# BART Determination Report:
Georgia Power Company Plant Bowen

Prepared by:

Southern Company Services and RMB Consulting and Research for Georgia Power Company

February 2007

### i 

# Contents

1.0 Executive Summary.......................................................................................................... ..1-1

2.0 Introduction ............................................................................................................... .........2-1
3.0 BART Exemption Modeling Results..................................................................................3-1
4.0 BART Determination Analysis for PM..............................................................................4-1
4.1 Overview of PM Species............................................................................................4-1
4.2 Potentially Available PM Controls.............................................................................4-4
4.3 Feasibility Analysis of Potential Controls................................................................4-11
4.4 Control Effectiveness of Feasible Options ...............................................................4-13
4.5 Impact Analysis for Feasible Options ......................................................................4-16
4.6 Visibility Impacts Modeling for Feasible Options ...................................................4-32
5.0 Proposed PM BART for the Plant Bowen EGUs...............................................................5-1

Appendix A Buoyancy of Moist Plumes: Equi valent Dry Effluent Temperature ..................A-1
Appendix B Exemption Modeling Delta-Deciview Values for the Top 20 Days - for Each Year/Each Class I Area and for the Top 25 Days - Over Three Years .................................B-1

Appendix C Agglomerator Cost Estimat e from Indigo Industries, Inc. ..................................C-1

Appendix D BART Determination Modeli ng Protocol: Georgia Power Company Plant Bowen.........................................................................................................D-1
Appendix E Determination Modeling Delta-Deciview Values for the Top 20 Days - for Each Year/Each Class I Area and for the Top 25 Days - Over Three Years ................................. E-1

### ii 

# List of Tables

Table 1-1 Summary of Bowen BART Determination Results .............................................1-2
Table 3-1 Summary of Results - Plant Bowe n Refined BART Exemption Modeling .........3-2
Table 4-1 Technically Feasible Upgr ade Options for Plant Bowen ...................................4-13
Table 4-2 Total Capital Cost Estimates, Units 1 / 2 ...........................................................4-20
Table 4-3 Total Capital Cost Estimates, Units 3 / 4 ...........................................................4-20
Table 4-4 Annualized Upgrade Cost Summa ry (2006 Dollars), Units 1 / 2.......................4-21
Table 4-5 Annualized Upgrade Cost Summa ry (2006 Dollars), Units 3 / 4.......................4-21
Table 4-6 2003-2005 Historical Emissions, Unit 1 ............................................................4-22
Table 4-7 2003-2005 Historical Emissions, Unit 2 ............................................................4-23
Table 4-8 2003-2005 Historical Emissions, Unit 3 ............................................................4-23
Table 4-9 2003-2005 Historical Emissions, Unit 4 ............................................................4-23
Table 4-10 Baseline Emissions [t ons per year], Units 1-4....................................................4-24

Table 4-11 Estimated Post -Retrofit Emissions [tons per year], Unit 1.................................4-24

Table 4-12 Estimated Post -Retrofit Emissions [tons per year], Unit 2.................................4-24

Table 4-13 Estimated Post -Retrofit Emissions [tons per year], Unit 3.................................4-25

Table 4-14 Estimated Post -Retrofit Emissions [tons per year], Unit 4.................................4-25
Table 4-15 Average Cost Effectiven ess of Upgrade Options, Unit 1...................................4-26
Table 4-16 Average Cost Effectiven ess of Upgrade Options, Unit 2...................................4-26
Table 4-17 Average Cost Effectiven ess of Upgrade Options, Unit 3...................................4-26
Table 4-18 Average Cost Effectiven ess of Upgrade Options, Unit 4...................................4-26
Table 4-19 Incremental Cost Effectiveness, Unit 1 ..............................................................4-31
Table 4-20 Incremental Cost Effectiveness, Unit 2 ..............................................................4-31
Table 4-21 Incremental Cost Effectiveness, Unit 3 ..............................................................4-31
Table 4-22 Incremental Cost Effectiveness, Unit 4 ..............................................................4-31
Table 4-23 Summary of Results – Plant Bowen BART Determination Modeling for Cohutta .........................................................................................4-33 Table 4-24 Summary of Results – Incremental Change from Baseline Plant Bowen BART Determination Modeling for Cohutta................................4-35 Table 5-1 Summary of Bowen BART Determination Results .............................................5-2

### iii 

# List of Figures

Figure 4-1 Least Cost Envelope, Unit 1...............................................................................4-29

Figure 4-2 Least Cost Envelope, Unit 2...............................................................................4-29

Figure 4-3 Least Cost Envelope, Unit 3...............................................................................4-30

Figure 4-4 Least Cost Envelope, Unit 4...............................................................................4-30

### 1-1 

# 1.0 Executive Summary

Under the regional haze regulations, determinations of best available retrofit technology (BART) for sources that are BART-eligible and subject to BART must be submitted by the states to EPA no later than December 17, 2007. Coal-fired electric generating units 1, 2, 3, and 4 at Plant Bowen, owned and operated by Georgia Powe r Company (GPC), are BART-eli gible units that are subject to BART. The Georgia Environm ental Protection Division (GEPD) is responsible for determining BART for each of the electric generating units (E GUs) at Plant Bowen. With respect to sulfur dioxide (SO 2 

### ) and nitrogen oxides (NO x 

### ), GEPD has indicated that it plans to adopt EPA’s regional haze rule that allows the cap-and-trade program of the Clean Air Interstate Rule (CAIR) to satisfy BART for SO 2 

### and NO x 

### at EGUs. Thus, no BART determination is required for SO 2 

### and NO x 

### emissions from Plant Bowen’s EGUs. For partic ulate matter (PM) emissions from Plant Bowen’s EGUs, GEPD has requested that GPC pr ovide a proposed BART determination.

The PM emissions from Plant Bowen’s EGUs are cu rrently controlled by electrostatic precipitators (ESPs). Wet flue gas desulfur ization (WFGD) scrubbers for SO 2 

### control, which also further reduce PM emissions, have been permitted for Plant Bowen’s EGUs and are under construction. Therefore, the PM BART determination analys is for Plant Bowen’s EGUs begins with the assumption that each EGU’s PM emissions are controlled with both an ESP and a scrubber.

GPC, with the assistance of Southern Co mpany Services (SCS) and RMB Consulting and Research, Inc. (RMB), has identifie d an additional six feasible PM control options for Units 1 and 2, and five feasible PM control options for Units 3 and 4. The co st of each option, the option’s potential for further reducing PM emissions , the resulting dollar-per-ton removed costeffectiveness, and the energy and non-air enviro nmental impacts of each option have also been analyzed. Using the least-cost-envelope curv e approach recommended by EPA, GPC narrowed down the possible BART options for each of the units to the following four: (1) high voltage power conditioners (Juice Cans), (2) a particle ag glomerator, (3) the combination of Juice Cans and an agglomerator, and (4) a wet electrostatic precipitator (WESP). To assess visibility improvement, two of those options (WESP and th e Juice Can/agglomerator combination) have been modeled using CALPUFF. Table 1-1 contains a summary of the cost, expected PM emissions reduction, cost-effectiveness (average dollars -per-ton removed), and modeled visibility improvement (delta-del ta-deciviews), for the control options at each of the Bowen EGUs.

Based on this information, GPC proposes that PM BART for the Plant Bowen EGUs be no additional controls. Each unit at Plant Bowen is already extremely well controlled for PM emissions. With regard to the WESP contro l option, although the modeling predicts a small improvement in visibility at the Cohutta Class I area associated with use of that option, the costeffectiveness of that option is well above any threshold for co st-effectiveness that could be considered reasonable for BART. In additi on, the WESP would consume additional energy and would create adverse non-air qu ality environmental impacts.

### 1-2 Table 1-1 Summary of Bowe n BART Determination Results 1

Upgrade Option Annualized Cost [$/yr] PM 10 Reduction [tpy] Average Cost [$/ton] Visibility Improvement from Baseline 2 [delta-delta-dv]
Bowen Unit 1 Results

Juice Can $76,860 11.3 $6,815 - Agglomerator $1,163,529 62.0 $18,759 - Agglomerator/Juice Cans $1,240,389 73.1 $16,921 -0.01
Wet ESP $12,474,580 261.0 $47,804 -0.14

Bowen Unit 2 Results

Juice Can $76,860 15.6 $4,923 - Agglomerator $1,163,529 85.9 $13,550 - Agglomerator/Juice Cans $1,240,389 101.5 $12,222 -0.01
Wet ESP $12,474,580 336.2 $37,107 -0.14

Bowen Unit 3 Results

Juice Can $98,820 21.1 $4,677 - Agglomerator $1,495,965 51.6 $28,965 - Agglomerator/Juice Can $1,594,785 72.8 $21,914 -0.01
Wet ESP $16,038,745 334.8 $47,909 -0.15

Bowen Unit 4 Results

Juice Can $98,820 24.0 $4,110 - Agglomerator $1,495,965 58.8 $25,452 - Agglomerator/Juice Cans $1,594,785 82.8 $19,256 -0.01
Wet ESP $16,038,745 360.5 $44,492 -0.16

Notes: 1. Visibility improvement results represent new IMPROVE equation and virtual stack temperature. 2. Incremental change is the “Highest of 8 th Highest delta-delta-dv for the 3-years.”



### With regard to the agglomerator option and the Juice Can/agglomeratorcombination, the costeffectiveness values exceed levels that have been considered reasonable even in the context of New Source Review (NSR) “Best Ava ilable Control Technol ogy” (BACT) assessments and the BARTlike determination conducted by Tampa Electric Company for evaluation of ESP-upgrade options at Big Bend Station. (The excep tion is for Unit 2, where the cost -effectiveness of these options falls within the upper range of values that could be considered reasonable for BACT.) Moreover, the CALPUFF modeling predicts virt ually no visibility improvement would occur with use of those options.

Finally, although the Juice Can option could be d eemed by GEPD to have a relatively moderate dollar-per-ton removed cost-effectiveness, that op tion reduces only a few tons of PM and is not expected to improve visibility at all.

For these reasons, GPC proposes that PM BART for Units 1-4 at Plant Bowen be no additional controls.

### 2-1 

# 2.0 Introduction

The regional haze regulations require a BART dete rmination for each BART-eligible source that emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility in a mandatory Federal Class I area. 1 

### The determination of BART for fossil-fuel fired power plants having a total gene rating capacity greater than 750 megawatts must be made pursuant to EPA’s “BART Guidelines ” found in Appendix Y to 40 C.F.R. Part 51. 2



### GPC owns and operates coal-fired EGUs 1, 2, 3, and 4 at Plant Bowen, near Cartersville, Georgia. To be BART-eligible, a source must fall within one of the 26 BART-eligible source categories identified in the Clean Air Act (for example, “fos sil-fuel fired steam electric plants of more than 250 million Btu per hour heat input”), the source must have been in existence on August 7, 1977 and have begun operation after August 7, 1962, and the source must have potential emissions of a visibility-impairing pollutant of 250 tons per year or more. All f our EGUs at Plant Bowen satisfy these criteria and thus are BART-eligible. The visibility-impairing pollutants emitted by Plant Bowen’s EGUs are SO 2 

### , NO x 

### , and PM. GEPD has determined that volatile organic compounds (VOCs) and, except in limited cases not applicable here, ammonia will not be considered visibilityimpairing pollutants in Georgia.

GEPD has announced its intention to participate in the CAIR cap-and -trade program and to use that program to satisfy BART for SO 2 

### and NO x 

### at BART-eligible EGUs, pursuant to 40 C.F.R. Section 51.308(e)(4). Based on guidance from EPA, if a state opts to allow CAIR to satisfy SO 2 

### and NO x 

### BART for an EGU, then a modeling analysis may be conducted to determine whether PM emissions from that EGU, by themselves, cause or contribute to visibility impairment in a Class I area (i.e., are subject to BART ). As a result, GPC undertook CALPUFF modeling to assess whether the PM emissions from Plant Bowen’s EGUs are subject to BART.

The Plant Bowen CALPUFF modeling was conducte d pursuant to a modeling protocol submitted by GPC to GEPD in May 2006, see “BART Modeling Protocol: Plant Bowen” (May 2006), with revisions to the procedures base d on later discussions with GEPD. 3 

### The results of that modeling indicate that the PM emissions from Plant Bo wen’s EGUs are subject to BART. Specifically, using GEPD’s 0.5 delta-deciview threshold (8 th 

### highest impact each year; 22 nd 

### highest impact over three years) for assessing whether a source contributes to visibility impairment in a Class I area, the visibility impact from Plant Bowen’s PM emissions exceed that threshold in one Class I area, the Cohutta wilderness area in North Georgia. Table 3-1, as presented later in Section 3.0 of this BART Determination Report, shows the results of the CALPUFF exemption modeling performed for Plant Bowen. Therefore, pursuant to EPA’ s BART Guidelines, a BART determination is required for PM emissions from each Plant Bowen EGU. 4

1 40 C.F.R. Section 51.308(e)

2 40 C.F.R. Section 51.308(e)(1)(ii)(B ); BART Guidelines, Section I.F.1

3 The revised procedures are reflect ed in the “BART Determination Mode ling Protocol” (December 2006) that was submitted to GEPD. See Section 4.6 of this report.

4 BART Guidelines, Section IV.B

### 2-2 BART is defined in the regional haze regulations as an emissi on limitation based on the degree of reduction achievable through the application of th e best system of continuous emission reduction for a pollutant emitted by a BART-eligible source. A BART emission limitation must be established on a case-by-case basis taking into consideration the te chnology available, the costs of compliance, the energy and non-air quality enviro nmental impacts of compliance, any pollution control equipment in use or in ex istence at the source, the remaini ng useful life of the source, and the degree of improvement in visibility which may reasonably be anticipated to result from the use of such technology. 5



### This document contains the analysis of these f actors for determining BART for PM emitted from each of Plant Bowen’s EGUs. Section 3.0 summar izes the exemption modeling performed using the baseline conditions. Section 4.0 identifies and provides an analysis of the feasible PM control technology options, as well as a di scussion of the considerations that must be examined for determining BART. Section 5.0 discusses GPC’ s proposal that PM BART for Plant Bowen’s EGUs be no additional controls.

5 BART Guidelines, Section IV.A

### 3-1 

# 3.0 BART Exemption Modeling Results

One factor in evaluating whether a source is subj ect to BART and which c ontrol options should be evaluated as BART is consider ation of any existing pollution c ontrol technology in use at the source. Currently, PM emissions from Plant Bowen’s EGUs are controlled with ESPs. However, SO 2 

### scrubbers, which also remove additional PM emissions, have been permitted and are under construction for Plant Bowen’s EGUs. Because the scrubbers will be in operation well before the expected implementation date for any additional BART PM control option that may be required, GPC has used the PM emissions from the scrubbed EGUs as the baseline for the PM BART modeling. The baseline condition also includes the stack parameters associated with the new stacks that will be constructed for the new scrubb ers. (“Good engineering practice” (GEP) stack height assumptions have been used for the modeling.)

The procedures contained in the “BART Modeling Protocol: Plant Bowen,” submitted to EPD on May 1, 2006, were used to conduct the BART exemption modeling. Based on discussions with EPD, it was agreed that the exem ption modeling would be done for th e units as scrubbed (the May 2006 Protocol as submitted discusses only unscr ubbed emissions), and the results would be presented using both the new and old IMPROVE equations. 6



### One issue that was not addressed in the modeling pr otocols was the stack temperature to use in the modeling. In the May 2006 and December 2006 Prot ocols (Tables 2-1 and 2-2, respectively), the stack temperature is reported as 327 K. This is the actual temperature. However, the virtual temperature of 349 K should have be en reported and was, in fact, used in the modeling. The plume rise equations in the existing EPA-approved air quality models describe the behavior of dry plumes. Since a scrubber plume is saturated and thus has greater buoyancy than dry flue gas, the virtual temperature of the stack gas is appropr iate for modeling of scrubbed plumes. Virtual temperature is the temperature that a dry plume would need to have to provide the same buoyancy as the wet gas. Research engineers at Southe rn Company’s Research an d Environmental Affairs Department created a calculation tool which dete rmines the virtual temperature of a moist plume based on fuel, operational, ambient and physical stack parameter inputs. This tool was used to calculate the virtual temperature at the stack exit of both of the new scrubber stacks. A description of the calculation tool is provide d in Appendix A. Since the temp erature does have a small effect on the results, the results using both the virtual and actual temperatures are presented below. However, the results representing the use of vi rtual temperature and th e new IMPROVE equation are considered the primary basis for the BART exemption (Section 3.0) analysis.

The exemption modeling results are provided in Table 3-1, and Appendix B lists delta-deciview results for the top 20 days for each year modeled and the top 25 days for the overall three years at each Class I area for the primary results (i.e., the virtual temperature, new IMPROVE equation case). The table indicates that both the 8 th 

### highest day’s impacts for each year and the 22 nd 

### highest day’s impacts over all three years are below 0.5 delta -dv for four of the five Class I areas within 300 km. However, the impacts are gr eater than 0.5 delta-dv at Cohu tta. These results demonstrate

6 Revisions to the modeling procedures (including estimates of PM emission reductions due to the scrubbers) are reflected in the “BART Determination Mo deling Protocol” (December 20 06), which is discussed in section 4.6 of this report.

### 3-2 Table 3-1 Summary of Results – Plant Bowen Refined BART Exemption Modeling

2001 2002 2003

Class I area Distance from source to Class I area boundary # of days and receptors beyond

98 th percentile with impact > 0.5 delta-dv 8 th Highest delta-dv # of days and receptors beyond

98 th percentile with impact > 0.5 delta-dv 8 th Highest delta-dv # of days and receptors beyond

98 th percentile with impact > 0.5 delta-dv 8 th Highest delta-dv Highest of 8 th Highest

delta-dv for the 3-

years 22 nd Highest delta-dv over 3-

year period

km Days Rec delta-dv Days Rec delta-dv Days Rec delta-dv delta-dv delta-dv

Cohutta

New Improve Virtual Temp 84.8 6 5 0.74 12 6 0.94 6 4 0.59 0.94 0.74

New Improve Actual Temp 84.8 10 6 0.78 15 7 1.05 13 5 0.73 1.05 0.82

Old Improve Virtual Temp 84.8 13 8 0.91 20 6 1.15 16 7 0.73 1.15 0.91

Old Improve Actual Temp 84.8 16 8 0.97 22 7 1.28 23 6 0.90 1.28 1.00

Great Smoky Mountains

New Improve Virtual Temp 175.9 0 0 0.28 0 0 0.23 0 0 0.23 0.28 0.24

New Improve Actual Temp 175.9 0 0 0.27 0 0 0.28 0 0 0.26 0.28 0.27

Old Improve Virtual Temp 175.9 0 0 0.35 0 0 0.29 0 0 0.28 0.35 0.30

Old Improve Actual Temp 175.9 0 0 0.33 0 0 0.34 0 0 0.34 0.34 0.34

Joyce Kilmer

New Improve Virtual Temp 162.3 0 0 0.27 0 0 0.31 0 0 0.25 0.31 0.27

New Improve Actual Temp 162.3 0 0 0.28 0 0 0.34 0 0 0.29 0.34 0.30

Old Improve Virtual Temp 162.3 0 0 0.34 0 0 0.39 0 0 0.31 0.39 0.34

Old Improve Actual Temp 162.3 0 0 0.35 0 0 0.43 0 0 0.37 0.43 0.38

Shining Rock

New Improve Virtual Temp 228.0 0 0 0.17 0 0 0.12 0 0 0.18 0.18 0.16

New Improve Actual Temp 228.0 0 0 0.17 0 0 0.14 0 0 0.19 0.19 0.18

Old Improve Virtual Temp 228.0 0 0 0.21 0 0 0.14 0 0 0.22 0.22 0.20

Old Improve Actual Temp 228.0 0 0 0.20 0 0 0.16 0 0 0.24 0.24 0.22

Sipsey

New Improve Virtual Temp 223.5 0 0 0.08 0 0 0.12 0 0 0.11 0.12 0.11

New Improve Actual Temp 223.5 0 0 0.08 0 0 0.12 0 0 0.12 0.12 0.11

Old Improve Virtual Temp 223.5 0 0 0.10 0 0 0.15 0 0 0.14 0.15 0.14

Old Improve Actual Temp 223.5 0 0 0.11 0 0 0.16 0 0 0.15 0.16 0.15

### 3-3 that Plant Bowen’s PM 10 

### emissions contribute to visibility impairment at Cohutta and Plant Bowen’s EGUs are therefore subject to PM BART and a PM BART determination analysis for Cohutta is required.

An external hard drive is being pr ovided separately that contains al l of the electronic data related to this application (i.e., the virtual temperature, new IMPROVE equation case). This drive contains all of the input and output f iles used in the modeling. A readme.txt file is also included on the hard drive that lists all of the files on the drive.

### 4-1 

# 4.0 BART Determination Analysis for PM

4.1 Overview of PM Species

The size of particulate resulting from coal-fir ed combustion will vary depending on coal type, boiler design, and mill grind. At the boile r exit, particles may range up to 200 microns 7 

### for a typical pulverized coal boiler. 8 

### Particle size distribution of flue gas particulate may be altered by control device type and operation, as certain types of PM controls (such as ESPs) have different size-specific collection e fficiencies. For an ESP-equipped boile r, PM sizes in the stack may range up to 100 microns, although 50-80% of the total mass is represented by particles less than 10 um. 9



### There are two distinct particle size ranges that ar e currently regulated by EPA. These are particles less than 10 microns in diameter (PM 10 

### ) and particles less than 2.5 microns in diameter (PM 2.5 

### ). EPA classifies PM between 10 and 2.5 microns as coarse PM and PM at or below 2.5 microns as fine PM.

PM emissions from fossil fuel fired boilers ar e generally characterized as ‘filterable’ and ‘condensable’ particulate. PM 10 

### coarse is essentially all filterable and PM 2.5 

### includes both filterable and condensable species.

Filterable PM Species

Filterable particulate refers to particulate that is measured us ing standard filter-based measurement techniques. 10 

### The particulate is captured from the flue gas on a filter and weighed for a direct mass measurement. Filterable PM is usually solid -phase material consisting of unburned carbon, ash, and other inorganic material. 11 

### Filterable PM measurement tec hniques do not catch vapors or some particles less than 0.3 microns. 12



### Condensable PM Species

Condensable PM refers to particulate that forms either by condensation of vapor-phase species or by chem ical reaction between gases. Primary condens able particulate forms either in the stack or shortly after discharge from the st ack, as hot flue gases are cool ed by ambient air. Secondary particulate is formed in the atmosphere through chemical reaction between ambient constituents.

7 A ‘micron’ is one millionth of a meter: 1 μ m = 1x10 -6 m

8 Applied Electrostatic Precipitation, Parker, K.R., 1997.

9 RMB analysis of SRI impactor data fo r a variety of coals and boiler types.

10 EPA Reference Method (RM) 5 (out of stack sampling) and RM-17 (in stack sampling)

11 While technically considered “condensable”, filterable me asurements may occasionally include suspended droplets and aerosols, such as scrubber carryover and condensed sulf uric acid, which are large en ough to be captured on the filter.

12 AP-42

### 4-2 Virtually all condensable partic ulate is less than one micron, and therefore may constitute a significant fraction of PM 2.5



### . Primary Condensable Species Primary condensable PM includes organic 13 

### compounds, sometimes referred to as primary organic aerosols (POA), and inorganic compounds. For coal-f ired applications, most primary condensable PM species is inorganic, representing roughly 80% of the total condensable PM 10



### . 14



### The primary organic components include a broad range of com pounds known as polycyclic organic matter (POM). The most commonly tested and re ported subset of POM are polycyclic aromatic hydrocarbons (PAH), which are compounds containing only carbon and hydrogen.

POM are products of incomplete combustion that are most often asso ciated with complex fuels that have a high carbon to hydrogen content a nd staged combustion, such as coal. 15 

### POM compounds exist in the solid phase under ambient conditions a nd may be emitted from the stack in either vapor form or solid form, depending on flue gas temperatur e. At typical flue ga s temperatures (> 300°F), most POM is emitted in the vapor phase. Howeve r, for scrubbed stacks where temperatures are often below the melting point of most POM (~ 200°F), POM compounds readily condense ontoexisting particulate. In this case, only a small portion of POM is emitted from the stack in vaporphase.

Primary inorganic compounds are typically condensed acids. For coal-fired applications, sulfuric acid (H 2 

### SO 4 

### ) and the sulfuric acid precursor SO 3 

### are most significant. During the combustion of fossil fuels, fuel-bound sulfur is oxidized to form SO 2 

### . A small fraction of SO 2 

### (< 1%) is further oxidized to form SO 3 

### in the convective regions of the furn ace, depending on coal sulfur content and excess air level. Additional SO 2 

### is oxidized in the economizer region of the boiler in a catalytic reaction that depends on SO 2 

### concentration

, 

### ash and boiler tube compositions, excess air level, and gas/tube surface temp erature and surface area. This additional oxidation may range up to 2%. 16 

### Upstream of the air pre-heater, SO 3 

### exists in the flue gas in the gaseous phase. However, as the gas passes through the air he ater and cools, the hygroscopic SO 3 

### combines with flue gas moisture to form vapor-phase sulfuric acid (H 2 

### SO 4 

### ).

Units equipped with SCR for NO x 

### control, particularly those firing high-sulfur coals, have a higher susceptibility to acid gas formation, as SCR catalys ts are known to cause additional oxidation of flue gas SO 2 

### . The SCR catalyst reduces NO x 

### (predominately NO) to molecular nitrogen, which provides additional oxygen for further SO 2 

### to SO 3 

### conversion. Test data have shown that this additional conversion may be as high as 3% de pending on the operating parameters, system design

13 Carbon containing

14 AP-42

15 U.S. Environmental Protection Agency. Locating and Estimating Air Emissions from Sources of Polycyclic Organic Matter . EPA-454/R-98-014. Office of Air Quality Planning an d Standards, Research Triangle Park, NC. 1998.

16 Emissions of Sulfur Trioxide from Coal-Fired Power Plants , Srivastava, R.K., Miller, C.A., Erickson, C., Jambhekar, R.

### 4-3 and catalyst formulation. This increa se can effectively double the existing SO 3 

### flue gas concentration. 17



### Measurement of primary condensable PM has become a more recent regulatory issue. The current version of the EPA reference method 18 

### for measuring condensable PM is known to have a measurement bias for flue gas containing SO 2 

### , NO x 

### , or soluble semi-volat ile organic compounds. A portion of these gases dissolve in the impinger solutions and oxidize to form artifacts that artificially inflate measured par ticulate. EPA is presently cons idering modification of RM-202 to reduce artifact formation.

Secondary Condensable Species Secondary condensable PM also includes both orga nic (“secondary organic aerosols”, SOA) and inorganic components. SOA are formed when th e oxidation products of certain volatile organic compounds 19 

### (VOCs) condense on pre-existing aerosols (u sually POA). The atmospheric behavior of SOA is currently not well unders tood and ambient SOA concentratio n is difficult to measure.

The most notable secondary inorga nic particulates associated w ith visibility impairment are ammonium sulfate, ammonium bisu lfate, and ammonium nitrate, e ach formed in the atmosphere through photochemical reactions. Unlike SOA, the mechanisms of sulfate and nitrate formation and their contributi on to atmospheric fine particles are well documented. 20



### Applicable PM Species for BART

Only direct PM emissions, including filtera ble and primary condensable PM species, are considered in the BART evaluation. Secondary i norganic species are not c onsidered PM emissions under the Clean Air Act, and thus are not considered for the purposes of the BART PM evaluation. These species are analyzed separately as part of BART evaluations for SO 2 

### and NO x 

### , which Plant Bowen is not required to undertake because SO 2 

### and NO x 

### BART are being satisfied through the State of Georgia’s implementation of the Clean Ai r Interstate Rule (CAI R). Secondary organic species (SOA), although considered PM in ambient air, is also not subjec t to this analysis since it is not considered to be direct PM emissions from a stack and is not include d in the current VISTAS modeling protocol. 21

17 Current Work on the Impacts of SO 3 Emissions from Selective Catalytic Reduction Systems, Erickson, C, Jambhekar, R.

18 EPA RM-202, Determination of Condensable Particulate Emissions from Stationary Sources

19 Organic species include volatile, semi-volatile, and (pri mary) condensable organic compounds. VOCs refers to those volatile and semi-volatile components that are em itted to the atmosphere in a gaseous phase.

20 EPA, ibid.

21 CALPUFF provides SOA modeling capa bility. However, the model was adapted from SOA formation from biogenic organic emissions, and, therefore, is not suited for modeling combustion-related SOA formation. The VISTAS protocol acknowledges that the application for combustion-related SOA formation requires additional investigation. Note that the organic carbon model input parameter refers to POA, not SOA.

### 4-4 Filterable Species The initial selection of BART options for filterable PM emissions is independent of particle size. Separate evaluations ar e not required for PM 10 

### and PM 2.5 

### . However, as part of the BART evaluation, the impact analysis requires a source to evaluate the visibility improvement of competing BART alternatives. Since fine particles have a greater effect on visibility, this suggests that any changes in the “baseline” particle size distribution should be cons idered when comparing BART alternatives.

Condensable Species The BART evaluation for primary condensable PM species will be ba sed on sulfuric acid emissions. Plant Bowen Units 1-4 are currently equipped with SCRs and Units 3 and 4 have flue gas conditioning. Also, all four units have permits for wet scrubbers and these scrubbers are currently under construction. Therefore, stack conditions may be favorable for relatively high concentrations of condensed sulfuric acid.

POA (POM) will not be considered in the selecti on of BART alternatives. SCS is unaware of any post-combustion controls that are specifical ly designed for POA reduction for coal-fired applications. Furthermore, for those control s cenarios where some theoretical reduction in POA may be possible (i.e. WESP, wet scrubber), SCS was unable to locate any additional information on removal efficiency. Given the re latively low concentrations of P OA in the flue gas, if any such data were available, it would likely be meani ngless because of measurement uncertainty.

4.2 Potentially Available PM Controls

The first step in the BART evaluation is to iden tify all available retrofit control technologies. “Available” refers to technologies “ with a practical potential for appl ication to the emissions unit and the regulated pollutant under evaluation .” 22 

### This limits the potential retrofit options to only those technologies that ar e field-proven and commercially available.

Based on the preceding discussion, this effort is li mited to the control technologies for filterable PM and sulfuric acid gas emissions. Many of the control technologies available for filterable PM removal have little or no removal of sulfuric acid emissions and vi ce-versa. In fact, while some controls may improve collection of one species, they may increas e emissions of the other (e.g. sorbent injection removes sulfuric ac id gas but increases filterable PM). 23 

### Therefore, in selecting the appropriate BART option, which could include one or more retrofit t echnologies, consideration will be given to the overall net PM removal and the net effects on visibility.

The following section includes a general discussion of the various potentia lly available control options for filterable PM and sulfuric acid emissions. Some of these options may not be applicable to certain affected units. A case-by-case analysis is discussed in Section 4. 3 as part of the BART evaluation to eliminate technically in feasible options for each unit.

22 BART Guidelines, Section IV.D.1.1.

23 The “appropriate” BART determination may include a combination of one or more PM control devices/upgrades (e.g. ESP upgrade and sorbent in jection). Note that the appropriate BART determination could also be a single control technology that does not provide removal capability of both PM species.

### 4-5 Filterable PM Controls

For coal-fired applications, all conventional PM contro ls are desi gned to remove primary, filterable particulate. In determining the initial list of ap propriate control technol ogies, SCS considered all field-proven, commercially availa ble upgrade options listed in the EPRI ESP upgrade guidelines manual. 24 

### These technologies have been applied at multiple coal-fired plants and the suppliers provide performance guarantees. Two additional technologies are included, because they have reached some degree of maturity in the period of time since the original guidelines documents were prepared: the Indigo Technologies’ particle agglomerator and th e BHA “Juice Can” filter.

Wet ESP Wet ESPs (“WESPs”) use the sam e basic operating principles as conventio nal, cold-side or hotside ESPs. However, instead of a plate rapping system, a flowing sheet of water covering each collecting plate is used to remove the collected ash. Collected material flows down the plate with the water film and is collected in a drain system . WESPs virtually eliminate reentrainment and back corona problems found in conventional precipitators.

A WESP system can be installed either as a separa te unit downstream of the existing ESP or, as a retrofit, in the outlet field of th e existing ESP casing. WESPs can be constructed in either a tubular or plate (similar to conventiona l ESPs) configuration. Tubular WESPs are suitable for vertical flow applications only. This de sign is slightly more compact, but more complicated to build than a flat plate design.

One of the critical design issues is the material used in the collection system. WESP internals must be made of a conductive material that will resi st corrosion due to the acid mist that might be present in the gas. Common materials include co nductive fiberglass, carbon steel, stainless steels, and various high-end alloys. For most multi-pollutant applications, an acid-gas scrubber is used to pre-treat the gas entering the WESP.

WESP technology has received particul ar interest as a multi-pollutant emissions control device capable of reducing emissions of acid fumes, fine particulate, mercury, and other metals. WESPs have also been considered a performance enhancem ent option for installations with either high ash resistivity or excessive reentrainment.

Although WESPs have been used successfully in some smaller-scale indus trial applications and widely used in the sulfuric acid industry to control SO 3 

### mist, historically, they have not been used in utility applications because of the high costs associated with th e corrosion-resistant metals used in their manufacture. Disposal of wastewater is also an issue.

Pulse Energization A conventional power supply for an ESP consists of a high voltage step up transform er to provide either a full wave or, in some ol der installations, half wave rectified electrical pow er input to the corona-generating electrode system. The comple te system includes a control system with an appropriate operating control algor ithm. The conventional power s upply provides input to the ESP system with a sinusoidal shaped varying voltage to transfer energy to the ESP. Electrical energy

24 Guidelines for Upgrading Electrostatic Precipitators: Volume 2, EPRI Report Number TR-113582.

### 4-6 flows to the collector when the applied voltage from the power s upply exceeds the residual voltage on the ESP electrode system. This conventiona l power supply is appropriate for most ESP installations.

Pulse energization is a technology th at is useful for in creasing the ESP collec tion efficiency for high resistivity fly ash particles. The advantage of pulse energizati on is that the voltage applied to the corona electrode is increased at such a fast rate that the process of individual tuft formation does not have time to develop before the entire cor ona electrode is brought into corona. It takes several microseconds of time for the individual tuft s to form so the entire corona electrode is generating corona before the shielding of individu al tufts can occur. This pulse corona process leads to the entire surf ace of the corona electrode supplying ca rrier ions and a much more nearly uniform current distribution over the surface of the dust layer.

In order to implement pulsed energization, it is necessary to purchase an d install the new power supplies. In some cases, portions of the existing power supplies can be reus ed, but in most cases, they are scrapped or installed on an other unit. If the ESP is operat ing with heavy back corona, the emissions can be significantly reduc ed with the addition of pulsers.

COHPAC COHPAC, a n acronym for Compact Hybrid Particulate Collector , is a technology that includes a small pulse jet fabric filter downstream of the ex isting ESP. There are two methods of installing COHPAC. The first method (“COHPAC I”) places the fabric filter downstream of the ESP in a new casing. The second method (“C OHPAC II”) requires the last mechanical field of the ESP to be replaced with th e fabric filter. 25 

### The pulse jet collector can be designed with a much higher gas to cloth ratio than a conventional collector because of two factors. First, the fly ash loading into the filter is much less than in a stand-alone fi lter because the ESP has re moved more than 90% of the ash. Second, the ash suspended in the gas st ream is electrically charged by the ESP and will form a much more porous dust cake than unc harged ash. PM removal efficiency (PM 2.5 

### and total PM) for COHPAC is consistent with the typical effi ciency of pulse jet fabr ic filter installations (~99% of existing emissions levels).

Particle Agglomerator The collection efficiency of an ESP varies as a func tion of the particle size of the material to be collected. This is because of a combination of f actors related to the electrical charging mechanisms and the ease with which particles of different sizes are able to move through the molecules in the gas stream. The collection efficiency for an ES P is high for large particles, decreases to a minimum for particles in the half micron diameter range, and then increases somewhat for the even smaller particles. The minimum collecting efficien cy just coincides with the wavelength of light that causes the greatest opacity of particles in the atmosphere.

The particle agglomerator is a t echnology that causes the smaller pa rticles to contact and adhere to the larger particles so that they will be removed with the large particles that are collected more

25 COHPAC II is not commercia lly available, and, therefore, does not meet the selection criteria as a viable retrofit technology.

### 4-7 efficiently than they would have been if remaining as individual particles. The principles used for particle coagulation are a combin ation of electrical and turbulent gas flow mechanisms that bring the oppositely charged particles into close prox imity and allow them to adhere together.

The system consists of a number of individual co rona discharge systems operating in parallel, with one channel operated with negative corona and the next with positive corona. This provides a gas stream with approximately half of the particles charged positively, with the remainder negative. When these gas streams are mixed, the oppositely ch arged particles are attracted together, then, upon contact, they adhere to each other and behave as one larger particle.

This device is installed in the ductwork leading to the ESP inlet plenum, where the gas velocity is relatively high – on the order of 20 to 40 feet pe r second. This region is designed to be highly turbulent to cause these gas str eams to mix and bring the oppositely charged particles together. The gas stream is next brought into a c onventional ESP for particle collection.

The specific agglomerator consider ed in this study is the Indigo Technologies system. There have been other types of agglomerators evaluated in the past; acoustic, electrical and sonic. The Indigo unit, using a combination of oppositely charged pa rticles together with a highly turbulent mixing region, has provided the best perf ormance in operating systems.

Vendor guarantees suggest a 50% reduction in pa rticles less than five microns. The resulting improvement in ESP collection efficiency will depend on the existing particle size distribution upstream of the agglomerator.

Gas Flow Optimization The gas velocity distribution in an ESP influen ces the overall removal efficiency in two ways. First, the ESP is an exponential type of collector; from collecti on considerations alone, the more nearly uniform the gas velocity distribution, th e higher the primary collection efficiency. The removal process for a dry collect or, however, consists of the pr imary removal coupled with the removal of the collected dust layer and deposition of this material into the ash hopper collection system. The optimization process depends on optimizing the combina tion of collection and removal to the hopper.

When the ash deposit is removed from the collec ting electrodes, the ash falls into the hopper by gravity. As the material falls, th e layer will break up to some degr ee, with some of the material carried away by the gas stream while the remainder falls into the hopper. Wh en this material falls into the hopper, the ash tends to break up and re-ent rain into the gas stream. This reentrainment is referred to as hopper “boil up.”

The amount of previously collected ash that is reen trained into the gas stre am is a function of the gas velocity where reentrainment occurs. The material collected on the top of the collecting electrode falls a greater distance in traveling to the hoppers than that collected on the bottom. Hopper boil up occurs in the region just above the collecting electrode . Therefore, there is a trend for more reentrainment to occur near the lower portion of the ESP than from the top. These considerations favor a gas velocity distribution with higher velocities in the top, minimizing the amount of material that must fa ll through the gas stream with lowe r gas velocities near the bottom, where higher collecting and lower reentrainment shoul d occur. These consid erations suggest that

### 4-8 the gas velocity distribution for optimized collection will not necessari ly be one that is uniform, but one that is somewhat skewed towards hi gher velocities in th e top of the ESP.

To optimize the gas flow in an ESP, the existi ng gas flow distribution must be measured and analyzed. The analysis is usually accomplished with a smaller scale model of the ESP system. When the desired gas velocity distribution is prod uced in the model study, the gas flow distribution baffles, turning vanes, etc. are designed and installe d in the full scale unit. Of course, the resultant gas velocity distribution must be verified in the full scale ESP installation. The estimated improvement in ESP performance will depend on the quality of the existing flow distribution and the existing amount of hopper reentrainment.

Juice Can Modern ESP power supply controls are programm ed to maintain the electrica l energization as high as possible to the point where some characteris tic of the ESP- particle combination limits the applied voltage or current. The two primary limitations on the ESP operating level are the mechanical spacing of the ESP components and th e electrical characteris tics of the ash being collected. The applied voltage can increase unt il one of the following occurs: (1) spark-over between the electrodes, (2) the cu rrent or voltage limit is reached on the power supplies, or (3) electrical breakdown occurs in the collected dust layer.

In conventional industrial negative corona ESPs, spark-over is initia ted at the positive or collecting electrode. If the applied voltage is raised, corona begins to flow at an applied voltage of about 20 kilovolts and increases somewhat exponentially until spark-over occurs or either the voltage or current reaches the power supply limits. If the collecting elect rodes are clean, spark-over will occur when the local value of the electric field adjacent to some location on the collecting electrode system reaches a value on the orde r of 10 kilovolts per centimeter, where sparking is initiated.

Sparking that is initiated by geom etrical constraints is limited by the instantaneous value of the applied voltage. The formation of the flare on the electrically positive surface occurs in a few microseconds. Therefore, any modification to the E SP system that can maintain the peak value of the applied voltage just below sp arking voltage while increasing th e average value of the voltage will increase the collecting effici ency of the ESP. This condition applies whenever the ESP is collecting low resistivity particles. This is th e condition where the “Juice Can” is an appropriate technology. 26



### The “Juice Can” consists of a cap acitor connected in para llel with the distribut ed capacitance of the ESP electrode system. The normal ESP electrode syst em has an equivalent el ectrical circuit of a parallel resistor-capacitor combination with a time constant on the order of 15 milliseconds. Since capacitances in parallel add, the addition of the Juice Can increases the electrode system capacitance, while the equivalent resistance remains nearly constant . This causes the electrical time constant of the electrode system to increase. If the Juice Can capacitanc e is equal to that of the ESP electrode system, the time constant is approximately doubled. Since the charging current flows from the power supply every 8.33 milliseconds , while the time constant of the ESP-Juice Can combination is doubled to 30 milliseconds, th e ESP voltage will decay less, perhaps leading to

26 The Juice Can may actually be detrimental to the performance of an ESP collecting high resistivity fly ash (unless the ash is conditioned).

