## 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 20111 The Reid Gardner Generating Station (R GGS), located in Moapa, Nevada, is a coal-fired power plant with a combin ed nominal generating capacity of 565 megawatts (MW). RGGS consists of th ree 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-NO X (oxides of nitrogen) burners (LNB) and over-fire air (OFA), a fabric filter (FF) for control of particulate matter (PM), and a venturi scrubber usin g soda ash for sulfur dioxide (SO 2 ) 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 Area s). 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) requiremen t. The EPA’s proposal would require RGGS to reduce emissions of NO X , SO 2 , and PM using various control technologies.

BART analyses were performed by CH2MHILL for RGGS units 1, 2, and 3 (CH2MHILL 2008). The modeling prot ocol for RGGS (CH2MHILL 2007) followed, to a large extent, the me thodology developed by the Western Regional Air Partnership (WRAP) for performing BART analyses (WRAP 2006). The RGGS BART analyses incl uded air dispersion modeling to estimate the visibility impacts at Cla ss I areas associated with emissions from RGGS, and the modeled improvem ent 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 mo deling was conducted with higher emissions rates for NO X and PM 10 than the NVE BART Limits). The BART analyses also considered a number of additional control options, however the CH2MHILL reports (CH2 MHILL 2008) state that “visibility modeling has not been performed for the combination of NO X , SO 2 and PM 10 ...” impacts associated with these control scenarios . The modeled visibility improvement in Grand Canyon NP for various NO X -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 de termination. The CH2MHILL reports do not make it clear, however how the dispersion modeling results were2 NO X SO 2 PM 10 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 “98 th 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 impa cts 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 a ssociated with all visibility-related pollutants (i.e., NO X , SO 2 and PM 10 ) 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 rate s, and (2) Current Title V Permit Limits (not actual emission levels, bu t rather enforceable operating limits). The Title V limits produce slightly lower NO X emissions but higher SO 2 and PM emissions than the WRAP baselin e scenario. The modeling results presented in the BART analyses show th at the Title V Limit emission scenario results in somewhat higher visibility impacts than the WRAP Baseline. The WRAP Baseline emission levels were us ed to represent baseline emissions for the BART analyses and also for the current RGGS visibility assessment.

The WRAP baseline emission rates for ea ch emitted pollutant were identified for each RGGS unit in the BART anal yses (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 repres ent baseline levels in Table 5-4 (for which the “reduction” is measured against), and (2) wh y does the “Base” scenario have a delta dv reduction associated with it (ranging fr om 0.354 to 0.386 for the three units)? Furthermore, how is delta dv assessed if only NO X 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 po llutant was obtained by multiplying the boiler heat inputs by the em ission rates shown in Table 1.

For the baseline emission s scenario, sulfate (SO 4 ) emissions were estimated to be 1.5 percent of the SO 2 emission rate (equivalent to 1 percent SO 2 conversion) following the methodology used in the RGGS BART analyses (CH2MHILL used this assumption for al l three of their modeled scenarios).

The PM 10 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 diamet er). In their BART analyses, CH2MHILL assumed that the PM25/PM 10 fraction was equal to 0.5758 (and PMC/PM 10 = 0.4242) for all of their modeled scenarios. The same fine/coarse fraction was used to split the PM 10 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) emi ssions 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 contro l options were considered in the BART analyses (CH2MHILL 2008), only three emission sce narios were evaluated at all five Class I areas within 300 km of RGGS using emissions from all visibilityimpairing pollutants. Additional mo deling was conducted by CH2MHILL to evaluate the visibility impacts associat ed 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 assessme nt, the visibility impacts associated with the SCR control option was evaluate d. The selective catalytic reduction control system chemically reduces NO X , to nitrogen and water using a

2 Modeling files for the three emission scenari os (WRAP Baseline, Ti tle V Limits, and NVE BART) were obtained (Malone 2011) and revi ewed as part of the current modeling assessment. Requests for similar mo deling files for the alternative NO X -only control options were made, however these file s 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 NO X 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 NO X 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 SO 2 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 (H 2 SO 4 ) production was estimated by combining the coal sulf ur 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, resultin g in an estimate of the amount of (uncontrolled) SO 2 manufactured as a result of combustion, which was then multiplied by a fuel factor (represent ing the percent of conversion from SO 2 to SO 3 ). An additional 0.5 percent of the uncontrolled SO 2 was assumed to be converted to SO 3 as a result of the catalyst, resulting in slightly higher SO 4 emission levels.

