# Appendix 9.5

BART Analysis for KCP&L - La Cygne Units 1 and 2

# CALPUFF BART M ODELING 

# P ROTOCOL 

#  K ANSAS 

# C ITY 

# P OWER 

# & L IGHT 

## L A 

## C YGNE 

## G ENERATING 

## S TATION



### V ERSION 

### 0

Prepared by:

Kasi Dubbs  Senior Consultant Mary Pham  Consultant

T RINITY 

### C ONSULTANTS 

### 25055 West Valley Parkway

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

November 2006

Project 061701.0125

### Kansas City Power & Light i Trinity Consultants

BART Modeling Protocol 

## T ABLE OF 

## C ONTENTS



### 1. I NTRODUCTION


### ................................................... ................................................... ..5 1.1 B ACKGROUND



### ................................................... ................................................... .....5 1.2 L OCATION OF 

### S OURCES AND RELEVANT 

### C LASS 

### I A REAS 

### ..........................................5 1.3 O BJECTIVE



### ................................................... ................................................... ..........6

2. CALPUFF M ODEL 

### S YSTEM


### ................................................... ................................7 2.1 M ODEL 

### V ERSIONS



### ................................................... ..................................................7 2.2 M ODELING 

### D OMAIN



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

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

................................................... ................9 3.1 G EOPHYSICAL 

### D ATA



### ................................................... ..............................................9 3.1.1 T ERRAIN 

### D ATA



### ................................................... ................................................... 9 3.1.2 L AND 

### U SE 

### D ATA



### ................................................... ............................................... 10 3.1.3 C OMPILING 

### T ERRAIN AND LAND 

### U SE 

### D ATA



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

### 3.2 M ETEOROLOGICAL 

### D ATA



### ................................................... ....................................11 3.2.1 M ESOSCALE 

### M ODEL 

### M ETEOROLOGICAL 

### D ATA 

### .................................................. 1 1 3.2.2 S URFACE 

### M ETEOROLOGICAL 

### D ATA



### ................................................... ................. 12 3.2.3 P RECIPITATION 

### M ETEOROLOGICAL 

### D ATA



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

### 3.3 S UMMARY OF 

### CALMET C ONTROL 

### P ARAMETERS 

### ..................................................1 4

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

................................................... ..............21 4.1 S OURCE 

### E MISSIONS 

### D ATA



### ................................................... ...................................21 4.1.1 SO 2 

### , NO X 

### , AND 

### PM 10 

### E MISSIONS



### ................................................... ...................... 21 4.1.2 S PECIATED 

### PM 10 

### E MISSIONS



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

### 4.2 GEP S TACK 

### H EIGHT



### ................................................... ............................................22 4.3 C LASS I 

### A REA 

### R ECEPTORS



### ................................................... ...................................24 4.4 B ACKGROUND 

### O ZONE



### ................................................... .........................................27 4.5 B ACKGROUND 

### A MMONIA



### ................................................... ....................................28 4.6 S UMMARY OF 

### CALPUFF C ONTROL 

### P ARAMETERS 

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

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

................................................... ..............43 5.1 L IGHT 

### E XTINCTION 

### A LGORITHM



### ................................................... ..........................43 5.2 CALPOST P ROCESSING METHOD



### ................................................... ........................43 5.3 N ATURAL 

### B ACKGROUND



### ................................................... .....................................44 5.4 S UMMARY OF 

### CALPOST C ONTROL 

### P ARAMETERS 

### .................................................44 5.5 E VALUATING 

### BART............................................... ................................................49

A PPENDIX 

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

................................................... ............................

A PPENDIX 

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

................................................... ............................

A PPENDIX 

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

................................................... ............................

### Kansas City Power & Light ii Trinity Consultants

BART Modeling Protocol 

### A PPENDIX 

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

................................................... ............................

### Kansas City Power & Light iii Trinity Consultants

BART Modeling Protocol 

## L IST OF 

## T ABLES

T ABLE 

### 1-1. D ISTANCE TO 

### C LASS 

### I A REAS



### ................................................... .......................................... 6

T ABLE 

### 2-1. CALPUFF M ODELING 

### S YSTEM 

### V ERSIONS



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

T ABLE 

### 3-1. S UMMARY OF 

### CALMET I NPUTS



### ................................................... ................................... 15

T ABLE 

### 4-1. S O 2, 

### NO X AND 

### PM 10 

### E MISSIONS



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

T ABLE 

### 4-2. H OURLY 

### PM 10 

### S PECIATED 

### E MISSIONS



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

T ABLE 

### 4-3. S UMMARY OF 

### S TACK 

### P ARAMETERS



### ................................................... .............................. 23

T ABLE 

### 4-4. S UMMARY OF 

### O ZONE 

### M ONITORS



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

T ABLE 

### 4-5. S UMMARY OF 

### CALPUFF I NPUTS



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

T ABLE 

### 5-1. M ONTHLY 

### H UMIDITY 

### F ACTORS



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

T ABLE 

### 5-2. D EFAULT 

### A VERAGE 

### A NNUAL 

### N ATURAL 

### B ACKGROUND 

### L EVELS 

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

T ABLE 

### 5-3. S UMMARY OF 

### CALPOST I NPUTS



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

T ABLE 

### A-1. L AND 

### U SE 

### D ATA 

### U SED IN 

### A NALYSIS



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

T ABLE 

### A-2. T ERRAIN 

### D ATA 

### U SED IN 

### A NALYSIS



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

T ABLE 

### B-1. L IST OF 

### S URFACE METEOROLOGCAL 

### S TATIONS



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

T ABLE 

### B-2. L IST OF 

### P RECIPITATION METEOROLOGICAL 

### S TATIONS



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

### Kansas City Power & Light iv Trinity Consultants

BART Modeling Protocol 

## L IST OF 

## F IGURES

F IGURE 

### 1-1. L OCATION OF 

### KCP&L L A 

### C YGNE 

### G ENERATING 

### S TATION AND 

### N EARBY 

### C LASS 

### I A REAS 

### 6

F IGURE 

### 2-1. P ROPOSED 

### M ODELING 

### D OMAIN



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

F IGURE 

### 3-1. P LOT OF 

### L AND 

### E LEVATION 

### U SING 

### USGS T ERRAIN 

### D ATA 

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

F IGURE 

### 3-2. P LOT OF 

### L AND 

### U SE 

### U SING 

### USGS LULC D ATA



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

F IGURE 

### 3-3. P LOT OF 

### S URFACE 

### S TATIONS



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

F IGURE 

### 3-4. P LOT OF 

### P RECIPITATION METOROLOGICAL 

### S TATIONS



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

F IGURE 

### 4-1. L A 

### C YGNE 

### S TACK AND 

### B UILDING 

### L OCATIONS



### ................................................... ............ 23

F IGURE 

### 4-2. H ERCULES 

### -G LADES 

### W ILDERNESS 

### R ECEPTOR 

### L OCATIONS 

### ............................................. 24

F IGURE 

### 4-3. W ICHITA 

### M OUNTAINS 

### R ECEPTOR 

### L OCATIONS



### ................................................... ............ 25

F IGURE 

### 4-4. U PPER 

### B UFFALO 

### R ECEPTOR 

### L OCATIONS



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

F IGURE 

### 4-5. C ANEY 

### C REEK 

### R ECEPTOR 

### L OCATIONS



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

F IGURE 

### 4-6. M INGO 

### R ECEPTOR 

### L OCATIONS



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

### Kansas City Power & Light 5 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.



## 1.2 L OCATION OF 

## S OURCES AND RELEVANT 

## C LASS 

## I A REAS

Kansas City Power & Light (KCP&L) has identified th e following sources that meet the three criteria for being BART-eligible sources:

 

### La Cygne Generating Station – Boiler Unit 1
 

### La Cygne Generating Station – Boiler Unit 2

Table 1-1 provides a summary of the distances from the La Cygne Generating Station to nearby Class I areas.

### Kansas City Power & Light 6 Trinity Consultants

BART Modeling Protocol T ABLE 

### 1-1. D ISTANCE TO 

### C LASS 

### I A REAS

Distance to Class I Area (km)

Source Wichita

Mountains HerculesGlades Upper

Buffalo Caney

Creek Mingo La Cygne 533.4 233.6 291.5 437.8 416.4



### Figure 1-1 provides a plot of the location of the L a Cygne Generating Station with respect to the listed Class I areas.



### F IGURE 

### 1-1. L OCATION OF 

### KCP&L L A 

### C YGNE 

### G ENERATING 

### S TATION AND 

### N EARBY 

### C LASS 

### I A REAS



## 1.3 O BJECTIVE

The objective of this document is to provide a prot ocol summarizing the modeling methods and procedures that KCP&L will follow as we evaluate th e visibility impacts attributable to La Cygne Unit 1 and La Cygne Unit 2 in the Class I areas lis ted in Table 1-1. Initially, KCP&L will use the methods in this protocol to determine the visibilit y impacts based on the existing emission rates and exhaust characteristics for the units (See Section 4). We will also use the methods in this protocol to evaluate BART control options. Since we are in the process of evaluating control options, no specific emission rates or exhaust characteristics based on BART control options are provided in this protocol.

### Kansas City Power & Light 7 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 h ourly 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 to model La Cygne Unit 1 and Unit 2 are listed in Table 2-1. Table 2-1 al so compares the program versions that will be used to model La Cygne Unit 1 and Unit 2 with the progra m versions recommended by CENRAP. Note that some of the program versions are not the same as the program versions 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 ot her as published. Specifically, the MM5 data extraction program (CALMM5) Version 2.4 is not compatible with CALMET Version 5.53a. CALMM5 Version 2.4 is compatible with a newer versi on of CALMET, Version 5.551. Note that meteorological data that is generated with CALMET V ersion 5.551 is not compatible with CALPUFF Version 5.711a. CALMET Version 5.551 is compatible with CALPUFF Version 5.727. In short, alternate program versions are required in order to accommodate the MM5 data extraction program version, so KCP&L will use alternate versions.

T ABLE 

### 2-1. CALPUFF M ODELING 

### S YSTEM 

### V ERSIONS

CENRAP Suggested KCP&L Analyses Reason for Differen ce Program Version Level Version Level 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 Modified code used by Alpine Geophysics CALMET 5.53a 040716 6.211 060414 Needed to process multiple CALMM5 files CALPUFF 5.711a 040716 6.112 060412 Needed to process CALMET output POSTUTIL 5.51 030709 6.131 060410 Needed to process CALPUFF output CALPOST 3.311 030709 3.311 030709

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BART Modeling Protocol 

## 2.2 M ODELING 

## D OMAIN

The modeling domain for the La Cygne Generating Sta tion is the same domain that has been calculated for other BART-eligible electric generat ing units in Kansas. The domain extends at least 50 km in all directions beyond the La Cygne Generat ing Station and the five Class I areas of interest. The map projection for the modeling domain is Lambe rt Conformal Conic (LCC) and the coordinate system is World Geodetic System 1984 (WGS84), which is an LCC projection. The meteorological grid spacing is 2.5 km.

The southwest corner of the modeling domain is Lati tude 33.92oN, 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 39.77oN, Longitude 89.29oW. At a grid spacing of 2.5 km, the number of X grid cells will be 346 and the number of Y grid cel ls will be 261.

Figure 2-1 provides a plot of the modeling domain w ith respect to the sources and Class I areas.



### F IGURE 

### 2-1. P ROPOSED 

### M ODELING 

### D OMAIN

0 200 400 600 800 East (km) 0 200 400 600North(km) La Cygne Generating Station

Hercules Glades

Wichita Mountains Upper Buffalo

Caney Creek Mingo

### Kansas City Power & Light 9 Trinity Consultants

BART Modeling Protocol 

## 3. CALMET

KCP&L will conduct a three-year CALMET analysis tha t incorporates both mesoscale model and observation meteorological data. The CALMET analys is will generate three years of data that will be input to CALPUFF. The CALMET model requires the in put of geophysical data, meteorological data, and model parameter settings. The CALMET mod eling 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 incorporate mesoscale model meteorological data (and no observation data). Since the CALMET a nalysis for KCP&L’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 th e 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 A. A pl ot of the land elevation for the modeling domain based on the referenced files is pr ovided in Figure 3-1.

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BART Modeling Protocol



### F IGURE 

### 3-1. P LOT OF 

### L AND 

### E LEVATION 

### U SING 

### USGS T ERRAIN 

### D ATA

0 100 200 300 400 500 600 700 800 East (m) 0 100 200 300 400 500 600North(k) La Cygne Generating Station

Hercules Glades Mingo NWR

Wichita Mountains Upper Buffalo Wilderness Area

Caney Creek

-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 A. A plot of the land use for the modeling domain based on the referenced files is provided in Figure 3-2.

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BART Modeling Protocol



### F IGURE 

### 3-2. P LOT OF 

### L AND 

### U SE 

### U SING 

### USGS LULC D ATA

0 100 200 300 400 500 600 700 800 East (km) 0 100 200 300 400 500 600North(km) La Cygne Generating Station

Hercules Glades Mingo NWR

Wichita Mountains Upper Buffalo Wilderness Area

Caney Creek 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, 20 02, and 2003 using mesoscale model output and National Weather Service (NWS) surface station obse rvations and precipitation station observations to develop the meteorological field.



### 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

### Kansas City Power & Light 12 Trinity Consultants

BART Modeling Protocol

field for the CALMET simulations. The following 5 th 

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

• 2001 MM5 data at 12 km resolution processed for EPA by Alpine Geophysics

• 2002 MM5 data at 36 km resolution processed by Iowa DNR

• 2003 MM5 data at 36 km resolution processed by the Midwest RPO

The MM5 data for the CENRAP region was extracted fr om the above MM5 data sets by Alpine Geophysics using the CALMM5 program. Trini ty will use this extracted MM5 data.



### 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 B. 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, Lev el: 050311 of EPA’s SMERGE
program.

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BART Modeling Protocol 

### F IGURE 

### 3-3. P LOT OF 

### S URFACE 

### S TATIONS

0 200 400 600 800 1000 East (km) -200 0 200 400 600 800North(km) La Cygne Generating Station

KDFW KICT KCOU KMCI

KSZL

KSHV

KACT KABI KFSM

KSPS KOKC KTUL

KPNC

KTXK KCNK

KEMP

KSGF

KTOP

KTYR

KOCH KPRX KHOT

KFYV KJLN

K1F0 KIRK

KSTJ

KCNU KMHK

KSLN

KMLC KHBR KUIN KSPI

KBLV KCPS KJEF

KAIZ KDMO

KVIH
KFAM KMDH

KCGI KPAH

KPOF

KDYR KARG

KBVX KJBR KMKL

KMEM

KSGT

KPBF KLIT KLRF KFLP KDEC

KMVN KSTL

Hercules Glades

Wichita Mountains Upper Buffalo

Caney Creek Mingo



### 3.2.3 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 analysi s. The precipitation stations from which data will be extracted are listed in Appendix B. The locations of the precipitation stations with respect to the modeling domain are sh own in Figure 3-4. 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.

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BART Modeling Protocol



### F IGURE 

### 3-4. P LOT OF 

### P RECIPITATION METOROLOGICAL 

### S TATIONS

0 200 400 600 800 1000 East (km) -200 0 200 400 600 800North(km) La Cygne Generating Station

ALUM

ANTO ARKA BLAK BOON BRIG

CAMD CLAR

DE Q DIER

FORE FORT HARR

LEWI MAGN MENA NIMR NORF

PRES

TEXA WALD ARLI

CALD CHAN CONC

COUN IONI

KIOW MARY

MILF SALI TOPE

UNIO WICH

MANS MIND

NATC RED SHRE APPL

BOLI

CASS COLU

DORA JOPL KANS KIRK

LEBA

MOUN NEVA

SPRI

TABL TRUM

ALTU AMES

ARDM BENG

CANE CART CHAN

CHIC CUST

DUNC ELK FORT

GEAR

HENN HOBA

KING LAKE

LEHI MACK MARS

MCAL MUSK OKAR OKEM OKLA PONC

ROFF TULS

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 SPRI JACK PITT CLAR GREE

CARL MORR

CARL

SPAR

MURP HANN CLAR LONG

MOBE

NEW FULT CAP

WASH ROSE

ROLL VIEN STEE POTO
VIBU SALE

ALLE CLEA

FARM

ADVA WAPP
WEST DORA MALD

HORN

ARKA

CALH CLAR

CLEV GREN

HICK HOLL MOUN

SARE UNIV BOLI BROW GREE

JACK MASO
MEMP MEMP MUNF SAMB UNIO

ALIC BATE

BEEB

BRIN CORN

DUMA GREE HARD

MONT PINE STUT WYNN Hercules Glades

Wichita Mountains Upper Buffalo

Caney Creek Mingo



## 3.3 S UMMARY OF 

## CALMET C ONTROL 

## P ARAMETERS

Table 3-1 provides a listing of the CALMET paramete rs 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.

