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# Technical Support Document US EPA Proposed Approval of the State of Kansas’

Regional Haze SIP
Prepared By: Tracey Casburn, Environm ental Scientist, US EPA, Region 7
August 12, 2011

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## Table of Contents Consultation Page 6 Emissions I nventory Page 7 Planned Burning Emissions I nventory Page 8 Ammonia Emissions I nventory Page 9 Mobile Source Emissions I nventory Page 9 Fugitive Dust Emissions Inventory Page 10 Point Source Emissions Inventory Page 10 Nonpoint Source Emissions I nventory Page 1 2 Affected Class I Areas and Source Apportionment Page 1 5 Best Available Retrofit Technology (BART) Page 20 BART Eligible Sources Page 2 3 BART Subject Sources Page 2 6 BART Determination for Westar JEC Units 1&2 Page 29 BART Determination for Westar GEC Unit 2 Page 3 4 BART Alternative for GEC Unit 2 Page 38 BART Determination for KCP&L Units 1&2 Page 4 2 Reasonable Progress/L ong Term Strategy Page 4 6

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## List of Figures Figure 1

- Geographical Area of Regional Planning Organizations Page 6 Figure 2

- 2002 Emissions of Pollutants Contributing to Regional Haze from Kansas Page 1 3 Nonpoint Sources Figure 3

- 2002 Percent Total Light Extinction at Eleven Class I Areas from Kansas Page 1 6 So urces, by Species, for All Source Categories during the Worst 20% Days Figure 4

- 2002 Percent Total Light Extinction at Eleven Class I Areas from Kansas Page 1 7 Air Emission Sources, by Source Category, for All Species during the Worst 20% Visibility D ays Figure 5

- 2018 Percent Total Light Extinction at Eleven Class I Areas from Kansas Page 1 8 Sources, by Species, for All Source Categories during the Worst 20% Days Figure 6

- 2018 Percent Total Light Extinction at Eleven Class I Areas from Kansas Page 1 9 Air Emission Sources, by Source Category, for All Species during the Worst 20% Visibility Days Figure 7

– 2002 VOC emissions by CENRAP State and S ource S ector Page 2 2 Figure 8

- 2002 Ammonia emissions by CENRAP state and S ource S ector Page 2 3 Figure 9

- Map of BART

- eligible S ources in Kansas Page 2 6 Figure 10

- Map of Class I A reas A nalyzed Page 2 7 

## List of Tables Table 1

- Baseline 2002 Kansas Emissions Summary, by Source Category and Pollutant Page 7 Table 2

- 2018 Kansas Project ed Emissions Summary, by Source Category and Pollutant Page 8 Table 3

- Total Acres Burned in Kansas with Estimated Annual Emissions Page 9 Table 4

- 2002 Locomotive Emissions in Kansas (tons/yr) Page 10 Table 5

- 2002 Nonroad Mobile Source Emissions in Kansas by Equipment Type (tons/yr) Page 10 Table 6

- Total 2018 Reductions in NO x and SO 2 from Kansas Emission Sources Subject to Page 1 1 Table 7

- Summary of Wichita Mountains Visibility Impact Results, in Percent Light Extinction Page 1 1 Increase, and in Deciview Increase
Table 8

- 2009 Emissions Summary of Ethanol and EGUs Page 1 2 Table 9

- 2002 Top PM 10 Emissions from Kansas Nonpoint (Area) Sources Page 1 3 Table 10

- 2002 Top VOC Emissions from Kansas Nonpoint (Area) Sources Page 1 4 Table 1 1

- 2002 Top NO x Emissions from Kansas Nonpoint (Area) Sources Page 1 4

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Table 12

- 2002 Top SO 2 Emissions from Kansas Nonpoint (Area) Sources Page 1 5 Table 13

- 2018 Percent Total Light Extinction > 0.5% Due to Kansas Sources for Page 20 Worst and Best 20% Visibility Days for the Wichita Mountains Class I Area Table 14

- Facilities with BART

- eligible U nits in the State of Kansas Page 2 5 Table 15

- List of 19 BART

- eligible S ource and T heir D istances from each of the Page 2 7 9 Class I Areas A nalyzed. Table 16

- Kansas BART

- Eligible Emission Units with at Least One > 0.5 dv Page 2 8 Visibility Impact Day on Selected Class I Areas during 2001

– 2003 Table 17

- BART Presumptive Limits and Operational Standards for NO x and SO 2 Page 29 at Subj ect
- to
- BART Em ission Units in Kansas Table 18

- Visibility Impairment Attributable to JEC Units 1 and 2 and GEC Unit 2 (2001

- 2003 Page 30 Table 19

- Pollutant Specific Contributions to Existing Visibility Impairment for Page 3 0 JEC Unit 1 and 2 (2001

- 200 3) Table 20

- Visibility Improvement from the Implementation of Presumptive SO 2 BA RT Page 3 1 Table 21

- Summaries of Cost effectiveness of NO x controls at JEC units 1 and 2 Page 3 2 Table 22

- Summary of Modeled Impacts from NO x Control for JEC Unit 1 (2001

- 2003) Page 3 3 Table 23

- Summary of Modeled Impacts from NO x Control for JEC Unit 2 (2001

- 2003) Page 3 3 Table 24

- Pollutant Specific Contributions to Existing Visibility Impairment Page 3 4 for GEC Unit 2 (2001

- 2003) Table 25

- Summary of Emission Rates Comparing Existing Control to a Fuel Page 3 5 Switch at GEC Unit 2 Table 26

- Summary of Cost Effectiveness for Switching from No. 6 Oil to one Page 3 5 percent sulfur fuel Table 27

- Summary of Modeled Impacts from SO2 control at GEC Unit 2 (2001

- 2003) Page 3 6 Table 28

- Summary of Cost Effectiveness for GEC Unit 2 NO x Control (SCR) Page 3 7 Table 29

- Summary of Modeled Visibility Im pacts from SCR control Page 38 Table 30

- Com

parison of Presumptive BART (1% Su lfur Fuel and LNB) to the Page 4 0 BART Alternative Table 31

- Com

parison of Presumptive BART (1% Sulfur Fuel and LNB) to the Page 4 1 BART Alternative at Five Class I area Table 32

- Visibility Impairment Attributable to KCP&L Units 1 and 2 (2001

- 2003) Page 4 2

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Table 33

- Pollutant Specific Contributions to Existing Visibility Impairment Page 4 2 for KCP& L Units 1 and 2 Table 34

- Visibility Improvement from the Implementation of Presumptive BART Page 4 3 Table 35

- Summary of Cost Effectiveness of NO x Control at KCP&L Units 2 Page 4 4 Table 36

- Summary of NOx Control at KCP&L Unit 2 Page 4 4 Table 37

- Kansas Emission Units Not Subject to BART Emitting at Least Page 47 500 Tons/Yr of NO x or SO 2 in 2002 Table 38

- Sources with Cost per Ton Reduced Greater than $10,000/ton Page 47 Table 39

- Most Effective Control Technologies with Ac ceptable Control Costs Page 48 for Kansas 500

- Ton NO x and SO 2 Em ission Units Table 40

- Overall Visibility Improvements Resulting from Application of the Most Page 50 Effective Controls at Selected Kansas 500

- Ton NO x and SO 2 Em ission Units. Table 41

- Selected Kansas 500

- ton NO x and SO 2 Emission Units Showing Significant Page 5 1 Visibility Improvement Resulting from Application of Most Effective Contro ls Table 42

- Summary of Each Unit’s C ost per ton per U nit of dv I mp rovement Page 5 2 Table 43

- Summary of Reasonable Progress Non

- Cost Statutory Factors Page 5 3 For Selected Kansas Point Sources Table 44

- Ranked List of Sources under the Reasonable Progress Analysis Page 5 4 Table 45

- Control or Work Practice Strategies for Westar Units to Meet Kansas Long Page 5 5 Term Strategy R equirements Table 46

- Estimated NO x and SO 2 Em ission Reductions for Implementation of Controls Page 5 6 or Work Practices required by Kansas’ Long Term Strategy

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## Consultation Because the pollutants that lead to regional haze can originate from sources located across broad geographic areas, EPA encouraged the States and Tribes to address visibility impa irment from a regional perspective. Five regional planning organizations (RPOs) were developed to address regional haze and related issues. These organizations first evaluated technical information to better understand how their States and Tribes impact visibility (regional haze) at national park and wilderness areas (Class I areas) across the country , and then pursued the development of regional strategies to reduce emissions of particulate matter and other pollutants leading to regional haze issues. The State of Kansas participated in the planning efforts of the Central Regional Air Planning Association (CENRAP) which is affiliated with the Central States Air Resource Agencies (CENSARA). This RPO includes nine states

- Nebraska, Kansas, Oklahoma, Texas, Minnesota, I owa, Missouri, Arkansas, and Louisiana. The RPOs, such as CENRAP, provided air quality modeling to the States to help them in determining whether sources located within the State can be reasonabl y expected to cause or contribute to visibilit y impairment in a Class I area. The modeling relied on baseline y ear (2002) and future planning ye ar (2018) emissions inventories that were prepared with the participation from each of the CENRAP states. States relied on these inventories to develop contr ol strategies to meet the visibility

improvement goals of the Regional Haze Rule (Rule) and to develop their State I mplementation Plans (SI Ps). This technical support document also relies heavil y on the data provided b y the CENRAP.

Figure 1

- Geographica l Area of Regional Planning Organizations

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## Emissions Inventory The inventory

(2002 and 2018) provided in the State’s Regional Haze SI P includes: Volatile Organic Compounds (VOCs), nitrogen oxides ( NO x ) 

### , sulfur dioxide ( SO 2 ) 

### , fine particulate matter ( PM 2.5 ) 

### , coarse particulate matter ( PM 10 ) 

### and ammonia (NH 3 

### ). The 2002 emissions inventory

and its improvements were developed b y CENRAP and its contractors as part of the development of a baseline inventory for the 2002 modeling inventory . The improvements were prepared by Sonoma Technology , Inc. (STI), a contractor retained to develop and improve three inventory categories of the baseline 2002 inventory : planned burning emissions inventory , a mmonia inventory , m obile source and fugitive dust emissions inven tory
. The complete 2002 baseline emissions inventory can be found in Appendix 7.1 of the SI P.

Table 1

- Baseline 2002 Kansas Emissions Summary, by Source Category and Pollutant 

### To estimate the 2018, future year emissions, the 2002 emissions were grown using the Economic Growth Analysis Sy stem (EGAS), MOBILE 6.2 vehicle emissions software, and the Integrated Planning Model (I PM) version 2.93 for EGUs. The complete 2018 emissions inventory can be found in Appendix 7.2 of the SIP.

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Table 2

- 2018 Kansas Projected Emissions Summary, by Source Category and Pollutant 

### Planned Burning Emissions Inventory Agricultural and prescribed burning activities, referred to as planned burning, li kel y contribute to episodes of impaired visibility in the CENRAP region. STI developed planned burning emissions inventories for the CENRAP region, including Kansas. In Kansas, planned burning is an important component of the nonpoint source category , with a significant amount of emissions. STI used a “bottom

- up” approach in preparing these emissions estimates. This preparation included using the First Order Fire Effects Model (FOFEM), incorporating emission factors and fuel loadings gathered from publishe d literature, geographic information sy stems (GI S) databases of land cover and vegetation, and activity data gathered through telephone survey s. In Kansas, a significant amount of the burning occurs on rangeland and cropland. To best characterize the amou nts burned, STI performed an agricultural burning survey of the count y agricultural extension service offices within the state. This survey included rangeland burning questions designed to determine the fraction of rangeland acreage t ypicall y burned each y ear and the timing of such burn events. Of the 105 total counties survey ed, 59 responded, which represents a 56% response rate. The results of these survey s can be found in Appendix 7.3 of the SI P. Assuming the responding counties comprise a representativ e sample, these two categories, wheat stubble burning and rangeland burning, together represent approximately 93% of the acres burned in Kansas. As can be seen in Table 3, these two categories also represent the majority of the burning inventory

emissions. The baseline 2002 source category “Area Source Fire” was grown to reflect the information gathered by STI and is represented in Appendix 7.1 of the SI P. Although area source fire emissions were shown to be significant in amount , photochemical modeling, d iscussed in greater detail below, did not show these emissions to have a significant impact on visibility at Class I areas.

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Table 3

- Total Acres Burned in Kansas with Estimated Annual Emissions 

### Ammonia Emissions Inventory It is widel y recognized that the formation of secondary particulate matter, which is generated from chemical transformations in the atmosphere of gaseous precursor species such as ammonia, NO x 

### , sulfur oxides (SO x) 

### , and VOCs , contributes significantl y to re gional haze issues in the CENRAP region. In Kansas, agriculture

- related ammonia emissions, coming from sources such as confined animal feeding operations (CAFOs) and crop fertilization, are significant in amount . Because ammonia can have an impact on the f ormation of secondary

particulate species, CENRAP and Kansas spent considerable resources on developing an accurate ammonia inventory . STI developed a comprehensive inventory for CENRAP. In Kansas, over 50% of the ammonia emissions come from livestock em issions, including CAFOs and free range grazing. In 2002, it is estimated that 91,000 tons per y ear (tpy or tons/y ear) of ammonia came from these two source categories. In addition, it is estimated that 65,000 tpy of ammonia emissions came from fertilizer emissions. The remainder of the ammonia emissions in Kansas is from biogenic and point sources categories. One of the important considerations in the development of the ammonia inventory was the temporal allocation of the ammonia emissions, both seasonall y and diurnall y. STI did considerable work in developing temporal profiles for the ammonia inventory in Kansas. These temporal profiles were then utilized during photochemical modeling to assess the impact of ammonia emissions on visibility impairment. Al though ammonia emissions were considered b y the State to be significant to the formation of visibility

impairing pollutants such as particulate matter, photochemical modeling, discussed in greater detail below, did not show that these ammonia formed pollut ant emissions have a significant impact on visibility at Class I areas. **Mobile Source Emissions Inventory** In Kansas, the emissions inventory for the on

- road mobile and nonroad mobile source categories are fairl y significant in amount for certain pollutants. STI prepared detailed inventories for these source categories, including refined spatial and temporal data. Table 4 has a summary of the locomotive emissions in Kansas, while Table 5 shows emissions from other nonroad source categories. There a re minimal emissions from recreational boating in Kansas,

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thus it is not summarized here. Photochemical modeling, discussed in greater detail below, did not show these emissions to have a significant impact on visibility at Class I areas.