### 4-9 a peak to average voltage ratio of only 1.15. Th is would raise the aver age operating voltage to 87% of the peak value. This simple change wi ll increase the migrati on velocity for individual particles by perhaps 15%. It increases the averag e operating current density as well. The estimated performance improvement of Juice Can technolo gy is unit-specific and depends on existing ash resistivity and field power levels.

Add Extra Collecting Field Adding an extra field m erely increases the si ze of the ESP and, thus, increases the overall collecting efficiency. This option can be consid ered for any ESP provided adequate space exists. The estimated performance improvement varies de pending on the performance of the existing ESP, the size of the additional field, a nd gas velocity through the ESP.

Moisture Injection It is well known that cooling a ga s stream in a cold side ESP re sults in a reduction of gas volume and the electrical resistivity of the ash, which impr oves the overall performan ce of the ESP. While moisture conditioning is based on sound theory, the dr awback is the difficulty with which the water must be injected into the flue gas without fo rming heavy deposits in the ductwork. Field experience with moisture conditioning systems ha s shown that the problems with the routine operation of these systems far outweigh the advantages.

Moisture conditioning was not seriously considered for use as a retrofit. It is only included in the discussion of potential retrofit technologies because it is usually mentioned as a low cost option.

Sulfuric Acid Gas Controls

Several methods are used to control emi ssions of condensed sulfuric acid and SO 3 

### , a sulfuric acid precursor. These methods are described briefly as follows:

Sorbent Injection Acid gas form ation can be controlled by the use of so rbent materials that are either injected directly into the boiler during the combustion process or into the flue gas stream upstream of the air heater. These sorbent materials are alkaline (basic) in na ture and combine with th e acid gas as particulate that is removed by the ESP or baghouse.

For furnace-injection technologies, al kaline sorbents are injected dire ctly into the boiler. Known sorbent materials for this methodology include limestone and calcium or magnesium-based slurries. For units with SCR, furnace injection technologies offer the potential benefit of allowing lower load operation and reduced SCR inlet temperat ures. However, preliminary data suggest that furnace-injected sorbents do not provide significant control of SCR generated SO 3



### . For post-furnace sorbent injection, alkaline sorbents can be injected upstream or downstream of the SCR, if so equipped, or upstream or downstream of the air heater. Pre-air heater injection offers the potential benefit of reducing the SO 3 

### generated by the SCR and lowering the acid dewpoint temperature, which allows for lower air heater ou tlet temperatures and the potential of increased plant efficiency. Known sorbents used for pre-ai r heater injection includ e ammonia, hydrated lime, limestone, magnesium oxide powder, and various sodium compounds.

### 4-10 Relatively high removal efficiencies (+80%) of SO 3 

### have been reported using sorbent-based control techniques. However, the injection of sorbent material can cause a reduction in ESP performance and in some cases may be the limiting factor in SO 3 

### reduction. This has been confirmed, to some extent, by existing field st udies by EPRI, DOE, and others. The potential effects on ESP inlet flue gas characteristics incl ude increased mass loading at the ESP inlet, a reduction in particle size distribut ion, increased particulate resistiv ity, and changes in particulate adhesion and cohesion properties. The combined effect of these issues suggests decreased ESP performance and an increase in ou tlet emissions. In addition, the collection of sorbent-containing ash may adversely affect the quality of salable flyash.

Flue Gas Humidification Another m ethod of sulfuric acid em issions control is to cool the fl ue gas below the acid dew point temperature by injecting water into the ductwork upstream of a cold-s ide ESP. The theory is to intentionally condense th e sulfuric acid from the flue gas.

Again, moisture conditioning was not seriously considered for use as a retrofit option. It is only included in the discussion of poten tial retrofit technologies because it is usually mentioned as a low cost option. While testing c onducted by DOE has shown some incremental improvement in sulfuric acid removal, there is insufficient data to assess the long-term feasibility of this option. Previous studies using humidification for improv ing ESP performance have shown sludge buildup in flue gas ductwork to be a significant problem. For sulfuric acid contro l, corrosion of internal ESP components is another significant concern.

SCR Catalyst Replacement Units equip ped with SCR for NO x 

### control, particularly those firing high-sulfur coals, have a higher susceptibility to acid gas formation, as SCR catalys ts are known to cause additional oxidation of flue gas SO 2 

### . The SCR catalyst reduces NO x 

### (predominately NO) to molecular nitrogen, which provides additional oxygen for further SO 2 

### to SO 3 

### conversion. Test data have shown that this additional conversion may be as high as 3% de pending on the operating parameters, system design and catalyst formulation. This increa se can effectively double the existing SO 3 

### flue gas concentration. 27



### SO 3 

### formation can also be contro lled by reducing the additional SO 2 

### conversion that takes place across the SCR. Catalysts are currently available from manufacturers that produce lower levels of SCR-formed SO 3, 

### in some cases, less than 0.5%.

Wet ESP WESPs are widely used in the su lfuric acid industry to control SO 3 

### mist. WESPs capture sulfuric acid by passing the flue gas thr ough an electrostatic precipitator after it has been cooled to saturation. By combining the electrostatic forc es with low temperature and water saturation, sulfuric acid can be captured at re latively high efficiencies (80+%).

27 Current Work on the Impacts of SO 3 Emissions from Selective Catalytic Reduction Systems, Erickson, C, Jambhekar, R.

### 4-11 4.3 Feasibility Analysis of Potential Controls

GPC may eliminate potential upgrade options from the BART determ ination analysis if such options are considered technically infeasible. 28 

### Technical feasibility depends on whether each technology is commercially availa ble and whether the technology can be applied to the affected source. Site-specific issues, such as space lim itations and flue gas properties, may preclude the application of certain commercially available tec hnologies. Upgrade options that are eliminated due to technical feasibility are exempt from subs equent analysis in the BART determination. The following is a summary of the feasibility of the various potential upgrade opti ons included in the previous section.

Wet ESP

WESP is considered a technically feasible option for all four Bowen units. Each boiler could be retrofitted with a WESP to reduce filterable par ticulate matter as well as condensable particulate matter. Depending on the available space, the WE SP would need to be integrated within the WFGD scrubber vessel or downstream of the WFGD as a grade-mounted installation. Additional analysis would be required to determine the available space, a lthough a preliminary investigation suggests that an integrated design may be applicable to Units 3 and 4 due to the confined layout.

Pulse Energization

Pulse energization is a technology th at is useful for in creasing the ESP collec tion efficiency for high resistivity fly ash particles. However, this is not an issue on any of the Bowen units because each unit is equipped with a flue gas conditioning system which cont rols fly ash resistivity. As a result, pulse energization is not considered a tech nically feasible option for any of the Bowen units.

COHPAC

COHPAC is available in two c onfigurations: COHPAC I and C OHPAC II. COHPAC I requires the installation of a fabric filter in a separate casing downstream of the existing ESP. COHPAC II is a retrofit of the outlet field of an existing ESP with a fabric filter. Of the two configurations, only COHPAC I is commercially available and ther efore COHPAC II is not considered as part of this analysis.

COHPAC I retrofit would be very difficult if not impossible due to the limited space existing between the ESPs and ID fans, especially on Units 3 and 4. Therefore, this option is considered technically infeasible.

Particle Agglomerator

The particle agglomerator is a technically feasib le option for all four of the Bowen units. The addition of an agglomerator shoul d improve the performance of the existing ESPs. Units 3 and 4 should benefit the most from this technology due to the relatively smaller si ze of the ESPs. Some

28 BART Guidelines, Section IV.D.2.

### 4-12 caution should be taken with this option due to the lack of experience in in stalling agglomerators on boilers of this size.

Gas Flow Optimization

Gas flow optimization is considered a technical feasible option on all of the Bowen units. However, since GPC has already conducted gas fl ow improvements, it is uncertain how much additional improvement can be made on these units . The excellent perfor mance of the existing ESPs brings to question the overall benefit possible with this option.

Juice Can

Juice Can technology is considered a technically feasible option on all of the Bowen units. Juice Cans have already been installed on some transf ormer/rectifier sets on Units 3 and 4 and have shown to improve the performance of the ESPs. Installation on Units 1 and 2 may show similar levels of performance improvement. However, fu rther improvement on Units 3 and 4 is uncertain due to the existing performance of these units.

Add Extra Collecting Field

Adding collection surface to any ESP will improve performance. This option was evaluated for Plant Bowen for Units 3 and 4 but was determined to be technically infeasib le due to inadequate space. Space is available for Units 1 and 2, and, th erefore, addition of an extra collecting field is considered technically feasible for these two units.

Moisture Injection (for PM and/or SO 3 

### control)

Field experience with moisture conditioning/flue gas humidification has shown this to be an infeasible technology due to the associated maintenance proble ms. Moisture injection has repeatedly been shown to cause heavy deposits of sludge in the ductwork. On the Bowen units, moisture injection would also cause additional sulfuric acid condensat ion, which would create corrosion problems in the ESP ductwork and casing.

Sorbent Injection

Sorbent injection for SO 3 

### control is considered a technically feasible opt ion for all of the Bowen units. However, while sorbent injection can provide a significant reduction in flue gas SO 3 

### concentration, it produces co llateral PM emissions due to the a dded particulate. In addition, since sorbent is typically injected upstream of the ESP, it can create a reduction in ESP performance due to the increased loading and adverse changes in fl ue gas properties. Because of these issues, SCS has assumed the sorbent would be injected dow nstream of the ESP between the ESP and the WFGD. SCS has also assumed that lime would be used as the sorbent material.

SCR Catalyst Replacement

Catalyst replacement may be considered a feasible option for units with a high SO 3 

### conversion across the SCR. However, the SO 3 

### conversion across the SCRs on the Bowen units is already relatively low (~1%). Switching to an ultra-low conversion cata lyst (~0.5%) is not considered

### 4-13 technically feasible at this time because such ca talysts are not well demons trated in the field. Furthermore, since SO 3 

### conversion is related to mercury oxidation, SCS is evaluating the potential co-benefit of the additional mercury oxidation across the SCR (and subsequent removal in the WFGD) as part of the long-term compliance strategy for CAMR.

Summary of Feasible Upgrade Options

Table 4-1 summarizes the feasible upgrade options for all four units. All other options are considered technically infeasib le and have been excluded from the remainder of the BART determination analysis.

Table 4-1 Technically Feasible Upgrade Options for Plant Bowen

Upgrade Option Applicable Unit WESP Units 1-4 Particle Agglomerator Units 1-4 Gas Flow Optimization Units 1-4 Juice Can Units 1-4 New Collection Field Units 1 and 2 Sorbent (Lime) Injection Units 1-4



### Combinations of Feasible Upgrade Options

The EPA BART Guidelines suggest that combinati ons of upgrade options should be considered as part of the feasibility analysis. EPA allows a ffected sources to determine the extent to which upgrade combinations are included but does not expe ct every possible control combination to be investigated. The selection of control combinations should be based on a reasonable consideration of estimated emissions reductions, the cost of th e various alternatives, and other plant impacts.

For Plant Bowen, any combination of the feasible upgr ade options listed in Ta ble 4-1 is technically possible since the effects on particulate removal ar e independent of each other. As EPA suggests, analysis of all possible control comb inations is unreasonable. As pa rt of the cost impact analysis, GPC will evaluate one or more combinations of th e lower cost control technologies and/or control technologies that provide lower levels of emissions reduction.

4.4 Control Effectiveness of Feasible Options

The evaluation of the various upgrade op tions requires a comp arison of total PM 10 

### (filterable and condensable) reduction of each option. The estimated total PM 10 

### reduction is determined by using an assumption of the incremental speciated removal efficiencies of filterable PM 10 

### , filterable PM 2.5 

### , and sulfuric acid for each upgrade option. For WESP and lime injection, speciated removal efficiencies reflect typical remova l efficiencies that have been de monstrated in practice. For ESP upgrade options, speciated removal efficiencies are based on ESP computer model results. Once the incremental removal efficiencies were determ ined, they were then applied to the baseline emissions data to determine the overall emissions reduction, expressed in terms of ‘tons per year’, for each upgrade option.

### 4-14 The following section summarizes the assumptions us ed to determine the control effectiveness and the expected emissions reduction of each feasible option:

Wet ESP

WESPs are capable of removing both filterable and condensable PM (sulfuric acid) species, although they are primarily consid ered for sulfuric acid remova l on units equipped with SCR and WFGD. Like conventional ESPs, WESP removal efficiency depends on a number of factors including size, gas velocity, and various flue gas propert ies. WESPs may be designed to achieve a specific emissions limit and/or th ey may be used to achieve the same emissions level as pre-SCR conditions. Since this analysis does not require a ta rget emissions rate for su lfuric acid or filterable PM emissions, SCS has assumed that the WESP would be used to limit sulfuric acid emissions to pre-SCR level. The estimated collection efficiency for this target emission rate is 80% removal of sulfuric acid at the WESP inlet. This removal ra te also represents the maximum, sustained removal efficiency, accounting for normal unit downtime, that can be expected using WESP technology. SCS has assumed the corresponding filterable PM 10 

### and filterable PM 2.5 

### collection efficiency to be 90%.

Lime Injection

Lime injection has shown to be an effective method of reducing flue gas SO 3 

### . However, the injection of lime causes an increase in filterable particulate emissions because the added particulate is not completely removed in the downstream PM control device. Since the assumed injection point is upstream of the WFGD, the WFGD repr esents the downstream PM control device. Emissions of total PM 10 

### (filterable and condensables) may increase or decrease depending on the speciation of the flue gas prior to the lime add ition, injection rate, and the filterable PM removal efficiency of the downstream control device. As with WESP design, lime injection rate can be adjusted to achieve a specific target SO 3 

### emission limit or to maintain SO 3 

### emissions to pre-SCR levels.

SCS calculated lime injection rates for each unit based on achieving pre-SCR levels of SO 3 

### . These injection rates translate to an estimated combined sulfuric acid collection efficiency of both the WFGD scrubber and lime injection of 75%. 29 

### SCS assumed that the added particulate was collected in the WFGD with a collection efficiency of 90% for both filterable PM 10 

### and filterable PM 2.5 

### . This removal efficiency is s lightly higher than the removal efficiency of 80% used in the baseline scrubber emissions estimates due to the la rger particle size distribution of the lime and the higher collection efficiency of the larger particulate in the scrubber.

ESP Upgrade Options

ESP upgrade options include those options that result in an improvement in ESP performance - Juice Can, new collection field, gas flow optimizati on, particle agglomerator. Since ESPs remove filterable PM species, estimated performance effects are considered only for filterable PM for these options. Generalized emissions reduction factors ar e not appropriate for anal ysis of these options

29 Based on testing conducted at Plant Yates in 2006.

### 4-15 because the effects of the various upgrade options on ESP removal efficiency are unit-specific and depend on a number of factors, in cluding ESP size and configurati on, inlet loading, particle size distribution, gas flow rate, and other flue gas properties.

In order to improve the accuracy of the estimated emissions reduction, SCS conducted computer modeling 30 

### of each upgrade option for each ESP desi gn. ESP computer models calculate performance from first principles and, therefore, have the capability to account for variations in flue gas properties. The output of the computer model was used to determine an estimated percent emissions reduction for each option. Given th e similarity in ESP design and flue gas characteristics between Units 1 a nd 2 (identical units) and Units 3 and 4 (identical units), modeling was conducted on a representative model for each set of units and the results were applied to the baseline emissions data for each unit. Certain up grade options may affect particle-size specific collection efficiency more than others. Therefor e, results were generated for both filterable PM 10 

### and filterable PM 2.5 

### species.

Particle Agglomerator The particle agglomerator improves overall filter able PM removal by exploiting the site-specific collection efficiency of the ESP. It is well known that fine PM collection efficiency in an ESP is lower than the overall PM collectio n efficiency. The particle aggl omerator combines some of the fine particulate into larger particulate, which is more readily collected in the ESP. This increases overall collection efficiency and reduces fine particle concentration at the ESP exit.

Vendor guarantees suggest a 50% reduction in PM 5 

### at the ESP inlets due to the particle agglomeration. This roughly translates to a 20% increase in the partic le size distribution mass mean diameter at the ESP inlet on each unit. SC S adjusted the inlet particle size distributions accordingly in each baseline model. For Units 1 and 2, the modeling results suggest a 33% reduction in filterable PM 10 

### and a 34% reduction in filterable PM 2.5 

### . For Units 3 and 4, the results suggest a 22% reducti on in filterable PM 10 

### and a 34% reduction in filterable PM 2.5



### . Gas Flow Optimization One of the primary benefits of gas flow optim ization is a reduction in rapping and steady reeentrainment losses from ESP hoppers. The degr ee of improvement is directly related to the existing flow profile within each ESP. Since SC S has already conducted prev ious flow studies on the Bowen units, it is not expected that this option will provide a significant performance improvement. As a conservative estimate, SCS assumed that gas flow optimization would produce a 15% reduction in reentrainment losses from the ESP.

For Units 1 and 2, the modeling results suggest a 3% reduction in filterable PM 10 

### and a 1% reduction in filterable PM 2.5 

### at the ESP outlet. For Units 3 and 4, the results suggest a 6% reduction in filterable PM 10 

### and a 2% reduction in filterable PM 2.5



### .

30 All modeling was conducted using EPRI’s ESPM Version 3 software using baseline models that were calibrated using measured emissions test data.

### 4-16 Juice Can The addition of Juice Can technology to the exis ting transformer-rectifier sets will increase the average operating voltages and currents within the ESP. EPRI research suggests an increase in the average voltage to ~90% of the peak operating voltage and an in crease in the operating current density of approximately 10%. 31 

### SCS adjusted the operating vo ltages of each calibrated model accordingly. For Units 1 and 2, operating currents were increased by 10%. For Units 3 and 4, operating currents were only increased by 5%, as a more conservative assumption, given the relatively high existing current levels. 32



### For Units 1 and 2, modeling results sugge st a 6% reduction in both filterable PM 10 

### and PM 2.5 

### . For Units 3 and 4, the results suggest a 9% reduction in both filterable PM 10 

### and PM 2.5



### .

New Collection Field Units 1 and 2 are each equipped with two ESPs a rranged in a para llel configurati on. Box ‘A’ is equipped with four fields in the di rection of gas flow and Box ‘B’ is equipped with six fields in the direction of gas flow. For the purpose of this analysis, SCS has assumed that the additional field would be added in a separate casing to the outle t of Box ‘A’ because it would provide a greater performance improvement over the a ddition to the outlet of Box ‘B’. SCS has also assumed that the mechanical and electrical characteristics of the new field would be id entical to those in the immediate upstream field in Box ‘A’.

For Units 1 and 2, modeling results s uggest a 35% reducti on in filterable PM 10 

### and a 32% reduction in filterable PM 2.5 

### . Modeling was not conducted for Units 3 and 4 because the addition of a new field was considered tec hnically infeasible on these units.

4.5 Impacts Analysis for Feasible Options

A BART determination requires consideration of cer tain statutory factors for each feasible control option. Among these are the costs of compliance and the energy and non-air quality environmentalimpacts of compliance. The following discussion analyzes these factors for the previouslyidentified feasible options.

4.5.1 Energy Impacts

Energy impacts may occur due to increased station service requirements and/or forced or extended unit outages that may be required to install a pa rticular upgrade technology. Energy impacts are considered to be negligible for all feasib le upgrade options, except the WESP, provided the upgrades can be conducted during a sc heduled outage. Upgrades such as installati on of the Juice Cans and gas flow optimization may be conducted dur ing a one to two week out age. Installation of the particle agglomerator, lime injection, and the addition of a new ESP field may require four to six weeks, which could be coordinated with the plant’ s major outage schedule.

31 Nichols, G. (1999), Guidelines for Upgrading Electrostatic Precipitator Performance: Volume 2: Electrostatic Precipitator Upgrade Guidelines , Electric Power Research Institute.

32 Some of the existing T/R sets are alr eady equipped with Juice Can technology.

### 4-17 WESP Energy Impacts WESP systems require additional station service to power the electrical fields and the various pumps associated with the water recycle and water treatment systems. Station service requirements are estimated at 0.5% of the gro ss turbine generating capacity (MW). 33



### WESP installations may require an extended un it outage depending on the WESP configuration. Grade-mounted installations minimize unit downtime. Virtually all construction can be conducted with the unit online and the WE SP may be tied-into the flue gas ductwork during a scheduled outage. The WESP tie-in could take four to six weeks, depending on complexity of the additional ductwork. Retrofit installations where the WESP is integrated into the WFGD vessel are more complicated than grade-mounted installations because additi onal custom field work (less modularization) is required. In addition, the inte grated design may require reinforcement of the scrubber foundation as the WFGD ma y not support the additional wei ght of the WESP. Integrated retrofits require the unit to be offline for the duration of the installa tion, which can result in extended unit outages. A more de tailed evaluation would be necessa ry to determine the potential unit downtime, but it is likely that such an inst allation would extend beyond the four weeks of a typical major outage at Bowen. 34 

### A preliminary analysis suggests that an integrated design may be needed on the Bowen units due to the available space.

4.5.2 Non-Air Quality Environmental Impacts

Non-air quality related environmental impacts are considered to be negligible for all upgrade options except the WESP. WESP operation is a water intensive process that requires an adequate supply of make-up water and disposal and/ or treatment of ash-laden byproducts.

WESP water systems are site-specific and may va ry considerably in complexity. The basic configuration may be either a once-through or closed-loop design. In a once-through design, fresh makeup water supplies 100% of the water used in the WESP. Make-up water may come from a nearby lake or stream or from other plant proces ses such as the condenser cooling water. While once-through systems typically pr ovide better performance and clean ing than closed-loop systems, water requirements are much higher. Water cons umption on a once-though system is estimated at 10 gpm/MW for adequate plate washing. 35



### Make-up water requirements can be minimized by using a closed-loop design, which recycles most of the water in the WESP. This may be suited for plants with a limite d supply of fresh makeup water. Some makeup water is still required in order to replace water lost through evaporation, leakage, and blow-down of recycl ed effluent. Make-up water re quirements in closed-loop systems depend largely on flue gas ash concentration and r ecycle water chemistry. Water consumption in a

33 L. Monroe (SCS). Estimate is supported by DOE estimates of 0.5% ( Economic Comparison of SO 3 Control Options for Coal-Fired Power Plants , presented at Air Quality IV Conference, Arlington, VA September 23, 2003).

34 Major scheduled outages occur 18-24 months and are typically 30 days in duration.

35 Harrison, W, et al. (1999), Field Pilot Test Results for a Utility Wet Electrostatic Precipitator: Water Treatment and Performance , Electric Power Research Institute.

### 4-18 closed-loop system may range from 1 to 2 gpm/MW for a typical bituminous coal application with WFGD. 36



### WESP ash disposal may represent a more signifi cant environmental concern for many plants. WESP effluent may either be sluiced to the plan t’s existing ash pond or filtered and shipped for landfill disposal. Unlike dry ESP flyash, where the ash can be sold for various commercial applications, the market for recycled WESP ash is likely to be limited because of the various substances in the ash.

4.5.3 Cost Impacts

SCS determined the cost effectiveness of each competing upgrade alternative. The cost components used in this analysis ar e consistent with those found in the EPA Air Pollution Control Cost Manual. For the purpose of this analysis, SCS has assumed a baseline generation rate of 700 MW for Units 1 and 2 and 900 MW for Units 3 and 4. Baseline emissions and unit capacity factors are based on historical data for the period 2003-2005. Baseline emissions data have been adjusted to account for future WFGD operation.

Total Capital Costs

The following section discusses the total capital co sts associated with each of the upgrade options, including direct and indi rect capital costs. Direct capital costs consist of basic equipment and installation costs, ductwork modifica tions (if applicable), various infrastructure costs incurred to accommodate the new equipment, design and engineer ing costs, and typical vendor contingencies. Indirect capital costs incl ude the loss of investment interest in curred during the c onstruction period.

Direct Capital Costs The direct capital cost information provided repres ents the latest cost information available from industry sources. Direct capital cost estimates for retrofits are highl y site-specific. The generalized estimates used in this analysis, expressed in terms of dollars per kilowatt ($/kW) of net generation capacity, are assumed to be accurate within 25% - 50%. 37 

### Considerable effort would be required to develop more accurate cost estimates. However, this level of accuracy should be sufficient to evaluate the cost-effectiv eness of various upgrade options since th e cost uncertaintie s affect all of the proposed upgrade options.

WESP. Capital costs for a WESP installation depend on a number of factors, including the configuration and size of the unit (based on require d efficiency and gas flow rate), the cost of corrosion resistant materials used in construction, the required level of water treatment, and market conditions. These factors may result in as much as an order of magnitude varia tion in capital costs. In addition, capital costs for retr ofit applications may be signi ficantly higher, depending on the difficulty of the retrofit. For this analysis, SCS has assumed that an integrated retrofit configuration would be required fo r all units. Capital cost for the WESP has been estimated at

36 Harrison, W., Ibid.

37 Nichols, G., Ibid.

### 4-19 $115/kW. 38 

### It should be noted that material costs can vary sign ificantly depending on market conditions. As demand for new power plant constr uction and pollution control devices increases, particularly for new WFGD system s, market prices for materi als and construction will also increase.

Lime Injection. Lime injection requires the installation of an injection manifold, ductwork modifications, various storage tanks , silos, blowers, and piping. SCS has assumed a capital cost of $10/kW for the addition of lime injection. 39



### ESP Upgrade Options. Capital costs for gas flow optimization and Juice Cans were assumed to be $1.5/kW and $1.0/kW, respectively. 40 

### Capital costs for the installation of a new collection field were assumed to be $40/kW. Capital costs for the particle agglomerator were assumed to be $13.3/kW. 41



### Indirect Capital Costs Indirect capital costs incl ude the loss of investment interest in curred during the construction period of the upgrade option. While indi rect capital costs can be a significant cost component for some construction projects, SCS has excluded them from this cost analysis. Almost all of the upgrade options can be completed within a four to six we ek timeframe and, therefor e, the indirect capital costs associated with these upgr ade options are considered minor . For the WESP option, while the installation may likely take longer than six weeks, SCS can not provide a reasonable estimate of the construction timeframe for the purpose of estimati ng indirect capital costs. Therefore, in order to provide a conservative cost estimate, SCS has excl uded this cost component from the analysis.

Capital Cost Summary The total capital investment (TCI) normally incl udes both the direct and indirect capital costs associated with upgrade option. Sinc e indirect capital costs have b een excluded from this analysis, TCI is based solely on direct capital costs. The TCI for each applicable upgrade option is summarized in Tables 4-2 and 4-3 for each unit.

38 Estimate is consistent with EPA data for WESP of $40/k W to $400/kW (EPA Air Pollution Technology Fact Sheet EPA-452/F-03-029). Higher cost estimate is justif ied due to complexity of integrated retrofit.

39 Data is consistent with DOE estimates for lime injec tion (upstream of A/H) of $9/kWh (Blythe, G. (2004). Furnace Injection of Alkaline Sorbents for Sulfuric Acid Removal, DOE Report).

40 Nichols, G., Ibid. Estimates are based on a model 300MW boiler equipped with 200 SCA (cold-side) ESP.

41 Based on vendor estimates provided by Indigo Technologies, Inc. for a 235 MW boiler equipped with a 110 SCA (cold-side) ESP (Appendix C). SCS has included an additional 20% cost adjustment to reflect in house facilities and engineering costs.

### 4-20 Table 4-2 Total Capital Cost Estimates, Units 1/2 Upgrade Option TCI Agglomerator $9,310,000 Optimize Gas Flow $1,068,065
Juice Cans $700,000
Add New Collection Field $28,000,000

WESP $80,500,000 Lime Injection $7,000,000

Table 4-3 Total Capital Cost Estimates, Units 3/4 Upgrade Option TCI Agglomerator $11,970,000 Optimize Gas Flow $1,373,226
Juice Cans $900,000

WESP $103,500,000 Lime Injection $9,000,000



### Annualized Costs

The cost impact analysis requires the determinati on of the annualized cost of each upgrade option. Annualized costs includ e operating and maintenance (O&M) co sts, annualized TCI costs and station service penalties associated with certain upgrade opt ions. Annualized costs are based on average capacity factor for the 2003 – 2005 historic al baseline timeframe (s ee discussion below). The following section discusses the annualized costs associated with each upgrade option.

Operation and Maintenance Costs O&M costs include the additional man-power and other resources (i.e. water requirements, sorbent materials, etc.) required to operate and maintain the equipment. 42 

### O&M costs for most of the upgrades are considered negligible except for the WESP and lime injection options. O&M costs for the WESP have been estimated at $0.47/MWh 43 

### . O&M costs for the lime injection system consist primarily of the added sorbent cost. SC S has assumed the O&M costs for lime injection to be $0.40/MWh.

Annualized TCI Costs Total capital costs (TCI) for major utility construction projects are typically referred to as “capital recovery costs” and are expressed on an annuali zed basis. Annual capita l recovery costs are equivalent to an annual payment that is sufficien t to finance the investme nt over the expected life of the equipment. Capital recovery costs are de termined by applying a cap ital recovery factor to the TCI cost. The capital recovery factor used in this analysis (0.1098) is derived from a simple interest formula assuming a “real” 44 

### interest rate of 7% and an equipment life of 15 years. The

42 Station service requirements for WESP a nd lime injection are not included in O&M cost estimates. These costs are accounted for separately as station service penalties.

43 Estimate is below EPA data for WESP O&M costs of $1.6 /MWh to $2.6/MWh (EPA Air Pollution Technology Fact Sheet EPA-452/F-03-029).

44 A “real” interest rate does not take into account the effects of inflation.

### 4-21 capital recovery cost ap proach does not include all revenues necessary to support an investment item such as administrative costs, proper ty taxes, and insurance expenses.

Capacity and Energy Penalties This analysis also includes the calculation of i ndirect capacity and energy pe nalties associated with the increased station service requirements of certa in upgrade options. Station service requirements for Units 1/2 were assumed to be 350 kW for bot h the agglomerator retrofit and the addition of a new collection field and 3,500 kW fo r the WESP. Station service re quirements for Units 3/4 were assumed to be 450 kW for the agglomerator retr ofit and 4,500 kW for the WESP. Station service requirements for all other upgrade were assumed to be negligible.

SCS assumed that the incremental capacity re duction would be made up by additional capacity constructed to offset the reduction. It is estimated that the cost of this capacity would be about $600/kW and that this capital woul d be recovered under the same financial assumptions as the control technologies being evaluated. In addition, the analysis includes an additional cost penalty associated with the energy production capability lo st from the capacity reduction. It is reasonable to assume that this energy will be made up by purchasing makeup power at an average cost of $0.05/kWh.

Annualized Cost Summary A breakdown of the total, annualized costs (in 2006 dollars) associat ed with each upgrade option is provided in Tables 4-4 and 4-5 for each unit.

Table 4-4 Annualized Upgrade Cost Summary (2006 Dollars), Units 1/2

Capacity and Energy Penalties

Upgrade Option O&M Costs TCI Capital Recovery Capital Cost of Additional

Capacity Capital Recovery of Additional

Capacity Cost of Makeup Energy Annualized Cost Agglomerator $1,022,238 $210,000 $23,058 $118,233 $1,163,529 Optimize Gas Flow $117,273 $117,273 Juice Cans $76,860 $76,860 Add New Collection Field $3,074,400 $210,000 $23,058 $118,233 $3,215,691

WESP $2,222,773 $8,838,900 $2,100,000 $230,580 $1,182,326 $12,474,580 Lime Injection $1,891,722 $768,600 $2,660,322

Table 4-5 Annualized Upgrade Cost Summary (2006 Dollars), Units 3/4

Capacity and Energy Penalties

Upgrade Option O&M Costs TCI Capital Recovery Capital Cost of Additional

Capacity Capital Recovery of Additional

Capacity Cost of Makeup Energy Annualized Cost Agglomerator $1,314,306 $270,000 $29,646 $152,013 $1,495,965 Optimize Gas Flow $150,780 $150,780 Juice Cans $98,820 $98,820 WESP $2,857,851 $11,364,300 $2,700,0 00 $296,460 $1,520,134 $16,038,745 Lime Injection $2,432,214 $988,200 $3,420,414

### 4-22 Other Cost Factors

There are a number of important factors that were not specifi cally included in the economic analysis. The most important of these relates to th e reduced reliability that results from the add-on of sulfuric acid control options. Reduced reliabi lity means these generati ng units would potentially be available for power generati on less often. To make up for this unavailability, additional capacity would have to be installed or energy pu rchases made in order to adequately serve the customer demand for electricity.

Cost Analysis

SCS performed the cost analysis of the various upgrade options in accordance with EPA’s BART Guidelines and the EPA Air Pollution Control Cost Manual . The objective of the cost analysis is to eliminate upgrade options from fu rther evaluation that are not cons idered cost effective. This analysis considers both average and incremental cost effectiveness using the annualized installed capital, operating, and other indirect annua lized costs and the estimated total PM 10 

### emissions reduction of each upgrade option. Estimated emissions reduction for each option are determined using baseline emissions with scrubber operation and the incremental improvement in collection efficiency identified in the previous section.

Baseline Emissions Baseline emissions for the cost an alysis are derived from histori cal emissions for the three-year period 2003 through 2005 and have been adjusted to account for future scrubber operation. First, total filterable PM emissions were calculated using emissions test results for total, filterable PM from 2002 and 2004 45 

### and actual heat input data as reported in the Acid Rain Program quarterly EDR files. The resulting estimate for total, filt erable PM emissions, in tons per year, along with corresponding NO x 

### and SO 2 

### emissions, is summarized in Tabl es 4-6 through 4-9 for the baseline historical period.

Table 4-6 2003 – 2005 Historical Emissions, Unit 1

Year Capacity Factor Boiler Heat Input PM Test Results Total, Filterable PM Emissions SO 2 Emissions NO x Emissions 2003 70.9% 42,878,960 0.029 622 34,644 4,695 2004 71.5% 43,260,104 0.081 1,752 34,447 5,129 2005 74.8% 45,277,146 0.081 1,834 39.451 5,344 Average 72.4% 43,805,403 0.064 1,403 36,181 5,056

45 SCS conducts biannual PM compliance testing for total, filte rable PM. Emissions test results, expressed in terms of ‘lb/mmBtu’ from 2002 were applied to hist orical heat input data for 2003 to de termine total, filterable PM emissions for 2003 in terms of ‘tons per year’. Emissions test results from 2004 were applied to historical heat input data for 2004 – 2005 to determine total, filterable PM emissions for 2004 – 2005.

### 4-23 Table 4-7 2003 – 2005 Historical Emissions, Unit 2

Year Capacity Factor Boiler Heat Input PM Test Results Total, Filterable PM Emissions SO 2 Emissions NO x Emissions 2003 70.6% 42,723,636 0.071 1,517 34,063 4,760 2004 76.9% 46,546,493 0.084 1,955 38,494 5,364 2005 92.6% 56,039,764 0.084 2,354 48,000 7,153 Average 80.1% 48,436,631 0.080 1,942 40,186 5,759

Table 4-8 2003 – 2005 Historical Emissions, Unit 3

Year Capacity Factor Boiler Heat Input PM Test Results Total, Filterable PM Emissions SO 2 Emissions NO x Emissions 2003 81.8% 59,294,767 0.086 2,550 46,724 7,308 2004 85.8% 62,211,838 0.046 1,431 50,603 7,979 2005 76.5% 55,427,706 0.046 1,275 48,714 6,597 Average 81.4% 58,978,104 0.059 1,752 48,680 7,295

Table 4-9 2003 – 2005 Historical Emissions, Unit 4

Year Capacity Factor Boiler Heat Input PM Test Results Total, Filterable PM Emissions SO 2 Emissions NO x Emissions 2003 85.9% 63,231,404 0.099 3,130 49,453 8,197 2004 71.5% 52,604,828 0.051 1,342 42,370 6,298 2005 80.4% 59,201,659 0.051 1,510 50,306 7,151 Average 79.3% 58,345,964 0.067 1,994 47,376 7,216



### The average total, historical filterable PM estim ate for the baseline historical period was combined with AP-42 emissions factors to calculate estimated historical emissions for the various PM species, including filterable PM 10, 

### fine PM, and organic carbon. 46 

### Baseline sulfuric acid emissions were calculated using the baseline SO 2 

### emissions (unadjusted for WFGD operation) and the assumption of 0.8% conversion of flue gas SO 2 

### to SO 3 

### in the boiler, a 51 % reduction in boilergenerated SO 3 

### across both the air pre-heater and ESP, and an additional 0.99% oxidation of flue gas SO 2 

### to SO 3 

### across the SCR. 47 

### Finally, the speciated PM emi ssions data were adjusted to account for future WFGD operation. 48 

### The resulting baseline emissions for each unit are summarized in Table 4-10.

46 The emissions factor used in the analysis for filterable PM 10 (0.67) was applied to the baseline estimate of total, filterable PM emissions. The emissions factor for filterable, fine PM (0.444) was applied to the calculated estimate of filterable PM 10 emissions. The emissions factor for organic carbon (0.0032) was applied to the baseline SO 2 emissions (unadjusted for WFGD operation).

47 The estimate of SCR-formed SO 3 has been adjusted to account for reac tion with ammonia, assuming an ammonia slip of 0.75 ppm at 6% O 2 .

48 Baseline emissions estimates with WFGD operation assume an additional 80% removal of PM 10 and an additional 40% removal of sulfuric acid emissions across the scrubber.

### 4-24 Table 4-10 Baseline Emissions [tons per year], Units 1–4

Filterable Species Condensable Species

Unit Total PM 10 Tot Filt PM 10 Coarse PM Fine PM Tot Cond PM 10 Sulfuric Acid OC
1 418.5 188.0 104.5 83.5 230.5 114.7 115.8
2 516.3 260.2 144.7 115.5 256.1 127.5 128.6 3 544.9 234.8 130.6 104.2 310.1 154.4 155.8 4 568.8 267.2 148.6 118.6 301.6 150.0 151.6



### Estimated Post-Retrofit Emissions The estimated PM 10 

### emissions for each upgrade option were calculated by applying the incremental removal efficiencies for filterable PM 10 

### , filterable fine PM, and sulfuric acid emissions (see Control Effectiveness of Feasible Options ) to the baseline emissions for each unit. Tables 4- 11 through 4-14 summarize the estimated emissions for the various PM species for each unit.

Table 4-11 Estimated Post-Retrofit Emissions [tons per year], Unit 1

Filterable Species Condensable Species

Upgrade Option Total PM 10 Tot Filt PM 10 Coarse PM Fine PM Tot Cond PM 10 Sulfuric Acid OC Agglomerator Retrofit 356.4 125.9 70.8 55.1 230.5 114.7 115.8 Optimize Gas Flow 412.8 182.3 99.7 82.6 230.5 114.7 115.8 Juice Can Retrofit 407.2 176.7 98.3 78.4 230.5 114.7 115.8 Add New Collection Field 352.7 122.2 65.5 56.7 230.5 114.7 115.8

WESP 157.5 18.8 10.5 8.3 138.7 22.9 115.8 Lime Injection 385.4 221.8 123.3 98.5 163.6 47.8 115.8

Table 4-12 Estimated Post-Retrofit Emissions [tons per year], Unit 2

Filterable Species Condensable Species

Upgrade Option Total PM 10 Tot Filt PM 10 Coarse PM Fine PM Tot Cond PM 10 Sulfuric Acid OC Agglomerator Retrofit 430.4 174.3 76.2 98.1 256.1 127.5 128.6 Optimize Gas Flow 508.5 252.4 114.4 138.0 256.1 127.5 128.6 Juice Can Retrofit 500.7 244.6 108.6 136.0 256.1 127.5 128.6 Add New Collection Field 425.2 169.1 78.5 90.6 256.1 127.5 128.6

WESP 180.1 26.0 11.5 14.5 154.1 25.5 128.6 Lime Injection 478.2 296.5 131.6 164.9 181.7 53.1 128.6

### 4-25 Table 4-13 Estimated Post-Retrofit Emissions [tons per year], Unit 3

Filterable Species Condensable Species

Upgrade Option Total PM 10 Tot Filt PM 10 Coarse PM Fine PM Tot Cond PM 10 Sulfuric Acid OC Agglomerator Retrofit 493.3 183.1 114.3 68.8 310.1 154.4 155.8 Optimize Gas Flow 530.8 220.7 118.6 102.1 310.1 154.4 155.8 Juice Can Retrofit 523.8 213.6 118.7 94.9 310.1 154.4 155.8 WESP 210.1 23.5 13.1 10.4 186.7 30.9 155.8 Lime Injection 500.3 280.2 155.8 124.4 220.1 64.3 155.8

Table 4-14 Estimated Post-Retrofit Emissions [tons per year], Unit 4

Filterable Species Condensable Species

Upgrade Option Total PM 10 Tot Filt PM 10 Coarse PM Fine PM Tot Cond PM 10 Sulfuric Acid OC Agglomerator Retrofit 510.0 208.4 130.1 78.3 301.6 150.0 151.6 Optimize Gas Flow 552.8 251.1 134.8 116.3 301.6 150.0 151.6 Juice Can Retrofit 544.8 243.1 135.2 107.9 301.6 150.0 151.6 WESP 208.3 26.7 14.8 11.9 181.6 30.0 151.6 Lime Injection 525.6 311.5 173.2 138.3 214.1 62.5 151.6



### Average Cost Effectiveness Average cost effectiveness refers to the total annualized costs of a control upgrade option, in dollars per year, divided by the estimated annual emissi ons reduction associated with the upgrade option, in tons per year. Averag e cost effectiveness is expresse d in terms of (annualized) dollars per ton of pollutant removed ($/ton).

