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 ofthe 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:

Subehanter 252:100-8,Part 1 • Best Avai able etro It 1 R fiTechnology

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

.-

Sbh u canter 252:1008• -1.1 Definiltlons

NOTICE PUBLIC HEARING GOVERNING BOARD

12/15/05 01/18106 Air QualityAdvisorv 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 ofrulemaking intent includes the date, time, and

location ofpublic hearings as well as information on how the public may submit written or oral comments

concerning proposed rules. The public commentperiod 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 ofthe 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 onrl;lcycled paperwllh soy Ink.

c~
Regional Haze Implementation Plan 

Revision 

State of Oklahoma 

Department of Environmental Quality 

February 2, 2010 
 Oklahoma 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 ............................................................................................. 37
A. 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 SO2 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 .............................................................................88
VIII. 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
LIST OF TABLES

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

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

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

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

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

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

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

Table III-8 Natural visibility conditions at the Wichita Mountains 

Table III-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 SO2

 emissions in Oklahoma 

Table IV-4 Leading sources of NH3 emissions in Oklahoma 

Table IV-5 Leading sources of NOx emissions in Oklahoma 

Table IV-6 Leading sources of VOC emissions in Oklahoma 

Table IV-7 Leading sources of direct PM10 - PM2.5 emissions in Oklahoma 

Table IV-8 Leading sources of direct PM2.5 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 2002)

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 

Table 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 (PM10)

Table VI-10 Visibility improvement in the 98th percentile with BART NOX controls 

Table VI-11 Visibility improvement in the 98th percentile with BART SO2 controls 
Table 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 days, 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 

LIST OF FIGURES

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) 

LIST OF APPENDICES

Appendix 1-1 Oklahoma Administrative Code and Statutes 

Appendix 4-1 2002 Oklahoma Emissions Inventory 
Appendix 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 regarding 

regional haze. The revision addresses regional planning, discusses state/tribe and FLM coordination, and 

contains 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 Stat. 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 Area, 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 area 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 to 
3 

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.” DEQ 
7 

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 Air 

Resource 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 its 

membership. 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 (NO3

-

), nitrite 

(NO2

-

), 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 carbon 

dioxide. 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 (PM2.5) 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 uniformly 
10

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 algorithm 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-absorbing” 

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 (NO2). 

1. Sulfurous Particulate 

The algorithm for calculating light extinction considers all sulfur detected via proton-induced xray emission as a part of (NH4)2SO4 (ammonium sulfate). The samplers do not measure ammonium 

cation concentration. This fully neutralized and ammoniated compound usually dominates in most field 

studies 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 (NH4)2SO4 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: fsml(RH)

for “small” particles and flrg(RH) for “large” particles 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 

correlation 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 (NH4NO3) 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 OP 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 

commonality 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 βext 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

[NO3

-

+ (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 from 

January 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

[O1+ (μg m

-3

) [O2+ (μg m

-3

) [O3+ (μg m

-3

) [O4+ (μg m

-3

) [OP+ (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 measurements 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

[E1+ (μg m

-3

) [E2+ (μg m

-3

) [E3+ (μg m

-3

) [OP+ (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

Baseline

.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

[PM10+ (μg m

-3

) [PM2.5+ (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 ParticulateTable 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

) [NO2

-

+ (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 .17
25

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 (NH4)2SO4 and NH4NO3 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 of 
29

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 to 
30

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

[NO3

-

+ (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 [O1] + [O2] + [O3] + [O4] + [OP+ (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 area 

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. 

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

Conditions

[E1] + [E2] + [E3] - [OP+ (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 relatively 
33

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

[PM10] - [PM2.5] (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 certain 
34

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. The 
36

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 7.53
37

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 (SO2), ammonia 

(NH3), nitrogen oxides (NOx), volatile organic compounds (VOCs), and particulate matter of aerodynamic 

diameters less than 10 μm (PM10) and less than 2.5 μm (PM2.5) 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) 

SO2 NH3 NOx VOCs PM10-PM2.5 PM2.5

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

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

Non-road 

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

On-road 

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

Biogenic 0 0 35,909 988,314 0 0

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

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-road 
38

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 emissions 

inventory 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 on 
39

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, dry 

cleaners, 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) 

SO2 NH3 NOx VOCs PM10 PM2.5

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 files 
40

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. SO2 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 SO2 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

fire 7,145 4.20%

general 4,634 2.73%

non-road mobile 4,773 2.81%

on-road mobile 4,708 2.77%

biogenic none

Statewide total 170,021 100.00%

2. NH3 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 NH3 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 1.40%

