### C

- 1 

# Appendix C:

Technical Support Document for

Visibility Modeling Analysis for

Entergy Independence Generating Station

Prepared by:

Michael Feldman

March 2015

### C

- 2 

# Table of Contents

List of Tables

................................ ................................ ................................ ...................... 3

Chapter 1: Introduction

................................ ................................ ................................ ... 4

Chapter 2: Modeling Protocol

................................ ................................ ........................ 5

2.1 Background and Introduction

................................ ................................ ............... 5

2.2 Calculation of Visibility Impact

................................ ................................ ........... 7

2.2.1 IMPROVE Equation

................................ ................................ ..................... 7

2.2.2 Revised IMPROVE Equation

................................ ................................ ....... 8

2.2.3 Deciview Impact Calculation

................................ ................................ ........ 9

2.3 CALMET/CALPUFF modeling protocol

................................ .......................... 11

Chapter 3: Emission Calculation Methodology

................................ ............................ 13

3.1 NO

x 

### and SO 2 

### emissions

................................ ................................ ...................... 13

3.1.1 Baseline Emissions

................................ ................................ ..................... 13

3.1.2 Controlled Emission Rates

................................ ................................ .......... 15

3.2 PM Emissions and Speciation

................................ ................................ ............ 15

3.3 Sulfuric Acid Emissions Calculations

................................ ................................ 17

Chapter 4: EPA Visibility Modeling

................................ ................................ ............ 18

4.1 EPA Modeling Scenarios

................................ ................................ ................... 18

4.2 EPA Visibility Results

................................ ................................ ....................... 18

4.2.1 Visibility Analysis of SO 2 

### Controls

................................ ............................ 19

4.2.2 Visibility Analysis of NOx Controls

................................ ........................... 21

4.2.3 Visibility Bene fit of NOx and SO 2 

### controls

................................ ............... 22

Attachment A. Emission Rates

................................ ................................ ........................ 24

Attac hment B. Visibility Modeling Results

................................ ................................ ..... 25

### C

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# List of Tables

Table 2

- 1. Distance (km) from source to Class I Area

................................ ...................... 5

Table 2

- 2. Class I Areas

................................ ................................ ................................ ...... 5

Table 2

- 3. Facility location

................................ ................................ ................................ . 5

Table 2

- 4. Stack Parameters for Each Unit

................................ ................................ ........ 5

Figure 2

- 1. Location o f Source and Class I Areas

................................ ............................. 6

Table 2

- 5. Natural Background Concentrations used for “Method 8”

............................ 10

Table 2

- 6. f

L 

### (RH) Large RH Adjustment Factors

................................ ............................ 10

Table 2

- 7. f

S 

### (RH) Small RH Adjustment Factors

................................ ............................ 10

Table 2

- 8. f

SS 

### (RH) Sea Salt RH Adjustment Fact ors

................................ ....................... 10

Table 2

- 9. Selected CALMET Settings Used

................................ ................................ ... 12

Table 3

- 1. Maximu m 24

- hr emissions

................................ ................................ .............. 14

Table 3

- 2. Annual emissions

................................ ................................ ............................. 14

Table 3

- 3. PM 10 

### Emission Calculation

................................ ................................ ............ 15

Table 3

- 4. PM Speciation for modeled control scenarios

................................ ................ 16

Table 3

- 5. Parameters for Sulfuric Aci d Emission Calculations (EPRI method)

............ 17

Table 4

- 1. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile Visibility

Impacts (∆dv) of SO 2 

### Controls (Facility

- w ide)

................................ ................................ 19

Table 4

- 2. Entergy Independence Unit 1: EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) of SO 2 

### Controls

................................ ................................ ......... 20

Table 4

- 3. Entergy Independence Unit 2: EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) of SO 2 

### Controls

................................ ................................ ......... 20

Table 4

- 4. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile Visibility

Impacts (∆dv) of SO 2 

### Controls (Facility

- wide) with BASE2

................................ ........... 21

Table 4

- 5. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile Visibility

Impacts (∆dv) of NOx Controls (Facility

- wide) with BASE2

................................ .......... 22

Table 4

- 6. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile Visibility

Impacts (∆dv) of NOx and SO 2 

### Controls (Facility

- wide) with BASE and BASE2

......... 23

### C

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## Chapter 1: Introd u c tion

Units 1 and 2 of the Entergy Independence plant are coal

- fired

, tangentially

- fired boilers

with a nameplate capacity of 90 0 MW each and a nominal heat input capacity of 8,950

million British thermal units per hour (MMBtu/hr) each. The boilers burn sub

- bituminous

coal as the primary fuel and No. 2 fuel oil or biofuel as a start

- up fuel. These units are currently equipped with electrostatic precipitators (ESPs).

EPA modeled the visibility impacts of the Entergy Independence plant to evaluate the

degree of visibility benefit anticipated due to the use of controls at the facility. This

visibility modeling effort is described in detail in this document

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- 5 

## Chapter 2: Modeling Protocol

2.1 B ACKGROUND AND 

### I NTRODUCTION



### We use d the CALPUFF model to analyze the visibility impacts and benefits of controls at

the Independence facility consistent with the approach recommended by the BART

Guidelines for comparing visibility improvement from control alternatives at a single

source. 1 

### The BART guidelines recommend use of the CALPUFF air quality dispersion

model to estimate the visibility improvements at each Class I area, typically within

roughly a 300 km (186 mile) radius of the source, of alternative control technologies, and

to compa re these to each other and to the impact of the baseline, or current, source

configuration. The four Class I Areas within approximately 300 km of the facility were

included in the modeling analysis. The facility and Class I areas are listed in Table 2

- 1

and 2

- 2 and shown in the map in Figure 2

- 1. Table 2

- 3 lists the facility’s location. Table

2

- 4 lists stack parameters of the facility utilized in the visibility modeling.

Table 2

- 1 . Distance (km) from source to Class I Area

Caney

Creek Hercules

-

Gl ades Upper

Buffalo Mingo

Independence 277 173 180 174



### Table 2

- 2

. Class I Areas

Class I Area State Agency IMPROVE

monitor

Caney Creek Wilderness Area AR USFS CACR

Hercules G l a des Wilderness Area MO USFS HEGL

Upper Buffalo Wilderness Area AR USFS UPBU

Mingo Wilderness Area MO FWS MING



### Table 2

- 3. Facility location 2

Facility Latitude Facility Longitude

35.6733

- 91.4083



### Table 2

- 4. Stack Parameters for Each Unit 3

stack height (m) base elevation (m) stack diameter (m) exit velocity (m/s) exit temp (K)

304.8 71.9 7.833 27.43 434

1 70 FR 39170 (July 6, 2005). CALPUFF is the single source air quality model that is recommended in the

BART Guidelines. Since CALPUFF was used for this analysis, the modeling results were post

- processed in

a manner consistent with the BART Guidelines.