Table 4

- 2002 Locomotive Emissions in Kansas (tons/yr)

Table 5

- 2002 Nonroad Mobile Source Emissions in Kansas by Equipment Type (tons/yr) 

### Fugitive Dust Emissions Inventory The final emissions category that STI anal yz ed dealt with fugitive PM2.5 emissions from agricultural sources. These emissions come from sources such as tilling operations. In Kansas, it is estimated that there are over 50,000 tpy of PM2.5 emissions from ag ricultural tilling operations. As was the case for ammonia emissions, the temporal distribution of these emissions is important as they

are seasonal in nature. Photochemical modeling, discussed in greater detail below, did not show fugitive dust emissions from agriculture to have a significant impact on visibility at Class I areas. **Point Source Inventory** The majority

of visibility

- impairing point source emissions in Kansas currently come from the electric utility

sector. This sector represented 93% of the reported SO 2 

### emissions and 57% of the NO x 

### emissions for the 2002 inventory y ear. The other large NO x 

### emissions category in Kansas is the natural gas compression industry , which represented 28% of total NO x 

### emissions for the 2002 inventory

y ear. For the point source sector, reductions in NO x 

### and SO 2 

### by 2018 are projected primarily

at the five EGU units the State found to be subject to BART controls. Table 6 details the reductions predicted from these emission units.

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**Subject -**  **to -**  **BART unit 2002**  **NO x**  **1** 

### 2002

**SO 2**  **1** 

### 2018

**NO x**  **2** 

### 2018

**SO 2**  **2** 

### NO **x**



### reduction **SO 2** 



### reduction tons/yr KCP&L

- La Cygne 1 30,058 6,648 2,576 3,948 27,482 2,700 KCP&L

- La Cygne 2 8,362 19,355 6,229 3,993 2,133 15,362 Westar

- Gordon Evans 2 2,023 3,211 138 0.0 1,886 3,211 Westar

- Jeffrey 1 9,602 20,459 4,268 3,532 5,334 16,927 Westar

- Jeffrey 2 10,892 23,715 4,040 3,465 6,852 20,251 **Total BART reductions 43,687**  **58,451** Table 6

- Total 2018 Reductions in NO x and SO 2 from Kansas Emission Sources Subject to 1 Data from EPA Clean Air Markets 2 Data from IPM 3.0 

### At the time this document was being prepared, an 895 MW coal

- fired EGU was in the permitting process in Kansas. This EGU, located in Holcomb in the southwest portion of the State, was anal yz ed for visibility impacts on the WI MO. The State has asserted that CAMx PSAT modeling showed that visibility impairment in the WI MO attributed to this new source would be below 0.5 delta deciview (Δdv) . The permit for the 895 MW EGU was approved by the Secretary

of the Kansas Department of Health and Environment. The visibility modeling results for the power plant at Holcomb is provided in Appendix 11.2 of the SI P.

Table 7

- Summary of Wichita Mountains Visibility Impact Results, in Percent Light Extinction Increase, and in Deciview Increase 

### Sev eral large natural gas compressor stations are located along pipelines crisscrossing the State, with numerous other stations clustered around the Hugoton and Panoma gas fields in the

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southwest. I n 2002, 19 EGUs reported NO x 

### emissions over 100 tons, wherea s in the same period 95 natural gas compressor stations reported NO x 

### emissions over 100 tons. From 1995 to 2005, production from the Hugoton gas field, the State’s largest natural gas production area, has decreased from approximately 460,000 billion cubic feet (Bcf) to 200,000 Bcf. Over the same period, actual NO x 

### emissions reported from the compressor stations have steadily decreased from 75,000 tons/y

r to 50,000 tons/y

r. Kansas expects emissions from this sector to continue to decline as production decl ines and equipment is replaced with new, more efficient designs. Additional information on this sector can be found in Appendix 7.4 of the SI P. In 2002, there were four ethanol plants in operation in Kansas, with a total production capacity of 164 million gallons per year (Mgal/ yr). That number grew to 14 facilities in 2007 with a total capacit y of 814 Mgal/ yr. Currently, there are 12 active ethanol facilities, 3 facilities under construction, 1 facility that has been permitted but not y et built, 1 facil ity built but not active, and 1 facility

whose permit application is pending. In 2008 and 2009, expansion of the fuel ethanol industry

in Kansas slowed. The actual emission rate of NO x 

### for these plants is quite low as compared to EGUs.

**NO x**  **PM 10**  **SO x** 



### EGU's 48,176.37 3,611.84 51,595.27 **Ethanol Plants** 374.03 294.27 59.93 Table 8

- 2009 Emissions Summary of Ethanol and EGUs 

### Nonpoint Source Inventory Nonpoint source emissions were compiled from the final 2002 National Emissions Inventory (NE I) database. Although PM 2.5 

### emissions values are not available from this data source, the NEI gives emissions estimates at a detailed level for source categories, so that source attribution is more easily

anal yz ed. (Note also the ratio of PM 2.5 

### to PM 10 

### , can for comp arative purposes be approximated as 0.2). Onl y NO x 

### , SO 2 

### , PM 10 

### , and VOC were considered in this anal ys is. The relative contributions of these pollutants within the nonpoint source category are shown in Figure 2 (below). The attribution of nonpoint source pollution on visibility at Class I areas is shown in Figure 4 ( provided on page 17 ).

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Figure 2

- 2002 Emissions of Pollutants Contributing to Regional Haze from Kansas Nonpoint Sources 

### As can be seen b y Figure 2, PM 10 

### accounts for the largest nonpoint source category, and can be attributed primarily to agricultural crop production (including burning of crop residue), unpaved and paved road dust, and dust from construction activities. Tables 9 through 12 give the relat ive contributions from the top sources of nonpoint emissions for the State for each of the four haze

- causing pollutants. These values are based on the 2002 National Emissions I nventory
. Figure 4 (provided on page 1 7 of this document) indicates that the v isibility impairment attributed to nonpoint source pollution from sources located in Kansas is almost equivalent to the visibility impairment attributed to point source pollution from sources in Kansas. However, although PM 10 

### emissions are the highest of the nonpoint source category emissions, compared to NO x 

### , VOC and SO 2 

### , Figure 3 (provided on page 1 6 of this document) indicates that a majorit y of the visibility

impairment causing pollution is SO 2 

### and NO x 

### and not necessarily PM 10 

### . So although nonpoint so urces in Kansas may contribute to visibility

impairment in Class I areas, nonpoint source PM 10 

### is not significant.

**Source classification tons/yr**  **% of nonpoint PM 10**  

### % of total PM **10** 

### Unpaved roads 275,026 37.9% 36.6% Agricultural crop production 253,845 35.0% 33.8% Agricultural burning 99,292 13.7% 13.2% Road construction 48,050 6.6% 6.4% Paved roads 32,892 4.5% 4.4% Mining and quarrying 7,539 1.0% 1.0% Table 9

- 2002 Top PM 10 Emissions from Kansas Nonpoint (Area) Sources 

### The nonpoint source category represents a large portion of the total anthropogenic emissions inventory

for VOC emissions. VOC emissions from nonpoint sources are projected to grow b y 20% in 2018 from the 2002 estimate. In 2018, the estimate is for approximately 105,000 tons /y

r of VOC from the nonpoint source category . Much of the nonpoint VOC is from 0 100, 000 200, 000 300, 000 400, 000 500, 000 600, 000 700, 000 800, 000

**P M 1 0**  **VO C**  **NO x**  **S O 2** **t o** **n s** **/ y** **r**

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solvent utilization and residential heating. Figure 3 below demonstrates that VOC is not anticipated to be a large contributor to visibility impairment in the Class I areas su rrounding Kansas so the remaining focus will be on NO x 

### and SO 2 

### nonpoint source emissions.

**Source classification tons/yr**  **% of nonpoint VOC**  **% of total VOC** Agricultural burning 55,058 41.4% 22.7% Residential fuel combustion 18,758 14.1% 7.7% Gasoline service stations 13,398 10.1% 5.5% Misc. commercial solvents 7,986 6.0% 3.3% Misc. consumer solvents 7,185 5.4% 3.0% Misc. industrial solvents 6,554 4.9% 2.7% Graphic arts solvents 6,476 4.9% 2.7% Surface coatings (solvent evap.) 5,704 4.3% 2.4% All other misc. solvent usage 4,044 3.0% 1.7% Table 10

- 2002 Top VOC Emissions from Kansas Nonpoint (Area) Sources 

### NO x 

### and SO 2 

### make up a relativel y small proportion of the State’s nonpoint emissions inventory . With the exception of agricultural burning, the major contributing source categories for NO x 

### are associated with residential, industrial, and commercial natural gas combus tion. These categories are projected to grow at a moderate pace between 2002 and 2018. For SO 2 

### , the major contributing categories are industrial fuel (primarily coal and distillate oil) combustion. These categories are also projected to grow at a modera te pace between 2002 and 2018. The nonpoint industrial coal combustion category in the NEI may

be overestimated, both in the 2002 and 2018 projected inventories, as most industrial sources utilizing coal are captured in the State’s point source inventory

. In addition, the distillate and residual oil combustion in the nonpoint sector are likel y to remain unchanged or decrease between 2002 and 2018. At worst, if the 2018 projections are correct, the nonpoint emissions for SO 2 

### are still only a fraction of t he point source SO 2 

### emissions.

**Source classification tons/yr**  **% of nonpoint NO**  **x** 

### % of total NO **x** 

### Agricultural burning 29,099 68.8% 7.6% Industrial fuel combustion 6,298 14.9% 1.6% Residential fuel combustion 4,203 9.9% 1.1% Table 11

- 2002 Top NO x Emissions from Kansas Nonpoint (Area) Sources

### 15 **Source classification tons/yr**  **% of nonpoint SO**  **2** 

### % of total

**SO 2**  

### Industrial fuel combustion 24,218 66.6% 12.9% Agricultural burning 10,949 30.1% 5.9% Table 12

- 2002 Top SO 2 Emissions from Kansas Nonpoint (Area) Sources 

## Affected Class I Areas and Source Apportionment There are no Class I areas hosted by the State of Kansas . States such as Kansas, that do not host Class I areas, are not required to identify reasonable progress goals or calculate baseline and natural visibility

conditions at Class I areas. However, States without Class I areas are still required to address their apportionment of visibility impact, from the emissions generated b y sources within the State ’s borders on Class I areas hosted by other States. The following are the Class I areas nearest to the State of Kansas in all directions around the State’s border:

 Caney Creek Wilderness Area, Arkansas (CACR)
 Upper Buffalo Wilderness Area, Arkansas (UPBU )
 Great Sands Dunes Wilderness Area, Colorado (GRSA)
 Rocky

Mountain National Park, Colorado (ROMO)
 Hercules Glades Wilderness Area, Missouri (HEGL)
 Mingo Wilderness Area, Missouri (MI NG)
 Wichita Mountains Wilderness Area, Oklahoma (WI MO)
 Badlands National Park, South Dakota (BADL)
 Wind Cave National Park, Texas (WI CA)
 Big Bend National Park, Texas (BIBE)
 Guadalupe Mountains National Park, Texas (GUMO) In order to address the issue of source attribution of regional haze for its member states, CENRAP contra cted with ENVIRON to carry out PM Source Apportionment Technology (PSAT) modeling, a well known “probing tool” for the CAMx photochemical model. ENVIRON delivered its results in the form of a customized Microsoft Access database query tool, hereinafter th e PSAT tool, which allows users to select among various inputs to produce charts and tables. Visibility impact can be represented in terms of absolute light extinction (B ext 

### ), inverse megameters (Mm - 1 

### ), and percent total light extinction. Data can be que ried from source categories within individual states on individual or grouped Class I area receptors, for the years 2002, 2000

- 2005 (baseline), and 2018 (end of current planning period). The PSAT tool used for this document is the August 27, 2007 version and is available at http://www.cenrap.org/html/projects.php ENVIRON used IMPROVE monitoring data for the 2000

- 2004 period to define baseline, natural, and 2018 conditions for each of the 11 Class I a reas. PM10 was speciated into six components (sulfate, particulate nitrate, organic carbon, elemental carbon, soil, and coarse mass) which were used to develop relative response factors (RRFs) between current and predicted concentrations for each component . The RRFs were multiplied by current baseline values to estimate future concentrations. The visibility improvements for 2018 were then calculated using

### 16

the original or new IMPROVE equation. Additional information about these anal ys es can be found in Chapt er 4 of the Technical Support Document (TSD) prepared b y ENVIRON and the Causes of Haze Assessment work performed b y STI . These documents can be found at http://www.cenrap.org/html/projects.php The EPA used the PSAT tool to find projected absolute and percent total light extinction values at eleven Class I areas. The anal ys is helped determine which Class I areas may be impacted b y Kansas sources, and also provides a quantifiable basis of discuss ion for meeting the reasonable progress goals for Class I areas potentially impacted by

emission sources within Kansas. The 20 % worst day

estimated percent light extinction, at these eleven Class I areas, attributed to emissions from sources in Kansas ( shown by pollutant species and source category ) is given below. The modeling (as shown in Figures 4 and 6 ) indicated that Kansas sources are most likely

to have the highest impact at the WI MO Class I area.

Figure 3

- 2002 Percent Total Light Extinction at Eleven Class I Areas from Kansas Sources, by Species, for All Source Categories during the Worst 20% Days

### 17

Figure 4

- 2002 Percent Total Light Extinction at Eleven Class I Areas from Kansas Air Emission Sources, by Source Category, for All Species during the Worst 20% Visibility Days

### 18

Figure 5

- 2018 Percent Total Light Extinction at Eleven Class I Areas from Kansas Sources, by Species, for All Source Categories during the Worst 20% Days

### 19

Figure 6

- 2018 Percent Total Light Extinction at Eleven Cl ass I Areas from Kansas Air Emission Sources, by Source Category, for All Species during the Worst 20% Visibility Days 

### Figures 3 through 6 reveal that the largest contributors to haze for WI MO from Kansas sources are nitrates (NO 3 

### ). To get a more precise idea of relative contributions, the PSAT tool was used to generate tables for individual species, for the best 20% visibility day s as well as for the worst 20% visibility

day s. Table 13 summarizes the PSAT tool

- generated tables, which are found in Append ix 10.1 of the State’s SI P. Table 13 shows percent total extinction values greater than 0.5% to represent percent total extinction for the respective source category and species combinations on the worst and best 20% visibility day s. Values of less than 0.5% were considered to be insignificant. This tool confirms that on the worst 20% visibility day s at WI MO, point sources of nitrates, followed by point sources of sulfates and primary organic aerosol (POA) have the greatest impact of pollutants attribute d to sources in Kansas. Sources of POA can be wildfires, mobile sources, industrial and commercial combustion, residential heating and open burning, burning of construction debris, industrial and commercial processes, agricultural burning, and fugitive dus t.

