## 10 FEB 25 PH 3: Or

February 17, 2010 STATE OF OKLAHOMA 

## ZfilD FEB 19 PH 2: 14

OFFICE OF THE SECRETARYOFENVIRONMEN,ITEi;NiIL :,FF.;:RS DIVISION BRADHENHY GOVEBNOH

Dr. Alfredo Armendariz, Regional Administrator (6RA) U.S. Environmental Protection Agency, Region 6 1445 Ross Avenue, Suite 1200 Dallas, TX 75202-2733

RE: Oklahoma Regional Haze State Implementation Plan

Dear Dr. Armendariz:

In a letter to Mr. Lawrence Starfield, Acting Regional Administrator, U.S. Environmental Protection Agency ("EPA"), dated April 8, 2009, Governor Brad Henry appointed me as his designee for the purpose of submitting documents to the EPA for approval and incorporation into the State Implementation Plan ("SIP") for the State of Oklahoma.

Accordingly, the State of Oklahoma submits for your review and approval under Section 110 of the federal Clean Air Act and 40 C.F.R. Part 51, the Oklahoma Regional Haze SIP and the associated evidence as required by 40 C.F.R. Part 51. Appendix V, 2.1. This SIP revision addresses the federal regulatory requirements applicable to Oklahoma in 40 C.F.R. Part 51, Subpart p. Protection of Visibility.

As required by 40 C.F.R. § 51.103(a), we have provided two hard copies under separate

cover and made available an identical electronic copy on the DEQ website at: http://www.deq.state.okus/AODnew/RulesAndPlanning/RegionaIHaze/index.htm

If you have questions. please do not hesitate to contact me or Eddie Terrill. Director of DEQ's Air Qua!ity Division, at (405) 702-4154.

Enclosures

cc: The Honorable Brad Henry, Governor of Oklahoma Steve Thompson, Executive Director, Department of Environmental Quality Eddie Terrill, Director, DEQ Air Quality Division Guy Donaldson, Section Chief, Air Planning Section, EPA Region VI (6PD-L) Jeff Robinson. Section Chief, Air Permits, EPA Region VI (6PD-R)

3800 North Classen Boulevard Oklahoma City, Oklahoma 73118 (405) 530-8995 fax (405) 530-8999



# .. \,,,--

### STEVEN A. THOMPSON Executive Oirector OKLAHOMA [ltpMIMt:NT Of**fNVIRO~1I.IENiAl** QIJAlITY 

## OKLAHOMA DEPARTMENT OF ENVIRONMENTAL QUALITY BRAO HENRY Governor

January 26, 2010

Dr. Alfredo Armendariz, Regional Administrator U.S. Environmental Protection Agency, Region 6 1445 Ross Avenue, Suite 1200 Dallas, TX 75202-2733

Subject: Oklahoma Regional Haze State Implementation Plan (SIP)

Dear Dr. Armendariz:

We certify that the rule making procedures for all the rules listed below were in compliance with the requirements of the Oklahoma Administrative Procedures Act, 75 O.S. 250.1 through 323, and 40 C.F.R. §§ 51.102(f), 51.102(d), and 40 C.F.R. Part 51, Appendix V, 2.1(g).

Each rule and its public hearing history are listed in the tables below:

R fiT 1 Subehanter 252:100-8, Part 1 • Best Avai able etro It echnology
NOTICE PUBLIC HEARING GOVERNING BOARD
12/15/05 01/18/06 Air Quality Advisory Council
12/15/05 02124/06 Environmental Quality Board
03/15/06 04/19/06 Air Quality Advisorv Council
08/22/06 Environmental Quality Board 

# .-

252 008 1 Defini Sbh

u canter :1 • -1. ltlons NOTICE PUBLIC HEARING GOVERNING BOARD
12/15/05 01/18106 Air Quality Advisorv Council 12/15/09 02/24/06 Environmental QualityBoard

Notices of intent to adopt new or amended rules were published in the Oklahoma Register. The Oklahoma Register is a semi-monthly publication prescribed by the Oklahoma Administrative Procedures Act in which all rule making actions and the associated documents must be published. This information is now accessible at www.sos.state.ok.us/oar. A notice of rulemaking intent includes the date, time, and location of public hearings as well as information on how the public may submit written or oral comments concerning proposed rules. The public comment period for all Air Quality Council meetings begins on the date of publication of the notice and ends on the date of the public hearing. The Environmental Quality Board (EQB) accepts comments on the date of the EQB hearing.

If you have questions or require additional information, please contact Cheryl Bradley, Environmental Programs Manager, at (405) 702-4100.

Eddie Terrill, Director Air Quality Division

ET:CB:gg
707 NORTH ROBINSON, P.O. BOX 1677, OKLAHOMACllY, OKLAHOMA 73101·1677

printed on rl;lcycled paperwllh soy Ink.**c~**

# Regional Haze Implementation Plan

Revision

State of Oklahoma

Department of Environmental Quality

February 2, 2010Oklahoma Department of Environmental Quality (DEQ) wishes to acknowledge Central Regional

Air Planning Association (CENRAP) and Mid-Atlantic/Northeast Visibility Union (MANE-VU) for providing the draft template that formed the basis for this implementation plan revision.



## Table of Contents

I. Background and Overview of the Federal Regional Haze Regulation ................................. 1

A. Class I Areas

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

B. Reasonably Attributable Visibility Impairment

..................................................................... 2

C. Regional Haze

........................................................................................................................ 2

D. Legal Authority

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

E. Central Regional Air Planning

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

F. History of Oklahoma Participation

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

G. State, Tribe, and Federal Land Manager Coordination

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

II. Visibility Monitoring and Conditions ................................................................................... 8

A. Monitoring Strategy

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

B. Monitor Operation

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

C. Calculating Light Extinction from Particulate Concentrations

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

D. Deciview Haze Index

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

E. Monitoring Data and Light Extinction Calculations

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

F. Baseline Visibility at the Wichita Mountains

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

III. Natural Conditions .............................................................................................................. 28

A. Contribution of Constituents to Natural Background

......................................................... 28

B. Estimate of Natural Visibility at the Wichita Mountains

.................................................... 34

C. Uncertainty and Error

......................................................................................................... 35

D. Uniform Rate of Improvement

............................................................................................ 36

IV. Emissions Inventory for 2002 ............................................................................................. 37A. Summary

............................................................................................................................. 37

B. Point Source Emissions

....................................................................................................... 38

C. Area Source Emissions

........................................................................................................ 38

D. On -road Mobile Source Emissions

...................................................................................... 39

E. Non-road Mobile Source Emissions

.................................................................................... 40

F. Biogenic Emissions

.............................................................................................................. 40

G. Discussion of Components

.................................................................................................. 40

H. Emissions Inventory for 2005

.............................................................................................. 47

V. Modeling Assessment ......................................................................................................... 48

A. Modeling Protocol

............................................................................................................... 49

B. Meteorological Modeling

.................................................................................................... 51

C. Emissions Modeling

............................................................................................................ 52

D. Air Chemistry Modeling

...................................................................................................... 54

E. Particulate Source Apportionment

..................................................................................... 57

F. Discussion of Components

.................................................................................................. 58

G. Other Class I Areas

.............................................................................................................. 64

H. Strategy for the Wichita Mountains

................................................................................... 66

I. International Transport

....................................................................................................... 67

VI. Best Available Retrofit Technology.................................................................................... 69

A. BART-eligible Sources in Oklahoma

.................................................................................... 69

B. Determination of Sources Required to Install BART

........................................................... 71

C. Determination of BART Requirements for Subject Sources

............................................... 75

D. Contingent SO 2 BART Determinations.

............................................................................... 82

E. Greater Reasonable Progress Alternative Determination.

................................................. 84

VII. Long-term Strategy with Emission Reduction .................................................................... 86

A. Permits, Compliance, and Enforcement

............................................................................. 86

B. Oklahoma Emissions Limitations and Visibility Impairment

............................................... 87

C. Ongoing Air Pollution Control Programs

............................................................................. 88VIII. Modeling of Regional Haze in 2018 ................................................................................... 91

A. Emissions Inventory

............................................................................................................ 91

B. Point Source Emissions for

.................................................................................................. 92

C. Other Emission Sources

...................................................................................................... 93

D. Meteorological and Air-quality Simulations

....................................................................... 94

E. Discussion of Components

.................................................................................................. 94

F. Long-term Strategy for Other Class I Areas

...................................................................... 100

G. Long-term Strategy Interpretation for the Wichita Mountains

........................................ 102

IX. Reasonable Progress Goal ................................................................................................. 104

A. Reasonable Progress Goal for the Wichita Mountains

..................................................... 104

B. Reductions Required to Meet the Uniform Rate of Progress

........................................... 107

C. Control Simulations

........................................................................................................... 107

D. Factors for Consideration

.................................................................................................. 111

E. Reasonableness of Controls

.............................................................................................. 112

F. Conclusion

......................................................................................................................... 114

X. Consultation and Comments ............................................................................................. 115

A. Long-term Strategy and Reasonable Progress Goals Consultation

................................... 115

B. Federal Land Manager Consultation

................................................................................. 117

C. Federal Land Manager Contact List

.................................................................................. 117

D. Public Hearing

................................................................................................................... 118

E. Public Comments

.............................................................................................................. 118

F. Future Consultation with Federal Land Managers

............................................................ 119

G. Periodic Updates of Emission Inventories

........................................................................ 119

H. Periodic Reporting and Determination of Adequacy

........................................................ 119

I. Comprehensive Periodic Revisions

................................................................................... 119

J. Conclusion Clause

............................................................................................................. 119

## L IST OF 

## T ABLES

Table I-1 Class I areas possibly affected by Oklahoma emissions

Table II-1 Monthly normal relative humidity functions at the Wichita Mountains

Table II-2 Measured fine particulate sulfur concentrations at the Wichita Mountains

Table II-3 Measured fine particulate nitrate concentrations at the Wichita Mountains

Table II-4 Measured fine particulate organic carbon concentrations at the Wichita Mountains

Table II-5 Measured fine particulate elemental carbon concentrations at the Wichita Mountains

Table II-6 Measured fine soil particulate concentrations at the Wichita Mountains

Table II-7 Measured coarse particulate concentrations at the Wichita Mountains

Table II-8 Measured fine chloride and chlorine particulate concentrations at the Wichita Mountains

Table II-9 Measured concentrations of various elements and nitrite in fine particulate matter at the Wichita

Mountains

Table II- 10 Average visibility conditions at the Wichita Mountains

Table II- 11 Best quintile visibility conditions at the Wichita Mountains

Table II- 12 Worst quintile visibility conditions at the Wichita Mountains

Table II I-1 Estimated natural sulfureous particulate concentrations at the Wichita Mountains

Table I II -2 Estimated natural nitrate particulate concentrations at the Wichita Mountains

Table II I-3 Estimated natural organic carbonaceous particulate concentrations at the Wichita Mountains

Table I II -4 Estimated natural elemental carbonaceous particulate concentrations at the Wichita Mountains

Table I II -5 Estimated natural fine soil particulate concentrations at the Wichita Mountains

Table I II -6 Estimated natural coarse particulate concentrations at the Wichita Mountains

Table I II -7 Estimated natural chloride particulate concentrations at the Wichita Mountains

Table I II -8 Natural visibility conditions at the Wichita Mountains

Table I II -9 Uniform rate of progress at Wichita Mountains (worst quintile, Western natural background)

Table IV-1 Summary of emissions from Oklahoma sources in 2002 (tons per year)

Table IV-2 Inventory of area-source emissions from Oklahoma for 2002 by subcategory (tons per year)Table IV-3 Leading sources of SO 2 emissions in Oklahoma

Table IV-4 Leading sources of NH 3 emissions in Oklahoma

Table IV-5 Leading sources of NO x emissions in Oklahoma

Table IV-6 Leading sources of VOC emissions in Oklahoma

Table IV-7 Leading sources of direct PM 10 - PM 2.5 emissions in Oklahoma

Table IV-8 Leading sources of direct PM 2.5 emissions in Oklahoma

Table V-1 Projected sulfureous sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

Table V-2 Projected nitrate sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

Table V-3 Projected organic carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days in

2002)

Table V- 13 Projected elemental carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days

in 2002)

Table V-5 Projected fine soil sources of light extinction at the Wichita Mountains (worst quintile days in 2 002)

Table V-6 Projected coarse particulate sources of light extinction at the Wichita Mountains (worst quintile days in

2002)

Table V-7 Projected contribution of Oklahoma emissions to visibility impairment at Class I areas (worst quintile days in

2002)

Table V-8 Projected sources of light extinction at Wichita Mountains (worst quintile days in 2002)

Table VI-1 Facilities with BART-eligible units in Oklahoma

Table VI-2 BART-eligible sources in Oklahoma granted waivers from BART based on dispersion modeling

Table VI-3 BART-eligible sources in Oklahoma granted waivers from BART with new proposed permitted emission

limits

T able VI- 14 Sources in Oklahoma required to install BART

Table VI -5 BART levels for sources subject to BART

Table VI- 6 Controls used to comply with BART

Table VI- 7 BART-level emissions reductions from the 2002 baseline, sulfur dioxide

Table VI- 8 BART-level emissions reductions from the 2002 baseline, nitrogen oxides

Table VI-9 BART-level emissions reductions from the 2002 baseline, particulate matter (PM 10 )

Table VI- 10 Visibility improvement in the 98th percentile with BART NO X controls

Table VI- 11 Visibility improvement in the 98th percentile with BART SO 2 controlsTable VII-1 Consent decree emissions reductions

Table VIII-1 Estimate of emissions from Oklahoma sources in 2018 (tons per year)

Table VIII-2 Comparison of Oklahoma emissions inventory for 2002 and estimates for 2018 (tons per year)

Table VIII-3 Projected sulfureous sources of light extinction at the Wichita Mountains (worst quintile d ays , estimate for

2018 )

Table VIII-4 Projected nitrate sources of light extinction at the Wichita Mountains (worst quintile days, estimate for

2018 )

Table VIII-5 Projected organic carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days

,

estimate for 2018)

Table VIII-6 Projected elemental carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days

,

estimate for 2018)

Table VIII-7 Projected fine soil sources of light extinction at the Wichita Mountains (worst quintile days, estimate for

2018 )

Table VIII-8 Projected coarse particulate sources of light extinction at the Wichita Mountains (worst quintile days

,

estimate for 2018)

Table VIII-9 Projected influence of emissions from Oklahoma on Class I areas (worst quintile days, 2018)

Table VIII- 10 Projected sources of light extinction at the Wichita Mountains (worst quintile days)

Table IX- 1 Reasonable progress goal for visibility at the Wichita Mountains (worst quintile days)

Table IX-2 Reasonable progress at the Wichita Mountains (best quintile days)

Table IX-3 Emissions reductions required to meet uniform rate of progress at the Wichita Mountains (worst quintile

days)

Table IX-4 Visibility at the Wichita Mountains under control scenario (worst quintile days)

Table IX-5 Sources considered for subjugation under reasonable progress goals



## L IST OF 

## F IGURES
Figure I-1 Geographical areas of regional planning organizations

Figure V-1 National regional planning organization 36-km modeling domain used in the CAMx and CMAQ modeling for

CENRAP

Figure IX-1 Glide path and reasonable progress goal at the Wichita Mountains (worst quintile days)



## L IST OF 

## A PPENDICES

Appendix 1-1 Oklahoma Administrative Code and Statutes

Appendix 4- 1 2002 Oklahoma Emissions InventoryAppendix 4- 2 Technical Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans

Appendix 6- 1 Oklahoma’s BART Rule and Administrative Materials

Appendix 6- 2 CENRAP BART Modeling Guidelines (CALPUFF Modeling Protocol)

Appendix 6-3 Waivers from BART based on dispersion modeling

Appendix 6- 4 BART Application Analysis for each facility required to install BART

Appendix 6-5 Regional Haze Agreements

Appendix 8- 1 Estimated Oklahoma Emissions Inventory for 2018

Appendix 10- 1 Copies of Correspondence

Appendix 10- 2 Summary of Comments from FLMs and Response

Appendix 10- 3 Summary of Comments from the Public and Responses

## I. Background and Overview of the Federal Regional Haze Regulation

In the 1977 Clean Air Act (CAA) amendments, Congress added §169A (42 USC §7491), setting forth, “Congress hereby declares as a national goal the prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Class I Federal areas which impairment results from

man- made air pollution.” Mandatory Class I Federal areas include national parks and wilderness areas which meet particular criteria.

Oklahoma’s only Class I Area is the Wichita Mountains Wilderness Area; however sources within the state may contribute to visibility impairment of Class I areas in other states. As the Oklahoma Department of Environmental Quality (DEQ) is empowered to administer the CAA within the State of Oklahoma, it is DEQ which implements rules to protect visibility at Class I areas. The current state implementation plan for addressing air quality concerns was approved by EPA on 8 November 1999. This implementation plan revision is set forth with the intention of addressing new legislation regardin g regional haze. The revision addresses regional planning, discusses state/tribe and FLM coordination, andcontains methods by which to provide plan revisions and adequacy determinations. DEQ has preexisting rules and processes which protect against visibility impairment; primary among them is the Best Available Retrofit Technology (BART) rule. Because regional haze is an issue which crosses jurisdictional boundaries, DEQ makes further efforts to reduce visibility impairment through interagency cooperation. Members of Oklahoma DEQ have been actively involved in the Central Regional Air Planning Association (CENRAP) since 1999 and cooperate with tribes and federal land managers (FLMs).



### A. Class I Areas Promulgated 23 October 1970, Public Law No. 91-504, 84 St at. 1104-06 designated 8,900 acres within the Wichita Mountains Wildlife Refuge, a tract of sixty-thousand acres of complex terrain in Comanche County, as a wilderness area. The federal Fish and Wildlife Service manages this Wichita
Mountains Wilderness A rea, hereinafter simply “Wichita Mountains.” Oklahoma contains no other Class I areas for the purpose of regional haze.

This implementation plan revision also addresses visibility impairment in mandatory Class I federal areas located outside Oklahoma. Transport of pollutants from Oklahoma could potentially influence visibility in Class I areas elsewhere. Prevention of significant deterioration regulations and rules, as practiced for each affected source in Oklahoma, require an evaluation of the visibility impairment at all Class I areas located within 300 km of such source. Class I areas typically evaluated for affected sources in Oklahoma include the Wichita Mountains and Caney Creek and Upper Buffalo in Arkansas. Other Class I areas potentially affected by Oklahoma emissions include Hercules Glades in Missouri, Salt Creek in New Mexico, and Guadalupe Mountains in Texas. Table I-I summarizes this
information on affected Class I areas.2

Table I-1 Class I areas possibly affected by Oklahoma emissions

Class I a rea State PSD review possible Oklahoma emissions effect on worst quintile days >2% E Wichita Mountains Oklahoma Yes Yes

Caney Creek Arkansas Yes No

Guadalupe Mountains Texas No Yes

Hercules Glades Missouri Yes Yes

Salt Creek New Mexico No Yes

Upper Buffalo Arkansas Yes No



### B. Reasonably Attributable Visibility Impairment During the past few decades, various government entities have taken modest steps to address the visibility problems in Class I areas. Control measures mainly addressed “plume blight” attributable to specific pollution sources and did little to address regional haze issues. Plume blight results from individual sources emitting pollutants into a stable atmosphere, forming a plume which impairs visibility.

Oklahoma Governor Henry L. Bellmon submitted an implementation plan revision to address reasonably attributable visibility impairment at the Wichita Mountains on 18 June 1990, and EPA approved this implementation plan revision on 8 November 1999 (64 FR 60683), codified as

40 C.F.R. §52.1920(c)(49). Oklahoma State Department of Health and DEQ provided review reports regarding visibility protection triennially from 1986 to 2003 in accordance with CAA §169A (42 USC §7491) and 40 C.F.R. §51.306. No reasonably attributable visibility impairment exists at the Wichita Mountains and the Wichita Mountains include no integral vistas.

DEQ rules already address visibility impairment through its permitting process, requiring the owner or operator of any new or modified major stationary source to comply with prevention of significant deterioration of air quality requirements. These preexisting rules limit the establishment of sources of air pollution that may contribute to visibility impairment. DEQ will continue to implement these rules to protect visibility at Wichita Mountains and Class I areas in other states.



### C. Regional Haze Congress amended the Clean Air Act in 1990, adding §169B (42 USC §7492), authorizing further research and regular assessments of progress toward its statutory visibility goal. Under 42 USC §7492(f), EPA established the Grand Canyon Visibility Transport Commission, involving several southwestern
states but not Oklahoma. This commission contributed valuable scientific and technical assessments and information to EPA and, after four years of research and policy development under

42 USC §7492(d), submitted a report in 1996. This report made recommendations for regulations to3

address long-range strategies for regional haze that EPA used in its development of the federal regional
haze rule.

EPA promulgated the regional haze rule (40 C.F.R. 51 , Subpart P), effective 30 August 1999, aiming to achieve national visibility goals by 2064. This rulemaking addressed the combined visibility effects of various pollution sources over a wide geographic region. This wide-reaching pollution net requires Oklahoma and every other State

— even those without Class I Areas

— to participate in haze reduction efforts. The regional haze rule also requires certain existing stationary facilities which contribute significantly to regional haze problems in Class I areas to install and maintain BART to reduce their respective contributions.

On 24 May 2002, the United States Circuit Court of Appeals for the District of Columbia ruled on the challenge that the American Corn Growers Association brought against the regional haze rule. See
American Corn Growers et al. v. EPA, 291 F.3d 1 (D.C. Cir. 2002). The Court remanded the BART provisions of the rule to EPA and denied the challenge of the industry to the regional haze rule goal of natural visibility without degradation. EPA promulgated revisions to the regional haze rule pursuant to
this remand.

To implement the national visibility goal enacted by Congress, the State of Oklahoma hereby submits this implementation plan revision to EPA in order to address requirements of the regional haze rule. This revision addresses regional planning, discusses State/Tribe and federal land manager coordination, and contains a commitment to provide plan revisions and adequacy determinations.4



### D. Legal Authority

27A O.S. §2 -5- 105 designates DEQ as the administrative agency for the Oklahoma CAA. DEQ’s Air Quality Division (AQD) handles the statutory authorities and responsibilities concerning air quality under OAC 252:4-1-3(c). The air quality division has the authority to carry out all duties, requirements, and responsibilities necessary and proper for the implementation of the Oklahoma CAA and fulfilling the requirements of the federal CAA under 27A O.S. §§1-3-101(B)(8), 2-3-101(E)(1), and 2-5-105. Upon recommendation of the Air Quality Advisory Council, the Environmental Quality Board has the authority under Oklahoma statutory law 27A O.S. §2 -5-106 to adopt air quality regulations for DEQ. DEQ has the authority under Oklahoma law to:

Enforce those regulations and orders of DEQ [27A OS §§2 -5-105(4) and 2-5-110]; Maintain and update an inventory of air emissions from stationary sources [27A O.S. §2 -5-105(19)]; Establish a permitting program [27A O.S. §2 -5-105(2)]; and Carry out all other duties, requirements and responsibilities necessary and proper for the implementation of the Oklahoma CAA and the fulfillment of the requirements of the Federal CAA [27A O.S. §§2-5-105(22)].

Specifically, the Environmental Quality Board and DEQ have the existing authority to:

Adopt emissions standards and regulations to implement the Oklahoma CAA and fulfill requirements of the Federal CAA [27A O.S. §§2-2-104, 2-5-105, 2-5-106, 2-5-107, and 2-5-114]; Enforce the relevant laws, regulations, standards, orders and compliance schedules authorized by the Oklahoma CAA [27A O.S. §§2-5-105(4) and 2-5-110], and seek injunctive relief when necessary [27A O.S. §§2-5-105(14) and 2-5-117(A)]; Abate pollutant emissions on evidence that the source is presenting an immediate, imminent and substantial endangerment to human health [27A O.S. §2 -5-105(15)]; Prevent construction, modification, or operation of a source in violation of the requirement to have a permit, or in violation of any substantive provision or condition of any permit issued pursuant to the Oklahoma CAA [27A O.S. §2 -5-117(A)(2)]; Obtain information necessary to determine compliance [27A O.S. §§2 -5-105(17), (18)]; Require recordkeeping, make inspections, and conduct tests [27A O.S. §2 -5-105(17)]; Require the installation, maintenance and use of monitors and require emissions reports of owners or operators [27A O.S. §2 -5-112(B)(5)]; and Make emissions data available to the public [51 O.S. §§24A.1 through 24A.27].

Appendix 1-1 contains these referenced statutes.5



### E. Central Regional Air Planning In the regional haze rule preamble, EPA acknowledged the key role of regional pollutant transport in contributing to haze at the Wichita Mountains and other Class I areas and recognized the need for interstate coordination for program planning and implementation. In response to this need, EPA designated five regional planning organizations to assist with the coordination and cooperation needed to address the visibility issue. The federal government funded the following five regional planning organizations:

Central Regional Air Planning Association (CENRAP), Western Regional Air Partnership (WRAP), Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Midwest Regional Planning Organization, and Mid-Atlantic/Northeast Visibility Union.

DEQ joined state clean 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. Figure I- 1 displays the membership of all five regional planning organizations on a map of the United States of America. CENRAP aims to provide information necessary to coordinate emissions reduction strategies to improve visibility at the Wichita Mountains and all other mandatory Class I areas.6

Figure I-1: Geographical areas of regional planning organizations

The policy oversight group governs CENRAP and comprises of 18 voting members representing the states and tribes within the CENRAP region and non-voting members representing local agencies, EPA, Fish and Wildlife Service, Forest Service, and National Park Service. The policy oversight group facilitates communication with federal land management personnel, steakholders, the public, and
CENRAP staff.

The committee structure of CENRAP addresses all technical and non-technical issues related to regional haze. CENRAP established five standing workgroups: monitoring, emissions inventory, modeling, communications, and implementation and control strategies. All interested parties can participate in and provide input openly to these workgroups.

This implementation plan revision uses data analysis, modeling results, and other technical support documents prepared for CENRAP members. DEQ coordinated with CENRAP and other regional planning organizations to ensure that its long-term strategy and BART determinations reduce emissions sufficiently to mitigate their visibility impairment at affected Class I areas. CENRAP provides data analyses, modeling results, and other technical support documents to members through its website and file transfer protocol.

In concurrence with EPA policy, bylaws of CENRAP state, “CENRAP has no regulatory authority
and recognizes that its members in accordance with existing law retain all legal authority.” DEQ7

therefore maintains sole authority and responsibility for the development of this implementation plan revision and regional haze rules.



### F. History of Oklahoma Participation DEQ began to participate actively in CENRAP upon its inception in 1999, when Central States AirResource Association (CenSARA) held the first workshop to develop the foundational policies and longrange plans for CENRAP. Former DEQ air quality director Larry Byrum served as the first director of CenSARA/CENRAP; current air quality director Eddie Terrill serves on the policy oversight group. DEQ employees actively consulted in many workgroups and served as chairs and co-chairs for those workgroups. DEQ assistant air quality director Beverly Botchlet-Smith assumed a leadership role in the communications workgroup, which former staff member Michelle Martinez also participated in. Ray Bishop led the monitoring workgroup before his retirement in 2007. DEQ engineering manager Lee Warden served as co-chair of the modeling workgroup, and staff manager Scott Thomas participated in the strategy and implementation workgroup. In addition to workgroup leadership and participation, members of DEQ staff actively participated in CenSARA/CENRAP meetings, and former DEQ staff member Annette Sharp now works as director of CenSARA/CENRAP.

DEQ provided emissions inventory information for all source categories to CENRAP and itsmembership. DEQ cooperated with all CENRAP states and tribes in developing information on baselineperiod visibility impairment, an estimate of natural conditions, and a projection of emissions and visibility impairment in 2018.



### G. State, Tribe, and Federal Land Manager Coordination

40 CFR § 51.308(i) requires coordination among states, tribes, and FLMs. FLMs serve on the policy oversight group of CENRAP and contribute to the membership on standing committees. This involvement enables FLMs to contribute to the development of technical and non-technical work of CENRAP. CENRAP additionally provided opportunities for FLMs to review and comment on each technical document developed toward the preparation of this implementation plan revision. In addition, the final draft of this implementation plan was uploaded to the internet for review by FLMs. FLMs were given thirty days to review and comment on the document prior to submittal. The FLM contact list used by DEQ in this process is provided in Chapter X, Consultation and Comments.8



## II. Visibility Monitoring and Conditions
The regional haze rule in 40 C.F.R. §51.308(d)(4) requires a monitoring strategy for measuring, characterizing, and reporting data representative of visibility impairment in the Wichita Mountains. An Interagency Monitoring of Protected Visual Environments (IMPROVE) monitor, established at Wichita Mountains in March 2001, fulfils this requirement. The IMPROVE monitor collects 24-hour samples every third calendar day. Later laboratory analysis gives dry concentrations for a wide variety of particulate components, including nitrate, various classes of carbonaceous aerosol, and many elements in the mixture. Application of the results then yields light extinction and visibility impairment during the sample period. This chapter details these analyses and calculations. Although the regional haze rule requires the establishment of baseline visibility conditions for 2000-2004, this implementation plan revision instead utilizes baseline conditions representative of 2002-2004 due to lack of data from previous years. Observations collected from the monitor indicate that sulfureous particulate causes a plurality of visibility impairment at the Wichita Mountains in every month of the year except January. On cold winter days, however, nitrate particulate dominates regional haze at the Wichita Mountains. Other components of regional haze spike during sporadic events. This chapter includes numerical values for all calculations, including baseline visibility.



### A. Monitoring Strategy Upon the creation of CENRAP, the Monitoring Workgroup identified a visibility data void in Oklahoma. At that time, an IMPROVE site for Upper Buffalo, Arkansas, in a wetter climate several hundred miles to the east-northeast, provided the closest data in the network. Lacking more proximate data, some analyses early in the regional haze planning process assigned this Arkansas data to the Wichita Mountains, but DEQ finds such data unrepresentative and inappropriate.

Wichita Mountains Wildlife Refuge personnel began operating an IMPROVE particle sampler in March 2001. Maintenance of this monitor with the current sampling protocol enables the assessment of reasonable progress toward addressing regional haze. This monitor represents conditions throughout the Wichita Mountains well, and because Oklahoma lacks any other Class I areas, this monitoring site alone provides all necessary data.

Within communications, technological, and financial constraints, this site will adhere to IMPROVE analysis methods and techniques. The IMPROVE program makes data available on the Internet and submits them to EPA’s air quality system. DEQ does not operate the IMPROVE monitor at the Wichita Mountains and therefore takes no responsibility for analyzing filters or disseminating and submitting data. DEQ nevertheless intends to ensure the operation of a visibility monitoring site at the Wichita Mountains as long as funding allows. DEQ cannot assess the achievement of reasonable progress at the Wichita Mountains without the continuation of this monitoring.9



### B. Monitor Operation Under IMPROVE protocol, Wichita Mountains Wildlife Refuge 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. Each monitoring site contains four independent modules connected only through a common clock. Each module contains a separate inlet, filtering system, and pump.

In module B

, a carbonate denuder removes nitric acid and other acidic gases before particles of aerodynamic diameter 0.2 μm to 2.5 μm collect on a nylon filter. This filter goes to the Research Triangle Institute for ion chromatography analysis to establish concentrations of nitrate (NO 3 - ), nitrite (NO 2 - ), and chloride (Cl - ) in the collected particulate. This arrangement of denuder and nylon filter best preserves the nitrate and nitrite particulates that ordinarily volatilize readily, especially in warm, dry conditions typical of indoor air.

