# SO 2 

# Characterization Modeling Analysis for the H.A. Wagner and Brandon Shores Power Plants

U.S. Environmental Protection Agency

Ambient Air Monitoring Program Air and Radiation Management Administration Maryland Dep

M ARYLAND 1800 Washington Boulevard | Baltimore, MD 21230 | w ww.mde.maryland.gov

410-537- 3000 | 800 Lawrence J. Hogan, Jr.,



### 1 

# Characterization Modeling Analysis for the H.A. Wagner and Brandon Shores Power Plants

Prepared for: U.S. Environmental Protection Agency

Prepared by: Ambient Air Monitoring Program Air and Radiation Management Administration Maryland Dep artment of the Environment

April 19, 2016

ARYLAND 

### D EPARTMENT OF THE 

### E NVIRONMENT 1800 Washington Boulevard | Baltimore, MD 21230 | w ww.mde.maryland.gov 3000 | 800 -633-6101 | TTY Users: 800-735-2258 Lawrence J. Hogan, Jr., Governor | Boyd K. Rutherford, Lt. Governor Ben Grumbles, Secretary 

# Characterization Modeling Analysis for the H.A. Wagner and Brandon Shores Power Plants

1800 Washington Boulevard | Baltimore, MD 21230 | w ww.mde.maryland.gov

### 2 

## Table of Contents

1 INTRODUCTION ......................................

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

1.1 Background ........................................

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



### 2 MODELING PROCEDURES AND RESULTS ...................

................................................... ......... 4

2.1 Dispersion Model Selection ........................

................................................... ............................................ 4

2.2 Emissions Data and Source Characterization ........

................................................... .................................. 4

2.3 Good Engineering Practice (GEP) Analysis

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

2.4 Meteorological Data Processing

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

2.5 Receptors to be Modeled

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

2.6 Model Configurations and Options

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

2.7 Background Concentrations

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

2.8 Results of SO 2 Characterization Analysis

.................................................. ........................................... 12



### Figures 2-1 – 2-5 .................................

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

### 3



## 1 Introduction

1.1 Background

The United States Environmental Protection Agency ( EPA) promulgated a 1-hour National Ambient Air Quality Standard (NAAQS) for SO 2 

### in 2010. The 1-hour SO 2 

### NAAQS has a level set at 75 ppb and the form of the standard is the a verage of the 99th percentile of the daily maximum 1-hour average concentrations realized in e ach of three consecutive calendar years (the “design value,” or DV).

The EPA is implementing the 2010 1-hour SO 2 

### National Ambient Air Quality Standard (NAAQS) in an approach that involves either a dispe rsion modeling or monitoring approach to characterize local SO 2 

### concentrations near isolated emission sources. On March 20, 2015, EPA informed affected states that certain emission sour ces within their states will be addressed in an expedited round of designations under the 1-hour SO 2 

### NAAQS due to terms of the SO 2 

### Consent Decree negotiated between the Sierra Club and EPA. 1 

### The EPA intends to designate the affected areas as either “attainment” (same as “unclassifiab le/attainment”), “nonattainment,” or “unclassifiable” by July 2, 2016 after a review of available modeling or monitoring data to support the SO 2 

### concentration characterizations.

One of the affected sources evaluated in this Conse nt Decree analysis is the H. A. Wagner Generating Station (“Wagner”). Due to its proximity to Wagner, the Brandon Shores Generating Station is also part of the SO 2 

### characterization process.

The Maryland Department of the Environment (MDE or the Department) has been working with Raven Power, owner of Wagner and Brandon Shores, wi th Raven Power’s consultant AECOM, and with EPA Region 3 to ensure the area around Wag ner is characterized appropriately. In addition, the Crane Generating Station (Crane) and another minor source in the vicinity of the Wagner area were included in the modeling analysis.

