## BART F IVE 

## F ACTOR 

## A NALYSIS



## A RKANSAS 

## E LECTRIC 

## C OOPERATIVE 

## C ORPORATION



## B AILEY AND 

## M C 

## C LELLAN 

## G ENERATING 

## S TATIONS

Prepared By:

T RINITY 

### C ONSULTANTS 

### , I NC



### .

120 East Sheridan, Suite 205 Oklahoma City, Oklahoma 73104 (405) 228-3292

T RINITY 

### C ONSULTANTS 

### , I NC



### .

977 Ridge Drive, Suite 380 Lenexa, Kansas 66219 (913) 894-4500

In conjunction with:

A RKANSAS 

### E LECTRIC COOPERATIVE CORPORATION 

### PO Box 194208 Little Rock, Arkansas (501) 570-2420

Trinity Project No. 123701.0036

December 2013
Version 3

### Arkansas Electric Cooperative Corporation i Trinity Consultants BART Analysis – Bailey and McClellan 

## T ABLE OF 

## C ONTENTS



### 1. E XECUTIVE 

### S UMMARY 

### ........................................................................................ 1-1

2. I NTRODUCTION AND 

### B ACKGROUND 

### .................................................................... 2-1

3. M ODELING 

### M ETHODOLOGIES AND 

### P ROCEDURES 

### ............................................. 3-1
3.1 CALMET AND 

### CALPUFF ................................................................................... 3-1
3.2 CALPOST ............................................................................................................ 3-1

4. E XISTING 

### E MISSIONS AND 

### V ISIBILITY 

### I MPAIRMENT 

### ......................................... 4-1
4.1 NO X 

### , SO 2 

### , AND 

### PM 10 

### B ASELINE 

### E MISSION 

### R ATES 

### ............................................... 4-1
4.2 B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### ....................................................................... 4-2

5. SO 2 

### BART E VALUATION 

### .................................................................................... 5-1
5.1 I DENTIFICATION OF 

### A VAILABLE 

### R ETROFIT 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES



### – F UEL 

### O IL COMBUSTION 

### .............................................................................................................. 5-1
5.2 E LIMINATE 

### T ECHNICALLY 

### I NFEASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

### ................ 5-1

5.2.1 D RY 

### S ORBENT 

### I NJECTION


### , S PRAY 

### D RYER 

### A BSORPTION 

### (SDA), W ET 

### S CRUBBER


### , C IRCULATING 

### D RY 

### S CRUBBER 

### (CDS) ............................................................................. 5-1

5.2.2 F UEL 

### S WITCHING 

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

### 5.3 R ANK OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### O PTIONS BY 

### E FFECTIVENESS 

### .. 5-2
5.4 E VALUATION OF 

### I MPACTS FOR 

### F EASIBLE 

### SO 2 

### C ONTROLS 

### ..................................... 5-3

5.4.1 C OST OF 

### C OMPLIANCE 

### ........................................................................................ 5-3

5.4.2 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS 

### ....................................... 5-10

5.4.3 R EMAINING 

### U SEFUL 

### L IFE 

### ................................................................................. 5-10


### 5.5 E VALUATION OF 

### V ISIBILITY 

### I MPACT OF 

### F EASIBLE 

### SO 2 

### C ONTROLS 

### .................... 5-10
5.6 P ROPOSED 

### B ART FOR 

### SO 2 

### ................................................................................... 5-14

6. NO X 

### BART E VALUATION 

### ................................................................................... 6-1

7. PM 10 

### BART E VALUATION 

### .................................................................................. 7-1
7.1 I DENTIFICATION OF 

### A VAILABLE 

### R ETROFIT 

### PM 10 

### C ONTROL 

### T ECHNOLOGIES 

### ........ 7-1
7.2 E LIMINATE 

### T ECHNICALLY 

### I NFEASIBLE 

### PM C ONTROL 

### T ECHNOLOGIES 

### ................. 7-1

7.2.1 D RY 

### E LECTROSTATIC 

### P RECIPITATORS 

### (ESP) ..................................................... 7-1

7.2.2 W ET 

### E LECTROSTATIC 

### P RECIPITATORS 

### (ESP) ..................................................... 7-1

7.2.3 M ECHANICAL 

### C OLLECTORS 

### (C YCLONES 

### ) .......................................................... 7-2

7.2.4 F ABRIC 

### F ILTER 

### .................................................................................................... 7-2

7.2.5 W ET 

### S CRUBBER 

### .................................................................................................. 7-2

7.2.6 F UEL 

### S WITCHING 

### ................................................................................................ 7-3 

### 7.3 R ANK OF 

### T ECHNICALLY FEASIBLE 

### PM 10 

### C ONTROL 

### O PTIONS BY 

### E FFECTIVENESS 

### . 7-3
7.4 E VALUATION OF 

### I MPACTS FOR 

### F EASIBLE 

### PM 10 

### C ONTROLS 

### ................................... 7-4

7.4.1 C OST OF 

### C OMPLIANCE 

### ........................................................................................ 7-4

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

### Arkansas Electric Cooperative Corporation ii Trinity Consultants BART Analysis – Bailey and McClellan 7.4.1.2 Remaining Useful Life ......................................................................... 7-5


### 7.5 E VALUATION OF 

### V ISIBILITY 

### I MPACT OF 

### F EASIBLE 

### PM 10 

### C ONTROLS 

### .................... 7-8
7.6 P ROPOSED 

### B ART FOR 

### PM 10 

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

A PPENDIX 

### A ......................................................................................................................... 7-1 4

A PPENDIX 

### B ......................................................................................................................... 7-1 5

### Arkansas Electric Cooperative Corporation iii Trinity Consultants BART Analysis – Bailey and McClellan 

## L IST OF 

## T ABLES

T ABLE 

### 1-1. E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### B AILEY 

### U NIT 

### 1 AND 

### M C 

### C LELLAN


### U NIT 

### 1 (2001-2003) ................................................................................................................ 1- 1

T ABLE 

### 3-1. A NNUAL 

### A VERAGE 

### B ACKGROUND 

### C ONCENTRATION 

### ..................................................... 3-2

T ABLE 

### 3-2. F L 

### (RH) L ARGE 

### RH A DJUSTMENT 

### F ACTORS



### ................................................................ 3-23-3

T ABLE 

### 3-3. F S 

### (RH) S MALL 

### RH A DJUSTMENT 

### F ACTORS 

### ..................................................................... 3-3

T ABLE 

### 3-4. F SS 

### (RH) S EA 

### S ALT 

### RH A DJUSTMENT 

### F ACTORS 

### ............................................................... 3-3

T ABLE 

### 4-1. B ASELINE 

### M AXIMUM 

### 24- HOUR 

### SO 2 

### , NO X 

### , AND 

### PM 10 

### E MISSION RATES 

### ( AS 

### H OURLY


### E QUIVALENTS 

### ) ....................................................................................................................... 4-2

T ABLE 

### 4-2. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### B AILEY


### , U NIT 

### 1 (2001-2003)

– N ATURAL 

### G AS 

### ........................................................................................................................ 4-3

T ABLE 

### 4-3. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### B AILEY


### , U NIT 

### 1 (2001-2003)

– F UEL 

### O IL



### .............................................................................................................................. ... 4-3

T ABLE 

### 4-4. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### M C 

### C LELLAN


### , U NIT 

### 1 (2001- 2003), N ATURAL 

### G AS 

### ............................................................................................................. 4-4

T ABLE 

### 4-5. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### M C 

### C LELLAN


### , U NIT 

### 1 (2001- 2003), F UEL 

### O IL 

### ..................................................................................................................... 4-4

TABLE 

### 5-2. A VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES FOR 

### B AILEY 

### U NIT 

### 1 A ND 

### M C 

### C LELLAN 

### U NIT


### 1 ............................................................................................................................. ................. 5-1

T ABLE 

### 5-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

### 5-3

T ABLE 

### 5-7. A VERAGE 

### S ULFUR 

### C ONTENT OF 

### F UEL 

### S TORED AT 

### B AILEY AND 

### M C 

### C LELLAN 

### .............. 5-4

T ABLE 

### 5-4. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### C URRENT 

### N O



### . 6

F UEL 

### O IL



### .............................................................................................................................. ... 5-5

T ABLE 

### 5-5. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### 1% S ULFUR 

### N O



### . 6

F UEL 

### O IL



### .............................................................................................................................. ... 5-5

T ABLE 

### 5-6. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### 0.5% S ULFUR 

### N O



### . 6

F UEL 

### O IL



### .............................................................................................................................. ... 5-6

