MEMORANDUM

TO: Steam Electric Rulemaking Record

FROM: Ryan Novak and Patrick Goodwin, ERG

DATE: September 30, 2015

SUBJECT: Technical Approach for Case Study Water Quality Modeling of Aquatic 

Systems (DCN SE05570)

1. INTRODUCTION/BACKGROUND

ERG is providing technical support to the U.S. Environmental Protection Agency’s 

Office of Water, Engineering and Analysis Division (EAD) for development of effluent 

limitations guidelines and standards (ELGs) for the Steam Electric Power Generating Industry.

In support of the development of the final ELGs, ERG, with support from subcontractor 

AQUA TERRA Consultants (AQUA TERRA), performed detailed water quality modeling of 

selected case study locations, the results of which support the Environmental Assessment (EA) 

for the final ELGs. Section 8 of the Environmental Assessment for the Effluent Limitations 

Guidelines and Standards for the Steam Electric Power Generating Point Source Category (EA 

Report) (EPA-821-R-15-006), describes ERG and AQUA TERRA’s technical approach for 

using EPA’s Water Quality Analysis Simulation Program (WASP) to perform case study 

modeling. This memorandum provides additional information not captured in the EA Report or 

supporting appendices about the selection of sites for case study modeling and the design, 

execution, and analysis of the six WASP models described in the EA Report. ERG organized the 

remainder of this memorandum into the following sections:

 Section 2 describes the methodology ERG used to identify representative sites for case 

study modeling, including the factors considered during site selection and assessment of 

representativeness of the six modeled sites.

 Section 3 discusses the development of the WASP models using U.S. Geological Survey 

(USGS) daily streamflow data and EPA’s Better Assessment Science Integrating Point 

and Nonpoint Sources (BASINS) interface and the incorporation of pollutant loadings 

data and water quality monitoring data into the WASP models.

 Section 4 describes the output generated by the WASP models and ERG’s use of the 

output data.

2. SELECTION OF SITES FOR CASE STUDY MODELING

As described in Section 8.1.1 of the EA Report, ERG identified six representative case 

study locations (five lotic sites and one lentic site) that capture improvements across multiple 
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September 30, 2015

Page 2

regulatory options, represent all four evaluated wastestreams (flue gas desulfurization (FGD) 

wastewater, fly ash transport water, bottom ash transport water, and combustion residual 

leachate), and represent both lentic and lotic aquatic environments. ERG selected the six sites for 

case study modeling by identifying sites that were suitable for modeling with WASP and then 

evaluating the remaining candidates for how representative they were of the collection of steam 

electric power plants evaluated in the EA. First, ERG developed and applied acceptance criteria 

to all combinations of steam electric power plants and their receiving waters (hereafter referred 

to as “plant-receiving waters”) to determine which sites were suitable for case study modeling 

with WASP. Plant-receiving waters that did not meet the criteria were eliminated from 

consideration for case study modeling. ERG assessed the remaining candidates to identify a 

collection of plant-receiving waters that, when evaluated as a group: 

 Represent a reasonable cross-section of the range of receiving waters evaluated in the 

EA.

 Illustrate pollutant removals across the regulatory options evaluated.

 Encompass discharges of all four wastestreams evaluated in the EA. 

 Demonstrate pollutant loadings that are representative of those discharged by steam 

electric power plants evaluated in the EA (i.e., discharges are typical of steam electric 

power plants and not outlier values). 

2.1 Identification of Sites Suitable for WASP Modeling

ERG reviewed all plant-receiving waters in the EA for the proposed rulemaking for 

factors that would negatively influence the ability to use WASP for case study water quality 

modeling or limit the ability to discuss the case study modeling results in a public document. 

Because water quality modeling of lentic (i.e., lakes, ponds, and reservoirs) or estuarine systems 

involves more complex hydrodynamics than systems with lotic characteristics (i.e., rivers and 

streams), ERG developed two different sets of acceptance criteria. Section 2.1.1 and 2.1.2

describe the acceptance criteria ERG applied to lotic and lentic/estuarine plant-receiving waters, 

respectively.

2.1.1 Acceptance Criteria for Lotic Aquatic Systems

ERG coordinated with AQUA TERRA to develop acceptance criteria for waterbody

characteristics, stream gage flow data, and plant data for lotic plant-receiving waters. ERG and 

AQUA TERRA reviewed all plant-receiving waters in the EA for the proposed rulemaking that 

were classified as a river or stream and assessed whether the plant-receiving water met all 

acceptance criteria for lotic case study modeling with WASP. Plant-receiving waters that did not 

meet all acceptance criteria were eliminated from consideration for case study modeling. The 

waterbody characteristics, stream gage flow data, and plant data acceptance criteria for lotic 

system modeling are further described below.

Receiving Water Characteristics. ERG and AQUA TERRA used EPA’s BASINS tool, 

National Hydrography Dataset Plus (NHDPlus Version 1) hydrography layers, and the “DMR 
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September 30, 2015

Page 3

and TRI Loadings Database for Case Study Modeling” (“DMR/TRI Loadings Database”) [ERG, 

2015a] to identify and eliminate plant-receiving waters that did not meet the following criteria:

 Downstream Waterbody Characteristics. WASP’s ability to accurately model water 

quality using stream gage flow data can be affected by major flow control structures 

such as dams that significantly affect the linear flow and transport of pollutants.

1

Additionally, as discussed in Section 2.1.2, ERG and AQUA TERRA had limited 

ability to model lentic and estuarine aquatic systems using WASP. The presence of 

these areas of concern downstream of plant-receiving waters could result in the need to 

truncate the modeling area. ERG and AQUA TERRA identified and eliminated plantreceiving waters whose downstream waterbodies exhibit these characteristics, unless 

the areas of concern were sufficiently downstream to allow for modeling of a 

reasonable distance before encountering the area of concern.

 Inability to Model Other Point Sources. ERG and AQUA TERRA identified plantreceiving waters that could be significantly influenced by discharges from other point 

sources (including other steam electric power plants) and evaluated whether those point 

sources could be accounted for using water quality monitoring data. If a receiving water 

would be significantly influenced by other point source discharges that could not be 

modeled because of waterbody characteristics (e.g., an upstream point source beyond 

a major flow control structure) or represented in the model by water quality monitoring 

data, EPA eliminated the plant-receiving water from consideration for case study 

modeling. If ERG deemed the pollutant loadings from the other point source discharges 

to be insignificant compared to the steam electric power plant pollutant loadings being 

evaluated, ERG did not eliminate the plant-receiving water from consideration.

