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 2004 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 snowcapped 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 SO2 Emission Rate...................................................................................... 34

6.1.3 Sulfuric Acid Emission Estimates .............................................................. 35

6.1.3.1 Assumptions used in Sulfuric 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

Attachment 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 Decree 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 98th 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 H2SO4 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 98th

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

Areas............................................................................................................... 33

Table 6-1. Assumptions used in Sulfuric acid (H2SO4) 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 98th

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 98th

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 - Summary 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 Method 8)................................. 48
6

Chapter 1: Background and Description of San Juan Generating 

Station

1.1 BACKGROUND

As described in our proposed action Federal Register notice, EPA is proposing to 

disapprove a portion of the State Implementation Plan (SIP) revision 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) and 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 part 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 (NOx) and sulfur 

dioxide (SO2) emission limits necessary at one source to prevent such interference. In 

addition, EPA is proposing sulfuric acid (H2SO4) and ammonia (NH3) hourly emission 

limits at the same source, to minimize the contribution of these compounds 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 technology (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 DESCRIPTION OF SAN JUAN GENERATING STATION

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 miles west of Farmington in northwest New 

Mexico. SJGS Units 1 and 2 have a unit capacity of 350 and 360 MW, respectively. 

These units are subcritical, wall-fired boilers that operate in a forced draft mode. SJGS 

Units 3 and 4 have a unit capacity of 544 MW 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 remaining 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 technology.

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-NOx burners (LNB) with overfire air (OFA) ports and a neural 

network (NN) system to reduce NOx 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 

SO2 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 SO2 control. 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 Wallfired/Babcock & 

Wilcox

Opposed Wallfired/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

NOx Post-consent 

decree baseline 

emission rate

0.30 lb/MMBtu for each unit 

SO2 Presumptive 

BART emission rate

90% annual average control, not to exceed 0.250 lb/MMBtu for a seven day 

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 April 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 enforceable 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 program of the New Mexico 

SIP. Specifically, the construction permit includes the NOx, SO2 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 includes the consent decree limits on 

NOx, SO2, and PM emissions and is federally enforceable.2 

 

1

 Consent Decree in The Grand Canyon Trust and Sierra Club, Plaintiffs, The State of New Mexico, 

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 Air 

Partnership (WRAP) photochemical modeling for the NOx and SO2 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 

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 visibility projections within the WRAP 

photochemical 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 the 

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.

Similarly, 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 SO2, which is much higher than the 0.15 lb/MMBtu emission rate utilized 

in the WRAP‘s photochemical 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 SO2 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 SO2 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 from 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 air 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 developing 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 commitment 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 emit 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 must 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; 

revised 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 specific presumptive limits for SO2 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 meet 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 Eligibility 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 identifying 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 modeling 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 years of modeling of 2001, 2002, and 2003.

• Visibility impacts due to emissions of SO2, NOx and primary PM emissions 

were calculated. 

• PM emissions were modeled as PM2.5.

• Visibility was calculated using the Original IMPROVE equation and Annual 

Average 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 contributes 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 change may still contribute to visibility impairment and thus be subject to 

BART. The modeling of each individual source complexes‘ combined SO2, NOx, and 

PM emissions was completed and results of the 98th 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 SO2, NOx, and PM 

emissions 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 

determination. The results of this analysis (Table 3-1) indicated that the San Juan 

Generating Station (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 Jia, 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_Apr21_2007.doc)
12

determining an appropriate threshold for determining 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 impairment 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 the installation of consent decree controls. 

