# Appendix 9.6

**BART Analysis for Westar Energy - Gordon Evans Unit 2**  **and Jeffrey Units 1 and 2 (including May 2009 adden dum** 

**for GEEC)**

# CALPUFF BART M ODELING 

# P ROTOCOL 

#  W ESTAR 

# E NERGY



## H UTCHINSON 

## , G ORDON 

## E VANS 

## , J EFFREY 

## , & L AWRENCE 

## E NERGY 

## C ENTERS



### V ERSION 

### 0

Prepared by:

Kasi Dubbs  Senior Consultant Kelli Johnson  Senior Consultant

T RINITY 

### C ONSULTANTS 

### 25055 West Valley Parkway

Suite 101 Olathe, Kansas 66061 (913) 390-9700

May 2006

Project 051701.0153

### Westar Energy, Inc. i Trinity Consultants

BART Modeling Protocol 

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION



### ................................................... .................................................1 1.1 B ACKGROUND



### ................................................... ................................................... 1 1.2 O BJECTIVE



### ................................................... ................................................... ......1 1.3 L OCATION OF 

### S OURCES AND RELEVANT 

### C LASS 

### I A REAS 

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

2. CALPUFF M ODEL 

### S YSTEM



### ................................................... .............................4 2.1 M ODEL 

### V ERSIONS



### ................................................... ..............................................4 2.2 M ODELING 

### D OMAIN



### ................................................... ...........................................5

3. CALMET.............................................

................................................... ............6 3.1 G EOPHYSICAL 

### D ATA



### ................................................... ..........................................6 3.1.1 T ERRAIN 

### D ATA



### ................................................... ............................................... 6 3.1.2 L AND 

### U SE 

### D ATA



### ................................................... ............................................. 7 3.1.3 C OMPILING 

### T ERRAIN AND LAND 

### U SE 

### D ATA



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

### 3.2 M ETEOROLOGICAL 

### D ATA



### ................................................... ...................................8 3.2.1 M ESOSCALE 

### M ODEL 

### M ETEOROLOGICAL 

### D ATA 

### .................................................. 9 3.2.2 S URFACE 

### M ETEOROLOGICAL 

### D ATA



### ................................................... ................ 9 3.2.3 U PPER 

### A IR 

### M ETEOROLOGICAL 

### D ATA



### ................................................... ............ 10 3.2.4 P RECIPITATION 

### M ETEOROLOGICAL 

### D ATA



### ................................................... ..... 12 

### 3.3 S UMMARY OF 

### CALMET C ONTROL 

### P ARAMETERS 

### ................................................13

4. CALPUFF ............................................

................................................... .........20 4.1 S OURCE 

### E MISSIONS 

### D ATA



### ................................................... ................................20 4.1.1 SO 2 AND 

### NO X 

### E MISSIONS



### ................................................... .............................. 20 4.1.2 T OTAL 

### PM 10 

### E MISSIONS



### ................................................... ................................ 20 4.1.3 S PECIATED 

### PM 10 

### E MISSIONS



### ................................................... ......................... 23 4.1.4 S TACK PARAMETERS



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

### 4.2 C LASS I 

### A REA 

### R ECEPTORS



### ................................................... ...............................31 4.3 B ACKGROUND 

### O ZONE



### ................................................... ......................................32 4.4 B ACKGROUND 

### A MMONIA



### ................................................... .................................33 4.5 S UMMARY OF 

### CALPUFF C ONTROL 

### P ARAMETERS 

### ...............................................33

5. CALPOST .............................................

................................................... ........48 5.1 L IGHT 

### E XTINCTION 

### A LGORITHM



### ................................................... ......................48 5.2 CALPOST P ROCESSING METHOD



### ................................................... .....................48 5.3 N ATURAL 

### B ACKGROUND



### ................................................... .................................49 5.4 E VALUATING 

### BART-E XEMPTION



### ................................................... .....................49 5.5 S UMMARY OF 

### CALPOST C ONTROL 

### P ARAMETERS 

### ...............................................49

A PPENDIX 

### A..................................................

................................................... .....................1

A PPENDIX 

### B ..................................................

................................................... .....................1

### Westar Energy, Inc. ii Trinity Consultants

BART Modeling Protocol 

### A PPENDIX 

### C..................................................

................................................... .....................1

A PPENDIX 

### D..................................................

................................................... .....................1

### Westar Energy, Inc. iii Trinity Consultants

BART Modeling Protocol 

## L IST OF 

## T ABLES

T ABLE 

### 1-1. D ISTANCE TO 

### C LASS 

### I A REAS



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

T ABLE 

### 2-1. CALPUFF M ODELING 

### S YSTEM 

### V ERSIONS



### ................................................... ................... 4

T ABLE 

### 3-1. S UMMARY OF 

### CALMET I NPUTS



### ................................................... ................................ 14

T ABLE 

### 4-1. W ESTAR 

### BART- ELIGIBLE 

### S OURCES 

### NO X AND 

### SO 2 

### M AXIMUM 

### A CTUAL 

### 24-H OUR 

### E MISSION 

### R ATES



### ................................................... ................................................... .......... 20

T ABLE 

### 4-2. A

NNUAL 

### T OTAL 

### P M 

### 10 E MISSION 

### E STIMATES



### ................................................... ............ 22

T ABLE 

### 4-3. S UMMARY OF 

### S PECIATED 

### PM 10 

### E MISSION 

### E STIMATES 

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

T ABLE 

### 4-4. HEC U

NIT 

### 4 N ORTH 

### S TACK 

### PM S PECIATION 

### A NALYSIS 

### (S AME FOR 

### S OUTH 

### S TACK 

### ) ..... 24

T ABLE 

### 4-5. GEEC U NIT 

### 2 A S TACK 

### PM S PECIATION 

### A NALYSIS 

### (S AME FOR 

### B S TACK 

### ).................... 24

T ABLE 

### 4-5. GEEC U NIT 

### 2 A S TACK 

### PM S PECIATION 

### A NALYSIS 

### (S AME FOR 

### B S TACK 

### ).................... 25

T ABLE 

### 4-6. JEC U

NIT 

### 1 PM S PECIATION 

### A NALYSIS



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

T ABLE 

### 4-7. JEC U

NIT 

### 2 PM S PECIATION 

### A NALYSIS



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

T ABLE 

### 4-8. LEC U

NIT 

### 5 PM S PECIATION 

### A NALYSIS



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

T ABLE 

### 4-8. LEC U

NIT 

### 5 PM S PECIATION 

### A NALYSIS



### ................................................... .................... 28

T ABLE 

### 4-9. S UMMARY OF 

### S PECIATED 

### PM 10 

### E MISSION 

### E STIMATES 

### .................................................. 3 0

T ABLE 

### 4-10. S UMMARY OF 

### O ZONE 

### M ONITORS



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

T ABLE 

### 4-11. S UMMARY OF 

### CALPUFF I NPUTS



### ................................................... ............................. 33

T ABLE 

### 5-1. M ONTHLY 

### H UMIDITY 

### F ACTORS



### ................................................... ................................. 48

T ABLE 

### 5-2. D EFAULT 

### W EST 

### A VERAGE 

### A NNUAL 

### N ATURAL 

### B ACKGROUND 

### L EVELS 

### ......................... 49

T ABLE 

### 5-3. S UMMARY OF 

### CALPOST I NPUTS



### ................................................... .............................. 49

T ABLE 

### A-1. D ETERMINATION OF 

### M ODELING 

### D OMAIN



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

T ABLE 

### C-1. L AND 

### U SE 

### D ATA 

### U SED IN 

### A NALYSIS



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

T ABLE 

### C-2. T ERRAIN 

### D ATA 

### U SED IN 

### A NALYSIS



### ................................................... ............................. 3

T ABLE 

### D-1. L IST OF 

### S URFACE METEOROLOGCAL 

### S TATIONS



### ................................................... ........... 1

### Westar Energy, Inc. iv Trinity Consultants

BART Modeling Protocol T ABLE 

### D-2. L IST OF 

### U PPER 

### A IR METEOROLOGICAL 

### S TATIONS



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

T ABLE 

### D-3. L IST OF 

### P RECIPITATION METEOROLOGICAL 

### S TATIONS



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

### Westar Energy, Inc. v Trinity Consultants

BART Modeling Protocol 

## L IST OF 

## F IGURES

F IGURE 

### 1-1. L OCATION OF 

### W ESTAR 

### F ACILITIES WITH 

### BART-E LIGIBLE 

### S OURCES AND 

### N EARBY 

### C LASS 

### I A REAS



### ................................................... ................................................... ............... 3

F IGURE 

### 2-1. P ROPOSED 

### M ODELING 

### D OMAIN



### ................................................... .................................. 5

F IGURE 

### 3-1. P LOT OF 

### L AND 

### E LEVATION 

### U SING 

### USGS T ERRAIN 

### D ATA 

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

F IGURE 

### 3-2. P LOT OF 

### L AND 

### U SE 

### U SING 

### USGS LULC D ATA



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

F IGURE 

### 3-3. P LOT OF 

### S URFACE 

### S TATIONS



### ................................................... .................................... 10

F IGURE 

### 3-4. P LOT OF 

### U PPER 

### A IR 

### S TATIONS



### ................................................... ................................. 11

F IGURE 

### 3-5. P LOT OF 

### P RECIPITATION METOROLOGICAL 

### S TATIONS 

### .................................................. 1 3

F IGURE 

### 4-1. H ERCULES 

### -G LADES 

### W ILDERNESS 

### R ECEPTOR 

### L OCATIONS 

### ........................................... 31

F IGURE 

### 4-2. W ICHITA 

### M OUNTAINS 

### R ECEPTOR 

### L OCATIONS



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

F IGURE 

### B-1. E XAMPLE 

### CALMM5 I NPUT 

### F ILE



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

### Westar Energy, Inc. 1 Trinity Consultants

BART Modeling Protocol 

## 1. I NTRODUCTION



## 1.1 B ACKGROUND

On July 1, 1999, the U.S. Environmental Protection Agency (EPA) published the final Regional Haze Rule (RHR). The objective of the RHR is to improve visibility in 156 specific areas across with United States, known as Class I areas. The Clean A ir Act defines Class I areas as certain national parks (over 6000 acres), wilderness areas (over 500 0 acres), national memorial parks (over 5000 acres), and international parks that were in existe nce on August 7, 1977.

On July 6, 2005, the EPA published amendments to it s 1999 RHR, often called the Best Available Retrofit Technology (BART) rule, which included gui dance for making source-specific BART determinations. The BART rule defines BART-eligibl e sources as sources that meet the following criteria:

(1) Have potential emissions of at least 250 tons per y ear of a visibility-impairing pollutant, (2) Began operation between August 7, 1962 and August 7 , 1977, and (3) Are listed as one of the 26 listed source categorie s in the guidance.

A BART-eligible source is not automatically subject to BART. Rather, BART-eligible sources are subject-to-BART if the sources are “reasonably anti cipated to cause or contribute to visibility impairment in any federal mandatory Class I area.” EPA has determined that sources are reasonably anticipated to cause or contribute to visibility im pairment if the visibility impacts from a source ar e greater than 0.5 deciviews (dv) when compared again st a natural background.

Air quality modeling is the tool that is used to de termine a source’s visibility impacts. States have the authority to exempt certain BART-eligible sources f rom installing BART controls if the results of the modeling demonstrate that the source cannot reasona bly be anticipated to cause or contribute to visibility impairment in a Class I area. Further, states also have the authority to define the modeli ng procedures for conducting modeling related to makin g BART determinations.

To promote consistency between states in the develo pment of BART modeling protocols and to harmonize the approaches between adjacent RPOs, CEN RAP developed BART Modeling Guidelines (December 15, 2005). The intent of the guidelines is to assist CENRAP states and source operators in the development of statewide and source-specific mo deling protocols.



## 1.2 O BJECTIVE

The objective of this document is to provide a prot ocol summarizing the modeling methods and procedures that Westar Energy (Westar) will follow as we evaluate whether BART applies to any of our BART-eligible sources. Westar will use the mod eling methods and procedures to determine if our BART-eligible sources can reasonably be anticip ated to cause or contribute to visibility impairment in a Class I area, and are thus subject to BART. We are proposing to consider a source

### Westar Energy, Inc. 2 Trinity Consultants

BART Modeling Protocol subject to BART if the 98 th 

### percentile of the visibility impacts predicted by the model are above EPA’s recommended visibility contribution threshold of 0.5 ' dv.



## 1.3 L OCATION OF 

## S OURCES AND RELEVANT 

## C LASS 

## I A REAS

Westar has identified the following sources that me et the three criteria for being BART-eligible

sources:

Ÿ 

### Hutchinson Energy Center (HEC) – Boiler Unit 4
Ÿ 

### Gordon Evans Energy Center (GEEC)– Boiler Unit 2
Ÿ 

### Jeffrey Energy Center (JEC) – Boiler Units 1 and 2
Ÿ 

### Lawrence Energy Center (LEC) – Boiler Unit 5

Table 1-1 provides a summary of the distances from each of our facilities with BART-eligible sources to nearby Class I areas.

T ABLE 

### 1-1. D ISTANCE TO 

### C LASS 

### I A REAS

Distance to Class I Area (km)

Energy Center Badlands

National Park Wind Cave

National Park Hercules-Glades

Wilderness Wichita

Mountains
GEEC 781.48 812.69 423.51 360.36
HEC 737.12 767.48 463.08 384.36
JEC 723.64 775.72 403.08 560.08
LEC 797.14 851.57 331.64 569.00



### In order to determine whether the BART eligible sou rces listed above are subject to BART, the visibility impacts in the two Class I areas that ar e within 600 km of the sources will be determined. The Class I areas within 600 km include Hercules-Gl ades Wilderness and Wichita Mountains. Figure 1-1 provides a plot of the location of the faciliti es listed above with respect to the Hercules Glades Wilderness and Wichita Mountains.

### Westar Energy, Inc. 3 Trinity Consultants

BART Modeling Protocol 

### F IGURE 

### 1-1. L OCATION OF 

### W ESTAR 

### F ACILITIES WITH 

### BART-E LIGIBLE 

### S OURCES AND 

### N EARBY 

### C LASS 

### I A REAS

### Westar Energy, Inc. 4 Trinity Consultants

BART Modeling Protocol 

## 2. CALPUFF M ODEL 

## S YSTEM

The main components of the CALPUFF modeling system are CALMET, CALPUFF, and CALPOST. CALMET is the meteorological model that generates ho urly three-dimensional meteorological fields such as wind and temperature. CALPUFF simulates th e non-steady state transport, dispersion, and chemical transformation of air pollutants emitted f rom a source in “puffs”. CALPUFF calculates hourly concentrations of visibility affecting pollu tants at each specified receptor in a modeling domain. CALPOST is the post-processor for CALPUFF, and CALPOST computes visibility impacts from a source based on the visibility affecting pol lutant concentrations that were produced by CALPUFF.



## 2.1 M ODEL 

## V ERSIONS

Earth Tech, Inc. is the primary developer of the CA LPUFF modeling system and all related programs. The versions of the CALPUFF modeling system program s that will be used for our modeling are listed in Table 2-1. Table 2-1 also compares the p rogram versions that will be used to model Westar’s sources with the program versions recommen ded by CENRAP. Note that some of the program versions are not the same as the program ve rsions recommended by CENRAP. The program versions are different due to the fact that several of the program versions recommended by CENRAP are incompatible with each other as published. Spe cifically, the MM5 data extraction program (CALMM5) Version 2.4 is not compatible with CALMET Ve rsion 5.53a. CALMM5 Version 2.4 is compatible with a newer version of CALMET, Version 5 .551. Note that meteorological data that is generated with CALMET Version 5.551 is not compatibl e with CALPUFF Version 5.711a. CALMET Version 5.551 is compatible with CALPUFF Vers ion 5.727. In short, alternate program versions are required in order to accommodate the MM5 data extraction program version, so Westar will use alternate versions.

T ABLE 

### 2-1. CALPUFF M ODELING 

### S YSTEM 

### V ERSIONS

CENRAP Suggested Westar Analyses Program Version Level Version Level Reason for Difference
TERREL 3.311 030709 3.311 030709 CTGCOMP 2.42 030709 2.22 030528 Version recommended is not
available CTGPROC 2.42 030709 2.42 030709 MAKEGEO 2.22 030709 2.22 030709 CALMM5 2.4 050413 2.4 050413 CALMET 5.53a 040716 5.551 050310 CALMM5 v2.4 is not compatible
with CALMET v5.53a CALPUFF 5.711a 040716 5.727 050309 Use version compatible with
CALMET v5.551 POSTUTIL 1.4 040818 1.4 040818 CALPOST 5.51 030709 5.636 050218 Use version compatible with
CALPUFF v5.636

### Westar Energy, Inc. 5 Trinity Consultants

BART Modeling Protocol 

## 2.2 M ODELING 

## D OMAIN

The modeling domain will extend 50 km in all direct ions beyond Westar’s BART-eligible sources and the two Class I areas of interest (HWG and WM). The map projection for the modeling domain will be Lambert Conformal Conic (LCC) and the coord inate system will be North American Datum 1927 (NAD27), which is an LCC projection. The mete orological grid spacing will be 2.5 km.

The southwest corner of the modeling domain is Lati tude 34.08oN, Longitude 99.35oW which will be assigned as the 0, 0 reference point for the domain . The northeast corner of the modeling domain is approximately Latitude 39.78oN, Longitude 92.05 oW. At a grid spacing of 2.5 km, the number of X grid cells will be 251 and the number of Y grid cel ls will be 246.

Calculations showing the determination of these dom ain parameters are included in Appendix A. Further, Figure 2-1 provides a plot of the modeling domain with respect to the sources and Class I

areas.



### F IGURE 

### 2-1. P ROPOSED 

### M ODELING 

### D OMAIN

-100 0 100 200 300 400 500 600 700 East (km) -100 0 100 200 300 400 500 600 700North(km) Gordon Evans Hutchinson Jeffrey

Lawrence

Hercules Glades

Wichita Mountains

### Westar Energy, Inc. 6 Trinity Consultants

BART Modeling Protocol 

## 3. CALMET

Westar will conduct a three-year CALMET analysis tha t incorporates both mesoscale model and observation meteorological data. The CALMET analysi s will generate three years of data that will be input to CALPUFF. The CALMET model requires the inp ut of geophysical data, meteorological data, and model parameter settings. The CALMET mode ling procedures that will be used will generally follow the recommendations in CENRAP’s pr otocol. However, some of CENRAP’s recommendations only apply to CALMET analyses that i ncorporate mesoscale model meteorological data (and no observation data). Since the CALMET an alysis for Westar’s modeling will be a hybrid analysis (mesoscale model data plus observation dat a), it is expected that some parameters will be different.



## 3.1 G EOPHYSICAL 

## D ATA

CALMET requires geophysical data to characterize the terrain and land use parameters that potentially affect dispersion. Terrain features af fect flows and create turbulence in the atmosphere and are potentially subjected to higher concentrati ons of elevated puffs. Different land uses exhibit variable characteristics such as surface roughness, albedo, Bowen ratio, and leaf-area index that also effect turbulence and dispersion.



### 3.1.1 T ERRAIN 

### D ATA



### Terrain data from the United States Geological Surv ey (USGS) in 1-degree (1:250,000 scale or approximately 90 meter resolution) digital format will be used. A list of the USGS terrain files is provided in Appendix C. A pl ot of the land elevation for the modeling domain based on the referenced files is pr ovided in Figure 3-1.

### Westar Energy, Inc. 7 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### 3-1. P LOT OF 

### L AND 

### E LEVATION 

### U SING 

### USGS T ERRAIN 

### D ATA

0 100 200 300 400 500 600 East (m) 0 100 200 300 400 500 600North(k) Gordon Evans

Hutchinson Jeffrey

Lawrence

Hercules Glades

Wichita Mountains -50 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750

Meters from MSL



### The USGS terrain data will be input into the TERREL program to generate grid-cell elevation averages across the modeling domain.



### 3.1.2 L AND 

### U SE 

### D ATA



### USGS Composite Theme Grid (CTG) format Land Use and Land Cover (LULC) data files at 1:250,000 resolution will be used, where availab le. Where 1:250,000 land use data is not available, USGS data at 1:100,000 resolution wi ll be used. A list of the USGS land use files is provided in Appendix C. A plot of the land use for the modeling domain based on the referenced files is provided in Figure 3-2.

### Westar Energy, Inc. 8 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### 3-2. P LOT OF 

### L AND 

### U SE 

### U SING 

### USGS LULC D ATA

0 100 200 300 400 500 600 East (km) 0 100 200 300 400 500 600North(km) Gordon Evans

Hutchinson Jeffrey

Lawrence

Hercules Glades

Wichita Mountains 10 to 17 20 to 24 30 to 33 40 to 43 50 to 55 Urban

Agricultural Rangeland Forestland Water



### The LULC data will be input into the CTGPROC progra m to generate land use for each grid cell across the modeling domain. The USGS CT G format LULC data files must be compressed prior to use in the CTGPROC utility proc essor; therefore the files will be compressed using the program CTGCOMP.



### 3.1.3 C OMPILING 

### T ERRAIN AND LAND 

### U SE 

### D ATA



### The terrain data files output by the TERELL program and the LULC files output by CTGPROC program will be input to the program MAKEGE O to create a geophysical data file that will be input to CALMET.



## 3.2 M ETEOROLOGICAL 

## D ATA

CALMET will be used to assimilate data for 2001, 200 2, and 2003 using mesoscale model output and National Weather Service (NWS) surface station obse rvations, upper air station observations, and precipitation station observations to develop the m eteorological field.

### Westar Energy, Inc. 9 Trinity Consultants

BART Modeling Protocol 

### 3.2.1 M ESOSCALE 

### M ODEL 

### M ETEOROLOGICAL 

### D ATA



### Hourly mesoscale data will be used to supplement th e hourly surface, upper air, and precipitation observation data. The mesoscale data will be used to define the initial guess field for the CALMET simulations. The following 5 th 

### generation mesoscale model (MM5) meteorological data sets will be used:

x 2001 MM5 data at 36 km resolution processed for EPA b y Alpine Geophysics

x 2002 MM5 data at 36 km resolution processed by Iowa D NR

x 2003 MM5 data set at 36 km resolution processed by th e Midwest RPO

The MM5 data for the modeling domain will be extracte d from the above MM5 data sets using the CALMM5 program. The MM5 data extraction wil l follow CENRAP’s recommendations, meaning that all vertical layers w ill be extracted and vertical velocity, relative humidity, cloud/rain fields, and ice/snow fields will be included. An example CALMM5 input file is provided in Appendix B.



### 3.2.2 S URFACE 

### M ETEOROLOGICAL 

### D ATA



### Parameters affecting turbulent dispersion that are observed hourly at surface stations include wind speed and direction, temperature, clou d cover and ceiling, relative humidity, and precipitation type. The surface stations from which data will be extracted are listed in Appendix D. The locations of the surface stations with respect to the modeling domain are shown in Figure 3-3. These stations were selected from the available data inventory to optimize spatial coverage and representation of the domain. Data from the stations will be processed for use in CALMET using Version 5.55, Leve l: 050311 of EPA’s SMERGE
program.

### Westar Energy, Inc. 10 Trinity Consultants

BART Modeling Protocol 

### F IGURE 

### 3-3. P LOT OF 

### S URFACE 

### S TATIONS

0 100 200 300 400 500 600 East (km) -200 -100 0 100 200 300 400 500 600 700North(km) GEEC HEC JEC LEC

KTYR KABI

KACT KCNK

KCNU KCOU

KDFW KEMP

KFSM KFYV

KHBR

KHOT KICT KIRK

KJLN KMCI

KMHK

KMLC

KOCH KOKC KPNC

KPRX KSGF

KSHV KSLN

KSPS KSTJ

KSZL KTOP

KTUL

KTXK K1F0 Hercules Glades

Wichita Mountains



### 3.2.3 U PPER 

### A IR 

### M ETEOROLOGICAL 

### D ATA



### Observations of meteorological conditions in the up per atmosphere provide a profile of turbulence from the surface through the depth of th e boundary layer in which dispersion occurs. Upper air data are collected by balloons l aunched simultaneously across the observation network at 0000 Greenwich Mean Time (GMT) (6 o’clock PM in Kansas) and 1200 GMT (6 o’clock AM in Kansas). Sensors observe pressure, wind speed and direction, and temperature (among other parameters) as the balloon rises through the

### Westar Energy, Inc. 11 Trinity Consultants

BART Modeling Protocol

atmosphere. The upper air observation network is l ess dense than surface observation points since upper air conditions vary less and are generally not as affected by local effects (e.g., terrain or water bodies). The upper air sta tions from data will be extracted are listed in Appendix D. The locations of the upper air stat ions with respect to the modeling domain are shown in Figure 3-4. These stations wer e selected from the available data inventory to optimize spatial coverage and represen tation of the domain. Data from the stations will be processed for use in CALMET using V ersion 5.52a, Level: 040716 of EPA’s READ62 program.



### F IGURE 

### 3-4. P LOT OF 

### U PPER 

### A IR 

### S TATIONS

0 100 200 300 400 500 600 East (km) -200 -100 0 100 200 300 400 500 600 700North(km) GEEC HEC JEC LEC

KOUN
KLZK

KFWD

KSHV KSGF KTOP

Hercules Glades

Wichita Mountains

### Westar Energy, Inc. 12 Trinity Consultants

BART Modeling Protocol 

### 3.2.4 P RECIPITATION 

### M ETEOROLOGICAL 

### D ATA



### The effects of chemical transformations and deposit ion processes on ambient pollutant concentrations will be considered in this analysis. Therefore, it is necessary to include observations of precipitation in the CALMET analysis . The precipitation stations from which data will be extracted are listed in Appendix D. The locations of the precipitation stations with respect to the modeling domain are sh own in Figure 3-5. These stations were selected from the available data inventory to optim ize spatial coverage and representation of the domain. Data from the stations will be proc essed for use in CALMET using Version 5.31, Level: 030528 of EPA’s PMERGE program.

