Air Dispersion Modeling Analysis

For Verifying Compliance with the 

One-Hour SO2 NAAQS

and Oklahoma SIP limits:

Muskogee Generating Station

Prepared by:

Lindsey Sears

June 3, 2014
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 2

TABLE OF CONTENTS 

1. Introduction..............................................................................................................3

2. Modeling Methodology ..........................................................................................4

2.1 Air Dispersion Model ..................................................................................4

2.2 AERMOD Input Control Options ...............................................................4

2.3 Output Options ............................................................................................5

3. Model inputs............................................................................................................5

3.1 Geographical Inputs ....................................................................................5

3.2 Emission Rates and Source Parameters.......................................................6

3.3 Building Dimensions for Downwash...........................................................8

3.4 Receptors......................................................................................................8

3.5 Meteorological Data.....................................................................................9

3.5.1 Surface Meteorology......................................................................10

3.5.2 Upper Air Data...............................................................................11

3.5.3 AERSURFACE..............................................................................11

3.5.4 Data Review...................................................................................12

4. Background Air Concentrations ............................................................................13

5. Modeling Results...................................................................................................13

5.1 NAAQS Modeling Results ........................................................................13

5.2 SIP Modeling Results ................................................................................15

FIGURES

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

2. OG&E Muskogee Facility: One-hour sulfur dioxide concentrations (μg/m3) from CAMD 

reported emissions, with background levels.

3. OG&E Muskogee Facility: One-hour sulfur dioxide concentrations (μg/m3) from allowable 

emissions, with background levels.

4. OG&E Muskogee Facility: One-hour sulfur dioxide concentrations (μg/m3) from 2013 

maximum emissions, with background levels.

ATTACHMENTS

1. Curriculum Vitae

2. Precipitation Conditions for Determining Seasonal Bowen Ratios

3. 2008 through 2012 Wind Roses
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 3

1. Introduction

Oklahoma Gas and Electric (OG&E) operates the Muskogee Generating Station, which is a major 

source of sulfur dioxide (SO2) emissions located in Muskogee County, Oklahoma. This facility, 

which is permitted by the Oklahoma Department of Environmental Quality (ODEQ), includes three

coal-fired boiler units. Emissions from each unit vent 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 SO2 emissions cause air impacts that exceed the recently 

promulgated one-hour National Ambient Air Quality Standard (NAAQS) for SO2, or would prevent 

maintenance of the standard. In response, I prepared air dispersion modeling analyses for 

calculating ambient SO2 air concentrations from the Muskogee facility. These modeled impacts are 

then compared with the one-hour SO2 NAAQS. This report presents my modeling results and 

discusses the technical methodology I used for performing these analyses.

The one-hour SO2 NAAQS takes the form of a three-year average of the 99th-percentile of the annual 

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/m3. The one-hour SO2 NAAQS of 75 ppb equals 196.2 μg/m3, and this 

is the value I used for determining whether modeled Muskogee impacts exceed the NAAQS.3 The 

99th-percentile of the annual distribution of daily maximum one-hour concentrations corresponds to 

the fourth-highest value at each receptor for a given year.

I was asked to perform three modeling scenarios for SO2 emissions to verify whether Muskogee is in 

compliance with the SO2 NAAQS: actual hour-by-hour emissions, allowable (or permitted) 

emissions; and 2013 reported maximum 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 SO2 NAAQS.

I was also asked to perform air dispersion modeling to verify whether actual hour-by-hour SO2

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/m3, 650 μg/m3, and 130 μg/m3, respectively.

 

1 USEPA, Clean Air Markets – Data and Maps, which can be accessed at: http://ampd.epa.gov/ampd. 

2 USEPA, Applicability of Appendix W Modeling Guidance for the 1-hour SO2 National Ambient Air Quality Standard, 

August 23, 2010.

3

The ppb to μg/m3conversion is found in the source code to AERMOD v. 13350, subroutine Modules. The conversion 

calculation is 75/0.3823 = 196.2 μg/m3.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 4

2. Modeling Methodology

This section describes the dispersion model, control options, and output options I used for verifying 

Muskogee’s compliance with the one-hour SO2 NAAQS and SIP limits.

2.1 Air Dispersion Model

I performed one-hour SO2 modeling of reported hour-by-hour emissions with USEPA’s AERMOD 

program, version 13350, obtained from the Support Center for Regulatory Atmospheric Modeling 

(SCRAM) website. AERMOD is the USEPA preferred air dispersion model for determining air 

impacts within 50 kilometers of air pollution emission sources.

