## Air Dispersion Modeling Analysis

For Verifying Compliance with the

One

- Hour SO 2 

## NAAQS

and Oklahoma SIP limits : Muskogee Generating Station

Prepared by:

Lindsey Sears

June 3 , 201 4M uskogee One

- Hour SO 2 NAAQS and SIP Analysis

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TABLE OF CONTENTS

1

. Introduction

................................ ................................ ................................ .............. 3
2

. Modeling Met hodology

................................ ................................ .......................... 4
2 .1 Air Dispersion Model

................................ ................................ .................. 4
2 .2 AERMOD Input Control Options

................................ ............................... 4
2 .3 Output Options

................................ ................................ ............................ 5
3

. Mode l inputs

................................ ................................ ................................ ............ 5
3 .1 Geographical Inputs

................................ ................................ .................... 5
3 .2 Emissio n Rates and Source Parameters

................................ ....................... 6 3 .3 Building Dimensions for Downwash

................................ ........................... 8
3 .4 Rece ptors

................................ ................................ ................................ ...... 8
3. 5 Meteoro lo g ical Data

................................ ................................ ..................... 9 3.5.1 Surface Meteorology

................................ ................................ ...... 10 3.5.2 Upper Air Data

................................ ................................ ............... 1 1 3.5.3 AERSURFACE

................................ ................................ .............. 1 1 3.5.4 Data Review

................................ ................................ ................... 1 2
4

. Background Air Concentrations

................................ ................................ ............ 1 3 5

. Modeling R esults

................................ ................................ ................................ ... 1 3 5.1 NAAQS Modeling Results

................................ ................................ ........ 1 3 5.2 SIP Modeling Results

................................ ................................ ................ 1 5

FIGURES
1

. OG&E Muskogee Facility : Aerial view of the plant.
2

. OG&E Muskogee Facility : One

- hour sulfur dioxide concentrations ( μg/m 3 ) from CAMD reported emissions, with background levels

. 3

. OG&E Muskogee Facility : One

- hour sulfur dioxide concentrations ( μg/m 3 ) from allowable emissions, with background levels.
4

. OG&E Muskogee Facility : One

- hou r sulfur dioxide concentrations ( μg/m 3 ) from 2013 maximum emissions, with background levels.

ATTACHMENTS
1. Curriculum Vitae
2. Precipitation Conditions for Determining Seasonal Bowen Ratios
3. 2008 th rough 2012 Wind RosesM uskogee One

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1. Introduction

Oklahoma Gas and El ectric (OG&E) operates the Muskogee Generating Station , which is a major source of sulfur dioxide (SO 2 ) emissions located in Muskogee County , Oklahoma

. Th is facility

, which is permitted by the Oklahoma Department of Environm ental Quality ( O DEQ ), includes three coal

- fired boiler unit s

. Emissions from each unit v ent to a dedicated stack

. 1 An aerial view of the Muskogee station is shown in Figure 1

.

I was asked to verify whether Muskogee’s SO 2 e missions cause air impacts that exceed the recently promulgate d one

- hour National Ambient Air Quality Standard ( NAAQS ) for SO 2 , or would prevent maintenance of the standard. In response, I prepared air dispersion modeling analyse s for

c alculating ambient SO 2 air concentrations from the Muskogee facility . These mode led impacts are then compared with the one

- hour SO 2 NAAQS . This report presents my modeling results and discusses the technical method ology I used for performing these analyse s

.

The one

- hour SO 2 NAAQS takes the form of a three

- year average of the 99 th

- pe rcentile of the annu al distribution of daily maximum one

- hour concentrations, which cannot exceed 75 ppb. 2 This standard is to be verified using USEPA’s AERMOD air dispersion model, which produces air
concentrations in units of μg/m 3 . The one

- hour SO 2 N A AQS of 75 ppb equals 196.2 μg/m 3 , and this is the value I used for determining whether modeled Muskogee impacts exceed the NAAQS. 3 The
99 th

- percentile of the annual distribution of daily maximum one

- hour concentrations corresponds to the fourth

- highest va lue at each receptor for a given year.

I was asked to perform three modeling scenarios for SO 2 emissions to verify whether Muskogee is in compliance with the SO 2 NAAQS : actual hour

- by

- hour emissions, allowable (or permitted) emissions ; and 2013 reported m aximum actual emissions

. In addition, I was asked to perform an iterative modeling process to determine the limits necessary to achieve and maintain compliance with the SO 2 NAAQS

.

I was also asked to perform air dispersion modeling to verify whether actu al hour

- by

- hour SO 2 emissions from the OG&E Muskogee facility are in violation Oklahoma’s State Implemented Plan (SIP) limits. In this analysis, I compared peak one

- hour, three

- hour, and 24

- hour average concentrations to the Oklahoma SIP limits of 1200 μg /m 3 , 650 μg/m 3 , and 130 μg/m 3 , respectively.

1 USEPA, Clean Air Markets

– D ata and Maps, which can be accessed at: http://ampd.epa.gov/ampd

. 2 USEPA, Applicability of Appendix W Modeling Guidance for the 1

- hour SO 2 National Ambient Air Quality Standard, August 23, 2010.
3 The ppb to μg/m 3 conversion is found in the source code to AERMOD v. 13350, subroutine Modules. The conversion calculation is 75/0.3823 = 196.2 μg/m 3

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2. M odeling Methodology

Th is section describes the dispersion model, control options, and output options I used for verifying Muskogee ’s compliance with the one

- hour SO 2 NAAQS and SIP limits

.

