# 2010 Primary Sulfur Dioxide (SO 2 

# )

National Ambient Air Quality Standard (NAAQS)

Technical Analyses and Information Supporting the

Designation Recommendation for Oklahoma

Comparison of Modeled and Monitored SO 2 

# Concentrations

In Muskogee, Oklahoma

Prepared by the Engineering Section of the Permitting Unit

Air Quality Division

Oklahoma Department of Environmental Quality

August 26 , 2015

## Table of Contents

1. WHAT IS THE PURPOSE OF THIS REPORT?

................................ ................................ .. 1

2 HOW WAS AREA SURROUNDING THE OG&E MUSKOGEE GENERATING

STATION MODELED?

................................ ................................ ................................ ......... 2

2.1 What air dispersion model was utilized for the modeling air quality

analysis?

................................ ................................ ................................ .................. 2

2.2 What receptors were utilized in the modeling air quality analysis?

................. 2

2.3 What terrain data was utilized for the modeling air quality analysis?

............. 2

2.4 What domain classification was utilized for the modeling air quality

analysis?

................................ ................................ ................................ .................. 3

2.5 What sources were included in the modeling air quality analysis?

.................. 3

2.5.1 How were hourly emissions from the modeled sources d etermined?

....... 4

2.5.2 How was GEP stack height addressed?

................................ .................... 5

2.6 Was building downwash included in the modeling analyses?

........................... 5

2.7 What meteorological data was utilized in the model ing air quality

analysis?

................................ ................................ ................................ .................. 5

2.7.1 How was the ISHD data processed?

................................ ......................... 6

2.7.2 Was AERMINUTE utilized in the modeling analysis?

............................ 6

2.7.3 What is Oklahoma Mesonet data and how was it utilized?

...................... 6

2.7.4 What upper air data was used?

................................ ................................ .. 7

2.7.5 How were surface characteristics determined?

................................ ......... 8

2.7.6 What was used to determine the surface moisture conditions?

................ 8

2.8 Was background monitoring data utilized in the modeling air quality

analysis?

................................ ................................ ................................ .................. 8

3. WHAT WERE THE MONITORED IMPACTS?

................................ ................................ .. 8

4. WHAT WERE THE MODELED IMPACTS AT THE MONITOR?

................................ .... 9

5 WHAT STEPS WERE TAKEN TO EVALUATE THE DIFFERENCE OF THE

MONITORED IMPA CTS AND THE MODELED IMPACTS?

................................ ........... 9

5.1 Was Representativeness of the Surface Meteorological Data Evaluated?

....... 9

5.2 Were the Effects of Terrain Evaluated?

................................ ............................ 10

5.3 Were the Sources of Upper Air Data Evaluated?

................................ ............. 11

### 5.4 How were the Modeled Ambient Impacts Compared to the Monitored

Ambient Impacts?

................................ ................................ ................................ 11

6. WHAT IS THE SUMMA RY OF THE REVIEW OF MONITORED IMPACTS AND

MODELED IMPACTS?

................................ ................................ ................................ ....... 13

7. WHAT REFERENCES WERE USED?

................................ ................................ ............... 14

APPENDICES

Appendix A

– Land Use/Land Cover Aerial Photo

Appendix B

– Aerial Photo Overlaid With Facility Data

Appendix C

– Wind Roses (Blowing To)

Appendix D

– Surface Characteristics

Appendix E

– Modeling Versus Monitoring Graphs

Appendix F

– Comparison of Hours with High Impacts at the Monitor with Sub

- Hourly Impact,

Meteorological, and Source Data

Appendix G

– Comparison of Hours with Modeled and Monitored High ImpactsMuskogee Monitoring Versus Modeling Report Page 1



## 1. WHAT IS THE PURPOSE OF THIS REPORT ?

On June 2 2 , 2010, the Environmental Protection Agency (EPA) revised the primary sulfur

dioxide (SO 2 

### ) National Ambient Air Quality Standard (NAAQS). T he EPA promulgated a new

1

- hour annual primary SO 2 

### standard at a level of 75 parts per billion (ppb) , based on the 3

- year

average of the annual 99 th 

### percentile of the daily maximum 1

- hour average concentrations. The

area designation process typically relies on air quality concentrations characterized by ambient

monitoring data to identify areas that are either meeting or violating the relevant standard.

However, a hybrid approach using modeling and monitoring for the designation process was

proposed because of the following:

 SO 2 

### impacts are considered t o be “ source

- oriented ” rather than “ regional ” ( peak

concentrations of SO 2 

### are commonly caused by one or a few major point sources in an area

and peak concentrations are typically observed relatively close to the source ) ;

 A mbient SO 2 

### concentrations can be modeled accurately using well understood air quality

modeling tools ; and

 Only a pproximately 35 % of the monitoring network w as addressing locations of maximum

(highest) concentrations of specific source s or group s of sources

.

On March 2, 2015, a Consent Decree (CD) was filed requiring the EPA to sign for publication in

the Federal Register no later than sixteen (16) months from March 2, 2015, a notice of EPA’s

promulgation of designations for the 2010 revised primary SO 2 

### NAAQS, for undesignated areas

whic h:

1) Based on air quality monitoring in the three (3) full calendar years preceding such deadline

have monitored violations of the 2010 revised primary SO 2 

### NAAQS; or

2) Contain any stationary source that has not been “announced for retirement” by the date of

t his Consent Decree, and that, according to the data in EPA’s Air Markets Database, either

a. Emitted more than 16,000 tons of SO 2 

### in 2012, or

b. Emitted more than 2,600 tons of SO 2 

### and had an annual average emission rate of 0.45

lbs SO 2 

### /MMBTU or higher in 2012.

By September 1 8 , 201 5

, the state may submit to EPA additional information regarding the

affected facilities and for which updated air quality analyses have been used to characterize the

air quality around the source either through monitoring, modeling

, or a combination of modeling

and monitoring

. To support the designations previously submitted by the State of Oklahoma, an

air quality analysis utilizing modeling was conducted to characterize the air quality of the areas

containing affected sources. Model ing of the area surrounding the OG&E Muskogee Generating

Station was one of the air quality analyses which w ere conducted. The area containing the

OG&E Muskogee Generating Station is unique because it also has an ambient SO 2 

### monitor

which can also be util ized to characterize the area. However, when comparing modeled impacts

to the monitored values from the Muskogee monitor (40

- 101

- 0167), there was poor correlation

between the modeled impacts and the monitored values. This report discuss es the analyses

co nducted concerning the modeling and trying to determine what may be contributing to the poor

correlation between the modeled impacts and the monitored values.Muskogee Monitoring Versus Modeling Report Page 2



## 2 HOW WAS AREA SURROUNDING THE OG&E MUSKOGEE

GENERATING STATION MODELED?

Modeling of the area s urrounding the OG&E Muskogee Generating Station was conducted

u tilizing 2012 to 2014 meteorological data and 2012 to 2014 actual emissions . The modeling

was conducted in accordance with the SO 2 

### NAAQS Designations Modeling Technical Assistance

Document (De cember 2013)

.

2

. 1 What a ir d ispersion m odel was utilized for the modeling air quality analysis ?

Given the source

- oriented nature of SO 2 

### , dispersion models are appropriate air quality modeling

tools to predict the near

- field concentrations. Therefore, f or the area designations under the 2010

1

- hour SO 2 

### primary NAAQS, the AMS/EPA Regulatory Model (AERMOD) w as used to

conduct the air quality analysis. AERMOD is the preferred air dispersion model , because it is

capable of handling rural and urban areas, fl at and complex terrain, surface and elevated releases,

and multiple sources (including, point, area, and volume sources)

.

The AERMOD modeling system includes the following components:

 AERMOD (Version 1 5 1 81 ) : the dispersion model ;

 AERMAP (Version 11103) : the terrain processor for AERMOD ; and

 AERMET (Version 1 5 1 81 ) : the meteorological data processor for AERMOD

.

For the OG&E Muskogee Generating Station these additional components were used in the

modeling analysis :

 BPIPPRIME (Version 04274) : the building input processor ;

 AERMINUTE (Version 14 3 37) : a 1

- minute ASOS winds pre

- processor to AERMET ;

 AERSURFACE (Version 13016) : the surface characteristics processor for AERMET ;

2 .2 What r eceptor s were utilized in the modeling air quality analysis ?

To simplify the comparison of predicted impacts from the modeling and the monitored

concentrations, a single receptor at the location of the monitor was used

. The monitor is located

3.7 km north

- northwest (341 degrees from the North ) of the OG&E Muskogee facility.

2 .3 What t errain d ata w as u tilized for the modeling air quality analysis ?

Terrain data was obtained from the USGS Seamless Data Server at

http://viewer.nationalmap.gov/viewer/ . The 1/3 arc

- second NED data w as obtained in the

GeoTIFF format for use in AERMAP. Interpolation of receptor and source heights from the 1/3

arc

- second NED elevation data w as based on the current AERMAP guidance in Section 4.4 of

the User’s Guide for the AERMOD Terrain Preprocessor (A ERMAP) (EPA

- 454/B

- 03

- 0003,

10/2004). AERMAP uses a distance weighted bilinear interpolation method. The area

surrounding the OG&E Muskogee Generating Station is located in UTM Z one 15. All

coordinates in the model w ere based on the North American Datum (NAD) 19 27 (NAD 27 ).Muskogee Monitoring Versus Modeling Report Page 3



### 2 .4 What d omain c lassification was utilized for the modeling air quality analysis ?

Determination of the whether or not the domain should be classified as urban or rural w as b ased

mainly on land use which is the p referred method

. The land use in the area surrounding the

OG&E Muskogee Generating Station is mainly classified as rural. Therefore, the modeling

analysis utilized a rural classification. However, the potential effects of the “urban heat island

affect” were reviewed because the OG&E Muskogee Generating Station is located next to a

metropolitan area. An A e rial photo of the area surround ing the OG&E Muskogee Generating

Station is included in Appendix B

.

