i

Technical Support Document

for the

Oklahoma and Texas Regional Haze Federal Implementation Plans

(FIP TSD)

Prepared and Reviewed by:

Erik Snyder

Michael Feldman

Joe Kordzi

November 2014
ii

Table of Contents

1 Introduction........................................................................................................................... 1

2 Overview of the Gaps in the Oklahoma and Texas Regional Haze SIPs......................... 1

2.1 Flaws in Texas’ Reasonable Progress Goal, Long Term Strategy, and Other Areas

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

2.2 Flaws in Oklahoma’s Flawed Reasonable Progress Goal .......................................... 1

3 Our FIPs Cure Defects in the Texas and Oklahoma Regional Haze SIPs....................... 2

3.1 Summary of the Texas FIP............................................................................................ 2

3.2 Summary of the Oklahoma FIP.................................................................................... 3

4 Technical Overview of the Oklahoma and Texas FIPs ..................................................... 3

4.1 Location of Sources........................................................................................................ 4

4.2 Approach to Reasonable Progress and Long term Strategy ...................................... 5

4.2.1 Time Necessary for Compliance, and the Oklahoma and Texas RPGs............. 7

4.2.2 Energy and Non-Air Quality Environmental Impacts of Compliance .............. 7

4.2.3 Remaining Useful Life ............................................................................................ 8

4.3 Analysis of the PPG Flat Glass Plant ........................................................................... 9

4.4 Approach to Technical Analysis................................................................................. 11

4.5 Use of Confidential Business Information ................................................................. 11

5 Reasonable Progress and Long Term Strategy Scrubber and DSI Cost Analyses....... 12

5.1 Big Brown Units 1 and 2.............................................................................................. 13

5.1.1 Emissions Summary.............................................................................................. 14

5.1.2 Analysis of the Cost of Compliance..................................................................... 14

5.2 Monticello Units 1 and 2 .............................................................................................. 15

5.2.1 Emissions Summary.............................................................................................. 16

5.2.2 Analysis of the Cost of Compliance..................................................................... 16

5.3 Coleto Creek ................................................................................................................. 17

5.3.1 Emissions Summary.............................................................................................. 18

5.3.2 Analysis of the Cost of Compliance..................................................................... 18

5.4 Tolk Units 171B and 172B........................................................................................... 19

5.4.1 Emissions Summary.............................................................................................. 20

5.4.2 Analysis of the Cost of Compliance..................................................................... 20

5.5 Welsh Units 1, 2, and 3................................................................................................. 21

5.5.1 Emissions Summary.............................................................................................. 22
iii

5.5.2 Analysis of the Cost of Compliance..................................................................... 23

5.6 W. A. Parish Units WAP5, WAP6, and WAP7 ......................................................... 24

5.6.1 Emissions Summary.............................................................................................. 25

5.6.2 Analysis of the Cost of Compliance..................................................................... 26

6 Summary of Scrubber Upgrade Cost Results.................................................................. 26

7 Modeled Benefits of Emission Controls............................................................................ 27

8 Proposed RP and LTS Determination for San Miguel.................................................... 29

9 Proposed RP and LTS Determination for Units other than San Miguel....................... 29

9.1 Proposed RP and LTS Determination for Scrubber Upgrades............................... 30

9.2 Proposed RP and LTS Determination for Scrubber Retrofits ................................ 30

10 Proposed Natural Conditions for the Texas Class I Areas ............................................. 32

11 Calculation of Natural Visibility Impairment for the Texas Class I Areas................... 33

12 Uniform Rates of Progress and the Emission Reductions Needed to Achieve Them ... 34

13 Reasonable Progress Goals for Oklahoma and Texas Class I Areas............................. 36

Appendix A. EPA’s Visibility Projection Modeling.............................................................. A-1

A.0 Background and Introduction .................................................................................. A-1

A.1 Emissions Data and EPA’s Q/D Analysis................................................................. A-2

A.2 Initial Source Apportionment Modeling for 38 Q/D sources............................... A-15

A.3 Our Evaluation of Modeling for 38 Facilities........................................................ A-27

A.4 Modeling Results – Selection of Sources for Further Evaluation........................ A-41

A.5 Preparation of Emissions Scenarios for Potential Controls - Additional visibility 

Modeling.................................................................................................................... A-54

A.6 Results for High/Low Control Runs and Final Control Analysis Benefits......... A-59
iv

Tables

Table 1. Sources undergoing RP and LTS analyses ................................................................. 4

Table 3. Annual SO2 and NOx emissions for Big Brown Units 1 and 2................................ 14

Table 4. Contrast in SO2 control cost effectiveness ................................................................ 14

Table 5. Annual SO2 and NOx emissions for Big Brown Units 1 and 2................................ 16

Table 6. Contrast in SO2 control cost effectiveness ................................................................ 16

Table 7. Annual SO2 and NOx emissions for Coleto Creek ................................................... 18

Table 8. Contrast in SO2 control cost effectiveness ................................................................ 18

Table 9. Annual SO2 and NOx emissions for Tolk Units 171B and 172B............................. 20

Table 10. Contrast in SO2 control cost effectiveness .............................................................. 20

Table 11. Annual SO2 and NOx emissions for Welsh Units 1, 2, and 3 ................................ 22

Table 12. Contrast in SO2 control cost effectiveness .............................................................. 23

Table 13. Annual SO2 and NOx emissions for W. A. Parish Units WAP5, WAP6, and 

WAP7 ........................................................................................................................ 25

Table 14. Contrast in SO2 control cost effectiveness .............................................................. 26

Table 15. Summary of Scrubber Upgrade Results................................................................. 27

Table 16. Proposed 30 Boiler Operating Day SO2 Emission Limits ..................................... 32

Table 18. Natural Conditions for the Guadalupe Mountains and Big Bend........................ 33

Table 19. Revised Visibility Metrics for the Class I Areas in Texas ..................................... 34

Figures

Figure 1 – Map of sources and Class I areas.............................................................................. 5

Figure 2. Aerial view of the Big Brown facility....................................................................... 13

Figure 3. Aerial view of the Monticello facility ....................................................................... 15

Figure 4. Aerial view of the Coleto Creek facility................................................................... 17

Figure 5. Aerial view of the Tolk Facility ................................................................................ 19

Figure 6. Aerial view of the Welsh facility............................................................................... 21

Figure 7. Aerial view of the W. A. Parish facility ................................................................... 24

Figure 8. URP for Big Bend ...................................................................................................... 35

Figure 9. URP for the Guadalupe Mountains......................................................................... 35
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Appendix A. EPA’s Visibility Projection Modeling

A.0 Background and Introduction........................................................................................ A-1

A.1 Emissions Data and EPA’s Q/D Analysis...................................................................... A-2

A.2 Initial Source Apportionment Modeling for 38 Q/D sources.................................... A-15

A.3 Our Evaluation of Modeling for 38 Facilities............................................................. A-27

A.4 Modeling Results – Selection of Sources for Further Evaluation............................. A-41

A.5 Preparation of Emissions Scenarios for Potential Controls - Additional visibility 

Modeling......................................................................................................................... A-54

A.6 Results for High/Low Control Runs and Final Control Analysis Benefits.............. A-59

Appendix Tables

Table A.1-1. Class I areas included in Q/D Analysis............................................................ A-3

Table A.1-2. Sources identified through EPA’s Q/D Analysis for inclusion in sourceapportionment analysis .................................................................................... A-5

Table A.4-1a. Top 10 Impacting sources ranked on % Extinction on Avg. 20% Worst 

Days at WIMO ................................................................................................ A-42

Table A.4-1b. Top 10 Impacting sources ranked on % Extinction on Avg. 20% Worst 

Days at BIBE ................................................................................................... A-43

Table A.4-1c. Top 10 Impacting sources ranked on % Extinction on Avg. 20% Worst 

Days at GUMO................................................................................................ A-44

Table A.4-2. 2008-2012 CAMD CEM emissions contrasted with Modeled emission rates for 

38 facilities modeled........................................................................................ A-47

Table A.4-3. Percent of extinction for the Avg. Impacts on W20% days.(Facilities) ..... A-51

Table A.4-4. Percent of extinction for the Avg. Impacts on W20% days.(Units) ........... A-52

Table A.5-1. EIA data and calculating potential emissions levels correlated to certain 

controls and efficiencies.................................................................................. A-56

Table A.5-2. Efficiency summary for Low and High Control Runs for each Unit.......... A-57

Table A.5-3. Emissions provided to ENVIRON for Low control and High Control visibility 

modeling runs.................................................................................................. A-58

Table A.6-1a. Average Change in Extinction levels at WIMO on W20% days for different 

controls............................................................................................................. A-60

Table A.6-1b. Average Change in Extinction levels at BIBE on W20% days for different 

controls............................................................................................................. A-61

Table A.6-1c. Average Change in Extinction levels at GUMO on W20% days for different 

controls............................................................................................................. A-62

Table A.6-1d. The Cumulative Average Change in Extinction levels at all other Class I 

areas (not WIMO, BIBE or GUMO) on W20% days for different controls 

.......................................................................................................................... A-63
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Table A.6-2a. Average Change in Deciview levels at WIMO on W20% days for different 

controls............................................................................................................. A-68

Table A.6-2b. Average Change in Deciview levels at BIBE on W20% days for different 

controls............................................................................................................. A-69

Table A.6-2c. Average Change in Deciview levels at GUMO on W20% days for different 

controls............................................................................................................. A-70

Table A.6-2d. The Cumulative Average Change in Deciview levels at all other Class I areas 

(not WIMO, BIBE or GUMO) on W20% days for different controls....... A-71

Table A.6-3. Deciview improvement at Class I areas for scrubber upgrades.................. A-73

Table A.6-4. Deciview Improvent due to differing levels of control on existing uncontrolled 

units.................................................................................................................. A-74

Table A.6-5. Net benefit of proposed controls on 2018 Visibility projections.................. A-76

Table A.6-6. Predicted benefit of all proposed controls beyond 2018 CENRAP projected 

visibility conditions (2018 ‘dirty’ background)............................................ A-77

Appendix Figures

Figure A.1-1. Class I areas included in EPA Q/D Analysis.................................................. A-3

Figure A.1-2. Location of Selected Sources........................................................................... A-6

Figure A.1-3a. Q/D for WIMO using 2009 Annual EI......................................................... A-7

Figure A.1-3b. Q/D for CACR using 2009 Annual EI.......................................................... A-8

Figure A.1-3c. Q/D for BIBE using 2009 Annual EI ............................................................ A-9

Figure A.1-3d. Q/D for GUMO using 2009 Annual EI....................................................... A-10

Figure A.1-3e. Q/D for CAVE using 2009 Annual EI......................................................... A-11

Figure A.1-3f. Q/D for BRET using 2009 Annual EI.......................................................... A-12

Figure A.1-3g. Q/D for SACR using 2009 Annual EI......................................................... A-13

Figure A.1-3h. Q/D for BOAP using 2009 Annual EI ........................................................ A-14

Figure A.2 -1. Source Contribution to 2018 Deciview over 20%Worst Days at WIMO, OK

........................................................................................................................ A-18

Figure A.2 -2. Source Contribution to 2018 Light Extinction by species over 20%Worst 

Days at WIMO, OK...................................................................................... A-18

Figure A.2 -3. Percentage of Total Extinction over 20%Worst Days at WIMO, OK ..... A-19

Figure A.2 -4. Percentage of Total Extinction by Species over 20%Worst Days at WIMO, 

OK.................................................................................................................. A-19

Figure A.2 -5. Maximum Source Contribution to 2018 Deciview on any day of W20% days 

at WIMO, OK............................................................................................... A-20

Figure A.2 -6. Uniform Rate of Progress and 2018 Projected Progress over 20%Worst 

Days at WIMO, OK...................................................................................... A-20

Figure A.2 -7. Source Contribution to 2018 Deciview over 20%Worst Days at BIBE, Texas

........................................................................................................................ A-21
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Figure A.2 -8. Source Contribution to 2018 Light Extinction by species over 20%Worst 

Days at BIBE, Texas..................................................................................... A-21

Figure A.2 -9. Percentage of Total Extinction over 20%Worst Days at BIBE, Texas .... A-22

Figure A.2 -10. Percentage of Total Extinction by Species over 20%Worst Days at BIBE, 

Texas.............................................................................................................. A-22

Figure A.2 -11. Maximum Source Contribution to 2018 Deciview on any day of W20% 

days at BIBE, Texas ..................................................................................... A-23

Figure A.2 -12. Uniform Rate of Progress and 2018 Projected Progress over 20%Worst 

Days at BIBE, Texas..................................................................................... A-23

Figure A.2 -13. Source Contribution to 2018 Deciview over 20%Worst Days at GUMO, 

Texas.............................................................................................................. A-24

Figure A.2 -14. Source Contribution to 2018 Light Extinction by species over 20%Worst 

Days at GUMO, Texas ................................................................................. A-24

Figure A.2 -15. Percentage of Total Extinction over 20%Worst Days at GUMO, TexasA-25

Figure A.2 -16. Percentage of Total Extinction by Species over 20%Worst Days at GUMO, 

Texas.............................................................................................................. A-25

Figure A.2 -17. Maximum Source Contribution to 2018 Deciview on any day of W20% 

days at GUMO, Texas.................................................................................. A-26

Figure A.2 -18. Uniform Rate of Progress and 2018 Projected Progress over 20%Worst 

Days at GUMO, Texas ................................................................................. A-26

Figure A.3-1a. CENRAP 2007 PSAT results % of extinction impacts at WIMO (W20%)

......................................................................................................................A-29

Figure A.3-1b. CENRAP 2007 PSAT results % of extinction impacts at BIBE (W20%) 

........................................................................................................................ A-30

Figure A.3-1c. CENRAP 2007 PSAT results % of extinction impacts at GUMO (W20%) 

........................................................................................................................ A-31

Figure A.3-2. Extinction Relative to Texas Influences and Texas Point Sources at WIMO 

(W20%) ......................................................................................................... A-32

Figure A.3-3. Extinction Relative to Texas Influences and Texas Point Sources at BIBE 

(W20%) ......................................................................................................... A-33

Figure A.3-4. Extinction Relative to Texas Influences and Texas Point Sources at GUMO 

(W20%) ......................................................................................................... A-34

Figure A.3-5. Example of Logarithmic nature of del-dv calculation ................................ A-40

Figure A.6-1e. Extinction level and percent of total extinction at WIMO for W20% days 

for the 9 facilities assessed in second modeling effort ............................... A-64

Figure A.6-1f. Extinction level and percent of total extinction at BIBE for W20% days for 

the 9 facilities assessed in second modeling effort ..................................... A-65

Figure A.6-1g. Extinction level and percent of total extinction at GUMO for W20% days 

for the 9 facilities assess in second modeling effort ................................... A-66
1

Oklahoma and Texas Regional Haze FIPs Technical Support Document

1 Introduction

This document provides an explanation for our proposed Federal Implementation Plans (FIPs) 

for the remaining portions of the Oklahoma Regional Haze SIP (OK RH SIP) that we either 

disapproved or did not act upon1in our previous action, and those portions of the Texas RH SIP 

for which we are proposing disapproval in this action.

2 Overview of the Gaps in the Oklahoma and Texas Regional Haze SIPs

Below, we list all of the portions of section 51.308 that we propose to disapprove for the Texas 

and Oklahoma RH SIPs. Please see our TX TSD and OK TSD documents for more information 

on why we believe these portions of the Texas and Oklahoma RH SIPs should be disapproved. 

We follow these sections with discussions of how we believe our FIPs fill these gaps.

2.1 Flaws in Texas’ Reasonable Progress Goal, Long Term Strategy, and Other Areas

In the TX TSD, we review the Texas RH SIP and discuss our rationale for proposing to 

disapprove the following parts of the Texas RH SIP:

 Section 51.308(d)(1)(i)(A), regarding Texas’ reasonable progress four factor analysis.

 Section 51.308(d)(1)(i)(B), regarding Texas’ calculation of the emission reductions 

needed to achieve the URPs for the Guadalupe Mountains and Big Bend.

 Section 51.308(d)(1)(ii), regarding Texas’ RPGs for the Guadalupe Mountains and 

Big Bend.

 Section 51.308(d)(2)(iii), regarding Texas’ calculation of the natural visibility 

conditions for the Guadalupe Mountains and Big Bend.

 Section 51.308(d)(2)(iv)(A) regarding Texas’ calculation of natural visibility 

impairment.

 Section 51.308(d)(3)(i) regarding Texas’ long-term strategy consultation.

 Section 51.308(d)(3)(ii) regarding Texas securing its share of reductions in other 

States’ RPGs.

 Section 51.308(d)(3)(iii) regarding Texas’ technical basis for its long-term strategy. 

 Section 51.308(d)(3)(v)(C), regarding Texas’ emissions limitations and schedules for 

compliance to achieve the RPGs for Big Bend and the Guadalupe Mountains.

2.2 Flaws in Oklahoma’s Flawed Reasonable Progress Goal

In the OK TSD, we do the following regarding our evaluation of the Oklahoma RH SIP:

 

1

 Previously, we proposed a partial approval and partial disapproval of, and a FIP for the Oklahoma SIP on March 

22, 2011 (76 FR 16168). We finalized that action on December 28, 2011 (76 FR 81728). 
2

 We review that we did not take action on whether Oklahoma satisfied the RP

requirements of section 51.308(d)(1) in our previous action, because we concluded we 

must first evaluate and act upon the RH SIP revision submitted by Texas.

 Having now reviewed the Texas RH SIP, we review Oklahoma’s submittal for satisfying 

the RP requirements of section 51.308(d)(1).

 We discuss our rationale for proposing to disapprove the RPGs for the Wichita 

Mountains set by Oklahoma in its regional haze SIP. In setting its RPG, we propose to 

find that Oklahoma generally did not meet the requirements of Section 51.308(d)(1) of 

the Oklahoma regional haze SIP, except for Section 51.308(d)(1)(vi).

3 Our FIPs Cure Defectsin the Texas and Oklahoma Regional Haze SIPs

Below we discuss why we believe our FIPs provide the information necessary to cure the defects 

in the Oklahoma and Texas RH SIPs that we have outlined above. 

3.1 Summary of the Texas FIP

We believe our proposed FIP and its rationale as presented here provide the technical analysis 

that was lacking in Texas’ development of its RPGs for the Guadalupe Mountains and Big Bend, 

and in its consultations with Oklahoma for the development of the RPGs for the Wichita 

Mountains, as well as addressing its long-term strategy. As Texas did in the development of its 

SIP, we have also used the same analysis to address both tasks. We began our review of Texas’ 

conclusions with an initial analysis of all point sources in Texas and an assessment of the 

visibility impact from those sources with the greatest potential to contribute to visibility 

impairment. A refinement of this analysis resulted in our focus on a much smaller group of 

sources that essentially reduced down to an analysis of whether, in light of the balance between 

the cost of control and visibility benefits of control at each source, additional SO2 controls should 

be installed on each of certain large coal fired EGUs in Texas in order to improve the visibility at 

these Class I areas. We conducted our analysis using the four reasonable progress factors listed 

in Section 51.308(d)(1)(i)(A). We propose to find that this portion of our proposed Texas FIP 

would make whole our disapproval of those portions of the Texas SIP intended to meet:

 Section 51.308(d)(1)(i)(A).

 Section 51.308(d)(3)(i).

 Section 51.308(d)(3)(ii).

 Section 51.308(d)(3)(iii). 

 Section 51.308(d)(3)(v)(C).

We also establish the natural visibility conditions for the Guadalupe Mountains and Big Bend. 

We then use those values and the analysis we have developed above to consider the emission 

reductions needed to achieve the URPs for the Guadalupe Mountains and Big Bend and establish 

their RPGs. We propose that these portions of our Texas FIP, developed below, make whole our 

disapproval of those portions of the Texas SIP intended to meet:

 Section 51.308(d)(2)(iii).

 Section 51.308(d)(2)(iv)(A).
3

 Section 51.308(d)(1)(i)(B).

 Section 51.308(d)(1)(ii).

3.2 Summary of the Oklahoma FIP

We believe some of the same portions of our proposed Texas FIP would also make whole the 

portions of the Oklahoma regional haze SIP we propose to disapprove. We believe that

Oklahoma’s flawed consultation with Texas denied it the knowledge it needed—the visibility 

impacts of individual sources in Texas with the largest potential to impact the visibility at the 

Wichita Mountains and the extent to which cost-effective controls were available—in order to 

properly construct its RPG for the Wichita Mountains. As indicated in the record, both the 

ODEQ and the TCEQ acknowledged during the development of their respective regional haze 

SIPs that Texas point sources have a significant visibility impact at the Wichita Mountains and 

that cost-effective controls were likely available for these sources. Armed with this knowledge, 

however, the ODEQ did not pursue the point in its consultations with the TCEQ under Section

51.308(d)(1)(iv). We believe that our proposed OK FIP would make whole the requirement in 

the Regional Haze Rule for states to adequately consult and to provide the information we 

believe should have resulted from those consultations. We propose that our analysis of potential 

controls for Texas sources allows us to reset Oklahoma’s RPG and demonstrate it is reasonable.

4 Technical Overview of the Oklahoma and Texas FIPs

As discussed in Appendix A to this TSD, we have determined that based on their visibility 

impacts, a smaller subset of the facilities that we have initially analyzed should be further 

evaluated to determine (1) if cost effective controls are available and (2) considering their 

projected visibility benefits, which, if any controls should be proposed. With one exception, the 

PPG Flat Glass plant in Wichita Falls, all of the facilities are coal fired power plants. Also as 

discussed in that section, we are limiting our analyses to the consideration of SO2 controls for 

these EGU sources, as our modeling indicates that the impacts from these sources on the 20% 

worst days are primarily due to sulfate emissions. In our Cost TSD, we conduct a SO2 cost

analyses for the following facilities and units:
4

Table 1. Sources undergoing RP and LTS analyses

Facility Units Scrubbed? Bypass?

Big Brown 1, 2

Sandow 4 1 Y Y

Monticello 1, 2

Monticello 3 Y Y

Martin Lake 1, 2, 3 Y Y

Coleto Creek 1

Limestone 1, 2 Y Y

San Miguel 1 Y N

Tolk 1, 2

Welsh 1, 2, 3

W. A. Parish 5, 6, 7

W. A. Parish 8 Y Y

In addition to these sources, we have examined the PPG Flat Glass Plant in Wichita Falls, Texas. 

This is the only non-EGU and the only source for which NOx controls are considered. For all of 

the sources we examined, visibility impacts were dominated by the impacts from SO2 emissions 

with the exception of the PPG Flat Glass Plant. Because of the proximity of this facility to 

Wichita Mountains, NOx and SO2 emissions from the facility were both responsible for the 

visibility impacts at Wichita Mountains. As discussed in more detail below, we evaluated these 

impacts and considered recent emissions and permit data in considering the potential need for 

additional controls for this facility. 

4.1 Location of Sources

The following is a map of Texas and the surrounding states that shows, with the exception of San 

Miguel,2the locations of the sources listed in Table 1 and selected Class I areas.

 

2

 For reasons we discuss elsewhere in this document, we are not proposing any additional controls on San Miguel.
5

Figure 1 – Map of sources and Class I areas

In the above map, the Wichita Mountains, Big Bend, and the Guadalupe Class I areas are 

abbreviated by WIMO1, BIBE1, GUMO1, respectively. These are the Class I areas most often 

referenced in our analysis.

4.2 Approach to Reasonable Progress and Long term Strategy 

We are simultaneously conducting reasonable progress and long-term strategy analyses. These 

analyses address both (1) the requirements to consider the four reasonable progress factors for 

the Texas Class I areas, and (2) the technical basis required to develop the long-term strategy for 

the Texas Class I areas and the Wichita Mountains in Oklahoma. We use the “four factor 

analysis” method outlined in 40 CFR 51.308(d)(1)(A) that States are directed to use in 

establishing a RPG:
6

(1) Reasonable progress goals. For each mandatory Class I Federal area located 

within the State, the State must establish goals (expressed in deciviews) that 

provide for reasonable progress towards achieving natural visibility conditions. 

The reasonable progress goals must provide for an improvement in visibility for 

the most impaired days over the period of the implementation plan and ensure no 

degradation in visibility for the least impaired days over the same period.

(i) In establishing a reasonable progress goal for any mandatory Class I Federal 

area within the State, the State must:

(A) Consider the costs of compliance, the time necessary for compliance, the 

energy and non-air quality ENVIRONmental impacts of compliance, and the 

remaining useful life of any potentially affected sources, and include a 

demonstration showing how these factors were taken into consideration in 

selecting the goal.

To assist in interpreting these reasonable progress factors, we will rely on our reasonable 

progress Guidance.3 Our Reasonable Progress Guidance notes the similarity between some of 

the reasonable progress factors and the BART factors contained in Section 51.308(e)(1)((ii)(A), 

and suggests that the BART Guidelines be consulted regarding cost, energy and non-air quality 

environmental impacts, and remaining useful life. We are therefore relying on our BART 

Guidelines for assistance in interpreting those reasonable progress factors, as applicable. 

We note that with one exception,4the issues relating to the evaluation of three of these factors: 

(1) time necessary for compliance, (2) energy and non-air quality environmental impacts of 

compliance, and (3) remaining useful life, are common to all the units we are analyzing. Thus, 

we are analyzing these factors for all the units simultaneously. 

In analyzing the remaining factor, cost of compliance, we are including in our evaluation a 

consideration of any control technology that may already be installed at the facility. Also, 

similar to a BART analysis, we are also considering the projected visibility benefit in our 

analysis. As we state in our Arizona proposal5:

While visibility is not an explicitly listed factor to consider when determining 

whether additional controls are reasonable, the purpose of the four-factor analysis 

is to determine what degree of progress toward natural visibility conditions is

reasonable. Therefore, it is appropriate to consider the projected visibility benefit 

of the controls when determining if the controls are needed to make reasonable 

progress.

For each unit, we are weighing the cost of compliance against the projected visibility benefit in a 

cost/benefit analysis.

 

3

 Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program, June 1, 2007.

4

 For reasons we discuss below, we believe that the Tolk facility may merit a special consideration of the energy 

and non-air quality environmental impacts of compliance.

5

 See 79 FR 9353, footnote 137. We also finalized our proposal in 79 FR 52420, using this same reasoning.
7

4.2.1 Time Necessary for Compliance, and the Oklahoma and Texas RPGs

We discuss the time necessary for compliance reasonable progress factor in our Reasonable 

Progress Guidance:6

It may be appropriate for you to use this factor to adjust the RPG to reflect the 

degree of improvement in visibility achievable within the period of the first SIP if 

the time needed for full implementation of a control measure (or measures) will 

extend beyond 2018. For example, if you anticipate that constraints on the 

availability of construction labor will preclude the installation of controls at all 

sources of a particular category by 2018, the visibility improvement anticipated 

from installation of controls at the percentage of sources that could be controlled 

within the strategy period should be considered in setting the RPG and in 

establishing the SIP requirements to meet the RPG. 

