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 85282-3397
ES-1

Executive Summary 

Background 

In response to the Regional Haze Rule and 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 generating 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 electric 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 following criteria pollutants: oxides of nitrogen 

(NOx) sulfur dioxide (SO2) and particulate matter less than 10 microns in aerodynamic diameter 

(PM10). BART emissions limits must be achieved within five years after the State Implementation 

Plan (SIP) is approved by the EPA. A compliance date of 2013 was assumed for this analysis. 

In completing the BART analysis, technology alternatives were investigated and potential 

reductions in NOx, SO2, and PM10 emissions rates were identified. The following technology 

alternatives were investigated, listed below by pollutant: 

NOx emission controls: 

• New/modified state-of-the-art low-NOx 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) 

SO2 emission controls: 

• Enhancements to the existing wet lime scrubber system 

PM10 emission controls: 

• Electrostatic Precipitator (ESP) 

• Fabric Filter 

BART Engineering Analysis 

The specific components of a BART engineering analysis are identified in the Code of Federal 

Regulations (CFR) at 40 CFR 51 Appendix Y, Section IV. The evaluation must include: 

1. The identification of available, technically feasible, retrofit control options 
BART 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 environmental impacts of compliance 

6. The degree of visibility improvement that may reasonably be anticipated from the use of 

BART 

These components are incorporated into the BART 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, technically feasible, retrofit 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 Effectiveness 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 environmental impacts of compliance 

Step 5 – Evaluate Visibility Impacts 

• The degree of visibility improvement that may reasonably be anticipated from the use of 

BART 

Separate analyses have been conducted for NOx, SO2, and PM10 emissions. All costs included in 

the BART analyses are in 2007 dollars, and costs have not been escalated to the assumed 2013 

BART implementation date. 

Coal Characteristics 

Sources of coal burned at Cholla 4 are McKinley, Lee Ranch, and El Segundo. The McKinley 

and the Lee Ranch mines are in western New Mexico, near the towns of Gallup and Grants 

respectively. The El Segundo mine is located adjacent to the Lee Ranch mine. 

Some of these coals may be classified as sub-bituminous, while demonstrating characteristics of 

bituminous coal which influences the level of NOx 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 characteristics of bituminous and sub-bituminous 

ES-2
BART ANALYSIS FOR CHOLLA UNIT 4 

coals planned to be burned at Cholla 4, and has evaluated the effect of these qualities on NOx 

formation and achievable emission rates. 

Recommendations 

NOx 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 projected significant reduction in NOx emissions, reasonable 

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

SO2 Emission Control 

Based on the analysis conducted, upgrade of the wet FGD system can achieve the BART 

emission level of 0.15 lb/MMBtu for SO2 emission control. 

PM10 Emission Control 

Based on the analysis conducted, the installation of a fabric filter can achieve the BART 

emission level of 0.015 lb/MMBtu for PM10 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 areas potentially affected are located more than 

50 kilometers (with the exception of Petrified Forest National Park), but less than 

300 kilometers, from the Cholla Power Plant. Petrified Forest National Park is approximately 

39 kilometers from the Cholla Power Plant). The 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 NOx, SO2, and PM10 emissions, post control 

visibility modeling scenarios were developed to cover the range of effectiveness for combining 

the individual NOx and SO2 control technologies under evaluation. These modeling scenarios, 

and the controls assumed, are as follows: 

ES-3
BART ANALYSIS FOR CHOLLA UNIT 4 

ES-4

• Scenario 1: New LNBs with SOFA system, upgraded wet lime FGD system, and fabric 

filter. 

• Scenario 2: New LNBs with SOFA system and ROFA, upgraded wet lime FGD system, and 

fabric filter. 

• Scenario 3: New LNBs with SOFA system and ROFA and Rotamix, upgraded wet lime 

FGD system, and fabric filter. 

• Scenario 4: New LNBs with SOFA system and SNCR, 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 Analysis Methodology as an accepted methodology 

for selecting the most reasonable, cost-effective controls. Incremental cost-effectiveness 

comparisons focus on annualized cost and emission reduction differences between dominant 

alternatives. The dominant set of control alternatives 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 alternatives identified in the BART analysis. 

To evaluate the impacts of the modeled control scenarios on the thirteen Class I areas, the total 

annualized cost, cost per deciview (dV) reduction, 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 percentile delta-deciview (ΔdV) 

reduction. 

Results of the least-cost analysis for the various NOx emission control scenarios confirm the 

selection of Scenario 1 (New LNB with SOFA), based on incremental cost and visibility 

improvements. All other NOx control scenarios are excluded on the basis of cost effectiveness. 

Just-Noticeable Differences in Atmospheric Haze 

Studies have been conducted that demonstrate only dV differences 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 person (Henry, 2002). Therefore, the modeling analysis results 

indicate that only minimal, if any, observable visibility improvements at the Class I areas studied 

would be expected under any of the control scenarios. Thus, the results indicate that even though 

APS will be spending many millions of dollars at this 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 ............................................................................................................. ES-1

Background ................................................................................................................ ES-1 

BART Engineering Analysis ..................................................................................... ES-1 

Coal Characteristics ................................................................................................... ES-2 

Recommendations...................................................................................................... ES-3 

NOx Emission Control ................................................................................... ES-3 

SO2 Emission Control .................................................................................... ES-3 

PM10 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 NOx Analysis.............................................................................3-2 

3.1.2 BART SO2 Analysis............................................................................3-13 

3.1.3 BART PM10 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 

ii
CONTENTS (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 NOx Control Technology Emission Rate Ranking 

Table 3-3 NOx Control Cost Comparison 

Table 3-4 SO2 Control Technology Emission Rate Ranking 

Table 3-5 SO2 Wet Lime FGD Costs 

Table 3-6 PM10 Control Technology Emission Rates 

Table 3-7 PM10 Fabric Filter Costs 

Table 4-1 User-Specified CALMET Options 

Table 4-2 Average Natural Levels of Aerosol Components 

Table 5-1 Emission Control Scenarios 

Table 5-2 Ranking of NOx Control Scenarios by Cost 

Table 5-3 NOx Control Scenario Results for Capitol Reef NP 

Table 5-4 NOx Control Scenario Results for Grand Canyon NP 

Table 5-5 NOx Control Scenario Results for Petrified Forest NP 

Table 5-6 NOx Control Scenario Results for Sycamore Canyon WA 

Table 5-7 Capitol Reef NP NOx Control Scenario Incremental Analysis Data 

Table 5-8 Grand Canyon NP NOx Control Scenario Incremental Analysis Data 

Table 5-9 Petrified Forest NP NOx Control Scenario Incremental Analysis Data 

Table 5-10 Sycamore Canyon WA NOx Control Scenario Incremental Analysis Data 

List of Figures 

Figure 3-1 Illustration of the Effect of Agglomeration on the Speed of Coal Combustion 

Figure 3-2 First Year Control Cost for NOX Air Pollution Control Options 

Figure 4-1 CALPUFF and CALMET Modeling Domains 

Figure 5-1 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at 

Capitol Reef NP 

Figure 5-2 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at 

Grand Canyon NP 

Figure 5-3 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at 

Petrified Forest NP 

iii
CONTENTS (CONTINUED) 

iv

Figure 5-4 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at 

Sycamore Canyon WA 

Figure 5-5 NOx Control Scenarios – Least-Cost Envelope Capitol Reef NP—Days 

Reduction 

Figure 5-6 NOx Control Scenarios—Least-Cost Envelope Capitol Reef NP—98th Percentile 