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 SO2 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 options:

 One-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 

Guideline on Air Quality Models.5

 

4 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.

5

Id., Section 7.2.3.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 5

2.3 Output Options

My AERMOD modeling analyses of the Muskogee facility includes a five-year span of 

meteorological data – years 2008 through 2012. Consistent with USEPA’s Modeling Guidance for 

SO2 NAAQS Designations, I used the MXDYBYYR (maximum day by year) output options to 

create a table of fourth-high one-hour SO2 impacts for each year of meteorological data modeled.

6

 

This provides five separate files of one-hour concentrations for each model run. I then averaged the

one-hour SO2 values for each receptor across the five years of modeled data to calculate 

concentrations in the form of the one-hour SO2 NAAQS. These output files also provide the data 

necessary for preparing air concentration isopleths.

I also used the MXDYBYYR (maximum day by year) output options to determine highest one-hour, 

three-hour, and 24-hour SO2 impacts for each year of meteorological data modeled. I compared 

these values with the Oklahoma SIP limits to determine compliance.

3. Model inputs

The AERMOD air dispersion model requires a lengthy list of input values. Key inputs to this 

dispersion model include local geography, air emission rates of the released pollutant, source 

parameters (how and where the material is released to the air), receptors (locations 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 establishing 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), and also to 

ascertain source to receptor distances and relationships.

I used the Universal Transverse Mercator (UTM) NAD83 zone 15 coordinate system for identifying 

the easting (x) and northing (y) coordinates of the modeled sources and receptors. I obtained 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, pp. 24-26.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 6

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 circles

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 Rates and Source Parameters

My modeling analyses are limited to SO2 emissions from the coal-fired boiler stacks 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 emission rate (g/s);

 Stack height (meters);

 Stack gas exit temperature (Kelvin);

 Stack gas exit velocity (meters/second);

 Stack 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 Muskogee coal-fired units.10 

First, I used actual hour-by-hour SO2 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 SO2 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, Appropriate and Necessary 

Analysis, March 16, 2011, p. 6.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 7

contemporaneous hourly meteorological data when running AERMOD. 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)

SO2

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 higher 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 also modeled permitted, also called allowable, SO2 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)

SO2

Emission 

Rate 

(g/s)

Release 

Ht. 

(meters)

Stack 

Temp. 

(K)

Stack 

Exit 

Vel. 

(m/s)

Stack 

Diam. 

(m)

4 293097.8 3959945.8 156.21 828.576 106.68 402.04 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 SO2 emission rates 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, pp. 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 Electric Muskogee Generating Station, January 15, 2010, p. 2.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 8

Third, I modeled 2013 actual maximum hourly SO2 emissions, obtained from USEPA’s CAMD.

The highest one-hour SO2 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)

SO2

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 24.84 6.55

Finally, I performed an iterative modeling process to determine what emission rate would be 

required for Muskogee to achieve compliance with the SO2 NAAQs. For this scenario, I modeled 

the same stack parameters as in the other scenarios, but varied the SO2 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 Building 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 elevation 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 receptors in 100 meter increments in a 10 km by 10 km Cartesian grid centered on the stack 

location for the Muskogee facility. Outside these grids, I generated a receptor grid in 500 meter 

 

13 USEPA, User’s Guide to the Building Profile Input Program, EPA-454/R-93-038, April 21, 2004.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 9

increments in a 20 km by 20 km area centered on the same point. The 500 meter grid of receptors 

encompasses the nested 100 meter receptors, 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 70 km area. The 1,000 meter grid of receptors encompasses

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.

Modeled source and receptor 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 extracted terrain elevations from the 

NED files using USEPA’s AERMAP program, v. 11103, with 1/3rdarc-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 grids.

3.5 Meteorological Data

USEPA’s definition of preferred meteorological data includes the most recent five years of National 

Weather Service (NWS) data. At the time of modeling, this condition was satisfied using 2008 

through 2012 Automated Surface Observing Station (ASOS) data collected at the most siteappropriate 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 available 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 and 
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 10

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 SO2 NAAQS 

designations, USEPA addresses the concern of calm hours in verifying compliance with the onehour SO2 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 SO2 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 instances 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 through 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: surface meteorological data, twicedaily 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. 13350, 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 (KMKO);

Upper air data: Norman/University of Oklahoma Westheimer Airport (KOUN).

