Technical Analysis for Arizona and Hawaii Regional 

Haze FIPs: Task 7: Five-Factor BART Analysis for TEP 

Sundt (Irvington)

Contract No: EP-D-07-102

Work Assignment: 5-12

Version: 1

Prepared for: Thomas Webb

Air Planning Office

U.S. EPA Region 9

75 Hawthorne Street

San Francisco, CA 94105

Prepared by: Uma Shankar, Elizabeth Adams, Mohammad Omary, 

Brian Naess and Saravanan Arunachalam

Institute for the Environment

University of North Carolina at Chapel Hill

Mark Lee, Juanita Haydel and Thomas Meyers

ICF International

Jim Staudt

Andover Technology Partners

Date delivered: October 9, 2012 
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Contents

Figures...........................................................................................................................................iii

Tables............................................................................................................................................ iv

Document Change Record ........................................................................................................... v

1 Introduction............................................................................................................................ 1

2 Work Performed and Deliverable Summary ...................................................................... 2

2.1 General Considerations for Visibility Modeling............................................................. 2

2.2 Five-Factor Analyses...................................................................................................... 3

2.2.1 Sundt Unit 4.........................................................................................................4

2.3 Conclusions................................................................................................................... 14

References.................................................................................................................................... 15

Appendix A.................................................................................................................................... 1

Emissions Data Tables............................................................................................................. 1

Appendix B.................................................................................................................................... 1

Andover Technology Partners Report Chapter 3: BART Analysis (NOx, SO2 and PM)....... 1
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Figures

Figure 2. Sundt Plant....................................................................................................................... 4
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Tables

Table 1. CIAs Within 300 km of Sundt.......................................................................................... 2

Table 13. Sundt Power Plant Data .................................................................................................. 4

Table 14. NOx Contol Technologies for Sundt Unit 4.................................................................... 6

Table 15. SO2 Contol Technologies for Sundt Unit 4 .................................................................... 7

Table 16. Control Scenarios Used in Sundt Visibility Modeling ................................................... 8

Table 17. Stack Parameters for Sundt Units 1-4............................................................................. 8

Table 18. 98th percentile Visibility Impairment (-dv) for Sundt Scenarios Using (a) IMPROVE 

Method 6(a) and (b) Method 8(b)................................................................................................. 10

Table 19. Visibility Benefits (dv) of Control Options for the Calculation Methods of Table 18. 11

Table 20. Control Effectiveness (million $/dv) for the Calculation Methods of Table 18........... 12

Table 21. Control Effectiveness (million $/dv) Summary for the Calculation Methods of Table 

18................................................................................................................................................... 13

Table A-2(a). NOx, SO2 and PM Emission Inputs for Base and Control Scenarios for Sundt Unit 

4 Visibility Modeling...................................................................................................................... 2

Table A-2(b). Speciated PM Emission Inputs for Base and Control Scenarios for Sundt Unit 4 

Visibility Modeling......................................................................................................................... 3
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Document Change Record

Revision Date Remarks

0.1 October 9, 2012 Draft version submitted to EPA
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1 Introduction

This report describes the 5-factor BART analyses of all technically feasible NOx control options 

for Sundt (TEP Irvington) Generating Station Unit 4

The five factors considered in these analyses are as follows:

1. Cost of compliance;

2. Energy and non-air quality impacts of compliance;

3. Any existing pollution control technology in use at each source;

4. Remaining useful life of the source, and 

5. Degree of visibility improvement that may be reasonably expected from the use of 

BART emissions controls.

Andover Technology Partners (ATP) estimated the cost of compliance for these units using data 

provided by Arizona Department of Environmental Quality (ADEQ) and the EPA Control Cost 

Manual (EPA, 2002). With assistance from ICF, ATP also examined data from EPA’s latest 

power sector modeling (Integrated Planning Model [IPM]) methods1, and other reasonable 

engineering methods to ensure consistency with the assumptions used in its control cost 

analyses. The control options were analyzed not only for the feasibility of implementation but 

also the first four factors listed above to estimate the total cost per year of each control 

technology and the cost of control per year for each.

Once all the feasible control options were identified, EPA reviewed and recommended control 

configurations on all applicable units to examine the visibility impacts of their emissions. UNC 

performed these impact assessments using the base configuration of existing controls as well as 

all EPA-approved control configurations for each BART facility at all Class I Areas (CIAs) 

within a 300-km radius through the use of dispersion modeling. Per EPA requirements these 

analyses used the following models and processors, and EPA-provided meteorological inputs 

available from a previous BART analysis done by ENSR (2009) using CALMET version 5.8:

 CALPUFF version 5.8 (v5.8) level 070623 puff dispersion model was used to estimate 

concentrations of visibility-impairing pollutants emitted by each facility at the relevant 

CIAs.