### Kansas City Power & Light 15 Trinity Consultants

BART Modeling Protocol T ABLE 

### 3-1. S UMMARY OF 

### CALMET I NPUTS

CALMET Variable Description Value Included in CENRAP Protocol Value KCP&L

Will Use Notes NUSTA Number of upper air data

sites 0 0 Will not 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 33.9195N Appropriate for domain

RLON0 Longitude (decimal

degrees) of projection origin 97W 99.3480W Appropriate for domain

XLAT1 Latitude of 1 st standard parallel 33N 33N Appropriate for domain

XLAT2 Latitude of 2 nd standard

parallel 45N 40N Appropriate for domain

DATUM Datum region for output

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

meteorological grid 300 346 Appropriate for domain

NY Number of Y grid cells in

meteorological grid 192 261 Appropriate for domain

DGRIDKM Grid spacing (km) 6.0 2.5 Refined grid spac ing 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

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BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value KCP&L

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 1 Will use surface observations only

NSSTA Number of meteorological

stations in SURF.DAT file 0 45 Number of stations

### Kansas City Power & Light 17 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value KCP&L

Will Use Notes NPSTA Number of precipitation

stations in PRECIP.DAT file 0 206 Number of stations

ICLOUD Gridded cloud fields (0 =

no, 3 = Gridded cloud cover from prognostic relative humidity) 3 0

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

### Kansas City Power & Light 18 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value KCP&L

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) 30 100

RMAX2 Maximum radius of

influence over land aloft (km) 30 500

RMAX3 Maximum radius of

influence over water (km) 50 100

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 80

R2 Weighting of first guess

aloft field (km) 1 200

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

### Kansas City Power & Light 19 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value KCP&L

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

### Kansas City Power & Light 20 Trinity Consultants

BART Modeling Protocol CALMET Variable Description Value Included in CENRAP Protocol Value KCP&L

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 1 Will use surface 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

### Kansas City Power & Light 21 Trinity Consultants

BART Modeling Protocol 

## 4. CALPUFF

KCP&L will conduct a three-year CALPUFF analysis. The CALPUFF model requires the input of meteorological data output by CALMET, source emissi ons data, receptor data, ozone and ammonia data, and model parameter settings.



## 4.1 S OURCE 

## E MISSIONS 

## D ATA

The BART rule indicates that if the PTE is greater than 250 tpy for any visibility-impairing pollutant

, a source is required to include emissions of all vi sibility impairing pollutants in the BART analysis. La Cygne Unit 1 and Unit 2 emit three primary visib ility-impairing pollutants: SO 2 

### , PM 10 

### , and NO X 

### . Since the PTE for at least one of these pollutants is above 250 tpy, KCP&L will include emissions of all three pollutants in the BART modeling analysis.



### 4.1.1 SO 2 

### , NO X 

### , AND 

### PM 10 

### E MISSIONS



### The BART rule indicates that KCP&L should model the highest actual 24-hour emission rate for each visibility impairing pollutant that occurred during a baseline period. Thus, the SO 2 

### , NO x 

### , and PM 10 

### emissions that will be modeled to determine the cur rent visibility impacts attributable to La Cygne Unit 1 and Unit 2 are the maximum of the 2002-2004 24-hour highest actual emissions rates. The SO 2 

### and NO x 

### emission rates will be based on CEMS data. The PM 10 

### emission rates will be based on actual fuel data from 2002-2004 and AP-42 emission factors. The PM 10 

### emission rates will include both the filterable and condensable fractions. Det ailed calculations for the PM 10 

### emission rates can be found in Appendix C. The SO 2 

### , NO x 

### , and PM 10 

### emissions are summarized in Table 4-1.

T ABLE 

### 4-1. S O 2, 

### NO X AND 

### PM 10 

### E MISSIONS

SO 2 NO X PM 10 (lb/hr) (lb/hr) (lb/hr) La Cygne - Unit 1 6,151.15 11,589.52 53.79 La Cygne - Unit 2 8,316.15 3,543.47 94.92



### 4.1.2 S PECIATED 

### PM 10 

### E MISSIONS



### The PM 10 

### emissions will be speciated to include the followi ng:

• Coarse particulate matter (PM C 

### )

• Fine particulate matter (PM f 

### )

• Sulfates (SO 4 

### )

• Nitrates (NO 3 

### )

• Secondary organic aerosols (SOA)

• Elemental carbon (EC)

The PM 10 

### emissions will be speciated according to the defau lt speciation profiles prepared by the Federal Land Managers (FLM) for a Cyclone Furnace w ith Flue Gas Desulfurization (FGD) and ESP

### Kansas City Power & Light 22 Trinity Consultants

BART Modeling Protocol (La Cygne Unit 1) and a dry bottom PC boiler with E SP (La Cygne Unit 2). It should be noted that Unit 1 is a cyclone furnace with wet FGD only; howe ver, there is not an FLM PM 10 

### speciation for this type of cyclone boiler. Therefore, the FLM PM 10 

### speciation for a Cyclone Furnace with FGD and ESP was used. Since the wet FGD controls PM, this speciation is appropriate for Unit 1. Table 4-2 provides a summary of the speciated emissions.

T ABLE 

### 4-2. H OURLY 

### PM 10 

### S PECIATED 

### E MISSIONS

Total

PM10 SO4 PM c PM f SOA EC

(lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) La Cygne - Unit 1 53.79 34.37 4.92 5.68 8.59 0.22 La Cygne - Unit 2 94.92 28.12 33.20 25.58 7.03 0.98



## 4.2 GEP S TACK 

## H EIGHT
Section 6.2.2 of the Guideline on Air Quality Model s 1 

### states the following:

"The use of stack height credit in excess of Good E ngineering Practice (GEP) stack height or credit resulting from any other dispersio n technique is prohibited in the development of emission limitations by 40 CFR 51.11 8 and 40 CFR 51.164."

Since this modeling is being used to determine if e missions limitations are needed, stack heights in excess of GEP stack heights should not be used in t he modeling. KCP&L has calculated the GEP stack heights of La Cygne Unit 1 and Unit 2 and det ermined that the actual heights are in excess of GEP stack height.

The EPA provides guidance for determining whether b uilding downwash will occur in Guideline for Determination of Good Engineering Practice Stack He ight. 2 

### The minimum stack height not subject to the effects of downwash (called the Good Enginee ring Practice or GEP stack height) is defined by the following formula:

GEP = H + 1.5L

Where: GEP = the minimum GEP stack height H = the height of the structure L = the lesser dimension of the structure (heigh t or projected width)

Stacks located more than 5L from any building are n ot subject to the effects of building downwash.

1 40 CFR 51, Appendix W

2 EPA, Office of Air Quality Planning and Standards. Guideline for Determination of Good Engineering Pra ctice Stack Height (Technical Support Document for the St ack Height Regulations) (Revised). Research Triangle Park, North Carolina. EPA 450/4-80-023R. June, 1985.

### Kansas City Power & Light 23 Trinity Consultants

BART Modeling Protocol Calculations for determining the direction-specific downwash parameters and GEP stack height were performed using BREEZE TM WAKE/BPIP software, which uses EPA’s Building Pro file Input Program (BPIP) downwash algorithm. Figure 4.1 show s the stacks and buildings that were included in the BPIP analysis. Since the GEP stack heights are less than the actual stack heights, the stacks will be modeled at GEP stack heights rather than th e actual stack heights. A summary of the stack parameters, including GEP and actual stack heights, can be found in Table 4-3. These parameters are only specific to the existing operations; as KCP&L evaluates BART control options, the parameters may be modified for each control option.



### F IGURE 

### 4-1. L A 

### C YGNE 

### S TACK AND 

### B UILDING 

### L OCATIONS

407.45 407.50 407.55 407.60 407.65 407.70 407.75 East (km) 487.55 487.60 487.65 487.70 487.75 487.80North(km) Boiler 1 Stack Height: 700 ft GEP: 591.2 ft

Boiler 2 Stack Height: 700 ft GEP: 597.8 ft



### T ABLE 

### 4-3. S UMMARY OF 

### S TACK 

### P ARAMETERS

La Cygne 1 La Cygne 2 Latitude (degrees) 38.3486 38.3476 Longitude (degrees) -94.6456 -94.6456 Actual Stack height (ft) 700 700 GEP Stack height (ft) 591.2 597.8 Stack Diameter (ft) 23 24 Exhaust Velocity (ft/s) 92.7 100.8 Exhaust Temperature (F) 127 281

### Kansas City Power & Light 24 Trinity Consultants

BART Modeling Protocol 

## 4.3 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 al l Class I areas will be downloaded from the NPS websi te, converted into the LCC WGS84 projection, and incorporated into the CALPUFF model. The recep tor locations for the Class I areas are shown in Figures 4-2 through 4-6.



### F IGURE 

### 4-2. H ERCULES 

### -G LADES 

### W ILDERNESS 

### R ECEPTOR 

### L OCATIONS

### Kansas City Power & Light 25 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### 4-3. W ICHITA 

### M OUNTAINS 

### R ECEPTOR 

### L OCATIONS

### Kansas City Power & Light 26 Trinity Consultants

BART Modeling Protocol



### F IGURE 

### 4-4. U PPER 

### B UFFALO 

### R ECEPTOR 

### L OCATIONS



### F IGURE 

### 4-5. C ANEY 

### C REEK 

### R ECEPTOR 

### L OCATIONS

### Kansas City Power & Light 27 Trinity Consultants

BART Modeling Protocol 

### F IGURE 

### 4-6. M INGO 

### R ECEPTOR 

### L OCATIONS



## 4.4 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 th e 2002 base year. KCP&L 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 4-4.

T ABLE 

### 4-4. 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

### Kansas City Power & Light 28 Trinity Consultants

BART Modeling Protocol Andy Hawkins of KDHE has made available processed o zone data files for 2001 through 2003 containing data from the above referenced stations. KCP&L is proposing to incorporate these files into the CALPUFF model.



## 4.5 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 th e 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 mod eling 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, KCP&L is proposing to us e 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

.



## 4.6 S UMMARY OF 

## CALPUFF C ONTROL 

## P ARAMETERS

Table 4-5 provides a listing of the CALPUFF paramet ers that KCP&L 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 

### 4-5. S UMMARY OF 

### CALPUFF I NPUTS

CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L Will Use Notes METRUN All model periods

in met files will be

run 0 0

IBYR Starting year 2001 Appropriate met year Years 2001, 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 9

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)

### Kansas City Power & Light 29 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L Will Use Notes ITEST Program is

executed after SETUP phase 2 2

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

### Kansas City Power & Light 30 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L Will Use Notes MTURBVW Use both σ v and

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

MDISP2 PG dispersion

coefficients 3 3

MROUGH PG σ y and σ 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 KCP&L

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 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

### Kansas City Power & Light 31 Trinity Consultants

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

cells in meteorological grid 66 346 Appropriate for domain and grid spacing

NY Number of Y grid

cells in meteorological grid 66 261 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 66 346 Appropriate for domain

JECOMP Y index of upper

right corner of the computational grid 66 261 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 66 346 Appropriate for domain

### Kansas City Power & Light 32 Trinity Consultants

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

right corner of sampling grid 66 261 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

### Kansas City Power & Light 33 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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

### Kansas City Power & Light 34 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 ( μ m) 0.48 0.48

NO3- Geometric mass

mean diameter of NO3- ( μ m) 0.48 0.48

PMC Geometric mass

mean diameter of PMC ( μ m) 6 6

PMF Geometric mass

mean diameter of PMF ( μ m) 0.48 0.48

EC Geometric mass

mean diameter of EC ( μ m) 0.48 0.48

SOA Geometric mass

mean diameter of SOA ( μ 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

### Kansas City Power & Light 35 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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-04 1.0E-04

### Kansas City Power & Light 36 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 ( μ 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

### Kansas City Power & Light 37 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 * *

### Kansas City Power & Light 38 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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

### Kansas City Power & Light 39 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 σ 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 19 Should be by hour of day – 1900 is hour 19 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

### Kansas City Power & Light 40 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 - -

### Kansas City Power & Light 41 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 - -

### Kansas City Power & Light 42 Trinity Consultants

BART Modeling Protocol CALPUFF Variable Description Value Included in CENRAP Protocol Value KCP&L 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 338

### Kansas City Power & Light 43 Trinity Consultants

BART Modeling Protocol 

## 5. CALPOST

KCP&L will conduct a three-year CALPOST analysis to determine the change in light extinction caused by La Cygne Unit 1 and Unit 2 when compared to a natural background. The CALPOST model requires the input of concentration data outp ut by CALPUFF.



## 5.1 L IGHT 

## E XTINCTION 

## A LGORITHM

KCP&L will utilize EPA’s currently approved algorit hm 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 La Cygne Unit 1 and Unit 2 and light extinction attributable to a natur al background. The change in deciviews based on the source and background light extinctions will be evaluated using the equation below.



### ∆ dv = 10*ln b b

b ext, background ext, source

ext, background 

### +   

  



## 5.2 CALPOST P ROCESSING METHOD

KCP&L will use CALPOST Method 6, which calculates h ourly light extinction impacts for the source and background using monthly average relativ e humidity adjustment factors. KCP&L 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 the Class I areas in this analys is 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 Upper Buffalo 3.3 3.0 2.7 2.8 3.4 3.4 3.4 3.4 3.6 3 .3 3.2 3.3 Caney Creek 3.4 3.1 2.9 3.0 3.6 3.6 3.4 3.4 3.6 3.5 3.4 3.5 Mingo 3.3 3.0 2.8 2.6 3.0 3.2 3.3 3.5 3.5 3.1 3.4 3 .3

### Kansas City Power & Light 44 Trinity Consultants

BART Modeling Protocol 

## 5.3 N ATURAL 

## B ACKGROUND

KCP&L will use EPA’s default average annual aerosol 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 for Wichita Mountains

. KCP&L will use EPA’s default average annual aerosol concentrations for the eastern 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 for Upper Buffalo, Caney Creek, Hercules Glades and Mingo

. The annual average concentrations are provided in Table 5-2.