2.5 What sources were included in the modeling air qual ity analysis?

When determining which affected sources should be included in the modeling air quality analysis, all

nearby sources w ere evaluated. Nearby sources a re those sources within 5 0 km of the 

### OG&E

Muskogee Generating Station that could cause or con tribute to a NAAQS violation in the vicinity of

the 

### OG&E Muskogee Generating Station

. All natural gas fired sources w ere excluded from the

modeling air quality analysis because they do not cause a significant concentration gradient at the

monitor location

. 

### The modeling air quality analysis did not include intermittent emissions sources,

such as emergency generators based on the recommendations in the March 1, 2011 memorandum

“Additional Clarification Regarding Application of Appendix W Modeling Guidance f or the 1

- hour

NO 2 

### National Ambient Air Quality Standard.”

A ll other sources of SO 2 

### w ere included in the modeling air quality analysis

. A list of all sources in

the modeling air quality analysis is given below

.



### Table 2

. 5

- 1

. Location, Elevation, & Emissions of Modeled Sources

Easting Northing Elevation SO 2



### Source ID Facility (m) (m) (m) (lb/hr)

UNIT 4 OG&E Muskogee 293116.7 3959729.8 156.2 2

, 010.98

UNIT 5 OG&E Muskogee 293172.0 3959739.3 156.8 1

, 912.21

UNIT 6 OG&E Muskogee 293240.4 3959757.7 157.0 2

, 206.57

DAL20 Dalitalia 284208.4 3953784.7 171.2 0.11

DAL23 Dalitalia 284164.9 3953711.7 170.8 0.05

DAL29 Dalitalia 284093.8 3953787.7 171.0 0.09

BORSCRUB Boral 282161.2 3951159.0 183.5 43.60

GPSTACK1 Georgia Pacific 292555.0 3956494.1 161.9 85.33

GPSTACK3 Georgia Pacific 292594.0 3956513.0 162.0 412.56

OBFURNA Owens Brockway 288620.7 3960596.6 179.6 9.30

OBFURNB Owens Brockway 288656.6 3960582.4 180.0 18.61

M1 US Lime 334096.8 3940641.2 203.6 20.31

K1 US Lime 334000.6 3940571.3 204.5 0.17

UNIT 1 GRDA 294138.0 4007357.0 189.5 2

, 881.26

UNIT 2 GRDA 294211.0 4007262.0 188.8 922.97Muskogee Monitoring Versus Modeling Report Page 4 

### Table 2

. 5

- 2

. Stack Parameters of Modeled Sources

St ac k

H eight St ac k

Temp

. Stack

Velocity St ac k

Dia meter

Source ID Facility (ft) (°F) (fps) (ft)

UNIT 4 OG&E Muskogee 350.0 264 44.96 24.0

UNIT 5 OG&E Muskogee 350.0 264 41.80 24.0

UNIT 6 OG&E Muskogee 500.0 264 55.01 21.5

DAL20 Dalitalia 46.3 130 29.80 5.0

DAL23 Dalitalia 4 6.0 130 45.08 2. 7

DAL29 Dalitalia 51.3 130 24.10 5.4

BORSCRUB Boral 70.0 355 36.29 6.2

GPSTACK1 Georgia Pacific 260.0 353 28.46 10.0

GPSTACK3 Georgia Pacific 260.0 306 28.24 13.8

OBFURNA Owens Brockway 150.0 918 29.16 5.3

OBFURNB Owens Brockway 80.0 389 28.89 7.1

M1 US Lime 150.0 266 57.09 5.3

K1 US Lime 96.0 155 31.20 7.0

UNIT 1 GRDA 504.0 300 68.14 20.0

UNIT 2 GRDA 507.0 190 90.98 20.0



### 2. 5. 1 How w ere h ourly e missions from the modeled s ources determined?

Actual emission data for input into AERMOD w as generated for each source included in the

modeling analysis . Most electric generating units (EGU) have continuous emissions monitoring

systems (CEMS). In the absence of CEMS data, simply dividing the annual emissions by the

number of hours in the year is not an accurate representation of actual emissions

. 

### For a ffected

sources without CEMS data, actual emissions w ere based on actual throughputs and the

calculated SO 2 

### emissions generally based on a mass balance. F or sources that vary their operation

throughout the year, AERMOD input variables w ere used to represent their actual operation

throughout the year. Use of AERMOD input variables such as hour of day (HROFDY), hour of day

and day of week (HRDOW), monthly hour of day and day of week (MHRDOW), w ere utilized to

represent actual operation for t hose sources without CEMS data. Varying emissions w ere based on

the available information such as production logs, fuel usage information, operating schedules, etc.



### CEMS data w as used to generate hourly emissions files for th ose sources with CEMS using th e

following methodology:

Step 1: T he emission data was downloaded from the Clean Air Markets Database (CAMD).



### Step 2 : T he monthly data was combined generating annual emission data files for each source at

an affected facility with CEMS data

. 

### Step 3 : There are three variables used in an hourly emission file, emissions

, velocity, and

temperature. These hourly values we re generated from the CAMD datasets and

formatted into the units used by AERMOD. The emissions we re converted from the

CAMD data lb/hr value s into g/s. The heat input given in the CAMD data was used with

Method 19, the CO 2 

### concentration, and stack temperature from recent relative accuracy

test audit s ( RATA ) , to generate the flow rate and resultant velocity. If a unit wa s

operating it was ass igned the normal stack temperature determined from RATA

. For theMuskogee Monitoring Versus Modeling Report Page 5 

### OG&E Muskogee Generating Station hourly velocity and temperature data from the

CEMS was used.



### Step 4 : The data wa s then reviewed for continuity and for missing data. If there was a single

h our of missing data

, it was replaced with the average of surrounding non

- missing hours

. For periods of missing data with more than a single missing value, operational data from

the facility was reviewed to fill the missing hours.



### 2

. 5. 1 .1 What operational data was used from the RATA?

The stack parameters used to generate the hourly emission file for AERMOD from RATA are

listed below. Only RATA data from the nearby EGU located at Grand River Dam Authority

(GRDA) was utilized in generating the hourly emissio n file.

Table 2 .7

- 1 Operational Stack Data from RATA

Stack Temp % Moisture % CO 2

Station Unit 2012 2013 2014 2012 2013 2014 2012 2013 2014

GRDA Unit 1 312 297 1 281 11.72 11.86 1 12.00 13.26 13.06 1 12.86

GRDA Unit 2 192 198 193 15.93 15.86 15.05 12.21 11.71 10.85

1

- The average of 2012 and 2014.



### 2. 5. 2 How w as GEP s tack h eight addressed?

Good Engineering Practice (GEP) stack height is the minimum stack height needed to prevent

the stack exhaust plume from being entrained in the wake of nearby obstructions. F or the

modeling analysis

, actual stack heights w ere used rather than follow ing the GEP stack height

policy . The GEP stack height policy use s GEP stack height for stack heights exceeding GEP and

actual stack height for stacks below GEP. Th e use of actual stack heights in the modeling

analysis mo re closely represent s a ctual ambient air quality conditions from the modeled sources

and the concentrations detected at the monitor would be reflective of the effect of emissions from

the actual stack heights

.

2. 6 Wa s b uilding d ownwash included in the modeling analyses?

Modeling that includes calculations for building downwash gives a more accurate representation

of pollutant impact s than does modeling that omits consideration of downwash affects.

Therefore, building downwash was included in the modeling analysis for the on

- site buildings

.

Not only are accurate stack parameters important but so are accurate building parameters

. Build ing parameters include location and orientation relative to stacks and building size. Other

parameters include tier heights and coordinates. These parameters are input into BPIP

- PRIME to

calculate building parameters for AERMOD. F acilit y data w as review ed and included in the

modeling analyses.

2

. 7 What m eteorological d ata w as u tilized in the modeling air quality analysis ?

The 2012

- 2014 meteorological data from the National Weather Service (NWS) Automated

Surface Observation Station (ASOS) located at the Davis Field Airport ( KMKO ) in Muskogee

was utilized for the 2010 1

- hour SO 2 

### primary NAAQS designations modeling.Muskogee Monitoring Versus Modeling Report Page 6



### When conducting air dispersion modeling, t he state of O klahoma generally utilizes

meteorological data from the following :

 Oklahoma Mesonet S urface D ata ;

 National Center s for Environmental Information (NCEI), formerly National Climatic Data

Center (NCDC)

, Integrated Surface Hourly Database (ISHD) S urface D ata ; and

 Earth System Research Laboratory (ESRL) Global Systems Division (GSD)

, formerly

Forecast Systems Laboratory (FSL)

, Upper Air (UA) data

.

Oklahoma Mesonet data is usually incorporated to help make more accurate forecasts of ambient

impacts from modeled sources . Incorporation of Oklahoma Mesonet data makes the AERMET

processed meteorol ogical data more accurate because the dataset s contain sub

- hourly values and

the sites are usually closer to the areas being modeled. Standard ISHD surface data usually only

contains a single two minute average recorded during an hour whereas Oklahoma Mes onet

datasets contain twelve five minute average s for each hour. There are a large number of

Oklahoma Mesonet stations which increases the potential of an Oklahoma Mesonet

meteorological station being closer to the area being modeled. Generally, the clos er a

meteorological station is to an area

, the more representative the meteorological data is to the area

being modeled

.

There are two types of ISHD surface stations ASOS and Automated Weather Observation

Stations (AWOS). All ASOS stations started record ing continuous sub

- hourly (2

- minute

averages) wind data in 2005. However, sub

- hourly wind data for AWOS stations is not

available. I f an ASOS station is closer or more representative than the nearest Oklahoma

Mesonet station

, data from the Oklahoma Meson et is not incorporated into the AERMET

processed meteorological data. Oklahoma Mesonet meteorological data was not utilized with the

Muskogee area ambient air quality modeling analysis because the ISHD ASOS KMKO Station

data is closer to and more represen tative than the nearest Oklahoma Mesonet Station and sub

hourly wind data is available for the KMKO Station. A wind rose for the KMKO station is

contained in Appendix F.

