NC0001422_FINAL Sutton Geochemical Rpt Oct 2016_20161001

47
synTerra
REFINED GEOCHEMICAL MODEL REPORT
L.V. SUTTON ENERGY COMPLEX
801 SUTTON STEAM PLANT ROAD
WILMINGTON,, NC 28401
OCTOBER 2016
PREPARED FOR
DUKE ENERGY PROGRESSF LLC.
410 S. WILMINGTON STREET/NC15
RALEIGH,F NORTH CAROLINA 27601
p DUKE
ENERGY
PROGRESS
NC
148 River Street, Suite 220 Greenville, SC 29601 [phone] Fax [phone] www.synterracorp.com

Refined Geochemical Model Report October 2016
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TABLE OF CONTENTS
SECTION PAGE
Executive Summary .............................................................................................................. ES-1
1.0 Background Information and Purpose ..................................................................... 1-1
1.1 Site Background ........................................................................................................ 1-1
1.2 Planned Closure and Groundwater Remediation ................................................ 1-2
1.3 Purpose of Site Specific Geochemical Conceptual Model ................................... 1-2
2.0 Geochemical Conceptual Model ................................................................................ 2-1
2.1 Distribution of Current Field Parameters and Observed
COI Concentrations .................................................................................................. 2-1
2.2 Current and Predicted COI Conditions ................................................................. 2-3
2.2.1 Arsenic .................................................................................................................. 2-3
2.2.2 Boron ..................................................................................................................... 2-5
2.2.3 Chromium ............................................................................................................ 2-6
2.2.4 Cobalt .................................................................................................................... 2-8
2.2.5 Selenium ............................................................................................................... 2-9
2.2.6 Thallium ............................................................................................................. 2-11
3.0 Conclusions and Recommendations ......................................................................... 3-1
4.0 References ....................................................................................................................... 4-1

LIST OF FIGURES
Figure 1 Site Geochemical Model Transect (or well layout map)

LIST OF TABLES
Table 1 Summary of Kd Values

LIST OF APPENDICES
Appendix A L.V. Sutton Energy Complex Geochemical Model Analysis
Appendix B Isoconcentration Maps (Arsenic, Boron, Cobalt, Selenium, Thallium)
Appendix C Historical Groundwater Data (Excel Format)
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EXECUTIVE SUMMARY
The Division of Water Resources from the North Carolina Department of
Environmental Quality (DEQ) provided comments to the geochemical modeling aspects
of the Sutton Corrective Action Plan (CAP) Part 2 report (SynTerra, 2016). This Refined
Geochemical Model report specifically addresses the DEQ global comments as well as
the site specific comments. The Refined Geochemical Model report describes the
current geochemical conditions (distribution of pH and Eh) along flow transects and
how these conditions help explain the constituent of interest (COI) concentration
distribution. The Refined Geochemical Model report also describes how the
geochemical conditions are expected to change after the proposed corrective action has
been implemented.
The flow and transport modeling in the CAP Part 2 provided conservative predictions
of COI distributions following corrective action implementation. The actual COI
concentrations may recede faster than predicted as the groundwater returns to natural
background conditions, the mobility of most of the COIs will generally decrease.
Changes in groundwater geochemistry as a result of corrective action are not
anticipated to be significant enough to substantially change the results of the flow and
transport model predictions. The revised geochemical model for Sutton (Powell, 2016)
is attached to this report as Appendix A, and the flow and transport model provided in
the CAP Part 2 are supported by the Refined Geochemical Model.
The site specific comments regarding the Sutton geochemical model are addressed in
the report provided as Appendix A.
The COIs specifically addressed herein are arsenic, boron, chromium, cobalt, selenium
and thallium as requested by DEQ.
Key elements of the proposed corrective action include:
1. Basin closure by excavation of the ash. Basin closure by ash excavation is
underway. It is anticipated that ash removal from both basins will be completed
by late 2019. Portions of the removed ash will be managed off site and the
balance will be placed in a permitted on-site landfill.
2. Groundwater extraction. As part of an accelerated remediation plan, a
groundwater extraction system is planned along the eastern property boundary
to control migration of groundwater. Once the ash is removed, groundwater
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extraction wells are planned near the footprint of the former basins to remediate
groundwater within the compliance boundary.
3. Monitored Natural Attenuation (MNA). MNA is planned for areas to the north,
south and west of the ash basins.
None of these corrective actions are expected to substantially change groundwater
geochemistry. The removal of the ash basins and the Former Ash Disposal Area
(FADA) will allow for the return of groundwater quality to background conditions.
The groundwater extraction systems will accelerate this process. Significant
geochemical changes that would result in mobilizing constituents are not anticipated.
The global and site-specific geochemical conceptual model (Appendix A) supports this
conclusion.
Primary Factors Controlling Changes in Groundwater Quality
The Eh-pH conditions in groundwater are two variables having the greatest impact on
the distribution coefficient (Kd) and therefore constituent mobility. In general, along
flow transects, pH decreases and Eh increases with distance from the ash basin source
areas. Variations in pH decrease and Eh increase will occur as groundwater migrates
radially away from the basins. As expected, as distance increases from the ash basins
the values of these two variables return to background conditions. Site specific
constituent information is summarized below:
Arsenic concentrations generally decrease with distance from the ash basins and
will continue to decrease once the ash basins have been closed. Values of Kd are
moderate (9 L/kg was used in the CAP flow and transport model) and are not
expected to be reduced as a result of closure or groundwater extraction activities.
Boron is a conservative ion and does not readily sorb to solid phase media; thus,
boron has a low Kd, which will not be reduced as a result of geochemical
variations. The groundwater flow and transport model used a Kd of 0 L/kg.
Geochemical conditions indicate chromium will remain primarily as Cr(III) with
a small portion of Cr(VI). There is not an observable plume of chromium
confirming this geochemical observation; therefore flow and transport modeling
was not conducted.
Cobalt has low source area concentrations. Possible geochemical changes with
corrective action may increase the mobility of cobalt, however very little source
material appears to be present. Removal of the source material mass should
prevent significant increases in cobalt concentrations in the future.
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Selenium is only found above detection limits in shallow geologic units. In units
with oxidizing conditions, selenium will readily sorb to solid phase media as will
be the case following corrective action. Selenium can be included in future flow
and transport modeling.
The occurrence of thallium is generally limited to the surficial zones southeast of
the 1971 basin and north of the 1984 basin; as well as background locations.
Available literature information does not support valid geochemical modeling
for thallium.
CAMA required post-closure groundwater monitoring can be used to verify
these findings.

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1.0 BACKGROUND INFORMATION AND PURPOSE
1.1 Site Background
Duke Energy Progress, LLC (Duke Energy) owns and operates the L.V. Sutton Energy
Complex (Site) located on approximately 3,300 acres near Wilmington, North Carolina.
The Site is located along the east bank of the Cape Fear River northwest of Wilmington
and west of US Highway 421. Figure 1 shows the layout of the property and
monitoring well network.
The Site started operations in 1954 with coal-fired boilers. Ash generated from coal
combustion was originally stored on-site in the 'former ash disposal area (FADA)', also
known as the ‘lay of land area’, then in the 1971 ash basin (old ash basin) and adjacent
1983 extension, and finally the 1984 ash basin (new ash basin). These ash storage areas
are referred to as the ash management area. The Site ceased burning coal in November
2013 and switched to natural gas for electricity generation, thus the facility no longer
generates coal ash.
The Corrective Action Plan (CAP) consisting of Parts 1 and 2, was prepared to
formulate the means to restore groundwater quality to the level of the standards, or as
close as is economically and technologically feasible in accordance with T15A NCAC
02L.0106. Exceedances of Subchapter 2L and Appendix 1 Subchapter 02L interim
maximum allowable concentration (IMAC) groundwater standards at or beyond the
compliance boundary is the basis for corrective action with the exception of parameters
for which naturally occurring background concentrations are greater than the
standards.
The CAP Part 1 (SynTerra, November 2015) included provisional background
concentrations for inorganic constituents in groundwater. Baseline constituent flow
and transport modeling was also conducted and compared to modeled simulations of
ash basin remedial alternatives to evaluate the remedial effects on groundwater
constituent concentrations. CAP Part 2 (SynTerra, February 2016) provided the
methods and results of a human health and ecological risk assessment; provided an
update to groundwater flow modeling; provided an update to geochemical modeling;
identified preferred corrective action; provided conceptual design information; and
described the steps necessary for implementation. The CAP Part 2 recommended
corrective actions including monitored natural attenuation (MNA) of groundwater in
some areas, groundwater extraction as containment and source removal and source
stabilization measures (e.g., ash basin closure) based on groundwater modeling and
baseline risk assessment results. Filling data gaps identified in the CSA Report were
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ongoing; the results were provided in a CSA Supplemental Report (SynTerra, August
2016).
1.2 Planned Closure and Groundwater Remediation
Remediation activities currently planned for the Site includes excavation of ash from
both ash basins and the FADA, and installation of groundwater extraction system(s).
Removal of the ash by excavation is underway, as required by CAMA. Currently the
ash is being disposed of offsite as permitted structural fill. In the future, the ash will be
placed in an on-site landfill following landfill construction and receipt of the Permit to
Operate. The former basins will either be left as open water (FADA and 1971 Basin)
and allowed to connect to the cooling pond or left as green areas (1984 Basin), graded to
the drain towards the cooling pond.
The groundwater extraction system will be installed in two phases; accelerated
remediation and source removal. The acceleration remediation will consist of the
installation of a line of extraction wells along the eastern boundary of the site property
to capture constituents before migration offsite and to recover offsite constituents.
Design of this system is ongoing and installation and startup is anticipated May 2017.
The source removal phase will consist of a line of recovery wells along the eastern
boundary of the ash basins to recover constituents from the former source area.
Excavation of the ash and groundwater extraction will improve groundwater quality.
The removal of ash will reduce leaching of constituents of interest into groundwater.
Source removal, removing constituent mass contribution to the groundwater system,
will allow pH and Eh to return to natural background conditions.
1.3 Purpose of Site Specific Geochemical Conceptual Model
DEQ provided global and site specific comments dated August 19,, 2016 (DEQ August
2016) for the Sutton Energy Complex geochemical modeling found in CAP 1, Appendix
D and CAP 2, Appendix C (SynTerra, November 2015 and SynTerra, February 2016). A
global geochemical model (including the seven DEP facilities) was submitted based on
the range of data available for seven DEP facilities. The DEQ global comment
applicable to the CAP was that a site specific geochemical conceptual model should be
provided to describe current and anticipated future changes to COI distribution in
response to geochemical changes which may occur as a result of corrective action
activities. The purpose of this report is to address this global comment.
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In addition, for Sutton, DEQ specifically requested modeling along three transects
(Figure 1). The site specific modeling requested is provided in Appendix A.
Observations along the transects begin within the source area, ABMW-01S/D, a well
cluster located in the 1971 ash basin. ABMW-01S was screened within the ash pore
water and ABMW-01D was screened within the Pee Dee upper geologic zone beneath
the source area. Both wells have been abandoned as a result of the ongoing excavation
of the ash basin. Data from 2015 was used for the purpose of source area modeling. No
source area wells are available for the 1984 ash basin, due to the presence of free-
standing water within the basin and the underlying clay liner. The 1971 ash basin was
unlined and extended to a depth of approximately 40 feet below the water table.
Therefore, groundwater constituent concentrations below these two source areas would
be expected to differ significantly. The 1971 ash basin data would be considered to
represent worst-case subsurface conditions among the two source areas.
DEQ requested modeling of three zones; a north zone, a transition zone and a south
zone. The following transects along the wells listed below address the zones requested:
North Transect
• ABMW-01S/D  MW-36B/C MW-27B/C  MW-38B/C/D  AW-08B/C
East (Transition) Transect
• ABMW-01S/D  MW-23B/C/D/E AW-06B/D/E & MW-12  SMW-
06B/C/D
Southeast Transect
• ABMW-01S/D  MW-18 MW-21C MW-28B/C MW-07A/B/C  AW-
09B/C/D
Along these transects parameter distribution and evolution of pH, Eh, resulting Kd
values and concentrations of hydrous ferric oxides and hydrous aluminum oxides (as
an indicator for sorption capacity) were considered for both current and future site
conditions. Current conditions establish the baseline geochemical environment, and
future conditions investigate the potential influence of the corrective actions on
constituent concentrations and speciation, in order to assess the ultimate impact on
groundwater quality over the remedial timeline.
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2.0 GEOCHEMICAL CONCEPTUAL MODEL
In addition to geochemistry and hydrogeology, structural or morphological conditions 
can influence the occurrence and distribution of COIs.  The groundwater table is 
shallow and flow is generally radial from the basins.  There is a strong lateral 
component to this flow due to the higher permeability of the surficial zone as compared 
to the lower Pee Dee.  As a result, much of the COI mass occurs within the lower 
surficial aquifer which is the primary focus of this report.   

The 1971 ash basin and the 1983 extension are unlined ash basins.  The ash within the 
1971 ash basin extends to depths up to approximately 40 feet below the water table and 
into most of the surficial aquifer; whereas the 1984 ash basin is lined and ash is not 
present below original site grade.  This provides insight on the higher Eh/pH zone 
southeast of the 1971 basin.  That the arsenic migration in groundwater extends beyond 
the ash basins only on the southeast (downgradient) side of the 1971/1983 extension 
suggests the migration is the result of mass loading, morphology, hydrology and 
geochemistry.   

The occurrence of selenium on the north side of the 1984 basin is unique to the site and 
is likely related to unique source area conditions. 

The geochemical site specific model along flow transects provides a qualitative 
comparison with ion concentrations measured in groundwater using data from the CSA 
Supplement (SynTerra, August 2016).  Site specific geochemical modeling results are 
presented in Appendix A.  The transect model shows qualitative agreement between 
the geochemical conceptual model and the site measured values.  

2.1 Distribution of Current Field Parameters and Observed COI
Concentrations
The Eh‐pH conditions in groundwater have the greatest impact on constituent Kd 
values.  An understanding of how the pH and redox potential change along flow 
transects away from the source areas is an important component to understanding the 
geochemical behavior of each constituent on a site specific scale.  
Observed ion concentrations response to variations in pH and Eh are as follows:  
A decrease in pH will generally cause a decrease in the mobility of anions and an 
increase in the mobility of cations.  Anions will have a greater electrostatic 
attraction and cations will have a lesser electrostatic attraction towards the 
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sorptive medium as mineral surfaces transition from a net negative to a net
positive surface charge.
An increase in Eh leads to the formation of more oxidized species. The redox
potential is rarely an indicator for mobility alone and is dependent on pH to
determine constituent species. For example, oxidation of the cation Cr3+ to the
soluble chromate anion CrO4-2 has the potential to enhance the mobility of
chromium. But this behavior is dependent on the sorption affinity of Cr(VI) which
is dependent on the pH of the source water.
Site-specific Kd values and the calculated retardation factor (R) are used to predict
contaminant migration and evaluate potential responses to remediation options.
The range of Kd values calculated in the PHREEQC geochemical model was
conducted using site-specific solid and aqueous phase concentrations at wells
along or near the three transects. Values used to formulate the R were
conservative to show the greatest potential for contaminant migration.
The sorption of metals retards contaminant transport in the subsurface. The
geochemical model estimates sorption based on hydrous ferric oxide (HFO) and
gibbsite (HAO) concentrations due to sorption capacity, common occurrence in
many geologic systems, and the availability of a self-consistent thermodynamic
sorption model considering most constituents of interest (Dzombak and Morel,
1990; Karamalidis and Dzombak, 2010). There may be no obvious trends along
transects for these minerals. Therefore, a generalized composite approach is used
assuming a generic surface site with an average reactivity of all minerals in the
solid phase, and that sorption occurs only to iron oxide minerals and sorption is to
only one site on both HFO and HAO.
In general, along flow transects pH decreases and Eh increases with distance from the
source area with a reasonable degree of variation. The decrease in pH toward
background conditions appears to cause an increase in the groundwater concentrations
of cationic species such as Co and a decrease in groundwater concentrations of anionic
species such as As and Se.
It should also be noted that in the Surficial aquifer, higher pH and Eh values are
pronounced in the south and southeast direction of the 1971 basin. This south-
southeast variation is likely due to the clay liner within the northern 1984 Basin
providing some protection of the surrounding groundwater.
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2.2 Current and Predicted COI Conditions
The occurrence and extent of arsenic, boron, chromium, cobalt, selenium and thallium
and changes anticipated by proposed corrective action measures are presented in the
following sections. Isoconcentration maps for these constituents (except chromium) in
the lower surficial zone from the CSA site work are included in Appendix B. A
summary table of historical groundwater data is presented in Appendix C.
2.2.1 Arsenic
Under current conditions, arsenic is detected in groundwater above the 2L
standard in monitoring wells near the southern and southeastern side of the 1971
ash basin and within the FADA area. The highest concentration of arsenic was
detected in the 1971 ash pore water well ABMW-01S (858 µg/l). The highest
concentration of arsenic in groundwater was detected beneath the FADA
(ABMW-02D) at concentrations ranging from 158 µg/l to 172 µg/l. The data
indicate that arsenic migration in groundwater is limited to the FADA waste
boundary and an area just southeast of the 1971 ash basin (MW-21C, lower
surficial aquifer). Recent site data (June 2016) confirms arsenic migration in
groundwater extends to MW-21C (47.3 µg/l).
The discharge canal and the cooling pond hydraulically confine the FADA area
(SynTerra August 2015). It is anticipated that the FADA will be excavated and
become part of the cooling pond and discharge canal system. Therefore, the area
of arsenic in groundwater associated with the FADA is anticipated to be
removed as part of closure. The 1984 ash basin was lined and built above the
water table. Arsenic does not tend to be present in groundwater downgradient
of the 1984 basin (SynTerra, August 2016).
The 1971 ash basin was constructed approximately 40 feet below the water table
and is not lined. Therefore, the greatest mass loading to the underlying aquifer
would be anticipated to occur in the area of the 1971 basin and radial
groundwater flow would be expected to produce constituent migration to the
southeast.
The global and site specific geochemical models, as demonstrated by the
Pourbaix diagrams, indicate the dominant arsenic species anticipated within the
aquifer system is As(V). This species is relatively immobile, having an elevated
Kd (as shown in the global and site specific geochemical model in Appendix A),
and thus is expected to sorb to the aquifer matrix. However, both As (III) and
As(V) would be expected to be present in the 1971 ash basin system due to the
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relatively high pH and variability in the Eh associated with the ash pore water.
As water migrates away from the basin, the pH will decrease toward
background concentrations and the Eh increases. As a result, it would be
expected that As(III) could migrate from the source area until the more natural,
background geochemical conditions are reached; thereafter, little if any As(III)
will persist as the systems approach equilibrium. These geochemical model
predictions explain the arsenic distributions detected in site groundwater.

