99,

27.72, 24.88, 23.37 !

Monthly ammonia concentrations

(Used only if MCHEM = 1, or 3)

(BCKNH3) in ppb Default: 12*10.

! BCKNH3 = 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50, 0.50,

0.50, 0.50 !

Nighttime SO2 loss rate (RNITE1)

in percent/hour Default: 0.2 ! RNITE1 = .2 !

Nighttime NOx loss rate (RNITE2)

in percent/hour Default: 2.0 ! RNITE2 = 2.0 !

Nighttime HNO3 formation rate (RNITE3)

in percent/hour Default: 2.0 ! RNITE3 = 2.0 !

H2O2 data input option (MH2O2) Default: 1 ! MH2O2 = 1 !

(Used only if MAQCHEM = 1)

0 = use a monthly background H2O2 value

1 = read hourly H2O2 concentrations from

the H2O2.DAT data file

Monthly H2O2 concentrations

(Used only if MQACHEM = 1 and

MH2O2 = 0 or MH2O2 = 1 and all hourly H2O2 data missing)

(BCKH2O2) in ppb Default: 12*1.

! BCKH2O2 = 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00,

1.00, 1.00 !

--- Data for SECONDARY ORGANIC AEROSOL (SOA) Option

(used only if MCHEM = 4)

The SOA module uses monthly values of:

Fine particulate concentration in ug/m^3 (BCKPMF)

Organic fraction of fine particulate (OFRAC)

VOC / NOX ratio (after reaction) (VCNX)

to characterize the air mass when computing

the formation of SOA from VOC emissions.

Typical values for several distinct air mass types are:

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

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Clean Continental

BCKPMF 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.

OFRAC .15 .15 .20 .20 .20 .20 .20 .20 .20 .20 .20 .15

VCNX 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50.

Clean Marine (surface)

BCKPMF .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5 .5

OFRAC .25 .25 .30 .30 .30 .30 .30 .30 .30 .30 .30 .25

VCNX 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50.

Urban - low biogenic (controls present)

BCKPMF 30. 30. 30. 30. 30. 30. 30. 30. 30. 30. 30. 30.

OFRAC .20 .20 .25 .25 .25 .25 .25 .25 .20 .20 .20 .20

VCNX 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.

18

Appendix H.6 - 1471
SCPWIN4KM02.inp 7/2/2007

Urban - high biogenic (controls present)

BCKPMF 60. 60. 60. 60. 60. 60. 60. 60. 60. 60. 60. 60.

OFRAC .25 .25 .30 .30 .30 .55 .55 .55 .35 .35 .35 .25

VCNX 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15.

Regional Plume

BCKPMF 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20.

OFRAC .20 .20 .25 .35 .25 .40 .40 .40 .30 .30 .30 .20

VCNX 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15. 15.

Urban - no controls present

BCKPMF 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100. 100.

OFRAC .30 .30 .35 .35 .35 .55 .55 .55 .35 .35 .35 .30

VCNX 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.

Default: Clean Continental

! BCKPMF = 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00,

1.00, 1.00 !

! OFRAC = 0.15, 0.15, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20, 0.20,

0.20, 0.15 !

! VCNX = 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00, 50.00,

50.00, 50.00, 50.00, 50.00 !

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 12 -- Misc. Dispersion and Computational Parameters

---------------

Horizontal size of puff (m) beyond which

time-dependent dispersion equations (Heffter)

are used to determine sigma-y and

sigma-z (SYTDEP) Default: 550. ! SYTDEP = 5.5E02 !

Switch for using Heffter equation for sigma z as above (0 = Not use Heffter; 1 = use Heffter

(MHFTSZ) Default: 0 ! MHFTSZ = 0

!

Stability class used to determine plume

growth rates for puffs above the boundary

layer (JSUP) Default: 5 ! JSUP = 5

!

Vertical dispersion constant for stable

conditions (k1 in Eqn. 2.7-3) (CONK1) Default: 0.01 ! CONK1 = .01

!

Vertical dispersion constant for neutral/

unstable conditions (k2 in Eqn. 2.7-4)

(CONK2) Default: 0.1 ! CONK2 = .1 !

Factor for determining Transition-point from

Schulman-Scire to Huber-Snyder Building Downwash

scheme (SS used for Hs < Hb + TBD * HL)

(TBD) Default: 0.5 ! TBD = .5 !

TBD < 0 ==> always use Huber-Snyder

TBD = 1.5 ==> always use Schulman-Scire

TBD = 0.5 ==> ISC Transition-point

Range of land use categories for which

19

Appendix H.6 - 1472
SCPWIN4KM02.inp 7/2/2007

urban dispersion is assumed

(IURB1, IURB2) Default: 10 ! IURB1 = 10

!

19 ! IURB2 = 19

!

Site characterization parameters for single-point Met data files

---------

(needed for METFM = 2,3,4,5)

Land use category for modeling domain

(ILANDUIN) Default: 20 ! ILANDUIN = 20 !

Roughness length (m) for modeling domain

(Z0IN) Default: 0.25 ! Z0IN = .25 !

Leaf area index for modeling domain

(XLAIIN) Default: 3.0 ! XLAIIN = 3.0

!

Elevation above sea level (m)

(ELEVIN) Default: 0.0 ! ELEVIN = .0

!

Latitude (degrees) for met location

(XLATIN) Default: -999. ! XLATIN =

-999.0 !

Longitude (degrees) for met location

(XLONIN) Default: -999. ! XLONIN =

-999.0 !

Specialized information for interpreting single-point Met data files

-----

Anemometer height (m) (Used only if METFM = 2,3)

(ANEMHT) Default: 10. ! ANEMHT = 10.0 !

Form of lateral turbulance data in PROFILE.DAT file

(Used only if METFM = 4,5 or MTURBVW = 1 or 3)

(ISIGMAV) Default: 1 ! ISIGMAV = 1

!

0 = read sigma-theta

1 = read sigma-v

Choice of mixing heights (Used only if METFM = 4)

(IMIXCTDM) Default: 0 ! IMIXCTDM =

0 !

0 = read PREDICTED mixing heights

1 = read OBSERVED mixing heights

Maximum length of a slug (met. grid units)

(XMXLEN) Default: 1.0 ! XMXLEN = 1.0

!

Maximum travel distance of a puff/slug (in

grid units) during one sampling step

(XSAMLEN) Default: 1.0 ! XSAMLEN =

1.0 !

Maximum Number of slugs/puffs release from

one source during one time step

(MXNEW) Default: 99 ! MXNEW = 99

!

20

Appendix H.6 - 1473
SCPWIN4KM02.inp 7/2/2007

Maximum Number of sampling steps for

one puff/slug during one time step

(MXSAM) Default: 99 ! MXSAM = 99

!

Number of iterations used when computing

the transport wind for a sampling step

that includes gradual rise (for CALMET

and PROFILE winds)

(NCOUNT) Default: 2 ! NCOUNT = 2

!

Minimum sigma y for a new puff/slug (m)

(SYMIN) Default: 1.0 ! SYMIN = 1.0

!

Minimum sigma z for a new puff/slug (m)

(SZMIN) Default: 1.0 ! SZMIN = 1.0

!

Default minimum turbulence velocities sigma-v and sigma-w

for each stability class over land and over water (m/s)

(SVMIN(12) and SWMIN(12))

---------- LAND ---------- --------- WATER ---------- Stab Class : A B C D E F A B C D E

F --- --- --- --- --- --- --- --- --- ---

--- ---

Default SVMIN : .50, .50, .50, .50, .50, .50, .37, .37, .37, .37,

.37, .37

Default SWMIN : .20, .12, .08, .06, .03, .016, .20, .12, .08, .06,

.03, .016

! SVMIN = 0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 0.370, 0.370,

0.370, 0.370, 0.370, 0.370!