### Westar Energy, Inc. 13 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### 3-5. P LOT OF 

### P RECIPITATION METOROLOGICAL 

### S TATIONS

0 100 200 300 400 500 600 East (km) -200 -100 0 100 200 300 400 500 600 700North(km) GEEC HEC JEC LEC

ALUM

ANTO ARKA BLAK BOON BRIG

CAMD CLAR

DE Q DIER

FORE FORT

LEWI

MAGN MENA NIMR

PRES

TEXA WALD

MANS

MIND

NATC RED SHRE ALTU

ARDM BENG

CANE CART CHAN

CHIC CUST

DUNC ELK

FORT

GEAR

HENN HOBA

KING LAKE

LEHI MACK MARS

MCAL MUSK OKAR

OKEM OKLA

ROFF TUSK WICH WIST WOLF

ABIL BONI

BURL CHIL

CLAR

COMM

CRAN CRES

CROS DALL DALL DENT

EAST FERR FORT GAIN

GILM GORD

GROE HICO HONE

JACK

KOPP LAKE

LONG MABA MINE

MOLI MOUN

NACO NAVA NEW

NEW

PALE PAT

PITT

PROC RED

RENO

SANT STAM

STEP SULP

SWAN TRUS

WACO WEAT WELL

WHIT WICH

WINC WOOD WRIG HARR NORF

ARLI

CALD CHAN CONC

COUN IONI

KIOW MARY

MILF

SALI TOPE

UNIO

WICH APPL

BOLI

CASS COLU

DORA JOPL KANS KIRK

LEBA

MOUN NEVA

SPRI

TABL TRUM

AMES PONC

TULS



## 3.3 S UMMARY OF 

## CALMET C ONTROL 

## P ARAMETERS

Table 3-1 provides a listing of the CALMET parameter s will be used in the modeling analysis. In addition to the parameters that will be used for th e modeling, the table also lists CENRAP’s recommended parameters for comparison. In cases wh ere a parameter to be used is different than what CENRAP recommended, a short explanation as to the difference is proved. Note that most of the differences from CENRAP’s recommended parameter s are due to the inclusion of observation data into the modeling analysis, since CENRAP’s par ameters are based on a no-observation analysis.

### Westar Energy, Inc. 14 Trinity Consultants

BART Modeling Protocol T ABLE 

### 3-1. S UMMARY OF 

### CALMET I NPUTS

CALMET Variable Description Value Included in CENRAP Protocol Value Westar

Will Use Notes NUSTA Number of upper air data

sites 0 6 Will use observations

NOWSTA Number of overwater data

sites 0 0

IBYR Starting year 2001 Appropriate met year Years 2001, 2002, 2003

IBMO Starting month 1 Appropriate month Due to file size, analysis

will be completed by month IBHR Starting hour 1 1 IBTZ Base time zone 6 6 IRLG Length of run 6 Varies with month Due to file size, analysis

will be completed by month IRTYPE Run type (1 for

CALPUFF) 1 1

LCALGRD Compute CALFRID data

fields (T = run CALGRID) F F

ITEST Stop run after SETUP to

do input QA (2 = run) 2 2

PMAP Map projection LCC LCC RLAT0 Latitude (decimal degrees)

of projection origin 40N 34.0825N Appropriate for domain

RLON0 Longitude (decimal

degrees) of projection origin 97W 99.3476W Appropriate for domain

XLAT1 Latitude of 1 st standard

parallel 33N 34N Appropriate for domain

XLAT2 Latitude of 2 nd standard

parallel 45N 40N Appropriate for domain

DATUM Datum region for output

coordinates WGS-G NAS-C Selected datum to match datum of land use data NX Number of X grid cells in

meteorological grid 300 251 Appropriate for domain

NY Number of Y grid cells in

meteorological grid 192 246 Appropriate for domain

DGRIDKM Grid spacing (km) 6.0 2.5 Refined grid spacin g XORIGKM Ref. coordinate of SW

corner of grid cell -1008 0 Appropriate for domain

YORIGKM Ref. coordinate of SW

corner of grid cell 0.0 0 Appropriate for domain

NZ Number of vertical layers 10 10 ZFACE Vertical cell face heights

(NZ + 1 values) 0, 20, 40, 80, 160, 320, 640, 1200, 2000, 3000, 4000 0, 20, 40, 80,

160, 320, 640, 1200, 2000, 3000, 4000

### Westar Energy, Inc. 15 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value Westar

Will Use Notes LSAVE Save met. data fields in an

unformatted file? T T

IFORMO Type of unformatted

output file (1 for CALPUFF) 1 1

LPRINT Print met. fields F F IPRINF Print intervals 1 1 IUVOUT(NZ) Specify layers of u,v wind

components to print NZ*0 NZ*0

IWOUT(NZ) Specify layers of w wind

component to print NZ*0 NZ*0

ITOUT(NZ) Specify layers of 3D

temperature field to print NZ*0 NZ*0

LDB Print met data and

variables F F

NN1 First time step for debug

data to be printed 1 1

NN2 Last time step for debug

data to be printed 1 2 Will generate debug data for a total of 2 time steps IOUTD Control variable for

writing test/debug wind fields 0 0

NZPRN2 Number of levels starting

at surface to print 0 1 Default

IPRO Print interpolated wind

components 0 0

IPR1 Print terrain adjusted

surface wind components 0 0

IPR2 Print initial divergence

fields 0 0

IPR3 Print final wind speed and

direction 0 0

IPR4 Print final divergence

fields 0 0

IPR5 Print winds after kinematic

effects 0 0

IPR6 Print winds after Froude

number adjustment 0 0

IPR7 Print winds after slope

flows are added 0 0

IPR8 Print final wind field

components 0 0

NOOBS No observation mode (2 =

No surface, overwater, or upper air observations; use MM5 for surface, overwater, and upper air data) 2 0 Will use observations

NSSTA Number of meteorological

stations in SURF.DAT file 0 32 Number of stations

### Westar Energy, Inc. 16 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value Westar

Will Use Notes NPSTA Number of precipitation

stations in PRECIP.DAT file 0 138 Number of stations

ICLOUD Gridded cloud fields (0 =

no, 3 = Gridded cloud cover from prognostic relative humidity) 3 3

IFORMS Format of surface data (2

= formatted) 2 2

IFORMP Format of precipitation

data (2 = formatted) 2 2

IFORMC Format of cloud data (2 =

formatted) 2 1 N/A - No cloud data used in model IWFCOD Generate winds by

diagnostic wind module? (1 = yes) 1 1

IFRADJ Adjust winds using Froude

number effects? (1 = yes) 1 1

IKINE Adjust winds using

kinematic effects? (0 = no) 0 1 Will compute kinematic effects in this analysis IOBR Use O’Brien procedure for

vertical winds? (0 = no) 0 0

ISLOPE Compute slope flows? (1 =

yes) 1 1

IEXTRP Extrapolate surface winds

to upper layers (-1 = no extrapolation and ignore layer 1 of upper air station data) -1 -4 -4 = Since observations are included in model, will use similarity theory and ignore layer 1 of upper air station data (FLAG default) ICALM Extrapolate surface winds

even if calm? (0 = no) 0 0

BIAS Layer dependent biases

weighting aloft
measurements 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 0, 0, 0, 0, 0, 0,

0, 0, 0, 0

RMIN2 Minimum vertical

extrapolation distance

Distance (km) around an upper air site where vertical extrapolation is excluded (set to –1 if IEXTRP = ± 4) -1 -1

IPROG Using prognostic or MMFDDA data? (14 = Use winds from MM5.DAT as initial guess wind field) 14 14

ISTEPPG Timestep (hours) of the

MM5 data 1 1

### Westar Energy, Inc. 17 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value Westar

Will Use Notes LVARY Use varying radius of

influence to develop surface winds? T F Use FLAG default

RMAX1 Maximum radius of

influence over land in surface layer (km) 30 36 Same as MM5 data spacing

RMAX2 Maximum radius of

influence over land aloft (km) 30 36 Same as MM5 data spacing

RMAX3 Maximum radius of

influence over water (km) 50 36 Same as MM5 data spacing RMIN Minimum radius of

influence used anywhere (km) 0.1 0.1

TERRAD Radius of influence of

terrain features (km) 12 12

R1 Weighting of first guess

surface field (km) 1 1

R2 Weighting of first guess

aloft field (km) 1 1

RPROG MM5 windfield weighting

parameter (km) 0 0

DIVLIM Maximum acceptable

divergence 5.E-6 5.E-6

NITER Max number of passes in

divergence minimization 50 50

NSMTH Number of passes through

smoothing filter in each layer of CALMET (NZ values) 2, 4, 4, 4, 4, 4, 4 2, 4, 4, 4, 4, 4,

4, 4, 4, 4

NITR2 Max number of stations

used in each layer for the interpolation of data to a grid point (NZ values) 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 5, 5, 5, 5, 5, 5,

5, 5, 5, 5

CRITFM Critical Froude number 1.0 1.0 ALPHA Kinematic effects

parameter 0.1 0.1

FEXTR2 Scaling factor for

extrapolating surface winds aloft NZ*0.0 NZ*0.0

NBAR Number of terrain barriers 0 0 IDIOTP1 Compute temperature

from observations (0 = true) 0 0

ISURFT Surface station to use for

surface temperature (between 1 and NSSTA) 4 4

IDIOPT2 Domain-averaged wind

component switch 0 0

### Westar Energy, Inc. 18 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value Westar

Will Use Notes IUPT Station for lapse rates

(between 1 and NUSTA) 2 2

ZUPT Depth through which lapse

rate is calculated 200 200

IDIOPT3 Domain averaged wind

component switch 0 0

IUPWND Upper air station for

domain winds -1 -1

ZUPWND Bottom and top of layer

through which the domain scale winds are computed 1., 1000. 1., 1000.

IDIOPT4 Observed surface wind

component switch 0 0

IDIOPT5 Observed aloft wind

component switch 0 0

LLBREZE Use lake breeze module? F F NBOX Number of lake breeze

regions 0 0

NLB Number of stations in the

region 0 0

METBXID(NLB) Station IDs in the region 0 0 CONSTB Neutral stability mixing

height coefficient 1.41 1.41

CONSTE Convective stability

mixing height coefficient 0.15 0.15

CONSTN Stable stability mixing

height coefficient 2400 2400

CONSTW Overwater mixing height

coefficient 0.16 0.16

FCORIOL Absolute value of Coriolis

parameter 1.E-4 1.E-4

IAVEZI Conduct spatial

averaging? (1 = yes) 1 1

MNMDAV Max search radius in

averaging process (number of grid cells) 10 10

HAFANG Half-angle of upwind

looking cone for averaging (degrees) 30 30

ILEVZI Layers of wind use in

upwind averaging (between 1 and NZ) 1 1

DPTMIN Minimum potential

temperature lapse rate in the stable layer above the current convective mixing height 0.001 0.001

### Westar Energy, Inc. 19 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value Westar

Will Use Notes DZZI Depth of layer above

current convective mixing height through which lapse rate is computed (m) 200 200

ZIMIN Minimum overland mixing

height (m) 50 50

ZIMAX Maximum overland

mixing height (m) 3000 3000

ZIMINW Minimum overwater

mixing height (m) 50 50

ZIMAXW Maximum overwater

missing height (m) 3000 3000

ITPROG 3D temperature from

observations or from MM5? 2 0 Will use surface and upper air observations

IRAD Type of interpolation (1 =

1/r) 1 1

TRADKM Temperature interpolation

radius of influence (km) 36 36

NUMTS Max number of stations

for temperature interpolations 5 5

IAVET Spatially average

temperature? (1 = yes) 1 1

TGDEFB Temperature gradient

below the mixing height over water (K/m) -.0098 -0.0098

TGDEFA Temperature gradient

above the mixing height over water (K/m) -.0045 -0.0045

JWAT1 Beginning land use

categories over water 55 55

JWAT2 Ending land use categories

for water 55 55

NFLAGP Precipitation interpolation

flag (2 = 1/r 2 ) 2 2

SIGMAP Radius of influence for

precipitation interpolation (km) 50 50

CUTP Minimum precipitation

rate cut off (mm/hr) 0.01 0.01

### Westar Energy, Inc. 20 Trinity Consultants

BART Modeling Protocol 

## 4. CALPUFF

Westar will conduct a three-year CALPUFF analysis. The CALPUFF model requires the input of meteorological data output by CALMET, source emissio ns data, receptor data, ozone and ammonia data, and model parameter settings.



## 4.1 S OURCE 

## E MISSIONS 

## D ATA

Westar will include emissions of SO 2 

### , NO x 

### , and PM 10 

### in the model.



### 4.1.1 SO 2 AND 

### NO X 

### E MISSIONS



### The SO 2 

### and NO x 

### emissions that will be included in the model are t he 98 th 

### percentile of the 2002- 2004 24-hour highest actual emissions rates, exclud ing periods of startup, shutdown, and malfunction. Table 4-1 provides a summary of these emission rate s. Note that these are the same emission rates that Westar has previously provided to the KDHE as part of KDHE’s request for information related to BART modeling.

T ABLE 

### 4-1. W ESTAR 

### BART- ELIGIBLE 

### S OURCES 

### NO X AND 

### SO 2 

### M AXIMUM 

### A CTUAL 

### 24-H OUR 

### E MISSION 

### R ATES

SO 2 (tons/24-hour) NO x

(tons/24-hour) SO 2

(lbs/hour) NO x

(lbs/hour)
LEC Unit 5 22.9 13.2 1908.3 1100.0
JEC Unit 1 78.2 41.2 6516.7 3433.3
JEC Unit 2 81.5 41.3 6791.7 3441.7 HEC Unit 4 (North Stack) 14.7 2.35 1225.0 195.8 HEC Unit 4 (South Stack) 14.7 2.35 1225.0 195.8 GEEC Evans Unit 2 (A Stack) 25.5 12.1 2125.0 1004.2 GEEC Evans Unit 2 (B Stack) 25.5 12.1 2125.0 1004.2



### 4.1.2 T OTAL 

### PM 10 

### E MISSIONS



### The PM 10 

### emissions that will be included in the model are b ased on an estimate as to the 98 th 

### percentile of the 2002-2004 24-hour highest actual emissions rates. The method for estimating the 98 th 

### percentile of the 2002-2004 24-hour highest actual PM 10 

### emissions rates is described below.

The 2002-2004 total annual calendar year PM 10 

### emission rates were calculated by multiplying the annual fuel throughputs by the specific filterable and condensable AP-42 emission factors. Once the total annual PM 10 

### emissions were determined based on AP-42, the annu al emissions were divided by 365 days to determine the average actual 24-hour em ission rate. Next, the average actual 24-hour

### Westar Energy, Inc. 21 Trinity Consultants

BART Modeling Protocol PM 10 

### emission rates for each calendar year were average d to determine the 2002-2004 actual average 24-hour emission rate. The maximum actual 24-hour emission rate was derived from the actual average 24-hour emission rate by multiplying the ac tual average 24-hour emission rate by a scaling factor. The scaling factor was derived as describe d below.

First, the 2002-2004 calendar year average actual 2 4-hour SO 2 

### emission rates (estimated as the actual annual emissions divided by 365 days per year) were determined. Next, the average 24-hour SO 2 

### emission rates for each calendar year were averaged to determine the 2002-2004 actual average 24- hour emission rate. Then, the 2002-2004 maximum ac tual 24-hour SO 2 

### emission rates that were listed in Table 4-1 were divided by the 2002-2004 a ctual average 24-hour emission rates. This value is the SO 2 

### scaling factor. The same procedure was followed t o determine the NO x 

### scaling factor. The average of the SO 2 

### and NO x 

### scaling factors was selected as the multiplier to convert the maximum actual average 24-hour PM 10 

### emission rates to the maximum actual 24-hour PM 10 

### emission rates.

Table 4-2 provides a summary of the total PM 10 

### emissions estimates using the methodology describe d above.Westar Energy, Inc. 22 Trinity ConsultantsBART Modeling ProtocolTABLE4-2.ANNUALTOTALPM10EMISSIONESTIMATES

### Westar Energy, Inc. 23 Trinity Consultants

BART Modeling Protocol 

### 4.1.3 S PECIATED 

### PM 10 

### E MISSIONS



### The PM 10 

### emissions will be speciated according to FLM guidan ce to include the following:

x Coarse particulate matter (PM C 

### )

x Fine particulate matter (PM f 

### )

x Sulfates (SO 4 

### )

x Secondary organic aerosols (SOA)

x Elemental carbon (EC)

The PM 10 

### emissions will be speciated according to the defau lt speciation profiles prepared by the FLM’s. Table 4-3 provides a summary of the proposed speciated emission rates that will be included in the model. Tables 4-4 through 4-8 provide the s peciation methodology using the FLM speciation guidelines. Note that there currently is no guidan ce for speciation of PM from a coal boiler with a wet scrubber (LEC Unit 5). Thus, Westar will speci ate PM from LEC Unit 5 using the speciation profile for PC boilers with ESPs.

T ABLE 

### 4-3. S UMMARY OF 

### S PECIATED 

### PM 10 

### E MISSION 

### E STIMATES

SO 4 (tons/ 24-hour) PM c

(tons/ 24-hour) PM f

(tons/ 24-hour) SOA

(tons/ 24-hour) EC

(tons/ 24-hour) Total PM 10

(tons/ 24-hour)
LEC Unit 5 1.152 0.551 0.425 0.288 0.016 2.43
JEC Unit 1 1.939 0.593 0.457 0.485 0.018 3.49
JEC Unit 2 1.849 0.565 0.436 0.462 0.017 3.33 HEC Unit 4 (North Stack) 0.070 0.206 0.513 0.012 0.0 41 0.84 HEC Unit 4 (South Stack) 0.070 0.206 0.513 0.012 0.0 41 0.84 GEEC Evans Unit 2 (A Stack) 0.125 0.338 0.842 0.022 0.067 1.39 GEEC Evans Unit 2 (B Stack) 0.125 0.338 0.842 0.022 0.067 1.39

SO 4 (lbs/hr) PM c

(lbs/hr) PM f

(lbs/hr) SOA

(lbs/hr) EC

(lbs/hr) Total PM 10

(lbs/hr)
LEC Unit 5 96.0 45.9 35.4 24.0 1.4 202.6
JEC Unit 1 161.6 49.4 38.1 40.4 1.5 290.9
JEC Unit 2 154.1 47.1 36.3 38.5 1.4 277.4 HEC Unit 4 (North Stack) 5.8 17.2 42.7 1.0 3.4 70.2 HEC Unit 4 (South Stack) 5.8 17.2 42.7 1.0 3.4 70.2 GEEC Evans Unit 2 (A Stack) 10.4 28.2 70.1 1.8 5.6 1 16.2 GEEC Evans Unit 2 (B Stack) 10.4 28.2 70.1 1.8 5.6 1 16.2Westar Energy, Inc.24Trinity ConsultantsBART Modeling ProtocolTABLE4-4.HECUNIT4NORTHSTACKPMSPECIATIONANALYSIS(SAME FORSOUTHSTACK)Controlled PM10 Speciation from AP-42 Tables 1.3-2& 1.3-4Uncontrolled Utility Residual Oil Boilerassumes firing of #6oil with a sulfur content of1.77%S; therefore, A =2.356133333 Assume heating value of156,270Btu/Gal f(RH) =1Uncontrolled PM10 Emissions(BoldValues from Tables 1.3-2 and 1.3-4.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext. Condensible CPM IOR Particle CPM ORType (lb/mGal) (lb/mGal) (lb/mGal) Coef. (lb/mGal) (lb/mGal) Coef. (lb/mGal) Coef. (lb/mGal) (lb/mGal) Type Ext.Coef. (lb/mGal)Utility15.4013.903.770.610.139.3810.75101.51.28SO43*f(RH)0.23Uncontrolled PM10 EmissionsBoiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext. Condensible CPM IOR Particle CPM ORType (% of Total) (% of Total) (% of Total) Coef. (% ofTotal) (% of Total) Coef. (% of Total) Coef. (% of Total) (% of Total) Type Ext.Coef. (% of Total)Utility100%90.3%24.5%0.665.8%60.9%14.9%109.7%8.3%SO43*f(RH)1.5%Uncontrolled PM10 EmissionsBoiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext. Condensible CPM IOR Particle CPM ORType (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (lb/ton) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type Ext.Coef. (lb/mmBtu)Utility0.100.090.020.60.060.0610.005100.010.01SO43*f(RH)0.001If you are given Total PM10 emissions in lb/hr:Uncontrolled PM10 Emissions(BoldValue isInputby user.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext. Condensible CPM IOR Particle CPM ORType (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (lb/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. (lb/hr)Utility70.263.3 17.2 0.6 46.2 42.7 1 3.4 10 6.8 5.8 SO4 3 1.0Weighted Extinction 10.342.7 34.217.4Coarse 24.5%Coarse 17.2Fine Soil 60.9%Fine Soil 42.7Fine EC 4.9%Fine EC 3.4CPM IOR 8.3%CPM IOR 5.8CPM OR 1.5%CPM OR 1.0100.0%70.2

## Westar Energy, Inc. 25 Trinity Consultants BART Modeling Protocol T ABLE 

## 4-5. GEEC U NIT 

## 2 A S TACK 

## PM S PECIATION 

## A NALYSIS 

## (S AME FOR 

## B S TACK 

## )

Controlled PM10 Speciation from AP-42 Tables 1.3-2 & 1.3-4 Uncontrolled Utility Residual Oil Boiler

assumes firing of # 6 oil with a sulfur content of 1.59 %S; therefore, A = 2.154533333 Assume heating value of 156,052 Btu/Gal f(RH) = 1

Uncontrolled PM10 Emissions (Bold Values from Tables 1.3-2 and 1.3-4.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/mGal) (lb/mGal) (lb/mGal) Coef. (lb/mGal) (lb/mG al) Coef. (lb/mGal) Coef. (lb/mGal) (lb/mGal) Type Ext.Coe f. Utility 14.21 12.71 3.45 0.6 9.26 8.58 1 0.69 10 1.5 1.28 SO4 3*f(RH)

Uncontrolled PM10 Emissions Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (% of Total) (% of Total) (% of Total) Coef. (% of Total) (% of Total) Coef. (% of Total) Coef. (% of Total ) (% of Total) Type Ext.Coef. Utility 100% 89.4% 24.3% 0.6 65.2% 60.4% 1 4.8% 10 10.6% 9.0% S O4 3*f(RH)

Uncontrolled PM10 Emissions Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (l b/ton) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type E xt.Coef. Utility 0.09 0.08 0.02 0.6 0.06 0.05 1 0.004 10 0.01 0.01 SO4 3* f(RH)

If you are given Total PM10 emissions in lb/hr: Uncontrolled PM10 Emissions (Bold Value is Input by user.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (l b/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. Utility 116.2 103.9 28.2 0.6 75.7 70.1 1 5.6 10 12.3 10.4 SO4 3

Weighted Extinction 16.9 70.1 56.0 31.3

Coarse 24.3% Coarse 28.2 Fine Soil 60.4% Fine Soil 70.1 Fine EC 4.8% Fine EC 5.6 CPM IOR 9.0% CPM IOR 10.4 CPM OR 1.6% CPM OR 1.8

100.0% 116.2

## Westar Energy, Inc. 26 Trinity Consultants BART Modeling Protocol T ABLE 

## 4-6. JEC U NIT 

## 1 PM S PECIATION 

## A NALYSIS

Controlled PM10 Speciation from AP-42 Tables 1.1-5 & 1.1-6 Dry Bottom Boiler burning Pulverized Coal using onl y ESP for Emissions control

assumes heating value of 12071.5 Btu/lb and a sulfur content of 0.55 % and an ash content of 4.82 % and f(RH) = 1

Controlled PM10 Emissions (Bold values from Table 1.1-5.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (l b/ton) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type E xt.Coef. PC-DB 0.0353 0.0108 0.0060 0.6 0.0048 0.0046 1 0.00018 10 0.025 0.020 SO4 3*f(RH)

Controlled PM10 Emissions (Bold Values from Table 1.1-6.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/ton) (lb/ton) (lb/ton) Coef. (lb/ton) (lb/ton) Co ef. (lb/ton) Coef. (lb/ton) (lb/ton) Type Ext.Coef. PC-DB 0.852 0.260 0.145 0.6 0.116 0.111 1 0.0043 10 0.592 0.473 SO4 3*f(RH)

Controlled PM10 Emissions Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (% of Total) (% of Total) (% of Total) Coef. (% of Total) (% of Total) Coef. (% of Total) Coef. (% of Total ) (% of Total) Type Ext.Coef. PC-DB 100% 30.6% 17.0% 0.6 13.6% 13.1% 1 0.5% 10 69.4% 55.5% SO4 3*f(RH)

If you are given Total PM10 emissions in lb/hr: Controlled PM10 Emissions (Bold Value is Input by user.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (l b/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. PC-DB 290.9 88.9 49.4 0.6 39.5 38.1 1 1.5 10 202.0 161.6 SO4 3

Weighted Extinction 29.6 38.1 14.6 484.7

Coarse 17.0% Coarse 49.4 Fine Soil 13.1% Fine Soil 38.1 Fine EC 0.5% Fine EC 1.5 CPM IOR 55.5% CPM IOR 161.6 CPM OR 13.9% CPM OR 40.4

100.0% 290.9

## Westar Energy, Inc. 27 Trinity Consultants BART Modeling Protocol T ABLE 

## 4-7. JEC U NIT 

## 2 PM S PECIATION 

## A NALYSIS

Controlled PM10 Speciation from AP-42 Tables 1.1-5 & 1.1-6 Dry Bottom Boiler burning Pulverized Coal using only ES P for Emissions control

assumes heating value of 12071.5 Btu/lb and a sulfur content of 0.55 % and an ash content of 4.82 % and f(RH) = 1

Controlled PM10 Emissions (Bold values from Table 1.1-5.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (l b/ton) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type E xt.Coef. PC-DB 0.0353 0.0108 0.0060 0.6 0.0048 0.0046 1 0.00018 10 0.025 0.020 SO4 3*f(RH)

Controlled PM10 Emissions (Bold Values from Table 1.1-6.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/ton) (lb/ton) (lb/ton) Coef. (lb/ton) (lb/ton) Co ef. (lb/ton) Coef. (lb/ton) (lb/ton) Type Ext.Coef. PC-DB 0.852 0.260 0.145 0.6 0.116 0.111 1 0.0043 10 0.592 0.473 SO4 3*f(RH)

Controlled PM10 Emissions Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (% of Total) (% of Total) (% of Total) Coef. (% of Total) (% of Total) Coef. (% of Total) Coef. (% of Total ) (% of Total) Type Ext.Coef. PC-DB 100% 30.6% 17.0% 0.6 13.6% 13.1% 1 0.5% 10 69.4% 55.5% SO4 3*f(RH)

If you are given Total PM10 emissions in lb/hr: Controlled PM10 Emissions (Bold Value is Input by user.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil E xt. Fine EC Ext. Condensible CPM IOR Particle Type (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (l b/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. PC-DB 277.4 84.8 47.1 0.6 37.7 36.3 1 1.4 10 192.6 154.1 SO4 3

Weighted Extinction 28.3 36.3 13.9 462.2

Coarse 17.0% Coarse 47.1 Fine Soil 13.1% Fine Soil 36.3 Fine EC 0.5% Fine EC 1.4 CPM IOR 55.5% CPM IOR 154.1 CPM OR 13.9% CPM OR 38.5

100.0% 277.4

## Westar Energy, Inc. 28 Trinity Consultants BART Modeling Protocol T ABLE 

## 4-8. LEC U NIT 

## 5 PM S PECIATION 

## A NALYSIS

Controlled PM10 Speciation from AP-42 Tables 1.1-5 & 1.1 -6 Dry Bottom Boiler burning Pulverized Coal using only ESP for Emissions control

assumes heating value of 12040 Btu/lb and a sulfur content of 0.49 % and an ash content of 5.84 % and f(RH) = 1