In addition to the initial list of technically feasible upgrade options, SCS evaluated the cost effectiveness of combining multiple options. 49 

### In evaluating which upgrade options to combine, SCS considered the combination of the two optio ns that would provide the maximum expected visibility improvement (WESP and lime injectio n) and the combination of the two most cost effective options on an individual basis (agg lomerator retrofit and Juice Cans).

Tables 4-15 through 4-18 summarize the average cost effectiveness of each upgrade option and upgrade combination. The results show that the most cost effective control option for all four units is the addition of Juice Cans. The average cost for this option ranges from $4,100/ton to $6,800/ton. The results also show that the least cost effec tive control option for all four units is the addition of lime injection. The average cost for the addition of lime injection ranges from $70,000/ton to $80,000/ton.

49 EPA’s BART Guidelines allow (Section IV.D.2) discretion in selecting the number of control combinations and the methodology used to determine these combinations.

### 4-26 Table 4-15 Average Cost Effectiveness of Upgrade Options, Unit 1

Upgrade Option Annualized Cost [$/yr] PM 10 Reduction [tpy] Average Cost [$/ton] Agglomerator $1,163,529 62.0 $18,759 Optimize Gas Flow $117,273 5.6 $20,798 Juice Cans $76,860 11.3 $6,815 Add New Collection Field $3,215,691 65.8 $48,881 WESP $12,474,580 261.0 $47,804 Lime Injection $2,660,322 33.1 $80,377 Agglomerator/Juice Cans $1,240,389 73.1 $16,921 WESP/Lime Injection $15,134,902 271.0 $55,857

Table 4-16 Average Cost Effectiveness of Upgrade Options, Unit 2

Upgrade Option Annualized Cost [$/yr] PM 10 Reduction [tpy] Average Cost [$/ton] Agglomerator $1,163,529 85.9 $13,550 Optimize Gas Flow $117,273 7.8 $15,023 Juice Cans $76,860 15.6 $4,923 Add New Collection Field $3,215,691 91.1 $35,308 WESP $12,474,580 336.2 $37,107 Lime Injection $2,660,322 38.1 $69,889 Agglomerator/Juice Cans $1,240,389 101.5 $12,222 WESP/Lime Injection $15,134,902 347.4 $43,563

Table 4-17 Average Cost Effectiveness of Upgrade Options, Unit 3

Upgrade Option Annualized Cost [$/yr] PM 10 Reduction [tpy] Average Cost [$/ton] Agglomerator Retrofit $1,495,965 51.6 $28,965 Optimize Gas Flow $150,780 14.1 $10,705 Juice Can Retrofit $98,820 21.1 $4,677 WESP $16,038,745 334.8 $47,909 Lime Injection $3,420,414 44.6 $76,652 Agglomerator/Juice Can $1,594,785 72.8 $21,914 WESP/Lime Injection $19,459,159 348.2 $55,878

Table 4-18 Average Cost Effectiveness of Upgrade Options, Unit 4

Upgrade Option Annualized Cost [$/yr] PM 10 Reduction [tpy] Average Cost [$/ton] Agglomerator $1,495,965 58.8 $25,452 Optimize Gas Flow $150,780 16.0 $9,406 Juice Cans $98,820 24.0 $4,110 WESP $16,038,745 360.5 $44,492 Lime Injection $3,420,414 43.2 $79,225 Agglomerator/Juice Cans $1,594,785 82.8 $19,256 WESP/Lime Injection $19,459,159 373.6 $52,092

### 4-27 EPA does not provide a “bright line” in determ ining acceptable control costs for BART. The average cost effectiveness should be compared to the cost effec tiveness of other similar BART determinations. However, such information is currently unavailable. SCS relies instead on the cost effectiveness of another “BART-like” determination in EPA Region 4 for this comparison. The PM BACT determination conducted by Tampa Electric Company (TECO) for ESP upgrades at Big Bend Station 50 

### showed an average cost effectiveness for upgrade options ranging from $2,800/ton to $5,100/ton 51 

### . The only upgrade option with simila r cost effectiveness for Bowen is the installation of Juice Can technology. The data suggests that all other upgrade options are not cost effective.

Incremental Cost Effectiveness Incremental cost effectiveness (ICE) is used to compare the cost and perf ormance of a particular upgrade option to the next most stringent upgrad e option. Incremental cost effectiveness is calculated according to the following formula:

) ( ) (

ion ringentOpt NextMostSt Option ion ringentOpt NextMostSt Option Emissions Emissions Cost Cost 

### ICE

− − 

### =

EPA recommends that both incremental and averag e cost effectiveness be used in combination when considering whether to eliminate poten tial upgrade options. The incremental cost effectiveness may be used to justify the eliminati on of a more stringent al ternative that may have comparable average cost effectiveness to the next most stringent option, but the incremental cost of the additional emissions reduction is excessive.

EPA recommends evaluating incremental cost effectiveness based on dominant control alternatives. 52 

### A dominant control alternative is a control option that has an average cost effectiveness that is consistent with the genera l relationship between cost and emissions reduction for all competing control options. Dominant cont rol alternatives can be identified by graphing the average cost effectiveness of each upgrade option. Dominant a lternatives will form a smooth, nonlinear curve known as the “l east-cost envelope”. Control options that lie inside of the least-cost envelope are considered inferior options because the cost per ton of particulate removed is inconsistent with other competing alternatives.

SCS determined the least-cost envelope for the technically feasible upgrade options by creating a graphical plot of the total annualized cost and total PM 10 

### emissions reductions for each upgrade option. An iterative procedure was then used to select the dominant options by including various combinations of upgrade options to achieve a leas t-cost envelope with the best curve fit.

50 TECO’s PM BACT analysis was conducted as a requiremen t of a consent decree that was issued in 2000. TECO applied BACT procedures to evaluate potential upgrade technologies for their existing ESPs on Units 1 - 3.

51 The BACT determination for all three units was a combination of upgrades including the installation of PC-based controls, improvements in ESP flow and temperature distribution, and upgrades to the ash handling system.

52 BART Guidelines, Section IV.D.4.e.2.

### 4-28 As shown in Figures 1 through 4, the dominant cont rol choices for all four units include WESP, the addition of Juice Can technology on existing tran sformer/rectifier (T/R) sets, the particle agglomerator, and the combination of Juice Can/ particle agglomerator. Gas flow optimization, lime injection, the addition of a ne w electrical field (on Units 1 and 2), and the combination of WESP/lime injection have been eliminated because they fall inside of the least-cost envelope. These options are considered inferior based on co st and emissions reduction and, therefore, have been eliminated from further analysis.

SCS calculated the incremental co st effectiveness of the dominan t control options, as shown in Tables 4-19 thru 4-22, sorted in ascending order by annualized cost. The results suggest that the WESP has both high average costs ($37,000/ton - $48,000/ton and high incremental costs ($47,000/ton - $60,000/ton) and should be eliminated due to the excessive cost. The results suggest that, while the incremental cost of the agglomerator/Juice Can co mbination is moderate ($4,100/ton - $6,800/ton), the average cost of this option in also rela tively high ($12,000/ton - $22,000/ton) and should be eliminated. Comparing the lowest cost option (Juice Cans) with the next most stringent option (a gglomerator retrofit) shows in cremental costs ranging from $15,000/ton to $46,000/ton. These incremental costs are also excessive – the incremental cost is approximately three to ten times higher th an the average cost of the Juice Cans.

Cost Analysis Summary The cost analysis suggests that the gas flow optimization, the addition of a new collection field (Units 1 and 2), lime injection, and the combin ation of WESP and lime injection should be eliminated from further consideration. These opti ons were identified as in ferior because they fall inside the least-cost envelope for each unit. Of the dominant upgrade options (Juice Cans, agglomerator retrofit, WESP, combination of Jui ce Cans/agglomerator), the Juice Can retrofit is the most cost-effective for all four units at Bo wen. Average costs for this option range from $4,100/ton to $6,800/ton, which is somewhat higher but comparable to the av erage costs shown in TECO’s BACT evaluation. Average and incremental costs for the remaining dominant alternatives appear to be excessive.

### 4-29 y = 156.21x 2 + 7050.1x - 3815.2
R 2 = 0.9997

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr] WESP

Agglomerator Gas Flow Opt Lime New Field WESP/Lime

Agg/JuiceCan

JuiceCan

Figure 4-1. Least Cost Envelope, Unit 1

y = 101.63x 2 + 2853.4x + 27626
R 2 = 0.9997

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr] WESP

Agglomerator
Gas Flow Opt Lime New Field WESP/Lime

Agg/JuiceCan
JuiceCan

Figure 4-2. Least Cost Envelope, Unit 2

### 4-30 y = 74.426x 2 + 23891x - 303588
R 2 = 0.9988

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000 $20,000,000 $22,000,000 $24,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr] WESP

Agglomerator

Gas Flow Opt Lime WESP/Lime

Agg/JuiceCan
JuiceCan

Figure 4-3. Least Cost Envelope, Unit 3

y = 71.151x 2 + 19601x - 276282
R 2 = 0.9987

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000 $20,000,000 $22,000,000 $24,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 450.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr] WESP

Agglomerator

Gas Flow Opt Lime WESP/Lime

Agg/JuiceCan JuiceCan

Figure 4-4. Least Cost Envelope, Unit 4

### 4-31 Table 4-19 Incremental Cost Effectiveness, Unit 1

Upgrade Option Annualized Cost [$/yr] Total PM 10 Reduction [tpy] Average Cost [$/ton] Incremental Cost [$/ton] Juice Cans $76,860 11.3 $6,815 NA Agglomerator $1,163,529 62.0 $18,759 $21,413 Agglomerator/Juice Cans $1,240,389 73.3 $16,921 $6,815 WESP $12,474,580 261.0 $47,804 $59,868

Table 4-20 Incremental Cost Effectiveness, Unit 2

Upgrade Option Annualized Cost [$/yr] Total PM 10 Reduction [tpy] Average Cost [$/ton] Incremental Cost [$/ton] Juice Cans $76,860 15.6 $4,923 NA Agglomerator $1,163,529 85.9 $13,550 $15,467 Agglomerator/Juice Cans $1,240,389 101.5 $12,222 $4,923 WESP $12,474,580 336.2 $37,107 $47,867

Table 4-21 Incremental Cost Effectiveness, Unit 3

Upgrade Option Annualized Cost [$/yr] Total PM 10 Reduction [tpy] Average Cost [$/ton] Incremental Cost [$/ton] Juice Cans $98,820 21.1 $4,677 NA Agglomerator $1,495,965 51.6 $28,965 $45,780 Agglomerator/Juice Cans $1,594,785 72.8 $21,914 $4,677 WESP $16,038,745 334.8 $47,909 $55,129

Table 4-22 Incremental Cost Effectiveness, Unit 4

Upgrade Option Annualized Cost [$/yr] Total PM 10 Reduction [tpy] Average Cost [$/ton] Incremental Cost [$/ton] Juice Cans $98,820 24.0 $4,110 NA Agglomerator $1,495,965 58.8 $25,452 $40,227 Agglomerator/Juice Cans $1,594,785 82.8 $19,256 $4,110 WESP $16,038,745 360.5 $44,492 $52,020

### 4-32 4.5.4 Remaining Useful Life

One of the factors that is considered in determ ining BART is the remaining useful life of the emissions unit. 53 

### In situations in which the unit will re tire before BART compliance is required, the “remaining useful life” factor would allow a stat e to determine that no control of that unit is required as BART. Similarly, in situations in which the unit will retire before conclusion of the assumed 15-year equipment life used for the capital recovery factor in annu alizing the cost of the control options, the state would c onsider the “remaining useful life” factor in determining the costeffectiveness of the control options being evaluated.

For each of the EGUs at Plant Bowen, neither situa tion exists. None of the units will be retired before BART compliance would be required, and none of the units will be retired before conclusion of the 15-year equipment life for annualizi ng the cost of control op tions. Thus, there is no basis for considering the “remaining useful lif e” factor in determini ng PM BART for any of Plant Bowen’s EGUs.

4.6 Visibility Impacts Mode ling for Feasible Options

In determining BART, states must consider the vi sibility improvement that would result from the potential control options under evaluation. 54 

### The visibility improvement modeling for Plant Bowen’s EGUs was conducted pursuant to th e “BART Determination Modeling Protocol” submitted to EPD on December 4, 2006 (Appendix D). (As was the case for the exemption modeling discussed in section 3.0 above, the stack temperature used in the determination modeling has been adjusted from actual to virtual temperature.) Although each possible control option could be modeled and that option’s visibility improveme nt could then be comp ared to the baseline visibility impact, the previous discussion suggests that the visibility improvement analysis for the Plant Bowen PM BART determination could be simplified by limiting the required modeling to a subset of the dominant control options for each un it. Specifically, modeling was conducted for the following two upgrade options for each unit: (1) th e combination of the part icle agglomerator and Juice Cans, which has the highest PM removal rate of the set of relatively lower cost controls; and (2) the WESP, the option with the highest total PM 10 

### removal. The December 2006 Modeling Protocol further explains this approach. As discussed below, this approach provides adequate information to evaluate the visibility improvement associated with each of the feasible control options.

Tables 4-23 and 4-24 summarize the results of th e visibility improvement modeling. For the agglomerator/Juice Can combination, the results show that there is virtually no visibility improvement associated with that option. Fo r each of the units, th e delta-delta-deciview improvement compared to the baseline is -0.01 dv (out to two decimal places, using the new IMPROVE equation and vi rtual temperature, 8 th 

### highest high). Given thes e results, it is clear that,

53 BART Guidelines, Section IV.D.4.k

54 BART Guidelines, Section IV.D.5.

### 4-33 Table 4-23 Summary of Results - Plant Bowen BART Determination Modeling for Cohutta

2001 2002 2003

8 th Highest delta-dv 8 th Highest delta-dv 8 th Highest delta-dv Highest of 8 th Highest

delta-dv for the 3-

years 22 nd Highest delta-dv over 3-

year period

Baseline - Scrubbed

Bowen 1-4 Result

New Improve Virtual Temp 0.74 0.94 0.59 0.94 0.74

New Improve Actual Temp 0.78 1.05 0.73 1.05 0.82

Old Improve Virtual Temp 0.91 1.15 0.73 1.15 0.91

Old Improve Actual Temp 0.97 1.28 0.90 1.28 1.00

Agglomerator/Juice Can

Bowen 1-4 Results - Unit 1 Controlled

New Improve Virtual Temp 0.73 0.94 0.58 0.94 0.73

New Improve Actual Temp 0.78 1.04 0.72 1.04 0.81

Old Improve Virtual Temp 0.91 1.14 0.73 1.14 0.91

Old Improve Actual Temp 0.96 1.27 0.89 1.27 0.99

Bowen 1-4 Results - Unit 2 Controlled

New Improve Virtual Temp 0.73 0.94 0.58 0.94 0.73

New Improve Actual Temp 0.78 1.04 0.72 1.04 0.81

Old Improve Virtual Temp 0.91 1.14 0.73 1.14 0.91

Old Improve Actual Temp 0.96 1.27 0.89 1.27 0.99

Bowen 1-4 Results - Unit 3 Controlled

New Improve Virtual Temp 0.73 0.94 0.59 0.94 0.73

New Improve Actual Temp 0.78 1.05 0.72 1.05 0.81

Old Improve Virtual Temp 0.91 1.15 0.73 1.15 0.91

Old Improve Actual Temp 0.96 1.28 0.90 1.28 1.00

Bowen 1-4 Results - Unit 4 Controlled

New Improve Virtual Temp 0.73 0.94 0.59 0.94 0.73

New Improve Actual Temp 0.78 1.05 0.72 1.05 0.81

Old Improve Virtual Temp 0.91 1.15 0.73 1.15 0.91

Old Improve Actual Temp 0.96 1.28 0.90 1.28 1.00

### 4-34 Table 4-23 (Continued) Summary of Results - Plant Bowen BART Determination Modeling for Cohutta

2001 2002 2003

8 th Highest delta-dv 8 th Highest delta-dv 8 th Highest delta-dv Highest of 8 th Highest

delta-dv for the 3-

years 22 nd Highest delta-dv over 3-

year period

Wet ESP

Bowen 1-4 Results - Unit 1 Controlled

New Improve Virtual Temp 0.64 0.80 0.50 0.80 0.64

New Improve Actual Temp 0.67 0.89 0.61 0.89 0.70

Old Improve Virtual Temp 0.78 0.98 0.62 0.98 0.78

Old Improve Actual Temp 0.83 1.09 0.76 1.09 0.85

Bowen 1-4 Results - Unit 2 Controlled

New Improve Virtual Temp 0.63 0.80 0.50 0.80 0.63

New Improve Actual Temp 0.67 0.89 0.61 0.89 0.69

Old Improve Virtual Temp 0.77 0.98 0.62 0.98 0.77

Old Improve Actual Temp 0.83 1.08 0.75 1.08 0.85

Bowen 1-4 Results - Unit 3 Controlled

New Improve Virtual Temp 0.63 0.79 0.50 0.79 0.63

New Improve Actual Temp 0.65 0.88 0.61 0.88 0.68

Old Improve Virtual Temp 0.78 0.96 0.61 0.96 0.78

Old Improve Actual Temp 0.81 1.08 0.77 1.08 0.84

Bowen 1-4 Results - Unit 4 Controlled

New Improve Virtual Temp 0.62 0.78 0.49 0.78 0.62

New Improve Actual Temp 0.65 0.87 0.61 0.87 0.67

Old Improve Virtual Temp 0.78 0.95 0.61 0.95 0.77

Old Improve Actual Temp 0.78 1.06 0.76 1.06 0.83

### 4-35 Table 4-24 Summary of Results – Visibili ty Improvement from Baseline

Plant Bowen BART Determination Modeling for Cohutta

2001 2002 2003

8 th Highest delta-dv 8 th Highest delta-dv 8 th Highest delta-dv Highest of 8 th Highest

delta-dv for the 3-

years 22 nd Highest delta-dv over 3-

year period

deltadelta-dv deltadelta-dv deltadelta-dv delta-deltadv delta-deltadv Agglomerator/Juice Can

Bowen 1-4 Results - Unit 1 Controlled

New Improve Virtual Temp -0.01 0.00 -0.01 -0.01 -0.01

New Improve Actual Temp 0.00 -0.01 -0.01 -0.01 -0.01

Old Improve Virtual Temp 0.00 -0.01 0.00 -0.01 0.00

Old Improve Actual Temp -0.01 -0.01 -0.01 -0.01 -0.01

Bowen 1-4 Results - Unit 2 Controlled

New Improve Virtual Temp -0.01 0.00 -0.01 -0.01 -0.01

New Improve Actual Temp 0.00 -0.01 -0.01 -0.01 -0.01

Old Improve Virtual Temp 0.00 -0.01 0.00 -0.01 0.00

Old Improve Actual Temp -0.01 -0.01 -0.01 -0.01 -0.01

Bowen 1-4 Results - Unit 3 Controlled

New Improve Virtual Temp -0.01 0.00 0.00 -0.01 -0.01

New Improve Actual Temp 0.00 0.00 -0.01 -0.01 -0.01

Old Improve Virtual Temp 0.00 0.00 0.00 0.00 0.00

Old Improve Actual Temp -0.01 0.00 0.00 -0.01 0.00

Bowen 1-4 Results - Unit 4 Controlled

New Improve Virtual Temp -0.01 0.00 0.00 -0.01 -0.01

New Improve Actual Temp 0.00 0.00 -0.01 -0.01 -0.01

Old Improve Virtual Temp 0.00 0.00 0.00 0.00 0.00

Old Improve Actual Temp -0.01 0.00 0.00 -0.01 0.00

### 4-36 Table 4-24 (Continued) Summary of Results – Visibili ty Improvement from Baseline

Plant Bowen BART Determination Modeling for Cohutta

2001 2002 2003

8 th Highest delta-dv 8 th Highest delta-dv 8 th Highest delta-dv Highest of 8 th Highest

delta-dv for the 3-

years 22 nd Highest delta-dv over 3-

year period

deltadelta-dv deltadelta-dv deltadelta-dv delta-deltadv delta-deltadv

Wet ESP

Bowen 1-4 Results - Unit 1 Controlled

New Improve Virtual Temp -0.10 -0.14 -0.09 -0.14 -0.10

New Improve Actual Temp -0.11 -0.16 -0.12 -0.16 -0.12

Old Improve Virtual Temp -0.13 -0.17 -0.11 -0.17 -0.13

Old Improve Actual Temp -0.14 -0.19 -0.14 -0.19 -0.15

Bowen 1-4 Results - Unit 2 Controlled

New Improve Virtual Temp -0.11 -0.14 -0.09 -0.14 -0.11

New Improve Actual Temp -0.11 -0.16 -0.12 -0.16 -0.13

Old Improve Virtual Temp -0.14 -0.17 -0.11 -0.17 -0.14

Old Improve Actual Temp -0.14 -0.20 -0.15 -0.20 -0.15

Bowen 1-4 Results - Unit 3 Controlled

New Improve Virtual Temp -0.11 -0.15 -0.09 -0.15 -0.11

New Improve Actual Temp -0.13 -0.17 -0.12 -0.17 -0.14

Old Improve Virtual Temp -0.13 -0.19 -0.12 -0.19 -0.13

Old Improve Actual Temp -0.16 -0.20 -0.13 -0.20 -0.16

Bowen 1-4 Results - Unit 4 Controlled

New Improve Virtual Temp -0.12 -0.16 -0.10 -0.16 -0.12

New Improve Actual Temp -0.13 -0.18 -0.12 -0.18 -0.15

Old Improve Virtual Temp -0.13 -0.20 -0.12 -0.20 -0.14

Old Improve Actual Temp -0.19 -0.22 -0.14 -0.22 -0.17

### 4-37 for each of the control options that provide fewer tons removed than does the agglomerator/Juice Can combination (i.e., the agglomerator alone an d the Juice Cans alone), there also will be no visibility improvement from those options.

For the WESP, the results show a small visibility improvement as compared to baseline visibility impacts. For Units 1 & 2 with a WESP, th e model predicts a - 0.14 delta-delta-deciview improvement compared to the baseline (8 th 

### highest high, new IMPROVE equation and virtual temperature). For Unit 3, the model predicts a -0.15 delta-delta-deciview improvement. Finally, for Unit 4, the model predicts a -0.16 delta-delta-deciview improvement.

Appendix E lists delta-deciview results for the top 20 days for each year modeled and the top 25 days for the overall three years at Cohutta for the primary results (i.e., the virtual temperature, new IMPROVE equation case).

An external hard drive is being pr ovided separately that contains al l of the electronic data related to this application (i.e., the virtual temperature, new IMPROVE equation case). This drive contains all of the input and output f iles used in the modeling. A readme.txt file is also included on the hard drive that lists all of the files on the drive.

### 5-1 

# 5.0 Proposed PM BART for the Plant Bowen EGUs

EPA’s BART Guidelines contain little guidance regarding how to select BART from among the control options under consideration. 55 

### The Guidelines simply indicate that, for each option, the emissions reduction, costs, energy and non-air quality environmental impacts, and modeled visibility improvement must be considered.

Section 4.0 of this document explains that PM emissions from Plant Bowen’s EGUs are already very well controlled. Moreover, the available control options for PM BART span a wide range of costs but provide relatively small additional PM emissions reductions. From a regional haze standpoint, none of the available control options would produce a significant improvement in visibility in the most impacted Class I area, Cohutta. This is not surprising given VISTAS’ conclusion that SO 2 

### emissions, not PM emissions, are primarily responsible for visibility impairment in the VISTAS’ Class I areas.

Table 5-1 summarizes the informati on that is relevant for assessing whether any of the feasible PM control options should be considered BART. Th e following discussion analyzes this information.

The cost of compliance is one of the considerati ons that must be taken into account in selecting BART. Because neither EPA nor GEPD has announced any cost threshold for selecting BART and cost information from other PM BART determin ations is unavailable, GPC has evaluated the BART options based on its general knowledge of previous NSR BACT cost thresholds that have been applied by regulators. It should be noted that GPC believes that NS R BACT cost evaluations are not directly relevant, and ar e conservative, for a BART analys is, given that NSR BACT applies to new sources and is part of a nationwide h ealth-and-welfare based ai r quality program, while BART is focused on retrofits to existing source s and is relevant to a single-component (i.e., visibility), welfare-based air quality program for only Federal Class I areas . Thus, if the costeffectiveness for a BART option exceeds the co st-effectiveness range that has been deemed reasonable in BACT determinations, then GPC believes that there is no reasonable basis for requiring those technologies under the regional h aze program. Under NSR BACT determinations, regulators typically have not requ ired installation of technologies that cost more than about $10,000/ton of pollutant removed.

As discussed previously, GPC has considered the cost information from the BART-like ESP upgrade analysis conducted by TECO for Big Bend Stat ion as part of the c onsent decree issued in 2000. This is perhaps the most relevant cost info rmation, since the objectiv e of the TECO analysis was to evaluate the cost-effectiveness of various PM control device upgrade technologies, which is very similar to BART. The TECO determinati on showed upgrade costs ranging from $2,800/ton to $5,100/ton.

The analysis described in sect ion 4.0 above reveals that the av erage cost-effectiveness of the WESP option ranges from about $37,000/ton (for Unit 2) to about $48,000/ton (for Units 1 and 3). In GPC’s view, such cost-effectiv eness values cannot be considered reasonable for BART. In addition, the WESP uses additional energy and has negative non-air environmental impacts

55 BART Guidelines, Section IV.E.

### 5-2 associated with wastewater dis posal. Finally, the WESP is estim ated to produce less than a -0.2 delta-delta-deciview improve ment in visibility in the closest Class I area, Cohutta, for any of the units. This level of improvement is well below th e level that EPA considers to be detectable

Table 5-1 Summary of Bowe n BART Determination Results 1

Upgrade Option Annualized Cost [$/yr] PM 10 Reduction [tpy] Average Cost [$/ton] Visibility Improvement from Baseline 2 [delta-delta-dv]
Bowen Unit 1 Results

Juice Can $76,860 11.3 $6,815 - Agglomerator $1,163,529 62.0 $18,759 - Agglomerator/Juice Cans $1,240,389 73.1 $16,921 -0.01
Wet ESP $12,474,580 261.0 $47,804 -0.14

Bowen Unit 2 Results

Juice Can $76,860 15.6 $4,923 - Agglomerator $1,163,529 85.9 $13,550 - Agglomerator/Juice Cans $1,240,389 101.5 $12,222 -0.01
Wet ESP $12,474,580 336.2 $37,107 -0.14

Bowen Unit 3 Results

Juice Can $98,820 21.1 $4,677 - Agglomerator $1,495,965 51.6 $28,965 - Agglomerator/Juice Can $1,594,785 72.8 $21,914 -0.01
Wet ESP $16,038,745 334.8 $47,909 -0.15

Bowen Unit 4 Results

Juice Can $98,820 24.0 $4,110 - Agglomerator $1,495,965 58.8 $25,452 - Agglomerator/Juice Cans $1,594,785 82.8 $19,256 -0.01
Wet ESP $16,038,745 360.5 $44,492 -0.16

Notes: 1. Visibility improvement results represent new IMPROVE equation and virtual stack temperature. 2. Incremental change is the “Highest of 8 th Highest delta-delta-dv for the 3-years.”



### (approximately one deciview), and is also be low the level (0.5 deciview) that EPA has recommended be used for visibil ity contribution analyses in th e “subject-to-BART” step. In addition, because VISTAS’ modeling shows that th e Cohutta Class I area is achieving, by a wide margin, the uniform rate of progress (glide slope goal) in 2018, the sma ll additional fraction of visibility improvement that would be associated with use of the WESP w ould, in GPC’s view, not be worth the significant additional cost.

The agglomerator and the Juice Can/agglomer ator combination both have average costeffectiveness values that exceed (Units 1, 3, & 4) or lie at th e upper limit (Unit 2) of costeffectiveness values considered to be reasonabl e for BACT and other BART-like determinations. As a result, GPC believes that th ese technologies are not cost-effec tive for BART. Moreover, these

### 5-3 technologies are estimated to produce virtually no visibility improvement in the Cohutta Class I area. Therefore, these options s hould also be rejected as BART.

Finally, the Juice Can option has an average co st-effectiveness ($4,110 - $6,815) that a regulator might consider reasonable for BACT, and whic h is close to the uppe r range for the TECO determination. However, even if this cost range were to be cons idered reasonable under the BART program, this option reduces PM emissions by only 11 to 24 tons (dependi ng on the unit), and the CALPUFF modeling of contro l options that reduce even more PM reveals that this option is not expected to improve visibility in the Cohutta Cla ss I area. The fact that the Juice Can option does not improve visibility in Cohutta should elimin ate the option from cons ideration, regardless of whether GEPD would or would not find the co st-effectiveness values to be reasonable.

Based on the foregoing discussion, GPC believes that none of the PM control options should be selected by EPD as BART for any of Plant Bowen’s EGUs.

## Appendix A

Description of Virtual Temperature Calculation Tool

## Appendix A

Buoyancy of Moist Plumes: E quivalent Dry Effluent Temperature Russell Noble, P.E.; Charles Boohaker, P.E. Southern Company Services, September 2003

Background Power plant stack effluent is typically treated in dispersion models as a ‘dry’ plume and moisture impacts are ignored. In high moisture plumes, such as those exiting a wet scrubber, the added buoyancy of water vapor compared to dry air or combustion product gases should be considered. One method of accounting for added buoyancy of hi gh moisture plumes is to calculate an equivalent dry plume temp erature. Simply put, a virtual temperature ( T v 

### ) can be defined and calculated 1 

### such that a moist plume of temperature T has the same density as a dry plume with temperature T v 

### . This paper will describe a calc ulation tool created to determine T v 

### of a moist plume based on fuel, operational, ambient a nd physical stack parameter inputs.

Input Data In order to calculate actual plume constituents an d concentrations, a fuel ultimate analysis (user input or provided selections) is needed along with expected excess combustion air. Local ambient dry bulb temperature and humidity ratio are required in addition to site base elevation, stack height and stack exit diameter. A flue gas temperatur e must be supplied and specified whether the temperature corresponds to the scru bber inlet or outlet (a lready saturated) location. The program is equipped with a ‘saturator’ module to calculate scrubber outlet conditions if unknown. Stack exit velocity will be calculated if a tota l flue gas flow is supplied. It is assumed that the given gas flow corresponds to the chosen scrubber inlet/out let location. Requir ed input summary:

Fuel ultimate analysis % by weight of constituents Flue gas temperature oF Temperature location Scrubber inlet or outlet (drop down box) Excess air % Flue gas flow lb/hr Ambient temperature oF Ambient humidity ratio lb water vapor 

### / lb dry air 

### Site base elevation ft above sea level Stack height ft above site base elevation Stack exit diameter ft

1 Rolland B. Stull, Meteorology for Scientists and Engineers , 2 nd Ed. (Pacific Grove, CA: Brooks/Cole, 2000), p. 8.

### Optional Data There is a provision for indirect reheat of the flue gas. In this case, the number of degrees of reheat (over saturation) is required. Note that reheat is indirect - no mo isture or hot gas is added and no supplemental fuel is fired. A reheat limit of 40o F over saturation is incorp orated into the tool.

Output Data For a scrubber ‘inlet’ specified i nput parameter set, the tool will sa turate the flue gas and provide the actual stack exit (saturated) gas temperature, stack exit virtual temperature, and average stack exit gas velocity. Given a scrubbe r ‘outlet’ parameter set, the tool will return stack exit virtual temperature and average stack exit gas velocity (actual saturated st ack exit temperature provided in parameter set).

Methodology Calculation of T v 

### can be broken down into four dis tinct areas or cal culation modules: determination of (1) fuel/air specific combusti on products, (2) moisture sa turation temperature of the combustion gas, (3) moisture properties and ab solute pressure at various elevations, and (4) final calculation of virtual temperature.

Data from the provided fuel analysis, along with excess air levels, are used to calculate the wet products of combustion. Comple te combustion is assumed and combustion air moisture is incorporated. Flue gas composition is now fully defined and can be expressed on a wet or dry basis.

If gas temperature is specified as scrubber ‘inlet ’, the moisture saturation temperature of the gas will be calculated (otherwise, it is given). Sa turation temperature is determined by assuming an adiabatic cooling process with a correction 2 

### for actual flue gas specific heat as a function of CO 2 

### content. Added moisture from the ‘saturator’ is incorporated into th e flue gas composition. Saturator calculations are performed at the site elevation pressure. No drop in saturated gas temperature occurs as the gas travels to the st ack exit elevation. This assumption is based on vendor modeling of heat transfer from the gas to atmosphere for a fiberglass stack liner inside a concrete shell design 3 

### . For other stack configurations, th is assumption should be reviewed.

Local barometric pressure will affect volumetric fl ow (exit gas velocity) a nd moisture properties. Absolute pressure at site and stack exit elevatio n was calculated from U.S. standard atmosphere lapse rate. Properties of moist air, along with lapse rate information were taken from ASHRAE tables 4 

### or formulae.

Final calculation of virtual temper ature is straightforward and is a function of absolute temperature of the gas and mixture humidity ratio (sometimes known as mixi ng ratio or specific humidity,

2 Sirozi Hatta, “New Humidity Chart Simplifies Combustion Gas Problems,” Chemical & Metallurgical Engineering, (March 1930), p. 165.

3 Hamon Custodis model of 500 ft, 130F stack system with 10F ambient indicated <0.3F temperature drop.

4 American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 1997 ASHRAE Handbook: Fundamentals , Inch Pound Edition, (1997), pp. 6.1-6.6.

### depending on the reference source). Humidity ratio is defined as the ratio of mass of water vapor to mass of dry gas. Tv is calculated as:



### )609.01( ω × + × = TTv

Where: T = absolute gas temperature, o K

ω = gas humidity ratio, lb water vapor 

### / lb dry gas

## Appendix B

Exemption Modeling
Delta-Deciview Values for the Top 20 Days – for Each Year/Each Class I Area and for the Top 25 Days – Over Three YearsNew IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.31 7.94 1.37 3.8 2.9 4.3 73.1 0.0 19.9 1.3 2.4 3.3 1 2001 113 1 8.60 7.60 0.99 2.8 2.3 3.6 67.2 0.0 24.2 1.6 3.0 4.0 2 2001 225 3 9.27 8.33 0.94 4.9 3.5 5.1 77.9 0.0 16.3 1.1 2.0 2.7 3 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.4 0.0 18.2 1.2 2.1 3.1 4 2001 114 4 8.43 7.60 0.82 2.8 2.3 3.6 67.5 0.0 24.3 1.6 2.6 4.0 5 2001 279 1 8.87 8.10 0.77 4.2 3.2 4.6 75.4 0.0 18.3 1.1 2.2 3.0 6 2001 220 54 9.10 8.33 0.76 4.9 3.5 5.1 77.9 0.0 16.3 1.1 1.9 2.8 7 2001 111 16 8.34 7.60 0.74 2.8 2.3 3.6 67.5 0.0 24.2 1.6 2.7 4.0 8 2001 294 1 8.79 8.10 0.69 4.2 3.2 4.6 75.2 0.0 18.2 1.2 2.3 3.1 9 2001 51 8 8.39 7.72 0.67 3.2 2.5 3.8 69.7 0.0 22.6 1.4 2.5 3.7 10 2001 102 9 8.20 7.60 0.60 2.8 2.3 3.6 67.4 0.0 24.2 1.6 2.7 4.1 11 2001 229 16 8.88 8.33 0.55 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.5 2.7 12 2001 110 3 8.12 7.60 0.52 2.8 2.3 3.6 67.3 0.0 24.2 1.6 2.7 4.1 13 2001 142 3 8.34 7.87 0.47 3.6 2.8 4.1 72.5 0.0 20.7 1.4 1.9 3.6 14 2001 112 35 8.08 7.60 0.47 2.8 2.3 3.6 67.5 0.0 24.2 1.6 2.6 4.1 15 2001 343 97 8.40 7.94 0.46 3.8 2.9 4.3 73.0 0.0 19.9 1.4 2.4 3.4 16 2001 158 25 8.56 8.10 0.46 4.2 3.2 4.6 75.4 0.0 18.3 1.2 2.2 2.9 17 2001 126 3 8.31 7.87 0.44 3.6 2.8 4.1 72.2 0.0 20.6 1.3 2.3 3.5 18 2001 236 25 8.77 8.33 0.43 4.9 3.5 5.1 77.8 0.0 16.3 1.1 2.1 2.7 19 2001 198 9 8.62 8.21 0.42 4.5 3.3 4.9 76.7 0.0 17.3 1.2 1.9 3.0 20

2002 224 3 9.67 8.33 1.33 4.9 3.5 5.1 77.9 0.0 16.3 1.1 2.0 2.7 1 2002 15 16 9.07 7.82 1.26 3.4 2.7 4.0 71.4 0.0 21.4 1.3 2.4 3.5 2 2002 227 8 9.52 8.33 1.19 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.9 2.7 3 2002 365 16 9.08 7.94 1.15 3.8 2.9 4.3 73.0 0.0 19.9 1.3 2.4 3.4 4 2002 67 3 8.72 7.67 1.05 3.0 2.5 3.7 68.7 0.0 23.2 1.5 2.7 3.9 5 2002 228 97 9.33 8.33 1.00 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.9 2.7 6 2002 230 3 9.31 8.33 0.98 4.9 3.5 5.1 77.8 0.0 16.3 1.1 2.0 2.8 7 2002 226 97 9.28 8.33 0.94 4.9 3.5 5.1 77.9 0.0 16.3 1.1 2.0 2.7 8 2002 314 2 8.76 7.87 0.89 3.6 2.8 4.1 72.1 0.0 20.6 1.3 2.6 3.5 9 2002 35 9 8.57 7.72 0.85 3.2 2.5 3.8 69.6 0.0 22.6 1.4 2.8 3.6 10 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.2 0.0 16.4 1.1 1.6 2.7 11 2002 28 3 8.59 7.82 0.77 3.4 2.7 4.0 71.3 0.0 21.3 1.3 2.5 3.6 12 2002 107 97 8.32 7.60 0.72 2.8 2.3 3.6 67.3 0.0 24.2 1.5 2.9 4.1 13 2002 207 9 8.90 8.21 0.69 4.5 3.3 4.9 76.8 0.0 17.4 1.1 1.7 3.0 14 2002 85 97 8.24 7.67 0.57 3.0 2.5 3.7 68.7 0.0 23.2 1.5 2.7 3.9 15 2002 323 3 8.43 7.87 0.55 3.6 2.8 4.1 72.1 0.0 20.6 1.3 2.5 3.4 16 2002 105 8 8.13 7.60 0.53 2.8 2.3 3.6 67.6 0.0 24.3 1.5 2.7 3.9 17 2002 191 16 8.72 8.21 0.51 4.5 3.3 4.9 77.1 0.0 17.4 1.1 1.5 2.9 18 2002 128 3 8.37 7.87 0.50 3.6 2.8 4.1 72.0 0.0 20.6 1.4 2.5 3.5 19 2002 106 8 8.08 7.60 0.48 2.8 2.3 3.6 67.2 0.0 24.2 1.6 3.0 4.1 20