Module C

collects particles of aerodynamic diameter 0.2 μm to 2.5 μm on a tandem quartz filter. Desert Research Institute analyzes these filters to measure carbonaceous particulate in eight temperature-based categories using the thermal optical reflectance combustion method. First placed in a pure helium atmosphere, the organic carbon combusts, producing measured quantities of carbondioxide. After heating the sample and adding some oxygen to the helium atmosphere, even lightabsorbing elemental carbon combusts, producing measurable carbon dioxide. Most error in these measurements results from the assignment of carbon among several temperature categories; the total organic carbon and total elemental carbon concentrations carry considerably less uncertainty.

Module D

collects particles of aerodynamic diameter less than 10 μm on a Teflon polytetrafluoroethylene filter; the Crocker Nuclear Laboratory uses a gravimetric electro-microbalance method to determine the mass. The larger particles on this filter generally represent local and regional windblown dust and sand but include pollen and other organic materials too.

Most information from IMPROVE filters in module A , a Teflon polytetrafluoroethylene filter that collects fine particles of aerodynamic diameter 0.2 μm to 2.5 μm . Gravimetric electro-microbalance mass analysis of this filter approximates but does not match the federal reference method for measuring fine particulate matter ( PM 2.5 ) within the national ambient air quality standard, so determinations of attainment and non-attainment cannot include this protocol. Most nitrates and some organics volatilize during and after sampling on this module.

On each module A

sample, Crocker Nuclear Laboratory performs proton elastic scattering analysis (PESA) for hydrogen. Proton induced x -ray emission yields values for sodium, magnesium, aluminum, silicon, phosphorus, sulfur, chlorine, potassium, calcium, titanium, vanadium, chromium, and

manganese. X

-ray fluorescence analysis gives mass concentrations of iron, cobalt, nickel, copper, zinc, arsenic, gallium, selenium, bromine, rubidium, strontium, zirconium, molybdenum, and lead.

When combined with measurements of the volume of air that passes through each filter, these analysis methods yield a mass concentration in μg m -3 for each constituent in the lower atmosphere. DEQ presumes that samples from the monitors contain chemical constituents distributed uniformly10

throughout the field of view and therefore represent visibility impairment throughout the Wichita Mountains. IMPROVE technique notably does not measure ammonium (NH + ) despite its significant contribution to visibility impairment. The protocol hereinafter described presumes that ammonium binds to all nitrate and sulfurous particulate.



### C. Calculating Light Extinction from Particulate Concentrations Throughout the planning process, CENRAP entertained several possible methods for determining a representative daily light extinction coefficient from the particle concentrations that IMPROVE data provides. In 40 C.F.R. §51.308(d)(2), the regional haze rule assigns the determination of visibility conditions from monitoring data to the States. DEQ chooses to follow the approach of
Pitchford et al. (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.). This approach utilizes an equation often called the “new” or “revised” IMPROVE algorit hm but herein simply called the “algorithm” unless otherwise noted.

The algorithm estimates additive extinction coefficients for each of several chemical constituents of particulate matter: sulfurous, nitrate, organic carbonaceous, elemental “light -absor bing” carbonaceous, fine soil, chlorine or chloride, and coarse matter. The algorithm 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. The monitor at the Wichita Mountains currently does not measure the light-absorbing gas nitrogen dioxide (NO 2 ).

1. Sulfurous Particulate The algorithm for calculating light extinction considers all sulfur detected via proton-induced x

ray emission as a part of (NH 4 ) 2 SO 4 (ammonium sulfate). The samplers do not measure ammonium cation concentration. This fully neutralized and ammoniated compound usually dominates in most fieldstudies elsewhere around the country, but sulfureous aerosols can form in ammonia- and ammoniumpoor environments. Some acidic sulfate particulate commonly mixes with ammonium sulfate, and the sulfurous particulate statistics also include any other particulate sulfur compounds. Accounting for the other elements in ammonium sulfate gives an assumed mass of (NH 4 ) 2 SO 4 equal to 4.125 times the
measured mass of sulfur.

At high concentrations, sulfurous particulate tends toward larger particles within the accumulation mode (generally 0.1-1.0 μm aerodynamic diameter) rather than occurring as more numerous smaller particles. The algorithm assumes that this shift progresses linearly with measured
sulfur concentration until the faux ammonium sulfate concentration reaches 20 μg m -3 , beyond which level all sulfurous particulate presumably takes the larger particle size. Because light extinction depends on the particle size distribution, the algorithm supplies two separate dry scattering efficiencies: f sml ( R H ) for “small” particles and f lrg ( R H ) for “large” partic les within the accumulation mode. A single large particle within the accumulation mode scatters light more efficiently than several smaller particles with
the same combined mass.11

Because its dipole between the ammonium cations and the sulfate anion resembles the polarity of the water molecule, ammonium sulfate effectively nucleates cloud droplets. Ammonium sulfate deliquesces at a relative humidity considerably below 100%, acquiring water and forming haze droplets that enhance visible light scattering efficiency. As the relative humidity approaches 100%, these haze droplets swell rapidly, and their scattering efficiency increases correspondingly. This growth enhances the scattering efficiency of smaller aerosols more effectively than that of their larger counterparts.

The algorithm includes lookup tables for the proportional enhancement of scattering efficiency as a function of relative humidity rounded to the nearest percent. Relative humidity varies considerably within each calendar day; minima of relative humidity typically occur with temperature maxima and conversely. The monitoring equipment, however, collects only one sample for an entire calendar day. As relative humidity approaches 100%, fog forms entirely naturally and restricts visibility quite severely even without anthropogenic visibility-impairing particulate.

Rather than attempt to address these issues regarding each particular day, the algorithm uses climatic monthly-average relative humidity functions for visibility restriction through vapor growth of haze droplets. Because of the nonlinearity of these functions of relative humidity, the average value of a function of relative humidity differs from the value of the function of the average relative humidity. The average values of the two functions moreover vary independently but similarly; despite a strong

co rrelation between the monthly-averaged value for small particles and that for larger particles within the accumulation mode, no strict relation exists between the time-averaged values of these functions.

The algorithm mathematically expresses the sulfurous contribution as:

The algorithm requires factors to account for the effects of relative humidity on light scattering specific to the Wichita Mountains, but IMPROVE generally does not deploy humidity sensors. EPA instead derived monthly climatological mean relative humidity function values from hourly relative humidity measurements in 1988-1997 at 292 National Weather Service sites, 29 IMPROVE sites (not including the Wichita Mountains), 48 Clean Air Status and Trends Network sites, and 13 National Park
Service sites. These interpolated monthly climatological “normal” relative humidity function values approximate deliquescence that increases the light extinction from suspended particles. Table II-1 gives climatic normal values of the functions of relative humidity.12

Table II -1: Monthly normal relative humidity functions at the Wichita Mountains

Month

January 3.17 2.39 3.35

February 2.94 2.25 3.12

March 2.69 2.10 2.91

April 2.68 2.11 2.94

May 3.15 2.39 3.40

June 2.86 2.24 3.21

July 2.49 2.02 2.84

August 2.70 2.13 3.01

September 3.07 2.35 3.32

October 2.87 2.22 3.10

November 2.97 2.28 3.20

December 3.20 2.41 3.40

2. Nitrate Particulate The algorithm uses nitrate concentrations derived from the ion chromatography analysis and maps them exclusively to ammonium nitrate (NH 4 NO 3 ) despite the presence of other nitrates. The mass of an ammonium nitrate molecule equals 1.29 times the mass of a nitrate anion. Nitrate aerosols exhibit similar hygroscopicity to sulfurous aerosols, and their size distribution shifts toward larger particles within the accumulation mode at greater concentrations. The algorithm treats these properties in a manner mathematically identical to its treatment of such properties in sulfurous aerosols; however, nitrate aerosols exhibit slightly greater dry extinction efficiency. As an equation:13

3. Organic Carbonaceous Particulate IMPROVE quantifies only the carbon in carbonaceous particulate. The thermal optical reflectance technique produces measurements of carbon derived from carbon dioxide produced when the particulate combusts within certain temperature ranges. Only oxygenated organic carbon compounds can combust in the pure helium atmosphere used for the first four temperature steps. This combustion and the attendant thermal energy induce pyrolysis that darkens the remaining sample with other organic carbon. After the introduction of some oxygen gas into the combustion chamber, this “pyrolyzed” organic carbon O P combusts. When the reflectance of the sample returns to its original level, the combustion of organic carbon ends and that of refractory carbon begins. The algorithm must approximate the contribution of organic carbonaceous aerosols to light extinction as a function of their mass concentration. Scientific techniques still cannot assess accurately the abundance of particular carbonaceous compounds among this diverse mixture; therefore, the algorithm must make rough assumptions based upon observation. The algorithm employs the same split between large and small particles within the accumulation mode as it does for sulfurous and nitrate particulate. Because of the

co mmonality of non-polar and weakly polar covalent bonds in carbonaceous aerosols, the algorithm assumes that they nucleate haze droplets poorly and do not swell whatsoever regardless of the relative humidity. The algorithm also assumes that the mass of the aggregate of this group approximately equals 1.8 times the mass of the carbon atoms contained therein.

The mathematical formula for the derivation of the light extinction attributed in this algorithm to organic carbonaceous aerosols follows:

The monitor at Wichita Mountains operates consistently under the same sampling protocol
whatever its artifacts and limitations.

4. Elemental Carbonaceous Particulate Refractory carbon combusts only in the presence of oxygen after the reflectance of the sample increases to or beyond its original value. The algorithm considers all such carbon as light-absorbing elemental carbon. IMPROVE provides elemental carbonaceous particulate data in three temperature bins; these three bins include the “pyrolyzed” carbon already considered among the organic carbonaceous particulate.14

The algorithm assumes that elemental carbonaceous particulate both absorbs and scatters visible light. The algorithm excludes the size-shifting phenomenon in extinction efficiency and assumes that these particles do not deliquesce. The equation follows:

5. Fine Soil Particulate Most windblown dust occupies the coarse mode with aerodynamic diameters too large for the accumulation-mode samplers. The Teflon filter on the accumulation mode sampler, however, does collect some fine soil particulate. Soils comprise many different components, most notably oxides and other compounds of silicon, aluminum, calcium, iron, and titanium. The coefficients in the equation effectively convert the molar mass of each element except iron into that of its oxide. Iron contributes to ferric and ferrous oxides, and the algorithm presumes an equal split between the two valence states. Potassium also commonly occurs in soils and smoke, so the algorithm assumes that the potassium concentration equals 60 percent of the iron concentration and adds an appropriate factor to the coefficient for iron. Most fine soils presumably scatter light rather inefficiently compared to the other fine components. The algorithm then divides the coefficient for each element by 0.86 to account for
miscellaneous soil elements.

The particle-induced x -ray emissions and x -ray fluorescence methods cannot detect the signals of elements that occur in such small quantities that the analysis cannot distinguish between noise and signal. The algorithm assumes that every sample contains each element in the soil equation even if the measurement does not detect it. If the raw data give a concentration of any component element less than the minimum detectable limit, the algorithm substitutes one-half the minimum detectable limit. Soil dust generally occurs most commonly in very dry environments and does not deliquesce readily.

6. Coarse Particulate The algorithm defines coarse particulate mass concentration as the difference between that of particulate with aerodynamic diameter less than 10 μm and that of particulate with aerodynamic
diameter less than 2.5 μm, both measured from gravimetric electro -microbalance on separate Teflon filters. IMPROVE does not determine routinely the chemical composition of coarse particulate. Limited studies elsewhere suggest that coarse matter comprises mostly soils, organic particulates, and nitrate particulate. The algorithm assumes that all coarse matter does not deliquesce and assigns it a relatively low dry scattering efficiency.15

7. Chlorine or Chloride Particulate Designed for use throughout the United States of America, the algorithm includes a provision for saline aerosols that account for a considerable proportion of the total fine particulate mass in coastal regions. To estimate the fine sea salt concentration, the algorithm uses the chloride anion measurement from ion chromatography and assumes that saline aerosol always takes the form of NaCl. Chlorides in the real atmosphere can take other chemical forms, but the algorithm includes all chlorides here. In the event of a missing chloride ion concentration or one below the minimum detectable limit, the algorithm can substitute chlorine concentration measured with proton-induced x -ray emissions from the Teflon filter. Hygroscopic growth of saline aerosol greatly enhances its scattering efficiency with increasing relative humidity. The algorithm estimates this effect in a manner analogous to its treatment of hygroscopic growth of large and small sulfurous aerosol but uses an entirely independent curve designed especially for sodium chloride aerosol.

8. Rayleigh scattering All gaseous molecules scatter light and Rayleigh theory describes this scattering, depending on atmospheric density and varying with actual meteorological conditions. The algorithm assumes a constant Rayleigh scattering for the Wichita Mountains based upon its elevation and mean annual
temperature.

(Malm 1999). Source: William Malm, Introduction to Visibility , 1999, National Park Service?]



### D. Deciview Haze Index The laws of physics that govern scattering of light allow for additive extinction coefficients. Expressed mathematically for the application to regional haze:

The regulatory law defines the deciview haze index such that:

In the case of visibility, aerosol light extinction β ext represents the inverse of the characteristic distance that light travels before reflection or absorption.

IMPROVE data includes concentrations of various trace elements not considered here. The algorithm ignores these measured concentrations entirely because their minimal concentrations correspond to an insignificantly small proportion of total visibility impairment.16

DEQ considered refining the algorithm to use hourly relative humidity data available from the remote automated weather sensor (RAWS) at the Wichita Mountains or from the Medicine Park site of the Oklahoma Mesonet. This potential change raises several issues related to discernment between humid haze and unpolluted fog or mist on some days and selects dramatically different days as the worst quintile for visibility impairment. Desiring to select days among the best and worst quintiles consistent with available guidance and with the methods of other States in CENRAP, DEQ decided against this approach for this implementation plan revision; however, DEQ may consider such
refinements in future submissions to EPA.



### E. Monitoring Data and Light Extinction Calculations The regional haze rule requires the establishment of baseline visibility conditions for 2000-2004. DEQ must establish the average visibility impairment on the best quintile and worst quintile of monitored days in each year. Progress toward natural visibility conditions requires a reduction in the five-year rolling average visibility impairment in deciviews. No monitor existed at the Wichita Mountains during the year 2000, and no data from any other monitor provides a reasonable substitute. Because monitoring began in March 2001, data from that year does not meet EPA’s completeness criteria. This implementation plan revision presents the incomplete visibility data for 2001, complete data for 2002-2004, and provisional data for 2005 and 2006. Baseline conditions represent the average
for 2002-2004.

1. Sulfureous Particulate
Table II -2 summarizes measurements of sulfureous particulate concentration and estimates of the light extinction attributed to sulfureous particulate. The values listed in several tables in this chapter include significant figures reflecting uncertainty in measurements.17

Table II -2: Measured fine particulate sulfur concentrations at the Wichita Mountains

Year *S+ (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

2001 .74 24.

2002 .79 .19 1.8 25. 5.4 62.

2003 .74 .185 1.32 22.3 5.0 43.

2004 .71 .18 1.28 21.4 5.0 42.

2005 1.00 .28 2.3 32. 7. 80

2006 .70 .21 1.3 21. 6. 43.

2007 .69 20.7

Baseline .75 .187 1.48 22.7 5.1 49.

Sulfureous aerosol bears culpability for half of visibility impairment at the Wichita Mountains and constitutes a majority on most days among the worst quintile. High sulfureous particulate concentrations generally occur on days when broad anticyclonic flow moves air from the Eastern states through Texas to the Wichita Mountains on prevailing southerly flow. Days with high sulfureous aerosol concentrations and consequent visibility impairment occur year-round but most commonly from April through October.

2. Nitrate Particulate
Table II -3 summarizes measurements of nitrate particulate concentration and estimates of the light extinction attributed to nitrate particulate.18

Table II -3: Measured fine particulate nitrate concentrations at the Wichita Mountains

Year [NO 3 - + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

2001 1.0 10.

2002 .91 .33 1.0 8.9 3.2 10.

2003 1.06 .29 2.6 10.8 2.7 29.

2004 1.10 .27 2.7 11.9 2.5 32.

2005 .8 .26 1.0 9. 2. 10.

2006 .72 .21 1.1 7. 2.0 11.

2007 1.00 10.8

Baseline 1.02 .30 2.1 10.5 2.8 24.

Nitrate particulate haze occurs primarily during the winter months, typically contributing just

2 Mm -1 to visibility impairment from June through September and more than ten times that fromJanuary through March. This constituent alone comprises a majority of visibility impairment on oneeighth of days among the worst quintile at the Wichita Mountains. Those days occur exclusively in December, January, and February. Lower temperatures and a relative lack of bright sunshine inhibit photochemical reactions involving oxides of nitrogen during the winter season. Prevailing southerly flow most frequently reverses into northerly winds behind Arctic cold fronts that slide down the Plains during this hibernal season. Most sources of nitrate aerosols and their precursors therefore must lie on the rural to remote regions of the Great Plains north of the Wichita Mountains.

3. Organic Carbonaceous Particulate
Table II -4 summarizes measurements of each category of organic carbonaceous particulate concentration and estimates of the light extinction attributed to organic carbonaceous particulate.19

Table II -4: Measured fine particulate organic carbon concentrations at the Wichita Mountains

Year [ O

1 + (μg m - 3 ) [ O 2 + (μg m - 3 ) [ O 3 + (μg m - 3 ) [ O 4 + (μg m - 3 ) [ O P + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 .07 .27 .44 .35 .20 8.0

2002 .069 .03 .16 .24 .09 .50 .38 .21 .71 .31 .14 .61 .22 .13 .29 7.2 3.2 15.

2003 .07 .03 .12 .26 .10 .44 .56 .26 .86 .40 .15 .65 .20 .14 .17 9.1 3.7 15.

2004 .08 .04 .18 .30 .11 .63 .57 .26 1.2 .42 .16 .9 .18 .13 .18 9.9 3.9 22.

2005 .08 .02 .2 .35 .14 .7 .26 .14 .4 .24 .10 .4 .44 .20 .8 8.3 3. 17.

2006 .05 .02 .2 .27 .09 .5 .27 .14 .4 .21 .10 .4 .33 .15 .6 6.7 2.7 13.

2007 .04 .26 .26 .20 .37 6.6

Baseline .070 .03 .15 .266 .10 .52 .50 .24 .92 .378 .15 .72 .201 .13 .21 8.7 3.6 17.

The organic carbonaceous particulate concentrations show some seasonal cycle with a pronounced hibernal minimum from November through early February. Extreme concentrations and related visibility impairment occur on a few days in March. Visibility impairment attributable to organic carbonaceous particulate generally averages double its hibernal minimum from April through September.

4. Elemental Carbonaceous Fine Particulate
Table II -5 summarizes meas u rements of each category of elemental carbonaceous particulate concentration and estimates of the light extinction attributed to elemental carbonaceous particulate.20

Table II -5: Measured fine particulate elemental carbon concentrations at the Wichita Mountains

Year [ E 1 + (μg m - 3 ) [ E 2 + (μg m - 3 ) [ E 3 + (μg m - 3 ) [ O P + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 .38 .079 .004 .20 2.7

2002 .36 .17 .63 .086 .08 .07 .006 .005 .002 .22 .13 .29 2.3 1.2 4.2

2003 .37 .17 .50 .107 .09 .11 .010 .010 .009 .20 .14 .17 3.0 1.3 4.5

2004 .36 .19 .58 .070 .06 .07 .004 .004 .005 .18 .13 .18 2.6 1.3 4.7

2005 .65 .26 1.3 .101 .09 .11 .004 .006 .002 .44 .20 .8 3.2 1.5 6.

2006 .51 .19 1.0 .089 .07 .12 .0023 .001 .005 .33 .15 .6 2.6 1.1 5.

2007 .53 .078 .002 .37 2.4

Baseline .364 .18 .57 .088 .079 .083 .0066 .007 .005 .201 .13 .21 2.6 1.2 4.5

Despite high dry extinction efficiency, elemental carbonaceous particulate contributes less than one-eighth of total visible light extinction on every day among the worst quintile. The total
concentration exhibits little seasonal variation.

5. Fine Soil Particulate
Table II -6 summarizes measurements of each category of fine soil particulate concentration and estimates of the light extinction attributed to fine soil particulate.21

Table II -6: Measured fine soil particulate concentrations at the Wichita Mountains

Year *Si+ (μg m - 3 ) *Al+ (μg m - 3 ) *Ca+ (μg m - 3 ) *Fe+ (μg m - 3 ) *Ti+ (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 .22 .072 .067 .056 .0080 .98

2002 .19 .065 .20 .057 .010 .03 .052 .026 .060 .042 .013 .037 .0045 .0012 .0035 .79 .26 .8

2003 .219 .088 .24 .028 .018 .009 .064 .038 .065 .051 .019 .049 .0050 .0018 .0045 .85 .38 .9

2004 .196 .072 .19 .063 .017 .026 .049 .024 .062 .041 .015 .033 .0040 .0015 .0024 .82 .30 .7

2005 .14 .09 .27 .045 .03 .04 .057 .05 .07 .037 .022 .04 .0035 .0020 .004 .64 .4 .7

2006 .21 .11 .3 .084 .041 .13 .078 .05 .11 .052 .025 .08 .0050 .002 .007 .98 .5 1.5

2007 .20 .070 .057 .048 .0048 .87

Base

line .201 .075 .21 .049 .015 .023 .055 .029 .062 .044 .016 .039 .0045 .0015 .0035 .82 .31 .79

Meteorological conditions explain the similarity between the mean concentration of fine soil particulate and the average concentration on the worst quintile days. Sulfates and wintertime nitrates dominate the current worst quintile days but tend to occur in flow that passes over the continental
eastern United States and Canada.

Back trajectories indicate that long-range transport of dust from the Sahara Desert also contributes significantly to the fine soil particulate, even on days with relatively low concentrations of coarse-mode particulate. Saharan dust reaches the Wichita Mountains regularly, beginning in late spring and continuing until hurricanes develop in the tropical Atlantic Ocean. Higher fine soil concentrations occur most notably in dry years with late or inactive Atlantic hurricane seasons.

Fine soil generally contributes less than 1 Mm -1 to light extinction from September through February and increases rapidly through June and July to peak at an average greater than 2 Mm -1

.

6. Coarse Particulate
Table II -7 summarizes measurements of coarse particulate concentration and estimates of the light extinction attributed to coarse particulate.22

Table II -7: Measured coarse particulate concentrations at the Wichita Mountains

Year [PM 10 + (μg m - 3 ) [PM 2.5 + (μg m - 3 ) ( Mm - 1 )

Mean Best

quintile Worst

quintile Mean Best

quintile Worst

quintile Mean Best

quintile Worst

quintile

2001 15.9 7.8 4.7

2002 14.7 6.5 24. 7.4 2.2 15. 4.4 2.5 5.

2003 15.3 8.0 21.0 7.9 2.5 14.0 4.5 3.3 4.

2004 14.1 5.5 22. 7.8 2.4 15.2 3.8 2.2 4.

2005 15.5 8. 26. 8.1 2.8 17. 4.5 4. 5.

2006 15.1 8. 24. 6.6 2.4 12. 5.1 4. 7.

2007 13.8 7.2 4.0

Baseline 14.7 6.7 22.4 7.71 2.35 14.6 4.2 2.7 4.6

Coarse particulate exhibits little seasonal variation with a normal minimum in January near

3 Mm -1 of visibility impairment and a broad peak near July with 6 Mm -1 of visibility impairment. Variation from day to day, however, overwhelms any seasonal trends. Dust storms which cause the most extreme concentrations of coarse particulate matter usually originate in the deserts and semiarid short-grass prairies of the High Plains, the American Southwest, and northern Mexico.

7. Chloride Particulate
Table II -8 summarizes measurements of chloride and chlorine particulate concentrations and estimates of consequent light extinction at the Wichita Mountains.23

Table II -8: Measured fine chloride and chlorine particulate concentrations at the Wichita Mountains

Year [Cl - + (μg m - 3 ) *Cl+ (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst

quintile Mean Best

quintile Worst

quintile Mean Best

quintile Worst

quintile

2001 .00 .0000 .06

2002 .00 .0 0. .0000 .0000 .0000 .006 .000 .000

2003 .02 .0 0. .0002 .0002 .0000 .07 .06 .02

2004 .041 .028 .048 .0003 .0002 .0003 .37 .23 .44

2005 .020 .01 .02 .00001 .0000 .0000 .20 .08 .2

2006 .023 .01 .05 .00003 .0001 .0000 .24 .09 .5

2007 .050 .00002 .52

Baseline .02 .00 0. .00016 .0002 .0001 .15 .10 .15

IMPROVE samples at the Wichita Mountains generally contain negligible or barely detectable chloride and chlorine concentrations. Considerable contributions occur only rarely, and the apparent increase over the past few years may indicate only improved detection.

Other Particulate Table II -9 summarizes measurements of various other chemicals that the IMPROVE monitor detects, using units of ng m -3 where appropriate because of their miniscule

concentrations.24

Table II -9: Measured concentrations of various elements and nitrite in fine particulate matter at the Wichita Mountains

Year *H+ (μg m - 3 ) [Na] (μg m - 3 ) *K+ (μg m - 3 ) [NO 2 - + (μg m - 3 ) *Mg+ (μg m - 3 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 .33 .056 .049 .013 .002

2002 .35 .12 .76 .08 .02 .17 .044 .016 .074 .012 .007 .016 .010 .007 .01

2003 .345 .124 .60 .10 .07 .18 .055 .020 .078 .017 .01 .02 .008 .01 .01

2004 .359 .134 .71 .05 .01 .11 .051 .020 .079 .013 .007 .01 .02 .01 .02

2005 .43 .16 .9 .031 .02 .04 .046 .023 .07 .003 .00 .004 .003 .005 .001

2006 .35 .15 .62 .064 .005 .12 .052 .022 .10 .002 .00 .000 .006 .003 .01

2007 .32 .055 .045 .003 .008

Baseline .352 .127 .69 .08 .03 .15 .050 .0186 .077 .014 .009 .016 .012 .010 .01

Year [Zn] ( ng m - 3 ) [Br] ( ng m - 3 ) [Pb] ( ng m - 3 ) [Mn] ( ng m - 3 ) [P] ( ng m - 3 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 3.5 2.01 1.39 1.5 .1

2002 3.42 1.7 5.6 1.95 .88 3.7 1.11 .69 1.6 .94 .34 1.0 2.1 1. 0.

2003 3.61 1.76 5.5 1.96 1.00 2.5 1.14 .50 1.70 .90 .46 .63 .1 .3 .0

2004 4.0 2.7 6.4 2.18 1.07 3.4 1.34 .58 2.0 1.05 .39 1.1 .5 .0 .0

2005 4.1 2.3 6. 2.06 1.1 3.2 1.24 .7 1.7 .98 .6 1.2 3.3 .0 5

2006 3.7 1.8 6. 1.82 .9 2.7 1.21 .5 1.9 1.27 .6 1.9 .1 .0 .0

2007 3.6 2.02 1.09 1.07 .9

Baseline 3.69 2.06 5.8 2.03 .98 3.2 1.19 .59 1.77 .96 .40 .92 .9 .5 .0

Year [Se] ( ng m - 3 ) [V] ( ng m - 3 ) [Cu] ( ng m - 3 ) [Sr] ( ng m - 3 ) [Rb] ( ng m - 3 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 .99 1.0 .51 .58 .15

2002 .68 .34 1.4 .64 .13 .8 .44 .25 .63 .47 .12 .55 .18 .06 .19

2003 .74 .28 .96 .49 .15 .36 .48 .22 .57 .48 .17 .47 .16 .06 .23

2004 .89 .42 1.7 .60 .21 .56 .56 .33 1.0 .43 .24 .38 .18 .13 .18

2005 .77 .35 1.4 .56 .3 .6 .53 .27 .7 .45 .28 .4 .15 .09 .1725

2006 .52 .21 .8 .54 .20 .8 .55 .32 .8 .58 .36 .8 .15 .12 .23

2007 .63 .60 .46 .50 .15

Baseline .77 .35 1.33 .57 .16 .59 .49 .26 .73 .46 .18 .47 .173 .08 .20

Year [As] ( ng m - 3 ) [Ni] ( ng m - 3 ) [Cr] ( ng m - 3 ) [Zr] ( ng m - 3 )

Mean Best quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile Mean Best

quintile Worst quintile

2001 .12 .08 .5 .06

2002 .09 .02 .17 .12 .01 .22 .09 .03 .16 .08 .03 .09

2003 .14 .09 .24 .091 .02 .08 .05 .05 .05 .02 .00 .02

2004 .12 .10 .18 .13 .05 .13 .053 .03 .06 .03 .00 .04

2005 .10 .04 .2 .13 .05 .15 .06 .03 .08 .02 .01 .00

2006 .11 .05 .19 .14 .05 .19 .07 .03 .08 .04 .01 .01

2007 .08 .123 .08 .08

Baseline .12 .07 .20 .113 .03 .14 .062 .04 .09 .04 .01 .05

The formulations (NH 4 ) 2 SO 4 and NH 4 NO 3 implicitly account for H, and NaCl already implicitly accounts for Na. The fine soil formulation considers K. IMPROVE monitor detects only negligible concentrations of other components of fine particulate matter; DEQ therefore finds that these trace components contribute only negligibly to regional haze.



### F. Baseline Visibility at the Wichita Mountains Using the data heretofore provided, DEQ calculates the baseline visibility conditions in the mean, on the best quintile visibility days, and on the worst quintile visibility days.26

Table II

- 10 : Average visibility conditions at the Wichita Mountains

Year ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) Haze index (deciview)

2001 24. 10. 8.0 2.7 .98 4.7 .06 11. 58. 16.4

2002 25. 8.9 7.2 2.3 .79 4.4 .006 11. 59. 16.5

2003 22.3 10.8 9.1 3.0 .85 4.5 .07 11. 62. 17.1

2004 21.4 11.9 9.9 2.6 .82 3.8 .37 11. 62. 16.8

2005 32. 9. 8.3 3.2 .64 4.5 .20 11. 69. 17.9

2006 21. 7. 6.7 2.6 .98 5.1 .24 11. 54. 15.9

2007 20.7 10.8 6.6 2.4 .87 4.0 .52 11. 57. 16.1

Baseline 22.7 10.5 8.7 2.6 .82 4.2 .15 11. 60.8 16.8

Fine particulate with sulfur and nitrate accounts for a majority of scattering of visible light in the atmosphere at the Wichita Mountains. Rayleigh scattering from air molecules also reduces visibility significantly.

Table II

- 11 : Best quintile visibility conditions at the Wichita Mountains

Year ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) Haze index (deciview)

2001

2002 5.4 3.2 3.2 1.2 .26 2.5 .000 11. 27. 9.8

2003 5.0 2.7 3.7 1.3 .38 3.3 .06 11. 27. 10.0

2004 5.0 2.5 3.9 1.3 .30 2.2 .23 11. 26. 9.6

2005 7. 2. 3. 1.5 .4 4. .08 11. 30. 10.6

2006 6. 2.0 2.7 1.1 .5 4. .09 11. 27. 9.8

2007

Baseline 5.1 2.8 3.6 1.2 .31 2.7 .10 11. 26.9 9.8

On the clearest quintile of days at the Wichita Mountains, Rayleigh scattering off air molecules exceeds the combined effects of sulfureous and nitrate aerosols on visibility.27

Table II

- 12 : Worst quintile visibility conditions at the Wichita Mountains

Year ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) ( Mm - 1 ) Haze index (deciview)

2001

2002 62. 10. 15. 4.2 .8 5. .000 11. 109. 23.6

2003 43. 29. 15. 4.5 .9 4. .02 11. 107. 23.6

2004 42. 32. 22. 4.7 .7 4. .44 11. 118. 24.2

2005 80 10. 17. 6. .7 5. .2 11. 135 26.

2006 43. 11. 13. 5. 1.5 7. .5 11. 92 22.