! SWMIN = 0.200, 0.120, 0.080, 0.060, 0.030, 0.016, 0.200, 0.120,

0.080, 0.060, 0.030, 0.016!

Divergence criterion for dw/dz across puff

used to initiate adjustment for horizontal

convergence (1/s)

Partial adjustment starts at CDIV(1), and

full adjustment is reached at CDIV(2)

(CDIV(2)) Default: 0.0,0.0 ! CDIV = .0,

.0 !

Minimum wind speed (m/s) allowed for

non-calm conditions. Also used as minimum

speed returned when using power-law

extrapolation toward surface

(WSCALM) Default: 0.5 ! WSCALM = .5

!

Maximum mixing height (m)

(XMAXZI) Default: 3000. ! XMAXZI =

3000.0 !

Minimum mixing height (m)

(XMINZI) Default: 50. ! XMINZI = 50.0 !

Default wind speed classes --

5 upper bounds (m/s) are entered;

the 6th class has no upper limit

(WSCAT(5)) Default :

21

Appendix H.6 - 1474
SCPWIN4KM02.inp 7/2/2007

ISC RURAL : 1.54, 3.09, 5.14, 8.23, 10.8

(10.8+)

Wind Speed Class : 1 2 3 4 5 --- --- --- --- --- ! WSCAT = 1.54, 3.09, 5.14, 8.23, 10.80

!

Default wind speed profile power-law

exponents for stabilities 1-6

(PLX0(6)) Default : ISC RURAL values

ISC RURAL : .07, .07, .10, .15, .35, .55

ISC URBAN : .15, .15, .20, .25, .30, .30

Stability Class : A B C D E

F

--- --- --- --- --- ---

! PLX0 = 0.07, 0.07, 0.10, 0.15, 0.35,

0.55 !

Default potential temperature gradient

for stable classes E, F (degK/m)

(PTG0(2)) Default: 0.020, 0.035

! PTG0 = 0.020, 0.035 !

Default plume path coefficients for

each stability class (used when option

for partial plume height terrain adjustment

is selected -- MCTADJ=3)

(PPC(6)) Stability Class : A B C D E

F

Default PPC : .50, .50, .50, .50, .35,

.35

--- --- --- --- --- --- ! PPC = 0.50, 0.50, 0.50, 0.50, 0.35,

0.35 !

Slug-to-puff transition criterion factor

equal to sigma-y/length of slug

(SL2PF) Default: 10. ! SL2PF = 5.0 !

Puff-splitting control variables ------------------------

VERTICAL SPLIT --------------

Number of puffs that result every time a puff

is split - nsplit=2 means that 1 puff splits

into 2

(NSPLIT) Default: 3 ! NSPLIT = 3

!

Time(s) of a day when split puffs are eligible to

be split once again; this is typically set once

per day, around sunset before nocturnal shear develops.

24 values: 0 is midnight (00:00) and 23 is 11 PM (23:00)

0=do not re-split 1=eligible for re-split

(IRESPLIT(24)) Default: Hour 17 = 1

! IRESPLIT = 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0 !

Split is allowed only if last hour's mixing

height (m) exceeds a minimum value

(ZISPLIT) Default: 100. ! ZISPLIT = 100.0 !

22

Appendix H.6 - 1475
SCPWIN4KM02.inp 7/2/2007

Split is allowed only if ratio of last hour's

mixing ht to the maximum mixing ht experienced

by the puff is less than a maximum value (this

postpones a split until a nocturnal layer develops)

(ROLDMAX) Default: 0.25 ! ROLDMAX = 0.25 !

HORIZONTAL SPLIT

----------------

Number of puffs that result every time a puff

is split - nsplith=5 means that 1 puff splits

into 5

(NSPLITH) Default: 5 ! NSPLITH = 5

!

Minimum sigma-y (Grid Cells Units) of puff

before it may be split

(SYSPLITH) Default: 1.0 ! SYSPLITH =

1.0 !

Minimum puff elongation rate (SYSPLITH/hr) due to

wind shear, before it may be split

(SHSPLITH) Default: 2. ! SHSPLITH = 2.0 !

Minimum concentration (g/m^3) of each

species in puff before it may be split

Enter array of NSPEC values; if a single value is

entered, it will be used for ALL species

(CNSPLITH) Default: 1.0E-07 ! CNSPLITH =

1.0E-07 !

Integration control variables ------------------------

Fractional convergence criterion for numerical SLUG

sampling integration

(EPSSLUG) Default: 1.0e-04 ! EPSSLUG =

1.0E-04 !

Fractional convergence criterion for numerical AREA

source integration

(EPSAREA) Default: 1.0e-06 ! EPSAREA =

1.0E-06 !

Trajectory step-length (m) used for numerical rise

integration

(DSRISE) Default: 1.0 ! DSRISE = 1.0

!

Boundary Condition (BC) Puff control variables ------------------------

Minimum height (m) to which BC puffs are mixed as they are emitted

(MBCON=2 ONLY). Actual height is reset to the current mixing height

at the release point if greater than this minimum.

(HTMINBC) Default: 500. ! HTMINBC =

500.0 !

Search radius (km) about a receptor for sampling nearest BC puff.

BC puffs are typically emitted with a spacing of one grid cell

length, so the search radius should be greater than DGRIDKM.

(RSAMPBC) Default: 10. ! RSAMPBC =

15.0 !

Near-Surface depletion adjustment to concentration profile used when

sampling BC puffs?

23

Appendix H.6 - 1476
SCPWIN4KM02.inp 7/2/2007

(MDEPBC) Default: 1 ! MDEPBC = 0

!

0 = Concentration is NOT adjusted for depletion

1 = Adjust Concentration for depletion

!END!

------------------------------------------------------------------------------

-

INPUT GROUPS: 13a, 13b, 13c, 13d -- Point source parameters

--------------------------------

---------------

Subgroup (13a) ---------------

Number of point sources with

parameters provided below (NPT1) No default ! NPT1 = 2 !

Units used for point source

emissions below (IPTU) Default: 1 ! IPTU = 1 !

1 = g/s

2 = kg/hr

3 = lb/hr

4 = tons/yr

5 = Odour Unit * m**3/s (vol. flux of odour compound)

6 = Odour Unit * m**3/min

7 = metric tons/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (13d) (NSPT1) Default: 0 ! NSPT1 = 0 !

Number of point sources with

variable emission parameters

provided in external file (NPT2) No default ! NPT2 = 0 !

(If NPT2 > 0, these point

source emissions are read from

the file: PTEMARB.DAT)

!END!

---------------

Subgroup (13b)

---------------

a

POINT SOURCE: CONSTANT DATA

-----------------------------

b

c

Source X Y Stack Base Stack Exit Exit

Bldg. Emission

No. Coordinate Coordinate Height Elevation Diameter Vel. Temp.

Dwash Rates

(km) (km) (m) (m) (m) (m/s) (deg. K) ------ ---------- ---------- ------ ------ -------- ----- -------- ----- --------

1 ! SRCNAM = WS01 !

1 ! X = 1627.285, -580.090, 123.14, 5.5, 6.10, 15.85, 324.8, .0,

0.00E+00, 2.15E+00, 0.00E+00, 0.00E+00, 0.00E+00,

2.68E-01, 2.68E-01, 0.00E+00, 0.00E+00,

24

Appendix H.6 - 1477
SCPWIN4KM02.inp 7/2/2007

1.71E+00, 2.11E+00, 1.21E+00, 3.02E-01, 2.01E-01, 1.10E+00,

1.03E-01 !

1 ! ZPLTFM = .0 !

1 ! FMFAC = 1.0 ! !END!

2 ! SRCNAM = WS02 !

2 ! X = 1627.285, -580.090, 123.14, 5.5, 6.10, 15.85, 324.8, .0,

0.00E+00, 2.15E+00, 0.00E+00, 0.00E+00, 0.00E+00,

2.68E-01, 2.68E-01, 0.00E+00, 0.00E+00,

1.71E+00, 2.11E+00, 1.21E+00, 3.02E-01, 2.01E-01, 1.10E+00,

1.04E-01 !

