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 (AOI s ) b ext 

### on CENRAP Worst 20% Days in 2004

Daily Variation in PM 2.5 

### Components at Upper Buf falo 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 b EXT on Worst 20% Days in 2004

0 25 50 75 100 125 150 175 200BIBEBWCABRETCACRGUMOHEGLMINGUPBUVOYA2WIMOMACASIPSISLEBADLGRSALOSTROMOSACRTHROWHPEWHITWICAbEXT(Mm-1) Coarse Mass
Soil
Elemental Carbon Organic Mass
Nitrate
Sulfateii 

### TABLE OF CONTENTS

LIST OF TABLES

................................ ................................ ................................ ....................... iv

LIST OF FIGURES

................................ ................................ ................................ .................... vi i

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 Litera ture 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 Developmen

t of the Areas of Influence (AOI)

................................ .......................... 21 5.2 Dev

elopment 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 P reparing Files for Control Plan Modeling

................................ ............ 33 6.3 Application of AirControlNET Technologies

................................ ........................... 36 6.4 Development of AOI

- Based Cost C urves ................................ ................................ .. 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

................................ ................................ ................................ . 41iii



### 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 SUMMAR Y 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: PM 2.5



### Compo sition 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

- 1iv 

### LIST OF TABLE S

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. SO

2 

### and NO X 

### Emissions Reduction Requirements (tons per year) Assuming Proportional Re ductions in Sulfate and Nitrate.

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

Table 5

- 3. SO

2 

### and NO

X 

### 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 SO 2 

### Control Efficiencies.

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

Table 6

- 7. IPM Post Processing Assigned Device Codes and Applied NO X 

### 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

. . ................................ ................................ ................................ ................................ ... 4 6

Table 7

- 3

. Subregional control strategy defined for Wichita Mountain

s SO4 AOI

- 1

. . ........ 4 6v



### Table 7

- 4

. Subregional control strategy defined for Breton Island

SO4 AOI

- 1

. . ................. 4 7

Table 7

- 5

. Subregional control strategy defined for Big Bend / Guadalupe Mountains SO4 AOI

- 1

. . ................................ ................................ ................................ ................................ ... 4 8vi 

### 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

... ............................. ...... .................. ..... . ......... ... i x

Figure ES

- 2. Level I Areas of Influence (AOI) for Nitrate A ssociated 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 Base d on IMPROVE Data. 20

Figure 5

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

Figure 5

- 2. Geocode d 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.

................................ ................................ ................................ ................................ .. 40vii 

### EXECUTIVE SUMMARY

The Implementation and Control Strategies (ICS) Workgroup of the Central Regional Air Planning Association (CENRA P), together with other workgroups and state , tribal and federal agencies, have been working for more than four years gathering information for developing

r egional haze (RH) control strategies for pertinent Class I areas within and adjacent to the CENRAP s tates and tribes

. In late February 2006, under the direction of the CENRAP Technical Director , Alpine Geophysics, LLC was contracted to assist the ICS in th is e ffort. Building upon information developed by the ICS and others, Alpine was charged with devel oping 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 submi tted 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

- impair ing 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 goals 1



### . We synthesized pertinent 'at tribution 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

- recept or impacts at the ten CENRAP Class I and twelve adjoining

areas.
Complement ing this task, we synthesized a number of r ecent regional modeling studies helpful in relating emissions reductions of visibility precursors (e.g. SO 2 

### , NO X 

### ) in upwind source region s ( A reas of I nfluence 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 Islan d, Voyageur s , 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 pr ecursor 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 glid e path.viii



### W e then deduced from available regional modeling studies 'rules of thumb' relating percentage or tonnage reductions in visibility reducing precursors (e.g., SO 2 

### , NO X 

### , ammonia, and VOCs) on the expected impact on visibility downwind. These 'rules of thumb' , i.e., s ource

- receptor relationships

, were essential in estimating the amounts of incremental precursor emissions reductions in regions upwind of each of the vario us 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 AOI s, 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 ben eficial 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 SO 2 

### and NO X 

### emissions and many of the Class I area AOIs intersect with States currently exclu ded 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

- s tate 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 application s are proposed to redu

ce 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 progre ss glide path 2 

### . 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 emi ssion 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

areas 3 

### will be unable to achieve a level of emissions reduction necessary to bring these are as 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 thes e 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,

e mission 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 necess arily 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 PM 2.5 

