# Technical Support Document

f or 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 2011ii 

### 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 Anal ysis: 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).......................................................1 7 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 Ret rofit 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)

................................ ...................... 56iii 

### Tables

Table 1 . Calculation of Uniform Rate of Progress

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

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

... ... ...... ... ..... . 2 3

Table 3 . Oklahoma’s 2018 Emissions Inventory

....................................... . ................2 4

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

the 20 Percent Worst D ays in 2018

................................. .. ...... ... .. .......... ....... 2 6

Table 5 . Facilities wi th BART

- eligible units in Oklahoma............................................. 3 6

Table 6 . Sources in Oklahoma required to install B ART

............... ... .............................3 9

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 P lanning 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 Musk ogee Generating Station, and AEP/PSO Northeastern Generating Station Appendix C

. SO 2 BART Cost Effectiveness Analysis for the Federal Implementa tion Plan Appendix D

. EPA Extension of SO 2 BART Cost Effectiveness Analysis Appendix E

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

BART Determination4



### Oklahoma Regional Haze T echnical Support Document (T SD )

I. Relationship of this TSD to our Proposal

We are proposing to partially approve and partially disapprove Oklahoma’s R egional H aze (RH) SIP revision submitted on February 19, 2010. Specifically, we are proposing to disapprove the Sulfur Dioxide ( SO 2 ) 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 prop osing to disapprove these SO 2 BART determinations because they do not comply with our regulations under 40 CFR 51.308(e).

We are al so proposing to disapprove the Long Term S trategy (LTS) under section 51.308(d)(3)

because Oklahoma has not shown that the s trategy 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 SO 2 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

dev elopment that assumed certain SO 2 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 SO 2 BART

emission limits on these sources. We are proposing that SO 2 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 SO 2 emission limit of 0.06 lbs/MMBtu that applies singly to each of these units on a 30 day rolling average. Additionally, we propose monitor ing, record

keeping, 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 solici t ed 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, w e solicited comments as to, considering the engineering and/or management challenges of such a fuel switch, whether the5

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

on the SO 2 BART c ost

- e ffectiveness 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 an alyses. 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 comp lete 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 r egard, 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 p roposal and are subject to change based on comments and other information we may receive during our public comment period.6

I I. 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 area s 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 Areas7 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 o n 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 visib ility 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 wi th 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 spec ified 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, wer e 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 O DEQ 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 fu lfill the requirements of the RH Rule.9

II I. 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‖ i n 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 Re gional 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. Emiss ions 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, Apri l 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

Page s 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

. Oklah oma 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 ar eas 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 degradation10

at the Hercules Glades in Missouri, app roximately 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 le vel of reductions of SO 2 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 SO 2 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 o n 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 TS D , 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

Stat e,” 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 a n 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 requirem ent

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 c onsider the costs of compliance, the time necessary for compl iance, the energy and non

- air quality environmental impacts of compliance, and the remaining useful life of11

any potentially affected sources, and include a demonstration showing how these factors were taken into consideration in selecting the goal.

EPA Fi ndings

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 a nalyze 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 mand atory 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

Pag es 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 calculat ed the URP to be 16.28 dv , as depicted by Table 1 , which

is a dapted 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 Findings12

We agree that ODEQ has

, i n establishing its RPG

, analyz ed and determine d the rate of progress needed to attain natural visibility conditions by the year 2064. ODEQ has calculate d this rate of

progress, and compare d baseline visibility conditions to natural visibility conditions for the

Wichita Mountains and determine d the URP need ed 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 cons ider 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

con ditions 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 p rovide 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 Oklah oma ’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 go al 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 fo r 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)(v i )

Oklah oma 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 S IP 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

b aseline 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 day s 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

Page s 8

- 27

.

Baseline conditions represent visibility for the best an d 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 t he 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 unif orm measure of light extinction caused by regional haze. This visibility metric expresses uniform changes in the14

degree of haze in terms of common increments across the entire range of visibility conditions, from pristine to extremely hazy conditions. Th e 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 u seful 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 Guidance 1 , as r equired by the RHR

. The baseline condition calculation begins with the calculation of light extinction, using the IMPROVE equation. The IMPROVE equ ation sums the light extinction resulting from individual pollutants, such as sulfates and nitrates

.

In 2 005 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 factor s 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 science 2 and 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 fo r 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 s cattering (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 s ubmittal 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, C olorado. 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/D ata Analysis Workgroup. September 2006 http://vista.cira.colostate.edu/impro ve/Publications/GrayLit/029_NaturalCondII/naturalhazelevelsIIreport.ppt15

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” a pproach

, 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 b egan 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 dat a for 2005 and 2006. The resulting b aseline conditions represent an average for 2002

- 2004. ODEQ calculated the baseline by first summing the light extinction ( where the extinction coefficient is expressed in Mm 1 ) for sulfurous, nitrate, organic

carbonac eous, elemental “light

- absorbing” carbonaceous, fine soil, chlorine or chloride, and

coarse matter

, using the following equation:

β ext = β ext S + β ext NO3 + β ext organic+ β ext elemental+ β ext SO il+ β ext coarse+ β ext Cl+ β ext Rayleigh

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

HI (dv) = 10 ln[ β ext / ( 10 Mm - 1 ) ]

ODEQ calculate d 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)(i ii )

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

n atural visibility conditions for the most impaired and least impaired days

. Natural visibility conditions must be calculated by estimating the degree of visibility impairment existing under16

natural conditions for the most impaired and least impaired days, based on available monitoring information and appropriate data analysis techniques

.

State Submittal

Page s 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 90 th percentile of the annual distribution represen ts 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 area s. 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 visibil ity pollutants.

As with the calculation of b aseline 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 7.5 3 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 Submittal17

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 value s 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

calcul ates the number of deciviews by which baseline conditions exceed natural visibility

conditions at the Wichita Mo untains 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 c onditions 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 e ach 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 o utside 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 mandat ory Class I Federal areas.

State Submittal

Page s 37

- 47, 58

- 67, 86

- 103

The LTS is a compilation of state

- specific control measures relied on by Oklahoma for achieving its RPG s . Oklahoma’s LTS for the first implementation period addresses the

emissions r eductions 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 inventor y, 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 t o 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 l imits 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 UR P.

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 a ddresses 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 t hrough 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 a dequately 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 stra tegies.

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

Page s 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 state19

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. O DE Q 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 fro m 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 corresponden ce, 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 Wil dlife 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 an d 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) re quires 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 consu ltation. ODEQ’s evaluation relied, however, upon SO 2 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 sec tion 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 cause s or contribute s 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 participated20

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

Page s 65, 115

- 117

As discussed in sections III.D.3

- 4, and dep icted 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 vis ibility 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 SO 2 BART levels, when in fact, after the consultation calls with tribes and other states in 2007 ODEQ found

that SO 2 BART was no control on these six units in their 2010 submittal . This original assumption that these six units would be controlled to presumptive SO 2 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 tri bes, 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 t o 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 neede d 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 tec hnical 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 regional21

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 abo ve, 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 fo r 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 consolidate d 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 invent ories 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 Section s 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 applyin g reductions expected from federal and state regulations affecting the emissions of the visibility

- impairing pollutants NO

x , PM, SO 2

, , and VOCs. The BART Guidelines direct states to exercise judgment in deciding whether VOCs and NH 3 impair visibility in t heir22

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 NH 3 do not significantly impair visibility in the CENRAP region. Therefore, Oklahoma did not consider ammonia among visibility

- impairing pollutants and did not further evaluate NH 3 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 th e 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 agricult ural 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 emis sions 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 met hods 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 impr ovement 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 invent ory. 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

Mo del with extensions (CAMx) model. To enhance accuracy of transportable dust estimates, the contractor also applied road and fugitive dust transport factors 3 to appropriate area source emissions. ODEQ and CENRAP developed the on

- road mobile source emissio ns 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 cont ractor 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.pdf23 Table 2 . Oklahoma’s 2002 Emissions Inventory

SO 2 NH 3 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 electri c 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 20 02 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

inclu ded 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. Similarl y, 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 f ederal standards, including the Tier 4 compression

- ignition engine standards and the exhaust emissions standards for large spark

ignition 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 SO 2 for six units in these

facilities.24

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

Table 3 . Oklahoma’s 2018 Emissions Inven tory

SO 2 NH 3 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 o

f 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 t he 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 reference s 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 use d for urban

- and regional

scale photochemical, PM 2.5 , 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 speci ated 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 capab le 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 transp arent 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 (Janu ary 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 L TS 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 measure ments 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 inp uts. 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 contr ibution 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

E missions Inventory Gui dance 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

- 00126

Table 4

. Projected Contribution from Oklahoma Emissions on Visibility Degradation for

the 20 Percent Worst D ays 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

Hercules

Glades 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 a l l other Class I

areas other than the Wichita Mountains . This info rmed 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 documen ted 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 sche dules;

(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 n et 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 , 11 4

. 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, adm inistrative 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 program s. O DEQ 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 impairment28

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

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 gasoline

powered 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 a nd 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 R ate (LAER) to major new and modified point sources of SO 2 , 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 co ntrol 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 vari ous 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, Contro l 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 SO 2 and NOx reductions.

We approved ODEQ’s SIP to address R easonably A ttributable V isibility I mpairment (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 no t incorporate any measures to specifically

address RAVI.

.29

(B) Measures to mitigate the impa cts of construction activities

We note that fine and coarse particulate, of which construction

- related activities are thought to be a small contributor , are them selves minor contributor s 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 achiev e 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 w hich may cause or contribute to regional
haze at the Wichita Mountains

. Unless permits specify a different schedule for compliance, O DEQ requires permitted sources to comply with their permits immediately upon issuance. O DEQ 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 SO 2 and NOx reductions

. CENRAP modeling in support of this SIP included these reductions. These enforceable consent decrees include compliance schedules.

(D) Source retir ement and replacement schedules

ODEQ considered source retirement and replacement schedules in developing its long

term strategy of emissions reductions. ODEQ took the position that it could not reliably predict the retirement or replacement of sources a nd 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 fo r 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 SO 2 , 4.1% of the total NH 3 , 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 PM 2.5 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 inten d to create a basic, voluntary smoke management program based on our Interim Air Quality Policy on Wildland and Prescribed Fires

.

(F) Enforceability of emissions limita tions 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

Tech nology (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 SO 2 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 lim its. 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 requiremen t[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 Cl ean 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 qual ity 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 anti cipated 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 emis sions 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 SO 2 BART levels, when in fact, ODEQ found

that SO 2 BART was no control. T his assumption of these units being controlled is31

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

P rotected 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 t o 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 projected32 emissions. The State must also inc lude 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 monitor ing s trategy 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 i n 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 t he 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 de scribing 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 establ ishment 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 moni toring 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 mo nitoring 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 c alculated using data representative of

2002

- 2004 due to lack of data from previous years.

Section 51.308(d)(4)(ii) requires that ODEQ establish p rocedures 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 Mounta ins 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 Interne t 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, an d 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 t he 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 f rom 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. Th e 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 re fer 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 inventori es periodically and review periodic emissions info rmation 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) R equirements : 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 reas onably 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 visibi lity conditions.

Note s : 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 of34

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)

O klahoma 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 de termination in more detail.

State Submittal

Pages, 69

- 71

Oklahoma’s BART rule, OAC 252:100

- 8, Part 11, and companion d efinitions 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. B elong ed to one of the twenty

- six BART categories listed below ; 2. D id not operate before 7 August 1962 but were in existence on 7 August 1977; and

3. H ad the potential to emit 250 tons per year or more of any visibility

- impairing pollutant.

O DEQ determined that the visibility

- impairing pollutants in Oklahoma include sulfur dioxide ( SO 2 ), nitrogen oxides (NOx), and particulate matter (PM10 and PM2.5). ODEQ determined using CENRAP modeling including source apportionment techniques and an ev aluation 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 d ata , Oklahoma decided to not consider ammonia among visibility

- impairing pollutants.

O DEQ initially determined which sources were BART eligible by screening its emissions inventory and permitting database to identify major facilities with emission units i n 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. Lim e 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 faciliti es.

O DEQ then used its databases and records to identify facilities with in 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, O DEQ 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 337

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 fede ral 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, Ok lahoma granted BART applicability waivers to a number of facilities. For sources that utilized BART modeling that did not include the inclusion of

meteor o logical observations using the 1 st High or maximum value from CALPUFF modeling was required to be bel ow 0.5 deciviews to obtain a waiver. For modeling that did include the meteorological observations, the 8 th High value from CALPUFF modeling was required to be below 0.5 deciviews to obtain a waiver. Based on these modeling considerations the state issue d

waivers to 11 facilities that are listed in Table 6

- 2 of Appendix A.

W ith 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

emi ssions 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 describe d below, each of these facilities then identified reductions in emissions that were subsequently secured through Title V permit modifications. Following this, a n updated BART modeling analysis, assuming those controls were in place, demonstrated a contrib ution 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 ha s 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 B

1 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 decivie ws 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, Air38

Quality Division operating Permi t, 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

, V alliant Paper Mill has three BART eligible boilers: EUG D1, Bark Boiler ; EUG D2, Power Boiler ; and EUG D3, P ackage 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 B ark 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 We stern Farmers Electric Coop (WFEC), Anadarko facility has three BART eligible combine cycle gas turbines, AN

- Unit 4

, AN

- U nit 5, and AN

- Unit 6. It also has a BART eligible internal combustion engine, AN ENG

- 1. WFEC has agreed to NOx, SO 2 , and PM

- 10 emissi on 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, whi ch 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

di scussed 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 and39

associated emission reductions achi evable 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, a ny 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 fro m Table VI

- 4 of the RH SIP, lists all of the sources determined by ODEQ to be subject to BART. Of these, the SO 2 BART determination s 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

NO X and PM10 BART determinations for these facilities and the NOx BART determina tions 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 SO 2

NOx

PM 10

OG&E Muskogee Generating Station Units 4

and 5 fossil fuel

- fired steam

electric plants SO 2

NOx

PM 10

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 SO 2

NOx

PM 10

AEP/ PSO Southwestern

Power Station Unit 3 fossil fuel

- fired steam electric plants NOx40

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 conside r “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 visibi lity which may reasonably be anticipated to result from the use of such technology. We disagree with ODEQ’s SO 2 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 Nor theastern plants. The SO 2 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 Determination s

State Submittal

Page s, 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 output s of 567 MW each. ODEQ determined that Low NOx Burners ( LNB ) and Over F ire 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. Lastl y, 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 hig her 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 NO x emissions from Seminole Units 1, 2 and 3 by approximately
5,5 13 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 a nd 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 an41 average cost effectiveness of $1,554

- $2,120 per ton of NO x removed for each unit over a twenty year operational life.

Combustion contro l 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.

NO x 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 NO

x 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 SO 2 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 S ooner Units 1 and 2 BART Determinations

State Submittal

Page s, 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 SO 2 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 complian ce, 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, base d 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 cou ld reduce overall NOX emissions from Sooner Units 1 and 2 by approximately 3,226 TPY beyond42 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

environmenta l 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 pres umptive limits

.

Annual NOx emission r eductions 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 additiona l 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 r evision. 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 SO 2 limits in

combination with ODEQ proposed limits for NOx and PM. This modeling s uggests small visibility improvements can be expected from also requiring the installation of SCR on these43

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, w e find that ODEQ ’s BART determin ations 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

Page s, 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 SO 2 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 S CR 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

environmen tal 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 f

rom 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

Wich ita 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 effect iveness 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 544 are justified meet the presumptive limits prescribed by us

.

Annual NOx emission reductions from new LNB with OFA on Units 4 and 5 are project ed 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 Unit s 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 filte rs 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 l bs/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

e nvironmental impacts, the remaining useful life and visibility modeling. EPA performed

additional CALPUFF visibility modeling that included our proposed SO 2 limits in combination with ODEQ proposed limits for NOx and PM. This modeling s uggests small visi bility 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 mo deling 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 Determination s

State Submittal

Page s, 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 also45

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

e valuated 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 investiga te 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 e ffectiveness 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 SO 2 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 Unit s 2 , 3, and 4 BART Determination s

State Submittal

Page s, 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 SO 2 BART determinations for Units 3 and 4 are covered under our pr oposed FIP. Therefore, we review ed 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 also46

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 e valuated 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 NO x 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 NO x 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 a n 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 da y 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 SO 2 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 exis ting 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 lim it 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 SO 2 limits in combination with ODEQ proposed limits for NOx and PM This modeling s uggests 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 add itional 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

Page s, 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 th e 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 NO x 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 OD EQ concluded that SCR control may not be as effective on boilers that operate as peaking units, as NO x 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 a n average cost effectiveness of $947 per ton of NO x 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 th ere 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 info rmation 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 e mission 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 SO 2 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 A EP/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

3,998 OG&E Seminole 2 (gas

- fired) 0.354

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) 0.6 13

Total NOx Reduction 27,04350 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

visibili ty 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 u nder the Regional Haze Rule).

State Submittal

Page s 69

- 85

All the facilities required to install BART that are identified above in Table 6, are fossil

- fuel fired steam electric generating plant s 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

T hese BART determinations are covered in Sections III.F.2.1 to III.F.2. 6

, above. We find that except for the SO 2 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 Nort heaster n 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 SO 2 BART determinations for these facilities are the subject of our proposed F IP

.

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 Fe deral area. All such sources are subject to BART.

Oklahoma is not required to make a determination of BART for SO 2 or for NOx if a BART

eligible source has the potential to emit less than 40 tons per year of such pollutant(s), or for

PM10 if a BART

- eligi ble 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 BAR T: 40 CFR 51.308(e)(1)(iv )

– (v )

E ach 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

Page s 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 t he Oklahoma Regional Haze SIP.” O DEQ 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 BAR T and achieve the associated BART emission within five years of our approval the SIP. Furthermore, O DEQ 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 e ach source subject to BART will (1) install and operate BART as expeditiously as practicable, but in no event later than 5 years af ter 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 SO 2 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 i n part 96 of this chapter, and is approved, in accordance with section 51.123(o)(1) or (2) (for the NO x annual program) and (aa)(1) or (2) (for the NO x ozone season program) and section 51.124(o)(1) or (2) (for the SO 2 program) or by remaining subject to th e 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, con sistent 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 prog ram 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 Subm ittal53

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 subje ct 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

t echniques, 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 tow ards 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 visibil ity 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 pa ragraph (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

O DEQ acknowledged this requirement on page 119 of the ir RH SIP.

H. Five Y ear 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 b y 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 requiremen ts 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 mand atory 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 Fede ral 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 mos t 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

im paired 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 a ny 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 the ir RH SIP. O DEQ stated it intends to submit the first fiv e

- year report by February 2015.

I. E xisting SIP A dequacy 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 vis ibility 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 rea sonable 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

S tates. 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 inadequa te to ensure reasonable progress due to emissions from sources in another country, it shall provide notification, along with available information, to the Administrator.

