BART FIVE FACTOR ANALYSIS

ARKANSAS ELECTRIC COOPERATIVE CORPORATION

BAILEY AND MCCLELLAN GENERATING STATIONS

Prepared By:

TRINITY CONSULTANTS, INC.

120 East Sheridan, Suite 205

Oklahoma City, Oklahoma 73104

(405) 228-3292

TRINITY CONSULTANTS, INC.

977 Ridge Drive, Suite 380

Lenexa, Kansas 66219

(913) 894-4500

In conjunction with:

ARKANSAS ELECTRIC COOPERATIVE CORPORATION

PO Box 194208

Little Rock, Arkansas

(501) 570-2420

Trinity Project No. 123701.0036

July 2012

Version 2
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BART Analysis – Bailey and McClellan

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY........................................................................................ 1-1

2. INTRODUCTION AND BACKGROUND.................................................................... 2-1

3. MODELING METHODOLOGIES AND PROCEDURES ............................................. 3-1

3.1 CALMET AND CALPUFF................................................................................... 3-1

3.2 CALPOST............................................................................................................ 3-1

4. EXISTING EMISSIONS AND VISIBILITY IMPAIRMENT ......................................... 4-1

4.1 NOX, SO2, AND PM10 BASELINE EMISSION RATES ............................................... 4-1

4.2 BASELINE VISIBILITY IMPAIRMENT....................................................................... 4-2

5. SO2 BART EVALUATION .................................................................................... 5-1

5.1 IDENTIFICATION OF AVAILABLE RETROFIT SO2 CONTROL TECHNOLOGIES – FUEL 

OIL COMBUSTION.............................................................................................................. 5-1

5.2 ELIMINATE TECHNICALLY INFEASIBLE SO2 CONTROL TECHNOLOGIES ................ 5-1

5.2.1 DRY SORBENT INJECTION, SPRAY DRYER ABSORPTION (SDA), WET SCRUBBER,

CIRCULATING DRY SCRUBBER (CDS).............................................................................5-1

5.2.2 FUEL SWITCHING ................................................................................................5-2

5.3 RANK OF TECHNICALLY FEASIBLE SO2 CONTROL OPTIONS BY EFFECTIVENESS .. 5-2

5.4 EVALUATION OF IMPACTS FOR FEASIBLE SO2 CONTROLS..................................... 5-3

5.4.1 COST OF COMPLIANCE ........................................................................................5-3

5.4.2 ENERGY IMPACTS AND NON-AIR QUALITY IMPACTS.........................................5-9

5.4.3 REMAINING USEFUL LIFE ...................................................................................5-9

5.5 EVALUATION OF VISIBILITY IMPACT OF FEASIBLE SO2 CONTROLS ...................... 5-9

5.6 PROPOSED BART FOR SO2 ................................................................................... 5-13

6. NOX BART EVALUATION................................................................................... 6-1

7. PM10 BART EVALUATION.................................................................................. 7-1

7.1 IDENTIFICATION OF AVAILABLE RETROFIT PM10 CONTROL TECHNOLOGIES ........ 7-1

7.2 ELIMINATE TECHNICALLY INFEASIBLE PM CONTROL TECHNOLOGIES................. 7-1

7.2.1 DRY ELECTROSTATIC PRECIPITATORS (ESP).....................................................7-1

7.2.2 WET ELECTROSTATIC PRECIPITATORS (ESP).....................................................7-2

7.2.3 MECHANICAL COLLECTORS (CYCLONES) ..........................................................7-2

7.2.4 FABRIC FILTER....................................................................................................7-2

7.2.5 WET SCRUBBER ..................................................................................................7-2

7.2.6 FUEL SWITCHING ................................................................................................7-3

7.3 RANK OF TECHNICALLY FEASIBLE PM10 CONTROL OPTIONS BY EFFECTIVENESS. 7-3

7.4 EVALUATION OF IMPACTS FOR FEASIBLE PM10 CONTROLS .................................. 7-4

7.4.1 COST OF COMPLIANCE ........................................................................................7-4
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BART Analysis – Bailey and McClellan

7.4.1.1 Energy Impacts and Non-Air Quality Impacts ....................................7-5

7.4.1.2 Remaining Useful Life.........................................................................7-5

7.5 EVALUATION OF VISIBILITY IMPACT OF FEASIBLE PM10 CONTROLS .................... 7-7

7.6 PROPOSED BART FOR PM10 ................................................................................. 7-11

APPENDIX A - SO2 CONTROL COST CALCULATIONS

APPENDIX B - PM10 CONTROL COST CALCULATIONS

APPENDIX C - MODELING PROTOCOL
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BART Analysis – Bailey and McClellan

LIST OF TABLES

TABLE 1-1. EXISTING VISIBILITY IMPAIRMENT ATTRIBUTABLE TO BAILEY UNIT 1 AND MCCLELLAN 

UNIT 1 (2001-2003)................................................................................................................1-1

TABLE 3-1. ANNUAL AVERAGE BACKGROUND CONCENTRATION.....................................................3-2

TABLE 3-2. FL(RH) LARGE RH ADJUSTMENT FACTORS.....................................................................3-3

TABLE 3-3. FS(RH) SMALL RH ADJUSTMENT FACTORS.....................................................................3-3

TABLE 3-4. FSS(RH) SEA SALT RH ADJUSTMENT FACTORS ...............................................................3-3

TABLE 4-1. BASELINE MAXIMUM 24-HOUR SO2, NOX, AND PM10 EMISSION RATES (AS HOURLY 

EQUIVALENTS) .......................................................................................................................4-2

TABLE 4-2. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO BAILEY, UNIT 1 (2001-2003) –

NATURAL GAS........................................................................................................................4-3

TABLE 4-3. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO BAILEY, UNIT 1 (2001-2003) –

FUEL OIL.................................................................................................................................4-3

TABLE 4-4. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO MCCLELLAN, UNIT 1 (2001-

2003), NATURAL GAS.............................................................................................................4-4

TABLE 4-5. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO MCCLELLAN, UNIT 1 (2001-

2003), FUEL OIL .....................................................................................................................4-4

TABLE 5-2. AVAILABLE SO2 CONTROL TECHNOLOGIES FOR BAILEY UNIT 1 AND MCCLELLAN UNIT 15-1

TABLE 5-3. CONTROL EFFECTIVENESS OF TECHNICALLY FEASIBLE SO2 CONTROL TECHNOLOGIES5-2

TABLE 5-4. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH CURRENT NO. 6

FUEL OIL.................................................................................................................................5-4

TABLE 5-5. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH 1% SULFUR NO. 6

FUEL OIL.................................................................................................................................5-4

TABLE 5-6. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH 0.5% SULFUR NO. 6

FUEL OIL.................................................................................................................................5-5

TABLE 5-7. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH DIESEL.................5-5

TABLE 5-8. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH NATURAL GAS.....5-5

FIGURE 5-1. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH NATURAL GAS....5-7
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BART Analysis – Bailey and McClellan

TABLE 5-9. SUMMARY OF COST EFFECTIVENESS FOR FUEL SWITCHING FOR CURRENT NO. 6 FUEL 

OIL AT BAILEY UNIT 1............................................................................................................5-8

TABLE 5-10. SUMMARY OF COST EFFECTIVENESS FOR FUEL SWITCHING FOR CURRENT NO. 6 FUEL 

OIL AT MCCLELLAN UNIT 1...................................................................................................5-8

TABLE 5-11. SUMMARY OF EMISSION RATES MODELED TO REFLECT FUEL SWITCHING FOR SO2

CONTROL AT BAILEY UNIT 1 ...............................................................................................5-10

TABLE 5-12. SUMMARY OF EMISSION RATES MODELED TO REFLECT FUEL SWITCHING FOR SO2

CONTROL AT MCCLELLAN UNIT 1 ......................................................................................5-10

TABLE 5-13. SUMMARY OF MODELED IMPACTS FROM SO2 CONTROL VISIBILITY IMPACT ANALYSIS 

FOR BAILEY UNIT 1 (2001-2003)..........................................................................................5-11

TABLE 5-14. SUMMARY OF MODELED IMPACTS FROM SO2 CONTROL VISIBILITY IMPACT ANALYSIS 

FOR MCCLELLAN UNIT 1 (2001-2003).................................................................................5-12

TABLE 7-1. AVAILABLE PM CONTROL TECHNOLOGIES FOR BAILEY UNIT 1 AND MCCLELLAN UNIT 17-1

TABLE 7-3. CONTROL EFFECTIVENESS OF TECHNICALLY FEASIBLE PM CONTROL TECHNOLOGIES 7-4

TABLE 7-4. SUMMARY OF COST EFFECTIVENESS FOR BAILEY UNIT 1 PM10 CONTROLS....................7-6

TABLE 7-5. SUMMARY OF COST EFFECTIVENESS FOR MCCLELLAN UNIT 1 PM10 CONTROLS ...........7-6

TABLE 7-5. SUMMARY OF EMISSION RATES MODELED TO REFLECT WET ESP FOR PM10 CONTROL7-7

TABLE 7-6. SUMMARY OF EMISSION RATES MODELED TO REFLECT WET SCRUBBER FOR PM10

CONTROL................................................................................................................................7-7

TABLE 7-7. SUMMARY OF EMISSION RATES MODELED TO REFLECT CYCLONE FOR PM10 CONTROL7-7

TABLE 7-8. SUMMARY OF MODELED IMPACTS FROM PM10 CONTROL VISIBILITY IMPACT ANALYSIS 

FOR BAILEY UNIT 1 (2001-2003)............................................................................................7-9

TABLE 7-9. SUMMARY OF MODELED IMPACTS FROM PM10 CONTROL VISIBILITY IMPACT ANALYSIS 

FOR MCCLELLAN UNIT 1 (2001-2003).................................................................................7-10
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BART Analysis – Bailey and McClellan

1. EXECUTIVE SUMMARY

This report documents the determination of the Best Available Retrofit Technology (BART) as 

proposed by Arkansas Electric Cooperative Corporation (AECC) for the Unit 1 Boiler at the Bailey

Generating Station and the Unit 1 Boiler at the McClellan Generating Station. Bailey Unit 1 is a 

wall-fired boiler with a maximum heat input of 1,350 million British thermal units per hour 

(MMBtu/hr) that burns natural gas and No. 6 fuel oil. McClellan Unit 1 is a wall-fired boiler with a 

maximum heat input of 1,436 MMBtu/hr that burns natural gas and No. 6 fuel oil. The ability to burn 

fuel oil at both Bailey and McClellan is important – even if the fuel oil is more expensive to burn than 

natural gas. During natural gas curtailments, natural gas infrastructure maintenance, and other 

emergencies, AECC relies on the fuel oil stored at the plants to maintain electrical reliability. 

Arkansas Department of Environmental Quality (ADEQ) has determined based on results of air 

dispersion modeling that cumulative emissions of nitrogen oxides (NOx), sulfur dioxide (SO2), and 

particulate matter with a mass mean diameter smaller than ten microns (PM10) from Bailey Unit 1 and 

McClellan Unit 1 each cause or contribute greater than 0.5 delta deciviews (∆dv) to visibility 

impairment in four Class I Areas: Caney Creek Wilderness (CACR), Upper Buffalo Wilderness 

(UPBU), Hercules Glades Wilderness (HERC), and Mingo Wilderness (MING). Since both Bailey 

Unit 1 and McClellan Unit 1 meet the three criteria that make a source BART-eligible, the fact that 

Bailey Unit 1 and McClellan Unit 1contribute to visibility impairment in a Class I area greater than 

0.5 ∆dv means that the boilers are subject to BART. 

A summary of the existing visibility impairment attributable to each boiler based on the default 

natural conditions is provided in Table 1-1. Note that the visibility impairment summarized in Table 

1-1 is based on recent modeling conducted by Trinity Consultants (Trinity) using emissions data 

based on a combination of stack testing, Continuous Emission Monitoring System (CEMS) data and 

AP-42 emission factors as further described in Section 4 of this report.

TABLE 1-1. EXISTING VISIBILITY IMPAIRMENT ATTRIBUTABLE TO BAILEY UNIT 1 AND 

MCCLELLAN UNIT 1 (2001-2003)

CACR UPBU HERC MING

Unit / Fuel Scenario

98th 

% 

∆dv

Days 

> 0.5 

∆dv

98th 

% 

∆dv

Days 

> 0.5 

∆dv

98th 

% 

∆dv

Days 

> 0.5 

∆dv

98th 

% 

∆dv

Days 

> 0.5 

∆dv

Bailey, Unit 1 – Natural 

Gas 0.083 0 0.072 0 0.073 0 0.102 0

Bailey, Unit 1 – Fuel Oil 0.330 8 0.347 7 0.367 6 0.378 12

McClellan, Unit 1 –

Natural Gas 0.125 3 0.052 0 0.040 0 0.058 0

McClellan, Unit 1 – Fuel 

Oil 0.621 22 0.266 5 0.230 2 0.227 2
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BART Analysis – Bailey and McClellan

Trinity used the EPA’s BART guidelines in 40 CFR Part 511 to determine BART for Bailey Unit 1 

and McClellan Unit 1. Specifically, Trinity conducted a five-step analysis to determine BART for 

SO2, NOx, and PM10 that included the following:

1. Identifying all available retrofit control technologies;

2. Eliminating technically infeasible control technologies;

3. Evaluating the control effectiveness of remaining control technologies;

4. Evaluating impacts and document the results;

5. Evaluating visibility impacts.

Based on the five-step analysis, the following were determined to be BART:

▲ SO2 – AECC has determined that BART for both Bailey Unit 1 and McClellan Unit 1 is 

switching fuels to fuels with 0.5% sulfur or less. 

▲ NOx – AECC has determined that the requirements of the Cross State Air Pollution Rule 

(CSAPR) satisfy BART for NOX from Bailey Unit 1and McClellan Unit 1

▲ PM10 –AECC has determined that no controls constitute BART. Neither a fuel change beyond 

that proposed for SO2 nor add-on controls are cost effective or result in an improvement to the 

visibility impairment attributable to the AECC boilers of greater than 0.011 Δdv, an insignificant 

improvement, as documented in Section 7.

 

1 The BART guidelines were published as amendments to the EPA’s RHR in 40 CFR Part 51, Section 308 on July 

6, 2005.
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BART Analysis – Bailey and McClellan

2. INTRODUCTION AND BACKGROUND

In the 1977 amendments to the Clean Air Act (CAA), Congress set a national goal to restore

national parks and wilderness areas to pristine conditions by preventing any future, and remedying 

any existing, man-made visibility impairment. On July 1, 1999, the U.S. EPA published the final 

Regional Haze Rule (RHR). The objective of the RHR is to restore visibility to pristine conditions in 

156 specific areas across the United States known as Class I areas. The CAA defines Class I areas as 

certain national parks (larger than 6,000 acres), wilderness areas (larger than 5,000 acres), national 

memorial parks (larger than 5,000 acres), and international parks that were in existence on 

August 7, 1977.

The RHR requires States to set goals that provide for reasonable progress towards achieving natural 

visibility conditions for each Class I area in their state. On July 6, 2005, the EPA published 

amendments to its 1999 RHR, often called the Best Available Retrofit Technology (BART) rule, 

which included guidance for making source-specific BART determinations. The BART rule defines 

BART-eligible sources as sources that meet the following criteria: 

(1) Have potential emissions of at least 250 tons per year of a visibility-impairing pollutant,

(2) Began operation between August 7, 1962 and August 7, 1977, and

(3) Are included as one of the 26 listed source categories in the guidance.

A BART-eligible source is subject to BART if the source is “reasonably anticipated to cause or 

contribute to visibility impairment in any federal mandatory Class I area.” EPA has determined that a 

source is reasonably anticipated to cause or contribute to visibility impairment if the 98th percentile 

visibility impacts from the source are greater than 0.5 delta deciviews (∆dv) when compared against a 

natural background.

2 Air quality modeling is the tool that is used to determine a source’s visibility 

impacts. 

Once it is determined that a source is subject to BART, a BART determination must address air 

pollution control measures for the source. The visibility regulations define BART as follows:

“...an emission limitation based on the degree of reduction achievable through the 

application of the best system of continuous emission reduction for each pollutant 

which is emitted by...[a BART-eligible source]. The emission limitation must be 

established on a case-by-case basis, taking into consideration the technology available, 

the cost of compliance, the energy and non air quality environmental impacts of 

compliance, any pollution control equipment in use or in existence at the source, the 

remaining useful life of the source, and the degree of improvement in visibility which 

may reasonable be anticipated to result from the use of such technology.

Specifically, the BART rule states that a BART determination should address the following five 

statutory factors:

1. Existing controls

2. Cost of controls

 

2 Note this is a change from the ADEQ protocol with the 2006 CENRAP data, as the original analysis for 

Arkansas reviewed the “High First High” impacts rather than the 98th percentile impacts
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BART Analysis – Bailey and McClellan

3. Energy and non-air quality environmental impacts

4. Remaining useful life of the source

5. Degree of visibility improvement as a result of controls

Further, the BART rule indicates that the five basic steps in a BART analysis can be summarized as 

follows:

1. Identify all available retrofit control technologies;

2. Eliminate technically infeasible control technologies;

3. Evaluate the control effectiveness of remaining control technologies;

4. Evaluate impacts and document the results;

5. Evaluate visibility impacts

A BART determination should be made for each visibility affecting pollutant (VAP) by following the 

five steps listed above for each VAP.

Bailey Unit 1 and McClellan Unit 1 meet the three BART-eligibility criteria described above. 

Further, the existing visibility impairment attributable to each Bailey Unit 1 and McClellan Unit 1 is 

greater than 0.5 dv in at least one Class I area. Thus, both Bailey Unit 1 and McClellan Unit 1 are

subject to BART. The details of the Bailey Unit 1 and McClellan Unit 1 existing/baseline emissions 

and the contribution of the emissions to visibility impairment can be found in Section 4. The VAPs 

emitted by Bailey Unit 1 and McClellan Unit 1 include NOx, SO2, and PM10 of various forms 

(filterable coarse particulate matter [PMc], filterable fine particle matter [PMf], elemental carbon 

[EC], inorganic condensable particulate matter [IOR CPM] as sulfates [SO4], and organic 

condensable particulate matter [OR CPM] also referred to as secondary organic aerosols [SOA]). The 

BART determinations for SO2, NOx, and PM10 can be found in Sections 5, 6, and 7, respectively. 

In accordance with the June 7, 2012 EPA published a final rule allowing states participating in the CrossState Air Pollution Rule (CSAPR) trading program to use CSAPR to satisfy BART, AECC is proposing 

to satisfy BART for NOx by complying with CSAPR at Bailey Unit 1 and McClellan Unit 1.
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BART Analysis – Bailey and McClellan

3. MODELING METHODOLOGIES AND PROCEDURES

This section summarizes the dispersion modeling methodologies and procedures applied in this 

BART analysis. All dispersion modeling has been conducted using the CALPUFF modeling system, 

consisting of the CALPUFF dispersion model, the CALMET meteorological data processor, and the 

CALPOST post-processing program. 

CALPUFF is a multi-layer, multi-species, non-steady-state puff dispersion model, which can simulate 

the effects of time and space varying meteorological conditions on pollutant transport, transformation, 

and removal. CALPUFF uses three-dimensional meteorological fields developed by the CALMET 

model. In addition to meteorological data, several other input files are used by the CALPUFF model 

to specify source and receptor parameters. The selection and control of CALPUFF options are 

determined by user-specific inputs contained in the control file. This file contains all of the necessary 

information to define a model run (e.g., starting date, run length, grid specifications, technical 

options, output options). CALPOST processes concentration, deposition, and visibility impacts based 

on pollutant specific concentrations predicted by CALPUFF. 

