1

Jeff Peltola

From: Gregory Stella [gms@alpinegeophysics.com]

Sent: Tuesday, May 09, 2006 3:53 PM

To: Jeff Peltola

Cc: 'Seltz, John'; mac@adeq.state.ar.us; 'T. W. Tesche'; 'Wilkinson, Jim'

Subject: Final CENRAP Regional Haze Control Strategy Analysis Plan

Attachments: Alpine Geophysics Final Report (9 May '06).pdf

Alpine Geophysics 

Final Report...

VIA E-MAIL

9 May 2006

Mr. Jeff Peltola

CENRAP Technical Director

10005 S. Pennsylvania, Ste C

Oklahoma City, OK 73159

Dear Jeff:

The scientists at Alpine Geophysics are pleased to submit the attached document titled 

"Final CENRAP Regional Haze Control Strategy Analysis Plan"

as outlined in Task 6 of our previously submitted quotation and work plan.

This document and associated materials are the product of our development and application 

of a quantitative procedure to identify and prioritize potential regional haze control 

strategies for Class I areas failing to meet visibility goal objectives. Additionally, we 

have addressed as many of the comments on the draft control strategy analysis plan as 

submitted to Alpine (April 25 and later) as we have determined to be within the scope of 

our original proposal to CENRAP.

To facilitate subsequent use of this methodology by CENRAP or others, this Final Report 

describes the various analytical steps and provides examples of this procedure (both in 

the body of the report and in supporting in appendixes). Document appendices and relevant

technical support information have archived on the Alpine Geophysics project website 

facilitating easy access by interested parties. The login and password to access these 

data is provided below.

ftp> ftp.alpinegeophysics.com

login> cenrap

pass> pass4ftp

Should you have any questions or problems accessing these files and supporting materials, 

please contact me at your convenience.

Respectfully yours,

Gregory Stella

Senior Scientist

Alpine Geophysics, LLC

Burnsville, NC 28714
Final Report

CENRAP REGIONAL HAZE CONTROL STRATEGY ANALYSIS PLAN

Wichita Mountains Areas of Influence (AOIs) bext on CENRAP Worst 20% Days in 2004

Daily Variation in PM2.5 Components at Upper Buffalo Wilderness

Prepared by

Mr. Gregory M. Stella

Dr. Jim Wilkinson

Dr. T. W. Tesche

Alpine Geophysics, LLC

Prepared for

CENRAP/CENSARA

10005 S. Pennsylvania, Ste C

Oklahoma City, OK 73159

9 May 2006

 

www.alpinegeophysics.com

IMPROVE Observed bEXT on Worst 20% Days in 2004 

0

25

50

75

100

125

150

175

200

BIBE

BWCA

BRET

CACR

GUMO

HEGL

MING

UPBU

VOYA2

WIMO

MACA

SIPS

ISLE

BADL

GRSA

LOST

ROMO

SACRTHRO

WHPE

WHIT

WICA

bEXT (Mm-1

)

Coarse Mass

Soil

Elemental Carbon

Organic Mass

Nitrate

Sulfate
ii

TABLE OF CONTENTS

LIST OF TABLES ....................................................................................................................... iv

LIST OF FIGURES .................................................................................................................... vii

EXECUTIVE SUMMARY........................................................................................................ vii

1.0 INTRODUCTION............................................................................................................. 1

1.1 Study Overview............................................................................................................. 2

1.2 Approach, Assumptions, and Constraints.................................................................. 4

1.3 Structure of Report ....................................................................................................... 5

1.4 Technical Support Resources....................................................................................... 6

2.0 CONTEXT FOR REGIONAL HAZE STRATEGY DEVELOPMENT..................... 7

2.1 Role of CENRAP and the Other Regional Planning Organizations (RPOs) .......... 7

2.2 Considerations in Designing Regional Haze Control Strategies............................... 8

2.3 Resources Available to this Study ............................................................................... 8

3.0 TECHNICAL APPROACH............................................................................................. 9

4.0 ESTIMATION OF RESIDUAL VISIBILITY IMPROVEMENT NEEDS .............. 11

4.1 Literature Review and Synthesis of Pertinent Source-Receptor Information...... 11

4.2 Preliminary Visibility Estimates for Class I Areas.................................................. 13

4.3 Estimation of Visibility-Impairing Concentration Increments .............................. 15

5.0 ESTIMATION OF EMISSIONS REDUCTION NEEDS........................................... 21

5.1 Development of the Areas of Influence (AOI).......................................................... 21

5.2 Development of Visibility Impairing Pollutant Concentrations to Precursor 

Emissions Sensitivity Coefficients ............................................................................. 24

5.3 Estimated Emissions Reductions Necessary to Attain 2018 Glide Path................ 26

6.0 PRIORITIZED CENRAP EMISSIONS REDUCTION SCENARIOS ..................... 30

6.1 Summary of Emission Inventories Used in Control Plan Development ................ 30

6.2 Process in Preparing Files for Control Plan Modeling............................................ 33

6.3 Application of AirControlNET Technologies........................................................... 36

6.4 Development of AOI-Based Cost Curves.................................................................. 38

6.5 Application of Cost Curves to Emission Reduction Needs...................................... 38

6.6 Four Factor Analysis for RPG................................................................................... 41

6.6.1 Cost of Compliance ................................................................................................. 41
iii

6.6.2 Time Necessary for Compliance ............................................................................ 41

6.6.3 Energy and Non-Air Quality Environmental Impacts of Compliance .............. 42

6.6.4 Remaining Useful Life of Potentially Affected Sources....................................... 42

7.0 SUMMARY AND RECOMMENDATIONS................................................................ 43

7.1 Summary...................................................................................................................... 43

7.2 Recommendations ....................................................................................................... 44

7.2.1 Regional Controls.................................................................................................... 44

7.2.2 Subregional Controls.............................................................................................. 44

8.0 REFERENCES................................................................................................................ 49

APPENDIX A: Methodology ................................................................................................. A-1

APPENDIX B: PM2.5 Composition and Visibility Projections at Class I Monitors ......... B-1

APPENDIX C: Area of Influence Data for each Class I Area of Interest......................... C-1

APPENDIX D: Pollutant-to-Precursor Emissions Sensitivity Calculations .....................D-1

APPENDIX E: Emission Summaries for CENRAP Class I Area AOIs.............................E-1

APPENDIX F: Illustrative Development of Incremental Control Cost Curve .................F-1

APPENDIX G: CENRAP Class I Area Cost Effectiveness Curves....................................G-1

APPENDIX H: Visibility Projections to Control Need Worksheet....................................H-1
iv

LIST OF TABLES

Table 1-1. Class I Areas Addressed in this Study. .................................................................... 1

Table 4-1. Reasonable Progress Goal Increments. ................................................................ 14

Table 4-2. Measured Extinction at Class I Areas.................................................................... 15

Table 4-3. Extinction Fraction for 20% Worst Days by Class I Area. ................................. 16

Table 4-4. Required Concentration Reductions: All Species................................................. 16

Table 4-5. Required Concentration Reductions: One Specie. ............................................... 17

Table 5-1. Synthesis of Sensitivity Values for Each Class I Area by AOI level.. ................. 26

Table 5-2. SO2 and NOX Emissions Reduction Requirements (tons per year) Assuming 

Proportional Reductions in Sulfate and Nitrate................................................................ 28

Table 5-3. SO2 and NOX Emissions Reduction Requirements (tons per year) Assuming a 

Single Chemical Species is Controlled................................................................................ 29

Table 6-1. CENRAP 2002 Base Year Annual Emissions Summary...................................... 30

Table 6-2. 2018 Base Case Annual Emissions Summary........................................................ 31

Table 6-3. CENRAP 2018 Base Case Annual Residual Emissions Contribution Summary.

................................................................................................................................................ 32

Table 6-4. CENRAP Annual Emissions Change (Tons)......................................................... 32

Table 6-5. CENRAP Annual Emissions Change (Percent). ................................................... 33

Table 6-6. IPM Post Processing Assigned Device Codes and Applied SO2 Control 

Efficiencies............................................................................................................................ 34

Table 6-7. IPM Post Processing Assigned Device Codes and Applied NOX Control 

Efficiencies............................................................................................................................ 35

Table 6-8. Emissions Weighted NETDC (MW) Association. ................................................. 37

Table 7-1. Subregional control strategy summary for single precursor emission reduction 

targets.................................................................................................................................... 43

Table 7-2. Subregional control strategy defined for Boundary Waters / Voyageurs SO4 

AOI-1..................................................................................................................................... 46

Table 7-3. Subregional control strategy defined for Wichita Mountains SO4 AOI-1.......... 46
v

Table 7-4. Subregional control strategy defined for Breton Island SO4 AOI-1.. ................. 47

Table 7-5. Subregional control strategy defined for Big Bend / Guadalupe Mountains SO4 

AOI-1..................................................................................................................................... 48
vi

LIST OF FIGURES

Figure ES-1. Level I Areas of Influence (AOI) for Sulfate associated with the Big

Bend, Guadalupe, Wichita Mountains, Breton Island, Voyageur, and

Boundary Waters Class I..........................................................................ix

Figure ES-2. Level I Areas of Influence (AOI) for Nitrate Associated with the Big

Bend, Guadalupe, Wichita Mountains, Breton Island, Voyageur, and

Boundary Waters Class I ..........................................................................x

Figure 1-1. Current Visibility Projections at CENRAP and Other Class I Sites (Source: 

Morris et al., 2006b). .............................................................................................................. 2

Figure 1-2. Preliminary Visibility Projections by State (Source: Morris et al., 2006b) ........ 3

Figure 4-1. Measured Extinction Coefficients at Class I Areas Based on IMPROVE Data.

................................................................................................................................................ 19

Figure 4-2. Measured Fractional Extinction at Class I Areas Based on IMPROVE Data. 20

Figure 5-1. Example Geocoded AOI for Boundary Waters and Voyageurs Class I areas 24

Figure 5-2. Geocoded AOIs for Big Bend, Guadalupe Mountain, Salt Creek, White 

Mountain, and Wheeler Peak. ........................................................................................... 27

Figure 6-1. Marginal Cost Curve for Wichita Mountain SO4/EC/OC AOI-1...................... 39

Figure 6-2. Marginal Cost Curve for Boundary Waters – Voyageurs SO4/EC/OC/CM/FS 

AOI-1. .................................................................................................................................. 40
vii

EXECUTIVE SUMMARY

The Implementation and Control Strategies (ICS) Workgroup of the Central Regional Air

Planning Association (CENRAP), together with other workgroups and state, tribal and federal 

agencies, have been working for more than four years gathering information for developing 

regional haze (RH) control strategies for pertinent Class I areas within and adjacent to the 

CENRAP states and tribes. In late February 2006, under the direction of the CENRAP Technical

Director, Alpine Geophysics, LLC was contracted to assist the ICS in this effort. Building upon 

information developed by the ICS and others, Alpine was charged with developing a quantitative 

procedure to identify and prioritize potential RH control strategies to be tested by CENRAP 

modelers. Alpine formulated a methodology for constructing control strategy recommendations 

based on presently available information and submitted a Work Plan detailing this approach to 

the ICS/CENRAP leadership for review and approval.

Using the results of preliminary and more recent CENRAP visibility projection modeling

together with current information on the composition of visibility-impairing fine particulate 

aerosols at 22 Class I monitors, Alpine identified residual visibility progress 'increments' that 

potentially require additional regional and/or subregional emission reductions to achieve

visibility goals1. We synthesized pertinent 'attribution of haze' documents, CENRAP

CAMx/CMAQ visibility modeling results, our own fine particulate modeling in the central U.S, 

and other technical reports, papers, and analyses bearing directly on the quantification of

emissions-source/visibility-receptor impacts at the ten CENRAP Class I and twelve adjoining 

areas. 

Complementing this task, we synthesized a number of recent regional modeling studies helpful 

in relating emissions reductions of visibility precursors (e.g. SO2, NOX) in upwind source regions 

(Areas of Influence or AOIs) to the improvement in visibility (in deciviews or Mm-1) at 

downwind Class I areas. Figures ES-1 and ES-2 present ‘level 1’ AOI plots for sulfate and 

nitrate impacts at the Big Bend, Guadalupe, Wichita Mountains, Breton Island, Voyageurs, and 

Boundary Waters Class I Areas, respectively. Three distinct levels of AOI have been estimated 

for each visibility precursor and Class I areas, but the controls most likely to be considered for 

modeling will be drawn from the closest (i.e., AOI level 1 or AOI-1) area of influence for each 

Class I area/visibility precursor pair.

 

1

 We use the term ‘increment’ to denote the difference between the modeled visibility at a Class I area in 2018 

compared to the value based on the Reasonable Progress Goal (RPG) glide path, evaluated at the same time period. 

A positive increment means that the modeled visibility at the Class I area is ‘poorer’ than the level associated with 

the linear FPG glide path. Accordingly, CENRAP may wish to consider recommending additional precursor 

controls to ameliorate such a positive visibility increment. In contrast, a negative increment suggests that the 

modeled growth and emissions controls by 2018 may produce better visibility conditions at the monitor when 

compared to the linear glide path. 
viii

We then deduced from available regional modeling studies 'rules of thumb' relating percentage or 

tonnage reductions in visibility reducing precursors (e.g., SO2, NOX, ammonia, and VOCs) on 

the expected impact on visibility downwind. These 'rules of thumb', i.e., source-receptor 

relationships, were essential in estimating the amounts of incremental precursor emissions 

reductions in regions upwind of each of the various Class I areas that CENRAP modelers should 

consider in the prescription of initial RH control strategy simulations.

Once an emissions reduction target was determined for each Class I area showing visibility 

projections above the uniform rate of progress line (i.e., a positive visibility increment), we 

applied a master list of controls on sources within the Class I AOIs to formulate the CENRAP 

Control Strategy plan, including cost-effectiveness as a key element.

Alpine’s analysis of the most recent CENRAP visibility projection data identified six Class I 

areas within the CENRAP domain whose projected visibility falls above the uniform rate of 

progress line (i.e., a projected positive visibility increment). On this basis, we quantified their 

associated AOIs, emission reduction estimates for reaching 2018 reasonable progress objectives, 

and potential incremental emission reductions worthy of annual CMAQ/CAMx modeling. For 

each area, sulfate and to a lesser extent, nitrate reductions were shown to be most beneficial 

during the 20 percent worst visibility days in 2002. 

As each of these areas (and all of the other Class I AOIs in the CENRAP domain) are dominated 

by EGU SO2 and NOX emissions and many of the Class I area AOIs intersect with States 

currently excluded by the EPA CAIR rule, a region-wide strategy for additional EGU emission 

reductions at CAIR levels for the non-CAIR EGUs may be beneficial to each Class I area in the 

CENRAP domain projected below the uniform rate of progress line. An alternate intra-state 

trading permutation of this regional approach is also recommended for review by CENRAP. 

In lieu of a single regional control option applied consistently across the entire CENRAP 

domain, individual subregional control applications are proposed to reduce emissions within 

certain Class I area AOIs. Based on the single precursor emission reduction target calculations 

defined by the ICS, subregional control strategies can be defined for three of the Class I areas 

projected to be above the reasonable progress glide path2. In each case, the marginal cost curves 

(based on the application of all available control options on all controllable industries and source 

types) allow the selection of control technologies which attains the ICS defined, AOI-1 specific 

emission reduction targets. 

However, the application of incremental control on all controllable point and area sources within 

certain AOIs still fails to meet the visibility objectives of three Class I areas modeled to be above 

the reasonable progress glide slope. In fact, as a result of the implementation of the exhaustive 

list of additional controls in each primary AOI, Alpine has determined that these three Class I 

areas3 will be unable to achieve a level of emissions reduction necessary to bring these areas 

under the reasonable progress line. Influences such as incrementally uncontrollable source

categories, cost-effectiveness limitations and international and inter-RPO emissions transport are 

barriers that prevent strategies from being configured for these Class I areas within the confines 

of the CENRAP domain.

 

2

 These areas include Boundary Waters, Wichita Mountains, and Voyageurs.

3

 These areas include Big Bend, Breton Island, and Guadalupe Mountains.
ix

Although application of the exhaustive list of available control technologies to sources within the 

AOIs for each of the Class I areas failing to achieve ICS identified emission reduction targets, 

emission reductions beyond the base case should not be forsaken as a result. Indeed, significant

emission reductions may be warranted in order to prepare impacted States and tribes for future 

attainment demonstrations where these measures may set the basis for defining and meeting 

future progress goals.

It should be noted that although this report and associated material includes controls for

particular sources or source categories as options to consider for further photochemical

modeling, it does not necessarily indicate that they will be modeled, and does not imply that 

these strategies ultimately will be implemented.

Finally, while the this methodology was developed and tested for regional haze control

programs, with very minor adaptation, the same methods can be used effectively to aid in the 

design of regional 8-hr ozone and annual PM2.5 NAAQS attainment strategies.

