Technical Support Document

for the

Oklahoma Regional Haze State Implementation Plan

and Federal Implementation Plan

Prepared and Reviewed by:

Joe Kordzi

Erik Snyder

Michael Feldman

Ellen Belk

Agustin Carbo-Lugo

March 2011
ii

Table of Contents

I. Relationship of this TSD to our Proposal................................................................................... 4

II. Background to Regional Haze................................................................................................... 6

III. Evaluation of State Submittal .................................................................................................. 9

A. Identification of Areas : 40 CFR 51.308(d).......................................................................... 9

B. Reasonable Progress Goals Development........................................................................... 10

B.1. Establishment of Reasonable Progress Goals: 40 CFR 51.308(d)(1) .......................... 10

B.2. Reasonable Progress Goals Four Factor Analysis: 40 CFR 51.308(d)(1)(i)(A) .......... 10

B.3. Uniform Rate of Progress Determination: 40 CFR 51.308(d)(1)(i)(B) ....................... 11

B.4. Uniform Rate of Progress Determination: 40 CFR 51.308(d)(1)(i)(B) ....................... 12

B.5 Analysis of Reasonable Progress Goal: 40 CFR 51.308(d)(1)(ii)-(iii) ......................... 12

B.6 Reasonable Progress State Consultation: 40 CFR 51.308(d)(1)(iv) ............................. 12

B.7 Reasonable Progress Goal Minimum: 40 CFR 51.308(d)(1)(vi).................................. 13

C. Baseline and Natural Visibility Conditions......................................................................... 13

C.1. Calculation of Baseline Conditions: 40 CFR 51.308(d)(2)(i)...................................... 13

C.2. Calculation of Natural Visibility Conditions: 40 CFR 51.308(d)(2)(iii) ..................... 15

C.3. Natural Visibility Impairment: 40 CFR 51.308(d)(2)(iv)(A)....................................... 16

D. Long Term Strategy: 40 CFR 51.308(d)(3).......................................................17

D.1 Long Term Strategy State Consultation: 40 CFR 51.308(d)(3)(i)................................ 18

D.2 Share of Reductions in Other States’ Progress Goals: 40 CFR 51.308(d)(3)(ii) .......... 19

D.3 Technical Basis for the Long Term Strategy: 40 CFR 51.308(d)(3)(iii) – (iv) ............ 21

D.4 Consideration of Long Term Strategy Factors: 40 CFR 51.308(d)(3)(v)..................... 27

E. Monitoring Strategy/Other SIP Requirements 40 CFR 51.308(d)(4) ................................. 31

F. Best Available Retrofit Technology (BART) Requirements: 40 CFR 51.308(e) ............... 33

F.1. BART Eligible Source Identification: 40 CFR 51.308(e)(1)(i).................................... 34

F.1.1. BART Sources Exempted Due to Permit Modifications....................................... 37

F.2. BART Five Factor Determination: 40 CFR 51.308(e)(1)(ii)(A).................................. 38

F.2.1. OG&E Seminole Units 1, 2, and 3 BART Determinations................................... 40

F.2.2. OG&E Sooner Units 1 and 2 BART Determinations............................................ 41

F.2.3. OG&E Muskogee Units 4 and 5 BART Determinations....................................... 43

F.2.4. AEP/PSO Comanche Units 1 and 2 BART Determinations ................................. 44

F.2.5. AEP/ PSO Northeastern Units 2, 3, and 4 BART Determinations........................ 45

F.2.6. AEP/PSO Southwestern Unit 3 BART Determination.......................................... 47

F.3. BART for Larger Fossil Fuel Power Plants: 40 CFR 51.308(e)(1)(ii)(B).................... 50

F.4. De Minimis BART: 40 CFR 51.308(e)(1)(ii)(C) ......................................................... 51

F.5. Design, Equipment, or Work Practice BART: 40 CFR 51.308(e)(1)(iii)..................... 51

F.6. Installation of BART: 40 CFR 51.308(e)(1)(iv) – (v).................................................. 51

F.7. CAIR for BART: 40 CFR 51.308(e)(4)........................................................................ 52

F.8. Reasonable Progress and BART: 40 CFR 51.308(e)(5)............................................... 52

F.9. BART Exemptions: 40 CFR 51.308(e)(6).................................................................... 53

G. 2018 Regional Haze SIP Revision: 40 CFR 51.308(f) ....................................................... 53

H. Five Year Progress Report 40 CFR 51.308(g).................................................................... 54

I. Existing SIP Adequacy Determination 40 CFR 51.308(h) .................................................. 55

J. Federal Land Manager Coordination 40 CFR 51.308(i)...................................................... 56
iii

Tables

Table 1. Calculation of Uniform Rate of Progress ......................................................11

Table 2. Summary of 2002 Emissions from Oklahoma Sources (tons per year) .....................23

Table 3. Oklahoma’s 2018 Emissions Inventory ........................................................24

Table 4. Projected Contribution from Oklahoma Emissions on Visibility Degradation for

the 20 Percent Worst Days in 2018 ...............................................................26

Table 5. Facilities with BART-eligible units in Oklahoma.............................................36

Table 6. Sources in Oklahoma required to install BART...............................................39

Table 7. NOx BART Reductions ...........................................................................49

Table 8. NOx BART Visibility Improvements ...........................................................50

Figures

Figure 1. Map of Class I Areas...............................................................................6

Figure 2. Location of the Class I Area in Oklahoma......................................................7

Figure 3. Map of Regional Planning Organization Boundaries.........................................8

Appendices

Appendix A. Review of Modeling and Emission Inventory Development for the Regional Haze

Implementation Plan for the State of Oklahoma

Appendix B. Refined Best Available Retrofit Technology (BART) Modeling Report: OG&E 

Sooner Generating Station, OG&E Muskogee Generating Station, and AEP/PSO 

Northeastern Generating Station

Appendix C. SO2 BART Cost Effectiveness Analysis for the Federal Implementation Plan

Appendix D. EPA Extension of SO2 BART Cost Effectiveness Analysis

Appendix E. Additional Information Provided by ODEQ for the AEP/PSO Southwestern Unit 3

BART Determination
4

Oklahoma Regional Haze Technical Support Document (TSD)

I. Relationship of this TSD to our Proposal

We are proposing to partially approve and partially disapprove Oklahoma’s Regional Haze (RH) 

SIP revision submitted on February 19, 2010. Specifically, we are proposing to disapprove the 

Sulfur Dioxide (SO2) Best Available Retrofit Technology (BART) determinations for Units 4 

and 5 of the Oklahoma Gas and Electric (OG&E) Muskogee plant; Units 1 and 2 of the OG&E 

Sooner plant; and Units 3 and 4 of the American Electric Power/Public Service Company of 

Oklahoma (AEP/PSO) Northeastern plant. We are proposing to disapprove these SO2 BART 

determinations because they do not comply with our regulations under 40 CFR 51.308(e).

We are also proposing to disapprove the Long Term Strategy (LTS) under section 51.308(d)(3) 

because Oklahoma has not shown that the strategy is adequate to achieve the reasonable progress 

goals set by Oklahoma and by other nearby States. The visibility modeling used by Oklahoma in 

support of its SIP revision submittal assumed SO2 reductions from the six sources as identified 

above that Oklahoma did not secure when making its BART determinations for these sources. 

As we discuss elsewhere, the Oklahoma Department of Environmental Quality (ODEQ)

participated in the Central Regional Air Planning Association (CENRAP) visibility modeling 

development that assumed certain SO2 reductions from these six BART sources. ODEQ also 

performed its consultations with other states with the understanding that these reductions would 

be secured. We propose a Federal Implementation Plan (FIP) to cure these defects in BART and 

the LTS.

We are also proposing to approve the remaining sections of the RH SIP submission, except as 

discussed below.

We are proposing to find that Units 4 and 5 of the OG&E Muskogee plant, Units 1 and 2 of the 

OG&E Sooner plant, and Units 3 and 4 of the AEP/PSO Northeastern plant are subject to BART 

under 40 CFR 51.308(e). Further, we are proposing a FIP that specifically imposes SO2 BART 

emission limits on these sources. We are proposing that SO2 BART for Units 4 and 5 of the 

OG&E Muskogee plant, Units 1 and 2 of the OG&E Sooner plant, and Units 3 and 4 of the 

AEP/PSO Northeastern plant is an SO2 emission limit of 0.06 lbs/MMBtu that applies singly to 

each of these units on a 30 day rolling average. Additionally, we propose monitoring, recordkeeping, and reporting requirements to ensure compliance with these emission limitations. 

We are proposing that compliance with the emission limits be within three (3) years of the 

effective date of our final rule. In our proposal we solicited comments on alternative timeframes, 

up to five (5) years from the effective date our final rule.

Should OG&E and/or AEP/PSO elect to reconfigure the above units to burn natural gas, as a 

means of satisfying their BART obligations under section 51.308(e), that conversion should be 

completed within the same time frame. In our proposal, we solicited comments as to, 

considering the engineering and/or management challenges of such a fuel switch, whether the 
5

full 5 years allowed under section 308(e)(1)(iv) following our final approval would be 

appropriate.

We are proposing to disapprove section VI.E of the Oklahoma RH SIP entitled, ―Greater 

Reasonable Progress Alternative Determination.‖ We are also proposing to disapprove the 

separate executed agreements between ODEQ and OG&E, and ODEQ and AEP/PSO entitled 

―OG&E Regional Haze Agreement, Case No. 10-024, and ―PSO Regional Haze Agreement, 

Case No. 10-025,‖ housed within Appendix 6-5 of the RH SIP. We are proposing that these 

portions of the submittal are severable from the BART determinations and the LTS; therefore, no 

FIP is required.

We are taking no action on whether Oklahoma has satisfied the reasonable progress requirements 

of our regional haze SIP requirements found at section 51.308(d)(1).

This TSD contains five appendices that support our proposal. Appendix A to this TSD contains 

a report describing our review of the emission inventory development, photochemical visibility 

modeling and the BART modeling performed by ODEQ. Appendix B to this TSD contains 

CALPUFF modeling that supports our proposed FIP. Appendix C to this TSD contains a report 

on the SO2 BART cost-effectiveness for Units 4 and 5 of the Oklahoma Gas and Electric 

Muskogee plant, Units 1 and 2 of the Oklahoma Gas and Electric Sooner plant, and Units 3 and 4 

of the American Electric Power/Public Service Company of Oklahoma Northeastern plant. 

Appendix D extends those analyses. Lastly, Appendix E contains additional information we 

received from the Oklahoma Department of Environmental Quality (ODEQ) that informed our 

BART proposal for the AEP/PSO Southwestern Unit 3 BART Determination.

This TSD is not meant to be a complete rationale for our decision. It merely provides additional 

information for some of the technical aspects of the basis for this action when needed. In some 

of the non-technical areas, our Federal Register notice(s) provides more detail than does this 

TSD. Also, this TSD treats the requirements of section 51.308 in the order in which they appear 

in that regulation, whereas our Federal Register notice(s) groups the requirements into related 

areas so the concepts can be understood more easily. In this regard, the TSD can serve as a 

checklist of whether the requirements have been addressed. Lastly, the TSD attempts to refer the 

reader to the specific portions of the Oklahoma’s regional haze SIP that we relied upon for our 

analysis of particular portions of section 51.308.

Throughout this document, we often use language such as, ―we find‖ or other similar phrases 

that on the surface would suggest a final determination has been made. However, all aspects of 

our TSD should be considered to be part of our proposal and are subject to change based on 

comments and other information we may receive during our public comment period.
6

II. Background to Regional Haze

In Section 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set forth a 

program for protecting visibility in Federal Class I areas which calls for the ―prevention of any 

future, and the remedying of any existing, impairment of visibility in mandatory Federal Class I 

areas which impairment results from manmade air pollution.‖ Congress adopted the visibility 

provisions to protect visibility in 156 Federal Class I areas, which include certain national parks, 

memorial parks, and wilderness areas over a certain size, and all international parks. These areas 

are defined at 40 CFR 81.400, are listed by State at 40 CFR 81.401-81.437, and are depicted in 

Figure 1, with the Class I area within Oklahoma, the Wichita Mountains National Wildlife 

Refuge, depicted in Figure 2:

Figure 1. Map of Class I Areas
7

Figure 2. Location of the Class I Area in Oklahoma

On December 2, 1980, we promulgated regulations to address visibility impairment that is 

―reasonably attributable‖ to a single source or small group of sources. (45 FR 80084) These 

regulations represented the first phase in addressing visibility impairment and deferred action on 

regional haze that emanates from a variety of sources until monitoring, modeling and scientific 

knowledge about the relationships between pollutants and visibility impairment improved. 

In the 1990 Amendments to the CAA, Congress added Section 169B and called on us to issue 

Regional Haze rules. The RH rule that we promulgated on July 1, 1999 (64 FR 35713) revised 

the existing visibility regulations to integrate provisions addressing regional haze visibility 

impairment and establish a comprehensive visibility protection program for Federal Class I 

areas. These regulations represented the second phase in addressing visibility impairment. 

States were required to submit state implementation plans (SIPs) to us that set out each state’s 

plan for complying with the RH Rule, including consultation and coordination with other states 

and with Federal Land Managers (FLMs). States were required to submit a Regional Haze SIP 

to us within three years after the date of designation of areas under the National Ambient Air 

Quality Standard (NAAQS) for fine particulate matter (PM2.5). We promulgated PM2.5 

designations on December 17, 2004, and thus, regional haze SIPs were to be submitted to us by 

December 17, 2007, which is also specified at 40 CFR 51.308(b).

The RH rule addressed the combined visibility effects of various pollution sources over a wide 

geographic region. Consequently, all 50 states, including those without Class I areas, 

Washington, D.C., and the Virgin Islands, were required to submit Regional Haze SIPs (40 CFR 

51.300(b)(3)). We designated five Regional Planning Organizations (RPOs) to assist with the 

coordination and cooperation needed to address the visibility issue, as shown by Figure 3.
8

Figure 3. Map of Regional Planning Organization Boundaries

Oklahoma, through the ODEQ joined state air agencies from Arkansas, Iowa, Kansas, Louisiana, 

Minnesota, Missouri, Nebraska, and Texas in forming CENRAP, in 1999. CENRAP also 

includes federally recognized Indian tribes located within the geographic boundaries of its 

member states. The State of Oklahoma and it’s agency, the Oklahoma Department of 

Environmental Quality, chose to participate in CENRAP to develop the technical analyses 

needed to fulfill the requirements of the RH Rule.
9

III. Evaluation of State Submittal

In evaluating the Oklahoma RH SIP, we reviewed all the parts of the document, including the 

body of the SIP itself, and all appendices. In addition, we drew upon the following additional 

documents that may not have been specifically discussed in our TSD and/or the Federal Register 

publication(s) of our decision:

1. 40 CFR Part 51.

2. Appendix V to 40 CFR Part 51.

3. The ―BART Guidelines‖ in Appendix Y to Part 51—Guidelines for BART

Determinations Under the Regional Haze Rule, 70 FR 39104, July 6, 2005.

4. Guidance for Tracking Progress Under the Regional Haze Rule, EPA-454/B-03-004, 

September, 2003.

5. Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional 

Haze, January 2, 2001.

6. Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule, 

EPA-454/B-03-005, September 2003.

7. The ―Baseline Memo,‖ which is entitled, ―2002 Base Year Emission Inventory SIP 

Planning: 8-Hour Ozone, PM2.5 and Regional Haze Programs, dated 11/18/2002, from 

Lydia Wegman to the Regional Air Directors.‖

8. Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program, 

dated June 1, 2007.

9. Visibility Monitoring Guidance, EPA-454/R-99-003, June 1999.

10. Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter 

National Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations, 

EPA-454/R-05-001, dated August, 2005.

11. Interim Air Quality Policy on Wildland and Prescribed Fires, April 23, 1998.

A. Identification of Areas : 40 CFR 51.308(d)

Oklahoma must address regional haze in each mandatory Class I Federal area located within it

and in each mandatory Class I Federal area located outside it which may be affected by 

emissions from within it.

State Submittal

Pages 1, 2, 100-102

The Oklahoma ODEQ is responsible for developing the Regional Haze SIP for Oklahoma and 

submitting it to the Oklahoma Air Pollution Control Board for approval. Oklahoma has one 

Class I area within its borders, the Wichita Mountains National Wildlife Refuge (Wichita 

Mountains), which it has identified in its SIP. ODEQ has also determined that Oklahoma 

emissions have a small potential to impact visibility at Class I areas outside of Oklahoma. 

Based on projections of visibility in 2018 for the 20% worst visibility days, ODEQ has projected 

that Oklahoma emissions are responsible for approximately 3.61% of the visibility degradation 
10

at the Hercules Glades in Missouri, approximately 2.53% of the visibility degradation at Salt 

Creek in New Mexico, and approximately 2.0% of the visibility degradation at Guadalupe 

Mountains in Texas. We note that these projections are based on modeling done by CENRAP 

that assumed a certain level of reductions of SO2 emissions from six sources that Oklahoma did 

not actually require in its submitted RH SIP revision. We expect that Oklahoma’s projected 

impacts on visibility at Class I areas outside of Oklahoma would be greater had these controls

and the associated SO2 emission reductions not been included in CENRAP’s visibility modeling. 

Additional detail can be found in Table VIII-9 of the RH SIP.

EPA Findings: We find that ODEQ has satisfied this requirement. 

B. Reasonable Progress Goals Development 

As we explain in our proposal, we are not taking action on Oklahoma’s submitted Reasonable 

Progress Goal (RPG) because, as described more fully therein, we must first evaluate and act 

upon the RH SIP revision submitted by the State of Texas. Therefore, with the exception of 

Oklahoma’s uniform rate of progress determination in section 51.308(d)(1)(i)(B), we do not 

further consider section 51.308(d)(1) in this TSD, other than to list the requirements.

Note: The language under Section 51.308(d)(1)(v), “The reasonable progress goals established 

by the State are not directly enforceable but will be considered by the Administrator in 

evaluating the adequacy of the measures in the SIP to achieve the progress goal adopted by the 

State,” is a statement relating to our review of RH SIPs. Since it does not require a response 

from the state, it is not separately evaluated here.

B.1. Establishment of Reasonable Progress Goals: 40 CFR 51.308(d)(1)

For each mandatory Class I Federal area located within the state, Oklahoma 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 SIP and ensure no degradation in visibility for the least 

impaired days over the same period.

EPA Findings

We are not proposing action on whether Oklahoma has met this requirement

B.2. Reasonable Progress Goals Four Factor Analysis: 40 CFR 51.308(d)(1)(i)(A)

In establishing a reasonable progress goal for any mandatory Class I Federal area within 

Oklahoma, it must 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 
11

any potentially affected sources, and include a demonstration showing how these factors were 

taken into consideration in selecting the goal.

EPA Findings

We are not proposing action on whether Oklahoma has met this requirement

B.3. Uniform Rate of Progress Determination: 40 CFR 51.308(d)(1)(i)(B)

In establishing a reasonable progress goal for any mandatory Class I Federal area within the 

State, Oklahoma must analyze and determine the rate of progress needed to attain natural 

visibility conditions by the year 2064. To calculate this rate of progress, Oklahoma must 

compare baseline visibility conditions to natural visibility conditions in the mandatory Federal 

Class I area and determine the uniform rate of visibility improvement (measured in deciviews) 

that would need to be maintained during each implementation period in order to attain natural 

visibility conditions by 2064. 

State Submittal

Pages 36, 104-106

See Sections III.C.1 – C.2 of this TSD for a discussion on ODEQ’s calculations of baseline and 

natural visibility conditions. To calculate the rate of progress needed to attain natural visibility 

conditions by the year 2064, also known as the Uniform Rate of Progress (URP), ODEQ 

compared baseline visibility conditions to natural visibility conditions at the Wichita Mountains

Class I area and determined the linear rate of visibility improvement (in deciviews) that would 

have to be maintained during each implementation period in order to attain natural visibility 

conditions by 2064. Using a baseline of 23.81 dv and a natural visibility value of 7.53 dv for the 

20 percent worst days, ODEQ calculated the URP to be 16.28 dv, as depicted by Table 1, which 

is adapted from Table III-9 of the SIP:

Table 1. Calculation of Uniform Rate of Progress

Baseline Conditions 23.81 dv

Natural Visibility 7.53 dv

Total Improvement by 2064 16.28 dv

Improvement for this SIP by 2018 3.80 dv

Uniform Rate of Progress 0.27 dv/year

This information is also depicted graphically in Figure IX-1 of the SIP.

EPA Findings
12

We agree that ODEQ has, in establishing its RPG, analyzed and determined the rate of progress 

needed to attain natural visibility conditions by the year 2064. ODEQ has calculated this rate of 

progress, and compared baseline visibility conditions to natural visibility conditions for the 

Wichita Mountains and determined the URP needed to be maintained during each 

implementation period in order to attain natural visibility conditions by 2064.

B.4. Uniform Rate of Progress Determination: 40 CFR 51.308(d)(1)(i)(B)

In establishing the reasonable progress goal, Oklahoma must consider the uniform rate of 

improvement in visibility and the emission reduction measures needed to achieve it for the 

period covered by the SIP.

EPA Findings

We are not proposing action on whether Oklahoma has met this requirement

B.5 Analysis of Reasonable Progress Goal: 40 CFR 51.308(d)(1)(ii)-(iii)

For the period of the SIP, if Oklahoma establishes a reasonable progress goal that provides for a 

slower rate of improvement in visibility than the rate that would be needed to attain natural 

conditions by 2064, it must demonstrate, based on the factors in section 51.308(d)(1)(i)(A), that 

the rate of progress for the SIP to attain natural conditions by 2064 is not reasonable; and that the 

progress goal adopted by it is reasonable. Oklahoma must provide to the public for review as 

part of its SIP an assessment of the number of years it would take to attain natural conditions if 

visibility improvement continues at the rate of progress selected by it as reasonable. 

In determining whether the Oklahoma’s goal for visibility improvement provides for reasonable 

progress towards natural visibility conditions, the Administrator will evaluate the demonstrations 

developed by it pursuant to Section 51.308(d)(1)(i) and (d)(1)(ii).

EPA Findings

We are not proposing action on whether Oklahoma has met this requirement

B.6 Reasonable Progress State Consultation: 40 CFR 51.308(d)(1)(iv)

In developing each reasonable progress goal, Oklahoma must consult with those States which 

may reasonably be anticipated to cause or contribute to visibility impairment in the mandatory 

Class I Federal area. In any situation in which Oklahoma cannot agree with another such State 

or group of States that a goal provides for reasonable progress, Oklahoma must describe in its 

submittal the actions taken to resolve the disagreement. In reviewing Oklahoma’s SIP submittal, 

the Administrator will take this information into account in determining whether its goal for 

visibility improvement provides for reasonable progress towards natural visibility conditions.
13

EPA Findings

We are not proposing action on whether Oklahoma has met this requirement

B.7 Reasonable Progress Goal Minimum: 40 CFR 51.308(d)(1)(vi)

Oklahoma may not adopt a reasonable progress goal that represents less visibility improvement 

than is expected to result from implementation of other requirements of the CAA during the 

applicable planning period.

EPA Findings

We are not proposing action on whether Oklahoma has met this requirement

C. Baseline and Natural Visibility Conditions

Note: 40 CFR 51.308(d)(2)(ii) is not applicable since this SIP was submitted after 2003, and 40 

CFR 51.308(d)(2)(iv)(B) is not applicable since this is the first SIP period.

C.1. Calculation of Baseline Conditions: 40 CFR 51.308(d)(2)(i)

For each mandatory Class I Federal area located within Oklahoma, it must determine the 

baseline visibility conditions for the most impaired and least impaired days. The period for 

establishing baseline visibility conditions is 2000 to 2004. Baseline visibility conditions must be 

calculated, using available monitoring data, by establishing the average degree of visibility 

impairment for the most and least impaired days for each calendar year from 2000 to 2004. The 

baseline visibility conditions are the average of these annual values. For mandatory Class I 

Federal areas without onsite monitoring data for 2000–2004, Oklahoma must establish baseline 

values using the most representative available monitoring data for 2000–2004, in consultation 

with the Administrator or his or her designee.

State Submittal

Pages 8 - 27.

Baseline conditions represent visibility for the best and worst days at the time the regional haze 

program is established. Baseline conditions are calculated using multiyear averaging of the 20 

percent of monitored days with the highest (most impaired, or worst days) and the 20 percent of 

monitored days with the lowest (least impaired, or best days) light extinction values, expressed in 

deciviews (dv), for the years 2000 through 2004. The RHR establishes the deciview (dv) as the 

principal metric for measuring visibility. See 70 FR 39104. A deciview is a uniform measure of 

light extinction caused by regional haze. This visibility metric expresses uniform changes in the 
14

degree of haze in terms of common increments across the entire range of visibility conditions, 

from pristine to extremely hazy conditions. The deciview scale approaches zero for no visibility 

degradation and increases as visibility is degraded. The deciview scale was designed similar to 

the decibel scale for sound, so that equal changes in deciview are equally perceptible. The 

deciview is a useful measure for tracking progress in improving visibility, because each deciview 

change is an equal incremental change in visibility perceived by the human eye. Most people 

can detect a change in visibility of one deciview. Common current values in the west are 15 to 

20 dv; common current values in the east are 25 to 30 dv. Visibility is also sometimes expressed 

in terms of the visual range, which is the greatest distance, in kilometers or miles, at which a 

dark object can just be distinguished against the sky.

The baseline is the starting point for this regional haze submittal, against which progress toward 

the national visibility goal is measured. ODEQ calculated its baseline conditions in accordance 

with our 2003 Natural Visibility Guidance1, as required by the RHR. The baseline condition 

calculation begins with the calculation of light extinction, using the IMPROVE equation. The 

IMPROVE equation sums the light extinction resulting from individual pollutants, such as 

sulfates and nitrates.

In 2005 the IMPROVE Steering Committee made recommendations for a refined equation that 

modifies the terms of the original equation to account for the most recent data. The purpose of 

this refinement is to provide more accurate estimates of the various factors that affect the 

calculation of light extinction. 

The new IMPROVE equation estimates additive extinction coefficients for each of several 

chemical constituents of particulate matter: sulfurous, nitrate, organic carbonaceous, elemental 

“light-absorbing” carbonaceous, fine soil, chlorine or chloride, and coarse matter. The 

equation also accounts for Rayleigh scattering of light and for deliquescence on sulfurous, 

nitrate, and chlorine or chloride particulates with an estimate of particle-bound water as a

function of relative humidity. It reflects the most recent review of the science2and it accounts for 

the effect of particle size distribution on light extinction efficiency of sulfate, nitrate, and organic 

carbon. It also adjusts the mass multiplier for organic carbon (particulate organic matter) by 

increasing it from 1.4 to 1.8. New terms are added to the equation to account for light extinction 

by sea salt and light absorption by gaseous nitrogen dioxide. Site-specific values are used for 

Rayleigh scattering (scattering of light due to atmospheric gases) to account for the site-specific 

effects of elevation and temperature. Separate relative humidity enhancement factors are used 

 

1

 Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program, EPA-454/B-03-005, 

September 2003.

2

 The science behind the revised IMPROVE equation is summarized in appendix B.2 of the Oklahoma Regional 

Haze submittal and in numerous published papers. See for example: Hand, J.L., and Malm, W.C.,2006, 

Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients - Final Report. March 

2006. Prepared for Interagency Monitoring of Protected Visual Environments (IMPROVE), Colorado State 

University, Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado.

http://vista.cira.colostate.edu/improve/publications/GrayLit/016_IMPROVEeqReview/IMPROVEeqReview.htm

; and Pitchford, Marc., 2006, Natural Haze Levels II: Application of the New IMPROVE Algorithm to Natural 

Species Concentrations Estimates. Final Report of the Natural Haze Levels II Committee to the RPO 

Monitoring/Data Analysis Workgroup. September 2006 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/029_NaturalCondII/naturalhazelevelsIIreport.ppt
15

for small and large size distributions of ammonium sulfate and ammonium nitrate and for sea 

salt. The terms for the remaining contributors, elemental carbon (light-absorbing carbon), fine 

soil, and coarse mass terms, do not change between the original and new IMPROVE equations. 

Further details and explanation of the Improve equations can be found in Appendices A and B.

As with the natural visibility conditions calculation, ODEQ chose to use the new IMPROVE 

equation. The choice between use of the old or the new equation for calculating the visibility 

metrics for each Class I area is made by the state in which the Class I area is located. ODEQ 

has chosen to use this new equation, referred to in the SIP as a “revised” approach, for 

calculating both the baseline and natural visibility conditions because it takes into account the 

most recent review of the science and because it is recommended by the IMPROVE Steering 

Committee.

Monitoring began at the Wichita Mountains in March 2001, and ODEQ concluded that no other 

monitor provided reasonable substitute that met our completeness criteria. As a consequence,

the Oklahoma RH SIP employs the incomplete visibility data for 2001, complete data for 2002-

2004, and provisional data for 2005 and 2006. The resulting baseline conditions represent an

average for 2002-2004. ODEQ calculated the baseline by first summing the light extinction 

(where the extinction coefficient is expressed in Mm1) for sulfurous, nitrate, organic 

carbonaceous, elemental “light-absorbing” carbonaceous, fine soil, chlorine or chloride, and 

coarse matter, using the following equation:

βext = βext S + βext NO3 + βext organic+ βext elemental+ βext SOil+ βext coarse+ βext Cl+ βext Rayleigh

ODEQ then converted the total light extinction values to deciviews (dv), using the Haze Index 

(HI) equation:

HI (dv) = 10 ln[βext/(10 Mm -1)]

ODEQ calculated the baseline conditions at the Wichita Mountains as 23.81 dv on the 20 

percent worst days, and 9.78 dv on the 20 percent best days.

EPA Findings

As stated above, ODEQ did not have access to a complete data set from 2000 – 2004. We agree 

with them that no adequate substitute for that monitoring data exists. Nevertheless, we feel that 

ODEQ adequately utilized the data they did have at their disposal and adequately calculated 

baseline conditions.

C.2. Calculation of Natural Visibility Conditions: 40 CFR 51.308(d)(2)(iii)

For each mandatory Class I Federal area located within the State, the State must determine the 

natural visibility conditions for the most impaired and least impaired days. Natural visibility 

conditions must be calculated by estimating the degree of visibility impairment existing under 
16

natural conditions for the most impaired and least impaired days, based on available monitoring 

information and appropriate data analysis techniques.

State Submittal

Pages 28-36

Natural background visibility, as defined in our natural visibility guidance, is based on annual 

average concentrations of fine particle components. Natural background visibility for the 20 

percent worst days is estimated by assuming that fine particle concentrations for natural 

background are normally distributed and the 90th percentile of the annual distribution represents 

natural background visibility on the 20 percent worst days (i.e, Method 1 prediction per our

natural visibility guidance).

In the natural visibility guidance, we provided default estimates for the natural visibility 

condition at almost all Class I areas. For the Wichita Mountains, the default natural visibility 

value for the 20 percent worst days is 11.07 dv and for the 20 percent best days is 3.39 dv. 

The natural visibility guidance also provided that states may use a “refined approach” to 

estimate the values that characterize the natural visibility conditions of the Class I areas. The 

purpose of such a refinement would be to provide more accurate estimates with changes to the 

extinction algorithm that may include the concentration values, factors to calculate extinction 

from a measured particular species and particle size, the extinction coefficients for certain 

compounds, geographical variation (by altitude) of a fixed value, and the addition of visibility 

pollutants.

As with the calculation of baseline conditions, ODEQ chose to use the new IMPROVE equation 

as the basis for its refined estimate because it takes into account the most recent review of the 

science and because it is recommended by the IMPROVE Steering Committee.

ODEQ calculated the natural conditions at the Wichita Mountains as 7.53 dv on the 20 percent 

worst days, and 4.2 dv on the 20 percent best days.

EPA Findings

We find that Oklahoma has satisfied this requirement.

C.3. Natural Visibility Impairment: 40 CFR 51.308(d)(2)(iv)(A)

For the first SIP addressing the requirements of 51.308(d) and (e) of this section, Oklahoma must 

calculate the number of deciviews by which baseline conditions exceed natural visibility 

conditions for the most impaired and least impaired days.

State Submittal
17

Page 36, 104-106.

As discussed in section III.C.1 of this TSD, ODEQ calculated the baseline conditions at the 

Wichita Mountains as 23.81 dv on the 20 percent worst days, and 9.78 dv on the 20 percent best 

days. Also discussed in section III.C.2, ODEQ calculated the natural conditions at the Wichita 

Mountains as 7.53 dv on the 20 percent worst days, and 4.2 dv on the 20 percent best days. 

Subtracting the natural visibility condition values from the baseline values results in the amount 

of visibility impairment. 

As summarized in Table III-9 and IX-1, and displayed in Figure IX-1 of its SIP, ODEQ 

calculates the number of deciviews by which baseline conditions exceed natural visibility 

conditions at the Wichita Mountains for the most impaired (20 percent worst) days to be 16.28 

dv (23.81 – 7.53).

Similarly, using the baseline and natural visibility conditions ODEQ calculated for the least 

impaired (20 percent best) days results in a calculation in which baseline conditions exceed 

natural visibility conditions at the Wichita Mountains for the least impaired (20 percent best) 

days to be 5.6 dv (9.78 – 4.2).

EPA Findings

We find that Oklahoma has satisfied this requirement.

D. Long Term Strategy: 40 CFR 51.308(d)(3).

Oklahoma, as with each State listed in section 51. 300(b)(3), must submit a long-term strategy 

that addresses regional haze visibility impairment for each mandatory Class I Federal area within 

it and for each mandatory Class I Federal area located outside it which may be affected by 

emissions from it. The long-term strategy must include enforceable emissions limitations, 

compliance schedules, and other measures as necessary to achieve the reasonable progress goals 

established by States having mandatory Class I Federal areas.

State Submittal

Pages 37-47, 58-67, 86-103

The LTS is a compilation of state-specific control measures relied on by Oklahoma for 

achieving its RPGs. Oklahoma’s LTS for the first implementation period addresses the 

emissions reductions from federal, state, and local controls that take effect in the state from the 

end of the baseline period starting in 2004 until 2018. The Oklahoma LTS was developed by 

ODEQ, in coordination with the CENRAP RPO, through an evaluation of the following 

components: (1) construction of a CENRAP 2002 baseline emission inventory; (2) construction 

of a CENRAP 2018 emission inventory, including reductions from CENRAP member state 

controls required or expected under federal and state regulations, (including BART); (3) 
18

Photochemical grid modeling that included both 2002 and 2018 analyses of CENRAP region 

and outside the CENRAP region to determine visibility improvement and apportion individual 

state contributions; (4) state consultation; and (5) application of the LTS factors.

EPA Findings

As discussed in section III.B, Oklahoma’s reasonable progress goal provides for a slower rate of

visibility improvement than does the uniform rate of progress. However, we feel it is clear that

sources originating outside of Oklahoma, contribute much more to the visibility degradation at 

the Wichita Mountains than do sources within Oklahoma. This limits Oklahoma’s ability to 

improve the visibility at the Wichita Mountains by controlling sources within its own borders. 

ODEQ has shown that even complete elimination of all anthropogenic emissions in Oklahoma 

would not result in Oklahoma meeting the URP. 

Consequently, we find that with the (1) the controls discussed above, (2) the addition of sources 

controlled through BART, and (3) those additional sources controlled through the FIP, ODEQ 

has demonstrated Oklahoma’s long term strategy sufficiently addresses Oklahoma’s share of 

emission reductions to reduce regional haze visibility impairment at the Wichita Mountains.

With regard to other mandatory Class I Federal areas located outside Oklahoma, Table VIII-9

of the RH SIP, “Projected influence of emissions from Oklahoma on Class I areas (worst quintile 

days, 2018)” shows that emissions from Oklahoma are projected to be responsible for no more 

than 3.61% of the visibility degradation at Class I areas outside of its borders (Hercules Glades

in 2018). We conclude this low level of visibility impairment does not merit additional controls. 

Therefore, we find that with the (1) the controls discussed above, (2) the addition of sources 

controlled through BART, and (3) those additional sources controlled through the FIP, ODEQ 

has demonstrated Oklahoma’s long term strategy sufficiently addresses Oklahoma’s share of 

emission reductions to reduce regional haze visibility impairment at mandatory Class I Federal 

area located its borders. We find that ODEQ has adequately addressed this requirement.

The remaining portions of section III.D expand upon our determination.

D.1 Long Term Strategy State Consultation: 40 CFR 51.308(d)(3)(i)

Where Oklahoma has emissions that are reasonably anticipated to contribute to visibility 

impairment in any mandatory Class I Federal area located in another State or States, it must 

consult with the other State(s) in order to develop coordinated emission management strategies. 

Oklahoma must consult with any other State having emissions that are reasonably anticipated to 

contribute to visibility impairment in any mandatory Class I Federal area within it.

State Submittal

Pages 115 – 117, Appendix 10-1

ODEQ used CENRAP as its main vehicle for facilitating collaboration with FLMs and other 

states in satisfying its LTS consultation requirement. This helped ODEQ and other state 
19

environmental agencies analyze emission apportionments at Class I areas and develop 

coordinated RH SIP strategies. CENRAP modeling concluded that emissions from Oklahoma 

are projected to have little effect on the visibility degradation at Class I areas outside of its

borders. ODEQ used this information to help inform its consultation strategy. ODEQ 

participated in consultations for Mingo and the Hercules Glades in Missouri; the Upper Buffalo,

and Caney Creek in Arkansas; and Big Bend and the Guadalupe Mountains in Texas. As 

described on page 115 of the RH SIP, ODEQ conducted four consultations. ODEQ held its first 

consultation, specifically directed to the tribal leaders in Oklahoma and their environmental 

managers, on 14 August 2007. Only the Delaware Nation of Oklahoma participated. ODEQ 

held the next three consultations as conference calls on 16 August, 30 August, and 25 September 

2007. In these consultations, ODEQ invited CENRAP member clean air agencies, us, and the 

tribes to participate. ODEQ invited personnel from clean-air agencies in Arkansas, Iowa, 

Kansas, Louisiana, Minnesota, Missouri, Nebraska, and Texas to participate in its consultations.

Appendix 10-1 of the RH SIP contains some correspondence related to these consultations. As 

part of that correspondence, the State of Texas agreed to provide ODEQ the opportunity to 

comment on Best Available Control Technology (BACT) determinations for Prevention of 

Significant Deterioration (PSD) sources that have significant impact on the Wichita Mountains 

National Wildlife Refuge (WMNWR). Specifically, ODEQ will be afforded the opportunity to 

review applications for sources if modeling predicts a five percent or higher impact on light 

extinction in a given year and provide comments to Texas during its public review and comment 

period. Texas also agreed to notify ODEQ whenever modeling indicates that a proposed source 

may significantly impact the WMNWR. ODEQ also requested that Class I impact reviews be 

required for all proposed PSD sources within 300 kilometers of a Class I area. However, this 

request was not agreed to by Texas, who cited the need for us to adopt significant impact levels 

for Class I reviews so that there is a consistent approach to requiring Class I reviews.

EPA Findings

Section 51.308(d)(3)(i) requires that Oklahoma consult with other states if its emissions are 

reasonably anticipated to contribute to visibility impairment at that state’s Class I area(s), and 

that Oklahoma consult with other states if their emissions are reasonably anticipated to 

contribute to visibility impairment at the Wichita Mountains. After evaluating whether emissions 

from Oklahoma sources contribute to visibility impairment in other states’ Class I areas, ODEQ 

concluded there was no contribution sufficient to require consultation. ODEQ’s evaluation 

relied, however, upon SO2 BART reductions from the six units of the OG&E and AEP/PSO three 

coal fired power plants. However, ODEQ did not secure these emissions reductions. Because 

this situation is tied to our analysis of section 51.308(d)(3)(ii), we combine our finding on 

section 51.308(d)(3)(i) with that of section 51.308(d)(3)(ii). 

D.2 Share of Reductions in Other States’ Progress Goals: 40 CFR 51.308(d)(3)(ii)

Where Oklahoma causes or contributes to impairment in a mandatory Class I Federal area, it

must demonstrate that it has included in its SIP all measures necessary to obtain its share of the 

emission reductions needed to meet the progress goal for the area. If Oklahoma has participated 
20

in a regional planning process, it must ensure it has included all measures needed to achieve its 

apportionment of emission reduction obligations agreed upon through that process.

State Submittal

Pages 65, 115-117

As discussed in sections III.D.3-4, and depicted in Table 4, below, ODEQ concluded that 

emissions from Oklahoma only minimally affect the visibility at other Class I areas. No states 

requested emission reductions from Oklahoma as part of their long term strategies. However, in 

performing this visibility projection modeling, in which other states relied, ODEQ assumed that 

Units 4 and 5 of the OG&E Muskogee plant, Units 1 and 2 of the OG&E Sooner plant, and Units 

3 and 4 of the AEP/PSO Northeastern plants, would be controlled at presumptive SO2 BART

levels, when in fact, after the consultation calls with tribes and other states in 2007 ODEQ found 

that SO2 BART was no control on these six units in their 2010 submittal. This original 

assumption that these six units would be controlled to presumptive SO2 limits is partially 

responsible for the low projected influence of Oklahoma’s emissions on the visibility of Class I 

areas outside of the state.

EPA Findings

As we discuss above in our analysis of section 51.308(d)(3)(i), ODEQ concluded there was no 

contribution sufficient to require consultation. Regardless of its conclusions regarding the 

impacts of Oklahoma emissions on other states’ Class I areas, however, Oklahoma did consult 

with other states and tribes, largely through the CENRAP process.

Section 51.308(d)(3)(ii) requires that if Oklahoma emissions cause or contribute to impairment 

in another state’s Class I area, Oklahoma must demonstrate that it has included in its RH SIP all 

measures necessary to obtain its share of the emission reductions needed to meet the progress 

goal for that Class I area. Section 51.308(d)(3)(ii) also requires that since Oklahoma 

participated in a regional planning process, it must ensure it has included all measures needed 

to achieve its apportionment of emission reduction obligations agreed upon through that 

process. As we state in the RHR , Oklahoma’s commitments to participate in CENRAP bind it to 

secure emission reductions agreed to as a result of that process, unless it proposes a separate 

process and performs its consultations on the basis of that process:

While States are not bound by the results of a regional planning effort, nor can the content of 

their SIPs be dictated by a regional planning body, we expect that a coordinated regional effort 

will likely produce results the States will find beneficial in developing their regional haze 

implementation plans. Any State choosing not to follow the recommendations of a regional body 

would need to provide a specific technical basis that its strategy nonetheless provides for 

reasonable progress based on the statutory factors. At the same time, we cannot require States 

to participate in regional planning efforts if the State prefers to develop a long-term strategy on 

its own. We note that any State that acts alone in this regard must conduct the necessary 

technical support to justify their apportionment, which generally will require regional 
21

inventories and a regional modeling analysis. Additionally, any such State must consult with 

other States before submitting its long-term strategy to us.

Consequently, because Oklahoma accepted and incorporated the CENRAP-developed visibility 

modeling into its RH SIP, which assumed controls for the six units discussed above that were not 

subsequently secured, we propose to disapprove Oklahoma’s long term strategy. 

However, our proposed FIP does include controls for the six units that at least achieve the level 

of control assumed in the CENRAP modeling. In addition, as described above, Oklahoma has 

required controls on additional sources as part of its BART evaluation. Therefore, we propose 

to find that with the addition of our proposed FIP, the requirements in section 51.308(d)(3)(i) 

and(ii) have been met.

D.3 Technical Basis for the Long Term Strategy: 40 CFR 51.308(d)(3)(iii) – (iv)

Oklahoma must document the technical basis, including modeling, monitoring and emissions 

information, on which the State is relying to determine its apportionment of emission reduction 

obligations necessary for achieving reasonable progress in each mandatory Class I Federal area it 

affects. It may meet this requirement by relying on technical analyses developed by the regional 

planning organization and approved by all State participants. Oklahoma must identify the 

baseline emissions inventory on which its strategies are based. The baseline emissions inventory 

year is presumed to be the most recent year of the consolidated periodic emissions inventory.

Oklahoma must also identify all anthropogenic sources of visibility impairment considered by 

the State in developing its long-term strategy. It should consider major and minor stationary 

sources, mobile sources, and area sources. The following discussion of emission inventories for 

the 2002 base and 2018 future year, 2002 and 2018 photochemical modeling results, and our 

review of the results is a brief summary of the work that was done and our conclusions. For a 

more detailed discussion of the technical analyses including the photochemical modeling and our 

review and conclusions, see Appendix A.

State Submittal

1. Emissions Inventory

Pages 37 – 68

Oklahoma met the requirements of Sections 51.308(d)(3)(iii) – (iv) mainly by relying on 

technical analyses developed by CENRAP and approved by all State participants. The emissions 

inventory used in the regional haze technical analyses was developed by CENRAP with 

assistance from Oklahoma. The 2018 emissions inventory was developed by projecting 2002 

emissions and applying reductions expected from federal and state regulations affecting the 

emissions of the visibility-impairing pollutants NOx, PM, SO2,, and VOCs. The BART Guidelines 

direct states to exercise judgment in deciding whether VOCs and NH3 impair visibility in their 
22

Class I area(s). 70 FR 391160. As discussed further in section V.C.3, CENRAP performed 

modeling sensitivity analyses and/or other analyses, which demonstrated that anthropogenic 

emissions of VOC and NH3 do not significantly impair visibility in the CENRAP region. 

Therefore, Oklahoma did not consider ammonia among visibility-impairing pollutants and did 

not further evaluate NH3 and VOC emissions sources for potential controls under BART or 

reasonable progress.

a. Oklahoma’s 2002 Emission Inventory

ODEQ and CENRAP developed an emission inventory for five inventory source 

classifications: point, area, non-road and on-road mobile sources, and biogenic sources for the 

baseline year of 2002. State, tribal, and local agencies submitted information to the national 

emissions inventory for 2002 which formed the basis of the CENRAP emissions inventory. A 

CENRAP contractor supplemented this inventory with non-point source inventories developed 

for CENRAP. These CENRAP-specific inventories addressed agricultural and prescribed 

burning, on-road and non-road mobile sources, agricultural tilling and livestock dust, and 

agricultural ammonia. For point source emissions, ODEQ rule OAC 252:100-5 requires 

individually permitted point sources to submit an annual emissions inventory, which was 

submitted the data to us as part of the 2002 national emissions inventory. A CENRAP contractor 

provided further refinement. The area-source inventory for 2002 used EPA-provided estimates 

for area-source emissions as a starting point, with refinement from ODEQ and CENRAP. 

ODEQ developed an oil and gas area-source emissions inventory, using county-level oil and gas 

production data from the Oklahoma Corporation Commission and the Oklahoma Tax 

Commission. Emissions calculation methods developed by the Texas Commission on 

Environmental Quality were applied to these data. ODEQ also developed area-source emissions 

estimates for gasoline distribution sources, dry cleaners, and wildfires, using the factors from 

emissions inventory improvement program and county-level activity data for Oklahoma. A 

CENRAP contractor consolidated the area-source inventories from these various inventory 

sources, conducted additional quality assurance, and worked with ODEQ to revise this 

consolidated inventory. Another CENRAP contractor removed selected sources from the 

consolidated area inventories to process them as separate subcategories to allow specialized 

processing to facilitate particulate source apportionment within the Comprehensive Air Quality 

Model with extensions (CAMx) model. To enhance accuracy of transportable dust estimates, the 

contractor also applied road and fugitive dust transport factors3to appropriate area source 

emissions. ODEQ and CENRAP developed the on-road mobile source emissions inventory for 

2002 with contractor support on a county basis. CENRAP contractors used our NONROAD 

model to prepare and develop broad emissions estimates for the non-road mobile source 

inventory generated the non-road emissions inventory. A CENRAP contractor ran our Biogenic 

Emissions Inventory System 3 (BEIS3) model to generate a biogenic emissions inventory. 

Oklahoma’s 2002 emissions inventory is summarized in Table 2:

 

3

 Using Methodology to Estimate the Transportable Fraction of Fugitive Dust Emissions for Regional and Urban 

Scale Air Quality Analyses, Thompson G. Pace, US EPA (8/3/2005 Revision), available at 

http://www.epa.gov/ttn/chief/emch/dustfractions/transportable_fraction_080305_rev.pdf
23

Table 2. Oklahoma’s 2002 Emissions Inventory

SO2 NH3 NOx VOCs PM10-

PM2.5 PM2.5

Point 148,761 24,102 158,818 37,794 8,026 8,636

Area 11,779 114,363 115,407 201,758 304,560 109,279

Non-road 

mobile

4,773 280 49,396 47,863 433 4,580

On-road 

mobile

4,708 4,434 142,592 99,924 879 2,459

Biogenic 0 0 35,909 988,314 0 0

Total 170,021 143,179 502,122 1,375,653 313,898 124,954

b. Oklahoma’s 2018 Emission Inventory

In general. ODEQ used a combination of our Economic Growth Analysis System (EGAS 

5), our mobile emissions model (MOBILE 6), our off-road emissions factor model (NONROAD), 

and the Integrated Planning Model (IPM) for electric generating units. ODEQ modified the 

projected emissions from the IPM modeling for OG&E Sooner and Muskogee electric power 

plants and the PSO Northeast electric power plants to reflect the application of presumptive 

BART controls.4 

More specifically, CENRAP developed emissions for five inventory source 

classifications: point, area, non-road and on-road mobile sources, and biogenic sources. 

CENRAP used its 2002 emission inventory, described above, to estimate emissions in 2018. 

CENRAP contractors, with input from us, compiled the growth and control assumptions and 

factors used to estimate point-source emissions in 2018 from the inventory for 2002. This 

included EGUs, internal combustion engines and other non-EGU point sources. CENRAP states 

provided data for the area source and non-road mobile emissions inventory for 2018, to which a 

CENRAP contractor applied area source growth and control factors. Similarly, CENRAP states 

provided data for the emissions inventory for 2018, to which a CENRAP contractor applied 

source growth and control factors for locomotives, aircraft, and commercial marine vessels. 

These control factors accounted for federal standards for commercial marine vessels and 

locomotives. For the remaining non-road mobile categories, the contractor ran our

NONROAD2004 model for 2018. This accounted for estimated growth in equipment populations 

and incorporated anticipated effects of most final federal standards, including the Tier 4 

compression-ignition engine standards and the exhaust emissions standards for large sparkignition engines, compression-ignition marine engines, and land-based recreational engines. 

ODEQ and CENRAP also developed the on-road mobile source emissions inventory for 2018 

with contractor support. Biogenic emissions were held constant from the 2002 emission 

 

4

 Note, our proposed FIP, discussed in section V.E, would require a stricter level of SO2 for six units in these 

facilities.
24

inventory. Oklahoma’s 20018 emissions inventory is summarized in Table 3:

Table 3. Oklahoma’s 2018 Emissions Inventory

SO2 NH3 NOx VOCs PM10-

PM2.5 PM2.5

Point 106,701 35,215 140,298 125,648 8,935 13,989

Area 12,374 141,532 128,257 400,056 275,844 127,018

Non-road 

mobile 156 40 25,387 28,489 2,914 292

On-road 

mobile 545 5,818 39,397 39,281 0 953

Biogenic 0 0 35,909 988,314 0 0

Total 119,776 182,605 369,248 1,581,788 287,693 142,252

CENRAP and ODEQ used this and other state’s 2018 emission inventories to construct 

visibility projection modeling for 2018. We find ODEQ’s 2018 emission inventory to be 

acceptable. 

2. Visibility Projection Modeling

Pages 48-68

Sections A through E of Chapter V of the ODEQ Regional Haze SIP discuss the modeling 

methods and protocol used by CENRAP and ODEQ in developing the assessment. Chapter IV 

describes the baseline emission inventory used in the Oklahoma analysis and submitted as 

Appendix 4-1 of the ODEQ RH SIP. A detailed description and discussion of the model 

selection, modeling protocol, quality assurance, performance evaluation, emission inventory 

development and data used in the regional haze analysis can be found in Appendix A and 

references therein. A short summary is provided below:

CENRAP performed modeling for the regional haze LTS for its member states, including 

Oklahoma. The modeling analysis is a complex technical evaluation that began with selection of 

the modeling system. CENRAP used the following modeling system:

Meteorological Model: The Pennsylvania State University/National Center for 

Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) is a 

nonhydrostatic, prognostic meteorological model routinely used for urban- and regionalscale photochemical, PM2.5, and regional haze regulatory modeling studies.

Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling 

system is an emissions modeling system that generates hourly gridded speciated emission 

inputs of mobile, non-road mobile, area, point, fire and biogenic emission sources for 

photochemical grid models.
25

Air Quality Model: EPA’s Models-3/ Community Multiscale Air Quality (CMAQ) 

modeling system is a photochemical grid model capable of addressing ozone, PM, 

visibility and acid deposition at a regional scale. The photochemical model selected for 

this study was CMAQ version 4.5. It was modified through CENRAP with a module for 

Secondary Organics Aerosols (SOA) in an open and transparent manner that was also 

subjected to outside peer review. The Comprehensive Air Quality Model with extensions 

(CAMx) Version 4.40 model, applied using similar options as used by CMAQ, was used 

as a secondary corroborative model. CAMx was also utilized with its Particulate Source 

Apportionment Technology (PSAT) tool to provide source apportionment of predicted 

nitrate and sulfate aerosol concentrations. 

CMAQ and CAMx modeling of regional haze in the CENRAP region for 2002 and 2018 was 

carried out on a grid of 36x36 kilometer (km) cells that covers the continental United States, 

portions of Canada and Mexico, and portions of the Atlantic and Pacific Oceans along the east 

and west coasts. Selection of a representative period of meteorology is crucial for evaluating 

baseline air quality conditions and projecting future changes in air quality due to changes in 

emissions of visibility-impairing pollutants. CENRAP conducted an in-depth analysis which 

resulted in the selection of the entire year of 2002 (January 1-December 31) as the best period of 

meteorology available for conducting the modeling. The CENRAP modeling was developed 

consistent with our guidance.5

CENRAP examined the model performance of the regional modeling for the areas of interest 

before determining whether the CMAQ model results were suitable for use in the regional haze 

assessment of the LTS and for use in the modeling assessment. The modeling assessment 

predicts future levels of emissions and visibility impairment used to support the LTS and to 

compare predicted, modeled visibility levels with those on the URP. In keeping with the 

objective of the CMAQ modeling platform, the air quality model performance was evaluated 

using graphical and statistical assessments based on ambient measurements of PM species,

gas-phase species and wet deposition from various monitoring networks and databases for the 

2002 base year. CENRAP used a diverse set of statistical parameters from our Modeling 

Guidance to stress and examine the model and modeling inputs. Once CENRAP determined the 

model performance to be acceptable, CENRAP used the model to assess the 2018 RPGs using 

the current and future year air quality modeling predictions, and compared the RPGs to the 

URP. Table 4 summarizes the projected contribution from Oklahoma emissions on visibility 

degradation at Class I areas for the 20 percent worst days in 2018. Note, this table only 

includes contributions of 0.15 deciviews or greater.

 

5

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

Ozone, PM2.5, and Regional Haze, (EPA-454/B-07-002), April 2007, located at 

http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf

 Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Air 

Quality Standards (NAAQS) and Regional Haze Regulations, August 2005, updated November 2005 (―our

Modeling Guidance‖), located at http://www.epa.gov/ttnchie1/eidocs/eiguid/index.html, EPA-454/R-05-001
26

Table 4. Projected Contribution from Oklahoma Emissions on Visibility Degradation for 

the 20 Percent Worst Days in 2018

Class I area State

Contribution

to light

Extinction

(Mm-1)

Total light

Extinction

(Mm-1)

Oklahoma

contribution

(Percent)

Deciview

contribution

Wichita

Mountains Oklahoma 12.28 86.56 14.19% 1.53

HerculesGlades Missouri 3.74 103.49 3.61% 0.37

Salt Creek New

Mexico 1.46 57.67 2.53% 0.26

Caney Creek Arkansas 2.23 96.84 2.30% 0.23

Upper 

Buffalo Arkansas 1.97 97.16 2.03% 0.21

Guadalupe

Mountains Texas 1.11 55.43 2.00% 0.20

Seney Michigan 1.74 95.27 1.83% 0.18

White

Mountain

New

Mexico 0.69 40.8 1.70% 0.17

Isle Royale Michigan 1.08 73.71 1.46% 0.15

This modeling indicates that emissions from Oklahoma contribute a small amount to visibility 

degradation at Class I areas in other states. In fact, this modeling indicates that Oklahoma’s 

emissions are responsible for 3.61% or less to the visibility degradation at all other Class I 

areas other than the Wichita Mountains. This informed ODEQ’s LTS and its state consultations. 

EPA Findings

We find ODEQ’s 2002 and 2018 emission inventory and the visibility projection modeling to be 

acceptable. We find that Oklahoma has adequately documented the technical basis, including 

modeling, monitoring, and emissions information, on which it relied upon to determine its 

apportionment of emission reduction obligations necessary for achieving reasonable progress in 

each mandatory Class I Federal area it affects. We also find that Oklahoma has adequately 

identified all anthropogenic sources of visibility impairment considered by it in developing its 

LTS. We therefore find that ODEQ has satisfied this requirement.
27

D.4 Consideration of Long Term Strategy Factors: 40 CFR 51.308(d)(3)(v)

Oklahoma must consider, at a minimum, the following factors in developing its long-term 

strategy:

(A) Emission reductions due to ongoing air pollution control programs, including 

measures to address reasonably attributable visibility impairment; 

(B) Measures to mitigate the impacts of construction activities; 

(C) Emissions limitations and schedules for compliance to achieve the reasonable 

progress goal; 

(D) Source retirement and replacement schedules;

(E) Smoke management techniques for agricultural and forestry management 

purposes including plans as currently exist within the State for these purposes;

(F) Enforceability of emissions limitations and control measures; and 

(G) The anticipated net effect on visibility due to projected changes in point, area, and 

mobile source emissions over the period addressed by the long-term strategy.

State Submittal

Pages 86-91, 114. Appendices 1-1, 6-1

In addition to its BART determinations and by extension our proposed FIP, Oklahoma’s LTS 

incorporates emission reductions due to a number of ongoing air pollution control programs. 

This includes enforceable emissions limitations, compliance schedules, administrative orders, 

the issuance and enforcement of permits limiting emissions from all known major sources in 

Oklahoma, state rules which specifically limit targeted emissions sources and categories, and 

several other ongoing air pollution control programs. ODEQ has promulgated rules in order to 

administer these programs. These rules govern ODEQ’s permitting process, including PSD, 

BACT and BART requirements, and implementation of federal requirements. ODEQ also has 

promulgated rules that limit emissions in order to comply with the National Ambient Air Quality 

Standards, which have ancillary visibility improving benefits. Other air pollution control 

programs, including federal mobile emissions programs, the Clean Air Interstate Rule, 

Maximum Achievable Control Technology, and Refinery Consent Decrees are implemented by 

ODEQ, which similarly have ancillary visibility improving benefits. 

ODEQ addresses the long term strategy factors in 40 CFR 51.308(d)(3)(v)(A)-(G) as follows:

(A) Emission reductions due to ongoing air pollution control programs, including measures 

to address reasonably attributable visibility impairment
28

ODEQ points out that since Oklahoma attains all national ambient air quality standards, 

it has not implemented many ongoing pollution-control programs beyond those required 

under federal regulations. These rules include motor vehicle emissions control 

programs, CAIR, and EPA requirements for cleaner non-road diesel and gasolinepowered engines. ODEQ’s rules under the Oklahoma Administrative Code (OAC) that 

limit opacity and particulate matter, sulfate, nitrate, and carbon emissions from point 

and area sources include:

OAC 252:100-7, Permits for Minor Facilities, applies to permitted and 

unpermitted minor sources.

OAC 252:100-8, Permits for Part 70 Sources, which contains provisions for PSD 

and BART requirements.