Availability of Stream Flow Data. Availability of a long-term, continuous stream flow 

record for both the receiving water being modeled and any significant downstream tributaries 

was a major factor in selecting case study locations because these data are needed to construct

the hydrodynamics in WASP. AQUA TERRA used USGS National Water Information System 

(NWIS) data sources to identify stations with available stream gage flow data in close proximity 

to each plant-receiving water. Plant-receiving waters that did not have any stream gage stations 

nearby were eliminated from consideration because there was not sufficient information to 

develop WASP model input. AQUA TERRA assessed the quality of stream gage flow data for 

use in case study modeling using the following criteria:

 Location of USGS Stream Gage Stations. AQUA TERRA evaluated whether the 

location of each identified stream gage station is appropriate for use in the model. If no 

stream gage stations are located on the river or within the vicinity of the plant-receiving 

 

1 ERG and AQUA TERRA used best professional judgment in determining whether a particular flow control 

structure would significantly impact the accuracy of WASP modeling. In the case of the Ohio River WASP model, 

ERG and AQUA TERRA identified two locks, the Montgomery Lock and New Cumberland Lock, within the 

modeling area. Since these structures still allow the flow of water to pass through and do not result in impoundment 

of the river, and because sufficient stream gage flow data are available at this site, ERG and AQUA TERRA 

determined that the locks would not compromise the WASP model’s ability to model pollutant transport.
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water that could be used to estimate the flow at the plant-receiving water, the plantreceiving water was eliminated from consideration. If the area downstream of the plantreceiving water had major tributaries and no stream gage stations are were adequately 

located to estimate the flow at the tributary’s convergence with the modeled waterbody, 

the plant-receiving water was eliminated from consideration. Ideal candidates have 

stream gage stations located upstream and downstream of the plant-receiving water and 

on all major tributaries near the convergence with the modeled waterway.

 Record Period of Stream Gage Flow Data. AQUA TERRA reviewed the period of 

record available for each identified stream gage station and assessed the quantity of 

data available and time period these data covered. Sufficient records for use in case 

study modeling covered more than ten years with little or no gaps in monitoring episode 

frequency. If no stream gage stations had sufficient record periods, the plant-receiving 

water was eliminated from consideration.

 Age of Stream Gage Flow Data. AQUA TERRA reviewed the age of the stream gage 

flow data in the record for each identified stream gage station and assessed if the data 

was collected after 1982 (the year of the last revision to the Steam Electric ELGs). ERG 

and AQUA TERRA did not accept stream gage flow data collected before 1982 

because these data were considered potentially unrepresentative of current and future 

flow conditions. If no stream gage stations had sufficient data collected after 1982, the 

plant-receiving water was eliminated from consideration.

Steam Electric Power Plant Data. ERG identified and eliminated from consideration 

those plant-receiving waters that did not met the following criteria:

 Confidential Business Information (CBI). ERG used the Proposed Rulemaking Steam 

Electric Survey Database to identify and eliminate steam electric power plants with 

CBI claims on discharge flow rate data for any of the four wastestreams modeled in 

case study modeling (i.e., flue gas desulfurization (FGD) wastewater, fly ash transport 

water, bottom ash transport water, combustion residual leachate).2 ERG eliminated 

plant-receiving waters with CBI claims from consideration for case study modeling 

because CBI data, including modeled water concentrations based on CBI data, cannot 

be discussed in a public document. For additional information about CBI and how it 

was handled for the EA, please refer to the “Review of Confidential Business 

Information (CBI) for the Final Environmental Assessment” [ERG, 2015b].

 Other CBI Designations. ERG identified plant-receiving waters that could be 

significantly influenced by discharges from other steam electric power plants in the EA

and evaluated whether those nearby plants claimed any discharge flow rate data as CBI. 

 

2 Because the CBI designations for the Final Steam Electric Questionnaire Database [ERG, 2015o] were not 

complete at the time of case study modeling site selection, the CBI designations in the Proposed Rulemaking Steam 

Electric Questionnaire Database [ERG, 2013b] represented the most updated, best available data. For the final 

rulemaking, some CBI designations on discharge flow rate data were removed. ERG had already completed case 

study modeling and did not evaluate the impact of the revised CBI designations on site selection.
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If EPA determined that a plant-receiving water would be significantly influenced by 

another plant’s discharges that could not be modeled because of CBI or represented in 

the model by water quality monitoring data, EPA eliminated the plant-receiving water 

from consideration for case study modeling.

 Retirements and Conversions. In development of the Steam Electric ELGs, ERG 

compiled publicly announced plans from the steam electric power generating industry 

to retire or modify steam electric generating units at specific power plants, including 

those that are expected to occur as a result of the Coal Combustion Residual (CCR) 

rulemaking by EPA’s Office of Solid Waste and Emergency Response (OSWER). ERG 

identified plants that were planning to retire, convert fuel types to eliminate fossil fuel 

combustion residuals (especially residuals from coal or petroleum coke), and/or 

upgrade wastewater treatment systems for all or most of their steam electric generating 

units prior to December 31, 2023. These plants were eliminated from the EA scope, 

including case study modeling, because the changes in pollutant removals at these 

plants would not be a direct result of the final rule. The ERG memorandum “Changes 

to Industry Profile for Steam Electric Generating Units for the Steam Electric Effluent 

Guidelines Final Rule” (“Industry Profile Changes Memo”) provides additional 

information about those steam electric generating units that will retire or convert prior 

to December 31, 2023.

After the application of the criteria above, ERG evaluated the remaining candidate plantreceiving waters as part of the representativeness analysis, which is described in Section 2.2.

2.1.2 Acceptance Criteria for Lentic and Estuarine Aquatic Systems

As described in Section 8.1.1 of the EA Report, water quality modeling of lentic systems 

or estuarine systems involves more complex hydrodynamics that would not be adequately 

represented by stream gage flow data. For this effort, modeling steam electric power plants that 

discharge to lentic or estuarine systems required the use of existing WASP models (or more 

specifically, the underlying hydrodynamic data) for the specific waterbodies of interest. 

Accordingly, ERG considered the availability of existing models a primary factor in selecting 

lentic and estuarine systems for case study water quality modeling. 

Through consultation with EPA, ERG identified one preexisting WASP model for a lake 

(Lake Sinclair, GA) that corresponds with a plant-receiving water that was modeled in the 

proposal EA [ERG, 2013a]. ERG requested and ultimately received the Lake Sinclair 

Environmental Fluid Dynamics Code (EFDC) hydrodynamic input for this WASP model 

through EPA from the Georgia Department of Natural Resources, Environmental Protection 

Division. In the proposal EA, ERG modeled steam electric power plant discharges from Georgia 

Power Company’s Plant Harllee Branch to Lake Sinclair. Based on ERG’s review of changes to 

the industry profile, this plant has decertified and retired all four of its coal-fired generating units 

as of April 16, 2015 [Georgia Power; 2013; Schwartz, 2012; Southern Company, 2013]. Despite 

the announced retirement of Plant Harllee Branch, ERG proceeded with an evaluation of the 

representativeness of Lake Sinclair to determine if this plant-receiving water remains a 
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September 30, 2015

Page 6

representative illustration of other lentic waterbodies that receive discharges of the evaluated 

wastestreams.