2 F acility location obtained from EPA Air Markets Program Data ( http://ampd.epa.gov/ampd/ )

3 Stack height, diameter, exit velocity, and temperature parameters are consistent with stack parameters

used for the sister facility (White Bluff) BART modeling. See “Revised BART Five Factor Analysis White

Bluff Steam Electric Station Redfield, Arkansas (AFIN 35

- 00110),” dated October 2013, prepared by

Trinity Consultants Inc. in conjunction with Entergy Services Inc. Elevation data obtained from Google

Earth.

### C

- 6 Figure 2

- 1. Location of Source and Class I Areas

T he measure of the visibility improvement to be used in comparisons between various

control scenarios is the 98 th 

### percentile of impacts expressed as 24

- hour averages of delta

deciviews relative to natural background, as estimated using the CALPUFF air quality

modeling system. The meaning of this is described next, followed by particulars of the

application of CALPUF F to the visibility analysis for Entergy Independence

.

### C

- 7

2.2 C ALCULATION OF 

### V ISIBILITY 

### I MPACT



### Under the RHR, visibility is measured in deciviews. Visibility is traditionally described

in terms of visual range in kilometers or miles. However, the visual range scale does not

correspond to how people perceive visibility: a given increase in visual r ange is perceived

differently depending on how good the original visibility was (that is, it is not on a linear

scale). The deciview scale is designed to address this problem. It is linear with respect to

perceived visual changes over its entire range, a nalogous to the decibel scale for sound: a

given change in deciviews will be perceived as the same amount of visibility change,

whatever the original visibility was. The defining equation is:

deciviews = 10*ln(b ext 

### / 10) (1)

where ln is the natur al logarithm, and extinction b ext 

### is the fraction of light scattered out

of a viewing path. Extinction increases with the amount of pollution (b ext 

### is in units of

1/Mm , “inverse megameters”). Lower deciview values represent better visibility, more

pristi ne atmospheres, and greater visual range, while increasing deciview values represent

increasingly poor visibility. An increase of 1 deciview corresponds to about a 10%

increase in extinction. (Deciviews are related to the more traditional visual range

ac cording to dv = 10*ln(391 / visual range)).

T he CALPUFF air quality model predicts 24

- hour average pollutant concentrations based

on source emissions and how they disperse in the atmosphere. The CALPUFF modeling

includes source emissions of the follow ing visibility

- impairing pollutants: SO 2 

### , SO 4



### , NO

x 

### , secondary organic aerosol (SOA), fine particulate matter (PMF), coarse particulate

matter (PMC), and elemental carbon (EC). CALPUFF incorporates a semi

- empirical

chemical module that simulates the conver sion of SO 2 

### to particulate sulfate and NO

x 

### to

particulate nitrate, at a rate dependent on meteorological conditions and background

ozone concentration. These concentrations are converted to deciviews by the CALPOS T

post

- processor in two steps.

2.2.1 IMPROVE Equation

Under the original IMPROVE 4 

### equation, extinction ( b ext 

### ) is estimated from the predicted

concentrations of various pollutants:

b ext 

### = 3 * f(RH) * [sulfate]

+ 3 * f(RH) * [nitrate]

+ 4 * [organic mass]

+ 10 * [ elemental carbon ]

+ 1 * [ fine soil ]

+ 0.6 * [ coarse mass ]

4 Interagency Monitoring of Protected Visual Environments (IMPROVE) is a network of monitor s in

various Class I Areas, established to assess visibility impairment and its causes. The IMPROVE equation

is used to converted monitored concentrations into extinction, a measure of visibility. See:

http://vista.cira.colostate.edu/improve/

### C

- 8 + 10

The 10 is for Rayleigh scattering

, which is due to the interaction of light with molecules

of air itself with no pollutants

. The f(RH) is a water growth factor for sulfate and nitrate ;

i ts value depends on relative humidity (RH) , ranging from 1 at low humidity to 18 at 98%

humidity.

2.2.2 Revised IMPROVE Equation

EPA used the “revised” IMPROVE equation 5 

### to calculate light extinction. Sometimes

the revised IMPROVE equation is called the “New” Improve equation. The revised

IMPROVE equation is used to convert measured or modeled concentrations into

extinction for each pollutant chemical species, and then total them up, accounting for the

effect of relative humidity

.

Revised IMPROVE equation:

b ext 

### = 2.2 * f

s 

### (RH) * [small sulfate] + 4.8 * f L 

### (R H) * [large sulfate]

+ 2.4 * f

s 

### (RH) * [small nitrate] + 5.1 * f L 

### (RH) * [large nitrate]

+ 2.8 * [small organic mass] + 6.1 * [large organic mass]

+ 10 * [ elemental carbon ]

+ 1 * [ fine soil ]

+ 1.7 * f

ss 

### (RH) * [sea salt]

+ 0.6 * [ coarse mass ]

+ Rayleigh scattering (site

- specific)

+ 0.33 * [NO 2 

### (ppb)]

Sulfate is assumed to be all “large sulfate” if total sulfate is over 20 μg/m 3 

### , otherwise its

fraction of the total is assumed to increase uniformly between 0 and 1 when the total i s in

the range between 0 and 20 (i .e. large sulfate = (total sulfate/20)*total ) . A similar

definition applies for nitrate and for organic mass. The organic mass is assumed to be 1.8

times the organic carbon mass that is measured by IMPROVE monitors, an i ncrease over

the old 1.4. Sea salt is estimated as 1.8 * [chloride] (or chlorine if chloride not available)

T he f

s 

### , f L 

### , f

ss 

### are water growth factors for small (“S”) and large (“L”) fractions of sulfate

and nitrate, and for sea salt (“SS”). Their values depend on relative humidity, ranging

from 1 at low humidity to over 5 at 95% humidity. Rayleigh scattering is due to the

interaction of light with molecules of air itself with no pollutants.

The IMPROVE program revised the IMPROVE equation after a scien tific assessment of

its implications for regional haze planning to reduce biases in light extinction estimates

compared to the old algorithm 6



### . In particular, when compared to nephelometer direct

5 Pitchfo rd, M. L., W. C. Malm, B. A. Schichtel, N. Kumar, D. Lowenthal and J. L. Hand, Revised

algorithm for estimating light extinction from IMPROVE particle speciation data, Journal of the Air &

Waste Management Association, 57 , 1326

- 1336, 2007.

6 Revised IMPRO VE algorithm for Esimating Light Extinction from Particle Speciation Data , IMPROVE,

January 2006 ( http://vista.cira.colostate.edu/improve/Publications/GrayLit/ gray_literature.htm ) ; Hand,

J.L., Douglas, S.G., 2006, Review of the IMPROVE Equation for Estimating Ambient Light Extinction

Coefficients

– Final Report

### C

- 9

measurements of visibility extinction, the original IMPROVE equation over

- predicts for

low extinction conditions and under

- predicts for high extinction. These biases have

direct relevance for estimates for the worst 20% visibility days that are used to as sess visibility impact. The new equation shows broader scatter overall, but less bias in

matching visibility measurements under high and low visibility conditions. The split

between small and large particles was the main factor in reducing the biases. Th e revised

IMPROVE equation has less bias, is more refined, accounts for more pollutants and

pollutant sizes , incorporates more recent data, and is based on considerations of relevance

for the calculations needed for assessing progress under the R egional H a ze R ule

.