The arsenic speciation data collected in 2015 indicate that where arsenic is
detected in groundwater, it is in the more mobile As(III) (see electronic historical
data table attached, Appendix C). The geochemical model predictions indicate
that the dominant As (V) species is sorbed to the aquifer matrix and would be at
low or below detectable concentrations in groundwater. Though the Kd values
for As(III) are lower than As(V), As(III) is still relatively immobile, with a
relatively high Kd. The As(III) Kd could range from approximately 0.03 to 697
L/kg compared to the As (V) Kd predicted range of approximately 14.6 to 2.9 x 106
L/kg as summarized from the geochemical modeling information (Table 1).
Note that mean value for Kd developed by UNCC (2016) is 48 and the value used
by Falta (2016), as part of the flow and transport model, is 9 L/kg. Using 9 L/kg
as the Kd, the retardation factor (R) likely varies from 37 to 73; meaning that the
sorption process reduces the concentration of arsenic by that factor and also
reduces the velocity of movement by that factor. Based on the Kd values
predicted by PHREEQC and measured by UNCC, the actual retardation factor is
As(V)
As(V)
As(V)
As(V)
As(III)
As(III)
Eh

(
v
o
l
t
s
)
More
Mobile
Less
Mobile
Global Geochemical Model Site Specific Geochemical Model
Eh

(
v
o
l
t
s
)
More
Mobile
Less
Mobile
As(V)
As(V)
As(V)
As(V)
As(III)
As(III)
Reference: Powell, B.A., Analysis of Geochemical Phenomena Controlling Mobility of Ions from Coal Ash Basins at the Duke Energy L. V. Sutton
Energy Complex, Wilmington, NC. October 14, 2016: Greenville, SC.
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likely greater than used in the flow and transport simulations provided in the
CAP (i.e., the CAP predictions are conservative estimates and actual migration
could be less and return to background conditions could be sooner than
predicted).
The site specific Pourbaix diagram shows the current site geochemistry favors As
(V); however there are a number of locations where the conditions are close to
favoring As (III). With ash basin closure by excavation, and the anticipated
groundwater extraction system(s), groundwater will return to the lower
background pH and higher Eh conditions, which will tend to drive the
geochemistry toward favoring the less mobile As (V) species.
The flow and transport modelling was conducted using a Kd of 9 for arsenic to
approximate current site observed conditions. Again, as the As V species is
anticipated to dominant the system under future conditions, with basin closure
and groundwater extraction system(s), the modeling conducted to date may have
over predicted the timeframe for arsenic remediation (was more conservative)
than would be anticipated following more conversion of As III to As V.
CAMA requires groundwater monitoring for 30 years following closure. The
groundwater monitoring can be used to confirm these predictions.
2.2.2 Boron
Boron is detected in the upper and lower surficial aquifer associated with
migration from the ash basins. Figure 1-11 from the Supplemental CSA report
(Appendix B) shows the extent of boron detections within the lower surficial.
The concentration distribution indicates that boron originates from both ash
basins.
As shown on the Pourbaix figure below, boron primarily exists as B(III) the
hydrolyzed species B(OH)3 or BO2-, which have low Kd values and are mobile in
groundwater regardless of geochemical conditions. Given the age of the basins
and the seepage velocities it appears that boron in groundwater is near
equilibrium conditions. Falta (2016) used a Kd of 0 for boron at Sutton; thus, the
decline in concentrations to the north, west and southeast of the ash basins is a
result of advection and dispersion (dilution processes). These processes will
continue to operate in the future. With the removal of the ash basins, boron
concentrations will diminish and ultimately reach background conditions.
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Per CAMA, ash will be removed from the two basins and the FADA.
Groundwater extraction wells are planned along the western property line and
near the source area once the ash has been removed. These corrective actions are
not anticipated to change the mobility of boron due to changes in groundwater
geochemistry.
2.2.3 Chromium
Chromium is not routinely detected in most wells and has not been detected
above 2L in surficial zone monitoring wells. In general, the occurrence of
chromium appears to be sporadic with limited apparent relationship to the ash
basins. As such, chromium isoconcentration maps have not been developed.
The Pourbaix diagrams below indicate the global and site specific geochemical
models for chromium are similar. The dominant chromium species anticipated
within the aquifer system is Cr(III). Cr(III) is less mobile than the Cr(VI) species.
Cr(VI) was detected in the ash pore samples; however, both Cr(III) and Cr(VI)
would be expected to be present in the ash basin and FADA due to the relatively
high pH and variability in the Eh associated with the ash pore water. As water
migrates away from the basin, the pH will decrease toward background
concentrations and the Eh increases. As with arsenic, it would be expected that
Cr(III) could migrate a short distance from the source area until the natural,
background geochemical conditions are reached.
Global Geochemical Model Site-Specific Geochemical Model
Reference: Powell, B.A., Analysis of Geochemical Phenomena Controlling Mobility of Ions from Coal Ash Basins at the Duke Energy L. V. Sutton
Energy Complex, Wilmington, NC. October 14, 2016: Greenville, SC.
B(III)
B(III)
B(III)
B(III)
Mobile
Mobile
Mobile
Mobile
Refined Geochemical Model Report October 2016
L.V. Sutton Energy Complex, Wilmington, NC SynTerra

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Speciation data collected indicate that both Cr(III) and Cr(VI) are present in Site
groundwaters with no particular pattern or trends with changing pH or Eh
(Appendix C). Consistent with the geochemical model predictions, Cr(III)
concentrations are greater than Cr(VI) concentrations where both are detectable.
The chromium concentrations in the source wells did not exceed the comparative
standards and it does not appear that there is a sufficient amount of chromium in
the source area to generate a contiguous plume in downgradient areas. Where
Cr(III) was detected, pH values were relatively high; where Cr(VI) was detected,
pH values were moderate to slightly low. The site specific and global
geochemical model predicts that the dominant Cr(III) species is sorbed to the
aquifer matrix and is less (or not) detectable in groundwater. Cr(III) is relatively
immobile, with a relatively high Kd. The Cr(III) Kd could range from
approximately 23 to 6.9 x 108 L/kg compared to the Cr(VI) Kd predicted range of
approximately 7.4 x 10-4 to 2.7 x 105 as summarized from the geochemical
modeling information (Table 1).
The site specific Pourbaix diagram shows the current site geochemistry favors
Cr(III) in most locations. A significant increase of Eh or pH would be required to
convert Cr(III) to the more mobile Cr(VI). Ash basin closures by excavation, and
the anticipated groundwater extraction system(s), are expected to result in
returning the source area groundwater to the lower background pH and higher
Kd conditions which favor the occurrence of Cr(III). Therefore, the proposed
Cr(VI)
Cr (VI)
Cr(VI)
Cr(III)
Cr(VI)
More
Mobile
Less
Mobile
Global Geochemical Model Site-Specific Geochemical Model
More
Mobile
Less
Mobile
Reference: Powell, B.A., Analysis of Geochemical Phenomena Controlling Mobility of Ions from Coal Ash Basins at the Duke Energy L. V. Sutton
Energy Complex, Wilmington, NC. October 14, 2016: Greenville, SC.
Cr(III)
Cr(III)
Cr(III)
Cr(VI)
Cr (VI)
Cr(VI)
Cr(III)
Cr(VI)
Cr(III)
Cr(III)
Cr(III)
Refined Geochemical Model Report October 2016
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corrective actions are anticipated to decrease the mobility of chromium due to
the groundwater geochemistry returning toward background conditions.
2.2.4 Cobalt
Cobalt is primarily detected in the lower surficial aquifer with concentration
distribution patterns not clearly linked to an ash basin source. There are two
areas with a number of measurable results – on the north side of the 1984 ash
basin and in a linear feature on the west side of the 1971 ash basin (Appendix B).
Samples of ash pore water, ABMW-1S (1971 basin), ABMW-2S (FADA), and
GWPZ-1B (closest to the north side of the 1984 basin) had no detectable
concentrations of cobalt.
Cobalt, Co(II) generally persists as the divalent cation Co2+, as shown on the
Pourbaix diagrams below. The site specific results indicate cobalt is almost
exclusively present at Co(II) with minor Co(III) concentration.

The measured groundwater concentrations of cobalt along the transects
generally decrease with increasing distance from the ash basins. This is
consistent with geochemical model predictions of decreasing sorption of Co2+
with decreasing pH (which is demonstrated by plotting the Kd values as a
function of pH in Appendix A). Due to the influence of pH on cobalt mobility,
the proposed corrective action work which will lower the pH of groundwater
may cause an increase in the aqueous concentration of cobalt. However, very
low concentrations appear to be present in the source areas, and with the ash
Global Geochemical Model Site-Specific Geochemical Model
Reference: Powell, B.A., Analysis of Geochemical Phenomena Controlling Mobility of Ions from Coal Ash Basins at the Duke Energy L. V. Sutton
Energy Complex, Wilmington, NC. October 14, 2016: Greenville, SC.
More
Mobile
More
Mobile
Less
Mobile
Co (II)
Co (II)
Co (II)
Co (II)
Co (II)Co (II)
Co (II)
Co (II)
Co (II)
Co (II)
Co (II)Co (II)
Less
Mobile
More
Mobile
More
Mobile
Less
Mobile
Less
Mobile
Refined Geochemical Model Report October 2016
L.V. Sutton Energy Complex, Wilmington, NC SynTerra

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removal and groundwater extraction, the remaining mass will be removed, thus
a large increase in downgradient concentrations as a result of the planned
corrective action is unlikely.
2.2.5 Selenium
Selenium is detected in groundwater above the 2L within the lower surficial
aquifer primarily north of the 1984 ash basin (Appendix B and Appendix C).
Selenium has not been detected greater than the 2L in the source area wells. The
1984 ash basin is lined and was constructed above the water table. Selenium is
known to have been detected intermittently in outfall SW-004 (CSA
Supplemental SynTerra, 2015). As such, the 1984 ash basin is the likely source.
The global and site specific geochemical models, as demonstrated by the
Pourbaix diagrams below, indicate the dominant selenium species anticipated
within the aquifer system is Se(IV) which is relatively immobile, having an
elevated Kd, and will sorb to the aquifer matrix. Both Se(IV) and Se(VI) could be
present in the 1984 ash basin system due to the relatively high pH and variable
Eh associated with the ash pore water. Actual speciation analysis indicates
Se(IV) was detectable in the 1971 basin ash pore water and Se(VI) was not
detected. As water migrates away from the basin, the pH will decrease toward
background concentrations as the Eh increases. As a result, it would be expected
that if Se(VI) is present, it could migrate from the source area under high
reduction potential conditions; thereafter, little if any Se(VI) will persist. These
geochemical model predictions explain the selenium distributions detected in
site groundwater. The selenium speciation data collected in 2015 indicate that
where selenium is detected in the groundwater, it can be present in either form
(Appendix C).
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In general, as the pH decreases and the Eh increases, the concentration of
selenium increases indicating the dominant species in the area north of the 1984
basin is likely selenium (IV).
Note in Table 1 that mean value for Kd as developed by UNCC (2016) is 10.2.
However, selenium was not included in the flow and transport model due to the
non-detectable source area concentrations and the lack of a discernable plume at
the time of the modeling. Using the Kd from UNCC (2016), the retardation factor
(R) likely varies from 42 to 83; meaning that the sorption process reduces the
concentration of selenium by the retardation factor and also reduces the velocity
of movement by the retardation factor. Based on the Kd values predicted by
PHREEQC and measured by UNCC, the actual retardation factor is likely to be
much greater.
The site specific Pourbaix diagram shows the current site geochemistry favors
Se(IV) and a few locations where the conditions are close to favoring Se(VI).
With ash basin closure by excavation, and the anticipated groundwater
extraction system(s), groundwater will return to the lower background pH and
moderate Eh conditions. The change in groundwater geochemistry is not
anticipated to increase selenium mobilization.
Global Geochemical Model Site-Specific Geochemical Model
Reference: Powell, B.A., Analysis of Geochemical Phenomena Controlling Mobility of Ions from Coal Ash Basins at the Duke Energy L. V. Sutton
Energy Complex, Wilmington, NC. October 14, 2016: Greenville, SC.
Less
Mobile
Se (II)
Se (II)
Se (VI)
Se (IV)Se (VI)
Se (IV)
More
Mobile
Se (IV)
Se (VI)
Se (VI)Se (IV)
Se (IV)
Se (IV)
Se (II)
Se (II)
More
Mobile
Less
Mobile
Refined Geochemical Model Report October 2016
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A flow and transport groundwater model was not conducted using selenium;
however selenium can be incorporated in future groundwater flow and transport
models.
CAMA requires groundwater monitoring for 30 years following closure. The
groundwater monitoring can be used to confirm these predictions.
2.2.6 Thallium
Thallium has been detected at concentrations slightly greater than the IMAC in
surficial monitoring wells located close to the southeast side of the 1971/1983 ash
basin (MW-22C and MW-23C), and close to the north and east side of the 1984
basin (GWPZ-1B, MW-36B and MW-40C). However, the data do not support a
significant areal distribution pattern away from the basins nor a strong
correlation with pH or Eh (Appendix B contains an isoconcentration map from
the CSA and Appendix C contains a historical data summary table). Due to the
small areal distribution pattern and the very low detectable concentrations, the
constituent was not evaluated as part of the flow and transport model in the
CAP.
The geochemistry of thallium (Tl) has not been studied as extensively as other
elements of interest to coal ash such as As, Cr, Co and Se. In particular, there are
few studies which have examined Tl sorption to solid phases of relevance. Since
sorption to solid phases is the primary means by which the mobility of most
constituents of interest are retarded, it is critical to have reliable and self-
consistent thermodynamic equilibrium constants describing the sorption
reactions. Specifically, equilibrium surface complexation constants for Tl-
ferrihydrate and Tl-gibbsite systems are needed to maintain self-consistency
within the modeling approach. However, there are no studies of Tl sorption to
gibbsite and only two that have been found examining Tl sorption to
ferrihydrate. A comparison of these studies reveals that the constants
determined vary by approximately one order of magnitude. There is also
considerable discussion within the peer reviewed literature regarding the
appropriateness and validity of several of the constants used to generate
Pourbaix predominance diagrams for the Tl system. Therefore, the currently
available literature is unreliable in its current form to utilize in the geochemical
analysis of Tl behavior in coal ash disposal sites. The discussion of Tl will be
restricted to empirical observations of the influence of pH and Eh.

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The historical data summary table does not indicate a strong concentration trend
associated with pH or Eh, only a trend of decreasing concentrations away from
the ash basins and over time. Due to these observations, and the very low
concentrations that have been detected in very limited areas, there is no reason to
suspect that the planned corrective actions will mobilize and create higher
concentrations of thallium in groundwater.

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3.0 CONCLUSIONS AND RECOMMENDATIONS
1. The planned corrective actions, excavation of ash basins and FADA, and
groundwater extraction, will remove COI mass from the source areas and
downgradient, promoting the return of natural background geochemistry of low pH
and moderate Eh. Along with mass removal, this will create optimal conditions for
most geochemically influenced constituents to become the less mobile species.
2. The mobility of boron, the primary constituent present in the aquifer system, is not
influenced by groundwater geochemistry. The potential changes in groundwater
geochemistry as a result of the corrective action will favor the more immobile
species of arsenic, chromium and selenium. Cobalt may become more mobile,
however the concentrations in the source area are negligible. There is insufficient
literature available for predictions involving thallium. However, the data suggest
small source area concentrations that will get even less with the source removal
measures.
3. The global water geochemistry model provided in the CAP has been augmented
with a site specific model. Both models are discussed in the report attached
(Appendix A). The site-specific model confirmed the results of the global model.
The results of the site specific model include a calculation of Kd values that in
aggregate are similar to or greater than the values used in the groundwater flow and
transport model provided in the CAP. This indicates that the models are
conservative and overestimate the concentration of the constituents of interest in
groundwater downgradient of the ash basins. No significant changes in Kd values
are anticipated as a result of closure and remedial actions.
4. Selenium can be included in future flow and transport modeling. It was not
previously modeled due to a lack of data indicating a discernable plume at the time.
5. CAMA requires groundwater monitoring for 30 years following closure. The
groundwater monitoring can be used to confirm the findings of this report.

Refined Geochemical Model Report October 2016
L.V. Sutton Energy Complex, Wilmington, NC SynTerra

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4.0 REFERENCES
DEQ, Division of Water Resources comments to Sutton Energy Complex Geochemical
Modeling. August 19, 2016. Charlotte, NC
Dzombak, D.A. and F.M.M. Morel, Surface complexation modeling : hydrous ferric oxide.
1990, New York: Wiley.
Falta, R. W., Brames, S. E., Graziano, R., Murdoch, L.C., Groundwater Flow and Transport
Modeling Report for L.V. Sutton Energy Complex. 2015.
Karamalidis, A.K. and D.A. Dzombak, Surface Complexation Modeling: Gibbsite. 2010,
Hoboken, NJ: John Wiley and Sons, Inc.
Powell, B.A., Analysis of Geochemical Phenomena Controlling Mobility of Ions from Coal Ash
Basins at the Duke Energy L. V. Sutton Energy Complex, Wilmington, NC. October 25,
2016: Greenville, SC.
SynTerra, Comprehensive Site Assessment Report – L.V. Sutton Energy Complex, Wilmington,
NC. September 2, 2015: Greenville, SC.
SynTerra, Comprehensive Site Assessment Report, Supplement 1– L.V. Sutton Energy
Complex, Wilmington, NC. August, 2016: Greenville, SC.
SynTerra, Corrective Action Plan Report Part 1– L.V. Sutton Energy Complex, Wilmington,
NC. November 2, 2015: Greenville, SC.
SynTerra, Corrective Action Plan Report Part 2– L.V. Sutton Energy Complex, Wilmington,
NC. February12, 2016: Greenville, SC.