Refined modeling performed by the New Mexico Environmental Department (NMED)

and by the Environmental 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 latest 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 for 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 Verde 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

Wheeler 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 Facility

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 0.32 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 BACKGROUND AND INTRODUCTION

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 that 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 quality 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 current, 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 10th (mean of Best 20% days) and 90th (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 10th Percentile

Deciview Impairment

90th Percentile

Deciview Impairment

Arches 2.9 11.3

Bandelier Wilderness 4.2 12.2

Black Canyon of the Gunnison 

Wilderness 2.2 10.5

Canyonlands 2.9 11.3

Capitol Reef 2.8 11.5

Grand Canyon 2.1 11.2

Great Sand Dunes National Monument 3.5 11.2

La Garita Wilderness 2.2 10.5

Maroon Bells Snowmass Wilderness 0.2 9.3

Mesa Verde 3.1 11.0

Pecos Wilderness 1.2 8.6

Petrified Forest 4.5 12.5

San Pedro Parks Wilderness 2.8 11.3

West Elk Wilderness 0.2 9.3

Weminuche Wilderness 2.2 10.5

Wheeler Peak Wilderness 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 

monitor13

Minimum 

Distance to 

SJGS (km)

Arches arch UT NPS CANY1 222

Bandelier Wilderness 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 another area is used.
16

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

sixteen Class I Areas 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 visibility 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. NMED 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 CALCULATION OF VISIBILITY IMPACT

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 deciviews will be perceived as the same amount of visibility change, 

whatever the original visibility was. The defining equation is: 

deciviews = 10*ln(bext / 10) 

where ln is the natural logarithm, and extinction bext is the fraction of light scattered out 

of a viewing path. Extinction increases with the amount of pollution (bext 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 source 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: SO2, SO4, NOx, 

secondary organic aerosol (SOA), fine particulate 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 SO2 to particulate sulfate and NOx to particulate 

nitrate, at a rate dependent on meteorological conditions and background ozone 

concentration. These concentrations are converted to deciviews by the CALPOST postprocessor in two steps. 

4.2.1 IMPROVE Equation

Under the original IMPROVE equation, extinction (bext) is estimated from the predicted 

concentrations of various pollutants: 16

bext = 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 scattering, 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; its 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:

bext = 2.2 * fs(RH) * [small sulfate] + 4.8 * fL(RH) * [large sulfate] 

+ 2.4 * fs(RH) * [small nitrate] + 5.1 * fL(RH) * [large nitrate]

+ 2.8 * [small organic mass] + 6.1 * [large organic mass]

+ 10 * [elemental carbon]

+ 1 * [fine soil]

+ 1.7 * fss(RH) * [sea salt]

+ 0.6 * [coarse mass]

+ Rayleigh scattering (site-specific)

+ 0.33 * [NO2(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/m3, otherwise its 

fraction of the total is assumed to increase uniformly between 0 and 1 when the total is 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 organic 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) 

The fs, fL, fss 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 molecules 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 particular, 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 worst 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 the 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 Regional Haze Rule. 

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 IMPROVE Equation for Estimating Ambient Light Extinction Coefficients –

Final Report 

(http://vista.cira.colostate.edu/improve/Publications/GrayLit/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. 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.
19

provided alternative calculations using the original IMPROVE 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 deciviews 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 8th highest day, visibility 

impacts expressed as 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 value 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 concentrations (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 factors from the 

Federal Land Managers‘ 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 parameter Pollutant Concentration (μg/m3)

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 for 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. 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)
20

Table 4-4. Natural Background Concentrations used by EPA 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 

Wilderness

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/CALPUFF 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 NMED.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 

modeling 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‖ 

(―no 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 

subdomain 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 

relied 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 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 Jia, August 15, 2006. Available on UCR Regional Modeling Center 

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/documents/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 from 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 that 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 is 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 

Workshop, May 16, 2007. 

(http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/archive/2007/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_assessment_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 indicated 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 parameters 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 sensible 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 used 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 upper 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 observed 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

km and 200 km, 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 performed for each unit separately. 

Pollution concentration predictions were then summed together to evaluate the facilitywide impacts before the post-processing steps described here. First, POSTUTIL 

considers ammonia background concentrations, temperature, and 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 equation. 

 

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

and 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 NMED31 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 recommendation

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 SJGS Best Available Retrofit Technology 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 H2SO4 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 CALPOST 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 similar 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 likely 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 98th percentile of daily maximum dv for 2001, 2002, and 2003) at 

Sixteen Class I Areas

Class I Area

Distance 

to SJGS 

(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 

National 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 2.96

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 (NH3) and sulfuric acid (H2SO4). 

