Modeling for the Reid Gardner Generating Station: 

Visibility Impacts in Class I Areas 

 

 

 

 

 

 

Prepared by 

 

H. Andrew Gray, Ph.D. 

Gray Sky Solutions 

San Rafael, CA 94903 

 

 

 

 

August 2011 
1

The Reid Gardner Generating Station (RGGS), located in Moapa, Nevada, is 

a coal-fired power plant with a combined nominal generating capacity of 565 

megawatts (MW). RGGS consists of three BART-eligible electric generating 

units with a nominal capacity of 110 MW each, and a fourth non-BARTeligible unit (265 MW). The three BART-eligible units are each equipped with 

a wall-fired pulverized coal boiler with low-NOX (oxides of nitrogen) burners 

(LNB) and over-fire air (OFA), a fabric filter (FF) for control of particulate 

matter (PM), and a venturi scrubber using soda ash for sulfur dioxide (SO2) 

removal. 

The Clean Air Act’s Regional Haze Rule (RHR) requires assessment of older 

sources that are causing or contributing to visibility impairment in National 

Parks and Wilderness Areas (Class I Areas). The US EPA has proposed that 

units 1, 2 and 3 of RGGS be subject to the Clean Air Act’s Best Available 

Retrofit Technology (BART) requirement. The EPA’s proposal would require 

RGGS to reduce emissions of NOX, SO2, and PM using various control 

technologies. 

BART analyses were performed by CH2MHILL for RGGS units 1, 2, and 3 

(CH2MHILL 2008). The modeling protocol for RGGS (CH2MHILL 2007) 

followed, to a large extent, the methodology developed by the Western 

Regional Air Partnership (WRAP) for performing BART analyses (WRAP 

2006). The RGGS BART analyses included air dispersion modeling to 

estimate the visibility impacts at Class I areas associated with emissions 

from RGGS, and the modeled improvement in visibility one would expect 

under various control scenarios. 

The BART analyses present the modeling results for three emission scenarios 

in which the visibility impacts due to all emitted visibility-related pollutants 

were considered. These modeling scenarios were: (1) WRAP baseline, (2) 

current Title V permit limits, and (3) the proposed Nevada Energy (NVE) 

BART Limits (although CH2MHILL’s modeling was conducted with higher 

emissions rates for NOX and PM10 than the NVE BART Limits). The BART 

analyses also considered a number of additional control options, however 

the CH2MHILL reports (CH2MHILL 2008) state that “visibility modeling has 

not been performed for the combination of NOX, SO2 and PM10...” impacts 

associated with these control scenarios. The modeled visibility improvement 

in Grand Canyon NP for various NOX-only control technologies was used to 

determine the cost effectiveness of each control scenario (Tables 5-4). 

These costs per visibility improvement (delta dv reduction) provided the 

ultimate basis for CH2MHILL’s BART determination. The CH2MHILL reports 

do not make it clear, however how the dispersion modeling results were 
2

NOX SO2 PM10

lb/MMBtu lb/MMBtu lb/MMBtu

Unit 1 0.591 0.329 0.0474

Unit 2 0.635 0.280 0.044

Unit 3 0.592 0.320 0.040

WRAP Baseline Emission Rates for Reid Gardner

used to develop the tabulated values of “98th Percentile delta dv Reduction” 

appearing in Tables 5-4.1 

This report describes the results of a dispersion modeling exercise that was 

undertaken to identify the visibility impacts attributable to the RGGS and the 

visibility improvements that would be expected for the selective catalytic 

reduction (SCR) control option. The modeling conducted for this report 

considered the controls and impacts associated with all visibility-related 

pollutants (i.e., NOX, SO2 and PM10) at all five Class I areas that are within 

300 km of the RGGS. 

Baseline Emissions 

In their BART analyses for RGGS, CH2MHILL identified two possible 

“baseline” modeling scenarios: (1) WRAP Baseline, representing historical 

maximum 24-hr average emission rates, and (2) Current Title V Permit 

Limits (not actual emission levels, but rather enforceable operating limits). 