Reduction 

Figure 5-7 NOx Control Scenarios—Least-Cost Envelope Grand Canyon NP—Days 

Reduction 

Figure 5-8 NOx Control Scenarios—Least-Cost Envelope Grand Canyon NP—98th

Percentile Reduction 

Figure 5-9 NOx Control Scenarios—Least-Cost Envelope Petrified Forest NP—Days 

Reduction 

Figure 5-10 NOx Control Scenarios—Least-Cost Envelope Petrified Forest NP—98th

Percentile Reduction 

Figure 5-11 NOx Control Scenarios—Least-Cost Envelope Sycamore Canyon WA—Days 

Reduction 

Figure 5-12 NOx Control Scenarios—Least-Cost Envelope Sycamore Canyon WA—98th

Percentile Reduction 
Acronyms and Abbreviations 

ADEQ Arizona Department of Environmental Quality 

APS Arizona Public Service 

BACT Best Available Control Technology 

BART Best Available Retrofit Technology 

CALDESK Program to display data and results 

CALMET Meteorological data preprocessing program for CALPUFF 

CALPOST Post-processing program for calculating visibility impacts 

CALPUFF Gaussian puff dispersion model 

COFA close-coupled over-fire air 

dV deciview 

ΔdV delta deciview, change in deciview 

ESP electrostatic precipitator 

EPA United States Environmental Protection Agency 

Fuel NOx 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-NOx burner 

LOI loss on ignition 

MMBtu Million British Thermal Units 

MM5 Mesoscale Meteorological Model, Version 5 

MW megawatts 

N2 nitrogen 

NM National Monument 

NO nitric oxide 

NOx oxides of nitrogen 

NP National Park 

v
ACRONYMS (CONTINUED) 

vi

NWS National Weather Service 

OFA over-fire air 

PM10 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-catalytic reduction system 

SOFA separated over-fire air 

SO2 sulfur dioxide 

SO3 sulfur trioxide 

Thermal NOx 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 visibility improvement in national 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 visibility 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, Congress 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 wilderness areas across the country classified as mandatory Class I 

areas. These regulations include requirements for States to establish goals for improving 

visibility in national parks and wilderness areas and to develop long-term strategies for 

reducing emissions of air pollutants that cause visibility impairment. 

One of the principal elements of the visibility 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 sources of air pollutants that are reasonably 

anticipated to contribute to visibility impairment in a Class I area); 

• An analysis of the emission reductions and changes in visibility that would result from 

“best retrofit” control levels on sources subject to BART; and 

• The BART emission limits for each subject source, 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, the 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 anticipated from the use of such technology (EPA, 

1999). 

In July 2005, EPA released specific 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, including Arizona, were required to submit a Regional 

Haze Rule State Implementation Plan (SIP) amendment that addresses BART implementation 

by December, 2007. In this plan amendment, States were to identify the facilities that will have 

to reduce emissions under BART and then set BART emissions limits for those facilities, 

and/or identify any alternative plan for reducing visibility impairing pollutants that would 

achieve greater reductions than those realized from BART emissions limits (EPA, 2005). 

1-1
BART ANALYSIS FOR CHOLLA UNIT 4 

1-2

Using information from the Western Regional 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 those sources to complete BART analyses to 

identify potential reductions for emissions of sulfur dioxide (SO2), nitrogen oxides (NOx) and 

particulate matter less than 10 microns in aerodynamic diameter (PM10) 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 December 2007. At this time, it is expected that 

Arizona’s SIP, when submitted, will address reduction of SO2 emissions at BART sources 

through an alternative measure in the form of a four-state backstop cap-and-trade program. 

Reduction of NOx and PM10 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 NOx, SO2, and PM10. 

Section 2.0 of this report provides a description of the present unit operation, including a 

discussion of coal sources and characteristics. The BART Engineering Analysis is provided in 

Section 3.0 by pollutant type. Section 4.0 provides 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 economic analysis (Appendix A) and the BART 

modeling protocol (Appendix B). 
2-1

2.0 Present Unit Operation 

The Cholla Power Plant consists of four electric 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 generating unit equipped with a tangentially-fired, dry bottom, boiler 

manufactured by Combustion Engineering. Current emissions control equipment includes a hot 

side electrostatic precipitator for particulate matter control. Close-coupled overfire air (COFA) 

is utilized for NOx control. One scrubber tower is in-service which treats approximately 36% 

of the flue gas. 

Cholla 4 was placed in service in 1981, with a projected remaining life 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 estimates the technologies will operate until 2033. 

Table 2-1 lists additional unit information and 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 106

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 NOx Controls COFA 

 NOx Emission Rate (lb/MMBtu) 5 0.415 

Current SO2 Controls Slip stream absorber 

 SO2 Emission Rate (lb/MMBtu) 5 0.734 

Current PM10 Controls Hot-side Electrostatic Precipitator 

PM10 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 average emissions during 2001-2003, provided by APS. 
BART ANALYSIS FOR CHOLLA UNIT 3 

2-2

For Table 2-1 above, emissions for the years 2001 to 2003 were analyzed to obtain the average 

Cholla 4 emissions. 

In the July 2005 EPA BART guidelines, EPA prescribed presumptive BART limits to be 

achieved at BART-eligible coal fired power plants with a total generating 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 NOx limit for dry bottom tangentially-fired boilers burning subbituminous coal is 0.15 lb/MMBtu, and the BART presumptive NOx 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 coal supply to burn solely El Segundo coal by the 

end of 2008. Burning El Segundo coal may result in uncontrolled SO2 emissions as high as 

2.5 lb/MMBtu. 

APS is planning to install new LNB with SOFA, a new SO2 absorber, and a new fabric filter in 

2008. 
TABLE 2-2 Coal Sources and Characteristics Cholla 4 

Ultimate Analysis (% dry basis) 

Mines Moist. % Ash % 

Volatile 

Matter % 

Fixed 

Carbon % Btu/lb Sulfur % Carbon Hydrogen Nitrogen Ch

McKinley Mine, NM 

13.90 14.28 32.45 39.90 9911 0.47 65.92 4.58 1.11 

Lee Ranch Mine, NM 15.30 17.80 33.50 33.40 9250 0.90 61.70 4.50 1.00 El Segundo Mine, NM 17.60 16.80 31.70 33.90 9215 1.10 62.13 4.62 1.00 
3.0 BART Engineering Analysis 

3.1 BART Process 

The specific components in a BART engineering analysis are identified in the Code of Federal 

Regulations (CFR) at 40 CFR 51 Appendix Y, Section IV. The evaluation must include: 

1. The identification of available, technically feasible, retrofit 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 environmental impacts of compliance, and 

6. The degree of visibility improvement which may reasonably be anticipated from the use of 

BART 

These components are incorporated into the BART 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, technically feasible, retrofit 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 Effectiveness 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 environmental impacts of compliance 

Step 5 – Evaluate Visibility Impacts 

• The degree of visibility improvement which may reasonably be anticipated 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, and 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. 

3-1
BART ANALYSIS FOR CHOLLA UNIT 4 

Separate analyses have been conducted for NOx, SO2, and PM10 emissions. All costs included in 

the BART analysis are in 2007 dollars, and costs 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 process, cost and expected emission information was 

developed for NOx, SO2, and PM10. This information is assembled 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 are several reasons for the wide range of cost 

estimates included in the BART analysis. This variability 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. Material 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 information. 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 actual project request for 

proposals. Also, variance in expected emissions can be dependent upon the pollutant under 

consideration; i.e., particulate emissions can generally be more accurately predicted than NOx

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 NOx Analysis 

NOx formation in coal-fired boilers is a complex process that is dependent on a number of 

variables, including operating conditions, equipment design, and coal characteristics. 

3.1.1.1 Formation of NOx

During coal combustion, NOx is formed in three different ways. The dominant source of NOx

formation is the oxidation of fuel-bound nitrogen (fuel NOx). During combustion, part of the 

fuel-bound nitrogen is released from the coal with the volatile matter, and part is retained in the 

solid portion (char). The nitrogen chemically bound in the coal is partially oxidized to nitrogen 

oxides (NO and NO2) and partially reduced to molecular nitrogen (N2). A smaller part of NOx

formation is due to high temperature fixation of atmospheric nitrogen in the combustion air 

(thermal NOx). A very small amount of NOx is called “prompt” NOx. Prompt NOx results from 

an interaction of hydrocarbon radicals, nitrogen, and oxygen. 

In a conventional pulverized coal burner, air is introduced with turbulence to promote good 

mixing of fuel and air, which provides 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 NOx. 

3-2
BART ANALYSIS FOR CHOLLA UNIT 4 

Coal characteristics directly and significantly affect NOx 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 NOx emissions than higher rank bituminous coals, due to 

their higher reactivity and lower nitrogen content. The fixed carbon to volatile matter ratio (fuel 

ratio), coal oxygen content, and rank are good relative indices of the reactivity of a coal. Lower 

rank coals release more organically bound nitrogen earlier in the combustion process than do 

higher rank bituminous coals. When used with low NOx burners, sub-bituminous coals create a 

longer time for the kinetics to promote more stable molecular nitrogen, and hence result in lower 

NOx emissions. 

