# BART Analysis for Cholla Unit 4

Prepared For:

P.O. Box 188 Joseph City, AZ 86032



## January 2008

Prepared By:

2625 South Plaza Drive
Suite 300 Tempe, AZ 85282-3397ES-1 

# Executive Summary

Background

In response to the Regional Haze Rule a nd Best Available Retrofit Technology (BART) regulations and guidelines, CH2M HILL was requested to perform a BART analysis for Arizona Public Service Company (APS) Cholla Unit 4 (hereafter referred to as Cholla 4). APS’s Cholla Power Plant includes four electric generati ng units, with a gross 1,150 megawatts (MW). The gross megawatt capacity ratings are as follows: Unit 1 at 125 MW, Unit 2 and 3 at 300 MW, and Unit 4 at 425 MW. Cholla 4 coal-fired steam electr ic generating unit utilizes coal as the primary fuel; however, diesel fuel oil is used for warm-up and stabilization. Cholla 4 also burns used oil which may be co-fired with coal.
The BART analysis for Cholla 4 addressed the fo llowing criteria pollutant s: oxides of nitrogen (NO x 

## ) sulfur dioxide (SO 2 

## ) and particulate matter less than 10 microns in aerodynamic diameter (PM 10 

## ). BART emissions limits must be achieved with in five years after the State Implementation Plan (SIP) is approved by the EPA. A complian ce date of 2013 was assumed for this analysis.
In completing the BART analysis, technology a lternatives were inve stigated a nd potential reductions in NO x 

## , SO 2 

## , and PM 10 

## emissions rates were identified. The following technology alternatives were investigat ed, listed below by pollutant:

NO x 

## emission controls:

• 

## New/modified state-of-the-art low-NO x 

## burners (LNB) with separated over-fire air (SOFA) system

• 

## Rotating Opposed Fire Air (ROFA)

• 

## Selective non-catalytic reduction system (SNCR)

• 

## Selective catalytic reduction (SCR) system

• 

## Neural Network Controls (Neural Net)

SO 2 

## emission controls:

• 

## Enhancements to the existing wet lime scrubber system

PM 10 

## emission controls:

• 

## Electrostatic Precipitator (ESP)

• 

## Fabric Filter 

# BART Engineering Analysis

The specific components of a BART engineering analysis are identi fied in the Code of Federal Regulations (CFR) at 40 CFR 51 Appendix Y, Section IV. The evalua tion must include:

1. The identification of available, techni cally feasible, retr ofit control optionsBART ANALYSIS FOR CHOLLA UNIT 4



## 2. Consideration of any pollution control equipment in use at the source (which affects the availability of options and their impacts)

3. The costs of compliance with the control options

4. The remaining useful life of the facility

5. The energy and non-air quality envi ronmental impacts of compliance

6. The degree of visibility improvement that ma y reasonably be anticipated from the use of BART

These components are incorporated into the BA RT analysis performed by CH2M HILL through the following steps:
Step 1 – Identify All Available Retrofit Control Technologies
Step 2 – Eliminate Technically Infeasible Options

• 

## The identification of available, techni cally feasible, retr ofit control options

• 

## Consideration of any pollution control equipment in use at the source (which affects the applicability of options and their impacts)
Step 3 – Evaluate Control Effectivene ss of Remaining Control Technologies
Step 4 – Evaluate Impacts and Document the Results

• 

## The costs of compliance with the control options

• 

## The remaining useful life of the facility

• 

## The energy and non-air quality envi ronmental impacts of compliance
Step 5 – Evaluate Visibility Impacts

• 

## The degree of visibility improvement that ma y reasonably be anticipated from the use of BART
Separate analyses have been conducted for NO x 

## , SO 2 

## , and PM 10 

## emissions. All costs included in the BART analyses are in 2007 dollars, and cost s have not been escalated to the assumed 2013 BART implementation date.


# Coal Characteristics

Sources of coal burned at Cholla 4 are McKi nley, Lee Ranch, and El Segundo. The McKinley and the Lee Ranch mines are in western New Me xico, near the towns of Gallup and Grants respectively. The El Segundo mine is lo cated adjacent to the Lee Ranch mine.
Some of these coals may be classified as sub- bituminous, while demonstra ting characteristics of bituminous coal which influences the level of NO x 

## emissions from the boiler. Bituminous coals typically have higher nitrogen content than sub- bituminous coals such as those from the PRB, which represent the bulk of sub-bituminous coal use in the U.S. and upon which the presumptive BART limit for sub-bituminous coals was based. This BART analysis has considered the higher nitrogen content and different combustion char acteristics of bituminous and sub-bituminous

ES-2BART ANALYSIS FOR CHOLLA UNIT 4



## coals planned to be burned at Cholla 4, and has evaluated the effect of these qualities on NO x 

## formation and achievable emission rates.



# Recommendations

NO x 

# Emission Control Based on the analysis conducted, new LNB with SOFA can achieve the BART emission level of 0.22 lb/MMBtu, based on the proj ected significant reduction in NO x 

## emissions, reasonable control costs, and the advantages of lack of non air quality environmental impacts.



# SO 2 

# Emission Control Based on the analysis conducte d, upgrade of the wet FGD sy stem can achieve the BART emission level of 0.15 lb/MMBtu for SO 2 

## emission control.



# PM 10



# Emission Control Based on the analysis conducted, the installati on of a fabric filter can achieve the BART emission level of 0.015 lb/MMBtu for PM 10 

## emission control. 

# BART Modeling Analysis

CH2M HILL used the CALPUFF modeling system to assess the visibility impacts of emissions from Cholla 4 at Class I areas. The Class I area s potentially affected are located more than 50 kilometers (with the exception of Petrif ied Forest National Park), but less than 300 kilometers, from the Cholla Power Plant. Pe trified Forest National Park is approximately 39 kilometers from the Cholla Power Plant). Th e Class I areas evaluated include the following:

• 

## Petrified Forest National Park (NP)

• 

## Sierra Ancha Wilderness Area (WA)

• 

## Mazatzal WA

• 

## Mount Baldy WA

• 

## Sycamore Canyon WA

• 

## Pine Mountain WA

• 

## Superstition WA

• 

## Grand Canyon NP

• 

## Gila WA

• 

## Galiuro WA

• 

## Mesa Verde NP

• 

## Capitol Reef NP

• 

## Saguaro NP
Because Cholla 4 will simultaneously control NO x 

## , SO 2 

## , and PM 10 

## emissions, post control visibility modeling scenarios were developed to cover the range of effectiveness for combining the individual NO x 

## and SO 2 

## control technologies under evalua tion. These modeling scenarios, and the controls assumed, are as follows:

ES-3BART ANALYSIS FOR CHOLLA UNIT 4

ES-4 • 

## Scenario 1 : New LNBs with SOFA system, upgrad ed wet lime FGD system, and fabric filter.

• 

## Scenario 2 : New LNBs with SOFA system and RO FA, upgraded wet lime FGD system, and fabric filter.

• 

## Scenario 3 : New LNBs with SOFA system and RO FA and Rotamix, upgraded wet lime FGD system, and fabric filter.

• 

## Scenario 4 : New LNBs with SOFA system and SN CR, upgraded wet lime FGD system, and fabric filter.

• 

## Scenario 5 : New LNBs with SOFA system and SCR, upgraded wet lime FGD system, and fabric filter.
Visibility improvements for all emission control scenarios were analyzed, and the results were compared utilizing a Least-Cost Envelope Analysis, as outlined in the draft EPA 1990 New Source Review Workshop Manual (NSR Manual). 

# Least-Cost Envelope Analysis

EPA has adopted the Least-Cost Envelope An alysis Methodology as an accepted methodology for selecting the most reasonable, cost-effec tive controls. Incremental cost-effectiveness comparisons focus on annualized cost and emi ssion reduction differences between dominant alternatives. The dominant set of control alternat ives is determined by generating what is called the envelope of least-cost alternatives. This is a graphical plot of total annualized costs for a total emissions reductions for all control altern atives identified in the BART analysis.
To evaluate the impacts of the modeled control sc enarios on the thirteen Class I areas, the total annualized cost, cost per deciview (dV) reducti on, and cost per reduction in number of days above 0.5 dV were analyzed. This report provides a comparison of the average incremental costs between relevant scenarios for the thirteen Class I areas; the total annualized cost versus number of days above 0.5 dV, and the total annualized cost versus 98th per centile delta-deciview ( Δ dV) reduction.
Results of the least-cost analysis for the various NO x 

## emission control scenarios confirm the selection of Scenario 1 (New LNB with SOFA), based on incremental cost and visibility improvements. All other NO x 

## control scenarios are excluded on the basis of cost effectiveness. 

# Just-Noticeable Differences in Atmospheric Haze

Studies have been conducted th at demonstrate only dV differe nces of approximately 1.5 to 2.0 dV or more are perceptible by the human eye. Deciview changes of less than 1.5 cannot be distinguished by the average pe rson (Henry, 2002). Therefore, the modeling analysis results indicate that only minimal, if a ny, observable visibility improveme nts at the Class I areas studied would be expected under any of th e control scenarios. Thus, the results indicate that even though APS will be spending many millions of dollars at th is single unit, and over a billion dollars when considering its entire coal fleet , only minimal discernable visibility improvements may result.

# Contents

Section Page
Executive Summary ............................................................................................................. E S-1 Background ................................................................................................................ ES-1 BART Engineering Analysis ..................................................................................... ES-1 Coal Characteristics ................................................................................................... ES-2 Recommendations...................................................................................................... ES-3 NO x 

## Emission Control ................................................................................... ES-3 SO 2 

## Emission Control .................................................................................... ES-3 PM 10 

## Emission Control .................................................................................. ES-3 BART Modeling Analysis.......................................................................................... ES-3 Least-Cost Envelope Analysis ................................................................................... ES-4 Just-Noticeable Differences in Atmospheric Haze .................................................... ES-4 Acronyms and Abbreviations ................................................................................................... v 1.0 Introduction.................................................................................................................1-1 2.0 Present Unit Operation...............................................................................................2-1 3.0 BART Engineering Analysis ......................................................................................3-1 3.1 BART Process...................................................................................................3-1 3.1.1 BART NO x 

## Analysis.............................................................................3-2 3.1.2 BART SO 2 

## Analysis............................................................................3-13 3.1.3 BART PM 10 

## Analysis..........................................................................3-15 4.0 BART Modeling Analysis...........................................................................................4-1 4.1 Introduction.......................................................................................................4-1 4.2 Model Selection ................................................................................................4-1 4.3 CALMET Methodology....................................................................................4-2 4.3.1 Dimensions of the Modeling Domain...................................................4-2 4.3.2 CALMET Input Data ............................................................................4-6 4.3.3 Validation of CALMET Wind Field.....................................................4-7 4.4 CALPUFF Methodology...................................................................................4-7 4.4.1 CALPUFF Modeling.............................................................................4-7 4.4.2 Receptor Grids and Coordinate Conversion .........................................4-8 4.5 Visibility Post-processing .................................................................................4-9 4.5.1 CALPOST.............................................................................................4-9 4.6 Results.............................................................................................................4-10 4.6.1 BART Least-Cost Analysis.................................................................4-10 5.0 Preliminary Assessment and Recommendations .....................................................5-1 5.1 Preliminary Recommended BART Controls ....................................................5-1 5.2 Analysis Baseline and Scenarios.......................................................................5-1 5.3 Least-Cost Envelope Analysis ..........................................................................5-7 5.3.1 Analysis Methodology ..........................................................................5-7 5.3.2 Analysis Results..................................................................................5-19 5.4 Recommendations...........................................................................................5-19

iiCONTENTS (CONTINUED)



## 5.4.1 NOx Emission Control........................................................................5-19 5.4.2 SO2 Emission Control ........................................................................5-19 5.4.3 PM10 Emission Control......................................................................5-19 5.5 Just-Noticeable Differences in Atmospheric Haze .........................................5-19 6.0 References ....................................................................................................................6 -1

Appendices
A Economic Analysis B BART Protocol C Additional BART Modeling Results

List of Tables
Table 2-1 Unit Operation and Study Assumptions Table 2-2 Coal Sources and Characteristics Table 3-1 Key Coal Characteristics Table 3-2 NO x 

## Control Technology Emission Rate Ranking Table 3-3 NO x 

## Control Cost Comparison Table 3-4 SO 2 

## Control Technology Emission Rate Ranking Table 3-5 SO 2 

## Wet Lime FGD Costs Table 3-6 PM 10 

## Control Technology Emission Rates Table 3-7 PM 10 

## Fabric Filter Costs Table 4-1 User-Specified CALMET Options Table 4-2 Average Natural Le vels of Aerosol Components Table 5-1 Emission Control Scenarios Table 5-2 Ranking of NO x 

## Control Scenarios by Cost Table 5-3 NO x 

## Control Scenario Results for Capitol Reef NP Table 5-4 NO x 

## Control Scenario Results for Grand Canyon NP Table 5-5 NO x 

## Control Scenario Results for Petrified Forest NP Table 5-6 NO x 

## Control Scenario Results for Sycamore Canyon WA Table 5-7 Capitol Reef NP NO x 

## Control Scenario Incremental Analysis Data Table 5-8 Grand Canyon NP NO x 

## Control Scenario Incremental Analysis Data Table 5-9 Petrified Forest NP NO x 

## Control Scenario Incremental Analysis Data Table 5-10 Sycamore Canyon WA NO x 

## Control Scenario Incr emental Analysis Data

List of Figures
Figure 3-1 Illustration of the Effect of A gglomeration on the Speed of Coal Combustion Figure 3-2 First Year Control Cost for NO X 

## Air Pollution Control Options Figure 4-1 CALPUFF and CA LMET Modeling Domains Figure 5-1 NO x 

## Control Scenarios—Maximum Contribut ions to Visual Range Reduction at Capitol Reef NP Figure 5-2 NO x 

## Control Scenarios—Maximum Contribut ions to Visual Range Reduction at Grand Canyon NP Figure 5-3 NO x 

## Control Scenarios—Maximum Contribut ions to Visual Range Reduction at Petrified Forest NP

iiiCONTENTS (CONTINUED)

iv 

## Figure 5-4 NO x 

## Control Scenarios—Maximum Contribut ions to Visual Range Reduction at Sycamore Canyon WA Figure 5-5 NO x 

## Control Scenarios – Least-Cost Envelope Capitol Reef NP—Days Reduction Figure 5-6 NO x 

## Control Scenarios—Least-Cost Envelope Capitol Reef NP—98 th 

## Percentile Reduction Figure 5-7 NO x 

## Control Scenarios—Least-Cost Envelope Grand Canyon NP—Days Reduction Figure 5-8 NO x 

## Control Scenarios—Least-Co st Envelope Grand Canyon NP—98 th


## Percentile Reduction Figure 5-9 NO x 

## Control Scenarios—Least-Cost E nvelope Petrified Forest NP—Days Reduction Figure 5-10 NO x 

## Control Scenarios—Least-Cost Envelope Petrified Forest NP—98 th


## Percentile Reduction Figure 5-11 NO x 

## Control Scenarios—Least-Cost Envelope Sycamore Canyon WA—Days Reduction Figure 5-12 NO x 

## Control Scenarios—Least-Cost Envelope Sycamore Canyon WA—98 th


## Percentile Reduction

# Acronyms and Abbreviations

ADEQ Arizona Department of Environmental Quality
APS Arizona Public Service
BACT Best Availa ble Control Technology
BART Best Availabl e Retrofit Technology CALDESK Program to display data and results CALMET Meteorological data pr eprocessing program for CALPUFF CALPOST Post-processing program fo r calculating visibility impacts CALPUFF Gaussian puff dispersion model COFA close-coupled over-fire air
dV deciview
Δ dV delta deciview, change in deciview ESP electrostatic precipitator EPA United States Environmental Protection Agency
Fuel NO x 

## oxidation of fuel bound nitrogen FGC flue gas conditioning FGD flue gas desulfurization f (RH) relative humidity factors
ID internal diameter
kW kilowatts
kW-Hr kilowatt-hour
LAER lowest achievable emission rate lb/MMBtu pounds per million British Thermal Units
LNB low-NO x 

## burner LOI loss on ignition
MMBtu Million British Thermal Units MM5 Mesoscale Meteorologi cal Model, Version 5 MW megawatts
N 2 

## nitrogen

NM National Monument

NO nitric oxide

NO x 

## oxides of nitrogen
NP National Park

vACRONYMS (CONTINUED)

vi 

## NWS National Weather Service

OFA over-fire air

PM 10 

## particulate matter less than 10 microns in aerodynamic diameter
PRB Powder River Basin ROFA Rotating Opposed Fire Air SCR selective catalytic reduction system SIP State Implementation Plan
SNCR selective non-cat alytic reduction system SOFA separated over-fire air
SO 2 

## sulfur dioxide
SO 3 

## sulfur trioxide
Thermal NO x 

## high temperature fixation of atmospheric nitrogen in combustion air USGS U.S. Geological Survey
WA Wilderness Area

# 1.0 Introduction

The Clean Air Act established goals for visibil ity improvement in nati onal parks, wilderness areas, and international parks. Through the 1977 amendments to the Clean Air Act in Section 169A, Congress set a national goal for visibility as “the prevention of any future, and the remedying of any existing, impairment of visibili ty in mandatory Class I Federal areas which impairment results from manmade air pollution.” The Amendments required the United States Environmental Protection Agency (EPA) to issue regulations to assure “reasonable progress” toward meeting the national goal. In 1990, C ongress again amended the Clean Air Act, providing additional emphasis on regional haze issues.
In 1999, EPA issued comprehensive regulations to improve visibility, or visual air quality, in the 156 national parks and wilderne ss areas across the country clas sified as mandatory Class I areas. These regulations include requirements for States to establish goals for improving visibility in national parks and wilderness ar eas and to develop long- term strategies for reducing emissions of air pollutants th at cause visibility impairment.
One of the principal elements of the visibili ty protection provisions of the Clean Air Act addresses installation of best available retrofit technology, or BART, for certain existing sources placed into operation between 1962 and 1977. The 1999 Regional Haze Rule requires three basic state plan elements related to BART:

• 

## A list of BART-eligible sources (includes s ources of air pollutant s that are reasonably anticipated to contribute to visibil ity impairment in a Class I area);

• 

## An analysis of the emission reductions and chan ges in visibility that would result from “best retrofit” control levels on sources subject to BART; and

• 

## The BART emission limits for each subject sour ce, or an alternative measure such as an emissions trading program for achieving greater reasonable progress in visibility protection than implementation of source -by-source BART controls.
In determining BART, the State can take into account several factors, including the existing control technology in place at the source, th e costs of compliance, energy and non-air environmental impacts of compliance, remaining useful life of the source, and the degree of visibility improvement that is reasonably anti cipated from the use of such technology (EPA, 1999).
In July 2005, EPA released speci fic BART guidelines for states to use when determining which facilities must install additional controls, and the type of controls that must be used. Under current regulatory deadlines, States, includi ng Arizona, were required to submit a Regional Haze Rule State Implementation Plan (SIP) am endment that addresses BART implementation by December, 2007. In this plan amendment, States were to identify the faci lities that will have to reduce emissions under BART and then set BA RT emissions limits for those facilities, and/or identify any alternative plan for reduci ng visibility impairing pollutants that would achieve greater reductions than those realized from BART emissions limits (EPA, 2005).