2

. 7 .1 How was the ISH D data processed ?

The ISH data files were downloaded from the NCDC ISHD web site:

ftp://ftp.ncdc.noaa.gov/pub/data/noaa

. The ISH data w as reviewed for completeness by

evaluating the number of hours that were recorded an d the number of cloud cover (GF1) values

that were recorded

. The data from KMKO did not have a significant number of missing values

for individual variables. Therefore, no missing data replace ment was performed.

2

. 7

. 2 W as AERMINUTE utilized in the model ing analysis ?

For this ASOS site , AERMINUTE data w as u tilized to incorporate continuous sub

- hourly wind

data

. The ASOS (6405) files were downloaded and then processed using AERMINUTE. The

ASOS 1

- minute files w ere download ed from http://www1.ncdc.noaa.gov/pub/data/ for each year.

2

. 7

. 3 What is Oklahoma Mesonet d ata and how wa s it u tilized ?

The Oklahoma Mesonet is a world

- class network of meteorological monitoring stations

. The

Oklahoma Mesonet is unique in its capability to measure a large variety of meteorologicalMuskogee Monitoring Versus Modeling Report Page 7



### conditions at so many sites across an area as large as Oklahoma. Oklahoma Mesonet data i s

provided courtesy of the Oklahoma Mesonet, a cooperative venture between Oklahoma St ate

University (OSU) and t he University of Oklahoma (OU) and supported by the taxpayers of

Oklahoma. At each site, the meteorological conditions are continuously measured and packaged

into 5

- minute observations

. These 5

- minute observations from the Oklah oma Mesonet we re

processed into a n AERMET acceptable format. The m eteorological data from sites surrounding

KMKO were utilized to evaluate the wind flow patterns in the Muskogee area.

T he f ive closest Mesonet site s in and around the Muskogee area are listed below

. Wind roses for

the specific stations are contained in Appendix F

.

Table 2.7.3

- 1 Oklahoma Mesonet Stations Near Muskogee, Oklahoma

STNM ID Name City County LAT LON ELEV

(m) DIST 1

(km)

118 PORT Porter Clarksville Wagoner 35.8257

- 95.5598 193 25. 5

44 HASK Haskell Haskell Muskogee 35.7480

- 95.6405 183 31.8

132 WEBR Webbers Falls Webbers Falls Muskogee 35.489 0

- 95.1233 145 33.8

92 TAHL Tahlequah Tahlequah Cherokee 35.9724

- 94.9867 290 35.8

31 COOK Cookson Marble City Cherokee 35.6800

- 94.849 0 299 40.7

1

- Distance from the OG&E Muskogee Generating Station (35.79194,

- 95.28794)

.



### 2

. 7 .4 What u pper a ir d ata w as u sed?

The re are three ESRL RAOB weather stations within 300 km of the Muskogee area. These

stations usually take soundings twice a day. The ESRL data files we re downloaded from the

ESRL ROAB web site: http://esrl.noaa.gov/raobs/

. The specific sites and distance from the

Muskogee area are shown below. The RAOB data from t he Max Westheimer Airport ( OUN)

located in Norman, Oklahoma was the upper air data utilized in the modeling analysis . The

OUN site is the closest station to the Muskogee area. However, other ESRL stations (LZK) were

evaluated to determine if they were mo re representative in terms of determining impacts within

the Muskogee area.

Table 2.7.4

- 1 Upper Air Station Location and Elevation and Distance to Muskogee 1



### Location Call Sign LAT LON ELEV (m) DIST (km)

Norman OUN 35.23

- 97.47 362 196

Springfield SGF 37.23

- 93.40 394 247

Little Rock LZK 34.83

- 92.27 172 295

1

- Distance from the KMKO Meteorological Station (35.6577,

- 95.3658).



### The UA data was reviewed for missing soundings. A single missing sounding will cause a

whole day (24 hours) of missing meteorological data values. To reduce the number of missing

meteorological data, replacement soundings were substituted for the missing soundings. The

replacement soundings were selected from a site with similar thermodynamic profiles. Each UA

data station was assigned a primary and a secondary replacement UA station. The primary

station is basically the station that i s closest to the station being reviewed. Each replacement

sounding was documented.Muskogee Monitoring Versus Modeling Report Page 8



### 2

. 7 .5 How w ere s urface c haracteristics d etermined?

When using AERMET

, to pr ocess meteorological data for AERMOD, three surface

characteristics ( Albedo, Bowen Ratio, and Surface Roughness Length ) must be determined for

the meteorological station . A lbedo is the fraction of total incident solar radiation reflected by the

surface back to space without absorption. Bowen ratio, an indicator of surface moisture, is the

ratio of sensible heat f

lux to latent heat flux. Surface roughness length is related to the height of

obstacles to the wind flow and is an important factor in determining the magnitude of mechanical

turbulence and the stability of the boundary layer. Albedo and Bowen Ratio are used for

determining planetary boundary layer parameters for convective conditions driven by the surface

sensible heat flux.

AERSURFACE uses land cover data from the U.S. Geological Survey (USGS) National Land

Cover Data 1992 archives (NLCD92) to determin e the land cover types for a specified location.

AERSURFACE matches the NLCD92 land cover categories to seasonal values of Albedo,

Bowen Ratio, and Surface Roughness and then calculates the surface characteristics for input

into AERMET. NLCD92 data in Geo TIFF format was downloaded from the Multi

- Resolution

Land Characteristics (MRLC) Consortium at the following link: http://www.mrlc.gov/viewerjs/

. When determining the surface roughness the default distance of 1 km was utilized. The surface

characteristics of the individual stations are included in Appendix G

.

2

. 7

. 6 What was u sed to d etermin e the s urface m oisture c onditions?

The monthly rainfall for each Mesonet site was analyzed from the beginning of the establishment

of the Mesonet program (approximately 20 years). The surface moisture conditions (Average,

Wet, Dry) for each of the Oklahoma Mesonet stations for each month w ere then determined

using the monthly rainfall amounts compared to the average rainfall. These determinations were

based on the guidance contained in the AERSURFACE Users Guide. T he Bowen Ratio was

then assigned as either average, dry, or wet based on th e monthly surface moisture conditions for

each Oklahoma Mesonet station

. The surface moisture conditions for the ISHD stations were

attributed to the surface moisture conditions of the nearest Oklahoma Mesonet station.

2. 8 Wa s b ackground m onitoring d ata u tilized in the modeling air quality analysis ?

When evaluating modeled source impacts at the monitor to the monitored impacts

, b ackground

concentrations were not added to modeled impacts. The main SO 2 

### sources contributing to the

impacts at the monitor wer e explicitly included in the modeling . S maller sources of SO 2



### emissions such as natural gas fired sources do not have a significant concentration gradient and

will not significantly impact the monitor. Therefore, background concentrations from another

mo nitor representative of area sources of SO 2 

### were not added to the modeled concentrations.



## 3. WHAT WERE THE MONITORED IMPACTS?

M onitoring data from the Muskogee monitor (40

- 101

- 0167) in the form of the standard is shown

below. Only data meeting the minimum data collection requirements or the minimum percent

observations were used when determining the design value.Muskogee Monitoring Versus Modeling Report Page 9 

### Table 3

- 1 Monitor Site Location and Elevation

Location ID LAT LON ELEV (m)

Muskogee 0167 35.79313

- 95.30228 158.5

Table 3

- 2 Monitoring Design Value

Pollutant Averaging Period Basis of Design Value

SO 2 

### 1

- hour 3 year average of 99 th 

### Percentile 1

- hour daily maximum

Table 3

- 3 Monitoring Site 40

- 101

- 0167 Design Value

Monitor ID 2012 2013 2014 3 Year

OBS 99 th 

### pp b OBS 99 th 

### pp b OBS 99 th 

### pp b Avg.

40

- 101

- 0167 28 5 H3H 63 365 H4H 37 357 H4H 48 49 .3

The 2012 to 2014 three year average of the 99 th 

### percentile 1

- hour daily maximum values is 49.3

ppb which is approximately 129.2 μg/m 3



### . The monitored concentration is approximately 66% of

the NAAQS (75 ppb).



## 4

. WHAT WE RE THE MODELED IMPACTS AT THE MONITOR ?

The modeled 2012 to 2014 three year average of the 99 th 

### percentile 1

- hour daily maximum

values is 1 49.5 μg/ m 3 

### which is approximatel y 57.1 ppb

. The modeled concentration is

approximately 7 6 % of the NAAQS (75 ppb). However, the modeled concentration is 15.7 %

different than the monitored concentration.



## 5 WHAT STEPS WERE TAKEN TO EVALUATE THE DIFFERENCE

OF THE MONITORED IMPACTS AND THE MODELED IMPACTS?

5

. 1 Was Representativeness of the Surface Meteorological Data Evaluated ?

The 2012

- 2014 wind roses for the five surrounding Oklahoma Mesonet meteorological stations

, the ISHD KMKO meteorological station

, and the ambient air monitoring site (40

- 101

- 0167)

w ere reviewed to determine the general wind pattern for the area. The majority of the wind roses

for Oklahoma represent a N

- S wind flow pattern. In the summer

, the wind out of the S to S E

dominates . In the winter

, the wind out of the N to N W dominates. A small percentage of the

time the wind flows in an E

- W direction.