The primary basis for coal rank classification of lower rank bituminous and all sub-bituminous 

coals by ASTM is gross calorific value determined on a moist mineral-matter-free basis. In the 

cases of both high volatile bituminous “C” and sub-bituminous “A” classifications, the gross 

calorific values on a moist mineral-matter-free basis 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 characteristic that classifies the coals as bituminous 

rather than sub-bituminous; that is, they are “agglomerating” as compared to “nonagglomerating”. Agglomerating as applied to coal is “the property of softening when it is heated 

to above about 400° C in a non-oxidizing atmosphere, and then appearing as a coherent mass 

after cooling to room temperature.” Because the agglomerating property of coals is the result of 

particles transforming into a plastic or semi-liquid state when heated, it reflects a change in 

surface area of the particle. Thus, with the application of heat, agglomerating 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, resulting in more favorable combustion conditions. This non-agglomerating 

property assists in making sub-bituminous coals more amenable to controlling NOx, by allowing 

less air to be introduced during the initial ignition portion of the combustion process. Since 

Cholla 4 may burn a blend of bituminous and marginally ranked sub-bituminous coals, NOx

emissions from combustion of these blended coals will vary depending on the resultant 

combined coal characteristics. 

3-3
BART 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 coals which are currently being burned on Cholla 4 

and a “typical” PRB sub-bituminous coal for comparison. 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 for this report did not indicate whether the coals 

were agglomerating or non-agglomerating. 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 moist mineral-matter-free values of 11,466 and 

11,279, respectively, which require the agglomerating determination in order to classify them. 

3-4
BART ANALYSIS FOR CHOLLA UNIT 4 

As shown in Table 3-1, the bituminous coals generally 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 NOx 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 NOx emissions, and they are also more conducive to reduction of 

NOx emissions through the use of combustion control measures, such as low NOx burners and 

over-fire air (OFA). These characteristics indicate that higher NOx formation is likely with 

bituminous rather than sub-bituminous coals. 

Coal quality characteristics also impact the design and operation of the boiler and associated 

auxiliary equipment. Minor changes in quality can sometimes be accommodated through 

operational adjustments or changes to equipment. It is important to note, however, that consistent 

variations in quality or assumptions of “average” quality for performance projections can be 

problematic. This is particularly troublesome when dealing with performance issues that are very 

sensitive to both coal quality and combustion conditions, such as NOx formation. 

Several of the coal quality characteristics and their effect on NOx formation have been previously 

discussed. There are additional considerations that illustrate the complexity of achieving and 

maintaining consistent low NOx 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 turbulence. These parameters 

along with a “design” coal are taken into consideration when designing a boiler and associated 

firing equipment such as fans, burners, and pulverizers. If a performance requirement such as 

NOx emission limits is subsequently changed, conflicts with other performance issues can result. 

Cholla 4 is located at an altitude 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 square 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 NOx using low NOx 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 NOx emissions is the fineness of the coal entering the 

burners. Fineness is influenced by the Hardgrove Grindability Index (HGI) of the coal. Finer coal 

particles promote release of volatiles and assist char burnout due to more surface area exposed to 

air. NOx 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 difficult to grind and can contribute to higher NOx

levels. In addition, coal fineness can deteriorate over time periods between pulverizer 

maintenance and service as pulverizer grinding surfaces wear. 

In summary, when all the factors of agglomeration versus non-agglomeration, nitrogen and 

oxygen content of the coals, and the grindability index are taken into account, this analysis 

demonstrates that, for the wide variability of coal supply to be utilized at Cholla 4, the more 

appropriate presumptive BART limit is 0.28 lb/MMBtu. This limit is referred to here only as a 

3-5
BART ANALYSIS FOR CHOLLA UNIT 4 

3-6

point of reference, and CH2M HILL recommends that this value be used in evaluation of the 

effectiveness of BART controls applied to Cholla 4. The BART analysis for NOx emissions from 

Cholla 4 is further described below. 

3.1.1.2 Step 1: Identify All Available Retrofit Control Technologies 

The first step of the BART process is to evaluate NOx control technologies with practical 

potential for application to Cholla 4, including those control technologies identified as Best 

Available Control Technology (BACT) or lowest 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 NOx emissions are currently controlled 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 NOx control technology options were considered: 

• New/modified state-of-the-art low-NOx burners (LNB) with SOFA 

• Rotating Opposed Fire Air (ROFA) 

• Selective non-catalytic reduction system (Rotamix & SNCR) 

• Selective catalytic reduction (SCR) system 

• Neural Network/Boiler Combustion Control (Neural Net) 

3.1.1.3 Step 2: Eliminate Technically Infeasible Options 

For Cholla 4, a tangentially-fired boiler burning a blend of bituminous and sub-bituminous rank 

coals, technical feasibility will primarily be determined by physical constraints, boiler 

configuration, and on the ability to achieve the regulatory presumptive limit of 0.28 lb 

NOx/MMBtu. Cholla 4 currently has an average - NOx emission rate of 0.415 lb/MMBtu. 

For this BART analysis, information pertaining to LNBs, OFA, SNCR, and SCR were based on 

a combination of vendor information and internal 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 BART analysis, along with projected NOx 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 

NOx 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/ SOFA3 Foster Wheeler 0.22 

ROFA4 Mobotec 0.16 

ROFA w/Rotamix4 Mobotec 0.12 

New LNB w/SOFA & SNCR2 Foster Wheeler, CH2M HILL 0.17 

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

Neural Net Controls1 NeuCo 0.30 

1 - NeuCo provides no guarantees; derived using 15% reduction from average NOx emissions 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 Effectiveness of Remaining Control Technologies 

Preliminary vendor proposals, such as those used to support portions of this BART analysis, may 

be technically feasible and provide expected or guaranteed emission rates; however, they include 

inherent uncertainties. These proposals are usually prepared in a limited time frame, may be 

based on incomplete information, may contain over-optimistic conclusions, and are non-binding. 

Therefore, emission rate values obtained in such preliminary proposals must be qualified, and it 

must be recognized that contractual guarantees are established only after more detailed analysis 

has been completed. 

Level of Confidence for Vendor Post-Control NOx Emissions Estimates. In order to determine the 

level of NOx emissions needed to consistently achieve compliance with an established goal, a 

review of typical NOx emissions from coal-fired generating units was completed. As a result of 

this review, it was noted that NOx emissions can vary significantly around an average emissions 

level. This variance can be attributed to many reasons, including coal characteristics, 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 NOx emissions value from vendor. 

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

3. Review and evaluate unit physical and operational characteristics and restrictions. The fewer 

variations there are in operations, coal supply, etc., the more predictable and less variant the 

NOx emissions are. 

3-7
BART ANALYSIS FOR CHOLLA UNIT 4 

4. For each technology expected value, there is a corresponding potential for actual NOx

emissions to vary from this expected value. From the vendor information presented, along 

with anticipated unit operational data, an adjustment to the expected value can be made. 

The following subsections describe the NOx control technologies and the control effectiveness 

evaluated in this BART analysis. 

New LNBs with OFA System. The mechanism used to lower NOx with low NOx 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 NOx. Fuel-rich conditions favor 

the conversion of fuel nitrogen to N2 instead of NOx. Additional air (OFA) is then introduced 

upstream or downstream in a lower temperature 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 would result in an expected NOx

emission rate of 0.22 lb/MMBtu. This emission rate represents a significant reduction from the 

current NOx emission rate, and is below the EPA presumptive NOx emission rate for bituminous 

coal of 0.28 lb/MMBtu. 

ROFA. Mobotec markets ROFA as an improved second 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 and 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 flames 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 NOx emissions. 

A typical ROFA installation will have a booster fan(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 NOx emission rate of 0.16 

lb/MMBtu. The operation of existing burners and 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 modification may be necessary. Computational fluid 

dynamics modeling will determine the quantity and location of new ROFA ports. Mobotec does 

not typically provide installation services because they believe that the Owner can more cost 

effectively contract for these services, however they did provide a budgetary price for installation 

labor. Mobotec provides one onsite construction supervisor during 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 

NOx to nitrogen and water. NOx reductions of up to 40 to 60 percent have been achieved, 

although 15 to 30 percent is more realistic for most applications. SNCR is typically applied on 

smaller units. Adequate reagent distribution in the furnaces of large units can be problematic. 