1-1BART ANALYSIS FOR CHOLLA UNIT 4

1-2 

## Using information from the Western Regiona l Air Partnership and its Regional Modeling Center, the State of Arizona has identified those eligible in-state sources that are required to reduce emissions under BART, and has directed t hose sources to complete BART analyses to identify potential reductions for emissions of sulfur dioxide (SO 2 

## ), nitrogen oxides (NO x 

## ) and particulate matter less than 10 micr ons in aerodynamic diameter (PM 10 

## ) that would be associated with addition of additional or new air pollution controls. This information will be included in the State’s SIP that was due in D ecember 2007. At this time, it is expected that Arizona’s SIP, when submitted, will address reduction of SO 2 

## emissions at BART sources through an alternative m easure in the form of a four-state backstop cap-and-trade program. Reduction of NO x 

## and PM 10 

## emissions will be addressed through establishment of BART emissions limits in source operating permits.
The EPA BART guidelines state that the BART emission limits established as a result of BART analyses must be fully implemented within five years of EPA’s approval of the SIP. For the purposes of this project, that date is assumed to be 2013.
This report documents the BART analysis that was performed on Cholla 4 on behalf of APS by CH2M HILL. The analysis was performed for the pollutants NO x 

## , SO 2 

## , and PM 10


## . Section 2.0 of this report provides a descrip tion of the present un it operation, including a discussion of coal sources and characteristics. The BART Engineering An alysis is provided in Section 3.0 by pollutant type. Section 4.0 provi des the methodology and results of the BART Modeling Analysis, followed by recommendations in Section 5.0. References are provided in Section 6.0. Appendices include the detailed eco nomic analysis (Appendix A) and the BART modeling protocol (Appendix B).2-1 

# 2.0 Present Unit Operation

The Cholla Power Plant consists of four elec tric generating units with a total generating capacity of 1,150 megawatts (MW). The power plant is located approximately 2 miles east of the town of Joseph City on Interstate 40, in Navajo County, Arizona. Cholla 4 is a 425 MW coal-fired steam electric genera ting unit equipped with a tangentially-fired, dry bottom, boiler manufactured by Combustion Engi neering. Current emissions cont rol equipment includes a hot side electrostatic precipitator for particulate matter control. Cl ose-coupled overfire air (COFA) is utilized for NOx control. One scrubber tower is in-service which tr eats approximately 36% of the flue gas.
Cholla 4 was placed in service in 1981, with a projected remaining lif e of 40 years or until 2047. This analysis is based on a 20-year life for BART control technologies. Assuming a BART implementation date of 2013, this estim ates the technologies will operate until 2033. Table 2-1 lists additional unit information a nd study assumptions for this analysis.

TABLE 2-1 

### Unit Operation and Study Assumptions Cholla 4

General Plant Data Site Elevation (feet above MSL) 5,019 Stack Height (feet) 1,3 550 Stack Exit ID (feet) /Exit Area (sq. ft.) 1,3 19.2 /289.4 Stack Exit Temperature (°F) 1,3 254.9 Stack Exit Velocity (ft/sec) 1,3 70.2 Stack Flow (ACFM) 3 1.2 x 10 6 Annual Unit Capacity Factor (%) 2 93.0 Gross Unit Output (MW) 425 Gross Unit Heat Rate (Btu/kW-Hr)(100% load) 4 10,042 Boiler Heat Input (MMBtu/Hr)(100% load) 4 4,268 Type of Boiler Tangential fired
Boiler Fuel Coal
Coal Sources See Table 2-2
Current NO xControls COFA
NO xEmission Rate (lb/MMBtu) 5 0.415
Current SO 2 Controls Slip stream absorber
SO 2 Emission Rate (lb/MMBtu) 5 0.734
Current PM 10 Controls Hot-side Electrostatic Precipitator
PM 10 Emission Rate (lb/MMBtu) 5 0.024 1 - Based on APS Cholla emission Reduction Project, August 2006 2 – Based on EPA Acid Rain Program 2001-2006 3 – Shared Stack with Unit 3 4 – Technical Support Documentation May 3, 2006 5 – Based on actual emissions, highest 24 hr av erage emissions during 2001- 2003, provided by APS.BART ANALYSIS FOR CHOLLA UNIT 3

2-2 

## For Table 2-1 above, emissions for the years 200 1 to 2003 were analyzed to obtain the average Cholla 4 emissions.
In the July 2005 EPA BART guidelines, EPA pr escribed presumptive BART limits to be achieved at BART-eligible coal fi red power plants with a total ge nerating capacity greater than 750 MW. Since the total generating capacity of the Cholla Power Plant is 1150 MW, the presumptive limits apply.
The BART presumptive NO x 

## limit for dry bottom tangentia lly-fired boilers burning subbituminous coal is 0.15 lb/MMBtu, and the BART presumptive NO x 

## limit for burning bituminous coal is 0.28 lb/MMBtu. Current sources of coal burned at Cholla 4 are summarized in Table 2-2, and APS is transitioning the co al supply to burn solely El Segundo coal by the end of 2008. Burning El Segundo coal may result in uncontrolled SO 2 

## emissions as high as 2.5 lb/MMBtu.
APS is planning to install new LNB with SOFA, a new SO 2 

## absorber, and a new fabric filter in 2008.BART ANALYSIS FOR CHOLLA UNIT 4TABLE 2-2Coal Sources and CharacteristicsCholla 4Ultimate Analysis (% dry basis)MinesMoist. %Ash %VolatileMatter %FixedCarbon %Btu/lbSulfur %CarbonHydrogenNitrogenChlorineSulfurAshOxygenMcKinley Mine,NM13.9014.2832.4539.9099110.4765.924.581.110.010.5516.5911.25Lee Ranch Mine,NM15.3017.8033.5033.4092500.9061.704.501.000.011.0621.0010.73El Segundo Mine,NM17.6016.8031.7033.9092151.1062.134.621.000.021.3420.4810.792-3

# 3.0 BART Engineering Analysis

3.1 BART Process

The specific components in a BART engineering anal ysis are identified in the Code of Federal Regulations (CFR) at 40 CFR 51 Appendix Y, Section IV. The evalua tion must include:

1. The identification of available, techni cally feasible, retr ofit control options 2. Consideration of any pollution control equipment in use at the source (which affects the availability of options and their impacts) 3. The costs of compliance with the control options 4. The remaining useful life of the facility 5. The energy and non-air quality environm ental impacts of compliance, and 6. The degree of visibility improvement which ma y reasonably be anticipa ted from the use of BART
These components are incorporated into the BA RT analysis performed by CH2M HILL through the following steps:
Step 1 – Identify All Available Retrofit Control Technologies
Step 2 – Eliminate Technically Infeasible Options

• 

## The identification of available, techni cally feasible, retr ofit control options

• 

## Consideration of any pollution control equipment in use at the source (which affects the applicability of options and their impacts)
Step 3 – Evaluate Control Effectivene ss of Remaining Control Technologies
Step 4 – Evaluate Impacts and Document the Results

• 

## The costs of compliance with the control options

• 

## The remaining useful life of the facility

• 

## The energy and non-air quality envi ronmental impacts of compliance
Step 5 – Evaluate Visibility Impacts

• 

## The degree of visibility improvement which ma y reasonably be anticipa ted from installation of BART controls.
In the evaluation, consideration was made of any pollution control equipment in use at the source, the costs of compliance associated with the control options, a nd the energy and non-air quality environmental impacts of compliance using these existing control devices. As a consequence, controls scenarios included enhancement of existing equipment, as well as addition of new control equipment.

3-1BART ANALYSIS FOR CHOLLA UNIT 4



## Separate analyses have been conducted for NO x 

## , SO 2 

## , and PM 10 

## emissions. All costs included in the BART analysis are in 2007 dollars, and cost s have not been escalated to the assumed 2013 BART implementation date.
Establishing Permit Emission Levels from BART Analysis Results As an integral part of the BART analysis proc ess, cost and expected emission information was developed for NO x 

## , SO 2 

## , and PM 10 

## . This information is asse mbled from various sources including emission reduction equipment vendors, APS operating and engineering data, and internal CH2M HILL historical information.
The level of accuracy of the cost estimate can be broadly classified as “Order of Magnitude”, which can be categorized as -30/+50%. There ar e several reasons for the wide range of cost estimates included in the BART analysis. This vari ability is primarily caused by the difficulty in receiving detailed and accurate information from equipment vendors. Due to the extremely active power industry marketplace, obtaining engineering and construction cost information is severely restricted due to vendor workload. Materi al and construction labor costs are also widely fluctuating in today’s active economy.
The accuracy of expected emissions may also be questionable, and is also attributable to the inability to gain timely and accurate informa tion. This is exemplified by the difficulty in obtaining background information, and the vendor time required to develop accurate emission projections for study purposes as opposed to their response to actua l project request for proposals. Also, variance in expected emissi ons can be dependent upon the pollutant under consideration; i.e., particulat e emissions can generally be more accurately predicted than NO x 

## emissions.
Therefore, when selecting emissions control technologies and establishing emission permitting levels, consideration of variability in cost and expected emissions information has been considered.



# 3.1.1 BART NO x 

# Analysis

NO x 

## formation in coal-fired boilers is a comp lex process that is dependent on a number of variables, including operating conditions, equipment design, and coal characteristics.

3.1.1.1 

## Formation of NO x 

## During coal combustion, NO x 

## is formed in three different ways. The dominant source of NO x 

## formation is the oxidation of fuel-bound nitrogen (fuel NO x 

## ). During combustion, part of the fuel-bound nitrogen is released from the coal with the volatile matte r, and part is retained in the solid portion (char). The nitrogen chemically bound in the coal is partially oxidized to nitrogen oxides (NO and NO 2 

## ) and partially reduced to molecular nitrogen (N 2 

## ). A smaller part of NO x 

## formation is due to high temper ature fixation of atmospheric n itrogen in the combustion air (thermal NO x 

## ). A very small amount of NO x 

## is called “prompt” NO x 

## . Prompt NO x 

## results from an interaction of hydrocarbon radicals, nitrogen, and oxygen.
In a conventional pulverized co al burner, air is introduced wi th turbulence to promote good mixing of fuel and air, which pr ovides stable combustion. However, not all of the oxygen in the air is used for combustion. Some of the oxygen combines with the fuel nitrogen to form NO x 

## .

3-2BART ANALYSIS FOR CHOLLA UNIT 4



## Coal characteristics directly and significantly affect NO x 

## emissions from coal combustion. Coal ranking as defined by The American Society for Testing and Materials (ASTM) is a means of classifying coals according to their degree of metamorphism in the natural series, from lignite to sub-bituminous to bituminous and on to anthracite. Lower rank coals, such as the sub-bituminous coals from the PRB, produce lower NO x 

## emissions than higher rank bituminous coals, due to their higher reactivit y and lower nitrogen content. The fixed carbon to volatile matter ratio (fuel ratio), coal oxygen content, and rank are good relativ e indices of the reactivity of a coal. Lower rank coals release more organically bound nitrog en earlier in the combustion process than do higher rank bituminous coals. When used with low NO x 

## burners, sub-bitumi nous coals create a longer time for the kinetics to promote more stab le molecular nitrogen, an d hence result in lower NO x 

## emissions.
The primary basis for coal rank classification of lower rank bi tuminous and all sub-bituminous coals by ASTM is gross calorific value determin ed on a moist mineral-matter-free basis. In the cases of both high volatile bituminous “C” and sub-bituminous “A” clas sifications, the gross calorific values on a moist mineral-matter-free ba sis must be greater than 10,500 Btu/lb and less than 11,500 Btu/lb. In order to classify these types of coals, a characteristic called agglomeration is used. Agglomeration is a distinguishing characte ristic that classifies the coals as bituminous rather than sub-bituminous; that is, they are “agglomerating” as compared to “nonagglomerating”. Agglomerating as a pplied to coal is “the property of softening when it is heated to above about 400° C in a non-oxidizing atmosphe re, and then appearin g as a coherent mass after cooling to room temperatur e.” Because the agglomerating prope rty of coals is the result of particles transforming into a plastic or semi-liq uid state when heated, it reflects a change in surface area of the particle. Thus, with the application of heat, a gglomerating coals would tend to develop a non-porous surface, while the surface of non-agglomerating coals would become even more porous with combustion. As shown by Figure 3-1, the increased porosity provides more particle surface area, re sulting in more favorable combusti on conditions. This non-agglomerating property assists in making sub-bituminous coals more amenable to controlling NO x 

## , by allowing less air to be introduced during the initial igni tion portion of the combustion process. Since Cholla 4 may burn a blend of bituminous and marginally ranked sub-bituminous coals, NO x 

## emissions from combustion of these blended coals will vary depending on the resultant combined coal characteristics.

3-3BART ANALYSIS FOR CHOLLA UNIT 4

FIGURE 3-1 

### Illustration of the Effect of Agglomeration on the Speed of Coal Combustion Cholla 4



## Table 3-1 shows key characteristics of the coal s which are currently being burned on Cholla 4 and a “typical” PRB sub-bituminous coal for co mparison. APS is currently transitioning to burn only El Segundo coal beginning in 2008.

TABLE 3-1 

### Key Coal Characteristics Cholla 4

Site Btu/Lb Ash (%) Sulfur (%) Nitrogen (%) Oxygen (%) Coal Rank

McKinley New Mexico 9911 14.28 0.47 0.96 9.96 Bit

Lee Ranch New Mexico 9250 17.80 0.90 0.85 9.09 Bit/Sub

El Segundo New Mexico 9215 16.80 1.10 0.82 8.86 Bit/Sub

Antelope Wyoming 8800 5.25 0.24 0.78 12.08 Sub



## The analyses shown above that were furnished fo r this report did not indi cate whether the coals were agglomerating or non-agglomer ating. Since the McKinley coal analysis results in a moist, mineral-matter-free heating value of 11,726 Btu/Lb, it is classified as high volatile C bituminous. The Lee Ranch and El Segundo coals have mo ist mineral-matter-free values of 11,466 and 11,279, respectively, which requ ire the agglomerating determination in order to classify them.

3-4BART ANALYSIS FOR CHOLLA UNIT 4



## As shown in Table 3-1, the bituminous coals gene rally exhibit higher nitrogen content and lower oxygen content than the sub-bituminous PRB coal.
The higher nitrogen content is an indication that more nitrogen is available to the combustion process and higher NO x 

## emissions are likely. Oxygen content can be correlated to the reactivity of the coal, with more reactive coals generally containing higher levels of oxygen. More reactive coals tend to produce lower NO x 

## emissions, and they are also more conducive to reduction of NO x 

## emissions through the use of combustion control measures, such as low NO x 

## burners and over-fire air (OFA). Th ese characteristics indicate that higher NO x 

## formation is likely with bituminous rather than sub-bituminous coals.
Coal quality characteristics also impact the de sign and operation of th e boiler and associated auxiliary equipment. Minor changes in qua lity can sometimes be accommodated through operational adjustments or changes to equipment. It is important to note, however, that consistent variations in quality or assump tions of “average” quality for performance projections can be problematic. This is particularly troublesome when dealing with pe rformance issues that are very sensitive to both coal quality a nd combustion conditions, such as NO x 

## formation.
Several of the coal quality char acteristics and their effect on NO x 

## formation have been previously discussed. There are additional considerations that illustrate the complexity of achieving and maintaining consistent low NO x 

## emissions with pulverized coal on a shorter term, such as a 30- day rolling average basis.
Good combustion is based on the “three Ts”: time, temperature and turbulen ce. These parameters along with a “design” coal are taken into cons ideration when designing a boiler and associated firing equipment such as fans, burners, and pulve rizers. If a performance requirement such as NO x 

## emission limits is subsequently changed, conflic ts with other performa nce issues can result.

Cholla 4 is located at an altit ude of 5,019 feet above sea level. At this elevation, atmospheric pressure is lower as compared with sea level pressure of 14.7 pounds per s quare inch. This lower pressure means that less oxygen is available for combustion for each volume of air. In order to provide adequate oxygen to meet the requirements for efficient combustion, larger volumes of air are required. When adjusting air flows and distribution to lower NO x 

## using low NO x 

## burners and overfire air (OFA), original boiler design restrictions again limit the modifications that can be made and still achieve satisfactory combustion performance.
Another significant factor in controlling NO x 

## emissions is the fineness of the coal entering the burners. Fineness is influenced by the Hardgrove Gr indability Index (HGI) of the coal. Finer coal particles promote release of vola tiles and assist char burnout due to more surface area exposed to air. NO x 

## reduction with high volatile coals is improved with greater fineness and with proper air staging. The lower rank sub-bituminous coals such as PRB coals are quite friable and easy to grind. Coals with lower HGI values, are more di fficult to grind and can contribute to higher NO x 

## levels. In addition, coal fineness can deteri orate over time periods between pulverizer maintenance and service as pul verizer grinding surfaces wear.
In summary, when all the factors of agglom eration versus non-agglomeration, nitrogen and oxygen content of the coals, and the grindability inde x are taken into acc ount, this analysis demonstrates that, for the wide variability of co al supply to be utilized at Cholla 4, the more appropriate presumptive BART lim it is 0.28 lb/MMBtu. This limit is referred to here only as a

3-5BART ANALYSIS FOR CHOLLA UNIT 4

3-6 

## point of reference, and CH2M HILL recommends th at this value be used in evaluation of the effectiveness of BART controls applied to Cholla 4. The BART analysis for NO x 

## emissions from Cholla 4 is further described below.

3.1.1.2 

## Step 1: Identify All Availabl e Retrofit Control Technologies The first step of the BART process is to evaluate NO x 

## control technologies with practical potential for application to Cho lla 4, including those control technologies identified as Best Available Control Technology (BACT) or lo west achievable emission rate (LAER) by permitting agencies across the United States. A broad range of information sources have been reviewed in an effort to identify potentially applicable emission control technologies.