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 12.90%

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 1.72%

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. NOx 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 NOx 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 NOx 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 28.38%

biogenic (model output) 35,909 7.15%

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 pollutants 
45

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 PM10 - PM2.5 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 PM2.5 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 Modeling 

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 

national 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 team 
50

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 PM2.5 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 PM2.5 and regional haze modeling guidance which served in as an interim 

placeholder. The goals for ozone attainment, annual and episodic PM2.5 attainment, and regional haze 

progress demonstrations generally share common modeling strategy. EPA ultimately issued final 

modeling guidance for the PM2.5 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 for 

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-road 
53

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 

modeling for 2002 to reflect typical operations of these critical sources, rather than using specific 
54

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 Domain 

CAMx 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 meteorological 

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 use 

this 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. This 
56

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 available 
57

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-1

Total

Point

Area

Non-road Mobile

On-road Mobile

Biogenic

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 

reflect 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 in 
60

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-1

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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 term 
61

“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-1

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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-1

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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-1

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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-1

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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 Area

State

Contribution to 

light extinction 

(Mm

-1

)

Total light 

extinction from all 

sources (Mm1

)

Oklahoma 

contribution (%)

Deciview 

contribution

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% .01
66

Class I Area

State

Contribution to 

light extinction 

(Mm

-1

)

Total light 

extinction from all 

sources (Mm1

)

Oklahoma 

contribution (%)

Deciview 

contribution

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 NOx 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-1

Sum

Sulfureous

Nitrate

Organic 

carbonaceous

Elemental 

carbonaceous

Soils

Coarse

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 (Distrito 
68

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 Determinations 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 (SO2), 

nitrogen oxides (NOx), and particulate matter (PM10 and PM2.5). CENRAP modeling shows that 

anthropogenic VOCs do not significantly impair visibility at the Wichita Mountains. Because of the 

limiting role of NOX and SO2 on PM2.5 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 process plants,

(22) Fossil-fuel boilers of more than 250 

million BTUs per hour heat input,

(23) Petroleum storage and transfer facilities 

with a capacity exceeding 300,000 

barrels,

(24) Taconite ore processing facilities,

(25) Glass fiber processing plants, and

(26) Charcoal production facilities.

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 

comprise the “BART-eligible sources” listed in Table VI-1. 
71

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

BART source category Facility name County Number of 

units

Fossil fuel-fired boilers of 

more than 250 

MMBTU/hr heat input

Georgia Pacific Consumer Products (formerly Fort James 

Operating) Muskogee Mill Muskogee 2

Kraft pulp mill International Paper (formerly Weyerhaeuser) Valliant 

Paper Mill McCurtain 5

Hydrofluoric, sulfuric, and 

nitric acid plants

Koch Nitrogen Enid Plant Garfield 7

Terra International Oklahoma Woodward Complex Woodward 11

Terra Nitrogen Partnership Verdigris Plant Rogers 12

Petroleum refineries

Sinclair Oil Tulsa Refinery Tulsa 7

Holly Refining and Marketing (formerly Sunoco) Tulsa 

Refinery Tulsa 25

Wynnewood Refining Garvin 14

Valero Refinery (formerly TPI Petroleum Inc) Ardmore 

Refinery Carter 24

Portland cement plants Lafarge Building Materials Tulsa Rogers City Line Rogers 10

Fossil fuel-fired steam 

electric plants of more 

than 250 MMBTU/hr heat 

input

OG&E Horseshoe Lake Generating Station Oklahoma 2

OG&E Muskogee Generating Station Muskogee 2

OG&E Seminole Generating Station Seminole 3

OG&E Sooner Generating Station Noble 2

PSO Comanche Power Station Comanche 2

PSO Northeastern Power Station Rogers 3

PSO Riverside Jenks Power Station Tulsa 2

PSO Southwestern Power Station Caddo 1

Western Farmers Electric Coop Anadarko Plant Caddo 3

Western Farmers Electric Coop Mooreland Station Woodward 3

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 or 
72

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 maximum 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 dispersion 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 98th 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 performed 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 Appendix 6-3. 