Reagent utilization, which is a measure of the efficiency with which the reagent reduces NOx, 

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

3-8
BART ANALYSIS FOR CHOLLA UNIT 4 

conditions, and allowable ammonia slip. With low reagent utilization, low temperatures, or 

inadequate mixing, ammonia slip occurs, allowing unreacted ammonia to create problems 

downstream. The ammonia may render fly ash unsaleable, and also react with sulfur to form 

ammonium bisulphate which can foul heat exchanger surfaces and/or create a visible stack 

plume. Reagent utilization can have a significant impact on economics, with higher levels of 

NOx reduction generally resulting in lower reagent utilization and higher operating cost. 

Reductions from higher baseline inlet NOx concentrations are lower in cost per ton, but result in 

higher operating costs, due to greater reagent consumption. 

Mobotec also provided information for their Rotamix SNCR system for Cholla 4. The expected 

NOx 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 

NOx 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 injected into the flue-gas stream, where it reduces 

NOx to nitrogen and water. Unlike the high temperatures required for SNCR, in SCR the reaction 

takes place on the surface of a vanadium/titanium-based 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 NOx emissions. The most common type of SCR is the high-dust configuration, where the 

catalyst is located downstream from the boiler economizer 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. For Cholla 4 the SCR would be installed before the 

air heater a high-particulate location. 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 catalyst 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 NOx emission levels as much as 

possible through combustion modifications, in order 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 boiler control system. Information regarding neural net controls has 

been previously received from NeuCo, Inc. While NeuCo offers several neural net products, 

CombustionOpt and SootOpt provide the potential for NOx reduction. NeuCo stated these 

products can be utilized on most control systems, and can be effective even in conjunction with 

other NOx reduction technologies. 

NeuCo predicts that CombustionOpt can reduce NOx by 15%, and SootOpt can provide an 

additional 5 to 10%. Since NeuCo does not offer guarantees on this projected emission reduction, 

a nominal reduction of 15% was assumed for evaluation 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. 

3-9
BART ANALYSIS FOR CHOLLA UNIT 4 

Since NeuCo does not guarantee NOx reduction, the estimated emission reduction levels 

provided can not be considered as reliable projections. Therefore, neural net should be 

considered as a supplementary or “polishing” technology, but not on a “stand-alone” basis. 

3.1.1.5 Step 4: Evaluate Impacts and Document the Results 

This step involves the consideration 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. Installation of new LNBs and SOFA system are not expected to significantly 

impact the boiler efficiency or forced draft fan power usage. Therefore, these technologies are 

not expected to have significant energy impacts. 

The Mobotec ROFA system requires installation and operation of one 4,800 Hp ROFA fan 

(3,579 kW total). Fuel Tech provided an estimate of 130 kW of additional auxiliary power, and 

the same estimate was used for Rotamix. SCR retrofit 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 increase significantly to an estimated 0.15 lb/MMBtu (based on a 

30-day average). APS completed a CO BACT review for this anticipated increase in CO 

emissions. 

Mobotec generally predicts that CO emissions, and unburned carbon in the ash commonly 

referred to as LOI (loss on ignition), 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 create a visible stack plume, which may negate other visibility 

improvements. Other environmental impacts involve the potential public and employee safety 

hazard associated with the storage of ammonia, especially anhydrous ammonia, and the 

transportation of the ammonia to the power plant site. 

Economic Impacts. A comparison of the technologies on the basis of costs, design control 

efficiencies, and tons of NOx removed is summarized in Table 3-3, and the first year control 

costs are shown in Figure 3-2. The complete Economic Analysis is contained in Appendix A. 

TABLE 3-3 

NOx Control Cost Comparison 

Cholla 4 

Factor 

LNB 

w/SOFA1 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 Million $0.8 Million $2.5 Million 

3-10
BART ANALYSIS FOR CHOLLA UNIT 4 

3-11

TABLE 3-3 

NOx Control Cost Comparison 

Cholla 4 

Factor 

LNB 

w/SOFA1 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 Million $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 

NOx Design Control Efficiency 47.0% 61.4% 71.1% 59.0% 83.1% 

NOx Removed per Year (Tons) 3,390 4,433 5,129 4,259 5,998 

First Year Average Control Cost 

($/Ton of NOx Removed) 242 717 885 670 2,206 

Incremental Control Cost 

($/Ton of NOx Removed) 242 1,878 1,953 2,337 10,004 

1 – Since installation of LNB is part of the planned APS environmental 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 based on its significant reduction in NOx emissions, reasonable 

control cost, and no additional power requirements or environmental impacts. New LNB 

w/SOFA meets the target EPA 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 Analysis) and 5.0 (Preliminary Assessment and 

Recommendations). 
FIGURE 3-2 First Year Control Cost for NOX Air Pollution Control Options 

Cholla 4 

0

2,0004,0006,0008,000

10,00012,000

LNB w/SOFA LNB w/SOFA & SNCR ROFA ROFA w/Rotamix 

Air Pollution Control Option

Control Cost ($/Ton NOx Removed)

First Year Average Control Cost First Year Incremental Control Cost
BART ANALYSIS FOR CHOLLA UNIT 4 

3.1.2 BART SO2 Analysis 

SO2 forms in the boiler during the combustion process from the oxidation of the sulfur present 

in the coal, and is primarily dependent on coal sulfur content. The BART analysis for SO2

emissions on Cholla 4 is described below. 

3.1.2.1 Step 1: Identify All Available Retrofit Control Technologies 

A broad range of information sources were reviewed, in an effort to identify potentially 

applicable emission control technologies for SO2 at Cholla 4. This included control 

technologies identified as BACT or LAER by permitting agencies across the United States. 

The following potential SO2 control technology option was evaluated: 

• Wet lime FGD 

Cholla 4 currently operates a partial scrubber for SO2 removal, which treats approximately 36% 

of the flue gas. For purposes of this BART analysis an SO2 emissions rate of 0.734 lb/MMBtu 

will be assumed. A new wet lime scrubber vessel designed to treat all 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 SO2 emissions, or 0.15 lb/MMBtu. Because Cholla 4 is currently operating with an 

SO2 emissions rate of approximately 0.734 lb/MMBtu, since only 36% of the flue gas is being 

scrubbed. A significant reduction of SO2 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 capable of the highest rate of SO2 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 SO2-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 scrubbers are capable of very high SO2 removal efficiencies, 

with a 95 percent removal efficiency assumed for this BART analysis. Based on the 

uncontrolled SO2 emissions rate of 2.5 lb/MMBtu, a new wet lime/limestone scrubber can 

achieve the presumptive limit for SO2 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 SO2 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 SO2 on a 

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

3-13
BART ANALYSIS FOR CHOLLA UNIT 4 

3-14

TABLE 3-4 

SO2 Control Technology Emission Rate Ranking 

Cholla 4 

Technology 

Projected 

Emission Rate 

(lb/MMBtu) 

Estimated SO2

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 consideration 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 expected 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 waste 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 

SO2 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 SO2 Design Control Efficiency 79.6% 

Incremental Tons SO2 Removed per Year 10,153 

First Year Average Control Cost 

($/Ton of SO2 Removed) 942 

Incremental Control Cost 

($/Ton of SO2 Removed) 942 
BART 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 SO2 control alternative for Cholla 4. This 

recommendation is based on significant reduction in SO2 emissions (meeting the presumptive 

limit of 0.15 lb/MMBtu) and reasonable control costs. 

3.1.2.5 Step 5: Evaluate Visibility Impacts 

Please see Sections 4.0 (BART Modeling Analysis) and 5.0 (Preliminary Assessment and 

Recommendations). 

3.1.3 BART PM10 Analysis 

Cholla 4 is currently equipped with a hot-side electrostatic precipitator for primary PM10

control. Fabric filters will be installed in 2008. Current PM10 emissions on Cholla 4 are 

approximately 0.024 lb/MMBtu, and the fabric filter will result in emissions estimated at 

0.015 lb/MMBtu. 

The BART analysis for PM10 emissions at Cholla 4 is described below. For the modeling 

analysis in Sections 4.0 and 5.0, PM10 and PM2.5 are used as indicators of PM emissions. 