ERG also identified one preexisting water WASP model for an estuary (Hillsborough 

Bay, FL) that receives steam electric power plant discharges. However, due to the hydrologic 

complexity of the model, and because estuarine systems represent less than 2 percent of the 

plant-receiving waters evaluated in the EA, ERG elected to develop only freshwater river and 

lake WASP models for this case study analysis. Additionally, the ecological risk modeling 

approach described in Section 5.2 of the EA Report is based on selenium bioaccumulation within 

freshwater environments and would not be appropriate to apply to estuarine or marine aquatic 

systems, which would limit the ability to analyze the ecological effects for the estuarine case 

study.

2.2 Representativeness of Selected Sites

Next, ERG assessed the representativeness of the plant-receiving waters that were not 

eliminated based on the criteria discussed in Section 2.1.1 and 2.1.2. While ERG considered 

many factors in evaluating the representativeness of the remaining plant-receiving waters, ERG 

determined that the water column concentrations, derived from the plant pollutant loadings and 

receiving water characteristics, were the primary factor for selecting representative plantreceiving waters. In addition to representativeness of the water column concentrations, receiving 

water, and pollutant loadings, ERG considered the types of evaluated wastestreams discharged 

by each plant (e.g., FGD wastewater, fly ash transport water, etc.). Plants that discharged more 

than one of the evaluated wastestreams and had loadings reductions under more than one of the 

regulatory options were more favorable for case study modeling because modeling of these sites 

would produce more data for comparison of improvements between case studies and across all 

the modeled sites. Finally, ERG considered the location of the plant-receiving waters to ensure 

that the final set of case study sites would represent a broad geographic distribution.

As a result of this review, ERG identified five case study locations for modeling as part 

of a representative set of steam electric power plants that discharge to lotic systems.

Additionally, ERG confirmed that Lake Sinclair was a representative waterbody to model as an 

example of lentic waterbodies that receive discharges of the evaluated wastestreams.

When evaluated as a group, the five lotic plant-receiving waters and Lake Sinclair 

demonstrate pollutant removals across all modeled regulatory options and all four evaluated 

wastestreams. Figure 1 and Table 1 present the six receiving waters that ERG selected for case 

study modeling. Section 0 and 2.2.2 further describe the evaluation of the representativeness of 

the lotic plant-receiving waters and Lake Sinclair, respectively.
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Figure 1. Overview of Case Study Modeling Locations
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Table 1. Locations Selected for Case Study Modeling

Case Study 

Location

Water

-body

Type

Steam Electric 

Power Plant(s) 

Modeled

Evaluated Wastestreams Discharged

Regulatory Options 

Demonstrating Removals

FGD

Fly 

Ash

Bottom 

Ash Leachate A B C D

Black Creek, 

MS Lotic

R.D. Morrow 

Sr. Generating 

Site

     

Etowah 

River, GA Lotic Plant Bowen     

Lick Creek 

& White 

River, IN

Lotic

Petersburg 

Generating 

Station

    

Ohio River, 

PA/WV/OH Lotic

Bruce 

Mansfield Plant 

& W.H. 

Sammis Plant



     

Mississippi 

River, 

MO/IL

Lotic Rush Island a    

Lake 

Sinclair, GA Lentic Plant Harllee Branch b       

Acronym: FGD (flue gas desulfurization); N/A (Not applicable).

a – ERG identified another steam electric power plant, Meramec, that discharges upstream of the Rush Island plant. 

ERG and AQUA TERRA incorporated the pollutant loadings of the Meramec plant to account for the upstream 

pollutant contributions. ERG did not evaluate the water quality, wildlife, or human health impacts associated with 

discharges from the Meramec plant because this plant was not selected using the case study selection methodology 

described in Section 2.

b – This steam electric power plant has decertified and retired all four of its coal-fired generating units as of April 

16, 2015. ERG selected this plant to represent the potential impacts of discharges of the evaluated wastestreams to 

lentic waterbodies because it meets all of the case study selection criteria.

2.2.1 Representativeness of Lotic Aquatic Systems

ERG determined the representativeness of the remaining lotic plant-receiving waters 

based on the water column concentrations output by the water quality module of the nationalscale immediate receiving water (IRW) model. These concentrations are derived, in part, from 

the immediate receiving water flow rates (as defined in NHDPlus Version 1) and pollutant 

loadings. Initially, ERG performed this review using the pollutant loadings, flow rates, and water 

concentrations from the proposal EA. For the final EA, ERG confirmed that the selected lotic 

plant-receiving waters remain representative when using the final pollutant loadings, flow rates, 

and national-scale water quality module results.3 Table 2 presents the immediate receiving water 

 

3 The baseline pollutant loadings for the plants in the case studies tend to be higher than the median; however, when 

the receiving water flow rates are taken into account (as they are with the water quality module), the pollutantspecific water column concentrations generally fall around the median. With the exception of Lick Creek (see 

footnote in Table 2), the pollutant-specific water column concentrations range from the 27th percentile to the 79th

percentile, with the median close to the 50th percentile.
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flow rates, baseline pollutant loadings of each modeled pollutant, and the water column 

concentration of each modeled pollutant (from the national-scale water quality module) for each 

of the selected lotic receiving waters. Additionally, Table 2 presents the percentile ranking of 

each of these values amongst all 183 lotic receiving waters modeled in the final EA.

2.2.2 Representativeness of Lake Sinclair

ERG determined the representativeness of Lake Sinclair based on the lake surface area

(as defined in NHDPlus Version 1), pollutant loadings, and water column concentrations

proposal output by the water quality module of the national-scale IRW model. Table 3 presents 

the lake surface area, baseline pollutant loadings of each modeled pollutant, and the water 

column concentration of each modeled pollutant for Lake Sinclair.4 Additionally, Table 3

presents the percentile ranking of each of these values amongst all lentic receiving waters 

modeled in the final EA. Based on this review, ERG determined that Lake Sinclair remains a 

representative illustration of lentic waterbodies that receive discharges of the evaluated 

wastestreams.