2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.8 2.7 1 2003 270 1 9.28 8.33 0.95 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.9 2.8 2 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.2 0.0 18.2 1.2 2.4 3.1 3 2003 356 16 8.61 7.94 0.67 3.8 2.9 4.3 73.2 0.0 19.9 1.3 2.4 3.3 4 2003 68 3 8.33 7.67 0.66 3.0 2.5 3.7 68.4 0.0 23.1 1.5 3.0 4.0 5 2003 246 3 8.98 8.33 0.65 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.7 2.9 6 2003 107 1 8.23 7.60 0.63 2.8 2.3 3.6 67.4 0.0 24.3 1.6 2.7 4.1 7 2003 163 3 8.69 8.10 0.59 4.2 3.2 4.6 75.5 0.0 18.2 1.2 2.0 3.1 8 2003 164 97 8.69 8.10 0.58 4.2 3.2 4.6 75.4 0.0 18.3 1.2 2.0 3.1 9 2003 187 3 8.79 8.21 0.58 4.5 3.3 4.9 76.4 0.0 17.3 1.1 2.2 2.9 10 2003 35 10 8.25 7.72 0.53 3.2 2.5 3.8 69.6 0.0 22.6 1.5 2.5 3.8 11 2003 242 16 8.84 8.33 0.51 4.9 3.5 5.1 78.2 0.0 16.3 1.0 1.7 2.7 12 2003 160 3 8.60 8.10 0.50 4.2 3.2 4.6 75.0 0.0 18.1 1.3 2.4 3.1 13 2003 326 1 8.36 7.87 0.49 3.6 2.8 4.1 71.9 0.0 20.6 1.3 2.7 3.5 14 2003 247 97 8.81 8.33 0.48 4.9 3.5 5.1 78.3 0.0 16.4 1.1 1.4 2.8 15 2003 304 1 8.56 8.10 0.46 4.2 3.2 4.6 75.4 0.0 18.3 1.2 2.2 3.0 16 2003 108 3 8.06 7.60 0.45 2.8 2.3 3.6 67.3 0.0 24.2 1.6 2.8 4.1 17 2003 259 3 8.78 8.33 0.45 4.9 3.5 5.1 77.7 0.0 16.3 1.1 2.1 2.8 18 2003 197 9 8.65 8.21 0.44 4.5 3.3 4.9 76.6 0.0 17.4 1.1 2.2 2.8 19 2003 166 46 8.54 8.10 0.44 4.2 3.2 4.6 75.3 0.0 18.2 1.2 2.1 3.1 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days Over 3 Years)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.31 7.94 1.37 3.8 2.9 4.3 73.1 0.0 19.9 1.3 2.4 3.3 1 2002 224 3 9.67 8.33 1.33 4.9 3.5 5.1 77.9 0.0 16.3 1.1 2.0 2.7 2 2002 15 16 9.07 7.82 1.26 3.4 2.7 4.0 71.4 0.0 21.4 1.3 2.4 3.5 3 2002 227 8 9.52 8.33 1.19 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.9 2.7 4 2002 365 16 9.08 7.94 1.15 3.8 2.9 4.3 73.0 0.0 19.9 1.3 2.4 3.4 5 2002 67 3 8.72 7.67 1.05 3.0 2.5 3.7 68.7 0.0 23.2 1.5 2.7 3.9 6 2002 228 97 9.33 8.33 1.00 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.9 2.7 7 2001 113 1 8.60 7.60 0.99 2.8 2.3 3.6 67.2 0.0 24.2 1.6 3.0 4.0 8 2002 230 3 9.31 8.33 0.98 4.9 3.5 5.1 77.8 0.0 16.3 1.1 2.0 2.8 9 2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.8 2.7 10 2003 270 1 9.28 8.33 0.95 4.9 3.5 5.1 78.0 0.0 16.3 1.1 1.9 2.8 11 2002 226 97 9.28 8.33 0.94 4.9 3.5 5.1 77.9 0.0 16.3 1.1 2.0 2.7 12 2001 225 3 9.27 8.33 0.94 4.9 3.5 5.1 77.9 0.0 16.3 1.1 2.0 2.7 13 2002 314 2 8.76 7.87 0.89 3.6 2.8 4.1 72.1 0.0 20.6 1.3 2.6 3.5 14 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.4 0.0 18.2 1.2 2.1 3.1 15 2002 35 9 8.57 7.72 0.85 3.2 2.5 3.8 69.6 0.0 22.6 1.4 2.8 3.6 16 2001 114 4 8.43 7.60 0.82 2.8 2.3 3.6 67.5 0.0 24.3 1.6 2.6 4.0 17 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.2 0.0 16.4 1.1 1.6 2.7 18 2002 28 3 8.59 7.82 0.77 3.4 2.7 4.0 71.3 0.0 21.3 1.3 2.5 3.6 19 2001 279 1 8.87 8.10 0.77 4.2 3.2 4.6 75.4 0.0 18.3 1.1 2.2 3.0 20 2001 220 54 9.10 8.33 0.76 4.9 3.5 5.1 77.9 0.0 16.3 1.1 1.9 2.8 21 2001 111 16 8.34 7.60 0.74 2.8 2.3 3.6 67.5 0.0 24.2 1.6 2.7 4.0 22 2002 107 97 8.32 7.60 0.72 2.8 2.3 3.6 67.3 0.0 24.2 1.5 2.9 4.1 23 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.2 0.0 18.2 1.2 2.4 3.1 24 2002 207 9 8.90 8.21 0.69 4.5 3.3 4.9 76.8 0.0 17.4 1.1 1.7 3.0 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for GSM (Top 20 Days Each Year)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 112 396 7.97 7.57 0.40 2.8 2.3 3.5 67.3 0.0 25.0 1.7 1.9 4.2 1 2001 98 350 7.96 7.57 0.39 2.8 2.3 3.5 67.0 0.0 24.9 1.6 2.4 4.1 2 2001 208 361 8.48 8.10 0.38 4.2 3.2 4.6 75.9 0.0 18.4 1.2 1.4 3.2 3 2001 113 609 7.89 7.57 0.32 2.8 2.3 3.5 67.3 0.0 25.0 1.7 1.8 4.2 4 2001 111 371 7.88 7.57 0.31 2.8 2.3 3.5 67.5 0.0 25.0 1.5 1.8 4.1 5 2001 341 606 8.17 7.87 0.30 3.6 2.8 4.1 72.9 0.0 20.8 1.4 1.4 3.5 6 2001 331 363 8.10 7.82 0.29 3.4 2.7 4.0 71.2 0.0 21.3 1.5 2.3 3.7 7 2001 297 514 8.39 8.10 0.28 4.2 3.2 4.6 76.0 0.0 18.5 1.3 1.1 3.2 8 2001 279 514 8.37 8.10 0.27 4.2 3.2 4.6 75.9 0.0 18.4 1.2 1.5 3.1 9 2001 343 609 8.11 7.87 0.24 3.6 2.8 4.1 72.7 0.0 20.8 1.3 1.7 3.5 10 2001 198 381 8.34 8.10 0.23 4.2 3.2 4.6 76.0 0.0 18.4 1.2 1.4 3.1 11 2001 199 435 8.33 8.10 0.22 4.2 3.2 4.6 76.1 0.0 18.5 1.2 1.2 3.0 12 2001 298 364 8.31 8.10 0.21 4.2 3.2 4.6 75.9 0.0 18.4 1.3 1.3 3.2 13 2001 143 364 7.97 7.76 0.21 3.3 2.6 4.0 70.9 0.0 22.1 1.4 1.8 3.8 14 2001 225 606 8.41 8.21 0.20 4.5 3.3 4.9 77.1 0.0 17.4 1.1 1.3 3.1 15 2001 263 371 8.54 8.33 0.20 4.9 3.5 5.1 78.2 0.0 16.4 1.1 1.5 2.8 16 2001 141 435 7.95 7.76 0.19 3.3 2.6 4.0 70.6 0.0 22.1 1.5 2.0 3.8 17 2001 102 710 7.75 7.57 0.18 2.8 2.3 3.5 67.2 0.0 25.0 1.6 2.1 4.2 18 2001 158 475 8.27 8.10 0.17 4.2 3.2 4.6 75.6 0.0 18.4 1.1 1.8 3.2 19 2001 239 814 8.37 8.21 0.16 4.5 3.3 4.9 77.1 0.0 17.4 1.1 1.4 3.0 20

2002 129 354 8.30 7.73 0.57 3.3 2.6 4.0 70.4 0.0 22.1 1.5 2.3 3.7 1 2002 228 609 8.67 8.17 0.50 4.5 3.3 4.9 77.2 0.0 17.4 1.1 1.3 2.9 2 2002 107 474 7.89 7.54 0.35 2.8 2.3 3.5 67.1 0.0 25.0 1.6 2.0 4.2 3 2002 67 474 7.93 7.61 0.33 2.9 2.4 3.6 68.9 0.0 24.0 1.6 1.6 3.9 4 2002 106 351 7.86 7.54 0.31 2.8 2.3 3.5 66.8 0.0 24.8 1.7 2.6 4.1 5 2002 31 371 8.08 7.78 0.30 3.4 2.7 4.0 71.6 0.0 21.4 1.4 1.9 3.7 6 2002 226 434 8.43 8.17 0.26 4.5 3.3 4.9 77.0 0.0 17.4 1.2 1.5 2.9 7 2002 85 514 7.84 7.61 0.23 2.9 2.4 3.6 68.9 0.0 24.0 1.5 1.7 3.9 8 2002 66 363 7.83 7.61 0.23 2.9 2.4 3.6 68.5 0.0 23.9 1.5 2.1 4.0 9 2002 230 609 8.40 8.17 0.22 4.5 3.3 4.9 77.2 0.0 17.4 1.2 1.4 2.8 10 2002 356 349 8.02 7.84 0.18 3.6 2.8 4.1 72.4 0.0 20.8 1.3 2.0 3.5 11 2002 224 435 8.35 8.17 0.18 4.5 3.3 4.9 76.9 0.0 17.4 1.2 1.5 2.9 12 2002 128 341 7.90 7.73 0.16 3.3 2.6 4.0 71.0 0.0 22.1 1.4 1.7 3.7 13 2002 93 434 7.70 7.54 0.16 2.8 2.3 3.5 67.3 0.0 24.9 1.5 2.1 4.2 14 2002 28 562 7.94 7.78 0.16 3.4 2.7 4.0 71.8 0.0 21.4 1.5 1.8 3.5 15 2002 229 574 8.32 8.17 0.15 4.5 3.3 4.9 77.0 0.0 17.4 1.2 1.5 3.0 16 2002 330 513 7.93 7.78 0.15 3.4 2.7 4.0 71.2 0.0 21.4 1.2 2.5 3.7 17 2002 324 763 7.93 7.78 0.15 3.4 2.7 4.0 72.1 0.0 21.7 1.2 1.6 3.4 18 2002 225 429 8.32 8.17 0.15 4.5 3.3 4.9 77.5 0.0 17.4 1.2 0.9 3.0 19 2002 164 340 8.21 8.07 0.15 4.2 3.2 4.6 75.8 0.0 18.4 1.2 1.5 3.1 20

2003 304 351 8.45 8.07 0.38 4.2 3.2 4.6 75.7 0.0 18.3 1.2 1.8 3.0 1 2003 327 475 8.10 7.78 0.32 3.4 2.7 4.0 71.6 0.0 21.4 1.3 2.2 3.5 2 2003 246 474 8.59 8.30 0.30 4.9 3.5 5.1 78.2 0.0 16.3 1.0 1.6 2.8 3 2003 247 423 8.56 8.30 0.27 4.9 3.5 5.1 78.3 0.0 16.3 1.2 1.5 2.8 4 2003 163 356 8.33 8.07 0.26 4.2 3.2 4.6 75.7 0.0 18.4 1.2 1.7 3.1 5 2003 178 353 8.30 8.07 0.23 4.2 3.2 4.6 75.5 0.0 18.2 1.2 2.0 3.1 6 2003 165 373 8.29 8.07 0.23 4.2 3.2 4.6 75.2 0.0 18.2 1.2 2.2 3.2 7 2003 6 367 8.01 7.78 0.23 3.4 2.7 4.0 71.2 0.0 21.4 1.4 2.3 3.7 8 2003 35 372 7.85 7.64 0.21 3.0 2.5 3.7 69.5 0.0 23.5 1.6 1.6 3.9 9 2003 188 353 8.27 8.07 0.21 4.2 3.2 4.6 75.8 0.0 18.3 1.1 1.7 3.1 10 2003 187 340 8.27 8.07 0.21 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.5 3.1 11 2003 85 350 7.81 7.61 0.20 2.9 2.4 3.6 68.3 0.0 23.9 1.7 2.1 4.0 12 2003 320 513 7.96 7.78 0.17 3.4 2.7 4.0 71.7 0.0 21.5 1.3 1.6 3.8 13 2003 68 366 7.78 7.61 0.17 2.9 2.4 3.6 68.3 0.0 23.9 1.7 1.9 4.1 14 2003 156 341 8.24 8.07 0.17 4.2 3.2 4.6 75.4 0.0 18.3 1.3 1.8 3.1 15 2003 321 346 7.95 7.78 0.16 3.4 2.7 4.0 71.7 0.0 21.4 1.5 2.0 3.5 16 2003 235 558 8.32 8.17 0.15 4.5 3.3 4.9 77.1 0.0 17.4 1.2 1.5 2.9 17 2003 240 322 8.32 8.17 0.15 4.5 3.3 4.9 77.2 0.0 17.4 1.2 1.2 3.0 18 2003 164 514 8.21 8.07 0.14 4.2 3.2 4.6 75.9 0.0 18.3 1.3 1.3 3.2 19 2003 166 609 8.21 8.07 0.14 4.2 3.2 4.6 75.5 0.0 18.4 1.3 1.6 3.2 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for GSM (Top 25 Days Over 3 Years)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2002 129 354 8.30 7.73 0.57 3.3 2.6 4.0 70.4 0.0 22.1 1.5 2.3 3.7 1 2002 228 609 8.67 8.17 0.50 4.5 3.3 4.9 77.2 0.0 17.4 1.1 1.3 2.9 2 2001 112 396 7.97 7.57 0.40 2.8 2.3 3.5 67.3 0.0 25.0 1.7 1.9 4.2 3 2001 98 350 7.96 7.57 0.39 2.8 2.3 3.5 67.0 0.0 24.9 1.6 2.4 4.1 4 2003 304 351 8.45 8.07 0.38 4.2 3.2 4.6 75.7 0.0 18.3 1.2 1.8 3.0 5 2001 208 361 8.48 8.10 0.38 4.2 3.2 4.6 75.9 0.0 18.4 1.2 1.4 3.2 6 2002 107 474 7.89 7.54 0.35 2.8 2.3 3.5 67.1 0.0 25.0 1.6 2.0 4.2 7 2002 67 474 7.93 7.61 0.33 2.9 2.4 3.6 68.9 0.0 24.0 1.6 1.6 3.9 8 2003 327 475 8.10 7.78 0.32 3.4 2.7 4.0 71.6 0.0 21.4 1.3 2.2 3.5 9 2001 113 609 7.89 7.57 0.32 2.8 2.3 3.5 67.3 0.0 25.0 1.7 1.8 4.2 10 2002 106 351 7.86 7.54 0.31 2.8 2.3 3.5 66.8 0.0 24.8 1.7 2.6 4.1 11 2001 111 371 7.88 7.57 0.31 2.8 2.3 3.5 67.5 0.0 25.0 1.5 1.8 4.1 12 2001 341 606 8.17 7.87 0.30 3.6 2.8 4.1 72.9 0.0 20.8 1.4 1.4 3.5 13 2002 31 371 8.08 7.78 0.30 3.4 2.7 4.0 71.6 0.0 21.4 1.4 1.9 3.7 14 2003 246 474 8.59 8.30 0.30 4.9 3.5 5.1 78.2 0.0 16.3 1.0 1.6 2.8 15 2001 331 363 8.10 7.82 0.29 3.4 2.7 4.0 71.2 0.0 21.3 1.5 2.3 3.7 16 2001 297 514 8.39 8.10 0.28 4.2 3.2 4.6 76.0 0.0 18.5 1.3 1.1 3.2 17 2001 279 514 8.37 8.10 0.27 4.2 3.2 4.6 75.9 0.0 18.4 1.2 1.5 3.1 18 2003 247 423 8.56 8.30 0.27 4.9 3.5 5.1 78.3 0.0 16.3 1.2 1.5 2.8 19 2003 163 356 8.33 8.07 0.26 4.2 3.2 4.6 75.7 0.0 18.4 1.2 1.7 3.1 20 2002 226 434 8.43 8.17 0.26 4.5 3.3 4.9 77.0 0.0 17.4 1.2 1.5 2.9 21 2001 343 609 8.11 7.87 0.24 3.6 2.8 4.1 72.7 0.0 20.8 1.3 1.7 3.5 22 2001 198 381 8.34 8.10 0.23 4.2 3.2 4.6 76.0 0.0 18.4 1.2 1.4 3.1 23 2003 178 353 8.30 8.07 0.23 4.2 3.2 4.6 75.5 0.0 18.2 1.2 2.0 3.1 24 2002 85 514 7.84 7.61 0.23 2.9 2.4 3.6 68.9 0.0 24.0 1.5 1.7 3.9 25New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Joyce-Kilmer (Top 20 Days Each Yr)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 112 240 7.98 7.54 0.44 2.8 2.3 3.5 67.1 0.0 24.9 1.6 2.2 4.2 1 2001 98 225 7.94 7.54 0.40 2.8 2.3 3.5 66.7 0.0 24.8 1.7 2.6 4.2 2 2001 113 317 7.89 7.54 0.34 2.8 2.3 3.5 67.3 0.0 25.0 1.7 1.9 4.1 3 2001 111 225 7.87 7.54 0.33 2.8 2.3 3.5 67.2 0.0 24.9 1.6 2.0 4.2 4 2001 279 278 8.39 8.07 0.33 4.2 3.2 4.6 75.9 0.0 18.3 1.1 1.6 3.0 5 2001 297 310 8.39 8.07 0.32 4.2 3.2 4.6 76.1 0.0 18.4 1.2 1.2 3.1 6 2001 343 310 8.22 7.90 0.31 3.8 2.9 4.3 73.4 0.0 20.0 1.3 1.9 3.4 7 2001 198 226 8.44 8.17 0.27 4.5 3.3 4.9 77.2 0.0 17.4 1.2 1.3 3.0 8 2001 341 317 8.17 7.90 0.27 3.8 2.9 4.3 74.0 0.0 20.1 1.2 1.4 3.3 9 2001 208 225 8.43 8.17 0.26 4.5 3.3 4.9 76.9 0.0 17.3 1.2 1.5 3.0 10 2001 263 221 8.55 8.30 0.26 4.9 3.5 5.1 78.4 0.0 16.3 1.0 1.5 2.7 11 2001 199 278 8.42 8.17 0.25 4.5 3.3 4.9 77.3 0.0 17.5 1.1 1.3 2.9 12 2001 225 278 8.54 8.30 0.24 4.9 3.5 5.1 78.3 0.0 16.3 1.1 1.5 2.7 13 2001 331 225 8.00 7.78 0.22 3.4 2.7 4.0 71.3 0.0 21.2 1.5 2.4 3.6 14 2001 141 221 8.00 7.78 0.21 3.4 2.7 4.0 71.4 0.0 21.4 1.3 2.2 3.7 15 2001 298 225 8.28 8.07 0.21 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.5 3.0 16 2001 332 239 7.98 7.78 0.19 3.4 2.7 4.0 71.3 0.0 21.4 1.5 2.2 3.6 17 2001 158 310 8.25 8.07 0.19 4.2 3.2 4.6 75.5 0.0 18.4 1.2 2.0 2.9 18 2001 12 286 7.96 7.78 0.18 3.4 2.7 4.0 72.1 0.0 21.6 1.3 1.3 3.7 19 2001 239 221 8.46 8.30 0.17 4.9 3.5 5.1 78.6 0.0 16.4 1.1 1.1 2.9 20

2002 228 317 8.85 8.30 0.56 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.3 2.7 1 2002 129 225 8.28 7.78 0.49 3.4 2.7 4.0 71.3 0.0 21.3 1.4 2.4 3.6 2 2002 107 286 7.95 7.54 0.41 2.8 2.3 3.5 67.1 0.0 24.9 1.6 2.2 4.2 3 2002 67 278 8.00 7.61 0.39 2.9 2.4 3.6 68.6 0.0 24.0 1.6 1.8 4.0 4 2002 226 239 8.68 8.30 0.38 4.9 3.5 5.1 78.3 0.0 16.3 1.0 1.6 2.8 5 2002 31 221 8.14 7.78 0.35 3.4 2.7 4.0 71.4 0.0 21.4 1.4 2.1 3.7 6 2002 224 226 8.61 8.30 0.32 4.9 3.5 5.1 78.3 0.0 16.4 1.1 1.5 2.8 7 2002 106 225 7.85 7.54 0.31 2.8 2.3 3.5 66.9 0.0 24.8 1.5 2.6 4.1 8 2002 230 310 8.54 8.30 0.24 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.4 2.7 9 2002 35 221 7.93 7.69 0.24 3.2 2.5 3.8 69.2 0.0 22.5 1.5 2.7 4.1 10 2002 85 317 7.84 7.61 0.23 2.9 2.4 3.6 68.8 0.0 24.0 1.6 1.6 3.9 11 2002 28 278 8.00 7.78 0.21 3.4 2.7 4.0 71.9 0.0 21.4 1.3 2.0 3.5 12 2002 164 222 8.27 8.07 0.20 4.2 3.2 4.6 75.7 0.0 18.4 1.1 1.6 3.2 13 2002 128 226 7.97 7.78 0.18 3.4 2.7 4.0 71.7 0.0 21.4 1.3 2.0 3.5 14 2002 330 222 7.97 7.78 0.18 3.4 2.7 4.0 71.3 0.0 21.3 1.3 2.6 3.6 15 2002 356 225 8.09 7.90 0.18 3.8 2.9 4.3 73.2 0.0 19.9 1.3 2.3 3.3 16 2002 225 239 8.47 8.30 0.18 4.9 3.5 5.1 79.0 0.0 16.5 1.0 0.8 2.8 17 2002 138 270 7.95 7.78 0.16 3.4 2.7 4.0 72.0 0.0 21.6 1.4 1.4 3.5 19 2002 93 278 7.71 7.54 0.16 2.8 2.3 3.5 66.9 0.0 24.9 1.8 2.3 4.1 18 2002 66 225 7.77 7.61 0.16 2.9 2.4 3.6 68.4 0.0 23.8 1.5 2.1 4.2 20

2003 246 270 8.63 8.30 0.34 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.7 2.7 1 2003 327 310 8.12 7.78 0.34 3.4 2.7 4.0 71.4 0.0 21.3 1.4 2.3 3.6 2 2003 304 225 8.38 8.07 0.31 4.2 3.2 4.6 75.7 0.0 18.3 1.1 1.9 3.0 3 2003 247 241 8.60 8.30 0.31 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.3 2.8 4 2003 165 225 8.37 8.07 0.31 4.2 3.2 4.6 75.3 0.0 18.3 1.2 2.2 3.1 5 2003 187 241 8.47 8.17 0.30 4.5 3.3 4.9 76.8 0.0 17.3 1.2 1.8 2.9 6 2003 178 221 8.34 8.07 0.27 4.2 3.2 4.6 75.4 0.0 18.2 1.2 2.1 3.1 7 2003 6 239 8.03 7.78 0.25 3.4 2.7 4.0 71.0 0.0 21.3 1.5 2.4 3.7 8 2003 68 221 7.84 7.61 0.23 2.9 2.4 3.6 68.2 0.0 23.8 1.6 2.2 4.1 9 2003 188 222 8.40 8.17 0.23 4.5 3.3 4.9 76.8 0.0 17.4 1.2 1.8 2.9 10 2003 85 225 7.83 7.61 0.22 2.9 2.4 3.6 68.6 0.0 23.9 1.5 1.9 4.1 11 2003 35 225 7.91 7.69 0.22 3.2 2.5 3.8 69.9 0.0 22.8 1.5 1.9 3.9 12 2003 156 221 8.28 8.07 0.21 4.2 3.2 4.6 75.5 0.0 18.3 1.3 1.9 3.0 13 2003 163 225 8.25 8.07 0.18 4.2 3.2 4.6 75.8 0.0 18.3 1.2 1.7 3.0 14 2003 321 225 7.96 7.78 0.18 3.4 2.7 4.0 71.6 0.0 21.3 1.3 2.1 3.7 15 2003 52 223 7.86 7.69 0.18 3.2 2.5 3.8 69.8 0.0 22.7 1.3 2.4 3.8 16 2003 240 225 8.47 8.30 0.17 4.9 3.5 5.1 78.6 0.0 16.5 1.0 1.0 2.9 17 2003 357 222 8.07 7.90 0.17 3.8 2.9 4.3 73.6 0.0 19.9 1.4 1.6 3.5 18 2003 235 317 8.46 8.30 0.16 4.9 3.5 5.1 78.5 0.0 16.3 1.1 1.4 2.7 19 2003 291 240 8.23 8.07 0.16 4.2 3.2 4.6 75.3 0.0 18.2 1.1 2.0 3.4 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Joyce-Kil (Top 25 Days Over 3 Yrs)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2002 228 317 8.85 8.30 0.56 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.3 2.7 1 2002 129 225 8.28 7.78 0.49 3.4 2.7 4.0 71.3 0.0 21.3 1.4 2.4 3.6 2 2001 112 240 7.98 7.54 0.44 2.8 2.3 3.5 67.1 0.0 24.9 1.6 2.2 4.2 3 2002 107 286 7.95 7.54 0.41 2.8 2.3 3.5 67.1 0.0 24.9 1.6 2.2 4.2 4 2001 98 225 7.94 7.54 0.40 2.8 2.3 3.5 66.7 0.0 24.8 1.7 2.6 4.2 5 2002 67 278 8.00 7.61 0.39 2.9 2.4 3.6 68.6 0.0 24.0 1.6 1.8 4.0 6 2002 226 239 8.68 8.30 0.38 4.9 3.5 5.1 78.3 0.0 16.3 1.0 1.6 2.8 7 2002 31 221 8.14 7.78 0.35 3.4 2.7 4.0 71.4 0.0 21.4 1.4 2.1 3.7 8 2001 113 317 7.89 7.54 0.34 2.8 2.3 3.5 67.3 0.0 25.0 1.7 1.9 4.1 9 2003 246 270 8.63 8.30 0.34 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.7 2.7 10 2003 327 310 8.12 7.78 0.34 3.4 2.7 4.0 71.4 0.0 21.3 1.4 2.3 3.6 11 2001 111 225 7.87 7.54 0.33 2.8 2.3 3.5 67.2 0.0 24.9 1.6 2.0 4.2 12 2001 279 278 8.39 8.07 0.33 4.2 3.2 4.6 75.9 0.0 18.3 1.1 1.6 3.0 13 2001 297 310 8.39 8.07 0.32 4.2 3.2 4.6 76.1 0.0 18.4 1.2 1.2 3.1 14 2002 224 226 8.61 8.30 0.32 4.9 3.5 5.1 78.3 0.0 16.4 1.1 1.5 2.8 15 2003 304 225 8.38 8.07 0.31 4.2 3.2 4.6 75.7 0.0 18.3 1.1 1.9 3.0 16 2001 343 310 8.22 7.90 0.31 3.8 2.9 4.3 73.4 0.0 20.0 1.3 1.9 3.4 17 2002 106 225 7.85 7.54 0.31 2.8 2.3 3.5 66.9 0.0 24.8 1.5 2.6 4.1 18 2003 247 241 8.60 8.30 0.31 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.3 2.8 19 2003 165 225 8.37 8.07 0.31 4.2 3.2 4.6 75.3 0.0 18.3 1.2 2.2 3.1 20 2003 187 241 8.47 8.17 0.30 4.5 3.3 4.9 76.8 0.0 17.3 1.2 1.8 2.9 21 2003 178 221 8.34 8.07 0.27 4.2 3.2 4.6 75.4 0.0 18.2 1.2 2.1 3.1 22 2001 198 226 8.44 8.17 0.27 4.5 3.3 4.9 77.2 0.0 17.4 1.2 1.3 3.0 23 2001 341 317 8.17 7.90 0.27 3.8 2.9 4.3 74.0 0.0 20.1 1.2 1.4 3.3 24 2001 208 225 8.43 8.17 0.26 4.5 3.3 4.9 76.9 0.0 17.3 1.2 1.5 3.0 25New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Shining Rk (Top 20 Days Each Yr)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 298 1265 7.89 7.65 0.24 4.2 3.2 4.6 76.0 0.0 18.4 1.2 1.3 3.1 1 2001 331 1207 7.59 7.35 0.24 3.4 2.7 4.0 71.5 0.0 21.4 1.4 2.2 3.4 2 2001 102 1265 7.32 7.09 0.23 2.8 2.3 3.5 67.1 0.0 25.0 1.5 2.2 4.1 3 2001 279 1266 7.84 7.65 0.19 4.2 3.2 4.6 75.5 0.0 18.2 1.2 1.9 3.1 4 2001 208 1313 7.94 7.76 0.19 4.5 3.3 4.9 77.7 0.0 17.5 1.0 0.7 3.0 5 2001 342 1208 7.59 7.41 0.18 3.6 2.8 4.1 72.6 0.0 20.8 1.3 1.8 3.4 6 2001 52 1210 7.37 7.20 0.17 3.0 2.5 3.7 69.3 0.0 23.3 1.4 2.0 4.0 7 2001 330 1308 7.52 7.35 0.17 3.4 2.7 4.0 71.6 0.0 21.3 1.4 2.0 3.7 8 2001 148 1210 7.57 7.41 0.17 3.6 2.8 4.1 72.6 0.0 20.8 1.4 1.7 3.5 9 2001 199 1265 7.89 7.76 0.13 4.5 3.3 4.9 77.8 0.0 17.6 1.1 0.7 2.8 10 2001 156 1265 7.77 7.65 0.12 4.2 3.2 4.6 75.9 0.0 18.4 1.1 1.5 3.1 11 2001 209 1305 7.88 7.76 0.12 4.5 3.3 4.9 76.6 0.0 17.3 1.2 1.9 3.1 12 2001 173 1216 7.76 7.65 0.12 4.2 3.2 4.6 76.2 0.0 18.5 1.2 0.8 3.3 13 2001 149 1266 7.51 7.41 0.11 3.6 2.8 4.1 72.0 0.0 20.7 1.4 2.3 3.6 14 2001 48 1313 7.30 7.20 0.10 3.0 2.5 3.7 69.6 0.0 23.7 1.5 1.5 3.9 15 2001 98 1210 7.19 7.09 0.10 2.8 2.3 3.5 67.7 0.0 25.2 1.5 1.5 4.1 17 2001 343 1302 7.50 7.41 0.10 3.6 2.8 4.1 72.0 0.0 20.5 1.5 2.5 3.5 18 2001 224 1210 8.15 8.05 0.10 5.4 3.8 5.4 80.4 0.0 15.5 0.9 0.5 2.7 16 2001 198 1265 7.85 7.76 0.09 4.5 3.3 4.9 77.4 0.0 17.6 1.0 1.0 3.0 19 2001 210 1277 7.85 7.76 0.09 4.5 3.3 4.9 77.0 0.0 17.4 1.0 1.5 3.1 20

2002 330 1228 7.61 7.35 0.26 3.4 2.7 4.0 71.4 0.0 21.3 1.5 2.2 3.7 1 2002 29 1210 7.61 7.35 0.26 3.4 2.7 4.0 72.2 0.0 21.6 1.3 1.3 3.6 2 2002 129 1313 7.57 7.41 0.16 3.6 2.8 4.1 73.0 0.0 20.8 1.5 1.2 3.5 3 2002 147 1216 7.57 7.41 0.16 3.6 2.8 4.1 72.4 0.0 20.8 1.5 1.8 3.5 4 2002 66 1302 7.32 7.16 0.16 2.9 2.4 3.6 68.2 0.0 23.8 1.5 2.5 4.0 5 2002 31 1235 7.49 7.35 0.14 3.4 2.7 4.0 71.6 0.0 21.3 1.4 2.0 3.7 6 2002 35 1206 7.34 7.20 0.14 3.0 2.5 3.7 69.3 0.0 23.4 1.4 2.1 3.9 7 2002 84 1210 7.28 7.16 0.12 2.9 2.4 3.6 68.4 0.0 24.0 1.7 1.7 4.2 8 2002 77 1210 7.27 7.16 0.11 2.9 2.4 3.6 68.6 0.0 24.1 1.4 1.8 4.1 9 2002 75 1210 7.24 7.16 0.08 2.9 2.4 3.6 68.2 0.0 23.8 1.8 1.8 4.3 10 2002 145 1210 7.49 7.41 0.08 3.6 2.8 4.1 72.5 0.0 20.9 1.2 1.8 3.6 11 2002 165 1206 7.72 7.65 0.08 4.2 3.2 4.6 76.1 0.0 18.3 1.2 1.2 3.1 12 2002 225 1265 8.12 8.05 0.07 5.4 3.8 5.4 80.3 0.0 15.5 1.2 0.6 2.4 13 2002 178 1265 7.72 7.65 0.07 4.2 3.2 4.6 75.7 0.0 18.4 1.3 1.3 3.3 14 2002 67 1308 7.23 7.16 0.07 2.9 2.4 3.6 68.9 0.0 23.8 1.5 2.2 3.7 15 2002 164 1265 7.70 7.65 0.06 4.2 3.2 4.6 76.6 0.0 18.5 0.8 0.8 3.3 17 2002 134 1210 7.46 7.41 0.06 3.6 2.8 4.1 73.0 0.0 21.2 1.7 0.8 3.3 16 2002 192 1210 7.81 7.76 0.06 4.5 3.3 4.9 77.4 0.0 17.7 0.8 0.8 3.3 18 2002 229 1300 8.10 8.05 0.05 5.4 3.8 5.4 80.3 0.0 15.4 0.9 0.9 2.6 19 2002 226 1313 8.10 8.05 0.05 5.4 3.8 5.4 80.3 0.0 15.4 0.9 0.9 2.6 20

2003 85 1212 7.42 7.16 0.26 2.9 2.4 3.6 68.3 0.0 23.8 1.6 2.3 4.1 1 2003 343 1212 7.66 7.41 0.25 3.6 2.8 4.1 72.2 0.0 20.6 1.3 2.5 3.4 2 2003 35 1211 7.45 7.20 0.25 3.0 2.5 3.7 69.2 0.0 23.4 1.6 1.9 3.9 3 2003 357 1210 7.62 7.41 0.21 3.6 2.8 4.1 72.1 0.0 20.7 1.4 2.3 3.6 4 2003 178 1251 7.85 7.65 0.20 4.2 3.2 4.6 76.4 0.0 18.5 1.2 0.9 3.0 5 2003 247 1266 8.09 7.89 0.20 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.4 2.8 6 2003 52 1206 7.38 7.20 0.19 3.0 2.5 3.7 68.9 0.0 23.3 1.6 2.1 4.0 7 2003 356 1208 7.59 7.41 0.18 3.6 2.8 4.1 72.2 0.0 20.6 1.3 2.4 3.5 8 2003 188 1206 7.93 7.76 0.18 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.3 2.9 9 2003 327 1206 7.51 7.35 0.17 3.4 2.7 4.0 71.5 0.0 21.5 1.5 2.0 3.5 10 2003 240 1211 8.21 8.05 0.16 5.4 3.8 5.4 80.3 0.0 15.3 1.1 0.8 2.5 11 2003 320 1266 7.49 7.35 0.15 3.4 2.7 4.0 71.6 0.0 21.4 1.3 2.0 3.7 12 2003 130 1210 7.54 7.41 0.13 3.6 2.8 4.1 72.2 0.0 20.6 1.5 2.2 3.6 13 2003 316 1209 7.47 7.35 0.12 3.4 2.7 4.0 71.9 0.0 21.6 1.2 1.6 3.7 14 2003 72 1208 7.28 7.16 0.12 2.9 2.4 3.6 68.1 0.0 23.9 1.7 2.5 3.8 15 2003 166 1314 7.76 7.65 0.12 4.2 3.2 4.6 76.3 0.0 18.4 1.2 1.2 2.9 16 2003 344 1312 7.52 7.41 0.11 3.6 2.8 4.1 73.0 0.0 20.7 1.3 1.7 3.4 17 2003 160 1206 7.76 7.65 0.11 4.2 3.2 4.6 75.9 0.0 18.2 1.3 1.7 2.9 18 2003 58 1252 7.30 7.20 0.10 3.0 2.5 3.7 69.0 0.0 23.3 1.4 2.4 3.8 19 2003 291 1214 7.75 7.65 0.10 4.2 3.2 4.6 76.1 0.0 18.4 0.9 1.8 2.7 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change - Shining Rk (Top 25 Days Over 3 Yrs)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2002 330 1228 7.61 7.35 0.26 3.4 2.7 4.0 71.4 0.0 21.3 1.5 2.2 3.7 1 2002 29 1210 7.61 7.35 0.26 3.4 2.7 4.0 72.2 0.0 21.6 1.3 1.3 3.6 2 2003 85 1212 7.42 7.16 0.26 2.9 2.4 3.6 68.3 0.0 23.8 1.6 2.3 4.1 3 2003 343 1212 7.66 7.41 0.25 3.6 2.8 4.1 72.2 0.0 20.6 1.3 2.5 3.4 4 2003 35 1211 7.45 7.20 0.25 3.0 2.5 3.7 69.2 0.0 23.4 1.6 1.9 3.9 5 2001 298 1265 7.89 7.65 0.24 4.2 3.2 4.6 76.0 0.0 18.4 1.2 1.3 3.1 6 2001 331 1207 7.59 7.35 0.24 3.4 2.7 4.0 71.5 0.0 21.4 1.4 2.2 3.4 7 2001 102 1265 7.32 7.09 0.23 2.8 2.3 3.5 67.1 0.0 25.0 1.5 2.2 4.1 8 2003 357 1210 7.62 7.41 0.21 3.6 2.8 4.1 72.1 0.0 20.7 1.4 2.3 3.6 9 2003 178 1251 7.85 7.65 0.20 4.2 3.2 4.6 76.4 0.0 18.5 1.2 0.9 3.0 10 2003 247 1266 8.09 7.89 0.20 4.9 3.5 5.1 78.4 0.0 16.4 1.1 1.4 2.8 11 2001 279 1266 7.84 7.65 0.19 4.2 3.2 4.6 75.5 0.0 18.2 1.2 1.9 3.1 12 2001 208 1313 7.94 7.76 0.19 4.5 3.3 4.9 77.7 0.0 17.5 1.0 0.7 3.0 13 2003 52 1206 7.38 7.20 0.19 3.0 2.5 3.7 68.9 0.0 23.3 1.6 2.1 4.0 14 2001 342 1208 7.59 7.41 0.18 3.6 2.8 4.1 72.6 0.0 20.8 1.3 1.8 3.4 15 2003 356 1208 7.59 7.41 0.18 3.6 2.8 4.1 72.2 0.0 20.6 1.3 2.4 3.5 16 2003 188 1206 7.93 7.76 0.18 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.3 2.9 17 2001 52 1210 7.37 7.20 0.17 3.0 2.5 3.7 69.3 0.0 23.3 1.4 2.0 4.0 18 2001 330 1308 7.52 7.35 0.17 3.4 2.7 4.0 71.6 0.0 21.3 1.4 2.0 3.7 19 2001 148 1210 7.57 7.41 0.17 3.6 2.8 4.1 72.6 0.0 20.8 1.4 1.7 3.5 20 2003 327 1206 7.51 7.35 0.17 3.4 2.7 4.0 71.5 0.0 21.5 1.5 2.0 3.5 21 2003 240 1211 8.21 8.05 0.16 5.4 3.8 5.4 80.3 0.0 15.3 1.1 0.8 2.5 22 2002 129 1313 7.57 7.41 0.16 3.6 2.8 4.1 73.0 0.0 20.8 1.5 1.2 3.5 23 2002 147 1216 7.57 7.41 0.16 3.6 2.8 4.1 72.4 0.0 20.8 1.5 1.8 3.5 24 2002 66 1302 7.32 7.16 0.16 2.9 2.4 3.6 68.2 0.0 23.8 1.5 2.5 4.0 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Sipsey (Top 20 Days Each Year)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 259 1058 8.39 8.17 0.22 4.2 3.2 4.6 75.9 0.0 18.4 1.2 1.4 3.1 1 2001 311 1165 8.01 7.88 0.13 3.4 2.7 4.0 71.6 0.0 21.3 1.4 2.1 3.6 2 2001 164 1077 8.20 8.08 0.12 4.0 3.0 4.5 74.5 0.0 19.1 1.1 1.9 3.4 3 2001 215 1205 8.28 8.17 0.11 4.2 3.2 4.6 76.2 0.0 18.5 1.2 0.8 3.2 4 2001 273 1199 8.27 8.17 0.10 4.2 3.2 4.6 75.3 0.0 18.3 1.4 1.8 3.2 5 2001 205 1058 8.27 8.17 0.10 4.2 3.2 4.6 75.6 0.0 18.3 1.4 1.4 3.3 6 2001 216 1147 8.26 8.17 0.09 4.2 3.2 4.6 76.3 0.0 18.4 1.1 1.1 3.2 7 2001 304 1129 8.16 8.08 0.08 4.0 3.0 4.5 75.0 0.0 19.2 1.2 1.2 3.5 8 2001 170 1092 8.16 8.08 0.08 4.0 3.0 4.5 75.2 0.0 19.5 1.2 1.2 3.0 9 2001 274 1201 8.24 8.17 0.07 4.2 3.2 4.6 75.3 0.0 18.3 1.3 1.9 3.2 10 2001 120 1092 7.73 7.66 0.07 2.8 2.3 3.6 68.0 0.0 24.3 1.4 2.1 4.2 11 2001 346 1205 8.00 7.94 0.06 3.6 2.8 4.1 72.3 0.0 20.4 1.5 2.2 3.7 12 2001 213 1204 8.23 8.17 0.06 4.2 3.2 4.6 76.1 0.0 18.2 0.8 1.6 3.2 13 2001 326 1129 7.93 7.88 0.05 3.4 2.7 4.0 71.5 0.0 21.2 1.8 1.8 3.7 14 2001 43 1092 7.82 7.78 0.05 3.2 2.5 3.8 70.7 0.0 23.3 1.0 1.0 4.0 15 2001 312 1092 7.92 7.88 0.04 3.4 2.7 4.0 72.5 0.0 21.7 1.2 1.2 3.5 16 2001 320 1129 7.92 7.88 0.04 3.4 2.7 4.0 72.6 0.0 21.4 1.2 1.2 3.6 17 2001 206 1147 8.21 8.17 0.04 4.2 3.2 4.6 76.0 0.0 18.1 1.2 1.2 3.5 18 2001 89 1058 7.73 7.69 0.04 2.9 2.4 3.6 68.3 0.0 24.2 1.3 2.5 3.8 19 2001 178 1204 8.12 8.08 0.04 4.0 3.0 4.5 74.9 0.0 19.0 1.2 1.2 3.7 20