### 20 **Source category / particulate species Percent total extinction > 0.5% (%)** 

Worst 20%

visibility days (total extinction = 72.01 Mm - 1 

### ) **Point sources**

**– NO**  **3** 

### 0.9572 **Point sources**

**– SO**  **4** 

### 0.8404 **Area sources**

**– POA**  0.7210

Best 20% visibility days (total extinction = 15.14 Mm - 1 

### ) **Point sources**

**– NO**  **3** 

### 0.7386 **Point sources**

**– SO**  **4** 

### 1.6518 **Area sources**

**– SO**  **4** 

### 0.5526 **Area sources**

**– POA**  1.6600 **Area sources**

**– EC**  0.6497 Table 13

- 2018 Percent Total Light Extinction > 0.5% Due to Kansas Sources for Worst and Best 20% Visibility Day s for the Wichita Mountains Class I Area 

## Best Available Retrofit Control Technology Section 169A of the CAA directs States to evaluate the use of retrofit controls at certain larger, often uncontrolled, older stationary sources in order to address visibility impacts from these sources. Specifically , the CAA requires that certain categories of existing stationary sources built between 1962 and 1977 procure, install, and operate the “best available retrof it technology ”, or BART, as determined by the State. 1 

### States are directed to conduct BART determinations for the sources that cause or contribute to any visibility impairment in a Class I area. On July

6, 2005, EPA published the Guidelines for BART De terminations Under the Regional Haze Rule at Appendix Y to 40 CFR Part 51 (hereinafter referred to as the “BART Guidelines”) to assist States in determining which of their sources should be subject to the BART requirements and in determining appropriate em issions limits for each such source. In

1 The set of “major stationary sources” potentially subject to BART are listed in CAA section 169A(g)(7). The 26 sour ce categories are: (1) Fossil

- fuel fired steam electric plants of more than 250 million British thermal units per hour heat input, (2) Coal cleaning plants (thermal dryers), (3) Kraft pulp mills, (4) Portland cement plants, (5) Primary zinc smelters, (6) I ron and steel mill plants, (7) Primary aluminum ore reduction plants, (8) Primary copper sm elters, (9) Municipal incinerators capable of charging more than 250 tons of refuse per day, (10) Hydrofluoric, sulfuric, and nitric acid plants, (11) Petroleum refi neries, (12) Lime plants, (13) Phosphate rock processing plants, (14) Coke oven batteries, (15) Sulfur recovery plants, (16) Carbon black plants (furnace process), (17) Primary lead sm elters, (18) Fuel conversion plants, (19) Sintering plants, (20) Seconda ry metal production facilities, (21) Chemical process plants, (22) Fossil

- fuel boilers of more than 250 million British thermal units per hour heat input, (23) Petroleum storage and transfer facilities with a capacity exceeding 300,000 barrels, (24) Taconi te ore processing facilities, (25) Glass fiber processing plants, and (26) Charcoal production facilities.

### 21

making a BART determination for a fossil fuel

- fired generating plant with a total generating capacity in excess of 750 megawatts, a State must use the approach set forth in the BART Guidelines. A State is not requ ired to follow the BART Guidelines in making BART determinations for other types of sources. The BART Guidelines provide five steps toward identify ing BART control for these very large EGUs. 1. Id entify

all available retrofit control technologies; 2. Eliminate technicall y infeasible control technologies; 3. Evaluate the control effectiveness of remaining control technologies; 4. Evaluate impacts and document the results; 5. Evaluate visibility

impacts In the BART determination process, states must address all significant visibility impairing pollutants. The EPA determined that presumptively , the most significant visibility impairing pollutants are SO2, NOx, and PM. As indicated b y the BART Guidelines 2 

### , consideration of whether VOC or NH 3 

### emissions impair visi bility in particular Class I areas is on a state
- by

- state basis. In its SI P the State determined that it did not need to consider VOC or NH 3 

### emissions when conducting its BART anal ys es. Based on its own review of VOC and NH 3 

### emissions in the State and the pollutant’s impacts on visibility at Class I areas, the EPA agrees with the State’s findings. The EPA’s anal ys is is described below. Figure 7 compares 2002 VOC emission sources from each of the CENRAP states by source sector. As explained by Table 2 (a

bove), and confirmed by

Figure 7, a majority

of the anthropogenic VOC emission from Kansas sources are emitted b y nonpoint sources such as area sources, area fire sources, and off road vehicles. Another major source of VOC emissions from the State can be attributed to on road vehicles. Since neither on road, off road, or area sources are included in the 26 major source categories describe by the BART rule, and since VOC emissions from nonpoint sources in Kansas are not expected to contribute significantly to visibility impairment (shown in Figure 3 ) the EPA agrees with the State that it needn’t consider VOC emission reductions as part of its BART determinations.

2 Appendix Y of Part 51

- States should exercise judgment in deciding whether the following pollutants impair visibility in an area :( 4) Volatile organic compounds (VOC), and (5) Ammonia and ammonia compounds. A State should use its best judgment in deciding whether VOC or ammonia emissions from a source are likely to have an im pact on visibility in an area. Certain types of VOC emissions, for examp le, are more likely to form secondary organic aerosols than others. Similarly, controlling ammonia emissions in some areas may not have a significant im pact on visibility. A State need not provide a formal showing of an individual decision that a source of VOC or ammonia emissions is not subject to BART review . Because air quality modeling may not be feasible for individual sources of VOC or ammonia, a state should also exercise its judgment in assessing the degree of visibility impacts due to emissions of VOC and emissions of ammonia or ammonia compounds. A state should fully document the basis for judging that a VOC or ammonia source merits BART review , including its assessment of the source's contribution to visibility impairment.

### 22

Figure 7

– 2002 VOC emissions by CENRAP State and S ource S ector 

### Figure 8 compares 2002 NH 3 

### e mission sources from each of the CENRAP states by source sector. As explained b y Table 2 (above) a majority of the anthropogenic emission from Kansas sources are emitted b y point and onroad mobile sources. A review of the 2002 NEI data shows that a majori ty

of the point source ammonia emissions come from “Agricultural Services”

. 

### Individuall y, these point sources generall y have less than 250 tpy of emissions. Further review of the 2002 NEI data shows that only one large point source of NH 3 

### is in one of the 26 major source categories the EPA has required states to consider when evaluating BART. This source is the Frontier Eldorado Refining Compan y and is discussed further in the BART

- eligibility discussion below. Based on this information the EPA agrees with the State that it needn’t consider NH 3 

### emission reductions as part of its BART determinations.

### 23

Figure 8

- 2002 Ammonia emissions by CENRAP state and S ource S ector 

### BART Eligible Sources The EPA has reviewed the State’s process for determining if a s ource is BART

- eligible and found that the State appropriatel y identified nineteen facilities as having units that were BART

- eligible. The State utilized the methodology in the BART Guidelines for determining which sources were BART

- eligible. The followi ng criterion was used to identify an emission source as BART

- eligible:

 One or more emissions units at the facility fit within one of the 26 categories listed in the BART Guidelines;
 The emission unit was in existence on August 7, 1977 and began operation at some point on or after August 7, 1962; and
 The limited potential emissions from all emission units identified in the previous two bullets were 250 tons or more per ye ar of an y of these visibility

- impairing pollutants: SO 2 

### , NO x 

### , or PM10. To identify

the sources that met the criteria above, a multi

- step search was conducted including a database query of the permitted air sources and a survey of the facilities. The first step to the BART

- eligible screening was done by query ing the point sources based on t he following criteria:

 The facility

is operating;
 The facility

is coded as being class A (controlled emissions > 100 tpy ), A1 (actual emissions < 100 tpy , potential emissions >100 tpy ), or A2 (actual/potential emission > major source threshold);

### 24

 The faci lity

is in one of the Standard Industrial Classification (SI C) codes or has processes within one of the Source Classification Codes (SCCs) that meet one of the 26 major source categories. The second step was to eliminate sources from the fossil fuel

- fire d boilers category with less than a 250 MMBtu/hr heat input rate. The boiler values were summed for each of the facilities. Facilities with aggregated boiler capacity of less than 250 MMBtu/hr were then eliminated. This resulted in a list of 97 facilities. Then, for the 97 facilities on the list, y ear 2000 actual emissions at the emission unit level were obtained and combined with 1999 Toxics Release Inventory

(TRI) ammonia emissions data at the facilit y level. At this point the petroleum storage and trans fer facilities with storage capacit y <300,000 barrels were eliminated, along with an y facilities where the potential to emit and operation/reconstruction date criteria were not met. The third step included sending the facilities survey s. Each of the 97 facilities that met the Regional Haze Rule’s source category criteria was sent a survey under a request for information about the air emission units at their facilities. The facilities were requested to submit potential to emit data for NO x 

### , SO 2 

### , PM 10 

### , VOCs, and NH 3 

### for each identified emission unit. The survey consisted of 4 steps: 1. Evaluate dates 2. Verify BART category 3. Enter potential emissions (tons/y r) 4. Provide boiler and tank information, if applicable In Appendix 9.2 of the SIP, Kansas acknowledged that although it asked the facilities to provide VOC and NH 3 

### emissions information, it did not intend on using that information in its BART

- subject anal ys is. Since only a fraction of the VOCs emission that would be reported actuall y form seco ndary

organic aerosols and because the screening model (CAL PUFF 3 

### ) cannot simulate formation of particles from anthropogenic VOCs, nor their visibility impacts it, the data would not be used in the BART determinations. NH 3 

### emissions were included as backgr ound in the modeling but emissions data from specific sources was not used as part of the evaluation process. Each of the facilities returned the survey forms, which were then anal yz ed to determine BART

- eligibility . The analy sis indicated there were 19 fa cilities in Kansas with BART

- eligible units. The nineteen facilities identified by this process are listed in Table 1 4

.

3 CALPUFF is a multi

- la yer, 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. CALPUFF can be applied on scales of tens to hundreds of kilometer s. It includes algorithms for sub grid scale effects (such as terrain im pingement), as well as longer range effects (such as pollutant removal due to wet scavenging and dry deposition, chemical transformation, and visibility effects of particulate matter c oncentrations). http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#calpuff

### 25

**BART Source Category**  **Nam e**  **Facility ID Facility Nam**  **e BART**  **- Eligible Emission Units** 

Fossil

- Fuel Fired Electric Generating Units 0090002 Aq uila (now Sunflower Electric)

- Arthur Mullergren Unit 3 (Stacks 1 and 2) 1750001 Aq uila (now Sunflower

Electric)

- Cimarron River Unit 1 0570001 Aq uila (now Sunflower

Electric)

- Judson Large Unit 4 2090008 Kansas City BPU

- Nearman Unit 1 2090048 Kansas City BPU

- Quindaro Unit 1 Unit 2 1070005 KCP&L

- La Cygne Unit 1 Unit 2 1130014 McPherson Municipal Pow er Plan #2 Unit 1 0550026 Sunflow er Electric

- Garden City Unit S2 1730012 Westar Energy

- Gordon Evans Unit 2 (Stacks 2 and 3) 1550033 Westar Energy

- Hutchinson Unit 4 (Stacks A and B) 1490001 Westar Energy

- Jeffrey Unit 1 Unit 2 0450014 Westar Energy

- Lawrence Unit 5 0350012 Winfield Municipal Pow er Plant #2 Unit 4
Portland Cement Plants 0010009 Monarch Cement Co. No. 4 Kiln Stack, No.4 Kiln Clinker Cooler, No.5 Kiln Stack, No. 5 Kiln Clinker Cooler, Raw Material Unloading, Clinker Grinding and Cement Handling, Stone Quarry Processing

Petroleum Refineries 0150004 Frontier El Dorado Refining Co. Boiler B

- 105, Boiler B

- 107, Plant Process Heaters,

Refinery Flare System B

- 1303, Plant Cooling Tow ers, Storage Tanks, Gas Oil Hydrotreater
1130003 National Cooperative Refinery

Assoc. (NCRA) Alky Heater HA

- 002, No.9 Boiler SB

- 009, No.12 Boiler SB

- 012, Coker IR Comp. CR

- 003, Plat Stab Boil Htr HP

- 003, Plat Charge Htr HP

- 006,

Fugitive Emissions Chemical Processing Plants 1730070 Basic Chemicals (now OxyChem

- Wichita) Boiler 1; Boiler 2; Boiler 3; Chloromethanes
0570003 Koch Nitrogen Ammonia plant

- primary reformer; Ammonia plant

- other; Nitric acid plant

- absorber tail gas; Ammonium nitrate plant

– neutralizer

Glass Fiber Processing Plants 2090010 Ow ens Corning 70 furnace

- N exhaust; 70 furnace

- S exhaust; 70 riser/channel/forehearth; 70 A forming; 70 B forming; 70 C forming; 70 D forming; 70 curing oven charge end; 70 curing oven discharge end; J5 furnace; J5 riser/channel/forehearth; J6 A forming; J6 B forming; J6 C forming; J6 curing oven charge end; J6 curing oven discharge end; J6 smoke stripper; J6 north co oling (A); J6 south cooling (B); J6 asphalt coating; Raw material processing Table 14

- Facilities with BART

- eligible U nits in the State of Kansas

### 26

Figure 9

- Map of BART

- eligible S ources in Ka nsas 

### BART Subject Sources The EPA has reviewed the State’s process for determining if a source was BART

- subject and has found that the State used a satisfactory method for determining which of the BART

eligible sources were BART

- subject. The State completed a modeling analy sis of all 19 sources determined to be BART

- eligible, using CAL PUFF. The State established a screening threshold of 0.5 d v (the same threshold described in the BART guidelines). If the modeling results showed that a source had at least a 0.5 dv or greater visib ility impact on at least one day in a three y ear period (2001

- 2003) further BART

- subject anal ys is was required. The one day in three

- ye ar period threshold, as well as the Class I areas selected for modeling, were based on recommendations made by staff at EPA Regions 6 and 7 in 2005. The nine Class I areas that were determined to be significant for determining impacts from potential BART

- subject sources were:

 Caney

Creek Wilderness Area, Arkansas (CACR)
 Upper Buffalo Wilderness Area, Arkansas (UPBU)
 Great Sand Dunes Wilderness Area, Colorado (GRSA)
 Rocky

Mountain National Park, Colorado (ROMO)
 Hercules

- Glades Wilderness Area, Missouri (HEGL)
 Mingo Wilderness Area, Missouri (MI NG)
 Wichita Mountains Wilderness Area, Oklahoma (WI MO)
 Badlands National Par k, South Dakota (BADL)
 Wind Cave National Park, South Dakota (WI CA)

### 27

Figure 10

- Map of Class I A reas A nalyzed

Table 1 5

- List of 19 BART

- eligible S ource and Their D istances from each of the 9 Class I Areas analyzed. Highlights i ndicate the Class I area that the s ource w as e valuated for to d etermine I mp acts.