The Title V limits produce slightly lower NOX emissions but higher SO2 and 

PM emissions than the WRAP baseline scenario. The modeling results 

presented in the BART analyses show that the Title V Limit emission scenario 

results in somewhat higher visibility impacts than the WRAP Baseline. The 

WRAP Baseline emission levels were used to represent baseline emissions for 

the BART analyses and also for the current RGGS visibility assessment. 

The WRAP baseline emission rates for each emitted pollutant were identified 

for each RGGS unit in the BART analyses (CH2MHILL 2008; Tables 5-1). 

These emission rates are shown in Table 1, below. 

Table 1. Modeled Baseline Emission Rates 

 

1 Specifically, (1) what is being used to represent baseline levels in Table 5-4 (for which the 

“reduction” is measured against), and (2) why does the “Base” scenario have a delta dv 

reduction associated with it (ranging from 0.354 to 0.386 for the three units)? 

Furthermore, how is delta dv assessed if only NOX controls have been are considered (i.e., 

are all pollutants included as appropriate in the modeled extinction and/or background 

extinction calculation)? 
3

The boiler heat inputs (at 100% load) are listed as 1,215 MMBtu/hr for units 

1 and 2, and 1,237 MMBtu/hr for unit 3 (CH2MHILL 2008). The baseline 

mass emission rate (lb/hr) for each pollutant was obtained by multiplying 

the boiler heat inputs by the emission rates shown in Table 1. 

For the baseline emissions scenario, sulfate (SO4) emissions were estimated 

to be 1.5 percent of the SO2 emission rate (equivalent to 1 percent SO2

conversion) following the methodology used in the RGGS BART analyses 

(CH2MHILL used this assumption for all three of their modeled scenarios). 

The PM10 emissions were assumed to be split into fine mode (PM25; particles 

smaller than 2.5 microns in diameter) and coarse mode (PMC; particles 

between 2.5 and 10 microns in diameter). In their BART analyses, 

CH2MHILL assumed that the PM25/PM10 fraction was equal to 0.5758 (and 

PMC/PM10 = 0.4242) for all of their modeled scenarios. The same 

fine/coarse fraction was used to split the PM10 emissions for both the 

baseline emissions scenario and the SCR control scenario. Elemental carbon 

emissions were assumed to be 3.8% of the PM25 emissions, also following 

the methodology used by CH2MHILL in the RGGS BART analyses. 

Secondary organic aerosol (SOA) emissions were assumed by CH2MHILL to 

be 0.16 lb/hr (= 0.02 g/s) for each unit for the baseline scenario and for 

each of their modeled control scenarios. This constant emission rate was 

also used for the two current modeling scenarios. 

Control Scenarios

Although a number of control options were considered in the BART analyses 

(CH2MHILL 2008), only three emission scenarios were evaluated at all five 

Class I areas within 300 km of RGGS using emissions from all visibilityimpairing pollutants. Additional modeling was conducted by CH2MHILL to 

evaluate the visibility impacts associated with alternative NOX-only control 

scenarios; however the results were only presented in the BART analyses for 

Grand Canyon NP.2

For the current RGGS modeling assessment, the visibility impacts associated 

with the SCR control option was evaluated. The selective catalytic reduction 

control system chemically reduces NOX, to nitrogen and water using a 

 

2 Modeling files for the three emission scenarios (WRAP Baseline, Title V Limits, and NVE 

BART) were obtained (Malone 2011) and reviewed as part of the current modeling 

assessment. Requests for similar modeling files for the alternative NOX-only control options 

were made, however these files were not made available, and could therefore not be 

reviewed, nor could the results be verified. 
4

catalyst to promote the chemical reaction, leading to higher NOX removal 

rates than for SNCR (selective non-catalytic reduction). SCR requires the 

injection of ammonia into the furnace or flue stream. 

For the current visibility assessment, it was assumed that SCR would result 

in a NOX emissions rate of 0.05 lb/MMBtu for all three RGGS units. The 

BART limit of 0.15 lb/MMBtu was assumed to be met for SO2 emissions 

under the SCR control scenario. The PM emission rate for the SCR scenario 

was assumed to be 0.015 lb/MMBtu (proposed BART limit). 

