### 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 approva l for its use from the EPA Regional Administrator. In response to these comme nts and for the reasons discussed below, the following information is provide d to assist SC DHEC in re questing formal approval for using this methodology for BART modeling an alyses in South Carolina. As you know, similar BART exemption modeling analyses ha ve 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 equati on, takes into account the latest scientific understa nding 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 calcu lated using each of the equations.
2. The new algorithm recognizes spatial and tem poral 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 re flect 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 NO 2 

### term to represent times when light absorption by NO 2 

### 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 impor tant 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 algorit hm. 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 e ligible emission units to haze at the Cape

### Mr. John P. Glass, Jr. Page 3 April 30, 2007

Romain Wildlife Refuge. EPA Region 4, Natio nal Park Service, and the US Fish & Wildlife Service determined that adding site-spe cific data to account for the impact of sea salt and Rayleigh scattering to the old IMPROVE equation is notappropriate, 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 modificati on 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 appropri ately implements the features of the new IMPROVE equation, with the exception of th e nitrogen dioxide term. EPA Region 4 further stated that with the addition of their recommendation for addressing the NO 2 

### term, the VISTAS methodology to incorporate the new IMPROVE equation is acceptable on a case-by-case basis upon consultation with the EP A 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 IMPR OVE equation and nephelometer data at several Class I areas is included in Enclos ure 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. Th e 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 th e equations versus measured data for Cape Romain is included in Enclosure 1.

Since the initial EPA revi ew of the methodology, Dr. Tombach has modified the spreadsheet to address the NO 2 

### contribution. A copy of th e 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 sodi um ion or strontium approach to estimate annual sea salt concentrations as discussed in Dr. Ivar Tombach’s March 5, 200 7 white paper entitled, “Evaluation of Alternative Methods for Estima ting Sea Salt Concentrations at Cape Romain” (Enclosure 4). The new IMPROVE approach usi ng either sodium or strontium measurements provides a more reliable estimate of sea salt a nd 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. Prio r to that data availability, it was still evident that the use of the chloride ion c oncentrations for determining the sea salt concentration term of the new IMPROVE equation has serious drawbacks, as documented in Dr. Tombach’s white paper. Th e chloride ion from s ea salt reacts with

### Mr. John P. Glass, Jr. Page 4 April 30, 2007

naturally-occurring and anthr opogenic acids (primarily sulfuric and nitric) in the atmosphere. This reaction converts the chlori de to several gaseous forms where it is no longer captured on the filter media. As su ch, technical/scientific experts (including VISTAS’ expert consultant) readily acknowledge that the chloride ion cannot be used to obtain a representative value for naturall y 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 Ro main. The results of the survey found that the strontium approach generally estimates aver age concentrations very close to average concentrations using VISTAS' "preferred" methods for applying measuremen ts of sodium ion and elemental sodium. A long history of reliable observations is available from Cape Romain, including a continuous record of stront ium measurements from September 1994 through September 2004. This data corroborates Ivar Tombach's average value of 0.77 ug/m 3 

### 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/m 3 

### 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 modeli ng results using the new IM PROVE equation and sea salt contribution included in the natural background visibility are summari zed in Tables 1 and 2 below. The modeling results continue to indicate that the IP Georgetown BART eligible emission units do not cause or contribut e 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 pr esented in this report, IP Georgetown requests that the SCDHEC evaluate the m odeling 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 file s 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 a nd the sodium ion sea salt calculation methodology and for Wolf Island using th e original guideline IMPROVE equation currently incorporated into the CALMET processor.

### 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 re quest 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

### Mr. John P. Glass, Jr. Page 6 April 30, 2007

units do not cause or contribu te to regional haze at any Cl ass I area. Thus, we are requesting that the IP Georgetown Mill be ex empt from the BART rule. Should you have any questions whatsoever about the completene ss 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. Nephe lometer Scattering Using New or Old IMPROVE Algorithm and Daily f(RH), Shining Rock, NC 1995-1999 Enclosure 3 Instructions: A Postpro cessor 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 – URSPrepared 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.: 12084-002-0101Prepared 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.: 12084-002-0101May 2006 i 

## Contents

1.0 Introd ucti on............................................................................................................... ................................ 1-1
1.1 Objectives..................................................................................................................... ................... 1-1
1.2 Location of source vs. re levant Cla ss I Areas ................................................................................ 1- 1
1.3 Organization of pr otocol do cument.............................................................................................. ... 1-2

2.0 Source description and emissi ons da ta ...................................................................................... ......... 2-1
2.1 Unit-specific source data...................................................................................................... ........... 2-1

3.0 Geophysical and meteorol ogical input to CALMET............................................................................ 3 -1
3.1 Modeling domain................................................................................................................ ............. 3-1
3.2 Terrain and land us e ........................................................................................................... ............ 3-1
3.3 Meteorologica l dat abase........................................................................................................ ......... 3-1 3.3.1 12-km regional CA LMET domain...................................................................................... 3-1 3.3.2 4-km sub-regional CALMET domain and finer grid resoluti ons ....................................... 3-2
3.4 Air quality database (background ozone and a mmonia) ............................................................... 3-3
3.5 Natural conditions and monthly f(RH) at Cla ss I Areas ................................................................. 3-3

4.0 Air quality mode ling pro cedures ............................................................................................ ............... 4-1
4.1 Model selection and feat ures................................................................................................... ....... 4-1
4.2 Modeling domain and rec eptors .................................................................................................. ... 4-1
4.3 Technical options us ed in the modeling ......................................................................................... 4-1
4.4 Light extinction and haze impact calcul ations ................................................................................ 4- 2

5.0 Presentation of modeling r esults........................................................................................... ................ 5-1

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

## List of Tables

Table 1-1 ISG Georgetown Inc., Georgetown, SC Mill – BART-eligib le 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 Georgeto wn............................................... 1-3May 2006 1-1 

## 1.0 Introduction

1.1 Objectives The Regional Haze Rule regulations require Best Avail able Retrofit Technology (BAR T) for any BART-eligible source that ‘‘emits any air pollutant which may reas onably 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 require ments based on dispersion modeling demonstrating that the source cannot reasonably be anticipa ted to cause or contribute to visibility impairment in a Class I area.
The ISG Georgetown Inc. Steel mill in Georgetown, SC (ISG Georgeto wn) has been identified as a BART eligible source. As noted above, a modeling demonstrat ion can be used to show t hat 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 followi ng 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 pot ential emissions for these BART-eligible units is provided in Table 1-1. This exemption modeling anal ysis will determine whether the source is subject to BART requirements, and if so, describe the modeling procedure to be used for the visibili ty impacts factor in the BART determination step. The modeling procedures are consist ent 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 pr otocol 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 progr am, has determined that units listed in Table 1-1 are BART-eligible for NO x , SO 2 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 an d, if necessary, the determination modeling will be conducted for each of these Class I areas in acco rdance with the referenced VISTAS common BART modeling protocol and the procedures described in this source-specific BART modeling protocol.May 2006 1-2 

### 1.3 Organization of protocol document Section 2 of this source-specific prot ocol 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 de scribes the air quality modeling procedures and Section 5 discusses the presentation of modeling results. Since a ll 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., Geor getown, SC Mill – BART-eligible Units

Allowable Emissions
Source Description Start-up Date

SO 2 NO X PM 10

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.May 2006 1-3 Figure 1-1 Location of PSD Class I Areas in relation to ISC GeorgetownMay 2006 2-1 

## 2.0 Source description and emissions data

2.1 Unit-specific source data
The emissions data used to assess the visi bility impacts at the Class I areas within 300 km of the subject plant is discussed in this section. The SO 2 , NO x and PM 10 emissions were provided by ISG Georgetown and are representative of the highest 24-hour average actual e mission rate for the years 2001 to 2003. Since various components of PM 10 emissions have different visibility extinction efficiencies, the PM 10 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, reas onably conservative engineering judgments were made to develop this information, as described below.

• Filterable PM 10 emissions are assumed to be the same as reported PM 10 emissions.

• Fine PM 10 is conservatively assumed to equal filterable PM 10 based on control by baghouses.

• Elemental carbon (EC) is assumed to be 5% of filterable fine PM 10 based on engineering judgment and data for combustion sources, Table 6 of “Cat alog 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.

• SO 4 emissions are assumed to be 5% of SO 2 emissions times the ratio 96/64 to account for molecular weight.

• Organic PM 10 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 PM 10 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 us ing 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.May 20062-2Table 2-1 ISG Georgetown Inc., Georgetown, SC Mill - Baseline BART Stack Parameters and Criteria Pollutant EmissionsLocation UTM(Zone 17 NAD-83)MaximumShort-Term EmissionsCaseSource /UnitUTMEastUTMNorthStackHtBaseElev.DiameterGasExitVel.StackGasExitTemp.SO2NOXPM10km km M ft m m/s degK lb/hr lb/hr lb/hrBaseline Unit 1 659055 3693488 24.4 10 232.8 0.01 355.4 28.1 20.1 16.9Baseline Unit 2a 659107 36933579.1 10 0.5 27.1293.2 0 0 1.23Baseline Unit 2b 659103 369337228.3 10 3.6 13.7417.0 3.39 77.4 0Baseline Unit 3 659139 36932909.4 10 0.6 10.5293.2 0 0 0.4Baseline Unit 4 659109 36933668.5 10 0.5 12.6293.2 0 0 0.7Baseline Unit 5No Emissions – Ore is Wet in this ProcessNotes:•Emission parameters are provided for the baselineconditions of the units listed below. Emissionparameters are not currently available for control cases as this information will be developed as part of theBART 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 Unit1 represents a non-vertical release.•Emissions reported are the highest 24-hour averageactual emission rate for the years 2001 to 2003.May 20062-3Table 2-2ISG Georgetown Inc., Georgetown, SC Mill- Baseline BART Particulate SpeciationParticleSpeciationFine Filterable PMCondensible PM10CaseUnitTotalFilterablePM10CoursePM10Total Soil EC Total Inorganic(SO4)OrganicAll Values in lb/hrBaselineUnit 116.9 0 16.9 16.1 0.85 11.84 2.11 9.73BaselineUnit 2a1.23 0 1.2 1.17 0.06 0.71 0 0.71BaselineUnit 2b0 0 0 0 0 0.25 0.25 0BaselineUnit 30.4 0 0.4 0.38 0.02 0 0 0BaselineUnit 40.7 0 0.7 0.67 0.04 0 0 0BaselineUnit 5No Emissions – Ore is Wet in this ProcessNotes:•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 averageactual emission rate for the years 2001 to 2003.•Filterable PM10emissions are assumed to be the same as reported PM10emissions.•Fine PM10is conservatively assumed to equal filterable PM10based on control by baghouses.•Elemental Carbon (EC) is assumed to be 5% of filterable fine PM10based on engineering judgment and data forcombustion sources, Table 6 of “Catalog of Global Emissions Inventories and Emission Inventory Tools for BlackCarbon”, William Battye and Kathy Boyer,EPA Contract No. 68-D-98-046, January 2002.•SO4emissions are assumed to be 5% of SO2emissions times the ratio 96/64 to account for molecular weight•Organic PM10is assumed to be equal to VOC emissions. VOC emissions for Units 1 and 2 are based on the ratio ofAP-42 emission factors (Draft Section 12.5.1) for VOC and filterable PM and the reported PM10emissions. VOCemissions for Units 3, 4, and 5 are expected to be zero.May 2006 3-1 

## 3.0 Geophysical and mete orological input to CALMET

3.1 Modeling domain The regional 12-km CALMET meteorological data wa s developed for three years (2001-2003) to cover all BART eligible sources and PSD Class I areas within the VI STAS 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-r egional 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 met eorological 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 regi onal and 4-km sub-regional CA LMET 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 deve loped by VISTAS with CALMET such that any Class I areas within VISTAS states and nearby areas could be ev aluated 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 VI STAS to develop the 12-km regional meteorological data that is eventually to be the basis for the regi onal 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 modeli ng 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 resoluti on 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 theMay 2006 3-2 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 ad just the initial wind field to be in agreement with actual observations. However, since these observati ons are already incorporated into the actual MM5 simulations (although they are part of a model outp ut), 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 VI STAS CALPUFF modeling. The development of the 12- km regional CALMET databases is discussed in more de tail in the Section 4 of the VISTAS protocol. However, due to the omission of the Step 2 process, VISTAS has determined that us e of this database is a screening step, so that the peak predicted source impact is to be com pared to the 0.5 deciview threshold for a contribution to visibility impai rment 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. Th ese sub-regional 4-km CALMET modeling domains were strategically developed to cover all potential BART eligib le 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 me teorological data, precipit ation 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 (f rom 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 defini ng the wind field. Domain-specific CALMET settings used for each sub-regional CALMET r un 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 environm ents. If a source is located within 50-100 km from a Class I area, the intervening terrain often plays an imp ortant 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 indi cate that peak concentrations could be better predicted with a CALPUFF run using a finer grid, due to terrain or ot her 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 domai n would need to be changed because of disk space restrictions, especially if only Cape Romain were the so le 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.May 2006 3-3 If CALMET needs to be run at even a finer grid resoluti on, then the appropriate model setting/files (specifically the GEO.DAT file) will be modified. A summary of thes e 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 data base (background ozone and ammonia) Hourly measurements of ozone from all non-urban moni tors, 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 CALPU FF. VISTAS intends to provid e 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 w ould be used without application of the ALM technique.



### 3.5 Natural conditions and mont hly f(RH) at Class I Areas For each of the Class I areas shown in Figure 1-1, nat ural background conditions must be established in order to determine a change in natural conditions related to a source’s emissions. E PA’s “Guidance for Estimating Natural Visibility Conditions Under the Regional Ha ze 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 preambl e 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.May 2006 4-1 

## 4.0 Air quality mo deling procedures

This section provides a summary of the modeling procedur es outlined in the VISTAS pr otocol 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 CA LPUFF 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 Servic e (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, CALP UFF, 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 domai n 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 gr id, we will provide a s upplement to this modeling protocol with t he 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 subdom ain 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 decid es to conduct additional modeling with a finer grid than 4 km, this modeling protocol will be updated to specif y 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 VIST AS common BART modeling protocol recommendations. The VISTAS protocol also notes th at 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 determi ned that building downwash effects do not need to be included in the CALPUFF modeling.
The POSTUTIL utility program will be used to repartition HNO 3 and NO 3 using VISTAS-provided ammonia concentrations derived from previ ous 2002 CMAQ modeling conducted by EPA. For any 12-km grid screening modeling, VISTAS would use a default ammonia backg round concentration of 0.5 ppb. For refined modeling with a 4-km grid, Class I-specific monthly ammonia concent ration values will be used as supplied by VISTAS.May 2006 4-2 

### 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 prima ry and secondary particulate matter concentrations on light extinction. The formula that is used is the existing (not the November 2005 revised) IMPROV E/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:

b ext = 3 f(RH) [(NH 4 )2SO 4 ] + 3 f(RH) [NH 4 NO 3 ] + 4[OC] + 1[Soil] + 0.6[Coarse Mass] + 10[EC] + b Ray The concentrations, in square brackets, are in μg/m3 and b ext is in units of Mm -1 . The Rayleigh scattering term (b Ray ) has a default value of 10 Mm -1 , as recommended in EPA guidance for tracking reasonable progress. However, as recommended in the VISTAS protocol, fo r refined 4-km grid (or smaller) CALPUFF runs if necessary, the Rayleigh scattering term will be modified fo r 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 ar eas 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 anticip ated 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 extincti on is calculated by first using the hygroscopic components of the source-caused c oncentrations, due to ammonium sulfate and nitrate, and monthly Class I area-specific f(RH) values. The c ontribution to the total s ource-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 element al 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 s ub-regional CALMET database, a source does not cause or contribute to visibility impai rment 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 BAR T modeling protocol presents a fl ow 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 Ge orgetown, SC mill does no t cause or contribute to visibility impairment, then the source will not be subject to BART requirements, and no further analysis is needed.May 2006 5-1 

## 5.0 Presentation of modeling results

The BART exemption and, if necessary, the BART dete rmination 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 modeli ng, most likely with a 4-km grid, and will provide a BART modeling report. If more refined modeling is requir ed, Mittal Steel wi ll have ENSR work with the South Carolina DHEC to provide the appr opriate 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 al l Class I areas in the VISTAS domain and those in neighboring states and impacts from the BART 4-km grid exemption m odeling at those Class I areas within 300 km of the source, as illustrat ed 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 98 th percentile days in each year (and the 98 th 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 98 th percentile for refined analyses) that th e 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, resu lts for those Class I areas for which impacts of the source exceeded 0.5 deciviews in the 4-km initial modeli ng. We would report the same type of results as provided for 4-km exemption modeling.
For BART determination modeling, if necessary, the object ive 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 relati ve 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 modeli ng contractor on CD-ROM or DVD media.May 2006 A -1 

## Appendix A: VISTAS BART Modeling Protocol (March 9, 2006 update)

# 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)Table of Contents iv 

## TABLE OF CONTENTS

Page

SUMMARY S

- 1

1. INTRODUCTION AND PRO TOCOL 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 CALP UFF 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 MODEL ING PROTOCOL 32 4.1 Overv iew 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, NH 3 

### , 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 Regi onal 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 Over view 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 Contri bution to CALPUFF Modeling of BART Eligible Sources 51

5

. SOURCE

- SPECIFIC MODELING PR OTOCOL 52Table 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 62Summary S

- 1 

## SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available R etrofit 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 i n 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

Be st 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 attri bution 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 visi bility 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 s ources, 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 V ISTAS 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, Christell e 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 Par k, 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 1 st 

### , 2005 VISTAS held another meeting with its participants in Nas hville, 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.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 mode ling

 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 in tended 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
spec ific 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 a re:

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 analys isSummary 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 enhancemen ts 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 i nclude 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 ra te 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 a vailable.

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 SO 4 

### and total NO 3 

### (HNO 3 

### + NO 3 

### ). CMAQ data in CALPUFF

- ready format will be provided for each Class I area by VISTAS. After running CALPUFF for an individual facility, repartition NO 3 

### in POSTUTIL using the CMAQ background data, including that for NH 3 

### .

• Use ozone data from non

- urban monitors as the b ackground ozone input.

• Use the Pasquill

- Gifford dispersion method. 1

1 The Fina l 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.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 backgro und 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 descr ibe, 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 determ ining if a BART

- eligible source is subject to BART CALPUFF modeling, use a two

tier 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 g rid 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 are a boundary (Q/d) as a presumptive indicator that a BART

- eligible source is subject to BART. If Q/d for SO 2 

### 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 pe rformed 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 p erform 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.Summary S

- 5 

### Not e 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. I f 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 a nd 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 98 th 

### 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 l ess) analyses, use the 98 th 

### percentile impact value for the 24

- hr average. Use either the 8 th 

### highest day in each year or the 22 nd 

### highest day in the 3

- year period, whichever is more conservative, for comparison to the exemption threshold.

Use the same mod el 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 co ntrol” 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).Introduction and Protocol Objectives 1 

## 1. INTRODUCTION AND PRO TOC OL 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 rul e 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 usef ul 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 pa rks 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” i s 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 “BART

eligible source”.

One of the listed categories i s 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 adde d together regardless of construction date. In this category, electric generating sources greater than 750 MW have presumptive SO 2 

### and NO x 

### emission limits. States may presume the same limits for EGU sources between 250

- 750 MW. However, units at those sou rces 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 SO 2 

### and NO x 

### . 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 iden tified 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 ar e subject to slight adjustments and refinements.) No BART sources are located on Tribal lands.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 protocol 2 

### and provides additional des cription 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 s ource 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.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 i n 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

- eligi ble 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 modeli ng of BART

eligible 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 m odel 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 alter native 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, th e 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 trad ing alternative under the proposed EPA trading guidance (70 FR 44154

- 44175) that is to be promulgated early in 2006.Review of EPA’s Guidance 4 

## 2. REVIEW OF EPA’S GUID ANCE FOR BART MODELI NG

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 inte nded 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

Th e 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” a ir 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 BART

eligible. Finally, a source is eligible for BAR T if potential e missions of visibility

- impairing air pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate matter (PM 10 

### ) and gaseous precursors to secondary fine particulate matter, such as SO 2 

### and NO x 

### . Whether a mmonia or volatile organic compounds (VOCs) should be included as visibility

impairing 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 PM 2.5 

### 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 SO 2 

### and NO x 

### and 15 TPY of PM 10 

### or PM 2.5 

### . This assumes that the other BART

- eligibility criteria are met.)

2) Determin e 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 quali ty 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 le vel 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 visibi lity 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.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 98 th 

### 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 impai rment and therefore is not subject to BART. Whether or not the 98 th 

### percentile value exceeds the threshold must be determined at each Class I area. Over an annual period, this implies the 8 th 

### highest 24

- hr value at a particular Class I area is compared to the contribution threshold. Over a 3

- year modeling period, the 98 th 

### percentile value may be interpreted as the highest of the three annual 98 th 

### percentile values at a particular Class I area or the 22 nd 

### highest value in the combined three year record, w hichever 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 opposi te, 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 pl ant analysis conducted by EPA in finalizing the BART rule. Under this option, sources with potential emissions of SO 2 

### plus NO x 

### of less than 500 tons and a distance from any Class I area greater than 50 kilometers or sources with SO 2 

### plus NO x 

### potential emi ssions 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 cons idered along with SO 2 

### and NO x 

### , so that a plant could be exempted if the combined potential emissions of SO 2 

### , NO x 

### , plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting the “best” al ternative, 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.Review of EPA’s Guidance 6 

### Note that if a source agrees to apply the most stringent c ontrols 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 20 07.

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 th at 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 th e 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 an d 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 rul e (70 FR 39138

- 39139) that emissions reductions under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO 2 

### and NO x 

### control for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR and therefore a BART anal ysis 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 so urce applications” (70 FR 39123). Photochemical grid models may beReview 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 invol ve 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. E PA’s role in the development of the protocol is only advisory as the “States better understand the BART

eligible 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 appropri ate 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 e valuated 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, frequenc y, 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 progr ess 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.

I f 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 CALP UFF 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 2 006.Ove rview of the CALPUFF Modeling System 8 

## 3. OVERVIEW OF THE CALP UFF 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 mo del 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 u se 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 vi sibility 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). CALPU FF 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 avai lable 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 m odeling 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 meteor ological 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 observationa l 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 puf f 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 incl udes 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.Ove rview of the CALPUFF Modeling System 9 

### Fig ure 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 nitrat e 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 combina tion 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 model ing components in more detail.Ove rview 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. T he 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 B oundary 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 Minimizati on

- 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, surfaceOve rview 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 base d 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 me thod 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

ver sion 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 laye r 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 mixi ng 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 meteorologi cal 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 fe atures 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 terrain

for ced vertical velocity, subject to an exponential, stability

- dependent decay function. The effects of terrain on the horizontal wind components are evaluated by applying a divergence

minimization scheme to the initial guess wind field. The divergence minimi zation 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 (19 82). This scheme includes bothOve rview 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 m agnitude 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 terra in 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 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 re gions 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 ob servational 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 CAL MET. 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, refle ct 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 variati on 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 me teorological 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. 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 o ne 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 Comp lex TerrainOve rview 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 bas ed 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 (H d 

### ) to determine which pollutant material is deflected around the sides of a hill (below H d 

### ) and which material is advected over the hill (above H d 

### ). The local flow (near the feature) used to define H d 

### is taken from the gridded CALMET fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Func tions: 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 rap idly

- 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 stat e 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 ( σ v 

### and σ w 

### ), the use of similarity theory to estimate σ v 

### and σ w 

### from modeled surface heat and momentum fluxes, or the use of Pasqui ll

- 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 a dditional 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.Ove rview 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

- specif ic 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 μ m diameter) from coarse particulate matter (2.5

- 10 μ m diame ter).

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 origi nal, 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.

We t 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 ty pe (i.e., frozen vs. liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical transformation effects using the five species scheme (SO 2 

### , SO 4 = 

### , NO x 

### , HNO 3 

### , and NO 3 - 

### ) employed in the MESOPUFF II model or a set of u ser

- 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 SO 2 

### and NO x 

### and thus allows analyses of visibility impacts. Ambie nt 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 rela tionship to partition nitric acid and nitrate on an hour

- by

- hour and receptor

- by

- receptor basis.Ove rview 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 o r 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, temperatu re)

- 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 emissio ns 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 w ildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient ( σ y , σ z ) options

- Direct measurements of σ v and σ w

- Estimated values of σ v and σ w based on similarity theor y

-- 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)

• Ve rtical 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/we akly stable flow in CTDMPLUS)Ove rview 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, H d , as in CTDMPLUS:

- Above H d , material flows over the hill and experiences alt ered diffusion rates

- Below H d , 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 resis tance 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 con ditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo

- first

- order chemical mechanism for SO 2 , SO = 4 , NO x , HNO 3 , and NO 3 (MESOPUFF II method)

- Pseudo

- first

- order chemical mech anism for SO 2 , SO = 4 , NO, NO 2 , HNO 3 , and NO 3 (RIVAD/ARM3 method)

- User

- specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function o f 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 visibility

related 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 de scribed 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.Ove rview 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 betwe en 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 effe cts 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. PO STUTIL 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 a nd 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 Appl icability 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 caut iously 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 pr esence 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 si nce 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 6 th Modeling Conference provides the background for this rec ommendation: “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 dista nces, 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.)Ove rview of the CALPUFF Modeling System 18 

### new puffs thus generated can increase the horiz ontal 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 puff s 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 shorte r 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 algorithm s 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 regula tory 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” situ ations 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, ad ditional 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 Pasqui ll

- 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 convectiveOve rview of the CALPUFF Modeling System 19 

### probability density function (pdf) mo dule, 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 Model 5 

### . The ISCST3 model and its PG dispersion coeffic ients 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 applicati on. 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 appro priate 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 start ing 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 a bove, 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 concent ration distribution will require a grid that is finer than 1 km. (For reference, the width of a Gaussian plume, 2 σ y 

### , is roughly 1 km after 10 km of travel distance, assuming Pasquill

- Gifford dispersion rates under neutral conditions.)

As an additional con sideration, 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 C ALPUFF 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 Terra in) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218

- 68261. 9 November 2005.Ove rview 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 optica l effects of emissions of particles, SO 2 

### , and NO x 

### 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 PM 2.5


### Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that CALPUFF can treat p rimary pollutants such as PM 10 

### . In actuality, CALPUFF can simulate PM 10 

### or PM 2.5 

### 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 PM 2.5 

### 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. PM 2.5 

### particles, which have a mass median diameter around 0.5 μ m, 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 o ther hand, coarse particles (those PM 10 

### particles larger than PM 2.5 

### ) 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 pa rticle 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 PM 10 

### , is acco unted for in the calculation of the deposition velocity. Effects of inertial impaction (important for the upper part of the PM 10 

### 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 μ m diameter), which has higher light extinction efficiency than coarse particulate matter (2.5

- 10 μ m diameters), should be treated separately in the model. CALPUFF allows for user

- specified s ize categories to be treated as

6 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE

- II for BART applicationOve rview of the CALPUFF Modeling System 21 

### separate species, which includes calculating size

- specific dry deposition velocities for each size category.
A primary PM 2.5 

### emission from coal

- fired electric generating units (EGUs) that is of relevance to visibility calcul ations 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 signi ficant formation of secondary sulfate conversion from SO 2 

### has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate

The MESOPUFF

- II chemistry algorithm used in CALPUFF 7 simulates the gas phase oxidation of sulfur dioxide to sulfate by a linear tr ansformation 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 easily

computed or observed parameters that are used as surrogates for the factors that control SO 2 

### 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 SO 2 

### oxidation is a function of OH radical concentrations. Ozone concentrations are correlated with OH radical concentrat ions during daytime hours, and their use in the daytime SO 2 

### 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, b ut 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
ot her variables in the relationship. They are surrogates for factors that the regression analysis has shown to be important in SO 2 

### oxidation rates. At night, the SO 2 

### conversion is set to a constant low value (default is 0.2%/hr). Aqueous phase oxidation of SO 2 

### 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 expl ained below, non

- linear behavior in nitrate formation can be modeled.

7 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) MESOPUFF

- II system and the 6 species RIVAD system (which treats NO and NO 2 separately). IWAQM recommends using the MESOPUFF

- II system with CALPUFF. The RIVAD system is believed to be more appr opriate 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.Ove rview 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 phas e oxidation of SO 2 

### . 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 wat er 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 s ulfate 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 NO x 

### concentrations in the plume are high and in actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO 2 

### ; and (2) lac k 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 de gree, 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 SO 2 

### 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 CALP UFF 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 applicati ons, the ozone concentrations required for the sulfate formation calculations are derived from ambient measurements, although concentrations simulated by regional models can be used.Ove rview of the CALPUFF Modeling System 23 

### Figure 3

- 2. Observed vs. CALPUFF

- predicted 24

- hour sulfate concentr ations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995.

NO x 

### and Secondary Ammonium Nitrate

The MESOPUFF

- II chemistry algorithm used in CALPUFF simulates the oxidation of NO x 

### to nitric acid and organic nitrates (both gases) by transformation r ates that depend on NO x 

### 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 betwee n 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 concentratio n 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 represe nting nitrate chemistry. Potential situations where this assumption may not be correct, however, include (1) plumes with high concentrations of NO x 

### that deplete the ambient ozone and thus limit theOve rview of the CALPUFF Modeling System 24 

### transformation of NO x 

### 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 NO x 

### 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 o bservational 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 amm onia 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 ex tinction 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 consid ered (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 esti mated 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 concentrati ons 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) o r 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 incr eased 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

measure ments, 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,Ove rview of the CALPUFF Modeling System 25 

### so defining the ammonia concentration throughout the Southeast would require ex tensive 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 i n 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 facto r 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)Ove rview of the CALPUFF Modeling System 26 

### Figure 3

- 3b. Observed vs. CALP UFF

- 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 Nati onal 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 significan t localized, short

- term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of the 1.4 μ g/m 3 

### concentration assumed for natural conditions by the EPA, which means that current fossil fuel burni ng 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 (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb)
CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4 

### [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IMPROVE 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 (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb) CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4Ove rview of the CALPUFF Modeling System 27 

### sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 d v) 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 ca pabilities that do exist include the following.

First, PM 10 

### 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 a nalyses 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 PM 10 

### emissions condense into organic PM 2.5 

### particles. The size of this organic frac tion 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, parame terized 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, accordin g to this rule, isoprene (C 5 

### H 8 

### ) 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 PM 10 

### and with biogenic VOCs, although the soundness of concentration estimates by these approaches when modeling a p lume 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 extend ed 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 emis sions of butane and ethane during petroleum processing

8 Recent researc h 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 descrip tion of this module.Ove rview 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 s uch 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 photoche mical 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 PM 10 

### emissions, though.

3.2.3 Regional Haze

Calculation of the impact of t he 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:

b ext 

### = 3 f(RH) [(NH 4 

### ) 2 

### SO 4 

### ] + 3 f(RH) [NH 4 

### NO 3 

### ] + 4[OC] + 1[Soil] + + 0.6[Coarse Mass] + 10[EC] + b Ray 

### (3

- 1)

The concentrations, in square brackets, are in μ g/m 3 

### and b ext 

### is in units of Mm - 1 

### . The Rayleigh scattering term (b Ray 

### ) 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 t he 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 alon e) 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 IMPR OVE 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 (H I) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (b ext 

### /10) (3

- 2)

where HI is in units of deciviews (dv) and b ext 

### 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.Ove rview 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 extin ction 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 avera ge 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 coefficie nt 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 (EP A, 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 thi s 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 concentra tions 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

- specifi c f(RH) from a 1993 IWAQM curve (a different oneOve rview 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, e xcept 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 backg round 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 r uling 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 p rime”.

Refined Estimates of Extinction and Natural Background Visibilit y
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).

Refinemen ts 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 var ies 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.Ove rview 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 measurem ents

- 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 si te

- 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 new ly 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.VISTAS’ Common Modeling Protocol 32 

### 4. VISTAS’ COMMON MODEL ING PROTOCOL 4.1 Overview of Common Modeling Appr oach

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 v isibility 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 (SO 2 

### , NO x 

### , PM, and in certain cases VOC and NH 3 

### ) that “may reasonably be antici pated 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 fr om individual sources exceeds a contribution threshold) for SO 2 

### , NO x 

### , 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 NH 3 

### 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 SO 2 

### , NO x 

### , or PM is sub ject to BART:

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d for SO 2 

### > 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.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 dom ain 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 asses sment 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 t o 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 ski pped and the analysis may proceed directly to the finer grid modeling analysis, which is described in Section 4.4.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 eval uating 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 exce ption 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 i mpacts 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 impr ovement 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 i n 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 f or 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 c ontrol 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 co ntrol 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, NH 3 

### , and PM

Volatile Organic Co mpounds

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. (Separate ly, condensable organic carbon can be calculated from PM 10 

### .)

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

- BAR T) 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 emissionsVISTAS’ 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 VISTA S 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 imp act 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 NH 3 

### 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 addr ess ammonia (NH 3 

### ) 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 NH 3 

### emissions from point s ources (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 NH 3 

### 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 NH 3 

### 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 BA RT

- 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 SO 2 

### and NO x 

### under the CAIR rule meet the BART requirements, but these EGUs may still be subje ct to BART for primary PM. To determine the potential impacts of PM from EGU and non

EGU 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 wi ll be reported at the same time as results for VOC and NH 3 

### . 

### 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 protoc ol. 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 re solution meteorological fields provided by VISTAS. In someVISTAS’ 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 anti cipate 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 q uestion.

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 pr otocol 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 so urce

- 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 meteorolog ical and dispersion options. A pre

- computed set of meteorological files and a pre

defined 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 sc reening 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)VISTAS’ Common Modeling Protocol 37 

### - 2002 MM5 dataset at 12 km and 36 km g rid (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 three

dimensional meteorological files will be available on external hard drives to the States and other parties.

The initial procedure t o determine if a BART

- eligible source is subject to BART uses the pre

computed 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 sim ulations 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 (whic h 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 larg e 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 th e 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.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

m ay set a lower threshold). The 98 th 

### percentile (8 th 

### 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 impactVISTAS’ 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 8 th 

### highest values or the 22 nd 

### highest value over all three years

com bined, whichever is greater.

For the purposes of the initial analysis, however, the highest value over the three

- year period (not the 98 th 

### percentile value) is to be compared to the contribution threshold. This ensures a significant measure of conservat ism 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 contributio n 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 CALM ET, 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 emi ssions from BART

eligible 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 inclu de 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.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 simulati ons 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 si mulations)

- 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: IW AQM 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 deali ng 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 lake s 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 spa tial 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 CALMETVISTAS’ 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 wil l 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 rec ommended for all

source

- receptor distances to be considered in the BART analyses.

- Chemical mechanism : MESOPUFF II module

- Background concentrations of SO 4 

### and TNO 3 

### (HNO 3 

### + NO 3 

### ) from CMAQ 2001

- 2003 annual runs

- Species modeled : SO 2 

### , SO 4 

### , NO x 

### , HNO 3 

### , NO 3 

### and particulate matter in size categories of <0.625 μ m, 0.625

- 1.0 μ m, 1.0

- 1.25 μ m, 1.25

- 2.5 μ m, 2.5

- 6.0 μ m and 6

- 10 μ m 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 μ m in diameter), coarse PM (between 2.5

- 10 μ m 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.) N ote 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 SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### .

Condensable emissions are consid ered 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 ava ilable, 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 SO 2 

### and NO x 

### and 15 tons per year for PM 10 

### ). De minimis levels are plant wide for each visibility

- impairing pollutant, so individual units may be mo deled 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: elementa l carbon (soot), “soil” (fineVISTAS’ Common Modeling Protocol 42 

### PM < 2.5 μ m diameter), coarse particulate matter (2.5

- 10 μ m diameter) and organics. The process is illustrated in Figure 4

- 3. If source

- specific emissions factors are not available, AP

42 factors can be used to estimate the PM speciation for those source sectors for which AP

42 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 μ m 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 disp ersion 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

H 2 SO 4 ) As Prim. SO4) Organic PM
(SOA)

EC Soil PMC SO 4 f(RH) Non

- SO 4 Inorganic OCVISTAS’ Common Modeling Protocol 43 

### - Puff representation: integrated puff s ampling 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 modeli ng)

- 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: SO 4 

### , NO 3 

### , 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 μ g/m 3) 

### into CALPOST that is numerically equal to thi s result. (Since the extinction efficiency of soil is 1 m 2 

### /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 algorit hm 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 Li miting 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 chang es 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 fi ner 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 s hown 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 andVISTAS’ 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 98 th 

### percentile change in deciviews value rather than the more conservative highest value used in the in itial 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 pri ority) 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 emi ssions

- 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 o ptions. 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 rate s 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 do mains 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 sourcesVISTAS’ 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 Stat e 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 eff ects 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 di stances 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

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 ea ch 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 coast al 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 CAL MET 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 NH 3 

### concentrations developed from the 2002 CMAQ regional mode ling 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 e valuating whether a source is contributing to visibility impairment is based on the 98 th 

### 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 re quired in the CALPOST postprocessor in order to allow the 98 th 

### 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 pro duced 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 model ing would be required.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 %_P MC %_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 4.93 9 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 79.78 2 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 3 64 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 = 1.219VISTAS’ 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 SO 4 

### , NO 3 

### , organics, eleme ntal 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 o f three years at six different Class I areas. The 98 th 

### percentile (8 th 

### 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 becaus e the highest 98 th 

### 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 sourc e

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 discu ssion of the number of Class I areas with visibility impairment from the source on 98 th 

### 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

v isibility impact attributed to the source).