2007

Baseline 49. 24. 17. 4.5 .79 4.6 .15 11. 111. 23.8

On the worst quintile of days, fine sulfureous aerosols account for almost as much visibility impairment as all particles do on an average day. Fine particles of nitrate and organic carbon also degrade visibility on these days. Although anthropogenic sources contribute greatly to particulate matter in the atmosphere, some degradation of visibility results from natural sources and processes.28



## III. Natural Conditions
DEQ estimates the average natural background visibility conditions for the most and least impaired quintiles of days based upon available monitoring data and appropriate analysis techniques. DEQ follows the “revised” natural haze levels approach, most consistent with the algorithm for estimating baseline conditions. EPA and DEQ base this approach on estimates that John C. Trijonis developed and included in his contribution to a National Acid Precipitation Assessment Program report in 1990.This chapter explains sources and magnitude of error in the algorithm and presents alternative estimates of natural conditions. Natural sources of visibility impairment include windblown dust, fires, active volcanoes, and biogenic emissions. Natural conditions also change, sometimes dramatically, on various temporal and spatial scales. This chapter also compares observed visibility statistics with natural background estimates to obtain a uniform rate of progress toward the goal of achieving natural visibility
conditions at the Wichita Mountains in 2064.

To summarize conclusions, DEQ data finds that visibility degradation at the Wichita Mountains is primarily caused by anthropogenic emissions of sulfur and nitrogen oxides but organic aerosols also contribute significantly in summer. Meeting the uniform rate of progress glidepath toward the statutory goal of no visibility impairment in 2064 necessitates an improvement of 34.18 Mm -1 in light extinction by 2018 and entails an average rate of improvement of 0.27 deciviews per year. This calculation assumes an ultimate goal to reach natural conditions at 7.53 deciviews by the year 2064. DEQ considers this analysis approach seriously flawed but lacks adequate understanding of the magnitude and location of natural sources of particulate matter. DEQ consequently chooses not to proceed with an alternative natural background estimate at the Wichita Mountains but may do so in later implementation plan revisions.



### A. Contribution of Constituents to Natural Background The algorithm bases its estimates of natural conditions on annual average estimates of Trijonis (1990) for the American West. This is consistent with EPA communications establishing 98°W longitude as the division between East and West but inconsistent with airflow patterns. In the work of Trijonis, the East extends one tier of states west beyond the Mississippi River. Desert or mountain arid areas of the Mountain and Pacific Time Zones define the West. A strong dichotomy in vegetation, humidity, and historic measured visibility between the two regions suggests different natural visibility conditions. Oklahoma observes Central Time but lies in the second tier of states west of the Mississippi River and therefore strictly belongs to neither region; Trijonis lacked sufficient data from the Great Plains states to place these states in any regional group.

The Trijonis estimates depend on an extensive literature review which concentrates on emissions inventories, measurements in remote areas, and tracer-based regression studies. Because of the extremely general character of the estimates and their inherent assumptions, they apply only as broad regional averages, not necessarily to specific points, and include large error, usually a factor of29

two without any quantified confidence. His assessment recommends significant further research to refine these estimates. No such research yet has been published.

The algorithm for estimating natural conditions assumes that natural conditions display the same distribution as observed conditions, true only to the extent that natural and anthropogenic sources geographically and temporally coincide. The algorithm attributes a constant proportion of each constituent of observed particulate to natural conditions in any given calendar year. The identity of the days among the best and worst quintiles, however, may differ between natural and observed conditions because a different proportion applies to each particulate constituent.

In the tables in this chapter, the best and worst quintiles refer to observed daily data unless otherwise indicated. The identity of the best and worst quintile of days under natural conditions differs markedly. This document, however, focuses on improving visibility on the existing worst quintile of days while preventing deterioration on the existing best quintile of days; therefore, DEQ finds comparisons to these respective quintiles advantageous for clarity. Unless otherwise indicated, the values in the tables in this chapter do not reflect the error factor associated with natural conditions.

1. Sulfureous Particulate Natural sulfur emissions include terrestrial and marine gaseous sulfur. Anthropogenic emissions clearly overwhelm their natural counterparts, especially in the East. Trijonis identified a denser network
of natural sulfur emissions in the East than in the West. Table III -1 assigns his regional averages to
observations at the Wichita Mountains.

Table III -1: Estimated natural sulfureous particulate concentrations at the Wichita Mountains

Conditions *S+ (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed .75 .187 1.48 22.7 5.1 49.

Natural

— East .056 .014 .11 1.5 .4 3.0

Natural

— West .029 .007 .06 .76 .19 1.5

Sulfureous particulate clearly arises overwhelmingly from anthropogenic sources. Known natural sources of sulfur in Oklahoma include gypsum and other sulfureous minerals in the soils, sulfur springs, petroleum seeps, and biological decay, mostly in swamps and other wetlands. Distant marine sources and active volcanoes also contribute to natural background concentrations.

High sulfureous particulate concentrations at the Wichita Mountains generally occur on days when broad anticyclonic flow moves air from the Eastern states through Texas to the Wichita Mountains on prevailing southerly winds. Any natural sulfureous emissions in this broad region contribute to30

decreased visibility at the Wichita Mountains. The natural sulfureous particulate concentration at the Wichita Mountains consequently fits better in the East than in the West.

2. Nitrate Particulate Trijonis estimated the average annual natural conditions from available data. Table III -2 applies his regional averages to observations at the Wichita Mountains.

Table III -2: Estimated natural nitrate particulate concentrations at the Wichita Mountains

Conditions [NO 3 - + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed 1.02 .30 2.1 10.5 2.8 24.

Natural

— East .08 .023 .16 .7 .21 1.5

Natural

— West .08 .023 .16 .7 .21 1.5

Nitrate particulate haze occurs primarily on cold, cloudy days during the winter but still frequently exceeds the target natural conditions of the algorithm on most hot summer days. A considerable proportion of the source region for nitrate aerosols and their precursors lie in the rural to remote regions of the Great Plains north of the Wichita Mountains. Grazing livestock and wild animals excrete significant amounts of ammonia and its precursors. Any urine readily decays into ammonia and
carbon dioxide. In the uncommon event of snow cover and an associated low inversion, the concentration of ammonium nitrate arising from this natural process may exceed mean natural conditions greatly, particularly if the meteorological conditions persist over several days or weeks. Natural sources of ammonium nitrate precursors also include soils and vegetation.

3. Organic Carbonaceous Particulate Organic carbonaceous particulate covers a broad selection of chemical constituents derived from various organisms. DEQ lacks the data to assign the observed organic particulate to any particular chemical species. Natural sources include plant waxes and terpenes, especially isoprene. Wetter and hotter areas support greater plant growth and related organic emissions; organic particulate
concentrations often reach 10 μg m -3 in tropical rain forests.

In the atmosphere, organic compounds and particulates partake in a particularly complex web of chemical reactions involving thousands of chemical species as they degrade or deposit. Trijonis cited
southwestern desert field studies with an ambient concentration around 3 μg m -3 , analyzed as largely petroleum-related organic carbon and significant wood smoke.

Table III -3 presents estimates of natural conditions at the Wichita Mountains for both eastern and
western divisions.31

Table III -3: Estimated natural organic carbonaceous particulate concentrations at the Wichita Mountains

Conditions [ O 1 ] + [ O 2 ] + [ O 3 ] + [ O 4 ] + [ O P + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile Baseline observed 1.41 .66 2.5 8.7 3.6 17. Natural

— East 1.0 .5 1.8 5.4 2.5 11. Natural

— West .33 .16 .6 1.8 .8 3.3

The error factor in the estimates of Trijonis describes a broad range of plausible natural
conditions between 0.5 μg m -3 and 2.0 μg m -3 (carbon-only mass in fine organic particulate) averaged over the entire East. The baseline organic aerosol concentration at the Wichita Mountains falls within the margin of error of this estimate of natural conditions for the eastern states. This assessment does not preclude an anthropogenic contribution, but quantifying the relatively small anthropogenic influence requires a reevaluation of natural conditions and a detailed inventory of anthropogenic sources. A prudent evaluation also requires a better understanding of gaseous, aqueous, and particulate organic chemistry in the atmosphere. Only an extensive site-specific field study can provide the necessary data.

A significant proportion of organic aerosol at the Wichita Mountains originates from are a sources, a category primarily containing fires. Estimates of natural visibility conditions include particulate emissions from naturally-occurring fires. The high correlation between organic and elemental carbon particulates suggests prevalent combustion-related emissions among organic carbon. Trijonis understandably attributed combustion to anthropogenic sources, but fire cessation would alter the ecosystem. Dendrochronology in the American Southwest suggests that fires occurred regularly in the mountains where rains fell and trees thrived during the first half of the second millennium. The species that now contribute to the “natural environment” of Oklahoma consequently now tolerate or even depend on fire. Fires effectively created and maintained Oklahoma prairies for millennia. The non-air-quality environmental effects of regulatory cessation of fire use in land management practices would cause the endangerment of numerous native species through loss of habitat. Consideration of fires as natural phenomena necessarily entails acceptance of slightly degraded visibility at the Wichita Mountains from organic and elemental carbonaceous particulate.

The other major component of natural organic carbonaceous particulate comes from vegetative emissions. The organic carbonaceous particulate concentrations show seasonal cycles with a pronounced hibernal minimum. This minimum again suggests a dominant non-anthropogenic contribution because anthropogenic sources tend to operate year-round, whereas most plants undergo dormancy during the winter. Biogenic production of organic aerosols varies according to species, life cycle, temperature, hydration, stressors, and other factors.32

4. Elemental Carbonaceous Particulate Only combustion releases elemental carbonaceous particulate; therefore, wildfires constitute its only significant natural source. The natural conditions in Table III -4 reflect information that Trijonis used in his estimates. Elemental carbonaceous particulate correlates strongly with sulfate particulate, suggesting a dominant anthropogenic component.

Tab le III -4: Estimated natural elemental carbonaceous particulate concentrations at the Wichita Mountains

Conditions [ E 1 ] + [ E 2 ] + [ E 3 ]

- [ O P + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed .26 .13 .45 2.6 1.3 4.5

Natural

— East .02 .010 .034 .20 .10 .34

Natural

— West .02 .010 .034 .20 .10 .34

Increasing incidence of fires may render the concentrations that Trijonis specified far too low.

5. Fine Soil Particulate Trijonis assumed that half of fine soil particulate originated from natural sources of wind-blown dust. Traffic, construction, agriculture, and anthropogenic land-use changes presumably raised the remainder. Fine soil concentrations did not vary between West and East. Fine soil aerosols over both the East and the West currently fit within the error factor of two.

Table III -5: Estimated natural fine soil particulate concentrations at the Wichita Mountains

Conditions 2.20[ Al ] + 2.49[ Si ] + 1.63[ Ca ] + 2.42[ Fe ] + 1.94[ Ti + (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed .81 .32 .79 .81 .32 .79

Natural

— East .50 .19 .5 .50 .19 .5

Natural

— West .50 .19 .5 .50 .19 .5

High fine soil concentrations generally occur on days without high sulfureous particulate concentrations because the two regional haze components originate in different geographical regions. Days with high sulfureous particulate concentrations and relatively low fine soil concentrations currently dominate the worst quintile visibility days at the Wichita Mountains. Because reaching natural conditions entails dramatic reduction in sulfureous particulate concentrations, other days with relatively33

high fine soil concentrations will displace the current set of days among the worst quintile. This exchange means that approaching natural conditions should increase the fine soil concentration on the worst quintile of days, even assuming no climate change, and even if a considerable proportion of fine soil particulate currently results from anthropogenic sources. With increasing effective reductions in sulfate and nitrate particulate pollution, Oklahoma anticipates that more days with high fine soil concentrations will qualify for the worst quintile; the fine-soil particulate concentrations in that worst quintile consequently should increase.

Because larger particles tend to settle readily from windblown dust, overseas deserts contribute far more fine soil than they do coarse particulate matter, whereas nearby arid regions contribute mostly coarse particulates. Saharan dust reaches the Wichita Mountains primarily during the late spring and early summer. Fine soils traceable to African sources tend to occur in higher concentrations during years with inactive hurricane seasons. A switch to a negative phase of the Atlantic multi-decadal oscillation consequently may increase the incidence of fine soils dramatically.

Other natural sources of fine soils, usually accompanying considerable or disproportionate coarse particulate, include fierce gales sweeping over dry lands on this continent. Asian dust occasionally reaches the Wichita Mountains as both fine soils and coarse particulate. Absent any contrary reliable information, DEQ considers fine soil particulate observed in the Wichita Mountains to originate mostly from various natural and foreign sources.

6. Coarse Particulate Trijonis assumed that natural organic coarse particulate concentrations in the Eastern states are
similar to dust-dominated coarse concentrations in the Western states.

Table III -6: Estimated natural coarse particulate concentrations at the Wichita Mountains

Conditions [ PM 10 ]

- [ PM 2.5 ] (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed 7. 4.5 8. 4.2 2.7 4.6

Natural

— East 3.0 1.9 3. 1.8 1.1 2.0

Natural

— West 3.0 1.9 3. 1.8 1.1 2.0

Dust storms in arid regions of the High Plains and the American Southwest occur naturally, most frequently in early spring after the typically dry High Plains winter when fierce gales raise soil from barren spots on the landscape. Westerly winds around developing strong spring storms frequently carry dust from West Texas and New Mexico into southwestern Oklahoma behind the dryline. The Dust Bowl of the 1930s taught Oklahoma farmers and ranchers the critical value of soil conservation and dust suppression efforts; however, adequately dense xeric vegetation simply cannot develop on certain34

climatologically desiccated landscapes. Bison, cattle, wapiti, other large herbivores, burrowing mammals, and even smaller animals disturb such denuded surfaces and kick dust into the atmosphere. Turbulence accompanying high winds in the surface layer of the atmosphere also removes soil from
denuded surfaces.

Biogenic coarse particulate includes pollen and mold spores. The species of grasses, shrubs, trees, and other natural vegetation in the Wichita Mountains and elsewhere downstream might produce especially large quantities of pollen or other organic coarse particulates. Absent more reliable information, DEQ contends that most coarse particulate matter currently observed in the Wichita Mountains likely originates from natural sources.

7. Chlorine or Chloride Particulate The Trijonis excluded sea-spray particles because of their lack of importance in inland regions, but the algorithm attributes all saline particulate to natural sources. The IMPROVE system in the Wichita Mountains rarely measured detectable levels of chloride particulate.

Table III -7: Estimated natural chloride particulate concentrations at the Wichita Mountains

Conditions [Cl] (μg m - 3 ) ( Mm - 1 )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed .02 .00 0. .15 .10 .15

Natural

— East .02 .00 0. .15 .10 .15

Natural

— West .02 .00 0. .15 .10 .15

All observed chloride particulate arises from natural sources.



### B. Estimate of Natural Visibility at the Wichita Mountains

Table III -8 presents the baseline visibility conditions for the Wichita Mountains and three estimates of natural conditions. The EPA default considers the Wichita Mountains as a Western site, but
the table also considers the Wichita Mountains as an Eastern site. Pseudo-natural conditions consider all organic carbonaceous particulate, coarse matter, and fine soils currently observed at the Wichita Mountains as natural and all sulfureous, nitrate, and elemental carbon particulate as anthropogenic. This implementation plan revision includes these pseudo-natural conditions because the organic carbonaceous, fine soil, and coarse matter components lack clearly identifiable and readily controllable anthropogenic sources, and their current concentrations lie within the margin of error of the natural conditions estimates used in the EPA default. This implementation plan revision identifies copious anthropogenic sources of sulfureous and nitrate particulate, and these anthropogenic sources overwhelm their natural counterparts.35

Table III -8: Natural visibility conditions at the Wichita Mountains

Conditions ( Mm - 1 ) Haze index ( deciview )

Mean Best quintile Worst quintile Mean Best quintile Worst quintile

Baseline observed 60.8 26.9 111. 16.8 9.8 23.8

Natural

— East 21 15 29 5.1 3.0 11.

Natural

— West 17 14 21 7.1 4.2 7.5

Pseudo

- natural 25 16 39 8.5 4.8 13.

After significant further reductions in emissions in Oklahoma and Texas, DEQ can begin to identify natural sources of sulfureous and nitrate aerosols and their precursors to reevaluate natural visibility conditions. As DEQ and other clean air agencies work to decrease the overwhelmingly anthropogenic components of regional haze, concentrations of those components already near natural conditions actually may increase among the worst quintile of days. These increases will occur because days with dense sulfureous or nitrate haze presently displace days with fires, dust storms, organic aerosols, and other natural impediments to visibility for placement among the worst quintile. The worst quintile days at the Wichita Mountains consequently increasingly will comprise of relatively minor dust
and smoke events.



### C. Uncertainty and Error DEQ may update the estimates of natural particulate mass concentrations for each component based on recent peer-reviewed literature rather than using the default values. DEQ also may rely upon any other scientifically and technically supportable method to refine the default approach. Because Oklahoma lies near the boundary between East and West, EPA advises DEQ to choose and adopt values from the most appropriate regional estimate. Trijonis restricted his Western averages to arid mountain and desert regions of the Mountain and Pacific Time zones. Although the Wichita Mountains perhaps qualified as peripherally semi-arid during the Dust Bowl era, the Wichita Mountains now receives a climatological normal of 35 inches of rain per year, more than many Eastern Class I areas.

EPA guidance allows natural sources to contribute no more particulate in any category than the least observed five-year average. This constraint does not affect default natural-background concentrations at the Wichita Mountains. Unique events like wildfires and volcanic eruptions, however, may produce protracted periods of naturally reduced visibility, and entirely natural climate change can modulate the occurrence and intensity of natural fires, dust storms, and organic emissions on timescales of decades and longer. The Wichita Mountains lie astride a sharp and ever-shifting gradient in precipitation between the swampy humid forests of eastern Oklahoma and deep east Texas and the partially barren semiarid short grass prairies and chaparral of west Texas and New Mexico. The36

chemical composition of natural particulate at the Wichita Mountains changes constantly with the evershifting climate.

Most natural concentrations of Trijonis (1990) contained an error factor of 2; a natural nitrate particulate concentration of 0.1 μg m -3 , for example, indicates a broad regional mean that lies probably
between 0.05 μg m -3 and 0.2 μg m -3 . The error inherent in these estimates allows for the possibility of natural background either much lower or much higher than the EPA estimates derived from his work. Current concentrations of all components of regional haze exceed the estimated regional natural background. The estimated natural concentrations with their error factors apply only as broad regional averages; actual natural conditions at the Wichita Mountains or at other points may exceed even the high end range of these estimates.



### D. Uniform Rate of Improvement A uniform rate of improvement (progress) under 40 C.F.R. § 51.308(d)(1)(i)(B) specifies a linear reduction in visibility impairment in deciviews on the worst quintile of days from baseline conditions to reach estimated natural visibility in 2064. Table III -9 shows the uniform rate of progress at the Wichita Mountains for the worst quintile of days with the Western natural background assumptions.

Table III -9: Uniform rate of progress at Wichita Mountains (worst quintile, Western natural background)

Conditions (Mm - 1 ) Haze index (deciviews) Presently observed (2002

- 2004) conditions 108.15 23.81 Improvement needed by 2018 assuming uniform rate of

progress 34.18 3.80
Progress (2004

- 2018) at uniform rate .27 per year Observed impairment above natural conditions 86.91 16.28 Ultimate goal of natural conditions (for 2064) 21.23 7.5337



## IV. Emissions Inventory for 2002
In fulfillment of 40 C.F.R. §51.308(d)(3)(iii), DEQ bases its long-term strategy on an identified baseline emissions inventory, Base G of the CENRAP inventory for 2002. In accordance with

40 C.F.R. §51.308(d)(3)(iv), DEQ identifies all anthropogenic sources of visibility impairment considered in developing the long-term strategy. To comply with 40 C.F.R. §51.308(d)(4)(v), DEQ submits an emissions inventory of pollutants from Oklahoma sources reasonably anticipated to cause or contribute to visibility impairment at the Wichita Mountains or any other mandatory Class I area. A table summarizing the 2002 Oklahoma Emissions Inventory values can be found in section A for this chapter, broken down into five main source categories. Further breakdown for each source category can be found in subsequent sections.



### A. Summary DEQ followed specific EPA guidance and inventoried emissions of sulfur dioxide (SO 2 ), ammonia (NH 3 ), nitrogen oxides (NO x ), volatile organic compounds (VOCs), and particulate matter of aerodynamic
diameters less than 10 μm ( PM 10 ) and less than 2.5 μm ( PM 2.5 ) from all anthropogenic and biogenic sources. DEQ and CENRAP developed an inventory for the baseline year of 2002. Table IV -1 summarizes this inventory. DEQ submits the complete Oklahoma statewide emissions inventory for 2002 as Appendix 4-1.

Table IV -1: Summary of emissions from Oklahoma sources (tons per year)

SO 2 NH 3 NO x VOCs PM 10 - PM 2.5 PM 2.5

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

Following convention, DEQ divides the emissions inventory into five primary categories: point, area, non-road mobile, on-road mobile, and biogenic. These divisions reflect differing inventory development methods, unifying attributes, and model processing needs.

State, tribal, and local agencies submitted information to the national emissions inventory for 2002 which formed the basis of the CENRAP emissions inventory. Sonoma Technology supplemented national emissions inventory data with non-point source inventories developed for CENRAP. These CENRAP-specific inventories addressed agricultural and prescribed burning, on-road and non-road38

mobile sources, agricultural tilling and livestock dust, and agricultural ammonia. Appendix 4-2, Technical Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans , fully documents the methods used in the development of the emissions inventory for 2002 and includes technical support documents.



### B. Point Source Emissions DEQ rule OAC 252:100-5 requires individually permitted point sources to submit an emissionsinventory annually with well-defined location and release parameters; therefore, DEQ bases this pointsource inventory for 2002 on this facility-reported information. DEQ followed internal quality assurance procedures and submitted the data to EPA for the 2002 national emissions inventory.

Following submission to the national emissions inventory, CENRAP provided additional quality assurance and revision opportunities for data improvement. E.H. Pechan & Associates, (Pechan) through a contract with CENRAP, obtained the Oklahoma point-source inventory and worked with DEQ to make necessary corrections. Revisions focused on updating and correcting facility coordinates and stack parameters. Pechan also prepared day- and hour-specific emissions for electric generating units based on continuous emissions monitoring data for 2002. Two documents referenced in Appendix 4-2

— The Consolidation of Emissions Inventories (April 28, 2005) and Refinement of CENRAP’s 2002 Emissions

Inventories (August 31, 2005)

— detail the work of Pechan on behalf of CENRAP.



### C. Area Source Emissions EPA treats each class of immobile area sources collectively by county. Neither EPA nor DEQ can collect data for each point of emission, and EPA consequently provides estimates of emissions over larger regions. Residential heating and architectural coatings exemplify stationary area sources. The area-source inventory for 2002 builds on EPA-provided estimates for area-source emissions. It includes emissions estimates that DEQ and CENRAP prepared and data from the national emissions inventory to fill gaps.

Sonoma Technology prepared the following reports. Referenced in Appendix 4-2, they contain the data and methods used to develop the prescribed burning, agricultural dust, and soil agricultural
ammonia inventories for CENRAP.

Research and Development of Planned Burning Emission Inventories for the Central States Regional Air Planning Association (July 30, 2004), Emission Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central
States (October 28, 2004), and Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning Association (October 30, 2003)

Departing from the primarily EPA-developed area source inventory, DEQ developed an oil and gas area-source emissions inventory. To produce this inventory, DEQ collected county-level oil and gas production data from the Oklahoma Corporation Commission and Oklahoma Tax Commission and combined these data with emissions calculation methods developed by the Texas Commission on39

Environmental Quality. Appendix 4-2 contains the calculation methods, excerpted from the Texas emissions inventory documentation from 1996 entitled Oil and Gas Production (East Texas, 1996 EI)

.
DEQ also developed area-source emissions estimates for gasoline distribution sources, drycleaners, and wildfires, using the factors from emissions inventory improvement program and countylevel activity data for Oklahoma.

In a contract with CENRAP, Pechan consolidated the area-source inventories from these various inventory sources, conducted additional quality assurance, and worked with DEQ to revise this consolidated inventory. Two documents referenced in Appendix 4-2 describe the work of Pechan in
detail: The Consolidation of Emissions Inventories (April 28, 2005) and Refinement of CENRAP’s 2002

Emissions Inventories (August 31, 2005).

University of California at Riverside (UCR), under contract with CENRAP, removed selected sources from the consolidated area inventories to process them as separate subcategories. UCR created these subcategories to allow specialized processing within the Sparse Matrix Operator Kernel Emissions (SMOKE) model in anticipation of particulate source apportionment within the Comprehensive Air Quality Model with extensions (CAMx). To enhance accuracy of transportable dust estimates, UCR also applied road and fugitive dust transport factors to appropriate area source emissions, following the Methodology to Estimate the Transportable Fraction of Fugitive Dust Emissions for Regional and Urban Scale Air Quality Analyses (Pace 2005). Table IV -2 summarizes area-source emissions from Oklahoma by subcategory.

Table IV -2: Inventory of area-source emissions from Oklahoma by subcategory (tons per year)

SO 2 NH 3 NO x VOCs PM 10 PM 2.5

General area 4,634 6,146 96,166 173,250 526 10,332
Fire 7,145 5,819 19,623 28,507 11,167 41,764
Ammonia 0 102,245 0 0 0 0 Road dust 0 0 0 0 216,289 38,057 Fugitive dust 0 0 0 0 76,578 19,126 Uncategorized 0 153

- 381 0 0 0 Total area sources 11,779 114,363 115,407 201,758 304,560 109,279



### D. On -road Mobile Source Emissions For vehicular sources which travel on roadways, DEQ can compute emissions either spread over a spatial extent or assigned to a line location called a link. Examples of these on-road mobile sources include light-duty gasoline vehicles and heavy-duty diesel vehicles.

DEQ and CENRAP developed the on-road mobile source emissions inventory for 2002 with contractor support. For all counties in Oklahoma and elsewhere throughout the CENRAP region, Sonoma Technology provided vehicle miles traveled data for all months of 2002 and MOBILE6 input files40

only for the months of January and July 2002, using the methods and data described in the report Emissions Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central States (October 28, 2004), referenced in Appendix 4-2. UCR prepared MOBILE6 input files for the remaining months of 2002 and processed the mobile emissions using the MOBILE6 model within the SMOKE
framework.



### E. Non-road Mobile Source Emissions DEQ and EPA include a broad selection of equipment in the non-road mobile source category. EPA developed emissions estimates for all aircraft operations, commercial and recreational marine vessels, and railroad locomotives for the national emissions inventory for 2002.

Sonoma Technology, under contract to CENRAP, used the NONROAD model to prepare and develop broad emissions estimates for the non-road mobile source inventory. Sonoma Technology documented the methods and data used to generate this inventory in the report Emissions Inventory Development for Mobile Sources and Agricultural Dust Sources for the Central States (October 28, 2004) referenced in Appendix 4-2.

Pechan consolidated the estimates of Sonoma Technology with those of EPA to generate the full non-road emissions inventory. In the process of consolidating the emissions estimates, Pechan assured quality of the data and worked with DEQ to make necessary corrections before creating SMOKE model formatted files. Pechan made specific corrections to the fuel oxygenate content used in the NONROAD
model. Two documents referenced in Appendix 4-2 describe the work of Pechan in detail: The Consolidation of Emissions Inventories (April 28, 2005) and Refinement of CENRAP’s 2002 Emissions

Inventories (August 31, 2005).



### F. Biogenic Emissions EPA calculates biogenic emissions based on land use data that characterize types of vegetation as county-total or grid-cell values.

As the emissions modeling contractor for CENRAP, UCR ran the Biogenic Emissions Inventory System 3 (BEIS3) model within the SMOKE framework to generate a biogenic emissions inventory. This system derives estimates of biogenic gas-phase emissions from land-use information, emissions factors for different plant species, and hourly meteorological data on a coordinate grid. The technical support document in Appendix 4-2 describes the development of the biogenic emissions inventory.



### G. Discussion of Components

1. SO 2 Emissions Four coal-fired electric power generators in or near the northeastern quarter of Oklahoma emitted half the total sulfur dioxide in this inventory. Other fossil fuel electric power generators and petroleum refiners also emitted sulfur compounds in 2002.41

Table IV -3: Leading sources of SO 2 emissions in Oklahoma

Category Source classification Source name County Tons per
year Percent of

total

point fossil fuel electric

power generation Public Service

Company of Oklahoma Rogers 34,512 20. 30 %

Oklahoma Gas and Electric Muskogee 27,522 16. 19 %

Oklahoma Gas and Electric Noble 18,392 10. 82 %

Grand River Dam Authority Mayes 16,227 9. 54 %

all other 14,180 8.34% petroleum refineries 12,350 7.26% all other 25,579 15.04%

area f ire 7,145 4. 20 %

g eneral 4,634 2. 73 %

non

- road mobile 4,773 2. 81 %

on

- road mobile 4,708 2.7 7 % b iogenic none Statewide total 17 0 , 021 100.00%

2. NH 3 Emissions Animals contributed approximately three-fifths of ammonia emissions in Oklahoma in 2002, mostly through excreta. A further one-fifth of this inventory concerns emissions from crop fertilizers. These sources mostly lie beyond the currently active regulatory purview of DEQ.42

Table IV -4: Leading sources of NH 3 emissions in Oklahoma

Category Source classification Source name County Tons per
year Percent of

total

point agricultural

products

— livestock and animal Oklahoma City West

Livestock Market Canadian 2,002 1.4 0 %

all Texas 9,698 6.77 % all other 8,165 5.70 % nitrogenous fertilizer manufacturing 2,850 1.99 %

all other 1,387 .97 %

area ammonia agriculture production livestock

— cattle and calves waste

— beef cows 18,241 12.74 %
agriculture production livestock

— cattle and calves waste

— steers and bulls 16,096 11. 24 %

agriculture production livestock

— cattle and calves waste

— heifers 10,678 7. 46 %
agriculture production livestock

— cattle and calves waste

— milk cows 2,549 1. 78 %

agriculture production livestock

— poultry waste

— broilers LeFlore 2,270 1. 59 %

McCurtain 1,980 1. 38 % all other 2,484 1. 73 % agriculture production livestock

— poultry

waste

— all other 1,852 1. 29 %
agriculture production livestock

— swine production composite 2,742 1. 92 %
agriculture production crops

— fertilizer

application

— urea 18,470 1 2

. 90 %
agriculture production crops

— fertilizer application

— nitrogen solutions 3,754 2. 62 %
agriculture production crops

— fertilizer application

— all other 3,931 2. 75 %

domestic animal waste

— dogs 2,465 1.7 2 % natural sources

— biogenic

— agricultural land crop and pasture Anderson use code 3,499 2.44 %

natural sources

— biogenic

— forest land deciduous Anderson use code 3,106 2.17 %

natural sources

— biogenic

— all other 3,018 2.11 %

all other 5,112 3.57 %

general industrial processes

— not

elsewhere classified Rogers 6,044 4.22 %

all other 102 .07 %

fire agriculture production crops

— field

burning whole set

— grasses technique

not important 3,890 2. 72 %

all other 1,929 1.35 %43

Category Source classification Source name County Tons per
year Percent of

total agriculture production crops

— fertilizer application

— all other (certain types and counties excluded from ammonia

subcategory) 153 .11 %

non

- road mobile 280 .20%

on

- road mobile (model output) 4,434 3. 10 % biogenic (model output) none Statewide total 143,179 100.00%

3. NO x Emissions

On -road mobile sources emitted more than one-quarter of all oxides of nitrogen in this statewide inventory for 2002. Four large coal-fired electric power generators in or near the northeastern quarter of Oklahoma contributed almost one-eighth of these emissions. Other large contributors included small (area-source) industrial natural gas boilers, several large fossil fuel electric power generators, non-road mobile equipment emissions, considerable biogenic emissions, and agricultural and prairie fires.44

Table IV -5: Leading sources of NO x emissions in Oklahoma

Category Subcategory Source name County Tons per
year Percent of

total

point fossil fuel electric

power generation Oklahoma Gas and Electric Muskogee 17,442 3.47%

Public Service Company of Oklahoma Rogers 16,827 3.35%

Grand River Dam Authority Mayes 14,160 2.82%

Oklahoma Gas and Electric Noble 12,184 2.42%

all other 24,931 4.96% natural gas liquid extraction 16,547 3.29% pipeline transportation of natural gas 15,038 2.99% crude petroleum and natural gas extraction 12,001 2.39%

all other 29 , 688 5.91%

area general industrial processes

— oil and gas production 66,480 13.23%
stationary source fuel combustion

— industrial

— natural gas
— total boilers and

internal engines 21,524 4.28%

all other 8,161 1.62 %

fire agriculture production crops

— field

burning whole set

— grasses technique

not important 16,885 3.36%

all other 2,738 .54 % wildfires (considered among fire area sources but not total

area sources for NO x purposes only) - 381

- .08 %

non

- road mobile off

- highway diesel agricultural equipment

— tractors 9,340 1.86 % all other 12,159 2.42 % railroad equipment diesel

— line haul locomotives class I operations 18,293 3.64 %

all other 9,605 1.91 %

on

- road mobile (model output) 142,592 28.3 8 % biogenic (model output) 35,909 7.1 5 % Statewide total 502, 504 100.00%

Point and area sources related to extraction, production, and transportation of crude oil, natural gas, and related fuels accounted for more than one-fifth of this inventory, much of it in counties in relative proximity to the Wichita Mountains. The statewide inventory includes numerous small-scale petroleum and natural gas extraction and pipeline operations in locations throughout western Oklahoma. Despite efforts to improve accuracy of area-source inventories, DEQ cannot assess their reliability nor use them as a basis for regulation without a lengthy field study to quantify pollutants45

released during petroleum and natural gas exploration, extraction, transport, delivery, and storage
under various conditions often encountered.