### 28

The modeling showed that eight of the nineteen BART

- eligible sources met the screening criteria (0.5 dv or greater visibility impact on at least one day in a three y ear perio d). Those sources are identified below in Table 1 6

. **Source CACR**  **UPBU GRSA**  **ROMO HEGL**  **MING WIMO**  **BADL WICA** 



### Number of days during 2001

- 2003 with visibility impact >0.5 dv Kansas City BPU

- Nearman Unit 1 23 21 3 1 30 16 15 3 2 Kansas City BPU

- Quindaro Units 1 & 2 13 13 1 1 18 6 9 0 0 KCP&L

- La Cygne Units 1 & 2 204 249 17 21 278 233 142 46 38 Monarch Cement Kilns 4 & 5 0 0 0 0 0 0 1 0 0 Westar Energy

- Gordon Evans Unit 2 33 30 11 13 28 17 102 32 24 Westar Energy

- Hutchinson Unit 4 14 7 6 5 6 3 17 9 4 Westar Energy

- Jeffrey Units 1 & 2 150 161 27 28 182 158 165 82 55 Westar Energy

- Lawrence Unit 5 14 14 1 1 17 7 9 2 1 Table 1 6

- Kansas BART

- Eligible Emission Units with at Least One > 0.5 dv Visibility Impact Day on Selected Class I Areas during 2001

– 2003 

### The State required each of those eight sources to submit refined modeling to the State for further review. The refined modeling anal ys is for each source is given in Appendix 9.8 of the State’s Regional Haze SIP. Each facility ’s refined modeling results were based on CAL PUFF modeling for individual source exemption provided in the State’s BART modeling protocol. Monarch Cement, Kansas City BPU

- Quindaro, Kansas City BPU

– Nearman, Westar Energy

- Hutchinson, an d Westar Energy

- Lawrence were able to show that the emissions from their BART

- eligible units are not anticipated to cause or contribute to visibility impairment in Class I areas. The remaining facility ’s refined modeling results did show that emissions from their BART

- eligible units were anticipated to cause or contribute to visibility impairment in Class I areas. Five units at three sources were determined to be BART

- subject and required BART determinations. Those five units are given below:

 Unit 1 a t Kansas City Power and Light (KCP&L), La C ygne, Facility I D 1070005
 Unit 2 at Kansas City Power and Light, La C ygne, Facilit y ID 1070005
 Unit 1 at Westar Energy (Westar), Jeffrey

Energy Center (JEC), Facility

I D 1490001
 Unit 2 at Westar Energy , Jeffrey En ergy Center, Facility

I D 1490001
 Unit 2 at Westar Energy , Gordon Evans Energy Center (GEC), Facility

I D 1730012 It should be mentioned that BPU submitted the Black & Veatch

- prepared document Best Available Retrofit Technology Engineering Analysis for its Nearman Facility Unit 1 to the State in February

2007. In November 2007, the State determined that there was a potential issue with

### 29

the hourly

emission rates used in the initial modeling determination resulting in Nearman Unit 1 qualify ing as a BART sourc e. The State informed BPU of this potential issue and BPU revised both the 24 hour maximum emission rates and the BART determination modeling they had performed. On December 19, 2007 the State received the revised 24 hour maximum emission rates from BPU f

or Nearman Unit 1. Along with the revised emissions rate, BPU also submitted additional CAL PUFF modeling for purposes of a BART determination. This new modeling indicated Nearman Unit 1 should not have been included as a BART source. The modeling BPU pe rformed was based on the approved CALPUFF modeling protocol. It was therefore determined that BPU’s Nearman Unit 1 is not a source subject to BART because it was not anticipated to have an visibility impairment of greater than 0.5 dv based on a 98 th 

### perce ntile. BART determinations for the five subject

- to
- BART units in Kansas were carried out b y third

- part y contractors on behalf of the sources’ two owners and submitted to the State for approval. Because all five of the BART units are EGUs and are located at 750 MW facilities, the State stipulated that the minimum BART control level would be the presumptive limits specified in the BART Guidelines. Kansas provided each owner with a guideline for conducting their determination, titled “ Guidance for Facilitie s Conducting a BART Analysis ” (found in Appendix 9.4 of the SI P). Although the guidance stated that if a facility was considered a presumptive EGU, and proposed controls at or bey ond the presumptive emission limits, then the facilit y need not take into ac count the remaining statutory factors (as the State assumed BART would be met) , the EPA has determined that each of the BART determinations did consider each of the five statutory factors. As mentioned above, the State is required to evaluate EGUs greater than 750 MW in accordance with the BART guidelines. The BART Guidelines provide five steps (discussed previousl y) toward identifying BART control for these very large EGUs. The resulting anal ys es are found in Appendix 9.5 (KCP&L) and Appendix 9.6 (Westar ) and are discussed below. The BART emission rates (established after consideration of each of the five statutory factors, discussed in detail below) are summarized in Table 1 7 and have been included in Consent Agreements with each source. **Subject -**  **to -**  **BART Emission Unit BART Presumptive Limit**  **BART Operational Standard NO**  **x** 

### SO **2** 

### NO **x** 

### SO **2**



### lb/MMBtu KCP&L

- La Cygne Unit 1 0.10 0.16* 0.15 0.15* 0.13* 0.10* KCP&L

- La Cygne Unit 2 0.23 0.15 Westar Energy

- Je ffrey Unit 1 0.15 0.15 0.15 0.15 Westar Energy

- Je ffrey Unit 2 0.15 0.15 0.15 0.15 * Weighted average Table 1 7

- BART Presumptive Limits and Operational Standards for NO x and SO 2 at Subject

- to
- BART Em ission Units in Kansas 

### BART Determination for Westar JEC Units 1 and 2 The State determined that JEC Units 1 and 2, located in St. Mary ’s, Kansas and GEEC Unit 2, located in Colwich, Kansas, meet the BART eligibility criteria and contribute greater than 0.5 ∆dv to visibility

impairment in a federall y protected Class I area wh en compared to a natural background. Therefore, these three units are subject to BART. JEC Units 1 and 2 are

### 30

each 720 MW coal

- f

ired boilers. GEEC Unit 2 is a 383 MW oil

- fired boiler (The GEEC facility has a capacit y greater than 750 MW) . Therefore each of the units is a presumptive BART unit. The emission limits (described in Table 1 7 above) determined to represent BART for JEC Units 1 and 2 are maximum emission rates for the visibility

- impairing pollutants NO x 

### and SO 2 

### because they

are coal fired units . Because GEC Unit 2 is an oil fired unit, it is subject to a fuel switch as BART. The State determined that added PM 2.5 

### controls would only improve visibility marginall y, and would not be cost effective. A summary

of the visibility

impairment attribu table to the JEC Units 1 and 2 and GEEC Unit 2 is provided in Table 1 8

. Wichita Mountains Hercules

Glades Wilderness Caney Creek

Wilderness Mingo NWR Upper Buffalo Wilderness 98th % ∆dv Day s > 0.5 ∆dv 98th % ∆dv Day s > 0.5 ∆dv 98th

% ∆dv Day s > 0.5 ∆dv 98th

% ∆dv Day s > 0.5 ∆dv 98 th

% ∆dv Day s > 0.5 ∆dv JEC Unit 1 and Unit 2 0.99 59 0.90 63 0.73 37 0.49 21 0.85 53 GEEC Unit 2 1.08 85 0.40 16 0.38 14 0.17 4 0.42 16 Table 1 8

- Visibility Impairment Attributable to JEC Units 1 and 2 and GEC Unit 2 (2001

- 2003). “BART Five Factor Analysis

- Wes tar Energy” Trinity Consultants



### SO 2 

### BART Determination

Table 1 9

- Pollutant Specific Contributions to Existing Visibility Impairment for JEC Unit 1 and 2 (2001

- 2003). “B ART Five Factor Analysis

- Wes tar Energy” Trinity Consultants 

### As shown in Table 1 9 , the most significant visibility impairment attributable to these units is dominated by

NO x 

### emissions followed by SO 2 

### . PM visibility

impairment attribution from these units is not significant. Because vis ibility impairment from PM is insignificant, the remainder of this section will focus the control strategies to reduce NO x 

### and SO 2. 

### The five factor anal ys is identified several available control technologies for SO 2 

### . This list included Dry

Sorbent I nject ion (DSI), Wet Scrubbing, Dry Scrubbing and Circulating Dry Scrubbing (CDS). It was found that all were technically feasible with scrubbing ranking as the control with the highest removal efficiency (Wet and Dry

scrubbing have removal efficiencies that ar e very

similar). Because scrubbing technology has the highest removal efficiency of the

### 31

above mentioned technologies, it was determined that the remainder of the anal ys is would evaluate onl y scrubbing for SO 2 

### BART control. As the State had determined that the highest efficiency control technology

would be BART, no cost comparison of lower efficiency controls was needed. The only non - air qualit y environmental impact raised as a potential issue was the handling of the waste product that results from scrubb ing. It was determined that this issue was related more to cost and did not prohibit the implementation of scrubbing. The remaining useful life of the units was considered as well. I t was determined that the useful life of the units were anticipated to b e at least as long as the capital cost recovery period, which is 20 y ears, so the useful life of the units was not a hindrance to control for SO 2 

### BART. Next, the analy sis considered the visibility improvement from scrubbing. As shown in Table 20 , the im plementation of scrubbing the units will result in at least a 0.68 dv improvement at the WI MO and a total visibility improvement of 2.8 dv improvement across all five Class I areas anal yz ed (considering maximum visibility impacts).

Table 20

- Visibility Im

provement from the Implementation of Presumptive SO 2 BA RT. “BART Five Factor Analysis

- Wes tar Energy” Trinity Consultants 

### Based on the consultant’s analy sis, the State determined that the SO 2 

### BART emission rate for JEC Unit 1 and Unit 2 would be 0.15 lb/MMBtu based on the operation of wet scrubbers. The State required (through consent agreements) the facilit y to meet this limit for each unit on a 30

- day rolling average, excluding periods of s tartup, shutdown and malfunction (SSM)

. Compliance will be demonstrated using data from the existing continuous emissions monitoring sy stems (CEMS). Excluding the language on startup, shutdown and malfunction (as discussed in the Federal Register notice) in the Consent Agreements and in the State’s SI P, the EPA finds that the proposed control strategy meets the presumptive limits established in the BART guidelines and sufficientl y considers each of the five statutory factors. NO x 

### BART Determination 

### For NO x 

### control, the State indentified Flue Gas Recirculation (FGR), Overfire Air (OFA), Low NO x 

### Burners (LNB), Selective Cataly tic Reduction (SCR) and Selective Non

-

### 32

Cataly tic Reduction (SNCR) as being reasonably available, technicall y feasible technologie s. The most efficient of these technologies was identified as being SCR followed by L NB s ys tems. Because they

were identified as being the most efficient, SCR and L NB were the onl y control technologies evaluated for cost. The life expectancy

considerat ions of these two units were discussed previously in this document. The State considered ammonia slip as a potential non

- air environmental impact from SCR and SNCR NO x 

### control but did not determine that it was significant enough an issue to eliminate SCR o r SNCR from consideration. In the BART guidelines, EPA states that most EGUs can meet the presumptive NO x 

### limits through the use of current combustion control technology , i.e. LNB. For units that cannot meet these rates using such technologies, the S tate should consider whether advanced combustion control technology should be considered. The EPA calculated that for all t ypes of boilers, other than cy clone boilers, combustion control technology is generally

more cost
effective than post

- combustion con trols. The EPA estimated that approximately 75 percent of the BART units (non

- cy clone) could meet the presumptive NO x 

### limits at a cost of $100 to $1,000 per ton of NO x 

### removed based on the use of combustion control technology 4 

### . The EPA estimated, for the units that could not meet the presumptive limits using combustion control technology , that almost all of these sources could meet the presumptive limits using advanced combustion controls; the EPA estimated that the cost of such controls are usuall y less t han $1,500 per ton removed 5 6



### .
Table 18

- Cost Effectiveness Calculations for JEC Units 1 and 2. “BART Five Factor Analysis

- Westar Energy” Trinity Consultants. 

### Table 17 indicates that the cost effectiveness of LNB s ys tems for JEC Unit 1 and Unit 2 is le ss than $1,500 per ton of NO X 

### removed. Tables 17 also indicates that the costs for SCR for JEC Unit 1 and Unit 2 are over $1500 per ton of NO X 

### removed (JEC Unit 1 SCR cost = $2,211/ton and JEC Unit 2 SCR cost = $1,738/ton). As shown in Table 18 and 19 the visibility improvement from implementation of LNB at Unit 1 is estimated to be 0.57 dv at WI MO and 1.82 dv across all five Class I areas analy zed (considering maximum visibility impacts). The addition of SCR would only benefit 0.27 dv for an incremental co st increase of $6,613/ton. Based on these facts the State determined that it was reasonable to determine that NOx BART for JEC Units 1 and 2 is the implementation of LNBs.