(4) W here Oklahoma determines that the SIP is or may be inadequate to ensure

reaso nable 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. O DEQ stated it intends to

s ubmit the first five

- year report required by Section 51.308(g) by February 2015. Using the56

findings of that report

, O DEQ 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 Fed eral 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 affec ted 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 conti nuing 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

Page s 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 For est 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 practice57

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 d raft 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 implementat ion 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 coordi nate 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 FL Ms 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 sat isfied 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 p rovided 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, SO 2 BART for the six OG&E and AEP/PSO coal fired units that are the subject of ou r FIP was determined by ODEQ to be dry SO 2 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 final58

RH SIP to us, those SO 2 BART determinations were changed to replac e the SO 2 scrubber requirements with an SO 2 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

whet her 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 Emissi on Inventory Development for the Regional Haze Implementa tion Plan for the State of

Oklahoma

Prepared and Reviewed by:
Erik Snyder

Michael FeldmanA-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 fr om IMPROVE Measurements ................................. 10

2.3 Baseline Visibility Conditions ........................................................................... 12

2.4 Natural Visibility Conditions ............................................................................. 13

2.5 Uniform Rate of Progress (G lidePath) 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 Selectio n 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 a nd 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 (SO 4 ) ................................................. 33

4.7.4.2 Model Performance for Nitrate (NO 3 ) ................................................. 34A-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 Pa rticulate 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 Change s Using Modeling Results ........................... 43

5.3 Reasonable Progress Goal and Pa th 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 .......................................................................................... 61A-iv 

### List of Tables Table 2.5. Uniform Rate of Progress for Wi chita Mountains (worst quintile, western natural visibility conditions) ............................................................................................. 14

Table 3-1. Emissions from Ok lahoma Sources (tons/yr) ................................................. 17

Table 3-2. Emission estimates for Ok lahoma sources in 2018 (tons/yr) ......................... 19 Table 5-1. Comparison of reas onable progress goal to unifor m rate of progress for 2018 (total extinction and deciviews) ........................................................................................ 45 Table 6-1. Projected light ex tinction for 20% worst days at Wichita Mountains in 2002 (Mm -1 ) ............................................................................................................................. .. 49 Table 6-2. Projected light ex tinction 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 fr om BART based on dispersion modeling. ......... 54 Table 7-3. Sources in Oklahoma subject to BART ........................................................... 56A-v 

### List of Figures Figure 1-1. Map of Regional Pl anning Organizations ........................................................ 7 Figure 4-1. National RPO 36-km modeli ng domain for CMAQ, CAMx, and SMOKE modeling. ..................................................................................................................... ..... 24 Figure 4-2. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for sulfate (SO 4 ). ................................................................................. 34 Figure 4-3. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for nitrate (NO 3 ). ................................................................................ 35 Figure 4-4. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for organic carbon (OC). .................................................................... 36 Figure 4-5. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for elemental carbon (EC). ................................................................. 37 Figure 4-6. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for soil. ................................................................................................ 38 Figure 4-7. CENRAP model performance (fac tional bias and error) of the Typ02f modeling scenario for course pa rticulate material (CM). ................................................. 39 Figure 4-8. Daily extinction model performan ce 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 ex tinction (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 resu lts by source region and source category for worst 20% days at Wichita Mountains in 2002. .......................................................... 50 Figure 6-3. Source apportionment modeling re sults by source region and species for worst 20% days at Wichita Mountains in 2002. ............................................................... 50 Figure 6-4. Source apportionment modeling resu lts by source region and source category for worst 20% days at Wichita Mountains in 2018. .......................................................... 51 Figure 6-5. Source apportionment modeling re sults by source region and species for worst 20% days at Wichita Mountains in 2018. ............................................................... 51 Figure 7-1. CENRAP CALMET 6km mete orological modeling domains ...................... 57A-6 

### Chapter 1: Background and Introduction

1.1 B ACKGROUND

Regional haze is visibility impairment that is caused overwhelmingly by fine particulates (PM2.5). Visibility impairment occurs when PM 2.5 in the atmosphere scatters and absorbs light, thereby creating haze. PM2.5 can be emitted in to the atmosphere directly as primary particulates, or it can be produced in the atmosphere from photoc hemical 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, a nd because the ability of PM2.5 to scatter light depends on particle size, with lig ht 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 rema in suspended in the atmosphere for longer periods and is transported long distances, thereb y contributing to region al-scale impacts of pollutant emissions on visibility.

The sources of PM2.5 are difficult to quantify because of the comp lex nature of their formation, transport, and removal from the atmosphere. This makes it difficult to simply use emissions data to determine which pollutants s hould be controlled to most ef fectively improve visibility. Photochemical air quality models offer opportuni ty to better understand the sources of PM2.5 by simulating the emissions of pollutants and the fo rmation, transport, and deposition of PM2.5. If an air quality model performs well for a histori cal 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 visibil ity goals. Although several type s of air quality modeling systems are available, the gridded, three-dimensional, Eu lerian models provide th e most complete spatial representation and the most comprehensive representation of pro cesses affecting PM2.5, especially for situations in which multiple pollutant sources inte ract to form PM2.5.

In Section 169A of the 1977 Amendments to CA A, 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 Federa l Class I areas which impairment results from manmade air pollution.” EPA pr omulgated a rule to address regional haze on July 1, 1999 (64 FR 35713), the Regional Haze Rule (RHR). Th e 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 fr om sources located across broad geographic areas, EPA has encouraged the States and Tribes across the Un ited States to address visibility impairment from a regional perspective. Five Regional Planni ng Organizations (RPOs) were developed to address regional haze and re lated 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 developi ng 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 fe deral agencies established to conduct data analyses, conduct pollutant transport modeling, and coordinate planni ng activities among the

central States. CENRAP members include the State governments of Nebraska, Kansas, Oklahoma, Texas, Minnesota, Iowa, Missouri, Ar kansas, and Louisiana and Tribal governments

included in these states.

1.2 T ECHNICAL R EQUIREMENTS FOR R EGIONAL H AZE SIP S

The RHR does not mandate specific milestones or rates of progress, but instead calls for States to establish goals that pr ovide for “reasonable progress” to ward achieving natural visibility conditions. In setting Reasona ble 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 visibili ty 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 visibili ty 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 Retrof it Technology (BART)

v. Current and Future (201 8) Emission Inventories

vi. Source Contribution to Haze

vii. Reasonable Progress Goals

The purpose of this document is to review th e technical products developed by the Oklahoma Department of Environmental Quality (ODEQ) a nd CENRAP for the central regional states, in support of their RH SIP. This document evalua ted the methods and procedures used by ODEQ and CENRAP to develop the modeling and emissi on inventory products th at assisted Oklahoma and the central regional States in addressing the required elements of a RH SIP. Specifically,

this document reviewed emission inventor y, meteorological, photochemical, and BART modeling conducted by CENRAP, evaluated the resu lts and determined if these models met applicable guidelines or protocols, and met mode ling standards at the time they were conducted.A-9 

### Chapter 2: Development of Baseline and Natur al Visibility Conditions and Glidepath

2.1 I NTRODUCTION

Under the Regional Haze Rule (RHR), each State is required to demonstr ate reasonable progress in visibility conditions for each of its Class I area s. The State is to determine a uniform rate of progress ("glide path", "glide slope") toward th e goal of natural visibility conditions in 2064. Considering various statutory factor s, the State is also to define a reasonable rate of progress, and compare this to the benchmark uniform rate; if pr ojected progress is less than the uniform rate, then the State is to explain why. Procedures for assessing progress are de scribed in the Regional Haze Rule and EPA guidance documents.

In brief, the guidance defines a metric to quantif y visibility conditions, to gether with procedures for determining a starting point and an ending poin t, between which progress is to be made. The metric used is the Haze Index, measured in de civiews, and is designe d to correspond to human perception of visibility cha nges. It is defined as: 10*ln(b ext /10) (1)

where b ext 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 av erage 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 de gradation on the best 20%.

The starting point for progress is current or ba seline visibility conditions, as monitored by the

I nteragency M onitoring of PRO tected V isual E nvironments (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 ex tinction using the IMPROVE equation, which adds up the contribution of each pollutant to extincti on, while accounting for the effect of relative humidity. This total extinction is then conve rted to deciviews in the Haze Index through equation 1. For each of the years of the ba seline period (2000-2004), the average of the deciviews on the worst 20% of days is calculat ed; the five-year average of these defines the baseline. This procedure is described in deta il in EPA's "Guidance fo r Tracking Progress Under the Regional Haze Rule". 1 The guidance also makes provisions for dealing with missing data, since monitoring instrument maintenance and malf unctions mean that data is not available for every scheduled measurement.

The end point for progress is th e goal of natural visi bility 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.pdfA-10 Conditions Under the Regional Haze Program". 2 Annual average natu ral 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 humi dity for each specific Class I area. These annual averages are then translated into estima tes for the best 20% and worst 20% days needed for the progress assessment. Ex tinction was assumed to have a lognormal frequency distribution;

deciviews would then have a norma l distribution, and its 10th and 90th percentiles were used as estimates of the average of the best 20% and wo rst 20% of days, respec tively. The result is a

table of best and worst 20% deciview values fo r 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 calcul ated as the difference between the baseline and natural conditions, spread over the 60 years be tween 2004 and 2064: uniform deciviews per year improvement = (current 2004 deciviews - natura l 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 Ru le is through 2018, so this uniform rate is multiplied by 14 to determine the first benchmark.

2.2 C ALCULATION OF V ISIBILITY FROM IMPROVE M EASUREMENTS

The CENRAP procedure used for developing a uni form 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 re vised IMPROVE algorithm was utilized rather than the original IMPROVE equation . The pr ocedure used is described in the Technical Support Document for CENRAP Em issions and Air Quality Mode ling 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 sp ecies, and then total them up, accounting for the effect of relative humidity, and including the Rayl eigh scattering that occurs in pure air. The

extinction total is then used to calculate deci views 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 Hi storical Conditions-Causes and Effects", chapter 24 in

NAPAP State of Science & Techno logy, Vol. III web page: http://vista.cira.colostate.edu/improve/Publications/Principle_pubs.htm

4 Hereafter “CENRAP TSD”: Environ Inte rnational 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 Qualit y Goals for Ozone, PM2.5, and Regional Haze” 6 states
that the use of either the IM PROVE or the revised IMPROVE equation is acceptable provided that the States supply documentation concerni ng the choice of equation and that the same algorithm is utilized for both the base a nd future extinction calculations.

The IMPROVE program revised the IMPROVE equa tion after a scientific assessment of its implications for regional haze planning to reduce bias es in light extinction estimates compared to the old algorithm. 7 In particular, when compared to nephelometer direct measurements of visibility extinction, the origin al IMPROVE equation over-predict s 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 fo r coastal sites; 2) incr eased organic carbon mass estimate, based on more recent data for remote areas; 3) Rayleigh scatte ring using site-specific elevation and temperature, a refinement over the older network-wide cons tant; and 4) separate estimates for small and large particles of visibi lity impacts and humidity-dependent particle size growth rates, which could affect es timates at the low and high ends. 8 The revised equation has

an additional term for inclus ion of NO2; however, none of th e CENRAP Class I areas have monitors that provide observations of NO2 so this term was not used.

The new equation shows broader scatter overa ll, but less bias in matching visibility measurements under high and low visibility cond itions. That is, though it has a somewhat worse fit considering all the data, it ha s a better fit under visibi lity 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 change d (IMPROVE technical subcommittee for algorithm review, 2001, pp. 11-12), and so th is makes little difference for assessing the contribution of emission sources to current conditions, and for pr ojecting 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 IM PROVE 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/scr am001/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 Ligh t 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/Publicati ons/GrayLit/016_IMPROVEEeqReview/IMPROVEeqReview.htm

8 Pitchford, Marc, 2006, "New IMPR OVE 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/Pub lications/NewsLetters/IMPNews4thQtr2005.pdfA-12 areas, the revised IMPROVE equation embeds an OM/OC factor of 1.8. At the Wichita Mountains site, fine sulfurous aer osol contributes the most to visibility impairment on the worst days during the baseline years. Nitrates and or ganic carbon are also sign ificant 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, nitrat e, 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 co nsiderations of relevance for the calculations needed for assessing progress under the RHR. EP A believes it is approp riate for the CENRAP states to use the revised IMPROVE equation. As the state of the scie nce evolves, it is recommended that this procedure is reevaluated to apply more current and site specific data as it becomes available. CENRAP provides alternat ive calculations using the original IMPROVE equation for comparison with thes e visibility calculation results.

2.3 B ASELINE V ISIBILITY C ONDITIONS

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 th e 5 year period. These steps are mostly straightforward and are briefly di scussed here with a more detail ed 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 medi ans 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 Missi ng 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 th e 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 comp lete 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 Mount ains 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 av ailable for the 2002-2004 porti on of this baselineA-13 period. Therefore, baseline visibility was calcula ted 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 vi sibility conditions de tailed in the GTP.

Every Class I area within the CENRAP states has an associated IMPROVE monitor. Results from analysis of samples collected at each monito r site are used to calculate extinction and haze index using the procedure desc ribed 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 completene ss 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 procedur es can be found at the VIEWS website: (http://vista.cira.colo state.edu/views/).

The CENRAP followed EPA guidance for estima ting baseline visibi lity conditions.

2.4 N ATURAL V ISIBILITY C ONDITIONS

EPA guidance set out a default procedure for esti mating natural conditions, but also describes circumstances when States might want to use a more refined approach, such as to reduce uncertainty when baseline visibil ity is already near na tural conditions, or when there is marked seasonality; these might be accomplished via altern ative estimates of natural concentrations, or use of temporally varying es timates (GENV sec. 3.1 and 3.2).

The natural conditions approach used the revi sed 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 na tural visibility conditions based on the annual average estimates of Trijonis ( 1990) for the American West.

The default procedure used has several acknowledge d limitations. 1) each chemical species can have one of only two possible background concentra tions, 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 co mponents; EPA guidance mentions the possibility of treating these separately (GENV sec. 3.4). ODEQ c onsidered 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 availabl e. This, too, is a potential area for future

efforts.

The majority of visibility impairment at th e 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 backgr ound concentrations of PM will improve. The current approach used by ODEQ follows EPA defa ult methods and is acceptable. As additionalA-14 information and more site-specific data becomes available, ODEQ is encouraged to pursue refinements in this approach to bette r quantify natural visibility conditions.

2.5 U NIFORM R ATE OF P ROGRESS (G LIDE P ATH ) C ALCULATION

The uniform rate of progress is calculated as th e 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. Ta ble 2.5 summarizes the calculations performed by ODEQ.

Table 2.5. Uniform Rate of Progress for Wichit a Mountains (worst qui ntile, 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 unifo rm rate .27 per year Improvement needed by 2018 assuming uniform rate of progress 34.18 3.80A-15 

### Chapter 3: Emission Inventory Development

3.1 I NTRODUCTION

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 repres ent the 2000-04 regional haze baseli ne 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 inventor ies has undergone a number of revisions throughout the developmen t process to arrive at the fi nal versions used in CMAQ and CAMx air quality modeling. In general, update d 2002 emissions data for the U.S. developed by the Regional Planning Organizati ons (RPOs), updated emissions data for Mexico from the

BRAVO 1999 emissions inve ntory, and version 2 of the 2000 em issions data for Canada were used to generate a 2002 annual emissions data base. 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 quali ty assurance (QA) work was based on the Quality Assurance Project Plan (QAPP) CENRAP Emissions and Air Quality Modeling 10 and Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling 11 (hereafter referred to as the “Modeling Protocol”). These prot ocols 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 deve loped to represent th e 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 re plicating observed particulate matter air quality. The base case includes actual day-specific emissions of SO 2 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 En vironmental Program (CEP), University of North Carolina(UNC), (http://www.cenrap.org/html/projects.php?mode=subcatdownl oad&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 Stat es. Sonoma Technology, Inc. ( http://cenrap.sonomatech.com/CEN RAP_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/CENRA P_Ammonia_NIF/FinalReport.pdf).

10 Morris, R.E. and G. Tonnesen. 2006. Quality Assuranc e Project Plan (Draft) for Ce ntral Regional Air Planning Association (CENRAP) Emissions and Air Quality Modeling. (http://pah.cert.ucr.edu/aqm/cenrap/doc s/CENRAP_QAPP_Rev3_Mar_29_2006.pdf )

11 Morris, R.E. et al. Modeling Protocol for the CENR AP 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 em issions scenario is referred to as “Typ02G”. The purpose of the Typ02G invent ory is to represent baseline emission patterns based on average, or “typical”, condi tions. This inventory provides a basis for comparison with the future year 2018 projecte d emissions, as well as to gauge reasonable progress with respect to future year visibili ty. 5-years of CEM data were analyzed and typical seasonal and diurnally vary ing emissions were defined.

• The 2018 future-year base case emissions scenar io is referred to as “2018 Base Case” or

“Base18G”. These emissions are used to re present conditions in future year 2018 with respect to sources of criter ia and particulate matter air pollutants, taking into consideration growth and contro ls. 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 categor ies are needed to support air quality modeling: stationary point-source emissions, stationary area-source emissions (a lso called nonpoint), mobile emissions for on-road sources, mobile em issions for nonroad source s (including aircraft, railroad, and marine vessels), and biogenic em issions. 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 Q uality Modeling to Support Regional Haze State Implementation Plans 12 (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 desc ription of emissions used in the 2002 typical (Typ02G) emissions inventory. Emissions invent ories 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 qua ntifying emissions of sulfur dioxide (SO 2 ), ammonia (NH3), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter of aerodynamic diameters less than 10 μ m (PM10) and less than 2.5 μ m (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 Technol ogy supplemented the 2002 NEI data with nonpoint source inventories to a ddress agricultural and prescribed burning, on-road and non-road mobile sources, agricultural tilling and livesto ck dust, and agricultu ral 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 Supp ort 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/CEN RAP_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)

SO 2 NH 3 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 informa tion 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 CE NRAP stationary-point invent ory 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-s pecific emission inventor ies for input into the Base02F inventory. The Typ02G inventory includes further pr ocessing of EGU emissions to develop a typical emission leve ls and temporal profiles.

Coal-fired point sources within the CENRAP states use a PM 2.5 speciation profile recently developed for MRPO by Carnegie Mellon that is representative of combustion of eastern

bituminous coal. Texas and North Dakota source s 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 pr epared for CENRAP mo deling 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), meteor ological data and other MOBILE6 16 inputs for calculation in

SMOKE/MOBILE6. Annual mobile emissions were pre-computed as part of the 1999 Mexico inventory and 2000 Canada inventor y. Seasonal mobile emissions calculated in MOBILE6 were provided for all 13 WRAP states. For all ot her RPOs, including CENRAP, county-level VMT were prepared and input into SMOKE/MOBILE 6. For the CENRAP states, Sonoma Technology provided monthly VMT data and MOBILE6 input f iles 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 stat e-of-the-science at the time the modeling was conducted and deemed acceptable at that time. EPA Office of Transporta tion and Air Quality is continuing to develop and update a new model, Motor Vehicle Emission Simulator (MOVES), which will replace the MOBILE6 model for estima ting emissions from on-road mobile sources.

3.2.3 Biogenic Emissions The BEIS3 system is utilized to estimate emissi ons from biogenic sources. BEIS3 is integrated into SMOKE for deriving biogenic emissions es timates given land use information, emissions factors for different plant species, and hourly, gr idded meteorology data. Land use data is from

the BELD3 land use database and emission factor s used are version 0.98 of the BELD emissions

factors. These land use data and emission f actors were developed by the WRAP during their preliminary modeling efforts. BEIS modeling pr oduces gridded, hourly emi ssions 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 operati ons, commercial and recreational marine vessels, and railroad

locomotives and other sources were devel oped by the EPA for the 2002 NEI. The EPA

NONROAD 19 model was utilized by Sonoma Tec hnology 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 bur ning, agricultural dust, and soil agricultural a mmonia 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/CEN RAP_Mobile/FinalReport.pdf) .; Reid, S.B et al. 2004. Research and Development of Planned Burning Emission Inventories for the Central States Regional Air PlanningA-19 based on county-level production data and emi ssion estimates for gaso line distribution, dry cleaning and wildfires. The Western Regional Ai r Partnership (WRAP) pr ovided 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 s ubcategories (general area, fire, ammonia, road dust, fugitive dust, uncategorized) to assist in particulate source apportionment modeling with CAMx.