Due to delays in processing the Texas regional haze SIP and the remaining portion of the 

Oklahoma regional haze SIP, we cannot assume that the SO2 controls we are proposing will be 

installed and operational within this planning period, which ends in 2018. For instance, typical 

SO2 scrubber installations can take up to five years to plan, construct and bring to operational 

readiness. This would mean that any such controls that we may require in our final action may

not be operational until after 2018. Therefore, although we are proposing revised RPGs for 

Oklahoma and Texas, we are proposing RPGs that only account for the scrubber upgrades 

included in this FIP anticipated to be completed by 2018. We request that Oklahoma and Texas 

consider the additional visibility improvements anticipated from any proposed FIP controls 

implemented after 2018 with the submission of their next regional haze SIPs due July 13, 2018.

4.2.2 Energy and Non-Air Quality Environmental Impacts of Compliance

Regarding the analysis of energy impacts, the BART Guidelines advise, “You should examine 

the energy requirements of the control technology and determine whether the use of that 

technology results in energy penalties or benefits.”7 As discussed below in our cost analyses for 

Dry Sorbent Injection (DSI) and Spray Dryer Absorber (SDA) SO2 scrubbers, our cost model 

allows for the inclusion or exclusion of the cost of the additional auxiliary power required for the 

pollution controls we considered to be included in the variable operating costs. We chose to 

include this additional auxiliary power in all cases. Consequently, we believe that any energy 

impacts of compliance have been adequately considered in our analyses.

Regarding the analysis of non-air quality environmental impacts, the BART Guidelines advise8:

Such environmental impacts include solid or hazardous waste generation and 

discharges of polluted water from a control device. You should identify any 

significant or unusual environmental impacts associated with a control alternative 

that have the potential to affect the selection or elimination of a control 

 

6

 Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program, June 1, 2007. Page 19.

7

 70 FR 39168 (July 6, 2005).

8

 70 FR 39169 (July 6, 2005).
8

alternative. Some control technologies may have potentially significant 

secondary environmental impacts. Scrubber effluent, for example, may affect 

water quality and land use. Alternatively, water availability may affect the 

feasibility and costs of wet scrubbers. Other examples of secondary 

environmental impacts could include hazardous waste discharges, such as spent 

catalysts or contaminated carbon. Generally, these types of environmental 

concerns become important when sensitive site-specific receptors exist or when 

the incremental emissions reductions potential of the more stringent control is 

only marginally greater than the next most-effective option. However, the fact 

that a control device creates liquid and solid waste that must be disposed of does 

not necessarily argue against selection of that technology as BART, particularly if 

the control device has been applied to similar facilities elsewhere and the solid or 

liquid waste is similar to those other applications. On the other hand, where you 

or the source owner can show that unusual circumstances at the proposed facility 

create greater problems than experienced elsewhere, this may provide a basis for 

the elimination of that control alternative as BART.

The SO2 control technologies we considered in our analysis – DSI and scrubbers – are in wide 

use in the coal-fired electricity generation industry. Both technologies add spent reagent to the 

waste stream already generated by the facilities we analyzed, but do not present any unusual 

environmental impacts. As discussed below in our cost analyses for DSI and SDA SO2

scrubbers, our cost model includes waste disposal costs in the variable operating costs. 

Consequently, we believe that with one possible exception, any non-air quality environmental 

impacts have been adequately considered in our analyses. An examination of the aerial photo of 

the Tolk facility, which we present in section 5.4, does not reveal any obvious source of surface 

water. We therefore assume that well water is used. In light of this and its potential relationship 

to the energy and non-air quality environmental impacts of compliance, we limit our SO2 control 

analysis for Tolk to DSI and dry scrubbers.

4.2.3 Remaining Useful Life 

Regarding the analysis of the remaining useful life, the BART Guidelines advise:

The ‘‘remaining useful life’’ of a source, if it represents a relatively short time 

period, may affect the annualized costs of retrofit controls. For example, the 

methods for calculating annualized costs in EPA’s OAQPS Control Cost Manual 

require the use of a specified time period for amortization that varies based upon 

the type of control. If the remaining useful life will clearly exceed this time 

period, the remaining useful life has essentially no effect on control costs and on 

the BART determination process. Where the remaining useful life is less than the 

time period for amortizing costs, you should use this shorter time period in your 

cost calculations.
9

In determining the cost of scrubbers in our prior Oklahoma FIP, we used a lifetime of 30 years. 

In so doing, we noted9that scrubber vendors indicate that the lifetime of a scrubber is equal to 

the lifetime of the boiler, which might easily be over 60 years. We also noted that many 

scrubbers that were installed between 1975 and 1986 are still in operation today (e.g., Coyote 

Station, H.L. Spurlock Unit 2, East Bend Unit 2, Laramie River Unit 3, Cholla 5, Basin Electric, 

Mitchell Unit 33, and all of the units in Table 30 that currently have scrubbers). Further, we 

noted that standard cost estimating handbooks and published papers report 30 years as a typical 

life for a scrubber and that many utilities routinely specify 30+ year lifetimes in requests for 

proposal and to evaluate proposals. We have used this 30 year lifetime approach in prior actions 

and we therefore adopted the same scrubber lifetime in our present analysis. See 76 FR 52388 

(Aug 22, 2011); 76 FR 81728 (Dec. 28, 2011); Oklahoma v. EPA, 723 F.3d 1201 (July 19, 

2013), cert. denied (U.S. May 27, 2014).

We see no reason to assume that a DSI system installation, which is a much less complex and 

costly (capital costs, as opposed to annualized costs) technology in comparison to a scrubber 

installation, should have a shorter lifetime. As with a scrubber, we expect the boiler to be the 

limiting factor when considering the lifetime of a coal-fired power plant. We have therefore 

similarly assumed that the lifetime of a DSI system is 30 years, as constrained by the boiler 

lifetime, as noted above.

The BART Guidelines provide further clarification:

Where this affects the BART determination, this date should be assured by a 

federally- or State-enforceable restriction preventing further operation. We

recognize that there may be situations where a source operator intends to shut 

down a source by a given date, but wishes to retain the flexibility to continue 

operating beyond that date in the event, for example, that market conditions 

change. Where this is the case, your BART analysis may account for this, but it 

must maintain consistency with the statutory requirement to install BART within 

5 years. Where the source chooses not to accept a federally enforceable condition 

requiring the source to shut down by a given date, it is necessary to determine 

whether a reduced time period for the remaining useful life changes the level of 

controls that would have been required as BART.

As in a BART determination, we propose to adopt the same requirement regarding the need for a 

federally enforceable restriction for any DSI or scrubber remaining useful life of less than 30 

years. 

4.3 Analysis of the PPG Flat Glass Plant

The Wichita Falls PPG flat glass plant is located in Wichita Falls, Texas. The plant began 

operations in 1974.10 The facility produces flat glass on two production lines, each with its own 

 

9

 Technical Support Document for the Oklahoma Regional Haze State Implementation Plan and Federal 

Implementation Plan. March 2011, p. 14.

10

 http://corporate.ppg.com/Our-Company/Worldwide-Operations/North-America/Wichita-Falls
10

natural gas furnace. A furnace typically lasts ten to twelve years until re-bricking is required. In 

2007, PPG applied to the TCEQ for a standard permit registration11 in order to obtain 

authorization for the implementation of a low-NOx oxy-fuel injection conversion to its Melting 

Furnace No. 1. As a result of this upgrade, PPG calculated its NOx emissions from Furnace No. 

1 would decrease by approximately 1,996 tpy to 894.25 tpy. PPG also further reduced their NOx 

emissions as a result of a fuel conservation project which occurred with the rebuilding of 

Furnace No. 2. This project lowered the NOx emissions of Furnace No. 2 from an allowable 

annual NOx limit of 3,236.82 tpy to 2,947.49 tpy. These reductions were incorporated into a 

permit alteration.12

Table 31 below compares the 2018 projected CENRAP emission inventory to the 2002 

CENRAP emission inventory, the current permit limits for the two furnaces, and average actual 

annual emissions for the facility. We projected the visibility impact from this facility at the 2018 

projected emission level to be 0.635 Mm-1at Wichita Mountains (using source apportionment). 

Permit allowable emissions for NOx for the two furnaces are much lower than projected and 

modeled for 2018 and lower than the 2002 emission level. The 2018 projected emissions for 

SO2 also exceed the permitted emissions for furnace No. 2. Average annual emissions are only 

44% of the projected 2018 emissions for NOx and 81% of the projected SO2 emissions. 

Therefore, we estimate that the current visibility impact due to the facility is significantly lower 

than the 2018 projected value. We are proposing to find that the Wichita Falls PPG flat glass 

plant is adequately controlled to address visibility impacts from this facility for the first planning 

period. We encourage the State of Texas to revisit this issue when Furnace No. 2 is scheduled 

for its next re-bricking

 

11

 Standard Permit Registration, PPG Industries, Inc., Wichita Falls Plant, Account No. WH-0040-R. Submitted by 

ENVIRON, dated October 11, 2007.

12

 Permit Alteration, Permit Number: 898, Flat Glass Manufacturing Facility, Wichita Falls, Wichita County, 

Regulated Entity Number: RN102522950, Customer Reference Number: CN600124614, Account Number: WH0040-R
11

Table 31. Emission comparison for PPG Flat Glass Plant

CENRAP 2002 

emission 

inventory (tpy)

CENRAP 2018 

emission inventory 

(tpy)

Permit Allowable13

(tpy)

Average Annual 

Emissions (tpy, 

2009-2012)14

NOx SO2 NOx SO2 NOx SO2 NOx SO2

Furnace 

No. 1

2,694.5 48.0 4,526.8 80.7 894.3 180.3 --- ---

Furnace 

No. 2

2,495.2 279.7 4,191.9 470.0 2,947.5 350.4 --- ---

Furnace 

No. 1 and 

No. 2

5,189.7 327.7 8,718.8 550.6 3,841.7 530.7 --- ---

Facility 

total

5,317.0 371.0 8,929.0 623.0 --- --- 3,887.8 501.9

4.4 Approach to Technical Analysis

We present a reasonable progress and long term strategy cost analyses for those units being 

analyzed for DSI or scrubber retrofits in which we assess the cost of DSI, SDA, and wet FGD. 

The modeled benefits that would result from the installation of those controls are reviewed, and 

the cost of the controls are weighed against their projected visibility benefits at a number of 

Class I areas. We then propose which units should install SO2 control equipment and the control 

level those units should achieve. Please see our Cost TSD for more detail on how we performed 

the cost analysis and Appendix A to this TSD for more details about how we conducted our 

visibility analysis.

We also present a summary of our scrubber upgrade cost analyses for those units in Table 1 that 

are already partially scrubbed. We present a similar cost/benefit analysis as we did for each unit 

we analyzed for scrubber upgrades. We propose which units should install SO2 scrubber 

upgrades and the control level those units should achieve. 

4.5 Use of Confidential Business Information

Within our Cost TSD, we calculate the SO2 removal efficiencies for the underperforming 

scrubbers listed in Table 1, and present information that discusses how these scrubbers have been 

historically upgraded and what kinds of equipment revisions are typically required. In order to 

assess the potential range of options available to upgrade the scrubbers in the facilities listed in 

Table 30, we must have an understanding of what upgrades may have already been performed. 

Because most of this information is not available publicly, we have requested it under authority 

 

13

 Permit Alteration, Permit Number: 898, Flat Glass Manufacturing Facility, Wichita Falls, Wichita County, 

Regulated Entity Number: RN102522950, Customer Reference Number: CN600124614, Account Number: WH0040-R

14

 TCEQ point source emission inventory. Downloaded from https://www.tceq.texas.gov/airquality/point-sourceei/psei.html and available in the docket for this action.
12

granted to us under Section 114(a) of the CAA. For each unit, we then conducted a cost analysis 

for eliminating any scrubber bypass and upgrading the units’ overall SO2 removal efficiency to at 

least 95%. As most of the information we received in response to our Section 114(a) requests 

was claimed as Confidential Business Information (CBI) under 40 C.F.R. Part 2, Subpart B, 

therefore we are limited in what we are able to publicly state in this analyses. Consequently, 

although our full cost analysis is available on a facility-by-facility basis for viewing by the 

companies who provided us with the CBI material, we can only provide a summary of it below. 

5 Reasonable Progress and Long Term Strategy Scrubber and DSI Cost Analyses

In Section 4, above, we discuss how we are simultaneously conducting RP and LTS analyses 

using the “four factor analysis” outlined in 40 CFR 51.308(d)(1)(A) that states are directed to use 

in establishing a RPG. We also discuss why we are considering visibility in our analysis. We 

considered the costs of compliance for DSI, SDA and wet FGD, the time necessary for 

compliance, the energy and non-air quality environmental impacts of compliance, and the 

remaining useful life of any potentially affected sources. 

In this section, for each unit with no SO2 control, we provide an overview of the facility based on 

data from our Air Markets Program Data website,15 and reporting to the EIA via Forms 860 and 

923. We develop our cost estimates for DSI, SDA, and wet FGD in our COST TSD. Here, we 

present the historical annual emissions,16 and contrast the cost of DSI, SDA, and wet FGD. 

As we discuss in Our Cost TSD, we evaluated each unit at its maximum recommended level of 

control, considering the type of SO2 control device:

 We evaluated each unit at its maximum recommended DSI performance level, according 

to the IPM DSI documentation, assuming milled trona: 80% SO2 removal for an ESP 

installation and 90% SO2 removal for a baghouse installation. This level of control is 

within that of SO2 scrubbers, and thus allows a better comparison of the costs of DSI and 

scrubbers.

 However, as we state above, we believe that the maximum performance level for DSI can 

only be determined after an onsite performance test. Therefore, we don’t know whether a 

given unit is actually capable of achieving these DSI control levels, and (2) we believe it 

is useful to evaluate lesser levels of DSI control (and correspondingly lower costs). We 

therefore also evaluated all the units at a DSI SO2 control level of 50%, which we believe 

is likely achievable for any unit.

 The SDA level of control was assumed to be either a maximum of 95% not to go below 

0.06 lbs/MMBtu.

 The wet FGD level of control was assumed to be a maximum of 98% not to go below 

0.04 lbs/MMBtu.

As we note in our Cost TSD, the cost effectiveness of DSI worsens (increasing $/ton) as the level 

of control goes up. For all but one of the units we analyzed, even at the lower level of control of 

50%, the cost effectiveness of DSI was worse than either SDA or wet FGD, even with the latter 

 

15

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

16

 Ibid.
13

options offering much greater levels of control. At the higher 80% or 90% level of control, the 

cost effectiveness of DSI was worse than either SDA or a wet FGD in all cases. 

5.1 Big Brown Units 1 and 2

The Big Brown facility is located in Fairfield, within Freestone County, Texas. It is comprised 

of two coal fired units. Unit 1, a tangentially-fired boiler rated at 572.9 MW, became operational 

in 1971 and Unit 2, also a tangentially-fired boiler rated at 572.9 MW, became operational in 

1972. Both units burn a mixture of Texas lignite and PRB coal. Neither unit has any SO2

control. Both units employ Low NOx Burners (LNB), Separated Overfire Air (SOFA), and 

Selective Non-Catalytic Reduction (SNCR) to control NOx. Both units also employ cold side 

ESPs (downstream of the air pre-heaters) and baghouses to control PM. Both units employ 

Activated Carbon Injection (ACI) to control mercury.

Figure 2. Aerial view of the Big Brown facility
14

5.1.1 Emissions Summary

Below are the annual SO2 for Big Brown Units 1 and 2:

Table 3. Annual SO2 and NOx emissions for Big Brown Units 1 and 2

Facility Unit Year

SO2

(tons)

Big Brown 1

2009 28,929

2010 31,131

2011 30,070

2012 32,100

2013 30,801

1

2

2009 26,619

2010 32,169

2011 34,127

2012 28,581

2013 31,693

5.1.2 Analysis of the Cost of Compliance

Below we summarize and contrasts the costs for installing DSI, SDA, and wet FGD:

Table 4. Contrast in SO2 control cost effectiveness

Facility Unit Control

Control 

level

(%)

SO2

reduction 

(tpy)

$/ton 

reduced

Big 

Brown

1

DSI 50.0 15,334 $2,223

DSI 90.0 27,600 $2,996

SDA 95.0 29,134 $1,377

Wet FGD 98.0 30,054 $1255

2

DSI 50.0 15,407 $2,201

DSI 90.0 27,733 $2,994

SDA 95.0 29,273 $1,373

Wet FGD 97.9 30,169 $1,257
15

5.2 Monticello Units 1 and 2

The Monticello facility is located in Mount Pleasant, within Titus County, Texas. It is comprised 

of three coal fired units. Units 1 and 2 do not have any SO2 control and are treated in this 

section, and Unit 3 is partially scrubbed for SO2 and is treated in section 4. Unit 1, a 

tangentially-fired boiler rated at 562.9 MW, became operational in 1974, and Unit 2, also a 

tangentially-fired boiler rated at 562.9 MW, became operational in 1975. Both units burn a 

mixture of Texas lignite and PRB coal. Units 1 and 2 employ LNB with SOFA and SNCR. 

Both units also employ cold side ESPs to control PM. Baghouses were installed in 1978-80 on 

Units 1 and 2 to accommodate 80% of the flow as the ESPs were not effective at controlling PM 

emissions.17 All three units employ Activated Carbon Injection (ACI) to control mercury.

Figure 3. Aerial view of the Monticello facility

 

17

 Larry G. Felix, Randy L. Merritt, and Kkent Duncan, Improving Baghouse Performance at the Monticello 

Generating Station, Journal of the Air Pollution Control Association, v. 36, no. 9, September 1986, pp. 1075 –

1085.
16

5.2.1 Emissions Summary

Below are the annual SO2 for Monticello Units 1 and 2:

Table 5. Annual SO2 and NOx emissions for Big Brown Units 1 and 2

Facility Unit Year

SO2

(tons)

Monticello 1

2009 20,509

2010 19,160

2011 21,897

2012 13,925

2013 6,683

1

2

2009 20,930

2010 19,872

2011 18,436

2012 10,980

2013 7,072

5.2.2 Analysis of the Cost of Compliance

Below we summarize and contrasts the costs for installing DSI, SDA, and wet FGD:

Table 6. Contrast in SO2 control cost effectiveness

Facility Unit Control

Control 

level 

(%)

SO2

reduction 

(tpy)

$/ton 

reduced

Monticello

1

DSI 50.0 8,933 $2,728

DSI 90.0 16,079 $3,420

SDA 95.0 16,972 $2,012

Wet FGD 97.0 17,328 $1,937

2

DSI 50.0 8,215 $3,086

DSI 90.0 14,786 $3,845

SDA 95.0 15,608 $2,254

Wet FGD 96.8 15,907 $2,170
17

5.3 Coleto Creek

The Coleto Creek facility is located near Fannin, within Goliad County, Texas. It is comprised 

of a single coal fired unit. The Coleto Creek facility has one unit, a tangentially-fired boiler 

rated at 629.5 MW which became operational in 1980. It burns PRB coal and does not have any 

SO2 control. It employs LNB with OFA to control NOx and a baghouse to control PM. 

Figure 4. Aerial view of the Coleto Creek facility
18

5.3.1 Emissions Summary

Below are the annual SO2 emissions for Coleto Creek:

Table 7. Annual SO2 and NOx emissions for Coleto Creek

Facility Unit Year

SO2

(tons)

Coleto Creek 1

2009 21,453

2010 17,616

2011 13,694

2012 16,218

2013 14,344

5.3.2 Analysis of the Cost of Compliance

Below we summarize and contrasts the costs for installing DSI, SDA, and wet FGD:

Table 8. Contrast in SO2 control cost effectiveness

Facility Unit Control

Control 

level 

(%)

SO2

reduction 

(tpy)

$/ton 

reduced

Coleto Creek 1

DSI 50.0 8,030 $2,792

DSI 90.0 14,453 $3,460

SDA 93.5 15,012 $2,356

Wet FGD 95.7 15,361 $2,278
19

5.4 Tolk Units 171B and 172B

The Tolk facility is located on County Road 65 between Earth and Muleshoe, within Lamb 

County, Texas. It is comprised of two coal fired units. Unit 171B, a tangentially-fired boiler 

rated at 533 MW, became operational in 1982 and Unit 172B, also a tangentially-fired boiler 

rated at 542.9 MW, became operational in 1985. Both units burn PRB coal. Neither unit has any 

SO2 control. Both units employ OFA to control NOx. Both units also employ baghouses to 

control PM. An aerial photo of the Tolk facility is shown below. Expanding the view of this 

photo does not reveal any obvious source of surface water. We therefore assume that well water 

is used. In light of this and its potential relationship to the energy and non-air quality 

environmental impacts of compliance, we limit our SO2 control analysis for Tolk to DSI and dry 

scrubbers.

Figure 5. Aerial view of the Tolk Facility
20

5.4.1 Emissions Summary

Below are the annual SO2 emissions for Tolk Units 171B and 172B.

Table 9. Annual SO2 and NOx emissions for Tolk Units 171B and 172B

Facility Unit Year

SO2

(tons)

Tolk

171B

2009 10,681

2010 12,412

2011 10,546

2012 8,613

2013 8,868

172B

2009 11,960

2010 12,062

2011 9,285

2012 10,555

2013 10,586

5.4.2 Analysis of the Cost of Compliance

Below we summarize and contrasts the costs for installing DSI, SDA, and wet FGD:

Table 10. Contrast in SO2 control cost effectiveness

Facility Unit Control

Control 

level 

(%)

SO2

reduction 

(tpy)

$/ton 

reduced

Tolk

171B

DSI 50.0 5,016 $3,084

DSI 90.0 9,028 $3,592

SDA 91.7 9,195 $3,178

Wet FGD 94.4 9,474 $3,204

172B

DSI 50.0 5,517 $2,828

DSI 90.0 9,931 $3,221

SDA 90.8 10,015 $2,998

Wet FGD 93.8 10,355 $3,019
21

5.5 Welsh Units 1, 2, and 3

The Welsh facility is located southeast of Mount Pleasant, within Titus County, Texas. It is 

comprised of three coal fired units. All three units are wall fired boilers. Unit 1 is rated at 521.6 

MW and became operational in 1977, Unit 2 is rated at 519 MW and became operational in 

1980, and Unit 3 is rated at 519 MW and became operational in 1982. All three units burn PRB 

coal. None of the units have any SO2 control, and all three units employ LNB with OFA to 

control NOx, and hot side ESPs to control PM. Unit 2 is scheduled to retire no later than 

December 31, 2016.18

Figure 6. Aerial view of the Welsh facility

 

18

 See Sierra Club et al v. U.S. Army Corps of Engineers, civil 4:10-cv-04017-RGK, also letter from John M. 

McManus to Mike Wilson, dated May 2, 2013. Under the terms of a consent decree, after the Turk Plant 

commences commercial operation, Unit 2 will be restricted to a 60% annual capacity factor during any rolling 12-

month period. Thereafter, Unit 2 must be retired no later than December 31, 2016.
22

5.5.1 Emissions Summary

Below are the annual SO2 emissions for Welsh Units 1, 2, and 3.

Table 11. Annual SO2 and NOx emissions for Welsh Units 1, 2, and 3

Facility Unit Year

SO2

(tons)

Welsh

1

2009 9,061

2010 8,361

2011 8,401

2012 7,491

2013 6,469

2

2009 9,453

2010 8,792

2011 8,386

2012 7,588

2013 6,159

3

2009 8,858

2010 9,534

2011 8,836

2012 8,133

2013 7,092
23

5.5.2 Analysis of the Cost of Compliance

Below we summarize and contrasts the costs for installing DSI, SDA, and wet FGD:

Table 12. Contrast in SO2 control cost effectiveness

Facility Unit Control

Control 

level 

(%)

SO2

reduction 

(tpy)

$/ton 

reduced

Welsh

1

DSI 50.0 4,042 $3,718

DSI 80.0 6,467 $4,019

SDA 88.7 7,169 $3,489

Wet FGD 92.5 7,474 $3,508

2

DSI 50.0 4,128 $3,611

DSI 80.0 6,605 $3,879

SDA 88.2 7,285 $3,438

Wet FGD 92.2 7,608 $3,454

3

DSI 50.0 4,305 $3,690

DSI 80.0 6,887 $3,998

SDA 88.7 7,634 $3,368

Wet FGD 92.5 7,959 $3,379
24

5.6 W. A. Parish Units WAP5, WAP6, and WAP7

The W. A. Parish facility is often cited as being the largest electricity generating facility in the 

U.S.19 It is located southeast of Houston, within Fort Bend County, Texas. It is comprised of 

nine units. Units 5, 6, 7, and 8 are coal fired but burn a small amount of natural gas. Units 5, 6, 

and 7 do not have any SO2 control and are treated in this section. Unit 8, which is partially 

scrubbed for SO2 is treated in Section 4. Unit 5 is a wall fired boiler that became operational in 

1977 and is rated at 638.7 MW. Unit 6 is also a wall fired boiler that became operational in 1978 

and is rated at 636.8 MW. Unit 7 is a tangentially fired boiler that became operational in 1980 

and is rated at 559.4 MW. All three units employ Selective Catalytic Reduction (SCR) to control 

NOx and baghouses to control PM. 

Figure 7. Aerial view of the W. A. Parish facility

 

19

 

https://online.platts.com/PPS/P=m&s=1029337384756.1478827&e=1092414376630.634056602174080316/?artnu

m=200vh40u811t1F30702VSa_1
25

5.6.1 Emissions Summary

Below are the annual SO2 emissions for W. A. Parish Units WAP5, WAP6, and WAP7.

Table 13. Annual SO2 and NOx emissions for W. A. Parish Units WAP5, WAP6, and WAP7

Facility Unit Year

SO2

(tons)

W. A. Parish

WAP5

2009 14,145

2010 16,232

2011 14,992

2012 12,774

2013 13,335

WAP6

2009 13,206

2010 17,149

2011 18,267

2012 12,695

2013 15,565

WAP7

2009 12,492

2010 13,200

2011 13,147

2012 10,391

2013 11,365
26

5.6.2 Analysis of the Cost of Compliance

Below we summarize and contrasts the costs for installing DSI, SDA, and wet FGD:

Table 14. Contrast in SO2 control cost effectiveness

Facility Unit Control

Control 

level 

(%)

SO2

reduction 

(tpy)

$/ton 

reduced

W. A. 

Parish

5

DSI 50.0 7,079 $2,559

DSI 90.0 12,741 $2,995

SDA 92.5 13,095 $2,441

Wet FGD 95.0 13,449 $2,389

6

DSI 50.0 7,654 $2,699

DSI 90.0 13,776 $3,229

SDA 93.1 14,251 $2,401

Wet FGD 95.4 14,603 $2,334

7

DSI 50.0 6,168 $2,805

DSI 90.0 11,102 $3,296

SDA 92.7 11,432 $2,559

Wet FGD 95.1 11,733 $2,542

6 Summary of Scrubber Upgrade Cost Results

In our Cost TSD, we analyze those units listed in Table 1 with an existing SO2 scrubber in order 

to determine if cost effective scrubber upgrades are available. Because all of the scrubber 

systems we evaluate are wet scrubbers, we limit our analyses of scrubber upgrades to wet 

scrubbers. Below, we present a summary of the results of that analysis.