 The POSTUTIL utility version 1.56 was used to adjust these concentrations for the 

phase-partitioning of HNO3 between gas and particle based on additional input 

information.

 The adjusted concentrations were used in the CALPOST visibility post-processor 

version 6.221 (v6.221) to calculate the visibility impairment in deciview (dv) relative to 

the background visibility at each CIA. 

The change in deciview, or -dv, corresponding to the visibility-impairing pollutant emissions

reduction for each control option relative to the base configuration was used along with the 

yearly cost of control to estimate the control cost effectiveness in $/dv at each CIA. It is also 

reported as the cost of the maximum dv benefit over all the CIAs, and for the dv benefit 

summed over all the CIAs. 

 

1 Documentation of EPA IPM Base Case v4.10. http://www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html#documentation
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2 Work Performed and Deliverable Summary

In the five-factor analysis performed in this task ATP examined NOx, SO2 and particulate

matter (PM) control options and their costs for each of the three facilities considering the first 

four factors listed in Section 1. UNC used these cost estimates along with its visibility impact 

assessments for each facility to estimate the control option effectiveness in a combined metric 

specified by EPA, namely, $/dv. The overarching considerations for the visibility impact 

assessments are provided in Section 2.1. The five-factor analyses for each plant are described in 

Section 2.2.

2.1 General Considerations for Visibility Modeling

In Task 4 of this WA UNC used a GIS software tool to determine the nearest distance D (km) to 

the boundaries of all the CIAs within a 300-km radius of the major emitting facilities; The 16 

CIAs and their nearest mean distances (as determined in the Task 4 screening) from the 

applicable BART facilities are listed in Table 1. The domain description and Lambert 

Conformal Conic (LCC) projection parameters to obtain the coordinates of the facilities and 

CIAs came from meteorological inputs for CALMET v5.8 provided by EPA from BART 

analysis by ENSR Corporation (ENSR, 2009) for the Navajo Generating Station for 2001-2003; 

this domain and meteorological inputs were also used in the CALPUFF modeling in this task. 

Table 1. CIAs Within 300 km of Sundt

The CALPUFF model set-up for the three facilities followed the guidance of the modeling 

protocol that was delivered to EPA on March 19, 2012 as a Task 5 deliverable. This modeling 

protocol invokes almost all the model input settings recommended in the WRAP BART Subject 

Protocol (2006) included in its Appendix A. Differences in model set-up from the 

CIA

Short 

Name

State

Bryce Canyon brca UT

Chiricahua NM chir AZ

Chiricahua Wilderness chrw AZ

Galiuro Wilderness gali AZ

Gila Wilderness gila NM

Grand Canyon NP grca AZ

Joshua Tree NP jotr CA

Mazatzal Wilderness maza AZ

Mount Baldy Wilderness moba AZ

Petrified Forest NP pefo AZ

Pine Mountain Wilderness pimo AZ

Saguaro NP sagu AZ

Sierra Ancha Wilderness sian AZ

Superstition Wilderness supe AZ

Sycamore Canyon 

Wilderness syca AZ

Zion zion UT
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recommendations of the WRAP protocol due to the CALPUFF version used, or other direction 

from the EPA are noted in this protocol. UNC verified and reconciled any discrepancies in the 

CALPUFF inputs through a series of test runs. Of note, CALPUFF modeling used a constant 

value of 1 ppb throughout for ammonia per EPA guidance. 

The CALPUFF model was run in each year 2001-2003, and the outputs were processed in the 

POSTUTIL processor to redistribute the HNO3 and PM nitrate using additional inputs. This 

step was necessitated by the limitations of CALPUFF v5.8 in modeling the gas-particle 

partitioning and formation of particulate nitrate. The annual concentration files output by

POSTUTIL were combined in APPEND into a composite 3-year concentration file for visibility

post-processing. CALPOST was used to determine the 98th percentile visibility impairment and 

the visibility benefits of control in the years 2001-2003, and in the composite period. Further 

facility-specific details on the visibility analyses are provided in Section 2.2.

2.2 Five-Factor Analyses

This section describes the control options analysis, followed by the details of the visibility

modeling and the results of the five-factor analysis for each facility. The visibility modeling 

used combinations of control technologies (control “scenarios”) specified by the EPA with 

respect to the emissions inputs. The costs of compliance and visibility impacts for each scenario 

were estimated based on the following assumptions:

 The costs and emission reductions (in tons reduced) for the individual technologies that 

make up each control configuration are additive, i.e., the control scenario costs and 

emission reductions can be expressed, respectively, as the sum of the component 

technology costs, and the sum of the associated emission reductions (in tons reduced). 

 The control effectiveness values are applicable to the baseline emission values used in 

the visibility modeling. 