Sulfate is emitted from each boiler unit either in the form of sulfuric acid (H 2 SO 4 ) or, if sufficient ammonia (NH 3 ) is present to neutralize the sulfate, as ammonium bisulfate ((NH 4 ) 2 SO 4 ) or ammonium sulfate (NH 4 SO 4 ). 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 NH 3 (assumed to be 0.75 ppm) reacts with H 2 SO 4 to produce ammonium sulfate or ammo nium bisulfate, with the remaining sulfate emitted as H 2 SO 4 . The penetration fa ctors for ammonium sulfate/bisulfate particles are assumed to be 0.36 (air pre-heater, APH) and 0.01 (FF); the penetration factors for emitted H 2 SO 4 mist are assumed to be 0.36 (APH), 0.10 (FF), and 0.65 (Venturi scrubber).

For the modeled SCR contro l scenario, the PM25/PM 10 and PMC/PM 10 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 wa s the case for the modeling performed by CH2MHILL for their BA RT analysis modeling).

The resulting emission estimates are sh own in Table 2 for each unit and for all three units combined for the two mo deled scenarios. SCR controls would achieve greater than 90 percent NO X reduction relative to baseline emission levels. The SCR control scenario would also reduce SO 2 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 simu lates the effects of time- and spacevarying meteorological conditions on po llutant transport, transformation and removal. CALPUFF has been adopted by the US EPA in its Guideline on Air Quality Models (US EPA, 2005a) as th e 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 th e following pollutants as a consequence of the emissions from RGGS for each modeled emission scenario (as shown in Table 2): SO 2 , SO 4 , NO 2 , NO 3 , HNO 3 , SOA, PM25, PMC, and EC.

The meteorological data files (MM5 da ta, 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 Prot ection (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 BA RT analysis modeling, including the same ozone data set, and the same set of Class I area receptors (i.e., the6 CALPUFF.INP control files were identica l except for the emission data and filenames). Following CALPUFF execut ion, the POSTUTIL program (part of the CALPUFF modeling system) was used to “correct” the balance between model-predicted hourly HNO 3 and NO 3 concentrations, based on the amount of available background ammonia (as well as temperature and relative humidity). The background ammonia le vel was set to the 1 ppb, following the same methodology used by CH2MHILL.

FLAG Guidelines for Determinatio n of Regional Vi sibility 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 area s include national parks that are over 6,000 acres and wilderness areas and me morial parks that are over 5,000 acres. Recently the FLMs released an updated set of guidelines for evaluating the effects on air qualit y related values (AQRVs), including visibility, in federal Class I areas (FLAG, 2010).

The FLAG guidelines require that mo deled 24-hour averaged visibility impacts be compared with existing conditions, (defined as “natural” conditions). An impact from a new (o r modified) source that increases the existing atmospheric light extinction (b ext ) levels by 5 percent or less (which corresponds to an increase in deciview s, dv, of approximately 0.5 or less) would be considered not to cause an ad verse visibility impact. An increase in light extinction of 10 percent or mo re (corresponding to approximately a one dv or greater change) would genera lly cause a noticeable impairment to existing visibility conditions. The 98 th percentile in 24-hour average change in extinction (or deciviews), which corresponds to the 8 th highest 24-hour average change, at any modeled receptor within a Class I area during each modeled year, is to be compared to th e 5 and 10 percent levels to determine if the source’s visibility impact will likely be of concern. If the 98 th percentile change in light extinction with resp ect to average natural conditions is greater than 5 percent, then, accordin g to BART guidelines, the impact is considered to be perceptible and th e source “contributes” to visibility impairment (US EPA, 2005b). If the 98 th percentile change in light extinction with respect to average na tural conditions is greater than 10 percent, then the impact of the source is considered to “cause” visibility impairment.

The FLMs recommend that measured av erage pollutant concentrations (and Rayleigh scattering) as measured in Class I areas by the IMPROVE network be used to represent natural condit ions at each Class I area. The recommended values for each Class area are tabulated in the revised FLAG guidelines (FLAG, 2010). The FLAG-recommended background7 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 curre nt modeling assessment of visibility impacts due to emissions from RGGS , along with the monthly average relative humidity adjustment factors sp ecific 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_Mod e=5) procedure wi thin the CALPOST postprocessor. 3

There were 2359 locations used as mode l receptors, spread throughout the five Class I areas. At each of thes e receptor locations, the hourly modelpredicted concentrations of six polluta nts (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 concentrat ions of sulfates, nitrates, organic and elemental carbon, fine and coarse PM, in each of the modeled Class I areas for three year s (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 visib ility calculations. FLAG guidelines require the use of Method 8 (M8_Mode=5). This affect s (1) the RH factor for extinction due to hygroscopic species (e.g., SO 4 , NO 3 ), 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 (b ext ) due to the sulfate, nitrate, organic and elemental carbon, and fine and coarse PM 10 concentrations attributed to each RGGS unit (and all units combin ed) at each Class I area, following FLAG guidelines (Method 8, M8_Mod e=5). The 24-hour averages were compared to existing annual average co nditions and the relative changes to extinction were then ordered from highest to lowest. The 8 th highest 24- hour average extinction change (or de lta dv) for each year corresponds to the 98 th percentile change in extinction th at must be compared to the 5% or 10% threshold, as per the FL AG (and BART) guidelines.