Controlled PM10 Emissions (Bold values from Table 1.1-5.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext . Fine EC Ext. Condensible CPM IOR Particle CPM OR Type (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (lb/ton ) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type Ext.Coef. (lb/mmBtu) PC-DB 0.0321 0.0131 0.0073 0.6 0.0058 0.0056 1 0.00022 10 0.019 0.015 SO4 3*f(RH) 0.004

Controlled PM10 Emissions (Bold Values from Table 1.1-6.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext . Fine EC Ext. Condensible CPM IOR Particle CPM OR Type (lb/ton) (lb/ton) (lb/ton) Coef. (lb/ton) (lb/ton) Coef. (lb/ton) Coef. (lb/ton) (lb/ton) Type Ext.Coef. (lb/ton) PC-DB 0.773 0.315 0.175 0.6 0.140 0.135 1 0.0052 10 0.458 0.366 SO4 3*f(RH) 0.092

Controlled PM10 Emissions Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext . Fine EC Ext. Condensible CPM IOR Particle CPM OR Type (% of Total) (% of Total) (% of Total) Coef. (% of Tot al) (% of Total) Coef. (% of Total) Coef. (% of Total) (% of Total) Type Ext.Coef. (% of Total) PC-DB 100% 40.8% 22.7% 0.6 18.1% 17.5% 1 0.7% 10 59.2% 47.4% SO4 3*f(RH) 11.8%

If you are given Total PM10 emissions in lb/hr: Controlled PM10 Emissions (Bold Value is Input by user.) Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext . Fine EC Ext. Condensible CPM IOR Particle CPM OR Type (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (lb/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. (lb/hr) PC-DB 202.6 82.7 45.9 0.6 36.7 35.4 1 1.4 10 120.0 96.0 SO4 3 24.0

Weighted Extinction 27.6 35.4 13.6 287.9

Coarse 22.7% Coarse 45.9 Fine Soil 17.5% Fine Soil 35.4 Fine EC 0.7% Fine EC 1.4 CPM IOR 47.4% CPM IOR 96.0 CPM OR 11.8% CPM OR 24.0

100.0% 202.6

### Westar Energy, Inc. 29 Trinity Consultants

BART Modeling Protocol 

### 4.1.4 S TACK PARAMETERS


### Table 4-9 provides a summary of the exhaust charact eristics that will be modeled, including a summary of the emission rates presented elsewhere i n this protocol for the BART-eligible sources.Westar Energy, Inc.30Trinity ConsultantsBART Modeling ProtocolTABLE4-9.SUMMARY OFSPECIATEDPM10EMISSIONESTIMATESLEC Unit 5 JEC Unit 1 JEC Unit 2HEC Unit 4(South Stack)HEC Unit 4(North Stack)GEEC Unit 2(A Stack)GEEC Unit 2(B Stack)Latitude (degrees) 39.007 39.287 39.287 38.092 38.092 37.793 37.793Longitude (degrees) 95.275 96.116 96.116 97.873 97.873 97.518 97.518Stack height (ft) 355 600 600 149 149 197 197Stack Diameter (ft) 18.5 26.0 26.0 8.0 8.0 13.0 13.0Exhaust Velocity (ft/s) 80.0 91.3 91.3 56.0 56.0 69.0 69.0Exhaust Temperature (F) 166 300 300 313 313 290 290NOx(lb/hr) 1100.0 3433.3 3441.7 195.8 195.8 1004.2 1004.2SO2(lb/hr) 1908.3 6516.7 6791.7 1225.0 1225.0 2125.02125.0SO4(lb/hr) 96.0 161.6 154.1 5.8 5.8 10.4 10.4PMc(lb/hr) 45.9 49.4 47.1 17.2 17.2 28.2 28.2PMf(lb/hr) 35.4 38.1 36.3 42.7 42.7 70.1 70.1SOA (lb/hr) 24.0 40.4 38.5 1.0 1.0 1.8 1.8EC (lb/hr) 1.4 1.5 1.4 3.4 3.4 5.6 5.6

### Westar Energy, Inc. 31 Trinity Consultants

BART Modeling Protocol 

## 4.2 C LASS I 

## A REA 

## R ECEPTORS

The National Park Service (NPS) has electronic file s for each Class I area available on their website containing the locations and elevations of discrete Class I area receptors. The receptor files for Hercules-Glades Wilderness and the Wichita Mountains will be downloaded from the NPS website, converted into the LCC NAD27 projection, and incorp orated into the CALPUFF model. The receptor locations for the Hercules-Glades Wilderness are sh ow in Figure 4-1, and the locations for Wichita Mountains are shown in Figure 4-2.



### F IGURE 

### 4-1. H ERCULES 

### -G LADES 

### W ILDERNESS 

### R ECEPTOR 

### L OCATIONS

### Westar Energy, Inc. 32 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### 4-2. W ICHITA 

### M OUNTAINS 

### R ECEPTOR 

### L OCATIONS



## 4.3 B ACKGROUND 

## O ZONE

Background ozone concentrations are required in ord er to model the photochemical conversion of SO 2 

### and NO x 

### to sulfates (SO 4 

### ) and nitrates (NO 3 

### ). CALPUFF can use either a single background value representative of an area or hourly ozone dat a from one or more ozone monitoring stations. CENRAP recommends either developing background ozon e estimates from ambient monitors located within the particular domain being modeled or devel oping background ammonia estimates from CENRAP’s most recent CMAQ or CAMx simulation for the 2002 base year. Westar is proposing to incorporate hourly ozone data from three rural ozon e monitors across the state of Kansas. The three monitors are listed in Table 4-10.

T ABLE 

### 4-10. S UMMARY OF 

### O ZONE 

### M ONITORS



### Monitor ID County Latitude Longitude 201910002 (Peck) Sumner 37.477 97.366 201950001 (Cedar Bluff) Trego 38.770 99.764 20107002 (Mine Creek) Linn 38.135 94.732

Andy Hawkins of KDHE has made available processed o zone data files for 2001 through 2003 containing data from the above referenced stations. Westar is proposing to incorporate these files in to the CALPUFF model.

### Westar Energy, Inc. 33 Trinity Consultants

BART Modeling Protocol 

## 4.4 B ACKGROUND 

## A MMONIA

Background ammonia concentrations are required to m odel the formation of ammonium sulfates and ammonium nitrates. CENRAP recommends developing ba ckground ammonia estimates from CENRAP’s most recent CMAQ or CAMx simulation for the 2002 base year. Since CMAQ/CAMx modeled and observed monthly averaged ammonia conce ntrations exhibit wide spatial variability, CENRAP recommends obtaining separate monthly-averag ed ammonia concentrations from CMAQ or CAMx for the CENRAP north, central and south mode ling domains, respectively. These would then be used as input to CALPUFF. Since the data f rom CENRAP’s CMAQ and CAMx simulations are not readily available, Westar is proposing to u se a conservative monthly background concentration of 3 ppb. This background concentrat ion is the value included in CENRAP’s protocol as a default background value for the CENRAP region

.



## 4.5 S UMMARY OF 

## CALPUFF C ONTROL 

## P ARAMETERS

Table 4-11 provides a listing of the CALPUFF parame ters that Westar proposes to use in the modeling analysis. In addition to the parameters t hat will be used, the table also lists CENRAP’s recommended parameters for comparison. In cases wh ere a parameter to be used is different than what CENRAP recommended, a short explanation as to the difference is proved.

T ABLE 

### 4-11. S UMMARY OF 

### CALPUFF I NPUTS

CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes METRUN All model periods

in met files will be

run 0 0

IBYR Starting year 2001 Appropriate met year Years 2 001, 2002, 2003 IBMO Starting month 1 1 IBDY Starting day 1 1 IBHR Starting hour 1 1 XBTZ Base time zone (6

= CST) 6 6

IRLG Length of run 8760 8760 NSPEC Number of

MESOPUFF II chemical species 10 10

NSE Number of

chemical species to be emitted 8 7 Appears to be an error in CENRAP’s count of the emitted species (only 7 listed in Table B-4 of protocol) ITEST Program is

executed after SETUP phase 2 2

### Westar Energy, Inc. 34 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes MRESTART Do not read or

write a restart file during run 0 0

NRESPD File written only at

last period 0 0

METFM CALMET binary

file (CALMET.MET) 1 1

AVET Averaging time in

minutes 60 60

PGTIME PG Averaging

time in minutes 60 60

MGAUSS Gaussian

distribution used in near field 1 1

MCTADJ Partial plume path

terrain adjustment 3 3

MCTSG Sub-grid-scale

complex terrain not modeled 0 0

MSLUG Near-field puffs

not modeled as elongated 0 0

MTRANS Transitional plume

rise modeled 1 1

MTIP Stack tip

downwash used 1 1

MSHEAR (0, 1) Vertical

wind shear (not modeled, modeled) 0 0

MSPLIT Puffs are not split 0 1 Included puff splitting due to significant distance between sources and Class I areas MCHEM MESOPUFF II

chemical parameterization scheme 1 1

MAQCHEM Aqueous phase

transformation not modeled 0 0

MWET Wet removal

modeled 1 1

MDRY Dry deposition

modeled 1 1

MDISP PG dispersion

coefficients 3 3

### Westar Energy, Inc. 35 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes MTURBVW Use both V v and

V w from PROFILE.DAT to compute V y and V z (n/a) 3 3

MDISP2 PG dispersion

coefficients 3 3

MROUGH PG V y and V z not

adjusted for roughness 0 0

MPARTL No partial plume

penetration of elevated inversion 1 1

MTINV Strength of

temperature inversion computed from default gradients 0 0

MPDF PDF not used for

dispersion under convective conditions 0 0

MSGTIBL Sub-grid TIBL

module not used for shoreline 0 0

MBCON Boundary

concentration conditions not modeled 0 0

MFOG Do not configure

for FOG model output 0 0

MREG Technical options

must conform to USEPA Long Range Transport (LRT) guidance 1 1

CSPEC CENRAP Westar

Output Group Species Modeled Emitted Dry Deposition Output

Group Species Modeled Emitted Dry Deposition SO 2 1 1 1 SO 2 1 1 1
SO4 1 0 2 SO4 1 1 2

NOX 1 1 1 NOX 1 1 1 HNO3 1 0 1 HNO3 1 0 1 NO3 1 0 2 NO3 1 0 2 NH3 1 1 1 NH3 1 0 1 PMC 1 1 2 PMC 1 1 2 PMF 1 1 2 PMF 1 1 2 EC 1 1 2 EC 1 1 2 SOA 1 1 2 SOA 1 1 2 PMAP Map projection UTM LCC

### Westar Energy, Inc. 36 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes NX Number of X grid

cells in meteorological grid 66 251 Appropriate for domain and grid spacing

NY Number of Y grid

cells in meteorological grid 66 246 Appropriate for domain and grid spacing

NZ Number of vertical

layers in meteorological grid 10 10

DGRIDKM Grid spacing (km) 6 2.5 Refined grid size ZFACE Cell face heights

in meteorological grid (m) 0, 20, 40, 80, 160,

320, 640, 1200, 2000, 3000, 4000 0, 20, 40, 80, 160,

320, 640, 1200, 2000, 3000, 4000 XORIGKM Reference X

coordinate for SW corner of grid cell of meteorological grid (km) 5 0 Appropriate for domain

YORIGKM Reference Y

coordinate for SW corner of grid cell of meteorological grid (km) 3327 0 Appropriate for domain

IUTMZN UTM zone of

coordinates (NAD83) 12 14 Appropriate for domain

IBCOMP X index of lower

left corner of the computational grid 1 1

JBCOMP Y index of lower

left corner of the computational grids 1 1

IECOMP X index of upper

right corner of the computational grid 66 251 Appropriate for domain

JECOMP Y index of upper

right corner of the computational grid 66 246 Appropriate for domain

LSAMP Sampling grid is

not used F F

IBSAMP X index of lower

left corner of sampling grid 1 1

JBSAMP Y index of lower

left corner of sampling grid 1 1

IESAMP X index of upper

right corner of sampling grid 66 251 Appropriate for domain

### Westar Energy, Inc. 37 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes JESAMP Y index of upper

right corner of sampling grid 66 246 Appropriate for domain

MESHDN Nesting factor of

sampling grid 1 1

ICON Output file

CONC.DAT containing concentrations is created 1 1

IDRY Output file

DFLX.DAT containing dry fluxes is created 1 1

IWET Output file

WFLX.DAT containing wet fluxes is created 1 1

IVIS Output file

containing relative humidity data is created 1 1

LCOMPRS Perform data

compression in output file T T

IMFLX Do not calculate

mass fluzes across specific boundaries 0 0

IMBAL Mass balances for

each species not reported hourly 0 0

ICPRT Print concentration

fields to output list file 1 1

IDPRT Do not print dry

flux fields to output list file 0 0

IWPRT Do not print wet

flux fields to output list file 0 0

ICFRQ Concentration

fields are printed to output list file every hour 1 1

IDFRQ Dry flux fields are

printed to output list file every 1 hour 1 1

IWFRQ Wet flux fields are

printed to output list file every 1 hour 1 1

### Westar Energy, Inc. 38 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes IPTRU Units for line

printer output are in g/m 3 for concentration and g/m 2 /s for deposition 3 3

IMESG Messages tracking

the progress of run written to screen 2 2

LDEBUG Logical value for

debug output F F

IPFDEB First puff to track 1 1 NPFDEB Number of puffs

to track 1 1

NN1 Meteorological

period to start output 1 1

NN2 Meteorological

period to end output 10 10

NHILL Number of terrain

features 0 0

NCTREC Number of special

complex terrain receptors 0 0

MHILL Input terrain and

receptor data for CTSG hills input in CTDM format 2 2

XHILL2M Conversion factor

for changing horizontal dimensions to
meters 1 1

ZHILL2M Conversion factor

for changing vertical dimensions to
meters 1 1

XCTDMKM X origin of CTDM

system relative to CALPUFF coordinate system (km) 0 0

YCTDMKM Y origin of CTDM

system relative to CALPUFF coordinate system (km) 0 0

SO2 Diffusivity 0.1509 0.1509 Alpha star 1000 1000 Reactivity 8 8

### Westar Energy, Inc. 39 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes Mesophyll

resistance 0 0

Henry’s Law

coefficient 0.04 0.04

NO X Diffusivity 0.1656 0.1656 Alpha star 1 1 Reactivity 8 8 Mesophyll

resistance 5 5

Henry’s Law

coefficient 3.5 3.5

HNO3 Diffusivity 0.1628 0.1628 Alpha star 1 1 Reactivity 18 18 Mesophyll

resistance 0 0

Henry’s Law

coefficient 8.e-8 8.e-8

SO4-2 Geomatric mass

mean diameter of SO4-2 ( P m) 0.48 0.48

NO3- Geometric mass

mean diameter of NO3- ( P m) 0.48 0.48

PMC Geometric mass

mean diameter of PMC ( P m) 6 6

PMF Geometric mass

mean diameter of PMF ( P m) 0.48 0.48

EC Geometric mass

mean diameter of EC ( P m) 0.48 0.48

SOA Geometric mass

mean diameter of SOA ( P m) 0.48 0.48

RCUTR Reference cuticle

resistance (s/cm) 30 30

RGR Reference ground

resistance (s/cm) 10 10

REACTR Reference

pollutant reactivity 8 8

NINT Number of particle

size intervals for effective particle deposition velocity 9 9

IVEG Vegetation in nonirrigated areas is active and unstressed 1 1

### Westar Energy, Inc. 40 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes SO 2 Scavenging

coefficient for liquid precipitation (s -1 ) 3.21E-05 3.E-05

Scavenging

coefficient for frozen precipitation (s -1 ) 0 0

SO4-2 Scavenging

coefficient for liquid precipitation (s -1 ) 1.0E-04 1.0E-04

Scavenging

coefficient for frozen precipitation (s -1 ) 3.0E-05 3.0E-05

HNO3 Scavenging

coefficient for liquid precipitation (s -1 ) 6.0E-05 6.0E-05

Scavenging

coefficient for frozen precipitation (s -1 ) 0 0

NO3- Scavenging

coefficient for liquid precipitation (s -1 ) 1.0E-04 1.0E-04

Scavenging

coefficient for frozen precipitation (s -1 ) 3.0E-05 3.0E-05

NH3 Scavenging

coefficient for liquid precipitation (s -1 ) 8.0E-05 NA

Scavenging

coefficient for frozen precipitation (s -1 ) 0 NA

PMC Scavenging

coefficient for liquid precipitation (s -1 ) 1.0E-4 1.0E-4

Scavenging

coefficient for frozen precipitation (s -1 ) 3.0E-05 3.0E-05

PMF Scavenging

coefficient for liquid precipitation (s -1 ) 1.0E-05 1.0E-05

### Westar Energy, Inc. 41 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes Scavenging

coefficient for frozen precipitation (s -1 ) 3.0E-05 3.0E-05

EC Scavenging

coefficient for liquid precipitation (s -1 ) 1.0E-04 1.0E-04

Scavenging

coefficient for frozen precipitation (s -1 ) 3.0E-05 3.0E-05

OC Scavenging

coefficient for liquid precipitation (s -1 ) 1.0E-04 1.0E-04

Scavenging

coefficient for frozen precipitation (s -1 ) 3.0E-05 3.0E-05

MOZ Read ozone

background concentrations from ozone.dat file (measured values) 1 1

BCKO3 Background ozone

concentration (ppb) 12*40 NA Used ozone data file

BCKNH3 Background

ammonia concentration (ppb) 12*3 12*3

RNITE1 Nighttime NO2

loss rate is %/hour 0.2 0.2

RNITE2 Nighttime NO X

loss rate is %/hour 2 2

RNITE3 Nighttime HNO3

loss rate is %/hour 2 2

MH2O2 Background H2O2

concentrations 1 0 Need to choose 0 in order to use monthly background value BCKH2O2 Background

monthly H2O2 concentrations 1 12*1

BCKPMF Fine particulate

concentration for SOA option ( P g/m 3 ) 1 1

OFRAC Organic fraction of

fine particulate for SOA option .2 0.15,0.15,0.2,0.2,0.2, 0.2,0.2,0.2,0.2,0.2, 0.2,0.15 Irrelevant, since

MCHEM not equal to 4

### Westar Energy, Inc. 42 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes VCNX VOC/NO X ratio

for SOA option 50 50

SYDEP Horizontal size of

a puff in meters beyond which the time dependant dispersion equation of Heffter is used 550 550

MHFTSZ Do not use Heffter

formulas for sigma

z 0 0

JSUP Stability class used

to determine dispersion rates for puffs above boundary layer 5 5

CONK1 Vertical dispersion

constant for stable conditions 0.01 0.01

CONK2 Vertical dispersion

constant for neutral/stable conditions 0.1 0.1

TBD Use ISC transition

point for determining the transition point between the Schulman-Scire to Huber-Snyder Building Downwash scheme 0.5 0.5

IURB1 Lower range of

land use categories for which urban dispersion is assumed 10 10

IURB2 Upper range of

land use categories for which urban dispersion is assumed 19 19

ILANDUIN Land use category

for modeling domain * *

XLAIIN Leaf area index for

modeling domain * *

ZOIN Roughness length

in meters for modeling domain * *

### Westar Energy, Inc. 43 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes ELEVIN Elevation above

sea level * *

XLATIN North latitude of

station in degrees - -

XLONIN South latitude of

station in degrees - -

ANEMHT Anemometer

height in meters 10 10

ISIGMAV Sigma-v is read

for lateral turbulence data 1 1

IMIXCTDM Predicted mixing

heights are used 0 0

XMXLEN Maximum length

of emitted slug in meteorological grid units 1 1

XSAMLEN Maximum travel

distance of slug or puff in meteorological grid units during one sampling unit 10 10

MXNEW Maximum number

of puffs or slugs released from one source during one time step 60 60

MXSAM Maximum number

of sampling steps during one time step for a puff or slug 60 60

NCOUNT Number of

iterations used when computing the transport wind for a sampling step that includes transitional plume rise 2 2

SYMIN Minimum sigma y

in meters for a new puff or slug 1 1

SZMIN Minimum sigma z

in meters for a new puff or slug 1 1

SVMIN Minimum lateral

turbulence velocities (m/s) 0.5 0.5

SWMIN Minimum vertical

turbulence velocities (m/s) 0.20, 0.12, 0.08, 0.06,

0.03, 0.016 0.20, 0.12, 0.08,

0.06, 0.03, 0.016

### Westar Energy, Inc. 44 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use NotesWSCALM Minimum noncalm wind speeds (m/s) 0.5 0.5

XMAXZI Maximum mixing

height (m) 3000 3000

XMINZI Minimum mixing

height (m) 20 20

SL2PF Maximum V y/puff

length 10 10

PLXO Wind speed

power-law exponents 0.07, 0.07, 0.10, 0.15,

0.35, 0.55 0.07, 0.07, 0.10,

0.15, 0.35, 0.55

WSCAT Upper bounds of

1 st 5 wind speed classes 1.54, 3.09, 5.14, 8.23,

10.80 1.54, 3.09, 5.14,

8.23, 10.80

PGGO Potential temp

gradients PG E & F (deg/km) 0.020, 0.035 0.020, 0.035

CDIV Divergence

criterion for dw/dz (1/s) 0.01 0.01

PPC Plume path

coefficients (only if MCTADJ = 3) 0.5, 0.5, 0.5, 0.5,

0.35, 0.35 0.5, 0.5, 0.5, 0.5,

0.35, 0.35

NSPLIT Number of puffs

when split 3 3

IRESPLIT Hours when puff

is eligible to split 1900 Hour 17 Should be by hour of day – 1900 is hour 17 ZISPLIT Previous hours

minimum mixing height, m 100 100

ROLDMAX Previous max

mixing height/current height ratio, must be less than this value to allow puff to split 0.25 0.25

NSPLITH Number of puffs

resulting from a split 5 5

SYSPLITH Minimum sigma-y

of puff before it may split 1.0 1.0

SHSPLITH Minimum puff

elongation rate from wind shear before puff may split 2.0 2.0

### Westar Energy, Inc. 45 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes CNSPLITH Minimum species

concentration before a puff may split 1.0E-07 1.0E-07

EPSSLUG Criterion for

SLUG sampling 1.0E-04 1.0E-04

EPSAREA Criterion for area

source integration 1.0E-06 1.0E-06

DSRISE Trajectory step

length for numerical site algorithm 1.0 1.0

NPT1 Number of point

sources with constant stack parameters or variable emission rate scale factors Varies by scenario Varies by scenario

IPTU Units for point

source emission rates are g/s 1 3 Used different units (3 = lb/hr)

NSPT1 Number of sourcespecies combinations with variable emissions scaling factors - -

NPT2 Number of point

sources with variable emission parameters provided in external file - -

MISC Other point source

inputs include stack height, stack diameter, exit temperature, exit velocity, downwash flag and emissions by species - -

NAR1 Number of

polygon area

sources Varies by scenario 0 None modeled

IARU Units for area

source emission rates are g/m 2

/s 1 1

NSAR1 Number of source

species combinations with variable emissions scaling factors - -

### Westar Energy, Inc. 46 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes NAR2 Number of

buoyant polygon area sources with variable location and emission parameters - -

NLN2 Number of

buoyant line sources with variable location and emission parameters - 0 None modeled

NLINES Number of

buoyant line

sources - -

ILNU Units for line

source emission rates in g/s - -

NSLN1 Number of sourcespecies combinations with variable emissions scaling factors - -

MXNSEG Maximum number

of segments used to model each line - -

NLRISE Number of

distance at which transitional rise is computed - -

XL Average line

source length (m) - -
HBL Average height of

line source height (m) - -

WBL Average building

width (m) - -
WML Average line

source width (m) - -
DXL Average

separation between buildings (m) - -

FPRIMEL Average buoyancy

parameter (m4/s3) - -

NVL1 Number of volume

sources - 0 None modeled

IVLU Units for volume

source emission rates in grams/second - -

### Westar Energy, Inc. 47 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value Westar Will Use NotesNSVL1 Number of sourcespecies combinations with variable emissions scaling factors - -

IGRDVL Gridded volume

source data is not used - -

VEFFHT Effective height of

emissions (m) - -
VSIGYI Initial sigma-y

value - -
VSIGZI Initial sigma-z

value - -

NREC Number of nongridded receptors 5630 139 Receptor data provided in Appendix

### Westar Energy, Inc. 48 Trinity Consultants

BART Modeling Protocol 

## 5. CALPOST

Westar will conduct a three-year CALPOST analysis t o determine the change in light extinction caused by our BART-eligible sources when compared t o a natural background. The CALPOST model requires the input of concentration data outp ut by CALPUFF.



## 5.1 L IGHT 

## E XTINCTION 

## A LGORITHM

Westar will utilize EPA’s currently approved algori thm for reconstructing light extinction (as opposed to the new equation for reconstructing ligh t extinction recommended by the IMPROVE Steering Committee). The light extinction equation is provided below.



### b = 3*f(RH) *[(NH SO + 3* f(RH) *[NH NO3 + 4*[OC] + 1*[P M + 0.6*[PM ] + 10*[EC] + b ext 4 4 4 f

c Ray 

### ) ] ] ] 2



### The algorithm will be used to calculate the daily l ight extinction attributable to Westar’s BARTeligible sources and light extinction attributable to a natural background. The change in deciviews based on the source and background light extinction s will be evaluated using the equation below.



### ' dv = 10*ln b b

b ext, background ext, source

ext, background 

###  a ¬ « « o

1⁄4 » »



## 5.2 CALPOST P ROCESSING METHOD

Westar will use CALPOST Method 6, which calculates h ourly light extinction impacts for the source and background using monthly average relative humid ity adjustment factors. Westar will use monthly Class I area-specific relative humidity adj ustment factors based on the centroid of the Class I areas as included in Table A-3 of EPA’s Guidance for Estimating Natural Visibility Conditio ns Under the Regional Haze Program . The factors for Hercules Glades Wilderness and W ichita Mountains are provided in Table 5-1.

T ABLE 

### 5-1. M ONTHLY 

### H UMIDITY 

### F ACTORS

Class I Area Jan Feb Mar Apr May Jun Jul Aug Sep Oc t Nov Dec Hercules-Glades 3.2 2.9 2.7 2.7 3.3 3.3 3.3 3.3 3.4 3.1 3.1 3.3
Wichita Mountains 2.7 2.6 2.4 2.4 3.0 2.7 2.3 2.5 2 .9 2.6 2.7 2.8

### Westar Energy, Inc. 49 Trinity Consultants

BART Modeling Protocol 

## 5.3 N ATURAL 

## B ACKGROUND

Westar will use EPA’s default average annual aeroso l concentrations for the western half of the U.S. that are included in Table 2-1 of EPA’s Guidance for Estimating Natural Visibility Conditio ns Under the Regional Haze Program. The annual average concentrations are provided in Table 5-2.