### 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, Vo yageur, and Boundary Waters Class I Areas.x



### Figure ES

- 2. Level I Areas of Influence (AOI

- 1 ) for Nitrate A ssociated with the Big Bend, Guadalupe, Wichita Mountains, Breton Island, Voyageur, and Boundary Waters Class I Areas.1



### 1.0 INTRODUCTION

The Implement ation 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 constructin g regional haze (RH) control strategies f or 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 contracte d 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 modeler s. 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 con trol 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 emis sions 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 Californ ia, Riverside) for quantitative testing with the SMOKE/CMAQ/CAMx regional modeling systems.2



### To facilitate subsequent use of this methodology

, this r eport describes the various analytical steps and provides examples (both in the body of the report and in s upporting 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 Wa ters) 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 mode ling 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%BIBE1GUMO1WIMO1CACR1UPBU1HEGL1MING1BRET1VOYA2MACA1SIPS1ISLE1SACR1WHIT1WHPE1GRSA1ROMO1WICA1BADL1THRO1LOST1Percent of target reduction achieved Base18b/Typ02a Base18d/Typ02b

CENRAP non-CENRAP3



### 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 t he following objectives:

> Analyze existing regional haze modeling inventories developed by CENR AP, the States, tribes, and other RPOs;

> Synthesize available and pertinent air quality and meteorological data and recent ‘attribution of haze studies’ by C ENRAP 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 emiss ions 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 projecti ons. 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.

T he 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 re cognition 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 visi bility

- impairing precursors (in tons/day) to ambient aerosol concentrations at each of the ten (10) CENRAP Class I monitor s . We also developed these quantitative source

- receptor relationships for a dozen Class I areas in adjoining RPOs to the extent possi

ble give available data, project resources and schedule. As of this writing,5



### C ENRAP 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 re

examine 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 thos e 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 r efinements 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 preclude d 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 200 6, 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 compositio n of PM 2.5 

### 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 t he 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/m 3 

### ).

Section 5 presents the quantitative methods for converting these concen tration 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, NO X 

### and SO 2 

### . In addition, the section describes the methods us ed 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 Southern6



### Appalachian Mountains Initiative (SAMI), the RPOs, and university scientists 4 

### . 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 Cla ss 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

format s . 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 

### T he AOI methodology was carried out by Dr. Jim Wilkinson of Alpine whose recent Ph.D. origi nal research and Dissertation from Georgia Tech focused on the development of the AOI methodology for regional haze, ozone , and PM 2.5 

### control strategy modeling in the eastern U.S.7



### 2.0 CONTEXT FOR REGIONAL HAZE STRATEGY DEVEL OPMENT

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 addr ess 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 long

standing EPA commitment to address regional haze under th e 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 n

ote 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 Tribe s 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 f

ine particulate (PM 2.5 

### ) 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 ma nagement 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.

Th e 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, R iverside (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 sho w 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 visibilit y 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 req uired 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
gene rally 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 initia lly 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 sour ces 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 techniqu

es 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 visibilit y 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

. Cer tain regio nal 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 th e 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 h aze’ documents , CENRAP CAMx/CMAQ visibility modeling r

esults, 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 sou rce

- 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 invol ved a concise summarization of e xisting regional haze modeling inventories and associated local, State, Tribal and Federal control programs to determine available incremental control s on sources or source types affecting visibility i ncrements (i.e., differ ences 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 p roduct 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 majo r 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 ty pes 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 cate gories 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 develope d 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



### Ta sk 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

. SO 2 

### , NO X 

### ) 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 perce ntage or tonnage reductions in visibility reducing precursors (e.g., SO 2 

### , NO X 

### , 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

a dditional 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 includ ing b oth regional and sub

regional 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. Th is plan identif ies 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 suffici ent to allow the CENRAP

m odeling contractor

s 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, up dated 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 participate d 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 : T o 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 RESIDU AL VISIBILITY IMPROV EMENT NEEDS

The estimation o

f 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 sou rce

- 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 modelin g 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 episo dically, seasonally, inter

- annually?

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

- How do the relative concentration s of the major components compare with the relative emission rates nearby and regionally?

> Wh

at 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 hazin ess?

- 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 wors t haze days in the CENRAP region ?

- Are the e m

ission 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 i n 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 t rends 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 ha ze levels?

With these questions in mind, we surve yed 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 rain

shadow);

- Inversion layers;

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

areas.