This modeling analysis was completed in consultatio n with EPA Region 3, to demonstrate that the size of the 1-hour SO 2 

### nonattainment area proposed by EPA in their March 1, 2016 Draft Technical Support Document entitled “Area Designati ons for the 2010 SO 2 

### Primary Ambient Air Quality Standard” 2 

### should be reduced in size to the immediate area su rrounding Wagner. This modeling analysis will use updated modeling procedu res and the dispersion modeling results that are summarized in this appendix, to characterize SO 2 

### concentrations in the area surrounding Wagner.

1 See Case No.: 3:13-cv-3953-SI, in the United States District Court for the Northern District of Califo rnia, San Francisco Division, filed March 2, 2015, available at http://www.4cleanair.org/sites/default/files/resour ces/Litigation-SO2- Designations_Deadline_Suit-Final_CD-030215.pdf

.

2 https://www3.epa.gov/so2designations/round2/03_MD_ tsd.pdf

### 4



## 2 Modeling Procedures and Results

2.1 Dispersion Model Selection

This modeling analysis utilized the most recent ver sion of the AERMOD dispersion model 3 

### (Version 15181) to evaluate air quality impacts fro m the emission sources of interest. The AERMOD modeling system consists of two preprocessor s and the dispersion model. AERMET is the meteorological preprocessor component and AE RMAP is the terrain pre-processor component that characterizes the terrain and genera tes receptor elevations along with critical hill heights for receptors.



### 2.2 Emissions Data and Source Characterization

The most recent three years (2013-2015) of actual e missions data for Crane, Brandon Shores, and Wagner, data that Raven Power’s consultant AECO M submitted to MDE, were used in the 1-hour SO 2 

### source characterization modeling analysis as per t he guidance in EPA’s SO 2 

### NAAQS Designations Modeling Technical Assistance Document (TAD) 4

. 

### Also, one additional source, Wheelabrator, was included in the modeling analysis . Actual emissions for the Wheelabrator source were not available, so the allowable emissio ns rate was used. Figure 2-1 

### (see the figures at the end of this appendix) shows the sources located in the Baltimore area. Table 2-1, on the next page, lists the sources and parameters modeled. Bra ndon Shores Units 1 and 2 exhaust to a common stack with height and internal exit diameter as reported in Table 2-1. When both units were operating, the combined emission rate, average flow rate and weighted average temperature were used in AERMOD, consistent with EPA Model Clea ringhouse Memo 91-II-01. When Unit 1 or 2 operated alone, the single flue diameter was used. AECOM updated the flue gas temperature and exit velocity data in the hourly em issions file. These data were derived via examination of 2013-2015 data collected using the c ertified flue gas flow monitors (CEMs data) installed in the Brandon Shores, Wagner, and Crane stacks.

The stack temperature data includes several periods of erroneous temperature data for Wagner Unit 3. Four hours erroneously reported a temperatu re of 0 degrees F (March 21, 2013 Hour 8, June 12, 2013 Hour 9, August 8, 2013 Hour 19, and S eptember 3, 2013 Hour 13). These values were replaced with the temperatures in the Departme nt’s emissions inventory files (289.99 degrees F / 416.48 K).

Intermittent sources and transient conditions such as emergency generators, auxiliary boilers, and startup/shutdown operations were not modeled as exp lained in the March 2011 EPA guidance document 5 

### for modeling 1-hour NO 2 

### and SO 2 

### . These emission sources are of insufficient duration and frequency to affect NAAQS compliance.

3 Available at http://www.epa.gov/ttn/scram/11thmodconf/presentati ons/1-5_Proposed_Updates_AERMOD_System.pdf

.

4 http://www3.epa.gov/airquality/sulfurdioxide/pdfs/S O2ModelingTAD.pdf

5 http://www3.epa.gov/scram001/guidance/clarification /Additional_Clarifications_AppendixW_Hourly-NO2- NAAQS_FINAL_03-01-2011.pdf

### 5 Table 2-1: Emissions and Stack Parameters for Input to AERMOD

a Actual hourly monitor values were used in the model ing, as provided by Raven Power
b Wagner Units 1 and 4 are not equipped with stack fl ow meters.

c Wagner Unit 2 emission rate was capped at 1.0 lb/MM BTU to represent future operations.