3.1 CALMET AND CALPUFF

The CALPUFF data and parameters are based on the 2005 BART modeling guidelines prepared for 

the Central States Regional Air Planning Association (CENRAP). The CALMET data and 

parameters are based on the modeling protocol included in Appendix C. Note that the protocol 

included in Appendix C summarizes modeling methods and procedures that were followed to predict 

visibility impairment for several BART-eligible sources located in Oklahoma as part of the BART 

analyses for these sources. In addition, several sources in Texas used the CALMET data that was 

generated in accordance with the protocol in their BART analyses. 

3.2 CALPOST

The CALPOST visibility processing completed for this BART analysis is based on the October 2010 

guidance from the Federal Land Managers Air Quality Related Values Workgroup (FLAG). The 

2010 FLAG guidance, which was issued in draft form on July 8, 2008 and published as final guidance 

in December 2010, makes technical revisions to the previous guidance issued in December 2000.

Visibility impairment is quantified using the light extinction coefficient (bext), which is expressed in 

terms of the haze index expressed in deciviews (dv). The haze index (HI) is calculated as follows:















10

(dv) 10 ln bext HI

The impact of a source is determined by comparing the HI attributable to a source relative to 

estimated natural background conditions. The change in the haze index, in deciviews, also referred to 

as “delta dv,” or ∆dv, based on the source and background light extinction is based on the following 

equation:
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BART Analysis – Bailey and McClellan  dv = 10*ln

b b

b

ext, background ext, source

ext, background

 















The Interagency Monitoring of Protected Visual Environments (IMPROVE) workgroup adopted an 

equation for predicting light extinction as part of the 2010 FLAG guidance (often referred to as the 

new IMPROVE equation). The new IMPROVE equation is as follows:

bext

         

     

         

     Site specificRayleigh Scattering 2

Small Large

4 3 Small 4 3 Large

4 4 2 Small 4 4 2 Large

1.4 Sea Salt 0.33 NO

2.8 OC 6.1 OC 10 EC 1 PMF 0.6 PMC

2.4 NH NO 5.1 NH NO

2.2 NH SO 4.8 NH SO

 

    

 

 

RH b f

f RH f RH

f RH f RH

S S

S L

S L

Visibility impairment predictions for Bailey Unit 1 and McClellan Unit 1 relied upon in this BART 

analysis used the equation shown above. The use of this equation is referred to as “Method 8” in the 

CALPOST control file. The use of Method 8 requires that one of five different “modes” be selected. 

The modes specify the approach for addressing the growth of hygroscopic particles due to moisture in 

the atmosphere. “Mode 5” has been used in this BART analysis. Mode 5 addresses moisture in the 

atmosphere in a similar way as to “Method 6”, where “Method 6” is specified as the preferred 

approach for use with the old IMPROVE equation in the CENRAP BART modeling protocol.

CALPOST Method 8, Mode 5 requires the following:

▲ Annual average concentrations reflecting natural background for various particles and for sea salt

▲ Monthly RH factors for large and small ammonium sulfates and nitrates and for sea salts

▲ Rayleigh scattering parameter corrected for site-specific elevation

Tables 3-1 to Table 3-4 below show the values for the data described above that were input to 

CALPOST for use with Method 8, Mode 5. The values were obtained from the 2010 FLAG guidance.

TABLE 3-1. ANNUAL AVERAGE BACKGROUND CONCENTRATION 

Class I Area (NH4)2SO4 NH4NO3 OM EC Soil CM Sea Salt

Rayleigh 

(Mm-1)

CACR 0.23 0.1 1.8 0.02 0.5 3 0.03 11

UPBU 0.23 0.1 1.8 0.02 0.5 3 0.03 11

HERC 0.23 0.1 1.8 0.02 0.5 3 0.02 11

MING 0.23 0.1 1.83 0.02 0.51 3.05 0.04 12
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TABLE 3-2. FL(RH) LARGE RH ADJUSTMENT FACTORS

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

CACR 2.77 2.53 2.37 2.43 2.68 2.71 2.59 2.6 2.71 2.69 2.67 2.79

UPBU 2.71 2.48 2.31 2.33 2.61 2.64 2.57 2.59 2.71 2.58 2.59 2.72

HERC 2.7 2.48 2.3 2.3 2.57 2.59 2.56 2.6 2.69 2.54 2.57 2.72

MING 2.73 2.52 2.34 2.28 2.53 2.6 2.64 2.67 2.71 2.56 2.56 2.73

TABLE 3-3. FS(RH) SMALL RH ADJUSTMENT FACTORS 

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

CACR 3.85 3.44 3.14 3.24 3.66 3.71 3.49 3.51 3.73 3.72 3.68 3.88

UPBU 3.73 3.33 3.03 3.07 3.54 3.57 3.43 3.5 3.71 3.51 3.52 3.74

HERC 3.7 3.33 3.01 3.01 3.47 3.48 3.41 3.51 3.67 3.43 3.46 3.73

MING 3.74 3.38 3.07 2.97 3.39 3.52 3.57 3.64 3.72 3.47 3.43 3.74

TABLE 3-4. FSS(RH) SEA SALT RH ADJUSTMENT FACTORS 

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

CACR 3.9 3.52 3.31 3.41 3.83 3.88 3.69 3.68 3.82 3.76 3.77 3.93

UPBU 3.85 3.47 3.23 3.27 3.72 3.78 3.69 3.7 3.84 3.64 3.67 3.86

HERC 3.86 3.51 3.23 3.22 3.66 3.72 3.69 3.73 3.81 3.57 3.65 3.88

MING 3.92 3.58 3.3 3.19 3.58 3.72 3.8 3.82 3.85 3.61 3.66 3.9
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BART Analysis – Bailey and McClellan

4. EXISTING EMISSIONS AND VISIBILITY IMPAIRMENT

This section summarizes the existing (i.e. baseline) visibility impairment attributable to Bailey Unit 1 

and McClellan Unit 1 based on air quality modeling conducted by Trinity. 

4.1 NOX, SO2, AND PM10 BASELINE EMISSION RATES

Table 4-1 summarizes the emission rates that were modeled for SO2, NOx, and PM10, including the 

speciated PM10 emissions. The SO2 and NOx emission rates are the highest actual 24-hour emission 

rates based on 2001-2003 continuous emissions monitoring system (CEMS) data – broken out to 

distinguish SO2 and NOx from burning No. 6 fuel oil and natural gas individually. 

The PM10 emission rates are from maximum burner capacities with emission factors from AP-42 1.3-

1 for No. 6 fuel oil and AP-42 1.4-2 for natural gas. 

For the No. 6 fuel oil case, the emission rates for the PM10 species shown in Table 4-1 reflect the 

breakdown of the PM10 determined from the National Park Service (NPS) “speciation spreadsheet” 

for Uncontrolled Utility Residual Oil Boilers.

3 More specifically, the NPS workbook shows the 

following baseline distributions for the PM species from No. 6 fuel oil at Bailey and McClellan, 

respectively:

▲ Coarse PM (PMC) = 24.5%, 23.9%

▲ Fine soil (modeled as PMF) = 61.0%, 64.3%

▲ Fine elemental carbon (modeled as EC) = 4.9 %, 4.8%

▲ Organic condensable PM (modeled as SOA) = 1.4%, 1.8%

▲ Inorganic condensable PM (modeled as SO4) = 8.2%, 10.0%

For the natural gas case the emission rates for the PM10 species reflect the breakdown of the filterable 

and condensable PM10 determined from AP-42 Table 1.4-2 Combustion of Natural Gas. All filterable 

PM was assumed to be elemental carbon, as this is the assumption that the NPS uses for filterable 

PM10 from natural gas fired combustion turbines. All of the condensable PM was assumed to be 

SOA, except for a small fraction of the condensable PM that was estimated to be SO4. One-third of

the estimated SO2 emissions were separated and adjusted for differences in molecular weight to 

represent SO4 emissions. This essentially double counts some of the fuel sulfur based emissions as 

SO2 but also as SO4. Since pipeline natural gas contains very little sulfur, both the SO2 and SO4

emission rates are very low.

 

3 The NPS Workbook, "Uncontrolled Utility Residual Oil Boiler.xls" updated 03/2006, was obtained from the 

NPS website: http://www.nature.nps.gov/air/Permits/ect/index.cfm. The following parameters were input into the 

workbook for speciation determination for Bailey: #6 oil with a sulfur content of 1.81%, and a heat input of 1,350 

MMBtu/hr and for McClellan: #6 oil with a sulfur content of 1.38%, and a heat input of 1,436 MMBtu/hr.
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BART Analysis – Bailey and McClellan

TABLE 4-1. BASELINE MAXIMUM 24-HOUR SO2, NOX, AND PM10 EMISSION RATES (AS HOURLY 

EQUIVALENTS)

Unit / Fuel SO2

4 NOx

5

Total 

PM10 SO4 PMc PMf SOA EC

Scenario (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)

Bailey, Unit 1 –

Natural Gas 0.5 443.8 10.2 0.3 0.0 0.0 7.4 2.5

Bailey, Unit 1 –

Fuel Oil 2,375.8 408.8 49.4 4.0 12.1 30.2 0.7 2.4

McClellan, Unit 1 –

Natural Gas 0.6 423.9 10.9 0.3 0.0 0.0 7.9 2.7

McClellan, Unit 1 –

Fuel Oil 2,747.5 579.8 48.2 4.8 11.5 28.7 0.8 2.3

4.2 BASELINE VISIBILITY IMPAIRMENT

Trinity conducted modeling to determine the visibility impairment attributable to Bailey Unit 1 and 

McClellan Unit 1 in four Class I Areas: Caney Creek Wilderness (CACR), Upper Buffalo 

Wilderness (UPBU), Hercules Glades Wilderness (HERC), and Mingo Wilderness (MING) using the 

CALPUFF dispersion model. 

Table 4-2 through Table 4-5 provide a summary of the modeled visibility impairment attributable to 

Bailey Unit 1 and McClellan Unit 1 at CACR, UPBU, HERC, and MING based on the emission rates 

shown in Table 4-1. Note that all of the CALMET, CALPUFF, and CALPOST modeling files are 

included as part of the electronic files submitted with this document. 

 

4 Hourly rates were derived from EPA’s Clean Air Market Database (CAMD) daily rates of 12 lb/day and 14 

lb/day from natural gas at Bailey and McClellan, respectively, and 57,018 lb/day and 65,940 lb/day from No. 6 fuel oil at 

Bailey and McClellan, respectively. 

5 Hourly rates were derived from EPA’s Clean Air Market Database (CAMD) daily rates of 10,650 lb/day and 

10,174 lb/day from natural gas at Bailey and McClellan, respectively, and 9,812lb/day and 13,914 lb/day from No. 6 fuel oil 

at Bailey and McClellan, respectively. 
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TABLE 4-2. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO BAILEY, UNIT 1 (2001-2003)

– NATURAL GAS

Year

Maximum

(Δdv)

98th 

Percentile

(Δdv)

No. of Day 

with Δdv ≥ 

0.5

98th 

Percentile

% SO4

98th 

Percentile

% NO3

98th 

Percentile

% PM10

98th 

Percentile

% NO2

Caney Creek Wilderness

2001 0.137 0.083 0 0.28 96.36 0.00 0.00

2002 0.219 0.075 0 0.31 95.93 0.00 0.54

2003 0.170 0.072 0 0.40 91.98 0.00 2.10

Upper Buffalo Wilderness

2001 0.089 0.04 0 0.23 95.01 0.00 1.72

2002 0.160 0.031 0 0.30 86.44 0.00 7.77

2003 0.170 0.072 0 0.29 95.02 0.00 1.26

Hercules Glades Wilderness

2001 0.238 0.056 0 0.23 96.39 0.00 0.31

2002 0.067 0.039 0 0.88 87.67 0.00 0.67

2003 0.175 0.073 0 0.22 92.76 0.00 3.35

Mingo Wilderness

2001 0.154 0.07 0 0.29 90.58 0.00 3.72

2002 0.443 0.084 0 0.43 83.07 0.00 8.58

2003 0.201 0.102 0 0.45 83.34 0.00 8.11

TABLE 4-3. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO BAILEY, UNIT 1 (2001-2003)

– FUEL OIL

Year

Maximum

(Δdv)

98th 

Percentile

(Δdv)

No. of Day 

with Δdv ≥ 

0.5

98th 

Percentile

% SO4

98th 

Percentile

% NO3

98th 

Percentile

% PM10

98th 

Percentile

% NO2

Caney Creek Wilderness

2001 0.683 0.306 2 75.75 22.53 0.68 0.44

2002 0.743 0.330 3 87.24 12.12 0.26 0.14

2003 0.969 0.326 3 98.84 0.81 0.18 0.00

Upper Buffalo Wilderness

2001 0.686 0.327 3 94.36 4.99 0.33 0.00

2002 0.667 0.303 1 73.86 21.43 1.56 1.65

2003 0.695 0.347 3 90.81 8.44 0.38 0.02

Hercules Glades Wilderness

2001 0.686 0.327 3 98.46 1.07 0.24 0.00

2002 0.634 0.248 2 80.45 18.66 0.41 0.13

2003 0.646 0.367 1 82.91 14.45 0.93 0.80

Mingo Wilderness

2001 0.522 0.354 1 89.73 8.38 0.74 0.41

2002 1.589 0.378 7 94.08 4.69 0.56 0.11

2003 0.695 0.347 3 66.29 29.30 1.26 1.88
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TABLE 4-4. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO MCCLELLAN, UNIT 1 (2001-

2003), NATURAL GAS

Year

Maximum

(Δdv)

98th 

Percentile

(Δdv)

No. of Day 

with Δdv ≥ 

0.5

98th 

Percentile

% SO4

98th 

Percentile

% NO3

98th 

Percentile

% PM10

98th 

Percentile

% NO2

Caney Creek Wilderness

2001 0.670 0.116 1 0.31 93.69 0.00 1.57

2002 0.175 0.092 0 0.55 82.94 0.00 8.15

2003 0.538 0.125 2 0.39 87.09 0.00 5.89

Upper Buffalo Wilderness

2001 0.096 0.048 0 0.38 92.78 0.00 1.41

2002 0.258 0.031 0 0.32 94.54 0.00 1.10

2003 0.112 0.052 0 0.34 91.78 0.00 3.05

Hercules Glades Wilderness

2001 0.064 0.034 0 0.29 93.50 0.00 1.79

2002 0.082 0.022 0 0.74 88.76 0.00 0.41

2003 0.092 0.04 0 0.74 86.01 0.00 3.07

Mingo Wilderness

2001 0.091 0.032 0 0.30 92.13 0.00 3.67

2002 0.132 0.058 0 0.33 91.96 0.00 2.58

2003 0.107 0.034 0 0.37 90.42 0.00 3.35

TABLE 4-5. BASELINE VISIBILITY IMPAIRMENT ATTRIBUTABLE TO MCCLELLAN, UNIT 1

(2001-2003), FUEL OIL

Year

Maximum

(Δdv)

98th 

Percentile

(Δdv)

No. of Day 

with Δdv ≥ 

0.5

98th 

Percentile

% SO4

98th 

Percentile

% NO3

98th 

Percentile

% PM10

98th 

Percentile

% NO2

Caney Creek Wilderness

2001 1.676 0.621 10 89.94 9.63 0.21 0.00

2002 1.018 0.386 3 86.53 11.33 0.69 0.74

2003 3.007 0.616 9 82.89 15.76 0.36 0.62

Upper Buffalo Wilderness

2001 0.603 0.257 2 84.16 15.04 0.42 0.01

2002 1.319 0.183 1 77.46 21.07 0.59 0.30

2003 0.598 0.266 2 98.58 0.95 0.24 0.00

Hercules Glades Wilderness

2001 0.510 0.230 1 78.80 20.24 0.50 0.01

2002 0.462 0.166 0 59.40 37.95 0.98 0.76

2003 0.660 0.209 1 76.44 20.41 1.05 1.09

Mingo Wilderness

2001 0.416 0.277 0 81.05 17.97 0.50 0.01

2002 0.546 0.212 2 59.52 37.19 0.98 1.39

2003 0.469 0.202 0 87.57 11.29 0.54 0.09
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5. SO2 BART EVALUATION

5.1 IDENTIFICATION OF AVAILABLE RETROFIT SO2 CONTROL 

TECHNOLOGIES – FUEL OIL COMBUSTION

Bailey Unit 1 and McClellan Unit 1 currently combust No. 6 fuel oil and natural gas. Because the 

SO2 emissions profile from natural gas is so small, no additional controls will be considered for

combustion of natural gas. This section concerns controlling SO2 emissions from the combustion of 

No. 6 fuel oil. 

Sulfur oxides, SOx, are generated during fuel oil combustion from the oxidation of sulfur contained in 

the fuel. SOx emissions are almost entirely dependent on the sulfur content of the fuel and are not 

affected by boiler size or burner design. SOx emission from conventional combustion systems are 

predominantly in the form of SO2. Since SO2 is the predominant sulfur compound emitted from the 

AECC boilers, the BART analysis is specific to emissions of SO2.

Step 1 of the top-down control review is to identify available retrofit control options for SO2. The 

available SO2 retrofit control technologies for the AECC boilers are summarized in Table 5-2. The 

retrofit controls include both add-on controls that eliminate SO2 after it is formed and switching to 

lower sulfur fuels which reduces the formation of SO2. 

TABLE 5-2. AVAILABLE SO2 CONTROL TECHNOLOGIES FOR BAILEY UNIT 1 AND MCCLELLAN 

UNIT 1

SO2 Control Technologies

Dry Sorbent Injection

Spray Dryer Absorber (SDA) i.e., Semi-Dry Scrubber

Wet Scrubber

Circulating Dry Scrubber (CDS)

Fuel Switching

5.2 ELIMINATE TECHNICALLY INFEASIBLE SO2 CONTROL TECHNOLOGIES

Step 2 of the BART determination is to eliminate technically infeasible SO2 control technologies that 

were identified in Step 1. 

5.2.1 DRY SORBENT INJECTION, SPRAY DRYER ABSORPTION (SDA), WET SCRUBBER,

CIRCULATING DRY SCRUBBER (CDS)

These technologies are collectively known as flue gas desulfurization (FGD) systems. 

FGD applications have not been used historically for SO2 control on oil-fired units in the 

U.S. electric industry. As there are no known FGD applications for oil-fired units, the 

performance of FGDs on oil-fired units is unknown. EPA took this into account when 

evaluating the presumptive SO2 emission rate for oil-fired units and determined that the 

presumptive emission rate should be based on the sulfur content of the fuel oil, rather than 
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on FGD rates.

6 Since there are no applications of FGD on oil-fired units in the U.S., FGDs 

are considered technically infeasible for the control of SO2 from Bailey Unit 1 and 

McClellan Unit 1 and are not considered further for BART.

5.2.2 FUEL SWITCHING 

The AECC boilers currently burn residual fuel oil with an average sulfur content of 1.81 

percent by weight at Bailey Unit 1 and 1.38 percent by weight at McClellan Unit 1. These 

values are representative of the average from 2009-2011 and are also consistent with the 

previous baseline years of 2001-2003. Switching to a fuel with lower sulfur content should 

reduce SO2 emissions in proportion to the reduction in the sulfur content of the fuel, 

assuming similar heat contents of the fuels. In the case of No. 2 fuel and natural gas with 

different fuel heat contents, NPS speciation in conjunction with sulfur content is used to 

determine the SO2 emissions. No. 2 fuel oil (diesel) is available and could be burned in the 

boilers. Lower sulfur No. 6 fuel oil is also available and could also be burned in the 

boilers. Natural gas has less sulfur than either No. 6 or No. 2 fuel oil and is a technically 

feasible option.