Cholla 4 NO x 

## emissions are currently c ontrolled through the use of a COFA system, and a new LNB system will be added in 2008. A SOFA upgrade is also planned for Cholla 4.
The following potential NO x 

## control technology options were considered:

• 

## New/modified state-of-the-art low-NO x 

## burners (LNB) with SOFA

• 

## Rotating Opposed Fire Air (ROFA)

• 

## Selective non-catalytic reducti on system (Rotamix & SNCR)

• 

## Selective catalytic reduction (SCR) system

• 

## Neural Network/Boiler Combus tion Control (Neural Net)

3.1.1.3 

## Step 2: Eliminate Technically Infeasible Options For Cholla 4, a tangentially-fired boiler burni ng a blend of bituminous and sub-bituminous rank coals, technical feasibility w ill primarily be determined by physical constraints, boiler configuration, and on the ability to achieve the regulatory presumptive limit of 0.28 lb NO x 

## /MMBtu. Cholla 4 currently has an average - NO x 

## emission rate of 0.415 lb/MMBtu.
For this BART analysis, information pertaini ng to LNBs, OFA, SNCR, and SCR were based on a combination of vendor information and intern al CH2M HILL information. Sources of cost estimates for Cholla 4 are listed below in Table 3-2, which also summarizes the control technology options evaluated in this BA RT analysis, along with projected NO x 

## emission rates. All technologies listed can meet the bituminous presumptive BART limit of 0.28 lb/MMBTU, except for the neural net boiler controls.BART ANALYSIS FOR CHOLLA UNIT 4

TABLE 3-2 

### NO x 

### Control Technology Emission Rate Ranking Cholla 4

Technology Source of Estimated Cost and Emissions Expected Emission Rate (lb/MMBtu)

Presumptive BART Limit 0.28

New LNB w/ SOFA 3 Foster Wheeler 0.22

ROFA 4 Mobotec 0.16

ROFA w/Rotamix 4 Mobotec 0.12

New LNB w/SOFA & SNCR 2 Foster Wheeler, CH2M HILL 0.17

New LNB w/SOFA & SCR Foster Wheeler, CH2M HILL 0.07

Neural Net Controls 1 NeuCo 0.30
1 - NeuCo provides no guarantees; derived using 15% reduction from average NO xemissions level

. 2 - A 25% removal efficiency was assumed from prior SNCR proposals 3 - Expected emission rate from APS environmental upgrades 4 - Potential guaranteed emission levels



### 3.1.1.4 

## Step 3: Evaluate Control Effectiven ess of Remaining Control Technologies Preliminary vendor proposals, such as those used to support porti ons of this BART analysis, may be technically feasible and provide expected or guaranteed emission rates; however, they include inherent uncertainties. These proposals are usua lly prepared in a limited time frame, may be based on incomplete information, may contain ov er-optimistic conclusions, and are non-binding. Therefore, emission rate values obtained in su ch preliminary proposals must be qualified, and it must be recognized that contractual guarantees ar e established only after mo re detailed analysis has been completed.

Level of Confidence for Vendor Post-Control NO x 

## Emissions Estimates. In order to determine the level of NO x 

## emissions needed to consistently achieve compliance with an established goal, a review of typical NO x 

## emissions from coal-fired generating units was completed. As a result of this review, it was noted that NO x 

## emissions can vary significan tly around an average emissions level. This variance can be attributed to many reasons, including coal ch aracteristics, unit load, boiler operation including excess air, boiler slagging, burner equipment condition, coal mill fineness, and so forth.
The steps utilized for determining a level of confidence for the vendor expected value are as follows:

1. Establish expected NO x 

## emissions value from vendor.

2. Evaluate vendor experience and historical basis for meeting expected values.

3. Review and evaluate unit physical and operationa l characteristics and restrictions. The fewer variations there are in operations, coal supply, etc., the mo re predictable and less variant the NO x 

## emissions are.

3-7BART ANALYSIS FOR CHOLLA UNIT 4



## 4. For each technology expected value, there is a corresponding potential for actual NO x 

## emissions to vary from this expected valu e. From the vendor information presented, along with anticipated unit operational data, an adju stment to the expected value can be made.
The following subsections describe the NO x 

## control technologies and the control effectiveness evaluated in this BART analysis. New LNBs with OFA System. The mechanism used to lower NO x 

## with low NO x 

## burners is to stage the combustion process and provide a fuel rich condition initially; this is so oxygen needed for combustion is not diverted to combine with nitrogen and form NO x 

## . Fuel-rich conditions favor the conversion of fuel nitrogen to N 2 

## instead of NO x 

## . Additional air (OFA) is then introduced upstream or downstream in a lower temp erature zone to burn out the char.

Both LNBs and OFA are considered to be a capital cost, combustion technology retrofit which may require boiler water wall tube replacement. Information provided to CH2M HILL by APS indicates that new LNB and SOFA modifications at Cholla 4 w ould result in an expected NO x 

## emission rate of 0.22 lb/MMBtu. This emission rate represents a significa nt reduction from the current NO x 

## emission rate, and is below the EPA presumptive NO x 

## emission rate for bituminous coal of 0.28 lb/MMBtu.

ROFA. Mobotec markets ROFA as an improved sec ond generation OFA system. Mobotec states that “the flue gas volume of the furnace is set in rotation by asymmetrically placed air nozzles.” Rotation is reported to prevent laminar flow a nd improve gas mixing, so that the entire volume of the furnace can be used more effectively for the combustion process. In addition, the swirling action reduces the maximum temperature of the fl ames and increases heat absorption. Mobotec expects that enhanced mixing will also result in reduction in hot/cold furnace zones, improved heat absorption and boiler efficiency, and lower CO and NO x 

## emissions.
A typical ROFA installation will have a booster fa n(s) to supply the high velocity air to the ROFA boxes. Mobotec proposed one 4,800 Hp fan for Cholla 4 located at grade, which would provide hot air at all boiler loads.
Utilizing ROFA technology, Mobotec offered an estimated NO x 

## emission rate of 0.16 lb/MMBtu. The operation of existing burners an d OFA ports will be analyzed, and OFA ports not planned for use would likely be blocked off. While a typical installation does not require modification to the existing burners, some modi fication may be necessary. Computational fluid dynamics modeling will determine the quantity and location of new RO FA ports. Mobotec does not typically provide inst allation services because they beli eve that the Owner can more cost effectively contract for these services, however they did provide a budgetary price for installation labor. Mobotec provides one ons ite construction supervisor du ring installation and startup.

SNCR. With SNCR, an amine-based reagent such as ammonia, or more commonly urea, is injected into the furnace within a temperature range of 1,600° F to 2,100° F, where it reduces NO x 

## to nitrogen and water. NO x 

## reductions of up to 40 to 60 percent have been achieved, although 15 to 30 percent is more realistic for mo st applications. SNCR is typically applied on smaller units. Adequate reagent distribution in th e furnaces of large units can be problematic.
Reagent utilization, which is a measure of the efficiency with which the reagent reduces NO x 

## , can range from 20 to 60 percent, depending on the amount of reduction, unit size, operating

3-8BART ANALYSIS FOR CHOLLA UNIT 4



## conditions, and allowable ammonia slip. With low reagent utilization, low temperatures, or inadequate mixing, ammonia slip occurs, allowing unreacted a mmonia to create problems downstream. The ammonia may render fly ash unsal eable, and also react w ith sulfur to form ammonium bisulphate which can foul heat exch anger surfaces and/or cr eate a visible stack plume. Reagent utilization can have a significant impact on economics, with higher levels of NO x 

## reduction generally resulti ng in lower reagent utilizat ion and higher operating cost. Reductions from higher baseline inlet NO x 

## concentrations are lower in cost per ton, but result in higher operating costs, due to greater reagent consumption.
Mobotec also provided information for their Rota mix SNCR system for Cholla 4. The expected NO x 

## emission rate for the Rotamix system, operating in conjunction with ROFA, is 0.12 lb/MMBtu. CH2M HILL utilized previous SNCR vendor proposals to develop cost and NO x 

## emission estimates.

SCR. SCR works on the same chemical principle as SNCR but SCR uses a catalyst to promote the chemical reaction. Ammonia or urea is inject ed into the flue-gas stream, where it reduces NO x 

## to nitrogen and water. Unlike the high temper atures required for SNCR, in SCR the reaction takes place on the surface of a vanadium/titanium-b ased catalyst at a temperature range between 580° F to 750° F. Due to the catalyst, the SCR process is more efficient than SNCR and results in lower NO x 

## emissions. The most common type of SCR is the high-dust configuration, where the catalyst is located downstream from the boiler ec onomizer and upstream of the air heater and any particulate control equipment. In this location, the SCR is exposed to the full concentration of fly ash in the flue gas that is leaving the boiler. Fo r Cholla 4 the SCR would be installed before the air heater a high-particulate locatio n. In a full-scale SCR, the flue ducts are routed to a separate large reactor containing the catalyst. With in-duct SCR, the catalyst is located in the existing gas duct, which may be expanded in the area of the ca talyst to reduce flue gas flow velocity and increase flue gas residence time. Due to the higher removal rate, a full-scale SCR was used as the basis for analysis at Cholla 4.
As with SNCR, it is generally more cost effective to reduce NO x 

## emission levels as much as possible through combustion modifications, in or der to minimize the catalyst surface area and ammonia requirements of the SCR.

Neural Net Controls/Boiler Combustion Control. Review of neural net and improved boiler combustion control are combined for purposes of this analysis under the potential implementation of neural net boile r control system. Information rega rding neural net controls has been previously received from NeuCo, Inc. While NeuCo offers several neural net products, CombustionOpt and SootOpt provide the potential for NO x 

## reduction. NeuCo stated these products can be utilized on most control systems, and can be eff ective even in conjunction with other NO x 

## reduction technologies.
NeuCo predicts that CombustionOpt can reduce NO x 

## by 15%, and SootOpt can provide an additional 5 to 10%. Since NeuCo does not offer guarantees on this projec ted emission reduction, a nominal reduction of 15% was assumed for ev aluation purposes. The budgetary price for CombustionOpt and SootOpt were $150,000 and $175,000 respectively, with an addition $200,000 cost for a process link to the unit control system.

3-9BART ANALYSIS FOR CHOLLA UNIT 4



## Since NeuCo does not guarantee NO x 

## reduction, the estimated emission reduction levels provided can not be considered as reliable pr ojections. Therefore, neural net should be considered as a supplementary or “polishi ng” technology, but not on a “stand-alone” basis.

3.1.1.5 

## Step 4: Evaluate Impacts and Document the Results This step involves the consider ation of energy, environmental, and economic impacts associated with each control technology. The re maining useful life of the plant is also considered during the evaluation. Energy Impacts. Installation of new LNBs and SOFA syst em are not expected to significantly impact the boiler efficiency or forced draft fa n power usage. Therefore, these technologies are not expected to have significant energy impacts.
The Mobotec ROFA system requires installa tion and operation of one 4,800 Hp ROFA fan (3,579 kW total). Fuel Tech provided an estimate of 130 kW of additiona l auxiliary power, and the same estimate was used for Rotamix. SCR retr ofit impacts the existing flue gas fan systems, due to the additional pressure drop associated with the catalyst, which is typically a 6- to 8-inch water gage increase. Environmental Impacts. With the planned installation of new LNBs and SOFA system, CO emissions are projected to incr ease significantly to an estimated 0.15 lb/MMBtu (based on a 30-day average). APS completed a CO BACT revi ew for this anticipated increase in CO emissions.
Mobotec generally predicts that CO emi ssions, and unburned carbon in the ash commonly referred to as LOI (loss on igniti on), would be the same or lower than prior levels for the ROFA system. SNCR and SCR installation could impact the salability and disposal of fly ash due to ammonia levels, and could potentially crea te a visible stack plume, whic h may negate other visibility improvements. Other environmental impacts invol ve the potential public and employee safety hazard associated with the storage of ammoni a, especially anhydrous ammonia, and the transportation of the ammonia to the power plant site. Economic Impacts. A comparison of the technologies on th e basis of costs, design control efficiencies, and tons of NO x 

## removed is summarized in Table 3-3, and the first year control costs are shown in Figure 3-2. The complete Econ omic Analysis is contained in Appendix A.

TABLE 3-3 

### NO x 

### Control Cost Comparison Cholla 4

Factor LNB w/SOFA 1 ROFA ROFA w/ Rotamix LNB w/SOFA & SNCR LNB w/SOFA & SCR
Major Materials and Design Costs $2.7 Million $5.7 Million $7.5 Million $8.4 Million $43.7 Million
Total Installed Capital Costs $6.8 Million $15.4 Million $22.7 Million $21.5 Million $112.6 Million

Total First Year Fixed & Variable O&M Costs $0.2 Million $1.7 Million $2.4 Mi llion $0.8 Million $2.5 Million

3-10BART ANALYSIS FOR CHOLLA UNIT 4

3-11 TABLE 3-3 

### NO x 

### Control Cost Comparison Cholla 4

Factor LNB w/SOFA 1 ROFA ROFA w/ Rotamix LNB w/SOFA & SNCR LNB w/SOFA & SCR

Total First Year Annualized Cost $0.8 Million $3.2 Million $4.5 Mi llion $2.9 Million $13.2 Million
Power Consumption (MW) --- 3.58 3.58 0.43 2.13
Annual Power Usage (1000 MW-Hr/Yr) --- 29.2 29.2 3.5 17.3

NO xDesign Control Efficiency 47.0% 61.4% 71.1% 59.0% 83.1%

NO xRemoved per Year (Tons) 3,390 4,433 5, 129 4,259 5,998
First Year Average Control Cost ($/Ton of NO x Removed) 242 717 885 670 2,206

Incremental Control Cost ($/Ton of NO x Removed) 242 1,878 1,953 2,337 10,004

1 – Since installation of LNB is part of the planned APS env ironmental upgrades, this option is assumed to have zero cost



## Preliminary BART Selection. The 4-step evaluation indicates new LNBs with SOFA would represent BART for Cholla 4 base d on its significant reduction in NO x 

## emissions, reasonable control cost, and no additional power requirements or environmental impacts. New LNB w/SOFA meets the target EP A presumptive limit of 0.28 lb/MMBtu for bituminous coal.

3.1.1.6 Step 5: Evaluate Visibility Impacts 

## Please see Sections 4.0 (BART Modeling Anal ysis) and 5.0 (Preliminary Assessment and Recommendations).BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 3-2First Year Control Cost for NOXAir Pollution Control OptionsCholla 402,0004,0006,0008,00010,00012,000LNB w/SOFALNB w/SOFA & SNCRROFAROFA w/RotamixLNB w/SOFA & SCRAir Pollution Control Option

Control Cost ($/Ton NOx Removed)First Year Average Control CostFirst Year Incremental Control Cost3-12BART ANALYSIS FOR CHOLLA UNIT 4



# 3.1.2 BART SO 2 

# Analysis

SO 2 

## forms in the boiler during the combustion pro cess from the oxidation of the sulfur present in the coal, and is primarily dependent on coal sulfur content. The BART analysis for SO 2 

## emissions on Cholla 4 is described below.

3.1.2.1 

## Step 1: Identify All Availabl e Retrofit Control Technologies A broad range of information sources were revi ewed, in an effort to identify potentially applicable emission control technologies for SO 2 

## at Cholla 4. This included control technologies identified as BACT or LAER by permitting agencies across the United States.
The following potential SO 2 

## control technology option was evaluated:

• Wet lime FGD
Cholla 4 currently operates a partial scrubber for SO 2 

## removal, which treats approximately 36% of the flue gas. For purposes of this BART analysis an SO 2 

## emissions rate of 0.734 lb/MMBtu will be assumed. A new wet lime sc rubber vessel designed to treat al l flue gas is planned to be installed in 2008.