### In April 2015, Raven Power reduced SO 2 

### emissions at Wagner Unit 2 by changing to Colorado coal, a lower chlorine and lower sulfur bituminous coal that will comply with the Mercury and Air Toxics Standards (MATS) Rule. Figure 2-2 

### shows the comparison of megawatt (MW) output
to SO 2 

### emissions for January 1 – September 30, 2015. Maxi mum SO 2 

### emissions before the change were on the order of 2,500 lb/hr and after t he maximum emission rate has been less than 1,500 lb/hr or less than 1.0 lb/MMBTU (~40% reductio n in SO 2 

### emissions) at the same MW output. Raven Power plans to continue burning this or similar coal in Wagner Unit 2 in order to meet MATS. For this modeling analysis no changes w ere made to the actual emissions for the Wagner, Brandon Shores and Crane sources.



### 2.3 Good Engineering Practice (GEP) Analysis

Federal stack height regulations limit the stack he ight used in performing dispersion modeling to predict the air quality impact of a source. Sour ces must be modeled at the actual physical stack height unless that height exceeds the Good En gineering Practice (GEP) formula stack height. If the physical stack height is less than t he formula GEP height, the potential for the source's plume to be affected by aerodynamic wakes created by the building(s) must be evaluated in the dispersion modeling analysis.

A GEP formula stack height analysis has been perfor med for sources of interest located at Brandon Shores, Wagner, and Crane in accordance wit h the EPA's "Guideline for Determination of Good Engineering Practice Stack He ight” (EPA, 1985) 6 

### . A GEP stack

6 Available at http://www.epa.gov/scram001/guidance/guide/gep.pdf

.

Stack SO 2

Emissions (g/s) Stack

Height (m) Exit Diameter (m) Exit

Temperature (K) Exit

Velocity (m/s)
Crane Unit 1 Variable a 107.59 3.328 Variable a Variable a

Crane Unit 2 Variable a 107.59 3.330 Variable a Variable a

Brandon Shores Unit 1 Variable a 121.92 9.50 Variable a Variable a

Brandon Shores Unit 2 Variable a 121.92 9.50 Variable a Variable a

Brandon Shores Merged Stack Variable a 121.92 13.435 Variable a Variable a

Wagner Unit 1 Variable a 87.48 3.099 330.00 30.48 Wagner Unit 2 Variable a,c 87.48 3.100 Variable a Variable a
Wagner Unit 3 Variable a 105.46 4.215 Variable a Variable a
Wagner Unit 4 Variable a 104.24 5.334 610.93 35.357

Wheelabrator 12.6 96.01 2.130 485.93 22.55

### 6

height is defined as the greater of 65 meters (213 feet), measured from the ground elevation of the stack, or the formula height (H g ), as determined from the following equation:

H g = H + 1.5 L

Where,

H is the height of the nearby structure which maxim izes H g , and L is the lesser dimension (height or projected widt h) of the building.

For a squat structure, i.e., height less than proje cted width, the formula reduces

to: Hg = 2.5H

In the absence of influencing structures, a “defaul t” GEP stack height is credited up to 65 meters (213 feet). Both the height and the width of the building are determined through a vertical cross-section perpendicular to the wind di rection. In all instances, the GEP formula height is based upon the highest value of H g as determined from H and L over all nearby buildings over the entire range of possible wind di rections. For the purposes of determining the GEP formula height, only buildings within 5L of the source of interest are considered.

The GEP analyses were conducted with the latest ver sion of the US EPA’s Building Profile Input Program software (BPIP-PRIME version 04274).