 The baseline periods used vary by source and unit, in order to account for differences in 

dates of installation of controls. This provides a more realistic assessment of “existing 

pollution control technology in use at each source” than a uniform baseline for all units.

 The baseline emissions used in the CALPUFF modeling are not identical to those used 

in the control options analyses because the BART Guidelines provide different methods 

for calculating baseline emissions for these two purposes. 

 The baseline emission inputs for NOx and SO2 use the 24-hour average emission rate 

from the highest emitting day of a representative baseline period (excluding periods of 

start-up, shutdown or malfunction). These data were extracted from the EPA Clean Air 

Market Division (CAMD) Continuous Emission Monitoring System (CEMS) database. 

 The baseline emission inputs for PM for visibility modeling are derived from the hourly

plant heat input (from the CEMS database) multiplied by emission limits specified in 

ADEQ’s BART determinations for these units. This approach to estimating PM 

emissions accounts for the fact that EPA is proposing to approve ADEQ’s BART 

emission limits for PM at these units, and that these emission limits will go into effect 

prior to any NOx or SO2 limitations resulting from EPA’s action.

 The control cost analysis uses annual average emissions (TPY) based on actual 

emissions from a baseline period representing anticipated future annual emissions.
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The visibility impacts of the control scenarios are discussed in the sections on visibility and

control effectiveness analysis for each facility.

2.2.1 Sundt Unit 4

2.2.1.1 General Plant Information

Unit 4 of the Sundt plant (formerly Irvington) shown in Figure 2 is a coal-fired boiler that was 

found to be subject to BART from our previous analyses in Task 6 of this WA. Table 13 

displays the modeled and reported data for Sundt Unit 4 as an average over 2008-2010, and as 

an annual average averaged for 2011. The capacity factor, which is important in performing cost 

effectiveness calculations, is determined by dividing total reported heat input by the potential 

heat input if the unit were operating at 100% capacity, 365 days a year.

Sundt Unit 4 has a baghouse (FF) for PM control. In addition it is equipped with LNB and 

overfire air (OFA) for NOx control.

Figure 2. Sundt Plant

Table 13. Sundt Power Plant Data

Unit

Capacity

Fuel

2008-2010 

Reported Avg

2011 Reported 

Rates

2008-

2011

NOx SO2 NOx SO2

Capacity 

Factor

MW gross lb/MMBtu lb/MMBtu %

Unit 4 173.3 Coal 0.361 0.477 0.445 0.729 37%
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2.2.1.2 NOx Control Options Analysis

2.2.1.2.1 Available NOx Control Technologies

Both combustion and post-combustion controls are available.

2.2.1.2.2 Feasible NOx Control Technologies

Since Sundt 4 already has combustion controls by way of LNB and OFA, the focus of ATP’s 

cost analysis was on post-combustion controls.

2.2.1.2.3 Evaluation of NOx Control Technologies

Although only post-combustion controls were examined, some advanced forms of OFA may

permit further reductions. The results of this evaluation are shown in Table 14.

For the cost estimation, baseline emissions and capacity factors for all electric utility units were 

developed from historical data as follows:

 The capacity factor is the average of that estimated for the period 2008-2011 using 

reported heat input and plant capacity information.

 NOx and SO2 emission rates were examined for the average period of 2008-2010 and 

2011. The 2011 rate was selected as the baseline rate for estimating cost effectiveness.

In addition, a capital recovery factor of 9.44% (equal to 7% over 20 years) plus tax and 

insurance costs of 1.2% were assumed in the cost analysis. This rate for taxes and insurance 

differs from what is used for industrial sources per the EPA Air Pollution Control Cost Manual, 

and is consistent with what has been determined for electric generating units per the IPM 

documentation. 2

2.2.1.2.4 Energy and Environmental Impacts

SNCR has negligible energy impact. SCR will incur an additional parasitic loss, mainly due to 

pressure drop across the SCR system, which is roughly equal to 0.59% of output. Although the 

IPM calculations for which these algorithms were developed treated this not as a cost, but as a 

reduction in capacity, in these calculations it has been treated as an increase in variable 

operating costs.

Environmental impacts of SNCR and SCR are primarily due to ammonia slip. The main 

environmental concern is regarding fly ash odor and potential impact on fly ash sales/disposal. 

Ammonia slip can be well controlled so that fly ash is not adversely impacted. SCR catalyst can 

be recycled and therefore is not a significant concern. 