3.3 2018 E MISSIONS I NVENTORY

An emission inventory for 2018 including antici pated changes due to population growth, emission controls and development of industry, energy, and natural resour ces is required to project the net effect on visi bility conditions by 2018. CENRAP developed an emission inventory for 2018 (Base18G) using a combinati on of EPA Economic Growth Analysis System (EGAS 5), MOBILE 6, NONROAD, and the Inte grated Planning Model (IPM) of ICF International for EGUs to project emissions fr om 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 Contro l Inputs for CENRAP 2018 Emissions draft technical support

document. 23

Table 3-2. Emission estimates for Oklahoma sources in 2018 (tons/yr)

SO 2 NH 3 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 En vironmental Program (CEP), University of North Carolina(UNC), (http://www.cenrap.org/html/projects.php?mode=subcatdownl oad&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

SO 2 NH 3 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 projec tions for CENRAP states;

• Annual Energy Outlook (AEO) projections for oil and gas production emissions;

• average historical values rather th an 2002 data for prescribed burning;

• Extrapolation of historical trends for unpaved roads;

• United States Department of Agriculture (U SDA) 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) re gulations were applied to those engines subject to MACT rules. Emi ssions for Canada are based on a shared 2020 emission inventory. 2018 EGU emissions were ba sed on the run 2.1.9 of the Integrated Planning Model (IPM) updated by the CENRAP states. Re ductions anticipated from BART controls for EGUs in Oklahoma, Arkansas, Kansas, and Ne braska were included in projections of 2018 emissions. These anticipated reductions were based on actual operating c onditions and estimated control efficiencies from utilities. Newly perm itted coal-fired utilities were included in 2018 projections. Conservatively, no IP M projected new units were rem oved from the simulation with the addition of the permitted facilities. Appendi x B of the CENRAP TSD lists the file names, data source, type and a description of emi ssions used in the 2018 (Base18G) emissions inventory. ODEQ modified th e emissions from the IPM for OG&E Sooner and Muskogee electric power plants and the PSO Northeast elect ric power plant to refl ect 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-agricul tural sources (i.e., natural wind blown fugitive dust);

• Off-shore emissions associated with off- shore marine and oi l 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) util ized 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 I NTRODUCTION

Meteorological, emission and photochemical models are essential tools in examining factors that impact visibility and for devel opment of effective control stra tegies 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-dime nsional air quality models to si mulate the transport and fate of key species that affect visibility in Class I Ar eas in the central U.S. This work included the development of meteorological data for input in to the model as well as creation and processing

of emission estimates for use in the model. The Modeling Protocol 24 describes the model selection, configuration, episode selection, and model evalua tion used in support of the

Oklahoma RHR SIP.

4.2 Q UALITY A SSURANCE P ROGRAM P LAN

The modeling team developed a qualit y assurance program plan (QAPP) 25 to develop clearly defined data quality objectives, documentation, and procedures. This QAPP was developed incorporating the following elements as describe d in the EPA guidance document for modeling:

• A systematic planning process including id entification of assessments and related performance criteria;

• Peer reviewed theory and equations;

• A carefully designed life-cycle developm ent process that minimizes errors;

• Documentation of any change s from original plans;

• Clear documentation of assumptions, theory, an d parameterization that is detailed enough so others can understand the model output;

• Input data and parameters that are accura te and appropriate for the problem; and

• Output data that can be used to help inform decision makers. The plan describes the data management and qual ity assurance/quality cont rol measures taken to assure high quality emission inve ntories and air quality modeli ng results for use in the RH analysis.

4.3 E PISODE S ELECTION

EPA guidance 26 describes the criteria that should be us ed to select a modeling episode. The modeling episode should: 1) reflect a variety of meteorological c onditions 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/CE NRAP_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 extensiv e 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 da ys). For regional haze modeling, the preferred approach is to simulate an entire representative y ear. 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 ba se case and baseline inventories covering the same y ear. Other RPOs selected the same modeling year, allowing for more direct comparison of modeling results and sh aring of modeling inputs. The availability of 2002 NEI also provides an additiona l resource in the development of emission inventories for the modeling episode. The Wichita M ountains site has a complete ye ar 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 EP A approves of the selection of 2002 for the regional haze modeling episode.

4.4 P HOTOCHEMICAL AND E MISSIONS M ODELING D OMAIN

CENRAP conducted emissions and air quality mode ling on the 36-km national regional planning organization (RPO) domain. This domain consis ts 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 additiona l computational resources required to run the model at the higher resolution. The use of highe r 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 dom ain for CMAQ, CAMx, and SMOKE modeling.

4.5 MM5 M ETEOROLOGICAL M ODEL

4.5.1 Model Selection

Photochemical grid models, such as CMAQ a nd CAMx, require inputs of three-dimensional gridded meteorological data, including wind, temperature, humi dity, cloud/precipitation, and boundary layer parameters. The Fifth-Genera tion 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 a pplications and has been used exte nsively in past local, state, regional, and national modeling efforts. MM5 ha s undergone extensive peer -review, with all of its components continually undergoing developm ent and scrutiny by the modeling community. In-depth descriptions of MM5 28 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 CA LMET/CALPUFF modeling sy stem. As discussed further in Section 6 of this review, CALM ET/CALPUFF was used to determine whether a BART eligible source contributes to visibility impairment at a Cla ss 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 Depart ment of Natural Resources (IDNR) and is described fully in the report entitled Meteorological Model Perf ormance 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, MM 5 was configured to run on the standard continental-scale Regional Planning Organizatio n (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 centra l 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 (eas t-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 dom ain with 12 km grid spacing covering the central states by EPA Region V II and the Texas Commission on Environmental Quality to examine model prediction sensitivity to grid resolution. The vert ical layer structure of the CENRAP meteorological modeling domain consis ts of 34 layers, a top level at 100 millibars, and increasing layer thickness with a ltitude. The vertical layer stru cture is further detailed in the Modeling Protocol.

4.5.3 Model Configuration

The final CENRAP MM5 modeling system conf iguration for the 2002 annual simulation is provided in the Modeling Prot ocol 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 result s showed an extreme

cold bias over the central U.S and unnatural diur nal profiles near shorelines. A number of sensitivity tests were performed by IDNR to reso lve 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 mode ling 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 n ecessary to prepare topo graphic, vegetative, initial condition, boundary conditi on, 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 distri bution on the MM5 domains was defined from the 24-category USGS vegetation data w ith a resolution of 2 minutes.

The 3-hour Eta analysis and surface fields availa ble from the National Center for Atmospheric Research (NCAR) were taken fr om the Eta Data Assimilation System (EDAS) and used to supply initial and boundary conditio ns to MM5, and for analysis nudging in the FDDA package. The EDAS analyses are devel oped from a wide variety of obs ervational sources, including standard surface and upper air measurements, prof iler networks, radar- and satellite-derived measurements, and ship and aircra ft reports. The wide array of data sources, coupled with the high time- and spatial resolution provided by E DAS, result in an anal ysis product that far

exceeds the level of detail found in tr aditional global-scale analyses.

Sea surface temperatures (SSTs) were a pproximated by ETA skin temperatures The annual simulation was generated from 95 inde pendent simulations in itialized at 12Z and integrated through five days. To allow for a pproximately a two week photochemical model spinup period, the simulation st arted 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 meteorolog ical conditions and precipitation. 33 Additional performance evaluation was done by CENRAP by compari ng 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 Meteorologi cal 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 fo r 2002, Task 3: Met Gatekeeper Report. http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/ CENRAP_VISTAS_WRAP_2002_36km_MM5_eval.pptA-27 The goal of the evaluation was to determine whethe r the meteorological fields are sufficiently accurate to properly characterize the trans port, chemistry, and removal processes in CMAQ/CAMx. If errors in the meteorological fiel ds are too large, the ability of the air quality model to replicate regional polluta nt levels over the entire base y ear will be severely hampered and the predicted impacts from future year growth and controls will be highly questionable. To provide a reasonable meteorological characteriza tion 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 forci ngs for mesoscale circulations;

• Mesoscale and regional wind, temperature, PB L height, humidity, and cloud/precipitation
patterns;

• Mesoscale circulations such as sea br eezes and mountain/drainage circulations;

• Diurnal cycles in PBL depth, temperature, and humidity.

For visibility applications, the moisture and conden sate fields are particularly important as they significantly impact PM chemical formation, rem oval, and light scattering efficiency. In addition, cloud and precipitation fi elds are a good measure of the integrated performance of the model since these are model-derived quantities and not nudged to observati ons. Because of the model’s coarse resolution of 36-km, the model ca nnot be expected to faithfully simulate the pattern or variability of th e convective precipitation, but should reproduce the synoptic precipitation and cloud patterns.

The IDNR evaluation of the MM5 model performa nce was limited to operational testing of the model, and not to a scientific evaluation. Previous peer-reviewed documentation of MM5 formulation, testing, and evaluati on provide the basis for its scie ntific validity. An operational

evaluation entails an assessment of the model's ability to correctly estimate surface and boundary layer wind, temperature, and moisture largel y independent of whet her the actual process descriptions in the model are ac curate. The operational evaluati on essentially tests whether the predicted meteorological fields ar e reasonable, consistent, and ag ree adequately with available observations in time and space. The process pr ovides only limited information about whether

the results are correct from a scie ntific perspective or whether th ey are the fortuitous product of compensating errors; thus a “successful” operatio nal evaluation is a nece ssary but insufficient condition for achieving a sound, reliabl e performance testing exercise.

The basis for the IDNR operational performan ce assessment entailed a comparison of the predicted meteorological fields to available surf ace and aloft data that are collected, analyzed, and disseminated by the National Weather Service. It was carried out both graphically and statistically to evaluate m odel 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 th e final 36 km CENRAP MM5 simulations exhibit reasonably good performance for the central U.S.

Comparison of CENRAP MM5 performance w ith 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 assimilati on of temperature in the interi m WRAP simulation resulted in the best performance in prediction of surface temperatures but the poorest performance for vertical temperature profiles. Surface data a ssimilation has since been dropped from the WRAP modeling protocol. The 2002 VISTAS MM5 simula tions 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 me teorological databases used for prior air quality modeling efforts. It is therefore de emed 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 modeli ng was conducted. The performance of the model was adequate for the purposes for which it was used and on par with other st udies 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 E MISSIONS M ODEL

CENRAP selected the Sparse Matrix Operator Kernel Emissions model 36 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 ava ilable emission inventory data to the resolution needed by the air quality model. SMOKE also ha s the ability to compute emissions for mobile on-road and biogenic sources. Biogenic emissi on modeling is perfor med through SMOKE with the Biogenic Emission Inventory System, version 3 (BEIS3) 37 using the Biogenic Emissions Landcover Database (BELD3) vege tative database. Mobile em issions can be calculated by

SMOKE from mobile-source activ ity data, using emission factor s from the MOBILE6 model. SMOKE supports the emission input formats required by the CAMX and CMAQ air quality

models.

4.7 A IR Q UALITY M ODEL

Photochemical air quality models offer opportunity to better unders tand the sources of particulate matter that impair visibility by simulating the em issions, formation, transport, and deposition of these pollutants. If an air qua lity model performs well for a hi storical episode, the model may then be useful for identifying the sources of pa rticulate matter and helping to select the most

effective emissions reduction stra tegies for attaining visibility goals. Although several types of air quality modeling systems are available, the gridded, three-di mensional, Eulerian models provide the most complete spatial representation and the most comprehensive representation of

36 Available at http://www.smoke-model.org/index.cfm

37 Available at http://www.epa.gov/ttn/chief/software.html#pcbeisA-29 processes affecting particulate matter, especial ly 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 dow nloadable to all stakeholders. Furthermore, the user must be able to revise the code to perf orm diagnostic analyses and/or to improve the model’s ability to

describe observations in a credible manner. Se veral additional prerequisi tes should be met for a model to be used to support an attainment demonstration or unif orm rate of progress assessment.

• It should have received and been revised in response to a scie ntific peer review.

• It should be appropr iate for the specific applic ation on a theoretical basis.

• It should be used with a data base which is adequate to supp ort its application.

• It should be shown to have performed we ll in past modeling applications. (If the application is the first for a particular m odel, then the State sh ould note why it believes the new model is expected to perform sufficiently.)

• It should be applied consis tently 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 Re gional 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, app lied 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 Sect ion 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 tr eatment of horizontal diffusion SOA formation mechanisms, and in grid nesting. EPA accep ts 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 EP A for this Regional Haze selection.

Both air quality models were set up and r un 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 incr ease in computer resources and time. CAMx

runs at 12-km resolution redu ced 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 ex ception, 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-S cale 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 provi de 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, visibi lity, 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 gr id cells and the number and thicknesses of layers are defined by the user, based in pa rt 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 nume rical 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-pha se chemistry: the Regional Acid Deposition Mechanism version 2 (RADM2), a fixed coeffici ent 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 (typi cal 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 car bon (OMC) concentrati ons. 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 configur ation 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) model 41 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, visi bility, 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, ae rosol formation, and dry and wet deposition. CAMx has seen extensive use within many of th e CENRAP states. The CAMx model is based

on well-established treatments of advecti on, diffusion, deposition, and chemistry. CENRAP used CAMx Version 4.40, applied using simila r 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 ae rosol 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 th e 34 layer vertical st ructure used in MM5 modeling to a smaller set of vertical layers . Sensitivity studie s by WRAP and VISTAS examined model performance looking at a variet y 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 collapsi ng 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 effi ciency and produces results almost identical to

the full vertical structure runs.

4.7.3 Initial and Boundary Conditions Initial conditions (ICs) are spec ified by the user for the first day of a model simulation. For continental-scale modeling using the RPO 36-km do main, the ICs can affect model results for as many as 15 days, although the eff ect typically becomes very sma ll 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 simu lation 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 us ed. 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 fl ow 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 substan tial contributor to visib ility impairment. For this study BC data generated in an annual simu lation of the global-scal e GEOS-Chem model for

41 ENVIRON, 2006. “User’s Guide – Comprehensive Air-qua lity 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 we re 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 cas e, for calendar year 2002—to demonstrate the suitability of the modeling systems for subse quent planning, sensitivity, and emissions control strategy modeling. Comparisons between the 2 002 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 repres entative of the final model performance. Therefore, model performan ce was evaluated using the T yp02F emission inventory.

Model performance evaluation is performed by co mparing output from model simulations with ambient air quality data for the same time peri od 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 differe nt monitoring networks for both PM and gaseous species, for an annual time period, and for a large number of sites. The fo cus 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 Nitrat e (NO3); Elemental Carbon (EC); Organic Mass Carbon (OMC); Other inorganic fi ne particulate (IP or Soil); a nd Coarse Matter (CM). The

model must be evaluated for both the worst visi bility 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 experiment ed 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 a nd model-predicted species concentrations

• Scatter plots showing model predictions on th e y-axis and ambient data on the x-axis

• Spatial analysis plots with ambien t data overlaid on model predictions

• Bar plots comparing the mean fractional bi as (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 contribut ions 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 pe rformance for CENRAP CMAQ modeling using the Typ02F emissions inventory. Below are si x 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 for2002 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 mode l output. The monitoring networks used for comparison are IMPROVE, CASTNET, and STN, and are treated separately because each monitoring network has different go als, siting criteria, and data co llection protocols. The model performance plots depicted here are “bugle plot s”, and depict model pe rformance (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 pe rformance goals or criteria (i.e., the ≤ 30%/50% and

≤ 60%/75% bias/error levels) as concentrations become greater than 2.5 μg/m 3

.

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 th an the IMPROVE network, but is also mostly sited at Class 1 Areas and as such, model pe rformance at CASTNET sites should also be considered important. The STN monitoring network is an urban network, and model performance relative to this networ k should be given less importance.

The model performance goals and criteria used by CENRAP were approp riate at the time the modeling was conducted and consistent with th e methods adopted by VI STAS and WRAP. The EPA agrees with the CENRAP model performance pr ocedures 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/cen rap/cmaq.shtml#cmaq_typ02f_mpe ).

4.7.4.1 Model Performance for Sulfate (SO 4 )

Figure 4-2 shows the monthly SO 4 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 perfor mance for all months is within the goals for

both fractional bias and error. Model performance for CASTN ET sites is within goals for

fractional error and within the criteria for fr actional 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 (factiona l bias and error) of the Typ02f modeling scenario for sulfate (SO 4 ). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averag ing all monitors in the CENRAP region. Solid lines represent CENRAP mo deling goals and criteria.

4.7.4.2 Model Performance for Nitrate (NO 3 )

Figure 4-3 shows the monthly NO 3 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 duri ng 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 wi nter bias is more significant because NO 3 concentrations tend to be a larg e component of visibility impair ment 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, pr edicting concentrations of NO 3 much higher than observed.

Figure 4-3. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for nitrate (NO 3 ). The 12 symbols for each monitor represent monthly average model performance for the year 2002, averag ing all monitors in the CENRAP region. Solid lines represent CENRAP mo deling 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 perf ormance 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 hi gher concentrations of OC then the ruralA-36 IMPROVE monitors. Model performance for ST N sites shows a negati ve bias throughout the year that fall within the model cr iteria for both bias and error.

Figure 4-4. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for organic carbon (OC). The 12 sy mbols for each monitor represent monthly average model performance for the year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENR AP 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 perf ormance 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 la rge model underprediction during the summer atA-37 the IMPROVE sites. However, EC concentrat ions at these sites are low putting the model performance within the goals for low concentrations.

Figure 4-5. CENRAP model performance (factiona l 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 CENR AP 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 an d 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 gett ing worse in the fall and spring. This may be

due to local effects near the monitor and diffi culties in capturing emissions accurately in theA-38 inventory. This is an area of c oncern, especially in areas where so il contributes significantly to visibility impairment.

Figure 4-6. CENRAP model performance (factiona l bias and error) of the Typ02f modeling

scenario for soil. The 12 symbols for each monitor re present monthly average model performance for the year 2002, averaging all monitors in the CENRAP region. Solid lines represent CENRAP m odeling 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 thro ughout the year. This may be due to localized

emissions near the monitor and difficulties in capturing these emissions accurately in theA-39 inventory. This is an area of c oncern, especially in areas where CM contributes significantly to visibility impairment.

Figure 4-7. CENRAP model performance (factiona l bias and error) of the Typ02f modeling scenario for course par ticulate 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 divers e geographic area may mask model performance issues occurring in smaller geographic sub-region s. 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 CENR AP 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, vi sibility impairment is primarily due to high soil and CM which are not well predicted by the m odel 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 fo r planning purposes. In general, model performance is acceptable for SO 4 , NO 3 , 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 invest igated the assumption that all

CM and soil are natural a nd 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 vari ous 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 Mount ains 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, SO 4 and OC underestimates results in an underprediction (-42% bias) on total extinction. There is an overe stimate of extinct ion (+21% bias) on the 20% best days due to an over-prediction of NO 3 . With a few exceptions, visibility impairment on the worst days is due to SO 4

.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 M ODEL S IMULATIONS

The 2018 future-year base case scenar io is referred to as “2018 Base Case” or “Base18G”. The purpose of the Base18G scenario is to simulate the air quality represen tative of conditions in future year 2018 with respect to sources of crit eria and particulate matte r air pollutants, taking into consideration growth and controls. Modeli ng results based on this emission inventory are used to define the future year ambien t air quality and vi sibility metrics.

Input data used for the 2018 Base Case model si mulations 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 (i ncluding science options, run scripts, simulation periods, and ancillary data) fo r the Base18 cases was identical to that used in the Typ02G modeling.

The purpose of modeling 2018 visibility is to comp are the 2018 visibility predictions to the 2002 typical-year visibility modeli ng results and compare 2018 visibili ty predictions to the URP goal for 2018, as discussed below. Some improvement s in visibility by 2018 are expected because of reductions in emissions due to currently planne d regulations and techno logy improvements. The methodology used by CENRAP in developing visi bility projections for 2018 and described below is consistent with EPA guidance.

5.2 V ISIBILITY P ROJECTIONS

The Regional Haze Rule (RHR) goals include ac hieving 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 vi sibility 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 r easonable 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) th at has at one end the observed visibility conditions during the mandated five-year ( 2000-2004) baseline period and at the other end natural visibility c onditions in 2064; the visibility value that occurs on the glide path at year 2018 is the URP goal.

CENRAP has made 2018 visibility projecti ons using Typ02G and Base18G CMAQ 36-km modeling results following EPA guidance 43 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/s cram/guidance/guide/final-03-pm-rh-guidance.pdf)A-43 response factors (RRFs), which are defined as th e ratio of the future-y ear modeling results for each component that affects visibility to the current-year modeling results. The calculated RRFs are applied to the baseline obs erved 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 in to 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 devel op RRFs between future and current predicted concentrations of each component.

• Component-specific RRFs are multiplied by curr ent monitored values to estimate future component concentrations.

• Estimates of future component concentrations are consolidated to pr ovide an estimate of future air quality.

• Future estimated air quality is compared with the goal for re gional 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 exti nction in the revised IMPROVE mass extinction equation 44 is scaled separately. Because the modele d 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 m odeled 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) resul ting from the modified SOA module used by

CENRAP are also included in the OC term.