Georgia Pacific Consumer Products LP – Muskogee Mill has agreed to limit NOX emissions to 

744lb/day from the BART-eligible units through the use of new low NOX 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 maximum 
73

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 NOX PM10 SO2

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 SO2 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 NOx 1.300

OG&E Sooner Generating 

Station

Units 1 and 

2

fossil fuel-fired steam 

electric plants

SO2 1.795

NOx 1.244

PM10 0.281

OG&E Muskogee 

Generating Station

Units 4 and 

5

fossil fuel-fired steam 

electric plants

SO2 1.724

NOx 0.210

PM10 1.469

PSO Comanche Power 

Station

Units 1 and 

2

fossil fuel-fired steam 

electric plants NOx 1.830

PSO Northeastern Power 

Station Unit 2 fossil fuel-fired steam electric plants NOx 0.809

PSO Northeastern Power 

Station

Units 3 and 

4

fossil fuel-fired steam 

electric plants

SO2 1.836

NOx 1.029

PM10 0.230

PSO Southwestern Power 

Station Unit 3 fossil fuel-fired steam electric plants NOx 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, and 
74

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 natural gas, there are no sulfur dioxide (“SO2”) or particulate matter 

(“PM”) emission control systems. Seminole Unit 3 was designed with flue gas recirculation (“FGR”) for 

nitrogen oxide (“NOX”) control. Each unit has the potential to emit 250 tons per year (“TPY”) or more of 

NOX. 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 NOX, SO2, 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 NOX, 

SO2, 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 SO2 or PM emission control systems. Both 

units have the potential to emit 250 TPY or more of NOX. 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 includes 

one (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 SO2 or PM emission control systems. The unit has the 

potential to emit 250 TPY or more of NOX. 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 three 
75

(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 SO2 or PM emission control systems. 

Unit 2 has the potential to emit 250 TPY or more of NOX. Northeastern Units 3 and 4 both fire coal as 

their primary fuel, and both units have the potential to emit 250 TPY or more of NOX, SO2, 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 SO2 or NOx per year or at least 15 tons of PM10 per year, then the regulation does not require 

DEQ to make a determination of BART for such pollutants. Specifically, each 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) years 
76

of 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

NOX Control Combustion controls 

including: Low-NOX

Burners, Overfire Air, and 

Flue Gas Recirculation

Combustion controls 

including: Low-NOX

Burners, Overfire Air, and 

Flue Gas Recirculation

Combustion controls 

including: Low-NOX

Burners, Overfire Air, and 

Flue Gas Recirculation

NOX 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 and emission limits for OG&E Sooner Generating Station Units 1 and 2

Control Unit 1 Unit 2

NOX Control Low NOX Burner with Over-fire Air Low NOX 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)

SO2 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

PM10 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 and emission limits for OG&E Muskogee Generating Station Units 4 and 5

Control Unit 4 Unit 5

NOX Control Low NOX Burner with Over-fire Air Low NOX 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)

SO2 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

PM10 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 and emission limits for PSO Comanche Power Station Units 1 and 2

Control Unit 1 Unit 2

NOX Control Dry Low-NOX Burners Dry Low-NOX 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 and emission limits for PSO Southwestern Power Station Units 3

Control Unit 3

NOX Control Low NOX 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 and emission limits for PSO Northeastern Power Station Units 2, 3, and 4

Control Unit 2 Unit 3 Unit 4

NOX Control Low NOX Burner with 

Over-fire Air

Low NOX Burner with Overfire AirNew Low NOX 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)

SO2 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

PM10 Control1– 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 test results.
80

The application of BART to all sources required to install it provides an estimated emission 

reduction of 12,224 tons of SO2 per year, and 27,043 tons of NOx per year from the baseline. Tables VI11 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

OG&E Seminole Generating Station 2 (gas-fired) 0.354 3,998

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 NOX controls, calculated as the 3-year average of the modeled visibility 

improvement at the 98th percentile. 
81

Table VI-13: Visibility improvement in the 98th percentile with BART NOX 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”) is not cost-effective 

for SO2 control for any of the six coal-fired steam electric units reviewed, i.e., OG&E Sooner Units 1 and 

2, OG&E Muskogee Units 4 and 5, and 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. SO2 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. SO2 source control cost effectiveness (in dollars per ton of SO2 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 

be 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 SO2 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 SO2 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 

SO2 (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 SO2 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 SO2 BART control technologies and emission limits for OG&E Sooner Generating Station Units 1 and 2

Control Unit 1 Unit 2

SO2 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)

PM10 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 SO2 BART control technologies and emission limits for OG&E Muskogee Generating Station Units 4 

and 5

Control Unit 4 Unit 5

SO2 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)