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L.V. Sutton Energy Complex, Wilmington, NC SynTerra

P:\Duke Energy Progress.1026\108. Sutton Ash Basin GW Assessment Plan\20.EG_CAP\CAP Site Specific
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FIGURE
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ABMW-1D/S(ABANDONED)
ABMW-2DABMW-2S
AW-1BAW-1C
AW-2BAW-2CAW-2D
AW-3BAW-3C
AW-4BAW-4C
AW-5BAW-5CAW-5DAW-5E
AW-6BAW-6DAW-6E
AW-7D
AW-8BAW-8C
AW-9BAW-9CAW-9D
GWPZ-1AGWPZ-1B
GWPZ-2AGWPZ-2B
GWPZ-3AGWPZ-3B
GWPZ-4AGWPZ-4B
MW-4AMW-4
MW-4B
MW-5AMW-5B
MW-5C
MW-5CDMW-5DMW-5E
MW-7C
MW-8
MW-9
MW-10
MW-11
MW-12
MW-15
MW-15D
MW-16MW-16D
MW-18
MW-19
MW-20MW-20D
MW-21C
MW-22BMW-22C
MW-23BMW-23C
MW-23DMW-23E
MW-24BMW-24C
MW-27B
MW-28BMW-28C
MW-28T
MW-31C
MW-32C
MW-33C
MW-34BMW-34C
MW-35BMW-35C
MW-36BMW-36C
MW-37BMW-37C MW-37CDMW-37DMW-37E
MW-38BMW-38CMW-38DMW-39BMW-39CMW-39D
MW-40BMW-40CMW-40D
PE-SW-5 PE-SW-6B
PE-SW-6D
PE-SW-6E
PZ-6D
PZ-6S
PZ-10D
PZ-10S
PZ-23D
PZ-INT
SMW-1BSMW-1C
SMW-2BSMW-2C
SMW-3BSMW-3C
SMW-4BSMW-4C
SMW-5BSMW-5C
SMW-6BSMW-6CSMW-6D
SW-004
SW-1C
SW-6A
SW-8A
SW-CF001
MW-24R-BMW-24R-C
FORM ER ASHDISPOSALAREA
1971 ASHBASIN
1984 ASHBASIN(LINED)
1984 ASHBASIN(LINED)
1983EXTENSION
DW-14
DW-15(PRW-1)
DW-09
DW-07
DW-10
DW-08
DW-06
PRW-2
PRW-3DW-11
DW-12
PRW-6 DW-13 PRW-7
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DW-02
DW-03
DW-05
DW-04
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DUKE ENERG Y PROGR ESS SUTTONPLANT SITE BOUN DARY
NOTES:
2014 AERIAL ORTHOPHOTOGR APHY OBTAINE D FROMUSDA NRCS GEOSPATIAL DATA GATE WAY(https://gdg.sc.egov.usda.gov/GDGOrder.aspx).
DRAWING HAS BE EN SET WITH A PROJE CTION OFNORTH CAROLINA STATE PLAN E COORDINATE S YSTEMFIPS 3200 (NAD83/2011).
MW-7A
NORTH TRANS ECT
DRAWN BY: A. FEIGLPROJECT MANAGER: P. WALDREPCHECKED BY: A. ALBERT
DATE: 10/19/2016
148 RIVER STREET, SUITE 220GREENVILLE, SOUTH CAROL INA 29601PHONE [phone]www.synterracorp.com
P:\Duke Energy Progress.1026\00 G IS BASE DATA\Sutton\Map_Docs\CSA_Supplement\FIG01_SGMODELTRANSECT_11x 17.mxd
500 0 500 1,000250
IN FEET
GRAPHIC SCALE
SOUTHEAST TRANSE CT
TRANSITION (EAST) TRA NSECT
FIGURE 1SITE GEOCHEMICAL MODEL TRANSECTL.V. SUTTON ENERGY COMPLEXWILMINGTON, NORTH CAROLINA
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TABLES
Refined Geochemical Model Report October 2016
L.V. Sutton Energy Complex, Wilmington, NC SynTerra

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TABLE 1
SUMMARY OF Kd VALUES
L.V. SUTTON ENERGY COMPLEX

Parameter
Reactive
Transport
Modeling
Derived Kd
Value
(L/kg)
Mean
Kd Value
Measured by
UNCC Batch
Experiments
(L/kg)
Range of Kd Values
From PHREEQC
Geochemical Model
(L/kg)
Arsenic 9 48 Total As: 14.65 to 2.9 x 106
As(V): 14.6 to 2.9 x 106
As(III): 0.03 to 697
Value for Average GW conditions:
As(III): 369;
As(V) 2.22 x 104
Boron 0 1.7 Range: 1.1 x 10-5 to 0.031
Geometric mean: 2.4 x 10-3
Value for average GW conditions: 0.006
Chromium Not Modeled Not Measured Total Cr: 23 to 6.9 x 108, Mean 1.6 x 106
Cr(III): 23 to 6.9 x 108, Mean 1.8 x 106
Cr(VI): 7.45 x 10-4 to 2.7 x 105, Mean 22
Average GW conditions: Total Cr: 7.15 x 107
Cobalt Not Modeled 141 Values varied with GW Simulants
Minimum Simulant GW: 2226
Average Simulant GW 32.5
Maximum Simulant GW 0.11
All Average GW 0.79
Selenium Not Modeled 10.2 Values varied with GW Simulants
Minimum GW simulant: 3415
Average GW simulant: 96
Maximum GW simulant: 3.1
Average groundwater conditions: 160
Prepared by: AA Checked by: PBW

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APPENDIX A
L.V. SUTTON ENERGY COMPLEX
GEOCHEMICAL MODEL ANALYSIS

Brian A. Powell, Ph.D.
112 Cherry Street
Pendleton, SC 29670
[phone]
[e-mail]

October 25, 2016

Kathy Webb, PG
SynTerra Corporation
148 River Street, Suite 220
Greenville, SC 29601

Dear Ms. Webb,

I write to address the comments of the letter from the North Carolina Department of
Environmental Quality (DEQ), Division of Water Resources dated August 19, 2016 with the
subject “Sutton Energy Complex, NPDES Permit No. NC0001422 New Hanover County, North
Carolina, Comments on Geochemical Modeling (Corrective Action Plan Part 1, Appendix D,
and Corrective Action Plan Part 2, Appendix C)”. I have carefully read the comments from DEQ
and revised our geochemical modeling approach accordingly. The revised model was submitted
to your office as report “Analysis of Geochemical Phenomena Controlling Mobility of Ions from
Coal Ash Basins at the Duke Energy L. V. Sutton Energy Complex”. In the text below, I have
provided a point-by-point description of how the comments from DEQ have been addressed in
this revised geochemical model. For clarity, the DEQ comments are provided below and the
description of our response is provided below each comment in italicized text.

Please let me know if you have any further questions or comments regarding this revised
geochemical model.

Sincerely

Brian A. Powell, Ph.D.
Attachment I
General and Site-Specific Comments regarding the Geochemical
Modeling Presented in the SynTerra Corrective Action Plan Part 1,
Appendix D, and SynTerra Corrective Action Plan Part 2, Appendix C

General Comments
1. Provide a site-specific conceptual geochemical model for areas of the Sutton site
associated with contaminant concentrations above 15A NCAC 02L (2L). Modeling should
describe, at the local, monitor well scale, the geochemical conditions (and/or other factors)
that explain the horizontal and vertical distribution of observed contaminant concentrations
along flowpath transects upgradient of the ash source, within the ash source, and
downgradient of the ash source. As part of this effort, identify the dominant mechanism(s)
that immobilize or otherwise reduce concentrations of a given constituent along the transect
(for example, adsorption/desorption and mineral precipitation/dissolution processes) and
discuss whether or not changes in pH, Eh, unstable soil oxyhydroxides, or other conditions
during or after the proposed corrective action would be expected to result in higher or lower
downgradient Contaminant of Interest (COI) concentrations in the future and how quickly
those effects would be expected to occur. The descriptions should include tables of the
specific well and boring data upon which the conceptual model is based and any calculations
(such as mineral saturation indices) that are made to develop the site-specific model. This
exercise should be done for each COL
Separate localized conceptual modeling is needed to explain the occurrence of isolated
elevated COI concentrations at locations not along obvious flowpaths, such as selenium
concentrations in the area of MW-27B (north of the new ash basin) and arsenic concentrations
in the area of MW-21C (south east of the old ash basin). The conceptual model should also
explain why pore water COI concentrations are lower than downgradient COI concentrations
in certain areas.
If the generalized understandings gleaned from site-wide data/modeling are verified in
specific areas of the site (using actual monitor well pH/Eh/COI data) where particularly
high concentrations of a given COI are observed and/or where pH or Eh conditions vary
substantially from that of other areas of the facility, they may be used as the basis for
CAP design. If the understandings do not hold, efforts should be made to explain why and
subsequently to correct or supplement them in those specific areas. Only those conceptual
models using site data, to accurately explain the causes and effects of COI concentration
distributions and mobility in individual areas of the site should be used as evidence in
support of a proposed CAP remedy.

Page 2 
Initially we have taken a global approach to the geochemical modeling simulations and
considered a range of pH, Eh, ion concentrations, and extractable Fe and Al
concentrations from seven coal ash disposal sites. The intent of this global model was to
provide a qualitative conceptual model of constituent behavior across a wide range of
conditions. The global model successfully predicts the influence of major geochemical
parameters such as pH and Eh on the partitioning of the constituents of interest
between solid and aqueous phases. Based on the comments above, we have run
additional simulations using site specific data and parameters. Three transects
originating from the well ABMW-1S/D within the coal ash basin where used.
Specifically, From ABMW-01S/D the flow paths chosen for the transect flow north, east,
and southeast as follows:

 North Transect
o ABMW-01S/D  MW-36B/C MW-27B/C  MW-38B/C/D  AW-08B/C
 East Transect
o ABMW-01S/D  MW-23B/C/D/E AW-06B/D/E & MW-12  SMW-06B/C/D
o with an additional branch from MW-23B/C/D/E  AW-04B/C  SMW-01B/C/D
 Southeast Transect
o ABMW-01S/D  MW-18 MW-21C MW-28B/C MW-07A/B/C  AW-
09B/C/D
The site-specific transect models use the measured pH, Eh, and ion concentrations data
from each well on a transect to simulate the geochemical behavior of As, B, Co, Cr, and
Se. Though there are some wells with measureable Tl at the site, we have not conducted
a rigorous analysis of Tl geochemical behavior due to the lack of reliable
thermodynamic data to support the model. Both the global model and the site-specific
transect models consider ion sorption to ferrihydrate and gibbsite. In the site-specific
transect model, the stability of the ferrihydrate and gibbsite phases was monitored by
coupling the modeled masses of these minerals to the sorption site density within
PHREEQC (the geochemical modeling program selected for this work) using the
EQUILUBRIUM_PHASES command. The initial Fe and Al model input concentrations
were based on the extractable Fe and Al concentrations in solids recovered from wells
along the transect. Therefore, this revised site-specific model provides an explicit
analysis of the site conditions.
As discussed below and in the report, this model was used to evaluate if ongoing or
planned remediation efforts have the potential to unintentionally mobilize any
constituents of interest. In almost all cases, the geochemical model predicted sorption
distribution coefficients (Kd) which were greater than those used in the flow and
transport modeling efforts.

Page 3 
2. The localized conceptual models discussed in #1 above should be used, along with the
appropriate monitor well and boring data, to inform and develop 1-D PHREEQC transport
models along selected flowpath transects. These evaluations are needed in support of the
proposed CAP. Once the computer models are developed and their accuracy verified (by
comparing observed to simulated dissolved concentrations in wells along the transect), the
models should be used to predict future concentrations based on changes to the geochemical
setting (pH, Eh, etc) that are expected to occur as a result of the proposed corrective actions.
Sensitivity analyses may be used as needed to bracket the plausible downgradient
concentrations expected in the future; this is particularly important if there is only limited
confidence in the models, input data, or assumed future conditions.
As noted above, we have performed modeling along specific transects and the model
accurately predicts changes in the aqueous concentrations of the constituents of
interest with respect to changes in geochemical parameters. However, we have not
performed a quantitative comparison of the data. This is because agreement between a
geochemical model of this nature and field data is essentially unachievable in such a
complex system. The key reasons for this are as follows:
1) The geochemical model assumes equilibrium conditions are met but this may not
be the case in localized environments.
2) The geochemical model considers pure mineral phases ferrihydrate and gibbsite
as the primary sorbing surfaces due to the availability of a self-consistent
thermochemical database of sorption equilibrium constants. However, within
the model these phases are an approximation of sorption sites whereas the true
sorption sites are likely a wide ranging mixture of minerals.
3) The field data has inherent variability in the spatial and temporal
concentrations of many constituents of concern which would be difficult if not
impossible to capture with an equilibrium modeling approach.
Despite the inability to provide a quantitative comparison between the geochemical model
and site specific data, the model accurately predicts trends in the sorption behavior of the
constituents of interest with respect to changing pH and Eh and thus was used in the report
to describe how future remediation efforts may influence the mobility of the constituents.
3. In some cases a single value of Kd is used to represent sorption along very long flow
paths. However, measured and modeled Kds are shown in the reports to have some very
large ranges (up to orders of magnitude). Sensitivity analyses should be conducted and
presented to demonstrate the effects of Kd on predicted COI concentrations.
The flow and transport model used the lowest measured Kd value in order to provide a
conservative estimate of the COI migration from the ash basins. The Sutton transport
model considered transport of boron, arsenic, and vanadium. Measured boron Kd values
ranged from ~0 to 1100 mL/g, a value that is not considered realistic. During the model

Page 4 
calibration, it was found that any boron Kd value larger than 0 would under predict the
observed extent of boron migration at the site.
The measured Kd values for arsenic at Sutton ranged from 8.7 to 500 mL/g. The transport
model used a low value of 9, which results in a retardation factor of 73. This level of
retardation allows the arsenic to migrate only a short distance from the ash basins in the
transport simulation, and the simulation was not able to reproduce the observed arsenic
values at MW-21C. The use of a higher value of Kd in the simulation would further restrict
the migration of arsenic. The use of a lower Kd value did not appear to be justified by the
experimental data or geochemical modeling.
The measured Kd for vanadium ranged from 1.9 to 1300 mL/g, and the transport simulation
used a value of 2.0 mL/g. This low value for Kd still results in a retardation factor of 17,
and the simulated vanadium plume migrated only a short distance from the ash basins. The
use of a higher Kd value in the model would only reduce the predicted migration.
4. The primary contaminant attenuation mechanism at Sutton is adsorption onto Fe and Al
hydroxides present in the aquifer solid phase. As a consequence, the stabilities of these
solids under site conditions are important because they affect the available concentration of
adsorbent. The solubility of Fe hydroxide is strongly pH and Eh dependent, while the
solubility of Al hydroxide is pH dependent over the ranges considered for the site
geochemical model. To this end, because the sorption model is built upon the presence of
HFO in the aquifer, it would be beneficial to discuss the stability of this solid phase under
the measured pH/Eh conditions found in the aquifer(s). What is the saturation index of HFO
(or ferrihydrite) for the range of water chemistries found across the site? A discussion should
be included in the report on Al and Fe adsorbent stabilities, perhaps with accompanying
Pourbaix diagrams for these elements. The effects of various remediation methods on the
solubilities of these minerals should also be addressed. See Section 4 of the CAP Part 1
Appendix D for context.