3.5.1 Surface Meteorology

I used 2008 through 2012 Integrated Surface Hourly (ISH) data obtained from the National Climatic 

Data Center (NCDC). From the ISH 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 USEPA, AERMINUTE User’s Instructions, v. 11325, p. 1.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 11

I also obtained 2008 through 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-free wind instrument start date (February 9, 2007) and used default 

settings 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 2012 upper air data from twice-daily radiosonde measurements obtained from 

the Norman/University of Oklahoma 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 “weather 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 AERSURFACE

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 necessary 

 

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 flux 

and drier areas have a higher Bowen ratio. Surface roughness, shown in shorthand as (“z0”), 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 purposes, z0 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 z0 -- on the order of 0.01 meter or 

less. Tall and irregular surfaces, which are a greater obstacle to wind flow, have higher values of z0 – up to 1.0 meter or 

more for forests.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 12

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 surrounding 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 kilometer 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

through 2012). I determined the seasonal moisture conditions (wet, average, dry) using 1981 

through 2012 climatic mean monthly rainfall data for the Muskogee/Davis Field Airport.

20

 For each 

season of each year, I compared the seasonal total rainfall to climatic 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-ready meteorological 

data sets I created, individually 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 meteorological 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 meteorological data I used 

(developed with one-minute winds) represent the best data available for modeling Muskogee’s SO2

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, Section 5.3.2, pp. 5-4 – 5-5.

23 USEPA, AERMOD Implementation Guide, March 19, 2009, pp. 3-4.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 13

4. Background Air Concentrations

In this modeling analysis, I used an SO2 background concentration that is consistent with USEPA’s

Modeling Guidance for SO2 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 SO2 concentration from a monitoring site in 

Tulsa County, OK (Site ID 40-143-0235).25 This site location is sufficiently distant to minimize 

double-counting impacts from OG&E’s Muskogee coal-fired power plant in the background SO2

concentrations.

The 2010 – 2012 SO2 background design value from Site ID 40-143-0235 is 30 ppb, which equals 

78.5 μg/m3. The design value (equal to the three-year average 99th-percentile of the measurements) 

preserves the form of the one-hour SO2 standard, and is added to the 99thpercentile 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 SO2 emissions, allowable SO2 emissions, and 

2013 actual maximum SO2 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 

actual hour-by-hour SO2 emissions 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 99th percentile modeled one-hour SO2 ambient air impacts from Muskogee’s actual hour-byhour emissions, allowable emissions, and 2013 reported maximum actual emissions are presented in 

the table below. 

For this analysis, the one-hour SO2 ambient air impacts (Facility H4H – highest 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.
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 14

are based on the 99th 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 SO2

ambient air impacts, using 2008 through 2012 KMKO/KOUN meteorological data, steady state stack 

exit velocities and temperatures associated with allowable emissions, and with background SO2

concentrations, are as follows:

Sulfur dioxide (SO2) KMKO 2008-2012 met, AERMINUTE

Scenario

Facility 

H4H Conc. 

(μg/m3)

XUTM 

(m)

YUTM 

(m)

Impacted Counties

(Not Designated 

Nonattainment)

Hour-by-Hour CAMD Emissions 459.2 293200 3961200

Wagoner, Cherokee, 

Muskogee

Allowable Emissions 1249.1 293300 3961100

Wagoner, Cherokee, 

Sequoyah, Muskogee, 

McIntosh

2013 CAMD Max Emissions 597.9 293300 3961100

Wagoner, Cherokee, 

Sequoyah, Muskogee

Background concentration of 78.5 μg/m3is included in results.

Accounting for background SO2 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 table:

Required total Facility Reduction 

Based on Allowable Emissions

Required Total Facility SO2

Emission Rate (lbs/hr)

Required Total Facility One-Hour 

Average SO2 Emission Factor

(lb/MMBTU)

90.0% 1976.2 0.12

Even if background levels were not included, OG&E’s Muskogee facility would still violate the onehour SO2 NAAQS in each scenario, as shown in the following table:
Muskogee One-Hour SO2 NAAQS and SIP Analysis

June 3, 2014

Page - 15

Sulfur dioxide (SO2) KMKO 2008-2012 met, AERMINUTE

Scenario

Facility 

H4H Conc. 

(μg/m3)

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. Figures 2 through 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 Golden Software’s Surfer, v. 10. I 

used kriging algorithms to grid the data for the isopleths.