EPA 2007 guidance 7 

### states that the use of either the IMPROVE or the revised IMPROVE

equation is acceptable provided that the same algorithm is utilized for both the base and

future extinction calculations. EPA believes it is appropriate to use t he revised

IMPROVE equation and that this is the preferred method

.

2.2.3 Deciview Impact Calculation

In the second CALPOST step this extinction is converted to deciviews, using the defining

equation (equation 1) for deciviews. The delta deciviews (Δdv) repr esents the impact on

visibility in deciviews of the source being evaluated, and is also provided by CALPUFF.

It is the change, or “delta”, between deciviews with and without the source. D elta

deciviews is calculated as the difference between deciviews in cluding the impact of the

source and natural background, and deciviews of the natural background alone. Each

modeled day and location in the Class I area will have an associated delta deciview. For

each day, the model finds the maximum visibility impact of all locations (receptors) in

the Class I area. From among these daily values, the BART guidelines recommend use

of the 98th percentile, roughly equivalent to the 8 th 

### highest day, visibility impacts

expressed as 24

- hour averages of delta deciviews relat ive to natural background, as

estimated using the CALPUFF air quality modeling system for comparing the base case

and the effects of various controls. The 98th percentile is recommended rather than the

maximum value to avoid undue influence from unu sual m eteorological conditions when

CALPUFF is executed according to EPA’s guideline on Air Quality Models (40 CFR

Part 51 Appendix W)

.

T he BART guidelines recommend that impacts of sources be estimated in deciviews

relative to natural background. EPA used ave rage background concentrations (Table 2

- 5 )

and relative humidity adjustment factors (Tables 2

- 6 , 2

- 7 , and 2

- 8 ) from the Federal Land

Manag ers’ Air Quality Related Values Work Group (FLAG) Phase I Report 8 

### for use with

the revised IMPROVE equation (“Method 8”)

.

( http://vista.cira.colostate.edu/improve/Publications/GrayLit/016_IMPROVEEeqReview/IMPROVEeqRevi

ew.htm )

7 Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals

for Ozone, PM2.5, and Regional Haze , EPA

- 454/B

- 07

- 002

, April 2007, EPA OAQPS;

( http://www.epa.gov/ttn/scram/guidance/guide/final

- 03

- pm

- rh

- guidance.pdf )

8 Federal Land Managers’ Air Quality Related Values Work Group (FLAG) Phase I Report

— Revised

(2010) Natural Resource Report NPS/NRPC/NRR

— 2010/232

( http://www.nature.nps.gov/air/Pubs/pdf/flag/FLAG_2010.pdf )

### C

- 10 Table 2

- 5 . Natural Background Concentrations used for “Method 8”

Class I Area CALPOST control input parameters (μg/m 3 )

B KSO4 BKNO3 BKOC BKEC BKSOIL BKCM BKSALT

Ammonium

sulfate Ammonium

nitrate Organic

carbon Elemental

carbon Soil Coarse

mass Sea salt

Caney Creek 0.23 0.10 1.80 0.02 0.50 3.00 0.03

Upper Buffalo 0.23 0.10 1.80 0.02 0.50 3.00 0.03

Hercules

Glades 0.23 0.10 1.80 0.02 0.50 3.00 0.02

Mingo 0.23 0.10 1.83 0.02 0.51 3.05 0.04



### Table 2

- 6

. f

L 

### (RH) Large RH Adjustment Factors

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

Caney Creek 2.77 2.53 2.37 2.43 2.68 2.71 2.59 2.6 2.71 2.69 2.67 2.79

Upper Buffalo 2.71 2.48 2.31 2.33 2.61 2.64 2.57 2.59 2.71 2.58 2.59 2.72

Hercules

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

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



### Table 2

- 7 . f

S 

### (RH) Small RH Adjustment Factors

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

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

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

Hercules

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

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



### Table 2

- 8 . f

SS 

### (RH) Sea Salt RH Adjustment Factors

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

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

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

Hercules

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

Mingo 3.92 3.58 3.3 3.19 3.58 3.72 3.8 3.82 3.85 3.61 3.66 3.9

### C

- 11

2.3 CALMET/CALPUFF MODELING PROTOCOL



### Trinity Consultants conducted modeling for certain facilities in Oklahoma according to

modeling protocols submitted and reviewed by ODEQ and EPA

. 9

, 10

, 11 

### Surface and upper

air observations were utilized (NO OBS = 0) for the source

- specific modeling, consistent

with EPA and FLM recommendations. Originally, modeling was performed with

CALMET v6 for use with CALPUFF v6.211. Problems have been identified in these

versions 12 

### therefore Trinity Consultants reverted to an existing CALMET data set that

utilized CALMET v5.53a which is an earlier regulator y version and compatible with the

regulatory version of CALPUFF (v5.8 .4 )

. 13

, 14 

### As with the visibility modeling performed

for Arkansas subject

- to BART sources by Trinity 15



### , we elected to utilize the CALMET

v5.53a output generating by Trinity Consultants and de scribed here for input into

CALPUFF for visibility modeling to assess the benefit of emission controls on visibility.

Rather than evaluate each unit subject to BART individually, we have elected to evaluate

visibility on a facility basis. We have also ev aluated the visibility benefit of SO 2 

### controls

on a unit basis to provide additional information on the sensitivity of the results to

changes in emissions and for comparison with the facility

- wide approach.

Table 2

- 9 shows selected CALMET settings used by Trinity for the meteorological

modeling EPA utilized for modeling visibility impacts from the facility . R1MAX and

R2MAX represent the radius of influence of surface and upper air observations,

respectively. R1 (R2) defines the distance from an observ ation at which the surface

(upper air) observation and the initial wind field are equally weighted and is typically set

to half the value of R1MAX(R2MAX). EPA and FLMs recommend the use of higher

values for R1MAX (100 km) and R2MAX (200 km) based on trace r evaluations and

9 Calpuff Modeling Report BART Determination, Okla homa Gas & Electric, Sooner Generating Station,

Trinity Consultants, March 17, 2008 (available at

http://www.deq.state.ok.us/aqdnew/permitting/application s/2003

- 274

- TVR_M

- 2/index.htm , as attachment

B of Sooner BART application submitted to ODEQ May 30, 2008)

10 Calpuff Modeling Report BART Determination, Oklahoma Gas & Electric, Muskogee Generating

Station, Trinity Consultants, March 17, 2008 (available at

http://www.deq.state.ok.us/aqdnew/permitting/applications/2005

- 271

- TVR_M

- 1/index.htm , as attachment

B of Muskogee BART application submitted to ODEQ May 30, 2008)

11 Best Available Retrofit Technology (BART) Determination, American Electric Power, Northeastern

Power Plant, N.N. Dharmarajan (AEP), Eugene Chen, Jeremy Townley (Trinity Consultants), May 30,

2008 ( http://www.deq.state.ok.us/aqdnew/permitting/ applications/96

- 288

- TV_M

- 3/index.htm )

12 CALPUFF: Status and Update , Atkinson, Dennis, Presentation at Regional/State/Local Modelers

Workshop, May 16, 2007.