T ABLE 

### 5-7. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### D IESEL 

### ................. 5-6

T ABLE 

### 5-8. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### N ATURAL 

### G AS 

### ..... 5-7

### Arkansas Electric Cooperative Corporation iv Trinity Consultants BART Analysis – Bailey and McClellan FIGURE 

### 5-1. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### N ATURAL 

### G AS 

### .... 5-8

T ABLE 

### 5-9. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### F UEL 

### S WITCHING FOR CURRENT 

### N O



### . 6 F UEL


### O IL AT 

### B AILEY 

### U NIT 

### 1 ............................................................................................................ 5-9

T ABLE 

### 5-10. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### F UEL 

### S WITCHING FOR CURRENT 

### N O



### . 6 F UEL


### O IL AT 

### M C 

### C LELLAN 

### U NIT 

### 1 ................................................................................................... 5-9

T ABLE 

### 5-11. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### F UEL 

### S WITCHING FOR 

### SO 2 

### C ONTROL 

### A T 

### B AILEY 

### U NIT 

### 1 ............................................................................................... 5-11

T ABLE 

### 5-12. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### F UEL 

### S WITCHING FOR 

### SO 2 

### C ONTROL 

### A T 

### M C 

### C LELLAN 

### U NIT 

### 1 ...................................................................................... 5-11

T ABLE 

### 5-13. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS

FOR 

### B AILEY 

### U NIT 

### 1 (2001-2003) .......................................................................................... 5-12

T ABLE 

### 5-14. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS

FOR 

### M C 

### C LELLAN 

### U NIT 

### 1 (2001-2003) ................................................................................. 5-13

T ABLE 

### 7-1. A VAILABLE 

### PM C ONTROL 

### T ECHNOLOGIES FOR 

### B AILEY 

### U NIT 

### 1 AND 

### M C 

### C LELLAN 

### U NIT


### 1 ............................................................................................................................. ................. 7-1

T ABLE 

### 7-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### PM C ONTROL 

### T ECHNOLOGIES 

### 7-4

T ABLE 

### 7-4. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### B AILEY 

### U NIT 

### 1 PM 10 

### C ONTROLS 

### .................... 7-6

T ABLE 

### 7-5. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### M C 

### C LELLAN 

### U NIT 

### 1 PM 10 

### C ONTROLS 

### ........... 7-7

T ABLE 

### 7-5. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### W ET 

### ESP FOR 

### PM 10 

### C ONTROL 

### 7-8

T ABLE 

### 7-6. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### W ET 

### S CRUBBER FOR 

### PM 10 

### C ONTROL



### .............................................................................................................................. .. 7-8

T ABLE 

### 7-7. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### C YCLONE FOR 

### PM 10 

### C ONTROL 

### . 7-

8

T ABLE 

### 7-8. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### PM 10 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS

FOR 

### B AILEY 

### U NIT 

### 1 (2001-2003) .......................................................................................... 7-10

T ABLE 

### 7-9. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### PM 10 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS

FOR 

### M C 

### C LELLAN 

### U NIT 

### 1 (2001-2003) ................................................................................. 7-11

### Arkansas Electric Cooperative Corporation 1-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 1. E XECUTIVE 

## S UMMARY

This report documents the determination of th e Best Available Retrofit Technology (BART) as proposed by Arkansas Electric Cooperative Corporat ion (AECC) for the Unit 1 Boiler at the Bailey Generating Station and the Unit 1 Boiler at the McCl ellan 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 oi l. McClellan Unit 1 is a wall-fired boiler with a maximum heat input of 1,436 MMBtu/hr that burns natu ral 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, na tural 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 (NO
x 

### ), sulfur dioxide (SO 2 

### ), and particulate matter with a mass mean diameter smaller than ten microns (PM 10 

### ) 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 visib ility 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 Trin ity Consultants (Trinity) using emissions data based on a combination of stack testing, Continu ous 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.

T ABLE 

### 1-1. E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### B AILEY 

### U NIT 

### 1 AND


### M C 

### C LELLAN 

### U NIT 

### 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. 34734
8 7 0.36 8
7 6 0.37 9
8 12 McClellan, Unit 1 – Natural
Gas 0.125 3 0.052 0 0.040 0 0.058 0

McClellan, Unit 1 – Fuel Oil 0. 62162
2 2224 0.266 5 0.23 1
0 2 0.22 8
7 2

### Arkansas Electric Cooperative Corporation 1-2 Trinity Consultants BART Analysis – Bailey and McClellan Trinity used the EPA’s BART guidelines in 40 CFR Part 51 1 

### to determine BART for Bailey Unit 1 and McClellan Unit 1. Specifically, Trinity conduc ted a five-step analysis to determine BART for
SO 2 

### , NO
x 

### , and PM 10 

### 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 fo llowing were determined to be BART:

▲ 

### SO 2 

### – AECC has determined that BART for both Bailey Unit 1 and McClellan Unit 1 is switching fuels to fuels with 0.5% sulfur or l essusing fuels with 0.5% sulfur or less (including natural gas)

.

▲ 

### NO
x 

### – AECC has determined that the requireme nts of the Cross State Air Pollution Rule (CSAPR) satisfy BART for NO X 

### from Bailey Unit 1and McClellan Unit 1

. 2

▲ 

### PM 10 

### –AECC has determined that no controls cons titute BART. Neither a fuel change beyond that proposed for SO 2 

### 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 Δ dv, 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 res ponse 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.

### Arkansas Electric Cooperative Corporation 2-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 2. I NTRODUCTION AND 

## B ACKGROUND

In the 1977 amendments to the Clean Air Act (CAA), Congress set a national goal to restore national parks and wilderness areas to pristine cond itions 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 in ternational 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 thei r state. On July 6, 2005, the EPA published amendments to its 1999 RHR, often called the B est 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 th e 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 c ontribute to visibility impairment if the 98 th 

### 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 vi sibility regulations define BART as follows:

“...an emission limitation based on the deg ree of reduction achievable through the application of the best system of continu ous emission reduction for each pollutant which is emitted by...[a BART-eligible sou rce]. The emission limitation must be established on a case-by-case basis, taking in to consideration the technology available, the cost of compliance, the energy and n on 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

3 Note this is a change from the ADEQ protocol with the 2006 CENRAP data, as th e original analysis for Arkansas reviewed the “High First High” impacts rather than the 98 th percentile impacts

### Arkansas Electric Cooperative Corporation 2-2 Trinity Consultants 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 b asic 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 visi bility affecting pollutant (VAP) by following the five steps listed above for each VAP.

Bailey Unit 1 and McClellan Unit 1 meet the th ree BART-eligibility criteria described above. Further, the existing visibility impairment attri butable 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 NO
x 

### , SO 2 

### , and PM 10 

### of various forms (filterable coarse particulate matter [PM
c 

### ], filterable fine particle matter [PM f 

### ], elemental carbon [EC], inorganic condensable particulat e matter [IOR CPM] as sulfates [SO 4 

### ], and organic condensable particulate matter [OR CPM] also refe rred to as secondary organic aerosols [SOA]). The
BART determinations for SO 2 

### , NO
x 

### , and PM 10 

### 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 sat isfy BART. Thus, AECC is proposing to satisfy
BART for NO
x 

### 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 be ing vacated on August 21, 2012, AECC submitted a five-factor analysis for NOx to ADEQ in September 2012 as an addendum to this analysis.

### Arkansas Electric Cooperative Corporation 3-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 3. M ODELING 

## M ETHODOLOGIES AND 

## P ROCEDURES

This section summarizes the dispersion modeling methodologies and procedures applied in this BART analysis. All dispersion modeling has b een 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-st ate 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 me teorological 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. Th e selection and control of CALPUFF options are determined by user-specific inputs contained in the cont rol file. This file contains all of the necessary information to define a model run (e.g., starti ng 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 A ssociation (CENRAP). The CALMET data and parameters are based on the modeli ng protocol included in Appendix CB . Note that the protocol included in Appendix C B summarizes modeling methods and procedures that were followed to predict visibility impairment for several BART-e ligible 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 protoc ol 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 Qu ality 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 us ing the light extinction coefficient ( b ext 

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

     

 10 ln 10 (dv) 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:

### Arkansas Electric Cooperative Corporation 3-2 Trinity Consultants 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:



##  ext 

## b     

#       

#     

##     

##              

##   

2 Scattering Rayleigh specific Site Large Small Large 3 4 Small 3 4 Large 2 4 4 Small 2 4 4



### NO 33

. 0 Salt Sea 4

. 1 PMC 6

. 0 PMF 1 EC 10 OC 1

. 6 OC 8

. 2 NO NH 1

. 5 NO NH 4

. 2 SO NH 8

. 4 SO NH 2

. 2

         



 

### b RH f RH f RH f RH f RH f

SS L S L S



### Visibility impairment predictions for Bailey Unit 1 and McClellan Unit 1 re lied 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 th at one of five different “modes” be selected. The modes specify the approach for addressing the growth of hygroscopi c 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”, wh ere “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.