 

2

From Integrated Planning Model ver 4.10 documentation, page 8-11: 0.30% for insurance and 0.90% for taxes.
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Table 14. NOx Contol Technologies for Sundt Unit 4 

Sundt 4 - NOx

SNCR SCR

Capacity Factor 37% 37%

Control Efficiency 30% 89%

Uncontrolled Rate

NOx Rate (lb/hr) 813 813

NOx Rate (lb/MMBtu) 0.445 0.445

NOx (TPY) 1318 1318

Controlled Rate

NOx Rate (lb/hr) 569 91

NOx Rate (lb/MMBtu) 0.31 0.05

NOx (TPY) 923 148

Tons/Year Removed 395 1,170

Total Cost, $/year $1,308,000 $5,712,715

$/Ton $3,307 $4,882

$/Ton (incremental) $3,307 $5,686

* SCR is actually capable of 90% removal. The model that we used set a 

lower limit of 0.05 lb/MMBtu, but lower NOx levels should be achievable

2.2.1.3 SO2 Control Options Analysis

SO2 emission rates increased in 2011 from the 2008-2010 average, indicating a change in coal.

2.2.1.3.1 Available SO2 Control Technologies

Available technologies for Sundt are DSI, wet and dry FGD, and a change to lower sulfur coal.

2.2.1.3.2 Feasible SO2 Control Technologies

All of the available technologies are feasible. In fact, DSI is well suited to this unit since it is 

equipped with FF control. A dry FGD at Sundt Unit 4 is assumed to include a new FF since 

there is probably not enough space to install a new absorber between the boiler and the existing 

FF. A wet FGD would be installed downstream of the existing FF.

2.2.1.3.3 Evaluation of of SO2 Control Technologies

A change in coal was not evaluated for cost since coal cost data was not available.

Table 15 shows the estimated cost of each of the SO2 control technologies. In light of the low 

capacity factor and high capital costs of FGD, the two FGD approaches are much more 

expensive than DSI (based on $/tons SO2 removed). DSI is expected to have high control

efficiency due to the existing FF. The FGD removal efficiencies are limited by assumptions in 

the Sargent & Lundy model developed for CAMD that was used for the FGD cost analysis. In 

practice, we would expect both dry and wet FGD to achieve higher capture efficiencies and 

therefore somewhat lower emissions levels, i.e., the Table 15 results are conservative.
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Table 15. SO2 Contol Technologies for Sundt Unit 4 

2.2.1.3.4 Energy and Environmental Impacts of Control Options

The energy impacts of DSI are small. FGD will have significant energy impacts (over 1% of 

output) due to pressure loss across the FGD system, and the additional pumping power and 

auxiliary loads involved. Energy costs are included for each technology analyzed in Table 15.

DSI using trona or SBC will result in increased solids collected by FF. These sodium-based 

materials may result in a by-product with a water-soluble component that may require some 

stabilization. The use of activated lime hydrate might avoid this problem. DSI has other benefits 

to air emissions.

Both the wet and dry scrubbers will have waste products. The dry scrubber waste is stable and 

can be disposed of in landfills. The wet scrubber waste can be dewatered and sold to the 

gypsum market. More information on these impacts is available in Section 2.1.2 of the ATP 

report attached in Appendix B.

2.2.1.4 PM Control Options 

Sundt Unit 4 is equipped with FF, and there is no technology available that would provide a 

higher degree of PM reduction than a properly designed and operated FF. Therefore, there are 

no changes assumed to the PM emission rates under the BART analysis.

Sundt 4 – SO2

DSI Dry FGD Wet FGD

Capacity Factor 37% 37% 37%

Control Efficiency 75% 89% 92%

Uncontrolled Rate

SO2 Rate (lb/hr) 1333 1333 1333

SO2 Rate (lb/MMBtu) 0.729 0.729 0.729

SO2 (TPY) 2160 2160 2160

Controlled Rate

SO2 Rate (lb/hr) 333 146 110

SO2 Rate (lb/MMBtu) 0.18 0.08 0.06

SO2 (TPY) 540 237 178

Tons/Year Removed 1620 1923 1982

% change stack velocity No Change -10% -20%

Exit temp No Change 175 F 130 F

Impact on PM speciation No Change Yes, FGD plus FF Yes, FGD plus FF 

Total Cost, $/year $2,967,501 $12,553,539 $13,986,681

$/Ton $1,832 $6,529 $7,057

$/Ton (incremental) $1,832 $31,647 $24,189
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2.2.1.5 Visibility Modeling

Sundt Unit 4 is subject to BART controls, but for realistic representation of the emission 

plumes all four units of the plant were included in the CALPUFF model inputs. EPA reviewed 

the control options presented in Tables 14 and 15 and recommended the control configurations 

for the modeling presented in Table 16. The following sections describe the control scenarios, 

and the input data assumptions and sources of data for the various visibility modeling 

components.

