## Technical Support Document

Visibility Modeling for BART Determination: San Juan Generating Station, New Mexico

Prepared by:

U.S. Environmental Protection Agency, Region 6

Michael Feldman Erik Snyder

### 2

Pictures taken by Erik Snyder January 2 004 from a vantage point Southeast of Bloomfield/Farmington area on Hwy 550 looking towards La Plata Mountains around Mesa Verde (previous page and above) and snow

capped mountains in the background are part of Weminuche Wilderness.

### 3 

# Table of Contents

Table of Contents

................................ ................................ ................................ ................ 3

List of Attachments

................................ ................................ ................................ ............. 4 List of Tables and Figures

................................ ................................ ................................ ... 5 Chapter 1: Background and Description of San Juan Generating Station

...................... 6

1.1 Background

................................ ................................ ................................ .......... 6

1.2 Description of San Juan Generating Station

................................ ........................ 6 Chapter 2: BART Eligible Determination

................................ ................................ ...... 9 Chapter 3: Subject

- to

- BART Determination

................................ ................................ 11 Chapter 4: BART Guidelines and Modeling Protocols

................................ ................ 14

4.1 Background and Introduction

................................ ................................ ............. 14

4.2 Calculation of Visibility Impact

................................ ................................ ......... 16

4.2.1 IMPROVE Equation

................................ ................................ ................... 17

4.2.2 Revised IMPROVE Equation

................................ ................................ ..... 17

4.2.3 Deciview Impact Calculation

................................ ................................ ...... 19

4.3 CALMET/CALPUFF MODELING

................................ ................................ .. 21

4.3.1 Modeling Protocol

................................ ................................ ...................... 21

4.3.2 Ammonia

................................ ................................ ................................ ..... 27

4.3.3 Sulfuric Acid

................................ ................................ ............................... 28 Chapter 5: B&V Approach

................................ ................................ ........................... 29

5.1 B&V Modeling Inputs

................................ ................................ ........................ 29

5.1.1 Sulfuric Acid Emission Estimates

................................ .............................. 29

5.1. 2 Background Ammonia Concentration

................................ ........................ 29

5.1.3 B&V Modeling Scenarios

................................ ................................ ........... 30

5.2 Results of NMED visibility modeling

................................ ............................... 32 Chapter 6: EPA approach

................................ ................................ ............................. 34

6.1 EPA Modeling Inputs

................................ ................................ ......................... 34

6.1.1 NOx Emission Rate

................................ ................................ ..................... 34

6.1.2 SO 2 

### Emission Rate

................................ ................................ ...................... 34

6.1.3 Sulfuric Acid Emission Estimates

................................ .............................. 35

6.1.3.1 Assumptions used in S ulfuric Acid Emission Calculations

................ 35

6.1.3.2 Comparison of Sulfuric Acid Emission Estimates

.............................. 37

6.1.3.3 Sulfuric Acid Emission Limit

................................ .............................. 38

6.1.4 Background Ammonia Concentrations

................................ ....................... 39

6.1.5 EPA Modeling Scenarios

................................ ................................ ............ 42

6.2 EPA Visibility Modeling Impacts of SJGS

................................ ........................ 43

6.2.1 Baseline to Revised (EPA) Baseline

................................ ........................... 44

6.2.2 Visibility Improvement from Operation of SCR

................................ ........ 46

### 4 

# List of Attachments

Atta chment 1 . Revised BART Cost Effectiveness Analysis for Selective Catalytic

Reduction at the Public Service Company of New Mexico San Juan Generating Station

- Final Report, November 2010 prepared by Phyllis Fox, Ph.D., P.E. for EPA Region 6.

Attachment 2 . Exhibit 1 for Cost Effectiveness Analysis Report

– Spreadsheet of SJGS

Cost Effectiveness Analysis.

Attachment 3

. EPA CALPUFF Visibility Modeling Results

### 5 

# List of Tables and Figures

Table 1

- 1. SJGS Operational Characteristics

................................ ................................ ..... 7

Table 3

- 1. WRAP BART CALPUFF screening results for SJGS

................................ ... 13

Table 3

- 2. EPA Consent Decr ee baseline visibility modeling (1ppb background ammonia

and Method 8)

................................ ................................ ................................ . 13

Table 4

- 1. Measured Visibility Impairment at Class I areas (2007

– 2008 average)

...... 14 Figure 4

- 1. Map of Class I Areas within 300 km of SJGS

................................ ............. 15

Table 4

- 2. Class I Areas in CALPUFF Modeling for SJGS

................................ ............ 15

Table 4

- 3. Natural Background Concentrations used by NMED and EPA for ―Method 6‖

................................ ................................ ................................ ......................... 19

Table 4

- 4. Natural Background Concentrations used by EPA for ―Method 8‖

............... 20 Figure 4

- 2. CALMET and CALPUFF computational domain.

................................ ....... 22

Table 4

- 5. Selected CALMET settings for SJGS

................................ ............................ 25

Table 4

- 6. Impacts of SJGS on Visibility using B&V post

- consent decree baseline

emissions (average of 98 th 

### percentile of daily maximum dv for 2001, 2002, and 2003) at Sixteen Class I Areas

................................ ................................ . 27

Table 5

- 1. Assumptions Used by B&V for H 2 

### SO 4 

### Emission Estimates

.......................... 29

Table 5

- 2. Ammonia background concentration in ppb

................................ .................. 29

Table 5

- 3. CALPUFF Modeling Emission Inputs (lb/hr)

................................ ................ 31

Table 5

- 4. NMED Modeling Results

-- Impacts of SJGS on Visibility (average of 98 th



### percentile of daily maxi mum dv for 2001, 2002, and 2003) at Sixteen Class I

Areas

................................ ................................ ................................ ............... 33

Table 6

- 1. Assumptions used in Sulfuric acid (H 2 

### SO 4 

### ) Emissions Calculations by NPS.

................................ ................................ ................................ ......................... 36

Table 6

- 2. SJGS Facility

- Wide Sulfuric Acid Emission Estimates

................................ . 38

Table 6

- 3. EPA Modeling Inputs (lb/hr)

................................ ................................ .......... 43

Table 6

- 4. EPA Modeling Results

– Impacts of SJGS on Visibility (maximum of 98 th



### Percentile of daily maximum dv of 2001, 2002, and 2003) at Sixteen Class I Areas (1ppb background ammonia concentration, Method 8)

........................ 45

Table 6

- 5. EPA Modeling Results

- Summary of total visibility impairment at all 16 Class

I sites.

................................ ................................ ................................ .............. 45

Table 6

- 6. EPA Modeling Results

– Impacts of SJGS on Visibility (maximum of 98 th



### Percentile of daily maximum dv of 2001, 2002, and 2003) at Sixteen Class I Areas (1ppb background ammonia concentration, Method 8)

........................ 47

Table 6

- 7. EPA Modeling Results

- Summa ry of total visibility impairment at all 16

Class I sites.

................................ ................................ ................................ .... 47

Table 6

- 8. Average number of days per year SJGS visibility impacts exceed 1.0 and 0.5

deciviews (1 ppb background ammonia and Met hod 8).

................................ 48

### 6



## Chapter 1: Background and Description of San Juan Generating

Station

1.1 B ACKGROUND



### As described in our proposed action Federal Register notice, EPA is proposing to disapprove a portion of the State Implementation Plan (SIP) re vision submitted by the State of New Mexico for the purpose of addressing the ―good neighbor‖ requirements of section 110(a)(2)(D)(i) of the Clean Air Act (CAA or Act) for the 1997 8

- hour ozone National Ambient Air Quality Standards (NAAQS or standards) an d the 1997 fine particulate matter (PM2.5) NAAQS. The SIP revision addresses the requirement that New Mexico‘s SIP must have adequate provisions to prohibit emissions from adversely affecting another state‘s air quality through interstate transport. EPA is proposing to disapprove the New Mexico Interstate Transport SIP provisions that address the requirement of section 110(a)(2)(D)(i)(II) that emissions from New Mexico sources do not interfere with measures required in the SIP of any other state under par t C of the CAA to protect visibility. EPA is also proposing to promulgate a Federal Implementation Plan (FIP) to prevent emissions from New Mexico sources from interfering with other states‘ measures to protect visibility, and to implement nitrogen oxides (NO x ) and sulfur dioxide (SO 2 

### ) emission limits necessary at one source to prevent such interference. In addition, EPA is proposing sulfuric acid (H 2 

### SO 4 

### ) and ammonia (NH 3 

### ) hourly emission limits at the same source, to minimize the contribution of these co mpounds to visibility impairment. EPA also proposes that compliance with the emission limits be within three (3) years of the effective date of our final rule. Furthermore, EPA is proposing the FIP to address the requirement for best available retrofit t echnology (BART) for NOx for this source. This action is being taken under section 110 and part C of the CAA. This Technical Support Document, including attachments and appendices, and other docket materials provides much of the support for our proposed actions.

1.2 D ESCRIPTION OF 

### S AN 

### J UAN 

### G ENERATING 

### S TATION



### The Public Service of New Mexico‘s (PNM) San Juan Generating Station (SJGS)

includes four coal

- fired boilers burning subbituminous coal from the San Juan Mine. SJGS is located in San Juan County, 15 mi les west of Farmington in northwest New Mexico. SJGS Units 1 and 2 have a unit capacity of 350 and 360 MW, respectively.

The se units are subcritical, wall

- fired boilers that operate in a forced draft mode. SJGS Units 3 and 4 have a unit capacity of 544 M W each and are equipped with subcritical, opposed wall

- fired boilers that operate in a forced draft mode. Chapter 2 of this document addresses the SJGS as being BART eligible and Chapter 3 discusses the evaluation of SJGS as being subject

- to

- BART. The r emaining chapters summarize the methodology and results of modeling to evaluate the degree of improvement in visibility which may reasonably be anticipated to result from the use of control t echnology

.

On March 10, 2005, PNM entered into a consent decree with the Grand Canyon Trust, Sierra Club, and New Mexico Environmental Department (NMED), resulting in the

installation of low

- NO x 

### burners (LNB) with overfire air (OFA) ports and a neural network (NN) system to reduce NO x 

### emissions, and a full

- sized pulse jet fabric filter

### 7

(PJFF) to reduce PM emissions. 1 

### The wet limestone scrubber was modified to eliminate flue gas bypass, and dibasic acid (DBA) was added to the scrubber process to improve

SO 2 

### removal. Installation of these controls on all four units was completed in the spring of 2009. Prior to installation of controls to meet the consent decree, all SGJS units were equipped with hot

- side electrostatic precipitators (ESP) for PM control

, and wet limestone flue gas desulfurization (FGD) systems for SO 2 

### co ntrol

. SJGS Units1, 3, and 4

also had LNB controls for NOx prior to the installation of controls for the consent decree.

Table 1

- 1

. SJGS Operational Characteristics

Unit #1 Unit #2 Unit #3 Unit #4
Unit Rating, MW (gross) 360 350 544 544

+Boiler Heat Input,

MBtu/hr 3,707 3,688 5,758 5,649

Type of

Boiler/Manufacturer Wall

- fired/ Foster

Wheeler Wall

- fired/ Foster

Wheeler Opposed Wall

fired/Babcock &

Wilcox Opposed Wall

fired/Babcock &

Wilcox Existing emissions controls (post

- consent decree):

NOx LNB/ OFA/NN for all units

SO2 Wet FGD for all units

PM PJFF for all units

NO

x Post

- consent

decree baseline

emission rate 0.30 lb/ MMBtu for each unit

SO 2 Presumptive

BART emission rate 90% annual average control, not to exceed 0.250 lb/ MMBtu for a seven da y block average for each unit

PM Post

- consent

decree baseline

emission rate 0.015 lb/ MMBtu for each unit



### In a permit modification to the construction permit for SJGS, NMED issued a revised construction permit (NSR Air Quality Permit No. 0063

- M6) on Apri l 22, 2008 to incorporate some of the conditions from the consent decree. The construction permit was issued by the Air Quality Bureau of the NMED to SJGS pursuant to the New Mexico Air Quality Control Act and regulations and is considered a federally enf orceable permit. EPA is not a party to the consent decree, but the inclusion of limits from the consent decree that have been included in the construction permit for the facility were issued pursuant to the federally approved construction permitting progr am of the New Mexico SIP. Specifically, the construction permit includes the NOx, SO 2 

### and PM limits from the

consent decree that are identified in Table 1

- 1. This permit has since been superceded by a further construction permit modification that also in cludes the consent decree limits on
NOx, SO 2 

### , and PM emissions and is federally enforceable. 2

1 Consent Decree in The Grand Canyon Trust and Sierra Club, Plaintiffs, The State of New Mexic o, Plaintiff

- Intervenor, v. Public Service Company of New Mexico, Defendant, (CV 02

- 552 BB/ACT (ACE)), lodged in the United States District Court, District of New Mexico, on March 10, 2005, at 15

- 16.

2 New Mexico Environment Department Air Quality Bureau NSR Air Quality Permit No. 0063

- M6R1 was issued on September 12, 2008 and superceded Permit No. 0063

- M6.

### 8

However, EPA does not believe this permit provides the necessary emission limits and enforceable mechanisms to ensure the levels used in the Western Regional A ir Partnership (WRAP) photochemical modeling for the NOx and SO 2 

### emissions of the

SJGS units. 3 

### Since other WRAP states are using levels modeled for the SJGS units in their demonstration of reasonable progress towards natural visibility conditions, any

dis crepancies between what was included in the WRAP photochemical modeling and what is presently enforceable, is a concern for this action. EPA must establish federally enforceable limits for pollutants that impact visibility projections within the WRAP phot ochemical modeling that were utilized by WRAP states to demonstrate reasonable progress towards natural visibility conditions. For the four SJGS units the consent decree

limits of 0.30 lb/MMBtu of NOx on a 30

- day rolling average is less restrictive than t he

0.27 and 0.28 lb/MMBtu of NOx emission rates that were included in the WRAP‘s photochemical modeling that are being used for assessing daily visibility impacts. Thus the NOx permit limits are at a higher level than what the WRAP modeling assumed.