T ABLE 

### 3-1. A NNUAL 

### A VERAGE 

### B ACKGROUND 

### C ONCENTRATION

Class I Area (NH 4 ) 2 SO 4 NH 4 NO 3 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



### T ABLE 

### 3-2. F L 

### (RH) L ARGE 

### RH A DJUSTMENT 

### F ACTORS

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

### Arkansas Electric Cooperative Corporation 3-3 Trinity Consultants BART Analysis – Bailey and McClellan 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



### T ABLE 

### 3-3. F S 

### (RH) S MALL 

### RH A DJUSTMENT 

### F ACTORS

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



### T ABLE 

### 3-4. F SS 

### (RH) S EA 

### S ALT 

### RH A DJUSTMENT 

### F ACTORS

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

### Arkansas Electric Cooperative Corporation 4-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 4. E XISTING 

## E MISSIONS AND 

## V ISIBILITY 

## I MPAIRMENT

This section summarizes the existing (i.e. baseline) vi sibility impairment attributable to Bailey Unit 1 and McClellan Unit 1 based on air quality modeling conducted by Trinity.



## 4.1 NO X 

## , SO 2 

## , AND 

## PM 10 

## B ASELINE 

## E MISSION 

## R ATES

Table 4-1 summarizes the emission rates that were modeled for SO 2 

### , NO
x 

### , and PM 10 

### , including the speciated PM 10 

### emissions. The SO 2 

### and NO
x 

### emission rates are the highest actual 24-hour emission
rates based on 2001-2003 continuous emissions monitoring system (CEMS) data – broken out to distinguish SO 2 

### and NO
x 

### from burning No. 6 fuel oil and natural gas individually.

The PM 10 

### emission rates for natural gas combustion are based on the emission factor for total PM 10 

### 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 PM 10 

### species shown in Table 4-1 reflect the breakdown of the filterable and
condensable PM 10 

### 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 PM 10 

### from natural gas fired combustion turbines. All of the condensable PM was assumed to be SOA, except for a small fraction of the conden sable PM that was estimated to be SO 4 

### . One-third of the
estimated SO 2 

### emissions were separated and adjusted for differences in molecular weight to represent
SO 4 

### emissions. This double counts some of the fuel sulfur based emissions as SO 2 

### but also as SO 4



### . Since pipeline natural gas contains very little sulfur, both the SO 2 

### and SO 4 

### emission rates are very
low.

The PM 10 

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

PM 10 

### conducted on Unit 1 at the McClellan plant on May 29, 2013. The total PM 10 

### emission rate determined during the testing was 59.4 lb/hr. Thus, a total PM 10 

### emission rate of 59.4 lb/hr was modeled for McCllellan. Stack testing was not c onducted at Bailey in 2013, however, the total PM 10 

### emission rate for Unit 1 at Bailey was scaled by th e ratio of the heat input for Bailey vs McClellan (1436/1350) to get a total PM 10 

### emission rate of 55.8 lb/hr. The emission rates for the PM 10 

### species

shown in Table 4-1 reflect the breakdown of the PM 10 

### 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 distributi ons for the PM species from No. 6 fuel oil at Bailey and McClellan, respectively:

▲ 

### Coarse PM (PM C 

### ) = 24.5%, 23.9%

▲ 

### Fine soil (modeled as PM F 

### ) = 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 SO 4 

### ) = 8.2%, 10.0%

5 The NPS Workbook, "Uncontrolled Util ity Residual Oil Boiler.xls" update d 03/2006, was obtained from the NPS website: http://www.nature.nps.gov/ai r/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 c ontent of 1.38%, and a heat input of 1,436 MMBtu/hr.

### Arkansas Electric Cooperative Corporation 4-2 Trinity Consultants BART Analysis – Bailey and McClellan

T ABLE 

### 4-1. B ASELINE 

### M AXIMUM 

### 24- HOUR 

### SO 2 

### , NO X 

### , AND 

### PM 10 

### E MISSION RATES 

### ( AS 

### H OURLY


### E QUIVALENTS 

### )

Unit / Fuel SO 2 6 

### NO
x 7 Total
PM 10 SO 4 PM c PM f 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 55.849.4 4.64.0 13.712.1 34.130.2 0.80.7 2.72.4

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 59.448.2 5.94.8 14.211.5 35.428.7 1.00.8 2.82.3



## 4.2 B ASELINE 

## V ISIBILITY 

## I MPAIRMENT

Trinity conducted modeling to determine the visibilit y impairment attributable to Bailey Unit 1 and McClellan Unit 1 in four Class I Areas: Caney Creek Wilderness (CACR), Upper Buffalo Wilderness (UPBU), Hercules Glad es Wilderness (HERC), and Mingo Wilderness (MING) using the CALPUFF dispersion model.

Table 4-2 through Table 4-5 provide a summary of th e modeled visibility impairment attributable to Bailey Unit 1 and McClellan Unit 1 at CACR, UP BU, HERC, and MING based on the emission rates
shown in Table 4-1. Note that all of th e 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 Mc Clellan, 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 ra tes of 10,650 lb/day and
10,174 lb/day from natural gas at Bailey an d McClellan, respecti vely, and 9,812lb/day and 13,914 lb/day from No. 6 fuel oil
at Bailey and McClellan, respectively.

### Arkansas Electric Cooperative Corporation 4-3 Trinity Consultants BART Analysis – Bailey and McClellan T ABLE 

### 4-2. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### B AILEY 

### , U NIT 

### 1 (2001-2003)

– N ATURAL 

### G AS

Year Maximum ( Δ dv) 98th
Percentile

( Δ dv) No. of Day
with Δ dv ≥
0.5 98th
Percentile

% SO 4 98th
Percentile

% NO 3 98th
Percentile

% PM 10 98th
Percentile

% NO 2 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



### T ABLE 

### 4-3. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### B AILEY 

### , U NIT 

### 1 (2001-2003)

– F UEL 

### O IL

Year Maximum ( Δ dv) 98th
Percentile

( Δ dv) No. of Day
with Δ dv ≥
0.5 98th
Percentile

% SO 4 98th
Percentile

% NO 3 98th
Percentile

% PM 10 98th
Percentile

% NO 2 Caney Creek Wilderness

2001 0.6840.683 0.3070.306 22 75.6675.75 22.4722.53 1.440.68 0.440.44

2002 0.7450.743 0.3300.330 33 87.1987.24 12.1112.12 0.570.26 0.140.14

2003 0.9700.969 0.3270.326 33 98.8098.84 0.810.81 0.400.18 00.00 Upper Buffalo Wilderness

2001 0.5780.686 0.2820.327 33 94.2994.36 4.994.99 0.730.33 0.000.00

2002 0.6680.667 0.3050.303 11 73.6573.86 21.2821.43 3.431.56 1.641.65

2003 0.6960.695 0.3480.347 33 90.7390.81 8.428.44 0.830.38 0.020.02

Hercules Glades Wilderness

2001 0.6870.686 0.3270.327 33 98.4098.46 1.071.07 0.520.24 00.00

2002 0.6350.634 0.2490.248 22 80.3880.45 18.6218.66 0.870.41 0.120.13

2003 0.6480.646 0.3680.367 11 82.7482.91 14.3914.45 2.080.93 0.790.80 Mingo Wilderness