( http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/archive/2007/presentations/Wednesda y

%20

- %20May%2016%202007/CALPUFF_status_update.pdf )

13 Letter to Lee Warden (ODEQ) from Trinity Consultants, January 23, 2008 RE: Oklahoma Gas and

Electric

– Multiple Sites, Best Available Retrofit Technology (BART) Determination, CALMET Data

Processing Pro tocol

14 CALPUFF v5.8 was the regulatory version at the time the Trinity modeling protocol was developed. On

December 4, 2013, EPA approved an update to v5.8.4 that contains bug fixes to the previous version. See

December 3, 2013 CALPUFF Update Memo for a discussion of model changes.

15 See Appendix B to the TSD, Technical Support Document for Arkansas Regional Haze Best Available

Retrofit Technology (BART) Modeling Protocol Review for a review of the BART modeling protocol.

### C

- 12

testing

. 16 

### We are aware of these deviations and weighed the potential differences that

may occur due to these deviations with the additional work and computing resources that

would be required to redo the analyses. We concluded that these deviations were

acceptable in this case due to time and work resources available.

Our modeling used a constant value of 3 ppb background concentration of ammonia for

the domain during the modeling period. Hourly ozone observational data provided by

ADEQ to Trinity over the 2001

- 2003 timeframe were used to define ba ckground ozone

concentrations.

Table 2

- 9 . Selected CALMET Settings Used

Option Description EPA default Trinity Consultants 17

version CALMET version 5.8 .4 , level 130731 5.53a, level 040716

NOOBS No Observation Mode 0 0

ICLOUD gridded cloud fields 0 0

IWFCOD diagnostic winds 1 1

IFRADJ Froude wind adj. 1 1

IKINE kinematic effects 0 0

IOBR O'Brien vertical wind adj. 0 0

ISLOPE slope flows 1 1

IEXTRP extrapolate wind to upper air

- 4

- 4

ICALM extrapolate calm to upper air 0 0

BIAS layer biases sfc vs. UA NZ*0 NZ*0

IPROG gridded initial prognostic 0 14

RMAX1 max surface radius of influence NA 20

RMAX2 max aloft radius of influence NA 50

RMAX3 max over

- water radius of influence. NA 100

RMIN min wind radius of influence. 0.1 0.1

RMIN2 min dist sfc winds extrap 4 4

TERRAD terrain radius of in influence NA 10

R1 weight surface Step 1 vs. obs NA 10

R2 weight aloft Step 1 vs. obs NA 25

ITPROG 3D T from obs or prognostic 0 0

TRADKM radius of influ. for T interp. 500 500

IAVET spatial T averaging 1 1

JWAT1 starting land use for T interp. over water 999 55

JWAT2 ending land use for T interp. over water 999 55

16 Memorandum from Tyler Fox (EPA OAQPS) “Clarification on EPA

- FLM Recommended Settings for

CALMET,” August 31, 2009

17 EPA utilized CALMET output from Trinity Consultants for use in CALPUFF simulations

### C

- 13 

## Chapter 3: Emissi on Calculation Methodology

This chapter discusses the approach to calculating emissions we used for the visibility

analysis for Entergy Independence

.

3.1 NO X AND 

### SO 2 EMISSIONS



### 3.1.1 Baseline Em issions

For evaluating SO 2 

### controls, the maximum 24

- hr actual emissions from 2001

- 2003 for

SO 2 

### a nd NOx were used to model the baseline visibility impacts from the facility

(“BASE”) . EPA reviewed annual and daily emission data for NOx and SO 2 

### for 2000

- 2013 for each unit at Independence from EPA’s Air Markets Program Data 18 

### and

identified the maximum 2 4

- hr emission rate (lb/hr) for each year. We note that recent

SO 2 

### emissions have increased from the baseline period as the facility has burned coal

with higher sulfur content in recent years. 19 

### Tables 3

- 1 and 3

- 2 summarize this data. The

BART Guidelines in 40 CFR 51 Appendix Y Part III state that emission estimates used in

the modeling analysis to determine visibility impairment impacts should reflect steady

state operating conditions during periods of high capacity utilization. These baseline SO 2



### emiss ion rates are representative of the highest 24

- hour block emissions reported during

the baseline period (the RHR defines the baseline period as 2000

- 2004 ) and are

consistent with the baseline emissions used in the BART analyses performed for the

subject

- to

- BART units in Arkansas. Had we modeled a more recent baseline for SO 2 

### emissions, the baseline visibility impact would be greater and the visibility benefits

modeled from the control scenarios would be greater.

For evaluating NOx controls, a second b aseline scenario was also modeled updating the

NOx emissions using more recent maximum 24

- hr actual emissions from 2011

- 2013

while holding the SO 2 

### emissions to the maximum 24

- hr actual emissions from 2001

- 2003

( “BASE2” ). This was done to account for any c hanges in operation that resulted in a n

observed decrease in NOx emissions from the previous baseline period when examining

the benefit due to NOx controls. 20 

### For further information about the modeled b aseline

and controlled emission rates see Attachment A of this report.

18 http://ampd.epa.gov/ampd/

19 See Independen ce_coal data.xls for summary of EIA data on coal properties (available in the docket for

this proposed rulemaking)

20 A similar approach was taken in the BART modeling for the White Bluff units to account for the addition

of a neural network and boiler tun ing. See the TSD and “Revised BART Five Factor Analysis White Bluff

Steam Electric Station Redfield, Arkansas (AFIN 35

- 00110),” dated October 2013, prepared by Trinity

Consultants Inc. in conjunction with Entergy Services Inc.