5.3 RANK OF TECHNICALLY FEASIBLE SO2 CONTROL OPTIONS BY 

EFFECTIVENESS

The third step in the BART analysis is to rank the technically feasible options according to 

effectiveness. Fuel switching is the only technically feasible control option. SO2 emissions from fuel 

combustion are generally proportional to the sulfur content of the fuel. For example, combusting

diesel oil (0.05 percent sulfur) should result in approximately a 96-97 percent reduction in SO2

emissions from the AECC boilers as compared to the combustion of the current No. 6 fuel oil 

(1.81 and 1.38 percent sulfur). 

Table 5-3 provides a ranking of the control levels for switching fuels in the AECC boilers.

TABLE 5-3. CONTROL EFFECTIVENESS OF TECHNICALLY FEASIBLE SO2 CONTROL 

TECHNOLOGIES

Fuel Switching to:

Estimated Control 

Efficiency

(Bailey, McClellan) 

1% sulfur No. 6 fuel oil 45%, 28%

0.5% sulfur No. 6 fuel oil 72%, 64%

0.05% sulfur diesel 97%, 96%

Natural gas 99.9%, 99.9%

 

6 Summary of Comments and Responses on the 2004 and 2001 Proposed Guidelines for Best Available Retrofit 

Technology (BART) Determinations Under the Regional Haze Regulations EPA Docket Number OAR-2002-0076.
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5.4 EVALUATION OF IMPACTS FOR FEASIBLE SO2 CONTROLS 

Step four of the BART analysis procedure is the impact analysis. The BART determination 

guidelines list the four factors to be considered in the impact analysis:

▲ Cost of compliance

▲ Energy impacts

▲ Non-air quality impacts; and

▲ The remaining useful life of the source

5.4.1 COST OF COMPLIANCE

Control Costs

The cost of the fuel switching that was used in the cost effectiveness calculations was 

determined by calculating the annual cost of the current No. 6 fuel oil and determining the 

increased cost of switching to the various lower sulfur fuels. Switching fuel to diesel will 

require changes to the burners and the fuel system. However, for this analysis capital 

expenses were not included. The details of the cost estimates are provided in Appendix A

of this report. 

As AECC currently burns both No. 6 fuel oil and natural gas at Bailey and McClellan, the 

costs for these fuels were based on historical pricing, as an average dollar per MMBtu from 

2000 to 2011. The supplier of the existing fuels (i.e., No. 6 fuel oil and natural gas)

provided cost estimates for lower sulfur No. 6 fuel oils and diesel. 

Annual Tons Reduced

The annual tons reduced used in the cost effectiveness calculations were determined by 

subtracting the estimated controlled annual emission rates from the baseline annual 

emission rates. 

The baseline and controlled annual emission rates were estimated by conducting a mass 

balance on the sulfur in the various fuels. The No. 2 fuel oil emission rate, for example, 

was determined by first using the No. 2 fuel oil heat content to determine the quantity of 

No. 2 fuel that would be used per year:

Average annual heat input from 2007-2011 / No. 2 oil heat content

 The tons per year of sulfur was calculated:

No. 2 fuel use per year * No. 2 oil density * Sulfur content in No. 2 fuel

Then the NPS speciation spreadsheet for distillate fuel was used to derive the percent of 

sulfur represented as SO4. The remaining sulfur is represented as SO2. Tables 5-4 through

and 5-8 provide a summary of the mass balance data and calculations for the future annual 

SO2 emission rates. Note that the percentage of sulfur that is indicated to be emitted as 

SO4 in both Tables 5-4 and 5-8 is based on the percentage of the baseline annual sulfur that 

was determined to be SO4, as described in Section 4.
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TABLE 5-4. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH CURRENT 

NO. 6 FUEL OIL 

Parameter Bailey McClellan

No. 6 Oil Heat Content (MMBtu/Mgal) 155 155

Fuel Use (gal/yr)

 

252,855 

 

1,882,146 

No. 6 Oil Density (lb/gal) 8.26 8.26 

Average Sulfur in No. 6 Oil (%) 1.81 1.38

Average Sulfur in No. 6 Oil (tpy) 18.90 107.27 

SO4 [Assume CPM IOR = SO4] (lb/hr) 20.90 27.80 

SO4[Assume CPM IOR = SO4] (tpy) 10.61 72.14 

SO4 as Sulfur in Fuel [Assume 1 mol S for each mol SO4] (tpy) 3.54 24.05 

% S as SO4 18.71% 22.42%

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 15.36 83.22 

% S as SO2 81.29% 77.58%

SO2 (tpy) 30.73 166.45 

TABLE 5-5. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH 1% SULFUR 

NO. 6 FUEL OIL 

Parameter Bailey McClellan

No. 6 Oil Heat Content (MMBtu/Mgal) 155 155

Fuel Use (gal/yr) 252,855 1,882,146 

No. 6 Oil Density (lb/gal) 8.26 8.26

Sulfur in No. 6 Oil (%) 1 1

Sulfur in No. 6 Oil (tpy) 10.44 77.73 

% S as SO4 12.39% 12.39%

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 9.15 68.10 

% S as SO2 87.61% 87.61%

SO2 (tpy) 18.30 136.20 
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TABLE 5-6. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH 0.5% SULFUR 

NO. 6 FUEL OIL 

Parameter Bailey McClellan

No. 6 Oil Heat Content (MMBtu/Mgal) 155 155

Fuel Use (gal/yr) 252,855 1,882,146 

No. 6 Oil Density (lb/gal) 8.26 8.26

Sulfur in No. 6 Oil (%) 0.5 0.5

Sulfur in No. 6 Oil (tpy) 5.22 38.87 

% S as SO4 18.25% 18.25%

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 4.27 31.77 

% S as SO2 81.75% 81.75%

SO2 (tpy) 8.54 63.55 

TABLE 5-7. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH DIESEL 

Parameter Bailey McClellan

No. 2 Oil Heat Content (MMBtu/Mgal) 136.15 136.15

Fuel Use (gal/yr) 287,863 2,142,730 

No. 2 Oil Density (lb/gal) 7.0 7.0

Sulfur in No. 2 Oil (%) 0.05 0.05

Sulfur in No. 2 Oil (tpy) 0.50 3.75 

% S as SO4 5.61% 5.61%

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 0.48 3.54 

% S as SO2 94.39% 94.39%

SO2 (tpy) 0.95 7.08 

TABLE 5-8. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH NATURAL GAS

Parameter Bailey McClellan

Natural Gas Heat Content (MMBtu/Mscf) 1,011.00 1,011.00

Fuel Use (scf/yr) 38,766 288,558 

Natural Gas Density (lb/scf) 0.5825 0.5798

Sulfur in N.G (%) 0.0437 0.0435

Sulfur in N.G. (tpy) 0.00 0.04 

% S as SO4 1.22% 1.33%

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 0.00 0.04 

% S as SO2 98.78% 98.67%

SO2 (tpy) 0.01 0.07 
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Cost Effectiveness

Table 5-9 presents a summary of the cost effectiveness of switching from the current No. 6 

fuel oil to the lower sulfur fuels. The cost effectiveness was determined by dividing the 

annual cost increase of fuel switching by the annual tons of SO2 reduced. Tables 5-9 and 

5-10 indicate that the cost of switching to lower sulfur No. 6 fuel oil is a minimum of 

$23,000/ton of SO2 reduced. Fuel switching to diesel is greater than $21,000/ton for 

Bailey Unit 1 and almost $28,000/ton for McClellan Unit 1. Fuel switching to natural gas 

is estimated to be nearly $2,000/ton for Bailey Unit 1 and $500/ton for McClellan Unit 1. 

Because fuel is a traded commodity, the price for fuel can vary greatly dependent upon 

factors such as supply, demand, as well as environmental and regulatory influences. The 

estimates provided by current fuel suppliers for lower sulfur fuel oils, while higher than the 

estimates provided in 2001-2003, are representative of today’s market available at Bailey 

and McClellan.7 

AECC believes for fuel switching analyses, it may not be prudent to compare pricing 

between natural gas and fuel oil due to the fuel price variability. It is important to note that

with fuel price variability the cost effectiveness values summarized above will vary from 

year to year. For instance, over the past ten years, there were periods of time when fuel oil 

was less expensive than natural gas. During those times, the cost effectiveness numbers 

would yield different results – with the natural gas cost effectiveness numbers being 

greater than the fuel oil cost effectiveness numbers.

This is demonstrated in Figure 5-1, below, which is a historical graph of costs of natural 

gas and fuel oil from years 2003 through 2012. In four out of the last ten years, natural gas 

prices have been higher than fuel oil prices.

 

7 Current vendor estimates (not quotes) for fuel oil with varying levels of sulfur include: 0.5% sulfur -

$18/MMBtu, 1.0% - $16.90/MMBtu, $1.5% - $16.50/MMBtu, 2.0% $16.00/MMbtu 
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FIGURE 5-1. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH NATURAL GAS
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TABLE 5-9. SUMMARY OF COST EFFECTIVENESS FOR FUEL SWITCHING FOR CURRENT NO. 6 FUEL OIL AT BAILEY UNIT 1

Average 

Sulfur 

ContentA

Baseline 

S O2 

Emission 

RateB

Controlled 

S O2 

Emission 

RateG

S O2 

Reduced

Baseline 

PM10 

Emission 

RateB

Controlled 

PM10 

Emission 

RateF

PM10 

Reduced

Annual Heat 

Input

Fuel Heating 

Value (HHV)C

Annual Fuel 

Usage Fuel Cost

Fuel Cost 

Differential 

from Current 

No. 6 OilD

Fuel Cost

Differential 

Cost of Fuel 

Switching

S O2 Cost 

EffectivenessE

PM10 Cost 

EffectivenessE

(%) (tpy) (tpy) (tpy) (tpy) (tpy) (tpy) (MMBtu/yr)

(MMBtu/Mgal) 

or 

(MMBtu/Mscf) (Mgal/yr) ($/MMBtu) ($/MMBtu) ($/yr) ($/yr) ($/ton) ($/ton)

Base CaseA 1.81 30.73 - - 2.51 - - 39,193 155.00 252.86 4.68 - $183,492 - - -

No. 6 - 1% 1.00 - 18.30 12.43 - 1.57 0.94 39,193 155.00 252.86 16.50 11.82 - $ 463,185 37,260 492,166

No. 6 - 0.5% 0.50 - 8.54 22.19 - 0.99 1.52 39,193 155.00 252.86 17.75 13.07 - $ 512,175 23,079 336,504

DieselA 0.05 - 0.95 29.78 - 0.03 2.48 39,193 136.15 287.86 20.95 16.27 - $ 637,592 21,411 256,578

Natural Gas - - 0.01 30.72 - 0.03 2.48 39,193 1,011.00 38.77 6.19 1.51 - $ 59,038 1,922 23,758

TABLE 5-10. SUMMARY OF COST EFFECTIVENESS FOR FUEL SWITCHING FOR CURRENT NO. 6 FUEL OIL AT MCCLELLAN UNIT 1

Average 

Sulfur 

ContentA

Baseline 

S O2 

Emission 

RateB

Controlled 

S O2 

Emission 

RateG

S O2 

Reduced

Baseline 

PM10 

Emission 

RateB

Controlled 

PM10 

Emission 

RateF

PM10 

Reduced

Annual Heat 

Input

Fuel Heating 

Value (HHV)C

Annual Fuel 

Usage Fuel Cost

Fuel Cost 

Differential 

from Current 

No. 6 OilD

Fuel Cost

Differential 

Cost of Fuel 

Switching

S O2 Cost 

EffectivenessE

PM10 Cost 

EffectivenessE

(%) (tpy) (tpy) (tpy) (tpy) (tpy) (tpy) (MMBtu/yr)

(MMBtu/Mgal) 

or 

(MMBtu/Mscf) (Mgal/yr) ($/MMBtu) ($/MMBtu) ($/yr) ($/yr) ($/ton) ($/ton)

Base CaseA 1.38 166.45 - - 14.97 - - 291,733 155.00 1882.15 5.70 - $1,663,937 - - -

No. 6 - 1% 1.00 - 136.20 30.25 - 11.68 3.29 291,733 155.00 1882.15 16.50 10.80 - 3,149,652 104,135 958,385

No. 6 - 0.5% 0.50 - 63.55 102.90 - 7.35 7.61 291,733 155.00 1882.15 17.75 12.05 - 3,514,317 34,152 461,763

DieselA 0.05 - 7.08 159.37 - 0.15 14.82 291,733 136.15 2142.73 20.95 15.25 - 4,447,862 27,909 300,217

Natural Gas - - 0.07 166.38 - 0.15 14.82 291,733 1,011.00 288.56 5.97 0.26 - 76,911 462 5,191

E Total SO2 and PM10 Cost Effectiveness for fuel switching to distillate includes yearly capital recovery costs associated with necessary infrastructure changes.

F

 Reductions in PM Species is based on default NPS profile.

G

 Controlled SO2 emission rate for natural is based on a 99.98% control efficiency from the current IFO (as seen in comparison of baseline emission rates of burning natural gas vs. IFO).

A Sulfur content of base case No. 6 fuel oil based on average of fuel burned in 2009- 2011. Sulfur content of diesel based on average sulfur in diesel burned at AECC Fitzhugh plant during the same timeframe since 

diesel is not burned at Bailey or McClellan. 

D

 The price differential for natural gas based on 10-year avarage pricing of current IFO and natural gas at Bailey and McClellan, each. The price differential for distillate based on distillate currently received at Fitzhugh.

C

 Higher heating value of residual oil based on average of fuel burned from supplier. Higher heating value of distillate oil average from Fitzhugh plant. Higher heating value of natural gas from 6.23.11 Bailey gas 

analysis and 7.12.11 gas analysis.

B The Baseline SO2 Emission Rate was developed using an average fuel usage from 2007 and 2011, the average heat content of the No. 6 fuel oil during that same time as well as the average sulfur contect of the fuel 

during that time. The Baseline PM10 Emission Rates are representative of emission rates determined during stack testing in 2010 and then scaled to reflect the maximum heat input for each boiler.
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5.4.2 ENERGY IMPACTS AND NON-AIR QUALITY IMPACTS

There are no energy or non-air quality impacts associated with fuel switching to 1% sulfur 

No. 6 fuel oil, 0.5% sulfur No. 6 fuel oil, or diesel. Switching to natural gas may have an 

impact during periods of natural gas curtailments. However, temporary permitted use of 

fuel oil would provide for electric grid reliability. The ability to burn fuel oil at both 

Bailey and McClellan is important – even if fuel oil is more expensive and difficult to burn 

than natural gas. During natural gas curtailments, natural gas infrastructure maintenance, 

and other emergencies, AECC relies on the fuel oil stored at the plants to maintain 

electrical reliability. 

5.4.3 REMAINING USEFUL LIFE

The remaining useful lives of Bailey Unit 1 and McClellan Unit 1 do not impact the 

annualized capital costs since it is assumed that fuel switching will not require any 

significant capital costs, and thus for the purpose of this analysis there is nothing to 

capitalize that would require a review of the life of the equipment.

5.5 EVALUATION OF VISIBILITY IMPACT OF FEASIBLE SO2 CONTROLS 

A final impact analysis was conducted to assess the visibility improvement associated with switching 

fuels. Tables 5-11 and 5-12 summarize the lb/hr emission rates that were modeled to reflect fuel 

switching as a control at Bailey and McClellan, respectively. The SO2 emission rate in lb/MMBtu 

associated with the combustion of a particular fuel was calculated by scaling the existing rolling 30-

day average emission rate from 2001 to 2003 by the ratio of the sulfur content of the new fuel and the 

current maximum annual average sulfur content from 2009-2011. 

The controlled 30-day lb/MMBtu was converted to lb/hr by dividing by the boiler design heat input. 

The calculation of the SO2 emission rate for the one percent sulfur fuel oil for Bailey Unit 1 is 

provided for an example:

Sulfur

Sulfur lb MMBtu

1.81%

(1.81% 1%) 1.592 / *

= 0.880 lb/MMBtu

0.880lb / MMBtu *1,350MMBtu / hr

= 1,187.62 lb/hr

The SO4 emission rate was determined assuming the reduction in SO4 is proportional to the reduction 

in SO2 from the baseline case to the controlled case. The NOx emission rate was modeled at the 

baseline rate. The PM species was modeled the same as the baseline No. 6 fuel oil case.8 

 

8 Modeled controlled emission rates of PMC, PMF and PM10, SOA, and EC, the speciated PM pollutants, for the 

lower sulfur oil scenarios are conservatively assumed to equal the “high” or baseline sulfur oil scenario. 
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TABLE 5-11. SUMMARY OF EMISSION RATES MODELED TO REFLECT FUEL SWITCHING FOR SO2

CONTROL AT BAILEY UNIT 1

Bailey Unit 1

SO2

(lb/hr)

SO4

(lb/hr)

NOx

(lb/hr)

PMC

(lb/hr)

PMF

(lb/hr)

SOA

(lb/hr)

EC

(lb/hr)

PM10, total

(lb/hr)

1% sulfur fuel oil 

No. 6 1,187.6 2.0 408.8 12.1 30.2 0.7 2.4 49.4

0.5% sulfur fuel 

oil No. 6 593.8 1.0 408.8 12.1 30.2 0.7 2.4 49.4

Diesel 59.4 0.1 408.8 12.1 30.2 0.7 2.4 49.4

Natural gas 0.5 0.3 443.8 0.0 0.0 7.4 2.5 10.2

TABLE 5-12. SUMMARY OF EMISSION RATES MODELED TO REFLECT FUEL SWITCHING FOR SO2

CONTROL AT MCCLELLAN UNIT 1

McClellan Unit 1

SO2

(lb/hr)

SO4

(lb/hr)

NOx

(lb/hr)

PMC

(lb/hr)

PMF

(lb/hr)

SOA

(lb/hr)

EC

(lb/hr)

PM10, total

(lb/hr)

1% sulfur fuel oil 

No. 6 2,317.1 4.0 579.8 11.5 28.7 0.8 2.3 48.2

0.5% sulfur fuel 

oil No. 6 1,158.5 2.0 579.8 11.5 28.7 0.8 2.3 48.2

Diesel 115.9 0.2 579.8 115.5 28.7 0.8 2.3 48.2

Natural gas 0.6 0.3 423.9 0.0 0.0 7.9 2.7 10.9

Visibility improvement was evaluated by comparing the visibility impairment from the baseline 

scenario to the impairment for a control scenario. The baseline rate used to establish the baseline 

visibility impairment reflects a peak 24-hour emission rate. Thus, it would make sense that the 

emission rates used in control scenarios would represent the peak emission rates associated with the 

controls. That being said, control effectiveness is typically not evaluated on a 24-hour basis. 

Typically, control effectiveness for EGUs for NOX/SOX is based on a longer term performance, with 

30-day being standard. While using rolling 30-day average emissions rates gives a lower emission 

rate than using peak rates, this methodology of comparing the peak to average is consistent with other 

accepted BART methodologies. 