Figure ES-1. Level I Areas of Influence (AOI-1) for Sulfate associated with the Big Bend, 

Guadalupe, Wichita Mountains, Breton Island, Voyageur, and Boundary Waters Class I Areas.
x

Figure ES-2. Level I Areas of Influence (AOI-1) for Nitrate Associated with the Big Bend, 

Guadalupe, Wichita Mountains, Breton Island, Voyageur, and Boundary Waters Class I Areas.
1

1.0 INTRODUCTION

The Implementation and Control Strategies (ICS) Workgroup of the Central Regional Air

Planning Association (CENRAP), together with other workgroups and state, tribal and federal 

agencies, have worked for more than four years in developing the foundation for constructing 

regional haze (RH) control strategies for pertinent Class I areas (Table 1-1) within and adjacent 

to the CENRAP states and tribes (Seltz, 2006a,b; Anderson; 2005; Sharp and Anderson, 2005). 

In late February 2006, Alpine Geophysics, LLC (AG) was contracted to assist the ICS in these 

ongoing efforts. Specifically, using information developed by the ICS and others, AG was 

charged with developing a quantitative procedure to identify and prioritize potential RH control 

strategies to be tested by CENRAP modelers. Alpine formulated a methodology for constructing 

control strategy recommendations based on presently available information and submitted a

Work Plan detailing this approach to the ICS/CENRAP leadership for review (Tesche and Stella, 

2006). 

Table 1-1. Class I Areas Addressed in this Study.

RPO Class I Area ST Name

CENRAP Big Bend Nat'l Park TX BIBE

CENRAP Boundary Waters MN BWCA

CENRAP Breton Island LA BRET

CENRAP Caney Creek AR CACR

CENRAP Guadalupe Mountains TX GUMO

CENRAP Hercules-Glades MO HEGL

CENRAP Mingo MO MING

CENRAP Upper Buffalo AR UPBU

CENRAP Voyageurs MN VOYA2

CENRAP Wichita Mountains OK WIMO

VISTAS Mammoth Cave KY MACA

VISTAS Sipsey Wilderness AL SIPS

MRPO Isle Royale MI ISLE

WRAP Badlands SD BADL

WRAP Great Sand Dunes CO GRSA

WRAP Lostwood Wilderness ND LOST

WRAP Rocky Mtn Nat'l Park CO ROMO

WRAP Salt Creek NM SACR

WRAP Theodore Roosevlt ND THRO

WRAP Wheeler Peak NM WHPE

WRAP White Mountain NM WHIT

WRAP Wind Cave SD WICA

Based on comments received, the approved Work Plan was implemented, culminating in the 

quantitative methodology for identifying potentially viable regional haze control strategies for 

the CENRAP states and tribes. Using the most pertinent aerometric, emissions and air quality 

modeling data available, we implemented this methodology and, in this report, present a set of 

recommendations for regional haze precursor emissions reduction strategies. These

recommendations, once reviewed and refined by the ICS and Modeling workgroup, will be 

passed on to the CENRAP Emissions and Air Quality Modeling contractors (ENVIRON

International Corporation and the University of California, Riverside) for quantitative testing 

with the SMOKE/CMAQ/CAMx regional modeling systems. 
2

To facilitate subsequent use of this methodology, this report describes the various analytical 

steps and provides examples (both in the body of the report and in supporting appendixes). In 

addition, relevant technical support information, data sets, and analysis software have been

supplied to CENRAP for posting on their project website for access by interested parties.

1.1 Study Overview

Preliminary (Typ02a) and more recent (Typ02b) modeling projections from the CMAQ

Base18b/Typ02 scenarios (Morris et al., 2006b) have indicated that some Class I areas within or 

near the CENRAP domain may achieve the 2018 Reasonable Progress Goals (RPG) under 

current ‘on-the-books’ and ‘on-the-way’ controls while others may not unless additional

emissions reductions are implemented (see Figures 1-1 and 1-2). As shown in Figure 1-1, six 

CENRAP Class I Areas (Big Bend, Guadalupe, Wichita Mountains, Breton Island, Voyageur, 

and Boundary Waters) are projected, by the latest CMAQ modeling, to have somewhat higher 

visibility metrics (deciviews) when compared to the 2018 RPG glide paths. While Boundary 

Waters does not explicitly appear in Figure 1-1 due to data base insufficiencies, recent modeling 

by various RPOs suggests that Boundary Waters responds similarly to Voyageurs. Accordingly, 

it is thus included as one of the six projected Class I areas where additional precursor controls 

might be considered by CENRAP/ICS.

Figure 1-1. Current Visibility Projections (Base 18d/Typ02b) at CENRAP and Other Class I 

Sites (Source: Morris et al., 2006b).

Method 1 old IMPROVE algorithm predictions for CENRAP+ sites

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

BIBE1

GUMO1

WIMO1CACR1UPBU1

HEGL1

MING1

BRET1

VOYA2

MACA1

SIPS1

ISLE1

SACR1

WHIT1

WHPE1

GRSA1

ROMO1

WICA1BADL1

THRO1

LOST1

Percent of target reduction achieved

Base18b/Typ02a

Base18d/Typ02b

CENRAP non-CENRAP
3

Figure 1-2. Preliminary Visibility Projections by State (Source: Morris et al., 2006b)
4

To prepare for the modeling of potential additional control strategies, an intensified effort has 

been undertaken by the ICS work group over the past two years to ‘set the stage’ for this activity 

(see for example ICS, 2005, Seltz, 2006). Consonant with these plans and on behalf of

CENRAP, the ICS workgroup seeks to integrate focused contractor support with ongoing

workgroup activities to accomplish the following objectives:

> Analyze existing regional haze modeling inventories developed by CENRAP, the 

States, tribes, and other RPOs;

> Synthesize available and pertinent air quality and meteorological data and recent 

‘attribution of haze studies’ by CENRAP and the other RPOs;

> Review preliminary 2018 RPG modeling by CENRAP and other RPOs to identify 

the key Class I areas for which additional emissions reductions may be needed; 

> Develop a prioritized set of regional and subregional precursor emissions control 

scenarios aimed at achieving the RPG at the CENRAP Class I areas; and

> Monitor the initial 2018 control strategy modeling performed by the CENRAP 

modeling team to ascertain whether subsequent strategies need to be refined or 

new strategies developed.

The project Work Plan (Tesche and Stella, 2006) describes in detail how these objectives have 

been addressed in cooperation with ICS and CENRAP.

1.2 Approach, Assumptions, and Constraints

Development of recommendations for potential CENRAP regional haze control strategy

simulations was a three-step process. First, we assembled available information useful in

quantifying the reductions in fine particulate aerosol concentrations needed to satisfy CENRAP’s 

preliminary regional haze visibility projections. Naturally, the principal focus was on the Class I 

areas within the CENRAP region that were estimated to not meet the 2018 Reasonable Further 

Progress (RFP) glide paths. Based on preliminary and more recent modeling (Morris et al., 

2006b), some Class I areas did meet the 2018 RFP glide paths while others did not. As new 

visibility projections for the Class I areas become available, the ICS may wish to re-examine this 

study’s strategy recommendations in order to account for more up-to-date estimates. 

The second step involved developing Areas of Influence (AOIs) upwind of each Class I area 

within which common ‘visibility precursor-Class I receptor’ impacts could be aggregated into 

similar groupings. We used results of numerous statistical and pattern recognition studies, as 

well as pertinent regional photochemical aerosol modeling by Alpine and ENVIRON scientists 

as well as other groups (including the RPOs). These analyses culminated in quantitative ‘rules of 

thumb’ relating emissions reductions of visibility-impairing precursors (in tons/day) to ambient 

aerosol concentrations at each of the ten (10) CENRAP Class I monitors. We also developed 

these quantitative source-receptor relationships for a dozen Class I areas in adjoining RPOs to 

the extent possible give available data, project resources and schedule. As of this writing, 
5

CENRAP Modeling contractors are still performing focused particulate source apportionment 

modeling (CAMx PSAT) over the region. Once this work is completed, the ICS may wish to reexamine our methodology and strategy recommendations to determine if refined source-receptor 

relationships alter in any way our present findings and conclusions. 

The third step synthesized the results of the first two, together with information on the estimated 

2018 CENRAP emissions inventory and the cost-effectiveness of various controls, to deduce a 

prioritized set of RH control strategies containing elements of both regional emissions reductions 

and targeted reductions within the AOIs closest to those six CENRAP Class I areas for which 

positive visibility increments were estimated (Morris et al., 2006b). We used the most up-to-date 

modeling inventory supplied by the CENRAP Modeling contractor; however, the current round 

of inventory corrections and refinements will undoubtedly lead to refined emissions data sets in 

coming months. Thus, another constraint limiting the ‘shelf-life’ of this study’s

recommendations is the accuracy and representativeness of the draft 2018 emissions data used in 

developing this plan’s precursor emissions control recommendations. 

While project work scope precluded re-running the strategy development process described in 

this report with updated CAMx/PSAT and CMAQ visibility projections expected in late May or 

early June 2006, the methodological tools are cataloged and archived should the ICS wish to 

undertake this activity at a later time. 

1.3 Structure of Report

This report is organized as follows. Section 2 provides a brief background on the Regional Haze 

Rule (RHR) and the role that CENRAP and the other RPOs are playing in developing strategies 

that will show progress in meeting Reasonable Progress Goals by 2018. We also discuss key 

considerations that influence the design of regional and subregional control strategies in the 

context of the RHR. Our technical approach is summarized in Section 3. Details of our 

methodology are given in the Work Plan (Tesche and Stella, 2006a). In Section 4 we describe 

the information available to characterize the daily and annul composition of PM2.5 constituents 

(sulfate, nitrate, elemental carbon, etc) at the various IMPROVE monitors in the CENRAP and 

adjoining Class I Areas. We also describe the method to relate the modeled deciview (dv) or 

extinction coefficient (Mm-1) – derived from the most recent CENRAP visibility projection

modeling – to the fine particulate component concentrations at each Class I area expressed in 

units of mass per unit volume (i.e., μg/m3). 

Section 5 presents the quantitative methods for converting these concentration increments

(whose reductions will likely achieve the individual Class I areas visibility goals by 2108) to 

mass emissions rate reductions for the primary particulate aerosol precursors, NOX and SO2. In 

addition, the section describes the methods used to construct Area of Influence (AOI) domains 

surrounding each Class I area based on historical data analysis, statistical pattern recognition 

studies, and various photochemical and aerosol modeling studies performed throughout the 

eastern U.S. by Alpine, ENVIRON, state, tribal and federal regulatory agencies, the Southern 
6

Appalachian Mountains Initiative (SAMI), the RPOs, and university scientists4. In Section 6, the 

information developed in the two preceding chapters is used, together with original ana lyses of 

the 2018 regional haze inventories and control technology cost-effectiveness information, to 

construct a series of curves from which quantitative estimates of suggested precursor emissions 

controls (within specific AOIs) are developed for each Class I Area in CENRAP projected above 

the reasonable progress glide path in 2018. Our summary and recommendations are presented in 

Section 7. 

1.4 Technical Support Resources

Several technical appendixes and support documents are provided to accommodate the ext ensive 

tabular and graphical information underpinning our methodology. Some appendixes constitute 

simple tabular data or emissions summaries (in Excel format) while other appendixes contain 

information in PowerPoint or Adobe Acrobat formats. Finally, the study’s Work Plan, Final 

Report, Technical Support Documents (i.e., the appendixes and other materials), and a

compilation of science reports, professional papers and journal articles have been transferred to 

CENRAP for uploading to their project ftp site.

 

4 The AOI methodology was carried out by Dr. Jim Wilkinson of Alpine whose recent Ph.D.

original research and Dissertation from Georgia Tech focused on the development of the AOI 

methodology for regional haze, ozone, and PM2.5 control strategy modeling in the eastern U.S.
7

2.0 CONTEXT FOR REGIONAL HAZE STRATEGY DEVELOPMENT

Section 169A of the Clean Air Act (CCA) sets forth a national goal for visibility which is the 

“prevention of any future, and the remedying of any existing, impairment of visibility in Class I 

areas which impairment results from manmade air pollution.” In 1999, EPA published a final 

rule to address a type of visibility impairment known as regional haze (64 FR 35714). The 

Regional Haze Rule (RHR) requires States to submit implementation plans (SIPs) to address 

regional haze visibility impairment in federally-protected parks and wilderness areas (i.e., the 

Class I scenic areas identified in the Clean Air Act). The 1999 rule was issued to fulfill a longstanding EPA commitment to address regional haze under the authority and requirements of 

sections 169A and 169B of the CAA. In essence, the RHR prescribes that states are to make 

efforts to improve visibility in 156 Class I areas at such rates that “natural conditions” would be 

achieved in each area by 2064. A ‘reasonable rate of progress’ corresponds to linear

improvement in visibility, as characterized in units of deciview (dv), between current conditions 

during the base period of 2000-2004 and natural conditions at the end point of 2064. It is 

important to note that a modeled 2018 visibility condition at a Class I monitor – numerically 

equaling the monitor’s RPG goal – is not meant to imply ‘attainment’ of any standard nor is 

lesser modeled progress in reaching a particular RPG indicative of ‘nonattainment’. Indeed, as 

will be discussed later, progress in attaining visibility improvements at some CENRAP monitors 

(in Texas and Minnesota) may be thwarted by substantial contributions of visibility precursors 

from Mexico and Canada over which the States and Tribes have no direct control.

2.1 Role of CENRAP and the Other Regional Planning Organizations (RPOs)

CENRAP is one of five Regional Planning Organizations (RPOs) that have responsibility for 

coordinating development of State Implementation Plans (SIPs) and Tribal Implementation Plans 

(TIPs) in selected areas of the U.S. to address the requirements of the Regional Haze Rule 

(RHR). The RHR visibility SIPs/TIPs are due in 2007/2008. CENRAP modeling results may 

also form the regional component for 8-hour ozone and fine particulate (PM2.5) SIPs/TIPs that 

are also expected to be due in 2007/2008. CENRAP is a regional partnership of states, tribes, 

federal agencies, stakeholders and citizen groups established to initiate and coordinate activities 

associated with the management of regional haze and other air quality issues within the

CENRAP states. The CENRAP region includes states and tribal lands located within the 

boundaries of Arkansas, Iowa, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma 

and Texas. 

The regional emissions and fine particulate/visibility modeling for CENRAP is being performed 

by the Emissions and Air Quality Modeling Contractor that is comprised of staff from

ENVIRON International Corporation (ENVIRON) and the University of California, Riverside 

(UCR). The ENVIRON/UCR team performs the emissions and air quality modeling simulations 

for states and tribes within the CENRAP region, providing analytical results used in developing 

implementation plans under the EPA Regional Haze Rule. Alpine Geophysics serves as the 

Technical Advisor to CENRAP, working interactively with the emissions and air quality

modelers at ENVIRON and UCR. 
8

2.2 Considerations in Designing Regional Haze Control Strategies 

Where the year 2018 base case modeling does not show an acceptable regional haze or visibility 

glide slope for a Class I area within or adjacent to the CENRAP domain, additional (and possibly 

substantial) emission reductions will most likely be required to show reasonable progress in 

meeting 2108 visibility goals. Due to the unique location, meteorology, and emission sources 

within an area of influence to each Class I area, individualized control strategies reducing

emissions from the remaining residual sources or source types are most likely to achieve required 

results. It is highly unlikely that a single cost effective “across-the-board” reduction strategy will 

achieve the visibility goals for every Class I area.

Although emissions located within areas of direct proximity to Class I area monitors will 

generally have the greatest influence on attaining visibility goals, these sources may not be the 

only ones with significant impact on the air quality. Using methods such as localized geography 

analysis (e.g., within 200km of Class I area boundaries) to initially identify source types and 

pollutants with the greatest influence will only provide part of the picture. In reality, other 

methods will also provide information related to transport sources impacting a Class I area. 

These other methods can include back trajectory analysis, residence time probability, source 

apportionment modeling (PSAT, OSAT, TSSA), and the cause of haze (COH) studies performed 

in the past two years by the various RPOs including CENRAP. Other geographic studies, such as 

identifying sources that have an impact on more than one Class I area are also warranted. These 

methods can also help to limit or refine geography, pollutants, or source categories of interest for 

additional reduction potential in each Class I area.

Using these techniques in addition to review of the future year base case emissions inventories 

and assigned control strategies will allow CENRAP and the ICS Workgroup to further define 

incremental reduction allowing for the attainment of Class I area air quality or visibility 

objectives.

2.3 Resources Available to this Study

The reference section of this report and the technical discussions in Sections 4 through 6 identify 

the major data bases, reports, modeling output files and other resources used in this study. 