4 70 CFR 39135 column 2 5 70 CFR 31935 column 3 6 Technical Support Document for BART NOx Limits fo r Electric Generating Units and Technical Support Document for BART NOx Limits for Electric Generating Units Excel Spreadsheet, Memorandum to Docket OAR 2002

- 0076, April 15, 2005

### 33

Table 2 1

- Summaries of Cost effectiveness of NO x controls at JEC units 1 an d 2. “BART Five Factor Analysis

Wes tar Energy” Trinity Consultants

Table 2 2

- Summary of Modeled Impacts from NO x Control for JEC Unit 1 (2001

- 2003). “BART Five Factor Analysis

- Wes tar Energy” Trinity Consultants

Table 2 3

- Summary of Modeled Impacts from NO x Control for JEC Unit 2 (2001

- 2003). “BART Five Factor Analysis

- Wes tar Energy” Trinity Consultants 

### Based on the consultant’s analy sis, the State has determined that the NO x 

### BART emission rate is 0.15 lb/MMBtu for both JEC Unit 1 and Unit 2. The State has required the facility (through the previousl y discussed consent agreement) to meet this limit for both JEC Unit 1 and Unit 2 by installing LNB sy stems. The State eliminated SCR as NO x 

### BART due to the high incremental cost of SCR over LNB s ys tems ($11,496,886 as an average over the two units) and the minimal increase in visibility improvement (0.05 dv improvement for both units). The EPA finds that the proposed control strategy meets the presumptive limits established in the BART guidelines and s ufficiently considers each of the five statutory factors. We also believe that the

### 34

State reasonabl y concluded that NOx BART is LNB in light of the incremental cost and the minimal impacts on visibility associated with SCR. **BART Determination for Westar GEC Unit 2**  SO 2 

### BART Determination

Table 2 4

- Pollutant Specific Contr ibutions to Existing Visibility Im pairment for GEC Unit 2 (2001

- 2003). “BART Five Factor Analysis

- Westar Energy” Trinity Consultants 

### As shown in Table 2 4 (above) , the most significant visibility impairing pollutant attributable to this unit is NO x 

### followed b y SO 2 

### . PM visibility impairment attribution from this unit is not significant. Because visibility impairment from PM is insignificant, the remainder of this section will focus on control strategies to reduce NO x 

### and SO 2. 

### The five factor anal ys is first identified several available control technologies for SO 2 

### . These were DSI, Wet Scrubbing, Dry Scrubbing, CDS, and fuel switching. DSI , Dry and Wet Scrubbing, as well as CDS are technologies collectively known as flue gas desulfurization (FGD) sy stems. FGD applications have not been used historically for SO 2 

### control in the U.S. electric industry on oil

- fired units. The EPA took this into account when establishing the presum ptive SO 2 

### emission rate 7 

### for oil

- fired units and determined that the presumptive emission rate should be based on the sulfur content of the fuel oil, rather than on FGD. Therefore, FGDs are considered technicall y infeasible for the control of SO 2 

### from GEE C Unit 2 and were not considered for BART for GEC Unit 2. The EPA identified, in the BART guidelines, that one percent sulfur fuel oil is the presumptive BART limit for oil

- fired units. The one percent sulfur oil is an alternative to the No. 6 fuel oil that is currently combusted b y GEEC Unit 2. The lower sulfur content of the one percent sulfur fuel oil would result in approximately a 33 percent reduction in SO 2 

### emissions from GEC Unit 2 (see Table 2 5 ) as compared to the combustion of the current No. 6 fuel oil, which contains approximately 1.5 percent sulfur. Fuel switching to 1 percent sulfur fuel oil is a technicall y feasible option for the control of SO 2 

### from GEC Unit 2.

7 Summary of Comments and Responses on the 2004 and 2001 Proposed Guidelines for Best Available Retrofit Technology (BART) Determinations Under the Regional Haze Regulations EPA Docket Number OAR

- 2002

- 0076.

### 35

**Unit Emission Rate Scenario**  **Emission Rate** SO 2 NO X PM 10 * (lb/hr) (lb/hr) (lb/hr) GEEC Unit 2 Existing Emission Rate 5,766.7 4,818.3 431.5

1 Percent Sulfur Fuel Oil 3,844.5 4,818.3 326.3 Table 2 5

- Summary of Emission Rates Comparing Existing Control to a Fuel Switch at GEC Unit 2. “BART Five Factor Analysis

- Wes tar Energy” Trinity Consultants *P M 10 emissions are calculated based on AP

- 42 em ission factors. 

### The anal ys is also considered the cost of SO 2 

### BART control of GEC Unit 2. The cost effectiveness of switching to one percent sulfur fuel oil has been evaluated. The cost of the fuel switching that was used in the cost effectiveness calculations was determined by calculating the cost of the current No. 6 fuel oil and determining the increased cost of switching to one percent sulfur fuel oil. I t was assumed in thi s analy sis that the fuel switch will not require an y capital expenses. The fuel costs for one percent fuel oil was determined from the fuel costs published by the Energy

I nformation Administration. The fuel cost for the No. 6 fuel oil is the market price on February

14, 2007. The annual tons reduced that were used in the cost effectiveness calculations were determined b y subtracting the estimated controlled annual emission rates from the existing annual emission rates. The existing annual emission rate s was the highest 365 day rolling totals as determined from CEMS data from 2002

- 2004. The controlled annual emission rate was estimated by

reducing the existing annual emission rate by 33 percent
. In the BART guidelines, EPA calculated that that the majo rity of BART

- eligible units could meet the presumptive limits at a cost of $400 to $2,000 per ton of SO 2 

### removed, based on the use of wet scrubbers and SDA sy stems 8 

### . Table 2 6 indicates that the cost of switching to one percent sulfur fuel oil is well abov e this range.

Estimated No. 6 Oil Hourly Usage Rate* Estimated

No. 6 Oil Annual Fuel Usage† No. 6 Oil Fuel

Cost Estimated

Annual No. 6 Oil Fuel Cost Estimated 1%

S Fuel Oil Hourly Usage Rate* Estimated 1%

S Fuel Oil Annual Fuel Usage‡ 1% S Fuel Oil

Cost Estimated

Annual 1% S Fuel Oil Cost Mgal/hr Mgal/yr Cents/gal $/yr Mgal/hr Mgal/yr Cents/gal $/yr 27.40* 108,011† 85 91,809,180 27.96‡ 110,215† 117.1 129,061,884

Annual Cost of Fuel Switching SO 2 Cost

Effectiveness ($/yr) ($/ton) 37,252,704 25,969 Table 2 6

- Summary of

C ost Effectiveness for Switching from No. 6 Oil to one percent sulfur fuel. “B ART Five Factor Analysis

- Westar Energy” Trinity Consul tants *4110 MMBtu/hr/150 MMBtu/Mgal; ‡4110 MMBtu/hr/147 MMBtu/Mgal; †A nnual fuel usage is based on the hourly fuel usage rate and 3,942 annual operating hours (Assuming unit operates at an annual 45% capacity factor, 45% * 8,760 hours = 3,942 hours)

8 70 FR 39132 column 3

### 36

There were no non

- air quality environmental impacts raised as potential issues for fuel switch ing. The anal ys is did consider the remaining useful life of the units. It was determined that the remaining useful life of GEC Unit 2 was not a deterrent for considering SO 2 

### BART control at GEC Unit 2 it was assumed that fuel switching would not require c apital costs, and therefore did not impact the annualized cost. Finally , the anal ys is considered the visibility improvement from a fuel switch. As shown in Table 26, the implementation of a fuel switch at GEC Unit 2 will result in at least a 0.14 dv imp rovement at the WI MO and a total visibility improvement of 0 .70 dv improvement across all five Class I areas analy zed (considering maximum visibility impacts). The State, after initial consideration of each of the five statutory factors, determined that S O 2 

### BART for GEC Unit 2 could be a fuel switch to one percent sulfur fuel oil. However, after consideration of a “BART Alternative” proposed by Westar (and discussed later in this document) the State’s final SO 2 

### BART determination for GEC Unit 2 is a fuel switch to natural gas combustion.

Table 27

- Summary of Modeled Impacts from SO 2 control at GEC Unit 2 (2001

- 2003). “BART Five Factor Analysis

- Westar Energy” Trinity Consultants 

### NO x 

### BART Determination 

### For NO x 

### control, the State indentified FGR, OFA, LNB, SCR and SNCR as being reasonabl y available, technically feasible technologies. The most efficient of these technologies was identified as being SCR followed by L NB s ys tems. Because it was identified as being t he most efficient, SCR was the only control technology

evaluated for cost. The capital cost and operating costs of SCR were estimated based on the State’s SCR installation experience. The capital costs were annualized over a 20

- ye ar period and then added to the annual operating costs to obtain the total annualized costs. The annual tons reduced that were used in the cost effectiveness calculations were estimated by subtracting the estimated controlled annual emission rates from the existing annual emissi on rates. The existing annual emission rates were the highest 365 day

rolling totals as determined from CEMS data from 2002

- 2004. **Wichita Mountains Hercules Glades**  **Wilderness Caney Creek**  **Wilderness Mingo NWR**  **Upper Buffalo Wilderness** MaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement*MaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement*MaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement*MaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement*MaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement*

Existing Em ission Rate 2.16 1.08 85

- 1.31 0.40 16

- 1.07 0.38 14

- 0.71 0.17 4

- 2.22 0.42 16

- One Per cent Sulfur Fuel Oil 2.02 0.94 71 13% 1.19 0.34 10 13% 0.87 0.34 9 10% 0.65 0.14 2 16% 2.04 0.38 15 10%

### 37

The controlled annual emission rates were estimated b y multiply ing estimated control efficiency for the SCR (90 percent) b y the existing annual emission rates. A sample of the controlled annual emission rate is shown as follows: %) 90 % 100 ( * 352 , 2

 tpy 

### =235 tpy The cost effectiveness for the SCR was determined by dividing the annual cost by the annual tons reduced. The cost is summarized in Table 2 8 . The cost of the SCR is greater than $5,300/ton of NO X 

### removed. Compared to the cost effectiveness range for NO x 

### BART control (established b y the EPA and previousl y discussed) the State determined that the cost of SCR was prohib itive.
Current Annual Emission Rate Controlled

Annual Emission Rate* Estimated

Reduced Emissions Capital

Cost† Annualized

Capital Cost‡ Annualized

Fixed O&M‡ Annualized

Variable O&M‡ Total

Annualized Cost Cost

Effectiveness (tpy) (tpy) (ton/yr) ($) ($/yr) ($/yr) ($/yr) ($/yr) ($/ton) SCR 2,352 235 2,117 81,000,000 9,514,260 218,932 1,627,628 11,360,820 5,367 Table 2 8

- Summary of Cost Effectiveness for GEC Unit 2 NO x Control (SCR). “B ART Five Factor Analysis

Westar Ener gy” Trinity Consultants *T he annual emission rate represents a control efficiency of 90%. †The SCR capital cost was determined from recent SCR installation experience ‡The costs are annualized in 2006 dollars. 

### The non

- air environmental impacts were evaluated for SCR. The five factor anal ys is briefl y describes some concern with electricit y consumption of SCR and the potential for SCR to potentially

cause environmental impacts related to the usage and storage of ammonia. Storage of aqueous ammonia above 10,000 lbs is regulated by a risk management program (RMP), since the accidental release of ammonia has the potential to cause serious injury and death to persons in the vicinity

of the release. Ammonia can also be emitted in the exhaust of boilers that operate with SCR or SNCR for NO x 

### control due to ammonia slip. Ammonia slip from SCR and SNCR sy stems occurs either from ammonia injection at temperatures too low for effective reaction with NO x , leading to an exce ss of un

- reacted ammonia, or from over

- injection of reagent leading to uneven distribution; which also leads to an excess of un

- reacted ammonia. Ammonia released from SCR and SNCR sy stems will react with sulfates and nitrates in the atmosphere to form amm onium sulfate which are sources of regional haze. The remaining useful life of GEEC Unit 2 does not impact the annualized capital costs of potential controls because the useful life of the unit is anticipated to be at least as long as the capital cost re co very

period, which is 20 y ears. The State compared the visibility improvement for existing emission rates to the emission rates associated with SCR . The results of this analysis are given in Table 2 9 (Table 3 1 shows the visibility improvement expected of LNB and 1% sulfur coal) . The existing emission rates and emission rates associated with SCR were modeled using CAL PUFF. The existing emission rates were the same rates that were modeled for the BART applicability anal ys is. The emission rate associat ed with the SCR for GEC Unit 2 was 0.12 lb/MMBtu. The emission rate for SCR was determined b y reducing the existing hourly equivalent of the maximum 24

- hour emission rate b y a control efficiency

of 90 percent.

### 38

Comparisons of the existing visibility imp acts and the visibility impacts associated with SCR, including the maximum modeled visibility impact, 98 th 

### percentile modeled visibility impact, and the number of day s with a modeled visibility impact greater than 0.5 Δdv, for each Class I area are provided in Table 29 for GEC Unit 2. As shown in Table 2 9 , the installation of SCR results in a 98 th 

### percentile modeled visibility im pact of 0.09 to 0.60 dv as compared to 0.17 to 1.08 dv (depending on the Class I area) at the existing emission rate, and attributable to GEC Unit 2. However, as previously documented in Table 2 8 , the cost of SCR for this unit is est imated at over $5,300/ton. This cost is high compared to the relatively minimal associated visibility improvement

.

Table 29

- Summary of Modeled Visibility Impacts from SCR control. “B ART Five Factor Analysis

- Westar Energy” Trinity Consultants 

### Since the cost of SCR was high given the associated visibility improvements , the State determined that LNB could achieve at least the presumptive emission rate at GEC Unit 2 and therefore LNB would be NO x 

### BART for GEC Unit 2. However, since the concurrent anal ys is for SO2 reduction (discussed later in this document) demonstrated that control through fuel switching to natural gas resulted in both SO 2 

### and NO x 

### emission reductions, and in visibility improvements bey ond those gained b y presumptive BART, the Stat e determined, and EPA agrees, that the fuel switch to natural gas meets the NOx BART requirements. **BART Alternative for GEC Unit 2** Based on the SO 2 

### and NO x 

### BART anal ys es, the State had determined that BART for SO 2 

### for GEC Unit 2 was likely the combustion of one percent sulfur fuel oil and BART for NO X 

### was the installation of L

NB. However, Westar proposed an alternative to the controls determined to meet BART for SO 2 

### and NO X 

### for GEC Unit 2. I n order for control strategies to be acceptable as an alternative to what is determined to meet BART, the alternatives must show greater visibility Wichita Mountains Hercules Glades Wilderness Caney Creek Wilderness Mingo NWR Upper Buffalo WildernessMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement

Existing Emission Rate 2.16 1.08 85

- 1.31 0.4 16

- 1.07 0.36 14

- 0.71 0.17 4

- 2.22 0.42 16

- SCR 1.34 0.60 28 45% 1.09 0.20 5 51% 0.65 0.21 4 46% 0.34 0.09 0 48% 1.07 0.20 8 51%
* Im provement is based on the 98% impact visibility improvement (Δdv) of SCR over the existing emission rate.