### C

- 14 Table 3

- 1

. Maximum 24

- hr emissions

Year 

### SO 2



### 24

- hr Max (lb/hr) NOx

24

- hr Max (lb/hr)

unit 1 unit 2 unit 1 unit 2

2000 3741 4543 3023 3341

2001 3633 3745 3370 4366

2002 5463 6337 3061 4502

2003 3913 5502 3045 4224

2004 5063 5521 3108 3096

2005 4438 4120 2534 2591

2006 6354 5754 3211 3269

2007 6481 6394 3984 3742

2008 5693 5983 3037 2656

2009 5620 5850 2743 2959

2010 5450 5785 2511 2456

2011 5649 5707 3165 2333

2012 6095 6669 2152 2479

2013 5401 5490 2203 2008

Max 2001

- 2003 5463 6337 3370 4502

Max 2011

- 2013 6095 6669 3165 2479



### Table 3

- 2

. Annual emissions

Year 

### SO 2



### (tpy) NOx

(tpy)

unit 1 unit 2 unit 1 unit 2

2000 10609 11252 7498 8160

2001 11186 11829 8131 10429

2002 11918 12695 7104 9211

2003 10652 11489 6635 9108

2004 11761 11827 7560 7961

2005 11006 11357 6578 6593

2006 12875 13297 7350 7313

2007 14682 14857 8740 8613

2008 13763 12685 7981 7142

2009 12254 15171 6610 7728

2010 14917 13758 7841 6778

2011 15308 15090 7013 6397

2012 16233 16741 5364 5702

2013 12581 16273 4737 5977

### C

- 15

3.1.2 Controlled Emission Rates

As discussed in the TSD and the Federal Registe r notice for this proposed rulemaking,

we concluded that installation of DFGD could achieve a 0.06 lb/mmBtu SO 2 

### emission

limit or the installation of WFGD could achieve a 0.04 lb/mmBtu SO 2 

### emission limit at

the Independence units, consistent with the findings for the White Bluff units. The

modeled SO 2 

### emission rates associated with wet scrubbing (wet FGD) and sem i

- dry

scrubbing (dry FGD) were calculated by multiplying the controlled emission level (i.e.,

0.04 lb/MMBtu for wet scrubbing and 0.06 lb/MMBtu for semi

- dry scrubbing) by the

boiler heat input of 8,950 MMBtu/hr. The NO X 

### emission rate was modeled at the bas eline

rate (using 2001

- 2003) for both the wet scrubbing and semi

- dry scrubb ing option

modeling scenarios

.

For NOx control scenarios, t he controlled emission level associated with LNB/SOFA is

0.15 lb/MMBtu

. Th is level w as multiplied by each unit’s maximu m heat input to derive

the hourly emission rate used in the modeling. The SO 2 

### emission rate was modeled at the

baseline rate (2001

- 2003) for the NOx control option modeling scenarios . For further

information about the b aseline and controlled emission rat es see Attachment A of this

report.

3.2 PM E MISSIONS AND 

### S PECIATION



### Baseline t otal PM 10 

### Emissions were estimated based on National Emission Inventory

(NEI) data 21 

### for annual facility emissions of PM 10



### . Heat input and hours of operation

data were obtained from EPA’s Air Markets Program Data. 22 

### The annual average

emission rate in pounds per hour was doubled to estimate the 24

- hr maximum emission

rate. 23 

### Table 3

- 3 below summarizes the NEI data for PM emissions and the estimated

emission rate.

Table 3

- 3

. PM 10 

### Emission Calculation

PM 10 (tpy) Heat Input

(MMBtu/yr) Annual Avg.

Emission rate

(lb/MMBtu) Hours of

Operation Annual Avg.

Emission rate (lb/hr)

Unit1 Unit2 Unit1 Unit2

2011 825.62 1.134E+08 0.0146 7816 8024 106.5 102.1

2008 779.99 1.190E+08 0.0131 7909 6957 102.6 107.6

2005 836.05 1.121E+08 0.0149 7957 8020 104.8 104.5

Avg. 104.6 104.7

2xAvg. 209.2 209.5

21 NEI data available at EPA’ s Clearinghouse for Inventories and Emission Factors (CHIEF) at

http://www.epa.gov/ttn/chief/eiinformation.html

22 http://ampd.epa.gov/ampd/ (Independence Emission data also available in Independence_EI 2000

- 2013.x ls spreadsheet in the docket for this action)

23 Based on evaluation of some sources that had both annual and maximum 24

- hour actual data, EPA

recommended that sources could use an emission rate that was double the ann ual emission rate to

approximate the maximum 24

- hour actual emission rates for some sources for CALPUFF modeling when

there was not enough data to generate a maximum 24

- hr actual emission rate. For additional information

on this approach see the Technical Support Document for the Oklahoma and Texas Regional Haze Federal

Implementation Plans, page A

- 36. Available at http://www.regulations.gov/#!documentDetail;D=EPA

R06

- OAR

- 2014

- 0754

- 0007

### C

- 16

For the SO 2 

### control scenarios for dry scrubbing and wet scrubbing, PM10 emissions were

reduced by 50% to estimate an anticipated additional removal of PM. While this is not a

refined estimate of the additional PM reduction anticipated for the control scenarios , PM

em ission s are already reduced significantly due to the existing ESP (estimated 99.5%

removal for PM filterable) 24 

### and the visibility impact from direct PM emissions are a very

small portion of the overall visibility impact due to the facilities emissions. Th erefore,

we do not anticipate a more refined approach to estimate PM emissions for these

scenarios would significantly impact the modeling results.

The speciated PM emissions were calculated using the National Park Service (NPS)

speciation worksheets corresponding to dry bottom boilers and the emission control

equipment present in each modeling scenario 25



### . PM speciation in these worksheets are

based on AP

- 42 Tables 1.1

- 5 and 1

- 1.6 (for dry

- bottom boilers) (See Table 3

- 4 )

. Emission factors within th ese tables vary by the type of emission control equipment

downstream and are functions of the coal’s sulfur and ash content. Coal properties of

8,950 Btu/lb heating value , sulfur content of 0.27%

, and ash content of 4.87% were used

in these calculations

. 26 

### PM coarse, PM fine, soil, elemental carbon and SOA emissions

were input into CALPUFF. ( These spreadsheets are available in the Docket for this

proposed rulemaking ) For further information about the b aseline and controlled emission

rates see Attachme nt A of this report

.

Table 3

- 4 . PM Speciation for modeled control scenarios

PM speciation used for

Baseline scenario PM speciation used for

dry scrubber scenario PM speciation used for

wet scrubber scenario

Coarse 32.8% 10.5% 23.3%

Fine Soil 25.3% 10.1% 17.9%

Fine EC 1.0% 0.4% 0.7%

CPM (inorganic

condensable) 32.8% 63.1% 46.5%

CPM OR

(organic

condensable) 8.2% 15.8% 11.6%

Heating Value

(BTU/lb) 8950 8950 8950

% sulfur 0.27 0.27 0.27

% ash 4.87 4.87 4.87

24 See “Revised BART Five Factor Analysis White Bluff Steam Electric Station Redfield, A rkansas (AFIN

35

- 00110),” dated October 2013, prepared by Trinity Consultants Inc. in conjunction with Entergy Services

Inc.

25 Original PM Speciation worksheet s available at

h ttp://www.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm . PM Speciation worksheets for the

modeled scenarios are available in the docket for this proposed rulemaking.

26 Coal Properties from Footnote 14 , “Revised BART Five Factor Analysis White Bluff Steam Electric

Station Redfield, Arkansas (AFIN 35

- 00110),” dated October 2013, prepared by Trinity Consultants Inc. in

conjunction with Entergy Services Inc.

