### 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. I NTRODUCTION /B ACKGROUND

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 deta iled water quality modeling of selected case study locations, the results of which support the Environmental Assessment (EA)

for the final ELGs. Section 8 of t he Environmental Assessment for the Effluent Limitations Guidelines and Standards for the Steam E lectric 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 addit ional 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 mem orandum into the following sections:

 Section 2 describes the methodology ERG used to identify representative sites for case study modeling, including the factors co nsidered during site selection and assessment of representativeness of the six modeled sites.

 Section 3 discusses the development of the WASP models using U.S. Geo logical 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 model s

.

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

.

2. S ELECTION OF S ITES F OR C ASE S TUDY M ODELING

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 multipleMemorandum

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 identif ying sites that were suitable for modeling with WASP and then

evaluat ing 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 plant s and their receiving waters (hereafter referred to as “plant

- receiving waters”) to determine which sites were suitable f or case study modeling

with WASP. P lant

- receiving waters that did not meet the c riteria were eliminated from

consideration for case stud y 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

E RG 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, a nd reservoirs) or estuarine systems involves more complex hydrodynamics than systems with lotic characteristics ( i.e. , rivers and streams) , E RG 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, respecti vely.

2.1.1 Acceptance Criteria for Lotic Aquatic Systems

ERG coordin ated 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 al l 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 mode ling 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 “DMRMemorandum

September 30, 2015 Page 3

and TRI Loadings Database for Case Study Modeling” ( “ DMR/TRI Loadings Database ” ) [ERG, 2015a] to identify and eliminate plant

- re ceiving waters that did not m e et the following criteria:

 Downstream Waterbody Characteristics

. W ASP’s ability to accura tely model water quality using stream gage flow data can be affected by major flow control structures

such a s 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

abi lity 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 plant

receiving waters whose downstream water bodies 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 plant

receiving 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 qualit y monitoring data. If a receiving water would be significantly influenced by other point source discha rges 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 electr ic 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 a nd any significant downstream tributaries

wa s 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 inform ation to develop WASP model input. AQUA TERRA assessed t he quality of stream gage flow data for use in case study modeling using the following criteria:

 Locati on of USGS Stream Gage Stations

. AQUA TERRA evaluated whether the

location of each identified str eam 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.Memorandum

September 30, 2015 Page 4

water that could be used to estimate the flow at the plant

- receiving water

, the plant

receiving water wa s eliminated from consideration. If the area downstream of the plant

receiving 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

- rec eiving 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 Gag e Flow Data . AQUA TERRA review ed 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. I f no stream gage stations had sufficient record periods, the plant

- receiving

water was eliminated from consideration.

 Age of Stream Gage Flow Data

. AQUA TERRA review ed the age of the str eam gag e 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

be cause 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. E RG identif ied and eliminate d 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 transpo rt 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 . E RG identified plant

- receiving wa ters 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] w ere not complete at the time of case s tudy 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.Memorandum

September 30, 2015 Page 5

If EPA determined that a plant

- receiving water would be signif icantly influenced by another plant’s disch arges 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 Con versions

. 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 p lants that were planning to retire, convert fuel types to eliminate fos sil fuel combustion residuals (especially residuals fr o m 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 scop e, 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.

A fter the application of the criteria above , ERG evaluated the rema ining candidate plant

receiving 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 eff ort, m odeling steam electric power plants that discharge to lentic or estuarine systems require d 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, 2013 a ]

. 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 discharge s 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 pr oceeded with an evaluation of the

repre sentativeness of Lake Sinclair to determine if this plant

- receiving water remains aMemorandum

September 30, 2015 Page 6

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

wastestreams

.

ERG also identified on e 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

- recei ving 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 bioaccumulat ion 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 as sessed 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 evaluati ng 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 plant

receiving waters

. In addition to representativeness of the water column concentrations, receiving

water

, and pollutant loadings, ERG co nsidered 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 regulator y 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

, E RG identif ied 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 a n example of lentic waterbodies that receive discharges of the evaluated wastestreams.