2 ! ZPLTFM = .0 !

2 ! FMFAC = 1.0 ! !END!

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

SRCNAM is a 12-character name for a source

(No default)

X is an array holding the source data listed by the column headings

(No default)

SIGYZI is an array holding the initial sigma-y and sigma-z (m)

(Default: 0.,0.)

ZPLTFM is the platform height (m) for sources influenced by an isolated

structure that has a significant open area between the surface

and the bulk of the structure, such as an offshore oil platform.

The Base Elevation is that of the surface (ground or ocean),

and the Stack Height is the release height above the Base (not

above the platform). Building heights entered in Subgroup 13c

must be those of the buildings on the platform, measured from

the platform deck. ZPLTFM is used only with MBDW=1 (ISC

downwash method) for sources with building downwash.

(Default: 0.0)

FMFAC is a vertical momentum flux factor (0. or 1.0) used to represent

the effect of rain-caps or other physical configurations that

reduce momentum rise associated with the actual exit velocity.

(Default: 1.0 -- full momentum used)

b

0. = No building downwash modeled, 1. = downwash modeled

NOTE: must be entered as a REAL number (i.e., with decimal point)

c

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by IPTU

(e.g. 1 for g/s).

---------------

Subgroup (13c) ---------------

BUILDING DIMENSION DATA FOR SOURCES SUBJECT TO DOWNWASH

------------------------------------------------------- Source a

No. Effective building height, width, length and X/Y offset (in meters)

every 10 degrees. LENGTH, XBADJ, and YBADJ are only needed for

MBDW=2 (PRIME downwash option)

------

--------------------------------------------------------------------

25

Appendix H.6 - 1478
SCPWIN4KM02.inp 7/2/2007

--------

a

Building height, width, length, and X/Y offset from the source are

treated

as a separate input subgroup for each source and therefore must end with

an input group terminator. The X/Y offset is the position, relative to

the

stack, of the center of the upwind face of the projected building, with

the

x-axis pointing along the flow direction.

---------------

Subgroup (13d) --------------- a

POINT SOURCE: VARIABLE EMISSIONS DATA ---------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 13b. Factors entered multiply the rates in 13b.

Skip sources here that have constant emissions. For more elaborate

variation in source parameters, use PTEMARB.DAT and NPT2 > 0.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

-------- a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 14a, 14b, 14c, 14d -- Area source parameters

--------------------------------

---------------

Subgroup (14a)

---------------

Number of polygon area sources with

parameters specified below (NAR1) No default ! NAR1 = 0 !

Units used for area source

emissions below (IARU) Default: 1 ! IARU = 1 !

1 = g/m**2/s

2 = kg/m**2/hr

3 = lb/m**2/hr

26

Appendix H.6 - 1479
SCPWIN4KM02.inp 7/2/2007

4 = tons/m**2/yr

5 = Odour Unit * m/s (vol. flux/m**2 of odour compound)

6 = Odour Unit * m/min

7 = metric tons/m**2/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (14d) (NSAR1) Default: 0 ! NSAR1 = 0 !

Number of buoyant polygon area sources

with variable location and emission

parameters (NAR2) No default ! NAR2 = 0 !

(If NAR2 > 0, ALL parameter data for

these sources are read from the file: BAEMARB.DAT)

!END!

---------------

Subgroup (14b)

--------------- a

AREA SOURCE: CONSTANT DATA ----------------------------

b

Source Effect. Base Initial Emission

No. Height Elevation Sigma z Rates

(m) (m) (m) ------- ------ ------ -------- ---------

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by IARU

(e.g. 1 for g/m**2/s).

---------------

Subgroup (14c) ---------------

COORDINATES (km) FOR EACH VERTEX(4) OF EACH POLYGON --------------------------------------------------------

Source a

No. Ordered list of X followed by list of Y, grouped by source

------ ------------------------------------------------------------

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

---------------

Subgroup (14d)

--------------- a

AREA SOURCE: VARIABLE EMISSIONS DATA

--------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 14b. Factors entered multiply the rates in 14b.

27

Appendix H.6 - 1480
SCPWIN4KM02.inp 7/2/2007

Skip sources here that have constant emissions. For more elaborate

variation in source parameters, use BAEMARB.DAT and NAR2 > 0.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

--------

a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 15a, 15b, 15c -- Line source parameters

---------------------------

---------------

Subgroup (15a) ---------------

Number of buoyant line sources

with variable location and emission

parameters (NLN2) No default ! NLN2 = 0

!

(If NLN2 > 0, ALL parameter data for

these sources are read from the file: LNEMARB.DAT)

Number of buoyant line sources (NLINES) No default ! NLINES = 0

!

Units used for line source

emissions below (ILNU) Default: 1 ! ILNU = 1

!

1 = g/s

2 = kg/hr

3 = lb/hr

4 = tons/yr

5 = Odour Unit * m**3/s (vol. flux of odour compound)

6 = Odour Unit * m**3/min

7 = metric tons/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (15c) (NSLN1) Default: 0 ! NSLN1 = 0 !

Maximum number of segments used to model

each line (MXNSEG) Default: 7 ! MXNSEG = 7

!

28

Appendix H.6 - 1481
SCPWIN4KM02.inp 7/2/2007

The following variables are required only if NLINES > 0. They are

used in the buoyant line source plume rise calculations.

Number of distances at which Default: 6 ! NLRISE = 6

!

transitional rise is computed

Average building length (XL) No default ! XL = .0 !

(in meters)

Average building height (HBL) No default ! HBL = .0 !

(in meters)

Average building width (WBL) No default ! WBL = .0 !

(in meters)

Average line source width (WML) No default ! WML = .0 !

(in meters)

Average separation between buildings (DXL) No default ! DXL = .0 !

(in meters)

Average buoyancy parameter (FPRIMEL) No default ! FPRIMEL =

.0 !

(in m**4/s**3)

!END!

---------------

Subgroup (15b)

---------------

BUOYANT LINE SOURCE: CONSTANT DATA

----------------------------------

a

Source Beg. X Beg. Y End. X End. Y Release Base

Emission

No. Coordinate Coordinate Coordinate Coordinate Height Elevation

Rates

(km) (km) (km) (km) (m) (m)

------ ---------- ---------- --------- ---------- ------- --------- ---------

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by ILNTU

(e.g. 1 for g/s).

---------------

Subgroup (15c) ---------------

a

BUOYANT LINE SOURCE: VARIABLE EMISSIONS DATA ----------------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 15b. Factors entered multiply the rates in 15b.

Skip sources here that have constant emissions.

29

Appendix H.6 - 1482
SCPWIN4KM02.inp 7/2/2007

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

--------

a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 16a, 16b, 16c -- Volume source parameters

---------------------------

---------------

Subgroup (16a)

---------------

Number of volume sources with

parameters provided in 16b,c (NVL1) No default ! NVL1 = 0 !

Units used for volume source

emissions below in 16b (IVLU) Default: 1 ! IVLU = 1 !

1 = g/s

2 = kg/hr

3 = lb/hr

4 = tons/yr

5 = Odour Unit * m**3/s (vol. flux of odour compound)

6 = Odour Unit * m**3/min

7 = metric tons/yr

Number of source-species

combinations with variable

emissions scaling factors

provided below in (16c) (NSVL1) Default: 0 ! NSVL1 = 0 !

Number of volume sources with

variable location and emission

parameters (NVL2) No default ! NVL2 = 0 !

(If NVL2 > 0, ALL parameter data for

these sources are read from the VOLEMARB.DAT file(s) )

!END!