> Visibili

ty

- 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% reconstru cted extinction days and seasonal patterns by site )

- S patial and seasonal patterns of aerosol components frequency distributions.

- A erosol component data in light of emissions sources, monitoring site settings, back trajectories

- Results of c luster , CA RT, and other pattern

- recognition

analys e s to group sites with similar patterns in aerosol component contributions to haze13



### > Back Trajectory Analyses

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

- B ack trajectory summar y statistics residence time by season, best 20% and worst 20% reconstructed extinction and aerosol components for all CENRAP Class I areas;

- C onditional probability maps for high and low extinction and aerosol components.

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

, etc., and

- Mesoscale meteorological analys e s complementing back trajectories

. Of course, complete answers to all these questions could not be developed in the course of this three week study; ho wever, 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 synth esized 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 es timates 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 the se more r ecent 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 Estimat es 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(b ext 

### /10) (4

- 1)

where HI is the haze index (deci

view s [dv]) and b ext 

### 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 b ext 

### , “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 I MPROVE site. The most recent measured extinction values (in Mm
- 1 

### ) for the va

rious 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 present ed as extinction totals for the individual visibility

- impairing chemical species: sulfate; nitrate; organic mass; elemental carbon; soil; and15



### 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. Figur es 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 emiss ions 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 vi sibility 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/m 3 

### ) 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 Increment17



### 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 t he volumetric mass concentration is as follows:

b Sulfate 

### = 3 • 

### f(RH) • 

### [SO4]

b Nitrate 

### = 3 • 

### f(RH) •



### [NO3]

b EC 

### = 10 • 

### [EC]

b OM 

### = 4 • 

### [OM]

b Soil 

### = 1 • 

### [Soil]

b CM 

### = 0.6 •



### [CM]

b Ray 

### = 10 Mm

- 1



### b ext 

### = b Ray 

### + b Sulfate



### + b Nitrate 

### + b EC 

### +b O M 

### + b Soil 

### + b CM



### 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 microgra ms 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



### R earranging yields a solution for the aerosol concentrations as a function of the me asured or modeled extinction:

[ SO4] = b Sulfate



### / [3 • 

### f(RH)]

[NO3] = b Nitrate



### / [3 • 

### f(RH)]

[EC] = b EC 

### / 10

[OM] = b OM 

### / 4

[Soil] = b Soil



### [CM] = b CM 

### / 0.6

Note that the sulfate (SO 4 

### ) and nitrate (NO 3 

### ) components are hygroscopic because their extinction c

oefficients depend upon relative humidity. T he concentrations, in square brackets, are in μg / m 3 

### and b ext 

### is in units of Mm
- 1 

### . The Rayleigh scattering term (b Ray 

### ) has a default value of 10 Mm

- 1 

### , as recommended in EPA guidance for tracking reasonable progre ss (EPA, 2003). The effect of relative humidity variability on the extinction coefficients for SO 4 

### and NO 3 

### can be estimated in several ways, but given the scope of this analysis, we calculated annual average Class I areas

- spe cific monthly f(RH) values (la st 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 b EXT 

### on Worst 20% Days in 2004

0 25 50 75 100 125 150 175 200BIBEBWCABRETCACRGUMOHEGLMINGUPBUVOYA2WIMOMACASIPSISLEBADLGRSALOSTROMOSACRTHROWHPEWHITWICAbEXT(Mm-1) Coarse Mass

Soil
Elemental Carbon Organic Mass

Nitrate
Sulfate20



### Figure 4

- 2. Measured Fractional Ext inction at Class I Areas Based on IMPROVE Data.



## IMPROVE Observed b EXT 

## 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 1.0BIBEBWCABRETCACRGUMOHEGLMINGUPBUVOYA2WIMOMACASIPSISLEBADLGRSALOSTROMOSACRTHROWHPEWHITWICAFraction of Total bEXT Sulfate Nitrate Organic Mass Elemental Carbon Soil Coarse Mass21



### 5.0 ESTIMATION OF EMISSI ONS REDUCTION NEEDS

5.1 Development of the Areas of Influence (AOI)

To quantify the incremental emissions reductions needed to ameliorate positive visibility i ncrement s at Class I area s , it wa s 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
t hat it provides a quantitative assessment of the impact of the emissions from a source region on such metrics as PM 2.5 

### concentration at a Class I area. This should not be confused with source apportionment where source regions are assigned quantitative cu lpability 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 SO 2 