With the exception of San Miguel, we are limited in what information we can include in this 

section, because in developing our scrubber cost estimates we used information that was claimed 

as CBI. This information was submitted in response to our Section 114(a) requests. We can 

therefore only present the following summary.

With the exception of San Miguel, we propose to find that for all the units we analyzed:

 The absorber system had either already been upgraded to perform at an SO2 removal 

efficiency of at least 95%, or it could be upgraded to perform at that level using proven 

equipment and techniques.

 The SO2 scrubber bypass could be eliminated, and the additional flue gas could be treated 

by the absorber system with at least a 95% removal efficiency.

 Additional modifications necessary to eliminate the bypass, such as adding fan capacity, 

upgrading the electrical distribution system, and conversion to a wet stack could be 

performed using proven equipment and techniques.
27

 The additional SO2 emission reductions resulting from the scrubber upgrade are 

substantial, ranging from 68% to 89% reduction from the current emission levels, and 

cost effective. 

A summary of our analyses is as follows:

Table 15. Summary of Scrubber Upgrade Results

Unit

2009-2013

3-yr Avg. 

SO2

Emissions 

(eliminate 

max and 

min)

(tons)

SO2

Emissions 

at 95% 

Control

(tons)

SO2

Emissions 

Reduction 

Due to 

Scrubber 

Upgrade

(tons)

SO2 Emission 

Rate at 95% 

Control 

(lbs/MMBtu)

W. A. Parish WAP8 2,586 836 1,750 0.04

Monticello 3 13,857 1,571 12,286 0.06

Sandow 4 22,289 4,625 17,664 0.20

Martin Lake 1 24,495 3,706 20,789 0.12

Martin Lake 2 21,580 3,664 17,917 0.12

Martin Lake 3 19,940 3,542 16,389 0.11

Limestone 1 10,913 2,466 8,446 0.08

Limestone 2 11,946 2,615 9,331 0.08

We calculated the cost effectiveness for each of these units. Because those calculations 

depended on information claimed by the companies as CBI we cannot present it here, except to 

note that in all cases, the cost effectiveness was less than $600/ton. We invite the facilities listed 

above to make arrangements with us to view our complete cost analysis for their units.

7 Modeled Benefits of Emission Controls

In Appendix A and attachments to Appendix A, we describe the different modeling runs we 

conducted for our review, our methodology and selection of emission rates, our modeling results, 

and our final modeling analysis that we use to evaluate the benefits of the controls and their 

associated emission decreases on visibility impairment values. Our modeling focused on 

calculating the extinction and visibility impacts and benefits at the Wichita Mountains, the 

Guadalupe Mountains, and Big Bend primarily, but also included analysis at a number of other 

Class I areas in states surrounding Texas. In evaluating the impacts and benefits of potential

controls, we evaluated a number of metrics such as change in deciviews in 2018 and natural 

conditions situations, change in extinction, change in percentage of total extinction, recent actual

emissions vs. CENRAP 2018 projected emissions, etc. For a full discussion on our review of all 

the modeling results, and factors that we considered in evaluating and weighing all the results, 

precedents, and other policy concerns please see Appendix A.
28

Our review of the impacts/benefits of scrubber upgrades on eight units at five facilities 

concluded that scrubber upgrades conducted at seven of the eight units would net significant 

visibility improvements at the Wichita Mountains. These seven units are: Limestone 1 and 2; 

Martin Lake 1, 2, and 3; Monticello 3; and Sandow 4. We project visibility benefits at Big Bend, 

the Guadalupe Mountains and other Class I areas, with the largest visibility benefit from these 

seven sources projected to occur at the Wichita Mountains. We consider the visibility 

improvement from a scrubber upgrade on W. A. Parish 8 would be relatively small in 

comparison to the other units we evaluated, and not large enough to consider as beneficial at this 

time.

We evaluated the visibility benefits of DSI, for the thirteen units that currently have no SO2

control, as described in section 5. We evaluated all the units using the same control levels we 

employed in our control cost analyses. In summary, we evaluated these units at a DSI SO2

control level of 50%, which we believe is likely achievable for any unit. We also evaluated each 

unit at its maximum recommended DSI performance level, of 80% SO2 removal for an ESP 

installation and 90% SO2 removal for a baghouse installation. As we discuss in our Cost TSD, 

we believe these are maximum performance levels for DSI but we do not know whether a given 

unit is actually capable of achieving these DSI control levels. We conducted this analysis, 

however, in order to be able to more closely compare DSI cost and performance with that of 

scrubbers. At the lower performance level we conclude that the corresponding visibility benefits 

from DSI would be close to half of the benefits from scrubbers. The visibility benefits from DSI 

are quantified specifically in Appendix A. Overall, the visibility benefits from scrubber retrofits 

are more beneficial. 20 

We also evaluated the visibility benefits for the thirteen scrubber retrofits listed in Tables A.6-

1a-d, A.6-2a-d, A.6-4; assuming control levels corresponding to SDA and wet FGD. We 

conclude that installing either wet FGD or SDA scrubbers on five of these units would yield 

significant visibility improvements at the Wichita Mountains. These five units are: Big Brown 1

and 2, Coleto Creek, and Monticello 1 and 2. We conclude that scrubber installations on Big 

Brown 1 and 2 would also yield significant benefits at the Guadalupe Mountains, and that a 

scrubber installation on the Coleto Creek unit would also yield significant visibility benefits at 

Big Bend. 

In comparison to the above five units, we propose to find that the visibility benefits from 

installing scrubbers on the W. A. Parish 5, 6, and 7 units; and Welsh 1, 2, and 3 units would not 

yield large enough visibility benefits to be considered at this time. 

We also evaluated the visibility benefits of installing scrubbers on Tolk units 171B and 172B, 

limiting our analysis to SDA as discussed in section 4.. The visibility benefits of SDA scrubbers 

on the Tolk units are projected to occur mainly at the Guadalupe Mountains. We note that the

deciview visibility benefits projected at the Guadalupe Mountains from controls on the Tolk 

units are smaller than those from scrubber upgrades at W. A. Parish or Welsh for impacts at the 

Wichita Mountains. However, when we evaluated other metrics, such as extinction benefit and

 

20 Our multiple CAMx runs yielded data on three or more levels of emissions (controlled and uncontrolled) on a 

number of facilities and based on the data a linear relationship between emission level and visibility impairment on a 

source specific basis is a reasonable analytical approach. See Appendix A for more details.
29

percent of extinction benefits, we believe that the overall visibility benefit for installing 

scrubbers on the Tolk units was superior to either the W. A. Parish or the Welsh units. In 

particular, the Wichita Mountains has a much higher total extinction for the baseline and the 

2018 projection than the Guadalupe Mountains, so the relative improvement in extinction levels 

is higher when the Tolk units are controlled for the Guadalupe Mountains, than if the W. A. 

Parish or the Welsh units were controlled for the Wichita Mountains. Therefore, considering all 

the visibility benefits relative to the respective Class I areas, we propose to find that the visibility 

benefits from installation of dry scrubbers on the Tolk units would be significant and beneficial

towards the goal of meeting natural visibility conditions at Guadalupe Mountains.

8 Proposed RP and LTS Determination for San Miguel

We propose to find that the San Miguel facility has upgraded its SO2 scrubber system to perform 

at the reasonably highest level that can be expected (94% based on a 2009 – 2013 average) based 

on the extremely high sulfur content of the coal being burned, and the technology currently 

available. We thus do not propose any further control. We propose to find that the San Miguel 

facility maintain a 30 Boiler Operating Day rolling average SO2 emission rate of 0.60 

lbs/MMBtu based on the most recent actual emissions data. We believe that based on the 

scrubber upgrades it has recently performed and its demonstrated ability to maintain an emission 

rate below this value on a monthly basis from December 2013 to June 2014 that it can 

consistently achieve this emission level. See our Cost TSD for more details about our analysis of 

the scrubber upgrades that San Miguel has performed on its unit. We are specifically soliciting 

comments on this proposed emission limit and the potential need for a slightly higher limit to 

provide sufficient operational headroom to demonstrate compliance. 

9 Proposed RP and LTS Determination for Units other than San Miguel

In section 5, we present the results of our SO2 control cost analysis for those units listed in Table 

30 with no SO2 control. In section 6, we present the results of our control cost analysis for 

upgrading those units equipped with underperforming wet FGD scrubbers. In Section 7, we 

present the results of our modeled visibility benefits for these controls. We believe that we have 

provided the technical analysis that was lacking in Texas’ development of its RPGs for the 

Guadalupe Mountains and Big Bend, and in its consultations with Oklahoma for the 

development of the RPG for the Wichita Mountains. Further, we believe that our proposed

control set, which we discuss below, developed through our reasonable progress four factor 

analysis, would ensure that Texas secures its share of the reductions needed for the RPGs of the 

Wichita Mountains, the Guadalupe Mountains, and Big Bend. Specifically, we propose to find

that our technical analysis and control set makes whole our disapproval of:

 Section 51.308(d)(1)(i)(A), regarding Texas’ reasonable progress four factor analysis.

 Section 51.308(d)(1)(i)(B), regarding Texas’ calculation of the emission reductions

needed to achieve the URPs for the Guadalupe Mountains and Big Bend.

 Section 51.308(d)(1)(ii), regarding Texas’ RPGs for the Guadalupe Mountains and 

Big Bend.

 Section 51.308(d)(3)(i) regarding Texas’ long-term strategy consultation.
30

 Section 51.308(d)(3)(ii) regarding Texas securing its share of reductions in other 

States’ RPGs.

 Section 51.308(d)(3)(iii) regarding Texas’ technical basis for its long-term strategy. 

 Section 51.308(d)(3)(v)(C), regarding Texas’ emissions limitations and schedules for 

compliance to achieve the RPGs for Big Bend and the Guadalupe Mountains.

We also believe that this technical analysis and control set makes whole our proposed 

disapproval of Oklahoma’s submission under Section 51.308(d)(1), except for Section

51.308(d)(1)(vi), which we propose to approve. We believe our technical analysis provides the 

information that Oklahoma should have had during its consultations with Texas in order to 

determine whether sources in Texas should have been controlled to improve the visibility at the 

Wichita Mountains. We believe our proposed control set would ensure that Texas’ share of the 

emission reductions are incorporated into Oklahoma’s RPGs.

As we note in section 5, for all but one of the units we analyzed that currently have no SO2

controls, even at the lower level of control of 50%, the cost-effectiveness of DSI was worse 

(higher $/ton) than either SDA or wet FGD, even with the latter options offering much greater 

levels of control and visibility benefit. At the higher 80% or 90% level of control, the costeffectiveness of DSI was worse than either SDA or wet FGD in all cases. Consequently, we are 

not proposing that DSI be installed at any unit.

With the exception of Tolk, all of the scrubber retrofits were analyzed on the basis of both SDA 

and wet scrubbers. The SDA level of control was assumed to be a maximum of 95% not to go 

below 0.06 lbs/MMBtu. The wet FGD level of control was assumed to be a maximum of 98% 

not to go below 0.04 lbs/MMBtu. As we discuss in our Cost TSD, the cost-effectiveness ($/ton) 

of wet FGD was better than SDA in all cases except for the Tolk and Welsh units, which burn 

Power River Basin (PRB) coal. However, even in those cases, the cost-effectiveness of wet FGD 

was only 0.5 to 0.8% greater than SDA. Given the greater visibility improvement of wet FGD 

over SDA, we propose to base our cost/benefit reasonable progress and long-term strategy

determination on wet FGD, except for the Tolk units, due to their potential water issue.

9.1 Proposed RP and LTS Determination for Scrubber Upgrades

We propose to find that the cost-effectiveness of the scrubber upgrades ($600/ton or less) to be 

reasonable, and that on an individual basis, any reasonable amount of visibility improvement due 

to their installation justifies their cost. We believe this is the case for all of the scrubber 

upgrades except for the Parish 8 unit. Despite the same level of cost-effectiveness of the Parish 8 

unit, we do not believe that the visibility benefits are large enough to justify the implementation 

of a scrubber upgrade on that unit. Therefore we propose that the scrubbers for the Sandow 4; 

Martin Lake 1, 2, 3; Monticello 3, and Limestone 1 and 2 units be upgraded to perform at a 95% 

control level. This level of control corresponds to the emission limits listed in Table 16, below.

9.2 Proposed RP and LTS Determination for Scrubber Retrofits

The cost-effectiveness of the scrubber retrofits for the Welsh and Parish units are within a $/ton 

range that we have previously found to be cost-effective in BART determinations. However, we 
31

do not believe that their individual projected visibility improvements merit the installation of 

scrubbers at this time. We encourage the State of Texas to re-evaluate this determination as part 

of its next regional haze SIP submittal.

Similar to the scrubber upgrades, we consider the scrubber retrofits for the Big Brown units to be 

cost effective and we find the projected visibility benefits to be significant. We therefore 

propose that the Big Brown units meet emission limits corresponding to this evaluation. Our 

proposed SO2 emission limits for the Big Brown units is shown in Table 16.

In comparison to the Big Brown units, the cost effectiveness of the scrubber retrofits for the 

Monticello, Coleto Creek, and Tolk units are less, although still well within the range that we 

have found acceptable for BART. Also, as we discuss in section 7, in comparison to the Big 

Brown units, the visibility improvements projected to occur due to the installation of the 

scrubber retrofits are less. For instance, as we discuss above, the visibility benefits of SDA 

scrubbers on the Tolk units are projected to occur mainly at the Guadalupe Mountains. Those 

visibility benefits are smaller than the visibility benefits at Wichita Mountains from scrubber 

upgrades at W. A. Parish or Welsh, which we are not proposing to control. However, when we 

evaluated other metrics, such as extinction benefit or percent of extinction benefits, we 

concluded that the overall visibility benefit for installing scrubbers on the Tolk units was 

superior to either the W. A. Parish or the Welsh units. Thus, we consider these visibility benefits 

to be significant. Consequently, we propose that the Monticello, Coleto Creek, and Tolk units 

meet SO2 emission limits corresponding to this evaluation. Our proposed SO2 emission limits 

for these units are shown in Table 16. In recognition of their lesser cost/benefit ratio, we are 

specifically soliciting comments on the appropriateness of one or more of these scrubber 

retrofits. 

We propose that compliance be based on a 30 Boiler Operating Day (BOD) period. As the 

BART Guidelines direct, “[y]ou should consider a boiler operating day to be any 24-hour period 

between 12:00 midnight and the following midnight during which any fuel is combusted at any 

time at the steam generating unit.”21 To calculate a 30 day rolling average based on boiler 

operating day, the average of the last 30 “boiler operating days” is used. In other words, days are 

skipped when the unit is down, as for maintenance. This, in effect, provides a margin of safety 

by eliminating spikes that occur at the beginning and end of outages. Although we are not 

conducting BART determinations, our reasonable progress guidance notes the similarity between 

some of the reasonable progress factors and the BART factors contained in Section

51.308(e)(1)((ii)(A), and suggests that the BART Guidelines be consulted regarding cost, energy 

and non-air quality environmental impacts, and remaining useful life. We are therefore relying 

on our BART Guidelines for assistance in establishing the emission limit averaging period as 

well.

 

21

 70 FR 39172 (July 6, 2005).
32

Table 16. Proposed 30 Boiler Operating Day SO2 Emission Limits

Unit

Proposed 

SO2 Emission 

Limit 

(lbs/MMBtu)

Scrubber Upgrades

Sandow 4 0.20

Martin Lake 1 0.12

Martin Lake 2 0.12

Martin Lake 3 0.11

Monticello 3 0,06

Limestone 2 0.08

Limestone 1 0.08

San Miguel* 0.60

Scrubber Retrofits

Big Brown 1 0.04

Big Brown 2 0.04

Monticello 1 0.04

Monticello 2 0.04

Coleto Creek 1 0.04

Tolk 172B 0.06

Tolk 171B 0.06

* As we note elsewhere, we do not anticipate that San Miguel will have to install 

any additional control in order to comply with this emission limit.

10 Proposed Natural Conditionsfor the Texas Class I Areas

As we discuss in our TX TSD, the TCEQ used a refined approach to calculating the natural 

conditions for the Guadalupe Mountains and Big Bend. This approach, among other things, 

requires knowledge about the amount of coarse mass and soil that is attributable to natural 

sources. The TCEQ has provided data that supports the conclusion that a large portion of dust 

impacting visibility at its Class I areas is likely due to natural sources. We agree that dust storms 

and other blown dust from deserts are a significant contributor to visibility impairment at the 

Texas Class I areas that may not be captured accurately by our default method. However, we do 

not believe, as the TCEQ asserts, that all coarse mass and soil can be attributable to 100% natural 

sources. 

Although we believe that some coarse mass and soil should be attributable to natural sources, we 

do not have the information necessary to determine how much should be attributable to natural 

sources. We therefore acknowledge that like the TCEQ, we cannot accurately reset the natural 

conditions for the Guadalupe Mountains and Big Bend by using the TCEQ’s methodology, 
33

which depends on this information. In lieu of this, we propose to rely on the adjusted default 

estimates for the new IMPROVE equation from the Natural Conditions II committee22, which 

was the starting point for the Texas natural visibility calculations, but solicit comment on the 

acceptability of alternate estimates in the range between our default estimates and the Texas 

estimates. We propose that the natural conditions for the Guadalupe Mountains and Big Bend be 

set as follows:

Table 18. Natural Conditions for the Guadalupe Mountains and Big Bend

Class 1 Area

20 Percent 

Best Days

(dv)

20 Percent 

Worst Days

(dv)

Guadalupe Mountains 0.99 6.65

Big Bend 1.62 7.16

We recommend that the State of Texas re-evaluate the natural conditions for its Class I areas in 

the next regional haze SIP.

11 Calculation of Natural Visibility Impairment for the Texas Class I Areas

Using our proposed natural visibility conditions for the Guadalupe Mountains and Big Bend, we 

reset the amount of natural visibility impairment for these Class I areas under section 

51.308(d)(2)(iv)(A). We do this by modifying the table we present in our TX TSD. We replace 

Texas’ calculations of natural visibility for its Class I areas, with the adjusted default values (NC 

II), discussed above. We retain the baseline visibility values we proposed to approve in our TX 

 

22

 Regional Haze Rule Natural Level Estimates Using the Revised IMPROVE Aerosol Reconstructed Light 

Extinction Algorithm, Copeland, S. A., et al, Final Paper # 48, available in our docket.; 

NC II, or new IMPROVE natural visibility conditions are available at: 

http://vista.cira.colostate.edu/Docs/IMPROVE/Aerosol/NaturalConditions/NaturalConditionsII_Format2_v2.xls, for 

which we have filtered the data for Texas Class I areas and which is also available in our docket.
34

TSD then recalculate the amount the baseline values exceed the natural visibility conditions. We 

propose that the natural visibility impairment for the Guadalupe Mountains and Big Bend be set 

as follows:

Table 19. Revised Visibility Metrics for the Class I Areas in Texas

Estimate of Natural Visibility Conditions

Class I Area Haze Index (deciviews)

Most Impaired Least Impaired

Big Bend 7.16 1.62

Guadalupe Mountains 6.65 0.99

Baseline Visibility Conditions, 2000–2004

Class I Area Haze Index (deciviews)

Most Impaired Least Impaired

Big Bend 17.30 5.78

Guadalupe Mountains 17.19 5.95

Estimate of Extent Baseline Exceeds Natural Visibility Conditions

Class I Area Haze Index (deciviews)

Most Impaired Least Impaired

Big Bend 10.14 4.16

Guadalupe Mountains 10.54 4.96

12 Uniform Rates of Progress and the Emission Reductions Needed to Achieve Them

Section 51.308(d)(1)(i)(B) requires that we analyze and determine the rates of progress needed to 

attain natural visibility conditions by the year 2064 and consider the uniform rate of 

improvement in visibility and the emission reduction measures needed to achieve them. Below, 

we present the URPs for the Guadalupe Mountains and Big Bend, using the natural conditions 

we propose to establish above:
35

Figure 8. URP for Big Bend

Figure 9. URP for the Guadalupe Mountains
36

We constructed these URPs in a spreadsheet using Texas’ baseline values and our reset natural 

conditions, shown above in Table 19.

23

 We then used the resulting equation of the line to project 

the values for the URP at the end of each planning period. Those values are displayed in the 

graphs above. 

13 Reasonable Progress Goals for Oklahoma and Texas Class I Areas

We are quantifying proposed RPGs (in deciviews) for the 20-percent worst days in 2018. The 

proposed RPGs for Oklahoma’s Class I area, the Wichita Mountains, and Texas’ two Class I 

areas, Big Bend and the Guadalupe Mountains, account for the emission reductions from the 

reasonable progress control measures identified above in our proposed regional haze FIPs. The 

proposed RPGs reflect the results of our reasonable progress analysis of point sources as 

described in detail in Appendix A. These proposed RPGs are established based on an adjustment 

of the 2018 RPGs established by Texas and Oklahoma that were based on the 2018 CENRAP 

modeling. We note that we do not anticipate implementation of the identified scrubber retrofits 

by the end of 2018. Therefore, we are only adjusting the RPGs established by the States to 

reflect the additional anticipated visibility benefit from the scrubber upgrades over the 2018 

projected visibility conditions. The tables below show the new adjusted RPGs as well as the 

additional improvement that is anticipated once all the scrubber retrofits have been implemented 

sometime after 2018. These new RPGs provide for an improvement in visibility on the worst 

days during this planning period. Table 20 below estimates the RPG if all proposed controls 

were implemented by 2018. 24 See Appendix A to this TSD for more information on our 

modeling and estimated visibility benefits from the controls proposed in this FIP.

Table 20. Proposed RPGs for 20% Worst Days based on predicted benefit of scrubber upgrades 

beyond 2018 CENRAP projected visibility conditions.

Baseline 

(dv)

2018 

CENRAP 

projection 

(dv)

Predicted 

additional 

benefit due 

only to FIP 

scrubber 

upgrades

(dv)

Proposed 

RPG

(dv)

Natural 

visibility

Number of 

years 

needed to 

reach 

natural 

visibility

Wichita 

Mountains 23.81 21.47 0.14 21.33 7.58 92

Big Bend 17.30 16.6 0.03 16.57 7.16 194

Guadalupe 

Mountains 17.19 16.3 0.04 16.26 6.65 159

 

23

 This spreadsheet, “TX URPs.xlsx,” is in our docket.

24 See Vis modeling summary.xlsx in the docket for this action for our calculations and estimates of visibility 

benefits from the examined levels of controls, and summary of visibility benefits from proposed controls.
37

Table 21. Calculated RPGs for 20% Worst Days based on predicted benefit of all proposed 

controls beyond 2018 CENRAP projected visibility conditions

Baseline 

(dv)

2018 

CENRAP 

projection 

(dv)

Predicted 

additional 

benefit due

only to FIP 

scrubber 

upgrades 

(dv)

Additional 

benefit 

predicted 

due to FIP 

scrubber 

retrofits 

(dv)

Total 

benefit 

from 

proposed 

controls

RPG 

assuming 

all 

controls 

in place 

by 2018

Natural 

visibility

Number 

of years 

needed to 

reach 

natural 

visibility

Wichita 

Mountains 23.81 21.47 0.14 0.30 0.45 21.03 7.58 82

Big Bend 17.3 16.6 0.03 0.09 0.12 16.48 7.16 173

Guadalupe 

Mountains 17.19 16.3 0.04 0.12 0.15 16.14 6.65 141

As discussed in more detail in Appendix A, current actual emissions for many of the units that 

we propose to control are higher than the projected CENRAP 2018 emission rate. Therefore, the 

actual visibility impact due to emissions from these sources and the anticipated benefit from 

controls are larger than the benefits calculated above based on the 2018 CENRAP projected 

visibility conditions. The table below summarizes the amount of visibility benefit we anticipate 

will occur from the implementation of our proposed FIP controls and the resulting emission 

reductions from the current actual average annual emissions. 

Table 22. Anticipated Visibility Benefit due to Emission Reductions from Actual Emission 

levels.

Predicted 

benefit due 

to FIP 

scrubber 

upgrades 

(dv)

Benefit 

predicted 

due to FIP 

scrubber 

retrofits (dv)

Total benefit 

from 

proposed 

controls (dv)

Wichita Mountains 0.28 0.33 0.62

Big Bend 0.07 0.10 0.17

Guadalupe Mountains 0.07 0.12 0.20

We propose to find that it is not reasonable to provide for rates of progress at the Wichita 

Mountains, Big Bend, or the Guadalupe Mountains that would attain natural visibility conditions 

by 2064 (i.e., the URP). Our demonstration that a slower rate of progress is reasonable is based 

on the reasonable progress analyses performed by us and the States that considered the four 

statutory reasonable progress factors, as described above. Although progress is slower than the 

URP, the proposed FIP would provide for RPGs that reflect an improved rate of progress and a 

shorter time period to reach natural visibility conditions at each of the Class I areas, compared 

with the RPGs established by Texas and Oklahoma in their regional haze SIPs. We have 

provided an estimate of the number of years needed to meet natural visibility conditions at the 

rate of progress proposed by us as reasonable. We have also estimated the RPG and the number 
38

of years to meet natural visibility conditions if all proposed controls were in place by 2018. We 

note that this does not take into account the visibility benefit from scrubber retrofits included in 

this proposed FIP that will be implemented after 2018. 
A-1

Appendix A. EPA’s Visibility Projection Modeling

A.0 Background and Introduction

TCEQ analyzed available monitor data and source apportionment modeling to identify the 

pollutants and source categories that most impact visibility at Class I areas in Texas and 

surrounding areas. The primary anthropogenic emissions that impact visibility are NOx and SO2

emissions from point sources. For further details of TCEQ’s analysis and conclusions see our 

TX TSD. Based on our review of TCEQ’s analysis and our assessment of TCEQ’s conclusions, 

we conducted our own analysis to identify those sources with the largest potential to impact 

visibility, evaluate the impacts of these select sources in Texas and determine if reasonable 

controls were available that were overlooked by Texas in their evaluation of sources that would 

lead to visibility impairment improvement at Class I areas in Texas and surrounding areas 

including WIMO.

In the process of developing the modeling analyses for evaluating the Texas regional haze plan, 

EPA Region 6 received assistance in conducting modeling runs from ENVIRON, a consultant to 

RTI International under contract EP-W-11-029, Work Assignment No. 3-09. 

Q/D ANALYSIS - GENERAL

EPA, States and RPOs have historically used a Q/D analysis to identify those facilities that have 

the potential to impact visibility at a Class I area based on their emissions and distance to the 

Class I area. These identified facilities could then be considered for further evaluation under the 

four factors for reasonable controls.