T ABLE 

### 5-2. D EFAULT 

### A VERAGE 

### A NNUAL 

### N ATURAL 

### B ACKGROUND 

### L EVELS

Component Average Annual

Natural Background –
Western ( μ g/m 3 ) Average Annual

Natural Background –
Eastern ( μ g/m 3 )
Ammonium Sulfate 0.12 0.23
Ammonium Nitrate 0.1 0.1 Organic Carbon Mass 0.47 1.4
Elemental Carbon 0.02 0.02
Soil 0.5 0.5
Coarse Mass 3 3



## 5.4 S UMMARY OF 

## CALPOST C ONTROL 

## P ARAMETERS

Table 5-3 provides a listing of the CALPOST paramet ers that KCP&L proposes to use in the modeling analysis. In addition to the parameters t hat will be used for the modeling, the table also l ists 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 KCP&L 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

NREP Process every hour

of data? 1 = yes 1 1

ASPEC Process species for

visibility VISIB VISIB

### Kansas City Power & Light 45 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value KCP&L Will Use Notes 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

LVNO3 Compute light

extinction for nitrate? T T

LVOC Compute light

extinction for organic carbon? T T

### Kansas City Power & Light 46 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value KCP&L Will Use Notes 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

RHFRAC % of particles

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

### Kansas City Power & Light 47 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value KCP&L Will Use Notes RHFAC Extinction

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

Area See Table 5-1 As appropriate for Class I area

BKSO4 Background sulfate

extinction coeff -
west 0.12 0.12 Used for Wichita Mountains

BKNO3 Background nitrate

extinction coeff –
west 0.10 0.10 Used for Wichita Mountains

BKPMC Background coarse

part. extinction coeff – west 3.00 3.00 Used for Wichita Mountains

BKSOC Background

organic carbon extinction coeff –
west 0.47 0.47 Used for Wichita Mountains

BKSOIL Background soil

extinction coeff –
west 0.50 0.50 Used for Wichita Mountains

BKSEC Background

elemental carbon extinction coeff –
west 0.02 0.02 Used for Wichita Mountains

BKSO4 Background sulfate

extinction coeff –
east 0.23 0.23 Used for Upper Buffalo, Caney Creek, Hercules Glades and Mingo BKNO3 Background nitrate

extinction coeff –
east 0.10 0.10 Used for Upper Buffalo, Caney Creek, Hercules Glades and Mingo BKPMC Background sulfate

extinction coeff –
west 3.00 3.00 Used for Upper Buffalo, Caney Creek, Hercules Glades and Mingo BKSOC Background

organic carbon extinction coeff –
east 1.40 1.40 Used for Upper Buffalo, Caney Creek, Hercules Glades and Mingo BKSSOIL Background soil

extinction coeff –
east 0.50 0.50 Used for Upper Buffalo, Caney Creek, Hercules Glades and Mingo BKSEC Background

elemental carbon extinction coeff –
east 0.02 0.02 Used for Upper Buffalo, Caney Creek, Hercules Glades and Mingo

### Kansas City Power & Light 48 Trinity Consultants

BART Modeling Protocol CALPOST Variable Description Value Included in CENRAP Protocol Value KCP&L 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

### Kansas City Power & Light 49 Trinity Consultants

BART Modeling Protocol 

## 5.5 E VALUATING 

## BART

KCP&L will perform modeling as outlined in this pro tocol to determine the visibility impacts based on the existing emission rates and exhaust characte ristics for La Cygne Unit 1 and Unit 2. The modeling methods outlined in this protocol will als o be used in the evaluation of BART control options. Since we are in the process of evaluating BART control options, specific data related to the control options are not provided in this protocol. KCP&L will provide data specific to the control options as part of the controls analysis.

### Kansas City Power & Light A - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## A

G EOPHYSICAL 

### D ATA

### Kansas City Power & Light A - 2 Trinity Consultants

BART Modeling Protocol T ABLE 

### A-1. L AND 

### U SE 

### D ATA 

### U SED IN 

### A NALYSIS



### 1:250,000 Scale Data Abilene Ardmore Belleville Beloit Blytheville Clinton Dallas Decatur Dyersburg El Dorado Enid Fort Smith Great Bend Greenwood Harrison Helena Hutchinson Jackson Jefferson City Joplin Kansas City Lawrence Lawton Little Rock Manhattan Memphis Meridian Moberly Oklahoma City Paducah Poplar Bluff Pratt Quincy Rolla Sherman Shreveport Springfield St. Louis Texarkana Tulsa Tupelo Tyler West Point Wichita Wichita Falls Woodward

1:100,000 Scale Data Antlers Conway DeQueen Fly Gap Mountains McAlester Mena Mountainview Russellville

### Kansas City Power & Light A - 2 Trinity Consultants

BART Modeling Protocol T ABLE 

### A-2. T ERRAIN 

### D ATA 

### U SED IN 

### A NALYSIS



### 1:250,000 Scale Data Abilene-E Abilene-W Ardmore-E Ardmore-W Belleville-E Belleville-W Beloit-E Beloit-W Blytheville-E Blytheville-W Clinton-E Clinton-W Dallas-E Dallas-W Decatur-E Decatur-W Dyersburg-E Dyersburg-W El Dorado-E El Dorado-W Enid-E Enid-W Fort Smith-E Fort Smith-W Great Bend-E Great Bend-W Greenwood-E Greenwood-W Harrison-E Harrison-W Helena-E Helena-W Hutchinson-E Hutchinson-W Jackson-E Jackson-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 Little Rock-E Little Rock-W Manhattan-E Manhattan-W McAlester-E McAlester-W Memphis-E Memphis-W Meridian-E Meridian-W Moberly-E Moberly-W Oklahoma City-E Oklahoma City-W Paducah-E Paducah-W Poplar Bluff-E Poplar Bluff-W Pratt-E Pratt-W Quincy-E Quincy-W Rolla-E Rolla-W Russellville-E Russellville-W Sherman-E Sherman-W Shreveport-E Shreveport-W Springfield-E Springfield-W Saint Louis-E Saint Louis-W Texarkana-E Texarkana-W Tulsa-E Tulsa-W Tupelo-E Tupelo-W Tyler-E Tyler-W West Point-E West Point-W Wichita Falls-E Wichita Falls-W Wichita-E Wichita-W Woodward-E Woodward-W

### Kansas City Power & Light B - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## B

T ABLE 

### B-1. L IST OF 

### S URFACE METEOROLOGCAL 

### S TATIONS

Station
ID Name ID Latitude Longitude X (km) Y (km)
3927 DALLAS-FORT WORTH INTL AP KDFW 32.900 -97.017 217.594 -110.664
3928 WICHITA MID-CONTINENT AP KICT 37.650 -97.433 1 68.277 415.757
3945 COLUMBIA REGIONAL AIRPORT KCOU 38.817 -92.217 616.499 566.49
3947 KANSAS CITY INT'L ARPT KMCI 39.300 -94.717 397 .998 606.872
13930 WHITEMAN AFB KSZL 38.717 -93.550 502.081 547. 665
13957 SHREVEPORT REGIONAL ARPT KSHV 32.450 -93.817 518.947 -148.434
13959 WACO REGIONAL AP KACT 31.617 -97.233 200.571 -253.861
13962 ABILENE REGIONAL AP KABI 32.417 -99.683 -31.4 6 -166.963
13964 FORT SMITH REGIONAL AP KFSM 35.333 -94.367 45 0.826 168.614
13966 WICHITA FALLS MUNICIPAL ARPT KSPS 33.983 -98. 500 78.106 7.4
13967 OKLAHOMA CITY WILL ROGERS WOR KOKC 35.383 -97 .600 158.172 163.934
13968 TULSA INTERNATIONAL AIRPORT KTUL 36.200 -95.8 83 310.224 258.752
13969 PONCA CITY MUNICIPAL AP KPNC 36.733 -97.100 1 99.91 314.65
13977 TEXARKANA WEBB FIELD KTXK 33.450 -94.000 495. 621 -38.391
13984 CONCORDIA BLOSSER MUNI AP KCNK 39.550 -97.650 145.489 626.359
13989 EMPORIA MUNICIPAL AP KEMP 38.333 -96.183 275. 593 494.432
13995 SPRINGFIELD REGIONAL ARPT KSGF 37.233 -93.383 526.725 384.127
13996 TOPEKA MUNICIPAL AP KTOP 39.067 -95.633 320.3 18 577.594
72244 TYLER/POUNDS FLD KTYR 32.350 -95.400 370.978 -166.863 72249 NACOGDOCHES (AWOS) KOCH 31.583 -94.717 439.21 7 -249.285
72258 COX FLD KPRX 33.633 -95.450 360.508 -24.527
72341 MEMORIAL FLD KHOT 34.467 -93.100 571.592 79.3 65
72344 FAYETTEVILLE DRAKE FIELD KFYV 36.000 -94.167 464.929 243.488
72349 JOPLIN MUNICIPAL AP KJLN 37.150 -94.500 428.6 48 369.387
72352 ARDMORE K1F0 34.150 -97.117 205.042 27.981
72445 KIRKSVILLE REGIONAL AP KIRK 40.100 -92.550 57 7.7 706.619
72449 ST JOSEPH ROSECRANS MEMORIAL KSTJ 39.767 -94. 900 379.885 657.971
72450 CHANUTE MARTIN JOHNSON AP KCNU 37.667 -95.483 339.483 422.786
72455 MANHATTAN RGNL KMHK 39.133 -96.667 230.987 58 1.961
72458 SALINA MUNICIPAL AP KSLN 38.817 -97.667 145.4 54 544.915
93950 MCALESTER MUNICIPAL AP KMLC 34.900 -95.783 32 4.515 114.893
93986 HOBART MUNICIPAL AP KHBR 35.000 -99.050 27.09 9 120.026 72439 QUINCY MUNICIPAL BALDWIN FLD KUIN 39.900 -91. 200 694.052 693.367
72439 SPRINGFIELD CAPITOL AP KSPI 39.800 -89.600 83 1.037 694.973
72433 BELLEVILLE SCOTT AFB KBLV 38.500 -89.800 828. 344 549.557

### Kansas City Power & Light B - 2 Trinity Consultants

BART Modeling Protocol Station
ID Name ID Latitude Longitude X (km) Y (km)
72531 KAHOKIA/ST. LOUIS KCPS 38.500 -90.100 802.399 547.016
72445 JEFFERSON CITY MEM KJEF 38.500 -92.100 629.24 3 532.143
72445 KAISER MEM KAIZ 38.100 -92.500 597.734 485.30 3
72445 SEDALIA MEM KDMO 38.700 -93.100 541.118 548.2 19
72445 VICHY ROLLA NATL APT KVIH 38.100 -91.700 667. 423 490.56
72445 FARMINGTON APT KFAM 37.700 -90.400 784.689 45 6.128
72433 CARBONDALE KMDH 37.700 -89.200 889.555 466.56 4
72348 CAPE GIRARDEAU MUNICIPAL APT KCGI 37.200 -89. 500 869.018 408.63
72435 PADUCAH BARKLEY REG AP KPAH 37.000 -88.700 94 1.785 394.085
72330 POPLAR BLUFF KPOF 36.700 -90.400 794.99 345.6 28
72334 DYERSBURG MUN AP KDYR 36.000 -89.400 891.548 277.081
72340 WALNUT RIDGE KARG 36.100 -90.900 756.52 275.2 88
72344 BATESVILLE KBVX 35.700 -91.600 697.546 225.76 7
72340 JONESBORO MUNICIPAL APT KJBR 35.800 -90.600 7 86.334 244.532
72334 JACKSON MCKELLAR-SIPES REGL KMKL 35.600 -88.9 00 940.995 237.698
72334 MEMPHIS INTL APT KMEM 35.000 -89.900 857.761 162.112
72341 STUTTGART KSGT 34.600 -91.500 716.472 104.798
72341 PINE BLUFF/GRIDER KPBF 34.100 -91.900 684.322 46.538
72340 LITTLE ROCK ADAMS FIELD KLIT 34.700 -92.200 6 51.867 110.892
72340 LITTLE ROCK AFB KLRF 34.900 -92.100 659.299 1 33.715
72344 FLIPPIN KFLP 36.300 -92.400 620.853 286.681
72531 DECATUR KDEC 39.800 -88.800 898.976 702.162
72433 MOUNT VERNON KMVN 38.300 -88.800 917.197 536. 538
72434 ST. LOUIS LAMBERT INTL APT KSTL 38.700 -90.30 0 783.013 567.477



### T ABLE 

### B-2. L IST OF 

### P RECIPITATION METEOROLOGICAL 

### S TATIONS

Station ID Name ID Latitude Longitude X (km) Y (km)
30130 ALUM FORK ALUM 34.800 -92.850 591.935 117.768
30178 ANTOINE ANTO 34.033 -93.417 545.626 29.461
30220 ARKADELPHIA 2 N ARKA 34.150 -93.050 578.461 4 4.534
30764 BLAKELY MOUNTAIN DAM BLAK 34.567 -93.200 561. 75 89.82
30832 BOONEVILLE 3 SSE BOON 35.100 -93.917 493.003 144.997
30900 BRIGGSVILLE BRIG 34.933 -93.500 531.902 128.7 45
31152 CAMDEN 1 CAMD 33.600 -92.817 604.003 -14.996
31457 CLARKSVILLE 6 NE CLAR 35.533 -93.400 536.873 195.774 31952 DE QUEEN DAM DE Q 34.100 -94.367 457.937 31.9 02
32020 DIERKS DAM DIER 34.150 -94.083 483.655 38.834
32544 FOREMAN FORE 33.717 -94.383 458.602 -10.703 32574 FORT SMITH MU, OK FORT 35.333 -94.367 450.855 168.653
33165 HARRISON BOONE CNTY AP HARR 36.267 -93.157 55 3.576 278.374
34185 LEWISVILLE LEWI 33.367 -93.567 536.29 -45.314

### Kansas City Power & Light B - 3 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
34548 MAGNOLIA 3 N MAGN 33.333 -93.250 565.86 -47.1 99
34756 MENA MENA 34.567 -94.267 464.388 84.135
35200 NIMROD DAM NIMR 34.950 -93.167 562.061 132.48 6
35228 NORFORK DAM NORF 36.249 -92.256 634.101 282.0 23
35908 PRESCOTT PRES 33.800 -93.383 550.293 3.781
37048 TEXARKANA TEXA 33.450 -94.000 495.621 -38.391
37488 WALDRON WALD 34.900 -94.100 477.588 121.905
140326 ARLINGTON ARLI 37.900 -98.267 94.684 442.364
141233 CALDWELL CALD 37.034 -97.616 153.468 347.056
141427 CHANUTE FAA AIRPORT CHAN 37.670 -95.484 339. 366 423.145
141767 CONCORDIA BLOSSER MUNI CONC 39.551 -97.651 1 45.415 626.512
141867 COUNCIL GROVE LAKE COUN 38.675 -96.526 244.6 36 531.493
143997 IONIA IONI 39.661 -98.348 85.529 637.868
144341 KIOWA KIOW 37.017 -98.485 76.494 344.138
145063 MARYSVILLE MARY 39.833 -96.633 231.722 659.7 97
145306 MILFORD LAKE MILF 39.075 -96.898 211.249 574 .959
147160 SALINA AP SALI 38.817 -97.667 145.454 544.91 5
148167 TOPEKA BILLARD MUNI AP TOPE 39.069 -95.639 3 19.802 577.785
148293 UNIONTOWN UNIO 37.848 -94.978 382.945 444.73
148830 WICHITA WICH 37.650 -97.433 168.277 415.757
165874 MANSFIELD MANS 32.033 -93.700 532.631 -194.0 82
166244 MINDEN MIND 32.600 -93.300 566.346 -128.864
166582 NATCHITOCHES NATC 31.767 -93.100 591.063 -22 0.21
167738 RED RIVER RSRCH STN RED 32.417 -93.633 536. 38 -151.129 168440 SHREVEPORT, LA SHRE 32.467 -94.317 472.014 - 149.154
230204 APPLETON CITY APPL 38.184 -94.026 464.144 48 6.148
230789 BOLIVAR 1 NE BOLI 37.617 -93.391 523.377 426 .582
231383 CASSVILLE RANGER STN CASS 36.673 -93.858 488 .393 319.564
231791 COLUMBIA REGIONAL AP COLU 38.817 -92.218 616 .384 566.475
232302 DORA DORA 36.780 -92.233 631.838 340.862
234315 JOPLIN REGIONAL AP JOPL 37.147 -94.502 428.4 71 369.008
234358 KANSAS CITY AP KANS 39.300 -94.717 397.998 6 06.872
234544 KIRKSVILLE KIRK 40.200 -92.567 575.475 717.6 09
234825 LEBANON 2 W LEBA 37.685 -92.694 584.043 438. 162
235834 MOUNTAIN GROVE 2 N MOUN 37.153 -92.264 626.0 6 381.947
235987 NEVADA WATER PLANT NEVA 37.839 -94.373 435.8 85 446.376
237976 SPRINGFIELD REG AP SPRI 37.240 -93.390 526.0 86 384.835
238252 TABLE ROCK DAM TABL 36.597 -93.308 537.808 3 14.127
238466 TRUMAN DAM & RESERVIOR TRUM 38.258 -93.399 5 18.297 497.601
340179 ALTUS IRIG RES STN ALTU 34.583 -99.333 1.342 73.726
340215 AMES AMES 36.250 -98.183 104.257 259.348
340292 ARDMORE ARDM 34.167 -97.133 203.499 29.797