### emissions has on sulfate concentration (μg/m 3 

### ) 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 c onsistent 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 t

hat 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 Speci es 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 Bac k Trajectory Residence Time (BTRT) plots. B ack trajectory analyses use meteorological fields to e stimate the most likely geographical path an air mass traversed to end at a particular receptor

. Of note, the meteorological field can be based on interpolat ion 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 trajectori

es o versimplif

y 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 produce s a series of endpoints representing longitude, latitude, and22



### 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, 2 005c). BTRT plots give the fraction of total hours that a n 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 com puted 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 ens emble backward trajectories with chemically speciated emissions data to estimate the trajectory

- emissions density

- weighted area likely to impact a r eceptor site

. PORSCH can do this for a single day or a suite of days though for purposes of this study, onl y data relevant to the 20% worst haze days were extracted.

As the name implies Tagged Species Source Apportionment (TSSA) uses “Tagged Chemical Species ,” or t racers , to track chemical transfor mations and transport of each chemical species or
precursor spec ies during an air quality model run

. K ey 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, d iffusion, deposition, chemical transformation), and the end results are three dimensional f ields in time showing source attribution of the chemical s

pecies for any grid cell in model domain. When chemical species are tagged by emissions source region, thi s 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 ar eas. 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 a ir 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 us ed to place a “break

point.” 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 a23



### 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; fin e 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.

On ce 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

s patial 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 “break

points.” Based on this reconciliation effort, the Level 1, 2, or gr eater “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 repres enting 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 cur rent 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 C lass 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 draw n 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 digiti zed.

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

- SO 2 

### and nitrate

- to

- NO X 

### s ensitivities.24 SO 2 – Level 1

SO 2 – Level 2 NO X – Level 2 NO X – Level 1 Boundary Waters

Voyageurs



### Figure 5

- 1

. Example G eocoded AOI for Boundary Waters and Voyageurs Class I areas. Green contours delineate areas of influence where NO X 

### emissions impact aerosol nitrate at the Class I

areas. Red contours delineate areas of influence where SO 2 

### 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 iden tified 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/m

3 

### of pollutant reduced per ton per day of precursor emissions reduced. For example,

- 0.001 μg/m 3 

### of sulfate per ton per day SO 2 

### reduced tells one that for each ton of SO 2 

### reduced within an AOI, the Class I area will exhibit a decrease of 0.001 μg/m 3 

### in 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 o f VISTAS. By systematically perturbing the global inventory (e.g., reducing global NO X 

### 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% NO X 

### reduction to actual tons per day NO X 

### reduction, which is simply done by taking 10% of the25



### emissions in the AQM

- ready emissions files, and dividing that into the peak concentration reduction, the sensitivity that is of most importance is realiz ed. 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 computi

ng 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 workboo k 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. Onc e 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/m 3 

### sulfate reduction per 10% reduction in SO 2 

### 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 VIST AS. 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, Uppe r 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 (2 006) 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 Appen dix 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 T able 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/m 3 

### ) per average daily ton reduction in NO X 

### (SO 2 

### ) 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 emissi ons reductions that are needed at each Class I area to attain the 2018 glide path. This is accomplished by dividing the visibility ‘ increment ’ b y the sensitivity coefficient and multiplying by 365.

Table 5

- 2 shows the required incremental reductions of SO 2 

### and NO X 

### emissions that are estimated to be required in order for the Class I areas to meet the glide slope by 2018. The estimated SO 2 

### and NO X 

### reductions in Table 5

- 2 are proportional to chemical species contributions during the 20% worst haze days. I n contrast, Table 5

- 3 shows the estimated SO 2 

### and NO X 

### 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 SO 2 

### emissions reductions (Table 5

- 2) from SO 2 

### emissions source27



### residing in the Level 1 AOI (Figure 5

- 2) are required assuming that incremental emissions reductions are d

eveloped based on a proportional reduction in the chemical species. Hence, in addition to the estimated incremental SO 2 

### emissions reductions of 73,000 tons per year, estimated incremental NO X 

### emissions reductions of 8,000 tons per year are also expected t o 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 r eductions were not estimated given that reasonably available emissions control scenarios exist only for NO X 

### and SO 2 

### . If only one chemical specie is controlled, for example sulfate, then precursor SO 2 

### incremental emissions reductions from emissions sources located within the SO 2 

### 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 NO X 

### incremental emissions reductions from emissions sources located within the NO X



### Level 1 A

OI (Figure 5

- 2) are estimated to be 210,000 tons per year.