---------------

Subgroup (16b) ---------------

a

30

Appendix H.6 - 1483
SCPWIN4KM02.inp 7/2/2007

VOLUME SOURCE: CONSTANT DATA

------------------------------

b

X Y Effect. Base Initial Initial

Emission

Coordinate Coordinate Height Elevation Sigma y Sigma z Rates

(km) (km) (m) (m) (m) (m) ---------- ---------- ------ ------ -------- --------

--------

--------

a

Data for each source are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

An emission rate must be entered for every pollutant modeled.

Enter emission rate of zero for secondary pollutants that are

modeled, but not emitted. Units are specified by IVLU

(e.g. 1 for g/s).

---------------

Subgroup (16c) --------------- a

VOLUME SOURCE: VARIABLE EMISSIONS DATA ----------------------------------------

Use this subgroup to describe temporal variations in the emission

rates given in 16b. Factors entered multiply the rates in 16b.

Skip sources here that have constant emissions. For more elaborate

variation in source parameters, use VOLEMARB.DAT and NVL2 > 0.

IVARY determines the type of variation, and is source-specific:

(IVARY) Default: 0

0 = Constant

1 = Diurnal cycle (24 scaling factors: hours 1-24)

2 = Monthly cycle (12 scaling factors: months 1-12)

3 = Hour & Season (4 groups of 24 hourly scaling factors,

where first group is DEC-JAN-FEB)

4 = Speed & Stab. (6 groups of 6 scaling factors, where

first group is Stability Class A,

and the speed classes have upper

bounds (m/s) defined in Group 12

5 = Temperature (12 scaling factors, where temperature

classes have upper bounds (C) of:

0, 5, 10, 15, 20, 25, 30, 35, 40,

45, 50, 50+)

--------

a

Data for each species are treated as a separate input subgroup

and therefore must end with an input group terminator.

------------------------------------------------------------------------------

-

INPUT GROUPS: 17a & 17b -- Non-gridded (discrete) receptor information

-----------------------

---------------

Subgroup (17a)

31

Appendix H.6 - 1484
SCPWIN4KM02.inp 7/2/2007

---------------

Number of non-gridded receptors (NREC) No default ! NREC = 194 !

!END!

---------------

Subgroup (17b) ---------------

a

NON-GRIDDED (DISCRETE) RECEPTOR DATA

------------------------------------

X Y Ground Height b

Receptor Coordinate Coordinate Elevation Above Ground

No. (km) (km) (m) (m)

-------- ---------- ---------- --------- ------------ 1 ! X = 1609.918, -633.619, 0.0, 0.00, ! !END!

2 ! X = 1610.682, -633.471, 1.0, 0.00, ! !END!

3 ! X = 1610.506, -632.561, 1.0, 0.00, ! !END!

4 ! X = 1611.270, -632.414, 1.0, 0.00, ! !END!

5 ! X = 1611.094, -631.504, 1.0, 0.00, ! !END!

6 ! X = 1611.858, -631.356, 1.0, 0.00, ! !END!

7 ! X = 1610.154, -630.742, 1.0, 0.00, ! !END!

8 ! X = 1610.918, -630.594, 1.0, 0.00, ! !END!

9 ! X = 1611.682, -630.446, 1.0, 0.00, ! !END!

10 ! X = 1612.446, -630.298, 1.0, 0.00, ! !END!

11 ! X = 1610.742, -629.684, 1.0, 0.00, ! !END!

12 ! X = 1611.506, -629.536, 1.0, 0.00, ! !END!

13 ! X = 1612.269, -629.389, 1.0, 0.00, ! !END!

14 ! X = 1610.566, -628.774, 0.0, 0.00, ! !END!

15 ! X = 1611.330, -628.627, 1.0, 0.00, ! !END!

16 ! X = 1612.093, -628.479, 1.0, 0.00, ! !END!

17 ! X = 1612.857, -628.331, 1.0, 0.00, ! !END!

18 ! X = 1610.390, -627.865, 1.0, 0.00, ! !END!

19 ! X = 1611.154, -627.717, 1.0, 0.00, ! !END!

20 ! X = 1610.214, -626.955, 1.0, 0.00, ! !END!

21 ! X = 1610.978, -626.807, 1.0, 0.00, ! !END!

22 ! X = 1611.741, -626.660, 1.0, 0.00, ! !END!

23 ! X = 1610.802, -625.898, 1.0, 0.00, ! !END!

24 ! X = 1611.565, -625.750, 1.0, 0.00, ! !END!

25 ! X = 1610.626, -624.988, 1.0, 0.00, ! !END!

26 ! X = 1611.389, -624.840, 2.0, 0.00, ! !END!

27 ! X = 1612.152, -624.692, 1.0, 0.00, ! !END!

28 ! X = 1615.968, -623.952, 0.0, 0.00, ! !END!

29 ! X = 1611.213, -623.931, 1.0, 0.00, ! !END!

30 ! X = 1611.976, -623.783, 1.0, 0.00, ! !END!

31 ! X = 1616.554, -622.895, 0.0, 0.00, ! !END!

32 ! X = 1611.037, -623.021, 1.0, 0.00, ! !END!

33 ! X = 1611.800, -622.873, 1.0, 0.00, ! !END!

34 ! X = 1612.386, -621.816, 1.0, 0.00, ! !END!

35 ! X = 1613.149, -621.668, 1.0, 0.00, ! !END!

36 ! X = 1617.727, -620.779, 0.0, 0.00, ! !END!

37 ! X = 1612.973, -620.758, 1.0, 0.00, ! !END!

38 ! X = 1613.736, -620.610, 1.0, 0.00, ! !END!

39 ! X = 1621.949, -618.069, 0.0, 0.00, ! !END!

40 ! X = 1622.711, -617.920, 1.0, 0.00, ! !END!

41 ! X = 1623.474, -617.771, 1.0, 0.00, ! !END!

42 ! X = 1614.146, -618.643, 1.0, 0.00, ! !END!

43 ! X = 1623.297, -616.862, 1.0, 0.00, ! !END!

44 ! X = 1624.059, -616.713, 1.0, 0.00, ! !END!

45 ! X = 1624.821, -616.564, 1.0, 0.00, ! !END!

46 ! X = 1625.584, -616.415, 1.0, 0.00, ! !END!

47 ! X = 1633.208, -614.921, 1.0, 0.00, ! !END!

48 ! X = 1633.970, -614.771, 0.0, 0.00, ! !END!

49 ! X = 1614.732, -617.586, 1.0, 0.00, ! !END!

50 ! X = 1621.594, -616.250, 0.0, 0.00, ! !END!

32

Appendix H.6 - 1485
SCPWIN4KM02.inp 7/2/2007

51 ! X = 1622.357, -616.102, 1.0, 0.00, ! !END!

52 ! X = 1623.119, -615.953, 1.0, 0.00, ! !END!

53 ! X = 1623.881, -615.804, 1.0, 0.00, ! !END!

54 ! X = 1624.644, -615.655, 1.0, 0.00, ! !END!

55 ! X = 1625.406, -615.506, 1.0, 0.00, ! !END!

56 ! X = 1626.169, -615.357, 1.0, 0.00, ! !END!

57 ! X = 1626.931, -615.208, 1.0, 0.00, ! !END!

58 ! X = 1627.693, -615.059, 1.0, 0.00, ! !END!

59 ! X = 1628.456, -614.909, 2.0, 0.00, ! !END!

60 ! X = 1629.218, -614.760, 2.0, 0.00, ! !END!

61 ! X = 1629.980, -614.611, 1.0, 0.00, ! !END!

62 ! X = 1630.742, -614.461, 2.0, 0.00, ! !END!

63 ! X = 1631.505, -614.311, 1.0, 0.00, ! !END!

64 ! X = 1633.791, -613.862, 0.0, 0.00, ! !END!

65 ! X = 1615.318, -616.528, 1.0, 0.00, ! !END!