Simi larly, in previous communications to NMED and the WRAP, PNM had indicated that the 90% annual average control would be expected to yield roughly a 0.195

lb/MMBtu of SO 2 

### , which is much higher than the 0.15 lb/ MMBtu emission rate utilized

in the WRAP‘s photo chemical modeling. 4 

### EPA notes that the 90% reduction is based on the annual average sulfur content in the coal combusted in the SJGS‘s four units and is not a hard limit. It is an annual average limit that can fluctuate depending upon the annual average of the sulfur content. The most restrictive of the emission limits within the current construction permit for SJGS is the 0.250 lb/MMBtu on a 7

- day block average

for each unit. This limit is much less restrictive than the 0.15 lb/MMBtu emission rate

that was used for daily impacts within the WRAP‘s photochemical modeling.

Thus, the NOx and SO 2 

### permit limits are at a higher level than what the WRAP photochemical modeling assumed. EPA does not see an enforceable mechanism in place to guarantee the NOx and SO 2 

### emission limits assumed in the WRAP modeling, which adjacent states are assuming in their reasonable progress demonstrations. Consequently, we are proposing to disapprove New Mexico‘s SIP as not meeting the requirement of prohibiting emissions fr om interfering with efforts to protect visibility in other states

. CAA section 110(a)(2)(D)(i)(II).

3 The Western Regional Air Partnership (WRAP) is a voluntary partnership of state, tribal, federal, and local air agencies dealing with regional ai r quality issues in the West. Member states include Alaska, Arizona, California, Colorado, Idaho, Montana, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, and Wyoming. The WRAP established various committees to assist in managing

and de veloping RH work products. New Mexico is a WRAP member.

4 Comments Received to

- Date on the Draft 2018 Base Case Projections, Version: December 21, 2005.

Available here: http://www.wrapair.org/forums/ssjf/documents/eictts/Projections/Summary%20of%20Comments_1221 05_final.pdf , pdf pagination 20.

### 9 

## Chapter 2: BART Eligible Determination

In 1999, EPA published a final rule to address a type of visibility impairment known as regional haze (64 FR 35714, July 1, 1999). The regional haze rule requires States to submit state implementation plans (SIPs) to address regional haze visibility impairment in 156 Federally

- protected parks and wilderness areas. The 1999 rule was issued to fulfill a long

- standing EPA commi tment to address regional haze under the authority and requirements of sections 169A and 169B of the Clean Air Act (CAA). As required by the CAA, EPA included in the final regional haze rule a requirement for Best Available Retrofit Technology (BART) for certain large stationary sources. The regulatory requirements for BART were codified at 40 CFR 50.308(e) and in definitions that appear

in 40 CFR 50.301.

40 CFR Part 51.301 defines BART

- eligible sources are those sources which have the potential to emi t 250 tons per year or more of a visibility impairing air pollutant, were put in place between August 7, 1962 and August 7, 1977, and whose operations fall within one or more of 26 specifically listed source categories. Under the CAA, BART is required for any BART

- eligible source which a State determines ―emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility in any such area.‖ Accordingly, for stationary sources meeting these criteria,

States mu st address the BART requirement when they develop their regional haze SIPs.

The EPA published a second rulemaking on July 6, 2005 that made changes to the Final Rule published July 1, 1999. The second rulemaking was in response to a U.S. District

Court of Appeals ruling that vacated part of the regional haze rule. The June 6, 2005 Final Rule required the BART analysis to include an analysis of the degree of visibility improvement resulting from the use of control technology at BART

- subject sources;

revi sed the BART provisions; included new BART Guidelines contained in a new Appendix Y to Part 51; and added the requirement that States use Appendix Y for determining BART at certain large electrical generating units (EGUs). The Guidelines also contained sp ecific presumptive limits for SO 2 

### and NOx for certain large EGUs based on fuel type, unit size, cost effectiveness, and presence or absence of pre

- existing controls. For NOx emissions, the EPA directs states to generally require owners and

operators to me et the presumptive limits at coal

- fired EGUs greater than 200 MW at power plants with a total generating capacity greater than 750 MW. The presumptive limits for NOx are based on coal type, boiler type and whether SCR or SNCR are already

installed at the source.

New Mexico and the WRAP identified the BART eligible sources in New Mexico. 5 

### In May 2006, NMED conducted an internal review of sources potentially subject to the BART rule and identified 11 source complexes that were BART eligible. The four coal

-

5 Draft Report ―Identification of BART

- Eligible Sources in the WRAP Region‖, prepared for the WRAP by Eastern Research Group, Inc.; April 4, 2005.

### 10

fired EGU units (Units 1

- 4) at the San Juan Generation Station were identified as one of these 11 BART eligible source complexes by NMED and the WRAP. 6

6 WRAP spreadsheets revised (09/21/06) Appendix K: Eligibility of Steam Electric Plants at the Unit

Level ―WRAP App_K_BART

- Eligible_EGUs. XLS ‖ and Appendix H: Master List of Sources and Their Eligi bility WRAP App_H_All_Sources_v5.XLS‖.

### 11 

## Chapter 3: Subject

- to

- BART Determination

Section III of the BART Guidelines outlines the steps involved in ident ifying sources that are subject to BART. Previously, the state of New Mexico and the WRAP identified sources subject to BART in New Mexico. Based on the list of identified sources (see previous chapter discussion), the WRAP performed the initial BART mod eling for the state of New Mexico. The procedures used are outlined in the WRAP Regional Modeling Center (RMC) BART Modeling Protocol. 7 

### The basic assumptions in the WRAP BART CALMET/CALPUFF modeling used for New Mexico are as follows:

• Use of three ye ars of modeling of 2001, 2002, and 2003.

• Visibility impacts due to emissions of SO 2 

### , NOx and primary PM emissions

were calculated.

• PM emissions were modeled as PM2.5.

• Visibility was calculated using the Original IMPROVE equation and Annual Averag e Natural Conditions.

Initial modeling was performed by the WRAP for the 11 source complexes in New Mexico that were considered BART eligible.

As we note in the BART Guidelines, one of the first steps in determining whether a

source causes or contribut es to visibility impairment is to establish a threshold (measured in deciviews). A single source that is responsible for a 1.0 deciview change or more should be considered to  ̳ ̳cause‘‘ visibility impairment; a source that causes less than a

1.0 deciview c hange may still contribute to visibility impairment and thus be subject to

BART. The modeling of each individual source complexes‘ combined SO 2 

### , NOx, and PM emissions was completed and results of the 98 th 

### percentile visibility impacts at any

Class I area were evaluated to determine if visibility impacts at any Class I area exceeded the 0.5 dv significance threshold. Of the 11 source complexes analyzed, only one source complex‘s visibility impacts at any Class I area due to combined SO 2 

### , NOx, and PM

emissi ons exceeded the 0.5 dv threshold (PNM San Juan Generating Station Boilers #1

- 4). Of the 10 other source complexes, none exceed a 0.33 dv impact. Consequently, only

the PNM San Juan Boilers #1

- 4 were determined by NMED to be subject to a BART

determinatio n. The results of this analysis (Table 3

- 1) indicated that the S an J uan G enerating S tation (SJGS) , on a facility

- wide basis, ―causes‖ visibility impairment at all

16 Class I areas within 300 km of the facility. 8



### Therefore, a conclusion was made that the units of the SJGS cause and/ or contribute to visibility impairment in many nearby Class I areas. We note in the BART Guidelines that states (and by extension EPA when promulgating a FIP) have flexibility in

7 ―CALMET/CALPUFF Protocol for BART Exemption Screening Analysis for Class I Areas in the Western United States‖, Western Regional Air Partnership (WRAP); Gail Tonnesen, Zion Wang; Ralph Morris, Abby Hoats and Yiqin J ia, August 15, 2006. Available at: http://pah.cert.ucr.edu/aqm/308/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf

8 Summary of WRAP RMC BART Modeling for New Mexico Draft#4, April 21, 2006 ( http://pah.cert.ucr.edu/aqm/308/bart/calpuff/summary/NM_summary_v4_Ap r21_2007.doc )

### 12

determining an appropriate threshold for determi ning whether a source ―contributes to any visibility impairment‖ for the purposes of BART. However, this threshold should not be higher than 0.5 deciviews. In the case of the SJGS, this decision point is moot, since visibility modeling indicates that even using the default contribution threshold of 0.5 deciviews, the SJGS contributes to visibility impairment at a number of Class I areas.

On November 9, 2006, New Mexico informed PNM that the modeling performed by the WRAP indicated the visibility impairm ent from the San Juan Generating Station (SJGS) was over the 0.5 dv threshold, and was therefore subject to a BART analysis

However, this modeling was based on the installed control technology at the time and

does not reflect emission reductions due to th e installation of consent decree controls.

Refined modeling performed by the N ew M exico Environmental Department (NM ED ) and by the E nvironmental Protection Agency (EPA) (Table 3

- 2) in 2010, including the controls required by the consent decree

, both show that each individual unit at SJGS still ―cause‖ visibility impairment at Mesa Verde National Park, and contribute to visibility impairment at other Class I areas. 9

, 10 

### As discussed further in following Chapters, EPA‘s modeling was updated to utilize the la test EPA approved version of CALPUFF because

of some technical concerns that had been raised with the later version of CALPUFF that was originally used in the WRAP‘s modeling. The changes in CALPUFF versions did not change the conclusions that were made f or the SGJS units. Therefore, we find that units 1, 2, 3, and 4 of the SJGS are subject to BART. Details on modeling protocols and inputs used are discussed later in this document.

9 Visibility modeling was primarily performed by the contractor Black and Veatch for PNM and submitted to NMED for review. Throughout this document, any references to ―NMED modeling‖ refers to modeling performed or reviewed by NMED.

10 PNM San Juan Generating Station Refined BART Visibility Results, Black and Veatch, March 31, 2008 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/03312008Final_BART_Modeling_Response_wi th_Attachments_0331.pdf )

### 13

Table 3

- 1

. WRAP BART CALPUFF screening results for SJGS

Class I Area Minimum

Distance 98th Percentile for Each

Year 3 year

average (km) 2001 2002 2003

Arches 222 4.06 3.71 3.59 3.79

Bandelier Wilderness 210 2.47 2.90 3.08 2.82
Black Canyon of the Gunnison

Wilderness 203 2.38 2.27 2.43 2.36

Canyonlands 170 6.21 4.33 4 .44 4.99
Capitol Reef 232 4.00 2.02 2.35 2.79
Grand Canyon 285 2.12 1.50 1.18 1.60

Great Sand Dunes National Monument 269 1.47 1.59 1.74 1.60

La Garita Wilderness 169 1.63 1.82 1.77 1.74

Maroon Bells

- Snowmass Wilderness 271 1.19 1.27 1.15 1.21

Mesa V erde 40 5.54 5.34 5.30 5.40

Pecos Wilderness 248 2.17 2.63 2.81 2.53

Petrified Forest 213 1.62 1.27 1.03 1.31

San Pedro Parks Wilderness 155 3.80 4.07 4.14 4.01

Weminuche Wilderness 98 2.24 2.99 2.41 2.55

West Elk Wilderness 216 2.14 1.90 2.20 2.08

W heeler Peak Wilderness 258 1.94 1.73 1.97 1.88



### Table 3

- 2. EPA Consent Decree baseline visibility modeling (1ppb background ammonia and Method 8)

Class I Area Average of 98th percentile for 2001, 2002, and 2003 (dv)

Unit 1 Unit 2 Unit 3 Unit 4 Facili ty

Arches 0.47 0.47 0.69 0.67 2.03

Bandelier Wilderness 0.27 0.27 0.41 0.41 1.27 Black Canyon of the Gunnison

Wilderness 0.23 0.23 0.37 0.36 1.15

Canyonlands 0.74 0.74 1.08 1.06 3.05 Capitol Reef 0.28 0.28 0.37 0.37 1.21

La Garita Wilderness 0.32 0.3 2 0.49 0.48 1.51 Grand Canyon 0.11 0.11 0.17 0.17 0.56

Great Sand Dunes National Monument 0.24 0.24 0.37 0.37 1.18

Mesa Verde 1.67 1.67 2.36 2.34 5.13

Pecos Wilderness 0.23 0.23 0.36 0.35 1.11

Petrified Forest 0.10 0.10 0.15 0.15 0.47

San Pedro Parks Wilderness 0.47 0.46 0.73 0.71 2.13

Maroon Bells Snowmass Wilderness 0.11 0.11 0.18 0.18 0.56

West Elk Wilderness 0.24 0.24 0.42 0.41 1.26

Weminuche Wilderness 0.58 0.58 0.85 0.83 2.41

Wheeler Peak Wilderness 0.22 0.22 0.34 0.33 1.07

### 14 

## Chapter 4: BART Guidelines and Modeling Protocols

4.1 B ACKGROUND AND 

### I NTRODUCTION



### The BART analysis includes engineering and modeling methods and procedures used to determine the appropriate controls for the SJGS Units to reduce the source‘s contribution to pollutant concentrations tha t result in visibility impairment in the surrounding Class I areas. The final factor to consider under EPA's BART Guidelines is the degree of visibility improvement from the BART control options. 11 

### The BART guidelines recommend use of the CALPUFF air qual ity dispersion model to estimate the visibility improvements at each Class I area, typically within a 300 km (186 mile) radius of the source, of alternative control technologies, and to compare these to each other and to the impact of the baseline, or curr ent, source configuration. There are 16 Class I areas within

300km of the SJGS. These are listed in Table 4

- 1 and 4

- 2 and shown in Figure 4

- 1. The measured visibility impairment (in deciviews) at each Class I area is presented in Table