SO 2 – Level 1 NO X – 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 SO 2 

### and NO X 

### are identified.28



### Table 5

- 2 . SO 2 

### and NO X 

### 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

. SO 2 

### and NO X



### 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 AOI30



### 6.0 PRIORITIZED CENRAP E MISSIONS REDUCTION S CENARIOS

6.1 Summary of Emission Inventories Used in Control Plan Development

A necessary component of t he 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 mod eling 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 SMOK E

- 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 C

ategory 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 High way 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 1,819,98331



### Table 6

- 2

. 2018 Base Case Annual Emissions Summary

. CENRAP 2018 Base Case Annual Emissions (Tons) Source Categor y 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 Process es 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 14,0 19 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 i dentify 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 i f 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.

Fina lly, 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 ident ified 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 B ART 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%

Misce llaneous 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

- 1,334,63 8

- 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

- 6,1 03,145

- 53,460

- 302,603

- 169,189 605,37633



### 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
Fu el 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 Pro cess 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 a nd 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 technolog ies 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 sim ilar 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 cont rol device applied to SO 2 

### and NO X 

### , 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 combin ations for SO 2 

### and NO X 

### , 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 C ENRAP 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 fl ow, 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 SO 2 

### 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 10035



### Table 6

- 7

. IPM Post Processing Assigned Device Codes and Applied NO X 

### Control Efficiencies

. Primary Device Code Secondary

Device Code Description CE R E

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 IN JECTION 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

13 9 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 R EDUCTION) + 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 RECIRCUL ATION 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 10036



### 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 source s 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, PM 10 

### , PM 2.5 

### , SO 2 

### , NO X 

### , as well as visibility impairment (regional haze) from point, area, and mobile sources. PM 10 

### and PM 2.5 

### 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 calc ulated 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 predetermin ed 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 table s 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 appli ed 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

- E GU 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 cost

effectiveness 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 me asures, 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 Descrip tion 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; Pulveriz ed 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; B ituminous/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

(Sub bituminous 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 Co mbustion 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 Genera tion; 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; Dist illate Oil (Diesel); Turbine: Exhaust 200
20100201 Internal Combustion Engines; Electric Generation; Natural Gas; Turbine 200

All other boilers 10038



### 6.4 Development of AOI

- Based Cost Curves

Each Class I area in the CENRAP modeling domain has an associ ated 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. Incrementa l 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 ap plication 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 avail able 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 e ach 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 sour ce. Additionally, the information provided in these cost curves is representative of the primary pollutant of control and does not reflect any co

control 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 1,000,000Millions

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 80,000 100,000 120,000 140,000 160,000 180,000 200,000Millions

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 ne cessary 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 c apital 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 t

he 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 esti mate, 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 Nec

essary 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 implemen tation 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 installatio n 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 partic ular 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 t o 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 d etermine 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 Po tentially 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 resu lting 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, t

ribal and site dependant and could not be addressed here.43



### 7.0 SUMMARY AND RECOMMEN DATIONS

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, Boundar y 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 a nd 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 re duction 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 reduct ion 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 em ission 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 uncontrollabl e 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 SO 2 

### sources account for up to 23% of the observed annual sulfate levels at Big Bend. During the summer months, Mexican SO 2 

### emissions sources can account for as much as 70% of the sulfate at Big Bend. Barna e

t al. also show that SO 2 

### emission sources for the Eastern U.S. are the biggest culprit to high sulfate at Big Bend during the high PM 2.5 

### summer days; and SO 2 

### from the Eastern US and Texas are the biggest contributor to high sulfate at Big Bend during the h igh PM 2.5 

### 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 reas onable progress glide path (and all of the other Class I AOIs in the CENRAP domain) are dominated by EGU SO 2 

### and NO X 

### emissions and many of these area AOIs intersect with States currently excluded by the EPA CAIR rule, we recommend that CENRAP consider a co ntrol 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 S tates 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 addi

tional IPM run configured to take into account the CAIR reductions within non

CAIR 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 de termine 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 in to 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 st rategies can be defined for three of the Class I areas projected to be above the reasonable progress glide path. In each case, the marginal cost45



### curves (based on the application of all available control options on all controllable industries and source typ es) 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 ass ociated 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 reasonab ly 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 t o 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 assumpti ons 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 approp riate, 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 Application47



### 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 Application48



### 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 Application49



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