66 ! X = 1616.080, -616.380, 1.0, 0.00, ! !END!

67 ! X = 1616.843, -616.232, 1.0, 0.00, ! !END!

68 ! X = 1620.654, -615.490, 0.0, 0.00, ! !END!

69 ! X = 1621.417, -615.341, 1.0, 0.00, ! !END!

70 ! X = 1622.179, -615.192, 1.0, 0.00, ! !END!

71 ! X = 1622.942, -615.044, 1.0, 0.00, ! !END!

72 ! X = 1623.704, -614.895, 1.0, 0.00, ! !END!

73 ! X = 1624.466, -614.746, 1.0, 0.00, ! !END!

74 ! X = 1625.228, -614.597, 1.0, 0.00, ! !END!

75 ! X = 1628.277, -614.000, 1.0, 0.00, ! !END!

76 ! X = 1629.040, -613.851, 1.0, 0.00, ! !END!

77 ! X = 1632.088, -613.253, 1.0, 0.00, ! !END!

78 ! X = 1634.375, -612.803, 0.0, 0.00, ! !END!

79 ! X = 1619.715, -614.729, 0.0, 0.00, ! !END!

80 ! X = 1620.477, -614.580, 1.0, 0.00, ! !END!

81 ! X = 1621.240, -614.432, 1.0, 0.00, ! !END!

82 ! X = 1622.002, -614.283, 1.0, 0.00, ! !END!

83 ! X = 1622.764, -614.134, 1.0, 0.00, ! !END!

84 ! X = 1623.526, -613.986, 1.0, 0.00, ! !END!

85 ! X = 1624.288, -613.837, 1.0, 0.00, ! !END!

86 ! X = 1625.051, -613.688, 1.0, 0.00, ! !END!

87 ! X = 1625.813, -613.539, 1.0, 0.00, ! !END!

88 ! X = 1626.575, -613.389, 1.0, 0.00, ! !END!

89 ! X = 1627.337, -613.240, 1.0, 0.00, ! !END!

90 ! X = 1629.624, -612.792, 1.0, 0.00, ! !END!

91 ! X = 1630.386, -612.643, 1.0, 0.00, ! !END!

92 ! X = 1631.148, -612.493, 1.0, 0.00, ! !END!

93 ! X = 1631.910, -612.344, 1.0, 0.00, ! !END!

94 ! X = 1634.196, -611.894, 0.0, 0.00, ! !END!

95 ! X = 1618.776, -613.968, 1.0, 0.00, ! !END!

96 ! X = 1619.538, -613.820, 1.0, 0.00, ! !END!

97 ! X = 1620.300, -613.671, 1.0, 0.00, ! !END!

98 ! X = 1621.062, -613.523, 1.0, 0.00, ! !END!

99 ! X = 1621.824, -613.374, 1.0, 0.00, ! !END!

100 ! X = 1622.587, -613.225, 1.0, 0.00, ! !END!

101 ! X = 1623.349, -613.076, 1.0, 0.00, ! !END!

102 ! X = 1624.111, -612.927, 1.0, 0.00, ! !END!

103 ! X = 1624.873, -612.778, 1.0, 0.00, ! !END!

104 ! X = 1625.635, -612.629, 1.0, 0.00, ! !END!

105 ! X = 1626.397, -612.480, 1.0, 0.00, ! !END!

106 ! X = 1627.159, -612.331, 1.0, 0.00, ! !END!

107 ! X = 1627.921, -612.182, 1.0, 0.00, ! !END!

108 ! X = 1628.683, -612.033, 1.0, 0.00, ! !END!

109 ! X = 1629.445, -611.883, 1.0, 0.00, ! !END!

110 ! X = 1630.207, -611.734, 1.0, 0.00, ! !END!

111 ! X = 1630.969, -611.584, 1.0, 0.00, ! !END!

112 ! X = 1634.017, -610.985, 0.0, 0.00, ! !END!

113 ! X = 1620.123, -612.762, 1.0, 0.00, ! !END!

114 ! X = 1620.885, -612.613, 1.0, 0.00, ! !END!

115 ! X = 1621.647, -612.465, 1.0, 0.00, ! !END!

116 ! X = 1622.409, -612.316, 1.0, 0.00, ! !END!

117 ! X = 1623.171, -612.167, 1.0, 0.00, ! !END!

33

Appendix H.6 - 1486
SCPWIN4KM02.inp 7/2/2007

118!X= 1623.933, -612.018, 1.0, 0.00,! !END!

119!X= 1624.695, -611.869, 1.0, 0.00,! !END!

120!X= 1625.457, -611.720, 1.0, 0.00,! !END!

121!X= 1626.219, -611.571, 1.0, 0.00,! !END!

122!X= 1626.981, -611.422, 1.0, 0.00,! !END!

123!X= 1627.743, -611.273, 1.0, 0.00,! !END!

124!X= 1628.505, -611.123, 1.0, 0.00,! !END!

125!X= 1633.077, -610.226, 0.0, 0.00,! !END!

126!X= 1633.839, -610.076, 1.0, 0.00,! !END!

127!X= 1634.600, -609.927, 1.0, 0.00,! !END!

128!X= 1621.470, -611.555, 1.0, 0.00,! !END!

129!X= 1622.232, -611.407, 1.0, 0.00,! !END!

130!X= 1622.993, -611.258, 1.0, 0.00,! !END!

131!X= 1623.755, -611.109, 1.0, 0.00,! !END!

132!X= 1624.517, -610.960, 1.0, 0.00,! !END!

133!X= 1625.279, -610.811, 1.0, 0.00,! !END!

134!X= 1626.041, -610.662, 1.0, 0.00,! !END!

135!X= 1626.803, -610.513, 1.0, 0.00,! !END!

136!X= 1627.565, -610.364, 1.0, 0.00,! !END!

137!X= 1628.327, -610.214, 1.0, 0.00,! !END!

138!X= 1629.089, -610.065, 1.0, 0.00,! !END!

139!X= 1629.851, -609.916, 1.0, 0.00,! !END!

140!X= 1630.613, -609.766, 1.0, 0.00,! !END!

141!X= 1634.422, -609.018, 0.0, 0.00,! !END!

142!X= 1624.340, -610.051, 1.0, 0.00,! !END!

143!X= 1625.102, -609.902, 1.0, 0.00,! !END!

144!X= 1625.863, -609.753, 1.0, 0.00,! !END!

145!X= 1626.625, -609.604, 1.0, 0.00,! !END!

146!X= 1627.387, -609.455, 1.0, 0.00,! !END!

147!X= 1628.149, -609.305, 1.0, 0.00,! !END!

148!X= 1628.911, -609.156, 1.0, 0.00,! !END!

149!X= 1629.672, -609.007, 1.0, 0.00,! !END!

150!X= 1630.434, -608.857, 1.0, 0.00,! !END!

151!X= 1634.243, -608.109, 0.0, 0.00,! !END!

152!X= 1628.732, -608.247, 1.0, 0.00,! !END!

153!X= 1629.494, -608.098, 1.0, 0.00,! !END!

154!X= 1630.256, -607.948, 1.0, 0.00,! !END!

155!X= 1631.017, -607.799, 1.0, 0.00,! !END!

156!X= 1629.316, -607.189, 1.0, 0.00,! !END!

157!X= 1630.077, -607.039, 1.0, 0.00,! !END!

158!X= 1630.839, -606.890, 1.0, 0.00,! !END!

159!X= 1631.600, -606.740, 1.0, 0.00,! !END!

160!X= 1629.899, -606.130, 1.0, 0.00,! !END!

161!X= 1630.660, -605.981, 1.0, 0.00,! !END!

162!X= 1631.422, -605.831, 1.0, 0.00,! !END!

163!X= 1629.721, -605.221, 1.0, 0.00,! !END!