5.2.2 Projecting Visibility Chan ges Using Modeling Results

RRFs are calculated as the ratio of the 2018 mode ling 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 fo r 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 guidance 45
summarize the general procedure used to project fu ture-year visibility fo r the 20% best and 20% worst visibility days:

1) For each Class I area, rank visibility (in d eciviews) 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 pe riod, 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 pe riod emissions and future emissions. Use the resulting information to develop relativ e response factors fo r 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 IMPR OVE 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 wors t days of each year. Then average the 5 years together to get the final future mean d eciview 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 visibili ty conditions. These methods were appropriate at the time the modeling was performed.

5.3 R EASONABLE P ROGRESS G OAL AND P ATH TO N ATURAL C ONDITIONS

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 pr ogress. Table 5-1 and figure 5-1 compare the URP using the western natural conditions descri bed 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 UR P point is not a requirement of the RHR SIPs, but it serves as a benchmark to compare pr ogress 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 Program 46 , “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 s hows 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/s cram/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/reasonabl e_progress_guid 071307.pdfA-45 deciviews by 2018. This is consiste nt with the requirement of no de gradation of visibility on the best days at Class I sites.

Table 5-1. Comparison of reasona ble progress goal to uniform ra te 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 extrap olated 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 resu lts of total extinction between the Base18F and Typ02F model predictions, including the contri butions 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 concentr ations 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 requ ires that four factors be considered:

• Cost of compliance

• Time necessary for complianceA-46 • Energy and non-air quality environm ental 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 na tural 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 ot her states in the regi on, future visibility projections will be evaluated to determine th e necessary reductions in Oklahoma emissions needed to satisfy the 2064 regional haze goal.

Figure 5-2. Differences in modeled total extin ction (Bext) between Base18F and Typ02F for 20% worst days at Wichita Mountains site.A-47 

### Chapter 6: Source Apportionment Modeling

6.1 I NTRODUCTION

Visibility impairment in Class I areas is the re sult of local air pollution as well as transport of regional pollution across long distance s. The relative contributions to visibility impairment from each source region and category is needed to deve lop effective control strategies to improve visibility. CENRAP used CAMx Version 4.40 with its Particulate Source Apportionment Technology (PSAT) tool to provide source a pportionment by geographic regions and major source category. CAMx was run with similar opt ions and inputs as the CAMQ modeling with

both the 2002 baseline and 2018 future case emissi on inventories. The CAMX model selection and performance are discussed briefly in secti on 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 operat e in parallel to the CAMx host model using the same emissions, transport, chemical transfor mation and deposition rates as the host model to account for the contributions of user speci fied 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 agai nst other apportionment techniques. 48,49 The goals of the

PSAT assessment are to evaluate the contributions of different geographi c regions and source categories to visibility impair ment at Class I areas in 2002 an d the projected 2018 case in order to identify those regions and s ource categories that, if contro lled, 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 we stern and eastern United States, the Gulf of Mexico, Canada and Mexico. Six source categories (eleva ted point sources; lowlevel point sources, on-road mobile, non-road mobile, area and natura l or non-anthropogenic sources) were tracked separately. The CENRAP PSAT 2002 and 2018 applica tions 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 an d 20% best days at e ach CENRAP and nearby

Class I area were extracted from the PSAT result s. The original IMPROVE equation was used

to calculate extinction coefficients from modele d 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 littl e effect on visibility predictions at the CENRAP class I areas.

Extinction due to soil and coarse material ch anges very little between 2002 and 2018. A PSAT Visualization Tool was developed that can be used by States, Tr ibes 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 W ith Extensions (CAMx) Version 4.30.” ENVIRON International Corporation, Novato, Calif ornia, 2006 (available at www.camx.com).

48 Morris, R.E., G.Y., C.E., G.W., B. K. 2005. “Recent Advances in On e-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 perc ent 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 S OURCE A PPORTIONMENT R ESULTS AT W ICHITA M OUNTAINS

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 speci es for the 20% worst days in 2002 and 2018. Visibility impairment at the Wi chita Mountains site in 2002 on th e 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 vi sibility 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 su lfate emissions from point sources in Texas, the eastern United States, Indiana, and Ohio acc ount for a decrease of 11 Mm-1 in total light extinction, or 47% of the total reduction be tween 2002 and 2018. Even with such large

50 available at http://www.cenrap.org/html/projects.phpA-49 reductions in SO4 from point sources in 2018, extinc tion due to point sources is still the highest contributor to visibility impairment on the wors t 20% days. Reductions in mobile sources of

NO3 contribute a decrease in tota l extinction of a pproximately 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 larg e computational expense. The use of higher resolution modeling should be reconsidered in future modeling efforts.

Table 6-1. Projected light exti nction for 20% worst days at Wichita Mountains in 2002 (Mm -1 )

Total 1 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 exti nction for 20% worst days at Wichita Mountains in 2018 (Mm -1 )

Total 1 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 matterA-50 Figure 6-2. Source apportionment modeling results by source re gion and source category for worst 20% days at Wichita Mountains in 2002.

Figure 6-3. Source apportionment modeling results by source regi on and species for worst 20% days at Wichita Mountains in 2002.A-51 Figure 6-4. Source apportionment modeling results by source re gion 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 regi on and species for worst 20% days at Wichita Mountains in 2018.A-52 6.3 C ONTRIBUTIONS TO VISIBILITY IMPAIRMENT AT OTHER C LASS 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 outsi de of the state.

Oklahoma sources are modeled to have small c ontributions to the othe r 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 20 18 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 unit s. We agree that additional emission reductions in Oklahoma, beyond those controlled through ODE Q’s BART determination’s or the proposed FIP, are not necessary to protect visibility at Class I areas outsi de of the state at this time.A-53 

### Chapter 7: BART Determination

7.1 I NTRODUCTION

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 Se ction 169A(b)(2)(A) of the act requires certain major stati onary sources in existence betw een 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-i mpairing pollutant (NOx, SO 2 , and PM) from any unit that was in existence on August 7, 1977 and began operatio n after August 7, 1962. Table 7-1 shows all facilities within Oklahoma that have units that are BART-eligible.

Table 7-1. Facilities with BA RT-eligible units in Oklahoma

Pursuant to federal regulations, states have th e option of exempting a BA RT-eligible source from the BART requirements based on dispersion m odeling demonstrating that the source cannot reasonably be anticipated to cause or contribute to visibility im pairment in a Class I area. According to 40 CFR Part 51, Appendix Y 51 , a BART eligible s ource is considered to “contribute” to visibility impairme nt in a Class I area if the m odeled 98th percentile change in

51 EPA Guidelines for BART Determinations Under the Regional Haze Rule; EPA, 2005A-54 deciviews is equal to or greater than the “contribution threshol d.” 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 contribut ion threshold of 0.5 change in deciview be used, although States have the option to establ ish 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 recomme nds use of the CALMET/CALPUFF modeling system; the main components of this modeling system are CALMET (a diagnostic threedimensional meteorological model), CALPU FF (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 repres entatives of the Federal Land Managers. ODEQ reviewed these BART analyses in accordan ce with this BART mode ling 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 f acilities that were granted waivers from BART base d 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 Paci fic Consumer Products LP – Muskogee Mill, International Paper – Vall iant Paper Mill, and Western Farmers Electric Coop (WFEC) – Anadarko Plant) showed contributions to visibility impair ment greater than the threshold value. Each facility has applied for a permit modification with reduced emissions to decrease their contributions to visibility impair ment to below the threshold value and qualify for a waiver from BART. Georgi a Pacific Consumer Products LP – Muskogee Mill will limit NOx emissions to 744 lb/day in order to lower their visibility impa irment contribution to below 0.5 dv. International Paper – Valliant Pa per Mill will limit the sulfur content of their fuel to meet the threshold. The WFEC – Anadarko facility has lowered the emission limits of NOx, SO 2 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 Pape r permit was issued Ma rch 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 D ISPERSION M ODELING P ROTOCOLA-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 guidance 52 and readily available mode ling data bases that were used by states and/or source opera tors in many cases to conduct th eir analyses. In development of these guidelines, CENRAP fo llowed the EPA BART guidelines 53 and the applicable CALMET/CALPUFF modeling guidance 54 in effect at the time the analysis was conducted. ODEQ required all owners or ope rators of BART-eligible sour ces to perform source-specific dispersion modeling using CALPU FF 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 screeni ng or refined analysis modeling to determine

whether or not the source cont ributes significantly (a bove the 0.5 dv threshol d) to visibility impairment at any Class I area. Visibility impa irment is calculated in relation to the natural background visibility conditions described in th e 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 mode ling was performed for those uni ts subject to BART (Table 7-3) by consultants on behalf of the owner/operato r to determine the visibility improvement that would result from installation of various opti ons 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 Modelin g 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 Regi onal 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 Tr ansport 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 fo ssil fuel-fired steam electric plants NOx

OG&E Sooner Units 1 and 2 foss il fuel-fired steam electric plants SO 2

NOx

PM 10 OG&E Muskogee Units 4 and 5 fossil fuel-fired steam electric plants SO 2

NOx

PM 10

AEP/PSO Comanche Units 1 and 2 foss il 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 fo ssil fuel-fired steam electric plants SO 2

NOx

PM 10

AEP/PSO Southwestern Unit 3 fossil fuel-fired steam electric plants NOx

7.2.2 Model Selection and Applicability

Relevant guidance 55 states that the CALPUFF model is gene rally 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 faciliti es 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 Cl ass I area are 50 km or greater for many receptors. Generally EPA doe s not recommend using CALPUFF within 50 km, but in this case it is a mix of less than and gr eater than 50 km distances and the nearest distance is 40 km. Furthermore, since we are assessing visibility and the distan ce is near the 50 km distance, EPA concurred with the use of CALP UFF 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 Regi onal 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 Tr ansport 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 overl apping CALMET/CALPUFF modeling domains in

the central United States coveri ng the southern, central and nor thern portion of the CENRAP region (Fig. 7-1). The dimensions of the domain we re selected to include the State of interest, plus Class I areas in nearby stat es, and to provide a sufficient buffer between the Class I areas and the domain boundaries (e.g., 50 km) to assure th at CALPUFF puffs are not eliminated that may temporarily leave the domain and later reemer ge and cause visibility impacts at a Class I area. The operator/owner may elect to restrict the computational CALPU FF 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 CA LMET 6km meteorologi cal modeling domains

For BART screening analyses, CALPUFF m odeling 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) resolu tion dataset and land use data from United States Geological Survey (USGS) Global La nd Cover Characterization (GLCC) version 2.0 database at 30 second (~1km) resolution. CENRAP used MM5 meteorology data for the years

2001 57 , 2002 58 and 2003 59 (at 36 km resolution to create 6-km resolution CALMET

56 Appendix 6-2 of the OK RH SIP, CENRAP BART Modelin g 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/scra m001/reports/2001_36_mm5_summary_fin al.pdf); “Annua l 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/meteor ology/metgridmodeling/an nual_prot_1.1.pdf).A-58 meteorological fields. Observational data was not blended with the MM5 data in the CALMET modeling. EPA recommends performing thr ee years of CALMET/CALPUFF modeling when using MM5 data and 2001 through 2003 were th e 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 BA RT modeling, source operators/o wners developed specialized modeling domains with finer reso lution meteorological fields. A 4-km resolution meteorological grid was used and the MM5 data was blended wi th surface and upper air observations. Terrain

data from the USGS at 90m resolution and la nd 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 emission s reflecting periods of start-up, shutdown, and malfunction be used, as such emission rate s could produce higher t han normal effects than would be typical of most facilities. We reco mmend that States use the 24 hour average actual emission rate from the highest emitting day of the meteorological peri od modeled, unless this rate reflects periods start- up, shutdown, or malfunction.” 60

The CENRAP BART modeling guidance reco mmends the following prioritization for

identification of the maximum 24-hr actual emissi ons 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 MM 5 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. Meteorol ogical 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 th e 2001-2003 meteorological period modeled. In-stack continuous emission monitoring (C EM) 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 recommen dations for regulatory application of the CALPUFF modeling system in effe ct at the time of the analysis with a few exceptions. Appendix A of the CENRAP BART modeling gu idance details the CALMET settings used and the EPA default values, and Appendix B detail s the recommended CALPUFF settings used for screening analyses and the EPA default sett ings. Development of the regional CALMET meteorological fields from MM5 data for sc reening 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 th e 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 al oft 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 a ssumes no water land use categories.

Refined source-specific modeling used a constant value of 3 ppb background concentration of ammonia for the domain during th e modeling period. Hourly ozone observational data provided by ODEQ over the 2001-2003 timeframe were used to define background oz one 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 util ized (NO OBS = 0) for the sour ce-specific modeling, consistent with EPA and FLM recommendations. Original ly, modeling was performed with CALMET v6

for use with CALPUFF v6.211. Problems have been identified in these versions 61 therefore Trinity Consultants reverted to an existi ng CALMET data set that utilized CALMET v5.53a which is an earlier regulatory version and compatible with th e regulatory version of CALPUFF (v5.8). EPA was made aware of these deviations and weighed the potentia l differences that may

occur due to these deviations with the additional work that would be required to redo the analyses. EPA concluded that thes e deviations were acceptable in th is case due to time and work

resources available at the time.

7.3 BART D ETERMINATION

61 Atkinson, Dennis, 2007. CALPUFF: Status and Update , Presentation at Regional/State/Local Modelers Workshop, May 16, 2007. (http://www.cleanairinfo.com/regionalstatelocalmodelin gworkshop/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 ar e minimal. Therefore, ODEQ determined that

SO 2 and PM BART are no additional control. A full BART analysis was conducted for NOx for the gas-fired subject-to-BART units. The resu lts of that analysis are discussed here.

The OG&E Seminole Units 1, 2 and 3 are BART-elig ible 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 th at 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 conjunc tion with other factor s including the technology available, the costs of complia nce, the energy and nonair quali ty environmental impacts of compliance, any pollution control eq uipment in use at the source, a nd the remaining useful life of

the source.

The AEP/PSO Comanche Units 1 and 2 are BART-e ligible 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 effectivene ss 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 usef ul 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 OF A, 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, a nd the remaining useful life. Lastly, ODEQ performed modeling to investigat e the visibility impacts. ODE Q found that NOx BART was the

installation of LNB with OFA for Units 2.

The AEP/PSO Southwestern Unit 3 is a BART-elig ible source. This unit is a gas fired boiler with a gross output of 332 MW. ODEQ dete rmined that LNB and OFA , and SCCR are technically feasible. ODEQ evaluated the cont rol effectiveness of these technologies. ODEQ then evaluated the impacts, including the cost s of compliance, the energy impacts, the non-air quality environmental impacts, and the remaining useful lif e. Lastly, ODEQ performed modeling to investigate the visibility impacts. ODEQ found that NOx BA RT was the installation

of LNB with OFA for Unit 3.

EPA reviewed the modeling and analysis perfor med in ODEQ’s BART determinations for the above units and agrees with the findings of BART contro ls 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 determinationsA-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 Americ an Electric Power/Public Service Company of Oklahoma Northeastern plant. ODEQ determined that th e installation of LNB with OFA to achieve an

emission limit of 0.15 lb/mmBTU is NOx BART fo r these 6 units. NOx control levels on 30-day rolling averages of 0.15 lb/mmBtu are justified meet the presumptive lim its prescribed by EPA

. The BART determination also identified the ex isting electrostatic prec ipitators (ESP) as PM BART for these 6 units. EPA reviewed ODEQ’s ba sis for these BART determinations, including the costs of compliance, the energy impacts, th e non-air quality environmental impacts, the remaining useful life and visibi lity modeling, and agreed with the conclusions that LNB w/OFA and ESP, are BART for these facili ties 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 drye r absorbers (“dry scrubbers”) no r 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 SO 2 removed ($/ton) to the visibility improvement at the nearest Class I areas. ODEQ determined that SO 2 BART for these units was no control and specified an SO 2 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 em ission 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 SO 2 BART assessment. The

AEP/PSO units have historical annual emission rate s that has been steadily decreasing to a point where the imposition of ODEQ’s proposed BART SO 2 emission rate of 0.65 lbs/MMBtu would result in very little in emission reduction.

EPA reviewed the modeling and cost analysis th at was the basis for these determinations. EPA disagrees with ODEQ’s SO 2 BART determination for thes e 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 issu es were noted. EPA re modeled the visibility impacts of the Sooner, Muskogee and Northeastern Generating Stat ions to rectify these issues and to address consistency w ith modeling guidance EPA has provided to the states. This visibility modeling effort is described in deta il in the report titled “Refined Best Available Retrofit Technology (BART) Modeling Repor t: OG&E Sooner Generating Station, OG&E Muskogee Generating Station, and AEP/PSO No rtheastern Generati ng Station,” found as appendix D to the Oklahoma Regional Haze Technical Support Document.

EPA has conducted modeling analyses for our pr oposal 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 si x units, which includes multiple scenarios, is included in Appendix B to the TSD.

## APPENDIX B

Refined Best Available Retrof it Technology (BART) Modeling Report: OG&E Sooner Generating St ation, OG&E Muskogee Generating Station, and AEP/ PSO Northeastern Generating

Station

Prepared by:

Erik Snyder

Michael FeldmanB-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 SO 2 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 va lues used for “Method 6” ........................................... 7 Table 2-2. Monthly Humidity Factor s 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 Su lfuric Acid Emission Calculations (EPRI method) ....................................................................................................................... ...... 13 Table 4-1. Average of 98 th percentile of daily maximum dv for 2001, 2002, and 2003,

Method 6 ...................................................................................................................... ..... 18 Table 4-2. Average of 98 th percentile of daily maximum dv for 2001, 2002, and 2003,

Method 8 ...................................................................................................................... ..... 19

Table 4-3. Maximum of 98 th percentile of daily maximum dv for 2001, 2002, and 2003,

Method 6 ...................................................................................................................... ..... 20

Table 4-4. Maximum of 98 th percentile of daily maximum dv for 2001, 2002, and 2003,

Method 8 ...................................................................................................................... ..... 21B-iiiB-1 

### Chapter 1: Introduction

Units 4 and 5 of the Oklahoma Gas and Elect ric (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) North eastern plant have been determined to be subject-to-BART. These units are tange ntially 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 SO 2 , respectively. The AEP/PSO units have the same controls, with the addition of 1 st generation low NOx burners with over fire air. After review of modeling and analysis pe rformed by the Oklahoma Department of Environmental Quality (ODEQ), EPA ag rees 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 sc rubbers”) as BART controls for control of SO 2 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 thes e three facilities’ (6 BART units) SO 2 BART determination, ODEQ concluded that neither dry flue gas desulfurization with spray dryer absorbers (“dry scrubbers”) nor wet flue gas desulfurization (“wet sc rubbers”) were cost effective for these units. ODEQ came to this decision based on their analysis of cost effectiveness and modeled visibility improve ment at the nearest Class I areas. ODEQ

determined that SO 2 BART for these units was no control and specified an SO 2 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 (P RB) of Wyoming, and alr eady have historical annual emission rates significantly below this li mit. Therefore, it is possible the OG&E units would be able to actually increase th eir emissions approximately 10-20%, and still be in compliance with ODEQ’s SO 2 BART assessment. The AEP/PSO units have

historical annual emission rate s that have been steadily decreasing to a point where the imposition of ODEQ’s proposed BART SO 2 emission rate of 0.65 lbs/MMBtu would result in very little in SO 2 emission reduction.