We also used a Q/D analysis as an initial screening test to identify emission sources that may 

impact air visibility at Class I areas. Where, 

 Q is the annual emissions in tons per year (tpy) 

 D is the nearest distance to a Class I Area in kilometers (km)

We used a Q divided by a value of ten as a threshold for initial identification of sources for 

further evaluation for RP controls, where Q is combined annual emissions of NOx and SO2. We 

selected this value based on guidance contained in the BART Guidelines, which states:

Based on our analyses, we believe that a State that has established 0.5 deciviews 

as a contribution threshold could reasonably exempt from the BART review 

process sources that emit less than 500 tpy of NOx or SO2 (or combined NOx and 

SO2), as long as these sources are located more than 50 kilometers from any Class 

I area; and sources that emit less than 1000 tpy of NOx or SO2 (or combined NOx 

and SO2) that are located more than 100 kilometers from any Class I area.25

The approach described above corresponds to a Q/D threshold of ten. This approach has also 

been recommended by the Federal Land Managers’ Air Quality Related Values Work Group 

 

25 See 40 CFR part 51, app. Y, § III (How to Identify Sources “Subject to BART”).
A-2

(FLAG)26 as an initial screening test to determine if an analysis is required to evaluate the 

potential impact of a new or modified source on air quality related value (AQRV) at a Class I 

area. For this purpose, a Q/D value is calculated using the combined annual emissions in tons 

per year of sulfur dioxide (SO2), oxides of nitrogen (NOx), particulate matter less than 10 

microns (PM10), and sulfuric acid mist (H2SO4) divided by the distance to the Class I area in km. 

A Q/D value greater than 10 requires a Class I area AQRV analysis.

In the Texas Regional Haze SIP, TCEQ performed a Q/D analysis based on 2018 projected 

emissions for SO2 and NOx as part of the analysis to identify point sources for potential control. 

TCEQ calculated Q/D for NOx and SO2 separately for each point source with emissions greater 

than 100 tons per year and compared that to a threshold of 5. Appendix 10-1 of the Texas RH 

SIP describes the two step process (reproduced below) utilized by TCEQ to identify sources: 

The best candidate sources for proposed control strategies were identified with a 

two step process. First, sources with potential control strategy costs greater than 

$2,700 per ton SO2 for NOX were initially screened out to limit the population to 

potential sources with relatively cost effective control strategies. The group of 

sources was further reduced to eliminate sources that are so distant from any of 

the ten Class I areas that any reduction in emissions would be unlikely to have a 

perceptible impact on visibility. The list was restricted to those sources with a 

ratio of estimated projected 2018 base annual emissions (tons) to distance 

(kilometers) greater than five to any Class I area. Also, any source with predicted 

2018 emissions less than 100 tons per year was excluded. The regulatory and 

logistical overhead associated with controlling these small sources would not be 

justified by the likely benefit.

For our review of TCEQ’s Q/D analysis and use of a cost threshold see the Texas TSD for this 

action and why we conducted our own Q/D analysis.

A.1 Emissions Data and EPA’s Q/D Analysis

CALCULATION

We evaluated annual emission inventory data for point sources available from the TCEQ. The 

Texas point sources are defined as industrial, commercial, or institutional sites that meet the 

reporting requirements of 30 Texas Administrative Code (TAC) §101.10. Permitted point 

sources in Texas are required to submit annual emissions inventories. The data are drawn from 

TCEQ’s computer-based State of Texas Air Retrieval System (STARS). Annual emission data 

from 200927 were utilized to calculate the Q/D value for all point sources with reported 

 

26 Federal Land Managers’ Air Quality Related Values Work Group (FLAG), Phase I Report—Revised (2010)

Natural Resource Report NPS/NRPC/NRR—2010/232, October 2010. Available at 

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

27 2009 emissions data available at 

http://www.tceq.texas.gov/assets/public/implementation/air/ie/pseisums/2009statesum.xlsx. Available in the docket 

as “2009statesum.xlsx”
A-3

emissions in Texas for all Class I areas within Texas and nearby Class I areas in surrounding 

states (Table A.1-1 and Figure A.1-1). Latitude and longitude for each facility was obtained 

from a separate STARS emission inventory28 with unit specific emissions. Distances between 

each facility and nearby Class I areas were then calculated using ArcGIS software. For plots for 

each Class I area see Figures A.1-3a-h at the end of Section A.1.

Table A.1-1. Class I areas included in Q/D Analysis

Site State Code

State

FIPS County

County 

FIPS

Latitud

e Longitude

LCP

X 

(km)

LCPY 

(km)

Breton Wilderness Area LA BRET1 22

St. 

Bernard 

Parish 87 29.1189 -89.2066 763 -1176

Big Bend National Park TX BIBE1 48

Brewster 

County 43 29.3027 -103.178 -604 -1167

Guadalupe Mountains TX

GUMO

1 48

Culberson 

County 109 31.833 -104.809 -738 -873

Wichita Mountains 

Wilderness OK

WIMO

1 40

Comanche 

County 31 34.7323 -98.713 -156 -581

Carlsbad Caverns NP. NM 

GUMO

1 31.833 -104.809 -738 -873

Caney Creek Wilderness 

Area AR

CACR

1 5

Polk 

County 113 34.4544 -94.1429 261 -610

Bosque del Apache 

Wilderness Area NM

BOAP

1 35

Socorro 

County 53 33.8695 -106.852 -906 -629

Salt Creek Wilderness 

Area NM SACR1 35

Grant 

County 17 33.4598 -104.404 -685 -696

Figure A.1-1. Class I areas included in EPA Q/D Analysis

 

28 Downloaded from TCEQ at ftp://ftp.tceq.texas.gov/pub/ChiefEngineer/adam/EPA-R6-Data/ in April 2011. 

Available in the docket as 2009TCEQpointSOURCEdata.mdb. 
A-4

RESULTS

We calculated a Q/D value for each point source and Class I area using the sum of actual 2009 

annual SO2 and NOx emissions. Those facilities with a Q/D value greater than 10 were 

identified for further analysis using source-apportionment modeling. This approach aims to 

identify those facilities with the largest potential to impact visibility at a Class I area. The Q/D

method does not take into account any specific conditions at the emitting source (e.g., stack 

parameters) and does not account for meteorology/transport phenomena. As further discussed in 

Section A.2 below, facilities identified through the Q/D analysis were then included in a 

photochemical modeling scenario utilizing source apportionment to quantify the visibility 

impacts from each source. Due to computation resource limitations, it is not possible to include 

a large number of facilities in the photochemical modeling episode utilizing source 

apportionment. The Q/D analysis and use of the threshold value of ten narrows the number of 

facilities to examine to those with the largest potential for impact and to a manageable number 

for this planning period. Table A.1- 2 below lists the emissions and the Q/D value for the nearest 

Class I area for the identified sources with Q/D greater than 10. Figure A.1-2 plots these 38 

facilities with a Q/D greater than 10 on a map of Texas and the surrounding area.
A-5

Table A.1-2. Sources identified through EPA’s Q/D Analysis for inclusion in sourceapportionment analysis

Map 

location

Facility NOx 

(tpy)

SO2 

(tpy)

Nearest 

Class I 

area

Distance 

(km)

Q/D

1 BIG BROWN STEAM ELECTRIC STATION 5794.68 55547.40 CACR 335 182.9

2 BIG SPRING CARBON BLACK 567.01 8876.88 CAVE 280 33.8

3 BORGER CARBON BLACK PLANT 361.98 3306.74 WIMO 262 14.0

4 BORGER CARBON BLACK PLT (Sid Richardson) 662.40 6150.70 WIMO 262 26.0

5 COLETO CREEK POWER STATION 4198.11 21453.35 BIBE 558 46.0

6 FAYETTE POWER PROJECT (Sam Seymor) 6224.53 27551.11 CACR 554 61.0

7 FULLERTON GAS PLANT 1659.65 869.00 CAVE 150 16.9

8 GIBBONS CREEK 2114.07 11930.82 CACR 456 30.8

9 GOLDSMITH GAS PLANT 957.50 999.98 CAVE 166 11.8

10 OXBOW CALCINING LLC (Great Lakes Carbon) 688.40 10333.08 BRET 483 22.8

11 GUADALUPE COMPRESSOR STATION 668.05 0.01 GUMO 5 138.5

12 HARRINGTON STATION POWER PLANT 7695.50 22188.86 WIMO 277 107.8

13 *MIDLOTHIAN PLANT (HOLCIM) 951.04 1661.31 WIMO 289

14 AEP PIRKEY POWER PLANT 3327.60 4363.10 CACR 215 35.8

15 KEYSTONE COMPRESSOR STATION 1661.97 0.29 CAVE 122 13.6

16 KEYSTONE GAS PLANT 1945.07 373.27 CAVE 128 18.1

17 LIGNITE FIRED POWER PLANT (San Miguel) 3102.52 11227.05 BIBE 435 32.9

18 MARTIN LAKE ELECTRICAL STATION 15710.02 71848.79 CACR 238 367.4

19 *MIDLOTHIAN PLANT (TXI) 1022.40 550.20 WIMO 289

20 MONTICELLO STEAM ELECTRIC STATION 11944.54 58269.05 CACR 165 425.4

21 NEWMAN STATION 1726.01 6.34 GUMO 133 13.0

22 MIDLOTHIAN PLANT (Ashgrove or North Texas 

Cement)

1266.20 2696.69 WIMO 289 13.7

23 ODESSA CEMENT PLANT 2352.56 225.29 CAVE 179 14.4

24 OKLAUNION POWER STATION 4318.44 2355.00 WIMO 79 85.0

25 PEGASUS GAS PLANT 2312.01 62.50 CAVE 219 10.8

26 LIMESTONE ELECTRIC GENERATION STATION 11900.92 20666.61 CACR 383 85.1

27 SANDOW STEAM ELECTRIC STATION 4916.02 25597.31 WIMO 484 63.0

28 SHERHAN GAS PLANT 2530.52 764.82 WIMO 310 10.6

29 CALAVERAS PLANT (Sommers Deely Spruce) 7259.44 17936.31 BIBE 443 56.9

30 STREETMAN PLANT 698.85 3560.79 CACR 342 12.5

31 TEXARKANA MILL 1602.26 90.62 CACR 123 13.7

32 TWIN OAKS POWER (TNP one) 1479.13 4705.71 CACR 436 14.2

33 TOLK STATION 3709.56 22639.37 SACR 177 148.5

34 WA PARISH ELECTRIC GENERATING STATION 5041.38 42484.17 CACR 563 84.3

35 WAHA FIELD PLANT 322.00 3478.76 CAVE 157 24.3

36 WELSH POWER PLANT 10383.40 26606.50 CACR 161 230.1

37 WORKS NO 4 5121.52 587.52 WIMO 79 72.4

38 **SANDOW 5 GENERATING PLANT 190.64 227.60 WIMO 484
A-6

*The Midlothian Plant site name is associated with three separate facilities: 

Ashgrove, TXI and Holcim. The Ashgrove facility is the only one of the three to 

meet the Q/D threshold but all three facilities were included in the sourceapportionment analysis to avoid confusion and to compare impacts from varying 

emission levels at three closely located emission points. 

** Sandow 5 Generating Station came online in 2009 and is well controlled. It 

was included in the source apportionment analysis for comparison with the 

impacts from higher emissions from the older Sandow Station unit located nearby.

Figure A.1-2. Location of Selected Sources
A-7

UPDATED EMISSIONS ANALYSIS

After the initial Q/D analysis was completed TCEQ emission inventories for 2010 and 2011 

became available.29 We recalculated Q/D values using the more recent facility annual emission 

inventories. Compared to the 2009 analysis, four facilities have decreased emissions in 2010 and 

2011 and fall below the Q/D threshold of 10: Keystone Compressor Station, Odessa Cement 

Plant, Sherhan Gas Plant and Waha Field Plant. In 2010, the two units at Oak Grove Power 

Station in Robertson County came online. This facility exceeds the Q/D threshold of 10 for 2010 

and 2011. However, these new units are equipped with scrubbers and selective catalytic 

reduction and are currently well controlled. One additional facility had an increase in emissions 

from 2009 and exceeds the Q/D threshold for 2011, the Echo Carbon Black Plant in Orange 

County. The Oxbow Calcining facility (formerly Great Lakes Carbon) is approximately 45 km 

south west of Echo Carbon Black and has about twice the emissions of SO2. Source 

apportionment results for the Oxbow facility will provide some indication of the potential 

impacts from the Echo Carbon Black Plant. 

Figure A.1-3a. Q/D for WIMO using 2009 Annual EI

 

29 29 2010 and 2011 emissions data available at 

http://www.tceq.texas.gov/assets/public/implementation/air/ie/pseisums/2010statesum.xlsx. And 

http://www.tceq.texas.gov/assets/public/implementation/air/ie/pseisums/2011statesum.xlsx. Available in the docket 

as “2010statesum.xlsx” and “2011statesum.xlsx”
A-8

Figure A.1-3b. Q/D for CACR using 2009 Annual EI
A-9

Figure A.1-3c. Q/D for BIBE using 2009 Annual EI
A-10

Figure A.1-3d. Q/D for GUMO using 2009 Annual EI
A-11

Figure A.1-3e. Q/D for CAVE using 2009 Annual EI
A-12

Figure A.1-3f. Q/D for BRET using 2009 Annual EI
A-13

Figure A.1-3g. Q/D for SACR using 2009 Annual EI
A-14

Figure A.1-3h. Q/D for BOAP using 2009 Annual EI
A-15

A.2 Initial Source Apportionment Modeling for 38 Q/D sources

After conducting the Q/D analysis, which resulted in identification of 38 facilities that were 

potentially the larger contributors to downwind Class I areas, we determined we should evaluate 

these sources for meteorology/transport to determine which of the 38 facilities had large impacts 

during the average 20% Worst Days and also their impacts on specific days within the 20% 

worst days. As mentioned above, we contracted with RTI/Environ to conduct the modeling 

analyses needed to evaluate the 38 facilities in Texas and assess their visibility impairment 

impacts at Class I areas in Texas and surrounding areas. Our current analysis builds upon 

modeling of 2002 and 2018 conducted previously for CENRAP by ENVIRON.

In particular, the CENRAP 2002 and 2018 36 km modeling databases for CAMx were enhanced 

to include a 12 km grid over Texas and nearby Class I areas. The overall approach to the project 

included the following steps: 

• Update CENRAP 2002 and 2018 modeling databases to use with the latest release of 

CAMx (v5.41)

• Conduct 2002 modeling with Plume-in-Grid (PiG) and a 12-km flexi-nest grid to 

provide the new 2002 baseline RH modeling

• Conduct 2018 modeling with PiG and the CAMx PM Source Apportionment 

Technology (PSAT) for 38 facilities selected by EPA’s Q/D analysis

• Evaluate impact of selected sources on visibility in Class I areas (further discussed in 

Sections A.3 and A.4)

2002

The ENVIRON memorandum titled “2002 Baseline CAMx Simulation, Texas Regional Haze 

Evaluation” documents the new 2002 baseline modeling setup and results.30 From this point this 

memo will be referred to as “ENVIRON 2002 Memo”. Please see the ENVIRON 2002 Memo 

for full details. We touch on some specific issues that we analyzed and made decisions in the 

discussion below.

We utilized PiG for all the 38 selected sources in order to utilize the PSAT within the PiG. We 

also utilized PiG for other large point sources of NOx and SO2 within the modeling domain for 

both the 2002 and 2018 model runs as would typically be done in current day SIP modeling. 

Selection of sources and emissions thresholds for PiG treatment (for other than the 38 sources) 

was based on balancing PiG treatment with model run time. For the 38 selected sources we used 

the CENRAP 2002 Typical G inventory emission rates as were previously modeled. 

Documentation of these emission rates, preprocessors and other model selection options is 

included in the ENVIRON 2002 Memo. While we expect slight differences in 2002 projections 

compared to the CENRAP 2002 projections due to the model and preprocessor updates, options 

selections, the use of a 12 km flexinest, etc., we conducted both the 2002 and 2018 model runs 

with these same new procedures, etc., to enable an ‘apples to apples’ comparison between the 

base and future runs. The ENVIRON 2002 Memo also included modeling results of annual 

average pollution levels, daily speciated visibility impairment on Best 20% (B20%) and Worst 

20% (W20%) days at different Class I areas. This information was compared with previous 

 

30

 Electronic file included in the docket as “Memo_TXHAZE_2002CAMx_ENV_29July2013.docx”
A-16

CENRAP modeling results for 2002 and overall were very similar, therefore validating the 

modeling had been replicated with the updated procedures and techniques.

2018

The ENVIRON memorandum titled “2018 Base Case CAMx Simulation, Texas Regional Haze 

Evaluation” documents the new 2018 future base case modeling setup and results.31 From this 

point this memo will be referred to as “ENVIRON 2018 Memo”. Please see the ENVIRON

2018 Memo for full details. We touch on some of the specific issues that we analyzed and made 

decisions in the discussion below.

In addition to things discussed above, EPA started with the CENRAP 2018 Emission Inventory 

and made some adjustments based on review of information that had changed for specific 

units/facilities. These included:

 Updated emissions to 8 facilities and added one new facility:

- One new unit at Sommers/Deely/Spruce power plant site

- Two new units at Sandow 5 Generating Plant (new plant)

- Three new units at WA Parish Station carried over from the 2002 CENRAP inventory 

and emission changes to one existing unit 

- Emission changes at North Texas Cement (Ash grove) to reflect shutting down two 

units and rebuilding the third unit

- Emission changes to reflect recently installed controls or improvements in control 

efficiencies on power plants at Sommers/Deely/Spruce, Big Brown, Gibbons Creek, 

Sandown Steam Electric Station, Monticello Steam Electric Station, and Fayette 

Power Project 

As we discuss in more detail below, we considered updating these emissions for the Cross State 

Air Pollution Rule (CSAPR) or the Mercury and Air Toxics Standards (MATS), but based on 

recent information and recent actual emissions from CEMS we were uncertain that any 

significant addition reductions would be expected from Texas EGU sources in the next couple of 

years. Also, based on recent comments from the TCEQ, it was also unclear if any further SO2 or 

NOx reductions would occur due to these rules even if all litigation was resolved. The TCEQ 

has provided extensive comments on recent emission inventory indicating that further significant 

reductions in SO2 were not expected due to CSAPR or MATS.32 We thought it was reasonable to 

continue to rely upon the initial CENRAP 2018 modeling inventory initially and update the 

information that we were more certain about as discussed above. We utilized 2009-2013 CEM 

data for EGUs in evaluation and selection of updated emission levels to model.33 Comparison of 

recent CEM data with CAIR projections indicated that the Texas EGUs were on track to meet the 

CAIR requirements without further substantial reductions. For the ENVIRON modeling we did 

not increase emissions for existing sources based on recent actuals but we did lower emissions 

 

31

 Electronic file included in the docket as “Memo_TXHAZE_2018CAMx.7Sept13.docx”

32 TCEQ comment letter to EPA on draft modeling platform dated June 24, 2014. ‘2018 EMP signed.pdf’

33 Emission rates/data used in modeling are included in the report and electronic file 

“Summary_emissions_for_39_selected_072913_ENV.xlsx” and CEM data included in file “TX Sources of Interest 

Emissions 2007-2012_msf_annual estimates.xls”
A-17

for some sources when controls had been installed and relied on post-control actuals to support 

modeled emission rates. TCEQ in recent ozone attainment modeling has also used recent CEM 

data to represent expected emissions levels from Texas EGUs for future year of 2018 in recent 

Houston and DFW area modeling.

As discussed in the workplan, ENVIRON ran PSAT with PiG with Chemistry to evaluate the 

impacts of the 38 selected sources at Class I areas in Texas and surrounding areas. ENVIRON

did some comparisons of the 2018 base case simulation and concluded that overall the air quality 

maps (by pollutant) show consistent spatial patterns between 2002 and 2018 with lower 

concentrations predicted in the 2018 base case. The modeling also showed that sulfate is the 

main constituent that contributes to visibility impairment at the Class I areas in Texas and in 

other nearby Class I areas for both the B20% and W20% days. Overall the 2018 projections 

match with what would be expected based on the CENRAP 2018 base case, therefore the new 

analysis comports with expectations and is acceptable for using to evaluate single facility/source 

impacts on visibility impairment. The full model results included source contribution to 2018 

Deciview Haze Index, source contribution to 2018 Light extinction by species, percent of total 

extinction, percent of total extinction by species, and URP/2018 RP for a number of Class I areas 

including WIMO, BIBE, and GUMO. The full modeling results are included in the ENVIRON

2018 Memo and spreadsheets that are attachments to the memo.34 We are including projections 

for WIMO, BIBE, and GUMO from the ENVIRON 2018 Memo as an example of the 

information in the following five Figures A.2-1 through A.2-18, for additional Class I areas see 

the ENVIRON 2018 Memo and associated spreadsheets.

 

34 Electronic files in the docket “EPA_txbart3612k_Vis_2002_2018_PSAT_Projected_072913.xlsx”, and 

“EPA_txbart3612k_Vis_2002_2018_PSAT_GlidePath_FOR_ENVIRON.xlsx”_
A-18

Figure A.2 -1. Source Contribution to 2018 Deciview over 20%Worst Days at WIMO, OK

Figure A.2 -2. Source Contribution to 2018 Light Extinction by species over 20%Worst Days at 

WIMO, OK
A-19

Figure A.2 -3. Percentage of Total Extinction over 20%Worst Days at WIMO, OK

Figure A.2 -4. Percentage of Total Extinction by Species over 20%Worst Days at WIMO, OK
A-20

Figure A.2 -5. Maximum Source Contribution to 2018 Deciview on any day of W20% days at 

WIMO, OK

Figure A.2 -6. Uniform Rate of Progress and 2018 Projected Progress over 20%Worst Days at 

WIMO, OK

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE... GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5... STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION... KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE dv

Maximum Source Contribution to 2018 Deciview Haze Index on any day at Wichita 

Mountains Wilderness , W20 Group
A-21

Figure A.2 -7. Source Contribution to 2018 Deciview over 20%Worst Days at BIBE, Texas

Figure A.2 -8. Source Contribution to 2018 Light Extinction by species over 20%Worst Days at 

BIBE, Texas

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE COMPR GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5 GENERA STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION POWER KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE Mm-1

Source Contribution to 2018 Light Extinction by Species at Big Bend National 

Park , W20 Group

E_oc E_so4 E_no3 E_ec E_soil E_cm E_salt

2018 Deciview Haze Index: 17.68 dv

0.0000

0.0050

0.0100

0.0150

0.0200

0.0250

0.0300

0.0350

0.0400

0.0450

0.0500 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE COMPR GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5 GENERA STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION POWER KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE dv

Source Contribution to 2018 Deciview Haze Index at Big Bend National Park , W20 

Group
A-22

Figure A.2 -9. Percentage of Total Extinction over 20%Worst Days at BIBE, Texas

Figure A.2 -10. Percentage of Total Extinction by Species over 20%Worst Days at BIBE, Texas

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60% FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE COMPR GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5 GENERA STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION POWER KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE

Percentage of Total Extinction at Big Bend National Park , W20 Group

2018 Deciview Haze Index: 17.68 dv

E_oc, 9.459, 

19.46%

E_so4, 24.820, 

51.06%

E_no3, 1.684, 

3.46% E_ec, 2.163, 

4.45%

E_soil, 

2.734, 

5.62%

E_cm, 7.747, 

15.94%

E_salt, 0.006, 

0.01%

Percent of total extinction by species

E_oc

E_so4

E_no3

E_ec

E_soil

E_cm

E_salt

2018 Total Light Extinction: 85.95 Mm-1, 

Rayleigh = 11
A-23

Figure A.2 -11. Maximum Source Contribution to 2018 Deciview on any day of W20% days at 

BIBE, Texas

Figure A.2 -12. Uniform Rate of Progress and 2018 Projected Progress over 20%Worst Days at 

BIBE, Texas

0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE... GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5... STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION... KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE dv

Maximum Source Contribution to 2018 Deciview Haze Index on any day at Big Bend 

National Park , W20 Group

17.30

16.62

14.93

13.24

11.39

9.86

8.17

7.16

5.78 5.78 5.78 5.78 5.78 5.78 5.78 5.78

17.30 16.80

5.63

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

Haze Index (dv)

Uniform Rate of Progress and 2018 Projected Progress

Big Bend National Park 

Glide path (20% Worst) Glide path (%20 Best) Observation Prediction (20% Worst) Prediction (20% Best)
A-24

Figure A.2 -13. Source Contribution to 2018 Deciview over 20%Worst Days at GUMO, Texas

Figure A.2 -14. Source Contribution to 2018 Light Extinction by species over 20%Worst Days 

at GUMO, Texas

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

0.0600 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE COMPR GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5 GENERA STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION POWER KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE dv

Source Contribution to 2018 Deciview Haze Index at Guadalupe Mountains , W20 Group

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0.3500 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE COMPR GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5 GENERA STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION POWER KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE Mm-1

Source Contribution to 2018 Light Extinction by Species at Guadalupe 

Mountains , W20 Group

E_oc E_so4 E_no3 E_ec E_soil E_cm E_salt

2018 Deciview Haze Index: 17.19 dv
A-25

Figure A.2 -15. Percentage of Total Extinction over 20%Worst Days at GUMO, Texas

Figure A.2 -16. Percentage of Total Extinction by Species over 20%Worst Days at GUMO, 

Texas

E_oc, 7.576, 

16.20%

E_so4, 14.557, 

31.12%

E_no3, 2.298, 

4.91%

E_ec, 1.050, 

2.25% E_soil, 5.208, 

11.13%

E_cm, 16.016, 

34.24%

E_salt, 0.070, 

0.15%

Percent of total extinction by species

E_oc

E_so4

E_no3

E_ec

E_soil

E_cm

E_salt

2018 Total Light Extinction: 85.95 Mm-1, 

Rayleigh = 11

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70% FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE COMPR GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5 GENERA STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION POWER KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE

Percentage of Total Extinction at Guadalupe Mountains , W20 Group

2018 Deciview Haze Index: 17.19 dv
A-26

Figure A.2 -17. Maximum Source Contribution to 2018 Deciview on any day of W20% days at 

GUMO, Texas

Figure A.2 -18. Uniform Rate of Progress and 2018 Projected Progress over 20%Worst Days at 

GUMO, Texas

0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200

0.1400

0.1600 FULLERTON GAS P LIGNITE-FIRED P SOMMERS DEELY S TEXARKANA MILL GUADALUPE... GOLDSMITH GASOL ODESSA CEMENT P NORTH TEXAS CEM MIDLOTHIAN PLAN HOLCIM (TEXAS) FAYETTE POWER P W A PARISH STAT BIG BROWN COLETO CREEK PL GIBBONS CREEK SHERHAN PLANT HW PIRKEY POWER BIG SPRING CARB BORGER CB BLK BORGER CB PLT GREAT LAKES CAR TOLK STATION RELIANT ENERGY PEGASUS GAS PLA SANDOW STEAM EL SANDOW 5... STREETMAN PLANT HARRINGTON STAT WAHA PLANT TNP ONE STEAM E MARTIN LAKE ELE MONTICELLO STM WELSH POWER PLA WORKS NO 4 OKLAUNION... KEYSTONE PLANT NEWMAN STATION KEYSTONE COMPRE dv

Maximum Source Contribution to 2018 Deciview Haze Index on any day at Guadalupe 

Mountains , W20 Group

17.19

16.49

14.74

12.99

11.06

9.48

7.73

6.68 6.08 6.08 6.08 6.08 6.08 6.08 6.08 6.08

17.19

16.36

5.88

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

Haze Index (dv)

Uniform Rate of Progress and 2018 Projected Progress

Guadalupe Mountains 

Glide path (20% Worst) Glide path (%20 Best) Observation Prediction (20% Worst) Prediction (20% Best)
A-27

A.3 Our Evaluation of Modeling for 38 Facilities

2018 Modeling Contribution Assessment

One of our points of inquiry was how much of the total impacts from Texas point sources is due

to these 38 facilities, considering Texas had over 1,600 facilities in their point source database

that we used in our Q/D analysis. We did not ask ENVIRON to redo the state level source 

apportionment modeling that was previously conducted by CENRAP for the 2018 Base G 

emission inventory in 2007, however, because we only made small changes to that inventory 

along with the modeling updates, we were able to do some comparisons to the original source 

apportionment results. As part of this work we did review and summarize some of the CENRAP 

source apportionment modeling results from late summer 2007.35 We combined the impacts 

from the three regions of Texas from CENRAP’s modeling (shared during consultation in 2007) 

that concluded that Texas is responsible for approximately 27.5% of the total impairment at 

WIMO and slightly over half of this (14%) total impairment was due to Texas Point Sources. 