4

- 1 as a range of the 10 th 

### (mean of Best 20% days) and 90 th 

### (mean of Worst 20% days) percentiles, averaged for 2007 and 2008.

Table 4

- 1. Measured Visibility Impairment at Class I areas (2007

– 2008 average) 12

Class I Area 10 th Percentile Deciview Impairment 90 th Percen tile Deciview Impairment

Arches 2.9 11.3

Bandelier W ilderness 4.2 12.2 Black Canyon of the Gunnison

W ilderness 2.2 10.5

Canyonlands 2.9 11.3 Capitol Reef 2.8 11.5 Grand Canyon 2.1 11.2

Great Sand Dunes N ational Monument 3.5 11.2

La Garita W ilde rness 2.2 10.5

Maroon Bells Snowmass W ilderness 0.2 9.3

Mesa Verde 3.1 11.0

Pecos W ilderness 1.2 8.6

Petrified Forest 4.5 12.5

San Pedro Parks W ilderness 2.8 11.3

West Elk W ilderness 0.2 9.3

Weminuche W ilderness 2.2 10.5

Wheeler Peak W ilderness 1 .2 8.6

11 59 FR 39104, 39170 (July 6, 2005)

12 IMPROVE monitoring network ( Regional Haze Rule Summary data , Revised (new) IMPROVE Algorithm

, http://vista.cira.colostate.edu/improve/Data/IMPROVE/summary_data.htm )

### 15

Figure 4

- 1. Map of Class I Areas within 300 km of SJGS

Table 4

- 2. Class I Areas in CALPUFF Modeling for SJGS

Class I Area Abbreviation State Agency IMPROVE

monitor 13 Minimum

Distance to SJGS (km)

Arches arch UT NPS CANY1 222

Bandelier Wild erness band NM NPS BAND1 210 Black Canyon of the

Gunnison Wilderness blca CO NPS WEMI1 203

Canyonlands cany UT NPS CANY1 170 Capitol Reef care UT NPS CAPI1 232 Grand Canyon grca AZ NPS GRCA2 285

Great Sand Dunes National

Monument grsa CO NPS GRSA1 269

La Garita Wilderness laga CO USFS WEMI1 169

Maroon Bells Snowmass

Wilderness mabe CO USFS WHRI1 271

Mesa Verde meve CO NPS MEVE1 40

Pecos Wilderness peco NM NPS WHPE1 248

Petrified Forest pefo AZ NPS PEFO1 213

San Pedro Parks Wilderness sape NM USFS SAPE1 155

Weminuche Wilderness wemi CO USFS WEMI1 98

West Elk Wilderness weel CO USFS WHRI1 216

Wheeler Peak Wilderness whpe NM USFS WHPE1 258

13 For a Class I area without an IMPROVE monitor of its own, the nearest monitor in anothe r area is used.

### 16

Visibility modeling was performed for the San Juan Generating Station and included the

sixteen Class I Ar eas within 300 km of the facility in the modeling analysis. Although not officially submitted to EPA, NMED completed a NOx and PM BART determination for the SJGS, which EPA found to be thorough and comprehensive. 14 

### Although EPA generally regards the visib ility modeling analyses performed by NMED to be of high quality, some minor issues were noted. EPA remodeled the visibility impacts of SJGS to rectify these issues in order to address consistency with modeling guidance EPA has provided to the states. NME D and EPA modeling protocols are discussed later in this chapter, followed by detailed discussion of model inputs and results in the following chapters.

4.2 C ALCULATION OF 

### V ISIBILITY 

### I MPACT



### Under the RHR, visibility is measured in deciviews. Visibility is traditionally described in terms of visual range in kilometers or miles. However, the visual range scale does not correspond to how people perceive visibility: a given increase in visual range is perceived differently depending on how good the original visibility was (that is, it is not on a linear scale). The deciview scale is designed to address this problem. It is linear with respect to perceived visual changes over its entire range, analogous to the decibel scale for sound: a given change in deciv iews will be perceived as the same amount of visibility change, whatever the original visibility was. The defining equation is:

deciviews = 10*ln(b ext 

### / 10)

where ln is the natural logarithm, and extinction b ext 

### is the fraction of light scattere d out of a viewing path. Extinction increases with the amount of pollution (b ext 

### is in units of

1/Mm , ―inverse megameters‖). Lower deciview values represent better visibility, more pristine atmospheres, and greater visual range, while increasing deciview values represent increasingly poor visibility. An increase of 1 deciview corresponds to about a 10% increase in extinction. (Deciviews are related to the more traditional visual range according to dv = 10*ln(391 / visual range)). An eligible BART sourc e with a predicted impact of 0.5 dv or more of impairment in a Class I area ―contributes‖ to visibility impairment and is subject to BART. 15



### Under the BART guidelines, deciviews are estimated using the CALPUFF air quality

model. CALPUFF predicts 24

- hour average pollutant concentrations based on source emissions and how they disperse in the atmosphere. The CALPUFF modeling includes source emissions of the following visibility

- impairing pollutants: SO 2 

### , SO 4 

### , NO

x 

### , secondary organic aerosol (SOA), fine part iculate matter (PMF), coarse particulate matter (PMC), and elemental carbon (EC). CALPUFF incorporates a semi

- empirical chemical

14 New Mexico Environment Department, Air Quality Bureau, BART Determination, Public Service Company of New Mexico, San Juan Generating Station, Units 1

- 4, June 21, 2010. Available here: http://www.nmenv.state.nm.us/aqb/reghaz/documents /AppxA_NM_SJGS_NOxBARTDetermination_ 06212010.pdf

15 70 FR 39104, 39121 (July 6, 2005)

.

### 17

module that simulates the conversion of SO 2 

### to particulate sulfate and NO

x 

### to particulate nitrate, at a rate dependent on mete orological conditions and background ozone concentration. These concentrations are converted to deciviews by the CALPOST post

processor in two steps.

4.2.1 IMPROVE Equation Under the original IMPROVE equation, extinction ( b ext 

### ) is estimated from the predicte d concentrations of various pollutants: 16



### b ext 

### = 3 * f(RH) * [sulfate] + 3 * f(RH) * [nitrate] + 4 * [organic mass] + 10 * [ elemental carbon ] + 1 * [ fine soil ] + 0.6 * [ coarse mass ]

+ 10

The 10 that is added accounts for Rayleigh scatteri ng
, which is due to the interaction of light with molecules of air itself with no pollutants

. The f(RH) is a water growth factor

for sulfate and nitrate ; i ts value depends on relative humidity (RH) , ranging from 1 at low humidity to 18 at 98% humidity.

4.2.2 Revised IMPROVE Equation

EPA used the ―revised‖ IMPROVE equation to calculate light extinction. 17 

### The revised IMPROVE equation is used to convert measured or modeled concentrations into extinction for each pollutant chemical species, and then total them up, accounting for the effect of relative humidity

.

Revised IMPROVE equation:

b ext 

### = 2.2 * f

s 

### (RH) * [small sulfate] + 4.8 * f L 

### (R H) * [large sulfate] + 2.4 * f

s 

### (RH) * [small nitrate] + 5.1 * f L 

### (RH) * [large nitrate] + 2.8 * [small organic mass] + 6.1 * [large organic mass] + 10 * [ elemental carbon ] + 1 * [ fine soil ] + 1.7 * f

ss 

### (RH) * [sea salt] + 0.6 * [ coarse mass ] + Rayleigh scattering (site

- specific) + 0.33 * [NO 2 

### (ppb)]

16 Interagency Monitoring of Protected Visual Environments (IMPROVE) is a network of monitors in various Class I Areas, established to assess visibility impairment and its causes. The IMPROVE equation is used to converted monitored concentrations into extinction, a measure of visibility. See: http://vista.cira.colostate.edu/improve/

17 Pitchford, M. L., W. C. Malm, B. A. Schichtel, N. Kumar, D. Lowenthal and J. L . Hand, Revised algorithm for estimating light extinction from IMPROVE particle speciation data, Journal of the Air & Waste Management Association, 57 , 1326

- 1336, 2007.

### 18

Sulfate is assumed to be all ―large sulfate‖ if total sulfate is over 20 μg/m 3



### , otherwise its fraction of the total is assumed to increase uniformly between 0 and 1 when the total i s in the range between 0 and 20 (i .e. large sulfate = (total sulfate/20)*total ) . A similar definition applies for nitrate and for organic mass. The organ ic mass is assumed to be 1.8 times the organic carbon mass that is measured by IMPROVE monitors, an increase over

the old 1.4. Sea salt is estimated as 1.8 * [chloride] (or chlorine if chloride not available)

T he f

s 

### , f L 

### , f

ss 

### are water growth factors for small (―S‖) and large (―L‖) fractions of sulfate and nitrate, and for sea salt (―SS‖). Their values depend on relative humidity, ranging from 1 at low humidity to over 5 at 95% humidity. Rayleigh scattering is due to the interaction of light with molecul es of air itself with no pollutants.

The IMPROVE program revised the IMPROVE equation after a scientific assessment of its implications for regional haze planning to reduce biases in light extinction estimates compared to the old algorithm. 18 

### In particul ar, when compared to nephelometer direct measurements of visibility extinction, the original IMPROVE equation over

- predicts for

low extinction conditions and under

- predicts for high extinction. These biases have

direct relevance for estimates for the wors t 20% visibility days that are used to assess visibility impact. The new equation shows broader scatter overall, but less bias in matching visibility measurements under high and low visibility conditions. The split between small and large particles was t he main factor in reducing the biases. The revised IMPROVE equation has less bias, is more refined, accounts for more pollutants, incorporates more recent data, and is based on considerations of relevance for the calculations needed for assessing progress under the R egional H aze R ule

.

EPA guidance states that the use of either the IMPROVE or the revised IMPROVE equation is acceptable provided that the same algorithm is utilized for both the base and

future extinction calculations. 19 

### Since EPA has redone the analysis we have been able to incorporate the revised IMPROVE equation, which we believe is appropriate and the more preferred method for analyses being conducted at this time. 20

, 21

, 22 

### EPA has

18 Revised IMPROVE algorithm for Esimating Light Extinction from Particle Speciation Data

, IMPROVE, January 2006 ( http://vista.cira.colostate.edu/improve/Publications/GrayLit/gray_literature.htm ) ; Hand, J.L., Douglas, S.G., 2006, Review of the I MPROVE Equation for Estimating Ambient Light Extinction Coefficients

– Final Report ( http://vista.cira.colostate.edu/improve/Publications/Gra yLit/016_IMPROVEEeqReview/IMPROVEeq

Review.htm )

19 Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze , EPA

- 454/B

- 07

- 002

, April 2007, EPA OAQPS; ( http://www.epa.gov/ttn/scram/guidance/guide/final

- 03

- pm

- rh

- guidance.pdf )

20 U.S. EPA. 2006a. Additional Regional Haze Questions. U.S. Environmental Protections Agency. August 3. (http://www. wrapair.org/forums/iwg/documents/Q_and_A_for_Regional_Haze_8

- 03

- 06.pdf#search=%22%22New%20IMPROVE%20equation%22%22).

21 WRAP presentation, ― Update on IMPROVE Light Extinction Equation and Natural Conditions

Estimates ‖ Tom Moore, May 23, 2006.

22 U.S. For est Service, National Park Service, and U.S. Fish and Wildlife Service. 2010. Federal land managers‘ air quality related values work group (FLAG): phase I report

— revised (2010). Natural Resource Report NPS/NRPC/NRR

— 2010/232. National Park Service, Denver, Colorado.

### 19

provid ed alternative calculations using the original IMPROV E equation for comparison with these visibility calculation results.

4.2.3 Deciview Impact Calculation

In the second CALPOST step this extinction is converted to deciviews, using the defining equation (equation 1) for deciviews. The delta deciviews (Δdv) represents the impact on visibility in deciviews of the source being evaluated, and is also provided by CALPUFF. It is the change, or ―delta‖, between deciviews with and without the source. Under the BART guidelines, delta deciv iews is the difference between deciviews including the impact of the source and natural background, and deciviews of the natural background alone. Each modeled day and location in the Class I area will have an associated delta deciview. For each day, the model finds the maximum visibility impact of all locations (receptors) in the Class I area. From among these daily values, the BART guidelines recommend use of the 98th percentile, roughly equivalent to the 8 th 

### highest day, visibility impacts expressed a s 24

- hour averages of delta deciviews relative to natural background, as estimated using the CALPUFF air quality modeling system for comparing the base case and the effects of various controls. The 98th percentile is recommended rather than

the maximum va lue to avoid undue influence from unusual meteorological conditions and conservative assumptions in the model.