EPA reviewed the modeling and cost an alysis that was the basis for these determinations. EPA disagrees with ODEQ’s SO 2 BART determination for these units, and is proposing to promulgate a FIP to reme dy 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 impact s 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 documentB-2 

### Chapter 2: BART Guidelines and Modeling Protocols

2.1 B ACKGROUND AND I NTRODUCTION

The BART analysis includes engineeri ng 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 fi nal factor to consider under EPA's BART Guidelines is the degree of visibility improveme nt from the BART control options 1 . The BART guidelines recommend use of the CALPUFF ai r quality dispersion model to estimate the visibility improvements at each Class I ar ea, typically within roughly a 300 km (186 mile) or larger radius of the source, of a lternative control technologies, and to compare these to each other and to the impact of th e baseline, or current , source configuration. 2
The four Class I Areas within approximately 300 km of the facilities were included in the modeling analysis. Thes e 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) fr om 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 imp acts 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 S ources and Class I Areas

Trinity Consultants conducted the visibi lity modeling for Sooner, Muskogee, and Northeastern Generating Stations and this modeling was reviewed by ODEQ as part of

their BART determination 3 . Although EPA generally rega rds the visibility modeling analyses performed by ODEQ to be of high quality, some issues were noted. EPA remodeled the visibility impacts of the S ooner, Muskogee and Northeastern Generating Stations to rectify these issues and to a ddress consistency with modeling guidance EPA has provided to the states. ODEQ and EPA mode ling 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 th e visibility improvement to be used in comparisons between various control scenarios is the 98 th percentile of impacts expressed as 24-hour averages of delta deciviews relati ve 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 Sta tions BART determinations.

3 Throughout this document, any reference to “ODEQ modeling” refers to modeling performed or reviewed by ODEQ.B-4 2.2 C ALCULATION OF V ISIBILITY I MPACT

Under the RHR, visibility is measured in deciviews. Visibility is traditionally described in terms of visual range in kilome ters or miles. However, the visual range scale does not correspond to how people percei ve 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 perceive d visual changes over its en tire range, analogous to the decibel scale for sound: a given change in deciviews will be perceived as the same amount of visibility change, wh atever the original visibility was. The defining equation

is:

deciviews = 10*ln(b ext / 10) (1)

where ln is the natural logarithm, and extinction b ext is the fraction of light scattered out of a viewing path. Extinction incr eases with the amount of pollution (b ext is in units of 1/Mm, “inverse megameters”). Lower deciview values represent better visibility, more pristine atmospheres, and greater visual rang e, while increasing deciview values represent increasingly poor visibility. An increase of 1 deciview corresponds to about a 10% increase in extinction. (Deciviews are rela ted 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, deciview s 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 followi ng visibility-impairing pollutants: SO 2 , SO 4 , NO

x , secondary organic aerosol (S OA), fine particulate matter (PMF), coarse particulate matter (PMC), and elemental carbon (EC). CALPUFF incorporates a semi-empirical

chemical module that simu lates the conversion of SO 2 to particulate sulfate and NO

x to particulate nitrate, at a rate dependent on meteorological c onditions and background

ozone concentration. These concentrations are converted to deci views by the CALPOST post-processor in two steps.

2.2.1 IMPROVE Equation

Under the original IMPROVE 4 equation, extinction (b ext ) is estimated from the predicted concentrations of various pollutants:

b ext = 3 * f(RH) * [sulfate]

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), ra nging from 1 at low humidity to 18 at 98% humidity.

2.2.2 Revised IMPROVE Equation

EPA used the “revised” IMPROVE equation 5 to calculate light extinction. Sometimes the revised IMPROVE equation is called the “New” Improve equation. The revised IMPROVE equation is used to convert measured or modeled concentrations into extinction for each pollutant chemical species , and then total them up, accounting for the effect of relative humidity.

Revised IMPROVE equation:

b ext = 2.2 * f

s (RH) * [small sulfate] + 4.8 * f L (RH) * [large sulfate] + 2.4 * f

s (RH) * [small nitrate] + 5.1 * f L (RH) * [large nitrate] + 2.8 * [small organic mass] + 6.1 * [large organic mass] + 10 * [elemental carbon] + 1 * [fine soil] + 1.7 * f

ss (RH) * [sea salt] + 0.6 * [coarse mass] + Rayleigh scatteri ng (site-specific) + 0.33 * [NO 2 (ppb)]

Sulfate is assumed to be all “large sulfate” if total sulfate is over 20 μ g/m 3 , 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 meas ured by IMPROVE monitors, an increase over the old 1.4. Sea salt is estimated as 1.8 * [chlor ide] (or chlorine if chloride not available)

The f

s , f L , f

ss are water growth factors for small (“S” ) and large (“L”) fractions of sulfate and nitrate, and for sea salt (“SS”). Thei r 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 IM PROVE equation afte r a scientific assessment of its implications for regiona l haze planning to reduce biases in light extinction estimates compared to the old algorithm 6 . In particular, when compared to nephelometer direct measurements of visi bility extinction, th e original IMPROVE equation over-predicts for low extincti on conditions and under-predicts for high

extinction. These biases have direct relevance for estimate s for the worst 20% visibility days that are used to asse ss visibility impact. The new equation shows broader scatter overall, but less bias in matc hing 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 ha s less bias, is more refined, accounts for more pollutants and pollutant sizes, incor porates more recent data, and is based on

considerations of relevance for the calcul ations needed for assessing progress under the Regional Haze Rule.

EPA 2007 guidance 7 states that the use of either the IMPROVE or the revised IMPROVE equation is acceptable provided that the same algorithm is utilized for both

the base and future extinction calculations. EPA believes it is appropriate to use 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 extincti on is converted to deciviews, using the defining equation (equation 1) for d eciviews. The delta deciviews ( Δ dv) represents the impact on visibility in deciviews of the s ource 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 decivi ews is the difference between deciviews including the impact of the source and natural background, a nd deciviews of the natural background alone. Each modeled day and lo cation 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 pe rcentile, roughly equivalent to the 8 th 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. Th e 98th percentile is

recommended rather than the maximum va lue 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 IMPROV E Equation for Estimating Ambient Light Extinction Coefficients – Final Report ( http://vista.cira.colostate.edu/improve/Publicati ons/GrayLit/016_IMPROVEEe qReview/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 impact s of sources be estimated in deciviews relative to natural background. ODEQ used b ackground concentrations (Table 2-1) from Table 2-1 and monthly area-specif ic relative humidity factors (Table 2-2) from Table A-3 of “Guidance for Estimating Natural Visi bility Conditions Under the Regional Haze

Rule” 8 . The factors listed in Table 2-2 a nd used by ODEQ are not the recommended values and are given in the guidance documen t as supplemental information. Table 2-3

shows the recommended values from Table A-2 of the guidance and used by EPA for visibility calculati ons 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 Creek 9 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 Fact ors 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 Ma nagers’ Air Quality Related Values Work Group (FLAG) Phase I Report 10 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 Estimat ing Natural Visibility Conditions Under the Regional Haze Rule places Caney Creek in the East.

10 Federal Land Managers’ Air Quality Related Valu es 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 s ubject 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 we re 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 identifie d in these versions 15 therefore Trinity Consultants reverted to an existing CALMET data set that utilized CA LMET v5.53a which is an earlier regulatory version and compatible with the re gulatory version of CALPUFF (v5.8). 16

11 Calpuff Modeling Report BART Determination, Ok lahoma 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 Determinati on, 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) Determ ination, 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/permi tting/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/RulesAndPlan ning/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/regionalstatelocalmode lingworkshop/archive/2007/presentations/Wednesday %20-%20May%2016%202007/CALPUFF_status_update.pdf)

16 Letter to Lee Warden (ODEQ) from Trinity Cons ultants, January 23, 2008 RE: Oklahoma Gas and Electric – Multiple Sites, Best Available Retrofit Technology (BART) Determination, CALMET Data Processing ProtocolB-9 Table 2-5 shows selected CALMET settings used by Trinity for modeling visibility impacts from the three facilities. R1MAX a nd R2MAX represent the radius of influence of surface and upper air observati ons, 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 requi red to redo the analyses. EPA concluded that these deviations were acceptable in th is case due to time and work resources

available. EPA elected to utilize th e 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 modeli ng, refined source-specific modeling used a constant value of 3 ppb background concentrat ion of ammonia for the domain during the modeling period. Hourly ozone observationa l data provided by ODEQ over the 2001-

2003 timeframe were used to define background ozone concentrations.

17 Memorandum from Tyler Fox (EPA OAQPS) “C larification on EPA-FLM Recommended Settings for CALMET,” August 31, 2009B-10 Table 2-5. Selected CALMET Settings Used
Option Description EPA default Trinity Consultants 18

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 Co nsultants for use in CALPUFF simulationsB-11 

### Chapter 3: Emission Calculation Methodology

This chapter discusses the differences in a pproaches to calculating emissions used by Trinity Consultants and EPA for the three fa cilities being evalua ted. 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 S PECIATION

3.1.1 Trinity Consultants Approach

For modeling scenarios that included only PM 10 emissions, PM coarse, PM fine, soil,

elemental carbon and SOA emissions were i nput 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 sp eciation 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 as h content. Trinity Consultants calculated PM sp eciation for Sooner and Musk ogee 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 presen t 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 contro l scenarios, and again the proper coal properties were not used in the calcul ation. 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 a nd 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 scenarios 19 PM speciation used for Muskogee Units for all

control scenarios 19 PM speciation used for

Northeastern Units for

all control scenarios 20

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 bo ilers and the emission control equipment present in each modeling scenario. Coal pr operties 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., PM 8.0 , PM 4.25 , PM 1.88 , PM 1.25 , PM 0.813 , and PM 0.625 ). After CALPUFF modeling, these modeled species are then gr ouped in POSTUTIL as to represent the appropriate visibility pollu tants (e.g., PMcourse = PM 8.0 + PM 4.25 and PMfine = PM 1.88 +

PM 1.25 + PM 0.813 + PM 0.625 ). Elemental carbon (EC) and secondary organic aerosols (SOA) are based on the PM 1.88 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 Ba seline emission rates and controlled emission

rates also see Attachment A of this report.

3.2 S ULFURIC A CID E MISSIONS C ALCULATIONS

3.2.1 Trinity Consultants Approach

Sulfuric acid emissions were calculated in two ways by Trinity Consultants. For modeling scenarios that included only SO 2 /SO 4 emissions, sulfuric acid emissions from the Sooner and Muskogee units were estimat ed based on an assumed 1% SO2 to SO3

conversion rate across the boiler. A sulfuric acid control e fficiency of 40% was assumed for the wet FGD and 90% for the dry FGD. E missions from the Northeastern units were

19 PC Wet Bottom ESP PM Speci ation worksheet from NPS http://www.nature.nps.gov/air/perm its/ect/ectCoalFiredBoiler.cfm

20 PC Dry Bottom ESP PM Speciation worksheet from NPS http://www.nature.nps.gov/air/perm its/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, 12/28/09B-13 calculated based on an assumed 3ppm sulfur c ontent conversion in the flue gas. In modeling scenarios including only PM emissi ons, all inorganic condensable PM were assumed to be sulfates by Trinity Consultants. As detailed above, e rrors were discovered in the PM speciation calculati ons which include the calculation of the emission rate of the inorganic condensable fraction.

3.2.2 EPA Approach

Sulfuric acid emissions from power plan ts are calculated by EPA by estimating the

amount of H 2 SO 4 produced and the amount of H 2 SO 4 removed by control equipment using information from the Electric Power Research Institute (EPRI). 22 These calculations rely on assumed values for the amount of fuel sulfur converted to SO 2 , the

amount of SO 2 oxidized to SO 3 , and the amount of H 2 SO 4 lost to (or mitigated by) the air pre-heater and applicable cont rol equipment, such as baghou ses, and FGDs. Baseline and post-control estimates of H 2 SO 4 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 emissi on rates and controlled emission rates, see Attachment A of this report.

Table 3-2. Parameters used by EPA in Su lfuric Acid Emission Calculations (EPRI method)

EPRI method parameter Value

% Sulfur 23 0.48%

% Ash 13 7.5% Heating value 13 8000 Btu/lb

Sulfer conversion to SO 2 24 0.875

F1 25 (Fuel Impact Factor, subbituminous/PRB all boilers) 0.0019

F2 25 (Technology Impact factors):

Control Scenario BaseA Base B_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 NO X E MISSIONS

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 North eastern 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 visi bility 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 incl usion of our proposed SO2 reduc tions, the analys is indicates small visibility improvements would be achie ved with also requiri ng SCR, therefore we are determining that the visibility improveme nts 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 be low 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 facil ity (including all visibility imp airing 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 Attachme nt A of this report.

3.4 SO 2 E MISSIONS

As discussed in the Main TSD and other A ppendices to the Main TSD, EPA concluded

that installation of DFGD c ould achieve a 0.06 lb/mmBtu SO 2 emission limit or the

installation of WFGD could achieve a 0.04 lb/mmBtu SO 2 emission limit at all six BART

units. We modeled a Baseline emission rate fo r comparison using emission rates that are documented in Attachment A to this report. Baseline SO 2 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 conjunc tion 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 estim ates used in the m odeling analysis to determine visibility impairment impacts s hould reflect steady-stat e operating conditions during periods of high capacity utilizati on. These baseline emission rates are representative of the highest 24-hour block em issions reported duri ng the baseline period (the RHR defines the baseline period as 2000-200 4) and are consistent with the baseline emissions used in each facility’s BART analys is. We note, these units currently burn a

low sulfur coal from the Powder River Ba sin (PRB) of Wyoming, and currently have

emission rates below this baseline rate. Ho wever, without a BART determination, there are no federally enforceable permits that prohi bit these units from re turning to a higher

sulfur coal and emission rates similar to thos e seen during the baseline modeling period.

For further information about the Baseline em ission 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 m odeling for each pollutant was carried out separately so that only NO

x emissions or only SO 2 /H 2 SO 4 emissions or only PM10 emissions were included in a modeling scen ario. Due to the nonlinear nature and complexity of atmospheric chemistry and chemical transformation among pollutants, CALPUFF modeling on a pollutant-specific basi s is not recommended. 26 Furthermore, this approach does not allow for predictions of total visibility impairment for different

control scenarios at Class I area rece ptors and the determination of the 98 th percentile day for visibility impairment. To model the vi sibility 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 M ODELING S CENARIOS

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. SO 2 controls evaluated were dry flue gas desulfurization (DFGD) and wet flue gas desulfurization (WF GD). We did not explicitly

model the SO 2 emissions if a unit was converted to combusting natural gas instead of coal, but the visibility impairment improve ments 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 emissi on rates for NOx, SO2, and PM10 are from Appendix E, of each facilities’ Best Available Retrofit Technology Evaluation report 27,28,29 . SO 4 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

27 Calpuff Modeling Report BART Determination, Ok lahoma 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 Determinati on, 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 a bove. PM speciation was calculated using the correct NPS PM speciatio n worksheet and coal properties.

2. LNB – Emission rates for NOx are at the LNB/OFA controlled value of

0.15 lb/mmBTU consistent with th e controlled rate assumed by the

facilities. All other emissions are un changed 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. SO 4 emissions were calculated for each unit based on the

EPRI method discussed above. PM speciation was calculated using the correct NPS PM speciation worksheet a nd 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 NO x are at the LNB/OFA controlled

value of 0.15 lb/mmBTU. SO2 emissions are controlled to 0.04

lb/mmBTU. SO 4 emissions were calculated for each unit based on the

EPRI method discussed above. PM speciation was calculated using the correct NPS PM speciation work sheet and coal properties.

The SO 2 emission rates suggested by the facili ties 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 th e application of contro l 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 provi de 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 asse ssed more or less in tuitively, weighing the various area impacts and the number of affected Class I areas.

29 Best Available Retrofit Technology (BART) Determ ination, 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/permi tting/applications/96-288-TV_M-3/index.htm)B-17 Tables 4-1 and 4-2 present the average value of the 98 th percentile of the daily maximum impact for the three modeled years ( 2001-2003) using “Method 6” and “Method 8,” respectively. SO2 controls in additi on 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 be tween the result s of the WFGD and DFGD in

the 98 th percentile values. At the low sulfur emission levels of these controls, nitrates are responsible for the majority of visibility impa irment so no benefit is seen in decreasing

SO 2 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 98 th 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 th e 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 scientifica lly robust “Method 8” version.B-18 Table 4-1. Average of 98 th 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 2.98B-19 Table 4-2. Average of 98 th 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 2.47B-20 Table 4-3. Maximum of 98 th 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 3.64B-21 Table 4-4. Maximum of 98 th 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 3.02B-22 

## Attachment AB-23SOONER UNIT 1 Control systems NOx SO2 PM10 H2SO45116 mmBTU/hrNOx SO2 lb/mmBTU lb/hr lb/mmBTUlb/hr lb/mmBTU lb/hr lb/hrBaseline A base base 0.601 3074.7160.860 4399.760 0.038 193.896 4.05Base B LNB LNB/OFA base 0.150 767.400 0.860 4399.760 0.038 193.896 4.05LNB DFGD LNB/OFA DFGD 0.150 767.400 0.060 306.960 0.012 61.392 0.06LNB WFGD LNB/OFA WFGD 0.150 767.400 0.040 204.640 0.038 193.896 1.62SOONER UNIT 1 Control systems PMCoarse (lb/hr) PM Fine (lb/hr)EC (lb/hr) SOA (lb/hr)NOx SO2 PM 8 PM 4.25 PM 1.88PM 1.25 PM 0.813 PM 0.625Baseline A base base 49.20 60.77 34.738.68 5.79 34.73 3.20 27.60Base B LNB LNB/OFA base 49.20 60.7734.73 8.68 5.7934.73 3.20 27.60LNB DFGD LNB/OFA DFGD 10.01 16.0214.68 4.00 7.349.34 1.10 26.20LNB WFGD LNB/OFA WFGD 24.58 30.0443.69 10.92 30.0454.62 5.10 3.50SOONER UNIT 2 Control systems NOx SO2 PM10 H2SO45116 mmBTU/hrNOx SO2 lb/mmBTU lb/hr lb/mmBTUlb/hr lb/mmBTU lb/hr lb/hrBaseline A base base 0.584 2987.7440.860 4399.760 0.039 200.036 4.05Base B LNB LNB/OFA base 0.150 767.400 0.860 4399.760 0.039 200.036 4.05LNB DFGD LNB/OFA DFGD 0.150 767.400 0.060 306.960 0.012 61.392 0.06LNB WFGD LNB/OFA WFGD 0.150 767.400 0.040 204.640 0.039 200.036 1.62SOONER UNIT 2 Control systems PMCoarse (lb/hr) PM Fine (lb/hr)EC (lb/hr) SOA (lb/hr)NOx SO2 PM 8 PM 4.25 PM 1.88PM 1.25 PM 0.813 PM 0.625Baseline A base base 50.63 62.54 35.748.93 5.96 35.74 3.30 28.40Base B LNB LNB/OFA base 50.63 62.5435.74 8.93 5.9635.74 3.30 28.40LNB DFGD LNB/OFA DFGD 10.01 16.0214.68 4.00 7.349.34 1.10 26.20LNB WFGD LNB/OFA WFGD 25.36 30.9945.08 11.27 30.9956.35 5.30 3.70B-24MUSKOGEEUNIT 4Control systems NOx SO2 PM10 H2SO45480 mmBTU/hrNOx SO2 lb/mmBTU lb/hr lb/mmBTUlb/hr lb/mmBTU lb/hr lb/hrBaseline A base base0.495 2712.600 0.800 4384.000 0.018 100.832 4.34Base B LNB LNB/OFA base0.150 822.000 0.800 4384.000 0.018 100.832 4.34LNB_DFGD LNB/OFA DFGD0.150 822.000 0.060 328.800 0.012 65.760 0.06LNB_WFGD LNB/OFA WFGD0.150 822.000 0.040 219.200 0.018 100.832 1.74MUSKOGEEUNIT 4Control systems PM Coarse (lb/hr) PM Fine (lb/hr)EC (lb/hr) SOA (lb/hr)NOx SO2 PM 8 PM 4.25 PM 1.88PM 1.25 PM 0.813 PM 0.625Baseline A base base25.58 31.60 18.06 4.51 3.01 18.06 1.70 14.30Base B LNB LNB/OFA base25.58 31.60 18.06 4.51 3.01 18.06 1.70 14.30LNB_DFGD LNB/OFA DFGD10.72 17.15 15.73 4.29 7.86 10.01 1.20 28.10LNB_WFGD LNB/OFA WFGD12.78 15.62 22.72 5.68 15.62 28.40 2.70 1.80MUSKOGEEUNIT 5Control systems NOx SO2 PM10 H2SO45480 mmBTU/hrNOx SO2 lb/mmBTU lb/hr lb/mmBTUlb/hr lb/mmBTU lb/hr lb/hrBaseline A base base0.522 2860.560 0.850 4658.000 0.024 133.712 4.34Base B LNB LNB/OFA base0.150 822.000 0.850 4658.000 0.024 133.712 4.34LNB_DFGD LNB/OFA DFGD0.150 822.000 0.060 328.800 0.012 65.760 0.06LNB_WFGD LNB/OFA WFGD0.150 822.000 0.040 219.200 0.024 133.712 1.74MUSKOGEEUNIT 5Control systems PM Coarse (lb/hr) PM Fine (lb/hr)EC (lb/hr) SOA (lb/hr)NOx SO2 PM 8 PM 4.25 PM 1.88PM 1.25 PM 0.813 PM 0.625Baseline A base base33.93 41.91 23.95 5.99 3.99 23.95 2.20 19.00Base B LNB LNB/OFA base33.93 41.91 23.95 5.99 3.99 23.95 2.20 19.00LNB_DFGD LNB/OFA DFGD10.72 17.15 15.73 4.29 7.86 10.01 1.20 28.10LNB_WFGD LNB/OFA WFGD16.95 20.72 30.13 7.53 20.72 37.67 3.50 2.40B-25NORTHEASTERNUNIT 3Control systems NOx SO2 PM10 H2SO45812 mmBTU/hrNOx SO2 lb/mmBTU lb/hr lb/mmBTUlb/hr lb/mmBTU lb/hr lb/hrBaseline A base base0.536 3115.232 0.900 5230.800 0.038 220.856 4.6Base B LNB LNB/OFA base0.150 871.800 0.900 5230.800 0.038 220.856 4.6LNB_DFGD LNB/OFA DFGD0.150 871.800 0.060 348.720 0.012 69.744 0.06LNB_WFGD LNB/OFA WFGD0.150 871.800 0.040 232.480 0.038 220.856 1.84NORTHEASTERNUNIT 3Control systems PM Coarse (lb/hr) PM Fine (lb/hr)EC (lb/hr) SOA (lb/hr)NOx SO2 PM 8 PM 4.25 PM 1.88PM 1.25 PM 0.813 PM 0.625Baseline A base base56.04 69.22 39.56 9.89 6.59 39.56 3.60 31.40Base B LNB LNB/OFA base56.04 69.22 39.56 9.89 6.59 39.56 3.60 31.40LNB_DFGD LNB/OFA DFGD11.37 18.19 16.68 4.55 8.34 10.61 1.30 29.80LNB_WFGD LNB/OFA WFGD28.00 34.22 49.77 12.44 34.22 62.21 5.80 4.00NORTHEASTERNUNIT 4Control systems NOx SO2 PM10 H2SO45594 mmBTU/hrNOx SO2 lb/mmBTU lb/hr lb/mmBTUlb/hr lb/mmBTU lb/hr lb/hrBaseline A base base0.491 2746.654 0.900 5034.600 0.059 342.908 4.43Base B LNB LNB/OFA base0.150 839.100 0.900 5034.600 0.059 342.908 4.43LNB_DFGD LNB/OFA DFGD0.150 839.100 0.060 335.640 0.012 69.744 0.06LNB_WFGD LNB/OFA WFGD0.150 839.100 0.040 223.760 0.059 342.908 1.77NORTHEASTERNUNIT 4Control systems PM Coarse (lb/hr) PM Fine (lb/hr)EC (lb/hr) SOA (lb/hr)NOx SO2 PM 8 PM 4.25 PM 1.88PM 1.25 PM 0.813 PM 0.625Baseline A base base83.74 103.45 59.11 14.78 9.85 59.11 5.40 46.90Base B LNB LNB/OFA base83.74 103.45 59.11 14.78 9.85 59.11 5.40 46.90LNB_DFGD LNB/OFA DFGD10.94 17.51 16.05 4.38 8.03 10.22 1.20 28.60LNB_WFGD LNB/OFA WFGD41.84 51.13 74.38 18.59 51.13 92.97 8.70 6.00B-26