2002 3 1092 8.17 7.94 0.24 3.6 2.8 4.1 72.8 0.0 20.8 1.3 1.5 3.5 1 2002 268 1092 8.39 8.17 0.22 4.2 3.2 4.6 75.7 0.0 18.3 1.2 1.6 3.1 2 2002 241 1067 8.35 8.17 0.18 4.2 3.2 4.6 75.7 0.0 18.3 1.2 1.5 3.2 3 2002 247 1147 8.32 8.17 0.15 4.2 3.2 4.6 76.0 0.0 18.5 1.2 1.2 3.1 4 2002 220 1110 8.32 8.17 0.15 4.2 3.2 4.6 75.5 0.0 18.4 1.2 1.8 3.1 5 2002 216 1065 8.31 8.17 0.13 4.2 3.2 4.6 75.7 0.0 18.3 1.3 1.7 3.0 6 2002 245 1147 8.30 8.17 0.13 4.2 3.2 4.6 76.4 0.0 18.5 1.0 1.0 3.1 7 2002 244 1147 8.29 8.17 0.12 4.2 3.2 4.6 76.1 0.0 18.5 1.2 1.2 3.1 8 2002 170 1092 8.19 8.08 0.11 4.0 3.0 4.5 74.7 0.0 19.1 1.2 1.6 3.3 9 2002 253 1065 8.28 8.17 0.11 4.2 3.2 4.6 76.3 0.0 18.3 1.2 1.2 2.9 10 2002 242 1058 8.28 8.17 0.11 4.2 3.2 4.6 76.0 0.0 18.5 1.3 1.3 2.9 11 2002 243 1092 8.27 8.17 0.10 4.2 3.2 4.6 75.9 0.0 18.4 1.3 1.3 3.1 12 2002 240 1129 8.26 8.17 0.09 4.2 3.2 4.6 76.6 0.0 18.4 1.0 1.0 3.0 13 2002 189 1147 8.23 8.17 0.06 4.2 3.2 4.6 75.2 0.0 18.3 1.4 2.1 2.9 14 2002 273 1165 8.23 8.17 0.06 4.2 3.2 4.6 75.1 0.0 18.4 1.4 2.2 2.9 15 2002 288 1093 8.14 8.08 0.05 4.0 3.0 4.5 74.4 0.0 19.4 0.9 1.8 3.6 16 2002 344 1205 7.99 7.94 0.05 3.6 2.8 4.1 71.9 0.0 20.7 0.9 2.8 3.7 17 2002 339 1205 7.98 7.94 0.04 3.6 2.8 4.1 73.1 0.0 20.8 1.0 2.1 3.1 18 2002 234 1092 8.21 8.17 0.04 4.2 3.2 4.6 75.8 0.0 17.9 1.1 2.1 3.2 19 2002 252 1065 8.21 8.17 0.04 4.2 3.2 4.6 76.2 0.0 18.4 1.1 1.1 3.2 20

2003 251 1148 8.48 8.17 0.31 4.2 3.2 4.6 76.1 0.0 18.4 1.2 1.2 3.2 1 2003 314 1058 8.11 7.88 0.23 3.4 2.7 4.0 71.8 0.0 21.6 1.4 1.6 3.6 3 2003 282 1067 8.32 8.08 0.23 4.0 3.0 4.5 74.3 0.0 19.1 1.4 2.0 3.3 2 2003 308 1147 8.07 7.88 0.19 3.4 2.7 4.0 71.9 0.0 21.5 1.5 1.5 3.7 4 2003 250 1201 8.31 8.17 0.14 4.2 3.2 4.6 75.6 0.0 18.3 1.3 1.6 3.2 5 2003 281 1092 8.22 8.08 0.14 4.0 3.0 4.5 74.9 0.0 19.1 1.3 1.3 3.3 6 2003 286 1201 8.20 8.08 0.12 4.0 3.0 4.5 74.6 0.0 19.2 1.1 1.5 3.4 7 2003 283 1201 8.19 8.08 0.11 4.0 3.0 4.5 74.4 0.0 19.0 1.2 2.1 3.3 8 2003 261 1201 8.25 8.17 0.08 4.2 3.2 4.6 76.0 0.0 18.3 1.1 1.7 2.9 9 2003 255 1147 8.25 8.17 0.08 4.2 3.2 4.6 76.9 0.0 18.5 1.2 0.6 2.9 10 2003 285 1204 8.15 8.08 0.06 4.0 3.0 4.5 74.5 0.0 19.2 1.4 1.4 3.5 11 2003 348 1201 7.99 7.94 0.06 3.6 2.8 4.1 72.2 0.0 20.6 1.6 2.4 3.2 12 2003 284 1204 8.14 8.08 0.05 4.0 3.0 4.5 75.6 0.0 19.1 0.9 0.9 3.5 13 2003 74 1147 7.74 7.69 0.05 2.9 2.4 3.6 69.0 0.0 23.8 2.1 1.0 4.1 14 2003 77 1204 7.74 7.69 0.04 2.9 2.4 3.6 67.9 0.0 23.4 2.2 2.2 4.3 15 2003 227 1147 8.21 8.17 0.04 4.2 3.2 4.6 76.0 0.0 18.6 1.1 1.1 3.3 16 2003 262 1201 8.21 8.17 0.04 4.2 3.2 4.6 75.6 0.0 18.7 1.1 1.1 3.4 17 2003 238 1147 8.21 8.17 0.03 4.2 3.2 4.6 76.8 0.0 18.0 1.3 1.3 2.6 18 2003 254 1147 8.20 8.17 0.03 4.2 3.2 4.6 75.8 0.0 18.6 1.4 1.4 2.8 19 2003 175 1205 8.12 8.08 0.03 4.0 3.0 4.5 74.0 0.0 18.9 1.4 2.9 2.9 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Sipsey (Top 25 Days Over 3 Years)

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2003 251 1148 8.48 8.17 0.31 4.2 3.2 4.6 76.1 0.0 18.4 1.2 1.2 3.2 1 2002 3 1092 8.17 7.94 0.24 3.6 2.8 4.1 72.8 0.0 20.8 1.3 1.5 3.5 2 2003 282 1067 8.32 8.08 0.23 4.0 3.0 4.5 74.3 0.0 19.1 1.4 2.0 3.3 3 2003 314 1058 8.11 7.88 0.23 3.4 2.7 4.0 71.8 0.0 21.6 1.4 1.6 3.6 4 2001 259 1058 8.39 8.17 0.22 4.2 3.2 4.6 75.9 0.0 18.4 1.2 1.4 3.1 5 2002 268 1092 8.39 8.17 0.22 4.2 3.2 4.6 75.7 0.0 18.3 1.2 1.6 3.1 6 2003 308 1147 8.07 7.88 0.19 3.4 2.7 4.0 71.9 0.0 21.5 1.5 1.5 3.7 7 2002 241 1067 8.35 8.17 0.18 4.2 3.2 4.6 75.7 0.0 18.3 1.2 1.5 3.2 8 2002 247 1147 8.32 8.17 0.15 4.2 3.2 4.6 76.0 0.0 18.5 1.2 1.2 3.1 9 2002 220 1110 8.32 8.17 0.15 4.2 3.2 4.6 75.5 0.0 18.4 1.2 1.8 3.1 10 2003 250 1201 8.31 8.17 0.14 4.2 3.2 4.6 75.6 0.0 18.3 1.3 1.6 3.2 11 2003 281 1092 8.22 8.08 0.14 4.0 3.0 4.5 74.9 0.0 19.1 1.3 1.3 3.3 12 2002 216 1065 8.31 8.17 0.13 4.2 3.2 4.6 75.7 0.0 18.3 1.3 1.7 3.0 13 2001 311 1165 8.01 7.88 0.13 3.4 2.7 4.0 71.6 0.0 21.3 1.4 2.1 3.6 14 2002 245 1147 8.30 8.17 0.13 4.2 3.2 4.6 76.4 0.0 18.5 1.0 1.0 3.1 15 2001 164 1077 8.20 8.08 0.12 4.0 3.0 4.5 74.5 0.0 19.1 1.1 1.9 3.4 16 2003 286 1201 8.20 8.08 0.12 4.0 3.0 4.5 74.6 0.0 19.2 1.1 1.5 3.4 17 2002 244 1147 8.29 8.17 0.12 4.2 3.2 4.6 76.1 0.0 18.5 1.2 1.2 3.1 18 2001 215 1205 8.28 8.17 0.11 4.2 3.2 4.6 76.2 0.0 18.5 1.2 0.8 3.2 19 2002 170 1092 8.19 8.08 0.11 4.0 3.0 4.5 74.7 0.0 19.1 1.2 1.6 3.3 20 2003 283 1201 8.19 8.08 0.11 4.0 3.0 4.5 74.4 0.0 19.0 1.2 2.1 3.3 21 2002 253 1065 8.28 8.17 0.11 4.2 3.2 4.6 76.3 0.0 18.3 1.2 1.2 2.9 22 2002 242 1058 8.28 8.17 0.11 4.2 3.2 4.6 76.0 0.0 18.5 1.3 1.3 2.9 23 2002 243 1092 8.27 8.17 0.10 4.2 3.2 4.6 75.9 0.0 18.4 1.3 1.3 3.1 24 2001 273 1199 8.27 8.17 0.10 4.2 3.2 4.6 75.3 0.0 18.3 1.4 1.8 3.2 25

## Appendix C

Agglomerator Cost Estimate from Indigo Industries, Inc.

## Appendix D

BART Determination Modeling Protocol: Georgia Power Company Plant Bowen

# BART Determination Modeling Protocol:
Georgia Power Company Plant Bowen

Prepared by:
Southern Company Services and ENSR Corporation for Georgia Power Company

November 2006

## Contents

1.0 Introduction

............................................................................................................................... ................ 1-1

1.1 Objectives

............................................................................................................................... ......... 1-1

1.2 Location of source vs. relevant Class I Areas ................................................................................ 1-1

1.3 Organization of protocol document ................................................................................................. 1-1

2.0 Source description and emissions data ............................................................................................... 2-1

2.1 BART determination baseline ......................................................................................................... 2-1

2.2 PM emissions controls to be modeled ............................................................................................ 2-1

2.3 Unit-specific source data ................................................................................................................. 2-1

3.0 Input data to the CALPUFF model ......................................................................................................... 3-1

3.1 General modeling procedures: ....................................................................................................... 3-1

3.2 Air quality database (background ozone and ammonia) ............................................................... 3-1

3.3 Natural conditions and monthl y f(RH) at Class I Areas ................................................................. 3-1

4.0 Air quality modeling procedures ........................................................................................................... 4-1

4.1 Model selection and features .......................................................................................................... 4-1

4.2 Modeling domain and receptors ..................................................................................................... 4-1

4.3 Technical options used in the modeling ......................................................................................... 4-1

4.4 Light extinction and haze impact calculations ................................................................................ 4-2

5.0 Presentation of modeling results ........................................................................................................... 5-1

Appendix A Basis for Source-Specifi c Sulfuric Acid Emissi ons.......................................................... A-1

Appendix B Estimated Emissions of Primary Total Carbon and Primary Sulfate From Coal-Fired Power Plants ..................................................................................................... ..B-1

i 113006 Bowen BART Determination Modeling Protocol.doc November 2006

## List of Tables

Table 2-1 Plant Bowen premiminary BART Cost Analysis Results ........................................................ 2-2

Table 2-2 Plant Bowen modeling emission parameters .......................................................................... 2-7



## List of Figures

Figure 1-1 Location of Class I Areas in Relation to Plant Bowen ............................................................. 1-2
Figure 2-1 Plant Bowen Unit 1 - Annualized Cost versus PM Removed ................................................. 2-3
Figure 2-2 Plant Bowen Unit 2 - Annualized Cost versus PM Removed ................................................. 2-3
Figure 2-3 Plant Bowen Unit 3 - Annualized Cost versus PM Removed ................................................. 2-4
Figure 2-4 Plant Bowen Unit 4 - Annualized Cost versus PM Removed ................................................. 2-4

ii 113006 Bowen BART Determination Modeling Protocol.doc November 2006

## 1.0 Introduction

1.1 Objectives

The Regional Haze Rule requires Best Available Retrofit Technology (BART) for any BART-eligible source that ‘‘emits any air pollutant which may reasonably be antici pated to cause or contribute to any impairment of visibility” in any mandatory Class I federal area. Purs uant to federal regulations, states have the option of exempting a BART-eligible source from the BART requirements based on dispersion modeling demonstrating that the source cannot reasonably be anti cipated to cause or contribute to visibility impairment in a Class I area. In addition, the Environmental Protection Agency (EPA) has promulgated a rule allowing states subject to the Clean Air Interstate Rule (CAIR) to determi ne that CAIR satisfies the BART requirements for SO 2 and NO x for electric generating units (EGUs). Preliminary feedback from the Georgia Environmental Protection Division indicates that they anticipate making the decision that CAIR satisfies BART for SO 2 and NO x for EGUs. Therefore, this modeling protocol focuses on performing the BART modeling analysis for particulate matter (PM) only.

Units 1, 2, 3, and 4 at Plant Bowen, located near Cartersville, which is owned and operated by Georgia Power Company, has been identified as a BART-eligible source. The purpose of this document is to summarize the procedures by which modeling analyses will be conducted for this source. Georgia Power has determined that Plant Bowen is subject to BART for PM. Therefor e, the procedures below will be used to evaluate the visibility improvement factor in the BART determin ation step (determination modeling). The modeling procedures are consistent with thos e outlined in the updated final VISTAS common BART modeling protocol (dated December 22, 2005, revision 3.2 – August 31, 2006), available at http://www.vistassesarm.org/BART/BARTModelingProtocol_rev3.2_31Aug2006.pdf . This source-specific BART modeling protocol references relevant portions of the common VISTAS modeling protocol.



### 1.2 Location of source vs. relevant Class I Areas

The Georgia Environmental Protection Division, whic h is in charge of the state’s BART program, has determined that Units 1, 2, 3, and 4 at Plant Bowen are BART-eligible for PM. Figure 1-1 shows a plot of Plant Bowen relative to nearby Class I Areas. There are five Class I areas within 300 km of the plant: Cohutta (84.8 km), Great Smoky Mountains (175.9 km), Joyce Kilme r (162.3 km), Shinning Rock (228.0 km), and Sipsey (223.5 km). Baseline modeling will be conducted for each of these Class I areas in accordance with the referenced VISTAS common BART modeling protocol and the procedures described in this source-specific BART modeling protocol. Vi sibility improvement modeling for the BART determination analysis will be performed for those Class I areas where the baseline modeling shows a greater than 0.5 deciview impact.



### 1.3 Organization of pr otocol document

Section 2 of this protocol describes the baseline to be used for the BART determination, identifies the PM emissions controls that will be model ed, and outlines the source emissions that will be used as input to the BART determination modeling. Section 3 describes th e input data to be used for the modeling including the modeling domain, terrain and land use, and meteorologica l data. Section 4 describes the air quality modeling procedures and Section 5 discusses how the modeling results will be presented. Since all of the references cited are also included in the VISTAS common BART mode ling protocol (Section 7.), no reference section is included in this document. Appendices A and B provid e additional information on the baseline source emissions.

113006 Bowen BART Determination Modeling Protocol.doc November 2006 1-1Figure 1-1 Location of Class I Areas in Relation to Plant Bowen

113006 Bowen BART Determination Modeling Protocol.doc November 2006 1-2

## 2.0 Source description and emissions data

2.1 BART determination baseline

SO 2 scrubbers have been permitted and are under construction for all four units at Plant Bowen. The scrubbers will go on line in 2008 for Units 3 and 4, 2009 for Unit 2, and 2010 for Unit 1. So, these scrubbers will be “existing” controls for Bowen well ahead of t he estimated implementation date for BART (~2014). It has been determined that impacts from Bowen 1-4 (the BART eligible source) will be greater than 0.5 deciview on at least one Class I area even with the PM emissions reduc tions that occur from scrubbers. In addition, using the scrubbers as baseline provides consistency that allo ws for straight forward (i.e., effect of emission reduction only) interpretation of results. That is, t here will be consistency in stack parameters and emissions that might otherwise confound interpretation of modeling results. Finally, this approach is consistent with the BART statutory factor that requires consideration of " any existing pollution control technology in use at the source." For these reasons, scrubbers on the Bowen units will be the starting poin t (baseline) for the PM BART determination and visib ility improvement modeling.


### 2.2 PM emissions contro ls to be modeled

Georgia Power has initiated the PM BART determinati on analysis for Bowen. Preliminary results include identification of technically feasible PM controls and performance of a remo val cost analysis for these controls. This preliminary cost analysis considers the installed capital and oper ating cost (including sorbent cost, where appropriate), capacity and energy penalties associated wi th station service impacts , PM species specific removal efficiencies for each control, emissions der ived based on 2003-2005 actuals adjusted by removal efficiencies, and financial assumptions consistent with the EPA Control Cost Manual and industry-accepted capital and operating cost estimates..

Table 2-1 summarizes preliminary cost analysis results for each technically feasible PM control. Due to space constraints, COHPAC (on all four uni ts) and the addition of a new electros tatic precipitator (ESP) collection field in a new case (on Units 3 and 4) were not considered. A detailed description of potentially available PM controls and their feasibility for Bowen and a detailed discussion of the cost analysis will be provided in the final BART determination analysis repor t to be submitted later. Furt her refinements to the analysis are possible, but it is not anticipated that the conclusions will be significantly different. As described in the EPA BART guidance, the data in Table 2-1 was used to creat e a graphical plot of the total annualized cost for the total PM emissions reductions for all feasible control alter natives (Figures 2-1 thru 2-4). A curve was fit to the data in order to identify a “least-cost envelope” of dominant control choices. Control options that lie inside of the least-cost envelope are considered inferior options based on cost because the cost per ton of particulate removed is inconsistent with ot her competing alternatives.

Figures 2-1 through 2-4 show that the dominant control c hoices for all four units in clude WESP, the addition of JuiceCan technology on existing tr ansformer/rectifier (T/R) sets, the particle agglomerator, and the combination of JuiceCan/particle agglomerator. Gas flow optimization, lime injection, the addition of a new electrical field (on Units 1 and 2), and the combinat ion of WESP/lime injectio n have been eliminated because they fall inside of the least-cost envelope. These opti ons are considered inferior based on cost and, therefore, have been eliminated from further consideration. Rath er than performing visibility improvement modeling for all of the remaining controls, modeling will be performed for two of the remaining options: (1) Agglomerator/Juice Can - the highest removal option of the set of relatively lower cost controls, and (2) Wet ESP - the remaining control with the overall highest total PM removal. This will bracket the overall visibility improvement results.



### 2.3 Unit-specific source data

The emissions data used to assess the vi sibility impacts at the Class I areas within 300 km of Plant Bowen are discussed in this section. As noted earlier, indications from the Georgia Environmental Protection Division are

113006 Bowen BART Determination Modeling Protocol.doc November 2006 2-1that they will issue rules stating that CAIR will suffice for EGU BART for SO 2 and NO x . Therefore, this protocol focuses only on PM 10 . Since various components of PM 10 emissions have different visibility extinction efficiencies, the PM 10 emissions are divided, or “speciated,” into several components (VISTAS common protocol Sections 4.3.3 and 4.4.2). The VISTAS protoc ol (Section 5.) allows for the use of source-specific emissions and speciation factors and/or default values from AP-42. The PM 10 emissions and speciation

Table 2- 1 Plant Bowen Preliminary BART Cost Analysis Results

Total PM10

Removed Annualized Cost Removal Cost Total PM10 Removed Annualized Cost Removal Cost Control Option Tons $/Yr $ per Ton Tons $/Yr $ per Ton Optimize Gas Flow 5.6 $125,600 $22,274 7.8 $125,600 $16,089 Juice Can Retrofit 11.3 $82,317 $7,299 15.6 $82,317 $5,272 Lime Injection 33.1 $2,714,893 $82,026 38.1 $2,714,893 $71,323 Agglomerator Retrofit 62.0 $1,236,108 $19,929 85.9 $1,236,108 $14,395 Add field in new casing 65.8 $3,433,973 $52,199 91.1 $3,433,973 $37,705 Agglomerator/JuiceCan 73.3 $1,318,425 $17,986 101.5 $1,318,425 $12,991 WESP 261.0 $13,102,142 $50,209 336.2 $13,102,142 $38,973 WESP/Lime Injection 271.0 $15,817,034 $58,375 347.4 $15,817,034 $45,527

Control Option Tons $/Yr $ per Ton Tons $/Yr $ per Ton Optimize Gas Flow 14.1 $161,486 $11,465 16.0 $161,486 $10,074 Juice Can Retrofit 21.1 $105,836 $5,009 24.0 $105,836 $4,402 Lime Injection 44.6 $3,490,576 $78,224 43.2 $3,490,576 $80,850 Agglomerator Retrofit 51.6 $1,589,281 $30,772 58.8 $1,589,281 $27,039 Agglomerator/JuiceCan 72.8 $1,695,117 $23,292 82.8 $1,695,117 $20,467 WESP 334.8 $16,845,611 $50,319 360.5 $16,845,611 $46,731 WESP/Lime Injection 348.2 $20,336,187 $58,396 373.6 $20,336,187 $54,440 Add field in new casing n/a n/a n/a n/a n/a n/a Bowen 3 Bowen 4 Bowen 1 Bowen 2

113006 Bowen BART Determination Modeling Protocol.doc November 2006 2-2Figure 2-1 Plant Bowen Unit 1 - Annualized Cost versus PM Removed

y = 162.81x 2 + 7738.6x - 6001.7

R 2 = 0.9997

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr]

Baseline: FGD WESP

Agglomerator Gas Flow Opt Lime New Field WESP/Lime

Agg/JuiceCan

JuiceCan

Figure 2-2 Plant Bowen Unit 2 - Annualized Cost versus PM Removed

y = 106.02x 2 + 3245.8x + 26909

R 2 = 0.9997

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr]

Baseline: FGD WESP

Agglomerator

Gas Flow Opt Lime New Field WESP/Lime

Agg/JuiceCan
JuiceCan

113006 Bowen BART Determination Modeling Protocol.doc November 2006 2-3Figure 2-3 Plant Bowen Unit 3 - Annualized Cost versus PM Removed

y = 76.74x 2 + 25591x - 325653

R 2 = 0.9988

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000 $20,000,000 $22,000,000 $24,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr]

Baseline: FGD WESP

Agglomerator

Gas Flow Opt Lime WESP/Lime

Agg/JuiceCan
JuiceCan

Figure 2-4 Plant Bowen Unit 4 - Annualized Cost versus PM Removed

y = 73.543x 2 + 21034x - 297166
R 2 = 0.9987

$- $2,000,000 $4,000,000 $6,000,000 $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000 $20,000,000 $22,000,000 $24,000,000

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 400.0 450.0 Incremental Total PM Reduction [tpy]Annualized Cost [$/yr]

Baseline: FGD WESP

Agglomerator

Gas Flow Opt Lime WESP/Lime

Agg/JuiceCan JuiceCan

113006 Bowen BART Determination Modeling Protocol.doc November 2006 2-4approach to be used for the modeling described in this pr otocol is indicated in the bullets below. Where default speciation values are used, the data represents a unit where curr ent (baseline) emission controls include ESPs and selective catalytic reducti on (SCR) systems, but no post-combustion SO 2 control equipment exists.

As indicated in Section 2.1, it has been determined that the baseline for the BART determination analysis and visibility improvement modeling will include scrubbers on all of the Bowen units. Therefore, the foundation for deriving the baseline and control option emissions fo r the BART determination modeling was to establish “maximum 24-hour average emission rates” based on the current configuration cons istent with the VISTAS common protocol and then to apply the species specific control efficiencies as appropriate. To establish emission rates for the BART determination baseline mode ling, scrubber control efficiencies were applied to the maximum 24-hour average rates.

• Total PM 10 is comprised of filterable and condensable emissions.

• Filterable PM 10 emissions are based on the highest stack test for the most recent 3-year period (2003- 2005). This stack test is combined with the highes t 24 hour heat input value for this period from CEMS data to calculate the “maximum 24-hour aver age emission rate” as required by the VISTAS protocol.

• Filterable PM 10 will be subdivided by size category consistent with the default approach from AP-42 Table 1-1.6, and as noted on pages 43 and 44 of the VISTAS common BART modeling protocol. The AP-42 Table 1-1.6 specifies for the emission controls indicated above that 55.6% of filterable PM 10 emissions is coarse (greater than 2.5 microns in size) and 44.4% is fine. Of the fine portion, 3.7% is elemental carbon and the remainder is inorganic fine particulates (soil).

• Condensable PM 10 consists of inorganic and organic compounds . The inorganic portion is by default assumed to be H 2 SO 4 , although other non-sulfate inorganic condensables could be present. The organic portion is modeled as organic aerosols.

• H 2 SO 4 emissions are calculated consis tent with the method used by Georgia Power to derive these emissions for TRI purposes. This approach assumes that the H 2 SO 4 emissions released from the stack are proportional to SO 2 emissions from combustion and are dependent on the fuel type and the removal of H 2 SO 4 by downstream equipment (i.e., ESP and ai r heater). For eastern bituminous coal the fundamental H 2 SO 4 release rate (without scrubbers or add-on PM controls) is in the range of 0.3 to 0.8% of the SO 2 emissions. Appendix A provides the basis for the site-specific values used.

• Emissions of condensable organics (the remaining portion of condensable PM 10 ) are derived based on the supporting field observational information in Appendix B and is estimated as 0.32% of SO 2 emitted.

• Coarse filterable particles (between 2.5 and 10 micr ons in size) will be modeled with a geometric mass mean diameter of 5 microns, while fine filterable and all condensable particles wi ll be modeled with a geometric mass mean diameter of 0.48 microns, consis tent with the CALPUFF default value for fine particles. The geometric standard deviation for both fine and coarse particles will be set to 2 microns, consistent with the CALPUFF default value. The 0.48 micron diameter value for fine particles comes from the default values in sample input files pr esented on the TRC web site. There is no default value presented for the coarse particles on the TRC web site. However, since 5 is the geometric mass mean diameter of 2.5 and 10 (the bounds of coarse particle sizes), it is a reasonable estimate for the geometric mass mean diameter for that class of particles.
In practice, CALPUFF allows for the user to input certain components of PM 10 as separate species and separate sizes, which will result in more accurate wet and dry deposition velocity results and also more accurate effects on light scattering. As noted above, th e particle size distribution information is provided in AP-42 Table 1-1.6, and will be used for the BART determination modeling.

113006 Bowen BART Determination Modeling Protocol.doc November 2006 2-5November 2006 2-6 113006 Bowen BART Determination Modeling Protocol.doc Table 2-2 provides a summary of the modeling emi ssion parameters to be used in the BART CALPUFF modeling, consistent with the source emissions data presented in Appendices A and B for the current configuration. The foundation for all of the emissions in Table 2-2 were derived from CEMS data for the 2003 to 2005 period and represent the maximum 24-hour aver age lb/hr rates (excluding days where startup, shutdown, or malfunctions occurred). For NO x and SO 2 the current configuration values are directly from CEMS. Filterable PM 10 emissions were calculated using the highest stack test over the 2003 to 2005 period and multiplying these values times the maximum 24-h our average heat input derived from CEMS. These values were then adjusted using AP-42 factor s from Table 1.1-6 that indicate that PM 10 is 67% of total PM for a pulverized coal unit with an ESP. PM 10 speciation was then performed as indicated above such that total Filterable PM 10 is made up of Coarse Soil plus total Fine PM and total Fine PM is m ade up of Fine Soil plus Elemental Carbon (EC). Since these units include SCRs , a consistent set of s easonal emissions data was developed representing periods with and without SCR oper ation. For these, the maximum 24-hour average rates were extracted from the seasonal (May - Sept ember) and non-seasonal (October - April) CEMS data. For visibility improvement modeling, only the e missions representing SCR operation was used as the foundation for establishing the baselin e (scrubbed) emission rates and the emission rates for PM controls under consideration.Table 2-2 Plant Bowen modeling emission parametersLocation UTM(Zone 16 NAD-83)Emissions1ParticleSpeciation2CaseSource/ UnitUTMEastUTMNorthActualStack HtBaseElev.FlueDiameterGasExitVel.StackGasExitTemp.SO2NOXPM10Filt.PM10CoarseSoilFinePMFine SoilECCond.PM10H2SO4Organicmmmmmm/sdegKlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrFUNDAMENTAL EMISSIONS DATAFundamental Data (Unit Basis)Current Config.Unit 1691,8933,778,033304.8219.87.620.8403.015374.221565.42510.21378.07210.20167.86161.656.21132.1482.9549.20Current Config.Unit 2691,8933,778,033304.8219.87.620.8403.016059.381253.44545.86407.51226.58180.94174.246.69138.3586.9651.39Current Config.Unit 3691,8933,778,033304.8219.87.627.1409.718519.37773.00478.86268.01149.01119.00114.594.40210.85151.5859.26Current Config.Unit 4691,8933,778,033304.8219.87.627.1409.719504.06971.92521.14297.26165.28131.98127.104.88223.88161.4762.41BART DETERMINATION BASELINE EMISSIONSScrubber Baseline Data (Unit Basis)BaselineUnit 1691,8933,778,033207.5219.57.612.5327.0768.711565.42174.5875.6142.0433.5732.331.2498.9749.7749.20BaselineUnit 2691,8933,778,033207.5219.57.612.5327.0802.971253.44185.0781.5045.3236.1934.851.34103.5652.1751.39BaselineUnit 3691,8933,778,033207.5219.57.615.8327.0925.97773.00173.0353.6029.8023.8022.920.88119.4360.1659.26BaselineUnit 4691,8933,778,033207.5219.57.615.8327.0975.20971.92185.1059.4533.0626.4025.420.98125.6563.2462.41Scrubber Baseline Data (Stack Basis)ModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85359.64157.1287.3669.7667.182.58202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92358.13113.0562.8650.2048.341.86245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1845.3219.8011.018.798.460.3325.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8645.1214.247.926.326.090.2330.8815.5515.33November 20062-7113006 Bowen BART Determination Modeling Protocol.docTable 2-2 (Continued) Plant Bowen modeling emission parametersLocation UTM(Zone 16 NAD-83)Emissions1ParticleSpeciation2CaseSource/ UnitUTMEastUTMNorthActualStack HtBaseElev.FlueDiameterGasExitVel.StackGasExitTemp.SO2NOXPM10Filt.PM10CoarseSoilFinePMFine SoilECCond.PM10H2SO4Organicmmmmmm/sdegKlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrEMISSIONS for MODELED CONTROL OPTIONSAgglomerator/Juice Can Data (Unit Basis)Agglom/JCUnit 1691,8933,778,033207.5219.57.612.5327.0768.711565.42145.0946.1225.9820.1419.400.7598.9749.7749.20Agglom/JCUnit 2691,8933,778,033207.5219.57.612.5327.0802.971253.44153.2849.7228.0021.7120.910.80103.5652.1751.39Agglom/JCUnit 3691,8933,778,033207.5219.57.615.8327.0925.97773.00156.4136.9923.4213.5713.060.50119.4360.1659.26Agglom/JCUnit 4691,8933,778,033207.5219.57.615.8327.0975.20971.92166.6741.0225.9815.0514.490.56125.6563.2462.41Agglomerator/Juice Can Data (Stack Basis) - Unit 1 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85330.16127.6371.3056.3354.252.08202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92358.13113.0562.8650.2048.341.86245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1841.6016.088.987.106.840.2625.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8645.1214.247.926.326.090.2330.8815.5515.33Agglomerator/Juice Can Data (Stack Basis) - Unit 2 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85327.86125.3370.0555.2853.242.05202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92358.13113.0562.8650.2048.341.86245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1841.3115.798.836.976.710.2625.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8645.1214.247.926.326.090.2330.8815.5515.33November 20062-8113006 Bowen BART Determination Modeling Protocol.docTable 2-2 (Continued) Plant Bowen modeling emission parametersLocation UTM(Zone 16 NAD-83)Emissions1ParticleSpeciation2CaseSource/ UnitUTMEastUTMNorthActualStack HtBaseElev.FlueDiameterGasExitVel.StackGasExitTemp.SO2NOXPM10Filt.PM10CoarseSoilFinePMFine SoilECCond.PM10H2SO4OrganicAgglomerator/Juice Can Data (Stack Basis) - Unit 3 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85359.64157.1287.3669.7667.182.58202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92341.5196.4456.4839.9638.481.48245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1845.3219.8011.018.798.460.3325.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8643.0312.157.125.044.850.1930.8815.5515.33Agglomerator/Juice Can Data (Stack Basis) - Unit 4 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85359.64157.1287.3669.7667.182.58202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92339.7094.6255.7838.8537.411.44245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1845.3219.8011.018.798.460.3325.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8642.8011.927.034.894.710.1830.8815.5515.33November 20062-9113006 Bowen BART Determination Modeling Protocol.docTable 2-2 (Continued) Plant Bowen modeling emission parametersLocation UTM(Zone 16 NAD-83)Emissions1ParticleSpeciation2CaseSource/ UnitUTMEastUTMNorthActualStack HtBaseElev.FlueDiameterGasExitVel.StackGasExitTemp.SO2NOXPM10Filt.PM10CoarseSoilFinePMFine SoilECCond.PM10H2SO4OrganicWet ESP Data (Unit Basis)Wet ESPUnit 1691,8933,778,033207.5219.57.612.5327.0768.711565.4266.717.564.203.363.230.1259.159.9549.20Wet ESPUnit 2691,8933,778,033207.5219.57.612.5327.0802.971253.4469.988.154.533.623.480.1361.8210.4351.39Wet ESPUnit 3691,8933,778,033207.5219.57.615.8327.0925.97773.0076.655.362.982.382.290.0971.2912.0359.26Wet ESPUnit 4691,8933,778,033207.5219.57.615.8327.0975.20971.9281.015.953.312.642.540.1075.0612.6562.41Wet ESP Data (Stack Basis) - Unit 1 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85251.7889.0649.5239.5438.081.46162.7262.13100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92358.13113.0562.8650.2048.341.86245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1831.7211.226.244.984.800.1820.507.8312.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8645.1214.247.926.326.090.2330.8815.5515.33Wet ESP Data (Stack Basis) - Unit 2 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85244.5583.7646.5737.1935.811.38160.7960.20100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92358.13113.0562.8650.2048.341.86245.08123.40121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033304.8219.514.212.5327.0198.03355.1830.8110.555.874.694.510.1720.267.5912.67Stack 23&4691,8933,778,033304.8219.514.215.8327.0239.55219.8645.1214.247.926.326.090.2330.8815.5515.33Wet ESP Data (Stack Basis) - Unit 3 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85359.64157.1287.3669.7667.182.58202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92261.7664.8136.0428.7827.711.06196.9475.27121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1845.3219.8011.018.798.460.3325.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8632.988.174.543.633.490.1324.819.4815.33November 20062-10113006 Bowen BART Determination Modeling Protocol.doc113006 BowLocation UTM(Zone 16 NAD-83)Emissions1ParticleSpeciation2CaseSource/ UnitUTMEastUTMNorthActualStack HtBaseElev.FlueDiameterGasExitVel.StackGasExitTemp.SO2NOXPM10Filt.PM10CoarseSoilFinePMFine SoilECCond.PM10H2SO4OrganicWet ESP Data (Stack Basis) - Unit 4 Only ControlledModeledStk Ht3Eq.Dia.mmmmmm/sdeg Klbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrlbs/hrStack 11&2691,8933,778,033207.5219.514.212.5327.01571.682818.85359.64157.1287.3669.7667.182.58202.53101.94100.59Stack 23&4691,8933,778,033207.5219.514.215.8327.01901.171744.92254.0359.5533.1126.4425.460.98194.4972.81121.67Stack Basis Emissions Converted to g/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secg/secStack 11&2691,8933,778,033207.5219.514.212.5327.0198.03355.1845.3219.8011.018.798.460.3325.5212.8412.67Stack 23&4691,8933,778,033207.5219.514.215.8327.0239.55219.8632.017.504.173.333.210.1224.519.1715.332Elemental carbon (EC) and Fine PM are a part of Filterable PM10and H2SO4and Organics are a part of Condensable PM10. Note that H2SO4is input to CALPUFF asSO4. The molecular weights of H2SO4and SO4are 98 and 96 respectively; therefore the conversion factor from H2SO4to SO4is 96/98.1SO2and NOxemissions are not BART-applicable for EGU sources in CAIR states, if the state agency agrees with EPA’s interpretation of theBART final rule. Theemissions for SO2and NOxare provided for information purposes, and for reference inthe computation of certain particle species such as H2SO4.November 20062-11en BART Determination Modeling Protocol.doc3Stack credit is equal to actual stack height since this stack is grandfathered.Table 2-2 (Continued) Plant Bowen modeling emission parametersNotes:

## 3.0 Input data to the CALPUFF model

3.1 General modeling procedures:

VISTAS has developed five sub-regional 4-km CALMET meteorological databases for three years (2001- 2003) (VISTAS common protocol Section 4.4.2). Th e sub-regional modeling domains are strategically designed to cover all potential BART eligible sources with in VISTAS states and all PSD Class I areas within 300 km of those sources (to the nearest edge). The ext ents of the 4-km sub-regional domains are shown in Figure 4-4 of the VISTAS common BART modeling protoc ol. The BART modeling for Plant Bowen will be done using the 4-km subdomain 4.

USGS 90-meter Digital Elevation Model (DEM) files were used by VISTAS to generate the terrain data at 4-km resolution for input to the 4-km sub-regional CALMET run. Likewise, USGS 90-meter Composite Theme Grid (CTG) files were used by VISTAS to generate the land us e data at 4-km resolution for input to the 4-km subregional CALMET run.

Three years of MM5 data (2001-2003) were used by VISTAS to generate the 4-km sub-regional meteorological datasets. See Sections 4.3.2 and 4. 4.2 in the VISTAS common BART modeling protocol for more detail on these issues.

It is intended that all of the modeling for Plant Bowen w ill use the 4-km subdomain 4. However, if the results indicate that the modeling could be improved with a CA LPUFF run using a finer grid, then refinements in the modeling procedures will be considered and the Georgia Environmental Protection Division will be asked to approve these refinements.

In the event that a finer grid resolution is used, CALM ET must be rerun. Other modifications to inputs of CALMET would include the extent of the modeling domain, the resolution of the terrain and land use data, and other relevant settings. The same MM5 data and obs ervations as used for the 4-km sub-regional CALMET simulations would be used. The extent of the modeling domain may need to be changed because of disk space restrictions. The size of the CALMET output is di rectly proportional to the grid resolution of the run. The domain would be limited to the source and the exclus ive Class I area(s) being assessed with a higher grid resolution, including a 50-km buffer in all directions.

If CALMET needs to be run at even a finer grid resoluti on, then the appropriate model setting/files (specifically the GEO.DAT file) will be modified. A summary of th ese modifications would be provided to the Georgia Environmental Protection Divi sion for review and approval.



### 3.2 Air quality databa se (background ozone and ammonia)

Hourly measurements of ozone from all non-urban moni tors, as generated by VISTAS and available on the VISTAS CALPUFF page on the Earth Tech web site (http://www.src.com/verio/download/sample_files.htm), will be used as input to CALPUFF. For ammonia, a 0.5 ppb background value as recommended by VISTAS will be used. However, since only PM emissions are being modeled, ozone and ammonia data is not really needed given that this data has no effect on PM results in CALPUFF.



### 3.3 Natural conditions and monthl y f(RH) at Class I Areas

For each of the applicable Class I areas, natural bac kground conditions must be established in order to determine a change in natural conditions related to a s ource’s emissions. The modeling described by this protocol document intends to use annual average natura l background light extinction (EPA 2003 values).

113006 Bowen BART Determination Modeling Protocol.doc November 2006 3-1To determine the input to CALPUFF, it is first necessa ry to convert the deciviews to extinction using the equation:

Extinction (Mm -1 ) = 10 exp(deciviews/10).

For example, the EPA guidance document indicates for Great Smoky Mountains National Park that the deciview value for the average of the days is 7.60. This is equivalent to an ex tinction of 21.38 inverse megameters (Mm -1 ).

This extinction includes the default 10 Mm-1 for Ra yleigh scattering. The remain ing extinction is due to naturally occurring particles, and should be held constant fo r the entire year’s simulation. Therefore, the data provided to CALPOST for Great Smoky Mountains woul d be the total natural background extinction minus 10 (expressed in Mm -1 ), or 11.38. This is most easily i nput as fine soil concentrations (11.38 μ g/m 3 ) in CALPOST, since the extinction efficiency of soil (PM-fine) is 1. 0 and there is no f(RH) component. The concentration entries for all other particle constituents would be set to zero, and the fine soil concentration would be kept the same for each month of the year. The monthly values for f(RH) that CALPOST needs will be taken from "Guidance for Tracking Progress Under the Regional Haze Rule" (EPA, 2003) Appendix A, Table A-3.