PM10 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 SO2 BART control technologies and emission limits for PSO Northeastern Power Station Units 3 and 4

Control Unit 3 Unit 4

SO2 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 SO2 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 

Appendix 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 SO2 emission limit that is equivalent to: (i) the SO2 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 SO2 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 SO2 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 SO2 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 SO2 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 SO2 emission limit that is 

equivalent to: (i) the SO2 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 SO2 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 SO2 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 SO2 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 SO2, NOx, VOCs, and particulate matter. BACT minimizes emissions of all 
87

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 sources subject to 

operating permit requirements under Title V of the Federal Clean Air Act as amended.” 27A O.S. § 1-3-

101(E)(8). The 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 Oklahoma 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 

OAC 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 rules 
89

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 NOx, 

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 NOx, 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 SO2

and NOx 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 SO2 in all covered 

areas except Arkansas, Delaware, New Jersey, and New England. CAIR will reduce SO2 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 SO2 and NOx will occur in two phases under a cap-andtrade system that EPA established. Emissions caps for SO2 will drop in 2010 and again in 2015. 

Allowable NOx emission will decrease in 2009 and again in 2015. Oklahoma is not a CAIR state; Electric 

generating unit allowances that cap annual NOx 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 SO2 and NOx. The NOx reductions 

generally apply as company-wide requirements. EPA and DEQ provided specific SO2 and NOx 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

NOx

(ton/year)

SO2

(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) will 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) 

SO2 NH3 NOx

Volatile organic 

compounds PM10-PM2.5 PM2.5

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

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

Non-road 

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

On-road 

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

Biogenic 0 0 35,909 988,314 0 0

Total 119,776 182,605 369,248 1,581,788 287,693 142,252
92

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 SO2 NH3 NOx

Volatile 

organic 

compounds

PM10-PM2.5 PM2.5

Inventory 

for 2002 171,707 137,435 502,656 1,603,591 308,921 148,279

Estimate 

for 2018 119,776 182,605 369,248 1,581,788 287,693 142,252

Net 

change -51,931 +45,170 -133,408 -21,803 -21,228 -6,027

Percent 

change -30.2440% +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 SO2 emissions; however, the modeling also anticipates NOx 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 NOx and SO2 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), NOx

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 EPA 
95

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)

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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)

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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)

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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)

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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)

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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)

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

Sum

Point

Area

Non-road mobile

On-road mobile

Biogenic

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 

undertake 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 area State Contribution to light extinction (Mm

-1

)

Total light extinction 

(Mm

-1

)

Oklahoma contribution 

(%)

Deciview contribution

Reduction in 

Oklahoma contribution 

from 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 .10
102 

Class I area

State

Contribution to light 

extinction (Mm

-1

)

Total light extinction 

(Mm

-1

)

Oklahoma contribution 

(%)

Deciview contribution

Reduction in 

Oklahoma contribution 

from 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-1

Sum

Sulfureous

Nitrate

Organic carbonaceous

Elemental carbonaceous

Soils

Coarse

Sum

Sulfureous

Nitrate

Organic carbonaceous

Elemental carbonaceous

Soils

Coarse

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 .28
104 

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 within 

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 Mountains 

DEQ 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) 

Visibility Impairment Projections

Worst Quintile Days at Wichita Mountains

23.81

22.72

20.01

17.30

14.58

11.87

9.16

7.53

23.81

23.14

21.47

19.81

18.14

16.47

14.80

13.80

7.53

0

5

10

15

20 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Year Deciview Haze Index

Uniform Rate of Progress Natural Condition

Observation Reasonable Progress Goal

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 SO2 and NOx 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 in 

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 meet 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 NOx and SO2 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 the 
108 

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 

not 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 NOx and SO2 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 NOx and SO2 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 NOx or SO2 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 

SO2 and less than 100 tons of NOx 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 NOx or SO2 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 SO2 and NOx 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 

controls 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 gasfired 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 NOx

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 

boilers

selective catalytic 

reduction, low NOx

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 NOx coal-and-air 

nozzles with closecoupled and separated 

over-fire air and flue 

gas desulfurization wet 

scrubber

shall install BART
111 

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 NOx coal-and-air 

nozzles with closecoupled 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 noncatalytic 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 capital 
112 

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 NOx 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 NOx 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 NOx 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 consulted 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 

Background 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 Mountains 
116 

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) Air 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-ofevidence 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 PM2.5 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 98th-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. 
117

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. This 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.gov
118 

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. The 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 contains 

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. 

 
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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.