2001 0.5240.522 0.3550.354 11 89.5789.73 8.358.38 1.670.74 0.410.41

2002 1.5921.589 0.3790.378 77 93.9594.08 4.684.69 1.260.56 0.110.11

2003 0.6890.695 0.3000.347 43 66.1766.29 29.1329.30 2.831.26 1.871.88

### Arkansas Electric Cooperative Corporation 4-4 Trinity Consultants BART Analysis – Bailey and McClellan T ABLE 

### 4-4. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### M C 

### C LELLAN 

### , U NIT 

### 1 (2001- 2003), N ATURAL 

### G AS

Year Maximum ( Δ dv) 98th
Percentile

( Δ dv) No. of Day
with Δ dv ≥
0.5 98th
Percentile

% SO 4 98th
Percentile

% NO 3 98th
Percentile

% PM 10 98th
Percentile

% NO 2 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



### T ABLE 

### 4-5. B ASELINE 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### M C 

### C LELLAN 

### , U NIT 

### 1 (2001-2003), F UEL 

### O IL

Year Maximum ( Δ dv) 98th
Percentile

( Δ dv) No. of Day
with Δ dv ≥
0.5 98th
Percentile

% SO 4 98th
Percentile

% NO 3 98th
Percentile

% PM 10 98th
Percentile

% NO 2 Caney Creek Wilderness

2001 1.6851.676 0.6220.621 1010 89.8689.94 9.629.63 0.530.21 0.000.00

2002 1.0211.018 0.3890.386 43 86.2986.53 11.2611.33 1.720.69 0.740.74

2003 3.007 0.616 9 82.89 15.76 0.36 0.62 Upper Buffalo Wilderness

2001 0.6040.603 0.2580.257 22 84.0284.16 14.9815.04 0.990.42 0.010.01

2002 1.3231.319 0.1840.183 11 77.3177.46 20.9621.07 1.430.59 0.300.30

2003 0.5990.598 0.2660.266 22 98.4798.58 0.950.95 0.580.24 0.000.00

Hercules Glades Wilderness

2001 0.5120.510 0.2310.230 11 78.6778.80 20.1620.24 1.170.50 0.010.01

2002 0.4630.462 0.1680.166 00 59.2859.40 37.6537.95 2.310.98 0.750.76

2003 0.6620.660 0.2110.209 11 76.1876.44 20.2220.41 2.511.05 1.081.09 Mingo Wilderness

2001 0.4170.416 0.2280.277 00 80.9081.05 17.8917.97 1.200.50 0.010.01

2002 0.5470.546 0.2130.212 22 59.4259.52 36.8837.19 2.320.98 1.381.39

2003 0.4710.469 0.2030.202 00 87.3987.57 11.2311.29 1.290.54 0.090.09

### Arkansas Electric Cooperative Corporation 5-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 5. SO 2 

## BART E VALUATION



## 5.1 I DENTIFICATION OF 

## A VAILABLE 

## R ETROFIT 

## SO 2 

## C ONTROL 

## T ECHNOLOGIES



## – F UEL 

## O IL COMBUSTION

Bailey Unit 1 and McClellan Unit 1 currently combust No. 6 fuel oil and natural gas. Because the
SO 2 

### emissions profile from natural gas is so small, no additional controls will be considered for combustion of natural gas. This section concerns controlling SO 2 

### emissions from the combustion of
No. 6 fuel oil.

Sulfur oxides, SO
x 

### , are generated during fuel oil combustion from the oxidation of sulfur contained in
the fuel. SO
x 

### emissions are almost entirely dependent on th e sulfur content of the fuel and are not affected by boiler size or burner design. SO
x 

### emission from conventional combustion systems are predominantly in the form of SO 2 

### . Since SO 2 

### is the predominant sulfur compound emitted from the AECC boilers, the BART analysis is specific to emissions of SO 2



### .

Step 1 of the top-down control review is to identify available retrofit control options for SO 2 

### . The
available SO 2 

### retrofit control technologies for the AECC boilers are summarized in Table 5-2. The
retrofit controls include both add- on controls that eliminate SO 2 

### after it is formed and switching to
lower sulfur fuels which reduces the formation of SO 2



### .

TABLE 

### 5-2. A VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES FOR 

### B AILEY 

### U NIT 

### 1 A ND 

### M C 

### C LELLAN 

### U NIT 

### 1

SO 2 Control Technologies

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

Wet Scrubber Circulating Dry Scrubber (CDS) Fuel Switching



## 5.2 E LIMINATE 

## T ECHNICALLY 

## I NFEASIBLE 

## SO 2 

## C ONTROL 

## T ECHNOLOGIES

Step 2 of the BART determination is to eliminate technically infeasible SO 2 

### control technologies that were identified in Step 1.



### 5.2.1 D RY 

### S ORBENT 

### I NJECTION 

### , S PRAY 

### D RYER 

### A BSORPTION 

### (SDA), W ET 

### S CRUBBER 

### , C IRCULATING 

### D RY 

### S CRUBBER 

### (CDS)

These technologies are collectively known as flue gas desulfurization (FGD) systems. FGD applications have not been used historically for SO 2 

### 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 SO 2 

### emission rate for oil-fired units and determined that the presumptive emission rate should be based on the su lfur content of the fuel oil, rather than

### Arkansas Electric Cooperative Corporation 5-2 Trinity Consultants BART Analysis – Bailey and McClellan 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 SO 2 

### from Bailey Unit 1 and
McClellan Unit 1 and are not considered further for BART.



### 5.2.2 F UEL 

### S WITCHING



### The AECC boilers currently burn some 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

. The last most recent fuel oil shipment fo r Bailey was in December of 2006, and the lastmost recent fuel oil shipment for McClellan was in April of 2009. The fuel oil that has been currently existsstored 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 at Bailey Unit 1 and 1.38 percent by weight at McClellan Unit 1The sulfur content of the fuel oil in the tanks Thus, the sulfur content of the fuel oil supplied to Bailey and McClellan has not changed since 2006 and 2009, respectively. 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 SO 2 

### 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 sulf ur No. 6 fuel oil, No. 2 fuel oil, or natural

gas. 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 SO 2 

### emissions. No. 2 fuel oil (diesel) is available and could be burne d 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 R ANK OF 

## T ECHNICALLY 

## F EASIBLE 

## SO 2 

## C ONTROL 

## O PTIONS BY 

## E FFECTIVENESS

The third step in the BART analysis is to rank the technically feasible options according to effectiveness. Fuel switching is the on ly technically feasible control option. SO 2 

### 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 SO 2 

### emissions from the AECC boilers as compared to th e 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 le vels for switching fuels in the AECC boilers.

8 Summary of Comments and Responses on the 2004 and 2001 Pr oposed Guidelines for Best Available Retrofit Technology (BART) Determinations Under the Regional Haze Regulations EPA Docket Number OAR-2002-0076.

### Arkansas Electric Cooperative Corporation 5-3 Trinity Consultants BART Analysis – Bailey and McClellan T ABLE 

### 5-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES

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 E VALUATION OF 

## I MPACTS FOR 

## F EASIBLE 

## SO 2 

## C ONTROLS

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 C OST OF 

### C OMPLIANCE



### Control Costs The cost of the fuel switching that was used in the cost effectiveness calculations was determined by calculating the annual cost of th e current No. 6 fuel oil and determining the increased cost of switching to the various lowe r sulfur fuels. Switching fuel to diesel will require changes to the burners and the fuel sy stem. However, for this analysis, capital expenses were not included.

As AECC currently burns both No. 6 fuel oil a nd 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 e ffectiveness calculations were determined by subtracting the estimated controlled annu al 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 av erage sulfur content of the No. 6 fuel oil

### Arkansas Electric Cooperative Corporation 5-4 Trinity Consultants BART Analysis – Bailey and McClellan 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 stor ed 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.

T ABLE 

### 5-7. A VERAGE 

### S ULFUR 

### C ONTENT OF 

### F UEL 

### S TORED AT 

### B AILEY AND 

### M C 

### C LELLAN



### 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 BA RT 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 shoul d 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 estim ate 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. SO 2 

### and SO 4 

### )

was calculated:

### Arkansas Electric Cooperative Corporation 5-5 Trinity Consultants BART Analysis – Bailey and McClellan No. 2 fuel use per year * No. 2 oil density * Sulfur content in No. 2 fuel

The mass of sulfur in the form of SO 4 

### was estimated and subtracted from the total sulfur to determine the quantity of sulfur that could form SO 2 

### . The SO 2 

### emission rate was estimated by multiplying the sulfur available to form SO 2 

### by the ratio of the molecular weight for

SO 2 

### vs. sulfur. The mass of sulfur in the form of SO 4 

### was estimated by reducing the

baseline SO 4 

### emission rate in proportion to the per cent reduction in fuel sulfur and then multiplying the SO 4 

### rate by the ratio of the molecular weight of sulfur vs. SO 4



### .

Then the NPS speciation spreadsheet for distillate fuel was used to derive the percent of sulfur represented as SO 4 