However, due to terrain affects

, ther e is a shift in the wind flow pattern south of Muskogee,

Oklahoma from a N

- S wind flow to a n E

- W wi nd flow. This can be seen in the Weber Falls

, Oklahoma (WEBR) wind rose where the main wind flow comes from the ESE to SE direction

and NW to NNW direction . As the wind move s toward Muskogee, the wind flow pattern is

shifted back to the normal Oklahoma w ind flow pattern as seen in the Davis Field ( KMKO ) wind

rose where it comes from the SE to SSE direction and NNW to N direction . As the wind

continues to move across the plains

, the wind flow pattern spreads out as can be seen in the

Porter, Oklahoma (POR T) and Haskell, Oklahoma (HASK) wind roses where the flow changes

to a SSW to SE direction and NNE to NNW direction

.Muskogee Monitoring Versus Modeling Report Page 10



### The Tahlequah, Oklahoma (TAHL) wind rose also shows a wider spread ing of the wind pattern

as the wind comes out onto the plain s off the Cookson Hills

. A similar affect can be seen in the

wind flow pattern for the Muskogee ambient air monitor (0167).

The Cookson, Oklahoma (COOK) meteorological station located in the Cookson Hills has less of

a spread than the sites located in the plains with the wind flow pattern from the SE to SSE

direction and the NW to N direction . The general wind flow pattern for COOK is similar to the

KMKO Station.

The meteorological data collected at the monitoring site does not meet the standard

meteorol ogical data collection requirements and was therefore not considered except as a means

to compare and evaluate the KMKO station meteorological data. Based on a review of modeling

conducted with the 0167 meteorological data, there was less correlation betw een the modeling

results using the 0167 meteorological data than the meteorological data from the KMKO Station.

The Muskogee ambient air monitor (0167) is actually located slightly below the surrounding

terrain a t 520 feet with the wind measurement instru ment located at a height of 2 0 feet. The

surrounding terrain within 1 mile of the monitor ranges from 490 feet to 550 feet. The hills

surrounding the area reach heights of 800 feet.

5

. 2 Were the Effects of Terrain E valuated ?

Muskogee , Oklahoma lies in a r iver v alley and has a low elevation compared to the surrounding

area. The Muskogee area is unique in that it is located north of the west end of the Arkansas

Valley and is nestled in the hook of the west end of the Cookson Hills (Lower Buffalo

Mountains ). The Ozarks are made up of the Boston Mountains (Upper and Lower) and define

the northern portion of the Arkansas River Valley. The Ouachita Mountains make up the

southern portion of the Arkansas Valley. The Cookson Hills are flat

- topped remnants of a ncient

plateaus and the Ouachita’s are folded ridges. To the east of Muskogee, the land opens up into

prairie grassland

. As the wind flows through the west end of the Arkansas River Valley

, it meets

the northern edge of the Ouachita Mountains and starts to flow toward the northwest in the open

plains.

In the northeast corner of Muskogee

, is the T hree R ivers A rea which is the confluence of the

Arkansas River, the Neosho River, and the Verdigris River . The three rivers con verge and

continue on as the Arka nsas River

, which winds through the Cookson Hills

. The east side of

Muskogee is bo rdered by the Arkansas River and the Cookson Hills. I n the southeast corner of

Muskogee

, the Arkansas River is bounded by local terrain ( Devil’s Peak and Braggs Mountain )

c reating a small gap through which the surface wind is funneled.

The wind speed from each meteorological station varies and is mainly due to the surface

roughness of the terrain surrounding the meteorological station. As the surface wind comes

across rugg ed terrain

, the average wind speed will be reduced as seen in the difference in the

average wind speed of the KMKO Station and the COOK Station. However, terrain features can

have other effects on wind speed such as funneling of surface wind s between two hills and

dropping of surface winds from mountainous areas into open plains.Muskogee Monitoring Versus Modeling Report Page 11



### The surface roughness of the individual meteorological stations was compared to the surface

roughness of the facility being modeled. The surface roughness of the KMKO meteorolog ical

station was lower than the surface roughness generated by AERSURFACE for the facility. The

surface roughness of all of the Oklahoma Mesonet stations was larger than the surface roughness

generated by AERSURFACE for the facility. In order to review the surface roughness

determination for the KMKO meteorological station , the area used to determine the surface

roughness was expanded out to two kilometers (km). At 2 km, the surface roughness of the

KMKO meteorological station closely resembled the surface roughness of the OG&E Muskogee

facility.

5. 3 Were the Sources of Upper Air Data Evaluated ?

The t hree closest meteorological stations w here upper air data is recorded are located

approximately 200, 250 and 300 km from the Muskogee ar ea. Muskogee is almost located

equidistance between these locations. Th e s e large distances can lead to distinct differences when

determining the structure of the atmosphere for the Muskogee area.

The meteorological station that has the closest elevation to the Muskogee area (~ 160 to 180

m eters above sea level ) is Little Rock, Arkansas (LZK). However, LZK is the farthest site from

the Muskogee area. The Springfield, Missouri station (SGF) and the Norman, Oklahoma station

(OUN) are at approximately the s ame elevation and are about 200 meters higher than the

Muskogee area. However, SGF is 30 meters higher and 50 km distant than OUN. These larges

differences in elevation can also lead to differences when determining the characteristics of the

upper air in the Muskogee area.

Depending on the wind patterns for specific days one upper air station may be more

representative than another upper air station (e.g. OUN may be more represen ta tive in the winter

and LZK may be more represen ta tive in the summer). The refore, use of a single location for a

whole year may not yield representative modeling results which can lead to differences in

modeled and monitored ambient impacts.

5

. 4 How were the Modeled Ambient Impacts Compared to the M onitored Ambient

Impacts ?

To review the correlation between the modeled and monitored ambient impacts, additional

modeling was conducted changing a single variable to determine which variable had the most

influence and brought the modeled ambient impacts closer to the monitored ambie nt impacts.

The modeled ambient impacts were generated using the following combinations of

meteorological data:

 KMKO Surface Data W/OUN Upper Air Data,

 KMKO Surface Data combined with wind data from the 40

- 101

- 0167 Monitoring Station

W/OUN Upper Air Data;

 KMKO Surface Data W/LZK Upper Air Data ;

 KMKO Surface Data W/SGF Upper Air Data;

 KMKO Surface Data W/OUN Upper Air Data W/2 km Surface Roughness ;Muskogee Monitoring Versus Modeling Report Page 12



###  KMKO Surface Data W/LZK Upper Air Data W/2 km Surface Roughness;

 KMKO Surface Data W/OUN Upper Air Data W/RO TANG option at 5 degrees; and

 KMKO Surface Data W/OUN Upper Air Data W/ROTANG option at 10 degrees.

Table 5.5

- 1 Modeled Impacts Versus Monitored Impacts Using Various Inputs

Surface

Station Upper Air

Station Additional

Data Modeled

Impacts

(μg/m 3 

### ) Monitored

Impacts

(μg/m 3 

### ) %

Difference

KMKO OUN 149.5 129.2 15.7

KMKO OUN 0167 18 8.7 129.2 46.1

KMKO LZK 145.4 129.2 12.5

KMKO SGF 147.0 129.2 13.8

KMKO OUN 2 km SR 137.0 129.2 6.0

KMKO LZK 2 km SR 137.0 129.2 6.0

KMKO OUN ROTANG5 138.3 129.2 7.0

KMKO OUN ROTANG10 159.0 129.2 23.1

The modeling scenarios closest to representing the design value recorded at the monitor were the

modeling scenarios that increase d the surface roughness to be more representative of the surface

roughness at the site. However, while the three year average of the H4H was very close to the

actual monitored impacts, the actual hourly impacts predicted at the monitor were not.

In order to evaluate the differences between and correlate the modeled and monitored ambient

impacts on an hourly basis , predicted modeled ambient impacts for each hour were plotted with

the monitored ambient impacts for each hour. Based on these graphs, it was determined that

there was not a significant correlation between the modeled ambient impacts and the monitored

ambient impacts. The modeled ambient impacts for each hour we re also plotted using the

monitored ambient impacts as the horizontal axis wh ich should have generate d a graph which

can easily be used to correlate the values . V alues that are equivalent will be plotted along a line

with a slope of 1 (@ 45°) extending from the origin

. Again these plots did not generate a straight

line and confir med that there was no correlation between the hourly modeled and monitored

ambient impacts.

Finally, to evaluate what was producing the differences between the monitored concentrations

and the modeled hourly concentrations, the meteorological data for the hours when the monitor

was predicting high impacts and the hours when the model was predicting high impacts were

reviewed. From this review the following was determined :

 T he monitor was experiencing high impacts when the wind was blowing directly from t he

OG&E facility towards the monitor at 162 degrees.

 The monitor was experiencing high impacts when the average wind speed was

approximately 5.2 m/s (Slightly Unstable to Neutral Conditions)

.  The mo del was predicting high impacts at the monitor when the wind was blowing directly

from the OG&E facility towards the monitor at 162 degrees.

 The mo del was predicting high impacts at the monitor when the average wind speed was

approximately 2.2 m/s (Very Unstable to Unstable Conditions)

.Muskogee Monitoring Versus Modeling Report Page 13



### The modeling s cenarios that predicted the highest impact were run with downwash and without

downwash. Removing downwash from the model did not change the predicted design value of

the model. However, removing downwash from the model did reduce the correlation between

the modeled and monitored hourly values.

The surface roughness

, wind speed, and wind direction significantly affect the modeling results.

The inability of the AERMOD model to predict on an hour

- by

- hour basis what is occurring at the

monitor suggests that there is some doubt concerning the representativeness of the

meteorological data used in the modeling analysis

.



## 6

. WHAT IS THE SUMMARY OF THE REVIEW OF MONITORED

IMPACTS AND MODELED IMPACTS ?

The area surrounding the OG&E Muskogee Generating Station is not adequately characterized

by the available meteorological data. The terrain effects on the wind direction and speed of the

area surrounding the modeling domain are not adequately characterized by the area surrounding

the NWS Station (KMKO). Also, t he surface roughness at the NWS Station (KMKO) is not

representative of the surface roughness of the modeling domain. A dditional meteorological

monitoring in the area of the OG&E Muskogee Generating Station is needed to address questions

regarding meteoro logical data representativeness

.