3.1.3.1 Step 1: Identify All Available Retrofit Control Technologies 

Two retrofit control technologies 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 precipitator may be capable of achieving a 

0.015 lb/MMBtu target, operational variations may not result in consistent compliance with this 

emissions rate. Any ESP upgrade or gas conditioning would also result in a PM10 removal 

system which is less reliable in achieving the 0.015 lb/MMBtu than a fabric filter. Therefore 

this option is not considered 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 feet 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 service with this replacement fabric filter 

option. 

3.1.3.3 Step 3: Evaluate Control Effectiveness 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 replacement fabric filter, or new ESP, PM10

emissions are expected to be approximately 0.015 lb/MMBtu. 

The PM10 control technology emission rates are summarized in Table 3-5, with the PM10

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

3-15
BART ANALYSIS FOR CHOLLA UNIT 4 

TABLE 3-6 

PM10 Control Technology Emission Rates 

Cholla 4 

Control Technology Expected PM10 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 consideration 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 additional pressure drop from the fabric 

filter replacement and associated ductwork. Therefore, fan upgrades may be required to 

overcome the additional pressure drop. An estimated 6 to 8 inches H2O additional total system 

pressure drop for the replacement fabric filter may be experienced. 

Environmental Impacts. There are no negative environmental impacts from the addition of a 

replacement ESP or fabric filter. 

Economic Impacts. A listing of the costs and PM10 removed for a fabric filter is shown in 

Table 3-6, with a graph of first year costs shown 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 PM10 reduction comparable to a fabric filter, costs for an 

ESP are not shown. 

TABLE 3-7 

PM10 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 

3-16
BART ANALYSIS FOR CHOLLA UNIT 4 

3-17

Preliminary BART Selection. The 4-step evaluation indicates installation of a replacement fabric 

filter represents BART for Cholla 4. This option has a high control cost, but APS already plans 

to install fabric filters in 2008. Based on this plan and the knowledge that a fabric filter will 

result in a significant reduction in PM emissions 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 Analysis) and 5.0 (Preliminary Assessment and 

Recommendations). 
4.0 BART Modeling Analysis 

4.1 Introduction 

This section presents the dispersion modeling methods and results for estimating the degree of 

visibility improvement from BART control 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 proposed deviations from that methodology are 

documented in this report. 

4.2 Model Selection 

CH2M HILL used the EPA-required CALPUFF modeling system to assess the visibility impacts 

at Class I areas. CALPUFF is a multi-layer, multi-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 available for predicting a single source’s contribution to visibility 

impairment and is currently the only EPA-approved model for use in estimating single source 

pollutant concentrations resulting from the long range transport of pollutants.” 

The CALPUFF modeling system includes the meteorological data preprocessing program for 

CALPUFF (CALMET) with algorithms for chemical transformation and deposition, and a post 

processor capable of calculating concentrations, visibility impacts, and deposition (CALPOST). 

The CALPUFF modeling system was applied 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 EPA-approved versions (Version 5). The FLM and 

others have noted that the EPA-approved Version 5 contained errors and that a newer version 

should be used. Consequently, it was decided to use the latest (as of April 2006) version of the 

CALPUFF modeling system (available 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 test case results provided with the source code 

(TRC, 2007), and the difference between the results was 0.03 percent. 

4-1
BART 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 those established for the Arizona 

BART modeling in WRAP (2006). In addition, 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 central meridian near the center of the domain. 

CH2M HILL used the CALMET model to generate 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-kilometers buffer around all Class I areas 

within 300 kilometers of the Cholla Power Plant. Grid resolution for this domain was 

4 kilometers. Figure 4-1 shows the extent of the modeling domain. 

4-2
BART ANALYSIS FOR CHOLLA UNIT 4 

FIGURE 4-1 

CALPUFF and CALMET Modeling Domains 

4-3
BART 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, 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), ZIMAX were set to 4,500 meters based on the Colorado 

Department of Health and Environment (CDPHE) analyses of soundings for summer ozone 

events 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 3,000 meters during the 

summer. For example, on some summer days, ozone 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. 

4-4
BART 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 

4-5
BART ANALYSIS FOR CHOLLA UNIT 4 

4.3.2 CALMET Input Data 

CH2M HILL ran the CALMET model to produce 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-kilometers resolution that were obtained from the 

contractor (Alpine Geophysics) who developed the nationwide data for the EPA. For 2002, 

CH2M HILL used 36-kilometers MM5 data obtained from Alpine Geophysics, originally 

developed for the WRAP. Data for 2003 (also from Alpine Geophysics), at 36-kilometers 

resolution, were developed by the Wisconsin Department 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 and land use effects to generate a Step 1 wind 

field, and then further refined using local surface observations to create 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) network that are in the domain. The surface data 

were obtained in abbreviated DATSAV3 format. A conversion routine available from the TRC 

website was used to convert the DATSAV3 files to CD 144 format for input to the SMERGE 

preprocessor and CALMET. 

Land use and terrain data were obtained from the 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 CALMET land use categories. Surface properties, 

such as albedo, Bowen ratio, roughness length, and leaf area index, were computed from the land 

use values. Terrain data were taken from USGS 1 degree Digital Elevation 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 for the modeling domain. The list of available 

stations and stations that have collected complete data varies by year, but CH2M HILL 

processed all available stations/data within the domain for each year. Precipitation data were 

prepared with the PXTRACT/PMERGE processors 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 fields. In the development of the 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 observations as part of the Four Dimensional Data Assimilation. 

This results in higher temporal (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 potentially producing spurious vertical velocities. 

4-6
BART ANALYSIS FOR CHOLLA UNIT 4 

4.3.3 Validation of CALMET Wind Field 

CH2M HILL used the CALDESK (program to display data and results) data display and analysis 

system (v2.97, Enviromodeling Ltda.) to view plots 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 Weather Maps Project (http://docs.lib.noaa.gov/ 

rescue/ dwm/ data_rescue_daily_weather_maps.html), 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 (Figure 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 SO2 and NOX transformation with the MESOPUFF II chemical transformation scheme. 

CH2M HILL used 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 relied on a monthly 

default value of 80 parts per billion. For background 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 analysis appeared to be related to natural gas 

combustion so it was necessary to replace these with more applicable values. The same stack 

data were used for all scenarios since none of the emission controls related to these scenarios 

would significantly affect the exhaust exit flows or temperatures. 

4-7
BART ANALYSIS FOR CHOLLA UNIT 4 

Pre-Control Emission Rates 

Pre-control emission rates reflect normal maximum capacity 24-hour emissions 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 models are intended to reflect steady-state 

operating conditions during periods of high-capacity utilization. We do not generally 

recommend that emissions reflecting periods of start-up, shutdown, and malfunction be 

used...” 

CH2M HILL used available CEM data to determine the baseline emission rates. Data reflect 

operations from 2001 through 2006. 

Emissions were modeled for the following species: 

• Sulfur dioxide (SO2)

• Oxides of nitrogen (NOx)

• Coarse particulate (diameter greater than PM2.5 and less than or equal to PM10) 

• Fine particulate (diameter less than or equal to PM2.5) 

• Elemental carbon (EC) 

• Organic aerosols (SOA) 

• Sulfates (SO4) 

Post-control Emission Rates 

Post-control emission rates reflected the effects of the emissions control scenario under 

consideration. Modeled pollutants were the same as listed for the pre-control scenario. 

Modeling Process 

The CALPUFF modeling for the control technology 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 scenarios 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 coordinates to LCC map 

coordinates for the meteorological stations and source locations. The USGS conversion program 

PROJ (version 4.4.6) was 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 kilometers of the Cholla Power Plant, discrete receptors 

for the CALPUFF modeling were taken from the National Park Service database for Class I area 

4-8
BART ANALYSIS FOR CHOLLA UNIT 4 

modeling receptors. The entire area of each Class I area that is within or intersects the 

300-kilometers circle (Figure 4-1) were included 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. The 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 (PM10) 0.6 

• PM fine (PM2.5) 1.0 

• Organic carbon 4.0 

• Elemental carbon 10.0 

CALPOST Visibility Method 6 (MVISBK=6) was used for the determination of visibility 

impacts. Monthly average relative humidity factors (f[RH]) were used in the light extinction 

calculations to account for the hygroscopic 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 determine the change in visual 

range background—or ∆dV—represent the average natural background concentration for 

western Class I areas. 

Table 4-2 lists the annual average species concentrations from the EPA Guidance. 