T ABLE 

### 5-2. D EFAULT 

### W EST 

### A VERAGE 

### A NNUAL 

### N ATURAL 

### B ACKGROUND 

### L EVELS

Component Average Annual

Natural Background ( P g/m 3 )
Ammonium Sulfate 0.12
Ammonium Nitrate 0.1 Organic Carbon Mass 0.47
Elemental Carbon 0.02
Soil 0.5
Coarse Mass 3



## 5.4 E VALUATING 

## BART-E XEMPTION

Westar will compare the 98 th 

### percentile of the 2001 through 2003 daily ' dv values output by CALPOST (22 nd



### highest daily value) to a contribution threshold o f 0.5 ' dv. If the 98 th 

### percentile daily ' dv values output by CALPOST is less 0.5 ' dv, then it will be concluded that the source is exempt from BART and that no further analysis is ne cessary. If the 98 th 

### percentile of the daily ' dv values output by CALPOST is greater than 0.5 ' dv, then it will be concluded that further analysis is
necessary.



## 5.5 S UMMARY OF 

## CALPOST C ONTROL 

## P ARAMETERS

Table 5-3 provides a listing of the CALPOST paramet ers that Westar proposes to use in the modeling analysis. In addition to the parameters that will be used for the modeling, the table also lists CENRAP’s recommended parameters for comparison. In cases where a parameter to be used is different than what CENRAP recommended, a short exp lanation as to the difference is proved.

T ABLE 

### 5-3. S UMMARY OF 

### CALPOST I NPUTS

CALPOST Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes ISYR Starting year 2001 Appropriate met year Years 2001, 2002,

2003 ISMO Starting month 1 1 ISDY Starting day 1 1 ISHR Starting hour 0 1 All CALPUFF periods will be included NPER Number of periods

to process 8760 8760

### Westar Energy, Inc. 50 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes NREP Process every hour

of data? 1 = yes 1 1

ASPEC Process species for

visibility VISIB VISIB

ILAYER Layer/deposition

code; 1 for CALPUFF concentrations 1 1

A Scaling factor,

slope 0 0

B Scaling factor,

intercept 0 0

LBACK Add hourly

background concentrations of fluxes? F F

LG Process gridded

receptors? F F

LD Process discrete

receptors? T T

LCT Process complex

terrain receptors? F F

LDRING Report receptor ring

results? F F

NDRECP Select all discrete

receptors -1 Varies As appropriate for Class I area being analyzed IBGRID X index of LL

corner of receptor grid -1 -1

JBGRID Y index of LL

corner of receptor grid -1 -1

IEGRID X index of UR

corner of receptor grid -1 -1

JEGRID Y index of UR

corner of receptor grid -1 -1

NGONOFF Number of gridded

receptor rows 0 0

NGXRECP Exclude specific

gridded receptors, Yes = 0 0 0

RHMAX Maximum RH%

used in particle growth curve 95 95

LVSO4 Compute light

extinction for sulfate? T T

### Westar Energy, Inc. 51 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes LVNO3 Compute light

extinction for nitrate? T T

LVOC Compute light

extinction for organic carbon? T T

LVMPC Compute light

extinction for coarse particles? T T

LVMPF Compute light

extinction for fine particles? T T

LVEC Compute light

extinction for elemental carbon? T T

LVBK Include background

in extinction calculation? T T

SPECPMC Coarse particulate

species PMC PMC

SPECPMF Fine particulate

species PM 10 PMF Notation difference

EEPMC Extinction

efficiency for coarse particulates 0.6 0.6

EEPMF Extinction

efficiency for fine particles? 1.0 1.0

EEPMCBCK Extinction

efficiency for coarse part. Background 0.6 0.6

EESO4 Extinction

efficiency for ammonium sulfate 3.0 3.0

EENO3 Extinction

efficiency for ammonium nitrate 3.0 3.0

EEOC Extinction

efficiency for organic carbon 4.0 4.0

EESOIL Extinction

efficiency for soil 1.0 1.0

EEEC Extinction

efficiency for elemental carbon 10.0 10.0

MVISBK Method 6 for

background light extinction 6 6

BEXTBTBK Background

extinction for MVISBK=1 12 Not Used Not necessary since MVISBK=6

### Westar Energy, Inc. 52 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes RHFRAC % of particles

affected by RH 10 Not Used Not necessary since MVISBK=6 RHFAC Extinction

coefficients for modeled and background hygroscopic species computed using EPA (2003) monthly RH adjustment factors Depends on Class I

Area For HerculesGlades: 3.2, 2.9, 2.7, 2.7, 3.3, 3.3, 3.3, 3.3, 3.4, 3.1, 3.1, 3.3 For Wichita Mtns: 2.7, 2.6, 2.4, 2.4, 3.0, 2.7, 2.3, 2.5, 2.9, 2.6, 2.7, 2.8 As appropriate for

Class I area

BKSO4 Background sulfate

extinction coeff -
west 0.12 0.12

BKNO3 Background nitrate

extinction coeff –
west 0.10 0.10

BKPMC Background coarse

part. extinction coeff – west 3.00 3.00

BKSOC Background

organic carbon extinction coeff –
west 0.47 0.47

BKSOIL Background soil

extinction coeff –
west 0.50 0.50

BKSEC Background

elemental carbon extinction coeff –
west 0.02 0.02

BKSO4 Background sulfate

extinction coeff –
east 0.23 Not Used West analysis only

BKNO3 Background nitrate

extinction coeff –
east 0.10 Not Used West analysis only

BKPMC Background sulfate

extinction coeff –
west 3.00 Not Used West analysis only

BKSOC Background

organic carbon extinction coeff –
east 1.40 Not Used West analysis only

BKSSOIL Background soil

extinction coeff –
east 0.50 Not Used West analysis only

BKSEC Background

elemental carbon extinction coeff –
east 0.02 Not Used West analysis only

### Westar Energy, Inc. 53 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value Westar Will Use Notes BEXTRAY Extinction due to

Rayleigh scattering (1/Mm) 10.0 10.0

LDOC Print documentation image? F F

IPTRU Print output units

for concentrations and for deposition 3 1 Units preference

L1HR Report 1 hr

averaging times F F

L3HR Report 3 hr

averaging times F F

L24HR Report 24 hr

averaging times T T

LRUNL Report run-length

averaging times F F

LT50 Top 50 table F F LTOPN Top N table F F NTOP Number of Top-N

values at each receptor 4 4

ITOP Ranks of Top-N

values at each receptor 1,2,3,4 1,2,3,4

LEXCD Threshold

exceedances counts F F

THRESH1 Averaging time

threshold for 1 hr
averages -1 -1

THRESH3 Averaging time

threshold for 3 hr
averages -1 -1

THRESH24 Averaging time

threshold for 24 hr
averages -1 -0.2 Lower threshold – no effect on results

THRESHN Averaging time

threshold for NAVG-hr averages -1 -1

NDAY Accumulation

period, days 0 0

NCOUNT Number of

exceedances allowed 1 1

LECHO Echo option F F LTIME Time series option F F LPLT Plot file option F F LGRD Use grid format

instead of DATA format F F

LDEBUG Output information

for debugging? F F

### Westar Energy, Inc. A - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## A

M ODELING 

### D OMAIN

### Westar Energy, Inc. A - 2 Trinity Consultants

BART Modeling Protocol

T ABLE 

### A-1. D ETERMINATION OF 

### M ODELING 

### D OMAIN

X Y Lat NAD27 Lon NAD27 Gordon Evans NAD 27, Zone 14 630.457 4183.633 37.793 97.518
Hutchinson NAD 27, Zone 14 598.828 4216.416 38.092 97.873 Jeffrey NAD 27, Zone 14 748.746 4352.838 39.287 96.116
Lawrence NAD 27, Zone 14 822.578 4323.950 39.007 95.275

Hercules-Glades Wilderness Area

UTM = NAD 27, Zone 14 Lat NAD27 Lon NAD27

SouthWest 1038.441 4073.213 36.654 92.979 NorthEast 1046.917 4081.226 36.721 92.879 SouthEast 1047.394 4073.782 36.654 92.879 NorthWest 1037.973 4080.657 36.721 92.979

Wichita Mountains Area

UTM = NAD 27, Zone 14 Lat NAD27 Lon NAD27

SouthWest 530.176 3840.082 34.7041 98.6705 NorthEast 520.233 3851.144 34.8041 98.7788 SouthEast 520.258 3840.055 34.7041 98.7788 NorthWest 530.140 3851.171 34.8041 98.6705

50 distance from facility 50 distance from Hercules-Glades WA

UTM = NAD 27, Zone 14 Lat NAD27 Lon NAD27 POINT UTM (KM) E UTM(KM) N Latitude (Decimal) Longitude (Decimal) A 470.233 3790.055 34.25 99.32 B 1097.394 3790.055 34.08 92.53 C 1097.394 4402.838 39.57 92.05 D 470.233 4402.838 39.78 99.35

2.5 km grid spacing MAX HORIZONTAL DIST (KM) 627.16 251 NX

MAX VERTICAL DIST (KM) 612.78 246 NY

Modeling Domain D 39.78 C

99.35 92.05

A B

34.08

### Westar Energy, Inc. B - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## B

E XAMPLE OF 

### CALMM5 I NPUT 

### F ILE

### Westar Energy, Inc. B - 2 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### B-1. E XAMPLE 

### CALMM5 I NPUT 

### F ILE

CALMM5 VER3 Output Sample Input File 33 ! Number of MM5 Output files (0 for auto) i:\mm5\MMOUT_DOMAIN1_01_2000123112-2001010600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2000123112-2001010600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2000123112-2001010600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_04_2000123112-2001010600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_05_2000123112-2001010600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_01_2001010512-2001011100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2001010512-2001011100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2001010512-2001011100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_04_2001010512-2001011100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_05_2001010512-2001011100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_01_2001011012-2001011600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2001011012-2001011600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2001011012-2001011600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_04_2001011012-2001011600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_05_2001011012-2001011600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_01_2001011512-2001012100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2001011512-2001012100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2001011512-2001012100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_04_2001011512-2001012100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_05_2001011512-2001012100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_01_2001012012-2001012600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2001012012-2001012600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2001012012-2001012600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_04_2001012012-2001012600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_05_2001012012-2001012600 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_01_2001012512-2001013100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2001012512-2001013100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2001012512-2001013100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_04_2001012512-2001013100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_05_2001012512-2001013100 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_01_2001013012-2001020500 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_02_2001013012-2001020500 ! MM5 input file name (no space before or within filenam e) i:\mm5\MMOUT_DOMAIN1_03_2001013012-2001020500 ! MM5 input file name (no space before or within filenam e) R:\WestarBART\CALMM5\2001Outputs\Jan.m3d ! CALMM5 o utput file name (no space before or within filenam e) R:\WestarBART\CALMM5\2001Outputs\Jan.lst ! CALMM5 l ist file name (no space before or within filename) 1 ! Options for selecting a region (1: use lat/long; 2: use J/I) 34.08 ! Southernmost latitude (in decimal, positive for NH), or J1/Y1 39.78 ! Northermost latitude (in decimal, positive for NH), or J2/Y2 -99.35 ! Westernmost longitude (in decimal, negative for WH), or I1/X1 -92.05 ! Easternmost longitude (in decimal , negative for WH), or I2/X2 2001010107 ! Starting date (year-month-day-UTC hour)(yymmddhh) 2001020106 ! Ending date 1 ! Output format (1,2,3,4,5, 6 - see readme.cm5 for details) Keep this line - The following lines vary depending on the output format selected 1 1 1 1 0 ! Output W, RH, cloud and rain, ice and snow, graupel 0 ! Flag for 2-D variables output (1/0 : output/not) 1 ! Lowest extraction level in MM5 34 ! Highest extraction level in MM5

### Westar Energy, Inc. C - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## C

G EOPHYSICAL 

### D ATA

### Westar Energy, Inc. C - 2 Trinity Consultants

BART Modeling Protocol T ABLE 

### C-1. L AND 

### U SE 

### D ATA 

### U SED IN 

### A NALYSIS

1:250,000 scale data 1:100,000 scale data Ardmore Antlers Beloit Conway Clinton Dequeen Enid Fly Gap Mountain Fort Smith McAlester Great Bend Mena Harrison Mountain View Helena Russellville Hutchinson Jefferson City Joplin Kansas City Lawrence Lawton Little Rock Manhattan Memphis Moberly Oklahoma City Poplar Bluff Pratt Quincy Rolla Springfield St. Louis Tulsa Wichita Woodward

### Westar Energy, Inc. C - 3 Trinity Consultants

BART Modeling Protocol T ABLE 

### C-2. T ERRAIN 

### D ATA 

### U SED IN 

### A NALYSIS

Ardmore-E Ardmore-W Beloit-E Beloit-W Clinton-E Clinton-W Enid-E Enid-W FortSmith-E FortSmith-W GreatBend-E GreatBend-W Harrison-E Harrison-W Helena-W Hutchinson-E Hutchinson-W Jefferson_City-E Jefferson_City-W Joplin-E Joplin-W Kansas_City-E Kansas_City-W Lawrence-E Lawrence-W Lawton-E Lawton-W LittleRock-E LittleRock-W Manhattan-E Manhattan-W Mcalester-E Mcalester-W Memphis-W Moberly-E Moberly-W OklahomaCity-E OklahomaCity-W Poplar_Bluff-W Pratt-E Pratt-W Quincy-W Rolla-W Russellville-E Russellville-W Saint_Louis-W Springfield-E Springfield-W Tulsa-E Tulsa-W Wichita-E Wichita-W Woodward-E Woodward-W

### Westar Energy, Inc. D - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## D

T ABLE 

### D-1. L IST OF 

### S URFACE METEOROLOGCAL 

### S TATIONS

Station
ID Name ID Latitude Longitude X (km) Y (km)
72244 TYLER/POUNDS FLD KTYR 32.35 -95.4 371.587 -18 4.785
13962 ABILENE REGIONAL AP KABI 32.417 -99.683 -31.309 -184.980
13959 WACO REGIONAL AP KACT 31.617 -97.233 201.010 - 271.953
13984 CONCORDIA BLOSSER MUNI AP KCNK 39.55 -97.65 145 .670 608.842
72450 CHANUTE MARTIN JOHNSON AP KCNU 37.667 -95.483 339.779 405.295
3945 COLUMBIA REGIONAL AIRPORT KCOU 38.817 -92.217 616 .771 549.227
3927 DALLAS-FORT WORTH INTL AP KDFW 32.9 -97.017 218 .011 -128.585
13989 EMPORIA MUNICIPAL AP KEMP 38.333 -96.183 275.837 476.935
13964 FORT SMITH REGIONAL AP KFSM 35.333 -94.367 451. 322 151.049
72344 FAYETTEVILLE DRAKE FIELD KFYV 36 -94.167 465.3 86 225.981
93986 HOBART MUNICIPAL AP KHBR 35 -99.05 27.301 102.2 82
72341 MEMORIAL FLD KHOT 34.467 -93.1 572.243 61.814
3928 WICHITA MID-CONTINENT AP KICT 37.65 -97.433 168.512 398.200
72445 KIRKSVILLE REGIONAL AP KIRK 40.1 -92.55 577.848 6 89.334
72349 JOPLIN MUNICIPAL AP KJLN 37.15 -94.5 429.008 351 .924
3947 KANSAS CITY INT'L ARPT KMCI 39.3 -94.717 398.209 589.456
72455 MANHATTAN RGNL KMHK 39.133 -96.667 231.189 564.465
93950 MCALESTER MUNICIPAL AP KMLC 34.9 -95.783 324.94 6 97.220 72249 NACOGDOCHES (AWOS) KOCH 31.583 -94.717 439.952 -267.268
13967 OKLAHOMA CITY WILL ROGERS WOR KOKC 35.383 -97.6 158.463 146.240
13969 PONCA CITY MUNICIPAL AP KPNC 36.733 -97.1 200.18 7 297.058
72258 COX FLD KPRX 33.633 -95.45 361.036 -42.304
13995 SPRINGFIELD REGIONAL ARPT KSGF 37.233 -93.383 52 7.118 366.736
13957 SHREVEPORT REGIONAL ARPT KSHV 32.45 -93.817 51 9.713 -166.247
72458 SALINA MUNICIPAL AP KSLN 38.817 -97.667 145.653 527.389
13966 WICHITA FALLS MUNICIPAL ARPT KSPS 33.983 -98.5 78 .361 -10.434
72449 ST JOSEPH ROSECRANS MEMORIAL KSTJ 39.767 -94.9 380.065 640.548
13930 WHITEMAN AFB KSZL 38.717 -93.55 502.346 530.30 6
13996 TOPEKA MUNICIPAL AP KTOP 39.067 -95.633 320.534 560.134
13968 TULSA INTERNATIONAL AIRPORT KTUL 36.2 -95.883 31 0.581 241.170
13977 TEXARKANA WEBB FIELD KTXK 33.45 -94 496.288 -5 6.103
72352 ARDMORE K1F0 34.15 -97.117 205.406 10.192

### Westar Energy, Inc. D - 2 Trinity Consultants

BART Modeling Protocol

T ABLE 

### D-2. L IST OF 

### U PPER 

### A IR METEOROLOGICAL 

### S TATIONS

Station ID Name ID Latitude Longitude X (km) Y (km) 3948 NORMAN, OK KOUN 35.233 -97.47 170.562 129.807 3952 LITTLE ROCK, AR KLZK 34.83 -92.27 645.107 107.3 61 3990 FT. WORTH, TX KFWD 32.8 -97.3 191.81 -140.322 13957 SHREVEPORT, LA KSHV 32.45 -93.817 519.713 -16 6.247 13995 SPRINGFIELD, MO KSGF 37.233 -93.383 527.118 366 .736 13996 TOPEKA, KS KTOP 39.067 -95.633 320.534 560.13 4



### T ABLE 

### D-3. L IST OF 

### P RECIPITATION METEOROLOGICAL 

### S TATIONS

Station ID Name ID Latitude Longitude X (km) Y (km)
30130 ALUM FORK ALUM 34.800 -92.850 592.573 100.264
30178 ANTOINE ANTO 34.033 -93.417 546.292 11.848
30220 ARKADELPHIA 2 N ARKA 34.150 -93.050 579.146 26 .959
30764 BLAKELY MOUNTAIN DAM BLAK 34.567 -93.200 562.3 85 72.271
30832 BOONEVILLE 3 SSE BOON 35.100 -93.917 493.544 1 27.441
30900 BRIGGSVILLE BRIG 34.933 -93.500 532.484 111.205
31152 CAMDEN 1 CAMD 33.600 -92.817 604.758 -32.605
31457 CLARKSVILLE 6 NE CLAR 35.533 -93.400 537.41 1 78.286 31952 DE QUEEN DAM DE Q 34.100 -94.367 458.524 14.2 32
32020 DIERKS DAM DIER 34.150 -94.083 484.26 21.186
32544 FOREMAN FORE 33.717 -94.383 459.215 -28.412 32574 FORT SMITH MU, OK FORT 35.333 -94.367 451.35 1 51.087
34185 LEWISVILLE LEWI 33.367 -93.567 537.001 -63.005
34548 MAGNOLIA 3 N MAGN 33.333 -93.250 566.603 -64.8 7
34756 MENA MENA 34.567 -94.267 464.947 66.513
35200 NIMROD DAM NIMR 34.950 -93.167 562.663 114.971
35908 PRESCOTT PRES 33.800 -93.383 550.983 -13.852
37048 TEXARKANA TEXA 33.450 -94.000 496.288 -56.103
37488 WALDRON WALD 34.900 -94.100 478.133 104.322
165874 MANSFIELD MANS 32.033 -93.700 533.444 -211.93 8
166244 MINDEN MIND 32.600 -93.300 567.151 -146.621
166582 NATCHITOCHES NATC 31.767 -93.100 591.967 -238 .055
167738 RED RIVER RSRCH STN RED 32.417 -93.633 537.1 67 -168.933 168440 SHREVEPORT, LA SHRE 32.467 -94.317 472.727 - 166.999
340179 ALTUS IRIG RES STN ALTU 34.583 -99.333 1.525 5 5.944
340292 ARDMORE ARDM 34.167 -97.133 203.861 12.009
340670 BENGAL BENG 34.850 -95.083 388.894 94.294
341437 CANEY CANE 34.233 -96.217 287.894 21.785
341544 CARTER TOWER CART 34.267 -94.783 419.327 30. 813
341684 CHANDLER 1 CHAN 35.700 -96.883 222.432 182.8 73
341750 CHICKASHA EXP STN CHIC 35.050 -97.917 130.348 108.775

### Westar Energy, Inc. D - 3 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
342334 CUSTER CITY CUST 35.650 -98.833 46.626 174.56 3
342654 DUNCAN AIRPORT DUNC 34.483 -97.967 126.717 45 .752
342849 ELK CITY ELK 35.383 -99.400 -4.528 144.821
343281 FORT COBB FORT 35.100 -98.433 83.311 113.749
343497 GEARY GEAR 35.633 -98.317 93.27 173.092
344052 HENNEPIN 5 N HENN 34.567 -97.350 183.003 56.0 15
344202 HOBART HOBA 35.033 -99.083 24.257 105.976
344865 KINGSTON KING 34.000 -96.733 241.149 -5.578
344975 LAKE EUFAULA LAKE 35.283 -95.433 355.031 141 .02
345108 LEHIGH LEHI 34.467 -96.217 287.04 47.705
345463 MACKIE 4 NNW MACK 35.750 -99.833 -43.561 185. 653
345589 MARSHALL MARS 36.150 -97.617 155.403 231.376
345664 MCALESTER MUNI AP MCAL 34.883 -95.783 324.985 95.368
346130 MUSKOGEE MUSK 35.767 -95.333 361.825 195.03
346620 OKARCHE OKAR 35.717 -97.983 123.223 182.725
346638 OKEMAH OKEM 35.433 -96.300 275.968 154.796 346661 OKLAHOMA CITY, OK OKLA 35.383 -97.600 158.462 146.277
347705 ROFF 2 WNW ROFF 34.633 -96.883 225.503 64.42 3
349023 TUSKAHOMA TUSK 34.633 -95.283 371.705 69.443
349629 WICHITA MTN WL REF WICH 34.733 -98.717 57.832 7 2.804
349724 WISTER WIST 34.950 -94.700 423.264 107.027
349748 WOLF 4 N WOLF 35.133 -96.667 243.74 120.487 410016 ABILENE MUN, TX ABIL 32.417 -99.683 -31.34 -18 5.017
410926 BONITA 4 NW BONI 33.833 -97.633 158.551 -25.9 95
411246 BURLESON BURL 32.550 -97.317 190.846 -168.18 4
411698 CHILDRESS MUNI AP CHIL 34.433 -100.283 -85.552 39.694
411773 CLARKSVILLE 1 W CLAR 33.617 -95.017 401.207 -42.383
411921 COMMERCE COMM 33.200 -95.933 318.041 -92.161
412086 CRANFILLS GAP CRAN 31.767 -97.833 143.748 -25 6.356
412096 CRESSON CRES 32.533 -97.617 162.725 -170.597
412131 CROSS PLAINS 2 CROS 32.133 -99.167 17.298 -21 6.619
412242 DALLAS-FORT WORTH/FORT. TX. DALL 32.900 -97. 017 218.043 -128.584
412244 DALLAS LOVE FIELD DALL 32.850 -96.850 233.76 -133.752
412404 DENTON 2 SE DENT 33.200 -97.100 209.467 -95. 394
412715 EASTLAND EAST 32.400 -98.817 50.155 -186.788
413133 FERRIS FERR 32.517 -96.667 251.944 -170.383
413285 FORT WORTH WSFO FORT 32.833 -97.300 191.73 - 136.613
413415 GAINESVILLE GAIN 33.633 -97.133 205.242 -47.275
413546 GILMER 2 W GILM 32.733 -94.983 408.8 -140.429
413642 GORDONVILLE GORD 33.800 -96.850 230.983 -28. 097
413771 GROESBECK 2 GROE 31.533 -96.533 267.703 -279 .557
414137 HICO HICO 31.983 -98.033 124.459 -232.49

### Westar Energy, Inc. D - 4 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
414257 HONEY GROVE HONE 33.583 -95.900 319.597 -49. 433
414520 JACKSBORO 1 NNE JACK 33.233 -98.150 111.677 -93.458
414866 KOPPERL KOPP 32.133 -97.483 176.115 -214.908
414972 LAKE BRIDGEPORT DAM LAKE 33.217 -97.833 141.1 74 -94.891
415348 LONGVIEW TX. LONG 32.350 -94.650 442.089 -181 .578
415463 MABANK 4 SW MABA 32.350 -96.117 304.197 -187 .329
415957 MINERAL WELLS 1 SSW MINE 32.783 -98.117 115.42 8 -143.496
415996 MOLINE MOLI 31.400 -98.317 98.384 -297.856
416108 MOUNT PLEASANT MOUN 33.167 -95.000 405.037 - 92.321
416177 NACOGDOCHES NACO 31.617 -94.650 446.124 -263 .206
416210 NAVARRO MILLS DAM NAVA 31.950 -96.700 250.573 -233.576
416270 NEW BOSTON NEW 33.450 -94.417 457.673 -58.1 92
416335 NEW SUMMERFIELD 2 W NEW 31.983 -95.133 398.4 67 -224.521
416757 PALESTINE 2 NE PALE 31.783 -95.600 355.27 -24 8.642
416834 PAT MAYSE DAM PAT 33.867 -95.517 353.821 -1 6.591
417066 PITTSBURG 5 S PITT 32.933 -94.933 412.444 -117 .974
417300 PROCTOR RESERVOIR PROC 31.967 -98.500 80.365 -234.849
417499 RED SPRINGS 2 ESE RED 33.600 -99.383 -3.088 -53.373
417556 RENO RENO 32.950 -97.567 166.549 -124.131
418047 SANTA ANNA SANT 31.750 -99.333 1.58 -259.359
418583 STAMFORD 1 STAM 32.933 -99.800 -42.038 -127. 448
418623 STEPHENVILLE 1 N STEP 32.250 -98.200 108.347 -202.983
418743 SULPHUR SPRINGS SULP 33.150 -95.633 346.171 - 96.673
418778 SWAN SWAN 32.450 -95.417 369.56 -173.721
419163 TRUSCOTT TRUS 33.750 -99.867 -47.772 -36.562
419419 WACOMADISON-COOPER TX. WACO 31.617 -97.233 20 0.979 -271.99
419532 WEATHERFORD WEAT 32.750 -97.767 148.243 -146 .722
419565 WELLINGTON WELL 34.833 -100.217 -79.036 84.08 2
419715 WHITNEY DAM WHIT 31.850 -97.367 187.779 -246.2 55
419729 WICHITA FALLS/SHEPS AFB TX WICH 33.983 -98.500 78.36 -10.397
419817 WINCHELL WINC 31.467 -99.167 17.441 -290.937
419893 WOODSON WOOD 33.017 -99.050 27.996 -118.228
419916 WRIGHT PATMAN WRIG 33.300 -94.167 481.752 -73. 621
33165 HARRISON BOONE CNTY AP HARR 36.267 -93.157 554 .062 260.951
35228 NORFORK DAM NORF 36.249 -92.256 634.629 264.6 68
140326 ARLINGTON ARLI 37.900 -98.267 94.889 424.802
141233 CALDWELL CALD 37.034 -97.616 153.714 329.468
141427 CHANUTE FAA AIRPORT CHAN 37.670 -95.484 339.6 61 405.655
141767 CONCORDIA BLOSSER MUNI CONC 39.551 -97.651 14 5.595 608.996
141867 COUNCIL GROVE LAKE COUN 38.675 -96.526 244.85 8 513.992
143997 IONIA IONI 39.661 -98.348 85.704 620.341
144341 KIOWA KIOW 37.017 -98.485 76.705 326.534