4. Volatile organic compound emissions Common anthropogenic emissions sources of organic carbon largely relate to small oil and gas producers. For forests, prairies, and other biogenic sources DEQ resorts to estimates from biogenic models. Estimates of natural biogenic emissions comprise a majority of this statewide inventory.

Table IV -6: Leading sources of VOC emissions in Oklahoma

Category Subcategory Source name Tons per
year Percent of

total point 37,794 2. 75 %

area general industrial processes
— oil and gas production 104,193 7.57 %

all others 69,057 5.02 % fire 28,507 2.07 %

non

- road mobile 47,863 3.48 %

on

- road mobile (model output) 99,924 7.26 % biogenic (model output) 988,314 71.84 % Statewide total 1, 375 , 652 100.00%

DEQ lacks the financial capacity to conduct an in-depth field study to assess the performance of biogenic models within Oklahoma. Biogenic sources emit most VOCs in Oklahoma and lie beyond the regulatory purview of DEQ. Because of enormous uncertainty in biogenic volatile organic emissions, however, DEQ recommends a detailed field study of such components, focusing on all poorly resolved sources and covering the entire warm season and possibly the remainder of the year. A biogenic study can focus on actual organic emissions from biota that live near the Wichita Mountains and in upstream ecosystems during actual meteorological conditions. DEQ also requires further study to identify anthropogenic emissions of VOCs and to determine which such compounds most efficaciously contribute to visibility impairment and ozone production.

5. Coarse particulate direct emissions An overwhelming majority of direct emissions of coarse particulate matter came from road and fugitive dust. These sources of direct emissions of coarse particulate do not differ chemically from natural sources. Although the inventory excludes biogenic sources, an unknown quantity of mold spores, pollen, and other poorly understood emissions fits into this category.46

Table IV -7: Leading sources of direct PM 10 - PM 2.5 emissions in Oklahoma

Category Subcategory Source name Tons per year Percent of total point 8,026 2. 56 %

area road dust mobile

— unpaved

— fugitives 200,479 63

. 87 % mobile

— paved

— fugitives 15,810 5.04%

fugitive dust agriculture production crops

— tilling 53,762 17.13 %

industrial processes
— construction 13,062 4.16%

all other 9,754 3.11% fire 11,167 3.56 % general 526

. 17 %

non

- road mobile 433 .14%

on

- road mobile (model output) 879 .28% biogenic (model output) none Statewide total 313,898 100.00%

6. Fine particulate direct emissions Direct emissions of fine particulate matter in the Oklahoma statewide inventory for 2002 originate from more varied sources than those for coarse particulate matter. Fires and dust nonetheless constitute a considerable majority of this inventory. Despite their inclusion among area sources in this inventory, one can argue for the inclusion of these fires and dust among natural conditions. The algorithm for computation of visibility impairment apportions directly emitted fine particulate matter among all categories except coarse particulate matter.

Table IV -8: Leading sources of direct PM 2.5 emissions in Oklahoma

Category Subcategory Source name Tons per
year Percent of

total point 8,636 6.99 %

area fire agriculture production crops

— field burning whole set

— grasses technique not important 28,443 23.04 %

all other 11,840 9.59%

road dust mobile

— unpaved

— fugitives 35,257 28.55%

mobile

— paved

— fugitives 2,800 2.27 %

fugitive dust agriculture production crops

— tilling 13,441 10.89% all other 5,685 4.60 % general 10,332 8.37 %

non

- road mobile 4,580 3. 71 %

on

- road mobile (model output) 2,459 1. 99 % biogenic (model output) none Statewide total 123,473 100.00%47



### H. Emissions Inventory for 2005 Oklahoma’s Emissions Inventory for 2005 is the most recent state -wide emissions inventory available. This inventory is available on EPA’s website at http://www.epa.gov/ttn/chief/eiinformation.html

.48



## V. Modeling Assessment
Sections A, B, C, D, and E of this chapter discuss in detail modeling methods and protocol used by DEQ in developing the assessment. Results primarily attribute sulfureous aerosol, nitrate aerosol, and elemental carbonaceous particulate to anthropogenic sources; organic carbonaceous particulate, fine soil particulate, and coarse particulate concentrations are attributed to natural and/or area sources. For most pollutants, the majority of visibility-impairing pollutants originate outside Oklahoma; prevailing winds transport a considerable proportion of visibility impairing aerosols from Texas, and more than one-tenth of visibility impairment at the Wichita Mountains results from international transport

. In comparison, the data in section G depicts Oklahoma as only a very minor contributor to visibility impairment for any Class I area outside of the state. Inside of Oklahoma, Texas alone contributes more to visibility impairment at the Wichita Mountains than Oklahoma does. Considering these results, any effective strategy for managing visibility impairment at the Wichita Mountains must address outside sources including regional and international transport.

Numerical modeling of regional haze comprises three steps: numerical weather simulation (meteorological modeling), mapping emissions inventories to the model grid (emissions modeling), and air-chemistry simulation. In the 1 July 1999 publication of the regional haze rule in the Federal Register, EPA endorsed the use of such regional modeling in analyses:

To calculate the degree of visibility improvement that emissions reductions will achieve at the Wichita Mountains and at other Class I areas; To determine emissions reductions needed to meet the progress goal at the Wichita Mountains and at other Class I areas; To determine the extent of emissions reductions needed from sources in Oklahoma, Texas, and other individual states; To support a conclusion a long-term strategy provides for reasonable progress; and To compare visibility improvement between proposed control strategies.49

CENRAP conducted regional air-quality modeling to provide these analyses, to support DEQ in the development of this implementation plan revision, and to support clean air agencies of other member states in the development of their respective regional haze plans. CENRAP in 2003 began modeling projects to obtain a better understanding of the causes of visibility impairment. The CENRAP emissions and air-quality modeling team comprised of Environ, UCR, and other consulting firms under contract to CENRAP; the CENRAP modeling workgroup supervised these contracts and comprised personnel from clean-air agencies and stakeholders unconnected to modeling team members. The CENRAP modeling team in 2004 began to perform:

Emissions processing and modeling; Air-quality and visibility modeling simulations; Analysis, display, and reporting of modeling results; and Storage and quality assurance of the modeling input and output files.

The modeling team developed Technical Support Document for CENRAP Emissions and Air Quality Modeling to Support Regional Haze SIP , included as Appendix 4- 2, to provide further detail on the modeling analyses.

This modeling attributes visibility impairment at the Wichita Mountains mainly to anthropogenic emissions of sulfureous and nitrate pollutants. Sources in Oklahoma contribute less than one-seventh of visibility impairment at the Wichita Mountains; emissions from Texas alone account for almost twice the impairment as those from all of Oklahoma.



### A. Modeling Protocol At the outset of the study, the modeling team prepared a protocol, following EPA guidance, to serve as an outline for performing CENRAP emissions and air quality modeling and to communicate the modeling plans to CENRAP members. The modeling protocol took the long-range plan of CENRAP and the modeling needs of this implementation plan revision into account. Appendix 4- 2 includes Modeli ng Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling

.

1. Quality Assurance Project Plan The modeling team prepared a quality-assurance project plan for the CENRAP emissions and air quality modeling study (Appendix 5-1 : Quality Assurance Project Plan for Central Regional Air Planning Association Emissions and Air Quality Modeling ). That project plan describes the quality management functions performed by the modeling team. The team based its quality-assurance project plan on the

nationa l consensus standards for quality assurance. The project plan follows EPA’s general and modeling guidelines for quality-assurance and takes into account recommendations from the North American Research Strategy for Tropospheric Ozone Quality Handbook . EPA and North American Research Strategy for Tropospheric Ozone developed their guidance documents specifically for modeling projects with quality assurance concerns different from those of environmental monitoring data collection projects. This project involved modeling work both at the basic research level and also for regulatory and planning applications. To use model outputs for these applications, the team50

followed a project-planning process described in EPA modeling guidance documents. The systematic planning process incorporates:

Identification of assessments and related performance criteria; Peer-reviewed theory and equations; Carefully designed life-cycle development process that minimizes errors; Documentation of any changes from original plans; Clear documentation of assumptions, theory, and parameterization; Input data and parameters accurate and appropriate for the analysis; and Output data.

2. Selection of Episodes CENRAP selected the entire calendar year of 2002 for regional haze modeling, as described in
the CENRAP Modeling Protocol . Applicable EPA guidance on PM 2 .5 and regional haze modeling identified specific goals to consider when selecting modeling periods for use in demonstrating reasonable progress in attaining the regional haze goal. During the modeling period selection process, EPA also published an updated summary of PM 2.5 and regional haze modeling guidance which served in as an interim placeholder. The goals for ozone attainment, annual and episodic PM 2

. 5 attainment, and regional haze progress demonstrations generally share common modeling strategy. EPA ultimately issued final modeling guidance for the PM 2.5 and ozone national ambient air quality standards and regional haze. This final common guidance document addresses selecting episodes for all three objectives, consistent for regional haze with interim EPA modeling guidance.

EPA guidance recommends that the selection of a modeling period derive from the following three principal criteria.

The modeled period should cover a variety of meteorological conditions, including those on the worst quintile and best quintile visibility days at the Wichita Mountains and at other Class I areas in the CENRAP region. The modeled period should represent the baseline period of 2000-2004. Monitoring data at the Wichita Mountains did not commence until March 2001, making 2002 the first year with complete data. The modeling periods should include sufficient available days to average relative response factors over more than 15 days among the worst quintile (and 15 days among the best quintile), but guidance suggests the preferred approach to model a full representative year for regional haze.51

EPA also lists several other considerations for choosing potential regional haze episodes, including choosing:

Periods already modeled, Days with available enhanced data beyond routine aerometric and emissions monitoring to the extent possible, Weekend days among those chosen, and Periods that meet as many episode selection criteria as possible at the Wichita Mountains and at other Class I areas.

Available resources allowed CENRAP to model a single calendar year. DEQ and CNERAP selected the modeling year from the five-year baseline of 2000-2004. CENRAP selected the calendar year 2002, the middle of the baseline period, for the following reasons:

The complete IMPROVE monitoring site at the Wichita Mountains entered operation before 2002 with partial data available from 2001 and none from 2000; CENRAP initiated modeling before IMPROVE data for 2003 and 2004 came available; The other regional planning organizations used the year 2002; and The year 2002 apparently typifies weather conditions for the five-year baseline period of 2000-
2004.



### B. Meteorological Modeling

1. Model Selection CENRAP based its selection of the meteorological model on a review of previous regional haze modeling studies performed in the region and elsewhere in the United States. CENRAP selected The Pennsylvania State University (PSU)/National Center for Atmospheric Research (NCAR) Mesoscale Model 5 (MM5 3.6 MPP), a non-hydrostatic, prognostic meteorological model routinely used for urban-scale and regional haze, and other regulatory modeling studies.

40 C.F.R. Part 51 Appendix W provides regional-scale modeling guidelines for particulate matter and visibility. The one-atmosphere photochemical grid approach of Models-3/CMAQ modeling system of EPA includes MM5 and can address particulate matter and visibility at a regional scale for extended periods.52

2. MM5 Meteorological Model Configuration MM5 originated from a mesoscale model developed at PSU in the early 1970s which thereafter underwent many changes designed to broaden its usage. These changes include:

multiple-nest capability, non-hydrostatic dynamics that allows the use of the model at a few-kilometer scale, multitasking capability on shared- and distributed-memory machines, four-dimensional data assimilation capability, and additional physics options

This implementation plan revision and its appended technical support document refer to several auxiliary programs that support MM5 collectively as the MM5 system. As a regional model, MM5 requires initial and lateral boundary conditions to run. A lateral boundary condition for a model run requires gridded data to cover the entire integration period of the model. CENRAP ran MM5 using 34 vertical layers from the surface to a top pressure level of 100 mb (approximately 15 km above ground level).

3. Model Inputs The Iowa Department of Natural Resources conducted the 36-km MM5 simulation for 2002 and performed a preliminary model performance evaluation. EPA Region VII and Texas Commission on Environmental Quality performed application of MM5 on a 12-km grid covering the central states for portions of 2002. CENRAP performed an additional MM5 evaluation of the 36-km MM5 simulation which included a comparative evaluation against the final VISTAS 36-km MM5 simulation and an interim
WRAP 36-km simulation for 2002.

Appendix 4-2 provides details of a conducted comparative evaluation of the CENRAP, WRAP, and VISTAS 36-km MM5 simulations for 2002. This study concluded that the VISTAS simulation performed best; the moderate CENRAP MM5 performance resembled that of VISTAS more than that of

WRAP.



### C. Emissions Modeling

1. Model Selection, Configuration, and Domain CENRAP based its selection of the emissions model on a review of previous modeling studies in the United States of America. CENRAP emissions modeling protocol provides details on the justification
for model selection and the formulation of different models.

CENRAP selected the SMOKE modeling system to generate hourly-gridded, speciated emissions inputs for point, area, non-road mobile, on-road mobile, and biogenic source categories fo r photochemical grid models. SMOKE system and similar emissions models generally do not simulate emissions from first principles and lack the science configuration options found in meteorological and air chemistry models. SMOKE only provides an efficient tool for converting base emissions inventories for each source category into the hourly, gridded, speciated, formatted emissions files that an air chemistry model requires. SMOKE system nevertheless uses meteorological and other inputs to estimate on-roa d53

mobile and biogenic emissions. Appendix 4- 2 summarizes the SMOKE system version and sources of data used in constructing the required modeling inventories.

CENRAP conducted emissions and air quality modeling on the 36-km national regional planning organization domain. This domain consists of a 148 × 112 array of 36- km × 36 -km grid cells and covers the continental United States. The technical support document, included as Appendix 4-2, provides additional information on the modeling domain.

Figure V-1: National regional planning organization 36-km modeling domain used in the CAMx and CMAQ modeling for
CENRAP

2. Emission Inventories UCR prepared emissions inputs for the air-chemistry model using the SMOKE system. The CENRAP modeling emissions inventory for 2002 consists of a base case dataset for model performance evaluation and a distinct typical-scenario dataset. These datasets differ only in emissions from electric generating units (EGUs). Carolina Environmental Program under contract to CENRAP obtained continuous emission monitoring data for 2001, 2002, and 2003; generated temporal profiles; and then applied them to annual emissions inventories for EGUs. CENRAP used these temporal profiles in the

mo deling for 2002 to reflect typical operations of these critical sources, rather than using specific54

operations in 2002 that might contain anomalous events. The inventory extends spatially throughout the regional planning organization 36-km modeling domain which covers the continental United States and portions of Canada and Mexico. Several rounds of CENRAP workgroup review and revision refined the initial Base A version of the inventory, culminating in the Base G inventory. The technical support document, found as Appendix 4-2, provides the methods for the SMOKE processing. Chapter 5 of the technical support document summarizes the development of the emissions inventory.

3. Quality Assurance Project Plan Key components of the CENRAP modeling quality-assurance project plan include the graphical display of model inputs and outputs and multiple peer reviews for each step of the modeling process. UCR displayed emissions plots, model outputs, and other work products on the CENRAP modeling website for the modeling team, modeling workgroup, and others.



### D. Air Chemistry Modeling

1. Model Selection CENRAP based its selection of the air-chemistry models on a review of previous studies. CENRAP air-chemistry modeling protocol provides details on the justification for model selection and the
formulation of the different models. 40 C.F.R. Part 51 Appendix W provides guidelines for conducting regional-scale modeling for particulate matter and visibility.

EPA recommends the use of Community Multi-scale Air Quality (CMAQ) model or Comprehensive Air-quality Model with Extensions (CAMx) to simulate aerosols impairing visibility. CENRAP modeling team used both CMAQ system as the primary air quality model and CAMx as the secondary corroborative model. The one-atmosphere photochemical grid approach of Models-3/CMAQ system of EPA can address particulate matter, visibility, and other applications at a regional scale for extended periods.

2. Air Chemistry Model Configuration CENRAP used CMAQ 4.5 with the secondary organic aerosol modules (SOAmods) enhancement described hereinafter and the configuration shown in Appendix 4-2. The modeling team set up and exercised the model on the same regional planning organization national 36-km grid that WRAP and VISTAS used. VISTAS performed CMAQ simulations for 2002 and found that the model greatly underestimated organic carbon concentrations, especially in the summer. A review of the CMAQ formulation found that it failed to treat secondary organic aerosol formation from sesquiterpenes and isoprene and failed to account for the polymerization of secondary organic aerosol; although no longer volatile, polymerized aerosols stay in the particle form. VISTAS thus updated CMAQ secondary organic aerosol module to include these missing processes and found much-improved performance regarding organic carbonaceous aerosols. CENRAP tested CMAQ 4.5 with this secondary organic aerosol modules enhancement and found that it performed much better for organic carbon than the standard versions of CMAQ 4.5. CENRAP therefore adopted CMAQ 4.5 model with the secondary organic aerosol modules enhancement. The standard CAMx and CMAQ runs typically use the PPM advection solver.55

3. Selection of Modeling Domain CENRAP conducted air-chemistry modeling on the same 36-km domain used for emissions modeling. CENRAP performed a cost-benefit analysis of nesting a 12-km grid, covering the entire CENRAP region, within the bounds of the national 36-km domain. Smaller grid resolutions substantially increase processing time but offer the possibility of improved accuracy. CENRAP modeling team modeled three episodes in 2002 with the 36-km and 12-km domains. The episodes represented spring, summer, and winter periods of poor visibility at Class I areas in the CENRAP region. The modeling team found CMAQ results to be insensitive to these grid resolutions, but 12-km resolution reduced the sulfate under-prediction bias in the summertime with CAMx. With this possible exception, CENRAP noted little benefit in model performance with use of the 12-km grid. CENRAP also considered in its decision that VISTAS also found similar visibility projections for 2018 using 36-km and 12-km grids, supporting the conclusion that costs outweighed benefits of the smaller grid resolution. The technical support document, found at Appendix 4-2, provides additional information on the modeling domain.

4. Vertical Structure of the Modeling DomainCAMx and CMAQ model configurations can collapse vertical layers from MM5 into thicker airchemistry model layers to improve computational efficiency. WRAP and VISTAS evaluated the sensitivity of CMAQ model estimates to the number of vertical layers. The modeling team performed CMAQ simulations without layer collapsing ( i.e. , with the same 34 layers that MM5 used) and with various layer collapsing schemes. These studies found that using 19 vertical layers upward to 100 mb (the same model top as MM5) and matching the eight lowest MM5 vertical layers near the surface produced results nearly identical to those without layer collapsing. They also found that very aggressive layer collapsing ( e.g. , 34 layers to 12 layers) produced results with substantial differences compared to those without layer collapsing. Based on WRAP and VISTAS sensitivity analyses, CENRAP adopted the

19 -vertical-layer configuration.

5. Initial Concentrations and Boundary Conditions CENRAP modeling team operated CAMx and CMAQ model separately for each quarter of 2002 using an approximate 15-day spin-up period to limit the influence of the assumed initial concentrations ( e.g. , start 15 June 2002 for the third quarter that began on 1 July). Sensitivity simulations with this

15 -day spin-up period demonstrated the minimal influence of initial concentrations using the 36-km regional planning organization national domain. CMAQ and CAMx modeling consequently specified clean initial concentrations using a 15-day spin-up period.

Boundary conditions result from the assumed concentrations along the lateral edges of the

36 -km modeling domain (see Figure V-1 ). CENRAP used the results for 2002 from GEOS-CHEM, a threedimensional global circulation and chemistry simulation driven by assimilated meteorologica l observations from the Goddard Earth Observing System (GEOS) of National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office. Research groups around the world usethis general circulation model for a wide range of atmospheric composition problems. The atmosphericchemistry modeling group at Harvard University provides central management and support of the model and applied it in a joint regional planning organization study for 2002 that VISTAS coordinated. This56

study retained the University of Houston to process the GEOS-CHEM output for 2002 into boundary conditions for the CMAQ model.

Boundary conditions include visibility-impairing emissions from Mexico City, the Yucatán Peninsula, much of central and southern Mexico, and further south into Mesoamerica.

Initial conditions represent the assumed three-dimensional concentrations throughout the modeling domain at the beginning of the simulation.

6. Quality and Performance Evaluation CENRAP air-quality modeling quality-assurance project plan includes the graphical display of model inputs and outputs as well as multiple peer reviews of each step of the modeling process. UCR displayed emissions plots, model outputs, and other work products on their CENRAP modeling website for the modeling team, modeling workgroup, and others. Other model valuations for CENRAP compared concentrations of various pollutants that CAMx and CMAQ model simulated with observations from:

IMPROVE, Clean Air Status and Trends Network (CASTNet), Speciated Trends Network (STN), Aerometric Information Retrieval Systems (AIRS), and South Eastern Aerosol Research and Characterization (SEARCH).

The modeling team of UCR and Environ evaluated CMAQ model and CAMx against ambient measurements of particulate constituents, gas-phase species, and wet deposition. The team conducted numerous base- and typical-case CMAQ model and CAMx simulations for 2002 and several performance evaluations during the course of the CENRAP modeling study. CENRAP shared the results of these evaluations on its modeling website, in presentations made to the public, and in previous reports. CAMx and CMAQ model generally performed well for sulfate and elemental carbonaceous aerosols. For nitrate aerosols, the model performance varied from underestimation in summer to overestimation in winter. Performance for organic carbonaceous aerosols also varied, but the inclusion of the secondary organic aerosol modules enhancement greatly improves this performance in summer. The model performed poorly for fine soil and coarse matter. The IMPROVE measurements partially result from local fugitive dust sources not captured in the emissions inventories or not well resolved in the 36-km model grid; this measurement/model incommensurability perhaps partially explains the poor performance for fine soil and especially for coarse mass. The technical support document in Appendix 4-2 details information on the model performance evaluations.

7. Model Simulations The modeling team conducted numerous runs on the successive versions of the base inventory. Modeling began in autumn of 2004 and concluded in summer of 2007 with the final Base G control strategy runs; during this process, the modeling team adopted many corrections and refinements of the inventory and enhancements of emissions and air-chemistry models. CENRAP workgroup chairs and the modeling team documented these changes through reports and presentations made publicly available57

on the CENRAP website, CENRAP modeling website, and listserv. Appendix 4-2 includes some of these documents. The technical support document discusses the evolution of the modeling analyses.

Because of the similarity between Base F and Base G for 2002 and resource constraints, CENRAP did not re-conduct the model evaluation for Base G; therefore, only the typical modeling run for 2002 and the base and control strategy modeling runs for 2018 reflect the Base G inventory. Chapter 3 and Appendix C of the technical support document provide specific details on the performance evaluation of the final Base F 36-km CMAQ base-case model for 2002.



### E. Particulate Source Apportionment

1. Model Selection and Configuration
A state- of -science one-atmosphere photochemical grid model from Environ, CAMx can address particulate matter and visibility impairment at a regional scale for extended periods. The CENRAP modeling team used CAMx, and CENRAP selected the system as the secondary corroborative air-quality model. EPA recommends its use to simulate pollutants impairing visibility.

The modeling team applied CAMx 4.40 using options similar to those used for CMAQ system. CAMx contains particulate-matter source apportionment technology widely used in CENRAP modeling. Appendix 4-2 lists the main CAMx configuration used for CENRAP annual modeling, selected in part for consistency with the CMAQ model configuration.

A 12-km resolution reduced the sulfate under-prediction bias in the summertime with the CAMx model. CENRAP nevertheless decided that use of the 12-km grid for regional haze modeling provided inadequate benefits to justify the computational expense. CAMx simulations consequently used the same modeling domain as the CMAQ simulations.

CAMx and CMAQ model configurations can collapse vertical layers from MM5 into thicker airchemistry model layers to improve computational efficiency. CAMx simulations used the same vertical structure as CMAQ simulations.

2. Input, Simulation, and Performance Evaluation CENRAP modeling team operated CAMx and CMAQ model separately for each quarter of 2002 using the same 15-day spin-up period. CAMx and CMAQ modeling specified clean initial concentrations.

The modeling team initially used CAMx in side- by -side comparisons with CMAQ. Environ presented comparative model performance results and other factors for CAMx 4 and CMAQ 4.4 with secondary organic aerosol modules enhancement at the 7 February 2006 CENRAP modeling workgroup meetings and concluded:58

Neither model outperformed the other consistently over all chemical constituents and averaging
times. Both models performed well for sulfate aerosol. CAMx tended to over-predict winter nitrate aerosol more than CMAQ model did. CAMx performed slightly better than CMAQ model for elemental carbonaceous aerosol. CMAQ performed much better than CAMx for organic carbonaceous aerosol. Both models over-predicted fine soil dust and under-predicted coarse matter. CMAQ ran faster than CAMx due to multiple-processor capability. CAMx required much less disk space than CMAQ.

Based on these factors, CENRAP selected CMAQ as the primary air-chemistry model for the CENRAP regional haze modeling and CAMx as the secondary corroborative model; however, CAMx also features particulate source apportionment technology capability that the CENRAP modeling used widely. The team used consistent model configuration where possible to support the corroborative use
of both models. Environ nevertheless used the Bott advection solver in the CAMx but the PPM advection solver in CMAQ. The high computational requirements of the CAMx particulate source apportionment technique runs dictated this choice of the highly computationally efficient Bott scheme.

Because of the similarity between Base F and Base G for 2002 and resource constraints, CENRAP did not re-conduct the model evaluation for Base G; therefore, only the typical modeling run for 2002 and the base and control strategy modeling runs for 2018 reflect the Base G inventory. Particulate source apportionment technique modeling reflects the Base F inventory. The technical support document, Appendix 4- 2, details information on the model performance evaluations.



### F. Discussion of Components The tables in this chapter show the modeled contributions of various areas and pollutants to visibility impairment at the Wichita Mountains on the worst quintile of monitored days in 2002. The projections shown in these tables use the modeling results scaled to measured pollutant concentrations according to the modeling guidelines of EPA.

1. Sulfureous Aerosol CENRAP modeling apportioned most sulfureous particulate to point sources in the United States, particularly Texas and several Eastern states. Anthropogenic sources in Oklahoma, however, contribute less than five percent of sulfureous particulate that reaches the Wichita Mountains. The modeling nevertheless missed one-third of the observed sulfureous particulate, revealing a lack of understanding of atmospheric gaseous and aqueous sulfur chemistry, inadequacies in emissions inventories, discrepancies between the modeled concentrations and the sampling method, or some other problems.59

Table V-1: Projected sulfureous sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1TotalPointAreaNon-road MobileOn-road MobileBiogenic

Texas 13.98 11.56 1.85 .28 .29 .00
East 7.49 6.90 .40 .09 .10 .00 boundary conditions 3.93
Louisiana 2.60 1.93 .56 .08 .03 .00
Indiana 2.41 2.29 .09 .02 .02 .00
Mexico 1.79 1.67 .09 .01 .01 .00
Oklahoma 1.77 1.28 .33 .06 .09 .01
Canada 1.62 1.50 .08 .02 .01 .00
Ohio 1.61 1.54 .02 .02 .02 .00
Illinois 1.55 1.47 .03 .03 .02 .00 Kentucky 1.35 1.23 .08 .02 .01 .00
Alabama 1.30 1.16 .11 .02 .02 .00
Tennessee 1.29 1.15 .08 .03 .02 .00
Missouri 1.13 .95 .14 .03 .02 .00
Arkansas 1.03 .73 .21 .04 .03 .01
Kansas .92 .71 .16 .04 .02 .00
Iowa .46 .42 .01 .02 .01 .00 Michigan .42 .37 .02 .02 .01 .00
West .37 .28 .03 .03 .01 .02 Mississippi .32 .25 .01 .04 .01 .00
Nebraska .32 .25 .03 .03 .01 .00
Minnesota .30 .24 .03 .02 .01 .01
Wisconsin .25 .23 .01 .01 .01 .00
North Dakota .23 .21 .01 .01 .00 .00 Wyoming .18 .15 .02 .01 .00 .00
all other .49 .28 .17 .03 .01 .00
total 49.12 38.76 4.59 1.00 .79 .05
Source: Particulate source apportionment technique

— 2002 base year

Table V-1 indicates that point sources contribute overwhelmingly to sulfureous particulate that impairs visibility at the Wichita Mountains. The high sulfureous particulate concentrations generally

re flect transport from Texas and the eastern two-thirds of the United States.

2. Nitrate Aerosol The overwhelming majority of nitrate aerosol at the Wichita Mountains comes from anthropogenic sources in the United States, three-tenths from Oklahoma. CENRAP model projection implicates on-road mobile sources for a plurality of emissions that impair visibility at the Wichita Mountains, significantly implicates point sources, and assigns some culpability to area sources in60

Oklahoma. This model projection nevertheless considerably overestimates nitrate particulate concentrations on the worst quintile of days.

Table V-2: Projected nitrate sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1SumPointAreaNon-road mobileOn-road mobileBiogenic

Texas 7.89 1.97 .96 .98 2.74 1.24
Oklahoma 6.43 1.80 1.54 .61 1.69 .78
Kansas 1.94 .66 .24 .41 .47 .15
Missouri 1.06 .39 .08 .16 .36 .07 boundary conditions .87
Nebraska .63 .16 .04 .20 .19 .04
Canada .58 .14 .14 .13 .15 .02
Iowa .57 .22 .01 .10 .20 .04
Arkansas .49 .13 .06 .08 .20 .02
Illinois .42 .20 .02 .09 .10 .01
Mexico .37 .24 .02 .01 .04 .07
Minnesota .31 .12 .03 .05 .10 .01
Louisiana .30 .12 .06 .03 .08 .01
West .29 .08 .02 .06 .11 .02
New Mexico .28 .09 .06 .05 .06 .03
Colorado .25 .08 .02 .03 .10 .02 Wyoming .16 .08 .02 .04 .02 .00
East .15 .05 .01 .02 .06 .01
North Dakota .14 .06 .01 .03 .03 .01
South Dakota .12 .01 .01 .03 .05 .03
all other .48 .20 .05 .08 .13 .01
total 23.72 6.80 3.41 3.16 6.87 2.61
Source: Particulate source apportionment technique

— 2002 base year

Nitrates from on-road mobile and point sources throughout the domain each contribute 7% of the visual impairment at the Wichita Mountains.