2 .1 Air Dispersion Model

I per formed one

- hour SO 2 modeling of reported hour

- by

- hour emissions with USEP A ’s AERMOD program, version 1 3350

, obtained from the Support Center for Regulatory Atmospheric Modeling (SCRAM) website. AERMOD is the USEPA preferred air dispersion mod el for determining air impacts within 50 kilometers of air pollution emission source s

. 4 At the time of this analysis, version
13350 was the latest version of the AERMOD model, which was completed on December 16, 2013 (Julian day 350 of 2013)

.

I performed one

- hour SO 2 modeling of allowable and 2013 maximum emissions with USEPA’s AERMOD program, version 14134, which was completed on May 14, 2014 (Julian day 134 of 2014).

2 .2 AERMOD Input Control Options

I ran AERMOD model with the following control option s:

 O ne

- hour average air concentrations

 Regulatory defaults

 Flagpole receptors

 Rural dispersion coefficients
To correspond to a representative inhalation level, I used a flagpole height of 1.5 meters for all modeled receptors. I added this parameter to the receptor file when running AERMAP, as described in Section 3.4.

I determined that Muskogee should be modeled with the default AERMOD rural dispersion control option. I reached this finding using USEPA’s methodology outlined in Section 7.2.3 of the
Guide line on Air Quality Models. 5

4 USEPA, Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terr ain) Dispersion Model and Other Revisions, Appendix W to 40 CFR Part 51, November 9, 2005.
5 Id

. , Section 7.2.3.M uskogee One

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2.3 Output Options

My AERM OD modeling analys e s of the Muskogee facility include s a five

- year span of meteorological data

– y ears 2008 through 2012

. Consistent with USEPA’s Modeling Guidance for
SO 2 NAAQS Designations, I used the MXDYBYYR (maxi mum day by year) output options to

create a table of fourth

- high one

- hour SO 2 impacts for each year of meteorological data modeled

. 6

This provide s five separate file s of one

- hour concentration s for each model run

. I then averaged the

one

- h our SO 2 value s for each receptor across the five years of modeled data to calculate concentrations in the form of the one

- hour SO 2 NAAQS . These output file s also provide the data necessary for preparing air concentration isopleths.

I also u sed the MXDYBY YR (maxi mum day by year) output options to determine highest one

- hour, three

- hour, and 24

- hour SO 2 impacts for each year of meteorological data modeled. I compared these values with the Oklahoma SIP limits to determine compliance.

3. Model input s

The AERM OD air dispersion model requires a lengthy list of input values. Key inputs to this dispersion model include local geo graphy, air emission rates of the released pollutant, source parameters (how and where the material is released to the air), receptors (l ocations where the offsite concentrations and deposition are calculated), and meteorological data (determines how and where the material is dispersed in the air). Each of these inputs is discussed below.

3 .1 Geographical Inputs

The “ground floor” of all air dispersion modeling analyses is establish ing a coordinate system for identifying the geographical location of emission sources and receptors. These geographical locations are used to determine local characteristics (such as land use and elevation), a nd also to ascertain source to receptor distances and relationships.

I used the Universal Transverse Mercator (UTM) NAD83 z one 15 coordina te system for identifying the easting (x) and northing (y) coordinates of the modeled source s and receptors. I obtai ned UTM source coordinates using ESRI’s ArcGIS geographic information system and NAIP orthoimagery. I verified the source coordinates using Google Earth Pro orthoimagery , which ensures consistency with the UTM NAD83 coordinate system

.

6 USEPA, Area Designations for the 2010 Revised Primary Sulfur Dioxide National Ambient Air Quality Standards, Attachment 3, March 24, 2011, p p . 24

- 26

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As mentioned above, I determined that Muskogee ’s emission sources should be modeled with rural dispersion coefficients. I studied the geographical setting in a three

- kilometer radius circle s surrounding the Muskogee facility location, examining both land use and population density characteristics. If less than 50% of the surrounding area is urban and developed, then a rural classification is supported. Also, the default rural option may apply if the population density in the three

- kilometer radius surrounding each facility is less than 750 people per square kilometer. Since both of these conditions apply to the Muskogee facility , I modeled the stack emissions with AERMOD’s default rural dispersion coefficients. 7

3 .2 Emission Rate s and Source Parameters

My modeling analys e s are limited to SO 2 emission s from the coal

- fired boiler stack s at the Muskogee facility

, as described in the Introduction

. 8

Coal

- fired boiler stacks are treated as point sources in AERMOD

. Point sources are modeled with the following stack parameters :

 Source Location X (Easting) coordinate (UTM NAD83 );

 Source Location Y (Northing) coordinate (UTM NAD83 );

 Source base elevation (meters above sea level );

 Stack e mission rate (g/s);

 Stack height (meters);

 Stack gas exit temperature (Kelvin);

 Stack gas exi t velocity (meters/second);

 Stac k diameter (meters). 9

Stack parameters for units 4, 5, and 6 were developed using USEPA’s Non

- Hg Case Study Chronic Inhalation Risk Assessment for the Utility MACT, which included AERMOD modeling for all three of OG&E’s Mus kogee coal