The BART guidelines recommend that impacts of sources be estimated in deciviews relative to natural background. NMED and EPA used background c oncentrations (Table

4

- 3) from Table 2

- 1 of ―Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule‖ for calculations using ―Method 6‖

. 23 

### EPA used average background concentrations (Table 4

- 4) and relative humidity adjustment fa ctors from the Federal Land Manag ers‘ Air Quality Related Values Work Group (FLAG) Phase I Report for use with the revised IMPROVE equation (―Method 8‖). 24



### Table 4

- 3. Natural Background Concentrations used by NMED and EPA for ―Method 6‖

CALPOST parame ter Pollutant Concentration (μg/m 3 )

BKSO4 Ammonium sulfate 0.12

BKNO3 Ammonium nitrate 0.10

BKPMC Coarse particulates 3.00

BKOC Organic carbon 0.47

BKSOIL Soil 0.50

BKEC Elemental carbon 0.02

23 U.S. Environmental Protection Agency, Guidance fo r Estimating Natural Visibility Conditions Under the Regional Haze Rule

, EPA

- 454/B

- 03

- 005, September 2003, on web page http://www.epa.gov/ttn/oarpg/t1/memoranda/rh_envcurhr_gd.pdf

24 U.S. Fore st Service, National Park Service, and U.S. Fish and Wildlife Service. 2010. Federal land managers‘ air quality related values work group (FLAG): phase I report

— revised (2010). Natural Resource Report NPS/NRPC/NRR

— 2010/232. National Park Service, Denver, C olorado ( http://www.nature.nps.gov/air/Pubs/pdf/flag/FLAG_2010.pdf )

### 20

Table 4

- 4. Natural Background Concentrations used by EP A for ―Method 8‖

Class I Area CALPOST parameters

BKSALT BKSO4 BKNO3 BKPMC BKOC BKSOIL BKEC

Salt Ammonium

sulfate Ammonium

nitrate Coarse

particulates Organic

carbon Soil Elemental

carbon

Arches 0.01 0.12 0.10 2.92 0.60 0.50 0.02

Bandelier

Wilderness 0.02 0.12 0.10 2.86 0.60 0.50 0.02

Black Canyon

of the Gunnison

Wilderness 0.01 0.12 0.10 2.59 0.60 0.50 0.02

Canyonlands 0.01 0.12 0.10 2.92 0.60 0.50 0.02
Capitol Reef 0.00 0.12 0.10 2.78 0.58 0.50 0.02
Grand Canyon 0.02 0.12 0.10 2.88 0.60 0.50 0.02

Great Sand

Dunes National

Monument 0.01 0.12 0.10 2.99 0.58 0.50 0.02

La Garita

Wilderness 0.01 0.12 0.10 2.59 0.60 0.49 0.02

Maroon Bells

Snowmass

Wilderness 0.00 0.12 0.10 2.59 0.60 0.48 0.02

Mesa Verde 0.00 0.12 0.10 2.88 0.60 0.50 0.02

Pecos

Wild erness 0.03 0.12 0.10 2.29 0.60 0.50 0.02

Petrified Forest 0.01 0.12 0.10 3.00 0.60 0.50 0.02

San Pedro

Parks

Wilderness 0.01 0.12 0.10 2.19 0.60 0.50 0.02

Weminuche

Wilderness 0.00 0.12 0.10 2.12 0.60 0.50 0.02

West Elk

Wilderness 0.00 0.12 0.10 2.12 0.60 0.50 0.02

Wheeler Peak

Wilderness 0.00 0.12 0.10 2.29 0.60 0.50 0.02

### 21

4.3 CALMET/CALPU FF MODELING

4.3.1 Modeling Protocol

The contractor, Black & Veatch (B&V), was hired by PNM toconduct modeling for the SJGS according to modeling protocols submitted to N MED. 25 

### NMED reviewed this modeling and performed additional modeling following the same general approach. This modeling analysis generally followed the BART protocol developed by the WRAP. 26



### The WRAP protocol received extensive scrutiny during 2005

- 2006 by various States, EPA Regions, and Federal Land Managers (FLMs). Some key features of the protocol are listed here. Meteorological fields were derived from the MM5 prognostic meteorological model, using a 36 km grid resolution, for the years 2001, 2002 , and 2003 and with the continental United States as the modeling domain. Subdomains were extracted for use by individual states, such as New Mexico. The MM5 fields were then processed using CALMET (meteorological pre

- processor, part of the CALPUFF

model ing system) as preparation for use in the CALPUFF air quality model, using a 4 km grid resolution. After much discussion, WRAP elected to blend meteorological station observations into the MM5 fields, instead of relying on the CALMET ―no observations‖ (―n o obs‖) mode.

The NMED modeling protocol followed the WRAP protocol. CALMET and CALPUFF settings were almost all the same. One difference to note is that the modeling domain was but a subset of the modeling domain used by the WRAP, the New Mexico

sub domain shown in figure 4

- 2 was used; all Class I Areas within 300 km of SJGS were included. The BART visibility modeling protocol used by B&V was revised to include variable background ammonia concentrations and nitrate repartitioning. 27 

### All modeling

reli ed on the same MM5 meteorological simulation data and terrain data developed and utilized by the WRAP.

25 Public Service Company of New Mexico San Juan Generating Station Best Available Retrofit Technology Analysis, Black and Veatch, June 2007 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/PNMBARTReport_AppendixO

- 1.pdf ) and PNM San Juan Generating Station Refined BART Visibility Results, Black and Veatch, March 31,

2008

26 ―CALMET/CALPUFF Pro tocol for BART Exemption Screening Analysis for Class I Areas in the Western United States‖, Western Regional Air Partnership (WRAP); Gail Tonnesen, Zion Wang; Ralph Morris, Abby Hoats and Yiqin Jia, August 15, 2006. Available on UCR Regional Modeling Cen ter web site, BART CALPUFF Modeling, http://pah.cert.ucr.edu/aqm/308/bart/WRAP_RMC_BART_Protocol_Aug15_2006.pdf

27 PNM San Juan Generating Station Refined BART Visibility Results, Black and Veatch, March 31, 2008 ( http://www.nmenv.state.nm.us/aqb/reghaz/d ocuments/03312008Final_BART_Modeling_Response_wi th_Attachments_0331.pdf )

### 22

Figure 4

- 2. CALMET and CALPUFF computational domain.

The protocol/modeling methodologies followed by the EPA for modeling visibility impacts f rom SJGS differs from that used by the WRAP and NMED. First, EPA used the EPA current approved regulatory versions of the CALPUFF (v. 5.8) modeling system which is composed of CALPUFF version 5.8, and CALMET version 5.8. EPA used

CALPOST version 6.221 th at was approved by the Federal Land Managers to allow for application of the revised IMPROVE equation (―Method 8‖). 28 

### Some problems have been identified in the CALPUFF modeling system (v6.2111) since the original WRAP CALPUFF modeling was conducted which i s also the version used by PNM‘s contractors

and NMED. 29

28 U.S. Forest Service, National Park Service, and U.S. Fish and Wildlife Service. 2010. Federal land managers‘ air quality related values work group (FLAG): phase I report

— revised ( 2010). Natural Resource Report NPS/NRPC/NRR

— 2010/232. National Park Service, Denver, Colorado ( http://www.nature.nps.gov/air/Pubs/pdf/flag/FLAG_2010.pdf )

29 ―CALPUFF: Status and Update‖ , Dennis Atkinson, Presentation at Regional/State/Local Modelers

Work shop, May 16, 2007. ( http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/archive/200 7/presentations/Wednes day%20

- %20May%2016%202007/CALPUFF_status_update.pdf ); EPA report, ―Assessment of the ―VISTAS‖ Version of the CALPUFF Modeling System‖, EPA

- 454/R

- 08

- 007, August 2008 available at ( http://www.epa.gov/ttn/scram/reports/calpuff_vistas_as sessment_report_final.pdf ); ― CALPUFF Regulatory Update ‖, Roger W. Brode, Presentation at Regional/State/Local Modelers Workshop, June

### 23

EPA previously performed an analysis of changes to the CALPUFF modeling system that were included in the CALPUFF v6.2111 model system (also referred to as the ―VISTAS version‖). EPA‘s review indica ted that significant differences between the changes incorporated in the VISTAS version and the previously EPA approved version of CALPUFF (v5.711a) were present. EPA‘s review determined that the majority of the concerns were related to the new default pa rameters THRESHL and THRESHW for the threshold buoyancy energy flux over land and water, respectively. The incorporation of these new thresholds and other code changes had set up situations where the boundary layer could artificially collapse if the sensi ble heat flux fell below a threshold. This impact could occur when the boundary layer is not actually collapsing. Because we wish to minimize technical concerns with the CALPUFF modeling system in making a critical decision to install controls, we have u sed the CALPUFF version that has been approved by EPA for regulatory purposes that does not include this boundary layer collapsing problem or other problems identified with the VISTA‘s version of CALPUFF.

Another difference in our modeling is the option ( NOOBS = 0) was selected by EPA in

order to utilize surface and upp er air station observations. For blending in meteorological station wind observations, NMED used a maximum radius of influence for stations of 50 km for surface stations (RMAX1) and 100 km for aloft (RMAX2). R1 (R2) defines the distance from an observation at which the surface (upper air) observation and the initial wind field are equally weighted and is typically set to half the value of RMAX1(RMAX2) in order to transition from the observe d value to the modeled value

over the distance RMAX1. R1 was set at 100 km and R2 at 200 km in the WRAP and NMED modeling which can lead to more sudden transitions in wind fields at the distance

of RMAX1. Per FLM and EPA‘s recommendations

, EPA increased these values to 100

k m and 200 k m, respectively. 30 

### Various relevant CALMET option settings are listed in

Table 4

- 5.

For visibility calculations, the pollution concentration predictions of CALPUFF are used in post

- processing. CALPUFF modeling was perfor med for each unit separately. Pollution concentration predictions were then summed together to evaluate the facility

wide impacts before the post

- processing steps described here. First, POSTUTIL considers ammonia background concentrations, temperature, a nd humidity to determine how much nitrate ends up as particulate ammonium nitrate; this is to reflect the competition between sulfate and nitrate for available ammonia and is referred to as ―Nitrate Repartitioning

. ‖ Ammonia background is discussed further below. Second, CALPOST uses the adjusted concentrations to calculate visibility using the IMPROVE equation, as described above. The WRAP and B&V protocols specified visibility calculation ―Method 6‖ in CALPOST, which utilizes the original IMPROVE equati on.

10

- 12, 2008, available at ( http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/archive/2008/presentations/BRODE _CA.pdf ).

30 Memorandum from Tyler Fox (EPA OAQPS) ―Clarification on EPA

- FLM Recommended Settings for CALMET,‖ August 31, 2009 ( http://www.epa.gov/ttn/scram/CALMET%20CLARIFICATION.pdf )

### 24

EPA ran CALPOST using ―Method 6‖ to allow comparison with previous WRAP, B&V and NM modeling. EPA also calculated visibility impacts using ―Method 8,‖ which uses the revised IMPROVE equation to calculate visibility using the adjusted concentrations

a nd used this approach in doing our evaluation of visibility impacts from proposed

reductions in NOx

.

### 25

Table 4

- 5. Selected CALMET settings for SJGS

Option Description EPA

default WRAP NMED 31 EPA Comments on EPA options

version CALMET version 5.8,

level

070623 6.211,

level

060414 6.211,

level

060414 5.8, level

070623

NOOBS No Observation

Mode 0 2 1 0 Per FLM recommendation use both surface and upper air

observations

ICLOUD gridded cloud fields 0 0 0 0

IWFCOD diagnostic winds 1 1 1 1

IFRADJ Froude wind adj. 1 1 1 1

IKINE kinematic effects 0 0 0 0

IOBR O'Brien vertical wind

adj. 0 0 0 0

ISLOPE slope flows 1 1 1 1

IEXTRP extrapolate wind to

upper air - 4 1 1

- 4

ICALM extrapolate calm to

upper air 0 0 0 0

BIAS layer biases sfc vs.

UA NZ*0 NZ *0 NZ*0

- 1,0,0,0,0,
0,0,0,0,0,0

IPROG gridded initial

prognostic 0 14 14 14 Use prognostic inputs from

MM5

RMAX1 max surface radius of

influence NA 50 50 100 Per FLM recommendation

RMAX2 max aloft radius of

influence NA 100 100 200 Per FLM recommendati on

RMAX3 max over

- water

radius of influence. NA 100 100 100

RMIN min wind radius of

influence. 0.1 0.1 0.1 .1

RMIN2 min dist sfc winds

extrap 4 4 4 4

TERRAD terrain radius of in

influence NA 10 10 10

R1 weight surface Step 1

vs. obs NA 100 100 50 Per FLM recommendation, equally weighs observation and prediction at midpoint of

RMAX1

R2 weight aloft Step 1

vs. obs NA 200 200 100 Per FLM recommendation, equally weighs observation and prediction at midpoint of

RMAX2

ITPROG 3D T from obs or

prognostic 0 1 or 2 implied 1 0

TRADKM radius of influ. for T

interp. 500 500 500 500

IAVET spatial T averaging 1 0 1 1 Smoothing turned on (WRAP thought MM5 already smooth

enough)

JWAT1 starting land use for

T interp. over water 999 51 55 55 This area has very little "water" land use; may set to 999 to

disable overwater feature

JWAT2 ending land use for T

interp. over water 999 55 55 55

31 PNM S JGS Best Available Retrofit Technolog y Analysis, Black and Veatch, June 2007, http://www.nmenv.state.nm.us/aqb/reghaz/documents/PNMBARTReport_AppendixO

- 1.pdf

### 26

To assess the variability from the different CALMET version change we utilized the same criteria pollutant emission rates and the same ammonia and H 2 

### SO 4 

### emission rates as documented in the B&V modeling. Table 4

- 6 shows the difference in visibility impacts due to the different CALMET version and settings used by EPA, including the inclusion of upper air data. CALPUFF and CA LPOST were run with the same post

- consent decree emissions rates and options as those used by B&V in their visibility modeling.

This does show some differences between the two approaches, but overall the level of visibility impact from the SJGS is simil ar in total delta deciview with the EPA approach yielding a value slightly lower than the B&V modeling (17.84 dv vs. 19.86 dv for the cumulative average). While impacts at individual Class I areas varied, the overall magnitude of impact was slightly less, but similar. This information helps to support the

conclusion that the differences due to the operation of CALMET in accordance with the WRAP protocol several years ago and the current regulatory approach that EPA has

conducted in this action would not l ikely change the original determination by the WRAP

and NMED of which sources screen out of BART and which are subject to a full BART analysis. Therefore EPA is proposing to conclude that of the BART eligible sources identified by NMED and the WRAP, only the SJGS units are subject to BART.