See Figure A.3-1a-c for CENRAP PSAT information for WIMO, BIBE, and GUMO. Figures in 

Section A.2 includes the speciated extinction analysis from the PSAT modeling of the 38 

facilities at WIMO and the two Texas Class I areas BIBE and GUMO from ENVIRON’s recent 

work for us. Information for the other Class I areas evaluated in this study can be reviewed in 

electronic file using the look-up tables.36 From the Figures and other information it is clear that 

most source impacts on the W20% days are dominated by impacts due to SO2 emissions with the 

exception of some of the closer sources to the Class I areas (such as the Glass Plant in Wichita 

Falls {Works #4} and WIMO). 

In comparison to the original CENRAP modeling PSAT work, we estimated that approximately 

75-80% of the impacts from Texas Point Sources were from this small group of 38 facilities for 

the W20% days at WIMO.

37 See Figure A.3-2 for individual percentage contributions based on 

2018 base case modeling. Similar analyses were also done for Texas Class I areas BIBE and 

GUMO. CENRAP’s modeling indicated that 22.8% of the total impairment at BIBE was due to 

Texas and 8% (less than 1/3 of Texas’ impacts) were from point sources on W20% days. The 38 

sources are approximately 50% of this 8% that represents all Texas point sources impairment at 

BIBE. CENRAP’s modeling also indicated that 34.6% of the total impairment at GUMO was 

due to Texas and 8.6% (approx. 1/4 of Texas’ impacts) were from point sources on W20% days. 

The 38 sources are approximately 50% of this 8.6% that represents all Texas point sources 

impairment at GUMO.38 See Figure A.3-3 and A.3-4 for individual % contributions based on 

2018 base case modeling.

In evaluating the data and Figures A.2 – 6, A.2-12, and A.2-18 it is clear that WIMO, BIBE, and 

GUMO are all still projected to be above the Glidepath point in 2018. Due to the minor changes 

 

35 Included in docket the CENRAP 2007 PSAT database

36 Included in the docket: “Extinction charts.xls” and 

“EPA_txbart3612k_Vis_2002_2018_PSAT_Projected_MSF_v5.xlsx”

37

 Ibid.

38 Ibid.
A-28

in modeling, 12km flexinest grid, etc.; there are slight differences in the projected values but the 

conclusions are consistent with the original CENRAP work.

In evaluating the impacts from individual sources it can be seen that even a smaller set of sources 

make up the majority of the total impairment impacts from the 38 facilities at these three Class I 

Areas. Therefore, we concluded it was worth investigating whether the installation of cost 

effective controls on a small group of sources, out of the universe of sources in Texas, would 

result in a significant reduction in Texas’ contribution to the visibility impairment at Class I 

areas.
A-29

Figure A.3-1a. CENRAP 2007 PSAT results % of extinction impacts at WIMO (W20%)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

13.0%

14.0%

15.0%

16.0%

17.0%

18.0%

19.0%

20.0%

21.0%

22.0%

23.0%

24.0%

25.0%

26.0%

27.0%

28.0%

% of total extinction to 2018 Deciview Haze Index at WIMO (CENRAP PSAT 

results)

Boundary Condition

Initial Concentration

SOAB

SOAA

Non-EGU Point (Tx only)

EGU Point (Tx only)

Area

Non-Road Mobile

O n-Road Mobile

Natural

Low-Level Point

Elevated Point

2018 Total Light Extinction: 75.56 Mm-1

TX Non-EGU point, 6.3%

TX EGU point, 7.7%

OK point, 3.9%
A-30

Figure A.3-1b. CENRAP 2007 PSAT results % of extinction impacts at BIBE (W20%)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

13.0%

14.0%

15.0%

16.0%

17.0%

18.0%

19.0%

20.0%

21.0%

22.0%

23.0%

24.0%

25.0%

26.0%

27.0%

28.0%

29.0%

% of total extinction to 2018 Deciview Haze Index at BIBE (CENRAP PSAT 

results)

Boundary Condition

Initial Concentration

SOAB

SOAA

Non-EGU Point (Tx only)

EGU Point (Tx only)

Area

Non-Road Mobile

O n-Road Mobile

Natural

Low-Level Point

Elevated Point

2018 Total Light Extinction: 44.23 Mm-1

TX Non-EGU point, 3.6%

TX EGU point, 4.5%

Mexico point, 16.8% 

(Carbon I and II ~ 9% 

from BRAVO study)
A-31

Figure A.3-1c. CENRAP 2007 PSAT results % of extinction impacts at GUMO (W20%)

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

13.0%

14.0%

15.0%

16.0%

17.0%

18.0%

19.0%

20.0%

21.0%

22.0%

23.0%

24.0%

25.0%

26.0%

27.0%

28.0%

29.0%

30.0%

31.0%

32.0%

33.0%

34.0%

35.0%

% of total extinction to 2018 Deciview Haze Index at GUMO (CENRAP PSAT 

results)

Boundary Condition

Initial Concentration

SOAB

SOAA

Non-EGU Point (Tx only)

EGU Point (Tx only)

Area

Non-Road Mobile

O n-Road Mobile

Natural

Low-Level Point

Elevated Point

2018 Total Light Extinction: 44.43 Mm-1

TX Non-EGU point, 4.6%

TX EGU point, 4.1%

Mexico point, 7.2%
A-32

Figure A.3-2. Extinction Relative to Texas Influences and Texas Point Sources at WIMO (W20%)

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

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W A PARISH STATION

BIG BROWN

COLETO CREEK PLANT

GIBBONS CREEK

SHERHAN PLANT

HW PIRKEY POWER PLT

BIG SPRING CARBON BLACK

BORGER CARBON BLACK

BORGER CARBON BLACK PLT

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TOLK STATION

RELIANT ENERGY LIMESTONE

PEGASUS GAS PLANT

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SANDOW 5 GENERATING PLANT

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MARTIN LAKE ELECTRICAL STATION

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WORKS NO 4

OKLAUNION POWER STATION

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NEWMAN STATION

KEYSTONE COMPRESSOR STN.

% of Texas extinction and Texas point source extinction to 2018 

Deciview Haze Index at Wichita Mountains Wilderness 

% of total

Texas

% of total

Texas Point
A-33

Figure A.3-3. Extinction Relative to Texas Influences and Texas Point Sources at BIBE (W20%)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

FULLERTON GAS PLANT

LIGNITE-FIRED POWER...

SOMMERS DEELY...

TEXARKANA MILL

GUADALUPE...

GOLDSMITH GASOLINE...

ODESSA CEMENT PLANT

NORTH TEXAS CEMENT...

MIDLOTHIAN PLANT

HOLCIM (TEXAS) LP

FAYETTE POWER PROJECT

W A PARISH STATION

BIG BROWN

COLETO CREEK PLANT

GIBBONS CREEK

SHERHAN PLANT

HW PIRKEY POWER PLT

BIG SPRING CARBON...

BORGER CARBON BLACKBORGER CARBON BLACK...

GREAT LAKES CARBON...

TOLK STATION

RELIANT ENERGY...

PEGASUS GAS PLANT

SANDOW STEAM...

SANDOW 5...

STREETMAN PLANT

HARRINGTON STATION

WAHA PLANT

TNP ONE STEAM...

MARTIN LAKE...

MONTICELLO STM ELE...

WELSH POWER PLANT

WORKS NO 4

OKLAUNION POWER...

KEYSTONE PLANT

NEWMAN STATION

KEYSTONE...

% of Texas extinction and Texas point source extinction to 2018 

Deciview Haze Index at Big Bend National Park 

% of total

Texas

% of total

Texas Point
A-34

Figure A.3-4. Extinction Relative to Texas Influences and Texas Point Sources at GUMO (W20%)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

FULLERTON GAS PLANT

LIGNITE-FIRED POWER...

SOMMERS DEELY...

TEXARKANA MILL

GUADALUPE...

GOLDSMITH GASOLINE...

ODESSA CEMENT PLANT

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MIDLOTHIAN PLANT

HOLCIM (TEXAS) LP

FAYETTE POWER...

W A PARISH STATION

BIG BROWN

COLETO CREEK PLANT

GIBBONS CREEK

SHERHAN PLANT

HW PIRKEY POWER PLT

BIG SPRING CARBON...

BORGER CARBON BLACK

BORGER CARBON...

GREAT LAKES CARBON...

TOLK STATION

RELIANT ENERGY...

PEGASUS GAS PLANT

SANDOW STEAM...

SANDOW 5...

STREETMAN PLANT

HARRINGTON STATION

WAHA PLANT

TNP ONE STEAM...

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WELSH POWER PLANT

WORKS NO 4

OKLAUNION POWER...

KEYSTONE PLANT

NEWMAN STATION

KEYSTONE...

% of Texas extinction and Texas point source extinction to 2018 

Deciview Haze Index at Guadalupe Mountains 

% of total

Texas

% of total

Texas Point
A-35

Reasonable Progress vs. BART Analysis Issues

Our analysis has determined that a handful of the point sources in Texas (less than 1%) have a 

very large percentage of the contribution to visibility impairment at impacted Class I areas in a 

relative sense. However, difficulties arise when these modeled visibility impact levels from RP 

analysis using CAMx’s photochemical modeling are compared to BART analysis using 

CALPUFF modeling for individual sources developed in support of other regional haze actions.

We have not established specific metrics for use in evaluating single facility impacts on visibility 

impairment (RP) at downwind Class I areas with a photochemical grid model such as CAMx or 

CMAQ to help assess the significance of a modeled impact level, so there is a tendency to try to 

compare these modeled impacts to those metrics established for BART analyses with CALPUFF 

modeled results.. A common metric used in BART visibility modeling using CALPUFF is the 

BART screening level of 0.5 del-dv used by most states for screening out facilities from further 

BART consideration. However, there are a number of factors that make the two analyses 

uniquely different and not comparable, invalidating the use of the BART screening metric, or 

other such comparisons with modeled visibility impacts for RP with CAMx or CMAQ. Because 

of these many differences cause RP analysis results using CAMx to be much lower in magnitude 

than BART analysis results using CALPUFF. We highlight these differences below. We also 

discuss why BART analyses are addressing a fundamentally different question than the RP

analyses, which makes BART and RP results not directly comparable.

POLICY QUESTION DIFFERENCE - BART analyses are targeted towards assessing the 

impacts of a single facility’s sources on Class I area(s) and result in ranked impacts based on the 

maximum or 98th percentile (also called High Eighth High and abbreviated as H8H) impacts of 

the facility independent of whether the Class I area was actually monitoring/modeling overall 

high visibility impairment on that day. Some of the highest impacting days from the source 

using CALPUFF for BART modeling could be days that overall visibility at the Class I area is 

not significantly impaired since CALPUFF does not conduct a full analysis of all emissions from 

all potential sources. RP modeling using a photochemical model (CAMx) typically evaluates 

impacts from a source (with all other sources also included in the modeling) on a Class I area’s 

W20% and B20% days (days selected from monitoring that is cumulative of all emissions 

sources impacts at the Class I area) and is not looking for the maximum or near maximum 

impact of the specific source, but the average impact on these best and worst monitored days at a 

Class I area. Reasonable Progress modeling specifically assesses using these W20% and B20% 

days, so it would be difficult to deviate from these metrics in our review of the Texas and 

Oklahoma SIPs. Specifically, the BART based analysis (typically CALPUFF) is focused on 

finding the highest impact (max or H8H) from a facility regardless of the monitored values at the 

Class I area, whereas the RP analysis (typically CAMx/CMAQ) is focused on the 20% best and 

20% worst monitored days regardless if the facility was having an impact during those days

(20% equates to 72 days out of 365 days, IMPROVE monitors usually monitor 1 in 3 days so 

this would equate to 21 days). The metric evaluated is the facility’s average impacts (not max or 

high distribution impact) for those 20% days identified by the monitor data. Therefore BART

analyses are focused on answering a policy question of what is the maximum or 98th percentile 

impacts (365 days/year) of the facility being analyzed regardless of overall visibility impairment 

levels at the Class I area, and RP analyses are focused on assessing the average impacts of a 

facility on 20% of the days in the year with the Best and Worst overall visibility impairment days 
A-36

at the Class I area regardless of whether the facility had large/sizeable impacts on any of those 

particular days. In some situations, the days that BART modeling maximum or 98th percentile 

value impacts of the facility occur may not coincide with any of the days that make up the days 

in the Worst 20% days at the Class I area in a RP analysis.

METRICS DIFFERENCE ISSUE – As mentioned above, because RP is using the average of 

the change in impacts from control on data representing up to the worst 72 days for total 

visibility impairment at the Class I area that were selected from monitored values in the base 

period, there is not a direct correlation that these days align with the days that a specific facility 

would impact a Class I area such as WIMO. Even if the worst 20% days based on monitoring 

(reflective of all pollutants in the air shed) did coincide with the 20% highest days from BART 

modeling with CALPUFF, there are still the fundamental differences between the metrics. 

BART modeling impacts with CALPUFF are based on running 3 years of meteorology and 

picking the 1st or 8th highest value of 365 data points for each year and then picking the highest 

of the three 1st or 8th highs. RP anlaysis with CAMx is using the 20% worst days for evaluation 

of a facility’s impacts (worst days of visibility impairment monitored at the Class I area from all 

days and meteorological/transport conditions in the base period). When daily BART modeling 

with CALPUFF impacts ranked from highest to lowest were examined, typically the change 

would be closer to an exponential rather than a linear change, so the 1st or 8th high (BART

metric) would be significantly higher than the average of the top 21 or 72 days impacts (RP

impacts). RP metric results (average impact over the 20% worst days) could easily be several 

times less than the CALPUFF based BART metrics (1st or 8th high single day impact).

39

EMISSIONS ISSUE – BART screening modeling of a facility following the BART guidelines 

uses maximum 24-hour emissions (over a 3 to 5-year period) which are significantly higher than 

what we are using in the RP analysis (annual average tpy). Typically, when a facility lacked 

adequate data for maximum 24-hour actuals guidance that EPA Regional modelers provided (and 

states/sources followed) was to examine available data and double the annual average lb/hr rates 

in order to arrive at an estimate of maximum 24-hour actuals. For the few cases where BART 

screening was done with CAMx, including the Texas BART screening with CAMx, the same 

multiplier of 2X annual average lb/hr in the CAMx modeling was used.

40 Due to this issue alone 

it is reasonable to conclude that the RP results using annual actual emissions would be 50% 

lower than BART modeling results as the CAMx modeling has shown a linear relationship with 

extinction levels and emissions changes.

Several of the EPA Regions, including we in Region 6, analyzed some of the available CEM 

data, utilization rates, and other information at the time (2004-2005) to support the 2 X multiplier

of the annual average emission rate to estimate 24-hour max actual emissions (lb/hr) and states 

agreed with this approach for BART screening. To further support this approach for this action 

we also looked at recent modeling we in Region 6 have done in our Oklahoma FIP. In our 

BART modeling with CALPUFF used to evaluate benefits of controls, we used 80/85% load 

factor for the Muskogee Units 4 & 5 respectively, although some modeling elsewhere has used 

higher values for modeling control benefits. Actual load factors vary but were more often in the 

60-75% range. In BART analyses, we modeled the emissions for 8760 hours and not based on 

 

39 Included in Docket “OK CALPUFF distribution results.xlsx”.

40 Page 2-10 Screening Analysis of Potential BART-Eligible Sources in Texas; “App9_5_rev.pdf”
A-37

actual hours of operation. The actual BART Baseline (pre-control) was based on the historical 

range of coal burned and the lb of SO2/MMBTU was 0.8/0.85 respectively for Units 4 & 5. This 

equated to annual SO2 emissions for the Baseline BART modeling of 19,202 tpy for Unit 4 and 

20,402 tpy for Unit 5. These units have switched to a lower sulfur content coal since they were 

burning the higher sulfur content coal in the early 2000s and recent actuals are much lower. 

Using SO2 emissions data based on CEM data (2009-13 annual actuals based on CEM data and 

then dropping the min and max years values and averaging the three remaining years), Muskogee 

Units 4 and 5 values are 7,687 tpy, and 8,093 tpy. These emissions are less than half of the value 

used as the baseline for estimating the benefits of controls for BART using CALPUFF in the OK 

FIP. Although each situation will be different, we believe this, and the multiplier of 2X actuals 

used in the screening, both support that RP results using the same metrics would be 50% or less 

than the BART based results just due to the differences in emissions modeled for a facility.

CHEMISTRY ISSUE – CALPUFF uses a rather simple chemistry mechanism and CAMx uses a 

significantly more complex chemistry mechanism. It is unclear how this ultimately impacts the 

model estimates between these two models as the two chemistry approaches are vastly different. 

The more technically sophisticated CAMx model’s chemistry provides many more reactions and 

alternate pathways for regional haze pre-cursor emissions to be consumed/reacted, but given all 

the differences in the chemistry mechanisms, pre-cursor concentrations, and other differences 

that would introduce variation in comparisons it is impossible to come up with an answer on how 

this issue should be factored into a comparison of model results from CAMx and CALPUFF 

except to conclude that they would likely give differing values.

We considered the above issues in deciding to employ CAMx for our analysis. Since the RP 

analysis is based on W20% and B20% and is typically evaluated with a photochemical model 

such as CMAQ or CAMx in most states RH SIPs including Texas and Oklahoma, we believe it is 

appropriate to conduct this analysis with a photochemical model. EPA’s guidance does 

recommend photochemical models for RP analyses over great distances. We also factored in that 

many of the sources being evaluated are beyond the typical range of 300- 400 km from a Class I 

area and at greater distances raising some concern that CALPUFF may be over-predicting or not 

as accurate. CAMx also gave us the capability of doing PiG with chemistry in the PiG and also 

full source apportionment. Therefore we chose to complete our analysis with CAMx and its 

available tools.

“CLEAN VS. DIRTY” BACKGROUND ISSUE- CALPUFF modeling (for BART and other 

analyses) is conducted to determine a facility’s impact on a Class I area with no consideration of 

other pollutants in the air (other than natural background conditions) to challenge and consume 

the pre-cursors that are modeled to react with the facility’s emissions. Because the ammonia and 

other pollutants are more fully available to react with the facility’s emissions and generate haze 

causing pollutants in a CALPUFF analysis, this is often termed a ‘clean background’ analysis. 

CAMx is a full photochemical model with all the other sources quantified and added to the 

modeling, such that emissions from other facilities, non-point sources, mobile sources, etc., all 

react with available pre-cursors such as ammonia. This limits the amount of ammonia (and other 

pre-cursors) that are available to react with the specific facility emissions that is being assessed. 

Because CAMx takes into account the entire pollution load in the atmosphere in 2018, we often 

refer to this as the “dirty background” analysis. A facility’s visibility impairment impacts are 
A-38

substantially lower with a dirty background analysis compared to a clean background analysis. 

The new Improve equation is used to calculate the extinction (Inverse Megameters) that is then 

converted to del-dv using a logarithmic relationship. This logarithmic relationship is dependent 

upon the point on the deciview-extinction curve where the analysis is completed. For example, 

see Figure A.3-5 which shows the del-dv change due to a 10 (1/Mm) change at both the 2018 

projected extinction level and the 2064 natural visibility conditions extinction level for the 

Wichita Mountains. In the ‘dirty background’ case the 10 (1/Mm) yields a 1.26 del-dv, whereas

in the ‘clean background’ case the same 10 (1/Mm) yields a 3.86 del-dv improvement. In this 

example, the ‘clean background’ situation yields a del-dv improvement 3 times greater than the 

‘dirty background’ for the same level of extinction improvement. In the context of evaluating 

potential controls for a source, both of these are important since any emission reductions in 2018 

from controls will continue to provide benefits as other pollutant concentrations decrease in the 

Class I area atmosphere which results in further reductions in the calculated extinction levels 

(using the new IMPROVE equation) and therefore yielding more del-dv benefit over time as the 

area approaches natural condition levels.

The ‘clean’ vs. ‘dirty’ background issue can be conceptualized in an analogy by realizing that the 

deciview scale of visibility is similar to the decibel scale of sound. If a pin is dropped on a table 

in a quiet room (analogous to a clean background CALPUFF run), it can be easily heard. If on 

the other hand, the same pin is dropped on the same table in a noisy room (analogous to a dirty 

background CAMx run), it will not seem as loud in a relative sense. In both cases, the dropped 

pin makes the same sound (analogous to extinction level), but in the latter case, that sound is 

partially obscured by the noisy room.

In the BART Rule, we wrote:41

Using existing conditions as the baseline for single source visibility impact

determinations would create the following paradox: the dirtier the existing air, the 

less likely it would be that any control is required. This is true because of the 

nonlinear nature of visibility impairment. In other words, as a Class I area 

becomes more polluted, any individual source's contribution to changes in 

impairment becomes geometrically less. Therefore the more polluted the Class I 

area would become, the less control would seem to be needed from an individual 

source. We agree that this kind of calculation would essentially raise the "cause or

contribute" applicability threshold to a level that would never allow enough 

emission control to significantly improve visibility. Such a reading would render 

the visibility provisions meaningless, as EPA and the States would be prevented

from assuring "reasonable progress" and fulfilling the statutorily-defined goals of 

the visibility program. Conversely, measuring improvement against clean 

conditions would ensure reasonable progress toward those clean conditions.

In evaluating benefits of potential controls in our analysis, we considered estimated deciview 

improvements based on both a degraded 2018 background and a “clean” background based on 

average annual natural conditions, as shown in the tables below. As discussed above, since our 

analysis is based on a full photo-chemical grid model that includes modeling all emissions in the 

 

41

 70 FR 39124
A-39

modeling domain, the model results are inherently a degraded background analysis and the 

results are impacted/lowered by emissions from other sources in our 2018 analysis. To estimate 

the full benefit of reductions on a source we have estimated the natural conditions to simulate 

“clean” background results based on the modeled extinction impact levels for each source and 

calculated the del-dv based on annual average natural conditions. Due to the inclusion of all 

these other sources at 2018 estimated emission levels, the estimated impacts from a source (or 

from controlling a source) are less than the results that would be obtained using emission levels 

of sources that would exist when natural conditions are achieved. We note that CALPUFF based 

modeling simulates ‘clean’ background conditions with no other sources included than the 

source(s) being evaluated. The deciview improvement based on the 2018 background conditions 

provides an estimate of the amount of benefit that can be anticipated in 2018 and the impact a 

control/emission reduciton may have on the established RPG for 2018. However, this estimate 

based on degraded or “dirty” background conditions underestimates the visibility improvement 

that would be realized for the control options under consideration. Because of the non-linear 

nature of the deciview metric, as a Class I area becomes more polluted the visibility impairment 

from an individual source in terms of deciviews becomes geometrically less. Results based 

solely on a degraded background, will rarely if ever demonstrate an appreciable effect on 

incremental visibility improvement in a given area. Rather than providing for incremental 

improvements towards the goal of natural visibility, degraded background results will serve to 

instead maintain those current degraded conditions. Therefore, the visibility benefit estimated 

based on natural or “clean” conditions is needed to assess the full benefit from potential controls. 

In our final decision for our North Dakota SIP and FIP,42 we explained this by noting:

This is true because of the nonlinear nature of visibility impairment. In other 

words, as a Class I area becomes more polluted, any individual source's 

contribution to changes in impairment becomes geometrically less. Therefore the 

more polluted the Class I area would become, the less control would seem to be 

needed from an individual source.

We were subsequently upheld on this point by the Eighth Circuit Court in North Dakota v. EPA. 

730 F.3d 750, 766 (8th Cir. 2013).

We have also considered this natural condition approach in other actions, such as Wyoming.

43

 

42

 77 FR 20912.

43

 77FR 33022-62, 78 FR34738-34794, 79 FR 5032-5222. 
A-40

Figure A.3-5. Example of Logarithmic nature of del-dv calculation

1

0

3.86 dv

1.26 dv

dv = 10*ln(Bext/10)

1

0

21.5 dv 

(2018 

CENRAP 

projected 

visibility for 

WIMO

7.53 dv (2064 

natural 

visibility goal 

for WIMO
A-41

A.4 Modeling Results – Selection of Sources for Further Evaluation

After our initial evaluation of modeling results from ENVIRON, we decided to examine the 

results in several different ways to help in identifying a subset of sources for further visibility 

modeling and control analysis. This second round of analysis was focused on looking at the 

largest impacting sources at WIMO, BIBE, and GUMO from the initial analysis and will be 

discussed further in Section A.5. This section will explain how we selected the subset of sources 

for further evaluation. 

IDENTIFICATION OF THE TOP 10 IMPACTING FACILITIES

We initially evaluated and ranked the top 10 impacting facilities for each of the three Class I 

areas. These Tables are included in Tables A.4-1a-c and include the average extinction on the 

20% Worst Days and also the maximum extinction during the 20% worst days. This information 

is provided in units of Inverse Megameters (1/Mm) and as percent of total extinction (avg. 

20%W). In comparing results between the Class I areas we provided the percent approach to 

somewhat normalize the total extinction differences between the differing Class I areas. For 

example, with regard to the clean vs. dirty background issue we discussed above, we consider a 

10 (1/Mm) extinction on a cleaner background area such as GUMO which has a relatively lower 

total extinction level more beneficial than a 10 (1/Mm) extinction at WIMO which has a much 

higher total extinction level. As we discussed previously it is important to consider where on the 

extinction/deciview curve the reductions would occur. Consequently, referring to Figure A.3-5, 

in GUMO’s case a 10 (1/Mm) extinction change results in a much larger deciview improvement. 

