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

March 2014

Version 4
Arkansas Electric Cooperative Corporation i Trinity Consultants

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..................................... 5-2

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

5.4.2 ENERGY IMPACTS AND NON-AIR QUALITY IMPACTS.......................................5-10

5.4.3 REMAINING USEFUL LIFE .................................................................................5-10

5.5 EVALUATION OF VISIBILITY IMPACT OF FEASIBLE SO2 CONTROLS .................... 5-10

5.6 PROPOSED BART FOR SO2 ................................................................................... 5-14

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).....................................................7-1

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

7.4.1.1 Energy Impacts and Non-Air Quality Impacts ....................................7-5
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BART Analysis – Bailey and McClellan

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......................................................................................................................... 7-12

APPENDIX B......................................................................................................................... 7-13
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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 5-7. AVERAGE SULFUR CONTENT OF FUEL STORED AT BAILEY AND MCCLELLAN ..............5-4

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

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

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

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

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

FUEL OIL.................................................................................................................................5-6

TABLE 5-7. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH DIESEL.................5-6

TABLE 5-8. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH NATURAL GAS.....5-7
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BART Analysis – Bailey and McClellan

FIGURE 5-1. SUMMARY OF FUTURE ANNUAL SO2 EMISSIONS ASSOCIATED WITH NATURAL GAS....5-8

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

OIL AT BAILEY UNIT 1............................................................................................................5-9

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

OIL AT MCCLELLAN UNIT 1...................................................................................................5-9

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

CONTROL AT BAILEY UNIT 1 ...............................................................................................5-11

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

CONTROL AT MCCLELLAN UNIT 1 ......................................................................................5-11

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

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

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

FOR MCCLELLAN UNIT 1 (2001-2003).................................................................................5-13

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 previous 

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. AECC recognizes that the 

recent modeling shows impacts for Bailey Unit 1 that are less than 0.5∆dv, the threshold that ADEQ 

used to classify a source as subject to BART. Nevertheless, AECC is continuing with the BART 

analysis.

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 0.348 7 0.368 6 0.379 12

McClellan, Unit 1 – Natural Gas 0.125 3 0.052 0 0.040 0 0.058 0

McClellan, Unit 1 – Fuel Oil 0.622 24 0.266 5 0.231 2 0.228 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 using 

fuels with 0.5% sulfur or less (including natural gas). 

▲ 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 1.2

▲ 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.

2 This determination was originally submitted on July 24, 2012. In response to CSAPR being vacated on August 

21, 2012, AECC submitted a five-factor analysis for NOx to ADEQ in September 2012 as an addendum to this analysis.
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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.3 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:

 3 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

1. Existing controls

2. Cost of controls

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. 

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. Thus, AECC is proposing to satisfy 

BART for NOx by complying with CSAPR at Bailey Unit 1 and McClellan Unit 1.4

 4 This proposal was originally submitted on July 24, 2012. In response to CSAPR being vacated on August 21, 

2012, AECC submitted a five-factor analysis for NOx to ADEQ in September 2012 as an addendum to this analysis.
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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 B. Note that the protocol 

included in Appendix B 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 ext b 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 specific Rayleigh 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

+ +

+ + + + +

+ +

+ +

− f RH b

f RH f RH

f RH f RH

SS

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 for natural gas combustion are based on the emission factor for total PM10 in 

Table 1.4-2 of AP-42, which is 7.6 lbs/MMscf, and the maximum heat inputs for the units. The 

emission rates for the PM10 species shown in Table 4-1 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 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.

The PM10 rates for fuel oil combustion are based on stack testing of both filterable and condensable

PM10 conducted on Unit 1 at the McClellan plant on May 29, 2013. The total PM10 emission rate 

determined during the testing was 59.4 lb/hr. Thus, a total PM10 emission rate of 59.4 lb/hr was 

modeled for McCllellan. Stack testing was not conducted at Bailey in 2013, however, the total PM10

emission rate for Unit 1 at Bailey was scaled by the ratio of the heat input for Bailey vs McClellan 

(1436/1350) to get a total PM10 emission rate of 55.8 lb/hr. 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.5 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%

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

EQUIVALENTS)

Unit / Fuel SO26 NOx7

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 2.6

Bailey, Unit 1 –

Fuel Oil 2,375.8 408.8 55.8 4.6 13.7 34.1 0.8 2.7

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 59.4 5.9 14.2 35.4 1.0 2.8

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. 

 

6 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. 