### Westar Energy, Inc. D - 5 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
145063 MARYSVILLE MARY 39.833 -96.633 231.897 642.3 07
145306 MILFORD LAKE MILF 39.075 -96.898 211.451 557.4 55
147160 SALINA AP SALI 38.817 -97.667 145.653 527.389
148167 TOPEKA BILLARD MUNI AP TOPE 39.069 -95.639 320 .018 560.323
148293 UNIONTOWN UNIO 37.848 -94.978 383.243 427.269
148830 WICHITA WICH 37.650 -97.433 168.512 398.2
230204 APPLETON CITY APPL 38.184 -94.026 464.443 46 8.749
230789 BOLIVAR 1 NE BOLI 37.617 -93.391 523.736 409. 205
231383 CASSVILLE RANGER STN CASS 36.673 -93.858 488. 813 302.115
231791 COLUMBIA REGIONAL AP COLU 38.817 -92.218 616.6 57 549.212
232302 DORA DORA 36.780 -92.233 632.313 323.535
234315 JOPLIN REGIONAL AP JOPL 37.147 -94.502 428.83 351.544
234358 KANSAS CITY AP KANS 39.300 -94.717 398.209 58 9.456
234544 KIRKSVILLE KIRK 40.200 -92.567 575.614 700.322
234825 LEBANON 2 W LEBA 37.685 -92.694 584.416 420. 837
235834 MOUNTAIN GROVE 2 N MOUN 37.153 -92.264 626.49 8 364.635
235987 NEVADA WATER PLANT NEVA 37.839 -94.373 436.2 01 428.946
237976 SPRINGFIELD REG AP SPRI 37.240 -93.390 526.478 367.443
238252 TABLE ROCK DAM TABL 36.597 -93.308 538.258 2 96.711
238466 TRUMAN DAM & RESERVIOR TRUM 38.258 -93.399 51 8.603 480.243
340215 AMES AMES 36.250 -98.183 104.495 241.705
347196 PONCA CITY PONC 36.717 -97.100 200.229 295.28 2
348992 TULSA INTL AIRPORT TULS 36.198 -95.888 310.16 240.97

# BART F IVE 

# F ACTOR 

# A NALYSIS 

# § W ESTAR 

# E NERGY 

## J EFFREY 

## E NERGY 

## C ENTER AND 

## G ORDON 

## E VANS 

## E NERGY 

## C ENTER



### V ERSION 

### 0

Prepared by:

T RINITY 

### C ONSULTANTS 

### 9777 Ridge Drive Suite 380 Lenexa, Kansas 66219 (913) 894-4500

August 2007

Project 051701.0153

### Westar Energy i Trinity Consultants

BART Analysis 

## T ABLE OF 

## C ONTENTS



### 1. E XECUTIVE 

### S UMMARY



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

2. I NTRODUCTION AND 

### B ACKGROUND



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

3. B ART 

### A PPLICABILITY 

### D ETERMINATION



### ................................................... ....... 3-1

4. JEC SO 2 

### BART E VALUATION



### ................................................... ...................... 4-1 4.1 I DENTIFICATION OF 

### A VAILABLE 

### R

ETROFIT 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

### ......... 4-1 4.2 E LIMINATE 

### T ECHNICALLY 

### I NFEASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

### ............... 4-1 4.2.1 D RY 

### S ORBENT 

### I NJECTION



### ................................................... .............................4-1 4.2.2 S PRAY 

### D RYER 

### A BSORPTION 

### (SDA).............................................. ...................4-2 4.2.3 W ET 

### S CRUBBER



### ................................................... ........................................... 4-2 4.2.4 C IRCULATING 

### D RY 

### S CRUBBER 

### (CDS) .............................................. ............... 4-2 

### 4.3 R

ANK OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### O PTIONS BY 

### E FFECTIVENESS 

### .. 4-3 4.4 E VALUATION OF 

### I MPACTS FOR 

### F EASIBLE 

### SO 2 

### C ONTROLS 

### ................................... 4-3 4.4.1 C OST OF 

### C OMPLIANCE



### ................................................... ................................. 4-3 4.4.2 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS 

### .......................................4-3 4.4.3 R EMAINING 

### U SEFUL 

### L IFE



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

### 4.5 E VALUATION OF 

### V ISIBILITY 

### I MPACT OF 

### F EASIBLE 

### SO 2 

### C ONTROLS 

### ..................... 4-4 4.6 P ROPOSED 

### B ART FOR 

### SO 2


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

5. JEC NO X 

### BART E VALUATION



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

### A VAILABLE 

### R

ETROFIT 

### NO X 

### C ONTROL 

### T ECHNOLOGIES 

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

### T ECHNICALLY 

### I NFEASIBLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES 

### .............. 5-1 5.2.1 C OMBUSTION 

### C ONTROLS



### ................................................... .............................5-2 5.2.1.1 Flue Gas Recirculation (FGR) ....................... ................................... 5-2 5.2.1.2 Overfire Air (OFA) ................................. .........................................5-2 5.2.1.3 Low and Ultra Low NO X 

### Burners........................................... .......... 5-2 5.2.2 P OST 

### C OMBUSTION 

### C ONTROLS



### ................................................... ....................5-3 5.2.2.1 Selective Catalytic Reduction...................... ..................................... 5-3 5.2.2.2 Selective Non-Catalytic Reduction .................. ................................. 5-4 

### 5.3 R

ANK OF 

### T ECHNICALLY FEASIBLE 

### NO X 

### C ONTROL 

### O PTIONS BY 

### E FFECTIVENESS 

### . 5-4 5.4 E VALUATION OF 

### I MPACTS FOR 

### F EASIBLE 

### NO X 

### C ONTROLS 

### .................................. 5-5 5.4.1 C OST OF 

### C OMPLIANCE



### ................................................... ................................. 5-5 5.4.2 E NERGY 

### I MPACTS 

### & N ON 

### -A IR 

### I MPACTS



### ................................................... ....... 5-9 5.4.3 R EMAINING 

### U SEFUL 

### L IFE



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

### 5.5 E VALUATION OF 

### V ISIBILITY 

### I MPACT OF 

### F EASIBLE 

### NO X 

### C ONTROLS 

### .................... 5-9 5.6 P ROPOSED 

### B ART FOR 

### NO X


### ................................................... ............................5-13

6. GEEC SO 2 

### BART E VALUATION



### ................................................... .................. 6-1 6.1 I DENTIFICATION OF 

### A VAILABLE 

### R

ETROFIT 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

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

### Westar Energy ii Trinity Consultants

BART Analysis 

### 6.2 E LIMINATE 

### T ECHNICALLY 

### I NFEASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

### ............... 6-1 6.2.1 D RY 

### S ORBENT 

### I NJECTION 

### , S PRAY 

### D RYER 

### A BSORPTION 

### (SDA), W ET 

### S CRUBBER 

### , C IRCULATING 

### D RY 

### S CRUBBER 

### (CDS).............................................. ............................6-1 6.2.2 F UEL 

### S WITCHING TO 

### O NE 

### P ERCENT 

### S ULFUR 

### F UEL 

### O IL 

### ....................................6-2 

### 6.3 R

ANK OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### O PTIONS BY 

### E FFECTIVENESS 

### .. 6-2 6.4 E VALUATION OF 

### I MPACTS FOR 

### F EASIBLE 

### SO 2 

### C ONTROLS 

### ................................... 6-2 6.4.1 C OST OF 

### C OMPLIANCE



### ................................................... ................................. 6-2 6.4.1.1 Energy Impacts and Non-Air Quality Impacts ......... .........................6-5 6.4.1.2 Remaining Useful Life .............................. .......................................6-5 

### 6.5 E VALUATION OF 

### V ISIBILITY 

### I MPACT OF 

### F EASIBLE 

### SO 2 

### C ONTROLS 

### ..................... 6-5 6.6 P ROPOSED 

### B ART FOR 

### SO 2


### ................................................... .............................. 6-8

7. GEEC NO X 

### BART E VALUATION



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

### A VAILABLE 

### R

ETROFIT 

### NO X 

### C ONTROL 

### T ECHNOLOGIES 

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

### T ECHNICALLY 

### I NFEASIBLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES 

### .............. 7-1 7.2.1 C OMBUSTION 

### C ONTROLS



### ................................................... .............................7-2 7.2.1.1 Flue Gas Recirculation (FGR) ....................... ................................... 7-2 7.2.1.2 Overfire Air (OFA) ................................. .........................................7-2 7.2.1.3 Low and Ultra Low NO X 

### Burners........................................... .......... 7-2 7.2.2 P OST 

### C OMBUSTION 

### C ONTROLS



### ................................................... ....................7-3 7.2.2.1 Selective Catalytic Reduction...................... ..................................... 7-3 7.2.2.2 Selective Non-Catalytic Reduction .................. ................................. 7-4 

### 7.3 R

ANK OF 

### T ECHNICALLY FEASIBLE 

### NO X 

### C ONTROL 

### O PTIONS BY 

### E FFECTIVENESS 

### . 7-4 7.4 E VALUATION OF 

### I MPACTS FOR 

### F EASIBLE 

### NO X 

### C ONTROLS 

### .................................. 7-5 7.4.1 C OST OF 

### C OMPLIANCE



### ................................................... ................................. 7-5 7.4.2 E NERGY 

### I MPACTS 

### & N ON 

### -A IR 

### I MPACTS



### ................................................... ....... 7-8 7.4.3 R EMAINING 

### U SEFUL 

### L IFE



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

### 7.5 E VALUATION OF 

### V ISIBILITY 

### I MPACT OF 

### F EASIBLE 

### NO X 

### C ONTROLS 

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

### B ART FOR 

### NO X


### ................................................... ............................7-11

8. PM 10 

### BART E VALUATION



### ................................................... ........................... 8-1

9. P ROPOSED 

### GEEC BART A LTERNATIVE



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

9.1.1.1 Compliance with Alternative BART Control Strategy.. ..................... 9-1 9.1.2 C OMPARISON OF 

### V ISIBILITY 

### I MPACTS FOR 

### BART AND 

### P ROPOSED 

### A LTERNATIVE 

### BART C ONTROL 

### S TRATEGIES



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

### Westar Energy iii Trinity Consultants

BART Analysis 

## L IST OF 

## T ABLES

T ABLE 

### 1-1. E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 AND 

### GEEC U

NIT 

### 2 (2001-2003)........................................

................................................... ......1-1

T ABLE 

### 1-2. V ISIBILITY 

### I MPAIRMENT 

### I MPROVEMENT FROM 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 (2001-2003)....1-2

T ABLE 

### 1-3. V ISIBILITY 

### I MPAIRMENT 

### I MPROVEMENT FROM 

### GEEC U NIT 

### 2 (2001-2003)................... 1-3

T ABLE 

### 3-1. E XISTING MAXIMUM 

### 24-H OUR 

### SO 2 

### , NO X 

### , AND 

### PM 10 

### E MISSIONS 

### (A S 

### H OURLY 

### E QUIVALENTS 

### ) ..................................................

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

T ABLE 

### 3-2. S UMMARY OF 

### E XISTING 

### S TACK 

### P ARAMETERS



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

T ABLE 

### 3-3. E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 AND 

### GEEC U

NIT 

### 2 (2001-2003)........................................

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

T ABLE 

### 3-4. B REAKDOWN OF 

### P OLLUTANT 

### S PECIFIC 

### C ONTRIBUTIONS TO 

### E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### F OR 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 (2001-2003)........................................ ............. 3-2

T ABLE 

### 3-5. B REAKDOWN OF 

### P OLLUTANT 

### S PECIFIC 

### C ONTRIBUTIONS TO 

### E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### F OR 

### GEEC U NIT 

### 2 (2001-2003) ........................................ ............................3-3

T ABLE 

### 4-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### SO 2 

### E MISSION 

### R ATES FOR 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 .....4-1

TABLE 

### 4-2. A

VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES FOR 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 ..................... 4-1

T ABLE 

### 4-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES
FOR 

### JEC U

NIT 

### 1 AND 

### U

NIT 

### 2 .................................................. ............................................ 4-3

T ABLE 

### 4-4. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS FOR 

### JEC U

NIT 

### 1 AND 

### U

NIT 

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

T ABLE 

### 4-5. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS
FOR 

### JEC U

NIT 

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

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

T ABLE 

### 4-6. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS
FOR 

### JEC U

NIT 

### 2 (2001-2003) ........................................

................................................... .. 4-6

T ABLE 

### 5-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### NO X 

### E MISSION 

### R ATE FOR 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 ......5-1

T ABLE 

### 5-2. A

VAILABLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES FOR 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2.................... 5-1

T ABLE 

### 5-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES 

### 5-5

TABLE 

### 5-4. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### JEC U NIT 

### 1 NO X 

### C ONTROLS 

### .......................... 5-8

### Westar Energy iv Trinity Consultants

BART Analysis TABLE 

### 5-5. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### JEC U NIT 

### 2 NO X 

### C ONTROLS 

### .......................... 5-8

T ABLE 

### 5-6. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS FOR 

### JEC U

NIT 

### 1 AND 

### JEC U

NIT 

### 2 .................................................. .................. 5-10

T ABLE 

### 5-7. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS
FOR 

### JEC U

NIT 

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

................................................... 5-11

T ABLE 

### 5-8. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS
FOR 

### JEC U

NIT 

### 2 (2001-2003) ........................................

................................................... 5-12

T ABLE 

### 6-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### SO 2 

### E MISSION 

### R ATES 

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

TABLE 

### 6-2. A

VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES FOR 

### GEEC U NIT 

### 2.....................................6-1

T ABLE 

### 6-3. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### S WITCHING 

### F ROM 

### N O



### . 6 O IL TO 

### 1% S ULFUR 

### F UEL 

### O IL



### ................................................... ................................................... ..................... 6-4

T ABLE 

### 6-4. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS



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

T ABLE 

### 6-5. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS
FOR 

### GEEC U

NIT 

### 2 (2001-2003)........................................ .................................................. 6 -7

T ABLE 

### 7-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### NO X 

### E MISSION 

### R ATE 

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

T ABLE 

### 7-2. A

VAILABLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES FOR 

### GEEC U NIT 

### 2...................................7-1

T ABLE 

### 7-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES 

### 7-5

TABLE 

### 7-4. S UMMARY OF 

### C OST 

### E FFECTIVENESS FOR 

### GEEC U NIT 

### 2 NO X 

### C ONTROLS 

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

T ABLE 

### 7-5. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS



### ................................................... ................................................... .................... 7-9

T ABLE 

### 7-6. S UMMARY OF 

### M ODELED 

### I MPACTS FROM 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS
FOR 

### GEEC U

NIT 

### 2 (2001-2003)........................................ ................................................ 7-1 0

T ABLE 

### 8-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### PM 10 

### E MISSION 

### R ATE 

### ..................................................8 -1

T ABLE 

### 8-2. VAP V ISIBILITY 

### I MPAIRMENT 

### C ONTRIBUTIONS FROM 

### JEC U NIT 

### 1 AND 

### U

NIT 

### 2 (2001- 2003)..............................................

................................................... ................................8-1

T ABLE 

### 8-3. V ISIBILITY 

### I MPAIRMENT 

### C ONTRIBUTIONS FROM 

### GEEC U NIT 

### 2(2001-2003) .................8-2

T ABLE 

### 9-1. M ODELED 

### I MPACTS 

### B ASED ON 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES AND 

### A LTERNATIVE 

### BART AT 

### GEEC U NIT 

### 2 (2001-2003)........................................ .................. 9-2

### Westar Energy v Trinity Consultants

BART Analysis

### Westar Energy 1-1 Trinity Consultants

BART Analysis 

## 1. E XECUTIVE 

## S UMMARY

This report documents the determination of the Best Available Retrofit Technology (BART) as proposed by Westar Energy (Westar) for the Jeffrey Energy Center (JEC) located in St. Mary’s, Kansas and the Gordon Evans Energy Center (GEEC) lo cated in Colwich, Kansas. There are two units at JEC and one unit at GEEC that are subject to BART. JEC Unit 1 and Unit 2 are each coalfired boilers with heat input ratings of 8,110 mill ion British thermal units per hour (MMBtu/hr). GEEC Unit 2 is an oil-fired boiler with a heat inpu t rating of 4,110 MMBtu/hr.

Westar has determined that JEC Unit 1 and Unit 2 an d GEEC Unit 2 contribute greater than 0.5 delta deciviews ( ∆ dv) to visibility impairment in a federally protect ed Class I area when compared to a natural background. Therefore, these three units a re subject to BART. A summary of the visibility impairment attributable to the JEC Unit 1 and Unit 2 and GEEC Unit 2 is provided in Table 1-1.

T ABLE 

### 1-1. E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2 AND 

### GEEC U NIT 

### 2 (2001-2003)

Wichita Mountains Hercules

Glades Wilderness Caney Creek

Wilderness Mingo NWR Upper Buffalo Wilderness

98th % ∆dv Days > 0.5 ∆dv 98th % ∆dv Days > 0.5 ∆dv 98th

% ∆dv Days > 0.5 ∆dv 98th

% ∆dv Days > 0.5 ∆dv 98 th

% ∆dv Days > 0.5 ∆dv JEC Unit 1 and Unit 2 0.99 59 0.90 63 0.73 37 0.49 21 0.85 53 GEEC Unit 2 1.08 85 0.40 16 0.38 14 0.17 4 0.42 16



### Westar used the U.S. Environmental Protection Agenc y’s (EPA’s) guidelines 1 

### in 40 CFR Part 51 to determine BART for JEC Unit 1 and Unit 2 and GEEC U nit 2. Specifically, Westar conducted a fivestep analysis to determine BART for SO 2 

### , NO X 

### , and PM 10 

### that included the following:

1. 

### Identifying all available retrofit control technolo gies;

2. 

### Eliminating technically infeasible control technolo gies;
3. 

### Evaluating the control effectiveness of remaining c ontrol technologies;
4. 

### Evaluating impacts and document the results;

5. 

### Evaluating visibility impacts

Based on the five-step analysis, Westar proposes th e following as BART:

JEC Unit 1:

• PM 10 

### – Westar proposes upgrades to the existing electro static precipitator (ESP).

• NO X 

### – Westar proposes to meet a BART limit of 0.15 lb /MMBtu by installing a low-NO X 

### burner (LNB) system.

• SO 2 

### – Westar proposes to meet a BART limit of 0.15 lb /MMBtu by rebuilding the existing wet scrubber.

1 40 CFR 51, Regional Haze Regulations and Guideline s for Best Available Retrofit Technology (BART) Determinations

### Westar Energy 1-2 Trinity Consultants

BART Analysis JEC Unit 2:

• PM 10 

### – Westar proposes upgrades to the existing ESP.

• NO X 

### – Westar proposes to meet a BART limit of 0.15 lb /MMBtu by installing a LNB system.

• SO 2 

### – Westar proposes to meet a BART limit of 0.15 lb /MMBtu by rebuilding the existing wet scrubber.

GEEC Unit 2:

• PM 10 

### – Westar proposes that no additional PM 10 

### controls, other than the fuel switching from No.6 fuel oil to natural gas proposed as a BART alt ernative, are required for PM 10 

### BART compliance. Additional PM controls for a gas-fired unit would provide little visibility improvement and require significant capital expendi tures.

• NO X 

### and SO 2 

### – Westar proposes to meet the BART control require ment by switching from combusting No. 6 fuel oil to combusting natural gas , exclusively, except as discussed in Section 9 of this document.

The proposed BART control strategies will result in reductions of the visibility impacts attributable to JEC Unit 1 and Unit 2 and GEEC Unit 2. A summary o f the visibility improvement based on the existing emission rates and proposed BART emission rates for JEC Unit 1 and Unit 2 is provided in Table 1-2. A summary of the visibility improvement based on the existing emission rates for GEEC Unit 2 and the BART control strategy for GEEC Unit 2 is provided in Table 1-3.

T ABLE 

### 1-2. V ISIBILITY 

### I MPAIRMENT 

### I MPROVEMENT FROM 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2 (2001-2003)

Caney Creek Wilderness Hercules Glades

Wilderness Mingo NWR Upper Buffalo Wilderness Wichita MountainsExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovement

Max Impact (Δdv) 2.74 0.85 69% 2.70 0.75 72% 1.44 0.44 69% 2.73 0.84 69% 3.51 1.19 66% 98% Impact (Δdv) 0.73 0.23 70% 0.90 0.30 67% 0.49 0.16 69% 0.85 0.25 71% 0.99 0.32 68% Days > 0.5 37 4 89% 63 4 94% 21 0 100% 53 3 94% 59 11 81%

### Westar Energy 1-3 Trinity Consultants

BART Analysis T ABLE 

### 1-3. V ISIBILITY 

### I MPAIRMENT 

### I MPROVEMENT FROM 

### GEEC U NIT 

### 2 (2001-2003)

Caney Creek Wilderness Hercules Glades

Wilderness Mingo NWR Upper Buffalo Wilderness Wichita MountainsExistingBART*ImprovementExistingBART*ImprovementExistingBART*ImprovementExistingBART*ImprovementExistingBART*Improvement

Max Impact (Δdv) 1.07 0.49 54% 1.31 0.93 29% 0.71 0.49 31% 2.22 1.62 27% 2.16 1.66 23% 98% Impact (Δdv) 0.38 0.25 34% 0.4 0.21 48% 0.17 0.08 55% 0.42 0.28 33% 1.08 0.69 36% Days > 0.5 14 0 100% 16 4 75% 4 0 100% 16 11 31% 85 44 48% * Based on the BART alternative presented in Section 9

### Westar Energy 2-1 Trinity Consultants

BART Analysis 

## 2. I NTRODUCTION AND 

## B ACKGROUND

On July 1, 1999, the U.S. EPA published the final R egional Haze Rule (RHR). The objective of the RHR is to improve visibility in 156 specific areas across with United States, known as Class I areas. The Clean Air Act defines Class I areas as certain national parks (over 6000 acres), wilderness areas (over 5000 acres), national memorial parks (over 50 00 acres), and international parks that were in existence on August 7, 1977.

On July 6, 2005, the EPA published amendments to it s 1999 RHR, often called the Best Available Retrofit Technology (BART) rule, which included gui dance for making source-specific BART determinations. The BART rule defines BART-eligibl e sources as sources that meet the following criteria:

(1) Have potential emissions of at least 250 tons per y ear 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 categor ies in the guidance.

A BART-eligible source is subject to BART if the so urce 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 contri bute 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. Air quality modeling is the to ol that is used to determine a source’s visibility impacts.

Once it is determined that a source is subject to B ART, a BART determination must address air pollution control measures for the source. The vis ibility regulations define BART as follows:

“...an emission limitation based on the degree of redu ction achievable through the application of the best system of continuous emissi on reduction for each pollutant which is emitted by...[a BART-eligible source]. The emission limitation must be established on a case-by-case basis, taking into co nsideration the technology available, the cost of compliance, the energy and non air qual ity 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 us e of such technology.

Specifically, the BART rule states that a BART dete rmination should address the following five statutory factors:

1. 

### Existing controls
2. 

### Cost of controls
3. 

### Energy and non-air quality environmental impacts
4. 

### Remaining useful life of the source
5. 

### Degree of visibility improvement as a result of con trols

Further, the BART rule indicates that the five basi c steps in a BART analysis can be summarized as follows:

### Westar Energy 2-2 Trinity Consultants

BART Analysis 1. 

### Identify all available retrofit control technologie s; 2. 

### Eliminate technically infeasible control technologi es; 3. 

### Evaluate the control effectiveness of remaining con trol technologies;
4. 

### Evaluate impacts and document the results;

5. 

### Evaluate visibility impacts

A BART determination should be made for each visibi lity affecting pollutant (VAP) by following the five steps listed above for each VAP.