Comparisons of the existing visibility impacts and the visibility impacts based on fuel switching, 

including the maximum modeled visibility impact, 98th percentile modeled visibility impact, and the 

number of days with a modeled visibility impact greater than 0.5 Δdv, for each Class I area are 

provided in Tables 5-13 and 5-14. The visibility improvement associated with fuel switching was 

calculated as the difference between the existing visibility impairment and the visibility impairment 

for the various fuels as measured by the 98th percentile modeled visibility impact. 
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TABLE 5-13. SUMMARY OF MODELED IMPACTS FROM SO2 CONTROL VISIBILITY IMPACT ANALYSIS FOR BAILEY UNIT 1 (2001-2003)

Caney Creek Wilderness Upper Buffalo Wilderness Hercules Glades Wilderness Mingo NWR Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv

Visibility Improvement*

Baseline Emission Rate –

(fuel oil) 0.969 0.330 8 - 0.695 0.347 7 - 0.686 0.367 6 - 1.589 0.378 12 -

1% sulfur fuel oil No. 6 0.547 0.195 1 40.91% 0.380 0.196 0 43.52% 0.411 0.208 0 43.32% 1.014 0.211 2 44.18%

0.5% sulfur fuel oil No. 6 0.340 0.145 0 56.06% 0.234 0.132 0 61.96% 0.286 0.140 0 56.06% 0.716 0.178 1 52.91%

Diesel 0.217 0.087 0 73.64% 0.161 0.074 0 78.67% 0.228 0.073 0 80.11% 0.441 0.100 0 73.54%

Natural gas 0.219 0.083 0 74.85% 0.170 0.072 0 79.25% 0.238 0.073 0 80.11% 0.443 0.102 0 73.02%

*Improvement is based on the 98th percentile impact (Δdv) for the control scenario compared to the 98th percentile impact (Δdv) baseline impact (Δdv). 

†The visibility improvement shown in the table has been calculated from 98th percentile baseline and controlled impacts that include more decimal places than 

what is shown in the table. Due to rounding of the baseline and controlled 98th percentile impacts shown in the table, the visibility improvement calculated from 

the baseline and controlled 98th percentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.
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TABLE 5-14. SUMMARY OF MODELED IMPACTS FROM SO2 CONTROL VISIBILITY IMPACT ANALYSIS FOR MCCLELLAN UNIT 1 (2001-2003)

Caney Creek Wilderness Upper Buffalo Wilderness Hercules Glades Wilderness Mingo NWR Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv

Visibility Improvement*

Baseline Emission Rate 3.007 0.621 22 - 1.319 0.266 5 - 0.660 0.230 2 - 0.546 0.227 2 -

1% sulfur fuel oil No. 6 2.675 0.538 18 13.37% 1.172 0.232 4 12.78% 0.563 0.202 1 12.17% 0.479 0.194 0 14.54%

0.5% sulfur fuel oil No. 6 1.737 0.325 8 47.67% 0.767 0.147 1 44.74% 0.296 0.117 0 49.13% 0.328 0.139 0 38.77%

Diesel 0.934 0.183 4 70.53% 0.391 0.076 0 71.43% 0.138 0.063 0 72.61% 0.194 0.083 0 71.43%

Natural gas 0.670 0.125 3 79.87% 0.258 0.052 0 80.45% 0.092 0.040 0 82.61% 0.132 0.058 0 74.45%

*Improvement is based on the 98th percentile impact (Δdv) for the control scenario compared to the 98th percentile impact (Δdv) baseline impact (Δdv)

†The visibility improvement shown in the table has been calculated from 98th percentile baseline and controlled impacts that include more decimal places than 

what is shown in the table. Due to rounding of the baseline and controlled 98th percentile impacts shown in the table, the visibility improvement calculated from 

the baseline and controlled 98th percentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.
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As shown in Table 5-13, based on visibility predictions from the CALPUFF modeling system, fuel 

switching at Bailey Unit 1 will result in up to a 44.18, 61.96, 80.11 or 80.11 percent improvement

(depending on the Class I area) to the existing visibility impairment attributable to this unit for fuel 

switching to 1% sulfur fuel oil, 0.5% sulfur fuel oil, diesel and natural gas, respectively. Please note 

that despite the varying levels of percent visibility improvement, the number of days of visibility 

impairment >Δ0.5 dv is 0 in many of the control cases. For example, at Hercules Glades Wilderness 

there are 0 days of visibility impairment greater than >Δ0.5 dv for the 1% sulfur fuel oil and also for 

the natural gas control although the visibility improvement varies from 43.52% to 79.25%.

As shown in Table 5-14, based on visibility predictions from the CALPUFF modeling system, fuel 

switching at McClellan Unit 1 will result in up to a 14.54, 47.68, 72.61, or 82.61 percent 

improvement (depending on the Class I area) to the existing visibility impairment attributable to this 

unit for fuel switching to 1% sulfur fuel oil, 0.5% sulfur fuel oil, diesel and natural gas, respectively. 

5.6 PROPOSED BART FOR SO2

AECC has determined that BART for Bailey Unit 1 and McClellan Unit 1 is fuel switching to fuels 

that have a sulfur content of 0.5% or less. As mentioned in the Section 5.5 of this report, fuel with a 

sulfur content of 0.5% or less will have visibility improvements in Class I areas of up to 61.96% for 

Bailey and 47.68% for McClellan. 

When the BART limits become effective, Bailey and McClellan would burn the existing supply of 

No. 6 fuel oil as the normal course of business dictates and in accordance with any operating 

restrictions enforced by ADEQ. Future fuel purchases will be fuels of 0.5% sulfur content or less. 

While EPA might have some hesitation comparing the visibility impairment from the baseline 

scenario on a peak 24-hour basis to visibility impairment due to control effectiveness on a 30-day 

rolling average basis, the increased visibility improvement did not have a significant bearing on 

AECC selecting to burn 0.5% sulfur fuel oil going forward. Because burning fuel oil is necessary in 

addition to using natural gas from a grid reliability perspective, AECC had to select a lower sulfur 

fuel oil than currently received fuel oil. And because the cost/ton of the 0.5% sulfur is lower than for 

1% sulfur, 0.5% sulfur is the appropriate option. 

Because AECC will not be limited to a lb/MMBtu limit but by a percent sulfur limit, revised 

modeling reflecting slightly different control rates should not impact the decision that 0.5% sulfur is 

BART for the AECC boilers.
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6. NOX BART EVALUATION

On June 7, 2012 EPA published a final rule allowing states participating in the Cross-State Air 

Pollution Rule (CSAPR) trading program to use CSAPR to satisfy BART. Arkansas is one of the 

states with units subject to CSAPR that will participate in a NOx trading program during the ozone 

season. EPA commented that “NOx control in the five ozone season-only states is achieved 

predominantly by combustion controls.”9 Due to the nature of combustion controls, plants typically 

keep combustion controls in place and running year-round, even if emission limitations are seasonal. 

Although Arkansas is an ozone season-only state, units with combustion controls would run anytime 

the unit is in operation. 

An email dated June 28, 2012 from ADEQ stated, “ADEQ agrees CSAPR is better than BART and 

the subject-to-BART sources do not need to include NOx in their five-factor analysis.”10 Therefore, 

AECC is not including NOx analyses in the Bailey and McClellan five-factor analyses.

On July 6, 2012 EPA published a final rule of the Nebraska Regional Haze Federal Implementation 

Plan (FIP).11 Nebraska is subject to CSAPR for annual SO2 and NOx. The FIP reviewed the 

Nebraska suggest BART for NOx, but ultimately stated that because CSAPR satisfies BART, CSAPR 

controls will equate with BART in the state.12 

AECC is proposing to satisfy BART for NOx by complying with CSAPR at Bailey Unit 1 and 

McClellan Unit 1.

 

9 “Regional Haze: Revisions to Provisions Governing Alternatives to Source-Specific Best Available Retrofit 

Technology (BART) Determination, Limited SIP Disapprovals, and Federal Implementation Plans.” CFR Vol. 77, No. 110. 

Thursday, June 7, 2012, Rules and Regulations. Page 33651.

10 Email from Mary Pettyjohn of ADEQ to subject-to-BART unit operators dated June 28, 2012. 

11 “Approval, Disapproval and Promulgation of Implementation Plans; State of Nebraska; Regional Haze State 

Implementation Plan; Federal Implementation Plan for Best Available Retrofit Technology Determination.” CFR Vol. 77, 

No. 130. Friday, July 6, 2012. Page 40150.

12 Ibid, 40151.
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7. PM10 BART EVALUATION

7.1 IDENTIFICATION OF AVAILABLE RETROFIT PM10 CONTROL 

TECHNOLOGIES

PM10 emissions are either “filterable” or “condensable”. Filterable PM10 is generally considered to be 

particles less than or equal to 10 microns in diameter that are trapped by a filter during testing of 

exhaust gas. Condensable PM is material that is emitted in the vapor state but that condenses in the 

atmosphere to form particles. Filterable PM10 emissions from fuel oil combustion depend 

predominantly on the grade of fuel oil fired. Combustion of lighter distillate oils results in 

significantly lower PM10 formation than does combustion of heavier residual oils. Among residual 

oils, firing of No. 4 or No. 5 oil usually produces less PM10 than does the firing of heavier residual oil. 

This is due to the higher ash and sulfur contents of residual oil compared to lighter oils. 

Step 1 of the BART determination is the identification of all available retrofit PM10 control 

technologies. The available retrofit PM10 control technologies are summarized in Table 6-2 for

Bailey Unit 1 and McClellan Unit 1. 

TABLE 7-1. AVAILABLE PM CONTROL TECHNOLOGIES FOR BAILEY UNIT 1 AND MCCLELLAN 

UNIT 1

PM10 Control Technologies

Dry Electrostatic Precipitator (ESP)

Wet Electrostatic Precipitator (ESP)

Fabric Filter

Wet Scrubber

Cyclone

Fuel Switching 

7.2 ELIMINATE TECHNICALLY INFEASIBLE PM CONTROL TECHNOLOGIES

Step 2 of the BART determination is to eliminate technically infeasible PM control technologies that 

were identified in Step 1. 

7.2.1 DRY ELECTROSTATIC PRECIPITATORS (ESP)

A dry ESP operates by first placing a charge on the particles through a series of electrodes, 

and then capturing the charged particles on collection plates. Particles from oil-fired 

boilers tend to be sticky and small. Because of these properties and a general lack of 

existence in practice, a dry ESP is not a good technological match for either Bailey Unit 1 

or McClellan Unit 1. 
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7.2.2 WET ELECTROSTATIC PRECIPITATORS (ESP)

A wet ESP operates similarly to a dry ESP but is a better technological match for oil-fired 

boilers because it is not sensitive to small and sticky particulates. A wet ESP utilizes water 

to collect and remove the particles, and will produce a waste-water product. Flue gas 

leaving wet ESPs will be saturated and may result in a visual steam plume exiting the 

stack. The estimated PM control efficiency is up to 90% for a wet ESP.

13 Wet ESP is a 

technically feasible option for control of PM10 for Bailey Unit 1 and McClellan Unit 1.

7.2.3 MECHANICAL COLLECTORS (CYCLONES)

Mechanical collectors, or cyclones, control particulates generated during soot blowing, 

during upset conditions, or when a heavy oil is fired. For these situations, high-efficiency 

cyclonic collectors can achieve up to 85% control of particulate.

14 This control is designed 

for the larger PM size fractions, and thus, when firing residual oil, the control will not be 

effective at controlling the smaller particles that are a source of visibility impairment. 

Further, when a clean oil is combusted, cyclonic collectors are not nearly so effective 

because of the high percentage of small particles (less than 3 micrometers in diameter) 

emitted. 

7.2.4 FABRIC FILTER

Fabric filters work by filtering the PM in flue gas through filter bags. The collected 

particles are periodically removed from the bag through a pulse jet or reverse flow 

mechanism. Due to the sticky nature of particles from oil-fired boilers and the associated 

hazard from flammability of their use, fabric filters are not used to control PM from boilers 

firing residual oil. Thus, fabric filters are not technically feasible for Bailey Unit 1 and 

McClellan Unit 1.

7.2.5 WET SCRUBBER

Wet scrubbers remove PM from flue gas by contacting it with a scrubbing liquid using one 

of several approaches: spraying the gas stream with the liquid, forcing the gas stream 

through a pool of liquid, or by some other contact method. PM in the gas stream is 

captured in the scrubbing liquid. The PM-laden scrubbing liquid is separated from the gas 

stream, and the resultant scrubbing liquid is treated prior to discharge or reuse in the plant. 

Problems associated with scrubbers include corrosion issues, high power requirements, and 

water-disposal challenges. However, the use of wet scrubbers for Bailey Unit 1 and 

McClellan Unit 1 is considered a technically feasible option. The estimated PM10 removal 

efficiency for a wet scrubber is 50-60%.15 

While wet scrubbers are considered technically feasible, it is worth noting the wet 

scrubbers are not very efficient at controlling submicron size particles. When drops of 

 

13 Ibid.

14 AP-42, Fifth Edition, Volume I, Chapter 1, Section 1.3.4.1

15 AP-42, Fifth Edition, Volume I, Chapter 1, Section 1.3.4.1
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water are suspended in a stream of air containing particles, such as they are in wet 

scrubbers, the air must go around the drops to pass through the scrubber. This creates 

streamlines of higher velocity air near each drop. For particles to be captured, they must 

push through these streamlines to the surface of the drop. Particles that are smaller than 1

micron are hardest to control because they follow the streamlines and avoid contact with 

the drop. As particle size decreases, more energy is needed to force contact with the drops. 

This makes conventional scrubbers ineffective for particles smaller than a few microns.16

While the majority of the PM emissions for Bailey Unit 1 and McClellan Unit 1 are not 

less than a few microns, particles of this size have the highest ability to impair visibility; 

thus, a wet scrubber may not be effective at controlling the particles that have the greatest 

ability to impair visibility.

7.2.6 FUEL SWITCHING 

Residual oil has inherent ash that contributes to the emissions of filterable PM. Lower 

grades of fuel oil have less ash and ultimately lower filterable PM emissions. Filterable 

PM emissions could be reduced by switching to a lower grade fuel oil or natural gas. 

Section 5 discussed the option of fuel switching with respect to reducing SO2 emissions. 

Distillate fuel oil has only trace amounts of ash.

17 It is estimated that filterable PM10

emissions would be reduced in proportion to the reduction in ash content. Based on the 

reduction in ash content, reductions of filterable PM10 would be expected to be greater than

99%. Reductions in filterable PM10 in No. 6 fuel oil are directly related to the sulfur 

content of the fuel, as seen in AP-42, 1.3-1. The percent reduction in filterable PM10 from 

fuel switching to natural gas is estimated from the reduced ash content in natural gas (trace 

amount) as compared to current No. 6 fuel, 0.035% ash content, for 99% control 

efficiency. 

7.3 RANK OF TECHNICALLY FEASIBLE PM10 CONTROL OPTIONS BY 

EFFECTIVENESS

Step 3 of the top-down control review is to rank the technically feasible options according to 

effectiveness. Table 7-3 provides a ranking of the control levels for the controls listed in the 

previous section.

 

16 http://www.tri-mer.com/q&a/comparing-electrostatic-precipitator.htm

17 Combustion-Fossil Power Systems, J.G. Singer published by Combustion Engineer, Inc,
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TABLE 7-3. CONTROL EFFECTIVENESS OF TECHNICALLY FEASIBLE PM CONTROL 

TECHNOLOGIES

Control Technology

Control Efficiency18 

(%)

Fuel Switching ≤99%

Wet ESP ≤90

Cyclone 85%

Wet Scrubber 55%

7.4 EVALUATION OF IMPACTS FOR FEASIBLE PM10 CONTROLS 

Step four for the BART analysis procedure is the impact analysis. The BART determination 

guidelines list the four factors to be considered in the impact analysis:

▲ Cost of compliance

▲ Energy impacts

▲ Non-air quality impacts; and

▲ The remaining useful life of the source

7.4.1 COST OF COMPLIANCE

The capital costs, operating costs, and cost effectiveness of wet ESPs, cyclones and wet 

scrubbers have been estimated for Bailey Unit 1 and McClellan Unit 1. The cost 

effectiveness of fuel switching to 1% sulfur fuel oil, 0.5% sulfur fuel oil, diesel and natural 

gas has also been estimated. 

Control Costs

The capital and operating costs of the wet ESP and wet scrubber were prepared by AECC 

using Electric Power Research Institute’s (EPRI) IECCOST Software, and cyclone 

estimates were derived from EPA estimates. The capital costs were annualized over a 15-

year period and then added to the annual operating costs to obtain the total annualized 

costs. The details of the capital and operating cost estimates are provided in Appendix B 

of this report. 

Annual Tons Reduced

The annual tons reduced that were used in the cost effectiveness calculations were 

determined by subtracting the estimated controlled annual emission rates from the baseline 

annual emission rates, as calculated from AP-42: 1.3-1. The controlled annual emission 

rates were estimated by reducing the existing annual emission rate by the control 

percentages shown in Table 7-3.

 

18AP-42, Fifth Edition, Volume I, Chapter 1, Section 1.3.4.1, 9.19(S)+3.22. For Bailey, the average sulfur 

content of fuel delievered in 2009-2011 was 1.81%, and the average fuel usage was 252,855 gal. For McClellan these 

values were 1.38% sulfur and 1,882,146 gal. Bailey: (9.19*(1.81)+3.22)*(252,855*103/200) = 2.51 tpy. McClellan: 

(9.19*(1.38)+3.22)*(1,882,146*103/200) = 14.97 tpy
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Cost Effectiveness

The cost effectiveness was determined by dividing the annualized cost by the annual tons 

reduced. The costs effectiveness analysis is summarized in Tables 7-4 and 7-5. 

Table 7-4 indicates that the cost effectiveness of switching to natural gas is over $5,000/ton 

for each boiler. Further, Tables 7-4 and 7-5 indicate that the cost effectiveness for all other 

controls is excessively expensive at $300,217/ton for fuel switching to diesel at McClellan 

Unit 1 to $36,326,871/ton for a wet scrubber at Bailey Unit 1.

7.4.1.1 ENERGY IMPACTS AND NON-AIR QUALITY IMPACTS

There are no energy or non-air quality impacts associated with fuel switching, 

but there are impacts associated with wet ESPs and wet scrubbers. ESPs by 

design apply energy to the particles they are collecting. This energy usage can 

be significant, especially if the wet ESP is designed to control submicron size 

particles where more energy is applied to collect more of the particles. Wet 

scrubbers also require a substantial amount of energy to force exhaust gases 

through the scrubber. 

Both wet ESPs and wet scrubber generate wastewater streams that must either 

be treated on-site or sent to a waste water treatment plant. Further, the 

wastewater treatment process will generate a filter cake that would likely 

require land-filling.