113006 Bowen BART Determination Modeling Protocol.doc November 2006 3-2

## 4.0 Air quality modeling procedures

This section provides a summary of the modeling procedur es outlined in the VISTAS pr otocol that will be used for the refined CALPUFF analysis to be conducted for Plant Bowen.



### 4.1 Model selection and features

As noted in the VISTAS protocol (Summary, Reco mmendations Section II.), VI STAS will use CALPUFF Version 5.754 and CALMET Version 5.7, which can be obtained at http://www.src.com/verio/download/download.htm#VISTAS_VERSION . These versions contain enhancements funded by the Minerals Management Service (MMS) and VISTAS. They are maintained on TRC’s Atmospheric Studies Group CALPUFF website fo r public access. This release includes CALMET, CALPUFF, CALPOST, CALSUM, and PO STUTIL as well as CALVIEW.

The major features of the CALPUFF modeling system , including those of CALMET and the post processors (CALPOST and POSTUTIL), are referenced in Section 3 of the VISTAS protocol.

The baseline BART modeling will be c onducted for Bowen Units 1 thru 4 (BART eligible units) for each Class I area within 300 km of the source. Unit 1 thru 4 will each be modeled separately for the visibility improvement modeling for the BART determination step for the Clas s I areas where baseline modeling shows a greater than 0.5 deciview impact.



### 4.2 Modeling domain and receptors

The initial Plant Bowen BART runs will use the sub-domain 4, 4-km CA LMET data supplied by VISTAS, as discussed above. This domain includes all Class I areas wi thin 300 km of the source, plus a 50-km buffer. If there is the need for a refined analysis with a finer grid, a supplement to this modeling protocol will be provided describing the proposed procedures.

The receptors used for each of the Class I areas are based on the NPS database of Class I receptors, as recommended by the VISTAS common protocol (Section 4.3.3).



### 4.3 Technical options us ed in the modeling

CALMET modeling for the VISTAS-provided 4-km subdomai ns will be performed per the procedures specified in the VISTAS common BART modeling protocol. If it is decided to conduct additional modeling with a finer grid than 4 km, this modeling protoc ol will be updated to specify the te chnical options to be used in the CALMET run, in order to allow for state agency review and approval.

For CALPUFF model options, Plant Bowen will follow the VISTAS common BART modeling protocol (Section 4.4.1), which states that we should use IWAQM (EPA, 1998) guidance. The VISTAS pr otocol (Section 4.3.3) also notes that building downwash effects are not required to be included unless the state directs the source to include these effects. Since Plant Bowen is more than 50 km from the nearest Class I area, building downwash effects will not be included in the CALPUFF modeling.

The POSTUTIL utility program (VISTAS common protocol Section 4.4.2) will be used to repartition HNO 3 and NO 3 using VISTAS-provided ammonia concentrations der ived from previous 2002 CMAQ modeling conducted by EPA or the alternate ammonia concentrations approach recommended by VISTAS, if the CMAQ data is unavailable. As indicated earlier, since only PM em issions are being modeled, the treatment of ammonia should not have an affect on PM results from CALPUFF.

113006 Bowen BART Determination Modeling Protocol.doc November 2006 4-1

### 4.4 Light extinction and haze impact calculations

The CALPOST postprocessor will be used as prescribed in the VISTAS protocol for the calculation of the impact from the modeled source’s prima ry and secondary particulate matter concentrations on light extinction. The formula that is currently used in CALPOST is the existing (not the November 2005 revised) IMPROVE/EPA formula, which is applied to determine a change in light extinction due to increases in the particulate matter component concentra tions. Using the notation of CALPOST, the formula is the following:

b ext = 3 f(RH) [(NH 4 )2SO 4 ] + 3 f(RH) [NH 4 NO 3 ] + 4[OC] + 1[Soil] + 0.6[Coarse Mass] + 10[EC] + b Ray

The concentrations, in square brackets, are in ug/m 3 and b ext is in units of Mm -1 . The Rayleigh scattering term (b Ray ) has a default value of 10 Mm -1 , as recommended in EPA guidance for tracking reasonable progress (EPA, 2003a).

The extinction formula shown above is known to be inadequat e in its representation of light extinction from sea salt and its usage of 1.4 as the organic mass to carbon mass ratio. Furthermore, guidance for this formula did not provide for site-specific Rayleigh scattering. In December of 2005, the IMPROVE Steering Committee adopted a new formula for determining light extinction that addresses these and other shortcomings. The new formula is shown below..



### )]([33.0 ) ( ] [6.0 ][)(7.1 ][1 ] [10 ] [1.6] [8.2 ] [)(1.5] [)(4.2 ] [)(8.4] [)(2.2

2 

### ppbNO SpecificSite Scattering Rayleigh Mass Course SaltSeaRHf SoilFine Carbon

Elemental Mass OrganicrgeLa Mass Organic Small NitratergeLaRHf Nitrate Small RHf SulfatergeLaRHf Sulfate Small RHf b

SS L S L S ext



### ×+ + ×+ ××+ ×+ ×+ ×+ ×+ ××+ ××+ × × + ××≈

The apportionment of the total concentration of sulfat e compounds into the concentrations of the small and large size fractions is accomp lished using the following equations.

3
3 

### /20] [], [ /20 ] [ ] [ mg Sulfate Total Sulfate Total mg Sulfate Total SulfatergeLa μ μ < × =

3 

### /20] [], [] [ mg Sulfate Total Sulfate Total SulfatergeLa μ ≥ =

] [] [] [ SulfatergeLa Sulfate Total Sulfate Small

− =

This revised version of the IMPROVE Equation will be us ed to calculate visibility improvement results for the BART determination modeling. Dr. Ivar Tombach (V ISTAS consultant) has produced a spreadsheet tool (September 29, 2006) to allow the new IMPROVE formul a results to be derived from the basic CALPOST

113006 Bowen BART Determination Modeling Protocol.doc November 2006 4-2outputs. Also, since the BART determinati on modeling is focused only on PM, NO 2 will be set to zero in the new formula. For informational purposes only, resu lts from the old equation will also be presented.

The assessment of visibility impacts at the Class I areas will use CALPOST Method 6 (VISTAS common protocol Section 4.3.2). Each hour’s source-caused exti nction is calculated by first using the hygroscopic components of the source-caused concentrations, due to ammonium sulfate and nitrate, and monthly Class I area-specific f(RH) values. The cont ribution to the total source-caused extinction from ammonium sulfate and nitrate is then added to the other, non-hygroscopic com ponents of the particulate concentration (from coarse and fine soil, secondary organic aerosols, and from elemental carbon) to yi eld the total hourly source-caused extinction.

The BART rule significance threshold for the contribution to visibility impairment is 0.5 deciviews. The VISTAS protocol (Section 4.3.2) indicates that with the use of the 4-km sub-regional CALMET database, a source does not cause or contribute to visibility impairment if the 98th percentile (or 8th highest) day’s change in extinction from natural conditions does not exceed 0.5 deciviews for any of the modeled years (an added check is: the 22nd highest prediction over the three years modeled should also not exceed 0.5 deciviews for a source to be exempted from a BART determination). Both the 98th percentile (or 8th highest) day's change in extinction from natural conditions for any modeled year and the 22nd highest prediction over the three years modeled will be evaluated.

Figure 4-1 of the VISTAS common BAR T modeling protocol presents a fl ow chart showing the components of that common protocol. The modeling for Plant Bowen will focus on Subregional Fine-Scale modeling as depicted in the lower half of the figure.

The source will perform BART deter mination modeling for the baseline and each control option in the manner described in this document.

113006 Bowen BART Determination Modeling Protocol.doc November 2006 4-3

## 5.0 Presentation of modeling results

The BART determination modeling results for Plant Bow en will be provided to the state agency in a manner as described in the VISTAS protocol (Section 4.5). The results will include the following elements (as suggested in the VISTAS protocol):

1. A map of the source location and Class I areas within 300 km of the source.

2. For the CALPUFF modeling domain, a table listing all Class I areas in the VISTAS domain and those in neighboring states and impacts from the BART 4-km grid baseline modeling at those Class I areas within 300 km of the source, as illust rated in Table 4-3 of the VISTAS protocol.

3. Identify from the baseline modeli ng the number of Class I areas wi th visibility impairment due to source emissions for the 98th percentile days in each year (and the 98th percentile over all three years modeled) greater than 0.5 dv.

4. For the Class I area with the maximum impact, identification of the number of days beyond those excluded (e.g., the 98th percentile for refined analyses) that the impact of the source exceeds 0.5 dv, the number of receptors in the Class I area where the impact exceeds 0.5 dv, and the maximum impact.

The BART determination modeling will be performed for those Class I areas shown in the baseline modeling to exceed 0.5 dv impact. The results pr esented will be a comparison of the 98 th percentile value for the baseline and each control option and emissions unit derived as is outlined above for the baseline modeling. A summary of the relative results among all emission scenarios run would be produced.

Additionally, the appropriate electronic files used to conduct the CALPUFF modeling will be submitted on CDROM or DVD media.

113006 Bowen BART Determination Modeling Protocol.doc November 2006 5-1

## Appendix A

Basis for Source-Specific Sulfuric Acid Emissions

113006 Bowen BART Determination Modeling Protocol.doc November 2006 A-1

### Appendix A

Basis for Source-Specific Sulfuric Acid Emissions

Sulfuric Acid (H2SO4) Emissions

During the combustion of sulfur-containing fuels, a percentage of the SO 2 formed is further oxidized to SO 3

. As the flue gas cools across the air heater, this SO 3 combines with flue gas mo isture to form vapor-phase and/or condensed sulfuric acid (H 2 SO 4 ). The H 2 SO 4 emissions shown in Table 2-1 of this BART modeling protocol were calculated consistent with the method used by Southern Company to derive these emissions for Toxics Release Inventory (TRI) purposes. This method is documented in a repor t titled Estimating Total Sulfuric Acid Emissions from Stationary Power Plants: Revision 3 (2005) prepared by Keith Harrison and Dr. Larry Monroe (Southern Company Services) and Edward Cichanowicz (Consultant). The approach described in this report assumes that H 2 SO 4 emissions released from the stack are proportional to SO 2 emissions from combustion and are dependent on the fuel type and the removal of H 2 SO 4 by downstream equipment (i.e., ESP and air heater) and add-on emissions control equipment (scrubber).

Since this facility contains post-combustion NOx control (SCR), the baseline sulfuric acid emissions estimate accounts for the manufacture of H 2 SO 4 through combustion and through further oxidation of SO 2 in the SCR. Calculated sulfuric acid releases then account for lo ss or removal within the system. The equations below show how the manufacture and release calculations are made. Table A-1 shows the resulting H 2 SO 4 emissions calculations .

Sulfuric Acid Manufactured from Combustion (EMComb): EMComb = K x F1 x E2 where, EMComb = total sulfuric acid manu factured from combustion, lbs/yr K = Molecular weight and units conversion constant = 98.07 / 64.04 * 2000 = 3,063 (98.07 = Molecular weight of sulfuric acid; 64.04 = Molecular weight of SO2; Conversion from tons per year to pounds per year – multiply by 2000.) F1 = Fuel Impact Factor (from the emissions estimating report) E2 = Sulfur dioxide emission s, tons (from CEMS data).

Sulfuric Acid Released from Combustion (ERComb) ERComb = EMComb x F2 (technology impact factors for air heater and ESP) ERComb = EMComb x (0.49) x (0.49)

Sulfuric Acid Manufactured by SCR (EMSCR) EMSCR = K * S2 * fs * E2 where, EMSCR = Total sulfuric acid manufactured from SCR, lbs per year K = Conversion factor = 3063 S2 = SCR catalyst SO2 oxidation rate (specified as a decimal) fs = Operating factor of SCR system, fraction of coal burn when SCR operates E2 = SO2 produced, tons per year

Sulfuric Acid Released from SCR (ERSCR) ERSCR = [EMSCR – (Ks * B * fs * SNH3)] * F2x where, ERSCR = Total sulfuric acid released from SCR, lbs per year EMSCR = Total sulfuric acid manufactured from SCR, lbs per year Ks = Conversion factor = 3799 B = Coal burn in TBtu/hr

113006 Bowen BART Determination Modeling Protocol.doc November 2006 A-2November 2006 A-3 113006 Bowen BART Determination Modeling Protocol.doc fs = Operating factor of SCR system, fraction of coal burn when SCR operates SNH3 = NH3 slip from SCR, ppmv at 3% O2 F2x = Technology Impact Factors, all that apply

Sulfuric Acid Manufactured from Flue Gas Conditioning (EMfgc): EMfgc = Ke x B x fe x Is where, EMfgc = total sulfuric acid manufactured fr om flue gas conditioning system, lbs/hr Ke = Conversion Factor = 3,799 B = Coal burn in TBtu/hr fe = Operating factor of FGC system, fraction of coal burned when FGC operates Is =SO3 injection rate in ppmv at 6% O2, wet

Sulfuric Acid Released from FGC (ERfgc) ERfgc = [EMfgc – (Ke * B * fe * I NH3 )] * F3 x F2 where, ERfgc = Total sulfuric acid released from FGC, lbs per hour EMfgc = Total sulfuric acid manufactured from FGC, lbs per hour Ke = Conversion factor = 3799 B = Coal burn in TBtu/hr fe = Operating factor of FGC system, fraction of coal burn when FGC operates I NH3 = NH3 injection for dual flue gas conditioning system, ppmv at 6% O2, wet (= 0 if no NH3 used) F3 = Technology Impact Factors for FGC F2 = Technology Impact Factors for equipment after ESP only If no control after ESP, F2 = 1

Total Sulfuric Acid Released (TSAR): TSAR = ERComb + ERSCR + ERfgc[Bowen 3 and 4 only]November 2006A-4006 Bowen BART Determination Modeling Protocol.docTable A-1 Plant Bowen sulfuric acid calculationsCaseSource/ UnitSO2Conv.FactorFuelImpactFactorManuffromCombut.APHFactorESPFactorReleasedFromCombust.SCROxid.RateSCROp.FactorManuf bySCRCoal Burn, BConv.FactorNH3SlipReleas.fromSCRlbs/hrKF1lbs/hrF2F2lbs/hrS2fslbs/hrTBtu/hrKsSNH3lbs/hrECNSUnit 117069.430630.008209.10.490.4950.20.007500.00.0068437990.750.0ECSUnit 115374.230630.008188.40.490.4945.20.00751176.60.0068437990.7537.7BaselineSUnit 115374.230630.008188.40.490.4945.20.00751176.60.0068437990.7537.7ECNSUnit 217247.630630.008211.30.490.4950.70.007500.00.0066937990.750.0ECSUnit 216059.430630.008196.80.490.4947.20.00751184.50.0066937990.7539.7BaselineSUnit 216059.430630.008196.80.490.4947.20.00751184.50.0066937990.7539.7ECNSUnit 320652.430630.008253.00.490.4960.80.007500.00.0077237990.750.0ECSUnit 318519.430630.008226.90.490.4954.50.00751212.70.0077237990.7545.8BaselineSUnit 318519.430630.008226.90.490.4954.50.00751212.70.0077237990.7545.8ECNSUnit 420097.230630.008246.20.490.4959.10.007500.00.0084337990.750.0ECSUnit 419504.130630.008239.00.490.4957.40.00751224.00.0084337990.7548.0BaselineSUnit 419504.130630.008239.00.490.4957.40.00751224.00.0084337990.7548.0EC= Existing Configuration (i.e., no scrubber) Baseline=Scrubbed NS = No SCR Operation S= SCR OperationCaseSource/ UnitConv.FactorFGCOp.FactorSO3InjectionRateManufbyFGCTechImpactFactorforFGCTechImpactFactor forEquipafter ESPNH3InjectionRateReleasedFrom FGCTotalReleasedwithoutScrubberRemovalRate forScrubberTotalReleasedafterScrubberKefeIslbs/hrF3F2INH3lbs/hrlbs/hr%lbs/hrECNSUnit 1n/an/an/an/an/an/an/an/a50.2ECSUnit 1n/an/an/an/an/an/an/an/a83.0BaselineSUnit 1n/an/an/an/an/an/an/an/a83.04049.8ECNSUnit 2n/an/an/an/an/an/an/an/a50.7ECSUnit 2n/an/an/an/an/an/an/an/a87.0BaselineSUnit 2n/an/an/an/an/an/an/an/a87.04052.2ECNSUnit 337991.07.0205.20.251.00.051.3112.1ECSUnit 337991.07.0205.50.251.00.051.3151.6BaselineSUnit 3n/an/an/an/an/an/an/an/a100.34060.2ECNSUnit 437991.07.0224.30.251.00.056.1115.2ECSUnit 437991.07.0224.30.251.00.056.1161.5BaselineSUnit 4n/an/an/an/an/an/an/an/a105.44063.2EC= Existing Configuration (i.e., no scrubber) Baseline=Scrubbed NS = No SCR Operation S= SCR Operation113

## Appendix B

Estimated Emissions of Primary Total Carbon and Primary Sulfate From Coal-Fired Power Plants

[The above titled paper is include d as a separate document along with this site specific BART modeling protoc ol. This paper was prepared for Southern Company by Er ic S. Edgerton of Atmospheric Research & Analysis, Inc.]

113006 Bowen BART Determination Modeling Protocol.doc November 2006 B-1

### Estimated Emissions of Condensab le Carbon and Condensable SO 3


### From Coal-Fired Power Plants

Prepared for

John J. Jansen
Southern Company

Birmingham, AL

Prepared by

Eric S. Edgerton
Atmospheric Research & Analysis, Inc.
Cary, NC

June 11, 20062 

### ABSTRACT

Data from the SEARCH network were used to estimate condensable carbon and condensable

SO 3 

### emissions from coal-fired power plan ts (CFPPs). Conti nuous trace gas and PM 2.5



### measurements were used to identify CFPP plumes and to quantify incremental fine particulate

total carbon (TC) and fine particulate total sulfate (SO 4 

### ) during the period October 2005-May

2006. As measured in the field, incremental TC includes emitted particulate OC, particulate EC

and condensable carbon as well as seconda ry organic aerosol (SOA). Incremental SO 4 

### includes

emitted particulate SO 4 

### , condensable SO 3 

### , and secondary SO 4. 

### As such, TC and SO 4 

### provide

upper bounds for CFPP emissions of c ondensable carbon and condensable SO 3 

### . Plume events

were selected so as to avoid confounding of TC and SO 4 

### signals by other sources, and to

minimize in-plume production of secondary SO 4 

### and SOA. Results are presented as ratios

relative to SO 2 

### , for example, pounds TC per pound SO 2 

### (lb TC/lb SO 2 

### ,). Plume increments can

be interpreted as emission ratios for TC and primary SO 4 

### . For TC, 14 plume events from 4 sites

and 7 CFPPs exhibited sufficiently stable data fo r analysis. Of these, 11 events yielded an

average TC/SO 2 

### emission ratio of 3.2 x 10 -3 

### lb/lb (range 1.1 x 10 -3 

### to 6.6 x 10 -3 

### ). In other words,

TC emissions represented about 0.32 percent of SO 2 

### emissions, on a mass basis. The 3

remaining events yielded negative emission ratios using the default approach, and an average

emission ratio of 1.5 x 10 -3 

### using an alternate approach. For SO 4 

### , a total of 20 events from 4

sites and 8 CFPPs were analyzed. Results showed an average SO 4 

### /SO 2 

### emission ratio of 6.4 x

10 -3 

### lb/lb (range 2.1 x 10 -3 

### to 15.0 x 10 -3 

### ). On average, SO 4 

### was found to represent about 0.64

percent of SO 2 

### emissions during the study period. Inferre d emission ratios should be considered

upper bound estimates because: 1) the measuremen ts include, in addition to the condensable3 

### carbon and condensable SO 3 

### emissions of interest, primary particulate carbon (EC and OC) and

primary particulate sulfate emitted by the CFPP; 2) may include secondary carbon and

secondary sulfate produced in the atmosphere; and 3) could be in flated due to preferential loss

of SO 2 

### from the plume (due to conversion and/or dr y deposition) in transit from the CFPP to the

research site.

INTRODUCTION

The Southeastern Aerosol Research and Characterization Study (SEARCH) was designed to

provide extensive, long-term data on the s ources and chemical characteristics of PM 2.5 

### and

PM coarse 

### for the southeastern U. S. SEAR CH is unique in that continuous PM 2.5 

### measurements of

all major components are made at urban/rural pairs of sites in and around four southeastern U. S.

cities. In conjunction with co-measured me teorological and trace gas data, continuous PM 2.5



### measurements provide opportuniti es for: (1) investigating sources and physico-chemical

dynamics of PM 2.5 

### ; (2) evaluating chemical transport an d transformation models; (3) assessing

the effectiveness of emissions reduction programs; and (4) examining rela tionships between PM

mass and composition and vari ous health end points.

CFPPs emit three forms of primary particulate carbon to the atmosphere: filterable organic

carbon (OC), filterable elemental carbon (EC) and condensable carbon. OC and EC are emitted

as particles, while condensable carbon is emitted in the vapor phase and is presumed to condense

rapidly onto pre-existing particles. These th ree forms of carbon, plus secondary organic aerosol4 

### (SOA), are measured collectively in the SE ARCH network, as total carbon (TC), using

continuous measurement techniques. CFPPs also emit two forms of primary particulate sulfate:

filterable sulfate and condensable sulfur trioxide (SO 3 

### ). In the atmosphere, condensable SO 3



### reacts more or less instantaneously with water va por to produce particulate sulfate. These forms

of sulfate, plus secondary sulfate from oxidation of SO 2 

### , are also measured in the SEARCH

network using continuous techniques.

This report uses SEARCH data to: (1) identify CFPP plumes obser ved at numerous sites during

the fall of 2005 through spring of 2006; and, (2) calcula te total carbon (TC) and total sulfate

(SO 4 

### ) associated with such plumes. Results are us ed to estimate CFPP emi ssion ratios of TC and

SO 4 

### , relative to SO 2 

### . Given that the measurement techni ques do not discriminate between the

various form of particulate car bon and particulate sulfate present in the plume, results can be

used as upper bound estimates of emission ratios for condensable carbon and condensable SO 3



### .

EXPERIMENTAL

Continuous measurements of trace gases fine particulate TC and fine particulate SO 4 

### were made

at the Southeastern Aerosol Research and Char acterization (SEARCH) s ites shown in Figure 1.

Analyzable plume events were observed at 5 of the 8 SEARCH sites between early October 2005

and early May 2006: Yorkville, GA; Jefferson Street, GA; Cent reville, AL; OLF, FL; and

Gulfport, MS. Brief descriptions for these 5 sites are provided below.5 

### Yorkville, GA - Yorkville (lat. 33.9283 N, long. 85.0456 W) is a rural/agricultural site 55 km

WNW and 40 km SSW of Atlanta, GA and Rome, GA, respectively. The site is on a broad ridge

(elev. 395 m) in a large (>150 ha) clearing devoted largely to pastur e. CFPPs in th e vicinity of

Yorkville are shown in Figure 2.

Centreville, AL – Centreville (lat. 32.9029 N, long. 87.2497 W) is located on private property in

rural Bibb County, approximately 85 km SSW of Birmingham, AL. The surrounding area

includes the Talladega National Forest and is heavily wooded with mixed deciduous (oakhickory) and loblolly pine. CFPPs in the vicinity of Cent reville are shown in Figure 2.

Jefferson Street (Atlanta), GA - Jefferson Street (lat. 33.7775 N, long. 84.4167 W) is an

urban/industrial-residential site 4.5 kilometers NW of downtown Atlanta, GA. The site is

located at 829 Jefferson Street NW, on Georgia Power Company property in a 70m by 125m

grass-covered clearing on a knoll 15 meters above street level. CFPPs in the vicinity of Jefferson

Street are shown in Figure 3.

Outlying Landing Field #8 (OLF), FL - OLF (lat. 30.5496 N, long. 87.3734 W) is a suburban

site 21 km NW of downtown Pensacola, FL and 20 km N of the Gulf of Mexico. The site is

adjacent to a paved, lightly traveled (< 200 vehi cles/day) road on the northern edge of a large

(>500 ha) grass-covered field. CFPPs in th e vicinity of OLF are shown in Figure 3.6 

### Gulfport, MS – Gulfport (lat. 30.3901 N, long. 89.0498 W) is located 1.5 km from the Gulf of

Mexico on the premises of the Harrison County Y outh Court at 47 Maples Ave. The area is

covered with sparse forest and grass, with single family homes to the east, an elementary school

to the north and athletic fields to the south. CFPPs in the vicin ity of OLF are shown in Figure 3.

Continuous Trace Gas and Particle Measurements

SO 2 

### , NO y 

### and CO are measured at each site and used to: 1) screen for periods of influence from

point sources (specifically CFPPs ) and non-point sources; 2) identify specific CFPPs based on

SO 2 

### :NO y 

### ratios; and 3) calculate TC/SO 2 

### and SO 4 

### /SO 2 

### ratios. Continuous (1-minute average)

measurements were made at a reference height of 10 m above ground level. Sample air is pulled

through a weather-proof inlet box and then into the equipment shelter via 1⁄4” o.d. heavy wall

PFA Teflon tubing. The inlet box contains cat alytic converters (for NOy), solenoids and

plumbing for introduction of zero ai r and calibrant gases. Calibra tion gases (+/- 1% for CO and

NO and +/- 2% for SO 2 

### ) were supplied by Scott-Marr in, Inc. (Riverside, CA).

SO 2 

### is measured via pulsed UV fl uorescence with a TEI Model 43ctl analyzer operated on a 0-

200 ppb scale. The instrument is calibrated ev ery third day by gas replacement and zeroed 10

out of every 90 minutes by diverting sample ai r through a sodium carbonate impregnated annular

denuder (URG, Carrboro, NC). The analyzer is also subjected to weekly multipoint gas

replacement calibrations (GRC).7 

### CO is measured via gas filter correlation with non-dispersive infrared detection using a TEI

Model 48ctl analyzer operated on a 0-3000 ppb scal e (0-10,000 ppb at JST). The analyzer is

calibrated and zeroed on the same schedule as the SO2 analyzer. Zeroing is performed by

diverting the sample stream through a heated (50-100C) trap containing approximately 200

grams of 1% Pt on alumina (DeG ussa, Seviersville, TN).

NO y 

### is measured via ozone-NO chemiluminescen ce following reduction to NO on a 350 oC Mo

catalytic converter, using a dua l-channel TEI Model ctl NO-NO x 

### analyzer operated on a 0-200

ppb scale. The analyzer is ze roed four times per day and cal ibrated every third day via gas

replacement. Converter efficiency is check ed once a week with n-propyl nitrate.

SO 4 

### is measured continuously usi ng a variation of the Harvard Sc hool of Public Health (HSPH)

approach. This method uses a 1000 °C inconel st eel tube to reduce particulate SO4 to sulfur

dioxide (SO 2 

### ). The SO 2 

### is then detected using a Ther mo-Environmental Instruments (TEI,

Franklin, MA) Model 43S or 43Ctl high sensitivity, pulsed ultra-violet fluorescence SO 2



### analyzer. Sample air is pulled through a 2.5 μm sharp- cut cyclone inlet (BGI, Atlanta, GA), then

through two 30 mm o.d., 254 mm long sodium carbonate and citric acid coat ed annular denuders

(URG, Carrboro, NC) followed by a 30 mm o.d., 100 mm long carbon honeycomb denuder

(MAST Carbon Ltd., Surrey, UK). The denuders effectively remove a wide range of

interferents, including SO 2 

### , reduced sulfur gases, nitrogen oxides and volatile organic

compounds. Sample air then passes through a 300 mm section of inconel tubing heated to 1000

°C in a Lindberg/Blue M horizontal tube furnace. Every 90 minutes, the system is zeroed for 108 

### minutes by diverting sample air through an in line filter upstream of the converter. The SO 2



### analyzer is subjected to manual and automated gas replacement audits on a weekly schedule.

Total carbon (TC) is measured continuously with a Sunset La boratory Model RT-OCEC Aerosol

Carbon Analyzer. This device ope rates on an hourly cycle, with 47 minutes devoted to sample

collection and 13 minutes devoted to sample anal ysis. In sample mode, ambient air is pulled

through an activated carbon monolith denuder (Novacarb TM 

### , Mast Carbon Ltd., UK) at a flow

rate of 8.5 lpm, then through dual qu artz fiber filters. In analysis mode, th e filters are heated

through several temperature plateaus to a final temperature of 900 oC. CO 2 

### produced during the

heating cycle is quantified with a non-dispersive infra-red (NDIR) detector and TC is calculated

based on CO 2 

### produced and sample volume. The TC an alyzer is automatically calibrated with

5% methane in helium after every analysis cycle.

Trajectory Calculations

Twenty-four hour back trajectories are generate d using the interactive version of the NOAA

HYSPLIT4 model on the NOAA-ARL web site ( 12 ). Back trajectories use EDAS 40 km

meteorological data and default vertical moti on, with starting heights of 1000 m, 500 m and 250

m, for the time (hour) of peak SO 2 

### concentration during each even t. The 250 m trajectory is

used to determine which CFPP affected the site, as well as time of emission at the CFPP.9 

### Event Selection and Data Reduction

Event selection attempted to identify episodes with minimal contamination from non-CFPP

sources. In general, this means that different episodes are used for TC and SO 4 

### analyses. For

TC, we look for clean, well-vent ilated conditions during the mi ddle of the day, with low and

stable CO concentrations. This avoids rush hour emissions and near-surface sources that tend to

accumulate under the nocturnal boundary layer. While some VOC to SOA conversion is

possible, the effect should be small during fall an d winter because of: 1) low biogenic precursor

emissions; and 2) low temperatures; and 3) low solar insolation. For SO 4 

### , in contrast, we are less

concerned with contamination from non-CFPP sour ces, but want to avoid strong sunlight and

consequent photochemical pr oduction of secondary SO 4 

### within the plume. Thus, the majority of

SO 4 

### events selected for this analysis occurred e ither at night or during the early morning hours.

TC emission ratios are calculated using the “ratio of deltas” method, as shown below,

ER TC 

### = (TC Plume 

### -TC Base 

### )/(SO2 Plume 

### -SO2 Base 

### ) = Δ TC/ Δ SO 2 

### , (Eq. 1)

where subscripts Plume and Base refer to concen trations measured during the plume event and

before or after the event, respectively. The t echnique is illustrated in Figure 4, which shows an

event that occurred at Yorkville on April 9, 2006. The upper panel shows SO 2 

### and CO during

the course of the day. Note that the regular gaps in the time series reflect zeroing cycles. SO 2



### concentrations were <5 ppb until about 1430 local standard time (LST), when they increased

sharply and remained above 40 ppb until about 1630, then fell below 5 ppb for the remainder of10 

### the day. CO concentrations were between 80 and 100 ppb for the entire day, indicating no

evidence of plumes from biomass burning, transportation and ot her activities.

The lower panel shows time series for SO 2 

### (red symbols) and TC (black bars), also for April 9,

2006. In this case, SO 2 

### concentrations have been averaged to coincide exactly with the 47-

minute Sunset collection period. The plume event is shown in the red box a nd the downward

facing arrows indicate th e two values used (i.e., averaged) to calculate Base concentration. The

symbols and bars at 1500 LST and 1600 LST are averaged to cal culate Plume concentration.

Base and Plume concentrations are then used to calculate the ratio of deltas, as shown in

Equation 1. Note that Δ TC during this event (0.22 μg/m 3 

### ) is quite small compared to the overall

range of TC observed during the day, despite the fact that average SO 2 

### concentrations exceeded

75 ppb for the 47-minute period beginning at 1600 LST. This is typical of CFPP plume events

and underscores the fact that CFPPs are minor s ources of particulate carbon. In other words,

large plumes are needed in order to even “see” an increase in TC. The small increment of TC

associated with CFPP events places a high premium on stable TC measurements.

For several CFPP events, Δ TC was negative, indicating that Ba se concentrations were slightly

higher than the Plume concentrations. Based on Equation 1, this implies a physically unrealistic

negative ER. For these events, we used the de tection limit for the Sunset analyzer (0.1 μg/m 3 

### ) in

the numerator of Equation 1.11 

### SO 4 

### emission ratios are calculated by linear least square regression of 1-minute SO 4



### concentrations versus 1-minute SO 2 

### concentrations. The regression slope is equivalent to the

primary SO 4 

### /SO 2 

### emission ratio and the intercept is equivalent to the baseline SO 4 

### concentration

in absence of the plume. Figure 5 illustrates an example SO 4 

### event which occurred at Yorkville

on February 25, 2006. In the upper panel, SO 2 

### concentration is < 5 ppb until approximately

0400 (LST), increases to nearly 50 ppb just before 0600, then falls below 5 ppb by 0900. SO 4



### concentrations (right hand scale) are < 1 ppb (3.9 μg/m 3 

### ) the entire day, but show several minor

excursions, some of which are associated with SO 2 

### excursions and some of which are not. The

lower panel shows the scattergram of SO 4 

### versus SO 2 

### and associated regression statistics. Data

for the regression correspond to th e red box in the upper panel. Re sults show a highly significant

relationship between SO 4 

### and SO 2 

### (p<0.01) with a regression slope of 0.0042 on a ppb/ppb basis.

Given that the molecular weight of SO 4 

### is 1.5 times that of SO 2 

### , the emission ratio for this event

is 0.0063 lb/lb or 0.63 %.

It should be noted that both the ratio of deltas approach and the linear regression approach give

upper bound estimates of TC and SO 4 

### . The principal reason for th is is dry deposition, which

removes gaseous SO 2 

### from the plume much faster than partic les. If we assume dry deposition to

be a first order loss process, then the effect is to reduce Δ SO 2 

### in the denominator of equation 1

and thereby inflate the ratio Δ TC/ Δ SO 2 

### . Another reason is photochemical or non-photochemical

production of secondary SO 4 

### and OC, which would increase SO 4 

### and, at the same time, decrease

SO 2 

### in the plume. Although events have been carefully selected to minimize these effects, we

cannot be certain they have been eliminated completely.12 

### RESULTS

Table 1 summarizes results for 14 TC plume events observed at 4 sites. Data include the site

which observed the CFPP plume, the likely sour ce of the plume (based on trajectory analyses

and SO 2 

### /NO y 

### ratios) and concentration data for th e ratio of deltas ca lculation. Mean Δ TC/ Δ SO 2



### for 11 events is 0.0032 +/- 0.0014 with a range of 0.0011 to 0.0066. OLF and Yorkville both

observed 5 events. At OLF, all 5 events were from the Crist CFPP and these gave an emission

ratio of 0.0020 +/- 0.0012 lb/lb. At Yo rkville, the plume events likel y originated from 3 different

CFPPs and these gave an average ratio of 0.003 3 +/- 0.0021 lb/lb. These events clearly show

that TC is a small and difficult to detect component of CFPP emissions.

Table 2 summarizes results for 20 SO 4 

### plume events observed at 4 sites and likely originating

from 8 different CFPPs. Data include the maximum observed 1-minute SO 2 

### concentration, plus

the regression slope and r-squared for SO 4 

### vs. SO 2 

### . Calculated values for Δ SO 4 

### / Δ SO 2 

### range

from 0.0030 to 0.0180 lb/lb with an average of 0.0064 lb/lb. In most cases, the regression is

highly significant; however, r-square tends to decr ease as slope decreases because instrument

noise starts to dominate the SO 4 

### signal. These events clearly show that SO 4 

### is a small and

difficult to detect component of CFPP emissions.

CONCLUSIONS

Continuous field measurements can be used to derive emission estimates for TC and SO 4 

### from

CFPPs which are upper bound estimates of condensable carbon and condensable SO 3 

### . Careful

attention must be paid to plum e event selection in order to a void contamination from non-CFPP13 

### sources (TC) and photochemical activity in the CFPP plume (SO 4 

### ). Optimal conditions for both

TC and SO 4 

### estimates appear to occur during the cool er months when photochemical activity is

low and persistent winds advect relatively fresh CFPP plumes to the research sites. Plume

analysis results show that prim ary TC emissions and primary SO 4 

### emissions from CFPPs are

well below 1% of SO2 on a mass basis. For primar y TC, analysis of 14 events from 7 different

CFPPs gave an overall average em ission ratio of 0.0032 lb TC/lb SO 2 

### (or 0.32% of SO2. For

primary SO 4 

### , analysis of 20 events from 8 different CFPPs gave an overall average emission

ratio of 0.0064 lb SO 4 

### /lb SO 2 

### (or 0.64% of SO 2 

### ).Figure 1. The SEARCH Network

35.0

32.5

30.0

90.0 87.5 85.0 82.5

14

### Figure 2. CFPPs observed at YRK (top) and CTR (bottom).

### Figure 3. CFPPs observed at JST (to p), OLF (middle) and GFP (bottom).

16Figure 4. CFPP plume event at YRK showing 1-minute SO 2 and CO (top), 47-minute SO 2 and TC (bottom).

0 40 80 120 160 200

4/9/06 0:00 4/9/06 3:00 4/9/06 6:00 4/9/06 9:00 4/9/06 12:00 4/9/06 15:00 4/9/06 18:00 4/9/06 21:00SO2 (ppb)

0 80 160 240 320 400CO (ppb) SO2 CO

0 10 20 30 40 50 60 70 80

4/9/06 0:00 4/9/06 3:00 4/9/06 6:00 4/9/06 9:00 4/9/06 12:00 4/9/06 15:00 4/9/06 18:00 4/9/06 21:00SO2 (ug/m3)

0 1 2 3 4TC (ug/m3) Sunset TC SO2

17Figure 5. CFPP plume event at YRK showing SO 2 and SO 4 (top) and SO 4 vs. SO 2 (bottom).

0 10 20 30 40 50

2/25/06 0:00 2/25/06 3:00 2/25/06 6:00 2/25/06 9:00 2/25/06 12:00 2/25/06 15:00 2/25/06 18:00 2/25/06 21:00SO2 (ppb) .

0 1 2 3 4 5SO4 (ppb) . SO2 SO4 ZERO

SO4 = 0.0042*SO2 + 0.43
R 2 = 0.68

0.00 0.25 0.50 0.75 1.00 1.25

0 1 0 2 0 3 0 4 0 5 0 6 0 SO2 (ppb) .SO4 (ppb) .

1819 Table 1. Summary of Total Carbon Events.

Site Date Probable CFPP Base SO 2 (ppb) Plume SO 2 (ppb) Base TC (μg/m3) Plume TC (μg/m3) Δ TC/ Δ SO 2 (lb/lb) Alternate Δ TC/ Δ SO 2

(lb/lb)

CTR 12/18/05 Gaston 15.6 51.2 2.96 3.30 3.7 x 10 -3

CTR 12/20/05 Gorgas 15.1 23.1 1.30 1.38 3.8 x 10 -3

CTR 02/23/06 Miller 5.1 20.6 1.71 1.49 < 0 2.5 x 10 -3

JST 05/06/06 McDonough 3.5 64.5 3.35 3.6 1.6 x 10 -3

OLF 11/25/05 Crist 11.9 38.9 2.22 2.38 2.3 x 10 -3

OLF 02/07/06 Crist 4.2 34.6 2.22 2.38 2.0 x 10 -3

OLF 02/24/06 Crist 11.1 35.0 1.48 1.70 3.5 x 10 -3

OLF 04/28/06 Crist 4.3 41.2 3.53 3.48 < 0 1.2 x 10 -3

OLF 05/06/06 Crist 3.3 85.3 3.31 3.55 1.1 x 10 -3

YRK 10/31/05 McDonough 6.3 48.8 2.72 3.45 6.59 x 10 -3

YRK 02/25/06 Bowen 4.7 39.5 2.24 2.52 3.08 x 10 -3

YRK 03/04/06 Bowen 5.5 33.4 3.49 3.72 3.15 x 10 -3

YRK 03/11/06 Wansley 1.8 52.2 4.06 3.87 < 0 7.6 x 10 -4

YRK 04/09/06 Bowen 1.7 61.6 2.12 2.34 3.63 x 10 -3

Mean
(s.d.) 3.2 x 10 -3

(1.4 x 10 -3 ) 1.5 x 10 -3

(0.9 x 10 -3 )

Note: Base and Peak concentrations based on 47-minute averages.Table 2. Summary of SO 4 Events.