Certain regional modeling and data analysis studies performed by the RPOs and their contractors 

were particularly useful in developing source-receptor relationships for the various Class I areas.

These include: (a) the recent (25 April 2006) visibility projections for the CENRAP and 

adjoining RPOs recently described by Morris et al. (2006b), (b) monitoring information for the 

various Class I areas of interest, summarized on the IMPROVE website, and (c) the most recent 

2018 SMOKE emissions inventory developed for CENRAP by various state, tribal and federal 

agencies and contractors.

 
9

3.0 TECHNICAL APPROACH

As described in the Work Plan (Tesche and Stella, 2006b), our technical approach consisted of 

six (6) tasks which are summarized briefly here to provide background for the more detailed 

technical discussions given in subsequent chapters. 

Task 1: Synthesize Relevant Regional Haze Aerometric Analyses: The objective of Task 1 

was to synthesize pertinent ‘attribution of haze’ documents, CENRAP CAMx/CMAQ visibility 

modeling results, and other technical reports, papers, and analyses bearing directly on the 

quantification of emissions-source/visibility-receptor impacts at the 10 CENRAP Class I areas 

and adjoining areas. This Task was aimed at quantifying what is known about source-receptor 

relationships at the 10 CENRAP Class I areas on the basis of emissions, air chemistry and 

meteorological statistical analyses and receptor modeling studies.

Task 2: Review Existing Inventories and Control Scenario Strategy Options: This involved 

a concise summarization of existing regional haze modeling inventories and associated local, 

State, Tribal and Federal control programs to determine available incremental controls on

sources or source types affecting visibility increments (i.e., differences between the modeled 

2018 visibility level and the RFP glide slope for the particular Class I Area). In addition, we 

attempted to confirm future year control plans and reduction scenarios necessary to accomplish 

incremental reduction analysis. The product of this effort was a set of suggestions for alternate 

incremental control strategies based on analysis of available emissions, monitoring, and modeled 

data. 

The Task 2 review was conducted in a top down fashion starting with an analysis of the major 

source categories in the domains of interest (based on results from Tasks 1 and 3) to determine 

which major categories have the highest residual contribution to the area. Once the highest 

source types were identified, subcategories within those source types were reviewed. In addition 

to reviewing the residual emission categories in the future year base, we also identified

reductions that have already occurred within each category or at specific units. This allows 

CENRAP to determine if certain source categories that have yet to be controlled under the base 

case have the potential for reduction or if source types already reduced have reached the full 

cost-effective potential. Finally, unit level tables of emission comparisons from 2002 to 2018 

were developed that facilitate ICS’s review of existing emission reductions and the assignment 

of new cost-effective controls to units using the best control for the scenario.

Once the list of potential sources available for reduction were identified, we used relevant 

control strategy information extracted from EPA’s AirControlNET (Pechan, 2005) and other 

sources to further define the most cost-effective strategies for these sources. Since

AirConrolNET does not allow for the interactive processing of new inventories (it comes 

preconfigured with inventories and control strategies applied), this extract was performed outside 

of the AirConrolNET model to assign incremental control programs. Finally, we ran every

accessible control strategy against the identified source list to develop incremental cost curves 

necessary to design command and control or cost-effectiveness based control strategies by source 

or domain. This master list of controls was then used in the development of our final control 

strategy recommendations.
10

Task 3: Synthesize Relevant Regional Haze Source Attribution Modeling: 

Complementing Task 1, work under Task 3 was aimed at synthesizing key results from recent 

regional modeling studies helpful relating emissions reductions of visibility precursors (e.g. SO2, 

NOX) in upwind source regions to the improvement in visibility (in deciviews or, alternatively, in 

Mm-1) at downwind Class I. More specifically, we attempted to extract from available regional 

modeling studies useful ‘rules of thumb’ relating percentage or tonnage reductions in visibility 

reducing precursors (e.g., SO2, NOX, ammonia, and VOCs) on the expected impact on visibility 

downwind. These ‘rules of thumb’ or source-receptor relationships were essential in estimating 

the amounts of precursor emissions to be reduced in regions upwind of each of the various Class 

I areas. 

Task 4: Develop CENRAP Control Strategy Plan: The objective of Task 4 was to assemble 

the findings and technical work products from Tasks 1 through 3, supplemented with any 

additional information provided by the ICS Workgroup or CENRAP Modeling contractors, and 

construct the CENRAP Control Strategy Plan. As described in subsequent chapters, this plan 

addresses feasible regional haze control strategies with each one including both regional and subregional elements. 

More specifically, using the results of the most recent CENRAP visibility projection modeling 

(Morris et al., 2006b), we identified six Class I areas that potentially require additional regional 

and/or subregional incremental emission reductions to achieve reasonable progress visibility 

goals. Once an emissions reduction target was determined for each Class I area, we used the 

master list of controls developed in Task 2 to formulate the CENRAP Control Strategy plan, 

including cost-effectiveness as a key element. This plan identifies specific source categories 

(e.g., SIC, SCC, plant ID), and emissions reductions to be implemented. The specificity of the 

prescribed control scenarios recommended in the plan is sufficient to allow the CENRAP 

modeling contractors to readily implement the suggested changes through the SMOKE model 

input stream.

The CENRAP Control Strategy Plan is intended to identify the specific sources and/or source 

categories where additional control is available with emphasis on known incremental reductions 

first (e.g., BART). Using this plan as a starting point, CENRAP is equipped to assess the present 

strategy recommendations and identify any new assumptions (recent or new facility

configurations, updated control strategy information from the states and tribes), emergent data 

sets (e.g., CAMx PSAT modeling; updated 2018 CMAQ visibility projections), corrected

modeling inventories, and so on that were unavailable during the three-week time period when 

this plan was developed.

Task 5: Review Control Strategy Plan With ICS: The project team participated in a

teleconference call on 13 April 2006 with the CENRAP ICS Workgroup to discuss the study 

methodology, findings, and recommendations. 

Task 6: Final Report: To the maximum extend feasible within this project’s work scope, we 

incorporated written responses from CENRAP on the 10 April draft report, culminating in this 

final document.
11

4.0 ESTIMATION OF RESIDUAL VISIBILITY IMPROVEMENT NEEDS

The estimation of residual visibility improvement needs (i.e., the aerosol species concentration 

reductions [mass per unit volume] at each Class I monitor) was performed through three

activities: (a) literature review and synthesis, (b) analysis of current CMAQ visibility projections 

and IMPROVE measurements at the Class I sites, and (c) integration of this information into a 

computational scheme for use in later tasks. 

4.1 Literature Review and Synthesis of Pertinent Source-Receptor Information

Our synthesis of existing source-receptor information for the CENRAP and adjacent Class I area 

was guided by the following set of questions for which specific answers were sought in recent 

reports, papers, RPO and science meeting presentations, as well as recent one-atmosphere 

modeling studies. These core questions include: 

> What aerosol components are responsible for haze?

- What are the major components for best, worst and average days visibility 

days across the CENRAP domain and how do they compare?

- How variable are they episodically, seasonally, inter-annually?

- What site characteristics best group sites with similar patterns of major 

components?

- How do the relative concentrations of the major components compare with 

the relative emission rates nearby and regionally?

> What is meteorology’s role in the causes of haze?

- How do meteorological conditions influencing the CENRAP Class I areas 

differ for best, worst and typical haze conditions?

- What empirical relationships are their between meteorological conditions 

and haziness?

- How well can haze conditions be predicted solely using meteorological 

factors?

- What characteristics best group CENRAP Class I sites with similar

relationships between meteorological conditions and haze?

- How well can inter-annual variations in ha ze be accounted for by

variations in meteorological conditions at the CENRAP Class I areas?

> What are the emission sources responsible for haze?

- What geographic areas are associated with transported air that arrives at 

sites on best, typical and worst haze days in the CENRAP region?

- Are the emission characteristics of the transport areas consistent with the 

aerosol components responsible for haze?

- What do the aerosol characteristics on best, typical and worst days indicate 

about CENERAP or upwind emissions sources?

- What does the spatial and temporal pattern analysis indicate about the 

locations and time periods associated with sources responsible for haze?

- What evidence is there for urban impacts on haze at the CENRAP Class I 

areas and what is the magnitude and frequency when evident?
12

- What connections can be made between sample periods with unusual

species concentrations and activity of highly sporadic sources (e.g. major 

fires, dust storms)?

- What can be inferred about impacts from sources in other states, other 

RPOs and other countries, particularly Mexico and Canada?

- What refinements to default natural haze levels can be made using

ambient monitoring and emission data?

> Are there detectable and/or statistically significant multi-year trends in the 

causes of haze?

- Are the aerosol components responsible for haze changing?

Where changes are seen, are they the result of meteorological or emissions 

changes?

- Where emissions are known to have changed, are there corresponding 

changes in haze levels?

With these questions in mind, we surveyed the literature relevant to the CENRAP Class I areas 

in order to summarize:

> Characteristics of Each CENRAP Monitoring Site

- Their representation of the Class I area and nearby Class I areas; 

- Relationship to terrain features, bodies of water, etc; 

- Proximity to major point sources, cities, etc.

> Meteorological Characteristics of Each CENRAP Monitoring Site

- Expected mesoscale flow patterns of interest (sea/land breeze,

mountain/valley winds, convergence zones, nocturnal jets, etc.);

- Orographic precipitation patterns (i.e. favored for precipitation, or in rainshadow);

- Inversion layers; 

- Potential for transport from cities and other significant sources/source 

areas.

> Visibility-Aerosol Related Data Analyses

- Descriptive statistics and interpretation for aerosol data- individual

components and reconstructed extinction

- Key aerosol species component spatial and seasonal patterns (e.g., Best 

20%, middle 60%, worst 20% reconstructed extinction days and seasonal 

patterns by site)

- Spatial and seasonal patterns of aerosol components frequency

distributions. 

- Aerosol component data in light of emissions sources, monitoring site 

settings, back trajectories

- Results of cluster, CART, and other pattern-recognition analyses to group 

sites with similar patterns in aerosol component contributions to haze 
13

> Back Trajectory Analyses

- Results of back trajectory end point data for each CENRAP Class I area;

- Back trajectory summary statistics residence time by season, best 20% and 

worst 20% reconstructed extinction and aerosol components for all

CENRAP Class I areas; 

- Conditional probability maps for high and low extinction and aerosol

components. 

- Results of emissions density maps giving location information, site setting 

information, etc., and

- Mesoscale meteorological analys es complementing back trajectories.

Of course, complete answers to all these questions could not be developed in the course of this 

three week study; however, sufficient information was available that, when distilled into key 

tabular and graphical summaries, provided a solid foundation for continued efforts in Task 1 and 

especially Task 2 (discussed in Section 5). Key reports and modeling summaries synthesized 

during this initial review were supplied to CENRAP for uploading onto the CENSARA project 

website for easy access by interested CENRAP workgroup members or stakeholders.

4.2 Preliminary Visibility Estimates for Class I Areas

The visibility projection estimates for 2018 available at the time this study was performed

(Typ02a) were developed in early 2006 by ENVIRON/UCR and presented at the February

CENRAP meetings in Baton Rouge, LA. Appendix B presents these preliminary visibility

projections for the ten (10) CENRAP Class I areas and the twelve (12) outlying Class I areas in 

the WRAP, MRPO, and VISTAS domains. After the draft report had been prepared, Morris et 

al., (2006b) published an updated set of visibility projections (Typ02b). Given the importance of 

using the most up to date projections possible, where feasible we repeated our technical work 

using the updated projections (See Table 1-1 for a visual comparison of the differences). Table 

4-1 lists the following information derived from these more recent CENRAP projections of 

Morris et al., (2006b).

> Visibility (in dv) on the 20% worst days in 2002;

> The 2000-2004 visibility baseline (in dv); 

> The 2018 visibility goal (in dv) based on the requirements of the Regional Haze 

Rule; 

> The CMAQ-forecasted 2018 visibility levels on the 20% worst days;

> The ‘increment’ in visibility, expressed in dv (calculated as the difference

between the 2018 goal and the 2018 forecast. Negative values (presented in red in 

Table 2) denote that additiona l visibility improvement needed to achieve the 

desired 2018 progress goal; and

> The ‘increment’ in visibility, expressed in units of inverse mega-meters (Mm-1).
14

Table 4-1. Reasonable Progress Goal Estimates and ‘Increments’.

W20% 2000/2004 2018 2018 Deciview Ext Annual

Bkgrnd Baseline Goal Forecast Incre Incre f(RH)

RPO Class I Area ST Name DV DV DV DV DV Mm-1

CENRAP Big Bend Nat'l Park TX BIBE 6.93 17.10 14.73 16.39 1.66 7.9 2.1

CENRAP Boundary Waters MN BWCA 11.21 18.30 16.62 17.54 0.92 5.1 3.3

CENRAP Breton Island LA BRET 11.53 25.59 22.31 22.45 0.14 1.3 3.8

CENRAP Caney Creek AR CACR 11.33 25.34 22.07 20.91 -1.16 -10.0 3.2

CENRAP Guadalupe Mountains TX GUMO 7.02 17.48 15.04 16.53 1.49 7.2 1.8

CENRAP Hercules-Glades MO HEGL 11.27 25.63 22.28 21.94 -0.34 -3.1 3.1

CENRAP Mingo MO MING 11.27 26.49 22.94 22.13 -0.81 -7.7 3.2

CENRAP Upper Buffalo AR UPBU 11.28 25.31 22.03 21.33 -0.70 -6.1 3.1

CENRAP Voyageurs MN VOYA2 11.09 18.46 16.74 17.43 0.69 3.8 3.4

CENRAP Wichita Mountains OK WIMO 11.07 23.06 20.26 20.47 0.21 1.6 2.6

VISTAS Mammoth Cave KY MACA 11.53 29.94 25.65 24.01 -1.64 -19.7 3.2

VISTAS Sipsey Wilderness AL SIPS 11.39 27.71 23.91 22.72 -1.19 -12.3 3.3

MRPO Isle Royale MI ISLE 11.22 20.28 18.16 18.74 0.58 3.7 3.5

WRAP Badlands SD BADL 7.30 17.00 14.74 16.37 1.63 7.7 2.6

WRAP Great Sand Dunes CO GRSA 7.10 13.20 11.78 12.96 1.18 4.1 2.0

WRAP Lostwood Wilderness ND LOST 7.33 19.49 16.66 19.28 2.62 15.8 2.9

WRAP Rocky Mtn Nat'l Park CO ROMO 7.05 14.15 12.49 13.51 1.02 3.7 2.1

WRAP Salt Creek NM SACR 6.99 18.05 15.47 17.59 2.12 11.1 1.8

WRAP Theodore Roosevlt ND THRO 7.31 17.66 15.24 17.40 2.16 11.1 3.7

WRAP Wheeler Peak NM WHPE 7.04 11.26 10.27 11.14 0.87 2.5 1.9

WRAP White Mountain NM WHIT 6.98 14.06 12.41 13.40 0.99 3.6 1.8

WRAP Wind Cave SD WICA 7.24 15.81 13.81 15.30 1.49 6.4 2.5

The relationship between deciviews (dv) and inverse megameters (Mm-1) is described in detail 

by Malm, (1999). Equation 4-1 defines the Haze Index (HI): 

HI = 10 ln(bext/10) (4-1)

where HI is the haze index (deciviews [dv]) and bext is the light extinction coefficient (Mm-1). 

Thus, one deciview is approximately equal to 11.05 Mm-1 and a change of one dv represents a 

change of approximately ten percent in bext, “which is a small but perceptible scenic change 

under many circumstances”. Malm (1999) provides the following graphical representation

between the extinction (Mm-1), deciviews, and visual range (km):

The measured light extinction at the Class I areas for the 20% worst days each year are available

at http://vista.cira.colostate.edu/views/web/AnnualSummaryDev/Composition.aspx, the

IMPROVE site. The most recent measured extinction values (in Mm-1) for the various Class I 

monitors are listed in Table 4-2, presented in Figure 4-1, and also given in Appendix B. For the 

most part, IMPROVE extinction measurements for the 20% worst days are available for 2004, 

the most recent year analyzed. These data are presented as extinction totals for the individual 

visibility-impairing chemical species: sulfate; nitrate; organic mass; elemental carbon; soil; and 
15

coarse mass. Table 4-3 lists the fractional extinction for each chemical species. Finally, the 

IMPROVE data for each species at the 22 Class I monitors are presented as a function of time in 

the appendices to this document. These time series plots reveal the seasonal and daily variation 

in the visibility-impairing components throughout the year at teach site. Figures 4-2 and 4-3 

present the absolute and fractional extinction values listed in Tables 4-2 and 4-3 in the form of 

stacked bar charts for ease of comparison. 

Table 4-2. Measured Extinction at Class I Areas.