### 2.4 Meteorological Data Processing

The meteorological data required for input to AERMO D were created with the latest version of AERMET (15181) using the adjusted u* option. This o ption is currently a beta non-guideline option; justification for its use is discussed belo w. Hourly surface observations from BaltimoreWashington International Thurgood Marshall Airport (BWI), MD along with concurrent upper air data from Sterling, VA were used as input to AE RMET. The surface data (wind direction, wind speed, temperature, sky cover, and relative hu midity) are measured 10 m above ground level. A wind rose for 2013-2015 is shown in Figure 2-3



### .

AERMET requires specification of site characteristi cs including surface roughness (zo), albedo (r), and Bowen ratio (Bo). These parameters were developed according to the guidance provided by US EPA in the recently revised AERMOD Implementation Guide 7 

### (AIG).

The AIG provides the following recommendations for determining the site characteristics:

1. The determination of the surface roughness length s hould be based on an inverse distance weighted geometric mean for a default upwi nd distance of 1 kilometer relative to the measurement site. Surface roughness length may be varied by sector

7 Available at http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_ implmtn_guide_19March2009.pdf

.

### 7 to account for variations in land cover near the me asurement site; however, the sector widths should be no smaller than 30 degrees.
2. The determination of the Bowen ratio should be base d on a simple un-weighted geometric mean (i.e., no direction or distance depe ndency) for a representative domain, with a default domain defined by a 10-km by 10-km region centered on the measurement site.
3. The determination of the albedo should be based on a simple un-weighted arithmetic mean (i.e., no direction or distance dep endency) for the same representative domain as defined for Bowen ratio, w ith a default domain defined by a 10-km by 10-km region centered on the measurem ent site.



### The AIG recommends that the surface characteristics be determined based on digitized land cover data. EPA has developed a tool called AERSURF ACE that can be used to determine the site characteristics based on digitized land co ver data in accordance with the recommendations from the AIG discussed above. Aersu rface 8 

### incorporates look-up tables of representative surface characteristic values by lan d cover category and seasonal category. AERSURFACE was applied with the instructions provid ed in the AERSURFACE User’s Guide.

The current version of AERSURFACE (Version 13016) s upports the use of land cover data from the USGS National Land Cover Data 1992 archive s 9 

### (NLCD92). The NLCD92 archive provides data at a spatial resolution of 30 meters based upon a 21-category classification scheme applied over the continental United States. The AIG recommends that the surface characteristics be determined based on the land use surrounding the site where the surface meteorological data were collected.

As recommended in the AIG for surface roughness, th e 1-km radius circular area centered at the meteorological station site can be divided into sectors for the analysis; the default 12 sectors was used for this analysis.

In AERSURFACE, the various land cover categories ar e linked to a set of seasonal surface characteristics. As such, AERSURFACE requires speci fication of the seasonal category for each month of the year. The following five seasonal categories are supported by AERSURFACE, with the applicable months of the year specified for this site.

1. 

### Midsummer with lush vegetation (June-August).

2. 

### Autumn with un-harvested cropland (September- Novem ber).

3. 

### Late autumn after frost and harvest, or winter with no snow (December - February)

4. 

### Winter with continuous snow on ground (none).

5. 

### Transitional spring with partial green coverage or short annuals (March - May).

8 Documentation available at http://www.epa.gov/ttn/scram/dispersion_related.htm #aersurface

.

9 See additional information at http://landcover.usgs.gov/natllandcover.php

.

### 8

For Bowen ratio, the land use values are linked to three categories of surface moisture corresponding with average, wet, and dry conditions . The surface moisture condition for the site may vary depending on the meteorological data period for which the surface characteristics should be applied. AERSURFACE appli es the surface moisture condition for the entire data period. Therefore, if the surface moisture condition varies significantly across the data period, then AERSURFACE can be applied mul tiple times to account for those variations.