4. For the Class I area with the maximum impact, discussion of the number of days below the 98 th 

### 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.VISTAS’ Common Modeling Protocol 49 

### Table 4

- 2. Example of Visibility Impact Ranking s at Six Class I Areas

Class I Area 2001 2002 2003
Delta

Deciview Ranks 1

- 8 Delta

Deciview Ranks 1

- 8 Delta

Deciview 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 0.19VISTAS’ Common Modeling Protocol 50 

### Table 4

- 3. Format of Summary of Results for CALPUFF Modeling in VISTAS’ 12

- k m 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 days 1 and # of receptors with impact > 0.5 dv in Class I area: 2001 # of days 1 and # of receptors with i mpact > 0.5 dv in Class I area: 2002 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2003 # of days 1 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 98 th percentile of days in each year or the three

- year modeling period, as appropriateVISTAS’ Common Modeling Protocol 51 

### States will provide further guidance on graphic presentation of results to simplify evaluation of effectivene ss 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 d ata 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 concernin g 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. V ersion 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 exercis e.

• 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 in clude 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 de scribed 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 NO 3 

### in POSTUTIL.

o Background concentrations files for use in POSTUTIL

o All other set up files used in VISTAS 12

- km CALPUFF runSource

- Specific Modeling Protocol 52 

## 5

. SOURCE

- SPECIFIC MODELING PR OTOCOL
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 re view. 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 un der 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 foll owing parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### ).

Additional building dimension information (buildi ng 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 th e 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 source

specific 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 lati tude/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 suita ble base maps can be found on the CALPUFF data site ( www.src.com ) in the section on “Aerial Photos”.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 Mo deling 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 neededSource

- 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, emi ssions 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.

D iscussions 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 SO 2 

### potential emissions (tpy) NO x 

### potential emissions (tpy) Total PM potential emissions (tpy)
Emissions source

name

... Total emissions
Potential BART

eligible emissionsQuality 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 Cl ass 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 product s 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 exe cution 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 f unding 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 as surance 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 qu ality 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 m ajor 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 b e 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 invol ves 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 un der 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 controlQuality 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 o thers 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 descr ibed 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 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 deve lopment 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 un derstanding 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
t echniques. 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 regulatoryQuality Assurance 57 

### guidance and default values (see referenc es 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 th at 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 t he 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 add ition, 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 prov ided 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 pa rt 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 Tr ansverse 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 d omain, the grid parameters will be provided as part of the source

- specific protocol.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 t he default value for each variable, a text description of the variable, the meaning of each variable option, the units of the variable and inter

relationships among variables indicating if/when the variable is used. Some third

- party commercial GUIs strip o ut 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 i s 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 t he 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 Q uality 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 th e 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 v ariables contain default values. Appendix B of the IWAQM report contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 ofQuality 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 s trip 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 POSTU TIL
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 meth od (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 co ntains 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 v alue of the change in extinction is shown at the bottom of the visibility table (see Table 4

- 1). For a finer grid simulation, the 98 th 

### percentile value (8 th 

### 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

ass urance 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., sulfat e, nitrate, organics, (as SOA), coarse and fine particulate matter and elemental carbon.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 usefu l 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 model ed 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 wei ghting 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,
t he 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 Alternati ve 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 includ e 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
prov iding 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 s ite

- 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 specifi c 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 dataQuality 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 l ight 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 th e 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, perf ormance 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 rath er 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 o f 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 conditi ons 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.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

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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, M on. Wea. Rev ., 132 , 495

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Carson, D.J., 1973: The development of a dry, inv ersion

- capped, convectively unstable boundary layer. Quart. J. Roy. Meteor. Soc ., 99 , 450

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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 Meteorol ogy , 42 (4): 453

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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. Cali fornia 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

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- 004, U.S. Environmental Protection Agency, Research Triangle Park, NC. Environmental Protection Ag ency, 2003b: Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. EPA

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Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommend ations for Modeling Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA

454/R

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### Escoffier

- Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitr ate Aerosol Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12 th 

### 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, Lawrenc e 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 CAP TEX 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. 96 th 

### A&WMA Annual Conference & Exhibition, 22

- 26 June 2003, San Diego, CA.

O'Brien, J.J., 1970: A note on the vertical struct ure of the eddy exchange coefficient in the planetary boundary layer. J. Atmos. Sci ., 27 , 1213

- 1215.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 Sp eciation 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 Algor ithms 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

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- 057, U.S. Environmental Protection Agency, Researc h 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 o f 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 Dispersi on 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.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.A Trusted Global Environmental, Health and Safety Partner 

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Ft. Collins Tox Lab (970) 416-0916 CT, Stamford (203) 323-6620 CT, Willington (860) 429-5323 FL. St. Petersburg (727) 577-5430 FL, Tallahassee (850) 385-5006 GA, Norcross (770) 381-1836 IL, Chicago (630) 836-1700 IL, Collinsville (618) 344-1545 LA, Baton Rouge (225) 298-1206 MA, Air Laboratory (978) 772-2345 MA, Sagamore Beach (508) 888-3900 MA, Westford (978) 589-3000 MA, Woods Hole (508) 457-7900 MD, Columbia (410) 884-9280 ME, Portland (207) 773-9501 MI, Detroit (269) 385-4245 MN, Minneapolis (952) 924-0117 NC, Charlotte (704) 529-1755 NC, Raleigh (919) 872-6600 NH, Gilford (603) 524-8866 NJ, Piscataway (732) 981-0200 NY, Albany (518) 453-6444 NY, Rochester (585) 381-2210 NY, Syracuse (315) 432-0506 NY, Syracuse Air Lab (315) 434-9834 OH, Cincinnati (513) 772-7800 PA, Langhorne (215) 757-4900 PA, Pittsburgh (412) 261-2910 RI, Providence (401) 274-5685 SC, Columbia (803) 216-0003 TX, Dallas (972) 509-2250 TX, Houston (713) 520-9900 VA, Chesapeake (757) 312-0063 WA, Redmond (425) 881-7700 WI, Milwaukee (262) 523-2040 Headquarters MA, Westford (978) 589-3000
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www .ensr.aecom.comPrepared for: ISG Georgetown Inc. Georgetown, SC



# Source-Specific BART Exemption Modeling Results: ISG Georgetown Inc., Georgetown, SC

ENSR Corporation September 2006 Document No.: 12084-002-0102Prepared 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.: 12084-002-0102i September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 

## Contents

1.0 Introduc tion ................................................................................................................... ......................... 1-1
1.1 Objectives ..................................................................................................................... ................... 1-1
1.2 Location of source vs. re levant Cla ss I Areas ................................................................................ 1- 1
1.3 Organization of pr otocol do cument.............................................................................................. ... 1-2

2.0 Source Description a nd Emissions Data.......................................................................................... .. 2-1
2.1 Unit-Specific Source Data ...................................................................................................... ......... 2-1

3.0 Geophysical and Meteorologi cal Input to CALMET .......................................................................... 3-1
3.1 Modeling Domain................................................................................................................ ............. 3-1
3.2 Terrain and Land Us e........................................................................................................... ........... 3-1
3.3 Meteorologica l Dat abase........................................................................................................ ......... 3-1
3.4 Air Quality Database (Backg round Ozone and A mmonia) ............................................................ 3-2
3.5 Natural Conditions and Monthly f(RH) at Cla ss I Areas ................................................................. 3-2

4.0 Air Quality Modeli ng Proc edures ................................................................................................ ........ 4-1
4.1 Model Selecti on and Feat ures ................................................................................................... ..... 4-1
4.2 Modeling Domain and Rec eptors.................................................................................................. .. 4-1
4.3 Technical Options Used in the M odeling ........................................................................................ 4 -1
4.4 Light Extinction and Haze Impact Calcul ations .............................................................................. 4-1

5.0 BART Exemption M odeling R esults................................................................................................ .... 5-1ii September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 

## List of Tables

Table 1-1: ISG Georgetown Inc., Georgetown, SC Mill – BART-eligib le 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 Exemptio n 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 Georgetow n ............................................. 1-31-1 September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 

## 1.0 Introduction

1.1 Objectives The Regional Haze Rule regulations require Best Avail able Retrofit Technology (BAR T) for any BART-eligible source that ‘‘emits any air pollutant which may reas onably 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 require ments based on dispersion modeling demonstrating that the source cannot reasonably be anticipa ted to cause or contribute to visibility impairment in a Class I area.
The ISG Georgetown Inc. Steel mill in Georgetown, SC (ISG Georgeto wn) 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, NO x , exceed 250 tons per year. As noted above, a modeling demonstration can be used to show that a source identif ied as being eligible for BART requirements can be exempt if modeling shows the source does not cause or c ontribute 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 resu lts 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 pot ential emissions for these BART-eligible units is provided in Table 1-1.
The exemption modeling analysis was conducted to de termine whether the source is subject to BART requirements. The modeling procedures are consistent with those out lined 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 progr am, has determined that units listed in Table 1-1 are BART-eligible for NOx, SO 2 and PM. Figure 1-1 shows a plot of t he 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 accordanc e with the referenced VISTAS common BART modeling protocol and the procedures described in th is source-specific BAR T modeling protocol.1-2 September 2006 ISG Georgetown SC – Source-Specific BART Exemption 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 m odeling results. Since all of the references cited are also included in the VISTAS common BART mode ling protocol, no additional references section is required in this document.
Table 1-1: ISG Georgetown Inc., Geor getown, SC Mill – BART-eligible Units

Allowable Emissions
Source Description Start-up Date

SO 2 NO X PM 10

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 1974.1-3 September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 Figure 1-1: Location of PSD Class I Areas in Relation to ISG Georgetown2-1 September 2006 ISG Georgetown SC – Source-Specific BART Exemption 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 visi bility impacts at the Class I areas within 300 km of the subject plant is discussed in this section. The SO 2 , NOx and PM 10 emissions were provided by ISG Georgetown and are representative of the highest 24-hour average actual e mission rate for the years 2001 to 2003. Since various components of PM 10 emissions have different visibility extinction efficiencies, the PM 10 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 PM 10 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.2-2August 2006ISG Georgetown SC – Source-Specific BART ExemptionModeling Results – 12084-002-0102Table 2-1: ISG Georgetown Inc., Georgetown, SC Mill – Baseline BART Stack Parameters and Criteria Pollutant EmissionsLocation UTM(Zone 17 NAD-83)MaximumShort-Term EmissionsStack IDSource /UnitUTMEastUTMNorthStackHtBaseElev.DiameterGasExitVel.StackGasExitTemp.SO2NOXPM10km Km M ft m m/s degK lb/hr lb/hr lb/hr6 Unit 1 659055 3693488 24.384 10232.751 0.010 355.3728.13 20.14 16.87471 Unit 2a 659107 3693357 9.14410 0.488 27.127 293.15 0 0 1.23PS Unit 2b 659103 3693372 28.34610 3.569 13.679 417.04 3.39 77.40 03 Unit 3 659139 3693290 9.44910 0.610 10.516 293.15 0 0 0.364 Unit 4 659109 3693366 8.53410 0.488 12.649 293.15 0 0 0.68Unit5No Emissions – Ore is Wet in this ProcessNotes:•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 volumesource with lateral dimension = 0.85 m andverticaldimension = 1.43 m•Emissions reported are the highest 24-hour averageactual emission rate for the years 2001 to 20033-1 September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 

## 3.0 Geophysical and Mete orological Input to CALMET

3.1 Modeling Domain The regional 12-km CALMET meteorological data wa s developed for three years (2001-2003) to cover all BART eligible sources and PSD Class I areas within the VI STAS 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 do mains are shown in Figure 4-4 of the VISTAS common BART modeling protocol. The selection of the met eorological 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 us e 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 VIST AS 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 modeli ng 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 loca tions 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 me teorological data, precipit ation 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 (f rom 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 defini ng the wind field. Domain-specific CALMET settings used for each sub-regional CALMET run were provid ed and used by the VISTAS modeling contractor.3-2 September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 

### 3.4 Air Quality Database (Background Ozone and Ammonia) Hourly measurements of ozone from all non-urban moni tors, as generated by VISTAS and available on the VISTAS CALPUFF page on the TRC web site (http:// www.src.com/verio/download/s ample_files.htm), were used as input to CALPUFF. A constant gaseous amm onia 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 Mont hly f(RH) at Class I Areas For each of the Class I areas shown in Figure 1-1, nat ural background conditions must be established in order to determine a change in natural conditions related to a source’s emissions. E PA’s “Guidance for Estimating Natural Visibility Conditions Under the Regional Ha ze 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 day s, or annual average value). ISG Georgetown Inc. elected to use the annual average natural background value.4-1 September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 

## 4.0 Air Quality Modeling Procedures

This section provides a summary of the modeling proce dures outlined in the VISTAS protocol that were used for the refined CALPUFF analysis that wa s conducted for ISG’s Georgetown, SC mill.



### 4.1 Model Selection and Features As noted in the VISTAS protocol, VISTAS used CA LPUFF 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. Thes e versions contain enhancements funded by the Minerals Management Service (MMS) and VISTAS. They are maintained on TRC’s Atmospheric Studies Group CALPUFF website fo r public access. This release includes CALMET, CALPUFF, CALPOST, CALSUM, and PO STUTIL 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 fac ility 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 data base of Class I receptors, as recommended by VISTAS. 

### 4.3 Technical Options Used in the Modeling CALMET modeling for the VISTAS-provided 4-km subdom ain 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 opti ons, ISG Georgetown proposed the use of the VISTAS common BART modeling protocol recommendations. T he 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 near est 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 backgr ound concentration of 0.5 ppb. 

### 4.4 Light Extinction and Ha ze 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 particu late matter concentrations on light extinction. The formula that is used was the existing (not the No vember 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:

b ext = 3 f(RH) [(NH 4 )2SO 4 ] + 3 f(RH) [NH 4 NO 3 ] + 4[OC] + 1[Soil] + 0.6[Coarse Mass] + 10[EC] + b Ray The concentrations, in square brackets, are in μg/m 3 and bext is in units of Mm -1 . The Rayleigh scattering term (b Ray ) 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 eit her the annual average or best 20% days’ natural background. ISG Georgetown Inc. elected to use the annual average natural background4-2 September 2006 ISG Georgetown SC – Source-Specific BART Exemption Modeling Results – 12084-002-0102 value. Each hour’s source-caused extinction is calcul ated 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 ex tinction from ammonium sulf ate 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 s ub-regional CALMET database, a source does not cause or contribute to visibility impai rment 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 BAR T modeling protocol presents a fl ow 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 cont ribute to visibility impairment would lead the conclusion that the source will not be subject to BART requirements, and no further analysis is needed.5-1 September 2006 ISG Georgetown SC – Source-Specific BART Exemption 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 valuesindicate that among the three years modeled, the highest 98th percentile predicted impact is 0.279 deltadeciview. These results demonstrate that ISG Geor getown’s peak daily emissions are well below the threshold that would cause or contribute to visibility imp airment. Therefore, the sour ce is not subject to BART, and no further BART analys is is required.
The VISTAS contractor has provided the appropriate electronic files used to co nduct the CALPUFF modeling to the South Carolina DHEC.
Table 5-1: Summary of Results – ISG Ge orgetown Refined BART Exemption Modeling

Class I Area 2001 2002 2003 Annual average background b ext 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

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### 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 M

e adWestvaco 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 MeadW estvaco 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 with in 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 po llutants. 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 prop osed 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 ar eas: 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 MeadWestv aco 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 BA RT 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 gener ally 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 prot ocol, 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 neces sary, Trinity and MeadWestvaco anticipate maintaining close communication with DHEC to en sure that refined analyses are conducted using mutually agreeable data resources and processing options.

### 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, pl ease 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)

## B EST 

## A VAILABLE 

## R ETROFIT 

## T ECHNOLOGY 

## A PPLICABILITY 

## A NALYSIS 

## A IR 

## Q UALITY 

## M ODELING 

## P ROTOCOL 

## M EAD 

## W ESTVACO 

## C ORPORATION 

##  N ORTH 

## C HARLESTON 

## O PERATIONS

T RINITY 

### C ONSULTANTS 

### 1100 Johnson Ferry Road Suite 685 Atlanta, Georgia 30342 (404) 256-1919

March 2006

Project 061101.0024

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION 

### ........................................................................................................1 1.1 O VERVIEW OF 

### R EGIONAL 

### H AZE 

### R ULE AND 

### BART R EQUIREMENTS 

### .........................2 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY 

### ............................................................... 2 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY 

### ...................................................................................................... 2 

### 1.2 S CHEDULE FOR 

### BART I MPLEMENTATION IN 

### S OUTH 

### C AROLINA 

### ...............................8 1.3 O RGANIZATION OF 

### M ODELING 

### P ROTOCOL 

### ................................................................9

2. BART-E LIGIBLE 

### S OURCE 

### D ESCRIPTION 

### .............................................................10 2.1 BART-E LIGIBLE 

### E MISSION 

### U NITS 

### ..........................................................................10 2.2 BART- ELIGIBLE 

### S OURCE 

### M ODEL 

### E MISSIONS 

### I NVENTORY 

### .....................................11 2.2.1 R ECOVERY 

### B OILER 

### N O 

### . 9 P ARTICULATE 

### S PECIATION 

### ........................................ 14 2.2.2 L IME 

### K ILN 

### P ARTICULATE 

### S PECIATION 

### ................................................................ 17 2.2.3 S MELT 

### D ISSOLVING 

### T ANK 

### P ARTICULATE 

### S PECIATION 

### ....................................... 18 

### 2.3 M ODELED 

### S TACK 

### P ARAMETERS AND 

### E MISSIONS 

### ....................................................21

3. G EOPHYSICAL AND 

### M ETEOROLOGICAL 

### D ATA 

### ....................................................23 3.1 T ERRAIN 

### E LEVATIONS WITHIN THE 

### M ODELING 

### D OMAIN 

### ........................................23 3.2 L AND 

### U SE AND 

### C OVER WITHIN THE 

### M ODELING 

### D OMAIN 

### .......................................25 3.3 M ETEOROLOGICAL 

### D ATABASE 

### ...............................................................................25 3.3.1 MM5 S IMULATIONS 

### ............................................................................................. 26 3.3.2 M EASUREMENTS AND 

### O BSERVATIONS 

### ................................................................ 26 

### 3.4 A IR 

### Q UALITY 

### D ATABASE 

### ........................................................................................28 3.4.1 O ZONE 

### B ACKGROUND 

### C ONCENTRATIONS 

### ........................................................... 28 3.4.2 A MMONIA 

### B ACKGROUND 

### C ONCENTRATIONS 

### ..................................................... 28 3.4.3 O THER 

### P OLLUTANT 

### B ACKGROUND AND 

### B OUNDARY 

### C ONDITIONS 

### .................... 29 

### 3.5 N ATURAL 

### C ONDITIONS AT 

### C LASS 

### I A REAS 

### .............................................................29

4. A IR 

### Q UALITY 

### M ODELING 

### M ETHODOLOGY 

### ..........................................................32 4.1 P LUME 

### M ODEL 

### S ELECTION 

### .....................................................................................32 4.1.1 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALMET ....................................................... 33 4.1.2 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALPUFF ...................................................... 35 

### 4.2 M ODELING 

### D OMAIN 

### C ONFIGURATION 

### ....................................................................37 4.3 CALMET M ETEOROLOGICAL 

### M ODELING 

### ..............................................................37 4.4 CALPUFF C OMPUTATIONAL 

### D OMAIN AND 

### R ECEPTORS 

### ........................................38 4.5 CALPUFF M ODELING 

### O PTION 

### S ELECTIONS 

### ..........................................................39 4.6 CALPOST P ROCESSING 

### O PTION 

### S ELECTIONS FOR 

### L IGHT 

### E XTINCTION AND 

### H AZE 

### I MPACT 

### C ALCULATIONS 

### ..........................................................................................40 4.7 M ODELING 

### P RODUCTS 

### ............................................................................................40

MeadWestvaco North Charleston Operations. i Trinity Consultants BART Applicability Modeling Protocol

### 5. Q UALITY 

### A SSURANCE 

### M ETHODS 

### ..........................................................................42 5.1 CALMET F IELDS 

### ...................................................................................................42 5.2 CALPUFF, CALPOST, AND 

### POSTUTIL R ESULTS 

### ..............................................43

6. R EFERENCES 

### ..........................................................................................................45

A PPENDIX 

### A VISTAS BART M ODELING 

### P ROTOCOL



### MeadWestvaco North Charleston Operations. ii Trinity Consultants BART Applicability Modeling Protocol

## 1. I NTRODUCTION

MeadWestvaco Corporation (MeadWestvaco) operat es 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 (DHE C). The facility is considered eligible to be regulated under the Best Available Retrofit T echnology (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 visib ility impairment at any federally protected Class I area, and hence whether a BART determination is n ecessary. This modeling protocol is presented to describe the procedures, analytical techniques, and data resources MeadWestvaco proposes to use to make the applicability determination. MeadW estvaco’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 Visib ility 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 Sour ce 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 refere nce for MeadWestvaco’s sourcespecific modeling protocol, and is presented in Appe ndix A of this modeling protocol document. This BART applicability modeling protocol is subm itted 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 fe derally-protected Class I ar eas 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 DHE C’s review and comment on this protocol, MeadWestvaco anticipates additional communica tion 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 A pplicability 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

## 1.1 O VERVIEW OF 

## R EGIONAL 

## H AZE 

## R ULE AND 

## BART R EQUIREMENTS
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 opera tion 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 (NO X 

### ), sulfur dioxide (SO 2 

### ), and particulate matter less than 10 micrometers in aerodynamic diameter (PM 10 

### ). 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 I ndustrial Classification 26 – Pa per and Allied Products. Chemical plants are also one of the listed BART-e ligible 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 peri od 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 D ETERMINATION OF 

### BART- ELIGIBILITY 

### The U.S. EPA BART guidelines define the fo llowing 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 un its operate at a Kraft pulp mill, were in existence on August 7, 1977, a nd began operation after August 7, 1962. This collection of emission units has potential emissions of 630 tpy NO X 

### , 592 tpy PM 10 

### , and 2,143 tpy SO 2 

### . Accordingly, the BART-eligible emission un its 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 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY 

### In its role as technical analysis coordi nator, 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

### and prescribes modeling techniques and data r esources 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 normali zed light extinction attributable to visibility affecting pollutants. To determine whethe r 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 s ource. 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 th e light extinction coefficient ( b ext 

### ), 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 HI for estimated natural background conditions with the impact of the source and without the impact of the source. The background extinction coefficient b ext, background 

### is affected by various chemical species and the Rayleigh scattering phenomenon and can be calculated as shown in the following equation.

rayap coarse soil OC NO SO background ext bbbbbbbkm b ++++++= −

3 4 )( 1

,


### where, 

## [ ] [ ] [ ] [ ] [ ]

3 Ray 34 424

μg/minion Concentrat ][ factor adjustment humidity relative f(RH) Scattering Rayleigh b Carbon] Elemental[01.0 Mass Coarse 0006.0 Soil001.0 OC004.0 )(NONH003.0 )(SO)(NH003.0

3 4

= =

= = = = = = =

ap Coarse Soil OC NO SO

b b b b RHf b RHf b



### MeadWestvaco North Charleston Operations 3 Trinity Consultants BART Applicability Modeling Protocol

### Values for the parameters listed above specifi c 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 c onditions particular to Class I areas potentially affected by MeadW estvaco’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 (SO 4 

### ) and nitrates (NO 3 

### ). MeadWestvaco’s calculation of speciated visibility affecting pollutant em issions is presented in Section 2 of this modeling protocol. The extinction coefficien t due to emissions of visibility affecting pollutants from a single BART-eligible source b ext,source 

### is calculated using an air quality model. The extinction due to the BART-e ligible will be calculated as shown in the following equation. 

# ( )
EC SOA PMF PMC NO SO sourceext bbbbbbkmb +++++= −

3 4 1

,


### where, 

## [ ] [ ]

3 34 424

μg/minion Concentrat ][ factor adjustment humidity relative)( ][01.0 ][004.0

][001.0 ][0006.0 )(NONH003.0 )(SO)(NH003.0

3 4

= = = =

= = = =

RHf EC b SOA b PMF b PMC b RHf b RHf b

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 cont ribution threshold is exceeded, and if so, the frequency, duration, and magnitude of any exceedance events. The U.S. EPA BART guidelines prescribe that the 98 th 

### 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 98 th 

### 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

### contribute to visibility impairment, MeadWestvaco’s applicability modeling analysis will quantify the top eight 24-hour average visibilit y impacts of each year modeled to illustrate the distribution (i.e., frequency, duration, a nd magnitude) of peak visibility impairment episodes attributable to the North Charleston Mill.

CALPUFF is a refined air quality modeling syst em 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 assess ment 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 Cl ass 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 lo cations 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 re ceptors, http://www2.nature. nps.gov/air/maps/receptors/. 

### MeadWestvaco North Charleston Operations 5 Trinity Consultants BART Applicability Modeling Protocol

### F IGURE 

### 1-1. L OCATION OF 

### M EAD 

### W ESTVACO 

### ’ S 

### N ORTH 

### C HARLESTON 

### M ILL



### MeadWestvaco North Charleston Operations 6 Trinity Consultants BART Applicability Modeling Protocol

### F IGURE 

### 1-2. L OCATION OF 

### M EAD 

### W ESTVACO 

### ’ S 

### N ORTH 

### C HARLESTON 

### O PERATIONS 

### R ELATIVE TO 

### C LASS 

### I A REAS WITHIN 

### 300 KM



### The initial screening analysis results will be u sed to demonstrate whether refined analysis is necessary, and whether the scope of the re fined 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 rela tively 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 su itable 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

### 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 s horter 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 di stances 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 me teorological data sets, boundary condition emissions and CMAQ-modeled background a mmonia 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 mode ling protocol submittal and applicability analysis deadline.



## 1.2 S CHEDULE FOR 

## BART I MPLEMENTATION IN 

## S OUTH 

## C AROLINA
Each state within the VISTAS region has established its own schedule for BART implementation, which includes separate deadlines for BART app licability 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 Mode ling 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 M odeling Protocol due August 15, 2006

V 

### BART Determination Modeling, Engineeri ng Analyses, and Permit Application due September 15, 2006

MeadWestvaco North Charleston Operations 8 Trinity Consultants BART Applicability Modeling Protocol

## 1.3 O RGANIZATION OF 

## M ODELING 

## P ROTOCOL
The remainder of this modeling protocol is or ganized as follows. Section 2 describes the BARTeligible emission units at the North Charleston M ill and the emission rates to be modeled in the BART applicability analysis. Section 3 describ es 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 presenta tion 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

## 2. BART-E LIGIBLE 

## S OURCE 

## D ESCRIPTION

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 sour ce will be represented in the modeling analysis.



## 2.1 BART-E LIGIBLE 

## E MISSION 

## U NITS

MeadWestvaco reviewed the criteri a 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.

T ABLE 

### 2-1. S UMMARY OF 

### BART- ELIGIBLE 

### E MISSION 

### U NITS

Emission Unit Source Code/ Stack ID BART Source Category* Date † Built or Modified Potential SO 2 Emissions (tpy) Potential NO X Emissions (tpy) Potential PM 10 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 Ta nk 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; cate gory 22 denotes large fossil fuel boilers, which for the purposes of BART eligibilit y include boilers individually greater than 250 MMB tu/hr that burn any amount of fossil fuel. † Dates in parenthesis indicate uncer tain, but assumed, dates of origin al construction or reconstruction. ‡ These emission units were subsequently modified in 1995; how ever, 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 NO X 

### , SO 2 

### , and PM 10, 

### which are considered visibilityaffecting pollutants. Note that many of the ot herwise BART-eligible emis sion units have potential emissions only of volatile organic compounds ( VOC), which VISTAS and South Carolina DHEC have determined are not visibility affec ting 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 elimin ating all VOC emissions from all point
MeadWestvaco North Charleston Operations 10 Trinity Consultants BART Applicability Modeling Protocol

### sources in a State is less than 0.5 dv, th en 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 remaini ng 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 

## S OURCE 

## M ODEL 

## E MISSIONS 

## I NVENTORY
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 NO X 

### , SO 2 

### , and PM 10 

### . 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 in dustry-specific emission factors compiled by the National Council for Air and Stream Improvement s (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.

T ABLE 

### 2-2. S UMMARY OF 

### 24- HOUR 

### A VERAGE 

### M AXIMUM 

### A CTUAL 

### E MISSION 

### R ATES

Emission Unit NO X Emissions

(lb/hr) SO 2 Emissions

(lb/hr) Total PM 10 Emissions

(lb/hr) Total PM 2.5 Emissions

(lb/hr) H 2 SO 4 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 burni ng fuel oil, which is a permitted operating scenario. The No. 9 Recovery Boiler typically fires black liquor solid s, which results in lower emission rates of NO X and SO 2 .



### MeadWestvaco North Charleston Operations 11 Trinity Consultants BART Applicability Modeling Protocol

### 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 PM 10 

### (TPM 10 

### ), which includes emissions of TPM 2.5 

### . Modeling of visibility impairment requires that the components of the exhaust stream be speciated because different types of partic ulate 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 m 2 

### /g for coarse particulate matter to 10 m 2 

### /g for elemental carbon. Fine particulate matter (1 m 2 

### /g) and organic aerosols (4 m 2 

### /g) scatter light with intermediate efficiencies, and ammonium sulfate and ammonium nitrate (that forms from precursor SO 2 

### and NO X 

### emissions in the presence of ambient am monia) are hygroscopic species that are particularly efficient light scatters in the presence of ambient water vapor (3 f

(RH) m 2 

### /g, where f

(RH) is a function of the relative humidity). Th e size distribution of particle species is also important, since smaller particles may be trans ported 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

.


### F IGURE 

### 2-1. P ARTICULATE 

### M ATTER 

### S PECIATION 

### ( AFTER 

### F IGURE 

### 4-3 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

Although the FLM have published speciation guidan ce 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

### 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 qua lity 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 visi bility impairment at Class I areas using CALPUFF modeling. The following analysis does not represe nt source test results for specific sources at the North Charleston Mill.

NO X 

### emissions from the North Charleston Mill’s BART-eligible sources result from combustion operations. Similarly, SO 2 

### emissions emanate from combustion sources, and result from the incomplete oxidation of fuel-bound sulfur. Primary em issions of sulfuric acid mists or vapors, if any, are assumed to occur as only a small percentage of the primary SO 2 

### emissions unless an emission factor has otherwise been published. Because of th e condensable nature of such emissions and the distinct effect on visibility caused by sulfates, pr imary sulfate is evaluated as a distinct, speciated particulate fraction. Condensable particulate matt er (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.

T ABLE 

### 2-3. N OMENCLATURE FOR 

### E MISSIONS 

### S PECIATION 

### A NALYSIS

Nomenclature Description

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μ m PM 10 Filterable particulate matter with an aerodynamic diameter < 10 μ m PM 6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μ m PM 2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μ m PM 2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μ m PM 1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μ m PM 1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μ m PM 0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μ m PM 0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μ m CPM Condensable particulate matt er (organic and inorganic) TPM 10 Filterable PM 10 + CPM TPM 2.5 Filterable PM 2.5 + CPM



### These PM classifications are necessar y in the Class I visibility analysis because each type of PM has a different effect on visibility as defined by the extin ction 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 a nd is distinguished from other PM types because of its light extinction characteristics. Coarse PM (PMC) comprises PM 2.5-6 

### and PM 6-10 

### . Fine PM (PMF) comprises PM 0.5-0.625 

### , PM 0.625-1 

### , PM 1-1.25 

### , and 

### PM 1.25-2.5 

### . CPM comprises both organic and inorganic species. The organic fraction of CP M is represented in CALPUFF as secondary organic aerosols (SOA), which are direct primary emissi ons but are called SOA by convention and due to

MeadWestvaco North Charleston Operations 13 Trinity Consultants BART Applicability Modeling Protocol

### their visibility affecting characteristics. Primary emissions of inorganic CPM may contain hygroscopic sulfates (SO 4 

### ) and nitrates (NO 3 

### ), 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 coefficien t than non-hygroscopic (i.e., non-sulfate and nonnitrate) species. Even the distinction between prim ary sulfate and nitrate emissions is important since primary nitrate emissions will be affected by the pa rtitioning of nitrate and nitric acid in the presence of ambient ammonia, which is modeled explic itly 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 em issions are hygroscopic and not affected by the partition of ambient nitrate, nitric acid, and am monia. In the refined analyses, if necessary, MeadWestvaco would distinguish primary emissions of sulfates and nitrates, which would beassigned to the appropriate modeled PM type (i.e., SO4 and NO3, respectively), from nonhygroscopic species (e.g., carbonates), which w ould 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.

T ABLE 

### 2-4. A SSIGNMENT OF 

### E MITTED 

### PM S PECIES TO 

### M ODELED 

### PM C ATEGORIES

Modeled PM Category Components Extinction Coefficient (m 2 /g)

PMC Filterable coarse particles (PM 6-10 , PM 2.5-6 ) 0.6

PMF Filterable fine particles (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 ) and non-hygroscopic primary condensable inorganic emissions* 1
SO4 Primary condensable inorganic emissions* 3 f

(RH) NO3 Primary condensable inorganic emissions of nitrates* 3 f

(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 emissio ns. The refined analysis, if necessary, would distinguish be tween primary sulfate, nitrate, and other emissions.



### 2.2.1 R ECOVERY 

### B OILER 

### N O 

### . 9 P ARTICULATE 

### S PECIATION 

### MeadWestvaco operates a direct contact eva porator (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 concentrat ion when analyzed to assess natural background visibility (Malm 1994). 

### MeadWestvaco North Charleston Operations 14 Trinity Consultants BART Applicability Modeling Protocol

### 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.

T ABLE 

### 2-5. R ECOVERY 

### B OILER 

### S PECIATION 

### D ATA

Speciation Data Value Reference

PM 10 as a % of TSP 75.0% PM 2.5 as a % of TSP 56.7% CPM (lb/TBLS) 0.38 Filterable portion of TPM 10 * 30.7% Condensable portion of TPM 10 * 69.3% Organic portion of CPM† 7.2% Inorganic portion of CPM† 92.8% NCASI Data, DCE Kraft Recovery Furnaces

PM 6-10 as a % of PM 10 9.1% PM 2.5-6 as a % of PM 10 19.2% PM 1.25-2.5 as a % of PM 10 17.7% PM 1-1.25 as a % of PM 10 8.4% PM 0.625-1 as a % of PM 10 16.0% PM 0.5-0.625 as a % of PM 10 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 PM 10 emissions or the CPM emissions (CPM factor x hourly capacity of unit) by the sum of the PM 10 emissions and the CPM emissions. † Values calculated based on the average amount of i norganic (water soluble) portion or organic (MeCl 2 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . Remainder of PM 10 that did not fall within any ot her category was assumed to be PM 0.5-0.625 . 

### Using the information presented in Table 2-5, MeadWestvaco first calculated TPM 10 

### and TPM 2.5 

### 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 TPM 10 

### and 45.47 lb/hr TPM 2.5 

### . Next, the organic CPM emissions were determined by multiplying the calculated TPM 10 

### emissions by the condensable percentage of TPM 10 

### and the organic percentage of CPM. Organic CPM was assumed to be evenly split between the two size categories, 0.625-1.0 μ m and 0.5-0.625 μ m. 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

### 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 μ m and 0.5-0.625 μ m.

Filterable PM 10 

### emissions were divided amongst six different size categories by multiplying TPM emissions by the filterable per centage and then by the percentage of PM 10 

### for each size category, yielding hourly emissions of PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625-1 

### , and PM 0.5-0.625 

### . A sample calculation for PM 6-10 

### 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 speciat ed 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 TPM 10 

### .
T ABLE 

### 2-6. N O 

### . 9 R ECOVERY 

### F URNACE 

### PM S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### )

Organic CPM ( μ m) Inorganic CPM ( μ m) Coarse Filterable PM ( μ m) Fine Filterable PM ( μ m) 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



### F IGURE 

### 2-2. N O 

### . 9 R ECOVERY 

### F URNACE 

### TPM 10 

### S PECIATION 

### ( 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

### 2.2.2 L IME 

### K ILN 

### P ARTICULATE 

### S PECIATION 

### MeadWestvaco operates the No. 4 Lime Kiln (C P001) 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 th e 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.