Amm Organic Elem Soil Coarse

RPO Class I Area ST Name Sulfate Nitrate Mass Carbon Mass Mass Total

CENRAP Big Bend Nat'l Park TX BIBE 25.86 1.57 5.85 1.80 2.21 4.55 41.84

CENRAP Boundary Waters MN BWCA 28.09 24.78 7.76 2.94 0.44 2.10 66.11

CENRAP Breton Island LA BRET 65.60 8.49 6.13 4.26 0.40 4.45 89.33

CENRAP Caney Creek AR CACR 65.68 15.43 17.95 4.27 0.79 2.66 106.78

CENRAP Guadalupe Mountains TX GUMO 15.92 4.98 5.51 1.30 2.83 9.99 40.53

CENRAP Hercules-Glades MO HEGL 67.23 21.92 21.14 5.12 0.88 2.85 119.14

CENRAP Mingo MO MING 80.44 35.11 26.10 8.95 1.55 8.40 160.55

CENRAP Upper Buffalo AR UPBU 64.43 17.39 16.47 4.48 0.90 7.23 110.90

CENRAP Voyageurs MN VOYA2 10.16 15.14 9.94 2.68 0.46 2.84 41.22

CENRAP Wichita Mountains OK WIMO 40.78 28.25 16.64 4.67 0.70 4.06 95.10

VISTAS Mammoth Cave KY MACA 146.48 10.78 15.58 5.33 1.04 1.76 180.97

VISTAS Sipsey Wilderness AL SIPS 109.27 8.09 20.22 7.06 0.95 2.66 148.25

MRPO Isle Royale MI ISLE 33.33 12.64 9.71 2.93 0.48 3.51 62.60

WRAP Badlands SD BADL 20.05 6.58 7.53 1.55 0.75 3.60 40.06

WRAP Great Sand Dunes CO GRSA 6.20 2.78 6.44 1.30 2.11 3.78 22.61

WRAP Lostwood Wilderness ND LOST 28.44 26.00 9.02 2.22 0.41 2.73 68.82

WRAP Rocky Mtn Nat'l Park CO ROMO 8.19 4.73 6.37 2.00 1.11 2.78 25.18

WRAP Salt Creek NM SACR 17.74 12.42 7.04 2.24 4.18 6.08 49.70

WRAP Theodore Roosevlt ND THRO 15.68 16.28 9.95 2.52 0.55 2.99 47.97

WRAP Wheeler Peak NM WHPE 5.69 1.26 4.98 2.05 1.59 1.29 16.86

WRAP White Mountain NM WHIT 8.77 2.49 8.52 2.11 1.58 3.81 27.28

WRAP Wind Cave SD WICA 14.27 8.91 8.35 3.17 0.79 2.08 37.57

Measured Extinction (Mm-1) on 20% Worst Days in 2004

4.3 Estimation of Visibility-Impairing Concentration Increments

The information in Tables 4-1 through 4-3 as well as other data provided in the appendices of 

this document was used to estimate the extent to which additional visibility-impairing precursor 

emissions reductions might be needed on the basis of current estimates of the projected positive 

increments and the chemical composition of fine particulate aerosol at the six CENRAP Class I 

monitors on the worst 20% days. The next step was to transform the visibility increment 

estimates into concentration increment estimates based on current IMPROVE algorithms. Using 

the modeled visibility increment (Mm-1) estimates and annual f(RH) values (Table 4-1) together 

with the measured sulfate, nitrate, OC, EC, soil, and course mass fractions from the IMPROVE 

Class I monitors (Tables 4-2 and 4-3), we deduced the atmospheric concentrations of the six 

species groups (μg/m3) using the standard IMPROVE equation (EPA, 2003). These

concentrations were calculated assuming: (a) the required concentration reductions would be met 

by each precursor in proportion to the most recent IMPROVE distribution at each Class I 

monitor (Table 4-4); and (b) the concentration reductions would be met by each precursor 

individually (Table 4-5). 
16

Table 4-3. Extinction Fraction for 20% Worst Days by Class I Area.

Amm Amm Organic Elem Soil Coarse

RPO Class I Area ST Name Sulfate Nitrate Mass Carbon Mass Mass

CENRAP Big Bend Nat'l Park TX BIBE 0.62 0.04 0.14 0.04 0.05 0.11

CENRAP Boundary Waters MN BWCA 0.42 0.37 0.12 0.04 0.01 0.03

CENRAP Breton Island LA BRET 0.73 0.10 0.07 0.05 0.00 0.05

CENRAP Caney Creek AR CACR 0.62 0.14 0.17 0.04 0.01 0.02

CENRAP Guadalupe Mountains TX GUMO 0.39 0.12 0.14 0.03 0.07 0.25

CENRAP Hercules-Glades MO HEGL 0.56 0.18 0.18 0.04 0.01 0.02

CENRAP Mingo MO MING 0.50 0.22 0.16 0.06 0.01 0.05

CENRAP Upper Buffalo AR UPBU 0.58 0.16 0.15 0.04 0.01 0.07

CENRAP Voyageurs MN VOYA2 0.25 0.37 0.24 0.07 0.01 0.07

CENRAP Wichita Mountains OK WIMO 0.43 0.30 0.17 0.05 0.01 0.04

VISTAS Mammoth Cave KY MACA 0.81 0.06 0.09 0.03 0.01 0.01

VISTAS Sipsey Wilderness AL SIPS 0.74 0.05 0.14 0.05 0.01 0.02

MRPO Isle Royale MI ISLE 0.53 0.20 0.16 0.05 0.01 0.06

WRAP Badlands SD BADL 0.50 0.16 0.19 0.04 0.02 0.09

WRAP Great Sand Dunes CO GRSA 0.27 0.12 0.28 0.06 0.09 0.17

WRAP Lostwood Wilderness ND LOST 0.41 0.38 0.13 0.03 0.01 0.04

WRAP Rocky Mtn Nat'l Park CO ROMO 0.33 0.19 0.25 0.08 0.04 0.11

WRAP Salt Creek NM SACR 0.36 0.25 0.14 0.05 0.08 0.12

WRAP Theodore Roosevlt ND THRO 0.33 0.34 0.21 0.05 0.01 0.06

WRAP Wheeler Peak NM WHPE 0.34 0.07 0.30 0.12 0.09 0.08

WRAP White Mountain NM WHIT 0.32 0.09 0.31 0.08 0.06 0.14

WRAP Wind Cave SD WICA 0.38 0.24 0.22 0.08 0.02 0.06

Extinction Fraction for 20% Worst Days by Class I Area

Table 4-4. Required Concentration Reductions: All Species.

RPO Class I Area ST Name Sulfate Nitrate OC EC Soil Coarse

CENRAP Big Bend Nat'l Park TX BIBE 0.77 0.05 0.28 0.03 0.42 1.43

CENRAP Boundary Waters MN BWCA 0.22 0.19 0.15 0.02 0.03 0.27

CENRAP Breton Island LA BRET 0.08 0.01 0.02 0.01 0.01 0.11

CENRAP Caney Creek AR CACR

CENRAP Guadalupe Mountains TX GUMO 0.53 0.16 0.25 0.02 0.50 2.97

CENRAP Hercules-Glades MO HEGL

CENRAP Mingo MO MING

CENRAP Upper Buffalo AR UPBU

CENRAP Voyageurs MN VOYA2 0.09 0.14 0.23 0.02 0.04 0.44

CENRAP Wichita Mountains OK WIMO 0.09 0.06 0.07 0.01 0.01 0.11

VISTAS Mammoth Cave KY MACA

VISTAS Sipsey Wilderness AL SIPS

MRPO Isle Royale MI ISLE 0.19 0.07 0.14 0.02 0.03 0.34

WRAP Badlands SD BADL 0.50 0.16 0.36 0.03 0.14 1.16

WRAP Great Sand Dunes CO GRSA 0.19 0.08 0.29 0.02 0.38 1.13

WRAP Lostwood Wilderness ND LOST 0.75 0.69 0.52 0.05 0.09 1.05

WRAP Rocky Mtn Nat'l Park CO ROMO 0.19 0.11 0.24 0.03 0.17 0.69

WRAP Salt Creek NM SACR 0.73 0.51 0.39 0.05 0.93 2.26

WRAP Theodore Roosevlt ND THRO 0.33 0.34 0.57 0.06 0.13 1.15

WRAP Wheeler Peak NM WHPE 0.15 0.03 0.19 0.03 0.24 0.32

WRAP White Mountain NM WHIT 0.21 0.06 0.28 0.03 0.21 0.84

WRAP Wind Cave SD WICA 0.32 0.20 0.36 0.05 0.13 0.59

Assuming Controls in Proportion of Area-Specific Composition

Reduction in All Species (μg/m3) to Eliminate DV Increment
17

Table 4-5. Required Concentration Reductions: One Specie.

RPO Class I Area ST Name Sulfate Nitrate OC EC Soil Coarse

CENRAP Big Bend Nat'l Park TX BIBE 1.25 1.25 1.97 0.79 7.88 13.13

CENRAP Boundary Waters MN BWCA 0.51 0.51 1.27 0.51 5.08 8.46

CENRAP Breton Island LA BRET 0.12 0.12 0.33 0.13 1.31 2.19

CENRAP Caney Creek AR CACR

CENRAP Guadalupe Mountains TX GUMO 1.34 1.34 1.81 0.72 7.23 12.05

CENRAP Hercules-Glades MO HEGL

CENRAP Mingo MO MING

CENRAP Upper Buffalo AR UPBU

CENRAP Voyageurs MN VOYA2 0.37 0.37 0.95 0.38 3.81 6.35

CENRAP Wichita Mountains OK WIMO 0.21 0.21 0.40 0.16 1.61 2.68

VISTAS Mammoth Cave KY MACA

VISTAS Sipsey Wilderness AL SIPS

MRPO Isle Royale MI ISLE 0.35 0.35 0.92 0.37 3.67 6.12

WRAP Badlands SD BADL 0.99 0.99 1.93 0.77 7.73 12.88

WRAP Great Sand Dunes CO GRSA 0.68 0.68 1.02 0.41 4.07 6.78

WRAP Lostwood Wilderness ND LOST 1.82 1.82 3.96 1.58 15.85 26.41

WRAP Rocky Mtn Nat'l Park CO ROMO 0.59 0.59 0.94 0.37 3.74 6.24

WRAP Salt Creek NM SACR 2.05 2.05 2.77 1.11 11.09 18.49

WRAP Theodore Roosevlt ND THRO 1.00 1.00 2.77 1.11 11.07 18.45

WRAP Wheeler Peak NM WHPE 0.45 0.45 0.63 0.25 2.54 4.23

WRAP White Mountain NM WHIT 0.67 0.67 0.90 0.36 3.60 6.00

WRAP Wind Cave SD WICA 0.85 0.85 1.60 0.64 6.39 10.65

Assuming Controls on Only 1 Specie 

Reduction in One Specie (μg/m3) to Eliminate DV Increment

Following the IMPROVE methodology, the relationship between the extinction (Mm-1) of an 

individual chemical species and the volumetric mass concentration is as follows:

bSulfate = 3 • f(RH) • [SO4] 

bNitrate = 3 • f(RH) • [NO3] 

bEC = 10 • [EC] 

bOM = 4 • [OM]

bSoil = 1 • [Soil]

bCM = 0.6 • [CM] 

bRay = 10 Mm-1

bext = bRay + bSulfate + bNitrate + bEC +bOM + bSoil + bCM

The numeric coefficient at the beginning of each equation is the dry scattering or absorption 

efficiency. The f(RH) term is a monthly-average relative humidity adjustment factor. The terms 

in the brackets are the concentrations in micrograms per cubic meter (μg/m

3

) that will need to be 

reduced on the 20% worst days at the Class I monitor to make up for the projected visibility 

‘increment’. 
18

Rearranging yields a solution for the aerosol concentrations as a function of the measured or 

modeled extinction:

[SO4] = bSulfate / [3 • f(RH)]

[NO3] = bNitrate / [3 • f(RH)]

[EC] = bEC / 10

[OM] = bOM / 4

[Soil] = bSoil

[CM] = bCM / 0.6

Note that the sulfate (SO4) and nitrate (NO3) components are hygroscopic because their

extinction coefficients depend upon relative humidity. The concentrations, in square brackets, 

are in μg/m

3

 and bext is in units of Mm-1. The Rayleigh scattering term (bRay) has a default value 

of 10 Mm-1, as recommended in EPA guidance for tracking reasonable progress (EPA, 2003). 

The effect of relative humidity variability on the extinction coefficients for SO4 and NO3 can be 

estimated in several ways, but given the scope of this analysis, we calculated annual average 

Class I areas-specific monthly f(RH) values (last column of Table 4-1) from the seasonal f(RH) 

data provided by EPA in the BART guidelines.
19

Figure 4-1. Measured Extinction Coefficients at Class I Areas Based on IMPROVE Data.

IMPROVE Observed bEXT on Worst 20% Days in 2004 

0

25

50

75

100

125

150

175

200

BIBE

BWCA

BRET

CACR

GUMO

HEGL

MING

UPBU

VOYA2

WIMO

MACA

SIPS

ISLE

BADL

GRSA

LOST

ROMO

SACRTHRO

WHPE

WHIT

WICA

bEXT (Mm-1

)

Coarse Mass

Soil

Elemental Carbon

Organic Mass

Nitrate

Sulfate
20

Figure 4-2. Measured Fractional Extinction at Class I Areas Based on IMPROVE Data.

IMPROVE Observed bEXT on Worst 20% Days in 2004

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

BIBE

BWCA

BRET

CACR

GUMO

HEGL

MING

UPBU

VOYA2

WIMO

MACA

SIPSISLE

BADL

GRSA

LOST

ROMO

SACRTHRO

WHPE

WHIT

WICA

Fraction of Total bEXT 

Sulfate Nitrate Organic Mass Elemental Carbon Soil Coarse Mass
21

5.0 ESTIMATION OF EMISSIONS REDUCTION NEEDS 

5.1 Development of the Areas of Influence (AOI)

To quantify the incremental emissions reductions needed to ameliorate positive visibility

increments at Class I areas, it was first necessary to identify those regions that adversely impact 

visibility at the Class I areas. These Areas of Influence (AOI) directly identify the source 

regions whose emissions impact a Class I area. Further, an AOI can also be constructed such 

that it provides a quantitative assessment of the impact of the emissions from a source region on 

such metrics as PM2.5 concentration at a Class I area. This should not be confused with source 

apportionment where source regions are assigned quantitative culpability to an overall air quality 

metric such as sulfate concentration or light extinction. Instead, an AOI ideally describes 

geographically the emissions source regions and magnitude of, say, the impact that a one ton 

reduction in SO2 emissions has on sulfate concentration (μg/m3) at a Class I area.

An AOI can be constructed based on a variety of data such as: sensitivities derived from the 

Decoupled Direct Method (DDM) (Yang et al., 1997; Mendoza et al., 2000); brute force 

sensitivities; various forms of back trajectory analysis which examine air mass residence time 

(e.g., Schichtel et al., 2006; DRI, 2005c); and methods that combine back trajectory analyses 

with such information as emissions impact potential (e.g., Raffuse et al., 2005). Over the last

two years, one or more of these methods has been used to construct AOIs or AOI-like diagrams 

for all the Class I areas of interest to this study. Therefore, it was necessary to identify, gather, 

and synthesize these data from the many sources so that a consistent set of AOIs could be 

constructed. 

Appendix C is a compendium of AOI data for each Class I area of interest that could be extracted 

from the body of literature that is available. The first six slides of Appendix C provide examples 

of the data that were available to construct the AOIs – references are provided on each slide. 

Ultimately, the Residence Time Difference plots (DRI, 2005c), the Probability of Regional 

Source Contribution to Haze (PORSCH) plots (Raffuse et al., 2005), the Tagged Species Source 

Apportionment (TSSA) results (Tonnesen and Wang, 2004; UCR, 2006), and a good deal of 

engineering judgment were used to construct a consistent set of AOIs for each Class I area. 

Residence Time Difference (RTD) plots were constructed based on Back Trajectory Residence 

Time (BTRT) plots. Back trajectory analyses use meteorological fields to estimate the most 

likely geographical path an air mass traversed to end at a particular receptor. Of note, the 

meteorological field can be based on interpolation of observations, modeled (e.g., from a

prognostic meteorological model such as MM5), or a hybrid field based on combined modeled 

and observed values. The method essentially reverses the wind field, moving an air mass

backward in time. Back trajectories oversimplify actual atmospheric conditions in that

dispersion is ignored. Further, the potential emissions source regions that impact a receptor are 

underestimated given that it is impossible to track every air parcel impacting the receptor. 