### . The remaining sulfur is represented as SO 2



### . Tables 5-4 through and 5-8 provide a summary of the mass balance data and calculations for the future annual

SO 2 

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

SO 4 

### in both Tables 5-4 and 5-8 is based on the percentage of the baseline annual sulfur that
was determined to be SO 4 

### , as described in Section 4.

T ABLE 

### 5-4. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### C URRENT 

### N O



### . 6 F UEL 

### O IL

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) 4.55 20.90 5.9227.80

SO4 [Assume CPM IOR = SO4] (tpy) 2.31 10.61 15.3572.14
SO4 as Sulfur in Fuel [Assume 1 mol S for each mol SO4] (tpy) 0.39 3.54 2.5624.05

% S as SO4 2.0418.71% 2.3922.42%
Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 18.52 15.36 104.7183.22

% S as SO2 97.9681.29% 97.6177.58%
SO2 (tpy) 37.03 30.73 209.43166.45



### T ABLE 

### 5-5. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### 1% S ULFUR 

### N O



### . 6 F UEL 

### O IL

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

### Arkansas Electric Cooperative Corporation 5-6 Trinity Consultants BART Analysis – Bailey and McClellan 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 1.0212.39% 1.1912.39%
Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 10.34 9.15 76.8168.10

% S as SO2 98.9887.61% 98.8187.61%
SO2 (tpy) 20.67 18.30 153.61136.20



### T ABLE 

### 5-6. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### 0.5% S ULFUR 

### N O



### . 6 F UEL 

### O IL

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 2.0418.25% 2.3918.25%
Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 5.12 4.27 37.9431.77

% S as SO2 97.9681.75% 97.6181.75%
SO2 (tpy) 10.23 8.54 75.8863.55



### T ABLE 

### 5-7. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### D IESEL

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

### Arkansas Electric Cooperative Corporation 5-7 Trinity Consultants BART Analysis – Bailey and McClellan 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 2.115.61% 2.475.61%
Sulfur Available for SO2 Formation [backing out Sulfur for SO4] 0.49 0.48 3.663.54

% S as SO2 0.9894.39% 0.9894.39%
SO2 (tpy) 0.99 0.95 7.317.08



### T ABLE 

### 5-8. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### N ATURAL 

### G AS

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 e ffectiveness of switching from the current No. 6
fuel oil to the lower sulfur fu els. The cost effectiveness was determined by dividing the annual cost increase of fuel switching by the annual tons of SO 2 

### reduced. Tables 5-9 and 5-10 indicate that the cost of switching to lower sulfur No. 6 fuel oil is a minimum of $2 32 ,000/ton of SO 2 

### reduced. Fuel switching to diesel is greater than $2 15 ,000/ton for Bailey Unit 1 and almost $ 2833 ,000/ton for McClellan Unit 1. Fuel switching to natural gas is estimated to be nearly over $2,000/ton for Bailey Unit 1 and over $500/ton for

McClellan Unit 1. Because fuel is a traded co mmodity, 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. 9



### 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 effect iveness values summarized above will vary from year to year. For instance, over the past ten y ears, there were periods of time when fuel oil

9 Current vendor estimates (not quotes ) for fuel oil with vary ing levels of sulfur include: 0.5% sulfur - $18/MMBtu, 1.0% - $16.90/MMBtu, $1.5% - $16.50/MMBtu, 2.0% $16.00/MMbtu

### Arkansas Electric Cooperative Corporation 5-8 Trinity Consultants BART Analysis – Bailey and McClellan was less expensive than natural gas. Duri ng 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, wh ich 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.

FIGURE 

### 5-1. S UMMARY OF 

### F UTURE 

### A NNUAL 

### SO 2 

### E MISSIONS 

### A SSOCIATED WITH 

### N ATURAL 

### G AS$/MMBTU

Year Fuel Price Variability 2003

‐ 2012

Natural Gas

Fuel OilArkansas Electric Cooperative Corporation 5-9 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE5-9.SUMMARY OFCOSTEFFECTIVENESS FORFUELSWITCHING FOR CURRENTNO.6FUELOIL ATBAILEYUNIT1TABLE5-10.SUMMARY OFCOSTEFFECTIVENESS FORFUELSWITCHING FOR CURRENTNO.6FUELOIL ATMCCLELLANUNIT1AverageSulfurContentABaselineSO2EmissionRateBControlledSO2EmissionRateGSO2ReducedBaselinePM10EmissionRateBControlledPM10EmissionRateFPM10ReducedAnnual HeatInputFuel HeatingValue (HHV)CAnnual FuelUsage Fuel CostFuel CostDifferentialfrom CurrentNo. 6 OilDFuel CostDifferentialCost of FuelSwitchingSO2CostEffectivenessEPM10 CostEffectivenessE(%) (tpy) (tpy) (tpy) (tpy) (tpy) (tpy) (MMBtu/yr)(MMBtu/Mgal)or(MMBtu/Mscf) (Mgal/yr) ($/MMBtu) ($/MMBtu) ($/yr) ($/yr) ($/ton) ($/ton)Base CaseA1.81 37.03 - - 25.63 - - 39,193 155.00 252.86 4.68 - $183,492 - - -No. 6 - 1% 1.00 - 20.67 16.36 - 8.80 16.83 39,193 155.00 252.86 16.50 11.82 - 463,185$ 28,312 27,528No. 6 - 0.5% 0.50 - 10.23 26.80 - 2.75 22.88 39,193 155.00 252.86 17.75 13.07 - 512,175$ 19,109 22,386DieselA0.05 - 0.99 36.05 - 0.13 25.50 39,193 136.15 287.86 20.95 16.27 - 637,592$ 17,688 25,004Natural Gas - - 0.01 37.02 - 0.26 25.37 39,193 1,011.00 38.77 6.19 1.51 - 59,038$ 1,595 2,327AverageSulfurContentABaselineSO2EmissionRateBControlledSO2EmissionRateGSO2ReducedBaselinePM10EmissionRateBControlledPM10EmissionRateFPM10ReducedAnnual HeatInputFuel HeatingValue (HHV)CAnnual FuelUsage Fuel CostFuel CostDifferentialfrom CurrentNo. 6 OilDFuel CostDifferentialCost of FuelSwitchingSO2CostEffectivenessEPM10 CostEffectivenessE(%) (tpy) (tpy) (tpy) (tpy) (tpy) (tpy) (MMBtu/yr)(MMBtu/Mgal)or(MMBtu/Mscf) (Mgal/yr) ($/MMBtu) ($/MMBtu) ($/yr) ($/yr) ($/ton) ($/ton)Base CaseA1.38 209.43 - - 136.08 - - 291,733 155.00 1882.15 5.70 - $1,663,937 - - -No. 6 - 1% 1.00 - 153.61 55.81 - 76.70 59.38 291,733 155.00 1882.15 16.50 10.80 - 3,149,652 56,431 53,044No. 6 - 0.5% 0.50 - 75.88 133.55 - 23.94 112.14 291,733 155.00 1882.15 17.75 12.05 - 3,514,317 26,315 31,338DieselA0.05 - 7.31 202.11 - 1.10 134.98 291,733 136.15 2142.73 20.95 15.25 - 4,447,862 22,007 32,952Natural Gas - - 0.07 209.35 - 1.36 134.72 291,733 1,011.00 288.56 5.97 0.26 - 76,911 367 571ETotal SO2 and PM10 Cost Effectiveness for fuel switching to distillate includes yearly capital recovery costs associated with necessary infrastructure changes.FReductions in PM Species is based on default NPS profile.GControlled 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).ASulfur 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 sincediesel is not burned at Bailey or McClellan.DThe 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.CHigher 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 gasanalysis and 7.12.11 gas analysis.BThe Baseline SO2 Emission Rates were developed using an average fuel usage from 2007 and 2011, the average heat content of theNo. 6 fuel oil during that same time, and the average sulfur content of the fuelduring 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).

### Arkansas Electric Cooperative Corporation 5-10 Trinity Consultants BART Analysis – Bailey and McClellan 

### 5.4.2 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS



### There are no energy or non-air quality impacts a ssociated 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 curtailm ents. However, temporary permitted use of fuel oil would provide for electric grid relia bility. The ability to burn fuel oil at both Bailey and McClellan is important – even if fu el oil is more expensive and difficult to burn than natural gas. During natural gas curtailm ents, natural gas infrastructure maintenance, and other emergencies, AECC relies on the fuel oil stored at the plants to maintain electrical reliability.



### 5.4.3 R EMAINING 

### U SEFUL 

### L IFE



### 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 purpo se of this analysis there is nothing to capitalize that would require a review of the life of the equipment.



