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Appendix C:

Technical Support Document for 

Visibility Modeling Analysis for

Entergy Independence Generating Station

Prepared by:

Michael Feldman

March 2015
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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 NOx and SO2 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 SO2 Controls............................................................ 19

4.2.2 Visibility Analysis of NOx Controls........................................................... 21

4.2.3 Visibility Benefit of NOx and SO2 controls ............................................... 22

Attachment A. Emission Rates........................................................................................ 24

Attachment B. Visibility Modeling Results..................................................................... 25
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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 of Source and Class I Areas............................................................. 6

Table 2-5. Natural Background Concentrations used for “Method 8” ............................ 10

Table 2-6. fL(RH) Large RH Adjustment Factors............................................................ 10

Table 2-7. fS(RH) Small RH Adjustment Factors............................................................ 10

Table 2-8. fSS(RH) Sea Salt RH Adjustment Factors....................................................... 10

Table 2-9. Selected CALMET Settings Used ................................................................... 12

Table 3-1. Maximum 24-hr emissions.............................................................................. 14

Table 3-2. Annual emissions............................................................................................. 14

Table 3-3. PM10 Emission Calculation ............................................................................ 15

Table 3-4. PM Speciation for modeled control scenarios................................................ 16

Table 3-5. Parameters for Sulfuric Acid Emission Calculations (EPRI method)............ 17

Table 4-1. Entergy Independence: EPA Modeled Maximum 98th Percentile Visibility 

Impacts (∆dv) of SO2 Controls (Facility-wide) ................................................................ 19

Table 4-2. Entergy Independence Unit 1: EPA Modeled Maximum 98th Percentile 

Visibility Impacts (∆dv) of SO2 Controls......................................................................... 20

Table 4-3. Entergy Independence Unit 2: EPA Modeled Maximum 98th Percentile 

Visibility Impacts (∆dv) of SO2 Controls......................................................................... 20

Table 4-4. Entergy Independence: EPA Modeled Maximum 98th Percentile Visibility 

Impacts (∆dv) of SO2 Controls (Facility-wide) with BASE2........................................... 21

Table 4-5. Entergy Independence: EPA Modeled Maximum 98th Percentile Visibility 

Impacts (∆dv) of NOx Controls (Facility-wide) with BASE2.......................................... 22

Table 4-6. Entergy Independence: EPA Modeled Maximum 98th Percentile Visibility 

Impacts (∆dv) of NOx and SO2 Controls (Facility-wide) with BASE and BASE2 ......... 23
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Chapter 1: Introduction 

Units 1 and 2 of the Entergy Independence plant are coal-fired, tangentially-fired boilers 

with a nameplate capacity of 900 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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Chapter 2: Modeling Protocol

2.1 BACKGROUND AND INTRODUCTION

We used 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 compare 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 -

Glades

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 Glades Wilderness Area MO USFS HEGL

Upper Buffalo Wilderness Area AR USFS UPBU

Mingo Wilderness Area MO FWS MING

Table 2-3. Facility location2

Facility Latitude Facility Longitude

35.6733 -91.4083

Table 2-4. Stack Parameters for Each Unit3

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 Facility 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. 
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Figure 2-1. Location of Source and Class I Areas

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

control scenarios is the 98th 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 CALPUFF to the visibility analysis for Entergy Independence.
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2.2 CALCULATION OF VISIBILITY IMPACT

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 range 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, analogous 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(bext / 10) (1)

where ln is the natural logarithm, and extinction bext is the fraction of light scattered out 

of a viewing path. Extinction increases with the amount of pollution (bext is in units of 

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

pristine 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 

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

The 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 following visibility-impairing pollutants: SO2, SO4, 

NOx, 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 conversion of SO2 to particulate sulfate and NOx to 

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

ozone concentration. These concentrations are converted to deciviews by the CALPOST 

post-processor in two steps.

2.2.1 IMPROVE Equation

Under the original IMPROVE4equation, extinction (bext) is estimated from the predicted 

concentrations of various pollutants: 

bext = 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 monitors 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/
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+ 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; 

its 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 equation5to 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:

bext = 2.2 * fs(RH) * [small sulfate] + 4.8 * fL(RH) * [large sulfate] 

+ 2.4 * fs(RH) * [small nitrate] + 5.1 * fL(RH) * [large nitrate]

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

+ 10 * [elemental carbon]

+ 1 * [fine soil]

+ 1.7 * fss(RH) * [sea salt]

+ 0.6 * [coarse mass]

+ Rayleigh scattering (site-specific)

+ 0.33 * [NO2(ppb)]

Sulfate is assumed to be all “large sulfate” if total sulfate is over 20 μg/m3, otherwise its 

fraction of the total is assumed to increase uniformly between 0 and 1 when the total is 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 increase over 

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

The fs, fL, fss 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 scientific assessment of 

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

compared to the old algorithm6. In particular, when compared to nephelometer direct 

 

5 Pitchford, 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 IMPROVE 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 
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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 assess 

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. The 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 Regional Haze Rule.