Westar performed a BART applicability analysis for JEC Unit 1 and Unit 2 and GEEC Unit 2 and determined the units are subject to BART. The deta ils of the applicability determination can be found in Section 3. Subsequently, Westar performed an an alysis to determine BART for each VAP for JEC Unit 1 and Unit 2 and GEEC Unit 2. The VAPs emitte d by JEC Unit 1 and Unit 2 and GEEC Unit 2 include NO x 

### , SO 2 

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

### ) of various forms (filterable coarse particulate mat ter [PM c 

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

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

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

### , NO X 

### , and PM 10 

### can be found in Sections 4, 5, and 6, respectively.

EPA established presumptive limits in the BART guid elines for coal-fired electric generating units (EGUs). The presumptive limits were established by reviewing BART-eligible units and determining a level of emissions reductions that would be cost effective. The EPA’s BART guidelines state the following with regard to presumptive BART for coal- fired EGU units for SO 2 

### :

“You must require 750 MW power plants to meet speci fic control levels for SO 2 

### of either 95 percent control or 0.15 lb/MMBtu... For coal fired EGUs with an existing post combustion SO 2 

### controls achieving less than 50 percent removal ef ficiencies, we recommend that you evaluate construction a new FGD system to meet the same emission limit as above (95 percent removal or 0.15 lb/MMBtu)”

For power plants greater than 750 MW, EPA requires t hat state agencies apply the presumptive BART limit as a floor for SO 2 

### . Thus, the SO 2 

### presumptive limit for both JEC Unit 1 and Unit 2 i s 0.15 lb/MMBtu.

Similarly, the guidelines provide presumptive NO X 

### limits for coal-fired EGUs. JEC Unit 1 and Unit 2 are tangential-fired units burning sub-b ituminous coal; the guidelines state that the NO X 

### presumptive limit is 0.15 lb/MMBtu for this type of EGU.

The BART guidelines state the following with regardto presumptive BART controls for oilfired boilers:

“For oil-fired and gas-fired EGUs larger than 200 M W, we believe that installation of current combustion control technology to control NO X 

### is generally highly cost-effective and should be considered in your determination of BART for these sources.”

EPA also established presumptive SO 2 

### controls for oil-fired EGUs. For oil-fired units, the guidelines state that sources of all sizes should evaluate lim iting the sulfur content of the fuel oil to 1 perce nt or

### Westar Energy 2-3 Trinity Consultants

BART Analysis less by weight as BART. Thus, the SO 2 

### presumptive limit for GEEC Unit 2 is fuel oil sulf ur content of 1 percent.

The BART guidelines do not specify presumptive BART limits for PM 10 

### emissions.

### Westar Energy 3-1 Trinity Consultants

BART Analysis 

## 3. B ART 

## A PPLICABILITY 

## D ETERMINATION

As stated in Section 2, a BART-eligible source is s ubject-to-BART if the source is “reasonably anticipated to cause or contribute to visibility im pairment in any federal mandatory Class I area.” EPA has determined that a source is reasonably anti cipated to cause or contribute to visibility impairment if the 98 th



### percentile of the visibility impacts from the sour ce is greater than 0.5 ∆dv when compared against a natural background. Westar cond ucted air quality modeling to predict the existing visibility impairment attributable to JEC Unit 1 and Unit 2 and GEEC Unit 2 in the following Class I areas:

▲ 

### Wichita Mountains ▲ 

### Hercules Glades Wilderness ▲ 

### Upper Buffalo Wilderness ▲ 

### Caney Creek Wilderness ▲ 

### Mingo National Wildlife Refuge (NWR)

The modeling methods and procedures that were follo wed were provided to the Kansas Department of Health and Environment (KDHE) in a modeling proto col in September 2006. Table 3-1 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 emis sion rates based on 2002-2004 continuous emissions monitoring system (CEMS) data. The PM 10 

### emission rates are the calculated highest emission rates based on fuel data from 2002-2004 and AP-42 emission factors. The total PM 10 

### emission rates include both the filterable and cond ensable fractions and are speciated into the following:

▲ 

### Coarse particulate matter (PM C 

### ) ▲ 

### Fine particulate matter (PM f 

### ) ▲ 

### Sulfates (SO 4 

### ) ▲ 

### Secondary organic aerosols (SOA) ▲ 

### Elemental carbon (EC)

T ABLE 

### 3-1. E XISTING MAXIMUM 

### 24-H OUR 

### SO 2 

### , NO X 

### , AND 

### PM 10 

### E MISSIONS 

### (A S 

### H OURLY 

### E QUIVALENTS 

### )

SO 2 NO X Total

PM 10 SO 4 PM c PM f SOA EC (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) JEC Unit 1 6,938.9 3,972.3 327.4 181.9 55.6 42.8 45.5 1.6 JEC Unit 2 7,128.2 3,924.0 303.9 168.8 51.6 39.8 42.2 1.5
GEEC Unit 2 5,766.7 4,818.3 431.5 38.7 104.7 260.5 6.8 20.8



### Table 3-2 summarizes the stack parameters that were used to model JEC Unit 1 and Unit 2 and GEEC Unit 2 (two stacks). It should be noted that the g ood engineering practice (GEP) stack heights were modeled instead of the actual stack heights for JEC Unit 1 and Unit 2 since the GEP stack heights are less than the actual stack heights.

### Westar Energy 3-2 Trinity Consultants

BART Analysis T ABLE 

### 3-2. S UMMARY OF 

### E XISTING 

### S TACK 

### P ARAMETERS

JEC Unit 1 JEC Unit 2 GEEC Unit 2 (Stack 2A) GEEC Unit 2

(Stack 2B) Latitude (degrees) 39.287 39.287 37.793 37.793 Longitude (degrees) 96.116 96.116 97.518 97.518 Actual Stack height (ft) 600 600 197 197 GEP Stack height (ft) 574 574 381 381 Stack Diameter (ft) 26 26 13 13 Exhaust Velocity (ft/s) 91.3 91.3 69 69 Exhaust Temperature (F) 300 300 290 290



### The results of the modeling are summarized in Table 3-3. The results of the modeling indicate that the 98 th 

### percentile of the visibility impacts attributable to JEC Unit 1 and Unit 2 and GEEC Unit 2 are greater than 0.5 Δ dv when compared against a natural background. Sin ce the visibility impacts are greater than 0.5 Δd

v, JEC Unit 1 and Unit 2 and GEEC Unit 2 are subjec t to BART.

T ABLE 

### 3-3. E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2 AND



### GEEC U NIT 

### 2 (2001-2003)

Class I Area Wichita

Mountains Hercules

Glades Wilderness Caney Creek

Wilderness Mingo NWR Upper Buffalo Wilderness 98th % ∆dv Days > 0.5 ∆dv 98th

% ∆dv Days > 0.5 ∆dv 98th

% ∆dv Days > 0.5 ∆dv 98th

% ∆dv Days > 0.5 ∆dv 98th

% ∆dv Days > 0.5 ∆dv JEC Unit 1 and Unit 2 0.99 59 0.90 63 0.73 37 0.49 21 0.85 53 GEEC Unit 2 1.08 85 0.40 16 0.38 14 0.17 4 0.42 16



### Tables 3-4 and 3-5 provide a breakdown of the visib ility impacts listed in Table 3-3 by each VAP for JEC and GEEC, respectively.

T ABLE 

### 3-4. B REAKDOWN OF 

### P OLLUTANT 

### S PECIFIC 

### C ONTRIBUTIONS TO 

### E XISTING 

### V ISIBILITY



### I MPAIRMENT 

### F OR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2 (2001-2003)

Visibility Impairment Attributable to SO 4 Visibility

Impairment Attributable to NO 3 Visibility

Impairment Attributable to SOA Visibility

Impairment Attributable to EC Visibility

Impairment Attributable to PM c Visibility

Impairment Attributable to PM f Total

Visibility Impairment 98% Class I Area (%) (%) (%) (%) (%) (%) ( 

### Δdv )
Wichita Mountains Wilderness 51.13 48.28 0.44 0.04 0 .01 0.1 0.99
Hercules Glades Wilderness 38.21 60.92 0.63 0.06 0.03 0.15 0.90 Caney Creek Wilderness 40.79 57.87 0.98 0.09 0.05 0.23 0.73 Mingo Wildlife 43.81 55.53 0.49 0.04 0.01 0.11 0.49 Upper Buffalo Wilderness 39.6 59.22 0.85 0.08 0.05 0.2 0.85

### Westar Energy 3-3 Trinity Consultants

BART Analysis T ABLE 

### 3-5. B REAKDOWN OF 

### P OLLUTANT 

### S PECIFIC 

### C ONTRIBUTIONS TO 

### E XISTING 

### V ISIBILITY 

### I MPAIRMENT 

### F OR 

### GEEC U NIT 

### 2 (2001-2003)

Visibility Impairment Attributable to SO 4 Visibility

Impairment Attributable to NO 3 Visibility

Impairment Attributable to SOA Visibility

Impairment Attributable to EC Visibility

Impairment Attributable to PM c Visibility

Impairment Attributable to PM f Total

Visibility Impairment 98% Class I Area (%) (%) (%) (%) (%) (%) ( 

### Δdv )
Wichita Mountains Wilderness 29.29 67.54 0.16 1.25 0 .19 1.57 1.08
Hercules Glades Wilderness 41.15 57.14 0.09 0.7 0.04 0.88 0.40 Caney Creek Wilderness 26.11 71.72 0.12 0.89 0.04 1.11 0.38 Mingo Wildlife 63.14 34.96 0.1 0.78 0.03 0.98 0.17 Upper Buffalo Wilderness 35.67 62.6 0.09 0.71 0.02 0.89 0.42



### As shown in Tables 3-4 and 3-5, the most significan t contributors to the visibility impairment are sulfates (SO 4 

### ) and nitrates (NO 3 

### ). The SO 4 

### contribution is primarily from the chemical conver sion of SO 2 

### emitted by JEC Unit 1 and Unit 2 and GEEC Unit 2 t o SO 4 

### ; a very small fraction is from SO 4 

### emitted as condensable particulate. The NO 3 

### contribution is entirely from the chemical convers ion of NO X 

### emitted from JEC Unit 1 and Unit 2 and GEEC Unit 2 . The contribution of PM 10 

### to the total visibility impairment can be estimated as the sum o f the contributions from SOA, EC, PM c 

### , and PM f 

### . The PM 10 

### contribution is less than the contribution from SO 2 

### and NO X 

### .

### Westar Energy 4-1 Trinity Consultants

BART Analysis 

## 4. JEC SO 2 

## BART E VALUATION

The existing maximum 24-hour SO 2 

### emission rates that were modeled for the BART appl icability determination are summarized in Table 4-1.

T ABLE 

### 4-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### SO 2 

### E MISSION 

### R ATES FOR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2

Heat Input SO 2 24-Hour

Emission Rate SO 2 Hourly

Emission Rate SO 2 Emission Rate (MMBtu/hr) (ton/24-hr) (lb/hr) (lb/MMBtu) JEC Unit 1 8,110 83.3 6,938.9 0.86 JEC Unit 2 8,110 85.5 7,128.2 0.88



## 4.1 I DENTIFICATION OF 

## A VAILABLE 

## R ETROFIT 

## SO 2 

## C ONTROL 

## T ECHNOLOGIES
Step 1 of the BART determination is the identificat ion of all available retrofit SO 2 

### control technologies. A list of control technologies was o btained by reviewing the U.S. EPA’s Clean Air Technology Center, control equipment vendor informa tion, publicly-available air permits, applications, and technical literature published by the U.S. EPA and Regional Planning Organizations (RPOs).

The available retrofit SO 2 

### control technologies are summarized in Table 4-2 f or JEC Unit 1 and 2.

TABLE 

### 4-2. A VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES FOR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2

SO 2 Control Technologies

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



### All of the technologies listed in Table 4-2 involve removing the SO 2 

### in the exhaust gas, which is known as flue gas desulfurization (FGD).



## 4.2 E LIMINATE 

## T ECHNICALLY 

## I NFEASIBLE 

## SO 2 

## C ONTROL 

## T ECHNOLOGIES
Step 2 of the BART determination is to eliminate te chnically infeasible SO 2 

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



### 4.2.1 D RY 

### S ORBENT 

### I NJECTION



### Dry sorbent injection involves the injection of a l ime or limestone powder into the exhaust gas stream where SO 2 

### becomes entrained in the lime. The stream is then passed through a fabric filter to remove the sorbent and entrained S O 2 

### . The process was developed as a lower cost FGD option because the mixing of the SO 2 

### and lime occurs directly in the

### Westar Energy 4-2 Trinity Consultants

BART Analysis

exhaust gas stream instead of in a separate tower. Depending on the residence time and gas stream temperature, sorbent injection control e fficiency is typically between 40 and 60 percent. 2 

### This control is a technically feasible option for the control of SO 2 

### from JEC Unit 1 and Unit 2.



### 4.2.2 S PRAY 

### D RYER 

### A BSORPTION 

### (SDA)

Spray dryer absorption is a semi-dry scrubbing syst em that sprays a fine mist of lime slurry into an absorption tower where the SO 2 

### is absorbed by the slurry droplets. The absorptio n of the SO 2 

### leads to the formation of calcium sulfite and calc ium sulfate within the droplets. The liquid-to-gas ratio is such that the heat from the exhaust gas causes the water to evaporate before the droplets reach the bottom of t he tower. This leads to the formation of a dry powder which is carried out with the gas and collected with a fabric filter. Existing spray dryer absorption control efficiencies range f rom 60 to 95 percent. 3 

### This control is a technically feasible option for the control of SO 2 

### from JEC Unit 1 and Unit 2.



### 4.2.3 W ET 

### S CRUBBER



### Wet scrubbing involves scrubbing the exhaust gas st ream with a slurry comprised of lime or limestone in suspension. The process takes plac e in a wet scrubbing tower located downstream of a PM control device such as a fabric f ilter or an ESP to prevent the plugging of spray nozzles and other problems caused by the presence of particulates in the scrubber. Similarly to the chemistry illustrated a bove for spray dryer absorption, the SO 2 

### in the gas stream reacts with the lime or limestone slurry to form calcium sulfite and calcium sulfate. Wet lime scrubbing is capable of achieving 80-95 percent control. 4 

### This control is a technically feasible option for the co ntrol of SO 2 

### from JEC Unit 1 and Unit 2.



### 4.2.4 C IRCULATING 

### D RY 

### S CRUBBER 

### (CDS)

In the circulating dry scrubbing process, the flue gas is introduced into the bottom of a reactor vessel at high velocity through a venturi n ozzle; the exhaust is mixed with water, hydrated lime, recycled flyash and CDS reaction pro ducts. The intensive gas-solid mixing that occurs in the reactor promotes the reaction of sulfur oxides in the flue gas with the dry lime particles. The mixture of reaction products ( calcium sulfite/sulfate), unreacted lime, and fly ash is carried out with the exhaust and col lected in an ESP or fabric filter. A large portion of the collected particles is recycled to t he reactor to sustain the bed and improve lime utilization. CDS absorbers have been installe d with both fabric filters and ESPs for particulate control. The control efficiency of a C DS is similar to that of an SDA. This control is a technically feasible option for the co ntrol of SO 2 

### from JEC Unit 1 and Unit 2.

2 "Assessment of Control Technology Options for BART -Eligible Sources: Steam Electric Boilers, Industri al Boilers, Cement Plants and Paper and Pulp Facilitie s" Northeast States for Coordinated Air Use Managem ent (NESCAUM), March 2005. 3 EPA Basic Concepts in Environmental Sciences, Modu le 6: Air Pollutants and Control Techniques http://www.epa.gov/eogapti1/module6/sulfur/control/ control.htm 4 EPA Basic Concepts in Environmental Sciences, Modu le 6: Air Pollutants and Control Techniques http://www.epa.gov/eogapti1/module6/sulfur/control/ control.htm

### Westar Energy 4-3 Trinity Consultants

BART Analysis 

## 4.3 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. Table 4-3 provides a ranking of the control efficiencies for the controls listed in th e previous section for JEC Unit 1 and 2.

T ABLE 

### 4-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL



### T ECHNOLOGIES FOR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2

Control Technology Estimated Control Efficiency

Wet Scrubber ~80-95% Spray Dryer Absorber (SDA) ~60-95% Circulating Dry Scrubber (CDS) ~60-95% Dry Sorbent Injection ~40-60%



### Since dry sorbent injection has the lowest control level of the controls listed in Table 4-3, this con trol will no longer be evaluated.



## 4.4 E VALUATION OF 

## I MPACTS FOR 

## F EASIBLE 

## SO 2 

## C ONTROLS
Step four for the BART analysis procedure is the im pact analysis. The BART determination guidelines list the four factors to be considered i n the impact analysis:

▲ 

### Cost of compliance ▲ 

### Energy impacts ▲ 

### Non-air quality impacts; and ▲ 

### The remaining useful life of the source



### 4.4.1 C OST OF 

### C OMPLIANCE



### The three remaining SO 2 

### control options (wet scrubbers, SDA, CDS) for JEC Unit 1 and Unit 2 are FGD systems capable of achieving similar maximum levels of SO 2 

### reductions. Westar will only evaluate wet scrubbers for BART. Since this control option achieves an equally high level of control as the other control options, cost analyses are not necessary.



### 4.4.2 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS



### FGD systems require electricity to operate the anci llary equipment. Additionally, wet FGD systems generate wastewater and sludge that mus t be treated. This places additional burdens on the wastewater treatment and solid waste management capabilities. If wet scrubbing produces calcium sulfite sludge, the slud ge will be water-laden, and it must be stabilized for landfilling. If wet scrubbing produ ces calcium sulfate sludge, it is stable and easy to dewater. However, control costs will be hi gher because additional equipment is required.

### Westar Energy 4-4 Trinity Consultants

BART Analysis 

### 4.4.3 R EMAINING 

### U SEFUL 

### L IFE



### The remaining useful life of JEC Unit 1 and Unit 2 does not impact the annualized capital costs because the useful lives of the units are ant icipated to be at least as long as the capital cost recovery period, which is 20 years.



## 4.5 E VALUATION OF 

## V ISIBILITY 

## I MPACT OF 

## F EASIBLE 

## SO 2 

## C ONTROLS
A final impact analysis was conducted to assess the visibility improvement for existing emission rates when compared to the emission rates of associated w ith a wet scrubber. The existing emission rates and emission rates associated with a wet scrubber w ere modeled using CALPUFF. The existing emission rates are the same rates that were modeled for the BART applicability analysis. The emission rate associated with the wet scrubber is 0 .15 lb/MMBtu multiplied by the maximum hourly heat inputs for JEC Unit 1 and Unit 2. A sample ca lculation of the SO 2 

### emission rate associated with a wet scrubber for JEC Unit 1 is provided as follow s:



### HI P * = 1,216.5 lb/hr Where: P (emission rate of wet scrubber) = 0.15 lb/MMBtu HI (hourly heat input) = 8,110 MMBtu/hr

The existing hourly equivalent of the maximum 24-ho ur emission rates and the hourly equivalent of the 24-hour emission rates associated with the wet scrubber that were utilized in the visibility impac t modeling are summarized in Table 4-4.

T ABLE 

### 4-4. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS FOR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2

Unit Emission Rate Scenario Emission Rate
SO 2 NO X PM 10 * (lb/hr) (lb/hr) (lb/hr) JEC Unit 1 Existing Emission Rate 6,938.9 3,972.3 327 .4 Wet Scrubber 1,216.5 3,972.3 327.4 JEC Unit 2 Existing Emission Rate 7,128.2 3,924.0 303 .9 Wet Scrubber 1,216.5 3,924.0 303.9

*PM 10 emissions are calculated based on AP-42 emission fa ctors.


### Comparisons of the existing visibility impacts and the visibility impacts based on the wet scrubber, 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 Δdv, for each Cl ass I area are provided in Tables 4-5 and 4-6 for JEC Unit 1 and J EC Unit 2, respectfully. The visibility improvement associated with the wet scrubber is als o shown in Tables 4-5 and 4-6; this value was calculated as the difference between the existing v isibility impairment and the visibility impairment for the wet scrubber as measured by the 98 th 

### percentile modeled visibility impact.Westar Energy4-5Trinity ConsultantsBART AnalysisTABLE4-5.SUMMARY OFMODELEDIMPACTS FROMSO2CONTROLVISIBILITYIMPACTANALYSIS FORJECUNIT1(2001-2003)Wichita MountainsHercules GladesWildernessCaney CreekWildernessMingo NWRUpper BuffaloWilderness

Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δd v) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement*Existing Emission Rate 1.91 0.51 24 - 1.44 0.47 19- 1.46 0.37 11 - 0.75 0.25 5 - 1.45 0.43 17 -Wet Scrubber1.22 0.34 10 33% 0.78 0.30 6 36% 0.95 0.22 5 42% 0.48 0.16 0 34% 0.88 0.27 6 39%*Improvement is based on the 98thpercentile visibilityimpact (Δdv) of thewet scrubber over the existing emission rate.

## Westar Energy 4-6 Trinity Consultants BART Analysis T ABLE 

## 4-6. S UMMARY OF 

## M ODELED 

## I MPACTS FROM 

## SO 2 

## C ONTROL 

## V ISIBILITY 

## I MPACT 

## A NALYSIS FOR 

## JEC U NIT 

## 2 (2001-2003)

Wichita Mountains Hercules Glades

Wilderness Caney Creek

Wilderness Mingo NWR Upper Buffalo
WildernessM a x i m u m I m p a c t ( Δ d v )9 8 % I m pa c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s >0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *



## Existing Emission Rate 1.91 0.51 24 - 1.45 0.46 20 - 1 .47 0.37 11 - 0.75 0.25 5 - 1.47 0.43 17 - Wet Scrubber 1.20 0.33 10 34% 0.77 0.30 4 36% 0.93 0.21 5 43% 0. 47 0.16 0 36% 0.87 0.26 6 40%

*Improvement is based on the 98 th 

### percentile visibility impact (Δdv) of a wet scrubber over the existing emission rate.

### Westar Energy 4-7 Trinity Consultants

BART Analysis As shown in Table 4-5, the operation of a wet scrub ber on JEC Unit 1 results in a 33 to 42 percent improvement (depending on the Class I area) to the existing visibility impairment attributable to this unit. Similarly, as shown in Table 4-6, the operat ion of wet scrubbers on JEC Unit 2 results in a 34 to 43 percent improvement (depending on the Class I ar ea) to the existing visibility impairment attributable to this unit.



## 4.6 P ROPOSED 

## B ART FOR 

## SO 2



### Westar has determined that the SO 2 

### BART emission rate for JEC Unit 1 and Unit 2 is 0. 15 lb/MMBtu based on the operation of wet scrubbers. Westar is proposing to meet this limit for each unit on a 30

- day rolling average, excluding periods of startup, shutdown and malfunction. Compliance will be demonstrated using data from the existing continuou s emissions monitoring systems (CEMS).

### Westar Energy 5-1 Trinity Consultants

BART Analysis 

## 5. JEC NO X 

## BART E VALUATION

The existing maximum daily NO X 

### emission rates that were modeled for the BART appl icability determination are summarized in Table 5-1.

T ABLE 

### 5-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### NO X 

### E MISSION 

### R ATE FOR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2

Heat Input NO X 24-Hour Emission Rate NO X Hourly Emission Rate NO X

Emission Rate (MMBtu/hr) (ton/24-hr) (lb/hr) (lb/MMBtu) JEC Unit 1 8,110 47.7 3,972.3 0.49 JEC Unit 2 8,110 47.1 3,924.0 0.48



## 5.1 I DENTIFICATION OF 

## A VAILABLE 

## R ETROFIT 

## NO X 

## C ONTROL 

## T ECHNOLOGIES
Step 1 of the BART determination is the identificat ion of all available retrofit NO X 

### control technologies. A list of control technologies was o btained by reviewing the U.S. EPA’s Clean Air Technology Center, control equipment vendor informa tion, publicly-available air permits, applications, and technical literature published by the U.S. EPA and the RPOs.

The available retrofit NO X 

### control technologies are summarized in Table 5-2 f or JEC Unit 1 and 2.

T ABLE 

### 5-2. A VAILABLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES FOR 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2

NO X Control Technologies

Flue Gas Recirculation (FGR) Overfire Air (OFA) Combustion Controls

Low NO X Burners (LNB) and Ultra Low NO X Burners (ULNB) Selective Catalytic Reduction (SCR) Post-Combustion Controls Selective Non-Catalytic Reduction (SNCR)



### NO X 

### emissions controls, as listed in Table 5-2, can be categorized as combustion or post-combustion controls. Combustion controls, including flue gas recirculation (FGR), overfire air (OFA), and Low NO X 

### Burners (LNB), reduce the peak flame temperature a nd excess air in the furnace which minimizes NO X 

### formation. Post-combustion controls, such as sele ctive catalytic reduction (SCR) and selective non-catalytic reduction (SNCR) convert NO X 

### in the flue gas to molecular nitrogen and
water.



## 5.2 E LIMINATE 

## T ECHNICALLY 

## I NFEASIBLE 

## NO X 

## C ONTROL 

## T ECHNOLOGIES
Step 2 of the BART determination is to eliminate te chnically infeasible NO X 

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

### Westar Energy 5-2 Trinity Consultants

BART Analysis 

### 5.2.1 C OMBUSTION 

### C ONTROLS



### 5.2.1.1 F LUE 

### G

AS 

### R ECIRCULATION 

### (FGR)

FGR uses flue gas as an inert material to reduce fl ame temperatures. In a typical flue gas recirculation system, flue gas is collected from the heater or stack and returned to the burner via a duct and blo wer. The addition of flue gas reduces the oxygen content of the “combustion air” (air + flue gas) in the burner. The lower oxygen level in the combustion z one reduces flame temperatures; which in turn reduces thermal NO X 

### formation. When operated without additional controls, the NO X 

### control efficiency range for FGR is 30 percent to 50 percent. When coupled with LNB the c ontrol efficiency increases to 50-72 percent. 5 

### This control is a technically feasible option for the control of NO X 

### from JEC Unit 1 and Unit 2.



### 5.2.1.2 O VERFIRE 

### A IR 

### (OFA)

OFA diverts a portion of the total combustion air f rom the burners and injects it through separate air ports above the top level of b urners. Staging of the combustion air creates an initial fuel-rich combust ion zone with a lower peak flame temperature. This reduces the formation of t hermal NO X 

### by lowering combustion temperature and limiting the availabilit y of oxygen in the combustion zone where NO X 

### is most likely to be formed.

OFA as a single NO X 

### control technique may reduce NO X 

### emissions by 25 to 55 percent. When combined with LNB, reductions of up to 60 percent may result. 6 

### This control is a technically feasible option for the control of NO X 

### from JEC Unit 1 and Unit 2.



### 5.2.1.3 L OW AND 

### U LTRA 

### L OW 

### NO X 

### B URNERS



### LNB technology utilizes advanced burner design to r educe NO X 

### formation through the restriction of oxygen, lowering of flam e temperature, and/or reduced residence time. LNB is a staged combustion process that is designed to split fuel combustion into two zones. In the prima ry zone, NO X 

### formation is limited by either one of two methods. Under staged fuel-rich conditions, low oxygen levels limit flame temperatures resulting in less NO X 

### formation. The primary zone is then followed by a secondary zone i n which the incomplete combustion products formed in the primary zone act as reducing agents. Alternatively, under staged fuel-lean conditions, e xcess air will reduce flame temperature to reduce NO X 

### formation. In the secondary zone, combustion

5 "Midwest Regional Planning Organization Boiler Bes t Available Retrofit Technology (BART) Engineering Analysis" MACTEC, March 30, 2005. 6 "Assessment of Control Technology Options for BART -Eligible Sources: Steam Electric Boilers, Industri al Boilers, Cement Plants and Paper and Pulp Facilitie s" Northeast States for Coordinated Air Use Managem ent (NESCAUM), March 2005

### Westar Energy 5-3 Trinity Consultants

BART Analysis products formed in the primary zone act to lower th e local oxygen

concentration, resulting in a decrease in NO X 

### formation. The estimated NO X 

### control efficiency for LNBs in high temperature app lications is 25 percent. However when coupled with FGR or SNCR these efficie ncies increase to 50-72 and 50-89 percent, respectively. 7



### ULNBs may incorporate a variety of techniques inclu ding induced FGR, steam injection, or a combination of techniques. These b urners combine the benefits of flue gas recirculation and LNB control technolog ies. Rather than a system of fans and blowers (like FGR), the burner is designed to recirculate hot, oxygen depleted flue gas from the flame or firebox back in to the combustion zone. This leads to a reduction in the average oxygen con centration in the flame without reducing the flame temperature below temper atures necessary for optimal combustion efficiency.