### C

- 17

3.3 S ULFURIC 

### A CID 

### E MISSIONS 

### C ALCULATIONS



### Sulfuric acid emissions from power plants are calculated by estimating the amount of

H 2 

### SO 4 

### produced and the amount of H 2 

### SO 4 

### removed by control equipment using

information from the Electric Power Research Institute (EPRI)

. 27 

### These calculations rely

on assumed values for the amount of fuel sulfur converted to SO 2 

### , the amount of SO 2



### oxidized to SO 3 

### , and the amount of H 2 

### SO 4 

### lost to (or mitigated by) the air pre

- heater and

applicable control equipment, such as baghouses, and FGD s. Baseline and post

- control

estimates of H 2 

### SO 4 

### from Independence are based on the best current information

available from EP RI and coal properties (Table 3

- 5 ). Coal properties of 8,950 Btu/lb

heating value , sulfur content of 0.27% and, ash content of 4 .87%. were used in these

calculations. 28 

### For further information about the baseline and controlled emission rates

see Attachment A of this report

.

Table 3

- 5

. Parameters for Sulfuric Acid Emission Calculations (EPRI method)

EPRI method parameter Value

% Sulfur 0. 27 %

% Ash 4.7 %

Heating value 8950 Btu/lb

Sulfer conversion to SO 2 29 0.875

F1 30 (Fuel Impact Factor,

subbituminous/PRB all boilers) 0.0019

F2 30 (Technology Impact factors):

Control Scenario Baseline LNB D FGD W FGD

Air Heater (PRB) 0.36 0.36 0.36 0.36

Cold

- side ESP (PRB) 0.72 0.72 1 0.72

Wet spray tower (PRB) 1 1 1 0.4

Dry FGD and baghouse 1 1 .01 1

27 Estimating Total Sulfuric Acid Emissions from Stationary Power Plants: Version 2010a. EPR I,

Palo Alto, CA: 2010. 1020636.

28 Coal Properties from Footnote 14 , “Revised BART Five Factor Analysis White Bluff Steam Electric

Station Redfield, Arkansas (AFIN 35

- 00110),” dated October 2013, prepared by Trinity Consultants Inc. in

conjunction with En tergy Services Inc.

29 I mpl icit from EPA AP

- 42 (EPA, “Compilation of Air Pollutant Emission Factors AP

- 42, Fifth Edition,

Volume I: Stationary Point and Area Sources.” http://www.epa.gov/ttnchie1/ap42pdf/c01s01.pdf.)

30 Estimating Total Sulfuric Acid Emissions from Stationary Power Plants: Version 2010a. EPRI,

Palo Alto, CA: 2010. 1020636.

### C

- 18 

## Chapter 4: EPA Visibility Modeling

4.1 EPA M ODELING 

### S CENARIOS



### We performed visibility modeling to evaluate the visibility benefit of additional emission

controls at the two units at Entergy Independence . For NOx control, operation of low

NOx burners with Separated Over

- Fire Air (LNB/ S OFA) was examined. SO 2 

### controls

evaluated were dry flue gas desulfurization ( dry scrubbing, DFGD) and wet flue gas

desulfurization ( wet scrubbing, WFGD).

These control scenarios are outlined below. Emission inputs for each scenario are

included in Attachment A of this report

.

Control Scenarios :

1. Baseline (BASE)

– Emission rates for NOx and SO 2 

### are from maximum actual

24

- hr emissions during the 2001

- 2003 period.

2. Dry Sc rubber (DFGD)

- Emission rates for NOx are maximum actual 24

- hr

emissions during the 2001

- 2003 period. SO 2 

### emissions are controlled to 0.06

lb/mmBTU.

3. Wet Scrubber (WFGD)

– Emission rates for NOx are maximum actual 24

- hr

emissions during the 2001

- 2003 peri od. SO 2 

### emissions are controlled to 0.04

lb/mmBTU.

4. Baseline 2 (BASE2)

– Emission rates for SO 2 

### are maximum actual 24

- hr

emissions during the 2001

- 2003 period. Emission rates for NOx are maximum

actual 24

- hr emissions during the 20011

- 2013 period.

5. LNB/S OFA (LNB)

– Emission rates for NOx are at the LNB/SOFA controlled

value of 0.15 lb/mmBTU. Emission rates for SO 2 

### are maximum actual 24

- hr

emissions during the 2001

- 2003 period.

6. LNB/SOFA and DFGD (LNB_DFGD)

- Emission rates for NOx are at the

LNB/ S OFA cont rolled value of 0.15 lb/mmBTU. SO 2 

### emissions are controlled to

0.06 lb/mmBTU.

7. Dry Scrubber and Baseline 2 (BASE2_DFGD)

- Emission rates for NOx are

maximum actual 24

- hr emissions during the 2011

- 2013 period. SO 2 

### emissions

are controlled to 0.06 lb/mmB TU

*BASE, DFGD, and WFGD scenarios were also modeled for each individual unit.

SO 4 

### emissions were calculated for each unit based on the EPRI method and the control

equipment present under each scenario as described above . Maximum 24

- hr total PM 10



### emissions were estimated based on NEI annual emission data as described above . PM

speciation was calculated using the NPS PM speciation worksheets and coal properties.

4.2 EPA V ISIBILITY 

### R ESULTS

### C

- 19

We present the visibility improvement from the application of control technologies in two

ways: (1) deciview improvement at each Class I area, and (2) the sum of the

improvements at all areas.

Deciview improvement at each Class I area is the metric that is most commonly

evaluated, but does not give the overall pi cture of improvement at all impacted Class I

areas . The motivation for the second metric, which involves summing over Class I areas,

is to provide a single number for comparing control scenarios to each other. There are

multiple Class I areas, with differ ent visibility impacts for each, but there is no

established metric for expressing visibility impact considering all areas together in a

single number. The overall impact can be assessed more or less intuitively, weighing the

various area impacts and the number of affected Class I area s.

4.2.1 Visibility Analysis of SO 2 

### Controls

Tables 4

- 1, 4

- 2

, and 4

- 3 present the maximum value of the 98 th 

### percentile of the daily

maximum impact for the three modeled years (2001

- 2003) for the facility and for each

individual unit for the baseline and SO2 control scenarios

. SO 2 

### controls provide for

improvements in visibility, lowering the impact the facility has on any single Class I area

by 0.87 to 1. 06 dv

. There is little difference between the results of the WFGD and DFGD

in the 98 th 

### percentile values . At the low sulfur emission levels of these controls, nitrates

are responsible for the majority of visibility impairment so little benefit is seen in

decreasing SO 2 

### emissions from the DFGD rate of 0.06 to the WFGD rate of 0.04

lb/mmBTU. Modeled visibility benefits of SO 2 

### controls are larger at unit 2 due to the

higher baseline emissions for this unit (6337 lb/hr compared to 5463 lb/hr for unit 1).