### 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 Prot ection 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 0209897.004.085iii 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 & Commissi oning Craft Support ...............................................................21 e. Operator Training & Manuals ................................................................................21

f. Other Factored Sums ..............................................................................................22 g. Initial Fabric Filter Replacement Materials ...........................................................22iv 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 FO R NORTHEASTERN DRY SCRUBBER 35

A. CAPITAL COST ...................................................................................................36

1. Unit Capital Cost..................................................................................................36

2. Capital Recovery Factor ......................................................................................36

B. OPERATING COSTS ...........................................................................................37

1. Water .....................................................................................................................3 7

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 ..................................................................................39v 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 .....................................................................................................................5 0

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 ....................................................................................57vi TABLES

Table 1. Dry Scrubber Control Opti ons Evaluated by Sargent & Lundy ...........................4

Table 2. Option 1 Revised Cost Effectivene ss, Increased Emission Reductions (91.5% SO2 Reduction from Scrubber Inlet of 1.176 lb/MMBtu) ...........................................................6

Table 3. Option 2 Revised Cost Effectivene ss, 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 Effectiv eness Values for Dry Scrubbers & Low Sulfur

Coal with Revised OG&E and AEP/PSO Values ..............................................................42

Table 8. Comparison of Average Cost Eff ectiveness 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 .........................................541 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 st ations: Sooner Units 1 and 2; Muskogee Units 4 and 5; and Northeastern Units 3 and 4. These ar e 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 fo r SO2 for these six units was satisfied by continuing to burn low-sulfur coal and meeti ng 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/MMB tu 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 co al-fired units without citing any unusual

circumstances. This conclusion is based on cost analyses performed by Sargent & Lundy 4 for Oklahoma Gas & Electric (OG&E) Sooner Un its 1 and 2 and Muskogee Units 4 and 5 and by Trinity Consultants 5 for American Electric Power/Public Service Company of Oklahoma (AEP/PSO) Northeastern Units 3 and 4. The focu s of these company analys es 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 collater al 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 Report 6 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 Implem entation Plan ("OK Regional Haze SIP").

2 OK Regional Haze SIP, Ta ble 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, Decemb er 28, 2009 ("12/28/09 S&L Followup Report").

5 Trinity Consultants, Best Available Retrofit Technol ogy (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 Technol ogy (BART) Determination, American Electric Power, Northeastern Power Plant, May 30, 2008 (5/30/08 Trinity Report).2 review, I prepared revised cost analyses, correcti ng some of the errors th at I found. It was not possible to correct all errors as the parties dec lined to provide copies of their spreadsheets disclosing their cost calculat ions 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 highlight ed in blue in these Appendixes. My basis is explained in the following sections.

My analysis indicates the cost to install dry scrubbers at all si x 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 th e 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 correct ed, the average cost effectiven ess 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 overestimate s that I identified due to lack of support and underlying calculations. My revise d cost effectiveness values are within th e range of values

found to be cost effective at othe r 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 f acilities nationwide indicate that the cost

effectiveness of a wet scrubber is about 9% higher than for a dr y scrubber. In cases where an existing electrostatic precipitator can be used in stead of a new fabric f ilter baghouse, the cost effectiveness of wet scrubbing is lower than dry scrubbing and the incremen tal cost effectiveness is zero. This is likely the case for the subjec t units, but this option was not evaluated by the companies.

I revised the wet scrubber co st analyses based on the ratio of my revised dry scrubber cost to the initial company costs. This lo wered 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 eff ectiveness analysis is cont ained in the "12/28/09 S&L FollowUp Report". 8 This report was prepared in respons e to Oklahoma's draft Regional Haze SIP, which required Sooner and Muskogee to in stall dry flue gas de sulfurization systems (scrubbers) to satisfy BART. Oklahoma rej ected Sargent & Lundy's earlier BART cost effectiveness analyses as they were "substant ially 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 st ill substantially higher th an 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 ca lculated according the EPA Air Pollution Control Cost Manual or "Cost Manual", 9 as stipulated in the BART Guid elines. A control technology is

considered to be “cost effectiv e” 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. Seco nd, 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 cost s for the control technology are consistent with

the claimed emission reductions.

In performing its BART analysis, Sargen t & Lundy assumed that the Sooner and Muskogee plants burned coal with a higher sulfur cont ent than they are currently burning. This necessitated costing a much bigge r scrubber than is necessary for the coal they are currently burning. However, Sargent & Lundy also assume d 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 word s, the costed scrubber is much more efficient than the one required to deliver the assumed BART performance level.

The FollowUp Report indicates that two separa te 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 cont aining 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 estim ate 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, Decemb er 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 represen ts a significant increase in the

sulfur content of the coal that is currently burn ed (0.51 lb/MMBtu). 10 The emission reductions that Sargent & Lundy used to calculate cost eff ectiveness, 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 cu rrently burned were costed, which skewed the cost effectiveness in do llars per ton to the high side. When calculating the tonnage removed, a lower scrubbing efficien cy, 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 w ould be needed to minimally satisfy BART.

This mismatch in design causes a dilemma in an alyzing the cost effectiveness that is not completely resolvable within the scope of this wo rk. An ideal analysis w ould be to contrast the cost effectiveness of the larger scrubbers, usi ng a range of coals to the cost effectiveness of smaller scrubbers burning the current coal. Th e basic decision point for EPA, however, is

whether scrubbers are cost effective and what em ission 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 s ections, I revised the Sa rgent & 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 7,4355 The first option, costing the la rger scrubber, as is demonstr ated 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 intend ed as it allows the owner to switch to higher sulfur coals in the future as the supply of lowe r sulfur coals diminishes or to use cheaper higher sulfur coals to reduce oper ating costs. This first option is car ried throughout the balance of this report because Sargent & Lundy assumed a higher su lfur coal would be burned when it costed the scrubbers. The second option, costing a smalle r scrubber, is discusse d 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). T hus, for a given annual cost, the calculated cost effectiveness in dollars per t on declines as emission reductions increase.

Cost effectiveness could be estimated usi ng the cost of the larg er scrubber and the

emission reductions that could be achieved with this larger sc rubber, assuming an outlet of 0.1 lb/MMBtu SO2. The use of higher sulfur co al, 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 effectivene ss 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 scrubbe r 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 incl uded 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 Tabl e 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 revision s, coupled with the increased SO2 emission

reductions shown in Table 2 result in an average cost effectivene ss 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 anal ysis 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 expone nt, which is typically close to 0.6. The capital cost of a process is relate d to capacity by the equation:

C b = C
a (S b /S
a ) n

where C b is the approximate cost ($) of a process or equipment having size S b and C

a is the known cost ($) of a process or equipment having corresponding size S

a. The ratio S b /S
a 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 sc rubber 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

Costs 13 ($/yr) Design

Basis

Reduction 14
(ton/yr) Revised

Cost

Effect. 15

($/ton)

Sooner 93,664,600 43,428 2,157 Muskogee 102,600,400 46,458 2,2087 as the size factor, which is dimensionless. 16 The exponent, n, varies depending upon the type of process unit. This equation indica tes that a log-log plot of capacity versus equipment cost for a given type of equipment should be a straight line with a slope equa l to the exponent n.

The exponent n is typically 0.8 to 0.9 for pro cesses that use a lot of mechanical work or gas compression. For typical petrochemical proce sses, 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 co st-capacity exponent ranges from less than 0.2 to greater than 1.0. I am not aware of any public data sp ecific to dry scrubbe rs. However, it is appropriate here as Sargent & Lundy did not supp ly any cost data for th e smaller scrubber case, the extrapolation is less than a 10-fold capacity range, and the scrubbers are similar with regard to type of construction, material s 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 mate rial balances in the 12/28/09 S&L FollowUp Report. This calculation yi elds a size factor of 0.51. 20 In other words, the fixed capital cost of the smaller scrubber, that can reduce the SO2 fr om 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, Cap ital 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 De sign: 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 summ arized 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 scrubbe r 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/h r 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

Costs 21 ($/yr) S&L

Emission

Reduction 22
(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 smalle r 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 co st 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 im prove 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 Guidelin es 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 scr ubber based on the higher sulfur coal as it is not only more cost effec tive, but it provides greater flexibility in future operations. While this is a good business strate gy, 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 coul d result in inflated cost-effectiveness figures." 24 Cost effectiveness is determ ined 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 effectivenes s for Sooner and Muskogee in Appendix 1.

B. COSTING METHOD

In evaluating cost effectiveness, it is im portant that a consistent set of rules be used. Otherwise, one runs the risk of compar ing apples with oranges when making the cost effectiveness determination. For power plants having a capacity in exces s of 750 MW, as here, Section 169A(b) of the CAA requires that Stat es 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 cont ained 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 e ffectiveness. For cost analyses, the Guidelines stipulate: "In order to maintain and improve c onsistency, 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 us ed for internal project justification or other accounting purposes but are not a ppropriate 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; faile d to optimize costs; dou ble counted costs; and used inflated factors. Oklahoma did not challeng e 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 Manual 28 sets out a generic method to estim ate the cost of pollution control equipment. It also provides specific cost al gorithms to estimate capital and O&M costs for some types of pollution control equipment. The Cost Manual does not include spec ific 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 Manua l, while relying on vendor quotes and other information to estimate scrubber capital a nd 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 de viates in important ways from the Cost Manual. This makes it appear that the cost to cont rol 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 pr ovided for "any unusual circumstances that exist for the source

that would lead to cost-effec tiveness estimates that would ex ceed 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 in terest. In contrast, Sargent & Lundy escalated capital costs from the year of estimate, 2010, to the year of construction; included various future cost s 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 recove ry factor. Revising th ese four factors alone significantly lowers annual costs.

1. Capital Cost: Failure To Follow Cost Manual

One of the factors that is evaluated in a BA RT determination is cost effectiveness of the control options. Cost effectiveness is determin ed 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 unle ss unusual circumstances exist at the source. 31 Thus, cost

effectiveness is a relativ e determination, based on costs borne by other similar facilities. To compare costs among units, a level playing field must be establis hed 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 Po wer 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 Co st 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 Ai r Quality, North Dakota Department of Health, Re: EPA's Comments on the North Dakota Departme nt 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, bo nd cost, and AFUDC; using an excessive contingency; and by annualizing costs using an inflated capital recove ry factor. Cost items such as escalation of costs, bond cost, and AFUDC are not part of th e 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 ty pes of costs create an unlevel playing field for comparison with other BART analyses. 33 The impact of these and ot her factors discussed in this report on the cost effectiveness anal ysis 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% fo r 2011-2015) to the year of expenditure. 34 The Cost Manual approach, recommended by the BA RT Guidance, explicitly excludes future escalation as cost comparisons are made on a current real dollar basis. In flation is not included in cost effectiveness analyses as they rely on the most accurate information available at current prices and do not try to extrapol ate 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 continge ncy and is double counted as discu ssed 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 e qual 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 constr uction 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 to tal capital expended to account for the cost of funding the cash flow during the construction period. AFUDC is an accounting method. Assets under c onstruction do not provide servi ce 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 th em 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, AF UDC 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 co st 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 co st-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 bon d costs estimated as 1% of total project

cost with escalation and con tingency, but without AFUDC. 38 The basis and justification for these costs is not stated. These costs appear to represent a portion of th e cost of financing the project and thus are similar to AFUDC. These cost s 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 ha ve set bond cost equal to zero in my cost analysis in Appendix 1, Line 42.

d. Contingency

The Sargent & Lundy capital cost estima tes 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 ra nge estimating techniques. 40 The details of how this value was calculated are not disclosed. However, it is unpr ecedented 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 compar ed 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 fo r Muskogee 4&5 as 14.085% of tota l 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/tech nical/rps/40R-08.pdf.13 management to estimate costs. Sargent & Lundy cl aims 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 for ecast 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 co sting 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 bu t 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 deliv ery of materials, but their arri val at the job site is not a problem unique to the retrofit. The standard c ontingency 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 us e 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 an d 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 th ese 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 in to a stream of equal annual payments over a given time at a given in terest 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 paymen t is calculated by multiplying the total capital investment by a capital recovery factor calculated from a form ula based on interest rate and equipment lifetime. 45 The longer the service life and the lower the inte rest 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 mentione d 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 re sulted in an annual levelized charge of 10.36%. 46
Thus, total capital costs were annualized by mu ltiplying them by 0.1036. This is equivalent to assuming a 20-year life and an interest rate of 8.23% in the capita l 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 se t 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 th e 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 effectivenes s. 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 co sts by about 30% (0.1036/ 0.0806), a significant amount when dealing with capital cost s of tens of milli ons 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. Th ere 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 handbooks 49 and published papers 50 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 & L undy cost estimate, for example, assumed a 25 year lifetime, which was lo wered to 20 years in the revised 2009 analysis

without comment. The BART determination fo r Big Stone Unit 1, on-line in 1974, assumed a 30 years scrubber life. Thus, I used 30 y ears in my revised cost estimates.

In response to questions from EPA, Sarg ent & 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 Spra y Dryer Absorber Material: Production, Characterization, Utilization Appli cations, 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 op eration 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 esta blished by the Office of Manageme nt and Budget for comparison of public policy issues. 53 Sargent & Lundy's cost estimate assu med an interest rate of 8.23% (when the effect of debt, equity and ta xes are included). It has long be en standard practice to use an interest rate of 7% in regulatory cost effectiv eness analyses, as this is the social cost of borrowing for governmental analyses. 54 In fact, permit applicati ons that AEP, the owner of Northeastern, submitted to the states of Ohio and We st Virginia, use the standard interest rate of

7%. 55 Increasing the interest rate to 8.26% from 7% while hold ing 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 construc t the project, including additional crane rental allowance, mobilization/demobilization, scaffoldi ng, temporary facilities, overtime inefficiency, per deim expense and BOP contractor's general & administrative costs, ar e 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 us ed by Sargent & Lundy are excessive for an open shop project in Oklahoma and the ge neral region, ranging from about $40 to $80/hr. Many public sources demonstrate th ese 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/o mb/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/o mb/circulars_a004_a-4/#d

.

55 Application to the West Virginia Department of En vironmental 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 Southe rn Power Company and Ohio Power Co mpany, 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 in dicates the second quarter 2010 total cost to employer for construction worker s is $31.44/hr. This is a nationa l average, incl uding 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-Bacon 58 prevailing wages on federally funded work in Oklahoma for carpenters, piledrivers, equipment operators, re inforcing steel, etc. is typically $7-11/hr throughout most of the state. The highest wage s I found were in Oklahoma County at $35/hr to $40/hr (including fringe benefits) for electricians, pipefitters, a nd sheetmetal workers. Sooner, Muskogee, and Northeastern are located in Noble, Muskogee, and Rogers counties, respectively. Open shop wages would be less than prevaili ng wages on federally f unded projects.

A 2008 wage survey 59 summarizes union and nonunion wage s by craft within regions. This survey, reproduced in Figure 1, shows that th e highest wage plus benefits in Region 6 (AR, LA, NM, OK, TX) for non-union construction work ers 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 th e wage rates assumed by Sargent & Lundy. Wages have genera lly 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, Availabl e 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 pr ojects are located.

59 FMI Management Consulting, Craft Worker Compensa tion Research Report, January 2008, Ex. 36, 41 - 54.

Available at: http://www.agc.org/galleries/laborhr/AGC%20Craft%2 0Worker%20Compensation%20Research%20Report%20- %20Final%201-4-08.pdf.17 Figure 1

The wages used in the Sargent & Lundy analys is are factors of two to three times higher than those reported in these sour ces. The files that I reviewed contain no support for these high labor rates. I did not change the labor rates in my revised cost anal ysis as the parties declined to supply the electronic spr eadsheets with embedded calculation s, which would have facilitated such a change. Further, the spec ific craft that is required for th e various tasks is not indicated, but rather only crew names. In addition to the very high wage starti ng point, Sargent & Lundy boosted the cost of labor even more by using hi gh labor escalation rate s 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 pe r diem is included in the wage. 60 However, the cost spreadsheet in Attachment A include certain "oth er 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 ar e about $50/day to $70/day. As the costs are based on six 10 hour days, this amounts of $100 pe r day for per diem, or about $18 million for each plant.