 

4 To determine the water column concentration of each modeled pollutant for Lake Sinclair, ERG first calculated the 

pollutant loadings to represent continued discharges from Plant Harllee Branch (despite the actual decertification 

and retirement of the coal-fired generating units at this plant). In calculating these loadings, ERG used the same 

methodology that is described in Section 10 of the TDD and assumed that the plant would continue operating the 

same generating units that were in the EA analyses for the proposed rulemaking. ERG the used the receiving water 

characteristic data (surface area, inflow, depth, and volume) from the proposed rulemaking EA and these revised 

loadings to estimate the water column concentrations for this evaluation of representativeness. 
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Table 2. Representativeness of Lotic Case Study Sites

Case Study 

Location

Steam Electric 

Power Plant

Immediate 

Receiving Water 

Receiving Water Flow Pollutant Plant Loadings Water Column 

Concentration

cfs Percentile a g/day Percentile a μg/L Percentile a

Black Creek 

(MS)

R.D. Morrow Sr. 

Generating Site Black Creek 251 34%

As 16.8 43% 0.0285 62%

Cd 105 80% 0.177 75%

Cu 25.4 51% 0.0430 65%

Pb 7.61 33% 0.0129 66%

Ni 819 78% 1.38 73%

Se 1,080 81% 1.82 79%

Tl 29.9 47% 0.0505 66%

Zn 1,310 80% 2.21 72%

Etowah River 

(GA) Plant Bowen Etowah River 2,430 48%

As 41.4 59% 0.00706 51%

Cd 413 95% 0.0704 69%

Cu 95.8 65% 0.0164 56%

Pb 29.6 50% 0.00506 59%

Ni 3,250 95% 0.555 68%

Se 4,250 95% 0.725 73%

Tl 115 60% 0.0196 56%

Zn 5,110 95% 0.872 68%

Lick Creek/

White River 

(IN)

Petersburg 

Generating Station

Lick Creek b 4.46 14%

As 52.6 62% 4.62 90%

Cd 27.4 60% 2.40 92%

Cu 176 78% 15.5 95%

Pb 80.7 65% 7.08 92%

Ni 155 52% 13.6 91%

Se 461 67% 40.4 92%

Tl 96.3 56% 8.45 92%

Zn 162 52% 14.2 85%

White River b 11,900 69%

As -- -- -- --

Cd -- -- -- --

Cu -- -- -- --

Pb -- -- -- --

Ni -- -- -- --

Se -- -- -- --

Tl -- -- -- --

Zn -- -- -- --
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Table 2. Representativeness of Lotic Case Study Sites

Case Study 

Location

Steam Electric 

Power Plant

Immediate 

Receiving Water 

Receiving Water Flow Pollutant Plant Loadings Water Column 

Concentration

cfs Percentile a g/day Percentile a μg/L Percentile a

Ohio River 

(WV/OH)

Bruce Mansfield 

Plant Ohio River 33,900 78%

As 79.3 65% 0.000961 27%

Cd 445 96% 0.00540 54%

Cu 145 74% 0.00175 35%

Pb 64.1 61% 0.000777 35%

Ni 3,600 96% 0.0436 56%

Se 4,500 95% 0.0546 52%

Tl 288 73% 0.00349 40%

Zn 5,520 96% 0.0670 54%

W.H. Sammis Plant Ohio River 34,100 79%

As 362 87% 0.00436 45%

Cd 102 79% 0.00123 39%

Cu 437 88% 0.00526 48%

Pb 329 86% 0.00396 55%

Ni 1,640 87% 0.0197 46%

Se 1,110 82% 0.0134 40%

Tl 1,670 93% 0.0201 57%

Zn 1,360 81% 0.0164 41%

Mississippi 

River (MO/IL) Rush Island Mississippi River 177,000 98%

As 2,730 99% 0.00632 50%

Cd 394 95% 0.000912 34%

Cu 1,870 99% 0.00434 45%

Pb 1,230 98% 0.00284 51%

Ni 1,480 84% 0.00344 29%

Se 1,180 84% 0.00274 27%

Tl 1,420 91% 0.00329 39%

Zn 3,450 93% 0.00798 30%

Sources: ERG, 2015d; ERG, 2015e; ERG, 2015f.

Acronyms: cfs (cubic feet per second); g/day (grams per day); μg/L (micrograms per liter).

Note: Flow rates, pollutant loadings, and concentration values are rounded to three significant figures. All percentile rankings are rounded to two significant figures.

a – The values in this column represent the percentile rank of the value amongst all 183 lotic receiving waters modeled in the national-scale IRW model, accounting for 

the CCR rule. For example, a receiving water flow that is of the 75% percentile rank is greater than or equal to 75% of all receiving water flow rates in the population.

b – Both the national-scale IRW model and case study model assign the loadings from Petersburg Generating Station to Lick Creek, the immediate receiving water. The

total length of the modeled segment of Lick Creek is less than 4 percent of the modeling area downstream of Petersburg Generating Station. At the point where Lick 

Creek flows into the White River, the average annual flow rate of the White River is more than 2,000 times larger than the average annual flow rate of Lick Creek. 
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Despite the high percentile ranking of the total water column concentration in Lick Creek, ERG still considered the White River and Lick Creek system as representative 

of small, effluent-dominated immediate receiving waters. 

Table 3. Representativeness of Lake Sinclair Case Study Site

Case Study 

Location

Steam Electric 

Power Plant

Immediate 

Receiving Water 

Receiving Water 

Surface Area Pollutant Plant Loadings Total Water Column Concentration

m2 Percentile g/day Percentile μg/L Percentile

Lake Sinclair 

(GA)

Plant Harllee 

Branch Lake Sinclair 3,190 82%

As 102 82% 0.0131 32%

Cd 540 96% 0.0693 53%

Cu 226 82% 0.0290 46%

Pb 81.9 80% 0.0105 34%

Ni 4,230 96% 0.543 53%

Se 5,440 96% 0.699 67%

Tl 182 67% 0.0233 39%

Zn 6,190 96% 0.886 57%

Sources: [ERG, 2015l]

Acronyms: m2(square meters); g/day (grams per day); μg/L (micrograms per liter).

Note: Surface area, pollutant loadings, and concentration values are rounded to three significant figures. All percentile rankings are rounded to two significant figures.

a – The values in this column represent the percentile rank of the value amongst the 26 lentic plant-receiving waters in the national-scale IRW model, plus Lake Sinclair.
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3. DEVELOPMENT OF WASP SIMULATION MODELS

This section presents additional information about the model development and input 

variables applicable to case study modeling performed using the ‘Simple Toxicant’ module of 

WASP. The WASP model calculates the water column and benthic pollutant concentrations of 

eight modeled pollutants using user-defined parameters and default assumption values. The 

process described in this section is based on using WASP Version 7.52 and BASINS Version 

4.1.5 Both represent the most current versions available at the time ERG and AQUA TERRA 

performed case study modeling.

Appendix G of the EA Report presents the WASP default parameters and values, 

including pollutant partition coefficients, pollutant densities, and solids constants and sediment 

transport parameters, that were used for all the case study models. Additional information 

regarding the site-specific design, site-specific input parameters (e.g., background pollutant 

concentrations, USGS time series flow data, steam electric power plant pollutant loadings), and 

model settings (e.g., solids constants and sediment transport parameters) for each of the WASP 

models is presented in Appendix G to the EA Report and the supporting documentation for each 

case study model [ERG, 2015g through 2015l].