OAC 252:100-8, Parts 7 and 9, which are ODEQ’s PSD NSR rules, require the 

application of BACT or Lowest Achievable Emission Rate (LAER) to major new 

and modified point sources of SO2, NOx, VOCs, and particulate matter. 

OAC 252:100-13, Open Burning, prohibits most open burning of refuse and other 

combustible materials.

OAC 252:100-17, 19, 23, 24, and 25 limits particulate matter and/or opacity of 

emissions from incinerators; fuel-burning units and industrial processes; cotton 

gins; fugitive dust from grain, feed, and seed operations; smoke, visible 

emissions, and particulates.

OAC 252:100-29, Control of Fugitive Dust, provides control over fugitive dust, 

including the minimization of blowing dust from construction activities.

OAC 252:100-31, Control of Emission of Sulfur Compounds, limits sulfur dioxide, 

hydrogen sulfide, total reduced sulfur compounds, and sulfuric acid from various 

sources, including: petroleum and natural gas related operation processes, pulp 

mills, electric generating units, limekilns, sulfuric acid plants, smelters, and sulfur 

recovery units at petroleum refineries and all other sources.

OAC 252:100-33, Control of Emission of Nitrogen Oxides, controls emissions of 

nitrogen oxides from fuel-burning equipment.

ODEQ administers applicable federal rules, including (1) motor vehicle emissions 

control programs under 40 C.F.R. Parts 80, 81, 86, 89, 90, 92, 94, 1039, and 1048; (2) 

reciprocating internal combustion engines regulation under 40 C.F.R. section 63.6580 et 

seq.; (3) turbine MACT regulations in 40 C.F.R. section 63.6080 et seq.; and (4) the new 

source performance standard for residential wood combustion under 40 C.F.R. Part 60, 

Subpart AAA. Oklahoma has also joined us in refinery consent decrees that result in 

significant SO2 and NOx reductions.

We approved ODEQ’s SIP to address Reasonably Attributable Visibility Impairment 

(RAVI) at the Wichita Mountains on November 8, 1999 (64 FR 60683), The FLMs did 

not identify any integral vistas in Oklahoma. In addition, the Wichita Mountains is not 

experiencing RAVI, nor are any Oklahoma sources affected by the RAVI provisions. 

Therefore, the Oklahoma RH SIP does not incorporate any measures to specifically 

address RAVI. .
29

(B) Measures to mitigate the impacts of construction activities

We note that fine and coarse particulate, of which construction-related activities are 

thought to be a small contributor, are themselves minor contributors to visibility 

impairment at the Wichita Mountains. ODEQ relies on fugitive dust control rules in 

OAC 252:100-29 to control and minimize dust from construction activities.

(C) Emissions limitations and schedules for compliance to achieve the reasonable progress 

goal

ODEQ implements an air permitting program that includes the issuance of permits to all 

known major point sources in Oklahoma. Each permit contains enforceable limitations 

on emissions of various pollutants, some of which may cause or contribute to regional 

haze at the Wichita Mountains. Unless permits specify a different schedule for 

compliance, ODEQ requires permitted sources to comply with their permits immediately 

upon issuance. ODEQ also enforces compliance schedules of relevant administrative and 

judicial orders, including consent decrees. ODEQ also enforces compliance schedules of 

relevant administrative and judicial orders, including consent decrees.

We and ODEQ have entered into refinery consent decrees with Sinclair Oil, Valero 

Refining, Sunoco Refinery, and Conoco, Inc. that include SO2 and NOx reductions. 

CENRAP modeling in support of this SIP included these reductions. These enforceable 

consent decrees include compliance schedules.

(D) Source retirement and replacement schedules

ODEQ considered source retirement and replacement schedules in developing its longterm strategy of emissions reductions. ODEQ took the position that it could not reliably 

predict the retirement or replacement of sources and did not rely on source retirement to 

achieve any reasonable progress goal. 

(E) Smoke management techniques for agricultural and forestry management purposes 

including plans as currently exist within the State for these purposes;

Fires are responsible for much of the directly emitted fine particulate matter in the 

Oklahoma emissions inventory. ODEQ considered smoke management techniques for 

the purposes of agricultural and forestry management in its LTS. OAC 252:100-13 

includes provisions regulating the burning of forestland, cropland, and rangeland. As 

Tables IV-1 and IV-2 in the Oklahoma RH SIP revision submittal indicate, all types of 

fire sources (wildfire, agricultural burning, rangeland burning, etc.) are responsible for 

approximately 4.2% of the total SO2, 4.1% of the total NH3, 3.9% of the total NOx, 2.1 % 

of the total VOCs, and 3.6% of the total PM10 emissions. In contrast, fire is responsible 

for about 33.4% of the total PM2.5 emissions. However, Table VIII-3 of the Oklahoma 

RH SIP indicates that all Oklahoma area sources combined, of which fire is only a part, 

account for less than 1% of the total visibility impact at the Wichita Mountains. 

Nevertheless, ODEQ states that it and the Oklahoma Department of Agriculture, Food, 
30

and Forestry intend to create a basic, voluntary smoke management program based on 

our Interim Air Quality Policy on Wildland and Prescribed Fires.

(F) Enforceability of emissions limitations and control measures

ODEQ has adopted rules to ensure the enforceability of these emission limitations. This 

includes rules that govern ODEQ’s permitting process for major and minor sources, 

Prevention of Significant Deterioration (PSD) provisions, Best Available Control 

Technology (BACT), and BART requirements. ODEQ issues permits to all major and 

minor point sources in Oklahoma, and each permit contains enforceable limitations on 

emissions of various pollutants, including those which cause or contribute to RH at the 

Wichita Mountains. Unless those permits specify a different schedule for compliance, 

ODEQ requires permitted sources to comply with their permits immediately upon 

issuance. ODEQ also enforces compliance schedules of relevant administrative and 

judicial orders, including consent decrees that result in significant SO2 and NOx 

reductions. ODEQ has committed to issuing enforceable Part 70 air quality permits 

requiring BART-eligible sources subject to BART to install BART and achieve the 

associated BART emission limits. Subject sources must achieve the BART emission limits 

referenced above within five years of our approval the SIP. Section 51.308(e)(1)(iv). 

Furthermore, the emission limitations we propose to require under our FIP are 

“applicable federal requirement[s]” under the CAA, and thus must be incorporated in 

those facilities’ Title V operating permits.

ODEQ has the statutory authority to carry out all “duties, requirements, and 

responsibilities necessary and proper for the implementation of the Oklahoma Clean Air 

Act and fulfilling the requirements of the Federal Clean Air Act.” 27A O.S. § 2-5-

105(22). This authority includes “sole environmental jurisdiction to regulate air 

emissions from all facilities and sources subject to operating permit requirements under 

Title V of the Federal Clean Air Act as amended.” 27A O.S. § 1-3-101(E)(8). The ODEQ 

also has authority to issue and renew air quality permits, see 27A O.S. §§ 2-5-112(B), 2-

5-105(22), and 2-14-202, as well as to reopen and revise existing air quality permits, see 

27A O.S. § 2-5-112(B)(3). It is unlawful for any entity to “operate any new or existing 

source of air contaminants except in compliance with a permit issued by the [ODEQ].” 

27A O.S. § 2-5-112(A); see also OAC 252:100-8-1.3.

(G) The anticipated net effect on visibility due to projected changes in point, area, and mobile 

source emissions over the period addressed by the long-term strategy.

As described above, CENRAP used its 2002 emissions inventory as the starting point for 

its 2018 emissions inventory. The 2018 emissions inventory was designed to capture the 

anticipated changes in point, area, and mobile sources emissions over the period 

addressed by the long term strategy. ODEQ assumed in the visibility projection 

modeling summarized above that Units 4 and 5 of the OG&E Muskogee plant, Units 1 

and 2 of the OG&E Sooner plant, and Units 3 and 4 of the AEP/PSO Northeastern 

plants, would be controlled at presumptive SO2 BART levels, when in fact, ODEQ found 

that SO2 BART was no control. This assumption of these units being controlled is 
31

partially responsible for the low projected influence of Oklahoma’s emissions on the 

visibility of Class I areas outside of the state. As we are remedying that flaw through our 

proposed FIP, we find ODEQ’s visibility projection modeling to be acceptable, and that 

ODEQ properly considered the anticipated net effect on visibility due to projected 

changes in point, area, and mobile source emissions over the period addressed by the 

long-term strategy, required by section 51.308(d)(3)(v)(G).

EPA Findings

We find that with the addition of the controls included under our proposed FIP, the ODEQ has 

adequately addressed factors A - G of 51.308(d)(3)(v) in developing its long-term strategy.

E. Monitoring Strategy/Other SIP Requirements 40 CFR 51.308(d)(4)

Oklahoma must submit with the SIP a monitoring strategy for measuring, characterizing, and 

reporting of regional haze visibility impairment that is representative of all mandatory Class I 

Federal areas within it. This monitoring strategy must be coordinated with the monitoring 

strategy required in section 51.305 for reasonably attributable visibility impairment. Compliance 

with this requirement may be met through participation in the Interagency Monitoring of 

Protected Visual Environments network. The SIP must also provide for the following: 

(i) The establishment of any additional monitoring sites or equipment needed to 

assess whether reasonable progress goals to address regional haze for all 

mandatory Class I Federal areas within Oklahoma are being achieved.

(ii) Procedures by which monitoring data and other information are used in 

determining the contribution of emissions from within Oklahoma to regional haze 

visibility impairment at mandatory Class I Federal areas both within and outside 

the State.

(iii) For a State with no mandatory Class I Federal areas, procedures by which 

monitoring data and other information are used in determining the contribution of 

emissions from within the State to regional haze visibility impairment at 

mandatory Class I Federal areas in other States.

(iv) The SIP must provide for the reporting of all visibility monitoring data to the 

Administrator at least annually for each mandatory Class I Federal area in the 

State. To the extent possible, Oklahoma should report visibility monitoring data 

electronically.

(v) A statewide inventory of emissions of pollutants that are reasonably anticipated to 

cause or contribute to visibility impairment in any mandatory Class I Federal area. 

The inventory must include emissions for a baseline year, emissions for the most 

recent year for which data are available, and estimates of future projected 
32

emissions. The State must also include a commitment to update the inventory 

periodically.

(vi) Other elements, including reporting, recordkeeping, and other measures, 

necessary to assess and report on visibility.

State Submittal

Pages 8-10, 119

Section 51.308(d)(4) requires the SIP contain a monitoring strategy for measuring, 

characterizing, and reporting of regional haze visibility impairment that is representative of all 

mandatory Class I Federal areas within the State. This monitoring strategy must be coordinated 

with the monitoring strategy required in Section 51.305 for reasonably attributable visibility 

impairment. As Section 51.308(d)(4) notes, compliance with this requirement may be met 

through participation in the IMPROVE network, which applies to the monitor at the Wichita

Mountains. ODEQ stated its intention to rely on the IMPROVE network for complying with the 

regional haze monitoring requirement in our RHR for the current and future regional haze 

implementation periods. The primary monitoring network for regional haze in CENRAP is the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) network. FLMs 

responsible for Class I areas joined us in 1985 in response to the 1977 CAA Amendments to form 

the IMPROVE Program. The IMPROVE Steering Committee consists of representatives from 

the FLMs, us, and regional-state agencies At the direction of the National Park Service, the 

Cooperative Institute for Research in the Atmosphere, at Colorado State University’s Foothills 

Campus in Fort Collins, Colorado, maintains the network plan on the IMPROVE Internet Web 

site.6 The primary objectives of the IMPROVE Web site are to provide federal, state, and air 

quality regulatory agencies as well as the general public access to IMPROVE data, data 

products, and metadata fully describing the IMPROVE database, including characteristics and 

history of all network sites. The Web site allows users to query data, review reports, and 

communicate with different members of the IMPROVE Program.

Section 51.308(d)(4)(i) requires the establishment of any additional monitoring sites or 

equipment needed to assess whether reasonable progress goals to address regional haze for all 

mandatory Class I Federal areas within the State are being achieved. Shortly after the creation 

of CENRAP, its monitoring workgroup noted the lack of a representative monitor for the Wichita 

Mountains. At that time, an IMPROVE site for Upper Buffalo, Arkansas, in a wetter climate 

several hundred miles to the east-northeast, provided the closest available visibility monitoring 

data. Because this monitoring data was deemed unrepresentative, a particle sampler monitor 

was established at the Wichita Mountains and began operating in March, 2001. As described in 

section III.C.1, above, baseline visibility conditions were calculated using data representative of 

2002-2004 due to lack of data from previous years.

Section 51.308(d)(4)(ii) requires that ODEQ establish procedures by which monitoring data and 

other information are used in determining the contribution of emissions from within Oklahoma 

to regional haze visibility impairment at mandatory Class I Federal areas both within and 

 

6

 http://vista.cira.colostate.edu/improve/
33

outside the State. The monitor at the Wichita Mountains is operated by Wichita Mountains

personnel. Under IMPROVE protocol, the Wichita Mountains personnel send filter modules for 

analysis to the Crocker Nuclear Laboratory at the University of California in Davis every 

Tuesday. The modules collect 24-hour samples every third calendar day. The IMPROVE 

program makes data available on the Internet via the site noted above and submits them to our

air quality system. This information is used to characterize the visibility at the Wichita 

Mountains and assess Oklahoma’s progress toward its RPG. The IMPROVE monitoring 

program is national in scope, and other states have similar monitoring and data reporting 

procedures, ensuring a consistent and robust monitoring data collection system. 

Section 51.308(d)(4)(iv) requires that the SIP must provide for the reporting of all visibility 

monitoring data to the Administrator at least annually for each mandatory Class I Federal area 

in the State. To the extent possible, Oklahoma should report visibility monitoring data 

electronically. Section 51.308(d)(4)(vi) also requires that ODEQ provide for other elements, 

including reporting, recordkeeping, and other measures, necessary to assess and report on 

visibility. Again, Oklahoma’s participation in the IMPROVE network ensure the monitoring 

data is reported at least annually and is available for download from the site referenced above.

Section 51.308(d)(4)(iv) requires that ODEQ maintain a statewide inventory of emissions of 

pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any 

mandatory Class I Federal area. The inventory must include emissions for a baseline year, 

emissions for the most recent year for which data are available, and estimates of future projected 

emissions. The State must also include a commitment to update the inventory periodically. 

Please refer to section III.D, above, where we discuss how ODEQ has constructed its emission 

inventory. ODEQ has stated on page 119 of its RH SIP that it intends to update the Oklahoma 

statewide emissions inventories periodically and review periodic emissions information from 

other states and future emissions projections.

EPA Findings

We find that ODEQ has satisfied the requirements of section 51.308(d)(4).

F. Best Available Retrofit Technology (BART) Requirements: 40 CFR 51.308(e)

Best Available Retrofit Technology (BART) requirements for regional haze visibility 

impairment. Oklahoma must submit a SIP containing emission limitations representing BART 

and schedules for compliance with BART for each BART eligible source that may reasonably be 

anticipated to cause or contribute to any impairment of visibility in any mandatory Class I 

Federal area, unless it demonstrates that an emissions trading program or other alternative will 

achieve greater reasonable progress toward natural visibility conditions.

Notes: The evaluation of whether the RH SIP contains ―adequate emission limitations 

representing BART and schedules for compliance with BART for each BART eligible 

source that may reasonably be anticipated to cause or contribute to any impairment of 
34

visibility in any mandatory Class I Federal area‖ is conducted within each BART 

evaluation in Sections III.F.2.1 to III.F.2.7.

40 CFR 51.308(e)(2) – (3) concerning the use of a trading program or other alternative to 

BART are not applicable to this SIP.

F.1. BART Eligible Source Identification: 40 CFR 51.308(e)(1)(i)

Oklahoma must submit a SIP containing a list of all BART-eligible sources within the State.

Note: This section is intended to cover the general question of whether the state identified which 

sources were subject to BART. Subsequent sections treat this determination in more detail.

State Submittal

Pages, 69-71

Oklahoma’s BART rule, OAC 252:100-8, Part 11, and companion definitions related to the 

BART rule added as OAC 252:100-8.1.1, implement the BART requirements of ODEQ’s regional 

haze rule. The Oklahoma BART rule sets out the criteria for identifying BART-eligible sources 

and incorporates by reference the guidelines contained in Appendix Y to 40 C.F.R. Part 51.

These rules can be found in Appendix 6-1. ODEQ identified sources that were BART eligible if 

they:

1. Belonged to one of the twenty-six BART categories listed below;

2. Did not operate before 7 August 1962 but were in existence on 7 August 1977; and 

3. Had the potential to emit 250 tons per year or more of any visibility-impairing pollutant.

ODEQ determined that the visibility-impairing pollutants in Oklahoma include sulfur dioxide 

(SO2), nitrogen oxides (NOx), and particulate matter (PM10 and PM2.5). ODEQ determined 

using CENRAP modeling including source apportionment techniques and an evaluation of 

monitoring data that anthropogenic VOCs do not significantly impair visibility at the Wichita 

Mountains. Because of the uncertainties in assessing the effect of ammonia emissions reductions 

on visibility and review of modeling an monitoring data, Oklahoma decided to not consider 

ammonia among visibility-impairing pollutants. 

ODEQ initially determined which sources were BART eligible by screening its emissions 

inventory and permitting database to identify major facilities with emission units in one or more 

of the 26 BART categories:

1. Fossil-fuel fired steam electric plants of more than 250 million British thermal 

units (BTU) per hour heat input,

2. Coal cleaning plants (thermal dryers),

3. Kraft pulp mills,

4. Portland cement plants,

5. Primary zinc smelters,
35

6. Iron and steel mill plants,

7. Primary aluminum ore reduction plants,

8. Primary copper smelters,

9. Municipal incinerators capable of charging more than 250 tons of refuse per day,

10. Hydrofluoric, sulfuric, and nitric acid plants,

11. Petroleum refineries,

12. Lime plants,

13. Phosphate rock processing plants,

14. Coke oven batteries,

15. Sulfur recovery plants,

16. Carbon black plants (furnace process),

17. Primary lead smelters,

18. Fuel conversion plants,

19. Sintering plants,

20. Secondary metal production facilities,

21. Chemical process plants,

22. Fossil-fuel boilers of more than 250 million BTUs per hour heat input,

23. Petroleum storage and transfer facilities with a capacity exceeding 300,000 

barrels,

24. Taconite ore processing facilities,

25. Glass fiber processing plants, and

26. Charcoal production facilities.

ODEQ then used its databases and records to identify facilities within these source categories 

with potential emissions of 250 tons per year or more for any visibility-impairing pollutant from 

any unit that was in existence on August 7, 1977 and began operation after August 7, 1962. As 

necessary, ODEQ contacted the sources to obtain or confirm pertinent data and information. 

These emissions units comprise the “BART-eligible sources” listed in Table 5, taken from Table 

VI-1 of the RH SIP:
36

Table 5. Facilities with BART-eligible units in Oklahoma

BART source category Facility name County Number 

of units

Fossil fuel-fired boilers 

of more than 250 

MMBTU/hr heat input

Georgia Pacific Consumer Products 

(formerly Fort James Operating) 

Muskogee Mill

Muskogee 2

Kraft pulp mill International Paper (formerly 

Weyerhaeuser) Valliant Paper Mill McCurtain 4

Hydrofluoric, sulfuric, 

and nitric acid plants 

Koch Nitrogen Enid Plant Garfield 7

Terra International Oklahoma 

Woodward Complex Woodward 11

Terra Nitrogen Partnership 

Verdigris Plant Rogers 12

Petroleum refineries 

Sinclair Oil Tulsa Refinery Tulsa 7

Holly Refining and Marketing 

(formerly Sunoco) Tulsa Refinery Tulsa 25

Wynnewood Refining Garvin 14

Valero Refinery (formerly TPI 

Petroleum Inc) Ardmore Refinery Carter 24

Portland cement plants Lafarge Building Materials Tulsa 

Rogers City Line Rogers 10

Fossil fuel-fired steam 

electric plants of more 

than 250 MMBTU/hr 

heat input 

OG&E Horseshoe Lake Generating 

Station Oklahoma 2

OG&E Muskogee Generating 

Station Muskogee 2

OG&E Seminole Generating Station Seminole 3

OG&E Sooner Generating Station Noble 2

PSO Comanche Power Station Comanche 2

PSO Northeastern Power Station Rogers 3

PSO Riverside Jenks Power Station Tulsa 2

PSO Southwestern Power Station Caddo 1

Western Farmers Electric Coop 

Anadarko Plant Caddo 3

Western Farmers Electric Coop 

Mooreland Station Woodward 3
37

EPA Findings

We find that ODEQ has adequately identified all BART-eligible sources within the State.

F.1.1. BART Sources Exempted Due to Modeling Waivers and Modeling Waivers with

Permit Modifications

State Submittal

Following the identification of those sources which were determined to be BART eligible, 

ODEQ required each sources to perform visibility modeling to determine whether it caused or 

contributed to visibility impairment at any Class I area. Pursuant to federal regulations, states 

have the option of exempting a BART-eligible source from the BART requirements based on 

dispersion modeling demonstrating that the source cannot reasonably be anticipated to cause or 

contribute to visibility impairment in a Class I area. Please see Appendix A for more details of 

how sources were granted waivers by the state and our analysis of this modeling.

Based on the approved BART modeling protocol between CENRAP, EPA, FLM representatives, 

and states participating in CENRAP, Oklahoma granted BART applicability waivers to a number 

of facilities. For sources that utilized BART modeling that did not include the inclusion of 

meteorological observations using the 1st High or maximum value from CALPUFF modeling 

was required to be below 0.5 deciviews to obtain a waiver. For modeling that did include the 

meteorological observations, the 8th High value from CALPUFF modeling was required to be 

below 0.5 deciviews to obtain a waiver. Based on these modeling considerations the state issued

waivers to 11 facilities that are listed in Table 6-2 of Appendix A.

With this initial BART screening modeling, ODEQ also identified three sources with a 

contribution of greater than 0.5 deciviews in visibility impairment that desired to limit their 

emissions in order to avoid a BART determination. These sources were (1) the Georgia Pacific 

Consumer Products LP, Muskogee Mill; (2) the International Paper, Valliant Paper Mill; and (3) 

the Western Farmers Electric Coop (WFEC), Anadarko Plant. As described below, each of these 

facilities then identified reductions in emissions that were subsequently secured through Title V 

permit modifications. Following this, an updated BART modeling analysis, assuming those 

controls were in place, demonstrated a contribution of less than 0.5 deciview of visibility 

impairment under all conditions. 

a. Georgia Pacific Consumer Products LP, Muskogee Mill

The Georgia Pacific, Muskogee Mill has two BART eligible boilers, Boiler B-1 and 

Boiler B-2. Georgia Pacific requested of ODEQ that an enforceable emission limit be imposed 

on Boiler B-1 to maintain emissions below the BART contribution threshold of 0.5 deciviews. 

Where previously Boiler B-1 was permitted to burn either No. 2 fuel oil or natural gas, Boiler B1 is now restricted to burning natural gas, which will reduce its NOx emissions. ODEQ has 

determined that under the Title V operating permit modification, this facility will have a 

visibility impairment contribution of less than 0.5 deciviews at any Class I area, which is below 

the contribution threshold used by ODEQ in their BART analyses. This emission reduction is 

housed in a modification to the facility’s Oklahoma Department of Environmental Quality, Air 
38

Quality Division operating Permit, No. 99-113-TV (M-5), issued January 5, 2011. This permit 

requires that this fuel switch be operational no more than five years following our final action on 

the Oklahoma RH SIP.

b. International Paper, Valliant Paper Mill

The International Paper, Valliant Paper Mill has three BART eligible boilers: EUG D1, 

Bark Boiler ; EUG D2, Power Boiler; and EUG D3, Package Boiler. It also has a BART eligible 

Lime Kiln, EUG E7a. The Valiant Paper Mill has accepted limits on the sulfur content of fuel to 

the Bark and Power boilers in order to reduce its visibility impact. ODEQ has determined that 

following this modification, this facility will have a visibility impairment contribution of less 

than 0.5 deciviews at any Class I area, which is below the contribution threshold used by ODEQ 

in their BART analyses. This emission reduction is housed in a modification to the facility’s 

Oklahoma Department of Environmental Quality, Air Quality Division operating Permit No. 97-

057-TV (M-10), issued March 24, 2010. This permit requires these controls be operational no 

more than five years following our final action on the Oklahoma RH SIP.

c. Western Farmers Electric Coop, Anadarko Plant

The Western Farmers Electric Coop (WFEC), Anadarko facility has three BART eligible 

combine cycle gas turbines, AN-Unit 4, AN-Unit 5, and AN-Unit 6. It also has a BART eligible 

internal combustion engine, AN ENG-1. WFEC has agreed to NOx, SO2, and PM-10 emission 

limits on the combined cycle gas turbines in order to reduce their visibility impact. ODEQ has 

determined that following this modification, this facility will have a visibility impairment 

contribution of less than 0.5 deciviews at any Class I area, which is below the contribution 

threshold used by ODEQ in their BART analyses. This emission reduction is housed in a 

modification to the facility’s Oklahoma Department of Environmental Quality, Air Quality 

Division operating Permit, No. 2005-037-TVR (M-1), issued July 9, 2010. This permit will 

require these controls be operational no more than five years following our final action on the 

Oklahoma RH SIP.

EPA Findings

We find that ODEQ has demonstrated that the modifications proposed in the three plants 

discussed above will result in emission reductions that are adequate to exempt these sources from 

being subject to BART using the 0.5 dv threshold for significant contribution to visibility 

impairment.

F.2. BART Five Factor Determination: 40 CFR 51.308(e)(1)(ii)(A)

For the sources that were not granted waivers ODEQ must make a determination of BART for 

each BART-eligible source in Oklahoma that emits any air pollutant which may reasonably be 

anticipated to cause or contribute to any impairment of visibility in any mandatory Class I 

Federal area. All such sources are subject to BART. The determination of BART must be based 

on an analysis of the best system of continuous emission control technology available and 
39

associated emission reductions achievable for each BART-eligible source that is subject to 

BART within the State. In this analysis, Oklahoma must take into consideration the technology 

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

compliance, any pollution control equipment in use at the source, the remaining useful life of the 

source, and the degree of improvement in visibility which may reasonably be anticipated to 

result from the use of such technology.

State Submittal

Table 7, adapted from Table VI-4 of the RH SIP, lists all of the sources determined by ODEQ to 

be subject to BART. Of these, the SO2 BART determinations for the Oklahoma Gas and Electric 

(OG&E) Sooner Generating Station Units 1 and 2, the OG&E Muskogee Generating Station

Units 4 and 5, and the American Electric Power/Public Service Company of Oklahoma 

(AEP/PSO) Northeastern Power Station Units 3 and 4 are the subject of our proposed FIP. The 

NOX and PM10 BART determinations for these facilities and the NOx BART determinations 

performed by ODEQ for the remaining facilities listed in Table 6 are evaluated in Sections 

III.F.2.1 – 2.6, below.

Table 6. Sources in Oklahoma required to install BART

Facility Name BART 

Emission 

Units 

Source Category Pollutants 

Evaluated 

OG&E Seminole 

Generating Station 

Units 1, 2, 

and 3 

fossil fuel-fired steam 

electric plants 

NOx 

OG&E Sooner 

Generating Station 

Units 1 

and 2 

fossil fuel-fired steam 

electric plants 

SO2

NOx 

PM10

OG&E Muskogee 

Generating Station 

Units 4 

and 5 

fossil fuel-fired steam 

electric plants 

SO2

NOx 

PM10

AEP/PSO Comanche 

Power Station 

Units 1 

and 2 

fossil fuel-fired steam 

electric plants 

NOx 

AEP/PSO Northeastern 

Power Station 

Unit 2 fossil fuel-fired steam 

electric plants 

NOx 

AEP/PSO Northeastern 

Power Station 

Units 3 

and 4 

fossil fuel-fired steam 

electric plants 

SO2

NOx 

PM10

AEP/PSO Southwestern 

Power Station 

Unit 3 fossil fuel-fired steam 

electric plants 

NOx 
40

EPA Findings

We find that ODEQ has adequately identified all sources subject to BART within the State. For 

each BART determination, except as we note below, we feel ODEQ adequately considered the 

requirements under section 51.308(e)(1)(ii)(A), to consider “the technology available, the costs 

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

pollution control equipment in use at the source, the remaining useful life of the source, and the 

degree of improvement in visibility which may reasonably be anticipated to result from the use of 

such technology. We disagree with ODEQ’s SO2 BART determinations for Units 4 and 5 of the 

OG&E Muskogee plant, Units 1 and 2 of the OG&E Sooner plant, and Units 3 and 4 of the 

AEP/PSO Northeastern plants. The SO2 BART determinations for these six units are the subject 

of our proposed FIP.

F.2.1. OG&E Seminole Units 1, 2, and 3 BART Determinations

State Submittal

Pages, 69 - 84, Appendix 6-4.

The OG&E Seminole Units 1, 2 and 3 are BART-eligible sources. These units are gas 

fired boiler with gross outputs of 567 MW each. ODEQ determined that Low NOx Burners 

(LNB) and Over Fire Air (OFA) are technically feasible for all three units. As Seminole Unit 3 

is currently fitted with Flue Gas Recirculation (FGR), ODEQ deemed it technically feasible for 

retrofit to the remaining units. ODEQ also determined that Selective Catalytic Reduction (SCR)

was technically feasible and that Selective Non-Catalytic Reduction (SNCR) was not technically 

feasible. ODEQ evaluated the control effectiveness of these technologies. ODEQ then evaluated 

the impacts, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, and the remaining useful life. Lastly, ODEQ performed modeling to 

investigate the visibility impacts. ODEQ found that NOx BART was the installation of LNB with 

OFA and FGR for Units 1, 2, and 3 based on the following conclusions:

The cost of installing SCR on each unit is significantly higher than the cost for 

LNB with OFA and FGR. Additional capital costs for SCR on Units 1-3 are on 

average $89,957,200 per unit. Based on projected actual emissions, SCR could 

reduce overall NOx emissions from Seminole Units 1, 2 and 3 by approximately 

5,513 tpy (compared to combustion controls and flue gas recirculation); however, 

the incremental cost associated with this reduction is approximately $44,534,600 

per year, or $8,078/ton.

The cumulative visibility improvement for SCR, as compared to LNB/OFA and 

FGR across Wichita Mountains and Caney Creek (based on the 98th percentile 

modeled results) was 0.56-0.60 Δdv for all three units.

Installation of new LNB with OFA and FGR was cost effective, with a capital cost 

of $16,977,200 per unit for units 1 and 2 and $9,468,600 for unit 3 and an 
41

average cost effectiveness of $1,554-$2,120 per ton of NOx removed for each unit 

over a twenty year operational life.

Combustion control using the LNB/OFA and FGR do not require non-air quality 

environmental mitigation for the use of chemical reagents (i.e., ammonia or urea) 

and there is minimal energy impact.

NOx control levels on 30-day rolling averages of 0.203 lb/mmBtu for Unit 1, 

0.212 lb/mmBtu for Unit 2 and 0.164 lb/mmBtu for Unit 3 are justified.

Annual NOx emission reductions from new LNB with OFA and FGR on Units 1, 

2, and 3 are 662-1,704 tons for a total annual reduction of 3,998 tons.

As Units 1, 2, and 3 are gas fired, ODEQ determined that SO2 and PM BART for them 

are no additional control. Oklahoma’s BART rule requires each source subject to BART to 

install and operate BART no later than 5 years after we approve this implementation plan 

revision. OAC 252-100-8-75(e). 

EPA Findings

We find that ODEQ adequately determined BART for the Seminole Units 1, 2, and 3.

F.2.2. OG&E Sooner Units 1 and 2 BART Determinations

State Submittal

Pages, 69 - 84, Appendix 6-4.

The OG&E Sooner Units 1 and 2 are BART-eligible sources. Both units are coal fired 

with a gross output of 570 MW. The SO2 BART determinations for Units 1 and 2 are covered 

under our proposed FIP. Therefore, we reviewed ODEQ’s NOx and PM BART determination 

for these units.

ODEQ determined that LNB and OFA are technically feasible for both units. ODEQ 

also determined that SCR was technically feasible and that SNCR was not technically feasible. 

ODEQ evaluated the control effectiveness of these technologies. ODEQ then evaluated the 

impacts, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, and the remaining useful life. Lastly, ODEQ performed modeling to 

investigate the visibility impacts. ODEQ found that NOx BART was the installation of LNB with 

OFA for Units 1 and 2, based on the following conclusions:

The cost of installing SCR on each unit is significantly higher than the cost for 

LNB with OFA. Additional capital costs for SCR on Units 1 and 2 are on average 

$192,018,500 per unit. Based on projected emissions, SCR could reduce overall 

NOX emissions from Sooner Units 1 and 2 by approximately 3,226 TPY beyond 
42

combustion controls; however, the incremental cost associated with this reduction 

is approximately $17,795/ton. 

The cumulative visibility improvement for SCR, as compared to LNB/OFA across 

Wichita Mountains and Caney Creek (based on the 98th percentile modeled 

results) was 0.15 Δdv for both units. 

Installation of new LNB with OFA was cost effective, with a capital cost of 

$14,055,900 per unit for units 1 and 2 and an average cost effectiveness of $493-

785 per ton of NOX removed for each unit over a twenty-five year operational 

life.

Combustion control using the LNB/OFA does not require non-air quality 

environmental mitigation for the use of chemical reagents (i.e., ammonia or urea) 

and there is minimal energy impact.

NOx control levels on 30-day rolling averages of 0.15 lb/mmBtu for Unit 1 and 2 

are justified and meet the presumptive limits.

Annual NOx emission reductions from new LNB with OFA on Units 1 and 2 are 

2,664-4,241 tons for a total annual reduction of 6,905 tons based on actual 

emissions from 2004-2006 and projected emissions at maximum heat input and 

90% capacity.

ODEQ considered the addition of fabric filters (baghouses) for PM control on Units 1 

and 2, which are currently equipped with Electrostatic Precipitators (ESPs). ODEQ considered 

fabric filters to be technically feasible. ODEQ then evaluated the additional cost versus the 

increased PM control. ODEQ estimated the cost for fabric filters was $9,324/ton over the 

existing ESP controls. Based on the high cost and the incrementally small improvement in PM 

control, ODEQ determined that PM BART for Units 1 and 2 was the existing ESPs. ODEQ 

specified additional BART emission limitations in lbs/hour and tons/year. Oklahoma’s BART 

rule requires each source subject to BART to install and operate BART no later than 5 years 

after we approve this implementation plan revision. OAC 252-100-8-75(e). 

EPA Findings

EPA reviewed the modeling associated with the NOx and PM BART determinations 

ODEQ made for Units 1 and 2 of the OG&E Sooner plant. ODEQ determined that the 

installation of LNB with OFA to achieve an emission limit of 0.15 lb/mmBTU is NOx BART for 

these units. The BART determination also identified the existing electrostatic precipitators 

(ESP) as PM BART for these units. EPA reviewed ODEQ’s basis for these BART 

determinations, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, the remaining useful life and visibility modeling. EPA performed 

additional visibility modeling with CALPUFF that included our proposed SO2 limits in 

combination with ODEQ proposed limits for NOx and PM. This modeling suggests small 

visibility improvements can be expected from also requiring the installation of SCR on these 
43

units. The small visibility improvements achieved are not justified due to the additional cost of 

SCR. See Appendix B for additional information on EPA visibility modeling for these units. 

With the inclusion of the SO2 BART determinations covered in our proposed FIP, we find that 

ODEQ’s BART determinations for NOx and PM for Sooner Units 1 and 2 are adequate.

F.2.3. OG&E Muskogee Units 4 and 5 BART Determinations

State Submittal

Pages, 69 - 84, Appendix 6-4.

The OG&E Muskogee Units 4 and 5 are BART-eligible sources. Both units are coal fired 

with a gross output of 572 MW. The SO2 BART determinations for Units 4 and 5 are covered 

under our proposed FIP. Therefore, we reviewed ODEQ’s NOx and PM BART determination 

for these units.

ODEQ determined that LNB and OFA are technically feasible for both units. ODEQ 

also determined that SCR was technically feasible and that SNCR was not technically feasible. 

ODEQ evaluated the control effectiveness of these technologies. ODEQ then evaluated the 

impacts, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, and the remaining useful life. Lastly,ODEQ performed modeling to 

investigate the visibility impacts. ODEQ found that NOx BART was the installation of LNB with 

OFA for Units 4 and 5, based on the following conclusions:

The cost of installing SCR on each unit is significantly higher than the cost for 

LNB with OFA. Additional capital costs for SCR on Units 4 and 5 are on average 

$193,077,000 per unit. Based on projected emissions, SCR could reduce overall 

NOX emissions from Muskogee Units 4 and 5 by approximately 3,456 TPY 

beyond combustion controls; however, the incremental cost associated with this 

reduction is approximately $16,611/ton. 

The cumulative visibility improvement for SCR, as compared to LNB/OFA across 

Wichita Mountains and Caney Creek (based on the 98th percentile modeled 

results) was 0.10 and 0.18 Δdv respectively. 

Installation of new LNB with OFA was cost effective, with a capital cost of 

$14,113,700 per unit for units 4 and 5 and an average cost effectiveness of $260-

$281 per ton of NOx removed for each unit over a twenty-five year operational 

life.

Combustion control using the LNB/OFA does not require non-air quality 

environmental mitigation for the use of chemical reagents (i.e., ammonia or urea) 

and there is minimal energy impact.

NOx control levels on 30-day rolling averages of 0.15 lb/mmBtu for Unit 4 and 5 
44

are justified meet the presumptive limits prescribed by us.

Annual NOx emission reductions from new LNB with OFA on Units 4 and 5 are 

projected to be 2,018-2,469 tons for a total annual reduction of 4,487 tons based 

on actual emissions from 2004-2006 and projected emissions at maximum heat 

input and 90% capacity.

ODEQ considered the addition of fabric filters (baghouses) for PM control on Units 4 

and 5, which are currently equipped with Electrostatic Precipitators (ESPs). ODEQ considered 

fabric filters to be technically feasible. ODEQ then evaluated the additional cost versus the 

increased PM control. ODEQ estimated the cost for fabric filters was $9,324/ton over the 

existing ESP controls. Based on the high cost and the incrementally small improvement in PM 

control, ODEQ determined that PM BART for Units 4 and 5 was the existing ESPs. ODEQ 

specified additional BART emission limitations in lbs/hour and tons/year. Oklahoma’s BART 

rule requires each source subject to BART to install and operate BART no later than 5 years 

after we approve this implementation plan revision. OAC 252-100-8-75(e). 

EPA Findings

EPA reviewed the modeling associated with the NOx and PM BART determinations 

ODEQ made for Units 4 and 5 of the OG&E Muskogee plant. ODEQ determined that the 

installation of LNB with OFA to achieve an emission limit of 0.15 lb/mmBTU is NOx BART for 

these units. The BART determination also identified the existing electrostatic precipitators 

(ESP) as PM BART for these units. EPA reviewed ODEQ’s basis for these BART 

determinations, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, the remaining useful life and visibility modeling. EPA performed 

additional CALPUFF visibility modeling that included our proposed SO2 limits in combination 

with ODEQ proposed limits for NOx and PM. This modeling suggests small visibility 

improvements can be expected from also requiring the installation of SCR on these units. The 

small visibility improvements achieved are not justified due to the additional cost of SCR. See 

Appendix B for additional information on EPA visibility modeling for these units. 

 

With the inclusion of the SO2 BART determinations covered in our proposed FIP, we find that 

ODEQ’s BART determinations for NOx and PM for Muskogee Units 4 and 5 are adequate.

.

F.2.4. AEP/PSO Comanche Units 1 and 2 BART Determinations

State Submittal

Pages, 69 - 84, Appendix 6-4.

The AEP/PSO Comanche Units 1 and 2 are BART-eligible sources. These units are gas 

fired turbines with duct burners and heat recovery steam generators with a gross output of 94 

MW each. ODEQ determined that dry LNBs are technically feasible for both units. ODEQ also 
45

accepted AEP/PSO’s explanation that AEP-PSO contends that it is technically infeasible to 

retrofit post combustion SCR NOx control without rebuilding the generating units. ODEQ 

evaluated the control effectiveness of dry LNBs. ODEQ then evaluated the impacts, including 

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

remaining useful life. Lastly, ODEQ performed modeling to investigate the visibility impacts. 

ODEQ found that NOx BART was the installation of LNB for Units 1 and 2, based on the 

following conclusions:

Installation of new DLNB was cost effective, with a capital cost of $34,660,000 

for units 1 and 2 and an average cost effectiveness of $2,600 per ton of NOx 

removed for each unit over a twenty year operational life.

Combustion control using the LNB does not require non-air quality 

environmental mitigation for the use of chemical reagents (i.e., ammonia or urea) 

and there is minimal energy impact.

NOx control levels on 30-day rolling averages of 0.15 lb/mmBtu for Unit 1 and 2 

are justified.

Annual NOx emission reductions from new LNB on Units 1, and 2 are a total of 

1,891 tons.

As Units 1 and 2 are gas fired, ODEQ determined that SO2 and PM BART for them are 

no additional control. Oklahoma’s BART rule requires each source subject to BART to install 

and operate BART no later than 5 years after we approve this implementation plan revision. 

OAC 252-100-8-75(e).

EPA Findings

We find that ODEQ adequately determined BART for the Comanche Units 1 and 2.

F.2.5. AEP/ PSO Northeastern Units 2, 3, and 4 BART Determinations

State Submittal

Pages, 69 - 84, Appendix 6-4.

The AEP/PSO Northeastern Units 2, 3, and 4 are BART-eligible sources. Unit 2 is gas 

fired boiler with a gross output of 495 MW. Units 3 and 4 are coal fired with gross outputs of 

490 MW. The SO2 BART determinations for Units 3 and 4 are covered under our proposed FIP. 

Therefore, we reviewed ODEQ’s NOx and PM BART determination for these units. 

Units 2, 3, and 4 all have first generation Low NOx Burners (LNB) and Over Fire Air 

(OFA). ODEQ considered both combustion and post combustion types of NOx controls. ODEQ 

determined that new LNB and OFA are technically feasible for all three units. ODEQ also 
46

determined that SCR was technically feasible for Units 2, 3 and 4 and that SNCR was technically 

feasible for Units 3 and 4. ODEQ did not make a determination that SNCR was not technically 

feasible for Unit 2, but did not further evaluate it. ODEQ evaluated the control effectiveness of 

these technologies. ODEQ then evaluated the impacts, including the costs of compliance, the 

energy impacts, the non-air quality environmental impacts, and the remaining useful life. Lastly, 

ODEQ performed modeling to investigate the visibility impacts. ODEQ found that NOx BART 

was the installation of LNB with OFA for Units 2, 3, and 4, based on the following conclusions:

The cost of installing SCR on each unit is significantly higher than the cost for 

LNB with OFA. Additional capital costs for SCR on Units 3 and 4 are on average 

$290,000,000. Based on projected emissions, SCR could reduce overall NOx

emissions from Northeast Units 3 and 4 by approximately 4,120 TPY beyond 

combustion controls; however, the incremental cost associated with this reduction 

is approximately $11,013/ton. SCR controls on Unit 2 would result in an 

incremental cost of $13,989. 

The cumulative visibility improvement for SCR, as compared to LNB/OFA across 

Wichita Mountains and Caney Creek (based on the 98th percentile modeled 

results) was 0.10 and 0.18 Δdv respectively. 

Installation of new LNB with OFA was cost effective at an average cost 

effectiveness of $303-313.

Combustion control using the LNB/OFA does not require non-air quality 

environmental mitigation for the use of chemical reagents (i.e., ammonia or urea) 

and there is minimal energy impact.

NOx control levels on 30-day rolling averages of 0.15 lb/mmBtu for Units 3 and 4 

and 0.28 lb/mmBtu on Unit 2 are justified meet the presumptive limits.

For Unit 2, ODEQ determined NOx BART is the retrofitting of new LNB and OFA with 

an emission rate of 0.28 lbs/MMBtu on a 30 day average. The BART Guidelines do not specify a 

presumptive NOx BART limit for gas fired power plants. As Unit 2 is gas fired, ODEQ 

determined that SO2 and PM BART for it is no additional control. 

For Units 3 and 4, ODEQ determined NOx BART is the retrofitting of new LNB and OFA 

with an emission rate of 0.15 lbs/MMBtu on a 30 day average, which meets the BART 

presumptive limit. ODEQ determined PM BART is the existing ESPs with an emission rate of 

0.1 lbs/MMBtu on a 3-hour average. ODEQ considered the addition of fabric filters 

(baghouses) for PM control on Units 3 and 4, which are currently equipped with Electrostatic 

Precipitators (ESPs). ODEQ considered fabric filters to be technically feasible. ODEQ then 

evaluated the additional cost versus the increased PM control. ODEQ estimated the cost for 

fabric filters was $12,565/ton over the existing ESP controls. Based on the high cost and the 

incrementally small improvement in PM control, ODEQ determined that PM BART for Units 3 

and 4 was the existing ESPs.
47

ODEQ specified additional BART emission limitations in lbs/hour and tons/year. 

Oklahoma’s BART rule requires each source subject to BART to install and operate BART no 

later than 5 years after we approve this implementation plan revision. OAC 252-100-8-75(e). 

EPA Findings

EPA reviewed the modeling associated with the NOx and PM BART determinations 

ODEQ made for Units 3 and 4 of the AEP/PSO Northeastern plant. ODEQ determined that the 

installation of LNB with OFA to achieve an emission limit of 0.15 lb/mmBTU is NOx BART for 

these units. The BART determination also identified the existing electrostatic precipitators 

(ESP) as PM BART for these units. EPA reviewed ODEQ’s basis for these BART 

determinations, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, the remaining useful life and visibility modeling. EPA performed 

additional CALPUFF visibility modeling that included our proposed SO2 limits in combination 

with ODEQ proposed limits for NOx and PM This modeling suggests small visibility 

improvements can be expected from also requiring the installation of SCR on these units. The 

small visibility improvements achieved are not justified due to the additional cost of SCR. See 

Appendix B for additional information on EPA visibility modeling for these units.

 

With the inclusion of the SO2 BART determinations covered in our proposed FIP, we find that 

ODEQ’s BART determinations for NOx and PM for Northeastern Units 3 and 4 are adequate.

We also find that ODEQ adequately determined BART for Northeastern Unit 2.

F.2.6. AEP/PSO Southwestern Unit 3 BART Determination

State Submittal

Pages, 69 - 84, Appendix 6-4.

The AEP/PSO Southwestern Unit 3 is a BART-eligible source. This unit is a gas fired 

boiler with a gross output of 332 MW. ODEQ determined that LNB and OFA are technically

feasible. ODEQ also determined that SCR was technically feasible. ODEQ did not address the 

feasibility of SNCR. ODEQ evaluated the control effectiveness of the remaining technologies. 

ODEQ then evaluated the impacts, including the costs of compliance, the energy impacts, the 

non-air quality environmental impacts, the remaining useful life, and visibility impacts. ODEQ 

found that NOx BART was the installation of LNB with OFA for Unit 3, based on the following 

conclusions:

The cost of installing SCR on each unit is significantly higher than the cost for 

LNB with OFA. Additional capital costs for SCR on Unit 3 are $65,968,400. 

Based on projected actual emissions, SCR could reduce overall NOx emissions 

from Southwestern Unit 3 by approximately 1,441 tpy (compared to combustion 

controls); however, the incremental cost associated with this reduction is 

approximately $10,281,677 per year, or $6,859/ton. 
48

ODEQ concluded that SCR control may not be as effective on boilers that operate 

as peaking units, as NOx reduction in an SCR is a function of flue gas 

temperature. 

Installation of new LNB with OFA was cost effective, with a capital cost of 

$3,000,000 and an average cost effectiveness of $947 per ton of NOx removed 

over a twenty year operational life.

Combustion control using the LNB/OFA does not require non-air quality 

environmental mitigation for the use of chemical reagents (i.e., ammonia or urea) 

and there is minimal energy impact.

NOx control levels on 30-day rolling averages of 0.45 lb/mmBtu for Unit 3 are 

justified.

Annual actual NOx emission reductions from new LNB with OFA on Unit 3 are 

450 tons.

Due to the relatively high NOx emission rate that ODEQ determined was BART, and the 

fact that it appeared the annual average emissions rates recorded with the Clean Air Markets 

Division indicate that the boiler can currently comply with the standard on an annual average 

basis, we asked for additional information justifying that rate. ODEQ responded with data 

detailing 9 years of emissions versus load data, that indicate that the boiler operates through a 

range where emissions can reach as much as 1.4 lb/MMBtu at full load. ODEQ stated, 

“The vendor guarantee of an expected emissions rate is in reference to the 

highest NOx emissions and the percent reduction “predicted” by the vendor for 

the site specific application. Once in operation, a similar graph of emissions 

versus load would indicate that the boiler would have maximum emissions near 

the 0.45 lbs/MMBtu level rather than annual averages. These new daily and 

annual averages would be expected to be much lower. We are not restricting 

load on the unit and therefore will permit AEP at the maximum emission rate for 

the proposed control technology on this specific boiler.”

After having examined the data, attached to Appendix E, we accept ODEQ’s explanation.

As Unit 3 is gas fired, ODEQ determined that SO2 and PM BART for it are no additional 

control. Oklahoma’s BART rule requires each source subject to BART to install and operate 

BART no later than 5 years after we approve this implementation plan revision. OAC 252-100-

8-75(e). 

EPA Findings

We find that ODEQ adequately determined BART for the AEP/PSO Southwestern Unit 3.
49

ODEQ BART Results and Summary

Table 7, modified from Table VI-12 of the RH SIP, summarizes the NOx reductions 

ODEQ calculated from baseline that result from the NOx BART determinations discussed above. 

Table 8, modified from Table VI-13 of the RH SIP summarizes the NOx BART visibility benefits 

ODEQ calculated as the 3-year average of the modeled visibility improvement at the 98th 

percentile.

Table 7. NOx BART Reductions

Facility Unit

Baseline

Emissions

(lb/MMBTU)

Emissions

Reduction

(tpy)

OG&E Muskogee 4 (coal-fired) 0.495

4,487

OG&E Muskogee 5 (coal-fired) 0.522

OG&E Seminole 1 (gas-fired) 0.339

OG&E Seminole 2 (gas-fired) 0.354 3,998

OG&E Seminole 3 (gas-fired) 0.219

OG&E Sooner 1 (coal-fired) 0.601

6,905

OG&E Sooner 2 (coal-fired) 0.584

PSO Northeastern 2 (gas-fired) 0.71 1,615

PSO Northeastern 3 (coal-fired) 0.536

7,697

PSO Northeastern 4 (coal-fired) 0.491

PSO Southwestern 3 (gas-fired) 0.57 450

PSO Comanche 1 (gas-fired) 0.696

1,891

PSO Comanche 1 (gas-fired) 0.613

Total NOx Reduction 27,043
50

Table 8. NOx BART Visibility Improvements

Facility

Wichita 

Mountains 

(Δ-dv)

Caney 

Creek 

(Δ-dv)

Upper 

Buffalo 

(Δ-dv)

Hercules 

Glades 

(Δ-dv)

Total 

(Δ-dv)

OG&E Muskogee

(coal fired) 0.44 0.77 0.55 0.33 2.09

OG&E Seminole

(gas fired) 0.37 0.25 0.14 0.12 0.88

OG&E Sooner

(coal fired) 0.83 0.38 0.31 0.28 1.80

PSO Northeastern

(gas fired) 0.22 0.40 0.21 0.21 1.04

PSO Northeastern

(coal fired) 0.39 0.64 0.41 0.30 1.74

PSO Southwestern

(gas fired) 1.93 0.14 0.11 0.08 2.26

PSO Comanche

(gas fired) 1.28 0.07 0.06 0.05 1.46

Totals 5.46 2.65 1.79 1.37 11.27

F.3. BART for Larger Fossil Fuel Power Plants: 40 CFR 51.308(e)(1)(ii)(B)

A determination of BART is required for each BART-eligible source in Oklahoma that emits any 

air pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility in any mandatory Class I Federal area. All such sources are subject to BART. The 

determination of BART for fossil-fuel fired power plants having a total generating capacity 

greater than 750 megawatts must be made pursuant to the guidelines in appendix Y of this part 

(Guidelines for BART Determinations under the Regional Haze Rule).

State Submittal

Pages 69 - 85

All the facilities required to install BART that are identified above in Table 6, are fossil-fuel fired 

steam electric generating plants with heat inputs greater than 250-mmBtu/hr, and with a total 

generating capacity in excess of 750 MW. ODEQ has stated it followed the requirements in 

Section 51.308(e)(1)(ii)(B) in preparing the BART determinations for these facilities.

EPA Findings

These BART determinations are covered in Sections III.F.2.1 to III.F.2.6, above. We find that 

except for the SO2 BART determinations for Units 4 and 5 of the OG&E Muskogee plant, Units 1 

and 2 of the OG&E Sooner plant, and Units 3 and 4 of the AEP/PSO Northeastern plants,
51

ODEQ has followed the requirements of Section 51.308(e)(1)(ii)(B) in preparing the BART 

determinations of all of its subject to BART sources. The SO2 BART determinations for these 

facilities are the subject of our proposed FIP.

F.4. De Minimis BART: 40 CFR 51.308(e)(1)(ii)(C)

A determination of BART for each BART-eligible source in Oklahoma that emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility in any mandatory Class I Federal area. All such sources are subject to BART. 

Oklahoma is not required to make a determination of BART for SO2 or for NOx if a BARTeligible source has the potential to emit less than 40 tons per year of such pollutant(s), or for 

PM10 if a BART-eligible source has the potential to emit less than 15 tons per year of such 

pollutant.

State Submittal

ODEQ did not specifically exclude any BART eligible sources under this provision.

F.5. Design, Equipment, or Work Practice BART: 40 CFR 51.308(e)(1)(iii)

If Oklahoma determines in establishing BART that technological or economic limitations on the 

applicability of measurement methodology to a particular source would make the imposition of 

an emission standard infeasible, it may instead prescribe a design, equipment, work practice, or 

other operational standard, or combination thereof, to require the application of BART. Such 

standard, to the degree possible, is to set forth the emission reduction to be achieved by 

implementation of such design, equipment, work practice or operation, and must provide for 

compliance by means which achieve equivalent results.

ODEQ did not prescribe a design, equipment, work practice, or other operational standard, or 

combination thereof, as BART.

F.6. Installation of BART: 40 CFR 51.308(e)(1)(iv) – (v)

Each source subject to BART must (1) install and operate BART as expeditiously as practicable, 

but in no event later than 5 years after approval of the SIP revision, and (2) maintain the control 

equipment required by this subpart and establish procedures to ensure such equipment is 

properly operated and maintained.

State Submittal

Pages 75-76, Appendix 6-1.
52

Oklahoma’s BART Rule, OAC 252:100-8-75(e), requires that “each BART-eligible source 

subject to BART shall install and operate BART no later than five years after EPA approves the 

Oklahoma Regional Haze SIP.” ODEQ has stated on page 87 of the RH SIP that it will issue 

enforceable Part 70 air quality permits requiring BART-eligible sources subject to BART to 

install BART and achieve the associated BART emission within five years of our approval the 

SIP. Furthermore, ODEQ has the authority to administratively and judicially enforce any 

provision of its issued air quality permits. See 27A O.S. §§ 2-5-110, 2-3-502, and 2-3-504.

EPA Findings

We find that ODEQ has demonstrated it has the authority and enforceable mechanisms in place 

to ensure each source subject to BART will (1) install and operate BART as expeditiously as 

practicable, but in no event later than 5 years after approval of the SIP revision, and (2) 

maintain the control equipment required by this subpart and establish procedures to ensure such 

equipment is properly operated and maintained.

F.7. CAIR for BART: 40 CFR 51.308(e)(4)

If Oklahoma chooses to meet the emission reduction requirements of the Clean Air Interstate 

Rule (CAIR) by participating in one or more of the EPA-administered CAIR trading programs 

for SO2 and NOx, it need not require BART-eligible EGUs subject to such trading programs in 

Oklahoma to install, operate, and maintain BART for the pollutants covered by such trading 

programs in it. Oklahoma may choose to participate in the EPA-administered CAIR trading 

programs either by submitting a SIP that incorporates the CAIR model trading rules in part 96 of 

this chapter, and is approved, in accordance with section51.123(o)(1) or (2) (for the NOx annual 

program) and (aa)(1) or (2) (for the NOx ozone season program) and section 51.124(o)(1) or (2) 

(for the SO2 program) or by remaining subject to the Federal implementation plan in part 97 of 

this chapter (which may be modified by a SIP approved in accordance with sections 51.123(p) 

and (ee) and 51.124(r)). If Oklahoma chooses to participate in such trading programs, it may 

also adopt provisions, consistent with such trading programs, for a geographic enhancement to 

the program to address the requirement under section 51.302(c) related to BART for reasonably 

attributable impairment from the pollutants covered by the CAIR cap-and-trade programs.

Note: This provision of the regional haze rule does not apply to Oklahoma since it did not 

participate in the CAIR.

F.8. Reasonable Progress and BART: 40 CFR 51.308(e)(5)

After a State has met the requirements for BART or implemented emissions trading program or 

other alternative measure that achieves more reasonable progress than the installation and 

operation of BART, BART-eligible sources will be subject to the requirements of paragraph (d) 

of this section in the same manner as other sources.

State Submittal
53

Note: There are no BART sources that ODEQ revisited for the purposes of obtaining additional 

reductions under Section 51.308(d).

F.9. BART Exemptions: 40 CFR 51.308(e)(6)

Any BART-eligible facility subject to the requirement under paragraph (e) of this section to 

install, operate, and maintain BART may apply to the Administrator for an exemption from that 

requirement. An application for an exemption will be subject to the requirements of section

51.303(a)(2)–(h).

Note: This provision of the regional haze rule was not used by ODEQ in the preparation of its 

RH SIP.

G. 2018 Regional Haze SIP Revision: 40 CFR 51.308(f)

Each State identified in section 51. 300(b)(3),which includes Oklahoma, must revise and submit 

its regional haze SIP revision to EPA by July 31, 2018 and every ten years thereafter. In each 

SIP revision, Oklahoma must evaluate and reassess all of the elements required in paragraph (d) 

of this section, taking into account improvements in monitoring data collection and analysis 

techniques, control technologies, and other relevant factors. In evaluating and reassessing these 

elements, Oklahoma must address the following: 

(1) Current visibility conditions for the most impaired and least impaired days, and 

actual progress made towards natural conditions during the previous 

implementation period. The period for calculating current visibility conditions is 

the most recent five year period preceding the required date of the SIP submittal 

for which data are available. Current visibility conditions must be calculated 

based on the annual average level of visibility impairment for the most and least 

impaired days for each of these five years. Current visibility conditions are the 

average of these annual values.

(2) The effectiveness of the long-term strategy for achieving reasonable progress 

goals over the prior implementation period(s); and 

(3) Affirmation of, or revision to, the reasonable progress goal in accordance with the 

procedures set forth in paragraph (d)(1) of this section. If Oklahoma established a 

reasonable progress goal for the prior period which provided a slower rate of 

progress than that needed to attain natural conditions by the year 2064, it must 

evaluate and determine the reasonableness, based on the factors in paragraph 

(d)(1)(i)(A) of this section, of additional measures that could be adopted to 

achieve the degree of visibility improvement projected by the analysis contained 

in the first SIP described in paragraph (d)(1)(i)(B) of this section.
54

State Submittal

ODEQ acknowledged this requirement on page 119 of their RH SIP.

H. Five Year Progress Report 40 CFR 51.308(g)

Each State identified in section 51. 300(b)(3), which includes Oklahoma, must submit a report to 

the Administrator every 5 years evaluating progress towards the reasonable progress goal for 

each mandatory Class I Federal area located within it and in each mandatory Class I Federal area 

located outside it which may be affected by emissions from within it. The first progress report is 

due 5 years from submittal of the initial SIP addressing paragraphs (d) and (e) of this section. 