This evaluation of adsorbent stability is consistent with the following statement from the CAP
Part 2 Appendix C Introduction, last sentence: "Therefore, the primary emphasis was to
quantify how changes in the system conditions will alter the speciation and mobility of each
constituent (particularly changes in pH and EH). This will allow us to determine if changes
occurring during remediation could mobilize any particular constituent." A critical component in 
this process is to calculate the solubilities of the adsorbents as a result of changing pH and Eh, 
which has not been discussed in the report and apparently was not part of the PHREEQC model 
calculations (i.e., the Fe and Al adsorbents were not specified as Equilibrium Phases in the 
PHREEQC simulations). 
In the global model, a range of Fe and Al concentrations were used to evaluate the impact
of changing concentrations on the mobility of constituents. With the exception of specific
low pH conditions where the input Fe concentrations were well below expected values for
the site, ferrihydrate and gibbsite remained saturated. For the revised site specific

Page 5 
transect model, we have explicitly monitored the stability of ferrihydrate and gibbsite and
furthermore we have linked the concentration of Fe and Al to the concentration of
sorption sites (hydrous ferric oxide, HFO and hydrous aluminum oxide, HAO sorption
sites which are related to the mass of ferrihydrate and gibbsite, respectively, in the
system). This was done by considering the sorbing phases using the
EQUILUBRIUM_PHASES command in PHREEQC as requested above. Therefore, the
concentration of sorption sites is directly related to the saturation state of ferrihydrate
and gibbsite. However, it is noteworthy that the Fe and Al, HFO and HAO, are only
proxies for adsorption sites and many other mineral phases may be influential as well.
5. The following comment pertains to any additional sampling and analysis of solid samples
that may be needed to augment the CSA, CAP, or geochemical modeling. To extend the
results of the lab-derived Kd terms to other soils and geochemical conditions, soil samples
were collected and analyzed for hydrous ferric oxide (HFO), which is believed to be the
dominant adsorbent of COPCs. Soil HFO is typically measured using a selective, sequential
extraction technique that dissolves the HFO so that its concentration can be quantified.
Because dissolving the HFO also releases to solution the species adsorbed to the solid, it is
recommended that those adsorbed species also be measured during the HFO determination.
This additional data can then be used to calculate adsorbed concentrations on the HFO, which
can be compared to the Kd-derived concentrations to provide assurance that there is
relationship between Kd and HFO concentration. Also, the measured adsorbed concentrations
can be used to validate computer calculations of surface complexation models developed to
simulate the adsorption process.
This is an insightful comment and these measurements could provide useful data.
Unfortunately, these measurements were not performed during the HFO and HAO
extractions. We do have limited data on the solid phase concentrations of constituents
using the US EPA Test Method 1312: Synthetic Precipitation Leaching Procedure. However,
there is insufficient data for both the solid phase and pore water concentrations of constituents for
the Kd estimations described above. This is due to either a spatial or temporal mismatch between
the pore water and solid phase concentration measurements and/or the pore water concentrations
of constituents below detection limits. Furthermore, higher temporal resolution may be hard to
achieve since solid phase data comes from soil boring samples during well installations.
6. A range of Kd values for each contaminant is calculated by PHREEQC based on
the variable input parameters pH, Eh, and major ion concentrations (See CAP Part 2
Appendix C, Tables 3.1, 4.1, 5.2, and 6.1). For each contaminant, the Kd range covers
several orders of magnitude. It appears that for the transport model calculations only a
single Kd values was used for each contaminant at each site (Tables 3.1, 4.1, 5.2, and
6.1). It is very likely that the Kd value is not constant along the flowpath away from
the source to the leading edge of each plume. Why is the constant Kd transport
modeling approach appropriate for simulating the current conditions and possible
future conditions after remediation? Choosing a low Kd for transport modeling may
be conservative from the standpoint of plume extent, but it is not at all conservative in

Page 6 
terms of the solid phase adsorbed contaminant concentration that will provide a long-
term source of contamination to groundwater following remediation. This source of
contamination in the solid phase of the aquifer will extend the amount of time necessary
for the groundwater to reach a target cleanup level.
The flow and transport models use single Kd values because there was insufficient
evidence to support multiple Kd values or spatially variable Kd values. We have identified
trends in constituent pore water concentrations with changes in geochemical parameters
(mainly pH and Eh). Future modeling efforts and field measurements will verify the
geochemical model output and revised flow and transport models will be developed as
necessary with model iterations to evaluate the impact of less or more conservative Kd
values.
7. Consideration should be given to the potential importance of groundwater residence time
and duration of mineral weathering in evaluating groundwater concentration levels. For
example, chemical weathering reactions generally consume H+ thereby increasing groundwater
pH. Also, chloride concentrations increase in groundwater as a result of leaching from
minerals, resulting in higher concentrations correlated with groundwater age. The overall
result is that groundwater pH and chloride concentrations increase along a groundwater
flowpath or between a shallow and deep aquifer. See the CAP Part 1, Appendix D, Section 3
for context.
The global geochemical model predicts how the Kd values will change as a result of
changes in pH, Eh, and ion concentrations across the range of values expected at the site
using geochemical data from wells placed and installed to assess CCR influences on
groundwater. The wide range of Kd values are primarily due to changes in pH. These same
trends were predicted when using site specific data and coupling the availability and
reactivity of sorbing Fe and Al mineral phase. Furthermore, we performed an explicit
analysis of the sorption capacity of Fe and Al minerals.
8. Molecular oxygen is rarely the dominant redox buffer in groundwater systems because of
slow reaction rates. Most likely, the redox buffers that have the greatest influence on Eh are
Fe or Mn. See CAP Part 1, Appendix D, Section 3 for context.
We agree with this comment. However, in our model simulations Eh, Fe, and Mn
concentrations were fixed based on site measured values. In order to maintain a constant
Eh within PHREEQC, a reagent must be added. We selected dissolved oxygen to fix the
measured Eh values so that we did not alter the measured Fe and Mn concentrations
which were used for model input.
9. Discuss whether or not an anoxic environment was maintained during sample transport
and lab Kd testing (glove box) for solid samples collected from locations characterized by
low dissolved oxygen. Discuss whether or not the model results are sensitive to the chosen
sample collection and testing methodology. If they are sensitive to the collection and testing

Page 7 
methods, how does this affect the conclusions that have been drawn about COI mobility and
model predictions?
The samples were not maintained in an anoxic environment and the experiments were
conducted on the benchtop under atmospheric conditions. Since we have not performed
experiments under anoxic conditions, we cannot definitively state if the measurements are
sensitive to data collection and testing methodology. The transport modeling used the
lowest measured Kd values for boron (0 mL/g), arsenic (9 mL/g) and vanadium (2 mL/g).
These were considered to be reasonable lower bounds for the likely Kd values at the site.
10. Please provide date and page numbers in the updated report.
This has been done in the revised report.

Site-Specific Comments
1. CAP Part 1, Appendix D. The Executive Summary states "The capacity of the aquifer
solids to sequester the constituents of interest was estimated by assuming the aquifer solids
contained 0.05 moles of sorption sites per mole of extractable iron. The number of moles of
several constituents of interest in the pore fluid was estimated assuming all constituents were
present at the NC2L standard levels. Assuming 100% sorption of the summation of the total moles
of all constituents, less than 1% of the total available sorption sites was occupied. Therefore, it
appears the aquifer solids have sufficient sorption capacity for high concentrations of all
constituents though the actual sorbed concentrations will vary based on the sorption affinity (i.e.,
distribution coefficient) of individual constituents". This conclusion appears to erroneously
neglect the fact that flowing groundwater has contributed this amount of moles of constituents
(mass flux) to available sorption sites for many decades and will continue to contribute more over
time. In other words, the sorption sites have been filling for a very long time and will continue to
do so. Only by accounting for the total mass of constituents contained in the ash can an appropriate
conclusion about the sequestration capacity of the aquifer be drawn.
Also, sorption sites may also be filled with constituents that are not COIs (major ions, etc.).
For all future solid sample extraction tests to quantify adsorption capacity, the concentrations
of ALL COIs in the extract should be measured along with the Fe or Al adsorbent
concentrations. This will allow the actual data to confirm and corroborate the modeled or lab-
observed sorptions for the various COIs from the collected solid samples.
We appreciate this insightful comment. The model accurately considered the extent of
sorption essentially within one pore volume of fluid but does not consider the exchange of
that fluid with addition mass of sorbing constituents. We have revised the estimations of
the capacity of aquifer solids to retain constituents in a new method in the revised report.
We have used the ADVECTION command in PHREEQC to simulate 500 pore volume
exchanges between the source waters from the ash basin well ABMW-1S/D through wells
along the east transect. The changes in solid phase concentrations of the constituents
along with the stability (i.e. saturation state) of the Fe and Al adsorbents were monitored.

Page 8 
2. CAP Part 1, Appendix D, Section 2, last paragraph states "In these Pourbaix diagrams, the
EH and pH measurements from the Sutton site are shown as individual data points. A generic
groundwater chemistry containing 500 ppb of each constituent of concern was used in the
simulations (Fable 2.1). These concentrations are generally higher than the concentrations
observed in Sutton groundwater samples." This statement appears to be inaccurate, at least for
concentrations of boron, iron, and manganese. Actual maximum observed groundwater
concentrations should be used for these COIs.
We have updated the comment to indicate that 500 ppb concentrations of B, Fe, and Mn
are lower than some measured values. However, we have not updated the model. Our
intent was to have a direct comparison between constituents. Therefore, we used relatively
low concentrations for most constituents.
3. CAP Part 1, Appendix D: Section 3, last paragraph. The report should provide additional
information on the filtration testing. Was filtering done in the field or in the lab? Were the
filtrates preserved with acid prior to analysis? How did the results of the 0.1 u filters compare
to those of the 0.45 u filters? What does this say about colloidal transport at the site? In
looking at filtered pore water results versus filtered down or side gradient groundwater
results, are the colloids associated with ash? Were filtered (dissolved) sample results used
exclusively in the PHREEQC model?
Filtration testing was performed in the field at the time of sample collection and
immediately preserved with acid using predetermined bottle ware. Results indicate that
the majority of constituents of interest are soluble. Concentrations of relatively insoluble
ions such as Fe and Al decreased upon filtration. These data come from the dissolved and
total concentrations of analytical results, and are discussed in the report. Most colloids
appear to be associated with ash, with highest concentrations down gradient of the basin
and lower concentrations side gradient of the basin, following groundwater flow from the
ash basin.
4. CAP Part 1, Appendix D: Section 4, second to last paragraph, last sentence states: "This
assumed value is similar to value of 0.11 molsites/molHFo (0.2 molsites/molFe) used by Dzombak and
Morel in a global model of ion sorption to HFO (discussed in section 6 below)". If all of the sites
are on HFO and HFO contains only one Fe in its formula, then the site densities on a mole scale
should be the same (0.20 molsites/molHFO). The assumed fraction of extractable Fe that is available
for sorption sites should be 0.20 molsites/molHFO in the equation.
This was corrected in the revised model and 0.2molesites/molferrihydrate was used.
5. CAP Part 1, Appendix D: Table 5.1 should include all COIs.
This table includes the COIs which were specifically considered in the report.
6. CAP Part 1, Appendix D: Section 4, last paragraph, the conversion from pe to Eh is
incorrect. The correct relationship is Eh(volts) = 0.059 x pe or Eh(mV) = 59 x pe (Appelo

Page 9 
CAJ and D Postma. 1993. Geochemistry, Groundwater, and Pollution. page 246). A pe of -5
corresponds to an Eh of -296 mV and a pe of 15 corresponds to an Eh of 888 mV.
This was a calculation error when making plots for the figures. Accurate values were used
in the geochemical model shown in the CAP 1 appendix and in the models described in
our revised report.
7. CAP Part 1, Appendix D: Section 5. Assuming that the adsorption calculations were all
run over ape range of -5 to 15, the Eh values in all the figures in this section are incorrect.
The Eh range should be -296 mV to 888 mV.
This is correct. An updated report with the correct values in the figures has been
produced and is available if needed. However, the revised global and site specific models
provide a more specific set of conditions and input values and should supersede these
model output shown in these figures.
8. CAP Part 1, Appendix D: Section 6, 2nd paragraph. The relationship between pe and Eh is
given as pe = Eh x 59 mv. As discussed above, the correct relationship is pe =
Eh(mV)/59mV. The incorrect relationship is used throughout the geochemical modeling
report, which means that the conversion of the site-measured Eh values to pe for Table 6.1 and
the use of that pe in PHREEQC to calculate the modeled fractions of As(III) and As(V) in the
table lead to incorrect results. Figure 6.1 derived from the data in Table 6.1 also requires
correction.
As stated above, the conversion error was only in terms of the plot axes. Accurate values
were used for model input.
9. CAP Part 1, Appendix D, Section 6, Figure 6.2. The x-axis on this figure is pH+ pe.
Because the pe was not calculated correctly from the measured Eh for these water samples,
the locations of the samples along the x-axis will change significantly. For example, the first
water sample has a pH of 7.9 and an Eh of-170 mV (pe = -2.87); therefore, the pH + pe = 7.9 +
-2.87 = 5.03. However, the data are plotted at a pH+ pe value of -2.10 because the incorrect
conversion of Eh resulted in a pe of -10.
As stated above, this conversion error has been fixed for the plots and the model input
used the correct pe values.
10. CAP Part 1, Appendix D, Section 6. A counterpart table to Figure 6.2 should be
provided that includes the numerical percent differences between modeled values of COI
species concentrations and those that are observed in various locations across the site,
including areas with COIs above 2L. This should be done for each COL Expectations are that
boron and chloride concentrations should match to within about 20% and arsenic, selenium,
and thallium within about 50%.
As discussed above with regards to general comment #2, a quantitative match between the
modeled and measured aqueous concentrations cannot be made because of inherent

Page 10 
assumptions in the model and a wide range of variability in the field data. Therefore, this
level of agreement between the model output and field measurements cannot be achieved.
11. CAP Part 1, Appendix D, Section 7, 1st paragraph. At the end of this paragraph, the
gibbsite adsorption site density is given as 0.41 molsites/molA1. However, using the formula in
this paragraph to calculate this value with the provided data results in a site density of 0.033
molsites/molAl. This lower value for gibbsite is more consistent with the HFO value of 0.2
molsites/molFe and with HFO 's higher surface area of 600 m2/g (Dzombak and Morel 1990)
compared to the gibbsite surface area of 32 m2/ g. If the correct gibbsite adsorption site
density is0.033 molsites/molA1, then the gibbsite adsorption capacities given in Table 7.1 will all
decrease by about a factor of ten, bringing them close to the HFO capacities. Table 7.2 for gibbsite
will also have to be revised
This is correct. The higher value was assumed in our initial model to approximate an
amorphous aluminum oxide phase. To remain consistent, we have updated the global
model and used 0.033 molsites/molAl in all simulations. This value was also used in the site
specific transect models. The figures and discussion of the global model in our revised
geochemical model report have been updated with these new model simulations.
12. CAP Part 1, Appendix D, Section 8. Summary.

• 2nd Bullet. The assumption that 5% of the extractable iron content is
available for adsorption and that this modeling assumption predicts
Kd values within an order of magnitude needs to be revisited after
using correct pe values for the Eh measurements.
• 3rd Bullet. Arsenic speciation modeling and speciation modeling of
other COIs needs to be reviewed to confirm that correct pe values
were used.
• Provide a summary of the conceptual site geochemical model for
Sutton developed from site data and the PHREEQC calculations.
All bulleted points above have been addressed or corrected in the revised global model
and the site specific transect model.
13. CAP Part 2, Appendix C, Page 7, last paragraph. "The variability of the pH and Eh
conditions at each site will essentially be "noise" considering the wide range of Kd values
predicted as a function of pH which are discussed below. Therefore, one "global" model
which shows the influence of Kd as a function of pH and Eh within the selected range is
appropriate for all sites." How does a "global" site model deal with "outliers" that have been
observed at the sites. For example, there is localized elevated arsenic in groundwater in the
area of monitoring well MW-21 and elevated selenium in the area of monitoring well MW-
27. The geochemical conditions that have produced these outliers need to be understood so that
responses to remediation can be estimated at these locations. Details in item 14.

Page 11 
The global model does not address these outliers. Using the site specific model of
transects based on the recommendations in item 14 below, we have attempted to explain
these observed localized high concentrations of specific constituents.
14. CAP 2 Part 2- Site conditions described below seems not to be adequately simulated by
the geochemical model and the Global Model Input parameters listed in Table 2.3.
Groundwater data at the Sutton site reflect that there are three distinctive zones: North,
Transition N-S, and South zones.
North Zone (north of New Ash Basin)
Site Conditions: The "north zone" reflects low pH values, ranging from 4.5 to 5.6 and Eh
values ranging from 425 to 505 mv. The zone has been characterized by relatively low iron
concentrations (when compared to the rest of the site), high manganese concentrations, and
one isolated area where selenium has been consistently above the 2L standards (MW-27B).
The model provided did not include selenium as a contaminant of concern. Understanding
of selenium mobilization is required.
Input parameters in Table 2.3: The closest representation to site conditions would be: (1)
pH=4; Eh=482 (2) pH=5. l ; Eh=372; and (3) pH=5.6; Eh= -21. Being pH a logarithmic
function, additional modeling ("with more pH resolution") would be required to represent
the site.
Transition N-S Zone
Site Conditions: Groundwater data from the "transition zone" have reported pH values
generally> 5.5 and Eh values from 350 to 460 mv. Notice that pH values in the same well at
different screened depths show marked differences in pH (i.e. MW-24 Band MW-24C). The
zone has reported high concentrations of boron, iron, and manganese. Thallium above the
IMAC value has been observed in well MW-24B and Cobalt above the IMAC value has
been reported in MW-24C.
Input parameters in Table 2.3: The closest representation to site conditions would be:
pH=5.l; Eh=372. Being pH a logarithmic function, small differences in pH would have a
large impact on the predicted Kd values. Additional modeling (" with more pH resolution")
would be required to properly represent the site. Moreover, differences in geochemical
behavior can vary at the same location at different depths. Include geochemical modeling
of thallium and cobalt as contaminant of concern. Understanding of thallium and cobalt
mobilization and potential attenuation mechanisms is required.
South Zone (South, South-east of Old Ash Basin)
Site Conditions: Groundwater data from the "south zone" have reported pH values generally>
6.3 and Eh values in the range of 350-400 mv and around of 150 mv (i.e. MW-21C). The zone
has reported high concentrations of boron, iron, and manganese. There is an area with
consistent high arsenic concentrations (i.e., MW-21C). Thallium and cobalt above IMAC

Page 12 
values have also been reported. Include geochemical modeling to understand the observed
site conditions.
We appreciate this comment and the careful consideration of trends in the data and have
incorporated these regions into the transect analysis in the site specific model described.
in the revised report.
15. The PHREEQC modeling code used to make the speciation calculations and
develop the geochemical model requires that the pe of the system be used to input the
redox potential of the groundwater. Eh and pe are related by the following equation
from Stumm and Morgan (Aquatic Chemistry 3rd Edition, 1996, p. 444):
ܧ݄ሺݒ݋݈ݐݏሻ ൌ൬2.3ܴܶ
ܨ ൰݌݁ 
Using appropriate values for R (1.987 x 10-3 kcal/deg-mol), T (298 deg), and F (23.06
kcal/volt) results in the equation Eh(volts) = 0.059 volts x pe, which upon re-arranging
and with E h in millivolts (mv) becomes pe = Eh(mv) / 59 mv. The Eh to pe conversion
used for the geochemical modeling calculations was, however, pe = Eh (V) x 59 mV,
note dimensional inconsistency. This was stated in an e-mail (subject: L.V. Sutton
Energy Complex - Corrective Action Plan- Part 1 (Appendix D) - Geochemistry
Model: Verify pe-Eh relationship) on *February 9, 2016* from Morella Sanchez King,
notifying Mr. Ed Sullivan of the error and implications for the model results. This
results in a calculation error that increases the actual pe by a factor of 3.5. Using the
incorrect pe in PHREEQC results in redox speciation calculations that do not represent
the geochemical system being modeled and in conclusions as to redox speciation,
mineral equilibrium, and adsorption of the redox-sensitive species that are not accurate.
A geochemical model of the aquifer system based on these calculations is not
representative of actual site conditions. This comment applies to the Sutton CAP1
Appendix D and to the Sutton CAP2 Appendix C, but may also apply to the models
developed for other Duke sites.
As stated above, this conversion was a mistake made when making plots in the CAP 1
report. This has been corrected in the plots. The accurate pe values were used in the
PHREEQC models. So the model results are accurate.