3.1 Development of the WASP Input File through BASINS

As described in Section 8.1.3 of the EA Report, AQUA TERRA designed each case 

study model using the BASINS setup tool for WASP, known as the WASP Model Builder, 

which allows the user to open WASP directly from the BASINS interface. This section explains 

the technical approach to developing the WASP input files through the BASIN interface, 

including the use or bypassing of specific model features and incorporation of stream flow data.

3.1.1 Design of Lotic WASP Models

The WASP Model Builder operates using a BASINS project for a single HUC8-level 

watershed with NHDPlus Version 1 Flowlines loaded and active. AQUA TERRA used NHDPlus 

Version 1 Flowlines as the basis for defining waterbody segments in WASP. AQUA TERRA 

selected the particular Flowline segments to be included in the WASP model based on the sitespecific modeling area and the availability of USGS stream gage flow data. To simplify the 

geographic extent of the modeling area, AQUA TERRA did not include any small tributaries 

with flow rates of less than five cubic feet per second (cfs).6 Once AQUA TERRA specified 

which Flowlines to include in the WASP model, these Flowlines were combined or divided into 

segments as desired based on the time that water takes to pass through the segment. The travel 

time of each segment is calculated using default values for the length, width, depth, slope, 

 

5 For more information about this version of WASP, refer to the following EPA website: 

http://www.epa.gov/athens/wwqtsc/html/wasp.html

6 Because tributaries of this size rarely have USGS stream gage flow data, this determination was made based on the 

mean annual flow rate data in NHDPlus Version 1.
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roughness, and velocity data from NHDPlus Version 1. Per a recommendation by EPA, AQUA 

TERRA designed the WASP simulations for each lotic case study site to have a minimum travel 

time of one tenth of a day per segment in order to minimize dispersion and model instability. In 

some cases, segment travel times were shorter than the desired minimum because the segment 

was located between an upstream and downstream tributary of some significance.

One of the goals of the case study modeling effort was to capture increases in pollutant 

concentrations within benthic sediment over time and the resulting influence on pollutant 

concentrations in the water column due to desorption and/or sediment resuspension. The WASP 

import file generated by the BASINS WASP Model Builder, however, does not include benthic 

sediment segments corresponding to each stream segment by default. To allow for modeling of 

sediment concentrations, ERG and AQUA TERRA coordinated with WASP model experts from 

EPA to develop a simple executable program that would add two additional sets of benthic 

segments to the existing riverine WASP input file [ERG, 2015m]. AQUA TERRA used this 

script to develop segments representing the upper and lower benthic segment layer. For each 

model, the assumed depths of the upper and lower benthic sediment layers are 0.03 meters (m)

and 0.25 m, respectively.

3.1.2 Design of Lake Sinclair WASP Model

As discussed in Section Error! Reference source not found., ERG and AQUA TERRA

relied on the availability of an existing water quality model to perform case study modeling of 

Lake Sinclair. The scope of the Lake Sinclair WASP model is defined by the boundaries and 

segments of the pre-existing EFDC hydrodynamics model. The modeling area encompasses the 

main body of Lake Sinclair, from Wallace Dam to Sinclair Dam, and the major tributaries 

feeding into the Lake. The three-dimensional EFDC model divides the waterbody into 1,235 

segments. Each segment represents a unique location and stratum within Lake Sinclair. The 

EFDC model uses stretch or sigma vertical coordinates and Cartesian coordinates to represent 

the physical characteristics of Lake Sinclair. The Lake Sinclair model does not have any 

segments representing benthic sediment.

3.2 Incorporation of Flow Data

AQUA TERRA used daily USGS stream gage flow data to represent inflows at the 

upstream end of the lotic WASP models and significant tributaries flowing into the model area. 

Where stream gage flow data were available, AQUA TERRA scaled the data to account for the 

difference in drainage area between the actual gage location and the point where the contributing 

flow enters the model. AQUA TERRA used stream gage flow data from the actual time period

(e.g., 1982 – 2014) to represent the baseline flow rate in the modeling area. AQUA TERRA 

reused the historical flow data to the extent necessary to complete the modeling period through 

the assumed compliance date (e.g., 2015 – 2020), preferentially selecting flow data from periods 

that excluded years of particularly high or low flow rates. Then AQUA TERRA reused the 

historical flow data to represent the period through the end of the model run (e.g., 2020 – 2036).

This approach ensured that the modeling periods before and after the assumed compliance date 

were based on similar flow data. Appendix G to the EA Report presents the identified stream 

gage flow data and the corresponding scaled flow data incorporated into each model.
Memorandum

September 30, 2015

Page 15

In contrast to the lotic case study models, the Lake Sinclair WASP model does not 

incorporate any USGS flow data. Instead, AQUA TERRA used the seven years of hydrodynamic 

and flow input integrated into the EFDC model.

3.3 Incorporation of Water Quality Monitoring Data

The STORET (STOrage and RETrieval) Data Warehouse is EPA’s repository of the 

water quality monitoring data collected by water resource management groups across the 

country. On February 12, 2014, ERG performed a pull of water quality and sub-surface water 

sediment concentration data for the eight modeled pollutants, total organic carbon (TOC), and 

total suspended solids (TSS) from EPA’s STORET monitoring database. This dataset represents 

more than five million sample results for the eight modeled pollutants, nearly one million sample 

results for TOC, and more than one million sample results for TSS collected across the United 

States. ERG compiled these data in the “STORET Monitoring Data Database for Case Study 

Modeling” (“STORET Monitoring Database”) [ERG, 2015n]. Data in the STORET Monitoring 

Database indicates the collection site (i.e., station ID, state, county, coordinate information), the 

sample medium (i.e., sediment, water), date of sample collection, purpose of collection, analyte, 

procedure (e.g., total, dissolved), analytical method, unit of measure, and result value for samples 

collected.7

As part of the development of each case study model, ERG used the latitude and 

longitude coordinates associated with the STORET monitoring data to identify station IDs with 

data within, or within the vicinity of, the modeling area. ERG queried all available concentration 

results for the EA modeled pollutants, TOC, and TSS for these station IDs and evaluated the 

monitoring data against a set of acceptance criteria. Only results that met all of the acceptance 

criteria described below were accepted for consideration for case study modeling:

 Sample Fraction. For the eight modeled pollutants and TOC, ERG only accepted results 

that were representative of total concentration. For TSS, ERG accepted sample results 

designated as total concentrations or suspended concentrations. Sample results 

representing partial results (e.g., dissolved, acid soluble, volatile) were not considered 

for use for case study modeling. 