The progress reports must be in the form of SIP revisions that comply with the procedural 

requirements of section 51. 102 and section 51. 103. Periodic progress reports must contain at a 

minimum the following elements: 

(1) A description of the status of implementation of all measures included in the SIP

for achieving reasonable progress goals for mandatory Class I Federal areas both 

within and outside Oklahoma.

(2) A summary of the emissions reductions achieved throughout Oklahoma through 

implementation of the measures described in paragraph (g)(1) of this section.

(3) For each mandatory Class I Federal area within Oklahoma, it must assess the 

following visibility conditions and changes, with values for most impaired and 

least impaired days expressed in terms of 5-year averages of these annual values.

(i) The current visibility conditions for the most impaired and least impaired 

days; 

(ii) The difference between current visibility conditions for the most impaired 

and least impaired days and baseline visibility conditions; 

(iii) The change in visibility impairment for the most impaired and least 

impaired days over the past 5 years; 

(4) An analysis tracking the change over the past 5 years in emissions of pollutants 

contributing to visibility impairment from all sources and activities within 

Oklahoma. Emissions changes should be identified by type of source or activity. 

The analysis must be based on the most recent updated emissions inventory, with 

estimates projected forward as necessary and appropriate, to account for 

emissions changes during the applicable 5-year period.

(5) An assessment of any significant changes in anthropogenic emissions within or 

outside Oklahoma that have occurred over the past 5 years that have limited or 

impeded progress in reducing pollutant emissions and improving visibility.
55

(6) An assessment of whether the current SIP elements and strategies are sufficient to 

enable Oklahoma, or other States with mandatory Federal Class I areas affected 

by emissions from it, to meet all established reasonable progress goals.

(7) A review of Oklahoma’s visibility monitoring strategy and any modifications to 

the strategy as necessary.

State Submittal

ODEQ acknowledged this requirement on page 119 of their RH SIP. ODEQ stated it intends to 

submit the first five-year report by February 2015.

I. Existing SIP Adequacy Determination 40 CFR 51.308(h)

At the same time Oklahoma is required to submit any 5-year progress report to us in accordance 

with paragraph (g) of this section, it must also take one of the following actions based upon the 

information presented in the progress report: 

(1) If Oklahoma determines that the existing SIP requires no further substantive 

revision at this time in order to achieve established goals for visibility 

improvement and emissions reductions, it must provide to the Administrator a 

negative declaration that further revision of the existing SIP is not needed at this 

time. 

(2) If Oklahoma determines that the SIP is or may be inadequate to ensure reasonable 

progress due to emissions from sources in another State(s) which participated in a 

regional planning process, it must provide notification to the Administrator and to 

the other State(s) which participated in the regional planning process with the 

States. Oklahoma must also collaborate with the other State(s) through the 

regional planning process for the purpose of developing additional strategies to 

address the plan’s deficiencies. 

(3) Where Oklahoma determines that the SIP is or may be inadequate to ensure 

reasonable progress due to emissions from sources in another country, it shall 

provide notification, along with available information, to the Administrator. 

(4) Where Oklahoma determines that the SIP is or may be inadequate to ensure 

reasonable progress due to emissions from sources within the State, the State shall 

revise its SIP to address the plan’s deficiencies within one year. 

State Submittal

ODEQ acknowledged this requirement on page 119 of the RH SIP. ODEQ stated it intends to 

submit the first five-year report required by Section 51.308(g) by February 2015. Using the 
56

findings of that report, ODEQ will make a determination regarding the adequacy of this RH SIP

and take an appropriate action based upon the requirements of Section 51.308(h).

J. Federal Land Manager Coordination 40 CFR 51.308(i)

(1) By November 29, 1999, Oklahoma must identify in writing to the Federal Land 

Managers the title of the official to which the Federal Land Manager of any mandatory 

Class I Federal area can submit any recommendations on the implementation of this 

subpart including, but not limited to: 

(i) Identification of impairment of visibility in any mandatory Class I Federal area(s); 

and

(ii) Identification of elements for inclusion in the visibility monitoring strategy 

required by section 51.305 and this section.

(2) Oklahoma must provide the Federal Land Manager with an opportunity for consultation, 

in person and at least 60 days prior to holding any public hearing on a SIP (or plan 

revision) for regional haze required by this subpart. This consultation must include the 

opportunity for the affected Federal Land Managers to discuss their: 

(i) Assessment of impairment of visibility in any mandatory Class I Federal area; and 

(ii) Recommendations on the development of the reasonable progress goal and on the 

development and implementation of strategies to address visibility impairment.

(3) In developing any SIP (or plan revision), Oklahoma must include a description of how it 

addressed any comments provided by the Federal Land Managers. 

(4) The SIP (or SIP revision) must provide procedures for continuing consultation between 

Oklahoma and Federal Land Manager(s) on the implementation of the visibility 

protection program required by this subpart, including development and review of SIP

revisions and 5-year progress reports, and on the implementation of other programs 

having the potential to contribute to impairment of visibility in mandatory Class I Federal 

areas.

State Submittal

Pages 117-119, Appendix 10-2.

Under Section 51.308(i)(2), the Oklahoma was obligated to provide the Fish and Wildlife 

Service with an opportunity for consultation, in person and at least 60 days prior to holding a 

public hearing on it RH SIP. In practice, state environmental agencies have usually provided all 

FLMs – the Forest Service, the Park Service, and the Fish and Wildlife Service, copies of their 

RH SIP, as the FLMs collectively have reviewed these RH SIPs. ODEQ followed this practice 
57

and sent its draft of this implementation plan revision to the federal land manager staff on 

October 1, 2009 and notified the federal land manager staff of the public hearing held on 

December 16, 2009. In its letter dated December 4, 2009, transmitting its comments, the Fish 

and Wildlife Service acknowledged having received Oklahoma’s draft RH SIP on October 5, 

2009.

Section 51.308(i)(3) requires that ODEQ provide in its RH SIP a description of how it addressed 

any comments provided by the FLMs. ODEQ has provided that information in Appendix 10-2 of 

its RH SIP.

Lastly, Section 51.308(i)(4) specifies the RH SIP must provide procedures for continuing 

consultation between the State and Federal Land Manager on the implementation of the visibility 

protection program required by section 51.308, including development and review of 

implementation plan revisions and 5-year progress reports, and on the implementation of other 

programs having the potential to contribute to impairment of visibility in the mandatory Class I 

Federal areas. ODEQ has stipulated in its RH SIP it will continue to coordinate and consult 

with the FLMs as required by section 51.308(i)(4). ODEQ states it intends to consult the FLMs 

in the development and review of implementation plan revisions; review of progress reports; and 

development and implementation of other programs that may contribute to impairment of 

visibility at the Wichita Mountains and other Class I areas. We find that ODEQ has satisfied 

section 51.308(i).

EPA Findings

The Wichita Mountains is one of more than 546 refuges throughout the United States managed

by the Fish and Wildlife Service, which is the Federal Land Manager (FLM) for this Class I 

area. Although the FLMs are very active in participating in the RPOs, the RH Rule grants the 

FLMs a special role in the review of the RH SIPs. We view both the FLMs and the state 

environmental agencies as our partners in the RH process.

We acknowledge that Section 51.308(i)(1) has been satisfied by all states via communication 

prior to this SIP. We find that ODEQ has satisfied the requirements of Section 51.308(i)(2)-(3) 

by providing the FLMs with a with an opportunity for consultation, in person and at least 60 

days prior to holding any public hearing on its RH SIP, and ODEQ has adequately addressed 

the FLM comments in its SIP. Further, we find that ODEQ has satisfied Section 51.308(i)(4) by 

committing to continuing consultation between the it and the FLMs on the implementation of the 

visibility protection program required by Section 51.308(i), including development and review of 

SIP revisions and 5-year progress reports, and on the implementation of other programs having 

the potential to contribute to impairment of visibility in mandatory Class I Federal areas.

The FLMs have communicated to us their dissatisfaction with the fact that the draft RH SIP they 

were provided by ODEQ was markedly different that the version ODEQ submitted to us as their 

final RH SIP. Specifically, the FLMs note that in the version of the SIP they reviewed, SO2

BART for the six OG&E and AEP/PSO coal fired units that are the subject of our FIP was 

determined by ODEQ to be dry SO2 scrubbers with an emission limit of 0.10 lbs/MMBtu for the 

OG&E units, and 0.153 lbs/MMBtu for the AEP-PSO units. When ODEQ submitted their final 
58

RH SIP to us, those SO2 BART determinations were changed to replace the SO2 scrubber 

requirements with an SO2 limit of 0.65 lbs/MMBtu on a 30 day rolling average that corresponds 

to uncontrolled low sulfur coal. We note the Fish and Wildlife Service has not requested that 

ODEQ re-open their 60 day comment period. We would like to address any question as to 

whether section 51.308(i)(2) has been satisfied. We believe, however, that our proposed FIP, as 

described in section V.F., above, may alleviate these concerns. We invite the FLMs to provide 

comment on this or other aspects of our proposal.
Appendix A. 

Review of Modeling and Emission Inventory Development for 

the Regional Haze Implementation Plan for the State of 

Oklahoma 

Prepared and Reviewed by: 

Erik Snyder 

Michael Feldman 
A-ii

Table of Contents 

Table of Contents ................................................................................................................ ii

List of Tables ..................................................................................................................... iv

List of Figures ..................................................................................................................... v

Chapter 1: Background and Introduction ..................................................................... 6

1.1 Background .......................................................................................................... 6

1.2 Technical Requirements for Regional Haze SIPs ................................................ 7

Chapter 2: Development of Baseline and Natural Visibility Conditions and Glidepath

 9

2.1 Introduction .......................................................................................................... 9

2.2 Calculation of Visibility from IMPROVE Measurements ................................. 10

2.3 Baseline Visibility Conditions ........................................................................... 12

2.4 Natural Visibility Conditions ............................................................................. 13

2.5 Uniform Rate of Progress (GlidePath) Calculation ............................................ 14

Chapter 3: Emission Inventory Development ............................................................. 15

3.1 Introduction ........................................................................................................ 15

3.2 2002 emissions inventory ................................................................................... 16

3.2.1 Stationary Point-Source Emissions ........................................................... 17

3.2.2 On-Road Mobile Emissions ...................................................................... 17

3.2.3 Biogenic Emissions ................................................................................... 18

3.2.4 Non-Road Mobile Emissions .................................................................... 18

3.2.5 Area Source Emissions ............................................................................. 18

3.3 2018 Emissions Inventory .................................................................................. 19

Chapter 4: Modeling Protocol, Episode Selection and Modeling Set-up Overview .. 22

4.1 Introduction ........................................................................................................ 22

4.2 Quality Assurance Program Plan ....................................................................... 22

4.3 Episode Selection ............................................................................................... 22

4.4 Photochemical and Emissions Modeling Domain ............................................. 23

4.5 MM5 Meteorological Model .............................................................................. 24

4.5.1 Model Selection ........................................................................................ 24

4.5.2 Meteorological Modeling Domain and Vertical Layer Structure ............. 25

4.5.3 Model Configuration ................................................................................. 25

4.5.4 MM5 Processing and Application ............................................................ 26

4.5.5 Model Performance ................................................................................... 26

4.6 SMOKE Emissions Model ................................................................................. 28

4.7 Air Quality Model .............................................................................................. 28

4.7.1 Model Selection ........................................................................................ 29

4.7.1.1 CMAQ Air Quality Model .................................................................. 30

4.7.1.2 CAMx Air Quality Model ................................................................... 30

4.7.2 Vertical Modeling Domain ....................................................................... 31

4.7.3 Initial and Boundary Conditions ............................................................... 31

4.7.4 Base Case/ Baseline Model Performance ................................................. 32

4.7.4.1 Model Performance for Sulfate (SO4) ................................................. 33

4.7.4.2 Model Performance for Nitrate (NO3) ................................................. 34
A-iii

4.7.4.3 Model Performance for Organic Carbon (OC) .................................... 35

4.7.4.4 Model Performance for Elemental Carbon (EC) ................................. 36

4.7.4.5 Model Performance for Soil ................................................................ 37

4.7.4.6 Model Performance for Course Particulate Material (CM) ................. 38

4.7.4.7 Model Performance for Prediction of Total Extinction ....................... 40

Chapter 5: 2018 Future Year Modeling ...................................................................... 42

5.1 2018 Model Simulations .................................................................................... 42

5.2 Visibility Projections .......................................................................................... 42

5.2.1 Mapping Model Results to IMPROVE Measurements ............................ 43

5.2.2 Projecting Visibility Changes Using Modeling Results ........................... 43

5.3 Reasonable Progress Goal and Path to Natural Conditions ............................... 44

Chapter 6: Source Apportionment Modeling ............................................................. 47

6.1 Introduction ........................................................................................................ 47

6.2 Source Apportionment Results at Wichita Mountains ....................................... 48

6.3 Contributions to visibility impairment at other Class I areas ............................. 52

Chapter 7: BART Determination ................................................................................ 53

7.1 Introduction ........................................................................................................ 53

7.2 BART Dispersion Modeling Protocol ................................................................ 54

7.2.1 Overview of Modeling Approach ............................................................. 55

7.2.2 Model Selection and Applicability ........................................................... 56

7.2.3 Modeling Domain and Meteorology Inputs .............................................. 56

7.2.4 Emissions Input ......................................................................................... 58

7.2.5 Calpuff/Calmet Model Settings ................................................................ 59

7.3 BART Determination ......................................................................................... 59

7.3.1 Gas – Fired Units ...................................................................................... 60

7.3.2 Coal-Fired Units ........................................................................................ 60

7.3.2.1 NOx and PM BART ............................................................................ 60

7.3.2.2 SO2 BART .......................................................................................... 61
A-iv

 List of Tables 

Table 2.5. Uniform Rate of Progress for Wichita Mountains (worst quintile, western 

natural visibility conditions) ............................................................................................. 14

Table 3-1. Emissions from Oklahoma Sources (tons/yr) ................................................. 17

Table 3-2. Emission estimates for Oklahoma sources in 2018 (tons/yr) ......................... 19

Table 5-1. Comparison of reasonable progress goal to uniform rate of progress for 2018 

(total extinction and deciviews) ........................................................................................ 45

Table 6-1. Projected light extinction for 20% worst days at Wichita Mountains in 2002 

(Mm-1) ............................................................................................................................... 49

Table 6-2. Projected light extinction for 20% worst days at Wichita Mountains in 2018 

(Mm-1) ............................................................................................................................... 49

Table 7-1. Facilities with BART-eligible units in Oklahoma .......................................... 53

Table 7-2. Facilities granted waivers from BART based on dispersion modeling. ......... 54

Table 7-3. Sources in Oklahoma subject to BART ........................................................... 56
A-v

List of Figures 

Figure 1-1. Map of Regional Planning Organizations ........................................................ 7

Figure 4-1. National RPO 36-km modeling domain for CMAQ, CAMx, and SMOKE 

modeling. .......................................................................................................................... 24

Figure 4-2. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for sulfate (SO4). ................................................................................. 34

Figure 4-3. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for nitrate (NO3). ................................................................................ 35

Figure 4-4. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for organic carbon (OC). .................................................................... 36

Figure 4-5. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for elemental carbon (EC). ................................................................. 37

Figure 4-6. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for soil. ................................................................................................ 38

Figure 4-7. CENRAP model performance (factional bias and error) of the Typ02f 

modeling scenario for course particulate material (CM). ................................................. 39

Figure 4-8. Daily extinction model performance at Wichita Mountains, OK for the worst 

(top) and best (bottom) 20% days during 2002 ................................................................ 41

Figure 5-1. Projections of visibility impairment for 20% worst days.............................. 45

Figure 5-2. Differences in modeled total extinction (Bext) between Base18F and Typ02F 

for 20% worst days at Wichita Mountains site. ................................................................ 46

Figure 6-1. Source Regions used in CAMx PSAT PM source apportionment modeling 48

Figure 6-2. Source apportionment modeling results by source region and source category 

for worst 20% days at Wichita Mountains in 2002. .......................................................... 50

Figure 6-3. Source apportionment modeling results by source region and species for 

worst 20% days at Wichita Mountains in 2002. ............................................................... 50

Figure 6-4. Source apportionment modeling results by source region and source category 

for worst 20% days at Wichita Mountains in 2018. .......................................................... 51

Figure 6-5. Source apportionment modeling results by source region and species for 

worst 20% days at Wichita Mountains in 2018. ............................................................... 51

Figure 7-1. CENRAP CALMET 6km meteorological modeling domains ...................... 57
A-6

Chapter 1: Background and Introduction 

1.1 BACKGROUND 

Regional haze is visibility impairment that is caused overwhelmingly by fine particulates 

(PM2.5). Visibility impairment occurs when PM2.5 in the atmosphere scatters and absorbs light, 

thereby creating haze. PM2.5 can be emitted into the atmosphere directly as primary 

particulates, or it can be produced in the atmosphere from photochemical reactions of gas-phase 

precursors and subsequent condensation to form secondary particulates. Examples of primary 

PM2.5 include crustal materials and elemental carbon; examples of secondary PM include 

ammonium nitrate, ammonium sulfates, and secondary organic aerosols (SOA). Secondary 

PM2.5 is generally smaller than primary PM2.5, and because the ability of PM2.5 to scatter light 

depends on particle size, with light scattering for fine particles being greater than for coarse 

particles, secondary PM2.5 plays an especially important role in visibility impairment. 

Moreover, the smaller secondary PM2.5 can remain suspended in the atmosphere for longer 

periods and is transported long distances, thereby contributing to regional-scale impacts of 

pollutant emissions on visibility. 

The sources of PM2.5 are difficult to quantify because of the complex nature of their formation, 

transport, and removal from the atmosphere. This makes it difficult to simply use emissions data 

to determine which pollutants should be controlled to most effectively improve visibility. 

Photochemical air quality models offer opportunity to better understand the sources of PM2.5 by 

simulating the emissions of pollutants and the formation, transport, and deposition of PM2.5. If 

an air quality model performs well for a historical episode, the model may then be useful for 

identifying the sources of PM2.5 and helping to select the most effective emissions reduction 

strategies for attaining visibility goals. Although several types of air quality modeling systems 

are available, the gridded, three-dimensional, Eulerian models provide the most complete spatial 

representation and the most comprehensive representation of processes affecting PM2.5, 

especially for situations in which multiple pollutant sources interact to form PM2.5. 

In Section 169A of the 1977 Amendments to CAA, Congress set forth a program for protecting 

visibility in the nation’s national parks and wilderness areas. This section of the CAA 

establishes as a national goal the “prevention of any future, and the remedying of any existing, 

impairment of visibility in mandatory Federal Class I areas which impairment results from 

manmade air pollution.” EPA promulgated a rule to address regional haze on July 1, 1999 (64 

FR 35713), the Regional Haze Rule (RHR). The RHR established the goal of achieving 

“natural” visibility conditions in all 156 Federal Class I areas by 2064. 

Because the pollutants that lead to regional haze can originate from sources located across broad 

geographic areas, EPA has encouraged the States and Tribes across the United States to address 

visibility impairment from a regional perspective. Five Regional Planning Organizations (RPOs) 

were developed to address regional haze and related issues (Figure 1-1). One of the main 

objectives of the RPOs is to analyze available data and conduct pollutant transport modeling to 

assist the States in developing their regional haze plans. 
A-7

Figure 1-1. Map of Regional Planning Organizations 

The Central Regional Air Planning Association (CENRAP) RPO is a collaborative effort of State 

governments, tribal governments, and various federal agencies established to conduct data 

analyses, conduct pollutant transport modeling, and coordinate planning activities among the 

central States. CENRAP members include the State governments of Nebraska, Kansas, 

Oklahoma, Texas, Minnesota, Iowa, Missouri, Arkansas, and Louisiana and Tribal governments 

included in these states. 

1.2 TECHNICAL REQUIREMENTS FOR REGIONAL HAZE SIPS 

The RHR does not mandate specific milestones or rates of progress, but instead calls for States to 

establish goals that provide for “reasonable progress” toward achieving natural visibility 

conditions. In setting Reasonable Progress Goals (RPGs), States must provide for an 

improvement in visibility for the most impaired days over the ten-year period of the SIP, and 

ensure no degradation in visibility for the least impaired days over the same period. In setting 

the RPGs for each 10-year period covered by a SIP, States must also compare the RPGs to the 

uniform rate of progress needed to reach natural visibility conditions by 2064, referred to as the 

“glide path”, which is the linear rate of reduction in visibility impairment (in deciviews) needed 

to achieve natural conditions by 2064. 
A-8

According to the RHR, Regional Haze SIPs must specifically identify and address the following 

elements: 

i. Baseline Visibility Conditions 

ii. Natural Visibility Conditions 

iii. Uniform Rate of Progress 

iv. Best Available Retrofit Technology (BART) 

v. Current and Future (2018) Emission Inventories 

vi. Source Contribution to Haze 

vii. Reasonable Progress Goals 

The purpose of this document is to review the technical products developed by the Oklahoma 

Department of Environmental Quality (ODEQ) and CENRAP for the central regional states, in 

support of their RH SIP. This document evaluated the methods and procedures used by ODEQ 

and CENRAP to develop the modeling and emission inventory products that assisted Oklahoma 

and the central regional States in addressing the required elements of a RH SIP. Specifically, 

this document reviewed emission inventory, meteorological, photochemical, and BART 

modeling conducted by CENRAP, evaluated the results and determined if these models met 

applicable guidelines or protocols, and met modeling standards at the time they were conducted. 
A-9

Chapter 2: Development of Baseline and Natural Visibility Conditions and 

Glidepath 

2.1 INTRODUCTION

Under the Regional Haze Rule (RHR), each State is required to demonstrate reasonable progress 

in visibility conditions for each of its Class I areas. The State is to determine a uniform rate of 

progress ("glide path", "glide slope") toward the goal of natural visibility conditions in 2064. 

Considering various statutory factors, the State is also to define a reasonable rate of progress, and 

compare this to the benchmark uniform rate; if projected progress is less than the uniform rate, 

then the State is to explain why. Procedures for assessing progress are described in the Regional 

Haze Rule and EPA guidance documents. 

In brief, the guidance defines a metric to quantify visibility conditions, together with procedures 

for determining a starting point and an ending point, between which progress is to be made. The 

metric used is the Haze Index, measured in deciviews, and is designed to correspond to human 

perception of visibility changes. It is defined as: 

10*ln(bext/10) (1) 

where bext is extinction, the fraction of light scattered out of a sight path due to pollutants over a 

given distance (with units of Mm-1 or "inverse megameters"); it is inversely related to visual 

range. A 24-hour average is used, so there is a deciview value for each day of the year; the 

average of the 20% most-impaired days, and the average of the 20% least-impaired days during a 

year are to be assessed. The Regional Haze Rule goal is to improve visibility on the worst 20% 

of days, while having no degradation on the best 20%. 

The starting point for progress is current or baseline visibility conditions, as monitored by the 

Interagency Monitoring of PROtected Visual Environments (IMPROVE) monitoring network 

(webpage and data access: http://vista.cira.colostate.edu/improve/Default.htm). 24-hour samples 

are collected every three days and are sent to a laboratory facility for analysis to obtain dry 

concentrations of a wide variety of species that impact visibility. Monitored pollutant 

concentrations are converted to visibility extinction using the IMPROVE equation, which adds 

up the contribution of each pollutant to extinction, while accounting for the effect of relative 

humidity. This total extinction is then converted to deciviews in the Haze Index through 

equation 1. For each of the years of the baseline period (2000-2004), the average of the 

deciviews on the worst 20% of days is calculated; the five-year average of these defines the 

baseline. This procedure is described in detail in EPA's "Guidance for Tracking Progress Under 

the Regional Haze Rule".1 The guidance also makes provisions for dealing with missing data, 

since monitoring instrument maintenance and malfunctions mean that data is not available for 

every scheduled measurement. 

The end point for progress is the goal of natural visibility conditions in 2064. The default 

approach for determining these is described in EPA's "Guidance for Estimating Natural Visibility 

 

1

 Hereafter “GTR”: EPA, 2003, Guidance for Tracking Progress Under the Regional Haze Rule, EPA-454/B-03-

004, September 2003, EPA OAQPS ; web page: http://www.epa.gov/ttn/oarpg/t1pgm.html 

direct link: http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_tpurhr_gd.pdf 
A-10

Conditions Under the Regional Haze Program".2

 Annual average natural background pollutant 

concentrations are estimated by Trijonis et al.3

 under NAPAP for the East and West parts of the 

country. Deciviews are calculated based on these natural background estimates with the 

IMPROVE equation, using the monthly relative humidity for each specific Class I area. These 

annual averages are then translated into estimates for the best 20% and worst 20% days needed 

for the progress assessment. Extinction was assumed to have a lognormal frequency distribution; 

deciviews would then have a normal distribution, and its 10th and 90th percentiles were used as 

estimates of the average of the best 20% and worst 20% of days, respectively. The result is a 

table of best and worst 20% deciview values for each Class I area, which appears in Appendix B 

of the guidance. The guidance also allows States to use a refined alternative to this default 

approach for estimating natural conditions. 

Finally, the uniform rate of progress is calculated as the difference between the baseline and 

natural conditions, spread over the 60 years between 2004 and 2064: uniform deciviews per year 

improvement = (current 2004 deciviews - natural 2064 deciviews) / 60. This rate is the 

benchmark against which visibility improvement is to be compared by the State; the first 

planning period envisaged by the Regional Haze Rule is through 2018, so this uniform rate is 

multiplied by 14 to determine the first benchmark. 

2.2 CALCULATION OF VISIBILITY FROM IMPROVE MEASUREMENTS

The CENRAP procedure used for developing a uniform rate of progress (URP, also known as 

"glide path" or "glide slope") for the State of Oklahoma followed EPA guidance contained in the 

GTR and GENVC with the exception that the revised IMPROVE algorithm was utilized rather 

than the original IMPROVE equation . The procedure used is described in the Technical 

Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze 

State Implementation Plans.4

 

CENRAP used the approach of Pitchford et al.5

. The equation utilized is referred to as the 

“revised” IMPROVE algorithm or equation and was used for estimates of both baseline and 

natural conditions. The revised IMPROVE equation is used to convert measured concentrations 

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

effect of relative humidity, and including the Rayleigh scattering that occurs in pure air. The 

extinction total is then used to calculate deciviews for use in visibility progress assessments 

through equation 1. EPA’s 2007 “Guidance on the Use of Models and Other Analyses for 

 

2

 Hereafter “GENVC”: EPA, 2003, Guidance for Estimating Natural Visibility Conditions Under the Regional Haze 

Program, EPA-454/B-03-005, September 2003, EPA OAQPS; web page: 

http://www.epa.gov/ttn/oarpg/t1pgm.html direct link: 

http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_envcurhr_gd.pdf 3

 Trijonis, J.C., et al., 1990, "Visibility: Existing and Historical Conditions-Causes and Effects", chapter 24 in 

NAPAP State of Science & Technology, Vol. III web page: 

http://vista.cira.colostate.edu/improve/Publications/Principle_pubs.htm 4

 Hereafter “CENRAP TSD”: Environ International Corp. and University of California at Riverside, September 

2007. 

5

 (2007) Pitchford, Marc; William Malm, Bret Schichtel, Naresh Kumar, Douglas Lowenthal, and Jenny Hand, 

2007: Revised algorithm for estimating light extinction from IMPROVE particle speciation data. J. Air & Waste 

Manage. Assoc., 57, 1326-1336. 
A-11

Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”6

 states 

that the use of either the IMPROVE or the revised IMPROVE equation is acceptable provided 

that the States supply documentation concerning the choice of equation and that the same 

algorithm is utilized for both the base and future extinction calculations. 

The IMPROVE program revised the IMPROVE equation after a scientific assessment of its 

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

the old algorithm.7 In particular, when compared to nephelometer direct measurements of 

visibility extinction, the original IMPROVE equation over-predicts for low extinction conditions 

and under-predicts for high extinction. These biases have direct relevance for estimates for the 

best 20% and worst 20% visibility days that are used to assess progress. 

The revised equation used by CENRAP has four changes: 1) greater completeness though the 

inclusion of sea salt, which can be important for coastal sites; 2) increased organic carbon mass 

estimate, based on more recent data for remote areas; 3) Rayleigh scattering using site-specific 

elevation and temperature, a refinement over the older network-wide constant; and 4) separate 

estimates for small and large particles of visibility impacts and humidity-dependent particle size 

growth rates, which could affect estimates at the low and high ends.8 The revised equation has 

an additional term for inclusion of NO2; however, none of the CENRAP Class I areas have 

monitors that provide observations of NO2 so this term was not used. 

The new equation shows broader scatter overall, but less bias in matching visibility 

measurements under high and low visibility conditions. That is, though it has a somewhat worse 

fit considering all the data, it has a better fit under visibility conditions most relevant to regional 

haze planning, the best and worst 20% of days. The looser overall fit can cause a slightly 

different set of days to be the ones chosen as the 20% worst, but the chemical species 

composition for such days is little changed (IMPROVE technical subcommittee for algorithm 

review, 2001, pp. 11-12), and so this makes little difference for assessing the contribution of 

emission sources to current conditions, and for projecting the effect of emission controls. The 

split between small and large particles was the main factor in reducing the biases. 

The organic carbon (OC) measured by the IMPROVE network does not include all organic 

matter (OM); based on 1970's urban data, a scaling factor of 1.4 is embedded in the old equation 

to account for the full mass. Based on recent data more relevant to relatively remote Class I 

 

6

 Hereafter “GOPMRH”: EPA, 2007, Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, EPA-454/B-07-002, April 2007, EPA 

OAQPS; web page: http://www.epa.gov/scram001/guidance_sip.htm direct link: 

http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf

7

 IMPROVE, 2006, Revised IMPROVE algorithm for Esimating Light Extinction from Particle Speciation Data, 

January 2006; http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm; Hand, J.L.; Douglas, 

S.G., 2006, Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients – Final 

Report, 

http://vista.cira.colostate.edu/improve/Publications/GrayLit/016_IMPROVEEeqReview/IMPROVEeqReview.htm

8

 Pitchford, Marc, 2006, "New IMPROVE algorithm for estimating light extinction approved for 

use", The IMPROVE Newsletter, Volume 14, Number 4, Air Resource Specialists, Inc.; web 

page: http://vista.cira.colostate.edu/improve/Publications/news_letters.htm 

direct link: http://vista.cira.colostate.edu/improve/Publications/NewsLetters/IMPNews4thQtr2005.pdf
A-12

areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. At the Wichita 

Mountains site, fine sulfurous aerosol contributes the most to visibility impairment on the worst 

days during the baseline years. Nitrates and organic carbon are also significant contributors to 

the total extinction on these days. The largest difference in results between the two algorithms is 

related to the separation of total concentrations of sulfate, nitrate, and organic carbon into small 

and large size distributions in the revised equation. 

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

incorporates more recent data, and is based on considerations of relevance for the calculations 

needed for assessing progress under the RHR. EPA believes it is appropriate for the CENRAP 

states to use the revised IMPROVE equation. As the state of the science evolves, it is 

recommended that this procedure is reevaluated to apply more current and site specific data as it 

becomes available. CENRAP provides alternative calculations using the original IMPROVE 

equation for comparison with these visibility calculation results.

2.3 BASELINE VISIBILITY CONDITIONS

Section 2 of the EPA's "Guidance for Tracking Progress Under the Regional Haze Rule" 

("GTP") describes a step-by-step process for calculating the visibility metric for the baseline 

period 2000-2004. The steps involve (1) assembly of daily species concentration data from the 

IMPROVE network, (2) inclusion of substitutions for missing data; (3) assessment of site data 

completeness (4) calculation of extinction via the IMPROVE equation; (5) calculation of the 

deciview Haze Index; (6) calculation of average deciviews for the 20% best and 20% worst days 

for each year; and (7) averaging these over the 5 year period. These steps are mostly 

straightforward and are briefly discussed here with a more detailed discussion on the differences 

between EPA guidance and CENRAP procedures. 

For data substitution, the EPA guidance describes two procedures. The first is the use of 

quarterly median concentrations. This is the median concentration from the quarter the data 

value is missing from, averaged with similar medians from the preceding four years, using only 

quarters with having at least 50% of the days available and no more than 10 consecutive missing 

days (GTP, "Step 3: Determine Quarterly Median Concentrations for Missing Variables", p.2-5). 

The second substitution involves using quarterly averages, as long as the substitution changes 

extinction values by less than 10% in 90% of the data (GRP, "Step 5 - Evaluate Feasibility of 

Substituting Average Values", pp. 2-7 - 2-8). 

Completeness requirements are also given in the EPA guidance: to be complete and included in 

the progress calculation, a year must have data for 50% of the sampling days in every quarter, 

75% of the days for the year overall, and no more than 10 consecutive missing sampling days; 

the overall 5-year period must have at least 3 complete years of data (GTP, "Step 7 - Check Data 

Completeness", pp. 2-8 - 2-9). 

The RHR defines the baseline period as the five year span from 2000-2004. An IMPROVE 

monitor was established at the Wichita Mountains Wilderness Area in 2001 and collected 

samples for the only Class I area in the state of Oklahoma. Data for the Wichita Mountains site 

that meet the completeness requirements are available for the 2002-2004 portion of this baseline 
A-13

period. Therefore, baseline visibility was calculated based on the average of the worst (best) 

20% of days for each of these three years. This meets the minimum overall data completeness 

requirements for calculation of the baseline visibility conditions detailed in the GTP. 

Every Class I area within the CENRAP states has an associated IMPROVE monitor. Results 

from analysis of samples collected at each monitor site are used to calculate extinction and haze 

index using the procedure described above. For those CENRAP sites (Breton Island (BRET), 

Louisiana; Boundary Waters (BOWA), Minnesota and Mingo (MING), Missouri) that did not 

have three valid years that met the completeness requirements for inclusion in the baseline 

visibility calculations, data filling was used to create at least three years of valid data. These data 

filled IMPROVE databases were prepared and made available on the VIEWS website. More 

information on the data filling procedures can be found at the VIEWS website: 

(http://vista.cira.colostate.edu/views/). 

The CENRAP followed EPA guidance for estimating baseline visibility conditions. 

2.4 NATURAL VISIBILITY CONDITIONS

EPA guidance set out a default procedure for estimating natural conditions, but also describes 

circumstances when States might want to use a more refined approach, such as to reduce 

uncertainty when baseline visibility is already near natural conditions, or when there is marked 

seasonality; these might be accomplished via alternative estimates of natural concentrations, or 

use of temporally varying estimates (GENV sec. 3.1 and 3.2). 

The natural conditions approach used the revised IMPROVE equation, so that progress between 

baseline conditions and natural conditions can be calculated on a consistent basis. ODEQ 

utilized the default EPA method to determine natural visibility conditions based on the annual 

average estimates of Trijonis (1990) for the American West. 

The default procedure used has several acknowledged limitations. 1) each chemical species can 

have one of only two possible background concentrations, one for the East and one for the West. 

Future efforts may provide for a larger number of geographic zones with differing 

concentrations. Oklahoma is near the boundary between East and West regions and background 

concentrations may not be captured as well by either region. 

A second potential limitation is that the same approach is used for both natural- and 

anthropogenic-dominated species components; EPA guidance mentions the possibility of treating 

these separately (GENV sec. 3.4). ODEQ considered a “pseudo-natural” condition where 

organic carbonaceous particulate, coarse matter, and fine soils are all assumed to come from 

natural sources and are held at constant levels. However, this alternative assumption requires 

more site-specific analysis than currently available. This, too, is a potential area for future 

efforts. 

The majority of visibility impairment at the Wichita Mountains site is currently from 

anthropogenic sources. As measures are taken to improve visibility and decrease emissions, the 

ability to identify natural sources and background concentrations of PM will improve. The 

current approach used by ODEQ follows EPA default methods and is acceptable. As additional 
A-14

information and more site-specific data becomes available, ODEQ is encouraged to pursue 

refinements in this approach to better quantify natural visibility conditions. 

2.5 UNIFORM RATE OF PROGRESS (GLIDEPATH) CALCULATION

The uniform rate of progress is calculated as the linear rate of progress (decrease in deciviews 

per year) required to reach natural visibility conditions in 2064, starting from the baseline 

conditions in 2004. The first benchmark year is 2018 and the calculated improvement required 

to attain the desired rate of progress is 3.80 deciviews. Table 2.5 summarizes the calculations 

performed by ODEQ. 

Table 2.5. Uniform Rate of Progress for Wichita Mountains (worst quintile, western natural 

visibility conditions) 

Conditions Total extinction 

(Mm-1) 

Haze Index 

(deciviews) 

Baseline (2002-2004) conditions 108.15 23.81 

Natural (for 2064) conditions 21.23 7.53 

Observed impairment above natural conditions 86.91 16.28 

Progress (2004-2018) at uniform rate .27 per year 

Improvement needed by 2018 assuming uniform 

rate of progress 

34.18 3.80 

 
A-15

Chapter 3: Emission Inventory Development 

3.1 INTRODUCTION

In support of the CENRAP Regional Haze air quality modeling efforts, air quality modeling 

inputs including annual meteorology and emissions inventories for a 2002 actual emissions base 

case, a planning case to represent the 2000-04 regional haze baseline period using averages for 

key emissions categories, and a 2018 base case of projected emissions are needed. All emission 

inventories were developed using the Sparse Matrix Operator Kernel Emissions (SMOKE) 

modeling system (See section 3.6). Each of these inventories has undergone a number of 

revisions throughout the development process to arrive at the final versions used in CMAQ and 

CAMx air quality modeling. In general, updated 2002 emissions data for the U.S. developed by 

the Regional Planning Organizations (RPOs), updated emissions data for Mexico from the 

BRAVO 1999 emissions inventory, and version 2 of the 2000 emissions data for Canada were 

used to generate a 2002 annual emissions database. The 2002 and 2018 emissions inventories 

and ancillary modeling data were provided by CENRAP emissions inventory contractors,9 other 

RPOs and EPA. Emission modeling and quality assurance (QA) work was based on the Quality 

Assurance Project Plan (QAPP) CENRAP Emissions and Air Quality Modeling10 and Protocol 

for the CENRAP 2002 Annual Emissions and Air Quality Modeling11 (hereafter referred to as the 

“Modeling Protocol”). These protocols were reviewed by the EPA Regions at the time they were 

developed. 

The development of each of these emission scenarios are as follows: 

• The 2002 base case emissions scenario was developed to represent the actual conditions 

in calendar year 2002 with respect to ambient air quality and the associated sources of 

criteria and particulate matter air pollutants. This emission inventory is used to validate 

the air quality model and associated databases and to demonstrate acceptable model 

performance with respect to replicating observed particulate matter air quality. The base 

case includes actual day-specific emissions of SO2 and NOx emissions for large 

stationary point sources based on measured continuous emissions monitoring (CEM) data 

along with actual 2002 fire emissions. 

 

9

 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and 

Associates, Inc. and Carolina Environmental Program (CEP), University of North Carolina(UNC), 

(http://www.cenrap.org/html/projects.php?mode=subcatdownload&id=50); Pechan and CEP. 2005. Refinements 

of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and 

CEP, UNC. (http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf); Reid, S.B. et al. 2004. Emission Inventory 

Development for Mobile Sources and Agricultural Dust Sources for the Central States. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf).; Reid, S.B et al. 2004. Research and 

Development of Planned Burning Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_PlannedBurnData/FinalReport.pdf).; Coe, D.L. and S.B. Reid. 2003. 

Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Ammonia_NIF/FinalReport.pdf). 10 Morris, R.E. and G. Tonnesen. 2006. Quality Assurance Project Plan (Draft) for Central Regional Air Planning 

Association (CENRAP) Emissions and Air Quality Modeling. 

(http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_QAPP_Rev3_Mar_29_2006.pdf ) 11 Morris, R.E. et al. Modeling Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling, Draft 

2.0. Web:http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_Draft2.0_Modeling_Protocol_120804.pdf).
A-16

 

• The 2000-04 baseline period planning case emissions scenario is referred to as 

“Typ02G”. The purpose of the Typ02G inventory is to represent baseline emission 

patterns based on average, or “typical”, conditions. This inventory provides a basis for 

comparison with the future year 2018 projected emissions, as well as to gauge reasonable 

progress with respect to future year visibility. 5-years of CEM data were analyzed and 

typical seasonal and diurnally varying emissions were defined. 

• The 2018 future-year base case emissions scenario is referred to as “2018 Base Case” or 

“Base18G”. These emissions are used to represent conditions in future year 2018 with 

respect to sources of criteria and particulate matter air pollutants, taking into 

consideration growth and controls. Modeling results based on this emission inventory are 

used to define the future year ambient air quality and visibility metrics. 

Emission inventory data from five general categories are needed to support air quality modeling: 

stationary point-source emissions, stationary area-source emissions (also called nonpoint), 

mobile emissions for on-road sources, mobile emissions for nonroad sources (including aircraft, 

railroad, and marine vessels), and biogenic emissions. The emission inventory development and 

emissions modeling steps can be different for each of these categories. The Technical Support 

Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze State 

Implementation Plans12 (hereafter referred to as the “CENRAP TSD”) describes the 

development of each source category inventory is detail. Appendix B of the CENRAP TSD lists 

the file names, data source, type and a description of emissions used in the 2002 typical 

(Typ02G) emissions inventory. Emissions inventories for each source category are described 

briefly in the following section. The CENRAP TSD is included as Appendix 4-2 of the ODEQ 

Regional Haze Implementation Plan Revision. 

3.2 2002 EMISSIONS INVENTORY

ODEQ developed an emission inventory for 2002 quantifying emissions of sulfur dioxide (SO2), 

ammonia (NH3), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate 

matter of aerodynamic diameters less than 10 μm (PM10) and less than 2.5 μm (PM2.5) for the 

State of Oklahoma. The 2002 national emissions inventory (2002 NEI), compiled from 

submitted inventories from states, tribal and local agencies was the original basis for the 

CENRAP emission inventory. Sonoma Technology supplemented the 2002 NEI data with nonpoint source inventories to address agricultural and prescribed burning, on-road and non-road 

mobile sources, agricultural tilling and livestock dust, and agricultural ammonia for the 

CENRAP inventory.13 

 

12 Environ International Corp. and University of California at Riverside, 2007. Technical Support Document for 

CENRAP Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans. 

(http://www.cenrap.org/html/projects.php?mode=download&id=87)

13 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for 

the Central States. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf).; Reid, S.B et al. 2004. Research and 

Development of Planned Burning Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_PlannedBurnData/FinalReport.pdf).; Coe, D.L. and S.B. Reid. 2003. 
A-17

Table 3-1. Emissions from Oklahoma Sources (tons/yr) 

SO2 NH3 NOx VOCs PM10-

PM2.5 PM2.5 

Point 148,761 24,102 158,818 37,794 8,026 8,636 

Area 11,779 114,363 115,407 201,758 304,560 109,279 

Non-road 

mobile 4,773 280 49,396 47,863 433 4,580 

On-road 

mobile 4,708 4,434 142,592 99,924 879 2,459 

Biogenic 0 0 35,909 988,314 0 0 

Total 170,021 143,179 502,122 1,375,653 313,898 124,954 

3.2.1 Stationary Point-Source Emissions 

Point sources are typically regulated and information on emissions and locations are available in 

regulatory reports. Permitted point sources in Oklahoma are required to submit annual emissions 

inventories. This data, along with similar data available from other states make the basis of the 

point source inventory. The CENRAP stationary-point inventory consisted of annual countylevel and tribal data provided in August of 2005.14 Point source inventories were developed by 

the other RPOs and shared with CENRAP. These inventories are typically further divided into 

EGU and non-EGU sources. For EGU sources, available continuous emissions monitoring 

(CEM) data is available to create day and hour-specific emission inventories for input into the 

Base02F inventory. The Typ02G inventory includes further processing of EGU emissions to 

develop a typical emission levels and temporal profiles. 

Coal-fired point sources within the CENRAP states use a PM2.5 speciation profile recently 

developed for MRPO by Carnegie Mellon that is representative of combustion of eastern 

bituminous coal. Texas and North Dakota sources that burn lignite coal used a modified 

NCOAL speciation.15 More specific speciation profiles should be utilized as they become 

available to accurately describe the speciation of PM2.5 from combustion of different types of 

coal utilized in Oklahoma. 

3.2.2 On-Road Mobile Emissions 

Emissions from mobile, on-road sources are prepared for CENRAP modeling in one of two 

ways: 1) pre-computed emissions supplied by an RPO or other group or 2) supplied vehicle 

 Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 14 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and 

Associates, Inc. and CEP, UNC, (http://www.cenrap.org/html/projects.php?mode=subcatdownload&id=50); 

Pechan and CEP. 2005. Refinements of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). 

E.H. Pechan and Associates, Inc. and CEP, UNC. (http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf) 15 Chow, J et al. 2004. Source Profiles for Industrial, Mobile, and Area Sources in the Big Bend Regional Aerosol 

Visibility and Observational Study. Chemosphere 54, 185-208. 
A-18

miles traveled (VMT), meteorological data and other MOBILE616 inputs for calculation in 

SMOKE/MOBILE6. Annual mobile emissions were pre-computed as part of the 1999 Mexico 

inventory and 2000 Canada inventory. Seasonal mobile emissions calculated in MOBILE6 were 

provided for all 13 WRAP states. For all other RPOs, including CENRAP, county-level VMT 

were prepared and input into SMOKE/MOBILE6. For the CENRAP states, Sonoma Technology 

provided monthly VMT data and MOBILE6 input files for the months of Janurary and July for 

all counties in the CENRAP region.17 MOBILE6 input files for the remaining months of 2002 

had to be generated. The EPA MOBILE6 was state-of-the-science at the time the modeling was 

conducted and deemed acceptable at that time. EPA Office of Transportation and Air Quality is 

continuing to develop and update a new model, Motor Vehicle Emission Simulator (MOVES), 

which will replace the MOBILE6 model for estimating emissions from on-road mobile sources. 

3.2.3 Biogenic Emissions 

The BEIS3 system is utilized to estimate emissions from biogenic sources. BEIS3 is integrated 

into SMOKE for deriving biogenic emissions estimates given land use information, emissions 

factors for different plant species, and hourly, gridded meteorology data. Land use data is from 

the BELD3 land use database and emission factors used are version 0.98 of the BELD emissions 

factors. These land use data and emission factors were developed by the WRAP during their 

preliminary modeling efforts. BEIS modeling produces gridded, hourly emissions for input into 

CMAQ and CAMx.18 The EPA approves of the use of BEIS3 by CENRAP in this SIP. 

3.2.4 Non-Road Mobile Emissions 

Emissions from aircraft operations, commercial and recreational marine vessels, and railroad 

locomotives and other sources were developed by the EPA for the 2002 NEI. The EPA 

NONROAD19 model was utilized by Sonoma Technology to develop a non-road emissions 

inventory for the CENRAP states.17 EPA and CENRAP emissions were consolidated by Pechan 

and CEP.20 

3.2.5 Area Source Emissions 

The area source inventory includes data from the EPA 2002 NEI and inventories prepared by 

ODEQ, CENRAP and other CENRAP states. Sonoma Technology prepared additional 

inventories of prescribed burning, agricultural dust, and soil agricultural ammonia for the 

CENRAP region.21 ODEQ also prepared an inventory of oil and gas production area sources 

 

16 EPA’s MOBILE6 model is available at http://www.epa.gov/OMSWWW/m6.htm 17 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for 

the Central States. Sonoma Technology, Inc. (http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf)

18 Tonnesen, G., et al. 2005. Final Report for the Western Regional Air Partnership (WRAP) Regional Modeling 

Center (RMC) for the Project Period March 1, 2004 through February 28, 2005. UCR. 

(http://pah.cert.ucr.edu/aqm/308/reports/final/2004_RMC_final_report_main_body.pdf). 19 NONROAD is available at http://www.epa.gov/otaq/nonrdmdl.htm 20 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and 

Associates, Inc. and CEP, UNC, (http://www.cenrap.org/html/projects.php?mode=subcatdownload&id=50) 

21 Reid, S.B. et al. 2004. Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for 

the Central States. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_Mobile/FinalReport.pdf).; Reid, S.B et al. 2004. Research and 

Development of Planned Burning Emission Inventories for the Central States Regional Air Planning 
A-19

based on county-level production data and emission estimates for gasoline distribution, dry 

cleaning and wildfires. The Western Regional Air Partnership (WRAP) provided an oil and gas 

production inventory for states within the WRAP that included a number of states in the 

CENRAP modeling domain. These emissions were consolidated by Pechan and CEP.22 UCR 

processed this inventory further to separate the inventory into subcategories (general area, fire, 

ammonia, road dust, fugitive dust, uncategorized) to assist in particulate source apportionment 

modeling with CAMx. 

3.3 2018 EMISSIONS INVENTORY

An emission inventory for 2018 including anticipated changes due to population growth, 

emission controls and development of industry, energy, and natural resources is required to 

project the net effect on visibility conditions by 2018. CENRAP developed an emission 

inventory for 2018 (Base18G) using a combination of EPA Economic Growth Analysis System 

(EGAS 5), MOBILE 6, NONROAD, and the Integrated Planning Model (IPM) of ICF 

International for EGUs to project emissions from 2002 to 2018. Emission projections for most 

source categories are based on growth and control factors compiled by Pechan and detailed in the 

Development of Growth and Control Inputs for CENRAP 2018 Emissions draft technical support 

document.23

Table 3-2. Emission estimates for Oklahoma sources in 2018 (tons/yr) 

 SO2 NH3 NOx VOCs PM10-

PM2.5 PM2.5 

Point 106,701 35,215 140,298 125,648 8,935 13,989 

Area 12,374 141,532 128,257 400,056 275,844 127,018 

Non-road 

mobile 156 40 25,387 28,489 2,914 292 

On-road 

mobile 545 5,818 39,397 39,281 0 953 

Biogenic 0 0 35,909 988,314 0 0 

Total 119,776 182,605 369,248 1,581,788 287,693 142,252 

 Association. Sonoma Technology, Inc. 

(http://cenrap.sonomatech.com/CENRAP_PlannedBurnData/FinalReport.pdf).; Coe, D.L. and S.B. Reid. 2003. 

Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning 

Association. Sonoma Technology, Inc. 22 Pechan and CEP. 2005. Consolidation of Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and 

Associates, Inc. and Carolina Environmental Program (CEP), University of North Carolina(UNC), 

(http://www.cenrap.org/html/projects.php?mode=subcatdownload&id=50); Pechan and CEP. 2005. Refinements 

of CENRAP’s 2002 Emissions Inventories (Schedule 9; Work Item 3). E.H. Pechan and Associates, Inc. and 

CEP, UNC. (http://www.dnr.mo.gov/env/apcp/docs/appendixh-3.pdf) 23 Pechan 2005. Development of Growth and Control Inputs for CENRAP 2018 Emissions, Draft Technical 

Support Document. E.H. Pechan and Associates, Inc. (http://www.dnr.mo.gov/env/apcp/docs/appendixh-4.pdf) 
A-20

Table 3-3. Change in Oklahoma emissions from 2002 to 2018 

SO2 NH3 NOx VOCs PM10-

PM2.5 PM2.5 

Inventory 

for 2002 171,707 137,435 502,656 1,603,591 308,921 148,279 

Estimate 

for 2018 119,776 182,605 369,248 1,581,788 287,693 142,252 

Net change -51,931 +45,170 -133,408 -21,803 -21,228 -6,027 

Percent 

Change -30.244% +32.866% -26.541% -1.360% -6.872% -4.065% 

Pechan used the following alternative data sources to replace EGAS default projections: 

• County-level population projections for CENRAP states; 

• Annual Energy Outlook (AEO) projections for oil and gas production emissions; 

• average historical values rather than 2002 data for prescribed burning; 

• Extrapolation of historical trends for unpaved roads; 

• United States Department of Agriculture (USDA) projections of planted acreage; for 

major crops for crop tilling emissions; 

• Onroad vehicle miles traveled projections for paved road fugitive dust emissions; 

• USDA livestock projections 

All control strategies expected to take effect prior to 2018 are included in the projected emission 

inventory. Maximum Achievable Control Technology (MACT) regulations were applied to 

those engines subject to MACT rules. Emissions for Canada are based on a shared 2020 

emission inventory. 2018 EGU emissions were based on the run 2.1.9 of the Integrated Planning 

Model (IPM) updated by the CENRAP states. Reductions anticipated from BART controls for 

EGUs in Oklahoma, Arkansas, Kansas, and Nebraska were included in projections of 2018 

emissions. These anticipated reductions were based on actual operating conditions and estimated 

control efficiencies from utilities. Newly permitted coal-fired utilities were included in 2018 

projections. Conservatively, no IPM projected new units were removed from the simulation with 

the addition of the permitted facilities. Appendix B of the CENRAP TSD lists the file names, 

data source, type and a description of emissions used in the 2018 (Base18G) emissions 

inventory. ODEQ modified the emissions from the IPM for OG&E Sooner and Muskogee 

electric power plants and the PSO Northeast electric power plant to reflect the application of 

presumptive BART controls. 

The following sources were assumed to remain constant between the 2002 and 2018 base case 

simulations: 

• Biogenic VOC and NOx emissions from the BEIS3 biogenic emissions model; 

• Wind blown dust associated with non-agricultural sources (i.e., natural wind blown 

fugitive dust); 

• Off-shore emissions associated with off-shore marine and oil and gas production 

activities; 

• Emissions from wildfires; 
A-21

• Emissions from Mexico; and 

• Global transport (i.e., emissions due to BCs from the 2002 GEOS-CHEM global 

chemistry model. 

The last future runs (2018G) utilized an inventory that had assumptions about BART controls in 

the CENRAP states. 
A-22

Chapter 4: Modeling Protocol, Episode Selection and Modeling Set-up 

Overview 

4.1 INTRODUCTION

Meteorological, emission and photochemical models are essential tools in examining factors that 

impact visibility and for development of effective control strategies to meet the goals and 

requirements of the RHR. CENRAP selected the team of ENVIRON and UCR to perform the 

needed emissions and air quality modeling. The team performed regional haze analyses by 

operating regional scale, three-dimensional air quality models to simulate the transport and fate 

of key species that affect visibility in Class I Areas in the central U.S. This work included the 

development of meteorological data for input into the model as well as creation and processing 

of emission estimates for use in the model. The Modeling Protocol24 describes the model 

selection, configuration, episode selection, and model evaluation used in support of the 

Oklahoma RHR SIP.

 

4.2 QUALITY ASSURANCE PROGRAM PLAN

The modeling team developed a quality assurance program plan (QAPP)25 to develop clearly 

defined data quality objectives, documentation, and procedures. This QAPP was developed 

incorporating the following elements as described in the EPA guidance document for modeling: 

• A systematic planning process including identification of assessments and related 

performance criteria; 

• Peer reviewed theory and equations; 

• A carefully designed life-cycle development process that minimizes errors; 

• Documentation of any changes from original plans; 

• Clear documentation of assumptions, theory, and parameterization that is detailed enough 

so others can understand the model output; 

• Input data and parameters that are accurate and appropriate for the problem; and 

• Output data that can be used to help inform decision makers. 

The plan describes the data management and quality assurance/quality control measures taken to 

assure high quality emission inventories and air quality modeling results for use in the RH 

analysis. 

4.3 EPISODE SELECTION

EPA guidance26 describes the criteria that should be used to select a modeling episode. The 

modeling episode should: 1) reflect a variety of meteorological conditions that are representative 

of the 20% worst and 20% best days in the Class I areas being modeled, 2) be representative of 

 

24 Morris, R.E. et al. Modeling Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling, Draft 

2.0. Web:http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_Draft2.0_Modeling_Protocol_120804.pdf). 25 Morris, R.E. and G. Tonnesen. 2004. Quality Assurance Project Plan (Draft) for Central Regional Air Planning 

Association (CENRAP) Emissions and Air Quality Modeling. 

(http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_QAPP_Nov_24_2004.pdf). December 23. 

26 EPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze, EPA-454/B-07-002, April 2007, EPA OAQPS; 

(http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf) 
A-23

the baseline period of 2000-2004, 3) cover a period where extensive air quality/meteorological 

data are available, 4) cover a long enough period so that relative response factors (RRF) can be 

averaged over a period several days (> 10 days). For regional haze modeling, the preferred 

approach is to simulate an entire representative year. This allows the states to base RRF values 

on the 20% best and 20% worst days of the year. 

CENRAP selected the entire calendar year of 2002 for regional haze modeling. This is 

consistent with the EPA guidance and has the added benefit of the base case and baseline 

inventories covering the same year. Other RPOs selected the same modeling year, allowing for 

more direct comparison of modeling results and sharing of modeling inputs. The availability of 

2002 NEI also provides an additional resource in the development of emission inventories for the 

modeling episode. The Wichita Mountains site has a complete year of observations from the 

IMPROVE monitor beginning in 2002. Furthermore, 2002 appears to be a more representative 

year when compared to 2003 and 2004. The EPA approves of the selection of 2002 for the 

regional haze modeling episode. 

4.4 PHOTOCHEMICAL AND EMISSIONS MODELING DOMAIN

CENRAP conducted emissions and air quality modeling on the 36-km national regional planning 

organization (RPO) domain. This domain consists of a 148 × 112 array of 36-km × 36-km grid 

cells and covers the continental United States. Additional photochemical modeling runs were 

performed on a 12-km domain covering the central states to examine the sensitivity of model 

results to domain resolution. These results were similar to the 36 km results so CENRAP 

determined that the 36-km modeling domain was sufficient for the 2002 annual modeling.27 

CENRAP’s choice of 36 km horizontal resolution was appropriate given the lack of improved 

performance at 12 km resolution and the additional computational resources required to run the 

model at the higher resolution. The use of higher spatial resolution modeling should be revisited 

in future modeling efforts as computational efficiency improves. 

 

27 Morris, R.E. et al. 2006. CENRAP Modeling: Need for 36 km versus 12 km Grid Resolution. Presented at 

CENRAP Modeling Work Group Meeting, Baton Rouge, Louisiana. 

(http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ Morris_36vs12km_Feb6-8_2006.ppt). 
A-24

Figure 4-1. National RPO 36-km modeling domain for CMAQ, CAMx, and SMOKE modeling. 

4.5 MM5 METEOROLOGICAL MODEL 

4.5.1 Model Selection 

Photochemical grid models, such as CMAQ and CAMx, require inputs of three-dimensional 

gridded meteorological data, including wind, temperature, humidity, cloud/precipitation, and 

boundary layer parameters. The Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) 

was used to develop these input fields for the CENRAP visibility modeling as well as inputs for 

the SMOKE emissions processing tool. MM5 is a state-of-the-science atmosphere model that 

has proven useful for air quality applications and has been used extensively in past local, state, 

regional, and national modeling efforts. MM5 has undergone extensive peer-review, with all of 

its components continually undergoing development and scrutiny by the modeling community. 

In-depth descriptions of MM528 can be found in Dudhia (1993)29 and Grell et al. (1994).30 All 

meteorological data used for the CENRAP air quality modeling efforts are derived from MM5 

model simulations. 

 

28 http://www.mmm.ucar.edu/mm5 29 Dudhia, J., 1993. “A non-hydrostatic version of the Penn State/NCAR Mesoscale Model: validation tests and 

simulation of an Atlantic cyclone and cold front.” Mon. Wea. Rev. 121, pp.1493-1513. 30 Grell, G.A., J. Dudhia, and D.R. Stauffer, 1994. “A description of the Fifth Generation Penn State/NCAR 

Mesoscale Model (MM5).” NCAR Technical Note, NCAR TN-398-STR, 138 pp. 
A-25

In addition to development of meteorological inputs for CMAQ and CAMx, MM5 was also used 

to develop meteorological inputs for the CALMET/CALPUFF modeling system. As discussed 

further in Section 6 of this review, CALMET/CALPUFF was used to determine whether a 

BART eligible source contributes to visibility impairment at a Class I area. Refer to Section 6 of 

this review for further information on the use of MM5 for BART modeling. 