3.1.2.2 

## Step 2: Eliminate Technically Infeasible Options Technical feasibility will primarily be based on the regulatory presumptive limit of 95 percent reduction in SO 2 

## emissions, or 0.15 lb/MMBtu. Because Cho lla 4 is currently operating with an SO 2 

## emissions rate of approximately 0.734 lb/MMBt u, since only 36% of the flue gas is being scrubbed. A significan t reduction of SO 2 

## emissions would be required meet a target of 0.15 lb/MMBtu.
APS anticipates the installation of a new wet lime scrubbing system to treat all of the flue gas. Since wet scrubber technology is ca pable of the highest rate of SO 2 

## removal, and is currently planned for installation at Cholla 4, no other alternative was considered.

New Wet Lime Scrubber. A typical wet lime scrubber consists of SO 2 

## -laden flue gas entering a scrubber vessel where it is sprayed with water/ calcium slurry. The calcium reacts to form calcium sulfite or sulfate, and is then either removed and disposed as scrubber waste or reclaimed as gypsum. Wet lime scr ubbers are capable of very high SO 2 

## removal efficiencies, with a 95 percent removal efficiency assu med for this BART analysis. Based on the uncontrolled SO 2 

## emissions rate of 2.5 lb/MMBtu, a new wet lime/limestone scrubber can achieve the presumptive limit for SO 2 

## emissions of 0.15 lb/MMBtu.

3.1.2.3 Step 3: Evaluate Control Effectiveness of Remaining Control Technologies 

## When evaluating the control effectiveness of SO 2 

## reduction technologies, each option can be compared against benchmarks of performance. One such benchmark is the presumptive BART emission limit. As indicated previously, the BART presumptive limit for SO 2 

## on a BART-eligible coal burning unit is 95 percent removal, or 0.15 lb/MMBtu.

3-13BART ANALYSIS FOR CHOLLA UNIT 4

3-14 TABLE 3-4 

### SO 2 

### Control Technology Emission Rate Ranking Cholla 4

Technology Projected Emission Rate

(lb/MMBtu) Estimated SO 2 Removal Efficiency (%)
BART Presumptive Limit Guideline 0.15 N/A

Wet Lime FGD 0.15 94



## 3.2.2.4 Step 4: Evaluate Impacts and Document the Results This step involves the cons ideration of energy, environmental, and economic impacts associated with each control technology. The remaining useful life of the plant is also considered during the evaluation. Energy Impacts. The upgraded wet lime FGD system is e xpected to result in additional power consumption. Environmental Impacts. Because of the upgrade in capacity of the wet lime FGD system, there will be incremental additions to scrubber wast e disposal and makeup water requirements, as well a reduction of the stack gas temperature if there is elimination of flue gas bypass. Economic Impacts

. There are no anticipated cost impacts attributable to upgraded scrubber operation.

TABLE 3-5 

### SO 2 

### Wet Lime FGD Costs Cholla 4

Factor Wet Lime FGD
Major Materials and Design Costs $27 Million
Total Installed Capital Costs $69.7 Million

Total First Year Fixed & Variable O&M Costs $2.9 Million

Total First Year Annualized Cost $9.6 Million
Additional Power Consumption (MW) 0.4
Additional Annual Power Usage (1000 MW-Hr/Yr) 3.3

Incremental SO 2 Design Control Efficiency 79.6%

Incremental Tons SO 2 Removed per Year 10,153
First Year Average Control Cost ($/Ton of SO 2 Removed) 942

Incremental Control Cost ($/Ton of SO 2 Removed) 942BART ANALYSIS FOR CHOLLA UNIT 4



## Preliminary Selection. Based upon the technical and economic analysis above, an upgrade of the wet lime system is the most attractive SO 2 

## control alternative for Cholla 4. This recommendation is based on si gnificant reduction in SO 2 

## emissions (meeting the presumptive limit of 0.15 lb/MMBtu) and r easonable control costs.

3.1.2.5 

## Step 5: Evaluate Visibility Impacts Please see Sections 4.0 (BART Modeling Anal ysis) and 5.0 (Preliminary Assessment and Recommendations).



# 3.1.3 BART PM 10



# Analysis Cholla 4 is currently equipped with a hot-s ide electrostatic precipitator for primary PM 10 

## control. Fabric filters will be installed in 2008. Current PM 10 

## emissions on Cholla 4 are approximately 0.024 lb/MMBtu, and the fabric f ilter will result in emissions estimated at 0.015 lb/MMBtu.
The BART analysis for PM 10 

## emissions at Cholla 4 is described below. For the modeling analysis in Sections 4.0 and 5.0, PM 10 

## and PM 2.5 

## are used as indicators of PM emissions.

3.1.3.1 

## Step 1: Identify All Availabl e Retrofit Control Technologies
Two retrofit control t echnologies have been identified for additional PM control:

• 

## ESP upgrade or replacement

• 

## Fabric Filter

3.1.3.2 

## Step 2: Eliminate Technically Infeasible Options
New ESP. While a replacement electrostatic preci pitator may be capable of achieving a 0.015 lb/MMBtu target, operational va riations may not result in consistent compliance with this emissions rate. Any ESP upgrade or gas conditioning would also result in a PM 10 

## removal system which is less reliable in achieving the 0.015 lb/MMBtu than a fabric filter. Therefore this option is not consider ed technically attractive.

Fabric Filter. A full-size pulse jet fabric filter could be installed as a replacement for the existing ESP on Cholla 4. This fabric filter would be sized for approximately 3.5 or 4:1 Air to Cloth (A/C) ratio (actual cubic f eet per minute of flue gas/square feet of fabric). An A/C ratio of 4:1 was used for this analysis. Fabric filters have been proven to provide highly effective and consistent particulate emissions reduction, with outlet emissions of approximately 0.015 lb/MMBtu. The ESP will be removed from se rvice with this repl acement fabric filter option.

3.1.3.3 

## Step 3: Evaluate Control Effectiven ess of Remaining Control Technologies The existing hot-side ESP at Cholla 4 is achieving a controlled PM emission rate of approximately 0.024 lb/MMBtu. Adding a repla cement fabric filter, or new ESP, PM 10 

## emissions are expected to be approximately 0.015 lb/MMBtu.

The PM 10 

## control technology emission rates are summarized in Table 3-5, with the PM 10 

## emissions rates shown for both a replacement fabric filter and ESP.

3-15BART ANALYSIS FOR CHOLLA UNIT 4

TABLE 3-6 

### PM 10



### Control Technology Emission Rates Cholla 4

Control Technology Expected PM 10 Emission Rate (Lb/MMBtu)

Electrostatic Precipitator >0.015

Fabric Filter 0.015



### 3.1.3.4 

## Step 4: Evaluate Impacts and Document the Results This step involves the cons ideration of energy, environmental, and economic impacts associated with each control technology. The remaining useful life of the plant is also considered during the evaluation. Energy Impacts. Energy is required to overcome the addi tional pressure drop from the fabric filter replacement and associat ed ductwork. Therefore, fan upgrades may be required to overcome the additional pressure dro p. An estimated 6 to 8 inches H 2 

## O additional total system pressure drop for the replacement fabric filter may be experienced. Environmental Impacts. There are no negative environmenta l impacts from the addition of a replacement ESP or fabric filter. Economic Impacts. A listing of the costs and PM 10 

## removed for a fabric filter is shown in Table 3-6, with a graph of first year costs show n in Figure 3-2. Capital cost information was used from previous CH2M HILL equipment estimates for the replacement fabric filter. Since an ESP is not capable of achieving PM 10 

## reduction comparable to a fabric filter, costs for an ESP are not shown.

TABLE 3-7 

### PM 10



### Fabric Filter Costs Cholla 4

Factor Fabric Filter Replacement
Major Materials and Design Costs $55.8 Million
Total Installed Capital Costs $144 Million

Total First Year Fixed & Variable O&M Costs $1.8 Million

Total First Year Annualized Cost $15.5 Million
Additional Power Consumption (MW) 0.4
Additional Annual Power Usage (1000 MW-Hr/Yr) 3.3
Incremental PM Design Control Efficiency 37.5%
Incremental Tons PM Removed per Year 156
First Year Average Control Cost ($/Ton of PM Removed) 99,100
Incremental Control Cost ($/Ton of PM Removed) 99,100

3-16BART ANALYSIS FOR CHOLLA UNIT 4

3-17 

## Preliminary BART Selection. The 4-step evaluation indicates inst allation of a replacement fabric filter represents BART for Cholla 4. This opti on has a high control cost , but APS already plans to install fabric filters in 2008. Based on this pl an and the knowledge that a fabric filter will result in a significant reduction in PM emissi ons and have no non-air quality environmental impacts, this option is selected as BART.

3.1.3.5 

## Step 5: Evaluate Visibility Impacts Please see Sections 4.0 (BART Modeling Anal ysis) and 5.0 (Preliminary Assessment and Recommendations).

# 4.0 BART Modeling Analysis

4.1 Introduction

This section presents the disp ersion modeling methods and results for estimating the degree of visibility improvement from BART cont rol technology options for the Cholla 4.
To a large extent, the modeling followed the methodology outlined in the WRAP protocol for performing BART analyses (WRAP, 2006). Any pr oposed deviations from that methodology are documented in this report.



# 4.2 Model Selection

CH2M HILL used the EPA-required CALPUFF mode ling system to assess the visibility impacts at Class I areas. CALPUFF is a multi-layer, mu lti-species non-steady-state puff dispersion model that simulates the effects of time- and space- varying meteorological conditions on pollution transport, transformation, and removal. BART guidance says, “CALPUFF is the best regulatory modeling application currently avai lable for predicting a single source ’s contribution to visibility impairment and is currently the only EPA-appr oved model for use in estimating single source pollutant concentrations resulting from the long range transport of pollutants.”
The CALPUFF modeling system includes the me teorological data preprocessing program for CALPUFF (CALMET) with algorithms for chemi cal transformation and deposition, and a post processor capable of calculating concentrations, visibility imp acts, and deposition (CALPOST). The CALPUFF modeling system was a pplied in a full, refined mode.
CH2M HILL used the latest version (Version 6) of the CALPUFF modeling system preprocessors and models in lieu of the EP A-approved versions (Version 5). The FLM and others have noted that the EP A-approved Version 5 contained e rrors and that a newer version should be used. Consequently, it wa s decided to use the latest (a s of April 2006) version of the CALPUFF modeling system (a vailable at www.src.com):

• CALMET Version 6.211 Level 060414

• CALPUFF Version 6.112 Level 060412
CALMET, CALPUFF, CALPOST, and POSTUTIL were recompiled with the Lahey/Fujitsu Fortran 95 Compiler (Release 7.10.02) to accommodate the large CALMET domain. The recompiled processors were tested against the te st case results provided with the source code (TRC, 2007), and the difference betw een the results was 0.03 percent.

4-1BART ANALYSIS FOR CHOLLA UNIT 4



# 4.3 CALMET Methodology

4.3.1 Dimensions of the Modeling Domain

CH2M HILL-defined domains for Mesoscale Meteorological Model, Version 5 (MM5), CALMET, and CALPUFF that were slightly different than thos e established for the Arizona BART modeling in WRAP (2006). In addi tion, the CALMET and CALPUFF Lambert Conformal Conic (LCC) map projection used in this analysis is based on a central meridian of 110° W rather than 97° W. This puts the centr al meridian near the center of the domain.
CH2M HILL used the CALMET model to genera te three-dimensional wind fields and other meteorological parameters suitable for us e by the CALPUFF model. A CALMET modeling domain has been defined to allow for at leas t a 50-kilometers buffer around all Class I areas within 300 kilometers of the Cholla Power Pl ant. Grid resolution for this domain was 4 kilometers. Figure 4-1 shows the extent of the modeling domain.

4-2BART ANALYSIS FOR CHOLLA UNIT 4

FIGURE 4-1 

### CALPUFF and CALMET Modeling Domains

4-3BART ANALYSIS FOR CHOLLA UNIT 4



## The technical options recommended in WRAP (2006) were used for CALMET. Vertical resolution of the wind field included 11 layers, w ith vertical cell face heights as follows (in meters):

• 0, 20, 100, 200, 350, 500, 750, 1000, 2000, 3000, 4000, 5000
Also, following WRAP (2006), ZIMAX were set to 4,500 meters based on the Colorado Department of Health and Environment (CDP HE) analyses of soundings for summer ozone events in the Denver area (CDPHE, 2005). The CD PHE analysis suggests mixing heights in the Denver area are often well above the CALM ET default value of 3,000 meters during the summer. For example, on some summer days, oz one levels are elevated to 6,000 meters mean sea level or beyond during some meteorological regimes, including some regimes associated with high-ozone episodes. It is assumed that, as in Denver, mixing heights in excess of the 3,000 meters AGL CALMET default maximum would occur in the domain used for this analysis.
Table 4-1 lists the key user-specified options.

4-4BART ANALYSIS FOR CHOLLA UNIT 4

TABLE 4-1 

### User-Specified CALMET Options

Description CALMET Input Parameter Value
CALMET Input Group 2
Map projection PMAP LCC
Grid spacing DGRIDKM 4
Number vertical layers NZ 11
Top of lowest layer (meters) 20
Top of highest layer (meters) 5,000
CALMET Input Group 4

Observation mode NOOBS 1
CALMET Input Group 5
Extrapolation of surface wind observations IEXTRP 4

Prognostic or MM-FDDA data switch IPROG 14

Max surface over-land extrapolation radius (kilometers) RMAX1 50

Max aloft over-land extrapolations radius (kilometers) RMAX2 50

Radius of influence of terrain features (kilometers) TERRAD 10

Relative weight at surface of Step 1 field and obs R1 25

Relative weight aloft of Step 1 field and obs R2 25

CALMET Input Group 6
Maximum over-land mixing height (meters) ZIMAX 4,500

4-5BART ANALYSIS FOR CHOLLA UNIT 4



# 4.3.2 CALMET Input Data CH2M HILL ran the CALMET model to pr oduce 3 years of analysis: 2001, 2002, and 2003. CH2M HILL used MM5 data as the basis for the CALMET wind fields. The horizontal resolution of the MM5 data is 36 kilometers.
For 2001, CH2M HILL used MM5 data at 36-kilomete rs resolution that were obtained from the contractor (Alpine Geophysics) who develope d the nationwide data for the EPA. For 2002, CH2M HILL used 36-kilometers MM5 data ob tained from Alpine Geophysics, originally developed for the WRAP. Data for 2003 (also from Alpine Geophysics) , at 36-kilometers resolution, were developed by the Wisconsin De partment of Natural Resources, the Illinois Environmental Protection Agency, and the Lake Michigan Air Directors Consortium (Midwest RPO).
The MM5 data were used as input to CALMET as the “initial guess” wind field. The initial guess field was adjusted by CALMET for local terrain a nd land use effects to generate a Step 1 wind field, and then further refined usi ng local surface observations to cr eate a final Step 2 wind field.
Surface data for 2001 through 2003 were obtained from the National Climatic Data Center. CH2M HILL processed data for all stations from the National Weather Service’s (NWS) Automated Surface Observing System (ASOS) networ k that are in the domain. The surface data were obtained in abbreviated DATSAV3 format. A conversion routine av ailable from the TRC website was used to convert th e DATSAV3 files to CD 144 form at for input to the SMERGE preprocessor and CALMET.

Land use and terrain data were obtained from th e U.S. Geological Survey (USGS). Land use data were obtained in Composite Theme Grid format from the USGS, and the Level I USGS land use categories were mapped into the 14 primary CA LMET land use categories. Surface properties, such as albedo, Bowen ratio, roughn ess length, and leaf area index, were computed from the land use values. Terrain data were taken from USGS 1 degree Digital Elevat ion Model data, which are primarily derived from USGS 1:250,000 scale topographic maps. Missing land use data were filled with a value that is appropriate for the missing area.
Precipitation data were ordered from the National Climatic Data Center. All available data in fixed-length, TD-3240 format were ordered fo r the modeling domain. The list of available stations and stations that ha ve collected complete data varies by year, but CH2M HILL processed all available stations/d ata within the domain for each year. Precipitation data were prepared with the PXTRACT/PMERGE processo rs in preparation for use within CALMET.
Following the methodology recommended in WRAP (2006), no observed upper-air meteorological observations were used as they are redundant to the MM5 data and may introduce spurious artifacts in the wind fi elds. In the development of th e MM5 data, the twice daily upperair meteorological observations were used as input with the MM5 model. The MM5 estimates were nudged to the upper-air obser vations as part of the Four Dimensional Data Assimilation. This results in higher te mporal (hourly versus. 12 hour) and spatial (36 kilometers versus ~300 kilometers) resolution for the upper-air meteorology in the MM5 field. These MM5 data are more dynamically balanced than those contained in the upper-air observations. Therefore, the use of the upper-air observations with CALMET is not needed, and in fact , will upset the dynamic balance of the meteorological fields potential ly producing spurious vertical velocities.

4-6BART ANALYSIS FOR CHOLLA UNIT 4



# 4.3.3 Validation of CALMET Wind Field CH2M HILL used the CALDESK (program to displa y data and results) data display and analysis system (v2.97, Enviromodeling Ltda.) to view pl ots of wind vectors and other meteorological parameters to evaluate the CALMET wind fields . CH2M HILL observed weather conditions, as depicted in surface and upper-air weather maps from the National Oceanic and Atmospheric Administration Central Library U.S. Daily W eather Maps Project (h ttp://docs.lib.noaa.gov/ rescue/ dwm/ data_rescue_daily_weather_maps.ht ml), to compare to the CALDESK displays.