164!X= 1630.482, -605.072, 1.0, 0.00,! !END!* End CR

165!X= 1488.690, -835.192, 1.0, 0.00,! !END!* Begin WI

166!X= 1489.472, -835.055, 1.0, 0.00,! !END!

167!X= 1488.530, -834.276, 1.0, 0.00,! !END!

168!X= 1487.428, -832.581, 3.0, 0.00,! !END!

169!X= 1488.210, -832.444, 2.0, 0.00,! !END!

170!X= 1488.993, -832.308, 1.0, 0.00,! !END!

171!X= 1489.775, -832.171, 1.0, 0.00,! !END!

172!X= 1486.486, -831.801, 1.0, 0.00,! !END!

173!X= 1487.268, -831.665, 1.0, 0.00,! !END!

174!X= 1488.051, -831.529, 1.0, 0.00,! !END!

175!X= 1488.833, -831.392, 1.0, 0.00,! !END!

176!X= 1489.615, -831.256, 1.0, 0.00,! !END!

177!X= 1486.327, -830.886, 1.0, 0.00,! !END!

178!X= 1487.109, -830.749, 1.0, 0.00,! !END!

179!X= 1487.891, -830.613, 1.0, 0.00,! !END!

180!X= 1488.673, -830.476, 1.0, 0.00,! !END!

181!X= 1489.455, -830.340, 1.0, 0.00,! !END!

182!X= 1485.385, -830.106, 1.0, 0.00,! !END!

183!X= 1486.167, -829.970, 1.0, 0.00,! !END!

184!X= 1486.949, -829.834, 1.0, 0.00,! !END!

34

Appendix H.6 - 1487
SCPWIN4KM02.inp 7/2/2007

185 ! X = 1487.731, -829.697, 1.0, 0.00, ! !END!

186 ! X = 1488.513, -829.561, 1.0, 0.00, ! !END!

187 ! X = 1489.295, -829.424, 1.0, 0.00, ! !END!

188 ! X = 1485.225, -829.190, 1.0, 0.00, ! !END!

189 ! X = 1486.007, -829.054, 1.0, 0.00, ! !END!

190 ! X = 1486.789, -828.918, 1.0, 0.00, ! !END!

191 ! X = 1487.571, -828.781, 1.0, 0.00, ! !END!

192 ! X = 1488.353, -828.645, 1.0, 0.00, ! !END!

193 ! X = 1489.135, -828.508, 1.0, 0.00, ! !END!

194 ! X = 1485.848, -828.138, 1.0, 0.00, ! !END! * End WI

-------------

a

Data for each receptor are treated as a separate input subgroup

and therefore must end with an input group terminator.

b

Receptor height above ground is optional. If no value is entered,

the receptor is placed on the ground.

35

Appendix H.6 - 1488
Santee Cooper – Grainger, Jefferies, Winyah A - 6 Trinity Consultants 

BART Applicability Modeling Analysis 

SAMPLE POSTUTIL INPUT FILE

SCPWIN4KMUT02.INP 

WINYAH 4-KM REFINED ANALYSIS YEAR 2002 

Appendix H.6 - 1489
SCPWIN4KMUT02.inp 3/21/2007

SANTEE COOPER WINYAH, SC

BART APPLICABILITY ANALYSIS 2002 - 4KM GRID

CAPE ROMAIN/WOLF ISLAND

---------------- Run title (3 lines) ------------------------------------------

POSTUTIL MODEL CONTROL FILE

---------------------------

------------------------------------------------------------------------------

-

INPUT GROUP: 0 -- Input and Output File Names

--------------

-------------

Subgroup (0a) -------------

Output Files

------------

File Default File Name ---- -----------------

List File POSTUTIL.LST ! UTLLST = T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KMUT02.LST !

Data File MODEL.DAT ! UTLDAT =

T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KMUT02.CON !

Input Files

-----------

A time-varying file of "background" concentrations can be included

when the ammonia-limiting method (ALM) for setting the HNO3/NO3

concentration partition is accomplished in 1 step. This option

is selected by setting MNITRATE=3 in Input Group 1.

Species required in the "background" concentration file are:

SO4, NO3, HNO3 and TNH3 (total NH3 = NH3gaseous + NH3particulate).

File Default File Name

---- ----------------- BCKG File BCKGALM.DAT * BCKGALM =BCKGALM.DAT *

A number of CALPUFF data files may be processed in this application.

The files may represent individual CALPUFF simulations that were made

for a specific set of species and/or sources. Specify the total number

of CALPUFF runs you wish to combine, and provide the filename for each

in subgroup 0b.

Number of CALPUFF data files (NFILES)

Default: 1 ! NFILES = 1 !

Meteorological data files are needed for the HNO3/NO3 partition option.

Three types of meteorological data files can be used:

METFM= 0 - CALMET.DAT

METFM= 1 - 1-D file with RH, Temp and Rhoair timeseries

METFM= 2 - 2-D files with either Rh, Temp or Rhoair in each

(3 2_D files are needed)

The default is to use CALMET.DAT files.

Default: 0 ! METFM = 0 !

1

Appendix H.6 - 1490
SCPWIN4KMUT02.inp 3/21/2007

Multiple meteorological data files may be used in sequence to span the

processing period. Specify the number of time-period files (NMET) that

you need to use, and provide a filename for each in subgroup 0b.

- NMET is 0 if no meteorological files are provided - NMET is 1 if METFM=1 (multiple file feature is not available) - NMET is 1 or more if METFM=0 or 2 (multiple CALMET files or 2DMET files)

Number of meteorological data file time-periods (NMET)

Default: 0 ! NMET = 0 !

All filenames will be converted to lower case if LCFILES = T

Otherwise, if LCFILES = F, filenames will be converted to UPPER CASE

Convert filenames to lower case? Default: T ! LCFILES = T !

T = lower case

F = UPPER CASE

!END!

-----------------------------------------------------------

NOTE: file/path names can be up to 70 characters in length -----------------------------------------------------------

-------------

Subgroup (0b)

-------------

NMET CALMET Data Files (METFM=0):

Input File Default File Name

---------- ----------------- 1 MET.DAT * UTLMET =CALMET.DAT *

NMET 1-D Data Files (METFM=1):

Input File Default File Name

---------- ----------------- 1 MET_1D.DAT * MET1D = MET_1D.DAT * *END*

NMET 2-D Data Files of Each Type (METFM=2):

Input File Default File Name ---------- -----------------

1 RHUMD.DAT * M2DRHU = RELHUM.DAT * *END*

1 TEMP.DAT * M2DTMP = TEMP.DAT * *END*

1 RHOAIR.DAT * M2DRHO = RHOAIR.DAT * *END*

NFILES CALPUFF Data Files:

Input File Default File Name ---------- -----------------

1 CALPUFF.DAT ! MODDAT =

T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KM02.con ! !END!

-------------

Note: provide NMET lines of the form * UTLMET = name * *END*

or * MET1D = name * *END*

or * M2DRHU = name * *END*

(and) * M2DTMP = name * *END*

2

Appendix H.6 - 1491
SCPWIN4KMUT02.inp 3/21/2007

(and) * M2DRHO = name * *END*

and NFILES lines of the form * MODDAT = name * *END*

where the * should be replaced with an exclamation point,

the special delimiter character.

------------------------------------------------------------------------------

--

INPUT GROUP: 1 -- General run control parameters --------------

Starting date: Year (ISYR) -- No default ! ISYR = 2002 !

Month (ISMO) -- No default ! ISMO = 1 !

Day (ISDY) -- No default ! ISDY = 1 !

Hour (ISHR) -- No default ! ISHR = 1 !

Number of periods to process

(NPER) -- No default ! NPER = 8760 !

Number of species to process from CALPUFF runs

(NSPECINP) -- No default ! NSPECINP = 16 !

Number of species to write to output file

(NSPECOUT) -- No default ! NSPECOUT = 9 !