Table 16. Control Scenarios Used in Sundt Visibility Modeling

Control 

Option

Unit 4 All Controls

SO2 Control -Emis SO2

(TPY) NOx Control -Emis NOx(TPY) 

SO2+NOx

-Emis 

(TPY) $/year

Base Case LNB+OFA; 

FF

0 LNB+OFA; FF 0 0 $0

Ctrl-1 DSI 1620 - 0 1620 $2,967,501

Ctrl-2 Dry FGD 1923 - 0 1923 $12,553,539

Ctrl-3 Wet FGD 1982 - 0 1982 $13,986,681

Ctrl-4 0 SNCR 395 395 $1,308,000

Ctrl-5 DSI 1620 SNCR 395 2015 $4,275,501

Ctrl-6 Dry FGD 1923 SNCR 395 2318 $13,861,539

Ctrl-7 Wet FGD 1982 SNCR 395 2377 $15,294,681

Ctrl-8 0 SCR 1170 1170 $5,712,715

Ctrl-9 DSI 1620 SCR 1170 2790 $8,680,216

Ctrl-10 Dry FGD 1923 SCR 1170 3093 $18,266,254

Ctrl-11 Wet FGD 1982 SCR 1170 3152 $19,699,396

2.2.1.5.1 Stack Parameters

Table 17 lists stack parameters for Sundt, taken from the National Emission Inventory 2008 

Version 2; stack elevation was taken from the WRAP BART analysis of 20073. The table 

includes changes in the exit velocity and temperature for each of the FGD control scenarios, as 

recommended in Table 15.

Table 17. Stack Parameters for Sundt Units 1-4

 

3

Summary of WRAP BART Modeling for Arizona, Draft #5, May 25, 2007. See 

http://pah.cert.ucr.edu/aqm/308/bart.shtml
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2.2.1.5.2 CALPUFF Emissions Inputs

The modeling requirements and data sources for emissions for Sundt Units 1-4 were specified 

by EPA. These data are documented in Table B-1 of the modeling protocol submitted as a 

Task 5 deliverable, and in the supporting spreadsheet documentation for this report. They are 

included for convenience in Table A-2, Appendix A. The input data were extensively qualityassured using scripts created for this purpose by UNC. The emission data for Sundt were input 

as follows:

 The baseline emission rates for NOx and SO2 for all four units were obtained from 

CEMS for the average hourly emission rate of the maximum emission day during a 

period of continuous operation from 2008-2010. Units 1-3 of Sundt are natural-gas fired 

boilers, and in all scenarios modeled their NOx and SO2emission rates were held at their 

baseline values. 

 The EPA’s initial guidance on the emissions for these scenarios called for the use of the 

WRAP BART analysis (WRAP, 2007) for PM emissions from all units. However, that 

analysis covered only Unit 4 and one out of three of the natural gas-fired units. Thus the 

WRAP emission rates were used only for Unit 4. 

o PM speciation for Unit 4 was derived using the NPS speciation spreadsheets for drybottom coal-fired boiler with fabric filter (see 

http://www.nature.nps.gov/air/Permits/ect/docs/coalBoiler/2006FinalDryBottomPC_

FFpmSpeciationProfile.xls).

o The heating value, ash content and sulfur content of Rout Company (CO) coal

needed for PM speciation were provided by EPA.

 As reliable PM emissions information was not available for Units 1-3, and their PM 

emission rates are very low compared to the NOx and SO2 emissions from the facility, 

EPA eventually recommended that the PM emissions from Units 1-3 be set to zero. Thus 

no PM emissions or speciation data are provided for these units. 

Following the CALPUFF modeling, the concentration files were input to the POSTUTIL 

processor for the repartitioning of nitrate between gas and particulate phases, as the version of 

CALPUFF used does not have the advancements in chemistry available in the later versions. 

The POSTUTIL output concentration files for 2001-2003 were combined in the APPEND 

utility to produce a composite 3-year file, which was provided along with the annual files for 

CALPOST visibility processing. 

2.2.1.5.3 CALPOST Setup

As with the CALPUFF modeling, the CALPOST model setup followed the modeling protocol. 

Post-processing of POSTUTIL output with CALPOST used both IMPROVE Methods 6 and 8 

(submode 5) for calculation and comparison of the incremental visibility impairment (-dv) 

relative to background at the affected CIAs. Each method used two sets of background visibility

conditions in these assessments, corresponding to (a) annual average background, and (b) 

background visibility on the 20% best visibility days at the CIAs. For both methods, CALPOST 

uses the input values of monthly-average relative humidity adjustment factor f(RH), rather than 

the hourly values on the VISB.DAT file generated by CALPUFF. CALPOST input data were 

extensively quality-assured using scripts created for this purpose by UNC.
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2.2.1.5.4 Extraction of Visibility Results with POSTCALPOST 

UNC extracted the results from the CALPOST visibility analysis for the four methods of 

calculating visibility using POSTCALPOST. The results for the 98th percentile incremental 

visibility impairment -dv relative to background visibility at the 9 CIAs listed in Table 1 

within 300-km of the Sundt Plant for each year modeled (2001-2003), and for the composite 

three-year period are documented in the supporting spreadsheet documentation submitted with 

this report. The results in this report are from Method 6(a) and 8(b) for the composite period 

2001-2003. Tables 18(a) and (b) summarize the incremental visibility impairment relative to 

background (22nd highest -dv, corresponding to the 98th percentile) at each CIA within 300 

km of the Sundt Plant for the base case and controls scenarios from Method 6(a) and Method 

8(b) respectively.