# 4.4 CALPUFF Methodology

4.4.1 CALPUFF Modeling

CH2M HILL ran the CALPUFF model with the meteorological output from CALMET over the CALPUFF modeling domain (Figur e 4-1). The CALPUFF model was used to predict visibility impacts for the pre-control (baseline) scenario for comparison to the predicted impacts for post-control scenarios.
Background Ozone and Ammonia Hourly values of background ozone concentrations were used by CALPUFF for the calculation of SO 2 

## and NO X 

## transformation with the MESOPUFF II chemical transformation scheme. CH2M HILL used the hourly ozone data ge nerated for the WRAP BART analysis for 2001, 2002, and 2003.
For periods of missing hourly ozone data, the chemical transformation relied on a monthly default value of 80 parts per billion. For bac kground ammonia the following monthly values were used:
Dec – Mar: 0.2 parts per billion (ppb) Apr – May: 0.5 ppb Jun – Sep: 1.0 ppb Oct – Nov: 0.5 ppb

Stack Parameters The baseline stack parameters for the baseline and post-control scenarios were supplied by APS staff. The parameters used in the WRAP anal ysis appeared to be related to natural gas combustion so it was necessary to replace these with more appli cable values. The same stack data were used for all scenario s since none of the emission contro ls related to these scenarios would significantly affect the exha ust exit flows or temperatures.

4-7BART ANALYSIS FOR CHOLLA UNIT 4



## Pre-Control Emission Rates
Pre-control emission rates reflect normal maxi mum capacity 24-hour emi ssions that may occur under the source’s current permit. The emission rates reflect actual emissions under normal operating conditions. As described by the EPA in the Regional Haze Regulations and Guidelines for BART Determinations; Final Rule (40 CFR Part 51; July 6, 2005, pg 39129):

“The emissions estimates used in the mode ls are intended to reflect steady-state operating conditions during peri ods of high-capacity utiliz ation. We do not generally recommend that emissions reflecting periods of start-up, shutdow n, and malfunction be used...”

CH2M HILL used available CEM data to determ ine the baseline emission rates. Data reflect operations from 2001 through 2006.
Emissions were modeled for the following species:

• Sulfur dioxide (SO 2)



## • Oxides of nitrogen (NO x)



## • Coarse particulate (diameter greater than PM 2.5 

## and less than or equal to PM 10 

## )

• Fine particulate (diameter less than or equal to PM 2.5 

## )

• Elemental carbon (EC)

• Organic aerosols (SOA)

• Sulfates (SO 4 

## )

Post-control Emission Rates
Post-control emission rates reflected the effect s of the emissions c ontrol scenario under consideration. Modeled pollutant s were the same as listed for the pre-control scenario.
Modeling Process The CALPUFF modeling for the control tec hnology options followed this sequence:

• Model WRAP-RMC parameters to verify results

• Model pre-control (baseline) emissions

• Determine the degree of visibility improvement

• Model other control scen arios if applicable

• Determine the degree of visibility improvement

• Factor visibility results into BART five-step evaluation



# 4.4.2 Receptor Grids and Coordinate Conversion The TRC COORDS program was used to convert the latitude/longitude c oordinates to LCC map coordinates for the meteorological stations a nd source locations. The USGS conversion program PROJ (version 4.4.6) was used to convert the Nati onal Park Service (NPS) receptor location data from latitude/longitude to LCC.

For the Class I areas that are within 300 kilometers of the Cholla Power Plant, discrete receptors for the CALPUFF modeling were taken from the Na tional Park Service database for Class I area

4-8BART ANALYSIS FOR CHOLLA UNIT 4



## modeling receptors. The entire ar ea of each Class I area that is within or intersects the 300-kilometers circle (Figure 4-1) were include d in the modeling analysis. The following lists the Class I areas that were modeled for the Cholla facility:

• Capitol Reef NP (care)

• Galiuro Wilderness (gali)

• Gila Wilderness (gila)

• Grand Canyon NP (grca)

• Mazatzal Wilderness (maza)

• Mesa Verde NP (meve)

• Mount Baldy Wilderness (moba)

• Petrified Forest NP (pefo)

• Pine Mountain Wilderness (pimo)

• Saguaro NP (sagu)

• Sierra Ancha Wilderness (sian)

• Superstition Wilderness (supe)

• Sycamore Canyon WA (syca)



# 4.5 Visibility Post-processing

4.5.1 CALPOST The CALPOST processor was used to determine 24- hour average visibility results. Output is specified in deciview (dV) units.
Calculations of light extinction were made for each pollutant modeled. Th e sum of all extinction values was used to calculate the delta-dv ( ∆ dV) change relative to natural background. The following default extinction coefficients for each species, as shown below, were used:

• Ammonium sulfate 3.0

• Ammonium nitrate 3.0

• PM coarse (PM 10 

## ) 0.6

• PM fine (PM 2.5 

## ) 1.0

• Organic carbon 4.0

• Elemental carbon 10.0
CALPOST Visibility Method 6 (M VISBK=6) was used for the determination of visibility impacts. Monthly average re lative humidity factors ( f

[RH]) were used in the light extinction calculations to account for the hygros copic characteristic of sulfate and nitrate particles. Monthly f

(RH) values, from the WRAP_RMC BART modeling, were used in CALPOST for the particular Class I area being modeled.
The natural background conditions used in the post-processing to de termine the change in visual range background—or ∆ dV—represent the average natu ral background concentration for western Class I areas.
Table 4-2 lists the annual average species concentrations from the EPA Guidance.

4-9BART ANALYSIS FOR CHOLLA UNIT 4

4-10 TABLE 4-2 

### Average Natural Levels of Aerosol Components

Aerosol Component Average Natural Concentration (μg/m3) for Western Class I Areas

Ammonium Sulfate 0.12

Ammonium Nitrate 0.10

Organic Carbon 0.47

Elemental Carbon 0.02

Soil 0.50

Coarse Mass 3.0
Taken from Table 2-1 of Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule. EPA-454/B-03-005, September 2003.



# 4.6 Results

Input and output files for the CALMET/CA LPUFF modeling and pos t-processing will be provided in electronic format to the Arizona Department of Environm ental Quality (ADEQ). Larger files, such as binary files genera ted by CALMET, have not been included on the submitted disks, but any omitte d files will be provided electronically upon request.



# 4.6.1 BART Least-Cost Analysis The results and comparisons of the CALPUFF modeling for the baseline emission rates and those for the alternative emission contro l scenarios are provided in Section 5.

# 5.0 Preliminary Assessment and Recommendations

5.1 Preliminary Recommended BART Controls

As a result of the completed technical and ec onomic evaluations, and consideration of the modeling analysis for Cholla 4, the preliminary recommended BART controls for NO x 

## , SO 2 

## , and PM 10 

## are as follows:

• The most cost-effective emissions control scenario for NO x 

## includes new LNB with SOFA.

• Installation of a replacement fabric filter for PM 10 

## emission control is recommended.

• Upgrade of the wet limestone FGD system for SO 2 

## removal is also recommended.

The above NO x 

## recommendation is identified as Scenar io 1 for the modeling analysis described in Section 4.0. Visibility improve ments for all emission control scenarios were analyzed, and the results are compared below, using a least-cost envelope, as outlined in the draft EPA New Source Review Workshop Manual(1990).



# 5.2 Analysis Baseline and Scenarios

Table 5-1 compares the six emission control scenarios with expected emission levels.

TABLE 5-1 

### Emission Control Scenarios Cholla 4

Case Description Expected NO x Emission (lb/MMBtu) Expected SO 2 Emissions (lb/MMBtu) Expected PM 10 Emissions (lb/MMBtu)

Baseline 0.415 0.734 0.024

Scenario 1 New LNB with SOFA 0.220 0.150 0.015

Scenario 2 ROFA 0.160 0.150 0.015

Scenario 3 ROFA with Rotamix 0.120 0.150 0.015

Scenario 4 New LNB with SOFA & SNCR 0.170 0.150 0.015

Scenario 5 New LNB with SOFA & SCR 0.070 0.150 0.015

5-1BART ANALYSIS FOR CHOLLA UNIT 4

5-2 

## The ranking of the different NO x 

## emission control scenarios based on annual costs, from lowest to highest cost, is presented on Table 5-2.

TABLE 5-2 

### Ranking of NO x 

### Control Scenarios by Cost Cholla 4

Rank Scenario Description Total Annual Cost

1 Scenario 1 New LNB with SOFA $820,389

2 Scenario 4 New LNB with SOFA & SNCR $2,852,164

3 Scenario 2 ROFA $3,178,713

4 Scenario 3 ROFA with Rotamix $4,536,932

5 Scenario 5 New LNB with SOFA & SCR $13,233,192



## The Baseline of this BART analysis was defined as the level of NO x 

## and PM 10 

## emission control that would be representative of future operations without the additional co st and level of control associated with the scenarios. Figures 5-1 th rough 5-4 compare the modeled contribution to visual range reduction for each Class I area for the Baseline and each NO x 

## emission control scenario.

Of the thirteen Class I areas included in this anal ysis, results from the anal ysis for four of these areas are presented in this section. These four ar eas were selected because they represented the maximum Baseline impacts. The results for the remaining nine areas are presented in Appendix C. The four selected areas include:

• Capitol Reef NP (care)

• Grand Canyon NP (grca)

• Petrified Forest NP (pefo)

• Sycamore Canyon WA (syca)
The following figures show the maximum impacts for each emission control scenario at these Class I areas.BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-1NOxControl Scenarios—Maximum Contributions to Visual Range Reduction at Capitol Reef NPCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Capitol Reef National Park0.000.501.001.502.002.503.00BaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 5Control Scenario

Maximum Impact ( Δ dv)5-3BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-2NOxControl Scenarios—Maximum Contributionsto Visual Range Reduction at Grand Canyon NPCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Grand Canyon National Park0.000.501.001.502.002.503.00BaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 5Control Scenario

Maximum Impact ( Δ dv)5-4BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-3NOxControl Scenarios—Maximum Contributions to Visual Range Reduction at Petrified Forest NPCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Petrified Forest National Park0.000.501.001.502.002.503.003.504.00BaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 5Control Scenario

Maximum Impact ( Δ dv)5-5BART ANALYSIS FOR CHOLLA UNIT 45-6NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Sycamore Canyon Wilderness0.000.501.001.502.002.503.00BaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE 5-4NOxControl Scenarios—Maximum Contributions toVisual Range Reduction at Sycamore Canyon WACholla 4BART ANALYSIS FOR CHOLLA UNIT 4



# 5.3 Least-Cost Envelope Analysis

The total annualized cost, cost per ∆ dV reduction, and cost per re duction in number of days above 0.5 ∆ dV for each of the NO x 

## emission control scenarios and each of the selected Class I areas are listed in Tables 5-3 through 5-6. Only costs for NO x 

## control scenarios are shown. A comparison of the incremental costs between rele vant scenarios is shown in Tables 5-7 through 5-10. The total annualized cost versus number of days above 0.5 ∆ dV, and the total annualized cost versus 98 th 

## percentile ∆ dV reduction are shown in Figures 5-5 through 5-12 for the four Class I areas.



# 5.3.1 Analysis Methodology On page B-41 of the New Source Review Workshop Manual(EPA, 1990), the EPA states that,
“Incremental cost-effectiveness compar isons should focus on annualized cost and emission reduction differences between dominant alternativ es. Dominant set of control alternatives are determined by generating w hat is called the envelope of least-cost alternatives. This is a graphical plot of total annualized costs for a total emissions reductions for all control alternatives identified in the BACT analysis...”
An analysis of incremental cost effectiveness has been conducted. This analysis was performed in the following manner. Control scenarios are sel ected from points that fall on the least-cost envelope curves (Figures 5-5 through 5-12). The incremental cost effectiveness data, expressed per day and per ∆ dV, represents a comparison of the different scenarios, and is summarized in Tables 5-7 through 5-10 for each of the Class I areas. Then the most reasonable smooth curve of least-cost control option scenario s is plotted for each analysis.

TABLE 5-3 

### NO x 

### Control Scenario Results for Capitol Reef NP Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98 th
Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No.

of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 26 0.000 0.000 0.000 0.000

1 New LNB with SOFA 3 0.558 0.820 0.036 1.470

2 ROFA 2 0.612 3.179 0.132 5.194

3 ROFA with Rotami x 2 0.656 4.537 0.189 6.916

4 New LNB with SOFA & SNCR 2 0.601 2.852 0.119 4.746

5 New LNB with SOFA & SCR 1 0.689 13.233 0.529 19.206

5-7BART ANALYSIS FOR CHOLLA UNIT 4

TABLE 5-4 

### NO x 

### Control Scenario Results for Grand Canyon NP Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98 th
Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No.

of Days Above 0.5 ∆ dV (Million$/

Day Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 29 0.000 0.000 0.000 0.000

1 New LNB with SOFA 12 0.758 0.820 0.048 1.082

2 ROFA 11 0.816 3.179 0.177 3.895

3 ROFA with Rotami x 11 0.855 4.537 0.252 5.306

4 New LNB with SOFA & SNCR 11 0.806 2.852 0.158 3.539

5 New LNB with SOFA & SCR 7 0.876 13.233 0.602 15.106

TABLE 5-5 

### NO x 

### Control Scenario Results for Petrified Forest NP Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98 th
Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No.

of Days Above 0.5 ∆ dV (Million$/

Day Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 176 0.000 0.000 0.000 0.000

1 New LNB with SOFA 29 1.254 0.820 0.006 0.654

2 ROFA 19 1.328 3.179 0.020 2.394

3 ROFA with Rotami x 14 1.383 4.537 0.028 3.281

4 New LNB with SOFA & SNCR 19 1.312 2.852 0.018 2.174

5 New LNB with SOFA & SCR 9 1.455 13.233 0.079 9.095

5-8BART ANALYSIS FOR CHOLLA UNIT 4

TABLE 5-6 

### NO x 

### Control Scenario Results for Sycamore Canyon WA Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98 th
Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 28 0.000 0.000 0.000 0.000

1 New LNB with SOFA 3 0.568 0.820 0.033 1.444

2 ROFA 2 0.581 3.179 0.122 5.471

3 ROFA with Rotami x 2 0.603 4.537 0.174 7.524

4 New LNB with SOFA & SNCR 2 0.576 2.852 0.110 4.952

5 New LNB with SOFA & SCR 2 0.640 13.233 0.509 20.677

TABLE 5-7 

### Capitol Reef NP NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above

0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Day) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseli ne 23 0.558 0.820 0.036 1.470

Scenario 3 vs. Scenario 1 0 0.044 1.358 NA 30.869

Scenario 5 vs. Scenario 3 1 0.033 8.696 8.696 263.523

5-9BART ANALYSIS FOR CHOLLA UNIT 4

5-10 TABLE 5-8 

### Grand Canyon NP NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above

0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Day) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseli ne 17 0.758 0.820 0.048 1.082

Scenario 3 vs. Scenario 1 0 0.039 1.358 NA 34.826

Scenario 5 vs. Scenario 3 4 0.021 8.696 2.174 414.107

TABLE 5-9 

### Petrified Forest NP NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above

0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Day) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseli ne 147 1.254 0.820 0.006 0.654

Scenario 3 vs. Scenario 1 5 0.055 1.358 0.272 24.695

Scenario 5 vs. Scenario 3 5 0.072 8.696 1.739 120.782

TABLE 5-10 

### Sycamore Canyon WA NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above

0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Day) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseli ne 25 0.568 0.820 0.033 1.444

Scenario 3 vs. Scenario 1 0 0.022 1.358 NA 61.737

Scenario 5 vs. Scenario 3 0 0.037 8.696 NA 235.034BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-5NOxControl Scenarios – Least-Cost Envelope Capitol Reef NP—Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Capitol Reef National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.0051015202530Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)5-11BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-6NOxControl Scenarios—Least-Cost Envelope Capitol Reef NP—98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Capitol Reef National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.000.100.200.300.400.500.600.700.80Reduction in Delta dv

Total Annualized Cost (MM $)5-12BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-7NOxControl Scenarios—Least-Cost Envelope Grand Canyon NP—Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Grand Canyon National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00510152025Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)5-13BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-8NOxControl Scenarios—Least-Cost Envelope Grand Canyon NP—98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Grand Canyon National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00Reduction in Delta dv

Total Annualized Cost (MM $)5-14BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-9NOxControl Scenarios—Least-Cost Envelope Petrified Forest NP—Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Petrified Forest National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.0020406080100120140160180Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)5-15BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-10NOxControl Scenarios—Least-Cost Envelope Petrified Forest NP—98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Petrified Forest National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.000.200.400.600.801.001.201.401.60Reduction in Delta dv

Total Annualized Cost (MM $)5-16BART ANALYSIS FOR CHOLLA UNIT 4FIGURE 5-11NOxControl Scenarios—Least-Cost Envelope Sycamore Canyon WA—Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Sycamore Canyon WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.0051015202530Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)5-17BART ANALYSIS FOR CHOLLA UNIT 45-18NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Sycamore Canyon WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.000.100.200.300.400.500.600.70Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE 5-12NOxControl Scenarios—Least-Cost Envelope Sycamore Canyon WA—98thPercentile ReductionCholla 4BART ANALYSIS FOR CHOLLA UNIT 4



# 5.3.2 Analysis Results Results of the least-cost analysis for the various NO x 

## emission control scenarios, shown in Tables 5-3 through 5-10 and Fi gures 5-5 through 5-12, confirm the selection of Scenario 1 (New LNB with SOFA), based on incremental cost and visibility improvements. All other NO x 

## control scenarios are excluded on the basis of cost effectiveness.
Analysis of the NO x 

## results for the four Class I areas in Tables 5-3 through 5-10 and Figures 5-5 through 5-12 illustrates the conclu sions stated above. For the Grand Canyon NP, the incremental cost differential for Scenario 1 compared to Baseline is reasonable at $1,082,000 per ∆ dV. The incremental cost effectiven ess between Scenario 3 (ROFA with Rotamix) and Scenario 1 shows a significant increase ($34,826,000 per ∆ dV). The incremental cost effectiveness of Scenarios 2 (ROFA) a nd Scenario 4 (New LN B with SOFA & SNCR) relative to Scenario 1 are similar to that of S cenario 3. The incremental cost effectiveness of Scenarios 5 (New LNB with SOFA & SCR) relative to Scenario 1 is quite high ($414,107,000 per ∆ dV ).
For Scenario 1 compared to the Baseline, the incremental cost for re duction of days with ∆ dV values greater than 0.5 dV is reasonable at $48,000 per day. As with the deciview improvements, the costs for reduced days of im pacts for the other control scenarios are much higher.
Therefore, because of the significant improvements related to Scenario 1, Scenario 1 represents NO x 

## control BART for Cholla 4.



# 5.4 Recommendations

5.4.1 NO x 

# Emission Control Based on the analysis conducted, new LNB with SOFA can achie ve the BART emission level of 0.22 lb/MMBtu, based on the projec ted significant reduction in NO x 

## emissions, reasonable control costs, and the advantages of lack of non air quality environmental impacts.



# 5.4.2 SO 2 

# Emission Control Based on the analysis conducte d, upgrade of the wet FGD sy stem can achieve the BART emission level of 0.15 lb/MMBtu for SO 2 

## emission control.



# 5.4.3 PM 10



# Emission Control Based on the analysis conducted, the installati on of a fabric filter can achieve the BART emission level of 0.015 lb/MMBtu for PM 10 

## emission control.



# 5.5 Just-Noticeable Differences in Atmospheric Haze

Studies have been conducted th at demonstrate only dV differe nces of approximately 1.5 to 2.0 dV or more are perceptible by the human eye (Henry, 2002). D eciview changes of less than 1.5 cannot be distinguished by the average person Therefore, the modeling analysis results indicate that only minimal, if any, observable visibility improvements at the Class I areas

5-19BART ANALYSIS FOR CHOLLA UNIT 4

5-20 

## studied would be expected under any of the scen arios. Thus the results indicate that even though many millions of dollars will be spent, only minimal, if any, noticeable visibility improvements may result.
Finally, it should be noted that none of the data were corrected for na tural obscuration where water in various forms (fog, clouds, snow, or ra in) or other naturally caused aerosols obscure the atmosphere. During the period of 2001 thr ough 2003, there were several mega-wildfires that lasted for many days and could have had a significant impact of background visibility in these Class I areas. If natural obscuration were to reduce the reduction in visibility impacts modeled for the Cholla 4 facility, the eff ect would be to increase the costs per ∆ dV reduction that are presented in this report.