Therefore in the analysis we have conducted, we do not believe it is enough to consider just the 

magnitude of extinction from a facility, we believe we must also consider the percent analysis. 

We provided the maximum extinction metrics to provide some general context between the 

average and maximum impacts for days that make up the 20% worst days. As discussed in 

Section A.3, we cannot evaluate CAMx modeling results for RP using CALPUFF metrics for 

BART, but examining the maximum does give some insight into the distribution of impacts. In 

addition, it is worth noting that a number of facilities made the top 10 for more than one Class I 

area.
A-42

Table A.4-1a. Top 10 Impacting sources ranked on % Extinction on Avg. 20% Worst Days at WIMO

Class I Wichita Mountains Wilderness 

Top 10 Plant Name Extinction % Contribution

Max extinction during 

20% worst days

Max % contribution 

during 20% worst days

1 MONTICELLO STM 1.275 1.73% 3.551 2.55%

2 BIG BROWN 1.169 1.59% 3.297 2.37%

3 MARTIN LAKE ELE 0.686 0.93% 1.900 1.36%

4 WORKS NO 4 0.635 0.86% 2.066 2.11%

5 RELIANT ENERGY 0.478 0.65% 1.310 0.94%

6 OKLAUNION POWER 0.417 0.57% 1.049 0.75%

7 SOMMERS DEELY S 0.410 0.56% 1.134 0.81%

8 HW PIRKEY POWER 0.368 0.50% 1.036 0.74%

9 COLETO CREEK PL 0.354 0.48% 0.995 0.71%

10 WELSH POWER PLA 0.349 0.47% 0.963 0.63%

Total Projected extinction in 2018 (Avg. 20% Worst Days) 73.547
A-43

Table A.4-1b. Top 10 Impacting sources ranked on % Extinction on Avg. 20% Worst Days at BIBE

Class I Big Bend National Park

Top 10 Plant Name Extinction % Contribution

Max extinction during 

20% worst days

Max % contribution 

during 20% worst days

1 SOMMERS DEELY S 0.276 0.57% 1.193 1.13%

2 COLETO CREEK PL 0.216 0.44% 0.937 0.89%

3 BIG BROWN 0.212 0.44% 0.923 0.87%

4 RELIANT ENERGY 0.103 0.21% 0.441 0.42%

5 LIGNITE-FIRED P 0.101 0.21% 0.428 0.41%

6 MONTICELLO STM 0.096 0.20% 0.413 0.39%

7 W A PARISH STAT 0.090 0.18% 0.385 0.36%

8 BIG SPRING CARB 0.084 0.17% 0.356 0.34%

9 SANDOW STEAM EL 0.080 0.16% 0.342 0.32%

10 MARTIN LAKE ELE 0.080 0.16% 0.342 0.32%

Total Projected extinction in 2018 (Avg. 20% Worst Days) 48.613
A-44

Table A.4-1c. Top 10 Impacting sources ranked on % Extinction on Avg. 20% Worst Days at GUMO

Class I Guadalupe Mountains

Top 10 Plant Name Extinction % Contribution

Max extinction during 

20% worst days

Max % contribution 

during 20% worst days

1 TOLK STATION 0.302 0.65% 1.004 1.55%

2 BIG BROWN 0.235 0.50% 0.809 1.25%

3 BIG SPRING CARB 0.226 0.48% 0.775 1.19%

4 SOMMERS DEELY S 0.208 0.44% 0.688 1.06%

5 HARRINGTON STAT 0.184 0.39% 0.606 0.93%

6 MONTICELLO STM 0.114 0.24% 0.391 0.60%

7 WAHA PLANT 0.113 0.24% 0.387 0.60%

8 RELIANT ENERGY 0.111 0.24% 0.372 0.57%

9 MARTIN LAKE ELE 0.104 0.22% 0.351 0.54%

10 COLETO CREEK PL 0.066 0.14% 0.227 0.35%

Total Projected extinction in 2018 (Avg. 20% Worst Days) 46.776
A-45

CONSIDERATION OF RECENT EMISSIONS

The CENRAP modeling was based on an IPM (Integrated Planning Model) that estimated EGU 

future emissions in 2018 including reductions for CAIR across the Eastern half of the United 

States. This analysis was conducted in 2006 and projected that Texas would actually be a 

purchaser of SO2 credits, and not as much high level controls would be placed on Texas EGU 

sources. Given the length of time between 2006 when the IPM analysis was conducted, and 

2013 when we were conducting this analysis, we had some concern that projections could be off 

for the EGUs in Texas. This was especially important considering that some of these same

EGUs made up the majority of sources that made the top 10 list for each of the three Class I 

areas. Information available also indicates that SO2 credits are much cheaper than originally 

projected, therefore more credits may have been used in lieu of emission reductions. We also 

weighed the technique that Texas has used in estimating emissions from EGUs for future years 

(including 2018) in ozone attainment demonstration SIPs in DFW and HGB44. For these 

photochemical modeling analyses with CAMx they have relied upon the recent CEM data that is 

also included in CAMD’s databases in conjunction with information on recently permitted EGUs 

for estimating the emissions to model for EGUs in Texas in 2018 as these emission levels are 

near CAIR Phase II control levels. 

At the time we were conducting this analysis the CSAPR was still being litigated and the future 

of the rule was uncertain. We were cognizant of the fact that even if CSAPR makes it 

completely through litigation and is upheld, the actual SO2 allowances for Texas are not much 

different than the CAIR Cap for Texas, so large additional reductions over current emission 

levels were not expected. Also, we have projected that controls for MATS may generate the 

installation of additional scrubbers in Texas that could potentially result in further SO2

reductions. Texas recently submitted comments to us on a more recent IPM projection that was 

going to be part of a new modeling platform for national rule making45. In these comments and 

comments from several EGU owners in Texas, the assertion was that no significant amount of 

additional SO2 controls are expected due to compliance with MATS. The comments also 

pointed out that as some of our cursory research had also indicated that no large SO2 control 

projects were planned at most of the sources we were evaluating. Therefore, based on Texas’ 

recent comments and other information, we concluded considerable uncertainty exists as to 

whether any further reductions of SO2 will occur beyond current emission levels as a result of

compliance with MATS or CSAPR. Overall this information supports looking at recent actual 

emissions to represent future emission levels in 2018.

 

44 HGB 1997 8-Hour Ozone standard attainment demonstration approved by EPA in 2013, see TSD materials for 

2010 “Appendix B Emission Modeling for the HGB Attainment Demonstrtion SIP Revision for the 1997 EightHour Ozone Standard” on page B-78, “09017SIP_ado_Appendix_B.pdf”.; DFW 1997 8-Hour Ozone standard 

attainment demonstration submitted to EPA, see TSD Appendix B: Emission Modeling for the DFW Attainment 

Demonstration SIP Revision for the 1997 Eight-Hour Ozone Standard, Page B-39, “AppB_EI_ado.pdf”; DFW 2008 

8-Hour Ozone standard attainment demonstration proposed for adoption Dec. 10, 2014 and posted October 2014, 

see TSD materials “Appendix B Emissions Modeling for the Dallas-Fort Worth Attainment Demonstration State 

Implementation Plan Revision for the 2008 Eight-Hour Ozone Standard” Starting Page 40.,DFWAD_SIP_Appendix 

B.pdf 

45 Texas comments on Draft IPM modeling conducted by EPA for potential national rule making platformprovided 

on June 26, 2014. In this docket materials as ““TCEQ comment letter to EPA on draft modeling platform dated 

June 24, 2014. ‘2018 EMP signed.pdf’
A-46

ENVIRON finished the initial 2018 source apportionment modeling for EPA in late summer 

2013. Therefore the latest full year of CEM data available was 2012. We evaluated recent 

emissions for the 38 sources that were available through CAMD’s CEM databases. We 

evaluated the average and maximum annual tpy for the 2008-2012 data (most recent 5 year 

period available). We contrasted this data with the emission rates that we had modeled. Table 

A.4-2 summarizes both the CAMD data and modeled emission rate in tpy.
A-47

Table A.4-2. 2008-2012 CAMD CEM emissions contrasted with Modeled emission rates for 38 facilities modeled
A-48

Table A.4-2. 2008-2012 CAMD CEM emissions contrasted with Modeled emission rates for 38 facilities modeled (continued)
A-49

We note that the data indicates that a number of facilities have actual emissions that are much 

higher than modeled. For instance, Big Brown, Sandow, and Martin Lake were all significantly 

higher than modeled rates, with Martin Lake having over 90% more SO2 emissions than 

modeled. Both Pirkey and Oklaunion had much smaller actual SO2 emissions than modeled. 

Other examples of the differences between actual and modeled emissions can also be seen.

FINAL SELECTION OF SOURCES FOR ADDITIONAL VISIBILITY ANALYSIS

Given the significant range in impacts among the 38 facilities on Class I areas, we wanted to 

narrow the source list for further visibility evaluation to a smaller subset of sources that would 

potentially yield the most visibility improvement. We were primarily interested in generating a 

smaller group of sources that could potentially yield visibility improvements at WIMO, BIBE, 

and GUMO.

In order to choose a subset of sources for further evaluation we considered several different 

things. Some individual source/facility analyses for RP have used CALPUFF modeling results. 

We have CAMx results that allow us to evaluate individual source impacts directly to the same 

metrics used for RP analysis (W20% and B20%). We examined our modeling results for any 

natural break points that indicated a significant drop-off in impacts that would allow us to select 

a natural subset of the largest impacting sources. This examination yielded break points around 

1%, 0.5% and 0.3%. We note that Texas used 0.5 Mm-1and Oklahoma used 1 Mm-1of 

extinction from all sources in a state as a threshold for inviting a state to consult.

46

 Depending 

on Class I area, these values would equate to a value of approximately 0.6% to 4%. Given that 

consultation was based on all emissions from an upwind state, not just point source emissions, 

the actual reductions from individual point sources in a state would only be a small percentage of 

this amount that would have been potentially discussed in consultation. For example if 1/3 of the 

impacts of a state came from one source, which is a conservative approach, that source might be 

considered for potential controls in consultation for impacts that could be approximately 0.2% to 

1.33% of the extinction at a Class I area. Without specific modeling, many states initially 

focused on the largest emissions sources in the state until they got to a point of diminishing 

visibility improvements. There are a number of different approaches used by states in 

development of sources for RP evaluation but it usually centered around the general premise of 

evaluating the biggest sources and the biggest impactors on visibility. For our analysis we tried 

utilizing an extinction percentage of 1% for a facility’s impacts with a consideration that some 

facilities have two or three units and this metric would equate to 0.5% or 0.33% extinction per 

unit. As discussed below, we concluded that this was a reasonable way to arrive at some 

common breakpoints/drop-offs in potential visibility improvements. This reduced the sources 

we had to examine to about a dozen facilities and helped set a context for what level of impacts 

may warrant further modeling to assist in a full four factor analysis for our RP/LTS analysis.

We continued to evaluate these sources, considering the different approaches highlighted in the 

information above: maximum actual emissions, extinction, and extinction percentage. In our 

final analyses we evaluated these sources based on facility impacts and also on a unit basis 

impact. We used a linear scalar approach to scale the impacts based on the modeled emission 

rate and associated impact level to result in an estimated impact based on actual emissions. 

 

46 See Texas Regional Haze SIP Appendix 4-1: Summary of Consultation Calls and Section X.A. of the Oklahoma 

Regional Haze SIP
A-50

Since as we have noted earlier, the extinction from most sources was almost all due to SO2

emissions, we scaled the impacts based on SO2 emissions.

Initially we evaluated the average extinction on the 20% worst days for each of the 38 facilities 

based on the modeled emissions and also adjusted based on the average of the 2008-2012 annual 

tpy SO2 emissions. Table A.4-3 includes an evaluation of the extinction levels as a percentage of 

total extinction at the Class I area for each of the 38 facilities in our initial modeling. This table 

includes this metric for each of the 3 Class I areas for both the modeled emissions and the 

estimated facility impact based upon the average of 2008-2012 CEM data. As discussed 

elsewhere, the model was responding linearly to emission changes, so we scaled the modeling 

based on CEM data to get CEM based impact estimates. The sources are ranked based on the 

highest to lowest modeled extinction at the highest percentage extinction at one of the three 

Class I areas. As detailed in the top of the table we have include a “>1%” next to the name of 

each facility that had above a 1% extinction impact on a Class I area based on either modeled or 

CEM data. From other information in the spreadsheet that this table was developed, we also 

factored in if the facility or a unit(s) at the facility had impacts above 0.5% with a “(+)” and a 

“(++)” when impacts were above 0.3%.47 We further shaded the extinction percentages in the 

table when a facility had units greater than 0.3%. As another point of context, the top 3-4 

facilities have more impact than all emissions sources in Louisiana on WIMO’s extinction levels.

When we examined the impacts, we noted that some source impacts are quite low and some 

impacts were spread among several sources at the facility, making individual unit impacts even 

smaller. We therefore concluded that some of these impacts did not warrant further evaluation 

for this planning period and dropped them from Table A.4-3. With this shortened list of 

facilities, we then scaled the ENVIRON modeling and the estimated impacts of each facility to 

estimate unit specific values for the percentage of extinction due to each unit at a facility. We 

did this because our four factor RP analysis evaluating potential controls would be completed on 

a unit specific basis. In Table A.4-4 we present the results of these estimates. We have shaded 

all individual sources/units at a facility that had impacts over a 0.3% threshold using the model 

based analysis or the estimate based on 2008-2012 average CEM data.

We concluded that any unit with an estimated impact greater than 0.3% would be further 

evaluated. We believe that using a percent impacts approach is appropriate because of its 

linkage to the reasonable progress concept. For example, a source that has a smaller absolute 

impact on a relatively cleaner area but a higher percentage impact might be considered for 

control so that the cleaner area can potentially make progress. Since we had recent actual 

emissions, and any feasibility of controls would likely be based on reductions from actuals, we 

weighed the estimated impacts based on actuals in addition to the modeled impact levels.

We used the 0.3% threshold only as a way to identify a reasonable set of sources to evaluate 

further. At this point, the resulting reasonably broad cutpoint served as a starting place from 

which to further analyze individual source impacts in the second round of modeling, and balance 

them against any cost effective controls that could be identified. We describe that process 

below.

 

47 Electronic file in the Docket named “Source selection analysis TX RH 1-31-14.xlsx” and “Source selection 

analysis TX RH-es-1-31-14.xlsx.
A-51

Table A.4-3. Percent of extinction for the Avg. Impacts on W20% days.(Facilities)

 Ranked by Facility Highest Avg impacts on W20% days for Class I areas in OK & TX

 1. Facilities with either modeled or estimated impacts based on recent EI that are above 1% have '( > 1% )'

 above 0.3% have '( ++ )'

 3. Facilities with unit(s) greater than 0.3% impact are shaded

Plantname

Most 

Impacted 

Area

2nd Most 

Impacted 

Area

3rd Most 

Impacted 

Area

Most 

Impacted 

Area

2nd Most 

Impacted 

Area

3rd Most 

Impacted 

Area

Most 

Impacted 

Area

2nd Most 

Impacted 

Area

3rd Most 

Impacted 

Area

MONTICELLO STM ELE STN ( >1% ) ( + ) 1.734% 0.244% 0.197% WIMO GUMO BIBE 1.834% 0.258% 0.209%

BIG BROWN ( >1% ) ( + ) 1.590% 0.502% 0.435% WIMO GUMO BIBE 2.060% 0.651% 0.564%

MARTIN LAKE ELECTRICAL STATION ( >1% ) ( + ) 0.932% 0.223% 0.164% WIMO GUMO BIBE 1.745% 0.417% 0.306%

WORKS NO 4 ( ++ ) 0.863% 0.015% 0.011% WIMO GUMO BIBE

RELIANT ENERGY LIMESTONE ( ++ ) 0.650% 0.238% 0.212% WIMO GUMO BIBE 0.797% 0.291% 0.260%

TOLK STATION ( ++ ) 0.646% 0.186% 0.096% GUMO WIMO BIBE 0.620% 0.179% 0.093%

SOMMERS DEELY SPRUCE PWR 0.569% 0.558% 0.444% BIBE WIMO GUMO 0.454% 0.446% 0.355%

OKLAUNION POWER STATION 0.567% 0.076% 0.075% WIMO BIBE GUMO 0.286% 0.039% 0.038%

HW PIRKEY POWER PLT 0.501% 0.130% 0.090% WIMO GUMO BIBE 0.097% 0.025% 0.017%

BIG SPRING CARBON BLACK 0.482% 0.304% 0.173% GUMO WIMO BIBE

COLETO CREEK PLANT ( + ) 0.481% 0.444% 0.141% WIMO BIBE GUMO 0.513% 0.473% 0.150%

WELSH POWER PLANT ( ++ ) 0.475% 0.071% 0.054% WIMO GUMO BIBE 0.862% 0.129% 0.099%

HARRINGTON STATION 0.393% 0.162% 0.070% GUMO WIMO BIBE 0.317% 0.131% 0.057%

SANDOW STEAM ELECTRIC ( + ) 0.376% 0.164% 0.119% WIMO BIBE GUMO 0.974% 0.426% 0.308%

W A PARISH STATION ( ++ ) 0.291% 0.185% 0.089% WIMO BIBE GUMO 0.881% 0.559% 0.268%

WAHA PLANT 0.242% 0.070% 0.036% GUMO BIBE WIMO

GREAT LAKES CARBON LLC 0.238% 0.093% 0.038% WIMO BIBE GUMO

HOLCIM (TEXAS) LP 0.217% 0.055% 0.042% WIMO GUMO BIBE

SAN MIGUEL ( ++ ) 0.207% 0.164% 0.107% BIBE WIMO GUMO 0.333% 0.263% 0.172%

STREETMAN PLANT 0.196% 0.047% 0.047% WIMO GUMO BIBE

MIDLOTHIAN PLANT 0.167% 0.044% 0.028% WIMO GUMO BIBE

FULLERTON GAS PLANT 0.106% 0.037% 0.029% GUMO BIBE WIMO

BORGER CARBON BLACK PLT 0.106% 0.057% 0.023% GUMO WIMO BIBE

SANDOW 5 GENERATING PLANT 0.100% 0.043% 0.027% WIMO BIBE GUMO 0.071% 0.031% 0.019%

TNP ONE STEAM ELECTRIC ST 0.082% 0.030% 0.023% WIMO BIBE GUMO

FAYETTE POWER PROJECT 0.072% 0.031% 0.031% WIMO GUMO BIBE 0.072% 0.031% 0.000%

GOLDSMITH GASOLINE PLANT 0.072% 0.022% 0.020% GUMO BIBE WIMO

BORGER CARBON BLACK 0.066% 0.038% 0.016% GUMO WIMO BIBE

ODESSA CEMENT PLANT 0.066% 0.021% 0.012% GUMO BIBE WIMO

KEYSTONE PLANT 0.055% 0.012% 0.010% GUMO BIBE WIMO

TEXARKANA MILL 0.036% 0.009% 0.005% WIMO BIBE GUMO

NORTH TEXAS CEMENT CO. 0.035% 0.007% 0.006% WIMO BIBE GUMO

KEYSTONE COMPRESSOR STN. 0.035% 0.006% 0.001% GUMO BIBE WIMO

GUADALUPE COMPRESSOR STATION 0.032% 0.000% 0.000% GUMO BIBE WIMO

SHERHAN PLANT 0.032% 0.012% 0.008% GUMO WIMO BIBE

GIBBONS CREEK 0.031% 0.014% 0.010% WIMO BIBE GUMO

PEGASUS GAS PLANT 0.015% 0.008% 0.003% BIBE GUMO WIMO

NEWMAN STATION 0.005% 0.000% 0.000% GUMO BIBE BIBE

Class I area Codes: OK & TX WIMO = Wichita Mtns.; GUMO = Guadalupe Mtns.; BIBE = Big Bend

Modeled Facility Impacts 

at each of the Class I areas 

in OK & TX 

Estimated Facility Impact 

Adjusted to reflect 2008-

2012 Avg. Emissions 

 2. Facilities that have unit(s) with modeled or estimated impacts based on recent EI above 0.5% have '( + )', and 
A-52

Table A.4-4. Percent of extinction for the Avg. Impacts on W20% days.(Units)

 Ranked by Estimated Unit Avg. Extinction % Impacts on W20% for Class I areas in OK & TX 

 1. Individual Units with impacts greater than 0.3% are shaded

Plantname Unit

% at Most 

Impacted 

Area

Most 

Impacted 

Area

% at 2nd 

Most 

Impacted 

Area

2nd Most 

Impacted 

Area

% at 3rd 

Most 

Impacted 

Area

3rd Most 

Impacted 

Area

Unit Max 

at most 

impacted 

Area

Unit Max 

at second 

most 

impacted 

Area

Unit Max 

at third 

most 

impacted 

Area

BIG BROWN 1 0.786% WIMO 0.249% GUMO 0.215% BIBE 1.030% 0.326% 0.282%

BIG BROWN 2 0.803% WIMO 0.254% GUMO 0.220% BIBE 1.030% 0.326% 0.282%

SANDOW STEAM ELECTRIC 1 0.376% WIMO 0.164% BIBE 0.119% GUMO 0.974% 0.426% 0.308%

MONTICELLO STM ELE STN 1 0.654% WIMO 0.092% GUMO 0.074% BIBE 0.691% 0.097% 0.079%

MONTICELLO STM ELE STN 2 0.673% WIMO 0.095% GUMO 0.077% BIBE 0.666% 0.094% 0.076%

MARTIN LAKE ELECTRICAL STATION 1 0.296% WIMO 0.071% GUMO 0.052% BIBE 0.609% 0.145% 0.107%

MARTIN LAKE ELECTRICAL STATION 2 0.313% WIMO 0.075% GUMO 0.055% BIBE 0.572% 0.137% 0.100%

MARTIN LAKE ELECTRICAL STATION 3 0.323% WIMO 0.077% GUMO 0.057% BIBE 0.564% 0.135% 0.099%

COLETO CREEK PLANT 1 0.481% WIMO 0.444% BIBE 0.141% GUMO 0.513% 0.473% 0.150%

MONTICELLO STM ELE STN 3 0.406% WIMO 0.057% GUMO 0.046% BIBE 0.477% 0.067% 0.054%

WORKS NO 4 1 0.448% WIMO 0.008% GUMO 0.002% BIBE

WORKS NO 4 2 0.415% WIMO 0.007% GUMO 0.010% BIBE

RELIANT ENERGY LIMESTONE 2 0.183% WIMO 0.067% GUMO 0.060% BIBE 0.414% 0.151% 0.135%

RELIANT ENERGY LIMESTONE 1 0.467% WIMO 0.171% GUMO 0.152% BIBE 0.383% 0.140% 0.125%

San Miguel 1 0.207% BIBE 0.164% WIMO 0.107% GUMO 0.333% 0.263% 0.172%

TOLK STATION 1 0.338% GUMO 0.097% WIMO 0.050% BIBE 0.312% 0.090% 0.047%

TOLK STATION 2 0.308% GUMO 0.089% WIMO 0.046% BIBE 0.308% 0.089% 0.046%

WELSH POWER PLANT 3 0.393% WIMO 0.059% GUMO 0.045% BIBE 0.301% 0.045% 0.034%

W A PARISH STATION 7 0.072% WIMO 0.046% BIBE 0.022% GUMO 0.298% 0.189% 0.091%

W A PARISH STATION 6 0.071% WIMO 0.045% BIBE 0.022% GUMO 0.289% 0.184% 0.088%

WELSH POWER PLANT 2 0.041% WIMO 0.006% GUMO 0.005% BIBE 0.287% 0.043% 0.033%

OKLAUNION POWER STATION 1 0.567% WIMO 0.076% BIBE 0.075% GUMO 0.286% 0.039% 0.038%

WELSH POWER PLANT 1 0.041% WIMO 0.006% GUMO 0.005% BIBE 0.274% 0.041% 0.031%

W A PARISH STATION 8 0.063% WIMO 0.040% BIBE 0.019% GUMO 0.240% 0.152% 0.073%

SOMMERS DEELY SPRUCE PWR 2 0.232% BIBE 0.228% WIMO 0.181% GUMO 0.201% 0.197% 0.157%

SOMMERS DEELY SPRUCE PWR 1 0.236% BIBE 0.232% WIMO 0.184% GUMO 0.188% 0.184% 0.147%

HARRINGTON STATION 3 0.134% GUMO 0.055% WIMO 0.024% BIBE 0.111% 0.046% 0.020%

HARRINGTON STATION 1 0.123% GUMO 0.051% WIMO 0.022% BIBE 0.103% 0.043% 0.019%

HARRINGTON STATION 2 0.137% GUMO 0.056% WIMO 0.024% BIBE 0.102% 0.042% 0.018%

HW PIRKEY POWER PLT 1 0.501% WIMO 0.130% GUMO 0.090% BIBE 0.097% 0.025% 0.017%

SOMMERS DEELY SPRUCE PWR 3 0.095% BIBE 0.093% WIMO 0.074% GUMO 0.061% 0.060% 0.048%

W A PARISH STATION 9 0.086% WIMO 0.054% BIBE 0.026% GUMO 0.053% 0.034% 0.016%

SANDOW 5 GENERATING PLANT 0.048% WIMO 0.021% BIBE 0.013% GUMO 0.036% 0.015% 0.010%

SANDOW 5 GENERATING PLANT 0.051% WIMO 0.022% BIBE 0.014% GUMO 0.035% 0.015% 0.010%

FAYETTE POWER PROJECT 2 0.025% WIMO 0.011% GUMO BIBE 0.025% 0.011% 0.000%

FAYETTE POWER PROJECT 1 0.024% WIMO 0.010% GUMO BIBE 0.024% 0.010% 0.000%

FAYETTE POWER PROJECT 3 0.023% WIMO 0.010% GUMO BIBE 0.023% 0.010% 0.000%

SOMMERS DEELY SPRUCE PWR 4 0.005% BIBE 0.005% WIMO 0.004% GUMO 0.004% 0.004% 0.003%

TNP ONE STEAM ELECTRIC ST 1 WIMO BIBE GUMO 0.000% 0.000% 0.000%

TNP ONE STEAM ELECTRIC ST 2 WIMO BIBE GUMO 0.000% 0.000% 0.000%

Estimated Unit Impact 

Adjusted to reflect 2008-

2012 Avg. Emissions 

Modeled Facility Impacts 

at each of the Class I 

areas in OK & TX 
A-53

After coming up with a list of sources/units based on Table A.4-4 we continued to evaluate 

whether to include or exclude sources that were close to the cutpoint, or for which we had 

additional information that would indicate they should be excluded in the second round of 

visibility modeling. In so doing, we noted the following:

 Martin Lake had two units above the cutpoint for both modeled emissions and recent 

actuals, we included Unit #1 because it was above based on actuals and very close to the 

cutpoint with modeled values.