### 39

improvement on average than what is determined to meet BART based on the following visibility metrics 9 

### :  The maximum visibility impact
 The 98th percentile visibility impact
 The number of day s where the visibility impacts are greater than 0.5 ∆dv The State chose to implement this test by evaluating whether the values for the metrics listed above for an alternative BART control strategy w ere equal to or better than the values for the BART control strategy

for each Class I area. As an alternative to combusting one percent fuel oil to reduce SO 2 

### and L NB to reduce NO x 

### for GEC Unit 2, Westar proposed to combust natural gas in GEC Unit 2. W estar proposed to demonstrate compliance with the alternative BART control strategy for GEC Unit 2 agreeing to submit annual certifications of compliance verify ing that natural gas was the only fuel combusted in GEC Unit 2 for the ye ar, except as provided below. In order to assure electric sy stem reliability , Westar requires the availability of an emergency

fuel for backup, as well as the abilit y to burn a limited amount of the fuel periodicall y during non

- emergencies to assure that the emergency s ys tem functions adequately . When the natural gas compan y implements an Operational Flow Order (OFO) or declares an emergency which could result in an impact to electric s ys tem reliability , Westar will combust No. 6 fuel oil for the time period of the emergency

. Westar will diminish the existing supply of No. 6 fuel oil, once diminished the emergency fuel will be replaced with a fuel oil containing 1 percent or less sulfur content. The modeled visibility

impacts of the BART control strategy and the proposed a lternative BART control strategy

are summarized in Table 30 . The visibility improvement associated with the BART and alternative BART control options are also shown in Table 30 ; this value was calculated as the difference between the existing visibility i mpairment and the visibility impairment for the BART and alternative BART control options as measured b y the 98 th 

### percentile modeled visibility impact. The visibility

impacts for each metric are lower in all five Class I areas for the alternate BART contr ol strategy and are shown in Table 3 1

. Because the maximum visibility impact, t he 98th percentile visibility impact and t he number of day s where the visibility impacts are greater than 0.5 ∆dv are equal to or better than the values for the presumptive BA RT control strategy (even though the emissions of NO x 

### are higher with the alternative than the presumptive BART strategy ), for each Class I area, the State determined that the BART alternative met the requirements of the Rule. Based on the consultant’s analy sis, the State has determined that the fuel switch to natural gas provides greater reasonable progress than NO x 

### and SO 2 

### BART presumptive limits combined

. The State has required the facilit y (through previously discussed consent agreement) to establis h

9 40 CFR § 51.308(e)

- Best Available Retrofit Technology (BART) requirements for regional haze visibili ty impairment. The State must submit an implementation plan containing emission limitations representing BART and schedules for compliance with BART for each BART

- eligible source that may reasonably be anticipated to cause or contribute to any impairment o f visibility in any mandatory Class I Federal area, unless the State demonstrates that an emissions trading program or other alternative will achieve greater reasonable progress tow ard natural visibility conditions.

### 40

compliance with this strategy by reporting to the State fuel oil usage at Unit 2 in accordance with K.A.R. 28

- 19

- 512. The EPA is proposing to find that the proposed control strategy meets the presumptive limits established in the BART guidelines and suf ficientl y considers each of the five statutory factors. EPA also is proposing to find that Kansas reasonabl y concluded that the BART alternative will provide for greater reasonable progress than BART

.

Presumptive BART Case

- 1% Sulfur, LNB at 0.8 lb/MMB Tu Presumptive BART Case

1% Sulfur, LNB at 0.2 lb/MMBTu Alternative BART Case

Natural Gas Maximum (in dv) 1.575 1.02 0.744 98% (in dv) 0.804 0.474 0.344 NO x 

### (lb/hr) 3288.0 822.0 2136.0 SO 2 

### (lb/hr) 3844.4 3844.4 1.7 PM (lb/hr) 324.7 326.3 30.6 Table 30

- Com

parison of Presumptive BART (1% Sulfur Fuel and LNB) to the BART Alternative

### 41 Wichita Mountains Hercules Glades Wilderness Caney Creek Wilderness Mingo NWR Upper Buffalo WildernessMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovementMaximumImpact(Δdv)98%Impact(Δdv)#Days>0.5ΔdvVisibilityImprovement

Existing Emission Rate 2.16 1.08 85

- 1.31 0.4 16

- 1.07 0.36 14

- 0.71 0.17 4

- 2.22 0.42 16

-
Existing BART

- LNB +1% sulfur fuel oil 2. 02 0.94 71 13% 1.19 0.34 10 13% 0.87 0.34 9 10% 0.65 0.14 2 16% 2.04 0.38 15 10%

Altnernative

- Natural Gas 1.66 0.69 44 36% 0.93 0.21 4 48% 0.49 0.25 0 34% 0.49 0.08 0 55% 1.62 0.28 11 33% * Improvement is based on the 98% impact visibility improvement (Δdv ) of SCR over the existing emission rate. Table 3 1

- Com

parison of Presumptive BART (1% Sulfur Fuel and LNB) to the BART Alternative at Five Class I area. “BART Five Factor Analysis

- Westar Energy” Trinity Consultant

### 42

**BART Dete rmination for KCP&**  **L Units 1 and 2** The State determined that KCP& L Units 1 and 2, located in La C ygne , Kansas met the BART eligibility

criteria and contribute g reater than 0.5 ∆dv to visibility impairment in a federally protected Class I area when compared to a natural background. Therefore, these units are subject to BART. KCP&L Unit 1 is an 840 MW boiler with a scrubber for SO 2 

### and PM control and overfire air that minimizes the formation of NO x 

### . Unit 2 is a 710 MW boiler that has an electrostatic pre cipitator (ESP) for PM control. Unit 1 burns a blend of Powder River Basin (PRB) coal and local coal. Unit 2 burns 100 percent PRB coal. Both units are presumptive units for BART. The emission limits determined to represent BART for KCP& L Units 1 and 2 are maximum emission rates for the visibility

- impairing pollutants NO x 

### and SO 2 

### because they are coal fired units. A summary

of the visibility

impairment attributable to the KCP& L Units 1 and 2 is provided in Table 3 2

.

Table 3 2

- Visibility Impairment Attributable to KCP&L Units 1 and 2 (2001

- 2003). “BART Five Factor Analysis

Kans as City Pow er & Light

- La Cygne ” Trinity Consultants


### SO 2 

### BART Determination

Table 3 3

- Pollutant Specific Contributions to Existing Visibility Impair ment for KCP& L Units 1 and 2. “BART Five Factor Analysis

- Kansas City Power & Light

- La Cygnes” Trinity Consultants 

### As shown in Table 3 3 , the most significant visibility impairment attributable to these units is pretty

evenl y dominated by NO x 

### and SO 2 

### . PM visibility impairment attribution from these units is not significant. Because visibility impairment from PM is insignificant, the remainder of this section will focus the control strategies to reduce NO x 

### and SO 2.

### 43

The five factor anal ys is identified several available control technologies for SO 2 

### . This list included DSI , Wet Scrubbing, Dry Scrubbing and CDS. I t was found that all were technicall y feasible with scrubbing ranking as the control with the highest removal efficiency (Wet and Dry

scrubbing have removal efficiencies that are very similar). Because scrubbing technology

has the highest removal efficiency of the above mentioned technologies, it was determined that the remainder of the anal ys is would evaluate onl y scrubbing for SO 2 

### BART control

. The State had determined that the highest efficiency control technology

would be BART, and no cost comparison of lower efficiency controls was needed. The only non - air qualit y environmental impact raised as a potential issue was the handling of the was te product that results from scrubbing. It was determined that this issue was related more to cost and did not prohibit the implementation of scrubbing. The remaining useful life of the units was considered as well. I t was determined that the useful life of the units were anticipated to be at least as long as the capital cost recovery period, which is 20 y ears, so the useful life of the units was not a hindrance to control for SO 2 

### BART. Next, the analy sis considered the visibility improvement from scru

bbing. As shown in Table 3 4 , the implementation of scrubbing the units will result in at least a 0.81 dv improvement at the WI MO and a total visibility improvement of 5.32 dv improvement across all five Class I areas anal yz ed (considering maximum visibilit y impacts).

Table 3 4

- Visibility Improvement from the Implementation of Presumptive BART. “BART Five Factor Analysis

Kansas City Pow er & Light

- La Cygnes” Trinity Consultants 

### Based on the consultant’s analy sis, the State determined that the SO 2 

### BART emission rate for KCP&L Unit 1 and Unit 2 would be 0.15 lb/MMBtu (as a weighted average) based on the operation of wet scrubbing technology . However, during the process of developing the consent agreement, KCP&L proposed a more restrictive emission limit than the presumptive limit of 0.15 lb/MMBtu (weighted average), to 0.10 lb/MMBtu on a 30

- day rolling weighted average. Compliance will be demonstrated b y reconfiguring the CEMS software to generate a dail y report showing the two

- unit 30

- day rolling average. The average must remain below 0.10 lb/MMBtu, excluding periods of startup and shutdown. Compliance will be demonstrated using data from the existing CEMS. Ex cluding the language on SSM in the Consent Agreement and in the State’s SI P (as discussed in the Federal Register notice) t he EPA finds that the proposed control strategy meets the presumptive limits established in the BART guidelines and sufficiently considers each of the five statutory factors.

### 44

NO x 

### BART Determination 

### For NO x 

### control, the State indentified FGR, OFA, LNB, SCR and SNCR as being reasonabl y available, technically feasible technologies. The most efficient of these technologies was identified as being SCR followed by L NB s ys tems. Because it was identified as being t he most efficient SCR was evaluated for cost. Because it was a combustion control technology capable of achieving the presumptive BART level for NO x 

### , LNB was also evaluated for cost. At the time of the five factor anal ys is, KCP&L was in the process of constructing an SCR sy stem on KCP&L Unit 1. Since SCR provides the highest level of control there was no need to evaluate the cost for other NO x 

### controls for Unit 1. The life expectancy considerations of these two units were discussed previously in this do cument. The State considered ammonia slip as a potential non

- air environmental impact from SCR control but did not determine that it was significant enough an issue to eliminate SCR or from consideration.

Table 3 5

- Summary of Cost Effectiveness of NO x Co ntrol at KCP&L Units 2. “B ART Five Factor Analysis

- Kansas City Power & Light

- La Cygnes” Trinity Consultants

Table 3 6

- Summary of NOx Control at KCP&L Unit 2. “B ART Five Factor Analysis

- Kansas City Pow er & Light

- La Cygnes” Trinity Consultants



### Kansas stated in its SI P that the EPA is on record stating that most EGUs can meet the presumptive NO x 

### limits through the use of current combustion control technology , i.e. LNB. For units that cannot meet these rates using such technologies, the State should consider whether advanced combustion control technology should be considered. The EPA estimated t hat approximately

75 percent of the BART units (non

- cy clone) could meet the presumptive NO x 

### limits at a cost of $100 to $1,000 per ton of NO x 

### removed based on the use of combustion control

### 45

technology 10 

### . The EPA estimated, for the units that could not meet t he presumptive limits using combustion control technology , that almost all of these sources could meet the presumptive limits using advanced combustion controls; the EPA estimated that the cost of such controls are usually less than $1,500 per ton removed 11 12



### . Kansas went on to conclude that s ince the cost effectiveness of SCR (and advanced combustion control technology ) implementation at KCP&L is greater than the $1,500 cost effectiveness threshold to meet the presumptive emission limits for a coal fired uni t, LNB are NO x 

### BART for KCP&L Unit 2. Although Kansas’ recitation of the Federal Register and guidance is correct, use of the above estimated cost ranges at which man y BART units across the nation could meet presumptive NO x 

### limits as a cost threshold to e liminate, outright, more stringent limits associated with technology such as SCR, is inappropriate. Based on the consultant’s analy sis, the State had determined that the NO x 

### BART emission rates were the presumptive emission rates of 0.10 lb/MMBtu and 0.2 3 lb/MMBtu for Unit 1 and Unit 2, respectively (or 0.16 lb/MMBtu as a weighted average) , which resulted in a combined (SO 2 

### and NO x 

### ) modeled visibility improvement of 78

- 81% (98 th 

### percentile visibility impact) and a reduction of the number of day s with a vi sibility impact greater than 0.5 dv from a range of 57

- 138 day s to 3

- 14 day s . D uring the course of negotiating an enforceable BART agreement, KCP& L proposed limits that were more restrictive than the presumptive BART limits. This proposal was made in order to be consistent with an agreement KCP&L ha s with the Sierra Club and with the knowledge that the Kansas City metro area ha s been close to nonat tainment for ozone. The State agreed that the alternative emission limits proposed by KCP&L were better than the presumptive BART limits and KDHE incorporated these limits into the KCP&L consent agreement. The agreement includes an emission rate of 0.13 lb /MMBtu on a 30

- day rolling weighted average using the alread y permitted SCR control for Unit 1 and some form of pre

- or post
- combustion control (e.g., low NO x 

### burner, low NO x 

### burner with overfire air, or SCR) for Unit 2. At the 0.13 lb/MMBtu weighted aver age rate for both units, which is bey ond the presumptive NOx rate of 0.23 lb/MMBtu, EPA would not an ticipate additional significant visibility improvement for the additional significant cost of installing SCR on Unit 2. Compliance will be demonstrated via reconfiguring the CEMS software to generate a daily report showing the two

- unit 30

- day rolling average. The average must remain below 0.13 lb/MMBtu, excluding periods of startup and shutdown. In the event Unit 2 suffers an outage in excess of 10 week s the facility will meet the 0.10 lb/MMBtu presumptive limit for NO x 

### at Unit 1. Excluding the language on SSM in the Consent Agreement and in the State’s SI P (as discussed in the Federal Register notice) the EPA finds that the proposed control strategy me ets or exceeds the presumptive limits established in the BART guidelines and sufficientl y considers each of the five statutory

factors.