7 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 9,812 lb/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 3.35 0.00

2002 0.219 0.075 0 0.31 95.93 3.22 0.54

2003 0.147 0.067 0 0.40 91.98 5.51 2.10

Upper Buffalo Wilderness

2001 0.089 0.04 0 0.23 95.01 3.05 1.72

2002 0.160 0.031 0 0.30 86.44 5.48 7.77

2003 0.170 0.072 0 0.29 95.02 3.43 1.26

Hercules Glades Wilderness

2001 0.238 0.056 0 0.23 96.39 3.08 0.31

2002 0.067 0.039 0 0.88 87.67 10.78 0.67

2003 0.175 0.073 0 0.22 92.76 3.67 3.35

Mingo Wilderness

2001 0.154 0.070 0 0.29 90.58 5.41 3.72

2002 0.443 0.084 0 0.43 83.07 7.92 8.58

2003 0.201 0.102 0 0.45 83.34 8.10 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 0.684 0.307 2 75.66 22.47 1.44 0.44

2002 0.745 0.330 3 87.19 12.11 0.57 0.14

2003 0.970 0.327 3 98.80 0.81 0.40 0

Upper Buffalo Wilderness

2001 0.578 0.282 3 94.29 4.99 0.73 0.00

2002 0.668 0.305 1 73.65 21.28 3.43 1.64

2003 0.696 0.348 3 90.73 8.42 0.83 0.02

Hercules Glades Wilderness

2001 0.687 0.327 3 98.40 1.07 0.52 0

2002 0.635 0.249 2 80.38 18.62 0.87 0.12

2003 0.648 0.368 1 82.74 14.39 2.08 0.79

Mingo Wilderness

2001 0.524 0.355 1 89.57 8.35 1.67 0.41

2002 1.592 0.379 7 93.95 4.68 1.26 0.11

2003 0.689 0.300 4 66.17 29.13 2.83 1.87
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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 4.43 1.57

2002 0.175 0.092 0 0.55 82.94 8.35 8.15

2003 0.538 0.125 2 0.39 87.09 6.63 5.89

Upper Buffalo Wilderness

2001 0.096 0.048 0 0.38 92.78 5.43 1.41

2002 0.258 0.031 0 0.32 94.54 4.04 1.10

2003 0.112 0.052 0 0.34 91.78 4.82 3.05

Hercules Glades Wilderness

2001 0.064 0.034 0 0.29 93.50 4.42 1.79

2002 0.082 0.022 0 0.74 88.76 10.09 0.41

2003 0.092 0.04 0 0.74 86.01 10.18 3.07

Mingo Wilderness

2001 0.091 0.032 0 0.30 92.13 3.91 3.67

2002 0.132 0.058 0 0.33 91.96 5.13 2.58

2003 0.107 0.034 0 0.37 90.42 5.85 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 1.685 0.622 10 89.86 9.62 0.53 0.00

2002 1.021 0.389 4 86.29 11.26 1.72 0.74

2003 3.007 0.616 9 82.89 15.76 0.36 0.62

Upper Buffalo Wilderness

2001 0.604 0.258 2 84.02 14.98 0.99 0.01

2002 1.323 0.184 1 77.31 20.96 1.43 0.30

2003 0.599 0.266 2 98.47 0.95 0.58 0.00

Hercules Glades Wilderness

2001 0.512 0.231 1 78.67 20.16 1.17 0.01

2002 0.463 0.168 0 59.28 37.65 2.31 0.75

2003 0.662 0.211 1 76.18 20.22 2.51 1.08

Mingo Wilderness

2001 0.417 0.228 0 80.90 17.89 1.20 0.01

2002 0.547 0.213 2 59.42 36.88 2.32 1.38

2003 0.471 0.203 0 87.39 11.23 1.29 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.8 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 some residual fuel oil. The most recent fuel oil shipment 

for Bailey was in December of 2006, and the most recent fuel oil shipment for McClellan 

was in April of 2009. The fuel oil that has been stored at Bailey since 2006 has an average 

sulfur content of 1.81 percent by weight, and the fuel oil that has been stored at McClellan 

since 2009 has an average sulfur content of 1.38 percent by weight. 

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. 

Fuels with lower sulfur content include lower sulfur No. 6 fuel oil, No. 2 fuel oil, or natural 

gas.

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 for Bailey and McClellan, respectively). 

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%

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:

 8 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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▲ 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. 

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 in phone calls with 

AECC staff. 