7.4.1.2 REMAINING USEFUL LIFE

The remaining useful lives of Bailey Unit 1 and McClellan Unit 1 do not 

impact the annualized capital costs of the wet ESP, wet scrubber, or cyclone

because the useful life of the boilers is anticipated to be at least as long as the 

capital cost recovery period, which is 15 years. Further, the remaining useful 

lives of Bailey Unit 1 and McClellan Unit 1 do not impact the annualized fuel 

cost, since it is assumed that fuel switching will not require any capital costs, 

and thus there is nothing to capitalize that would require a review of the life of 

the equipment.
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TABLE 7-4. SUMMARY OF COST EFFECTIVENESS FOR BAILEY UNIT 1 PM10 CONTROLS

Baseline 

Emission 

Rate

Control 

Efficiency

Annual Heat 

InputA

Controlled 

Emission Rate P M10 Reduced Capital Cost Total Annual Cost

Cost 

Effectiveness

(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) ($/ton)

Wet ESP 2.51 90.00 39,193 0.25 2.26 105,141,431 22,638,340 10,021,076

Wet Scrubber 2.51 55.00 39,193 1.13 1.38 140,957,713 50,150,862 36,326,871

Cyclone 2.51 85.00 39,193 0.38 2.13 989,479 1,188,630 557,109

No. 6 Fuel Oil - 1% 2.51 - 39,193 1.57 0.94 - 463184.75 492,166

No 6. Fuel Oil - 0.5% 2.51 - 39,193 0.99 1.52 - 512175.45 336,504

Diesel 2.51 - 39,193 0.03 2.48 - 637591.63 256,578

Natural Gas 2.51 - 39,193 0.03 2.48 - 59038.24 23,758

A

 Annual Heat Input derived for 2007-2011 average fuel usage times the heat content of No. 6 fuel oil

TABLE 7-5. SUMMARY OF COST EFFECTIVENESS FOR MCCLELLAN UNIT 1 PM10 CONTROLS

Baseline 

Emission 

Rate

Control 

Efficiency

Annual Heat 

InputA

 Controlled 

Emission Rate P M10 Reduced Capital Cost Total Annual Cost

Cost 

Effectiveness

(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) ($/ton)

Wet ESP 14.97 90.00 291,733 1.50 13.47 151,509,333 32,605,907 2,420,880

Wet Scrubber 14.97 55.00 291,733 6.73 8.23 146,303,011 52,056,542 6,324,586

Cyclone 14.97 85.00 291,733 2.24 12.72 1,432,971 1,721,384 135,325

No. 6 Fuel Oil - 1% 14.97 - 291,733 11.68 3.29 - 3149651.56 958,385

No 6. Fuel Oil - 0.5% 14.97 - 291,733 7.35 7.61 - 3514317.35 461,763

Diesel 14.97 - 291,733 0.15 14.82 - 4447861.76 300,217

Natural Gas 14.97 - 291,733 0.15 14.82 - 76911.33 5,191

A

 Annual Heat Input derived for 2007-2011 average fuel usage times the heat content of No. 6 fuel oil
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7.5 EVALUATION OF VISIBILITY IMPACT OF FEASIBLE PM10 CONTROLS 

A final impact analysis was conducted to assess the visibility improvement associated with wet ESPs,

wet scrubbers, and cyclones. Note that fuel switching has impacts on both SO2 and PM, as shown in 

Section 5 of this report. Section 4 of this report documented the existing visibility impairment 

attributable to Bailey Unit 1 and McClellan Unit 1. 

In order to assess the visibility improvement associated with wet ESPs, scrubbers, and cyclones the 

maximum short-term PM10 emission rates associated with these controls were modeled using 

CALPUFF. The maximum short-term PM10 emission rates associated with wet ESPs, scrubbers, and 

cyclones were calculated by reducing the uncontrolled yearly PM10 emission rates, in Table 7-4, by 

the control percentages shown in Table 7-3. Tables 7-5 through 7-7 summarize the emission rates 

that were modeled to reflect the wet ESPs, wet scrubbers, and cyclones, respectively. The emission 

rates for the pollutants shown in Tables 7-5 through 7-7 for NOX and SO2 that are not PM are the 

same as in the baseline modeling. 

TABLE 7-5. SUMMARY OF EMISSION RATES MODELED TO REFLECT WET ESP FOR 

PM10 CONTROL

Unit

SO2

(lb/hr)

SO4

(lb/hr)

NOX

(lb/hr)

PMC

(lb/hr)

PMF

(lb/hr)

SOA

(lb/hr)

EC

(lb/hr)

PM10, total

(lb/hr)

Bailey Unit 1 2,375.8 0.4 408.8 1.2 3.0 0.1 0.2 4.9

McClellan Unit 1 2,747.5 0.5 579.8 1.2 2.9 0.1 0.2 4.8

TABLE 7-6. SUMMARY OF EMISSION RATES MODELED TO REFLECT WET SCRUBBER FOR 

PM10 CONTROL

Unit

SO2

(lb/hr)

SO4

(lb/hr)

NOX

(lb/hr)

PMC

(lb/hr)

PMF

(lb/hr)

SOA

(lb/hr)

EC

(lb/hr)

PM10, total

(lb/hr)

Bailey Unit 1 2,375.8 1.8 408.8 5.5 13.6 0.3 1.1 22.2

McClellan Unit 1 2,747.5 2.2 579.8 5.2 12.9 0.4 1.0 21.7

TABLE 7-7. SUMMARY OF EMISSION RATES MODELED TO REFLECT CYCLONE FOR 

PM10 CONTROL

Unit

SO2

(lb/hr)

SO4

(lb/hr)

NOX

(lb/hr)

PMC

(lb/hr)

PMF

(lb/hr)

SOA

(lb/hr)

EC

(lb/hr)

PM10, total

(lb/hr)

Bailey Unit 1 2,375.8 4.0 408.8 1.8 4.5 0.7 0.4 7.4

McClellan Unit 1 2,747.5 4.8 579.8 1.7 4.3 0.8 0.3 7.2

Comparisons of the existing visibility impacts and the visibility impacts for PM-specific controls, 

excluding fuels switching which are included in Section 5, including the maximum modeled visibility 

impact, 98th percentile modeled visibility impact, and the number of days with a modeled visibility 

impact greater than 0.5 Δdv, for each Class I area are provided in Tables 7-8 and 7-9. The visibility 
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BART Analysis – Bailey and McClellan

improvement associated with PM controls was calculated as the difference between the existing 

visibility impairment and the visibility impairment for the control as measured by the 98th percentile 

modeled visibility impact. 
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TABLE 7-8. SUMMARY OF MODELED IMPACTS FROM PM10 CONTROL VISIBILITY IMPACT ANALYSIS FOR BAILEY UNIT 1 (2001-2003)

Caney Creek Wilderness Upper Buffalo Wilderness

Hercules Glades 

Wilderness Mingo NWR

Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*

Baseline Emission Rate 0.969 0.330 8 - 0.695 0.347 7 - 0.686 0.367 6 - 1.589 0.378 12 -

Wet ESP 0.961 0.327 8 0.91% 0.687 0.343 6 1.15% 0.677 0.356 5 3.00% 1.572 0.371 12 1.85%

Wet Scrubber 0.964 0.328 8 0.61% 0.690 0.345 6 0.58% 0.681 0.360 5 1.91% 1.579 0.374 12 1.06%

Cyclone 0.965 0.328 8 0.61% 0.691 0.345 7 0.58% 0.682 0.361 5 1.63% 1.580 0.374 12 1.06%

*Improvement is based on the 98th percentile impact (Δdv) for the control scenario compared to the 98th percentile impact (Δdv) baseline impact (Δdv)

†The visibility improvement shown in the table has been calculated from 98th percentile baseline and controlled impacts that include more decimal places than 

what is shown in the table. Due to rounding of the baseline and controlled 98th percentile impacts shown in the table, the visibility improvement calculated from 

the baseline and controlled 98th percentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.
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TABLE 7-9. SUMMARY OF MODELED IMPACTS FROM PM10 CONTROL VISIBILITY IMPACT ANALYSIS FOR MCCLELLAN UNIT 1 (2001-2003)

Caney Creek Wilderness Upper Buffalo Wilderness

Hercules Glades 

Wilderness Mingo NWR

Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*Maximum Impact (Δdv)98% Impact (Δdv)# Days > 0.5 ΔdvVisibility Improvement*

Baseline Emission Rate 3.007 0.621 22 - 1.319 0.266 5 - 0.660 0.230 2 - 0.546 0.227 2 -

Wet ESP 2.977 0.617 21 0.64% 1.305 0.263 5 1.13% 0.656 0.227 2 1.30% 0.540 0.223 2 1.76%

Wet Scrubber 2.989 0.619 21 0.32% 1.311 0.264 5 0.75% 0.657 0.228 2 0.87% 0.542 0.224 2 1.32%

Cyclone 2.993 0.619 21 0.32% 1.313 0.265 5 0.38% 0.658 0.229 2 0.43% 0.543 0.225 2 0.88%

*Improvement is based on the 98th percentile impact (Δdv) for the control scenario compared to the 98th percentile impact (Δdv) baseline impact (Δdv)

†The visibility improvement shown in the table has been calculated from 98th percentile baseline and controlled impacts that include more decimal places than 

what is shown in the table. Due to rounding of the baseline and controlled 98th percentile impacts shown in the table, the visibility improvement calculated from 

the baseline and controlled 98th percentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.
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As shown in Table 7-8, the operation of wet ESPs results in an estimated 0.003 to 0.004 Δdv 

improvement (0.64 to 1.76 percent) of the 98th percentile visibility impairment attributable to Bailey 

Unit 1 at the applicable Class I areas. Further, as shown in Table 7-8, the operation of wet scrubbers 

results in an estimated 0.002 to 0.003 Δdv improvement (0.32 to 1.32 percent) of the 98th percentile 

visibility impairment attributable to Bailey Unit 1, and the operation of cyclones results in an 

estimated 0.001 to 0.002 Δdv improvement (0.32 to 0.88 percent) of the 98th percentile visibility 

impairment attributable to Bailey Unit 1. 

As shown in Table 7-9, the operation of wet ESPs results in an estimated 0.003 to 0.011 Δdv 

improvement (0.91 to 3.00 percent) of the 98th percentile visibility impairment attributable to

McClellan Unit 1 at the applicable Class I areas. Further, as shown in Table 7-9, the operation of wet 

scrubbers results in an estimated 0.002 to 0.007 Δdv improvement (0.58 to 1.91 percent) of the 98th

percentile visibility impairment attributable to McClellan Unit 1, and the operation of cyclones results

in an estimated 0.002 to 0.006 Δdv improvement (0.58 to 1.63 percent) of the 98th percentile visibility 

impairment attributable to McClellan Unit 1. 

7.6 PROPOSED BART FOR PM10

The cost effectiveness of all the PM controls evaluated for both the boilers is greater than $5,000/ton, 

and for most controls is much greater than $5,000/ton. Based on the low PM10 emission from the 

boilers (less than 15 tpy for either Bailey Unit 1 or McClellan Unit 1) and the related low 

improvement to the visibility impairment attributable to the boilers based on the application of the 

controls (only up to 0.011 Δdv), none of the controls are determined to satisfy BART. Thus, there are 

no fuel changes or add-on controls proposed as BART for PM10 for Bailey Unit 1 or McClellan Unit 

1.

19

 

19 However, AECC is proposing fuel switching to 0.5% sulfur fuel oil as BART for SO2

.
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BART Analysis – Bailey and McClellan

APPENDIX A

SO2CONTROL COST CALCULATIONS
Bailey Unit 1

Average 

Sulfur 

ContentA

Baseline 

SO2 

Emission 

RateB

Controlled 

SO2 

Emission 

RateG

SO2 

Reduced

Baseline 

PM10 

Emission 

RateB

Controlled 

PM10 

Emission 

RateF

PM10 

Reduced

Annual Heat 

Input

Fuel Heating 

Value (HHV)C

Annual Fuel 

Usage Fuel Cost

Fuel Cost 

Differential 

from Current 

No. 6 OilD

Fuel Cost

Differential 

Cost of Fuel 

Switching

Eff

(%) (tpy) (tpy) (tpy) (tpy) (tpy) (tpy) (MMBtu/yr)

(MMBtu/Mgal) 

or 

(MMBtu/Mscf) (Mgal/yr) ($/MMBtu) ($/MMBtu) ($/yr) ($/yr) 

Base CaseA 1.81 30.73 - - 2.51 - - 39,193 155.00 252.86 4.68 - $183,492 - 

No. 6 - 1% 1.00 - 18.30 12.43 - 1.57 0.94 39,193 155.00 252.86 16.50 11.82 - 463,185 $ 

No. 6 - 0.5% 0.50 - 8.54 22.19 - 0.99 1.52 39,193 155.00 252.86 17.75 13.07 - 512,175 $ DieselA 0.05 - 0.95 29.78 - 0.03 2.48 39,193 136.15 287.86 20.95 16.27 - 637,592 $ 

Natural Gas - - 0.01 30.72 - 0.03 2.48 39,193 1,011.00 38.77 6.19 1.51 - 59,038 $ McClellan Unit 1

Average 

Sulfur 

ContentA

Baseline 

SO2 

Emission 

RateB

Controlled 

SO2 

Emission 

RateG

SO2 

Reduced

Baseline 

PM10 

Emission 

RateB

Controlled 

PM10 

Emission 

RateF

PM10 

Reduced

Annual Heat 

Input

Fuel Heating 

Value (HHV)C

Annual Fuel 

Usage Fuel Cost

Fuel Cost 

Differential 

from Current 

No. 6 OilD

Fuel Cost

Differential 

Cost of Fuel 

Switching

Eff

(%) (tpy) (tpy) (tpy) (tpy) (tpy) (tpy) (MMBtu/yr)

(MMBtu/Mgal) 

or 

(MMBtu/Mscf) (Mgal/yr) ($/MMBtu) ($/MMBtu) ($/yr) ($/yr) 

Base CaseA 1.38 166.45 - - 14.97 - - 291,733 155.00 1882.15 5.70 - $1,663,937 - 

No. 6 - 1% 1.00 - 136.20 30.25 - 11.68 3.29 291,733 155.00 1882.15 16.50 10.80 - 3,149,652 No. 6 - 0.5% 0.50 - 63.55 102.90 - 7.35 7.61 291,733 155.00 1882.15 17.75 12.05 - 3,514,317 DieselA 0.05 - 7.08 159.37 - 0.15 14.82 291,733 136.15 2142.73 20.95 15.25 - 4,447,862 Natural Gas - - 0.07 166.38 - 0.15 14.82 291,733 1,011.00 288.56 5.97 0.26 - 76,911 

E Total SO2 and PM10 Cost Effectiveness for fuel switching to distillate includes yearly capital recovery costs associated with necessary infrastructure changes.F Reductions in PM Species is based on default NPS profile.G Controlled SO2 emission rate for natural is based on a 99.98% control efficiency from the current IFO (as seen in comparison of baseline emission rates of burning natural gas vs. IFO).A Sulfur content of base case No. 6 fuel oil based on average of fuel burned in 2009- 2011. Sulfur content of diesel based on average sulfur in diesel burned at AECC Fitzhugh plant during the same timeframe sin

not burned at Bailey or McClellan. 

D The price differential for natural gas based on 10-year avarage pricing of current IFO and natural gas at Bailey and McClellan, each. The price differential for distillate based on distillate currently received at FitzC Higher heating value of residual oil based on average of fuel burned from supplier. Higher heating value of distillate oil average from Fitzhugh plant. Higher heating value of natural gas from 6.23.11 Bailey gas 

and 7.12.11 gas analysis.

B The Baseline SO2 Emission Rate was developed using an average fuel usage from 2007 and 2011, the average heat content of the No. 6 fuel oil during that same time as well as the average sulfur contect of the 

that time. The Baseline PM10 Emission Rates are representative of emission rates determined during stack testing in 2010 and then scaled to reflect the maximum heat input for each boiler.
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BART Analysis – Bailey and McClellan

APPENDIX B

PM10 CONTROL COST CALCULATIONS
Bailey Unit 1

Baseline 

Emission 

Rate

Control 

Efficiency

Annual Heat 

InputA

Controlled 

Emission Rate PM10 Reduced Capital Cost Total Annual Cost

(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) 

Wet ESP 2.51 90.00 39,193 0.25 2.26 105,141,431 22,638,340 

Wet Scrubber 2.51 55.00 39,193 1.13 1.38 140,957,713 50,150,862 

Cyclone 2.51 85.00 39,193 0.38 2.13 989,479 1,188,630 

No. 6 Fuel Oil - 1% 2.51 - 39,193 1.57 0.94 - 463184.75 

No 6. Fuel Oil - 0.5% 2.51 - 39,193 0.99 1.52 - 512175.45 

Diesel 2.51 - 39,193 0.03 2.48 - 637591.63 

Natural Gas 2.51 - 39,193 0.03 2.48 - 59038.24 

McClellan Unit 1

Baseline 

Emission 

Rate

Control 

Efficiency

Annual Heat 

InputA

 Controlled 

Emission Rate PM10 Reduced Capital Cost Total Annual Cost

(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) 

Wet ESP 14.97 90.00 291,733 1.50 13.47 151,509,333 32,605,907 Wet Scrubber 14.97 55.00 291,733 6.73 8.23 146,303,011 52,056,542 

Cyclone 14.97 85.00 291,733 2.24 12.72 1,432,971 1,721,384 No. 6 Fuel Oil - 1% 14.97 - 291,733 11.68 3.29 - 3149651.56 No 6. Fuel Oil - 0.5% 14.97 - 291,733 7.35 7.61 - 3514317.35 Diesel 14.97 - 291,733 0.15 14.82 - 4447861.76 Natural Gas 14.97 - 291,733 0.15 14.82 - 76911.33 

A Annual Heat Input derived for 2007-2011 average fuel usage times the heat content of No. 6 fuel oil
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APPENDIX C

MODELING PROTOCOL
CALMET DATA PROCESSING PROTOCOL  BART DETERMINATION

OKLAHOMA GAS & ELECTRIC

MUSKOGEE GENERATING STATION

SEMINOLE GENERATING STATION

SOONER GENERATING STATION

Prepared by:

Vern Choquette  Principal Consultant

Eugene Chen, PE  Senior Consultant

Jeremy Townley  Consultant

TRINITY CONSULTANTS

120 East Sheridan

Suite 205

Oklahoma City, OK 73104

(405) 228-3292

January 23, 2008

Project 083701.0004
OG&E i Trinity Consultants

CALMET Processing Protocol 083701.0004

TABLE OF CONTENTS

1. INTRODUCTION.................................................................................................... 1-1

1.1 BEST AVAILABLE RETROFIT TECHNOLOGY RULE BACKGROUND ......................... 1-1

1.2 OBJECTIVE ............................................................................................................ 1-2

1.3 LOCATION OF SOURCES AND RELEVANT CLASS I AREAS...................................... 1-2

2. CALPUFF MODEL SYSTEM ............................................................................... 2-1

2.1 MODEL VERSIONS................................................................................................. 2-1

2.2 MODELING DOMAIN ............................................................................................. 2-1

3. CALMET............................................................................................................ 3-1

3.1 GEOPHYSICAL DATA............................................................................................. 3-1

3.1.1 TERRAIN DATA ...................................................................................................3-1

3.1.2 LAND USE DATA.................................................................................................3-2

3.1.3 COMPILING TERRAIN AND LAND USE DATA.......................................................3-3

3.2 METEOROLOGICAL DATA ..................................................................................... 3-3

3.2.1 MESOSCALE MODEL METEOROLOGICAL DATA .................................................3-3

3.2.2 SURFACE METEOROLOGICAL DATA ...................................................................3-4

3.2.3 UPPER AIR METEOROLOGICAL DATA.................................................................3-5

3.2.4 PRECIPITATION METEOROLOGICAL DATA..........................................................3-6

3.2.5 BUOY METEOROLOGICAL DATA.........................................................................3-7

3.3 CALMET CONTROL PARAMETERS....................................................................... 3-8

3.3.1 VERTICAL METEOROLOGICAL PROFILE..............................................................3-8

3.3.2 INFLUENCES OF OBSERVATIONS .........................................................................3-9

APPENDIX A- METEOROLOGICAL STATIONS

APPENDIX B – SAMPLE CALMET CONTROL FILE (CALPUFF VERSION 5.8)

APPENDIX C – SAMPLE CALMET CONTROL FILE (CALPUFF VERSION 6)
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CALMET Processing Protocol 083701.0004

LIST OF TABLES

TABLE 1-1. BART-ELIGIBLE SOURCES ...............................................................................................1-2

TABLE 1-2. DISTANCE FROM STATION TO SURROUNDING CLASS I AREAS .......................................1-2

TABLE 2-1. CALPUFF MODELING SYSTEM VERSIONS .....................................................................2-1

TABLE 3-1. VERTICAL LAYERS OF THE CALMET METEOROLOGICAL DOMAIN................................3-9

TABLE A-1. LIST OF SURFACE METEOROLOGICAL STATIONS ...........................................................A-1

TABLE A-2. LIST OF UPPER AIR METEOROLOGICAL STATIONS.........................................................A-5

TABLE A-3. LIST OF PRECIPITATION METEOROLOGICAL STATIONS..................................................A-6

TABLE A-4. LIST OF OVER WATER METEOROLOGICAL STATIONS..................................................A-14
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CALMET Processing Protocol 083701.0004

LIST OF FIGURES

FIGURE 1-1. PLOT OF SOURCES AND NEAREST CLASS I AREAS .........................................................1-3

FIGURE 2-1. REFINED METEOROLOGICAL MODELING DOMAIN.........................................................2-2

FIGURE 3-1. PLOT OF LAND ELEVATION USING USGS TERRAIN DATA ............................................3-2

FIGURE 3-2. PLOT OF LAND USE USING USGS LULC DATA.............................................................3-3

FIGURE 3-3. PLOT OF SURFACE STATION LOCATIONS........................................................................3-5

FIGURE 3-4. PLOT OF UPPER AIR STATIONS LOCATIONS ...................................................................3-6

FIGURE 3-5. PLOT OF PRECIPITATION METEOROLOGICAL STATIONS .................................................3-7

FIGURE 3-6. PLOT OF BUOY METEOROLOGICAL STATIONS ................................................................3-8
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CALMET Processing Protocol 083701.0004

1. INTRODUCTION

Oklahoma Gas & Electric (OG&E) owns and operates three electric generating stations near

Muskogee, Oklahoma (Muskogee Generating Station), Seminole, Oklahoma (Seminole Generating 

Station), and Stillwater, Oklahoma (Sooner Generating Station). These generating stations are

considered eligible to be regulated under the U.S. Environmental Protection Agency’s (EPA) Best 

Available Retrofit Technology (BART) provisions of the Regional Haze Rule. This protocol 

describes the proposed methodology for conducting the CALMET data processing for the refined 

CALPUFF BART modeling analysis for OG&E’s Muskogee, Seminole, and Sooner Generating 

Stations. A detailed CALPUFF BART modeling protocol will be submitted in the near future and 

will include a discussion of the CALPUFF parameters as well as the post processing methodologies 

to be used in the refined modeling analysis for each station.