As such, the surface moisture condition for each se ason was determined by comparing precipitation for the period of data to be processe d to the 30-year climatological record, selecting “wet” conditions if precipitation was in the upper 30 th 

### -percentile, “dry” conditions if precipitation was in the lower 30 th 

### - percentile, and “average” conditions if precipit ation was in the middle 40 th 

### -percentile. The 30-year precipitation data set to be used in this modeling was taken from the National Climatic Data Center 10



### .

The monthly designations of surface moisture that w ere input to AERSURFACE are summarized in Table 2-2.

Table 2-2: AERSURFACE Bowen Ratio Condition Des ignations

Month Bowen Ratio Category

2013 2014 2015

January Wet Average Wet
February Dry Wet Average
March Average Average Average
April Dry Wet Wet
May Average Average Dry

June Wet Wet Wet
July Average Average Average
August Dry Wet Wet
September Dry Average Average
October Wet Average Average
November Average Average Average
December Wet Average Wet

10 http://www.ncdc.noaa.gov/cdo-web/

### 9



### 2.5 Receptors to be Modeled

Receptors were placed in nested Cartesian grids cen tered on the Fort Smallwood Complex and Crane with the following spacing:

• 

### Every 25 meters along the property boundary

• 

### Every 100 meters out to a distance of 2 km

• 

### Every 250 meters between 2 and 5 km, and

• 

### Every 500 meters between 5 and 10 km.

Additional receptors were placed at the outer edges to the northwest of Wagner in order to make sure that the maximum 1-hour SO 2 

### concentrations were accurately modeled.

The original 10,600 receptors were included in the modeling grid. After the initial modeling showed high 1-hour SO 2 

### concentrations to the northwest close to the model ing domain boundary, an additional 6,600 receptors were located there to assure the maximum 1-hour SO 2 

### concentrations were modeled. This brought the tota l number of receptors used in this analysis to 17,000.

The current version of AERMAP has the ability to pr ocess USGS National Elevation Dataset (NED) data in place of Digital Elevation Model file s. The appropriate file for 1-arc-second, or 30-m, NED data were obtained from the Multi-Resolut ion Land Characteristics Consortium (MRLC) link at http://www.mrlc.gov/viewerjs/

.

Per EPA’s SO 2 

### Technical Assistance Document for modeling 11 

### , receptors in inaccessible areas such as over water and on Aberdeen Proving Gr ound were removed for this modeling analysis as shown in Figure 2-4



### .



### 2.6 Model Configurations and Options

AERMET and AERMOD (Versions 15181) were run with th e default options and the Adjust U* (ADJ_U*) option in AERMET. In accordance with Appe ndix W, Section 3.2.2, the Department has submitted a request to the EPA Region 3 Regiona l Administrator to be given approval to use the non-regulatory default ADJ_U* option in this mo deling analysis. 12



### 2.7 Background Concentrations

The Beltsville, MD monitor (Site #24-033-0030), whi ch is located about 33 km to the southwest of the Fort Smallwood Complex, was used to determin e the uniform regional background

11 https://www3.epa.gov/airquality/sulfurdioxide/pdfs/ SO2ModelingTAD.pdf

12 Letter from Mr. Ben Grumbles (MDE Secretary) to Re gional Administrator Mr. Shawn Garvin, April 14, 20 16. See Appendix C of the document, “MDE Technical Support Document Regarding the Designation of the Area of t he Herbert A. Wagner Generating Plant for 1-Hour Sulfu r Dioxide.”

### 10

component for the NAAQS SO 2 

### modeling. EPA’s March 2011 clarification memo 13 

### regarding 1- hour SO 2 

### NAAQS modeling allows for an approach using the 99 th 

### percentile monitored values whereby the background values vary by season and by hour of the day. MDE used the AECOM approach and applied it to the modeling, using data from the 3-year period of 2013 – 2015. The
SO 2 

### concentrations that were used are listed in Table 2-3. According to the EPA’s “Table 5c. Monitoring Site Listing for Sulfur Dioxide 1-Hour N AAQS”, 14 

### the completeness criteria for 2013 and 2014 (Column W) are satisfied, therefore, the Beltsville 1-hour SO 2 

### monitoring data is complete and is acceptable to use in the modeling. For 2015, the Beltsville monitor recorded data for 8,334 hours (95% complete).