10 70 CFR 39135 column 2 11 70 CFR 31935 column 3 12 Technical Support Document for BART NOx Limits for Electric Generating Units and Technical Support Document for BART NOx Limits for Electric Generating Units Excel Spreadsheet, Memorandum to Docket OAR 2002

- 0076, April 15, 2005

### 46



## Reasonable Progress/Long Term Strategy Since Kansas does not host a Class I area, it is not required to set Reasona ble Progress Goals (RPGs). However, it is required to determine the emission reductions needed from sources within the State that are necessary to meet the RPGs of Class I areas hosted by other states. Kansas must provide the emission reduction strategy it will undertake to obtain those emissions in its L ong Term Strategy (LTS). A six step process was used to identify which point sources needed to be controlled as part of the LTS. The results of each of these steps of the process will be described in se quence below. First, the State identified all point sources with emissions of greater than 500 tpy NO x 

### and/or SO 2 

### by

using the 2002 base y ear inventory (previously

described in this TSD). The State chose 500 tp y as a screening criterion because it was determined b y modeling (previousl y discussed in this TSD) that a source emitting less than 500 tpy of either pollutant would not significantl y impact (great than 0.5 delta dv) Class I areas hosted by another State. Kansas identified a total of 30 NO x 

### uni ts and 28 SO 2 

### units. Of this set of units, 8 NO x 

### and 10 SO 2 

### units were removed from further review for the following reasons:

 6 of the NO x 

### units and 6 of the SO 2 

### units were alread y identified as BART

- subject;
 2 of the NO x 

### units and 2 of the SO 2 

### units had installed controls since 2002 and emitted less than 500 tpy of either pollutant.
 2 of the SO 2 

### units were determined to have no commercially available controls. The remaining set of 22 NO x 

### units included 11 EGUs, 6 cement kilns, 2 gas compressor engines, 1 refinery

fluid

- bed catal yt ic cracking unit (FCCU), 1 ammonia plant, and 1 glass furnace, all located at 15 separate facilities. The 18 SO 2 

### units were comprised of 13 EGUs, 4 cement kilns, and 1 refiner FCCU, all located at 12 facilities. A lis t of these sources is provided below in Table 3 7

. **Source ID Facility**  **Unit 2002 NO**  **x** 

### 2002 SO **2** 

### ton/yr 0910057 AGC Flat Glass North America Glass furnace 1,375 [234.2] * 

### 1330001 Ash Grove Cement Kiln 2,121 747 0210002 Empire District Electric

–

Riverton Unit 7 582 2,393 0210002 Empire District Electric

-

Riverton Unit 8 1,040 866 0150004 Frontier El Dorado Refinery FCCU 688 856 1250015 Heartland Cement Kiln 928 1,007 2090048 Kansas City BPU

- Quindaro Unit 1 1,599 1,288 2090048 Kansas City BPU

- Quindaro Unit 2 1,330 2,123 2090008 Kansas City BPU

– Nearman Unit 1 3,860 7,625 0930012 Kinder Morgan

- Lakin Station EU

- 2 579 [0.1] 0570003 Koch Nitrogen Ammonia plant 585 [0.5] 2057022 Lafarge Midwest

- Fredonia Kiln 1 596 660 2057022 Lafarge Midwest

- Fredonia Kiln 2 883 978 0010009 Monarch Cement Kiln 4 652 [74.7]

### 47

0010009 Monarch Cement Kiln 5 972 [254.6] 0670035 ONEOK

- Ulysses Station Clark 1 619 [0.1] 0550023 Sunflower Electric

- Holcomb Unit 1 3,781 1,669 1730012 Westar Energy

- Gordon Evans Unit 1 [208.5] 618 1550033 Westar Energy

- Hutchinson Unit 4 [209.2] 734 1490001 Westar Energy

– Jeffrey Unit 3 10,481 23,206 0450014 Westar Energy

– Lawrence Unit 3 738 1,965 0450014 Westar Energy

– Lawrence Unit 4 2,037 1,430 0450014 Westar Energy

– Lawrence Unit 5 3,762 4,354 1770030 Westar Energy

– Tecumseh Unit 7 1,531 2,693 1770030 Westar Energy

– Tecumseh Unit 8 1,877 4,515
* Values within brackets indicate 2002 emissions less than 500 tpy
. Table 37

- Kansas Emission Units Not Subject to BART Emitting at Least 500 Tons/Yr of NO x or SO 2 in 2002 

### During the second step, the State matched each of the remaining units with the emission control technology

selected for it b y a CENRAP contractor, Alpine Geophysics, utilizing t he least marginal cost. For units that were not identified by Alpine Geophy sics, the units were matched with control technologies, control efficiencies and control cost as determined by EPA’s AirControlNET version 4.1 13 

### . Units whose cost of control was de termined to be $10,000/ton reduced or greater were screened out in this step. It was agreed b y members of CENRAP that $10,000/ton was an excessive cost of control. Eight SO 2 

### emission units were removed from the list of candidate units, and are listed in Table 3 8 , which shows each unit’s $/ton over $10,000/ton.
**SO 2**  

### emission unit **Cost per ton reduced**

Frontier El Dorado Refinery FCCU $14,069 Empire District Electric

- Riverton Unit 7 $11,066 Heartland Cement Kiln $28,435 Westar Energy

- Lawrence Unit 4 $40,598 Sunflower Electric

- Holcomb Unit 1 $10,767 Ash Grove Cement Kiln $24,305 Lafarge Midwest

- Fredonia Kiln 1 $20,127 Lafarge Midwest

- Fredonia Kiln 2 $13,586 Table 38

- Sources with Cost per Ton Reduced Greater than $10,000/ton 

### Calculations for fuel switching at Westar’s Hutchinson Energy Center Unit 4 and Gordon Evans Energy

Center Unit 1 involved analy sis of the price of natural gas and #6 residual o il based on publicly

available information from the Energy I nformation Administration 14



### . Calculations for Hutchinson Unit 4 included switching to all natural gas with the equivalent heat input of the 4,195 thousand gallons (Mgal) of #6 oil and 1,240 million cubic feet (MMcf) of natural gas burned in 2002. Gordon Evans Unit 1 included fuel switching to all natural gas based on 3,930 Mgal #6 oil and 865 MMcf natural gas in 2002. The price of natural gas used in the

13 

### “ Final CENRAP Control Strategy Analysis Plan

- 9 May 2006 ” page 36. http://www.cenrap.org/html/projects.php
14 http://www.eia.doe.gov/

### 48

calculations was $1,200/Mgal for #6 oil and was $8,000/MMscf for natural gas. Prices from June 2005 to November 2006 were used to reflect more current prices. Negative cost results for both units (

- $135/ton and

- $37/ton, respectivel y) imply that the residual oil burned in 2002 was purchased at a lower price than the amount used for the anal ys is. Also, although fuel switching for these two units was selected as a control strategy for SO 2 

### , some control of NO x 

### was achieved as well, and was included in the CAL PUFF modeling in next step of the analy si s.

**Source ID Facility**  **Unit Selected**  **control measure**  **Control**

**efficiency (%)**  **Cost per ton**

**reduced ($2002 AirControlNET**  **or $2005 Alpine) Source** 

**NO x**  **sources** 0910057 

### AGC Flat

Glass North Am erica Glass

Furnace SNCR 40.0 $1,041 AirControlNET
1330001 Ash Grove

Cement Cement

Kiln LNB 25.0 $509 AirControlNET
0210002 Em

pire District Electric

- Riverton Unit 7 LNBO 55.9 $1,398 Alpine

0210002 Em

pire District Electric

- Riverton Unit 8 LNC3 53.1 $873 Alpine

0150004 Frontier El

Dorado Refinery FCCU LNB + FGR 55.0 $5,314 Alpine
1250015 Heartland

Cement Cement

Kiln Mid

- kiln firing 30.0 $83 Alpine
2090008 Kansas City

BPU

– Nearman Unit 1 LNB+OFA+NN 45.2 $750 BPU

– 2006 dollars
2090048 Kansas City

BPU

- Quindaro Unit 1 NGR 50.0 $579 Alpine

2090048 Kansas City

BPU

- Quindaro Unit 2 LNB 40.3 $849 AirControlNET
0930012 Kinder Morgan

- Lakin Station EU

- 2 A/F + IR 30.0 $2,027 AirControlNET 0570003 Koch Nitrogen Ammonia Plant OT + WI 65.0 $688 Alpine
2057022 Lafarge

Midw est
- Fredonia Kiln 1 Mid

- kiln firing 30.0 $83 Alpine

2057022 Lafarge

Midw est
- Fredonia Kiln 2 Mid

- kiln firing 30.0 $83 Alpine
0010009 Monarch

Cement Kiln 4 Biosolid injection 23.0 $466 Alpine 0010009 Monarch Kiln 5 Biosolid 23.0 $466 Alpine

### 49 Cement injection 0670035 ONEOK

-

Ulysses Station Clark 1 A/F + IR 30.0 $323 Alpine
0550023 Sunflow er

Electric

- Holcomb Unit 1 LNB 16.0 $1,383 AirControlNET
1490001 Westar Energy

- Jeffrey Unit 3 LNC3 58.3 $1,560 Alpine 0450014 Westar Energy

- La wr ence Unit 3 LNC1 33.1 $683 Alpine 0450014 Westar Energy

- La wr ence Unit 4 LNC1 43.3 $325 Alpine 0450014 Westar Energy

- La wr ence Unit 5 LNC3 58.3 $1,628 Alpine 1770030 Westar Energy

- Tecum seh Unit 7 LNC1 33.1 $432 Alpine 1770030 Westar Energy

- Tecum seh Unit 8 LNC3 53.1 $518 Alpine **SO 2**  **sources** 0210002 Em

pire District Electric

- Riverton Unit 8 FGD wet scrubber 90.0 $5,782 Alpine

2090008 Kansas City

BPU

– Nearman Unit 1 Sem i

- Dry scrubber 88.5 $2,024 BPU

- 2006 dollars
2090048 Kansas City

BPU

- Quindaro Unit 1 FGD wet scrubber 90.0 $5,825 Alpine

2090048 Kansas City

BPU

- Quindaro Unit 2 FGD wet scrubber 90.0 $3,522 Alpine
1730012 Westar Energy

- Gordon Evans Unit 1 Fuel switching 99.9 ($37) KDHE 1550033 Westar Energy

- Hutchinson Unit 4 Fuel switching 99.9 ($135) KDHE 1490001 Westar Energy

- Jeffrey Unit 3 FGD wet scrubber 75.0 $1,695 AirControlNET 0450014 Westar Energy

- La wr ence Unit 3 FGD wet scrubber 90.0 $5,339 Alpine 0450014 Westar Energy

- La wr ence Unit 5 FGD wet scrubber 79.2 $9,265 AirControlNET 1770030 Westar Energy

- Tecum seh Unit 7 FGD wet scrubber 90.0 $4,519 Alpine 1770030 Westar Energy

- Tecum seh Unit 8 FGD wet scrubber 90.0 $3,715 Alpine Table 3 9

- Most Effective Control Technologies with Acceptable Control Costs for Kansas 500

- Ton NO x and SO 2 Em ission Units 

### As part of the third step, the 22 NO x 

### and 10 SO 2 

### units within 17 facilities remaining were evaluated for visibility

impacts at Class I areas. Essentially the same CALPUFF protocol was followed as for BART screening, so modeling was carried out on a facility

- by

- facilit y basis, and

### 50

NO x 

### and SO 2 

### impacts we re calculated together. Post

- control model inputs for emissions were calculated using the control efficiencies included with each control technology selected in the second step of this anal ys is (previously described). I nstead of looking at number of day s with greater than 1.0 dv impact as was done for BART screening, in this anal ys is actual deciview values for each facility ’s modeled impacts were pulled from the post

- processing file and examined by

the State. Pre

- and post

- control 98 th 

### percentile decivie w values for the Class I area with greatest impact were record ed, and are presented in Table 40 . Respective differences in deciview are labeled as visibility improvements and the Class I areas with greatest impact are shown.

**Source ID**  **Facility Max. 98th**  **percentile pre**  **- control**  **Max. 98th**

**percentile post**  **- control**  **Visibility**

**im provemen**  **t (pre -**  **post) Class I area impacted**  dv dv dv 0910057 AGC Flat Glass

North America 0.065 0.043 0.022 Upper Buffalo 1330001 Ash Grove Cement 0.110 0.086 0.024 Upper Buffalo 0210002 Em

pire District Electric

- Riverton 0.257 0.165 0.092 Hercules

- Glades 0150004 Frontier El Dorado

Refinery 0.068 0.053 0.015 Wichita Mountains 1250015 Heartland Cement 0.069 0.053 0.016 Hercules

- Glades 2090048 Kansas City BPU

-

Quindaro 0.215 0.082 0.133 Hercules

- Glades 0930012 Kinder Morgan

-

Lakin Station 0.021 0.015 0.006 Wichita Mountains 0570003 Koch Nitrogen 0.028 0.010 0.018 Wichita Mountains 2057022 Lafarge Midwest

-

Fredonia 0.111 0.105 0.006 Wichita Mountains 0010009 Monarch Cement 0.074 0.066 0.008 Upper Buffalo 0670035 ONEOK

- Ulysses

Station 0.026 0.018 0.008 Wichita Mountains 0550023 Sunflow er Electric

-

Holcomb 0.189 0.173 0.016 Wind Cave 1730012 Westar Energy

-

Gordon Evans 0.117 0.006 0.111 Wichita Mountains 1550033 Westar Energy

-

Hutchinson 0.039 0.008 0.031 Wichita Mountains 1490001 Westar Energy

-

Jeffrey 1.174 0.341 0.833 Wichita Mountains 0450014 Westar Energy

-

Lawrence 0.428 0.285 0.143 Hercules

- Glades 1770033 Westar Energy

-

Tecumseh 0.308 0.094 0.214 Hercules

- Glades Table 40

- Overall Visibility Improvements Resulting from Application of Most Effective Controls at Selected Kansas 500

- Ton NO x and SO 2 Em ission Units

. Note that Kansas City BPU Nearman was not evaluated in this table because at the tim e th e m odeling was performed the Nearman facility was determined to be a BART source

.

### 51

As a refinement to the modeling, the State re

- ran CAL PUFF for the remaining sources considering the impacts of NO x 

### and SO2 separately . The State considered the pollutant emissions’ visibility

impacts separatel y because potential controls for a facility , to meet reasonable progress goals in a Class I area hosted by another State, could be pollutant dependent.