1.1 BEST AVAILABLE RETROFIT TECHNOLOGY RULE BACKGROUND

On July 1, 1999, the U.S. Environmental EPA published the final Regional Haze Rule (RHR). The 

objective of the RHR is to improve visibility in 156 specific areas across with United States, known 

as Class I areas. The Clean Air Act defines Class I areas as certain national parks (over 6000 acres), 

wilderness areas (over 5000 acres), national memorial parks (over 5000 acres), and international 

parks that were in existence on August 7, 1977.

On July 6, 2005, the EPA published amendments to its 1999 RHR, often called the BART rule, which 

included guidance for making source-specific Best Available Retrofit Technology (BART) 

determinations. The BART rule defines BART-eligible sources as sources that meet the following 

criteria: 

(1) Have potential emissions of at least 250 tons per year of a visibility-impairing pollutant,

(2) Began operation between August 7, 1962 and August 7, 1977, and

(3) Are listed as one of the 26 listed source categories in the guidance.

A BART-eligible source is not automatically subject to BART. Rather, BART-eligible sources are 

subject-to-BART if the sources are “reasonably anticipated to cause or contribute to visibility 

impairment in any federal mandatory Class I area.” EPA has determined that sources are reasonably 

anticipated to cause or contribute to visibility impairment if the visibility impacts from a source are 

greater than 0.5 deciviews (dv) when compared against a natural background.

Air quality modeling is the tool that is used to determine a source’s visibility impacts. States have the 

authority to exempt certain BART-eligible sources from installing BART controls if the results of the 

dispersion modeling demonstrate that the source cannot reasonably be anticipated to cause or 

contribute to visibility impairment in a Class I area. Further, states also have the authority to define 

the modeling procedures for conducting modeling related to making BART determinations. 
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CALMET Processing Protocol 083701.0004

1.2 OBJECTIVE

The objective of this document is to provide a protocol summarizing the modeling methods and 

procedures that will be followed to conduct the CALMET data processing necessary to complete a

refined CALPUFF modeling analysis for the OG&E generating stations discussed above. The 

modeling methods and procedures contained in this protocol and the CALPUFF protocol yet to be 

submitted will be used to determine appropriate controls for OG&E’s BART-eligible sources that can 

reasonably be anticipated to reduce the sources’ effects on or contribution to visibility impairment in 

the surrounding Class I areas. It is OG&E’s intent to determine a combination of emissions controls 

that will reduce the impact of each generating station to a degree that the 98th percentile of the 

visibility impact predicted by the model due to all the BART eligible sources at each station 

collectively is below EPA’s recommended visibility contribution threshold of 0.5 dv.

1.3 LOCATION OF SOURCES AND RELEVANT CLASS I AREAS

The sources listed in Table 1-1 are the sources that have been identified by OG&E as sources that 

meet the three criteria for BART-eligible sources.

TABLE 1-1. BART-ELIGIBLE SOURCES

EPN Description

Muskogee Sources

Unit 4 5,480 MMBtu/hr Coal Fired Boiler

Unit 5 5,480 MMBtu/hr Coal Fired Boiler

Seminole Sources

SM1 5,480 MMBtu/hr Natural Gas Fired Boiler

SM2 5,480 MMBtu/hr Natural Gas Fired Boiler

SM3 5,496 MMBtu/hr Natural Gas Fired Boiler

Sooner Sources

Unit 1 5,116 MMBtu/hr Coal Fired Boiler

Unit 2 5,116 MMBtu/hr Coal Fired Boiler

As required in CENRAP’s BART Modeling Guidelines, Class I areas within 300 km of each station 

will be included in each analysis. The following table summarizes the distances of the four closest 

Class I areas to each station. As seen from this summary, some Class I areas are more than 300 km 

from the certain stations. However, in order to demonstrate that each station will not have an adverse 

effect on the visibility at any of the four nearest Class I areas, OG&E has opted to include those Class 

I areas more than 300 km away in this analysis. Note that the distances listed in the table below are 

the distances between the stations and the closest border of the Class I areas. 

TABLE 1-2. DISTANCE FROM STATION TO SURROUNDING CLASS I AREAS

CACR HEGL UPBU WIMO

Muskogee 180 230 164 324

Seminole 242 386 310 178

Sooner 345 363 327 234
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CALMET Processing Protocol 083701.0004

A plot of the Class I areas with respect to the each station is provided in Figure 1-1.

 FIGURE 1-1. PLOT OF SOURCES AND NEAREST CLASS I AREAS

-800 -600 -400 -200 0 200 400 600 800

LCC Easting (km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

LCC Northing (km)

Seminole Station

Muskogee Station

Sooner Station

WIMO

CACR

UPBU

HEGL

Class I Areas
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CALMET Processing Protocol 083701.0004

2. CALPUFF MODEL SYSTEM

The main components of the CALPUFF modeling system are CALMET, CALPUFF, and CALPOST. 

CALMET is the meteorological model that generates hourly three-dimensional meteorological fields 

such as wind and temperature. CALPUFF simulates the non-steady state transport, dispersion, and 

chemical transformation of air pollutants emitted from a source in “puffs”. CALPUFF calculates 

hourly concentrations of visibility affecting pollutants at each specified receptor in a modeling 

domain. CALPOST is the post-processor for CALPUFF that computes visibility impacts from a 

source based on the visibility affecting pollutant concentrations that were output by CALPUFF.

2.1 MODEL VERSIONS

The versions of the CALPUFF modeling system programs that are proposed for conducting OG&E’s 

BART modeling are listed in Table 2-1. A detailed refined CALPUFF BART modeling protocol will 

be submitted in the near future.

TABLE 2-1. CALPUFF MODELING SYSTEM VERSIONS

Processor Version Level

TERREL 3.3 030402

CTGCOMP 2.21 030402

CTGPROC 2.63 050128

MAKEGEO 2.2 030402

CALMET 5.53a 040716

CALPUFF 5.8 070623

POSTUTIL 1.3 030402

CALPOST 5.51 030709

2.2 MODELING DOMAIN

The CALPUFF modeling system utilizes three modeling grids: the meteorological grid, the 

computational grid, and the sampling grid. The meteorological grid is the system of grid points at 

which meteorological fields are developed with CALMET. The computational grid determines the 

computational area for a CALPUFF run. Puffs are advected and tracked only while within the 

computational grid. The meteorological grid is defined so that it covers the areas of concern and 

gives enough marginal buffer area for puff transport and dispersion. A plot of the proposed 

meteorological modeling domain with respect to the Class I areas being modeled is also provided in 
OG&E 2-2 Trinity Consultants

CALMET Processing Protocol 083701.0004

Figure 2-1. The computational domain will be set to extend at least 50 km in all directions beyond 

the Muskogee, Seminole, and Sooner Generating Stations and the Class I areas of interest. Note that 

the map projection for the modeling domain will be Lambert Conformal Conic (LCC) and the datum 

will be the World Geodetic System 84 (WGS-84). The reference point for the modeling domain is

Latitude 40oN, Longitude 97oW. The southwest corner will be set to -951.547 km LCC, -1646.637 

km LCC corresponding to Latitude 24.813 oN and Longitude 87.778oW. The meteorological grid 

spacing will be 4 km, resulting in 462 grid points in the X direction and 376 grid points in the Y 

direction.

FIGURE 2-1. REFINED METEOROLOGICAL MODELING DOMAIN

-800 -600 -400 -200 0 200 400 600 800

LCC Easting (km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

L

C

C

N

o

r

t

h

i

n

g

(

k

)m

Seminole Station

Muskogee Station

Sooner Station

WIMO

CACR

UPBU

HEGL

Class I Areas

Meteorological Modeling Domain

Computational Modeling Domain 1
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CALMET Processing Protocol 083701.0004

3. CALMET

The EPA Approved Version of the CALMET meteorological processor will be used to generate the 

meteorological data for CALPUFF. CALMET is the meteorological processor that compiles 

meteorological data from raw observations of surface and upper air conditions, precipitation 

measurements, mesoscale model output, and geophysical parameters into a single hourly, gridded 

data set for input into CALPUFF. CALMET will be used to assimilate data for 2001- 2003 using 

National Weather Service (NWS) surface station observations, upper air station observations, 

precipitation station observations, buoy station observations (for overwater areas), and mesoscale 

model output to develop the meteorological field. 

3.1 GEOPHYSICAL DATA

CALMET requires geophysical data to characterize the terrain and land use parameters that 

potentially affect dispersion. Terrain features affect flows and create turbulence in the atmosphere 

and are potentially subjected to higher concentrations of elevated puffs. Different land uses exhibit 

variable characteristics such as surface roughness, albedo, Bowen ratio, and leaf-area index that also 

effect turbulence and dispersion. 

3.1.1 TERRAIN DATA

Terrain data will be obtained from the United States Geological Survey (USGS) in 

1-degree (1:250,000 scale or approximately 90 meter resolution) digital format. The

USGS terrain data will then be processed by the TERREL program to generate grid-cell 

elevation averages across the modeling domain. A plot of the land elevations based on the 

USGS data for the modeling domain is provided in Figure 3-1.
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CALMET Processing Protocol 083701.0004

FIGURE 3-1. PLOT OF LAND ELEVATION USING USGS TERRAIN DATA

3.1.2 LAND USE DATA

The land use land cover (LULC) data from the USGS North American land cover 

characteristics data base in the Lambert Azimuthal equal area map projection will be used 

in order to determine the land use within the modeling domain. The LULC data will be 

processed by the CTGPROC program which will generate land use for each grid cell 

across the modeling domain. A plot of the land use based on the USGS data for the 

modeling domain is provided in Figure 3-2.

-800 -600 -400 -200 0 200 400 600 800

LCC Easting(km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

L

C

C

N

o

r

t

h

i

n

g

(

k

)m

Seminole Station

Muskogee Station

Sooner Station

0

982

1960

2950

3930

Terrain 

Elevation (m)
OG&E 3-3 Trinity Consultants

CALMET Processing Protocol 083701.0004

FIGURE 3-2. PLOT OF LAND USE USING USGS LULC DATA

3.1.3 COMPILING TERRAIN AND LAND USE DATA

The terrain data files output by the TERELL program and the LULC files output by the 

CTGPROC program will be uploaded into the MAKEGEO program to create a 

geophysical data file that will be input into CALMET. 

3.2 METEOROLOGICAL DATA 

CALMET will be used to assimilate data for 2001, 2002, and 2003 using mesoscale model output and 

National Weather Service (NWS) surface station observations, upper air station observations,

precipitation station observations, and National Oceanic and Atmosphere Administrations (NOAA) 

buoy station observations to develop the meteorological field. 

3.2.1 MESOSCALE MODEL METEOROLOGICAL DATA

Hourly mesoscale data will also be used as the initial guess field in developing the 

CALMET meteorological data. It is OG&E’s intent to use the following 5th generation 

mesoscale model meteorological data sets (or MM5 data) in the analysis:

 2001 MM5 data at 12 km resolution generated by the U.S. EPA 

 2002 MM5 data at 36 km resolution generated by the Iowa DNR

-800 -600 -400 -200 0 200 400 600 800

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 2003 MM5 data set at 36 km resolution generated by the Midwest RPO

The specific MM5 data that will be used are subsets of the data listed above. As the 

contractor to CENRAP for developing the meteorological data sets for the BART 

modeling, Alpine Geophysics extracted three subsets of MM5 data for each year from 

2001 to 2003 from the data sets listed above using the CALMM5 extraction program. The 

three subsets covered the northern, central, and southern portions of CENRAP. TXI is 

proposing to use the southern set of the extracted MM5 data. 

The 2001 southern subset of the extracted MM5 data includes 30 files that are broken into 

10 to 11 day increments (3 files per month). The 2002 and 2003 southern subsets of 

extracted MM5 data include 12 files each of which are broken into 30 to 31 day increment

files (1 file per month). Note that the 2001 to 2003 MM5 data extracted by Alpine

Geophysics will not be able to be used directly in the modeling analysis. To run the Alpine 

Geophysics extracted MM data in the EPA approved CALMET program, each of the MM5 

files will need to be adjusted by appending an additional six (6) hours, at a minimum, to 

the end of each file to account for the shift in time zones from the Greenwich Mean Time 

(GMT) prepared Alpine Geophysics data to Time Zone 6 for this analysis. No change to 

the data will occur. 

The time periods covered by the data in each of the MM5 files extracted by Alpine 

Geophysics include a specific number of calendar days, where the data starts at Hour 0 in 

GMT for the first calendar day and ends at Hour 23 in GMT on the last calendar day. In 

order to run CALMET in the local standard time (LST), which is necessary since the 

surface meteorological observations are recorded in LST, there must be hours of MM5 data 

referenced in a CALMET run that match the LST observation hours. Since the LST hours 

in Central Standard Time (CST) are 6 hours behind GMT, it is necessary to adjust the data 

in each MM5 file so that the time periods covered in the files match CST. 

Based on the above discussion, the Alpine Geophysics MM5 data will not be used directly. 

Instead the data files will be modified to add 8 additional hours of data to the end of each

file from the beginning of the subsequent file. CALMET will then be run using the 

appended MM5 data to generate a contiguous set of CALMET output files. The converted 

MM5 data files occupy approximately 1.2 terabytes (TB) of hard drive space.

3.2.2 SURFACE METEOROLOGICAL DATA

Parameters affecting turbulent dispersion that are observed hourly at surface stations 

include wind speed and direction, temperature, cloud cover and ceiling, relative humidity, 

and precipitation type. It is OG&E’s intent to use the surface stations listed in Table A-1 

of Appendix A. The locations of the surface stations with respect to the modeling domain 

are shown in Figure 3-3. The stations were selected from the available data inventory to 

optimize spatial coverage and representation of the domain. Data from the stations will be 

processed for use in CALMET using EPA’s SMERGE program.
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FIGURE 3-3. PLOT OF SURFACE STATION LOCATIONS

3.2.3 UPPER AIR METEOROLOGICAL DATA

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) (6 o’clock PM in Oklahoma) 

and 1200 GMT (6 o’clock AM in Oklahoma). Sensors observe pressure, wind speed and 

direction, and temperature (among other parameters) as the balloon rises through the 

atmosphere. The upper air observation network is less dense than surface observation 

points since upper air conditions vary less and are generally not as affected by local effects 

(e.g., terrain or water bodies). The upper air stations that are proposed for this analysis are 

listed in Table A-2 of Appendix A. The locations of the upper air stations with respect to 

the modeling domain are shown in Figure 3-4. These stations were selected from the 

available data inventory to optimize spatial coverage and representation of the domain. 

Data from the stations will be processed for use in CALMET using EPA’s READ62 

program.

-800 -600 -400 -200 0 200 400 600 800

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Muskogee Station

Sooner Station

WIMO

CACR

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Class I Areas

Surface Stations
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FIGURE 3-4. PLOT OF UPPER AIR STATIONS LOCATIONS

-800 -600 -400 -200 0 200 400 600 800

LCC Easting (km)

-1600

-1400

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LCC Northing (km)

Seminole Station

Muskogee Station

Sooner Station

WIMO

CACR

UPBU

HEGL

Class I Areas

Upper Air Stations

3.2.4 PRECIPITATION METEOROLOGICAL DATA

The effects of chemical transformation and deposition processes on ambient pollutant 

concentrations will be considered in this analysis. Therefore, it is necessary to include 

observations of precipitation in the CALMET analysis. The precipitation stations that are 

proposed for this analysis are listed in Table A-3 of Appendix A. The locations of the 

precipitation stations with respect to the modeling domain are shown in Figure 3-5. These 

stations were selected from the available data inventory to optimize spatial coverage and 

representation of the domain. Data from the stations will be processed for use in 

CALMET using EPA’s PMERGE program.
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FIGURE 3-5. PLOT OF PRECIPITATION METEOROLOGICAL STATIONS

-800 -600 -400 -200 0 200 400 600 800

LCC Easting (km)

-1600

-1400

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-1000

-800

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-400

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LCC Northing (km)

Seminole Station

Muskogee Station

Sooner Station

WIMO

CACR

UPBU

HEGL

Class I Areas

Precipitation Stations

3.2.5 BUOY METEOROLOGICAL DATA

The effects of land/sea breeze on ambient pollutant concentrations will be considered in 

this analysis. Therefore, it is necessary to include observations of buoy stations in the 

CALMET analysis. The buoy stations that are proposed for this analysis are listed in Table 

A-4 of Appendix A. The locations of the buoy stations with respect to the modeling 

domain are shown in Figure 3-6. These stations were selected from the available data 

inventory to optimize spatial coverage and representation of the domain along the 

coastline. Data from the stations will be prepared by filling missing hour records with the 

CALMET missing parameter value (9999). No adjustments to the data will occur. 
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FIGURE 3-6. PLOT OF BUOY METEOROLOGICAL STATIONS

-800 -600 -400 -200 0 200 400 600 800

LCC Easting (km)

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LCC Northing (km)

Seminole Station

Muskogee Station

Sooner Station

WIMO

CACR

UPBU

HEGL

Class I Areas

Buoy Stations

3.3 CALMET CONTROL PARAMETERS

Appendix B provides a sample CALMET input file used in OG&E’s modeling analysis. A few 

details of the CALMET model setup for sensitive parameters are also discussed below. 

3.3.1 VERTICAL METEOROLOGICAL PROFILE

The height of the top vertical layer will be set to 3,500 meters. This height corresponds to 

the top sounding pressure level for which upper air observation data will be relied upon. 