Table 4

- 1 . Enter gy Independence: EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) 

### of SO 2 

### Controls (Facility

- wide)

Class I Area Distance

(km) Visibility Impact Visibility

Improvement

Over Baseline Incremental

Visibility

Improvement

of Wet FGD

vs. Dry FGD BASE Dry

FGD Wet

FGD Dry

FGD Wet

FGD

Caney Creek 277 2.412 1.474 1.442 0.938 0.97 0.032

Upper Buffalo 180 1.764 0.876 0.86 0 0.888 0.904 0.016

Hercules

- Glades 173 1.704 0.648 0.608 1.056 1.096 0.04

Mingo 174 1.547 0.676 0.649 0.871 0.898 0.027

Total

- 7.427 3.674 3.559 3.753 3.868 0.115

### C

- 20

Table 4

- 2 . Entergy Independence Unit 1 : EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) 

### of SO 2 

### Controls

Class I Area Distance

(km) Visibility Impact Visibility

Improvement

Over Baseline Incremental

Visibility

Improvement

of Wet FGD

vs. Dry FGD BASE Dry

FGD Wet

FGD Dry

FGD Wet

FGD

Caney Creek 277 1.133 0.657 0.64 0.476 0.493 0.017

Upper Buffalo 180 0.845 0.385 0.377 0.46 0.468 0.008

Hercules

- Glades 173 0.793 0.295 0.267 0.498 0.526 0.028

Mingo 174 0.739 0.298 0.284 0.441 0.455 0.014

Total

- 3.51 1.635 1.568 1.875 1.942 0.067



### Table 4

- 3 . Entergy Independence Unit 2 : EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) 

### of SO 2 

### Controls

Class I Area Distance

(km) Visibility Impact Visibility

Improvement

Over Baseline Incremental

Visibility

Improvement

of Wet FGD

vs. Dry FGD 

### BASE Dry

FGD Wet

FGD Dry

FGD Wet

FGD

Caney Creek 277 1.412 0.865 0.843 0.547 0.569 0.022

Upper Buffalo 180 0.997 0.509 0.499 0.488 0.498 0.01

Hercules

- Glades 173 0.977 0.364 0.355 0.613 0.622 0.009

Mingo 174 0.883 0.388 0.374 0.495 0.509 0.014

Total

- 4.269 2.126 2.071 2.143 2.198 0.055



### Table 4

- 4 presents the maximum value of the 98 th 

### percentile of the daily impact for the

three modeled years for the baseline and DFGD control scenarios utilizing more recent

emissions data for the NOx emissions (BASE2) . These results utilize the maximum 24

-

hr NOx emissions from the 2011

- 2013 period, wh ich are lower than emission rates from

the 2001

- 2003 baseline. Modeled v isibility benefits from the use of DFGD are similar to

### C

- 21

those modeled with the 2001

- 2003 baseline NOx emissions values shown in Table 4

- 1.

Overall, the DFGD visibility benefits are la rger using the lower NOx emissions, rang ing

from 0.97 to 1.08 at any single Class I area. Again, we note that had we modeled a more

recent baseline for SO 2 

### emissions, the baseline visibility impact would be greater and the

visibility benefits modeled from the control scenarios would be greater.

Table 4

- 4. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) 

### of SO 2 

### Controls (Facility

- wide) with BASE2

Class I Area Distance

(km) Visibility Impact Visibility Improvement

Over Baseline B ASE2 31 

### Dry FGD 32

Caney Creek 277 2.054 1. 086 0.96 8

Upper Buffalo 180 1.724 0. 641 0.975

Hercules

- Glades 173 1.482 0.507 0.988

Mingo 174 1.492 0.504 1.083

Total

- 6.752 2.738 4.014



### 4.2.2 Visibility Analysis of NOx Controls

Table 4

- 5 presents the maximum value of the 98 th 

### percentile of the daily maximum

impact for the three modeled years (2001

- 2003) for the facility for the baseline and NOx

control scenarios . The baseline results utilize the maximum 24

- hr NOx emissions from

the 2011

- 2 013 period , which are lower than emission rates from the 2001

- 2003 baseline.

LNB/ S OFA provides for improvements in visibility on any single Class I area ranging

from 0.21 to 0.46 dv.

31 Baseline NOx emissions were updated to the maximum 24

- hr emissions from 2011

- 2013 for the

evaluation of the anticipated benefit fro m NOx controls.

32 Baseline NOx emissions were updated to the maximum 24

- hr emissions from 2011

- 2013 for the

evaluation of the anticipated benefit from NOx controls.

### C

- 22 Table 4

- 5. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) 

### of NOx Controls (Facility

- wide) with BASE2

Class I Area Distance

(km) Visibility Impact Visibility Improvement

of LNB/SOFA Over

Baseline B ASE2 33 

### LNB/SOFA

Caney Creek 277 2.054 1.593 0.461

Upper Buffalo 180 1.724 1.476 0.248

Hercules

- Glades 173 1.482 1.218 0.264

Mingo 174 1.492 1.279 0.213

Total

- 6.752 5.566 1.186



### 4.2.3 Visibility Benefit of NOx and SO 2 

### controls

Table 4

- 6 presents the maximum value of the 98 th 

### percentile of the daily maximum

impact for the three modeled years (2001

- 2003) for the facility for the two different

baselines modeled and a control scenario with both LNB/ S OFA and DFGD. The

“BASE” results utilize the maximum 24

- hr SO 2 

### and NOx emissions from the 2001

- 2003

period.

The “BASE2” results utilize the maximum 24

- hr NOx emissions from the 2011

- 2013

period, which are lower than emission rates from the 2001

- 2003 baseline

. We note that

had we modeled a more recent baseline for SO 2 

### emissions, the baseline visibility impacts

would be greater and the visibility benefits modeled from the control scenarios would

also be greater. Modeling of both LNB/ S OFA and DFGD shows visibility benefits

ranging from 1.18 to 1.48 dv at each Class I area when compared to BASE2 , compared to

visibility benefits ranging from 0.87 to 1.06 dv for only DFGD when compared to BASE

and 0.97 to 1.08 dv for only DFGD when compared to BASE2

.

33 Baseline NOx emissions were updated to the maximum 24

- hr emissions from 2011

- 2013 fo r the

evaluation of the anticipated benefit from NOx controls.

### C

- 23 Table 4

- 6. Entergy Independence: EPA Modeled Maximum 98 th 

### Percentile

Visibility Impacts (∆dv) 

### of NOx and SO 2 

### Controls (Facility

- wide) with BASE

and BASE2

Class I

Area Distance

(km) Visibility Impact Visibility

Improvement

of LNB/SOFA

and DFGD

Over BASE Visibility

Improvement of

LNB/SOFA and

DFGD Over

BASE2 BASE BASE2 34 

### LNB/SOFA

and DFGD

Caney

Creek 277 2.412 2.054 0.570 1.842 1.484

Upper

Buffalo 180 1.764 1.724 0.330 1.434 1.394

Hercules

Glades 173 1.704 1.482 0.301 1.403 1.181

Mingo 174 1.547 1.492 0.281 1.266 1.211

Total

- 7.427 6.752 1.482 5.945 5.270

34 Baseline NOx emissions were updated to the maximum 24

- hr emissions from 2011

- 2013 for the

evaluation of the anticipated benefit from NOx controls.