### 27

Table 4

- 6. Impacts of SJGS on Visibility using B&V post

- consent decree baseline

emissions ( average of 98 th 

### p ercentile of daily maximum dv for 2001, 2002, and 2003 ) at

Sixteen Class I Areas

Class I Area Distance

to SJ GS (km) B&V modeling with CALMET

6.211 EPA modeling with CALMET

5.8

2001 2002 2003 Average 2001 2002 2003 Average

Arches 222 1.69 1.65 1.49 1.61 1.99 0.81 0.98 1.26

Bandelier Wilderness 210 1.04 1.56 1.20 1.27 1.12 1.02 0.84 1.00

Black Canyon of the

Gunnison Wilderness 203 0.95 1.15 1.07 1.05 0.96 1.27 0.63 0.95

Canyonlands 170 2.26 1.73 1.68 1.89 2.32 1.35 1.28 1.65

Capitol Reef 232 1.81 0.82 1.05 1.23 1.64 0.48 0.72 0.95

Grand Canyon 285 0.97 0.76 0.57 0.77 0.73 0.29 0.35 0.46

Great Sand Dunes

N ational Monument 269 0.63 0.71 0.64 0.66 0.87 1.28 1.04 1.06

La Garita Wilderness 169 0.86 0.94 0.90 0.9 0.95 1.64 0.82 1.14

Maroon Bells

Snowmass

Wilderness 271 0.54 0.56 0.51 0.54 0.61 0.58 0.31 0.50

Mesa Verde 40 3.38 3.53 3.8 3.57 2.68 3.21 2.99 2.9 6

Pecos Wilderness 248 1.05 1.09 1.00 1.05 0.74 1.02 0.8 0.85

Petrified Forest 213 0.82 0.60 0.53 0.65 0.52 0.38 0.42 0.44

San Pedro Parks

Wilderness 155 1.40 2.01 1.56 1.66 1.35 1.42 1.25 1.34

Weminuche

Wilderness 98 1.15 1.48 1.34 1.33 1.43 1.96 1.41 1.60

West Elk Wilderness 216 0.8 0.91 0.83 0.85 0.88 1.09 0.52 0.83

Wheeler Peak

Wilderness 258 0.75 0.86 0.89 0.83 0.88 0.95 0.75 0.86

Total Delta dv 20.1 20.36 19.06 19.86 19.67 18.75 15.10 17.84



### In addition to the general modeling procedures, we must address two additional pollutants when estimating the impact of various control technologies on visibility improvement: ammonia (NH 3 

### ) and sulfuric acid (H 2 

### SO 4 

### ).

4.3.2 Ammonia

Ammonia is important because it is a precursor to particulate ammonium sulfate and ammonium nitrate, both of which degrade visibility. It is present in the atmosphere from both natural and anthropogenic sources. The latter may include livestock operations, fertilizer application associated with farming, and ammonia slip (or un

- rea cted ammonia)

from its use in SCR or SNCR to control NO

x 

### emissions.

### 28

4.3.3 Sulfuric Acid

Sulfuric acid (H 2 

### SO 4 

### ) is formed from coal

- fired power plants from the oxidation of sulfur dioxide (SO 2 

### ) to sulfur trioxide (SO 3 

### ) and the subsequent reaction of SO 3 

### with water vapor (SO 3 

### + H 2 

### O = H 2 

### SO 4 

### ). Sulfuric acid (also known as sulfuric acid mist) can combine with ammonia to form particulate ammonium sulfate, which is an important contributor to visibility impairment. CALPUFF models emissions of H 2 

### SO 4 

### as inorganic

pa rticulate sulfate.

We will address these two additional pollutants when estimating the impact of various control technologies on visibility improvement: ammonia (NH 3 

### ) and sulfuric acid (H 2 

### SO 4 

### ) in greater detail in the next Chapters.

### 29 

## Chapter 5: B&V Approach

5.1 B& V M ODELING 

### I NPUTS



### 5.1.1 Sulfuric Acid Emission Estimates

B&V estimated sulfuric acid released from combustion by using the U.S. National Park Service (NPS) speciation worksheet and assuming that all inorganic condensable PM is

H 2 

### SO 4



### . 32 

### Sulfuric acid due to SCR operation was calculated assuming 1% conversion of

SO 2 

### to SO 3 

### based on the economizer outlet emission of SO 2 

### . Removal efficiencies for the PJFF and FGD system were applied to the sulfuric acid produced by the SCR. Table

5

- 1 outlines the assumptions used by B&V for the sulfuric acid calculation.

Table 5

- 1. Assumptions Used by B&V for H 2 

### SO 4 

### Emission Estimates

Unit 1 Unit 2 Unit 3 Unit 4
Controlled SO3 emissions (lb/hr) from boiler 33 40.5 40.3 62.9 61.7

Economizer SO 2 emissions (lb/hr) 34 5,884 5,854 9,1 40 8,967

Conversion of SO 2 to SO 3 due to SCR (%) assumed by B&V

based on economizer SO 2 emissions 35 1% 1% 1% 1%

% Removal by PJFF (for inherent removal scenario) 36 50% 50% 50% 50% % Removal by FGD system (for inherent removal scenario) 37 40% 40% 40% 40%



### 5.1.2 Background Ammonia Concentration

As discussed above, in addition to emissions from the facility, the background ammonia concentrations must be specified for the model. The IWAQM default value for ammonia background in this region is 1 ppb year

- round. Th e revised B&V modeling used background ammonia concentrations that vary monthly based on concentrations used for the PSD ambient air quality impacts analysis for the Desert Rock Energy Center. Table

5

- 2 compares the IWAQM default values with the ammonia v alues used by B&V.

Table 5

- 2. Ammonia background concentration in ppb

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
IWAQM default 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

B&V values 0.2 0.2 0.5 0.5 1.0 1.0 1.0 1.0 1.0 0.5 0.5 0.2

32 From Dry Bottom PC w FGD+FF PM Spe ciation Profile, PM10 Speciation is from AP

- 42 Tables 1.1

- 5

& 1.1

- 6 ( http://www.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm ). All inorganic

condensable PM was assumed to be H2SO4.

33 Public Service Company of New Mexico San Juan Generating Station Best Available Retrofit Technology Analysis, Black and Veatch, June 2007

34 Id.

35 Id.

36 PNM San Juan Generating Station Review of Inherent SO3 Removal Scenarios and S orbent Injection for SO3 Removal, Black and Veatch, March 16, 2009, ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/COMPLETE_Final_SO3_Removal_Report_031 609.pdf )

37 Id.

### 30

5.1.3 B&V Model ing Scenarios

Although not officially submitted to us, NMED completed a NOx and PM BART determination for the SJGS (referred to herein as the ―NMED BART evaluation‖), which we have found to be thorough and comprehensive. 38 

### In making our NOx BART

determin ation for the SJGS, we drew heavily upon the NOx BART portion of that document, and used it to help inform our NOx BART determination for the SJGS. NMED concluded that, other than SCR, none of the NOx control technologies were capable of significantly imp roving the visibility at any of the 16 Class I areas, therefore, we did not further evaluate them. We note that SCR was capable of uniformly improving the visibility at all of the 16 Class I areas.

Table 5

- 3 summarizes the emission inputs NMED used for visibility modeling for SJGS. Although NMED included a number of control scenarios in addition to SCR in their modeling, we focused on the SCR control scenarios as detailed above. We note that this modeling conducted by NMED was performed in accordance with the WRAP protocol and therefore did differ from the CALPUFF modeling system approach that EPA is utilizing in this action. As we noted in the last section, we conducted a sensitivity test to assess the potential changes in delta deciview that may occ ur due to these differences and concluded that the results from the two approaches give similar results that when weighed in a relative sense, yield the same conclusions. Therefore, EPA believes that even if we had redone the NMED analysis for controls ot her than SCR based controls, that our conclusion that controls utilizing SCR (or equivalent technology) and the proposed emission limit levels would be the only scenario that would be beneficial in reducing visibility impacts and being cost effective.

The modeling scenarios presented here are the B&V baseline (post consent decree) emissions, B&V emission inputs for the SCR control case, 39 

### SCR with removal of SO 3 

### from inherent controls, and SCR with sorbent injection. 40 

### B&V assumed an SCR

controlled emission rate of 0.07 lb/MMBtu for NOx. SO 2 

### and total filterable particulate (PM10) emissions were held at the post

- consent decree rates of 0.18 lb/MMBtu for SO 2 

### and 0.015 lb/MMBtu for PM10 for visibility modeling.

38 New Mexico Environment Department, Air Quality Bureau, BART Determination, Pu blic Service Company of New Mexico, San Juan Generating Station, Units 1

- 4, June 21, 2010. Available here: http://www.nmenv.state.nm.us/aqb/reghaz/documents/AppxA_NM_SJGS_NOxBARTDetermination_0 6212010.pdf

39 Public Service Company of New Mexico San Juan Generating Station Best Available Retrofit Technology Analysis, Black and Veatch, June 2007 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/PNMBARTReport_Appen dixO

- 1.pdf ) and PNM San Juan Generating Station Refined BART Visibility Results, Black and Veatch, March 31,

2008 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/03312008Final_BART_Modeling_Response_wi th_Attachments_0331.pdf )

40 PNM San Juan Generati ng Station Review of Inherent SO3 Removal Scenarios and Sorbent Injection for SO3 Removal, Black and Veatch, March 16, 2009 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/COMPLETE_Final_SO3_Removal_Report_031 609.pdf )

### 31

Table 5

- 3. CALPUFF Modeling Emission I nputs (lb/hr)

Unit 1 Unit 2 Unit 3 Unit 4 

### B&V Baseline (post

- consent decree controls)

SO 2 667.26 663.84 1036.44 1016.82

SO 4 40.5 40.3 62.9 61.7

NO

x 1223.31 1217.04 1900.14 1864.17

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23. 5 36.6 35.9

EC 1.0 1.0 1.6 1.6



### B&V SCR control scenario

SO 2 667.26 663.84 1036.44 1016.82

SO 4 114.2 113.6 177.3 174

NO

x 259.49 258.16 403.06 395.43

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23.5 36.6 35.9

EC 1.0 1.0 1.6 1.6



### B&V SCR w/ inherent removal control scenario

SO 2 667.26 663.84 1036.44 1016.82

SO 4 62.61 62.29 97.22 95.39

NO

x 259.49 258.16 403.06 395.43

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23.5 36.6 35.9

EC 1.0 1.0 1.6 1.6



### B&V SC R w/ sorbent injection control scenario

SO 2 667.26 663.84 1036.44 1016.82

SO 4 16.09 16.01 24.99 24.52

NO

x 259.49 258.16 403.06 395.43

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23.5 36.6 35.9

EC 1.0 1.0 1.6 1.6



### Note that all particulate modeling was performed using 1 gram per second nominal emission rates in CALPUFF for the specific size ranges presented in the NPS speciation spreadsheets (i.e., PM 8.0 

### , PM 4.25 

### , PM 1.88 

### , PM 1.25 

### , PM 0.813 

### , and PM 0.625 

### ). 41 

### These modeled species ar e grouped in POSTUTIL as to represent the appropriate visibility pollutants (e.g., PMcourse = PM 8.0 

### + PM 4.25 

### and PMfine = PM 1.88 

### + PM 1.25 

### + PM 0.813 

### + PM 0.625 

### ) and

are then scaled with the emission rates derived from the NPS spreadsheets. EC and SOA

are de rived from the PM 1.88 

### species and scaled with the emission rates derived from the NPS spreadsheets.

41 Dry Bottom PC w FGD+FF PM Spec iation Profile, PM10 Speciation is from AP

- 42 Tables 1.1

- 5 &

1.1

- 6 ( http://www.nature.nps.gov/air/permits/ect/ectCoalFiredBoiler.cfm )

### 32

Although the control technology scenarios may be the same (e.g. SCR on Units 1

– 4), the assumptions and control level emission rates will differ between t he scenarios modeled by B&V previously and the final scenarios that EPA has conducted for this analysis. For example, based on more recent information, different assumptions and methodology in calculating emissions of H 2 

### SO 4 

### resulted in different H 2 

### SO 4 

### emi ssion rate inputs for visibility modeling.

5.2 R ESULTS OF 

### NMED VISIBILITY MODELING



### NMED presented various control scenarios‘ visibility improvement s in terms of the 98 th


### percentile (8 th 

### highest) of the daily maximum delta deciviews from CALPUFF for each

C lass I area, averaged over 2001

– 2003 and the maximum of the 98 th 

### Percentile of daily

maximum dv for 2001, 2002 and 2003 . As outlined in the 1999 Regional Haze rule (64 FR 35725, July 1, 1999), a one deciview change in visibility impairment is a small bu t perceptible change under most circumstances when viewing scenes in a Class I area.

Table 5

- 4 presents the impact of SJGS on visibility at each Class I area, based on the 98 th


### percentile of daily maxima from 2001

– 2003

, as modeled by NMED

. NMED modelin g indicates significant improvements in visibility are achieved through the use of SCR and sorbent injection. Based on all factors, including visibility impacts, NMED proposed to conclude that BART for the SJGS was SCR plus sorbent injection.

Except fo r the following points, we agree with NMED‘s proposed conclusions regarding

BART for SJGS:

NMED evaluated the visibility benefits of SCR at the SJGS based on a NOx emission limit of 0.07 lbs/MMBtu, but noted the potential for greater control at

rates as l ow as 0.03 lbs/MMBtu. We have concluded that a NOx emission limit of 0.05 lbs/MMBtu is achievable for the SJGS through operation of SCR, and performed our visibility modeling on that basis.

NMED concluded BART for the SJGS was SCR plus sorbent injection to remove sulfur trioxide (SO 3 

### ) in the flue gas by reaction with an alkaline material. We have concluded that sorbent injection is not necessary, as the SJGS burns a low sulfur coal, and catalysts are available with a low SO 2 

### to SO 3 

### conversion rate.

Our estimates of sulfuric acid emissions from SJGS with SCR installed are significantly lower than those presented by NMED in their analysis, but are closer in magnitude to the emission rates that PNM has reported for the SJGS for EPA‘s

annual Toxic Release In ventory (TRI). 42



### Theses points are discussed further in the following chapter.

42 TRI data is available at http://www.epa.gov/tri/tr idata/current_data/index.html . 2009 data is considered preliminary.