Number of species to compute from those modeled

(must be no greater than NSPECOUT)

(NSPECCMP) -- No default ! NSPECCMP = 4 !

When multiple files are used, a species name may appear in more than

one file. Data for this species will be summed (appropriate if the

CALPUFF runs use different source groups). If this summing is not

appropriate, remove duplicate species from the file(s).

Stop run if duplicate species names

are found? (MDUPLCT) Default: 0 ! MDUPLCT = 0 !

0 = no (i.e., duplicate species are summed)

1 = yes (i.e., run is halted)

Data for each species in a CALPUFF data file may also be scaled as

they are read. This can be done to alter the emission rate of all

sources that were modeled in a particular CALPUFF application.

The scaling factor for each species is entered in Subgroup (2d), for

each file for which scaling is requested.

Number of CALPUFF data files that will be scaled

(must be no greater than NFILES)

(NSCALED) Default: 0 ! NSCALED = 0 !

Ammonia-Limiting Method Option to recompute the HNO3/NO3 concentration

partition prior to performing other actions is controlled by MNITRATE.

This option will NOT alter any deposition fluxes contained in the CALPUFF

file(s). Three partition selections are provided. The first two are

typically used in sequence (POSTUTIL is run more than once). The first

selection (MNITRATE=1) computes the partition for the TOTAL (all sources)

concentration fields (SO4, NO3, HNO3; NH3), and the second (MNITRATE=2)

uses this partition (from the previous application of POSTUTIL) to

compute the partition for individual source groups. The third selection

(MNITRATE=3) can be used instead in a single POSTUTIL application if a

file of background concentrations is provided (BCKGALM in Input Group 0).

Required information for MNITRATE=1 includes:

3

Appendix H.6 - 1492
SCPWIN4KMUT02.inp 3/21/2007

species NO3, HNO3, and SO4

NH3 concentration(s)

met. data file for RH and T

Required information for MNITRATE=2 includes:

species NO3 and HNO3 for a source group

species NO3ALL and HNO3ALL for all source groups, properly

partitioned

Required information for MNITRATE=3 includes:

species NO3, HNO3, and SO4 for a source group

species NO3, HNO3, SO4 and TNH3 from the background BCKGALM file

If TNH3 is not in the background BCKGALM file, monthly TNH3

concentrations are used (BCKTNH3)

TNH3= total NH3 = NH3gaseous+NH3particulate

Recompute the HNO3/NO3 partition for concentrations?

(MNITRATE) Default: 0 ! MNITRATE = 0 !

0 = no

1 = yes, for all sources combined

2 = yes, for a source group

3 = yes, ALM application in one step

SOURCE OF AMMONIA:

Ammonia may be available as a modeled species in the CALPUFF files,

and it may or may not be appropriate to use it for repartitioning NO3/HNO3

(in option MNITRATE=1 or MNITRATE=3). Its use is contolled by NH3TYP.

When NH3 is listed as a processed species in Subgroup (2a), as one of

the NSPECINP ASPECI entries, and the right option is chosen for NH3TYP,

the NH3 modeled values from the CALPUFF concentration files will be used

in the chemical equilibrium calculation.

NH3TYP also controls when monthly background ammonia values are used. Both

gaseous (NH3) and total (TNH3=NH3gaseous+NH3particulate) ammonia can be

provided

monthly as BCKNH3/BCKTNH3.

What is the input source of Ammonia?

(NH3TYP) No Default ! NH3TYP = 0 !

0 = No background will be used.

ONLY NH3 or TNH3 from the concentration

files listed in Subgroup (2a&2b) as

a processed species will be used.

(Cannot be used with MNITRATE=3)

1 = NH3 Monthly averaged background (BCKNH3)

listed below will be added to NH3 from

concentration files listed in Subgroup (2a)

2 = NH3 from background concentration file BCKGALM

will be added to NH3 from concentration files

listed in Subgroup (2a&2b)

(ONLY possible for MNITRATE=3)

3 = NH3 Monthly averaged background (BCKNH3)

listed below will be used alone.

4 = NH3 from background concentration file BCKGALM

will be used alone

(ONLY possible for MNITRATE=3)

| OPTION | NH3 or TNH3 CONC | BCKNH3 or BCKTNH3 | TNH3/BCKGALM or BCKTNH3 |

|--------|------------------|-------------------|-------------------------|

| 0 | X | 0 | 0 |

|--------|------------------|-------------------|-------------------------|

4

Appendix H.6 - 1493
SCPWIN4KMUT02.inp 3/21/2007

| 1 | X | X | 0 |

|--------|------------------|-------------------|-------------------------|

| 2 | X | 0 | X |

|--------|------------------|-------------------|-------------------------|

| 3 | 0 | X | 0 |

|--------|------------------|-------------------|-------------------------|

| 4 | 0 | 0 | X |

|--------|------------------|-------------------|-------------------------|

Default monthly (12 values) background ammonia concentration (ppb)

used for HNO3/NO3 partition (need to choose one or the other):

Gaseous NH3 (BCKNH3) Default: -999

! BCKNH3 = 12*0.5 !

Total TNH3 (BCKTNH3) Default: -999 * BCKTNH3 = 1., 1., 1., 1.1, 1.4, 1.3, 1.3, 1.2, 4*1. *

If a single value is entered, this is used for all 12 months.

Month 1 is JANUARY, Month 12 is DECEMBER.

!END!

------------------------------------------------------------------------------

-

INPUT GROUP: 2 -- Species Processing Information

--------------

-------------

Subgroup (2a)

-------------

The following NSPECINP species will be processed:

! ASPECI = SO2 ! !END!

! ASPECI = SO4 ! !END!

! ASPECI = NOX ! !END!

! ASPECI = HNO3 ! !END!

! ASPECI = NO3 ! !END!

! ASPECI = POC081 ! !END!

! ASPECI = POC056 ! !END!

! ASPECI = PIC081 ! !END!

! ASPECI = PIC056 ! !END!

! ASPECI = PMC800 ! !END!

! ASPECI = PMC425 ! !END!

! ASPECI = PMF187 ! !END!

! ASPECI = PMF112 ! !END!

! ASPECI = PMF081 ! !END!

! ASPECI = PMF056 ! !END!

! ASPECI = EC056 ! !END!

-------------

Subgroup (2b) -------------

The following NSPECOUT species will be written:

! ASPECO = SO2 ! !END!

! ASPECO = SO4 ! !END!

! ASPECO = NOX ! !END!

! ASPECO = HNO3 ! !END!

! ASPECO = NO3 ! !END!

! ASPECO = EC ! !END!

! ASPECO = SOIL ! !END!

! ASPECO = SOA ! !END!

5

Appendix H.6 - 1494
SCPWIN4KMUT02.inp 3/21/2007

! ASPECO = PMC ! !END!

-------------

Subgroup (2c) -------------

The following NSPECCMP species will be computed by scaling and summing

one or more of the processed input species. Identify the name(s) of

the computed species and provide the scaling factors for each of the

NSPECINP input species (NSPECCMP groups of NSPECINP+1 lines each):

! CSPECCMP = EC !

! EC056 = 1.0000 !

!END!

! CSPECCMP = SOIL !

! PMF187 = 1.0000 !

! PMF112 = 1.0000 !

! PMF081 = 1.0000 !

! PMF056 = 1.0000 !

! PIC081 = 1.0000 !

! PIC056 = 1.0000 !

!END!

! CSPECCMP = SOA !

! POC081 = 1.0000 !

! POC056 = 1.0000 !

!END!

! CSPECCMP = PMC !

! PMC800 = 1.0000 !

! PMC425 = 1.0000 !

!END!

-------------

Subgroup (2d) -------------

Each species in NSCALED CALPUFF data files may be scaled before being

processed (e.g., to change the emission rate for all sources modeled

in the run that produced a data file). For each file, identify the

file name and then provide the name(s) of the scaled species and the

corresponding scaling factors (A,B where x' = Ax+B).