3. Organic Carbonaceous Particulate In the atmosphere, organic aerosols and compounds partake in a complex web of chemical reactions involving thousands of chemical species as they usually degrade or deposit. Available modeling algorithms greatly simplify these processes and may not represent them adequately to assign culpability with sufficient confidence to justify pursuing reductions in the already small concentrations of organic carbonaceous particulate. CENRAP modeling classifies organic aerosols as primary or secondary. The term “primary” refers to particles emitted directly into the atmosphere. The term61

“secondary” refers to particles formed in the atmosphere, usually through chemical reactions, some within condensed water droplets.

CENRAP particulate source apportionment modeling for 2002 traces less than 5% of baseline concentrations to point, on-road mobile, or non-road mobile sources in the United States. Trijonis cited a considerable petroleum-based component to organic aerosols in a desert field study during the 1980s, presumably near major cities of Los Angeles, Riverside, and San Diego. A lack of cities of such population in Oklahoma, decades of federally mandated improvement in motor vehicle emissions, the increasing prevalence of catalytic converters and fuel-injection systems, and other efforts to decrease emissions from petroleum combustion, however, lend credibility to our modeling suggesting only a
trivial contribution from mobile sources.

CENRAP model projection captures organic aerosol largely from wildfires. The model projects that more than four-fifths of primary organic aerosol at the Wichita Mountains originates outside Oklahoma. Major source regions include Texas and areas outside the boundary of the domain. These boundary conditions include extensive agricultural burning and occasional wildfire emissions throughout Mesoamerica and the Caribbean that occur each spring. Questionable accuracy of fire emission inventories nevertheless yields uncertainty regarding the modeled effect of fire emissions on primary organic aerosol concentrations.

CENRAP modeling attributes a significant proportion of organic aerosols at the Wichita Mountains to the anthropogenic area category, including fires. It attributes a further one-third of organic particulate to “secondary” aerosol production.62

Table V-3 : Projected organic carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1SumPointAreaNon-road mobileOn-road mobileBiogenic

boundary conditions 3.61
Texas 3.05 .34 2.37 .19 .12 .03 secondary biogenic 2.91 secondary anthropogenic 2.57 Oklahoma 2.26 .13 1.75 .19 .08 .12
Kansas .57 .05 .49 .02 .01 .01
Louisiana .42 .17 .18 .04 .01 .03
East .34 .04 .25 .03 .01 .01 Arkansas .31 .03 .11 .03 .01 .13
West .20 .00 .04 .01 .01 .14
Missouri .19 .00 .13 .05 .00 .00
Minnesota .11 .00 .04 .01 .00 .05 Mississippi .10 .01 .06 .01 .00 .01 Canada .09 .01 .04 .01 .00 .03 Nebraska .08 .02 .04 .01 .00 .00 all other .48 .06 .28 .07 .03 .03 total 17.28 .86 5.79 .66 .30 .60
Source: Particulate source apportionment technique

— 2002 base year Primary aerosols only except where “secondary” explicitly indicated

A detailed field study may identify the species of gaseous and particulate organic compounds in the atmosphere near the Wichita Mountains, locate their sources and sinks, and improve understanding of their chemistry; however, DEQ presently lacks the financial and other resources to undertake such study.

4. Elemental Carbonaceous Particulate Modeling apportions more than three-quarters of elemental carbon particulate to United States anthropogenic sources, largely to area sources, a category that includes fires.63

Table V-4: Projected elemental carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1SumPointAreaNon-road mobileOn-road mobileBiogenic

Texas 1.42 .03 .83 .36 .20 .01
Oklahoma 1.08 .03 .56 .32 .14 .03 boundary conditions .69
Kansas .30 .00 .23 .06 .01 .00
Louisiana .14 .01 .08 .03 .02 .01
East .13 .01 .05 .05 .02 .00
Arkansas .13 .01 .03 .03 .02 .03
West .07 .00 .01 .02 .01 .03
Missouri .07 .00 .03 .03 .01 .00
all other .44 .01 .08 .25 .08 .03
total 4.47 .10 1.89 1.14 .51 .14
Source: Particulate source apportionment technique

— 2002 base year

5. Fine Soil Particulate Available atmospheric chemistry modeling systems generally do not capture natural wind erosion. CENRAP modeling attributes the overwhelming majority of fine soils to grossly overestimated

area sources.

Table V-5: Projected fine soil sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1SumPointAreaNon-road mobileOn-road mobileBiogenic

Texas .29 .02 .22 .00 .00 .05
Oklahoma .26 .00 .24 .00 .00 .01
all other .24 .02 .18 .00 .00 .02
total .79 .05 .65 .00 .00 .07
Source: Particulate source apportionment technique

— 2002 base year

6. Coarse Particulate CENRAP modeling attributes practically all coarse particulate matter to natural and area sources. The coarse matter in the atmosphere at the Wichita Mountains comes primarily from natural dust storms that modeling simulates poorly.64

Table V-6: Projected coarse particulate sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1SumPointAreaNon-road mobileOn-road mobileBiogenic

Oklahoma 2.91 .01 2.73 .00 .00 .17
Texas 1.51 .02 1.15 .00 .00 .34 all other .17 .01 .13 .00 .00 .03 total 4.60 .04 4.01 .00 .01 .53
Source: Particulate source apportionment technique

— 2002 base year



### G. Other Class I Areas

Table V-7 illustrates that emissions from Oklahoma sources only insignificantly impair visibility at all Class I areas in other states. DEQ consequently need not undertake any emission reduction action to protect Class I areas in other states.65

Table V-7: Projected contribution of Oklahoma emissions to visibility impairment at Class I areas (worst quintile days in 2002)Class I AreaStateContribution tolight extinction(Mm-1)Total lightextinction from allsources (Mm1)Oklahomacontribution (%)Deciviewcontribution

Wichita Mountains Oklahoma 14.72 111.03 13.25% 1.42
Hercules

- Glades Missouri 4.84 151.05 3.20% .33 Salt Creek New Mexico 1.71 63.50 2.69% .27 Guadalupe Mountains Texas 1.18 58.80 2.01% .20 Seney Michigan 2.10 116.35 1.80% .18 Caney Creek Arkansas 2.61 144.93 1.80% .18 White Mountain New Mexico .76 43.91 1.72% .17 Upper Buffalo Arkansas 2.43 142.79 1.70% .17 Isle Royale Michigan 1.17 81.92 1.43% .14 Badlands South Dakota .72 55.87 1.28% .13 Wind Cave South Dakota .65 51.71 1.26% .13 Big Bend Texas .68 58.79 1.16% .12 Wheeler Peak New Mexico .33 32.96 .99% .10
Breton Louisiana 1.34 134.99 .99% .10 Mingo Missouri 1.62 170.83 .95% .10 Bosque del Apache New Mexico .36 41.71 .86% .09 Great Sand Dunes Colorado .32 38.88 .82% .08 San Pedro Parks New Mexico .23 31.82 .74% .07 Rocky Mountain Colorado .29 43.13 .68% .07 Boundary Waters Canoe Area Minnesota .48 72.33 .66% .07 Voyageurs Minnesota .34 70.50 .48% .05 Bandelier New Mexico .18 40.97 .44% .04 Everglades Florida .38 115.24 .33% .03 Galiuro Arizona .13 40.51 .32% .03 Chassahowitzka Florida .39 125.17 .31% .03 Wolf Island Georgia .45 149.00 .30% .03 Theodore Roosevelt North Dakota .18 61.33 .29% .03 UL Bend Montana .12 48.42 .24% .02 Mammoth Cave Kentucky .52 218.58 .24% .02 Lye Brook Vermont .29 123.08 .24% .02 Presidential Range

- Dry River New Hampshire .22 112.46 .20% .02 Saint Marks Florida .27 140.35 .19% .02 Roosevelt Campobello Maine .17 90.67 .19% .02 Rawah Colorado .06 34.23 .17% .02 Cape Romain South Carolina .22 134.64 .16% .02 Cohutta Georgia .32 218.72 .15% .01 Acadia Maine .15 100.97 .15% .01 Swanquarter North Carolina .17 120.86 .14% .0166Class I AreaStateContribution tolight extinction(Mm-1)Total lightextinction from allsources (Mm1)Oklahomacontribution (%)Deciviewcontribution

Brigantine New Jersey .22 178.06 .12% .01
Pine Mountain Arizona .05 39.84 .12% .01 Sipsey Alabama .22 187.03 .12% .01 Saguaro Arizona .05 47.10 .11% .01 Lostwood North Dakota .08 72.91 .11% .01 Shining Rock North Carolina .18 182.63 .10% .01 Medicine Lake Montana .06 60.56 .10% .01
James River Face Virginia .17 189.25 .09% .01 West Elk Colorado .03 30.67 .09% .01 Joyce Kilmer

- Slickrock North Carolina .17 193.84 .09% .01 Linville Gorge North Carolina .13 183.46 .07% .01 Shenandoah Virginia .13 197.41 .07% .01 Otter Creek West Virginia .11 177.35 .06% .01 Petrified Forest Arizona .03 42.36 .06% .01 Weminuche Colorado .02 31.72 .06% .01 Gila New Mexico .02 42.24 .05% .01 Washakie Wyoming .02 34.73 .05% .01

Source: Particulate source apportionment technique

— 2002 base year Oklahoma contribution is less than .10 Mm -1 and less than .1% at every unlisted Class I area.



### H. Strategy for the Wichita Mountains

As Table V-8 indicates, sulfureous emissions clearly most importantly impair visibility at the Wichita Mountains. Nitrate particulate matter forms from NO x emissions but occurs predominantly during the winter months; sulfureous aerosol comprises a plurality during the rest of the year. Organic carbonaceous aerosols also contribute significantly to visibility impairment at the Wichita Mountains. Texas sources bear culpability for the largest proportion of visibility impairment. In every category except coarse particulate matter, sources in Texas (and other states) notably contribute more than those within Oklahoma do. Several other states each emit sulfureous aerosols which impair visibility at
the Wichita Mountains more than emissions from all Oklahoma sources do.67

Table V-8: Projected sources of light extinction at the Wichita Mountains (worst quintile days in 2002)

All values in Mm - 1SumSulfureousNitrateOrganiccarbonaceousElementalcarbonaceousSoilsCoarse

Texas 28.15 13.98 7.89 3.05 1.42 .29 1.51 Oklahoma 14.72 1.77 6.43 2.26 1.08 .26 2.91 Rayleigh scattering 11.00 boundary conditions 9.17 3.93 .87 3.61 .69 .02 .05
East 8.12 7.49 .15 .34 .13 .01 .00
Kansas 3.82 .92 1.94 .57 .30 .05 .05
Louisiana 3.47 2.60 .30 .42 .14 .01 .00 secondary biogenic 2.91 2.91 secondary anthropogenic 2.57 2.57 Indiana 2.53 2.41 .07 .02 .02 .00 .00
Missouri 2.47 1.13 1.06 .19 .07 .02 .01 Canada 2.35 1.62 .58 .09 .05 .01 .00
Mexico 2.25 1.79 .37 .04 .02 .01 .02 Illinois 2.05 1.55 .42 .04 .03 .00 .00 Arkansas 1.97 1.03 .49 .31 .13 .01 .01 Ohio 1.68 1.61 .02 .02 .02 .00 .00
Tennessee 1.51 1.29 .11 .07 .03 .00 .00 Kentucky 1.47 1.35 .05 .04 .02 .00 .00 Alabama 1.41 1.30 .03 .06 .02 .00 .00
Iowa 1.16 .46 .57 .06 .03 .03 .01 Nebraska 1.11 .32 .63 .08 .04 .02 .02
West .93 .37 .29 .20 .07 .01 .01
Minnesota .78 .30 .31 .11 .04 .01 .00 Mississippi .52 .32 .06 .10 .03 .00 .00 Michigan .47 .42 .02 .01 .01 .00 .00
New Mexico .46 .14 .28 .02 .01 .00 .01 Colorado .45 .16 .25 .02 .01 .00 .00 North Dakota .41 .23 .14 .02 .01 .00 .00 Wyoming .36 .18 .16 .01 .00 .00 .00
Wisconsin .33 .25 .05 .02 .01 .00 .00 South Dakota .23 .05 .12 .02 .02 .01 .01 Gulf of Mexico .22 .15 .06 .01 .00 .00 .00 initial conditions .00 .00 .00 .00 .00 .00 .00 total 111.03 49.12 23.72 17.28 4.47 .79 4.64 Source: Particulate source apportionment technique

— 2002 base year



### I. International Transport

Table V-8 shows some contribution from sources in southern Canada, northern México, and especially the boundary conditions outside the CENRAP modeling domain. The boundary conditions include particulate from much of central and all of southern México, including Ciudad México (Distrito68

Federal), the Mexican Yucatán Peninsula, Mesoamerica, the Caribbean region, Africa, the People’s Republic of China, and other Asian and international sources. International transport contributes more than one-tenth of the regional haze on the worst quintile of days at the Wichita Mountains.

The contribution of international transport of anthropogenic regional haze into Oklahoma, Texas, and other states makes reaching natural conditions at the Wichita Mountains difficult without reductions in this transport. Approaching natural conditions ultimately may necessitate reductions in international transport of particulate matter and its precursors to parallel the reductions in American anthropogenic regional haze.69



## VI. Best Available Retrofit Technology
The federal regional haze rule requires certain older emission sources, largely unregulated under other provisions of the CAA, to apply additional controls. On 6 July 2005, EPA published final amendments to its regional haze rule, which requires emission sources that fit specific criteria, including reasonably anticipated contribution to visibility impairment in any Class I area, to install best available retrofit technology (BART). The final rule provides direction on determining which sources must install BART, and on determining what such sources must do to meet the BART requirement. The rule also includes Appendix Y to 40 C.F.R. Part 51, “Guidelines for BART Det erminations under the Regional Haze
Rule. ” Oklahoma’s BART rule, OAC 252:100 -8, Part 11, became effective as an emergency rule on 8 October 2006 and as a permanent rule on 15 June 2007. Definitions related to the BART rule were added in the Air Quality Rules general definitions section in OAC 252:100-8.1.1, and became effective as a permanent rule on 15 June 2006. These rules implement the BART requirements of EPA’s regional haze rule and incorporate by reference 40 C.F.R. Part 51, Appendix Y. The rules also provide that the resulting source- specific requirements be incorporated into that source’s air quality permit. A copy of
Oklahoma’s BART rule and relevant definitions, along with administrative materials required under 40 C.F.R. Part 51, Appendix V, Section 2.1(b) through (h), are included in Appendix 6-1.-



### A. BART-eligible Sources in Oklahoma 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. The Oklahoma
rule affects sources that:

Belong to one of twenty-six industry source categories; Did not operate before 7 August 1962 but were in existence on 7 August 1977; and Have the potential to emit 250 tons per year or more of any regulated air pollutant. (The Appendix Y guidance narrows this to any visibility-impairing pollutant.)

DEQ determined that the visibility-impairing pollutants in Oklahoma include sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and particulate matter ( PM 10 and PM 2 .5 ). CENRAP modeling shows that anthropogenic VOCs do not significantly impair visibility at the Wichita Mountains. Because of the limiting role of NO X and SO 2 on PM 2.5 formation and the uncertainties in assessing the effect of ammonia emissions reductions on visibility, Oklahoma does not consider ammonia among visibility-impairing pollutants.

Subsection II.A of Appendix Y to 40 C.F.R. Part 51 describes the steps necessary to derive a list of BART-eligible sources. DEQ initially screened its emissions inventory and permitting database to identify major facilities with emission units in one or more of the BART categories:70

(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, (6) Iron and steel mill plants, (7) Primary aluminum ore reduction plants, (8) Primary copper smelters, (9) Municipal incinerators capable of charging more than 250 tons of refuse per day, (10) Hydrofluoric, sulfuric, and nitric acid plants, (11) Petroleum refineries, (12) Lime plants, (13) Phosphate rock processing plants, (14) Coke oven batteries, (15) Sulfur recovery plants, (16) Carbon black plants (furnace process), (17) Primary lead smelters, (18) Fuel conversion plants, (19) Sintering plants, (20) Secondary metal production facilities, (21) Chemical pr ocess plants, (22) Fossil

- fuel boilers of more than 250 million BTUs per hour heat input, (23) Petroleum storage and transfer facilities with a capacity exceeding 300,000 barrels, (24) Taconite ore processing facilities, (25) Glass fiber processing plants, and (26) Charcoal production facilities.

The BART requirement applies only to sources in these specific categories. The BART requirement does not apply to sources in other source categories, regardless of their emissions.

Next, DEQ used agency databases and records to identify facilities 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, DEQ contacted the sources to obtain or confirm pertinent data and information. These emissions units

com prise the “BART -eligible sources ” listed in Table VI-1

.71

Table VI-1: Facilities with BART-eligible u ni ts in Oklahoma

BART source category Facility name County Number of units
F ossil fuel

- fired boilers of more than 250 MMBTU/hr heat input Georgia Pacific Consumer Products (formerly Fort James Operating) Muskogee Mill Muskogee 2

K raft pulp mill International Paper (formerly Weyerhaeuser) Valliant Paper Mill McCurtain 5

Hydro fluoric , 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

P etroleum refiner ies 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 In c ) Ardmore

Refinery Carter 24

Portland cement plant s Lafarge Building Materials Tulsa Rogers City Line Rogers 10

F ossil fuel

- fired steam electric plants of more than 250 MMBTU/hr heat

input OG&E Horseshoe Lake Generating Station Oklahoma 2 OG&E Muskogee Generating Station Muskogee 2 OG&E Seminole Generating Station Seminole 3 OG&E Sooner Generating Station Noble 2
PSO Comanche Power Station Comanche 2
PSO Northeastern Power Station Rogers 3
PSO Riverside Jenks Power Station Tulsa 2
PSO Southwestern Power Station Caddo 1 Western Farmers Electric Coop Anadarko Plant Caddo 3 Western Farmers Electric Coop Mooreland Station Woodward 3



### B. Determination of Sources Required to Install BART Federal guidelines (Section III of Appendix Y to 40 CFR Part 51) give DEQ two options regarding BART-eligible sources:

Make BART determinations for all sources, or Consider exempting some sources from BART because they do not cause or contribute to visibility impairment at the Wichita Mountains or any other Class I area.

DEQ chose to exempt some sources from BART. The regulatory guidelines suggest three options for exempting an eligible source from BART:

Individual source attribution approach (dispersion modeling), Use of model plants to exempt sources with common characteristics, and Cumulative modeling to exclude every source in Oklahoma from BART.

DEQ chose the first option and required the owner or operator of each BART-eligible source to perform a source-specific analysis, using the CALPUFF model to determine whether it causes or contributes to visibility impairment. DEQ will require any BART-eligible source determined to cause or72

contribute to visibility impairment at the Wichita Mountains or any other Class I area to install BART. Appendix 6-2 includes the CALPUFF modeling protocol used for determining facilities subject to BART. Subsection III.A of the regulatory guidelines in Appendix Y to 40 C.F.R. Part 51 provides DEQ the
discretion to set a m aximum contribution threshold below 0.5 deciview if “the location of a large number of BART-eligible sources within the State and in proximity to a Class I area justifies this approach.” With only three Oklahoma BART -eligible sources within close proximity of the Wichita Mountains, DEQ allowed sources to use the 0.5-deciview threshold; therefore, BART-eligible sources that always contribute less than 0.5 deciview of visibility impairment need not install BART. Some sources used screening and/or refined di spersion modeling to determine that they fell below the 0.5 deciview threshold, and DEQ granted waivers to the facilities listed in Table VI-2. These waivers are in Appendix 6-3. Each application includes a modeling analysis.

Table VI-2: BART-eligible Sources in Oklahoma granted waivers from BART based on dispersion modeling

Company Facility Greatest Impact Koch Nitrogen Enid Plant 0.489 dv Terra International Oklahoma Inc. Woodward Complex 0.405 dv Terra Nitrogen Ltd Partnership Verdigris Plant 0.427 dv Sinclair Oil Corporation Tulsa Refinery 0.119 dv Holly Refining and Marketing Tulsa Refinery 0.335 dv Wynnewood Refining Company Wynnewood Refinery 0.267 dv Valero Refinery Company Ardmore Refinery 0.22 dv Oklahoma Gas & Electric Horseshoe Lake Generating Station 0.359 dv* American Electric Power PSO Riverside Jenks Power Station 0.239 dv* Western Farmers Electric Coop Mooreland Station 0.18 dv* Lafarge Building Materials Tulsa Rogers City Line 0.121 dv*

* The indicated facilities demonstrated this maximum visibility impairment contribution at the 98 th Percentile

Initial modeling results for three of the BART-eligible facilities (Georgia Pacific Consumer
Products LP

–

Muskogee Mill, International Paper

– Valliant Paper Mill, and Western Farmers Electric Coop (WFEC)

–

Anadarko Plant) indicated a contribution of more than 0.5 deciview of visibility impairment under currently permitted operations. As described below, each facility has identified available reductions in potential emissions, and has perform ed an updated source-specific analysis, using the CALPUFF model and taking into account these emission reductions. The updated analyses demonstrated a contribution of less than 0.5 deciview of visibility impairment under all conditions. Accordingly, each facility has applied for a permit modification to include federally enforceable limits on potential emissions and allow them to qualify for similar waivers from installing BART. These waivers
are also in Ap pendix 6-3.

Georgia Pacific Consumer Products LP

– Muskogee Mill has agreed to limit NO X emissions to 744lb/day from the BART-eligible units through the use of new low NO X burners. The facility will have a modeled visibility impairment of 0.498 dv from subject units after control.

The International Paper

– Valliant Paper Mill has accepted limits on sulfur content of fuel to the BART unit. Emission limitations are based on 3 different operating scenarios, and reflect maximum73

pound per hour rather than ton per year emissions. Sulfur content has decreased substantially and maximum impacts under all permitted scenarios result in less than 0.5 dv of impairment at any Class I

area.

The WFEC Anadarko Facility has proposed to accept the emission limitations on Units 4, 5, and 6 listed in Table VI-3. This change limits modeled visibility impairment to less than 0.5 dv.

Table VI-3 : New proposed permitted emission limits for WFEC Anadarko Facility’s BART-eligible units

Emission Unit NO X PM 10 SO 2

lb/hr TPY lb/hr TPY lb/hr TPY

AN

- Unit 4 190.00 833 12.18 54 1.57 6.87

AN

- Unit 5 190.00 833 12.18 54 1.57 6.87

AN

- Unit 6 190.00 833 12.18 54 1.57 6.87

Table VI- 4 lists the facilities with BART-eligible units that must install BART, and a brief description of each of these “BART

- subject” facilities follows . Note that the SO 2 and PM emissions from natural gas-fired steam electric plants do not significantly contribute to visibility impairment and were
therefore not further evaluated.

Table VI -4: Sources in Oklahoma required to install BART

Facility Name BART

Emission Units Source Category Pollutants Evaluated 98th Percentile

Contribution (deciview)

OG&E Seminole Generating Station Units 1, 2, and 3 fossil fuel

- fired steam

electric plants NO x 1.300

OG&E Sooner Generating
Station Units 1 and
2 fossil fuel

- fired steam

electric plants SO 2 1.795
NO x 1.244
PM 10 0.281

OG&E Muskogee Generating Station Units 4 and
5 fossil fuel

- fired steam

electric plants SO 2 1.724
NO x 0.210
PM 10 1.469 PSO Comanche Power
Station Units 1 and
2 fossil fuel

- fired steam

electric plants NO x 1.830

PSO Northeastern Power
Station Unit 2 fossil fuel

- fired steam electric plants NO x 0.809

PSO Northeastern Power
Station Units 3 and
4 fossil fuel

- fired steam

electric plants SO 2 1.836
NO x 1.029
PM 10 0.230 PSO Southwestern Power
Station Unit 3 fossil fuel

- fired steam electric plants NO x 3.863

OG&E Seminole Generating Station is located northeast of the City of Konawa, Seminole County, Oklahoma. The station includes three (3) nominal 567 megawatts (“MW”) steam electric generating units designated as Seminole Units 1, 2, and 3. Seminole Units 1 and 2 became operational in 1968, and74

Unit 3 became operational in 1970. All three (3) units are Babcock & Wilcox wall-fired boilers that fire natural gas as their primary fuel. Each unit is a fossil-fuel fired boiler with heat inputs greater than 250 mmBtu/hr. Because the units fire na tural gas, there are no sulfur dioxide (“SO 2 ”) or particulate matter (“PM”) emission control systems. Seminole Unit 3 was designed with flue gas recirculation (“FGR”) for nitrogen oxide (“NO X ”) control. Each unit has the potential to emit 250 tons per year (“TPY” ) or more o f

NO X . The facility is currently permitted to operate under DEQ Air Quality Permit No. 2003-400-TVR, which was issued on May 8, 2006.

OG&E Sooner Generating Station is located in Red Rock, Noble County, Oklahoma. The station includes two (2) 570 MW steam electric generating units designated as Sooner Units 1 and 2. Each unit is a fossil-fuel fired boiler with heat inputs greater than 250 mmBtu/hr. Sooner Units 1 and 2 were in existence prior to August 7, 1977, but not in operation prior to August 7, 1962. Both units fire coal as their primary fuel, and both units have the potential to emit 250 TPY or more of NO X , SO 2 , and PM. The facility is currently permitted to operate under DEQ Air Quality Permit No. 2003-274-TVR, which was issued on February 11, 2006.

OG&E Muskogee Generating Station is located in Muskogee, Muskogee County, Oklahoma. The station includes two (2) 572 MW steam electric generating units designated as Muskogee Units 4 and 5. Each unit is a fossil-fuel fired boiler with heat inputs greater than 250 mmBtu/hr. Both Muskogee Units 4 and 5 were in existence prior to August 7, 1977, but not in operation prior to August 7, 1962. Both units fire coal as their primary fuel, and both units have the potential to emit 250 TPY or more of NO X , SO 2 , and PM. The facility is currently permitted to operate under DEQ Air Quality Permit No. 2005-271- TVR, which was issued on September 8, 2009.

PSO Comanche Power Station is located in Comanche County, Oklahoma. The station includes two (2) 94 MW combustion turbine generating units designated as Comanche Units 1 and 2. Both units were in existence prior to August 7, 1977, but not in operation prior to August 7, 1962. Each unit is a fossil-fuel fired boiler with heat inputs greater than 250 mmBtu/hr and each unit fires natural gas as its primary fuel. Because the units fire natural gas, there are no SO 2 or PM emission control systems. Both units have the potential to emit 250 TPY or more of NO X . The facility is currently permitted to operate under DEQ Air Quality Permit No. 2003-261-TVR, which was issued on April 27, 2006.

PSO Southwestern Power Station is located in Caddo County, Oklahoma. The station includesone (1) 332 MW steam electric generating unit designated as Southwestern Unit 3. The unit is a fossilfuel fired boiler with heat inputs greater than 250 mmBtu/hr. The unit was in existence prior to August 7, 1977, but not in operation prior to August 7, 1962. The unit fires natural gas as its primary fuel. Because the unit fires natural gas, there are no SO 2 or PM emission control systems. The unit has the potential to emit 250 TPY or more of NO X . The facility is currently permitted to operate under DEQ Air Quality Permit No. 2003-403-TVR (M-3), which was issued on July, 20, 2009.

PSO Northeastern Power Station is located in Rogers County, Oklahoma. The station includes one (1) 495 MW gas-fired steam electric generating unit designated as Northeastern Unit 2 and two (2) 490 MW coal-fired steam electric generating units designated as Northeastern Units 3 and 4. All three75

(3) units are fossil-fuel fired boilers with heat inputs greater than 250 mmBtu/hr. All three (3) units were in existence prior to August 7, 1977, but not in operation prior to August 7, 1962. Northeastern Unit 2 fires natural gas as its primary fuel; consequently, it has no SO 2 or PM emission control systems. Unit 2 has the potential to emit 250 TPY or more of NO X . Northeastern Units 3 and 4 both fire coal as
th eir primary fuel, and both units have the potential to emit 250 TPY or more of NO X , SO 2 , and PM. The facility is currently permitted to operate under DEQ Air Quality Permit No. 2003-410-TVR, which was issued on February 4, 2008.



### C. Determination of BART Requirements for Subject Sources State rules require that each BART-subject facility submit a BART determination that includes an analysis of emissions control alternatives for each unit that is subject to BART. This BART determination identifies an emission limit for each pollutant based on the degree of reduction achievable through the application of the best system of continuous emission reduction. These limits consider the technology available, the costs of compliance, the energy required for compliance, the non-air-quality environmental effects of compliance, any pollution control equipment in use or in existence at the source, the remaining useful life of the source, and the resultant improvement in visibility reasonably anticipated from the use of such technology. If a BART-eligible source lacks the potential to emit at least
40 tons of SO 2 or NO x per year or at least 15 tons of PM 10 per year, then the regulation does not require DEQ to make a determination of BART for such pollutants. Specifically, e ach BART analysis includes “the Five Basic Steps of a Case- by

- Case BART Analysis,” as listed in Section IV.D of Appendix Y to 40 C.F.R. Part

51:
STEP 1

— Identify All Available Retrofit Control Technologies,

STEP 2

— Eliminate Technically Infeasible Options,

STEP 3

— Evaluate Control Effectiveness of Remaining Control Technologies,

STEP 4

— Evaluate Impacts and Document the Results, and

STEP 5

— Evaluate Visibility Impacts.

Each of the facilities required to install BART (identified in Table VI-4) is a fossil-fuel fired steam electric generating plant with heat inputs greater than 250-mmBtu/hr, and with a total generating capacity in excess of 750 MW. Therefore

, 40CFR § 51.308(e)(1)(ii)(B) required that the Appendix Y guidelines be used to prepare BART determinations for each facility. Based on an evaluation of potentially feasible retrofit control technologies, including an assessment of the costs and visibility improvements associated therewith, the control technologies and emission limits described in DEQ’s analysis of the BART Determination for each of the six (6) facilities (Appendix 6-4 ; collectively “BART Determinations”) have been determined to be BART. Oklahoma’s BART rule requires each source subject to BART to install and operate BART no later than 5 years after EPA approves this implementation plan revision. See OAC 252-100-8-75(e). Tables VI-5 through VI-10 summarize the BART control technologies and emission limits. DEQ has issued enforceable administrative orders with the owners and operators of each BART-eligible source subject to BART requiring the installation and operation of BART and the achievement of the associated BART emission limitations within five (5) years76

o f EPA’s approval of this SIP revision. See OG&E Regional Haze Agreement, Case No. 10-024 (attached as Item 1 in Appendix 6-5) and PSO Regional Haze Agreement, Case No. 10-025 (attached as Item 2 in Appendix 6-5). These Regional Haze Agreements also allow subject sources the option of implementing the Greater Reasonable Progress Alternative set forth in § VI(E) of this SIP by December 31, 2026, in lieu of installing and operating BART. Furthermore, the Regional Haze Agreements require each subject source to obtain necessary permit modifications that will also include a requirement, schedule, and procedures to ensure that the source properly installs, operates, and maintains any required control equipment. The Oklahoma BART rule also contains a compliance schedule and is another enforceable
mechanism.