LNBs may also be coupled with neural net systems to further optimize combustion. Neural net systems are computer automa ted systems that measure certain operational parameters associated with comb ustion. Based on these measured parameters, the neural net systems can eit her automatically adjust operational parameters to achieve optimal operation or provide recommendations to operators of changes to boiler c ontrol elements. By accepting the recommendations, NO X 

### and unit heat rate can be optimized for best overall performance.

The estimated NO X 

### control efficiency for ULNBs in high temperature applications is 50 percent. Newer designs have yie lded efficiencies of between 75-85 percent. When coupled with SCR, efficiencies in the range of 85-97 percent can be obtained. 8



### LNB systems are technically feasible for tangential and wall-fired boilers of various sizes, but are not feasible for other boile r types such as cyclone or stoker. 9 

### LNB systems are technically feasible for the cont rol of NO x 

### from JEC Unit 1 and Unit 2.



### 5.2.2 P OST 

### C OMBUSTION 

### C ONTROLS



### 5.2.2.1 S ELECTIVE 

### C ATALYTIC 

### R EDUCTION



### SCR refers to the process in which NO x 

### is reduced by ammonia over a heterogeneous catalyst in the presence of oxygen. The process is termed

7 "Midwest Regional Planning Organization Boiler Bes t Available Retrofit Technology (BART) Engineering Analysis" MACTEC, March 30, 2005. 8 Interim White Paper “Source Category: Electric Gen erating Units” Midwest RPO Candidate Control Measur es, December 9, 2005 9 AP 42, Fifth Edition, Volume I Chapter 1 Section 1. 1.4.3

### Westar Energy 5-4 Trinity Consultants

BART Analysis selective because the ammonia preferentially reacts with NO x 

### rather than

oxygen, although the oxygen enhances the reaction a nd is a necessary component of the process. The overall reactions ca n be written:

4NO + 4NH 3 + O 2 à 4N 2 + 6H 2 O
2NO 2 + 4NH 3 + O 2 à 3N 2 + 6H 2 O



### The SCR process requires a reactor, a catalyst, and an ammonia storage and injection system. The effectiveness of an SCR syst em is dependent on a variety of factors, including the inlet NO x 

### concentration, the exhaust temperature, the ammonia injection rate, and the type of catalyst. The NO X 

### control efficiency range for SCR is 70 to 90 percent. 10 

### This control is a technically feasible option for the control of NO X 

### from JEC Unit 1 and Unit 2.



### 5.2.2.2 S ELECTIVE 

### N ON 

### -C ATALYTIC 

### R EDUCTION



### In SNCR systems, a reagent is injected into the flu e gas in the furnace within an appropriate temperature window. The NO x 

### and reagent (ammonia or urea) react to form nitrogen and water. A typical SNCR s ystem consists of reagent storage, multi-level reagent-injection equipment, a nd associated control instrumentation. The SNCR reagent storage and hand ling systems are similar to those for SCR systems. However, because of high er stoichiometric ratios, both ammonia and urea SNCR processes require three or four times as much reagent as SCR systems to achieve similar NO x 

### reductions. The NO X 

### control efficiency range for SNCR is 25 to 50 percent. 11 

### This control is a technically feasible option for the control of NO X 

### from JEC Unit 1 and Unit 2.



## 5.3 R ANK OF 

## T ECHNICALLY FEASIBLE 

## NO X 

## C ONTROL 

## O PTIONS BY



## E FFECTIVENESS
The third step in the BART analysis is to rank the technically feasible options according to effectiveness. Table 5-3 provides a ranking of the NO x 

### control efficiencies for JEC Unit 1 and JEC Unit 2.

10 Ibid.

11 Interim White Paper “Source Category: Electric Gen erating Units” Midwest RPO Candidate Control Measur es, December 9, 2005.

### Westar Energy 5-5 Trinity Consultants

BART Analysis T ABLE 

### 5-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES

Control Technology Estimated Control Efficiency (%)

SCR ~70-90 LNB Systems ~30-60 FGR ~30-50 OFA ~25-55 LNB Only ~25-50 SNCR ~25-50



## 5.4 E VALUATION OF 

## I MPACTS FOR 

## F EASIBLE 

## NO X 

## C ONTROLS
Step four for the BART analysis procedure is the im pact analysis. The BART determination guidelines list four factors to be considered in th e 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



### The capital costs, operating costs, and cost effect iveness of LNB systems and SCR were estimated for the cost analysis. LNB systems refer to a control system which includes LNB and possibly OFA or neural net systems. These control options were included in the analysis because they provide the highest levels of control and are commonly used for NO X 

### control in large utility boilers.

Control Costs The capital cost of the LNB systems for JEC Unit 1 and Unit 2 was estimated based on Westar’s experience with the capital costs for a si milar project on JEC Unit 3, and the operating costs were estimated using an EPA cost es timate method. 12 

### Westar is still experiencing what type of operating costs to expect from a LNB system project, but project specific data from which to base the annual operati ng costs over the operating life of the system does not yet exist, so an EPA estimate was r elied upon.

The capital cost for the SCR was determined from re cent SCR installation experience. The operating and maintenance costs for the SCR were es timated using an EPA cost method. 13 

### The EPA estimates for the operating and maintenance costs are considered to be study grade, which is +/- 30 percent accuracy.

12 Nitrogen Oxides (NO X ), Why and How They Are Controlled . EPA 456/F-99-006R, November 1999. 13 Cost of Selective Catalytic Reduction (SCR) Applica tion for NOx Control on Coal-fired Boiler. EPA 600/R01/087, October 2001.

### Westar Energy 5-6 Trinity Consultants

BART Analysis

The capital costs were annualized over a 20-year pe riod and then added to the annual operating costs to obtain the total annualized cost s for each technology.

Annual Tons Reduced The annual tons reduced that were used in the cost effectiveness calculations were estimated by subtracting the estimated controlled a nnual emission rates from the existing annual emission rates. The existing annual emissi on rates were the highest 365 day rolling totals as determined from CEMS data from 2002-2004.

The controlled annual emission rates were estimated based on NO X 

### emission rates of 0.15 lb/MMBtu for LNB systems and 0.10 lb/MMBtu for SCR. Th ese emission rates were multiplied by the maximum hourly heat input (8,110 MMBtu/hr) and then multiplied by the annual number of operating hours. The annual n umber of operating hours was 8,760. The annual operating hours were based on the maximu m number of annual operating hours of JEC Unit 1and Unit 2 from 2002-2004. The maximu m annual operating time was approximately 94 percent for JEC Unit 1 and 96 perc ent for JEC Unit 2; therefore, an estimated 100 percent factor was used for both unit s. A sample of the controlled annual emission rate is shown as follows for a LNB system for JEC Unit 1:



### % 100 * 000 ,2 * 760 ,8 * / 110 ,8 * / 15 .0 lb ton

yr hrs hr MMBtu MMBtu lb =5,220 tpy

Cost Effectiveness The cost effectiveness for the remaining two contro l options was determined by dividing the annual cost by the annual tons reduced. The in cremental cost effectiveness was also calculated for the two control options. In this ca se, the incremental cost analysis was performed to show the incremental increase in costs between the SCR and the LNB system. The costs are summarized for JEC Unit 1 an d JEC Unit 2 in Tables 5-4 and 5-5, respectively.

In the BART guidelines, EPA calculated that for all types of boilers other than cyclone boilers, combustion control technology is generallymore cost-effective than postcombustion controls. EPA estimates that approximat ely 75 percent of the BART units (non-cyclone) could meet the presumptive NO X 

### limits at a cost of $100 to $1,000 per ton of NO X 

### removed based on the use of combustion control tec hnology. For the units that could not meet the presumptive limits using combustion co ntrol technology, EPA estimates that almost all of these sources could meet the presumpt ive limits using advanced combustion controls; the EPA estimates that the cost of such c ontrols are usually less than $1,500 per ton removed.

Tables 5-4 and 5-5 indicate that the cost effective ness of LNB systems for JEC Unit 1 and Unit 2 is less than $1,500 per ton of NO X 

### removed. Tables 5-4 and 5-5 also indicate that the costs for SCR for JEC Unit 1 and Unit 2 are ove r $1500 per ton of NO X 

### removed (JEC Unit 1 SCR cost = $2,211/ton and JEC Unit 2 SCR cost = $1,738/ton). Additionally, the

### Westar Energy 5-7 Trinity Consultants

BART Analysis

incremental costs of the SCR over the LNB systems a re greater than $6,600 per ton of NO X 

### removed for JEC Unit 1 and Unit 2. Westar believes that the incremental costs are excessive.Westar Energy5-8Trinity ConsultantsBART AnalysisTABLE5-4.SUMMARY OFCOSTEFFECTIVENESS FORJECUNIT1NOXCONTROLSCurrentAnnualEmissionRateControlledAnnualEmissionRateAnnualEmissionsReducedCapitalCostAnnualizedCapitalCost*AnnualizedFixedO&M*AnnualizedVariableO&M*TotalAnnualizedCost*CostEffectivenessIncrementalCost(tpy) (tpy) (ton/yr) ($) ($/yr) ($/yr) ($/yr) ($/yr) ($/ton) ($/ton)LNB System 9,524 5,220† 4,304 11,500,000 1,350,790506,709 0 1,857,499 432 -SCR 9,524 3,480‡6,044 81,000,000 9,514,260 338,492 3,510,068 13,362,820 2,211 6,613*The costs are annualized in 2006 dollars.†The LNB system represents an emission rate of 0.15lb/MMBtu.‡The SCR represents an emission rate of 0.1 lb/MMBtu.TABLE5-5.SUMMARY OFCOSTEFFECTIVENESS FORJECUNIT2NOXCONTROLSCurrentAnnualEmissionRateControlledAnnualEmissionRateReducedEmissionsCapitalCostAnnualizedCapitalCost*AnnualizedFixedO&M*AnnualizedVariableO&M*TotalAnnualizedCost*CostEffectivenessIncrementalCost(tpy) (tpy) (ton/yr) ($) ($/yr) ($/yr) ($/yr) ($/yr) ($/ton) ($/ton)LNB System** 11,115 5,156† 5,959 11,500,000 1,350,790 506,709 0 1,857,499 312 -SCR*** 11,115 3,437‡7,677 81,000,000 9,514,260 338,420 3,493,270 13,345,950 1,738 6,684*The costs are annualized in 2006 dollars.†The LNB w/ OFA emission rate represents an emissionrate of 0.15 lb/MMBtu.‡ The SCR represents an emission rate of 0.1 lb/MMBtu.

### Westar Energy 5-9 Trinity Consultants

BART Analysis 

### 5.4.2 E NERGY 

### I MPACTS 

### & N ON 

### -A IR 

### I MPACTS



### SCR systems require electricity to operate the anci llary equipment. Additionally, the SCR can potentially cause significant environmental imp acts related to the usage and storage of ammonia. Storage of aqueous ammonia above 10,000 l bs is regulated by a risk management program (RMP), since the accidental relea se of ammonia has the potential to cause serious injury and death to persons in the vi cinity of the release. Ammonia can also be emitted in the exhaust of boilers that operate w ith SCR or SNCR for NO X 

### control due to ammonia slip.

Ammonia slip from SCR and SNCR systems occurs eithe r from ammonia injection at temperatures too low for effective reaction with NO x 

### , leading to an excess of unreacted ammonia, or from over-injection of reagent leading to uneven distribution; which also leads to an excess of unreacted ammonia. Ammonia r eleased from SCR and SNCR systems will react with sulfates and nitrates in th e atmosphere to form ammonium sulfate. Together, ammonium sulfate and ammonium nitrate are the predominant sources of regional haze.



### 5.4.3 R EMAINING 

### U SEFUL 

### L IFE



### The remaining useful life of JEC Unit 1 and Unit 2 do not impact the annualized capital costs of potential controls because the useful live s of the units are anticipated to be at least as long as the capital cost recovery period, which is 20 years.



## 5.5 E VALUATION OF 

## V ISIBILITY 

## I MPACT OF 

## F EASIBLE 

## NO X 

## C ONTROLS
The final impact analysis was conducted to assess t he visibility improvement for existing emission rates when compared to the emission rates associate d with SCR and LNB systems. The existing emission rates and emission rates associated with S CR and LNB systems were modeled using CALPUFF. The existing emission rates are the same rates that were modeled for the BART applicability analysis. The emission rates associa ted with SCR and LNB systems for JEC Unit 1 and Unit 2 are the presumptive limit of 0.15 lb/MMBtu for LNB systems and 0.10 lb/MMBtu for SCR. These rates were multiplied by the maximum hourly h eat inputs for JEC Unit 1 and Unit 2 to obtain the modeled hourly emission rate. A sample calcula tion of the NO X 

### emission rate associated with a LNB system for JEC Unit 1 is provided as follows:



### HI E * = 1,216.5 lb/hr Where: E (emission rate of LNB system) = 0.15 lb/MMBtu HI (hourly heat input) = 8,110 MMBtu/hr

### Westar Energy 5-10 Trinity Consultants

BART Analysis T ABLE 

### 5-6. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS FOR 

### JEC U NIT 

### 1 AND 

### JEC U NIT 

### 2

Unit Emission Rate Scenario Emission Rate
SO 2 NO X PM 10 (lb/hr) (lb/hr) (lb/hr) JEC Unit 1 Existing Emission Rate 6,938.9 3,972.3 327 .4 LNB System 6,938.9 1,216.5 327.4
SCR 6,938.9 811.0 327.4 JEC Unit 2 Existing Emission Rate 7,128.2 3,924.0 303 .9 LNB System 7,128.2 1,216.5 303.9
SCR 7,128.2 811.0 303.9



### Comparisons of the existing visibility impacts and the visibility impacts associated with SCR and LNB systems, including the maximum modeled visibili ty impact, 98 th 

### percentile modeled visibility impact, and the number of days with a m odeled visibility impact greater than 0.5 Δdv, for each Class I area are provided in Tables 5-8 and 5-9 for JEC U nit 1 and JEC Unit 2, respectively. The visibility improvement associated with SCR and LNB systems are also shown in Tables 5-7 and 5-8; this value was calculated as the difference between the existi ng visibility impairment and the visibility impairment associated with SCR and LNB systems as m easured by the 98 th 

### percentile modeled visibility impact.Westar Energy5-11Trinity ConsultantsBART AnalysisTABLE5-7.SUMMARY OFMODELEDIMPACTS FROMNOXCONTROLVISIBILITYIMPACTANALYSIS FORJECUNIT1(2001-2003)Wichita MountainsHercules GladesWildernessCaney CreekWildernessMingo NWRUpper BuffaloWilderness

Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv ) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement*Existing Emission Rate 1.91 0.51 24 - 1.44 0.47 19- 1.46 0.37 11 - 0.75 0.25 5 - 1.45 0.43 17 -LNB System1.34 0.34 11 33% 1.08 0.35 4 26% 1.04 0.25 6 33% 0.50 0.18 0 27% 1.23 0.32 4 25%SCR1.26 0.31 11 38% 1.03 0.32 4 31% 0.97 0.23 6 39% 0.46 0.18 0 30% 1.20 0.31 3 29%*Improvement is based on the 98thpercentile impact visibilityimpact (Δdv) ofan LNB system and SCR over the existing emission rate.

## Westar Energy 5-12 Trinity Consultants BART Analysis T ABLE 

## 5-8. S UMMARY OF 

## M ODELED 

## I MPACTS FROM 

## NO X 

## C ONTROL 

## V ISIBILITY 

## I MPACT 

## A NALYSIS FOR 

## JEC U NIT 

## 2 (2001-2003)

Wichita Mountains Hercules Glades

Wilderness Caney Creek

Wilderness Mingo NWR Upper Buffalo
WildernessM a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v)9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *M a x i m u m I m p a c t ( Δ d v )9 8 % I m p a c t ( Δ d v )# D a y s > 0 . 5 Δ d vV i s i b i l i t y I m p r o v e m e n t *



## Existing Emission Rate 1.91 0.51 24 - 1.45 0.46 20 - 1.47 0.37 11 - 0.75 0.25 5 - 1.47 0.43 17 - LNB System 1.36 0.34 11 32% 1.10 0.34 4 25% 1.05 0.25 6 32% 0. 50 0.19 1 25% 1.24 0.33 4 23%
SCR 1.27 0.32 11 37% 1.04 0.33 4 28% 0.99 0.23 6 38% 0. 47 0.18 0 29% 1.21 0.31 3 28%

*Improvement is based on the 98 th 

### percentile impact visibility impact (Δdv) of an LNB system and SCR over the existing emission ra te.

### Westar Energy 5-13 Trinity Consultants

BART Analysis As shown in Table 5-7, the installation of a LNB sy stem on JEC Unit 1 results in a 25 to 33 percent improvement (depending on the Class I area) to the existing visibility impairment attributable to this unit. The installation of SCR results in 29 to 39 percent visibility improvement. Similarly, as show n in Table 5-8, the installation of a LNB system on J EC Unit 2 results in a 23 to 32 percent improvement (depending on the Class I area) to the existing visibility impairment attributable to this unit. The installation of SCR results in 28 to 38 percent visibility improvement. In short, the visibility improvement based on the installation of LNB systems for JEC Unit 1 and JEC Unit 2 is only slightly better than the visibility improvemen t based on the installation of SCR for the units. The slight increase in visibility improvement does not offset the large incremental cost of installing SCR over LNB systems.



## 5.6 P ROPOSED 

## B ART FOR 

## NO X



### Westar has determined that the NO X 

### BART emission rate is 0.15 lb/MMBtu for both JEC Uni t 1 and Unit 2. Westar proposes to meet this limit for bot h JEC Unit 1 and Unit 2 by installing LNB systems. Westar has eliminated SCR as BART due to the high i ncremental cost of SCR over LNB systems and the minimal increase in visibility improvement.

### Westar Energy 6-1 Trinity Consultants

BART Analysis 

## 6. GEEC SO 2 

## BART E VALUATION

The existing maximum 24-hour SO 2 

### emission rates that were modeled for the BART appl icability determination are summarized in Table 6-1.

T ABLE 

### 6-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### SO 2 

### E MISSION 

### R ATES

Heat Input SO 2 24-Hour

Emission Rate SO 2 Hourly

Emission Rate SO 2 Emission Rate (MMBtu/hr) (ton/24-hr) (lb/hr) (lb/MMBtu) GEEC Unit 2 4,110 69.2 5,766.7 1.40



## 6.1 I DENTIFICATION OF 

## A VAILABLE 

## R ETROFIT 

## SO 2 

## C ONTROL 

## T ECHNOLOGIES
Step 1 of the BART determination is the identificat ion of all available retrofit SO 2 

### control technologies. A list of control technologies was o btained by reviewing the U.S. EPA’s Clean Air Technology Center, control equipment vendor informa tion, publicly-available air permits, applications, and technical literature published by the U.S. EPA and Regional Planning Organizations (RPOs).

The available retrofit SO 2 

### control technologies are summarized in Table 6-2 f or GEEC Unit 2.

TABLE 

### 6-2. A VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES FOR 

### GEEC U NIT 

### 2

SO 2 Control Technologies
Dry Sorbent Injection Spray Dryer Absorber (SDA) i.e., Semi-Dry Scrubber

Wet Scrubber Circulating Dry Scrubber (CDS) Fuel Switching



## 6.2 E LIMINATE 

## T ECHNICALLY 

## I NFEASIBLE 

## SO 2 

## C ONTROL 

## T ECHNOLOGIES
Step 2 of the BART determination is to eliminate te chnically infeasible SO 2 

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



### 6.2.1 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 g as desulfurization (FGD) systems. FGD applications have not been used historically fo r SO 2 

### control in the U.S. electric industry on oil-fired units. 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

### Westar Energy 6-2 Trinity Consultants

BART Analysis

evaluating the presumptive SO 2 

### emission rate 14 

### 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 on FGD. Therefore, FGDs are considered technically infeasible for the control of SO 2 

### from GEEC Unit 2 and will no longer be considered f or BART.



### 6.2.2 F UEL 

### S WITCHING TO 

### O NE 

### P ERCENT 

### S ULFUR 

### F UEL 

### O IL



### One percent sulfur fuel oil is listed by EPA as the presumptive BART limit for oil-fired units. The one percent sulfur oil is an alternativ e to the No. 6 fuel oil that is currently combusted by GEEC Unit 2. The lower sulfur content of the one percent sulfur fuel oil should result in approximately a 33 percent reducti on in SO 2 

### emissions from GEEC Unit 2 as compared to the combustion of the current No. 6 fuel oil, which contains approximately 1.5 percent sulfur. Fuel switching to 1 percent su lfur fuel oil is a technically feasible option for the control of SO 2 

### from GEEC Unit 2.



## 6.3 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 only remainin g technically feasible control option GEEC Unit 2. Westar has estimated that switching to one percent sulfur oil, consistent with the EPA’s presumptive BART determination for GEEC Unit 2, would result in approximately a 33% reduction in SO 2 

### emissions from GEEC Unit 2.



## 6.4 E VALUATION OF 

## I MPACTS FOR 

## F EASIBLE 

## SO 2 

## C ONTROLS
Step four for the BART analysis procedure is the im pact analysis. The BART determination guidelines list the four factors to be considered i n the impact analysis:

▲ 

### Cost of compliance ▲ 

### Energy impacts ▲ 

### Non-air quality impacts; and ▲ 

### The remaining useful life of the source



### 6.4.1 C OST OF 

### C OMPLIANCE



### The cost effectiveness of switching to one percent sulfur fuel oil has been evaluated.

Control Cost The cost of the fuel switching that was used in the cost effectiveness calculations was determined by calculating the cost of the current N o. 6 fuel oil and determining the increased cost of switching to one percent sulfur f uel oil. It was assumed in this analysis that the fuel switch will not require any capital e xpenses. The fuel costs for 1 percent fuel oil was determined from the most recent fuel costs published by the Energy Information

14 Summary of Comments and Responses on the 2004 and 2 001 Proposed Guidelines for Best Available Retrofit Technology (BART) Determinations Under the Regional Haze Regulations EPA Docket Number OAR-2002-0076.

### Westar Energy 6-3 Trinity Consultants

BART Analysis

Administration. The fuel cost for the No. 6 fuel o il is the market price on February 14, 2007.

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 existing annual emission rates. The existing annual emissio n rates was the highest 365 day rolling totals as determined from CEMS data from 2002-2004. The controlled annual emission rate was estimated by reducing the existing annual emission rate by 33%.

Cost Effectiveness In the BART guidelines, EPA calculated that that th e majority of BART-eligible units could meet the presumptive limits at a cost of $400 to $2,000 per ton of SO 2 

### removed, based on the use of wet scrubbers and SDA systems. Table 6-3 indicates that the cost of switching to 1% sulfur fuel oil is well above this range.Westar Energy6-4Trinity ConsultantsBART AnalysisTABLE6-3.SUMMARY OFCOSTEFFECTIVENESS FORSWITCHINGFROMNO.6OIL TO1%SULFURFUELOILCurrent/UncontrolledAnnual SO2Emission RateControlled AnnualSO2EmissionRateEstimated AnnualSO2Tons ReducedEstimatedSO2Control(tpy) (tpy) (tpy) (%)4,303 2,869 1,434 33.33Estimated No. 6Oil Hourly UsageRate*Estimated No.6 Oil AnnualFuel Usage†No. 6 Oil FuelCostEstimated AnnualNo. 6 Oil FuelCostEstimated 1% SFuel Oil HourlyUsage Rate*Estimated 1% SFuel Oil AnnualFuel Usage‡1% S Fuel OilCostEstimated Annual1% S Fuel OilCostMgal/hr Mgal/yr Cents/gal $/yr Mgal/hr Mgal/yr Cents/gal $/yr27.40* 108,011† 85 91,809,180 27.96‡ 110,215† 117.1129,061,884*4110 MMBtu/hr/150 MMBtu/Mgal†Annual fuel usage is based on the hourly fuel usage rate and 3,942 annual operating hours (Assuming unit operates at an annual 45% capacity factor, 45%* 8,760 hours = 3,942 hours)‡4110 MMBtu/hr/147 MMBtu/MgalAnnual Cost of FuelSwitchingSO2CostEffectiveness($/yr) ($/ton)37,252,704 25,969

### Westar Energy 6-5 Trinity Consultants

BART Analysis



### 6.4.1.1 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS



### There are no energy or non-air quality impacts asso ciated with fuel switching to one percent sulfur fuel oil.



### 6.4.1.2 R EMAINING 

### U SEFUL 

### L IFE



### The remaining useful life of GEEC Unit 2 does not i mpact the annualized cost for this analysis, since it is assumed that fuel sw itching will not require any capital costs.



## 6.5 E VALUATION OF 

## V ISIBILITY 

## I MPACT OF 

## F EASIBLE 

## SO 2 

## C ONTROLS
A final impact analysis was conducted to assess the visibility improvement for existing emission rates when compared to the emission rates associated with the combustion of one percent sulfur fuel oil. The existing emission rate and emission rate associ ated with the combustion of one percent sulfur fuel oil were modeled using CALPUFF. The existing emiss ion rates are the same rates that were modeled for the BART applicability analysis.

The SO 2 

### emission rate associated with the combustion of one percent sulfur fuel oil was calculated by scaling the hourly equivalent of the maximum 24-hou r emission rate for GEEC Unit 2 by the ratio of the one percent sulfur fuel oil content and the cur rent maximum sulfur content (1.5%). The calculation of the SO 2 

### emission rate for the one percent sulfur fuel oil for GEEC Unit 2 is provided as follows:



### Sulfur Sulfur hr lb % 5.1 % 1 * / 676 ,5 = 3,845 lb/hr

The existing hourly equivalent of the maximum 24-ho ur emission rates and the hourly equivalent of the 24-hour emission rates associated with the rema ining control option that was utilized in the visibility impact modeling are summarized in Table 6-4.

T ABLE 

### 6-4. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### SO 2 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS

Unit Emission Rate Scenario Emission Rate
SO 2 NO X PM 10 * (lb/hr) (lb/hr) (lb/hr) GEEC Unit 2 Existing Emission Rate 5,766.7 4,818.3 43 1.5

1 Percent Sulfur Fuel Oil 3,844.5 4,818.3 326.3

*PM 10 emissions are calculated based on AP-42 emission fa ctors.


### Comparisons of the existing visibility impacts and the visibility impacts associated with the combustion of one percent sulfur fuel oil, includin g the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the numbe r of days with a modeled visibility impact greater than 0.5 Δdv, for each Cla ss I area are provided in Table 6-5 for GEEC Unit 2 . The visibility improvement associated with the combustion of one p ercent sulfur fuel oil is also shown in Table 6-5;

### Westar Energy 6-6 Trinity Consultants

BART Analysis this value was calculated as the difference between the existing visibility impairment and the visibility impairment for the remaining control opt ion emission rates as measured by the 98 th 

### percentile modeled visibility impact.Westar Energy6-7Trinity ConsultantsBART AnalysisTABLE6-5.SUMMARY OFMODELEDIMPACTS FROMSO2CONTROLVISIBILITYIMPACTANALYSIS FORGEECUNIT2(2001-2003)Wichita MountainsHercules GladesWildernessCaney CreekWildernessMingo NWRUpper BuffaloWilderness

Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv ) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement*Existing Emission Rate 2.16 1.08 85 - 1.31 0.40 16- 1.07 0.38 14 - 0.71 0.17 4 - 2.22 0.42 16 -One Percent Sulfur Fuel Oil2.02 0.94 71 13% 1.19 0.34 10 13% 0.87 0.34 9 10% 0.65 0.14 2 16% 2.04 0.38 15 10%*Improvement is based on the 98thpercentile impact visibilityimpact (Δdv) ofone percent sulfur fuel oil over the existing emission rate.