A(Default=1.0) B(Default=0.0)

-------------- --------------

6

Appendix H.6 - 1495
Santee Cooper – Grainger, Jefferies, Winyah A - 7 Trinity Consultants 

BART Applicability Modeling Analysis 

SAMPLE CALPOST INPUT FILE

SCPWIN4KMVANCR02.INP 

WINYAH CAPE ROMAIN 4-KM REFINED ANALYSIS YEAR 2002 

ANNUAL AVERAGE NATURAL BACKGROUND 

Appendix H.6 - 1496
SCPWIN4KMVANCR02.inp 3/21/2007

SANTEE COOPER WINYAH, SC

BART APPLICABILITY ANALYSIS - 4KM GRID

CAPE ROMAIN VISTAS ANNUAL AVERAGE NATURAL BACKGROUND

---------------- Run title (3 lines) ------------------------------------------

CALPOST MODEL CONTROL FILE

--------------------------

------------------------------------------------------------------------------

-

INPUT GROUP: 0 -- Input and Output File Names

--------------

Input Files

-----------

File Default File Name ---- -----------------

Conc/Dep Flux File MODEL.DAT ! MODDAT = T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KMUT02.con !

Relative Humidity File VISB.DAT * VISDAT = * Background Data File BACK.DAT * BACKDAT = *

Transmissometer or VSRN.DAT * VSRDAT = * Nephelometer Data File or

DATSAV Weather Data File or

Prognostic Weather File

Output Files

------------

File Default File Name ---- -----------------

List File CALPOST.LST ! PSTLST = T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KMVANCR02.lst !

Pathname for Timeseries Files (blank) * TSPATH = * (activate with exclamation points only if

providing NON-BLANK character string)

Pathname for Plot Files (blank) * PLPATH = *

(activate with exclamation points only if

providing NON-BLANK character string)

User Character String (U) to augment default filenames

(activate with exclamation points only if

providing NON-BLANK character string)

Timeseries TSERIES_ASPEC_ttHR_CONC_TSUNAM.DAT

Peak Value PEAKVAL_ASPEC_ttHR_CONC_TSUNAM.DAT

* TSUNAM = *

Top Nth Rank Plot RANK(ALL)_ASPEC_ttHR_CONC_TUNAM.DAT

or RANK(ii)_ASPEC_ttHR_CONC_TUNAM.GRD

* TUNAM = *

Exceedance Plot EXCEED_ASPEC_ttHR_CONC_XUNAM.DAT

or EXCEED_ASPEC_ttHR_CONC_XUNAM.GRD

* XUNAM = *

Echo Plot

(Specific Days)

yyyy_Mmm_Ddd_hh00(UTCszzzz)_L00_ASPEC_ttHR_CONC.DAT

1

Appendix H.6 - 1497
SCPWIN4KMVANCR02.inp 3/21/2007

or yyyy_Mmm_Ddd_hh00(UTCszzzz)_L00_ASPEC_ttHR_CONC.GRD

Visibility Plot DAILY_VISIB_VUNAM.DAT * VUNAM = * (Daily Peak Summary)

Auxiliary Output Files ----------------------

File Default File Name

---- ----------------- Visibility Change DELVIS.DAT ! DVISDAT =

T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KMVANCR02.del !

------------------------------------------------------------------------------

--

All file names will be converted to lower case if LCFILES = T

Otherwise, if LCFILES = F, file names will be converted to UPPER CASE

T = lower case ! LCFILES = T !

F = UPPER CASE

NOTE: (1) file/path names can be up to 132 characters in length

NOTE: (2) Filenames for ALL PLOT and TIMESERIES FILES are constructed

using a template that includes a pathname, user-supplied

character(s), and context-specific strings, where

ASPEC = Species Name

CONC = CONC Or WFLX Or DFLX Or TFLX

tt = Averaging Period (e.g. 03)

ii = Rank (e.g. 02)

hh = Hour(ending) in LST

szzzz = Base time zone shift from UTC (EST is -0500)

yyyy = Year(LST) mm = Month(LST)

dd = day of month (LST)

are determined internally based on selections made below.

If a path or user-supplied character(s) are supplied, each

must contain at least 1 non-blank character.

!END!

------------------------------------------------------------------------------

--

INPUT GROUP: 1 -- General run control parameters

--------------

Option to run all periods found

in the met. file(s) (METRUN) Default: 0 ! METRUN = 1 !

METRUN = 0 - Run period explicitly defined below

METRUN = 1 - Run all periods in CALPUFF data file(s)

Starting date: Year (ISYR) -- No default ! ISYR = 2002 !

(used only if Month (ISMO) -- No default ! ISMO = 1 !

METRUN = 0) Day (ISDY) -- No default ! ISDY = 1 !

Hour (ISHR) -- No default ! ISHR = 1 !

Number of hours to process (NHRS) -- No default ! NHRS = 8760 !

Process every hour of data?(NREP) -- Default: 1 ! NREP = 1 !

(1 = every hour processed,

2 = every 2nd hour processed,

5 = every 5th hour processed, etc.)

Species & Concentration/Deposition Information ----------------------------------------------

2

Appendix H.6 - 1498
SCPWIN4KMVANCR02.inp 3/21/2007

Species to process (ASPEC) -- No default ! ASPEC = VISIB !

(ASPEC = VISIB for visibility processing)

Layer/deposition code (ILAYER) -- Default: 1 ! ILAYER = 1 !

'1' for CALPUFF concentrations,

'-1' for dry deposition fluxes,

'-2' for wet deposition fluxes,

'-3' for wet+dry deposition fluxes.

Scaling factors of the form: -- Defaults: ! A = 0.0 !

X(new) = X(old) * A + B A = 0.0 ! B = 0.0 !

(NOT applied if A = B = 0.0) B = 0.0

Add Hourly Background Concentrations/Fluxes?

(LBACK) -- Default: F ! LBACK = F !

Source information ------------------

Option to process source contributions:

0 = Process only total reported contributions

1 = Sum all individual source contributions and process

2 = Run in TRACEBACK mode to identify source

contributions at a SINGLE receptor

(MSOURCE) -- Default: 0 ! MSOURCE = 0 !

Receptor information

--------------------

Gridded receptors processed? (LG) -- Default: F ! LG = F !

Discrete receptors processed? (LD) -- Default: F ! LD = T !

CTSG Complex terrain receptors processed?

(LCT) -- Default: F ! LCT = F !

--Report results by DISCRETE receptor RING?

(only used when LD = T) (LDRING) -- Default: F ! LDRING = F !

--Select range of DISCRETE receptors (only used when LD = T):

Select ALL DISCRETE receptors by setting NDRECP flag to -1;

OR

Select SPECIFIC DISCRETE receptors by entering a flag (0,1) for each

0 = discrete receptor not processed

1 = discrete receptor processed

using repeated value notation to select blocks of receptors:

23*1, 15*0, 12*1

Flag for all receptors after the last one assigned is set to 0

(NDRECP) -- Default: -1

! NDRECP = 164*1, 30*0 !

--Select range of GRIDDED receptors (only used when LG = T):

X index of LL corner (IBGRID) -- Default: -1 ! IBGRID = -1 !

(-1 OR 1 <= IBGRID <= NX)

Y index of LL corner (JBGRID) -- Default: -1 ! JBGRID = -1 !

(-1 OR 1 <= JBGRID <= NY)

X index of UR corner (IEGRID) -- Default: -1 ! IEGRID = -1 !

(-1 OR 1 <= IEGRID <= NX)

Y index of UR corner (JEGRID) -- Default: -1 ! JEGRID = -1 !

(-1 OR 1 <= JEGRID <= NY)

Note: Entire grid is processed if IBGRID=JBGRID=IEGRID=JEGRID=-1

3

Appendix H.6 - 1499