T ABLE 

### 2-7. L IME 

### K ILN 

### S PECIATION 

### D ATA

Speciation Data Value Reference

TPM 10 as a % of TSP 101% TPM 2.5 as a % of TSP 88% Filterable portion of TPM 10 97.9% Condensable portion of TPM 10 2.1% Organic portion of CPM* 7.4% Inorganic portion of CPM* 92.6% NCASI Data, Lime Kilns with Wet Scrubbers

PM 6-10 as a % of PM 10 0.1% PM 2.5-6 as a % of PM 10 2.2% PM 1.25-2.5 as a % of PM 10 11.2% PM 1-1.25 as a % of PM 10 6.2% PM 0.625-1 as a % of PM 10 25.0% PM 0.5-0.625 as a % of PM 10 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 i norganic (water soluble) portion or organic (MeCl 2 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . Remainder of PM 10 that did not fall within any ot her category was assumed to be PM 0.5-0.625 .



### Using the information presented in Table 2-7, MeadWestvaco first calculated TPM 10 

### and TPM 2.5 

### emissions by multiplying the maximum hourly TSP emission rate of 9.48 lb/hr by 101% and 88%, respectively, yielding 9.57 lb/hr TPM 10 

### and 8.34 lb/hr TPM 2.5 

### . Next, the organic CPM emissions were determined by multiplying the calculated TPM 10 

### emissions by the condensable percentage of TPM 10 

### and the organic percentage of CPM. Organic CPM was assumed to be evenly split between the two size categories, 0.625-1.0 μ m and 0.5-0.625 μ m. 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

### 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 μ m and 0.5-0.625 μ m.

Filterable PM 10 

### emissions were divided amongst six different size categories by multiplying TPM 10 

### emissions by the filterable % and then by the % of PM 10 

### for each size category, yielding hourly emissions of PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625-1 

### , and PM 0.5-0.625 

### . A sample calculation for PM 6-10 

### 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 TPM 10 

### .
T ABLE 

### 2-8. N O 

### . 4 L IME 

### K ILN 

### PM S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### )

Organic CPM ( μ m) Inorganic CPM ( μ m) Coarse Filterable PM ( μ m) Fine Filterable PM ( μ m) 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



### F IGURE 

### 2-3. N O 

### . 4 L IME 

### K ILN 

### TPM 10 

### S PECIATION 

### ( 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 S MELT 

### D ISSOLVING 

### T ANK 

### P ARTICULATE 

### S PECIATION 

### MeadWestvaco operates two smelt dissolving tanks, the No. 9 East Smelt Dissolving Tank (REC008) and No. 9 West Smelt Dissolving Ta nk (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

### 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.

T ABLE 

### 2-9. S MELT 

### D ISSOLVING 

### T ANK 

### S PECIATION 

### D ATA

Speciation Data Value Reference

TPM 10 as a % of TSP 84.0% TPM 2.5 as a % of TSP 72.0% Filterable portion of TPM 10 98.1% Condensable portion of TPM 10 1.9% Organic portion of CPM* 26.0% Inorganic portion of CPM* 74.0% NCASI Data, Smelt Dissolving Tanks

PM 6-10 as a % of PM 10 1.2% PM 2.5-6 as a % of PM 10 7.9% PM 1.25-2.5 as a % of PM 10 19.9% PM 1-1.25 as a % of PM 10 9.8% PM 0.625-1 as a % of PM 10 17.9% PM 0.5-0.625 as a % of PM 10 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 (MeCl 2 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . Remainder of PM 10 that did not fall within any ot her category was assumed to be PM 0.5-0.625 .



### Using the information presented in Table 2-9, MeadWestvaco first calculated TPM 10 

### and TPM 2.5 

### 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 TPM 10 

### and 21.59 lb/hr TPM 2.5 

### . For the No. 9 West Smelt Dissolving Tank, PM emissions of 29.83 lb/hr result in 25.06 lb/hr TPM 10 

### and 21.48 lb/hr TPM 2.5 

### . Next, the organic CPM emissions for each smelt dissolving tank were determined by multiplying the calculated TPM 10 

### emissions by the condensable % of TPM 10 

### and the organic % of CPM. Organic CPM was assumed to be evenly split between the two size categories, 0.625-1.0 μ m and 0.5-0.625 μ m. 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

### 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 μ m and 0.5-0.625 μ m, 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 PM 10 

### emissions were divided amongst six different size categories by multiplying TPM 10 

### emissions by the filterable percentage and then by the percentage of PM 10 

### for each size category, yielding hourly emissions of PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625-1 

### , and PM 0.5-0.625 

### . A sample calculation for PM 6-10 

### 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 TPM 10 

### emissions of 25.19 lb/hr and 25.06 lb/hr for the No. 9 East and West Smelt Dissolving Tanks, respectively.

T ABLE 

### 2-10. N O 

### . 9 E AST 

### S MELT 

### D ISSOLVING 

### T ANKS 

### PM S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### )

Organic CPM ( μ m) Inorganic CPM ( μ m) Coarse Filterable PM ( μ m) Fine Filterable PM ( μ m) 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



### F IGURE 

### 2-4. N O 

### . 9 E AST 

### S MELT 

### D ISSOLVING 

### T ANK 

### TPM 10 

### S PECIATION 

### ( 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

### T ABLE 

### 2-11. N O 

### . 9 W EST 

### S MELT 

### D ISSOLVING 

### T ANKS 

### PM S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### )

Organic CPM ( μ m) Inorganic CPM ( μ m) Coarse Filterable PM ( μ m) Fine Filterable PM ( μ m) 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



### F IGURE 

### 2-5. N O 

### . 9 W EST 

### S MELT 

### D ISSOLVING 

### T ANK 

### TPM 10 

### S PECIATION 

### ( 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 M ODELED 

## S TACK 

## P ARAMETERS AND 

## E MISSIONS
Actual stack parameters will be input to the CA LPUFF 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 dischar ges vertically without obstruction. Since the nearest Class I area is located 30 km to the fac ility, 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 ProtocolTABLE2-12. STACKPARAMETERS FORBART-ELIGIBLEEMISSIONUNITSEmission UnitStack IDLCC East(km)LCC North(km)StackHeight(feet)StackDiameter(feet)ExhaustTemperature(°F)ExhaustVelocity(feet/sec)No. 4 Lime KilnCP0011580.925-637.769113.55.8315226.18No. 9 Recovery BoilerREC0071581.068-637.835257.09.9632081.80No. 9 East Smelt Dissolving TankREC0081581.032-637.932251.06.491718.37No. 9 West Smelt Dissolving TankREC0091581.017-637.924251.06.491718.37TABLE2-13. MODELEDEMISSIONRATES OFSPECIATED FORBART-ELIGIBLEEMISSIONUNITSPMC (lb/hr)PMF (lb/hr)SO4(lb/hr)SOA (lb/hr)ECEmission UnitStack ID6-10μm2.5-6μm1.25-2.5μm1.0-1.25μm0.625-1.0μm0.5-0.625μm0.625-1.0μm0.5-0.625μm0.625-1.0μm0.5-0.625μm(lb/hr)No. 4 Lime KilnCP0010.00950.211.050.582.355.180.0940.0940.00750.0075--No. 9 Recovery BoilerREC0071.372.902.681.272.424.4715.8015.801.231.23--No. 9 East Smelt Dissolving TankREC0080.301.964.912.434.4210.680.180.180.0630.063--No. 9 West Smelt Dissolving TankREC0090.301.954.892.424.3910.630.180.180.0630.063--MeadWestvaco North Charleston Operations22Trinity ConsultantsBART Applicability Modeling Protocol

## 3. G EOPHYSICAL AND 

## M ETEOROLOGICAL 

## D ATA

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 sour ce-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. Te rrain features affect flows and create turbulence in the atmosphere and are potentially subjected to highe r 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 T ERRAIN 

## E LEVATIONS WITHIN THE 

## M ODELING 

## D OMAIN
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, appr oximately 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 preproces sors to format and assimilate these data into a single geophysical data file for processing by CALM ET. 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 app licability 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-re gional 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 Charl eston 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

### An application for source-receptor distances within about 50 km may require a grid resolution less than 1 km if complex terrain e ffects are likely to be important. This determination should be made on a case-by-c ase basis. There is not a single distance at which a particular grid size is appropr iate. It depends on factors such as the complexity of the terrain, the source-recept or 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.



### F IGURE 

### 3-1. T ERRAIN 

### R EPRESENTATION IN THE 

### 12- KM 

### R EGIONAL 

### S CREENING 

### G RID 

### (A FTER 

### F IGURE 

### 4-2 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

MeadWestvaco North Charleston Operations 24 Trinity Consultants BART Applicability Modeling Protocol

## 3.2 L AND 

## U SE AND 

## C OVER WITHIN THE 

## M ODELING 

## D OMAIN
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. Ge ological 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, al bedo, soil heat flux) that affect turbulent dispersion in the boundary layer.

Refined analyses may be necessary as part of the MeadWestvaco BART app licability analysis, in which case CALMET grid resolutions of 4 km or l ess (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 s ub-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 resolu tion LULC data would be used for a refined analysis; therefore the 200-meter CTG format w ill provide adequate resolution for CALMET grid spacing down to 1 km. Therefore, refined analy ses performed by MeadWest vaco, if necessary, will utilize the same CTG LULC data.



## 3.3 M ETEOROLOGICAL 

## D ATABASE
CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields from mesoscale model (MM) output, raw obser vations of surface and upper air conditions, precipitation measurements, and geop hysical 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 me teorological fields are available, as discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used in conjunction with available standar d [National Weather Service] NWS or comparable meteorological observations w ithin 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 data base and consistent CALPU FF modeling options. The CALMET modeling output files in the form of CA LPUFF-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, M eadWestvaco 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 spaci ng, 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

### 3.3.1 MM5 S IMULATIONS



### MM data are used as “observed” or “fir st-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 mete orological fields from MM5 data were conducted in No-Observations (“No-Obs”) m ode since the MM5 data already reflect assimilation of observational data and are likel y 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 r esolution 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 te rrain 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 M EASUREMENTS AND 

### O BSERVATIONS 

### The finer grid (4 km) CALMET simulations, which MeadWestvaco anticipates utilizing as part of a refined analysis, will be run by VI STAS 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 wh ich 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, temperatur e, cloud cover and ceiling, relative humidity,

MeadWestvaco North Charleston Operations 26 Trinity Consultants BART Applicability Modeling Protocol

### and precipitation type. Surface da ta 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 Ce nter (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 va ry 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 optimi ze 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 tr ansformations and wet deposition processes on ambient pollutant concentrations are an important part of the BART applicability analysis. Therefore, it is necessary to include observati ons of precipitation in the CALMET analysis. Precipitation data would be collected from se lected surface meteorological data stations included in the analysis, plus Cooperative Ob servation Network (COOP) stations nearer to or within the domain. Precipitation data we re extracted from among the NCDC’s available data inventory to optimize spatial covera ge 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 bounda ry layers, and atmospheric dispersion phenomena occurring within these distinct regi mes, 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 envi ronments that affect pollutant dispersion include land/sea-breezes that cause recirculation of pollutant mass, temperature moderation that results in sharp gradients and mixing he ight discontinuities at the land-sea interface, and thermal internal boundary layers that could cause severe fumigation under certain conditions. The CALMET processor is equippe d to assimilate overwater data obtained from coastal, near-shore, and offshore observation platforms. CALMET uses a profilemethod to simulate boundary layer effects by computing the friction velocity, MoninObukhov length, surface roughness, and mixing he ight 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

### 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, an d the overwater temperature gradients above and below the overwater mixing height. For pr actical 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 temperatur e 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 prov ided in addition to the hourly surface meteorological observations, precipitation obser vations, and twice-daily upper air sounding data due to the location of the North Charelston Mill on the Atlantic Ocean coastline.



## 3.4 A IR 

## Q UALITY 

## D ATABASE
The CALPUFF model is capable of simulating linear chemical transformation effects by using pseudo-first-order chemical reaction m echanisms for the conversions of SO 2 

### to SO 4 

### , and NO X 

### , which consists of nitrogen oxide ( NO) and nitrogen dioxide (NO 2 

### ), to nitrate (NO 3 

### ) and nitric acid (HNO 3 

### ). In this study, chemical transformations involving five species (SO 2 

### , SO 4 

### , NO X 

### , HNO 3 

### , and NO 3 

### ) will be modeled using the MESOPUFF II chemical transf ormation scheme. Ambient concentrations of ammonia and ozone concentrations as represented in the model affect the MESOPUFF II chemical transformation simulation.



### 3.4.1 O ZONE 

### B ACKGROUND 

### C ONCENTRATIONS 

### Both screening and refined analyses will u tilize observed ozone data for 2001 through 2003 from non-urban CASTNet and AIRS sta tions 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 co mputed by month) for substitution should all observations be missing for a particular hour of the dataset.



### 3.4.2 A MMONIA 

### B ACKGROUND 

### C ONCENTRATIONS 

### In the screening and refined analyses, a consta nt background value (0.5 ppb) for ammonia will be utilized. The VISTAS BART Modeling Protocol envisions that CMAQ-modeled NH 3 

### data for each Class I area would be u sed during postprocessing to repartition HNO 3 

### and NO 3 

### using the ammonia limiting method. Howe ver, 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 m odeled background leve ls 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

### available during the time between the mode ling protocol submittal and applicability analysis deadline to ensure a mutually agreeable approach for representing ammonia is used in the analyses.



### 3.4.3 O THER 

### P OLLUTANT 

### B ACKGROUND AND 

### B OUNDARY 

### C ONDITIONS



### 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 antic ipates completing refined analyses for the BART-eligible source at North Charleston, appr opriate boundary conditions will be used in the refined analyses and appropriate docum entation will be provided in the BART applicability modeling final report.



## 3.5 N ATURAL 

## C ONDITIONS AT 

## C LASS 

## I A REAS
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 atmospheri c 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 No rth 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

.
T ABLE 

### 3-1. N ATURAL 

### B ACKGROUND 

### C ONCENTRATIONS OF 

### V ISIBILITY 

### A FFECTING 

### P OLLUTANTS

Dry Extinction West East Error Efficiency Component (μg/m 3 ) (μg/m 3 ) Factor (m 2 /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

### As is described in Section 4 of this protocol, the effects of relative humidity to amplify the visibility impairment of hygroscopic sulfates and nitrat es will be characterized using “Method 6,” which computes ∆ b ext 

### using a monthly average relative humidity adjustment particular to each Class I area applied to background and modeled sulfate and nitr ate. 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

.
T ABLE 

### 3-2. M ONTHLY 

### A VERAGE 

### f (RH) FOR 

### S ELECTED 

### C LASS 

### I A REAS 

### *

Class I Area January February Ma rch 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.

rayap coarse soil OC NO SO background ext bbbbbbbkm b ++++++= −

3 4 )( 1

,


### where, 

## [ ] [ ] [ ] [ ] [ ]

3 Ray 34 424

μg/minion Concentrat ][ factor adjustment humidity relative f(RH) Scattering Rayleigh b Carbon] Elemental[01.0 Mass Coarse 0006.0 Soil001.0 OC004.0 )(NONH003.0 )(SO)(NH003.0

3 4

= =

= = = = = = =

ap Coarse Soil OC NO SO

b b b b RHf b RHf b



### As noted in the VISTAS BART Modeling Protocol , the U.S. EPA and the Regional Planning Organizations (including VISTAS) are evaluating wh ether refinements are warranted to the methods recommended in U.S. EPA’s guidance to calculate de fault estimates of natural background visibility. In addition, the Interagency Monitoring of Prot ected 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 IMPR OVE 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 ProtocolV 

### 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 fo rmula, f(RH), for sulfates and nitrates

V 

### Revising the extinction efficiencies (the numeri cal 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 recomme nded 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

## 4. A IR 

## Q UALITY 

## M ODELING 

## M ETHODOLOGY

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 sour ce-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 modeli ng system is recommended as the preferred modeling approach for use in the BART analyses.



## 4.1 P LUME 

## M ODEL 

## S ELECTION
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 im pacts. 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 applic ation 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 appropr iate 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 Appe ndix A of this document. The following sections present a brief summary of major features of the CALMET and CALPUFF models, and further detailed inform ation 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 specifi es CALMET Version 5.7 a nd CALPUFF Version 5.8, as of the date of this modeling protoc ol, 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

### 4.1.1 M AJOR 

### R ELEVANT 

### F EATURES 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 tocompute 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 laye r parameters in the marine environment. A profile technique, using air-sea temperature differences, is used in CALMET to compute the micro-meteorological parameters in th e 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 enha ncements 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 firs t 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 pro cedure 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. No rmally, 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 te rrain 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 effectsof terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind fi eld. The divergence minimization scheme is applied iteratively until the three-dimensiona l divergence is less than a threshold value.

MeadWestvaco North Charleston Operations 33 Trinity Consultants BART Applicability Modeling Protocol

### 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 ad justments 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, obser vational data are not used in the Step 2 calculations. The No-Obs mode is appropria te 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 relativ ely large i.e., coarser than the scale of variation of the true wind field, which is partic ularly 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 ad justments 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 reflect ed in the MM5 dataset. In this case, the MM5 winds will be interpolated by CA LMET to the CALMET layers and CALMET’s boundary layer modules will compute mixing he ights, turbulence parameters and other meteorological parameters that are require d by CALPUFF. For 2003, the 36-km MM5 data were used as CALMET’s initial guess fi eld 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

### required, will utilize the MM5 data as the fi rst-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 M AJOR 

### R ELEVANT 

### F EATURES OF 

### CALPUFF
By its puff-based formulation and through th e use of three-dimensional meteorological data developed by the CALMET meteorologi cal model, CALPUFF can simulate the effects of time- and space-varying meteorol ogical 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 gene ral “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical dispersion derived from the weakly-stratified flow and dispersion m odule 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 recep tors using a dividing streamline height (H d 

### ) to determine which pollutant material is deflected around the sides of a hill (below H d 

### ) and which material is advected over the hill (above H d 

### ). The local flow (near the feature) used to define H d 

### is taken from the gridded CALMET fields. As in CTDMPLUS, each feature is modeled in is olation 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 ( σ v 

### and σ w 

### ), the use of similarity theory to estimate σ v 

### and σ w 

### from modeled surface

MeadWestvaco North Charleston Operations 35 Trinity Consultants BART Applicability Modeling Protocol

### 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 u se the AERMOD turbulence profiles for determining dispersion rates, which is the mo st 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 c onditions, 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 μ m diameter) from coarse particulate matter (2.5-10
μ m 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 trans port among the “new” puffs generated from the original, well-mixed puff can substa ntially increase the effective rate of horizontal spread of the material. Puffs ma y 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 co efficient approach is used in CALPUFF to compute the depletion and wet depositi on 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 includ es options for parameterizing chemical transformation effects using the five species scheme (SO 2 

### , SO 4 = 

### , NO X 

### , HNO 3 

### , and NO 3 - 

### ) 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 SO 2 

### and NO X 

### 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

## 4.2 M ODELING 

## D OMAIN 

## C ONFIGURATION
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 CA LPUFF 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 de grees 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 th e default maximum mixing height of 3,000 meters.

T ABLE 

### 4-1. V ERTICAL 

### G RID 

### S TRUCTURE

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 wh ich case the appropriate subregional grid would be utilized. Additional runs at higher resoluti on (e.g., 1 km) may be performed and the modeling subdomain would be selected appropria tely from the VISTAS regional grid.



## 4.3 CALMET M ETEOROLOGICAL 

## M ODELING
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 th is protocol, and the geophysical and meteorological databases were described in Section 3. CALMET processing w as 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 ProtocolV 

### 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, ex cept 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 a nd 2002 simulation (using 12-km MM5 data)

V 

### CALMET options dealing with radius of in fluence parameters (R1, R2, RMAX1, RMAX2, RMAX3), BIAS, ICALM parameters are not used in No-Observations mode.

V 

### TERRAD (terrain scale) is required for runs w ith 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 simula tions based on sensitivity tests. The purpose of the testing was to optimize the variable to allo w 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, appropria te CALMET processing options will be evaluated and documented in the final applicability modeling report.



## 4.4 CALPUFF C OMPUTATIONAL 

## D OMAIN AND 

## R ECEPTORS
CALPUFF analyses to assess the visibility impacts attr ibutable 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 encomp ass 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 recircula tion 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 recep tors 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/ai r/maps/Receptors/index.htm 

### MeadWestvaco North Charleston Operations 38 Trinity Consultants BART Applicability Modeling Protocol

## 4.5 CALPUFF M ODELING 

## O PTION 

## S ELECTIONS
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 SO 4 

### and TNO 3 

### (HNO 3 

### + NO 3 

### ) from CMAQ 2001-2003 annual runs. Note that as of the date of this pr otocol, 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: SO 2 

### , SO 4 

### , NO X 

### , HNO 3 

### , NO 3 

### and particulate matter in size categories of <0.625 μ m, 0.625-1.0 μ m, 1.0-1.25 μ m, 1.25-2.5 μ m, 2.5-6.0 μ m and 6-10 μ m aerodynamic diameters.

V 

### Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual emission rate with normal operations from th e highest emitting day of the meteorological period modeled (excluding days where star t-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 maximu m emissions are used for modeling purposes (70 FR 39162). Pollutants considered include SO 2 

### , H 2 

### SO 4 

### , NO X 

### and PM 10 

### . The basis for modeled emission rates was described in Section 2 of this protocol.

V 

### Condensable emissions are considered as prim ary fine particulate matter and allocated equally to the two sub-micrometer particle si ze 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 SO 2 

### and NO X 

### and 15 tons per year for PM 10 

### ). 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 ca tegories: elemental carbon (soot), “soil” (fine PM < 2.5 μ m diameter), coarse particulate matter (2.5-10 μ m 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 functi on (pdf) dispersion, as used in the AERMOD model.

V 

### Ozone dataset: use observed ozone data fo r 2001 through 2003 from CASTNet and AIRS stations. Only non-urban ozone stations shoul d 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 NH 3 

### data will be used for each Class I area in POSTUTIL to repartition HNO 3 

### and NO 3 

### . Note that as of the date of this protocol, the appropriate background levels

MeadWestvaco North Charleston Operations 39 Trinity Consultants BART Applicability Modeling Protocol

### for each Class I area have not yet been sp ecified. MeadWestvaco requests that DHEC confirm the appropriate values in r esponse 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 P ROCESSING 

## O PTION 

## S ELECTIONS FOR 

## L IGHT 

## E XTINCTION AND 

## H AZE 

## I MPACT 

## C ALCULATIONS
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: SO 4 

### , NO 3 

### , 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 re presented at each Class I area within 300 km of MeadWestvaco’s North Charleston Mill as descr ibed in Section 3.5 of this protocol.

V 

### Light extinction efficiencies: Use EPA (2003a) va lues. 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 pe rformed outside CALPOST unless CALPOST is modified to accommodate the new algorithm.

V 

### Ammonia Limiting Method: Use ammonia from CMAQ to define NH 3 

### 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 fo r each Class I area considered. Predicted changes exceeding the “contribution” threshold (0.5 decivi ews) will trigger a finer grid CALPUFF modeling analysis. To assess whether BART-eligible ope rations contribute to visibility impairment, MeadWestvaco’s applicability modeling analysis will demonstrate the top eight 24-hour average visibility impacts of each year modeled to illustra te the distribution (i.e., frequency, duration, and magnitude) of peak visibility impairment episod es attributable to the North Charleston Mill. Only the 98 th 

### percentile 24-hour average visibility impact w ould be evaluated in the refined analysis, if necessary.



## 4.7 M ODELING 

## P RODUCTS
MeadWestvaco will prepare and submit a BART appl icability analysis result describing the modeling procedures, data resources, and results of screen ing 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

### 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 exem ption 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 98 th 

### percentile days in each year greater than 0.5 dv (total visibility impairment minus impairment on 20% best days for natural background visib ility 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 98 th 

### percentile that the impact of the source ex ceeds 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

## 5. Q UALITY 

## A SSURANCE 

## M ETHODS

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 Mead Westvaco’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 F IELDS

Section 4 of the VISTAS BART Modeling Protocol describes the methods and procedures for use in conducting regional scale screening modeling to dete rmine 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 fi elds 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 ac tivity of end-users is again reduced from what would be required in the development of the dat aset. It is not expected that the quality assurance steps in the development will be repeated in each application. The VISTA S-provided regional and subregional meteorological fields will include a t est 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-observa tions 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), includi ng 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 su ch 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 da ta and the physical processes expected to be important at that location will be conducted as part of th e site-specific QA plan development.

MeadWestvaco North Charleston Operations 42 Trinity Consultants BART Applicability Modeling Protocol

## 5.2 CALPUFF, CALPOST, AND 

## POSTUTIL R ESULTS
Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report contains a list of recommended CALPUFF switch setti ngs. 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. Th e 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 cont rol file be reviewed manually as part of the CALPUFF QA analysis. To facilitate this pr ocess, 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 or der 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 fo r computing background extinction and monthly relative humidity factors for hygroscopic aerosol s. 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 an alysis. 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 98 th 

### percentile value (8 th 

### 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 spre adsheet to rank the dail y change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that will be carefully ch ecked 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 ma tter, 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

### POSTUTIL allows the user to sum the contributi ons 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., diffe rent particle sizes) into speci es-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 inpu t into CALPOST. This process, although simple, requires a careful review of the weighting factors for each source. PO STUTIL also allows a repartitioning of nitric acid and nitrate to account for the eff ects of ammonia limiting conditions.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, performance of the model in reproducing observe d CASTNet or IMPROVE sulfate and nitrate concentrations at measurement sites within the s ite-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 ev aluation of the model performance relative to the techniques in the VISTAS common protocol.

MeadWestvaco North Charleston Operations 44 Trinity Consultants BART Applicability Modeling Protocol

## 6. R EFERENCES

The following guidance documents, regulations, and technical publications were referenced in preparation of this BART Applicability M odeling Protocol for MeadWestvaco and the VISTAS BART Modeling Protocol

.
Allwine, K.J. and C.D. Whiteman, 1985: MELSA R: 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. Ma nikin, 2004: An Hourly Assimilation Forecast Cycle: the RUC, M on. Wea. Rev ., 132 , 495-518.

Carson, D.J., 1973: The development of a dry, in version-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 Assist ant 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 Controvers ies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidan ce 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: Guidan ce for Estimating Natural Visibility Conditions under the Regional Haze Rule. EPA- 454/B-03-005. U.S. Environmental Protection Agency, Research Triangle Park, NC.

MeadWestvaco North Charleston Operations 45 Trinity Consultants BART Applicability Modeling Protocol

### Environmental Protection Agency, 1998: Ph ase 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 E ffects of Ammonia Limitation on Nitrate Aerosol Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12 th 

### 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. Grach ev, 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 Live rmore National Laboratory. FLAG, 2000. Federal Land Managers’ Air Quality Related Values Wor kgroup (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: Parameteri zation of the Formation Potential of Secondary Organic Aerosols

. Atmos. Environ. , 23 , 1733-1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A si mple 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: Eval uation 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 Pa th Forward. Mystic, CT, 22-24 October 2003.

Morrison, K., Z-X Wu, J.S. Scire, J. Chenie r and T. Jeffs-Schonewille, 2003: CALPUFF-Based Predictive and Reactive Emissions Control System. 96 th 

### A&WMA Annual Conference & Exhibition, 22-26 June 2003, San Diego, CA.

MeadWestvaco North Charleston Operations 46 Trinity Consultants BART Applicability Modeling Protocol

### National Council for Air and Stream Improvement. Performance of EPA stack sampling methods for PM 10 

### , PM 2.5 

### 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 (Tec hnical Bulletin No. 898), March 2005.

National Council for Air and Stream Improvement. Compilation of criteria air pollutant emissions data for sources at pulp and paper mills including boilers (Technical Bulletin No. 884), April 2005 (Revised).

O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coe fficient 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 Extinctio n 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 (CTDMPLU S) 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. Envi ronmental Protection Agency, Research Triangle Park, NC.

Scire, J.S. and F.R. Robe, 1997: Fine-Scale App lication of the CALMET Meteorological Model to a Complex Terrain Site. Paper 97-A1313. Air & Wast e 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

### Scire, J.S., Z.-X. Wu, 2003: Evaluation of th e 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: Th e 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, 1 998: 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

## A PPENDIX 

## A

VISTAS BART M ODELING 

### P ROTOCOL



### MeadWestvaco North Charleston Operations A - 1 Trinity Consultants BART Applicability Modeling Protocol

# 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)Table of Contents iv 

## TABLE OF CONTENTS

Page

SUMMAR Y S

- 1

1. INTRODUCTION AND PRO TOCOL OBJECTIVES 1 1.1 Background 1 1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUID ANCE FOR BART M ODELING 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 CALP UFF MODELING SYSTEM 8 3.1 Capabilities and features of CALPUFF 8 3.1.1 Major Features of CALMET 10 3.1.2 Major Featur es 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 Dif fusion 17 3.2.2 Aerosol Constituents 20 3.2.3 Regional Haze 28

4. VISTAS’ COMMON MODEL ING 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, NH 3 

### , and PM 34 4.2 Op tional 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 Mod eling 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 Mode ling of BART Eligible Sources 51

5

. SOURCE

- SPECIFIC MODELING PR OTOCOL 52Table 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 Co ntrol 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 62Summary S

- 1 

## SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BA RT) 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 Appen dix 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, “ Y ou 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 BA RT

- 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 conta cts, 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 P rotocol 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 a nd discussed with VISTAS participants and federal partners. VISTAS gratefully acknowledges the very useful contributions of those that provided comments. On November 1 st 

### , 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.Summary S

- 2 

### Objectives

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

 A con sistent 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 expectatio ns 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 summ ary 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

Fol low 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. Ident ify 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 analysisSummary 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 Mineral s 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 publicl y 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 inp ut 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 surfac e 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 m odel 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 operati on 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 f rom 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 SO 4 

### and total NO 3 

### (HNO 3 

### + NO 3 

### ). CMAQ data in CALPUFF

- ready format wi ll be provided for each Class I area by VISTAS. After running CALPUFF for an individual facility, repartition NO 3 

### in POSTUTIL using the CMAQ background data, including that for NH 3 

### .

• 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.Summary S

- 4 

### • Use EPA default calcul ations 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).

Prov ide 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 im pacts > 0.5 dv
III. CALPUFF Application for BART

For determining if a BART

- eligible source is subject to BART CALPUFF modeling, use a two

tier approach. For the initial exemption modeling use CALPUFF with 12

- km grid CALMET. For finer resolution of meteoro logical 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 SO 2 

### is greater than 10 for 2002 actual annual emissions, then the State presumes that the

so urce 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 e ngineering 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 exempt ion 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.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 a ll 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 exceedan ces of the threshold value for exemption purposes, up to no more than the 98 th 

### 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 wer e projected in the 12

- km modeling.

For finer grid (4 km or less) analyses, use the 98 th 

### percentile impact value for the 24

- hr average. Use either the 8 th 

### highest day in each year or the 22 nd 

### 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 v isibility 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).Introduction and Protocol Objectives 1 

## 1. INTRODUCTION AND PRO TOCOL 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 visib ility

- 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 contrib ute 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 mee ts 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 defini tion “BART

eligible 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 capac ities 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 SO 2 

### and NO x 

### 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 SO 2 

### and NO x 

### . 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 indust rial sources. (Note that these numbers are not final and are subject to slight adjustments and refinements.) No BART sources are located on Tribal lands.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 proced ures 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 rev iewing 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 rec eived on VISTAS’ draft protocol 2 

### 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 BAR T

- 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, 200 5.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 d etermine 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 situat ions:

• 

### 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 t he 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 d escribed in Chapter 4. The procedures include initial modeling of BART

eligible 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, fu rther 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 wan ts 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 VI STAS 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.Review of EPA’s Guidance 4 

## 2. REVIEW OF EPA’S GUID ANCE FOR BART MODELI NG

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 Chap ter 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 magnit ude 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 BART

eligible. Finally, a source is eligible for BART if potential e missions of visibility

- impairing air pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate matter (PM 10 

### ) and gaseous precursors to secon dary fine particulate matter, such as SO 2 

### and NO x 

### . Whether ammonia or volatile organic compounds (VOCs) should be included as visibility

impairing pollutants for BART eligibility is left for the States to determine on a case

- by

- case basis. The guidance st ates that high molecular weight VOCs with 25 or more carbon atoms and low vapor pressure should be considered as primary PM 2.5 

### 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 SO 2 

### and NO x 

### and 15 TPY of PM 10 

### or PM 2.5 

### . This assumes th at 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 “nat ural 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.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 propose d 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 “dr iven” by the contribution level, since if a source “causes”, by definition it “contributes”.

EPA recommends that the 98 th 

### percentile value from the modeling be compared to the contribution threshold of 0.5 deciviews (or a lower level set by a State) to d etermine if a source does not contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98 th 

### percentile value exceeds the threshold must be determined at each Class I area. Over an annual period, this implies the 8 th 

### hig hest day at a particular Class I area is compared to the contribution threshold. Over a 3

- year modeling period, the 98 th 

### percentile value may be interpreted as the highest of the three annual 98 th 

### percentile values at a particular receptor or the 22 nd 

### hi ghest 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 optio n 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 SO 2 

### plus NO x 

### of less than 500 tons and a distance from any Class I area greater than 50 kilom eters or sources with SO 2 

### plus NO x 

### 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 discuss ions with EPA staff indicate that PM may be considered along with SO 2 

### and NO x 

### , so that a plant could be exempted if the combined potential emissions of SO 2 

### , NO x 

### , plus PM meet the criteria above.

3) Determine BART controls for the source by considering var ious 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.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 Pla n 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 emissio ns 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 improv ements 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 purp ose.
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 SO 2 

### and NO x 

### 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 guida nce 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 Sta te 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 P LUVUE

- 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 h ave 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 a nd “demonstrated to be appropriate for single source applications” (70 FR 39123). Photochemical grid models may beReview 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 BART

eligible source configurations” and factors affecting their particular Class I areas (70 FR 39126).

I n 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 “e ncouraged 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 S tates 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 th at 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 differen t 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. Fr om 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.Overview of the CALPUFF Modeling System 8 

## 3. OVERVIEW OF THE CALP UFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities and limitations. It does not include spec ific 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 reco mmended 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 de position, 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 recommend s 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 dete rminations.

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

- dimen sional 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 effe cts. 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 fiel ds.

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 t o 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 eval uated at user

- specified receptor locations.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 no n

- 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 contribut ions 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 th e capabilities and features of the CALPUFF modeling components in more detail.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 lay ers. 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 met eorological 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

-- Conve ctive 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

- Terr ain 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 (19 83) is used to compute hourly gridded fields of the sensible heat flux, surfaceOverview 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 soun dings 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 properti es 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 p arameters 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 enhance ments 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 av eraging 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 computati on 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 VIS TAS 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 gue ss 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 divergence

minimization 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) b ased on the shooting flow algorithm of Mahrt (1982). This scheme includes bothOverview 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 Effe cts : 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 o f 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 f ield 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 f inal 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 f ollowed 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 obse rvations, 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 relative ly 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

- dimensiona l 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. 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 d iscrete 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

- s tratified flow and dispersion module of the Complex TerrainOverview 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
t he 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 r esolved 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 (H d 

### ) to determine which pollutant material is deflected around the sides of a hill (below H d 

### ) and which material is advected over the hill (above H d 

### ). The local flow (near the feature) used to define H d 

### is taken from the gridded CALMET fields. As in CTDMPLUS, each feature is modeled in isolation w ith 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 r eleases. 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 stac k

- 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 ( σ v 

### and σ w 

### ), the use of similarity theory to estimate σ v 

### and σ w 

### from modeled surfac e 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 ad dition, 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.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 μ m diameter) from coarse particulate matter (2.5

- 10 μ m 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 transpor t 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 (SO 2 

### , SO 4 = 

### , NO x 

### , HNO 3 

### , and NO 3 - 

### ) 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 SO 2 

### and NO x 

### and th us 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.Overview of the CALPUFF Modeling System 15 

### Table 3

- 2. Major Features of the CALPUFF Dispersion Model

• Source types

- Point sources (constant or v ariable 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 fiel ds 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 dispers ion 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 hea t flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient ( σ y , σ z ) options

- Direct measurements of σ v and σ w

- Estimat ed values of σ v and σ w 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"

- O ptional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)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, H d , as in CTDMPLUS:

- Above H d , material flows over the hill and experiences altered diffusion rates

- Below H d , material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of spac e 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 SO 2 , SO = 4 , NO x , HNO 3 , and NO 3 (MESOPUF F II method)

- Pseudo

- first

- order chemical mechanism for SO 2 , SO = 4 , NO, NO 2 , HNO 3 , and NO 3 (RIVAD/ARM3 method)

- User

- specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coe fficient 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 p rograms. 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 visibili ty

related 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 evalua ting 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.Overview of the CALPUFF Modeling System 17 

### The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources from different simulation s 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 with out 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 al lows 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 Thr esholds (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 tr ansport 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 bas ic 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 actua l 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 situati ons, 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 6 th Modeling Conference provides the background f or 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 time s 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 3 00 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.)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 additi onal 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 dista nces 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 reversal s, 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 d efault" 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 tur bulence

- 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 convectiveOverview 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 Model 5



### . 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 throu gh 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 CAL PUFF 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 p lume 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 st andard 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, 2 σ y 

### , is roughly 1 km after 10 km of travel distance,

ass uming 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 rece ptor 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 exti nction 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 Co mplex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218

- 68261. 9 November 2005.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 

### PLUV UE

- II is a Gaussian model that simulates the dispersion, chemical conversion, and optical effects of emissions of particles, SO 2 

### , and NO x 

### 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 PM 2.5


### Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that CALPUFF can treat primary pollutants such as PM 10 

### . In actuality, CALPUFF can simulate PM 10 

### or PM 2.5 

### or some other size range, because the assumed size distribution of the particles is a u ser input. The smaller the particles, the more they disperse like an inert gas. In most cases, the dispersion of inert PM 2.5 

### particles will be only minutely different from that of an inert gas, but the behavior of larger particles will differ.