The vertical dimension of the domain will be divided into 12 layers with the maximum 

elevations for each layer shown in Table 3-1. The vertical dimensions are weighted 

towards the surface to resolve the mixing layer while using a somewhat coarser resolution 

for the layers aloft. 
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TABLE 3-1. VERTICAL LAYERS OF THE CALMET METEOROLOGICAL DOMAIN

Layer Elevation (m)

1 20 

2 40

3 60

4 80

5 100

6 150

7 200

8 250

9 500

10 1000

11 2000

12 3500

CALMET allows for a bias value to be applied to each of the vertical layers. The bias 

settings for each vertical layer determine the relative weight given to the vertically 

extrapolated surface and upper air wind and temperature observations. The initial guess 

fields are computed with an inverse distance weighting (1/r

2

) of the surface and upper air 

data. The initial guess fields may be modified by a layer dependent bias factor. Values for 

the bias factor may range from -1 to +1. A bias of -1 eliminates upper-air observations in

the 1/r2interpolations used to initialize the vertical wind fields. Conversely, a bias of +1 

eliminates the surface observations in the interpolations for this layer. Normally, bias is set 

to zero (0) for each vertical layer, such that the upper air and surface observations are given 

equal weight in the 1/r2interpolations. The biases for each layer of the proposed modeling 

domain will be set to zero.

CALMET allows for vertical extrapolation of surface wind observations to layers aloft to 

be skipped if the surface station is close to the upper air station. Alternatively, CALMET 

allows data from all surface stations to be extrapolated. The CALMET parameter that 

controls this setting is IEXTRP. Setting IEXTRP to a value less than zero (0) means that 

layer 1 data from upper air soundings is ignored in any vertical extrapolations. IEXTRP 

will be set to -4 for this analysis (i.e., the similarity theory is used to extrapolate the surface 

winds into the layers aloft, which provides more information on observed local effects to 

the upper layers).

3.3.2 INFLUENCES OF OBSERVATIONS

Step 1 wind fields will be based on an initial guess using MM5 data and refined to reflect 

terrain affects. Step 2 wind fields will adjust the Step 1 wind field by incorporating the 

influence of local observations. An inverse distance method is used to determine the 

influence of observations to the Step 1 wind field. RMAX1 and RMAX2 define the radius 

of influence for data from surface stations to land in the surface layer and data from upper 

air stations to land in the layers aloft. In general, RMAX1 and RMAX2 are used to 

exclude observations from being inappropriately included in the development of the Step 2
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CALMET Processing Protocol 083701.0004

wind field if the distance from an observation station to a grid point exceeds the maximum 

radius of influence. 

If the distance from an observation station to a grid point is less than the value set for 

RMAX, the observation data will be used in the development of the Step 2 wind field. R1 

represents the distance from a surface observation station at which the surface observation 

and the Step 1 wind field are weighted equally. R2 represents the comparable distance for 

winds aloft. R1 and R2 are used to weight the observation data with respect to the MM5 

data that was used to generate the Step 1 wind field. Large values for R1 and R2 give 

more weight to the observations, where as small values give more weight to the MM5 data. 

In this BART modeling analysis, RMAX 1 will be set to 20 km, and R1 will be set to 10 

km. This will limit the influence of the surface observation data from all surface stations to 

20 km from each station, and will equally weight the MM5 and observation data at 10 km. 

RMAX2 will be set to 50 km, and R2 will be set to 25 km. This will limit the influence of 

the upper air observation data from all surface stations to 50 km from each station, and will 

equally weight the MM5 and observation data at 25 km. These settings of radius of 

influence will allow for adequate weighting of the MM5 data and the observation data 

across the modeling domain due to the vast domain to be modeled. RAMX 3 will be set to 

500 km. 
OG&E A-1 Trinity Consultants

CALMET Processing Protocol 083701.0004

APPENDIX A- METEOROLOGICAL STATIONS

TABLE A-1. LIST OF SURFACE METEOROLOGICAL STATIONS

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC North

(km) Long Lat

1 KDYS 69019 -267.672 -834.095 96.9968 39.9925

2 KNPA 72222 932.565 -1020.909 97.0110 39.9908

3 KBFM 72223 857.471 -996.829 97.0101 39.9910

4 KGZH 72227 946.767 -899.515 97.0112 39.9919

5 KTCL 72228 870.843 -706.104 97.0103 39.9936

6 KNEW 53917 674.172 -1078.342 97.0080 39.9903

7 KNBG 12958 677.719 -1104.227 97.0080 39.9900

8 BVE 12884 741.996 -1153.463 97.0088 39.9896

9 KPTN 72232 550.88 -1124.295 97.0065 39.9898

10 KMEI 13865 774.911 -814.225 97.0092 39.9926

11 KPIB 72234 728.416 -915.165 97.0086 39.9917

12 KGLH 72235 557.072 -703.097 97.0066 39.9936

13 KHEZ 11111 540.777 -912.22 97.0064 39.9918

14 KMCB 11112 622.755 -949.618 97.0074 39.9914

15 KGWO 11113 640.102 -695.286 97.0076 39.9937

16 KASD 72236 692.381 -1043.261 97.0082 39.9906

17 KPOE 72239 363.294 -984.839 97.0043 39.9911

18 KBAZ 72241 -102.133 -1140.886 96.9988 39.9897

19 KGLS 72242 215.108 -1185.604 97.0025 39.9893

20 KDWH 11114 140.413 -1101.174 97.0017 39.9900

21 KIAH 12960 158.266 -1108.37 97.0019 39.9900

22 KHOU 72243 167.147 -1147.402 97.0020 39.9896

23 KEFD 12906 178.551 -1152.782 97.0021 39.9896

24 KCXO 72244 152.739 -1069.309 97.0018 39.9903

25 KCLL 11115 60.898 -1044.381 97.0007 39.9906

26 KLFK 93987 214.643 -969.355 97.0025 39.9912

27 KUTS 11116 136.056 -1026.773 97.0016 39.9907

28 KTYR 11117 150.451 -846.207 97.0018 39.9924

29 KCRS 72246 56.655 -882.642 97.0007 39.9920

30 KGGG 72247 214.572 -841.163 97.0025 39.9924

31 KGKY 11118 -9.365 -812.25 96.9999 39.9927

32 KDTN 72248 304.827 -821.713 97.0036 39.9926

33 KBAD 11119 312.743 -825.101 97.0037 39.9925

34 KMLU 11120 465.834 -816.211 97.0055 39.9926

35 KTVR 11121 561.446 -840.225 97.0066 39.9924

36 KTRL 11122 68.599 -806.417 97.0008 39.9927

37 KOCH 72249 216.81 -930.252 97.0026 39.9916

38 KBRO 12919 -44.167 -1571.387 96.9995 39.9858
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Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC North

(km) Long Lat

39 KALI 72251 -103.012 -1363.74 96.9988 39.9877

40 KLRD 12920 -246.548 -1381.603 96.9971 39.9875

41 KSSF 72252 -143.386 -1183.35 96.9983 39.9893

42 KRKP 11123 -4.965 -1324.914 96.9999 39.9880

43 KCOT 11124 -219.097 -1280.964 96.9974 39.9884

44 KLBX 11125 150.245 -1207.466 97.0018 39.9891

45 KSAT 12921 -143.024 -1160.935 96.9983 39.9895

46 KHDO 12962 -211.702 -1178.172 96.9975 39.9894

47 KSKF 72253 -154.625 -1177.555 96.9982 39.9894

48 KHYI 11126 -84.156 -1122.487 96.9990 39.9899

49 KTKI 72254 38.788 -754.791 97.0005 39.9932

50 KBMQ 11127 -118.39 -1027.031 96.9986 39.9907

51 KATT 11128 -67.587 -1075.97 96.9992 39.9903

52 KSGR 11129 131.478 -1151.702 97.0016 39.9896

53 KGTU 11130 -65.624 -1033.173 96.9992 39.9907

54 KVCT 12912 6.587 -1236.788 97.0001 39.9888

55 KPSX 72255 73.878 -1253.33 97.0009 39.9887

56 KACT 13959 -22.12 -929.156 96.9997 39.9916

57 KPWG 72256 -30.147 -944.073 96.9996 39.9915

58 KILE 72257 -65.288 -988.507 96.9992 39.9911

59 KGRK 11131 -79.643 -990.173 96.9991 39.9911

60 KTPL 11132 -38.203 -981.19 96.9996 39.9911

61 KPRX 13960 143.317 -703.663 97.0017 39.9936

62 KDTO 72258 -17.018 -752.974 96.9998 39.9932

63 KAFW 11133 -29.564 -777.061 96.9997 39.9930

64 KFTW 72259 -34.302 -795.502 96.9996 39.9928

65 KMWL 11134 -99.769 -798.767 96.9988 39.9928

66 KRBD 11135 12.453 -810.467 97.0002 39.9927

67 KDRT 11136 -384.069 -1170.59 96.9955 39.9894

68 KFST 22010 -566.418 -988.838 96.9933 39.9911

69 KGDP 72261 -739.127 -873.302 96.9913 39.9921

70 KSJT 72262 -333.338 -952.54 96.9961 39.9914

71 KMRF 23034 -676.265 -1042.616 96.9920 39.9906

72 KMAF 72264 -489.668 -878.107 96.9942 39.9921

73 KINK 23023 -586.882 -890.654 96.9931 39.9920

74 KABI 72265 -252.044 -836.353 96.9970 39.9924

75 KLBB 13962 -445.006 -689.313 96.9948 39.9938

76 KATS 11137 -696.818 -763.258 96.9918 39.9931

77 KCQC 11138 -785.757 -515.724 96.9907 39.9953

78 KROW 23009 -698.822 -712.898 96.9918 39.9936

79 KSRR 72268 -789.593 -686.226 96.9907 39.9938

80 KCNM 11139 -682.79 -822.109 96.9919 39.9926

81 KALM 36870 -838.056 -752.338 96.9901 39.9932

82 KLRU 72269 -931.527 -804.112 96.9890 39.9927

83 KTCS 72271 -952.353 -695.469 96.9888 39.9937
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Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC North

(km) Long Lat

84 KSVC 93063 -1042.03 -752.033 96.9877 39.9932

85 KDMN 72272 -1006.77 -799.231 96.9881 39.9928

86 KMSL 72323 854.846 -536.687 97.0101 39.9952

87 KPOF 72330 578.62 -336.733 97.0068 39.9970

88 KGTR 11140 779.065 -689.108 97.0092 39.9938

89 KTUP 93862 753.875 -600.337 97.0089 39.9946

90 KMKL 72334 727.051 -454.383 97.0086 39.9959

91 KLRF 72340 440.654 -550.661 97.0052 39.9950

92 KHKA 11141 643.365 -424.419 97.0076 39.9962

93 KHOT 72341 358.094 -604.603 97.0042 39.9945

94 KTXK 11142 278.022 -720.623 97.0033 39.9935

95 KLLQ 72342 488.655 -698.008 97.0058 39.9937

96 KMWT 72343 254.18 -599.224 97.0030 39.9946

97 KFSM 13964 237.97 -512.87 97.0028 39.9954

98 KSLG 72344 224.881 -419.064 97.0027 39.9962

99 KVBT 11143 248.074 -399.892 97.0029 39.9964

100 KHRO 11144 343.525 -405.601 97.0041 39.9963

101 KFLP 11145 404.239 -399.142 97.0048 39.9964

102 KBVX 11146 480.712 -457.853 97.0057 39.9959

103 KROG 11147 258.44 -397.685 97.0031 39.9964

104 KSPS 13966 -138.053 -664.886 96.9984 39.9940

105 KHBR 72352 -186.121 -551.123 96.9978 39.9950

106 KCSM 11148 -198.844 -513.911 96.9977 39.9954

107 KFDR 11149 -181.653 -625.205 96.9979 39.9944

108 KGOK 72353 -35.905 -458.97 96.9996 39.9959

109 KTIK 72354 -34.581 -506.938 96.9996 39.9954

110 KPWA 11150 -58.596 -493.951 96.9993 39.9955

111 KSWO 11151 -7.42 -425.828 96.9999 39.9962

112 KMKO 72355 146.972 -479.879 97.0017 39.9957

113 KRVS 72356 91.059 -438.276 97.0011 39.9960

114 KBVO 11152 87.136 -357.069 97.0010 39.9968

115 KMLC 11153 110.647 -563.566 97.0013 39.9949

116 KOUN 72357 -40.731 -527.298 96.9995 39.9952

117 KLAW 11154 -129.405 -600.222 96.9985 39.9946

118 KCDS 72360 -300.297 -610.668 96.9965 39.9945

119 KGNT 72362 -985.117 -475.563 96.9884 39.9957

120 KGUP 11155 -1059.48 -427.151 96.9875 39.9961

121 KAMA 23047 -425.319 -518.171 96.9950 39.9953

122 KBGD 72363 -395.603 -466.083 96.9953 39.9958

123 KFMN 72365 -993.449 -297.944 96.9883 39.9973

124 KSKX 72366 -770.464 -355.855 96.9909 39.9968

125 KTCC 23048 -597.271 -511.241 96.9930 39.9954

126 KLVS 23054 -732.565 -448.329 96.9914 39.9960

127 KEHR 72423 812.573 -199.695 97.0096 39.9982

128 KEVV 93817 822.929 -172.715 97.0097 39.9984
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Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC North

(km) Long Lat

129 KMVN 72433 704.666 -154.54 97.0083 39.9986

130 KMDH 11156 676.745 -218.041 97.0080 39.9980

131 KBLV 11157 617.659 -136.018 97.0073 39.9988

132 KSUS 3966 547.898 -130.122 97.0065 39.9988

133 KPAH 3816 725.985 -293.319 97.0086 39.9974

134 KJEF 72445 419.01 -145.496 97.0050 39.9987

135 KAIZ 11158 387.096 -200.609 97.0046 39.9982

136 KIXD 72447 182.322 -126.913 97.0022 39.9989

137 KWLD 72450 0 -298.57 97.0000 39.9973

138 KAAO 11159 -18.976 -248.773 96.9998 39.9978

139 KIAB 11160 -23.392 -263.471 96.9997 39.9976

140 KEWK 11161 -24.645 -215.58 96.9997 39.9981

141 KGBD 72451 -161.892 -180.781 96.9981 39.9984

142 KHYS 11162 -195.191 -124.723 96.9977 39.9989

143 KCFV 11163 126.442 -319.698 97.0015 39.9971

144 KFOE 72456 114.618 -115.26 97.0014 39.9990

145 KEHA 72460 -432.761 -320.089 96.9949 39.9971

146 KALS 72462 -777.592 -245.892 96.9908 39.9978

147 KDRO 11164 -945.713 -259.163 96.9888 39.9977

148 KLHX 72463 -568.426 -195.178 96.9933 39.9982

149 KSPD 2128 -494.076 -285.176 96.9942 39.9974

150 KCOS 93037 -664.022 -102.596 96.9922 39.9991

151 KGUC 72467 -857.452 -115.301 96.9899 39.9990

152 KMTJ 93013 -940.981 -109.358 96.9889 39.9990

153 KCEZ 72476 -1020.87 -233.14 96.9880 39.9979

154 KCPS 72531 591.652 -136.14 97.0070 39.9988

155 KLWV 72534 808.939 -94.46 97.0096 39.9992

156 KPPF 74543 130.433 -293.855 97.0015 39.9973

157 KHOP 74671 841.751 -324.569 97.0099 39.9971

158 KBIX 74768 778.252 -1028.514 97.0092 39.9907

159 KPQL 11165 814.599 -1019.583 97.0096 39.9908

160 MMPG 76243 -348.007 -1248.779 96.9959 39.9887

161 MMMV 76342 -446.576 -1449.334 96.9947 39.9869

162 MMMY 76394 -316.664 -1581.176 96.9963 39.9857
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TABLE A-2. LIST OF UPPER AIR METEOROLOGICAL STATIONS