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-reacted ammonia) 

from its use in SCR or SNCR to control NOx emissions. 
28

4.3.3 Sulfuric Acid

Sulfuric acid (H2SO4) is formed from coal-fired power plants from the oxidation of sulfur 

dioxide (SO2) to sulfur trioxide (SO3) and the subsequent reaction of SO3 with water 

vapor (SO3 + H2O = H2SO4). 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 H2SO4 as inorganic 

particulate sulfate. 

We will address these two additional pollutants when estimating the impact of various 

control technologies on visibility improvement: ammonia (NH3) and sulfuric acid 

(H2SO4) in greater detail in the next Chapters. 
29

Chapter 5: B&V Approach 

5.1 B&V MODELING INPUTS

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 

H2SO4.

32

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

SO2 to SO3 based on the economizer outlet emission of SO2. 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 H2SO4 Emission Estimates 

Unit 1 Unit 2 Unit 3 Unit 4

Controlled SO3 emissions (lb/hr) from boiler33 40.5 40.3 62.9 61.7

Economizer SO2 emissions (lb/hr)34 5,884 5,854 9,140 8,967

Conversion of SO2 to SO3 due to SCR (%) assumed by B&V 

based on economizer SO2 emissions35 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. The 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 values 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 Speciation 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 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)

37

 Id.
30

5.1.3 B&V Modeling 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 

determination 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 improving 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 occur 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 other 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 SO3

from inherent controls, and SCR with sorbent injection.40 B&V assumed an SCR 

controlled emission rate of 0.07 lb/MMBtu for NOx. SO2 and total filterable particulate 

(PM10) emissions were held at the post-consent decree rates of 0.18 lb/MMBtu for SO2

and 0.015 lb/MMBtu for PM10 for visibility modeling. 

 

38

 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_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_AppendixO-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 Generating 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 Inputs (lb/hr)

Unit 1 Unit 2 Unit 3 Unit 4

B&V Baseline (post-consent decree controls)

SO2 667.26 663.84 1036.44 1016.82

SO4 40.5 40.3 62.9 61.7

NOx 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

SO2 667.26 663.84 1036.44 1016.82

SO4 114.2 113.6 177.3 174

NOx 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

SO2 667.26 663.84 1036.44 1016.82

SO4 62.61 62.29 97.22 95.39

NOx 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/ sorbent injection control scenario

SO2 667.26 663.84 1036.44 1016.82

SO4 16.09 16.01 24.99 24.52

NOx 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., PM8.0, PM4.25, PM1.88, PM1.25, PM0.813, and PM0.625). 41 These modeled 

species are grouped in POSTUTIL as to represent the appropriate visibility pollutants 

(e.g., PMcourse = PM8.0 + PM4.25 and PMfine = PM1.88 + PM1.25 + PM0.813 + PM0.625) and 

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

are derived from the PM1.88 species and scaled with the emission rates derived from the 

NPS spreadsheets.

 

41

 Dry Bottom PC w FGD+FF PM Speciation 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 the 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 H2SO4 resulted in different H2SO4 emission rate 

inputs for visibility modeling. 

5.2 RESULTS OF NMED VISIBILITY MODELING

NMED presented various control scenarios‘ visibility improvements in terms of the 98th

percentile (8th highest) of the daily maximum delta deciviews from CALPUFF for each

Class I area, averaged over 2001 – 2003 and the maximum of the 98th 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 but 

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 98th

percentile of daily maxima from 2001 – 2003, as modeled by NMED. NMED modeling 

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 for 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 low 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 (SO3) 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 SO2 to SO3 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 Inventory (TRI).42

Theses points are discussed further in the following chapter. 