13 Available at http://www.epa.gov/ttn/scram/guidance/clarification /Additional_Clarifications_AppendixW_HourlyNO2-NAAQS_FINAL_03-01-2011.pdf

.

14 See http://www3.epa.gov/airtrends/pdfs/SO2_DesignValues _20122014_FINAL_8_3_15.xlsx

.

### 11

Table 2-3: 1-hr SO 2 Ambient Background Concentrations for Beltsville M onitor (2013-2015)

Hour 3-Year Averaged

Hourly Values for Winter ( μ g/m3) 3-Year Averaged

Hourly Values for Spring ( μ g/m3) 3-Year Averaged

Hourly Values for Summer ( μ g/m3) 3-Year Averaged

Hourly Values for Fall ( μ g/m3)

1 10.31 6.81 3.14 6.38

2 6.46 8.21 2.27 4.93

3 11.79 8.30 2.88 3.49

4 11.09 7.07 3.23 3.58

5 10.74 6.81 2.79 3.76

6 12.58 7.07 2.79 3.93

7 11.62 8.47 4.10 3.49

8 10.92 7.07 7.16 4.37

9 10.57 12.31 7.51 6.72

10 13.54 11.79 8.82 10.13

11 17.64 11.27 9.26 13.27

12 14.50 10.65 6.55 14.76

13 15.55 13.10 6.38 11.96

14 13.45 12.14 7.77 10.65

15 12.93 10.39 5.24 9.34

16 13.54 9.08 5.76 10.65

17 13.45 11.35 5.76 8.56

18 11.53 14.24 4.10 7.16

19 14.58 11.70 3.58 5.94

20 14.50 9.34 3.23 4.54

21 12.75 8.12 3.41 4.80

22 11.79 8.03 3.14 5.33

23 15.72 8.21 2.97 4.45

24 11.53 6.55 3.06 4.28

### 12



### 2.8 Results of SO 2 

### Characterization Analysis

The results of this SO 2 

### characterization analysis using modeling can infor m the decision as to proper designation of the Wagner area for the 1-hou r SO 2 

### NAAQS based on three years (2013- 2015) of actual emissions for Wagner, Brandon Shore s and Crane. This modeling process has some conservative features included, such as:

• Use of allowable emission rates for background sour ces (Wheelabrator).

• Use of actual emissions for Wagner Unit #2, 1-hour SO 2 

### emissions have been dramatically reduced by the use of low chlorine coa l, with low sulfur content, for compliance with MATS.

Therefore, with these conservative assumptions, the modeling results show that the 99 th 

### percentile peak daily 1-hour maximum concentration around Wagner modeled to be 255.58 μg/m 3 

### (including background) which exceeds the 1-hour SO 2 

### NAAQS of 196.2 μg/m 3



### . However, also based on modeling, the 1-hour SO 2 

### nonattainment area should be limited to only the area immediately surrounding Wagner. Provided in Figure 2-5 is the area not demonstrating compliance with the 1-hour SO 2 

### NAAQS.

### 13 

# Figures 2-1 – 2-5

Wagner Brandon Shores Crane

Wheelabrator

Figure 2

- 1. Locations of SO2 Sources Used in the Modeling A nalysisFigure 2

- 2: Wagner Unit 2 Emission Reductions in 2015



### 14

### 15 Figure 2

- 3. BWI Airport Wind Rose

### 16 Figure 2

- 4. Receptor Locations

### 17 Figure 2

- 5. 99th Percentile SO2 Modeling Results Using Adju st U*