 Collection Date. ERG only accepted sample results that were collected in or after 1982 

(the year of the last revision to the Steam Electric ELGs). ERG did not accept STORET 

monitoring data collected before 1982 because these data were considered potentially 

unrepresentative of current and future water concentrations entering the modeling area.

 Routine Samples. ERG did not accept sample results that were designated as duplicates 

or quality control samples, which may have been collected at the same time and 

submitted in addition to the routine sample results.

 

7 Data in the STORET Warehouse are of documented quality, meaning that a certain level of metadata, including 

where, how, why, when, and what was monitored must be included with all data submissions. Some data also 

include information of interest such as sampling and analytical methods used. 
Memorandum

September 30, 2015

Page 16

 Duplicative Data. ERG identified that, in some cases, more than one organization 

reported identical sample results from the same collection date. ERG screened the 

duplicative data from both reporting organizations and accepted the more complete 

data set.

 Insufficient Information. ERG did not acceptsample records that did not have sufficient 

information to use in calculation (e.g., sample reported as above detection limit without 

a specific value) or that lacked sufficient information to confirm the sample met other 

acceptance criteria (e.g., samples reported without the year of collection).

 Outlier Data. ERG identified results that were significantly (e.g., 10 times) larger than 

all other sample results collected in the data set. ERG flagged these results as potential 

outliers that may not be representative of typical water quality conditions and removed 

these results.

After removal of all STORET monitoring data that did not meet the acceptance criteria, 

ERG divided the remaining data into two categories. Accepted monitoring data from stations 

located within the WASP model boundaries were used for calibration of the solids transport 

parameters and validation of the WASP model water and sediment concentration output, which 

is further described in Section 3.5.1. Accepted monitoring data from stations located near the 

WASP model boundaries were used to represent pollutant contributions flowing into the 

modeling area from upstream point sources, nonpoint sources, and background sources. The 

remainder of this section describes further manipulation of the monitoring data for use in case 

study modeling.

For STORET monitoring data that met all acceptance criteria and were collected from 

monitoring stations at or near the boundaries of the WASP model, ERG used the accepted results 

to calculate a station-specific average concentration for each pollutant. For stations with two or 

more accepted sample results for a pollutant, ERG used the average of those sample results. In 

this calculation, ERG replaced any results that were non-detections or flagged for a detection 

below the quantitation limit (QL) with a value equal to one-half the pollutant-specific method 

detection limit (MDL) for EPA 1638, EPA’s preferred low-level analytical method.8 For TOC 

and TSS, ERG used a value of zero for any sample result that was a non-detection or flagged as a 

detection below the QL. If only one result was accepted for a pollutant, the value of this result 

was used as the average concentration. If all results at a station were non-detections or detected 

below the QL, the average concentration for a pollutant at that station was equal to one half the 

MDL for EPA 1638. ERG did not calculate a station-specific average for any pollutants where 

ERG rejected use of all sample results based on the STORET monitoring data acceptance 

criteria. 

ERG then calculated a model boundary-specific weighted average concentration for each 

pollutant using the station-specific count of accepted samples and station-specific average 

 

8 For arsenic, EPA used a value equal to one half of the MDL for EPA 1632, a low-level analytical method specific 

for arsenic. For the other seven modeled pollutants (cadmium, copper, nickel, lead, selenium, thallium, and zinc), 

EPA used the pollutant-specific MDL for EPA 1638.
Memorandum

September 30, 2015

Page 17

concentrations. For example, Station A, Station B, and Station C are all STORET monitoring 

stations located near an upstream modeling boundary of a fictitious case study model. ERG 

calculated the station-specific average concentrations for arsenic based on the monitoring data 

that met all acceptance criteria at each station, as described above. The average arsenic 

concentration for Station A was calculated as 10 micrograms per liter (μg/L) based on two 

accepted sample results. The average arsenic concentration for Station B was calculated as 4 

μg/L based on one sample result. None of the arsenic sample results at Station C were accepted 

for use. For this example, ERG would have calculated a WASP model boundary-specific 

weighted average concentration of 8 μg/L for arsenic, as demonstrated below:

[(10 μg/L x 2 samples) + (4 μg/L x 1 sample)] / (3 total samples) = 8 μg/L average

ERG did not calculate a model boundary average concentration for any pollutants where 

all accepted sample results were non-detections or detections below the QL.

Prior to incorporation into the WASP model, ERG converted the calculated average 

pollutant concentrations to a mass loading (for all pollutants except TOC and TSS) using the 

average annual flow rate for the stream reach represented by the monitoring stations. This 

approach ensured that the modeled pollutant concentrations flowing into the modeling area 

would vary with changes in the stream flow rate.

The supporting documentation for each case study model present the STORET 

monitoring data used, the station-specific average concentrations, the station-specific sample 

counts, and the concentration or mass loading incorporated into each WASP model [ERG, 2015g

through 2015l].

3.4 Incorporation of Pollutant Loadings Data

As described in Section 8.1.2 of the EA Report, ERG used pollutant loadings from the 

evaluated wastestreams (see Section 10 of the Technical Development Document for the Final 

Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point 

Source Category (TDD) [EPA 821-R-15-007]) and 2011 TRI and DMR loadings data as a 

representative set of discharge conditions. EPA acknowledges that these data may not reflect the 

actual pollutant loadings over the entire modeling period; however, they represent an appropriate 

estimation of annual pollutant loadings and how discharges may affect individual aquatic 

systems over time. Section 3.4.1 and 3.4.2 describe ERG’s methodology for incorporating

pollutant loadings from steam electric power plants and other point sources, respectively.

3.4.1 Steam Electric Power Plant Loadings

In support of the final rule, ERG estimated the pollutant loadings discharged from steam 

electric power plants for the evaluated wastestreams, as described in Section 10 of the TDD. The 

resources used by ERG to calculate baseline and regulatory option pollutant loadings for plantreceiving waters are described in the memorandum “Pollutant Loads Supporting Documentation” 

[ERG, 2015p]. ERG and AQUA TERRA performed case study water quality modeling for the 
Memorandum

September 30, 2015

Page 18

following eight pollutants (or “toxicants” as defined in the WASP model), which were also 

included in the IRW model:

 Arsenic (As).

 Cadmium (Cd).

 Copper (Cu).

 Lead (Pb).

 Nickel (Ni).

 Selenium (Se).

 Thallium (Tl).

 Zinc (Zn).