# 6.0 References

Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule

. Protocol for BART-Related Vi sibility Improvemen t Modeling Analysis in North Dakota . North Dakota Department of Health. October 26, 2005. Regional Haze Regulations and Guidelines for Best Available Retrofit Technology Determinations; Final Rule (40 CFR Part 51; July 6, 2005, pg 39129) United States Environmental Protection Agency, 1990. New Source Review Workshop Manual –Prevention of Significant Deterioration and Nonattainment Area Permitting . October 1990. United States Environmental Protection Agency, 1999. Fact Sheet: Final Regional Haze Regulations for Protection of Visibility in National Parks and Wilderness Areas. June 2, 1999.
United States Environmental Protection Agency, 2005. Fact Sheet: Final Amendments to the Regional Haze Rule and Guidelines for Be st Available Retrofit Technology (BART) Determinations. July, 2005. Henry, R.C. 2002. Just-Noticeable Differences in Atmospheric Haze . Journal Air and Waste Management Association. Vol. 52, Pages 1238-1243. October 2002.

TRC 2007. Atmospheric Studies Group at TRC . The CALPUFF Modeling System. http://www.src.com/calpuff/download/download.htm . Accessed July 2007. Western Regional Air Pa rtnership (WRAP) 2006. Draft Final Modeling Protocol, CALMET/CALPUFF Protocol for BART Exemption Sc reening Analysis for Class I Areas in the Western United States . Western Regional Air Partnershi p, Air Quality Modeling Forum, Regional Modeling Center, August 15, 2006.

6-1

# Appendices

## APPENDIX A 

# Economic AnalysisECONOMIC ANALYSIS SUMMARYCholla Unit 4Boiler Design: Tangential FiredSO2 Control PM ControlLNB w/SOFALNB w/SOFA& SNCRROFAROFAw/RotamixLNB w/SOFA &SCRUpgrades to WetLime SystemFabric FilterCase12 345 6 7 8NOx Emission Control SystemLNB w/COFA LNB w/SOFALNB w/SOFA &SNCR ROFA ROFA w/Rotamix LNB w/SOFA & SCR ---- ---SO2 Emission Control SystemAbsorber --- --- --- --- ---Upgrades to Wet LimeSystem ---PM Emission Control SystemESP --- --- --- --- --- --- Fabric FilterTOTAL INSTALLED CAPITAL COST ($)0 6,837,000 21,543,000 15,404,911 22,698,330 112,617,000 69,660,000 143,964,000FIRST YEAR O&M COST ($)Operating Labor ($) 0 42,500 85,000 63,750 85,000 106,250 212,500 170,000Maintenance Material ($) 0 85,000 170,000 127,500 170,000 212,500 425,000 340,000Maintenance Labor ($) 0 42,500 85,000 63,750 85,000 106,250 212,500 170,000Administrative Labor ($) 0 0 0 0 0 0 0 0TOTAL FIXED O&M COST 0 170,000 340,000 255,000 340,000 425,000 850,000 680,000Makeup Water Cost 0 0 0 0 0 0 166,519 0Reagent Cost 0 0 289,706 0 579,412 592,084 1,132,549 0SCR Catalyst / FF Bag Cost 0 0 0 0 0 637,500 0 968,000Waste Disposal Cost 0 0 0 0 0 0 621,696 0Electric Power Cost 0 0 173,120 1,458,277 1,458,277 865,598 162,936 162,936TOTAL VARIABLE O&M COST0 0 462,825 1,458,277 2,037,689 2,095,182 2,083,700 1,130,936TOTAL FIRST YEAR O&M COST 0 170,000 802,825 1,713,277 2,377,689 2,520,182 2,933,700 1,810,936FIRST YEAR DEBT SERVICE ($)0 650,389 2,049,339 1,465,436 2,159,243 10,713,010 6,626,604 13,694,982TOTAL FIRST YEAR COST ($)0 820,389 2,852,164 3,178,713 4,536,932 13,233,192 9,560,304 15,505,918Power Consumption (MW)0.0 0.0 0.4 3.6 3.6 2.1 0.4 0.4Annual Power Usage (kW-Hr/Yr) 0.0 0.0 3.5 29.2 29.2 17.3 3.3 3.3CONTROL COST ($/Ton Removed)NOx Removal Rate (%) 0.0% 47.0% 59.0% 61.4% 71.1% 83.1% 0.0% 0.0%NOx Removed (Tons/Yr) 0 3,390 4,259 4,433 5,129 5,998 0 0First Year Average Control Cost ($/Ton NOx Rem.) 0 242 670 717 885 2,206 0 0Incremental Control Cost ($/Ton NOx Removed) Base 242 2,337 1,878 1,953 10,004 0 0Case Comparison2-1 3-2 4-3 5-4 6-5SO2 Removal Rate (%) 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 79.6% 0.0%SO2 Removed (Tons/Yr) 0 0 0 0 0 0 10,153 0First Year Average Control Cost ($/Ton SO2 Rem.) 0 0 0 0 0 0 942 0Incremental Control Cost ($/Ton SO2 Removed) Base 0 0 0 0 0 942 0Case Comparison7-1PM Removal Rate (%) 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 37.50%PM Removed (Tons/Yr) 0 0 0 0 0 0 0 156First Year Average Control Cost ($/Ton PM Rem.) 0 0 0 0 0 0 0 99,100Incremental Control Cost ($/Ton PM Removed) Base 0 0 0 0 0 0 99,100Case Comparison8-1PRESENT WORTH COST ($)0 8,914,035 31,351,803 36,337,481 51,748,586 143,408,209 105,503,513 166,089,750SO2 and PM efficiencies shown are only incrementalParameterCurrentOperationNOx ControlCholla Economic Analysis Model1 of 11/29/2008 4:15 PM

### INPUT CALCULATIONS

Cholla Unit 4 Boiler Design: Tangential Fired SO2 Control PM Control

LNB w/SOFA LNB w/SOFA & SNCR ROFA ROFA w/Rotamix LNB w/SOFA & SCR Upgrades to Wet Lime System Fabric Filter

Case 123456 7 8

NOx Emission Control System LNB w/COFA LNB w/SOFA LNB w/SOFA & SNCR ROFA ROFA w/Rotamix LNB w/SOFA & SCR

---- ---
SO2 Emission Control System Absorber

--- --- --- --- --- Upgrades to Wet Lime System --- PM Emission Control System ESP

--- --- --- --- --- --- Fabric Filte r Unit Design and Coal Characteristics Type of Unit PC PC PC PC PC PC PC PC Net Power Output (kW) 425,000 425,000 425,000 425,000 425,000 425,000 425,000 425,000 Net Plant Heat Rate (Btu/kW-Hr) 10,042 10,042 10,042 10,042 10,042 10,042 10,042 10,042 Boiler Fuel El Segundo El Segundo El Segundo El Segundo El Segundo El Segundo El Segundo El Segundo Coal Heating Value (Btu/Lb) 9,215 9,215 9,215 9,215 9,215 9,215 9,215 9,215 Coal Sulfur Content (wt.%) 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% 1.10% Coal Ash Content (wt.%) 16.80% 16.80% 16.80% 16.80% 16.80% 16.80% 16.80% 16.80% Boiler Heat Input, each (MMBtu/Hr) 4,268 4,268 4,268 4,268 4,268 4,268 4,268 4,268 Coal Flow Rate (Lb/Hr) 463,158 463,158 463,158 463,158 463,158 463,158 463,158 463,158 (Ton/Yr) 1,886,627 1,886,627 1,886,627 1,886,627 1,886,627 1,886,627 1,886,627 1,886,627 (MMBtu/Yr) 34,770,542 34,770,542 34,770,542 34,770,542 34,770,542 34,770,542 34,770,542 34,770,542
Emissions Uncontrolled SO2 (Lb/Hr) 3,133 3,133 3,133 3,133 3,133 3,133 3,133 3,133 (Lb/MMBtu) 0.73 0.73 0.73 0.73 0.73 0.73 0.73 0.73 (Lb Moles/Hr) 48.90 48.90 48.90 48.90 48.90 48.90 48.90 48.90 (Tons/Yr) 12,761 12,761 12,761 12,761 12,761 12,761 12,761 12,761 SO2 Removal Rate (%) 0% 0.0% 0.0% 0.0% 0.0% 0.0% 79.6% 0.0% (Lb/Hr) 0 0 0 0 0 0 2,493 0 (Ton/Yr) 0 0 0 0 0 0 10,153 0 SO2 Emission Rate (Lb/Hr) 3,133 3,133 3,133 3,133 3,133 3,133 640 3,133 (Lb/MMBtu) 0.73 0.73 0.73 0.73 0.73 0.73 0.15 0.73 (Ton/Yr) 12,761 12,761 12,761 12,761 12,761 12,761 2,608 12,761 Uncontrolled NOx (Lb/Hr) 1,771 1,771 1,771 1,771 1,771 1,771 1,771 1,771 (Lb/MMBtu) 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 (Lb Moles/Hr) 59.02 59.02 59.02 59.02 59.02 59.02 59.02 59.02 (Tons/Yr) 7,215 7,215 7,215 7,215 7,215 7,215 7,215 7,215 NOx Removal Rate (%) 0.0% 47.0% 59.0% 61.4% 71.1% 83.1% 0.0% 0.0% (Lb/Hr) 0 832 1,046 1,088 1,259 1,472 0 0 (Lb Moles/Hr) 0.00 27.73 34.84 36.27 41.95 49.07 0.00 0.00 (Ton/Yr) 0 3,390 4,259 4,433 5,129 5,998 0 0 NOx Emission Rate (Lb/Hr) 1,771 939 726 683 512 299 1,771 1,771 (Lb/MMBtu) 0.42 0.22 0.17 0.16 0.12 0.07 0.42 0.42 (Ton/Yr) 7,215 3,825 2,955 2,782 2,086 1,217 7,215 7,215 Uncontrolled Fly Ash (Lb/Hr) 62,248 102 102 102 102 102 102 102 (Lb/MMBtu) 0.02 0.024 0.024 0.024 0.024 0.024 0.024 0.024 (Lb Moles/Hr) 2,074.3 3.4 3.4 3.4 3.4 3.4 3.4 3.4 (Tons/Yr) 253,563 417 417 417 417 417 417 417 Fly Ash Removal Rate (%) 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 37.50% (Lb/Hr) 62,146 0 0 0 0 0 0 38 (Ton/Yr) 253,145 0 0 0 0 0 0 156 Fly Ash Emission Rate (Lb/Hr) 102 102 102 102 102 102 102 64 (Lb/MMBtu) 0.024 0.024 0.024 0.024 0.024 0.024 0.024 0.015 (Ton/Yr) 417 417 417 417 417 417 417 261 General Plant Data Annual Operation (Hours/Year) 8,147 8,147 8,147 8,147 8,147 8,147 8,147 8,147 A nnual On-Site Power Plant Capacity Factor 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 Economic Factors Interest Rate (%) 7.10% 7.10% 7.10% 7.10% 7.10% 7.10% 7.10% 7.10% Discount Rate (%) 7.10% 7.10% 7.10% 7.10% 7.10% 7.10% 7.10% 7.10% Plant Economic Life (Years) 20 20 20 20 20 20 20 20 Installed Capital Costs NOx Emission Control System ($2007) 0 6,837,000 21,543,000 15,404,911 22,698,330 112,617,000 0 0 SO2 Emission Control System ($2007) 0 0 0 0 0 0 69,660,000 0 PM Emission Control System ($2007) 0 0 0 0 0 0 0 143,964,000 Total Emission Control Systems ($2007) 0 6,837,000 21,543,000 15,404,911 22,698,330 112,617,000 69,660,000 143,964,000 NOx Emission Control System ($/kW) 0 16 51 36 53 265 0 0 SO2 Emission Control System ($/kW) 0 0 0 0 0 0 164 0 PM Emission Control System ($/kW) 0 0 0 0 0 0 0 339 Total Emission Control Systems ($/kW) 0 16 51 36 53 265 164 339 Total Fixed Operating & Maintenance Costs Operating Labor ($) 0 42,500 85,000 63,750 85,000 106,250 212,500 170,000 Maintenance Material ($) 0 85,000 170,000 127,500 170,000 212,500 425,000 340,000 Maintenance Labor ($) 0 42,500 85,000 63,750 85,000 106,250 212,500 170,000 A dministrative Labor ($) 000000 0 0 Total Fixed O&M Cost ($) 0 170,000 340,000 255,000 340,000 425,000 850,000 680,000 A nnual Fixed O&M Cost Escalation Rate (%) 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% Water Cost Makeup Water Usage (Gpm) 0 0 0 0 0 0 280 0 Unit Price ($/1000 Gallons) 1.22 1.22 1.22 1.22 1.22 1.22 1.22 1.22 First Year Water Cost ($) 0 0 0 0 0 0 166,519 0 A nnual Water Cost Escalation Rate (%) 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00%

Reagent Cost None None Urea None Urea Anhydrous NH3 Lime None Unit Cost ($/Ton) 0.00 0.00 370 0.00 370 400 91.25 0.00 ($/Lb) 0.000 0.000 0.185 0.000 0.185 0.200 0.046 0.000 Molar Stoichiometry 0.00 0.00 0.45 0.00 0.45 1.00 1.00 0.00 Reagent Purity (Wt.%) 100% 100% 100% 100% 100% 100% 90% 90% Reagent Usage (Lb/Hr) 0 0 192 0 384 363 3,047 0 First Year Reagent Cost ($) 0 0 289,706 0 579,412 592,084 1,132,549 0 A nnual Reagent Cost Escalation Rate (%) 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% SCR Catalyst / FF Bag Replacement Cost SCR Catalyst Annual SCR Catalyst (m3) / No. FF Bags/No. Rolls 00000 213 0 2,200 SCR Catalyst ($/m3) / Bag Cost ($/ea.) / Roll Cost ($/ea.) 3,000 3,000 3,000 3,000 3,000 3,000 3,000 440 First Year SCR Catalyst / Bag Replace. Cost ($) 0 0 0 0 0 637,500 0 968,000 A nnual SCR Catalyst / Bag Cost Esc. Rate (%) 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% FGD Waste Disposal Cost FGD Solid Waste Disposal Rate, Dry (Lb/Hr) 0 0 0 0 0 0 6,272 0 FGD Waste Disposal Unit Cost ($/Dry Ton) 24.33 24.33 24.33 24.33 24.33 24.33 24.33 24.33 First Year FGD Waste Disposal Cost ($) 0 0 0 0 0 0 621,696 0 A nnual Waste Disposal Cost Esc. Rate (%) 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% Auxiliary Power Cost Auxiliary Power Requirement (% of Plant Output) 0.00% 0.00% 0.10% 0.84% 0.84% 0.50% 0.09% 0.09% (MW) 0.00 0.00 0.43 3.58 3.58 2.13 0.40 0.40 Unit Cost ($2007/MW-Hr) 50.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 First Year Auxiliary Power Cost ($) 0 0 173,120 1,458,277 1,458,277 865,598 162,936 162,936 A nnual Power Cost Escalation Rate (%) 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% 2.00% Parameter Comments Current Operation NOx Control