A particular ly important contributor to PM concentrations is the rate of deposition to the surface. PM 2.5 

### particles, which have a mass median diameter around 0.5 μ m, 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 PM 10 

### particles larger than PM 2.5 

### ) have an average deposition velocity of more than 1 m/min (or 1440 m/day), which is significant, eve n 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. Gravitatio nal settling, which is of particular importance for the coarse fraction of PM 10 

### , is accounted for in the calculation of the deposition velocity. Effects of inertial impaction (important for the upper part of the PM 10 

### distribution) and Brownian motion (impo rtant for small, sub

- micron particles) and wet scavenging are also addressed. The BART guidance recommends that fine particulate matter (less than 2.5 μ m diameter), which has higher light extinction efficiency than coarse particulate matter (2.5

- 10 μ m dia meters), 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 applicationOverview of the CALPUFF Modeling System 21 

### separate species, which includes calculating size

- specific dry deposition velocities for each size category.
A primary PM 2.5 

### emission fr om 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 impor tant to simulate their effect with CALPUFF, especially at shorter distances before significant formation of secondary sulfate conversion from SO 2 

### has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate

The MESOPUFF

- II chemistry algorithm used i n CALPUFF 7 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 easily

computed or observed parameters that are used as surrogates for the factors that control SO 2 

### 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 SO 2 

### 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 SO 2 

### conversion rate in CALPUFF is based on this correlation relationship. The philosophy is that OH radical mea surements 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 SO 2 

### oxidation rates. At night, the SO 2 

### co nversion is set to a constant low value (default is 0.2%/hr). Aqueous phase oxidation of SO 2 

### 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 parameterizin g chemical transformations: the 5 species (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) MESOPUFF

- II system and the 6 species RIVAD system (which treats NO and NO 2 separately). IWAQM recommends using the MESOPUFF

- II system with CALPUFF. The RIVAD system is believed to b e 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.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 SO 2 

### . 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 u nderestimated 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 te rm 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 NO x 

### concentrations in the plume are high and in actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO 2 

### ; 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 import ant 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 SO 2 

### sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et al., 2001). Ov erall, 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 r esults 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 ex tinction 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 model s can be used.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.

NO x 

### and Secondary Ammonium Nitrate

The MESOPUFF

- II chemistry algorithm used in CALPUFF simulates t he oxidation of NO x 

### to nitric acid and organic nitrates (both gases) by transformation rates that depend on NO x 

### 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 a mbient 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 NO x 

### that deplete the amb ient ozone and thus limit theOverview of the CALPUFF Modeling System 24 

### transformation of NO x 

### 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 (Morr is 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 NO x 

### emissions at those times, especially in the colder northern states

. This potential overestimate of nitrat e 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 amoun t 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 co mbined 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, betwee n 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 po stprocessing 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 i ncluded 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 t hat 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 inferre d 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 i s large,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 amm onia 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 o verpredictions 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 usi ng the ammonia limiting method. (Scire et al., 2001)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 fo r 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 μ g/m 3 

### concentrati on 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 fro m 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 (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb)
CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4 

### [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IMPROVE 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 (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb) CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4Overview 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 visibil ity

- impairing organic particles from some forms of volatile organic carbon (VOC). The capabilities that do exist include the following.

First, PM 10 

### emissions (such as from power plants) are often divided into filterable and condensable components, with t he 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 PM 10 

### emissions condense into organic PM 2.5 

### 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 e t 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 ca rbon atoms can form significant SOA (Grosjean and Seinfeld, 1989). For example, according to this rule, isoprene (C 5 

### H 8 

### ) 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 PM 10 

### 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, thi s eliminates many anthropogenic sources of VOC emissions. For example, the fugitive emissions of butane and ethane during petroleum processing

8 Rec ent 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 fo r a description of this module.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 me chanism. 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 condensabl e fraction of PM 10 

### 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/EP A 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:

b ext 

### = 3 f(RH) [(NH 4 

### ) 2 

### SO 4 

### ] + 3 f(RH) [NH 4 

### NO 3 

### ] + 4[OC] + 1[Soil] + + 0.6[ Coarse Mass] + 10[EC] + b Ray 

### (3

- 1)

The concentrations, in square brackets, are in μ g/m 3 

### and b ext 

### is in units of Mm - 1 

### . The Rayleigh scattering term (b Ray 

### ) 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 th e 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 (b ext 

### /10) (3

- 2)

where HI is in units of deciviews (dv) and b ext 

### 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.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 conditi ons” 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.

Metho d 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 h ourly 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 res ults 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 def ault 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 back ground extinction is then calculated using these concentrations and hourly, site

- specific f(RH) from a 1993 IWAQM curve (a different oneOverview 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% (defaul t) 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 a re 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 wit h 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 r eflect 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 vi sibility 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 averag e natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibilit y
Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations

are e valuating 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) th at 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.Overview of the CALPUFF Modeling System 31 

### - Increasing the factor used to calcu late 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 n atural 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 fo rmula. 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.VISTAS’ Common Modeling Protocol 32 

### 4. VISTAS’ COMMON MODEL ING 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 p ollutant (SO 2 

### , NO x 

### , PM, and in certain cases VOC and NH 3 

### ) 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 sour ces 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 threshol d value.

VISTAS States intend to follow Option C (determine if the visibility impact from individual sources exceeds a contribution threshold) for SO 2 

### , NO x 

### , 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 NH 3 

### 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 SO 2 

### , NO x 

### , 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 SO 2 

### > 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 e ngineering 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.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 mod eling 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 underta ken, 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 impair ment 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 opt ion 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.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 m odel 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 th e 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
compar ison 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 ar e 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 mode ling, is not necessary.
4.1.3 VISTAS’ Treatment of VOC, NH 3 

### , 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 VO C emissions is not adequately tested, especially for anthropogenic emissions. (Separately, condensable organic carbon can be calculated from PM 10 

### .)

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 emissionsVISTAS’ Common Modeling Protocol 35 

### from all anthropogenic sources in the VISTAS 12

- km modeling dom ain (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 NH 3 

### and PM have been completed, a technical justification for treatment of all three of these pollutants will be presente d to EPA and FLMs for their review.

Ammonia

EPA has given states the option to address ammonia (NH 3 

### ) 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 NH 3 

### 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 NH 3 

### emissi ons 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 NH 3 

### emissions not be subject to BART.

Primary Particulate Matter

Primary particulate matter is cons idered 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 SO 2 

### a nd NO x 

### 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 non

EGU sources in the VISTAS states, two CMAQ sensitivity runs are underway for the f irst 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 an y, 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 NH 3 

### . 

### 4.2 Optional Source

- Specific Modeling

In some circumstances, a source may want to apply techniques design ed 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 someVISTAS’ Common Modeling Protocol 36 

### situations, higher resolution MM5 or other prognostic meteorological datasets may be available than the stan dard 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 is sue, 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 t o 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 pre

defined 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 an d 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 uni versally 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 VI STAS 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 da taset at 12 km and 36 km grid (developed for EPA)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 three

dimensional 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 pre

computed CALMET meteorological fields for the years 2001

- 2003 on the 12

- km CALMET domain in Figu re 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 devel opment 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 ar e 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 w inds. 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 C ALMET 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.VISTAS’ Common Modeling Protocol 38 

### Figure 4

- 2. VISTAS Regional 12

- km Resolut ion 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 “contrib utes” to visibility impairment is 0.5 dv change from natural conditions (although states may set a lower threshold). The 98 th 

### percentile (8 th 

### 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 impactVISTAS’ 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 8 th 

### highest values or the 22 nd 

### 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 98 th 

### percentile value) is to b e 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 e xemption 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 BART

eligible sources. The regional CALMET domain will be unchanged by these adjustments.

Also, the initial approach is designed to significantly reduce t he 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 req uire 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 opti on in CALMET allows finer grid CALMET input files to be calculated from the 12

- km CALMET files.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 Lambe rt 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 w ith 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 fie ld 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 CALMETVISTAS’ 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 CALPUF F 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 SO 4 

### and TNO 3 

### (HNO 3 

### + NO 3 

### ) from CMAQ 2001

- 2003 annual runs

- Species modeled : SO 2 

### , SO 4 

### , NO x 

### , HNO 3 

### , NO 3 

### and particulate matter in size categories of <0.625 μ m, 0.625

- 1.0 μ m, 1.0

- 1.25 μ m, 1.25

- 2.5 μ m, 2.5

- 6.0 μ m and 6

- 10 μ m aerodynamic diameters. As noted below, the particulate matter emi ssions 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 μ m in diameter), coarse PM (between 2.5

- 10 μ m in diameter) and organics (called secondary organic aeros ols (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 st art

- 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 considere d include SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### . 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 b ased 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 investigate d 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 SO 2 

### and NO x 

### and 15 tons per year for PM 10 

### ). 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 condensab le particulate matter into the following species categories: elemental carbon (soot), “soil” (fineVISTAS’ Common Modeling Protocol 42 

### PM < 2.5 μ m diameter), coarse particulate matter (2.5

- 10 μ m diameter) and organics. The process is illustrated in Figure 4

- 3. If source

- specific emissions f actors are not available, AP

42 factors can be used to estimate the PM speciation for those source sectors for which AP

42 emissions factors have been developed. Otherwise assumptions will need to be proposed by the source, and reviewed and approved by th e state.

Figure 4

- 3. Speciation of PM

- 10 Emissions. (PMC is coarse particulate matter

-- 2.5 to 10 μ m diameter.)

- Class I receptors : Use FLM Class I receptor list with receptor elevations provided (available from the NPS).

- CALPUFF model options : U se IWAQM (EPA, 1998) guidance, except use turbulence

based 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. Onl y 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 mo nth). PM

- 10

Filterable Condensable

EC PM

- 2.5 PMC PM
(including

H 2 SO 4 ) As Prim. SO4) Organic PM
(SOA)

EC Soil PMC SO 4 f(RH) Non

- SO 4 Inorganic OCVISTAS’ Common Modeling Protocol 43 

### - Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) values for ammonia. (But, as indicated below, u se CMAQ NH 3 

### data for each Class I area in POSTUTIL to repartition HNO 3 

### and NO 3 

### .)

- Puff representation: integrated puff sampli ng 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 exemptio n 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: SO 4 

### , NO 3 

### , EC, SOA (i.e., condensable organic emissions), soil, coa rse 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.

- Li ght 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

ou tside CALPOST or CALPOST would need to be modified to accommodate the new algorithm.)

- Ammonia Limiting Method: Use ammonia from CMAQ to define NH 3 

### 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 modelingVISTAS’ Common Modeling Protocol 44 

### results are not below the threshold for visibility impacts, the next step is to co nduct 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 98 th 

### 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 m odeling 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 E mission 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 Be nefits 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, distan ce of the source from Class I areas, and total size of the subregional modeling domain.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 elig ible 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 domain s 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 applica tion 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, theVISTAS’ Common Modeling Protocol 46 

### physical stack parameters (e.g., a tall stack in com plex 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.

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 ob servations 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 th e 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 refineme nts 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, C lass 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 98 th 

### percentil e 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 98 th 

### 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 thres hold, 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 impa ct 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.VISTAS’ Common Modeling Protocol 47 

### Table 4

- 1. Example of CALPOST Output, Showing Maximum Daily Impacts of S ource 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 = 1.219VISTAS’ 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 hygr oscopic aerosols (F(RH)), and the contribution of each species to light extinction (in percent of the total source contribution) for SO 4 

### , NO 3 

### , organics, elemental carbon, coarse and fine particulate matter.

For the finer grid analysis, the data in the tabl e 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 98 th 

### percentile (8 th 

### highest value) in the
sort ed 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 98 th 

### percentile visibility impact is below the contribution threshold of 0.5 dv.

Th e 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 are as 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 98 th 

### 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, discuss ion of the number of days below the 98 th 

### 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 t hose 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 t abular 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 effective ness of candidate control options are to be compared to each other, not to a specific target improvement.VISTAS’ Common Modeling Protocol 49 

### Table 4

- 2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003
Delta

Deciview Ranks 1

- 8 Delta

Deciview Rank s 1

- 8 Delta

Deciview 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 0.3 1 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 0.19VISTAS’ 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 Distanc e (km) from source to Class I

area boundary # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2001 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2002 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2003 # of days 1 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
Si psey, 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 98 th percentile of days in each year o r the three

- year modeling period, as appropriateVISTAS’ 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 contr olled 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 mak e 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 la st 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 un less 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 gene rate 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 w ith 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 ru n CALPUFF and to partition NO 3 

### in POSTUTIL.

o Background concentrations files for use in POSTUTIL

o All other set up files used in VISTAS 12

- km CALPUFF runSource

- Specific Modeling Protocol 52 

## 5

. SOURCE

- SPECIFIC MODELING PR OTOCOL
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 configurat ion, 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 g round

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### ).

Additional building dimension information (building width, length, height and corner locations) is needed for short stacks that are less than Good En gineering 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 distan ce 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. T he 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 source

specific domains ma y 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 grap hics 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”.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 E valuation 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 Measurement s 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 an d 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 MODELI NG PROTOCOL A.2 ... other appendices as neededSource

- 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 w ebsite, 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 SO 2 

### potential emissions (tpy) NO x 

### potential emissions (tpy) Total PM pote ntial emissions (tpy)
Emissions source

name

... Total emissions
Potential BART

eligible emissionsQuality Assurance 55 

## 6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol i s 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 dete rmine 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 obj ectives 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 sit e

- 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 mode ler 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 v arious 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 p art 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 s ystem. 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 depen d 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 exec ute 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 informati on 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 controlQuality Assurance 56 

### technology decisions and assessments. Finally, some of the modeling steps may be carried out by

con tractors 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 e fforts 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 (Secti on 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 CALPUFF

ready meteoro logical 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 o f 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 meteorol ogical 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. En vironmental 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. VIS TAS 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

assum ptions 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 regulatoryQuality Assurance 57 

### guidance and default values (see references in Section 4.3.3). However, remodeling of the boundary conditions is not required for VISTAS

- prov ided 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 assur ance 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

co nfirmation 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 fo r 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 cont ains 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 m odel. 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 r epeated 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, lat itude/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.Quality Assurance 58 

### Appendix A of t he 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 CA LMET 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 inter

relationships 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 fo r 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 outp ut 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 pr eprocessing 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 u ser 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 ou tput 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 pr ovided, 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 CALPU FF switch settings. Except as modified in Chapter 4 ofQuality 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” wit h 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 essent ial 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
stat istics 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 b ackground 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 rece ptors 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 s ubset 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). Fo r a finer grid simulation, the 98 th 

### percentile value (8 th 

### 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

- spec ific 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.Quality Assurance 60 

### - Appropri ate 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 recept ors 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 so urces 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 parti culate 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 particulat e 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 co mmon 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 w ill 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 meteorologica l (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 app roach 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 i s 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 in clude preparation of wind rose (using observed, MM5 and CALMET

- derived data), including examination of the dataQuality 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 illustrati ng 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 per formance 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 perf ormance 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 CA LPUFF. 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 predicti ng 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, si nce a comparable effect will be included in both the base and sensitivity simulations.References 62 

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-- Overview, Technical De scription and User's Guide. Pacific Northwest Laboratory, Richland, Washington.

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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. Sm ith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast Cycle: the RUC, M on. Wea. Rev ., 132 , 495

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Carson, D.J., 1973: The development of a dry, inversion

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Chan g, 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

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Department of the Interior, 2003: Letter from the Assistant Secretary of the I nterior 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 an d 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 T racking 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. EP A

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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

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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, 12 th 

### AMS/A&WMA Conferen ce 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 Wor kgroup (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.

Gro sjean, 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. Gryni ng 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 Mod eling 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 Reac tive Emissions Control System. 96 th 

### A&WMA Annual Conference & Exhibition, 22

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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

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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. Ad ams, 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

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- 057, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Scire, J.S. and F.R. Robe, 1997: Fine

- Scale Application of the CALMET Meteoro logical 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, M A.

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 R egulatory 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 Polluti on Meteorology, Phoenix, Arizona. American Meteorological Society. 11

- 16 January 1998.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 Dif ferences. Paper presented at the AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7

- 11 February 2005.

### EPA Region 4 BART CALPUFF Modeling Protocol Comments

Facility: MeadWestvaco Corporation, North Charleston, South Carolina Protocol Developer: Trinity Consultants Reviewers: Brenda Johns on 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 scr eening modeling of visibility at all Class I areas within 300 km, but also anticipates conducting refi ned 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 fo r 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 de veloped a number for finer-scale 4-km CALMET domains. One of these domain s (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 CA LPUFF. The March 16, 2006 EPA Model Clearinghouse memo, “Dispersion Coefficients for Regulatory Air Quality Modeling in CALPUFF,” from Tyler Fox to Kay Prin ce, discusses the Agency’s position on dispersion coefficients for CALPUFF. The me mo 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/scr am/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.

## B EST 

## A VAILABLE 

## R ETROFIT 

## T ECHNOLOGY 

## A PPLICABILITY 

## A NALYSIS 

## A IR 

## Q UALITY 

## M ODELING 

## P ROTOCOL 

## (R EVISED 

## ) S ANTEE 

## C OOPER 

##  G RAINGER 

## , J EFFERIES 

## , AND 

## W INYAH 

## S TATIONS

T RINITY 

### C ONSULTANTS 

### 1100 Johnson Ferry Road Suite 685 Atlanta, GA 30342 (404) 256-1919

July 2007

Project 061101.0117

### Santee Cooper – Grainger, Jefferies, Winyah i Trinity Consultants BART Applicability Modeling Protocol 

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION 

### ....................................................................................................................1 1.1 O VERVIEW OF 

### R EGIONAL 

### H AZE 

### R ULE AND 

### BART R EQUIREMENTS 

### .........................2 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY



### ........................................................ 3 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY



### ................................................................................................ 3 1.1.3 P OTENTIALLY 

### A FFECTED 

### C LASS 

### I A REAS



### ....................................................... 5 1.1.4 CALPUFF M ODELING 

### A NALYSES



### .................................................................. 7 

### 1.2 O RGANIZATION OF 

### BART A PPLICABILITY 

### A NALYSIS 

### M ODELING 

### P ROTOCOL 

### ..........8

2. BART-E LIGIBLE 

### S OURCE 

### D ESCRIPTION 

### ...........................................................................9 2.1 BART-E LIGIBLE 

### E MISSION 

### U NITS 

### ............................................................................9 2.2 BART- ELIGIBLE 

### S OURCE 

### M ODEL 

### E MISSIONS 

### I NVENTORY 

### .....................................12 2.2.1 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP O NLY



### ..... 15 2.2.2 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP AND 

### FGD 18 

### 2.3 M ODELED 

### S TACK 

### P ARAMETERS AND 

### E MISSIONS 

### ....................................................21

3. A IR 

### Q UALITY 

### M ODELING 

### A NALYSES 

### ...............................................................................25 3.1 A IR 

### Q UALITY 

### M ODELING 

### S YSTEM 

### ..........................................................................25 3.2 S CREENING AND 

### R EFINED 

### A NALYSIS 

### T ECHNIQUES 

### .................................................25 3.2.1 V ISIBILITY 

### A SSESSMENT 

### M ETRIC



### ................................................................. 26 3.2.2 M ETEOROLOGICAL AND 

### C OMPUTATIONAL 

### G RIDS



### ........................................ 26 3.2.3 M ODEL 

### P ROCESSING AND 

### P OSTPROCESSING 

### M ETHODS



### ............................... 27 

### 3.3 CALMET M ETEOROLOGICAL 

### P ROCESSING 

### ............................................................27 3.3.1 CALMET M ETEOROLOGICAL 

### D OMAINS



### ...................................................... 28 3.3.2 MM5 S IMULATIONS



### ...................................................................................... 29 3.3.3 G EOPHYSICAL 

### D ATA



### ..................................................................................... 30 3.3.4 12- KM 

### S CREENING 

### A NALYSIS 

### CALMET P ROCESSING



### ................................. 30 3.3.5 4- KM 

### R EFINED 

### G RID 

### CALMET P ROCESSING



### ............................................... 31 3.3.6 Q UALITY 

### A SSURANCE OF 

### CALMET A NALYSES



### .......................................... 32 

### 3.4 CALPUFF M ODEL 

### P ROCESSING 

### .............................................................................32 3.4.1 M ODELED 

### E MISSIONS AND 

### C HEMICAL 

### T RANSFORMATIONS



### ........................ 32 3.4.2 CALPUFF D ISPERSION 

### A LGORITHMS



### .......................................................... 36 3.4.3 B UILDING DOWNWASH



### .................................................................................. 36 3.4.4 CALPUFF M ODELING 

### D OMAINS AND 

### C LASS 

### I A REA 

### R ECEPTORS



### .............. 36 3.4.5 B ACKGROUND 

### O ZONE AND 

### A MMONIA 

### C ONCENTRATIONS



### ........................... 41 3.4.6 P UFF 

### R EPRESENTATION



### ................................................................................. 43 3.4.7 Q UALITY 

### A SSURANCE OF 

### CALPUFF A NALYSES



### ......................................... 44 

### 3.5 CALPOST P OSTPROCESSING AND 

### N ATURAL 

### B ACKGROUND 

### C ONDITIONS FOR 

### L IGHT 

### E XTINCTION AND 

### H AZE 

### I NDEX 

### C ALCULATIONS 

### ...........................................44 3.5.1 POSTUTIL P ROCESSING



### ............................................................................... 44 3.5.2 C LASS 

### I A REA



### - SPECIFIC 

### N ATURAL 

### B ACKGROUND 

### C ONDITIONS



### .................. 45

### Santee Cooper – Grainger, Jefferies, Winyah ii Trinity Consultants BART Applicability Modeling Protocol 3.5.3 V ISIBILITY 

### I MPACT 

### C ALCULATION AND 

### R EVISED 

### IMPROVE L IGHT 

### E XTINCTION 

### A LGORITHM



### .............................................................................. 47 3.5.3.1 Background NO 2 

### Concentrations ......................................................... 50 3.5.3.2 Site-Specific Rayleigh Scattering......................................................... 50 3.5.3.3 Background Sea Salt Aerosol Concentrations...................................... 50 3.5.4 Q UALITY 

### A SSURANCE OF 

### P OSTPROCESSING 

### A NALYSES



### ............................... 55



### A PPENDIX 

### A VISTAS BART Modeling Protocol (Revision 3.2, August 31, 2006)

A PPENDIX 

### B CPM Factor Analysis

### Santee Cooper – Grainger, Jefferies, Winyah 1 Trinity Consultants BART Applicability Modeling Protocol 

## 1. I NTRODUCTION

Santee Cooper operates several electrical genera ting stations in South Carolina, including the Grainger, Jefferies, and Winyah Generating Stati ons. Each of the three facilities operates under a Title V Air Quality Permit issued by the South Ca rolina Department of Health and Environmental Control (DHEC) and consists of coal-fired utility boilers and supporting operati ons (e.g., coal storage and handling operations).

South Carolina DHEC considers the Santee Coope r 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 contri bute 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 a nd the proposed modeling methods to determine applicability of BART as described in this prot ocol are consistent w ith 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 prot ocol, and is presented in Appendix A. The VISTAS

### 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 website 1 

### ) establish common technical approaches for quantifying emissions from BART-eligible emission units and conducting screening and re fined 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 guidan ce to the source-specific analysis of visibility impacts attributable to the Grainger, Jefferies, and Winyah Generating Stations.

Santee Cooper submits this revised BART appli cability 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 Manage rs (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 accord ance with a mutually agreeable schedule.

To facilitate South Carolina DHEC’s review of th is 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 e ligibility of emission units at each of the facilities. Additional detail was added to Ta ble 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 O VERVIEW OF 

## R EGIONAL 

## H AZE 

## R ULE AND 

## BART R EQUIREMENTS
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 opera tion 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 (NO X 

### ), sulfur dioxide (SO 2 

### ), and particulate matter less than 10 micrometers in aerodynamic diameter (PM 10 

### ). Fossil-fueled electric generating units (EGUs) are one of the lis ted source categories, and fall under SIC code 4911 – Electric Services.

1 http://vistas-sesarm.org/BART/index.asp

### 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 th e relevant time period. The BART-eligible source may include multiple emission units but need not include the entire facility.



### 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY 

### The U.S. EPA BART guidelines define the fo llowing 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 differentthan BART-eligible sources at other facilities on the list of 26. The Santee Cooper BARTeligible units are EGUs that, in addition to be ing subject to the BART Rule, are subject to the Clean Air Interstate Rule (CAIR), which targets emission reductions for both SO 2 

### and NO X 

### (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 NO X 

### and SO 2 

### emissions; thus South Carolina DHEC has stated that the evaluation of BART for EGUs should be restricted to PM 10 

### emissions only. Therefore, this BART-eligibility analysis only includes evaluations in terms of direct PM 10 

### emissions from Santee Cooper’s BARTeligible EGU sources, and excludes all dir ect emissions from, and secondary reactions with, SO 2 

### and NO X 

### 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, we re 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 PM 10 

### . 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 Sec tion 2 of this BART Applicability Modeling Protocol.



### 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY 

### In its role as technical analysis coordi nator, 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.

### 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 deciview s (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 determ ine 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 CA LPUFF modeling system, to quantify the impacts attributable to a single BART-eligibl e 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 ( b ext 

### ), 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 backgr ound conditions. The background extinction coefficient b ext, background 

### is affected by various chemical species and the Rayleigh scattering phenomenon and can be calculated as sh own in the following equation. 

# ( ) Ray ap Coarse Soil OC NO SO background ext 

### bbbbbbbMm b ++++++= − 3 4 1

,


### where, 

## ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ] [ ] [ ] 

# ( ) ( ) 

## [ ] 3 1 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative defaultby Mm 10 Scattering Rayleigh EC10 Mass Coarse6.0 Soil1 OC4 NONH3 SONH3

= =

= = = = = = =

− RHf b b b b b RHf b RHf b

Ray

ap Coarse Soil OC NO SO 

## ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ]
molecules air todue scattering is Scattering Rayleigh carbon elemental ofion concentrat thedenotes EC dusts coarse ofion concentrat thedenotes Mass Coarse soils fine ofion concentrat thedenotes Soil carbon organic ofion concentrat thedenotes OC ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4



### Values for the parameters listed above specifi c to the natural background conditions at the Class I areas to be considered in this analysis are provided on an annual average basis in

### Santee Cooper – Grainger, Jefferies, Winyah 5 Trinity Consultants BART Applicability Modeling Protocol the U.S. EPA’s Guidance for Estimating Na tural Visibility Conditions under the Regional Haze Rule

. 3 

### More detailed information about the natural background c onditions 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 partic ulate matter (PMF), and elemental carbon (EC), as well as precursors to secondary organic aeros ols (SOA) and fine particulate matter such as SO 2 

### and NO X 

### . 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 b ext,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. 

# ( )
EC SOA PMF PMC NO SO sourceext 

### bbbbbbMmb +++++= − 3 4 1

,


### where, 

## ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ]

[ ] [ ] ( ) 

## [ ] 3 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative EC10

PMC6.0 PMF1 SOA4 NONH3 SONH3

= =

=

= = = = =

RHf b b b b RHf b RHf b

EC

PMC PMF SOA NO SO 

## ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ] carbon elemental ofion concentrat thedenotes EC PM coarse ofion concentrat thedenotes PMC PM fine ofion concentrat thedenotes PMF aerosols organic secondary ofion concentrat thedenotes SOA ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4



### 1.1.3 P OTENTIALLY 

### A FFECTED 

### C LASS 

### I A REAS

### 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.

### Santee Cooper – Grainger, Jefferies, Winyah 6 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 1-1. D ISTANCES 

### ( KILOMETERS 

### ) S EPARATING 

### VISTAS C LASS 

### I A REAS AND 

### S ANTEE 

### C OOPER 

### ’ S 

### J EFFERIES 

### , G RAINGER 

### , AND 

### W INYAH 

### G ENERATING 

### S TATIONS

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 VI STAS 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 Ro main, Wolf Island, and Swanquarter National Wildlife Refuges. 4

4 National Park Service compilation of Class I area re ceptors, http://www2.nature. nps.gov/air/maps/receptors/.

### Santee Cooper – Grainger, Jefferies, Winyah 7 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 1-1. L OCATION OF 

### S ANTEE 

### C OOPER 

### ’ S 

### BART-E LIGIBLE 

### F ACILITIES 

### R ELATIVE TO 

### VISTAS C LASS 

### I A REAS

1,300 1,400 1,500 1,600 1,700 1,800 

### Lambert Conformal Coordinate East (km) -800 -700 -600 -500 -400 -300 -200Lambert Conformal Coordinate North (km)

Grainger

Jefferies Winyah

Wolf Island Cape Romain Swanquarter



### 1.1.4 CALPUFF M ODELING 

### A NALYSES 

### 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 freque ncy, 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 po llutant emissions from a single source and is therefore preferred for BART applicability and determination analyses.

### Santee Cooper – Grainger, Jefferies, Winyah 8 Trinity Consultants BART Applicability Modeling Protocol The U.S. EPA BART guidelines prescribe that the 98 th 

### percentile, 24-hour average, visibility impact computed in a modeling analysis that evaluates three years of meteorological data should be compared to th e contribution threshold. However, VISTAS prescribes that the maximum computed visibility impact be used as the basis for comparison in the screening analysis and the 98 th 

### 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 th at the same CALPUFF modeling analysis techniques have been determined to be su itable 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 a pplicability 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 O RGANIZATION OF 

## BART A PPLICABILITY 

## A NALYSIS 

## M ODELING 

## P ROTOCOL
The remainder of this BART applicability m odeling 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.

### Santee Cooper – Grainger, Jefferies, Winyah 9 Trinity Consultants BART Applicability Modeling Protocol 

## 2. BART-E LIGIBLE 

## S OURCE 

## D ESCRIPTION

This section of the BART Applicability Analysis modeling protocol describes the emission units that comprise the BART-eligible source at Santee Coope r’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-E LIGIBLE 

## E MISSION 

## U NITS
Santee Cooper and South Carolina DHEC reviewed th e 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.

T ABLE 

### 2-1. S UMMARY OF 

### BART- ELIGIBLE 

### E MISSION 

### U NITS AND 

### P OTENTIAL 

### E MISSIONS

Facility Emission Unit Source Code BART Source Category* Date Built or Modified SO 2 Emissions (tpy) NO X Emissions (tpy) PM 10 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 To tal Potential Emissions (tpy) 27,008 7,280 320 Jefferies BART-Eligible Emission Units To tal Potential Emissions (tpy) 47,002 12,674 548 Winyah BART-Eligible Emission Units To tal Potential Emissions (tpy) 46,790 18,748 684
* Category 1 denotes fossil fuel-fired steam electri c plants of more than 250 MMBtu/hr heat input. † Emissions not listed since emission unit was construc ted prior to or after the BART-eligibility period.

5 While the Winyah boilers are also pe rmitted to combust fuel oil, they primarily com bust bituminous coal.

### Santee Cooper – Grainger, Jefferies, Winyah 10 Trinity Consultants 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 NO X 

### , SO 2 

### , and total PM 10 

### (condensable and filterable). Therefore, all three of the Santee Cooper facilities are BART-e ligible since potential emissions exceed 250 tpy for at least one (in fact, all) of NO X 

### , SO 2 

### , and PM 10, 

### 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 supporti ng 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 c overed by BART have potential PM 10 

### 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 PM 10 

### ) 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 SO 2 

### , NO X 

### , and PM 10 

### , 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 visib ility-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 contri bute to visibility impacts, and other than these 13 facilities, ammonia emissions from VISTAS sour ces do not contribute to visibility impairment within the VISTAS region: Based on these analyses, the VISTAS states re commend 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.

### Santee Cooper – Grainger, Jefferies, Winyah 11 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 2-2. T ITLE 

### V I NSIGNIFICANT 

### E MISSION 

### U NITS 

### E XCLUDED FROM 

### BART A NALYSIS

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 Unload ing 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 Unload ing 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 Ha ndling Reclaim Hoppers < 0.2 Handling C01 – C08 Coal Hand ling 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 F eed Conveyors C-1, C-2, & C-3 < 0.2, each Handling P12/P13, P21 Coal Crushe rs (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 Co nveyors 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 dur ing the BART applicability period of August 7, 1962 – August 7, 197 7. † Potential emissions are based on app lication of a safety factor to actual emissions. Values are rounded.

### Santee Cooper – Grainger, Jefferies, Winyah 12 Trinity Consultants BART Applicability Modeling Protocol 

## 2.2 BART- ELIGIBLE 

## S OURCE 

## M ODEL 

## E MISSIONS 

## I NVENTORY
Whereas the BART eligibility determination relies on potential emissions of visibility-affecting pollutants, the BART applicability analysis utili zes maximum actual 24-hour average emission rates of NO X 

### , SO 2 

### , and PM 10 

### . The VISTAS BART Modeling Protocol specifies the following hierarchy of resources to establish the maximum actual 24-hour av erage 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, PM 10 

### 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 PM 10 

### emissions in the BART applicability modeling. If the use of 24-hour controlled potential PM 10 

### 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 PM 10 

### emission rates from each BART-eligible emission unit that will be used in the analyses.

T ABLE 

### 2-3. S UMMARY OF 

### 24- HOUR 

### A VERAGE 

### M AXIMUM 

### P OTENTIAL 

### BART E MISSION 

### R ATES

Facility Emission Unit NO X Emissions* (lb/hr) SO 2 Emissions* (lb/hr) Total PM 10 Emissions† (lb/hr) Total PM 2.5 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

* NO X and SO 2 emissions for EGUs are not required to be included in the BART analysis. † Emissions include both filterable a nd condensable particulate matter.



### Table 2-3 includes 24-hour maximum emission rates and distinguishes the emission rates of Total PM 10 

### (TPM 10 

### ), which includes emissions of Total PM less than 2.5 microns in aerodynamic diameter (TPM 2.5 

### ) and all filterable and condensable components. Modeling of visibility impairment requires that the components of the exhaust stream be speci ated because different types of particulate matter affect visibility to varying extents. Figure 2-1 de picts the speciation of visibility-affecting pollutant

### 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, Sant ee Cooper adapted the speciation presented in the following sections.

Note that the organic fraction of condensable pa rticulate 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 (SO 4 

### ) and nitrates (NO 3 

### ), 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 sulfat e 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 par ticulate species used in the remaining discussion about particulate speciation.



### F IGURE 

### 2-1. P ARTICULATE 

### M ATTER 

### S PECIATION 

### ( AFTER 

### F IGURE 

### 4-3 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

### Santee Cooper – Grainger, Jefferies, Winyah 14 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 2-4. N OMENCLATURE FOR 

### E MISSIONS 

### S PECIATION 

### A NALYSIS

Nomenclature Description

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μ m PM 10 Filterable particulate matter with an aerodynamic diameter < 10 μ m PM 6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μ m PM 2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μ m PM 2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μ m PM 1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μ m PM 1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μ m PM 0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μ m PM 0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μ m PMC Filterable Coarse particulate matter = PM 6-10 + PM 2.5-6 PMF Filterable Fine particulate matter = PM 6-10 + PM 2.5-6 CPM Condensable particulate matt er (organic and inorganic) POC Primary organic condensable emissions PIC Primary non-hygroscopic inorganic condensable emissions TPM 10 Filterable PM 10 + CPM TPM 2.5 Filterable PM 2.5 + CPM SO4 Inorganic Sulfate CPM NO3 Inorganic Nitrate CPM EC Elemental Carbon



### The FLM have published speciation guidance for certa in source types (e.g., gas-, oil-, and coal-fired combustion sources) which can be used to create sp eciation 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 H 2 

### SO 4 

### 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 hy groscopic 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 Res ources (NCDENR), and evaluated the mean and median factors from the larger data sets.