The CENRAP meteorological modeling used for input to photochemical modeling and emission 

processing was performed by the Iowa Department of Natural Resources (IDNR) and is 

described fully in the report entitled Meteorological Model Performance Evaluation of an Annual 

2002 MM5 (version 3.6.3) Simulation (hereafter referred to as the Meteorological Model 

Performance Evaluation report) .31

4.5.2 Meteorological Modeling Domain and Vertical Layer Structure 

In the IDNR 36-km meteorological modeling, MM5 was configured to run on the standard 

continental-scale Regional Planning Organization (RPO) National Grid with 36-km grid point 

spacing. The RPO National Grid is defined on a Lambert conformal projection, with true 

latitudes at 33°N and 45°N, and the central latitude and longitude at 40°N and 97°W, 

respectively. The grid point spacing is 36-km. The continental expanse of this domain results in 

a grid of 165 (east-west) by 129 (north-south) dot points, and 164 (east-west) by 128 (northsouth) cross points. Overall, the domain covers 5904 km by 4608 km. The MM5 domain 

provides overlap of the CMAQ and CAMx air quality modeling grid (described in section 3.3) to 

alleviate any numerical boundary artifacts that may be present in the MM5 output fields. 

Meteorology modeling was also completed on a regional-scale domain with 12 km grid spacing 

covering the central states by EPA Region VII and the Texas Commission on Environmental 

Quality to examine model prediction sensitivity to grid resolution. The vertical layer structure of 

the CENRAP meteorological modeling domain consists of 34 layers, a top level at 100 millibars, 

and increasing layer thickness with altitude. The vertical layer structure is further detailed in the 

Modeling Protocol. 

4.5.3 Model Configuration 

The final CENRAP MM5 modeling system configuration for the 2002 annual simulation is 

provided in the Modeling Protocol and the Meteorological Model Performance Evaluation 

report. Early MM5 simulations by the State of Iowa and the Lake Michigan Air Directors 

Consortium (LADCO) and further sensitivity tests were performed to identify an MM5 

configuration for annual runs. 

The initial 2002 36-km IDNR simulation results showed that MM5 results showed an extreme 

cold bias over the central U.S and unnatural diurnal profiles near shorelines. A number of 

sensitivity tests were performed by IDNR to resolve performance issues identified in the initial 

simulation. At the same time, sensitivity tests were performed in support of the development of 

 31 Johnson, M. 2007. Meteorological Model Performance Evaluation of an Annual 2002\ MM5 (version 

3.6.3) Simulation. Iowa Department of Natural Resources, Air Quality Bureau. 

(http://www.iowadnr.gov/air/prof/progdev/files/IDNR.2002mm5v363.evaluation.v204.p f) 

. 
A-26

meteorological modeling for VISTAS.32 The combination of all of these studies led to the 

configuration used by CENRAP for MM5 modeling detailed in the CENRAP TSD and the 

Modeling Protocol. 

 

4.5.4 MM5 Processing and Application 

Several preprocessing steps are necessary to prepare input data for an MM5 simulation. The 

MM5 modeling system provides all of the tools necessary to prepare topographic, vegetative, 

initial condition, boundary condition, and FDDA nudging input files. 

Global topographic data at 2-minute (latitude/longitude) resolution were used to define terrain 

elevations on the 36-km grid. Land use distribution on the MM5 domains was defined from the 

24-category USGS vegetation data with a resolution of 2 minutes. 

The 3-hour Eta analysis and surface fields available from the National Center for Atmospheric 

Research (NCAR) were taken from the Eta Data Assimilation System (EDAS) and used to 

supply initial and boundary conditions to MM5, and for analysis nudging in the FDDA package. 

The EDAS analyses are developed from a wide variety of observational sources, including 

standard surface and upper air measurements, profiler networks, radar- and satellite-derived 

measurements, and ship and aircraft reports. The wide array of data sources, coupled with the 

high time- and spatial resolution provided by EDAS, result in an analysis product that far 

exceeds the level of detail found in traditional global-scale analyses. 

Sea surface temperatures (SSTs) were approximated by ETA skin temperatures 

The annual simulation was generated from 95 independent simulations initialized at 12Z and 

integrated through five days. To allow for approximately a two week photochemical model spinup period, the simulation started at 12/16/2001 12Z. 

4.5.5 Model Performance 

Model performance evaluation was performed by IDNR through comparison with observations 

of surface and upper-air meteorological conditions and precipitation.33 Additional performance 

evaluation was done by CENRAP by comparing the 2002 CENRAP MM5 simulation with the 

2002 VISTAS MM5 and the interim 2002 WRAP simulations.34 Details on this comparison can 

be found in the CENRAP TSD, Appendix A. 

 

 

32 Olerud, D., Sims, A., 2004. MM5 2002 Modeling in Support of VISTAS (Visibility Improvement—State and 

Tribal Association). Baron Advanced Meteorological Systems, LLC, Research Triangle Park, NC. 

http://www.baronams.com/projects/VISTAS/reports/VISTAS_TASK3f_final.pdf 

33 Johnson, M. 2007. Meteorological Model Performance Evaluation of an Annual 2002\ MM5 (version 3.6.3) 

Simulation. Iowa Department of Natural Resources, Air Quality Bureau. 

(http://www.iowadnr.gov/air/prof/progdev/files/IDNR.2002mm5v363.evaluation.v204.pdf)

34 Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen, 2005. Comparison of CENRAP, 

VISTAS and WRAP 36 km MM5 Model Runs for 2002, Task 3: Met Gatekeeper Report. 

http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VISTAS_WRAP_2002_36km_MM5_eval.ppt 
A-27

The goal of the evaluation was to determine whether the meteorological fields are sufficiently 

accurate to properly characterize the transport, chemistry, and removal processes in 

CMAQ/CAMx. If errors in the meteorological fields are too large, the ability of the air quality 

model to replicate regional pollutant levels over the entire base year will be severely hampered 

and the predicted impacts from future year growth and controls will be highly questionable. To 

provide a reasonable meteorological characterization to the photochemical/visibility model, 

MM5 must represent with some fidelity the: 

• Large-scale weather patterns (i.e., synoptic patterns depicted in the 850-300 mb height 

fields), as these are key forcings for mesoscale circulations; 

• Mesoscale and regional wind, temperature, PBL height, humidity, and cloud/precipitation 

patterns; 

• Mesoscale circulations such as sea breezes and mountain/drainage circulations; 

• Diurnal cycles in PBL depth, temperature, and humidity. 

For visibility applications, the moisture and condensate fields are particularly important as they 

significantly impact PM chemical formation, removal, and light scattering efficiency. In 

addition, cloud and precipitation fields are a good measure of the integrated performance of the 

model since these are model-derived quantities and not nudged to observations. Because of the 

model’s coarse resolution of 36-km, the model cannot be expected to faithfully simulate the 

pattern or variability of the convective precipitation, but should reproduce the synoptic 

precipitation and cloud patterns. 

The IDNR evaluation of the MM5 model performance was limited to operational testing of the 

model, and not to a scientific evaluation. Previous peer-reviewed documentation of MM5 

formulation, testing, and evaluation provide the basis for its scientific validity. An operational 

evaluation entails an assessment of the model's ability to correctly estimate surface and boundary 

layer wind, temperature, and moisture largely independent of whether the actual process 

descriptions in the model are accurate. The operational evaluation essentially tests whether the 

predicted meteorological fields are reasonable, consistent, and agree adequately with available 

observations in time and space. The process provides only limited information about whether 

the results are correct from a scientific perspective or whether they are the fortuitous product of 

compensating errors; thus a “successful” operational evaluation is a necessary but insufficient 

condition for achieving a sound, reliable performance testing exercise. 

The basis for the IDNR operational performance assessment entailed a comparison of the 

predicted meteorological fields to available surface and aloft data that are collected, analyzed, 

and disseminated by the National Weather Service. It was carried out both graphically and 

statistically to evaluate model performance for winds, temperatures, humidity, and the 

placement, intensity, and evolution of key weather phenomena. The MM5 results were 

compared to a specific set of statistics that have been identified for use in establishing 

benchmarks for acceptable MM5 model performance.35 The IDNR concluded, based on the 

 

35 Emery, C.A., E. Tai, and G. Yarwood. 2001. “Enhanced meteorological modeling and performance evaluation for 

two Texas ozone episodes.” Prepared for the Texas Natural Resource Conservation Commission, by ENVIRON 

International Corporation. 
A-28

results of the performance evaluation, that the final 36 km CENRAP MM5 simulations exhibit 

reasonably good performance for the central U.S. 

Comparison of CENRAP MM5 performance with similar modeling efforts by WRAP and 

VISTAS revealed comparable performance across all three simulations. The three simulations 

showed similar performance for prediction of surface wind speed, wind direction and humidity. 

The use of surface data assimilation of temperature in the interim WRAP simulation resulted in 

the best performance in prediction of surface temperatures but the poorest performance for 

vertical temperature profiles. Surface data assimilation has since been dropped from the WRAP 

modeling protocol. The 2002 VISTAS MM5 simulations showed the best performance and the 

CENRAP performance more closely resembled that of the VISTAS than the WRAP. 

The 2002 CENRAP MM5 model results are within the bounds of other meteorological databases 

used for prior air quality modeling efforts. It is therefore deemed reasonable to proceed with its 

use as inputs for visibility modeling. The EPA accepts the use of MM5 in this configuration and 

selected modeling domain and recognizes that the MM5 meteorological model used by CENRAP 

was state-of-the-science at the time the modeling was conducted. The performance of the model 

was adequate for the purposes for which it was used and on par with other studies at the time. A 

new meteorological model, the Weather Research Forecast model (WRF), has been developed to 

address the some of the limitations of the MM5 model and should be considered as a possible 

alternative for future meteorological modeling efforts. 

4.6 SMOKE EMISSIONS MODEL 

CENRAP selected the Sparse Matrix Operator Kernel Emissions model36 to generate gridded 

hourly speciated emission estimates for mobile, non-road, area, point, fire and biogenic emission 

sources for use as inputs for photochemical grid models. The purpose of SMOKE is to convert 

the spatial and temporal resolution of the available emission inventory data to the resolution 

needed by the air quality model. SMOKE also has the ability to compute emissions for mobile 

on-road and biogenic sources. Biogenic emission modeling is performed through SMOKE with 

the Biogenic Emission Inventory System, version 3 (BEIS3)37 using the Biogenic Emissions 

Landcover Database (BELD3) vegetative database. Mobile emissions can be calculated by 

SMOKE from mobile-source activity data, using emission factors from the MOBILE6 model. 

SMOKE supports the emission input formats required by the CAMX and CMAQ air quality 

models. 

 

4.7 AIR QUALITY MODEL

Photochemical air quality models offer opportunity to better understand the sources of particulate 

matter that impair visibility by simulating the emissions, formation, transport, and deposition of 

these pollutants. If an air quality model performs well for a historical episode, the model may 

then be useful for identifying the sources of particulate matter and helping to select the most 

effective emissions reduction strategies for attaining visibility goals. Although several types of 

air quality modeling systems are available, the gridded, three-dimensional, Eulerian models 

provide the most complete spatial representation and the most comprehensive representation of 

 

36 Available at http://www.smoke-model.org/index.cfm 

37Available at http://www.epa.gov/ttn/chief/software.html#pcbeis
A-29

processes affecting particulate matter, especially for situations in which multiple pollutant 

sources interact to form particulate matter. 

4.7.1 Model Selection 

Guidance from the EPA requires that the air quality model should be selected based on intended 

application and must be freely downloadable to all stakeholders. Furthermore, the user must be 

able to revise the code to perform diagnostic analyses and/or to improve the model’s ability to 

describe observations in a credible manner. Several additional prerequisites should be met for a 

model to be used to support an attainment demonstration or uniform rate of progress assessment. 

• It should have received and been revised in response to a scientific peer review. 

• It should be appropriate for the specific application on a theoretical basis. 

• It should be used with a data base which is adequate to support its application. 

• It should be shown to have performed well in past modeling applications. (If the 

application is the first for a particular model, then the State should note why it believes 

the new model is expected to perform sufficiently.) 

• It should be applied consistently with a protocol on methods and procedures. 

The Guideline on Air Quality Models (GAQM - 40 CFR Part 51 Appendix W) does not indicate 

a preferred photochemical grid model for Regional Haze applications. The CMAQ and CAMx 

models have been accepted by EPA for numerous regulatory air quality modeling applications 

and were considered by CENRAP for use in regional haze modeling. CENRAP selected CMAQ 

Version 4.5 with “SOAmod enhancements” as the primary air quality model for regional haze 

modeling and the CAMx Version 4.40 model, applied using similar options as used by CMAQ, 

as a secondary corroborative model. CAMx was also utilized with its Particulate Source 

Apportionment Technology (PSAT) tool to provide source apportionment with both the baseline 

and future case emissions inventories (See Section 5). EPA concurred with the selection of 

CAMx for the CENRAP regional haze modeling as it has been extensively used within the 

region and has been proven to be an acceptable model. The selection of CMAQ was based on 

review of previous and concurrent studies within CENRAP and other RPOs, as well as 

comparisons with CAMx model results.38 Major differences between the two models that still 

exist are in the basic model code, in the treatment of horizontal diffusion SOA formation 

mechanisms, and in grid nesting. EPA accepts the choice of CMAQ as it satisfies the 

requirements and guidelines detailed above. The versions of CMAQ and CAMx used by 

CENRAP in its visibility modeling were the state-of-the-science at the time they were 

implemented and are acceptable to EPA for this Regional Haze selection. 

Both air quality models were set up and run on the RPO national 36–km modeling domain 

described in section 3.3. This modeling domain is also used by WRAP and VISTAS. Sensitivity 

runs performed by CENRAP for CMAQ run on a 12km modeling domain revealed limited 

improvement over the 36-km runs and a large increase in computer resources and time. CAMx 

runs at 12-km resolution reduced the sulfate under-prediction bias in the summertime when 

 38 Morris, R.E., et al. 2006. CENRAP Modeling Update: CMAQ versus CAMx Model Performance Evaluation. 

Presented at CENRAP Modeling Work Group Meeting, Baton Rouge, Louisiana. 

(http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ Morris_MPE_Feb6-8_2006.ppt) 
A-30

compared to 36-km runs. With this possible exception, CENRAP noted little benefit in overall 

model performance with use of the 12-km grid. Therefore, the 36km domain was selected for all 

CENRAP CMAQ and CAMx runs.39

These air quality models are discussed in more detail below. 

4.7.1.1 CMAQ Air Quality Model 

EPA initially developed the Community Multi-Scale Air Quality (CMAQ) modeling system in 

the late 1990s. The model source code and supporting data can be downloaded from the 

Community Modeling and Analysis System (CMAS) Center (http://www.cmascenter.org/), 

which is funded by EPA to distribute and provide limited support for CMAQ users. CMAQ was 

designed as a “one atmosphere” modeling system to encompass modeling of multiple pollutants 

and issues, including ozone, PM, visibility, and air toxics. This is in contrast to many earlier air 

quality models that focused on single-pollutant issues (e.g., ozone modeling by the Urban 

Airshed Model). CMAQ is an Eulerian model—that is, it is a grid-based model in which the 

frame of reference is a fixed, three-dimensional (3-D) grid with uniformly sized horizontal grid 

cells and variable vertical layer thicknesses. The number and size of grid cells and the number 

and thicknesses of layers are defined by the user, based in part on the size of the modeling 

domain to be used for each modeling project. The key science processes included in CMAQ are 

emissions, advection and dispersion, photochemical transformation, aerosol thermodynamics and 

phase transfer, aqueous chemistry, and wet and dry deposition of trace species. CMAQ offers a 

variety of choices in the numerical algorithms for treating many of these processes, and it is 

designed so that new algorithms can be included in the model. CMAQ offers a choice of three 

photochemical mechanisms for solving gas-phase chemistry: the Regional Acid Deposition 

Mechanism version 2 (RADM2), a fixed coefficient version of the SAPRC90 mechanism, and 

the Carbon Bond IV mechanism (CB-IV). 

CENRAP used CMAQ Version 4.5 with a “SOAmods enhancement” for 2002 base case (actual 

emissions), 2002 baseline (typical emissions) and 2018 future case (projected emissions) 

modeling. The “SOAmods enhancement” was the result of work by VISTAS investigating the 

model’s underestimate of organic mass carbon (OMC) concentrations. The updated CMAQ 

secondary organic aerosol (SOA) module led to improved estimation of OMC in VISTA 

modeling. CENRAP examined the use of the enhanced SOA module and found similar 

improvements in model performance over the original CMAQ Version 4.5 model. CENRAP 

decided to use the CMAQ Version 4.5 with the “SOAmods enhancement”40 for CENRAP 

modeling. Details of the CMAQ model configuration used by CENRAP can be found in the 

CENRAP TSD and the Modeling Protocol. 

4.7.1.2 CAMx Air Quality Model 

 

39 Morris, R.E., et al. 2006. CENRAP Modeling: Need for 36 km versus 12 km Grid Resolution. Presented at 

CENRAP Modeling Work Group Meeting, Baton Rouge, Louisiana. 

(http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ Morris_36vs12km_Feb6-8_2006.ppt) 40 Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T.W. Tesche, G. Tonnesen, J. Boylan and P. 

Brewer. 2006. Model Sensitivity Evaluation for Organic Carbon using Two MultiPollutant Air Quality Models 

that Simulate Regional Haze in the Southeastern United States. Atmos. Env. 40 (2006) 4960-4972. 
A-31

The Comprehensive Air Quality Model with extensions (CAMx) model41 was initially developed 

by ENVIRON in the late 1990s as a nested-grid, gas-phase, Eulerian photochemical grid model. 

ENVIRON later revised CAMx to treat PM, visibility, and air toxics. While there are many 

similarities between the CMAQ and CAMx systems, there are also some significant differences 

in their treatment of advection, dispersion, aerosol formation, and dry and wet deposition. 

CAMx has seen extensive use within many of the CENRAP states. The CAMx model is based 

on well-established treatments of advection, diffusion, deposition, and chemistry. CENRAP 

used CAMx Version 4.40, applied using similar options as used by CMAQ, as a secondary 

corroborative model and utilized CAMx with its Particulate Source Apportionment Technology 

(PSAT) tool to provide source apportionment of nitrate and sulfate aerosol with both the 2002 

baseline and 2018 future case emission inventories (See section 5). Details of the CAMx model 

configuration used by CENRAP can be found in the CENRAP TSD and the Modeling Protocol. 

4.7.2 Vertical Modeling Domain 

CMAQ and CAMx have the ability to collapse the 34 layer vertical structure used in MM5 

modeling to a smaller set of vertical layers. Sensitivity studies by WRAP and VISTAS 

examined model performance looking at a variety of vertical modeling domains ranging from 

modeling all 34 vertical layers to collapsing the structure down to 12 vertical layers. Results of 

this study showed that collapsing the vertical structure down to 19 layers while matching the 8 

bottom most vertical layers produced results nearly identical to the full 34 layer runs. The more 

aggressive layer collapsing scheme of 12 layers produced substantially different results. Based 

on these results, CENRAP selected the 19 layer vertical structure described in the CENRAP 

TSD. This selection improves computational efficiency and produces results almost identical to 

the full vertical structure runs. 

4.7.3 Initial and Boundary Conditions 

Initial conditions (ICs) are specified by the user for the first day of a model simulation. For 

continental-scale modeling using the RPO 36-km domain, the ICs can affect model results for as 

many as 15 days, although the effect typically becomes very small after about 7 days. A model 

spin-up period is included in each simulation to eliminate any effects from the ICs. For the 

CENRAP modeling, the annual simulation is divided into four quarters, and included a 15-day 

spin-up period for the quarters beginning in April, July, and October. For the quarter beginning 

in January 2002, a spin-up period covering December 16-31, 2001, using meteorology and 

emissions data developed for CENRAP were used. We agree that the 15 day spin-up period 

employed by CENRAP was sufficient to minimize the effects of the IC on model results given 

the size of the modeling domain. 

Boundary conditions (BCs) specify the concentrations of gas and PM species at the four lateral 

boundaries of the model domain. BCs determine the amounts of gas and PM species that are 

transported into the model domain when winds flow into the domain. Boundary conditions have 

a much larger effect on model simulations than do ICs. For some areas in the CENRAP region 

and for clean conditions, the BCs can be a substantial contributor to visibility impairment. For 

this study BC data generated in an annual simulation of the global-scale GEOS-Chem model for 

 

41 ENVIRON, 2006. “User’s Guide – Comprehensive Air-quality Model with extensions, Version 4.30.” ENVIRON 

International Corporation, Novato, California. (available at http://www.camx.com). 
A-32

calendar year 2002 were applied.42 The BCs employed by CENRAP were state-of-the-science at 

the time they were implemented. 

4.7.4 Base Case/ Baseline Model Performance 

The 2002 Base Case modeling efforts were used to evaluate air quality/visibility modeling 

systems for a historical episode—in this case, for calendar year 2002—to demonstrate the 

suitability of the modeling systems for subsequent planning, sensitivity, and emissions control 

strategy modeling. Comparisons between the 2002 Base F actual emissions model performance 

with the 2002 typical emissions (Typ02F) revealed little difference in model performance. The 

2002 F model predictions are nearly identical to 2002 G results so model performance evaluation 

performed with 2002 Base F emissions is representative of the final model performance. 

Therefore, model performance was evaluated using the Typ02F emission inventory. 

 

Model performance evaluation is performed by comparing output from model simulations with 

ambient air quality data for the same time period to determine whether the model’s performance 

is sufficiently accurate to justify using the model for simulating future conditions. There are a 

number of challenges in completing an annual MPE for regional haze. The model must be 

compared to ambient data from several different monitoring networks for both PM and gaseous 

species, for an annual time period, and for a large number of sites. The focus of the performance 

evaluation is on the six components of particulate matter that are used to characterize visibility at 

Class I areas: Sulfate (SO4); Particulate Nitrate (NO3); Elemental Carbon (EC); Organic Mass 

Carbon (OMC); Other inorganic fine particulate (IP or Soil); and Coarse Matter (CM). The 

model must be evaluated for both the worst visibility conditions and for very clean conditions. 

Finally, final guidance on how to perform an MPE for fine-particulate models is not available 

from EPA. Therefore, the CENRAP experimented with many different approaches for showing 

model performance results. 

The plot types that were found to be the most useful are the following: 

• Time-series plots comparing the measured and model-predicted species concentrations 

• Scatter plots showing model predictions on the y-axis and ambient data on the x-axis 

• Spatial analysis plots with ambient data overlaid on model predictions 

• Bar plots comparing the mean fractional bias (MFB) or mean fractional error (MFE) 

performance metrics 

• “Bugle plots” showing how model performance varies as a function of the PM species 

concentration 

• Stacked-bar plots of contributions to light extinction for the average of the best-20% 

visibility days or the worst-20% visibility days at each site; the higher the light extinction, the 

lower the visibility 

The following plots depict summary model performance for CENRAP CMAQ modeling using 

the Typ02F emissions inventory. Below are six sets of model fractional bias and model 

fractional error plots. Each set of plots compares the measured chemically speciated aerosol data 

 

42 Jacob, D.J., R. Park and J.A. Logan. 2005. Documentation and Evaluation of the GEOS-CHEM Simulation for 

2002 Provided to the VISTAS Group. Harvard University (http://www.vistassesarm.org/documents/Harvard_GEOS-CHEM_FinalReport_20050624.doc) 
A-33

from a monitoring network with the corresponding model output. The monitoring networks used 

for comparison are IMPROVE, CASTNET, and STN, and are treated separately because each 

monitoring network has different goals, siting criteria, and data collection protocols. The model 

performance plots depicted here are “bugle plots”, and depict model performance (symbols) and 

model performance standards (curves) on the y axis relative to measured concentration on the x 

axis. Model performance standards are of greater latitude at lower concentrations because of the 

higher relative uncertainties in the data at lower concentrations. Performance goals or criteria 

approach 200% error and ≤200% bias as observed concentrations approach zero and 

asymptotically approach the proposed performance goals or criteria (i.e., the ≤30%/50% and 

≤60%/75% bias/error levels) as concentrations become greater than 2.5 μg/m3

.

 

Model performance at IMPROVE monitors is of highest importance, because these monitors are 

sited to be representative of the visibility conditions impacting each Class 1 Area. The 

CASTNET monitoring network is more sparse than the IMPROVE network, but is also mostly 

sited at Class 1 Areas and as such, model performance at CASTNET sites should also be 

considered important. The STN monitoring network is an urban network, and model 

performance relative to this network should be given less importance. 

The model performance goals and criteria used by CENRAP were appropriate at the time the 

modeling was conducted and consistent with the methods adopted by VISTAS and WRAP. The 

EPA agrees with the CENRAP model performance procedures and analysis. Detailed results of 

the model performance evaluation can be found in Appendix C of the CENRAP TSD and on the 

University of California, Riverside CENRAP visibility modeling website 

(http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml#cmaq_typ02f_mpe). 

4.7.4.1 Model Performance for Sulfate (SO4) 

 

Figure 4-2 shows the monthly SO4 fractional error and bias for the STN, IMPROVE and 

CASTNET monitoring networks as well as the proposed performance goals and criteria. 

In general, there is an under-prediction bias that is more pronounced during the spring and 

summer months. For the STN network, model performance for all months is within the goals for 

both fractional bias and error. Model performance for CASTNET sites is within goals for 

fractional error and within the criteria for fractional bias as is model performance for the 

IMPROVE sites with the exception of two months that lie within the criteria but beyond the goal 

for fractional error. 
A-34

Figure 4-2. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for sulfate (SO4). The 12 symbols for each monitor represent monthly average 

model performance for the year 2002, averaging all monitors in the CENRAP region. 

Solid lines represent CENRAP modeling goals and criteria. 

4.7.4.2 Model Performance for Nitrate (NO3) 

 

Figure 4-3 shows the monthly NO3 fractional error and bias for the STN, IMPROVE and 

CASTNET monitoring networks as well as the proposed performance goals and criteria. 

NO3 model performance is variable. There is an underprediction during the summer months, 

approaching a fractional bias of -140% in June and July and an overprediction with bias of 

approximately 50% in the winter. The winter bias is more significant because NO3

concentrations tend to be a large component of visibility impairment during the winter months. 
A-35

In general, winter model performance does not meet the performance goals and in some cases 

does not meet the criteria, predicting concentrations of NO3 much higher than observed. 

Figure 4-3. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for nitrate (NO3). The 12 symbols for each monitor represent monthly average 

model performance for the year 2002, averaging all monitors in the CENRAP region. 

Solid lines represent CENRAP modeling goals and criteria. 

4.7.4.3 Model Performance for Organic Carbon (OC) 

 

Figure 4-4 shows the monthly OC fractional error and bias for the STN and IMPROVE 

monitoring networks as well as the proposed performance goals and criteria. For the IMPROVE 

network, model performance for all months is within the goals for both fractional bias and error. 

The STN monitors in urban areas measured higher concentrations of OC then the rural 
A-36

IMPROVE monitors. Model performance for STN sites shows a negative bias throughout the 

year that fall within the model criteria for both bias and error. 

Figure 4-4. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for organic carbon (OC). The 12 symbols for each monitor represent monthly 

average model performance for the year 2002, averaging all monitors in the CENRAP 

region. Solid lines represent CENRAP modeling goals and criteria. 

4.7.4.4 Model Performance for Elemental Carbon (EC) 

 

Figure 4-5 shows the monthly EC fractional error and bias for the STN and IMPROVE 

monitoring networks as well as the proposed performance goals and criteria. Model performance 

for EC falls within the proposed performance goals. Fractional bias for the STN sites is small 

with a fractional error around 50%. There is a large model underprediction during the summer at 
A-37

the IMPROVE sites. However, EC concentrations at these sites are low putting the model 

performance within the goals for low concentrations.

Figure 4-5. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for elemental carbon (EC). The 12 symbols for each monitor represent monthly 

average model performance for the year 2002, averaging all monitors in the CENRAP 

region. Solid lines represent CENRAP modeling goals and criteria. 

4.7.4.5 Model Performance for Soil 

 

Figure 4-6 shows the monthly soil fractional error and bias for the monitoring network as well as 

the proposed performance goals and criteria. Model performance for the winter months is poor 

with large overpredictions of soil concentrations. The summer months are within the goals for 

both fractional bias and error with performance getting worse in the fall and spring. This may be 

due to local effects near the monitor and difficulties in capturing emissions accurately in the 
A-38

inventory. This is an area of concern, especially in areas where soil contributes significantly to 

visibility impairment. 

Figure 4-6. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for soil. The 12 symbols for each monitor represent monthly average model 

performance for the year 2002, averaging all monitors in the CENRAP region. Solid 

lines represent CENRAP modeling goals and criteria. 

4.7.4.6 Model Performance for Course Particulate Material (CM) 

 

Figure 4-7 shows the monthly CM fractional error and bias for IMPROVE and monitoring 

network as well as the proposed performance goals and criteria. Model performance is poor with 

large underpredictions of CM concentrations throughout the year. This may be due to localized 

emissions near the monitor and difficulties in capturing these emissions accurately in the 
A-39

inventory. This is an area of concern, especially in areas where CM contributes significantly to 

visibility impairment. 

Figure 4-7. CENRAP model performance (factional bias and error) of the Typ02f modeling 

scenario for course particulate material (CM). The 12 symbols for each monitor represent 

monthly average model performance for the year 2002, averaging all monitors in the 

CENRAP region. Solid lines represent CENRAP modeling goals and criteria. 
A-40

4.7.4.7 Model Performance for Prediction of Total Extinction 

The above model performance summary includes all sites within the CENRAP. However, a 

model performance summary over such a diverse geographic area may mask model performance 

issues occurring in smaller geographic sub-regions. CENRAP also evaluated model performance 

in predicting total extinction on the 20% best and 20% worst days at each Class I site. 

Performance for the worst 20% days at the CENRAP Class I areas is generally characterized by 

an underestimation bias. Performance at the Breton Island (BRET), LA, Big Bend (BIBE), TX 

and Guadalupe Mountains (GUMO), TX Class I areas for the worst 20 percent days is 

particularly poor. At GUMO, visibility impairment is primarily due to high soil and CM which 

are not well predicted by the model across the CENRAP area. At the BRET and BIBE sites, all 

components are under-predicted, leading to an under-prediction in total extinction. Model 

predictions at these sites are less reliable than at other CENRAP sites for planning purposes. In 

general, model performance is acceptable for SO4, NO3, OMC and EC at the Class I areas. The 

model was not able to accurately predict CM and soil concentrations in the CENRAP region. 

In order to address this model performance issue, CENRAP investigated the assumption that all 

CM and soil are natural and their concentrations remain constant for future projections as well as 

assuming that only a portion of the soil was from natural sources. Results of this sensitivity 

analysis showed that these various projections of CM and soil had little effect on visibility 

predictions at the CENRAP class I areas. See section 5.5.1 of the CENRAP TSD for results of 

this sensitivity analysis.

Within the state of Oklahoma, the Wichita Mountains site is the only Class I area. Model 

performance at predicting total extinction at this site during the worst 20% and best 20% days is 

shown in Figure 4-8. For the 20% worst days, SO4 and OC underestimates results in an underprediction (-42% bias) on total extinction. There is an overestimate of extinction (+21% bias) on 

the 20% best days due to an over-prediction of NO3. With a few exceptions, visibility 

impairment on the worst days is due to SO4. 
A-41

Figure 4-8. Daily extinction model performance at Wichita Mountains, OK for the worst (top) 

and best (bottom) 20% days during 2002. 
A-42

Chapter 5: 2018 Future Year Modeling 

5.1 2018 MODEL SIMULATIONS

The 2018 future-year base case scenario is referred to as “2018 Base Case” or “Base18G”. The 

purpose of the Base18G scenario is to simulate the air quality representative of conditions in 

future year 2018 with respect to sources of criteria and particulate matter air pollutants, taking 

into consideration growth and controls. Modeling results based on this emission inventory are 

used to define the future year ambient air quality and visibility metrics. 

Input data used for the 2018 Base Case model simulations consisted of the same meteorology as 

for the 2002 Base Case and the Base18 emission inventories described under the Emissions 

Modeling section (Section 3). The setup of the CMAQ model (including science options, run 

scripts, simulation periods, and ancillary data) for the Base18 cases was identical to that used in 

the Typ02G modeling. 

The purpose of modeling 2018 visibility is to compare the 2018 visibility predictions to the 2002 

typical-year visibility modeling results and compare 2018 visibility predictions to the URP goal 

for 2018, as discussed below. Some improvements in visibility by 2018 are expected because of 

reductions in emissions due to currently planned regulations and technology improvements. The 

methodology used by CENRAP in developing visibility projections for 2018 and described 

below is consistent with EPA guidance. 

 

5.2 VISIBILITY PROJECTIONS

The Regional Haze Rule (RHR) goals include achieving natural visibility conditions at 156 

federally mandated Class I areas by 2064. In more specific terms, that RHR goal is defined as 

(1) visibility improvement toward natural conditions for the 20% of days that have the worst 

visibility (termed “20% worst” visibility days) and (2) no worsening in visibility for the 20% of 

days that have the best visibility (“20% best” visibility days). One component of the states’ 

demonstration to EPA that they are making reasonable progress toward this 2064 goal is the 

comparison of modeled visibility projections for the first milestone year of 2018 with what is 

termed a uniform rate of progress (URP) goal. As explained in detail in Section 2, the 2018 URP 

goal is obtained by constructing a “linear glide path” (in deciviews) that has at one end the 

observed visibility conditions during the mandated five-year (2000-2004) baseline period and at 

the other end natural visibility conditions in 2064; the visibility value that occurs on the glide 

path at year 2018 is the URP goal. 

CENRAP has made 2018 visibility projections using Typ02G and Base18G CMAQ 36-km 

modeling results following EPA guidance43 that recommends applying the modeling results in a 

relative sense to project future-year visibility conditions. Projections are made using relative 

 

43 US EPA, 2006. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze, US EPA, Office of Air Quality and Planning Standards, EPA-454/B07-002, EPA, April 2007, (http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf) 
A-43

response factors (RRFs), which are defined as the ratio of the future-year modeling results for 

each component that affects visibility to the current-year modeling results. The calculated RRFs 

are applied to the baseline observed visibility conditions to project future-year observed 

visibility. These projections can then be used to assess the effectiveness of the simulated 

emission control strategies that were included in the future-year modeling. The major features of 

our recommended visibility projections and guidance are as follows: 

• Monitoring data should be used to define current air quality. 

• Monitored concentrations of PM10 are divided into six major components; the first five are 

assumed to be PM2.5 and the sixth is PM2.5-10. 

o SO4 (sulfate) 

o NO3 (particulate nitrate) 

o OC (organic carbon) 

o EC (elemental carbon) 

o Soil (other fine particulate or soil) 

o CM (coarse matter). 

• Models are used in a relative sense to develop RRFs between future and current predicted 

concentrations of each component. 

• Component-specific RRFs are multiplied by current monitored values to estimate future 

component concentrations. 

• Estimates of future component concentrations are consolidated to provide an estimate of 

future air quality. 

• Future estimated air quality is compared with the goal for regional haze to see whether the 

simulated control strategy would result in the goal being met. 

5.2.1 Mapping Model Results to IMPROVE Measurements 

Each of the six PM components of light extinction in the revised IMPROVE mass extinction 

equation44 is scaled separately. Because the modeled species do not exactly match up with the 

IMPROVE measured PM species, assumptions must be made to map the modeled PM species to 

the IMPROVE measured species for the purpose of projecting visibility improvements. Table 4-

2 of the CENRAP TSD shows the assumptions used to relate modeled species in CMAQ Version 

4.5 to the species used in the equation to estimate visibility. Some additional species (described 

in section 4.3.1 of the CENRAP TSD) resulting from the modified SOA module used by 

CENRAP are also included in the OC term. 

5.2.2 Projecting Visibility Changes Using Modeling Results 

RRFs are calculated as the ratio of the 2018 modeling results to the 2002 modeling results, and 

are specific to each Class I area and each PM species. These RRFs are applied to the baseline 

period visibility conditions calculated from observed PM species levels to project future-year PM 

levels. The projected PM levels are used to estimate visibility conditions in 2018 through the 

 

44 IMPROVE technical subcommittee for algorithm review, 2006. Revised IMPROVE Algorithm for Estimating 

Light Extinction from Particle Speciation Data. 

(http://vista.cira.colostate.edu/improve/Publications/GrayLit/019_RevisedIMPROVEeq/RevisedIMPROVEAlgorith

m3.doc) 
A-44

revised IMPROVE equation. The following six steps found in the modeling guidance45

summarize the general procedure used to project future-year visibility for the 20% best and 20% 

worst visibility days: 

1) For each Class I area, rank visibility (in deciviews) on each day with observed speciated 

PM data for each of the 5 years of the base period. 

2) For each of the 5 years comprising the base period, calculate the mean deciviews for the 

20% worst and 20% best visibility days. For each Class I area, calculate the 5 year mean 

deciviews for the worst and best days from the 5 year-specific values. 

3) Use an air quality model to simulate base period emissions and future emissions. Use the 

resulting information to develop relative response factors for each component of 

particulate matter identified in the IMPROVE equation. 

4) Multiply the relative response factors times the measured species concentration data 

during the base period (for the 20% best and worst days). This results in daily future year 

species concentrations data. 

5) Using the results in Step 4 and the IMPROVE algorithm calculate the future daily 

extinction coefficients for the 20% best and worst visibility days in each of the five base 

years. 

6) Calculate daily deciview values (from total daily extinction) and then compute the future 

average mean deciviews for the best and worst days of each year. Then average the 5 

years together to get the final future mean deciview value for the best and worst days. 

The six steps listed above from national EPA modeling guidance for regional haze were followed 

by CENRAP to estimate projected future visibility conditions. These methods were appropriate 

at the time the modeling was performed.

5.3 REASONABLE PROGRESS GOAL AND PATH TO NATURAL CONDITIONS

A linear URP from the Baseline Conditions in 2004 to Natural Conditions in 2064 is assumed, 

and the value on the glide path at 2018 is the presumptive URP visibility target that the modeled 

2018 projections are compared against to judge progress. Table 5-1 and figure 5-1 compare the 

URP using the western natural conditions described in section 2 to the modeled reasonable 

progress goal. The baseline visibility (2002-2004) is 23.8 dv and the estimated 2018 URP is 20 

dv. The modeling predicts a visibility improvement of 2.33 dv, compared to the URP 

improvement of 3.8 dv by 2018. Achieving the 2018 URP point is not a requirement of the RHR 

SIPs, but it serves as a benchmark to compare progress toward natural visibility conditions in 

2064 and is designed to help states in selecting their 2018 RPGs. As stated in EPA Guidance for 

Setting Reasonable Progress Goals Under the Regional Haze Program46, “The glidepath is not a 

presumptive target, and States may establish a RPG that provides for greater, lesser, or 

equivalent improvement as that described by the glidepath.” The reasonable progress goal 

established by ODEQ for the 20% best days shows an improvement in visibility of 0.54 

 

45 US EPA, 2006. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze, US EPA, Office of Air Quality and Planning Standards, EPA-454/B07-002, EPA, April 2007, (http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf) 46 US EPA, 2007, Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program, EPA, June 

2007. http://www.epa.gov/ttncaaa1/t1/memoranda/reasonable_progress_guid071307.pdf 
A-45

deciviews by 2018. This is consistent with the requirement of no degradation of visibility on the 

best days at Class I sites. 

Table 5-1. Comparison of reasonable progress goal to uniform rate of progress for 2018 (total 

extinction and deciviews) 

Change by 2018 (reasonable progress goal) -22.52 Mm-1 -2.33 deciviews 

Change by 2018 at uniform rate of progress -34.18 Mm-1 -3.80 deciviews 

Projected rate of change (2004-2018) -0.17 deciviews/yr 

Change needed to reach natural conditions -86.91 Mm-1 -16.28 deciviews 

Change by 2064 extrapolated from reasonable progress goal -68.38 Mm-1 -10.01 deciviews 

Visibility in 2064 from extrapolated reasonable progress goal +39.76 Mm-1 +13.80 deciviews 

Extrapolated from reasonable progress goal for 2102 (natural 

conditions) +21.23 Mm-1 +7.53 deciviews 

Figure 5-1. Projections of visibility impairment for 20% worst days 

 

Figures 5-2 shows the differences in model results of total extinction between the Base18F and 

Typ02F model predictions, including the contributions from each component specie of the 

IMPROVE algorithm. On most days, visibility improvements are due to reductions in sulfate. 

Three days exhibit differences in nitrate concentrations being the most significant contribution to 

improved visibility. 

The estimated visibility impairment in 2018 (reasonable progress goal) is higher than the 

calculated URP (Section 2). The URP acts as a benchmark for evaluating the reasonable 

progress towards reaching natural conditions by 2064. In determining reasonable progress, 

section 169A(g) of the Clean Air Act requires that four factors be considered: 

• Cost of compliance 

• Time necessary for compliance 
A-46

• Energy and non-air quality environmental impacts of compliance 

• Remaining useful life of existing sources that contribute to visibility impairment. 

 

As discussed in the following chapter on source apportionment, visibility impairment at Wichita 

Mountains is due to emissions and transport from outside of Oklahoma as well as in state 

sources. Modeling results indicate that at present, the unrealistic scenario of elimination of all 

anthropogenic emissions within Oklahoma would be an insufficient reduction in emissions to 

meet the URP. Efforts to meet the goal of natural visibility by 2064 will require further emission 

reductions within Oklahoma and other states in the region. As additional emission control 

strategies are implemented nationally and across other states in the region, future visibility 

projections will be evaluated to determine the necessary reductions in Oklahoma emissions 

needed to satisfy the 2064 regional haze goal. 

Figure 5-2. Differences in modeled total extinction (Bext) between Base18F and Typ02F for 

20% worst days at Wichita Mountains site. 
A-47

Chapter 6: Source Apportionment Modeling 

6.1 INTRODUCTION

Visibility impairment in Class I areas is the result of local air pollution as well as transport of 

regional pollution across long distances. The relative contributions to visibility impairment from 

each source region and category is needed to develop effective control strategies to improve 

visibility. CENRAP used CAMx Version 4.40 with its Particulate Source Apportionment 

Technology (PSAT) tool to provide source apportionment by geographic regions and major 

source category. CAMx was run with similar options and inputs as the CAMQ modeling with 

both the 2002 baseline and 2018 future case emission inventories. The CAMX model selection 

and performance are discussed briefly in section 3.7 of this document and details of the CAMx 

model configuration used by CENRAP can be found in the CENRAP TSD and the Modeling 

Protocol. PSAT uses reactive tracers that operate in parallel to the CAMx host model using the 

same emissions, transport, chemical transformation and deposition rates as the host model to 

account for the contributions of user specified source regions and categories to PM 

concentrations throughout the modeling domain. Details on the formulation of the CAMx PSAT 

source apportionment can be found in the CAMx user’s guidance.47 The CAMx PSAT analysis 

has been tested and evaluated against other apportionment techniques.48,49 The goals of the 

PSAT assessment are to evaluate the contributions of different geographic regions and source 

categories to visibility impairment at Class I areas in 2002 and the projected 2018 case in order 

to identify those regions and source categories that, if controlled, would produce the greatest 

improvements in visibility. 

CENRAP defined 30 geographical source regions (Fig 5-1) consisting of CENRAP and nearby 

states, with Texas divided into 3 regions, the remainder of the western and eastern United States, 

the Gulf of Mexico, Canada and Mexico. Six source categories (elevated point sources; lowlevel point sources, on-road mobile, non-road mobile, area and natural or non-anthropogenic 

sources) were tracked separately. The CENRAP PSAT 2002 and 2018 applications used three of 

the PSAT families of tracers: 1) sulfate, 2) nitrate and ammonium, and 3) secondary organic 

aerosols (SOA). SOA was portioned into an anthropogenic (SOAA) and biogenic (SOAB) 

components. Contributions for the 20% worst and 20% best days at each CENRAP and nearby 

Class I area were extracted from the PSAT results. The original IMPROVE equation was used 

to calculate extinction coefficients from modeled concentrations. Modeling performance is poor 

for soil and coarse material. As discussed in Section 3 of this document, results of various 

projections of CM and soil had little effect on visibility predictions at the CENRAP class I areas. 

Extinction due to soil and coarse material changes very little between 2002 and 2018. A PSAT 

Visualization Tool was developed that can be used by States, Tribes and others to generate 

displays of the contributions of source regions and categories to visibility impairment for the 

 

47 “User’s Guide Comprehensive Air Quality Model With Extensions (CAMx) Version 4.30.” ENVIRON 

International Corporation, Novato, California, 2006 (available at www.camx.com). 

48 Morris, R.E., G.Y., C.E., G.W., B.K. 2005. “Recent Advances in One-Atmospheric Modeling Using the 

Comprehensive Air-quality Model with Extensions.” Presented at the 98th Annual Air and Waste Management 

Conference, Minneapolis, MN. June.

49 Yarwood, G., R.E. Morris, G. Wilson. 2004. “Particulate Matter Source Apportionment Technology (PSAT) in 

the CAMx Photochemical Grid Model." Presented at the ITM 27th NATO Conference- Banff Centre, Canada, 

October. (http://www.camx.com/publ/pdfs/___Yarwood_ITM_paper.pdf) 
A-48

average of the worst 20 percent and best 20 percent days at each CENRAP and nearby Class I 

areas.50 EPA believes the selection and application of CAMx for source apportionment analysis 

is appropriate. 

Figure 6-1. Source Regions used in CAMx PSAT PM source apportionment modeling 

6.2 SOURCE APPORTIONMENT RESULTS AT WICHITA MOUNTAINS

Tables 6-1 and 6-2 show the contributions to total extinction for each source category and 

species for 2002 and 2018, respectively. Figures 6-2, 6-3, 6-4, and 6-5 show the geographical 

source apportionment by source category and species for the 20% worst days in 2002 and 2018. 

Visibility impairment at the Wichita Mountains site in 2002 on the worst 20% days is largely due 

to sulfate (49.12 Mm-1 of the 111.03 Mm-1 total extinction), primarily from point sources. The 

largest contributions of sulfate come from Texas (13.98 Mm-1 from all source categories) and the 

eastern United States (7.49 Mm-1). Overall, the largest source region contributions to visibility 

impairment in 2002 are from Texas (28.15 Mm-1, 16.76 Mm-1 from East Texas) and Oklahoma 

(14.72 Mm-1). 

In 2018, Oklahoma sources contribute more to visibility impairment than East Texas as 

reductions in East Texas outpace reductions in Oklahoma. The 2018 projection shows the total 

extinction at Wichita Mountains for the worst 20 % days is estimated to be 87.54 Mm-1, a 

reduction of approximately 27%. Reductions of sulfate emissions from point sources in Texas, 

the eastern United States, Indiana, and Ohio account for a decrease of 11 Mm-1 in total light 

extinction, or 47% of the total reduction between 2002 and 2018. Even with such large 

 

50 available at http://www.cenrap.org/html/projects.php
A-49

reductions in SO4 from point sources in 2018, extinction due to point sources is still the highest 

contributor to visibility impairment on the worst 20% days. Reductions in mobile sources of 

NO3 contribute a decrease in total extinction of approximately 5 Mm-1. There is an underprediction bias in the model that must be considered when examining source apportionment 

results for sulfate. Use of a 12km resolution modeling grid in CAMX reduced the summertime 

sulfate bias but required large computational expense. The use of higher resolution modeling 

should be reconsidered in future modeling efforts. 

Table 6-1. Projected light extinction for 20% worst days at Wichita Mountains in 2002 (Mm-1) 

Total1

 Point Area Non-road On-road Biogenic 

SO4 49.12 38.76 4.59 1.0 0.79 0.05 

NO3 23.72 6.8 3.41 3.16 6.87 2.61 

POA 17.28 0.86 5.79 0.66 0.3 0.6 

EC 4.47 0.1 1.89 1.14 0.51 0.14 

Soil 0.79 0.05 0.65 0.0 0.0 0.07 

CM 4.60 0.04 4.01 0.0 0.01 0.53 

Rayleigh 

scattering 11 

Sum 111.03 46.61 20.34 5.96 8.48 4.0 1

Totals include contributions from boundary conditions and secondary organic matter 

Table 6-2. Projected light extinction for 20% worst days at Wichita Mountains in 2018 (Mm-1) 

Total1

 Point Area Non-road On-road Biogenic 

SO4 33.33 24.64 4.51 .28 0.1 0.04 

NO3 18.10 6.25 3.96 2.18 1.92 2.76 

POA 15.98 1.19 4.99 0.61 0.11 0.47 

EC 3.00 0.09 1.45 0.55 0.11 0.11 

Soil 0.79 0.08 0.62 0.0 0.0 0.07 

CM 4.30 0.05 3.70 0.0 0.01 0.53 

Rayleigh 

scattering 11 

Sum 87.54 32.3 19.23 3.62 2.25 3.98 1

Totals include contributions from boundary conditions and secondary organic matter 
A-50

Figure 6-2. Source apportionment modeling results by source region and source category for 

worst 20% days at Wichita Mountains in 2002. 

Figure 6-3. Source apportionment modeling results by source region and species for worst 20% 

days at Wichita Mountains in 2002. 
A-51

Figure 6-4. Source apportionment modeling results by source region and source category for 

worst 20% days at Wichita Mountains in 2018. 

*2018 projections for Texas Point sources are divided into EGU and Non-EGU point sources rather than 

elevated and low-level. 

Figure 6-5. Source apportionment modeling results by source region and species for worst 20% 

days at Wichita Mountains in 2018. 
A-52

6.3 CONTRIBUTIONS TO VISIBILITY IMPAIRMENT AT OTHER CLASS I AREAS

CAMx PSAT results are also utilized to evaluate the impact of Oklahoma emission 

sources in 2002 and 2018 on visibility impairment at Class I areas outside of the state. 

Oklahoma sources are modeled to have small contributions to the other Class I areas. The 

largest contribution is at the Hercules-Glades Class I area in Missouri that amounts to 3.2 % of 

visibility impairment in 2002 and 3.61% in 2018. We note that these 2018 projections include 

reductions from six coal-fired units that Oklahoma assumed in its visibility modeling, but did not 

secure in its BART determinations for these units. We agree that additional emission reductions 

in Oklahoma, beyond those controlled through ODEQ’s BART determination’s or the proposed 

FIP, are not necessary to protect visibility at Class I areas outside of the state at this time. 
A-53

Chapter 7: BART Determination 

7.1 INTRODUCTION

The Clean Air Act establishes the national goal of eliminating man-made visibility impairment 

from all Class 1 areas. As a part of the plan for achieving this goal Section 169A(b)(2)(A) of the 

act requires certain major stationary sources in existence between 1962 and 1977 to be reviewed 

for Best Available Retrofit Technology (BART). Oklahoma DEQ identified facilities in the 

affected source categories with potential emissions over the BART rule threshold of 250 tons per 

year for any visibility-impairing pollutant (NOx, SO2, and PM) from any unit that was in 

existence on August 7, 1977 and began operation after August 7, 1962. Table 7-1 shows all 

facilities within Oklahoma that have units that are BART-eligible. 

Table 7-1. Facilities with BART-eligible units in Oklahoma 

 

Pursuant to federal regulations, states have the option of exempting a BART-eligible source from 

the BART requirements based on dispersion modeling demonstrating that the source cannot 

reasonably be anticipated to cause or contribute to visibility impairment in a Class I area. 

According to 40 CFR Part 51, Appendix Y51 , a BART eligible source is considered to 

“contribute” to visibility impairment in a Class I area if the modeled 98th percentile change in 

 

51 EPA Guidelines for BART Determinations Under the Regional Haze Rule; EPA, 2005 
A-54

deciviews is equal to or greater than the “contribution threshold.” Any BART-eligible source 

determined to cause or contribute to visibility impairment in any Class I area is subject to BART. 

The EPA BART Guidelines recommend a contribution threshold of 0.5 change in deciview be 

used, although States have the option to establish alternative thresholds. ODEQ applied the 0.5 

deciview threshold to the BART-eligible sources. To determine whether a source exceeds the 

BART contribution threshold, EPA recommends use of the CALMET/CALPUFF modeling 

system; the main components of this modeling system are CALMET (a diagnostic threedimensional meteorological model), CALPUFF (an air quality dispersion model), and 

CALPOST (a post-processing package). CENRAP developed a BART modeling protocol in 

2005 with input from EPA Region 6, 7 and representatives of the Federal Land Managers. 

ODEQ reviewed these BART analyses in accordance with this BART modeling protocol (or in a 

few cases alternate protocols were submitted) for sources that requested waivers based on 

visibility impacts from the CALPUFF modeling. Table 7-2 shows those facilities that were 

granted waivers from BART based on dispersion modeling results. 

Table 7-2. Facilities granted waivers from BART based on dispersion modeling. 

Initial modeling results for three BART-eligible facilities (Georgia Pacific Consumer Products 

LP – Muskogee Mill, International Paper – Valliant Paper Mill, and Western Farmers Electric 

Coop (WFEC) – Anadarko Plant) showed contributions to visibility impairment greater than the 

threshold value. Each facility has applied for a permit modification with reduced emissions to 

decrease their contributions to visibility impairment to below the threshold value and qualify for 

a waiver from BART. Georgia Pacific Consumer Products LP – Muskogee Mill will limit NOx 

emissions to 744 lb/day in order to lower their visibility impairment contribution to below 0.5 

dv. International Paper – Valliant Paper Mill will limit the sulfur content of their fuel to meet the 

threshold. The WFEC – Anadarko facility has lowered the emission limits of NOx, SO2 and 

PM10 to meet a contribution less than 0.5 dv. ODEQ has issued modified permits for these three 

facilities and the revised permits are available as part of the materials in the docket for this 

action. The International Paper permit was issued March 24, 2010, the Georgia Pacific facility 

permit modification was issued October 12, 2010, and the WFEC modified permit was issued 

July 9, 2010 and further modified on September 9, 2010. 

7.2 BART DISPERSION MODELING PROTOCOL
A-55

7.2.1 Overview of Modeling Approach 

In the CENRAP region, the states themselves took the lead in the BART modeling, but 

CENRAP provided modeling guidance52 and readily available modeling data bases that were 

used by states and/or source operators in many cases to conduct their analyses. In development 

of these guidelines, CENRAP followed the EPA BART guidelines53 and the applicable 

CALMET/CALPUFF modeling guidance 54 in effect at the time the analysis was conducted. 

ODEQ required all owners or operators of BART-eligible sources to perform source-specific 

dispersion modeling using CALPUFF to determine if the source causes or contributes to 

visibility impairment in any Class I area. 

Using the procedures in the modeling guidelines, the owner or operator of each BART-eligible 

source conducted initial subject-to-BART screening or refined analysis modeling to determine 

whether or not the source contributes significantly (above the 0.5 dv threshold) to visibility 

impairment at any Class I area. Visibility impairment is calculated in relation to the natural 

background visibility conditions described in the GENV. The results were submitted to ODEQ 

and ODEQ used those results, in conjunction with other information, to determine whether or not 

the individual BART-eligible source will be subject to BART control requirements. 

Refined source-specific BART modeling was performed for those units subject to BART (Table 

7-3) by consultants on behalf of the owner/operator to determine the visibility improvement that 

would result from installation of various options for control technology. ODEQ’s evaluation of 

each of these facilities’ BART applications can be found in Appendix 6-4 of the OK RH SIP. 

 

 

52 Appendix 6-2 of the OK RH SIP, CENRAP BART Modeling Guidelines, Tesche, T. W., Dennis E. McNally, and 

George J. Schewe, Alpine Geophysics, LLC, 2005. 

53 EPA. 2005. “Regional Haze Regulations and Guidelines for Best Available Technology (BART) Determinations”. 

Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104-39172. 40 CFR Part 51, FRL7925-9, RIN AJ31. 

54 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations 

for Modeling Long-Range Transport and Impacts on Regional Visibility, EPA- 454/R-98-019, IWAQM, 1998; 

“Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)”: Phase I Report, FLAG, USDI – 

National Park Service, Air Resources Division, Denver, CO., 2000.

http://www.nature.nps.gov/air/Pubs/pdf/flag/FlagFinal.pdf;. EPA, 2003. “Revisions to the Guideline on Air 

Quality Models: Adoption of a Preferred Long Range Transport Model and Other Resources”; Final Rule. Fed. 

Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations. 40 CFR51. 
A-56

Table 7-3. Sources in Oklahoma subject to BART 

Facility Name BART

Emission Units Source Category Pollutants Evaluated

OG&E Seminole Units 1, 2, and 3 fossil fuel-fired steam electric plants NOx 

OG&E Sooner Units 1 and 2 fossil fuel-fired steam electric plants SO2

NOx 

PM10

OG&E Muskogee Units 4 and 5 fossil fuel-fired steam electric plants SO2

NOx 

PM10

AEP/PSO Comanche Units 1 and 2 fossil fuel-fired steam electric plants NOx 

AEP/PSO Northeastern Unit 2 fossil fuel-fired steam electric plants NOx 

AEP/PSO Northeastern Units 3 and 4 fossil fuel-fired steam electric plants SO2

NOx 

PM10

AEP/PSO Southwestern Unit 3 fossil fuel-fired steam electric plants NOx 

7.2.2 Model Selection and Applicability 

Relevant guidance55 states that the CALPUFF model is generally applicable at distances from 50 

km to at least 200-300 km downwind of a source. BART screening analyses were performed 

with the latest versions of CALMET/CALPUFF. The BART modeling conducted by consultants 

(Trinity consultants) for OG&E and PSO facilities subject to BART were performed using 

CALPUFF version 5.8, our current regulatory version. Wichita Mountain, Caney Creek, Upper 

Buffalo and Hercules Glade are the closest Class I areas to these facilities and each facility is 

within 300 km of at least one of these sites. The PSO Comanche and Southwestern Power 

Stations are 40 km and 44 km, respectively from the closest border of the Wichita Mountains 

Class I area. The distance to other receptors in the Wichita Mountains Class I area are 50 km or 

greater for many receptors. Generally EPA does not recommend using CALPUFF within 50 km, 

but in this case it is a mix of less than and greater than 50 km distances and the nearest distance 

is 40 km. Furthermore, since we are assessing visibility and the distance is near the 50 km 

distance, EPA concurred with the use of CALPUFF in the context of conducting these BART 

analyses. 

7.2.3 Modeling Domain and Meteorology Inputs 

 

55 Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations 

for Modeling Long-Range Transport and Impacts on Regional Visibility, EPA- 454/R-98-019, IWAQM, 1998; 

“Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)”: Phase I Report, FLAG, USDI – 

National Park Service, Air Resources Division, Denver, CO., 2000.

http://www.nature.nps.gov/air/Pubs/pdf/flag/FlagFinal.pdf;. EPA, 2003. “Revisions to the Guideline on Air 

Quality Models: Adoption of a Preferred Long Range Transport Model and Other Resources”; Final Rule. Fed. 

Reg./Vol. 68, No. 72/Tuesday April 15, 2003/Rules and Regulations. 40 CFR51. 
A-57

The CENRAP developed a set of three overlapping CALMET/CALPUFF modeling domains in 

the central United States covering the southern, central and northern portion of the CENRAP 

region (Fig. 7-1). The dimensions of the domain were selected to include the State of interest, 

plus Class I areas in nearby states, and to provide a sufficient buffer between the Class I areas 

and the domain boundaries (e.g., 50 km) to assure that CALPUFF puffs are not eliminated that 

may temporarily leave the domain and later reemerge and cause visibility impacts at a Class I 

area. The operator/owner may elect to restrict the computational CALPUFF domain to a subset 

of the CALMET domain to include the facilities of interest and only areas where significant 

impacts are feasible, extending at least 50 km in all directions beyond the class I areas within 

300km of a source. 