4-9
BART 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/CALPUFF modeling and post-processing will be 

provided in electronic format to the Arizona Department of Environmental Quality (ADEQ). 

Larger files, such as binary files generated by CALMET, have not been included on the 

submitted disks, but any omitted 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 control 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 economic evaluations, and consideration of the 

modeling analysis for Cholla 4, the preliminary recommended BART controls for NOx, SO2, and 

PM10 are as follows: 

• The most cost-effective emissions control scenario for NOx includes new LNB with SOFA. 

• Installation of a replacement fabric filter for PM10 emission control is recommended. 

• Upgrade of the wet limestone FGD system for SO2 removal is also recommended. 

The above NOx recommendation is identified as Scenario 1 for the modeling analysis described 

in Section 4.0. Visibility improvements 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 NOx

Emission 

(lb/MMBtu) 

Expected SO2

Emissions 

(lb/MMBtu) 

Expected PM10

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 

5-1
BART ANALYSIS FOR CHOLLA UNIT 4 

5-2

The ranking of the different NOx emission control scenarios based on annual costs, from lowest 

to highest cost, is presented on Table 5-2. 

TABLE 5-2 

Ranking of NOx 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 NOx and PM10 emission control 

that would be representative of future operations without the additional cost and level of control 

associated with the scenarios. Figures 5-1 through 5-4 compare the modeled contribution to 

visual range reduction for each Class I area for the Baseline and each NOx emission control 

scenario. 

Of the thirteen Class I areas included in this analysis, results from the analysis for four of these 

areas are presented in this section. These four areas 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. 
FIGURE 5-1 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at Capitol Reef NP 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Capitol Reef National Park 

0.000.501.001.502.002.503.00

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scena

Control Scenario

Maximum Impact (Δdv)
FIGURE 5-2 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at Grand Canyon NP 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Grand Canyon National Park 

0.000.501.001.502.002.503.00

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scena

Control Scenario

Maximum Impact (Δdv)
FIGURE 5-3 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at Petrified Forest NP 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Petrified Forest National Park 

0.000.501.001.502.002.503.003.504.00

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scena

Control Scenario

Maximum Impact (Δdv)
NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Sycamore Canyon Wilderness 

0.000.501.001.502.002.503.00

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scena

Control Scenario

Maximum Impact (Δdv)

FIGURE 5-4 NOx Control Scenarios—Maximum Contributions to Visual Range Reduction at Sycamore Canyon WA 

Cholla 4 
BART ANALYSIS FOR CHOLLA UNIT 4 

5.3 Least-Cost Envelope Analysis 

The total annualized cost, cost per ∆dV reduction, and cost per reduction in number of days 

above 0.5 ∆dV for each of the NOx emission control scenarios and each of the selected Class I 

areas are listed in Tables 5-3 through 5-6. Only costs for NOx control scenarios are shown. A 

comparison of the incremental costs between relevant 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 98th 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 comparisons should focus on annualized cost and 

emission reduction differences between dominant alternatives. Dominant set of control 

alternatives are 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 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 selected 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 scenarios is plotted for each analysis. 

TABLE 5-3 

NOx Control Scenario Results for Capitol Reef 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 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 Rotamix 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 

5-7
BART ANALYSIS FOR CHOLLA UNIT 4 

TABLE 5-4 

NOx Control Scenario Results for Grand Canyon 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 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 Rotamix 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 

NOx Control Scenario Results for Petrified Forest 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 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 Rotamix 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 

5-8
BART ANALYSIS FOR CHOLLA UNIT 4 

TABLE 5-6 

NOx Control Scenario Results for Sycamore Canyon WA 

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 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 Rotamix 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 NOx 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. Baseline 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 

5-9
BART ANALYSIS FOR CHOLLA UNIT 4 

5-10

TABLE 5-8 

Grand Canyon NP NOx 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. Baseline 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 NOx 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. Baseline 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 NOx 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. Baseline 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 235.034 
FIGURE 5-5 NOx Control Scenarios – Least-Cost Envelope Capitol Reef NP—Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Capitol Reef National Park 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 25 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE 5-6 NOx Control Scenarios—Least-Cost Envelope Capitol Reef NP—98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Capitol Reef National Park 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE 5-7 NOx Control Scenarios—Least-Cost Envelope Grand Canyon NP—Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Grand Canyon National Park 

Baseline

Scenario 1

Scenario 2

Scenario 3Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE 5-8 NOx Control Scenarios—Least-Cost Envelope Grand Canyon NP—98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Grand Canyon National Park 

Baseline

Scenario 1

Scenario 2

Scenario 

Scenario 4

Scena

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE 5-9 NOx Control Scenarios—Least-Cost Envelope Petrified Forest NP—Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Petrified Forest National Park 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scena

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 20 40 60 80 100 120 140 160 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE 5-10 NOx Control Scenarios—Least-Cost Envelope Petrified Forest NP—98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Petrified Forest National Park 

Baseline

Scenario 1

Scenario 2

Scenari

Scenario 4

Sc

0.02.04.06.08.0

10.012.014.0

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE 5-11 NOx Control Scenarios—Least-Cost Envelope Sycamore Canyon WA—Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Sycamore Canyon Wilderness 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenar

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 25 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Sycamore Canyon Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 

Scenario 4

S

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 

Reduction in Delta dv

Total Annualized Cost (MM $)

FIGURE 5-12 NOx Control Scenarios—Least-Cost Envelope Sycamore Canyon WA—98th Percentile Reduction 

Cholla 4 
BART ANALYSIS FOR CHOLLA UNIT 4 

5.3.2 Analysis Results 

Results of the least-cost analysis for the various NOx emission control scenarios, shown in 

Tables 5-3 through 5-10 and Figures 5-5 through 5-12, confirm the selection of Scenario 1 

(New LNB with SOFA), based on incremental cost and visibility improvements. All other NOx

control scenarios are excluded on the basis of cost effectiveness. 

Analysis of the NOx results for the four Class I areas in Tables 5-3 through 5-10 and 

Figures 5-5 through 5-12 illustrates the conclusions 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 effectiveness 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) and Scenario 4 (New LNB with SOFA & SNCR) 

relative to Scenario 1 are similar to that of Scenario 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 reduction 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 impacts for the other control scenarios are much 

higher. 

Therefore, because of the significant improvements related to Scenario 1, Scenario 1 represents 

NOx control BART for Cholla 4. 

5.4 Recommendations 

5.4.1 NOx 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 projected significant reduction in NOx emissions, reasonable 

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

5.4.2 SO2 Emission Control 

Based on the analysis conducted, upgrade of the wet FGD system can achieve the BART 

emission level of 0.15 lb/MMBtu for SO2 emission control. 

5.4.3 PM10 Emission Control 

Based on the analysis conducted, the installation of a fabric filter can achieve the BART 

emission level of 0.015 lb/MMBtu for PM10 emission control. 

5.5 Just-Noticeable Differences in Atmospheric Haze 

Studies have been conducted that demonstrate only dV differences of approximately 1.5 to 

2.0 dV or more are perceptible by the human eye (Henry, 2002). Deciview 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 

5-19
BART ANALYSIS FOR CHOLLA UNIT 4 

5-20

studied would be expected under any of the scenarios. 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 natural obscuration where 

water in various forms (fog, clouds, snow, or rain) or other naturally caused aerosols obscure 

the atmosphere. During the period of 2001 through 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 effect 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 Visibility Improvement 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 Best 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 Partnership (WRAP) 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. 