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

1 KABQ 23050 -869.46 -501.713 96.9897 39.9955

2 KAMA 23047 -425.319 -518.171 96.9950 39.9953

3 KBMX 53823 951.609 -702.935 97.0112 39.9936

4 KBNA 13897 920.739 -377.164 97.0109 39.9966

5 KBRO 12919 -44.167 -1571.39 96.9995 39.9858

6 KCRP 12924 -51.535 -1360.35 96.9994 39.9877

7 KDDC 13985 -259.352 -242.681 96.9969 39.9978

8 KDRT 22010 -384.069 -1170.59 96.9955 39.9894

9 KEPZ 3020 -914.558 -852.552 96.9892 39.9923

10 KFWD 3990 -28.034 -793.745 96.9997 39.9928

11 KJAN 3940 650.105 -826.452 97.0077 39.9925

12 KLCH 3937 364.461 -1089.15 97.0043 39.9902

13 KLZK 3952 432.063 -560.441 97.0051 39.9949

14 KMAF 23023 -489.668 -878.107 96.9942 39.9921

15 KOUN 3948 -40.731 -527.298 96.9995 39.9952

16 KSHV 13957 298.869 -831.166 97.0035 39.9925

17 KSIL 53813 698.079 -1054.03 97.0082 39.9905
OG&E A-6 Trinity Consultants

CALMET Processing Protocol 083701.0004

TABLE A-3. LIST OF PRECIPITATION METEOROLOGICAL STATIONS

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

1 ADDI 10063 906.825 -601.428 97.0107 39.9946

2 ALBE 10140 917.606 -821.64 97.0108 39.9926

3 BERR 10748 892.454 -683.388 97.0105 39.9938

4 HALE 13620 881.928 -601.878 97.0104 39.9946

5 HAMT 13645 863.663 -612.725 97.0102 39.9945

6 JACK 14193 898.014 -915.623 97.0106 39.9917

7 MBLE 15478 851.953 -1022.41 97.0101 39.9908

8 MUSC 15749 880.113 -567.484 97.0104 39.9949

9 PETE 16370 935.558 -908.259 97.0110 39.9918

10 THOM 18178 900.858 -915.326 97.0106 39.9917

11 TUSC 18385 895.631 -713.223 97.0106 39.9936

12 VERN 18517 825.585 -685.773 97.0098 39.9938

13 BEEB 30530 462.394 -532.485 97.0055 39.9952

14 BRIG 30900 318.015 -554.857 97.0038 39.9950

15 CALI 31140 419.619 -731.44 97.0050 39.9934

16 CAMD 31152 386.546 -699.659 97.0046 39.9937

17 DIER 32020 268.114 -643.184 97.0032 39.9942

18 EURE 32356 286.738 -390.862 97.0034 39.9965

19 GILB 32794 383.362 -435.625 97.0045 39.9961

20 GREE 32978 450.594 -483.201 97.0053 39.9956

21 STUT 36920 509.943 -596.328 97.0060 39.9946

22 TEXA 37048 278.022 -720.623 97.0033 39.9935

23 ALAM 50130 -749.044 -267.856 96.9912 39.9976

24 ARAP 50304 -441.903 -152.324 96.9948 39.9986

25 COCH 51713 -819.794 -148.582 96.9903 39.9987

26 CRES 51959 -828.107 -119.911 96.9902 39.9989

27 GRAN 53477 -451.781 -203.82 96.9947 39.9982

28 GUNN 53662 -829.573 -141.995 96.9902 39.9987

29 HUGO 54172 -539.364 -81.948 96.9936 39.9993

30 JOHN 54388 -483.95 -201.915 96.9943 39.9982

31 KIM 54538 -544.501 -283.337 96.9936 39.9974

32 MESA 55531 -993.391 -256.696 96.9883 39.9977

33 ORDW 56136 -549.552 -55.741 96.9935 39.9995

34 OURA 56203 -904.197 -168.246 96.9893 39.9985

35 PLEA 56591 -1005.94 -229.472 96.9881 39.9979

36 PUEB 56740 -633.961 -176.872 96.9925 39.9984

37 TYE 57320 -662.095 -242.254 96.9922 39.9978

38 SAGU 57337 -790.269 -176.061 96.9907 39.9984
OG&E A-7 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

39 SANL 57428 -726.777 -285.47 96.9914 39.9974

40 SHEP 57572 -714.046 -252.189 96.9916 39.9977

41 TELL 58204 -920.205 -215.382 96.9891 39.9981

42 TERC 58220 -708.229 -296.023 96.9916 39.9973

43 TRIN 58429 -642.489 -293.805 96.9924 39.9973

44 TRLK 58436 -646.185 -295.727 96.9924 39.9973

45 WALS 58781 -654.989 -262.821 96.9923 39.9976

46 WHIT 58997 -619.615 -250.12 96.9927 39.9977

47 ASHL 110281 684.787 -169.285 97.0081 39.9985

48 CAIR 111166 697.177 -301.436 97.0082 39.9973

49 CARM 111302 772.938 -177.782 97.0091 39.9984

50 CISN 111664 758.146 -151.446 97.0090 39.9986

51 FLOR 113109 751.801 -139.837 97.0089 39.9987

52 HARR 113879 762.044 -246.62 97.0090 39.9978

53 KASK 114629 650.464 -239.886 97.0077 39.9978

54 LAWR 114957 829.038 -128.708 97.0098 39.9988

55 MTCA 115888 827.797 -149.966 97.0098 39.9986

56 MURP 115983 682.261 -251.649 97.0081 39.9977

57 NEWT 116159 766.098 -72.902 97.0090 39.9993

58 REND 117187 731.633 -185.058 97.0086 39.9983

59 SMIT 118020 770.027 -283.638 97.0091 39.9974

60 SPAR 118147 658.275 -185.973 97.0078 39.9983

61 VAND 118781 685.449 -127.048 97.0081 39.9989

62 WEST 119193 778.655 -147.215 97.0092 39.9987

63 EVAN 122738 842.476 -172.871 97.0100 39.9984

64 NEWB 126151 855.854 -223.713 97.0101 39.9980

65 PRIN 127125 836.901 -153.449 97.0099 39.9986

66 STEN 128442 859.099 -156.613 97.0101 39.9986

67 JTML 128967 788.703 -239.572 97.0093 39.9978

68 ARLI 140326 -101.734 -271.373 96.9988 39.9976

69 BAZI 140620 -210.423 -201.758 96.9975 39.9982

70 BEAU 140637 59.762 -288.39 97.0007 39.9974

71 BONN 140957 211.236 -103.29 97.0025 39.9991

72 CALD 141233 -32.689 -330.586 96.9996 39.9970

73 CASS 141351 54.006 -217.645 97.0006 39.9980

74 CENT 141404 170.503 -206.038 97.0020 39.9981

75 CHAN 141427 150.257 -286.094 97.0018 39.9974

76 CLIN 141612 155.623 -157.682 97.0018 39.9986

77 COLL 141730 -265.465 -156.95 96.9969 39.9986

78 COLU 141740 220.541 -316.555 97.0026 39.9971
OG&E A-8 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

79 CONC 141867 58.918 -175.589 97.0007 39.9984

80 DODG 142164 -226.497 -277.655 96.9973 39.9975

81 ELKH 142432 -400.112 -321.784 96.9953 39.9971

82 ENGL 142560 -264.927 -324.066 96.9969 39.9971

83 ERIE 142582 162.669 -291.383 97.0019 39.9974

84 FALL 142686 83.491 -288.177 97.0010 39.9974

85 GALA 142938 -136.931 -176.83 96.9984 39.9984

86 GARD 142980 -304.059 -215.308 96.9964 39.9981

87 GREN 143248 64.308 -307.161 97.0008 39.9972

88 HAYS 143527 -190.307 -161.342 96.9978 39.9985

89 HEAL 143554 -292.133 -175.921 96.9966 39.9984

90 HILL 143686 214.018 -174.006 97.0025 39.9984

91 INDE 143954 139.335 -315.058 97.0016 39.9972

92 IOLA 143984 153.451 -269.438 97.0018 39.9976

93 JOHR 144104 134.784 -203.41 97.0016 39.9982

94 KANO 144178 -50.289 -181.177 96.9994 39.9984

95 KIOW 144341 -113.967 -329.843 96.9987 39.9970

96 MARI 145039 -4.343 -195.712 97.0000 39.9982

97 MELV 145210 137.104 -186.781 97.0016 39.9983

98 MILF 145306 39.504 -106.05 97.0005 39.9990

99 MOUD 145536 152.624 -318.136 97.0018 39.9971

100 OAKL 145888 -306.378 -96.814 96.9964 39.9991

101 OTTA 146128 158.639 -178.635 97.0019 39.9984

102 POMO 146498 143.864 -176.707 97.0017 39.9984

103 SALI 147160 -29.426 -166.908 96.9997 39.9985

104 SMOL 147551 -34.639 -171.31 96.9996 39.9985

105 STAN 147756 225.026 -164.85 97.0027 39.9985

106 SUBL 147922 -303.514 -292.808 96.9964 39.9974

107 TOPE 148167 139.116 -104.91 97.0016 39.9991

108 TRIB 148235 -387.855 -180.643 96.9954 39.9984

109 UNIO 148293 211.43 -272.537 97.0025 39.9975

110 WALL 148535 -376.076 -152.432 96.9956 39.9986

111 WICH 148830 -23.729 -288.579 96.9997 39.9974

112 WILS 148946 -111.502 -156.22 96.9987 39.9986

113 BENT 150611 781.608 -348.109 97.0092 39.9969

114 CALH 151227 865.268 -261.635 97.0102 39.9976

115 CLTN 151631 749.287 -365.634 97.0088 39.9967

116 HERN 153798 859.01 -352.458 97.0101 39.9968

117 MADI 155067 854.116 -265.064 97.0101 39.9976

118 PADU 156110 753.185 -293.024 97.0089 39.9974
OG&E A-9 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

119 PCTN 156580 834.464 -280.496 97.0099 39.9975

120 ALEX 160103 433.824 -959.253 97.0051 39.9913

121 BATN 160549 562.794 -1032.4 97.0066 39.9907

122 CALH 161411 436.113 -817.451 97.0052 39.9926

123 CLNT 161899 578.969 -999.986 97.0068 39.9910

124 JENA 164696 455.225 -912.366 97.0054 39.9918

125 LACM 165078 364.784 -1089.92 97.0043 39.9901

126 MIND 166244 346.708 -812.651 97.0041 39.9927

127 MONR 166314 463.225 -814.905 97.0055 39.9926

128 NATC 166582 369.451 -905.316 97.0044 39.9918

129 SHRE 168440 299.526 -831.143 97.0035 39.9925

130 WINN 169803 408.309 -884.596 97.0048 39.9920

131 BROK 221094 621.827 -914.236 97.0073 39.9917

132 CONE 221900 737.007 -823.513 97.0087 39.9926

133 JAKS 224472 650.361 -826.097 97.0077 39.9925

134 LEAK 224966 805.886 -943.78 97.0095 39.9915

135 MERI 225776 774.942 -814.558 97.0092 39.9926

136 SARD 227815 658.33 -593.661 97.0078 39.9946

137 SAUC 227840 763.399 -1005.93 97.0090 39.9909

138 TUPE 229003 753.571 -600.03 97.0089 39.9946

139 ADVA 230022 657.892 -298.102 97.0078 39.9973

140 ALEY 230088 505.348 -305.864 97.0060 39.9972

141 BOLI 230789 331.651 -291.689 97.0039 39.9974

142 CASV 231383 310.855 -392.187 97.0037 39.9965

143 CLER 231674 575.868 -302.209 97.0068 39.9973

144 CLTT 231711 307.465 -190.83 97.0036 39.9983

145 COLU 231791 421.287 -155.672 97.0050 39.9986

146 DREX 232331 228.23 -185.776 97.0027 39.9983

147 ELM 232568 257.758 -159.419 97.0030 39.9986

148 FULT 233079 470.408 -150.668 97.0056 39.9986

149 HOME 233999 619.93 -415.469 97.0073 39.9962

150 JEFF 234271 424.774 -172.095 97.0050 39.9984

151 JOPL 234315 238.245 -318.262 97.0028 39.9971

152 LEBA 234825 402.239 -276.263 97.0048 39.9975

153 LICK 234919 480.849 -280.775 97.0057 39.9975

154 LOCK 235027 302.048 -300.612 97.0036 39.9973

155 MALD 235207 659.982 -377.876 97.0078 39.9966

156 MARS 235298 332.062 -94.655 97.0039 39.9991

157 MAFD 235307 391.968 -300.033 97.0046 39.9973

158 MCES 235415 471.737 -143.942 97.0056 39.9987
OG&E A-10 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

159 MILL 235594 309.516 -311.398 97.0037 39.9972

160 MTGV 235834 426.937 -310.43 97.0050 39.9972

161 NVAD 235987 243.915 -272.715 97.0029 39.9975

162 OZRK 236460 349.133 -390.626 97.0041 39.9965

163 PDTD 236777 334.055 -265.018 97.0039 39.9976

164 POTO 236826 572.215 -251.455 97.0068 39.9977

165 ROLL 237263 484.503 -253.958 97.0057 39.9977

166 ROSE 237300 500.59 -175.393 97.0059 39.9984

167 SALE 237506 498.94 -274.122 97.0059 39.9975

168 SENE 237656 233.959 -383.703 97.0028 39.9965

169 SPRC 237967 238.112 -373.616 97.0028 39.9966

170 SPVL 237976 332.385 -309.374 97.0039 39.9972

171 STEE 238043 503.354 -205.135 97.0059 39.9981

172 STOK 238082 310.911 -279.239 97.0037 39.9975

173 SWSP 238223 324.053 -150.325 97.0038 39.9986

174 TRKD 238252 340.418 -395.428 97.0040 39.9964

175 TRUM 238466 326.883 -197.796 97.0039 39.9982

176 UNIT 238524 238.567 -154.494 97.0028 39.9986

177 VIBU 238609 519.633 -267.258 97.0061 39.9976

178 VIEN 238620 470.383 -193.872 97.0056 39.9983

179 WAPP 238700 606.68 -358.746 97.0072 39.9968

180 WASG 238746 556.425 -164.993 97.0066 39.9985

181 WEST 238880 489.373 -377.809 97.0058 39.9966

182 ALBU 290234 -869.46 -501.713 96.9897 39.9955

183 ARTE 290600 -689.529 -773.897 96.9919 39.9930

184 AUGU 290640 -973.07 -598.391 96.9885 39.9946

185 CARL 291469 -680.335 -811.474 96.9920 39.9927

186 CARR 291515 -819.836 -665.132 96.9903 39.9940

187 CLAY 291887 -547.124 -374.102 96.9935 39.9966

188 CLOV 291939 -566.973 -599.296 96.9933 39.9946

189 CUBA 292241 -890.304 -392.495 96.9895 39.9965

190 CUBE 292250 -951.142 -489.293 96.9888 39.9956

191 DEMI 292436 -1007.99 -799.087 96.9881 39.9928

192 DURA 292665 -767.148 -577.618 96.9909 39.9948

193 EANT 292700 -735.089 -366.94 96.9913 39.9967

194 LAVG 294862 -738.245 -461.163 96.9913 39.9958

195 PROG 297094 -811.39 -578.971 96.9904 39.9948

196 RAMO 297254 -733.737 -615.175 96.9913 39.9944

197 ROSW 297610 -698.544 -712.921 96.9918 39.9936

198 ROY 297638 -644.735 -422.422 96.9924 39.9962
OG&E A-11 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

199 SANT 298085 -807.375 -445.708 96.9905 39.9960

200 SPRI 298501 -676.681 -374.272 96.9920 39.9966

201 STAY 298518 -810.491 -495.501 96.9904 39.9955

202 TNMN 299031 -912.488 -413.425 96.9892 39.9963

203 TUCU 299156 -604.359 -508.834 96.9929 39.9954

204 WAST 299569 -638.605 -820.288 96.9925 39.9926

205 WISD 299686 -856.967 -756.366 96.9899 39.9932

206 AIRS 340179 -212.731 -597.062 96.9975 39.9946

207 ARDM 340292 -12.242 -645.633 96.9999 39.9942

208 BENG 340670 174.368 -568.011 97.0021 39.9949

209 CANE 341437 71.857 -637.935 97.0009 39.9942

210 CHRT 341544 203.233 -632.067 97.0024 39.9943

211 CHAN 341684 10.494 -475.655 97.0001 39.9957

212 CHIK 341750 -83.175 -547.26 96.9990 39.9951

213 CCTY 342334 -165 -479.536 96.9981 39.9957

214 DUNC 342654 -88.38 -610.04 96.9990 39.9945

215 ELKC 342849 -216.769 -507.879 96.9974 39.9954

216 FORT 343281 -129.964 -541.113 96.9985 39.9951

217 GEAR 343497 -118.53 -482.187 96.9986 39.9956

218 HENN 344052 -31.964 -601.206 96.9996 39.9946

219 HOBA 344202 -189.062 -547.36 96.9978 39.9951

220 KING 344865 24.538 -664.103 97.0003 39.9940

221 LKEU 344975 141.702 -520.6 97.0017 39.9953

222 LEHI 345108 71.634 -612.05 97.0009 39.9945

223 MACI 345463 -254.63 -466.154 96.9970 39.9958

224 MALL 345589 -55.127 -425.644 96.9994 39.9962

225 MAYF 345648 -258.49 -512.583 96.9970 39.9954

226 MUSK 346130 149.764 -466.905 97.0018 39.9958

227 NOWA 346485 121.551 -364.038 97.0014 39.9967

228 OKAR 346620 -88.424 -473.338 96.9990 39.9957

229 OKEM 346638 63.188 -504.958 97.0008 39.9954

230 OKLA 346661 -54.198 -510.562 96.9994 39.9954

231 PAOL 346859 -23.665 -573.142 96.9997 39.9948

232 PAWH 346935 57.704 -369.174 97.0007 39.9967

233 PAWN 346944 16.927 -398.139 97.0002 39.9964

234 PONC 347196 -8.871 -363.068 96.9999 39.9967

235 PRYO 347309 150.763 -407.824 97.0018 39.9963

236 SHAT 348101 -256.963 -407.368 96.9970 39.9963

237 STIG 348497 171.02 -523.736 97.0020 39.9953

238 TULS 348992 99.361 -419.873 97.0012 39.9962
OG&E A-12 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

239 TUSK 349023 156.629 -592.395 97.0019 39.9946

240 WMWR 349629 -156.42 -581.308 96.9982 39.9947

241 WOLF 349748 30.212 -538.388 97.0004 39.9951

242 BOLI 400876 760.886 -500.256 97.0090 39.9955

243 BROW 401150 710.048 -480.346 97.0084 39.9957

244 CETR 401587 877.35 -456.294 97.0104 39.9959

245 DICS 402489 872.14 -391.132 97.0103 39.9965

246 DYER 402680 695.792 -409.316 97.0082 39.9963

247 GRNF 403697 760.795 -395.69 97.0090 39.9964

248 JSNN 404561 765.932 -476.414 97.0090 39.9957

249 LWER 405089 885.291 -487.757 97.0105 39.9956

250 LEXI 405210 790.003 -471.897 97.0093 39.9957

251 MASO 405720 694.163 -496.166 97.0082 39.9955

252 MEMP 405954 671.8 -522.492 97.0079 39.9953

253 MWFO 405956 681.292 -516.15 97.0080 39.9953

254 MUNF 406358 678.65 -495.241 97.0080 39.9955

255 SAMB 408065 697.077 -382.536 97.0082 39.9965

256 SAVA 408108 800.788 -498.682 97.0095 39.9955

257 UNCY 409219 711.595 -384.605 97.0084 39.9965

258 ABIL 410016 -251.753 -836.027 96.9970 39.9924

259 AMAR 410211 -425.302 -517.839 96.9950 39.9953

260 AUST 410428 -67.587 -1075.97 96.9992 39.9903

261 BRWN 411136 -43.861 -1571.39 96.9995 39.9858

262 COST 411889 60.611 -1044.72 97.0007 39.9906

263 COCR 412015 -51.832 -1360.01 96.9994 39.9877

264 CROS 412131 -204.599 -868.469 96.9976 39.9922

265 DFWT 412242 -1.867 -786.341 97.0000 39.9929

266 EAST 412715 -171.024 -840.253 96.9980 39.9924

267 ELPA 412797 -886.583 -860.763 96.9895 39.9922

268 HICO 414137 -97.323 -888.181 96.9989 39.9920

269 HUST 414300 157.976 -1108.38 97.0019 39.9900

270 KRES 414880 -434.746 -611.717 96.9949 39.9945

271 LKCK 414975 99.734 -693.521 97.0012 39.9937

272 LNGV 415348 220.962 -844.674 97.0026 39.9924

273 LUFK 415424 214.652 -969.69 97.0025 39.9912

274 MATH 415661 -86.438 -1330.47 96.9990 39.9880

275 MIDR 415890 -489.385 -878.123 96.9942 39.9921

276 MTLK 416104 -672.024 -1008.98 96.9921 39.9909

277 NACO 416177 223.065 -925.966 97.0026 39.9916

278 NAVA 416210 28.358 -892.028 97.0003 39.9919
OG&E A-13 Trinity Consultants

CALMET Processing Protocol 083701.0004

Number

Station 

Acronym

Station 

ID

LCC 

East 

(km)

LCC 

North

(km) Long Lat

279 NEWB 416270 239.111 -721.818 97.0028 39.9935

280 BPAT 417174 288.962 -1110.65 97.0034 39.9900

281 RANK 417431 -472.048 -959.488 96.9944 39.9913

282 SAAG 417943 -333.338 -952.54 96.9961 39.9914

283 SAAT 417945 -143.322 -1161.27 96.9983 39.9895

284 SHEF 418252 -463.759 -1019.19 96.9945 39.9908

285 STEP 418623 -112.988 -857.918 96.9987 39.9922

286 STER 418630 -376.683 -897.195 96.9956 39.9919

287 VALE 419270 -720.749 -1015.17 96.9915 39.9908

288 VICT 419364 6.882 -1236.45 97.0001 39.9888

289 WACO 419419 -21.834 -928.823 96.9997 39.9916

290 WATR 419499 -353.767 -916.015 96.9958 39.9917

291 WHEE 419665 57.489 -1008.99 97.0007 39.9909

292 WPDM 419916 262.792 -737.786 97.0031 39.9933

293 DORA 232302 433.256 -378.797 97.0051 39.9966

294 DIXN 112353 756.057 -267.193 97.0089 39.9976

295 DAUP 12172 864.408 -1050.41 97.0102 39.9905

296 FREV 123104 847.031 -117.884 97.0100 39.9989

297 WARR 18673 890.447 -788.703 97.0105 39.9929

298 MDTN 235562 493.264 -87.222 97.0058 39.9992
OG&E A-14 Trinity Consultants

CALMET Processing Protocol 083701.0004

TABLE A-4. LIST OF OVER WATER METEOROLOGICAL STATIONS

Number

Station 

ID

Input file 

Name

LCC 

East 

(km)

LCC North

(km) Long Lat

1 42001 42001 746.874 -1541.35 89.67 25.9

2 42002 42002 265.486 -1650.616 94.42 25.19

3 42007 42007 795.674 -1063.667 88.77 30.09

4 42019 42019 163.178 -1342.917 95.36 27.91

5 42020 42020 30.212 -1453.738 96.7 26.94

6 42035 42035 254.465 -1193.539 94.41 29.25

7 42040 42040 859.497 -1160.066 88.21 29.18

8 BURL1 42045 743.116 -1202.117 89.43 28.9

9 DPIA1 42046 861.385 -1039.466 88.07 30.25

10 GDIL1 42047 687.984 -1164.910 89.96 29.27

11 PTAT2 42048 -4.980 -1353.398 97.05 27.83

12 SRST2 42049 288.163 -1175.682 94.05 29.67