Site Date Probable CFPP 1-min Max. SO 2 (ppb) SO 4 vs. SO 2 Slope SO 4 vs. SO 2 R 2 Δ SO 4 / Δ SO 2 (lb/lb)

CTR 12/07/05 Gorgas 49.7 5.6 x 10 -3 0.77 8.4 x 10 -3

CTR 12/17/05 Gorgas 21.4 2.0 x 10 -3 0.02 3.0 x 10 -3

CTR 12/17/05 Miller 29.6 2.5 x 10 -3 0.09 3.7 x 10 -3

CTR 12/18/05 Gaston 55.3 4.4 x 10 -3 0.70 6.6 x 10 -3

CTR 12/19/05 Gorgas 30.1 3.6 x 10 -3 0.13 5.4 x 10 -3

CTR 12/20/05 Gorgas 43.3 5.9 x 10 -3 0.81 8.9 x 10 -3

CTR 01/27/06 Miller 20.2 5.1 x 10 -3 0.20 7.7 x 10 -3

GFP 01/26/06 Watson 137.1 3.8 x 10-3 0.95 5.7 x 10 -3

GFP 02/19/06 Watson 49.9 3.6 x 10-3 0.34 5.4 x 10 -3

OLF 11/19/05 Crist 42.8 2.5 x 10 -3 0.08 3.7 x 10 -3

OLF 02/07/06 Crist 52.1 1.4 x 10 -3 0.02 2.1 x 10 -3

OLF 02/24/06 Crist 59.1 4.3 x 10 -3 0.29 6.5 x 10 -3

OLF 4/13/06 Crist 186. 5.4 x 10 -3 0.68 8.1 x 10 -3

YRK 10/09/05 Bowen 33.8 1.2 x 10 -3 0.10 1.8 x 10 -3

YRK 10/31/05 McDonough 73.4 10.0 x 10 -3 0.90 15.0 x 10 -3

YRK 11/11/05 McDonough 48.3 3.3 x 10 -3 0.43 4.9 x 10 -3

YRK 12/18/05 Bowen 202.8 6.6 x 10 -3 0.96 9.9 x 10 -3

YRK 02/08/06 Hammond 31.2 7.6 x 10 -3 0.64 11.4 x 10 -3

YRK 02/25/06 Bowen 47.4 4.4 x 10 -3 0.69 6.6 x 10 -3

YRK 03/04/06 Bowen 60.9 2.4 x 10 -3 0.09 3.6 x 10 -3

Mean
(s.d.) 6.4 x 10 -3

(3.3 x 10 -3 )

## Appendix E

Determination Modeling
Delta-Deciview Values for the Top 20 Days – for Each Year/Each Class I Area and for the Top 25 Days – Over Three YearsNew IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year) Bowen 1-4 Results - Unit 1 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.36 3.8 2.9 4.3 73.7 0.0 20.1 1.1 2.2 3.0 1 2001 113 1 8.59 7.60 0.98 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.7 3.6 2 2001 225 3 9.27 8.33 0.93 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.4 3 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.9 0.0 18.4 1.1 1.9 2.8 4 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 68.2 0.0 24.5 1.4 2.3 3.6 5 2001 279 1 8.87 8.10 0.76 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.0 2.7 6 2001 220 54 9.09 8.33 0.76 4.9 3.5 5.1 78.5 0.0 16.4 1.0 1.7 2.5 7 2001 111 16 8.33 7.60 0.73 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.4 3.6 8 2001 294 1 8.78 8.10 0.68 4.2 3.2 4.6 75.8 0.0 18.3 1.0 2.1 2.8 9 2001 51 8 8.38 7.72 0.67 3.2 2.5 3.8 70.3 0.0 22.8 1.2 2.3 3.3 10 2001 102 9 8.19 7.60 0.59 2.8 2.3 3.6 68.1 0.0 24.5 1.3 2.5 3.6 11 2001 229 16 8.88 8.33 0.55 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.3 2.4 12 2001 110 3 8.12 7.60 0.51 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.4 3.6 13 2001 142 3 8.34 7.87 0.47 3.6 2.8 4.1 73.1 0.0 20.9 1.2 1.8 3.1 14 2001 112 35 8.07 7.60 0.47 2.8 2.3 3.6 68.2 0.0 24.5 1.4 2.3 3.6 15 2001 343 97 8.40 7.94 0.46 3.8 2.9 4.3 73.6 0.0 20.0 1.2 2.1 3.1 16 2001 158 25 8.56 8.10 0.45 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.1 2.6 17 2001 126 3 8.31 7.87 0.44 3.6 2.8 4.1 72.9 0.0 20.8 1.2 2.0 3.1 18 2001 236 25 8.77 8.33 0.43 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.9 2.4 19 2001 198 9 8.62 8.21 0.41 4.5 3.3 4.9 77.2 0.0 17.5 1.0 1.7 2.7 20

2002 224 3 9.66 8.33 1.32 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 1 2002 15 16 9.06 7.82 1.25 3.4 2.7 4.0 72.0 0.0 21.5 1.2 2.2 3.1 2 2002 227 8 9.51 8.33 1.18 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 3 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.6 0.0 20.0 1.2 2.2 3.0 4 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.3 0.0 23.4 1.3 2.5 3.4 5 2002 228 97 9.33 8.33 0.99 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.5 6 2002 230 3 9.31 8.33 0.97 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 7 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 8 2002 314 2 8.75 7.87 0.88 3.6 2.8 4.1 72.7 0.0 20.8 1.2 2.3 3.1 9 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.1 0.0 22.7 1.2 2.6 3.3 10 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.4 2.4 11 2002 28 3 8.58 7.82 0.76 3.4 2.7 4.0 71.9 0.0 21.5 1.2 2.2 3.2 12 2002 107 97 8.32 7.60 0.71 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.6 3.7 13 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.5 2.7 14 2002 85 97 8.24 7.67 0.56 3.0 2.5 3.7 69.4 0.0 23.4 1.3 2.5 3.5 15 2002 323 3 8.42 7.87 0.55 3.6 2.8 4.1 72.7 0.0 20.8 1.2 2.3 3.1 16 2002 105 8 8.13 7.60 0.52 2.8 2.3 3.6 68.1 0.0 24.5 1.3 2.4 3.6 17 2002 191 16 8.72 8.21 0.51 4.5 3.3 4.9 77.6 0.0 17.5 0.9 1.3 2.6 18 2002 128 3 8.37 7.87 0.50 3.6 2.8 4.1 72.6 0.0 20.8 1.2 2.3 3.1 19 2002 106 8 8.08 7.60 0.47 2.8 2.3 3.6 67.8 0.0 24.4 1.4 2.7 3.7 20

2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 1 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.5 2 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.8 0.0 18.3 1.0 2.1 2.8 3 2003 356 16 8.60 7.94 0.67 3.8 2.9 4.3 73.7 0.0 20.0 1.1 2.2 3.0 4 2003 68 3 8.32 7.67 0.65 3.0 2.5 3.7 69.1 0.0 23.3 1.3 2.7 3.5 5 2003 246 3 8.98 8.33 0.64 4.9 3.5 5.1 78.6 0.0 16.5 0.9 1.5 2.5 6 2003 107 1 8.23 7.60 0.62 2.8 2.3 3.6 68.0 0.0 24.5 1.3 2.5 3.7 7 2003 163 3 8.69 8.10 0.58 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.8 2.7 8 2003 164 97 8.68 8.10 0.58 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.8 2.7 9 2003 187 3 8.79 8.21 0.58 4.5 3.3 4.9 77.0 0.0 17.4 1.0 2.0 2.6 10 2003 35 10 8.24 7.72 0.53 3.2 2.5 3.8 70.2 0.0 22.8 1.3 2.3 3.3 11 2003 242 16 8.84 8.33 0.50 4.9 3.5 5.1 78.6 0.0 16.4 0.9 1.5 2.5 12 2003 160 3 8.59 8.10 0.49 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.2 2.8 13 2003 326 1 8.35 7.87 0.48 3.6 2.8 4.1 72.5 0.0 20.7 1.1 2.5 3.1 14 2003 247 97 8.81 8.33 0.47 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.3 2.5 15 2003 304 1 8.56 8.10 0.46 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.0 2.7 16 2003 108 3 8.05 7.60 0.45 2.8 2.3 3.6 68.0 0.0 24.5 1.4 2.5 3.7 17 2003 259 3 8.78 8.33 0.44 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.9 2.4 18 2003 197 9 8.65 8.21 0.44 4.5 3.3 4.9 77.0 0.0 17.5 1.0 2.0 2.5 19 2003 166 46 8.53 8.10 0.43 4.2 3.2 4.6 75.9 0.0 18.4 1.0 1.9 2.8 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years) Bowen 1-4 Results - Unit 1 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.36 3.8 2.9 4.3 73.7 0.0 20.1 1.1 2.2 3.0 1 2002 224 3 9.66 8.33 1.32 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 2 2002 15 16 9.06 7.82 1.25 3.4 2.7 4.0 72.0 0.0 21.5 1.2 2.2 3.1 3 2002 227 8 9.51 8.33 1.18 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 4 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.6 0.0 20.0 1.2 2.2 3.0 5 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.3 0.0 23.4 1.3 2.5 3.4 6 2002 228 97 9.33 8.33 0.99 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.5 7 2001 113 1 8.59 7.60 0.98 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.7 3.6 8 2002 230 3 9.31 8.33 0.97 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 9 2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 10 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.5 11 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 12 2001 225 3 9.27 8.33 0.93 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.4 13 2002 314 2 8.75 7.87 0.88 3.6 2.8 4.1 72.7 0.0 20.8 1.2 2.3 3.1 14 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.9 0.0 18.4 1.1 1.9 2.8 15 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.1 0.0 22.7 1.2 2.6 3.3 16 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 68.2 0.0 24.5 1.4 2.3 3.6 17 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.4 2.4 18 2002 28 3 8.58 7.82 0.76 3.4 2.7 4.0 71.9 0.0 21.5 1.2 2.2 3.2 19 2001 279 1 8.87 8.10 0.76 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.0 2.7 20 2001 220 54 9.09 8.33 0.76 4.9 3.5 5.1 78.5 0.0 16.4 1.0 1.7 2.5 21 2001 111 16 8.33 7.60 0.73 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.4 3.6 22 2002 107 97 8.32 7.60 0.71 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.6 3.7 23 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.8 0.0 18.3 1.0 2.1 2.8 24 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.5 2.7 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year) Bowen 1-4 Results - Unit 2 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.36 3.8 2.9 4.3 73.7 0.0 20.1 1.1 2.2 3.0 1 2001 113 1 8.59 7.60 0.98 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.7 3.6 2 2001 225 3 9.27 8.33 0.93 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.4 3 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.9 2.7 4 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 68.2 0.0 24.5 1.4 2.3 3.6 5 2001 279 1 8.87 8.10 0.76 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.0 2.7 6 2001 220 54 9.09 8.33 0.76 4.9 3.5 5.1 78.5 0.0 16.4 1.0 1.6 2.5 7 2001 111 16 8.33 7.60 0.73 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.4 3.6 8 2001 294 1 8.78 8.10 0.68 4.2 3.2 4.6 75.9 0.0 18.3 1.0 2.0 2.8 9 2001 51 8 8.38 7.72 0.67 3.2 2.5 3.8 70.3 0.0 22.8 1.2 2.3 3.3 10 2001 102 9 8.19 7.60 0.59 2.8 2.3 3.6 68.1 0.0 24.5 1.3 2.5 3.6 11 2001 229 16 8.88 8.33 0.55 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.3 2.4 12 2001 110 3 8.12 7.60 0.51 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.4 3.6 13 2001 142 3 8.34 7.87 0.47 3.6 2.8 4.1 73.2 0.0 20.9 1.2 1.7 3.1 14 2001 112 35 8.07 7.60 0.47 2.8 2.3 3.6 68.2 0.0 24.5 1.4 2.3 3.6 15 2001 343 97 8.39 7.94 0.46 3.8 2.9 4.3 73.7 0.0 20.1 1.2 2.1 3.0 16 2001 158 25 8.56 8.10 0.45 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.1 2.6 17 2001 126 3 8.31 7.87 0.44 3.6 2.8 4.1 72.9 0.0 20.8 1.2 2.0 3.1 18 2001 236 25 8.77 8.33 0.43 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.9 2.4 19 2001 198 9 8.62 8.21 0.41 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.7 2.6 20

2002 224 3 9.66 8.33 1.32 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 1 2002 15 16 9.06 7.82 1.25 3.4 2.7 4.0 72.0 0.0 21.5 1.2 2.1 3.1 2 2002 227 8 9.51 8.33 1.18 4.9 3.5 5.1 78.5 0.0 16.5 0.9 1.7 2.4 3 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.7 0.0 20.1 1.2 2.2 3.0 4 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.4 0.0 23.4 1.3 2.4 3.5 5 2002 228 97 9.33 8.33 0.99 4.9 3.5 5.1 78.6 0.0 16.4 0.9 1.7 2.4 6 2002 230 3 9.31 8.33 0.97 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 7 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 8 2002 314 2 8.75 7.87 0.88 3.6 2.8 4.1 72.7 0.0 20.8 1.2 2.3 3.0 9 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.2 0.0 22.8 1.2 2.5 3.3 10 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.4 2.4 11 2002 28 3 8.58 7.82 0.76 3.4 2.7 4.0 71.9 0.0 21.5 1.2 2.2 3.1 12 2002 107 97 8.32 7.60 0.71 2.8 2.3 3.6 68.0 0.0 24.5 1.4 2.6 3.6 13 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.5 2.6 14 2002 85 97 8.24 7.67 0.56 3.0 2.5 3.7 69.4 0.0 23.4 1.3 2.5 3.5 15 2002 323 3 8.42 7.87 0.55 3.6 2.8 4.1 72.8 0.0 20.8 1.2 2.2 3.1 16 2002 105 8 8.13 7.60 0.52 2.8 2.3 3.6 68.2 0.0 24.6 1.3 2.4 3.5 17 2002 191 16 8.72 8.21 0.51 4.5 3.3 4.9 77.6 0.0 17.5 0.9 1.3 2.6 18 2002 128 3 8.37 7.87 0.50 3.6 2.8 4.1 72.6 0.0 20.8 1.2 2.3 3.1 19 2002 313 8 8.34 7.87 0.47 3.6 2.8 4.1 72.9 0.0 20.9 1.2 2.1 3.0 20

2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.5 0.0 16.5 0.9 1.6 2.4 1 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 2 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.8 0.0 18.4 1.0 2.1 2.7 3 2003 356 16 8.60 7.94 0.67 3.8 2.9 4.3 73.7 0.0 20.1 1.1 2.2 2.9 4 2003 68 3 8.32 7.67 0.65 3.0 2.5 3.7 69.2 0.0 23.3 1.4 2.6 3.5 5 2003 246 3 8.98 8.33 0.64 4.9 3.5 5.1 78.6 0.0 16.5 0.9 1.5 2.5 6 2003 107 1 8.23 7.60 0.62 2.8 2.3 3.6 68.1 0.0 24.5 1.3 2.5 3.6 7 2003 163 3 8.69 8.10 0.58 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.8 2.7 8 2003 164 97 8.68 8.10 0.58 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.8 2.7 9 2003 187 3 8.79 8.21 0.58 4.5 3.3 4.9 77.0 0.0 17.4 1.0 2.0 2.6 10 2003 35 10 8.24 7.72 0.53 3.2 2.5 3.8 70.2 0.0 22.8 1.3 2.3 3.3 11 2003 242 16 8.84 8.33 0.50 4.9 3.5 5.1 78.7 0.0 16.4 0.9 1.5 2.4 12 2003 160 3 8.59 8.10 0.49 4.2 3.2 4.6 75.8 0.0 18.3 1.1 2.1 2.7 13 2003 326 1 8.35 7.87 0.48 3.6 2.8 4.1 72.7 0.0 20.8 1.1 2.4 3.0 14 2003 247 97 8.81 8.33 0.47 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.3 2.5 15 2003 304 1 8.56 8.10 0.46 4.2 3.2 4.6 76.0 0.0 18.4 1.0 2.0 2.6 16 2003 108 3 8.05 7.60 0.45 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.5 3.6 17 2003 259 3 8.78 8.33 0.44 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.9 2.4 18 2003 197 9 8.65 8.21 0.44 4.5 3.3 4.9 77.0 0.0 17.5 1.0 2.0 2.5 19 2003 166 46 8.53 8.10 0.43 4.2 3.2 4.6 76.0 0.0 18.4 1.0 1.9 2.7 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years) Bowen 1-4 Results - Unit 2 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.36 3.8 2.9 4.3 73.7 0.0 20.1 1.1 2.2 3.0 1 2002 224 3 9.66 8.33 1.32 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 2 2002 15 16 9.06 7.82 1.25 3.4 2.7 4.0 72.0 0.0 21.5 1.2 2.1 3.1 3 2002 227 8 9.51 8.33 1.18 4.9 3.5 5.1 78.5 0.0 16.5 0.9 1.7 2.4 4 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.7 0.0 20.1 1.2 2.2 3.0 5 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.4 0.0 23.4 1.3 2.4 3.5 6 2002 228 97 9.33 8.33 0.99 4.9 3.5 5.1 78.6 0.0 16.4 0.9 1.7 2.4 7 2001 113 1 8.59 7.60 0.98 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.7 3.6 8 2002 230 3 9.31 8.33 0.97 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 9 2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.5 0.0 16.5 0.9 1.6 2.4 10 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.7 2.4 11 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.4 0.0 16.4 0.9 1.8 2.4 12 2001 225 3 9.27 8.33 0.93 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.4 13 2002 314 2 8.75 7.87 0.88 3.6 2.8 4.1 72.7 0.0 20.8 1.2 2.3 3.0 14 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 76.0 0.0 18.4 1.1 1.9 2.7 15 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.2 0.0 22.8 1.2 2.5 3.3 16 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 68.2 0.0 24.5 1.4 2.3 3.6 17 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.8 0.0 16.5 0.9 1.4 2.4 18 2002 28 3 8.58 7.82 0.76 3.4 2.7 4.0 71.9 0.0 21.5 1.2 2.2 3.1 19 2001 279 1 8.87 8.10 0.76 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.0 2.7 20 2001 220 54 9.09 8.33 0.76 4.9 3.5 5.1 78.5 0.0 16.4 1.0 1.6 2.5 21 2001 111 16 8.33 7.60 0.73 2.8 2.3 3.6 68.1 0.0 24.5 1.4 2.4 3.6 22 2002 107 97 8.32 7.60 0.71 2.8 2.3 3.6 68.0 0.0 24.5 1.4 2.6 3.6 23 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.8 0.0 18.4 1.0 2.1 2.7 24 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.3 0.0 17.5 1.0 1.5 2.6 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year) Bowen 1-4 Results - Unit 3 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.37 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.1 1 2001 113 1 8.59 7.60 0.99 2.8 2.3 3.6 67.6 0.0 24.3 1.4 2.9 3.7 2 2001 225 3 9.27 8.33 0.94 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 3 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 4 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 67.9 0.0 24.4 1.5 2.5 3.7 5 2001 279 1 8.87 8.10 0.77 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.1 2.7 6 2001 220 54 9.10 8.33 0.76 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.8 2.6 7 2001 111 16 8.34 7.60 0.73 2.8 2.3 3.6 67.8 0.0 24.4 1.5 2.6 3.7 8 2001 294 1 8.79 8.10 0.69 4.2 3.2 4.6 75.5 0.0 18.3 1.2 2.2 2.9 9 2001 51 8 8.39 7.72 0.67 3.2 2.5 3.8 70.1 0.0 22.8 1.3 2.4 3.4 10 2001 102 9 8.20 7.60 0.59 2.8 2.3 3.6 67.8 0.0 24.4 1.5 2.6 3.8 11 2001 229 16 8.88 8.33 0.55 4.9 3.5 5.1 78.6 0.0 16.5 1.0 1.4 2.5 12 2001 110 3 8.12 7.60 0.52 2.8 2.3 3.6 67.8 0.0 24.4 1.5 2.5 3.8 13 2001 142 3 8.34 7.87 0.47 3.6 2.8 4.1 72.8 0.0 20.8 1.3 1.8 3.3 14 2001 112 35 8.07 7.60 0.47 2.8 2.3 3.6 67.8 0.0 24.4 1.5 2.5 3.8 15 2001 343 97 8.40 7.94 0.46 3.8 2.9 4.3 73.3 0.0 20.0 1.3 2.3 3.2 16 2001 158 25 8.56 8.10 0.46 4.2 3.2 4.6 75.8 0.0 18.4 1.1 2.1 2.6 17 2001 126 3 8.31 7.87 0.44 3.6 2.8 4.1 72.6 0.0 20.7 1.2 2.2 3.2 18 2001 236 25 8.77 8.33 0.43 4.9 3.5 5.1 78.2 0.0 16.3 1.0 2.0 2.5 19 2001 198 9 8.62 8.21 0.42 4.5 3.3 4.9 76.9 0.0 17.4 1.1 1.9 2.8 20

2002 224 3 9.66 8.33 1.33 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 1 2002 15 16 9.07 7.82 1.25 3.4 2.7 4.0 71.8 0.0 21.5 1.3 2.3 3.2 2 2002 227 8 9.52 8.33 1.18 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 3 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.1 4 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.1 0.0 23.3 1.4 2.7 3.6 5 2002 228 97 9.33 8.33 1.00 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 6 2002 230 3 9.31 8.33 0.98 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 7 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 8 2002 314 2 8.76 7.87 0.88 3.6 2.8 4.1 72.4 0.0 20.7 1.3 2.5 3.2 9 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.0 0.0 22.7 1.3 2.7 3.3 10 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.5 0.0 16.4 1.0 1.5 2.5 11 2002 28 3 8.58 7.82 0.77 3.4 2.7 4.0 71.7 0.0 21.5 1.3 2.4 3.2 12 2002 107 97 8.32 7.60 0.72 2.8 2.3 3.6 67.6 0.0 24.3 1.5 2.8 3.8 13 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.1 0.0 17.4 1.1 1.6 2.8 14 2002 85 97 8.24 7.67 0.57 3.0 2.5 3.7 69.1 0.0 23.3 1.4 2.6 3.6 15 2002 323 3 8.42 7.87 0.55 3.6 2.8 4.1 72.4 0.0 20.7 1.2 2.5 3.2 16 2002 105 8 8.13 7.60 0.52 2.8 2.3 3.6 68.1 0.0 24.5 1.3 2.6 3.5 17 2002 191 16 8.72 8.21 0.51 4.5 3.3 4.9 77.4 0.0 17.5 1.0 1.4 2.7 18 2002 128 3 8.37 7.87 0.50 3.6 2.8 4.1 72.4 0.0 20.7 1.3 2.5 3.2 19 2002 106 8 8.08 7.60 0.47 2.8 2.3 3.6 67.5 0.0 24.3 1.5 2.9 3.8 20

2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 1 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.6 2 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.5 0.0 18.3 1.1 2.3 2.8 3 2003 356 16 8.60 7.94 0.67 3.8 2.9 4.3 73.6 0.0 20.0 1.1 2.3 3.0 4 2003 68 3 8.33 7.67 0.65 3.0 2.5 3.7 68.8 0.0 23.2 1.4 2.9 3.7 5 2003 246 3 8.98 8.33 0.65 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.7 2.7 6 2003 107 1 8.23 7.60 0.63 2.8 2.3 3.6 67.7 0.0 24.4 1.5 2.7 3.8 7 2003 163 3 8.69 8.10 0.59 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 8 2003 164 97 8.68 8.10 0.58 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 9 2003 187 3 8.79 8.21 0.58 4.5 3.3 4.9 76.7 0.0 17.4 1.1 2.2 2.7 10 2003 35 10 8.24 7.72 0.53 3.2 2.5 3.8 70.0 0.0 22.8 1.3 2.5 3.4 11 2003 242 16 8.84 8.33 0.50 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.6 2.6 12 2003 160 3 8.60 8.10 0.49 4.2 3.2 4.6 75.4 0.0 18.2 1.2 2.3 2.9 13 2003 326 1 8.35 7.87 0.48 3.6 2.8 4.1 72.3 0.0 20.7 1.2 2.6 3.2 14 2003 247 97 8.81 8.33 0.48 4.9 3.5 5.1 78.5 0.0 16.5 1.0 1.4 2.6 15 2003 304 1 8.56 8.10 0.46 4.2 3.2 4.6 75.7 0.0 18.4 1.1 2.1 2.7 16 2003 108 3 8.05 7.60 0.45 2.8 2.3 3.6 67.7 0.0 24.4 1.5 2.7 3.8 17 2003 259 3 8.78 8.33 0.45 4.9 3.5 5.1 78.1 0.0 16.3 1.0 2.0 2.5 18 2003 197 9 8.65 8.21 0.44 4.5 3.3 4.9 77.0 0.0 17.4 1.0 2.1 2.5 19 2003 166 46 8.54 8.10 0.43 4.2 3.2 4.6 75.6 0.0 18.3 1.1 2.1 2.9 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years) Bowen 1-4 Results - Unit 3 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.37 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.1 1 2002 224 3 9.66 8.33 1.33 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 2 2002 15 16 9.07 7.82 1.25 3.4 2.7 4.0 71.8 0.0 21.5 1.3 2.3 3.2 3 2002 227 8 9.52 8.33 1.18 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 4 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.1 5 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.1 0.0 23.3 1.4 2.7 3.6 6 2002 228 97 9.33 8.33 1.00 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 7 2001 113 1 8.59 7.60 0.99 2.8 2.3 3.6 67.6 0.0 24.3 1.4 2.9 3.7 8 2002 230 3 9.31 8.33 0.98 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 9 2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 10 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.6 11 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 12 2001 225 3 9.27 8.33 0.94 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 13 2002 314 2 8.76 7.87 0.88 3.6 2.8 4.1 72.4 0.0 20.7 1.3 2.5 3.2 14 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 15 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.0 0.0 22.7 1.3 2.7 3.3 16 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 67.9 0.0 24.4 1.5 2.5 3.7 17 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.5 0.0 16.4 1.0 1.5 2.5 18 2002 28 3 8.58 7.82 0.77 3.4 2.7 4.0 71.7 0.0 21.5 1.3 2.4 3.2 19 2001 279 1 8.87 8.10 0.77 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.1 2.7 20 2001 220 54 9.10 8.33 0.76 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.8 2.6 21 2001 111 16 8.34 7.60 0.73 2.8 2.3 3.6 67.8 0.0 24.4 1.5 2.6 3.7 22 2002 107 97 8.32 7.60 0.72 2.8 2.3 3.6 67.6 0.0 24.3 1.5 2.8 3.8 23 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.5 0.0 18.3 1.1 2.3 2.8 24 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.1 0.0 17.4 1.1 1.6 2.8 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year) Bowen 1-4 Results - Unit 4 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.37 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.0 1 2001 113 1 8.59 7.60 0.99 2.8 2.3 3.6 67.7 0.0 24.3 1.4 2.9 3.7 2 2001 225 3 9.27 8.33 0.94 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 3 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 4 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.5 3.7 5 2001 279 1 8.87 8.10 0.76 4.2 3.2 4.6 75.8 0.0 18.4 1.0 2.1 2.7 6 2001 220 54 9.09 8.33 0.76 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.8 2.6 7 2001 111 16 8.34 7.60 0.73 2.8 2.3 3.6 67.9 0.0 24.4 1.5 2.6 3.7 8 2001 294 1 8.79 8.10 0.68 4.2 3.2 4.6 75.6 0.0 18.3 1.1 2.2 2.9 9 2001 51 8 8.39 7.72 0.67 3.2 2.5 3.8 70.2 0.0 22.8 1.3 2.4 3.3 10 2001 102 9 8.20 7.60 0.59 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.6 3.7 11 2001 229 16 8.88 8.33 0.55 4.9 3.5 5.1 78.6 0.0 16.5 0.9 1.4 2.5 12 2001 110 3 8.12 7.60 0.52 2.8 2.3 3.6 67.8 0.0 24.4 1.5 2.5 3.8 13 2001 142 3 8.34 7.87 0.47 3.6 2.8 4.1 72.8 0.0 20.8 1.3 1.8 3.3 14 2001 112 35 8.07 7.60 0.47 2.8 2.3 3.6 68.0 0.0 24.4 1.4 2.5 3.7 15 2001 343 97 8.40 7.94 0.46 3.8 2.9 4.3 73.3 0.0 20.0 1.3 2.3 3.2 16 2001 158 25 8.56 8.10 0.45 4.2 3.2 4.6 75.9 0.0 18.4 1.0 2.2 2.6 17 2001 126 3 8.31 7.87 0.44 3.6 2.8 4.1 72.6 0.0 20.7 1.2 2.2 3.2 18 2001 236 25 8.77 8.33 0.43 4.9 3.5 5.1 78.2 0.0 16.3 1.0 2.0 2.5 19 2001 198 9 8.62 8.21 0.42 4.5 3.3 4.9 76.9 0.0 17.4 1.1 1.9 2.8 20

2002 224 3 9.66 8.33 1.33 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.9 2.5 1 2002 15 16 9.06 7.82 1.25 3.4 2.7 4.0 71.8 0.0 21.5 1.2 2.3 3.2 2 2002 227 8 9.52 8.33 1.18 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 3 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.1 4 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.1 0.0 23.3 1.4 2.7 3.5 5 2002 228 97 9.33 8.33 1.00 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.5 6 2002 230 3 9.31 8.33 0.98 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 7 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.3 0.0 16.4 0.9 1.9 2.5 8 2002 314 2 8.76 7.87 0.88 3.6 2.8 4.1 72.5 0.0 20.7 1.2 2.5 3.1 9 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.0 0.0 22.7 1.3 2.7 3.3 10 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.6 0.0 16.4 1.0 1.5 2.5 11 2002 28 3 8.58 7.82 0.77 3.4 2.7 4.0 71.7 0.0 21.5 1.2 2.4 3.2 12 2002 107 97 8.32 7.60 0.71 2.8 2.3 3.6 67.7 0.0 24.3 1.4 2.8 3.8 13 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.1 0.0 17.5 1.1 1.6 2.7 14 2002 85 97 8.24 7.67 0.57 3.0 2.5 3.7 69.1 0.0 23.3 1.4 2.6 3.6 15 2002 323 3 8.42 7.87 0.55 3.6 2.8 4.1 72.5 0.0 20.7 1.2 2.5 3.1 16 2002 105 8 8.13 7.60 0.52 2.8 2.3 3.6 68.1 0.0 24.5 1.3 2.6 3.5 17 2002 191 16 8.72 8.21 0.51 4.5 3.3 4.9 77.4 0.0 17.5 1.0 1.4 2.7 18 2002 128 3 8.37 7.87 0.50 3.6 2.8 4.1 72.4 0.0 20.7 1.3 2.5 3.2 19 2002 106 8 8.08 7.60 0.47 2.8 2.3 3.6 67.5 0.0 24.3 1.5 2.9 3.8 20

2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.5 1 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 2 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.5 0.0 18.3 1.1 2.3 2.8 3 2003 356 16 8.60 7.94 0.67 3.8 2.9 4.3 73.6 0.0 20.0 1.1 2.3 3.0 4 2003 68 3 8.33 7.67 0.65 3.0 2.5 3.7 68.8 0.0 23.2 1.4 2.9 3.7 5 2003 246 3 8.98 8.33 0.65 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.7 2.6 6 2003 107 1 8.23 7.60 0.62 2.8 2.3 3.6 67.8 0.0 24.4 1.4 2.6 3.7 7 2003 163 3 8.69 8.10 0.59 4.2 3.2 4.6 75.9 0.0 18.3 1.1 2.0 2.8 8 2003 164 97 8.68 8.10 0.58 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 9 2003 187 3 8.79 8.21 0.58 4.5 3.3 4.9 76.7 0.0 17.4 1.1 2.2 2.7 10 2003 35 10 8.24 7.72 0.53 3.2 2.5 3.8 70.0 0.0 22.8 1.3 2.5 3.4 11 2003 242 16 8.84 8.33 0.50 4.9 3.5 5.1 78.5 0.0 16.4 0.9 1.6 2.6 12 2003 160 3 8.60 8.10 0.49 4.2 3.2 4.6 75.5 0.0 18.2 1.1 2.3 2.9 13 2003 326 1 8.35 7.87 0.48 3.6 2.8 4.1 72.3 0.0 20.7 1.2 2.5 3.2 14 2003 247 97 8.81 8.33 0.48 4.9 3.5 5.1 78.5 0.0 16.5 1.0 1.4 2.6 15 2003 304 1 8.56 8.10 0.46 4.2 3.2 4.6 75.7 0.0 18.4 1.1 2.1 2.7 16 2003 108 3 8.05 7.60 0.45 2.8 2.3 3.6 67.7 0.0 24.4 1.5 2.7 3.8 17 2003 259 3 8.78 8.33 0.45 4.9 3.5 5.1 78.1 0.0 16.3 1.0 2.0 2.5 18 2003 197 9 8.65 8.21 0.44 4.5 3.3 4.9 77.0 0.0 17.4 1.0 2.1 2.5 19 2003 166 46 8.54 8.10 0.43 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.1 2.8 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years) Bowen 1-4 Results - Unit 4 Controlled with Agglomerator/Juice Can

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.30 7.94 1.37 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.0 1 2002 224 3 9.66 8.33 1.33 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.9 2.5 2 2002 15 16 9.06 7.82 1.25 3.4 2.7 4.0 71.8 0.0 21.5 1.2 2.3 3.2 3 2002 227 8 9.52 8.33 1.18 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 4 2002 365 16 9.08 7.94 1.14 3.8 2.9 4.3 73.4 0.0 20.0 1.2 2.3 3.1 5 2002 67 3 8.71 7.67 1.04 3.0 2.5 3.7 69.1 0.0 23.3 1.4 2.7 3.5 6 2002 228 97 9.33 8.33 1.00 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.5 7 2001 113 1 8.59 7.60 0.99 2.8 2.3 3.6 67.7 0.0 24.3 1.4 2.9 3.7 8 2002 230 3 9.31 8.33 0.98 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 9 2003 271 3 9.29 8.33 0.96 4.9 3.5 5.1 78.4 0.0 16.4 1.0 1.8 2.5 10 2003 270 1 9.28 8.33 0.94 4.9 3.5 5.1 78.3 0.0 16.4 1.0 1.8 2.5 11 2002 226 97 9.27 8.33 0.94 4.9 3.5 5.1 78.3 0.0 16.4 0.9 1.9 2.5 12 2001 225 3 9.27 8.33 0.94 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.9 2.5 13 2002 314 2 8.76 7.87 0.88 3.6 2.8 4.1 72.5 0.0 20.7 1.2 2.5 3.1 14 2001 297 3 8.97 8.10 0.87 4.2 3.2 4.6 75.7 0.0 18.3 1.1 2.0 2.9 15 2002 35 9 8.56 7.72 0.84 3.2 2.5 3.8 70.0 0.0 22.7 1.3 2.7 3.3 16 2001 114 4 8.42 7.60 0.82 2.8 2.3 3.6 67.9 0.0 24.4 1.4 2.5 3.7 17 2002 222 16 9.12 8.33 0.79 4.9 3.5 5.1 78.6 0.0 16.4 1.0 1.5 2.5 18 2002 28 3 8.58 7.82 0.77 3.4 2.7 4.0 71.7 0.0 21.5 1.2 2.4 3.2 19 2001 279 1 8.87 8.10 0.76 4.2 3.2 4.6 75.8 0.0 18.4 1.0 2.1 2.7 20 2001 220 54 9.09 8.33 0.76 4.9 3.5 5.1 78.2 0.0 16.4 1.0 1.8 2.6 21 2001 111 16 8.34 7.60 0.73 2.8 2.3 3.6 67.9 0.0 24.4 1.5 2.6 3.7 22 2002 107 97 8.32 7.60 0.71 2.8 2.3 3.6 67.7 0.0 24.3 1.4 2.8 3.8 23 2003 178 3 8.79 8.10 0.69 4.2 3.2 4.6 75.5 0.0 18.3 1.1 2.3 2.8 24 2002 207 9 8.89 8.21 0.69 4.5 3.3 4.9 77.1 0.0 17.5 1.1 1.6 2.7 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year)
Bowen 1-4 Results - Unit 1 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.12 7.94 1.18 3.8 2.9 4.3 70.4 0.0 23.5 1.1 2.1 2.9 1 2001 113 1 8.46 7.60 0.86 2.8 2.3 3.6 64.3 0.0 28.2 1.3 2.6 3.5 2 2001 225 3 9.13 8.33 0.80 4.9 3.5 5.1 75.5 0.0 19.4 0.9 1.8 2.4 3 2001 297 3 8.84 8.10 0.74 4.2 3.2 4.6 72.6 0.0 21.8 1.0 1.8 2.7 4 2001 114 4 8.31 7.60 0.71 2.8 2.3 3.6 64.6 0.0 28.4 1.3 2.2 3.5 5 2001 279 1 8.77 8.10 0.67 4.2 3.2 4.6 73.2 0.0 21.2 1.0 1.9 2.6 6 2001 111 16 8.24 7.60 0.64 2.8 2.3 3.6 64.5 0.0 28.3 1.3 2.4 3.5 7 2001 220 54 8.97 8.33 0.64 4.9 3.5 5.1 75.2 0.0 19.7 0.9 1.7 2.4 8 2001 51 8 8.31 7.72 0.59 3.2 2.5 3.8 67.3 0.0 26.0 1.3 2.3 3.2 9 2001 294 1 8.68 8.10 0.58 4.2 3.2 4.6 72.4 0.0 21.9 1.0 2.0 2.8 10 2001 102 9 8.12 7.60 0.51 2.8 2.3 3.6 64.4 0.0 28.3 1.4 2.4 3.6 11 2001 229 16 8.80 8.33 0.47 4.9 3.5 5.1 76.0 0.0 19.4 0.9 1.3 2.4 12 2001 110 3 8.05 7.60 0.45 2.8 2.3 3.6 64.2 0.0 28.5 1.4 2.3 3.6 13 2001 112 35 8.01 7.60 0.41 2.8 2.3 3.6 64.6 0.0 28.4 1.3 2.2 3.5 14 2001 142 3 8.27 7.87 0.40 3.6 2.8 4.1 69.4 0.0 24.6 1.1 1.7 3.1 15 2001 158 25 8.50 8.10 0.40 4.2 3.2 4.6 73.4 0.0 20.9 1.0 2.0 2.7 16 2001 343 97 8.32 7.94 0.39 3.8 2.9 4.3 69.8 0.0 23.9 1.2 2.1 3.0 17 2001 126 3 8.24 7.87 0.37 3.6 2.8 4.1 69.3 0.0 24.5 1.1 2.0 3.1 18 2001 236 25 8.70 8.33 0.37 4.9 3.5 5.1 75.6 0.0 19.2 0.9 1.9 2.4 19 2001 198 9 8.56 8.21 0.35 4.5 3.3 4.9 74.0 0.0 20.7 1.0 1.8 2.5 20

2002 224 3 9.47 8.33 1.13 4.9 3.5 5.1 75.5 0.0 19.4 0.9 1.7 2.4 1 2002 15 16 8.91 7.82 1.09 3.4 2.7 4.0 68.9 0.0 24.8 1.2 2.1 3.1 2 2002 227 8 9.35 8.33 1.01 4.9 3.5 5.1 75.7 0.0 19.3 0.9 1.7 2.4 3 2002 365 16 8.92 7.94 0.98 3.8 2.9 4.3 70.3 0.0 23.6 1.1 2.1 2.9 4 2002 67 3 8.58 7.67 0.90 3.0 2.5 3.7 65.8 0.0 27.2 1.3 2.4 3.4 5 2002 228 97 9.19 8.33 0.85 4.9 3.5 5.1 75.7 0.0 19.4 0.9 1.7 2.4 6 2002 230 3 9.17 8.33 0.83 4.9 3.5 5.1 75.5 0.0 19.4 0.9 1.8 2.4 7 2002 226 97 9.14 8.33 0.80 4.9 3.5 5.1 75.6 0.0 19.3 0.9 1.8 2.4 8 2002 314 2 8.63 7.87 0.76 3.6 2.8 4.1 69.4 0.0 24.2 1.1 2.2 3.0 9 2002 35 9 8.46 7.72 0.75 3.2 2.5 3.8 67.2 0.0 25.8 1.2 2.5 3.3 10 2002 28 3 8.48 7.82 0.66 3.4 2.7 4.0 68.6 0.0 24.9 1.2 2.2 3.1 11 2002 222 16 9.01 8.33 0.67 4.9 3.5 5.1 75.9 0.0 19.4 0.9 1.4 2.4 12 2002 107 97 8.22 7.60 0.62 2.8 2.3 3.6 64.1 0.0 28.4 1.4 2.5 3.6 13 2002 207 9 8.79 8.21 0.58 4.5 3.3 4.9 74.0 0.0 20.9 1.0 1.5 2.6 14 2002 85 97 8.16 7.67 0.49 3.0 2.5 3.7 65.7 0.0 27.2 1.2 2.4 3.4 15 2002 323 3 8.35 7.87 0.48 3.6 2.8 4.1 69.5 0.0 24.1 1.2 2.2 3.0 16 2002 105 8 8.07 7.60 0.47 2.8 2.3 3.6 65.3 0.0 27.6 1.3 2.4 3.4 17 2002 191 16 8.65 8.21 0.44 4.5 3.3 4.9 74.6 0.0 20.5 1.0 1.3 2.6 18 2002 128 3 8.30 7.87 0.43 3.6 2.8 4.1 69.2 0.0 24.4 1.2 2.3 3.0 19 2002 313 8 8.29 7.87 0.42 3.6 2.8 4.1 70.1 0.0 23.7 1.1 2.1 3.0 20