- fired units. 10

First, I used actual hour

- by

- hour SO 2 emissions from the EPA’s Clean Air Market Database (CAMD) to determine NAAQS and SIP compliance

. These hourly emissions were coupled with

7 USEPA, Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions, Appendix W to 40 CFR Part 51, November 9, 2005 , Section 7.2.3.
8 There are other SO 2 emission sources near the facility that would tend to add to model impacts.
9 USEPA, User’s Guide for the AMS/EPA Regulatory Model

– AERMOD, EPA

- 454/B

- 03

- 101, September 2004 (with revisions), pp. 3

- 16

– 3

- 18.
10 USEPA, Non

- Hg Case Study Chronic Inhalation Risk Assessment for the Utility MACT, App ropriate and Necessary Analysis, March 16, 2011, p. 6.M uskogee One

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contemporaneous hourly meteorological data when run ning AERMOD. For this scenario, I modeled the following emissions and stack parameters for Muskogee’ s coal

- fired boiler stack s :

Unit XUTM (meters) YUTM

(meters) Base

Elevation (meters) SO 2

Emission Rate (g/s) Release

Ht. (meters) Stack

Temp. (K ) Stack

Exit Vel. (m/s) Stack

Diam. (m)
4 293097.8 3959945.8 156.21 hourly 106.68 402.04 17.13 7.32 5 293152.3 3959955.7 156.76 hourly 106.68 402.04 17.13 7.32
6 293225.2 3959976.2 156.95 hourly 152.40 402.04 24.84 6.55

As discussed above, I modeled actual hourly emissions (using the HOUREMIS option in AERMOD’s source pathway). I did not vary the stack gas exit velocity and temperature, instead using steady

- state maximum allowable load values. Since plume rise will be h igher with maximum allowable load values, this will result in a tendency to under

- predict hour

- by

- hour impacts using actual emissions (where the hourly stack gas exit velocity and temperature are likely to be less than maximum allowable load values).

I al so modeled permitted, also called allowable, SO 2 emissions from Muskogee Units 4, 5, and 6

. Allowable emissions based on permitted heat inputs and emission factors in lb per MMBTU are one basis for verifying compliance with the one

- hour NAAQS. 11 For this scenario, I modeled the following emissions and stack parameters for Muskogee’ s coal

- fired boiler stacks:

Unit XUTM (meters) YUTM

(meters) Base

Elevation (meters) SO 2

Emission Rate (g/s) Release

Ht. (meters) Stack

Temp. (K) Stack

Exit Ve l. (m/s) Stack

Diam. (m)
4 293097.8 3959945.8 156.21 828.576 106.68 402.04 17.13 7.32 5 293152.3 3959955.7 156.76 828.576 106.68 402.04 17.13 7.32
6 293225.2 3959976.2 156.95 828.576 152.40 402.04 24.84 6.55

The SO 2 emission ra tes are based on permitted emission limits of 1.2 lb/MMBTU, and a boiler heat input of 5,480 MMBTU/hr for each unit. 12

11 USEPA, Area Designations for the 2010 Revised Primary Sulfur Dioxide National Ambient Air Quality Standards, Attachment 3, March 24, 2011, p p. 9

- 11.
12 ODEQ, Air Quality Division, Permit Memorandum, Evaluation of Permit Application No. 97

- 136

- TV (M

- 4), Oklahoma Gas and Electric Muskogee Generating Station, June 16, 2006. See also, ODEQ, Air Quality Division, BART Application Analysis, Oklahoma Gas and Electri c Muskogee Generating Station, January 15, 2010, p. 2.M uskogee One

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Third, I modeled 2013 actual maximum hourly SO 2 emissions, obtained from USEPA’s CAMD. The highest one

- hour SO 2 emissions occurred on hour ten of September 13, 2013, totaling 8,524.4 lb/hr or 1074.1 g/s

. The emissions and parameters that I modeled for the individual units are as follows:

Unit XUTM (meters) YUTM

(meters) Base

Elevation (meters) SO 2

Emission Rate (g/s) Release

Ht. (meters) Stack

Temp. (K) Stack

Exit Vel. (m/s) Stack

Diam. (m)
4 293097.8 3959945.8 156.21 370.062 106.68 402.04 17.13 7.32 5 293152.3 3959955.7 156.76 374.056 106.68 402.04 17.13 7.32
6 293225.2 3959976.2 156.95 329.956 152.40 402.04 2 4.84 6.55

Finally, I performed an iterative modeling process to determine what emission rate would be required for Muskogee to achieve compliance with the SO 2 NAAQs. For this scenario, I modeled the same stack parameters as in the oth er scenarios, but varied the SO 2 emission rate until I found the rate at which Muskogee would be in compliance with the NAAQS. For each run, I modeled the same emission rate for the three stacks, reflecting the equal allowable emission limits.

3 .3 Bui lding Dimensions for Downwash

Adjacent buildings and other structures may cause plume downwash, a condition where plumes can be dispersed towards the ground in the downwind wake

- effect from these buildings. USEPA’s Building Profile Input Program (BPIPPRM v. 04274 with Plume Rise Model Enhancement (PRIME)) is used to determine stack

- specific good engineering practice (GEP) values and wind direction

- specific building downwash parameters for each 10

- degree azimuth. 13

Using oblique GIS orthoimagery and elevat ion photos, it appears that building downwash may be a factor for the Muskogee facility. I used downwash values obtained from previous Sierra Club analyses.