16. CAP Part 2, Appendix C: Page i and Table 2.2. The site density of the Al adsorbent is given
as 0.4 moles of Al sites per mole of solid phase Al and this value is used in Table 2.2 to calculate the
Al adsorbent concentration (1.16 E-05 molsites/gsolid). The site density in molsite/molA1 can be
calculated from values of site density (8 sites/nm2 and aluminum hydroxide surface area of 32 m2/g
provided in Karamalidis and Dzombak (2010, Surface Complexation Modeling - Gibbsite). The
calculated site density from these values for the Al adsorbent is 0.033 molsite/molA1, not the 0.4
molsite/molA1 used to calculate the adsorbent concentration used in the PHREEQC modeling. Using
the correct site density, the Al adsorbent concentration should be 9.34 E-07 molsite/gsolid, which is a

Page 13 
factor of 12 less than that used for the Al adsorption modeling. This modeling should be redone with
the correct Al adsorbent concentration.
As stated above, the global model has been rerun with the recommended value from above
and the Kd values have been adjusted accordingly. Since sorption is dominated by the iron
oxide phases in most cases, there was relatively little change in the Kd values.
17. CAP Part 2, Appendix C: Page ii, Arsenic bullet, second sentence states: "1) increased
sorption of As(V) relative to As(III) which would remove all As(V) from the groundwater
and prevent As(V) measurements in samples". Sorption cannot remove all the As(V) from
groundwater. There will always be some dissolved in groundwater in equilibrium with the
adsorbed concentration. Sorption might remove As(V) to below the analytical detection limit.
The text has been modified to note As(V) sorption decreasing the aqueous concentration
to below detection limits.
18. CAP Part 2, Appendix C: Page ii, Arsenic bullet, middle of paragraph. " . .. the minerals
scorodite (FeAs04.2H20) and mansfieldite (AlAs0 4. 2H20 ) are near saturation under some
pH and EH conditions examined in this model and measured in the field." Figure 3.8 shows
that scorodite is more than 100 times undersaturated (Saturation Index < -2) under the
range of pH and Eh conditions considered. Mansfieldite is even further from saturation
under these conditions. It is highly doubtful that they could form and limit dissolved arsenic
in the aquifer.
We agree with this assessment and have removed this text from the revised geochemical
model description.
19. CAP Part 2, Appendix C: Page 2, first paragraph. Why were both the WATEQ4F and
MINTEQ v4 databases used? Why not use only the more comprehensive MINTEQ v4
database, which has all the necessary thermodynamic data?
The MINTEQ v4 database has some internal consistencies and the WATEQ4F database is
the primary database used for the original release of PHREEQC. Therefore, we chose to
use WATEQ4F and update the database as necessary.
20. CAP Part 2, Appendix C: Page 3, last paragraph. Why is the concentration of the solid
phase assumed to be 50 g/L? In a typical aquifer, the concentration is on the order of 8,000
g- solid/L-gw (based on a bulk density of 2 kg/L and a porosity of 0.25).
A concentration of 50 g/L was selected to provide a comparison with the experimental
batch sorption experiments performed by our colleagues at University of North Carolina-
Charlotte. However, it is noteworthy that we did use the bulk density and porosity values
from the flow and transport modeling in the site specific transect models and the updated
capacity estimations.
21. CAP Part 2, Appendix C: Page 5, first paragraph, third sentence states", modeling
groundwater concentrations of each constituent of interest at the 2L Standard Level and

Page 14 
conservatively assuming 100% sorption, the capacity of the solid phases to sorb the
constituents of interest was determined. In all cases, less than 1% of the total sorption capacity
of the solid phases was occupied by the constituents of interest [28-34]." This assumes that
adsorption only occurs between the solid phase and constituents in one pore volume of
groundwater. Flow of groundwater replaces that pore volume of water with fresh dissolved
constituents that have adsorbed since contaminants began entering the aquifer decades ago.
The method of calculating adsorption capacity used in the report is not at all conservative from
the standpoint of estimating consumption of the adsorption capacity by contaminants in
groundwater over the past few decades.
As stated above, we have revised this model and believe that the current approach
accurately accounts for the exchange of multiple pore volumes occurring over decades of
exposure.
22. CAP Part 2, Appendix C: Page 23, first paragraph. This discussion concerns major
ions competing with arsenic for adsorption sites thereby lowering arsenic adsorption;
however, arsenic adsorption is not shown on Figures 3.6 and 3.7 called out in the
paragraph. If the concentration is too low to plot, at least provide the arsenic adsorption
concentrations under the different scenarios.
A revised discussion of the As sorption behavior is provided in the revised geochemical
report. The modeled As concentrations are provided along with the detection limits and
groundwater standard values for comparison.
23. CAP Part 2, Appendix C: Page 23, middle of last paragraph. " ... a saturation index of
1 or greater indicates that the solution is saturated with respect to that ion and will
precipitate." A saturation index of O or greater indicates saturation. The conclusions in this
paragraph based on the saturation index of 1 should be revised.
This conclusion section has been revised accordingly.
24. CAP Part 2, Appendix C: Page 23, middle of last paragraph. The arsenic mineral
scorodite discussed in this paragraph as a possible solid phase limiting dissolved arsenic
concentration only forms under acidic, highly oxidizing conditions and would not form in
the site environments.
We agree and have revised this discussion to note that it is unlikely that scorodite will
influence As behavior in these systems.
25. CAP Part 2, Appendix C: Page 25, Figure 3.7. The HAO adsorption calculations need
to be redone using the correct adsorbent concentration as discussed in the first Specific
Comment above.
As discussed above, this was done in our revised global model and the recommended
values were used in the site specific transect model.

Page 15 
26. CAP Part 2, Appendix C: Page 26, Section 3.3. Because of the discrepancy between
measured and modeled arsenic redox species, would it be more realistic to run PHREEQC
using the measured As(III) and As(V) concentrations and not couple arsenic redox to the pe
entered for the run? This method may more closely simulate site conditions in which
dissolved arsenic in the groundwater appears to be primarily As(III).
We agree this would be a more representative simulation of the pore water. However, in
many cases, the concentrations of both As(III) and/or As(V) are below detection limits to
reliable values are not available. Furthermore, the simulation would not include the solid
phase speciation and concentration of As since those values are unknown. We believe that
the sorption of As(V) to the solid phase is the primary reason for this discrepancy.
27. CAP Part 2, Appendix C: Page 28, second paragraph. "The calculated and measured Eh
values are shown in Figure 3.10. From these data, it is clear that the expected Eh values based
on the Fe redox couple are higher than the measured values." Because measured Eh values are
primarily lower than calculated Eh by 100 to 200 mV, confirm that field measured ORPs
were correctly converted to Eh values. The conversion factor typically requires adding about
200 mV to the field-measured ORP, and may bring the two values into closer alignment.
The field measured ORP values were correctly converted to EH. These values were then
correctly incorporated into the PHREEQC simulations.
28. CAP Part 2, Appendix C: Page 29, third paragraph, first sentence. Briefly describe how
the Kds used in reactive transport modeling were derived if they did not come from lab
measurements or PHREEQC modeling of adsorption.
The transport modeling used the lowest measured Kd values for boron (0 mL/g), arsenic (9 mL/g)
and vanadium (2 mL/g). These were considered to be reasonable lower bounds for the likely Kd
values at the site.
29. CAP Part 2, Appendix C: Page 30, last paragraph. "In the models of the average and
maximum major ion concentrations from Table 2.5, competition for (boron) sorption sites
by other major ions results in a decrease in the observed Kd values." What specific ions
compete for the boron adsorption sites? There are dramatic decreases in calculated boron
Kds with changing groundwater compositions suggesting that the boron Kd may change
along groundwater flowpaths away from the ash landfills. It appears from Table 4.1 that in
most cases a constant Kd was used for transport modeling across entire sites. How is this
representative of site conditions?
With so many constituents simultaneously run in the geochemical model, it is difficult to
state which specific ions are competing with boron. Our analysis indicates that sulfate is a
likely competitor but this is not confirmed through modeling or experimental approaches.
The range of boron Kd values essentially produce a Kd value of zero (and a resulting
retardation factor of 1). This value was used in the flow and transport model to
approximate the boron plume extending from the ash basin.

Page 16 
30. CAP Part 2, Appendix C: Page 31, second paragraph. "Sorption of boron to
aluminum hydroxides was predicted to be significantly higher than iron oxides as shown
in Figure 4.4." Revisit this conclusion after revising the HAO site concentrations
discussed above in the first Specific Comment.
As expected for the lower site density, the concentration of boron on HAO sites was
predicted to be lower but still comparable to HFO sites. The text was modified
accordingly.
31. CAP Part 2, Appendix C: Page 39, first paragraph. "Based on the groundwater
concentrations listed in Tables 2.4 and 2.5, only Fe2CrO4 is predicted to have a solubility
product greater than one (indicating precipitation is possible)." Text should read " ..
.saturation index greater than zero.. .", instead of "solubility product greater than one."
This text has been modified accordingly.
32. CAP Part 2, Appendix C: Page 44, last paragraph. "However, rhodocrocite generally
occurs in hydrothermal systems and is unexpected to form under these site conditions."
Rhodochrosite is known to form under moderately reducing conditions caused by
contamination (e.g., beneath landfills) and may form under some of the site conditions.
We have not explicitly considered Mn in the revised geochemical model report provided
with this letter. However, this description of rhodocrocite stability will be updated in the
revised global model in future reports where Mn is discussed for specific sites.
33. CAP Part 2, Appendix C: Page 46, Arsenic Box. Reconsider checking the
Chemical Precipitation box because it is highly unlikely that any arsenic mineral will
form in the site environments. The arsenic minerals are orders of magnitude
undersaturated (Figure 3.8) in all the pH/Eh conditions and should not be considered
equilibrium phases in the model.
We have revised this table and removed the “check” under precipitation.
34. CAP Part 2, Appendix C: Page 47, Chromium Box. "This concentration range is similar
to what was modeled in PHREEQC and indicates that formation of mineral phases containing
Cr may occur under high pH conditions with relatively high Cr concentrations." What
chromium mineral phases might form? Why do they only occur under high pH and high
chromium concentrations? Amorphous Cr(OH)3 is known to limit dissolved chromium to low
concentrations over a wide range of typical groundwater pH values.
This statement is considering the model output and the specific Cr concentrations used
where Cr(OH)3 was only saturated under high pH and high Cr conditions.

Analysis of Geochemical Phenomena Controlling Mobility of Ions
from Coal Ash Basins at the Duke Energy L. V. Sutton Energy
Complex

Brian A. Powell, Ph.D.
112 Cherry Street
Pendleton, SC 29670
[phone]
[e-mail]

October 25, 2016

TABLE OF CONTENTS
EXECUTIVE SUMMARY .......................................................................................................... i
1. INTRODUCTION ............................................................................................................ 1
2. Observations from groundwater measurements ................................................................. 1
3. GEOCHEMICAL MODEL DEVELOPMENT ................................................................. 8
3.1. General Sorption Model Description ............................................................................. 8
3.2. Sorption model development for analysis of site-specific transects .............................. 10
3.3. Sorption model development for global model development ....................................... 15
3.4. Global geochemical model parameterization ............................................................... 18
3.5. Pourbaix diagram modeling ........................................................................................ 26
3.6. Flow transect model .................................................................................................... 27
3.7. The use of Kd values ................................................................................................... 31
4. GEOCHEMICAL MODELING of ARSENIC ................................................................ 32
4.1. Pourbaix diagram analysis .......................................................................................... 32
4.2. Transect model analysis .............................................................................................. 33
4.3. PHREEQC global model analysis ............................................................................... 40
4.4. Comparison of measured and calculated As speciation ................................................ 47
4.5. Comparison modeled and experimental Kd values for arsenic ...................................... 50
5. GEOCHEMICAL MODELING of BORON ................................................................... 51
5.1. Pourbaix diagram analysis .......................................................................................... 51
5.2. Transect model analysis .............................................................................................. 51
5.3. PHREEQC global model analysis ............................................................................... 55
5.4. Comparison between modeled and experimental Kd values for boron .......................... 56
6. GEOCHEMICAL MODELING of CHROMIUM ........................................................... 58
6.1. Pourbaix diagram analysis .......................................................................................... 58
6.2. Transect model analysis .............................................................................................. 59
6.3. PHREEQC global model analysis ............................................................................... 63
6.4. Comparison between modeled and experimental Kd values for chromium ................... 68
7. GEOCHEMICAL MODELING of COBALT ................................................................. 70
7.1. Pourbaix diagram analysis .......................................................................................... 70
7.2. Transect model analysis .............................................................................................. 71
7.3. PHREEQC global model analysis ............................................................................... 75
7.4. Comparison between modeled and experimental Kd values for cobalt .......................... 78

8. GEOCHEMICAL MODELING of SELENIUM ............................................................. 79
8.1. Pourbaix diagram analysis .......................................................................................... 79
8.2. Transect model analysis .............................................................................................. 80
8.3. PHREEQC global model analysis ............................................................................... 84
8.4. Comparison between modeled and experimental Kd values for selenium ..................... 87
9. EXAMINATION of SORPTION CAPACITY ................................................................ 89
10. POTENTIAL EFFECTS of ACCELERATED REMEDIATION ..................................... 94
11. SUMMARY ................................................................................................................... 96
12. REFERENCES ............................................................................................................... 98

Page i
EXECUTIVE SUMMARY
The goal of this geochemical modeling effort is to describe the geochemical behavior and
subsurface mobility of several constituents of interest in the subsurface by considering sorption of the
constituent to the aquifer solids, oxidation/reduction reactions, and precipitation/coprecipitation in
mineral phases using the United States Geological Survey program PHREEQC. This report has three
major sections:

1. A description of the geochemical behavior of arsenic, boron, cobalt, chromium, and selenium
along three flow transects at the L. V. Sutton site. This modeling provides a direct
comparison with the measured concentrations of these ions in pore waters along the transects.
2. A global geochemical model which uses data from seven coal ash basin storage sites, which
will be referred to in this report by the abbreviated plant names: HF Lee, Weatherspoon,
Mayo, Cape Fear, Sutton, Asheville, and Roxboro.
3. An analysis of the sorption capacity of the solid phases assuming pure ferrihydrate and
gibbsite as the dominant minerals.