Table 4 shows the 98 th percentile (the 8 th 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 base line 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 98 th 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 98 th percentile delta dv for the combined impact of all three RGGS uni ts exceeded 2.0 within Grand Canyon NP, providing strong evidence that R GGS “causes” visibility impairment in Grand Canyon NP. Similarly, the 98 th 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 23 rd highest 24-hr delta dv out of a ll three model years to produce the 98 th percentile delta dv data for each Class I area in Tables 5-3 of the BART Analyses, rather than the average of the three 8 th 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 2001-2003 2001 2002 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 2001-2003 2001 2002 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 Visibili ty Impacts: Baseline Emissions

Table 5 shows the modeled visibility impa cts 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 98 th 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 im pact due to emissions from all three RGGS units combined would be below th e 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 dv 1 Ndays=>0.5 Delta dv 1 Ndays=>0.5 Delta dv 1 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 98 th percentile (the 8 th highest 24-hour period) delta dv for the three modeled years within each Class I area for the SCR control scenario as compared to th e baseline impacts. The table shows the visibility impact (delta dv) for the baseline emission level and for the SCR control scenario, along with the impr ovement 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 th e “contribution” threshold) due to the addition of SCR. SCR is predicted to reduce the 98 th percentile delta dv in the Grand Canyon by 1.79, which would re sult 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 indicati on 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 cu mulative 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 fi ve Class I areas (for all three RGGS units combined).

Summary

The model results using baseline emissi ons indicate that emissions from the three RGGS units causes noticeable vi sibility impairment on over 40 days each year in the Grand Canyon NP. The average 98 th percentile delta dv in Grand Canyon NP for all three units combined was estimated to be 2.17.

The improvement in visibility that wo uld be experienced due to SCR would be significant in Grand Canyon NP. The average 98 th 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 vi sibility relative to the baseline levels. According to the model results, emissi ons from the RGGS would no longer “contribute” to visibility impa irment 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 Impact 1 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 Impact 1
km Delta dv Delta d v 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 Impact 1
km Delta dv Delta d v 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 Impact 1
km Delta dv Delta d v 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 Imp acts: Baseline vs. SCR Control Scenario13 References

CH2MHILL. 2007. Modeling Protocol for BART Control Technology Improvement Modeling Analys es for the Reid Gardner Power Plant. Prepared for Sierra Pacific and Nevada Power by CH2MHILL, Henderson, NV. May 2007.

CH2MHILL. 2008. BART Analysis for Reid Gardner Station Units 1, 2 and 3. Prepared for NV Energy by CH2MHILL, Henderson, NV. Technical Support Document Docket Number: EPA-R 09-OAR-2011-0130. Revised Report, October 2008. http://www.regulatio ns.gov/#!documentDetail;D=EPA-R09- OAR-2011-0130-0007

FLAG. 2010. U.S. Forest Service, U.S. National Park Service, and the U.S. Fish and Wildlife Service. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report – Revised (2010). http://www.nature.nps.gov/ai r/Pubs/pdf/flag/FLAG_2010.pdf

Malone, Adele. 2011. personal communication. Nevada Division of Environmental Protection, Carson City, NV. Forwarded computer hard drive containing CALMET, CALPUFF, POSTUTIL , and CALPOST data files. August, 2011.

U.S. Environmental Protection Agency . 2005a. Guideline on Air Quality Models, 40 CFR Part 51, Appendix W. Published in the Federal Register, Vol. 70, No. 216, November 9, 2005.

U.S. Environmental Protection Agency . 2005b. Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Determinations. Published in the Federal Register, July, 6, 2005. http://www.federalregister.gov /articles/1999/07/01/99-13941/regionalhaze-regulations

Western Regional Air Partnership (WRAP). 2006. Draft Final Modeling Protocol, CALMET/CALPUFF Protocol for BART Exemption Screening Analysis for Class I Areas in the Western Unit ed States. Western Regional Air Partnership Air Quality Modeling Forum, Regional Modeling Center, August 15, 2006. http://www.regulations.go v/#!documentDetail;D=EPA-R09-OAR2011-0130-0012