## 5.5 E VALUATION OF 

## V ISIBILITY 

## I MPACT OF 

## F EASIBLE 

## SO 2 

## C ONTROLS

A final impact analysis was conducted to assess th e 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 SO 2 

### emission rate in lb/MMBtu associated with the combustion of a particular fu el 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 dividing multiplying by the boiler design heat input. The calculation of the SO 2 

### emission rate for the one percent sulfur fuel oil for Bailey Unit 1 is provided for an example:



## Sulfur Sulfur MMBtu lb % 81

. 1 %) 1 % 81

. 1 ( * / 592

. 1  = 0.880 lb/MMBtu

hr MMBtu MMBtu lb / 350

, 1 * / 880

. 0 

### = 1,187.62 lb/hr

The SO 4 

### emission rate was determined assuming the reduction in SO 4 

### is proportional to the reduction
in SO 2 

### 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 th e 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 NO
x 

### emission
rate was modeled at the baseline rate. The PM species was were modeled the same as the baseline
No. 6 fuel oil case. 10

10 Modeled controlled eEmission rates of PM C , PM F and PM 10 , SOA, and EC, the speciated PM pollutants, for the lower sulfur oil scenarios are conservatively assumed to equal the “h igh” or baseline sulfur oil scenario. 80% dependingon the SO 2 control and depending omnt eh Class I areaffff

### Arkansas Electric Cooperative Corporation 5-11 Trinity Consultants BART Analysis – Bailey and McClellan T ABLE 

### 5-11. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### F UEL 

### S WITCHING FOR 

### SO 2 

### C ONTROL 

### A T 

### B AILEY 

### U NIT 

### 1

Bailey Unit 1 SO 2 (lb/hr) SO 4 (lb/hr) 

### NO
x (lb/hr) PM C (lb/hr) PM F (lb/hr) SOA (lb/hr) EC (lb/hr) PM 10, total (lb/hr)
1% sulfur fuel oil No. 6 1,187.6 2.02.5 408.8 4.712.1 11.730.2 0.40.7 0.92.4 20.349.4

0.5% sulfur fuel
oil No. 6 593.8 1.01.3 408.8 1.512.1 3.730.2 0.20.7 0.32.4 6.949.4

Diesel 59.4 0.1 408.8 0.112.1 0.230.2 0.00.7 0.02.4 0.449.4 Natural gas 0.5 0.3 443.8 0.00.0 0.00.0 7.47.4 2.62.5 10.310.2



### T ABLE 

### 5-12. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### F UEL 

### S WITCHING FOR 

### SO 2 

### C ONTROL 

### A T 

### M C 

### C LELLAN 

### U NIT 

### 1

McClellan Unit 1 SO 2 (lb/hr) SO 4 (lb/hr) 

### NO
x (lb/hr) PM C (lb/hr) PM F (lb/hr) SOA (lb/hr) EC (lb/hr) PM 10, total (lb/hr)
1% sulfur fuel oil
No. 6 2,317.1 4.04.3 579.8 8.011.5 19.928.7 0.80.8 1.62.3 34.648.2

0.5% sulfur fuel oil No. 6 1,158.5 2.02.1 579.8 2.511.5 6.228.7 0.40.8 0.52.3 11.748.2

Diesel 115.9 0.2 579.8 0.111.5 0.328.7 0.00.8 0.02.3 0.748.2 Natural gas 0.6 0.3 423.9 0.00.0 0.00.0 7.97.9 2.72.7 10.910.9



### Visibility improvement was evaluated by compari ng 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 em ission rate. Thus, it would make sense that the emission rates used in control scenarios would repr esent the peak emission ra tes associated with the controls. That being said, control effectiveness is typically not evaluated on a 24-hour basis. Typically, control effectiveness for EGUs for NO X 

### /SO X 

### 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 comp aring the peak to average is consistent with other accepted BART methodologies.

Comparisons of the existing visibility impacts a nd the visibility impacts based on fuel switching, including the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the number of days with a modeled visibility impact greater than 0.5 Δ dv, 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 vi sibility impairment and the visibility impairment for the various fuels as measured by the 98 th 

### percentile modeled visibility impact.Arkansas Electric Cooperative Corporation 5-12 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE5-13.SUMMARY OFMODELEDIMPACTS FROMSO2CONTROLVISIBILITYIMPACTANALYSIS FORBAILEYUNIT1(2001-2003)Caney Creek WildernessUpper Buffalo WildernessHercules Glades WildernessMingo NWR

Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement*Baseline Emission Rate – (fuel oil)0.9700.3308-0.6960.3487-0.6870.3686-1.5920.37912-1% sulfur fuel oil No. 60.5440.193141.52%0.3770.194044.25%0.4080.206044.02%1.0080.206245.65%0.5% sulfur fuel oil No. 60.3330.142056.97%0.2270.127063.51%0.2790.135063.32%0.7060.170155.15%Diesel0.2080.084074.55%0.1560.069080.17%0.2150.069081.25%0.4290.095074.93%Natural gas0.2190.083074.85%0.1700.072079.31%0.2380.073080.16%0.4430.102073.09%*Improvement is based on the 98thpercentile impact (Δdv) for the control scenario compared to the 98thpercentile impact (Δdv) baseline impact (Δdv).†The visibility improvement shown in the table has been calculated from 98thpercentile baseline and controlled impactsthat include more decimal places thanwhat is shown in the table. Due to rounding of the baseline and controlled 98thpercentile impacts shown in the table, the visibility improvement calculated fromthe baseline and controlled 98thpercentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.Arkansas Electric Cooperative Corporation 5-13 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE5-14.SUMMARY OFMODELEDIMPACTS FROMSO2CONTROLVISIBILITYIMPACTANALYSIS FORMCCLELLANUNIT1(2001-2003)Caney Creek WildernessUpper Buffalo WildernessHercules Glades WildernessMingo NWR

Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement*Baseline Emission Rate3.0150.62224-1.3230.2665-0.6620.2312-0.5470.2282-1% sulfur fuel oil No. 62.6710.5371813.67%1.1700.231413.16%0.5620.202112.55%0.4780.193015.35%0.5% sulfur fuel oil No. 61.7220.322848.23%0.7610.146145.11%0.2940.115050.22%0.3240.136040.35%Diesel0.9090.174472.03%0.3820.073072.56%0.1360.062073.16%0.1900.080064.91%Natural gas0.6700.125379.90%0.2580.052080.45%0.0920.040082.68%0.1320.058074.56%*Improvement is based on the 98thpercentile impact (Δdv) for the control scenario compared to the 98thpercentile impact (Δdv) baseline impact (Δdv)†The visibility improvement shown in the table has been calculated from 98thpercentile baseline and controlled impactsthat include more decimal places thanwhat is shown in the table. Due to rounding of the baseline and controlled 98thpercentile impacts shown in the table, the visibility improvement calculated fromthe baseline and controlled 98thpercentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.

### Arkansas Electric Cooperative Corporation 5-14 Trinity Consultants BART Analysis – Bailey and McClellan As shown in Table 5-13, based on visibility predictions from th e CALPUFF modeling system, fuel switching at Bailey Unit 1 will result in up to a 44.1845.65

, 61.9663.51

, 80.1181.25 or 80. 11 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 o il, 0.5% sulfur fuel oil, diesel and natural gas, respectively. Please note that despite the varyi ng levels of percent visibility improvement, the number of days of visibility impairment > Δ 0.5 dv is 0 in many of the control cases. For example, at Hercules Glades Wilderness there are 0 days of visibility impairment greater than > Δ 0.5 dv for the 1% sulfur fuel oil and also for the natural gas control although the visibility improvement varies from
43.5244.02 % to 79.2581.25 %.

As shown in Table 5-14, based on visibility predictions from th e CALPUFF modeling system, fuel switching at McClellan Unit 1 will result in up to a 14.5415.35

, 47.6850.22

, 72.6173.16 , or 82. 61 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 o il, 0.5% sulfur fuel oil, diesel and natural gas, respectively.



## 5.6 P ROPOSED 

## B ART FOR 

## SO 2



### 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). 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 61.9663.51 % for Bailey and 47.6850.22 % 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 compari ng the visibility impairment from the baseline scenario on a peak 24-hour basis to visibility im pairment due to control effectiveness on a 30-day rolling average basis, the increas ed 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 becau se 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 s hould not impact the decision that 0.5% sulfur is
BART for the AECC boilers.