3 .4 Receptors

I created r eceptors in 100 meter increments in a 10 km by 10 km Cartesian grid cen tered on the stack location for the Muskogee facility

. Outside the s e grid s , I generated a receptor grid in 5 00 meter

13 USEPA, User’s Guide to the Building Profile Input Program, EPA

- 454/R

- 93

- 038, April 21, 2004.M uskogee One

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increments in a 20 km by 20 km area centered on the same point

. The 500 meter grid of receptors

encompass es the nested 100 meter receptor s, so any duplicate receptors with the exact same location were extracted from the data set. And outside the grid of 500 meter receptors, I created receptors in 1,000 meter increments in a 70 km by 7 0 km area. The 1,000 meter grid of receptors encompass e s the nested 100 and 500 meter receptors, so any duplicate receptors with the exact same location were extracted from the data set. As discussed earlier, I used a flagpole height of 1.5 meters for all modeled ground

- level receptors.

M odeled source and re ceptor locations require terrain elevation data, in meters above sea level. I obtained terrain elevation data for these locations using National Elevation Dataset (NED) GeoTiff data for the area encompassing the Muskogee facility and the modeled receptors

. GeoTiff is a binary file that includes data descriptors and geo

- referencing information necessary for extracting terrain elevations. For the 100 meter and 500 meter receptors, I extract ed terrain elevations from the
NED files using USEPA’s AERMAP progr am, v. 11103, with 1/3 rd arc

- second (10 meter horizontal ) resolution

. I used 1 arc

- second (30 meter horizontal resolution) NED files for extracting terrain elevations for the 1,000 meter receptor grid s

.

3 .5 Meteorological Data

USEPA’s definition of pre ferred meteorological data includes the most recent five years of National Weather Service (NWS) da ta. At the time of modeling, this condition wa s satisfied using 2008 through 2012 Automated Surface Observing Station (ASOS) data collected at the most site

appropriate airport

. From Section 8.3.1.2 of the Guideline on Air Quality Models:

a. Five years of representative meteorological data should be used when estimating concentrations with an air quality model. Consecutive years from the most recent, readily a vailable 5

- year period are preferred. The meteorological data should be adequately representative , and may be site specific or from a nearby NWS station. Where professional judgment indicates NWS

- collected ASOS (automated surface observing stations) data are inadequate [for cloud cover observations], the most recent 5 years of NWS data that are observer

- based may be considered for use.

The use of 5 years of NWS meteorological data or at least l year of site specific data is required. If one year or more ( including partial years), up to five years, of site specific data is available, these data are preferred for use in air quality analyses. Such data should have been subjected to quality assurance procedures as described in subsection 8.3.3.2. (Italics in original.) 14

14 USEPA, Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat an dM uskogee One

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More importantly, pre

- 2006 meteorological data are usually based on airport wind measurements that include an over

- stated number of calm conditions. In their modeling guidance for SO 2 NAAQS designations, USEPA addresses the concern of calm hou rs in verifying compliance with the one

hour SO 2 NAAQS:

In AERMOD, concentrations are not calculated for variable wind (i.e., missing wind direction) and calm conditions, resulting in zero concentrations for those hours. Since the SO 2 NAAQS is a one hour standard, these light wind conditions may be the controlling meteorological circumstances in some cases because of the limited dilution that occurs under low wind speeds which can lead to higher concentrations. The exclusion of a greater number of instance s of near

- calm conditions from the modeled concentration distribution may therefore lead to underestimation of daily maximum 1

- hour concentrations for calculation of the design value. 15

To address USEPA’s concerns regarding calm winds, I developed 2008 thr ough 2012 meteorological data that incorporate methods to reduce calm and missing hours (e.g. use one

- minute data and USEPA’s AERMINUTE program). 16 The meteorological data required by AERMOD is prepared by AERMET. Required data inputs to AERMET are: surfa ce meteorological data, twice

daily soundings of upper air data, and the micrometeorological parameters surface roughness, albedo, and Bowen ratio. AERMET creates the model

- ready surface and profile data files required by AERMOD.

Using AERMET v. 1 3350

, I created an AERMOD

- ready meteorological data set to model the Muskogee facility . This data set covered five years, 2008 through 2012 , and is summarized as follows:

Meteorological data used for modeling the Muskogee facility : Surface data: Muskogee/Davis Field Airport (K MKO ); Upper air data: Norman/University of Oklahoma Westheimer Airport (K OUN ).

3.5.1 Surface Meteorology

I used 2008 through 2012 Integra ted Surface Hourly (ISH) data obtained from the National Climatic Data Center (NCDC). From the IS H dataset, I extracted ASOS data from the Muskogee/Davis Field Airport in Muskogee, OK . Based on distance and site characteristics, I determined that this airport is the most site

- appropriate for modeling the Muskogee facility

.