Further, as per diem pays for living expens es, 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 fo r open shop jobs in today's soft labor market. Thus, additionally adding per diem, on top of the hi gh wage rates used to cal culate 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 pa id for a portion of the labor force, as some of the construction worker s will be drawn from the surrounding areas. This factor was not adjusted in my revised cost an alysis as the record c ontains 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 inab ility to attract labor, weather conditions, etc. 61
This is unprecedented in a cost effectiveness an alysis and is another form of contingency, specifically disallowed by the Co st Manual, as discussed above. It also is double counting as a general contingency is included else where in the cost analysis. It is unlikely that there would be any trouble attracting labor in the current and r easonably foreseeable labor market. Further, the Sargent & Lundy Design and Cost Estimate Basi s 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 tota l productivity-adjuste d 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, spec ifically disallowed by the Cost Manual, as discussed above. It also double counts, as a general contingency is in cluded elsewhere in the cost analysis. The new scrubbers would be installed during normal main tenance 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 electroni c 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 Musk ogee, 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 standa rd 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 Sargen t & Lundy's cost estimates are factored from other costs, such as total direct costs, reductions in item s included in these sums

lead to reductions in the factor ed costs. These various issues are discussed below for capital

costs.

a. Owner's Cost

The Sargent & Lundy cost analyses include ow ner'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 expe nded in other BART analyses. 66 These are reportedly costs incurred by the owner in managing and implementi ng 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 im plement 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 constructio n/field expenses are estimated as 10% of purchased equipment costs). Owner's cost is part of the fact ored estimate for engineering, procurement, and construction management. Owner's costs are not se parately 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 exam ple, EPA rejected the inclusion of "owner's costs" in a cost analysis fo r 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 a nd procurement as 10% of the purchased equipment cost. Sargent & Lundy estimated engin eering 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 & Aux iliary 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 Comme nts on the North Dakota Department of Health's April 2010 Draft BACT Determin ation for NOx for the Milton R. Young Station, May 10,

2010.20 include much more than just the purchas ed 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 estima tes using the Cost Manual approach of 10% of purchased equipment cost. The Cost Manual es timates 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 & L undy engineering/procurement co sts 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 cost s are typically reported on an all-in basis. Thus, because Sargent & Lundy also separately calculated engineering/procurement, the engineering and procurement costs for the vendo r-supplied portion of the capital costs are double

counted. The 6.5% factor should only apply to the balance of pl ant materials and labor, which exclude the scrubber system, fabric filter, a nd the scrubber contractor's scope for the ash recycling system.

The engineering and procurement costs, base d only on the balance of plant equipment,

material and labor costs as revi sed in Appendix 1, Line 34, are $8,371,563 71 for Sooner and $8,840,090 72 for Muskogee, or less than half of th e Sargent & Lundy estimate. This change reduces the engineering and procurement costs by $12,773,100 at Sooner 73 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 ma nagement 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", Sa rgent & 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 remove d 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 12/2 8/09 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 & L undy's analysis for "BOP contractor's general &

admin". 75

The Sargent & Lundy construction management co sts are factored from total direct costs,

calculated as 2% of total di rect 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 cons truction management costs in A ppendix 1, Line 35, to account for

other revisions I made to total direct costs. These revised fees, $5,462,095 at Sooner 76 and $5,685,568 at Muskogee, 77 were estimated as 2% of revised to tal direct costs. This reduces the construction management costs in Appendix 1, Line 35, by $1,043,955 at Sooner 78 and by $1,017,314 at Muskogee. 79

d. Startup & Commi ssioning Craft Support

Sargent & Lundy estimated startup and commi ssioning 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 Soone r). 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 a nd 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 pr oposals 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 av ailable as Sargent & Lundy did not provide an electronic copy of the Excel cost spreadsheets that contained the form ulas. 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 al l 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 f eet), 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, de positing 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 th e 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 ope rating, the vendor's cost estimate for the baghouse includes the cost of these replacement parts. Ho wever, 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 mi nimize 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 f acilities, use of exis ting equipment, and c onsideration of other scrubbing options. If the scrubbers were designed to minimize cap ital 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 & L undy were estimated on a per generating unit basis and doubled for the plant. However, cons iderable 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 a nd 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 pul verized 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 desi gning, procuring, and installing all four

identical OG&E units from the same vendor. Th e 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 s hop 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 re quired information. However, based on my experience, vendors would offer at least a 5% di scount on equipment cost for multiple identical

units. This reduction is incl uded in Appendix 1, Line 24.

b. Common Facilities

Capital and labor costs also could be re duced 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 a nd Muskogee instead of doubling the design of a single unit would reduce equipment, material, a nd 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 pieces24 of equipment. See Section III.A.2. The compan y'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 w ould be cheaper than tw o smaller ones with the same output. The reduction in costs for this fact or is included in the vendor discount adjustment in Appendix 1, Line 24.

d. Other Cost Options

There are more economical options for design ing 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-pas s 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 ch amber or absorber wher e a reagent is sprayed into the gas stream and reacts with SO2. An el ectrostatic 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 thes e 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 co al are collected in a dust collection device and can be recycled to the spray chamber to reduce li me demand. In a recycle system, these solids are collected, mixed with wa ter, and reinjected into the spray ch amber. 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 equi pment required to recycle the solids and more power and water required to operate the recycle syst em. 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 w ith recycle operations are greater than the additional lime and disposal costs without recycle. The Springerville dry scrubber was designed to achieve SO2 removal effi ciencies as high as 9 3.3% without the use of recycle. 86 Other vendors have noted th e 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 Achi eving 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 ne ed 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 (re moved 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 devi ces together are referred to as a dry scrubber or dry flue gas desulfurization unit (DFGD). The costs for th e 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 precipi tators, a dust collection device th at 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 si gnificant difference in an incr emental cost comparison with a wet scrubber, which does not require a downstream baghouse.

Existing electrostatic precipi tators are strong candidates fo r dust collection in retrofit applications. While extra collect ing surface may be needed in so me cases, they generally do not require any upgrade as the resist ivity 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 Re port 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 re move particulate would likely be much less than the cost of new baghouses. This option was not co nsidered 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 fo r Naughton 1 using the existing ESP.

Table 5 Naughton BART Analysis 91

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. Maur in, 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 Gu idelines that "it is important to include control options that involve improvement s to existing controls and not to limit the control options only to those measures that i nvolve a complete replacement of control devices." 93

iii. Fabric Filter Baghouse

The baghouse was designed to achieve a f ilterable particulate emission rate of 0.010 lb/MMBtu. However, the targeted BART filte rable emission limit is 0.10 lb/MMBtu, or ten times higher, met with the exis ting electrostatic precipitators. Thus, the baghouse included in the OG&E dry scrubber costs is over designed fo r its function in the dry scrubber. Baghouse removal efficiency increases in direct proportion to gas filtra tion 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 expens ive 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 Indust rial 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 co mprise the sum of variable O&M, fixed O&M, and indirect O&M. The Sargent & Lund y O&M cost estimates re quire adjustment of

both variable and indi rect 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/MMB tu to 0.100 lb/MMBtu (80% SO2 control or Option 2).

The Sargent & Lundy variable O&M cost s 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 pe r ton of SO2 removed, improving

the cost effectiveness of the dry scrubber. The Sargent & Lundy variable O&M costs also contain other errors, which cance l some of the O&M cost undere stimate. Each component of variable O&M cost that I adjusted to address these issues is di scussed 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 woul d be required to operate a scrubber designed to remove 80% of the SO2 fr om 0.51 lb/MMBtu coal (80% control case) while the capital costs were ba sed on a scrubber designed to re move 91% of the SO2 from a 1.18 lb/MMBtu coal (91% control cas e). As a result, Sargent & L undy 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 estim ated that 2.0 ton/hr of lime w ould 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 De sign 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. Sa rgent & Lundy used this value to estimate lime demand in a study of scrubber technol ogy 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 co st 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/yr 100 or by $4,682,111/yr for the total plant. It

increases the lime costs at Mu skogee from $1,730,600/yr to $3,968,632/yr 101 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 wa ter use are based on th e 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 S ooner 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 uppe r 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, recycl e 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 othe r similar dry scrubbers. 106 The vendor design was based on an average makeup demand within the usual report ed range for other si milar scrubbers.

Using the vendor water demand decreases th e per unit cost of water at Sooner from $95,100/yr to $85,299/yr or by $9,801/yr. At Muskogee, this adjustment d ecreases the per unit annual cost of water from $534,100/yr 107 to $443,735/yr 108 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 F GD waste disposal are based on solids to landfill (stream 12) for the 80% SO2 control opt ion and the unit cost to landfill the solids. 109

These costs assume that the Sooner scrubbe r 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 sc rubber 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 So oner increases from $1,733,500/yr to $4,102,860/yr 111 or by $2,369,360/yr . At Muskogee, this adjustment increas es the per unit cost from $1,849,300/yr to $4,504,643/yr 112 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 F GD solids is high compared to other similar BART cost analyses. The unit disposal co st 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 un it disposal cost as no information was provided about the on-site landf illing 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 agri culture, concrete, concrete products, flowable fill, mining applications, soil stabilization, stru ctural fills and embankments, and synthetic
aggregate. 115 Selling the FGD solids would reduce or el iminate 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 cleani ng. I was unable to re produce Sargent & Lundy's costs with the reported informa tion as it did not include the fl owrate 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 uni t 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 au xiliary power cost, accoun ted 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 decreas es the per unit repl acement cost from $653,300/yr to $544,026/yr 118 or by $109,274/yr . For Muskogee, my costing approach decreases the per unit replacement cost from $675,100/yr to $599,020/yr 119 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 App lications, 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 ]/[(1.07 3 - 1] = 0.3811.

118 Revised Sooner replacement cost s: [(1,724,05 1 ACFM)($3.22/ft 2 )(0.9)(0.3811)/3.5 ACFM/ft 2 = $544,026/yr

.

119 Revised Muskogee replacement cost s: [(1,846,716 ACFM)($3.31/ft 2 )(0.9)(0.3811)/3.5 ACFM/ft 2 = $599,020/yr

.31 The decrease in bag and cage replacement cost is included in my revi sed cost estimate in Appendix 1, Line 54.

v. Auxiliary Power Costs

Auxiliary power is the sum of the dema nd 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 co sts are overestimated for two r easons. 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 th an 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 Do cument from individual motors based on the 91% SO2 control case; 121 (3) assumed by Sargent & Lundy in the National Lime Association Report for simila r plants (1.2% gross output); 122 and (4) estimated by the vendors. The various estimates of auxiliary power, a ssuming 90% capacity factor, are summarized in

Table 6.

Table 6 Auxiliary Power Per Unit (MWh/yr)

Estimate Sooner Muskogee Source
S&L Cost Estimate 67,290 123 67,644 12/28/09 S&L Followup Report, pdf 101-104

S&L Cost Estimate Based on Stated Assumptions 67,408 124 67,645 12/28/09 S&L Followup Report, pdf 101-104 S&L Design Document 58,342 125 61,495 12/28/09 S&L Followup Report, pdf 84-87

S&L National L ime Report 53,927 126 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 & L undy's cost estimate is also high compared to other estimates. They assumed $85.93/MW h 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 regul ated 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 wi nter 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 an alyses typically estimate auxiliary power at $50/kW or less. The Northeastern cost effec tiveness 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 aux iliary power costs in the Follo wUp Report indicates the range

for other similar faciliti es 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 analys es 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% (M uscogee) of total capital investment and insurance as 0.0105% of total capital investment. As total capital investment was revised as discussed above, the fact ors that rely on them must also change. This is accommodated in my cost analysis in Appe ndix 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/residen tial-customers/billing-and-pa yment/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 anal yses relied on in Oklahoma's SO2 BART determination significantly overstate the cost of the proposed contro l. My revisions, summarized in Appendix 1, lower the calculated cost effectiveness for Sooner from $6,724/ton 130 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/h r, 1.44 lb SO2/MMBtu) in a March 2007 report prepared for the National Lime Association. In th is study, the calculated co st effectiveness of a 500 MW retrofit dry scrubber on low sulfur PR B coal (1.44 lb/MMB tu) based on 95% SO2 control in 2006 dollars was $1,320/ton. 131 Adjusting to 2009 dollars, th e 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 appl es-to-apples cost effectiveness analysis prepared pursuant to the EPA Cost Manual.

Capital costs are commonly expressed as dollars per kilowatt of capacit y. The total direct capital costs, when reduced to dollars pe r 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 seri ous retrofit issues. The plot plans indicate no retrofit issu es that would justify such hi gh 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) pres ented a graphic of capital cost as a function of generating capacity in 2007 that su mmarizes a wide range of scrubber cost data (2004-2005), reported in year-end 2005 do llars 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 estima te for a ready-to-operate syst em 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, J une 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 da ta, 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-appl es comparison with the Sargent & Lundy data reported in the UARG study, my unit cost woul d increase from $265/kW to $362/kW, at the upper end of the range. The much higher va lues on Figure 2, >$350/kW to $500/kW, are for smaller units on sites with signifi cant retrofit issu es. Sargent & Lundy's capital cost estimates for Sooner and Muskogee correspond to those exp ected for small units, 200 MW to 400 MW on sites with significant retrofit issues, which are no t present at either Sooner or Muskogee.

IV. REVISED COST ANALYSIS FO R NORTHEASTERN DRY SCRUBBER

The Northeastern BART cost effectiveness analysis is contained in the 5/30/08 Trinity Report, Appendix F. It concluded the cost eff ectiveness 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 ca lculations for their modification. Thus, my review is based on the 5/30/08 Trinity Report. The Trinity analys is significantly overestimates the dollars per

ton cost to control SO2 with a dr y scrubber for the reasons discusse d below. The format of my Northeastern review differs from my Sooner an d Muskogee review due to differences in the costing methodologies.

A. CAPITAL COST

The total capital investment and annual capital cost are bot h 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 s upported, but simply state d. It is reported as $555/kW in the Trinity Report 137 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 i ssues. These elevated values correspond to smaller units with significant retrofit issues. AEP, the owner of Northeastern, has pub licly 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 w ould lead to such a high unit capital cost.

The unit capital cost for the tw o coal-fired Northeastern units should be about the same as for Sooner and Muskogee. These six units are sim ilar-sized, tangentially-fi red 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 esti mate using the average capital cost from my revised Sooner and Muskogee analysis in Append ix 1, Line 65. This value is $265/kW, without
escalation, AFUDC, bond costs, an d 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 (ODE Q), 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.

138 1/19/10 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). Th e Trinity analysis uses th e correct capital recovery method, but uses incorrect inputs.

The capital recovery factor depends on th e service life of the equipment and the public interest rate, 7%. The higher th e 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 previous ly explained in Section III.B .1.h. The revised Northeastern analysis in Appendix 2, Line 67, is based on capita l 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 a nd 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 omi ssions are discussed below and corrected in Appendix 2.

1. Water

A dry scrubber requires water to prepare th e reagent slurry and to cool the flue gas stream. The cost to supply water was not include d in the Trinity analysis. I added water cost,

scaled from the water demand for Sooner and Mus kogee. See Section III.B.4.a.ii. The average cost of water, based on Sargent & Lundy's tabulati on 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 ei ght 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 furthe r can be optimized. Thus, they w ould be expected to fall at the lower end of the reported range, or $5/ton. Th erefore, in Appendix 2, Line 24, I estimated the cost of on-site landfill dispos al assuming that 5 pounds of solid s would be generated for each pound of SO2 removed and that it would co st $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 th e 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 sa me as those calculated by Sargent & Lundy for Sooner and Muskogee, or an average of about $4 m illion 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 an alysis for Sooner and Muskogee indicates that

insurance rates for similar facili ties are about a factor of ten lower, or 0.0105% of total capital

investment. 143 Thus, I revised the Northeastern insura nce 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 capita l investment, based on the Cost Manual default. The Sargent & Lundy an alysis 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 revi sed cost estimate assume s the upper end of the OG&E range or 0.85% of total capital investme nt. 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 discu ssed for Sooner and Muskogee exist here. The capital and O&M costs for Northeastern were sc aled 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. Th is 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 scr ubber. 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 effec tiveness, 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 dollars 147 or $1,340/ton 148 in 2009 dollars.

V. COST EFFECTIVEN ESS: 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 Bl uff 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 dolla rs 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 Appendi x 1, Line 63 for Sooner and Muskogee and in Appendix 2, Line 47 for Northeastern. A control t echnology is considered to be "cost effective" if it falls within a reasonable range of cost-e ffectiveness estimates. "A reasonable range would be a range that is consistent wi th the range of cost effectivene ss 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 app lications in the utility industry. 150 Thus, dry scrubbing is clearly cost effective, barring an unusual, site specific condi tion. 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 Northeaste rn Units 3 and 4. Oklahoma reported costs far above the usual range for similar fac ilities, 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 analys es 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 misce llaneous 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 effectivene ss values for dry scrubbers on other similar facilities burning low sulfur Powder River Basin co al. Most of these determinations, prepared by engineering firms to estimate actual costs rath er than regulatory costs, incorrectly contain escalation, contingency, and AFUDC deviations from the Cost Manua l, 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 revi sed numbers, I added back in AFUDC, escalation, and contingency to my estimat es 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 Northe astern ($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 App lications, 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 accuracy 152 of the cost estimate (+/-30%). 153 They are also well within the range of

values that have been found to be cost eff ective 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 scr ubbers at Northeastern, projected to be in service by 2012-2014. 154

VI. BART EMISSION LIMIT: DRY SCRUBBER

The Oklahoma Regional Haze SIP concluded th at BART for SO2 for Sooner, Muskogee, and Northeastern is a 30-day ro lling average emission rate (at the stack) of 0.65 lb/MMBtu and a 12-month rolling average of 0.55 lb/MMBt u, achieved using low sulfur coal. 155 These emission limits are higher than the presumptive BART limits and higher than achievabl e with current coal. A dry scrubber was eliminated based on cost effec tiveness. The underlying analysis is not valid as costs were significantly overe stimated, as explained above. Th is section discusses what the BART emission limit should be for a dry scrubber, gi ven that it is cost eff ective. As explained below, a wet scrubber could achieve a lower emi ssion limit and is cost e ffective and otherwise

feasible.

152 Cost estimates that are within the acc uracy 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/to n for a dry scrubber at Northeastern. However, the upper end of this range is in cremental cost effectiveness fo r a wet scrubber, not average cost effectiveness for a dry sc rubber. Incremen tal 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, Tabl es VI-6, VI-7, and VI-10.41 Table 7 Comparison of SO2 BART Co st 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&2 156 2x750 1.88 92 0.15 1,280 Big Stone 1 157 475 0.86 90 0.09 1,462 Nebraska City 1 158 650 0.815 88 0.10 1,636

Boswell 3 159 375 0.947 90 0.09 1,640 Sooner (Ex. 1) 2x570 1.18 91 0.10 1,670 Presumptive Muskogee 160 650 0.02 1,694 Muskogee (Ex. 1) 2x572 1.18 91 0.10 1,738 Naughton 1 161 176 1.18 87 0.15 1,877 Naughton 2 162 234 1.18 87 0.15 1,882

Stanton 1 163 188 1.2 90 0.12 2,006 Northeastern 3&4 2x470 0.91 83 0.15 2,232

White Bluff 1 & 2 164 2x850 0.68 92.5 0.15 2,430

156 ENSR Corporation, BART Analysis for the White Bl uff 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 Generatin g Station Unit 1, Best Available Retrofit Technology Analysis, November 2, 2009, Section 3.0.

158 Nebraska Department of Environm ental 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, D ecember 2009, Appendix 9.2,

Memorandum from Anne Jackson to File Re: BART Dete rmination 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.

160 Technical Support Document for the Best Available Re trofit Technology (BART) Notice of Final Rulemaking, Setting BART SO2 Limits for Electric Generating Units: Co ntrol Technology and Cost-E ffectiveness, 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 H aze 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 Pl an, 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 7 165 142 0.83 85 0.13 2,544

Gerald Gentleman 166 2x750 0.75 80 0.15 2,726

Drake 6 167 85 0.82 85 0.13 2,816

Boardman 168 617 0.61 80 0.12 3,055 Silver Bay 2 169 75 0.60 90 0.06 3,547

The BART analysis must include the maxi mum level of control th at each technology is capable of achieving. 170 In analyzing a technology, "you take into account the most stringent

emission control level that the t echnology is capable of achieving." 171 This is important as "[a]n unrealistically low assessment of the emission reduction potentia l of a certain technology could result in inflated cost-effectiveness figures." 172 This has happened here.