 Works #4 (glass plant) had modeled NOx values much higher than recent actuals. As we 

describe elsewhere in our TSD, one of the two furnaces (the main NOx sources) had been 

rebuilt and NOx controls had been installed. The plant is located in Wichita Falls, TX 

and is relatively close to WIMO, so it was one of the few sources that did have a sizeable 

amount of impact due to modeled NOx emissions. We note though that the modeled NOx 

was over five times higher than recent actuals and permit limits. There is some amount 

of SO2 impacts, but taking into account the large decrease in NOx emissions compared to 

modeled emissions and that the glass furnaces are only rebuilt approximately every 10 

years, we determined it was acceptable to drop this source from consideration this time. 

We suggest that the TCEQ evaluate this source in the future as part of a future SIP RP 

analysis.

 San Miguel was below the 0.3% threshold based on the modeled inventory and slightly 

above the threshold based on recent actuals. As noted elsewhere and included in the 

docket, San Miguel has completed SO2 scrubber upgrades and brought their scrubber 

systems to near current day standards, so we did not think further control should be 

considered as part of this RP review and are making existing controls enforceable as part 

of this proposed action.

 Welsh had 1 unit above the 0.3% threshold but the other two units were below the 

threshold. The CENRAP RPO IPM projections appears to assume that two of the three 

units would add scrubbers. This was not in the EPA IPM run so we concluded it must 

have been a correction made in the RPO IPM run. Recent emissions are approximately 

equal across all three units. As we discuss elsewhere in our TSD, Unit 2 is under a 

consent decree to shut down, so it was not considered further. Nevertheless, we included 

all three units in the additional visibility modeling to be conservative and be able to 

provide a visibility analysis if needed.

 Several units at W. A. Parish were very close to 0.3% based on actual emissions. We 

therefore included the Parish units in order to provide visibility information if needed.

 Oklaunion had model based impacts above 0.3% but just below 0.3% with the actual 

emissions analysis. Oklaunion is close to WIMO and its impacts are a combination of 

NOx and SO2 impacts. However, we concluded that if just the impacts from SO2 were 

examined, the facility’s impacts would be below the 0.3% value. Therefore Oklaunion 

was not included in additional visibility modeling.

 The Sommers Deely Spruce complex is below the 0.3% value for each Class I area, but it 

does impact all three Class I areas at about 0.2-0.23% which raised a question concerning

its cumulative impacts. However, Sommers Deely Spruce has indicated that they are 

shutting down two of their dirtiest sources by 2018. Therefore we did not include the 

Sommers Deely Spruce units in our additional visibility modeling.
A-54

 Pirkey had high modeled emissions and was above 0.3%, but the value was less than 

0.1% for the value based on actuals, so we did not include in our additional visibility 

modeling.

 Big Spring carbon’s facility impacts were above 0.3% with modeled emissions but there 

are 9 units with sizeable emissions. However it was unclear whether they could be 

controlled through one scrubber or would be treated as 9 units with individual impacts 

much smaller. Therefore we did not include them in our additional visibility modeling.

A.5 Preparation of Emissions Scenarios for Potential Controls - Additional visibility 

Modeling

The above described exercise narrowed the potential sources for further visibility modeling down 

to 9 facilities with 21 units total. We then developed emission scenarios to model that would 

result in multiple visibility data points, spanning any likely control scenarios for each unit. This 

information would be used to estimate the amount of visibility benefit that could be expected 

from installation of controls on each of the 21 units. Based on the general premise that any 

potential controls would be for SO2, we formulated model runs that would span the range of 

potential controls/emissions we planned to examine. Our goal was to maximize the amount of 

data points so we could estimate the visibility benefit at Class I areas from any decrease in 

emissions that would result from any SO2 control we would examine. ENVIRON assisted EPA 

in conducting two additional runs, with one run being based on using a low SO2 control such as 

DSI and one run based on a high SO2 control that would reflect SDA or wet FGD scrubbing.

We noted that some of the units we were evaluating were already equipped with older 

underperforming scrubbers, most of which were partially bypassed. For the units with scrubber

bypasses, we lacked information on scrubber absorber efficiencies represented by the recent 

actual emission levels and the amount of bypass that has occurred. This made it impossible to 

estimate the current base control efficiency and the amount of additional reductions that would 

occur if the unit was further controlled based soley on the actual emissions data.. Therefore, as is 

described in our Cost TSD, we used Coal Data from EIA Form 923. This information enabled us 

to assess the total sulfur levels coming into a unit, to determine an uncontrolled baseline, and 

then estimate the tpy emissions for the low and high control scenarios, to cover the potential 

range of upgraded scrubber control efficiencies.

Table A.5-1 displays a summary of the EIA data. The baseline SO2 emissions were calculated 

by evaluating the 2008-2012 data, removing the minimum and maximum values, and then 

averaging the three remaining annual values to result in an annual average tpy uncontrolled 

baseline. We used a general estimate for what low controls and high controls might achieve. In 

the case when we had basically identical units at a facility we sometimes chose two different 

efficiencies to give more data points for creating a unit/facility specific mathmatical relationship. 

For example, Big Brown has two units that are nearly identical and do not have a large variation 

between their 3 year annual uncontrolled baseline average. In this case we chose to model Unit 1 

at 40% for DSI controls and Unit 2 at 60% for DSI controls, in effect giving us more data points 

to create the “x” emissions yields “y” deciview benefit relationship. We also considered the 

emission level modeled in the 2018 basecase projection described above and selected a low-level 

controlled emission level such that we would not duplicate a modeled emission level. For 

example, the Sandow 4 unit was modeled at an emission level that represents approximately 90% 
A-55

control in the 2018 basecase, therefore we selected a low-level control of 75% rather than the 

90% we selected for upgraded scrubber units at other facilities to generate another useful data 

point for creating a linear relationship. In the high control cases we also estimated the scrubbers 

as 95% control but also evaluated a floor emission rate of 0.06 lb of SO2/MMBtu and used the 

higher of the two rates for the modeling analysis (highlighted in red in the table below). 

Table A.5-2 summarizes the efficiencies used in both the high and low control scenarios for each 

unit. Table A.5-3 summarizes the actual emissions provided to ENVIRON for each of the 

control scenario runs.
A-56

Table A.5-1. EIA data and calculating potential emissions levels correlated to certain controls and efficiencies

2008-2012 

Avg.

3-yr avg annual 

emissions 

(eliminate max 

and min)

low 

modeled 

control 

efficiency

low 

controlled 

emissions

emission 

rate (5 yr 

avg heat 

input)

high 

modeled 

control 

efficiency

High 

control 

emissions

high 

control 

emission 

rate

1 Big Brown 30561 30591 33513 23328 40% 20108 0.90 95% 1676 0.075

2 Big Brown 30554 30674 33357 23831 60% 13343 0.59 95% 1668 0.074

1 Coleto Creek 17280 17084 19678 16225 50% 9839 0.40 92% 1492 0.060

1 Limestone 10500 10599 49484 Y Y 12817 85% 7423 0.23 95% 2474 0.075

2 Limestone 11349 11014 52299 Y Y 5023 90% 5230 0.16 95% 2615 0.080

1 Martin Lake 23517 24872 77111 Y Y 11442 75% 19278 0.60 95% 3856 0.119

2 Martin Lake 22105 23208 77680 Y Y 12080 85% 11652 0.35 95% 3884 0.117

3 Martin Lake 21801 21597 74442 Y Y 12495 90% 7444 0.25 95% 3722 0.126

1 Monticello 20388 20522 22810 19298 40% 13686 0.61 94% 1355 0.060

2 Monticello 19643 19746 23002 19853 60% 9201 0.41 94% 1346 0.060

3 Monticello 14073 15019 35308 Y Y 11978 90% 3531 0.11 95% 1851 0.060

1 Sandow 4 21969 22629 91912 Y Y 8477 75% 22978 0.96 95% 4596 0.191

1 Tolk 10702 10829 12417 11584 40% 7450 0.37 90% 1209 0.060

2 Tolk 10556 10600 11300 10549 60% 4520 0.25 90% 1103 0.060

5 WA Parish 15375 15123 18240 3763 40% 10944 0.47 92% 1397 0.060

6 WA Parish 15835 16071 18557 3840 60% 7423 0.31 92% 1419 0.060

7 WA Parish 12750 12947 16215 3324 50% 8108 0.39 92% 1244 0.060

8 WA Parish 2821 2822 17904 Y Y 4548 90% 1790 0.08 92% 1371 0.060

1 Welsh 8244 8222 9821 1236 40% 5893 0.32 89% 1110 0.060

2 Welsh 8653 8741 9934 1233 60% 3974 0.21 89% 1117 0.060

3 Welsh 9051 9076 10028 11815 50% 5014 0.27 89% 1124 0.060

Unit # Facility

3 yr avg. coal 

data 

uncontrolled 

annual 

emissions(eli

minate max 

and min)

scrubbed

? bypass

CEM CAMD Low High 

CENRAP 

2018
A-57

Table A.5-2. Efficiency summary for Low and High Control Runs for each Unit

Units Model Run

Low High

Big Brown 1 & 2 40% 60% 95%

Coleto Creek 50% 92%

Limestone 1 & 2 85% 90% 95%

Martin Lake 1,2,&3 75% 85% 90% 95%

Monticello 1 & 2 40% 60% 94%

Monticello 3 90% 95%

Sandow 4 75% 95%

Tolk 1 & 2 40% 60% 90%

WA Parish 5 & 6 40% 60% 92%

WA Parish 7 50% 92%

WA Parish 8 90% 92%

Welsh 1 & 2 40% 60% 89%

Welsh 3 50% 89%
A-58

Table A.5-3. Emissions provided to ENVIRON for Low control and High Control visibility 

modeling runs.

adjusted SO2 emissions (tpy)

Unit # Facility

EPA/ CENRAP 

2018 Phase 1 

modeled 

emissions

low controlled 

emissions

High control 

emissions

1 Big Brown 23328 20107.64 1675.64

2 Big Brown 23831 13342.79 1667.85

1 Coleto Creek 16225 9838.94 1492.34

lim 1 Limestone 12817 7422.61 2474.20

lim 2 Limestone 5023 5229.94 2614.97

1 Martin Lake 11442 19277.73 3855.55

2 Martin Lake 12080 11652.05 3884.02

3 Martin Lake 12495 7444.15 3722.08

1 Monticello 19298 13685.89 1355.30

2 Monticello 19853 9200.89 1345.64

3 Monticello 11978 3530.81 1851.27

4 Sandow 4 8477 22978.12 4595.62

171b Tolk 11584 7450.19 1209.35

172b Tolk 10549 4520.10 1103.07

5 WA Parish 3763 10943.82 1396.72

6 WA Parish 3840 7422.94 1419.24

7 WA Parish 3324 8107.70 1244.01

8 WA Parish 4548 1790.38 1371.38

1 Welsh 1236 5892.87 1110.16

2 Welsh 1233 3973.62 1117.20

3 Welsh 11815 5014.24 1123.85
A-59

A.6 Results for High/Low Control Runs and Final Control Analysis Benefits

ENVIRON provided an additional report to us to document the modeling set-up, emissions 

processed and modeling results. These are available in the Docket and the report will be referred 

to as ENVIRON 2018 Control Cases.48

After we received the modeling we updated the baseline uncontrolled emissions for each unit 

based on CEM data for 2009-2013. Again, we discarded the maximum and minimum emissions 

years and averaged the three remaining years. We utilized this baseline to estimate the amount 

of tons reduced and the amount of visibility improvement.

Using the results of the unit level High and Low modeled visibility impacts and the 2018 facility 

level modeling described above in Section A.2, we examined the relationship between the level 

of emissions from a modeled site and the modeled visibility impact at each Class I area. For 

each facility and Class I area, the modeled data was linear with high correlation. Therefore we 

used the linear fit to extrapolate the anticipated visibility impact/benefit from a given level of 

emission/control.49

The modeling results for the amount of reduction in extinction from the different control levels 

are included in Table A.6-1a, b, and c for WIMO, BIBE and GUMO respectively. We also 

included Table A.6-1d which gives the same information for cumulative benefit at all the other 

Class I areas that were evaluated in the modeling. These tables include the extinction change 

based on the recent actual emissions (2009-2013) in the mid-section of the table and on the right 

side of the table are the change in extinction due to differing controls based the original 

CENRAP 2018 projections for these units.

 

48 The Report is in the docket as “Memo_TXHAZE_2018low_highControls_CAMx_12Ausg14.docx” and included 

spreadsheet files with the modeling results “EPA_txbart3612k_Vis_2002_2018low_PSAT_Projectedv1.xlsx”, 

“EPA_txbart3612k_Vis_2002_2018low.GlidePath.v1.xlsx”, 

“EPA_txbart3612k_Vis_2002_2018high_PSAT_Projectedv2.xlsx”, and 

“EPA_txbart3612k_Vis_2002_2018high.GlidePath.v1.xlsx”

49 See Vis modeling summary.xlsx in the docket for this action for our calculations and estimates of visibility 

benefits from the examined levels of controls.
A-60

Table A.6-1a. Average Change in Extinction levels at WIMO on W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.378 0.680 0.718 0.741 #N/A 0.197 0.499 0.537 0.560 #N/A

2 Big Brown Big Brown 2 0.380 0.683 0.721 0.743 #N/A 0.208 0.511 0.549 0.571 #N/A

1 Coleto Creek Coleto Creek 1 0.176 0.317 0.329 0.336 #N/A 0.179 0.320 0.332 0.340 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.226 #N/A #N/A #N/A #N/A 0.277

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.249 #N/A #N/A #N/A #N/A 0.064

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.393 #N/A #N/A #N/A #N/A 0.146

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.339 #N/A #N/A #N/A #N/A 0.159

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.310 #N/A #N/A #N/A #N/A 0.169

1 Monticello Monticello 1 0.220 0.397 0.419 0.428 #N/A 0.256 0.432 0.454 0.463 #N/A

2 Monticello Monticello 2 0.203 0.365 0.385 0.393 #N/A 0.287 0.449 0.470 0.477 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.303 #N/A #N/A #N/A #N/A 0.257

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.526 #N/A #N/A #N/A #N/A 0.115

171b Tolk Tolk 171b 0.030 0.053 0.054 0.056 #N/A 0.039 0.063 0.064 0.065 #N/A

172b Tolk Tolk 172b 0.033 0.059 0.059 0.061 #N/A 0.030 0.056 0.056 0.058 #N/A

5 WA Parish WA Parish 5 0.103 0.185 0.190 0.195 #N/A -0.048 0.034 0.039 0.044 #N/A

6 WA Parish WA Parish 6 0.111 0.200 0.207 0.212 #N/A -0.055 0.034 0.040 0.046 #N/A

7 WA Parish WA Parish 7 0.090 0.161 0.166 0.170 #N/A -0.041 0.030 0.035 0.040 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.025 #N/A #N/A #N/A #N/A 0.054

1 Welsh Welsh 1 0.099 0.158 0.175 0.182 #N/A -0.068 -0.009 0.008 0.015 #N/A

2 Welsh Welsh 2 0.101 0.161 0.178 0.186 #N/A -0.071 -0.010 0.006 0.014 #N/A

3 Welsh Welsh 3 0.105 0.168 0.186 0.194 #N/A 0.183 0.246 0.264 0.272 #N/A

estimated change in extinction from actual emissions change in extinction from 2018 projection (environ ) with CAIR
A-61

Table A.6-1b. Average Change in Extinction levels at BIBE on W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.068 0.123 0.130 0.134 #N/A 0.036 0.090 0.097 0.101 #N/A

2 Big Brown Big Brown 2 0.069 0.123 0.130 0.134 #N/A 0.037 0.092 0.099 0.103 #N/A

1 Coleto Creek Coleto Creek 1 0.107 0.192 0.199 0.204 #N/A 0.109 0.194 0.202 0.206 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.049 #N/A #N/A #N/A #N/A 0.060

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.054 #N/A #N/A #N/A #N/A 0.014

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.044 #N/A #N/A #N/A #N/A 0.017

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.038 #N/A #N/A #N/A #N/A 0.018

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.035 #N/A #N/A #N/A #N/A 0.019

1 Monticello Monticello 1 0.017 0.030 0.032 0.032 #N/A 0.019 0.033 0.034 0.035 #N/A

2 Monticello Monticello 2 0.015 0.028 0.029 0.030 #N/A 0.022 0.034 0.036 0.036 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.023 #N/A #N/A #N/A #N/A 0.019

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.153 #N/A #N/A #N/A #N/A 0.033

171b Tolk Tolk 171b 0.010 0.018 0.019 0.019 #N/A 0.013 0.022 0.022 0.023 #N/A

172b Tolk Tolk 172b 0.011 0.020 0.020 0.021 #N/A 0.010 0.019 0.019 0.020 #N/A

5 WA Parish WA Parish 5 0.042 0.076 0.078 0.081 #N/A -0.020 0.014 0.016 0.018 #N/A

6 WA Parish WA Parish 6 0.046 0.083 0.085 0.087 #N/A -0.023 0.014 0.017 0.019 #N/A

7 WA Parish WA Parish 7 0.037 0.067 0.068 0.070 #N/A -0.017 0.013 0.015 0.016 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.010 #N/A #N/A #N/A #N/A 0.022

1 Welsh Welsh 1 0.007 0.011 0.013 0.013 #N/A -0.005 -0.001 0.001 0.001 #N/A

2 Welsh Welsh 2 0.007 0.012 0.013 0.013 #N/A -0.005 -0.001 0.000 0.001 #N/A

3 Welsh Welsh 3 0.008 0.012 0.013 0.014 #N/A 0.013 0.018 0.019 0.019 #N/A

estimated change in extinction from actual emissions change in extinction from 2018 projection (environ ) with CAIR
A-62

Table A.6-1c. Average Change in Extinction levels at GUMO on W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.076 0.136 0.143 0.148 #N/A 0.039 0.100 0.107 0.112 #N/A

2 Big Brown Big Brown 2 0.076 0.137 0.144 0.149 #N/A 0.041 0.102 0.110 0.114 #N/A

1 Coleto Creek Coleto Creek 1 0.032 0.058 0.060 0.062 #N/A 0.033 0.059 0.061 0.062 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.052 #N/A #N/A #N/A #N/A 0.064

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.057 #N/A #N/A #N/A #N/A 0.015

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.058 #N/A #N/A #N/A #N/A 0.022

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.050 #N/A #N/A #N/A #N/A 0.024

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.046 #N/A #N/A #N/A #N/A 0.025

1 Monticello Monticello 1 0.020 0.035 0.037 0.038 #N/A 0.023 0.039 0.041 0.041 #N/A

2 Monticello Monticello 2 0.018 0.033 0.034 0.035 #N/A 0.026 0.040 0.042 0.043 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.027 #N/A #N/A #N/A #N/A 0.023

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.097 #N/A #N/A #N/A #N/A 0.021

171b Tolk Tolk 171b 0.067 0.121 0.123 0.127 #N/A 0.088 0.141 0.144 0.147 #N/A

172b Tolk Tolk 172b 0.074 0.133 0.134 0.138 #N/A 0.067 0.126 0.127 0.132 #N/A

5 WA Parish WA Parish 5 0.018 0.033 0.034 0.034 #N/A -0.008 0.006 0.007 0.008 #N/A

6 WA Parish WA Parish 6 0.020 0.035 0.037 0.037 #N/A -0.010 0.006 0.007 0.008 #N/A

7 WA Parish WA Parish 7 0.016 0.028 0.029 0.030 #N/A -0.007 0.005 0.006 0.007 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.004 #N/A #N/A #N/A #N/A 0.010

1 Welsh Welsh 1 0.009 0.015 0.016 0.017 #N/A -0.006 -0.001 0.001 0.001 #N/A

2 Welsh Welsh 2 0.009 0.015 0.017 0.017 #N/A -0.007 -0.001 0.001 0.001 #N/A

3 Welsh Welsh 3 0.010 0.016 0.017 0.018 #N/A 0.017 0.023 0.025 0.026 #N/A

estimated change in extinction from actual emissions change in extinction from 2018 projection (environ ) with CAIR
A-63

Table A.6-1d. The Cumulative Average Change in Extinction levels at all other Class I areas (not WIMO, BIBE or GUMO) on 

W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.581 1.046 1.104 1.139 #N/A 0.303 0.768 0.826 0.861 #N/A

2 Big Brown Big Brown 2 0.584 1.051 1.109 1.143 #N/A 0.319 0.786 0.845 0.879 #N/A

1 Coleto Creek Coleto Creek 1 0.143 0.258 0.268 0.274 #N/A 0.146 0.261 0.271 0.277 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.370 #N/A #N/A #N/A #N/A 0.453

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.408 #N/A #N/A #N/A #N/A 0.105

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 1.676 #N/A #N/A #N/A #N/A 0.624

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 1.444 #N/A #N/A #N/A #N/A 0.678

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 1.321 #N/A #N/A #N/A #N/A 0.721

1 Monticello Monticello 1 0.596 1.072 1.132 1.156 #N/A 0.691 1.168 1.228 1.251 #N/A

2 Monticello Monticello 2 0.548 0.986 1.041 1.061 #N/A 0.776 1.215 1.269 1.289 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.819 #N/A #N/A #N/A #N/A 0.694

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.465 #N/A #N/A #N/A #N/A 0.101

171b Tolk Tolk 171b 0.313 0.563 0.573 0.591 #N/A 0.410 0.660 0.670 0.688 #N/A

172b Tolk Tolk 172b 0.344 0.619 0.625 0.646 #N/A 0.314 0.589 0.594 0.616 #N/A

5 WA Parish WA Parish 5 0.181 0.326 0.335 0.344 #N/A -0.085 0.060 0.069 0.078 #N/A

6 WA Parish WA Parish 6 0.196 0.353 0.365 0.374 #N/A -0.098 0.059 0.071 0.080 #N/A

7 WA Parish WA Parish 7 0.158 0.284 0.293 0.300 #N/A -0.073 0.054 0.062 0.070 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.045 #N/A #N/A #N/A #N/A 0.095

1 Welsh Welsh 1 0.300 0.480 0.532 0.555 #N/A -0.208 -0.028 0.024 0.047 #N/A

2 Welsh Welsh 2 0.307 0.491 0.541 0.565 #N/A -0.215 -0.031 0.019 0.044 #N/A

3 Welsh Welsh 3 0.320 0.512 0.567 0.591 #N/A 0.558 0.750 0.805 0.829 #N/A

estimated change in extinction from actual emissions change in extinction from 2018 projection (environ ) with CAIR
A-64

Figure A.6-1e. Extinction level and percent of total extinction at WIMO for W20% days for the 

9 facilities assessed in second modeling effort

Shown in (Figure A.6-1e) are the percentage contribution to total visibility impairment due to all 

sources (type and category). The 2018 CENRAP projection for total extinction at WIMO is 

75.56 Mm-1. Texas point sources contribute 10.58 Mm-1 (14%) to the total extinction. The 

blue bars on the left (ten bars on the left) are results from our initial source apportionment 

modeling showing the extinction due to selected facilities (9 sources and the other 29 sources). 

The ‘remaining 1600’ is actually for the 1600+ facilities originally included in our Q/D analysis 

minus the 38 sources we explicitly modeled. This was calculated using CENRAP 2018 data for 

all sources and subtracting out the 38 explicitly modeled sources. Almost all of these Texas 

point sources were also present in the Cenrap 2018 projection. The five bars on the far right 

show the impact from all point sources from the five largest contributing regions outside of 

Texas from the 2018 CENRAP source apportionment modeling. The orange bars show 

estimated extinction due to these facilities based on recent actual emissions. The recommended 

controls address 5.8% of total visibility impairment and 41.4% of the impact from all Texas 

point sources (based on 2018 CENRAP projected emission levels and our modeling of 38 Texas 

point source facilities).
A-65

Figure A.6-1f. Extinction level and percent of total extinction at BIBE for W20% days for the 9 

facilities assessed in second modeling effort

Shown above (Figure A.6-1f) are the percent contribution to total visibility impairment due to all 

sources (type and category). The 2018 CENRAP projection for total extinction at BIBE is 44.23

Mm-1. Texas point sources contribute 3.56 Mm-1 (8%) to the total extinction. The blue bars on 

the left are results from our initial source apportionment modeling showing the extinction due to 

selected facilities (9 sources and the other 29 sources). The ‘remaining 1600’ is actually for the 

1600+ facilities originally included in our Q/D analysis minus the 38 sources we explicitly 

modeled. Almost all of these Texas point sources were also present in the Cenrap 2018 

projection. The five bars on the far right show the impact from all point sources from the five 

largest contributing regions outside of Texas from the 2018 CENRAP source apportionment 

modeling. The orange bars show estimated extinction due to these facilities based on recent 

actual emissions. Mexico’s impacts are beyond the range of the chart. Note that the one unit at 

Coleto Creek has similar impacts as the two units at Big Brown. The recommended controls 

address 1.88% of total visibility impairment and 23.4% of impact from all Texas point sources 

(based on 2018 CENRAP projected emission levels). Coleto Creek accounts for over 6% of the 

total Texas point source impact.
A-66

Figure A.6-1g. Extinction level and percent of total extinction at GUMO for W20% days for the 

9 facilities assess in second modeling effort

Shown above (Figure A.6-1g) are the percent contribution to total visibility impairment due to all 

sources (type and category). The 2018 CENRAP projection for total extinction at GUMO is 

44.43 Mm-1. Texas point sources contribute 3.84 Mm-1 (9%) to the total extinction.The blue 

bars on the left are results from our initial source apportionment modeling showing the extinction 

due to selected facilities (9 sources and the other 29 sources). The ‘remaining 1600’ is actually 

for the 1600+ facilities originally included in our Q/D analysis minus the 38 sources we 

explicitly modeled. Almost all of these Texas point sources were also present in the Cenrap 

2018 projection. The five bars on the far right show the impact from all point sources from the 

five largest contributing regions outside of Texas from the 2018 CENRAP source apportionment 

modeling. The orange bars show estimated extinction due to these facilities based on recent 

actual emissions. Mexico’s impacts are beyond the range of the chart. The recommended

controls address 2.22% of total visibility impairment and 25.74% of impact from all Texas point 

sources (based on 2018 CENRAP projected emission levels). Tolk accounts for nearly 8% of the 

total Texas point source impact.