When evaluated as a group, the five lotic plant

- re ceiving waters and Lake Sinclair demonstrate pollutant removals across all modeled regulatory options and all four evaluated

wastestreams. Fi gure 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.Memorandum

September 30, 2015 Page 7

Fi gure 1

. Overview of Case Study Modeling LocationsMemorandum

September 30, 2015 Page 8

Table 1

. Locations Selected for Case Study Modeling

C ase 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

– E RG identified another steam electric power plant, M eramec
, that discharges upstream of the Rush Island plant

. E RG and AQUA TERRA incorporated the pollutant loadings of the Meramec plant to account for the upstream pollutant contributions . E RG did not evaluate the water quality, wildlife, or human health impacts associated with discharges from the Meramec plant because this plant wa s 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 . E RG 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 national

scale immediate receiving water (IRW) model

. These concen trations 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 pollutant

specific 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 27 th percentile to the 79 th percentile, with the median close to the 50 th percentile.Memorandum

September 30, 2015 Page 9

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 18 3 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 wate r 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

represen tative illustration of lentic waterbodies that receive discharges of the evaluated

wastestreams.

4 To determine the wa ter 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 un its 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 rulemaki ng. 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.Memorandum September 30, 2015 Page 10

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 4 3 % 0.0285 6 2 %

Cd 105 80 % 0.177 75%

Cu 25.4 5 1 % 0.0430 65%

Pb 7.61 3 3 % 0.0129 66%

Ni 819 78% 1.38 73%

Se 1

, 080 81% 1.82 79%

Tl 29.9 4 7 % 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 6 5 % 0.0164 56%

Pb 29.6 50 % 0.00506 59%

Ni 3

, 250 95 % 0.555 6 8 %

Se 4

, 250 9 5 % 0.725 7 3 %

Tl 115 6 0 % 0.0196 5 6 %

Zn 5

, 110 9 5 % 0.872 6 8 %

Lick Creek/

White River (IN) Petersburg

Generating Station Lick Creek b 4.46 1 4 % As 52.6 6 2 % 4.62 90 %

Cd 27.4 6 0 % 2.40 92%

Cu 176 78% 15.5 9 5 %

Pb 80.7 65% 7.08 92%

Ni 155 5 2 % 13.6 91%

Se 461 67 % 40.4 92%

Tl 96.3 56 % 8.45 9 2 %

Zn 162 52% 14.2 85%

White River b 11,900 69% As

-- -- -- -- Cd

-- -- -- -- Cu

-- -- -- -- Pb

-- -- -- -- Ni

-- -- -- -- Se

-- -- -- -- Tl

-- -- -- -- Zn

-- -- -- --Memorandum September 30, 2015 Page 11

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 6 5 % 0.000961 27%

Cd 445 96% 0.00540 54%

Cu 145 74% 0.00175 35%

Pb 64.1 6 1 % 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 9 6 % 0.0670 54%

W.H. Sammis Plant Ohio River 34,100 7 9 % As 362 8 7 % 0.00436 45%

Cd 102 79% 0.00123 39%

Cu 437 88% 0.00526 48%

Pb 329 86% 0.00396 55%

Ni 1,640 8 7 % 0.0197 46%

Se 1,110 82% 0.0134 40%

Tl 1,670 93% 0.0201 57%

Zn 1,360 8 1 % 0.0164 41%

Mississippi River (MO/IL) Rush Island Mississippi River 177,000 9 8 % As 2,730 99% 0.00632 50%

Cd 394 9 5 % 0.000912 34%

Cu 1,870 9 9 % 0.00434 45%

Pb 1,230 98% 0.00284 51%

Ni 1,480 8 4 % 0.00344 29%

Se 1,180 8 4 % 0.00274 27%

Tl 1,420 9 1 % 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 ar e rounded to two significant figures.

a

– The values in this column represent the percentile rank of the value amongst all 18 3 lotic receiving waters modeled in the national

- scale IRW model , accounting for

the CCR rule . For example, a receiving water flow that is o f 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 re ceiving water . Th e total length of the modeled segment of Lick Creek is less than 4 percent of the modeling area downstream of Petersburg Genera ting 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 av erage annual flow rate of Lick Creek.Memorandum September 30, 2015 Page 12

Despite the high percentile ranking of the total water column concentration in Lick Cree k, 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 E lectric