### 33

Table 5

- 4. NMED Modeling Results

-- Impacts of SJGS on Visibility ( average of 98 th 

### p ercentile of daily maximum dv for 2001, 2002, and 2003 ) at Sixteen Class I Areas

Class I Area Distance to

SJGS (km) Visibility Impact ( dv) after applying: Visibility improvement over baseline ( dv)

Baseline (post

consent decree) SCR 43 SCR (w/ inherent reductions) 44 SCR (w/

sorbent injection) 45 SCR SCR (w/

inherent reductions) SCR (w/

sorbent injection)

Arches 222 1.61 1.54 1.25 0.96 0.07 0.36 0.65

Bandelier Wilderness 210 1.27 1.16 0.92 0.72 0.11 0.35 0.55
Black Canyon of the

Gunnison Wilderness 203 1.05 0.85 0.71 0.57 0.2 0.34 0.48

Canyonlands 170 1.89 2.01 1.61 1.32

- 0.12 0.28 0.57
Cap itol Reef 232 1.23 1.06 0.93 0.78 0.17 0.3 0.45
Grand Canyon 285 0.77 0.63 0.54 0.46 0.14 0.23 0.31

Great Sand Dunes

National Monument 269 0.66 0.53 0.46 0.35 0.13 0.2 0.31

La Garita Wilderness 169 0.9 0.66 0.54 0.43 0.24 0.36 0.47

Maroon Bells Snowmas s Wilderness 271 0.54 0.39 0.33 0.26 0.15 0.21 0.28

Mesa Verde 40 3.57 5.55 4.02 2.37

- 1.98

- 0.45 1.2

Pecos Wilderness 248 1.05 0.92 0.77 0.60 0.13 0.28 0.45

Petrified Forest 213 0.65 0.61 0.51 0.43 0.04 0.14 0.22

San Pedro Parks

Wilderness 155 1.66 1. 87 1.46 1.09

- 0.21 0.2 0.57

Weminuche Wilderness 98 1.33 1.4 0.99 0.70

- 0.07 0.34 0.63

West Elk Wilderness 216 0.85 0.64 0.53 0.41 .21 0.32 0.44

Wheeler Peak

Wilderness 258 0.83 0.67 0.58 0.47 0.16 0.25 0.36

Total Delta dv 19.86 20.49 16.15 11.92

- 0.63 3.71 7.94

43 PNM San Juan Generating Station Refined BART Visibility Results, Black and Veatch, March 31, 2008 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/03312008Final_BART_Modeling_Respo nse_with_Attachments_0331.pdf )

44 PNM San Juan Generating Station Review of Inherent SO3 Removal Scenarios and Sorbent Injection for SO3 Removal, Black and Vea tch, March 16, 2009 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/COMPLETE_Final_SO3 _Removal_Report_031609.pdf

45 Id.

### 34 

## Chapter 6: EPA approach

6.1 EPA M ODELING 

### I NPUTS



### 6.1.1 NOx Emission Rate

PNM assumed a controlled emission rate resulting from installation of SCR of 0.07

lb/ MMBtu

. NMED evaluated the visibility benefits of SCR at the SJGS based on this emission limit, but note d the potential for greater control at rates as low as 0.03

lbs/ MMBtu

. NOx continuous emission monitor (CEM) d ata from the EPA C lean Air

Markets Division show that SCR retrofits routinely result in 90% removal efficiency

. We

have concluded that a NOx emi ssion limit of 0.05 lbs/ MMBtu on a 30

- day rolling

average for each unit is achievable at the SJGS (~83% control from baseline of 0.30) , and performed ou r visibility modeling on that basis. Additional supporting information on the attainable efficiency of SCR is available in the cost effectiveness analysis. 46



### 6.1.2 SO 2 

### Emission Rate

As previously discussed, there are no federally enforceable limits that restrict the SJGS‘s

SO 2 

### emissions to a maximum emission rate of 0.15 lbs/MMBtu or less, the rate assumed by the WRAP in its photochemical modeling, or the 0.18 lbs/ MMBtu that was used for modeling visibility impacts of SJGS by NMED.

In a permit modification to the construction permit for SJGS, NMED issued a revised construction permit (NSR Air Quality Per mit No. 0063

- M6) on April 22, 2008 to incorporate some of the conditions from the consent decree that include a 90% reduction

in SO 2 

### emissions based on the annual average sulfur content in the coal combusted in the

SJGS‘s four units and 0.250 lb/ MMBtu on a 7

- day block average for each unit

. 47 

### We

also note that for the four SJGS units the consent decree limits of 0.30 lb/MMBtu of NOx

on a 30

- day rolling average is less restrictive than the 0.27 and 0.28 lb/MMBtu of NOx

emission rates that were included in th e WRAP‘s photochemical modeling that is being used for assessing daily visibility impacts. Thus the NOx permit limits are also at a higher level than what the WRAP modeling assumed. This permit has since been superceded by a further construction permit m odification that also includes the consent

decree limits on NOx and SO 2 

### emissions and is federally enforceable. 48



### However as previously discussed, EPA does not believe this permit, or other applicable requirements, provides the necessary emission limits and enforceable mechanisms to ensure the levels used in the WRAP photochemical modeling for the NOx and SO 2

46 Revised BART Cost Effectiveness Analysis for Selective Catalytic Reduction at the Public Service Company of New Mexico San Juan Generating Station Final Report, Phyllis Fox, November 2010.

47 NOx limit of 0.30 lb/ MMB tu on a 30

- day rolling average for each of the four units; SO2 limit of 90% annual average control for each unit, with a short

- term limit not to exceed 0.250 lb/ MMBtu for a seven

day block average.

48 New Mexico Environment Department Air Quality Bureau N SR Air Quality Permit No. 0063

- M6R1 was issued on September 12, 2008 and superceded Permit No. 0063

- M6.

### 35

emissions of the SJGS units. Since other WRAP states are using levels modeled for the SJGS units in their demonstration of reasonable progress towa rds natural visibility conditions, any discrepancies between what was included in the WRAP photochemical modeling and what is presently enforceable, is a concern for this action. EPA must establish federally enforceable limits for pollutants that impact v isibility projections within the WRAP photochemical modeling.

EPA used the proposed emission rate of 0.15 lb/ MMBtu, which is consistent with the presumptive BART limit that was assumed by the WRAP

, for modeling visibility impacts

of SJGS

. EPA notes t hat a n examination of the SJGS‘s actual emission rates based on emissions reported by EPA‘s Clean Air Markets Division indicates units 1, 2, 3, and 4 of the SJGS are already meeting these lower SO 2 

### emission limits. 49



### 6.1.3 Sulfuric Acid Emission Estimates

EPA used calculations by the NPS submitted to NMED in response to the proposed Regional Haze SIP and later revised that differed from certain assumptions and methodology used by NMED for SJGS to calculate the sulfuric acid emissions. 50

, 51 

### In particular, the a mount of sulfuric acid produced during combustion, the rate of SO 2 

### to
SO 3 

### oxidation from the SCR catalyst and the amount of sulfuric acid that penetrates through (or is lost to) the downstream equipment differed from SJGS and NMED‘s.

Sulfuric acid emissio ns from power plants were calculated by NPS by estimating the

amount of H 2 

### SO 4 

### produced and the amount of H 2 

### SO 4 

### removed by control equipment using information from the Electric Power Research Institute (EPRI). 52 

### These calculations rely on assumed values for the amount of fuel sulfur converted to SO 2 

### , the

amount of SO 2 

### oxidized to SO 3 

### , and the amount of H 2 

### SO 4 

### lost to (or mitigated by) the air preheater and applicable control equipment, such as baghouses, and FGDs. Baseline and

post

- control estimates of H 2 

### SO 4 

### from SJGS are based on the best current information available from EPRI and coal properties (Table 6

- 1).

6.1.3.1 Assumptions used in Sulfuric Acid Emission Calculations

49 http://camddataandmaps.epa.gov/gdm/index.cfm

50 Comments of the National Park Service and US Fish and Wildlife Service Regarding the New Mexico

Re gional Haze State Implementation Plan August 17, 2010 ( http://www.nmenv.state.nm.us/aqb/reghaz/documents/FWS

- NPS_Comments

NewMexicoRegionalHazeSIP

- 17AUG2010.pdf )

51 Don Shepherd to Michael Feldman, EPA Region 6 (electronic mail) dated Nov. 1, 2010

– revis ed NPS calculations to correct for errors in penetration factors.

52 Electric Power Research Institute, Estimating Total Sulfuric Acid Emissions from Stationary Power Plants, 1016384, technical Update, March 2008. (from this point forward this document is referred to as EPRI 2008).

### 36 Table 6

- 1. Assumptions used in Sulfuric acid (H 2 

### SO 4 

### ) Emissions Calculation s by

NPS. 53

EPRI Parameter EPA (revised

NPS calculations)
Fuel Impact Factor (SO 3 formation from

combustion) 0.00111

Coal sulfur content (%) 0.77

Coal S conversion to SO 2 0.875

Conversion of SO 2 to SO 3 from SCR catalyst

(%) 0.5

Penetration factorthrough Scrubber 54 0. 4 Penetration factor through Air Preheater 0.49 Penetration factor through Baghouse 0.1



### The following discussion provides a brief summary of each assumption used in the

sulfuric acid emissions calculation.

The amount of fuel sulfur that is oxidized to SO 2 

### is estimated for three different coal types. Hardman et al. (1998) report sulfur conversion to SO 2 

### (based on AP

- 42) to be 0.95 for bituminous coal, 0.875 for subbituminous coal, and 0.55

– 0.85 for lignite coal, based on sodium (Na) content. NPS c alculations relied on the value of 0.875. 55



### For coal

- fired power plants without SCR, SO 3 

### is only formed during the combustion process. The amount of SO 2 

### that is oxidized to SO 3 

### during combustion depends on coal type and the type of boiler. SO 3 

### reacts rapidly with water to form sulfuric acid.

Measurements of SO 3 

### production show a wide range (0.00111

– 0.016) but many of the values reported in the EPRI 2008 document are based on single measurements. EPRI 2008 and Harrison 2005 both report the SO 3 

### produ ction factor from Western bituminous coal from dry bottom boilers to be 0.00111, or 0.111% of SO 2 

### production, and NPS used

this value. 56



### For coal

- fired power plants with SCR, SO 3 

### is also formed as a result of oxidation of SO 2 

### to SO 3 

### on the SCR catalyst. N MED assumed a 1% conversion of SO 2 

### to SO 3 

### across the SCR catalyst layers, whereas NPS used 0.5% conversion efficiency because low conversion catalysts are commercially available to specifically reduce H 2 

### SO 4 

### formation following the application of SCR. 57 

### In a paper presented in the Proceedings of the 2006

53 Don Shepherd to Michael Feldman, EPA Region 6 (electronic mail) dated Nov. 1, 2010

– revised NPS calculations to correct for errors in penetration factors.

54 Penetration factor = 1

- Control Efficiency

55 Hardman, R. , R. Stacy, E. Dismukes. Estimating Total Sulfuric Acid Emissions from Coal

- Fired Power

Plants. Southern Company Services, Revised September 1998.

56 Harrison, K.E., L.S. Monroe, J.E. Cichanowicz, Estimating Total Sulfuric Acid Emissions from Stationary Power Plants: Revision 3 (2005). Southern Company Services, October 2005. (from this point forward this document is referred to as Harrison 2005).

57 Emails between Anita Lee, EPA Region 9 and Anthony C. Favale P.E., Director

- SCR Products,

Hitachi Powe r Systems America, Ltd. Favale: ―Catalyst development has progressed over the last few

### 37

Environmental Controls Conference, written by Hitachi Power Systems America, American Electric Power, and Babcock

- Hitachi, 58 

### the authors state that conversion rates as low as 0.1% have been achieved. Althoug h NPS and EPA recognize that the rate achieved may be much lower than the guarantee, to be conservative, EPA has determined that a 0.5% conversion rate is most appropriate for sulfuric acid estimates used in the visibility modeling.

At SJGS, H 2 

### SO 4 

### rem oval can occur in the air preheaters, scrubbers and baghouses. Removal efficiencies from the air preheaters are not available from EPRI 2008 and other

references for non

- Powder River Basin (PRB) Western bituminous coal, and only one datum point is availab le for PRB coal. EPRI 2008 reports penetration factors of 0.49 (Low Sulfur Eastern Bituminous coal), 0.85 (Med

- High Sulfur Eastern Bituminous coal) and 0.56 (PRB coal). Most data is available for low

- sulfur Eastern bituminous coal: measurements from eigh t units show a range of percent removal from 0

– 69%. Before calculating the average percent removal of H 2 

### SO 4 

### observed from low

- sulfur Eastern bituminous coal, EPRI 2008 and Harrison 2005 excluded the 0% removal value because ―the data point showing 0% re moval at approximately 600 ppm SO 2 

### is believed suspect, as an identical companion unit firing the same coal exhibited sulfuric acid capture of 38%‖. Because data from other coal types was not available, EPRI 2008 and Harrison 2005 suggested that western c oals with acidic ash (e.g., bituminous) may consider adopting the low sulfur, eastern bituminous value of 0.49 for the H 2 

### SO 4 

### penetration (1

- removal), which is equivalent to 51% removal. NPS used the 0.49 value for the air preheater penetration factor sug gested for coal with low alkalinity ash.

Downstream of the air preheater, H 2 

### SO 4 

### is also reduced by PM and SO 2 

### control equipment. All SJGS Units are equipped with wet limestone scrubbers and baghouses for

SO 2 

### and PM control. NPS used the EPRI

- recommende d penetration factors of 0.4 through the wet scrubber and 0.1 through the baghouses.

6.1.3.2 Comparison of Sulfuric Acid Emission Estimates

NMED concluded BART for the SJGS was SCR plus sorbent injection to remove sulfur trioxide (SO 3 

### ) in the flue gas. We hav e concluded that sorbent injection is not necessary based on the the coal having a low sulfur content, the availability of a low reaction SCR catalyst and calculations performed by NPS. Sulfuric acid emissions calculated by the NPS using the above methodo logy are significantly lower than those calculated by NMED based on their assumptions and methodology.