### C

- 24 

# Attachment A . Emission Rates

Table A

- 1 Emission R ates M odeled for E ach C ontrol S cenari o

Unit SO 2 

### (lb/hr) NO X



### (lb/hr) Total

PM 10 

### (lb/hr) SO 4



### (lb/hr) PMc

(lb/hr) PMf

(lb/hr) SOA

(lb/hr) EC

(lb/hr)

BASE 1 5462.83 3370.08 209 3.56 68.6 52.8 17.1 2

2 6337.17 4502.00 209 3.56 68.6 52.8 17.1 2 WFGD 1 358.0 3370 .08 105 1.43 24.4 18.8 12.2 0.7

2 358.0 4502 .00 105 1.43 24.4 18.8 12.2 0.7 DFGD 1 537.0 3370 .08 105 0.05 11.1 10.7 16.6 0.4

2 537.0 4502 .00 105 0.05 11.1 10.7 16.6 0.4

BASE 2 1 5462.8 3164.67 209 3.6 68.6 52.8 17.1 2.0

2 6337.2 2478.75 209 3.6 68.6 52.8 17.1 2.0

LNB/ SOFA 1 5462.8 1342.50 209 3.6 68.6 52.8 17.1 2.0

2 6337.2 1342.50 209 3.6 68.6 52.8 17.1 2.0 BASE 2

& DFGD 1 537.0 3164.67 105 0.05 11.1 10.7 16.6 0.4

2 537.0 2478.75 105 0.05 11.1 10.7 16.6 0.4 DFGD & LNB/ SOFA 1 537.0 1342.50 105 0.05 11.1 10.7 16.6 0.4

2 537.0 1342.50 105 0.05 11.1 10.7 16.6 0.4

### C

- 25 

# Attachment B. Visibility Modeling Results

B.1 Facility Modeling R esults

BASE

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 2.412 1.637 1.744 1.931 2.412

Upper Buffalo 1.517 1.309 1.764 1.530 1.764

Hercules

- Glades 1.438 0.853 1.704 1.332 1.704

Mingo 1.547 1.281 1.524 1.451 1.547

Sum 6.914 5.080 6.736 6.243 6.914



### DFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 1.474 0.797 1.018 1.096 1.474

Upper Buffalo 0.710 0.637 0.876 0.741 0.876

Hercules

- Glades 0.648 0.372 0.619 0.546 0.648

Mingo 0.658 0.556 0.676 0.630 0.676

Sum 3.490 2.362 3.189 3.014 3.490



### WFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 1.442 0.779 0.966 1.062 1.442

Upper Buffalo 0.686 0.605 0.860 0.717 0.860

Hercules

- Glades 0.608 0.361 0.567 0.512 0.608

Mingo 0.638 0.548 0.649 0.612 0.649

Sum 3.374 2.293 3.042 2.903 3.374



### BASE2

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 2.054 1.452 1.623 1.710 2.054

Upper Buffalo 1.503 1.166 1.724 1.464 1.724

Hercules

- Glades 1.315 0.829 1.482 1.209 1.482

Mingo 1.492 1.138 1.334 1.321 1.492

Sum 6.364 4.585 6.163 5.704 6.364

### C

- 26 LNB /SOFA

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 1.593 1.203 1.458 1.418 1.593

Upper Buffalo 1.349 0.932 1.476 1.252 1.476

Hercules

- Glades 1.176 0.775 1.218 1.056 1.218

Mingo 1.279 0.963 1.086 1.109 1.279

Sum 5.397 3.873 5.238 4.836 5.397



### DFGD_BASE2

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 1.086 0.592 0.784 0.821 1.086

Upper Buffalo 0.539 0.472 0.641 0.551 0.641

Hercules

- Glades 0.507 0.278 0.481 0.422 0.507

Mingo 0.491 0.409 0.504 0.468 0.504

Sum 2.623 1.751 2.410 2.261 2.623



### LNB /SOFA _DFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 0.570 0.327 0.412 0.436 0.570

Upper Buffalo 0.296 0.263 0.330 0.296 0.330

Hercules

- Glades 0.267 0.152 0.301 0.240 0.301

Mingo 0.268 0.229 0.281 0.259 0.281

Sum 1.401 0.971 1.324 1.232 1.401

### C

- 27 B.2 Unit 1 Modeling Results

BASE

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 0.817 0.592 0.784 0.731 0.817

Upper Buffalo 0.725 0.618 0.845 0.729 0.845

Hercules

- Glades 0.676 0.402 0.793 0.624 0.793

Mingo 0.739 0.589 0.707 0.678 0.739

Sum 2.957 2.201 3.129 2.762 3.129



### DFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 0.657 0.350 0.460 0.489 0.657

Upper Buffalo 0.316 0.280 0.385 0.327 0.385

Hercules

- Glades 0.295 0.163 0.281 0.246 0.295

Mingo 0.290 0.244 0.298 0.277 0.298

Sum 1.558 1.037 1.424 1.340 1.558



### WFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 0.640 0.340 0.432 0.471 0.640

Upper Buffalo 0.303 0.272 0.377 0.317 0.377

Hercules

- Glades 0.267 0.158 0.254 0.226 0.267

Mingo 0.280 0.240 0.284 0.268 0.284

Sum 1.490 1.010 1.347 1.282 1.490

### C

- 28 B.3 Unit 2 Modeling Results

BASE

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 1.412 0.931 0.999 1.114 1.412

Upper Buffalo 0.851 0.727 0.997 0.858 0.997

Hercules

Glades 0.810 0.467 0.977 0.751 0.977

Mingo 0.868 0.723 0.883 0.825 0.883

Sum 3.941 2.848 3.856 3.548 3.941



### DFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 0.865 0.461 0.584 0.637 0.865

Upper Buffalo 0.405 0.366 0.509 0.427 0.509

Hercules

Glades 0.364 0.212 0.349 0.308 0.364

Mingo 0.377 0.320 0.388 0.362 0.388

Sum 2.011 1.359 1.830 1.733 2.011



### WFGD

Class I Area 98th Percentile for Each Year 3 year

average 3 year

maximum 2001 2002 2003

Caney Creek 0.843 0.452 0.557 0.617 0.843

Upper Buffalo 0.393 0.343 0.499 0.412 0.499

Hercules

Glades 0.355 0.206 0.322 0.294 0.355

Mingo 0.367 0.316 0.374 0.352 0.374

Sum 1.958 1.317 1.752 1.676 1.958