In estimating the historical pollutant loadings with the FGD wastestream, ERG accounted 

for the fact that steam electric power plants may have installed FGD systems after the start of the 

modeling period and, therefore, the baseline loadings from this wastestream may not be 

representative of the plants historical operations. FGD wastewater is not generated unless a plant 

is operating a wet scrubber system. ERG performed a review of the responses to the Steam 

Electric Survey to determine the date the modeled plants installed and began operation of each 

scrubber system that produces FGD wastewater and the proportional flow associated with each 

system compared to the total FGD wastewater flow rate under baseline conditions. ERG assumed 

that the proportional pollutant loadings of each FGD system were equivalent to the proportional 

flow each system contributed. Therefore, ERG estimated that an FGD system that contributes 

one half of the total FGD wastewater flow at a plant would also contribute one half of the 

loadings for each pollutant. ERG then incorporated the loadings calculated for each FGD system 

based on the year each FGD system was installed. If a plant installed multiple FGD systems 

during the modeling period, ERG assumed that the annual, historical FGD wastewater pollutant 

loadings associated with each individual system were proportional to that system’s flow rate 

contribution compared to the total FGD wastewater flow rate under baseline conditions. The 

supporting documentation for each case study model presents the FGD system installation and 

flow data used to calculate the historical loadings for this wastestream at each plant [ERG, 2015g

through 2015l].

For all other evaluated wastestreams, ERG assumed the annual, historical pollutant 

loadings associated with fly ash transport water, bottom ash transport water, and combustion 

residual leachate discharges were equal to the baseline pollutant loadings calculated for these

wastestreams. ERG did not calculate the historical loadings using the methodology described for 

FGD wastewater because the impoundment and discharge of these wastestreams has been a 

standard technique practiced since before 1982. ERG did not attempt to determine whether a 

modeled plant had historical discharges of ash transport water or leachate that are not represented 

in the baseline pollutant loadings. 

ERG and AQUA TERRA assigned the loadings for the eight modeled pollutants to the 

WASP segment that most closely matched the immediate receiving water COMID used in the 

national-scale IRW model for that plant, which is described in the ERG memorandum “Water 

Quality Module: Plant and Receiving Water Characteristics” [ERG, 2015d]. The baseline and 
Memorandum

September 30, 2015

Page 19

regulatory option pollutant loadings used for each case study are presented in the supporting 

documentation for each case study model [ERG, 2015g through 2015l].

As described in Section 2.1.2, Plant Harllee Branch retired all of its coal-fired steam 

electric generating units in April 2015. Despite the retirement of this plant, ERG and AQUA 

TERRA proceeded with case study modeling of Lake Sinclair to represent the potential impacts 

of steam electric discharges on lentic waterbodies. ERG calculated the pollutant loadings for 

each wastestream at baseline and each regulatory option using the same loadings methodology 

that ERG used for other plants in the loadings analysis, based on the generating units that were in 

the EA for the proposed rulemaking. ERG did not include Plant Harllee Branch in any other EA 

quantitative and qualitative analyses for the final rulemaking.

3.4.2 Other Point Source Pollutant Loadings

ERG used publicly-available pollutant loadings data from Discharge Monitoring Reports 

(DMR) and the Toxics Release Inventory (TRI) to account for pollutant loadings from nonstream electric point sources within the WASP modeling boundaries. ERG downloaded four data 

sets using EPA’s DMR Pollutant Loading Tool that represented 2011 DMR and TRI loadings 

data for all facilities discharging any of the eight pollutants modeled in case study modeling.9

These data were compiled in the DMR/TRI Loadings Database [ERG, 2015a]. Data in the DMR 

& TRI Loadings Database include facility identification data (e.g., DMR/TRI ID, permit number, 

facility name), facility location (i.e., state, county, coordinate information), major/minor 

discharger indicator flags, and annual pollutant loadings (in pounds per year). 

The DMR pollutant loadings data collected by ERG represent wastewater monitoring 

data from publicly owned treatment works (POTWs) and industrial sources that directly 

discharge to surface waters. These reports require facilities to submit data only for those 

pollutants that are listed in the facility’s National Pollutant Discharge Elimination System 

(NPDES) permit requirements. In addition, states (or other permitting authorities) have some 

discretion as to which data they make available (or enter) to the national database (i.e., Permit 

Compliance System (PCS) and Integrated Compliance Information System for the National 

Pollutant Discharge Elimination System (ICIS-NPDES)). For example, permitting authorities 

enter DMR and permit information for facilities that are considered major dischargers. However, 

they do not necessarily enter DMR or permit information into PCS for minor dischargers or 

facilities covered by a general permit.

In TRI, facilities report annual loads released to the environment for each toxic chemical 

or chemical category that meets reporting requirements. The TRI pollutant loadings data 

collected by ERG represent facility-collected estimates of wastewater loadings data for both 

direct and indirect dischargers. The TRI data set does not include loadings from facilities with 

total annual chemical releases of less than 500 pounds and incorporates assumptions regarding 

 

9 EPA’s Office of Water (OW) reviews the DMR and TRI data used to calculate effluent loads and TWPE for 

accuracy as part of the Effluent Limitations Guidelines Program. At the time of case study model development, the 

2011 DMR and TRI loadings data sets represented the most recent data that had undergone quality review as part of 

the Effluent Limitations Guidelines annual review process.
Memorandum

September 30, 2015

Page 20

plants with annual releases of less than 1,000 pounds. The point source loadings from smaller 

facilities, therefore, may not be well represented in the TRI database.

10

As part of the development of each case study model, ERG used the latitude and 

longitude coordinates associated with the DMR and TRI data to identify facilities with data 

within or within the vicinity of the modeling area. For these facilities, ERG queried all pollutant 

loadings data available for the eight modeled pollutants.

ERG conducted a review of all DMR and TRI pollutant data to identify instances where 

both datasets include pollutant loadings data for the same facility. For each case study, ERG 

reviewed all facilities that directly discharge one or more of the modeled pollutants, were located 

within or in the vicinity of the WASP modeling area, and were represented in both the DMR and 

TRI data sets. For these facilities, ERG evaluated whether any of the eight modeled pollutants 

had pollutant loadings data in both data sets. In this case, ERG accounted for the pollutant 

loadings data from the DMR data set only.

11 ERG used best professional judgment in 

determining which non-steam-electric point source loadings were appropriate to include in the 

case study modeling, and in some cases, expanding the geographic boundaries of the model to 

encompass the receiving waters for upstream non-stream-electric point sources. In making this 

determination, ERG considered the type and quantity of pollutants discharged, the distance 

upstream from the steam electric power plant that is the subject of the case study, and the 

flowrate of the receiving water. Refer to the supporting documentation for each of the case study 

models for additional information regarding ERG’s review of non-steam-electric point sources 

for each case study [ERG, 2015g through 2015l].

To represent non-steam-electric point sources within the modeling area, ERG assigned 

the TRI and DMR pollutant loadings to the stream reach (as represented in NHDPlus Version 1) 

that was closest to the location of the point source (based on the coordinate data in the DMR/TRI 

Loadings Database).