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 sulfur content used for baseline was 1.81% for Bailey Unit 1 and 1.38% for McClellan 

Unit 1. Table 5-4 below summarizes the annual average sulfur content of the No. 6 fuel oil 

historically used at Bailey and McClellan. The most recent fuel oil shipment for Bailey 

was in December of 2006, and the most recent fuel oil shipment for McClellan was in 

April of 2009. The fuel oil that has been stored at Bailey since 2006 has an average sulfur 

content of 1.81 percent by weight, and the fuel oil that has been stored at McClellan since 

2009 has an average sulfur content of 1.38 percent by weight. 
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TABLE 5-7. AVERAGE SULFUR CONTENT OF FUEL STORED AT BAILEY AND MCCLELLAN

Bailey McClellan

2000 1.59 1.84

2001 1.30 1.70

2002 1.69 2.21

2003 1.89 1.67

2004 1.07 1.60

2005 1.45 1.94

2006 1.33 2.08

2007 1.81 2.06

2008 1.81 2.18

2009 1.81 1.38

2010 1.81 1.38

2011 1.81 1.38

2001 - 2003 average 1.63 1.86

2009 - 2011 average 1.81 1.38

In the EPA’s 2005 Regional Haze Rule BART Guidelines, EPA described baseline 

emissions as follows:

“The baseline emissions rate should represent a realistic depiction of anticipated annual 

emissions for the source... In the absence of enforceable limitations, you calculate baseline 

emissions based upon continuation of past practice.”

Since EPA states that baseline emissions should be based on anticipated annual emissions 

and a continuation of past practice, AECC used the sulfur content of the fuel oil currently 

stored at Bailey and McClellan to estimate baseline emissions for Bailey Unit 1 and 

McClellan Unit 1.

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 that is available to form sulfur compounds (i.e. SO2 and SO4) 

was calculated:

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

The mass of sulfur in the form of SO4 was estimated and subtracted from the total sulfur to 

determine the quantity of sulfur that could form SO2. The SO2 emission rate was estimated 

by multiplying the sulfur available to form SO2 by the ratio of the molecular weight for 
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SO2 vs. sulfur. The mass of sulfur in the form of SO4 was estimated by reducing the 

baseline SO4 emission rate in proportion to the percent reduction in fuel sulfur and then 

multiplying the SO4 rate by the ratio of the molecular weight of sulfur vs. SO4.

Tables 5-4 through and 5-8 provide a summary of the mass balance data and calculations 

for the future annual SO2 emission rates. 

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 (lb/hr) 4.55 5.92

SO4 (tpy) 2.31 15.35

SO4 as Sulfur in Fuel [Assume 1 mol S for each mol SO4] (tpy) 0.39 2.56

% S as SO4 2.04 2.39

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 18.52 104.71

% S as SO2 97.96 97.61

SO2 (tpy) 37.03 209.43

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 

SO4 (lb/hr) 1.26 2.14

SO4 (tpy) 0.64 5.56

SO4 as Sulfur in Fuel [Assume 1 mol S for each mol SO4] (tpy) 0.11 0.93

% S as SO4 1.02 1.19

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 10.34 76.81

% S as SO2 98.98 98.81

SO2 (tpy) 20.67 153.61
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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 

SO4 (lb/hr) 1.26 2.14

SO4 (tpy) 0.64 5.56

SO4 as Sulfur in Fuel [Assume 1 mol S for each mol SO4] (tpy) 0.11 0.93

% S as SO4 2.04 2.39

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 5.12 37.94

% S as SO2 97.96 97.61

SO2 (tpy) 10.23 75.88

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 

SO4 (lb/hr) 0.13 0.21

SO4 (tpy) 0.06 0.56

SO4 as Sulfur in Fuel [Assume 1 mol S for each mol SO4] (tpy) 0.01 0.09

% S as SO4 2.11 2.47

Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 0.49 3.66

% S as SO2 0.98 0.98

SO2 (tpy) 0.99 7.31
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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 

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 over 1,000/ton of 

SO2 reduced for Bailey Unit 1 and over $2,000/ton for McClellan Unit 1; switching to 

diesel is greater than $7,000/ton for Bailey Unit 1 and over $10,000/ton for McClellan Unit 

1, and switching to natural gas would save AECC money.9 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.10 

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.

 9 Although AECC would save money under this scenario, the option to burn fuel oil must be maintained for 

electricity reliability purposes in case natural gas is not available (such as during a natural gas curtailment). 