For the modeled SCR control scenario, sulfuric acid (H2SO4) production was 

estimated by combining the coal sulfur content (assumed to be 0.57% for 

Skyline coal), the heating value of the coal (11,712 Btu/lb for Skyline coal), 

and each boiler’s heat input, resulting in an estimate of the amount of 

(uncontrolled) SO2 manufactured as a result of combustion, which was then 

multiplied by a fuel factor (representing the percent of conversion from SO2

to SO3). An additional 0.5 percent of the uncontrolled SO2 was assumed to 

be converted to SO3 as a result of the catalyst, resulting in slightly higher 

SO4 emission levels. 

Sulfate is emitted from each boiler unit either in the form of sulfuric acid 

(H2SO4) or, if sufficient ammonia (NH3) is present to neutralize the sulfate, 

as ammonium bisulfate ((NH4)2SO4) or ammonium sulfate (NH4SO4). The 

estimate of sulfate emissions for the modeled SCR control scenario included 

the effects of ammonia slip on the formation of ammonium bisulfate (or 

ammonium sulfate). The NH3 (assumed to be 0.75 ppm) reacts with H2SO4

to produce ammonium sulfate or ammonium bisulfate, with the remaining 

sulfate emitted as H2SO4. The penetration factors for ammonium 

sulfate/bisulfate particles are assumed to be 0.36 (air pre-heater, APH) and 

0.01 (FF); the penetration factors for emitted H2SO4 mist are assumed to be 

0.36 (APH), 0.10 (FF), and 0.65 (Venturi scrubber). 

 

For the modeled SCR control scenario, the PM25/PM10 and PMC/PM10

fractions (0.5758 and 0.4242, respectively), the EC emission rate (3.8% of 

PM25 emissions), and the SOA emissions (0.16 lb/hr) were all assumed to 

be the same as for the baseline (as was the case for the modeling performed 

by CH2MHILL for their BART analysis modeling). 

The resulting emission estimates are shown in Table 2 for each unit and for 

all three units combined for the two modeled scenarios. SCR controls would 

achieve greater than 90 percent NOX reduction relative to baseline emission 

levels. The SCR control scenario would also reduce SO2 emissions by more 

than 50 percent relative to baseline levels. 
5

Baseline Emissions

(lb/hr) SO2 SO4 NOX PMC SOA PM25 EC

Unit 1 399.74 6.00 718.07 24.43 0.16 33.16 1.26

Unit 2 340.20 5.10 771.53 22.68 0.16 30.78 1.17

Unit 3 395.84 5.94 732.30 20.99 0.16 28.49 1.08

All 3 Units 1135.78 17.04 2221.90 68.10 0.48 92.43 3.51

Scenario E2: SCR

(lb/hr) SO2 SO4 NOX PMC SOA PM25 EC

Unit 1 182.25 0.18 60.75 7.73 0.16 10.49 0.40

Unit 2 182.25 0.18 60.75 7.73 0.16 10.49 0.40

Unit 3 185.55 0.18 61.85 7.87 0.16 10.68 0.41

All 3 Units 550.05 0.54 183.35 23.33 0.48 31.66 1.21

 Table 2. Emissions for Modeled Baseline and SCR Control Scenario 

Regional Dispersion Modeling 

The CALPUFF computer modeling system is an advanced, non-steady-state 

Gaussian dispersion model that simulates the effects of time- and spacevarying meteorological conditions on pollutant transport, transformation and 

removal. CALPUFF has been adopted by the US EPA in its Guideline on Air 

Quality Models (US EPA, 2005a) as the preferred model for assessing the 

long range transport of pollutants and for assessing regional haze. 

The CALPUFF dispersion model (Version 6.262, July 2008) was run following 

the same modeling protocol as used by CH2MHill (CH2MHILL 2007) in their 

assessment of the visibility impacts associated with RGGS’s emissions 

(CH2MHILL 2008). The model accounted for the emissions, atmospheric 

transport, chemical transformation, and deposition in order to predict hourly 

concentrations of the modeled pollutants at hundreds of locations in each of 

the five Class I areas within 300 km of the power plant. The model 

predicted hourly concentrations of the following pollutants as a consequence 

of the emissions from RGGS for each modeled emission scenario (as shown 

in Table 2): SO2, SO4, NO2, NO3, HNO3, SOA, PM25, PMC, and EC. 