### Santee Cooper – Grainger, Jefferies, Winyah 15 Trinity Consultants BART Applicability Modeling Protocol matter (NO 3 

### ). 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 (NO 3 

### ) and/or other non-hygroscopic primary inorganic CPM (PIC) emissions (such as carbonates).

The individual particulate species will be modele d in CALPUFF and then grouped into categories according to visibility-affecting characteristics usi ng 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 e ach of the BART-eligible sources at the three Santee Cooper facilities is presented in th e following sections of this protocol.

T ABLE 

### 2-5. A SSIGNMENT OF 

### E MITTED 

### PM S PECIES TO 

### M ODELED 

### PM C ATEGORIES

Modeled PM Category † Components Output Category ‡ Extinction Coefficient (m 2 /g)
PMC Filterable coarse particles (PM 6-10 , PM 2.5-6 ) PMC 0.6 PMF Filterable fine particles (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 ) SOIL 1 PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 SO4 Primary inorganic condensable emissions of sulfates* SO4 3 f

(RH) NO3 Primary inorganic condensable emissions of nitrates* NO3 3 f

(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 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP O NLY 

### 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 TPM 10 

### 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 fo r the North Carolina EGUs, which are most representative of the Santee Cooper EGUs. Refe r 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/dec rees/civil/caa/santee-cd.pdf

### Santee Cooper – Grainger, Jefferies, Winyah 16 Trinity Consultants BART Applicability Modeling Protocol of the CPM factor development. The percenta ge 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 boile rs at the Grainger and Jefferies Generating Stations.

T ABLE 

### 2-6. C OAL 

### B OILERS WITH 

### ESP S PECIATION 

### D ATA

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 Co al 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 Co al Properties*

CPM Factor (lb/MMBtu) 0.02 Santee Cooper Custom CPM Factor PM 10 as a % of PM 67.0% EC as a % of Fine PM 10 3.7% EC as a % of PM 10 ‡ 1.6% Organic portion of CPM 20.0% Inorganic portion of CPM 80.0% PM 6-10 as a % of PM 10 25.4% PM 2.5-6 as a % of PM 10 31.3% PM 1.25-2.5 as a % of PM 10 17.9% PM 1-1.25 as a % of PM 10 4.5% PM 0.625-1 as a % of PM 10 3.0% PM 0.5-0.625 as a % of PM 10 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 minimu m value for the period of 2001, 2003-2005, whichever values result in the higher factors. Although 2002 data was not av ailable, the variation between the existing coal data, calculated as the standard deviation divided by the mean valu e, was less than 8% for all pa rameters. 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . ‡ Value calculated based on the percentage of EC composing PM 2.5 multiplied by the percentage of PM 10 that is PM 2.5 .



### The site-specific information presented in Table 2-6 was input into the FLM speciation profile workbook, which uses the coal data a nd site-specific filterable and condensable PM factors to determine the TPM 10 

### emissions on a pound per hour basis. 9 

### The TPM 10 

### emissions are then speciated in the various filterable and condensable PM components. Table 2-7 presents the TPM 10 

### percentages and site-specific PM 10 

### , CPM, and TPM 10 

### emissions for the Grainger and Jefferies boilers.

9 Workbook is available at the NPS website: www2.natur e.nps.gov/air/permits/ect/e ctCoalFiredBoiler.cfm.

### Santee Cooper – Grainger, Jefferies, Winyah 17 Trinity Consultants BART Applicability Modeling Protocol

T ABLE 

### 2-7. D ATA 

### U SED TO 

### C ALCULATE 

### TPM 10 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### C OAL 

### B OILER WITH 

### ESP (G RAINGER AND 

### J EFFRIES 

### )

% of TPM 10 that is PM 10 * % of TPM 10 that is CPM* Boiler Heat Input (MMBtu/hr) PM 10 Emissions

(lb/hr)† TPM 10 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) × (PM 10 as a % of PM). ‡ Calculated as: (PM 10 emissions, lb/hr) / (% of TPM 10 that is PM 10 ). ** 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 μ m and 0.5-0.625 μ m. PIC emissions were calculated using a similar methodology, using the inorganic condensable percentage of 80%.

The filterable PM 10 

### emissions were then divided amongst six different size categories by multiplying filterable PM 10 

### emissions by the percentage of PM 10 

### for each size category (refer to Table 2-5), yielding hourly emissions of PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625-1 

### , and PM 0.5-0.625 

### . 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 speciatedPM emissions for the BART eligible coalfired boilers at the Grainger and Jefferies Ge nerating Stations while Figures 2-2 and 2-3 present graphical representations of the specia tion. Note that the values presented in the table and figures are for each boiler at the facility.

T ABLE 

### 2-8. TPM 10 

### S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### C OAL 

### B OILER WITH 

### ESP (G RAINGER AND 

### J EFFRIES 

### )

POC ( μ m) PIC (sulfate) ( μ m) PMC ( μ m) PMF ( μ m) 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

### Santee Cooper – Grainger, Jefferies, Winyah 18 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 2-2. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### G RAINGER 

### B OILER

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



### F IGURE 

### 2-3. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### J EFFERIES 

### B OILER

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 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP AND 

### FGD
The Winyah Generating Station operates two BA RT-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

### 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 TPM 10 

### 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 SO 2 

### 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 contro ls and will have greater sulfate (PIC) emissions and thus, greater CPM emissions th an the Winyah EGUs. The percentage of elemental carbon in the PM emissions was al so 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.

T ABLE 

### 2-9. C OAL 

### B OILERS WITH 

### ESP & FGD S PECIATION 

### D ATA

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 Aver age 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 Aver age Coal Properties* CPM Factor (lb/MMBtu) 0.008 Santee Cooper Custom CPM Factor PM 10 as a % of PM 67.0% EC as a % of Fine PM 10 3.7% EC as a % of PM 10 ‡ 1.6% Organic portion of CPM 20.0% Inorganic portion of CPM 80.0% PM 6-10 as a % of PM 10 25.4% PM 2.5-6 as a % of PM 10 31.3% PM 1.25-2.5 as a % of PM 10 17.9% PM 1-1.25 as a % of PM 10 4.5% PM 0.625-1 as a % of PM 10 3.0% PM 0.5-0.625 as a % of PM 10 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 minimu m value for the period of 2001, 2003-2005, whichever values result in the higher emission factors. Although 2002 data wa s 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . ‡ Value calculated based on the percentage of EC composing PM 2.5 multiplied by the percentage of PM 10 that is PM 2.5 .

10 Consent decree between EPA and Santee Cooper, June 24, 2004. www.epa.gov/Compliance/resources/dec rees/civil/caa/santee-cd.pdf

### 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 a nd site-specific filterable and condensable PM factors to determine the TPM 10 

### emissions on a pound per hour basis. 11 

### The TPM 10 

### emissions are then speciated in the various filterable and condensable PM components. Table 2-10 presents the TPM 10 

### percentages and site-specific PM 10 

### , CPM, and TPM 10 

### emissions for the Winyah boilers.

T ABLE 

### 2-10. D ATA 

### U SED TO 

### C ALCULATE 

### TPM 10 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### C OAL 

### B OILER WITH 

### ESP (W INYAH 

### )

% of TPM 10 that is PM 10 * % of TPM 10 that is CPM* Boiler Heat Input (MMBtu/hr) PM 10 Emissions

(lb/hr)† TPM 10 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) × (PM 10 as a % of PM). ‡ Calculated as: (PM 10 emissions, lb/hr) / (% of TPM 10 that is PM 10 ). ** Calculated as: (Boiler heat input, MMBtu/hr) × (CPM factor, lb/MMBtu).



### The POC, PIC, and various filterable PM 10 

### size grouping emissions for the Winyah boilers were determined using the sa me 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 Sta tion while Figures 2-4 and 2-5 present graphical representations of the speciation.

T ABLE 

### 2-11. TPM 10 

### S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### W INYAH 

### C OAL 

### B OILERS WITH 

### ESP & FGD

POC ( μ m) PIC (sulfate) ( μ m) PMC ( μ m) PMF ( μ m) 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.natur e.nps.gov/air/permits/ect/e ctCoalFiredBoiler.cfm.

### Santee Cooper – Grainger, Jefferies, Winyah 21 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 2-4. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### W INYAH 

### B OILER 

### 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



### F IGURE 

### 2-5. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### W INYAH 

### B OILER 

### 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 M ODELED 

## S TACK 

## P ARAMETERS AND 

## E MISSIONS
Actual stack parameters will be input to the CA LPUFF 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 ex haust discharges vertically without obstruction.

### 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 TPM 10 

### 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.Santee Cooper – Grainger, Jefferies, Winyah23Trinity ConsultantsBART Applicability Modeling ProtocolTABLE2-12.STACKPARAMETERS FORBART-ELIGIBLEEMISSIONUNITSFacilityEmissionUnitStack IDLCC East(km)LCC North(km)BaseElevation(feet)StackHeight(feet)StackDiameter(feet)ExhaustTemperature(°F)ExhaustVelocity(feet/sec)Grainger Boiler 1 GS01 1,644.618 -520.823 40 300 10.0 309 69.2Boiler2 GS02 1,644.643 -520.818 40 300 10.0 315 70.0Jefferies Boiler 3 JS03 1,572.042 -600.906 56 300 13.0 325 56.2Boiler4 JS04 1,572.073 -600.932 56 300 13.0 325 56.2Winyah Boiler 1 WS01 1,627.285 -580.090 18 404 20.0 125 52.0Boiler2 WS02 1,627.285 -580.090 18 404 20.0 125 52.0TABLE2-13.MODELEDEMISSIONRATES FORBART-ELIGIBLEEMISSIONUNITSEmission Stack PMC (lb/hr)PMF(lb/hr)PIC (as sulfate) (lb/hr)POC (lb/hr)ECFacility Unit ID6-10μm 2.5-6μm 1.25-2.5μm 1.0-1.25μm 0.625-1.0μm 0.5-0.625μm 0.625-1.0μm 0.5-0.625μm 0.625-1.0μm 0.5-0.625μm(lb/hr)Grainger Boiler 1 GS01 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04 1.76 1.76 0.28Boiler 2 GS02 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04 1.76 1.76 0.28Jefferies Boiler 3 JS03 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26 3.07 3.07 0.52Boiler 4 JS04 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26 3.07 3.07 0.52Winyah Boiler 1 WS01 13.57 16.76 9.58 2.39 1.60 8.76 8.51 8.51 2.13 2.13 0.82Boiler 2 WS02 13.57 16.76 9.58 2.39 1.60 8.75 8.51 8.51 2.13 2.13 0.83

### 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’ befo re December 31, 1970” and to “stack heights or dispersion techniques at sources which were rec onstructed 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 H g 

### = H + 1.5 L , where H g 

### = 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 regul ation does apply to the tw o Winyah boilers, which became “in existence” in 1971 and 1974. Therefor e, 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:

H

g 

### = H + 1.5 L = 66.14 m + 1.5(66.14 m) = 165.35 m

This calculation demonstrates that the Winyah bo ilers can be modeled at actual stack height (123.14 m) since creditable GEP stack height is not exceeded.

### Santee Cooper – Grainger, Jefferies, Winyah 25 Trinity Consultants BART Applicability Modeling Protocol 

## 3. A IR 

## Q UALITY 

## M ODELING 

## A NALYSES

Section 3 of this BART applicability modeling prot ocol 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 fo llowing the methods described in the VISTAS BART Modeling Protocol as revised.



## 3.1 A IR 

## Q UALITY 

## M ODELING 

## S YSTEM
CALPUFF is a multi-layer, multi-species, non-steady-state Lagrangian puff model, which can simulate the effects of time- and space-varying me teorological 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 dist ances, 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 distan ces. 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 m odeling 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. Sp ecifically, CALMET Version 5.726/060801, CALPUFF Version 5.756/060725, POSTUTIL Version 1.52/ 060412, and CALPOST Version 5.6393/060202 13 

### are designated as the “VISTAS Recommended Ve rsion for BART Modeling” by the VISTAS Technical Contractor and will be used in th e CALPUFF BART applicability analyses for Santee Cooper’s Grainger, Jefferies, a nd Winyah Generating Stations. 14



## 3.2 S CREENING AND 

## R EFINED 

## A NALYSIS 

## T ECHNIQUES

The VISTAS BART Modeling Protocol distinguishes between screening and refined applications of the CALPUFF model for the purposes of BART App licability 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 th e 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/downloa d/download.htm#VISTAS_VERSION

### 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 V ISIBILITY 

### A SSESSMENT 

### M ETRIC 

### The U.S. EPA BART guidelines prescribe that the 98 th 

### percentile, 24-hour average visibility impact computed in a modeling analysis that evaluates three years of meteorological data should be compared to th e 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 representati on of visibility impairment. The VISTAS refined approach prescribes that the 98 th 

### percentile impact be evaluated in refined analyses as the eighth-highest 24-hour average visibility impact of each of three years modeled, or the 22 nd 

### -highest 24-hour average visibility impact over the three years modeled, whichever is more conservative.

To assess whether BART-eligible operations c ontribute 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 separat ely in comparison to the 0.5 dv contribution threshold to assess BART applicability.



### 3.2.2 M ETEOROLOGICAL AND 

### C OMPUTATIONAL 

### G RIDS



### 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 transf ormation of visibility-affecting pollutants in the CALPUFF meteorological model. Th e VISTAS Technical Contractor conducted meteorological modeling using both screening 12 -km and refined 4-km grids to provide common data resources to eligible sour ces 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.

### Santee Cooper – Grainger, Jefferies, Winyah 27 Trinity Consultants BART Applicability Modeling Protocol 

### 3.2.3 M ODEL 

### P ROCESSING AND 

### P OSTPROCESSING 

### M ETHODS


### 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 th e 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 scr eening 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. Di fferential rates of transport among the new puffs thus generated can increase the horizontal spr ead 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 accu rately 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 c oncentrations of visibility-affecting pollutants, the POSTUTIL postprocessing utility can be used to apply the ammonia limiting method (ALM) by re-partitioning the distribution of HNO 3 

### and NO 3 

### 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 us ing the default ammonia background level of 0.5 ppb will be conducted in refined analyses if nitrate species are modeled.



## 3.3 CALMET M ETEOROLOGICAL 

## P ROCESSING
CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields from mesoscale model (MM) output, raw obser vations of surface and upper air conditions, precipitation measurements, and geop hysical 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 me teorological fields are available, as discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used in conjunction with available standar d [National Weather Service] NWS or comparable meteorological observations w ithin 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

### Santee Cooper – Grainger, Jefferies, Winyah 28 Trinity Consultants BART Applicability Modeling Protocol assured MM5 data for the entire VISTAS area. Fo r 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 M ETEOROLOGICAL 

### D OMAINS 

### 12-km screening and 4-km refined CALMET mete orological 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 fo r 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.



### F IGURE 

### 3-1. 12- KM 

### R EGIONAL 

### S CREENING 

### A NALYSIS AND 

### 4- KM 

### S UB 

### - REGIONAL 

### R EFINED 

### A NALYSIS 

### CALMET D OMAINS



### The VISTAS regional modeling domain was designed to allow any Class I areas within the VISTAS area to be evaluated with a single meteorological databa se and consistent CALPUFF modeling options. The horizontal dom ain is comprised of grid cells, each containing a central grid point at which mete orological and computational parameters are

### Santee Cooper – Grainger, Jefferies, Winyah 29 Trinity Consultants 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 structur e 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.

T ABLE 

### 3-1. V ERTICAL 

### G RID 

### S TRUCTURE

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 u tilize for its refined analyses, contain all portions of South Carolina, Georgia, and No rth Carolina in which the BART-eligible sources and potentially affected Class I areas are located.



### 3.3.2 MM5 S IMULATIONS



### MM data are used as “observed” or “fir st-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional grid. The following three years of MM5 me teorological 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

### Santee Cooper – Grainger, Jefferies, Winyah 30 Trinity Consultants BART Applicability Modeling Protocol subsequently used to run CALMET for the 12- km screening and 4-km sub-regional refined analyses.



### 3.3.3 G EOPHYSICAL 

### D ATA 

### 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 da ta 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 (appr oximately 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 assimila te 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 th e 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 calcul ate 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 

### S CREENING 

### A NALYSIS 

### CALMET P ROCESSING 

### The development of the regional screening CALMET meteorological fields from MM5 data were conducted in No-Ob servations (“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 meteorol ogical 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).

### Santee Cooper – Grainger, Jefferies, Winyah 31 Trinity Consultants BART Applicability Modeling Protocol 

### 3.3.5 4- KM 

### R EFINED 

### G RID 

### CALMET P ROCESSING 

### 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 wi nd fields, and NWS meteorological observational data in the Step 2 smoothing calculations. Inthis manner, actual observations of threedimensional meteorological conditions were u sed 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, temperatur e, cloud cover and ceiling, relative humidity, and precipitation type. Surface data were sel ected from the available data inventory to optimize spatial coverage and representation of the domain. Raw observations were obtained from the National Climatic Data Ce nter (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 va ry less and are generally not as affected by local effects (e.g., terrain or coastlines). U pper 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 analys is. Therefore, it is necessary to include observations of precipitation in the CALMET an alysis. Precipitation data were collected from selected surface meteorological data stations included in the analysis, plus Cooperative Observation Network (COOP) stati ons 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

### Santee Cooper – Grainger, Jefferies, Winyah 32 Trinity Consultants 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 di stinct 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 CA LMET processor is equipped to assimilate overwater data obtained from coastal, near-s hore, and offshore observation platforms. CALMET uses a profile method to simulate boundary layer effects by computing the friction velocity, Monin-Obukhov length, su rface roughness, and mixing height over the water surface. The details of the formulati on 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, an d the overwater temperature gradients above and below the overwater mixing height. For pr actical 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 temperatur e 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 Q UALITY 

### A SSURANCE OF 

### CALMET A NALYSES 

### 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 M ODEL 

## P ROCESSING
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 M ODELED 

### E MISSIONS AND 

### C HEMICAL 

### T RANSFORMATIONS



### Section 2 of this document described the BA RT-eligible emission units operated by Santee Cooper and the visibility-affecting pollutant s considered in the CALPUFF analysis. Emission rates were calculated following U.S. EPA and South Carolina DHEC BART

### Santee Cooper – Grainger, Jefferies, Winyah 33 Trinity Consultants BART Applicability Modeling Protocol guidance for primary emissions of PM 10 

### . 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 algor ithms, which are considered the default for regulatory CALPUFF modeling, will be em ployed. However, preliminary analyses will not include pollutants that undergo chemic al transformation. Background levels of ozone and ammonia, which drive the simu lated 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 (SO 2 

### , SO 4 

### , and NO X 

### ) 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 PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625 

### - 1 

### , and PM 0.5-0.625 

### , 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.

T ABLE 

### 3-2. R EPRESENTATION OF 

### PM S IZE 

### C ATEGORIES IN 

### CALPUFF

Computed Geometric Mass Geometric Standa rd Precipitation Scav enging 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 u tilizes 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 multiplicativ e 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, inorga nic condensable PM, and elemental carbon).

15 http://www.src.com/verio/downloa d/sample_files.ht m#EXAMPLE_BART

### Santee Cooper – Grainger, Jefferies, Winyah 34 Trinity Consultants 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 m odeling a single point source; however, for facilities with multiple emission point sources, the preceding approach is not appropriate. Specifically, for sources with multiple emi ssion points that have different exhaust characteristics (e.g., stack height, diameter, ve locity, 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 alternativ e to this approach for sources with multiple emission points is to model each emission po int 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 alternativ e 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.

T ABLE 

### 3-3. E XPLICIT 

### M ODELING OF 

### PM T YPES AND 

### S IZE 

### C ATEGORIES

Modeled PM Category Components

PMC800 PMC425 Filterable coarse particles divided between two size categories (PM 6-10 , PM 2.5-6 ) PMF187 PMF112 PMF081 PMF056 Filterable fine particles divided among four size categories (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 )

POC081 POC056 Primary condensable organi c emissions divided between two size categories (PM 0.625-1 and PM 0.5-0.625 ) PIC081 PIC056 Primary condensable inorganic emissions divided between tw o size categories (PM 0.625-1 and PM 0.5-0.625 ) EC187 EC112 EC081 EC056 Primary elemental carbon emissions di vided among four size categories (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 )



### 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.

### Santee Cooper – Grainger, Jefferies, Winyah 35 Trinity Consultants BART Applicability Modeling Protocol

T ABLE 

### 3-4. R EPRESENTATION OF 

### E XPLICITLY 

### M ODELED 

### PM S IZE 

### C ATEGORIES 

### IN CALPUFF

Computed Geometric Mass Geometric Standa rd Precipitation Scav enging 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 u tilizes the deposition velocity associated with the geometric mass mean diameter. † Santee Cooper is proposing to preliminarily model all PIC as su lfate. Should Santee Cooper elect to segregate PIC into sulfa te and non-sulfate species, the PIC species would be modeled as shown in this table.



### To post-process the CALPUFF output concentr ations that result from explicitly modeled multiple emission points, POSTUTIL will be u sed 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 emissi on 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 CA LPOST to compute light extinction due to the multiple emission points at the source.

### Santee Cooper – Grainger, Jefferies, Winyah 36 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 3-5. A SSIGNMENT OF 

### M ODELED 

### PM S PECIES TO 

### L IGHT 

### E XTINCTION 

### G ROUPS

Modeled Components CALPOST Light Extinction Group Extinction Coefficient (m 2 /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 preliminar ily 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 D ISPERSION 

### A LGORITHMS 

### As specified in a March 16, 2006 U.S. EPA memorandum 16 

### 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 defau lt option in screening and refined CALPUFF analyses.



### 3.4.3 B UILDING DOWNWASH 

### Per VISTAS guidance, the effects of buildi ng downwash will not be considered in the Santee Cooper BART applicability analyses. 17



### 3.4.4 CALPUFF M ODELING 

### D OMAINS AND 

### C LASS 

### I A REA 

### R ECEPTORS 

### 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 Modelin g 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.

### Santee Cooper – Grainger, Jefferies, Winyah 37 Trinity Consultants BART Applicability Modeling Protocol encompass the Santee Cooper facilities and the Cl ass 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 ver tical 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.



### F IGURE 

### 3-2. T ERRAIN 

### R EPRESENTATION IN THE 

### 12- KM 

### R EGIONAL 

### S CREENING 

### G RID 

### (A FTER 

### F IGURE 

### 4-2 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

### Santee Cooper – Grainger, Jefferies, Winyah 38 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 3-3. 12- KM 

### S CREENING 

### A NALYSIS 

### C OMPUTATIONAL 

### D OMAIN

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200


### Lambert Confor mal Coordinate East (km) -1,200 -1,100 -1,000

-900 -800 -700 -600 -500 -400 -300 -200 -100 0Lambert Conformal Coordinate North(km)

Grainger

Jefferies Winyah

Wolf Island Cape Romain Swanquarter

12-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 39 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 3-4. 4- KM 

### R EFINED 

### A NALYSIS 

### C OMPUTATIONAL 

### D OMAIN FOR 

### J EFFERIES 

### & W INYAH 

### G ENERATING 

### S TATIONS

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 Co ordinate East (km) -1,100 -1,050 -1,000

-950 -900 -850 -800 -750 -700 -650 -600 -550 -500 -450 -400 -350Lambert Conformal Coordinate North(km) Winyah Jefferies

Wolf Island Cape Romain

4-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 40 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 3-5. 4- KM 

### R EFINED 

### A NALYSIS 

### C OMPUTATIONAL 

### D OMAIN FOR 

### G RAINGER 

### G ENERATING 

### S TATION

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 -100Lambert Conformal Coordinate North(km)

Grainger

Cape Romain Swanquarter

4-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 41 Trinity Consultants BART Applicability Modeling Protocol 

### 3.4.5 B ACKGROUND 

### O ZONE AND 

### A MMONIA 

### C ONCENTRATIONS 

### The CALPUFF model is capable of simulating linear chemical transformation effects by using pseudo-first-order chemical reacti on mechanisms for the conversions of SO 2 

### to SO 4 

### , and NO X 

### , which consists of nitrogen oxide (NO) and nitrogen dioxide (NO 2 

### ), to NO 3 

### and HNO 3 

### . For the BART applicability analyses, ch emical transformations involving five species (SO 2 

### , SO 4 

### , NO X 

### , HNO 3 

### , and NO 3 

### ) may be modeled using the MESOPUFF II chemical transformation scheme. Ambien t concentrations of ammonia and ozone concentrations as represented in the model drive the MESOPUFF II chemical transformation simulation.

Both screening and refined analyses will u tilize observed ozone data for 2001 through 2003 from non-urban CASTNet and AIRS sta tions compiled by the VISTAS Technical Contractor for the regional domain. Sa ntee 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.

### Santee Cooper – Grainger, Jefferies, Winyah 42 Trinity Consultants BART Applicability Modeling Protocol 

### F IGURE 

### 3-6. O ZONE 

### D ATA 

### S TATIONS TO BE UTILIZED IN



### S ANTEE 

### C OOPER 

### ’ S 

### BART A PPLICABILITY 

### A NALYSES

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200


### Lambert Confor mal Coordinate East (km) -1,200 -1,100 -1,000

-900 -800 -700 -600 -500 -400 -300 -200 -100 0Lambert Conformal Coordinate North(km)

Grainger

Jefferies Winyah

Wolf Island Cape Romain Swanquarter

12-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 43 Trinity Consultants BART Applicability Modeling Protocol Monthly average ozone background values w ill 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.

T ABLE 

### 3-6. M ONTHLY 

### A VERAGE 

### S UBSTITUTE 

### O ZONE 

### V ALUES FOR 

### S CREENING 

### A NALYSIS

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 Wor kgroup has since concluded that modeled background and bounda ry conditions should not be utilized for BART modeling purposes because “EPA and FLM recommend that ALM approach using CMAQ concentration data (SO X 

### , NO X 

### , total NH 3 

### ) 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 ofthis document, only refined analyses will utilize the POSTUTIL postprocessing tool to repartition the HNO 3 

### and NO 3 

### equilibrium distributions using only a constant 0.5 ppb ammonia background level.



### 3.4.6 P UFF 

### R EPRESENTATION 

### As recommended by the VISTAS BART Modeling Protocol , the default integrated puff sampling methodology will be enabled for bot h screening and refined CALPUFF analyses. In the screening analyses, the default setti ng to disable puff splitting algorithms will be retained. In the refined analyses, puff splitting may be evaluated.

### Santee Cooper – Grainger, Jefferies, Winyah 44 Trinity Consultants BART Applicability Modeling Protocol 

### 3.4.7 Q UALITY 

### A SSURANCE OF 

### CALPUFF A NALYSES 

### 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 w ith that report. Comparison of the CALPUFF model inputs will be conducted to ensure th at 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 ve rified with the information presented in Section 2. The location and elevations of Cl ass I area receptors will also be verified by comparison to NPS maps.



## 3.5 CALPOST P OSTPROCESSING AND 

## N ATURAL 

## B ACKGROUND 

## C ONDITIONS FOR 

## L IGHT 

## E XTINCTION AND 

## H AZE 

## I NDEX 

## C ALCULATIONS
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 extincti on attributable to the natural background and source will occur as generally described in this protocol.



### 3.5.1 POSTUTIL P ROCESSING 

### The first postprocessing step will involves running POSTUTIL to calculate the concentrations of visibility-affecting spec ies SO4, NO3, SOA, EC, PMF, and PMC, as described in Section 3.4.1. Specifically, PO STUTIL will be used to group modeled species into visibility-affecting pollutant groups as or iginally shown as Table 3-5, reproduced here as Table 3-7.

### Santee Cooper – Grainger, Jefferies, Winyah 45 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 3-7. A SSIGNMENT OF 

### M ODELED 

### PM S PECIES TO 

### L IGHT 

### E XTINCTION 

### G ROUPS IN 

### POSTUTIL

Modeled Components CALPOST Light Extinction Group Extinction Coefficient (m 2 /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 preliminar ily 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 HNO 3 

### and NO 3 

### 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 w ill be conducted in refined analyses.



### 3.5.2 C LASS 

### I A REA 

### - SPECIFIC 

### N ATURAL 

### B ACKGROUND 

### C ONDITIONS 

### The visibility goal of the Clean Air Act is both the remedying of existing visibility impairment and prevention of futu re 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 inte nd to limit States to the use of the 20% best visibility days...States may use 20% best vi sibility 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 natu ral background conditions computed from

18 U.S. EPA Memorandum from Mr. Joseph Paisie to Ms. Kay Prince, as Atta chment 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.

### 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 pr oposes 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 calcula te natural background conditions.

T ABLE 

### 3-8. A NNUAL 

### A VERAGE 

### B ACKGROUND 

### C ONCENTRATIONS FOR 

### C LASS 

### I A REAS 

### P OTENTIALLY 

### A FFECTED BY THE 

### S ANTEE 

### C OOPER 

### F ACILITIES

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 us ing the following equations.

⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= 10

exp10 HI b back 

### ,

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total b back 

### for the best days at Cape Romain, Wolf Island, and Swanquarter, including the Rayleigh scattering coefficient is calculated according to the a bove equation. A sample calculation for Cape Romain is shown below for the annual average natural background.

Romain Capefor Mm 21.11 Mm 10 Mm 21.21 10 52.7 exp10 1 1 1

− − − 

### =⇒+

=+= = ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= soil soil soil ray back 

### bb bb b



### The effects of relative humidity to amplify the visibility impairment of hygroscopic sulfates and nitrates are characterized using CALPOST “Method 6,” which computes Δ b ext 

### 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

### 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

.
T ABLE 

### 3-9. M ONTHLY 

### A VERAGE 

### f (RH) FOR 

### S ELECTED 

### C LASS 

### I A REA 

### *

Class I Area January February Ma rch 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 V ISIBILITY 

### I MPACT 

### C ALCULATION AND 

### R EVISED 

### IMPROVE L IGHT 

### E XTINCTION 

### A LGORITHM 

### CALPOST will be run separately for each Class I area to obtain the necessary visibility statistics for evaluating compliance with the BA RT visibility impairment thresholds. The inputs to CALPOST involve selection of the visi bility 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 allo w 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 98 th 

### percentile 24-hour average visibility impact for each year and Class I area. The previously described Option (1) was used to quantify visibility impacts relati ve to annual average natural background conditions. As described in Section 3.2. 1, the default interpretation of the 98 th 

### percentile (8 th 

### -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 ( b ext 

### ) results from particulate species emitted from anthropogenic sources including coarse particulate matter (PMC), fine particulate matter (PMF), and elemental

### Santee Cooper – Grainger, Jefferies, Winyah 48 Trinity Consultants BART Applicability Modeling Protocol carbon (EC), as well as precursors to sec ondary organic aerosols (SOA) and fine particulate matter such as SO 2 

### and NO X 

### that form sulfates (SO 4 

### ) and nitrates (NO 3 

### ) after chemical transformations take place. The extinction coefficient due to emissions of visibility-affecting pollutants from a single BART-eligible source b ext,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. 

# ( )
EC SOA PMF PMC NO SO sourceext 

### bbbbbbMmb +++++= − 3 4 1

,


### where, 

### ( ) 

## [ ] ( ) 

### [ ] ( ) 

### [ ] [ ]

[ ] [ ] ( ) 

### [ ] 3 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative EC10

PMC6.0 PMF1 SOA4 NONH3 SONH3

= =

=

= = = = =

RHf b b b b RHf b RHf b

EC

PMC PMF SOA NO SO 

## ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ] carbon elemental ofion concentrat thedenotes EC PM coarse ofion concentrat thedenotes PMC PM fine ofion concentrat thedenotes PMF aerosols organic secondary ofion concentrat thedenotes SOA ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4



### The relative impact of a BART-eligible source is determined by comparing the HI attributable to a source relative to es timated natural background conditions. The background extinction coefficient b ext,background 

### is affected by various chemical species and the Rayleigh scattering phenomenon and was initially calculated as shown in the following default equation. 

# ( ) Ray ap Coarse Soil OC NO SO background ext 

### bbbbbbbMm b ++++++= − 3 4 1

, 

### where,



### ( ) 

### [ ] ( ) 

### [ ] ( ) 

### [ ] [ ] [ ] [ ] 

## ( ) ( ) 

### [ ] 3 1 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative defaultby Mm 10 Scattering Rayleigh EC10

Mass Coarse6.0 Soil1 OC4 NONH3 SONH3

= =

= = = = = = =

− RHf b b b b b RHf b RHf b

Ray

ap Coarse Soil OC NO SO 

### ( ) 

### [ ] [ ] [ ] [ ] [ ] [ ]
molecules air todue scattering is Scattering Rayleigh carbon elemental ofion concentrat thedenotes EC dusts coarse ofion concentrat thedenotes Mass Coarse soils fine ofion concentrat thedenotes Soil carbon organic ofion concentrat thedenotes OC ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4

2 4



### Values for the parameters listed above specifi c 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 c onditions 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.

### Santee Cooper – Grainger, Jefferies, Winyah 49 Trinity Consultants BART Applicability Modeling Protocol The preceding equation represents what has b een termed the “default” light extinction calculation that has been utilized for computa tion 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 visibilit y at Class I areas as part of its reasonable progress analyses, VISTAS intends to present regional air quality m odeling results using both the default and the newly revised IMPROVE light extinction formula.

In December 2005, the IMPROVE workgroup issu ed a report describing an alternative to the default formula used to estimate extinc tion 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 provisi onal 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 (NO 2 

### ).

The revised IMPROVE light extinctio n algorithm takes the following form:

≈ ext b 

## ( ) ( ) 

## [ ] ( ) ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ] [ ] [ ][ ] [ ] ( ) 

## [ ] [ ] 2 Scattering Rayleigh specific Site Large Small Large 34 Small 34 Large 2 44 Small 2 44

NO33.0 Salt Sea 4.1 PMC6.0PMF1EC10OC1.6OC8.2 NONH 1.5NONH 4.2 SONH 8.4 SONH 2.2

+ + + +++ + + + +

+

− b RHf RHf RHf RHf RHf

SS L S L S



### To facilitate the use of the revised IMPROVE algorithm for assessing BART applicability, the VISTAS Technical Contractor developed and distributed a spreadsheet tool toimplement 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 Estima ting Ambient Light Ex tinction Coefficients - Final Report , March 2006.

### Santee Cooper – Grainger, Jefferies, Winyah 50 Trinity Consultants 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 require d: background concentration of gaseous NO 2 

### , Rayleigh scattering parameter corrected for site-s pecific elevation, and an estimate of the average background sea salt aerosol concentration.



### 3.5.3.1 B ACKGROUND 

### NO 2 

### C ONCENTRATIONS 

### 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 S ITE 

### -S PECIFIC 

### R AYLEIGH 

### S CATTERING 

### The 2005 IMPROVE report 21 

### 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 de nsity, 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. Th e temperature data were obtained from the nearest weather stations for time pe riods 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, Wo lf Island, and Swanquarter.



### 3.5.3.3 B ACKGROUND 

### S EA 

### S ALT 

### A EROSOL 

### C ONCENTRATIONS 

### 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 po tentially 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 na tural 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 focu s within the scientific community in

21 http://vista.cira.colostate.edu/IMPROVE/Publicati ons/GrayLit/019_RevisedIMPROVEeq/RevisedIMPROVEAl gorithm3.doc

### 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 ( f

SS 

### (RH)) for sea salt aerosols based on observations in remote marine environments and thermodynamic mode ls. Although there is reasonable confidence in these components of the calculation of light extinction due to sea salt aerosols, there is greater uncer tainty 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 leve l. VISTAS states, including South Carolina, are using annual average natu ral 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 survey ed 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 sa lt 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 μ g/m 3 

### using Cl - 

### as the marker, which is recognized to

### 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 μ g/m 3 

### , 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 μ g/m 3 

### 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 concentr ation for soil-derived sodium provided

as: 

## [ ] [ ] Soil0283.0Na = Soil 

### ,

where 0.0283 is the crustal reference com position of sodium. Using this value, the ambient sodium attributable to sea salt is computed as: 

## [ ] [ ] [ ] Soil Salt Sea NaNa Na

− =



### 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[Na non-soil 

### ],

where [Na non-soil 

### ] = [Na] – 0.57[Fe] and [Fe] is the measured iron concentration.

22 Tombach, I. Estimating the Role of Sea Salt when Determin ing the Visibility Impa ct 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 Con centrations for Consideration in Determination of Natural background Levels at Eastern Class I Areas , August 2, 2006.

### Santee Cooper – Grainger, Jefferies, Winyah 53 Trinity Consultants BART Applicability Modeling Protocol The VISTAS Technical Contractor 25 

### 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[Na non-soil 

### ], where [Na non-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 μ g/m 3 

### using the Na + 

### method, but does not recommend a longe r-term average value. The VISTAS Technical Contractor demonstrates that the 2004 annual average sea salt concentrations of 0.35 μ g/m 3 

### estimated using [Cl - 

### ] and 0.11 μ g/m 3 

### estimated using [Cl] exhibit the expected underestimation bias due to the analysis technique. The elemental sodium (Na) a pproach 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 b ackground sea salt concentration, Santee Cooper applied reliable estim ation techniques over the period of record when such measurements were taken. Add itionally, 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 usi ng strontium (Sr), which is measured accurately using XRF and for which measurements are available over the long-

25 Tombach, I. Evaluation of Alternative Met hods for Estimating Sea Salt Con centrations 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.