Cholla Economic Analysis Model 1 of 1 1/28/2008 8:06 PMCAPITAL COSTCholla Unit 4CaseNOx Emission Control SystemSO2 Emission Control SystemPM Emission Control SystemCAPITAL COST COMPONENTFactor/Source Cost Factor/Source Cost Factor/Source Cost Factor/Source Cost Factor/Source Cost Factor/Source Cost Factor/Source CostLNB w/OFA or ROFALNB LNB ROFA ROFA LNBMajor Materials Design and SupplyVendor $2,650,000 Vendor $2,650,000 Vendor $5,694,888 Vendor $5,694,888 Vendor $2,650,000 Vendor $0 Vendor $0Construction50.0%$1,325,00050.0%$1,325,000Vendor $2,420,567 Vendor $2,420,567 50.0%$1,325,00050.0%$050.0%$0Balance of Plant50.0%$1,325,00050.0%$1,325,00030.0%$1,708,46630.0%$1,708,46650.0%$1,325,00050.0%$050.0%$0Electrical (Allowance)5.0%$132,5005.0%$132,50030.0%$1,708,46630.0%$1,708,4665.0%$132,5005.0%$05.0%$0Owner's Costs10.0%$265,00010.0%$265,00010.0%$569,48910.0%$569,48910.0%$265,00010.0%$010.0%$0Surcharge16.0%$424,00016.0%$424,00016.0%$911,18216.0%$911,18216.0%$424,00016.0%$016.0%$0AFUDC12.0%$318,00012.0%$318,00012.0%$683,38712.0%$683,38712.0%$318,00012.0%$012.0%$0Subtotal $6,439,500 $6,439,500 $13,696,445 $13,696,445 $6,439,500 $0 $0Contingency15.0%$397,50015.0%$397,50030.0%$1,708,46630.0%$1,708,46615.0%$397,50015.0%$015.0%$0Total Capital Cost for LNB w/OFA or ROFA $6,837,000 $6,837,000 $15,404,911 $15,404,911 $6,837,000 $0 $0SNCR or SCR or RotamixSNCR Rotamix SCRMajor Materials Design and SupplyVendor $0 Vendor $5,700,000 Vendor $0 Vendor $1,823,317 Vendor $41,000,000 Vendor $0 Vendor $0Construction50.0%$050.0%$2,850,00050.0%$0Vendor $3,227,422 50.0%$20,500,00050.0%$050.0%$0Balance of Plant50.0%$050.0%$2,850,00050.0%$050.0%$911,65950.0%$20,500,00050.0%$050.0%$0Electrical (Allowance)5.0%$05.0%$285,0005.0%$05.0%$91,1665.0%$2,050,0005.0%$05.0%$0Owner's Costs10.0%$010.0%$570,00010.0%$010.0%$182,33210.0%$4,100,00010.0%$010.0%$0Surcharge16.0%$016.0%$912,00016.0%$016.0%$291,73116.0%$6,560,00016.0%$016.0%$0AFUDC12.0%$012.0%$684,00012.0%$012.0%$218,79812.0%$4,920,00012.0%$012.0%$0Subtotal$0 $13,851,000 $0 $6,746,424 $99,630,000 $0 $0Contingency15.0%$015.0%$855,00015.0%$030.0%$546,99515.0%$6,150,00015.0%$015.0%$0Total Capital Cost for SNCR or SCR or Rotamix $0 $14,706,000 $0 $7,293,419 $105,780,000 $0 $0Dry or Wet FGD, FGC or Fabric FilterMajor Materials Design and SupplyVendor $0 Vendor $0 Vendor $0 Vendor $0 Vendor $0 Vendor $27,000,000 Vendor $55,800,000Construction50.0%$050.0%$050.0%$050.0%$050.0%$050.0%$13,500,00050.0%$27,900,000Balance of Plant50.0%$050.0%$050.0%$050.0%$050.0%$050.0%$13,500,00050.0%$27,900,000Electrical (Allowance)5.0%$05.0%$05.0%$05.0%$05.0%$05.0%$1,350,0005.0%$2,790,000Owner's Costs10.0%$010.0%$010.0%$010.0%$010.0%$010.0%$2,700,00010.0%$5,580,000Surcharge16.0%$016.0%$016.0%$016.0%$016.0%$016.0%$4,320,00016.0%$8,928,000AFUDC12.0%$012.0%$012.0%$012.0%$012.0%$012.0%$3,240,00012.0%$6,696,000Subtotal$0 $0 $0 $0 $0 $65,610,000 $135,594,000Contingency15.0%$015.0%$015.0%$015.0%$015.0%$015.0%$4,050,00015.0%$8,370,000Total Capital Cost for Dry/Wet FGD, FGC or FF $0 $0 $0 $0 $0 $69,660,000 $143,964,000LNB w/SOFA------ ---LNB w/SOFA & SNCR---ParameterLNB w/SOFA LNB w/SOFA & SNCR ROFA w/RotamixNOx ControlLNB w/SOFA & SCRROFALNB w/SOFA & SCR--------- ---ROFA w/Rotamix---ROFA---Upgrades to Wet LimeSystem62734 5SO2 Control PM ControlFabric FilterFabric Filter8------Upgrades to Wet Lime System-------Cholla Economic Analysis Model1 of 11/28/2008 8:05 PM

## APPENDIX B 

# BART Protocol

# Modeling Protocol for BART Control Technology Improvement Modeling Analyses for the APS Cholla Power Plant

Prepared for

Prepared by

October 2007

# Contents

Section Page

Introduction......................................................................................................................................1-1

Model Selection ................................................................................................................ ...............2-1 CALMET Methodology............................................................................................................. .....3-1
3.1 Dimensions of the Modeling Domain .................................................................3-1 3.2 CALMET Input Data..............................................................................................3-4 3.3 Validation of CALMET Wind Field.....................................................................3-5 CALPUFF Methodology............................................................................................................ .....4-1
4.1 CALPUFF Modeling ..............................................................................................4-1 4.1.1 Background Ozone and Ammonia .........................................................4-1 4.1.2 Stack Parameters........................................................................................4-1 4.1.3 Pre-Control Emission Rates .....................................................................4-1 4.1.4 Post Control Emission Rates....................................................................4-2 4.1.5 Modeling Process ......................................................................................4-2 4.2 Receptor Grids and Coordinate Conversion ......................................................4-2 Visibility Post-processing..................................................................................................... .........5-1
5.1 CALPOST ................................................................................................................5-1
Presentation of Results........................................................................................................ ...........6-1

References.........................................................................................................................................7-1



# Tables 3-1 User-Specified CALMET Options 5-1 Average Natural Levels of Aerosol Components 

# Figures

3-1 CALMET and CALPUFF Domains

ii

### SECTION 1.0 

# Introduction

This document presents a modeling protocol for estimating the degree of visibility improvement from Best Available Retrofit Technology (BART) control technology options for the Arizona Public Service Company (APS) Cholla Power Plant Units 2, 3, and 4. The Arizona Department of Environmental Quality (A DEQ) has identified that these three boiler units at the Cholla Power Plant are BART eligible and must perform a BART analysis.
This protocol outlines the proposed approa ch for the modeling analysis for the Cholla Power Plant. To a large extent, this protocol follows the methodology outlined in the Western Regional Air Partnership (WRAP) protocol for performing BART analyses (WRAP 2006). Any proposed deviations from that meth odology are documented in this protocol. Section 2.0 describes the modeling system (CALPUFF) that will be used for the analyses. Sections 3.0 and 4.0 describe the proposed methodology for the CALMET meteorological model and the CALPUFF model, respectively . Section 5.0 presents a summary of the proposed approach for the CALPOST post-p rocessor and Section 6.0 presents a brief description of the final report format for submittal to ADEQ. Section 7.0 contains a list of references cited in the protocol document.

1-1

### SECTION 2.0 

# Model Selection

CH2M HILL will use the CALPUFF modeling system to assess the visibility impacts at Class I areas. Workgroups that represent the interests of the Federal Land Managers (FLM) recommend that an analysis of Class I area air quality and air quality related values (AQRVs) be performed for major sources located more than 50 km from these areas (USEPA 1998). The CALPUFF model is the only model recommended by EPA for these types of regulatory analyses.
The CALPUFF modeling system includes the CALMET meteorological model, a Gaussian puff dispersion model (CALPUFF) with al gorithms for chemical transformation and deposition, and a post processor capable of ca lculating concentrations, visibility impacts, and deposition (CALPOST). The CALPUFF mo deling system will be applied in a full, refined mode.
CH2M HILL will use the latest version (Version 6) of the CALPUFF modeling system preprocessors and models in lieu of the EPA- approved versions (Version 5). The Federal Land Managers (FLMs) and others have noted that the EPA-approved Version 5 contained errors and that a newer version should be us ed. In addition, Version 6 was used in the Subject-To-BART (exemption) modeling analysis conducting by the WRAP Regional Modeling Center. Consequently, it was decided to use the latest (as of April, 2007) version of the CALPUFF modeling system (available at www.src.com):

• CALMET Version 6.211 Level 060414

• CALPUFF Version 6.112 Level 060412

2-1

### SECTION 3.0 

# CALMET Methodology

3.1 Dimensions of the Modeling Domain

CH2M HILL will define domains for Mesoscale Model data (MM5), CALMET, and CALPUFF that will be slightly different than those established for the Arizona BART modeling in WRAP 2006. In addition, the CALMET and CALPUFF Lambert Conformal Conic (LCC) map projection will be based on a central meridian of 110 W rather than 97 W. This will put the central meridian near the center of the domain.
CH2M HILL will use the CALMET model to ge nerate three-dimensional wind fields and other meteorological parameters suitable for use by the CALPUFF model. A CALMET modeling domain has been defined to allow for at least a 50-km buffer around all Class I areas within 300 km of the Cholla Power Plant. Gr id resolution for this domain will be 4-km. Figure 3-1 shows the extent of the proposed modeling domain.
The technical options recommended in WRAP 2006 will be used for CALMET. Vertical resolution of the wind field will include eleven layers, with vertical cell face heights as follows (in meters):

• 

### 0, 20, 100, 200, 350, 500, 750, 1000, 2000, 3000, 4000, 5000 Also, following WRAP 2006, the maximum over-land mixing height (ZIMAX) will be set to 4500 meters based on the Colorado Department of Health and Environment (CDPHE) analyses of soundings for summer ozone even ts in the Denver area (CDPHE, 2005). The CDPHE analysis suggests mixing heights in the Denver area are often well above the CALMET default value of 3000 meters during the summer. For example, on some summer days, ozone levels are elevated all the way to 6000 meters MSL or beyond during some meteorological regimes, including some regimes associated with high ozone episodes. It is assumed that, like in Denver, mixing heights in excess of the 3,000 m AGL CALMET default maximum would occur in the domain s considered for this analysis.
For the APS analysis, we propose to modify IEXTRP, R1, R2, RMAX1, and RMAX2 from the values in WRAP 2006. WRAP 2006 has R1 and R2 values that are larger than the RMAX1 and RMAX2 values. This means at the RMAX distances, the surface stations are weighted greater than the MM5 data. Defining the parameters in this way causes a noticeable boundary in the wind field at the RMAX distances. This effect is known as crop circling in the wind field because there is a well defined circle around the meteorological data station in the processed wind vector map, where there is a discrepancy between the surface station data and the MM5 data.
Crop circles in the wind field may result in inaccurate results from the CALPUFF modeling because the wind field may be either shifti ng the plume transport too greatly between individual time steps, or may push the plume ba ck to the original cell in a small time step.

3-1FIGURE 3-1 

### CALMET and CALPUFF Domains

3-2

### To alleviate this problem, it is proposed that the R1, R2, RMAX1, and RMAX2 values be modified to allow better smoothing in the wind field.
In addition, by using an IEXTRP value of 1, the WRAP CALMET proc essing prevents the surface stations from influencing the meteorological data above the surface layer. We are proposing to use an IEXTRP value of 4 (the CALMET default value) which allows some influence of the surface data on the layers above the surface.
Table 3-1 lists the key user-specified CALMET options.

TABLE 3-1 

### User-Specified CALMET Options Description CALMET Input Parameter Value
CALMET Input Group 2

Map projection PMAP Lambert Conformal (LCC)

Grid spacing DGRIDKM 4
Number vertical layers NZ 11
Top of lowest layer (m) 20
Top of highest layer (m) 5000
CALMET Input Group 4

Observation mode NOOBS 1
CALMET Input Group 5

Prognostic or MM-FDDA data switch IPROG 14

Max surface over-land extrapolation radius (km) RMAX1 50
Max aloft over-land extrapolations radius (km) RMAX2 50

Radius of influence of terrain features (km) TERRAD 10
Relative weight at surface of Step 1 field and obs (km) R1 25
Relative weight aloft of Step 1 field and obs (km) R2 25
Extrapolation of surface wind observations to upper layers IEXTRP 4
CALMET Input Group 6

Maximum over-land mixing height (m) ZIMAX 4500

3-3

# 3.2 CALMET Input Data

CH2M HILL will run the CALMET model to pr oduce three years of analysis: 2001, 2002, and 2003. CH2M HILL will use MM5 data as th e basis for the CALMET wind fields. The horizontal resolution of the MM5 data is 36-km.

For 2001, CH2M HILL will use MM5 data at 36-km resolution that were obtained from the contractor (Alpine Geophysics) who developed the nationwide data for the EPA. For 2002, CH2M HILL will use 36-km MM5 data obtain ed from Alpine Geophysics, originally developed for WRAP. Data to be used for 2003 (also from Alpine Geophysics), at 36-km resolution, were developed by the Wisconsin De partment of Natural Resources, the Illinois Environmental Protection Agency, and the Lake Michigan Air Directors Consortium (Midwest RPO).
The MM5 data will be used as input to CALMET as the “initial guess” wind field. The initial guess field will be adjusted by CALMET for loca l terrain and land use effects to generate a Step 1 wind field, and then further refined usin g local surface observations to create a final Step 2 wind field.

Surface data for 2001-2003 will be obtained from the National Climatic Data Center (NCDC). CH2M HILL will process data for all stations from the National Weather Service’s (NWS) Automated Surface Observing System (ASOS) ne twork that are in the domain. The surface data will be obtained in abbreviated DATSAV3 format. A conver sion routine available from the TRC website will be used to convert the DATSAV3 files to CD-144 format for input to the SMERGE preprocessor and CALMET.
Land use and terrain data will be obtained from the U.S. Geological Survey (USGS). Land use data will be obtained in Composite Them e Grid (CTG) format from the USGS, and the Level I USGS land use categories will be ma pped into the 14 primary CALMET land use categories. Surface properties such as albedo, Bowen ratio, roughness length, and leaf area index will be computed from the land use values. Terrain data will be taken from USGS 1-degree Digital Elevation Model (DEM ) data, which are primarily derived from USGS 1:250,000 scale topographic maps. Missing land use data will be filled with a value that is appropriate for the missing area.
Precipitation data will be ordered from the National Climatic Data Center (NCDC). All available data in fixed-length, TD-3240 format will be ordered for the modeling domain. The list of available stations and stations that hav e collected complete data varies by year, but CH2M HILL will process all available stations / data within the domain for each year. Precipitation data will be prepared with th e PXTRACT/PMERGE processors in preparation for use within CALMET.
Following the methodology recommended in WRAP 2006, no observed upper-air meteorological observations will be used as they are redundant to the MM5 data, and may introduce spurious artifacts in the wind fiel ds. In the development of the MM5 data, the twice daily upper-air meteorological observatio ns are used as input with the MM5 model. The MM5 estimates are nudged to the upper-air observations as part of the Four Dimensional Data Assimilation (FDDA). This re sults in higher tempor al (hourly vs. 12-hour) and spatial (36 km vs. ~300 km) resolution for the upper-air meteorology in the MM5 field.

3-43-5 

### These MM5 data are more dynamically balanc ed than those contained in the upper-air observations. Therefore the use of the upper-air observations with CALMET is not needed, and, in fact, will upset the dynamic balance of the meteorological fields potentially producing spurious vertical velocities.



# 3.3 Validation of CALMET Wind Field

CH2M HILL will use the CalDESK data display and analysis system (v2.97, Enviromodeling Ltd.) to view plots of wind vectors and othe r meteorological parameters to evaluate the CALMET wind fields. We will use observed weat her conditions, as depicted in surface and upper-air weather maps from the National Oceanic and Atmospheric Administration (NOAA) Central Library U.S. Daily Weathe r Maps Project (http://docs.lib.noaa.gov /

rescue / dwm / data_rescue_daily_weather_maps.html), to compare to the CalDESK displays.

### SECTION 4.0 

# CALPUFF Methodology

4.1 CALPUFF Modeling

CH2M HILL will drive the CALPUFF model with the meteorological output from CALMET over the CALPUFF modeling domain (Figure 3-1). The CALPUFF model will be used to predict visibility impacts for the pre-control (baseline) scenario for comparison to the predicted impacts for post-control scenarios.


# 4.1.1 Background Ozone and Ammonia Hourly values of background ozone concen trations will be used by CALPUFF for the calculation of SO 2 

### and NO X 

### transformation with the MESOPUFF II chemical transformation scheme. CH2M HILL will use the hourly ozone data generated for the WRAP BART analysis for 2001, 2002, and 2003.
For periods of missing hourly ozone data, the chemical transformation will rely on a monthly default value of 80 ppb. Background ammonia will be set to 1 ppb as recommended in WRAP 2006.



# 4.1.2 Stack Parameters The baseline stack parameters will be the same as those used in the WRAP-RMC exemption modeling unless more representative data are av ailable. Post-control stack parameters will reflect any anticipated changes from operation of the control technology alternatives that are being evaluated.



# 4.1.3 Pre-Control Emission Rates Pre-control emission rates will reflect normal maximum capacity 24- hour emissions that may occur under the source’s current permit. Th e emission rates will re flect actual emissions under normal operating conditions. As described by the EPA in the Regional Haze Regulations and Guidelines for Best Available Ret rofit Technology (BART) Determinations; Final Rule (40 CFR Part 51; July 6, 2005, pg 39129):

The emissions estimates used in the mode ls are intended to reflect steady-state operating conditions during periods of high- capacity utilization. We do not generally recommend that emissions reflecting period s of start-up, shutdown, and malfunction be used...