EPA 2007 guidance7states 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 the 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) represents 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. Delta 

deciviews is calculated as the difference between deciviews including 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 8th highest day, visibility impacts 

expressed as 24-hour averages of delta deciviews relative 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 unusual meteorological conditions when 

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

Part 51 Appendix W).

The BART guidelines recommend that impacts of sources be estimated in deciviews 

relative to natural background. EPA used average background concentrations (Table 2-5) 

and relative humidity adjustment factors (Tables 2-6, 2-7, and 2-8) from the Federal Land 

Managers’ Air Quality Related Values Work Group (FLAG) Phase I Report8for 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)
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Table 2-5. Natural Background Concentrations used for “Method 8”

Class I Area CALPOST control input parameters (μg/m3)

BKSO4 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

HerculesGlades0.23 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. fL(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

HerculesGlades2.7 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. fS(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

HerculesGlades3.7 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. fSS(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

HerculesGlades3.86 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
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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 

versions12 therefore Trinity Consultants reverted to an existing CALMET data set that 

utilized CALMET v5.53a which is an earlier regulatory 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 Trinity15, we elected to utilize the CALMET 

v5.53a output generating by Trinity Consultants and described 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 evaluated the visibility benefit of SO2 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 observation 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 tracer evaluations and 

 

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

Trinity Consultants, March 17, 2008 (available at 

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

%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 Protocol

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.
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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 background ozone 

concentrations.

Table 2-9. Selected CALMET Settings Used

Option Description EPA default Trinity Consultants17

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
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Chapter 3: Emission Calculation Methodology

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

analysis for Entergy Independence. 

3.1 NOX AND SO2 EMISSIONS

3.1.1 Baseline Emissions

For evaluating SO2 controls, the maximum 24-hr actual emissions from 2001-2003 for 

SO2 and NOx were used to model the baseline visibility impacts from the facility

(“BASE”). EPA reviewed annual and daily emission data for NOx and SO2 for 2000-

2013 for each unit at Independence from EPA’s Air Markets Program Data18 and 

identified the maximum 24-hr emission rate (lb/hr) for each year. We note that recent 

SO2 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 steadystate operating conditions during periods of high capacity utilization. These baseline SO2

emission 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 SO2

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 baseline scenario was also modeled updating the 

NOx emissions using more recent maximum 24-hr actual emissions from 2011-2013

while holding the SO2 emissions to the maximum 24-hr actual emissions from 2001-2003 

(“BASE2”). This was done to account for any changes in operation that resulted in an 

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 baseline 

and controlled emission rates see Attachment A of this report. 

 

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

19 See Independence_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 tuning. 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.
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 Table 3-1. Maximum 24-hr emissions

Year SO2

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

SO2

(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
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3.1.2 Controlled Emission Rates

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

we concluded that installation of DFGD could achieve a 0.06 lb/mmBtu SO2 emission 

limit or the installation of WFGD could achieve a 0.04 lb/mmBtu SO2 emission limit at 

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

modeled SO2 emission rates associated with wet scrubbing (wet FGD) and semi-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 NOX emission rate was modeled at the baseline 

rate (using 2001-2003) for both the wet scrubbing and semi-dry scrubbing option 

modeling scenarios. 

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

0.15 lb/MMBtu. This level was multiplied by each unit’s maximum heat input to derive 

the hourly emission rate used in the modeling. The SO2 emission rate was modeled at the 

baseline rate (2001-2003) for the NOx control option modeling scenarios. For further 

information about the baseline and controlled emission rates see Attachment A of this 

report. 

3.2 PM EMISSIONS AND SPECIATION

Baseline total PM10 Emissions were estimated based on National Emission Inventory 

(NEI) data21 for annual facility emissions of PM10. 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. PM10 Emission Calculation

PM10

(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.xls 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 annual 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=EPAR06-OAR-2014-0754-0007
C-16

For the SO2 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 

emissions 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. Therefore, 

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 scenario25. 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 these 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 baseline and controlled emission 

rates see Attachment 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, Arkansas (AFIN 

35-00110),” dated October 2013, prepared by Trinity Consultants Inc. in conjunction with Entergy Services 

Inc.