Table 18 shows greater visibility impairment for Method 6(a) compared to Method 8(b). The 

maximum visibility impairment due to Sundt is seen at Saguaro for both visibility methods. The 

lowest values of visibility impairment at this CIA are obtained with SCR controls combined 

with DSI. However, at CIAs that are farther away such as Mazatzal and Mount Baldy, the 

visibility impairment is least for SCR with (dry or wet) FGD controls.

Table 18. 98th percentile Visibility Impairment (-dv) for Sundt Scenarios Using 

(a) IMPROVE Method 6(a) and (b) Method 8(b)

(a)

(b)
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The above conclusion is confirmed by Table 19, which shows that the visibility benefits are 

greatest at Saguaro for SCR combined with DSI, followed by only SCR control. This applies to 

both visibility methods. At the more distant CIAs, the benefits are comparable among several 

technologies, including, in addition to these, SO2 control options such as dry and wet FGD in 

combination with the post-combustion NOx controls. 

Table 19. Visibility Benefits (dv) of Control Options for the Calculation Methods of Table 18

(a)

(b)
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2.2.1.6 Control Effectiveness Results

This section discusses the control cost effectiveness in $/dv for each control option at each CIA, 

at the CIA with the maximum dv impact, and for the dv impact summed over all CIAs. Tables 

20(a) and (b) show the comparisons on a CIA-specific basis. The most effective control options 

when costs are considered are, in order, SNCR without and with dry FGD when considering 

impacts over all CIAs. At the most impaired CIA, SNCR is still the most cost-effective, 

followed by SCR alone, and SCR combined with DSI. The post-combustion NOx control 

outweighs the effectiveness of SO2 controls, because of the higher NOx emissions and their 

commensurate impact as well as the control efficiencies of these technologies. These results 

hold for both visibility methods. 

Table 20. Control Effectiveness (million $/dv) for the Calculation Methods of Table 18

(a)

(b)

The summary results are presented in Table 21. A comparison of the $/dv value using the 

summed visibility impacts over all CIAs shows that the SO2 controls (DSI, wet and dry FGD) 

are clearly more cost-effective for the CIAs farther away than for nearest ones, and that DSI is 

much more cost-effective at the nearest CIA in combination with SCR and SNCR, rather than as 

a sole control option. SCR, due to its much higher cost is not as cost-effective as SNCR despite 

its much greater control efficiency. Finally, Method 8(b) shows greater effectiveness than 

Method 6(a) for all control options and CIAs.
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Table 21. Control Effectiveness (million $/dv) Summary for the 

Calculation Methods of Table 18

(a)

(b)
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2.3 Conclusions

The five-factor BART analysis of Sundt Unit 4 led to the following conclusions:

 The visibility impacts are greatest for each facility at the nearest CIAs, and the control 

options are least effective (greatest in million $/dv) at the farthest ones. 

 The following are the technologies found to be most cost-effective at the highest impact 

CIA at each facility:

o SNCR, and SNCR in combination with DSI at Sundt Unit 4, followed by SCR 

with DSI

 In the case of Sundt 4 the control effectiveness of SO2 controls such as wet and dry FGD 

is greater at the CIAs farther away from the source. NOx control technologies such as 

SCR and SNCR have a lot more effectiveness at nearby CIAs.

 Visibility Method 8(b) estimates greater impairment and shows greater benefits of 

control options in almost all case relative to Method 6(b).
EMAQ LOE (EPA Contract EP-D-07-102) WA 5-12: Task 7 Five-Factor BART Analysis Report

UNC-EMAQ 5-12-026.v1 15 October 9, 2012

References

ENSR, 2009: Revised BART Analysis for the Navajo Generating Station Units 1-3, ENSR 

Corporation, Document No. 05830-012-300, January 2009, Salt River Project – Navajo 

Generating Station, Tempe, AZ.

EPA, 2002: EPA Air Pollution Control Cost Manual (Sixth Edition), Report No. EPA/452/B02-001, U.S. Environmental Protection Agency. 

IL EPA, 2007: Technical Support Documentation for Control of Nitrogen Oxide Emissions 

from Lime Kilns”, June 2007, AQPSTR 07-04, Illinois Environmental Protection Agency.

Staudt, J., and C. Erickson, 2008: Selective Catalytic Reduction System Performance and 

Reliability Review. Presented at the 2008 International Power Generation Conference, 

December 2-4, 2008, Orlando, FL.