A major effort was undertaken to describe the chemical speciation expected under the variable
conditions (particularly with respect to changes in pH and Eh) and to relate the expected speciation to
observed behavior of each constituent. The model also considers the influence of background major ion
concentrations on the sorption of constituents of interest to iron and aluminum hydroxide solid phases. To
provide a self-consistent set of thermodynamic constants for sorption reactions, all sorption was modeled
assuming ferrihydrate (HFO) and hydrous aluminum oxide (HAO, using gibbsite as a surrogate) were the
dominant sorbing surfaces based on the databases developed by Dzomback and Morel [1] and
Karamalidis and Dzomback [2]. All three modeling efforts described above provide an analysis of the
stability of the ferrihydrate and gibbsite minerals which represent the sorbing surfaces as a function of
changing geochemical conditions. In a few notable scenarios, ferrihydrate becomes unsaturated and
dissolves, resulting in the loss of that sorbing surface and a coincident increase in the aqueous
concentration of the constituents.
The geochemical model along flow transects provides a qualitative comparison with ion
concentrations measured in pore waters along the transect. The transect model was able to qualitatively
match the observed behavior of As, Co, Cr, B, and Se. The three flow transects considered in the model
all originated from well ABMW-1 within the ash basin. Generally, the pH of the pore water decreased
with increasing distance from the ash basin. Though there is a significant amount of scatter in the data, the
decrease in pH appeared to cause an increase in the pore water concentrations of cationic species Co and
Cr and a decrease in pore water concentrations of anionic species As and Se. This behavior was verified
using the PHREEQC model. The geochemical model predicted little sorption and reactivity of boron and
essentially a Kd value of zero, similar to the value used in reactive transport modeling at the site.
The global model input requires initial concentrations of all ions of interest, pH, redox potential
(Eh), and a concentration of sorption sites. The field data from seven sites was compared and it was
demonstrated that each of these parameters were relatively consistent between the seven sites. Therefore,
an average range of geochemical conditions were used to predict the minimum and maximum distribution
coefficients (Kd) for each constituent of interest. The initial concentrations of ions were determined by
examining the minimum, average, and maximum concentrations observed by compiling the data from all
seven sites under consideration. A range of pH and Eh values were selected to capture the range of

Page ii
conditions observed at all seven sites. Selection of an appropriate pH and Eh range is vital because these
two variables have the greatest impact on constituent Kd values. Thus the pH and Eh values selected for
the model represented a wide range capturing minimum and maximum values. The concentration of iron
(Fe) and aluminum (Al) sorption sites was estimated based on the average extractable iron and aluminum
content of the solid phases retrieved from all seven sites.
The partitioning and solubility of constituents is highly dependent on the pH of the ground water.
This is because the majority of constituents of interest exist as anionic or cationic species. Sorption of
charged species to mineral surfaces changes with pH because the surface charge of all mineral surfaces
transitions from a positively charged surface at low pH to a negatively charged surface at high pH.
Therefore, sorption of anionic species will be stronger at low pH where the anions are attracted to the
positively charged surfaces (vice versa regarding the cationic species). Similarly, the solubility of a
mineral phase will also be pH dependent because lower pH values tend to favor the formation of more
soluble cationic species of most alkali elements, alkali earth elements, and transition metals. Conversely,
low pH values will facilitate protonation of most oxoanions (such as the conjugate bases AsO43-, SeO32-)
which can form neutrally charged H3AsO4 and H2SeO3 species at low pH. At higher pH values, these
oxoanions deprotonate and persist as anionic species which are generally very soluble and will only
weakly sorb to mineral surfaces. Therefore, generally low pH conditions will favor higher aqueous
concentrations of cationic constituents (e.g., Ba2+, Cr3+, Co2+, Fe2+/Fe3+) whereas higher aqueous
concentrations of anionic species (e.g., AsO43-, SeO32-, H2VO42-, H2BO3-) will be expected in higher pH
ground waters.
Since the partitioning of these constituents is highly dependent on the pH and the chemical
speciation of the constituent, consideration of potential changes in the constituent chemical species due to
changes in oxidation state is imperative. For example, Cr(III) generally exists as the cation Cr3+ which is
relatively insoluble and sorbs strongly to mineral surfaces. However, upon oxidation to Cr(VI), the
oxyanion chromate CrO42- becomes the dominant species which can become mobile under neutral to high
pH conditions. The geochemical model developed in this work considers changes in oxidation state for all
redox active constituents of interest (Se, As, B, Cr, Co) and changes in chemical speciation for all
constituents. Some specific observations are as follows:

· Arsenic: The PHREEQC model predicts As(V) as the dominant oxidation state of arsenic under
the field measured Eh and pH conditions but As(III) is the dominant species measured in ground
waters. The reason for this discrepancy is proposed to be due to 1) increased sorption of As(V)
relative to As(III) which would remove As(V) from the ground water resulting in As(V) values
reported as non-detect and/or 2) a kinetic limitation with respect to the As(III)/As(V)
oxidation/reduction reactions which prevents the system from reaching chemical equilibrium.
However, the observation of As(III) is consistent with the relatively lower Kd values required in
the reactive transport modeling efforts compared with the higher Kd values predicted by
PHREEQC. Therefore, the reactive transport model represents a conservative estimate. Due to the
stronger sorption of As(V), the tendency of the element to move in the subsurface, will decrease
as As(III) becomes oxidized to As(V) and sorbs to mineral surfaces.

· Boron: Boron exhibits relatively simple chemistry existing as either neutrally charged boric acid,
noted in the literature as either B(OH)3 or H3BO3, or as a borate anion H2BO3- which persists
above pH 9. Borate exhibits no redox reactions and solely exists as B(III). The relatively simple

Page iii
aqueous speciation of borate is due to lack of affinity to form complexes with other ions. This
lack of chemical reactivity also limits borate sorption to mineral surfaces. Thus boron is
essentially inert and behaves as a highly mobile ion in the subsurface. Boron exists only in the
B(III) oxidation state and generally persists as the neutrally charged chemical species boric acid
(H3BO3), which is a weak acid and exhibits minimal sorption to mineral surfaces. As the system
pH increases, H3BO3 will deprotonate (i.e., release an H+ ion) to form H2BO3- which also sorbs
weakly. Boric acid and H2BO3
- are the only two aqueous species of boron predicted to occur in
this model. Thus, the PHREEQC predicted Kd values for boron are low (1.1 x 10-5 to 0.031 L/kg).
These values are lower, but generally consistent, with the values chosen for reactive transport
modeling [3-9] and those measured in batch laboratory experiments [10-16]. Precipitation of any
boron containing mineral phases is not expected to occur. Therefore, physical attenuation and
sorption are the two primary processes which will control the movement of boron in the
subsurface.

· Chromium: Chromium is dominated by the trivalent and hexavalent oxidation states (Cr(III) and
Cr(VI)). The trivalent state exists as a triply charged ion at low pH and undergoes hydrolysis to
form neutrally charged Cr(OH)3(aq) and anionic Cr(OH)4- species with increasing pH. These
hydrolysis reactions are also potentially coupled with sorption of the cationic species to mineral
surfaces with increasing pH and/or formation of discrete precipitates (i.e. Cr2O3) provided the
concentration of Cr is sufficiently high. The hexavalent phase exists as the anions HCrO4- and
CrO4-2 at environmentally relevant pH values. These are generally soluble states but exhibit
moderately strong sorption affinity to metal oxide minerals such as iron oxides [1, 17]. The
ground water measurements from all seven sites indicate Cr(III) is the dominant oxidation state
which is in agreement with the PHREEQC model. The sorption of Cr(III) is significantly stronger
than Cr(VI) because Cr(III) persists as a highly charged cation (Cr3+) which readily sorbs to
mineral surfaces as the pH increases from acidic to basic conditions. This behavior is in stark
contrast to that of Cr(VI) which persists as a weakly sorbing anion (CrO4-) and decreases sorption
from acidic to basic conditions. This high charge density of Cr3+ also causes a propensity to form
aqueous complexes with anions such as SO4
2- and Cl- which can influence sorption behavior. For
example, formation of CrSO4+ appears to be responsible for a decreased Kd relative to baseline
conditions in the PHREEQC model presented in this work. The measured aqueous concentrations
in groundwater from the seven sites range from below detection to approximately 100 mg/L. This
concentration range is similar to what was modeled in PHREEQC and indicates that formation of
mineral phases containing Cr may occur under high pH conditions with relatively high Cr
concentrations.

· Cobalt: The dominant cobalt species predicted by the PHREEQC model is Co2+. Essentially no
oxidized Co(III) is expected to be present within the Eh range of the groundwater from the seven
sites. Thus, the behavior of cobalt exhibits relatively little influence on redox potential except for
the few cases where reaction between Co and some other reduced species (such as Se(-II) occurs.
The predicted Kd value of 0.79 L/kg using all average groundwater values (i.e., average pH,
average Eh, and average ion concentrations from all seven sites) agrees favorably with the value
of 2.5 L/kg used in the reactive transport model of the Asheville site. Note that Asheville is the
only site where the reactive transport model considered cobalt. The experimentally derived Kd

Page iv
values from batch experiments are generally higher than the value used in reactive transport
modeling. Given the high standard deviations from experimentally determined Kd values, there is
acceptable agreement between the average value of 32.5 L/kg from the experimental systems and
the average value of 533 L/kg from the PHREEQC modeling effort [11]. Overall cobalt is
expected to exhibit minimal transport in these systems relative to more mobile species such as
manganese and boron. Despite having an accessible Co(III) oxidation state, the dominant
oxidation state of cobalt under all conditions is Co(II). The dominant ions at neutral pH are Co2
+
and HCoO2- (Figure 2.4). As a cationic species, Co2+ sorption will increase with increasing pH.
This is a manifestation of the attraction of Co2+ to mineral surfaces as the surface transitions from
a net positive charge to a net negative charge with increasing pH. Formation of CoS2 (Cattierite),
CoSe (freboldite), and CoAs2 (safflorite) precipitates are favored under reducing conditions.
However, it is noteworthy that these simulations were run with significantly higher Co, As, and
Se concentrations than what has been measured in groundwater at the Sutton site. Therefore,
sufficiently high concentrations of cobalt, arsenide, and selenide may not persist to form discrete
precipitates.

· Selenium: Selenium geochemical behavior is highly dependent on the Eh of the groundwater.
Under reducing conditions, selenium can persist as the reduced Se(-II) or Se(0) species which are
generally insoluble. Therefore, selenium is expected to be relatively immobile under very
reducing conditions. Under mildly reducing to oxidizing conditions Se(IV) and Se(VI) exist as
anionic species. The dominant form is SeO4-2, though below pH 2 protonation to form HSeO4-
occurs. Selenite is a weaker acid than selenate and persists as H2SeO3, HSeO3-, and SeO3-2 in the
pH ranges 0-3, 3-8.5, and >8.5, respectively. Similar to arsenic’s behavior, both selenite and
selenate sorb to mineral surfaces (primarily iron oxides) [17-19]. The sorption of these anions
decreases with increasing pH. However, sorption is also influenced by competition with other
sorbing ions like sulfate. Therefore, selenium Kd values for either Se(IV) or Se(VI) exhibits a
wide range due to the influence of pH and ion competition. The PHREEQC model predictions
show Se(IV) as the dominant species under approximately neutral pH conditions but the fraction
of Se(VI) increases with increasing pH and Eh. This behavior is consistent with the selenium
oxidation state speciation analysis measured in field samples. Overall the range of Kd values
predicted by PHREEQC agrees with the values determined experimentally from batch sorption
tests.

Based on the conceptual models of the geochemical behavior of these constituents of interest, the
final chapter of this report describes potential impacts of enhanced remediation efforts including
installation of pump and treat systems and removal of ash from the basins. Assuming ash removal will
result in an increase in the Eh of the system and potentially a decrease in pH, the potential changes in the
mobility of each constituent are discussed.

Page 1
1. INTRODUCTION
A geochemical modeling effort was undertaken to describe the chemical speciation expected
under the variable conditions at the L. V. Sutton Energy Complex. The primary emphasis of the
geochemical modeling effort was to understand the influences of pH and redox potential (Eh) on the
aqueous speciation, sorption, and solubility of several constituents of interest using the United States
Geologic Survey (USGS) geochemical modeling program PHREEQC. Hydrous ferric oxide (HFO) and
gibbsite (HAO) minerals were used as the basis for sorption and capacity determination because of the
available thermochemical databases for surface complexation modeling of many constituents of interest
[1, 2]. Two modeling efforts are described in this report. The first is a global model using averaged
concentrations of constituents from seven Duke Energy Progress power plant sites (Sutton,
Weatherspoon, H. F. Lee, Mayo, Cape Fear, Asheville, and Roxboro). This model was used to develop a
conceptual understanding of how changes in major geochemical parameters such as pH and Eh can
influence the expected mobility of constituents of interest. In a second modeling effort, specific transects
at the L. V. Sutton Energy complex were examined by incorporating measured ion concentrations and
geochemical parameters from wells along each transect. In this site-specific transect model the
concentrations of HAO and HFO sorbents were fixed using the Fe and Al concentrations in solids
obtained from wells along the transect. In the global model, the concentrations of HFO and HAO were
constrained based on average extractable Al and Fe concentrations from many solid phases from all seven
sites measured by collaborators at the University of North Carolina – Charlotte [10-16].
The approach taken in this “global” modeling effort was to understand how changes in pH, redox
potential, and dissolved ion concentrations influence the sorption, aqueous speciation, and solubility of
several constituents of interest. The pH, Eh, and ion concentrations from all seven sites are compared
below to demonstrate that they are all relatively similar. Therefore, a fixed range of values was used to
perform the geochemical modeling discussed in this report. The logic of this model is that it is essentially
impossible to predict a Kd from first principles to use in a reactive transport model considering the
multitude of chemical, physical, and potentially biological processes occurring at the coal ash basin sites.
Therefore, the primary emphasis was to quantify how changes in the system conditions will alter the
speciation and mobility of each constituent (particularly changes in pH and Eh). This will allow us to
determine if changes occurring during remediation could mobilize any particular constituent. In the
discussion below, the global model is presented to provide a conceptual framework for the behavior of
several constituents of interest and then the site-specific transects are discussed in terms of the conceptual
understanding of the constituent geochemistry.
2. Observations from groundwater measurements
There are several notable features of the groundwater measurements at the Sutton site which help to
edify the mobility of major constituents. These features are discussed below along with the relevant
implications.

Correlation between dissolved oxygen (DO) concentrations in the water with pH clearly shows that
high dissolved oxygen conditions are only maintained for samples below pH 7 (Figure 2.1). This is
supported by Eh measurements in the same waters. Fitting a trendline to all the available Eh vs. pH data
points in Figure 2.2 gives a slope of 69 mV per unit change in pH. This is close to the Nernstian slope of
59 mV for oxidation or reduction of water. Therefore, it is assumed from this data that oxygen is one of

Page 2
the major redox buffers in this system. The absence of strongly oxidizing conditions at high pH indicate
that when describing the transport of constituents in this system, potentially highly mobile or highly
immobile species which persist under high pH/high Eh conditions do not necessarily need to be
considered. For example, it is unlikely the system will reach sufficiently high pH and Eh conditions to
facilitate significant Se(IV) oxidation to Se(VI). As discussed below, stabilization of Se(VI) primarily
occurs at higher pH values. Therefore, it is unlikely that the sustained higher Eh/low pH conditions would
facilitate formation of the higher mobile Se(VI). This can have an impact on Se mobility since Se(VI) is
generally more mobile that Se(IV). Additional discussion of such changes in redox speciation and the
subsequent influence on sorption is provided below in the context of PHREEQC speciation modeling.

Figure 2.1 : Correlation between pH and dissolved oxygen (DO) groundwater measurements at the
Sutton site.

0
1
2
3
4
5
6
7
8
3 4 5 6 7 8 9 10 11
Di
s
s
o
l
v
e
d

O
x
y
g
e
n

(
m
g
/
L
)
pH

Page 3

Figure 2.2: Correlation between pH and Eh groundwater measurements at the Sutton site.

The general behavior of many species can be delineated via comparisons of the dissolved
concentration in groundwater samples versus pH. Such plots for the Sutton Energy Complex site are
shown below for several major ions and constituents of interest (Figure 2.3Figure 2.6, due to the large
number of constituents of interest, the data are separated into four plots). While there is significant scatter
in the data which is an inherent function of the heterogeneous nature of the site, general trends in ion
behavior become apparent. These trends are speculative based on known behavior of the ions but require
further experimental effort to prove. Some noteworthy observations from these data are:
· Aluminum and iron concentrations decrease with increasing pH. This is consistent with the pH
dependent dissolution of iron and aluminum oxides and aluminosilicate minerals.

· Arsenic concentrations appear to remain relatively constant. Sorption of As(III) and As(V) will
decrease with increasing pH. Therefore, the relatively constant concertation could be indicative of
a solubility control. This may be a manifestation of sorption of the anionic species at low pH and
precipitation/coprecipitation with metal oxide minerals at high pH (consistent with the decreasing
concentrations of aluminum and iron as discussed above.

· Boron aqueous concentrations remain relatively constant around 1000 ppb above pH 5. Below pH
5, sorption of the neutrally charged H3BO3 or anionic H2BO3- complexes likely reduces the
aqueous concentration.

· Chloride concentrations are highly variable but generally increase with pH. This is consistent
with a small degree of chloride sorption to metal oxide surfaces which will have net positive
surface charges at lower pH values.

· Few samples contained concentrations of thallium above detection limits. Samples which were
above detection limits were generally at low pH indicating sorption of cobalt and thallium may be
-200
-100
0
100
200
300
400
500
600
3 4 5 6 7 8 9 10 11
Eh

(
m
V
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower

Page 4
a dominant control of the aqueous concentrations. Dissolved concentrations of zinc are also very
low and close to detection limits.

· Concentrations of divalent ions (Ca2+, Sr2+, Zn2+, Ni2+) are highly variable. Generally these ions
exhibit a decrease in concentration with decreasing pH. This could indicate sorption to
phyllosilicate minerals via ion exchange which should increase as the pH decreases to mildly
acidic conditions.

Figure 2.3: Correlation between dissolved concentrations of aluminum, arsenic, boron, and chloride
in Sutton site groundwater with changes in pH.
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
3 4 5 6 7 8 9 10 11
Di
s
s
o
l
v
e
d

C
o
n
c
e
n
t
r
a
t
i
o
n

Al
,

A
s
,

a
n
d

B

u
n
i
t
s

i
n

u g/
L

Cl

u
n
i
t
s

i
n

m
g
/
L
pH
Aluminum
Arsenic
Boron
Chloride

Page 5

Figure 2.4: Correlation between dissolved concentrations of calcium, cobalt, iron, and molybdenum
in Sutton site groundwater with changes in pH.

Figure 2.5: Correlation between dissolved concentrations of nickel, potassium, and selenium in
Sutton site groundwater with changes in pH.

1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
3 4 5 6 7 8 9 10 11
Di
s
s
o
l
v
e
d

c
o
n
c
e
n
t
r
a
t
i
o
n

Co
,

M
o
,

a
n
d

F
e

u
n
i
t
s

i
n

u g/
L

Ca

u
n
i
t
s

i
n

m
g
/
L

pH
Calcium
Cobalt
Iron
Molybdenum
1.0E-01
1.0E+00
1.0E+01
1.0E+02
3 4 5 6 7 8 9 10 11
Di
s
s
o
l
v
e
d

c
o
n
c
e
n
t
r
a
t
i
o
n

Ni

a
n
d

S
e

u
n
i
t
s

i
n

u g/
L

K
u
n
i
t
s

i
n

m
g
/
L

pH
Nickel
Potassium
Selenium

Page 6

Figure 2.6: Correlation between dissolved concentrations of strontium, thallium, vanadium, and zinc
in Sutton site groundwater with changes in pH. The majority of waters contained zinc
concentrations below the detection limit of 0.005 mg/L.