**Source ID**  **Facility Max. 98th**  **percentile pre**  **- control**  **Max. 98 th** 

**percentile post**  **- control**  **Visibility**

**im provement**  **Class I area**

**im pacted**  Dv NO x SO 2 NO x SO 2 1330001 Ash Grove Cement 0.110 0.090 0.024 Upper Buffalo 0210002 Em

pire District Electric

- Riverton 0.257 0.205 0.145 0.052 0.112 Hercules

- Glades 2090048 Kansas City BPU

-

Quindaro 0.215 0.174 0.142 0.041 0.073 Hercules

- Glades 2090008 Kansas City BPU

Nearman 0.378 0.318 0.198 0.060 0.180 Hercules

- Glades 2057022 Lafarge Midwest

-

Fredonia 0.111 0.105 0.006 Wichita Mountains 0550023 Sunflow er Electric

-

Holcomb 0.189 0.170 0.016 Wind Cave 1730012 Westar Energy

- Gordon

Evans 0.117 0.010 0.111 Wichita Mountains 1490001 Westar Energy

- Jeffrey 1.174 1.06 0.469 0.114 0.705 Wichita Mountains 0450014 Westar Energy

-

Lawrence 0.428 0.329 0.310 0.099 0.118 Hercules

- Glades 1770033 Westar Energy

-

Tecumseh 0.308 0.271 0.159 0.037 0.149 Hercules

- Glades Table 4 1

- Selected Kansas 500

- ton NO x and SO 2 Emission Units Showing Significant Visibility Improvement Resulting from Application of Most Effective Controls 

### In the fourth step, the State calculated the cost per ton per unit of dv improvement ($/ton/dv). The State estimated that the single value o f $/ton/dv combined the cost and visibility improvement in a way

that its numerical value increases: a. as the cost of controls increases and b. as the visibility

improvement decreases. The State determined that the facility with the lowest $/ton/dv would be the first to be reviewed for possible controls to meet reasonable progress goals in Class I areas hosted by other States.

### 52

**Source ID**  **Facility Pollutant**  **Emission unit(s)**  **Cost per**

**ton 1**  **Visibility im**  **provement Class I area** 

**(98 th**  **percentile) Cos**  **t per**

**ton per dv** $2002 Dv $2002/ton

dv 1330001 Ash Grove Cement NO x Cement kiln $509 0.024 Upper Buffalo $21,208 0210002 Em

pire District Electric

- Riverton NO x Units 7 & 8 $984 2 0.052 Hercules

Glades $18,923 0210002 Em

pire District Electric

- Riverton SO 2 Unit 8 $5,326 0.112 Hercules

Glades $47,554 2090048 Kansas City BPU

-

Quindaro NO x Units 1 & 2 $660 0.041 Hercules

Glades $16,098 2090048 Kansas City BPU

-

Quindaro SO 2 Units 1 & 2 $4,045 0.073 Hercules

Glades $55,411 2090008 Kansas City BPU

-

Nearman NO x Unit 1 $743 3 0.060 Hercules

Glades $12,383 2090008 Kansas City BPU

-

Nearman SO 2 Unit 1 $2,024 3 0.213 Caney Creek $9 , 502 2057022 Lafarge Midwest

-

Fredonia NO x Kilns 1 & 2 $76 0.006 Wichita Mountains $12,667 0550023 Sunflow er Electric

- Holcomb NO x Unit 1 $1,383 0.016 Wind Cave $86,438
1730012 Westar Energy

-

Gordon Evans SO 2 Unit 1 ($37) 0.111 Wichita Mountains ($333)
1490001 Westar Energy

-

Jeffrey NO x Unit 3 $1,437 0.114 Wichita Mountains $12,605 1490001 Westar Energy

-

Jeffrey SO 2 Unit 3 $1,695 0.705 Wichita Mountains $2,404 0450014 Westar Energy

-

Lawrence NO x Units 3, 4, & 5 $989 0.099 Hercules

Glades $9,990 0450014 Westar Energy

-

Lawrence SO 2 Units 3 & 5 $7,791 0.118 Hercules

Glades $66,025 1770030 Westar Energy

-

Tecumseh NO x Units 7 & 8 $450 0.037 Hercules

Glades $12,162 1770030 Westar Energy

-

Tecumseh SO 2 Units 7 & 8 $3,699 0.149 Hercules

Glades $24,826 Ta ble 4 2

- Summary of Each Unit’s cost per ton per U nit of dv I mp rovement 1 Alpine Geophysics reported costs in $2005, which have here been adjusted to 2002 dollars using a factor derived the ratio of Consumer Price Index average price data values for the two years, or 179.9/195.3 = 0.92115 (8) 2 Empire submitted estimates of $2 ,427 (60% efficient) and $1,826 (30% efficient) for NOx controls on Units 7 and 8, respectively. The values shown are from Alpine, however, since further visibility modeling was not carried out for the source. 3 Cost supplied by Kansas City BPU in an engi neering analysis report. Costs are in 2006 dollars. 

### As part of the fifth step, Kansas evaluated non

- cost factors such as non

- air environmental impacts, the remaining life of the unit and the compliance timeframe. Table 4 3 presents a summary of the facto rs for the sources remaining after cost and visibility impact was considered.

### 53

**Source ID**  **Facility Pollutant**  **Emission unit(s)**  **Selected**

**control measure**  **Compliance**

**Timeframe Non**  **- air** 

**quality environmental**  **im pacts**  **Rem aining** 

**useful life NO**  **x**
1330001 Ash Grove

Cement NO x Cement kiln LNB

3

- 4 y ears following SIP approval Potential

permitting issues, control device energy requirements 10

- 30

years

0210002 Em

pire District Electric

- Riverton NO x Unit 7 LNBO Potential permitting issues, reduction in electricity production capacity 5

- 10 years 0210002 Em

pire District Electric

- Riverton NO x Unit 8 LNC3

2090048 Kansas

City BPU

- Quindaro NO x Unit 1 NGR Potential permitting issues, reduction in electricity production capacity 30

- 50

years 2090048 Kansas

City BPU

- Quindaro NO x Unit 2 LNB

2090008 Kansas

City BPU

- Nearman NO x Unit 1 LNB+OFA+NN 30

- 50 years
2057022 Lafarge

Midw est
- Fredonia NO x Kilns 1 & 2 Mid

- kiln firing Control device energy requirements 10

- 30

years
0550023 Sunflow er

Electric

- Holcomb NO x Unit 1 LNB

Potential permitting issues, reduction in electricity production capacity 30

- 50

years 1490001 Westar

Energy

- Jeffrey NO x Unit 3 LNC3

0450014 Westar

Energy

- Lawrence NO x Units 3 & 4 LNC1

1770030 Westar

Energy

- Tecumseh NO x Unit 7 LNC1

1770030 Westar

Energy

- Tecumseh NO x Unit 8 LNC3 **SO 2** 
0210002 Em

pire District Electric

- Riverton SO 2 Unit 8 FGD wet scrubber 3

- 4 y ears

following SIP approval Potential

permitting & wa stewater issues, reduction in electricity production capacity 5

- 10 years

2090048 Kansas

City BPU

- Quindaro SO 2 Units 1 & 2 FGD wet

scrubber 30

- 50 years

### 54

2090008 Kansas

City BPU

- Nearman SO 2 Unit 1 Sem i

- Dry scrubber 3

- 4 y ears

following SIP approval Potential

permitting issues, reduction in electricity production capacity
1730012 Westar

Energy

- Gordon Evans SO 2 Unit 1 Fuel switching Currently feasible —

1490001 Westar

Energy

- Jeffrey SO 2 Unit 3 FGD wet scrubber 3

- 4 y ears

following SIP approval Potential

permitting & wa stewater issues, reduction in electricity production capacity 1770030 Westar

Energy

- Tecumseh SO 2 Units 7 & 8 FGD wet

scrubber
Table 4 3

- Summary of Reasonable Progress Non

- Cost Statutory Factors for Selected Kansas Point Sources 

### The two EGUs at Empire District

- Riverton, Units 7 and 8, have startup dates of 1950 and 1954, respectivel y, and it is likely they will be retired before 2018. It is unknown why the IPM 3.0 future year modeling did not reflect this, but merely indicated reduced (controlled) SO 2 

### emissions for Unit 7 starting in 2010. In addition, there is currentl y a question about Westar Energy ’s abilit y to use wet scrubbers at its Jeffrey facility at Unit 3 due to environmental concerns for wastewater sulfur levels. In the final step , the State ranked the remaining sources listing the sources from the previous step in increasing order of $/ton/dv and using $15,000/ton/dv as a max allowable cost. The final ranked list is given below in Table 4 4

.

**Rank Source** 

**ID Facility**  **Emission unit(s)**  **Pollutant Visibil**  **ity im**  **provement Cost per ton per**  **deciview** Dv $2002/ton

- dv 1 1730012 Westar Energy

-

Gordon Evans Unit 1 SO 2 0.111 ($333)
2 1490001 Westar Energy

–

Jeffrey Unit 3 SO 2 0.705 $2,404 3 2090008 Kansas City BPU

-

Nearman Unit 1 SO 2 0.213 $9,502 *
4 0450014 Westar Energy

–

Lawrence Units 3, 4, &

5 NO x 0.099 $9,990
5 1770030 Westar Energy

–

Tecumseh Units 7 /9 &

8 /10 NO x 0.037 $12,162 6 2090008 Kansas City BPU

-

Nearman Unit 1 NO x 0.060 $12,383
7 1490001 Westar Energy

–

Jeffrey Unit 3 NO x 0.114 $12,605

### 55

8 2057022 Lafarge Midwest

–

Fredonia Kilns 1 & 2 NO x 0.006 $12,667 Table 4 4

- Ranked List of Sources under the Reasonable Progress Analysis * Cost is based on 2006 dollars at Caney Creek. Hercules

- Glades cost is $11,244 again in 2006 dollars 

### The results of this process are described below. Kansas determined that controls or fuel switching on the sources listed below are reasonable and will result in significant visibility improvements at the WIMO.

Facility/Unit Emission rate or work practice

Gordon Evans Energy Center

- Unit 1 

### a fuel switch to natural gas at all times, with the exception of a gas curtailment order from the gas supplier, in which case the facilit y will be allowed to

utilize backup # 6 fuel oil
Hutchinson

- Unit 4 

### a fuel switch to natural gas at all times, with the exception of a gas curtailment order from the gas supplier, in which case the facilit y will be allowed to

utilize backup # 6 fuel oil Murray Gill

- Units 1,2,3 and 4 

### a fuel switch to natural gas at all times, with the exception of a gas curtailment order from the gas supplier, in which case the facilit y will be allowed to

utilize backup # 6 fuel oil
Neosho

- Unit 7 

### a fuel switch to natural gas at all times, with the exception of a gas curtailment order from the gas supplier, in which case the facilit y will be allowed to

utilize backup # 6 fuel oil Jeffery Energy Center

- Unit 3 

### an emission limit of 0.15 lbs/MMBtu for both SO 2 

### and NO x 

### Lawrence

- Unit3 

### an emission limit of 0.18 lbs/MMBtu for SO 2 

### Lawrence

- Unit 4 

### an emission limit of 0.18 lbs/MMBtu for SO 2 

### ; an emission limit of 0.15 lbs/MMBtu for NO x 

### Lawrence

- Unit 5 

### an emission limit of 0.15 lbs/MMBtu for both SO 2 

### and NO x 

### Tecumseh

- Unit 7/9 

### an emission limit of 0.18 lbs/MMBtu for SO 2 

### Tecumseh

- Units 8/10 

### an emission limit of 0.18 lbs/MMBtu for SO 2 Table 4 5

- Control or Work Practice Strategies for Westar Units to Meet Kansas Long Term Strategy R equirements



###  Murray

Gill, Hutchinson and Tecumseh Units

- were added to the list due to their inclusion in a previous Consent Agreement Westar has with the Sierra Club.

### 56

 BPU

- Nearman Unit 1

- this source primaril y impacts the Class I areas in Missouri and Arkansas. Thr ough Kansas’ consultation process with Missouri and Arkansas it was determined that no additional controls bey ond expected BART and on the books controls would be needed to meet the Reasonable Progress Goals for the Class I areas in these states. However, with the vacatur of the Clean Air Interstate Rule (CAI R ) and subsequent remanding of the case back to EPA without vacatur, Kansas suspects that it may

be necessary

to revisit this issue through the consultation process with Missouri and Arkansas

. This di scussion would occur once a final CAIR decision (or replacement rule) is finalized by the EPA and would be initiated by Missouri and/or Arkansas.

 Lafarge Midwest

- F redonia Kiln Unit 1 & 2

- The facility and the State have concerns regarding design and lay

- out constraints of some of the control technologies identified for this ty

pe of facility

. The small level of improvement in visibility gained by control at this facility

(0.006 dv for Wichita Mountains), in combination with the control sy stem design conc erns, led to the State to deci de not to require controls at this time. Additional technical and cost evaluations will be conducted during the five

- ye ar progress review. Consent Agreements (given in Appendix 9.7 to the SI P) provide a mechanism to enforce these determinations and sets the compliance schedules for these measures. The controls detailed above are expected to achieve approximately 10,409 tpy

of NO x 

### and 22,812 tpy

of SO 2 

### reductions.

Facility Unit 2002 NO x Emissions (tpy) 2002 SO 2

Emissions (tp y) Post Control

NO x (tpy ) Post Control

SO 2 (tpy) NO x

Reductions (tpy) SO 2

Reductions (tpy) Gordo n Evans 1 258.7 617.7 211.9 0.5 46.8 617.2 Hutchinson 4 267.1 734.3 158.5 0.6 108.5 733.7 Jeffrey 3 10,807.4 23,206.0 4,913.1 4,913.1 5,894.3 18,292.9 Lawrence 3 728.4 1,965.4 0.0 1,965.4 728.4 0.0 Lawrence 4 1,986.5 1,430.0 835.4 835.4 984.1 594.7 Lawrence 5 3,546.3 4546.3 2,564.7 2,564.7 981.6 1,789.0 Murray Gill 1 0.0 0.0 0.0 0.0 0.0 0.0 Murray Gill 2 4.5 0.0 4.0 0.0 0.5 0.0 Murray Gill 3 181.6 452.1 148.6 0.3 33.0 4,51.8 Murray Gill 4 103.8 333.3 85.2 0.2 18.7 333.1 Neosho 7 0.0 0.0 0.0 0.0 0.0 0.0 Tecumseh 7 1,530.6 2,692.7 691.6 2,692.7 839.0 0.0 Tecumseh 8 1,876.9 4,514.9 1,103.1 4,514.9 773.8 0.0 Total 10,408.7 22,812.4 Table 4 6

- Estimated NO x and SO 2 Emission Reductions for Im plem entation of Controls or Work Practices R equired by Kansas’ Long Term Strategy