A federal law called the Emergency Planning and Community Right to Know Act (EPCRA) requires that facilities report information on the toxic chemicals being released into the environment. The law requires facilities in certain industries, which

years to the point that an initial SO 2 conversion rate of 0.5% can be guaranteed with 80 to 90% NOx

reduction.‖

58 Application and Operating Results of Low SO2 to SO3 Conversion Rate Catalyst for DeNOx Application at AEP Gavin Unit 1, Anthony C. Favale, P.E. Presenter ( http://www.netl.doe.gov/publications/proceedings/06/ecc/pdfs/Favale_Summary.pdf )

### 38

manufacture, process, or use significant amounts of toxic chemicals, to report annually on their releases of these chemicals. The reports contain information a bout the types and amounts of toxic chemicals that are released each year. EPA maintains this information in a database called the Toxics Release Inventory (TRI). Reporting sulfuric acid emissions are included in this requirement. SJGS reports annual es timates of emissions

of sulfuric acid and other toxic chemicals to the TRI. Table 6

- 2 compares the estimated sulfuric acid emissions from the TRI with those used in visibility modeling. Emissions estimated by NMED are much higher than those submitted by SJGS to the TRI. EPA emission estimates include removal of sulfuric acid due to downstream equipment including, the air preheater, scrubbers, and baghouse. Installation of this equipment on all four units was not complete until spring of 2009.

Tab le 6

- 2. SJGS Facility

- Wide Sulfuric Acid Emission Estimates

Reported emissions 59 Hourly emissions 60

TRI 2007 7 2 000 lb/yr 9.7 lb/hr

TRI 2008 55000 lb/yr 7.4 lb/hr

TRI 2009 35553 lb/yr 4.8 lb/hr
NMED post

- consent decree baseline

--- 205.4 lb/hr EPA post

- consent decree baseline

--- 0.8 7 lb/hr



### 6.1.3.3 Sulfuric Acid Emission Limit

EPA believe s that these estimates of H 2 

### SO 4 

### emissions are appropriate based on the use of low reactivity catalyst that will reduce the rate of SO 2 

### to SO 3 

### conversion. To e nsure these

l evels are met, EPA is proposing that emissions of H 2 

### SO 4 

### be limited to 1.06 x 10 - 4



### lb/MMBtu. Th is emission limit is based on the most current information from EPRI

, 

### information on the sulfur content of the coal, and assuming a maximum of 0.5% SO2 to

SO3 co nversion efficiency of the SCR catalyst . These emission limits are consistent with that used in visibility modeling discussed in this section.

EPA notes that there are some assumptions and uncertainty in calculations of sulfuric

acid mist emissions . T herefore, an alternate approach based on information from PNM‘s TRI report and previous PNM calculations of the amount of additional sulfuric acid mist

due to addition of SCR was taken for the purposes of identifying a reasonable upper bound for a proposed limit. Emissions reported in the TRI are 35,553lb/yr or 2.79 x10 - 4



### lb/MMBtu for the SJGS for 2009. Addition of the SCR is estimated by PNM to increase

sulf uric acid emissions by about 182 % over baseline levels. 61 

### Using this 182 % increase

factor we esti mate an upper range of sulfuric acid mist emissions of 7.87 x10 - 4 

### lb/MMBtu using PNM‘s TRI data. The 2009 TRI emission estimate is based on estimates of sulfuric acid manufactured in the boiler and subsequent removal by downstream equipment. The

59 TRI data is available at http://www.epa.gov/tri/tridata/current_data/index.html . 2009 data is considered preliminary.

60 Hourly emissions calculated assuming that each unit is operating 85% of the year (7446 hr/yr)

61 PNM previously estimated emissions of sulfuric acid for the baseline at 205.4 lb/hr and an additional 373.99 lb/hr due to the SCR for all four units. Using this information, EPA has developed a ratio of the

increase in sulfuric acid emiss ions over baseline levels of 182 % increase (373.99/2 05.4 ≈ 1.8 2)

### 39

same rem oval factors will apply to any additional H 2 

### SO 4 

### generated by the operation of

SCR.

While a direct comparison to other facilities is not possible due to differences in system design and operating conditions, such comparison can be informative and provide an expected range for emissions. Recent stack tests at the Navajo Generating Station showed an average emission rate of 9x10 - 5 

### lb/MMBtu for H 2 

### SO 4 

### across the three units. 62


### This measured rate is lower than that estimated by the EPRI method of 2.71x10 - 4



### l b/MMBtu. 63 

### These units are larger (750 MW) than those at SJGS but have a similar design, with air preheater, wet scrubbers and hot side ESP (compared to baghouses at SJGS) currently installed. The smaller size of the units and the improved removal

efficie ncy of the baghouses over the hotside ESP, result in a lower expected emission rate

for the SJGS units.

6.1.4 Background Ammonia Concentrations

Because v ery little monitored ammonia data is available, we used t he IWAQM Phase 2

document which recommend s a default ammonia background value of 1 p art per billion (p pb ) for arid regions. 64 

### Alternative levels may be used if supported by data. B&V used lower background ammonia concentrations during the winter months based on

concentrations used for the PSD ambien t air quality impacts analysis for the Desert Rock Energy Center. EPA performed visibility modeling using both the B&V suggested background ammonia concentrations and the IWAQM default value.

We note that there is some debate about what to use historic ally for background ammonia levels and concern with the sensitivity of CALPUFF modeling estimates to the background ammonia level. EPA and others have even collected ammonia data in both the close vicinity of the power plants (SJGS and Four Corners) and i n other areas further from the SJGS and Four Corners power plants, but within the region impacted by these sources. The Desert Rock Energy Center used some of this data to argue for the use of

lower ammonia levels in the winter time. EPA has further rese arched the ammonia data collected and determined that the monitoring data validates the use of a 1 ppb threshold for background in calculating delta deciviews at receptors in Class I areas for the SJGS impacts. The following discussion in this section dis cusses our basis for the use of 1 ppb as a reasonable background number for impacts of the SJGS units on surrounding Class I

areas.

62 Salt River Project Navajo Generating Station

– Acid Mist Emissions Test Report by Catalyst Air Management, Inc. Report Number 264

- 001 (December 31, 2009).

63 BART Analysis for the Navajo Generating Station Units 1

– 3, ENSR Corporation, No vember 2007,

Document No.: 05830

- 012

- 300 ( http://www.wrapair.org/forums/ssjf/documents/bart/2007

- 11_SRP_Navajo_BART_Analysis_Report.pdf )

64 Interagency Workgroup On Air Quality Modeling (IWAQM) Phase 2 Summary Report And Recommendations For Modeling Long Range Transport Impacts (EPA

- 454/R

- 98

- 019 ) , EPA OAQPS, December 1998, http://www.epa.gov/scram001/7thconf/calpuff/phase2.pdf

### 40

Many analyses with CALPUFF have been conducted that highlight the sensitivity of CALPUFF visibility results to ammonia leve ls. The Colorado Department of Public Health and Environment (CDPHE) recently performed a sensitivity modeling analysis to explore the effect of the ammonia concentration input to CALPUFF on the predicted visibility impacts for a source with high NOx emis sions relative to SO 2 

### emissions (similar to the post

- consent decree baseline SJGS units). 65 

### The results of CPDHE‘s sensitivity modeling indicated that the largest sensitivity occurs for ammonia input values between 1 and 0.1 ppb. In that range, the differe nce in the peak visibility impacts predicted by CALPUFF is slightly more than a factor of 3 between ammonia concentration input values of 1 and 0.1 ppb. CPDHE‘s sensitivity analysis shows that the choice of background ammonia is important in terms of the magnitude of visibility impacts predicted by CALPUFF. As part of the supporting information for this action we also performed CALPUFF sensitivity modeling with the lower variable ammonia level in addition to our primary baseline with the constant 1 ppb le vel and confirmed some difference exists as well, but not necessarily as significant as in the CPDHE‘s situation.

We note that a recent report cited this CDPHE report and indicated lower ammonia levels

should be considered for the Colorado Plateau. 66 

### We h ave reviewed this material and consider it in making our proposal.

There are several factors to consider with selecting the appropriate ammonia background for estimating visibility impacts, including the length and temporal resolution of the

ammonia data collected, if the ammonia data varies depending on location of collection in comparison to proximity of SJGS plumes, fluctuation of levels throughout the year, and importance of plume chemistry from the point of emissions that react with background ammonia to the downwind receptor.

The ammonia data was based on three week integrated passive ammonia samples that were taken at five sites in the Four Corners area from December, 2006 through December, 2007 (January, 2008 for two sites). The study included a v ariety of site types: a regional background site in the Four Corners area (Mesa Verde National Park); oil and gas development impacted sites in the Four Corners area (Navajo Lake and Southern Ute, the Southern Ute site was also impacted by mobile source em issions); a coal

- fired power plant impacted site in the Four Corners area (Substation

– near the Four Corners and SJGS power plants); and an urban site in the Four Corners area (Farmington Airport). 67



### Annual mean ammonia concentrations for all Four Corners area sites for the 2007 study ranged from 0.21 ppb to 1.5 ppb. With the Substation and Farmington airport sites

65 Supplemental BART Analysis: CALPUFF Protocol for Class I Federal Area Visibility Improvement Modeling Analysis (DRAFT), revised June 25, 2010, available at http://www.colorado.gov/airquality/documents/Draft

‐ ColoradoSupplementalBARTAnalysisCALPUF

FProtoco l

‐ 25June2010.pdf.

66 ― Measurements of ambient background ammonia on the Colorado Plateai and visibility modeling implications ‖ ; Dr. Ivar Tombach, and Robert Paine for the Salt River Project, September 2010.

67 Journal article in Journal of Environmental Mo nitoring, ―Baseline ambient gaseous ammonia concentrations in the Four Corners area and eastern Oklahoma, USA‖, Sather, et. al.; September 25 th
2008. (from this point forward this document is referred to as  ̳Sather 2008‘).

### 41

having annual mean ammonia concentrations of 1.13 ppb and 1.47 ppb respectively. This is substantially higher than the annual mean value of 0 .21ppb at Mesa Verde, and the monitoring data suggests that the higher ammonia is due to the sources around Farmington and Substation monitors. The data at all the monitors including the more distant monitors is a 3 week integrated average and it is reaso nable to say that the winds are only transporting the ammonia levels from around the SJGS and Farmington areas to the Mesa Verde monitor some of the time and during those periods we would expect the values to be much higher than the 3 week integrated value or the annual mean values. It is during these periods when the wind transports emissions from the SJGS and elevated ammonia levels to the Class I areas, such as Mesa Verde, that we are interested in for determining the days that are used in the 98 th 

### perc entile visibility analysis of baseline levels. We also note that we are evaluating worst 24

- hour impact periods, and not 3 week integrated average periods, so daily variation due to transport support a higher level than

even the 3 week data set and certai nly higher than the annual mean levels.

We also note that while visibility is calculated at the Class I area by CALPUFF, Mesa Verde for example, the actual chemistry that impacts the generation of visibility impairment at the Class I area is actually mor e sensitive to the ammonia levels that interact with the plume when it is more highly concentrated close to the source than at the far downwind receptors at Class I areas. Therefore the actual ammonia concentrations at

and around the SJGS units are much m ore representative of the ammonia levels that will impact the chemistry and generate visibility impairing molecules, such as ammonia nitrate and ammonia sulfate. The CALPUFF system calculates the sensitivity to the ammonia levels at the Class I area, but this is an artifact of the simplified chemistry approach utilized by CALPUFF.

Further evaluation of the data that supported Sather 2008 indicates that the average at the five ammonia gathering sites (not including the Mesa Verde site) is 0.88 ppb on ann ual basis. The data also shows that the monthly maximums at the five sites ranged from 1.3 ppb to 3.5 ppb with an annual average monthly maximum of 1.74 ppb. We also evaluated the average of each 3 week period data from the Substation and Farmington

site s, which ranged from a low of 0.8 ppb to 1.75 ppb, with the average of the annual mean at these two sites being 1.3 ppb. This additional data highlights that annual average data underestimates potential levels of ammonia. It also highlights that when a s ubset of the ammonia data around the predominant amount of ammonia sources in the area of the emission source are evaluated that the average value monitored at the two sites did not drop below 0.8 ppb for any individual 3 week period that data was collecte d in 2007.

From our review we conclude that the ammonia levels around the emission source are of greater concern for determining visibility impacts from the source due to the actual atmospheric chemistry that occurs than the ammonia levels at isolated dow nwind Class I areas. Since the source‘s emissions and surrounding background ammonia are transported together to a Class I area on a specific day, and the chemistry will be most reactive with the source‘s emissions near the point of emission, it seems app ropriate to use a background level for ammonia that is representative of the area around the source. In this case the annual average values at the Substation and Farmington sites are above

### 42

the 1 ppb levels that we have chosen to model and therefore we bel ieve the level we modeled is very reasonable.

We are proposing NOx reductions that would likely be met with the installation of SCR and would have associated ammonia slip (ammonia that is not fully reacted and is emitted to atmosphere). The ammonia sli p limit may be higher than 1 ppb, but we don‘t think it would significantly impact visibility improvements due to the reductions in NOx and SO 2 

### at the SJGS units. .

6.1.5 EPA Modeling Scenarios

EPA remodeled the visibility impacts of SJGS using revised emi ssion estimates as discussed above and meteorology results from the regulatory version of the CALMET model. Briefly, we applied the EPRI methodology to estimate the sulfuric acid released from combustion in the boiler for all scenarios and for operation o f the SCR, assuming a 0.5% SO2 to SO3 conversion efficiency of the SCR catalyst (compared to 1% assumed by B&V). EPA assumed that the SCR could achieve an emission rate of 0.05 lb NOx/MMBtu based on emission data from existing SCR retrofits (compared to 0 .07 lb NOx /MMBtu assumed by B&V). EPA modeled a revised baseline, with SO 2 

### emissions lowered to the BART presumptive limit 0.15 lb SO 2 

### /MMBtu that was assumed by the WRAP states for regional visibility modeling to achieve reasonable progress towards

natur al visibility conditions. Finally, modeling was performed utilizing both the default background ammonia concentration of 1ppb, as well as the monthly variable concentration proposed by B&V and both ―Method 6‖ and Method 8‖ to calculate visibility impairme nt from the modeled pollutant concentrations.