### 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: 

## [ ] [ ] [ ] Si 0.0013528 SrxSr

− = 

### ,

where 0.0013528 is the crustal reference c oncentration of Sr and Si represents the silicon composition that is presumabl y 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 beca use 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 (2 006) 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 usi ng strontium for the available data period September 1994 through December 2004. Table 3-10 summarizes data from each approach over the Septembe r 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 Manage ment Association , 56 : 636-642, May 2006.

28 Mason, B. Principles of Geochemistry , 4 th ed. Wiley & Sons, Inc.: New York, 1982, p.344.

### Santee Cooper – Grainger, Jefferies, Winyah 55 Trinity Consultants BART Applicability Modeling Protocol T ABLE 

### 3-10. S UMMARY OF 

### S EA 

### S ALT 

### C ONCENTRATIONS 

### C OMPUTED USING 

### V ARIOUS 

### M ETHODS 

### , S EPTEMBER 

### 1994

– D ECEMBER 

### 2004

[Sea Salt] ( μ g/m 3 )

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 av erage background concentration of sea salt is estimated to be 1.1 μ g/m 3 

### . 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 μ g/m 3 

### as estimated by the VISTAS Technical Contractor and 1.1 μ g/m 3 

### as estimated bySantee Cooper) to represent that background condition in the CALPOSTIMPROVE tool as described in the following section of this report.



### 3.5.4 Q UALITY 

### A SSURANCE OF 

### P OSTPROCESSING 

### A NALYSES 

### Quality assurance of postprocessing analyses w ill 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 calcu lated 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.

### Santee Cooper – Grainger, Jefferies, Winyah Trinity Consultants BART Applicability Modeling Protocol 

## A PPENDIX 

## A

VISTAS BART M ODELING 

### P ROTOCOL 

### (R EVISION 

### 3.2, A UGUST 

### 31, 2006)

# 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)Table of Contents iv 

## TABLE OF CONTENTS

Page

SUMMARY 1

1. INTRODUCTION AND PRO TOCOL OBJECTIVES 1 1.1 Background 1 1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR B ART 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 CALP UFF 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 MODEL ING PROTOCOL 34 4.1 Overview of Co mmon Modeling Approach 34 4.1.1 BART Exemption Analysis 34 4.1.2 BART Control Evaluation 36 4.1.3 VISTAS’ Treatment of VOC, NH 3 

### , 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 Exempt ion 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 Mo deling 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 Modeli ng of BART Eligible Sources 54

5

. SOURCE

- SPECIFIC MODELING PR OTOCOL 55Table 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 Ass essment of Uncertainty in Modeling Results 64

7. REFERENCES 65Summary S

- 1 

## SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART) for the VI STAS 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 Appe ndix 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 dis persion 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 so urce 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 de termination 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, particula rly 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 1 st 

### , 2005 VISTAS held another meeting with its participants in Nashville, TN to present and discuss t he comments being considered for inclusion in the Protocol. No formal document will be prepared to address all the comments received on the Protocol.Summary S

- 2 

### Objectives

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

 A consistent appro ach 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 report ing 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 backgr ound 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 BA RT 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 feasib le 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 analysisSummary S

- 3 

### II. CALPUFF Model Configurat ion

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 Manage ment 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 en tire 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 deve loped 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 incorp orated 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 f iles 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, C ALPOST, and POSTUTIL configurations are highlighted below.

• After running CALPUFF for an individual facility, repartition NO 3 

### 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 SO 2 , NO 3 and NH 3 in POSTUTIL. After extensive discussion with the EPA and FLMs in early 2 006, 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 NH 3 concentrations specified on page 14 of the IWAQM Phase 2 repo rt (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 (Fl at 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

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 are a 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 two

tier 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 thresh old 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 sour ce is subject to BART. If Q/d for SO 2 

### 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 presumptio n, 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.

Fo r 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 sour ces 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 hig her Q/d values until a Q/d value

based dispersion methods would be considered a non

- guideline applica tion 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.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 BAR T

- 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 aver age 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 d v, 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 98 th 

### percentile value.

The 12

- km model ing 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 98 th 

### percentile impact value for the 24

- hr average. Use either the 8 th 

### highest day in each year or the 22 nd 

### 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 B ART 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 c onservative assumptions. Individual States ultimately have responsibility to determine which, if any, BART controls are recommended in their State Implementation Plans (SIPs).Introduction and Protocol Objectives 1 

## 1. INTRODUCTION AND PRO TOCOL OBJECTIVES

1.1 Background

Under regional haze regul ations, 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
sta tionary 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 improvem ent 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 un its at a single facility. If a source has several emission units, only those that meet the BART

- eligible criteria are included in the definition “BART

eligible 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 cat egory, electric generating sources greater than 750 MW have presumptive SO 2 

### and NO x 

### 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 n ot 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 SO 2 

### and NO x 

### . VISTAS states are tentatively accepting this guid ance. 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 i nto 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 B ART sources are located on Tribal lands.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 3 1 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 protocol 4 

### 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 guida nce 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 CA LPUFF 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.Introduction and Protocol Objectives 3 

### source

- specific data on source location, stack parameters, and emissions. The methods of the VISTAS co mmon 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 procedure s 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 control s.
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 b e 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 f or 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 BART

eligible sources using CALPUFF run in a conservative mode with regi onal 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 mode ls. 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 tr ade program. Such a cap and trade program is not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTASIntroduction and Protocol Objectives 4 

### States are not pursuing a regional trading alternative under the proposed EPA trading guidance (70 FR 44154

- 44175) th at is to be promulgated in 2006.Review of EPA’s Guidance 5 

## 2. REVIEW OF EPA’S GUID ANCE FOR BART MODELI NG

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 m odeling 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 addre ss 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) Ident ify 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 t hat are BART

- eligible. Sources must have been put in service between August 7, 1962 and August 7, 1977 in order to be BART

eligible. Finally, a source is eligible for BART if potential e missions of visibility

- impairing air pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate matter (PM 10 

### ) and gaseous precursors to secondary fine particulate matter, such as SO 2 

### and NO x 

### . Whether ammonia or volatile organic compounds (VOCs) should be included as visibility

impairi ng 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 PM 2.5 

### 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 em issions are less than the de minimis levels (40 TPY for SO 2 

### and NO x 

### and 15 TPY of PM 10 

### or PM 2.5 

### . 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 es timated 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 resol ves an ambiguity in EPA’s BART guidance, where t he BART guidelineReview 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 a ffecting 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 98 th 

### percentile value fro m 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 98 th 

### percentile valu e exceeds the threshold must be determined at each Class I area. Over an annual period, this implies the 8 th 

### highest 24

- hr value at a particular Class I area is compared to the contribution threshold. Over a 3

- year modeling period, the 98 th 

### percentile va lue may be interpreted as the highest of the three annual 98 th 

### percentile values at a particular Class I area or the 22 nd 

### 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 Stat e 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, sourc es with potential emissions of SO 2 

### plus NO x 

### of less than 500 tons and a distance from any Class I area greater than 50 kilometers or sources with SO 2 

### plus NO x 

### 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 SO 2 

### and NO x 

### , so that a plant could be exempted if the combined pot ential emissions of SO 2 

### , NO x 

### , 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.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 Sta tes) 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 t he 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 g eographic 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 441 60.) 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 SO 2 

### and NO x 

### 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 Analysi s
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 justificati on 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” andReview 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 BART

eligible 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 rec ent 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, CALP UFF 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 b ased 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 tha t 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.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.Overview of the CALPUFF Modeling System 10 

## 3. OVERVIEW OF THE CALP UFF MODELING SYSTEM

This chapter conta ins 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 ha ndle 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 effect s 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 su ch 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 dis tance range of the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition fluxes evaluated at user

- specified receptor locations.Overview of the CALPUFF Modeling System 11 

### Figure 3

- 1. CALPUFF modeling system components.

A set of postprocessing programs associat ed 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 chemi cal 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 incr ementally 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.Overview of the CALPUFF Modeling System 12 

### 3.1.1 Major Features of CALMET

The CALMET meteorological m odel 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 Lamb ert 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 F eatures 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)

-- Prec ipitation 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

- S cale 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, surfaceOverview of the CALPUFF Modeling System 13 

### friction velocity, Monin

- Obukhov length, and convective v elocity 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 (PG T) 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 environme nt. 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 e valuation 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 adv ective 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 te rrain, 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 fie ld 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 res olution 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 descr ibed 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 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 divergence

minimization 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 bothOverview 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 terr ain 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 Wh iteman, 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 adju stments 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
op tional 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 spa tial 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 al ong 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 time

and space

- varying meteorolo gical 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 th at 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 TerrainOverview 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 (H d 

### ) to determine which pollutant material is deflected around the sides of a hill (below H d 

### ) and which material is advected over the hill (above H d 

### ). The local flow (near the feature) used to define H d 

### 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 puf f 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 Schu lman

- 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 disp ersion coefficients, including the use of turbulence measurements ( σ v 

### and σ w 

### ), the use of similarity theory to estimate σ v 

### and σ w 

### from modeled surface heat and momentum fluxes, or the use of Pasquill

- Gifford (PG) or McElroy

- Pooler (MP) dispersion coefficie nts, 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 A ERMOD 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. (20 05).

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.Overview of the CALPUFF Modeling System 16 

### Dry Deposition: A resistance model is provided in CALPU FF 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 resi stance model. Of particular interest for BART analyses is the ability to separately model the deposition of fine particulate matter (< 2.5 μ m diameter) from coarse particulate matter (2.5

- 10 μ m diameter).

Wind Shear Effects: CALPUFF contains an optional p uff 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 effec tive 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 approac h 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 Tran sformation: CALPUFF includes options for parameterizing chemical transformation effects using the five species scheme (SO 2 

### , SO 4 = 

### , NO x 

### , HNO 3 

### , and NO 3 - 

### ) employed in the MESOPUFF II model or a set of user

- specified, diurnally

- varying transformation rates. T he MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine particulate matter (sulfate and nitrate) from emissions of SO 2 

### and NO x 

### and thus allows analyses of visibility impacts. Ambient ozone concentrations are used in the parameterized che mical 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.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 v ariable 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, fri ction 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

- Tempora l 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

- Integrate d puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient ( σ y , σ z ) options

- Direct measurements of σ v and σ w

- Estimated values of σ v and σ w based on similarity theory

-- AERMOD turbulence profiles

-- Original turbu lence 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)Overview of the CALPUFF Modeling System 18 

### Table 3

- 2. Major F eatures of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, H d , as in CTDMPLUS:

- Above H d , material flows over the hill and experiences altered diffusion rates

- Below H d , material defl ects 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 fo r 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 SO 2 , SO = 4 , NO x , HNO 3 , and NO 3 (MESOPUFF II method)

- Pseudo

- first

- order chemical mechanism for SO 2 , SO = 4 , NO, NO 2 , HNO 3 , and NO 3 (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 visibility

related modeling, CALPOST uses concentrations from CALPUFF to compute light extinction and relat ed 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 av erage 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 ofOverview of the CALPUFF Modeling System 19 

### predicted impacts (useful for evalua ting 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

so urces 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 comput e 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 ex ample 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 Diffu sion

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 inv olved.” 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 recep tors 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 ef fects 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

ve rtical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is

6 The IWAQM presentation at EPA’s 6 th 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 concentra tion 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.)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 variatio ns 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 s ubstantially, 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 shor ter 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 s upported 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, buildi ng 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 Qual ity 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 orientati on) 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 per formed 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 5 0 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 f ew 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 coef ficients. 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 (Sc ire 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 underpredi ction of peak concentrations.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 Model 7 

### . The ISCST3 model and its PG dispersion coefficients are being phased out as an acceptable regulat ory 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 turbul ence 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 B ART 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 reas ons 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 a nd 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 3 0 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 fin er than 1 km. (For reference, the width of a Gaussian plume, 2 σ y 

### , 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
appro priate. 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 Guidelin e 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.Overview of the CALPUFF Modeling System 22 

### The U.S. EPA has suggested that the plume visibilit y 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, SO 2 

### , and NO x 

### f rom 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 depe nd 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 thre sholds 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 sour ce impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM 2.5


### Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that CALPUFF can treat primary pollutants such as PM 10 

### . In actuality, CALPU FF can simulate PM 10 

### or PM 2.5 

### 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 PM 2.5 

### 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. PM 2.5 

### particles, which have a mass median diameter around 0.5 μ m, 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 PM 10 

### particles l arger than PM 2.5 

### ) 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 PM 10 

### , is accounted for in the calculation of the deposition velo city. Effects of inertial impaction (important for the upper part of the PM 10 

### distribution) and Brownian motion (important for small, sub

- micron particles) and wet scavenging are also addressed. The BART guidance recommends that fine particulate matter (l ess than 2.5 μ m diameter), which has higher light extinction efficiency than coarse particulate matter (2.5

- 10 μ m 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 applicationOverview of the CALPUFF Modeling System 23 

### separate species, w hich includes calculating size

- specific dry deposition velocities for each size category.
A primary PM 2.5 

### 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 fr om SO 2 

### has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate

The MESOPUFF

- II chemistry algorithm used in CALPUFF 9 simulates the gas phase oxidation of sulfur dioxide to sulfate by a linear transformation rate that was developed using regressi on 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 relation ships are based on easily

computed or observed parameters that are used as surrogates for the factors that control SO 2 

### oxidation.

The surrogate factors included in the parameterized chemistry during the daytime hours include solar radiation intensity, am bient ozone concentration, and atmospheric stability class. For example, gas phase SO 2 

### oxidation is a function of OH radical concentrations. Ozone concentrations are correlated with OH radical concentrations during daytime hours, and their use in the day time SO 2 

### 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 countr y, 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 surrog ates for factors that the regression analysis has shown to be important in SO 2 

### oxidation rates. At night, the SO 2 

### conversion is set to a constant low value (default is 0.2%/hr). Aqueous phase oxidation of SO 2 

### 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 formati on can be modeled.

9 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) MESOPUFF

- II system and the 6 species RIVAD system (which treats NO and NO 2 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.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 SO 2 

### . 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, liqu id 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 NO x 

### concentrations in the plume are high and in actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO 2 

### ; and (2) lack of direct consideration of the effect of temperat ure 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 SO 2 

### 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 measurem ents 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 availa bility 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 sulf ate formation calculations are derived from ambient measurements, although concentrations simulated by regional models can be used.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.

NO x 

### and Secondary Ammonium Nitrate

The MESOPUFF

- II chemistry algorithm used in CALPUFF simulates the oxidation of NO x 

### to nitric acid and organic nitrates (both gases) by transformation rates that depend on NO x 

### concentration, ambient ozon e 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 reac ts 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 NO x 

### that deplete the ambient ozone and thus limit theOverview of the CALPUFF Modeling System 26 

### transformation of NO x 

### to nitric acid in the plume; and (2) when ambient temperature is below 10 C, and thus t he 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 nit rate 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 imp acts of NO x 

### 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 over lap, 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

- limit ing 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 b oundary 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 p ollutants, 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 photochemic al model such as CMAQ or CAMx. The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large,Overview of the CALPUFF Modeling System 27 

### so defining the ammonia concentration throughout the Southeast would require extensive interpolation or extrapolation from the mea sured 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 eval uated 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 wi th 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)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) fou nd 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

o rganic 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 μ g/m 3 

### concentration assumed for natural conditions by the EPA, which means that current fossil fuel burning would contribute less than 2% to visibility in a n 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 (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb)
CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4 

### [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IMPROVE 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 (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb) CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4Overview 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 s ource 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.

Fi rst, PM 10 

### 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 PM 10 

### emissions condense into organic PM 2.5 

### 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 seco ndary 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 (C 5 

### H 8 

### ) does not make SOA b ut 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 app roaches for dealing with condensable VOC emissions that are characterized as condensable PM 10 

### 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 emissio ns 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 process ing

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.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 e missions is presented in Section 4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction of PM 10 

### emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component con centrations 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 CAL POST, the formula is the following:

b ext 

### = 3 f(RH) [(NH 4 

### ) 2 

### SO 4 

### ] + 3 f(RH) [NH 4 

### NO 3 

### ] + 4[OC] + 1[Soil] + + 0.6[Coarse Mass] + 10[EC] + b Ray 

### (3

- 1)

The concentrations, in square brackets, are in μ g/m 3 

### and b ext 

### is in units of Mm - 1 

### . The Rayleigh

s cattering term (b Ray 

### ) 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
synonymo us 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 do cuments). 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 (b ext 

### /1 0) (3

- 2)

where HI is in units of deciviews (dv) and b ext 

### 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.Overview of the CALPUFF Modeling System 31 

### CALPOST Meth ods

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 c urrent 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 compo nent 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 hygroscopi c 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 assumpt ion 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 descri be 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 oneOverview of the CALPUFF Modeling System 32 

### than that in Method 1) or, optionally, the EP A 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 climatologi cally 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 sulfa tes 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., sulfat e 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 v isibility 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 de veloped 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 Visibilit y
Separate from the BAR T 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 r ecently 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.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

- Revisi ng 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 fo r 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 cur rent 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.VISTAS’ Common Modeling Protocol 34 

### 4. VISTAS’ COMMON MODEL ING 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 opti ons.
For purpose 1), States must determine whether a source emits any air pollutant (SO 2 

### , NO x 

### , PM, and in certain cases VOC and NH 3 

### ) 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 SO 2 

### and NO x 

### emissions. The meth ods 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, NH 3 

### 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 NH 3 

### emissions, NH 3 

### . 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 s ource of SO 2 

### , NO x 

### , 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 SO 2 

### > 10 for 2002 actual emissions, then the State presumes that the source is subject to BART. If the sour ce 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 sourceVISTAS’ Common Modeling Protocol 35 

### ma y 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 so urce 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 wo uld 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.VISTAS’ Common Modeling Protocol 36 

### For large sources that will clearly exceed the initial screening t hresholds, 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 BA RT 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 ana lysis 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” com parison, 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 re sults 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 visibilit y 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 quant ify 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, NH 3 

### , 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 anth ropogenic emissions. (Separately, condensable organic carbon can be calculated from PM 10 

### .)

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 sour ces (BART

- eligible and

non

- BART) in each State do not contribute to visibility impairment. Emissions sensitivityVISTAS’ 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 Janu ary 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 an alyses 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., elimi nated). 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 n ot need to be included in BART analyses.

Ammonia

EPA has given states the option to address ammonia (NH 3 

### ) emissions from BART

- eligible sources. VISTAS also contracted with Georgia Tech to calculate NH 3 

### emissions sensitivities using CMAQ v 4.4 with a re fined SOA module and the same Jun

- Jul and Nov

- Dec periods in 2002 that were used for the VOC sensitivity evaluation. The NH 3 

### emissions from all point sources (BART

- eligible and not

- BART) in every State were reduced by 100% for these analyses. This sensiti vity evaluation showed that the collective impact of all VISTAS region point NH 3 

### emissions is greater than 0.5 dv (compared to annual average natural background) at several Class I areas. When the NH 3 

### emissions were scaled to represent 100% reduction from only the BART

eligible 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 NH 3 

### sources. In the absence of those 13 fac ilities, the scaled NH 3 

### 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, NH 3 

### emissions not be included in BART modeling. States will provide instructio ns to those 13 sources as to how to evaluate contributions of their NH 3 

### emissions to visibility impairment. For documentation purposes, in summer 2006 VISTAS is repeating the NH 3 

### emissions sensitivity calculations, using CMAQ v4.5 with Base F emissions

an d reducing 100% of NH 3 

### 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 SO 2 

### and NO x 

### under the CAIR rule meet the BART requirements, but these EGUs may still be subject to BART f or primary PM. To determine the potential impacts of PM from EGU and non

EGU 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 AlpineVISTAS’ Common Modeling Protocol 38 

### Geophy sics 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

co ntribute 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

I n 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 f or 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

- specifi c 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 describ e 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 in itial 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 pre

defined CALPUFF inp ut 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 VI STAS 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, ifVISTAS’ Common Modeling Protocol 39 

### initial modeling indicates a source may impact visibility significantl y, 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 pr oduce 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 three

dimensional 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 pre

computed CALMET meteorological f ields 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 condit ions 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 w ind field.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 di agnostic 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 w ill 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.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 conditions 14 

### (although States may set a lower threshold). The 98 th 

### percentile (8 th 

### highest annual) 24

- hr average predicted impact at th e 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 model ing values to be compared with the threshold are the greatest of the three annual 8 th 

### highest values or the 22 nd 

### 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 98 th 

### 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 BART

eligible sources. The regional CALMET domain will be unchanged by these adjustments.

Also, the initial approach is design ed 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.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 numb er 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

- k m 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 inpu ts 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 Model ing 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.

- CALM ET 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 dat a), 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.VISTAS’ Common Modeling Protocol 43 

### - TERR AD (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

r egional 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 terr ain 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 Configu ration (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 modele d : SO 2 

### , SO 4 

### , NO x 

### , HNO 3 

### , NO 3 

### and particulate matter in size categories of <0.625 μ m, 0.625

- 1.0 μ m, 1.0

- 1.25 μ m, 1.25

- 2.5 μ m, 2.5

- 6.0 μ m and 6

- 10 μ m aerodynamic diameters. As noted below, the particulate matter emissions by size category will be combined in to the appropriate species for the visibility analysis (i.e., elemental carbon (EC), fine PM or “soil” (< 2.5 μ m in diameter), coarse PM (between 2.5

- 10 μ m 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

s ometime 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 SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### .

Conde nsable 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 sizeVISTAS’ 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 pollu tants with plant

- wide emissions less than de minimis levels (40 tons per year for SO 2 

### and NO x 

### and 15 tons per year for PM 10 

### ). De minimis levels are plant wide for each visibility

- impairing pollutant, so individual units may be modeled even if they have emi ssions 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” (fi ne PM < 2.5 μ m diameter), coarse particulate matter (2.5

- 10 μ m 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 Nati onal 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 Emissio ns. (PMC is coarse particulate matter

-- 2.5 to 10 μ m diameter.) PM

- 10

Filterable Condensabl e

EC PM

- 2.5 PMC PM
(including

H 2 SO 4 ) As Prim. SO4) Organic PM
(SOA)

EC Soil PMC SO 4 f(RH) Non

- SO 4 Inorganic OCVISTAS’ 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 follow ing:

 Speciation profiles developed by the SMOKE emissions model for use in VISTAS’ CMAQ regional air quality modeling (available at http://www.vistas

sesarm.org/BART/calpuff.asp ).

 The approach described in a memo available at http://www.vistas

sesarm.org/BART/calpuff.asp , which provides reasonably conservative estimates in situations where data are incomplete.

- Class I receptors : Us e 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 200 1

- 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 unle ss 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 visibil ity analysis: SO 4 

### , NO 3 

### , 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 aver age natural background as calculated by CALPOST under Method 6, based on annual average default naturalVISTAS’ 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 guid ance 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 coeffici ents 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 necessar y), subtract the Rayleigh scattering value of 10 Mm - 1 

### , and enter a soil concentration (in μ g/m 3 

### ) into CALPOST that is numerically equal to this result. (Since the extinction efficiency of soil is 1 m 2 

### /g, Equation 3

- 1 shows that this process produces a back ground 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, th e 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 alg orithm.)

- 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 a rea 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 t wo 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 ex cluded 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 treatm ent 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 98 th 

### percentile change in deciviews value rather than the more conservat ive 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 followingVISTAS’ Common Modeling Protocol 47 

### emissions inf ormation (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 d ata

- 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 b y 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 configur ation 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 Setti ngs 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 resolutio n 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.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 f or 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 parti cular 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 t errain 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.

The finer grid CALMET simulations were run in hybrid mode, using b oth 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

- da ily upper air sounding data.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 dep ends 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 d efine 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 obse rvations 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 acc uracy 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 sour ce 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 b e 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 v isibility impairment is based on the 98 th 

### 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 compute s 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. Ther efore 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 c ontains 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 aeroso ls (f(RH)), and the contribution of each species to light extinction (in percent of the total source contribution) for SO 4 

### , NO 3 

### , organics, elemental carbon, coarse and fine particulate matter.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 5.0 8

.

.

.

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 48.1 2 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 = 1.219VISTAS’ 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 98 th 

### percentile (8 th 

### highest value) in the sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the e xample shown in this table, the source passes the finer grid analysis because the highest 98 th 

### percentile visibility impact is below the contribution threshold of 0.5 dv.

The Results section of the CALPUFF modeling report should contain the following info rmation:

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 98 th 

### percentile days in each year greater than 0.5 dv (total visibility impairment minus impairme nt 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 98 th 

### 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 in itial 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 speci fic 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.VISTAS’ Common Modeling Protocol 52 

### Table 4

- 2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003
Delta

Deciview Ranks 1

- 8 Delta

Deciview Ranks 1

- 8 Delta

Deciview 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 0.19VISTAS’ Common Modeling Protocol 53 

### Table 4

- 3. Format of Summa ry 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 days 1 and # of receptors with impact > 0.5 dv in Class I area: 2001 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2002 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2003 # of days 1 and # of receptors with impact > 1.0 dv in Class I area for 3

- yr period Ma x. 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
Linvi lle 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 98 th percentile of days in each year or the three

- year modeling period, as appropriateVISTAS’ Common Modeling Protocol 54 

### 4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligib le 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 versi on 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 disper sion, 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

- el igible 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 s ettings 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 availa ble 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.s rc.com/verio/download/sample_files.htm ).Source

- Specific Modeling Protocol 55 

## 5

. SOURCE

- SPECIFIC MODELING PR OTOCOL
Sources are required to submit a source

- specific protocol to the State for review and approval prior to source

- specific modeling. States will provide t he 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

- deta iled 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 (SO 2 

### , H 2 

### SO 4 

### , NO x 

### and PM 10 

### ).

Addit ional 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 build ing 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 pro jection origin, coordinate datum, and false Easting and Northing (if used) of the projection origin. Subregional and source

specific domains may be using either an LCC or UTM projection.

The CALPUFF Graphical User Interface (GUI) system provides softwar e (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 sou rce 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”.Source

- Specific Modeling Protocol 56 

### Table 5

- 1

. Sample Table of Contents of a Sourc e - 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 Co ncentrations

– 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 Ma jor 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 Modelin g 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 neededSource

- 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 for mat 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 fo r BART Eligible Source.

Unit name and/or description Start

- up dates SO 2 

### potential emissions (tpy) NO x 

### potential emissions (tpy) Total PM potential emissions (tpy)
Emissions source

name

... Total emissions
Potential BART

eligible emissionsQuality 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 c ontribute 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 esta blish 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 m ost 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 datas ets 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 option s 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 c ontains 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

s witches 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 detail ed 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 versio ns 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 meteor ological 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 wi ll need to be actively involved in controlQuality 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 tha t 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
asp ects 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 th e 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

- defi ned 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 de velopment 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 th at 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 ju dgment 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 refine d 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 wi th 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 regulatoryQuality 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 condi tions 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, includi ng 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 lis t 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 p art 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 ma y 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 projec tion 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 regi onal grid system. For a source

- specific domain, the grid parameters will be provided as part of the source

- specific protocol.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 CA LPUFF.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 inter

relationships 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 mo deling 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/pos tprocessor 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 m odel 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 doma in 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 v arious 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 ofQuality 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 tha t 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 str ucture. 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 C ontrol 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 CALPOS T 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 rela tive 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 ar e 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 outpu t 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 singl e 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 98 th 

### p ercentile value (8 th 

### 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 suc h 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 Me thod 6

- Background light extinction valuesQuality 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 simulati ons 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 of ten 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 li miting 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 t he 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 vi sibility 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 spatialQuality 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 circulat ion 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 configura tion 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 i mportant 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

- speci fic 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

- s pecific 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 inclu ded in both the base and sensitivity simulations.References 65 

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### AMS/A&WMA Conference on the Applications of Air Polluti on 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

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IWAQM, 1998: Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts. EPA

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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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- Scale Application of the CALMET Meteorological Model to a Complex Terrain Site. Paper 97

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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. Yamar tino, 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

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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

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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

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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

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### Santee Cooper – Grainger, Jefferies, Winyah Trinity Consultants BART Applicability Modeling Protocol 

## A PPENDIX 

## B

CPM F ACTOR 

### A NALYSIS

### Santee Cooper – Grainger, Jefferies, Winyah Trinity Consultants BART Applicability Modeling Protocol 

## A PPENDIX 

## B

CPM F ACTOR 

### A NALYSISTable B-1. AP-42 Reference Tests Used to Develop CPM FactorsSantee Cooper - CPM AnalysisRef. No.Cited Date(s)Company NameFacilityUnit(s) TestedLocationControl Type(s)78Nov. 1994Public Service Electric and Gas Co.Mercer Generating StationUnit No. 2Hamilton Township, NJPM only79Dec. 6, 1994Madison Gas and Electric Co.Blount Street StationUnits 7, 8, 9Madison, WIPM only80Jan. 23-25, 1990Marshfield Electric and Water Dept.Wildwood StationUnit 5Marshfield, WIPM only81Jan. 6, 1995Dairyland Power CooperativeJ.P. MadgettAlma, WIPM only82Jan. 25, 1994Portland General ElectricBoardman Power PlantBoardman, ORPM only83Dec. 11, 1989Marshfield Electric and Water Dept.Wildwood StationUnit 5Marshfield, WIPM only84Aug. 24-27, 1995Portland General ElectricBoardman Power PlantUnit 1Boardman, ORPM only85Sept. 19, 1996Portland General ElectricBoardman Power PlantUnit 1Boardman, ORPM only86Oct. 17, 1990Portland General ElectricBoardman Power PlantBoardman, ORPM only87Jan. 29, 1991Portland General ElectricBoardman Power PlantBoardman, ORPM only88Jan. 14, 1991Portland General ElectricBoardman Power PlantBoardman, ORPM only89Apr. 3, 1991Portland General ElectricBoardman Power PlantBoardman, ORPM only90Jan. 21, 1992Portland General ElectricBoardman Power PlantBoardman, ORPM only91Apr. 4, 1992Portland General ElectricBoardman Power PlantBoardman, ORPM only92Sept. 9, 1992Portland General ElectricBoardman Power PlantBoardman, ORPM only93Nov. 6, 1992Portland General ElectricBoardman Power PlantBoardman, ORPM only94Jan. 26, 1993Portland General ElectricBoardman Power PlantBoardman, ORPM only95Nov. 1994Keystone Cogeneration FacilityKeystone CogenerationLogan Township, NJPM and SO296Dec. 1992San Antonio City Public Service BoardJ.K. SpruceUnit 1Bexar, TXPM and SO2Trinity ConsultantsCPM Factor Analysis (2007-06-27).xls / AP-42 ReferencesTable 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 SO 2 * * * * N/A - not located 96 Dec. 1992 San Antonio City Public Service Board J.K. Spruce PM and SO 2 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 SO 2 0.0052 0.0052 0.0052 0.0052 PSD Permit Comments KCPL-2 Aug. 15, 2002 Kansas City Power & Light Hawthorn PM and SO 2 0.0026 0.0026 0.0026 0.0026 PSD Permit Comments KCPL-3 Apr. 15, 2003 Kansas City Power & Light Hawthorn PM and SO 2 0.0036 0.0036 0.0036 0.0036 PSD Permit Comments KCPL-4 May 24-25, 2004 Kansas City Power & Light Hawthorn PM and SO 2 0.0062 0.0062 0.0062 0.0062 PSD Permit Comments DPC-7 May 11, 2006 Dairyland Power Cooperative Genoa PM and SO 2 0.0030 0.0030 0.0030 0.0030 State Agency DPC-8 Aug. 3, 2004 Dairyland Power Cooperative Genoa PM and SO 2 0.0060 0.0070 0.0060 0.0063 State Agency DPC-9 Mar. 7, 2002 Dairyland Power Cooperative Genoa PM and SO 2 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 DataTable 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)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)

### 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 T echnology (BART) determin ation 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 Sant ee Cooper will calculate results using two alternative sea salt estimations based on non-default sodium and st rontium 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 CA LPOST-newIMPROVE calculation spreadsheet, in addition to any calculations performed using altern ative 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 conde nsable 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 dete rmination 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 w ill 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

### Henry Porter, Bureau of Air Quality

## B EST 

## A VAILABLE 

## R ETROFIT 

## T ECHNOLOGY 

## A PPLICABILITY 

## A NALYSIS 

## S ANTEE 

## C OOPER 

##  G RAINGER 

## , J EFFERIES 

## , AND 

## W INYAH 

## S TATIONS

T RINITY 

### C ONSULTANTS 

### 1100 Johnson Ferry Road Suite 685 Atlanta, GA 30342 (404) 256-1919

July 2007

Project 061101.0117

### Santee Cooper – Grainger, Jefferies, Winyah i Trinity Consultants BART Applicability Analysis 

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION 

### ....................................................................................................................1 1.1 O VERVIEW OF 

### R EGIONAL 

### H AZE 

### R ULE AND 

### BART R EQUIREMENTS 

### .........................2 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY 

### ........................................................ 2 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY 

### ................................................................................................ 3 1.1.3 P OTENTIALLY 

### A FFECTED 

### C LASS 

### I A REAS 

### ....................................................... 6 1.1.4 CALPUFF M ODELING 

### A NALYSES 

### .................................................................. 7 

### 1.2 O RGANIZATION OF 

### BART A PPLICABILITY 

### A NALYSIS 

### R EPORT 

### .................................8

2. BART-E LIGIBLE 

### S OURCE 

### D ESCRIPTION 

### ...........................................................................9 2.1 BART-E LIGIBLE 

### E MISSION 

### U NITS 

### ............................................................................9 2.2 BART- ELIGIBLE 

### S OURCE 

### M ODEL 

### E MISSIONS 

### I NVENTORY 

### .....................................12 2.2.1 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP O NLY 

### ..... 15 2.2.2 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP AND 

### FGD18 

### 2.3 M ODELED 

### S TACK 

### P ARAMETERS AND 

### E MISSIONS 

### ....................................................22

3. A IR 

### Q UALITY 

### M ODELING 

### A NALYSES 

### ...............................................................................25 3.1 A IR 

### Q UALITY 

### M ODELING 

### S YSTEM 

### ..........................................................................25 3.2 S CREENING AND 

### R EFINED 

### A NALYSIS 

### T ECHNIQUES 

### .................................................25 3.2.1 V ISIBILITY 

### A SSESSMENT 

### M ETRIC 

### ................................................................. 26 3.2.2 M ETEOROLOGICAL AND 

### C OMPUTATIONAL 

### G RIDS 

### ........................................ 26 3.2.3 M ODEL 

### P ROCESSING AND 

### P OSTPROCESSING 

### M ETHODS 

### ............................... 27 

### 3.3 CALMET M ETEOROLOGICAL 

### P ROCESSING 

### ............................................................27 3.3.1 CALMET M ETEOROLOGICAL 

### D OMAINS 

### ...................................................... 27 3.3.2 MM5 S IMULATIONS 

### ...................................................................................... 29 3.3.3 G EOPHYSICAL 

### D ATA 

### ..................................................................................... 29 3.3.4 12- KM 

### S CREENING 

### A NALYSIS 

### CALMET P ROCESSING 

### ................................. 30 3.3.5 4- KM 

### R EFINED 

### G RID 

### CALMET P ROCESSING 

### ............................................... 30 3.3.6 Q UALITY 

### A SSURANCE OF 

### CALMET A NALYSES 

### .......................................... 32 

### 3.4 CALPUFF M ODEL 

### P ROCESSING 

### .............................................................................32 3.4.1 M ODELED 

### E MISSIONS AND 

### C HEMICAL 

### T RANSFORMATIONS 

### ........................ 32 3.4.2 CALPUFF D ISPERSION 

### A LGORITHMS 

### .......................................................... 36 3.4.3 B UILDING DOWNWASH 

### .................................................................................. 36 3.4.4 CALPUFF M ODELING 

### D OMAINS AND 

### C LASS 

### I A REA 

### R ECEPTORS 

### .............. 36 3.4.5 B ACKGROUND 

### O ZONE AND 

### A MMONIA 

### C ONCENTRATIONS 

### ........................... 40 3.4.6 P UFF 

### R EPRESENTATION 

### ................................................................................. 44 3.4.7 Q UALITY 

### A SSURANCE OF 

### CALPUFF A NALYSES 

### ......................................... 44 

### 3.5 CALPOST P OSTPROCESSING AND 

### N ATURAL 

### B ACKGROUND 

### C ONDITIONS FOR 

### L IGHT 

### E XTINCTION AND 

### H AZE 

### I NDEX 

### C ALCULATIONS 

### ...........................................44 3.5.1 POSTUTIL P ROCESSING 

### ............................................................................... 45 3.5.2 C LASS 

### I A REA



### - SPECIFIC 

### N ATURAL 

### B ACKGROUND 

### C ONDITIONS 

### .................. 45

### Santee Cooper – Grainger, Jefferies, Winyah ii Trinity Consultants BART Applicability Analysis 3.5.3 V ISIBILITY 

### I MPACT 

### C ALCULATION AND 

### R EVISED 

### IMPROVE L IGHT 

### E XTINCTION 

### A LGORITHM 

### .............................................................................. 47 3.5.3.1 Site-Specific Rayleigh Scattering......................................................... 50 3.5.3.2 Background Sea Salt Aerosol Concentrations...................................... 50 3.5.4 Q UALITY 

### A SSURANCE OF 

### P OSTPROCESSING 

### A NALYSES 

### ............................... 55



### 4. S CREENING 

### A NALYSIS 

### R ESULTS 

### .......................................................................................56 4.1 G RAINGER 

### G ENERATING 

### S TATION 

### ..........................................................................56 4.1.1 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### C APE 

### R OMAIN 

### ...................................... 56 4.1.2 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### S WANQUARTER 

### .................................... 56 

### 4.2 J EFFERIES 

### G ENERATING 

### S TATION 

### ...........................................................................57 4.2.1 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### C APE 

### R OMAIN 

### ...................................... 57 4.2.2 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### W OLF 

### I SLAND 

### ...................................... 57 

### 4.3 W INYAH 

### G ENERATING 

### S TATION 

### .............................................................................58 4.3.1 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### C APE 

### R OMAIN 

### ...................................... 58 4.3.2 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### W OLF 

### I SLAND 

### ...................................... 58 

### 4.4 S UMMARY OF 

### S CREENING 

### A NALYSES 

### .....................................................................59

5. R EFINED 

### A NALYSIS 

### R ESULTS 

### ...........................................................................................60 5.1 J EFFERIES 

### G ENERATING 

### S TATION 

### ...........................................................................60 5.2 W INYAH 

### G ENERATING 

### S TATION 

### .............................................................................62

6. C ONCLUSIONS 

### ....................................................................................................................64

A PPENDIX 

### A Sample CALPUFF/POSTUTIL/CALPOST Input Files

A PPENDIX 

### B CPM Factor Analysis

A PPENDIX 

### C Electronic Media File Index

### Santee Cooper – Grainger, Jefferies, Winyah 1 Trinity Consultants BART Applicability Modeling Analysis 

## 1. I NTRODUCTION

Santee Cooper operates several electrical genera ting stations in South Carolina, including the Grainger, Jefferies, and Winyah Generating Stati ons. Each of the three facilities operates under a Title V Air Quality Permit issued by the South Ca rolina Department of Health and Environmental Control (DHEC) and consists of coal-fired utility boilers and supporting operati ons (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 subm itted 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 a dditional 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-elig ible units should not be considered subject to BART because the analyses described in this report demonstrate th at 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 re port 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

### Santee Cooper – Grainger, Jefferies, Winyah 2 Trinity Consultants BART Applicability Modeling Analysis related information (generally made available at the VISTAS BART website 1 

### ) 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 ad apted per South Carolina DHEC guidance to the source-specific analysis of visibility impacts attributable to the Grainger, Jefferies, and Winyah Generating Stations.