The BTRT estimates that were developed by DRI (2005b) and used in this study were estimated 

using HYSPLIT (Draxler and Hess, 1997; NOAA, 2006). HYSPLIT uses archived three 

dimensional meteorological fields generated from observations and short-term meteorological

forecasts. The model produces a series of endpoints representing longitude, latitude, and
22

elevation of the parcel at one-hour intervals. BTRT plots at each site were calculated for all 

days, by month, and by best and worst twenty percentile days (DRI, 2005c). BTRT plots give 

the fraction of total hours that an air parcel resided over each specific geographical area. RTD 

plots were created by subtracting the map for all days at a site from the map for the 20% worst 

days by pollutant. RTD plots were computed for the twenty percentile worst sulfate, nitrate, 

organic carbon, elemental carbon, fine soil, and coarse mass days.

The worst twenty percentile sulfate RTD plots, for example, shows the difference in residence 

time between the worst sulfate days and all days. If the number is positive, then the residence 

time on the worst sulfate days is greater than on all days. The residence time difference map 

simply shows the areas that air was more frequently (positive numbers) passing over on worst 

case days compared to all days. 

The PORSCH system is a suite of GIS tools that combines modeled backward wind trajectories, 

monitored concentrations, meteorological conditions, and emissions estimates to estimate

probable regions of influence. PORSCH combines ensemble backward trajectories with

chemically speciated emissions data to estimate the trajectory-emissions density-weighted area 

likely to impact a receptor site. PORSCH can do this for a single day or a suite of days though 

for purposes of this study, only data relevant to the 20% worst haze days were extracted.

As the name implies Tagged Species Source Apportionment (TSSA) uses “Tagged Chemical 

Species,” or tracers, to track chemical transformations and transport of each chemical species or 

precursor species during an air quality model run. Key chemical species are identified for 

specific emissions source regions or emissions source categories. These tagged chemical species 

are tracked during all phases of the air quality modeling run (e.g., advection, diffusion, 

deposition, chemical transformation), and the end results are three dimensional fields in time

showing source attribution of the chemical species for any grid cell in model domain. When 

chemical species are tagged by emissions source region, this provides valuable corroborative 

evidence for identifying key AOI regions.

Slides 8 through 82 of Appendix C contain the raw data that was extracted from the literature 

base, which served as the foundation to develop the AOIs for the ten CENRAP Class I areas. 

Slides 84 through 184 of Appendix C contain the raw data from which AOIs were synthesized 

for the nine WRAP and two VISTAS Class I areas that border the CENRAP states. Because 

RTD plots were available for the entire suite of twenty-one Class I areas, they served as the 

primary basis from which the AOIs were estimated. The RTD plots were manually examined to 

determine “natural break-points” in residence time difference (only positive values were

considered in these plots as positive values indicate air mass residence was greatest in these 

geographical areas on the 20% worst haze days). 

In many cases, these “natural break-points” were difficult to determine given that the scales on 

the RTD plots were not consistent; hence, engineering judgment was used to place a “breakpoint.” For virtually all Class I areas, it was possible to determine at least two “break-points” 

and in some instances, three and four “break-points” were determined. For purposes of this 

effort, a “break-point” was generally placed where the residence time difference transition was 

on the order of a factor of ten and over large geographical areas. Little pockets of large RTD 

transitions, such as might occur over Lake Michigan or the Gulf of Mexico, were merged into a 
23

larger “break-point.” Once a “break-point” was determined, a hand drawn contour was placed 

on the plot to indicate the Level 1, 2, or greater “break-point.” This was done for each of the 

chemical species classes: sulfate; nitrate; organic carbon; elemental carbon; fine soils; and coarse 

material, at each Class I area. For clarification purposes, the Level 1 “break-point” is always the 

smallest polygon closest to the Class I area, and subsequent Level 2, 3, or greater “break-points” 

cover progressively larger areas.

Once the RTD “break-points” were determined, the plots were manually compared to the 

supporting PORSCH and TSSA data in order to determine if a “break-point” needed to

expanded, contracted, or moved. The PORSCH data were used primarily to determine if the 

spatial extent of a “break-point” was adequate and the TSSA data were used to determine if the 

areas of emissions impact potential were captured within the spatial extent of the RTD “breakpoints.” Based on this reconciliation effort, the Level 1, 2, or greater “break-point” contours 

were manually adjusted on the plots. Again, a great deal of engineering judgment was used in 

how these data were combined. This initial effort resulted in the development of 126 plots (six 

pollutants times twenty-one Class I areas) consisting of one or more “break-point” contours.

Next, each plot was manually compared to the remaining plots to determine if any of the Level 1, 

2 or greater “break-point” contours were similar in their geographic placement. If a set of 

contours from different Class I areas had similar geographic placement, the plots were combined 

into a single set of contours. In many cases, the “break-point” contours were again manually 

adjusted so that different plots could be combined into a single set representing multiple Class I 

areas and multiple pollutants.

This final set of manually created, combined “break-point” contours is what is referred to as the 

Area of Influence (AOI) for each Class I area. However, these hand drawn AOIs are useless in 

their current form since it would have been far too time consuming to try to manually extract the 

counties over which an AOI passed – a step which is necessary if one is to determine the 

emissions impact potential from a geographic area (i.e., AOI) that impacts a Class I site. 

Therefore, it is necessary to convert the hand drawn AOIs into a geocoded, electronic file.

Geocoding of the hand drawn AOIs is accomplished by first scanning the image into an

electronic file. The scanned image is then registered to a known set of geographical objects. In 

this case, the geographical objects are the political boundaries of the United States. The function 

of registering the scanned image, which itself is a political boundaries map of the United States 

with a set of hand drawn AOIs, is performed using a Geographic Information System (GIS). 

Secondly, the registered scanned image is rectified so that the image retains its geographic 

relationship to real world coordinates. Finally, the contours of the rectified image are digitized. 

The final set of AOIs is shown in Slides 136 to 143 of Appendix C. These represent the 

geocoded AOIs that are used to extract a list of counties whose emissions sources have the 

greatest potential to impact the air quality at a Class I area. Again, ARC/Info was used to extract 

the counties within each AOI. Figure 5-1 is an example geocoded AOI for the Boundary Waters 

and Voyageurs Class I areas. Note the distinction between the Level 1 and Level 2 AOIs for 

both sulfate-to-SO2 and nitrate-to-NOX sensitivities.
24

SO2 – Level 1

SO2 – Level 2

NOX – Level 2

NOX – Level 1

Boundary Waters

Voyageurs

Figure 5-1. Example Geocoded AOI for Boundary Waters and Voyageurs Class I areas. Green 

contours delineate areas of influence where NOX emissions impact aerosol nitrate at the Class I 

areas. Red contours delineate areas of influence where SO2 emissions impact aerosol sulfate at 

Class I areas.

5.2 Development of Visibility Impairing Pollutant Concentrations to Precursor 

Emissions Sensitivity Coefficients

Though a list of counties can now be identified whose emissions sources have the greatest 

potential to impact air quality at a Class I area, this list has limited value until a quantitative 

value to associate emissions to air quality is estimated. Ideally, these associative values take the 

form of μg/m3 of pollutant reduced per ton per day of precursor emissions reduced. For 

example, -0.001 μg/m3of sulfate per ton per day SO2 reduced tells one that for each ton of SO2

reduced within an AOI, the Class I area will exhibit a decrease of 0.001 μg/m3in sulfate 

concentration. This value is referred to as a sensitivity value and is very powerful at informing 

efforts such as those pursued in this study. A great deal of work has been performed to ascertain 

such sensitivities, and it is from this body of knowledge that sensitivities specific to the current 

efforts have been derived.

Tesche et al. (2003c) conducted a suite of brute force sensitivity runs using the CAMx and 

CMAQ air quality modeling (AQM) systems over the eastern United States on behalf of 

VISTAS. By systematically perturbing the global inventory (e.g., reducing global NOX

emissions by 10%) and rerunning the AQM, they developed a suite of metrics that provided the 

maximum reduction to say the peak, modeled ammonium nitrate. By converting the 10% NOX

reduction to actual tons per day NOX reduction, which is simply done by taking 10% of the 
25

emissions in the AQM-ready emissions files, and dividing that into the peak concentration

reduction, the sensitivity that is of most importance is realized. Though this value is a more 

global sensitivity, its use is still valid for our needs. Indeed, by assuming that such a sensitivity is 

valid across the domain, this general purpose sensitivity value can be extended to all the AOIs of 

interest by computing the value of a 10% reduction in each of the AOIs and dividing this number 

into the general sensitivity value derived from the average of all the sensitivities, by pollutant of 

course, estimated by Tesche et al. (2003c).

Appendix D shows an Excel workbook containing the summary data (i.e., worksheet named 

“General”) from Tesche et al. (2003c). The worksheet shows the results of the specific 

sensitivity analyses conducted, and the results of our efforts to compute a general purpose 

sensitivity value. Once a general purpose sensitivity value was computed, it was recast in a form 

specific to the Class I areas of interest. This was done by assuming that the general purpose 

sensitivity (e.g., μg/m3sulfate reduction per 10% reduction in SO2 emissions) was va lid across 

the domain and dividing this number by the tons per day value deduced from a 10% reduction of 

a precursor pollutant in the AOI of interest.

Though a general purpose sensitivity value was estimated for all Class I areas and AOIs of 

interest, othe r sensitivity information that was more specific to certain Class I areas was

available from work done at the Georgia Institute of Technology (GIT, 2006). Researchers at 

GIT conducted numerous brute force sensitivity runs of the CMAQ AQM on behalf of VISTAS. 

 One component of these efforts was to conduct specific emissions source region and emissions 

source category sensitivity experiments to determine light extinction sensitivities to a reduction 

in one ton of precursor emissions at Mingo Wilderness, Upper Buffalo, Caney Creek, Hercules 

Glade, Breton Island, Sipsey, and Mammoth Cave. The emissions source regions for the GIT 

efforts (GIT, 2006) included the individual VISTAS states, the clustered CENRAP states, and 

the clustered MANE-VU states. The GIT (2006) results were extracted and summaries were 

prepared for the combined Mingo Wilderness-Upper Buffalo-Caney Creek-Hercules Glade

AOIs, the Breton Island AOI, the Sipsey AOI, and the Mammoth Cave AOI. The results of these 

efforts were summarized in Appendix D, Excel worksheet “Class I Specific.”

Finally, the results of the sensitivity summary efforts were combined in order to prepare a

consistent set of sensitivity values by AOI. This summary is presented in Appendix D, Excel 

worksheet “Summary” and in Table 5-1.
26

Table 5-1. Synthesis of Sensitivity Values for Each Class I Area by AOI level. Units should be 

interpreted as reduction in nitrate (sulfate) concentration (μg/m3) per average daily ton reduction 

in NOX (SO2) emissions in the specified AOI Level (see Figure 4-5 for an example of the 

delineation of the AOI Level).

Level 1 Level 1 Level 2 Level 2

Abb Class I RPO NOX SO2 NOX SO2

ug/m**3/ton ug/m**3/ton ug/m**3/ton ug/m**3/ton

badl Badlands WRAP -0.001 -0.008 -0.003 -0.002

bibe Big Bend CENRAP -0.002 -0.004 -0.001 -0.001

bowa Boundary Waters CENRAP -0.002 -0.006 -0.004 -0.002

bret Breton Island CENRAP -0.00008 -0.002 -0.00005 -0.0007

cacr Caney Creek CENRAP -0.0004 -0.003 -0.002 -0.002

grsa Great Sand Dunes WRAP -0.003 -0.02 -- -0.0005

gumo Guadalupe Mountains CENRAP -0.01 -0.004 -0.002 -0.001

herc Hercules Glade CENRAP -0.0004 -0.003 -0.002 -0.002

lost Lostwood Wilderness WRAP -0.01 -0.008 -0.003 -0.002

maca Mammoth Cave VISTAS -0.001 -0.005 -0.0008 -0.005

ming Mingo Wilderness CENRAP -0.0004 -0.003 -0.002 -0.002

romo Rocky Mountain WRAP -0.007 -0.02 -0.003 -0.0005

sacr Salt Creek WRAP -0.01 -0.08 -0.002 -0.0007

sips Sipsey Wilderness VISTAS -0.001 -0.007 -0.0008 -0.005

thro Theodore Roosevelt WRAP -0.01 -0.008 -0.003 -0.002

upbu Upper Buffalo CENRAP -0.0004 -0.003 -0.002 -0.002

voya Voyageurs CENRAP -0.002 -0.006 -0.004 -0.002

whmo White Mountain WRAP -0.01 -0.08 -0.002 -0.0007

whpe Wheeler Peak WRAP -0.01 -0.08 -0.002 -0.0007

wica Wind Cave WRAP -0.001 -0.008 -0.003 -0.002

wich Wichita Mountain CENRAP -0.005 -0.001 -0.003 -0.0004

5.3 Estimated Emissions Reductions Necessary to Attain 2018 Glide Path

Now that the visibility ‘increment’ (Table 4-4 [proportional species reduction] and Table 4-5 

[single specie reduction]) and the chemical species-to-precursor emissions sensitivity

coefficients (Table 5-1) are known by Class I area, it is a simple matter to compute the 

annualized, incremental emissions reductions that are needed at each Class I area to attain the 

2018 glide path. This is accomplished by dividing the visibility ‘increment’ by the sensitivity 

coefficient and multiplying by 365. 

Table 5-2 shows the required incremental reductions of SO2 and NOX emissions that are 

estimated to be required in order for the Class I areas to meet the glide slope by 2018. The 

estimated SO2 and NOX reductions in Table 5-2 are proportional to chemical species

contributions during the 20% worst haze days. In contrast, Table 5-3 shows the estimated SO2

and NOX emissions reductions if only one chemical species is reduced. The emissions

reductions requirements in Tables 5-2 and 5-3 are reported to two significant figures.

For example, in order for Big Bend to meet the 2018 visibility glide path, approximately 73,000 

tons per year of incremental SO2 emissions reductions (Table 5-2) from SO2 emissions source 
27

residing in the Level 1 AOI (Figure 5-2) are required assuming that incremental emissions 

reductions are developed based on a proportional reduction in the chemical species. Hence, in 

addition to the estimated incremental SO2 emissions reductions of 73,000 tons per year,

estimated incremental NOX emissions reductions of 8,000 tons per year are also expected to be 

required. Additionally, incremental emissions reductions in coarse material, soil, elemental

carbon, and organic compounds are also necessary if, again, emissions reductions are based on 

proportional reductions in the chemical species, though these reductions were not estimated 

given that reasonably available emissions control scenarios exist only for NOX and SO2.

If only one chemical specie is controlled, for example sulfate, then precursor SO2 incremental 

emissions reductions from emissions sources located within the SO2 Level 1 AOI (Figure 5-2) 

are estimated to be 120,000 tons per year (Table 5-3). On the other hand, if only nitrate is 

controlled, precursor NOX incremental emissions reductions from emissions sources located 

within the NOX Level 1 AOI (Figure 5-2) are estimated to be 210,000 tons per year.

SO2 – Level 1

NOX – Level 1

Figure 5-2. Geocoded AOIs for Big Bend, Guadalupe Mountain, Salt Creek, White Mountain, 

and Wheeler Peak. The Big Bend Level 1 AOI for SO2 and NOX are identified.
28

Table 5-2. SO2 and NOX Emissions Reduction Requirements (tons per year) Assuming Proportional Reductions in Sulfate and 

Nitrate.

Required SO2 Required NOX

sulfate-to-SO2 nitrate-to-NOX Emissions Reductions Emissions Reductions

Class I Area ST Sulfate Nitrate OC EC Soil Coarse (tons / year) (tons / year)

Big Bend Nat'l Park TX 0.77 0.05 0.28 0.03 0.42 1.43 -0.004 -0.002 73,000 8,000

Boundary Waters MN 0.22 0.19 0.15 0.02 0.03 0.27 -0.006 -0.004 13,000 19,000

Breton Island LA 0.08 0.01 0.02 0.01 0.01 0.11 -0.0001 -0.000007 226,000 572,000

Caney Creek AR -0.0002 -0.00001

Guadalupe Mountains TX 0.53 0.16 0.25 0.02 0.50 2.97 -0.004 -0.01 50,000 4,000

Hercules-Glades MO -0.00019 0.0000

Mingo MO -0.0002 -0.00001

Upper Buffalo AR -0.0002 -0.00001

Voyageurs MN 0.09 0.14 0.23 0.02 0.04 0.44 -0.006 -0.004 5,700 14,000

Wichita Mountains OK 0.09 0.06 0.07 0.01 0.01 0.11 -0.001 -0.005 32,000 4,500

Mammoth Cave KY -0.005 -0.001

Sipsey Wilderness AL -0.007 -0.001

Isle Royale MI 0.19 0.07 0.14 0.02 0.03 0.34 -0.006 -0.004 11,000 7,000

Badlands SD 0.50 0.16 0.36 0.03 0.14 1.16 -0.008 -0.001 23,000 45,000

Great Sand Dunes CO 0.19 0.08 0.29 0.02 0.38 1.13 -0.02 -0.003 3,400 10,000

Lostwood Wilderness ND 0.75 0.69 0.52 0.05 0.09 1.05 -0.008 -0.01 35,000 19,000

Rocky Mtn Nat'l Park CO 0.19 0.11 0.24 0.03 0.17 0.69 -0.02 -0.007 3,500 5,800

Salt Creek NM 0.73 0.51 0.39 0.05 0.93 2.26 -0.004 -0.01 68,800 13,000

Theodore Roosevlt ND 0.33 0.34 0.57 0.06 0.13 1.15 -0.008 -0.01 15,000 12,000

Wheeler Peak NM 0.15 0.03 0.19 0.03 0.24 0.32 -0.08 -0.01 690 800

White Mountain NM 0.21 0.06 0.28 0.03 0.21 0.84 -0.08 -0.01 990 1,500

Wind Cave SD 0.32 0.20 0.36 0.05 0.13 0.59 -0.008 -0.001 15,000 56,000

Level 1 AOI

Proportional Reduction Requirements (ug/m3)

(ug/m3/ton reduced)
29

Table 5-3. SO2 and NOX Emissions Reduction Requirements (tons per year) Assuming a Single Chemical Species is Controlled.