### Arkansas Electric Cooperative Corporation 6-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 6. NO X 

## BART E VALUATION

On June 7, 2012 EPA published a final rule allo wing states participating in the Cross-State Air Pollution Rule (CSAPR) trading program to use CS APR to satisfy BART. Arkansas is one of the states with units subject to CSAPR that will participate in a NO
x 

### trading program during the ozone
season. EPA commented that “NO
x 

### 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-r ound, even if emission limitations are seasonal. Although Arkansas is an ozone season-only state, un its 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 SO 2 

### and NO
x 

### . The FIP reviewed the Nebraska suggest BART for NO
x 

### , but ultimately stated that b ecause CSAPR satisfies BART, CSAPR controls will equate with BART in the state. 14



### AECC is proposing to satisfy BART for NO
x 

### by complying with CSAPR at Bailey Unit 1 and
McClellan Unit 1. 15

11 “Regional Haze: Revisions to Provi sions Governing Alternatives to Sour ce-Specific Best Available Retrofit Technology (BART) Determination, Lim ited SIP Disapprovals, and Federal Implemen tation 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 Im plementation 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 be ing vacated on August 21, 2012, AECC submitted a five-factor analysis for NOx to ADEQ in September 2012 as an addendum to this analysis.

### Arkansas Electric Cooperative Corporation 7-1 Trinity Consultants BART Analysis – Bailey and McClellan 

## 7. PM 10 

## BART E VALUATION



## 7.1 I DENTIFICATION OF 

## A VAILABLE 

## R ETROFIT 

## PM 10 

## C ONTROL 

## T ECHNOLOGIES

PM 10 

### emissions are either “filterable” or “condensable”. Filterable PM 10 

### is generally considered to be particles less than or equal to 10 microns in diam eter 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 PM 10 

### emissions from fuel oil combustion depend predominantly on the grade of fuel oil fired. Co mbustion of lighter distillate oils results in significantly lower PM 10 

### formation than does combustion of heavier residual oils. Among residual oils, firing of No. 4 or No. 5 oil usually produces less PM 10 

### 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 PM 10 

### control technologies. The available retrofit PM 10 

### control technologies are summarized in Table 6-2 for Bailey Unit 1 and McClellan Unit 1.

T ABLE 

### 7-1. A VAILABLE 

### PM C ONTROL 

### T ECHNOLOGIES FOR 

### B AILEY 

### U NIT 

### 1 AND 

### M C 

### C LELLAN 

### U NIT 

### 1

PM 10 Control Technologies

Dry Electrostatic Precipitator (ESP) Wet Electrostatic Precipitator (ESP)
Fabric Filter
Wet Scrubber Cyclone Fuel Switching



## 7.2 E LIMINATE 

## T ECHNICALLY 

## I NFEASIBLE 

## PM C ONTROL 

## T ECHNOLOGIES

Step 2 of the BART determination is to eliminate technically infeasible PM control technologies that were identified in Step 1.



### 7.2.1 D RY 

### E LECTROSTATIC 

### P RECIPITATORS 

### (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. Beca use 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 W ET 

### E LECTROSTATIC 

### P RECIPITATORS 

### (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

### Arkansas Electric Cooperative Corporation 7-2 Trinity Consultants BART Analysis – Bailey and McClellan 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 e fficiency is up to 90% for a wet ESP. 16 

### Wet ESP is a technically feasible option for control of PM 10 

### for Bailey Unit 1 and McClellan Unit 1.



### 7.2.3 M ECHANICAL 

### C OLLECTORS 

### (C YCLONES 

### )

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 co mbusted, 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 F ABRIC 

### F ILTER



### 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 par ticles from oil-fired boilers and the associated hazard from flammability of their use, fabric filte rs 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 W ET 

### S CRUBBER



### Wet scrubbers remove PM from flue gas by c ontacting it with a scrubbing liquid using one of several approaches: spraying the gas stre am 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 corro sion 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 PM 10 

### 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 controlli ng 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 th e drop. Particles that are smaller than 1

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

### Arkansas Electric Cooperative Corporation 7-3 Trinity Consultants BART Analysis – Bailey and McClellan micron are hardest to control because they fo llow 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. 19 

### While the majority of the PM emissions fo r 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 c ontrolling the particles that have the greatest ability to impair visibility.



### 7.2.6 F UEL 

### S WITCHING



### Residual oil has inherent ash that contributes to the emissions of filterable PM. Lower grades of fuel oil have less ash and ultimatel y 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 SO 2 

### emissions.

Distillate fuel oil has only trace amounts of ash. 20 

### It is estimated that filterable PM 10 

### emissions would be reduced in proportion to the reduction in ash content. Based on the reduction in ash content, reductions of filterable PM 10 

### would be expected to be greater than
99%. Reductions in filterable PM 10 

### 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 PM 10 

### 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 R ANK OF 

## T ECHNICALLY FEASIBLE 

## PM 10 

## C ONTROL 

## O PTIONS BY 

## E FFECTIVENESS

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 th e control levels for the controls listed in the previous section.

19 http://www.tri-mer.com/ q&a/comparing-electrosta tic-precipitator.htm
20 Combustion-Fossil Power Systems, J.G. Singer published by Combustion Engineer, Inc.

### Arkansas Electric Cooperative Corporation 7-4 Trinity Consultants BART Analysis – Bailey and McClellan T ABLE 

### 7-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### PM C ONTROL 

### T ECHNOLOGIES

Control Technology Control Efficiency 21

(%)

Fuel Switching ≤ 99%

Wet ESP ≤ 90 Cyclone 85%

Wet Scrubber 55%



## 7.4 E VALUATION OF 

## I MPACTS FOR 

## F EASIBLE 

## PM 10 

## C ONTROLS

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 C OST OF 

### C OMPLIANCE



### 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 operati ng 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 exis ting annual emission rate by the control percentages shown in Table 7-3.

21 AP-42, Fifth Edition, Volume I, Ch apter 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 aver age 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*10 3 /200) = 2.51 tpy. McClellan: (9.19*(1.38)+3.22)*(1,882,146*10 3 /200) = 14.97 tpy

### Arkansas Electric Cooperative Corporation 7-5 Trinity Consultants BART Analysis – Bailey and McClellan Cost Effectiveness The cost effectiveness was determined by dividi ng 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 in dicate 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 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS



### There are no energy or non-air quality im pacts 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 amo unt 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 R EMAINING 

### U SEFUL 

### L IFE



### 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 switc hing will not require any capital costs, and thus there is nothing to capitalize that would require a review of the life of the equipment.Arkansas Electric Cooperative Corporation 7-6 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE7-4.SUMMARY OFCOSTEFFECTIVENESS FORBAILEYUNIT1PM10CONTROLSBaselineEmissionRateControlEfficiencyAnnual HeatInputAControlledEmission RatePM10ReducedCapital Cost Total Annual CostCostEffectiveness(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) ($/ton)Wet ESP 25.63 90.00 39,193 2.56 23.06 105,141,431 22,638,340 981,583Wet Scrubber 25.63 55.00 39,193 11.53 14.09 140,957,713 50,150,862 3,558,286Cyclone 25.63 85.00 39,193 3.84 21.78 989,479 1,188,630 54,570No. 6 Fuel Oil - 1% 25.63 - 39,193 8.80 16.83 - 463,185 27,528No 6. Fuel Oil - 0.5% 25.63 - 39,193 2.75 22.88 - 512,175 22,386Diesel 25.63 - 39,193 0.13 25.50 - 637,592 25,004Natural Gas25.63 -39,1930.26 25.37 - 59,038 2,327BaselineEmissionRateControlEfficiencyAnnual HeatInputAControlledEmission RatePM10ReducedCapital Cost Total Annual CostCostEffectiveness(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) ($/ton)Wet ESP 2.51 90.00 39,193 0.25 2.26 105,141,431 22,638,340 10,021,076Wet Scrubber 2.51 55.00 39,193 1.13 1.38 140,957,713 50,150,862 36,326,871Cyclone 2.51 85.00 39,193 0.38 2.13 989,479 1,188,630 557,109No. 6 Fuel Oil - 1% 2.51 - 39,193 1.57 0.94 - 463184.75 492,166No 6. Fuel Oil - 0.5% 2.51 - 39,193 0.99 1.52 - 512175.45 336,504Diesel 2.51 - 39,193 0.03 2.48 - 637591.63 256,578Natural Gas2.51 -39,1930.03 2.48 - 59038.24 23,758AAnnual Heat Input derived for 2007-2011 average fuel usage times the heat content of No. 6 fuel oilArkansas Electric Cooperative Corporation 7-7 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE7-5.SUMMARY OFCOSTEFFECTIVENESS FORMCCLELLANUNIT1PM10CONTROLSBaselineEmissionRateControlEfficiencyAnnual HeatInputAControlledEmission RatePM10ReducedCapital Cost Total Annual CostCostEffectiveness(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) ($/ton)Wet ESP 136.08 90.00 291,733 13.61 122.47 151,509,333 32,605,907 266,237Wet Scrubber 136.08 55.00 291,733 61.23 74.84 146,303,011 52,056,542 695,549Cyclone 136.08 85.00 291,733 20.41 115.67 1,432,971 1,721,384 14,882No. 6 Fuel Oil - 1% 136.08 - 291,733 76.70 59.38 - 3,149,652 53,044No 6. Fuel Oil - 0.5% 136.08 - 291,733 23.94 112.14 - 3,514,317 31,338Diesel 136.08 - 291,733 1.10 134.98 - 4,447,862 32,952Natural Gas136.08 - 291,733 1.36 134.72 - 76,911 571BaselineEmissionRateControlEfficiencyAnnual HeatInputAControlledEmission RatePM10ReducedCapital Cost Total Annual CostCostEffectiveness(tpy) % (MMBtu/yr) (tpy) (ton/yr) ($) ($/yr) ($/ton)Wet ESP 14.97 90.00 291,733 1.50 13.47 151,509,333 32,605,907 2,420,880Wet Scrubber 14.97 55.00 291,733 6.73 8.23 146,303,011 52,056,542 6,324,586Cyclone 14.97 85.00 291,733 2.24 12.72 1,432,971 1,721,384 135,325No. 6 Fuel Oil - 1% 14.97 - 291,733 11.68 3.29 - 3149651.56 958,385No 6. Fuel Oil - 0.5% 14.97 - 291,733 7.35 7.61 - 3514317.35 461,763Diesel 14.97 - 291,733 0.15 14.82 - 4447861.76 300,217Natural Gas14.97 - 291,733 0.15 14.82 - 76911.33 5,191AAnnual Heat Input derived for 2007-2011 average fuel usage times the heat content of No. 6 fuel oil