Figure 7-1. CENRAP CALMET 6km meteorological modeling domains 

For BART screening analyses, CALPUFF modeling was performed by owner/operators 

consistent with the procedures described in section 6 of the CENRAP BART modeling 

guidance.56 The CALMET modeling used terrain data from the Shuttle Radar Topography 

Mission (SRTM)-GTOPO 30 second (~1 km) resolution dataset and land use data from United 

States Geological Survey (USGS) Global Land Cover Characterization (GLCC) version 2.0 

database at 30 second (~1km) resolution. CENRAP used MM5 meteorology data for the years 

200157, 200258 and 200359 (at 36 km resolution to create 6-km resolution CALMET 

 

56 Appendix 6-2 of the OK RH SIP, CENRAP BART Modeling Guidelines, Tesche, T. W., Dennis E. McNally, and 

George J. Schewe, Alpine Geophysics, LLC, 2005. 

57 “Annual Application of MM5 for Calendar Year 2001”. McNally, D. E., Alpine Geophysics, LLC, Arvada, CO. 

178 pp., 2003 (http://www.epa.gov/scram001/reports/2001_36_mm5_summary_final.pdf); “Annual Meteorological 

Modeling Protocol: Annual Application of the MM5 to the Continental United States”, McNally, D. E., and T. W. 

Tesche, 2002. (http://www.epa.gov/scram001/meteorology/metgridmodeling/annual_prot_1.1.pdf). 
A-58

meteorological fields. Observational data was not blended with the MM5 data in the CALMET 

modeling. EPA recommends performing three years of CALMET/CALPUFF modeling when 

using MM5 data and 2001 through 2003 were the most recent three years with MM5 data 

available at the time this modeling was initiated. The model-ready meteorological data sets for 

each domain were provided by CENRAP to expedite the screening analysis and provide for a 

consistent approach. To evaluate impacts at each Class I area, receptor data including 

coordinates and elevations that has been developed by the National Park Service. 

For refined source-specific BART modeling, source operators/owners developed specialized 

modeling domains with finer resolution meteorological fields. A 4-km resolution meteorological 

grid was used and the MM5 data was blended with surface and upper air observations. Terrain 

data from the USGS at 90m resolution and land use data from the USGS North American land 

cover characteristics data base were used for the higher resolution CALMET modeling. 

7.2.4 Emissions Input 

According to the EPA BART Guidelines: “The emissions estimates used in the models are 

intended to reflect steady-state operating conditions during periods of high capacity utilization. 

We do not generally recommend that emissions reflecting periods of start-up, shutdown, and 

malfunction be used, as such emission rates could produce higher than normal effects than 

would be typical of most facilities. We recommend that States use the 24 hour average actual 

emission rate from the highest emitting day of the meteorological period modeled, unless this 

rate reflects periods start-up, shutdown, or malfunction.”60

The CENRAP BART modeling guidance recommends the following prioritization for 

identification of the maximum 24-hr actual emissions rates for the most recent 3 or 5 years, 

according to the following prioritization: 

• Continuous Emissions Monitoring (CEM) data; 

• Facility emissions tests; 

• Emissions factors; 

• Permit limits; or lastly, 

• Potential to emit. 

 58 Meteorological modeling protocol: IDNR 2002 annual MM5 application, Johnson, M. T., Iowa Department of 

Natural Resources, Air Quality Bureau, Des Moines, IA, 2003;. Iowa DNR 2002 Annual MM5 Modeling Project. 

Johnson, M. T., Presented at the August 11th, 2003 CENRAP Workgroup Meeting in Bloomington, Minnesota, 

2003. (http://www.epa.gov/scram001/adhoc/johnson_2003.pdf) 

59 Baker, K., 2005. MM5 Model Performance: Great Lakes Region: 2002-04. Ad-Hoc Meteorological Modelers 

Group Meeting, Lakewood, Colorado. 11 June. (http://www.epa.gov/scram001/adhoc/baker_2005.pdf); Baker, K., 

M. Johnson, et al. 2005. Meteorological modeling performance summary for application to PM2.5/haze/ozone 

modeling projects, Prepared by the Lake Michigan Air Directors Consortium and the Midwest Regional Planning 

Organization, Des Plaines, IL. 

(http://www.deq.state.va.us/export/sites/default/info/pdf/vchec/BoardBook/attachments/Attachment_8.pdf) 

60 EPA. 2005. “Regional Haze Regulations and Guidelines for Best Available Technology (BART) Determinations”. 

Fed. Reg./Vol. 70, No. 128/Wed. July 6, 2005, Rules and Regulations, pp. 39104-39172. 40 CFR Part 51, FRL7925-9, RIN AJ31. 
A-59

Sources determined to be BART eligible were generally modeled using emission estimates based 

on the maximum 24 hour emission rates during the 2001-2003 meteorological period modeled. 

In-stack continuous emission monitoring (CEM) data were used where available. 

7.2.5 CALPUFF/CALMET Model Settings 

The CALMET and CALPUFF model settings used by CENRAP and included in the CENRAP 

modeling protocol complied with our recommendations for regulatory application of the 

CALPUFF modeling system in effect at the time of the analysis with a few exceptions. 

Appendix A of the CENRAP BART modeling guidance details the CALMET settings used and 

the EPA default values, and Appendix B details the recommended CALPUFF settings used for 

screening analyses and the EPA default settings. Development of the regional CALMET 

meteorological fields from MM5 data for screening applications was conducted in NoObservations (“No-Obs”) mode. Some of the differences between the CENRAP and EPA 

default CALMET modeling are detailed below. 

i. The EPA default assumes no MM5 data will be used (IPROG=0). In the CENRAP BART 

screening analysis, MM5 data were used as an initial-guess field (IPROG=14); 

ii. Gridded cloud data were inferred from the MM5 relative humidity fields (ICLOUD=3); 

iii. CENRAP used the hourly 36-km MM5 data to define the upper-level winds, thus the 

extrapolation of the surface wind data aloft was not needed (IEXTRP= -1); 

iv. The beginning and ending water land use categories were changed to 55, rather than 

using the EPA default (999) that assumes no water land use categories. 

Refined source-specific modeling used a constant value of 3 ppb background concentration of 

ammonia for the domain during the modeling period. Hourly ozone observational data provided 

by ODEQ over the 2001-2003 timeframe were used to define background ozone concentrations. 

Trinity Consultants conducted the refined modeling for each of the OG&E and AEP/PSO 

facilities of interest according to modeling protocols submitted and reviewed by ODEQ. The 

modeling analysis generally followed the BART protocol developed by CENRAP. Surface and 

upper air observations were utilized (NO OBS = 0) for the source-specific modeling, consistent 

with EPA and FLM recommendations. Originally, modeling was performed with CALMET v6 

for use with CALPUFF v6.211. Problems have been identified in these versions61 therefore 

Trinity Consultants reverted to an existing CALMET data set that utilized CALMET v5.53a 

which is an earlier regulatory version and compatible with the regulatory version of CALPUFF 

(v5.8). EPA was made aware of these deviations and weighed the potential differences that may 

occur due to these deviations with the additional work that would be required to redo the 

analyses. EPA concluded that these deviations were acceptable in this case due to time and work 

resources available at the time. 

7.3 BART DETERMINATION

 

61 Atkinson, Dennis, 2007. CALPUFF: Status and Update , Presentation at Regional/State/Local Modelers 

Workshop, May 16, 2007. 

(http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/archive/2007/presentations/Wednesday%20-

%20May%2016%202007/CALPUFF_status_update.pdf) 
A-60

7.3.1 Gas – Fired Units 

For gas-fired units, emissions of SO2 and PM are minimal. Therefore, ODEQ determined that 

SO2 and PM BART are no additional control. A full BART analysis was conducted for NOx for 

the gas-fired subject-to-BART units. The results of that analysis are discussed here. 

The OG&E Seminole Units 1, 2 and 3 are BART-eligible sources. These units are gas fired 

boiler with gross outputs of 567 MW each. ODEQ performed modeling to investigate the 

visibility impacts of controls and found that NOx BART was the installation of Low NOx 

Burners (LNB) with Over fire Air (OFA) and Flue Gas Recirculation (FGR). This determination 

was made based on modeling results in conjunction with other factors including the technology 

available, the costs of compliance, the energy and nonair quality environmental impacts of 

compliance, any pollution control equipment in use at the source, and the remaining useful life of 

the source. 

The AEP/PSO Comanche Units 1 and 2 are BART-eligible sources. These units are gas fired 

turbines with duct burners and heat recovery steam generators with a gross output of 94 MW 

each. ODEQ evaluated the control effectiveness of dry LNBs. ODEQ then evaluated the 

impacts, including the costs of compliance, the energy impacts, the non-air quality 

environmental impacts, and the remaining useful life. Lastly, ODEQ performed modeling to 

investigate the visibility impacts. ODEQ found that NOx BART was the installation of LNB for 

Units 1 and 2. 

The AEP/PSO Northeastern Unit 2 is a BART-eligible source. Unit 2 is gas fired boiler with a 

gross output of 495 MW. ODEQ considered both combustion and post combustion types of NOx 

controls. ODEQ determined that new LNB and OFA, and SCR are technically feasible for this 

unit. ODEQ then evaluated the impacts, including the costs of compliance, the energy impacts, 

the non-air quality environmental impacts, and the remaining useful life. Lastly, ODEQ 

performed modeling to investigate the visibility impacts. ODEQ found that NOx BART was the 

installation of LNB with OFA for Units 2. 

The AEP/PSO Southwestern Unit 3 is a BART-eligible source. This unit is a gas fired boiler 

with a gross output of 332 MW. ODEQ determined that LNB and OFA , and SCCR are 

technically feasible. ODEQ evaluated the control effectiveness of these technologies. ODEQ 

then evaluated the impacts, including the costs of compliance, the energy impacts, the non-air 

quality environmental impacts, and the remaining useful life. Lastly, ODEQ performed 

modeling to investigate the visibility impacts. ODEQ found that NOx BART was the installation 

of LNB with OFA for Unit 3. 

EPA reviewed the modeling and analysis performed in ODEQ’s BART determinations for the 

above units and agrees with the findings of BART controls for these gas-fired units. 

7.3.2 Coal-Fired Units 

7.3.2.1 NOx and PM BART 

EPA reviewed the modeling associated with the NOx and PM BART determinations 
A-61

ODEQ made for the coal-fired subject-to-BART units: Units 4 and 5 of the Oklahoma Gas and 

Electric Muskogee plant; Units 1 and 2 of the Oklahoma Gas and Electric Sooner plant; and 

Units 3 and 4 of the American Electric Power/Public Service Company of Oklahoma 

Northeastern plant. ODEQ determined that the installation of LNB with OFA to achieve an 

emission limit of 0.15 lb/mmBTU is NOx BART for these 6 units. NOx control levels on 30-day 

rolling averages of 0.15 lb/mmBtu are justified meet the presumptive limits prescribed by EPA. 

The BART determination also identified the existing electrostatic precipitators (ESP) as PM 

BART for these 6 units. EPA reviewed ODEQ’s basis for these BART determinations, including 

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

remaining useful life and visibility modeling, and agreed with the conclusions that LNB w/OFA 

and ESP, are BART for these facilities for NOx and PM, respectively. 

7.3.2.2 SO2 BART 

In their SO2 BART determination, ODEQ concluded that neither dry flue gas 

desulfurization with spray dryer absorbers (“dry scrubbers”) nor wet flue gas desulfurization 

(“wet scrubbers”) was cost effective for these coal-fired units. ODEQ came to this decision after 

comparing the cost effectiveness in annualized dollars per ton of SO2 removed ($/ton) to the 

visibility improvement at the nearest Class I areas. ODEQ determined that SO2 BART for these 

units was no control and specified an SO2 limit of 0.65 lbs/MMBtu on a 30 day rolling average. 

The OG&E units currently burn a low sulfur coal from the Powder River Basin (PRB) of 

Wyoming, and already have historical annual emission rates significantly below this limit. 

Therefore, it is possible the OG&E units would be able to actually increase their emissions 

approximately 10-20%, and still be in compliance with ODEQ’s SO2 BART assessment. The 

AEP/PSO units have historical annual emission rates that has been steadily decreasing to a point 

where the imposition of ODEQ’s proposed BART SO2 emission rate of 0.65 lbs/MMBtu would 

result in very little in emission reduction. 

EPA reviewed the modeling and cost analysis that was the basis for these determinations. 

EPA disagrees with ODEQ’s SO2 BART determination for these units, and is proposing to 

promulgate a FIP to remedy this defect in the Oklahoma RH SIP. Although EPA generally 

regards the visibility modeling analyses performed by ODEQ in support of the BART 

determination to be of high quality, some issues were noted. EPA remodeled the visibility 

impacts of the Sooner, Muskogee and Northeastern Generating Stations to rectify these issues 

and to address consistency with modeling guidance EPA has provided to the states. This 

visibility modeling effort is described in detail in the report titled “Refined Best Available 

Retrofit Technology (BART) Modeling Report: OG&E Sooner Generating Station, OG&E 

Muskogee Generating Station, and AEP/PSO Northeastern Generating Station,” found as 

appendix D to the Oklahoma Regional Haze Technical Support Document. 

 

EPA has conducted modeling analyses for our proposal that includes an assessment of the 

impacts of the installation of dry scrubbers and wet scrubbers on visibility impairment for these 

six units. EPA’s CALPUFF modeling for these six units, which includes multiple scenarios, is 

included in Appendix B to the TSD. 
APPENDIX B 

Refined Best Available Retrofit Technology (BART) Modeling 

Report: 

OG&E Sooner Generating Station, OG&E Muskogee 

Generating Station, and AEP/PSO Northeastern Generating 

Station 

Prepared by: 

Erik Snyder 

Michael Feldman 
B-ii 

Table of Contents 

Table of Contents ................................................................................................................ ii

List of Tables ...................................................................................................................... ii

Chapter 1: Introduction ................................................................................................... 1

Chapter 2: BART Guidelines and Modeling Protocols .................................................. 2

2.1 Background and Introduction ............................................................................... 2

2.2 Calculation of Visibility Impact ........................................................................... 4

2.2.1 IMPROVE Equation ..................................................................................... 4

2.2.2 Revised IMPROVE Equation ....................................................................... 5

2.2.3 Deciview Impact Calculation ........................................................................ 6

2.3 CALMET/CALPUFF modeling protocol ............................................................ 8

Chapter 3: Emission Calculation Methodology ............................................................ 11

3.1 PM Speciation .................................................................................................... 11

3.1.1 Trinity Consultants Approach ..................................................................... 11

3.1.2 EPA Approach ............................................................................................ 12

3.2 Sulfuric Acid Emissions Calculations ................................................................ 12

3.2.1 Trinity Consultants Approach ..................................................................... 12

3.2.2 EPA Approach ............................................................................................ 13

3.3 NOx Emissions ................................................................................................... 13

3.4 SO2 Emissions .................................................................................................... 14

Chapter 4: EPA Visibility Modeling ............................................................................ 15

4.1 EPA Modeling Scenarios ................................................................................... 15

4.1.1 EPA Visibility Results ................................................................................ 16

Attachment A .................................................................................................................... 22

List of Tables 

Table 1-1. Distance (km) from source to Class I Area ...................................................... 2

Table 1-2. Class I Areas ...................................................................................................... 2

Table 2-1. Natural Background values used for “Method 6” ........................................... 7

Table 2-2. Monthly Humidity Factors used by Trinity Consultants .................................. 7

Table 2-3. Monthly Humidity Factors used by EPA ......................................................... 7

Table 2-4. Natural Background Concentrations used by EPA for “Method 8” ................. 8

Table 2-5. Selected CALMET Settings Used ................................................................... 10

Table 3-1. PM Speciation values used by Trinity Consultants ........................................ 12

Table 3-2. Parameters used by EPA in Sulfuric Acid Emission Calculations (EPRI 

method) ............................................................................................................................. 13

Table 4-1. Average of 98th percentile of daily maximum dv for 2001, 2002, and 2003, 

Method 6 ........................................................................................................................... 18

Table 4-2. Average of 98th percentile of daily maximum dv for 2001, 2002, and 2003, 

Method 8 ........................................................................................................................... 19

Table 4-3. Maximum of 98th percentile of daily maximum dv for 2001, 2002, and 2003, 

Method 6 ........................................................................................................................... 20

Table 4-4. Maximum of 98th percentile of daily maximum dv for 2001, 2002, and 2003, 

Method 8 ........................................................................................................................... 21
B-iii 
B-1 

Chapter 1: Introduction 

Units 4 and 5 of the Oklahoma Gas and Electric (OG&E) Muskogee plant, Units 1 and 2 

of the OG&E Sooner plant, and Units 3 and 4 of the American Electric Power/Public 

Service Company of Oklahoma (AEP/PSO) Northeastern plant have been determined to 

be subject-to-BART. These units are tangentially fired, dry-bottom boilers. The OG&E 

units currently depend on combustion controls, electrostatic precipitator, and low-sulfur 

coal to control emissions of NOx, PM, and SO2, respectively. The AEP/PSO units have 

the same controls, with the addition of 1st generation low NOx burners with over fire air. 

After review of modeling and analysis performed by the Oklahoma Department of 

Environmental Quality (ODEQ), EPA agrees with the NOx and PM BART 

determinations made by ODEQ for these units. 

ODEQ originally proposed as part of their Regional Haze SIP dry flue gas 

desulfurization (“dry scrubbers”) as BART controls for control of SO2 at the OG&E 

Sooner and Muskogee BART units and also on the AEP/PSO Northeastern plant BART 

units. In their final RH SIP for these three facilities’ (6 BART units) SO2 BART 

determination, ODEQ concluded that neither dry flue gas desulfurization with spray dryer 

absorbers (“dry scrubbers”) nor wet flue gas desulfurization (“wet scrubbers”) were cost 

effective for these units. ODEQ came to this decision based on their analysis of cost 

effectiveness and modeled visibility improvement at the nearest Class I areas. ODEQ 

determined that SO2 BART for these units was no control and specified an SO2 limit of 

0.65 lbs/MMBtu on a 30 day rolling average. The OG&E units currently burn a low 

sulfur coal from the Powder River Basin (PRB) of Wyoming, and already have historical 

annual emission rates significantly below this limit. Therefore, it is possible the OG&E 

units would be able to actually increase their emissions approximately 10-20%, and still 

be in compliance with ODEQ’s SO2 BART assessment. The AEP/PSO units have 

historical annual emission rates that have been steadily decreasing to a point where the 

imposition of ODEQ’s proposed BART SO2 emission rate of 0.65 lbs/MMBtu would 

result in very little in SO2 emission reduction. 

EPA reviewed the modeling and cost analysis that was the basis for these 

determinations. EPA disagrees with ODEQ’s SO2 BART determination for these units, 

and is proposing to promulgate a FIP to remedy this defect in the Oklahoma RH SIP. 

Although EPA generally regards the visibility modeling analyses performed by ODEQ in 

support of BART determinations to be of high quality, some deviations from EPA 

guidance and errors in emission calculations were noted for analyses of these 6 BART 

units. EPA remodeled the visibility impacts of the Sooner, Muskogee and Northeastern 

Generating Stations to rectify these issues and to address consistency with modeling 

guidance EPA has provided to the states. This visibility modeling effort is described in 

detail in this document 

 
B-2 

Chapter 2: BART Guidelines and Modeling Protocols 

2.1 BACKGROUND AND INTRODUCTION

 The BART analysis includes engineering and modeling methods and procedures 

used to determine the appropriate controls for the subject-to-BART units to reduce the 

source’s contribution to pollutant concentrations that result in visibility impairment in the 

surrounding Class I areas. The final factor to consider under EPA's BART Guidelines is 

the degree of visibility improvement from the BART control options1

. The BART 

guidelines recommend use of the CALPUFF air quality dispersion model to estimate the 

visibility improvements at each Class I area, typically within roughly a 300 km (186 

mile) or larger radius of the source, of alternative control technologies, and to compare 

these to each other and to the impact of the baseline, or current, source configuration.2

 

The four Class I Areas within approximately 300 km of the facilities were included in the 

modeling analysis. These facilities and Class I areas are listed in Table 1-1 and 1-2 and 

shown in area map in Figure 1-1. 

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

Caney 

Creek 

Hercules - 

Glades 

Upper 

Buffalo 

Wichita 

Mountains 

Northeastern 263 244 211 323 

Sooner 345 363 327 234 

Muskogee 180 230 164 324 

Table 1-2. Class I Areas 

Class I Area State Agency IMPROVE 

monitor 

Caney Creek Wilderness Area AR USFS CACR 

Hercules Glades Wilderness Area MO USFS HEGL 

Upper Buffalo Wilderness Area AR USFS UPBU 

Wichita Mountains Wilderness Area OK FWS WIMO 

 

 

1

 59 FR 39104, 39170 (July 6, 2005). 

2

 CENRAP, ODEQ, and the facilities all modeled impacts at these four Class I areas. EPA and FLM 

representative guidance is that using CALPUFF beyond 300 km is appropriate for large emission sources. 
B-3 

Figure 1-1. Location of Sources and Class I Areas 

Trinity Consultants conducted the visibility modeling for Sooner, Muskogee, and 

Northeastern Generating Stations and this modeling was reviewed by ODEQ as part of 

their BART determination3. Although EPA generally regards the visibility modeling 

analyses performed by ODEQ to be of high quality, some issues were noted. EPA 

remodeled the visibility impacts of the Sooner, Muskogee and Northeastern Generating 

Stations to rectify these issues and to address consistency with modeling guidance EPA 

has provided to the states. ODEQ and EPA modeling protocols are discussed later in this 

chapter, followed by detailed discussion of model inputs and results in the following 

chapters. 

Under the BART guidelines, the measure of the visibility improvement to be used in 

comparisons between various control scenarios is the 98th percentile of impacts expressed 

as 24-hour averages of delta deciviews relative to natural background, as estimated using 

the CALPUFF air quality modeling system. The meaning of this is described next, 

followed by particulars of the application of CALPUFF to the Sooner, Muskogee, and 

Northeastern Generating Stations BART determinations. 

 

3

 Throughout this document, any reference to “ODEQ modeling” refers to modeling performed or reviewed 

by ODEQ. 
B-4 

2.2 CALCULATION OF VISIBILITY IMPACT

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

described in terms of visual range in kilometers or miles. However, the visual range 

scale does not correspond to how people perceive visibility: a given increase in visual 

range is perceived differently depending on how good the original visibility was (that is, 

it is not on a linear scale). The deciview scale is designed to address this problem. It is 

linear with respect to perceived visual changes over its entire range, analogous to the 

decibel scale for sound: a given change in deciviews will be perceived as the same 

amount of visibility change, whatever the original visibility was. The defining equation 

is: 

deciviews = 10*ln(bext / 10) (1) 

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

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

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

pristine atmospheres, and greater visual range, while increasing deciview values represent 

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

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

according to dv = 10*ln(391 / visual range)). An eligible BART source with a predicted 

impact of 0.5 dv or more of impairment in a Class I area “contributes” to visibility 

impairment and is subject to BART. 70 FR 39104, 39121 (July 6, 2005). 

 

 Under the BART guidelines, deciviews are estimated using the CALPUFF air 

quality model. CALPUFF predicts 24-hour average pollutant concentrations based on 

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

includes source emissions of the following visibility-impairing pollutants: SO2, SO4, 

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

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

chemical module that simulates the conversion of SO2 to particulate sulfate and NOx to 

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

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

post-processor in two steps. 

2.2.1 IMPROVE Equation 

 Under the original IMPROVE4

 equation, extinction (bext) is estimated from the 

predicted concentrations of various pollutants: 

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

 

4

 Interagency Monitoring of Protected Visual Environments (IMPROVE) is a network of monitors in 

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

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

http://vista.cira.colostate.edu/improve/ 
B-5 

 + 3 * f(RH) * [nitrate] 

 + 4 * [organic mass] 

 + 10 * [elemental carbon] 

 + 1 * [fine soil] 

 + 0.6 * [coarse mass] 

 + 10 

The 10 is for Rayleigh scattering, which is due to the interaction of light with molecules 

of air itself with no pollutants. The f(RH) is a water growth factor for sulfate and nitrate; 

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

humidity. 

2.2.2 Revised IMPROVE Equation 

EPA used the “revised” IMPROVE equation5

 to calculate light extinction. 

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

revised IMPROVE equation is used to convert measured or modeled concentrations into 

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

effect of relative humidity. 

Revised IMPROVE equation: 

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

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

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

+ 10 * [elemental carbon] 

+ 1 * [fine soil] 

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

+ 0.6 * [coarse mass] 

+ Rayleigh scattering (site-specific) 

+ 0.33 * [NO2(ppb)] 

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

, otherwise its 

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

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

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

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

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

The fs, fL, fss are water growth factors for small (“S”) and large (“L”) fractions of sulfate 

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

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

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

 

5

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

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

Waste Management Association, 57, 1326-1336, 2007. 
B-6 

The IMPROVE program revised the IMPROVE equation after a scientific 

assessment of its implications for regional haze planning to reduce biases in light 

extinction estimates compared to the old algorithm6

. In particular, when compared to 

nephelometer direct measurements of visibility extinction, the original IMPROVE 

equation over-predicts for low extinction conditions and under-predicts for high 

extinction. These biases have direct relevance for estimates for the worst 20% visibility 

days that are used to assess visibility impact. The new equation shows broader scatter 

overall, but less bias in matching visibility measurements under high and low visibility 

conditions. The split between small and large particles was the main factor in reducing 

the biases. The revised IMPROVE equation has less bias, is more refined, accounts for 

more pollutants and pollutant sizes, incorporates more recent data, and is based on 

considerations of relevance for the calculations needed for assessing progress under the 

Regional Haze Rule. 

EPA 2007 guidance7 states that the use of either the IMPROVE or the revised 

IMPROVE equation is acceptable provided that the same algorithm is utilized for both 

the base and future extinction calculations. EPA believes it is appropriate to use the 

revised IMPROVE equation and that this is the preferred method. EPA provides 

alternative calculations using the original IMPROVE equation for comparison with these 

visibility calculation results. 

2.2.3 Deciview Impact Calculation 

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

defining equation (equation 1) for deciviews. The delta deciviews (Δdv) represents the 

impact on visibility in deciviews of the source being evaluated, and is also provided by 

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

Under the BART guidelines, delta deciviews is the difference between deciviews 

including the impact of the source and natural background, and deciviews of the natural 

background alone. Each modeled day and location in the Class I area will have an 

associated delta deciview. For each day, the model finds the maximum visibility impact 

of all locations (receptors) in the Class I area. From among these daily values, the BART 

guidelines recommend use of the 98th percentile, roughly equivalent to the 8th highest 

day, visibility impacts expressed as 24-hour averages of delta deciviews relative to 

natural background, as estimated using the CALPUFF air quality modeling system for 

comparing the base case and the effects of various controls. The 98th percentile is 

recommended rather than the maximum value to avoid undue influence from unusual 

 

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

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

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

Coefficients – Final Report 

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

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

for Ozone, PM2.5, and Regional Haze, EPA-454/B-07-002, April 2007, EPA OAQPS; 

(http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf) 
B-7 

meteorological conditions when CALPUFF is executed according to EPA’s guideline on 

Air Quality Models (40 CFR Part 51 Appendix W). 

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

relative to natural background. ODEQ used background concentrations (Table 2-1) from 

Table 2-1 and monthly area-specific relative humidity factors (Table 2-2) from Table A-3 

of “Guidance for Estimating Natural Visibility Conditions Under the Regional Haze 

Rule”8

. The factors listed in Table 2-2 and used by ODEQ are not the recommended 

values and are given in the guidance document as supplemental information. Table 2-3 

shows the recommended values from Table A-2 of the guidance and used by EPA for 

visibility calculations using “Method 6.” 

 

Table 2-1. Natural Background values used for “Method 6” 

Class I Area Region SO4 NO3 OC EC Soil Coarse Mass 

Caney Creek9 East 0.23 0.1 1.4 0.02 0.5 3.00 

Hercules-Glades East 0.23 0.1 1.4 0.02 0.5 3.00 

Upper Buffalo East 0.23 0.1 1.4 0.02 0.5 3.00 

Wichita Mountains West 0.12 0.1 0.47 0.02 0.5 3.00 

Table 2-2. Monthly Humidity Factors used by Trinity Consultants 

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

Caney Creek 3.4 3.1 2.9 3.0 3.6 3.6 3.4 3.4 3.6 3.5 3.4 3.5 

Hercules-Glades 3.2 2.9 2.7 2.7 3.3 3.3 3.3 3.3 3.4 3.1 3.1 3.3 

Upper Buffalo 3.3 3.0 2.7 2.8 3.4 3.4 3.4 3.4 3.6 3.3 3.2 3.3 

Wichita Mountains 2.7 2.6 2.4 2.4 3.0 2.7 2.3 2.5 2.9 2.6 2.7 2.8 

 

Table 2-3. Monthly Humidity Factors used by EPA 

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

Caney Creek 3.3 3.0 2.7 2.8 3.2 3.2 3.0 3.0 3.2 3.2 3.1 3.3 

Hercules-Glades 3.2 2.9 2.6 2.6 3.0 3.0 3.0 3.0 3.2 2.9 3.0 3.2 

Upper Buffalo 3.2 2.9 2.6 2.7 3.1 3.1 3.0 3.0 3.2 3.0 3.0 3.2 

Wichita Mountains 2.8 2.6 2.4 2.4 2.7 2.5 2.2 2.4 2.7 2.5 2.6 2.8 

EPA used average background concentrations (Table 2-4) and relative humidity 

adjustment factors from the Federal Land Managers’ Air Quality Related Values Work 

Group (FLAG) Phase I Report10 for use with the revised IMPROVE equation (“Method 

8”). 

 

8

 U.S. Environmental Protection Agency, EPA-454/B-03-005, September 2003, on web page 

http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_envcurhr_gd.pdf 

9

Trinity Consultants used “West” values for Caney Creek visibility calculations when modeling Muskogee 

and Sooner Stations. The EPA Guidance for Estimating Natural Visibility Conditions Under the Regional 

Haze Rule places Caney Creek in the East. 

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

(2010) Natural Resource Report NPS/NRPC/NRR—2010/232 

(http://www.nature.nps.gov/air/Pubs/pdf/flag/FLAG_2010.pdf) 
B-8 

Table 2-4. Natural Background Concentrations used by EPA for “Method 8” 

Class I 

Area 

BKSALT BKSO4 BKNO3 BKPMC BKOC BKSOIL BKEC 

Salt Ammonium 

sulfate 

Ammonium 

nitrate 

Coarse 

particulates 

Organic 

carbon Soil Elemental 

carbon 

Caney 

Creek .03 0.23 0.10 3.0 1.8 0.5 0.02 

HerculesGlades .02 0.23 0.10 3.0 1.8 0.5 0.02 

Upper 

Buffalo .03 0.23 0.10 3.0 1.8 0.5 0.02 

Wichita 

Mountains .03 0.12 0.10 3.0 0.6 0.5 0.02 

2.3 CALMET/CALPUFF MODELING PROTOCOL

Trinity Consultants conducted modeling for each of the facilities of interest (Sooner, 

Muskogee and Northeastern) according to modeling protocols submitted and reviewed by 

ODEQ.11,12,13 Rather than evaluate each unit subject to BART individually, OG&E and 

AEP elected to evaluate cost effectiveness and visibility on a facility basis. The selected 

control technology will be installed on each of the affected units at the facility being 

modeled. The modeling analysis generally followed the BART protocol developed by 

CENRAP.14 Surface and upper air observations were utilized (NO OBS = 0) for the 

source-specific modeling, consistent with EPA and FLM recommendations. Originally, 

modeling was performed with CALMET v6 for use with CALPUFF v6.211. Problems 

have been identified in these versions15 therefore Trinity Consultants reverted to an 

existing CALMET data set that utilized CALMET v5.53a which is an earlier regulatory 

version and compatible with the regulatory version of CALPUFF (v5.8).16 

 

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

Trinity Consultants, March 17, 2008 (available at 

http://www.deq.state.ok.us/aqdnew/permitting/applications/2003-274-TVR_M-2/index.htm, as attachment 

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

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

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

http://www.deq.state.ok.us/aqdnew/permitting/applications/2005-271-TVR_M-1/index.htm, as attachment 

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

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

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

2008 (http://www.deq.state.ok.us/aqdnew/permitting/applications/96-288-TV_M-3/index.htm) 14 CENRAP BART Modeling Guidelines, T. W. Tesche, D. E. McNally, and G. J. Schewe (Alpine 

Geophysics LLC), December 15, 2005 

(http://www.deq.state.ok.us/aqdnew/RulesAndPlanning/Regional_Haze/SIP/Appendices/index.htm) 15 CALPUFF: Status and Update , Atkinson, Dennis, Presentation at Regional/State/Local Modelers 

Workshop, May 16, 2007. 

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

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

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

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

Processing Protocol 
B-9 

Table 2-5 shows selected CALMET settings used by Trinity for modeling visibility 

impacts from the three facilities. R1MAX and R2MAX represent the radius of influence 

of surface and upper air observations, respectively. R1 (R2) defines the distance from an 

observation at which the surface (upper air) observation and the initial wind field are 

equally weighted and is typically set to half the value of R1MAX(R2MAX). EPA and 

FLMs recommend the use of higher values for R1MAX (100 km) and R2MAX (200 km) 

based on tracer evaluations and testing.17 EPA is aware of these deviations and weighed 

the potential differences that may occur due to these deviations with the additional work 

and computing resources that would be required to redo the analyses. EPA concluded 

that these deviations were acceptable in this case due to time and work resources 

available. EPA elected to utilize the CALMET v5.53a output generating by Trinity 

Consultants and described here for input into CALPUFF for visibility modeling to assess 

the benefit of emission controls on visibility. 

For both Trinity Consultants and EPA modeling, refined source-specific modeling used a 

constant value of 3 ppb background concentration of ammonia for the domain during the 

modeling period. Hourly ozone observational data provided by ODEQ over the 2001-

2003 timeframe were used to define background ozone concentrations. 

 

17 Memorandum from Tyler Fox (EPA OAQPS) “Clarification on EPA-FLM Recommended Settings for 

CALMET,” August 31, 2009 
B-10 

Table 2-5. Selected CALMET Settings Used 

Option Description EPA default Trinity Consultants18

version CALMET version 5.8, level 070623 5.53a, level 040716 

NOOBS No Observation Mode 0 0 

ICLOUD gridded cloud fields 0 0 

IWFCOD diagnostic winds 1 1 

IFRADJ Froude wind adj. 1 1 

IKINE kinematic effects 0 0 

IOBR O'Brien vertical wind adj. 0 0 

ISLOPE slope flows 1 1 

IEXTRP extrapolate wind to upper air -4 -4 

ICALM extrapolate calm to upper air 0 0 

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

IPROG gridded initial prognostic 0 14 

RMAX1 max surface radius of influence NA 20 

RMAX2 max aloft radius of influence NA 50 

RMAX3 max over-water radius of influence. NA 100 

RMIN min wind radius of influence. 0.1 0.1 

RMIN2 min dist sfc winds extrap 4 4 

TERRAD terrain radius of in influence NA 10 

R1 weight surface Step 1 vs. obs NA 10 

R2 weight aloft Step 1 vs. obs NA 25 

ITPROG 3D T from obs or prognostic 0 0 

TRADKM radius of influ. for T interp. 500 500 

IAVET spatial T averaging 1 1 

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

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

 

18 EPA utilized CALMET output from Trinity Consultants for use in CALPUFF simulations 
B-11 

Chapter 3: Emission Calculation Methodology 

This chapter discusses the differences in approaches to calculating emissions used by 

Trinity Consultants and EPA for the three facilities being evaluated. Errors were 

identified in the emission calculations performed by Trinity Consultants for PM10 

speciation which impacted the emission estimates for species of particulate matter and 

sulfuric acid emissions. 

3.1 PM SPECIATION

3.1.1 Trinity Consultants Approach 

For modeling scenarios that included only PM10 emissions, PM coarse, PM fine, soil, 

elemental carbon and SOA emissions were input into CALPUFF. The speciated PM 

emissions were calculated using the National Park Service (NPS) speciation worksheets. 

Review of these values (Table 3-1) revealed that the incorrect speciation worksheet was 

utilized and the proper coal properties were not input into the calculations. PM 

speciations in these worksheets are based on AP-42 Tables 1.1-5 and 1-1.6 (for drybottom boilers). Emission factors within these tables vary by the type of emission control 

equipment downstream and are functions of the coal’s sulfur and ash content. Trinity 

Consultants calculated PM speciation for Sooner and Muskogee Station units for all 

control scenarios using emission factors for wet bottom boilers (AP-42 Table 1.1-7) with 

ESP and used the placeholder values present in the worksheets, rather than inputting 

specific data for the type of coal used. For the Northeastern units, the dry-bottom 

speciation worksheet was used for all control scenarios, and again the proper coal 

properties were not used in the calculation. All units considered here are dry bottom 

tangentially-fired pulverized coal boilers and the appropriate PM speciation worksheet should 

be used to utilize the proper emission factor from AP-42 Table 1.1-6 for the control scenario 

being considered (e.g. “PC Dry Bottom FGD and FF” NPS worksheet for control scenario 

with dry FGD and polishing filter).
B-12 

Table 3-1. PM Speciation values used by Trinity Consultants 

PM speciation used for 

Sooner Units for all 

control scenarios19

PM speciation used for 

Muskogee Units for all 

control scenarios19

PM speciation used for 

Northeastern Units for 

all control scenarios20

Coarse 10.99% 10.99% 15.46% 

Fine Soil 11.64% 11.64% 11.91% 

Fine EC 0.45% 0.45% 0.46% 

CPM (inorganic 

condensable) 61.54% 61.54% 57.73% 

CPM OR (organic 

condensable) 15.38% 15.38% 14.43% 

Heating Value 

(BTU/lb) 10000 10000 10000 

% sulfur 1 1 1 

% ash 10 10 10 

 

3.1.2 EPA Approach 

All particulate modeling was performed with the emission rates derived from the NPS 

spreadsheets corresponding to dry bottom boilers and the emission control equipment 

present in each modeling scenario. Coal properties of 8000 BTU/lb heating value, 0.48% 

sulfur and 7.5% ash content were used in these calculations.21 Emission rates were 

entered in CALPUFF for the specific size ranges presented in the NPS speciation 

spreadsheets (i.e., PM8.0, PM4.25, PM1.88, PM1.25, PM0.813, and PM0.625). After CALPUFF 

modeling, these modeled species are then grouped in POSTUTIL as to represent the 

appropriate visibility pollutants (e.g., PMcourse = PM8.0 + PM4.25 and PMfine = PM1.88 + 

PM1.25 + PM0.813 + PM0.625). Elemental carbon (EC) and secondary organic aerosols 

(SOA) are based on the PM1.88 species and scaled with the emission rates derived from 

the NPS spreadsheets. (These spreadsheets are available in the Docket for this proposed 

action) For further information about the Baseline emission rates and controlled emission 

rates also see Attachment A of this report. 

3.2 SULFURIC ACID EMISSIONS CALCULATIONS

3.2.1 Trinity Consultants Approach 

Sulfuric acid emissions were calculated in two ways by Trinity Consultants. For 

modeling scenarios that included only SO2/SO4 emissions, sulfuric acid emissions from 

the Sooner and Muskogee units were estimated based on an assumed 1% SO2 to SO3 

conversion rate across the boiler. A sulfuric acid control efficiency of 40% was assumed 

for the wet FGD and 90% for the dry FGD. Emissions from the Northeastern units were 

 

19PC Wet Bottom ESP PM Speciation worksheet from NPS 

http://www.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm 20 PC Dry Bottom ESP PM Speciation worksheet from NPS 

http://www.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm 21 Worst Case Coal Properties from Table 2.1-1 Sooner Units 1 & 2, Muskogee Units 4 & 5 Dry FGD 

BART Analysis Follow-Up Report, Sargent & Lundy, 12/28/09 
B-13 

calculated based on an assumed 3ppm sulfur content conversion in the flue gas. In 

modeling scenarios including only PM emissions, all inorganic condensable PM were 

assumed to be sulfates by Trinity Consultants. As detailed above, errors were discovered 

in the PM speciation calculations which include the calculation of the emission rate of the 

inorganic condensable fraction. 

3.2.2 EPA Approach 

Sulfuric acid emissions from power plants are calculated by EPA by estimating the 

amount of H2SO4 produced and the amount of H2SO4 removed by control equipment 

using information from the Electric Power Research Institute (EPRI).22 These 

calculations rely on assumed values for the amount of fuel sulfur converted to SO2, the 

amount of SO2 oxidized to SO3, and the amount of H2SO4 lost to (or mitigated by) the air 

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

post-control estimates of H2SO4 from Sooner, Muskogee and Northeastern are based on 

the best current information available from EPRI and coal properties (Table 3-2). For 

further information about the Baseline emission rates and controlled emission rates, see 

Attachment A of this report. 

Table 3-2. Parameters used by EPA in Sulfuric Acid Emission Calculations (EPRI 

method) 

EPRI method parameter Value 

% Sulfur23 0.48% 

% Ash13 7.5% 

Heating value13 8000 Btu/lb 

Sulfer conversion to SO2

24 0.875 

F125 (Fuel Impact Factor, 

subbituminous/PRB all boilers) 0.0019 

F225 (Technology Impact factors): 

Control Scenario BaseA BaseB_LNB LNB_WFGD LNB_DFGD 

Air Heater (PRB) 0.36 0.36 0.36 0.36 

Cold-side ESP (PRB) 0.72 0.72 0.72 1 

Wet spray tower (PRB) 1 1 0.4 1 

Dry FGD and baghouse 1 1 1 0.01 

3.3 NOX EMISSIONS

EPA reviewed the modeling associated with the NOx BART determinations ODEQ made 

for Units 1 and 2 of the OG&E Sooner plant, Units 4 and 5 of the OG&E Muskogee 

 

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

Palo Alto, CA: 2010. 1020636. 

23 Worst Case Coal Properties from Table 2.1-1 Sooner Units 1 & 2, Muskogee Units 4 & 5 Dry FGD 

BART Analysis Follow-Up Report, Sargent & Lundy, 12/28/09 

24 Implicit from EPA AP-42 (EPA, “Compilation of Air Pollutant Emission Factors AP-42, Fifth Edition, 

Volume I: Stationary Point and Area Sources.” http://www.epa.gov/ttnchie1/ap42pdf/c01s01.pdf.) 25 Estimating Total Sulfuric Acid Emissions from Stationary Power Plants: Version 2010a. EPRI, 

Palo Alto, CA: 2010. 1020636. 
B-14 

plant, and Units 3 and 4 of AEP/PSO Northeastern plant. ODEQ determined that the 

installation of LNB with OFA to achieve an emission limit of 0.15 lb/mmBTU is NOx 

BART for these units. EPA reviewed ODEQ’s basis for these BART determinations, 

including the costs of compliance, the energy impacts, the non-air quality environmental 

impacts, the remaining useful life and visibility modeling. EPA performed additional 

visibility modeling with CALPUFF that included our proposed SO2 limits in 

combination with ODEQ proposed limits for NOx and PM. This modeling suggests 

small visibility improvements can be expected from also requiring the installation of SCR 

on these units. With the inclusion of our proposed SO2 reductions, the analysis indicates 

small visibility improvements would be achieved with also requiring SCR, therefore we 

are determining that the visibility improvements do not justify when weighed against the 

additional cost of SCR. As can be seen from the results of LNB with OFA and Dry 

Scrubbers in Tables 4-1 to 4-4 and in Attachment A of this document, the total visibility 

impact with these controls in place is below 0.5 deciview. Evaluation of the BART 

modeling output files indicates a number of the highest days are now nitrate limited, but 

the overall impacts from the facility (including all visibility impairing pollutants) is fairly 

small (less than 0.5 deciview on a Class I area). Therefore the nitrate portion of visibility 

impairment, which is just part of the total visibility impairment, is fairly small for each 

facility. For further information about the Baseline emission rates and controlled 

emission rates, see Attachment A of this report. 

3.4 SO2 EMISSIONS

As discussed in the Main TSD and other Appendices to the Main TSD, EPA concluded 

that installation of DFGD could achieve a 0.06 lb/mmBtu SO2 emission limit or the 

installation of WFGD could achieve a 0.04 lb/mmBtu SO2 emission limit at all six BART 

units. We modeled a Baseline emission rate for comparison using emission rates that are 

documented in Attachment A to this report. Baseline SO2 emission rates of 0.86 

lb/mmBtu for the Sooner units, 0.80 lb/mmBtu and 0.85 lb/mmBtu for Muskogee units 4 

& 5, and 0.90 lb/mmBtu for Northeastern units were used in conjunction with firing rate 

information to determine the lb/hr emission rate modeled. EPA guidelines in 40 CFR 51 

Appendix Y Part III state that emission estimates used in the modeling analysis to 

determine visibility impairment impacts should reflect steady-state operating conditions 

during periods of high capacity utilization. These baseline emission rates are 

representative of the highest 24-hour block emissions reported during the baseline period 

(the RHR defines the baseline period as 2000-2004) and are consistent with the baseline 

emissions used in each facility’s BART analysis. We note, these units currently burn a 

low sulfur coal from the Powder River Basin (PRB) of Wyoming, and currently have 

emission rates below this baseline rate. However, without a BART determination, there 

are no federally enforceable permits that prohibit these units from returning to a higher 

sulfur coal and emission rates similar to those seen during the baseline modeling period. 

For further information about the Baseline emission rates and controlled emission rates, 

see Attachment A of this report. 
B-15 

Chapter 4: EPA Visibility Modeling 

EPA’s CALPUFF modeling approach differed from that used by ODEQ. In ODEQ’s 

modeling approach, CALPUFF visibility modeling for each pollutant was carried out 

separately so that only NOx emissions or only SO2/H2SO4 emissions or only PM10 

emissions were included in a modeling scenario. Due to the nonlinear nature and 

complexity of atmospheric chemistry and chemical transformation among pollutants, 

CALPUFF modeling on a pollutant-specific basis is not recommended.26 Furthermore, 

this approach does not allow for predictions of total visibility impairment for different 

control scenarios at Class I area receptors and the determination of the 98th percentile day 

for visibility impairment. To model the visibility benefit of emission controls, all 

relevant pollutants should be modeled together to determine the total visibility impact at 

each Class I area receptor. 

4.1 EPA MODELING SCENARIOS

EPA performed visibility modeling to evaluate the visibility benefit of additional 

emission controls for each facility. For NOx control, operation of low NOx burners with 

Over-Fire Air (LNB/OFA) was examined. SO2 controls evaluated were dry flue gas 

desulfurization (DFGD) and wet flue gas desulfurization (WFGD). We did not explicitly 

model the SO2 emissions if a unit was converted to combusting natural gas instead of 

coal, but the visibility impairment improvements would be more than the improvements 

from WFGD. EPA’s proposal indicates that a conversion from combusting coal to 

combusting natural gas would be acceptable as an alternative to installing either DFGD 

or WFGD. 

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

facility are included in Attachment A of this report. 

Control Scenarios: 

1. Baseline – Heat Input and emission rates for NOx, SO2, and PM10 are 

from Appendix E, of each facilities’ Best Available Retrofit Technology 

Evaluation report27,28,29. SO4 emissions were calculated for each unit 

 

26 Memo from Joseph Paisie (Geographic Strategies Group, OAQPS) to Kay Prince (Branch Chief EPA 

Region 4) on Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) 

Determinations, July 19, 2006 

27Calpuff Modeling Report BART Determination, Oklahoma Gas & Electric, Sooner Generating Station, 

Trinity Consultants, March 17, 2008 (available as attachment B of Sooner BART application submitted to 

ODEQ May 30, 2008 at http://www.deq.state.ok.us/aqdnew/permitting/applications/2003-274-TVR_M2/index.htm) 

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

Station, Trinity Consultants, March 17, 2008 (available as attachment B of Muskogee BART application 

submitted to ODEQ May 30, 2008 at http://www.deq.state.ok.us/aqdnew/permitting/applications/2005-

271-TVR_M-1/index.htm) 
B-16 

based on the EPRI method discussed above. PM speciation was calculated 

using the correct NPS PM speciation worksheet and coal properties. 

2. LNB – Emission rates for NOx are at the LNB/OFA controlled value of 

0.15 lb/mmBTU consistent with the controlled rate assumed by the 

facilities. All other emissions are unchanged from the Baseline scenario 

above. 

3. LNB DFGD - Emission rates for NOx are at the LNB/OFA controlled 

value of 0.15 lb/mmBTU. SO2 emissions are controlled to 0.06 

lb/mmBTU. SO4 emissions were calculated for each unit based on the 

EPRI method discussed above. PM speciation was calculated using the 

correct NPS PM speciation worksheet and coal properties. Installation of 

a polishing filter is included in this scenario as it is required for operation 

of the DFGD. 

4. LNB WFGD – Emission rates for NOx are at the LNB/OFA controlled 

value of 0.15 lb/mmBTU. SO2 emissions are controlled to 0.04 

lb/mmBTU. SO4 emissions were calculated for each unit based on the 

EPRI method discussed above. PM speciation was calculated using the 

correct NPS PM speciation worksheet and coal properties. 

The SO2 emission rates suggested by the facilities were 0.10 for the DFGD and 0.08 for 

the WFGD. EPA’s review of factors and current state of what is being achieved at 

similar facilities informed EPA of the proposed levels that can be achieved with DFGD 

or WFGD. For further details see the main TSD and other appendices of the main TSD. 

4.1.1 EPA Visibility Results 

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

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

improvements at all areas. 

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

evaluated for BART impacts, but does not give the overall picture of improvement at all 

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

over Class I areas, is to provide a single number for comparing control scenarios to each 

other. There are multiple Class I areas, with different visibility impacts for each, but there 

is no established metric for expressing visibility impact considering all areas together in a 

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

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

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

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

2008 (http://www.deq.state.ok.us/aqdnew/permitting/applications/96-288-TV_M-3/index.htm) 
B-17 

Tables 4-1 and 4-2 present the average value of the 98th percentile of the daily maximum 

impact for the three modeled years (2001-2003) using “Method 6” and “Method 8,” 

respectively. SO2 controls in addition to LNB/OFA provide for significant 

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

to below 0.5 dv. There is little difference between the results of the WFGD and DFGD in 

the 98th percentile values. At the low sulfur emission levels of these controls, nitrates are 

responsible for the majority of visibility impairment so no benefit is seen in decreasing 

SO2 emissions from the DFGD rate of 0.06 to the WFGD rate of 0.04 lb/mmBTU. 

Tables 4-3 and 4-4 present the maximum value of the 98th percentile of the daily 

maximum impact for the three modeled years (2001-2003) using “Method 6” and 

“Method 8,” respectively. As discussed above we are including the results from the 

original IMPROVE equation with “Method 6” results, and the revised IMPROVE 

equation with the “Method 8” results. EPA’s analysis and conclusions are based on the 

results of the more scientifically robust “Method 8” version. 
B-18 

Table 4-1. Average of 98th percentile of daily maximum dv for 2001, 2002, and 2003, 

Method 6 

Visibility Impact (Δ dv) Visibility Improvement (Δ dv) 

Baseline LNB LNB & 

DFGD 

LNB & 

WFGD 

LNB over 

Baseline 

DFGD over 

LNB 

WFGD over 

LNB 

Sooner Units 1&2

Caney Creek 0.73 0.48 0.13 0.12 0.25 0.35 0.36 

HerculesGlades 0.64 0.39 0.11 0.10 0.26 0.27 0.28 

Upper Buffalo 0.69 0.46 0.11 0.10 0.23 0.35 0.36 

Wichita 

Mountains 2.06 1.45 0.40 0.38 0.60 1.06 1.08 

Sum 4.12 2.78 0.75 0.70 1.34 2.03 2.08 

Muskogee Units 4&5

Caney Creek 1.52 1.10 0.35 0.39 0.42 0.75 0.71 

HerculesGlades 1.15 0.96 0.19 0.17 0.19 0.77 0.79 

Upper Buffalo 1.60 1.24 0.32 0.28 0.36 0.92 0.96 

Wichita 

Mountains 1.45 1.18 0.25 0.25 0.26 0.93 0.94 

Sum 5.72 4.48 1.11 1.09 1.24 3.37 3.39 

Northeastern Units 3&4

Caney Creek 1.56 1.00 0.27 0.28 0.56 0.73 0.72 

HerculesGlades 0.98 0.84 0.17 0.17 0.15 0.67 0.66 

Upper Buffalo 1.20 0.86 0.20 0.20 0.34 0.66 0.66 

Wichita 

Mountains 1.38 1.18 0.23 0.24 0.20 0.95 0.94 

Sum 5.12 3.88 0.87 0.90 1.24 3.01 2.98 
B-19 

Table 4-2. Average of 98th percentile of daily maximum dv for 2001, 2002, and 2003, 

Method 8 

Visibility Impact (Δ dv) Visibility Improvement (Δ dv) 

Baseline LNB LNB & 

DFGD 

LNB & 

WFGD 

LNB over 

Baseline 

DFGD over 

LNB 

WFGD over 

LNB 

Sooner Units 1&2

Caney Creek 0.64 0.43 0.12 0.11 0.21 0.31 0.31 

HerculesGlades 0.57 0.33 0.10 0.09 0.24 0.23 0.24 

Upper Buffalo 0.62 0.39 0.10 0.09 0.22 0.29 0.30 

Wichita 

Mountains 1.78 1.21 0.35 0.33 0.57 0.86 0.87 

Sum 3.60 2.36 0.67 0.63 1.25 1.69 1.72 

Muskogee Units 4&5

Caney Creek 1.36 0.94 0.33 0.36 0.42 0.61 0.58 

HerculesGlades 0.99 0.82 0.17 0.16 0.18 0.65 0.66 

Upper Buffalo 1.41 1.09 0.29 0.25 0.32 0.80 0.83 

Wichita 

Mountains 1.23 0.98 0.21 0.22 0.26 0.76 0.76 

Sum 4.99 3.82 1.00 0.99 1.17 2.82 2.83 

Northeastern Units 3&4

Caney Creek 1.43 0.89 0.25 0.26 0.54 0.64 0.63 

HerculesGlades 0.85 0.71 0.15 0.16 0.14 0.56 0.55 

Upper Buffalo 1.07 0.72 0.19 0.18 0.35 0.54 0.54 

Wichita 

Mountains 1.17 0.98 0.20 0.22 0.19 0.78 0.76 

Sum 4.51 3.29 0.78 0.82 1.22 2.52 2.47 
B-20 

Table 4-3. Maximum of 98th percentile of daily maximum dv for 2001, 2002, and 

2003, Method 6 

Visibility Impact (Δ dv) Visibility Improvement (Δ dv) 

Baseline LNB LNB & 

DFGD 

LNB & 

WFGD 

LNB over 

Baseline 

DFGD over 

LNB 

WFGD over 

LNB 

Sooner Units 1&2

Caney Creek 0.85 0.58 0.14 0.14 0.27 0.44 0.44 

HerculesGlades 0.79 0.50 0.14 0.13 0.29 0.36 0.37 

Upper Buffalo 0.87 0.57 0.14 0.13 0.30 0.43 0.44 

Wichita 

Mountains 2.41 1.75 0.46 0.39 0.66 1.29 1.36 

Sum 4.92 3.40 0.89 0.80 1.51 2.52 2.60 

Muskogee Units 4&5

Caney Creek 1.72 1.39 0.47 0.56 0.33 0.91 0.83 

HerculesGlades 1.25 1.09 0.20 0.20 0.16 0.89 0.88 

Upper Buffalo 1.68 1.36 0.39 0.36 0.32 0.97 1.00 

Wichita 

Mountains 1.52 1.25 0.34 0.38 0.27 0.91 0.87 

Sum 6.17 5.09 1.40 1.51 1.08 3.69 3.58 

Northeastern Units 3&4

Caney Creek 1.86 1.11 0.33 0.33 0.75 0.78 0.77 

HerculesGlades 1.10 1.05 0.20 0.21 0.05 0.85 0.84 

Upper Buffalo 1.67 1.01 0.30 0.30 0.66 0.71 0.72 

Wichita 

Mountains 1.95 1.67 0.35 0.36 0.27 1.32 1.31 

Sum 6.57 4.84 1.18 1.20 1.73 3.66 3.64 
B-21 

Table 4-4. Maximum of 98th percentile of daily maximum dv for 2001, 2002, and 

2003, Method 8 

Visibility Impact (Δ dv) Visibility Improvement (Δ dv) 