Table VI-5: BART control technologies and emission limits for OG&E Seminole Generating Station Units 1, 2, and 3

Control Unit 1 Unit 2 Unit 3

NO X Control Combustion controls including: Low

- NO X Burners
, Overfire Air, and
Flue Gas Recirculation Combustion controls including: Low

- NO X Burners
, Overfire Air, and
Flue Gas Recirculation Combustion controls including: Low

- NO X Burners
, Overfire Air, and
Flue Gas Recirculation

NO X Emission Rate (lb/mmBtu) 0.203 lb/mmBtu (30

- day rolling average) 0.212 lb/mmBtu (30

- day rolling average) 0.164 lb/mmBtu (30

- day rolling average)77

Table VI-6: BART control technologies a nd emission limits for OG&E Sooner Generating Station Units 1 and 2

Control Unit 1 Unit 2

NO X Control Low NO X Burner with Over

-

fire Air Low NO X Burner with Over

-

fire Air

Emission Rate (lb/mmBtu) 0.15 lb/mmBtu (30

- day rolling average) 0.15 lb/mmBtu (30

- day rolling average) Emission Rate lb/hr 767 lb/hr (30

- day rolling average) 767 lb/hr (30

- day rolling average)

Emission Rate TPY 3,361 TPY (12

- month rolling) 3,361 TPY (12

- month rolling)

SO 2 Control Low Sulfur Coal Low Sulfur Coal Hourly Emission Rate (lb/mmBtu) 0.65 lb/mmBtu (30

- day rolling average) 0.65 lb/mmBtu (30

- day rolling average) Emission Rate (lb/hr) 3,325 lb/hr (30

- day rolling average) 3,325 lb/hr (30

- day rolling average) Annual Emission Rate (lb/mmBtu) 0.55 lb/mmBtu (12

- month rolling average) 0.55 lb/mmBtu (12

- month rolling average)
Combined Annual Emission Rate (TPY) 19,736 TPY

PM 10 Control Existing Electrostatic Precipitator Existing Electrostatic Precipitator Emission Rate (lb/mmBtu) 0.10 lb/mmBtu (3

- hour rolling average) 0.10 lb/mmBtu (3

- hour rolling average) Emission Rate lb/hr 512 lb/hr (3

- hour rolling average) 512 lb/hr (3

- hour rolling average)

Emission Rate TPY 2,241 TPY (12

- month rolling average) 2,241 TPY (12

- month rolling average)78

Table VI-7: BART control technologies a nd emission limits for OG&E Muskogee Generating Station Units 4 and 5

Control Unit 4 Unit 5

NO X Control Low NO X Burner with Over

-

fire Air Low NO X Burner with Over

-

fire Air Emission Rate (lb/mmBtu) 0.15 lb/mmBtu (30

- day rolling average) 0.15 lb/mmBtu (30

- day rolling average) Emission Rate lb/hr 822 lb/hr (30

- day rolling average) 822 lb/hr (30

- day rolling average)

Emission Rate TPY 3,600 TPY (12

- month rolling) 3,600 TPY (12

- month rolling)

SO 2 Control Low Sulfur Coal Low Sulfur Coal Hourly Emission Rate (lb/mmBtu) 0.65 lb/mmBtu (30

- day rolling average) 0.65 lb/mmBtu (30

- day rolling average) Emission Rate (lb/hr) 3,562 lb/hr (30

- day rolling average) 3,562 lb/hr (30

- day rolling average) Annual Emission Rate (lb/mmBtu) 0.55 lb/mmBtu (12

- month rolling average) 0.55 lbmmBtu (12

- month rolling average)
Combined Annual Emission Rate (TPY) 18,096 TPY

PM 10 Control Existing Electrostatic Precipitator Existing Electrostatic Precipitator Emission Rate (lb/mmBtu) 0.10 lb/mmBtu (3

- hour rolling average) 0.10 lb/mmBtu (3

- hour rolling average) Emission Rate lb/hr 548 lb/hr (3

- hour rolling average) 548 lb/hr (3

- hour rolling average)

Emission Rate TPY 2,400 TPY (12

- month rolling average) 2,400 TPY (12

- month rolling average)

Table VI-8: BART control technologies a nd emission limits fo r PSO Comanche Power Station Units 1 and 2

Control Unit 1 Unit 2

NO X Control Dry Low

- NO X Burners Dry Low

- NO X Burners

NOx Emission Rate (lb/mmBtu) 0.15 lb/mmBtu (30

- day rolling average) 0.15 lb/mmBtu (30

- day rolling average)

Table VI-9: BART control technologies a nd emission limits f or PSO Southwestern Power Station Units 3

Control Unit 3

NO X Control Low NO X Burner with Over

-

fire Air NOx Emission Rate (lb/mmBtu) 0.45 lb/mmBtu (30

- day rolling average)79

Table VI- 10 : BART control technologies a nd emission limits f or PSO Northeastern Power Station Units 2, 3, and 4

Control Unit 2 Unit 3 Unit 4

NO X Control Low NO X Burner with

Over

- fire Air Low NO X Burner with Over

fire Air New Low NO X Burner with

Over

- fire Air Emission Rate (lb/mmBtu) 0.28 lb/mmBtu (30

- day rolling average) 0.15 lb/mmBtu

(30

- day rolling average) 0.15 lb/mmBtu (30

- day rolling average)

Emission Rate lb/hr 1331 lb/hr (30

- day rolling average) 716 lb/hr

(30

- day rolling average) 716 lb/hr (30

- day rolling average)

Emission Rate TPY 5,830 TPY (12

- month rolling) 6,274 TPY (12

- month rolling)

SO 2 Control

– Low Sulfur Coal Low Sulfur Coal Hourly Emission Rate (lb/mmBtu) – 0.65 lb/mmBtu (30

- day rolling average) 0.65 lb/mmBtu (30

- day rolling average) Emission Rate lb/hr

– 3,104 lb/hr (30

- day rolling average) 3,104 lb/hr (30

- day rolling average)
Annual Emission Rate (lb/mmBtu) – 0.55 lb/mmBtu (12

- month rolling average) 0.55 lb/mmBtu (12

- month rolling average)
Combined Annual Emission Rate (TPY) – 23,006 TPY

PM 10 Control 1

– Existing Electrostatic Precipitator Existing Electrostatic Precipitator Emission Rate (lb/mmBtu)

– 0.10 lb/mmBtu (3

- hour rolling average) 0.10 lb/mmBtu (3

- hour rolling average) Emission Rate lb/hr

– 478 lb/hr (3

- hour rolling average) 478 lb/hr (3

- hour rolling average)
Combined Annual Emission

Rate TPY – 4,183 TPY (12

- month rolling average)

1 Current emissions limits for ESPs are based on minimum NSPS requirements for front half catch. As part of the permitting process, PSO will be required to propose emission limits for front and back half reflective of the control technology and consistent with performance te st results.80

The application of BART to all sources required to install it provides an estimated emission reduction of 12,224 tons of SO 2 per year, and 27,043 tons of NO x per year from the baseline. Tables VI

11 and VI

- 12 show these reductions for each source.

Table VI

- 11 : BART-Level emissions reductions from the baseline, sulfur dioxide

Facility Unit Baseline Emissions lb/MMBTU Emission

Reductions
TPY PSO Northeast Generating Station 3 (coal

- fired) 0.9 12,224 PSO Northeast Generating Station 4 (coal

- fired) 0.9

Table VI

- 12 : BART-level emissions reductions from the baseline, nitrogen oxides

Facility Unit Baseline Emissions lb/MMBTU Emissions

Reduction
TPY

OG&E Muskogee Generating Station 4 (coal

- fired) 0.495 4,487 OG&E Muskogee Generating Station 5 (coal

- fired) 0.522
OG&E Seminole Generating Station 1 (gas

- fired) 0.339

3,998 OG&E Seminole Generating Station 2 (gas

- fired) 0.354
OG&E Seminole Generating Station 3 (gas

- fired) 0.219

OG&E Sooner Generating Station 1 (coal

- fired) 0.601 6,905 OG&E Sooner Generating Station 2 (coal

- fired) 0.584
PSO Northeastern Generating Station 2 (gas

- fired) 0.71 1,615
PSO Northeastern Generating Station 3 (coal

- fired) 0.536 7,697 PSO Northeastern Generating Station 4 (coal

- fired) 0.491
PSO Southwestern Generating Station 3 (gas

- fired) 0.57 450
PSO Comanche Generating Station 1 (gas

- fired) 0.696 1,891 PSO Comanche Generating Station 2 (gas

- fired) 0.613

Table VI -13 lists the anticipated improvement in visibility at mandatory federal Class I areas due
to the installation of BART NO X controls, calculated as the 3-year average of the modeled visibility improvement at the 98 th percentile.81

Table VI -13: Visibility improvement in the 98th percentile with BART NO X controls

Facility Wichita Mountains
Δ

- dv Caney Creek

Δ

- dv Upper Buffalo

Δ

- dv Hercules Glades

Δ

- dv OG&E Muskogee (coal

- fired) 0.44 0.77 0.55 0.33

OG&E Seminole (gas

- fired) 0.37 0.25 0.14 0.12

OG&E Sooner (coal

- fired) 0.83 0.38 0.31 0.28

PSO Northeastern (gas

- fired) 0.22 0.40 0.21 0.21

PSO Northeastern ( coal

- fired) 0.39 0.64 0.41 0.302

PSO Southwestern (gas

- fired) 1.93 0.14 0.11 0.08

PSO Comanche (gas

- fired) 1.28 0.07 0.06 0.05

As outlined in the previous section and described in detail in Appendix 6-4, DEQ conducted a thorough case- by -case five-factor BART analysis for each of the BART-subject units. DEQ determined that Dry- Flue Gas Desulfurization with Spray Dryer Absorber (“Dry FGD with SDA”) i s not cost-effective
for SO 2 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 PSO Northeastern Units 3 and 4. This determination is based on the capital cost of add-on controls, the cost effectiveness both in dollars per ton and dollars per deciview of add-on controls, and the long term viability of coal with respect to other environmental programs, and national commitments. In addition to information provided prior to the public hearing, DEQ considered public comments, and additional information provided by the affected facilities in response to questions raised by the commentors and DEQ staff. Revised cost estimates were provided by the affected facilities that are based on vendor quotes and go well beyond the default methodology recommended by EPA guidance. The cost estimates are credible, detailed, and specific for the individual facilities. The final estimate for Dry FGD with SDA for the six coal-fired units was on average 153 % greater than the high end costs assumed by DEQ in the Draft SIP. These costs put the projects well above costs reported for other BART determinations, and above the levels DEQ considered reasonable for cost effectiveness both in terms of dollars per ton of pollutant removed and dollars per deciview(e.g., $10,000,000/dv) of improved visibilty. Tables VI-14 and VI-15 give data on these measures of costeffectiveness.82

Table VI-14. SO 2 source control cost effectiveness (in dollars per deciview)

Source Wichita

Mountains

Δ

- dv Caney Creek

Δ

- dv Upper

Buffalo

Δ

- dv Hercules

Glades

Δ

- dv Total

Δ

- dv Cost

Effectiveness $/dv OG&E Muskogee (DFGD/SDA) 0.979 1.182 1.053 0.849 4.063 $25,252,375

OG&E Sooner (DFGD/SDA) 1.19 0.531 0.394 0.303 2.418 $38,736,394

PSO Northeastern (DFGD/SDA) 1.068 1.052 0.842 0.837 3.799 $22,835,694

Table VI-15. SO 2 source control cost effectiveness (in dollars per ton of SO 2 removed)

Source Baseline Emissions lb/MMBTU Emission Reductions

TPY Average Cost

Effectiveness $/Ton OG&E Muskogee (DFGD/SDA) 0.507

– 0.514 31,324 $7,378

– $7,493 OG&E Sooner (DFGD/SDA) 0.509

– 0.516 30,654 $6,348

– $7,147 PSO Northeastern (DFGD/SDA) 0.900 26,599 $3,294

– $6,146

DEQ has determined that the cost for DFGD is too high and the benefit too low. These costs would further extend the life expectancy of coal as the primary fuel in the Sooner facility for at least 20 years and beyond. Consequently, DEQ has determined BART for the six coal-fired steam electric units to
b e the use (or continued use) of low sulfur coal. Additional explanation of DEQ’s rationale and conclusions is included in the BART Determinations in Appendix 6-4.



### D. Contingent SO 2 

### BART Determinations.

In the event that: (i) EPA disapproves the DEQ determination described in the BART Determinations that Dry- Flue Gas Desulfurization with Spray Dryer Absorber (“Dry FGD with SDA”) is not
cost-effective for SO 2 control and (ii) all administrative and judicial appeals of EPA’s disapproval have been exhausted, then the low-sulfur coal requirement in Section VI.C and the BART Determinations for

SO 2 (and the related electrostatic precipitator requirement for PM) shall be replaced with a requirement that OG&E Sooner Units 1 and 2, OG&E Muskogee Units 4 and 5, and PSO Northeastern Units 3 and 4 shall, at the election of the owner and operator of the Unit, either: (i) install Dry FGD with SDA (and install fabric filters for PM control at OG&E Sooner and Muskogee) or meet the corresponding SO 2 and PM emission limits listed below (and further described in the Section IV(F) Contingent BART Determinations in the corresponding BART Determinations in Appendix 6-4) by January 1, 2018; or (ii) comply with the approved alternative described in Section VI.E, Greater Reasonable Progress Alternative Determination, prior to December 31, 2026.83

Table VI- 16 : Contingent SO 2 BART control technologies a nd emission limits for OG&E Sooner Generating Station Units 1 and 2

Control Unit 1 Unit 2

SO 2 Control Dry FGD with SDA Dry FGD with SDA Emission Rate (lb/mmBtu) 0.1 lb/mmBtu (30

- day rolling average) 0.1 lb/mmBtu (30

- day rolling average) Emission Rate lb/hr 512 lb/hr (30

- day rolling average) 512 lb/hr (30

- day rolling average)

Emission Rate TPY 2,241 TPY (12

- month rolling average) 2,241 TPY (12

- month rolling average)

PM 10 Control Fabric Filter Fabric Filter Emission Rate (lb/mmBtu) 0.015 lb/mmBtu (3

- hour rolling average) 0.015 lb/mmBtu (30

- hour rolling average) Emission Rate lb/hr 77 lb/hr (30

- hour rolling average) 77 lb/hr (30

- hour rolling average)

Emission Rate TPY 336 TPY (12

- month rolling average) 336 TPY (12

- month rolling average)

Table VI- 17 : Contingent SO 2 BART control technologies a nd emission limits for OG&E Muskogee Generating Station Units 4
and 5

Control Unit 4 Unit 5

SO 2 Control Dry FGD with SDA Dry FGD with SDA Emission Rate (lb/mmBtu) 0.1 lb/mmBtu (30

- day rolling average) 0.1 lb/mmBtu (30

- day rolling average) Emission Rate lb/hr 548 lb/hr (30

- day rolling average) 548 lb/hr (30

- day rolling average)

Emission Rate TPY 2,400 TPY (12

- month rolling average) 2,400 TPY (12

- month rolling average)

PM 10 Control Fabric Filter Fabric Filter Emission Rate (lb/mmBtu) 0.015 lb/mmBtu (3

- hour rolling average) 0.015 lb/mmBtu (30

- hour rolling average)

Emission Rate lb/hr 82 lb/hr (30

- hour rolling average) 82 lb/hr (30

- hour rolling average)

Emission Rate TPY 360 TPY (12

- month rolling average) 360 TPY (12

- month rolling average)84

Table VI- 18 : Contingent SO 2 BART control technologies a nd emission limits f or PSO Northeastern Power Station Units 3 and 4

Control Unit 3 Unit 4

SO 2 Control Dry FGD with SDA Dry FGD with SDA Emission Rate (lb/mmBtu) 0.1 lb/mmBtu (30

- day rolling average) 0.1 lb/mmBtu (30

- day rolling average) Emission Rate lb/hr 478 lb/hr (30

- day rolling average) 478 lb/hr (30

- day rolling average)

Emission Rate TPY 2,091 TPY (12

- month rolling average) 2,091 TPY (12

- month rolling average)



### E. Greater Reasonable Progress Alternative Determination. OG&E Sooner Units 1 and 2 and OG&E Muskogee Units 4 and 5 In lieu of installing and operating BART for SO 2 and PM control at the four (4) coal-fired units (i.e., OG&E Sooner Units 1 and 2 and OG&E Muskogee Units 4 and 5), the owner and operator of the unit may elect to implement the fuel switching alternative approved pursuant to 40 C.F.R. § 51.308(e)(2) and as part of the long-term strategy in fulfillment of 40 C.F.R. § 51.308(d)(3). See Greater Reasonable Progress Alternative Determination, § IV(G) of the OG&E Sooner and Muskogee BART Determinations in Ap pendix 6- 4 (collectively “Alternative Determinations”); see also OG&E Regional Haze Agreement, Case No. 10-024, ¶¶ 14, 26 (attached as Item 1 in Appendix 6- 5) . As detailed in the Alternative Determinations, implementation of this alternative requires OG&E to achieve by December 31, 2026, a combined annual SO 2 emission limit that is equivalent to: (i) the SO 2 emission limits provided in § VI.D for installing and operating Dry FGD with SDA on two of these coal-fired units; and (ii) being at or below
the SO 2 emissions that would result from switching the other two of the coal-fired units to natural gas. By adopting the emission limits described in the previous sentence, DEQ and OG&E expect the
cumulative SO 2 emissions from Sooner Units 1 and 2 and Muskogee Units 4 and 5 to be approximately fifty-seven percent (57%) less than would be achieved through the installation and operation of Dry FGD with SDA at all four (4) units. 1 See Alternative Determinations

.

PSO Northeastern Units 3 and 4 In lieu of installing and operating BART for SO 2 control at the two (2) coal-fired units (i.e., PSO Northeastern Units 3 and 4), the owner and operator of the unit may elect to implement the fuel switching alternative approved pursuant to 40 C.F.R. § 51.308(e)(2) and as part of the long-term strategy in fulfillment of 40 C.F.R. § 51.308(d)(3). See Greater Reasonable Progress Alternative Determination, §

1 If OG&E has elected to comply with the emission limits provided in this Section and if, prior to January 1, 2022, any of these units is required by any environmental law other than the Regional Haze Rule to install flue gas desulfurization equipment or achieve an SO 2 emissions rate lower than 0.10 lb/mmBtu, and if OG&E proceeds to take all necessary steps to comply with such legal requirement, the enforceable emission limits adopted pursuant to this Section in the operating permits for the affected coal units shall be adjusted, with the reasonable consent of DEQ and OG&E, as appropriate to reflect the installation of that equipment or the emission rates specified under such legal requirement. See OG&E Regional Haze Agreement, Case No. 10-024, ¶ 14 (attached as Item 1 in Appendix 6-5).85

IV(G) of the PSO Northeastern BART Determination in Appendix 6- 4 (“Alternative Determination”); see
also PSO Regional Haze Agreement, Case No. 10-025, ¶¶ 14, 26 (attached as Item 2 in Appendix 6- 5)

. As detailed in the Alternative Determination and the PSO Regional Haze Agreement, implementation of this alternative requires PSO to achieve by December 31, 2026, a combined annual SO 2 emission limit that is equivalent to: (i) the SO 2 emission limits provided in § VI.D for installing and operating Dry FGD with SDA on one (1) of these coal-fired units; and (ii) being at or below the SO 2 emissions that would result from switching the other one (1) coal-fired unit to natural gas. By adopting the emission limits described in the previous sentence, DEQ and PSO expect the cumulative SO 2 emissions from Northeastern Units 3 and 4 to be approximately forty-three percent (43%) less than would be achieved through the installation and operation of Dry FGD with SDA at both units. 2 See Alternative Determination

.

2 If PSO has elected to comply with the emission limits provided in this Section and if, prior to January 1, 2022, any of these units is required by any environmental law other than the Regional Haze Rule to install flue gas desulfurization equipment or achieve an SO 2 emissions rate lower than 0.10 lb/mmBtu, and if PSO proceeds to take all necessary steps to comply with such legal requirement, the enforceable emission limits adopted pursuant to this Section in the operating permit for the affected coal units shall be adjusted, with the reasonable consent of DEQ and PSO, as appropriate to reflect the installation of that equipment or the emission rates specified under such legal requirement. See PSO Regional Haze Agreement, Case No. 10-025, ¶ 14 (attached as Item 2 Appendix 6-5).86



## VII. Long-term Strategy with Emission Reduction
The long-term strategy for this implementation plan revision addresses regional haze visibility impairment at the Wichita Mountains and covers the period through 2018 in fulfillment of

40 C.F.R. §51.308(d)(3). DEQ considered several regulatory factors in 40 C.F.R. §51.308(d)(3)(v) when developing this long-term strategy, which includes enforceable emissions limitations, compliance schedules, administrative orders, and other measures necessary to achieve the reasonable progress goal for the Wichita Mountains. This strategy includes issuance and enforcement of permits limiting emissions from all known major sources in Oklahoma, state rules which specifically limit targeted emissions sources and categories, and several other ongoing air pollution control programs. In order to administer this strategy, DEQ already has rules in place; citations for those rules are throughout the chapter in appropriate sections. These citations refer to DEQ’s current permitting process, prevention of significant deterioration (PSD) provisions, BACT and BART requirements. These rules have corresponding compliance schedules and enforcement protocol which are also discussed in section A. Section B discusses emissions limitations set forth for various pollutants of sources within the state of Oklahoma; these emissions limitations are based upon the National Ambient Air Quality Standards. Section C contains details regarding other ongoing air pollution control programs. Such programs include federal mobile emissions programs, the Clean Air Interstate Rule, Maximum Achievable Control Technology, and Refinery Consent Decrees. This strategy should enable the achievement of a reasonable progress goal for 2018 toward the elimination of visibility impairment at the Wichita Mountains. Because emissions from Oklahoma only insignificantly impair visibility at all other Class I areas, this long-term strategy for achievement of reasonable progress goals in other Class I areas requires no further rules or action from
DEQ.



### A. Permits, Compliance, and Enforcement This implementation plan revision includes a state regulatory strategy to minimize emissions that cause or contribute to visibility impairment. DEQ will continue to apply rules that already afford protection to visibility at the Wichita Mountains and at other Class I areas.

1. Permits and Emissions Limitations Various state rules limit opacity and particulate matter emissions from point and area sources. Oklahoma Administrative Code (OAC) 252:100-7, Permits for Minor Facilities, applies to permitted and unpermitted minor sources. OAC 252:100-8, Permits for Part 70 Sources, contains provisions for the prevention of significant deterioration and the BART requirements directly applicable for the Wichita
Mountains and other Class I areas.

DEQ’s PSD new source review rules in OAC 252:100-8, Parts 7 and 9, require application of best available control technology (BACT) or Lowest Achievable Emission Rate (LAER) to major new and modified point sources of SO 2 , NO x , VOCs, and particulate matter. BACT minimizes emissions of all87

pollutants impacting attainment of the national ambient air quality standards, hence minimizing their contribution to regional haze.

2. Compliance Schedules DEQ issues permits to all known major point sources in Oklahoma, and each permit contains enforceable limitations on emissions of various pollutants, some of which may cause or contribute to regional haze at the Wichita Mountains. Unless permits specify a different schedule for compliance, DEQ requires permitted sources to comply with their permits immediately upon issuance. DEQ also enforces compliance schedules of relevant administrative and judicial orders, including consent decrees.

3. Enforceability
40 CFR § 51.308(d)(3)(v)(F) requires Oklahoma to ensure the enforceability of emissions limitations and control measures used to meet reasonable progress goals. As previously stated in Chapter VI(B) of the State Implementation Plan, DEQ will issue enforceable Part 70 air quality permits requiring BART-eligible sources subject to BART to install BART and achieve the associated BART emission limits. Subject sources must achieve the BART emission limits referenced above within five (5) years of EPA’s approval the SIP.

In general, DEQ has the statutory authority to carry out all “duties, requirements and responsibilities necessary and proper for the implementation of the Oklahoma Clean Air Act and fulfilling the requirements of the Federal Clean Air Act.” 27A O.S. § 2 -5-105(22). This authority includes “sole environmental jurisdiction to regulate air emissions from all facilities and sou rces 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 DEQ also has authority to issue and renew air quality permits, see 27A O.S. §§ 2-5-112(B), 2-5-105(22), and 2- 14 -202, as well as to reopen and revise existing air quality permits, see 27A O.S. § 2-

5- 112(B)(3). It is unlawful for any entity to “operate any new or existing source of air contaminants except in compliance with a permit issued by the *DEQ+.” 27A O.S. § 2 -5-112(A); see also OAC 252:100-

8-1.3.

DEQ has the authority to administratively and judicially enforce any provision of a DEQ issued air quality permits. See 27A O.S. §§ 2-5-110, 2-3-502, and 2-3-504. In addition to these enforceable permit provisions, the Ok lahoma Administrative Code requires that “each BART -eligible source subject to BART shall install and operate BART no later than five years after EPA approves the Oklahoma Regional Haze
SIP.” OAC 252:100 -8-75(e).

Lastly, DEQ has issued enforceable administrative orders to the BART-subject sources requiring the instllation and operation of BART or an approved alternative prior to specified deadlines. These
administrative orders are further discussed in the BART section of this SIP.



### B. Oklahoma Emissions Limitations and Visibility Impairment Various state rules limit emissions of pollutants which may contribute to regional haze at the
Wichita Mountains and elsewhere.88

1. Sulfureous Emissions OAC 252:100-31 limits sulfur dioxide, hydrogen sulfide, total reduced sulfur compounds, and sulfuric acid from various sources, including:

Petroleum and natural gas related operation processes, Pulp mills, Electric generating units, Limekilns, Sulfuric acid plants, Smelters, and Sulfur recovery units at petroleum refineries and all other sources.

Together with many lower emissions limitations in permits for new and modified sources, these rules limit the contribution of Oklahoma sources to sulfureous particulate at the Wichita Mountains.

2. Nitrate Precursor Emissions
O AC 252:100-33 controls emissions of nitrogen oxides from fuel-burning equipment.

3. Carbonaceous Emissions OAC 252:100-13 prohibits most open burning of refuse and other combustible materials. The regulation 40 C.F.R. §51.308 (d)(3)(v)(E) requires DEQ to consider smoke management techniques for the purposes of agricultural and forestry management in developing a reasonable progress goal.

OAC 252:100- 13 -7(4) includes provisions for the burning of forestland, cropland, and rangeland. In addition, the DEQ is developing an Oklahoma Smoke Management Plan in cooperation with the Oklahoma Department of Agriculture, Food & Forestry intended to minimize the impacts from wildland and prescribed fires.

4. Direct Emissions of Particulate Matter OAC 252:100-17 limits particulate matter and opacity of emissions from incinerators. OAC 252:100-19 controls emission of particulate matter from fuel-burning units and industrial processes. OAC 252:100-23 controls opacity of emissions from cotton gins. OAC 252:100-24 limits emissions opacity and fugitive dust from grain, feed, and seed operations. OAC 252:100-25 limits opacity of smoke, visible emissions, and particulates.

5. Fugitive Dust from Construction Activities
40 C.F.R. §51.308(d)(3)(v)(B) requires Oklahoma to consider measures to mitigate the impacts of construction activities. DEQ relies on fugitive dust control rules in OAC 252:100-29 to control and minimize the air quality effects of blowing dust from construction activities.



### C. Ongoing Air Pollution Control Programs

40 C.F.R. §51.308(d)(3)(v)(A) requires DEQ to consider emissions reductions from ongoing pollution-control programs. Because Oklahoma attains all national ambient air quality standards, DEQ has not implemented many ongoing pollution-control programs beyond those required under federal regulations. If circumstances warranted, however, DEQ may consider such programs. Federal rules89

include motor vehicle emissions control programs, CAIR, and EPA requirements for cleaner non-road diesel and gasoline-powered engines.

1. Federal Mobile Emissions Reduction Programs The federal motor vehicle emissions control program sets stringent limits on emissions of NO x , particulate matter, and VOCs from new on-road motor vehicles. The lower federal limits on sulfur content for gasoline and diesel fuel continue to reduce the sulfur input to internal combustion engines
and therefore reduce their total sulfureous emissions. The lower-sulfur fuels also contribute to lower
emissions of NO x , particulate matter, and VOCs for diesel and gasoline on-road motor vehicles and nonroad engines. The regulations 40 C.F.R. Parts 80, 81, 86, 89, 90, 92, 94, 1039, and 1048 contain several significant federal programs intended to reduce air pollution emissions.

2. Clean Air Interstate Rule Reductions On 10 March 2005, EPA issued the Clean Air Interstate Rule (CAIR), requiring reductions in SO 2 and NO x emissions from electric generating units in 28 states and the District of Columbia (70 FR 25162-25405), including Texas and most states east of Oklahoma. CAIR applies to SO 2 in all covered areas except Arkansas, Delaware, New Jersey, and New England. CAIR will reduce SO 2 emission allowances from applicable states by more than 60 percent below federal acid-rain cap levels for 2003. CAIR states may participate in an EPA-administered cap-and-trade program for electric generating units to meet regulatory requirements. Reductions of SO 2 and NO x will occur in two phases under a cap-andtrade system that EPA established. Emissions caps for SO 2 will drop in 2010 and again in 2015.
Allowable NO x emission will decrease in 2009 and again in 2015. Oklahoma is not a CAIR state; Electric generating unit allowances that cap annual NO x emissions do not apply in Oklahoma under CAIR but apply to most emissions affecting the Wichita Mountains.

On 11 July 2008, the United States Court of Appeals for the District of Columbia Circuit issued a decree vacating and remanding the CAIR as a violation of the CAA. After petitions for rehearing, however, the Court on 23 December 2008 conceded per curiam a stay of its decree in lieu of vacatur. The Court did not impose a deadline for EPA to promulgate a replacement rule that addresses the fundamental flaws of the CAIR but emphasized that it did not grant an indefinite stay; EPA pronounced intent to attempt to initiate progress toward preliminary compliance within two years. Expected emissions reductions and limitations from other states depend critically upon CAIR, and DEQ cannot submit this implementation plan revision without relying on these reductions. DEQ anticipates

— and any long-term strategy depends upon the assumption

— that any replacement rule requires electric generators or other emissions facilities in other states sufficiently to achieve at least as much visibility improvement as CAIR otherwise might achieve. In the time interval between the submittal of this plan revision and promulgation of the replacement rule, any conclusions and strategies involving CAIR will be accurate and effective because CAIR remains in effect until replaced.90

3. Consent Decrees Joint EPA and DEQ refinery consent decrees cover both SO 2 and NO x . The NO x reductions generally apply as company-wide requirements. EPA and DEQ provided specific SO 2 and NO x reductions for each refinery emissions point subject to these consent decrees. Final CENRAP modeling in support of this implementation plan revision accounted for and included these EPA-estimated reductions. These enforceable consent decrees include compliance schedules.

Table VII-1: Consent decree emissions reductions

Facility County NO x (ton/year) SO 2

(ton/year) PM

(ton/year) Consent decree

Sinclair Oil Corp Tulsa 760 2,100 0 06

- 2002

- 3744

Valero Refining Company Carter 0 156 412 06

- 2000

- 1220

Sunoco Refinery Tulsa 517 2,242 0 06

- 2002

- 3747

Conoco Inc Kay 1,400 1,124 0 06

- 2002

- 3717

4. Maximum Achievable Control Technology EPA promulgated the reciprocating internal combustion engines regulation in 40 C.F.R. §63.6580
et seq. and the turbine maximum achievable control technology (MACT) regulation in 40 C.F.R. §63.6080
et seq. Rule changes in July 2006 affected both standards. For subject natural gas-powered turbines, these MACT regulations apply only to new sources. The rule changes did not affect four-cycle rich burn engines. The revised rule applies only to new four- and two-cycle lean burn engines and new compression-ignition engines.