Complex Terrain) Dispersion Model and Other Revisions

, Appendix W to 40 CFR Part 51, November 9, 2005.
15 USEPA, Area Designations for the 2010 Revised Primary Sulfur Dioxide National Ambient Air Quality Standards, Attachment 3, March 24, 2011, p. 19.
16 U SEPA , AERMINUTE User’s Instructions, v. 11325, p. 1.M uskogee One

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I als o obtained 2008 throu gh 2012 one

- minute ASOS wind data from the Muskogee/Davis Field Airport , which I processed with AERMINUTE v. 11325. I downloaded these one

- minute data from the NCDC. 17 I input the ice

- fre e wind instrum ent start date ( February 9, 2007 ) and used default

set tings with AERMINUTE. As a quality assurance measure, I compared values developed from the

one

- minute data with the corresponding ISH data file.

I processed the ISH data through AERMET Stage 1, which performs data extraction and quality control checks. I merged the AERMINUTE output files with the processed AERMET Stage 1 ISH and upper air data in AERMET stage 2.

3.5.2 Upper Air Data

I used 2008 through 201 2 upp er air data from twice

- daily radiosonde measurements obtained from the Norman/University of O klahoma Westheimer Airport . These data are in Forecast Systems Laboratory (FSL) format which I downloaded in ASCII text format from NOAA’s FSL website

. 18 I downloaded and processed all reporting levels with AERMET.

Upper

- air data are collected by a “weat her balloon” that is released twice per day at selected locations. As the balloon is released, it rises through the atmosphere, and radios the data back to the surface. The measuring and transmitting device is known as either a radiosonde, or rawindsonde

. Data collected and radioed back include: air pressure, height, temperature, dew point, wind speed, and wind direction. I processed the FSL upper air data through AERMET Stage 1, which performs data extraction and quality control checks.

3.5.3 AERSURF ACE

AERSURFACE is USEPA’s program for extracting surface roughness, albedo, and daytime Bowen
ratio for an area surrounding a given location. 19 AERSURFACE uses land use and land cover (LULC) data in the U.S. Geological Survey’s 1992 NLCD to extract the ne cessary

17 See: ftp://ftp.ncdc.noaa.gov/pub/data/asos

- onemin/
18 Available at: http://esrl.noaa.gov/raobs/
19 Albedo is the fraction of total incident solar radiation reflected by the surface back to space (whiter surfaces have higher albedo). The Bowen ratio is an indicator of surface moisture. It is the ratio of sensible heat flux to latent heat fl ux and d rier areas have a higher Bowen ratio. Surface roughness, shown in shorthand as (“ z 0 ”), is an essential parameter in estimating turbulence and diffusion. Technically, it’s the height above the ground that the log wind law extrapolates to zero. For our pu rposes, z 0 can be thought of as a measure of how much the surface characteristics interfere with the wind flow. Very smooth surfaces, like short grass or calm ponds, have very low values of z 0 -- on the order of 0.01 meter or less. Tall and irregular sur faces, which are a greater obstacle to wind flow, have higher values of z 0 – up to 1.0 meter or more for forests.M uskogee One

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micrometeorological data. I used these 1992 LULC data for processing meteorological data sets which then serve as input to AERMOD.

I used AERSURFACE v. 13016 to develop surface roughness, albedo, and daytime Bowen ratio values in a region surroun ding the meteorological data collection site ( Muskogee/Davis Field Airport ). Using AERSURFACE, I extracted surface roughness in a one kilometer radius surrounding the data collection site. I also extracted Bowen ratio and albedo for a 10 kilometer by
10 k ilometer area centered on the meteorological data collection site. I processed these micrometeorological data for seasonal periods using 30

- degree sectors.

I developed variable Bowen ratios, based on precipitation for each season and each year ( 2008 thr ough 201 2 ). I determined the seasonal moisture conditions (wet, avera ge, dry) using 1981 through 2012 climatic me an monthly rainfall data for the Muskogee/Davis Field Airport

. 20 For each season of each year, I compared the seasonal total rainfall to clima tic means for that season. Seasonal rainfall less than 75% of climatic means was assessed as dry. I assessed seasonal rainfall greater than 125% of climatic means as wet. 21 Tables of the precipitation conditions for determining seasonal Bowen ratios from the Muskogee/Davis Field Airport are included in Attachment 2.

3.5.4 Data Review

I did not fill missing hours in the meteorological data sets as the data files easily exceed USEPA’s 90% data completeness requirement. 22 Annual wind roses of the AERMOD

- re ady meteorological data sets I created, individu ally by year for 2008 through 2012 for Muskogee/Norman , are included in Attachment 3.

The representativeness of airport meteorological data is a potential concern in modeling industrial source sites. 23 The m eteorological data sources I used are the most site

- appropriate available for modeling the Muskogee facility . In addition, I modeled the facilities with AERMOD’s default rural dispersion option. Given these considerations, I believe that the meteorologic al data I used (developed with one

- minute winds) represent the best data available for modeling Muskogee ’s SO 2 emissions.