The modeling results for the amount of deciview improvement from the different control levels 

are included in Table A.6-2a, b, and c for WIMO, BIBE and GUMO respectively. We also 
A-67

included Table A.6-2d which gives the same information for cumulative benefit at all the other 

Class I areas that were evaluated in the modeling. These tables include the deciview change 

based on the recent actual emissions (2009-2013) in the mid-section of the table and on the right 

side of the table are the change in deciviews due to differing controls based the original 

CENRAP 2018 projections for these units.
A-68

Table A.6-2a. Average Change in Deciview levels at WIMO on W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.045 0.081 0.085 0.088 #N/A 0.225 0.401 0.423 0.436 #N/A

2 Big Brown Big Brown 2 0.045 0.081 0.086 0.088 #N/A 0.226 0.403 0.425 0.438 #N/A

1 Coleto Creek Coleto Creek 1 0.021 0.038 0.039 0.040 #N/A 0.105 0.189 0.196 0.200 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.027 #N/A #N/A #N/A #N/A 0.135

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.030 #N/A #N/A #N/A #N/A 0.149

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.047 #N/A #N/A #N/A #N/A 0.234

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.040 #N/A #N/A #N/A #N/A 0.202

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.037 #N/A #N/A #N/A #N/A 0.185

1 Monticello Monticello 1 0.026 0.047 0.050 0.051 #N/A 0.132 0.236 0.249 0.254 #N/A

2 Monticello Monticello 2 0.024 0.043 0.046 0.047 #N/A 0.121 0.217 0.229 0.233 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.036 #N/A #N/A #N/A #N/A 0.181

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.062 #N/A #N/A #N/A #N/A 0.312

171b Tolk Tolk 171b 0.004 0.006 0.006 0.007 #N/A 0.018 0.032 0.033 0.034 #N/A

172b Tolk Tolk 172b 0.004 0.007 0.007 0.007 #N/A 0.020 0.035 0.036 0.037 #N/A

5 WA Parish WA Parish 5 0.012 0.022 0.023 0.023 #N/A 0.062 0.111 0.114 0.117 #N/A

6 WA Parish WA Parish 6 0.013 0.024 0.025 0.025 #N/A 0.067 0.120 0.124 0.127 #N/A

7 WA Parish WA Parish 7 0.011 0.019 0.020 0.020 #N/A 0.054 0.097 0.099 0.102 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.003 #N/A #N/A #N/A #N/A 0.015

1 Welsh Welsh 1 0.012 0.019 0.021 0.022 #N/A 0.059 0.094 0.105 0.109 #N/A

2 Welsh Welsh 2 0.012 0.019 0.021 0.022 #N/A 0.060 0.096 0.106 0.111 #N/A

3 Welsh Welsh 3 0.012 0.020 0.022 0.023 #N/A 0.063 0.101 0.111 0.116 #N/A

dv improvement 2018 background (environ) dv improvement (avg. natural conditions)
A-69

Table A.6-2b. Average Change in Deciview levels at BIBE on W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.012 0.021 0.022 0.023 #N/A 0.046 0.082 0.086 0.089 #N/A

2 Big Brown Big Brown 2 0.012 0.021 0.022 0.023 #N/A 0.046 0.082 0.087 0.089 #N/A

1 Coleto Creek Coleto Creek 1 0.018 0.033 0.034 0.035 #N/A 0.071 0.128 0.133 0.136 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.008 #N/A #N/A #N/A #N/A 0.033

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.009 #N/A #N/A #N/A #N/A 0.036

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.008 #N/A #N/A #N/A #N/A 0.030

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.007 #N/A #N/A #N/A #N/A 0.026

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.006 #N/A #N/A #N/A #N/A 0.023

1 Monticello Monticello 1 0.003 0.005 0.005 0.006 #N/A 0.011 0.020 0.021 0.022 #N/A

2 Monticello Monticello 2 0.003 0.005 0.005 0.005 #N/A 0.010 0.018 0.019 0.020 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.004 #N/A #N/A #N/A #N/A 0.015

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.026 #N/A #N/A #N/A #N/A 0.102

171b Tolk Tolk 171b 0.002 0.003 0.003 0.003 #N/A 0.007 0.012 0.013 0.013 #N/A

172b Tolk Tolk 172b 0.002 0.003 0.003 0.004 #N/A 0.008 0.014 0.014 0.014 #N/A

5 WA Parish WA Parish 5 0.007 0.013 0.013 0.014 #N/A 0.028 0.051 0.052 0.054 #N/A

6 WA Parish WA Parish 6 0.008 0.014 0.015 0.015 #N/A 0.031 0.055 0.057 0.058 #N/A

7 WA Parish WA Parish 7 0.006 0.011 0.012 0.012 #N/A 0.025 0.044 0.046 0.047 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.002 #N/A #N/A #N/A #N/A 0.007

1 Welsh Welsh 1 0.001 0.002 0.002 0.002 #N/A 0.005 0.008 0.008 0.009 #N/A

2 Welsh Welsh 2 0.001 0.002 0.002 0.002 #N/A 0.005 0.008 0.009 0.009 #N/A

3 Welsh Welsh 3 0.001 0.002 0.002 0.002 #N/A 0.005 0.008 0.009 0.009 #N/A

dv improvement 2018 background (environ) dv improvement (avg. natural conditions)
A-70

Table A.6-2c. Average Change in Deciview levels at GUMO on W20% days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.014 0.024 0.026 0.027 #N/A 0.054 0.096 0.101 0.105 #N/A

2 Big Brown Big Brown 2 0.014 0.025 0.026 0.027 #N/A 0.054 0.097 0.102 0.105 #N/A

1 Coleto Creek Coleto Creek 1 0.006 0.010 0.011 0.011 #N/A 0.023 0.041 0.043 0.044 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.009 #N/A #N/A #N/A #N/A 0.037

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.010 #N/A #N/A #N/A #N/A 0.041

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.010 #N/A #N/A #N/A #N/A 0.041

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.009 #N/A #N/A #N/A #N/A 0.036

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.008 #N/A #N/A #N/A #N/A 0.033

1 Monticello Monticello 1 0.004 0.006 0.007 0.007 #N/A 0.014 0.025 0.027 0.027 #N/A

2 Monticello Monticello 2 0.003 0.006 0.006 0.006 #N/A 0.013 0.023 0.024 0.025 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.005 #N/A #N/A #N/A #N/A 0.019

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.017 #N/A #N/A #N/A #N/A 0.069

171b Tolk Tolk 171b 0.012 0.022 0.022 0.023 #N/A 0.048 0.085 0.087 0.090 #N/A

172b Tolk Tolk 172b 0.013 0.024 0.024 0.025 #N/A 0.052 0.094 0.095 0.098 #N/A

5 WA Parish WA Parish 5 0.003 0.006 0.006 0.006 #N/A 0.013 0.023 0.024 0.024 #N/A

6 WA Parish WA Parish 6 0.004 0.006 0.007 0.007 #N/A 0.014 0.025 0.026 0.027 #N/A

7 WA Parish WA Parish 7 0.003 0.005 0.005 0.005 #N/A 0.011 0.020 0.021 0.021 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.001 #N/A #N/A #N/A #N/A 0.003

1 Welsh Welsh 1 0.002 0.003 0.003 0.003 #N/A 0.007 0.011 0.012 0.012 #N/A

2 Welsh Welsh 2 0.002 0.003 0.003 0.003 #N/A 0.007 0.011 0.012 0.012 #N/A

3 Welsh Welsh 3 0.002 0.003 0.003 0.003 #N/A 0.007 0.011 0.012 0.013 #N/A

dv improvement 2018 background (environ) dv improvement (avg. natural conditions)
A-71

Table A.6-2d. The Cumulative Average Change in Deciview levels at all other Class I areas (not WIMO, BIBE or GUMO) on W20% 

days for different controls

Visibility modeling results: 

Unit # Facility DSI_low DSI_high SDA WFGD WFGD_upgrade DSI_low DSI_high SDA WFGD

WFGD_u

pgrade

1 Big Brown Big Brown 1 0.073 0.131 0.138 0.143 #N/A 0.308 0.553 0.584 0.602 #N/A

2 Big Brown Big Brown 2 0.073 0.132 0.139 0.143 #N/A 0.309 0.556 0.586 0.604 #N/A

1 Coleto Creek Coleto Creek 1 0.026 0.047 0.049 0.050 #N/A 0.092 0.165 0.171 0.175 #N/A

lim 1 Limestone Limestone lim 1 #N/A #N/A #N/A #N/A 0.046 #N/A #N/A #N/A #N/A 0.197

lim 2 Limestone Limestone lim 2 #N/A #N/A #N/A #N/A 0.051 #N/A #N/A #N/A #N/A 0.218

1 Martin Lake Martin Lake 1 #N/A #N/A #N/A #N/A 0.173 #N/A #N/A #N/A #N/A 0.800

2 Martin Lake Martin Lake 2 #N/A #N/A #N/A #N/A 0.149 #N/A #N/A #N/A #N/A 0.691

3 Martin Lake Martin Lake 3 #N/A #N/A #N/A #N/A 0.137 #N/A #N/A #N/A #N/A 0.632

1 Monticello Monticello 1 0.063 0.114 0.120 0.123 #N/A 0.290 0.520 0.549 0.561 #N/A

2 Monticello Monticello 2 0.058 0.105 0.111 0.113 #N/A 0.267 0.479 0.505 0.515 #N/A

3 Monticello Monticello 3 #N/A #N/A #N/A #N/A 0.087 #N/A #N/A #N/A #N/A 0.398

4 Sandow Sandow 4 #N/A #N/A #N/A #N/A 0.074 #N/A #N/A #N/A #N/A 0.277

171b Tolk Tolk 171b 0.066 0.119 0.121 0.125 #N/A 0.214 0.385 0.392 0.404 #N/A

172b Tolk Tolk 172b 0.073 0.131 0.132 0.137 #N/A 0.236 0.424 0.427 0.442 #N/A

5 WA Parish WA Parish 5 0.024 0.044 0.045 0.046 #N/A 0.098 0.176 0.181 0.186 #N/A

6 WA Parish WA Parish 6 0.026 0.047 0.049 0.050 #N/A 0.106 0.191 0.197 0.202 #N/A

7 WA Parish WA Parish 7 0.021 0.038 0.039 0.040 #N/A 0.085 0.154 0.158 0.162 #N/A

8 WA Parish WA Parish 8 #N/A #N/A #N/A #N/A 0.006 #N/A #N/A #N/A #N/A 0.024

1 Welsh Welsh 1 0.032 0.051 0.056 0.058 #N/A 0.146 0.233 0.258 0.269 #N/A

2 Welsh Welsh 2 0.032 0.052 0.057 0.059 #N/A 0.149 0.238 0.263 0.274 #N/A

3 Welsh Welsh 3 0.034 0.054 0.060 0.062 #N/A 0.155 0.248 0.275 0.287 #N/A

dv improvement 2018 background (environ) dv improvement (avg. natural conditions)
A-72

We also evaluated the deciview change for both a “dirty background” (2018 analysis) and a 

“clean background,” the latter also referred to as Natural Conditions (NC) based on the estimated 

average annual natural conditions.

50

 Building on our previous “clean vs. dirty” discussion above, 

one way to view these two situations is that the deciview change for the 2018 model projections 

(dirty background) is the minimum level of deciview improvement that will be achieved from the 

controls and the NC (clean background) is the upper end of the deciview benefit from controls. 

These two values provide the range in visibility benefit that will result from any reduction in 

emissions due to the range of controls we considered.

Of the 21 units evaluated, eight had existing scrubbers, so the main control evaluated for these 

eight facilities was upgrading the scrubber control to 95% level controls (see above in this TSD 

and also our Cost TSD for further information). Table A.6-3 includes the amount of SO2

emission reductions and the amount of deciview improvement due to scrubber upgrades on these 

eight units. The deciview improvements are reported for both the 2018 (dirty background) and 

the average NCs. This includes information for the thee Class I areas and also the cumulative of 

the 19 Class I areas (WIMO, GUMO, BIBE and 16 other Class I areas).

Table A.6-4 shows the deciview improvements from the different control scenarios evaluated for 

each unit not already equipped with SO2 controls. This includes benefits for installing Dry 

Sorbent Injection (DSI) with a high and low control level, SDA and wet FGD. Overall DSI 

(with 40-60% control) achieves substantially less visibility and extinction improvement then 

either SDA or wet FGD scrubbers. We weigh this significant difference in visibility benefit 

when we consider if and what controls are reasonable in our cost/benefit analysis.

 

50 NC II, or new IMPROVE natural visibility conditions are available at: 

http://vista.cira.colostate.edu/Docs/IMPROVE/Aerosol/NaturalConditions/NaturalConditionsII_Format2_v2.xls
A-73

Table A.6-3. Deciview improvement at Class I areas for scrubber upgrades

Emission 

Unit

Control 

(%)

SO2 

Reductio

n (tpy)

2018 avg. NC 2018 avg. NC 2018 avg. NC 2018 avg. NC 2018 avg. NC Class I area

Limestone 

lim 1 95 8,446 0.027 0.135 0.008 0.033 0.009 0.037 0.091 0.401 0.014 0.070 CACR

Limestone 

lim 2 95 9,331 0.030 0.149 0.009 0.036 0.010 0.041 0.100 0.443 0.015 0.077 CACR

Martin Lake 

1

95 20,789 0.047 0.234 0.008 0.030 0.010 0.041 0.238 1.105 0.089 0.441 CACR

Martin Lake 

2

95 17,917 0.040 0.202 0.007 0.026 0.009 0.036 0.205 0.954 0.077 0.381 CACR

Martin Lake 

3

95 16,389 0.037 0.185 0.006 0.023 0.008 0.033 0.188 0.873 0.070 0.349 CACR

Monticello 3 95 12,286 0.036 0.181 0.004 0.015 0.005 0.019 0.132 0.614 0.037 0.188 CACR

Sandow 4 95 17,664 0.062 0.312 0.026 0.102 0.017 0.069 0.180 0.759 0.017 0.069 CAVE

WA Parish 8 95 1,750 0.003 0.015 0.002 0.007 0.001 0.003 0.012 0.050 0.001 0.007 CACR

highest remaining

Estimated deciview improvement from actual emissions (3-yr average annual tpy2009-2013 eliminating min and max yr)

WIMO BIBE GUMO Cumulative (19 areas)
A-74

Table A.6-4. Deciview Improvent due to differing levels of control on existing uncontrolled 

units

Emission 

Unit

Control 

(%)

Control

SO2 

Reduction 

(tpy)

dv 

improve

ment 

2018 

backgrou

nd

dv 

improvement 

(avg. natural 

conditions 

background)

dv 

improvement 

2018 

background

dv 

improvement 

(avg. natural 

conditions 

background)

dv 

improvement 

2018 

background

dv 

improvement 

(avg. natural 

conditions 

background)

dv 

improvement 

2018 

background

dv 

improvement 

(avg. natural 

conditions 

background)

dv 

improvement 

2018 

background

dv 

improvemen

t (avg. 

natural 

conditions 

background

)

Class I 

area

50 DSI 15,334 0.045 0.225 0.012 0.046 0.014 0.054 0.143 0.632 0.018 0.092 CACR

90 DSI 27,600 0.081 0.401 0.021 0.082 0.024 0.096 0.257 1.132 0.033 0.164 CACR

95 SDA 29,134 0.085 0.423 0.022 0.086 0.026 0.101 0.272 1.194 0.035 0.173 CACR

98 WFGD 30,054 0.088 0.436 0.023 0.089 0.027 0.105 0.280 1.232 0.036 0.179 CACR

50 DSI 15,407 0.045 0.226 0.012 0.046 0.014 0.054 0.143 0.635 0.018 0.092 CACR

90 DSI 27,733 0.081 0.403 0.021 0.082 0.025 0.097 0.258 1.137 0.033 0.165 CACR

95 SDA 29,273 0.086 0.425 0.022 0.087 0.026 0.102 0.273 1.200 0.035 0.174 CACR

97.9 WFGD 30,169 0.088 0.438 0.023 0.089 0.027 0.105 0.281 1.236 0.036 0.179 CACR

50 DSI 8,030 0.021 0.105 0.018 0.071 0.006 0.023 0.071 0.291 0.006 0.023 CAVE

90 DSI 14,453 0.038 0.189 0.033 0.128 0.010 0.041 0.128 0.523 0.010 0.041 CAVE

93.5 SDA 15,012 0.039 0.196 0.034 0.133 0.011 0.043 0.133 0.543 0.011 0.043 CAVE

95.7 WFGD 15,361 0.040 0.200 0.035 0.136 0.011 0.044 0.136 0.555 0.011 0.044 CAVE

50 DSI 8,933 0.026 0.132 0.003 0.011 0.004 0.014 0.096 0.447 0.027 0.137 CACR

90 DSI 16,079 0.047 0.236 0.005 0.020 0.006 0.025 0.173 0.802 0.049 0.245 CACR

95 SDA 16,972 0.050 0.249 0.005 0.021 0.007 0.027 0.182 0.846 0.052 0.258 CACR

97 WFGD 17,328 0.051 0.254 0.006 0.022 0.007 0.027 0.186 0.863 0.053 0.264 CACR

50 DSI 8,215 0.024 0.121 0.003 0.010 0.003 0.013 0.088 0.411 0.025 0.126 CACR

90 DSI 14,786 0.043 0.217 0.005 0.018 0.006 0.023 0.159 0.738 0.045 0.226 CACR

95 SDA 15,608 0.046 0.229 0.005 0.019 0.006 0.024 0.168 0.778 0.048 0.238 CACR

96.8 WFGD 15,907 0.047 0.233 0.005 0.020 0.006 0.025 0.171 0.793 0.048 0.242 CACR

50 DSI 5,016 0.004 0.018 0.002 0.007 0.012 0.048 0.083 0.286 0.017 0.060 SACR

90 DSI 9,028 0.006 0.032 0.003 0.012 0.022 0.085 0.150 0.515 0.030 0.108 SACR

91.7 SDA 9195 0.006 0.033 0.003 0.013 0.022 0.087 0.153 0.524 0.031 0.110 SACR

94.4 WFGD 9474 0.007 0.034 0.003 0.013 0.023 0.090 0.158 0.540 0.032 0.113 SACR

50 DSI 5,517 0.004 0.020 0.002 0.008 0.013 0.052 0.092 0.315 0.018 0.066 SACR

90 DSI 9,931 0.007 0.035 0.003 0.014 0.024 0.094 0.165 0.566 0.033 0.119 SACR

90.8 SDA 10015 0.007 0.036 0.003 0.014 0.024 0.095 0.167 0.571 0.034 0.120 SACR

93.8 WFGD 10355 0.007 0.037 0.004 0.014 0.025 0.098 0.172 0.590 0.035 0.124 SACR

50 DSI 7,079 0.012 0.062 0.007 0.028 0.003 0.013 0.047 0.201 0.006 0.030 CACR

90 DSI 12,741 0.022 0.111 0.013 0.051 0.006 0.023 0.084 0.361 0.011 0.054 CACR

92.5 SDA 13095 0.023 0.114 0.013 0.052 0.006 0.024 0.087 0.371 0.011 0.055 CACR

95 WFGD 13449 0.023 0.117 0.014 0.054 0.006 0.024 0.089 0.381 0.011 0.057 CACR

50 DSI 7,654 0.013 0.067 0.008 0.031 0.004 0.014 0.051 0.217 0.006 0.032 CACR

90 DSI 13,776 0.024 0.120 0.014 0.055 0.006 0.025 0.091 0.390 0.011 0.058 CACR

93.1 SDA 14251 0.025 0.124 0.015 0.057 0.007 0.026 0.095 0.404 0.012 0.060 CACR

95.4 WFGD 14603 0.025 0.127 0.015 0.058 0.007 0.027 0.097 0.414 0.012 0.061 CACR

50 DSI 6,168 0.011 0.054 0.006 0.025 0.003 0.011 0.041 0.175 0.005 0.026 CACR

90 DSI 11,102 0.019 0.097 0.011 0.044 0.005 0.020 0.074 0.315 0.009 0.047 CACR

92.7 SDA 11432 0.020 0.099 0.012 0.046 0.005 0.021 0.076 0.324 0.010 0.048 CACR

95.1 WFGD 11733 0.020 0.102 0.012 0.047 0.005 0.021 0.078 0.333 0.010 0.049 CACR

50 DSI 4,042 0.012 0.059 0.001 0.005 0.002 0.007 0.046 0.216 0.015 0.074 CACR

80 DSI 6,467 0.019 0.094 0.002 0.008 0.003 0.011 0.074 0.346 0.024 0.119 CACR

88.7 SDA 7169 0.021 0.105 0.002 0.008 0.003 0.012 0.082 0.383 0.026 0.132 CACR

92.5 WFGD 7474 0.022 0.109 0.002 0.009 0.003 0.012 0.085 0.399 0.027 0.137 CACR

50 DSI 4,128 0.012 0.060 0.001 0.005 0.002 0.007 0.047 0.221 0.015 0.076 CACR

80 DSI 6,605 0.019 0.096 0.002 0.008 0.003 0.011 0.075 0.353 0.024 0.121 CACR

88.2 SDA 7285 0.021 0.106 0.002 0.009 0.003 0.012 0.083 0.389 0.027 0.134 CACR

92.2 WFGD 7608 0.022 0.111 0.002 0.009 0.003 0.012 0.087 0.406 0.028 0.140 CACR

50 DSI 4,305 0.012 0.063 0.001 0.005 0.002 0.007 0.049 0.230 0.016 0.079 CACR

80 DSI 6,887 0.020 0.101 0.002 0.008 0.003 0.011 0.079 0.368 0.025 0.126 CACR

88.7 SDA 7634 0.022 0.111 0.002 0.009 0.003 0.012 0.087 0.408 0.028 0.140 CACR

92.5 WFGD 7959 0.023 0.116 0.002 0.009 0.003 0.013 0.091 0.425 0.029 0.146 CACR

WA Parish 6

WA Parish 7

Welsh 1

Welsh 2

WIMO BIBE GUMO Cumulative (19 areas)

Estimated deciview improvement from actual emissions (3-yr average annual tpy2009-2013 eliminating min and max yr)

Big Brown 1

highest remaining

Welsh 3

Big Brown 2

Coleto Creek 1

Monticello 1

Monticello 2

Tolk 171B

Tolk 172B

WA Parish 5
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As we have discussed above in our “RP vs. BART” section, we acknowledge that the deldeciview values (improvement in visibility) may seem small compared to BART CALPUFF 

based analysis impacts/metric. Also, as discussed above, there is no way to directly compare RP 

results with CAMx with BART analyses with CALPUFF. We have evaluated the deciview 

improvement for the natural conditions (“clean background”) conditions which we believe helps

factor in the approach that CALPUFF uses in the sense that all CALPUFF analyses are typically 

performed based on a ‘clean background’ situation.

We have also evaluated these impacts considering the amount of extinction improvement that 

could be achieved due to controls and compared these values to extinction levels supported by 

consultation metrics, etc., and concluded that these impacts are in the ballpark of potential 

benefits that may have been evaluated in a consultation process between states that had the 

potential to result in additional controls. For example, Oklahoma invited those states projected 

to contribute greater than 1 inverse megameter of light extinction (from all sources in the state 

combined) at the Wichita Mountains in 2018 to consultations. Similarly, Texas invited states

with greater than 0.5 inverse megameter impact on one of Texas’ Class I areas to consult.51 

We also evaluated recent FIPs that have included controls for RP. In our FIP for Wyoming, we 

controlled some sources that had a benefit of 0.3 dv using CALPUFF52. Given the modeled 

emissions differences between RP with CAMx and BART with CALPUFF and difference in 

metrics it can be argued that a 0.3 dv benefit with CALPUFF would be on the order of 0.1-0.15 

deciview benefit with CAMx modeling (this is an estimate just based on emissions and metrics 

differences and ignoring the other differences discussed above.)

We also recently finalized a FIP in Arizona that included controls that resulted in a 0.18 del-dv 

and 0.24 del-dv benefit based on CALPUFF modeling53. Again, given the modeled emissions 

differences between RP analysis with CAMx and BART analysis with CALPUFF and difference 

in metrics it can be argued that a 0.18-0.24 del-dv benefit with CALPUFF would be on the order 

of 0.1 del-dv or less benefit with CAMx modeling (this is an estimate just based on emissions 

and metrics differences and ignoring the other differences discussed above).

Weighing this information with the deciview benefit and extinction benefit on a (1/Mm) and 

percentage basis with the other information analyzed, we conclude that all of the scrubber 

upgrades in Table A.6.3 would yield visibility benefits, with the exception of WA Parish Unit 8 

which has a very small benefit.

Weighing this information with the deciview benefit and extinction benefit on a (1/Mm) and 

percentage basis with the other information analyzed, we conclude that many of the scrubber 

retrofits in Table A.6.4 would yield visibility benefits. We note a decrease in visibility 

improvement benefits at the three Class I areas for the W. A. Parish and Welsh units compared to 

the benefits at other facilities that mainly impact WIMO. Tolk has a smaller deciview benefit at 

WIMO and controlling Tolk mainly benefits GUMO, but in assessing Tolk, we must also weigh 

the percent of extinction that would be achieved as well, since GUMO has a much lower overall 

 

51 See Appendix 4-1 of the TX RH SIP.

52 Wyoming Final FIP FR Vol. 78, No. 111; pages 34785-,34789.

53 Arizona Final FIP FR Vol. 79, No. 170; pages 52464-52477
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extinction compared to WIMO. Coleto Creek and some of the other higher impacting sources 

have benefits at more than one of the three Class I areas (WIMO, BIBE, and GUMO), therefore 

we also consider the benefits when they occurred at two or three Class I areas in determining 

whether the visibility improvements are potentially worth achieving if we determined that cost 

effective controls are available.

Table A.6-5 Includes the net benefit on the proposed control level proposed in Section 6 on the 

projected visibility conditions based on reducing actual emission levels.

Table A.6-5. Net benefit of proposed controls on 2018 Visibility projections

Estimated deciview improvement (avg 20% Worst days) from actual emissions (3-yr average 

annual tpy 2009-2013 eliminating min and max yr)

Wichita Mtns. Big Bend Guadalupe Mtn. 

1

2018 'dirty' 

background 

Avg. 

natural 

conditions 

'clean' 

background

2018 'dirty' 

background 

Avg. 

natural 

conditions

'clean' 

background

2018 'dirty' 

background 

Avg. 

natural 

conditions 

'clean' 

background

Total Visibility 

Recommended 

Scrubber 

Retrofits 0.331 1.629 0.098

0.382

(Coleto 

Creek 1-

0.133) 0.124

0.487 (Tolk 

1&2 –

0.187)

Total Visibility 

Recommended 

Scrubber 

Upgrades 0.281 1.396 0.068 0.265 0.070 0.275

Total Benefit 

(delta dv) 0.622 3.03 0.167 0.646 0.195 0.763

We note that current actual emissions for many of the units that we propose to control are higher 

than the projected CENRAP 2018 emission rate. Therefore, the actual visibility impact due to 

emissions from these sources and the anticipated benefit from controls are larger than the 

benefits calculated based on the 2018 CENRAP projected visibility conditions.
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Table A.6-6 Includes the net benefit on the proposed controls proposed in Section 6 on the 2018 

projected visibility conditions based on reducing emission levels from the projected 2018 

CENRAP levels.

Table A.6-6. Predicted benefit of all proposed controls beyond 2018 CENRAP projected 

visibility conditions (2018 ‘dirty’ background)

Predicted 

additional 

benefit due

only to FIP 

scrubber 

upgrades 

(dv)

Additional 

benefit 

predicted 

due to FIP 

scrubber 

retrofits 

(dv)

Total 

benefit 

from 

proposed 

controls

Wichita 

Mountains 0.14 0.30 0.45

Big Bend 0.03 0.09 0.12

Guadalupe 

Mountains 0.04 0.12 0.15