### Arkansas Electric Cooperative Corporation 7-8 Trinity Consultants BART Analysis – Bailey and McClellan 

## 7.5 E VALUATION OF 

## V ISIBILITY 

## I MPACT OF 

## F EASIBLE 

## PM 10 

## C ONTROLS

A final impact analysis was conducted to assess th e visibility improvement associated with wet ESPs, wet scrubbers, and cyclones. Note that fuel switching has impacts on both SO 2 

### and PM, as shown in Section 5 of this report. Section 4 of this re port documented the existing visibility impairment attributable to Bailey Unit 1 and McClellan Unit 1.

In order to assess the visibility improvement asso ciated with wet ESPs, scrubbers, and cyclones the
maximum short-term PM 10 

### emission rates associated with these controls were modeled using
CALPUFF. The maximum short-term PM 10 

### emission rates associated with wet ESPs, scrubbers, and cyclones were calculated by reducing the uncontrolled yearly PM 10 

### 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 scr ubbers, and cyclones, respectively. The emission rates for the pollutants shown in Tables 7-5 through 7-7 for NO X 

### and SO 2 

### that are not PM are the same as in the baseline modeling.

T ABLE 

### 7-5. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### W ET 

### ESP FOR


### PM 10 

### C ONTROL

Unit SO 2 (lb/hr) SO 4 (lb/hr) NO X (lb/hr) PM C (lb/hr) PM F (lb/hr) SOA (lb/hr) EC (lb/hr) PM 10, 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



### T ABLE 

### 7-6. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### W ET 

### S CRUBBER FOR


### PM 10 

### C ONTROL

Unit SO 2 (lb/hr) SO 4 (lb/hr) NO X (lb/hr) PM C (lb/hr) PM F (lb/hr) SOA (lb/hr) EC (lb/hr) PM 10, 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



### T ABLE 

### 7-7. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED TO 

### R EFLECT 

### C YCLONE FOR


### PM 10 

### C ONTROL

Unit SO 2 (lb/hr) SO 4 (lb/hr) NO X (lb/hr) PM C (lb/hr) PM F (lb/hr) SOA (lb/hr) EC (lb/hr) PM 10, 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 Sect ion 5, including the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the number of days with a modeled visibility impact greater than 0.5 Δ dv, for each Class I area are provided in Tables 7-8 and 7-9. The visibility improvement associated with PM controls was ca lculated as the difference between the existing

### Arkansas Electric Cooperative Corporation 7-9 Trinity Consultants BART Analysis – Bailey and McClellan visibility impairment and the visibility impa irment for the control as measured by the 98 th 

### percentile modeled visibility impact.Arkansas Electric Cooperative Corporation 7-10 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE7-8.SUMMARY OFMODELEDIMPACTS FROMPM10CONTROLVISIBILITYIMPACTANALYSIS FORBAILEYUNIT1(2001-2003)Caney Creek Wilderness Upper Buffalo WildernessHercules GladesWilderness Mingo NWR

Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility 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 ESP0.961 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 98thpercentile impact (Δdv) for the control scenario compared to the 98thpercentile impact (Δdv) baseline impact (Δdv)†The visibility improvement shown in the table has been calculated from 98thpercentile baseline and controlled impactsthat include more decimal places thanwhat is shown in the table. Due to rounding of the baseline and controlled 98thpercentile impacts shown in the table, the visibility improvement calculated fromthe baseline and controlled 98thpercentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.Arkansas Electric Cooperative Corporation 7-11 Trinity ConsultantsBART Analysis – Bailey and McClellanTABLE7-9.SUMMARY OFMODELEDIMPACTS FROMPM10CONTROLVISIBILITYIMPACTANALYSIS FORMCCLELLANUNIT1(2001-2003)Caney Creek Wilderness Upper Buffalo WildernessHercules GladesWilderness Mingo NWR

Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv Visibility Improvement* Maximum Impact ( Δ dv) 98% Impact ( Δ dv) # Days > 0.5 Δ dv 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 -Wet ESP2.977 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 98thpercentile impact (Δdv) for the control scenario compared to the 98thpercentile impact (Δdv) baseline impact (Δdv)†The visibility improvement shown in the table has been calculated from 98thpercentile baseline and controlled impactsthat include more decimal places thanwhat is shown in the table. Due to rounding of the baseline and controlled 98thpercentile impacts shown in the table, the visibility improvement calculated fromthe baseline and controlled 98thpercentile impacts shown in the table may be slightly different than the visibility improvement reflected in the table.

### Arkansas Electric Cooperative Corporation 7-12 Trinity Consultants 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 Δ dv improvement (0.64 to 1.76 percent) of the 98 th 

### 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 Δ dv improvement (0.32 to 1.32 percent) of the 98 th 

### percentile visibility impairment attributable to Bailey Unit 1, and the operation of cyclones results in an
estimated 0.001 to 0.002 Δ dv improvement (0.32 to 0.88 percent) of the 98 th 

### 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 Δ dv improvement (0.91 to 3.00 percent) of the 98 th 

### percentile visibility impairment attributable to McClellan Unit 1 at the applicable Class I areas. Fu rther, as shown in Table 7-9, the operation of wet
scrubbers results in an estimated 0.002 to 0.007 Δ dv improvement (0.58 to 1.91 percent) of the 98 th 

### percentile visibility impairment attributable to Mc Clellan Unit 1, and the operation of cyclones results
in an estimated 0.002 to 0.006 Δ dv improvement (0.58 to 1.63 percent) of the 98 th 

### percentile visibility impairment attributable to McClellan Unit 1.



## 7.6 P ROPOSED 

## B ART FOR 

## PM 10



### The cost effectiveness of all the PM controls evaluate d 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 PM 10 

### 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 attributab le to the boilers based on the application of the controls (only up to 0.011 Δ dv), none of the controls are determin ed to satisfy BART. Thus, there are no fuel changes or add-on controls proposed as BART for PM 10 

### 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 SO 2

.

### Arkansas Electric Cooperative Corporation Trinity Consultants BART Analysis – Bailey and McClellan 

## A PPENDIX 

## A

SO 2 

### C ONTROL 

### C OST 

### C ALCULATIONS

### Arkansas Electric Cooperative Corporation Trinity Consultants BART Analysis – Bailey and McClellan 

## A PPENDIX 

## BA

PM 10 

### C ONTROL 

### C OST 

### C ALCULATIONS

### Arkansas Electric Cooperative Corporation Trinity Consultants BART Analysis – Bailey and McClellan 

## A PPENDIX 

## CB

M ODELING 

### P ROTOCOL