Baseline LNB LNB & 

DFGD 

LNB & 

WFGD 

LNB over 

Baseline 

DFGD over 

LNB 

WFGD over 

LNB 

Sooner Units 1&2

Caney Creek 0.73 0.50 0.13 0.13 0.23 0.37 0.38 

HerculesGlades 0.71 0.43 0.13 0.12 0.28 0.30 0.31 

Upper Buffalo 0.77 0.49 0.13 0.12 0.28 0.35 0.37 

Wichita 

Mountains 2.08 1.46 0.41 0.35 0.62 1.05 1.11 

Sum 4.28 2.88 0.80 0.71 1.41 2.08 2.16 

Muskogee Units 4&5

Caney Creek 1.48 1.19 0.45 0.51 0.29 0.74 0.69 

HerculesGlades 1.07 0.92 0.19 0.19 0.14 0.74 0.73 

Upper Buffalo 1.52 1.20 0.37 0.33 0.31 0.84 0.87 

Wichita 

Mountains 1.31 1.03 0.29 0.34 0.27 0.75 0.70 

Sum 5.37 4.35 1.29 1.37 1.02 3.06 2.98 

Northeastern Units 3&4

Caney Creek 1.70 0.99 0.29 0.30 0.71 0.70 0.69 

HerculesGlades 0.92 0.88 0.18 0.20 0.04 0.70 0.68 

Upper Buffalo 1.52 0.85 0.28 0.28 0.67 0.57 0.57 

Wichita 

Mountains 1.66 1.39 0.30 0.31 0.27 1.09 1.08 

Sum 5.80 4.11 1.05 1.09 1.69 3.06 3.02 
B-22 

Attachment A
B-23 

SOONER UNIT 1 Control systems NOx SO2 PM10 

5116 mmBTU/hr NOx SO2 lb/mmBTU lb/hr lb/mmBTU lb/hr lb/mmBTU lb/hr 

Baseline A base base 0.601 3074.716 0.860 4399.760 0.038 193.896 

Base B LNB LNB/OFA base 0.150 767.400 0.860 4399.760 0.038 193.896 

LNB DFGD LNB/OFA DFGD 0.150 767.400 0.060 306.960 0.012 61.392 

LNB WFGD LNB/OFA WFGD 0.150 767.400 0.040 204.640 0.038 193.896 

SOONER UNIT 1 Control systems PM Coarse (lb/hr) PM Fine (lb/hr) EC (lb/

NOx SO2 PM 8 PM 4.25 PM 1.88 PM 1.25 PM 0.813 PM 0.625 

Baseline A base base 49.20 60.77 34.73 8.68 5.79 34.73 3.20 

Base B LNB LNB/OFA base 49.20 60.77 34.73 8.68 5.79 34.73 3.20 

LNB DFGD LNB/OFA DFGD 10.01 16.02 14.68 4.00 7.34 9.34 1.10 

LNB WFGD LNB/OFA WFGD 24.58 30.04 43.69 10.92 30.04 54.62 5.10 

SOONER UNIT 2 Control systems NOx SO2 PM10 

5116 mmBTU/hr NOx SO2 lb/mmBTU lb/hr lb/mmBTU lb/hr lb/mmBTU lb/hr 

Baseline A base base 0.584 2987.744 0.860 4399.760 0.039 200.036 

Base B LNB LNB/OFA base 0.150 767.400 0.860 4399.760 0.039 200.036 

LNB DFGD LNB/OFA DFGD 0.150 767.400 0.060 306.960 0.012 61.392 

LNB WFGD LNB/OFA WFGD 0.150 767.400 0.040 204.640 0.039 200.036 

SOONER UNIT 2 Control systems PM Coarse (lb/hr) PM Fine (lb/hr) EC (lb/

NOx SO2 PM 8 PM 4.25 PM 1.88 PM 1.25 PM 0.813 PM 0.625 

Baseline A base base 50.63 62.54 35.74 8.93 5.96 35.74 3.30 

Base B LNB LNB/OFA base 50.63 62.54 35.74 8.93 5.96 35.74 3.30 

LNB DFGD LNB/OFA DFGD 10.01 16.02 14.68 4.00 7.34 9.34 1.10 

LNB WFGD LNB/OFA WFGD 25.36 30.99 45.08 11.27 30.99 56.35 5.30 
B-24 

MUSKOGEE 

UNIT 4 Control systems NOx SO2 PM10 

5480 mmBTU/hr NOx SO2 lb/mmBTU lb/hr lb/mmBTU lb/hr lb/mmBTU lb/hr 

Baseline A base base 0.495 2712.600 0.800 4384.000 0.018 100.832 

Base B LNB LNB/OFA base 0.150 822.000 0.800 4384.000 0.018 100.832 

LNB_DFGD LNB/OFA DFGD 0.150 822.000 0.060 328.800 0.012 65.760 

LNB_WFGD LNB/OFA WFGD 0.150 822.000 0.040 219.200 0.018 100.832 

MUSKOGEE 

UNIT 4 Control systems PM Coarse (lb/hr) PM Fine (lb/hr) EC (lb/h

NOx SO2 PM 8 PM 4.25 PM 1.88 PM 1.25 PM 0.813 PM 0.625 

Baseline A base base 25.58 31.60 18.06 4.51 3.01 18.06 1.70 

Base B LNB LNB/OFA base 25.58 31.60 18.06 4.51 3.01 18.06 1.70 

LNB_DFGD LNB/OFA DFGD 10.72 17.15 15.73 4.29 7.86 10.01 1.20 

LNB_WFGD LNB/OFA WFGD 12.78 15.62 22.72 5.68 15.62 28.40 2.70 

MUSKOGEE 

UNIT 5 Control systems NOx SO2 PM10 

5480 mmBTU/hr NOx SO2 lb/mmBTU lb/hr lb/mmBTU lb/hr lb/mmBTU lb/hr 

Baseline A base base 0.522 2860.560 0.850 4658.000 0.024 133.712 

Base B LNB LNB/OFA base 0.150 822.000 0.850 4658.000 0.024 133.712 

LNB_DFGD LNB/OFA DFGD 0.150 822.000 0.060 328.800 0.012 65.760 

LNB_WFGD LNB/OFA WFGD 0.150 822.000 0.040 219.200 0.024 133.712 

MUSKOGEE 

UNIT 5 Control systems PM Coarse (lb/hr) PM Fine (lb/hr) EC (lb/h

NOx SO2 PM 8 PM 4.25 PM 1.88 PM 1.25 PM 0.813 PM 0.625 

Baseline A base base 33.93 41.91 23.95 5.99 3.99 23.95 2.20 

Base B LNB LNB/OFA base 33.93 41.91 23.95 5.99 3.99 23.95 2.20 

LNB_DFGD LNB/OFA DFGD 10.72 17.15 15.73 4.29 7.86 10.01 1.20 

LNB_WFGD LNB/OFA WFGD 16.95 20.72 30.13 7.53 20.72 37.67 3.50 
B-25 

NORTHEASTERN 

UNIT 3 Control systems NOx SO2 PM10 

5812 mmBTU/hr NOx SO2 lb/mmBTU lb/hr lb/mmBTU lb/hr lb/mmBTU lb/hr 

Baseline A base base 0.536 3115.232 0.900 5230.800 0.038 220.856 

Base B LNB LNB/OFA base 0.150 871.800 0.900 5230.800 0.038 220.856 

LNB_DFGD LNB/OFA DFGD 0.150 871.800 0.060 348.720 0.012 69.744 

LNB_WFGD LNB/OFA WFGD 0.150 871.800 0.040 232.480 0.038 220.856 

NORTHEASTERN 

UNIT 3 Control systems PM Coarse (lb/hr) PM Fine (lb/hr) EC (

NOx SO2 PM 8 PM 4.25 PM 1.88 PM 1.25 PM 0.813 PM 0.625 

Baseline A base base 56.04 69.22 39.56 9.89 6.59 39.56 3

Base B LNB LNB/OFA base 56.04 69.22 39.56 9.89 6.59 39.56 3

LNB_DFGD LNB/OFA DFGD 11.37 18.19 16.68 4.55 8.34 10.61 1

LNB_WFGD LNB/OFA WFGD 28.00 34.22 49.77 12.44 34.22 62.21 5

NORTHEASTERN 

UNIT 4 Control systems NOx SO2 PM10 

5594 mmBTU/hr NOx SO2 lb/mmBTU lb/hr lb/mmBTU lb/hr lb/mmBTU lb/hr 

Baseline A base base 0.491 2746.654 0.900 5034.600 0.059 342.908 

Base B LNB LNB/OFA base 0.150 839.100 0.900 5034.600 0.059 342.908 

LNB_DFGD LNB/OFA DFGD 0.150 839.100 0.060 335.640 0.012 69.744 

LNB_WFGD LNB/OFA WFGD 0.150 839.100 0.040 223.760 0.059 342.908 

NORTHEASTERN 

UNIT 4 Control systems PM Coarse (lb/hr) PM Fine (lb/hr) EC (

NOx SO2 PM 8 PM 4.25 PM 1.88 PM 1.25 PM 0.813 PM 0.625 

Baseline A base base 83.74 103.45 59.11 14.78 9.85 59.11 5

Base B LNB LNB/OFA base 83.74 103.45 59.11 14.78 9.85 59.11 5

LNB_DFGD LNB/OFA DFGD 10.94 17.51 16.05 4.38 8.03 10.22 1

LNB_WFGD LNB/OFA WFGD 41.84 51.13 74.38 18.59 51.13 92.97 8
Appendix C 

SO2 BART Cost Effectiveness Analysis for the Federal Implementation Plan 
October 2010 

Revised BART 

Cost-Effectiveness Analysis for 

Flue Gas Desulfurization at 

Coal-Fired Electric Generating 

Units in Oklahoma: 

Sooner Units 1 & 2 

Muskogee Units 4 & 5 

Northeastern Units 3 & 4 

Final Report

Prepared for 

Ellen Belk 

U.S. Environmental Protection Agency Region 6 

Multi-media Planning and Permitting Division 

1445 Ross Avenue, Suite 1200 

Mail Code: 6PD 

Dallas, TX 75202-2733 

Prepared by 

Phyllis Fox, Ph.D., PE 

(Consultant) 

RTI Project Number 0209897.004.085 
iii 

TABLE OF CONTENTS 

I. INTRODUCTION..................................................................................................1 

II. SUMMARY & CONCLUSIONS..........................................................................2 

III. REVISED COST ANALYSIS FOR SOONER AND MUSKOGEE DRY 

 SCRUBBER ............................................................................................................3 

A. DESIGN BASIS.......................................................................................................3 

1. Option 1: Increased Emission Reductions ...........................................................5 

2. Option 2: Decreased Scrubber Cost .....................................................................6 

B. COSTING METHOD ..............................................................................................9 

1. Capital Cost: Failure to Follow Cost Manual ...................................................10 

a. Escalation ...............................................................................................................11

b. Allowance for Funds Used During Construction (AFUDC) .................................11 

c. Bond Costs .............................................................................................................12 

d. Contingency ...........................................................................................................12 

e. BOP Profits ............................................................................................................13 

f. Capital Recovery Factor ........................................................................................13 

 i. Scrubber Lifetime ......................................................................................14 

 ii. Interest Rate ...............................................................................................15 

g. Construction Costs .................................................................................................15 

 i Labor Rates ................................................................................................15 

 ii. Per Deim Expense ......................................................................................16 

 iii. Labor Productivity .....................................................................................18 

 iv. Overtime Inefficiency ................................................................................18 

2. Capital Cost: Double Counting ..........................................................................19 

a. Owner's Cost ..........................................................................................................19 

b. Engineering/Procurement ......................................................................................20 

c. Construction Management .....................................................................................20 

d. Startup & Commissioning Craft Support ...............................................................21 

e. Operator Training & Manuals ................................................................................21 

f. Other Factored Sums ..............................................................................................22 

g. Initial Fabric Filter Replacement Materials ...........................................................22 
iv

3. Capital Cost: Overestimating .............................................................................22 

a. Multiple Unit Discounts .........................................................................................23 

b. Common Facilities .................................................................................................23 

c. Cost Capacity ........................................................................................................24 

d. Other Cost Options ................................................................................................24 

 i. Recycle Scrubber .......................................................................................24 

 ii. Electrostatic Precipitator (ESP) .................................................................25 

 iii. Fabric Filter Baghouse ...............................................................................26 

4. Operating and Maintenance Costs (O&M) .......................................................27 

a. Variable O&M Costs .............................................................................................27 

 i. Lime Costs .................................................................................................27 

 ii. Water Cost .................................................................................................28 

 iii. FGD Waste Disposal..................................................................................29 

 iv. Bag and Cage Replacement Cost ...............................................................30 

 v. Auxiliary Power Costs ...............................................................................31 

b. Indirect Operating Costs ........................................................................................33 

5. OG&E Cost Summary .........................................................................................33 

IV. REVISED COST ANALYSIS FOR NORTHEASTERN DRY SCRUBBER 35 

A. CAPITAL COST ...................................................................................................36 

1. Unit Capital Cost..................................................................................................36 

2. Capital Recovery Factor ......................................................................................36 

B. OPERATING COSTS ...........................................................................................37 

1. Water .....................................................................................................................37 

2. FGD Waste Disposal ............................................................................................38 

3. Bag and Cage Replacement .................................................................................38 

4. Fixed O&M (Labor) ............................................................................................38 

5. Insurance ..............................................................................................................38 

 

6. Property Taxes .....................................................................................................39 

C. EMISSION REDUCTIONS ..................................................................................39 
v

D. NORTHEASTERN COST SUMMARY ...............................................................39 

V. COST EFFECTIVENESS: DRY SCRUBBER .................................................40 

VI. BART EMISSION LIMIT: DRY SCRUBBER ................................................41 

VII. WET SCRUBBING .............................................................................................47 

A. CONTROL EFFICIENCY .....................................................................................47 

B. COST EFFECTIVENESS OF WET SCRUBBING ..............................................48 

C. COLLATERAL EFFECTS OF WET SCRUBBING ............................................49 

1. Sulfuric Acid Mist ................................................................................................49 

2. Water .....................................................................................................................50 

3. Wastewater ...........................................................................................................51 

4. Excess Particulate Emissions ..............................................................................51 

5. Reactant ................................................................................................................51 

6. Plume Issues .........................................................................................................52 

D. BENEFITS OF WET SCRUBBING .....................................................................52 

1. Reliability ..............................................................................................................52

2. Maintenance Emissions .......................................................................................53 

VIII. BART EMISSION LIMIT: WET SCRUBBING ..............................................53 

APPENDIXES 

1: Revised Cost Effectiveness Analysis for Dry Flue Gas Desulfurization at 

Sooner Units 1 and 2 and Muskogee Units 4 and 5 ...........................................55 

2: Revised Cost Effectiveness Analysis for Flue Gas Desulfurization at 

Northeastern Units 3 & 4 ....................................................................................57 
vi

TABLES 

Table 1. Dry Scrubber Control Options Evaluated by Sargent & Lundy ...........................4 

Table 2. Option 1 Revised Cost Effectiveness, Increased Emission Reductions (91.5% SO2 

Reduction from Scrubber Inlet of 1.176 lb/MMBtu) ...........................................................6 

Table 3. Option 2 Revised Cost Effectiveness, Increased Emission Reductions (80% SO2 

Reduction from Scrubber Inlet of 0.509 lb/MMBtu) ...........................................................8 

Table 4. Oklahoma BART-Eligible Coal-Fired Electric Generating Units .......................23 

Table 5. Naughton BART Analysis ...................................................................................26 

Table 6. Auxiliary Power Per Unit (MWh/yr) ...................................................................32 

Table 7. Comparison of SO2 BART Cost Effectiveness Values for Dry Scrubbers & Low Sulfur 

Coal with Revised OG&E and AEP/PSO Values ..............................................................42 

Table 8. Comparison of Average Cost Effectiveness for Dry and Wet Scrubbers ............48 

FIGURES 

Figure 1. Non-Union Construction Hourly Wage & Benefit Rates by Region by Position - 

Region 6 .............................................................................................................................17 

Figure 2. Wet and Dry Scrubber Unit Cost ........................................................................35 

Figure 3. Relation Between Inlet SO2 and SO2 Removal Efficiency for a Dry Scrubber 44 

Figure 4. Newmont TS Power Plant, Unit 1, SO2 Rate, 2008 - June 2010 .......................46 

Figure 5. Pleasant Prairie Unit 1, SO2 Rate, 2007 - June 2010 .........................................54 
1 

I. INTRODUCTION 

 On February 17, 2010, Oklahoma submitted its Regional Haze State Implementation Plan 

("SIP") to EPA.1

 This SIP revision concluded that Oklahoma BART-subject sources included 

the following coal-fired electric generating stations: Sooner Units 1 and 2; Muskogee Units 4 

and 5; and Northeastern Units 3 and 4. These are all similar tangentially fired pulverized coalfired boilers ranging in size from 490 MW to 572 MW gross that burn low sulfur, Powder River 

Basin (PRB) sub-bituminous coal. 

 This SIP revision concluded that BART for SO2 for these six units was satisfied by 

continuing to burn low-sulfur coal and meeting an SO2 emission limit of 0.65 lb/MMBtu, based 

on a 30-day rolling average.2

 This determination does not comply with presumptive BART 

(95% SO2 reduction or an SO2 emission limit of 0.15 lb/MMBtu) and allows an increase in SO2 

emissions from current levels of 0.51 lb/MMBtu to 0.65 lb/MMBtu at Muskogee and Sooner. 

 This SIP revision concluded that both dry and wet scrubbers, widely used in similar 

applications,3

 are not cost effective for these six coal-fired units without citing any unusual 

circumstances. This conclusion is based on cost analyses performed by Sargent & Lundy4

 for 

Oklahoma Gas & Electric (OG&E) Sooner Units 1 and 2 and Muskogee Units 4 and 5 and by 

Trinity Consultants5 for American Electric Power/Public Service Company of Oklahoma 

(AEP/PSO) Northeastern Units 3 and 4. The focus of these company analyses is the use of dry 

scrubbing (or dry wet flue gas desulfurization) to control SO2. Wet scrubbing was eliminated 

based on incremental cost and adverse collateral impacts in earlier, 2008 analyses. The 

company's cost estimates for dry and wet scrubbers are much higher than costs estimated at other 

similar facilities, calling the reliability of these estimates into question. Thus, the following 

sections review and revise the company's cost estimates. 

II. SUMMARY AND CONCLUSIONS 

 

 I reviewed the dry scrubber cost analyses prepared by the companies: (1) Sargent & 

Lundy for the Sooner and Muskogee plants in the 12/28/09 S&L FollowUp Report6

 and (2) 

Trinity Consultants for Northeastern Units 3 and 4 in the 5/30/08 Trinity Report.7 Based on this 

 

1

 Regional Haze Implementation Plan Revision, State of Oklahoma, February 2, 2010, Submitted via Letter from 

J.D. Strong, Oklahoma Secretary of Environment, to Alfredo Armendariz, Regional Administrator, EPA Region 6, 

Re: Oklahoma Regional Haze State Implementation Plan ("OK Regional Haze SIP"). 

2

 OK Regional Haze SIP, Table IV-6, IV-7, and IV-10. 

3

 Carl Weilert & Emily Meyer, Burns & McDonnell, Utility FGD Design Trends, Power Engineering, August 2010, 

Table 1: Currently Operating FGD Units in the United States. 

4

 Sargent & Lundy, Sooner Units 1 & 2, Muskogee Units 4 & 5 Dry FGD BART Analysis Follow-Up Report, 

Prepared for Oklahoma Gas & Electric, December 28, 2009 ("12/28/09 S&L Followup Report"). 

5

 Trinity Consultants, Best Available Retrofit Technology (BART) Determination, American Electric Power, 

Northeastern Power Plant, May 30, 2008 (5/30/08 Trinity Report). 

6

 12/28/09 S&L FollowUp Report. 

7

 Trinity Consultants, Best Available Retrofit Technology (BART) Determination, American Electric Power, 

Northeastern Power Plant, May 30, 2008 (5/30/08 Trinity Report). 
2

review, I prepared revised cost analyses, correcting some of the errors that I found. It was not 

possible to correct all errors as the parties declined to provide copies of their spreadsheets 

disclosing their cost calculations and failed to support many critical assumptions. 

 This report summarizes the results of my review and recalculation. My revised cost 

analysis for Sooner and Muskogee is presented in Appendix 1 and for Northeastern in Appendix 

2. Each cost component that I revised is highlighted in blue in these Appendixes. My basis is 

explained in the following sections. 

 

 My analysis indicates the cost to install dry scrubbers at all six of these units is 

significantly overestimated for four principal reasons. First, a much bigger scrubber was costed 

than assumed in the BART analysis, resulting in a mismatch between annual costs and emission 

reductions. Second, the companies did not follow standard BART cost estimating guidelines. 

Third, some costs were significantly outside of the range of the actual costs. Fourth, the cost 

estimates contain double counting. Fifth, the cost estimates failed to evaluate the most cost 

effective options. 

 When these errors are corrected, the average cost effectiveness for dry scrubbing declines 

at Sooner from $6,747/ton to $1,291/ton; at Muskogee from $7,435/ton to $1,317/ton; and at 

Northeastern from $3,294/ton to $1,544/ton, in 2009 dollars. Actual costs could be even lower 

as I was unable to correct all of the overestimates that I identified due to lack of support and 

underlying calculations. My revised cost effectiveness values are within the range of values 

found to be cost effective at other similar facilities. In addition, my analysis indicates that a dry 

scrubber at any of these units could achieve a BART emission limit of 0.06 lb/MMBtu, based on 

30-day rolling average, when burning low sulfur coal. 

 BART analyses performed for many similar facilities nationwide indicate that the cost 

effectiveness of a wet scrubber is about 9% higher than for a dry scrubber. In cases where an 

existing electrostatic precipitator can be used instead of a new fabric filter baghouse, the cost 

effectiveness of wet scrubbing is lower than dry scrubbing and the incremental cost effectiveness 

is zero. This is likely the case for the subject units, but this option was not evaluated by the 

companies. 

 I revised the wet scrubber cost analyses based on the ratio of my revised dry scrubber 

cost to the initial company costs. This lowered the cost of wet scrubbing at Sooner from 

$8,101/ton to $1,555/ton; at Muskogee from $8,001/ton to $1,417/ton; and at Northeastern from 

$3,625/ton to $1,699/ton. Wet scrubbers can remove up to 98% of the SO2 from low sulfur 

coals and meet SO2 emission limits of 0.03 lb/MMBtu based on a 365-day rolling average and 

0.06 lb/MMBtu based on a 24-hour average. 

 
3

III. REVISED COST ANALYSIS FOR SOONER AND MUSKOGEE 

 DRY SCRUBBER 

 The Sargent & Lundy cost effectiveness analysis is contained in the "12/28/09 S&L 

FollowUp Report".8

 This report was prepared in response to Oklahoma's draft Regional Haze 

SIP, which required Sooner and Muskogee to install dry flue gas desulfurization systems 

(scrubbers) to satisfy BART. Oklahoma rejected Sargent & Lundy's earlier BART cost 

effectiveness analyses as they were "substantially higher than those reported for similar 

projects." The FollowUp Report is a response to Oklahoma's concern about the cost analyses. 

The revised costs, while somewhat lower, are still substantially higher than those reported for 

similar projects without the presence of any "unusual circumstances." 

A. DESIGN BASIS 

 Cost effectiveness analysis evaluates the cost of controlling pollution in dollars per ton of 

pollutant removed. Cost effectiveness is calculated according the EPA Air Pollution Control 

Cost Manual or "Cost Manual",9

 as stipulated in the BART Guidelines. A control technology is 

considered to be “cost effective” if it falls within a reasonable range of cost-effectiveness 

estimates. 

 Cost effectiveness determinations include several steps. First, the capital cost is 

estimated and annualized using a capital recovery factor. Second, the operating and maintenance 

(O&M) costs are estimated. Third, these costs are summed and divided by the tons of pollutant 

removed. This calculation requires that the costs for the control technology are consistent with 

the claimed emission reductions. 

 In performing its BART analysis, Sargent & Lundy assumed that the Sooner and 

Muskogee plants burned coal with a higher sulfur content than they are currently burning. This 

necessitated costing a much bigger scrubber than is necessary for the coal they are currently 

burning. However, Sargent & Lundy also assumed the emissions corresponded to these plants’ 

presently lower sulfur coal. This mismatch overestimated cost effectiveness (i.e., resulted in a 

higher dollars per ton) by about a factor of three. In other words, the costed scrubber is much 

more efficient than the one required to deliver the assumed BART performance level. 

 The FollowUp Report indicates that two separate cases were considered as summarized 

in Table 1: (1) reduction of SO2 from coal containing 1.176 lb/MMBtu SO2 to 0.10 lb/MMBtu 

and (2) reduction of SO2 from coal containing 0.509 lb/MMBtu SO2 to 0.10 lb/MMBtu. 

However, in the Sargent & Lundy cost effectiveness analysis, Option 1 was used to estimate 

capital cost and Option 2 was used to estimate O&M costs and emission reductions. This 

mismatch significantly overestimates the calculated cost effectiveness of the dry scrubber. The 

 

8

 Sargent & Lundy, Sooner Units 1 & 2, Muskogee Units 4 & 5 Dry FGD BART Analysis Follow-Up Report, 

Prepared for Oklahoma Gas & Electric, December 28, 2009 ("12/28/09 S&L Followup Report"). 

9

 U.S. EPA, EPA Air Pollution Control Cost Manual, Report EPA/452/B-02-001, 6th Ed., January 2002 ("Cost 

Manual"), The EPA Air Pollution Control Cost Manual is the current name for what was previously known as the 

OAQPS Control Cost Manual, the name for the Cost Manual in previous (pre-2002) editions of the Cost Manual. 
4

high sulfur design-basis coal used to cost the scrubbers represents a significant increase in the 

sulfur content of the coal that is currently burned (0.51 lb/MMBtu).10 The emission reductions 

that Sargent & Lundy used to calculate cost effectiveness, on the other hand, were based on only 

a 76% to 79% reduction in SO2 from a coal containing 0.51 lb/MMBtu SO2.11 

Table 1 

Dry Scrubber Control Options 

Evaluated by Sargent & Lundy 

In other words, larger scrubbers than are needed for the coal currently burned were 

costed, which skewed the cost effectiveness in dollars per ton to the high side. When calculating 

the tonnage removed, a lower scrubbing efficiency, corresponding to a smaller scrubber was 

used, which skewed the cost effectiveness to the high side. The use of a larger scrubber provides 

the operator with the flexibility to burn higher sulfur coals than are currently being burned. 

However, that increases the cost over what would be needed to minimally satisfy BART. 

This mismatch in design causes a dilemma in analyzing the cost effectiveness that is not 

completely resolvable within the scope of this work. An ideal analysis would be to contrast the 

cost effectiveness of the larger scrubbers, using a range of coals to the cost effectiveness of 

smaller scrubbers burning the current coal. The basic decision point for EPA, however, is 

whether scrubbers are cost effective and what emission limit should be specified. Therefore, in 

this section, I construct two costing options to bound the problem, within the parameters 

introduced by Sargent & Lundy. In subsequent sections, I revised the Sargent & Lundy analysis, 

which uses Option 1 for cost data and Option 2 for emissions data. 

 

10 12/28/10 S&L FollowUp Report, Table 9, pdf 21. 

11 12/28/10 S&L FollowUp Report, Table 5. Emission reductions calculated as 100(annual emission 

reductions)/baseline annual SO2 emissions. For Muskogee 4, SO2 reduction = 100(6953/9113) = 76.3%, for 

Muskogee 5, SO2 reduction = 100(6846/9006) =76.0%, for Sooner 1 = 100(7377/9349) = 78.9%, and for Sooner 2 

= 100(6553/8570) = 76.5%.

Option 

1 

Option 

2 

S&L 

Analysis 

Inlet S (lb/MMBtu) 1.176 0.509 

Outlet S (lb/MMBtu) 0.10 0.10 

SO2 Reduction (%) 91.5% 80% 

Annual Cost ($/yr) 

 Sooner 93,664,600 63,988,436 93,664,600 

 Muskogee 102,600,400 70,396,326 102,600,400 

Emission Reduction (ton/yr) 

 Sooner 43,428 13,930 13,930 

 Muskogee 46,458 13,799 13,799 

Cost Effectiveness ($/ton) 

 Sooner 2,157 4,594 7,147 

 Muskogee 2,208 5,102 7,435 
5

 

 The first option, costing the larger scrubber, as is demonstrated below, is more cost 

effective and it provides the owner much greater flexibility in coal selection, allowing the use of 

higher sulfur coals. This option was likely intended as it allows the owner to switch to higher 

sulfur coals in the future as the supply of lower sulfur coals diminishes or to use cheaper higher 

sulfur coals to reduce operating costs. This first option is carried throughout the balance of this 

report because Sargent & Lundy assumed a higher sulfur coal would be burned when it costed 

the scrubbers. The second option, costing a smaller scrubber, is discussed in Section III.A.2 to 

demonstrate that it is less cost effective. 

1. Option 1: Increased Emission Reductions Relative to Costed Case 

 Table 1 shows that a scrubber designed to remove 91.5% of the SO2 from an inlet of 

1.176 lb/MMBtu (Option 1) would remove more SO2 than a scrubber designed to remove only 

80% of the SO2 from an inlet of 0.51 lb/MMBtu (Option 2). Thus, for a given annual cost, the 

calculated cost effectiveness in dollars per ton declines as emission reductions increase. 

 Cost effectiveness could be estimated using the cost of the larger scrubber and the 

emission reductions that could be achieved with this larger scrubber, assuming an outlet of 0.1 

lb/MMBtu SO2. The use of higher sulfur coal, 1.176 lb/MMBtu SO2, differs from past 

practices, burning a 0.509 lb/MMBtu SO2 coal. Thus, the new baseline for purposes of 

estimating emission reductions is 1.176 lb/MMBtu. 

 The impact of using actual emission reductions for Option 1 on calculated cost 

effectiveness is summarized in Table 2, assuming all else remains the same. This table indicates 

that the mismatch reduces the cost effectiveness in dollars per ton for the OG&E plants from 

about $7,100/ton to about $2,100/ton (Table 2), or by over a factor of three.12 Actual emission 

reductions that could be achieved with the Option 1 scrubber are larger than stated in Table 2 as 

the costs correspond to a 95% efficient scrubber and an outlet emission limit lower than 0.1 

lb/MMBtu is feasible. 

 

12 Actual cost effectiveness would be somewhat higher as the O&M costs included in the Option 1 annual costs are 

for Option 2. This is corrected in subsequent sections of this report. 
6

Table 2 

Option 1 

Revised Cost Effectiveness 

Increased Emission Reductions 

(91.5% SO2 Reduction from 

Scrubber Inlet of 1.176 lb/MMBtu)

 The revised cost effectiveness values in Table 2 do not include any of the other revisions 

I make below to align costs with Regional Haze Guidelines and to eliminate double counting and 

other overestimates. These additional revisions, coupled with the increased SO2 emission 

reductions shown in Table 2 result in an average cost effectiveness value for the two plants of 

$1,304/ton, which is within the range of cost effectiveness values for other similar scrubbers. 

2. Option 2: Decreased Scrubber Cost Relative to Costed Case 

 Alternatively, cost effectiveness could be estimated for Option 2, removing 80% of the 

SO2 from an inlet of 0.51 lb/MMBtu, by costing a smaller scrubber. This would result in a 

higher dollars per ton value than estimated in Table 2 for the higher sulfur case. Sargent & 

Lundy did not provide any scrubber cost analysis for this current coal option. 

 A widely used approximation can be used to estimate the cost of the smaller scrubber. 

The fixed capital of a process having one capacity can be scaled from that with another capacity 

by using the "six-tenth rule," named for the exponent, which is typically close to 0.6. The capital 

cost of a process is related to capacity by the equation: 

 Cb = Ca(Sb/Sa)

n

where Cb is the approximate cost ($) of a process or equipment having size Sb and Ca is the 

known cost ($) of a process or equipment having corresponding size Sa. The ratio Sb/Sa is known 

 

13 12/28/09 S&L FollowUp Report, Table 6, Column labeled "Annual Cost." 

14 The per unit SO2 emissions at the inlet of each Muskogee scrubber is 6,440 lb/hr and at each Sooner scrubber it is 

6,020 lb/hr. 12/28/09 S&L FollowUp Report, pdf 76, 78. Thus, the potential SO2 emission reductions, assuming 

91.5% SO2 reduction and a 90% capacity factor: Muskogee: (0.9)(0.915)(6,440 lb/hr)(8,760 hr/yr)/2,000 lb/ton = 

23,229 ton/yr; Sooner: (0.9)(0.915)(6,020 lb/hr)(8,760 hr/yr)/2,000 lb/ton = 21,714 ton/yr. 

15 Total Annual Cost divided by Design Basis Reduction. 

Unit Total 

Annual 

Costs13

($/yr) 

Design 

Basis 

Reduction14

(ton/yr) 

Revised 

Cost 

Effect.15

($/ton) 

 

Sooner 93,664,600 43,428 2,157 

Muskogee 102,600,400 46,458 2,208 
7

as the size factor, which is dimensionless.16 The exponent, n, varies depending upon the type of 

process unit. This equation indicates that a log-log plot of capacity versus equipment cost for a 

given type of equipment should be a straight line with a slope equal to the exponent n. 

 The exponent n is typically 0.8 to 0.9 for processes that use a lot of mechanical work or 

gas compression. For typical petrochemical processes, n is usually about 0.7. For small-scale, 

highly instrumented processes, n is about 0.6.17 The average exponent for a large number of 

different processes is 0.69,18 which I use in my analysis.19 The use of this rule is an 

oversimplification as the actual values of the cost-capacity exponent ranges from less than 0.2 to 

greater than 1.0. I am not aware of any public data specific to dry scrubbers. However, it is 

appropriate here as Sargent & Lundy did not supply any cost data for the smaller scrubber case, 

the extrapolation is less than a 10-fold capacity range, and the scrubbers are similar with regard 

to type of construction, materials of construction, temperature and pressure operating range, and 

other key variables. 

 I based my analysis on the capacity of the scrubber system to remove SO2 in pounds per 

hour calculated from Sargent & Lundy's material balances in the 12/28/09 S&L FollowUp 

Report. This calculation yields a size factor of 0.51.20 In other words, the fixed capital cost of 

the smaller scrubber, that can reduce the SO2 from 0.51 to 0.10 lb/MMBtu, is 51% of the cost of 

the larger scrubber, that can reduce the SO2 from 1.18 to 0.10 lb/MMBtu. The cost effectiveness 

is re-calculated in Table 3, using all of Sargent & Lundy's assumptions, modifying only the 

annual capital. 

 

16 Gael D. Ulrich and Palligarnai T. Vasudevan, Capital Costs Quickly Calculated, Chemical Engineering, April 

2009, pp. 46 - 52; Max S. Peters, Klaus D. Timmerhaus, and Ronald E. West, Plant Design and Economics for 

Chemical Engineers, McGraw-Hill, Inc., 5th Edition, 2003, pp. 242 - 244 and Kenneth K. Humphreys and Paul 

Wellman, Basic Cost Engineering, 3rd Ed., Marcel Dekker, Inc., 1996, pp. 8-20. 

17 Gavin Towler and Ray Sinnott, Chemical Engineering Design: Principles, Practice and Economics of Plant and 

Process Design, 2008, pp. 307-308. 

18 Kenneth K. Humphreys and Paul Wellman, Basic Cost Engineering, 3rd Ed., Marcel Dekker, Inc., 1996, Table 

2.1. 

19 This value is consistent with wet scrubber data summarized in Shoou-Yun Chang and Angel L. Rivera, Theory 

and Evidence of Economies of Scale in the Development of Waste Management Systems, October 1989, Figure 13. 

The slope of the line, n, from Figure 3, for 400 to 600 MW: (88/116) = (400/600)n Thus, n = ln(88/116)/ln(400/600) 

= 0.68. 

20 For Sooner, from 12/28/09 S&L FollowUp Report, pdf 76 - 77, high sulfur case (Option 1), the scrubber inlet SO2 

("ID Fan Outlet") is 6,015 lb/hr and the outlet ("Stack Inlet") is 511 lb/hr. Thus, 6,015 - 511 = 5,504 lb/hr of SO2 is 

removed. For Sooner, low sulfur case (Option 2), the scrubber inlet SO2 is 2,605 lb/hr and the outlet is 513 lb/hr. 

Thus, 2,605 - 513 = 2,092 lb/hr of SO2 is removed. The size factor is: (2,092/5,504)0.69 = 0.51. For Muskogee, 

from 12/28/09 S&L FollowUp Report, pdf 78-79, high sulfur case, the scrubber inlet SO2 ("ID Fan Outlet") is 6,442 

lb/hr and the outlet ("Stack Inlet") is 548 lb/hr. Thus, 6,442 - 548 = 5,894 lb/hr of SO2 is removed. For Muskogee, 

low sulfur case, the scrubber inlet SO2 is 2,778 lb/hr and the outlet is 550 lb/hr. Thus, 2,778 - 550 = 2,228 lb/hr of 

SO2 is removed. The size factor for Muskogee is: (2,228/5,894)0.69 = 0.51.
8

Table 3 

Option 2 

Revised Cost Effectiveness 

Reduction In Scrubber Cost 

(80% SO2 Reduction from 

Scrubber Inlet of 0.509 lb/MMBtu)

Unit Revised 

Annual 

Costs21

($/yr) 

S&L 

Emission 

Reduction22

(ton/yr) 

Revised 

Cost 

Effect.23

($/ton) 

Sooner 63,988,436 13,930 4,594 

Muskogee 70,396,326 13,799 5,102 

 This table indicates that costing a smaller scrubber would reduce the dollar-per-ton cost 

effectiveness at Sooner from $6,747/ton to $4,594/ton and at Muskogee from $7,435/ton to 

$5,102/ton or by about one third. Thus, it is more cost effective to build a larger scrubber and 

burn a higher sulfur coal (Table 2) than to build a smaller scrubber for a lower sulfur coal (Table 

3), all else being held constant. Higher sulfur coal would improve the economics of Option 1. 

The revised cost effectiveness values in Table 3 do not include any of the other revisions I make 

below to align costs with Regional Haze Guidelines and to eliminate double counting and other 

overestimates. These additional revisions would significantly reduce these values. 

 Thus, it would be in the owner's interest to build the larger scrubber based on the higher 

sulfur coal as it is not only more cost effective, but it provides greater flexibility in future 

operations. While this is a good business strategy, it contravenes the BART Guidelines, which 

require that reasonable assumptions be made on control efficiencies as "An unrealistically low 

assessment of the emission reduction potential of a certain technology could result in inflated 

cost-effectiveness figures."24 Cost effectiveness is determined by dividing the cost of the 

designed scrubbed by the emission reductions that that design can achieve, not by emission 

reductions from a much smaller scrubber. Thus, I used the increased emission reductions in 

Table 2 to estimate cost effectiveness for Sooner and Muskogee in Appendix 1. 

 

B. COSTING METHOD 

 In evaluating cost effectiveness, it is important that a consistent set of rules be 

used. Otherwise, one runs the risk of comparing apples with oranges when making the cost 

effectiveness determination. For power plants having a capacity in excess of 750 MW, as here, 

Section 169A(b) of the CAA requires that States follow guidelines promulgated by EPA for 

 

21 Revised Annual Costs for Sooner: ($60,563,600/yr)(0.51) + $33,101,000/yr = $63,988,436/yr. Revised Annual 

Costs for Muskogee: ($65,722,600)(0.51) + $36,877,800/yr = $70,396,326/yr. 

22 12/28/09 S&L FollowUp Report, Table 6. 

23 Revised Annual Cost divided by S&L Emission Reduction. 

24 70 FR 39168 (July 6, 2005). 
9

determining BART.25 The responsive guidelines are contained in Appendix Y to Part 51, 

"Guidelines for BART Determinations Under the Regional Haze Rule." These Guidelines 

include methods to determine both cost and cost effectiveness. For cost analyses, the Guidelines 

stipulate: "In order to maintain and improve consistency, cost estimates should be based on the 

OAQPS Cost Control Manual, where possible."26 

 Sargent & Lundy asserted that they followed these Guidelines "[t]o the extent possible."27 

However, as demonstrated below, they did not follow these Guidelines, but rather followed 

utility industry cost estimating practices such as the levelized cost method, a method developed 

by the Electric Power Research Institute (EPRI) for estimating capital investment and O&M 

costs specific to utilities. These methods are used for internal project justification or other 

accounting purposes but are not appropriate for use in a BART analysis. Sargent & Lundy 

escalated costs to future build dates (BART is based on present dollars); included contingencies 

that are excessive for BART determinations; failed to optimize costs; double counted costs; and 

used inflated factors. Oklahoma did not challenge these estimates and used them as the basis of 

its BART determination. However, the costs that Oklahoma relied on to determine BART are 

overestimated and not comparable to those in other BART determinations. 

 The Cost Manual28 sets out a generic method to estimate the cost of pollution control 

equipment. It also provides specific cost algorithms to estimate capital and O&M costs for some 

types of pollution control equipment. The Cost Manual does not include specific cost algorithms 

for SO2 scrubbers, considered as BART for the OG&E units. Regardless, it is possible to follow 

the generic costing method in the Cost Manual, while relying on vendor quotes and other 

information to estimate scrubber capital and O&M costs. Sargent & Lundy used vendor 

estimates for equipment costs but did not follow the generic costing method. 

 Sargent & Lundy used a costing method that deviates in important ways from the Cost 

Manual. This makes it appear that the cost to control SO2 is higher than at other similar sources. 

However, these apparently higher costs are due in part to the costing method, not to any unique 

circumstances at the units that make retrofit of scrubber unusually costly. The Guidelines 

require that documentation be provided for "any unusual circumstances that exist for the source 

that would lead to cost-effectiveness estimates that would exceed that for recent retrofits."29 No 

unique circumstance is identified in any of the documentation to the BART determinations. 

 These deviations resulted in excessively high scrubber costs compared to estimates in 

other BART analyses and in published cost surveys.30 The cost metric estimated in the Manual 

is real or constant-dollar costs in that the effect of inflation has been removed. Capital cost are 

annualized using a capital recovery factor based on a 7% social rate of interest. In contrast, 

Sargent & Lundy escalated capital costs from the year of estimate, 2010, to the year of 

construction; included various future costs of borrowing money; used an unreasonable 

 

25 See, e.g., 70 FR 39158 (July 6, 2005) ("States must follow the guidelines in making BART determinations on a 

source-by-source basis for 750 megawatt (MW) power plants...") 

26 70 FR 39166 (July 6, 2005). 

27 12/28/09 S&L FollowUp Report, pp. 1 - 2. 
10

contingency; and used an incorrect capital recovery factor. Revising these four factors alone 

significantly lowers annual costs. 

 

1. Capital Cost: Failure To Follow Cost Manual 

 One of the factors that is evaluated in a BART determination is cost effectiveness of the 

control options. Cost effectiveness is determined by comparing annual cost per ton of pollutant 

removed for the source of interest to the range of cost effectiveness values for other similar 

permit decisions. If a given cost effectiveness value falls within the range of costs borne by 

others, it is per se cost effective unless unusual circumstances exist at the source.31 Thus, cost 

effectiveness is a relative determination, based on costs borne by other similar facilities. To 

compare costs among units, a level playing field must be established by following the same cost 

rules in each determination.32 The Sargent & Lundy cost estimating method deviates from the 

standard procedure used to establish the range of acceptable cost effectiveness values. In other 

words, the Sargent & Lundy cost effectiveness analyses compare apples and oranges. 

 28 U.S. EPA, EPA Air Pollution Control Cost Manual, Report EPA/452/B-02-001, 6th Ed., January 2002 ("Cost 

Manual"), p. 2-21. The EPA Air Pollution Control Cost Manual is the current name for what was previously known 

as the OAQPS Control Cost Manual, the name for the Cost Manual in previous (pre-2002) editions of the Cost 

Manual. 

29 70 FR 39168 (July 6, 2005). 

30 Sharp, George, “Update: What’s That Scrubber Going to Cost?”, Power Magazine, March 1, 2009; J. Edward 

Cichanowicz, Current Capital Cost and Cost Effectiveness of Power Plant Emissions Control Technologies, 

Prepared for Utility Air Regulatory Group, June 2007, Section 6, Flue Gas Desulfurization Costs; 3/07 S&L 

National Lime Association Report.

31 70 FR 39168 (July 6, 2005). 

32 See discussion of this issue in Letter from John Bunyak and Sandra V. Silva, Fish & Wildlife Service, to Mary 

Uhl, New Mexico Environmental Department, August 17, 2010, p. 5, footnote 9 ("November 7, 2007, statement 

from EPA Region 8 to the North Dakota Department of Health: "...in order to maintain and improve consistence, 

cost estimates should be based on the OAQPS Cost Control Manual. Therefore, these analyses should be revised to 

adhere to the Cost Manual methodology."), p. 6 (quoting a May 10, 2010 EPA letter to North Dakota Department of 

Health: "These accounting items [owner's cost] are unauthorized under the Cost Control Manual, create an unlevel 

playing field for comparison with other BACT analyses and alone account for an increase in capital costs from the 

Cost Control Manual by a factor of 1.6."). See discussion in: Letter from Andrew M. Gaydosh, Assistant Regional 

Administrator, EPA Region 8, to Terry O'Clair, Director, Division of Air Quality, North Dakota Department of 

Health, Re: EPA's Comments on the North Dakota Department of Health's April 2010 Draft BACT Determination 

for NOx for the Milton R. Young Station, May 10, 2010, pp. 14-16. 
11

 The Sargent & Lundy estimate of capital cost failed to follow the BART Guidelines Cost 

Manual by including: escalation, bond cost, and AFUDC; using an excessive contingency; and 

by annualizing costs using an inflated capital recovery factor. Cost items such as escalation of 

costs, bond cost, and AFUDC are not part of the Cost Manual methodology for estimating costs. 

These items are strictly contingencies, future transfers of monies, and accounting procedures not 

included in cost effectiveness analyses. These types of costs create an unlevel playing field for 

comparison with other BART analyses.33 The impact of these and other factors discussed in this 

report on the cost effectiveness analysis are summarized in Appendix 1. 

 

a. Escalation 

 The Sargent & Lundy cost estimates include escalation of equipment (2.5% for 2010 and 

4% for 2011 - 2014), material (3% for 2011 and 5% for >2012), labor (3% for 2010 and 4% for 

2011-2014), and indirects (3% for 2010 and 4% for 2011-2015) to the year of expenditure.34 The 

Cost Manual approach, recommended by the BART Guidance, explicitly excludes future 

escalation as cost comparisons are made on a current real dollar basis. Inflation is not included 

in cost effectiveness analyses as they rely on the most accurate information available at current 

prices and do not try to extrapolate those prices into the future.35 The assumed wage escalation, 

for example, is much higher than recent reports of 1.3% for union construction workers.36 

Further, escalation is a contingency and is double counted as discussed elsewhere. Escalation of 

costs from past to the current year of analysis is permitted, as costs are compared based on the 

time of estimate, but future escalation is not. Thus, I have set escalation equal to zero in my cost 

analysis in Appendix 1, Line 40. 

 

33 Ibid. 

34 3/28/09 S&L FollowUp Report, Appendix A, Dry FGD Design and Cost Estimate Basis Document, Sec. 8.3.8, 

Escalation Rates, pp. 7-8, pdf 66 - 68. 

35 See, e.g., Cost Manual, p. 2-36, pdf 50. 

36 Ken Simonson, Associated General Contractors of America, Beige Book Finds Construction Still Weak; Wages 

Cool: Hiring Up But Outlook Stays Dim, September 10, 2010. See also: Construction Labor Research Council at 

www.circdata.org ("Wage and benefit settlements for construction union employees so far in 2010 have resulted in 

an average first-year increase of $0.64 or 1.3%.") 
12

b. Allowance for Funds Used During Construction (AFUDC)

 The Sargent & Lundy cost estimates include a charge of 8.66% of total capital expended 

to account for the cost of funding the cash flow during the construction period. AFUDC is an 

accounting method. Assets under construction do not provide service to current customers and 

thus associated interest and allowed return on equity are not charged to current customers. 

Instead, AFUDC capitalizes these costs and adds them to the rate base so that they can be 

recovered from future customers when the assets are used. Thus, these charges represent future 

cash income to the utility. In other words, AFUDC is the accumulated cost of carrying capital 

and holding it waiting to spend, so money can be made in the future, by selling electricity. 

Future income should not be charged against the cost of a scrubber in a BART cost effectiveness 

analysis. 

 These costs are not part of the constant dollar approach found in the EPA Control Cost 

Manual and should not be included in BART cost-effectiveness analyses. The most recently 

issued sections of the Manual, for example, explicitly set AFUDC equal to zero.37 Thus, I have 

set AFUDC equal to zero in my cost analysis in Appendix 1, Line 43. 

c. Bond Costs

 The Sargent & Lundy cost estimates include bond costs estimated as 1% of total project 

cost with escalation and contingency, but without AFUDC.38 The basis and justification for 

these costs is not stated. These costs appear to represent a portion of the cost of financing the 

project and thus are similar to AFUDC. These costs are transfer payments and thus are not valid 

under the constant dollar approach in the Cost Manual and should not be included in BART cost 

effectiveness analyses. Thus, I have set bond cost equal to zero in my cost analysis in Appendix 

1, Line 42. 

d. Contingency

 The Sargent & Lundy capital cost estimates include a contingency of about 14%39 of the 

total project capital cost plus escalation. This value corresponds to the upper 95% confidence 

level, calculated using range estimating techniques.40 The details of how this value was 

calculated are not disclosed. However, it is unprecedented to use range estimating, a financial 

risk management and decision making technique,41 in a cost effectiveness analysis. This use 

inflates the cost of the OG&E scrubbers compared to other estimates that do not use risk 

 

37 Cost Manual, Section 4, Chapter 1, Table 1.4, pdf 424 and Chapter 2, Table 2.5, pdf 486. 

38 12/28/09 S&L Followup Report, Attach A, pdf 41, 56. 

39 12/28/09 S&L FollowUp Report, Appendix A. The contingency for Sooner 1&2 was estimated as 13.97% of total 

project capital plus escalation and for Muskogee 4&5 as 14.085% of total project capital plus escalation. 

40 12/28/09 S&L FollowUp Report, p. 5. 

41 Kenneth K. Humphreys, Project Risk Management - Advantages and Pitfalls, AACE Transactions, 1998, 

Available at: http://www.icoste.org/ICMJ%20Papers/Humphreys-Slovenia.pdf. See also AACE International 

Recommended Practice No. 40R-08, Contingency Estimating - General Principles, Available at: 

http://www.aacei.org/technical/rps/40R-08.pdf. 
13

management to estimate costs. Sargent & Lundy claims their revised cost estimate is accurate to 

within +/-20%.42 However, the need for a 14% contingency would seem to be inconsistent with 

this claim. 

 Thus, while this method may be chosen by a project proponent to forecast final costs at 

an early planning stage, before committing capital, it is not appropriate for determining BART 

cost effectiveness, which must follow the Cost Manual, as the resulting cost will ultimately be 

compared to costs borne by others. The same costing method must be used to assure like is 

compared to like. While BART Guidelines only require the use of the Cost Manual approach "if 

possible", it is certainly possible to follow the standard EPA contingency guidance. 

 A contingency factor in a cost effectiveness analysis "should be reserved (and applied to) 

only those items that could incur a reasonable but unanticipated increase but are not directly 

related to the demolition, fabrication, and installation of the system."43 An example would be a 

hundred year flood that postpones delivery of materials, but their arrival at the job site is not a 

problem unique to the retrofit. The standard contingency factor used in a cost effectiveness 

analysis, when it is included, is 3% of purchased equipment cost.44 Thus, I revised the 

contingency in my analysis to use 3%, shown in Appendix 1, Line 41. 

e. BOP Profits

 The Sargent & Lundy cost analyses include a balance of plant (BOP) contractor's profit 

figured as 10% of balance of plant materials and labor, exclusive of equipment costs. These 

costs amount to $14,817,240 for Sooner and $15,710,910 for Muskogee. Profits are commonly 

embedded in labor rates, supplies and materials. No support or justification for these estimates 

was provided. I did not adjust these costs as inadequate information was supplied to determine 

the basis of balance of plant material and labor. However, the costs are overestimated as they are 

factored from labor costs that are high due to inflated labor productivity and wage rates, 

discussed elsewhere. The BOP profits should be documented and critically evaluated in future 

work. 

f. Capital Recovery Factor

 A capital recovery factor or CRF is used to convert a present value into a stream of equal 

annual payments over a given time at a given interest rate. Under the Cost Manual methodology, 

total capital costs are annualized by calculating an annual payment sufficient to finance the 

investment over its entire life. This payment is calculated by multiplying the total capital 

investment by a capital recovery factor calculated from a formula based on interest rate and 

equipment lifetime.45 The longer the service life and the lower the interest rate, the lower the 

annualized capital costs and the lower the cost effectiveness in $/ton. 

 

42 12/28/09 S&L FollowUp Report, p. 2. 

43 Cost Manual, Chapter 2, Cost Estimation: Concepts and Methodology, Sec. 2.5.4, p. 2-30, pdf 44. 

44 See, for example, Cost Manual, Section 5.2, Post-Combustion Controls, Chapter 1: Wet Scrubbers for Acid Gas, 

Table 1.3 and Section 6, Chapter 1: Baghouses and Filters, Table 1.9. 

45 Cost Manual, p. 2-21, pdf 35. 
14

 Sargent & Lundy did not use the standard capital recovery method as required by the 

Manual, but rather (as mentioned earlier) a levelized annual cost method that included not only 

capital recovery, but also debt return, equity return, and income taxes, transfer payments not 

allowed under the Cost Manual. This method resulted in an annual levelized charge of 10.36%.46 

Thus, total capital costs were annualized by multiplying them by 0.1036. This is equivalent to 

assuming a 20-year life and an interest rate of 8.23% in the capital recovery method. I 

recalculated the capital recovery factor as 0.0806, assuming a scrubber lifetime of 30 years and a 

social interest rate of 7%, for the reasons set out below. My revised CRF is included in 

Appendix 1, Line 45 and the basis for my assumptions are discussed below. 

i. Scrubber Lifetime

 The lifetime used to calculate the capital recovery factor is the actual or service life of the 

equipment, not the depreciable life used by Sargent & Lundy.47 The lifetime is important 

because the shorter the lifetime, the higher the capital recovery factor and the higher the annual 

capital cost used to determine cost effectiveness. I used 30 years compared to 20 years used by 

Sargent & Lundy. Increasing the lifetime from 20 years to 30 years while holding all else 

constant would decrease annual capital costs by about 30% (0.1036/0.0806), a significant 

amount when dealing with capital costs of tens of millions of dollars. 

Scrubber vendors indicate that the lifetime of a scrubber is equal to the lifetime of the 

boiler, which might easily be over 60 years. There are, for example, many scrubbers that were 

installed between 1975 and 1985 that are still in operation today (e.g., Coyote Station, H.L. 

Spurlock Unit 2, East Bend Unit 2, Laramie River Unit 3,48 Cholla 5, Basin Electric, Mitchell 

Unit 33). Standard cost estimating handbooks49 and published papers50 report 30 years as a 

typical life for a scrubber. Further, many utilities routinely specify 30+ year lifetimes in requests 

for proposal and to evaluate proposals. The original Sargent & Lundy cost estimate, for 

example, assumed a 25 year lifetime, which was lowered to 20 years in the revised 2009 analysis 

without comment. The BART determination for Big Stone Unit 1, on-line in 1974, assumed a 30 

years scrubber life. Thus, I used 30 years in my revised cost estimates. 

In response to questions from EPA, Sargent & Lundy justified the 20 years as the 

"estimated remaining depreciable life for Sooner and Muskogee Stations as of 2014/2015 

according to OG&E's depreciation studies of those plants."51 The depreciable life is not used in 

capital recovery calculations, but rather the actual service life. Further, when the lifetime of the 

 

46 12/28/09 S&L FollowUp Report, Table 3 and Appx. B, Table B-5. 

47 12/28/09 S&L FollowUp Report, pdf 9-10. 

48 See, for example, EPRI, A Review of Literature Related to the Use of Spray Dryer Absorber Material: Production, 

Characterization, Utilization Applications, Barriers, and Recommendations, December 6, 2006, Table 1-2. 

49 William M. Vatavuk, Estimating Costs of Air Pollution Control, Lewis Publishers, 1990, p. 198. 

50 J. Warych and M. Szymanowski, Optimum Values of Process Parameters of the “Wet Limestone Flue Gas 

Desulfurization System, Chemical Engineering & Technology, v. 25, 2002, pp. 427-432. 

51 2/8/10 S&L Memorandum, Question 1. 
15

scrubber effects the BART determination, as here, this would require a federally-enforceable 

restriction preventing further operation after a date certain.52 Such a date has not been proposed. 

 ii. Interest Rate

 The interest rate used to calculate the capital recovery factor is the cost of borrowing or 

the social interest rate established by the Office of Management and Budget for comparison of 

public policy issues.53 Sargent & Lundy's cost estimate assumed an interest rate of 8.23% (when 

the effect of debt, equity and taxes are included). It has long been standard practice to use an 

interest rate of 7% in regulatory cost effectiveness analyses, as this is the social cost of 

borrowing for governmental analyses.54 In fact, permit applications that AEP, the owner of 

Northeastern, submitted to the states of Ohio and West Virginia, use the standard interest rate of 

7%.55 Increasing the interest rate to 8.26% from 7% while holding all else constant would 

increase the capital costs by about 13% (0.0911/0.08059), a significant amount when dealing 

with capital costs of tens of millions of dollars. 

g. Construction Costs

 Certain "other costs" required to construct the project, including additional crane rental 

allowance, mobilization/demobilization, scaffolding, temporary facilities, overtime inefficiency, 

per deim expense and BOP contractor's general & administrative costs, are overestimated. Some 

of these overestimates are discussed below. 

 i. Labor Rates

 The FollowUp Report states that labor cost estimates were developed using Oklahoma 

area wage rates.56 However, the labor rates actually used by Sargent & Lundy are excessive for 

an open shop project in Oklahoma and the general region, ranging from about $40 to $80/hr. 

Many public sources demonstrate these are excessive. No support is provided for these out-ofrange rates. 

 

52 70 FR 39169 (July 6, 2005). Where the date that a facility permanently stops operation affects the BART 

determination, "this date should be assured by a federally- or State-enforceable restriction preventing further 

operation.". 

53 Cost Manual, pdf 26; Circular A-4, U.S. Office of Management and Budget, September 2003. Available at: 

http://www.whitehouse.gov/omb/circulars_a004_a-4/#d. 

54 Cost Manual, pdf 27 and Circular A-4, U.S. Office of Management and Budget, September 2003. Available at: 

http://www.whitehouse.gov/omb/circulars_a004_a-4/#d. 

55 Application to the West Virginia Department of Environmental Protection for a Permit to Construct, AEP – 

Appalachian Power Company Mountaineer IGCC Facility, September 2006, p. AII-13; Letter from Welch, Dennis 

E., Vice President – Columbus Southern Power Company and Ohio Power Company, subsidiaries of American 

Electric Power to Ponchak, Dean, Ohio EPA, Division of Air Pollution Control, Southeast Division. AEP Great 

Bend IGCC Project – Permit to Install Application, p. 5-11. 

56 12/28/09 S&L FollowUp Report, p. 6. 
16

 The Bureau of Labor Statistics website indicates the second quarter 2010 total cost to 

employer for construction workers is $31.44/hr. This is a national average, including heavily 

unionized California and the northeastern states. Oklahoma is below the average. This same 

website shows the average nonunion wage is $15.25/hr. Construction managers top out the slate 

at $39.36/hr.57 

 The Davis-Bacon58 prevailing wages on federally funded work in Oklahoma for 

carpenters, piledrivers, equipment operators, reinforcing steel, etc. is typically $7-11/hr 

throughout most of the state. The highest wages I found were in Oklahoma County at $35/hr to 

$40/hr (including fringe benefits) for electricians, pipefitters, and sheetmetal workers. Sooner, 

Muskogee, and Northeastern are located in Noble, Muskogee, and Rogers counties, respectively. 

Open shop wages would be less than prevailing wages on federally funded projects. 

 A 2008 wage survey59 summarizes union and nonunion wages by craft within regions. 

This survey, reproduced in Figure 1, shows that the highest wage plus benefits in Region 6 (AR, 

LA, NM, OK, TX) for non-union construction workers is about $25/hr to $27/hr for masons, 

boilermakers, electricians, heavy equipment operators, ironworkers, millwrights, pipefitters, and 

riggers. None of the skilled craft workers in this survey approach the wage rates assumed by 

Sargent & Lundy. Wages have generally fallen since this survey. 

 

57 Bureau of Labor Statistics, Construction: NAICS 23. Available at: 

http://www.bls.gov/iag/tgs/iag23.htm#earnings. 

58 Davis-Bacon Wage Determinations by State, Available at: http://www.gpo.gov/davisbacon/OK.html. See, for 

example, general decisions OK201000023, OK20100024, and OK20100030 for some of the highest wages in the 

counties where the projects are located. 

59 FMI Management Consulting, Craft Worker Compensation Research Report, January 2008, Ex. 36, 41 - 54. 

Available at: 

http://www.agc.org/galleries/laborhr/AGC%20Craft%20Worker%20Compensation%20Research%20Report%20-

%20Final%201-4-08.pdf. 
17

Figure 1 

 The wages used in the Sargent & Lundy analysis are factors of two to three times higher 

than those reported in these sources. The files that I reviewed contain no support for these high 

labor rates. I did not change the labor rates in my revised cost analysis as the parties declined to 

supply the electronic spreadsheets with embedded calculations, which would have facilitated 

such a change. Further, the specific craft that is required for the various tasks is not indicated, 

but rather only crew names. In addition to the very high wage starting point, Sargent & Lundy 

boosted the cost of labor even more by using high labor escalation rates and adding additional 

charges in the "other cost" portion of its cost spreadsheets. These are discussed below. 

 ii. Per Deim Expense

 The FollowUp Report states that per diem is included in the wage.60 However, the cost 

spreadsheet in Attachment A include certain "other costs", which include a per diem expense of 

$10/hr that is added to the already high labor rates for every single hour (1,790,700 hrs for 

Sooner and 1,854,271 hrs for Muskogee) required to build the project. This is excessive for the 

Oklahoma market where current per deim rates are about $50/day to $70/day. As the costs are 

based on six 10 hour days, this amounts of $100 per day for per diem, or about $18 million for 

each plant. 

 Further, as per diem pays for living expenses, this implies that 100% of the workforce 

will be drawn from outside of the area. This is not believable, particularly in the current job 

market. Finally, per diem is not being paid for open shop jobs in today's soft labor market. 