Power Plant Immediate

Receiving Water Receiving Water

Surface Area Pollutant Plant Loadings Total Water Column

Concentration

m 2 Percentile g/day Percentile μg/L Percentile

Lake Sinclair (GA) Plant Harllee

Branch Lake Sinclair 3,19 0 82% As 102 82 % 0.0 131 32 %

Cd 540 96 % 0. 0693 53 %

Cu 226 82 % 0.0290 46 %

Pb 81.9 80 % 0.0 105 34 %

Ni 4,230 96 % 0.543 53 %

Se 5,440 96 % 0.699 67 %

Tl 182 67 % 0.0 233 39 %

Zn 6,190 96 % 0.886 57 %
Sources: [ERG, 2015 l ] Acronyms: m 2 (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 S inclair

.Memorandum

September 30, 2015 Page 13

3. D EVELOPMENT OF WASP S IMULATION M ODELS

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 Ve rsion

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 ea ch

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

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 Builde r, 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 incorporati on 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 F lowlines loaded and active. AQUA TERRA used NHDPlus

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

selected the particular F lowline segments to be included in the WASP model based on t he site

specific modeling area and the availability of USGS stream gage flow data. To simplify the geographic extent of the m odeling 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 F lowlines to include in the WAS P model, these F lowlines 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 flo w data, this determination was made based on the

mean annual flow rate data in NHDPlus Version 1.Memorandum

September 30, 2015 Page 14

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 case s, 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 th e case study modeling effort was to capture increases in pollutant

conce ntrations 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 inclu de benthic sediment segment s 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 additi onal 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 depth s of the upper and lower benthic s ediment 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. T he 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 flowi ng 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 strea m gage flow data from the actual time period ( e.g. , 1982

– 201 4 ) 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 dat e ( 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 t he 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 q uality 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 t hese 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 collect ion , 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 moni toring 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 pollutant s , 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 d id 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 d esignated 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 mu st 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 accept sample 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 lack ed 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.

A fter 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 calibrat ion of the solid s 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 represe nt 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 pollu tant. For stations w ith 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 t o 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 weight ed 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 ar senic. 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 model ing 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 S tation A was calculated as 10 micrograms per liter (μ g/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 accept ed 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 concentrati ons 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

wou ld 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 loa ding incorporated into each WASP model [ ERG, 2015g through 2015 l ].

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 Develop ment 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 acknow ledges 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 , E RG estimate d 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 calculat e baseline and regulatory option pollutant loadings for plant

receiving waters are d escribed in the memorandum “Pollutant Loads Supporting Documentation”
[ ERG, 2015p ]

. E RG and AQUA TERRA performed case study water quality modeling for theMemorandum

September 30, 2015 Page 18

following eight pollutant s (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 wastestre am, 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 w et 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 e ach 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 a n 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 ins talled. If a plant installed multiple FGD systems during the modeling period, E RG assumed that the annual, historical FGD wastewater pollutant loadings associated with each individual system were proportional to that system’s flow rate contribution compare d 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 2015 l]

.

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 po llutant 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 b efore 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 p ollutants 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 andMemorandum

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 2015 l]

.

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 Si nclair to represent the potential impacts

of steam electric d ischarges 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 plan ts 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 Pollutan t 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 non

stream 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 D atabase [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 requiremen ts. 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

Pol lutant 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 mino r 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 meet s reporting requirements. The TRI pollutant loadings data collected by ERG rep resent 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 re garding

9 EPA’s Office of Water (OW) reviews the DMR and TRI data used to calculate effluent loads and TW PE 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 Limitati ons 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

l ongitude 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 con ducted 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 pollutant s, 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 set s

. In t his 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 poin t 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 th e 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 estimate s, 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 ce rtain 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 relea ses 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 actu al 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 fo r 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. A SSESSMENT OF WASP M ODEL O UTPUTS

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 wa ter 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 mod eling for the

pre

- compliance date baseline scenario, post

- compliance date baseline scenario, and four post

compliance date scenario s 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 support ing documentation for each case study model present s the WASP model outputs and analyses performed on these output s [ ERG, 2015g through 2015 l ].

5. R EFERENCES

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

Water Characterist ics 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. “Chan ges 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. 2015 d

. Eastern Research Group, Inc. “Water Quality Module: Plant and Re ceiving 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 Qual ity 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. “Supportin g 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. Easte rn 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

SE0 64 17.

16. ERG. 2015 n . 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 Unit s 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.Memorandum

September 30, 2015 Page 23

21. Southern Company. 2013. “Branch Unit Closing Marks Symbolic Milesto ne.” ( September 30). DCN SE04945.