The BART analysis evaluated a control effici ency 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 emi ssion control levels that a dry scrubber is capable of achieving.

In its report for the National Lime Asso ciation, 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, D ecember 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 wate r 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 betw een 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 al so 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 rem oval efficiencies, up to 96%, have been demonstrated at a 600 MW PRB coal fired plan t 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 selec ting coal to assure the limit is met. Similarly, a dry scrubber designed to remove 93% of the SO2 from an in let of 0.91 lb/MMBtu woul d 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 SO 2 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 SO 2 from a 1.18 lb/MMBtu coal would routinely achieve 95% when burning this coal. However, if the sulf ur 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 manuf acturers 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 averag e, when burning coal containing 0.51 to 1.18

lb/MMBtu SO2. The same is true for Northeaster n. The reduction in removal efficiency of the dry scrubber would be offset by th e 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 comparab le levels that are bui lt or being built.

The Nevada Bureau of Air Pollution Contro l, for example, issued a PSD Operating

Permit to Construct to Newmont Nevada Ener gy 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 aver age and 95% minimum SO2 removal efficiency, when combusting coal with a su lfur content equal to or greater than 0.45% (30-day rolling period)

• 0.065 lb/MMBtu on a 24-hour rolling aver age and 91% minimum SO2 removal efficiency, when combustion coal with a sulfur conten t 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 th e 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 Qu ality 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 0.201/1/20083/1/20085/1/20087/1/20089/1/200811/1/20081/1/20093/1/20095/1/20097/1/20099/1/200911/1/20091/1/20103/1/20105/1/20107/1/20109/1/201011/1/2010

TimeSO2 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 supe rcritical boilers that w ill 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. Th e SO2 BACT limits are tiered based on sulfur content as follows:

 0.065 lb/MMBtu on a 30-day rolling aver age when the uncontrolled sulfur

dioxide emission rate is less th an 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/MMB tu but less than 1.25 lb/MMBtu;

 0.105 lb/MMBtu on a 30-day rolling aver age when the uncontrolled sulfur dioxide emission rate is gr eater 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 emi ssion 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.g a.us/gaairpermits/PermitPDF .aspx?id=PDF-PI-18499.46 VII. WET SCRUBBING

There are two general classes of technol ogies that are widely used to control SO 2 emissions: dry flue gas desulfurization (“DF GD” 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 dr y to wet scrubbing was judged unacceptable; and wet scrubbing was alleged to have certain adve rse 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 ha s the same adverse impacts as all other wet scrubbers, none of which are unique or unusual. The BART analysis mu st 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 e fficiency. 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 in flated cost-effectiveness figures." 178 These Guidelines further require that "in analyzing the tec hnology you take into account the most stringent

emission control level that the t echnology 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 achie ve 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 si milar low sulfur PRB co al (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 gu arantees, from the original eq uipment 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 Pr airie 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 Nort heastern. This level of control would yield an emission limit of 0.04 lb/MMBtu, which has b een achieved at Pleasant Prairie (Fig. 4).

B. COST EFFECTIVENESS OF WET SCRUBBING

The companies and ODEQ estimated the averag e and incremental cost effectiveness of both dry and wet scrubbing. In general, the averag e 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 larg e absorber is used for the wet scrubber 183 while two smaller absorbers are required for the dry scrubber or in retrofit applicatio ns that have existing electrostatic precipitato rs. I would expect th is 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 sc rubbing 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 we t scrubber is typically about 9% higher than for a dry scrubber, excep t 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 scrubbi ng 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 analys es 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 effectiven ess for wet scrubbing based on the ratio of my revised value for dry scrubbing to the company estimate for dry scrubbi ng. 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 co llateral impacts including: (1) increased sulfuric acid mist (SAM) in the flue gas; (2) excess pa rticulate emitted due to location of scrubber downstream of particulate control device; (3) uses more reactant, generating more fugitive dust; (4) uses significantly more wate r; (5) generates a wastewater st ream that must be treated; (6) creates higher visibility impairment due to lowe r exit velocity, lower stack temperature, and higher SAM emissions. These claimed adverse imp acts are either wrong or overstated. Further, ODEQ failed to point out the benefits of wet FG D 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 su lfuric 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 leve ls 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. Er ickson, 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 Research49 Further, Sargent & Lundy note: "low-and medium -sulfur coals [which include the PRB coals used by OG&E and AEP/PSO]...typically pr oduce 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 contro l efficiency increases from a dry scrubber, SAM control decreases. A much hi gher SO2 control efficiency can be obtained

with wet scrubbers without the risk of increased SAM emissions.

Further, as discussed below, a dry scrubber mu st be taken off-line pe riodically to change out the atomizer. During these changeout peri ods, 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 or der to meet the SO2 limit, these very high changeout emissions must be averaged out by oper ating the dry scrubber at a higher SO2 control efficiency, up to 98%, for enough time to offset the short-term, uncontro lled 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 emission s, increasing the cost of the dry scrubber

relative to the wet scrubber.

2. Water

The ODEQ alleges that wet scrubbers use "si gnificantly 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 we t scrubber is a small fract ion of the total plant water demand. Typically, about 90% of the freshwat er water is used for wet cooling, 2% for wet scrubbing, and the balance for ash sluicing, bo iler 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, Opera tional Limits on Semi-dry Absorbers, p. 1.

191 1/30/07 Meadows E-mail, p. 2. ("Th ere 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 th e 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 gene rates 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 saleab le gypsum is not produced. This wastewater can be reused elsewhere in the plant.

There are several options for treating FGD wa stewater: (1) eliminate the discharge using a zero liquid discharge system; 192 (2) treat the wastewater wi th the power plant combined wastewater, thus not requ iring a separate wastewater treatment system; (3) treat and discharge the wastewater separately (the only option men tioned); 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 wi ll 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 rem oved by the mist eliminator at the top of the scrubber, which is an integral part of a wet scrubbe r. This mist eliminator is typically specified to require that outle t particulate loading is no higher than inlet particulate load ing. Therefore, by design, the wet scrubber makes no net c ontribution 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 utilizati on of a dry system is poorer compared to a wet system, i.e., it takes more tons of reagent to re move 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 re sult 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 reduc tions 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 a ffect 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 th roughout Europe and could be used with either wet or dry scrubbing to improve plume buoya ncy. This option was not evaluated.

D. BENEFITS OF WET SCRUBBING

The beneficial and adverse impacts of both sc rubbing technologies must be evaluated. The ODEQ only identified advers e impacts of wet scrubbing compared to dry scrubbing. However, wet scrubbing has many beneficial imp acts 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 emissi on rates during atomizer change-out at a dry scrubber, creates a reusable bypr oduct, and does not contaminate th e flyash. Furthermore, some types of wet scrubbers, such as the Chiyoda bubb ling jet reactor and the double contact absorber, have fewer environmental impacts than the c onventional wet scrubber that was evaluated by ODEQ, e.g., uses less power, removes more sulfur ic acid mist, particulate matter, and mercury.

1. Reliability

Wet scrubbers have a much longer and mo re extensive operating history than dry scrubbers. Wet scrubbing systems have demonstrat ed 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 un its, maintained an availability of only 95%. 196

2. Maintenance Emissions

Dry scrubbers have very high short-term emi ssions and typically have permits that allow high 1-hour, 3-hour, and 24-hour emission lim its 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 re moved, 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 Ma nuel 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, reduc ing the control efficiency of the dry scrubber by up to 50%. 198

At the subject plants, two dry scrubber modul es 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 asse mbly. During this time, approxi mately half of the flue gas stream is uncontrolled, although some SO2 removal may still take place with in the filter cake in the baghouse, if a baghouse is us ed to control particulate matt er. The wet scrubber does not require similar routine maintenance and t hus does not have elevated short-term SO 2 emissions, a substantial advantage of the wet scrubbing over the dry system. Mitigating these short-term emissions would require a spare absorber or exce ss 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 scrubbe r as it is capable of greater SO2 removal efficiency, up to 98%. Pleasa nt Prairie Units 1 and 2, similar boilers that burn a low sulfur PRB coal similar, were retr ofit 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 scr ubber easily meets a 365-day rolling average of less than 0.03 lb/MMBtu. Comparably low SO2 em ission limits have been established in BACT analyses and required in PSD permits, achieved w ith 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 0.0501/1/20073/1/20075/1/20077/1/20079/1/200711/1/20071/1/20083/1/20085/1/20087/1/20089/1/200811/1/20081/1/20093/1/20095/1/20097/1/20099/1/200911/1/20091/1/20103/1/20105/1/20107/1/20109/1/201011/1/2010

TimeSO2 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 mo st stringent combination of c ontrol 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 averag e, 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, compar e the blue line (30-day average) with the red line (24-hr average). This comp arison indicates that a 30-day av erage is generally lower than a 24-hour average. The longer averaging time allo ws short-term spikes to be averaged out.

199 U.S. EPA, Region 9, Ambient Air Quality Imp act Report, Desert Rock, p. 18. Available at: http://www.regulations. gov/search/Regs/home.html#docketD etail?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.5455 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 5 203

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 86,859,60 0 86,859,600 86,741,200 86,741,200 13 Fabric Filter 46,335,00 0 46,335,000 45,872,900 45,872,900

14 Ash Recycling System - DFGD Contractor's Scope 11,116,90 0 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 42,463,00 0 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 Co mparison 351 37257 1 

### APPENDIX 2

2 Revised Cost Effectiveness Analysis for

3 Flue Gas Desulfurization at

4 Northeastern Units 3 & 4 204

5

6

7 Trinity a 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, BA RT Economic Analysis for DFGD (SO2). Modified to replace $555/kW with $582/kW, based on 1/19/10 ODEQ BA RT Report, Table 11.1 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 Muskogee 4&5 Notes

7 S&L Fox EPA S&L Fox EPA 1

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 Man-hour 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 Cos t 4,786,700 0 5,111,300 0

43 AFUDC 101,133,800 0 118,148,300 0

44 Total Project Cost 584,589,400 298,530,748 153,594,070 634,386,800 307,153,696 158,829,176 2

45 CRF 0.1036 0.0806 0.1036 0.0806

46 A nnual Capital 60,563,462 24,057,519 12,377,594 65,722,472 24,752,412 12,799,472

47 Variable O&M 19,899,400 19,899,400 19,899,400 21,292,200 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 8,439,200

49 Indirect O&M 5,174,200 5,174,200 5,174,200 7,146,400 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 36,877,800

51 Lime 4,682,110 0 4,476,064 0 3

52 Water -19,602 0 -180,730 0 4

53 FGD Waste Disposal Cost 4,738,721 0 5,310,685 0 5

54 Bag & Cage Replacement Cost -218,548 -218,548 -152,160 -152,160 6

55 Auxiliary Power -5,368,400 -5,368,400 -5,886,800 -5,886,800 7

56 Property Taxes -4,862,997 -7,326,921 -3,926,797 -5,706,691 8

57 Insurance -60,072 -90,509 -68,719 -99,867 9

58 Revised O&M 33,101,000 31,992,212 20,096,622 36,877,800 36,449,343 25,032,281

59 Total Annual Cost ($) 93,664,462 56,049,731 32,474,216 102,600,272 61,201,755 37,831,754

60 SO2 Emission Reduction (ton/yr) 13,930 13,930 13,799 13,799

61 Revised Emission Reduction (ton/yr) 43,428 15,860 46,458 15,989 10

62 S&L Cost Effectiveness ($/ton) 6,724 7,435

63 Revised Cost Effectiveness ($/ton) 1,291 2,048 1,317 2,366

64 Revised (AFUDC, escalation, contingency) 1,670 1,738

65 Cost ($/kw) 513 262 555 268 66 Cost ($/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.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. 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 evaluation 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 evaluation for those lower inlet values, the correction doesn't apply.

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. 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.

9. Modified Fox insurance correction by recalculating the total project cost, since S&L assumed property taxes were a percent of that figure. 8. Modified Fox property tax correction by using a recalculated total project cost, since S&L assumed property 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 9/3 0/10

2:15 PMD-1 

### Appendix D

EPA Extension of Cost Effectiveness Analysis

I. Background

As is noted in Appendix C to this TSD, OG& E costed a scrubber th at was larger than necessary to control SO 2 from the coal they currently burn. As it appears that was OG&E’s preferred option, perhaps for flexib ility in future coal selection, it was carried forward for the bulk of the analysis as Option 1. Option 2, es timating cost effectiven ess on the basis of a minimally sized scrubber to treat the coal currently burned, was al so 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, an d (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 cu rrently being burned. Therefore, we extended Option 2 to account for some of the issues that we re 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 exte nsion of the cost effectiveness of Option 2 is housed in a spreadsheet entitled, “EPA Modificati on 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 th e "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 recalculatio n of the size factor,

S b /S
a , that appears in the equation:

C b = C
a (S b /S
a ) n

For the Sooner units, the scrubber inlet rema ins 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 SO 2 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 sli ght safety factor. For the low sulfur case of Option 2, the scrubber inlet SO 2 remains at 2,605 lb/hr. Therefore, the SO 2 removed is calculated as: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 SO 2 remains at 2,778 lb/hr. Therefore, the SO 2 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 appl ied 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 SO 2 emission reductions. These adjustme nts are explained as notes that follow the copy of the spreadsheet below.D-3 1 EPA modification of Exhibit 1

2 Revised Cost Effectiveness Analysis fo r 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 Muskog
ee 4&5 Muskog
ee 4&5 Muskog
ee 4&5 Not

es

7 S&L Fox EPA S&L Fox EPA 1

8

9

1 0 Capacity (MW) 570 570 572 572

1 1 Vendor Quote 70,500,00

0 70,500,0

00 71,200,0

00 71,200,0

00

1 2 Dry FGD System 86,859,60

0 86,859,6

00 86,741,2

00 86,741,2

00

1
3 Fabric Filter 46,335,00

0 46,335,0

00 45,872,9

00 45,872,9

00

1

4 Ash Recycling System - DFGD Contractor's Scope 11,116,90

0 11,116,9

00 12,102,9

00 12,102,9

00

1

5 Waste Ash and Fly Ash Handling System 14,265,60

0 14,265,6

00 14,311,5

00 14,311,5

00

1 6 Ash Recycling - BOP Scope 955,600 955,600

1 7 Flue Gas System 42,463,00

0 42,463,0

00 52,238,2

00 52,238,2

00

1 8 Mechanical - Balance of Plant 6,891,300 6,891,30

0 6,201,40

0 6,201,40

0

1

9 Electrical:Auxiliary Power and
Balance of Plant Electrical
Work 41,935,10

0 41,935,1

00 37,923,6

00 37,923,6

00

2 0 Controls & Instrumentation 5,088,500 5,088,50

0 5,088,50

0 5,088,50

0

2 1 Civil Work 3,597,400 3,597,40

0 3,597,40

0 3,597,40

0

2

2 Removal & Relocation of Existing Equipment & Infrastructure 2,047,600 2,047,60

0 4,134,80

0 4,134,80

0

2

3 Manhour Productivity Factor - 14,276,5

30 - 14,918,1

00

2

4 Vendor Discount Equipment - 3,525,00

0 - 3,560,00

0

2

5 Optimizing Design Vendor Scope - 8,734,80

0 - 9,073,56

0

2

6 Subtotal Equipment Material &
Labor Costs 261,555,6

00 235,019,

270 268,212,

400 240,660,

740

2
7 Other Costs 61,016,90

0 61,016,9

00 63,987,8

00 63,987,8

00

2

8 Overtime Inefficiency - 9,518,00

0 - 9,945,00

0

2

9 BOP Contractor's General &

Admin - 7,408,60 - 7,855,50D-4 0 0

3

0 Spare Parts, Special Tools % Consumables 794,000 794,000 807,000 807,000

3 1 Freight, Duties & Taxes 1,936,000 1,936,00

0 2,136,90

0 2,136,90

0

3

2 325,302,5

00 281,839,
570 335,144,

100 289,791,
940

3
3 Total Indirect Costs 48,090,10

0 48,090,1

00 49,601,9

00 49,601,9

00

3

4 Engineering/Procurement - 12,205,3

38 - 12,354,4

95

3

5 Construction Management -869,259 -907,043

3

6 Startup & Commissioning Craft Support - 3,273,90

0 - 3,417,60

0

3

7 Operator Training & Manuals -900,300 -939,800

3

8 Owner's Cost - 16,265,1

25 - 16,757,2

05

3

9 Revised Total Indirect Costs 48,090,10
0 14,576,1
78 49,601,9
00 15,225,7
56

4

0 Escalation 46,602,90

0 0 63,277,1

00 0

4

1 Contingency 58,673,40

0 2,115,00

0 63,104,1

00 2,136,00

0

4

2 Bond Cost 4,786,700 0 5,111,30

0 0

4

3 AFUDC 101,133,8

00 0 118,148,

300 0

4

4 Total Project Cost 584,589,4
00 298,530,
748 153,594,

070 634,386,
800 307,153,
696 158,829,

176 2

4
5 CRF 0.1036 0.0806 0.1036 0.0806

4 6 Annual Capital 60,563,46

2 24,057,5

19 12,377,5

94 65,722,4

72 24,752,4

12 12,799,4

72

4
7 Variable O&M 19,899,40

0 19,899,4

00 19,899,4

00 21,292,2

00 21,292,2

00 21,292,2

00

4 8 Fixed O&M 8,027,400 8,027,40

0 8,027,40

0 8,439,20

0 8,439,20

0 8,439,20

0

4 9 Indirect O&M 5,174,200 5,174,20

0 5,174,20

0 7,146,40

0 7,146,40

0 7,146,40

0

5
0 Total O&M 33,101,00

0 33,101,0

00 33,101,0

00 36,877,8

00 36,877,8

00 36,877,8

00

5

1 Lime 4,682,11

0 0 4,476,06

4 0 3

5

2 Water -19,602 0 -180,730 0 4

5
3 FGD Waste Disposal Cost 4,738,72

1 0 5,310,68

5 0 5

5

4 Bag & Cage Replacement Cost -218,548 -218,548 -152,160 -152,160 6

5

5 Auxiliary Power - 5,368,40

0 - 5,368,40

0 - 5,886,80

0 - 5,886,80

0 7

5
6 Property Taxes - 4,862,99

7 - 7,326,92

1 - 3,926,79

7 - 5,706,69

1 8

5

7 Insurance -60,072 -90,509 -68,719 -99,867 9D-5 5
8 Revised O&M 33,101,00

0 31,992,2

12 20,096,6

22 36,877,8

00 36,449,3

43 25,032,2

81

5 9 Total Annual Cost ($) 93,664,46

2 56,049,7

31 32,474,2

16 102,600,

272 61,201,7

55 37,831,7

54

6

0 SO2 Emission Reduction (ton/yr) 13,930 13,930 13,799 13,799

6

1 Revised Emission Reduction (ton/yr) 43,428 15,860 46,458 15,989 10

6 2 S&L Cost Effectiveness ($/ton) 6,724 7,435

6

3 Revised Cost Effectiveness ($/ton) 1,291 2,048 1,317 2,366

6

4 Revised (AFUDC, escalation, contingency) 1,670 1,738

6 5 Cost ($/kw) 513 262 555 268

6 6 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 Cost-Effectiveness Analysis for Flue Gas Desulfurization at Coal-Fired Electric Generating Units in Oklahoma: S ooner 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 t he "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 in let 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% contro l case would actually be slightly larger due to the slight decrease in the flow rate thro ugh the baghouse OG&E uses in their design basis
for the 80% control cases vs the 91% cont rol cases on pdf 76 - 79 of their report.

7. Retained Fox auxiliary power correction, as t he 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

9. Modified Fox insurance correction by recalculating the total project cost, since S&L assumed ptoperty taxes were a percent of that figure.D-6 10. The baseline emissions in line 61 are tak en 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 multip lying 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 pr oposed BART determination for this unit.E-2E-3E-4E-5E-6E-7E-8E-9E-10