20 See http://www.ncdc.noaa.gov/oa/climate/normals/usnormals.html
21 USEPA, Non

- Hg Case Study Chronic Inhalation Risk Assessment for the Utility MACT Appropriate and Necessary Analysis, March 16, 2011, p. 11.
22 USEPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications, EPA

- 454/R

- 99

- 05, February 2000, Se ction 5.3.2, p p. 5

- 4

– 5

- 5.
23 USEPA

, AERMOD Implementation Guide , March 19, 2009, pp. 3

- 4.M uskogee One

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4

. Background Air Concentrations

I n this modeling analysis, I used an SO 2 background concentration that is consistent with USEPA’s Modeling Guidance for SO 2 NAAQS Designations. The appropriate background value is a uniform concentration based on the monitored design value for the latest three

- year period. 24 For the OG&E Muskogee facility, I used the 2010

– 2012 design value SO 2 conc entration from a monitoring site in Tulsa County , OK (Site ID 40

- 143

- 0 2 35 ). 25 This site location is sufficiently distant to minimize double

- counting impacts from OG&E ’s Muskogee coal

- fired power plant in the background SO 2 concentrations.

The 2010

– 2012 SO 2 background design value from Site ID 40

- 143

- 0 2 35 is 30 ppb, which equals 78.5 μg/m 3 . The design value ( equal to the three

- year average 99 th

- percentile of the measurements) preserves the form of the one

- hour SO 2 standard, and is added to the 99 th perce ntile of the annual distribution of daily maximum one

- hour concentrations averaged across the number of years modeled. 26

5

. Modeling Results

As discussed above, I modeled actual hour

- by

- hour SO 2 emissions

, allowable SO 2 emissions , and 2013 actual maximum SO 2 emissions from the OG&E Muskogee facility in order to determine whether Muskogee is in violation of the NAAQS. I performed an iterative modeling process to determine a limit at which Muskogee would be in compliance with the NAAQS. I also modeled

act ual hour

- by

- hour SO 2 emission s to determine compliance with Oklahoma’s SIP limits. The results regarding NAAQS compliance are discussed below, followed by results regarding SIP limits compliance.

5.1 NAAQS Modeling Results

The 99 th percentile modeled on e

- hour SO 2 ambient air impacts from Muskogee’s actual hour

- by

hour emissions, allowable emissions, and 2013 reported maximum actual emissions are presented in the table below.

For this analysis, the one

- hour SO 2 ambient air impacts (Facility H4H

– highes t fourth high value)

24 USEPA, Area Designations for the 2010 Revised Primary Sulfur Dioxide National Ambient Air Quality Standards, Attachment 3, March 24, 2011, pp. 20

- 23. 25 See http://www.epa.gov/airtrends/values.html

. 26 USEPA, Area Designations for the 2010 Revised Primary Sulfur Dioxide National Ambient Air Quality Standards, Attachment 3, March 24, 2011, pp. 20

- 23.M uskogee One

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are based on the 99 th percentile of the annual distribution of daily maximum one

- hour concentrations averaged across the five years of modeled meteorological data. The peak modeled one

- hour SO 2 ambient air impact s , using 2008 through 2012 K MKO /K OUN meteorological data, steady state stack exit velocities and temperatures associated with allowable emissions, and with background SO 2 concentrations, are as follows :

Sulfur dioxide (SO 2 ) K MKO 2008

- 2012 met, AERMINUTE

Scenario Facility
H4H Conc. (μg/m 3 ) XUTM

(m) YUTM

(m) Impacted Counties

( N ot Designated Nonattainment)

Hour

- by

- Hour CAMD Emissions 459.2 293200 3961200 Wagoner, Cherokee,
M uskogee

Allowable Emissions 1249.1 293300 3961100 Wagoner, Cherokee, Sequoyah, M uskogee

, McIntosh

2013 CAMD Max Emissions 597.9 293300 3961100 Wagoner, Cherokee, Sequoyah, M uskogee

Background concentration of 78.5 μg/m 3 is included in resul ts.

Accounting for background SO 2 concentrations, I determined that the Muskogee facility would need to have a total emission rate of no more than 249 g/s or 1976.2 lb/hr in order to comply with the NAAQS. These results are summarized in the following t able:

Required total Facility Reduction
Based on Allowable Emissions Required Total Facility SO 2 Emission Rate (lbs/hr) Required Total Facility One

- Hour Average SO 2 Emission Factor (lb/MMBTU)

90.0 % 1976.2 0. 12

Even if b ackground levels were not include d, OG&E’s Muskogee facility would still violate the one

hour SO 2 NAAQS in each scenario, as shown in the following table:M uskogee One

- Hour SO 2 NAAQS and SIP Analysis

June 3 , 2014 Page

- 15

Sulfur dioxide (SO 2 ) K MKO 2008

- 2012 met, AERMINUTE

Scenario Facility
H4H Conc. (μg/m 3 ) XUTM

(m) YUTM

(m)

Hour

- by

- Hour CAMD Emissions 380.7 293200 3961200

Allowable Emissions 1170.6 293300 3961100

2013 CAMD Max Emissions 519.4 293300 3961100

Background concentration is not included in results.

The modeled impacts can also be shown graphically. Figure s 2 t hrough 9 are maps showing isopleths (lines of equal air concentration) overlaid onto ESRI basemaps included with ArcMap v.
10

. I created the isopleths using AERMOD output plotfiles and G olden Software’s Surfer, v. 10. I used kriging algorithms to grid th e data for the isopleths.

Figure 2 shows m odeled one

- hour SO 2 concentrations from Muskogee’s 2008

- 2012 reported hourly
CAMD em issions, with background concentrations

. This map shows two SO 2 concentration levels: 196 .2 and 3 00 μg/m 3

. The regions within e ach isopleth have air concentrations that exceed the levels found on each isopleth. The areas encompassed by the 196 μg/m 3 isopleth exceed the one

hour SO 2 NAAQS and could result in a designation of nonattainment for that region.