As part of the review of point source loadings data, ERG used the DMR/TRI Loadings 

Database to identify the name and location of POTWs that receive indirect loadings from 

facilities in the TRI data set. If ERG identified that the TRI indirect loadings for any pollutant 

from a facility were sent to a POTW that had loadings for that pollutant in the DMR data set, 

ERG only accounted for the pollutant loadings data in the DMR data set. If ERG identified that 

the TRI indirect loadings were sent to a POTW that is not within the modeling area, ERG did not 

 

10 Other limitations of the data collected in TRI include the following: small establishments are not required to 

report, nor are facilities that do not meet reporting thresholds; releases reported are based on estimates, not 

measurements; certain chemicals are reported as a class, not as individual compounds; facilities are identified by 

North American Industrial Classification System (NAICS) code, not point source category; and TRI requires 

facilities to only report certain chemicals and therefore all pollutants discharged from a facility may not be captured.

11 Facilities reporting to TRI are not required to sample and analyze waste streams to determine the quantities of 

toxic chemicals released. They may estimate releases based on mass balance calculations, published emission 

factors, site-specific emission factors, or other approaches. Facilities are required to indicate, by a reporting code, 

the basis of their release estimate. Since DMR pollutant data represents actual measurements, and in some cases 

multiple measurements over time, the DMR data are perceived to be a better estimate of the actual pollutant loadings 

form the facility.
Memorandum

September 30, 2015

Page 21

expand the case study modeling area to account for these loadings. If ERG did not have 

sufficient information to identify the name or location that TRI indirect loadings were discharged 

from, ERG did not account for these loadings in the model. If ERG was able to confirm that 

indirect pollutant loadings were sent to a POTW within the WASP modeling area and loadings 

for this pollutant were not accounted for in the DMR data set, ERG assigned the TRI pollutant 

loadings to the stream reach (as represented in NHDPlus Version 1) that was closest to the 

location of the POTW.

4. ASSESSMENT OF WASP MODEL OUTPUTS

ERG and AQUA TERRA developed the WASP water quality models (for both lotic and 

lentic systems) to provide output data for pollutant concentration (total, dissolved, and sorbed) in 

the water column and benthic segments on a daily output time step. The WASP models generate 

these outputs for every segment in the model, including the segment identified as the immediate 

receiving water and every downstream segment. ERG performed case study modeling for the 

pre-compliance date baseline scenario, post-compliance date baseline scenario, and four postcompliance date scenarios representing each of the four regulatory options included in the case 

study modeling (Option A, Option B, Option C, Option D). To ensure that the output from the 

five post-compliance date scenarios could be compared, all parameters and flows used in the 

post-compliance date baseline scenario and regulatory option scenarios were identical except for 

the pollutant loadings from the steam electric power plants modeled.

ERG’s methodology for assessing the water quality, wildlife, and human health impacts 

under baseline conditions and improvements under each regulatory option are described in 

Appendix G of the EA Report. The supporting documentation for each case study model presents

the WASP model outputs and analyses performed on these outputs [ERG, 2015g through 2015l].

5. REFERENCES

1. ERG. 2013a. Eastern Research Group, Inc. “Water Quality Model: Plant and Receiving 

Water Characteristics Memorandum”. DCN SE01955.

2. ERG. 2013b. Eastern Research Group, Inc. Proposed Steam Electric Questionnaire 

Database. (April). DCN SE01958.

3. ERG. 2015a. Eastern Research Group, Inc. “DMR and TRI Loadings Database for Case 

Study Modeling” (“DMR and TRI Loadings Database”). (September 30). DCN SE05589.

4. ERG. 2015b. Eastern Research Group, Inc. Review of Confidential Business Information 

(CBI) for the Final Environmental Assessment. (September 30). DCN SE04514.

5. ERG. 2015c. Eastern Research Group, Inc. “Changes to Industry Profile for Steam 

Electric Generating Units for the Steam Electric Effluent Guidelines Final Rule” 

(“Industry Profile Changes Memo”). (September 30). DCN SE050569.
Memorandum

September 30, 2015

Page 22

6. ERG. 2015d. Eastern Research Group, Inc. “Water Quality Module: Plant and Receiving 

Water Characteristics.” (September 30). DCN SE04513.

7. ERG. 2015e. Eastern Research Group, Inc. “Loadings Analysis for the Steam Electric 

ELGs Environmental Assessment” (September 30). DCN SE04446.

8. ERG. 2015f. Eastern Research Group, Inc. “Water Quality Module Supporting 

Documentation” (September 30). DCN SE04451.

9. ERG. 2015g. Eastern Research Group, Inc. “Supporting Documentation for the Black 

Creek, MS Case Study Model.” (September 30). DCN SE05590.

10. ERG. 2015h. Eastern Research Group, Inc. “Supporting Documentation for the Etowah 

River, GA Case Study Model.” (September 30). DCN SE05591.

11. ERG. 2015i. Eastern Research Group, Inc. “Supporting Documentation for the Lick 

Creek/White River, IN Case Study Model.” (September 30). DCN SE05592.

12. ERG. 2015j. Eastern Research Group, Inc. “Supporting Documentation for the Ohio 

River, WV/OH Case Study Model.” (September 30). DCN SE05593.

13. ERG. 2015k. Eastern Research Group, Inc. “Supporting Documentation for the 

Mississippi River, MO/IL Case Study Model.” (September 30). DCN SE05594.

14. ERG. 2015l. Eastern Research Group, Inc. “Supporting Documentation for the Lake 

Sinclair, GA Case Study Model.” (September 30). DCN SE05595.

15. ERG. 2015m. Eastern Research Group, Inc. “Supporting Documentation for EA Case 

Study Modeling, Benthic Sediment Executable Program.” (September 30). DCN 

SE06417.

16. ERG. 2015n. Eastern Research Group, Inc. “STORET Monitoring Data Database for 

Case Study Modeling” (“STORET Monitoring Database”). (September 30). DCN 

SE05588.

17. ERG. 2015o. Eastern Research Group, Inc. Final Steam Electric Questionnaire Database. 

(September 30). DCN SE05903.

18. ERG. 2015p. Eastern Research Group, Inc. Pollutant Loads Supporting Documentation. 

(September 30). DCN SE04515.

19. Georgia Power. 2013. “Georgia Power Asks to Retire 15 Units at Four Plants.” (January 

7). DCN SE04930.

20. Swartz, Kristi E. 2012. “Georgia Power Closes Units at Plant Branch and Plant Mitchell.” 

Atlanta Journal-Constitution. (March 20). DCN SE01334.
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September 30, 2015

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21. Southern Company. 2013. “Branch Unit Closing Marks Symbolic Milestone.” 

(September 30). DCN SE04945.