10 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 $/MMBTU

Year

Fuel Price Variability 2003-2012

Natural Gas

Fuel Oil
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TABLE 5-9. SUMMARY OF COST EFFECTIVENESS FOR FUEL SWITCHING FOR CURRENT NO. 6 FUEL OIL AT BAILEY UNIT 1

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

Differential 

Cost of Fuel 

Switching

SO2 Cost 

EffectivenessE

PM10 Cost 

EffectivenessE

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

(MMBtu/Mgal) 

or 

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

Base CaseA 1.81 37.03 - - 25.63 - - 39,193 155.00 252.86 16.00 - - -

No. 6 - 1% 1.00 - 20.67 16.36 - 8.80 16.83 39,193 155.00 252.86 16.50 $ 19,596 1,198 1,165

No. 6 - 0.5% 0.50 - 10.23 26.80 - 2.75 22.88 39,193 155.00 252.86 17.75 $ 68,587 2,559 2,998

DieselA 0.05 - 0.99 36.05 - 0.13 25.50 39,193 136.15 287.86 20.95 $ 194,003 5,382 7,608

Natural Gas - - 0.01 37.02 - 0.26 25.37 39,193 1,011.00 38.77 6.19 $ (384,550) -10,387 -15,158

Average 

Sulfur 

ContentA

Baseline 

SO2 

Emission 

RateB

Controlled 

SO2 

Emission 

Rate

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

Differential 

Cost of Fuel 

Switching

SO2 Cost 

Effectiveness

PM10 Cost 

Effectiveness

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

(MMBtu/Mgal) 

or 

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

Base Case 1.38 209.43 - - 136.08 - - 291,733 155.00 1882.15 16.00 - - -

No. 6 - 1% 1.00 - 153.61 55.81 - 76.70 59.38 291,733 155.00 1882.15 16.50 145,866 2,613 2,457

No. 6 - 0.5% 0.50 - 75.88 133.55 - 23.94 112.14 291,733 155.00 1882.15 17.75 510,532 3,823 4,553

DieselA 0.05 - 7.31 202.11 - 1.10 134.98 291,733 136.15 2142.73 20.95 1,444,077 7,145 10,698

Natural Gas 0.04 - 0.07 209.35 - 1.36 134.72 291,733 1,011.00 288.56 5.97 -2,926,874 -13,980 -21,726

F Reductions in PM Species are based on default NPS profile.

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. 

C Higher heating value of residual oil based on data from supplier. Higher heating value of diesel is the 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 rates were calcauted using the average fuel usage from 2007 to 2011, the average heat content of the No. 6 fuel oil during that same time, and the average 

sulfur content of the fuel during that time. The baseline PM10 emission rates are the sum of the filterable PM species as predicted by the NPS workbook (based on total PM10 rates input 

to the workbook).
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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 to 2011. 

The controlled 30-day lb/MMBtu was converted to lb/hr by multiplying 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. Once the SO4 emission rate was determined, 

this rate was assumed to be IOR CPM and the emission rate was divided by the percentage of the total 

PM that NPS workbook indicates is IOR CPM to get the total PM rate. The total PM rate was then 

entered into the NPS workbook to get the emission rates for all of the PM species. The NOx emission 

rate was modeled at the baseline rate. 
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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 2.5 408.8 4.7 11.7 0.4 0.9 20.3

0.5% sulfur fuel 

oil No. 6 593.8 1.3 408.8 1.5 3.7 0.2 0.3 6.9

Diesel 59.4 0.1 408.8 0.1 0.2 0.0 0.0 0.4

Natural gas 0.5 0.3 443.8 0.0 0.0 7.4 2.6 10.3

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 4.3 579.8 8.0 19.9 0.8 1.6 34.6

0.5% sulfur fuel 

oil No. 6 1,158.5 2.1 579.8 2.5 6.2 0.4 0.5 11.7

Diesel 115.9 0.2 579.8 0.1 0.3 0.0 0.0 0.7

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) 0.970 0.330 8 - 0.696 0.348 7 - 0.687 0.368 6 - 1.592 0.379 12 -

1% sulfur fuel oil No. 6 0.544 0.193 1 41.52% 0.377 0.194 0 44.25% 0.408 0.206 0 44.02% 1.008 0.206 2 45.65%

0.5% sulfur fuel oil No. 6 0.333 0.142 0 56.97% 0.227 0.127 0 63.51% 0.279 0.135 0 63.32% 0.706 0.170 1 55.15%

Diesel 0.208 0.084 0 74.55% 0.156 0.069 0 80.17% 0.215 0.069 0 81.25% 0.429 0.095 0 74.93%

Natural gas 0.219 0.083 0 74.85% 0.170 0.072 0 79.31% 0.238 0.073 0 80.16% 0.443 0.102 0 73.09%