Figure 3 shows modeled one

- hour SO 2 concentrations fro m Muskogee’s allowable emissions, with background concentrations. This map shows four SO 2 concentration levels: 196 .2, 300, 400, and
5 00 μg/m 3

. The regions within each isopleth have air concentrations that exceed the levels found on each isopleth. The a reas encompassed by the 196 μg/m 3 isopleth exceed the one

- hour SO 2 NAAQS and could result in a designation of nonattainment for that region.

Figure 4 shows modeled one

- hour SO 2 concentrations from Muskogee’s 2013 maximum actual emissions, with background concentrations. This map shows two SO 2 concentration levels: 196 .2 and 300 μg/m 3

. The regions within each isopleth have air concentrations that exceed the levels found on each isopleth. The areas encompassed by the 196 μg/m 3 isopleth exceed the one

- hour SO 2 NAAQS and could result in a designation of nonat tainment for that region.

5

. 2 SIP Modeling Results

The modeled hour

- by

- hour SO 2 ambient air impacts from Muskogee’s emissions are presented in the table below. Concentrations are for surface

- based receptors with a flagpole height of 1.5 meters, andM uskogee One

- Hour SO 2 NAAQS and SIP Analysis

June 3 , 2014 Page

- 16

are in the form of the SIP limits.

For this analysis, I used 2008 through 2012 K MKO /K OUN meteorological data, steady state stack exit velocities and temperatures associated with allowable emissions, and no background SO 2 concentrations to determine highest one

- hour, three

- hour, and 24

- hour SO 2 impacts for each year of meteorological data modeled. The highest results are as follows:

Sulfur dioxide (SO 2 ) K MKO 2008

- 2012 met, AERMINUTE

Scenario Facility
H4H Conc. (μg/m 3 ) SIP limit

(μg/m 3 ) XUTM

(m) YU TM

(m)

1

- hour averaging period 6 65.2 1200 293400 3958200

3

- hour averaging period 5 11.4 650 293300 3960900

24

- hour averaging period 2 71.4 130 293100 3961000

Background concentration is not included in results.Figure 1Figure 2Figure 3Figure 4

## ATTACHMENT 1:

Curriculum VitaeLindsey Sears

6355 De Soto Ave. Apt A418

Woodland Hills, CA 91367

(805) 798

- 4646

Lindsey.Sears@yahoo.com

EDUCATION California State University, Northridge M.A. in Geography, with distinction 2012 Emphasis in GIS

Thesis: “ Diesel Trucks: Health Risk and Environmental Equity ”

 I used USEPA’s AERMOD model to determine concentrations of diesel particulate matter (DPM) around several Southern California freeways, focusing on pollution from port

- related diesel truck traffic. I also performed population analyses, examining inequities related to race and income groups exposed to DPM.

California State University, Northridge
B. M. in Oboe Performance, cum laude 2007

SOFTWARE SKILLS

 Microsoft Office

 Adobe Creative Suite

 AERMOD

 ArcGIS

 Golden Software Surfer

 Google Earth Pro

 ERDAS Imagine

 SPSS

EXPERIENCE

Consultant  Provid ing GIS analyses including creating, gathering, integrating, and interpreting spatial data

 Air Quality modeling using dispersion models such as USEPA’s AERMOD

 Creating maps and graphics

Past projects include:

• Mapped excess cancer risk from hexavalent chromium and benzene 2009

- Presentaround Exide Technologies in Vernon, CA

• Modeled and mapped SO 2 concentrations around Hunter Station Power Plant (Castle Dale, UT)

• Modeled and mapped SO 2 concentrations around Seward Power Station (Seward, PA)

• Georeferenced, digitized, and analyzed historic California vegetation maps for publication

• Mode led and mapped SO 2 concentrations around OG&E Muskogee Power Plant (Muskogee, OK)

• Modeled and mapped SO 2 concentrations around OG&E Sooner Power Plant (Red Rock, OK)

• Mapped Thorium and Uranium soil deposition around Coldwater Creek in Missouri

• Prepared ma ps and population analysis for the AEP Rockport Facility in Indiana

• Mapped SO 2 concentrations for the Potomac River Generating Station (Alexandria, VA )

• Prepared MET data to be used in AERMOD for Baton Rouge, LA for use in AERMOD

• Modeled and mapped PM 10 co ncentrations in Waimea, Kauai County, HI

• Modeled and mapped SO 2 and NO 2 concentrations around Colstrip Power Plant in Montana

• Analyzed land cover/population and prepared MET data to be used in AERMOD for Colorado Springs, CO

• Prepared maps showing SO 2 conce ntrations around the E.D. Edwards and Wood River power plants in IL

• Analyzed land cover/population density and mapped SO 2 concentrations around several Michigan power plants including MSU, J.R. Whiting, and St. Clair/Belle River

• Prepared maps of natural ga s pipelines in North Dakota and Berkeley, CA

• Mapped SO 2 concentrations around the Homer City, PA power plant

• Mapped SO 2 and PM 2.5 concentrations around the proposed Taylorville Energy Center facility in IL

• Mapped SO 2 concentrations around Ashtabula, Avon Lake, and Lakeshore power plants in OH