Western Regional Air Partnership (WRAP) Regional Modeling Center, 2006: 

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; 

http://pah.cert.ucr.edu/aqm/308/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf
Appendix A 

Emissions Data Tables
UNC-EMAQ 5-12-026.v1 A-2 October 9, 2012

This appendix provides the emissions data for CALPUFF modeling of the base case(s) and control scenarios analyzed for the three 

facilities. These data were extracted from a master spreadsheet that is being delivered as part of the documentation for this task. 

Table A-2(a). NOx, SO2 and PM Emission Inputs for Base and Control Scenarios for Sundt Unit 4 Visibility Modeling 
EMAQ LOE (EPA Contract EP-D-07-102) WA 5-12: Task 7 Five-Factor BART Analysis Report

UNC-EMAQ 5-12-026.v1 A-3 October 9, 2012

Table A-2(b). Speciated PM Emission Inputs for Base and Control Scenarios for Sundt Unit 4 Visibility Modeling
Appendix B

Andover Technology Partners Report Chapter 3: 

BART Analysis (NOx, SO2 and PM)
3-2 www.AndoverTechnology.com

Chapter 3. BART Analysis (NOx, SO2 and PM)

Task 7: Determine BART Control Options (SO2, NOx, PM)

Under this task, Andover Technology Partners will conduct a full 5-factor BART analysis of all 

technically feasible control options for 

 Sundt (Unit 4).

Cost estimates performed in this section utilize methods consistent with the US EPA Control 

Cost Manual,4cost estimating methods used in EPA’s latest Integrated Planning Model (IPM) 

methods5, and other reasonable engineering methods.

. 

 

4

EPA. 2002. EPA Air Pollution Control Cost Manual (Sixth Edition). EPA/452/B-02-001

5 Documentation of EPA IPM Base Case v4.10. http://www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html#documentation
3-3 www.AndoverTechnology.com

3.3 H. Wilson Sundt Power Plant

H. Wilson Sundt (Sundt) unit 4 requires a BART analysis, and it is the unit that is located at the 

bottom of Figure 3-3. Table 3-12 shows the modeled and reported data for Sundt unit 4. 2008-

2010 data was averaged and 2011 data is the average over that year. Capacity factor is 

determined by dividing total reported heat input by the potential heat input if the unit were 

operating at 100% capacity 365 days a year. Capacity factor is important in performing cost 

effectiveness calculations.

Sundt 4 is coal fired. Sundt Unit 4 is also equipped with a fabric filter, low NOx burners and 

overfire air. 

Figure 3-3. Sundt Power Plant.

Table 3-12. Sundt Power Plant Data

Unit Capacity

Fuel

2008-2010 

Reported Avg

2011 Reported 

Rates

2008-

2011

NOx SO2 NOx SO2

Capacity

Factor

MW gross lb/MMBtu lb/MMBtu %

Unit 4 173.3 Coal 0.361 0.477 0.445 0.729 37%
3-4 www.AndoverTechnology.com

3.3.1 NOx Control

Available Controls

Available controls include combustion controls and post-combustion controls.

Feasible Controls

Because Sundt 4 already has combustion controls, the focus will be post-combustion controls.

Evaluation of NOx Controls

Sundt 4 is reported to already have low NOx burners and Overfire Air, and emissions rates are 

consistent with that level of control for a wall fired boiler. Therefore, only post-combustion 

controls are examined, although some advanced forms of OFA may permit further reductions. 

Results are shown in Table 3-13.

For the purpose of cost estimation, baseline emissions and capacity factors for all electric utility 

units were developed from historical data in this manner.

 Capacity factor will be the average of that estimated for the period 2008-2011 using 

reported heat input and plant capacity information.

 NOx and SO2 emission rates were examined for the average period of 2008-2010 and 

2011. In the case of Sundt Unit 4, the 2011 rate was selected as the baseline rate for 

estimating cost effectiveness.

Also, a capital recovery factor of 9.44% (equal to 7% over 20 years) plus tax and insurance 

costs of 1.2% were assumed in the cost analysis. This charge rate for taxes and insurance 

differs from what is used for industrial sources per the EPA Air Pollution Control Cost Manual 

and is consistent with what has been determined for electric generating units per the Integrated 

Planning Model documentation. 6

Energy Effects

SNCR has negligible energy impact. SCR will incur an additional parasitic loss, mainly due to 

pressure drop across the SCR system, that is roughly equal to 0.59% of output. Although this is 

not treated as a cost in the IPM calculations that these algorithms were developed for, rather as 

a reduction in capacity, it has been treated as an increase in variable operating cost for these 

calculations.