## 1.1 O VERVIEW OF 

## R EGIONAL 

## H AZE 

## R ULE AND 

## BART R EQUIREMENTS
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 opera tion 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 (NO X 

### ), sulfur dioxide (SO 2 

### ), and particulate matter less than 10 micrometers in aerodynamic diameter (PM 10 

### ). Fossil-fueled electric generating units (EGUs) are one of the lis ted 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 th e relevant time period. The BART-eligible source may include multiple emission units but need not include the entire facility.



### 1.1.1 D ETERMINATION OF 

### BART- ELIGIBILITY 

### The U.S. EPA BART guidelines define the fo llowing 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 differentthan BART-eligible sources at other facilities on the list of 26. The Santee Cooper BARTeligible units are EGUs that, in addition to be ing subject to the BART Rule, are subject to the Clean Air Interstate Rule (CAIR), which targets emission reductions for both SO 2 

### and NO X 

### (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 NO X 

### and SO 2

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.

### 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 PM 10 

### emissions only. Therefore, this BART-eligibility analysis only includes evaluations in terms of direct PM 10 

### emissions from Santee Cooper’s BARTeligible EGU sources, and excludes all dir ect emissions from, and secondary reactions with, SO 2 

### and NO X 

### emissions.

Santee Cooper has developed an inventory of six EGUs that meet the preceding criteria and are BART-eligible since they ar e fossil-fueled EGUs, were in existence on August 7, 1977, and began operation after August 7, 1962. E ach source also has potential emissions of greater than 250 tpy of total PM 10 

### . Thus, each of the BART-eligible EGUs was analyzed to evaluate whether any of the three f acilities are exempt from BART. Specific information about these emission units is provided in Section 2 of this BART Applicability Analysis Report.



### 1.1.2 A SSESSMENT OF 

### C ONTRIBUTION TO 

### V ISIBILITY 

### I MPAIRMENT AND 

### BART A PPLICABILITY 

### In its role as technical analysis coordi nator, 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 deciview s (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 determ ine 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 CA LPUFF modeling system, to quantify the impacts attributable to a single BART-eligibl e 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 ( b ext 

### ), 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 backgr ound conditions. The background extinction coefficient b ext, background 

### is affected by various chemical species and the Rayleigh scattering phenomenon and can be calculated as sh own in the following equation.

### Santee Cooper – Grainger, Jefferies, Winyah 4 Trinity Consultants BART Applicability Modeling Analysis 

# ( ) Ray ap Coarse Soil OC NO SO background ext 

### bbbbbbbMm b ++++++= − 3 4 1

,


### where, 

## ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ] [ ] [ ] 

# ( ) ( ) 

## [ ] 3 1 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative defaultby Mm 10 Scattering Rayleigh EC10 Mass Coarse6.0 Soil1 OC4 NONH3 SONH3

= =

= = = = = = =

− RHf b b b b b RHf b RHf b

Ray

ap Coarse Soil OC NO SO 

## ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ]
molecules air todue scattering is Scattering Rayleigh carbon elemental ofion concentrat thedenotes EC dusts coarse ofion concentrat thedenotes Mass Coarse soils fine ofion concentrat thedenotes Soil carbon organic ofion concentrat thedenotes OC ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4



### Values for the parameters listed above specifi c 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 Na tural Visibility Conditions under the Regional Haze Rule

. 3 

### More detailed information about the natural background c onditions 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 equati on 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 a nd 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 (NO 2 

### )

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/P ublications/GrayLit/gray_literature.htm.

### Santee Cooper – Grainger, Jefferies, Winyah 5 Trinity Consultants BART Applicability Modeling Analysis The revised IMPROVE light extinctio n algorithm takes the following form:

≈ ext b 

## ( ) ( ) 

## [ ] ( ) ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ] [ ] [ ][ ] [ ] ( ) 

## [ ] [ ] 2 Scattering Rayleigh specific Site Large Small Large 34 Small 34 Large 2 44 Small 2 44

NO33.0 Salt Sea 4.1 PMC6.0PMF1EC10OC1.6OC8.2 NONH 1.5NONH 4.2 SONH 8.4 SONH 2.2

+ + + +++ + + + +

+

− b RHf RHf RHf RHf RHf

SS L S L S



### 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 defa ult 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 consid ered 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 implementati on 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 partic ulate matter (PMF), and elemental carbon (EC), as well as precursors to secondary organic aeros ols (SOA) and fine particulate matter such as SO 2 

### and NO X 

### . 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 b ext,source 

### is calculated using an air quality model. The extinction due to the BART-eligible source was calculated as shown in the following equation. 

# ( )
EC SOA PMF PMC NO SO sourceext 

### bbbbbbMmb +++++= − 3 4 1

,


### where, 

## ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ]

[ ] [ ] ( ) 

## [ ] 3 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative EC10

PMC6.0 PMF1 SOA4 NONH3 SONH3

= =

=

= = = = =

RHf b b b b RHf b RHf b

EC

PMC PMF SOA NO SO 

## ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ] carbon elemental ofion concentrat thedenotes EC PM coarse ofion concentrat thedenotes PMC PM fine ofion concentrat thedenotes PMF aerosols organic secondary ofion concentrat thedenotes SOA ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

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.

### Santee Cooper – Grainger, Jefferies, Winyah 6 Trinity Consultants BART Applicability Modeling Analysis 

### 1.1.3 P OTENTIALLY 

### A FFECTED 

### C LASS 

### I A REAS 

### 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. Tabl e 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.

T ABLE 

### 1-1. D ISTANCES 

### ( KILOMETERS 

### ) S EPARATING 

### VISTAS C LASS 

### I A REAS AND 

### S ANTEE 

### C OOPER 

### ’ S 

### J EFFERIES 

### , G RAINGER 

### , AND 

### W INYAH 

### G ENERATING 

### S TATIONS

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 lo cations evaluated in the modeling analysis were determined by, and were obtained from, th e 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 re ceptors, http://www2.nature. nps.gov/air/maps/receptors/.

### Santee Cooper – Grainger, Jefferies, Winyah 7 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 1-1. L OCATION OF 

### S ANTEE 

### C OOPER 

### ’ S 

### BART-E LIGIBLE 

### F ACILITIES 

### R ELATIVE TO 

### VISTAS C LASS 

### I A REAS

1,300 1,400 1,500 1,600 1,700 1,800 

### Lambert Conformal Coordinate East (km) -800 -700 -600 -500 -400 -300 -200Lambert Conformal Coordinate North (km)

Grainger

Jefferies Winyah

Wolf Island Cape Romain Swanquarter



### 1.1.4 CALPUFF M ODELING 

### A NALYSES 

### 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 sy stem 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 freque ncy, 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 po llutant emissions from a single source and is therefore preferred for BART applicability and determination analyses.

### Santee Cooper – Grainger, Jefferies, Winyah 8 Trinity Consultants BART Applicability Modeling Analysis The U.S. EPA BART guidelines prescribe that the 98 th 

### percentile, 24-hour average, visibility impact computed in a modeling analysis that evaluates three years of meteorological data should be compared to th e contribution threshold. However, VISTAS prescribes that the maximum computed visibility impact be used as the basis for comparison in the screening analysis and the 98 th 

### 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 a ttributable 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 th at the same CALPUFF modeling analysis techniques have been determined to be su itable 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 a pplicability 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 O RGANIZATION OF 

## BART A PPLICABILITY 

## A NALYSIS 

## R EPORT
The remainder of this BART Applicability An alysis 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 th e 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 a nd conclusion that the Grainger, Jefferies, and Winyah Generati ng Stations are not subject to BART.

Supplemental information is provided in several a ppendices 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.

### Santee Cooper – Grainger, Jefferies, Winyah 9 Trinity Consultants BART Applicability Modeling Analysis 

## 2. BART-E LIGIBLE 

## S OURCE 

## D ESCRIPTION

This section of the BART Applicability Analysis Report describes the emission units that comprise the BART-eligible sources at Santee Cooper’s Grainge r, 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-E LIGIBLE 

## E MISSION 

## U NITS
Santee Cooper reviewed the criteria for BART-eligibilit y 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.

T ABLE 

### 2-1. S UMMARY OF 

### BART- ELIGIBLE 

### E MISSION 

### U NITS AND 

### P OTENTIAL 

### E MISSIONS

Facility Emission Unit Source Code BART Source Category* Date Built or Modified SO 2 Emissions (tpy) NO X Emissions (tpy) PM 10 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 To tal Potential Emissions (tpy) 27,008 7,280 320 Jefferies BART-Eligible Emission Units To tal Potential Emissions (tpy) 47,002 12,674 548 Winyah BART-Eligible Emission Units To tal Potential Emissions (tpy) 46,790 18,748 684
* Category 1 denotes fossil fuel-fired steam electri c plants of more than 250 MMBtu/hr heat input. † Emissions not listed since emission unit was construc ted prior to or after the BART-eligibility period.

7 While the Winyah boilers are also pe rmitted to combust fuel oil, they primarily com bust bituminous coal.

### 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 NO X 

### , SO 2 

### , and total PM 10 

### (condensable and filterable). Therefore, all three of the Santee Cooper facilities are BART-e ligible since potential emissions exceed 250 tpy for at least one (in fact, all) of NO X 

### , SO 2 

### , and PM 10, 

### 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 supporti ng 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 c overed by BART have potential PM 10 

### 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 PM 10 

### ) 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 SO 2 

### , NO X 

### , and PM 10 

### , 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 visib ility-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 contri bute to visibility impacts, and other than these 13 facilities, ammonia emissions from VISTAS sour ces do not contribute to visibility impairment within the VISTAS region: Based on these analyses, the VISTAS states re commend 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.

### Santee Cooper – Grainger, Jefferies, Winyah 11 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 2-2. T ITLE 

### V I NSIGNIFICANT 

### E MISSION 

### U NITS 

### E XCLUDED FROM 

### BART A NALYSIS

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 Unload ing 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 Unload ing 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 Ha ndling Reclaim Hoppers < 0.2 Handling C01 – C08 Coal Hand ling 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 F eed Conveyors C-1, C-2, & C-3 < 0.2, each Handling P12/P13, P21 Coal Crushe rs (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 Co nveyors 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 dur ing the BART applicability period of August 7, 1962 – August 7, 197 7. † Potential emissions are based on app lication of a safety factor to actual emissions. Values are rounded.

### Santee Cooper – Grainger, Jefferies, Winyah 12 Trinity Consultants BART Applicability Modeling Analysis 

## 2.2 BART- ELIGIBLE 

## S OURCE 

## M ODEL 

## E MISSIONS 

## I NVENTORY
Whereas the BART eligibility determination relies on potential emissions of visibility-affecting pollutants, the BART applicability analysis utili zes maximum actual 24-hour average emission rates of NO X 

### , SO 2 

### , and PM 10 

### . The VISTAS BART Modeling Protocol specifies the following hierarchy of resources to establish the maximum actual 24-hour av erage 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, PM 10 

### emissions are the only emissions that were included in the BART applicability modeling for the boilers since they ar e EGUs subject to CAIR. Santee Cooper utilized the 24-hour controlled potential PM 10 

### emissions in the BART applicability modeling. Usage of the 24-hour controlled potential PM 10 

### emissions results in overly conserva tive visibility impacts; that is, modeling of 24-hour actual average emissions woul d result in smaller visibility impacts. Thus modeled impacts are conservatively high. Table 2-3 summarizes the 24-hour controlled potential PM 10 

### emission rates used in the analyses for each modeled BART-eligible emission unit.

T ABLE 

### 2-3. S UMMARY OF 

### 24- HOUR 

### A VERAGE 

### M AXIMUM 

### P OTENTIAL 

### BART E MISSION 

### R ATES

Facility Emission Unit NO X Emissions* (lb/hr) SO 2 Emissions* (lb/hr) Total PM 10 Emissions† (lb/hr) Total PM 2.5 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

* NO X and SO 2 emissions for EGUs are not required to be included in the BART analysis. † Emissions include both filterable a nd condensable particulate matter.



### Table 2-3 includes 24-hour maximum emission rates and distinguishes the emission rates of Total PM 10 

### (TPM 10 

### ), which includes emissions of Total PM less than 2.5 microns in aerodynamic diameter (TPM 2.5 

### ) and all filterable and condensable components. Modeling of visibility impairment requires that the components of the exhaust stream be speci ated because different types of particulate matter affect visibility to varying extents. Figure 2-1 de picts the speciation of visibility-affecting pollutant

### 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, Sant ee Cooper adapted the speciation presented in the following sections.

Note that the organic fraction of condensable pa rticulate 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 (SO 4 

### ) and nitrates (NO 3 

### ), as well as non-hygroscopic (PIC) particles. Since hygroscopic species have diffe rent 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 mode ling 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 par ticulate species used in the remaining discussion about particulate speciation.



### F IGURE 

### 2-1. P ARTICULATE 

### M ATTER 

### S PECIATION 

### ( AFTER 

### F IGURE 

### 4-3 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

### Santee Cooper – Grainger, Jefferies, Winyah 14 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 2-4. N OMENCLATURE FOR 

### E MISSIONS 

### S PECIATION 

### A NALYSIS

Nomenclature Description

TSP Total suspended particulate, filterable PM with an aerodynamic diameter < 30 μ m PM 10 Filterable particulate matter with an aerodynamic diameter < 10 μ m PM 6-10 Filterable particulate matter with an aerodynamic diameter > 6 and < 10 μ m PM 2.5-6 Filterable particulate matter with an aerodynamic diameter > 2.5 and < 6 μ m PM 2.5 Filterable particulate matter with an aerodynamic diameter < 2.5 μ m PM 1.25-2.5 Filterable particulate matter with an aerodynamic diameter > 1.25 and < 2.5 μ m PM 1-1.25 Filterable particulate matter with an aerodynamic diameter > 1.0 and < 1.25 μ m PM 0.625-1 Filterable particulate matter with an aerodynamic diameter > 0.625 and < 1.0 μ m PM 0.5-0.625 Filterable particulate matter with an aerodynamic diameter > 0.5 and < 0.625 μ m PMC Filterable Coarse particulate matter = PM 6-10 + PM 2.5-6 PMF Filterable Fine particulate matter = PM 6-10 + PM 2.5-6 CPM Condensable particulate matt er (organic and inorganic) POC Primary organic condensable emissions PIC Primary non-hygroscopic inorganic condensable emissions TPM 10 Filterable PM 10 + CPM TPM 2.5 Filterable PM 2.5 + CPM SO4 Inorganic Sulfate CPM NO3 Inorganic Nitrate CPM EC Elemental Carbon



### The FLM have published speciation guidance for certa in source types (e.g., gas-, oil-, and coal-fired combustion sources) which can be used to create sp eciation 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 H 2 

### SO 4 

### 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 hy groscopic 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 Res ources (NCDENR), and evaluated the mean and median factors from the larger data sets.

### Santee Cooper – Grainger, Jefferies, Winyah 15 Trinity Consultants BART Applicability Modeling Analysis matter (NO 3 

### ). However, Santee Cooper utilized the more conservative assumption that all primary inorganic CPM is hygroscopic sulfate (this assumpti on results in worst-case visibility impacts). That is, no distinction of non-sulfate inorga nic CPM between primary nitrate (NO 3 

### ) 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 usi ng 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 e ach of the BART-eligible sources at the three Santee Cooper facilities is presented in th e following sections of this document.

T ABLE 

### 2-5. A SSIGNMENT OF 

### E MITTED 

### PM S PECIES TO 

### M ODELED 

### PM C ATEGORIES

Modeled PM Category † Components Output Category ‡ Extinction Coefficient (m 2 /g)
PMC Filterable coarse particles (PM 6-10 , PM 2.5-6 ) PMC 0.6 PMF Filterable fine particles (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 ) SOIL 1 PIC Non-hygroscopic, primary inorganic condensable (PIC) emissions* SOIL 1 SO4 Primary inorganic condensable emissions of sulfates* SO4 3 f

(RH) NO3 Primary inorganic condensable emissions of nitrates* NO3 3 f

(RH) POC Primary organic condensable emissions SOA 4 EC Uncombusted carbonaceous fuel EC 10
* All condensable inorganic emi ssions 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 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP O NLY 

### 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 TPM 10 

### 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 fo r the North Carolina EGUs, which are most representative of the Santee Cooper EGUs. Refe r to Table B-3 in Appendix B for details of the CPM factor development. The percenta ge 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/dec rees/civil/caa/santee-cd.pdf

### Santee Cooper – Grainger, Jefferies, Winyah 16 Trinity Consultants BART Applicability Modeling Analysis summarizes the relevant data for the boile rs at the Grainger and Jefferies Generating Stations.

T ABLE 

### 2-6. C OAL 

### B OILERS WITH 

### ESP S PECIATION 

### D ATA

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 Co al 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 Co al Properties*

CPM Factor (lb/MMBtu) 0.02 Santee Cooper Custom CPM Factor PM 10 as a % of PM 67.0% EC as a % of Fine PM 10 3.7% EC as a % of PM 10 ‡ 1.6% Organic portion of CPM 20.0% Inorganic portion of CPM 80.0% PM 6-10 as a % of PM 10 25.4% PM 2.5-6 as a % of PM 10 31.3% PM 1.25-2.5 as a % of PM 10 17.9% PM 1-1.25 as a % of PM 10 4.5% PM 0.625-1 as a % of PM 10 3.0% PM 0.5-0.625 as a % of PM 10 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 minimu m value for the period of 2001, 2003-2005, whichever values result in the higher factors. Although 2002 data was not av ailable, the variation between the existing coal data, calculated as the standard deviation divided by the mean valu e, was less than 8% for all pa rameters. 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . ‡ Value calculated based on the percentage of EC composing PM 2.5 multiplied by the percentage of PM 10 that is PM 2.5 .



### The site-specific information presented in Table 2-6 was input into the FLM speciation profile workbook, which uses the coal data a nd site-specific filterable and condensable PM factors to determine the TPM 10 

### emissions on a pound per hour basis. 11 

### The TPM 10 

### emissions were then speciated in the various filterable and condensable PM components. Table 2-7 presents the TPM 10 

### percentages and site-specific PM 10 

### , CPM, and TPM 10 

### emissions for the Grainger and Jefferies boilers.

11 Workbook is available at the NPS website: www2.natur e.nps.gov/air/permits/ect/e ctCoalFiredBoiler.cfm.

### Santee Cooper – Grainger, Jefferies, Winyah 17 Trinity Consultants BART Applicability Modeling Analysis

T ABLE 

### 2-7. D ATA 

### U SED TO 

### C ALCULATE 

### TPM 10 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### C OAL 

### B OILER WITH 

### ESP (G RAINGER AND 

### J EFFRIES 

### )

% of TPM 10 that is PM 10 * % of TPM 10 that is CPM* Boiler Heat Input (MMBtu/hr) PM 10 Emissions

(lb/hr)† TPM 10 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) × (PM 10 as a % of PM). ‡ Calculated as: (PM 10 emissions, lb/hr) / (% of TPM 10 that is PM 10 ). ** 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 μ m and 0.5-0.625 μ m. PIC emissions were calculated using a similar methodology, using the inorganic condensable percentage of 80%.

The filterable PM 10 

### emissions were then divided amongst six different size categories by multiplying filterable PM 10 

### emissions by the percentage of PM 10 

### for each size category (refer to Table 2-5), yielding hourly emissions of PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625-1 

### , and PM 0.5-0.625 

### . 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 speciatedPM emissions for the BART-eligible coalfired boilers at the Grainger and Jefferies Ge nerating Stations while Figures 2-2 and 2-3 present graphical representations of the specia tion. Note that the values presented in the table and figures are for each boiler at the facility.

T ABLE 

### 2-8. TPM 10 

### S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### C OAL 

### B OILER WITH 

### ESP (G RAINGER AND 

### J EFFRIES 

### )

POC ( μ m) PIC (sulfate) ( μ m) PMC ( μ m) PMF ( μ m) 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

### Santee Cooper – Grainger, Jefferies, Winyah 18 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 2-2. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### G RAINGER 

### B OILER

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



### F IGURE 

### 2-3. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### J EFFERIES 

### B OILER

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 P ARTICULATE 

### S PECIATION FOR 

### C OAL 

### -F IRED 

### B OILERS WITH 

### ESP AND 

### FGD
The Winyah Generating Station operates two BA RT-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

### 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 TPM 10 

### 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 SO 2 

### 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 defau lt speciation profile compiled by VISTAS. Table 2-9 summarizes the relevant data for the two boilers at the Winyah Generating Station.

T ABLE 

### 2-9. C OAL 

### B OILERS WITH 

### ESP & FGD S PECIATION 

### D ATA

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 Aver age 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 Aver age Coal Properties* CPM Factor (lb/MMBtu) 0.008 Santee Cooper Custom CPM Factor PM 10 as a % of PM 67.0% EC as a % of Fine PM 10 3.7% EC as a % of PM 10 ‡ 1.6% Organic portion of CPM 20.0% Inorganic portion of CPM 80.0% PM 6-10 as a % of PM 10 25.4% PM 2.5-6 as a % of PM 10 31.3% PM 1.25-2.5 as a % of PM 10 17.9% PM 1-1.25 as a % of PM 10 4.5% PM 0.625-1 as a % of PM 10 3.0% PM 0.5-0.625 as a % of PM 10 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 minimu m value for the period of 2001, 2003-2005, whichever values result in the higher emission factors. Although 2002 data wa s 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 PM 10 by dividing by the % of TSP that is PM 10 (as shown in the AP-42 table). For example, PM 6-10 is calculated as (PM 10 % - PM 6 %)/(PM 10 %) = PM 6-10 as % of PM 10 . ‡ Value calculated based on the percentage of EC composing PM 2.5 multiplied by the percentage of PM 10 that is PM 2.5 .

12 Consent decree between EPA and Santee Cooper, June 24, 2004. www.epa.gov/Compliance/resources/dec rees/civil/caa/santee-cd.pdf

### 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 a nd site-specific filterable and condensable PM factors to determine the TPM 10 

### emissions on a pound per hour basis. 13 

### The TPM 10 

### emissions were then speciated in the various filterable and condensable PM components. Table 2-10 presents the TPM 10 

### percentages and site-specific PM 10 

### , CPM, and TPM 10 

### emissions for the Winyah boilers.

T ABLE 

### 2-10. D ATA 

### U SED TO 

### C ALCULATE 

### TPM 10 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### E ACH 

### C OAL 

### B OILER WITH 

### ESP (W INYAH 

### )

% of TPM 10 that is PM 10 * % of TPM 10 that is CPM* Boiler Heat Input (MMBtu/hr) PM 10 Emissions

(lb/hr)† TPM 10 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) × (PM 10 as a % of PM). ‡ Calculated as: (PM 10 emissions, lb/hr) / (% of TPM 10 that is PM 10 ). ** Calculated as: (Boiler heat input, MMBtu/hr) × (CPM factor, lb/MMBtu).



### The POC, PIC, and various filterable PM 10 

### size grouping emissions for the Winyah boilers were determined using the sa me 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 Sta tion while Figures 2-4 and 2-5 present graphical representations of the speciation.

T ABLE 

### 2-11. TPM 10 

### S PECIATED 

### E MISSIONS 

### ( LB 

### / HR 

### ) FOR 

### W INYAH 

### C OAL 

### B OILERS WITH 

### ESP & FGD

POC ( μ m) PIC (sulfate) ( μ m) PMC ( μ m) PMF ( μ m) 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.natur e.nps.gov/air/permits/ect/e ctCoalFiredBoiler.cfm.

### Santee Cooper – Grainger, Jefferies, Winyah 21 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 2-4. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### W INYAH 

### B OILER 

### 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



### F IGURE 

### 2-5. TPM 10 

### S PECIATION 

### ( LB 

### / HR 

### ) FOR 

### W INYAH 

### B OILER 

### 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

### Santee Cooper – Grainger, Jefferies, Winyah 22 Trinity Consultants BART Applicability Modeling Analysis 

## 2.3 M ODELED 

## S TACK 

## P ARAMETERS AND 

## E MISSIONS
Actual stack parameters were input to the CA LPUFF 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 c onsidered in the BART Applicability Analyses. 14



### Table 2-12 summarizes the stack parameters and Table 2-13 summarizes the modeled emission rates of TPM 10 

### 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.”Santee Cooper – Grainger, Jefferies, Winyah23Trinity ConsultantsBART Applicability Modeling AnalysisTABLE2-12.STACKPARAMETERS FORBART-ELIGIBLEEMISSIONUNITSFacilityEmissionUnitStack IDLCC East(km)LCC North(km)BaseElevation(feet)StackHeight(feet)StackDiameter(feet)ExhaustTemperature(°F)ExhaustVelocity(feet/sec)Grainger Boiler 1 GS01 1,644.618 -520.823 40 300 10.0 309 69.2Boiler2 GS02 1,644.643 -520.818 40 300 10.0 315 70.0Jefferies Boiler 3 JS03 1,572.042 -600.906 56 300 13.0 325 56.2Boiler4 JS04 1,572.073 -600.932 56 300 13.0 325 56.2Winyah Boiler 1 WS01 1,627.285 -580.090 18 404 20.0 125 52.0Boiler2 WS02 1,627.285 -580.090 18 404 20.0 125 52.0TABLE2-13.MODELEDEMISSIONRATES FORBART-ELIGIBLEEMISSIONUNITSEmission Stack PMC (lb/hr)PMF(lb/hr)PIC (as sulfate) (lb/hr)POC (lb/hr)ECFacility Unit ID6-10μm 2.5-6μm 1.25-2.5μm 1.0-1.25μm 0.625-1.0μm 0.5-0.625μm 0.625-1.0μm 0.5-0.625μm 0.625-1.0μm 0.5-0.625μm(lb/hr)Grainger Boiler 1 GS01 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04 1.76 1.76 0.28Boiler 2 GS02 4.49 5.54 3.17 0.79 0.53 2.89 7.04 7.04 1.76 1.76 0.28Jefferies Boiler 3 JS03 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26 3.07 3.07 0.52Boiler 4 JS04 7.82 9.66 5.52 1.38 0.92 5.00 12.26 12.26 3.07 3.07 0.52Winyah Boiler 1 WS01 13.57 16.76 9.58 2.39 1.60 8.76 8.51 8.51 2.13 2.13 0.82Boiler 2 WS02 13.57 16.76 9.58 2.39 1.60 8.75 8.51 8.51 2.13 2.13 0.83

### 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’ befo re December 31, 1970” and to “stack heights or dispersion techniques at sources which were rec onstructed 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 H g 

### = H + 1.5 L , where H g 

### = 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 regul ation does apply to the tw o Winyah boilers, which became “in existence” in 1971 and 1974. Therefor e, 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:

H

g 

### = H + 1.5 L = 66.14 m + 1.5(66.14 m) = 165.35 m

This calculation demonstrates that the Winyah bo ilers 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.

### Santee Cooper – Grainger, Jefferies, Winyah 25 Trinity Consultants BART Applicability Modeling Analysis 

## 3. A IR 

## Q UALITY 

## M ODELING 

## A NALYSES

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 refine d analyses to assess visibility impacts. Air quality modeling was conducted gene rally following the methods described in the VISTAS BART Modeling Protocol as revised.



## 3.1 A IR 

## Q UALITY 

## M ODELING 

## S YSTEM
CALPUFF is a multi-layer, multi-species, non-steady-state Lagrangian puff model, which can simulate the effects of time- and space-varying me teorological 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 dist ances, 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 distan ces. 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 m odeling 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 s ource-specific analysis of visibility impacts attributable to the three facilities. Sp ecifically, CALMET Version 5.726/060801, CALPUFF Version 5.756/060725, POSTUTIL Version 1.52/ 060412, and CALPOST Version 5.6393/060202 15 

### are designated as the “VISTAS Recommended Ve rsion for BART Modeling” by the VISTAS Technical Contractor and were used in the CALPUFF BART applicability analyses for Santee Cooper’s Grainger, Jefferies, a nd Winyah Generating Stations. 16



## 3.2 S CREENING AND 

## R EFINED 

## A NALYSIS 

## T ECHNIQUES

The VISTAS BART Modeling Protocol distinguishes between screening and refined applications of the CALPUFF model for the purposes of BART App licability 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 th e 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/downloa d/download.htm#VISTAS_VERSION

### 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 V ISIBILITY 

### A SSESSMENT 

### M ETRIC 

### The U.S. EPA BART guidelines prescribe that the 98 th 

### percentile, 24-hour average visibility impact computed in a modeling analysis that evaluates three years of meteorological data should be compared to th e 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 representati on of visibility impairment. The VISTAS refined approach prescribes that the 98 th 

### percentile impact be evaluated in refined analyses as the eighth-highest 24-hour average visibility impact of each of three years modeled, or the 22 nd 

### -highest 24-hour average visibility impact over the three years modeled, whichever is more conservative.

To assess whether BART-eligible operations c ontribute 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 M ETEOROLOGICAL AND 

### C OMPUTATIONAL 

### G RIDS



### 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 transf ormation of visibility-affecting pollutants in the CALPUFF meteorological model. Th e VISTAS Technical Contractor conducted meteorological modeling using both screening 12 -km and refined 4-km grids to provide common data resources to eligible sour ces 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.

### Santee Cooper – Grainger, Jefferies, Winyah 27 Trinity Consultants BART Applicability Modeling Analysis 

### 3.2.3 M ODEL 

### P ROCESSING AND 

### P OSTPROCESSING 

### M ETHODS


### 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 th e 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 sc reening 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. Di fferential rates of transport among the new puffs thus generated can increase the horizontal spr ead 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 accu rately 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 M ETEOROLOGICAL 

## P ROCESSING
CALMET is the meteorological preprocessor that compiles three-dimensional meteorological fields from mesoscale model (MM) output, raw obser vations of surface and upper air conditions, precipitation measurements, and geop hysical 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 me teorological fields are available, as discussed in paragraph 9.3(c). These mesoscale meteorological fields should be used in conjunction with available standar d [National Weather Service] NWS or comparable meteorological observations w ithin 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. Fo r its BART applicability analyses, Santee Cooper utilized 12-km screening and 4-km CALMET data pr epared by the VISTAS Technical Contractor using the following data resources and CALMET modeling techniques.



### 3.3.1 CALMET M ETEOROLOGICAL 

### D OMAINS 

### 12-km screening and 4-km refined CALMET mete orological data sets were prepared for the domains depicted in Figure 3-1. Sant ee Cooper’s BART applicability analyses utilized the Regional Screening Domain 0 for 12 -km screening analyses and the refined

### Santee Cooper – Grainger, Jefferies, Winyah 28 Trinity Consultants 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 dom ains as described in the following section of this document.



### F IGURE 

### 3-1. 12- KM 

### R EGIONAL 

### S CREENING 

### A NALYSIS AND 

### 4- KM 

### S UB 

### - REGIONAL 

### R EFINED 

### A NALYSIS 

### CALMET D OMAINS



### The VISTAS regional modeling domain was designed to allow any Class I areas within the VISTAS area to be evaluated with a single meteorological databa se and consistent CALPUFF modeling options. The horizontal dom ain is comprised of grid cells, each containing a central grid point at which mete orological 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 structur e 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

### Santee Cooper – Grainger, Jefferies, Winyah 29 Trinity Consultants 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.

T ABLE 

### 3-1. V ERTICAL 

### G RID 

### S TRUCTURE

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 S IMULATIONS



### MM data are used as “observed” or “fir st-guess” fields in CALMET due to its highresolution representation of meteorological conditions on a uniform three-dimensional grid. The following three years of MM5 me teorological 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 G EOPHYSICAL 

### D ATA 

### 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 da ta set that spans the globe from 60° north

### Santee Cooper – Grainger, Jefferies, Winyah 30 Trinity Consultants 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 (appr oximately 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 assimila te 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 th e 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 calcul ate 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 

### S CREENING 

### A NALYSIS 

### CALMET P ROCESSING 

### The development of the regional screening CALMET meteorological fields from MM5 data were conducted in No-Ob servations (“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 r esolution 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 wi nds were interpolated by CALMET to the CALMET layers and CALMET’s boundary la yer modules computed mixing heights, turbulence parameters, and other meteorol ogical 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 

### R EFINED 

### G RID 

### CALMET P ROCESSING 

### 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 wi nd fields, and NWS meteorological observational data in the Step 2 smoothing calculations. Inthis manner, actual observations of threedimensional meteorological conditions were u sed in the model to smooth the coarse MM5 resolution to better represent areas in which terrain features and coastlines may have an

### Santee Cooper – Grainger, Jefferies, Winyah 31 Trinity Consultants 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, temperatur e, cloud cover and ceiling, relative humidity, and precipitation type. Surface data were sel ected from the available data inventory to optimize spatial coverage and representation of the domain. Raw observations were obtained from the National Climatic Data Ce nter (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 va ry less and are generally not as affected by local effects (e.g., terrain or coastlines). U pper 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 analys is. Therefore, it is necessary to include observations of precipitation in the CALMET an alysis. Precipitation data were collected from selected surface meteorological data stations included in the analysis, plus Cooperative Observation Network (COOP) stati ons 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 di stinct 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

### Santee Cooper – Grainger, Jefferies, Winyah 32 Trinity Consultants BART Applicability Modeling Analysis fumigation under certain conditions. The CA LMET processor is equipped to assimilate overwater data obtained from coastal, near-s hore, and offshore observation platforms. CALMET uses a profile method to simulate boundary layer effects by computing the friction velocity, Monin-Obukhov length, su rface roughness, and mixing height over the water surface. The details of the formulati on 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, an d the overwater temperature gradients above and below the overwater mixing height. For pr actical 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 temperatur e 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 Q UALITY 

### A SSURANCE OF 

### CALMET A NALYSES 

### 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 techniqu es used by the contractor to validate the model’s performance.