A meteorological st ation located at or near the center of the domain would enhance the accuracy

of the modeling

. The meteorological data should include upper air data to characterize the

atmospheric conditions within the modeling domain

. The station should be equipped with a

meteorological tower of sufficient height to characterize the vertical structure of the atmosphere

(100 m) or a SODAR (SOnic Detection And Ranging ) instrument

, radar wind profiler (RWP),

radio acousti c sounding systems (RASS), or similar instrument to characterize the

thermodynamic structure of the lower layer of the atmosphere.Muskogee Monitoring Versus Modeling Report Page 14



## 7

. WHAT REFERENCES WERE USED?

 Additional Clarification Regarding Applicability of Appendix W Modeling Guidance for

the 1

- hour NO 2 

### NAAQS (March 1 , 201 1);

o http://www.epa.gov/ttn/scram/guidance/clarification/Additional_Clarificatio ns_Appe

ndixW_Hourly

- NO2

- NAAQS_FINAL_03

- 01

- 2011.pdf

 Guidance Concerning the Implementation of the 1

- hour SO 2 

### NAAQS for the Prevention of

Significant Deterioration Program ( August 23, 2010 );

o http://www.epa.gov/region07/air/nsr/nsrmemos/appwso2.pdf

 Applicability of Appendix W Modeling Guidance for the 1

- hour S O 2 

### NAAQS (August 23,

2010);

o http://www.epa.gov/ttn/scram/guidance/clarification/ClarificationMemo_AppendixW

_Hourly

- SO2

- NAAQS_FINAL_08

- 23

- 2010.pdf

 SO 2 

### NAAQS Designations Modeling Technical Assistance Document (December 2013) ;

o http://www.epa.gov/oaqps001/sulfurdioxide/pdfs/SO2ModelingTAD.pdf

 Guidance for 1

- Hour SO2 NAAQS SIP Submissions (September 22, 2011);

o http://www.epa.gov/oaqps001/sulfurdioxide/pdfs/DraftSO2Guidance_9

- 22

- 11.pdf

 User’s Guide for the AMS/EPA Regulatory Model

- AERMOD

o http://www.epa.gov/ttn/scram/models/aermod/aermod_userguide.zip

.  User’s Guide for the AERMOD Meteorological Data Preprocessor ( AERMET )

o http://www.epa.gov/ttn/scram/7thconf/aermod/aermet_userguide.zip

.  AERMINUTE User’s Instruction

o http://www.epa.gov/ttn/scram/7thconf/aermod /aerminute_14337.zip

.  AERSURFACE User’s Guide

o http://www.epa.gov/ttn/scram/7thconf/aermod/aersurface_userguide.pdf

.Muskogee Monitoring Versus Modeling Report



# Appendix A

– Land Use/Land Cover Aerial PhotoMuskogee Monitoring Versus Modeling Report Page A

- 1 

# OG&E

– Muskogee Generating StationMuskogee Monitoring Versus Modeling Report 

# Appendix B

– Aerial Photo Overlaid With Facility DataMuskogee Monitoring Versus Modeling Report Page B

- 1 

# OG&E

– Muskogee Generating StationMuskogee Monitoring Versus Modeling Report 

# Appendix C

– Wind Roses (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 1 

### 2012

- 2014 KMKO Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 2 

### 2012

- 2014 0167 Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 3 

### 2012

- 2014 PORT Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 4 

### 2012

- 2014 HASK Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 5 

### 2012

- 2014 WEBR Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 6 

### 2012

- 2014 TALH Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report Page C

- 7 

### 2012

- 2014 COOK Wind Rose (Blowing To)Muskogee Monitoring Versus Modeling Report 

# Appendix D

– Surface CharacteristicsMuskogee Monitoring Versus Modeling Report Page D

- 1 

### S urface Characteristics

Albedo AVG Wet Dry Surface

OG&E

Winter 0.17 0.65 0.39 1.85 0.043

Spring 0.14 0.39 0.24 1.12 0.051

Summer 0.17 0.44 0.26 1.01 0.065

Fall 0.17 0.65 0.39 1.85 0.060

KMKO

Winter 0.18 0.72 0.41 1.94 0.016

Spring 0.14 0.33 0.22 1.03 0.023

Summer 0.19 0.50 0.30 1.42 0.037

Fall 0.19 0.72 0.41 1.94 0.031

0167

Winter 0.17 0.64 0.37 1.47 0.021

Spring 0.14 0.37 0.24 0.93 0.027

Summer 0.18 0.43 0.28 1.01 0.064

Fall 0.18 0.67 0.37 1.47 0.064

PORT

Winter 0.18 0.73 0.39 1.77 0.026

Spring 0.15 0.36 0.23 1.01 0.042

Summer 0.18 0.45 0.28 1.16 0.183

Fall 0.18 0.73 0.39 1.77 0.183

HASK

Winter 0.18 0.72 0.39 1.78 0.023

Spring 0.15 0.35 0.22 1.00 0.035

Summer 0.19 0.46 0.28 1.22 0.157

Fall 0.19 0.72 0.39 1.78 0.157

WEBR

Winter 0.16 0.58 0.32 1.24 0.022

Spring 0.14 0.31 0.20 0.77 0.032

Summer 0.17 0.38 0.24 0.83 0.170

Fall 0.17 0.58 0.32 1.24 0.170

TAHL

Winter 0.1 7 0. 79 0.40 1.9 8 0.0 36

Spring 0.1 5 0. 41 0.2 4 1. 16 0. 053

Summer 0.1 8 0. 4 3 0.2 7 1.13 0. 219

Fall 0.1 8 0. 79 0.40 1.9 8 0. 219

COOK

Winter 0.17 0.95 0.40 1.97 0.090

Spring 0.15 0.63 0.29 1.44 0.139

Summer 0.16 0.33 0.22 0.70 0.382

Fall 0.16 0.95 0.40 1.97 0.382Muskogee Monitoring Versus Modeling Report 

# Appendix E

– Modeling Versus Monitoring GraphsMuskogee Monitoring Versus Modeling Report Page E

- 1 

### 2012 Monitor V ersus Modeled Impacts ( ppb )

KMKO

– Hourly impacts at the monitor site predicted by AERMOD using KMKO meteorological data.

Monitor

– Hourly impacts measured at th e monitor. 0 10 20 30 40 50 60 70 80 90

KMKO

MonitorMuskogee Monitoring Versus Modeling Report Page E

- 2 

### 201 3 Monitor V ersus Modeled Impacts ( ppb )

KMKO

– Hourly impacts at the monitor site predicted by AERMOD using KMKO meteorological data.

Monitor

– Hourly impacts measured at the monitor. 0 10 20 30 40 50 60 70 80 90

KMKO

MonitorMuskogee Monitoring Versus Modeling Report Page E

- 3 

### 201 4 Monitor V ersus Modeled Impacts (pp b )

KMKO

– Hourly impacts at the monitor site predicted by AERMOD using KMKO meteorological data.