*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 3.015 0.622 24 - 1.323 0.266 5 - 0.662 0.231 2 - 0.547 0.228 2 -

1% sulfur fuel oil No. 6 2.671 0.537 18 13.67% 1.170 0.231 4 13.16% 0.562 0.202 1 12.55% 0.478 0.193 0 15.35%

0.5% sulfur fuel oil No. 6 1.722 0.322 8 48.23% 0.761 0.146 1 45.11% 0.294 0.115 0 50.22% 0.324 0.136 0 40.35%

Diesel 0.909 0.174 4 72.03% 0.382 0.073 0 72.56% 0.136 0.062 0 73.16% 0.190 0.080 0 64.91%

Natural gas 0.670 0.125 3 79.90% 0.258 0.052 0 80.45% 0.092 0.040 0 82.68% 0.132 0.058 0 74.56%

*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 45.65, 63.51, 81.25 or 80.16 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 44.02% to 81.25%.

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 15.35, 50.22, 73.16, or 82.68 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 using 

fuels with 0.5% sulfur or less (including natural gas). 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 

63.51% for Bailey and 50.22% 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. 
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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.”11 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.”12 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).13 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.14 

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

McClellan Unit 1.15

 11 “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.

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

13 “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.

14 Ibid, 40151.

15 This proposal was originally submitted on July 24, 2012. In response to CSAPR being vacated on August 21, 

2012, AECC submitted a five-factor analysis for NOx to ADEQ in September 2012 as an addendum to this analysis.
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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. 

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 
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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.16 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.17 This control is designed 

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

effective at controlling the smaller particles that are the primary 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%.18 

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 

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 

 16 Ibid.

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

18 AP-42, Fifth Edition, Volume I, Chapter 1, Section 1.3.4.1
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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.19

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.20 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.

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

20 Combustion-Fossil Power Systems, J.G. Singer published by Combustion Engineer, Inc.21AP-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 delivered 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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TABLE 7-3. CONTROL EFFECTIVENESS OF TECHNICALLY FEASIBLE PM CONTROL 

TECHNOLOGIES

Control Technology

Control Efficiency21 

(%)

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.

 21AP-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 delivered 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

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

Baseline 

Emission 

Rate

Control 

Efficiency

Annual Heat 

InputA

Controlled 

Emission Rate PM10 Reduced Capital Cost Total Annual Cost

Cost 

Effectiveness

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

Wet ESP 25.63 90.00 39,193 2.56 23.06 105,141,431 22,638,340 981,583

Wet Scrubber 25.63 55.00 39,193 11.53 14.09 140,957,713 50,150,862 3,558,286

Cyclone 25.63 85.00 39,193 3.84 21.78 989,479 1,188,630 54,570

No. 6 Fuel Oil - 1% 25.63 - 39,193 8.80 16.83 - 463,185 27,528

No 6. Fuel Oil - 0.5% 25.63 - 39,193 2.75 22.88 - 512,175 22,386

Diesel 25.63 - 39,193 0.13 25.50 - 637,592 25,004

Natural Gas 25.63 - 39,193 0.26 25.37 - 59,038 2,327

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

Baseline 

Emission 

Rate

Control 

Efficiency

Annual Heat 

InputA

 Controlled 

Emission Rate PM10 Reduced Capital Cost Total Annual Cost

Cost 

Effectiveness

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

Wet ESP 136.08 90.00 291,733 13.61 122.47 151,509,333 32,605,907 266,237

Wet Scrubber 136.08 55.00 291,733 61.23 74.84 146,303,011 52,056,542 695,549

Cyclone 136.08 85.00 291,733 20.41 115.67 1,432,971 1,721,384 14,882

No. 6 Fuel Oil - 1% 136.08 - 291,733 76.70 59.38 - 3,149,652 53,044

No 6. Fuel Oil - 0.5% 136.08 - 291,733 23.94 112.14 - 3,514,317 31,338

Diesel 136.08 - 291,733 1.10 134.98 - 4,447,862 32,952

Natural Gas 136.08 - 291,733 1.36 134.72 - 76,911 571

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 

improvement associated with PM controls was calculated as the difference between the existing 
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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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BART Analysis – Bailey and McClellan

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 

1.22

 22 However, AECC is proposing fuel switching to 0.5% sulfur fuel oil as BART for SO2.
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APPENDIX A

PM10 CONTROL COST CALCULATIONS
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APPENDIX B

MODELING PROTOCOL