### Westar Energy 6-8 Trinity Consultants

BART Analysis As shown in Table 6-5, the combustion of 1 percent sulfur fuel oil results in a 10 to 16 percent improvement (depending on the Class I area) to the existing visibility impairment attributable to GEEC Unit 2.



## 6.6 P ROPOSED 

## B ART FOR 

## SO 2



### Westar has determined that SO 2 

### BART for GEEC Unit 2 is fuel switching to 1 percen t sulfur fuel oil. However, Westar is proposing an alternative to BART for GEEC Unit 2. The alternative control for GEEC Unit 2 is natural gas combustion; the details of this alternative can be found in Section 9.

### Westar Energy 7-1 Trinity Consultants

BART Analysis 

## 7. GEEC NO X 

## BART E VALUATION

The existing maximum daily NO X 

### emission rates that were modeled for the BART appl icability determination are summarized in Table 7-1.

T ABLE 

### 7-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### NO X 

### E MISSION 

### R ATE

Heat Input NO X 24-Hour Emission Rate NO X Hourly Emission Rate NO X

Emission Rate (MMBtu/hr) (ton/24-hr) (lb/hr) (lb/MMBtu) GEEC Unit 2 4,110 57.8 4,818.3 1.17



## 7.1 I DENTIFICATION OF 

## A VAILABLE 

## R ETROFIT 

## NO X 

## C ONTROL 

## T ECHNOLOGIES
Step 1 of the BART determination is the identificat ion of all available retrofit NO X 

### control technologies. A list of control technologies was o btained by reviewing the U.S. EPA’s Clean Air Technology Center, control equipment vendor informa tion, publicly-available air permits, applications, and technical literature published by the U.S. EPA and the RPOs.

The available retrofit NO X 

### control technologies are summarized in Table 7-2 f or GEEC Unit 2.

T ABLE 

### 7-2. A VAILABLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES FOR 

### GEEC U NIT 

### 2

NO X Control Technologies

Flue Gas Recirculation (FGR) Overfire Air (OFA) Combustion Controls

Low NO X Burners (LNB) and Ultra Low NO X Burners (ULNB) Selective Catalytic Reduction (SCR) Post-Combustion Controls Selective Non-Catalytic Reduction (SNCR)



### NO X 

### emissions controls, as listed in Table 5-2, can be categorized as combustion or post-combustion controls. Combustion controls, including flue gas recirculation (FGR), overfire air (OFA), and Low NO X 

### Burners (LNB), reduce the peak flame temperature a nd excess air in the furnace which minimizes NO X 

### formation. Post-combustion controls, such as sele ctive catalytic reduction (SCR) and selective non-catalytic reduction (SNCR) convert NO X 

### in the flue gas to molecular nitrogen and
water.



## 7.2 E LIMINATE 

## T ECHNICALLY 

## I NFEASIBLE 

## NO X 

## C ONTROL 

## T ECHNOLOGIES
Step 2 of the BART determination is to eliminate te chnically infeasible NO X 

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

### Westar Energy 7-2 Trinity Consultants

BART Analysis 

### 7.2.1 C OMBUSTION 

### C ONTROLS



### 7.2.1.1 F LUE 

### G

AS 

### R ECIRCULATION 

### (FGR)
FGR uses flue gas as an inert material to reduce fl ame temperatures. In a typical flue gas recirculation system, flue gas is collected from the heater or stack and returned to the burner via a duct and blo wer. The addition of flue gas reduces the oxygen content of the “combustion air” (air + flue gas) in the burner. The lower oxygen level in the combustion z one reduces flame temperatures; which in turn reduces thermal NO X 

### formation. When operated without additional controls, the NO X 

### control efficiency range for FGR is 30 percent to 50 percent. When coupled with LNB the c ontrol efficiency increases to 50-72 percent. 15 

### This control is a technically feasible option for the control of NO X 

### from GEEC Unit 2.



### 7.2.1.2 O VERFIRE 

### A IR 

### (OFA)

OFA diverts a portion of the total combustion air f rom the burners and injects it through separate air ports above the top level of b urners. Staging of the combustion air creates an initial fuel-rich combust ion zone with a lower peak flame temperature. This reduces the formation of t hermal NO X 

### by lowering combustion temperature and limiting the availabilit y of oxygen in the combustion zone where NO X 

### is most likely to be formed.

OFA as a single NO X 

### control technique may reduce NO X 

### emissions by 25 to 55 percent. When combined with LNB, reductions of up to 60 percent may result. 16 

### This control is a technically feasible option for the control of NO X 

### from GEEC Unit 2.



### 7.2.1.3 L OW AND 

### U LTRA 

### L OW 

### NO X 

### B URNERS



### LNB technology utilizes advanced burner design to r educe NO X 

### formation through the restriction of oxygen, lowering of flam e temperature, and/or reduced residence time. LNB is a staged combustion process that is designed to split fuel combustion into two zones. In the prima ry zone, NO X 

### formation is limited by either one of two methods. Under staged fuel-rich conditions, low oxygen levels limit flame temperatures resulting in less NO X 

### formation. The primary zone is then followed by a secondary zone i n which the incomplete combustion products formed in the primary zone act as reducing agents. Alternatively, under staged fuel-lean conditions, e xcess air will reduce flame temperature to reduce NO X 

### formation. In the secondary zone, combustion products formed in the primary zone act to lower th e local oxygen concentration, resulting in a decrease in NO X 

### formation. The estimated NO X 

### control efficiency for LNBs in high temperature app lications is 25 percent.

15 "Midwest Regional Planning Organization Boiler Bes t Available Retrofit Technology (BART) Engineering Analysis" MACTEC, March 30, 2005.

16 "Assessment of Control Technology Options for BART -Eligible Sources: Steam Electric Boilers, Industri al Boilers, Cement Plants and Paper and Pulp Facilitie s" Northeast States for Coordinated Air Use Managem ent (NESCAUM), March 2005

### Westar Energy 7-3 Trinity Consultants

BART Analysis However when coupled with FGR or SNCR these efficie ncies increase to 50-72

and 50-89 percent, respectively. 17



### ULNBs may incorporate a variety of techniques inclu ding induced FGR, steam injection, or a combination of techniques. These b urners combine the benefits of flue gas recirculation and LNB control technolog ies. Rather than a system of fans and blowers (like FGR), the burner is designed to recirculate hot, oxygen depleted flue gas from the flame or firebox back in to the combustion zone. This leads to a reduction in the average oxygen con centration in the flame without reducing the flame temperature below temper atures necessary for optimal combustion efficiency.

LNBs may also be coupled with neural net systems to further optimize combustion. Neural net systems are computer automa ted systems that measure certain operational parameters associated with comb ustion. Based on these measured parameters, the neural net systems can eit her automatically adjust operational parameters to achieve optimal operation or provide recommendations to operators of changes to boiler c ontrol elements. By accepting the recommendations, NO X 

### and unit heat rate can be optimized for best overall performance.

The estimated NO X 

### control efficiency for ULNBs in high temperature applications is 50 percent. Newer designs have yie lded efficiencies of between 75-85 percent. When coupled with SCR, efficiencies in the range of 85-97 percent can be obtained. 18



### LNB systems are technically feasible for tangential and wall-fired boilers of various sizes, but are not feasible for other boile r types such as cyclone or stoker. 19 

### LNB systems are technically feasible for the cont rol of NO x 

### from GEEC Unit 2.



### 7.2.2 P OST 

### C OMBUSTION 

### C ONTROLS



### 7.2.2.1 S ELECTIVE 

### C ATALYTIC 

### R EDUCTION



### SCR refers to the process in which NO x 

### is reduced by ammonia over a heterogeneous catalyst in the presence of oxygen. The process is termed selective because the ammonia preferentially reacts with NO x 

### rather than oxygen, although the oxygen enhances the reaction a nd is a necessary component of the process. The overall reactions ca n be written:

4NO + 4NH 3 + O 2 à 4N 2 + 6H 2 O

17 "Midwest Regional Planning Organization Boiler Bes t Available Retrofit Technology (BART) Engineering Analysis" MACTEC, March 30, 2005.

18 Interim White Paper “Source Category: Electric Gen erating Units” Midwest RPO Candidate Control Measur es, December 9, 2005 19 AP 42, Fifth Edition, Volume I Chapter 1 Section 1. 1.4.3

### Westar Energy 7-4 Trinity Consultants

BART Analysis 2NO 2 + 4NH 3 + O 2 à 3N 2 + 6H 2 O



### The SCR process requires a reactor, a catalyst, and an ammonia storage and injection system. The effectiveness of an SCR syst em is dependent on a variety of factors, including the inlet NO x 

### concentration, the exhaust temperature, the ammonia injection rate, and the type of catalyst. The NO X 

### control efficiency range for SCR is 70 to 90 percent. 20 

### This control is a technically feasible option for the control of NO X 

### from GEEC Unit 2.



### 7.2.2.2 S ELECTIVE 

### N ON 

### -C ATALYTIC 

### R EDUCTION



### In SNCR systems, a reagent is injected into the flu e gas in the furnace within an appropriate temperature window. The NO x 

### and reagent (ammonia or urea) react to form nitrogen and water. A typical SNCR s ystem consists of reagent storage, multi-level reagent-injection equipment, a nd associated control instrumentation. The SNCR reagent storage and hand ling systems are similar to those for SCR systems. However, because of high er stoichiometric ratios, both ammonia and urea SNCR processes require three or four times as much reagent as SCR systems to achieve similar NO x 

### reductions. The NO X 

### control efficiency range for SNCR is 25 to 50 percent. 21 

### This control is a technically feasible option for the control of NO X 

### from GEEC Unit 2.



## 7.3 R ANK OF 

## T ECHNICALLY FEASIBLE 

## NO X 

## C ONTROL 

## O PTIONS BY



## E FFECTIVENESS
The third step in the BART analysis is to rank the technically feasible options according to effectiveness. Table 7-3 provides a ranking of th e control efficiencies for the controls listed in t he previous section for GEEC 2.

20 Ibid. 21 Interim White Paper “Source Category: Electric Gen erating Units” Midwest RPO Candidate Control Measur es, December 9, 2005.

### Westar Energy 7-5 Trinity Consultants

BART Analysis T ABLE 

### 7-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES

Control Technology Estimated Control Efficiency (%)

SCR ~70-90 LNB Systems ~30-60 FGR ~30-50 OFA ~25-55 LNB Only ~25-50 SNCR ~25-50



## 7.4 E VALUATION OF 

## I MPACTS FOR 

## F EASIBLE 

## NO X 

## C ONTROLS
Step four for the BART analysis procedure is the im pact analysis. The BART determination guidelines list four factors to be considered in th e 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



### As mentioned in Section 2 of this report, EPA has c oncluded that “for oil-fired and gasfired EGUs larger than 200 MW, we believe that insta llation of current combustion control technology to control NO X 

### is generally highly cost-effective and should be c onsidered in your determination of BART for these sources”. Thu s, Westar is proposing that BART for GEEC Unit 2 is the operation of LNB.

For purposes of this 5 factor analysis, the capital costs, operating costs, and cost effectiveness of an SCR have been estimated. This control option is the only control option included in the analysis because it provides the highest level of NO x 

### control and it is the only technology with control efficiency higher than that of LNB systems. Should the analysis conclude that SCR is not BART, the next be st control to SCR is LNB, and this will be selected as BART.

Control Costs The capital cost and operating costs of SCR were es timated based on recent SCR installation experience. The capital costs were an nualized over a 20-year period and then added to the annual operating costs to obtain the t otal annualized costs.

Annual Tons Reduced The annual tons reduced that were used in the cost effectiveness calculations were estimated by subtracting the estimated controlled a nnual emission rates from the existing

### Westar Energy 7-6 Trinity Consultants

BART Analysis

annual emission rates. The existing annual emissio n rates were the highest 365 day rolling totals as determined from CEMS data from 2002-2004.

The controlled annual emission rates were estimated by multiplying an estimated control efficiency for the SCR (90 percent) by the existing annual emission rates. A sample of the controlled annual emission rate is shown as follows :


### %) 90 % 100 (* 352 ,2

− tpy =235 tpy

Cost Effectiveness The cost effectiveness for the SCR was determined b y dividing the annual cost by the annual tons reduced. The cost is summarized in Tab le 7-4. The cost of the SCR is greater than $5,300/ton of NO X 

### removed. Westar believes this cost is excessive.Westar Energy7-7Trinity ConsultantsBART AnalysisTABLE7-4.SUMMARY OFCOSTEFFECTIVENESS FORGEECUNIT2NOXCONTROLSCurrentAnnualEmissionRateControlledAnnualEmissionRate*EstimatedReducedEmissions Capital Cost†AnnualizedCapitalCost‡AnnualizedFixedO&M‡AnnualizedVariableO&M‡TotalAnnualizedCostCostEffectiveness(tpy) (tpy) (ton/yr) ($) ($/yr) ($/yr) ($/yr) ($/yr) ($/ton)SCR 2,352 235 2,117 81,000,000 9,514,260 218,932 1,627,628 11,360,820 5,367*The annual emission rate represents a control efficiency of 90%.†The SCR capital cost was determined from recent SCR installation experience‡The costs are annualized in 2006 dollars.

### Westar Energy 7-8 Trinity Consultants

BART Analysis 

### 7.4.2 E NERGY 

### I MPACTS 

### & N ON 

### -A IR 

### I MPACTS



### SCR systems require electricity to operate the anci llary equipment. Additionally, the SCR can potentially cause significant environmental imp acts related to the usage and storage of ammonia. Storage of aqueous ammonia above 10,000 l bs is regulated by a risk management program (RMP), since the accidental relea se of ammonia has the potential to cause serious injury and death to persons in the vi cinity of the release. Ammonia can also be emitted in the exhaust of boilers that operate w ith SCR or SNCR for NO X 

### control due to ammonia slip.

Ammonia slip from SCR and SNCR systems occurs eithe r from ammonia injection at temperatures too low for effective reaction with NO x 

### , leading to an excess of unreacted ammonia, or from over-injection of reagent leading to uneven distribution; which also leads to an excess of unreacted ammonia. Ammonia r eleased from SCR and SNCR systems will react with sulfates and nitrates in th e atmosphere to form ammonium sulfate. Together, ammonium sulfate and ammonium nitrate are the predominant sources of regional haze.



### 7.4.3 R EMAINING 

### U SEFUL 

### L IFE



### The remaining useful life of GEEC Unit 2 does not i mpact the annualized capital costs of potential controls because the useful life of the u nit is anticipated to be at least as long as the capital cost recovery period, which is 20 years

.



## 7.5 E VALUATION OF 

## V ISIBILITY 

## I MPACT OF 

## F EASIBLE 

## NO X 

## C ONTROLS
The final impact analysis was conducted to assess t he visibility improvement for existing emission rates when compared to the emission rates associate d with SCR. The existing emission rates and emission rates associated with SCR were modeled usi ng CALPUFF. The existing emission rates are the same rates that were modeled for the BART appli cability analysis.

The emission rate associated with the SCR for GEEC Unit 2 is 0.12 lb/MMBtu. The emission rate for SCR was determined by reducing the existing hourly equivalent of the maximum 24-hour emission rate by a control efficiency of 90 percent.

The existing hourly equivalent of the maximum 24-ho ur emission rates and the hourly equivalent of the 24-hour emission rates associated with SCR are summarized in Table 7-5.

### Westar Energy 7-9 Trinity Consultants

BART Analysis T ABLE 

### 7-5. S UMMARY OF 

### E MISSION 

### R ATES 

### M ODELED 

### I N 

### NO X 

### C ONTROL 

### V ISIBILITY 

### I MPACT 

### A NALYSIS

Unit Emission Rate Scenario Emission Rate
SO 2 NO X PM 10 (lb/hr) (lb/hr) (lb/hr) GEEC Unit 2 Existing Emission Rate 5,766.7 4,818.3 43 1.5 SCR 5,766.7 481.8 431.5



### Comparisons of the existing visibility impacts and the visibility impacts associated with SCR, including the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the number of days with a modeled visibility impact gre ater than 0.5 Δdv, for each Clas s I area are provided in Table 7-6 for GEEC Unit 2. The visibil ity improvement associated with SCR is also shown in Table 7-6; this value was calculated as th e difference between the existing visibility impairment and the visibility impairment for the re maining control options as measured by the 98 th 

### percentile modeled visibility impact.Westar Energy7-10Trinity ConsultantsBART AnalysisTABLE7-6.SUMMARY OFMODELEDIMPACTS FROMNOXCONTROLVISIBILITYIMPACTANALYSIS FORGEECUNIT2(2001-2003)Wichita MountainsHercules GladesWildernessCaney CreekWildernessMingo NWRUpper BuffaloWilderness

Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv ) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement*Existing Emission Rate 2.16 1.08 85-1.31 0.40 16-1.07 0.38 14-0.71 0.17 4-2.22 0.42 16-SCR1.34 0.60 28 45% 1.09 0.20 5 51% 0.65 0.21 4 46% 0.34 0.09 0 48% 1.07 0.20 8 51%*Improvement is based on the 98thpercentile impact visibilityimpact (Δdv) ofSCR over the existing emission rate.

### Westar Energy 7-11 Trinity Consultants

BART Analysis As shown in Table 7-6, the installation of SCR resu lts in 45 to 51 percent visibility improvement (depending on the Class I area) to the existing vis ibility impairment attributable to GEEC Unit 2. However, as documented in Table 7-4, the cost of SC R for this unit is estimated at over $5,300/ton. This large cost does not offset the visibility impr ovement that would be obtained by controlling NO x 

### using an SCR. Further, the future visibility impai rment attributable to GEEC Unit 2 will be significantly improved from the existing visibility impairment based on the switch from the combustion of No. 6 fuel oil to the combustion of n atural gas, and the large corresponding reduction in SO 2 

### emissions.



## 7.6 P ROPOSED 

## B ART FOR 

## NO X 

### Since SCR is not cost effective for GEEC Unit 2, We star has determined that NO x 

### BART for GEEC Unit 2 is a LNB. However, Westar is proposing an a lternative to BART for GEEC Unit 2. The alternative control for GEEC Unit 2 is natural gas combustion; the details of this alternative can be found in Section 9.

### Westar Energy 8-1 Trinity Consultants

BART Analysis 

## 8. PM 10 

## BART E VALUATION

The primary source of PM from JEC Unit 1 and Unit 2 is the fly ash in the flue gas. Other sources of PM include unburned carbon present in the flue gas, which is the result of incomplete combustion, and reactions of SO 2 

### and NO X 

### compounds to form fine PM in the form of nitrates, sulfur trioxide, and sulfates. PM emissions from JEC Unit 1 and Unit 2 are currently controlled by electrostatic precipitators (ESP). Similarly, PM emissions from G EEC Unit 2 are particles generated during the combustion of the No. 6 fuel oil. PM emissions from GEEC Unit 2 are currently uncontrolled.

The maximum daily PM 10 

### emission rates that were modeled for the BART appl icability determination are summarized in Table 8-1.

T ABLE 

### 8-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### PM 10 

### E MISSION 

### R ATE

Heat Input PM 10 24-Hr

Emission Rate PM 10 Hourly

Emission Rate PM 10 Emission Rate (MMBtu/hr) (ton/24-hr) (lb/hr) (lb/MMBtu) JEC Unit 1 8,110 3.93 327.4 0.04 JEC Unit 2 8,110 3.65 303.9 0.04 GEEC Unit 2 4,110 5.2 431.5 0.10



### From Table 8-1 it can be seen that the current PM 10 

### emission rates for JEC Unit 1 and Unit 2 and GEEC Unit 2 are much less than the current emission rates of SO 2 

### and NO X 

### . The low PM 10 

### emission rates correspond to low visibility impacts attribut able to PM 10 

### when compared to the impacts attributable to SO 2 

### and NO X 

### , from JEC Unit 1 and Unit 2 and GEEC Unit 2, as sh own in Tables 8-2 and 8-3.

T ABLE 

### 8-2. VAP V ISIBILITY 

### I MPAIRMENT 

### C ONTRIBUTIONS FROM 

### JEC U NIT 

### 1 AND 

### U NIT 

### 2 (2001- 2003)

Visibility

Impairment Attributable to SO 4 1 Visibility

Impairment Attributable to NO 3 2 Visibility

Impairment Attributable to PM 10 2 (%) (%) (%) Wichita Mountains Wilderness 51.13 48.28 0.59 Hercules Glades Wilderness 38.21 60.92 0.87 Caney Creek Wilderness 40.79 57.87 1.35 Mingo Wildlife 43.81 55.53 0.65 Upper Buffalo Wilderness 39.6 59.22 1.18 1 The visibility impairment attributable to SO 4 is primarily from SO 2 emissions. A very small portion is from SO 4 emitted as condensable particulate. 2 The visibility impairment attributable to NO 3 is entirely from NO X emissions. 3 The visibility impairment attributable to PM 10 is the sum of the visibility impairment attributable to all modeled primary PM species (PMc, PMf, EC, and SOA).

### Westar Energy 8-2 Trinity Consultants

BART Analysis T ABLE 

### 8-3. V ISIBILITY 

### I MPAIRMENT 

### C ONTRIBUTIONS FROM 

### GEEC U NIT 

### 2(2001-2003)

Visibility

Impairment Attributable to SO 4 1 Visibility

Impairment Attributable to NO 3 2 Visibility

Impairment Attributable to PM 10 2 (%) (%) (%) Wichita Mountains Wilderness 29.29 67.54 3.17 Hercules Glades Wilderness 41.15 57.14 1.71 Caney Creek Wilderness 26.11 71.72 2.16 Mingo Wildlife 63.14 34.96 1.89 Upper Buffalo Wilderness 35.67 62.6 1.71 1 The visibility impairment attributable to SO 4 is primarily from SO 2 emissions. A very small portion is from SO 4 emitted as condensable particulate. 2 The visibility impairment attributable to NO 3 is entirely from NO X emissions. 3 The visibility impairment attributable to PM 10 is the sum of the visibility impairment attributable to all modeled primary PM species (PMc, PMf, EC, and SOA).



### Westar proposes to upgrade the existing ESPs on JEC Unit 1 and Unit 2 in order to improve the PM control from these units. The upgrades to the exis ting ESPs will improve control of PM from JEC Unit 1 and Unit 2, thereby improving the visibility impacts from JEC Unit 1 and Unit 2.

Westar proposes that no additional PM control techno logies are required for GEEC Unit 2, which is currently uncontrolled. The fuel switching from fu el oil no. 6 to natural gas proposed as a BART alternative for SO 2 

### (see Section 9) will significantly reduce the PM em issions from GEEC Unit 2. Given the small PM emission rates from natural gas combustion, Westar believes that the reduced PM emissions from the fuel switching combined with the cost of retrofitting GEEC Unit 2 with a new PM control technology would provide little visibility improvement and require significant capital expenditures.

### Westar Energy 9-1 Trinity Consultants

BART Analysis 

## 9. P ROPOSED 

## GEEC BART A LTERNATIVE

Based on the GEEC SO 2 

### and NO x 

### BART analyses, Westar has determined that BART for SO 2 

### for GEEC Unit 2 is the combustion of 1 percent sulfur f uel oil and BART for NO X 

### is a LNB. Westar is proposing an alternative to the controls determined to meet BART for SO 2 

### and NO X 

### for GEEC Unit 2. In order for control strategies to be acceptabl e as an alternative to what is determined to meet BART, the alternatives must show greater visibility improvement than what is determined to meet BART based on the following visibility metrics:

▲ 

### The maximum visibility impact

▲ 

### The 98th percentile visibility impact

▲ 

### The number of days where the visibility impacts are greater than 0.5 ∆dv
In other words, the values for the metrics listed a bove for an alternative BART control strategy must be equal to or better than the values for the BART control strategy for each Class I area.

As an alternative to combusting 1 percent fuel oil to reduce SO 2 

### and LNB to reduce NO X 

### for GEEC Unit 2, Westar is proposing to combust natural gas in GEEC Unit 2. The switch to natural gas will provide greater than 95 percent SO 2 

### control from GEEC Unit 2.



### 9.1.1.1 C OMPLIANCE WITH 

### A LTERNATIVE 

### BART C ONTROL 

### S TRATEGY


### Westar is proposing to demonstrate compliance with the alternative BART control strategy for GEEC Unit 2 that includes switching from No. 6 fuel oil to natural gas by submitting annual certifications of compliance verifying that natural gas was the only fuel combusted in GEEC Unit 2 for the year, except as provided below.

In order to assure electric system reliability, Wes tar requires the availability of an emergency fuel for backup, as well as the ability to burn a limited am ount of the fuel periodically during nonemergencies to assure that the emergency system fun ctions adequately. When the natural gas company implements an Operational Flow Order (OFO) or declares an emergency which could result in an impact to electric system reliability, Westar will combust No. 6 fuel oil for the time period of the emergency. Westar will diminish the existing s upply of No. 6 fuel oil, once diminished the emergency fuel will be replaced with a fuel oil con taining 1% or less sulfur content.



### 9.1.2 C OMPARISON OF 

### V ISIBILITY 

### I MPACTS FOR 

### BART AND 

### P ROPOSED 

### A LTERNATIVE 

### BART C ONTROL 

### S TRATEGIES


### The modeled visibility impacts of the BART control strategy and the proposed alternative BART control strategy are summarized in Table 9-1. The visibility improvement associated with the BART and alternative BART control options are also shown in Table 9-1; this value was calculated as the difference between the existing visibility impairme nt and the visibility impairment for the BART and alternative BART control options as measured by the 98 th 

### percentile modeled visibility impact. The visibility impacts for each metric are lower in all five Class I areas for the alternate BART control strategy.Westar Energy 9-2 Trinity ConsultantsBART AnalysisTABLE9-1.MODELEDIMPACTSBASED ONPRESUMPTIVEBARTEMISSIONRATES ANDALTERNATIVEBARTATGEECUNIT2(2001-2003)Wichita MountainsHercules GladesWildernessCaney CreekWildernessMingo NWRUpper BuffaloWilderness

Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δdv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement* Maximum Impact (Δ dv) 98% Impact (Δdv) # Days > 0.5 Δdv Visibility Improvement*Existing 2.16 1.08 85 - 1.31 0.40 16 - 1.07 0.38 14 - 0.71 0.17 4 - 2.22 0.42 16 -BART - LNB + 1% sulfurfuel oil 2.02 0.94 71 13% 1.19 0.34 10 13% 0.87 0.34 9 10% 0.65 0.14 2 16% 2.04 0.38 15 10%Alternative - Natural gas1.66 0.69 44 36% 0.93 0.21 4 48% 0.49 0.25 0 34% 0.49 0.08 0 55% 1.62 0.28 11 33%