CH2M HILL will use available CEM data to de termine the baseline 24-hour emission rates. Data will reflect operations from 2001 through 2003.
Although the Wrap Exemption Mo deling evaluated emissions of NO X 

### , SO 2 

### , and PM 2.5 

### , particulate matter speciation data from the US EPA or National Park Service are proposed

4-1

### for this analysis (USEPA 2007, NPS 2007). Th erefore emissions will be modeled for the following species:

• Sulfur dioxide (SO 2 

### )

• Nitrogen oxides (NO x 

### )

• Coarse particulate (PM 2.5 

### < diameter ≤ PM 10 

### )

• Fine particulate (diameter ≤ PM 2.5 

### )

• Elemental carbon (EC)

• Organic aerosols (SOA)

• Sulfates (SO 4 

### )



# 4.1.4 Post Control Emission Rates

Post-control emission rates will reflect the e ffects of the emissions control scenario under consideration. Modeled pollutants will be the same as listed for the pre-control scenario.


# 4.1.5 Modeling Process The CALPUFF modeling for the control techno logy options will follow this sequence:

• Model pre-control (baseline) emissions

• Determine the degree of visibility improvement

• Model other control scenarios if applicable

• Determine the degree of visibility improvement

• Factor visibility results into BART “5-step” evaluation 

# 4.2 Receptor Grids and Coordinate Conversion

The TRC COORDS program will be used to conv ert the latitude/ longitude coordinates to LCC coordinates for the meteorological stations and source locations. The USGS conversion program PROJ (version 4.4.6) will be used to convert the National Park Service (NPS) receptor location data from latitude/longitude to LCC.

For the Class I areas that are within 300 km of the Cholla Power Plant, discrete receptors for the CALPUFF modeling will be taken from the NPS database for Class I area modeling receptors. The entire area of each Class I area that is within or intersects the 300 km circle (Figure 3-1) will be included in the modeling analysis. The following lists the Class I areas that will be modeled for the Cholla Power Plant:

• Capitol Reef National Park

• Galiuro Wilderness

• Saguaro National Park

• Gila Wilderness

• Superstition Wilderness

• Mount Baldy Wilderness

• Sierra Ancha Wilderness

• Mazatzal Wilderness

• Grand Canyon National Park

• Mesa Verde National Park

4-24-3 

### • Petrified Forest National Park

• Pine Mountain Wilderness

• Sycamore Canyon Wilderness

### SECTION 5.0 

# Visibility Post-processing

5.1 CALPOST

The CALPOST processor will be used to dete rmine 24-hour average visibility results. Output will be specified in deciview (dv) units.
Calculations of light extinction will be made for each pollutant modeled. The sum of all extinction values will be used to calculat e the delta-dv change relative to natural background. Default extinction coefficients for each species, as shown below, will be used.

• Ammonium sulfate 3.0

• Ammonium nitrate 3.0

• PM coarse (PM 10 

### ) 0.6

• PM fine (PM 2.5 

### ) 1.0

• Organic carbon 4.0

• Elemental carbon 10.0 CALPOST visibility Method 6 (MVISBK=6) will be used for the determination of visibility impacts. Monthly average relative humidity factors [f(RH)] will be used in the light extinction calculations to account for the hygr oscopic characteristic of sulfate and nitrate particles. Monthly f(RH) values will be the same as the Class I area specific values used in the WRAP-RMC BART modeling.
The natural background conditions as a refere nce for determination of the delta-dv change will represent the average natural concentration for western Class I areas. Table 5-1 lists the annual average species concentrations from the EPA Guidance.

TABLE 5-1 

### Average Natural Levels of Aerosol Components Aerosol Component Average Natural Concentration (μg/m 3 ) for Western Class I Areas

Ammonium Sulfate 0.12

Ammonium Nitrate 0.10
Organic Carbon 0.47

Elemental Carbon 0.02

Soil 0.50

Coarse Mass 3.0
Note: Taken from Table 2-1 of Guidance for Estimating Natu ral Visibility Conditions Under the Regional Haze Rule.

5-1

### SECTION 6.0 

# Presentation of Results

The results for a given year of meteorology, each emission control scenario, and each Class I area will be presented as the maximum Δ dv and 98 th 

### percentile Δ dv over the 3-year period, as well as the maximum number of days per year that the maximum Δ dv exceeds 0.5 Δ dv.
For the BART analysis, the model results for each emission control scenario will be compared to those for the baseline scenario. Incremental differences between increasing levels of control will also be evaluated.
The methodology and results of the CALPUFF modeling analyses will be presented in a technical report for each unit that is subj ect to BART. Input and output files for the CALMET/CALPUFF modeling and post-processing will be provided in electronic format to the ADEQ. Larger files such as binary files generated by CALMET will not be included on the submitted disks, but any omitted files will be provided electronically upon request.

6-1

### SECTION 7.0 

# References

Western Regional Air Partnership (WRA P) 2006. Draft Final Modeling Protocol, CALMET/CALPUFF Protocol for BART Exemption Screening Analysis for Class I Areas in the Western United States. Western Regional Air Partnership, Air Quality Modeling Forum, Regional Modeling Center, August 15, 2006.
Colorado Department of Public Health and Environment (CDPHE) 2005. CALMET/CALPUFF BART Protocol for Class I Fe deral Area Individual Source Attribution Visibility Impairment Modeling Analysis. Colo rado Department of Public Health and Environment, Air Pollution Control Division, Denver, Colorado. October 24.
National Park Service (NPS) 2007. Nature & Science, Air, Permits, Particulate Matter Speciation. http://www2.nature.nps.gov/air/Pe rmits/ect/ectCoalFiredBoiler.cfm

. Accessed 7/13/2007.
US Environmental Protection Agency (USE PA) 2003a. Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. USEPA. EPA-454/B-03-005. September 2003. USEPA 2003b. Guidance for Tracking Progress un der the Regional Haze Rule. USEPA. EPA454/B-03-004. September 2003.
USEPA 1998. Interagency Workgroup on Ai r Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long-Range Transport Impacts. U.S. Environmental Protection Agency, Air Qualit y Modeling Group (MD-14), Research Triangle Park, North Carolina; National Park Service, Air Resources Division, Denver, Colorado; USDA Forest Service, Air Program, Fort Collins, Colorado; and U.S. Fish and Wildlife Service, Air Quality Branch De nver, Colorado. December, 1998.
USEPA 2007. AP 42, Fifth Edition, Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area Sources. USEPA Te chnology Transfer Network Clearinghouse for Inventories & Emissions Factors, Emissions Factors & AP 42. http://www.epa.gov/ttn/chief/ap42/index.html . Accessed 7/20/2007.

7-1

## APPENDIX C 

# Additional BART Modeling ResultsFIGURE C-1NOxControl Scenarios - Maximum Contributionsto Visual Range Reduction at Gila WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Gila Wilderness0.000.501.001.502.002.50Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-2NOxControl Scenarios - Maximum Contributions toVisual Range Reduction at Mount Baldy WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Mount Baldy Wilderness0.000.501.001.502.002.50Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-3NOxControl Scenarios - Maximum Contributions toVisual Range Reduction at Sierra Ancha WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Sierra Ancha Wilderness0.000.501.001.502.002.50Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-4NOxControl Scenarios - Maximum Contributions toVisual Range Reduction at Mazatzal WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Mazatzal Wilderness0.000.200.400.600.801.001.201.401.601.802.00Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-5NOxControl Scenarios - Maximum Contributions to Visual Range Reduction at Pine Mountain WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Pine Mountain Wilderness0.000.200.400.600.801.001.201.401.601.802.00Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-6NOxControl Scenarios - Maximum Contributions to Visual Range Reduction at Superstition WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Superstition Wilderness0.000.501.001.502.002.50Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-7NOxControl Scenarios - Maximum Contributions to Visual Range Reduction at Galiuro WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Galiuro Wilderness0.000.200.400.600.801.001.201.40Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-8NOxControl Scenarios - Maximum Contributions toVisual Range Reduction at Mesa Verde WildernessCholla 4NOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Mesa Verde National Park0.000.200.400.600.801.001.201.401.60Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)FIGURE C-9NOxControl Scenarios - Maximum Contributions to Visual Range Reduction at Saguaro NPNOx Control ScenariosMaximum Contributions to Visual Range ReductionCholla Unit 4Saguaro National Park0.000.200.400.600.801.001.201.40Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5Control Scenario

Maximum Impact ( Δ dv)Cholla 4TABLE C-1 

### NO x 

### Control Scenario Results for Gila Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 23 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 1 0.542 0.820 0.037 1.514

2 ROFA 1 0.565 3.179 0.144 5.626

3 ROFA with Rotamix 1 0.568 4.537 0.206 7.988

4 LNB with OFA & SNCR 1 0.557 2.852 0.130 5.121

5 LNB with OFA & SCR 0 0.595 13.233 0.575 22.241

TABLE C-2 

### NO x 

### Control Scenario Results for Mount Baldy Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 22 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 2 0.612 0.820 0.041 1.341

2 ROFA 1 0.631 3.179 0.151 5.038

3 ROFA with Rotamix 1 0.646 4.537 0.216 7.023

4 LNB with OFA & SNCR 1 0.629 2.852 0.136 4.534

5 LNB with OFA & SCR 1 0.679 13.233 0.630 19.489TABLE C-3 

### NO x 

### Control Scenario Results for Sierra Ancha Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 34 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 4 0.727 0.820 0.027 1.128

2 ROFA 1 0.749 3.179 0.096 4.244

3 ROFA with Rotamix 0 0.772 4.537 0.133 5.877

4 LNB with OFA & SNCR 2 0.746 2.852 0.089 3.823

5 LNB with OFA & SCR 0 0.830 13.233 0.389 15.944

TABLE C-4 

### NO x 

### Control Scenario Results for Mazatzal Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 32 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 3 0.749 0.820 0.028 1.095

2 ROFA 2 0.779 3.179 0.106 4.081

3 ROFA with Rotamix 2 0.804 4.537 0.151 5.643

4 LNB with OFA & SNCR 3 0.775 2.852 0.098 3.680

5 LNB with OFA & SCR 1 0.827 13.233 0.427 16.001TABLE C-5 

### NO x 

### Control Scenario Results for Pine Mountain Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 23 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 3 0.565 0.820 0.041 1.452

2 ROFA 2 0.574 3.179 0.151 5.538

3 ROFA with Rotamix 1 0.587 4.537 0.206 7.729

4 LNB with OFA & SNCR 2 0.571 2.852 0.136 4.995

5 LNB with OFA & SCR 1 0.599 13.233 0.602 22.092

TABLE C-6 

### NO x 

### Control Scenario Results for Superstition Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 27 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 3 0.772 0.820 0.034 1.063

2 ROFA 2 0.790 3.179 0.127 4.024

3 ROFA with Rotamix 2 0.818 4.537 0.181 5.546

4 LNB with OFA & SNCR 3 0.787 2.852 0.119 3.624

5 LNB with OFA & SCR 1 0.847 13.233 0.509 15.624TABLE C-7 

### NO x 

### Control Scenario Results for Galiuro Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 10 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 0 0.366 0.820 0.082 2.241

2 ROFA 0 0.381 3.179 0.318 8.343

3 ROFA with Rotamix 0 0.382 4.537 0.454 11.877

4 LNB with OFA & SNCR 0 0.379 2.852 0.285 7.525

5 LNB with OFA & SCR 0 0.389 13.233 1.323 34.018

TABLE C-8 

### NO x 

### Control Scenario Results for Mesa Verde Wilderness Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 25 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 1 0.503 0.820 0.034 1.631

2 ROFA 0 0.550 3.179 0.127 5.779

3 ROFA with Rotamix 0 0.580 4.537 0.181 7.822

4 LNB with OFA & SNCR 0 0.542 2.852 0.114 5.262

5 LNB with OFA & SCR 0 0.613 13.233 0.529 21.588TABLE C-9 

### NO x 

### Control Scenario Results for Saguaro NP Cholla 4

Scenario Controls Average Number of Days Above 0.5 ∆ dV (Days) 98th Percentile ∆ dV Reduction Total Annualized Cost (Million$) Cost per Reduction in No. of Days Above 0.5 ∆ dV (Million$/Day

Reduced) Cost per ∆ dV Reduction (Million$/dV

Reduced)

Base 9 0.000 0.000 0.000 0.000

1 LNB with Existing OFA 0 0.296 0.820 0.091 2.772

2 ROFA 0 0.304 3.179 0.353 10.456

3 ROFA with Rotamix 0 0.312 4.537 0.504 14.541

4 LNB with OFA & SNCR 0 0.301 2.852 0.317 9.476

5 LNB with OFA & SCR 0 0.322 13.233 1.470 41.097

TABLE C-10 

### Gila Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 22 0.542 0.820 0.037 1.514

Scenario 2 vs. Scenario 1 0 0.023 2.358 NA 102.536

Scenario 5 vs. Scenario 2 1 0.027 8.696 8.696 322.084

TABLE C-11 

### Mount Baldy Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 20 0.612 0.820 0.041 1.341

Scenario 3 vs. Scenario 1 0 0.015 1.358 NA 90.548

Scenario 5 vs. Scenario 3 0 0.033 8.696 NA 263.523TABLE C-12 

### Sierra Ancha Wilderness Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 30 0.727 0.820 0.027 1.128

Scenario 3 vs. Scenario 1 1 0.023 1.358 1.358 59.053

Scenario 5 vs. Scenario 3 0 0.058 8.696 NA 149.936

TABLE C-13 

### Mazatzal Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 29 0.749 0.820 0.028 1.095

Scenario 3 vs. Scenario 1 0 0.025 1.358 NA 54.329

Scenario 5 vs. Scenario 3 1 0.023 8.696 8.696 378.098

TABLE C-14 

### Pine Mountain Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 20 0.565 0.820 0.041 1.452

Scenario 3 vs. Scenario 1 1 0.013 1.358 1.358 104.478

Scenario 5 vs. Scenario 3 0 0.012 8.696 NA 724.690TABLE C-15 

### Superstition Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 24 0.772 0.820 0.034 1.063

Scenario 3 vs. Scenario 1 0 0.028 1.358 NA 48.508

Scenario 5 vs. Scenario 3 1 0.029 8.696 8.696 299.871

TABLE C-16 

### Galiuro Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 10 0.366 0.820 0.082 2.241

Scenario 2 vs. Scenario 1 0 0.015 2.358 NA 157.221

Scenario 5 vs. Scenario 2 0 0.007 8.696 NA 1242.323

TABLE C-17 

### Mesa Verde Wilderness NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 24 0.503 0.820 0.034 1.631

Scenario 3 vs. Scenario 1 0 0.030 1.358 NA 45.274

Scenario 5 vs. Scenario 3 0 0.033 8.696 NA 263.523TABLE C-18 

### Saguaro NP NO x 

### Control Scenario Incremental Analysis Data Cholla 4

Options Compared Incremental Reduction in Days Above 0.5 ∆ dV (Days) Incremental ∆ dV Reductions (dV) Incremental Cost (Million$) Incremental Cost Effectiveness (Million$/Days) Incremental Cost Effectiveness (Million$/dV)

Scenario 1 vs. Baseline 9 0.296 0.820 0.091 2.772

Scenario 3 vs. Scenario 1 0 0.008 1.358 NA 169.778

Scenario 5 vs. Scenario 3 0 0.010 8.696 NA 869.624FIGURE C-10NOxControl Scenarios - Least Cost Envelope Gila Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Gila WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-11NOxControl Scenarios - Least Cost Envelope Gila Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Gila WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-12NOxControl Scenarios - Least Cost Envelope Mount Baldy Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Mount Baldy WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-13NOxControl Scenarios - Least Cost Envelope Mount Baldy Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Mount Baldy WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-14NOxControl Scenarios - Least Cost Envelope Sierra Ancha Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Sierra Ancha WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25 30 35 40Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-15NOxControl Scenarios - Least Cost Envelope Sierra Ancha Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Sierra Ancha WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-16NOxControl Scenarios - Least Cost EnvelopeMazatzal Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Mazatzal WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25 30 35Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-17NOxControl Scenarios - Least Cost Envelope Mazatzal Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Mazatzal WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-18NOxControl Scenarios - Least Cost EnvelopePine Mountain Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Pine Mountain WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-19NOxControl Scenarios - Least Cost Envelope Pine Mountain Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Pine Mountain WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-20NOxControl Scenarios - Least Cost Envelope Superstition Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Superstition WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25 30Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-21NOxControl Scenarios - Least Cost Envelope Superstition Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Superstition WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-22NOxControl Scenarios - Least Cost Envelope Galiuro Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Galiuro WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 2 4 6 8 10 12Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-23NOxControl Scenarios - Least Cost Envelope Galiuro Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Galiuro WildernessBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-24NOxControl Scenarios - Least Cost Envelope Mesa Verde Wilderness - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Mesa Verde National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 5 10 15 20 25 30Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-25NOxControl Scenarios - Least Cost Envelope Mesa Verde Wilderness - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Mesa Verde National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70Reduction in Delta dv

Total Annualized Cost (MM $)FIGURE C-26NOxControl Scenarios - Least Cost Envelope Saguaro NP - Days ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Saguaro National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00 1 2 3 4 5 6 7 8 9 10Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)FIGURE C-27NOxControl Scenarios - Least Cost Envelope Saguaro NP - 98thPercentile ReductionCholla 4NOx Control ScenariosLeast Cost EnvelopeCholla Unit 4Saguaro National ParkBaselineScenario 1Scenario 2Scenario 3Scenario 4Scenario 50.02.04.06.08.010.012.014.00.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35Reduction in Delta dv

Total Annualized Cost (MM $)