### Kansas City Power & Light B - 4 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
340670 BENGAL BENG 34.850 -95.083 388.412 111.938
341437 CANEY CANE 34.233 -96.217 287.462 39.536
341544 CARTER TOWER CART 34.267 -94.783 418.786 48. 493
341684 CHANDLER 1 CHAN 35.700 -96.883 222.108 200.5 24
341750 CHICKASHA EXP STN CHIC 35.050 -97.917 130.07 126.502
342334 CUSTER CITY CUST 35.650 -98.833 46.415 192.2 55
342654 DUNCAN AIRPORT DUNC 34.483 -97.967 126.429 6 3.529
342849 ELK CITY ELK 35.383 -99.400 -4.705 162.536
343281 FORT COBB FORT 35.100 -98.433 83.068 131.48
343497 GEARY GEAR 35.633 -98.317 93.029 190.78
344052 HENNEPIN 5 N HENN 34.567 -97.350 182.671 73. 771
344202 HOBART HOBA 35.033 -99.083 24.058 123.718
344865 KINGSTON KING 34.000 -96.733 240.748 12.214
344975 LAKE EUFAULA LAKE 35.283 -95.433 354.598 158 .646
345108 LEHIGH LEHI 34.467 -96.217 286.619 65.434
345463 MACKIE 4 NNW MACK 35.750 -99.833 -43.712 203 .34
345589 MARSHALL MARS 36.150 -97.617 155.133 249.015
345664 MCALESTER MUNI AP MCAL 34.883 -95.783 324.55 4 113.043
346130 MUSKOGEE MUSK 35.767 -95.333 361.415 212.616
346620 OKARCHE OKAR 35.717 -97.983 122.963 200.402
346638 OKEMAH OKEM 35.433 -96.300 275.597 172.448 346661 OKLAHOMA CITY, OK OKLA 35.383 -97.600 158.17 1 163.971
347196 PONCA CITY PONC 36.717 -97.100 199.951 312.8 75
347705 ROFF 2 WNW ROFF 34.633 -96.883 225.139 82.16
348992 TULSA INTL AIRPORT TULS 36.198 -95.888 309.8 03 258.552
349023 TUSKAHOMA TUSK 34.633 -95.283 371.223 87.116
349629 WICHITA MTN WL REF WICH 34.733 -98.717 57.60 4 90.569
349724 WISTER WIST 34.950 -94.700 422.763 124.642
349748 WOLF 4 N WOLF 35.133 -96.667 243.38 138.175 410016 ABILENE MUN, TX ABIL 32.417 -99.683 -31.492 -167
410926 BONITA 4 NW BONI 33.833 -97.633 158.221 -8.1 61
411246 BURLESON BURL 32.550 -97.317 190.447 -150.21 3
411698 CHILDRESS MUNI AP CHIL 34.433 -100.283 -85.6 62 57.486
411773 CLARKSVILLE 1 W CLAR 33.617 -95.017 400.641 -24.627
411921 COMMERCE COMM 33.200 -95.933 317.534 -74.315
412086 CRANFILLS GAP CRAN 31.767 -97.833 143.384 -2 38.27
412096 CRESSON CRES 32.533 -97.617 162.357 -152.616
412131 CROSS PLAINS 2 CROS 32.133 -99.167 17.09 -19 8.565
412242 DALLAS-FORT WORTH/FORT. TX. DALL 32.900 -97. 017 217.625 -110.663
412244 DALLAS LOVE FIELD DALL 32.850 -96.850 233.32 4 -115.831
412404 DENTON 2 SE DENT 33.200 -97.100 209.069 -77. 506
412715 EASTLAND EAST 32.400 -98.817 49.911 -168.771

### Kansas City Power & Light B - 5 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
413133 FERRIS FERR 32.517 -96.667 251.477 -152.428
413285 FORT WORTH WSFO FORT 32.833 -97.300 191.339 -118.676
413415 GAINESVILLE GAIN 33.633 -97.133 204.862 -29. 433
413546 GILMER 2 W GILM 32.733 -94.983 408.173 -122. 577
413642 GORDONVILLE GORD 33.800 -96.850 230.585 -10. 281
413771 GROESBECK 2 GROE 31.533 -96.533 267.178 -261 .477
414137 HICO HICO 31.983 -98.033 124.123 -214.429
414257 HONEY GROVE HONE 33.583 -95.900 319.106 -31. 63
414520 JACKSBORO 1 NNE JACK 33.233 -98.150 111.378 -75.549
414866 KOPPERL KOPP 32.133 -97.483 175.721 -196.88
414972 LAKE BRIDGEPORT DAM LAKE 33.217 -97.833 140. 846 -76.986
415348 LONGVIEW TX. LONG 32.350 -94.650 441.401 -16 3.699
415463 MABANK 4 SW MABA 32.350 -96.117 303.664 -169 .375
415957 MINERAL WELLS 1 SSW MINE 32.783 -98.117 115. 117 -125.535
415996 MOLINE MOLI 31.400 -98.317 98.07 -279.711
416108 MOUNT PLEASANT MOUN 33.167 -95.000 404.44 -7 4.517
416177 NACOGDOCHES NACO 31.617 -94.650 445.383 -245 .232
416210 NAVARRO MILLS DAM NAVA 31.950 -96.700 250.08 5 -215.548
416270 NEW BOSTON NEW 33.450 -94.417 457.042 -40.4 53
416335 NEW SUMMERFIELD 2 W NEW 31.983 -95.133 397. 806 -206.567
416757 PALESTINE 2 NE PALE 31.783 -95.600 354.65 -2 30.638
416834 PAT MAYSE DAM PAT 33.867 -95.517 353.313 1. 165
417066 PITTSBURG 5 S PITT 32.933 -94.933 411.826 -1 00.148
417300 PROCTOR RESERVOIR PROC 31.967 -98.500 80.081 -216.779
417499 RED SPRINGS 2 ESE RED 33.600 -99.383 -3.269 -35.493
417556 RENO RENO 32.950 -97.567 166.188 -106.202
418047 SANTA ANNA SANT 31.750 -99.333 1.39 -241.254
418583 STAMFORD 1 STAM 32.933 -99.800 -42.179 -109. 493
418623 STEPHENVILLE 1 N STEP 32.250 -98.200 108.034 -184.954
418743 SULPHUR SPRINGS SULP 33.150 -95.633 345.632 -78.835
418778 SWAN SWAN 32.450 -95.417 368.959 -155.811
419163 TRUSCOTT TRUS 33.750 -99.867 -47.911 -18.699
419419 WACOMADISON-COOPER TX. WACO 31.617 -97.233 2 00.54 -253.899
419532 WEATHERFORD WEAT 32.750 -97.767 147.896 -128 .764
419565 WELLINGTON WELL 34.833 -100.217 -79.156 101. 839
419715 WHITNEY DAM WHIT 31.850 -97.367 187.363 -228 .192
419729 WICHITA FALLS/SHEPS AFB TX WICH 33.983 -98.5 00 78.106 7.437
419817 WINCHELL WINC 31.467 -99.167 17.23 -272.793
419893 WOODSON WOOD 33.017 -99.050 27.781 -100.284
419916 WRIGHT PATMAN WRIG 33.300 -94.167 481.088 -5 5.882
118179 SPRINGFIELD SPRI 39.850 -89.680 823.683 69 9.815
114442 JACKSONVILLE JACK 39.730 -90.200 780.783 682 .207

### Kansas City Power & Light B - 6 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km)
116837 PITTSFIELD PITT 39.610 -90.800 730.895 664.2 16
231640 CLARKSVILLE L&D CLAR 39.360 -90.900 724.802 635.788
113666 GREENFIELD GREE 39.350 -90.210 783.935 640.0 88
111284 CARLINVILLE CARL 39.280 -89.880 812.919 635. 084
115841 MORRISONVILLE MORR 39.410 -89.460 847.375 65 3.077
111290 CARLYLE RESERVOIR CARL 38.630 -89.360 864.88 7 567.788
118147 SPARTA SPAR 38.110 -89.710 840.454 507.252
115983 MURPHYSBORO MURP 37.760 -89.360 874.891 471. 721
233601 HANNIBAL WATER WORKS HANN 39.710 -91.360 682 .205 671.178
231600 CLARENCE CANNON DAM CLAR 39.530 -91.630 660. 799 649.357
235050 LONG BRANCH RESERVOIR LONG 39.750 -92.510 58 3.855 668.049
235671 MOBERLY MOBE 39.400 -92.430 593.467 629.742
236012 NEW FRANKLIN NEW 39.010 -92.750 569.027 584 .59
233079 FULTON FULT 38.850 -91.930 640.981 572.019
231283 CAP AU GRIS CAP 39.000 -90.680 747.223 597. 635
238746 WASHINGTON WASH 38.550 -90.980 725.766 545.5 6
237300 ROSEBUD ROSE 38.450 -91.380 692.073 531.55
237263 ROLLA UNIV OF MO ROLL 37.950 -91.780 661.764 473.409
238620 VIENNA VIEN 38.200 -91.980 642.188 499.724
238043 STEELVILLE STEE 38.000 -91.360 697.947 481.9 07
236826 POTOSI POTO 37.960 -90.760 750.633 481.998
238609 VIBURNUM VIBU 37.710 -91.130 720.74 451.526
237506 SALEM SALE 37.550 -91.880 656.478 428.461
230088 ALLEY SPRINGS ALLE 37.150 -91.450 697.833 38 7.234
231674 CLEARWATER DAM CLEA 37.130 -90.760 758.841 3 90.244
232809 FARMINGTON FARM 37.800 -90.410 782.785 467.0 99
230022 ADVANCE ADVA 37.100 -89.900 834.924 394.048
238700 WAPPAPELLO DAM WAPP 36.930 -90.280 803.219 3 72.036
238880 WEST PLAINS WEST 36.750 -91.830 667.791 340. 292
232302 DORA DORA 36.780 -92.230 632.081 340.911
235207 MALDEN MUNICIPAL MALD 36.610 -89.980 833.16 339.238
233999 HORNERSVILLE HORN 36.050 -90.110 827.589 276 .263 220237 ARKABUTLA DAM, MS ARKA 34.450 -90.080 847.34 99.742
221314 CALHOUN CITY 2 NW CALH 33.520 -89.210 937.45 9 5.027
221707 CLARKSDALE CLAR 34.120 -90.340 827.064 60.99 8
221743 CLEVELAND 3 N CLEV 33.480 -90.430 825.402 -1 0.568
223650 GRENADA DAM GREN 33.480 -89.460 914.88 -1.8
224001 HICKORY FLAT HICK 34.370 -89.110 936.648 99. 892
224173 HOLLY SPRINGS 4 N HOLL 34.490 -89.260 921.58 2 111.695
226084 MOUNT PLEASANT 4 SW MOUN 34.540 -89.330 914. 634 116.549
227820 SAREPTA 1 NNE SARE 34.070 -89.180 933.781 66 .081 229079 UNIVERSITY, MS UNIV 34.230 -89.320 919.123 8 2.406

### Kansas City Power & Light B - 7 Trinity Consultants

BART Modeling Protocol Station ID Name ID Latitude Longitude X (km) Y (km) 400876 BOLIVAR WATERWORKS, TN BOLI 35.160 -88.590 9 74.247 192.241
401150 BROWNSVILLE SEWAGE P BROW 35.350 -89.160 920 .598 207.596
403697 GREENFIELD GREE 36.100 -88.470 973.304 297.1 22
404556 JACKSON MCKELLAR-SIP JACK 35.360 -88.550 975 .369 214.706
404561 JACKSON EXP STA JACK 35.370 -88.500 979.742 216.317
405720 MASON MASO 35.240 -89.320 907.463 193.934
405954 MEMPHIS INTL ARPT MEMP 35.030 -90.000 848.38 8 164.54
405956 MEMPHIS WSFO MEMP 35.080 -89.480 894.852 174 .771
406358 MUNFORD MUNF 35.270 -89.490 891.793 195.658
406750 OAK RIDGE OAK 36.000 -84.150 1358.823 338.4 61
408065 SAMBURG WILDLIFE REF SAMB 36.270 -89.190 907 .143 308.843 409219 UNION CITY, TN UNIO 36.240 -89.020 922.623 3 07.148 30064 ALICIA, AR ALIC 35.540 -91.050 748.472 212.20 2
30458 BATESVILLE LIVESTOCK BATE 35.500 -91.460 711. 939 204.668
30530 BEEBE BEEB 35.040 -91.540 708.871 153.189
30936 BRINKLEY BRIN 34.530 -91.110 752.656 100.029
31632 CORNING CORN 36.260 -90.350 803.976 297.429
32148 DUMAS DUMA 33.530 -91.290 745.537 -12.05
32978 GREERS FERRY DAM GREE 35.310 -92.000 664.907 179.784
33132 HARDY HARD 36.170 -91.280 721.923 280.109
34900 MONTICELLO 3 SW MONT 33.360 -91.480 729.54 -3 2.333
35754 PINE BLUFF PINE 34.140 -92.010 673.896 50.191
36920 STUTTGART 9 ESE STUT 34.280 -91.250 742.228 7 1.28 38052 WYNNE, AR WYNN 35.150 -90.480 803.727 173.682