• Mapped SO 2 concentrations around six Illinois Midwest Generation coal plants including detailed maps of Crawford and Fisk

• Mapped pesticide exposure in Oahu, HI

• Geocoded addresses and created Google Earth layers repres enting residents in Kauai County, Kauai

## ATTACHMENT 2:

Precipitation Conditions for Determining Seasonal Bowen Ratios

## ATTACHMENT 3:

2008 through 2012 Wind Roses

Surface m et: K MKO

– Muskogee/Davis Field Airport

Upper air: K OUN

– Norman/University of Oklahoma Westheimer Airpor t

Used as AERMOD inputs for:

OG&E Muskogee Generating StationWRPLOT View - Lakes Environmental Software WIND ROSE PLOT: Surface met: KMKO, Muskogee/Davis Field Airport Upper air: KOUN, Norman/University of Oklahoma Westheimer Airpo rt

COMMENTS:
Using 1-minute ASOS winds with AERMINUTE v.11325 COMPANY NAME:

MODELER:

DATE:

5/9/2014 PROJECT NO.: NORTH

SOUTH WEST EAST 5% 10% 15% 20% 25%

WIND SPEED (Knots)

>= 22

17 - 21

11 - 17

7 - 11

4 - 7

1 - 4

Calms: 0.93%

TOTAL COUNT:

8768 hrs. CALM WINDS:

0.93% DATA PERIOD:

Start Date: 1/1/2008 - 00:00 End Date: 12/31/2008 - 23:00

AVG. WIND SPEED:

8.64 Knots DISPLAY: Wind Speed Direction (blowing from)WRPLOT View - Lakes Environmental Software WIND ROSE PLOT: Surface met: KMKO, Muskogee/Davis Field Airport Upper air: KOUN, Norman/University of Oklahoma Westheimer Airpo rt

COMMENTS:
Using 1-minute ASOS winds with AERMINUTE v.11325 COMPANY NAME:

MODELER:

DATE:

5/9/2014 PROJECT NO.: NORTH

SOUTH WEST EAST 4% 8% 12% 16% 20%

WIND SPEED (Knots)

>= 22

17 - 21

11 - 17

7 - 11

4 - 7

1 - 4

Calms: 0.76%

TOTAL COUNT:

8630 hrs. CALM WINDS:

0.76% DATA PERIOD:

Start Date: 1/1/2009 - 00:00 End Date: 12/31/2009 - 23:00

AVG. WIND SPEED:

8.41 Knots DISPLAY: Wind Speed Direction (blowing from)WRPLOT View - Lakes Environmental Software WIND ROSE PLOT: Surface met: KMKO, Muskogee/Davis Field Airport Upper air: KOUN, Norman/University of Oklahoma Westheimer Airpo rt

COMMENTS:
Using 1-minute ASOS winds with AERMINUTE v.11325 COMPANY NAME:

MODELER:

DATE:

5/9/2014 PROJECT NO.: NORTH

SOUTH WEST EAST 5% 10% 15% 20% 25%

WIND SPEED (Knots)

>= 22

17 - 21

11 - 17

7 - 11

4 - 7

1 - 4

Calms: 0.99%

TOTAL COUNT:

8752 hrs. CALM WINDS:

0.99% DATA PERIOD:

Start Date: 1/1/2010 - 00:00 End Date: 12/31/2010 - 23:00

AVG. WIND SPEED:

7.89 Knots DISPLAY: Wind Speed Direction (blowing from)WRPLOT View - Lakes Environmental Software WIND ROSE PLOT: Surface met: KMKO, Muskogee/Davis Field Airport Upper air: KOUN, Norman/University of Oklahoma Westheimer Airpo rt

COMMENTS:
Using 1-minute ASOS winds with AERMINUTE v.11325 COMPANY NAME:

MODELER:

DATE:

5/9/2014 PROJECT NO.: NORTH

SOUTH WEST EAST 5% 10% 15% 20% 25%

WIND SPEED (Knots)

>= 22

17 - 21

11 - 17

7 - 11

4 - 7

1 - 4

Calms: 0.90%

TOTAL COUNT:

8719 hrs. CALM WINDS:

0.90% DATA PERIOD:

Start Date: 1/1/2011 - 00:00 End Date: 12/31/2011 - 23:00

AVG. WIND SPEED:

8.60 Knots DISPLAY: Wind Speed Direction (blowing from)WRPLOT View - Lakes Environmental Software WIND ROSE PLOT: Surface met: KMKO, Muskogee/Davis Field Airport Upper air: KOUN, Norman/University of Oklahoma Westheimer Airpo rt

COMMENTS:
Using 1-minute ASOS winds with AERMINUTE v.11325 COMPANY NAME:

MODELER:

DATE:

5/9/2014 PROJECT NO.: NORTH

SOUTH WEST EAST 5% 10% 15% 20% 25%

WIND SPEED (Knots)

>= 22

17 - 21

11 - 17

7 - 11

4 - 7

1 - 4

Calms: 0.57%

TOTAL COUNT:

8708 hrs. CALM WINDS:

0.57% DATA PERIOD:

Start Date: 1/1/2012 - 00:00 End Date: 12/31/2012 - 23:00

AVG. WIND SPEED:

7.87 Knots DISPLAY: Wind Speed Direction (blowing from)