EPA performed visibility modeling to evaluate the visibility benefit of additional NOx

emission controls for each unit. The control scenarios modeled are outlined below and

shown in Table 6

- 3.

Control Scenarios:

1. Baseline

– Emission rates are the same as the B&V post

- consent decree emissions with the exception of sulfuric acid emissions. Sulfuric acid emissions were calculated for each unit using the EPRI method discussed

above

2. EPA Revised Baseline or EPA Baseline

– Emission rates are the same as B&V post

- consent decree emissions with two exceptions: 1) Sulfuric acid emissions were calculated for each unit using the EPRI method discussed above; 2) SO 2 

### emission rate of 0.15 lb/MMBtu was used rather than 0.18

lb/MMBtu.

3. SCR

– Emissions are same as EPA Baseline with the exception of NOx

emissions. NOx emissions were lowered to 0.5 lb/MMBtu to reflect the control of NOx by SCR operation.

### 43

Table 6

- 3. EPA Modeling Inputs (lb/hr)

Unit 1 Unit 2 Unit 3 Uni t 4 

### EPA Baseline (post

- consent decree controls)

SO 2 667.26 663.84 1036.44 1016.82

SO 4 0.17 0.17 0.27 0.26

NO

x 1223.31 1217.04 1900.14 1864.17

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23.5 36.6 35.9

EC 1.0 1.0 1.6 1.6



### EPA Ad justed Baseline

SO 2 556.05 553.2 863.7 847.35

SO 4 0.17 0.17 0.27 0.26

NO

x 1223.31 1217.04 1900.14 1864.17

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23.5 36.6 35.9

EC 1.0 1.0 1.6 1.6

SO 2 667.26 663.84 1036.44 1016.82



### EPA SCR control scenario

SO 2 556.05 553.2 863.7 847.35

SO 4 0.39 0.39 0.61 0.60

NO

x 185.35 184.4 287.9 282.45

SOA 10.1 10.1 15.7 15.4

PM fine 32 31.9 49.8 48.8

PM coarse 23.6 23.5 36.6 35.9

EC 1.0 1.0 1.6 1.6 

### All particulate modeling was performed using 1 g ram per second nominal emission rates in CALPUFF for the specific size ranges presented in the NPS speciation spreadsheets (i.e., PM 8.0 

### , PM 4.25 

### , PM 1.88 

### , PM 1.25 

### , PM 0.813 

### , and PM 0.625 

### ). These modeled species are grouped in POSTUTIL as to represent the appro priate visibility pollutants (e.g.,

PMcourse = PM 8.0 

### + PM 4.25 

### and PMfine = PM 1.88 

### + PM 1.25 

### + PM 0.813 

### + PM 0.625 

### ) and are then scaled with the emission rates derived from the NPS spreadsheets. EC and SOA are derived from the PM 1.88 

### species and scaled with t he emission rates derived from the NPS spreadsheets.

6.2 EPA V ISIBILITY 

### M ODELING 

### I MPACTS OF 

### SJGS

We present the visibility improvement from the modeling scenarios in two ways: (1) deciview improvement at each Class I area, and (2) the sum of the improvemen ts at all areas. Deciview improvement at each Class I area is the primary metric under the BART guidelines.

The motivation for the second metric, which involves summing over Class I areas, is to provide a single number for comparing control scenarios t o each other. There are multiple Class I areas, with different visibility impacts for each, but there is no established metric for expressing visibility impact considering all areas together in a single number. The overall impact can be assessed more or less intuitively, weighing the

### 44

various area impacts and the number of affected Class I areas. Modeling results for each unit and the facility for all modeling scenarios are included in Attachment 3 to this

document.

6.2.1 Baseline to Revised (EPA) Baselin e

EPA modeled a revised baseline scenario to incorporate the proposed lower SO 2 

### emission rate of 0.15 lb/MMBtu rather than the 0.18 lb/MMBtu included in the original post

consent decree baseline modeled by NMED. The purpose of this was to separate any

vi sibility benefit from lowering the SO 2 

### emission rate from the benefit received from the operation of the SCR.

Table 6

- 4 shows the maximum of the 98 th 

### percentile of the daily maximum visibility impairment for the baseline scenario and the revised baseline for the 1 ppb background ammonia concentration modeled using ―Method 8‖ for visibility calculations. The results show minimal improvement in visibility due to the slightly lower SO 2 

### emission rate.

Table 6

- 5 summarizes the average and maximum results of v isibility impairment at all Class I areas for all background ammonia and visibility calculation method combinations. Results of visibility modeling at each Class I area and for individual units are available in

the attachment to this document.

### 45

Table 6

- 4. EPA Modeling Results

– Impacts of SJGS on Visibility ( maximum of 98 th


### Percentile of daily maximum dv of 2001, 2002, and 2003 ) at Sixteen Class I Areas (1ppb background ammonia concentration, Method 8)

Class I Area Distance

to SJGS (km) Visibility Impact ( dv)

after applying: Revised Baseline visibility improvement over
baseline ( dv) Baseline (post

consent decree) Revised

Baseline

Arches 222 3.55 3.50 0.05

Bandelier Wilderness 210 1.44 1.39 0.05
Black Canyon of the Gunnison Wilderness 203 1.44 1.4 1 0.03

Canyonlands 170 4.69 4.64 0.05
Capitol Reef 232 2.43 2.38 0.05

La Garita Wilderness 169 1.97 1.93 0.04
Grand Canyon 285 0.97 0.93 0.04

Great Sand Dunes National Monument 269 1.56 1.53 0.03

Mesa Verde 40 5.18 5.15 0.03

Pecos Wilderness 248 1.3 2 1.27 0.05

Petrified Forest 213 0.54 0.52 0.02

San Pedro Parks Wilderness 155 2.23 2.20 0.03

Maroon Bells Snowmass Wilderness 271 0.71 0.70 0.01

West Elk Wilderness 216 1.64 1.59 0.05

Weminuche Wilderness 98 2.94 2.92 0.02

Wheeler Peak Wilderness 25 8 1.16 1.12 0.04

Total Delta dv 33.76 33.18 0.59



### Table 6

- 5. EPA Modeling Results

- Summary of total visibility impairment at all 16 Class

I sites.

Visibility Modeling Results Baseline Revised

Baseline Revised Baseline

improvement over
baseline ( dv)
Variable Background Ammonia, Method 6, average 15.82 15.38 0.45 Variable Background Ammonia, Method 8, average 17.49 17.11 0.34 Variable Background Ammonia, Method 6,

maximum 20.65 20.20 0.43

Variable Background Ammonia, Method 8,

maximum 22.74 22.40 0. 34

1 ppb Background Ammonia, Method 6, average 23.59 23.01 0.58 1 ppb Background Ammonia, Method 8, average 26.09 25.56 0.54 1 ppb Background Ammonia, Method 6, maximum 30.82 30.11 0.70 1 ppb Background Ammonia, Method 8, maximum 33.76 33.18 0.59

### 46

6.2.2 Visibility Improvement from Operation of SCR

Modeled impacts on the Class I areas of SJGS are shown in Table 6

- 6. The table shows

the maximum of the 98 th 

### percentile daily delta deciview impacts from the three modeled years using the default background ammonia concentration of 1 ppb and Method 8 to calculate visibility.

Impacts generally decline with distance from SJGS, as would be expected since the plume disperses as it travels, though conditions can be more or less favorable for transport to Class I areas in particular directions. Mesa Verde is the closest area and shows the highest impact, while Maroon Bells Snowmass and the Grand Canyon are the most distant and have low impacts. Visibility improvement due to installation of SCR is significant, i ncluding a 3.11 dv improvement at Canyonlands and 2.88 dv at Mesa Verde. Total deciview improvement at all Class I areas within 300km of the facility is 21.69 dv, a decrease in visibility impairment of 65% from the revised baseline. Improvements at

indiv idual Class I areas exceed 0.5 dv for all areas except Maroon Bells Snowmass and

Petrified Forest.

Table 6

- 7 summarizes the average and maximum results of EPA modeling of visibility impairment at all Class I areas for all background ammonia and visibi lity calculation method combinations. Impacts depend strongly on the background ammonia assumed, increasing as ammonia does. This effect is strongest in the baseline case due to the higher NOx emissions and the effect of available ammonia on the concentra tion of nitrate particulates. Results of visibility modeling at each Class I area and for individual units

are available in the attachment to this document.

Summarizing all of the EPA modeling results, the visibility improvement from applyi ng a 0.05 lb NOx/MMBtu (achievable with SCR or equivalent controls) is significant. The majority of 0.05 lb NOx/MMBtu emission limit benefits are approximately 0.5 dv or higher using the lower background ammonia values and ―Method 6‖. Visibility benefits from 0.05 lb NOx/MMBtu emission limit are higher as background ammonia increases and/or if ―Method 8‖ is used. Despite uncertainty over background ammonia concentrations, several conclusions can be drawn. There would be a significant visibility improvem ent from applying SCR to SJGS. SCR improves visibility by a perceptible

amount even for the lower ammonia scenario at many Class I areas, and by more

noticeable amounts for the 1ppb background ammonia scenario . Large improvements in visibility are expect ed at individual Class I areas from the installation of SCR.

### 47

Table 6

- 6. EPA Modeling Results

– Impacts of SJGS on Visibility ( maximum of 98 th


### Percentile of daily maximum dv of 2001, 2002, and 2003 ) at Sixteen Class I Areas (1ppb background ammonia conc entration, Method 8)

Class I Area Distance

to SJGS (km) Visibility Impact ( dv)

after applying: SCR visibility improvement over
revised baseline ( dv) Revised

Baseline SCR

Arches 222 3.50 1.12 2.38

Bandelier Wilderness 210 1.39 0.48 0.91
Black Canyo n of the

Gunnison Wilderness 203 1.41 0.42 0.99

Canyonlands 170 4.64 1.53 3.11
Capitol Reef 232 2.38 0.82 1.56

La Garita Wilderness 169 1.93 0.57 1.36
Grand Canyon 285 0.93 0.33 0.60

Great Sand Dunes National

Monument 269 1.53 0.49 1.04

Mesa Verde 40 5.15 2.27 2.88

Pecos Wilderness 248 1.27 0.47 0.80

Petrified Forest 213 0.52 0.21 0.31

San Pedro Parks Wilderness 155 2.20 0.74 1.46

Maroon Bells Snowmass

Wilderness 271 0.70 0.28 0.42

West Elk Wilderness 216 1.59 0.45 1.14

Weminuche Wilderness 98 2 .92 0.87 2.05

Wheeler Peak Wilderness 258 1.12 0.44 0.68

Total Delta dv 33.18 11.48 21.69



### Table 6

- 7. EPA Modeling Results

- Summary of total visibility impairment at all 16

Class I sites.

Visibility Modeling Results Revised

Baseline SCR SCR improvemen t

over revised baseline
( dv)
Variable Background Ammonia, Method 6, average 15.38 8.02 7.35 Variable Background Ammonia, Method 8, average 17.11 8.18 8.97 Variable Background Ammonia, Method 6, maximum 20.20 9.90 10.33 Variable Background Ammonia, Met hod 8, maximum 22.40 10.07 12.34

1 ppb Background Ammonia, Method 6, average 23.01 8.86 14.14 1 ppb Background Ammonia, Method 8, average 25.56 9.17 16.38 1 ppb Background Ammonia, Method 6, maximum 30.11 11.05 19.06 1 ppb Background Ammonia, Meth od 8, maximum 33.18 11.48 21.69

### 48

We also evaluated the amount of improvement in the number of days that the SJGS units would either cause a visible impact (1 deciview or greater) and also the number of days they would significantly contribute to a visible impact (0.5 deciview or greater). As

detailed in Table 6

- 8 below, EPA‘s proposed reductions would decrease the number of days that the SJGS units have a 1 deciview impact (or greater) by over 80%. EPA‘s proposed reductions would also decrease the number of days that the SJGS units have a

0.5 deciview impact (or greater) by over 68 %. We note that at 7 of the 16 parks within 300 km, our proposed reductions would decrease the average number of days per year that the SJGS units cause a visibility impairment to zero days per year.

Table 6

- 8. Average number of days per year SJGS visibility impacts exceed 1.0 and 0.5 deciviews (1 ppb background ammonia and Method 8).

Class I Area Distance

to SJGS (km) Average # of days/yr > 1.0

dv Average # of days/yr > 0.5

dv

Revised

Baseline SCR Revised

Baseline SCR

Arches 222 18 5 25 12

Bandelier Wilderness 210 12 1 33 6 Black Canyon of the

Gunnison Wilderness 203 9 1 26 4

Canyonlands 170 28 8 42 22 Capitol Reef 232 8 2 13 5 Grand Canyon 285 3 0 6 2

Great Sand D unes National Monument 269 11 1 38 3

La Garita Wilderness 169 19 0 53 6

Maroon Bells Snowmass

Wilderness 271 2 0 9 0

Mesa Verde 40 115 43 175 105

Pecos Wilderness 248 9 0 30 4

Petrified Forest 213 1 0 6 1

San Pedro Parks

Wilderness 155 33 4 65 17

We minuche Wilderness 98 65 3 126 29

West Elk Wilderness 216 8 0 26 3

Wheeler Peak Wilderness 258 7 0 29 3 TOTAL Average # of days/yr 348 68 702 222