 

42

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

preliminary.
33

Table 5-4. NMED Modeling Results -- Impacts of SJGS on Visibility (average of 98th percentile 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 (postconsent decree) SCR43 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

Capitol 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 Snowmass 

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_Response_with_Attachments_0331.pdf)

44

 PNM San Juan Generating 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_031609.pdf

45

 Id.
34

Chapter 6: EPA approach 

6.1 EPA MODELING INPUTS

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 noted the potential for greater control at rates as low as 0.03 

lbs/MMBtu. NOx continuous emission monitor (CEM) data from the EPA Clean Air 

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

have concluded that a NOx emission 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 our 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 SO2 Emission Rate

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

SO2 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 Permit No. 0063-M6) on April 22, 2008 to 

incorporate some of the conditions from the consent decree that include a 90% reduction 

in SO2 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 the 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 modification that also includes the consent 

decree limits on NOx and SO2 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 SO2

 

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/MMBtu 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 sevenday block average.

48

 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.
35

emissions of the SJGS units. Since other WRAP states are using levels modeled for the 

SJGS units in their demonstration of reasonable progress towards 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 visibility 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 that an 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 SO2 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 amount of sulfuric acid produced during combustion, the rate of SO2 to 

SO3 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 emissions from power plants were calculated by NPS by estimating the 

amount of H2SO4 produced and the amount of H2SO4 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 SO2, the 

amount of SO2 oxidized to SO3, and the amount of H2SO4 lost to (or mitigated by) the air 

preheater and applicable control equipment, such as baghouses, and FGDs. Baseline and 

post-control estimates of H2SO4 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 

Regional Haze State Implementation Plan August 17, 2010 

(http://www.nmenv.state.nm.us/aqb/reghaz/documents/FWS-NPS_CommentsNewMexicoRegionalHazeSIP-17AUG2010.pdf)

51

 Don Shepherd to Michael Feldman, EPA Region 6 (electronic mail) dated Nov. 1, 2010 – revised 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 (H2SO4) Emissions Calculations by 

NPS.53

EPRI Parameter EPA (revised 

NPS 

calculations)

Fuel Impact Factor (SO3 formation from 

combustion)

0.00111

Coal sulfur content (%) 0.77

Coal S conversion to SO2 0.875

Conversion of SO2 to SO3 from SCR catalyst 

(%)

0.5

Penetration factorthrough Scrubber54 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 SO2 is estimated for three different coal 

types. Hardman et al. (1998) report sulfur conversion to SO2 (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 calculations relied on the value of 0.875.55 

For coal-fired power plants without SCR, SO3 is only formed during the combustion 

process. The amount of SO2 that is oxidized to SO3 during combustion depends on coal 

type and the type of boiler. SO3 reacts rapidly with water to form sulfuric acid. 

Measurements of SO3 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 SO3 production factor from Western bituminous 

coal from dry bottom boilers to be 0.00111, or 0.111% of SO2 production, and NPS used 

this value.56

For coal-fired power plants with SCR, SO3 is also formed as a result of oxidation of SO2

to SO3 on the SCR catalyst. NMED assumed a 1% conversion of SO2 to SO3 across the 

SCR catalyst layers, whereas NPS used 0.5% conversion efficiency because low 

conversion catalysts are commercially available to specifically reduce H2SO4 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 Power 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. Although 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, H2SO4 removal 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 available 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 eight units show a range of percent removal from 0 – 69%. Before 

calculating the average percent removal of H2SO4 observed from low-sulfur Eastern 

bituminous coal, EPRI 2008 and Harrison 2005 excluded the 0% removal value because 

―the data point showing 0% removal at approximately 600 ppm SO2 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 coals with acidic ash (e.g., bituminous) may consider 

adopting the low sulfur, eastern bituminous value of 0.49 for the H2SO4 penetration (1-

removal), which is equivalent to 51% removal. NPS used the 0.49 value for the air 

preheater penetration factor suggested for coal with low alkalinity ash. 

Downstream of the air preheater, H2SO4 is also reduced by PM and SO2 control 

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

SO2 and PM control. NPS used the EPRI-recommended 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 (SO3) in the flue gas. We have 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 methodology 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 SO2 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 about 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 estimates 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. 