The meteorological data files (MM5 data, surface and upper air data, land 

use and terrain data, precipitation data, and CALMET files for 2001-2003) 

that CH2MHILL developed and used in their BART analyses were obtained 

from the Nevada Division of Environmental Protection (NDEP) (Malone 

2011). The exact same modeling domain and meteorological data were 

used for the current modeling assessment. 

 

The CALPUFF dispersion model was run using the exact same set of input 

options that CH2MHILL used for their BART analysis modeling, including the 

same ozone data set, and the same set of Class I area receptors (i.e., the 
6

CALPUFF.INP control files were identical except for the emission data and 

filenames). Following CALPUFF execution, the POSTUTIL program (part of 

the CALPUFF modeling system) was used to “correct” the balance between 

model-predicted hourly HNO3 and NO3 concentrations, based on the amount 

of available background ammonia (as well as temperature and relative 

humidity). The background ammonia level was set to the 1 ppb, following 

the same methodology used by CH2MHILL. 

FLAG Guidelines for Determination of Regional Visibility Impacts 

The Federal Land Managers (FLMs) are responsible for protecting and 

maintaining the air quality related resources within the nation’s parks and 

wilderness areas. Federal Class I areas include national parks that are over 

6,000 acres and wilderness areas and memorial parks that are over 5,000 

acres. Recently the FLMs released an updated set of guidelines for 

evaluating the effects on air quality related values (AQRVs), including 

visibility, in federal Class I areas (FLAG, 2010). 

The FLAG guidelines require that modeled 24-hour averaged visibility 

impacts be compared with existing conditions, (defined as “natural” 

conditions). An impact from a new (or modified) source that increases the 

existing atmospheric light extinction (bext) levels by 5 percent or less (which 

corresponds to an increase in deciviews, dv, of approximately 0.5 or less) 

would be considered not to cause an adverse visibility impact. An increase 

in light extinction of 10 percent or more (corresponding to approximately a 

one dv or greater change) would generally cause a noticeable impairment to 

existing visibility conditions. The 98th percentile in 24-hour average change 

in extinction (or deciviews), which corresponds to the 8th highest 24-hour 

average change, at any modeled receptor within a Class I area during each 

modeled year, is to be compared to the 5 and 10 percent levels to determine 

if the source’s visibility impact will likely be of concern. If the 98th percentile 

change in light extinction with respect to average natural conditions is 

greater than 5 percent, then, according to BART guidelines, the impact is 

considered to be perceptible and the source “contributes” to visibility 

impairment (US EPA, 2005b). If the 98th percentile change in light 

extinction with respect to average natural conditions is greater than 10 

percent, then the impact of the source is considered to “cause” visibility 

impairment. 

 

The FLMs recommend that measured average pollutant concentrations (and 

Rayleigh scattering) as measured in Class I areas by the IMPROVE network 

be used to represent natural conditions at each Class I area. The 

recommended values for each Class area are tabulated in the revised FLAG 

guidelines (FLAG, 2010). The FLAG-recommended background 
7

Class I Area

Distance to 

RGPP (km)

Grand Canyon NP 86

Zion NP 148

Bryce Canyon NP 227

Sycamore Canyon W 289

Joshua Tree NP 292

concentrations were used for the current modeling assessment of visibility 

impacts due to emissions from RGGS, along with the monthly average 

relative humidity adjustment factors specific for each Class I area, also as 

recommended by the FLAG guidelines. The recommended FLAG 

methodology for specifying background concentrations and relative humidity 

corresponds to the Method 8 (M8_Mode=5) procedure within the CALPOST 

postprocessor.3

There were 2359 locations used as model receptors, spread throughout the 

five Class I areas. At each of these receptor locations, the hourly modelpredicted concentrations of six pollutants (sulfates, nitrates, elemental and 

organic carbon, fine and coarse particulate matter) as contributed by 

emissions from each of the RGGS units, were used to assess the visibility 

impacts using the FLAG-recommended methodology. 