6-1
Appendices 
APPENDIX A 

Economic Analysis 
ECONOMIC ANALYSIS SUMMARY

Cholla Unit 4 Boiler Design: Tangential Fired

SO2 

LNB w/SOFA LNB w/SOFA & SNCR ROFA ROFA w/Rotamix LNB w/SOFA & SCR Upgra

Lim

Case 12 345 6 

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 --- --- --- --- ---

Upgrad

PM Emission Control System ESP --- --- --- --- --- 

TOTAL INSTALLED CAPITAL COST ($) 0 6,837,000 21,543,000 15,404,911 22,698,330 112,617,000 69

FIRST YEAR O&M COST ($)

Operating Labor ($) 0 42,500 85,000 63,750 85,000 106,250 Maintenance Material ($) 0 85,000 170,000 127,500 170,000 212,500 Maintenance Labor ($) 0 42,500 85,000 63,750 85,000 106,250 Administrative Labor ($) 0 0 0 0 0 0 TOTAL FIXED O&M COST 0 170,000 340,000 255,000 340,000 425,000 Makeup Water Cost 0 0 0 0 0 0 Reagent Cost 0 0 289,706 0 579,412 592,084 1SCR Catalyst / FF Bag Cost 0 0 0 0 0 637,500 Waste Disposal Cost 0 0 0 0 0 0 Electric Power Cost 0 0 173,120 1,458,277 1,458,277 865,598 TOTAL VARIABLE O&M COST 0 0 462,825 1,458,277 2,037,689 2,095,182 2

TOTAL FIRST YEAR O&M COST 0 170,000 802,825 1,713,277 2,377,689 2,520,182 2

FIRST YEAR DEBT SERVICE ($) 0 650,389 2,049,339 1,465,436 2,159,243 10,713,010 6

TOTAL FIRST YEAR COST ($) 0 820,389 2,852,164 3,178,713 4,536,932 13,233,192 9

Power Consumption (MW) 0.0 0.0 0.4 3.6 3.6 2.1 Annual Power Usage (kW-Hr/Yr) 0.0 0.0 3.5 29.2 29.2 17.3 

CONTROL COST ($/Ton Removed)

NOx Removal Rate (%) 0.0% 47.0% 59.0% 61.4% 71.1% 83.1% NOx Removed (Tons/Yr) 0 3,390 4,259 4,433 5,129 5,998 First Year Average Control Cost ($/Ton NOx Rem.) 0 242 670 717 885 2,206 Incremental Control Cost ($/Ton NOx Removed) Base 242 2,337 1,878 1,953 10,004 Case Comparison 2-1 3-2 4-3 5-4 6-5SO2 Removal Rate (%) 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% SO2 Removed (Tons/Yr) 0 0 0 0 0 0 First Year Average Control Cost ($/Ton SO2 Rem.) 0 0 0 0 0 0 Incremental Control Cost ($/Ton SO2 Removed) Base 0 0 0 0 0 Case Comparison PM Removal Rate (%) 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% PM Removed (Tons/Yr) 0 0 0 0 0 0 First Year Average Control Cost ($/Ton PM Rem.) 0 0 0 0 0 0 Incremental Control Cost ($/Ton PM Removed) Base 0 0 0 0 0 Case Comparison 

PRESENT WORTH COST ($) 0 8,914,035 31,351,803 36,337,481 51,748,586 143,408,209 10

SO2 and PM efficiencies shown are only incremental

Parameter Current Operation

NOx Control

Cholla Economic Analysis Model 1 of 1 
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 Filter

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

Annual 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

Administrative Labor ($) 000000 0 0

Total Fixed O&M Cost ($) 0 170,000 340,000 255,000 340,000 425,000 850,000 680,000

Annual 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

Annual 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

Annual 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

Annual 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

Annual 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

Annual 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 PM
CAPITAL COST

Cholla Unit 4

CaseNOx Emission Control SystemSO2 Emission Control SystemPM Emission Control System

CAPITAL COST COMPONENT Factor/Source Cost Factor/Source Cost Factor/Source Cost Factor/Source Cost Factor/Source Cost Factor/SLNB w/OFA or ROFA LNB LNB ROFA ROFA LNB

Major Materials Design and Supply Vendor $2,650,000 Vendor $2,650,000 Vendor $5,694,888 Vendor $5,694,888 Vendor $2,650,000 VendConstruction 50.0% $1,325,000 50.0% $1,325,000 Vendor $2,420,567 Vendor $2,420,567 50.0% $1,325,000 50.0

Balance of Plant 50.0% $1,325,000 50.0% $1,325,000 30.0% $1,708,466 30.0% $1,708,466 50.0% $1,325,000 50.0

Electrical (Allowance) 5.0% $132,500 5.0% $132,500 30.0% $1,708,466 30.0% $1,708,466 5.0% $132,500 5.0% Owner's Costs 10.0% $265,000 10.0% $265,000 10.0% $569,489 10.0% $569,489 10.0% $265,000 10.0

Surcharge 16.0% $424,000 16.0% $424,000 16.0% $911,182 16.0% $911,182 16.0% $424,000 16.0

AFUDC 12.0% $318,000 12.0% $318,000 12.0% $683,387 12.0% $683,387 12.0% $318,000 12.0

Subtotal $6,439,500 $6,439,500 $13,696,445 $13,696,445 $6,439,500 Contingency 15.0% $397,500 15.0% $397,500 30.0% $1,708,466 30.0% $1,708,466 15.0% $397,500 15.0

Total Capital Cost for LNB w/OFA or ROFA $6,837,000 $6,837,000 $15,404,911 $15,404,911 $6,837,000 

SNCR or SCR or Rotamix SNCR Rotamix SCR

Major Materials Design and Supply Vendor $0 Vendor $5,700,000 Vendor $0 Vendor $1,823,317 Vendor $41,000,000 Vend

Construction 50.0% $0 50.0% $2,850,000 50.0% $0 Vendor $3,227,422 50.0% $20,500,000 50.0

Balance of Plant 50.0% $0 50.0% $2,850,000 50.0% $0 50.0% $911,659 50.0% $20,500,000 50.0

Electrical (Allowance) 5.0% $0 5.0% $285,000 5.0% $0 5.0% $91,166 5.0% $2,050,000 5.0% 

Owner's Costs 10.0% $0 10.0% $570,000 10.0% $0 10.0% $182,332 10.0% $4,100,000 10.0

Surcharge 16.0% $0 16.0% $912,000 16.0% $0 16.0% $291,731 16.0% $6,560,000 16.0

AFUDC 12.0% $0 12.0% $684,000 12.0% $0 12.0% $218,798 12.0% $4,920,000 12.0

Subtotal $0 $13,851,000 $0 $6,746,424 $99,630,000 Contingency 15.0% $0 15.0% $855,000 15.0% $0 30.0% $546,995 15.0% $6,150,000 15.0

Total Capital Cost for SNCR or SCR or Rotamix $0 $14,706,000 $0 $7,293,419 $105,780,000 

Dry or Wet FGD, FGC or Fabric Filter

Major Materials Design and Supply Vendor $0 Vendor $0 Vendor $0 Vendor $0 Vendor $0 VendConstruction 50.0% $0 50.0% $0 50.0% $0 50.0% $0 50.0% $0 50.0

Balance of Plant 50.0% $0 50.0% $0 50.0% $0 50.0% $0 50.0% $0 50.0

Electrical (Allowance) 5.0% $0 5.0% $0 5.0% $0 5.0% $0 5.0% $0 5.0% 

Owner's Costs 10.0% $0 10.0% $0 10.0% $0 10.0% $0 10.0% $0 10.0

Surcharge 16.0% $0 16.0% $0 16.0% $0 16.0% $0 16.0% $0 16.0

AFUDC 12.0% $0 12.0% $0 12.0% $0 12.0% $0 12.0% $0 12.0

Subtotal $0 $0 $0 $0 $0 Contingency 15.0% $0 15.0% $0 15.0% $0 15.0% $0 15.0% $0 15.0

Total Capital Cost for Dry/Wet FGD, FGC or FF $0 $0 $0 $0 $0 

LNB w/SOFA

------ ---

LNB w/SOFA & SNCR

---

Parameter

LNB w/SOFA LNB w/SOFA & SNCR ROFA w/Rotamix

NOx Control

ROFA LNB w/SOFA & SCR

LNB w/SOFA & SCR

---

--- --- ---

ROFA w/Rotamix

---

ROFA

---

Upgra

2 34 5 6 

SO

Upgrade

Cholla Economic Analysis Model 1 of 1 
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 (ADEQ) 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 approach 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 methodology 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-processor 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 algorithms for chemical transformation and 

deposition, and a post processor capable of calculating concentrations, visibility impacts, 

and deposition (CALPOST). The CALPUFF modeling 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 used. 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 generate 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. Grid 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 events 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 domains 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 shifting the plume transport too greatly between 

individual time steps, or may push the plume back to the original cell in a small time step. 