 

6

From Integrated Planning Model ver 4.10 documentation, page 8-11. 0.30% for insurance and 0.90% for taxes.
3-5 www.AndoverTechnology.com

Environmental Impacts

Environmental impacts of SNCR and SCR are primarily due to ammonia slip. The main 

environmental concern is regarding fly ash odor and potential impact on fly ash sales/disposal. 

Generally, ammonia slip can be well controlled from both of these technologies so that fly ash is 

not adversely impacted.

SCR catalyst can be recycled and therefore is not a significant concern. 

Table 3-13. NOx Control for Sundt 4

Sundt 4 - NOx

SNCR SCR

Capacity Factor 37% 37%

Control Efficiency 30% 89%

Uncontrolled Rate

NOx Rate (lb/hr) 813 813

NOx Rate (lb/MMBtu) 0.445 0.445

NOx (TPY) 1318 1318

Controlled Rate

NOx Rate (lb/hr) 569 91

NOx Rate (lb/MMBtu) 0.31 0.05

NOx (TPY) 923 148

Tons/Year Removed 395 1,170

Total Cost, $/year $1,308,000 $5,712,715

$/Ton $3,307 $4,882

$/Ton (incremental) $3,307 $5,686

* SCR is actually capable of 90% removal. The model that we used set a lower 

limit of 0.05 lb/MMBtu, but lower NOx levels should be achievable
3-6 www.AndoverTechnology.com

3.3.2 SO2 Control

SO2 emission rates increased in 2011 from the 2008-2010 average, indicating a change in coal. 

Available Technologies

Available Technologies for Sundt 4 include dry sorbent injection, wet FGD and dry FGD. Sundt 

could also change to lower sulfur coal.

Feasible Technologies

All of the above available technologies are also feasible. In fact, since Sundt 4 has a fabric 

filter, dry sorbent injection may be well suited to this unit. A dry FGD is assumed at Sundt 4 to 

include a new fabric filter since there likely is not enough space to install a new absorber 

between the boiler and the existing fabric filter. A wet FGD would be installed downstream of 

the existing fabric filter.

Evaluation of Technologies

A change in coal was not evaluated for cost since coal cost data was not available.

Table 3-14 shows the estimated cost of each of the SO2 control technologies. In light of the low 

capacity factor at Sundt 4 and the high capital costs of FGD, the two FGD approaches are 

significantly more expensive (on a $/ton of SO2 removed basis), than DSI. DSI is expected to 

deliver high removal efficiencies due to the fabric filter.

The FGD removal efficiencies are limited by assumptions in the Sargent & Lundy model 

developed for EPA’s Clean Air Markets Division that was used for the FGD cost analysis. In 

practice, we would expect both dry and wet FGD to achieve somewhat lower emissions levels 

(and higher capture efficiencies). In this respect these results are conservative.

Energy Impacts

The energy impacts of dry sorbent injection are small. FGD will have significant energy impacts 

(over 1% of output) due to pressure loss across the FGD system and due to additional pumping 

power and other auxiliary loads. In all cases energy costs are included in the results of Table 3-

14.

Environmental Impacts

DSI using trona or sodium bicarbonate will result in increased solids collected by the fabric filter. 

These sodium based materials may result in a by-product with a water soluble component that 

may require some stabilization. Activated lime hydrate, if used, might avoid that issue. DSI has 

other benefits to air emissions as described in Section 2.1.2.

Both the wet and dry scrubbers will have a by-product. The dry scrubber by product is stable 

and can be landfilled. The wet scrubber waste can be dewatered and sold to the gypsum 

market. More information is available in Section 2.1.2.
3-7 www.AndoverTechnology.com

Table 3-14. SO2 Control for Sundt 4

Sundt 4 – SO2

DSI Dry FGD Wet FGD

Capacity Factor 37% 37% 37%

Control Efficiency 75% 89% 92%

Uncontrolled Rate

SO2 Rate (lb/hr) 1333 1333 1333

SO2 Rate (lb/MMBtu) 0.729 0.729 0.729

SO2 (TPY) 2160 2160 2160

Controlled Rate

SO2 Rate (lb/hr) 333 146 110

SO2 Rate (lb/MMBtu) 0.18 0.08 0.06

SO2 (TPY) 540 237 178

Tons/Year Removed 1620 1923 1982

% change stack velocity No Change -10% -20%

Exit temp No Change 175 F 130 F

Impact on PM speciation No Change Yes, FGD plus FF Yes, FGD plus FF 

Total Cost, $/year $2,967,501 $12,553,539 $13,986,681

$/Ton $1,832 $6,529 $7,057

$/Ton (incremental) $1,832 $31,647 $24,189

3.3.3 PM Control

Sundt 4 is equipped with a fabric filter, and there is no technology available that would provide a 

higher degree of PM reduction than a properly designed and operated fabric filter. Therefore, 

there are no changes to PM emission rates under the BART analysis.