Select samples were passed through 450 nm and 100 nm filters in order to monitor formation of
precipitates or association with suspended particles. The fraction of each analyte passing through a 100
nm filter is shown in Figure 2.7. The number of samples with measureable analyte concentrations in the
filtrate is listed above each data point (n = x where x is the number of samples). The error bars represent
the standard deviation of all measureable samples. Consistent with the data in Figure 2.3 and Figure 2.4,
demonstrating that dissolved concentrations of Al and Fe are likely controlled by solubility, as a large
fraction of Al and Fe is removed from solution by filtration at 100 nm. Thus, a significant fraction of Fe
and Al could be present as a particulate phase. A large number of other ions are completely soluble as
indicated by having approximately 100% pass through a 100 nm filter for multiple samples. These soluble
ions include: antimony, arsenic, barium, boron, manganese, molybdenum, selenium, strontium, and
thallium. Cobalt, vanadium, and zinc all showed some removal by filtration but not on the same extent
observed for Fe and Al. This behavior is consistent with the moderate sorption expected of these ions
based on the groundwater measurements discussed above and the sorption modeling discussed below.
However, it is noteworthy that despite some removal by filtration, 77% ± 23% Ni (n=2) and 84% ± 25%
Co (n=4) remained soluble. Therefore, it is unclear if significant fractions of Co and Ni are associated
with particles greater than 100 nm. Some removal of V from groundwater upon filtration at 100 nm was
observed though 62% ± 21% (n=9) remained soluble. It is noteworthy that association of V and
potentially Co and Ni with colloidal particles does not necessarily indicate enhanced mobility. The
colloidal particles must also be mobile and recent studies have demonstrated that without stabilizing
surficial coatings, iron oxide colloids are generally immobile in soils [20, 21].
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
3 4 5 6 7 8 9 10 11
Di
s
s
o
l
v
e
d

c
o
n
c
e
n
t
r
a
t
i
o
n

Tl

a
n
d

V

u
n
i
t
s

i
n

u
g
/
L

Sr

a
n
d

Z
n

u
n
i
t
s

i
n

m
g
/
L

pH
Strontium
Thalium
Vanadium
Zinc

Page 7

Figure 2.7: Soluble fraction of several constituents of interest defined as the fraction passing through
a 100nm filter.

Page 8
3. GEOCHEMICAL MODEL DEVELOPMENT
3.1. General Sorption Model Description
To examine the sorption behavior of multiple ions of interest in these systems, a combined
aqueous speciation and surface complexation model was developed using the USGS geochemical
modeling program PHREEQC. Equilibrium constants for aqueous speciation reactions were taken from
the USGS WATEQ4F database. This database contained the reactions for most elements of interest
except for Co, Sb, V, and Cr. Constants for aqueous reactions and mineral formation for these elements
were taken from the MINTEQ v4 database which is also issued with PHREEQC. The constants were all
checked to provide a self-consistent incorporation into the revised database. The source of the MINTEQ
v4 database is primarily the well-known NIST 46 database [22].
Sorption reactions were modeled using a double layer surface complexation model. To ensure
self-consistency in the sorption model, a single database of constants was used as opposed to searching
out individual constants from the literature. The diffuse double layer model describing ion sorption to
HFO and HAO by Dzomback and Morel [1] and Karamalidis and Dzombak [2], respectively, was
selected for this effort. Many surface complexation reactions for ions of interest on HFO are included in
the standard release of the PHREEQC database. Constants for Co, V, Cr, and Sb were added to the
modified database as well as all constants involving ion sorption to HAO.
The geochemistry of Thallium (Tl) has not be studied as extensively as other elements of interest
to coal ash disposal sites such as Fe, Mn, As, or Se. In particular, there are few studies which have
examined Tl sorption to solid phases of relevance to these sites. Since sorption to solid phases is the
primary means by which the mobility of most constituents of interest is retarded, it is critical to have
reliable and self-consistent thermodynamic equilibrium constants describing the sorption reactions.
Specifically, equilibrium surface complexation constants for Tl-ferrihydrate and Tl-gibbsite systems are
needed to maintain self-consistency within our modeling approach [1, 2]. However, there are no studies of
Tl sorption to gibbsite and only two examining Tl sorption to ferrihydrate [23, 24]. A comparison of these
studies reveals that the constants determined from this work vary by approximately one order of
magnitude. There is also considerable discussion within the peer reviewed literature regarding the
appropriateness and validity of several of the constants used to generate Pourbaix predominance diagrams
for the Tl system [25, 26]. Therefore, we find the currently available literature unreliable in it’s current
form to utilize in the geochemical analysis of Tl behavior in coal ash disposal sites. Our discussion of Tl
will be restricted to empirical observations of the influence of pH and EH.
Using surface complexation models, the sorption of an element is written as a standard chemical
reaction such as those shown in Table 3.1. In these equations, ≡SOH represents a site on the HFO or
HAO mineral surface where sorption can occur. Speciation models utilize this reaction convention to
describe a “concentration” of surface sites to be used in a thermochemical approach to sorption modeling
[1, 27-29]. The primary difficulty in this approach is quantifying the concentration of reactive surface
sites. Many approaches have been used, the most common being potentiometric titrations of the solid
phase to quantify surface site concentrations using proton sorption/desorption behavior and surface area
analysis. These studies are typically done on pure, synthetic mineral phases and still exhibit large
variations in the surface site density determined from the data. Therefore, determination of surface site
densities for complex mineral assemblages cannot be accurately performed using currently available
techniques.

Page 9
The model proposed by Dzomback and Morel [1] assumes that all surfaces have a combination of
strong sorption sites and weak sorption sites. As discussed above, quantifying the reactive surface site
density for complex mineral assemblages such as those used in this work, is difficult if not impossible.
Therefore, attempting to delineate between mineral surfaces, let alone strong and weak sites on such
surfaces, would add unnecessary uncertainty and fitting parameters to the models. Therefore, sorption to
only one site on both HFO and HAO is considered.
There are two primary approaches to modeling complex mineral assemblages such as those
considered in this work. The component additivity approach considers sorption reactions to all mineral
phases present in a sample [27]. Such an approach requires separate reactions for each analyte sorbing to
each mineral phase present in a sample. These can be very complicated but robust models provided a
means for determining the surface site density of each mineral phase is available. A simpler alternative is
the generalized composite approach wherein data are modeled assuming a generic surface site (i.e.,
≡SOH) which represents an average reactivity of all minerals in the solid assemblage [27]. This modeling
approach still combines the flexibility of an aqueous speciation model with a sorption model under a
thermochemical framework. This work assumes that sorption occurs only to iron oxide minerals. Other
mineral surfaces can be considered and modeled. However, in the absence of data with sufficient
resolution to determine the presence of these mineral phases and accurate methods to determine the
surface site density for the minerals being considered, fitting additional surface reactions becomes a curve
fitting exercise with a high probability of a non-unique solution. By modeling ion sorption to HFO and
HAO based on extractable metal content but not considering other phases, the model is essentially a
combined generalized composite and component additivity model.

Table 3.1: Example reactions used in surface complexation modeling
(where ºSOH represents a sorption site).
Reaction Type Reaction Expression Stability constant
Surface protonation (i.e.,
develops positive surface
charge at low pH)
ºSOH + H+ Û ºSOH2+

Surface deprotonation (i.e.,
develops negative surface
charge at high pH)
ºSOH Û ºSO- + H+

Cation sorption ºSOH + Mn+ Û ºSOMn-1 + H+

Anion Sorption
ºSOH + H+ + A- Û ºSOH2+A-
or
ºSOH + A- Û ºSA + OH-

K = SOH A
SOH H [A ]exp − Ψ

+
2
++
[SOH ]FψK = exp[SOH]{H } RT
æ öç ÷è ø
- +
-
[SO ]{H } FψK = exp -[SOH] RT
æ öç ÷è ø
+
n-1 +
M
[SOM ]{H } FψK = exp ( 1)[SOH]{M } RT
n n+
æ ö-ç ÷è ø

Page 10

To constrain the number of sorption sites to be used in this model, a concentration of surface sites
(ºSOH) must be calculated in units of mol/L for application in the aforementioned chemical equations.
Such a concentration is conceptually difficult because ºSOH represents a point on a solid particle where
another ion may sorb, not an aqueous species as indicated by the units of mol/L. So to make this
transition, a density of sorption sites on the mineral surface must be assumed (e.g. “x” moles of sorption
sites per mole of total iron or aluminum in the solid phase). Additionally, in order to calculate a Kd value
to compare with reactive transport models and batch laboratory data, a solid phase concentration in gsolid/L
must also be assumed. The model proposed by Dzomback and Morel [1] assumes that all surfaces have a
combination of strong sorption sites and weak sorption sites. As discussed above, quantifying the reactive
surface site density for complex mineral assemblages such as those used in this work, is difficult if not
impossible. Therefore, attempting to delineate between mineral surfaces, let alone strong and weak sites
on such surfaces, would add unnecessary uncertainty and fitting parameters to the models. Therefore,
sorption to only one site on both HFO and HAO is considered.

3.2. Sorption model development for analysis of site-specific transects
Three transects at the L. V. Sutton site were selected for analysis (Figure 3.1). As discussed
below, the initial concentrations of major ions, constituents of interest, pH, and Eh were fixed in the
model based on measured values in wells along the transects. Multiple methods were attempted to provide
a technically reliable and consistent method of constraining the total sorption site density within the
PHREEQC model using site specific data. To clarify these methods are discussed briefly below, but the
method selected for the site-specific transect modeling presented in subsequent chapters was Option 1.
Modeling Option 1: This approach uses the total Fe and Al solid phase concentrations in specific
wells to identify how much sorbent is available. The EQUILIBRIUM_PHASES command within
PHREEQC can be used to react a specified amount of a mineral with the aqueous phase. The utility of the
EQUILUIBRIUM_PHASES command, in this case, is that it can provide the total number of sorption
sites to produce HFO and HAO, and it is linked to the stability of the gibbsite and ferrihydrate solid
phases which are used to approximate the sorption sites. So in effect, the model is calculating the aqueous
concentrations of Fe(II), Fe(III), and Al(III) as well as moles of gibbsite and ferrihydrate by combining
the total aqueous Fe and Al concentrations from well measurements with the total Fe and Al available as
solid phases measured from each well (listed in Table 3.3Table 3.5). To convert from mgFe/kgsolid into a
sorption site concentration in mol/L for use in PHREEQC, a site density of 0.2 moles Fe sites per 1 mol

Page 11

Figure 3.1: L. V. Sutton Site map showing three transects used for site-specific analysis

Page 12
ferrihydrate in the solid and 0.033 moles Al sites per 1 mol gibbsite (defined as Al(OH)3(s)) in the solid
we used. Note these values are 0.16 mol FeOHsites/molHFO and 0.011 mol AlOHsites/molHAO if defined as
the moles of Fe and Al instead of moles of ferrihydrate and gibbsite. Surface areas of 600 m2/g and 32
m2/g for HFO and HAO, respectively, were assumed based on a self-consistent application of these
surface complexation models [1, 2]. The site density of 0.2 molesites/molFe and 0.033 molesites/molAl were
determined using specific surface area and site density values recommended for modeling sorption to
HAO and HFO [1, 2]). These values were calculated using equation 2.1 and the values listed in Table 3.2

= ∗
∗ ∗ ∗
. Eqn. 2.1

where SD is the site density in sites/nm2, SA is the surface area in m2/g, and MW is the molecular weight
in g/mol.

Table 3.2: Surface area, site density, and molecular weight values used for conversions to
sorption site density.

Ferrihydrate, Fe2O3.H2O(s) Gibbsite, Al(OH)3(s)
Molecular Weight (g/mol) 89g/mol Fe2O3.H2O(s) or
55.845 g/mol Fe
78g/mol Al(OH)3(s)
or 26.98 g/mol Al
Site Density (sites/nm2) 2.31 8
Surface Area 600 32
Conversion to molsites/molmineral 0.205 0.033

One correction which was applied was to link the measured Fe and Al in the solid phases to a
consistent concentration in PHREEQC. To provide the most direct comparison with reactive transport
modeling efforts, the same bulk density and porosity were used to calculate input values for the
PHREEQC geochemical model. At the Sutton site, uniform values of 1.6 g/cm3 and 0.2 were used for the
bulk density and porosity, respectively [6]. Assuming a 1000 cm3 volume as an example, this would yield
1.6 kg of solid material and 0.2 L of liquid volume. Therefore, we can estimate a solid phase
concentration of 8 kg/L (1.6 kg / 0.2 L). Therefore, to normalize all aqueous concentrations to the default
value of 1000 cm3 in PHREEQC, the total moles of Fe and Al used as input to the
EQUILIBRIUM_PHASES pool was obtained by multiplying the moles/kg of Al and Fe by 1.6 to
approximate the 1.6 kg of solid present in the assumed 1000 cm3 simulation volume. Data were not
available for each specific zone as a function of depth. Therefore, these values are listed in Table 3.335
and represent the average of multiple solid samples obtained from the same well when possible.

Page 13
Table 3.3: Measured Fe and Al solid phase concentrations in wells along east transect and estimated
sorption site concentrations. *Solid phase data for well MW-23 was unavailable. Therefore, value for
Fe extraction measured by University of North Carolina Charlotte was used.
Transect Well ID(s) Average Solid Phase Aluminum
Concentration (mg/kg)
Average Solid Phase Iron
Concentration (mg/kg)
ABMW-01S/D 2505 5580
MW-23B/C/D/E* 528.7 1550
AW-6B/D/E and MW-12 2823 5551
SMW-1B/C 733 791
SMW-6B/C/D 3210 9450

Table 3.4: Measured Fe and Al solid phase concentrations in wells along north transect and
estimated sorption site concentrations. *Data not available for MW-36B/C and MW-27 B/C.
Therefore, values for MW-38B/C/D used due to proximity of wells to each other.
Transect Well ID(s) Average Solid Phase Aluminum
Concentration (mg/kg)
Average Solid Phase Iron
Concentration (mg/kg)
ABMW-01S/D 2505 5580
MW-36B/C* 1511 3491
MW-27B/C* 1511 3491
MW-38B/C/D* 1511 3491
AW-08B/C 288 239

Table 3.5: Measured Fe and Al solid phase concentrations in wells along southeast transect and
estimated sorption site concentrations. *Data not available for MW-18, MW-21C, MW-28B/C, and
MW-7A/B/C. Therefore, values for well AW-09B/C/D used as an approximation.

Modeling Option #2: This approach specifically noted a concentration of HFO and HAO sorption
sites available in the PHREEQC input file. The moles of HFO and HAO sites were calculated using the
fixed values discussed for modeling option #1 above (i.e. 0.2 mol FeOHsites/mol HFO and 0.033 mol
AlOHsites/mol HAO). Then the total moles of each site was divided by the assumed 0.2 L aqueous volume
in a 1000 cm3 volume of the subsurface (estimated from the porosity of 0.2 used in reactive transport
modeling of the site) [6]. While modeling option #2 does provide an explicit and consistent concentration
of HFO and HAO sites along the transects, it does not explicitly link the sorption site availability to the
presence of ferrihydrate and gibbsite. Since sorption to these solid phases is a fundamental assumption of
this model, linking together the saturation state (i.e. stability) of these two solids phases to the aqueous
geochemical conditions was desirable. However, the results were generally comparable and only deviated
Transect Well ID(s) Average Solid Phase Aluminum
Concentration (mg/kg)
Average Solid Phase Iron
Concentration (mg/kg)
ABMW-01S/D 2505 5580
MW-18* 458 224
MW-21C* 458 224
MW-28B/C* 458 224
MW-7A/B/C* 458 224
AW-09B/C/D 458 224

Page 14
in a few cases where the systems became unsaturated with respect to ferrihydrate in option #1. Thus,
option #1 was selected as a better approximation method because of explicitly modeling both the aqueous
and solid phase Fe and Al and using the amount of gibbsite and ferrihydrate present in the systems to
constrain the concentration of surface sorption sites. It is noteworthy that this option is the option chosen
for the global model discussed below because average Fe and Al solid phase concentrations were
assumed for the input HAO and HFO concentrations based on extractable Fe and Al data from seven
Duke Energy Progress sites.
Modeling Option #3: To examine the saturation state of ferrihydrate and gibbsite, a third method
was used to define the HFO and HAO sorption site concentrations. The measured total Al and Fe
concentrations in each well were used as input values and the EQUILIBRIUM_PHASES command in
PHREEQC was used to calculate the amount of ferrihydrate and gibbsite present for sorption to occur. No
additional Fe or Al was added to the EQUILIBRIUM PHASES command. Therefore, the solutions would
only produce ferrihydrate and gibbsite if the initial aqueous phase was supersaturated. While this is the
case for a number of samples, this approach generally predicted higher aqueous phase concentrations of
all constituents in the model because it does not account for the Fe and Al which are present in the solid
phase. Therefore, this model was not used for further analysis since it does not appropriately represent the
system.
The concentrations of ions, pH, and Eh values used for the PHREEQC model input were based on
measured values in wells along three transects. The input concentrations of several major ions (Al, Ba,
Ca, Cl, Fe, Mg, Mn, K, Na, K, Sr, or sulfate) used site specific data for wells along the transects. In the
event that a measurement for one of these ions was not performed, then the median value for all
measurements along the transect was used (Table 3.6). For many of the constituents of interest including
As, B, Cr, Co, Ni, nitrate, Se, V, and Zn, the concentration of the ion was frequently below detection
limit. Therefore, in order to produce a conservative model, the maximum concentration of selected ions
for all wells along the transect, listed in Table 3.6, were used in the PHREEQC input file. Note, the
concentrations from well ABMW-1S/D within the ash basin are included and have some of the highest
concentrations of B and As. Therefore, that maximum concentration is used in all three transects.