Modeled Class I Areas 

The visibility impacts due to emissions from the RGGS were evaluated at the 

five Class I areas that are within 300 km of the power plant. These five 

modeled Class I Areas are listed in Table 3. 

 Table 3. Class I Areas within 300 km of RGGS 

Model Results 

The CALPUFF model was used to estimate hourly concentrations of sulfates, 

nitrates, organic and elemental carbon, fine and coarse PM, in each of the 

modeled Class I areas for three years (2001-2003) due to emissions from 

each of the RGGS’s three units, as was shown in Table 2. In addition, the 

 

3

 CH2MHILL used Method 6 for the CALPOST visibility calculations. FLAG guidelines require 

the use of Method 8 (M8_Mode=5). This affects (1) the RH factor for extinction due to 

hygroscopic species (e.g., SO4, NO3), and (2) the values used to represent background 

extinction (Method 8 computes background extinction using speciated PM concentrations 

and the IMPROVE (2006) variable extinction efficiency formulation). Using CALPOST Method 

6 instead of Method 8 has the effect of significantly increasing background visibility levels. 
8

combined impact of all three units was evaluated by combining the 

concentrations from all three units on an hourly basis at each model 

receptor. 

The CALPOST postprocessor (Version 6.221) was used to compute the 24-

hour average light extinction (bext) due to the sulfate, nitrate, organic and 

elemental carbon, and fine and coarse PM10 concentrations attributed to 

each RGGS unit (and all units combined) at each Class I area, following 

FLAG guidelines (Method 8, M8_Mode=5). The 24-hour averages were 

compared to existing annual average conditions and the relative changes to 

extinction were then ordered from highest to lowest. The 8th highest 24-

hour average extinction change (or delta dv) for each year corresponds to 

the 98th percentile change in extinction that must be compared to the 5% or 

10% threshold, as per the FLAG (and BART) guidelines. 

Table 4 shows the 98th percentile (the 8th highest 24-hour period) delta dv 

for each year within each of the modeled Class I areas, as estimated by the 

CALPUFF modeling system using the baseline emissions for each unit and for 

all three units combined, as shown in Table 2. The number of days each 

year for which delta dv exceeds 0.5 is also shown in the table for each unit 

and for all three units combined. The rightmost pair of columns shows (1) 

the average of the 98th percentile delta dv over the three modeled years, 

and (2) the total number of days delta dv exceeded 0.5 for all three years 

combined.4

 As can be seen from Table 4, the 98th percentile delta dv for the 

combined impact of all three RGGS units exceeded 2.0 within Grand Canyon 

NP, providing strong evidence that RGGS “causes” visibility impairment in 

Grand Canyon NP. Similarly, the 98th percentile delta dv for all three units 

exceeded 0.5 at Joshua Tree NP (for all three modeled years) and Zion NP 

(for two of the three years, as well as the average of the three years), 

providing an indication that emissions from RGGS “contributes” to visibility 

impairment at these two national parks. 

 

4

 CH2MHILL used the 23rd highest 24-hr delta dv out of all three model years to produce the 

98th percentile delta dv data for each Class I area in Tables 5-3 of the BART Analyses, rather 

than the average of the three 8th highest delta dv values, as was done in Table 4, above. 
9

UNIT 1 Baseline Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.75 17 0.68 14 0.90 16 0.77 47

Zion NP 148 0.15 1 0.14 1 0.26 2 0.18 4

Bryce Canyon NP 227 0.12 0 0.09 0 0.14 0 0.12 0

Sycamore Canyon W 289 0.04 0 0.05 0 0.06 0 0.05 0

Joshua Tree NP 292 0.35 3 0.27 1 0.18 0 0.27 4

2001 2002 2003 2001-2003

UNIT 2 Baseline Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.77 18 0.74 12 0.86 15 0.79 45

Zion NP 148 0.18 1 0.15 1 0.27 2 0.20 4

Bryce Canyon NP 227 0.12 0 0.09 0 0.13 0 0.11 0

Sycamore Canyon W 289 0.04 0 0.05 0 0.06 0 0.05 0

Joshua Tree NP 292 0.32 3 0.28 1 0.17 0 0.26 4

2001 2002 2003 2001-2003

UNIT 3 Baseline Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.65 14 0.82 12 0.73 13 0.73 39