Thus, additionally adding per diem, on top of the high wage rates used to calculate direct labor is 

 

60 12/28/09 S&L FollowUp Report, p. 13. 
18

double counting. Per diem should either not be paid at all or only be paid for a portion of the 

labor force, as some of the construction workers will be drawn from the surrounding areas. This 

factor was not adjusted in my revised cost analysis as the record contains no basis for the 

assumed labor rate. 

 iii. Labor Productivity

 Sargent & Lundy increased the estimate of manhours to install equipment by 15% to 

account for "productivity," such factors as inability to attract labor, weather conditions, etc.61 

This is unprecedented in a cost effectiveness analysis and is another form of contingency, 

specifically disallowed by the Cost Manual, as discussed above. It also is double counting as a 

general contingency is included elsewhere in the cost analysis. It is unlikely that there would be 

any trouble attracting labor in the current and reasonably foreseeable labor market. Further, the 

Sargent & Lundy Design and Cost Estimate Basis Document in the 12/28/09 S&L FollowUp 

Report states that "no allowances have been made to cover items such as the loss in 

productivity..."62 Thus, I adjusted the cost to use unadjusted manhours. 

 For Sooner, the difference between total productivity-adjusted manhours (1,647,446 hrs) 

and actual manhours (1,432,562 hrs) is 214,884 hours. The value of these hours is $14,276,530, 

based on Sargent & Lundy's calculations.63 For Muskogee, the difference between total 

productivity-adjusted manhours (1,705,929 hrs) and actual manhours (1,483,417 hrs) is 222,512 

hours. The value of these hours at Muskogee is $14,918,100, based on Sargent & Lundy's 

calculations.64 Thus, I subtracted the 15% productivity contingency from the estimated capital 

costs in Appendix 1, Line 23. 

 iv. Overtime Inefficiency

 The Sargent & Lundy cost analyses contain another contingency in the "other costs" 

portion of its spreadsheets, called "cost due to overtime inefficiency working 5x10s."65 This is 

another form of contingency, specifically disallowed by the Cost Manual, as discussed above. It 

also double counts, as a general contingency is included elsewhere in the cost analysis. The new 

scrubbers would be installed during normal maintenance outages by working five 10 hour days. 

Efficient scheduling of the labor pool should not result in an overtime inefficiency. The 

12/28/09 S&L FollowUp Report contains no support for overtime inefficiency. Thus, I have set 

overtime inefficiency equal to 0 in Appendix 1, Line 28. 

 

61 12/28/09 S&L FollowUp Report, Attach. A, pdf 28 - 56. 

62 12/28/09 S&L FollowUp Report, Attach. A, pdf 66. 

63 12/28/09 S&L FollowUp Report, Attach. A. For Sooner, calculated from the electronic spreadsheet, line 585 as 

$109,453,400 - $109,453,400 /1.15 = $14,276,530. 

64 12/28/09 S&L FollowUp Report, Attach. A. For Muskogee, calculated from the electronic spreadsheet, line 611 

as $114,372,100 - $114,372,100 /1.15 = $14,918,100. 

65 12/28/09 S&L FollowUp Report, Attach. A, pdf 40, 55. 
19

2. Cost: Double Counting 

 In addition to failing to follow the standard cost estimating protocol required by the 

Guidelines, Sargent & Lundy also double counted some costs and overestimated other due to 

erroneous assumptions. Further, as many components of Sargent & Lundy's cost estimates are 

factored from other costs, such as total direct costs, reductions in items included in these sums 

lead to reductions in the factored costs. These various issues are discussed below for capital 

costs. 

a. Owner's Cost

 The Sargent & Lundy cost analyses include owner's costs as a separate indirect capital 

cost, estimated as 5% of total capital expended. These costs have been estimated by Sargent & 

Lundy as 2% of total capital expended in other BART analyses.66 These are reportedly costs 

incurred by the owner in managing and implementing a capital project. They include staff for 

site coordination during construction, equipment receiving, contract management, interface with 

regulatory agencies, owner engineering costs, warehousing, normal overhead of a corporation, 

and accounting support.67 

 While owners do incur some costs, they have been double counted and overestimated 

here. Owners generally do not manage and implement capital projects, but rather retain 

engineering firms, called the owner's engineer, to perform these functions. The costs that owners 

incur are included under "indirect capital cost," which also includes engineering/procurement 

estimated at 6.5% of total direct costs and construction management, estimated as 2% of total 

direct costs. These costs are high compared to costs that result when using the Cost Manual 

(both engineering and construction/field expenses are estimated as 10% of purchased equipment 

costs). Owner's cost is part of the factored estimate for engineering, procurement, and 

construction management. Owner's costs are not separately included in BART cost effectiveness 

analyses and have been more than double counted here. In its May 10, 2010 formal comments to 

the North Dakota Department of Health, for example, EPA rejected the inclusion of "owner's 

costs" in a cost analysis for the Milton R. Young facility.68 Thus, I set owner's cost equal to zero 

in Appendix 1, Line 38. 

b. Engineering/Procurement

 The Cost Manual estimates engineering and procurement as 10% of the purchased 

equipment cost. Sargent & Lundy estimated engineering and procurement, an "indirect capital 

cost", as 6.5% of the total direct costs. As can be seen from examining Appendix 1, direct costs 

 

66 See, e.g., Sargent & Lundy, Dry FGD and ID Fan & Auxiliary Power Conceptual Design Costs, Conceptual Cost 

Estimate, Prepared for Nebraska Public Power District, Gerald Gentleman Station, Units 1&2, February 7, 2008. 

67 12/28/09 S&L FollowUp Report, pdf 40, 56, 66, 67. 

68 Letter from Andrew M. Gaydosh, Assistant Regional Administrator, EPA Region 8, to Terry O'Clair, Director, 

Division of Air Quality, North Dakota Department of Health, Re: EPA's Comments on the North Dakota 

Department of Health's April 2010 Draft BACT Determination for NOx for the Milton R. Young Station, May 10, 

2010. 
20

include much more than just the purchased equipment costs. The Sargent & Lundy 

engineering/procurement costs are $21,144,700 for Sooner and $21,784,400 for Muskogee.69 

These values are very high compared to estimates using the Cost Manual approach of 10% of 

purchased equipment cost. The Cost Manual estimates are one-third of the Sargent & Lundy 

estimates, or $7,050,000 for Sooner and $7,120,000 for Muskogee.70

 A portion of the Sargent & Lundy engineering/procurement costs is double counted. The 

vendor proposals that Sargent & Lundy relied on include the cost to both design and supply the 

dry scrubber and baghouse. This is common, and costs are typically reported on an all-in basis. 

Thus, because Sargent & Lundy also separately calculated engineering/procurement, the 

engineering and procurement costs for the vendor-supplied portion of the capital costs are double 

counted. The 6.5% factor should only apply to the balance of plant materials and labor, which 

exclude the scrubber system, fabric filter, and the scrubber contractor's scope for the ash 

recycling system. 

 The engineering and procurement costs, based only on the balance of plant equipment, 

material and labor costs as revised in Appendix 1, Line 34, are $8,371,56371 for Sooner and 

$8,840,09072 for Muskogee, or less than half of the Sargent & Lundy estimate. This change 

reduces the engineering and procurement costs by $12,773,100 at Sooner73 and by $12,944,277

at Muskogee.74 These values are still high compared to estimates based on the Cost Manual 

method of 10% of purchased equipment cost. 

c. Construction Management

 The balance of plant (BOP) construction management costs were double counted. They 

occur in two places. Under "total indirect costs", Sargent & Lundy estimated construction 

management as 2% of the total direct costs, which include both vendor and balance of plant 

costs. Elsewhere, under "other costs", Sargent & Lundy estimated the balance of plant 

contractor's general and administrative costs, a subset of construction management, as 5% of 

balance of plant materials and labor costs. This double counts the balance of plant contractor's 

general and administrative costs and is removed in my cost analysis, Appendix 1, Line 29, by 

 

69 12/28/09 S&L FollowUp Report, pdf 40, 56. 

70 Purchased equipment cost for Sooner reported in 12/28/09 S&L FollowUp Report, pdf 94. Comparable data was 

not reported for Muskogee. Muskogee purchased equipment cost assumed to be about 3% higher based on vendor 

quotes. 

71 Revised Engineering/Procurement Cost Sooner: (0.065)[(273,104,770 - 86,859,600-46,335,000-11,116,900)] = 

$8,371,563. 

72 Revised Engineering/Procurement Cost Muskogee: (0.065)[(280,718,380 - 86,741,200-45,872,900-12,102,900)] = 

$8,840,090. 

73 Reduction in Engineering/Procurement Cost Sooner, Appendix 1, Line 34: (0.065)[(273,104,770 - 86,859,600-

46,335,000-11,116,900)] - 0.065(325,302,500) = -$12,773,100. 

74 Reduction in Engineering/Procurement Cost Muskogee, Appendix 1, Line 34: (0.065)[(280,718,380 - 86,741,200-

45,872,900-12,102,900)] - 0.065(335,144,100) = -$12,944,277. 
21

subtracting the values assumed in Sargent & Lundy's analysis for "BOP contractor's general & 

admin".75 

 The Sargent & Lundy construction management costs are factored from total direct costs, 

calculated as 2% of total direct costs, or $6,506,100 at Sooner and $6,702,900 at Muskogee. As 

my revisions lower total direct costs, other items factored from total direct costs must be 

adjusted. Thus, I revised construction management costs in Appendix 1, Line 35, to account for 

other revisions I made to total direct costs. These revised fees, $5,462,095 at Sooner76 and 

$5,685,568 at Muskogee,77 were estimated as 2% of revised total direct costs. This reduces the 

construction management costs in Appendix 1, Line 35, by $1,043,955 at Sooner78 and by 

$1,017,314 at Muskogee.79 

d. Startup & Commissioning Craft Support

 Sargent & Lundy estimated startup and commissioning craft support, an indirect cost, as 

2% of an unidentified capital sum about 8% less than the vendor portion of equipment, materials 

and labor costs (e.g., $163,695,000 for Sooner). These costs amounted to $3,273,900 for Sooner 

and $3,417,600 for Muskogee.80 However, the vendor proposals that Sargent & Lundy relied on 

include the cost to commission the units. Thus, these costs are double counted and are 

eliminated in Appendix 1, Line 36. 

e. Operator Training & Manuals

 Sargent & Lundy estimated operator training and manuals, an indirect cost, as 0.55% of 

the total construction & erection cost portion of total equipment, material and labor, or 

$163,695,000 for Sooner and $170,881,000 for Muskogee. These costs amounted to $900,300

for Sooner and $939,800 for Muskogee.81 However, the vendor proposals that Sargent & Lundy 

relied on include these costs. Thus, these costs are double counted and are eliminated in 

Appendix 1, Line 37. 

f. Other Factored Sums

 There are other items that are factored from various unidentified sums, including 

additional crane rental allowance, mobilization/demobilization, scaffolding, temporary facilities 

and construction utilities, consumables, BOP contractor's profit, and freight to site.82 The 

 

75 12/28/09 S&L FollowUp Report, pdf 40 (Sooner) and pdf 55 (Muskogee). 

76 Revised Construction Management at Sooner: (0.02)(273,104,770) = $5,462,095. 

77 Revised Construction Management at Muskogee: (0.02)(280,718,380) = $5,614,368. 

78 Reduction in Construction Management Costs at Sooner: (0.02)[273,104,770 - 325,302,500] = -$1,043,955. 

79 Reduction in Construction Management Costs at Muskogee: (0.02)[280,718,380 - 335,144,100] = -$1,088,514. 

80 12/28/09 S&L FollowUp Report, pdf 40, 56. 

81 12/28/09 S&L FollowUp Report, pdf 40, 56. 

82 12/28/09 S&L FollowUp Report, Attach. A. 
22

underlying sums are not available as Sargent & Lundy did not provide an electronic copy of the 

Excel cost spreadsheets that contained the formulas. Thus, the origin of the underlying sums, 

necessary to modify them, can only be determined through trial and error, which is beyond the 

scope of my work. However, it is likely that all of these factored items would decline, further 

reducing the dollar-per-ton cost effectiveness analyses. 

g. Initial Fabric Filter Replacement Materials

 The spray chamber (i.e., scrubber absorber) is followed by a fabric filter baghouse to 

remove fly ash and scrubber solids. A fabric filter baghouse is a multi-compartment enclosure, 

each containing up to several thousand long (10-26 feet), vertically supported, small diameter (5-

6 inches) fabric bags supported internally from a metal wire cage to prevent bag collapse. 

Particulate-laden gases pass through the bags, depositing 99.9%+ of the entrained solids on the 

bags. Thus, the bags and their cages must be periodically changed out, every 2 to 3 years. 

 The Sargent & Lundy cost estimates include charges for replacing the bags and cages 

every 3 years as an operating and maintenance (O&M) expense.83 The annual cost of 

replacement parts is estimated the same way as the annual cost of capital, by multiplying the 

current cost of replacing all of the bags/cages by a capital recovery factor based on the life of the 

bags and the social interest rate of borrowing. As the bags and cages are part of the baghouse 

and must be in place when the units start operating, the vendor's cost estimate for the baghouse 

includes the cost of these replacement parts. However, this same cost is included in the first 

year's annualized replacement costs. Thus, it is double counted. The annualized cost of the 

baghouse should be reported net of the cost of replacement materials needed at the beginning of 

operations to prevent this double counting.84 I did not make this change due to the complexity of 

revising costs without the original electronic spreadsheets. However, in future cost estimates, the 

cost of the baghouse should be reported net the annualized cost of bag/cage replacement. 

3. Capital Cost: Overestimating 

 Capital projects should be designed to minimize capital and construction costs. The 

company estimates made no apparent effort to minimize costs and overlooked many obvious 

opportunities to lower costs, including factoring in capital cost reductions due to multiple 

identical units, use of common facilities, use of existing equipment, and consideration of other 

scrubbing options. If the scrubbers were designed to minimize capital costs, the general design 

would be different and the cost of materials and labor much less. These types of issues are 

explicitly recognized in one of the vendor proposals that Sargent & Lundy relied on. 

a. Multiple Unit Discounts

 The vendor costs relied on by Sargent & Lundy were estimated on a per generating unit 

basis and doubled for the plant. However, considerable savings could be achieved in capital 

 

83 12/28/09 S&L FollowUp Report, Attach. B. 

84 Cost Manual, pdf 47. 
23

costs and labor by designing and constructing both units in one fell swoop. This section 

discusses multiple unit discounts and the next section discusses combined facilities. 

 The six units consist of nearly identical pulverized coal fired boilers installed around the 

same time that burn similar low sulfur PRB coal, as summarized in Table 4. 

Table 4 

Oklahoma BART-Eligible 

Coal-Fired Electric Generating Units 

Unit Install 

Date 

Capacity 

MWg 

Firing 

Rate 

(MMBtu/hr) 

Firing 

Mode 

Baseline 

Coal S 

(lb/MMBtu) 

Muskogee 4 1978 572 5480 Tang 0.51 

Muskogee 5 1979 572 5480 Tang 0.51 

Sooner 1 1979 570 5116 Tang 0.51 

Sooner 2 1980 570 5116 Tang 0.51 

Northeastern 3 1979 490 4775 Tang 0.91 

Northeastern 4 1980 490 4775 Tang 0.91 

 There are many economies involved in designing, procuring, and installing all four 

identical OG&E units from the same vendor. The scrubbing systems installed on the four OG&E 

units would be identical. Thus, only one system must be designed, which could be replicated for 

the other three, reducing engineering and shop setup time. Thus, vendors typically offer 

discounts for awards of multiples of the same system. 

 I cannot precisely estimate the reduction in cost from taking into account multiple units 

because Sargent & Lundy did not supply the required information. However, based on my 

experience, vendors would offer at least a 5% discount on equipment cost for multiple identical 

units. This reduction is included in Appendix 1, Line 24. 

b. Common Facilities

 Capital and labor costs also could be reduced by combining processing units, e.g., a 

single lime preparation system, a single ash handling system, a single fabric filter. Installing two 

separate units essentially doubles the balance of plant labor and increases capital costs. Common 

facilities should have been designed and costed. 

 I cannot precisely estimate the reduction in cost from taking into account common 

facility issues because Sargent & Lundy did not supply the required information. Optimizing the 

design to use single systems serving Sooner and Muskogee instead of doubling the design of a 

single unit would reduce equipment, material, and labor costs by about 5% compared to the 

current design. This reduction is included in Appendix 1, Line 25. 

c. Cost Capacity

 The per unit cost of a given piece of equipment drops as the capacity increases. This 

relationship, known as the "six-tenth-factor rule," results in lower capital costs for larger pieces 
24

of equipment. See Section III.A.2. The company's cost estimates were based on per unit costs 

and doubled for each plant. This overestimates costs by basing them on unit costs for smaller 

equipment. For example, one large compressor would be cheaper than two smaller ones with the 

same output. The reduction in costs for this factor is included in the vendor discount adjustment 

in Appendix 1, Line 24. 

d. Other Cost Options 

 There are more economical options for designing and integrating the scrubbers into the 

plant that were not explored. These include using the existing electrostatic precipitators instead 

of installing new baghouses and using a one-pass scrubber rather than the more expensive 

recycle system. The reductions in cost due to the use of these options are not included in my 

revised cost analyses as there is inadequate basis. 

 

 i. Recycle Scrubber

 In dry scrubbing, flue gases enter a spray chamber or absorber where a reagent is sprayed 

into the gas stream and reacts with SO2. An electrostatic precipitator or fabric filter baghouse 

typically follows the spray chamber to collect the reagent, flyash, and reaction products. Lime is 

the primary reagent used in these systems and is proposed here. Dry scrubbing systems that use 

only lime as the reagent are known as "single pass systems." Some of the lime does not react 

with SO2 after a single pass through the spray chamber. This unreacted lime and other high 

alkalinity solids from burning low sulfur PRB coal are collected in a dust collection device and 

can be recycled to the spray chamber to reduce lime demand. In a recycle system, these solids 

are collected, mixed with water, and reinjected into the spray chamber. This type of dry scrubber 

is referred to as a "recycle system."85 The dry scrubbers that were evaluated for Sooner and 

Muskogee are recycle systems. 

 Recycle systems generally have higher capital costs and are more expensive to operate 

than single pass systems, due to the extra equipment required to recycle the solids and more 

power and water required to operate the recycle system. The capital cost of the recycle system is 

high, amounting to about 6-10% of the purchased equipment costs. Springerville Unit 3 (454 

MW), which burns a similar low sulfur PRB coal, found that O&M costs with recycle operations 

are greater than the additional lime and disposal costs without recycle. The Springerville dry 

scrubber was designed to achieve SO2 removal efficiencies as high as 93.3% without the use of 

recycle.86 Other vendors have noted the same in BART analyses.87 A single pass option was not 

evaluated for Sooner or Muskogee and should be. 

 

85 Babcock & Wilcox, Steam Its Generation and Use, 2005, pp. 35-12 to 35-18. 

86 Phil Rader and Caio Botelho, FGD Technologies Achieving SO2 Compliance at the Lowest Lifecycle Cost, 

Power-Gen Latin America 2003, November 11-13, 2003, p. 5. 

87 Colorado Air Pollution Control Division, Best Available Retrofit Technology (BART) Analysis of Control 

Options for Colorado Springs Utilities -- Drake Plant, ("The March 2007 submittal also incorporates budgetary 

quotes from vendors for the major pieces of equipment as well as noting the need for a non-recycling LSD due to the 

ash removal system's operation at a very high capacity factor. As depicted in Figure 3, a non-recycling LSD would 

eliminate slurry solids; instead the FGD solids (removed in the baghouse) are immediately disposed.") 
25

 ii. Electrostatic Precipitator (ESP)

 As noted above, a dry scrubbing system consists of a spray chamber followed by a dust 

collection device. These two devices together are referred to as a dry scrubber or dry flue gas 

desulfurization unit (DFGD). The costs for the Sooner and Muskogee dry scrubbers include a 

new fabric filter baghouse at each unit. This baghouse accounts for about one-third of the 

purchased equipment cost.88 However, all of the BART-subject, coal-fired units are currently 

equipped with electrostatic precipitators, a dust collection device that could be used instead of 

the new baghouse. The costs for a new baghouse, when an acceptable dust control device is 

already present, could make a significant difference in an incremental cost comparison with a 

wet scrubber, which does not require a downstream baghouse. 

 Existing electrostatic precipitators are strong candidates for dust collection in retrofit 

applications. While extra collecting surface may be needed in some cases, they generally do not 

require any upgrade as the resistivity of the dust is drastically reduced during dry scrubbing, 

improving collection efficiency.89 The only extra cost would be duct work to tie in the spray 

chamber. The plot plans in the S&L Followup Report suggest no retrofit issues for this option at 

Sooner or Muskogee. No plot plans were provided for Northeastern. The cost to use the existing 

electrostatic precipitators to remove particulate would likely be much less than the cost of new 

baghouses. This option was not considered but should have been.90 

 This option was evaluated for Naughton Units 1 and 2 in Wyoming. The results are 

summarized in Table 5, extracted from Wyoming's BART analysis. This table shows that the 

use of the existing electrostatic precipitator, instead of adding a new fabric filter baghouse, 

reduces the cost effectiveness by $734/ton and that the cost effectiveness of dry and wet 

scrubbing are the same when a new baghouse is not included in the cost of the dry scrubber. A 

wet scrubber was selected as BART for Naughton 1 using the existing ESP. 

Table 5 

Naughton BART Analysis91

 

88 12/28/09 S&L Followup Report, Attach. 5, Summary of DFGD Vendor Quotations, of Attach. A. 

89 Stefan Ahman, Charles B. Barranger, and Peter G. Maurin, Alstom Power's Flash Dryer Absorber For Flue Gas 

Desulfurization, Proceedings of 2002 International Joint Power Generation Conference, June 24-26, 2002. 

90 See 12/4/09 USFWS Comments, Attachment, pp. 3, 6. 

91 Wyoming Department of Environmental Quality, BART Application Analysis, May 28, 2009, Table 20. 
26

 The BART determination must consider "any pollution control equipment in use or in 

existence at the source..."92 The EPA explained in its BART Guidelines that "it is important to 

include control options that involve improvements to existing controls and not to limit the 

control options only to those measures that involve a complete replacement of control devices."93 

 iii. Fabric Filter Baghouse

 The baghouse was designed to achieve a filterable particulate emission rate of 0.010 

lb/MMBtu. However, the targeted BART filterable emission limit is 0.10 lb/MMBtu, or ten 

times higher, met with the existing electrostatic precipitators. Thus, the baghouse included in 

the OG&E dry scrubber costs is over designed for its function in the dry scrubber. Baghouse 

removal efficiency increases in direct proportion to gas filtration velocity and bag cleaning 

method.94 Thus, a baghouse designed to remove ten times more particulate than required to 

support the scrubber is significantly more expensive than a baghouse designed to meet the BART 

limit. I have not modified the baghouse cost due to inadequate information in the documents I 

reviewed. This issue should be addressed in future work. 

 

92 OK Regional Haze SIP, p. 75; 70 FR 39157 (July 6, 2005) ("In identifying a level of control as BART, States are 

required by section 169A(g) of the CAA to consider:...(c) Any existing pollution control technology in use at the 

source..."; CAA Section 169A(g) ("States must identify the best system of continuous emission control technology 

for each source subject to BART taking into account the technology available, the costs of compliance, the energy 

and non-air quality environmental impacts of compliance, any pollution control equipment in use at the source...") 

93 70 FR 39164 (July 6, 2005). 

94 Mario G. Cora and Yung-Tse Hung, Controlling Industrial Particulate Emissions: A Practical Overview of 

Baghouse Technology, Environmental Quality Management, Summer 2002, pp. 53 - 64; Kenneth E. Noll, 

Fundamentals of Air Quality Systems: Design of Air Pollution Control Devices, American Academy of 

Environmental Engineers, 1999, Chapter 6: Fabric Filters. 
27

4. Operating and Maintenance Costs (O&M) 

 Operating and maintenance (O&M) costs comprise the sum of variable O&M, fixed 

O&M, and indirect O&M. The Sargent & Lundy O&M cost estimates require adjustment of 

both variable and indirect O&M costs. 

a. Variable O&M Costs

 Variable O&M costs are those that vary in proportion to the amount of SO2 that is 

removed. The Sargent & Lundy variable O&M costs were estimated from material mass 

balances in Attachment 1 to the Basis Document.95 The Basis Document contains mass balances 

for two cases: (1) reducing SO2 from 1.176 lb/MMBtu to 0.10 lb/MMBtu (91% SO2 control or 

Option 1) and (2) reducing SO2 from 0.509 lb/MMBtu to 0.100 lb/MMBtu (80% SO2 control or 

Option 2). 

 The Sargent & Lundy variable O&M costs and emission reductions were based on 

Option 2, the lowest performance option, while the scrubber sizing and capital costs were based 

on Option 1, the highest performance option. This mismatch error results in underestimating 

emission reductions and fixed O&M costs and overestimating capital costs. The net effect of 

correcting these errors is to significantly decrease the cost per ton of SO2 removed, improving 

the cost effectiveness of the dry scrubber. The Sargent & Lundy variable O&M costs also 

contain other errors, which cancel some of the O&M cost underestimate. Each component of 

variable O&M cost that I adjusted to address these issues is discussed below and shown in blue 

highlighting in Appendix 1. 

 i. Lime Cost

 Sargent & Lundy estimated lime cost based on lime feed rates (stream 1) from the 

"average sulfur case" mass balance ("80% SO2 control case" or Option 2) and delivered lime 

costs based on budgetary quotations.96 There are two problems with this estimate. 

 First, Sargent & Lundy estimated the amount of lime that would be required to operate a 

scrubber designed to remove 80% of the SO2 from 0.51 lb/MMBtu coal (80% control case) 

while the capital costs were based on a scrubber designed to remove 91% of the SO2 from a 1.18 

lb/MMBtu coal (91% control case). As a result, Sargent & Lundy underestimated the amount of 

lime that would be required to operate the scrubbers that were costed. 

 Second, Sargent & Lundy overestimated the amount of lime required for the 80% SO2 

case that was costed. It estimated that 2.0 ton/hr of lime would be required at Sooner and 2.1 

ton/hr at Muskogee, based on 90% capacity. This corresponds to an unrealistically high 

stoichiometric ratio. For a dry scrubber, the amount of lime required to remove a specific 

amount of SO2 is expressed as the ratio of moles of calcium in the lime to moles of sulfur at the 

 

95 12/28/09 S&L FollowUp Report, Attach. A, Dry FGD Design and Cost Estimate Basis Document, Attach. 1, 

Mass Balance Diagrams for Sooner Station and Muskogee Station. 

96 2/8/10 Memo from Snell and Wroble to Benham, Q10; 12/28/09 S&L FollowUp Report, p. 9 (pdf 11). 
28

inlet to the scrubber. The rule of thumb for this ratio is 1.2 for a retrofit dry scrubber burning 

low sulfur Powder River Basin coal, as here. Sargent & Lundy used this value to estimate lime 

demand in a study of scrubber technology for the National Lime Association.97 However, in the 

mass balances for Sooner and Muskogee, Sargent & Lundy used a lime stoichiometric ratio of 

1.5.98 

 The net effect of these two items is an underestimate in lime costs. Sargent & Lundy 

estimated that each Sooner unit would require 2.0 ton/hr of lime and each Muskogee unit would 

require 2.1 ton/hr of lime for the 80% SO2 control case (Option 2).99 The design basis for the 

91% control case is 4.45 ton/hr at each Sooner unit and 4.77 ton/yr at each Muskogee unit. As 

the 91% control case (Option 1) is the basis of the cost analysis, it should be used to estimate 

lime costs. 

 I revised the lime cost using the Option 1 lime feed rates. This increases the lime costs at 

each Sooner unit from $1,826,900 to $4,167,955/yr100 or by $4,682,111/yr for the total plant. It 

increases the lime costs at Muskogee from $1,730,600/yr to $3,968,632/yr101 or by 4,476,064/yr. 

The resulting increase in lime cost is added to my revised cost estimate in Appendix 1, Line 51. 

 ii. Water Cost

 The Sargent & Lundy cost estimates for water use are based on the total water demand 

(stream 13) 102 for the 80% SO2 control option (Option 2) and the cost of water per 1,000 gallons 

as estimated by OG&E.103 These costs assume that the Sooner scrubber would require 410 gpm 

and the Muskogee scrubber would require 439 gpm.104 This works out to 5.3 gallons of water 

for every MMBtu of heat input (5.3 gal/MMBtu).105 This is beyond the upper end of the range of 

 

97 Sargent & Lundy, Flue Gas Desulfurization Technology Evaluation: Dry Lime vs. Wet Limestone FGD, March 

2007 ("3/07 S&L National Lime Association Report"), Table 5.1-1. 

98 Sargent & Lundy stoichiometric ratio (based on Sooner): moles/hr of S at scrubber inlet = [(2,605 lb SO2/hr)(32 

lb S/mol/64 lb SO2/mol)]/32 lb S/mol = 40.7 mol S/hr; moles/hr of Ca in lime = (3,511 lb CaO/hr)(40 lb Ca/mol/56 

lb CaO/mol)]/40 lb Ca/mol = 62.7 mol Ca/hr. Stoichiometric ratio = 62.7/40/7 = 1.54 mol Ca/mol S. Calculations 

based on SO2 and CaO as reported in 12/28/09 S&L FollowUp Report, pdf 77 and used in fixed O&M calculations 

at pdf 101. 

99 12/28/09 S&L FollowUp Report, pdf 76-79. 

100 Revised Sooner per unit lime costs: (4.45 ton/hr)($118.8/ton)(8,760 hr/yr)(0.9) = $4,167,955/yr. 

101 Revised Muskogee per unit lime costs: (4.77 ton/hr)($105.53/ton)(8,760 hr/yr)(0.9) = $3,968,632/yr. 

102 Sum of slaker water, recycle water, and pin mixer water. 

103 12/28/09 S&L FollowUp Report, p. 9 (pdf 11) and pdf 101-104. The Muskogee water cost includes the cost of 

purchasing water, while Sooner has no water purchase costs. 

104 Compare 12/28/10 S&L FollowUp Report, pdf 77 (410 gpm) & 79 (439 gpm) with the gpm backcalculated from 

the water costs for Muskogee at pdf 101-102 ($534,100/yr/[($2.57/1000 gal)(8760 hr/yr)(60 min/hr)(0.9)] = 439 

gpm) and for Sooner at pdf 103-104 ($95,100/yr/[($0.49/1000 gal)(8760 hr/hr)(60 min/hr)(0.9)] = 410 gpm. 

105 Sooner unit water use = (410 gal/min)(60 min/hr)/(5116 MMBtu/hr)(0.9) = 5.34 gal/MMBtu. 
29

3.6 to 4.4 gal/MMBtu for other similar dry scrubbers.106 The vendor design was based on an 

average makeup demand within the usual reported range for other similar scrubbers. 

 Using the vendor water demand decreases the per unit cost of water at Sooner from 

$95,100/yr to $85,299/yr or by $9,801/yr. At Muskogee, this adjustment decreases the per unit 

annual cost of water from $534,100/yr107 to $443,735/yr108 or by $90,365$/yr. The decrease in 

water cost is added to my revised cost estimate in Appendix 1, Line 52. The accuracy of the unit 

water cost cannot be evaluated as the basis is not stated in the documents that I have reviewed. 

 iii. FGD Waste Disposal Cost

 The Sargent & Lundy cost estimates for FGD waste disposal are based on solids to 

landfill (stream 12) for the 80% SO2 control option and the unit cost to landfill the solids.109 

These costs assume that the Sooner scrubber would generate 11,105 lb/hr and the Muskogee 

scrubber would generate 11,839 lb/hr of FGD solids for landfilling.110 There are three problems 

with this estimate. 

 First, the amount of solids produced by the scrubber is underestimated as it is based on 

the 80% SO2 control case, rather than the case that was costed. The 91% SO2 control case 

(Option 1) indicates that 26,283 lb/hr would be generated at Sooner and 28,153 lb/hr at 

Muskogee, or about 13.6 ton/hr per unit. 

 Using Sargent & Lundy's unit cost to landfill and the 91% SO2 control case, the per unit 

cost of landfilling FGD wastes at Sooner increases from $1,733,500/yr to $4,102,860/yr111 or by 

$2,369,360/yr. At Muskogee, this adjustment increases the per unit cost from $1,849,300/yr to 

$4,504,643/yr112 or by $2,655,343/yr. The increase in landfill cost is added to my revised cost 

estimate in Appendix 1, Line 53. 

 

106 E-mail from Michael Meadows, Black & Veatch, January 30, 2007; Babcock & Wilcox, Steam, Its Generation 

and Use, 2005, p. 35-17, Table 8 (total water demand = 48+269 = 317 gpm for a 500 MW net unit which would 

have a heat rate of about 4,800 MMBtu/hr. Thus, unit water demand (317 gpm)(60 min/hr)/(4,800) MMBtu/hr)(0.9) 

= 4.4 gal/MMBtu). 

107 12/28/10 S&L FollowUp Report, pdf 101-102. 

108 Revised Sooner per unit water cost: (368 gpm)(60 min/hr)(8,760 hr/yr)(0.9)($0.49/1000 gal) = $85,299/yr. 

Revised Muskogee per unit water cost: (365 gpm)(60 min/hr)(8,760 hr/yr)(0.9)($2.57/1000 gal) = $443,735/yr. 

109 12/28/09 S&L FollowUp Report, p. 9 (pdf 11) and pdf 101-104. 

110 Compare 12/28/09 S&L FollowUp Report, pdf 77 (11,105 lb/hr) & 79 (11,839) with the lb/hr of landfilled solids 

backcalculated from the FGD waste disposal costs for Muskogee at pdf 101-102 (($1,894,300/yr)/($40.59/ton)[2000 

lb/ton/(8,760 hr/yr)(0.9)] = 11,839 lb/hr) and for Sooner at pdf 103-104 ($1,733,500)/($39.60/ton)[2000 lb/ton/8,760 

hr/yr)(0.9)] = 11,105 lb/hr). 

111 Revised Sooner FGD Waste Disposal Cost: [(26,283 lb/hr)/(2000 lb/ton)](8760 hr/yr)(0.9)($39.60/ton) = 

$4,102,860/yr. 

112 Revised Muskogee FGD Waste Disposal Cost: [(28,153 lb/hr)/(2000 lb/ton)](8760 hr/yr)(0.9)($40.59/ton) = 

$4,504,643/yr.
30

 Second, the unit cost to dispose of the FGD solids is high compared to other similar 

BART cost analyses. The unit disposal cost for Sooner is $39.60/ton and for Muskogee 

$40.59/ton for on-site disposal.113 Other similar BART cost analyses, as summarized by Sargent 

& Lundy, show a range from $5/ton to $24.33/ton.114 The basis for Sargent & Lundy's high 

estimate for Sooner and Muskogee is not provided. I did not adjust the unit disposal cost as no 

information was provided about the on-site landfilling operations. These unit costs should be 

critically evaluated in future work. 

 Third, the estimate assumes that 100% of the FGD solids will be disposed in a landfill. 

Dry FGD solids are currently being used in agriculture, concrete, concrete products, flowable 

fill, mining applications, soil stabilization, structural fills and embankments, and synthetic 

aggregate.115 Selling the FGD solids would reduce or eliminate the cost of landfilling. A market 

study should be conducted to explore this option. 

 iv. Bag and Cage Replacement Cost

 Sargent & Lundy estimated the cost to replace fabric filter bags and cages at Sooner and 

Muskogee to be about $700,000/yr per unit.116 The estimates assumed an air-to-cloth ratio of 

3.5, a dollars per square foot replacement cost, a 3-year bag life, an unspecified exhaust gas 

flowrate, and a 4% contingency for bag cleaning. I was unable to reproduce Sargent & Lundy's 

costs with the reported information as it did not include the flowrate and the basis of the 

contingency (i.e., 4% of what?). 

 However, I used the reported information to make an independent calculation. I used the 

same air-to-cloth ratio and replacement part unit cost, the design SDA outlet flowrate for the 

91% SO2 control case (Option 2), and eliminated the contingency. The contingency is not 

warranted as the cost of bag cleaning is an auxiliary power cost, accounted for elsewhere. 

Further, such contingencies are not consistent with the Cost Manual approach, as explained 

elsewhere. I annualized the cost of replacement parts (bags, cages) using the capital recovery 

approach, assuming a social interest rate of 7% and a 3 year bag life.117 

 For Sooner, my costing approach decreases the per unit replacement cost from 

$653,300/yr to $544,026/yr118 or by $109,274/yr. For Muskogee, my costing approach 

decreases the per unit replacement cost from $675,100/yr to $599,020/yr119 or by $76,080/yr. 

 

113 12/28/09 S&L FollowUp Report, Attach. B, pdf 102-104. 

114 12/28/09 S&L FollowUp Report, Attach. C, pdf 109 (Gerald Gentleman - $5/ton; White Bluff - $8.58/ton; 

Boardman - $10/ton; Naughton - $24.33/ton; Nebraska City - $9/ton). 

115 Electric Power Research Institute (EPRI), A Review of Literature Related to the Use of Spray Dryer Absorber 

Material: Production, Characterization, Utilization Applications, Barriers, and Recommendations, December 6, 

2006. 

116 12/28/10 S&L FollowUp Report, pdf 101-104. 

117 Capital recovery factor for replacement parts: [(0.07(1.07)3

]/[(1.073 - 1] = 0.3811. 

118 Revised Sooner replacement costs: [(1,724,051 ACFM)($3.22/ft2

)(0.9)(0.3811)/3.5 ACFM/ft2 = $544,026/yr. 

119 Revised Muskogee replacement costs: [(1,846,716 ACFM)($3.31/ft2

)(0.9)(0.3811)/3.5 ACFM/ft2 = $599,020/yr. 
31

The decrease in bag and cage replacement cost is included in my revised cost estimate in 

Appendix 1, Line 54. 

 v. Auxiliary Power Costs

 Auxiliary power is the sum of the demand by the scrubber, baghouse, and booster fans, 

the latter required to overcome the increase in backpressure from adding these controls. The 

Sargent & Lundy auxiliary power costs are overestimated for two reasons. First, the auxiliary 

power demand used to calculate O&M costs is much higher than usually assumed for dry 

scrubbers. Second, the unit cost of electricity is much higher than the cost to OG&E to produce 

electricity. 

 The auxiliary power requirements used in the Sargent & Lundy cost analyses are much 

higher than those: (1) derived from the stated assumptions (1% gross power for DFGD and 0.5% 

gross power for baghouse);120 (2) calculated in the Design Document from individual motors 

based on the 91% SO2 control case;121 (3) assumed by Sargent & Lundy in the National Lime 

Association Report for similar plants (1.2% gross output);122 and (4) estimated by the vendors. 

The various estimates of auxiliary power, assuming 90% capacity factor, are summarized in 

Table 6. 

Table 6 

Auxiliary Power Per Unit (MWh/yr) 

Estimate Sooner Muskogee Source 

S&L Cost Estimate 67,290123 67,644 12/28/09 S&L Followup 

Report, pdf 101-104 

S&L Cost Estimate Based 

on Stated Assumptions 

67,408124 67,645 12/28/09 S&L Followup 

Report, pdf 101-104 

S&L Design Document 58,342125 61,495 12/28/09 S&L Followup 

Report, pdf 84-87 

S&L National Lime Report 53,927126 54,116 3/7 S&L Report, 

 Table 5.2-2 

Average Vendors 59,134 57,372 Vendor quotes 

 

120 12/28/09 S&L FollowUp Report, pdf 101-104 (8.5 MW for Sooner and 8.6 MW for Muskogee). 

121 12/28/09 S&L FollowUp Report, pdf 84-87 (7.4 MW for Sooner and 7.8 MW for Muskogee). 

122 3/07 S&L National Lime Association Report, Table 5.2-2 (1.20% of gross plant output, which is 6.84 MW for 

Sooner and 6.86 MW for Muskogee). 

123 $5,640,900/yr/$83.83 MWh = 67,289.75 MWh/yr. 

124 1% gross for DFGD (570 MW)(0.01)(8760 hr/yr)(0.9) = 44,938.8 MWh/hr; 0.5% gross for baghouse (570 

MW)(0.005)(8760 hr/yr)(0.9) = 22,469.4 MWh/hr. Total auxiliary power = 44,938.8 + 22,469.4 = 67,408.2 

MWh/yr. 

125 Reported as 7.4 MW for Sooner. Thus, (7.4 MW)(8760 hr/yr)(0.9) = 58,341.6 MWh/hr. 

126 Reported as 1.2% of gross plant output. Thus, (570 MW)(0.012)(8760 hr/yr)(0.9) = 53,926.5 MWh/yr. 
32

As the vendor quotes were the basis of the capital cost analyses, the vendor estimate of auxiliary 

power should also be used to estimate O&M costs. The vendor estimate is also close to the 

middle of the range of reported values. 

 The cost of electricity used in Sargent & Lundy's cost estimate is also high compared to 

other estimates. They assumed $85.93/MWh for Sooner and $83.83/MWh for Muskogee. 

Auxiliary power is the power required to run the plant, or power not sold. Cost effectiveness 

analyses are based on the cost to the owner to produce the electricity, or the busbar cost, not 

market retail rates, which is apparently what was used. 

 Oklahoma Gas and Electric (OG&E) is a regulated utility and its electricity rates are 

posted on its website.127 The posted rates for energy charges for various types of off-peak uses 

are under $50/MWh, as low as $32/MWh for power & light uses in the winter and $36/MWh for 

public school summer offpeak and all winter demand. Thus, the busbar cost to produce 

electricity is under $50/MWh. Cost effectiveness analyses typically estimate auxiliary power at 

$50/kW or less. The Northeastern cost effectiveness analysis used $50/MWh. Sargent & Lundy 

assumed $45/MWh for Sooner and Muskogee in its initial 2008 cost analysis.128 Further, 

Sargent & Lundy's summary of auxiliary power costs in the FollowUp Report indicates the range 

for other similar facilities is $30/MWh to $50/Mwh.129 Thus, I estimated the cost of auxiliary 

power using $50/MWh, an upper bound consistent with Northeastern and other BART 

determinations. 

 Correcting these two items reduces the per unit auxiliary power cost at Sooner from 

$5,640,900/yr to $2,956,700/yr or by $2,684,200/yr. At Muskogee, it reduces the per unit 

auxiliary power cost from $5,812,000 to $2,868,600 or by $2,943,400/yr. These reductions are 

included in my cost analyses in Appendix 1, Line 55. 

b. Indirect Operating Costs

 Property taxes, insurance, and administrative charges are fixed annual charges, estimated 

as a percentage of certain other costs in the spreadsheets in the FollowUp Report, Attach. A. 

Sargent & Lundy estimated property taxes as 0.6% (Sooner) to 0.85% (Muscogee) of total 

capital investment and insurance as 0.0105% of total capital investment. As total capital 

investment was revised as discussed above, the factors that rely on them must also change. This 

is accommodated in my cost analysis in Appendix 1, Lines 56-58, by recalculating property taxes 

and insurance using Sargent & Lundy's factors and my revised subtotals. 

 

127 Oklahoma Rate Tariffs, http://www.oge.com/residential-customers/billing-and-payment/Pages/RateInfo.aspx. 

128 Sargent & Lundy and Trinity Consultants, Sooner Generating Station Best Available Retrofit Control 

Technology Evaluation, May 27, 2008, pp. A-11/12 and Sargent & Lundy and Trinity Consultants, Muskogee 

Generating Station Best Available Retrofit Control Technology Evaluation, May 28, 2008, pp. A-11/12. 

129 12/28/09 S&L FollowUp Report, Attach. C, pdf 109 (Gerald Gentleman - $45.65/MWh; White Bluff - 

$47/MWh; Boardman/Northeastern/Naughton - $50/MWh; Nebraska City - $30/MWh). 
33

5. OG&E Cost Summary 

 The Sargent & Lundy dry scrubber cost analyses relied on in Oklahoma's SO2 BART 

determination significantly overstate the cost of the proposed control. My revisions, summarized 

in Appendix 1, lower the calculated cost effectiveness for Sooner from $6,724/ton130 to 

$1,291/ton and for Muskogee from $7,435/ton to $1,317/ton. 

 

 My revised estimates are consistent with BART Guidance and estimates made by others. 

Sargent & Lundy, for example, calculated the cost of a retrofit dry scrubber very similar to the 

subject BART sources (500 MW, 5186 MMBtu/hr, 1.44 lb SO2/MMBtu) in a March 2007 report 

prepared for the National Lime Association. In this study, the calculated cost effectiveness of a 

500 MW retrofit dry scrubber on low sulfur PRB coal (1.44 lb/MMBtu) based on 95% SO2 

control in 2006 dollars was $1,320/ton.131 Adjusting to 2009 dollars, the cost effectiveness of a 

nearly identical retrofit calculated by the same engineering firm is $1,379/ton.132 This value 

contains cost adders (e.g., AFUDC) that are not allowed in an apples-to-apples cost effectiveness 

analysis prepared pursuant to the EPA Cost Manual. 

Capital costs are commonly expressed as dollars per kilowatt of capacity. The total direct 

capital costs, when reduced to dollars per kilowatt of capacity dropped from $534/kW to 

$265/kW. Sargent & Lundy's estimates of $513/kW for Sooner and $555/kW for Muskogee are 

outside of the range of actual costs for similar sized plants with no serious retrofit issues. The 

plot plans indicate no retrofit issues that would justify such high capital costs. The 12/28/09 S&L 

FollowUp Report notes: "In degree of difficulty, the retrofits at Sooner and Muskogee could be 

described as average."133 

The Utility Air Regulatory Group (UARG) presented a graphic of capital cost as a 

function of generating capacity in 2007 that summarizes a wide range of scrubber cost data 

(2004-2005), reported in year-end 2005 dollars and shown below in Figure 2.134 The costs in this 

figure for dry scrubbers are for lime-based spray dryer systems with fabric filter baghouses and 

represent the owner's best estimate for a ready-to-operate system accounting for all direct and 

indirect charges. These figures include the results of a detailed study by Sargent & Lundy of 

three midwestern systems (Systems A, B, and C) that included 24 dry scrubbers for generating 

capacities between 200 and 800 MW. 

 

130 The cost effectiveness estimated from the 12/28/09 S&L FollowUp Report is $6,747.50/ton. The value estimated 

in Appendix 1, using the input values from the 12/28/09 S&L FollowUp Report, Attachment A yield slightly 

different results, $6,724/ton. The difference is due to differences in rounding in the two spreadsheets. 

131 3/07 S&L National Lime Association Report, Table 7.3-4. 

132 Adjusting Sargent & Lundy's 2006 dollar estimate of the cost effectiveness of a retrofit dry scrubber on a 500 

MW PRB fired boiler to 2009 dollars using the Chemical Engineering Plant Cost Index (CEPCI) as reported in 

Chemical Engineering: (1,320)(521.9/499.6) = $1,379/ton. 

133 12/28/09 S&L FollowUp Report, p. 4, pdf 6. 

134 J. Edward Cichanowicz, Current Capital Cost and Cost Effectiveness of Power Plant Emissions Control 

Technologies, Prepared for Utility Air Regulatory Group, June 2007, Section 6, Flue Gas Desulfurization Costs, p. 

21 and Figure 6.1. 
34

 

Figure 2 

Wet and Dry Scrubber Unit Cost 

This figure shows that Sargent & Lundy's data, for Systems A, B and C, ranges from 

about $220/kW to $390/kW when adjusted to 2009 dollars, for units between 500 and 600 

MW.135 My revised estimates are at the lower end of this range. If just AFUDC were added 

back into my estimate, for an apples-to-apples comparison with the Sargent & Lundy data 

reported in the UARG study, my unit cost would increase from $265/kW to $362/kW, at the 

upper end of the range. The much higher values on Figure 2, >$350/kW to $500/kW, are for 

smaller units on sites with significant retrofit issues. Sargent & Lundy's capital cost estimates for 

Sooner and Muskogee correspond to those expected for small units, 200 MW to 400 MW on 

sites with significant retrofit issues, which are not present at either Sooner or Muskogee. 

IV. REVISED COST ANALYSIS FOR NORTHEASTERN DRY SCRUBBER 

 The Northeastern BART cost effectiveness analysis is contained in the 5/30/08 Trinity 

Report, Appendix F. It concluded the cost effectiveness of a dry scrubber at these units is 

 

135 The Sargent & Lundy plotted points range from about $200/kW to $350/kW. Conservatively assuming 2005 

dollars (when 2006 were reported by author) and using the CEPCI index (521.9/468.2), the 2009 range is $223/kW 

to $390/kW. The CEPCI index is recommended by the EPA (8/17/10 Fish & Wildlife Service Letter of New 

Mexico Environment Dept., p. 5, footnote 8). 
35

$3,266/ton. This analysis was slightly modified by the ODEQ, to $3,294/ton,136 but the record I 

reviewed contained no supporting calculations for their modification. Thus, my review is based 

on the 5/30/08 Trinity Report. The Trinity analysis significantly overestimates the dollars per 

ton cost to control SO2 with a dry scrubber for the reasons discussed below. The format of my 

Northeastern review differs from my Sooner and Muskogee review due to differences in the 

costing methodologies. 

A. CAPITAL COST 

 The total capital investment and annual capital cost are both overestimated, as explained 

below. 

1. Unit Capital Cost 

 The Northeastern analysis starts with an assumed unit capital cost, expressed in dollars 

per kilowatt of capacity ($/kW), that is not supported, but simply stated. It is reported as 

$555/kW in the Trinity Report137 and as $582/kW in the ODEQ BART analysis.138 As explained 

above, capital costs of this magnitude are outside of the range of actual costs for similar sized 

plants with no serious retrofit issues. These elevated values correspond to smaller units with 

significant retrofit issues. AEP, the owner of Northeastern, has publicly reported the capital cost 

of scrubber technology is about $262/kW.139 The information I reviewed does not identify any 

retrofit issues or unusual circumstances that would lead to such a high unit capital cost. 

 The unit capital cost for the two coal-fired Northeastern units should be about the same as 

for Sooner and Muskogee. These six units are similar-sized, tangentially-fired boilers that burn 

similar low sulfur PRB coal and were built about the same time (Table 4). They all use 

electrostatic precipitators for particulate control. Thus, absent a demonstration to the contrary, 

the unit capital cost for the Northeastern units should be about the same as for the Sooner and 

Muskogee units. 

 Thus, I revised the Trinity capital cost estimate using the average capital cost from my 

revised Sooner and Muskogee analysis in Appendix 1, Line 65. This value is $265/kW, without 

escalation, AFUDC, bond costs, and excessive contingency, which are not appropriate for BART 

cost effective analyses. The revised Northeastern capital cost is shown in Appendix 2, Line 12. 

2. Capital Recovery Factor 

 As explained above, a capital recovery factor, or CRF, is used to convert a present value 

(total capital investment) into a series of equal annual payments over a given time (service life) 

 

136 Oklahoma Department of Environmental Quality (ODEQ), BART Application Analysis, Northeastern Power 

Plant, January 19, 2010 (1/19/10 ODEQ Northeastern BART Report), Table 11. 

137 5/30/08 Trinity Report, Appx. F, pdf 155. 

1381/19/10 ODEQ Northeastern BART Report, Table 11. 

139 American Electric Power, 2008 Fact Book, 43rd EEI Financial Conference, Phoenix, AZ, p. 94, pdf 103. 
36

at a given interest rate (social interest rate). The Trinity analysis uses the correct capital recovery 

method, but uses incorrect inputs. 

 The capital recovery factor depends on the service life of the equipment and the public 

interest rate, 7%. The higher the interest rate and the shorter the service life, the higher the 

annual cost and the higher the dollars per ton cost effectiveness. The Trinity analysis 

overestimated annual costs by assuming a service life of only 20 years and an interest rate of 8%. 

These are incorrect, as previously explained in Section III.B.1.h. The revised Northeastern 

analysis in Appendix 2, Line 67, is based on capital recovery assuming a service life of 30 years 

and an interest rate of 7%. 

B. OPERATING COSTS 

 Operating and maintenance costs comprise the sum of variable O&M (utilities and 

replacement parts), fixed O&M (operating and maintenance labor), and indirect O&M 

(administrative/overhead). The Trinity analysis omitted some of the components of O&M and 

overestimated others. These errors and omissions are discussed below and corrected in 

Appendix 2. 

1. Water 

 A dry scrubber requires water to prepare the reagent slurry and to cool the flue gas 

stream. The cost to supply water was not included in the Trinity analysis. I added water cost, 

scaled from the water demand for Sooner and Muskogee. See Section III.B.4.a.ii. The average 

cost of water, based on Sargent & Lundy's tabulation of water cost for six similar facilities is 

$1.10 per 1,000 gallons.140 The water cost, calculated using these assumptions, is included in 

Appendix 2, Line 23. 

 

140 12/28/08 S&L FollowUp Report, pdf 109 (BART Cost Evaluation). 
37

2. FGD Waste Disposal 

 A dry scrubber generates about 5 pounds of solids per pound of SO2 removed.141 These 

solids may be reused, but most commonly, are disposed of at an on-site or off-site landfill. The 

capital cost estimates for Northeastern include costs for an on-site landfill. Sargent & Lundy 

summarized the O&M landfill disposal costs for eight similar facilities, including Sooner and 

Muskogee. The costs range from $5/ton at Gerald Gentleman to $40.59/ton at Muskogee.142 As 

the Northeastern costs include $25 million for an on-site landfill, disposal costs would be lower 

than off-site disposal and further can be optimized. Thus, they would be expected to fall at the 

lower end of the reported range, or $5/ton. Therefore, in Appendix 2, Line 24, I estimated the 

cost of on-site landfill disposal assuming that 5 pounds of solids would be generated for each 

pound of SO2 removed and that it would cost $5 per ton to dispose of them. 

3. Bag and Cage Replacement 

 The fabric filter baghouse, which follows the spray chamber, contains filter bags and 

supporting cages that are changed out every three years. These O&M costs were not included in 

the Northeastern cost analysis. These costs should be similar to those that I estimated for Sooner 

and Muskogee as the plants are similar. I estimated the cost of bag and cage replacement at 

Sooner to be $544,026/yr and at Muskogee to be $599,020/yr (Sec. III.B.4.a.iv). Thus, in 

Appendix 2, Line 25, I assume the cost of bag and cage replacement at Northeastern to be 

$572,000/yr. This should slightly overestimate the cost at Northeastern, as it has a smaller 

capacity. 

4. Fixed O&M (Labor) 

 The Trinity analysis did not include costs for the labor to operate and maintain the 

scrubber. These charges should be about the same as those calculated by Sargent & Lundy for 

Sooner and Muskogee, or an average of about $4 million per year. These costs are added to total 

direct costs in Appendix 2, Line 26. 

5. Insurance 

 Insurance for Northeastern was estimated as 1% of the total capital investment, based on 

the Cost Manual default. The Sargent & Lundy analysis for Sooner and Muskogee indicates that 

insurance rates for similar facilities are about a factor of ten lower, or 0.0105% of total capital 

investment.143 Thus, I revised the Northeastern insurance cost to use this value in Appendix 2, 

Line 32. 

 

141 12/28/09 S&L FollowUp Report, pdf 76-79 (mass balances for Sooner & Muskogee). 

142 12/28/09 S&L FollowUp Report, pdf 109 (BART Cost Evaluation). 

143 12/28/09 S&L FollowUp Report, p. 11, pdf 12. 
38

6. Property Taxes 

 Property tax for Northeastern was estimated as 1% of total capital investment, based on 

the Cost Manual default. The Sargent & Lundy analysis for Sooner and Muskogee indicates that 

property taxes in Oklahoma are lower, estimated as 0.60% of total capital investment at Sooner 

and 0.85% of total capital investment at Muskogee, based on actual rates.144 As the actual tax 

rate was not reported for Northeastern, my revised cost estimate assumes the upper end of the 

OG&E range or 0.85% of total capital investment. My revised estimate for Northeastern 

property tax is shown in Appendix 2, Line 33. 

C. EMISSION REDUCTIONS 

 The Northeastern cost effectiveness analysis for dry scrubbing is based on an 83% SO2 

control efficiency, resulting in emission reductions of 26,599 ton/yr.145 See Appendix 2, Lines 

45 and 59. However, BART requires the most stringent emission control level.146 As discussed 

elsewhere, a dry scrubber can achieve 95% SO2 control. 

 Further, the same mismatch problem discussed for Sooner and Muskogee exist here. The 

capital and O&M costs for Northeastern were scaled from capital costs and utility demands for 

the Sooner and Muskogee scrubbers, which were based on 91% SO2 control. Thus, I revised 

the SO2 control efficiency to 91% in Appendix 2, Line 59. This increases the SO2 emission 

reductions from 26,559 ton/yr to 29,119 ton/yr in Appendix 2, Line 45. 

D. NORTHEASTERN COST SUMMARY 

 The Northeastern cost analysis relied on in Oklahoma's SO2 BART determination 

overstates the cost of a dry scrubber. Trinity and ODEQ estimated the cost effectiveness of a dry 

scrubber at Northeastern to range from $3,266/ton (Trinity) to $3,294/ton (ODEQ). My revised 

analysis in Appendix 2, Line 47, indicates that the BART cost effectiveness, based on the 

Control Manual approach, is $1,544/ton. This value is consistent with a similar analysis 

prepared for AEP's White Bluff Units 1 and 2, which was submitted to EPA as part of the 

Arkansas SIP. This analysis reported a cost effectiveness value for a retrofit dry scrubber on 

similar units of $1,280/ton in 2006 dollars147 or $1,340/ton148 in 2009 dollars. 

V. COST EFFECTIVENESS: DRY SCRUBBER 

 

144 12/28/09 S&L FollowUp Report, p. 11, pdf 12. 

145 5/30/08 Trinity Northeastern BART Evaluation, Table 9-1 and Appx. F, BART Economic Analysis for DFGD, 

note 2. 

146 70 FR 39166 (July 6, 2005). 

147 ENSR Corporation, BART Analysis for the White Bluff Steam Electric Station, December 2006, Table 3-3. 

148 Adjusting 2006 dollars to 2009 dollars using the Chemical Engineering Plant Index: (521.9/499.6)(1,280) = 

$1,337/ton. 
39

 Cost effectiveness is measured in dollars per ton of pollutant removed, calculated 

according to the Cost Manual, as stipulated in the BART Guidelines. My revised cost 

effectiveness values are reported in Appendix 1, Line 63 for Sooner and Muskogee and in 

Appendix 2, Line 47 for Northeastern. A control technology is considered to be "cost effective" 

if it falls within a reasonable range of cost-effectiveness estimates. "A reasonable range would 

be a range that is consistent with the range of cost effectiveness values used in other similar 

permit decisions over a period of time."149 

 Dry scrubbers are being successfully applied to virtually every kind of stationary source 

worldwide, including many similar applications in the utility industry.150 Thus, dry scrubbing is 

clearly cost effective, barring an unusual, site specific condition. Without identifying any 

unusual condition, Oklahoma concluded that a dry scrubber is not cost-effective for SO2 control 

for any of the six coal-fired steam electric units reviewed, i.e., OG&E Sooner Units 1 and 2, 

OG&E Muskogee Units 4 and 5, and AEP Northeastern Units 3 and 4. Oklahoma reported costs 

far above the usual range for similar facilities, or $6,348/ton to $7,493/ton for Sooner and 

Muskogee and $3,294/ton for Northeastern.151 

 Oklahoma's conclusion, however, is based on flawed cost estimates from OG&E and 

AEP/PSO. As discussed above, I revised the company cost analyses to match up the design basis 

and SO2 reductions, conform the analysis to the Cost Manual, eliminate double counting, 

consider economies of scale, and adjust miscellaneous errors. My revised costs are compared 

with the companies' analyses in Appendixes 1 and 2. My revisions lower the cost effectiveness 

for Sooner from $6,747/ton to $1,291/ton, for Muskogee from $7,435/ton to $1,317/ton, and for 

Northeastern from $3,294/ton to $1,544/ton. These revisions also lowered the unit capital cost 

per kilowatt of capacity. 