Monitor

– Hourly impacts measured at the monitor. 0 10 20 30 40 50 60 70 80 90

KMKO

MonitorMuskogee Monitoring Versus Modeling Report Page E

- 4 0 20 40 60 80 100 120

0 20 40 60 80 100 120



# KMKO

KMKOMuskogee Monitoring Versus Modeling Report



# Appendix F

– Comparison of Hours with High Impacts at the Monitor with Sub

- Hourly

Impact, Meteorological

, and Source DataMuskogee Monitoring Versus Modeling Report Page F

- 1 

### 2014 Monitor Data for Highest Monitored Impacts & Main Contributing Source Data for the Hour

Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp

. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

2014 3 21 7 42 6:00 2 5 131 52 33 4 304.2 415 19.56

6:05 3 7 137 53 33 5 238.6 400 16.45

6:10 11 8 145 53 32 6 0.0 296 1.50

6:15 25 9 144 53 32 GP1 0.3 452 3.48

6:20 50 8 154 54 32 GP2 58.9 416 11.26

6:25 49 7 154 54 32

6:30 44 7 157 54 34

6:35 50 6 160 54 35

6:40 66 8 154 54 35

6:45 66 10 157 54 33

6:50 74 10 156 55 33

6:55 67 10 156 55 33

2014 3 21 8 58 7:00 59 11 158 55 32 4 304.6 417 21.03

7:05 65 9 161 55 33 5 237.3 398 17.04

7:10 49 9 164 55 35 6 0.0 296 0.62

7:15 24 11 162 55 35 GP1 0.3 452 3.48

7:20 15 10 162 55 35 GP2 58.9 416 11.26

7:25 41 8 161 55 35

7:30 72 8 153 56 35

7:35 82 8 153 56 34

7:40 80 9 157 56 34

7:45 85 9 157 56 34

7:50 AN 7 164 56 35

7:55 BA 9 164 56 36

2014 3 21 9 BF 8:00 BA 9 161 56 35 4 300.7 417 20.99

8:05 BA 11 166 57 35 5 237.9 399 16.84

8:10 BA 12 166 57 34 6 0.0 296 0.53

8:15 BA 11 163 57 34 GP1 0.3 452 3.48

8:20 BA 12 164 58 34 GP2 58.9 416 11.26

8:25 BA 11 168 58 34

8:30 BA 12 164 58 33

8:35 BA 11 161 58 33

8:40 BA 11 158 58 33Muskogee Monitoring Versus Modeling Report Page F

- 2 Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp

. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

8:45 BA 13 168 58 33

8:50 BA 13 164 59 33

8:55 BA 13 165 59 33

2014 3 31 3 10 2:00 2 11 147 60 43 4 194.1 406 14.77

2:05 2 14 146 60 43 5 16.1 375 9.19

2:10 4 12 153 60 43 6 210.6 398 16.12

2:15 4 11 150 60 43 GP1 17.5 452 15.74

2:20 3 12 149 60 43 GP2 31.5 416 6.12

2:25 3 12 152 59 43

2:30 5 11 150 59 44

2:35 13 11 150 60 44

2:40 15 12 149 60 43

2:45 14 10 150 60 44

2:50 23 10 152 60 44

2:55 36 11 152 60 44

2014 3 31 4 70 3:00 40 10 157 60 45 4 197.3 405 14.67

3:05 59 9 158 60 45 5 76.8 378 10.70

3:10 72 11 161 60 45 6 211.3 397 16.33

3:15 73 10 158 60 45 GP1 17.5 452 15.74

3:20 77 13 157 60 45 GP2 31.5 416 6.12

3:25 83 11 156 60 46

3:30 84 13 158 60 46

3:35 84 13 156 60 46

3:40 72 12 162 60 47

3:45 46 12 163 59 47

3:50 71 11 155 59 47

3:55 84 11 157 59 47

2014 3 31 5 33 4:00 74 11 162 59 48 4 215.7 404 15.34

4:05 64 12 159 59 49 5 86.5 386 10.69

4:10 65 12 158 59 49 6 251.5 395 18.45

4:15 59 11 164 59 49 GP1 17.5 452 15.74

4:20 42 11 168 59 50 GP2 31.5 416 6.12

4:25 26 12 166 59 50Muskogee Monitoring Versus Modeling Report Page F

- 3 Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp

. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

4:30 16 12 164 59 50

4:35 10 12 163 58 50

4:40 11 14 164 58 50

4:45 10 15 166 59 49

4:50 8 14 162 58 50

4:55 7 13 163 58 50

2014 6 16 21 23 20:00 8 10 150 82 61 4 312.4 435 22.87

20:05 7 9 154 82 62 5 341.4 424 22.70

20:10 7 10 148 82 62 6 324.1 430 22.27

20:15 7 11 152 82 62 GP1 13.2 452 8.80

20:20 10 11 153 82 63 GP2 51.3 416 9.89

20:25 11 11 158 82 63

20:30 15 11 153 82 62

20:35 28 10 155 82 63

20:40 35 10 155 82 64

20:45 56 11 152 82 64

20:50 49 10 152 82 64

20:55 48 12 150 82 64

2014 6 16 22 58 21:00 34 11 155 82 64 4 316.7 435 22.85

21:05 47 11 159 82 65 5 345.7 424 22.72

21:10 59 13 158 82 65 6 320.0 427 21.93

21:15 80 10 158 82 65 GP1 13.2 452 8.80

21:20 70 9 151 82 66 GP2 51.3 416 9.89

21:25 69 11 156 81 67

21:30 64 12 149 81 67

21:35 35 12 153 81 67

21:40 34 11 157 81 68

21:45 50 11 154 81 68

21:50 71 10 157 81 68

21:55 77 11 155 81 69

2014 6 16 23 67 22:00 80 11 153 81 69 4 316.0 434 22.45

22:05 91 11 154 81 69 5 365.8 424 22.70

22:10 89 11 157 81 69 6 322.2 427 21.92Muskogee Monitoring Versus Modeling Report Page F

- 4 Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp

. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

22:15 84 10 157 81 70 GP1 13.2 452 8.80

22:20 79 10 163 81 70 GP2 51.3 416 9.89

22:25 81 10 159 81 70

22:30 89 9 155 80 71

22:35 87 9 163 80 71

22:40 62 11 168 80 71

22:45 30 12 168 80 72

22:50 21 13 169 80 72

22:55 15 13 167 80 71

2014 6 16 24 14 23:00 12 11 167 80 72 4 351.4 433 22.49

23:05 11 11 166 80 73 5 382.8 423 22.50

23:10 9 11 166 80 73 6 247.0 424 17.97

23:15 9 10 172 80 73 GP1 13.2 452 8.80

23:20 10 10 163 79 74 GP2 51.3 416 9.89

23:25 12 12 163 79 74

23:30 10 11 161 79 74

23:35 11 8 161 79 74

23:40 12 5 149 79 75

23:45 19 6 149 78 76

23:50 26 7 151 78 76

23:55 30 7 149 78 77

WSPD

- Wind Speed; WDIR

- Wind Direction; TAIR

- Temperature of Air; RH

- Relative Humidity.

AN

– Machine Malfunction

. BA

- Maintenance/Routine Repairs

. BF

- Precision/Zero/Span

4, 5, 6

- OG&E Electrical Generating Units 4, 5, & 6, respectively.

GP1, GP2

- Georgia Pacific Stacks 1 and 2, respectively.

The hourly values for the monitor data are shifted one hour because the monitor time is 0

- 23 hours and the modeling time is 1

- 24 hours.

The data includes the hour prior to and the hour after the highest impacts.

The hours with the highest impacts are highlighted.Muskogee Monitoring Versus Modeling Report Page F

- 5 

### 201 3 Monitor Data for Highest Monitored Impacts & Main Contri buting Source Data for the Hour

Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

2013 3 7 10 22 9:00 1 12 164 48 35 4 0.0 282 0.60

9:05 1 12 154 48 34 5 223.8 402 17.51

9:10 4 11 156 49 34 6 240.1 410 19.88

9:15 5 10 162 49 34 GP1 14.1 452 9.37

9:20 4 12 162 50 33 GP2 49.6 416 9.60

9:25 11 14 161 50 33

9:30 13 13 166 50 33

9:35 15 11 163 51 33

9:40 30 10 177 51 32

9:45 60 10 171 52 32

9:50 48 14 180 52 31

9:55 70 12 175 52 31

2013 3 7 11 45 10:00 69 13 182 52 31 4 0.0 283 0.44

10:05 89 15 178 53 30 5 232.5 404 18.11

10:10 66 13 177 53 31 6 242.5 413 20.22

10:15 77 13 177 53 30 GP1 14.1 452 9.37

10:20 59 13 171 53 30 GP2 49.6 416 9.60

10:25 26 15 180 53 30

10:30 52 13 175 53 30

10:35 35 12 178 53 31

10:40 46 13 179 54 31

10:45 11 13 180 54 31

10:50 8 15 182 54 30

10:55 6 15 189 54 31

2013 3 7 12 30 11:00 11 12 174 54 31 4 0.0 283 0.38

11:05 16 11 179 55 31 5 238.2 408 19.08

11:10 31 12 181 55 31 6 244.3 414 20.12

11:15 51 13 177 55 30 GP1 14.1 452 9.37

11:20 20 13 182 55 30 GP2 49.6 416 9.60

11:25 54 12 177 56 30

11:30 35 10 168 56 30

11:35 27 14 185 56 29

11:40 13 12 181 57 30

11:45 10 11 169 57 29Muskogee Monitoring Versus Modeling Report Page F

- 6 Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

11:50 32 13 167 57 29

11:55 57 14 177 57 29

2 013 5 27 22 39 21:00 31 13 158 77 62 4 0.0 308 0.07

21:05 35 11 158 77 62 5 258.5 429 24.13

21:10 39 11 159 77 62 6 292.3 416 21.64

21:15 35 14 165 77 61 GP1 13.5 452 9.01

21:20 42 15 161 77 61 GP2 61.0 416 11.63

21:25 46 16 160 77 61

21:30 35 15 162 78 61

21:35 40 14 160 77 61

21:40 45 14 160 77 62

21:45 47 11 158 77 62

21:50 41 10 153 77 64

21:55 34 10 161 76 65

2013 5 27 23 46 22:00 40 12 161 76 64 4 0.0 308 0.00

22:05 47 13 159 76 64 5 219.5 431 21.13

22:10 50 14 159 76 63 6 237.5 411 18.88

22:15 55 14 162 77 62 GP1 13.5 452 9.01

22:20 52 12 156 77 63 GP2 61.0 416 11.63

22:25 62 11 160 76 64

22:30 57 11 159 77 63

22:35 62 10 155 76 64

22:40 57 10 154 76 64

22:45 31 11 152 76 64

22:50 21 14 151 76 64

22:55 17 13 149 76 64

2013 5 27 24 9 23:00 12 14 156 76 64 4 0.0 308 0.09

23:05 11 13 147 76 64 5 176.6 425 18.21

23:10 9 15 147 76 63 6 165.1 402 15.44

23:15 7 12 149 76 63 GP1 13.5 452 9.01

23:20 8 14 154 76 63 GP2 61.0 416 11.63

23:25 8 11 154 76 63

23:30 8 12 155 76 63

23:35 9 12 155 76 63

23:40 8 10 152 76 63Muskogee Monitoring Versus Modeling Report Page F

- 7 Monitor Data Original 5

- Minute On

- Site Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR TAIR RH ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG °F % g/s K m/s

23:45 9 13 154 76 63

23:50 10 14 151 76 63

23:55 9 11 159 76 62

WSPD

- Wind Speed; WDIR

- Wind Direction; TAIR

- Temperature of Air; RH

- Relative Humidity.

4, 5, 6

- OG&E Electrical Generating Units 4, 5, & 6, respectively.

GP1, GP2

- Georgia Pacific Stacks 1 and 2, respectively.

The hourly values for the monitor data are shifted one hour because the monitor time is 0

- 23 hours and the modeling time is 1

- 24 hours.

The data includes the hour prior to and the hour after the highest impacts.