25 Original PM Speciation worksheets available at 

http://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 SULFURIC ACID EMISSIONS CALCULATIONS

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

H2SO4 produced and the amount of H2SO4 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 SO2, the amount of SO2

oxidized to SO3, and the amount of H2SO4 lost to (or mitigated by) the air pre-heater and 

applicable control equipment, such as baghouses, and FGDs. Baseline and post-control 

estimates of H2SO4 from Independence are based on the best current information 

available from EPRI 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 SO2

29 0.875

F130 (Fuel Impact Factor, 

subbituminous/PRB all boilers) 0.0019

F230 (Technology Impact factors):

Control Scenario Baseline LNB DFGD WFGD

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. EPRI,

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 Entergy Services Inc.

29 Implicit 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 MODELING SCENARIOS

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/SOFA) was examined. SO2 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 SO2 are from maximum actual 

24-hr emissions during the 2001-2003 period. 

2. Dry Scrubber (DFGD) - Emission rates for NOx are maximum actual 24-hr 

emissions during the 2001-2003 period. SO2 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 period. SO2 emissions are controlled to 0.04 

lb/mmBTU. 

4. Baseline 2 (BASE2) –Emission rates for SO2 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/SOFA (LNB) – Emission rates for NOx are at the LNB/SOFA controlled 

value of 0.15 lb/mmBTU. Emission rates for SO2 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/SOFA controlled value of 0.15 lb/mmBTU. SO2 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. SO2 emissions 

are controlled to 0.06 lb/mmBTU

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

SO4 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 PM10

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 VISIBILITY RESULTS
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 picture 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 different 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 areas. 

4.2.1 Visibility Analysis of SO2 Controls

Tables 4-1, 4-2, and 4-3 present the maximum value of the 98th 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. SO2 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 98th 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 SO2 emissions from the DFGD rate of 0.06 to the WFGD rate of 0.04 

lb/mmBTU. Modeled visibility benefits of SO2 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. Entergy Independence: EPA Modeled Maximum 98th Percentile 

Visibility Impacts (∆dv) of SO2 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 0.860 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 98th Percentile 

Visibility Impacts (∆dv) of SO2 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 98th Percentile 

Visibility Impacts (∆dv) of SO2 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 98th 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, which are lower than emission rates from 

the 2001-2003 baseline. Modeled visibility 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 larger using the lower NOx emissions, ranging

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

recent baseline for SO2 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 98th Percentile 

Visibility Impacts (∆dv) of SO2 Controls (Facility-wide) with BASE2

Class I Area Distance 

(km)

Visibility Impact Visibility Improvement 

BASE2 Over Baseline 31 Dry FGD32

Caney Creek 277 2.054 1.086 0.968

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 98th 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 2011-2013 period, which are lower than emission rates from the 2001-2003 baseline. 

LNB/SOFA 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 from 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 98th 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 BASE233 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 SO2 controls

Table 4-6 presents the maximum value of the 98th 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/SOFA and DFGD. The 

“BASE” results utilize the maximum 24-hr SO2 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 SO2 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/SOFA 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 for the 

evaluation of the anticipated benefit from NOx controls. 
C-23

Table 4-6. Entergy Independence: EPA Modeled Maximum 98th Percentile

Visibility Impacts (∆dv) of NOx and SO2 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 BASE234 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

HerculesGlades 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 Rates Modeled for Each Control Scenario

Unit SO2

(lb/hr)

NOX

(lb/hr)

Total 

PM10

(lb/hr)

SO4

(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 Results

BASE

Class I Area

98th Percentile for Each Year 3 year 

average

3 year 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

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 

2001 2002 2003 maximum

Caney Creek 1.412 0.931 0.999 1.114 1.412

Upper Buffalo 0.851 0.727 0.997 0.858 0.997

HerculesGlades 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 

2001 2002 2003 maximum

Caney Creek 0.865 0.461 0.584 0.637 0.865

Upper Buffalo 0.405 0.366 0.509 0.427 0.509

HerculesGlades 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 

2001 2002 2003 maximum

Caney Creek 0.843 0.452 0.557 0.617 0.843

Upper Buffalo 0.393 0.343 0.499 0.412 0.499

HerculesGlades 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