## 3.4 CALPUFF M ODEL 

## P ROCESSING
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 Appli cability 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 opti ons were used, including the following important model options.



### 3.4.1 M ODELED 

### E MISSIONS AND 

### C HEMICAL 

### T RANSFORMATIONS



### Section 2 of this document described the BA RT-eligible emission units operated by Santee Cooper and the visibility-affecting pollutant s considered in the CALPUFF analysis. Emission rates were calculated following U.S. EPA and South Carolina DHEC BART guidance for primary emissions of PM 10 

### . 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 algor ithms, which are considered the default

### Santee Cooper – Grainger, Jefferies, Winyah 33 Trinity Consultants BART Applicability Modeling Analysis for regulatory CALPUFF modeling, were employ ed. However, analyses did not include pollutants that undergo chemical transforma tion. 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 (SO 2 

### , SO 4 

### , and NO X 

### ) 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 PM 6-10 

### , PM 2.5-6 

### , PM 1.25-2.5 

### , PM 1-1.25 

### , PM 0.625 

### - 1 

### , and PM 0.5-0.625 

### , 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.

T ABLE 

### 3-2. R EPRESENTATION OF 

### PM S IZE 

### C ATEGORIES IN 

### CALPUFF

Computed Geometric Mass Geometric Standa rd Precipitation Scav enging 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 u tilizes 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 multiplicativ e 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, inorga nic 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 m odeling a single point source; however, for facilities with multiple emission point sources, the preceding approach is not appropriate.

17 http://www.src.com/verio/downloa d/sample_files.ht m#EXAMPLE_BART

### Santee Cooper – Grainger, Jefferies, Winyah 34 Trinity Consultants BART Applicability Modeling Analysis Specifically, for sources with multiple emi ssion points that have different exhaust characteristics (e.g., stack height, diameter, ve locity, 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 alternativ e to this approach for sources with multiple emission points is to model each emission po int 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 alternativ e to the preceding approaches, the BART applicability analyses for Santee Cooper’s fac ilities 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.

T ABLE 

### 3-3. E XPLICIT 

### M ODELING OF 

### PM T YPES AND 

### S IZE 

### C ATEGORIES

Modeled PM Category Components

PMC800 PMC425 Filterable coarse particles divided between two size categories (PM 6-10 , PM 2.5-6 ) PMF187 PMF112 PMF081 PMF056 Filterable fine particles divided among four size categories (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 )

POC081 POC056 Primary condensable organi c emissions divided between two size categories (PM 0.625-1 and PM 0.5-0.625 ) PIC081 PIC056 Primary condensable inorganic emissions divided between tw o size categories (PM 0.625-1 and PM 0.5-0.625 ) EC187 EC112 EC081 EC056 Primary elemental carbon emissions di vided among four size categories (PM 1.25-2.5 , PM 1-1.25 , PM 0.625-1 , PM 0.5-0.625 )



### So that explicit modeling of the 14 par ticle 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.

### Santee Cooper – Grainger, Jefferies, Winyah 35 Trinity Consultants BART Applicability Modeling Analysis

T ABLE 

### 3-4. R EPRESENTATION OF 

### E XPLICITLY 

### M ODELED 

### PM S IZE 

### C ATEGORIES 

### IN CALPUFF

Computed Geometric Mass Geometric Standa rd Precipitation Scav enging 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 u tilizes the deposition velocity associated with the geometric mass mean diameter. † Santee Cooper is proposing to preliminarily model all PIC as su lfate. Should Santee Cooper elect to segregate PIC into sulfa te and non-sulfate species, the PIC species would be modeled as shown in this table.



### To post-process the CALPUFF output concentr ations that result from explicitly modeled multiple emission points, POSTUTIL was used only to group modeled PM into light extinction groups. Unit scaling factors we re used in POSTUTIL and there was no adjustment to the explicitly modeled emissi on 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.

### Santee Cooper – Grainger, Jefferies, Winyah 36 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 3-5. A SSIGNMENT OF 

### M ODELED 

### PM S PECIES TO 

### L IGHT 

### E XTINCTION 

### G ROUPS

Modeled Components CALPOST Light Extinction Group Extinction Coefficient (m 2 /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 D ISPERSION 

### A LGORITHMS 

### As specified in a March 16, 2006 U.S. EPA memorandum 18 

### 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 B UILDING 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 M ODELING 

### D OMAINS AND 

### C LASS 

### I A REA 

### R ECEPTORS 

### 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 Cl ass 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 Modelin g 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.”

### Santee Cooper – Grainger, Jefferies, Winyah 37 Trinity Consultants 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 illustra te 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.



### F IGURE 

### 3-2. T ERRAIN 

### R EPRESENTATION IN THE 

### 12- KM 

### R EGIONAL 

### S CREENING 

### G RID 

### (A FTER 

### F IGURE 

### 4-2 OF THE 

### VISTAS BART M ODELING 

### P ROTOCOL 

### )

### Santee Cooper – Grainger, Jefferies, Winyah 38 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 3-3. 12- KM 

### S CREENING 

### A NALYSIS 

### C OMPUTATIONAL 

### D OMAIN

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200


### Lambert Confor mal Coordinate East (km) -1,200 -1,100 -1,000

-900 -800 -700 -600 -500 -400 -300 -200 -100 0Lambert Conformal Coordinate North(km)

Grainger

Jefferies Winyah

Wolf Island Cape Romain Swanquarter

12-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 39 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 3-4. 4- KM 

### R EFINED 

### A NALYSIS 

### C OMPUTATIONAL 

### D OMAIN FOR 

### J EFFERIES 

### & W INYAH 

### G ENERATING 

### S TATIONS

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 Co ordinate East (km) -1,100 -1,050 -1,000

-950 -900 -850 -800 -750 -700 -650 -600 -550 -500 -450 -400 -350Lambert Conformal Coordinate North(km) Winyah Jefferies

Wolf Island Cape Romain

4-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 40 Trinity Consultants BART Applicability Modeling Analysis 

### 3.4.5 B ACKGROUND 

### O ZONE AND 

### A MMONIA 

### C ONCENTRATIONS 

### The CALPUFF model is capable of simulating linear chemical transformation effects by using pseudo-first-order chemical reacti on mechanisms for the conversions of SO 2 

### to SO 4 

### , and NO X 

### , which consists of nitrogen oxide (NO) and nitrogen dioxide (NO 2 

### ), to NO 3 

### and HNO 3 

### . For the BART applicability analyses, ch emical transformations involving five species (SO 2 

### , SO 4 

### , NO X 

### , HNO 3 

### , and NO 3 

### ) were modeled using the MESOPUFF II chemical transformation scheme. Ambien t 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 stati ons 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.

### Santee Cooper – Grainger, Jefferies, Winyah 41 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 3-5. O ZONE 

### D ATA 

### S TATIONS 

### U TILIZED IN 

### S ANTEE 

### C OOPER 

### ’ S 

### BART S CREENING 

### A PPLICABILITY 

### A NALYSES

1,200 1,300 1,400 1,500 1,600 1,700 1,800 1,900 2,000 2,100 2,200


### Lambert Confor mal Coordinate East (km) -1,200 -1,100 -1,000

-900 -800 -700 -600 -500 -400 -300 -200 -100 0Lambert Conformal Coordinate North(km)

Grainger

Jefferies Winyah

Wolf Island Cape Romain Swanquarter

12-km Domain

### Santee Cooper – Grainger, Jefferies, Winyah 42 Trinity Consultants BART Applicability Modeling Analysis 

### F IGURE 

### 3-6. O ZONE 

### D ATA 

### S TATIONS 

### U TILIZED IN 

### S ANTEE 

### C OOPER 

### ’ S 

### BART R EFINED 

### A PPLICABILITY 

### A NALYSES

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 Co ordinate East (km) -1,100 -1,050 -1,000

-950 -900 -850 -800 -750 -700 -650 -600 -550 -500 -450 -400 -350Lambert Conformal Coordinate North(km) Winyah Jefferies

### Santee Cooper – Grainger, Jefferies, Winyah 43 Trinity Consultants BART Applicability Modeling Analysis Monthly average ozone background values we re 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 screen ing analysis, while Table 3-7 summarizes the values computed for substitution in the re fined analysis for Jefferies and Winyah.

T ABLE 

### 3-6. M ONTHLY 

### A VERAGE 

### S UBSTITUTE 

### O ZONE 

### V ALUES FOR 

### S CREENING 

### A NALYSIS

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



### T ABLE 

### 3-7. M ONTHLY 

### A VERAGE 

### S UBSTITUTE 

### O ZONE 

### V ALUES FOR 

### R EFINED 

### A NALYSIS

2001 Substitute 2002 Subs titute 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

### 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 conditio ns of ammonia and sulfates in the refined analysis of chemical transformations involving these species and nitrates. However, VISTAS’ Technical Analysis Workgroup h as since concluded that modeled background and boundary conditions shoul d not be utilized for BART modeling purposes because “EPA and FLM recommend that ALM approach using CMAQ concentration data (SO X 

### , NO X 

### , total NH 3 

### ) 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 ofthis document, only refined analyses utilized the POSTUTIL postprocessing tool to repartition the HNO 3 

### and NO 3 

### equilibrium distributions using only a constant 0.5 ppb ammonia background level.



### 3.4.6 P UFF 

### R EPRESENTATION 

### As recommended by the VISTAS BART Modeling Protocol , the default integrated puff sampling methodology was enabled for both scr eening 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 evalua ted 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 Q UALITY 

### A SSURANCE OF 

### CALPUFF A NALYSES 

### Sample CALPUFF input files for the screeni ng 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 valu es, 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 do cument. Source parameters input for each BART-eligible emissions unit were verified w ith the information presented in Section 2. The location and elevations of Class I area rece ptors were also verified by comparison to NPS maps.



## 3.5 CALPOST P OSTPROCESSING AND 

## N ATURAL 

## B ACKGROUND 

## C ONDITIONS FOR 

## L IGHT 

## E XTINCTION AND 

## H AZE 

## I NDEX 

## C ALCULATIONS
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 extincti on attributable to the natural background and source occurred as generally described in this document.

### Santee Cooper – Grainger, Jefferies, Winyah 45 Trinity Consultants BART Applicability Modeling Analysis 

### 3.5.1 POSTUTIL P ROCESSING 

### The first postprocessing step involved running POSTUTIL to calculate the concentrations of visibility-affecting species SO4, NO3, SOA, EC, PMF, and PMC, as described inSection 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.

T ABLE 

### 3-8. A SSIGNMENT OF 

### M ODELED 

### PM S PECIES TO 

### L IGHT 

### E XTINCTION 

### G ROUPS IN 

### POSTUTIL

Modeled Components CALPOST Light Extinction Group Extinction Coefficient (m 2 /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 w as only run once to achieve this pollutant grouping. The ammonia limiting method (ALM ), which is used to re-partition the distribution of HNO 3 

### and NO 3 

### concentrations at each Class I area as a function of the temperature, relative humidity, and free amm onia 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 C LASS 

### I A REA 

### - SPECIFIC 

### N ATURAL 

### B ACKGROUND 

### C ONDITIONS 

### The visibility goal of the Clean Air Act is both the remedying of existing visibility impairment and prevention of futu re 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 inte nd to limit States to the use of the 20% best visibility days...States may use 20% best vi sibility 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 Atta chment 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.

### 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 natu ral 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 Coope r 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 calcula te natural background conditions.

T ABLE 

### 3-9. A NNUAL 

### A VERAGE 

### B ACKGROUND 

### C ONCENTRATIONS FOR 

### C LASS 

### I A REAS 

### P OTENTIALLY 

### A FFECTED BY THE 

### S ANTEE 

### C OOPER 

### F ACILITIES

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 us ing the following equations.

⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= 10

exp10 HI b back 

### ,

where HI is Haze Index expressed in units of deciviews (dv). Therefore, total b back 

### 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.

Romain Capefor Mm 21.11 Mm 10 Mm 21.21 10 52.7 exp10 1 1 1

− − − 

### =⇒+

=+= = ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= soil soil soil ray back 

### bb bb b



### The effects of relative humidity to amplify the visibility impairment of hygroscopic sulfates and nitrates are characterized using CALPOST “Method 6,” which computes Δ b ext 

### using a monthly average relative humidity adjustment particular to each Class I area

### 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 app lied 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

.
T ABLE 

### 3-10. M ONTHLY 

### A VERAGE 

### f (RH) FOR 

### S ELECTED 

### C LASS 

### I A REA

Class I Area January February Ma rch 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 V ISIBILITY 

### I MPACT 

### C ALCULATION AND 

### R EVISED 

### IMPROVE L IGHT 

### E XTINCTION 

### A LGORITHM 

### CALPOST was run separately for each Class I area to obtain the necessary visibility statistics for evaluating compliance with the BA RT visibility impairment thresholds. The inputs to CALPOST involve selection of the visi bility 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 allo w 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 98 th 

### 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 98 th 

### percentile (8 th 

### -highest day regardless of variability at each rece ptor) 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 ( b ext 

### ) 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 sec ondary organic aerosols (SOA) and fine

### Santee Cooper – Grainger, Jefferies, Winyah 48 Trinity Consultants BART Applicability Modeling Analysis particulate matter such as SO 2 

### and NO X 

### that form sulfates (SO 4 

### ) and nitrates (NO 3 

### ) after chemical transformations take place. The extinction coefficient due to emissions of visibility-affecting pollutants from a single BART-eligible source b ext,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. 

# ( )
EC SOA PMF PMC NO SO sourceext 

### bbbbbbMmb +++++= − 3 4 1

,


### where, 

### ( ) 

## [ ] ( ) 

### [ ] ( ) 

### [ ] [ ]

[ ] [ ] ( ) 

### [ ] 3 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative EC10

PMC6.0 PMF1 SOA4 NONH3 SONH3

= =

=

= = = = =

RHf b b b b RHf b RHf b

EC

PMC PMF SOA NO SO 

## ( ) 

## [ ] [ ] [ ] [ ] [ ] [ ] carbon elemental ofion concentrat thedenotes EC PM coarse ofion concentrat thedenotes PMC PM fine ofion concentrat thedenotes PMF aerosols organic secondary ofion concentrat thedenotes SOA ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4 2 4



### The relative impact of a BART-eligible source is determined by comparing the HI attributable to a source relative to es timated natural background conditions. The background extinction coefficient b ext,background 

### is affected by various chemical species and the Rayleigh scattering phenomenon and was initially calculated as shown in the following default equation. 

# ( ) Ray ap Coarse Soil OC NO SO background ext 

### bbbbbbbMm b ++++++= − 3 4 1

, 

### where,



### ( ) 

### [ ] ( ) 

### [ ] ( ) 

### [ ] [ ] [ ] [ ] 

## ( ) ( ) 

### [ ] 3 1 34 3 4 2 4 4

m μ ginion Concentrat Function Humidity Relative defaultby Mm 10 Scattering Rayleigh EC10

Mass Coarse6.0 Soil1 OC4 NONH3 SONH3

= =

= = = = = = =

− RHf b b b b b RHf b RHf b

Ray

ap Coarse Soil OC NO SO 

### ( ) 

### [ ] [ ] [ ] [ ] [ ] [ ]
molecules air todue scattering is Scattering Rayleigh carbon elemental ofion concentrat thedenotes EC dusts coarse ofion concentrat thedenotes Mass Coarse soils fine ofion concentrat thedenotes Soil carbon organic ofion concentrat thedenotes OC ion concentrat nitrate ammonium thedenotes NONH ion concentrat sulfate ammonium thedenotes SONH

34 4

2 4



### Values for the parameters listed above specifi c 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 c onditions particular to Class I areas potentially affected by Santee Cooper’s operations were provided in Tables 3-9 and 3-10 to represent annual aver age natural background conditions at Cape Romain, Wolf Island, and Swanquarter.

### Santee Cooper – Grainger, Jefferies, Winyah 49 Trinity Consultants BART Applicability Modeling Analysis The preceding equation represents what has b een termed the “default” light extinction calculation that has been utilized for computa tion 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 visibilit y at Class I areas as part of its reasonable progress analyses, VISTAS intends to present regional air quality m odeling results using both the default and the newly revised IMPROVE light extinction formula.

In December 2005, the IMPROVE workgroup issu ed a report describing an alternative to the default formula used to estimate extinc tion 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 provisi onal 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 (NO 2 

### ).

The revised IMPROVE light extinctio n algorithm takes the following form:

≈ ext b 

## ( ) ( ) 

## [ ] ( ) ( ) 

## [ ] ( ) 

## [ ] ( ) 

## [ ] [ ] [ ] [ ][ ] [ ] ( ) 

## [ ] [ ] 2 Scattering Rayleigh specific Site Large Small Large 34 Small 34 Large 2 44 Small 2 44

NO33.0 Salt Sea 4.1 PMC6.0PMF1EC10OC1.6OC8.2 NONH 1.5NONH 4.2 SONH 8.4 SONH 2.2

+ + + +++ + + + +

+

− b RHf RHf RHf RHf RHf

SS L S L S



### To facilitate the use of the revised IMPROVE algorithm for assessing BART applicability, the VISTAS Technical Contractor developed and distributed a spreadsheet tool toimplement 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 Estima ting Ambient Light Ex tinction Coefficients - Final Report , March 2006.

### Santee Cooper – Grainger, Jefferies, Winyah 50 Trinity Consultants BART Applicability Modeling Analysis current version of the CALPOST postprocessor. To implement the CALPOST-IMPROVE tool, two additional data points are require d: 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 NO 2 

### may be incorporated. However, since NO X 

### and NO 3 

### were not explicitly modeled in the Santee Cooper BART Applicability Analyses, the background NO 2 

### concentrations were not utilized.



### 3.5.3.1 S ITE 

### -S PECIFIC 

### R AYLEIGH 

### S CATTERING 

### The 2005 IMPROVE report 23 

### 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 de nsity, 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. Th e temperature data were obtained from the nearest weather stations for time pe riods 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, Wo lf Island, and Swanquarter.



### 3.5.3.2 B ACKGROUND 

### S EA 

### S ALT 

### A EROSOL 

### C ONCENTRATIONS 

### 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 po tentially 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 na tural 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 focu s 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 ( f

SS 

### (RH)) for sea salt aerosols based on observations in remote marine

23 http://vista.cira.colostate.edu/IMPROVE/Publicati ons/GrayLit/019_RevisedIMPROVEeq/RevisedIMPROVEAl gorithm3.doc

### Santee Cooper – Grainger, Jefferies, Winyah 51 Trinity Consultants BART Applicability Modeling Analysis environments and thermodynamic mode ls. Although there is reasonable confidence in these components of the calculation of light extinction due to sea salt aerosols, there is greater uncer tainty 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 leve l. VISTAS states, including South Carolina, are using annual average natu ral 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 survey ed 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 sa lt 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 μ g/m 3 

### using Cl - 

### as the marker, which is recognized to

### 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 μ g/m 3 

### , 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 μ g/m 3 

### 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 com position of sodium. Using this value, the ambient sodium attributable to sea salt is computed as: 

## [ ] [ ] [ ] Soil Salt Sea NaNa Na

− =



### 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[Na non-soil 

### ],

where [Na non-soil 

### ] = [Na] – 0.57[Fe] and [Fe] is the measured iron concentration.

24 Tombach, I. Estimating the Role of Sea Salt when Determin ing the Visibility Impa ct 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 Con centrations for Consideration in Determination of Natural background Levels at Eastern Class I Areas , August 2, 2006.

### Santee Cooper – Grainger, Jefferies, Winyah 53 Trinity Consultants BART Applicability Modeling Analysis The VISTAS Technical Contractor 27 

### 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[Na non-soil 

### ], where [Na non-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 μ g/m 3 

### using the Na + 

### method, but does not recommend a longe r-term average value. The VISTAS Technical Contractor demonstrates that the 2004 annual average sea salt concentrations of 0.35 μ g/m 3 

### estimated using [Cl - 

### ] and 0.11 μ g/m 3 

### estimated using [Cl] exhibit the expected underestimation bias due to the analysis technique. The elemental sodium (Na) a pproach 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 b ackground sea salt concentration, Santee Cooper applied reliable estim ation techniques over the period of record when such measurements were taken. Add itionally, 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 usi ng strontium (Sr), which is measuredaccurately 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 Met hods for Estimating Sea Salt Con centrations 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 Manage ment Association , 56 : 636-642, May 2006.

### Santee Cooper – Grainger, Jefferies, Winyah 54 Trinity Consultants BART Applicability Modeling Analysis 

## [ ] [ ] [ ] Si 0.0013528 SrxSr

− = 

### ,

where 0.0013528 is the crustal reference c oncentration of Sr and Si represents the silicon composition that is presumabl y 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 beca use 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 a pproach 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 su mmarizes data from each approach over the September 1994 through December 2001 time period during which reliable analytical techniques were in use for each species.

T ABLE 

### 3-11. S UMMARY OF 

### S EA 

### S ALT 

### C ONCENTRATIONS 

### C OMPUTED USING 

### V ARIOUS 

### M ETHODS 

### , S EPTEMBER 

### 1994

– D ECEMBER 

### 2004

[Sea Salt] ( μ g/m 3 )

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 , 4 th ed. Wiley & Sons, Inc.: New York, 1982, p.344.

### 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 av erage background concentration of sea salt is estimated to be 1.1 μ g/m 3 

### . 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 μ g/m 3 

### as estimated by the VISTAS Technical Contractor and 1.1 μ g/m 3 

### as estimated bySantee Cooper) to represent that background condition in the CALPOSTIMPROVE tool as described in the following section of this repor t. In addition, for reference only, results are presente d using the default IMPROVE value of 0.2 μ g/m 3



### . 

### 3.5.4 Q UALITY 

### A SSURANCE OF 

### P OSTPROCESSING 

### A NALYSES 

### Quality assurance of postprocessing analyses was conducted by verifying the sequence of postprocessing (i.e., POSTUTIL, including AL M 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 calculate d 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 concentrati ons and light extinction and relative source impact

### Santee Cooper – Grainger, Jefferies, Winyah 56 Trinity Consultants BART Applicability Modeling Analysis 

## 4. S CREENING 

## A NALYSIS 

## R ESULTS

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 represente d 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 G RAINGER 

## G ENERATING 

## S TATION



### 4.1.1 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### C APE 

### R OMAIN 

### 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 th e default annual average natural background described in Section 3.5 for representing na tural background condit ions. 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.

T ABLE 

### 4-1. G RAINGER 

### ’ S 

### P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN

Visibility Impact 2001 Δ dv (> 1.0 dv, > 0.5 dv) 2002 Δ dv (> 1.0 dv, > 0.5 dv) 2003 Δ dv (> 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 Ca pe Romain since the peak visibility impact estimated using the default screening approach is less than the 0.5 dv contribution threshold.



### 4.1.2 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### S WANQUARTER 

### 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 th e default annual average natural background described in Section 3.5 for representing na tural background condit ions. 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.

### Santee Cooper – Grainger, Jefferies, Winyah 57 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 4-2. G RAINGER 

### ’ S 

### P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### S WANQUARTER

Visibility Impact 2001 Δ dv (> 1.0 dv, > 0.5 dv) 2002 Δ dv (> 1.0 dv, > 0.5 dv) 2003 Δ dv (> 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 J EFFERIES 

## G ENERATING 

## S TATION



### 4.2.1 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### C APE 

### R OMAIN 

### 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 th e default annual average natural background described in Section 3.5 for representing na tural background condit ions. 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.

T ABLE 

### 4-3. J EFFERIES 

### ’ P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN

Visibility Impact 2001 Δ dv (> 1.0 dv, > 0.5 dv) 2002 Δ dv (> 1.0 dv, > 0.5 dv) 2003 Δ dv (> 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 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### W OLF 

### I SLAND 

### 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 a nnual 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.

### Santee Cooper – Grainger, Jefferies, Winyah 58 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 4-4. J EFFERIES 

### ’ P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### W OLF 

### I SLAND

Visibility Impact 2001 Δ dv (> 1.0 dv, > 0.5 dv) 2002 Δ dv (> 1.0 dv, > 0.5 dv) 2003 Δ dv (> 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 Wo lf Island since the peak visibility impact estimated using the default screening approach is less than the 0.5 dv contribution threshold.



## 4.3 W INYAH 

## G ENERATING 

## S TATION



### 4.3.1 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### C APE 

### R OMAIN 

### 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 th e default annual average natural background described in Section 3.5 for representing na tural background condit ions. 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.

T ABLE 

### 4-5. W INYAH 

### ’ S 

### P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN

Visibility Impact 2001 Δ dv (> 1.0 dv, > 0.5 dv) 2002 Δ dv (> 1.0 dv, > 0.5 dv) 2003 Δ dv (> 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 S CREENING 

### A NALYSIS 

### I MPACTS AT 

### W OLF 

### I SLAND 

### 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 a nnual 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.

### Santee Cooper – Grainger, Jefferies, Winyah 59 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 4-6. W INYAH 

### ’ S 

### P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### W OLF 

### I SLAND

Visibility Impact 2001 Δ dv (> 1.0 dv, > 0.5 dv) 2002 Δ dv (> 1.0 dv, > 0.5 dv) 2003 Δ dv (> 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 S UMMARY OF 

## S CREENING 

## A NALYSES
Because screening analyses of visibility impacts from Santee Cooper’s Jefferies and Winyah Generating Stations indicated peak 24-hour averag e 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 contributio ns 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 Wi nyah Generating Stations do not contribute to visibility impairment at Wolf Island NWR, and re fined analyses were not required for this Class I

area.

### Santee Cooper – Grainger, Jefferies, Winyah 60 Trinity Consultants BART Applicability Modeling Analysis 

## 5. R EFINED 

## A NALYSIS 

## R ESULTS

Screening analysis results described in Secti on 4 of this BART Applicability Analysis Report indicated that the 0.5 dv contribution threshold w as exceeded only at the Cape Romain NWR for both the Jefferies and Winyah Generating Stations. Th erefore, refined analyses were conducted for both facilities for Cape Romain using the refined postpro cessing methods described in Section 3.5 of this report to assess the 98 th 

### percentile, 24-hour average impacts in comparison to the 0.5 dv contribution threshold.



## 5.1 J EFFERIES 

## G ENERATING 

## S TATION
Table 5-1 summarizes the top eight modeled 24-hour average visibility impacts of each data analysis year at Cape Romain NWR attributable to Sant ee Cooper’s Jefferies Generating Station using refined modeling methods as described in this report. The 98 th 

### percentile impact is represented as the maximum 8 th 

### -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 μ g/m 3 

### as estimated by the VISTAS Technical Contractor, 1.1 μ g/m 3 

### as estimated by Santee Cooper In addition, for reference only, results are presented using the default IMPROVE value of 0.2 μ g/m 3



### .

.
T ABLE 

### 5-1. P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN

Ranked 2001 Δ dv Ranked 2002 Δ dv Ranked 2003 Δ dv Maximum 8 th -High Δ dv 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3

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 98 th 

### percentile, Table 5-2 summarizes a ranking of the top 25 daily average impacts over the three- year period modeled to quantify the 22 nd 

### -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 8 th highest change is 0.450 dv (in 2003), while the 22 nd highest ranked change is 0.448 dv.

### Santee Cooper – Grainger, Jefferies, Winyah 61 Trinity Consultants BART Applicability Modeling Analysis using various estimates of the annual aver age sea salt concentration, i.e., 0.77 μ g/m 3 

### as estimated by the VISTAS Technical Contractor, 1.1 μ g/m 3 

### as estimated by Santee Cooper, and 0.2 μ g/m 3 

### as estimated to be the default revised IMPR OVE concentration for Cape Romain NWR.

T ABLE 

### 5-2. R ANKED 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN OVER 

### 2001 THROUGH 

### 2003 D ATA 

### A NALYSIS 

### P ERIOD

Overall Rank Δ dv (0.77 μ g/m 3 ) Δ dv (1.1 μ g/m 3 ) Δ dv (0.2 μ g/m 3 )

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.

T ABLE 

### 5-3. R EFINED 

### V ISIBILITY 

### A NALYSIS 

### S UMMARY FOR 

### C APE 

### R OMAIN

Sea Salt 2001 8th High 2001 8th High 2001 8th High 22nd High Concentration Δ dv Δ dv Δ dv Δ dv

0.77 μ g/m 3 0.245 0.257 0.273 0.273
1.1 μ g/m 3 0.225 0.237 0.252 0.252
0.2 μ g/m 3 0.285 0.299 0.318 0.316

### 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 W INYAH 

## G ENERATING 

## S TATION
Table 5-4 summarizes the top eight modeled 24-hour average visibility impacts of each data analysis year at Cape Romain NWR attributable to Sant ee Cooper’s Winyah Generating Station using refined modeling methods as described in this report. The 98 th 

### percentile impact is represented as the maximum 8 th 

### -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 μ g/m 3 

### as estimated by the VISTAS Technical Contractor, 1.1 μ g/m 3 

### as estimated by Santee Cooper. In addition, for reference only, results are presented using the default IMPROVE value of 0.2 μ g/m 3



### .

.
T ABLE 

### 5-4. P EAK 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN

Ranked 2001 Δ dv Ranked 2002 Δ dv Ranked 2003 Δ dv Maximum 8 th -High Δ dv 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3 0.77 μ g/m 3 1.1 μ g/m 3 0.2 μ g/m 3

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 98 th 

### percentile, Table 5-5 summarizes a ranking of the top 25 daily average impacts over the three- year period modeled to quantify the 22 nd 

### -high impact over three years when the revised IMPROVE algorithm is applied. Results are tabulated for analyses using various estimates of the annual aver age sea salt concentration, i.e., 0.77 μ g/m 3 

### as estimated by the VISTAS Technical Contractor, 1.1 μ g/m 3 

### as estimated by Santee Cooper, and 0.2 μ g/m 3 

### as estimated to be the default revised IMPR OVE concentration for Cape Romain NWR.

### Santee Cooper – Grainger, Jefferies, Winyah 63 Trinity Consultants BART Applicability Modeling Analysis T ABLE 

### 5-5. R ANKED 

### 24- HOUR 

### A VERAGE 

### V ISIBILITY 

### I MPACTS AT 

### C APE 

### R OMAIN OVER 

### 2001 THROUGH 

### 2003 D ATA 

### A NALYSIS 

### P ERIOD

Overall Rank Δ dv (0.77 μ g/m 3 ) Δ dv (1.1 μ g/m 3 ) Δ dv (0.2 μ g/m 3 )

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.

T ABLE 

### 5-6. R EFINED 

### V ISIBILITY 

### A NALYSIS 

### S UMMARY FOR 

### C APE 

### R OMAIN

Sea Salt 2001 8th High 2001 8th High 2001 8th High 22nd High Concentration Δ dv Δ dv Δ dv Δ dv

0.77 μ g/m 3 0.401 0.377 0.476 0.449
1.1 μ g/m 3 0.371 0.347 0.435 0.410
0.2 μ g/m 3 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 repr esentative sea salt concentrations of 0.77 μ g/m 3 

### as estimated by the VISTAS Technical Contractor and 1.1 μ g/m 3 

### as estimated by Santee Cooper are utilized because the facility does not contribute to visibilit y impairment at Cape Romain as estimated using refined modeling techniques.

### Santee Cooper – Grainger, Jefferies, Winyah 64 Trinity Consultants BART Applicability Modeling Analysis 

## 6. C ONCLUSIONS

Using analysis methods prescribed by the U.S. EPA, VISTAS, and South Carolina DHEC, Santee Cooper conducted BART Applicability Analyses of em issions 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 An alysis Report include the most updated emissions inventory based on updated emission factor gui dance and enforceable permit conditions, most updated versions of the CALPUFF modeling sy stem, 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 Sw anquarter 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 Ca pe Romain NWR as demonstrated by the refined modeling analyses that quantified the 98 th 

### percentile 24-hour average visibility impact as being less than the 0.5 dv contribution threshold using refine d estimates of light extinction based on the revised IMPROVE algorithm and analyses of the most re presentative background sea salt concentrations.

### Santee Cooper – Grainger, Jefferies, Winyah A - 1 Trinity Consultants BART Applicability Modeling Analysis 

## A PPENDIX 

## A

S AMPLE 

### CALPUFF, POSTUTIL, AND 

### CALPOST I NPUT 

### F ILES 

### CALPOST-IMPROVE P ROCESSOR 

### W ORKSHEETS

### Santee Cooper – Grainger, Jefferies, Winyah A - 2 Trinity Consultants BART Applicability Modeling Analysis 

## SAMPLE CALPUFF INPUT FILE

SCPWIN12KM02.INP WINYAH 12-KM SCREENIN G ANALYSIS YEAR 2002SCPWIN12KM02.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

1SCPWIN12KM02.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 !

2SCPWIN12KM02.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!

3SCPWIN12KM02.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)

4SCPWIN12KM02.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 !

5SCPWIN12KM02.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)

6SCPWIN12KM02.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!

7SCPWIN12KM02.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

8SCPWIN12KM02.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

9SCPWIN12KM02.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.

10SCPWIN12KM02.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 !

11SCPWIN12KM02.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?

12SCPWIN12KM02.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)

---------------

13SCPWIN12KM02.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

14SCPWIN12KM02.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. !

15SCPWIN12KM02.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 !

16SCPWIN12KM02.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:

17SCPWIN12KM02.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

18SCPWIN12KM02.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)

19SCPWIN12KM02.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 !

20SCPWIN12KM02.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

--------------

21SCPWIN12KM02.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

22SCPWIN12KM02.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!

---------------

23SCPWIN12KM02.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

24SCPWIN12KM02.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.

25SCPWIN12KM02.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

26SCPWIN12KM02.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 !

27SCPWIN12KM02.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

28SCPWIN12KM02.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

29SCPWIN12KM02.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:

30SCPWIN12KM02.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!

31SCPWIN12KM02.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!

32SCPWIN12KM02.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

33SCPWIN12KM02.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!

34SCPWIN12KM02.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

### Santee Cooper – Grainger, Jefferies, Winyah A - 3 Trinity Consultants BART Applicability Modeling Analysis 

## SAMPLE POSTUTIL INPUT FILE

SCPWIN12KMUT02.INP WINYAH 12-KM SCREENIN G ANALYSIS YEAR 2002SCPWIN12KMUT02.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 !

1SCPWIN12KMUT02.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*

2SCPWIN12KMUT02.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:

3SCPWIN12KMUT02.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 | |--------|------------------|-------------------|-------------------------|

4SCPWIN12KMUT02.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!

5SCPWIN12KMUT02.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

### 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 NAT URAL BACKGROUNDSCPWIN12KMVANCR02.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

1SCPWIN12KMVANCR02.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

----------------------------------------------

2SCPWIN12KMVANCR02.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

3SCPWIN12KMVANCR02.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 !

4SCPWIN12KMVANCR02.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)

5SCPWIN12KMVANCR02.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,

6SCPWIN12KMVANCR02.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

7SCPWIN12KMVANCR02.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]

8SCPWIN12KMVANCR02.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

### 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 2002SCPWIN4KM02.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

1SCPWIN4KM02.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

2SCPWIN4KM02.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

3SCPWIN4KM02.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

4SCPWIN4KM02.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)

5SCPWIN4KM02.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

6SCPWIN4KM02.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)

7SCPWIN4KM02.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

8SCPWIN4KM02.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

9SCPWIN4KM02.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

10SCPWIN4KM02.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)

11SCPWIN4KM02.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)

12SCPWIN4KM02.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,

13SCPWIN4KM02.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.

14SCPWIN4KM02.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

15SCPWIN4KM02.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!

------------------------------------------------------------------------------

-

16SCPWIN4KM02.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

17SCPWIN4KM02.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.

18SCPWIN4KM02.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

19SCPWIN4KM02.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 !

20SCPWIN4KM02.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 :

21SCPWIN4KM02.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 !

22SCPWIN4KM02.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?

23SCPWIN4KM02.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,

24SCPWIN4KM02.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)

------

--------------------------------------------------------------------

25SCPWIN4KM02.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

26SCPWIN4KM02.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.

27SCPWIN4KM02.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 !

28SCPWIN4KM02.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.

29SCPWIN4KM02.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

30SCPWIN4KM02.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)

31SCPWIN4KM02.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!

32SCPWIN4KM02.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!

33SCPWIN4KM02.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!

34SCPWIN4KM02.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

### 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 2002SCPWIN4KMUT02.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 !

1SCPWIN4KMUT02.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*

2SCPWIN4KMUT02.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:

3SCPWIN4KMUT02.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 | |--------|------------------|-------------------|-------------------------|

4SCPWIN4KMUT02.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!

5SCPWIN4KMUT02.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

### 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 NAT URAL BACKGROUNDSCPWIN4KMVANCR02.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

1SCPWIN4KMVANCR02.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

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File Default File Name

---- ----------------- Visibility Change DELVIS.DAT ! DVISDAT = T:\BART\SNTCPRSC\4KM_CPM\SCPWIN4KMVANCR02.del !

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-- 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!

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--

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

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2SCPWIN4KMVANCR02.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

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