Zion NP 148 0.19 1 0.14 0 0.26 2 0.19 3

Bryce Canyon NP 227 0.12 0 0.09 0 0.16 0 0.12 0

Sycamore Canyon W 289 0.03 0 0.06 0 0.07 0 0.05 0

Joshua Tree NP 292 0.31 3 0.28 1 0.19 0 0.26 4

2001 2002 2003 2001-2003

ALL UNITS Baseline Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 2.13 46 2.05 42 2.33 44 2.17 132

Zion NP 148 0.51 8 0.42 5 0.76 13 0.56 26

Bryce Canyon NP 227 0.35 4 0.25 1 0.39 4 0.33 9

Sycamore Canyon W 289 0.11 0 0.17 1 0.17 0 0.15 1

Joshua Tree NP 292 0.91 32 0.82 24 0.51 8 0.75 64 1

 -- 98th percentile (8th high)

2001 2002 2003 2001-2003

 Table 4. Modeled Visibility Impacts: Baseline Emissions 

Table 5 shows the modeled visibility impacts for the SCR control scenario for 

each RGGS unit (and for all three units combined). The results demonstrate 

that SCR would have a significant effect on the 98th percentile delta dv and 

also on the number of days each year in which delta dv exceeds 0.5. Under 

the SCR control scenario, the visibility impact due to emissions from all three 

RGGS units combined would be below the threshold level at all Class I areas 

(i.e., emissions from RGGS would no longer “contribute” to visibility 

impairment at any Class I area). In addition, the number of days each year 

in which delta dv exceeds 0.5 would be almost completely eliminated. 
10

UNIT 1

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.12 0 0.13 0 0.14 0 0.13 0

Zion NP 148 0.04 0 0.04 0 0.05 0 0.04 0

Bryce Canyon NP 227 0.03 0 0.02 0 0.03 0 0.03 0

Sycamore Canyon W 289 0.01 0 0.01 0 0.02 0 0.01 0

Joshua Tree NP 292 0.08 0 0.07 0 0.05 0 0.07 0

E2: SCR Visibility Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

2001 2002 2003 2001-2003

UNIT 2

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.11 0 0.15 0 0.14 0 0.13 0

Zion NP 148 0.04 0 0.04 0 0.05 0 0.04 0

Bryce Canyon NP 227 0.03 0 0.02 0 0.03 0 0.03 0

Sycamore Canyon W 289 0.01 0 0.01 0 0.02 0 0.01 0

Joshua Tree NP 292 0.08 0 0.07 0 0.05 0 0.07 0

E2: SCR Visibility Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

2001 2002 2003 2001-2003

UNIT 3

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.11 0 0.12 0 0.13 0 0.12 0

Zion NP 148 0.04 0 0.04 0 0.05 0 0.04 0

Bryce Canyon NP 227 0.03 0 0.02 0 0.03 0 0.03 0

Sycamore Canyon W 289 0.01 0 0.01 0 0.02 0 0.01 0

Joshua Tree NP 292 0.08 0 0.08 0 0.05 0 0.07 0

E2: SCR Visibility Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

2001 2002 2003 2001-2003

ALL UNITS

Class I Area

Distance to 

RGPP

km Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Delta dv1 Ndays=>0.5 Avg Delta dv Ndays=>0.5

Grand Canyon NP 86 0.34 2 0.41 4 0.39 3 0.38 9

Zion NP 148 0.12 0 0.11 0 0.16 0 0.13 0

Bryce Canyon NP 227 0.09 0 0.07 0 0.09 0 0.08 0

Sycamore Canyon W 289 0.03 0 0.04 0 0.05 0 0.04 0

Joshua Tree NP 292 0.24 0 0.22 1 0.15 0 0.20 1 1

 -- 98th percentile (8th high)

E2: SCR Visibility Impact (Delta dv) and Number of Days Delta dv Exceeded 0.5 for 2001-2003

2001 2002 2003 2001-2003

 Table 5. Modeled Visibility Impacts: SCR Control Scenario 

Table 6 shows the average of the 98th percentile (the 8th highest 24-hour 

period) delta dv for the three modeled years within each Class I area for the 

SCR control scenario as compared to the baseline impacts. The table shows 

the visibility impact (delta dv) for the baseline emission level and for the 

SCR control scenario, along with the improvement in delta dv relative to the 

baseline. An improvement in delta dv that exceeds 1.0 would be considered 

a noticeable improvement. 