 

 Table 7 summarizes BART cost effectiveness values for dry scrubbers on other similar 

facilities burning low sulfur Powder River Basin coal. Most of these determinations, prepared by 

engineering firms to estimate actual costs rather than regulatory costs, incorrectly contain 

escalation, contingency, and AFUDC deviations from the Cost Manual, as discussed above. In 

some cases, the Cost Manual methodology was not mandatory as the plant capacity was under 

750 MW. 

 Thus, to make an apples-to-apples comparison with my revised numbers, I added back in 

AFUDC, escalation, and contingency to my estimates to level the playing field. These revised 

dollars per ton values for Sooner ($1,670/ton), Muskogee ($1,738/ton), and Northeastern 

($2,232/ton) fall within the range of values found to be cost effective in other similar BART 

permit decisions, as summarized in Table 7. My calculated cost effectiveness values for Sooner 

($1,291/ton), Muskogee ($1,317/ton), and Northeastern ($1,544/ton), excluding AFUDC, 

 

149 70 FR 39168 (July 6, 2005). 

150 Electric Power Research Institute (EPRI), A Review of Literature Related to the Use of Spray Dryer Absorber 

Material: Production, Characterization, Utilization Applications, Barriers, and Recommendations, December 6, 

2006, Table 1-2. 

151 OK Regional Haze SIP, pp. 81-82, Table VI-15. 
40

escalation and contingency, fall at the lower end of the range. Thus, a dry scrubber is cost 

effective for Sooner, Muskogee, and Northeastern. 

The unadjusted company's and ODEQ's estimates of $3,266/ton to $3,294/ton for 

Northeastern are also cost effective as they fall within the range of reported values, within the 

limit of accuracy152 of the cost estimate (+/-30%).153 They are also well within the range of 

values that have been found to be cost effective in numerous BART determinations, as 

summarized by the National Park Service. The owner of Northeastern, American Electric 

Power, stated in a financial conference that it planned to install scrubbers at Northeastern, 

projected to be in service by 2012-2014.154 

VI. BART EMISSION LIMIT: DRY SCRUBBER 

 The Oklahoma Regional Haze SIP concluded that BART for SO2 for Sooner, Muskogee, 

and Northeastern is a 30-day rolling average emission rate (at the stack) of 0.65 lb/MMBtu and a 

12-month rolling average of 0.55 lb/MMBtu, achieved using low sulfur coal.155 These emission 

limits are higher than the presumptive BART limits and higher than achievable with current coal. 

A dry scrubber was eliminated based on cost effectiveness. The underlying analysis is not valid 

as costs were significantly overestimated, as explained above. This section discusses what the 

BART emission limit should be for a dry scrubber, given that it is cost effective. As explained 

below, a wet scrubber could achieve a lower emission limit and is cost effective and otherwise 

feasible. 

 

152 Cost estimates that are within the accuracy of the estimate are considered to be indistinguishable when comparing 

costs. See NSR Manual at B.44. 

153 The OK Regional Haze SIP, Table VI-15, reports a range of $3,294/ton to $6,146/ton for a dry scrubber at 

Northeastern. However, the upper end of this range is incremental cost effectiveness for a wet scrubber, not average 

cost effectiveness for a dry scrubber. Incremental cost effectiveness is irrelevant for the dry scrubber as it is the 

technology against which other more efficient controls are compared. 

154 American Electric Power, 2008 Fact Book, 43rd EEI Financial Conference, Phoenix, AZ, pp. 91, 93. 

155 OK Regional Haze SIP, Tables VI-6, VI-7, and VI-10. 
41

Table 7 

Comparison of SO2 BART Cost Effectiveness Values 

for Dry Scrubbers & Low Sulfur Coal 

with Revised OG&E and AEP/PSO Values

Unit Rating 

(Gross)

(MW) 

Coal 

Sulfur 

(lb/MMBtu)

Control 

Efficiency

(%) 

BART 

(lb/MMBtu) 

Cost 

Effect.

($/ton)

White Bluff 1&2156 2x750 1.88 92 0.15 1,280 

Big Stone 1157 475 0.86 90 0.09 1,462 

Nebraska City 1158 650 0.815 88 0.10 1,636 

Boswell 3159 375 0.947 90 0.09 1,640

Sooner (Ex. 1) 2x570 1.18 91 0.10 1,670

Presumptive Muskogee160 650 0.02 1,694 

Muskogee (Ex. 1) 2x572 1.18 91 0.10 1,738

Naughton 1161 176 1.18 87 0.15 1,877 

Naughton 2162 234 1.18 87 0.15 1,882 

Stanton 1163 188 1.2 90 0.12 2,006 

Northeastern 3&4 2x470 0.91 83 0.15 2,232 

White Bluff 1 & 2164 2x850 0.68 92.5 0.15 2,430 

 

156 ENSR Corporation, BART Analysis for the White Bluff Steam Electric Station, December 2006, Table 3-3. 

157 South Dakota's Regional Haze State Implementation Plan, Draft, Sec. 6.3.2 and Appendix C, Burns & 

McDonnell, Otter Tail Power Company Big Stone Generating Station Unit 1, Best Available Retrofit Technology 

Analysis, November 2, 2009, Section 3.0. 

158 Nebraska Department of Environmental Quality, Fact Sheet, BART & PSD Analysis for the Omaha Public 

Power District Nebraska City Station Unit #1, February 26, 2009, pp. 4, 11, 15 and Response to Comments, 

February 26, 2009. 

159 Minnesota Pollution Control Agency, Regional Haze State Implementation Plan, December 2009, Appendix 9.2, 

Memorandum from Anne Jackson to File Re: BART Determination for Minnesota Power Boswell Unit 3, October 

26, 2009, Table 4 and Minnesota Pollution Control Agency, MPCA Review of Minnesota Power's Boswell 3 

Emission Reduction Plan, April 25, 2007. The costs are for a wet scrubber. 

160Technical Support Document for the Best Available Retrofit Technology (BART) Notice of Final Rulemaking, 

Setting BART SO2 Limits for Electric Generating Units: Control Technology and Cost-Effectiveness, April 2005 

("TSD"), Appx. D. 

161 Wyoming Department of Environmental Quality, BART Application Analysis, May 28, 2009, Table 20. A wet 

scrubber was selected as BART for Naughton 1 ($1,707/ton using existing ESP). The costs are for the dry scrubber 

option that was also analyzed. 

162 Wyoming Department of Environmental Quality, BART Application Analysis, May 28, 2009, Table 21. A wet 

scrubber was selected as BART for Naughton 2 ($1,700/ton, using existing ESP). The costs are for the dry scrubber 

option that was also analyzed. 

163 North Dakota Department of Health, A Plan for Implementing the Regional Haze Program Requirements of 

Section 308 of 40 CFR Part 51, Subpart P - Protection of Visibility, February 24, 2010, Appendix B.1, Department 

BART Determination for Stanton Unit 1, November 18, 2009 and Appendix C.3, Great River Energy, Stanton 

Station Unit 1, Best Available Retrofit Technology Analysis, Revised January 2008, Available at: 

http://www.ndhealth.gov/AQ/RegionalHaze/. 

164 Arkansas Regional Haze Rule State Implementation Plan, September 9, 2008, Table 9.3a, p. 52 and 

ENSR/AECOM, Revised BART Analysis for the White Bluff Steam Electric Station, August 2008, Table A-1. 
42

Drake 7165 142 0.83 85 0.13 2,544 

Gerald Gentleman166 2x750 0.75 80 0.15 2,726 

Drake 6167 85 0.82 85 0.13 2,816 

Boardman168 617 0.61 80 0.12 3,055 

Silver Bay 2169 75 0.60 90 0.06 3,547 

 

 The BART analysis must include the maximum level of control that each technology is 

capable of achieving.170 In analyzing a technology, "you take into account the most stringent 

emission control level that the technology is capable of achieving."171 This is important as "[a]n 

unrealistically low assessment of the emission reduction potential of a certain technology could 

result in inflated cost-effectiveness figures."172 This has happened here. 

 The BART analysis evaluated a control efficiency of less than 80% for the dry scrubber 

at Sooner and Muskogee and at 83% at Northeastern. However, 95% SO2 control is achievable 

with a dry scrubber. 173 This is not the most stringent emission control levels that a dry scrubber 

is capable of achieving. 

 

 In its report for the National Lime Association, Sargent & Lundy stated that vendors 

guarantee SO2 reduction efficiencies of up to 95% or as low as 0.06 lb/MMBtu SO2 for dry 

 

165 Colorado Regional Haze SIP, Chapter 6, pp. 70-71; Colorado Air Pollution Control Division, BART Analysis of 

Control Options for Colorado Springs Utilities -- Drake Plant. 

166 Sargent & Lundy, Revised Best Available Retrofit Technology (BART) Analysis for Units 1 & 2 Gerald 

Gentleman Station, Sutherland, Nebraska, February 2008, Tables 4 and 8 and Nebraska Department of 

Environmental Quality, Fact Sheet, BART & PSD Analysis for the Nebraska Public Power District Gerald 

Gentleman Station, May 11, 2010. 

167 Colorado Regional Haze SIP, Chapter 6, pp. 70-71; Colorado Air Pollution Control Division, BART Analysis of 

Control Options for Colorado Springs Utilities -- Drake Plant. 

168 Oregon Department of Environmental Quality, Oregon Regional Haze Plan for Implementing Section 308 

(40CFR 51.308) of the Regional Haze Rule, Adopted by the Environmental Quality Commission June 19, 2009, pp. 

149-158 and Appx. D.1, Table 15, pdf 82. 

169 Minnesota Pollution Control Agency, Regional Haze State Implementation Plan, December 2009, Appendix 9.2, 

BART Determination for Northshore Mining Bay Power Plant, October 5, 2009, Table 4. The dry scrubber was 

eliminated due to "substantial difficulties" in securing the necessary water discharge permits. BART was set at 0.48 

lb/MMBtu. 

170 70 FR 39164, note 12 (July 6, 2005). 

171 70 FR 39166 (July 6, 2005). 

172 70 FR 39168 (July 6, 2005). 

173 Sargent & Lundy, IPM Model - Revisions to Cost and Performance for APC Technologies, SDA FGD Cost 

Development Methodology, Final, August 2010, p. 1 ("A least squares curve fit of proprietary in-house cost data 

was defined as a "typical" SDA FGD retrofit for removal of 95% of the inlet sulfur."). Available at: 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v410/Appendix51B.pdf. 
43

scrubbers.174 This report included a chart showing the relationship between inlet SO2 and 

removal efficiency, reproduced in Figure 3.175 

Figure 3 

Relation Between Inlet SO2 and SO2 Removal Efficiency 

for a Dry Scrubber.

 This figure shows that a dry scrubber on the Sooner and Muskogee units can remove 

about 90% of the SO2 when burning current coal (0.51 lb/MMBtu), 91.5% when burning the 

proposed BART coal (0.65 lb/MMBtu), and 95% when burning the coal used to size the scrubber 

(1.18 lb/MMBtu). This figure also indicates that a dry scrubber on the Northeastern units can 

remove 93% of the SO2 when burning current coal (0.9 lb/MMBtu) and about 91.5% when 

burning the proposed BART coal. Higher removal efficiencies, up to 96%, have been 

demonstrated at a 600 MW PRB coal fired plant in the United States and 98% on a 350 MW 

coal-fired boiler in Denmark for an entire year. Thus, the BART requirement to evaluate the 

maximum degree of reduction has not been satisfied. 

 

 A dry scrubber designed to remove 95% of the SO2 from an inlet of 1.18 lb/MMBtu 

would yield an outlet of 0.06 lb/MMBtu based on a 30-day rolling average. The Sooner and 

Muskogee units can meet an SO2 emission limit of 0.06 lb/MMBtu by operating the dry scrubber 

at its design efficiency and selecting coal to assure the limit is met. Similarly, a dry scrubber 

designed to remove 93% of the SO2 from an inlet of 0.91 lb/MMBtu would yield an outlet of 

0.06 lb/MMBtu. 

 The control efficiency is constant at and near the design removal efficiency when burning 

the design coal, but can drop when inlet SO2 loading is significantly lower than design. 

However, emissions also drop as the scrubber inlet SO2 drops. For example, a scrubber 

designed to remove 95% of the SO2 from a 1.18 lb/MMBtu coal would routinely achieve 95% 

when burning this coal. However, if the sulfur content dropped to 0.51 lb/MMBtu or less, the 

removal efficiency would decline to about 90% yielding an outlet of 0.05 lb/MMBtu. Thus, a 

 

174 See also Sargent & Lundy, IPM Model - Revisions to Cost and Performance for APC Technologies, SDA FGD 

Cost Development Methodology, Final, August 2010, p. 1 ("It should be noted that the lowest available SO2 

emission guarantees, from the original equipment manufacturers of SDA FGD systems, are 0.06 lb/MMBtu."). 

175 Sargent & Lundy, Flue Gas Desulfurization Technology, Dry Lime vs. Wet Limestone FGD, Prepared for 

National Lime Association, March 2007, pp. 6, 14 (Table 4.1-1), 25 (Table 5.1-1), p. 41 (Table 7.2) 
44

dry scrubber at Sooner or Muskogee, designed as costed, could meet an SO2 emission limit of 

0.06 lb/MMBtu based on 30-day rolling average, when burning coal containing 0.51 to 1.18 

lb/MMBtu SO2. The same is true for Northeastern. The reduction in removal efficiency of the 

dry scrubber would be offset by the reduction of sulfur in the coal. The owner is responsible for 

selecting coals that assure the BART limit is satisfied. In other words, low inlet sulfur loading is 

not a valid excuse for failing to select the top control option. 

 Final permits have been issued for two plants that will fire low sulfur PRB coal and use a 

dry scrubber to control SO2 emissions to comparable levels that are built or being built. 

 The Nevada Bureau of Air Pollution Control, for example, issued a PSD Operating 

Permit to Construct to Newmont Nevada Energy Investment in May 2005 for a 200 MW PRBfired boiler equipped with a spray dryer absorber.176 The Permit contains a two-tiered SO2 

BACT limit as follows: 

• 0.09 lb/MMBtu on a 24-hour rolling average and 95% minimum SO2 removal 

efficiency, when combusting coal with a sulfur content equal to or greater than 

0.45% (30-day rolling period) 

• 0.065 lb/MMBtu on a 24-hour rolling average and 91% minimum SO2 removal 

efficiency, when combustion coal with a sulfur content less than 0.45% (30-day 

rolling period). 

The Newmont facility has been operating since May 2008. Figure 4 shows that it has 

consistently achieved an SO2 emission rate of less than 0.06 lb/MMBtu based on a 30-day 

rolling average. Clean Air Markets SO2 CEMS data indicates that the dry scrubber at the 

Hopewell Power Station is also achieving an emission limit of 0.06 lb/MMBtu, based on a 30-

day average. 

 

176 Nevada Bureau of Air Pollution Control, Class I Air Quality Operating Permit to Construct, No. AP4911-1349, 

Issued to Newmont Nevada Energy Investment, LLC. 
45

Figure 4 

(Newmont) TS Power Plant, Unit 1, SO2 Rate, 2008 - June 2010

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

1/1/20083/1/20085/1/20087/1/20089/1/2008

11/1/2008

1/1/20093/1/20095/1/20097/1/20099/1/2009

11/1/2009

1/1/20103/1/20105/1/20107/1/20109/1/2010

11/1/2010

Time

SO2 Rate (Lbs/mmBTU)

SO2_Rate_Hourly SO2_24HR_Avg SO2_30DAY_Avg SO2_365DAY_Avg

The Georgia Department of Natural Resources issued a final PSD permit for the proposed 

Longleaf plant in May 2007.177 The permit was appealed and the final permit was only recently 

confirmed. This plant consists of two 600-MW supercritical boilers that will be designed to fire 

either 100% PRB or a blend of PRB and low sulfur Appalachian coals. A spray dryer absorber 

will be used to control SO2. The SO2 BACT limits are tiered based on sulfur content as follows: 

 

 0.065 lb/MMBtu on a 30-day rolling average when the uncontrolled sulfur 

dioxide emission rate is less than or equal to 1 lb/MMBtu; 

 0.08 lb/MMBtu on a 30-day rolling average when the uncontrolled sulfur dioxide 

rate is greater than 1 lb/MMBtu but less than 1.25 lb/MMBtu; 

 0.105 lb/MMBtu on a 30-day rolling average when the uncontrolled sulfur 

dioxide emission rate is greater than 1.25 lb/MMBtu but less than 1.6 lb/MMBtu 

on a 30-day rolling average. 

 0.12 lb/MMBtu during any 24-hour average. 

 As the baseline coal sulfur content at all of the subject units is less than 1 lb/MMBtu SO2 

or 0.45% sulfur, the lowest reported emission limit, 0.065 lb/MMBtu based on both a 24-hour 

and a 30-day average, would be most applicable to the subject units. A 24-hour limit of 0.065 

lb/MMBtu corresponds to a much lower limit than a 30-day rolling average as the 30-day periods 

gives more opportunity to average out spikes. 

 

177 Georgia Environmental Protection Division, Longleaf Energy Station, Permit No. 4911-099-0033-P-01-0, April 

9, 2010. Available at: http://airpermit.dnr.state.ga.us/gaairpermits/PermitPDF.aspx?id=PDF-PI-18499. 
46

VII. WET SCRUBBING 

 There are two general classes of technologies that are widely used to control SO2

emissions: dry flue gas desulfurization (“DFGD” or “dry scrubbing”) and wet flue gas 

desulfurization (“WFGD” or “wet scrubbing”). The companies' and ODEQ's BART analyses 

eliminated wet scrubbing because the dollars per ton cost effectiveness was higher than for dry 

scrubbing; the incremental cost to go from dry to wet scrubbing was judged unacceptable; and 

wet scrubbing was alleged to have certain adverse impacts that dry scrubbing did not have. 

However, a viable and widely used control technology such as wet scrubbing cannot be 

eliminated simply because it costs more and has the same adverse impacts as all other wet 

scrubbers, none of which are unique or unusual. The BART analysis must identify the best 

available retrofit technology. These conclusions are incorrect for the reasons set out below. 

A. CONTROL EFFICIENCY 

 Cost effectiveness depends, in part, on the assumed SO2 control efficiency. Generally, 

cost effectiveness declines as control efficiency increases because emissions increase in direct 

proportion to control efficiency while cost increase much more gradually. The BART 

Guidelines note: "An unrealistically low assessment of the emission reduction potential of a 

certain technology could result in inflated cost-effectiveness figures."178 These Guidelines 

further require that "in analyzing the technology you take into account the most stringent 

emission control level that the technology is capable of achieving."179 The wet scrubber was 

assumed to remove only 95% of the SO2 in the BART analyses. 

 However, wet scrubbers on units burning low sulfur coal can achieve 97% - 98% removal 

efficiency or an outlet SO2 of 0.04 lb/MMBtu when burning low sulfur coals.180 The latest 

Integrated Planning Model (IPM), version 4.10, used by the U.S. EPA to estimate scrubber costs 

in various rulemakings, assumes 98% control of SO2 by wet scrubbers.181 Further, CEMS data 

for Pleasant Prairie Units 1 and 2 in Wisconsin show this level of performance has been 

demonstrated.182 These units are similar to Sooner, Muskogee, and Northeastern. They are 620 

MW pulverized coal fired boilers that burn similar low sulfur PRB coal (0.5 - 0.7 lb/MMBtu) 

that were placed into service in 1980 and 1985, respectively. They were retrofit with a wet 

scrubber in 2006 and 2007, respectively. 

 

 

178 70 FR 39168 (July 6, 2005). 

179 70 FR 39166 (July 6, 2005). 

180 3/07 S&L National Lime Association Report, p. 10 and Table 4.1-1; Sargent & Lundy, IPM Model - Revisions to 

Cost and Performance for APC Technologies, Wet FGD Cost Development Methodology, Final, August 2010, p. 1 

("It should be noted that the lowest available SO2 guarantees, from the original equipment manufacturers of wet 

FGD systems, are 0.04 lb/MMBtu."). 

181 U.S. EPA, Documentation for EPA Base Case v.4.10 Using the Integrated Planning Model, August 2010, p. 5-2. 

Available at: http://www.epa.gov/airmarkets/progsregs/epa-ipm/BaseCasev410.html#documentation. 

182 Inlet and Outlet NOx and SO2 CEMS Data, Pleasant Prairie Units 1 and 2, General Average Report, 1/1/07 – 

12/31/07, Wisconsin PSC Ref. #93460. 
47

 The average and incremental calculated cost effectiveness values would have been much 

lower if they had been based on 97% reduction of SO2 from the 1.176 lb/MMBtu coal at Sooner 

and Muskogee or from 0.90 lb/MMBtu coal at Northeastern. This level of control would yield 

an emission limit of 0.04 lb/MMBtu, which has been achieved at Pleasant Prairie (Fig. 4). 

B. COST EFFECTIVENESS OF WET SCRUBBING 

 The companies and ODEQ estimated the average and incremental cost effectiveness of 

both dry and wet scrubbing. In general, the average cost effectiveness of wet scrubbing is about 

10% higher than dry scrubbing. However, in some cases, wet scrubbing is more cost effective. 

These include cases in which a single large absorber is used for the wet scrubber183 while two 

smaller absorbers are required for the dry scrubber or in retrofit applications that have existing 

electrostatic precipitators. I would expect this to be true for Sooner, Muskogee, and 

Northeastern. However, the companies failed to evaluate these options. The relative magnitude 

of wet scrubbing compared to dry scrubbing is demonstrated in Table 8 which compares the 

average cost effectiveness of dry and wet scrubbing as calculated in 13 cost effectiveness 

analyses. 

Table 8 

Comparison of Average Cost Effectiveness 

for Dry and Wet Scrubbers 

($/ton) 

Plant Dry Wet Ratio

Sooner 1 6,348 7,413 1.17 

Sooner 2 7,147 8,788 1.23 

Muskogee 4 7,378 8,724 1.18 

Muskogee 5 7,493 7,278 0.97 

Northeastern 3,294 3,625 1.10 

3/7 S&L Report 1,320 1,230 0.93 

White Bluff 1,736 1,926 1.11 

Gerald Gentleman 2,726 2,724 1.00 

Boardman 3,055 3,933 1.29 

Naughton w/FF 1,882 1,700 0.90 

Naughton w/ESP 1,700 1,700 1.00 

Stanton 2,006 2,232 1.11 

Big Stone 1,462 1,699 1.16 

Average 1.09 

 Table 8 shows that the average calculated cost effectiveness of a wet scrubber is typically 

about 9% higher than for a dry scrubber, except in those cases where an existing ESP can 

 

183 The largest absorber module currently used for dry scrubbers is about 400 MW while single wet scrubber 

modules can exceed 1000 MW. Thus, for the subject units, two absorber modules would be required for dry 

scrubbing while only would be required for wet scrubbing. The cost per module decreases as the capacity increases. 
48

substitute for a new baghouse (see Naughton w/FF compared to Naughton w/ESP). I reviewed 

and revised the company's and ODEQ's dry scrubbing cost estimates in Appendixes 1 and 2. I 

did not revise the wet scrubbing cost estimates as wet scrubbing was eliminated early in Sargent 

& Lundy's analysis and thus detailed cost analyses were not prepared by the companies. 

However, the same sources of error are present in both sets of estimates, as discussed above. 

 Thus, I estimated the revised cost effectiveness for wet scrubbing based on the ratio of 

my revised value for dry scrubbing to the company estimate for dry scrubbing. This yields cost 

effectiveness values for wet scrubbing of $1,555/ton at Sooner;184 $1,417/ton at Muskogee,185

and $1,699/ton at Northeastern.186 While these values are about 9% higher than dry scrubbing, 

they are cost effective per se as they fall within the range of values found to be cost effective in 

other determinations. 

 

C. COLLATERAL EFFECTS OF WET SCRUBBING 

 The ODEQ determined that wet scrubbing was not BART for SO2 for any of the subject 

units, in part, based on several alleged adverse collateral impacts including: (1) increased sulfuric 

acid mist (SAM) in the flue gas; (2) excess particulate emitted due to location of scrubber 

downstream of particulate control device; (3) uses more reactant, generating more fugitive dust; 

(4) uses significantly more water; (5) generates a wastewater stream that must be treated; (6) 

creates higher visibility impairment due to lower exit velocity, lower stack temperature, and 

higher SAM emissions. These claimed adverse impacts are either wrong or overstated. Further, 

ODEQ failed to point out the benefits of wet FGD and the adverse collateral impacts associated 

with dry scrubbing.187 Some of these issues are discussed below. 

1. Sulfuric Acid Mist (SAM) 

 The ODEQ alleges that there will be more sulfuric acid mist in the flue gas from a wet 

scrubber than from a dry scrubber. This is a red herring. The PRB coals that these units burn 

have very low sulfur content and very high levels of alkalinity. This means that very little 

sulfuric acid mist will form and what does form is absorbed before it reaches the stack.188 

 

184 From Appendix 1 and OG&E 1/15/10 Sooner BART Analysis, the revised Sooner wet scrubbing cost 

effectiveness: (1,291/6,724)(8,101) = $1,555/ton. 

185 From Appendix 1 and OG&E 1/15/10 Muskogee BART Analysis, the revised Muskogee wet scrubbing cost 

effectiveness: (1,317/7,435)(8,001) = $1,417/ton.

186 From Appendix 2 and OG&E 1/19/10 Northeastern BART Analysis, the revised Northeastern wet scrubbing cost 

effectiveness: (1,544/3,294)(3,625) = $1,699/ton.

187 The adverse collateral impacts of dry scrubbing include: largest absorber module limited to 400 MW, requiring 

two smaller modules to treat flue gases from 570 MW to 572 MW units; uses more expensive lime reagent; reagent 

utilization is poorer compared to a wet limestone system; FGD solids have fewer uses; combined removal of flyash 

and byproducts precludes commercial sale of flyash; higher hazardous air pollutant (e.g., Hg) emissions; and higher 

short-term emissions of SO2, SAM, and particulates due to atomizer changeout. See 3/07 S&L National Lime 

Association Report, pp. 12-13. 

188 R.K. Srivastava, C.A. Miller, C. Erickson, and R. Jambhekar, Emissions of Sulfur Trioxide from Coal-Fired 

Power Plants, J. Air & Waste Management Association, v. 54, pp. 750-762 at p. 757; Electric Power Research 
49

Further, Sargent & Lundy note: "low-and medium-sulfur coals [which include the PRB coals 

used by OG&E and AEP/PSO]...typically produce insufficient SO3 to be a problem."189

 However, in fact, the opposite is true. Dry scrubbers create significant sulfuric acid mist 

when they are operated at maximum efficiency. In order to maximize the SO2 control from a 

dry scrubber, the scrubber exit temperature is decreased until it approaches the adiabatic 

saturation temperature.190 This increases the time for SO2 and lime to react. Id. However, this 

also increases sulfuric acid mist. Id. Therefore, as the SO2 control efficiency increases from a 

dry scrubber, SAM control decreases. A much higher SO2 control efficiency can be obtained 

with wet scrubbers without the risk of increased SAM emissions. 

 Further, as discussed below, a dry scrubber must be taken off-line periodically to change 

out the atomizer. During these changeout periods, no SAM control occurs at the offline dry 

scrubber. Thus, any SAM that did form, would not be removed. The wet scrubber, on the other 

hand, does not require these changeouts. In order to meet the SO2 limit, these very high 

changeout emissions must be averaged out by operating the dry scrubber at a higher SO2 control 

efficiency, up to 98%, for enough time to offset the short-term, uncontrolled SO2 spikes. These 

periods of higher control efficiency generate more SAM than present in wet scrubbers as they do 

not have this maintenance issue. If SAM were an issue due to these factors, a sorbent injection 

system would be required to control the emissions, increasing the cost of the dry scrubber 

relative to the wet scrubber. 

2. Water 

The ODEQ alleges that wet scrubbers use "significantly more" water than dry systems. 

This is not true. A wet scrubber does use more water than a dry scrubber in an absolute sense, 

roughly 50% more, primarily because it removes more SO2. However, about 90% of the wet 

scrubber demand can be recycled wastewater, compared to only 20% for the dry system.191 

Thus, a wet scrubber would decrease fresh water demand of the scrubber by about 80%. 

Further, freshwater (or new water) demand for a wet scrubber is a small fraction of the total plant 

water demand. Typically, about 90% of the freshwater water is used for wet cooling, 2% for wet 

scrubbing, and the balance for ash sluicing, boiler makeup, and other plant uses. When 

expressed on a gallon per ton SO2 removed basis, the total (reused plus fresh) water demand for 

the two systems is about the same, as the wet scrubber removes much more SO2. 

 Institute, Estimating Total Sulfuric Acid Emissions from Stationary Power Plants, 2010, p. 4-3, Table 4-1, and 

Figure 4-2. 

189 3/07 S&L National Lime Association Report, p. 19. 

190 Black & Veatch, Operational Limits on Semi-dry Absorbers, p. 1. 

191 1/30/07 Meadows E-mail, p. 2. ("There is an appreciable difference in the water quality demands of wet and 

semi-dry system. Wet systems need plant service quality water only for oxidation air humiliation water and vacuum 

filter seal water, which constitute about 10 percent of the total plant demand. The remainder can be cooling tower 

blowdown water, plant drain water, ash sluice water, etc. Semi-dry systems need portable quality water for lime 

slaking (about 15% of the total makeup demand) and plant service water for the remainder."). 
50

3. Wastewater 

The ODEQ alleges that the wet scrubber generates a wastewater stream that must be 

treated and discharged. This is not necessarily true. A purge stream is required for a wet 

scrubber to control the buildup of soluble chlorides. The purge stream from a wet scrubber is 

very small, typically only about 10-15 gpm in a 600 MW plant that produces saleable gypsum 

and half that amount if saleable gypsum is not produced. This wastewater can be reused 

elsewhere in the plant. 

There are several options for treating FGD wastewater: (1) eliminate the discharge using 

a zero liquid discharge system;192 (2) treat the wastewater with the power plant combined 

wastewater, thus not requiring a separate wastewater treatment system; (3) treat and discharge 

the wastewater separately (the only option mentioned); and (4) treat the wastewater and recycle 

within the plant.193 These other options were not evaluated. 

4. Excess Particulate Emissions 

The ODEQ alleges that total PM emissions will be higher with a wet scrubber as the 

absorber is located downstream from the baghouse. This is misleading. Gypsum fines and other 

particulate matter exiting a wet scrubber are removed by the mist eliminator at the top of the 

scrubber, which is an integral part of a wet scrubber. This mist eliminator is typically specified 

to require that outlet particulate loading is no higher than inlet particulate loading. Therefore, by 

design, the wet scrubber makes no net contribution to particulate emissions. 

5. Reactant 

The ODEQ alleges that wet scrubbers use more reactant (limestone) than do dry systems, 

thus generating more fugitive dust from limestone storage and handling. This is wrong. Dry 

scrubbers use lime, while wet scrubbers use limestone. The reagent utilization of a dry system is 

poorer compared to a wet system, i.e., it takes more tons of reagent to remove a ton of SO2 in a 

dry system than a wet system.194 Thus, the opposite is true. Regardless, the fugitive particulate 

matter emissions from reagent handling are de minimus. In fact, the operating costs of a dry 

scrubber are higher than those of a wet scrubber because the dry scrubber uses more costly lime 

and requires 20% more lime on a per mole basis than does the wet scrubber to accomplish 

comparable SO2 reductions. 

6. Plume Issues 

 The ODEQ alleges that a wet scrubber would result in higher visibility impairment due to 

reduced plume rise from a lower exit temperature. A wet FGD will result in a visible moisture 

 

192 Bryan D. Hansen, Burns & McDonnell, Minimizing FGD Costs, CoalGen 2006, Attachments, p. 7, FGD System 

Blowdown rate versus Unit size with Wyoming PRB Coal. 

193 Kuman Sinha, Colleen Layman, and Colleen Chapman, Bechtel Power Corp., Environmentally Acceptable 

Treatment Options for FGD Wastewater, Electric Power Conference, 2007. 

194 3/7 S&L National Lime Association Report, p. 13 and Table 5.1-1. 
51

plume and lower exit temperatures. However, the impact on visibility in Class I areas should be 

de minimus and offset by the much higher reductions in SO2 that is achievable with wet 

scrubbing. Further, dry scrubbers generate excess sulfuric acid mist emission not present with 

wet scrubber, which also offset any affect from lower exit temperatures. 

 However, if this issue were demonstrated to result in visibility impairment, which is 

unlikely, the owner could use stack gas reheat to raise the temperature of the gases above the 

condensation point. This is a common practice throughout Europe and could be used with either 

wet or dry scrubbing to improve plume buoyancy. This option was not evaluated. 

D. BENEFITS OF WET SCRUBBING 

The beneficial and adverse impacts of both scrubbing technologies must be evaluated. 

The ODEQ only identified adverse impacts of wet scrubbing compared to dry scrubbing. 

However, wet scrubbing has many beneficial impacts compared to dry scrubbing. Further, dry 

scrubbing has adverse impacts that were not discussed. Wet scrubbing achieves better mercury 

control, avoids the high short-term SO2 emission rates during atomizer change-out at a dry 

scrubber, creates a reusable byproduct, and does not contaminate the flyash. Furthermore, some 

types of wet scrubbers, such as the Chiyoda bubbling jet reactor and the double contact absorber, 

have fewer environmental impacts than the conventional wet scrubber that was evaluated by 

ODEQ, e.g., uses less power, removes more sulfuric acid mist, particulate matter, and mercury. 

1. Reliability

Wet scrubbers have a much longer and more extensive operating history than dry 

scrubbers. Wet scrubbing systems have demonstrated high availability (i.e., the number of hours 

the scrubber is available to operate), and all of them constructed in recent years have been able to 

maintain better than 98% availability.195 Operating data from six facilities using dry FGD was 

reviewed. This data showed these facilities were available much less than 98% of the time. The 

Hayden station, one of the better performing units, maintained an availability of only 95%.196 

 

2. Maintenance Emissions

 Dry scrubbers have very high short-term emissions and typically have permits that allow 

high 1-hour, 3-hour, and 24-hour emission limits to accommodate this maintenance.197 See, e.g., 

the permits issued to the Weston 4, Whelan, and Roundup facilities. Due to the abrasive nature 

of lime slurry, nozzle assemblies must be removed, cleaned and flushed, and replaced on a 

 

195 See, for example, Carl Weilert, FGD Systems Utilizing single Absorber Modules: The Wave of the Future for 

New Boilers?, Burns & McDonnell Technical Paper, 2002. 

196 See, e.g., James E. Staudt, Sikander R. Khan, and Manuel J. Oliva, Reliability of Selective Catalytic Reduction 

(SCR) and Flue Gas Desulfurization (FGD) Systems for High Pollutant Removal Efficiencies on Coal Fired Utility 

Boilers, 2004 Mega Symposium, 

197 E-mail from Gary T. Van Helvoirt, WPSC, to Rajen Vakharia, WDNR, Re: Weston 4 air permit submittal, 

February 9, 2004. 
52

routine basis, usually every 4 to 8 weeks, reducing the control efficiency of the dry scrubber by 

up to 50%.198 

 At the subject plants, two dry scrubber modules would likely service each unit. Atomizer 

maintenance requires one of the two modules to be removed from service for 1 to 2 hours so it 

can be swapped with another assembly. During this time, approximately half of the flue gas 

stream is uncontrolled, although some SO2 removal may still take place within the filter cake in 

the baghouse, if a baghouse is used to control particulate matter. The wet scrubber does not 

require similar routine maintenance and thus does not have elevated short-term SO2 emissions, a 

substantial advantage of the wet scrubbing over the dry system. Mitigating these short-term 

emissions would require a spare absorber or excess capacity in a two-absorber train. This would 

raise the cost of dry scrubbing compared to wet scrubbing, which does not have any atomizer 

emissions. 

VIII. BART EMISSION LIMIT: WET SCRUBBER 

 A wet scrubber can meet a lower SO2 emission rate than a dry scrubber as it is capable of 

greater SO2 removal efficiency, up to 98%. Pleasant Prairie Units 1 and 2, similar boilers that 

burn a low sulfur PRB coal similar, were retrofit with wet scrubbers in 2006 and 2007. The 

Clean Air Markets SO2 emissions data for Unit 1 for the period 2007 through June 2010 is 

shown in Figure 54. These data show the wet scrubber easily meets a 365-day rolling average of 

less than 0.03 lb/MMBtu. Comparably low SO2 emission limits have been established in BACT 

analyses and required in PSD permits, achieved with low sulfur coal and wet scrubbing. 

 

 

198 Letter from Kevin E. Redinger, Babcock & Wilcox, to Gary T. Van Helvoirt, Wisconsin Public Service 

Corporation, Re: Recommended Atomizer Maintenance, May 5, 2005; Atomizer Change-Out Interval Information 

from B&W. 
53

Figure 5 

Pleasant Prairie, Unit 1, SO2 Rate, 2007 - June 2010

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

1/1/20073/1/20075/1/20077/1/20079/1/2007

11/1/2007

1/1/20083/1/20085/1/20087/1/20089/1/2008

11/1/2008

1/1/20093/1/20095/1/20097/1/20099/1/2009

11/1/2009

1/1/20103/1/20105/1/20107/1/20109/1/2010

11/1/2010

Time

SO2 Rate (Lbs/mmBTU)

SO2_Rate_Hourly SO2_24HR_Avg SO2_30DAY_Avg SO2_365DAY_Avg

 

 In a 2008 BACT determination for Desert Rock, the U.S. EPA concluded that low sulfur 

coal and wet scrubbing were the most stringent combination of control technologies available for 

pulverized coal fired boilers.199 A final PSD permit was issued based on this determination with 

an SO2 emission limit of 0.060 lb/MMBtu, averaged over a 24-hour period.200 This limit 

corresponds to an SO2 emission reduction of 97% from a coal containing 1.88 lb 

SO2/MMBtu.201 

 The State of Nevada issued a draft PSD permit for Toquop Energy with an SO2 emission 

limit of 0.06 lb/MMBtu based on a 24-hour average, achieved using low sulfur PRB coal (0.69 

lb/MMBtu) and wet scrubbing.202 A 24-hour average is much more stringent than a 30-day 

rolling average. For example, on Figure 54, compare the blue line (30-day average) with the red 

line (24-hr average). This comparison indicates that a 30-day average is generally lower than a 

24-hour average. The longer averaging time allows short-term spikes to be averaged out. 

 

199 U.S. EPA, Region 9, Ambient Air Quality Impact Report, Desert Rock, p. 18. Available at: 

http://www.regulations.gov/search/Regs/home.html#docketDetail?R=EPA-R09-OAR-2007-1110. 

200 U.S. EPA, Region 9, Prevention of Significant Deterioration Permit, Desert Rock Energy Company, July 31, 

2008. Available at: http://www.regulations.gov/search/Regs/home.html#docketDetail?R=EPA-R09-OAR-2007-

1110. 

201 The coal quality for Desert Rock is reported in the PSD Application for Cottonwood Energy Center, March 

2004, Table 1-1. This application is in the Desert Rock docket. It reports the average SO2 content of Navajo mine 

coal of 1.84 lb/MMBtu of SO2. The SO2 removal efficiency is: 100[1-(0.06/1.84)] = 96.7%. Available at: 

Available at: http://www.regulations.gov/search/Regs/home.html#docketDetail?R=EPA-R09-OAR-2007-1110. 

202 Nevada Bureau of Air Pollution Control, Draft Permit No. AP4911-1146, Toquop Energy LLP, Special 

Conditions, December 2007 and ENSR, Toquop Energy Project, Class I-B Operating Permit to Construction 

Application, July 2007, Appendix 10, BACT Analysis. Available at: http://ndep.nv.gov/bapc/toq.html. 
54
55

1 APPENDIX 1 

2 Revised Cost Effectiveness Analysis for 

3 Dry Flue Gas Desulfurization at 

4 Sooner Units 1 and 2 and Muskogee Units 4 and 5203

5 

6 

Sooner 1 

&2 Sooner 1&2 

Muskagee 

4&5 

Muskagee 

4&5 

7 S&L Fox S&L Fox 

8 

9 

10 Capacity (MW) 570 570 572 572

11 Vendor Quote 70,500,000 70,500,000 71,200,000 71,200,000

12 Dry FGD System 86,859,600 86,859,600 86,741,200 86,741,200

13 Fabric Filter 46,335,000 46,335,000 45,872,900 45,872,900

14 

Ash Recycling System - DFGD 

Contractor's Scope 11,116,900 11,116,900 12,102,900 12,102,900

15 

Waste Ash and Fly Ash Handling 

System 14,265,600 14,265,600 14,311,500 14,311,500

16 Ash Recycling - BOP Scope 955,600 955,600 

17 Flue Gas System 42,463,000 42,463,000 52,238,200 52,238,200

18 Mechanical - Balance of Plant 6,891,300 6,891,300 6,201,400 6,201,400

19 

Electrical:Auxiliary Power and 

Balance of Plant Electrical Work 41,935,100 41,935,100 37,923,600 37,923,600

20 Controls & Instrumentation 5,088,500 5,088,500 5,088,500 5,088,500

21 Civil Work 3,597,400 3,597,400 3,597,400 3,597,400

22 

Removal & Relocation of Existing 

Equipment & Infrastructure 2,047,600 2,047,600 4,134,800 4,134,800

23 Manhour Productivity Factor -14,276,530 -14,918,100

24 Vendor Discount Equipment -3,525,000 -3,560,000

25 Optimizing Design Vendor Scope -8,734,800 -9,073,560

26 

Subtotal Equipment Material & 

Labor Costs 261,555,600 235,019,270 268,212,400 240,660,740

27 Other Costs 61,016,900 61,016,900 63,987,800 63,987,800

28 Overtime Inefficiency -9,518,000 -9,945,000

29 

BOP Contractor's General & 

Admin -7,408,600 -7,855,500

30 

Spare Parts, Special Tools % 

Consumables 794,000 794,000 807,000 807,000

31 Freight, Duties & Taxes 1,936,000 1,936,000 2,136,900 2,136,900

32 325,302,500 281,839,570 335,144,100 289,791,940

33 Total Indirect Costs 48,090,100 48,090,100 49,601,900 49,601,900

34 Engineering/Procurement -12,205,338 -12,354,495

35 Construction Management -869,259 -907,043

36 

Startup & Commissioning Craft 

Support -3,273,900 -3,417,600

37 Operator Training & Manuals -900,300 -939,800

38 Owner's Cost -16,265,125 -16,757,205

39 Revised Total Indirect Costs 48,090,100 14,576,178 49,601,900 15,225,756

40 Escalation 46,602,900 0 63,277,100 0

41 Contingency 58,673,400 2,115,000 63,104,100 2,136,000

42 Bond Cost 4,786,700 0 5,111,300 0

43 AFUDC 101,133,800 0 118,148,300 0

 

203 For electronic version of this spreadsheet, including calculations, see file Appendix1.xls. 
56

44 Total Project Cost 584,589,400 298,530,748 634,386,800 307,153,696

45 CRF 0.1036 0.0806 0.1036 0.0806

46 Annual Capital 60,563,462 24,057,519 65,722,472 24,752,412

47 Variable O&M 19,899,400 19,899,400 21,292,200 21,292,200

48 Fixed O&M 8,027,400 8,027,400 8,439,200 8,439,200

49 Indirect O&M 5,174,200 5,174,200 7,146,400 7,146,400

50 Total O&M 33,101,000 33,101,000 36,877,800 36,877,800

51 Lime 4,682,110 4,476,064

52 Water -19,602 -180,730

53 FGD Waste Disposal Cost 4,738,721 5,310,685

54 Bag & Cage Replacement Cost -218,548 -152,160

55 Auxiliary Power -5,368,400 -5,886,800

56 Property Taxes -4,862,997 -3,926,797

57 Insurance -60,072 -68,719

58 Revised O&M 33,101,000 31,992,212 36,877,800 36,449,343

59 Total Annual Cost ($) 93,664,462 56,049,731 102,600,272 61,201,755

60 SO2 Emission Reduction (ton/yr) 13,930 13,930 13,799 13,799

61 

Revised Emission Reduction 

(ton/yr) 43,428 46,458

62 S&L Cost Effectiveness ($/ton) 6,724 7,435 

63 

Revised Cost Effectiveness 

($/ton) 1,291 1,317

64 

Revised (AFUDC, escalation, 

contingency) 1,670 1,738

65 Cost ($/kw) 513 262 555 268

66 Cost ($/kw) Utility Comparison 351 372
57

1 APPENDIX 2 

2 Revised Cost Effectiveness Analysis for 

3 Flue Gas Desulfurization at 

4 Northeastern Units 3 & 4204

5 

6 

7 Trinitya

 Fox Section

8 

9 

10 CAPITAL COST SUMMARY 

11 

12 Purchased Equipment Cost (PEC) 547,080,000 249,100,000 

13 Landfill Construction 25,000,000 25,000,000 

14 

15 TOTAL CAPITAL INVESTMENT (TCI) 572,080,000 274,100,000 

16 

17 

18 ANNUAL COST SUMMARY 

19 

20 DIRECT OPERATING COST 

21 Lime Injection 6,178,600 6,178,600 

22 Operating Electricity 3,022,200 3,022,200 

23 Water 423,100 

24 FGD Waste Disposal 727,981 

25 Bag & Cage Replacement 572,000 

26 Fixed O&M 4,116,350 

27 

28 TOTAL DIRECT COST (DC) 9,200,800 15,040,232 

29 

30 INDIRECT OPERATING COSTS 

31 Administrative Charges (2% TCI) 11,441,600 5,482,000 

32 Insurance (1% TCI) 5,720,800 28,781 

33 Property Taxes (1% TCI) 5,720,800 2,329,850 

34 Capital Recovery (CRFxTCI) 58,267,612 22,088,733 

35 

36 TOTAL INDIRECT COST (IC) 81,150,812 29,929,364 

37 

38 TOTAL ANNUALIZED COST (DC+IC) 90,351,612 44,969,595 

39 

40 

41 COST EFFECTIVENESS SUMMARY 

42 

43 Annual Control Cost 90,351,612 44,969,595 

44 

45 SO2 to be Removed (ton/yr) 26,559 29,119 

46 

47 CONTROL COST EFFECTIVENESS ($/ton) 3,402 1,544 

48 

49 

50 

 

204 For electronic version of this spreadsheet, including calculations, see file Appendix2.xls. 
58

51 INPUTS 

52 DFGD Capital Cost ($/kW) 582 265 

53 Net Rating (MW) 940 940 

54 Landfill costs estimated by AEP ($) 25,000,000 25,000,000 

55 Estimated Lime Usage (ton/yr) 30,893 30,893 

56 Lime Cost ($/ton) 200 200 

57 Estimated Electricity Usage (kW/hr) 6,900 6,900 

58 Cost of Electricity ($/kW) 0.05 0.05 

59 SO2 Removal Rate 0.83 0.91 

60 Water (gal/MMBtu) 4.25 

61 Water Cost ($1.40/1000 gal) 1.40 

62 Solids Generated (lb solids/lb SO2) 5 

63 Solids Disposal Cost ($/ton) 5 

64 Baseline emissions (lb SO2/MMBtu) 0.90 0.90 

65 Annual Average Firing Rate (MMBtu/hr) 4,775 4,775 

66 Capacity Factor 0.85 0.85 

67 Capital Recovery Factor 0.1019 0.0806 

68 Interest Rate 0.08 0.07 

69 Scrubber Lifetime (yr) 20 30 

70 

(a) 5/30/08 Trinity Report, Appx. F, BART Economic Analysis for DFGD (SO2). 

 Modified to replace $555/kW with $582/kW, based on 1/19/10 ODEQ BART Report, Table 11. 
Appendix D 

EPA Extension of Cost Effectiveness Analysis 

I. Background 

As is noted in Appendix C to this TSD, OG&E costed a scrubber that was larger than 

necessary to control SO2 from the coal they currently burn. As it appears that was OG&E’s 

preferred option, perhaps for flexibility in future coal selection, it was carried forward for the 

bulk of the analysis as Option 1. Option 2, estimating cost effectiveness on the basis of a 

minimally sized scrubber to treat the coal currently burned, was also investigated. This was done 

by application of the "six-tenth rule,” to the annualized capital cost. 

Unlike Option 1, due to contracting limitations, Option 2 was not further adjusted within 

Appendix C to correct for cost estimating errors, such as (1) not following the Air Pollution 

Control Cost Manual, (2) adjusting costs that were outside of the range of the actual costs, (3) 

eliminating double counted costs, and (4) failing to evaluate the most cost effective options. 

However, even though it appeared that costing the larger scrubber was OG&E’s preferred option, 

we did not wish to propose our decision solely on that basis, as we wanted to investigate whether 

it was still cost effective to scrub the coal currently being burned. Therefore, we extended 

Option 2 to account for some of the issues that were rectified in Option 1. Also, as Option 2 was 

performed on the basis of a scrubber outlet of 0.10 lbs/MMBtu, and we determined that an 

emission rate of 0.06 lbs/MMBtu was appropriate, we also further adjusted Option 2 to account 

for a scrubber outlet of 0.06 lbs/MMBtu. This extension of the cost effectiveness of Option 2 is 

housed in a spreadsheet entitled, “EPA Modification of Appendix 1.xls,” which is present in our 

docket. A copy of that spreadsheet is attached to this Appendix (see primarily Columns F and I). 

II. Reapplication of the "Six-Tenth Rule." 

Please refer to Appendix C for a discussion of the "six-tenth rule." As discussed above, 

we reapplied the “six-tenth rule,” to adjust the assumed controlled emission rate of 0.10 

lbs/MMBtu were reduced to 0.06 lbs/MMBtu. This required recalculation of the size factor, 

Sb/Sa, that appears in the equation: 

Cb = Ca(Sb/Sa)

n

For the Sooner units, the scrubber inlet remains at 6,015 lb/hr. The scrubber outlet is 

calculated as: 

(5,611 MMBtu/hr)(0.055 lb/MMBtu) = 308.6 lb/hr 

In the above calculation, a controlled SO2 emission rate of 0.55 lb/MMBtu was used, in 

lieu of the design emission rate of 0.06 lbs/MMBtu, in order to build in a slight safety factor. For 

the low sulfur case of Option 2, the scrubber inlet SO2 remains at 2,605 lb/hr. Therefore, the SO2

removed is calculated as: 

D-1 
D-2 

2,605 lb/hr - 309 lb/hr = 2,296 lb/hr 

The size factor is then calculated as: 

(2,296 lbs/hr/6,015 lbs/hr)0.69 = 0.5145 

Similarly, for the Muskogee units, the scrubber outlet is calculated as: 

(5,480 MMBtu/hr)(0.055 lbs/MMBtu) = 301.4 lb/hr 

For the low sulfur case of Option 2, the scrubber inlet SO2 remains at 2,778 lb/hr. 

Therefore, the SO2 removed is calculated as: 

2,778 lbs/hr – 301 lbs/hr = 2,477 lb/hr 

The size factor is then calculated as: 

(2,477 lbs/hr/6,442 lbs/hr)0.69 = 0.5171 

These size factors were then applied to the Option 1 Total Project Costs, in cells F44 and I44. 

III. Remaining Cost Adjustments 

The remaining cost adjustments were made to the lime, water, FGD waste disposal, property 

taxes, insurance, and SO2 emission reductions. These adjustments are explained as notes that 

follow the copy of the spreadsheet below. 
1 EPA modification of Exhibit 1 

2 Revised Cost Effectiveness Analysis for Dry Flue Gas Desulfurization at 3 Sooner Units 1 and 2 and Muskogee Units 4 and 5 4 to Extend Fox Option 2 Case 5 6 Sooner 1 &2 Sooner 1&2 Sooner 1&2 

Muskogee 

4&5 

Muskogee 

4&5 

7 S&L Fox EPA S&L Fox 8 9 

10 Capacity (MW) 570 570 572 57211 Vendor Quote 70,500,000 70,500,000 71,200,000 71,200,00012 Dry FGD System 86,859,600 86,859,600 86,741,200 86,741,20013 Fabric Filter 46,335,000 46,335,000 45,872,900 45,872,90014 

Ash Recycling System - DFGD Contractor's Scope 11,116,900 11,116,900 12,102,900 12,102,900

15 Waste Ash and Fly Ash Handling System 14,265,600 14,265,600 14,311,500 14,311,50016 Ash Recycling - BOP Scope 955,600 955,60017 Flue Gas System 42,463,000 42,463,000 52,238,200 52,238,20018 Mechanical - Balance of Plant 6,891,300 6,891,300 6,201,400 6,201,40019 

Electrical:Auxiliary Power and Balance of Plant Electrical Work 41,935,100 41,935,100 37,923,600 37,923,600

20 Controls & Instrumentation 5,088,500 5,088,500 5,088,500 5,088,50021 Civil Work 3,597,400 3,597,400 3,597,400 3,597,40022 

Removal & Relocation of Existing Equipment & Infrastructure 2,047,600 2,047,600 4,134,800 4,134,800

23 Manhour Productivity Factor -14,276,530 -14,918,10024 Vendor Discount Equipment -3,525,000 -3,560,00025 Optimizing Design Vendor Scope -8,734,800 -9,073,56026 

Subtotal Equipment Material & Labor Costs 261,555,600 235,019,270 268,212,400 240,660,740

27 Other Costs 61,016,900 61,016,900 63,987,800 63,987,80028 Overtime Inefficiency -9,518,000 -9,945,000

D-3 
29 BOP Contractor's General & Admin -7,408,600 -7,855,500

30 

Spare Parts, Special Tools % Consumables 794,000 794,000 807,000 807,000

31 Freight, Duties & Taxes 1,936,000 1,936,000 2,136,900 2,136,90032 325,302,500 281,839,570 335,144,100 289,791,94033 Total Indirect Costs 48,090,100 48,090,100 49,601,900 49,601,90034 Engineering/Procurement -12,205,338 -12,354,49535 Construction Management -869,259 -907,04336 Startup & Commissioning Craft Support -3,273,900 -3,417,60037 Operator Training & Manuals -900,300 -939,80038 Owner's Cost -16,265,125 -16,757,20539 Revised Total Indirect Costs 48,090,100 14,576,178 49,601,900 15,225,75640 Escalation 46,602,900 0 63,277,100 041 Contingency 58,673,400 2,115,000 63,104,100 2,136,00042 Bond Cost 4,786,700 0 5,111,300 043 AFUDC 101,133,800 0 118,148,300 044 Total Project Cost 584,589,400 298,530,748 153,594,070 634,386,800 307,153,696 145 CRF 0.1036 0.0806 0.1036 0.080646 Annual Capital 60,563,462 24,057,519 12,377,594 65,722,472 24,752,412 47 Variable O&M 19,899,400 19,899,400 19,899,400 21,292,200 21,292,200 48 Fixed O&M 8,027,400 8,027,400 8,027,400 8,439,200 8,439,200 49 Indirect O&M 5,174,200 5,174,200 5,174,200 7,146,400 7,146,400 50 Total O&M 33,101,000 33,101,000 33,101,000 36,877,800 36,877,800 51 Lime 4,682,110 0 4,476,064 52 Water -19,602 0 -180,730 53 FGD Waste Disposal Cost 4,738,721 0 5,310,685 54 Bag & Cage Replacement Cost -218,548 -218,548 -152,160 55 Auxiliary Power -5,368,400 -5,368,400 -5,886,800 56 Property Taxes -4,862,997 -7,326,921 -3,926,797 57 Insurance -60,072 -90,509 -68,719 58 Revised O&M 33,101,000 31,992,212 20,096,622 36,877,800 36,449,343 59 Total Annual Cost ($) 93,664,462 56,049,731 32,474,216 102,600,272 61,201,755 60 SO2 Emission Reduction (ton/yr) 13,930 13,930 13,799 13,79961 Revised Emission Reduction (ton/yr) 43,428 15,860 46,458 62 S&L Cost Effectiveness ($/ton) 6,724 7,435

D-4 
63 Revised Cost Effectiveness ($/ton) 1,291 2,048 1,317 

64 

Revised (AFUDC, escalation, contingency) 1,670 1,738

65 Cost ($/kw) 513 262 555 26866 Cost ($/kw) Utility Comparison 351 372

1. This spreadsheet is a modification of "Exhibit 1," prepared by Phyllis Fox as part of her report "Revised BART CostEffectiveness Analysis for Flue Gas Desulfurization at Coal-Fired Electric Generating Units in Oklahoma: Sooner Units 1 & 2, Muskogee Units 4 & 5, Northeastern Units 3 & 4," October 2010, RTI Project Number 0209897.004.085. This spreadsheet is used to extend her cost analysis as part of EPA's proposed SO2 BART FIP for Units 4 and 5 of the 

OG&E Muskogee plant, Units 1 and 2 of the OG&E Sooner plant, and Units 3 and 4 of the AEP/PSO Northeastern plant. Any errors contained in this spreadsheet are solely the fault of EPA.2. Modified total project cost by application of the "6/10" rule as per email from Phyllis Fox to Joe Kordzi on 9/30/10 2:15 PM.3. Zeroed out Fox correction for lime, as the correction was needed to correct a S&L error for assuming lime use based 

on an inlet of 0.509 lbs/MMBtu SO2 at Sooner and 0.507 lbs/MMBtu at Muskogee to an inlet of 1.176 lbs/MMBtu. Since 

this is an extension of the cost evalution for those lower inlet values, the correction doesn't apply.4. Zeroed out Fox correction for water for the sake of expediency. This assumption biases the $/ton calculation upward.

5. Zeroed out Fox correction for FGD waste disposal, as the correction was needed to correct a S&L error for assuming FGD waste disposal based on an inlet of 0.509 lbs/MMBtu SO2 at Sooner and 0.507 lbs/MMBtu at Muskogee to an inlet of 1.176 lbs/MMBtu. Since this is an extension of the cost evalution for those lower inlet values, the correction doesn't 

apply.6. Retained Fox bag and cage correction. This is slightly conservative towards OG&E because the correction in going to the 80% control case would actually be slightly larger due to the slight decrease in the flow rate through the baghouse OG&E uses in their design basis for the 80% control cases vs the 91% control cases on pdf 76 - 79 of their report. 

7. Retained Fox auxiliary power correction, as the correction as calculated is independent of the size of the scrubber.8. Modified Fox property tax correction by using a recalculated total project cost, since S&L assumed ptoperty taxes were a percent of that figure. Project cost was recalculated using the "6/10" rule as per email from Phyllis Fox to Joe 

Kordzi on 9/30/10 2:15 PM 

D-5 
D-6 

9. Modified Fox insurance correction by recalculating the total project cost, since S&L assumed ptoperty taxes were a 

percent of that figure.10. The baseline emissions in line 61 are taken from the S&L report, Table 5. These baseline emissions correspond to 

uncontrolled emissions rates of 0.509 lbs/MMBtu for Sooner 1, 0.516 for Sooner 2, 0.507 for Muskogee 4, and 0.514 for Muskogee 5 (see Table 9, S&L Report 12/28/09). EPA's reductions in line 61 are based on 0.06 lbs/MMBtu. The post 

control emissions are calculated by multiplying the baseline emissions by (0.06/0.509) for Sooner 1, (0.06/0.516) for Sooner 2, (0.06/0.507) for Muskogee 4, and (0.06/0.514) for Muskogee 5. The emissions reductions are then calculated 

by subtracting the post control emissions from the baseline emissions.
E-1 

Appendix E 

The nine graphs contained in this appendix depict nine years of emissions versus load data for 

the AEP/PSO Southwestern Unit 3. Please see section III.F.2.6 for a discussion of how this 

information was incorporated into our proposed BART determination for this unit. 

 
E-2 
E-3 
E-4 
E-5 
E-6 

 
E-7 
E-8 

 
E-9 
E-10