The hours with the highest impacts are highlighted.Muskogee Monitoring Versus Modeling Report Page F

- 8 

### 201 2 Monitor Da ta for Highest Monitored Impacts & Main Contributing Source Data for the Hour

Monitor Data 0167 Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG g/s K m/s

2012 1 15 11 27 10:00 2 12 153 4 205. 2 39 6 18.62

10:05 5 5 221.5 387 15.91

10:10 7 6 325.4 393 20.59

10:15 10 GP1 1 4

. 0 45 2 9.3 1

10:20 24 GP2 54.6 41 6 10.5 2

10:25 34

10:30 31

10:35 15

10:40 27

10:45 75

10:50 39

10:55 62

2012 1 15 12 80 11:00 66 14 168 4 209. 9 39 6 18.28

11:05 66 5 223.8 38 9 15.98

11:10 68 6 277. 6 390 17.4 5

11:15 76 GP1 1 4

. 0 45 2 9.3 1

11:20 99 GP2 54.6 41 6 10.5 2

11:25 86

11:30 70

11:35 51

11:40 72

11:45 79

11:50 93

11:55 136

2012 1 15 13 39 12:00 89 12 165 4 187. 7 39 8 16.99

12:05 28 5 184. 6 390 15.0 2

12:10 25 6 218. 2 385 14.42

12:15 37 GP1 1 4

. 0 45 2 9.3 1

12:20 53 GP2 54.6 41 6 10.5 2

12:25 40

12:30 23Muskogee Monitoring Versus Modeling Report Page F

- 9 Monitor Data 0167 Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG g/s K m/s

12:35 47

12:40 6

12:45 16

12:50 33

12:55 77

2012 1 15 18 5 17:00 5 12 154 4 180.3 39 2 16.55

17:05 2 5 159.4 38 7 13.83

17:10 5 6 193.0 378 12.5 5

17:15 17 GP1 1 4

. 0 45 2 9.3 1

17:20 10 GP2 54.6 41 6 10.5 2

17:25 16

17:30 5

17:35 2

17:40 2

17:45 1

17:50 2

17:55 2

2012 1 15 19 68 18:00 3 11 157 4 204.0 39 2 17.47

18:05 8 5 213. 8 38 7 15.5 1

18:10 11 6 246. 4 381 15.01

18:15 18 GP1 1 4

. 0 45 2 9.3 1

18:20 98 GP2 54.6 41 6 10.5 2

18:25 91

18:30 59

18:35 81

18:40 105

18:45 114

18:50 114

18:55 115

2012 1 15 20 65 19:00 125 10 162 4 184.2 39 3 16.9 7

19:05 72 5 207.1 39 0 15.3 7

19:10 21 6 212.0 383 13.9 2Muskogee Monitoring Versus Modeling Report Page F

- 10 Monitor Data 0167 Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG g/s K m/s

19:15 76 GP1 1 4

. 0 45 2 9.3 1

19:20 78 GP2 54.6 41 6 10.5 2

19:25 69

19:30 74

19:35 66

19:40 73

19:45 44

19:50 35

19:55 55

2012 1 15 21 27 20:00 67 9 158 4 171.0 39 2 16.58

20:05 55 5 204.3 391 15.2 5

20:10 35 6 195.0 38 1 13.1 9

20:15 16 GP1 1 4

. 0 45 2 9.3 1

20:20 13 GP2 54.6 41 6 10.5 2

20:25 35

20:30 37

20:35 19

20:40 13

20:45 9

20:50 8

20:55 24

2012 5 23 10 22 9:00 27 13 166 4 202.0 400 16.9 3

9:05 12 5 0 .0 30 4 0 .00

9:10 3 6 360.1 42 2 24.05

9:15 6 GP1 13.5 45 2 9.0 3

9:20 10 GP2 50.3 41 6 9.79

9:25 8

9:30 17

9:35 20

9:40 7

9:45 27

9:50 66

9:55 58Muskogee Monitoring Versus Modeling Report Page F

- 11 Monitor Data 0167 Data Source Parameters

Conc. 5

- Min Impact Missing WSPD WDIR ID Emissions Temp. Velocity

Year Month Day Hour ppb v Hour ppb v Code MPH DEG g/s K m/s

2012 5 23 11 69 10:00 98 13 161 4 205. 9 40 2 17.3 3

10:05 98 5 0 .0 30 5 0

10:10 109 6 365.9 36 1 23.98

10:15 65 GP1 13.5 45 2 9.0 3

10:20 81 GP2 50.3 41 6 9.79

10:25 33

10:30 40

10:35 64

10:40 92

10:45 58

10:50 42

10:55 50

2012 5 23 12 32 11:00 68 13 157 4 206. 8 406 17.60

11:05 84 5 0 .0 306 0 .0

11:10 11 6 366.8 42 3 24.0 4

11:15 12 GP1 13.5 45 2 9.0 3

11:20 26 GP2 50.3 41 6 9.79

11:25 12

11:30 13

11:35 44

11:40 30

11:45 11

11:50 23

11:55 51

WSPD

- Wind Speed; WDIR

- Wind Direction; TAIR

- Temperature of Air; RH

- Relative Humidity.

4, 5, 6

- OG&E Electrical Generating Units 4, 5, & 6, respectively.

GP1, GP2

- Georgia Pacific Stacks 1 and 2, respectively.

The hourly values for the monitor data are shifted one hour because the monitor time is 0

- 23 hours and the modeling time is 1

- 24 hours.

The wind speed data for the monitor was not available in 5

- minute intervals for 2012.

The data includes the hour prior to and the hour after the highest impacts.

The hours with the highest impacts are highlighted.Muskogee Monitoring Versus Modeling Report



# Appendix G

– Comparison of Hours with Modeled and Monitored High ImpactsMuskogee Monitoring Versus Modeling Report Page G

- 1 

### 2014 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Sensible

Heat

flux Surface

Friction

Velocity Convective

Velocity

Scale Vertical

Potential

Temp.

Gradient Height of

Convectively

Generated

Boundary

Layer Height of

Mechanically

Generated

Boundary

Layer Monin

Obukhov

Length Surface

Roughness

Length

Modeled Monitor H u* w* VPTG Zic Zim L zo

YR MO DY HR ppb ppb Station W/m2 m/s m/s K/m m m m m

14 3 21 8 9 58 KMKO 6.6 0.347 0.241 0.007 74 490

- 558.5 0.023

0167 6.4 0.272 0.238 0.016 74 341

- 277.2 0.027

14 3 31 4 2 70 KMKO

- 35.4 0.327

- 9

- 9

- 999 450 87.2 0.023

0167

- 33.6 0.312

- 9

- 9

- 999 418 79.4 0.027

14 6 16 22 0 58 KMKO

- 36.6 0.35 0

- 9

- 9

- 999 498 104.1 0.037

0167

- 37 0.357

- 9

- 9

- 999 511 108.3 0.064

14 6 16 23 4 67 KMKO

- 39.2 0.375

- 9

- 9

- 999 552 119.2 0.037

0167

- 36.3 0.35 0

- 9

- 9

- 999 497 104.2 0.064



### 2014 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Bowen

Ratio Albedo Wind

Speed Wind

Direction Temp erature Relative

Humidity Press ure Cloud

Cover

Modeled Monitor Bo r Ws Wd temp rh pres ccvr

YR MO DY HR ppb ppb Station m/s deg. K % m b tenths

14 3 21 8 9 58 KMKO 0.33 0.32 5.21 161 284.9 46 990 0

0167 0.37 0.31 3.94 1 60 286.2 35 988 9

14 3 31 4 2 70 KMKO 0.33 1 5.44 157 287.5 55 989 0

0167 0.37 1 5.1 15 9 288.5 46 988 0

14 6 16 22 0 58 KMKO 0.3 0 1 5.33 155 299.2 76 991 0

0167 0.28 1 4.92 155 300.5 67 989 0

14 6 16 23 4 67 KMKO 0.3 0 1 5.65 160 298.8 81 991 0

0167 0.28 1 4.84 162 300.1 71 990 0Muskogee Monitoring Versus Modeling Report Page G

- 2 

### 201 3 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Sensible

Heat

flux Surface

Friction

Velocity Convective

Velocity

Scale Vertical

Potential

Temp.

Gradient Height of

Convectively

Generated

Boundary

Layer Height of

Mechanically

Generated

Boundary

Layer Monin

Obukhov

Length Surface

Roughness

Length

Modeled Monitor H u* w* VPTG Zic Zim L zo

YR MO DY HR ppb ppb Station W/m2 m/s m/s K/m m m m m

13 3 7 11 5 45 KMKO 83.2 0.51 0.806 0.01 225 874

- 142.9 0.023

0167 108.1 0.436 0.936 0.01 270 689

- 68.2 0.027

13 5 27 23 0 46 KMKO

- 39.4 0.376

- 9

- 9

- 999 553 118.7 0.023

0167

- 35.7 0.342

- 9

- 9

- 999 481 98.7 0.027



### 201 3 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Bowen

Ratio Albedo Wind

Speed Wind

Direction Temp erature Relative

Humidity Press ure Cloud

Cover

Modeled Monitor Bo r Ws Wd temp rh pres ccvr

YR MO DY HR ppb ppb Station m/s deg. K % m b tenths

13 3 7 11 5 45 KMKO 0.33 0.15 7.48 153 285.4 37 1002 0

0167 0.37 0.15 6.03 178 285.1 30 1001 0

13 5 27 23 0 46 KMKO 0.33 1 6.11 154 296.4 78 987 0

0167 0.37 1 5.49 156 297.7 64 986 0Muskogee Monitoring Versus Modeling Report Page G

- 3 

### 201 2 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Sensible

Heat

flux Surface

Friction

Velocity Convective

Velocity

Scale Vertical

Potential

Temp.