Figure 2 shows modeled one-hour SO2 concentrations from Muskogee’s 2008-2012 reported hourly 

CAMD emissions, with background concentrations. This map shows two SO2 concentration levels: 

196.2 and 300 μg/m3. The regions within each isopleth have air concentrations that exceed the 

levels found on each isopleth. The areas encompassed by the 196 μg/m3isopleth exceed the onehour SO2 NAAQS and could result in a designation of nonattainment for that region.

Figure 3 shows modeled one-hour SO2 concentrations from Muskogee’s allowable emissions, with 

background concentrations. This map shows four SO2 concentration levels: 196.2, 300, 400, and 

500 μg/m3. The regions within each isopleth have air concentrations that exceed the levels found on 

each isopleth. The areas encompassed by the 196 μg/m3isopleth exceed the one-hour SO2 NAAQS 

and could result in a designation of nonattainment for that region. 

Figure 4 shows modeled one-hour SO2 concentrations from Muskogee’s 2013 maximum actual 

emissions, with background concentrations. This map shows two SO2 concentration levels: 196.2 

and 300 μg/m3. The regions within each isopleth have air concentrations that exceed the levels 

found on each isopleth. The areas encompassed by the 196 μg/m3isopleth exceed the one-hour SO2

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

5.2 SIP Modeling Results

The modeled hour-by-hour SO2 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, and 
Muskogee One-Hour SO2 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 KMKO/KOUN meteorological data, steady state stack 

exit velocities and temperatures associated with allowable emissions, and no background SO2

concentrations to determine highest one-hour, three-hour, and 24-hour SO2 impacts for each year of 

meteorological data modeled. The highest results are as follows:

Sulfur dioxide (SO2) KMKO 2008-2012 met, AERMINUTE

Scenario

Facility 

H4H Conc. 

(μg/m3)

SIP limit

(μg/m3)

XUTM 

(m)

YUTM 

(m)

1-hour averaging period 665.2 1200 293400 3958200

3-hour averaging period 511.4 650 293300 3960900

24-hour averaging period 271.4 130 293100 3961000

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

Curriculum Vitae
Lindsey 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

 Providing 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 - Present
around Exide Technologies in Vernon, CA

• Modeled and mapped SO2 concentrations around Hunter Station 

Power Plant (Castle Dale, UT)

• Modeled and mapped SO2 concentrations around Seward Power 

Station (Seward, PA)

• Georeferenced, digitized, and analyzed historic California vegetation 

maps for publication

• Modeled and mapped SO2 concentrations around OG&E Muskogee 

Power Plant (Muskogee, OK)

• Modeled and mapped SO2 concentrations around OG&E Sooner Power 

Plant (Red Rock, OK)

• Mapped Thorium and Uranium soil deposition around Coldwater Creek 

in Missouri

• Prepared maps and population analysis for the AEP Rockport Facility 

in Indiana

• Mapped SO2 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 PM10 concentrations in Waimea, Kauai County, 

HI

• Modeled and mapped SO2 and NO2 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 SO2 concentrations around the E.D. Edwards 

and Wood River power plants in IL

• Analyzed land cover/population density and mapped SO2

concentrations around several Michigan power plants including MSU, 

J.R. Whiting, and St. Clair/Belle River

• Prepared maps of natural gas pipelines in North Dakota and Berkeley, 

CA 

• Mapped SO2 concentrations around the Homer City, PA power plant

• Mapped SO2 and PM2.5 concentrations around the proposed Taylorville 

Energy Center facility in IL

• Mapped SO2 concentrations around Ashtabula, Avon Lake, and 

Lakeshore power plants in OH

• Mapped SO2 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 representing 

residents in Kauai County, Kauai
ATTACHMENT 2:

Precipitation Conditions for Determining Seasonal Bowen Ratios

ATTACHMENT 3:

2008 through 2012 Wind Roses

Surface met: KMKO – Muskogee/Davis Field Airport

Upper air: KOUN – Norman/University of Oklahoma Westheimer Airport

Used as AERMOD inputs for:

OG&E Muskogee Generating Station
WRPLOT View - Lakes Environmental Software

WIND ROSE PLOT:

Surface met: KMKO, Muskogee/Davis Field Airport

Upper air: KOUN, Norman/University of Oklahoma Westheimer Airport

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 Airport

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 Airport

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 Airport

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 Airport

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)