3-1 
FIGURE 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 processing 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 produce three years of analysis: 2001, 2002, 

and 2003. CH2M HILL will use MM5 data as the 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 obtained 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 Department 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 local terrain and land use effects to generate a 

Step 1 wind field, and then further refined using 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) network that are in the domain. The surface 

data will be obtained in abbreviated DATSAV3 format. A conversion 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 Theme Grid (CTG) format from the USGS, and the 

Level I USGS land use categories will be mapped 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 have 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 the 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 fields. In the development of the MM5 data, the 

twice daily upper-air meteorological observations 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 results in higher temporal (hourly vs. 12-hour) 

and spatial (36 km vs. ~300 km) resolution for the upper-air meteorology in the MM5 field. 

3-4 
3-5 

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 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 other meteorological parameters to evaluate the 

CALMET wind fields. We will use observed weather conditions, as depicted in surface and 

upper-air weather maps from the National Oceanic and Atmospheric Administration 

(NOAA) Central Library U.S. Daily Weather 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 concentrations will be used by CALPUFF for the 

calculation of SO2 and NOX 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 available. 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. The emission rates will reflect actual emissions 

under normal operating conditions. As described by the EPA in the Regional Haze 

Regulations and Guidelines for Best Available Retrofit Technology (BART) Determinations; Final 

Rule (40 CFR Part 51; July 6, 2005, pg 39129): 

The emissions estimates used in the models are intended to reflect steady-state 

operating conditions during periods of high-capacity utilization. We do not generally 

recommend that emissions reflecting periods of start-up, shutdown, and malfunction 

be used... 

CH2M HILL will use available CEM data to determine the baseline 24-hour emission rates. 

Data will reflect operations from 2001 through 2003. 

Although the Wrap Exemption Modeling evaluated emissions of NOX, SO2, and PM2.5, 

particulate matter speciation data from the USEPA or National Park Service are proposed 

4-1 
for this analysis (USEPA 2007, NPS 2007). Therefore emissions will be modeled for the 

following species: 

• Sulfur dioxide (SO2) 

• Nitrogen oxides (NOx) 

• Coarse particulate (PM2.5 < diameter ≤ PM10) 

• Fine particulate (diameter ≤ PM2.5) 

• Elemental carbon (EC) 

• Organic aerosols (SOA) 

• Sulfates (SO4) 

4.1.4 Post Control Emission Rates 

Post-control emission rates will reflect the effects 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 technology 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 convert 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 

4-2 
4-3 

• 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 determine 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 calculate 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 (PM10) 0.6 

• PM fine (PM2.5) 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 hygroscopic 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 reference 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/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 

Note: Taken from Table 2-1 of Guidance for Estimating Natural 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 Δdv and 98th percentile Δdv over the 3-year period, 

as well as the maximum number of days per year that the maximum Δdv exceeds 0.5 Δdv. 

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 subject 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 (WRAP) 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 Federal Area Individual Source Attribution 

Visibility Impairment Modeling Analysis. Colorado 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/Permits/ect/ectCoalFiredBoiler.cfm. 

Accessed 7/13/2007. 

US Environmental Protection Agency (USEPA) 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 under the Regional Haze Rule. USEPA. EPA454/B-03-004. September 2003. 

USEPA 1998. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 

Summary Report and Recommendations for Modeling Long-Range Transport Impacts. U.S. 

Environmental Protection Agency, Air Quality 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 Denver, Colorado. December, 1998. 

USEPA 2007. AP 42, Fifth Edition, Compilation of Air Pollutant Emission Factors, Volume 1: 

Stationary Point and Area Sources. USEPA Technology 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 Results 
FIGURE C-1 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Gila Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Gila Wilderness 

0.000.501.001.502.002.50

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-2 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Mount Baldy Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Mount Baldy Wilderness 

0.000.501.001.502.002.50

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-3 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Sierra Ancha Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Sierra Ancha Wilderness 

0.000.501.001.502.002.50

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-4 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Mazatzal Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Mazatzal Wilderness 

0.000.200.400.600.801.001.201.401.601.802.00

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-5 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Pine Mountain Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Pine Mountain Wilderness 

0.000.200.400.600.801.001.201.401.601.802.00

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-6 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Superstition Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Superstition Wilderness 

0.000.501.001.502.002.50

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-7 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Galiuro Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Galiuro Wilderness 

0.000.200.400.600.801.001.201.40

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-8 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Mesa Verde Wilderness 

Cholla 4 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Mesa Verde National Park 

0.000.200.400.600.801.001.201.401.60

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
FIGURE C-9 

NOx Control Scenarios - Maximum Contributions to Visual Range Reduction at Saguaro NP 

NOx Control Scenarios

 Maximum Contributions to Visual Range Reduction

 Cholla Unit 4

 Saguaro National Park

0.000.200.400.600.801.001.201.40

Baseline Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 

Control Scenario

Maximum Impact (Δdv)
TABLE C-1 

NOx 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 

NOx 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 19.489 
TABLE C-3 

NOx 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 

NOx 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 16.001 
TABLE C-5 

NOx 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 

NOx 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 15.624 
TABLE C-7 

NOx 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 

NOx 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 21.588 
TABLE C-9 

NOx 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 NOx 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 NOx 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 263.523 
TABLE 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 NOx 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 NOx 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 724.690 
TABLE C-15 

Superstition Wilderness NOx 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 NOx 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 NOx 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 263.523 
TABLE C-18 

Saguaro NP NOx 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 869.624 
FIGURE C-10 

NOx Control Scenarios - Least Cost Envelope Gila Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Gila Wilderness 

Baseline

Scenario 

Scenario 2Scenario 3

Scenario 

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-11 

NOx Control Scenarios - Least Cost Envelope Gila Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Gila Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-12 

NOx Control Scenarios - Least Cost Envelope Mount Baldy Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Mount Baldy Wilderness 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-13 

NOx Control Scenarios - Least Cost Envelope Mount Baldy Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Mount Baldy Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-14 

NOx Control Scenarios - Least Cost Envelope Sierra Ancha Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Sierra Ancha Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 25 30 35 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-15 

NOx Control Scenarios - Least Cost Envelope Sierra Ancha Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Sierra Ancha Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-16 

NOx Control Scenarios - Least Cost Envelope Mazatzal Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Mazatzal Wilderness 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 25 30 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-17 

NOx Control Scenarios - Least Cost Envelope Mazatzal Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Mazatzal Wilderness 

Baseline

Scenario 1

Scenario 2

Scenari

Scenario 4

Scen

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-18 

NOx Control Scenarios - Least Cost Envelope Pine Mountain Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Pine Mountain Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-19 

NOx Control Scenarios - Least Cost Envelope Pine Mountain Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Pine Mountain Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-20 

NOx Control Scenarios - Least Cost Envelope Superstition Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Superstition Wilderness 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 25 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-21 

NOx Control Scenarios - Least Cost Envelope Superstition Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Superstition Wilderness 

Baseline

Scenario 1

Scenario 2

Scena

Scenario 4

Sc

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-22 

NOx Control Scenarios - Least Cost Envelope Galiuro Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Galiuro Wilderness 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 2 4 6 8 10 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-23 

NOx Control Scenarios - Least Cost Envelope Galiuro Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Galiuro Wilderness 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-24 

NOx Control Scenarios - Least Cost Envelope Mesa Verde Wilderness - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Mesa Verde National Park 

Baseline

Scenario 1

Scenario 2Scenario 3

Scenario 4

Scenario 5

0.02.04.06.08.0

10.012.014.0

0 5 10 15 20 25 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-25 

NOx Control Scenarios - Least Cost Envelope Mesa Verde Wilderness - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

Mesa Verde National Park 

Baseline

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 

0.02.04.06.08.0

10.012.014.0

0.00 0.10 0.20 0.30 0.40 0.50 0.60 

Reduction in Delta dv

Total Annualized Cost (MM $)
FIGURE C-26 

NOx Control Scenarios - Least Cost Envelope Saguaro NP - Days Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Saguaro National Park

Baseline

ScenaScenaScenaScenaScena

0.02.04.06.08.0

10.012.014.0

0 1 2 3 4 5 6 7 8 9 

Reduction in Days Above 0.5 Delta dv

Total Annualized Cost (MM $)
FIGURE C-27 

NOx Control Scenarios - Least Cost Envelope Saguaro NP - 98th Percentile Reduction 

Cholla 4 

NOx Control Scenarios

 Least Cost Envelope

 Cholla Unit 4

 Saguaro National Park

Baseline

Scenario 1

Scenario 2

Scenari

Scenario 4

Sce

0.02.04.06.08.0

10.012.014.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 

Reduction in Delta dv

Total Annualized Cost (MM $)