The visibility impact in Grand Canyon NP from all three RGGS units was 

estimated to improve from the baseline delta dv of 2.17 (a “noticeable” 
11

impact) to a delta dv of 0.38 (under the “contribution” threshold) due to the 

addition of SCR. SCR is predicted to reduce the 98th percentile delta dv in 

the Grand Canyon by 1.79, which would result in a noticeable change to the 

majority of park visitors. 

Table 6 also shows the total delta dv, summed over all five modeled Class I 

areas. This metric provides an indication of the total visibility impact in the 

five modeled Class I areas for each scenario. For example, the model 

indicates that SCR would reduce the cumulative delta dv impact from 3.96 

(for all three units) to 0.83. This would result in an overall visibility 

improvement of 3.12 dv within the five Class I areas (for all three RGGS 

units combined). 

Summary 

The model results using baseline emissions indicate that emissions from the 

three RGGS units causes noticeable visibility impairment on over 40 days 

each year in the Grand Canyon NP. The average 98th percentile delta dv in 

Grand Canyon NP for all three units combined was estimated to be 2.17. 

The improvement in visibility that would be experienced due to SCR would 

be significant in Grand Canyon NP. The average 98th percentile delta dv 

from all three units would be lowered from 2.17 (baseline) to 0.38, which 

would result in a noticeable change in visibility relative to the baseline levels. 

According to the model results, emissions from the RGGS would no longer 

“contribute” to visibility impairment in Grand Canyon NP. 
12

Average 98th Percentile (8th Highest) Delta dv Compared to Baseline Impacts for 2001-2003

UNIT 1

Class I Area

Distance to 

RGPP

Baseline 

Impact1

km Delta dv Delta dv Improvement

Grand Canyon NP 86 0.77 0.13 0.65

Zion NP 148 0.18 0.04 0.14

Bryce Canyon NP 227 0.12 0.03 0.09

Sycamore Canyon W 289 0.05 0.01 0.04

Joshua Tree NP 292 0.27 0.07 0.20

Total Delta dv 1.39 0.28 1.11

E2: SCR 

UNIT 2

Class I Area

Distance to 

RGPP

Baseline 

Impact1

km Delta dv Delta dv Improvement

Grand Canyon NP 86 0.79 0.13 0.66

Zion NP 148 0.20 0.04 0.16

Bryce Canyon NP 227 0.11 0.03 0.09

Sycamore Canyon W 289 0.05 0.01 0.04

Joshua Tree NP 292 0.26 0.07 0.19

Total Delta dv 1.41 0.28 1.12

E2: SCR 

UNIT 3

Class I Area

Distance to 

RGPP

Baseline 

Impact1

km Delta dv Delta dv Improvement

Grand Canyon NP 86 0.73 0.12 0.61

Zion NP 148 0.19 0.04 0.15

Bryce Canyon NP 227 0.12 0.03 0.09

Sycamore Canyon W 289 0.05 0.01 0.04

Joshua Tree NP 292 0.26 0.07 0.19

Total Delta dv 1.36 0.27 1.08

E2: SCR 

ALL UNITS

Class I Area

Distance to 

RGPP

Baseline 

Impact1

km Delta dv Delta dv Improvement

Grand Canyon NP 86 2.17 0.38 1.79

Zion NP 148 0.56 0.13 0.43

Bryce Canyon NP 227 0.33 0.08 0.25

Sycamore Canyon W 289 0.15 0.04 0.11

Joshua Tree NP 292 0.75 0.20 0.54

Total Delta dv 3.96 0.83 3.12 1

 -- Average 98th percentile (8th high) for the three modeled years

E2: SCR 

 Table 6. Modeled Visibility Impacts: Baseline vs. SCR Control Scenario 
13

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