Required SO2 Required NOX

sulfate-to-SO2 nitrate-to-NOX Emissions Reductions Emissions Reductions

Class I Area ST Sulfate Nitrate OC EC Soil Coarse (tons / year) (tons / year)

Big Bend Nat'l Park TX 1.25 1.25 1.97 0.79 7.88 13.13 -0.004 -0.002 120,000 210,000

Boundary Waters MN 0.51 0.51 1.27 0.51 5.08 8.46 -0.006 -0.004 32,000 51,000

Breton Island LA 0.12 0.12 0.33 0.13 1.31 2.19 -0.0001 -0.000007 308,000 6,010,000

Caney Creek AR -0.0002 -0.00001

Guadalupe Mountains TX 1.34 1.34 1.81 0.72 7.23 12.05 -0.004 -0.01 130,000 33,000

Hercules-Glades MO -0.00019 0.0000

Mingo MO -0.0002 -0.00001

Upper Buffalo AR -0.0002 -0.00001

Voyageurs MN 0.37 0.37 0.95 0.38 3.81 6.35 -0.006 -0.004 23,000 37,000

Wichita Mountains OK 0.21 0.21 0.40 0.16 1.61 2.68 -0.001 -0.005 75,000 15,000

Mammoth Cave KY -0.005 -0.001

Sipsey Wilderness AL -0.007 -0.001

Isle Royale MI 0.35 0.35 0.92 0.37 3.67 6.12 -0.006 -0.004 22,000 35,000

Badlands SD 0.99 0.99 1.93 0.77 7.73 12.88 -0.008 -0.001 46,000 280,000

Great Sand Dunes CO 0.68 0.68 1.02 0.41 4.07 6.78 -0.02 -0.003 12,000 82,000

Lostwood Wilderness ND 1.82 1.82 3.96 1.58 15.85 26.41 -0.008 -0.01 84,000 52,000

Rocky Mtn Nat'l Park CO 0.59 0.59 0.94 0.37 3.74 6.24 -0.02 -0.007 11,000 31,000

Salt Creek NM 2.05 2.05 2.77 1.11 11.09 18.49 -0.004 -0.01 192,800 50,000

Theodore Roosevlt ND 1.00 1.00 2.77 1.11 11.07 18.45 -0.008 -0.01 45,000 36,000

Wheeler Peak NM 0.45 0.45 0.63 0.25 2.54 4.23 -0.08 -0.01 2,100 11,000

White Mountain NM 0.67 0.67 0.90 0.36 3.60 6.00 -0.08 -0.01 3,100 16,000

Wind Cave SD 0.85 0.85 1.60 0.64 6.39 10.65 -0.008 -0.001 39,000 240,000

Control (ug/m3)

(ug/m3/ton reduced)

Reduction Requirement Assuming Single Species Level 1 AOI
30

6.0 PRIORITIZED CENRAP EMISSIONS REDUCTION SCENARIOS

6.1 Summary of Emission Inventories Used in Control Plan Development

A necessary component of the control strategy design is a thorough review of the emission 

inventories that are used in the modeling of the future year base case. This inventory can shed 

light on the residual emissions from sources or source categories defined to be within areas of

transport or impact of a Class I area. We obtained and used the current CENRAP future year 

(2018) base case and 2002 base year emissions to conduct a review of the top emitting categories 

and pollutants within identified impact areas.

The SMOKE-ready modeling files for both 2002 and 2018 base year and base cases were 

obtained from CENRAP’s emissions modeling contractor (UCR) in addition to a supplementary 

county level summary of onroad source emissions produced from the gridded, temporalized 

MOBILE6-based emissions output. Using the annualization methods confirmed with UCR and 

identified in the SMOKE file headers, each SMOKE input file was converted to annual

emissions and summed for the geography and domain of interest. 

Tables 6-1 and 6-2 present the ma jor source category breakdown of these emissions for the entire 

CENRAP domain. AOI-specific breakdowns are presented in Appendix E of this document for 

those CENRAP Class I areas projected to be above the reasonable progress glide slope. Because 

the SMOKE-ready files were used in this analysis, the particulate matter transport factor is 

included in the PM emission summaries. This factor is applied to account for the removal of a 

substantial portion of fugitive dust emissions near a source by surrounding vegetation and 

structures when such emissions are used in regional scale modeling analyses.

Table 6-1. CENRAP 2002 Base Year Annual Emissions Summary.

CENRAP 2002 Base Year Annual Emissions (Tons)

Source Category VOC NOx CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 13,838 1,006,914 290,478 1,545,327 79,429 53,475 4,462

Fuel Comb. Industrial 74,226 907,445 387,579 568,270 118,626 78,412 6,243

Fuel Comb. Other 151,527 98,457 435,320 34,605 67,380 65,556 4,870

Chemical & Allied Product Mfg 56,154 37,002 117,918 140,403 10,946 8,503 13,254

Metals Processing 8,178 16,197 115,827 86,425 14,930 6,486 4

Petroleum & Related Industries 486,785 306,947 274,187 81,950 10,442 7,408 819

Other Industrial Processes 150,388 107,908 119,678 89,127 235,401 74,228 206,676

Solvent Utilization 799,050 392 248 21 1,338 1,110 17

Storage & Transport 200,946 9,023 39,075 2,416 17,321 5,294 220

Waste Disposal & Recycling 58,790 16,836 248,560 5,319 57,500 53,804 9,914

Highway Vehicles 985,527 1,780,289 13,178,713 51,829 100,256 94,514 51,512

Off-highway 660,216 966,296 4,358,200 95,522 83,090 76,924 1,365

Natural Sources 0 0 0 0 0 0 80,213

Miscellaneous 310,871 150,474 4,538,131 47,040 4,325,839 1,062,364 1,440,416

CENRAP Total 3,956,494 5,404,181 24,103,914 2,748,255 5,122,496 1,588,078 1,819,983
31

Table 6-2. 2018 Base Case Annual Emissions Summary.

CENRAP 2018 Base Case Annual Emissions (Tons)

 Source Category

VOC NOx CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 15,963 800,509 231,161 1,397,945 125,999 106,402 12,188

Fuel Comb. Industrial 87,300 985,108 470,053 562,732 134,652 93,244 7,942

Fuel Comb. Other 139,826 93,527 348,628 33,555 57,292 55,498 4,932

Chemical & Allied Product Mfg 91,937 52,915 200,036 229,435 17,361 13,383 23,977

Metals Processing 14,600 24,603 200,166 154,071 23,811 10,838 6

Petroleum & Related Industries 519,225 320,126 287,198 106,536 13,818 9,753 1,077

Other Industrial Processes 215,126 162,931 163,154 133,203 316,220 100,922 285,113

Solvent Utilization 1,095,270 663 426 35 2,563 2,116 19

Storage & Transport 227,269 12,122 69,548 3,325 23,808 7,380 298

Waste Disposal & Recycling 73,117 19,379 296,493 7,704 67,637 63,084 14,019

Highway Vehicles 447,496 445,651 7,466,397 7,335 24,845 12,522 73,128

Off-highway 384,203 263,701 5,067,432 995 43,831 40,311 606

Natural Sources 0 0 0 0 0 0 80,213

Miscellaneous 212,436 107,761 3,200,076 57,923 3,968,055 903,434 1,921,843

CENRAP Total 3,523,767 3,288,994 18,000,769 2,694,795 4,819,893 1,418,889 2,425,360

As 2002 pre- and post-modeled emission summaries were provided on the input data files, we 

were able to verify the emission totals for each State and SCC in the modeling domain (Pechan, 

2006). However, as 2018 summaries were not available in time to review the files for this 

analysis, we have not confirmed that these 2018 emission totals are as expected by the ICS.

Our review was conducted in a top down fashion starting with an analysis of the major source 

categories in the domains of interest to determine which major categories have the highest 

residual contribution to the area. Once the highest source types were identified, subcategories 

within those source types were reviewed. Again, a ranking of the highest residual sub source 

types was performed and additional analyses on these categories were conducted. Table 6-3 

presents a percentage based contribution of residual emissions by major source category for the 

CENRAP domain. Tables for each CENRAP Class I AOI projected to be above the glide slope 

for reasonable progress are presented in Appendix E of this document.

In addition to reviewing the residual emission categories in the future year base, it was important 

to identify reductions that have already occurred within each category or at specific units. This 

will allow the ICS to determine if certain source categories that have yet to be controlled under 

the future year base case have the potential for reduction or if source types already reduced have 

reached the full cost-effective potential. Table 6-4 presents this information in annual tons for all 

sources in the CENRAP domain, while Table 6-5 presents the same information in terms of 

percent change from 2002.

Finally, once each subcategory was identified, unit level tables of emission comparisons from 

2002 to 2018 were developed allowing the ICS to review existing emission reductions and 

providing the ability to assign new cost-effective controls to units using the best control for the 

scenario. These tables present comparisons of 2002 and 2018 emission levels, by pollutant, and 

future year control technology assignment (by IPM forecasting) for EGU sources. Since unit-
32

specific technology assignments were not identified in the SMOKE control packets nor in

documentation obtained for use in this project, these units do not have associated future year 

technology identification data.

Ultimately, the ICS’ final control strategy decisions will include the application of BART 

applicable source reductions in the future year base case. However, as these sources and their 

associated reductions were unavailable for this project, they too are not included in this analysis.

Table 6-3. CENRAP 2018 Base Case Annual Residual Emissions Contribution Summary.

CENRAP 2018 Base Case Annual Emissions (Percent of Total)

Source Category VOC NOx CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 0% 24% 1% 52% 3% 7% 1%

Fuel Comb. Industrial 2% 30% 3% 21% 3% 7% 0%

Fuel Comb. Other 4% 3% 2% 1% 1% 4% 0%

Chemical & Allied Product Mfg 3% 2% 1% 9% 0% 1% 1%

Metals Processing 0% 1% 1% 6% 0% 1% 0%

Petroleum & Related Industries 15% 10% 2% 4% 0% 1% 0%

Other Industrial Processes 6% 5% 1% 5% 7% 7% 12%

Solvent Utilization 31% 0% 0% 0% 0% 0% 0%

Storage & Transport 6% 0% 0% 0% 0% 1% 0%

Waste Disposal & Recycling 2% 1% 2% 0% 1% 4% 1%

Highway Vehicles 13% 14% 41% 0% 1% 1% 3%

Off-highway 11% 8% 28% 0% 1% 3% 0%

Natural Sources 0% 0% 0% 0% 0% 0% 3%

Miscellaneous 6% 3% 18% 2% 82% 64% 79%

CENRAP Total 100% 100% 100% 100% 100% 100% 100%

Table 6-4. CENRAP Annual Emissions Change (Tons).

CENRAP Annual Emissions Change -- 2002 to 2018 (Tons)

Source Category VOC NOx CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 2,125 -206,405 -59,317 -147,382 46,570 52,927 7,727

Fuel Comb. Industrial 13,075 77,663 82,475 -5,538 16,025 14,832 1,699

Fuel Comb. Other -11,701 -4,930 -86,692 -1,050 -10,087 -10,058 62

Chemical & Allied Product Mfg 35,783 15,913 82,118 89,032 6,416 4,880 10,723

Metals Processing 6,422 8,405 84,338 67,647 8,882 4,352 3

Petroleum & Related Industries 32,441 13,179 13,011 24,587 3,377 2,346 258

Other Industrial Processes 64,738 55,023 43,475 44,076 80,819 26,694 78,437

Solvent Utilization 296,220 271 178 14 1,225 1,006 2

Storage & Transport 26,323 3,099 30,473 909 6,487 2,086 77

Waste Disposal & Recycling 14,328 2,542 47,933 2,385 10,137 9,281 4,105

Highway Vehicles -538,032 -1,334,638 -5,712,316 -44,495 -75,411 -81,992 21,616

Off-highway -276,012 -702,595 709,233 -94,527 -39,258 -36,612 -759

Natural Sources 0 0 0 0 0 0 0

Miscellaneous -98,436 -42,714 -1,338,055 10,883 -357,784 -158,930 481,427

CENRAP Total -432,727 -2,115,187 -6,103,145 -53,460 -302,603 -169,189 605,376
33

Table 6-5. CENRAP Annual Emissions Change (Percent).

CENRAP Annual Emissions Change -- 2002 to 2018 (Percent)

Source Category VOC NOx CO SO2 PM-10 PM-2.5 NH3

Fuel Comb. Elec. Util. 15% -20% -20% -10% 59% 99% 173%

Fuel Comb. Industrial 18% 9% 21% -1% 14% 19% 27%

Fuel Comb. Other -8% -5% -20% -3% -15% -15% 1%

Chemical & Allied Product Mfg 64% 43% 70% 63% 59% 57% 81%

Metals Processing 79% 52% 73% 78% 59% 67% 67%

Petroleum & Related Industries 7% 4% 5% 30% 32% 32% 31%

Other Industrial Processes 43% 51% 36% 49% 34% 36% 38%

Solvent Utilization 37% 69% 72% 66% 92% 91% 13%

Storage & Transport 13% 34% 78% 38% 37% 39% 35%

Waste Disposal & Recycling 24% 15% 19% 45% 18% 17% 41%

Highway Vehicles -55% -75% -43% -86% -75% -87% 42%

Off-highway -42% -73% 16% -99% -47% -48% -56%

Natural Sources 0% 0% 0% 0% 0% 0% 0%

Miscellaneous -32% -28% -29% 23% -8% -15% 33%

CENRAP Total -11% -39% -25% -2% -6% -11% 33%

6.2 Process in Preparing Files for Control Plan Modeling

In addition to the SMOKE emission files, the 2018 growth and control packets were obtained 

from UCR for additional application and verification of future year scenario assignment. Since 

the CENRAP utilized version of the SMOKE processor does not replace control efficiency, rule 

effectiveness, and rule penetration values in the output files generated using the growth and 

control modules of the model, Alpine manually applied these values to the 2018 non-EGU and 

stationary area source files for which the packets were applied. This step was necessary to 

duplicate the inventories that went into the results of CENRAP’s reasonable progress modeling 

and to ensure that any incremental assignment of control technologies did not duplicate emission 

reductions already assumed in the future year base case. 

The 2018 IPM file used by CENRAP for EGU sources was also obtained and matched to the 

2018 base case inventory of EGU sources. This step was conducted for reasons similar to those 

identified above for non-EGU and stationary area sources and to ensure that incremental controls 

assigned to these source types did not duplicate existing base case assumptions. Because IPM 

does not assign a control efficiency with each control device applied to SO2 and NOX, we made 

some assumptions, based on IPM documentation, as to what pollutant specific level of reduction 

was applied in the future year base case runs. These assumptions, by primary and secondary 

control device code combinations for SO2 and NOX, are presented in Tables 6-6 and 6-7, 

respectively. 

Since many of the control technology control cost equations within AirControlNET require 

additional unit-level characteristic data, we also made matches of the SMOKE IDA files to 

CENRAP NIF, EPA NEI, or EPA CAMD CEM data sets to obtain these variables when missing. 
34

Unit level boiler capacity (MMBtu/hr) or NETDC (MW) values are required for capital and 

operating and maintenance cost calculations for many of the EGU technologies. In cases where 

these nameplate capacity values could not be identified, emission weighted (based on the final 

EPA 2002 NEI) were assigned to boilers using a primary (highest emitting) SCC. Table 6-8 

presents these weighted capacities. Additionally, stack flow, sulfur content, and primary SCC 

assignment were necessary to cross-reference available incremental control technologies to the 

base case emissions inventory data. These variables were obtained where matches could be 

found, in priority order of CENRAP, CAMD, and EPA datasets, respectively.

Table 6-6. IPM Post Processing Assigned Device Codes and Applied SO2 Control Efficiencies.