### Kansas City Power & Light C - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## C

D ETAILED 

### E MISSION 

### C ALCULATIONSTable C-1. Model Inputs SummarySource NameBaseElevationLatitudeNAD83LongitudeNAD83Stack Ht.GEPStackHeightStackDiameterExitVelocityGasTemperatureftdec. deg. dec. deg.ft ft ft ft/sec °FLa Cygne - Unit 1850 38.349 -94.646 700 591.2 23.0 92.7 127La Cygne - Unit 285038.348-94.646700597.824.0100.8281BART-EligibleSource NameSO2NOXPM10SO2NOXPM10SO4PMCPMfSOA ECLa Cygne - Unit 173.81 139.07 0.65 6,151.15 11,589.52 53.79 34.37 4.92 5.68 8.59 0.22La Cygne - Unit 299.7942.521.148,316.153,543.4794.9228.1233.2025.587.030.98¶The Highest 24-hr Actual Mass Emission Rates of SO2and NOXare determined from actual monitored data from 2002-2004. The Highest 24-hr Actual Mass Emission Rate of PM10is calculated based on actual fuelusage data (the maximum daily heat input from 2002-2004, the minimum annual average fuel heat contentfrom 2002-2004, the maximum annual average ash content from 2002 to 2004, and the maximum annual averagesulfur content from 2002-2004) and AP-42 emission factors (see Table C-2).Highest 24 hr ActualEmission Rate¶2002-2004ton/24 hrInput to CALPUFFHourly Mass Emission Ratelb/hrTable C-2.PM10 Emissions24-Hr Fuel UsageMaximum 24-HrCoal UsageMaximum 24-HrHeat Input(ton/24hr) (MMBtu/24hr)La Cygne 1 12,806 223,488La Cygne 211,739198,911The maximum daily coal usage is calculated by dividing the maximum daily heat input from 2002-2004 bythe minimum of the annual average heat contents from 2002-2004.The maximum daily heat input is the maximum daily heat input from the years 2002-2004.PM10 Filterable and Condensable Emission FactorsPM10 FilterableEmission Factor*Emission FactorUnitsEmission FactorSourcePM10 CondensibleEmission Factor** Units SourceSulfurContent§(%)Ash Content¥(%)ControlEfficiencyFilterableControlEfficiencyCondensableLa Cygne 1 2.17 lb/tonAP-42 Table 1.1-40.02 lb/MMBtuAP-42 Table 1.1-51.21 8.35 96% 96%La Cygne 212.63lb/tonAP-42 Table 1.1-40.004lb/MMBtuAP-42 Table 1.1-50.345.4999%0%*PM10 filterable emission factors are for "Cyclonefurnace" boiler (La Cygne 1) and "PC-fired, dry bottom, wall-fired" boiler" (La Cygne 2).**PM10 condensable emission factor for La Cygne 1 is for PC boiler with FGD control; the factor for LaCygne 2 is for PC boiler without FGD control.§The coal sulfur content is the maximum of the annual average sulfur contents from 2002-2004.¥The coal ash content is the maximum of the annualaverage ash contents from 2002-2004.24-Hr EmissionsPM10 Filterable 24-Hr Emissions(Uncontrolled)PM10 Condensable24-Hr Emissions(Uncontrolled)Total PM10(Uncontrolled)PM10 FilterableEmissions(Controlled)PM10 CondensableEmissions(Controlled)Total PM10(Controlled)(lb/24-hr) (lb/24-hr) (lb/24-hr) (lb/24-hr) (lb/24-hr) (lb/24-hr)La Cygne 1 27,802 4,470 32,271 1,112 179 1,291La Cygne 2148,232796149,0281,4827962,278Hourly Emissions (Based on 24-hr emissions dividedby 24hrs)PM10 Filterable 24-Hr Emissions(Uncontrolled)PM10 Condensable24-Hr Emissions(Uncontrolled)Total PM10(Uncontrolled)PM10 FilterableEmissions(Controlled)PM10 CondensableEmissions(Controlled)Total PM10(Controlled)(lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr)La Cygne 1 1,158 186 1,345 46 7 54La Cygne 26,176336,2096233.1595Table C-3.La Cygne Unit 1 PM10 SpeciationControlled PM10 Speciation from AP-42 Tables 1.1-5 & 1.1-8Cyclone Furnace w FGD + ESP for Emissions controlassumes heating value of8,794Btu/lb and a sulfur content of1.03% and an ash content of8.05% and a heat inp9,312mmBtu/hr and f(RH) =1Controlled PM10 Emissions(Boldvalues from Table 1.1-5.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (lb/ton) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type Ext.Coef. (lb/mmBtu) Type Ext.Coef.Cyclone 0.0250 0.0050 0.0023 0.6 0.0027 0.00265 1 0.00010 100.020 0.016SO4 3*f(RH)0.004SOA 4Controlled PM10 Emissions(BoldValues from Table 1.1-8.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (lb/ton) (lb/ton) (lb/ton) Coef. (lb/ton) (lb/ton) Coef. (lb/ton) Coef. (lb/ton) (lb/ton) Type Ext.Coef. (lb/ton) Type Ext.Coef.Cyclone 0.4400.0890.040 0.60.0480.0465 1 0.0018 10 0.352 0.281 SO4 3*f(RH) 0.070 SOA 4Controlled PM10 EmissionsBoiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (% of Total) (% of Total) (% of Total) Coef. (% of Total) (% of Total) Coef. (% of Total) Coef. (% of Total) (% of Total) Type Ext.Coef. (% of Total) Type Ext.Coef.Cyclone 100% 20.1% 9.1% 0.6 11.0% 10.6% 1 0.4% 10 79.9% 63.9% SO4 3*f(RH) 16.0% SOA 4If you are given Total PM10 emissions in lb/hr:Controlled PM10 Emissions(BoldValue isInputby user.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (lb/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. (lb/hr) Type Ext.Coef.Cyclone53.810.82 4.92 0.6 5.90 5.68 1 0.22 10 43.0 34.4 SO4 3 8.6 SOA 4Weighted Extinction 3.05.7 2.2103.1 34.4 2.8Coarse 9.1%Coarse 4.9Fine Soil 10.6%Fine Soil 5.7Fine EC 0.4%Fine EC 0.2CPM IOR 63.9%CPM IOR 34.4CPM OR 16.0%CPM OR 8.6100.0%53.8Table C-4.La Cygne Unit 2 PM10 SpeciationControlled PM10 Speciation from AP-42 Tables 1.1-5 & 1.1-6Dry Bottom Boiler burning Pulverized Coal using only ESP for Emissions controlassumes heating value of8596Btu/lb and a sulfur content of0.33% and an ash content of5.41% and a heat inputo8,288mmBtu/hr and f(RH) =1Controlled PM10 Emissions(Boldvalues from Table 1.1-5.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (lb/mmBtu) (lb/mmBtu) (lb/mmBtu) Coef. (lb/mmBtu) (lb/ton) Coef. (lb/mmBtu) Coef. (lb/mmBtu) (lb/mmBtu) Type Ext.Coef. (lb/mmBtu) Type Ext.Coef.PC-DB 0.0270 0.0170 0.0094 0.6 0.0076 0.0073 1 0.0003 100.010 0.008SO4 3*f(RH)0.002SOA 4Controlled PM10 Emissions(BoldValues from Table 1.1-6.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (lb/ton) (lb/ton) (lb/ton) Coef. (lb/ton) (lb/ton) Coef. (lb/ton) Coef. (lb/ton) (lb/ton) Type Ext.Coef. (lb/ton) Type Ext.Coef.PC-DB 0.4640.2920.162 0.60.1300.125 1 0.005 10 0.172 0.138 SO4 3*f(RH) 0.034 SOA 4Controlled PM10 EmissionsBoiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (% of Total) (% of Total) (% of Total) Coef. (% of Total) (% of Total) Coef. (% of Total) Coef. (% of Total) (% of Total) Type Ext.Coef. (% of Total) Type Ext.Coef.PC-DB 100% 63.0% 35.0% 0.6 28.0% 26.9% 1 1.0% 10 37.0% 29.6% SO4 3*f(RH) 7.4% SOA 4If you are given Total PM10 emissions in lb/hr:Controlled PM10 Emissions(BoldValue isInputby user.)Boiler Total PM10 Filterable Coarse Ext. Fine Fine Soil Ext. Fine EC Ext.Condensible CPM IOR Particle CPM OR ParticleType (lb/hr) (lb/hr) (lb/hr) Coef. (lb/hr) (lb/hr) Coef. (lb/hr) Coef. (lb/hr) (lb/hr) Type Ext.Coef. (lb/hr) Type Ext.Coef.PC-DB94.959.8 33.2 0.6 26.6 25.6 1 1.0 10 35.2 28.1 SO4 3 7.0 SOA 4Weighted Extinction 19.9 25.6 9.884.4 28.1 1.8Coarse 35.0%Coarse 33.2Fine Soil 26.9%Fine Soil 25.6Fine EC 1.0%Fine EC 1.0CPM IOR 29.6%CPM IOR 28.1CPM OR 7.4%CPM OR 7.0100.0%94.9

### Kansas City Power & Light D - 1 Trinity Consultants

BART Modeling Protocol 

## A PPENDIX 

## D

R ECEPTOR 

### D ATA Receptor Latitude

(deg) Longitude

(deg) Elevation

(m)
1 36.654

- 92.946 274 2 36.654

- 92.9 38 299 3 36.654

- 92.929 328 4 36.654

- 92.921 365 5 36.663

- 92.963 250 6 36.663

- 92.954 278 7 36.663

- 92.946 335 8 36.663

- 92.938 307 9 36.663

- 92.929 345 10 36.671

- 92.971 261 11 36.671

- 92.963 271 12 36.671

- 92.954 274 13 36.671

- 92.946 331 14 36.671

- 92.938 327 15 36.671

- 92.929 304 16 36.671

- 92.921 335 17 36.671

- 92.913 312 18 36.671

- 92.904 340 19 36.671

- 92.896 361 20 36.671

- 92.888 382 21 36.679

- 92.971 274 22 36.679

- 92.963 274 23 36.679

- 92.954 335 24 36.679

- 92.946 294 25 36 .679

- 92.938 304 26 36.679

- 92.929 279 27 36.679

- 92.921 304 28 36.679

- 92.913 318 29 36.679

- 92.904 335 30 36.679

- 92.896 347 31 36.679

- 92.888 340 32 36.688

- 92.954 247 33 36.688

- 92.946 271 34 36.688

- 92.938 275 35 36.688

- 92.929 274 36 36.68 8

- 92.921 277 37 36.688

- 92.913 304 38 36.688

- 92.904 330 39 36.688

- 92.896 357 40 36.688

- 92.888 384 41 36.688

- 92.879 372 42 36.696

- 92.979 274 43 36.696

- 92.971 293 44 36.696

- 92.963 272 45 36.696

- 92.954 271 46 36.696

- 92.946 274 47 36.696

- 92.938 327

### Kansas City Power & Light D - 2 Trinity Consultants

BART Modeling Protocol Receptor Latitude (deg) Longitude

(deg) Elevation

(m)
48 36.696

- 92.929 316 49 36.696

- 92.921 304 50 36.696

- 92.913 354 51 36.696

- 92.904 346 52 36.696

- 92.896 335 53 36.696

- 92.888 344 54 36.696

- 92.879 364 55 36.704

- 92.971 243 56 36.704

- 92.963 335 57 36.704

- 92.954 324 58 36.704

- 92. 946 335 59 36.704

- 92.938 341 60 36.704

- 92.929 333 61 36.704

- 92.921 306 62 36.704

- 92.913 304 63 36.704

- 92.904 365 64 36.704

- 92.896 304 65 36.704

- 92.888 309 66 36.704

- 92.879 307 67 36.713

- 92.971 270 68 36.713

- 92.963 274 69 36.713

- 92.954 301 70 36.713

- 92.946 274 71 36.713

- 92.938 274 72 36.713

- 92.929 312 73 36.713

- 92.921 274 74 36.713

- 92.913 322 75 36.713

- 92.904 304 76 36.713

- 92.896 275 77 36.713

- 92.888 304 78 36.713

- 92.879 290 79 36.721

- 92.913 249 80 36.721

- 92.904 27 4 81 34.704

- 98.754 454 82 34.704

- 98.746 486 83 34.704

- 98.738 487 84 34.704

- 98.729 478 85 34.704

- 98.721 518 86 34.704

- 98.713 518 87 34.713

- 98.771 510 88 34.713

- 98.763 493 89 34.713

- 98.754 488 90 34.713

- 98.746 615 91 34.713

- 98.738 522 92 34.713

- 98.729 494 93 34.713

- 98.721 609 94 34.713

- 98.713 518 95 34.721

- 98.779 487 96 34.721

- 98.771 518 97 34.721

- 98.763 609 98 34.721

- 98.754 554 99 34.721

- 98.746 578 100 34.721

- 98.738 557 101 34.721

- 98.729 571

### Kansas City Power & Light D - 3 Trinity Consultants

BART Modeling Protocol Receptor Latitude (deg) Longitude

(deg) Elevation

(m)
102 34.721

- 98.721 670 103 34.721

- 98.713 518 104 34.729

- 98.763 518 105 34.729

- 98.754 548 106 34.729

- 98.746 548 107 34.729

- 98.738 518 108 34.738

- 98.763 517 109 34.738

- 98.754 579 110 34.738

- 98.746 613 111 34.738

- 98.738 548 112 34.738

- 98.729 523 113 34.746

- 98.7 71 542 114 34.746

- 98.763 545 115 34.746

- 98.754 552 116 34.771

- 98.679 579 117 34.779

- 98.713 609 118 34.779

- 98.704 654 119 34.779

- 98.696 621 120 34.779

- 98.688 629 121 34.779

- 98.679 579 122 34.779

- 98.671 560 123 34.788

- 98.721 615 124 34.7 88

- 98.713 641 125 34.788

- 98.704 640 126 34.788

- 98.696 662 127 34.788

- 98.688 618 128 34.788

- 98.679 630 129 34.788

- 98.671 534 130 34.796

- 98.721 606 131 34.796

- 98.713 566 132 34.796

- 98.704 633 133 34.796

- 98.696 670 134 34.796

- 98.688 609 135 34.796

- 98.679 579 136 34.796

- 98.671 535 137 34.804

- 98.704 548 138 34.804

- 98.696 518 139 34.804

- 98.688 506 140 35.821

- 93.454 555 141 35.821

- 93.446 589 142 35.821

- 93.421 563 143 35.829

- 93.454 549 144 35.829

- 93.446 487 145 35.829

- 93.4 38 487 146 35.829

- 93.429 490 147 35.838

- 93.454 650 148 35.838

- 93.446 563 149 35.838

- 93.438 540 150 35.838

- 93.429 502 151 35.838

- 93.421 526 152 35.838

- 93.404 534 153 35.838

- 93.396 563 154 35.846

- 93.454 548 155 35.846

- 93.446 628

### Kansas City Power & Light D - 4 Trinity Consultants

BART Modeling Protocol Receptor Latitude (deg) Longitude

(deg) Elevation

(m)
156 35.8 46

- 93.438 623 157 35.846

- 93.429 579 158 35.846

- 93.421 469 159 35.846

- 93.413 457 160 35.846

- 93.404 573 161 35.846

- 93.396 605 162 35.846

- 93.388 588 163 35.854

- 93.454 608 164 35.854

- 93.446 660 165 35.854

- 93.438 598 166 35.854

- 93.429 599 167 35.854

- 93.421 639 168 35.854

- 93.413 457 169 35.854

- 93.404 568 170 35.863

- 93.454 730 171 35.863

- 93.446 681 172 35.863

- 93.438 640 173 35.863

- 93.429 625 174 35.863

- 93.421 426 175 35.863

- 93.413 555 176 35.863

- 93.404 612 177 35.871

- 93.4 63 667 178 35.871

- 93.454 580 179 35.871

- 93.446 656 180 35.871

- 93.438 640 181 35.871

- 93.429 487 182 35.871

- 93.421 457 183 35.871

- 93.413 654 184 35.871

- 93.404 548 185 35.871

- 93.396 622 186 35.871

- 93.388 683 187 35.879

- 93.463 579 188 35.8 79

- 93.454 554 189 35.879

- 93.446 609 190 35.879

- 93.438 622 191 35.879

- 93.429 427 192 35.879

- 93.421 555 193 35.879

- 93.413 502 194 35.879

- 93.404 639 195 35.879

- 93.396 580 196 35.879

- 93.388 639 197 35.888

- 93.446 548 198 35.888

- 93.438 548 199 35.888

- 93.429 438 200 35.888

- 93.421 579 201 35.888

- 93.404 620 202 35.896

- 93.429 579 203 35.896

- 93.421 426 204 35.896

- 93.413 611 205 35.904

- 93.446 604 206 35.904

- 93.438 548 207 35.904

- 93.429 488 208 35.904

- 93.421 402 209 35.904

- 93.4 13 579

### Kansas City Power & Light D - 5 Trinity Consultants

BART Modeling Protocol Receptor Latitude (deg) Longitude

(deg) Elevation

(m)
210 35.904

- 93.404 573 211 35.904

- 93.396 609 212 34.371

- 94.054 365 213 34.371

- 94.046 365 214 34.371

- 94.038 368 215 34.379

- 94.071 411 216 34.379

- 94.063 462 217 34.379

- 94.054 431 218 34.379

- 94.046 518 219 34.379

- 94.038 487 220 34.3 79

- 94.029 396 221 34.388

- 94.104 518 222 34.388

- 94.096 523 223 34.388

- 94.088 548 224 34.388

- 94.079 579 225 34.388

- 94.071 547 226 34.388

- 94.063 538 227 34.388

- 94.054 640 228 34.388

- 94.046 608 229 34.396

- 94.163 335 230 34.396

- 94.154 431 231 34.396

- 94.146 457 232 34.396

- 94.138 414 233 34.396

- 94.129 426 234 34.396

- 94.121 426 235 34.396

- 94.113 388 236 34.396

- 94.104 388 237 34.396

- 94.096 365 238 34.396

- 94.088 386 239 34.396

- 94.079 396 240 34.396

- 94.071 426 241 34.396

- 94.0 63 446 242 34.396

- 94.054 441 243 34.396

- 94.046 457 244 34.396

- 94.038 465 245 34.396

- 94.029 442 246 34.396

- 94.021 426 247 34.404

- 94.163 304 248 34.404

- 94.154 304 249 34.404

- 94.146 319 250 34.404

- 94.138 334 251 34.404

- 94.129 370 252 34.4 04

- 94.121 405 253 34.404

- 94.113 409 254 34.404

- 94.104 450 255 34.404

- 94.096 518 256 34.404

- 94.088 609 257 34.404

- 94.079 534 258 34.404

- 94.071 517 259 34.404

- 94.063 575 260 34.404

- 94.054 600 261 34.404

- 94.046 609 262 34.404

- 94.038 609 263 34.404

- 94.029 561

### Kansas City Power & Light D - 6 Trinity Consultants

BART Modeling Protocol Receptor Latitude (deg) Longitude

(deg) Elevation

(m)
264 34.413

- 94.154 335 265 34.413

- 94.146 432 266 34.413

- 94.138 487 267 34.413

- 94.129 499 268 34.413

- 94.121 514 269 34.413

- 94.113 442 270 34.413

- 94.104 439 271 34.413

- 94.096 395 272 34.413

- 94.088 400 273 34.413

- 94.0 79 426 274 34.413

- 94.071 487 275 34.413

- 94.063 548 276 34.413

- 94.054 548 277 34.413

- 94.046 548 278 34.413

- 94.038 535 279 34.421

- 94.146 304 280 34.421

- 94.138 334 281 34.421

- 94.129 396 282 34.421

- 94.121 457 283 34.421

- 94.113 457 284 34.4 21

- 94.104 426 285 34.421

- 94.096 411 286 34.421

- 94.088 406 287 34.421

- 94.079 396 288 34.421

- 94.071 401 289 34.421

- 94.063 397 290 34.429

- 94.146 322 291 34.429

- 94.138 334 292 36.946

- 90.246 106 293 36.946

- 90.229 102 294 36.954

- 90.238 105 295 36.954

- 90.229 102 296 36.954

- 90.221 102 297 36.963

- 90.238 114 298 36.963

- 90.229 104 299 36.963

- 90.221 102 300 36.963

- 90.213 102 301 36.963

- 90.204 102 302 36.963

- 90.196 103 303 36.971

- 90.229 108 304 36.971

- 90.221 105 305 36.971

- 90.2 13 102 306 36.971

- 90.204 102 307 36.971

- 90.196 102 308 36.971

- 90.188 102 309 36.971

- 90.179 102 310 36.971

- 90.171 102 311 36.979

- 90.221 121 312 36.979

- 90.213 104 313 36.979

- 90.204 102 314 36.979

- 90.196 102 315 36.979

- 90.188 102 316 36.9 79

- 90.179 102 317 36.979

- 90.171 102

### Kansas City Power & Light D - 7 Trinity Consultants

BART Modeling Protocol Receptor Latitude (deg) Longitude

(deg) Elevation

(m)
318 36.988

- 90.213 121 319 36.988

- 90.204 105 320 36.988

- 90.196 102 321 36.988

- 90.188 102 322 36.988

- 90.179 102 323 36.988

- 90.171 101 324 36.996

- 90.204 117 325 36.996

- 90.196 101 326 36.996

- 90.188 101 327 36.996

- 90.179 102 328 36.996

- 90.171 101 329 37.004

- 90.196 106 330 37.004

- 90.188 102 331 37.004

- 90.179 102 332 37.004

- 90.171 102 333 37.013

- 90.188 103 334 37.013

- 90.179 102 335 37.013

- 90.171 102 336 37.021

- 90.179 103 337 37.021

- 90.171 102 338 37.029

- 90.171 103

# BART F IVE 

# F ACTOR 

# A NALYSIS 

#  K ANSAS 

# C ITY 

# P OWER 

# & L IGHT 

## L A 

## C YGNE 

## G ENERATING 

## S TATION



### V ERSION 

### 0

Prepared by:

Kasi Dubbs  Managing Consultant

Mary Pham  Consultant

T RINITY 

### C ONSULTANTS 

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

August 2007

Project 061701.0125

### Kansas City Power & Light 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 2.1 P RESUMPTIVE 

### L IMITS



### ................................................... ......................................... 2-2 2.2 E XISTING 

### C ONTROLS



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

3. B ART 

### A PPLICABILITY 

### D ETERMINATION


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

4. 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



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

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


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

5. 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 

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

### F EASIBLE 

### NO X 

### C ONTROLS 

### I MPACT ON 

### V ISIBILITY 

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

### B ART FOR 

### NO X 

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

### Kansas City Power & Light ii Trinity Consultants

BART Analysis 

### 6. PM BART E VALUATION


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

7. P ROPOSED 

### BART L IMITS 

### C OMPLIANCE SUMMARY 

### .......................................... 7-1 7.1 SO 2 

### BART C OMPLIANCE 

### S UMMARY



### ................................................... ................. 7-1 7.2 NO X 

### BART C OMPLIANCE 

### S UMMARY



### ................................................... ................ 7-2 7.3 PM 10 

### BART C OMPLIANCE 

### S UMMARY



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

### Kansas City Power & Light iii Trinity Consultants

BART Analysis 

## L IST OF 

## T ABLES

T ABLE 

### 1-1. V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### L A 

### C YGNE 

### G ENERATING 

### S TATION 

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

T ABLE 

### 1-2. V ISIBILITY 

### I MPAIRMENT 

### I MPROVEMENT



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

T ABLE 

### 3-1. H IGHEST 

### A CTUAL 

### 24-H OUR 

### SO 2 AND 

### NO X 

### E MISSIONS AND 

### C ALCULATED 

### H IGHEST 

### PM 10 

### E MISSIONS 

### (A S AN 

### H OURLY 

### E QUIVALENT 

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

T ABLE 

### 3-2. S UMMARY OF 

### S TACK 

### P ARAMETERS



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

T ABLE 

### 3-3. S UMMARY OF 

### 98 TH 

### P ERCENTILE 

### V ISIBILITY 

### I MPACTS AND 

### N UMBER OF 

### D AYS WITH 

### V ISIBILITY 

### I MPACT 

### G REATER THAN 

### 0.5  DV



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

T ABLE 

### 3-4. B REAKDOWN OF 

### P OLLUTANT 

### S PECIFIC 

### C ONTRIBUTIONS TO 

### V ISIBILITY 

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

T ABLE 

### 4-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### SO 2 

### E MISSION 

### R ATES



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

TABLE 

### 4-2. A VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES



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

T ABLE 

### 4-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES 

### 4-3

T ABLE 

### 4-4. E XISTING AND 

### PRESUMPTIVE BART E MISSION 

### R ATES 

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

T ABLE 

### 4-5.M ODELED 

### I MPACTS 

### B ASED ON 

### E XISTING AND 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES 

### ..4-5

T ABLE 

### 5-1. E XISTING 

### M AXIMUM 

### 24-H OUR 

### NO X 

### E MISSION 

### R ATE



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

T ABLE 

### 5-2. A VAILABLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES



### ................................................... ................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. NO X 

### C ONTROL 

### T ECHNOLOGY 

### C OST 

### F ACTORS 

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

TABLE 

### 5-5. NO X 

### C ONTROL 

### T ECHNOLOGY 

### C OST 

### S UMMARY 

### ( IN 

### 2005 D OLLARS 

### ) FOR 

### L A 

### C YGNE 

### U NIT 

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

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

T ABLE 

### 5-6. E XISTING AND 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES



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

T ABLE 

### 5-7.M ODELED 

### I MPACTS 

### B ASED ON 

### E XISTING AND 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES 

### 5-10

T ABLE 

### 6-1. H ISTORICAL 

### M AXIMUM 

### 24-H OUR 

### PM 10 

### E MISSION 

### R ATE 

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

T ABLE 

### 6-2. V ISIBILITY 

### I MPAIRMENT 

### C ONTRIBUTIONS



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

### Kansas City Power & Light 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 Kansas City Power & Light (KCP&L) for t he La Cygne Generating Station located in La Cygne, Kansas. There are two boilers at La Cygne. Unit 1 is an 840 MW supercritical cyclone coal boiler that was manufactured in 1973 by Babcock and Wilcox with a scrubber for sulfur dioxide (SO 2 

### ) and particulate matter (PM) control and overfire air that minimizes the formation of thermal nitrogen oxides (NO X 

### ). Unit 2 is a 710 MW radiant opposed-fired pulver ized coal (PC) boiler that was manufactured in 1976 by Babcock and Wilcox with an electrostatic precipitator (ESP) for PM control. Unit 1 burns a blend of Powder River Basi n (PRB) coal and local coal. Unit 2 burns 100 percent PRB coal.

KCP&L has determined that the two boilers at the La Cygne Generating Station contribute greater than 0.5 deciviews (dv) to visibility impairment in a federally protected Class I area when compared to a natural background. Therefore, these two sour ces are subject to BART. A summary of the visibility impairment attributable to the boilers i s provided in Table 1-1.

T ABLE 

### 1-1. V ISIBILITY 

### I MPAIRMENT 

### A TTRIBUTABLE TO 

### L A 

### C YGNE 

### G ENERATING 

### S TATION

Visibility Impairment

Class I Area 98th % 

###  dv Days > 

### 0.5

 dv

Wichita Mountains Wilderness 1.02 57
Hercules Glades Wilderness 1.74 138 Caney Creek Wilderness 1.14 63 Upper Buffalo Wilderness 1.92 125 Mingo Wilderness 0.92 62



### KCP&L used the guidelines in 40 CFR Part 51 1 

### to determine BART for the boilers. Specifically, KCP&L conducted a five-step analysis to determine B ART 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, KCP&L proposes the following as BART:

• NO X 

### – KCP&L proposes to meet the U.S. Environmental P rotection Agency’s (EPA’s) presumptive BART limits of 0.10 lb/MMBtu and 0.23 l b/MMBtu for Unit 1 and Unit 2, respectively, by complying with a combined Unit 1 a nd Unit 2 weighted average limit. This

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

### Kansas City Power & Light 1-2 Trinity Consultants

BART Analysis will be achieved by operating the currently permitt ed SCR on Unit 1 and by installing NO X 

### controls on Unit 2.

• SO 2 

### – KCP&L proposes to meet the U.S. EPA’s presumpti ve BART limit of 0.15 lb/MMBtu for Unit 1 and Unit 2 by complying with a combined Unit 1 and Unit 2 weighted average limit by installing either wet scrubbers or spray d ryer absorbers (SDA) on both units.

• PM – KCP&L proposes that no additional PM controls are required for BART compliance because the PM controls would provide little visibi lity improvement while requiring significant capital expenditures. Although not req uired for BART compliance, KCP&L plans to install baghouses on Unit 1 and Unit 2 for compl iance with other environmental regulations.

The proposed presumptive BART emission rates will r esult in reductions of the visibility impacts attributable to La Cygne Unit 1 and Unit 2. A summ ary of the visibility improvement based on the existing emission rates and presumptive BART emissi on rates is provided in Table 1-2.

T ABLE 

### 1-2. V ISIBILITY 

### I MPAIRMENT 

### I MPROVEMENT

Caney Creek Wilderness Hercules Glades

Wilderness Mingo Wilderness Upper Buffalo Wilderness Wichita Mountains

WildernessExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovement

Max Impact (  dv) 5.16 1.278 75% 4.479 0.998 78% 3.82 0.868 77% 6.359 1.563 75% 8.404 2.117 75% 98% Impact (  dv) 1.138 0.227 80% 1.738 0.357 79% 0.915 0.177 81% 1.923 0.415 78% 1.017 0.205 80% Days > 0.5 63 5 92% 138 6 96% 62 3 95% 125 14 89% 57 7 88%

### Kansas City Power & Light 2-1 Trinity Consultants

BART Analysis 

## 2. I NTRODUCTION AND 

## 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 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 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 red uction 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:

### Kansas City Power & Light 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.

KCP&L performed a BART applicability analysis for L a Cygne Unit 1 and Unit 2 and determined the units are subject to BART. The details of the appl icability determination can be found in Section 3. Subsequently, KCP&L performed an analysis to determ ine BART for each VAP for Unit 1 and Unit 2. The VAPs emitted by La Cygne Unit 1 and 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 matter [PM c 

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

### ], elemental carbon [EC], inorganic condensable particulate matter [IOR CMP] as sulfates [SO 4 

### ], and organic condensable particulate matter [OR CMP] also 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

.


## 2.1 P RESUMPTIVE 

## L IMITS
EPA established presumptive limits in the BART guid elines for electric generating units (EGUs). The presumptive limits were established by reviewin g 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 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 coa l 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 that state agencies apply the presumptive BART limit as a floor for SO 2 

### . The SO 2 

### presumptive limit for both Unit 1 and Unit 2 is 0. 15 lb/MMBtu.

Similarly for NO X 

### , the guidelines state:

“For coal-fired EGUs greater than 200 MW located at greater than 750 MW power plants and operating without post-combustion contro ls (i.e. selective catalytic reduction or selective non-catalytic reduction), we have provided presumptive NO X 

### limits differentiated by boiler design and type of coal burned.”

The guidelines go on to state for cyclone boilers:

“Because of the relatively high NO X 

### emission rates of cyclone units, SCR is more cost-effective than the use of current combustion c ontrol technology for these units. The use of SCRs at cyclone units burning bituminous coal, sub-bituminous coal, and lignite should enable the units to cost-effectively meet NO X 

### rates of 0.10 lbs/MMBtu.

### Kansas City Power & Light 2-3 Trinity Consultants

BART Analysis As a result, we are establishing a presumptive NO X 

### limit of 0.10 lb/MMBtu based on the use of SCR for coal-fired cyclone units greater than 200 MW located at 750 MW power plants.

Therefore, for units greater than 200 MW located at power plants greater than 750 MW, the presumptive limits are also a floor for NO X 

### . Since La Cygne Unit 1 is a cyclone boiler, the presumptive limit is 0.10 lb/MMBtu. For La Cygne U nit 2, which is a dry-bottom wall-fired boiler combusting sub-bituminous coal, the NO X 

### presumptive limit is 0.23 lb/MMBtu.

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

### emissions.



## 2.2 E XISTING 

## C ONTROLS
La Cygne Unit 1 and Unit 2 have existing emission c ontrols. Unit 1 is currently equipped with a scrubber for SO 2 

### and particulate control and overfire air that mini mizes the formation of thermal NO X 

### . KCPL is in the process of voluntarily constructi ng an SCR system for Unit 1 which will further reduce NO X 

### emissions. The SCR project is scheduled to be ope rational by May 2007. Unit 2 is equipped with an electrostatic precipitator (ESP) f or particulate control.

### Kansas City Power & Light 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 deciviews (dv) when compared against a natural background. K CP&L conducted air quality modeling to predict the existing visibility impairment attributable to La Cygne Unit 1 and Unit 2 in the following Class I

areas:



### Wichita Mountains Wilderness (Fish and Wildlife Ser vice [FWS])

### Hercules Glades Wilderness (Forest Service [FS])

### Upper Buffalo Wilderness (FS)

### Caney Creek Wilderness (FS)

### Mingo Wilderness (FWS)

The modeling methods and procedures that were follo wed were provided to the Kansas Department of Health and Environment (KDHE) in a November 2006 modeling protocol. In response to comments provided to KCP&L by KDHE regarding the mo deling protocol, the only change made to the modeling methods and procedures documented in t he protocol was to correct a typographical error for the November monthly humidity factor for Mingo from 3.4 to the correct value of 3.1. Since this change did not warrant a new version of the protocol, the documentation of this change is provided in this report. 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 emission rates based on 2002-2004 co ntinuous 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 condensable fractions and are speciated into the fo llowing:



### Coarse particulate matter (PM C 

### )

### Fine particulate matter (PM f 

### )

### Sulfates (SO 4 

### )

### Secondary organic aerosols (SOA)

### Elemental carbon (EC)

T ABLE 

### 3-1. H IGHEST 

### A CTUAL 

### 24-H OUR 

### SO 2 AND 

### NO X 

### E MISSIONS AND 

### C ALCULATED 

### H IGHEST



### PM 10 

### E MISSIONS 

### (A S AN 

### H OURLY 

### E QUIVALENT 

### )

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) La Cygne - Unit 1 6,151.15 11,589.52 53.79 34.37 4.92 5.68 8.59 0.22 La Cygne - Unit 2 8,316.15 3,543.47 94.92 28.12 33.20 25.58 7.03 0.98



### Table 3-2 summarizes the stack parameters that were used to model La Cygne Unit 1 and Unit 2. It should be noted that the good engineering practice (GEP) stack heights were modeled instead of the actual stack heights since the GEP stack heights ar e less than the actual stack heights.

### Kansas City Power & Light 3-2 Trinity Consultants

BART Analysis T ABLE 

### 3-2. S UMMARY OF 

### S TACK 

### P ARAMETERS

La Cygne Unit 1 La Cygne Unit 2 Latitude (degrees) 38.3486 38.3476 Longitude (degrees) -94.6456 -94.6456 Actual Stack height (ft) 700 700 GEP Stack height (ft) 591.2 597.8 Stack Diameter (ft) 23 24 Exhaust Velocity (ft/s) 92.7 100.8 Exhaust Temperature (F) 127 281



### The results of the modeling indicate that the 98 th 

### percentile of the visibility impacts attributable to La Cygne Unit 1 and Unit 2 are greater than 0.5 dv when compared against a natural background. Since the visibility impacts are greater than 0.5  dv, La Cygne Unit 1 and Unit 2 are subject to BART. The results of the modeling are summarized i n Table 3-3.