2003 271 3 9.15 8.33 0.82 4.9 3.5 5.1 75.7 0.0 19.4 0.9 1.6 2.4 1 2003 270 1 9.14 8.33 0.80 4.9 3.5 5.1 75.6 0.0 19.4 0.9 1.6 2.4 2 2003 178 3 8.69 8.10 0.59 4.2 3.2 4.6 72.7 0.0 21.5 1.0 2.1 2.7 3 2003 356 16 8.52 7.94 0.58 3.8 2.9 4.3 70.9 0.0 23.1 1.1 2.1 2.9 4 2003 68 3 8.23 7.67 0.56 3.0 2.5 3.7 65.2 0.0 27.5 1.3 2.6 3.4 5 2003 107 1 8.14 7.60 0.54 2.8 2.3 3.6 64.3 0.0 28.4 1.3 2.3 3.6 6 2003 246 3 8.87 8.33 0.54 4.9 3.5 5.1 75.2 0.0 19.8 1.0 1.5 2.5 7 2003 163 3 8.60 8.10 0.50 4.2 3.2 4.6 73.0 0.0 21.6 1.0 1.8 2.7 8 2003 164 97 8.60 8.10 0.49 4.2 3.2 4.6 72.7 0.0 21.8 1.0 1.8 2.7 9 2003 187 3 8.70 8.21 0.49 4.5 3.3 4.9 73.8 0.0 20.7 1.0 2.0 2.6 10 2003 35 10 8.18 7.72 0.46 3.2 2.5 3.8 67.0 0.0 26.3 1.2 2.2 3.3 11 2003 242 16 8.76 8.33 0.43 4.9 3.5 5.1 75.6 0.0 19.5 0.9 1.5 2.4 12 2003 326 1 8.29 7.87 0.41 3.6 2.8 4.1 69.3 0.0 24.3 1.1 2.3 3.0 13 2003 160 3 8.52 8.10 0.41 4.2 3.2 4.6 72.3 0.0 21.9 1.1 2.1 2.7 14 2003 304 1 8.50 8.10 0.40 4.2 3.2 4.6 73.3 0.0 21.1 1.0 2.0 2.7 15 2003 247 97 8.73 8.33 0.40 4.9 3.5 5.1 75.5 0.0 19.8 0.9 1.3 2.5 16 2003 108 3 7.99 7.60 0.39 2.8 2.3 3.6 64.3 0.0 28.4 1.3 2.4 3.5 17 2003 197 9 8.59 8.21 0.39 4.5 3.3 4.9 74.7 0.0 19.9 0.9 1.9 2.5 18 2003 320 25 8.25 7.87 0.38 3.6 2.8 4.1 69.9 0.0 23.7 1.1 2.3 2.9 19 2003 259 3 8.71 8.33 0.38 4.9 3.5 5.1 75.3 0.0 19.5 0.9 1.9 2.4 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years)
Bowen 1-4 Results - Unit 1 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.12 7.94 1.18 3.8 2.9 4.3 70.4 0.0 23.5 1.1 2.1 2.9 1 2002 224 3 9.47 8.33 1.13 4.9 3.5 5.1 75.5 0.0 19.4 0.9 1.7 2.4 2 2002 15 16 8.91 7.82 1.09 3.4 2.7 4.0 68.9 0.0 24.8 1.2 2.1 3.1 3 2002 227 8 9.35 8.33 1.01 4.9 3.5 5.1 75.7 0.0 19.3 0.9 1.7 2.4 4 2002 365 16 8.92 7.94 0.98 3.8 2.9 4.3 70.3 0.0 23.6 1.1 2.1 2.9 5 2002 67 3 8.58 7.67 0.90 3.0 2.5 3.7 65.8 0.0 27.2 1.3 2.4 3.4 6 2001 113 1 8.46 7.60 0.86 2.8 2.3 3.6 64.3 0.0 28.2 1.3 2.6 3.5 7 2002 228 97 9.19 8.33 0.85 4.9 3.5 5.1 75.7 0.0 19.4 0.9 1.7 2.4 8 2002 230 3 9.17 8.33 0.83 4.9 3.5 5.1 75.5 0.0 19.4 0.9 1.8 2.4 9 2003 271 3 9.15 8.33 0.82 4.9 3.5 5.1 75.7 0.0 19.4 0.9 1.6 2.4 10 2003 270 1 9.14 8.33 0.80 4.9 3.5 5.1 75.6 0.0 19.4 0.9 1.6 2.4 11 2002 226 97 9.14 8.33 0.80 4.9 3.5 5.1 75.6 0.0 19.3 0.9 1.8 2.4 12 2001 225 3 9.13 8.33 0.80 4.9 3.5 5.1 75.5 0.0 19.4 0.9 1.8 2.4 13 2002 314 2 8.63 7.87 0.76 3.6 2.8 4.1 69.4 0.0 24.2 1.1 2.2 3.0 14 2002 35 9 8.46 7.72 0.75 3.2 2.5 3.8 67.2 0.0 25.8 1.2 2.5 3.3 15 2001 297 3 8.84 8.10 0.74 4.2 3.2 4.6 72.6 0.0 21.8 1.0 1.8 2.7 16 2001 114 4 8.31 7.60 0.71 2.8 2.3 3.6 64.6 0.0 28.4 1.3 2.2 3.5 17 2002 222 16 9.01 8.33 0.67 4.9 3.5 5.1 75.9 0.0 19.4 0.9 1.4 2.4 18 2001 279 1 8.77 8.10 0.67 4.2 3.2 4.6 73.2 0.0 21.2 1.0 1.9 2.6 19 2002 28 3 8.48 7.82 0.66 3.4 2.7 4.0 68.6 0.0 24.9 1.2 2.2 3.1 20 2001 220 54 8.97 8.33 0.64 4.9 3.5 5.1 75.2 0.0 19.7 0.9 1.7 2.4 21 2001 111 16 8.24 7.60 0.64 2.8 2.3 3.6 64.5 0.0 28.3 1.3 2.4 3.5 22 2002 107 97 8.22 7.60 0.62 2.8 2.3 3.6 64.1 0.0 28.4 1.4 2.5 3.6 23 2001 51 8 8.31 7.72 0.59 3.2 2.5 3.8 67.3 0.0 26.0 1.3 2.3 3.2 24 2003 178 3 8.69 8.10 0.59 4.2 3.2 4.6 72.7 0.0 21.5 1.0 2.1 2.7 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year)
Bowen 1-4 Results - Unit 2 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.11 7.94 1.17 3.8 2.9 4.3 70.3 0.0 23.7 1.1 2.1 2.8 1 2001 113 1 8.46 7.60 0.85 2.8 2.3 3.6 64.2 0.0 28.5 1.3 2.6 3.4 2 2001 225 3 9.12 8.33 0.79 4.9 3.5 5.1 75.3 0.0 19.6 0.9 1.7 2.4 3 2001 297 3 8.83 8.10 0.73 4.2 3.2 4.6 72.5 0.0 22.0 1.0 1.8 2.7 4 2001 114 4 8.31 7.60 0.71 2.8 2.3 3.6 64.4 0.0 28.6 1.3 2.2 3.4 5 2001 279 1 8.76 8.10 0.66 4.2 3.2 4.6 73.0 0.0 21.4 1.0 1.9 2.6 6 2001 111 16 8.24 7.60 0.63 2.8 2.3 3.6 64.4 0.0 28.5 1.3 2.3 3.5 7 2001 220 54 8.96 8.33 0.63 4.9 3.5 5.1 75.2 0.0 19.9 0.9 1.6 2.4 8 2001 51 8 8.30 7.72 0.59 3.2 2.5 3.8 67.2 0.0 26.2 1.2 2.2 3.2 9 2001 294 1 8.67 8.10 0.57 4.2 3.2 4.6 72.3 0.0 22.1 1.0 1.9 2.6 10 2001 102 9 8.11 7.60 0.51 2.8 2.3 3.6 64.3 0.0 28.6 1.3 2.3 3.5 11 2001 229 16 8.80 8.33 0.46 4.9 3.5 5.1 75.9 0.0 19.5 0.9 1.3 2.3 12 2001 110 3 8.05 7.60 0.44 2.8 2.3 3.6 64.1 0.0 28.8 1.3 2.2 3.5 13 2001 112 35 8.01 7.60 0.40 2.8 2.3 3.6 64.4 0.0 28.6 1.3 2.2 3.5 14 2001 142 3 8.27 7.87 0.40 3.6 2.8 4.1 69.3 0.0 24.9 1.2 1.6 3.0 16 2001 158 25 8.50 8.10 0.40 4.2 3.2 4.6 73.4 0.0 21.1 1.0 1.9 2.6 15 2001 343 97 8.32 7.94 0.38 3.8 2.9 4.3 69.8 0.0 24.2 1.1 2.0 2.9 17 2001 126 3 8.24 7.87 0.37 3.6 2.8 4.1 69.2 0.0 24.8 1.1 1.9 3.0 18 2001 236 25 8.70 8.33 0.37 4.9 3.5 5.1 75.4 0.0 19.4 1.0 1.8 2.4 19 2001 198 9 8.55 8.21 0.35 4.5 3.3 4.9 73.8 0.0 20.9 1.0 1.7 2.6 20

2002 224 3 9.46 8.33 1.12 4.9 3.5 5.1 75.5 0.0 19.6 0.9 1.7 2.4 1 2002 15 16 8.90 7.82 1.09 3.4 2.7 4.0 68.8 0.0 25.0 1.2 2.1 3.0 2 2002 227 8 9.34 8.33 1.00 4.9 3.5 5.1 75.6 0.0 19.5 0.9 1.6 2.4 3 2002 365 16 8.91 7.94 0.97 3.8 2.9 4.3 70.1 0.0 23.8 1.1 2.1 2.9 4 2002 67 3 8.57 7.67 0.90 3.0 2.5 3.7 65.7 0.0 27.4 1.2 2.4 3.3 5 2002 228 97 9.18 8.33 0.84 4.9 3.5 5.1 75.5 0.0 19.6 0.9 1.6 2.4 6 2002 230 3 9.16 8.33 0.82 4.9 3.5 5.1 75.4 0.0 19.6 0.9 1.8 2.4 7 2002 226 97 9.13 8.33 0.80 4.9 3.5 5.1 75.5 0.0 19.5 0.9 1.7 2.4 8 2002 314 2 8.63 7.87 0.76 3.6 2.8 4.1 69.2 0.0 24.5 1.1 2.2 3.0 9 2002 35 9 8.46 7.72 0.74 3.2 2.5 3.8 67.1 0.0 26.1 1.2 2.5 3.2 10 2002 28 3 8.47 7.82 0.66 3.4 2.7 4.0 68.5 0.0 25.2 1.2 2.1 3.0 12 2002 222 16 9.00 8.33 0.67 4.9 3.5 5.1 75.8 0.0 19.5 0.9 1.4 2.4 11 2002 107 97 8.22 7.60 0.61 2.8 2.3 3.6 64.1 0.0 28.7 1.3 2.5 3.4 13 2002 207 9 8.78 8.21 0.57 4.5 3.3 4.9 73.9 0.0 21.1 1.0 1.5 2.5 14 2002 85 97 8.16 7.67 0.48 3.0 2.5 3.7 65.6 0.0 27.5 1.3 2.3 3.4 15 2002 323 3 8.34 7.87 0.47 3.6 2.8 4.1 69.3 0.0 24.3 1.2 2.2 3.0 16 2002 105 8 8.07 7.60 0.46 2.8 2.3 3.6 65.1 0.0 27.8 1.3 2.3 3.4 17 2002 191 16 8.64 8.21 0.43 4.5 3.3 4.9 74.6 0.0 20.7 0.9 1.2 2.5 18 2002 128 3 8.29 7.87 0.42 3.6 2.8 4.1 69.1 0.0 24.6 1.1 2.2 2.9 19 2002 313 8 8.28 7.87 0.41 3.6 2.8 4.1 70.1 0.0 23.9 1.1 2.0 2.9 20

2003 271 3 9.15 8.33 0.81 4.9 3.5 5.1 75.6 0.0 19.5 0.9 1.6 2.4 1 2003 270 1 9.13 8.33 0.80 4.9 3.5 5.1 75.5 0.0 19.6 0.9 1.6 2.4 2 2003 178 3 8.69 8.10 0.58 4.2 3.2 4.6 72.6 0.0 21.7 1.0 2.0 2.7 3 2003 356 16 8.51 7.94 0.58 3.8 2.9 4.3 70.7 0.0 23.3 1.1 2.1 2.8 4 2003 68 3 8.22 7.67 0.55 3.0 2.5 3.7 65.0 0.0 27.8 1.3 2.5 3.4 5 2003 107 1 8.14 7.60 0.54 2.8 2.3 3.6 64.2 0.0 28.7 1.3 2.4 3.5 6 2003 246 3 8.87 8.33 0.53 4.9 3.5 5.1 75.1 0.0 20.0 0.9 1.5 2.4 7 2003 163 3 8.60 8.10 0.50 4.2 3.2 4.6 72.8 0.0 21.7 1.0 1.8 2.7 8 2003 164 97 8.59 8.10 0.49 4.2 3.2 4.6 72.5 0.0 22.0 1.0 1.8 2.6 9 2003 187 3 8.69 8.21 0.49 4.5 3.3 4.9 73.7 0.0 20.9 1.0 1.9 2.5 10 2003 35 10 8.17 7.72 0.45 3.2 2.5 3.8 66.8 0.0 26.5 1.2 2.2 3.3 11 2003 242 16 8.76 8.33 0.42 4.9 3.5 5.1 75.5 0.0 19.7 0.9 1.5 2.4 12 2003 326 1 8.28 7.87 0.41 3.6 2.8 4.1 69.0 0.0 24.5 1.1 2.3 3.0 13 2003 160 3 8.51 8.10 0.41 4.2 3.2 4.6 72.1 0.0 22.1 1.0 2.1 2.7 14 2003 304 1 8.50 8.10 0.40 4.2 3.2 4.6 73.3 0.0 21.2 1.0 1.9 2.6 15 2003 247 97 8.73 8.33 0.39 4.9 3.5 5.1 75.4 0.0 20.0 0.9 1.2 2.4 16 2003 108 3 7.99 7.60 0.39 2.8 2.3 3.6 64.2 0.0 28.7 1.4 2.3 3.4 17 2003 197 9 8.59 8.21 0.38 4.5 3.3 4.9 74.6 0.0 20.1 0.9 2.0 2.4 18 2003 320 25 8.25 7.87 0.37 3.6 2.8 4.1 69.8 0.0 23.9 1.1 2.2 3.0 19 2003 259 3 8.71 8.33 0.37 4.9 3.5 5.1 75.3 0.0 19.7 0.9 1.8 2.3 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years)
Bowen 1-4 Results - Unit 2 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 2 9.11 7.94 1.17 3.8 2.9 4.3 70.3 0.0 23.7 1.1 2.1 2.8 1 2002 224 3 9.46 8.33 1.12 4.9 3.5 5.1 75.5 0.0 19.6 0.9 1.7 2.4 2 2002 15 16 8.90 7.82 1.09 3.4 2.7 4.0 68.8 0.0 25.0 1.2 2.1 3.0 3 2002 227 8 9.34 8.33 1.00 4.9 3.5 5.1 75.6 0.0 19.5 0.9 1.6 2.4 4 2002 365 16 8.91 7.94 0.97 3.8 2.9 4.3 70.1 0.0 23.8 1.1 2.1 2.9 5 2002 67 3 8.57 7.67 0.90 3.0 2.5 3.7 65.7 0.0 27.4 1.2 2.4 3.3 6 2001 113 1 8.46 7.60 0.85 2.8 2.3 3.6 64.2 0.0 28.5 1.3 2.6 3.4 7 2002 228 97 9.18 8.33 0.84 4.9 3.5 5.1 75.5 0.0 19.6 0.9 1.6 2.4 8 2002 230 3 9.16 8.33 0.82 4.9 3.5 5.1 75.4 0.0 19.6 0.9 1.8 2.4 9 2003 271 3 9.15 8.33 0.81 4.9 3.5 5.1 75.6 0.0 19.5 0.9 1.6 2.4 10 2003 270 1 9.13 8.33 0.80 4.9 3.5 5.1 75.5 0.0 19.6 0.9 1.6 2.4 11 2002 226 97 9.13 8.33 0.80 4.9 3.5 5.1 75.5 0.0 19.5 0.9 1.7 2.4 12 2001 225 3 9.12 8.33 0.79 4.9 3.5 5.1 75.3 0.0 19.6 0.9 1.7 2.4 13 2002 314 2 8.63 7.87 0.76 3.6 2.8 4.1 69.2 0.0 24.5 1.1 2.2 3.0 14 2002 35 9 8.46 7.72 0.74 3.2 2.5 3.8 67.1 0.0 26.1 1.2 2.5 3.2 15 2001 297 3 8.83 8.10 0.73 4.2 3.2 4.6 72.5 0.0 22.0 1.0 1.8 2.7 16 2001 114 4 8.31 7.60 0.71 2.8 2.3 3.6 64.4 0.0 28.6 1.3 2.2 3.4 17 2002 222 16 9.00 8.33 0.67 4.9 3.5 5.1 75.8 0.0 19.5 0.9 1.4 2.4 18 2001 279 1 8.76 8.10 0.66 4.2 3.2 4.6 73.0 0.0 21.4 1.0 1.9 2.6 19 2002 28 3 8.47 7.82 0.66 3.4 2.7 4.0 68.5 0.0 25.2 1.2 2.1 3.0 20 2001 111 16 8.24 7.60 0.63 2.8 2.3 3.6 64.4 0.0 28.5 1.3 2.3 3.5 21 2001 220 54 8.96 8.33 0.63 4.9 3.5 5.1 75.2 0.0 19.9 0.9 1.6 2.4 22 2002 107 97 8.22 7.60 0.61 2.8 2.3 3.6 64.1 0.0 28.7 1.3 2.5 3.4 23 2001 51 8 8.30 7.72 0.59 3.2 2.5 3.8 67.2 0.0 26.2 1.2 2.2 3.2 24 2003 178 3 8.69 8.10 0.58 4.2 3.2 4.6 72.6 0.0 21.7 1.0 2.0 2.7 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year)
Bowen 1-4 Results - Unit 3 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 1 9.10 7.94 1.16 3.8 2.9 4.3 69.2 0.0 23.8 1.3 2.4 3.3 1 2001 113 1 8.45 7.60 0.84 2.8 2.3 3.6 62.9 0.0 28.7 1.5 3.0 3.9 2 2001 225 3 9.12 8.33 0.79 4.9 3.5 5.1 74.5 0.0 19.6 1.1 2.0 2.8 3 2001 297 3 8.84 8.10 0.74 4.2 3.2 4.6 71.9 0.0 21.7 1.2 2.1 3.1 4 2001 114 4 8.30 7.60 0.70 2.8 2.3 3.6 63.2 0.0 28.8 1.5 2.5 4.0 5 2001 220 54 8.98 8.33 0.65 4.9 3.5 5.1 74.9 0.0 19.3 1.1 1.9 2.8 6 2001 279 4 8.74 8.10 0.64 4.2 3.2 4.6 71.5 0.0 22.2 1.2 2.2 3.0 7 2001 111 16 8.23 7.60 0.63 2.8 2.3 3.6 63.1 0.0 28.8 1.5 2.6 4.0 8 2001 294 1 8.69 8.10 0.59 4.2 3.2 4.6 71.9 0.0 21.5 1.2 2.3 3.1 9 2001 51 8 8.28 7.72 0.56 3.2 2.5 3.8 65.0 0.0 27.6 1.4 2.4 3.6 10 2001 102 9 8.11 7.60 0.51 2.8 2.3 3.6 63.1 0.0 28.7 1.6 2.7 4.0 11 2001 229 16 8.79 8.33 0.46 4.9 3.5 5.1 74.8 0.0 19.9 1.0 1.5 2.8 12 2001 110 3 8.05 7.60 0.45 2.8 2.3 3.6 63.2 0.0 28.5 1.6 2.7 4.0 13 2001 142 3 8.28 7.87 0.40 3.6 2.8 4.1 68.8 0.0 24.4 1.4 1.9 3.5 14 2001 112 35 8.00 7.60 0.40 2.8 2.3 3.6 63.2 0.0 28.7 1.5 2.5 4.0 15 2001 343 97 8.33 7.94 0.40 3.8 2.9 4.3 69.5 0.0 23.3 1.4 2.4 3.4 16 2001 126 3 8.25 7.87 0.38 3.6 2.8 4.1 68.5 0.0 24.4 1.3 2.2 3.6 17 2001 158 25 8.47 8.10 0.37 4.2 3.2 4.6 71.1 0.0 22.7 1.1 2.2 2.9 18 2001 236 25 8.70 8.33 0.36 4.9 3.5 5.1 74.4 0.0 19.7 1.1 2.1 2.8 19 2001 198 9 8.56 8.21 0.35 4.5 3.3 4.9 73.3 0.0 20.6 1.1 1.9 3.0 20

2002 224 3 9.45 8.33 1.12 4.9 3.5 5.1 74.5 0.0 19.7 1.1 2.0 2.7 1 2002 15 16 8.87 7.82 1.05 3.4 2.7 4.0 67.1 0.0 25.8 1.3 2.3 3.4 2 2002 227 8 9.33 8.33 0.99 4.9 3.5 5.1 74.5 0.0 19.8 1.1 1.9 2.7 3 2002 365 16 8.91 7.94 0.97 3.8 2.9 4.3 69.3 0.0 23.7 1.3 2.4 3.3 4 2002 67 2 8.57 7.67 0.89 3.0 2.5 3.7 64.5 0.0 27.5 1.5 2.7 3.8 5 2002 228 97 9.17 8.33 0.84 4.9 3.5 5.1 74.6 0.0 19.7 1.1 1.9 2.7 6 2002 230 3 9.15 8.33 0.82 4.9 3.5 5.1 74.5 0.0 19.7 1.0 2.0 2.8 7 2002 226 97 9.12 8.33 0.79 4.9 3.5 5.1 74.6 0.0 19.8 1.0 2.0 2.7 8 2002 314 2 8.62 7.87 0.75 3.6 2.8 4.1 68.1 0.0 24.6 1.3 2.5 3.4 9 2002 35 9 8.42 7.72 0.70 3.2 2.5 3.8 64.8 0.0 27.5 1.4 2.7 3.5 10 2002 222 16 8.99 8.33 0.66 4.9 3.5 5.1 74.8 0.0 19.8 1.0 1.6 2.7 11 2002 28 3 8.46 7.82 0.65 3.4 2.7 4.0 67.1 0.0 25.6 1.3 2.4 3.5 12 2002 107 97 8.22 7.60 0.62 2.8 2.3 3.6 63.1 0.0 28.5 1.5 2.8 4.0 13 2002 207 9 8.79 8.21 0.59 4.5 3.3 4.9 73.5 0.0 20.7 1.1 1.7 3.0 14 2002 85 97 8.16 7.67 0.48 3.0 2.5 3.7 64.5 0.0 27.5 1.4 2.7 3.9 15 2002 323 3 8.34 7.87 0.47 3.6 2.8 4.1 68.1 0.0 24.7 1.3 2.6 3.4 16 2002 105 16 8.04 7.60 0.44 2.8 2.3 3.6 62.7 0.0 29.3 1.5 2.7 3.9 17 2002 191 16 8.63 8.21 0.43 4.5 3.3 4.9 73.4 0.0 21.1 1.1 1.5 2.9 18 2002 128 3 8.30 7.87 0.42 3.6 2.8 4.1 68.2 0.0 24.5 1.3 2.6 3.5 19 2002 106 8 8.01 7.60 0.41 2.8 2.3 3.6 63.0 0.0 28.6 1.5 2.9 4.0 20

2003 271 3 9.14 8.33 0.80 4.9 3.5 5.1 74.6 0.0 19.8 1.1 1.9 2.7 1 2003 270 1 9.13 8.33 0.79 4.9 3.5 5.1 74.6 0.0 19.7 1.1 1.9 2.7 2 2003 178 3 8.68 8.10 0.58 4.2 3.2 4.6 71.5 0.0 21.9 1.1 2.4 3.1 3 2003 68 3 8.24 7.67 0.57 3.0 2.5 3.7 64.7 0.0 26.9 1.5 3.0 3.9 4 2003 356 16 8.49 7.94 0.56 3.8 2.9 4.3 69.0 0.0 24.2 1.2 2.4 3.3 5 2003 246 3 8.88 8.33 0.55 4.9 3.5 5.1 75.1 0.0 19.3 1.1 1.6 2.8 6 2003 107 1 8.14 7.60 0.54 2.8 2.3 3.6 63.1 0.0 28.6 1.6 2.7 4.0 7 2003 164 97 8.60 8.10 0.50 4.2 3.2 4.6 71.9 0.0 21.7 1.2 2.1 3.1 8 2003 163 3 8.60 8.10 0.49 4.2 3.2 4.6 71.7 0.0 22.0 1.2 2.1 3.1 9 2003 187 3 8.70 8.21 0.49 4.5 3.3 4.9 73.0 0.0 20.6 1.2 2.2 3.0 10 2003 35 10 8.16 7.72 0.45 3.2 2.5 3.8 65.3 0.0 27.1 1.5 2.5 3.7 11 2003 242 16 8.76 8.33 0.42 4.9 3.5 5.1 74.8 0.0 19.6 1.0 1.7 2.8 12 2003 160 3 8.52 8.10 0.42 4.2 3.2 4.6 71.8 0.0 21.4 1.2 2.4 3.2 13 2003 326 1 8.28 7.87 0.41 3.6 2.8 4.1 68.0 0.0 24.5 1.3 2.7 3.5 14 2003 247 97 8.74 8.33 0.41 4.9 3.5 5.1 75.3 0.0 19.4 1.1 1.4 2.8 15 2003 108 1 7.99 7.60 0.39 2.8 2.3 3.6 63.2 0.0 28.4 1.6 2.7 4.0 16 2003 304 25 8.48 8.10 0.38 4.2 3.2 4.6 71.3 0.0 22.4 1.2 2.1 3.0 17 2003 259 3 8.71 8.33 0.38 4.9 3.5 5.1 74.6 0.0 19.5 1.0 2.1 2.8 18 2003 316 97 8.24 7.87 0.37 3.6 2.8 4.1 68.4 0.0 24.3 1.4 2.5 3.5 19 2003 166 46 8.47 8.10 0.37 4.2 3.2 4.6 71.8 0.0 21.9 1.2 2.1 3.1 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years)
Bowen 1-4 Results - Unit 3 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 1 9.10 7.94 1.16 3.8 2.9 4.3 69.2 0.0 23.8 1.3 2.4 3.3 1 2002 224 3 9.45 8.33 1.12 4.9 3.5 5.1 74.5 0.0 19.7 1.1 2.0 2.7 2 2002 15 16 8.87 7.82 1.05 3.4 2.7 4.0 67.1 0.0 25.8 1.3 2.3 3.4 3 2002 227 8 9.33 8.33 0.99 4.9 3.5 5.1 74.5 0.0 19.8 1.1 1.9 2.7 4 2002 365 16 8.91 7.94 0.97 3.8 2.9 4.3 69.3 0.0 23.7 1.3 2.4 3.3 5 2002 67 2 8.57 7.67 0.89 3.0 2.5 3.7 64.5 0.0 27.5 1.5 2.7 3.8 6 2001 113 1 8.45 7.60 0.84 2.8 2.3 3.6 62.9 0.0 28.7 1.5 3.0 3.9 7 2002 228 97 9.17 8.33 0.84 4.9 3.5 5.1 74.6 0.0 19.7 1.1 1.9 2.7 8 2002 230 3 9.15 8.33 0.82 4.9 3.5 5.1 74.5 0.0 19.7 1.0 2.0 2.8 9 2003 271 3 9.14 8.33 0.80 4.9 3.5 5.1 74.6 0.0 19.8 1.1 1.9 2.7 10 2003 270 1 9.13 8.33 0.79 4.9 3.5 5.1 74.6 0.0 19.7 1.1 1.9 2.7 11 2001 225 3 9.12 8.33 0.79 4.9 3.5 5.1 74.5 0.0 19.6 1.1 2.0 2.8 12 2002 226 97 9.12 8.33 0.79 4.9 3.5 5.1 74.6 0.0 19.8 1.0 2.0 2.7 13 2002 314 2 8.62 7.87 0.75 3.6 2.8 4.1 68.1 0.0 24.6 1.3 2.5 3.4 14 2001 297 3 8.84 8.10 0.74 4.2 3.2 4.6 71.9 0.0 21.7 1.2 2.1 3.1 15 2002 35 9 8.42 7.72 0.70 3.2 2.5 3.8 64.8 0.0 27.5 1.4 2.7 3.5 16 2001 114 4 8.30 7.60 0.70 2.8 2.3 3.6 63.2 0.0 28.8 1.5 2.5 4.0 17 2002 222 16 8.99 8.33 0.66 4.9 3.5 5.1 74.8 0.0 19.8 1.0 1.6 2.7 18 2001 220 54 8.98 8.33 0.65 4.9 3.5 5.1 74.9 0.0 19.3 1.1 1.9 2.8 19 2002 28 3 8.46 7.82 0.65 3.4 2.7 4.0 67.1 0.0 25.6 1.3 2.4 3.5 20 2001 279 4 8.74 8.10 0.64 4.2 3.2 4.6 71.5 0.0 22.2 1.2 2.2 3.0 21 2001 111 16 8.23 7.60 0.63 2.8 2.3 3.6 63.1 0.0 28.8 1.5 2.6 4.0 22 2002 107 97 8.22 7.60 0.62 2.8 2.3 3.6 63.1 0.0 28.5 1.5 2.8 4.0 23 2001 294 1 8.69 8.10 0.59 4.2 3.2 4.6 71.9 0.0 21.5 1.2 2.3 3.1 24 2002 207 9 8.79 8.21 0.59 4.5 3.3 4.9 73.5 0.0 20.7 1.1 1.7 3.0 25New IMPROVE/Virtual Temp - Ranked Daily Visib ility Change for Cohutta (Top 20 Days Each Year)
Bowen 1-4 Results - Unit 4 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 1 9.09 7.94 1.15 3.8 2.9 4.3 69.0 0.0 24.1 1.3 2.4 3.3 1 2001 113 1 8.44 7.60 0.84 2.8 2.3 3.6 62.7 0.0 29.0 1.5 2.9 3.9 2 2001 225 3 9.11 8.33 0.78 4.9 3.5 5.1 74.4 0.0 19.8 1.0 2.0 2.7 3 2001 297 3 8.83 8.10 0.73 4.2 3.2 4.6 71.8 0.0 21.9 1.2 2.0 3.1 4 2001 114 4 8.30 7.60 0.69 2.8 2.3 3.6 63.0 0.0 29.1 1.5 2.5 3.9 5 2001 220 54 8.98 8.33 0.64 4.9 3.5 5.1 74.8 0.0 19.5 1.1 1.8 2.8 6 2001 279 4 8.73 8.10 0.63 4.2 3.2 4.6 71.3 0.0 22.5 1.1 2.1 3.0 7 2001 111 16 8.22 7.60 0.62 2.8 2.3 3.6 62.9 0.0 29.1 1.5 2.6 3.9 8 2001 294 1 8.68 8.10 0.58 4.2 3.2 4.6 71.6 0.0 21.7 1.2 2.3 3.1 9 2001 51 8 8.27 7.72 0.55 3.2 2.5 3.8 64.8 0.0 27.9 1.3 2.4 3.5 10 2001 102 9 8.11 7.60 0.50 2.8 2.3 3.6 62.9 0.0 29.0 1.5 2.6 3.9 11 2001 229 16 8.79 8.33 0.45 4.9 3.5 5.1 74.7 0.0 20.1 1.1 1.4 2.7 12 2001 110 3 8.04 7.60 0.44 2.8 2.3 3.6 63.1 0.0 28.9 1.5 2.6 4.0 13 2001 142 3 8.27 7.87 0.40 3.6 2.8 4.1 68.7 0.0 24.6 1.4 1.8 3.4 14 2001 112 35 8.00 7.60 0.40 2.8 2.3 3.6 63.0 0.0 29.0 1.6 2.5 3.9 15 2001 343 97 8.33 7.94 0.39 3.8 2.9 4.3 69.5 0.0 23.5 1.3 2.3 3.4 16 2001 126 3 8.24 7.87 0.37 3.6 2.8 4.1 68.3 0.0 24.6 1.4 2.2 3.5 17 2001 158 25 8.47 8.10 0.37 4.2 3.2 4.6 70.9 0.0 23.0 1.1 2.2 2.8 18 2001 236 25 8.69 8.33 0.36 4.9 3.5 5.1 74.2 0.0 19.9 1.1 2.1 2.7 19 2001 198 9 8.56 8.21 0.35 4.5 3.3 4.9 73.2 0.0 20.9 1.1 1.9 2.9 20

2002 224 3 9.44 8.33 1.11 4.9 3.5 5.1 74.4 0.0 19.9 1.1 1.9 2.7 1 2002 15 16 8.86 7.82 1.04 3.4 2.7 4.0 66.9 0.0 26.2 1.3 2.3 3.4 2 2002 227 8 9.31 8.33 0.98 4.9 3.5 5.1 74.4 0.0 20.1 1.0 1.9 2.7 3 2002 365 16 8.90 7.94 0.97 3.8 2.9 4.3 69.1 0.0 23.9 1.3 2.4 3.3 4 2002 67 2 8.56 7.67 0.88 3.0 2.5 3.7 64.3 0.0 27.8 1.5 2.6 3.8 5 2002 228 97 9.16 8.33 0.83 4.9 3.5 5.1 74.4 0.0 20.0 1.0 1.9 2.7 6 2002 230 3 9.15 8.33 0.81 4.9 3.5 5.1 74.3 0.0 19.9 1.1 2.0 2.7 7 2002 226 97 9.11 8.33 0.78 4.9 3.5 5.1 74.4 0.0 20.0 1.0 1.9 2.7 8 2002 314 2 8.61 7.87 0.74 3.6 2.8 4.1 67.9 0.0 24.9 1.3 2.5 3.3 9 2002 35 9 8.41 7.72 0.70 3.2 2.5 3.8 64.6 0.0 27.8 1.3 2.7 3.5 10 2002 222 16 8.98 8.33 0.65 4.9 3.5 5.1 74.6 0.0 20.1 1.1 1.5 2.7 11 2002 28 3 8.46 7.82 0.64 3.4 2.7 4.0 66.9 0.0 25.9 1.4 2.3 3.5 12 2002 107 97 8.21 7.60 0.61 2.8 2.3 3.6 62.8 0.0 28.8 1.5 2.8 4.0 13 2002 207 9 8.79 8.21 0.58 4.5 3.3 4.9 73.4 0.0 20.9 1.1 1.7 2.9 14 2002 85 97 8.15 7.67 0.48 3.0 2.5 3.7 64.3 0.0 27.8 1.5 2.6 3.8 15 2002 323 3 8.33 7.87 0.46 3.6 2.8 4.1 67.9 0.0 25.0 1.3 2.5 3.4 16 2002 105 16 8.04 7.60 0.44 2.8 2.3 3.6 62.5 0.0 29.6 1.5 2.6 3.8 17 2002 191 16 8.63 8.21 0.42 4.5 3.3 4.9 73.3 0.0 21.4 1.2 1.4 2.8 18 2002 128 3 8.29 7.87 0.42 3.6 2.8 4.1 68.1 0.0 24.7 1.3 2.5 3.4 19 2002 106 8 8.00 7.60 0.40 2.8 2.3 3.6 62.8 0.0 28.9 1.5 2.8 3.9 20

2003 271 3 9.13 8.33 0.79 4.9 3.5 5.1 74.4 0.0 20.0 1.0 1.8 2.7 1 2003 270 1 9.12 8.33 0.79 4.9 3.5 5.1 74.5 0.0 19.9 1.0 1.8 2.7 2 2003 178 3 8.68 8.10 0.57 4.2 3.2 4.6 71.4 0.0 22.1 1.2 2.3 3.0 3 2003 68 3 8.24 7.67 0.56 3.0 2.5 3.7 64.5 0.0 27.2 1.5 3.0 3.9 4 2003 356 16 8.49 7.94 0.55 3.8 2.9 4.3 68.7 0.0 24.5 1.2 2.4 3.2 5 2003 246 3 8.88 8.33 0.55 4.9 3.5 5.1 74.9 0.0 19.5 1.1 1.7 2.8 6 2003 107 1 8.14 7.60 0.53 2.8 2.3 3.6 62.9 0.0 28.9 1.5 2.7 4.0 7 2003 164 97 8.59 8.10 0.49 4.2 3.2 4.6 71.8 0.0 22.0 1.2 2.0 3.1 8 2003 163 3 8.59 8.10 0.49 4.2 3.2 4.6 71.6 0.0 22.2 1.2 2.0 3.0 9 2003 187 3 8.69 8.21 0.49 4.5 3.3 4.9 72.9 0.0 20.9 1.1 2.2 2.9 10 2003 35 10 8.16 7.72 0.44 3.2 2.5 3.8 65.1 0.0 27.4 1.4 2.4 3.6 11 2003 242 16 8.75 8.33 0.42 4.9 3.5 5.1 74.7 0.0 19.9 1.0 1.7 2.7 12 2003 160 3 8.52 8.10 0.42 4.2 3.2 4.6 71.7 0.0 21.7 1.2 2.3 3.1 13 2003 326 1 8.28 7.87 0.41 3.6 2.8 4.1 67.8 0.0 24.8 1.4 2.6 3.4 14 2003 247 97 8.74 8.33 0.40 4.9 3.5 5.1 75.1 0.0 19.6 1.1 1.4 2.8 15 2003 108 1 7.99 7.60 0.38 2.8 2.3 3.6 63.1 0.0 28.8 1.5 2.7 4.0 16 2003 304 25 8.48 8.10 0.37 4.2 3.2 4.6 71.2 0.0 22.7 1.1 2.2 2.9 17 2003 259 3 8.70 8.33 0.37 4.9 3.5 5.1 74.4 0.0 19.7 1.1 2.1 2.7 18 2003 316 97 8.24 7.87 0.37 3.6 2.8 4.1 68.1 0.0 24.5 1.4 2.5 3.5 19 2003 166 46 8.46 8.10 0.36 4.2 3.2 4.6 71.6 0.0 22.2 1.1 2.1 3.0 20New IMPROVE/Virtual Temp - Ranked Daily Visibilit y Change for Cohutta (Top 25 Days for 3 Years)
Bowen 1-4 Results - Unit 4 Controlled with WESP

F(RH) % of Modeled Extinction by Species

YEAR DAY REC DV(Total) DV(BKG) DELTA DV S L SS %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF Rank

2001 341 1 9.09 7.94 1.15 3.8 2.9 4.3 69.0 0.0 24.1 1.3 2.4 3.3 1 2002 224 3 9.44 8.33 1.11 4.9 3.5 5.1 74.4 0.0 19.9 1.1 1.9 2.7 2 2002 15 16 8.86 7.82 1.04 3.4 2.7 4.0 66.9 0.0 26.2 1.3 2.3 3.4 3 2002 227 8 9.31 8.33 0.98 4.9 3.5 5.1 74.4 0.0 20.1 1.0 1.9 2.7 4 2002 365 16 8.90 7.94 0.97 3.8 2.9 4.3 69.1 0.0 23.9 1.3 2.4 3.3 5 2002 67 2 8.56 7.67 0.88 3.0 2.5 3.7 64.3 0.0 27.8 1.5 2.6 3.8 6 2001 113 1 8.44 7.60 0.84 2.8 2.3 3.6 62.7 0.0 29.0 1.5 2.9 3.9 7 2002 228 97 9.16 8.33 0.83 4.9 3.5 5.1 74.4 0.0 20.0 1.0 1.9 2.7 8 2002 230 3 9.15 8.33 0.81 4.9 3.5 5.1 74.3 0.0 19.9 1.1 2.0 2.7 9 2003 271 3 9.13 8.33 0.79 4.9 3.5 5.1 74.4 0.0 20.0 1.0 1.8 2.7 10 2003 270 1 9.12 8.33 0.79 4.9 3.5 5.1 74.5 0.0 19.9 1.0 1.8 2.7 11 2001 225 3 9.11 8.33 0.78 4.9 3.5 5.1 74.4 0.0 19.8 1.0 2.0 2.7 12 2002 226 97 9.11 8.33 0.78 4.9 3.5 5.1 74.4 0.0 20.0 1.0 1.9 2.7 13 2002 314 2 8.61 7.87 0.74 3.6 2.8 4.1 67.9 0.0 24.9 1.3 2.5 3.3 14 2001 297 3 8.83 8.10 0.73 4.2 3.2 4.6 71.8 0.0 21.9 1.2 2.0 3.1 15 2002 35 9 8.41 7.72 0.70 3.2 2.5 3.8 64.6 0.0 27.8 1.3 2.7 3.5 16 2001 114 4 8.30 7.60 0.69 2.8 2.3 3.6 63.0 0.0 29.1 1.5 2.5 3.9 17 2002 222 16 8.98 8.33 0.65 4.9 3.5 5.1 74.6 0.0 20.1 1.1 1.5 2.7 18 2001 220 54 8.98 8.33 0.64 4.9 3.5 5.1 74.8 0.0 19.5 1.1 1.8 2.8 19 2002 28 3 8.46 7.82 0.64 3.4 2.7 4.0 66.9 0.0 25.9 1.4 2.3 3.5 20 2001 279 4 8.73 8.10 0.63 4.2 3.2 4.6 71.3 0.0 22.5 1.1 2.1 3.0 21 2001 111 16 8.22 7.60 0.62 2.8 2.3 3.6 62.9 0.0 29.1 1.5 2.6 3.9 22 2002 107 97 8.21 7.60 0.61 2.8 2.3 3.6 62.8 0.0 28.8 1.5 2.8 4.0 23 2001 294 1 8.68 8.10 0.58 4.2 3.2 4.6 71.6 0.0 21.7 1.2 2.3 3.1 24 2002 207 9 8.79 8.21 0.58 4.5 3.3 4.9 73.4 0.0 20.9 1.1 1.7 2.9 25