Primary Device Code Secondary Device Code Description CE RE

0 0 No Control 0 0

119 0 Dry Scrubber 90 100

141 0 Wet Scrubber 90 100
35

Table 6-7. IPM Post Processing Assigned Device Codes and Applied NOX Control Efficiencies.

Primary 

Device 

Code

Secondary 

Device 

Code

Description CE RE

0 0 UNCONTROLLED 0 0

26 0 FLUE GAS RECIRCULATION 35 100

26 29 FLUE GAS RECIRCULATION + LOW EXCESS AIR FIRING 35 100

26 204 FLUE GAS RECIRCULATION + OVERFIRE AIR 40 100

28 0 STEAM OR WATER INJECTION 65 100

28 32 STEAM OR WATER INJECTION + AMMONIA INJECTION 65 100

28 204 STEAM OR WATER INJECTION + OVERFIRE AIR 90 100

28 205 STEAM OR WATER INJECTION + LOW NOX BURNERS 90 100

29 0 LOW EXCESS AIR FIRING 35 100

32 0 AMMONIA INJECTION 55 100

32 28 AMMONIA INJECTION + STEAM OR WATER INJECTION 65 100

139 0 SCR (SELECTIVE CATALYTIC REDUCTION) 90 100

139 28 SCR (SELECTIVE CATALYTIC REDUCTION) + STEAM OR WATER INJECTION 95 100

139 71 SCR (SELECTIVE CATALYTIC REDUCTION) + FLUID BED DRY SCRUBBER 90 100

139 204 SCR (SELECTIVE CATALYTIC REDUCTION) + OVERFIRE AIR 90 100

139 205 SCR (SELECTIVE CATALYTIC REDUCTION) + LOW NOX BURNERS 94 100

140 0 NSCR (NON-SELECTIVE CATALYTIC REDUCTION) 90 100

140 29 NSCR (NON-SELECTIVE CATALYTIC REDUCTION) + LOW EXCESS AIR FIRING 90 100

140 71 NSCR (NON-SELECTIVE CATALYTIC REDUCTION) + FLUID BED DRY SCRUBBER 90 100

140 204 NSCR (NON-SELECTIVE CATALYTIC REDUCTION) + OVERFIRE AIR 90 100

140 205 NSCR (NON-SELECTIVE CATALYTIC REDUCTION) + LOW NOX BURNERS 90 100

204 0 OVERFIRE AIR 40 100

204 26 OVERFIRE AIR + FLUE GAS RECIRCULATION 40 100

204 205 OVERFIRE AIR + LOW NOX BURNERS 50 100

205 0 LOW NOX BURNERS 50 100

205 26 LOW NOX BURNERS + FLUE GAS RECIRCULATION 60 100

205 28 LOW NOX BURNERS + STEAM OR WATER INJECTION 50 100

205 32 LOW NOX BURNERS + AMMONIA INJECTION 50 100

205 204 LOW NOX BURNERS + OVERFIRE AIR 50 100
36

6.3 Application of AirControlNET Technologies

AirControlNET is a control technology analysis tool developed to support the U.S. EPA in its 

analyses of air pollution policies and regulations (Pechan, 2005). The tool provides data on 

emission sources, potential pollution control measures and emission reductions, and the costs of 

implementing those controls.

The core of AirControlNET is a relational database system in which control technologies are 

linked to sources within EPA emissions inventories. The system contains a database of control 

measure applicability, efficiency, and cost information for reducing the emissions contributing to 

ambient concentrations of ozone, PM10, PM2.5, SO2, NOX, as well as visibility impairment 

(regional haze) from point, area, and mobile sources. PM10 and PM2.5 as included in

AirControlNET represent primary emissions of PM. The control measure data file in

AirControlNET includes not only the technology's control efficiency, and calculated emission 

reductions for that source, but also estimates the costs (annual and capital) for application of the 

control measure.

Since the existing version of AirControlNET contains the preprocessed application of control 

technologies to a predetermined set of EPA emission inventories, direct use of the model in this 

analysis was not possible. However, Alpine received approval from EPA’s Innovative Strategies 

and Economics Group (ISEG) to modify the AirControlNET version 4.1 source code and data 

tables in order to make it useful to this study (Sorrels, 2006). The results of the application of 

this modified version of the code still retain the applicability, efficiency, and cost information 

from the unmodified version of the source code, but were applied to the CENRAP modeling 

inventories with updated price index scalars to reflect control costs in 2005-dollars.

Using the modified inventories identified in Section 6.2 above, we ran every available control 

strategy in AirControlNET against the EGU, non-EGU point, and stationary area source

inventories to develop a master list of available, incremental control strategies for the entire 

CENRAP 36 km domain necessary for the ICS to design command-and-control or costeffectiveness based control strategies by source or domain. Mobile source controls were not 

processed under this assignment as it would have required multiple iterative runs of the EPA 

NONROAD and MOBILE6 models to generate the appropriate information. This master list of 

controls was used in the final development of the control strategy plan as described in the 

following sections.

Since AirControlNET’s control cost equations take into consideration the useful remaining life 

of installed equipment and estimate the costs of compliance with these measures, two of the four 

reasonable progress goal considerations (see Section 6.6) are directly met through the results of 

the model’s output.
37

Table 6-8. Emissions Weighted NETDC (MW) Association.

SCC Description NETDC (MW)

10100201 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal) 200

10100202 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Bituminous Coal) 500

10100203 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cyclone Furnace (Bituminous Coal) 200

10100212

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Tangential) 

(Bituminous Coal) 500

10100215 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cell Burner (Bituminous Coal) 1300

10100218

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Atmospheric Fluidized Bed Combustion: 

Circulating Bed (Bitum. Coal) 200

10100222

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Subbituminous 

Coal) 400

10100223 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cyclone Furnace (Subbituminous Coal) 400

10100226

External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom Tangential 

(Subbituminous Coal) 500

10100401 External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Normal Firing 400

10100404 External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Tangential Firing 500

10100501 External Combustion Boilers; Electric Generation; Distillate Oil; Grades 1 and 2 Oil 400

10100601 External Combustion Boilers; Electric Generation; Natural Gas; Boilers > 100 Million Btu/hr except Tangential 400

10100701 External Combustion Boilers; Electric Generation; Process Gas; Boilers > 100 Million Btu/hr 200

10100801 External Combustion Boilers; Electric Generation; Petroleum Coke; All Boiler Sizes 600

10101204 External Combustion Boilers; Electric Generation; Solid Waste; Tire Derived Fuel : Shredded 200

10300811 External Combustion Boilers; Commercial/Institutional; Landfill Gas; Landfill Gas 200

20100101 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine 200

20100109 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine: Exhaust 200

20100201 Internal Combustion Engines; Electric Generation; Natural Gas; Turbine 200

All other boilers 100
38

6.4 Development of AOI-Based Cost Curves

Each Class I area in the CENRAP modeling domain has an associated set of AOIs as identified 

in other areas of this document. In order to best determine where emission reduction has the 

greatest benefit, this geography was designed to limit the available source type list from

including all sources within the entire domain.

Using a geocoded county list from these AOIs, we parsed the master list of incremental control 

measures from all non-mobile source types and sources located within the boundaries of the 

AOIs. This parsed list was then sorted on in incremental cost-effectiveness (marginal cost) basis 

to determine the most cost effective control suite necessary to attain emission reduction targets 

for specific pollutants within each AOI. Each individual source or source category (unit or 

county-SCC combination) had its own cost effectiveness curve generated. In aggregate, the 

results of these applications are cost curves for each visibility impairing pollutant for all EGU, 

non-EGU point, and stationary area source within the geographic domain of the AOI. 

Incremental controls on mobile sources were not considered in this analysis. An illustrative 

example of the steps involved with the cost effectiveness curve design can be found in the 

Appendix F of this document. Figures 6-1, 6-2 and Appendix G present actual cost curves for 

AOI-1 areas associated with the six CENRAP Class I areas projected to be above the reasonable 

progress glide path.

6.5 Application of Cost Curves to Emission Reduction Needs

Two sets of cost curves have been developed for each pollutant-Class I AOI-1 combination 

identified as of interest to the ICS. The first marginal cost curve includes the application of all 

available control measures to all applicable source types within the AOI. The second curve is 

the result of limiting the control measure application to only the top three residual emission 

subcategories identified in the 2018 base case for each AOI-pollutant combination. These two 

curves will allow the ICS to determine if limiting the control scenario to only the highest residual 

categories will attain reasonable glide path emission reduction objectives while presumably

minimizing the number and type of controlled sources in each AOI.

Within each AOI, an emissions reduction target has been established based on the review of 

relevant and available regional haze aerometric analyses and source attribution modeling. Each 

emissions reduction target sets the “solve point” of the cost curve and allows us to identify the 

most cost effective sources of reduction for the pollutants of interest within each impacted AOI. 

It is noted that each pollutant-based cost curve developed for this analysis is mutually exclusive 

of each other pollutant’s cost curve and does not consider the feasibility of multiple control 

technologies being applied to any one source. Additionally, the information provided in these 

cost curves is representative of the primary pollutant of control and does not reflect any cocontrol applicability or disbenefit as a result of the application of that control.
39

Wichita Mountain 

SO4/EC/OC AOI-1

$0

$5,000

$10,000

$15,000

$20,000

$25,000

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000

Millions

Annual Emission Reduction (Tons)

Control Cost (2005$)

Top Residual Categories Only

All Categories

Figure 6-1. Marginal Cost Curve for Wichita Mountain SO4/EC/OC AOI-1.
40

Boundary Waters-Voyageurs

SO4/EC/OC/CM/FS AOI-1

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

$10,000

0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

Millions

Annual Emission Reduction (Tons)

Control Cost (2005$)

Top Residual Categories Only

All Categories

Figure 6-2. Marginal Cost Curve for Boundary Waters – Voyageurs SO4/EC/OC/CM/FS AOI-1.
41

6.6 Four Factor Analysis for RPG

As part of the regional haze program requirements outlined in 40 CFR 51.308, there are four 

factors which have been identified as mandatory for purposes of establishing a reasonable 

progress goal for any mandatory Class I area within a State. 

40 CFR 51.308(d)(1)(i)(A) Consider the costs of compliance, the time necessary for compliance, 

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

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

were taken into consideration in selecting the goal.

6.6.1 Cost of Compliance

The cost of compliance factor is used to determine whether compliance costs for sources are 

reasonable compared to the emission reductions and visibility improvement they will achieve. 

Costs should be determined for one-time capital costs and ongoing annual operation,

maintenance, and upkeep costs.

Through the application of control technologies using the cost equations from the

AirControlNET source code, we have identified individual units for control application,

identified the design parameters for emission controls, and developed cost estimates based on 

those design parameters. An estimation of annualized cost of control, based on a one-time capital 

cost and continual operating and maintenance costs are included in this estimate, where

parameters were available in the AirControlNET equations. This application of control cost 

analysis as applied to the incremental reduction sources defined in this study meets the

application of the cost of compliance statutory factor.

6.6.2 Time Necessary for Compliance

The time necessary for compliance factor may be used to adjust the reasonable progress goals to 

reflect the degree of improvement achievable within the long term strategy period, as opposed to 

the improvement expected at full implementation of a control measure, if the time needed for full 

compliance exceeds the length of the long term strategy period. For example, if vendor

availability within the period of the long term strategy could not meet the full requirements of the 

installation schedule outlined by the control strategy, the reasonable progress goals should reflect 

the visibility improvement anticipated from installation of controls at the percentage of sources 

that could be controlled within the strategy period. 

In this particular analysis, a time necessary for compliance factor could not be determined simply 

based on the emissions inventory and a list of control measures applicable to controllable 

sources. An eventual SIP could include control strategies that extend beyond the 2018 milestone 

and the visibility improvement anticipated from installation of controls at the percentage of 

sources that could not be controlled within the first strategy period would have to be counted in a 

later SIP. Each of these elements would need to be determined on a unit by unit basis.
42

6.6.3 Energy and Non-Air Quality Environmental Impacts of Compliance

The energy and non-air impacts factor is meant to consider whether the energy requirements (the 

amount, type, and availability of energy) of the control technology result in energy penalties or 

benefits. For example, a particular control may require a fuel, water may be required for a 

cooling tower, or a landfill may be required for disposal of solid waste byproduct, each which are 

directly unavailable in the area. Since these impacts are State and site specific, they are not 

addressed in this analysis. Upon the final configuration of the control strategies by the ICS, each 

participating State, tribe and affected entity should review the control plan to determine whether 

significant energy burdens or benefits comes as a direct result of the application of a control 

technology. If determined to be so, the State should quantify this value and include it in the final 

submitted SIP.

6.6.4 Remaining Useful Life of Potentially Affected Sources

The statutory factor of the remaining useful life of the source is applicable only to those 

measures which would require retrofitting of control devices at existing sources. The remaining 

useful life of a source affects the annua lized costs of retrofit controls and is included in the 

methods used for calculating annualized costs in the control cost equations modified from EPA’s 

AirControlNET. 

CENRAP’s emission projections, as well as the control cost equations applied by Alpine, 

account for the remaining useful life between the year of the reasonable progress analysis and the 

date the facility permanently stops operations. Since source specific retirements are taken into 

consideration with the CENRAP forecasts (units are shut down in the year of their retirement) 

and average retirement rates are applied to control technologies within the control analysis 

equations, the statutory factor of the remaining useful life of the source has been considered.

In summary, the basis of our resulting control strategy recommendations provide a

demonstration of those reasonable progress goal requirements which could be taken into

consideration to meet visibility objectives with the data provided for this analysis. The remaining 

factors are State, tribal and site dependant and could not be addressed here.
43

7.0 SUMMARY AND RECOMMENDATIONS

7.1 Summary

Alpine’s review of all data discussed in the previous sections of this document have identified 

six Class I areas (Big Bend National Park, Breton Island, Boundary Waters, Guadalupe

Mountains, Wichita Mountain, and Voyageurs) within the CENRAP domain, their particular 

AOIs, ICS defined emission reduction targets, and potential incremental emission reductions 

recommended for CENRAP modeling. For each area, sulfate and to a lesser extent, nitrate 

reductions were shown to be most beneficial during the 20 percent worst visibility days in 2002. 

Alpine has configured subregional control strategies based on direction provided by the ICS to 

use single precursor emission reduction assumptions with a marginal cost per ton cutoff of 

$5,000 per ton reduced. Emission targets were identified by the ICS for each Class I area AOI to 

exceed the reasonable progress glide slope. These targets were established as 25 percent more 

reduction than was identified in Table 5-3 and were to be taken from any available source, not 

just those identified as having the highest residual emissions contribution to the Class I area AOI. 

Table 7-1 presents a summary of each of these strategies.

Table 7-1. Subregional control strategy summary for single precursor emission reduction targets.

Control Strategy

ICS Established Subregional Control Control Strategy Average Cost Per Ton

Class I Area ST Reduction Target Strategy Reductions Total Cost ($2005) ($/ton reduced)

Breton Island LA 385,000 119,966 $203,443,093 $1,696

Boundary Waters MN 40,000

Voyageurs MN 28,750

Wichita Mountains OK 93,750 99,479 $21,752,713 $219

Guadalupe Mountains TX 162,500

Big Bend Nat'l Park TX 150,000

46,301 $107,233,124 $2,316

115,936 $319,001,184 $2,752

SO2 Annual Emission Reduction (Tons)

For three of the six CENRAP Class I areas projected to be above the reasonable progress glide 

slope in 2018, control strategies have been prepared which meet the emission reduction targets 

recommended by the ICS. These areas (Boundary Waters, Wichita Mountains, and Voyageurs) 

all can meet the ICS defined targets while staying within the single precursor, $5,000 per ton 

reduced limitations.

We also have determined tha t as a result of the implementation of the list of additional point and 

area source controls in each primary AOI the remaining three Class I areas within the CENRAP 

domain (Big Bend National Park, Breton Island, and Guadalupe Mountains) modeled to be 

above the reasonable progress glide slope will be unable to achieve a level of emissions

reduction necessary to bring these areas under the glide slope by 2018 using the ICS identified 

control strategy definitions. Influences such as incrementally uncontrollable source categories, 

marginal cost effectiveness values greater than $5,000 per ton reduced, and international and 

inter-RPO emission transport prevent strategies from being configured for these Class I areas.
44

In particular, recent BRAVO research (see, for example Barna et al. 2006) shows that Mexican 

SO2 sources account for up to 23% of the observed annual sulfate levels at Big Bend. During the 

summer months, Mexican SO2 emissions sources can account for as much as 70% of the sulfate 

at Big Bend. Barna et al. also show that SO2 emission sources for the Eastern U.S. are the 

biggest culprit to high sulfate at Big Bend during the high PM2.5 summer days; and SO2 from the 

Eastern US and Texas are the biggest contributor to high sulfate at Big Bend during the high 

PM2.5 fall days.