5. Area Sources On 26 February 1988, EPA promulgated new source performance standard for residential wood combustion in 40 C.F.R. Part 60, Subpart AAA, which contributes to minimizing emissions from residential woodburning. Other new source performance standards may apply to Oklahoma area sources; however, their identity and applicability cannot be ascertained. OAC 252:100- 13 -7(4), which addresses the burning of forestland, cropland, and rangeland will continue to remain in effect. When finalized, the Oklahoma Smoke Management Plan (currently in draft) w ill further address area sources resulting from fire.91



## VIII. Modeling of Regional Haze in 2018

40 C.F.R. § 51.308(d)(3)(v)(G) requires DEQ to consider and address the anticipated net effect on visibility resulting from changes projected in point, area, and mobile source emissions by 2018. These changes will result from population growth, land management evolution, air pollution control, and development of industry, energy and natural resources. In order to assess the effect of these changes, CENRAP developed an estimated emissions inventory for the year 2018. While a full estimated emissions inventory is included as Appendix 8-1, a summary of this inventory is included below in
Section A of this chapter. Assumptions made which reflect future control technology for point source emissions are included in section B. For 2018 modeling, 2002 meteorological observations were utilized. Section C contains numerical values for the components of 2018 modeling. Similar to modeling for 2002, modeling for 2018 continues to predict that emissions from Oklahoma do not significantly impact
Class I Areas in other states. Table VIII -9 supports this conclusion. Table VIII

- 10 attributes anticipated regional haze at the Wichita Mountains to sources in several states and other regions in 2018.



### A. Emissions Inventory CENRAP developed an estimated emissions inventory for 2018. Appendix 4-2 fully documents the methods for this development and includes technical support documents.

To estimate emissions in 2018 from the inventory for 2002, CENRAP used a combination of EPA Economic Growth Analysis System (EGAS 5), EPA mobile emissions factor model (MOBILE 6) , EPA offroad emissions factor model (Nonroad), and Integrated Planning Model (IPM) of ICF International for electric generating units. Control strategies expected to take effect before 2018 may offset growth in each emissions category. Table VIII -1 summarizes the estimated inventory for 2018. DEQ submits complete emissions estimates for 2018 as Appendix 8-1.

Table VIII-1 : Estimate of emissions from Oklahoma sources in 2018 (tons per year)

SO 2 NH 3 NO x Volatile organic compounds PM 10 - PM 2.5 PM 2.5

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 1,581,788 287,693 142,25292

CENRAP generated a distinct base-case scenario emissions dataset for 2018. Carolina Environmental Program generated “typical” temporal profiles for 2002 under contract to CENRAP, which used these profiles in all modeling for 2018.

Table VIII-2: Comparison of Oklahoma emissions inventory for 2002 and estimates for 2018 (tons per year)

Year SO 2 NH 3 NO x Volatile organic compounds PM 10 - PM 2.5 PM 2.5

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 - 30.2440% +32.8664%

- 26.541%

- 1.35964%

- 6.8717%

- 4.0646%

Table VIII -2 illustrates the substantial emissions reductions anticipated from Oklahoma sources.



### B. Point Source Emissions for Under contract to CENRAP, Pechan compiled the growth and control assumptions and factors used to estimate point-source emissions in 2018 from the inventory for 2002. A report in Appendix 4-2

, entitled Development of Growth and Control Inputs for CENRAP 2018 Emissions Draft Technical Support

Document (May 2005), documents this estimation. The control factors for point sources other than
EGUs account for MACT standards.

EPA provided control assumptions which CENRAP then applied to the inventory for 2018. Using SMOKE model, CENRAP modeling team applied the agreed growth-and-control factors to the inventory for 2002 to estimate emissions from point sources other than EGUs in 2018. CENRAP modeling did not incorporate the effect of the refinery global settlements until the Base G simulations. These efforts will

reduce SO 2 emissions; however, the modeling also anticipates NO x reductions in Oklahoma, Louisiana, and Minnesota. The technical support document in Appendix 4-2 describes modeling contractor work on the point source inventory for 2018.

1. Maximum Achievable Control Technology Pechan developed control factors for engines in 2005 based on the reciprocating internal combustion engines regulation in 40 C.F.R. § 63.6580 et seq. , applicable to new four- and two-cycle leanburn engines and to new compression-ignition engines. Subsequent regulatory changes in July 2006 affected this standard. This regulation does not apply to four-cycle rich burn engines, so relevant estimates for 2018 reflect control factors based on the original Pechan estimates. Because the revised regulation applies only to certain classes of new engines, the CENRAP modeling workgroup chose to apply control parameters only to relevant grown emissions.93

Pechan also developed control factors for turbines in 2005 based on the MACT regulation in

40 C.F.R. § 63.6080 et seq. Subsequent regulatory changes in July 2006 also affected this standard. For natural gas-powered turbines subject to this regulation, the CENRAP modeling workgroup modified assumptions to apply controls to only the grown portion of the emissions estimates, reflecting the applicability of the regulation to new sources only.

2. Electric Generating Unit Projections ICF International developed the proprietary Integrated Planning Model (IPM 2.1.9) to examine various issues facing the electric power sector. EPA used this model to simulate electric power generation and distribution scenarios based upon least-cost assumptions for future years and to generate estimates of NO x and SO 2 emissions associated with these scenarios. ICF International conducted a run under contract to the regional planning organizations which specifically addressed emissions reductions through implementation of the acid rain program (Title IV

– Phases I and II), NO x state implementation plan call (63 FR 207 (27 October 98) p. 57356), and numerous state and local regulations. This run also incorporated unit-level updates that electric power company stakeholders provided.

CENRAP used the output from this Integrated Planning Model run to estimate EGU emissions for 2018. Pechan prepared SMOKE inventory data analyzer-formatted version of the file for 2018 for CENRAP. The Pechan report in Appendix 4-2, Refinement of CENRAP’s 2002 Emissions Inventories (August 31, 2005), gives more information.

3. Presumptive BART DEQ modified the projected emissions from the Integrated Planning Model for OG&E Sooner and Muskogee electric power plants and the PSO Northeast electric power plants to reflect the application of presumptive BART controls.



### C. Other Emission Sources

1. Area Source Inventory for 2018 CENRAP member state clean-air agencies provided data which formed the basis for the area source emissions inventory for 2018. Pechan prepared area-source growth-and-control factors, documented in the report in Appendix 4-2 entitled Development of Growth and Control Inputs for CENRAP 2018 Emissions Draft Technical Support Document (May 2005). The control factors reflect new source performance standards for residential wood combustion. The modeling team applied the growth-and-control factors for area sources within SMOKE model. The technical support document in Appendix 4-2 describes work on the area-source inventory for 2018. CENRAP held windblown dust from nonagricultural land-use categories and fire emissions constant from 2002 to 2018.94

Pechan developed the original growth-and-control factors and applied them to residential wood-stove categories; CENRAP modeling workgroup agreed to modify these factors following guidance from the Office of Air Quality, Planning, and Standards (OAQPS) of EPA.

2. Non-road Mobile Emissions for 2018 CENRAP based the non-road mobile emissions inventory for 2018 on inputs from DEQ and member state clean-air agencies. Pechan prepared factors for growth and control of emissions from locomotives, aircraft, and commercial marine vessels. The control factors accounted for federal standards for commercial marine vessels and locomotives. For the remaining non-road mobile categories, Pechan ran EPA’s NONROAD2004 model for 2018. This model accounts for estimated growth in equipment populations and incorporates anticipated effects of most final federal 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. The following report referenced in Appendix 4-2 describes methods of Pechan in detail: Development of Growth and Control Inputs for CENRAP 2018 Emissions Draft Technical Support Document (May 2005).

3. On -road Mobile Source Emissions for 2018 DEQ and CENRAP developed the on-road mobile source emissions inventory for 2018 with contractor support. Pechan prepared and provided the vehicle-miles-traveled growth factors and MOBILE6 input files in SMOKE format for the on-road mobile-source emissions inventory for 2018. For each county or group of counties modeled, Pechan prepared one MOBILE6 file representing July conditions and another such file representing January conditions. The following report referenced in Appendix 4-2 describes methods of Pechan in detail: Development of Growth and Control Inputs for CENRAP 2018 Emissions Draft Technical Support Document (May 2005). Modeling contractors ran the MOBILE6 model within SMOKE framework to prepare input files for the remaining months of 2018 and to process the on-road mobile emissions for the entire year. SMOKE applies the vehicle-miles-traveled growth factors. MOBILE6 accounts for federal motor vehicle controls, including light-duty spark-ignition engine standards, low-sulfur gasoline and diesel fuel, heavy-duty compression-ignition engine standards, and other components of the programs. The technical support document in Appendix 4-2 describes the

on -road mobile emissions inventory processing conducted by CENRAP contractors.

4. Biogenic Emissions CENRAP held biogenic emissions constant from 2002 to 2018.



### D. Meteorological and Air-quality Simulations CENRAP modeling uses the same meteorological fields for 2002 and 2018. The future-year modeling for 2018 employs the same options as that for 2002 with only the inventories changed.



### E. Discussion of Components The tables in this chapter show model-projected contributions of various areas and pollutants to visibility impairment at the Wichita Mountains on the worst quintile days in 2018. The projections shown in the tables use modeling results scaled to measured pollutant concentrations according to EPA95

guidelines. These tables present modeled estimates of the relative response of concentrations of components of particulate matter to anticipated changes in emissions.

1. Sulfureous Particulate CENRAP modeling suggests that CAIR and other promulgated efforts to reduce sulfur emissions from other states will reduce visibility impairment from sulfureous particulate at the Wichita Mountains. These programs should reduce contributions from distant eastern states drastically.96

Table VIII-3: Sulfureous sources of light extinction at the Wichita Mountains (worst quintile days, estimate for 2018)

All values in
Mm - 1 Model Projection (2018) Projected reduction (2002 to 2018) (negative reduction indicates an increase)SumPointAreaNon-road mobileOn-road mobileBiogenicSumPointAreaNon-road mobileOn-road mobileBiogenic

Texas 9.68 7.83 1.73 .08 .04 .00 4.30 3.73 .12 .20 .25 .00
East 2.67 2.27 .37 .01 .01 .00 4.82 4.63 .03 .08 .09 .00 boundary conditions 3.77 .16

Louisiana 2.77 2.16 .56 .05 .00 .00

- .17

- .23

- .01 .04 .03 .00 Indiana .86 .78 .07 .00 .00 .00 1.55 1.51 .02 .01 .01 .00
Mexico 1.72 1.61 .09 .01 .00 .00 .07 .06 .00 .00 .01 .00 Oklahoma 1.58 1.23 .33 .01 .01 .00 .19 .05 .00 .06 .08 .00
Canada 1.39 1.28 .08 .02 .00 .00 .23 .21 .00 .00 .01 .00 Ohio .45 .41 .03 .01 .00 .00 1.17 1.13 .00 .02 .02 .00 Illinois .78 .74 .03 .00 .00 .00 .77 .73 .00 .03 .01 .00 Kentucky .63 .54 .07 .01 .00 .00 .73 .69 .01 .01 .01 .00 Alabama .47 .37 .09 .01 .00 .00 .83 .79 .02 .01 .01 .00
Tennessee .65 .56 .07 .01 .00 .00 .64 .60 .01 .02 .02 .00
Missouri 1.15 1.00 .14 .00 .00 .00

- .02

- .05 .00 .02 .02 .00 Arkansas 1.04 .82 .21 .01 .00 .01

- .01

- .08 .00 .04 .03 .00
Kansas .63 .46 .17 .00 .00 .00 .29 .25

- .01 .04 .02 .00
Iowa .49 .48 .01 .00 .00 .00

- .03

- .06 .00 .02 .01 .00 Michigan .38 .35 .02 .01 .00 .00 .05 .02 .00 .01 .01 .00
West .33 .28 .03 .00 .00 .02 .04 .00 .00 .02 .01 .00 Mississippi .19 .16 .01 .02 .00 .00 .13 .10 .00 .02 .01 .00 Nebraska .25 .21 .04 .00 .00 .00 .06 .04

- .01 .03 .01 .00
Minnesota .31 .27 .04 .00 .00 .00

- .01

- .03 .00 .01 .00 .00
Wisconsin .21 .20 .01 .00 .00 .00 .05 .04 .00 .01 .00 .00 North Dakota .23 .22 .01 .00 .00 .00 .00

- .01 .00 .01 .00 .00 Wyoming .21 .18 .02 .00 .00 .00

- .02

- .03

- .01 .01 .00 .00 all other .50 .23 .27 .00 .00 .00

- .01 .05

- .09 .02 .00 .00
total 33.33 24.64 4.51 .28 .10 .04 15.79 14.12 .08 .72 .69 .02

A large majority of sulfureous particulate will still come from point sources. Federal motor vehicle and gasoline regulations should drastically reduce the contribution to sulfureous particulate
from on- and non-road mobile sources.97

2. Nitrate Particulate Planned programs should reduce visibility impairment attributable to American anthropogenic nitrate emissions by two-sevenths. These programs also should reduce emissions from Oklahoma sources. These programs aim to reduce nitrate particulate primarily from on- and non-road mobile

sources.

Table VIII-4: Projected nitrate sources of light extinction at the Wichita Mountains (worst quintile days, estimate for 2018)

All values in Mm - 1 Model Projection (2018) Projected reduction (2002 to 2018) (negative reduction indicates an increase)SumPointAreaNon-road mobileOn-road mobileBiogenicSumPointAreaNon-road mobileOn-road mobileBiogenic

Texas 6.08 2.17 1.02 .85 .71 1.33 1.81

- .19

- .06 .12 2.03

- .09 Oklahoma 4.99 1.52 1.82 .34 .49 .83 1.44 .28

- .27 .27 1.20

- .05
Kansas 1.36 .56 .27 .20 .16 .16 .58 .10

- .03 .21 .30

- .01
Missouri .71 .30 .09 .10 .14 .07 .35 .08

- .01 .05 .22 .00 boundary conditions 1.03

- .16

Nebraska .42 .17 .06 .10 .05 .04 .21

- .01

- .01 .09 .13 .00 Canada .46 .17 .14 .12 .00 .02 .12

- .03 .00 .00 .15 .00
Iowa .39 .20 .02 .06 .07 .04 .18 .02 .00 .04 .13 .00 Arkansas .30 .12 .06 .04 .06 .02 .19 .02 .00 .04 .13 .00 Illinois .22 .10 .02 .06 .03 .01 .20 .10 .00 .03 .08 .00
Mexico .30 .21 .02 .01 .00 .07 .07 .03 .00 .00 .03 .00
Minnesota .22 .11 .03 .03 .03 .01 .09 .01

- .01 .02 .07 .00
Louisiana .30 .14 .08 .04 .03 .01 .00

- .02

- .03

- .01 .05 .00
West .22 .08 .04 .04 .04 .02 .07

- .01

- .02 .02 .07 .00
New Mexico .23 .06 .09 .03 .02 .03 .06 .02

- .03 .02 .04 .00

Colorado .16 .07 .03 .02 .03 .01 .09 .01

- .01 .01 .07 .00 Wyoming .14 .07 .04 .03 .00 .00 .02 .02

- .02 .01 .01 .00
East .07 .03 .01 .01 .01 .01 .08 .03 .00 .01 .04 .00 North Dakota .12 .07 .02 .02 .01 .01 .02 .00 .00 .01 .02 .00

South Dakota .08 .01 .01 .02 .01 .03 .04

- .01 .00 .01 .04 .00

all other .30 .09 .08 .06 .02 .00 .19 .11

- .04 .01 .10 .00

total 18.10 6.25 3.96 2.18 1.92 2.76 5.62 .54

- .55 .98 4.95

- .15

Beyond the nitrate emissions reductions modeled, additional controls at point and area sources may result from ozone-reduction programs not yet conceived.98

3. Organic Carbonaceous Particulate Modeling for 2018 suggests a slight reduction in organic carbonaceous particulate at the Wichita Mountains; however, much of the particulate probably results from such natural sources as trees and
fires.

Table VIII-5: Organic carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days, estimate for
2018 )

All values in Mm - 1 Model projection (2018) Projected reduction (2002 to 2018)SumPointAreaNon-road mobileOn-road mobileBiogenicSumPointAreaNon-road mobileOn-road mobileBiogenic

Texas 2.57 .49 1.74 .26 .05 .02 .48

- .15 .63

- .07 .07 .01 boundary conditions 3.55 .06 secondary biogenic 2.84 .06 secondary anthropogenic 2.22 .35

Oklahoma 2.10 .18 1.64 .14 .02 .11 .17

- .05 .11 .05 .06 .01
Missouri .17 .01 .12 .04 .00 .00 .02 .00 .01 .01 .00 .00
Kansas .56 .06 .48 .01 .00 .01 .01

- .01 .01 .01 .00 .00
Louisiana .45 .21 .17 .03 .00 .03

- .03

- .04 .00 .00 .01 .00
East .31 .05 .23 .02 .00 .01 .03

- .01 .02 .01 .01 .00 Arkansas .27 .04 .12 .02 .00 .09 .04

- .01 .00 .01 .01 .04
West .18 .00 .04 .01 .01 .13 .01 .00 .00 .00 .00 .01
Minnesota .07 .01 .04 .00 .00 .02 .04 .00 .00 .00 .00 .03 Mississippi .10 .02 .06 .01 .00 .01 .00

- .01 .00 .00 .00 .00 Canada .09 .01 .04 .01 .00 .03 .00 .00 .00 .00 .00 .00 Nebraska .07 .02 .04 .01 .00 .00 .01

- .01 .00 .01 .00 .00 all other .43 .08 .26 .01 .00 .00 .03 .00 .00 .00 .00 .01
total 15.98 1.19 4.99 .61 .11 .47 1.30

- .33 .79 .04 .19 .13

A detailed field study may identify the particular chemical mixture of gaseous and particulate organic compounds in the atmosphere near the Wichita Mountains, locate their sources and sinks, and improve understanding of their chemistry. Such study would provide data necessary before Texas Commission on Environmental Quality or DEQ could devise an effective strategy to reduce anthropogenic emissions.

4. Elemental Carbonaceous Particulate Modeling indicates a sizable reduction in elemental carbonaceous particulate reaching the Wichita Mountains by 2018.99

Table VIII-6: Elemental carbonaceous sources of light extinction at the Wichita Mountains (worst quintile days, estimate for
2018 )

All values in Mm - 1 Model projection (2018) Projected reduction (2002 to 2018)SumPointAreaNon-road mobileOn-road mobileBiogenicSumPointAreaNon-road mobileOn-road mobileBiogenic

Texas .68 .03 .44 .15 .05 .00 .74

- .01 .39 .21 .15 .00 Oklahoma .72 .01 .52 .14 .02 .03 .36 .03 .04 .18 .11 .00 boundary conditions .69 .00
Kansas .26 .00 .22 .02 .00 .00 .04 .00 .00 .03 .01 .00
Louisiana .12 .01 .08 .02 .00 .01 .02 .00 .00 .01 .01 .00
East .08 .01 .04 .03 .00 .00 .05 .00 .01 .02 .02 .00 Arkansas .08 .01 .03 .02 .00 .02 .04 .00 .00 .02 .02 .01
West .05 .00 .01 .01 .01 .03 .02 .00 .00 .01 .01 .00
Missouri .04 .00 .03 .01 .00 .00 .02 .00 .00 .01 .01 .00 all other .27 .00 .07 .17 .00 .02 .16 .00 .00 .12 .02 .01
total 3.00 .09 1.45 .55 .11 .11 1.47 .01 .44 .59 .40 .03

5. Fine Soil Particulate Because fine soil particulate originates mostly from natural sources and blowing dust, DEQ cannot predict its prevalence in 2018; however, Table VIII -7 presents the modeling results.

Table VIII-7: Fine soil sources of light extinction at the Wichita Mountains (worst quintile days, estimate for 2018 )

All values in Mm - 1 Model projection (2018) Projected reduction (2002 to 2018)SumPointAreaNon-road mobileOn-road mobileBiogenicSumPointAreaNon-road mobileOn-road mobileBiogenic

Texas .30 .03 .21 .00 .00 .05 .00

- .01 .01 .00 .00 .00 Oklahoma .24 .01 .23 .00 .00 .01 .01 .00 .02 .00 .00 .00
all other .22 .01 .17 .00 .00 .00

- .01

- .02 .00 .00 .00 .00
total .79 .08 .62 .00 .00 .07 .00

- .03 .03 .00 .00 .00

6. Coarse Particulate Most coarse particulate matter observed at the Wichita Mountains originates from natural sources and blowing sand, so DEQ anticipates little change in light extinction from coarse matter in the
future.100

Table VIII-8: Coarse particulate sources of light extinction at the Wichita Mountains (worst quintile days, estimate for 2018)

All values in
Mm - 1 Model projection (2018) Projected reduction (2002 to 2018)SumPointAreaNon-road mobileOn-road mobileBiogenicSumPointAreaNon-road mobileOn-road mobileBiogenic

Oklahoma .72 .02 2.45 .00 .00 .17 .27 .00 .27 .00 .00 .00
Texas .68 .03 1.12 .00 .00 .34 .02 .00 .03 .00 .00 .00 all other 1.60 .01 .33 .00 .00 .03 .00 .00 .00 .00 .00 .00
total 4.30 .05 3.70 .00 .01 .53 .29

- .01 .30 .00 .00 .00

7. Chlorine Particulate and Rayleigh Scatter CENRAP modeling assumed no change in chlorine particulate concentration between 2002 and 2018. Because DEQ does not anticipate significant changes in atmospheric structure or density, this implementation plan revision assumes constant Rayleigh scattering, reducing visibility with an additive
extinction coefficient around 11 Mm -1

.



### F. Long-term Strategy for Other Class I Areas The analysis of model output presented in Table VIII -9 demonstrates that Oklahoma emissions will impair visibility only insignificantly at all Class I areas in other states. DEQ consequently need not
und ertake any emission reduction action to protect other Class I areas.101

Table VIII-9: Projected influence of emissions from Oklahoma on Class I areas (worst quintile days, 2018)Class I areaStateContributionto lightextinction (Mm-1)Total light extinction(Mm-1)Oklahoma contribution(%)Deciview contributionReduction inOklahoma contributionfrom 2002 (Mm-1)

Wichita Mountains Oklahoma 12.28 86.56 14.19% 1.53 2.43
Hercules

- Glades Missouri 3.74 103.49 3.61% .37 1.10 Salt Creek New Mexico 1.46 57.67 2.53% .26 .25 Guadalupe Mountains Texas 1.11 55.43 2.00% .20 .07 Seney Michigan 1.74 95.27 1.83% .18 .36 Caney Creek Arkansas 2.23 96.84 2.30% .23 .38 White Mountain New Mexico .69 40.80 1.70% .17 .06 Upper Buffalo Arkansas 1.97 97.16 2.03% .21 .45 Isle Royale Michigan 1.08 73.71 1.46% .15 .09 Badlands South Dakota .60 52.20 1.14% .11 .12 Wind Cave South Dakota .52 48.16 1.08% .11 .13 Big Bend Texas .60 55.23 1.08% .11 .08 Wheeler Peak New Mexico .29 31.80 .92% .09 .04
Breton Louisiana .99 105.06 .94% .09 .35 Mingo Missouri 1.22 110.24 1.11% .11 .40 Bosque del Apache New Mexico .33 40.13 .82% .08 .03 Great Sand Dunes Colorado .27 37.77 .71% .07 .05 San Pedro Parks New Mexico .22 31.21 .70% .07 .02 Rocky Mountain Colorado .25 40.41 .63% .06 .04 Boundary Waters Canoe Area Minnesota .38 64.32 .59% .06 .10 Voyageurs Minnesota .26 63.10 .41% .04 .08 Bandelier New Mexico .17 40.25 .41% .04 .01 Everglades Florida .32 87.23 .36% .04 .06 Galiuro Arizona .12 39.75 .30% .03 .01 Chassahowitzka Florida .39 91.00 .43% .04 .01 Wolf Island Georgia .45 112.84 .40% .04

- .00 Theodore Roosevelt North Dakota .15 57.80 .25% .03 .03 UL Bend Montana .09 45.16 .21% .02 .02 Mammoth Cave Kentucky .41 134.80 .30% .03 .11 Lye Brook Vermont .24 86.33 .28% .03 .05 Presidential Range

- Dry River New Hampshire .17 76.10 .23% .02 .05 Saint Marks Florida .21 94.24 .23% .02 .06 Roosevelt Campobello Maine .15 67.68 .22% .02 .02 Rawah Colorado .05 32.71 .14% .01 .01 Cape Romain South Carolina .20 101.82 .19% .02 .03 Cohutta Georgia .22 112.69 .20% .02 .10102Class I areaStateContributionto lightextinction (Mm-1)Total light extinction(Mm-1)Oklahoma contribution(%)Deciview contributionReduction inOklahoma contributionfrom 2002 (Mm-1)

Acadia Maine .12 70.17 .17% .02 .03 Swanquarter North Carolina .13 77.53 .16% .02 .04 Brigantine New Jersey .20 122.69 .16% .02 .02
Pine Mountain Arizona .04 39.96 .10% .01 .01 Sipsey Alabama .16 114.75 .14% .01 .06 Saguaro Arizona .05 42.92 .11% .01 .01 Lostwood North Dakota .07 69.15 .10% .01 .01 Shining Rock North Carolina .15 96.10 .16% .02 .03 Medicine Lake Montana .05 57.81 .08% .01 .01
James River Face Virginia .12 100.27 .12% .01 .05 West Elk Colorado .02 29.75 .08% .01 .00 Joyce Kilmer

- Slickrock North Carolina .13 104.67 .12% .01 .04 Linville Gorge North Carolina .09 90.25 .10% .01 .04 Shenandoah Virginia .09 95.58 .09% .01 .04 Otter Creek West Virginia .09 99.34 .09% .01 .02 Petrified Forest Arizona .02 41.56 .05% .01 .00 Weminuche Colorado .02 30.59 .05% .01 .00 Gila New Mexico .02 39.79 .05% .00 .00 Washakie Wyoming .02 33.76 .05% .00 .00



### G. Long-term Strategy Interpretation for the Wichita Mountains

Table VIII

- 10 illustrates modeled results of the combined long-term regional haze strategies of several states. At the Wichita Mountains, visibility impairment should decrease significantly.103

Table VIII- 10 : Projected sources of light extinction at the Wichita Mountains (worst quintile days)

Model projection, 2018 Projected reduction, 2002 to 2018

All values in
Mm - 1SumSulfureousNitrateOrganic carbonaceousElemental carbonaceousSoilsCoarseSumSulfureousNitrateOrganic carbonaceousElemental carbonaceousSoilsCoarse

Texas 20.79 9.68 6.08 2.57 .68 .30 1.49 7.36 4.30 1.81 .48 .74 .00 .02 Oklahoma 12.28 1.58 4.99 2.10 .72 .24 2.65 2.43 .19 1.44 .17 .36 .01 .27 Rayleigh scattering 11.00 .00

boundary conditions 9.11 3.77 1.03 3.55 .69 .02 .05 .06 .16

- .16 .06 .00 .00 .00

East 3.15 2.67 .07 .31 .08 .01 .00 4.97 4.82 .08 .03 .05 .00 .00
Kansas 2.90 .63 1.36 .56 .26 .05 .05 .92 .29 .58 .01 .04 .00 .00
Louisiana 3.65 2.77 .30 .45 .12 .01 .00

- .18

- .17 .00

- .03 .02 .00 .00 secondary biogenic 2.84 2.84 .06 .06

secondary anthropogenic 2.22 2.22 .35 .35

Indiana .92 .86 .02 .02 .01 .00 .00 1.61 1.55 .05 .00 .01 .00 .00
Missouri 2.10 1.15 .71 .17 .04 .02 .01 .37

- .02 .35 .02 .02 .00 .00 Canada 2.00 1.39 .46 .09 .05 .01 .00 .36 .23 .12 .00 .00 .00 .00
Mexico 2.10 1.72 .30 .04 .02 .01 .02 .15 .07 .07 .00 .00 .00 .00 Illinois 1.06 .78 .22 .04 .02 .00 .00 .99 .77 .20 .00 .02 .00 .00 Arkansas 1.71 1.04 .30 .27 .08 .01 .01 .26

- .01 .19 .04 .04 .00 .00 Ohio .49 .45 .01 .02 .01 .00 .00 1.19 1.17 .01 .00 .01 .00 .00
Tennessee .79 .65 .05 .07 .02 .00 .00 .72 .64 .07 .00 .01 .00 .00 Kentucky .70 .63 .03 .03 .01 .00 .00 .76 .73 .03 .00 .01 .00 .00 Alabama .55 .47 .01 .05 .01 .00 .00 .86 .83 .01 .01 .01 .00 .00
Iowa .99 .49 .39 .05 .02 .03 .01 .17

- .03 .18 .01 .01 .00 .00 Nebraska .81 .25 .42 .07 .02 .02 .02 .30 .06 .21 .01 .02 .00 .00
West .80 .33 .22 .18 .05 .01 .01 .13 .04 .07 .01 .02 .00 .00
Minnesota .64 .31 .22 .07 .02 .01 .00 .14

- .01 .09 .04 .02 .00 .00 Mississippi .36 .19 .04 .10 .02 .00 .00 .16 .13 .02 .00 .01 .00 .00 Michigan .40 .38 .01 .01 .00 .00 .00 .06 .05 .01 .00 .00 .00 .00
New Mexico .39 .13 .23 .01 .00 .00 .01 .07 .00 .06 .01 .00 .00 .00 Colorado .31 .11 .16 .02 .01 .00 .00 .14 .05 .09 .00 .01 .00 .00 North Dakota .38 .23 .12 .01 .01 .00 .00 .03 .00 .02 .00 .01 .00 .00 Wyoming .80 .33 .22 .18 .05 .01 .01

- .44

- .15

- .06

- .18

- .04

- .01 .00
Wisconsin .80 .33 .22 .18 .05 .01 .01

- .47

- .08

- .17

- .16

- .04

- .01

- .01 South Dakota .16 .04 .08 .02 .01 .01 .01 .06 .01 .04 .00 .01 .00 .00 Gulf of Mexico .33 .22 .10 .00 .00 .00 .00

- .11

- .07

- .04 .00 .00 .00 .00 initial conditions .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00
total 87.54 33.58 18.38 16.33 3.09 .80 4.36 23.49 15.54 5.34 .96 1.38

- .01 .28104



## IX. Reasonable Progress Goal
The federal regional haze regulation aims to achieve the national goal of natural visibility at the Wichita Mountains and other Class I areas by 2064. This regulation requires DEQ to show reasonable progress toward this goal by 2018 and to set a specific goal for visibility improvement at the Wichita Mountains. Unfortunately, this goal falls short of the uniform rate of progress toward achieving natural visibility in 2064. Section A of this chapter depicts the reasonable progress goal in table and graph form, comparing it to the uniform rate of progress. The reasonable progress goal for 2018 is calculated at 9.23 deciviews for the best quintile days. Linear extrapolation of the deciview glide path for the reasonable progress goal suggests natural conditions will be met circa 2102. Section B discusses CAIR and associated uncertainties. Reductions required to meet the uniform rate of progress are shown in section C; here it is important to note that even the elimination of all anthropogenic sources withi n Oklahoma is not sufficient to comply with uniform rate of progress. Section D details control simulations; sources eligible for additional emission controls are identified and visibility projections are given for 2018 taking into consideration these expected emissions reductions. These simulations further confirm that the uniform rate of progress cannot be met for the Wichita Mountains. Additional factors taken under consideration in reaching this conclusion are discussed in section E; such factors include source retirement and replacement schedules, basis for emissions reductions obligations, and other statutory factors. The reasonableness of controls and weight of evidence are discussed in sections F and G, respectively.



### A. Reasonable Progress Goal for the Wichita MountainsDEQ considers the visibility improvement by 2018 at the Wichita Mountains shown in Tables IX1 and Figure IX-1 reasonable and intends to achieve this goal. This reasonable progress goal derives from the CENRAP modeling with estimated emissions for 2018. It reflects visibility improvement resulting from emissions reduction programs associated with the federal CAA and Oklahoma CAA

, including long-term strategies of Oklahoma, Texas, and other states and presumptive emissions reductions from the Oklahoma BART rule. The emissions reductions included in this implementation plan revision demonstrably will reduce emissions from Oklahoma sources sufficiently to attain the reasonable progress goal at the Wichita Mountains in conformity with 40 C.F.R. § 51.308(d)(3)(ii).