T ABLE 

### 3-3. S UMMARY OF 

### 98 TH 

### P ERCENTILE 

### V ISIBILITY 

### I MPACTS AND 

### N UMBER OF 

### D AYS WITH 

### V ISIBILITY 

### I MPACT 

### G REATER THAN 

### 0.5  DV

Visibility Impairment

Class I Area 98th % 

###  dv Days > 

### 0.5

 dv

Wichita Mountains Wilderness 1.02 57
Hercules Glades Wilderness 1.74 138 Caney Creek Wilderness 1.14 63 Upper Buffalo Wilderness 1.92 125 Mingo Wilderness 0.92 62



### Table 3-4 provides a breakdown of the visibility im pacts listed in Table 3-3 by each VAP.

T ABLE 

### 3-4. B REAKDOWN OF 

### P OLLUTANT 

### S PECIFIC 

### C ONTRIBUTIONS TO 

### V ISIBILITY

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

Class I Area (%) (%) (%) (%) (%) (%) ( 

###  dv ) Wichita Mountains Wilderness 82.89 17.02 0.05 0.01 0.01 0.03 1.017 Hercules Glades Wilderness 19.63 79.99 0.20 0.04 0.04 0.10 1.738 Caney Creek Wilderness 24.32 75.46 0.12 0.02 0.02 0.06 1.138 Upper Buffalo Wilderness 20.20 79.53 0.15 0.03 0.02 0.07 1.923 Mingo Wilderness 42.26 57.61 0.07 0.01 0.00 0.03 0. 915



### As shown in Table 3-4, the most significant contrib utors 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 Unit 1 and Unit 2 to 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 Unit

### Kansas City Power & Light 3-3 Trinity Consultants

BART Analysis 1 and Unit 2. The contribution of PM 10 

### to the total visibility impairment can be estimate d as the sum of the contributions from SOA, EC, PMc, and PMf. T he PM 10 

### contribution is much smaller (<1%) than the contribution from SO 2 

### and NO X 

### .

### Kansas City Power & Light 4-1 Trinity Consultants

BART Analysis 

## 4. SO 2 

## BART E VALUATION

SO 2 

### emissions at coal-fired EGUs are the result of the oxidation of the sulfur compounds in the coal during the combustion process.

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

Maximum 24-

Hour Heat Input SO 2

Emission Rate SO 2

Emission Rate (MMBtu/24hr) (lb/hr) (lb/MMBtu) La Cygne - Unit 1 223,488 6,151.15 0.66 La Cygne - Unit 2 198,911 8,316.15 1.00



## 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 BART analyses, and technical lite rature published by the U.S. EPA and Regional Planning Organizations (RPOs).

The available retrofit SO 2 

### control technologies are summarized in Table 4-2.

TABLE 

### 4-2. A VAILABLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES

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 also 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.

### Kansas City Power & Light 4-2 Trinity Consultants

BART Analysis 

### 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 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 La Cygne 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 La Cygne 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 filter 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. 3 

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

### from La Cygne 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

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 Module 6: Air Pollutants and Control Technique s

### Kansas City Power & Light 4-3 Trinity Consultants

BART Analysis

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

### from La Cygne Unit 1 and Unit 2.



## 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.

T ABLE 

### 4-3. C ONTROL 

### E FFECTIVENESS OF 

### T ECHNICALLY 

### F EASIBLE 

### SO 2 

### C ONTROL 

### T ECHNOLOGIES

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%



### As seen in Table 4-3, dry sorbent injection has the lowest estimated control efficiency and will therefore no longer be considered for BART.

It should be noted that Unit 1 has an existing scru bber for SO 2 

### control; however, the current control efficiency of the scrubber is below the efficiencie s for the FGD controls listed in Table 4-3.



## 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 cost of compliance was evaluated for the two te chnologies with the highest SO 2 

### control efficiencies: wet scrubbers and SDA systems . The typical annual cost effectiveness for both wet scrubbers and SDA systems is $200 to $ 500 per ton of SO 2 

### removed at the highest removal efficiencies. The cost effectivene ss was estimated from a published cost in a technical paper. 4 

### This cost estimate is considered to be study grad e, which is +/- 30 percent accuracy.

4 "Assessment of Control Technology Options for BAR T-Eligible Sources: Steam Electric Boilers, Industr ial Boilers, Cement Plants and Paper and Pulp Facilitie s" Northeast States for Coordinated Air Use Managem ent (NESCAUM), March 2005.

### Kansas City Power & Light 4-4 Trinity Consultants

BART Analysis

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. Based on EPA’s guidelines as to what is considered cost effective for SO 2 

### removal and the annual cost effectiveness of $200 to $500 published in the technical paper, wet scrub bers and SDA systems are cost effective.



### 4.4.2 E NERGY 

### I MPACTS AND 

### N ON 

### -A IR 

### Q UALITY 

### I MPACTS



### FGD systems require electricity to operate the blow ers and pumps needed for the operation of the scrubbers. The generation of the electricit y will most likely involve fuel combustion, which will cause emissions. While the required electricity will result in emissions, the emissions should be small compared t o the reduction in SO 2 

### that would be gained by operating an FGD system.

Wet FGD systems generate wastewater and sludge that must be treated. This places additional burdens on the wastewater treatment and solid waste management capabilities. If wet scrubbing produces calcium sulfite sludge, t he sludge will be water-laden, and it must be stabilized for landfilling. If wet scrubbi ng produces calcium sulfate sludge, it is stable and easy to dewater. However, control costs will be higher because additional equipment is required.

Disposal of material from dry FGD systems is also r equired and will result in landfill impacts.



### 4.4.3 R EMAINING 

### U SEFUL 

### L IFE



### The remaining useful life of Unit 1 and Unit 2 do n ot impact the annualized capital costs because the useful lives of the units are anticipat ed 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
The final impact analysis was conducted to assess t he visibility improvement for existing emission rates when compared to the presumptive BART emissio n rates. The existing and presumptive BART emission rates were modeled using CALPUFF. The exi sting emission rates are the same rates that were modeled for the BART applicability analysis. The BART rates are the presumptive limits (in lb/MMBtu) multiplied by the historical maximum dail y heat inputs. A sample calculation of the SO 2 

### presumptive BART hourly equivalent emission rate fo r Unit 1 is provided as follows:



### hr day HI P 24 * * = 1,396.8 lb/hr

Where: P (Presumptive BART Emission Rate) = 0.15 lb/MMBtu HI (2002-2004 maximum daily heat input) = 223,488 MMBtu/day

### Kansas City Power & Light 4-5 Trinity Consultants

BART Analysis The existing and presumptive BART emission rates ar e summarized in Table 4-4.

T ABLE 

### 4-4. E XISTING AND 

### PRESUMPTIVE BART E MISSION 

### R ATES

Existing Emission Rate Presumptive BART Emission Ra te SO 2 NO X PM 10 SO 2 NO X PM 10 (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) La Cygne - Unit 1 6,151.15 11,589.52 53.79 1,396.80 931.20 53.79 La Cygne - Unit 2 8,316.15 3,543.47 94.92 1,243.19 1,906.23 94.92



### The visibility improvement due to the presumptive B ART limits was calculated as the difference between the existing visibility impairment and the visibility impairment for the presumptive rates. A comparison of the existing visibility impacts and t he visibility impacts based on the presumptive BART emission rates, including the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the number of days with a mo deled visibility impact greater than 0.5  dv, for each Class I area is provided in Table 4-5. It sho uld be noted that the visibility impacts presented in Table 4-5 are based on the application of the presu mptive BART limits for both SO 2 

### and NO x 

### . The analysis included the presumptive BART limits from both SO 2 

### and NO X 

### in order to determine the overall visibility improvement that would be gained from applying BART to both pollutants. The presumptive BART limits for NO X 

### are discussed in Section 5.

T ABLE 

### 4-5.M ODELED 

### I MPACTS 

### B ASED ON 

### E XISTING AND 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES

Caney Creek Wilderness Hercules Glades

Wilderness Mingo Wilderness Upper Buffalo Wilderness Wichita Mountains

WildernessExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovement

Max Impact (  dv) 5.16 1.28 75% 4.48 1.00 78% 3.82 0.87 77% 6.36 1.56 75% 8.40 2.12 75% 98% Impact (  dv) 1.14 0.23 80% 1.74 0.36 79% 0.92 0.18 81% 1.92 0.42 78% 1.02 0.21 80% Days > 0.5 63 5 92% 138 6 96% 62 3 95% 125 14 89% 57 7 88%



### As seen in Table 4-5, the visibility impacts (the m aximum visibility impact, 98 th 

### percentile visibility impact, and the number of days with a visibility im pact greater than 0.5  dv) for each Class I area are lower for the presumptive BART emission rates than for the existing emission rates.



## 4.6 P ROPOSED 

## B ART FOR 

## SO 2



### KCP&L has determined that the SO 2 

### BART emission rate for Unit 1 and Unit 2 is the pr esumptive emission rate of 0.15 lb/MMBtu. KCP&L is proposing to meet the presumptive BART SO 2 

### emission rate of 0.15 lb/MMBtu for each unit by complying wi th a combined Unit 1 and Unit 2 weighted average limit. KCP&L will meet this limit by insta lling scrubbing technology (i.e., either wet scrubber or SDA). Wet scrubbers and SDA systems ac hieve the highest levels of SO 2 

### control. In addition, the costs of compliance associated with b oth controls are similar, so no cost effectiveness is

### Kansas City Power & Light 4-6 Trinity Consultants

BART Analysis gained by choosing one control over the other. KCP &L will select one of these control options at a later date pending several factors, including antic ipated performance, availability, and market conditions. Performance, availability, and market conditions at the time of the BART implementation may drive the selection of the control option. At that time, a more detailed study may determine that one control is more favorable than the other.

### Kansas City Power & Light 5-1 Trinity Consultants

BART Analysis 

## 5. NO X 

## BART E VALUATION

NO x 

### from coal-fired EGUs is formed by three fundamental ly different mechanisms. The principle NO x 

### formation mechanism, thermal NO x 

### , arises from the thermal dissociation and subseque nt reaction of nitrogen (N 2 

### ) and oxygen (O 2 

### ) molecules in the combustion air. Most thermal NO x 

### forms in the highest temperature regions of the combustio n chamber (i.e., the air/fuel interface). The seco nd NO x 

### formation mechanism, prompt NO x 

### , arises from early reactions of nitrogen intermedi aries and hydrocarbon radicals in the fuel. The final NO x 

### formation mechanism, fuel NO x 

### , arises from the evolution and reaction of fuel bound nitrogen compo unds with oxygen.

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

Maximum 24- Hour Heat Input NO X

Emission Rate NO X

Emission Rate (MMBtu/24hr) (lb/hr) (lb/MMBtu)

La Cygne - Unit 1 223,488 11,589.52 1.24 La Cygne - Unit 2 198,911 3,543.47 0.43



## 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 BART analyses, and technical lite rature published by the U.S. EPA and the RPOs.

The available retrofit NO X 

### control technologies are summarized in Table 5-2.

T ABLE 

### 5-2. A VAILABLE 

### NO X 

### C ONTROL 

### T ECHNOLOGIES

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

### Kansas City Power & Light 5-2 Trinity Consultants

BART Analysis 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.



### 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 La Cygne 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 

### KCP&L currently uses OFA on La Cygne Unit 1, and this is a technically feasible option for the control of NO X 

### from La Cygne 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

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

### Kansas City Power & Light 5-3 Trinity Consultants

BART Analysis 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. 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.

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



### LNBs and ULNBs are technically feasible for tangent ial and wall-fired boilers of various sizes, but are not feasible for other bo iler types such as cyclone or stoker. 9 

### Since La Cygne Unit 1 is a cyclone boiler, LNBs a nd ULNBs are not technically feasible for the control of NO x 

### from this boiler. LNBs and ULNBs are technically feasible for the control of NO x 

### from La Cygne Unit 2.



### 5.2.2 P OST 

### C OMBUSTION 

### C ONTROLS



### NO X 

### can be reduced to molecular nitrogen (N 2 

### ) in add-on systems located downstream of the furnace. The two main post-combustion control techniques in commercial service are SCR and SNCR.



### 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

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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 4N 2 + 6H 2 O
2NO 2 + 4NH 3 + O 2 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 La Cygne 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 La Cygne 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.

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

### Kansas City Power & Light 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 OFA/LNB* ~30-60 LNB* ~25-50
FGR ~30-50 SNCR ~25-50 OFA ~25-55 *LNBs are technically feasible for La Cygne Unit 2 only, they are not technically feasible for La Cygne Unit 1.



## 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



### Control Costs The capital costs, operating costs, and cost effect iveness for combustion controls and SCR were estimated for La Cygne Unit 2 using an EPA cos t estimate method outlined in the document Nitrogen Oxides (NO X 

### ), Why and How They Are Controlled

. 12 

### These control options were selected because they provide the high est levels of control and are commonly used for NO X 

### control in large utility boilers. For the purpose s of this analysis, LNB with OFA was used to represent a combustion controls sys tem capable of achieving a NO X 

### emission rate of 0.23 lb/MMBtu.

The EPA cost method relies on cost factors for capi tal costs, annual fixed operating and maintenance costs, and annual variable operating an d maintenance costs for each of the control technologies. A summary of the cost factor s is provided in Table 5-4.

12 Nitrogen Oxides (NO X ), Why and How They Are Controlled . EPA 456/F-99-006R, November 1999.Kansas City Power & Light5-6Trinity ConsultantsBART AnalysisTABLE5-4.NOXCONTROLTECHNOLOGYCOSTFACTORS*ControlTechnologyCapacity(MW)CapitalCost($/kW)FixedO&M($/kW/yr)VariableO&M(mils/kWh)Capital Cost($)AnnualizedCapitalCost**($/yr)Annual FixedO&M Cost($/yr)AnnualVariableO&M Cost($/yr)TotalAnnualCost($/yr)CombustionControls System§750 22.8 0.35 0.07 17,100,000 2,008,566 262,500 459,900 2,730,966SCR 750 69.7 6.12 0.24 52,275,000 6,140,222 4,590,000 1,576,800 12,307,022*These cost factors are in 1997 dollars. Table 5-5shows the capital costs, annual fixed O&M costs, and annual variable O&M costs in 2005 dollars.**The annualized cost is based on a recovery periodof 20 years, assuming 10% interest. The capital recovery factor (CRF) of 0.11746 was obtained from EPA's Air Pollution Control Cost Manual,Sixth Edition. EPA 452/B-02-001, January 2002.§For the purposes of this analysis, the cost of a combustion control system is represented by a LNB w/OFA system capable of achieving a NOXemission rate of 0.23 lb/MMBtu. As KCP&L isproposing not to commit to specific combustion controls at this time, KCP&L is using LNB w/ OFA to represent combustion control equipment that can achieve a NOXrate of 0.23 lb/MMBtu.

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BART Analysis

These cost estimates are considered to be study gra de, which is +/- 30 percent accuracy. It is likely that these costs are low, since the costs may not reflect the current high market price for steel and other increased costs associate d with high demand.

KCP&L is in the process of constructing an SCR syst em on La Cygne Unit 1. Since SCR provides the highest level of control there is no n eed to evaluate the cost for other controls. Construction on the SCR system is scheduled to be o perational in May 2007.

Annual Tons Reduced The annual tons reduced that were used in the cost effectiveness calculations for NO X 

### controls on La Cygne Unit 2 were estimated by subtr acting the estimated controlled annual emission rates from the existing annual emission ra tes. The existing annual emission rates were the highest 365-day rolling totals as determin ed from CEMS data from 2002-2004.

The controlled annual emission rates were estimated based on the controlled NO X 

### emission rates for each control method in lb/MMBtu. These e mission rates were multiplied by the maximum 365-day rolling heat input as determined fr om CEMS data from 2002-2004.

A sample of the controlled annual emission rate is provided below for combustion controls with a controlled emission rate of 0.23 lb/MMBtu:



### ton lb yr MMBtu MMBtu lb / 000 ,2 / 314

, 507

, 63 / 23 .0 ÷

× =7,303 tpy

Cost Effectiveness 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. The cost effectiveness for a combustion controls system (rep resented by LNB with OFA) and SCR were determined by dividing the total annualized co st by the annual tons reduced. The control technology costs are summarized for Unit 2 in Table 5-5.

In the BART guidelines, EPA calculated that for all types of boilers other than cyclone boilers, combustion control technology is generally more 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.