Gradient Height of

Convectively

Generated

Boundary

Layer Height of

Mechanically

Generated

Boundary

Layer Monin

Obukhov

Length Surface

Roughness

Length

Modeled Monitor H u* w* VPTG Zic Zim L zo

YR MO DY HR ppb ppb Station W/m2 m/s m/s K/m m m m m

12 1 15 12 0 80 KMKO 97.3 0.522 0.781 0.007 175 907

- 130.7 0.016

0167 91.8 0.43 0 0.759 0.005 171 676

- 77.4 0.021

12 1 15 19 0 68 KMKO

- 31.8 0.289

- 9

- 9

- 999 373 67.2 0.016

0167

- 31.2 0.284

- 9

- 9

- 999 364 65.3 0.021

12 1 15 20 0 65 KMKO

- 31.7 0.287

- 9

- 9

- 999 370 66.5 0.016

0167

- 27.5 0.25 0

- 9

- 9

- 999 300 50.4 0.021

12 5 23 11 11 69 KMKO 266.4 0.526 1.887 0.005 888 915

- 48 .0 0.023

0167 251.6 0.44 0 1.79 0 0.005 805 700

- 29.8 0.027



### 201 2 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Bowen

Ratio Albedo Wind

Speed Wind

Direction Temp erature Relative

Humidity Press ure Cloud

Cover

Modeled Monitor Bo r Ws Wd temp rh pres ccvr

YR MO DY HR ppb ppb Station m/s deg. K % m b tenths

12 1 15 12 0 80 KMKO 0.72 0.21 8.11 143 284.2 40 1000 0

0167 0.64 0.2 6.26 168 284.9 32 1003 0

12 1 15 19 0 68 KMKO 0.72 1 5.19 155 284.2 46 995 0

0167 0.64 1 4.92 157 285.9 36 998 0

12 1 15 20 0 65 KMKO 0.72 1 5.17 152 284.2 46 995 0

0167 0.64 1 4.47 162 285.4 39 998 0

12 5 23 11 11 69 KMKO 1.03 0.14 7.37 152 301.4 48 985 0

0167 0.93 0.14 5.81 161 299.2 42 988 0Muskogee Monitoring Versus Modeling Report Page G

- 4 

### 2014 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Sensible

Heat

flux Surface

Friction

Velocity Convective

Velocity

Scale Vertical

Potential

Temp.

Gradient Height of

Convectively

Generated

Boundary

Layer Height of

Mechanically

Generated

Boundary

Layer Monin

Obukhov

Length Surface

Roughness

Length

Modeled Monitor H u* w* VPTG Zic Zim L zo

YR MO DY HR ppb ppb Station W/m2 m/s m/s K/m m m m m

14 1 10 14 97 0 KMKO 32.9 0.168 0.587 0.017 215 176

- 12.5 0.016

0167 30 .0 0.213 0.522 0.016 165 237

- 28.3 0.021

14 3 4 13 46 4 KMKO 99.3 0.294 1.054 0.033 420 384

- 22.8 0.023

0167 133.8 0.226 1.19 0.033 448 260

- 7.7 0.027

14 6 25 9 54 1 KMKO 73.2 0.222 0.814 0.009 262 251

- 13.2 0.037

0167 71 .0 0.221 0.78 0 0.009 237 250

- 13.5 0.037

14 8 13 9 46 1 KMKO 88.7 0.166 0.876 0.006 269 163

- 4.6 0.037

0167 82.1 0.165 0.851 0.006 267 161

- 4.9 0.037



### 2014 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Bowen

Ratio Albedo Wind

Speed Wind

Direction Temp erature Relative

Humidity Press ure Cloud

Cover

Modeled Monitor Bo r Ws Wd temp rh pres ccvr

YR MO DY HR ppb ppb Station m/s deg. K % m b tenths

14 1 10 14 97 0 KMKO 1.94 0.21 2.28 162 284.2 100 982 10

0167 1.47 0.2 2.94 167 284.5 91 980 10

14 3 4 13 46 4 KMKO 0.33 0.15 3.93 161 275.4 49 999 0

0167 0.37 0.15 2.64 175 275 47 998 0

14 6 25 9 54 1 KMKO 0.3 0 0.2 0 2.57 171 299.9 73 996 0

0167 0.28 0.19 2.57 171 299.8 70 995 0

14 8 13 9 46 1 KMKO 0.5 0 0.21 1.7 0 153 296.4 63 999 0

0167 0.43 0.2 0 1.7 0 153 296 .0 62 998 0Muskogee Monitoring Versus Modeling Report Page G

- 5 

### 201 3 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Sensible

Heat

flux Surface

Friction

Velocity Convective

Velocity

Scale Vertical

Potential

Temp.

Gradient Height of

Convectively

Generated

Boundary

Layer Height of

Mechanically

Generated

Boundary

Layer Monin

Obukhov

Length Surface

Roughness

Length

Modeled Monitor H u* w* VPTG Zic Zim L zo

YR MO DY HR ppb ppb Station W/m2 m/s m/s K/m m m m m

13 7 21 11 64 2 KMKO 41.2 0.163 0.74 0 0.007 347 174

- 9.3 0.037

0167 13.7 0.12 0 0.422 0.013 193 108

- 11.2 0.064

13 7 25 9 59 2 KMKO 96.9 0.194 0.959 0.005 324 204

- 6.6 0.037

0167 36.3 0.179 0.564 0.006 176 181

- 13.9 0.037

13 8 12 9 63 2 KMKO 93.8 0.182 0.921 0.005 296 186

- 5.7 0.037

0167 57.6 0.136 0.627 0.005 152 121

- 3.9 0.064

13 12 10 13 61 2 KMKO 66.7 0.217 0.776 0.017 252 243

- 13.8 0.016

0167 79.6 0.202 0.848 0.016 275 218

- 9.3 0.021



### 201 3 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Bowen

Ratio Albedo Wind

Speed Wind

Direction Temp erature Relative

Humidity Press ure Cloud

Cover

Modeled Monitor Bo r Ws Wd temp rh pres ccvr

YR MO DY HR ppb ppb Station m/s deg. K % m b tenths

13 7 21 11 64 2 KMKO 0.5 0 0.19 1.82 169 299.2 84 992 10

0167 0.43 0.18 1.21 9 297.6 85 991 10

13 7 25 9 59 2 KMKO 0.5 0 0.21 2.08 160 298.1 93 996 0

0167 0.43 0.2 0 2.08 160 298.8 78 995 9

13 8 12 9 63 2 KMKO 0.5 0 0.21 1.91 159 300.9 81 995 2

0167 0.43 0.2 0 1.18 18 3 300.8 74 994 8

13 12 10 13 61 2 KMKO 0.41 0.21 2.98 158 272.5 56 1008 0

0167 0.37 0.2 0 2.53 183 271.6 52 1006 0Muskogee Monitoring Versus Modeling Report Page G

- 6 

### 201 2 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Sensible

Heat

flux Surface

Friction

Velocity Convective

Velocity

Scale Vertical

Potential

Temp.

Gradient Height of

Convectively

Generated

Boundary

Layer Height of

Mechanically

Generated

Boundary

Layer Monin

Obukhov

Length Surface

Roughness

Length

Modeled Monitor H u* w* VPTG Zic Zim L zo

YR MO DY HR ppb ppb Station W/m2 m/s m/s K/m m m m m

12 2 14 13 60 23 KMKO 134 .0 0.239 0.918 0.016 204 280

- 9 .0 0.016

0167 126.2 0.191 0.886 0.017 196 201

- 4.9 0.021

12 5 5 8 65 0 KMKO 89 .0 0.222 0.851 0.013 244 251

- 10.8 0.023

0167 86 .0 0.133 0.836 0.013 241 117

- 2.4 0.027

12 7 13 8 76 1 KMKO 105 .0 0.158 0.886 0.007 235 151

- 3.4 0.037

0167 91.4 0.156 0.823 0.007 217 148

- 3.7 0.037

12 9 26 9 60 0 KMKO 83.2 0.131 0.921 0.014 333 115

- 2.4 0.031

0167 78.7 0.131 0.9 00 0.014 328 114

- 2.5 0.031

12 11 24 10 67 0 KMKO 85.4 0.167 0.768 0.018 190 163

- 4.9 0.031

0167 78.2 0.165 0.73 0 0.018 179 161

- 5.2 0.031

12 12 7 15 91 2 KMKO 21.7 0.117 0.613 0.009 374 97

- 6.5 0.016

0167 19.7 0.116 0.585 0.009 357 96

- 7.1 0.016

12 12 7 16 116 0 KMKO 7 .0 0.083 0.424 0.009 384 58

- 7.3 0.016

0167 6.3 0.083 0.404 0.009 365 57

- 7.9 0.016Muskogee Monitoring Versus Modeling Report Page G

- 7 

### 201 2 Monitored Impact/Modeled Impact at Monitor & Associated Surface Meteorological Data

Bowen

Ratio Albedo Wind

Speed Wind

Direction Temp erature Relative

Humidity Press ure Cloud

Cover

Modeled Monitor Bo r Ws Wd temp rh pres ccvr

YR MO DY HR ppb ppb Station m/s deg. K % m b tenths

12 2 14 13 60 23 KMKO 0.72 0.19 3.15 158 280.4 73 993 5

0167 0.64 0.18 2.24 182 279.9 67 996 5

12 5 5 8 65 0 KMKO 1.03 0.19 2.78 156 294.2 93 990 0

0167 0.93 0.19 1.34 142 296.4 68 993 0

12 7 13 8 76 1 KMKO 1.42 0.24 1.55 162 299.9 70 995 0

0167 1.01 0.23 1.55 162 299.2 67 998 0

12 9 26 9 60 0 KMKO 0.72 0.23 1.28 170 297.5 63 994 5

0167 0.64 0.22 1.28 170 297.5 63 994 5

12 11 24 10 67 0 KMKO 1.94 0.25 1.79 155 276.4 50 1007 0

0167 1.47 0.24 1.79 155 276.4 50 1007 0

12 12 7 15 91 2 KMKO 1.94 0.24 1.5 167 287.5 86 987 10

0167 1.47 0.23 1.5 167 287.5 86 987 10

12 12 7 16 116 0 KMKO 1.94 0.31 1.08 166 288.1 83 988 10

0167 1.47 0.3 0 1.08 166 288.1 83 988 10