1. Worst Quintile Days
Table IX- 1 compares the reasonable progress goal for the worst quintile days at the Wichita Mountains to the corresponding uniform rate of progress.105

Table IX -1: Reasonable progress goal for visibility at the Wichita Mountains (worst quintile days)

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)

- .17 deciviews/year Change needed to reach natural conditions

- 86.91 Mm - 1

- 16.28 deciviews Change by 2064 extrapolated from reasonable

progress goal - 68.38 Mm - 1

- 10.01 deciviews

Visibility in 2064 from extrapolated reasonable

progress goal +39.76 Mm - 1 +13.80 deciviews
Extrapolated from reasonable progress goal for 2102

(natural conditions) +21.23 Mm - 1 + 7.53 deciviews

Figure IX- 1 graphically compares the reasonable progress goal to the uniform rate of progress glide path for the worst quintile days at the Wichita Mountains. It uses the default (western) natural conditions estimate. Natural conditions did not factor into setting the reasonable progress goal; however, extrapolation thereof suggests attaining natural conditions circa 2102. If natural conditions account for a greater proportion of presently observed visibility impairment, then extrapolation of this reasonable progress goal suggests achieving natural visibility conditions circa 2064. See Chapter III.106

Figure IX -1: Glide path and reasonable progress goal at the Wichita Mountains (worst quintile days)

The reasonable progress goal excludes the substantial anticipated effects of the installation of BART at three natural-gas-fired electric power generation facilities in Caddo, Comanche, and Seminole Counties. CENRAP modeling of regional haze for 2018 did not include or quantify these emissions
reductions.

2. Best Quintile Days
Table IX- 2 provides the reasonable progress goal for the best quintile of days at the Wichita

Mountains.

Table IX -2: Reasonable progress at the Wichita Mountains (best quintile days)

Baseline visibility 26.58 Mm - 1 9.78 deciviews Projected 2018 visibility (reasonable progress goal) 25.17 Mm - 1 9.23 deciviews Improvement by 2018 (reasonable progress goal) 1.41 Mm - 1 .54 deciviews

CENRAP modeling for 2018 suggests compliance with the regulatory requirement in

40 C.F.R. § 51.308(d)(1) ensuring no degradation in visibility on the best quintile days at the Wichita

Mountains.107

3. Uncertainty Related to Clean Air Interstate Rule CAIR presumably provides the majority of the emissions reductions underlying the predicted visibility improvements. CAIR allows trading of SO 2 and NO x emissions allowances among electric generating units, leaving uncertainty regarding improvement in visibility at the Wichita Mountains. The program is expected to reduce visibility-impairing emissions across Texas and most eastern states. Because of its relatively low emissions compared to those from most industrial eastern states, EPA exempted Oklahoma from participation in the reductions. Texas contributes more to visibility impairment at the Wichita Mountains than Oklahoma or any other state does. Uncertainty in the geographical location of emissions reductions within the CAIR region contributes to uncertainty i n estimated visibility in 2018. The temporary legal status of and likely replacement regulation for CAIR further compound this uncertainty; however, any substantial improvement in visibility at the Wichita Mountains depends critically upon large emissions reductions in the affected region.



### B. Reductions Required to Meet the Uniform Rate of Progress Ongoing programs alone cannot suffice to meet the uniform rate of progress at the Wichita Mountains. The model-extracted data in Table IX-3 suggest that even complete elimination of all anthropogenic emissions in Oklahoma likely would fail to meet this uniform rate of progress.

Table IX -3: Emissions reductions required to meet uniform rate of progress at the Wichita Mountains (worst quintile days)

Improvement projected by 2018 using reasonable progress goal 22.52 Mm - 1 2.33 deciviews Improvement by 2018 at uniform rate of progress 34.18 Mm - 1 3.80 deciviews Unreasonable improvement needed to m eet uniform rate 11.66 Mm - 1 1.46 deciviews Contribution from non

- biogenic source categories in Oklahoma

(modeled for 2018) 11.13 Mm - 1

Contribution from biogenic sources in Oklahoma (modeled for 2018) 1.15 Mm - 1

Unreasonable reductions imply severe control of Oklahoma sources to compensate for the influence of pollution from other states. The vast majority of visibility impairment at the Wichita Mountains comes from sources beyond the borders of the State of Oklahoma. The federal regional haze rule in 40 C.F.R. § 51.308(d)(3)(ii) does not require DEQ to compensate for the lack of control of emissions in Texas, other states, and foreign countries. Table IX-3 also reinforces that progress at the Wichita Mountains depends on reducing emissions from Texas and elsewhere. Given the significant contribution of emissions from Texas and other areas outside DEQ jurisdiction, the uncertainty in the effect of CAIR, and the economic and energy cost of additional point-source controls, DEQ finds additional controls for regional haze inappropriate and unreasonable.



### C. Control Simulations CENRAP conducted a control-sensitivity evaluation of the effect of reducing point-source
emissions of NO x and SO 2 only with existing emissions-control technology. CENRAP limited this evaluation to control technologies estimated to cost less than $5,000 per ton of emissions reduced. CENRAP thereby intended to generate information on the effect of possible strategies in support of the108

consultation process. CENRAP did not design this modeling to prescribe control strategies but only to inform possible discussions that certainly would require much greater refinement of control options. CENRAP grouped the strategies together under a common set of criteria; DEQ and other members did

no t identify specific strategies.

1. Identification of Point Sources for Control Evaluation CENRAP contracted with Alpine Geophysics to provide an evaluation of possible additional controls for the point-source inventory from member states for 2018. These controls would add to the long-term strategy of emissions reductions assumed in the scenario already modeled for 2018 and used to set the reasonable progress goal for the Wichita Mountains. The study overlaid a detailed EPA control-measure database on CENRAP emissions inventories to compute source- and pollutant-specific emissions reductions and associated costs at various geographic levels.

This CENRAP study used the latest revised version of AirControlNet, a database tool which EPA released in 2006 to enable cost-benefit analyses of potential emissions-control measures and strategies. AirControlNet estimates the cost per ton of emissions of NO x and SO 2 reduced, based on new construction. The contractor enhanced Base F inventory files for 2018 with additional information on base-level controls and then linked these enhanced files with potential strategies from AirControlNet. CENRAP used AirControlNet to estimate cost per ton of emissions of the relevant pollutants reduced with potential add-on devices to control appropriate emissions generating units.

Alpine extrapolated cost estimates for NO x and SO 2 emissions reductions in 2005 dollars for point sources. CENRAP evaluated various control levels in terms of cost per mass of emissions reductions and chose to base its sensitivity analysis on a maximum estimated cost of $5,000 per ton of

emissions of NO x or SO 2 reduced. CENRAP made this threshold with the understanding that this process
underestimates the true cost of retrofit controls and does not consider recent market fluctuations in cost of control equipment and construction.

This control strategy analysis also excluded every source predicted to emit less than 100 tons of

SO 2 and less than 100 tons of NO x in the year 2018. The imperceptibly minute visibility benefit does not justify the regulatory and logistical overhead associated with more aggressively controlling these small

sources.

Iowa Department of Natural Resources and Kansas Department of Health and Environment staff
added area- of -influence data and distance calculations to each Class I area in every CENRAP member state. CENRAP refined the selection further, considering controls only to those sources with emissions

of NO x or SO 2 greater than or equal to five tons per year per kilometer of distance to the Wichita Mountains or the nearest other Class I area. This distance-weighting criterion limited the sensitivity evaluation to sources with the greatest likely influence on visibility. CENRAP did not consider additional
controls outside the borders of its member states in this evaluation.109

2. Control Sensitivity Results
Table IX- 4 presents visibility projections for 2018 based on the CMAQ control-sensitivity simulations for Base G. This control scenario would reduce mainly SO 2 and NO x emissions from point sources in the CENRAP member states, consequently limiting visibility improvements mainly to
reductions in sulfureous and nitrate aerosols.

Table IX -4: Visibility at the Wichita Mountains under control scenario (worst quintile days)

Observed visibility in 2002

- 2004 108.15 Mm - 1 23.81 deciviews Visibility projected by 2018 using reasonable progress goal 85.63 Mm - 1 21.47 deciviews Visibility by 2018 at uniform rate of progress 73.97 Mm - 1 20.01 deciviews Visibility projected by 2018 under control scenario 81.40 Mm - 1 20.97 deciviews

The modeling projects that this control scenario would reduce visibility impairment only slightly on the worst quintile days compared to the reasonable progress goal. Even if all CENRAP member states compelled sources to install and use controls as effectively as this scenario envisions, then the Wichita Mountains still would fall significantly short of meeting the uniform rate of progress glide path for the worst quintile days in 2018. This preliminary scenario moreover includes at least some already implemented, prohibitively costly, technically unfeasible, or otherwise unreasonable controls. Most
sources under consideration for control in this scenario lie in Texas or other states outside the territorial jurisdiction of DEQ.

3. Further Refinement of Sources to Control DEQ used this CENRAP control model to start its own analysis of additional controls. DEQ reviewed cost information from CENRAP and made changes based on knowledge of the particular facilities and experience with implementing ozone limitation strategies. This additional analysis focused on moderate-cost controls for sources likely to contribute to visibility impairment at the Wichita Mountains. DEQ already considered most of these sources under the BART review process, refinery consent decrees, or other controls included in the reasonable progress goal. DEQ considers further emissions reductions from such sources currently unreasonable.

Eliminating exorbitantly costly controls and sources already subject to reasonable controls left very few existing sources in Oklahoma among the set still under consideration. The cost of retrofit

co ntrols at these few sources likely would prove unreasonably high.110

Table IX-5: Sources considered for (additional) controls under reasonable progress goals

Company County Industrial classification Possible Control Equipment Disposition for this

implementation plan revision Public Service Company of Oklahoma Caddo electric services utility 2 oil or gas

fired boilers selective catalytic reduction subject to BART;

additional emissions reductions planned

Western Farmers Electric Cooperative Choctaw electric services utility coal

- fired boiler selective catalytic

reduction and flue gas desulfurization wet scrubber Not subject to BART;

no additional emissions reductions planned

Public Service Company of Oklahoma Comanche electric services utility
2 natural gas

- fired combustion turbines selective catalytic

reduction plus water injection shall install BART

Great Lakes Carbon Corporation Garfield products of petroleum and coal not elsewhere classified industry 2 process units flue gas desulfurization
twice Not subject to BART;

no additional emissions reductions planned

International Paper (formerly Weyerhaeuser ) Valliant Paper Mill McCurtain paperboard mills iwood bark, residual oil and natural gas

- fired boilers selective non

- catalytic

reduction urea based, selective catalytic reduction, low NO x burners, and wet flue gas desulfurization kraft pulp mill at this

facility granted BART waiver and agreed to reduce emissions

Grand River Dam Authority Mayes electric services utility coal

- fired boiler s selective catalytic

reduction, low NO x burner with over

- fire air, and flue gas desulfurization wet scrubber Not subject to BART

Associated Electric Cooperative Incorporated Mayes electric services utility natural gas

- fired combustion turbines selective catalytic

reduction plus water injection Not subject to BART;

no additional emissions reductions planned

Oklahoma Gas & Electric Muskogee electric services utility 3 coal

- fired boilers low NO x coal

- and

- air

nozzles with close

coupled and separated

over

- fire air and flue gas desulfurization wet scrubber shall install BART111

Company County Industrial classification Possible Control Equipment Disposition for this

implementation plan revision

Oklahoma Gas & Electric Noble electric services utility 2 coal

- fired boilers low NO x coal

- and

- air

nozzles with close

coupled and separated

over

- fire air shall install BART

Holcim United
States Incorporated Pontotoc cement manufacturing coal

- fired kilns selective catalytic

reduction and flue gas desulfurization Not subject to BART;
no emissions reductions planned

LaFarge Building Materials Rogers cement manufacturing coal

- fired kilns both selective non

catalytic reduction urea based and selective catalytic reduction Portland cement

plant at this location granted waiver from
BART Public Service Company of Oklahoma Rogers electric services utility 2 coal

- fired boilers selective catalytic reduction shall install BART

Sinclair Oil Corporation Tulsa petroleum refining industry flue gas desulfurization granted waiver from BART but emission reductions under refinery consent decrees



### D. Factors for Consideration

1. Source Retirement and Replacement Schedules Pursuant to 40 C.F.R. § 51.308(d)(3)(v)(D), DEQ considered source retirement and replacement schedules in developing its long-term strategy of emissions reductions. DEQ cannot reliably predict the retirement or replacement of sources and consequently does not rely on source retirement to achieve any reasonable progress goal. DEQ will manage replacement of sources in conformance with applicable rules and regulations, including prevention of significant deterioration and new source review.

2. Statutory Factors
The federal CAA in § 169A(g)(1), 42 U.S.C. § 7491(g)(1), requires DEQ to consider these factors in determining a reasonable progress goal:

Costs of compliance, Time necessary for compliance, Energy effects of compliance, Non-air quality environmental effects of compliance, and Remaining useful life of existing sources.

Compliance costs: Including cost estimates for each source adds weight against the finding of reasonableness in applying additional controls. Compelling facilities to expend large amounts of capital112

on pollution reduction technology likely would cause some facilities to cease operations and further compound unemployment and other economic problems in the communities. Emissions controls that increase production costs may impair economic competitiveness or viability of facilities or lead to drastic increases in consumer prices. In consideration of these concerns, DEQ relied on the control basis developed by CENRAP in its initial control strategy simulations for an initial list of sources for further

review.

Time for Compliance: In the reasonable progress goal for the Wichita Mountains, DEQ cannot rely on reductions that facilities cannot realize before 2018.

Energy effects of compliance and cost of energy: The cost estimates include energy necessary for additional controls to the extent quantifiable.

Non-air quality effects of compliance: Potential additional controls cannot be determined to have detrimental non-air-quality environmental impacts.

Remaining source life: CENRAP modeling for mobile sources considers this factor as it assumes reduced emissions per vehicle mile traveled due to the turnover of the on-road mobile source fleet. This consideration weighs more heavily against a determination of reasonableness in controlling sources with relatively short useful life remaining. Inclusion of sources with planned unit shutdowns raises cost estimates. None of the considered sources for additional reductions have indicated plans to shutdown.



### E. Reasonableness of Controls

1. Emissions of sulfur compounds The three largest point sources of sulfureous emissions in 2002 fall under the BART rule. Controls for Grand River Dam Authority, an electric power generator, would produce little visibility benefit at enormous monetary and energy cost, especially considering the vast distance between Mayes County and the Wichita Mountains; moreover, the facility already uses flue gas desulfurization. Consent decrees promise considerable emissions reductions from petroleum refineries. Other point sources emit sulfur and fall under existing rules limiting emissions. Retrofitting smaller facilities would impose prohibitively unreasonable costs with imperceptible visibility improvement.

2. Emissions of ammonia EPA currently does not consider ammonia for regulation as a precursor of fine particulate matter. DEQ follows the lead of EPA on this matter. The IMPROVE monitor at the Wichita Mountains measures neither ammonia nor ammonium, and DEQ cannot restrict emissions reasonably without such monitoring data.

3. Emissions of nitrogen oxides Three of the four largest point sources of emissions of nitrogen oxides in 2002 are to install BART. Controls for Grand River Dam Authority would entail considerable cost for too little benefit. Some smaller electric power generators closer to the Wichita Mountains also are to install BART.113

At other point sources, the overwhelming majority of NO x emissions originate from stationary combustion engines, including those used to extract and transport natural gas, petroleum, and natural gas liquids. Federal regulations of reciprocating internal combustion engines include new source performance standards and national emissions standards for hazardous air pollutants. These regulations generally apply only to new engines, but increasingly comprehensive or stringent limits likely will result in lower future emissions than those assumed in the reasonable progress goal. EPA may continue to promulgate increasingly stringent limits on various engines at point and area sources. These restrictions likely will result in the application of technology which reduces NO x emissions beyond those estimated for the reasonable progress goal. Macroeconomic and geopolitical trends will modulate natural gas and petroleum extraction and production, so DEQ offers no further prediction of net emissions reductions from the hydrocarbon fuels sector.

Numerous other point sources emit nitrogen oxides and fall under various existing rules and regulations limiting emissions. Retrofitting these facilities would impose prohibitively unreasonable costs for negligible visibility improvement. See Emission Inventory Section.

Despite the considerable contribution of area sources, especially oil and gas industrial processes, to the Oklahoma NO x emissions inventory presented in this implementation plan revision, DEQ lacks confidence in these estimates. Before targeting area sources for state rules requiring emissions limitations, DEQ desires a quality-assured enhanced inventory with confidence in its comprehensiveness and accuracy. Although DEQ possesses authority to conduct such inventory, time and resources do not suffice to complete such effort before the submission deadline of this implementation plan revision. Emissions controls on existing area sources also entail enormous cost to detect and enforce. As an initial attempt at refining this inventory, CENRAP conducted an oil and gas inventory project in 2008. Future control assessments will build on this information; however, this assessment lacks sufficient information on current levels of control to support a need for additional controls under this implementation plan revision.

DEQ lacks jurisdiction over non-road and on-road mobile sources. Because many of these sources often move to and from other states, DEQ relies on the federal motor vehicle emissions control programs and other EPA regulations to limit emissions from these sectors. DEQ also does not control fuel prices or other inhibitors to mobile source usage. DEQ considers biogenic and fire sources as
natural conditions or otherwise uncontrollable.

4. Volatile organic emissions The emissions inventory associated with this implementation plan revision assigns most emissions of VOCs to biogenic sources. DEQ considers these sources as natural and therefore
uncontrollable.

A minority of VOC emissions in Oklahoma originate from area, industrial, point, and mobile sources. These sources largely already employ controls under various federal mandates. Considering the small and uncertain contribution of anthropogenic sources of VOC to visibility impairment at the Wichita Mountains, DEQ does not find further controls reasonable.114

5. Coarse particulate emissions DEQ believes that most coarse particulate matter observed at the Wichita Mountains originates from natural conditions and recognizes that dust storms occasionally occur naturally in dry environments, especially those west of the Wichita Mountains. DEQ lacks confidence in the inventory which assigns most coarse particulate emissions to road dust. Unpaved roads typically lack the traffic necessary to justify the expense of pavement.

6. Fine particulate emissions and smoke management Fires cause most directly emitted fine particulate matter in the Oklahoma emissions inventory. DEQ recognizes many fires as natural, uncontrollable, or essential to ecosystem management or agricultural production. Pursuant to 40 C.F.R. § 51.308(d)(3)(v)(E), DEQ considered smoke management techniques for the purposes of agricultural and forestry management in developing this reasonable progress goal. Despite their prominence in the emissions inventory, agricultural burning and wildfires in Oklahoma do not contribute significantly to regional haze at the Wichita Mountains nor at any other
Class I area. OAC 252:100- 13 -7(4) includes provisions for the burning of forestland, cropland, and rangeland. Additionally, DEQ and the Oklahoma Department of Agriculture, Food, and Forestry intend to create a basic, voluntary smoke management program based on EPA’s Interim Air Quality Policy on
Wildland and Prescribed Fires

.
Most emissions of fine soil particulate matter also originate from natural sources (including the Sahara Desert), and even those in Oklahoma lie beyond the regulatory purview of DEQ. Other direct emissions of fine particulate contribute miniscule visibility impairment at the Wichita Mountains.



### F. Conclusion The reasonable progress goal for the Wichita Mountains provides for a slower rate of improvement in visibility than the uniform rate of progress; but the preceding discourse clearly demonstrates the reasonableness of this goal.

Given the lack of data from the south central US in the formulation of natural visibility estimates, DEQ believes that the default western target does not represent the actual conditions at the Wichita Mountains. Further, the uniform rate of progress for implementation by 2064 is not reasonable because the vast majority of anthropogenic contributors to the visibility impairment at the Wichita Mountains lie outside the territorial jurisdiction of DEQ. Additional controls at the remaining sources in Oklahoma are not reasonable given associated costs of controls and the resultant minimal visibility improvement.115



## X. Consultation and Comments

This chapter describes how this state implementation plan revision met consultation requirements in 40 C.F.R. § 51.308(d)(3)(i) and 40 C.F.R. § 51.308(i )( 2). DEQ consult ed with other state clean air agencies and tribal entities to develop coordinated emissions-reduction strategies as provided

in 40 C.F.R. § 51.308(d)(3)(i). This requirement is particularly important in regard to emissions from Texas and other states which contribute to visibility impairment at the Wichita Mountains. It also provided for consultation on emissions from Oklahoma reasonably anticipated to contribute to visibility impairment in other Class I areas. To facilitate coordination with other agencies and tribes, DEQ joined CENRAP. DEQ participated in CENRAP to consult with members to develop technical information necessary for coordinated strategies. DEQ also coordinated with CENRAP and other regional planning organizations on a weight- of -evidence analysis used to develop its long-term strategy. DEQ also coordinated with federal land manager (FLM) staff on long-term strategy development. Details are provided for applicable correspondence, notifications and public hearings as well as for DEQ participation in regional planning organizations. Consultation with FLMs is specifically addressed in sections C, D, and G. Public comments on this plan revision are included in section F. DEQ sets forth intentions of updating emission inventories and submitting periodic reports in sections H and J.



### A. Long-term Strategy and Reasonable Progress Goals Consultation DEQ participated in consultations for Class I areas located in Arkansas, Missouri, and Texas. Missouri Department of Natural Resources and Arkansas Department of Environmental Quality held consultations in concert for Mingo (Missouri), Hercules Glades (Missouri), Upper Buffalo (Arkansas), and Caney Creek (Arkansas) as telephone conferences on 3 April, 11 May, and 7 June 2007. Texas Commission on Environmental Quality held consultations for Big Bend and Guadalupe Mountains National Parks on 11, 18, and 31 July 2007.

DEQ conducted four consultations in compliance with 40 C.F.R. § 51.308(d) and

40 C.F.R. § 51.308(i). DEQ 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. Oklahoma held the next three consultations as conference calls on 16 August,

30 August, and 25 September 2007. In these consultations, DEQ invited CENRAP member clean air agencies, EPA, and the tribes to participate. DEQ invited personnel from clean-air agencies in Arkansas, Iowa, Kansas, Louisiana, Minnesota, Missouri, Nebraska, and Texas to participate in its consultations. Representatives from CENRAP, the U.S. Fish and Wildlife Service, and all eight states invited to participate attended these consultations

.
Notification of consultations included a United States Postal Service mailing and electronic notification to states projected to contribute greater than 1 Mm -1 of light extinction at the Wichita Mountains in 2018. The initial mailing included a consultation plan and agenda for the first meeting. DEQ emailed and posted subsequent meeting materials, including a white paper entitled “Natural Backgr ound for Wichita Mountains of Oklahoma,” before the meetings. DEQ posted all materials, including mp3 recordings of the consultations, to its regional haze webpage. The Wichita Mountains116

Wilderness Area Regional Haze Planning document, included as appendices, includes a list of contacts for state clean air agencies, tribes, and FLM staff. Not all participants provided formal comments. Comments received by DEQ are summarized below.

Arkansas Department of Environmental Quality (ADEQ) A ir Division Chief Mike Bates sent a letter dated 17 August 2007, which DEQ received on 20 September 2007. This letter asserted that Arkansas sources do not contribute significantly to visibility impairment at the Wichita Mountains and that Arkansas did not plan to require additional control of any source. ADEQ based this position on the modeling projections for the worst quintile days in 2018 at Wichita Mountains that indicate that
Arkansas sources contribute less than 1.5 Mm -1 of visibility impairment, which ADEQ estimates correspond to 0.2 deciview.

DEQ evaluated the modeling projections and the information that representatives of the Iowa Department of Natural Resources provided during the consultations. DEQ then sent a letter dated

25 February 2008 to Iowa Department of Natural Resources air quality bureau chief Catharine Fitzsimmons, stating that DEQ reasonably does not anticipate anthropogenic sources from Iowa to contribute significantly to visibility impairment at the Wichita Mountains.

On 24 September 2007, DEQ received a letter dated 18 September 2007 from James L. Kavanugh, director of air-pollution control program at Missouri Department of Natural Resources. Missouri questioned Oklahoma using 1 Mm -1 as the threshold for determining that a state contributes significantly to visibility impairment at the Wichita Mountains. They recommended a weight- of

evidence approach. Missouri Department of Natural Resources did not identify any emissions reductions beyond those considered in the modeling but did express intent to consider additional reasonably achievable control technology for sulfureous aerosols and their precursors for several sources as part of their implementation plan for Saint Louis to attain the PM 2.5 national ambient air quality standard.

In a letter dated 3 August 2007, DEQ requested that Texas Commission on Environmental Quality require new and modified PSD sources to conduct analyses for visibility impairment at the Wichita Mountains using federal land manager guidance. DEQ requested an opportunity to review and comment on best available control technology determinations for the proposed projects for which these analyses suggest that the 98 th -percentile values for the change in light extinction are higher than 5% for any year. DEQ also asked Texas Commission on Environmental Quality to expand its evaluation to
effects of new or modified sources within 100 km of the Wichita Mountains to all such sources within 300 km, as set out in the FLM guidance. Glenn Shankle, executive director of Texas Commission on Environmental Quality, provided the response for his agency in a letter dated 25 October 2007. Texas Commission on Environmental Quality agreed to provide DEQ an opportunity to review best available control technology determinations for prevention of significant deterioration sources with projected visibility impairment, as DEQ requested, but did not commit to expanding its evaluations to include new
and modified sources within 300 km.1 17

In her letter dated 25 March 2008, Susanna Hildebrand, director of air quality division of Texas Commission on Environmental Quality, requested concurrence of Oklahoma that DEQ did not rely on any additional reductions from Texas sources in meeting the reasonable progress goal at the Wichita Mountains. DEQ responded in a letter dated 25 April 2008, confirming that DEQ accounted for all expected reductions.

Each state considered reasonable additional emissions reductions under the factors listed in

40 C.F.R. § 51.308(d)(1). DEQ accepted the projected emissions reductions from Texas and all other states in these consultations concerning visibility impairment at the Wichita Mountains; however, DEQ hopes for emissions reductions from Texas sources beyond those herein explicitly anticipated. Appendix

10

- 1 includes copies of referenced correspondence.



### B. Federal Land Manager Consultation In development of this implementation plan revision, DEQ consulted the designated federal land manager staff personnel in accordance with the provisions of 40 C.F.R. § 51.308 (i) (2). DEQ provided an opportunity to federal land managers for consultation in person and at least 60 days before holding any public hearing on this implementation plan revision. Th is consultation gave the federal land managers the opportunity to discuss their assessment of :

Impairment of visibility at the Wichita Mountains and at other Class I areas; Recommendations on the development of reasonable progress goals; and Recommendations on strategies to address visibility impairment.

DEQ sent its draft of this implementation plan revision to the federal land manager staff on 1 Oct 2009. DEQ notified the federal land manager staff of the public hearing held on December 16, 2009. DEQ received written comments from U.S. Department of the Interior, U.S. Fish and Wildlife Service (FWS), in consultation with the National Park Service on 4 December 2009. DEQ considered or incorporated the comments of the federal land managers on this implementation plan. Comments received from the FWS were posted on the DEQ webpage at http://deq.state.ok.us/aqdnew/RulesAndPlanning/Regional_Haze/index.htm on 7 December 2009 and from the FS on 11 December 2009 responses are included as Appendix 10-2.



### C. Federal Land Manager Contact List

Tim Allen U.S. Fish & Wildlife Service 7333 W Jefferson Ave Ste 375 Lakewood, CO 80225 Tim_Allen@fws.gov

CDR Meredith Bond, P.E., USPHS U.S. Fish and Wildlife Service Deputy Chief, Branch of Air Quality 7333 W Jefferson Ave Ste 375 Lakewood, CO 80235 303- 914 -3808 303- 969 -5444 fax Meredith_Bond@fws.gov118

Bruce Polkowsky National Park Service Policy Planning and Permit Review Branch Air Resources Division National Park Service Phone (303) 987 6944 Fax (303) 969 2822 PO Box 25287 Denver, CO 80225 12795 W Alameda Pkwy Lakewood, CO 80228 Bruce_Polkowsky@nps.gov

Scott Copeland USDA Forest Service Visibility Data Analyst/COTR Washakie Ranger District 333 E Main St Lander, WY 82520 Work: (307) 332 9737 Cell: (307) 349 3595 copeland@cira.colostate.edu



### D. Public Hearing

DEQ provided notice of the public hearing and opportunity to comment on the implementation plan revision on Friday, 13 November 2009 as required by 40 C.F.R. §51.102. DEQ held a public hearing regarding the implementation plan revision on 16 December 2009. Notice was provided in the Lawton Constitution, Oklahoman, and Tulsa World newspapers on 13 November 2009. DEQ also posted a copy of the notice on its website and provided notification via e-mails to those persons who expressed an interest in this SIP revision and supplied their e-mail addresses. Th e notice also included information on the availability of this proposed implementation plan revision for public inspection at 707 N. Robinson Ave, Oklahoma City, OK, and on the DEQ webpage at http://deq.state.ok.us/aqdnew/RulesAndPlanning/Regional_Haze/index.htm

. Appendix 10-3 conta in s copies of the notice, proofs of publication, and transcript and sign-in sheets from this hearing

.



### E. Public Comments

DEQ received written comments and oral comments from the public and posted those on its website at http://deq.state.ok.us/aqdnew/RulesAndPlanning/Regional_Haze/index.htm . Copies of those comments and a summary of DEQ’s responses to them is in in Appendix 10-4.119



### F. Future Consultation with Federal Land Managers

DEQ will continue to coordinate and consult with the federal land managers as provided in 40 C.F.R §51.308(i)(4). DEQ intends to consult the federal land managers in the following instances:

Development and review of implementation plan revisions; Review of quinquennial 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.



### G. Periodic Updates of Emission Inventories

Recognizing the importance of maintaining current, valid emissions information, DEQ intends to update the Oklahoma statewide emissions inventories periodically. DEQ updates the point source inventories annually. DEQ also coordinates with EPA to update the area and on- and off-road mobile inventories triennially and conforms to emission reporting requirements of EPA.

In addition to completing regular updates of emissions inventory of Oklahoma, DEQ intends to review periodic emissions information from other states and future emissions projections. This effort will consist of reviewing any technical data and assumptions regarding emissions growth rates, implementation of emissions controls, and geographic distribution of emissions. DEQ may coordinate the periodic reviews with other state clean-air agencies and consultation partners in conjunction with the quinquennial progress reports. DEQ finds participation in CENRAP beneficial toward these ends.



### H. Periodic Reporting and Determination of Adequacy

40 C.F.R. § 51.308(g) requires quinquennial reports evaluating progress towards the reasonable progress goal established for the Wichita Mountains. DEQ intends to submit the first five-year report by February 2015. Using the findings of the quinquennial progress report, DEQ will be able to make a determination regarding the adequacy of the existing plan and take an appropriate action based upon that determination as specified in 51.308(h).



### I. Comprehensive Periodic Revisions

The regional haze rule requires periodic progress reports and implementation plan revisions.

40 C.F.R. § 51.308(f) currently requires DEQ to submit a major revision to its implementation plan to EPA by 31 July 2018 and decennially thereafter.



### J. Conclusion Clause

Now, having fulfilled all requirements of the federal regional haze rule insofar as it is capable, DEQ hereby submits this plan through J.D. Strong, Secretary of the Environment, designee for Governor Brad Henry, for consideration and approval by the EPA.

# The Appendices for the Oklahoma Regional **Haze Implementation Plan Revision are in separate documents.**