Table 6-2. SJGS Facility-Wide Sulfuric Acid Emission Estimates

Reported emissions59 Hourly emissions60

TRI 2007 72000 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 --- 0.87 lb/hr

6.1.3.3 Sulfuric Acid Emission Limit

EPA believes that these estimates of H2SO4 emissions are appropriate based on the use of 

low reactivity catalyst that will reduce the rate of SO2 to SO3 conversion. To ensure these 

levels are met, EPA is proposing that emissions of H2SO4 be limited to 1.06 x 10-4

lb/MMBtu. This 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 conversion 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. Therefore, 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 

sulfuric acid emissions by about 182% over baseline levels.61 Using this 182% increase 

factor we estimate an upper range of sulfuric acid mist emissions of 7.87 x10-4lb/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 emissions over baseline levels of 182% increase (373.99/205.4 ≈1.82)
39

same removal factors will apply to any additional H2SO4 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 9x10-5lb/MMBtu for H2SO4 across the three units.62 

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

lb/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 

efficiency 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 very little monitored ammonia data is available, we used the IWAQM Phase 2

document which recommends a default ammonia background value of 1 part per billion 

(ppb) 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 ambient 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 historically 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 in 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 researched 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 discusses 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, November 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 levels. 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 emissions relative to SO2 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 difference 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 level 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 have 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 variety 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 emissions); 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

FProtocol‐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 Monitoring, ―Baseline ambient gaseous ammonia 

concentrations in the Four Corners area and eastern Oklahoma, USA‖, Sather, et. al.; September 25th

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 reasonable 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 98th percentile 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 certainly 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 more 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 more 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 annual 

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 

sites, 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 subset 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 collected 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 downwind 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 appropriate 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 believe 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 slip 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 SO2

at the SJGS units. . 

6.1.5 EPA Modeling Scenarios

EPA remodeled the visibility impacts of SJGS using revised emission 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 of 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 SO2 emissions 

lowered to the BART presumptive limit 0.15 lb SO2/MMBtu that was assumed by the 

WRAP states for regional visibility modeling to achieve reasonable progress towards 

natural 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 impairment 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) SO2 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 Unit 4

EPA Baseline (post-consent decree controls)

SO2 667.26 663.84 1036.44 1016.82

SO4 0.17 0.17 0.27 0.26

NOx 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 Adjusted Baseline

SO2 556.05 553.2 863.7 847.35

SO4 0.17 0.17 0.27 0.26

NOx 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

SO2 667.26 663.84 1036.44 1016.82

EPA SCR control scenario

SO2 556.05 553.2 863.7 847.35

SO4 0.39 0.39 0.61 0.60

NOx 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 gram per second nominal emission rates 

in CALPUFF for the specific size ranges presented in the NPS speciation spreadsheets 

(i.e., PM8.0, PM4.25, PM1.88, PM1.25, PM0.813, and PM0.625). These modeled species are

grouped in POSTUTIL as to represent the appropriate visibility pollutants (e.g., 

PMcourse = PM8.0 + PM4.25 and PMfine = PM1.88 + PM1.25 + PM0.813 + PM0.625) and are 

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

derived from the PM1.88 species and scaled with the emission rates derived from the NPS 

spreadsheets.

6.2 EPA VISIBILITY MODELING IMPACTS 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 improvements 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 to 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) Baseline

EPA modeled a revised baseline scenario to incorporate the proposed lower SO2 emission 

rate of 0.15 lb/MMBtu rather than the 0.18 lb/MMBtu included in the original postconsent decree baseline modeled by NMED. The purpose of this was to separate any 

visibility benefit from lowering the SO2 emission rate from the benefit received from the 

operation of the SCR.

Table 6-4 shows the maximum of the 98th 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 SO2 emission rate. 

Table 6-5 summarizes the average and maximum results of visibility 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 98th

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 

(postconsent 

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 1.41 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 1.32 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 258 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 98th 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, including 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 

individual 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 visibility 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 concentration 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 applying 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 

improvement 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 expected at individual Class I areas from the installation of SCR. 
47

Table 6-6. EPA Modeling Results – Impacts of SJGS on Visibility (maximum of 98th

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: 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 Canyon 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 improvement 

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, Method 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, Method 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 Dunes 

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

Weminuche 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