In both of these episode examples, regardless of the emissions reduction achieved by CENRAP 

with the available source category and technology applications, there still is an emissions 

component which is directly out of their control. Additional consultation with inter-RPO and 

international agencies may be required to adequately co-configure strategies to bring these areas 

into attainment.

7.2 Recommendations

7.2.1 Regional Controls

As each of the six Class I areas projected to be above the reasonable progress glide path (and all 

of the other Class I AOIs in the CENRAP domain) are dominated by EGU SO2 and NOX

emissions and many of these area AOIs intersect with States currently excluded by the EPA 

CAIR rule, we recommend that CENRAP consider a control scenario which would reduce EGU 

emissions in non-CAIR States to levels comparable to those promulgated by EPA in the final 

CAIR regulation. In addition to this regional strategy proposal, we further recommend that the 

ICS consider individual CENRAP States within Class I area AOIs projected above the

reasonable progress glide slope to meet CAIR emissions budgets without the interstate trading 

aspect of the rule. This nuance may prevent emission reductions from being transferred to areas 

outside of the influential zones of the affected Class I areas and focus the reductions in those 

upwind areas with greatest impact on meeting visibility objective goals.

These regional controls could be modeled in multiple ways. Two noted methods being to 

develop an additional IPM run configured to take into account the CAIR reductions within nonCAIR States with or without the constraint of trading noted above. The second method would be 

to determine an emission budget (following EPA methods in the CAIR final rule) to determine 

State level targets for emission reduction. Using these targets, CENRAP could then apply the 

marginal cost curves developed for this analysis, but limit the solution to only EGU sources 

identified as “CAIR eligible”. This approach would not take into account any trading or 

participation in the bank and trade system, but would give an estimate of the regional emission 

reductions associated with the strategy.

7.2.2 Subregional Controls

In lieu of a single regional control option applied consistently across the entire CENRAP 

domain, individual subregional controls could be applied to reduce emissions within certain 

Class I area AOIs. Based on the single precursor emission reduction target calculations defined 

elsewhere in this document, subregional control strategies can be defined for three of the Class I 

areas projected to be above the reasonable progress glide path. In each case, the marginal cost 
45

curves (based on the application of all available control options on all controllable industries and 

source types) allow the selection of control technologies for sources within an AOI-1 that attains 

the ICS defined emission reduction targets. Details of these control strategies are presented in 

Tables 7-2 and 7-3. Note that as Boundary Waters and Voyageurs are associated within the same 

AOI-1, the larger of the two emission reduction targets was used to configure a control strategy 

that would meet both areas’ needs.

However, as noted in this document, the application of incremental control on all controllable 

point and area sources within the AOIs still fails to meet the visibility objectives of three Class I 

areas modeled to be above the reasonable progress glide slope. For this reason, we additionally 

recommend that the ICS consider applying the remaining reasonably cost effective control

technologies to sources within States and tribal lands contained in the boundaries of the three 

target Class I area AOIs. As part of the demonstration of reasonable progress, the application of 

reasonably cost effective controls to all emission sources and source types through a process as 

described in this document appears to provide support that the four reasonable progress goal 

considerations were taken into account where available. As is demonstrated for the Boundary 

Waters and Voyageurs AOI-1 above, the AOI-1 for Big Bend and Guadalupe Mountains share 

the same emission reduction target. In this case, however, the target cannot be fully achieved. 

Tables 7-4 and 7-5 present the details of these strategies.

For those Class I areas outside of CENRAP’s domain who based on CENRAP modeling did not 

forecast below the reasonable progress glide slope, we submit to the ICS our data of incremental 

control strategy application and cost curves based on existing modeling and inventory

assumptions provided by CENRAP to date for purposes of consultation with those States in 

which the affected Class I areas are located. We have not presented these non-CENRAP data as 

part of this document but much of the basic information is presented, where appropriate, in the 

supporting appendixes.
46

Table 7-2. Subregional control strategy defined for Boundary Waters / Voyageurs SO4 AOI-1.

FIPSST FIPSCNTY State County Plant ID Plant Name Point ID SIC Control Measure Ton Reduced Cost ($2005) Marginal CPT

27 037 Minnesota Dakota Co 2703700011 FLINT HILLS RESOURCES LP - PINE BEND EU111 2911 Sulfur Recovery and/or Tail Gas Treatment 290 $401,526 $1,383

27 037 Minnesota Dakota Co 2703700011 FLINT HILLS RESOURCES LP - PINE BEND EU045 2911 Sulfur Recovery and/or Tail Gas Treatment 286 $395,189 $1,383

27 037 Minnesota Dakota Co 2703700011 FLINT HILLS RESOURCES LP - PINE BEND EU088 2911 Sulfur Recovery and/or Tail Gas Treatment 62 $86,034 $1,383

27 163 Minnesota Washington Co 2716300003 MARATHON ASHLAND PETROLEUM LLC EU019 2911 Sulfur Recovery and/or Tail Gas Treatment 11 $14,854 $1,383

55 123 Wisconsin Vernon Co 663020930 DAIRYLAND POWER COOP GENOA STATION-EOP B20 4911 FGD Wet Scrubber 16,904 $28,492,444 $1,686

19 179 Iowa Wapello Co 90-07-001 IPL - OTTUMWA GENERATING STATION 143977 4911 FGD Wet Scrubber 15,897 $28,492,444 $1,792

19 113 Iowa Linn Co 57-01-004 0 0 0 FGD 2,042 $4,302,128 $2,107

55 123 Wisconsin Vernon Co 663020930 DAIRYLAND POWER COOP GENOA STATION-EOP B20 4911 FGD Wet Scrubber 12,569 $28,492,444 $2,267

31 109 Nebraska Lancaster Co 0005 NPPD SHELDON STATION 001 4911 FGD Wet Scrubber 6,079 $16,556,061 $2,724

19 193 Iowa Woodbury Co 97-04-010 MIDAMERICAN ENERGY CO. - GEORGE NEAL NOR 148780 4911 FGD Wet Scrubber 9,065 $28,492,444 $3,143

Overall Control Strategy 46,301 $107,233,124 $2,316

Duplicate entry in 2018d modeling inventory.

BOWA/VOYA SO2 Control Application

Table 7-3. Subregional control strategy defined for Wichita Mountains SO4 AOI-1.

FIPSST FIPSCNTY State County Plant ID Plant Name Point ID SIC Control Measure Ton Reduced Cost ($2005) Marginal CPT

29 093 Missouri Iron Co 0008 DOE RUN COMPANY-GLOVER SMELTER 8390 3339 FGD 51,834 $4,351,167 $84

48 201 Texas Harris Co 37 HOUSTON PLANT 000008 2819 Increase % Conversion to Meet NSPS (99.7) 3,486 $670,008 $192

22 033 Louisiana East Baton Rouge Par 0033 RHODIA INC/BR FAC 02 2869 Increase % Conversion to Meet NSPS (99.7) 7,090 $1,884,093 $266

22 005 Louisiana Ascension Par 0007 DUPONT CHEMICALS/BURNSIDE PLANT 01 2819 Increase % Conversion to Meet NSPS (99.7) 11,284 $3,896,018 $345

29 099 Missouri Jefferson Co 0003 DOE RUN COMPANY-HERCULANEUM SMELTER 11722 3339 FGD 10,653 $4,320,204 $406

48 201 Texas Harris Co 37 HOUSTON PLANT 000011 2819 Increase % Conversion to Meet NSPS (99.7) 5,953 $2,510,908 $422

22 005 Louisiana Ascension Par 0028 PCS NITROGEN FERTILIZER,L.P./GEISMAR 01 2873 Increase % Conversion to Meet NSPS (99.7) 9,179 $4,120,315 $449

Overall Control Strategy 99,479 $21,752,713 $219

WIMO SO2 Control Application
47

Table 7-4. Subregional control strategy defined for Breton Island SO4 AOI-1.

FIPSST FIPSCNTY State County Plant ID Plant Name Point ID SIC Control Measure Ton Reduced Cost ($2005) Marginal CPT

22 033 Louisiana East Baton Rouge Par 0033 RHODIA INC/BR FAC 02 2869 Increase % Conversion to Meet NSPS (99.7) 7,090 $1,884,093 $266

22 005 Louisiana Ascension Par 0007 DUPONT CHEMICALS/BURNSIDE PLANT 01 2819 Increase % Conversion to Meet NSPS (99.7) 11,284 $3,896,018 $345

22 005 Louisiana Ascension Par 0028 PCS NITROGEN FERTILIZER,L.P./GEISMAR 01 2873 Increase % Conversion to Meet NSPS (99.7) 9,179 $4,120,315 $449

22 033 Louisiana East Baton Rouge Par 0033 RHODIA INC/BR FAC 03 2869 Increase % Conversion to Meet NSPS (99.7) 2,693 $1,884,093 $700

01 097 Alabama Mobile Co 5009 AKZO NOBEL CHEMICALS INC 004 2819 Increase % Conversion to Meet NSPS (99.7) 2,183 $1,817,521 $832

12 113 Florida Santa Rosa Co 1130005 EXXONMOBIL PRODUCTION COMPANY 34 1311 Sulfur Recovery and/or Tail Gas Treatment 1,702 $2,354,901 $1,383

22 033 Louisiana East Baton Rouge Par 0015 EXXONMOBIL REF & SUPPLY CO/B R REFINERY 68 2911 Sulfur Recovery and/or Tail Gas Treatment 64 $88,364 $1,383

22 033 Louisiana East Baton Rouge Par 0015 EXXONMOBIL REF & SUPPLY CO/B R REFINERY 69 2911 Sulfur Recovery and/or Tail Gas Treatment 64 $88,364 $1,383

22 095 Louisiana St. John The Baptist 0013 MARATHON ASHLAND PETROLEUM LLC/LA REFINI 14 2911 Sulfur Recovery and/or Tail Gas Treatment 47 $64,441 $1,383

22 095 Louisiana St. John The Baptist 0013 MARATHON ASHLAND PETROLEUM LLC/LA REFINI 70 2911 Sulfur Recovery and/or Tail Gas Treatment 31 $42,396 $1,383

22 095 Louisiana St. John The Baptist 0013 MARATHON ASHLAND PETROLEUM LLC/LA REFINI V2 2911 Sulfur Recovery and/or Tail Gas Treatment 26 $35,613 $1,383

22 077 Louisiana Pointe Coupee Par 0005 LA GENERATING LLC/BIG CAJUN 2 PWR PLNT 01 4911 FGD Wet Scrubber 16,126 $28,492,444 $1,767

22 077 Louisiana Pointe Coupee Par 0005 LA GENERATING LLC/BIG CAJUN 2 PWR PLNT 02 4911 FGD Wet Scrubber 15,618 $28,492,444 $1,824

12 033 Florida Escambia Co 0330045 GULF POWER COMPANY CRIST ELECTRIC GENERA 6 4911 FGD Wet Scrubber 11,179 $20,964,424 $1,875

22 077 Louisiana Pointe Coupee Par 0005 LA GENERATING LLC/BIG CAJUN 2 PWR PLNT 03 4911 FGD Wet Scrubber 15,022 $28,492,444 $1,897

01 097 Alabama Mobile Co 1001 ALABAMA POWER COMPANY - BARRY 004 4911 FGD Wet Scrubber 8,396 $18,827,395 $2,242

28 059 Mississippi Jackson Co 2805900058 CHEVRON PRODUCTS COMPANY, PASCAGOULA REF 051 2911 FGD 1,638 $4,349,179 $2,655

22 051 Louisiana Jefferson Par 0004 CYTEC INDUSTRIES,INC/FORTIER PLNT 57 2821 Increase % Conversion to Meet NSPS (99.7) 1,087 $3,027,047 $2,784

01 097 Alabama Mobile Co 1001 ALABAMA POWER COMPANY - BARRY 003 4911 FGD Wet Scrubber 4,712 $13,574,846 $2,881

01 097 Alabama Mobile Co 1001 ALABAMA POWER COMPANY - BARRY 002 4911 FGD Wet Scrubber 4,631 $13,522,645 $2,920

01 047 Alabama Dallas Co 0003 INTERNATIONAL PAPER COMPANY 003 2611 FGD 1,971 $7,156,048 $3,630

12 033 Florida Escambia Co 0330045 GULF POWER COMPANY CRIST ELECTRIC GENERA 4 4911 FGD Wet Scrubber 2,734 $10,069,644 $3,683

12 033 Florida Escambia Co 0330045 GULF POWER COMPANY CRIST ELECTRIC GENERA 5 4911 FGD Wet Scrubber 2,489 $10,198,414 $4,097

Overall Control Strategy 119,966 $203,443,093 $1,696

BRET SO2 Control Application
48

Table 7-5. Subregional control strategy defined for Big Bend / Guadalupe Mountains SO4 AOI-1.

FIPSST FIPSCNTY State County Plant ID Plant Name Point ID SIC Control Measure Ton Reduced Cost ($2005) Marginal CPT

48 201 Texas Harris Co 37 HOUSTON PLANT 000008 2819 Increase % Conversion to Meet NSPS (99.7) 3,486 $670,008 $192

48 201 Texas Harris Co 37 HOUSTON PLANT 000011 2819 Increase % Conversion to Meet NSPS (99.7) 5,953 $2,510,908 $422

48 039 Texas Brazoria Co 10 SWEENY REFINERY PETROCHEM 000203 2911 FGD 883 $429,763 $487

48 355 Texas Nueces Co 3 CORPUS CHRISTI REFINERY 000174 2911 Sulfur Recovery and/or Tail Gas Treatment 1,430 $1,978,038 $1,383

48 167 Texas Galveston Co 1 TEXAS CITY REFINERY 000239 2911 Sulfur Recovery and/or Tail Gas Treatment 478 $660,954 $1,383

48 039 Texas Brazoria Co 10 SWEENY REFINERY PETROCHEM 000205 2911 Sulfur Recovery and/or Tail Gas Treatment 374 $518,052 $1,383

48 161 Texas Freestone Co 9 EMBRIDGE ENERGY TEAGUE PL 000004 1311 Sulfur Recovery and/or Tail Gas Treatment 324 $448,705 $1,383

48 355 Texas Nueces Co 3 CORPUS CHRISTI REFINERY 000174 2911 Sulfur Recovery and/or Tail Gas Treatment 63 $86,977 $1,383

48 201 Texas Harris Co 39 DEER PARK PLANT 001295 2911 Sulfur Recovery and/or Tail Gas Treatment 56 $77,549 $1,383

48 355 Texas Nueces Co 3 CORPUS CHRISTI REFINERY 000174 2911 Sulfur Recovery and/or Tail Gas Treatment 49 $67,251 $1,383

48 355 Texas Nueces Co 20 CORPUS CHRISTI EAST PLANT 000156 2911 Sulfur Recovery and/or Tail Gas Treatment 27 $37,762 $1,383

48 201 Texas Harris Co 39 DEER PARK PLANT 000208 2911 FGD 4,942 $8,474,217 $1,715

48 175 Texas Goliad Co 2 COLETO CREEK PLANT 000001 4911 FGD Wet Scrubber 14,490 $28,492,444 $1,966

48 389 Texas Reeves Co 2 WAHA PLANT 000031 4922 FGD 3,653 $8,153,168 $2,232

48 167 Texas Galveston Co 5 TEXAS CITY REFINERY 000068 2911 FGD 2,293 $5,993,771 $2,614

48 029 Texas Bexar Co 63 SOMMERS DEELY SPRUCE PWR 000002 4911 FGD Wet Scrubber 9,755 $28,492,444 $2,921

48 029 Texas Bexar Co 63 SOMMERS DEELY SPRUCE PWR 000004 4911 FGD Wet Scrubber 9,595 $28,492,444 $2,970

48 029 Texas Bexar Co 63 SOMMERS DEELY SPRUCE PWR 000004 4911 FGD Wet Scrubber 9,128 $28,492,444 $3,121

48 331 Texas Milam Co 1 ALCOA SANDOW PLANT 000011 3334 FGD 14,306 $49,048,714 $3,429

48 331 Texas Milam Co 1 ALCOA SANDOW PLANT 000010 3334 FGD 14,305 $49,048,714 $3,429

48 331 Texas Milam Co 1 ALCOA SANDOW PLANT 000012 3334 FGD 14,143 $49,048,714 $3,468

48 349 Texas Navarro Co 11 STREETMAN PLANT 000015 3295 FGD 2,443 $9,903,980 $4,054

48 227 Texas Howard Co 1 BIG SPRING REFINERY 000267 2911 FGD 2,060 $9,638,812 $4,679

48 135 Texas Ector Co 22 GOLDSMITH GASOLINE PLANT 000133 1321 FGD 1,700 $8,235,351 $4,844

Overall Control Strategy 115,936 $319,001,184 $2,752

BIBE/GUMO SO2 Control Application
49

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