Table 5-5 indicates that the cost effectiveness of a combustion controls system (represented by LNB with OFA) for La Cygne Unit 2 is less than $ 1,500 per ton of NO X 

### removed; however, the cost for SCR is over $1500 per ton of NO X 

### removed, which is greater than EPA’s cost estimate for advanced combustion control s.Kansas City Power & Light5-8Trinity ConsultantsBART AnalysisTABLE5-5.NOXCONTROLTECHNOLOGYCOSTSUMMARY(IN2005DOLLARS)FORLACYGNEUNIT2CurrentAnnualEmissionRateEstimated ControlledEmission RateAnnualEmissionsReducedCapitalCostAnnualizedFixedO&MAnnualizedVariableO&MTotalAnnualizedCost*CostEffectiveness(tpy) (lb/MMBtu) (tpy) (ton/yr) ($) ($/yr) ($/yr)($/yr) ($/ton)CombustionControls System** 10,669 0.23 7,303 3,365 20,714,670 317,988 557,116 3,308,249 983SCR 10,669 0.10 3,175 7,493 63,325,110 5,560,254 1,910,111 14,908,532 1,990*The costs are annualized in 2005 dollars.**For the purposes of this analysis, the cost of a combustion control system is represented by a LNB w/OFA system capable of achieving a NOXemission rate of 0.23 lb/MMBtu.

### Kansas City Power & Light 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 blow ers and pumps needed to operate the systems. The generation of the electricity will mo st likely involve fuel combustion, which will cause emissions. While the required electrici ty will result in the emissions, the emissions should be small compared to the reduction in NO x 

### that would be gained by operating an SCR system

SCR can potentially cause significant environmental impacts related to the usage and storage of ammonia. Storage of aqueous ammonia abo ve 10,000 lbs is regulated by a risk management program (RMP), since the accidental rele ase 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 Unit 1 and Unit 2 do n ot impact the annualized capital costs of potential controls because the useful lives of the units are anticipated to be at least as long as the capital cost recovery period, which is 20 ye ars.



## 5.5 E VALUATION OF 

## F EASIBLE 

## NO X 

## C ONTROLS 

## I MPACT ON 

## V ISIBILITY
The final impact analysis was conducted to assess t he visibility improvement for existing emission rates when compared to the presumptive BART emissio n rates. The existing and presumptive BART emission levels were modeled using CALPUFF. The ex isting emission rates are the same rates that were modeled for the BART applicability analysis. The BART rates are the presumptive limits (in lb/MMBtu) multiplied by the historical maximum dail y heat inputs. The existing and BART emission rates are summarized in Table 5-6.

T ABLE 

### 5-6. E XISTING AND 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES

Existing Emission Rate Presumptive BART Emission Ra te SO 2 NO X PM 10 SO 2 NO X PM 10 (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) (lb/hr) La Cygne - Unit 1 6,151.15 11,589.52 53.79 1,396.80 931.20 53.79 La Cygne - Unit 2 8,316.15 3,543.47 94.92 1,243.19 1,906.23 94.92

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BART Analysis The visibility improvement due to the presumptive B ART limits was calculated as the difference between the existing visibility impairment and the visibility impairment for the presumptive rates. A comparison of the existing visibility impacts and t he visibility impacts based on the presumptive BART emission rates, including the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the modeled number of days w ith a modeled visibility impact greater than 0.5  dv, for each Class I area is provided in Table 5-7. It should be noted that the visibility impacts presented in Table 5-7 are based on the application of the presumptive BART limits for both SO 2 

### and NO x 

### . The analysis included the presumptive BART limit s from both SO 2 

### and NO X 

### in order to determine the overall visibility improvement that w ould be gained from applying BART to both pollutants. The presumptive BART limits for SO 2 

### are discussed in Section 4.

T ABLE 

### 5-7.M ODELED 

### I MPACTS 

### B ASED ON 

### E XISTING AND 

### P RESUMPTIVE 

### BART E MISSION 

### R ATES

Caney Creek Wilderness Hercules Glades

Wilderness Mingo Wilderness Upper Buffalo Wilderness Wichita Mountains

WildernessExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovementExistingBARTImprovement

Max Impact (  dv) 5.16 1.28 75% 4.48 1.00 78% 3.82 0.87 77% 6.36 1.56 75% 8.40 2.12 75% 98% Impact (  dv) 1.14 0.23 80% 1.74 0.36 79% 0.92 0.18 81% 1.92 0.42 78% 1.02 0.21 80% Days > 0.5 63 5 92% 138 6 96% 62 3 95% 125 14 89% 57 7 88%



### As seen in Table 5-6, the visibility impacts (the m aximum visibility impact, 98 th 

### percentile visibility impact, and the number of days with a visibility im pact greater than 0.5  dv) for each Class I area are lower for the presumptive BART emission rates than for the existing emission rates.



## 5.6 P ROPOSED 

## B ART FOR 

## NO X



### KCP&L has determined that the NO X 

### BART emission rates are the presumptive emission r ates of 0.10 lb/MMBtu and 0.23 lb/MMBtu for Unit 1 and Unit 2, respectively. The presumptive NO X 

### limit of 0.10 lb/MMBtu for cyclone units is based on the use of SCR and the NO X 

### limit of 0.23 lb/MMBtu is based on combustion controls. Therefore, KCP&L proposes that the currently permitted SCR is equivalent to BART controls for Unit 1 and that com bustion controls, such as LNB or LNB and OFA, or post-combustion controls, such as an SCR, are eq uivalent to BART controls for Unit 2.

Although KCP&L proposes that combustion controls ar e equivalent to BART for Unit 2, KCP&L proposes to install an SCR for Unit 2 and further s tudy the installation of combustion controls on Uni t 2. KCP&L will further study combustion control opt ions at a later date pending several factors, including other regulatory requirements, availabili ty, and market conditions. The concerns surrounding these factors are described below.

La Cygne Generating Station is located just south o f the Kansas City metropolitan area. Based on ozone monitoring data for the past several ozone se asons, KCP&L anticipates that the Kansas City metropolitan area may soon implement regulatory req uirements to reduce volatile organic compound (VOC) and NO X 

### emissions in the area in order to avoid designatio n as a nonattainment area for the

### Kansas City Power & Light 5-11 Trinity Consultants

BART Analysis ozone National Ambient Air Quality Standard (NAAQS) . Controls at La Cygne Generating Station have already been proposed as part of the Kansas Ci ty Ozone Maintenance Plan, which has been submitted and approved by EPA. This may require KC P&L to install NO X 

### controls sooner than the BART deadline for controls implementation. The ext ent of NO X 

### reductions and the exact timeline for implementation that would be required are unknown a t this time. KCP&L would like to retain the flexibility to choose a control option based on upc oming ozone standard regulatory requirements that may require additional NO X 

### controls prior to the BART deadline.

Lastly, market conditions and vendor availability a t the time of the BART implementation may drive the selection of combustion control options. At th at time, a more detailed study analysis may determine that one combustion control is more favor able than the other.

Additional details on the proposed compliance demon stration methods are provided in Section 7.

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BART Analysis 

## 6. PM BART E VALUATION

The primary source of PM from 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, wh ich 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, sulfates, and sulfuric acid mist.

The maximum daily PM 10 

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

T ABLE 

### 6-1. H ISTORICAL 

### M AXIMUM 

### 24-H OUR 

### PM 10 

### E MISSION 

### R ATE

Maximum 24-

Hour Heat Input PM 10

Emission Rate PM 10

Emission Rate (MMBtu/24hr) (lb/hr) (lb/MMBtu)

La Cygne - Unit 1 223,488 53.79 0.006 La Cygne - Unit 2 198,911 94.92 0.011



### From Table 6-1 it can be seen that the current PM 10 

### emission rates for Unit 1 and Unit 2 are much less than the current emission rates of SO 2 

### and NO X 

### . The low PM 10 

### emission rates correspond to very low visibility impacts attributable to PM 10 

### when compared to the impacts attributable to SO 2 

### and NO X 

### , as shown in Table 6-2.

T ABLE 

### 6-2. V ISIBILITY 

### I MPAIRMENT 

### C ONTRIBUTIONS

Year Visibility

Impairment Attributable to SO 4 1 Visibility

Impairment Attributable to NO 3 2 Visibility

Impairment Attributable to PM 10 2 (%) (%) (%) Wichita Mountains 82.89 17.02 0.10 Hercules Glades 19.63 79.99 0.38 Caney Creek 24.32 75.46 0.22 Upper Buffalo 20.20 79.53 0.27 Mingo Wildlife 42.26 57.61 0.11 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 attributabl e to all modeled primary PM species (PMc, PMf, EC, and SOA).



### Given the small PM 10 

### emission rates compared to SO 2 

### and NO X 

### and the small contribution of PM 10 

### to the visibility impacts, any additional control tech nology would provide little visibility improvement and require significant capital expenditures. Ther efore, additional control technologies would not be cost effective, and are not required as PM control technologies for BART compliance. Although not

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BART Analysis required for BART compliance, KCP&L plans to instal l a baghouse on Unit 1 and Unit 2 for compliance with other environmental regulations.

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BART Analysis 

## 7. P ROPOSED 

## BART L IMITS 

## C OMPLIANCE SUMMARY



## 7.1 SO 2 

## BART C OMPLIANCE 

## S UMMARY

Based on the SO 2 

### BART analysis, KCP&L has determined that the BART limit for SO 2 

### for Unit 1 and Unit 2 is the presumptive emission limit of 0.15 lb /MMBtu. Per the BART guidelines, states may allow sources to “average” emissions across any set of BART-eligible emission units within a fenceline, so long as the emission reductions from each pollutant being controlled for BART would be equal to those reductions that would be obtained by simply controlling each of the BART-eligible units that constitute a BART-eligible source. The weighted average of the BART limits for Unit 1 and Unit 2 is 0.15 lbs/MMBtu.

Even though the weighted average of the presumptive BART limits for Unit 1 and Unit 2 is 0.15 lb/MMBtu, KCP&L proposes to demonstrate compliance with a limit that is more stringent than this limit. Specifically, KCP&L proposes a weighted ave rage limit of 0.10 lb/MMBtu on a 30-day rolling average for Unit 1 and Unit 2. The proposed weight ed average emission limit is below the presumptive BART emission rates which will result i n reductions of the visibility impacts attributable to La Cygne Unit 1 and Unit 2 in excess of those in dicated in Sections 4 and 5.

KCP&L proposes to demonstrate compliance with the U nit 1 and Unit 2 weighted average emission limit of 0.10 lbs/MMBtu using the existing CEMS wit h a new 30-day rolling average report. The CEMS software will be configured to generate a two unit weighted average. The daily average SO 2 

### rate for each unit will be the average of the hourl y SO 2 

### rates for each hour of a particular boiler operating day, excluding periods of start-up and sh ut-down. A boiler operating day will be any day that the unit combusts fuel for any period of time, excluding periods of start-up and shut-down. The two-unit daily average will be calculated using the following equation:

((SO 2 

### Day#1 x HI sum-day#1 ) + (SO 2 

### Day#2 x HI sum-day#2 ))

_____________________________________ (HI sum-day#1 + HI sum-day#2 )

Where: SO 2 

### day#1 = Daily average of hourly SO 2 

### rates for Unit 1
SO 2 

### day#2 = Daily average of hourly SO 2 

### rates for Unit 2
HI sum-day#1 = Summation of hourly heat inputs for the day for Unit 1
HI sum-day#2 = Summation of hourly heat inputs for the day for U nit 2

The 30-day rolling average will be the simple avera ge of the last 30 days of two-unit daily averages and will be in compliance if it is less than 0.10 l bs/MMBtu, excluding periods of start-up and shutdown.

Periods of start-up and shut-down for Unit 1 and Un it 2 will be based on good industry practice or the manufacturer's recommendations.

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BART Analysis 

## 7.2 NO X 

## BART C OMPLIANCE 

## S UMMARY

Based on the NO X 

### BART analysis, KCP&L has determined that the BART limits for NO X 

### are the presumptive emission limits of 0.10 lb/MMBtu and 0. 23 lb/MMBtu for Unit 1 and Unit 2, respectively. Per the BART guidelines, states may a llow sources to “average” emissions across any set of BART-eligible emission units within a fencel ine, so long as the emission reductions from each pollutant being controlled for BART would be equal to those reductions that would be obtained by simply controlling each of the BART-eligible units that constitute a BART-eligible source. The weighted average of the presumptive BART limits for Unit 1 and Unit 2 is 0.16 lbs/MMBtu. The Unit 1 + Unit 2 weighted average emission limit has been calculated as follows:



#  

= 

### ×

2 2

1

i im i im ip



### HI HI R

Where R ip 

### = Presumptive BART emission rate for unit i (lb/MM Btu) HI im 

### = Maximum daily heat input from 2002-2004 (MMBtu/d ay) for unit i

The detailed calculation of the weighted presumptiv e emission limit is shown below.



# ( ) ( ) ( ) = + × + ×

m

m m

p m p 

## HI HI HI R HI R

2

1 2

2 1 1 

### 0.16 lb/MMBtu

Where: R 1p 

### = Presumptive BART emission rate for Unit 1 (lb/MM Btu) = 0.10 lb/MMBtu HI 1m 

### = Maximum daily heat input from 2002-2004 (MMBtu/d ay) for Unit 1 = 223,488 MMBtu/day R 2p 

### = Presumptive BART emission rate for Unit 2 (lb/MM Btu) = 0.23 lb/MMBtu HI 2m 

### = Maximum daily heat input from 2002-2004 (MMBtu/d ay) for Unit 2 = 198,911 MMBtu/day

Even though the weighted average of the presumptive BART limits for Unit 1 and Unit 2 is 0.16 lb/MMBtu, KCP&L proposes to demonstrate compliance with a limit that is more stringent than this limit. Specifically, KCP&L proposes a weighted ave rage limit of 0.13 lb/MMBtu on a 30-day rolling average for Unit 1 and Unit 2. The proposed weight ed average emission limit is below the presumptive BART emission rates which will result i n reductions of the visibility impacts attributable to La Cygne Unit 1 and Unit 2 in excess of those in dicated in Sections 4 and 5.

KCP&L proposes to demonstrate compliance with the U nit 1 and Unit 2 weighted average emission limit of 0.13 lbs/MMBtu using the existing CEMS wit h a new 30-day rolling average report. The CEMS software will be configured to generate a two unit weighted average. The daily average NO X 

### rate for each unit will be the average of the hourl y NO X 

### rates for each hour of a particular boiler

### Kansas City Power & Light 7-3 Trinity Consultants

BART Analysis operating day, excluding periods of start-up and sh ut-down. A boiler operating day will be any day that the unit combusts fuel for any period of time, excluding periods of start-up and shut-down. The two-unit daily average will be calculated using the following equation:

((NO X 

### Day#1 x HI sum-day#1 ) + (NO X 

### Day#2 x HI sum-day#2 ))

_____________________________________ (HI sum-day#1 + HI sum-day#2 )

Where: NO X 

### day#1 = Daily average of hourly NO X 

### rates for Unit 1
NO X 

### day#2 = Daily average of hourly NO X 

### rates for Unit 2
HI sum-day#1 = Summation of hourly heat inputs for the day for Unit 1
HI sum-day#2 = Summation of hourly heat inputs for the day for Unit 2

The 30-day rolling average will be the simple avera ge of the last 30 days of two-unit daily averages and will be in compliance if it is less than 0.13 l bs/MMBtu, excluding periods of start-up and shutdown.

In the event of an extraordinary situation involvin g an extended outage of Unit 2 (duration in excess of 10 weeks) KCP&L will submit a plan to KDHE to ac hieve compliance with a La Cygne Unit 1 NO X 

### presumptive limit of 0.10 lbs/MMBtu on a 30-day ro lling average excluding periods of start-up and shut-down. The 10-week period is intended to cover all anticipated scheduled outages so that this scenario would only come up in the case of a u nit retirement or a catastrophic loss of one unit.

Periods of start-up and shut-down for Unit 1 and Un it 2 will be based on good industry practice or the manufacturer's recommendations.



## 7.3 PM 10 

## BART C OMPLIANCE 

## S UMMARY

KCP&L proposes that no additional PM controls are r equired for BART compliance. Although not required for BART compliance, KCP&L plans to instal l a baghouse on Unit 1 and Unit 2 for compliance with other environmental regulations.

KCP&L proposes to demonstrate compliance with the L a Cygne Unit 1 and Unit 2 average emission limit of 0.015 lbs/MMBtu for PM 10 

### (filterable) and 0.024 lbs/MMBtu for PM 10 

### (total) by conducting annual stack tests. Compliance is demonstrated if the weighted average stack test results of the two units combined meets the emission limits. Annual s tack test results will be the average of three onehour stack tests for each unit.