Page 15
Table 3.6: List of input values for constituent concentrations and geochemical parameters for
PHREEQC modeling of three transects at the L. V. Sutton site. Units are in ug/L unless otherwise
noted.
East Transect

North Transect

Southeast Transect

Value used
in Model
Value used in
model if
measurement
was not
made
Value used in
Model
Value used in
model if
measurement
was not
made
Value used
in Model
Value used in
model if
measurement
was not
made
Aluminum Site specific 181.5 Site specific 222 Site specific 83
Arsenic 858 858 858
Barium Site specific 5 Site specific 33 Site specific 31
Boron 3960 3960 3960
Calcium Site specific 12400 Site specific 16600 Site specific 9490
Chloride Site specific 30500 Site specific 11000 Site specific 11500
Chromium 1.71 2.42 1.71 Cobalt 12.4 22.5 10.6 Iron Site specific 1148 Site specific 284 Site specific 977
Magnesium Site specific 7669 Site specific 2315 Site specific 2580
Manganese Site specific 194 Site specific 48.5 Site specific 55.5
Nickel 2.54 3.31 2.015 Nitrate
(as N) 0.962 1.4 0.28
Potassium Site specific 8897 Site specific 3550 Site specific 2680
Selenium 10.3 65.3 1
Sodium Site specific 73930 Site specific 6585 Site specific 11200
Strontium Site specific 819 Site specific 157 Site specific 126.5
Sulfate Site specific 56025 Site specific 23000 Site specific 30500
Vanadium 8.72 8.72 8.72
Zinc 55 55 55 pH Site specific Site specific Site specific Eh Site specific Site specific Site specific Dissolved
Oxygen
(mg/L)
Site specific Site specific Site specific

3.3. Sorption model development for global model development

A second modeling effort was undertaken to examine general behavior of several constituents of
interest under changing geochemical conditions. This modeling effort is refered to as a global model
because it considers the range of ion concentrations, pH, Eh, and extractable Fe and Al concentrations
from all seven coal ash disposal sites considered in this project. Using a similar approach to that described
above for the site specific transect analysis, the concentration of sorption sites based on extractable Fe and
Al concentrations is based on the following equations:

Page 16

≡ = ∗ ∗

∗
.
∗ . ≡

Eqn. 2.1

≡ = ∗ ∗

∗
.
∗ . ≡

Eqn. 2.2
where [Solid] is the solid phase suspension concentration in g/L assumed for the model, [Fe]extr is the
extractable iron concentration in mgFe/gsolid, and [≡FeOH] is the concentration of iron surface sites in the
model input. (Note in this conversion, the molecular weight of iron is used instead of the value of 89
g/mol used by Dzomback and Morel [1] because extractable Fe instead of total ferrihydrate is used in the
above equation.) However, the model output is in mol/L of sorbed ions and mol/L of aqueous ions.
Therefore, to convert to a Kd value, we must convert the mol/L of sorbed ions to mol/kgsolid. This is done
by using the solid phase concentration assumed in the above reaction to keep the model self-consistent
and essentially cancel the assumed solid phase concentration as noted above. This is the approach used in
this model with an assumed solid phase concentration of 50 g/L.

An average sorption site concentration of HFO and HAO for the model was determined by
comparing the extractable Al and Fe concentrations in solids from seven Duke Energy Progress sites (HF
Lee, Weatherspoon, Mayo, Cape Fear, Sutton, Asheville, and Roxboro). The average concentrations of
extractable Al and Fe from solids obtained from all seven sites are shown in Figure 3.2 and the global
average of all seven sites is listed in Table 3.7. These data indicate that the average concentrations are
relatively similar though there is a significant amount of variation at each site. To see the data in finer
resolution, the minimum, mean, average, and maximum values are shown in Figure 3.3. Due to the
relatively similar values of extractable Fe and Al at each site, a “global” sorption model was selected
using the average extractable Fe and Al from the data from all seven sites. This average was used to
determine the input concentration of sorption sites for the PHREEQC model.

Table 3.7: Average extractable Fe and Al concentrations from all sites and calculated molar site
concentrations for PHREEQC model. Values are based on extractable Fe and Al measurements
reported by Langley and Shubhasini [10-16].

mgFe-Al/
kgsolid
molFe-Al/
gsolid
molsites/
gsolid
Assuming 50 gsolid/L, site
concentration in mol/L
Extractable Fe 1002 1.79E-05 3.67E-06 1.79E-04
Extractable Al 762 2.83E-05 9.37E-07 4.68E-05

Page 17

Figure 3.2: Average extractable Al and Fe concentrations and standard deviation in solids from all
seven sites.

0
500
1000
1500
2000
2500
3000
3500
4000
Ex
t
r
a
c
t
a
b
l
e

A
l

o
r

F
e

(
m
g
/
k
g
so
l
i
d
)
Average Extractable Fe Average Extractable Al

Page 18

Figure 3.3: Minimum, mean, and maximum extractable Fe (top) and Al (bottom)))
concentrations in solids from all seven sites. Based on data from [10-16]

3.4. Global geochemical model parameterization
The main geochemical parameters influencing sorption are pH, Eh, and the availability of
sorption sites. The pH and Eh of numerous ground water samples have been measured at each of the
seven sites along with other relevant geochemical parameters including the dissolved ion concentrations
and the oxidation state speciation of redox active ions such as As, Cr, and Se [30-36]. Similar to the
method described above to examine the similarities of extractable Fe and Al concentrations from solid
phases, the measured pH and Eh values from the seven sites are compared in Figure 3.4.
0
1000
2000
3000
4000
5000
6000
MIN GEOMEAN AVG MAX
Ex
t
r
a
c
t
a
b
l
e

F
e

(
m
g
Fe
/k
g
so
l
i
d
)
Asheville Sutton Lee Mayo Roxboro Cape Fear Weatherspoon
0
1000
2000
3000
4000
5000
6000
MIN GEOMEAN AVG MAX
Ex
t
r
a
c
t
a
b
l
e

A
l

(
m
g
Al
/k
g
so
l
i
d
)
Asheville Sutton Mayo Roxboro Cape Fear Weatherspoon

Page 19

Figure 3.4: Measured pH and Eh values at all seven sites. The dashed lines represent the
conditions where water is stable (i.e. below the bottom line water will reduce to H2(g) and above
the top dashed line water will oxidize to O2(g)). The inset box represents an approximate range
of pH and Eh values that will capture the majority of conditions of the site. The open symbols
represent the values selected for PHREEQC modeling. Based on data from [30-36].

The values from each site all fall within a similar range and there is not a particular site with
drastically different values than the others. Therefore, a reasonable range of pH and Eh values was
selected for all sites to parameterize the speciation model which will cover the expected range of chemical
speciation expected at the sites. It is a reasonable assumption that if the speciation models are run using
the approximate Eh and pH conditions within the inset box of Figure 3.4, the geochemical behavior of
each constituent can be determined. The variability of the pH and Eh conditions at each site will
essentially be “noise” considering the wide range of Kd values predicted as a function of pH which are
discussed below. Therefore, one “global” model which shows the influence of Kd as a function of pH and
Eh within the selected range is appropriate for all sites. The range of selected pH and Eh values listed in
Table 3.8 are depicted in Figure 3.4 with the black open circles. This range was chosen by selecting
values to represent a global average, a high pH/low Eh extreme, a high pH/high Eh extreme, a low
pH/low Eh extreme, a low pH, high Eh extreme, and values representing the pH at 25% and 75%
cumulative fractions from the histograms in Figure 3.5 andFigure 3.6.
Such a model could also be used to delineate between geologic units and choose the Kd values
which are best represented by the Eh and pH values of that unit. However, the reactive transport modeling
-350
-150
50
250
450
650
2 4 6 8 10 12
Eh

(
m
V
)
pH
Weatherspoon
Sutton
Lee
Cape Fear
Asheville
Mayo
Roxboro
Water Oxidation
Water Reduction
Selected Values

Page 20
and similarity in the pH and Eh conditions between the different geologic units at each site do not
necessarily justify this effort. Thus, the approach taken in this report is to compare the Kd values required
by the reactive transport model, Kd values from the PHREEQC geochemical model, and Kd values
measured in laboratory experiments to ensure that the trends, which are a manifestation of the underlying
geochemical behavior, are similar.

Figure 3.5: Histograms of Eh values measured at all seven sites. The bottom figure shows the
mean Eh values and +/- 1 standard deviation in the blue and black lines, respectively. Values at
approximately 25% and 75% for each for each pH and Eh condition were included in
geochemical modeling at each site (Table 3.8). Based on data from [30-36].

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
10
20
30
40
50
60
-400 -300 -200 -100 0 100 200 300 400 500 600 700 800
Cu
m
u
l
a
t
i
v
e

F
r
a
c
t
i
o
n
#
o
f

Oc
c
u
r
r
e
n
c
e
s
Eh (mV)
Histogram
Cumulative
Fraction
0
10
20
30
40
50
60
70
-400 -300 -200 -100 0 100 200 300 400 500 600 700 800
#
o
f

Oc
c
u
r
r
e
n
c
e
s
Eh (mV)
Histogram
EH + 1 SD
EH mean value
EH - 1 SD

Page 21

Figure 3.6: Histograms of pH values measured at all seven sites. The bottom figure shows the mean
pH values and +/- 1 standard deviation in the blue and black lines, respectively. Values at
approximately 25% and 75% for each for each pH and Eh condition were included in geochemical
modeling at each site (Table 3.8). Based on data from [30-36].

Table 3.8: pH and Eh Values for Global Model Input. Note that Eh was entered into PHREEQC
using pe (-log(e-) based on the equation Eh = 59 mV*pe
pH Eh (mV) pe Notes
4 482 8.16 low pH, high Eh value
5.6 -21 -0.35 low pH, low Eh value
6.47 220 3.72 global average pH and Eh
6.9 514 8.69 high pH, high Eh value
9.1 -104 -1.75 high pH, low Eh value
5.1 372 6.29 pH range covering 25-75% of sites from Figure 3.4 7.1 75.5 1.28

In addition to the pH and Eh range, a range of ion concentrations must also be selected for the
PHREEQC modeling. Similar to the model parameterization discussed above, the measured values from all
seven sites were compared to determine if there was significant variability. Hundreds of data points from
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
5
10
15
20
25
30
35
40
45
2 3 4 5 6 7 8 9 10 11 12
Cu
m
u
l
a
t
i
v
e

F
r
a
c
t
i
o
n
#
o
f

Oc
c
u
r
r
e
n
c
e
s
pH
Histogram
Cumulative
Fraction
0
5
10
15
20
25
30
35
40
45
50
2 3 4 5 6 7 8 9 10 11 12
#
o
f

Oc
c
u
r
r
e
n
c
e
s
pH
Histogram
pH + 1 SD
pH mean value
pH - 1 SD

Page 22
various geologic units at all seven sites were plotted together in the following series of figures (Figure 3.7
and Figure 3.8). There are wide variations in the ion concentrations at each site, but the average values from
site to site are relatively constant. Therefore, it was assumed that a global set of average values could be used
to approximate the geochemical behavior at each site. Using these values, a set of three conditions were used
as input values for the model (Table 3.9 and Table 3.10). The concentrations of major ions (e.g., Ca2+, Na+,
Fe(II/III), Cl-, SO42-) were varied to consider the range of potential values. The concentrations of several
trace ions and constituents of interest were not varied so that the model could examine the potential for
competition for sorption sites between the varying major ion concentration conditions and a fixed condition
for the trace elements. For example, sorption of Fe2+ and SO42- can effectively block sites for cation and
anion sorption, respectively. Therefore, by considering the ranges of ferrous iron and sulfate listed below in
Table 3.10, the potential for sulfate to outcompete another anion (e.g., arsenate AsO4
-3) can be examined. In
the model output described below, this impact is demonstrated by comparison of the Kd values measured
under “low”, “average” and “maximum” groundwater ion concentrations based on the values in Table 3.9
Table 3.10.

Page 23

Figure 3.7: Minimum, average, and maximum Al, Sb, As, Ba, B, Cd, Cr, and Co concentrations in
groundwater at all seven sites. Based on data from [30-36].

0.1
1
10
100
1000
Aluminum Antimony Arsenic Barium Boron Cadmium Chromium Cobalt
Mi
n
i
m
u
m

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville
1
10
100
1000
10000
Aluminum Antimony Arsenic Barium Boron Cadmium Chromium Cobalt
Av
e
r
a
g
e

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville
1
10
100
1000
10000
100000
Aluminum Antimony Arsenic Barium Boron Cadmium Chromium Cobalt
Ma
x
i
m
u
m

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville

Page 24

Figure 3.8: Minimum, average, and maximum Cu, Pb, Mn, Mo, Ni, Se, Sr, and V concentrations in
groundwater at all seven sites. Based on data from [30-36].

0.001
0.01
0.1
1
10
100
Mi
n
i
m
u
m

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville
0.1
1
10
100
1000
10000
Av
e
r
a
g
e

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville
1
10
100
1000
10000
100000
Ma
x
i
m
u
m

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville

Page 25
Table 3.9: Constituents to hold constant at average
concentrations in PHREEQC geochemical model
Constituent
Molecular
Weight
Average
(µg/L)
Average
(mol/L)
Antimony 121.76 2.28E+00 1.87E-08
Arsenic 74.92 8.46E+01 1.13E-06
Beryllium 9.01 1.94E+01 2.15E-06
Boron 10.81 1.42E+03 1.32E-04
Cadmium 112.41 1.82E+00 1.62E-08
Chromium 52.00 1.22E+01 2.34E-07
Cobalt 58.93 2.72E+01 4.61E-07
Copper 63.55 8.90E+00 1.40E-07
Lead 207.20 6.24E+00 3.01E-08
Mercury 200.59 1.52E-01 7.60E-10
Molybdenum 95.94 4.74E+01 4.94E-07
Nickel 58.69 2.54E+01 4.32E-07
Selenium 78.96 7.78E+00 9.86E-08
Strontium 87.62 6.22E+02 7.10E-06
Thallium 204.38 5.12E-01 2.51E-09
Vanadium 50.94 9.96E+00 1.96E-07
Zinc 65.41 6.68E+01 1.02E-06

Table 3.10: Constituents to vary in concentration to between minimum, average, and maximum
ground water concentrations to check for sorption competition in PHREEQC geochemical model
Constituent
Mol.
Weight
(g/mol)
Minimum
(mg/L)
Average
(mg/L)
Maximum
(mg/L)
Minimum
(mol/L)
Average
(mol/L)
Maximum
(mol/L)
Aluminum 26.98 5.00E-03 1.88E+00 5.74E+01 1.85E-07 6.98E-05 2.13E-03
Barium 137.33 6.00E-03 9.56E-02 1.92E+00 4.37E-08 6.96E-07 1.40E-05
Calcium 40.08 4.40E-02 5.10E+01 5.64E+02 1.10E-06 1.27E-03 1.41E-02
Carbonate
Alkalinity 60.01 0.00E+00 1.44E+02 3.80E+02 0.00E+00 2.41E-03 6.33E-03
Chloride 35.45 1.10E+00 3.70E+01 5.70E+02 3.10E-05 1.04E-03 1.61E-02
Iron 55.85 1.00E-02 8.42E+00 2.14E+03 1.79E-07 1.51E-04 3.83E-02
Magnesium 24.31 7.00E-03 1.40E+01 2.81E+02 2.88E-07 5.76E-04 1.16E-02
Manganese 54.94 5.00E-03 1.28E+00 4.55E+01 9.10E-08 2.32E-05 8.28E-04
Nitrate
(as N) 14.01 1.00E-02 5.75E-01 2.50E+01 7.14E-07 4.10E-05 1.78E-03
Potassium 39.10 1.23E-01 4.82E+00 1.91E+02 3.15E-06 1.23E-04 4.89E-03
Sodium 22.99 4.52E-01 3.15E+01 5.61E+02 1.97E-05 1.37E-03 2.44E-02
Sulfate 96.06 1.10E-01 1.31E+02 1.80E+04 1.15E-06 1.36E-03 1.87E-01
Sulfide 32.07 1.00E-01 4.29E-01 4.18E+00 3.12E-06 1.34E-05 1.30E-04

Page 26
3.5. Pourbaix diagram modeling
To gain an understanding of the aqueous chemical species of each constituent of interest,
Pourbaix diagrams were generated using Geochemist Workbench v10. To perform these simulations, the
WATEQ4F database was utilized because this is the same database used in PHREEQC modeling of the
sorption behavior described below. However, Se and V were not available in the Geochemist Workbench
database. Instead, the LLNL.v8.r6+ database was used to generate the Pourbaix diagrams for Se and V
described below. Constants for Se and V were added to the PHREEQC database for the sorption
modeling below. However, based on the similarity of the revised WATEQ4F database used in PHREEQC
modeling and the LLNL.v8.r6+ database, the speciation exhibited in the Pourbaix diagrams below is
representative of the species.
In these Pourbaix diagrams, the Eh and pH measurements from Table 3.8 are shown as individual
data points. A generic groundwater chemistry containing 500 ppb of each constituent of concern was used
in the simulations (Table 3.11). These concentrations are generally higher than the concentrations
observed in groundwater samples from the sites considered in this report with the exception of B, Fe, and
Mn. However, fixed concentrations were used for most constituents to provide a direct comparison of the
model output. If precipitation is not observed in these diagrams for the Eh-pH regions of interest, it will
not be occurring for lower concentrations which would be less saturated. The dominant aqueous species is
shown in the blue regions and dominant precipitated solid phases are shown in yellow regions.

Table 3.11: Concentrations of reagents used to generate Pourbaix diagrams
Species
Concentration
(ppm)
Concentration
(mol/L)
CaSO4. 2H2O 20.0 1.47 x 10-4
MgSO4 5.0 4.17 x 10-5
Na(HCO3) 10.0 1.19 x 10-4
Arsenic 0.5 6.67 x 10-6
Barium 0.5 3.64 x 10-6
Boron 0.5 4.62 x 10-5
Cobalt 0.5 8.49 x 10-6
Selenium 0.5 6.33 x 10-6
Vanadium 0.5 9.82 x 10-6
Chromium 0.5 9.66 x 10-6
Nitrate 1.5 2.43 x 10-5
Manganese 0.5 9.00 x 10-6

It is important to note in these diagrams that only the most abundant aqueous species is shown in
these plots. There are numerous aqueous and mineral species contributing to the reactivity of these
systems. These diagrams only serve to show major trends in the speciation. More detailed calculations
using PHREEQC consider all aqueous species involved and changes with respect to Eh and pH as done in
these Pourbaix diagrams. However, in those models sorption is considered and distribution coefficients
are calculated which consider all of the chemical species present under a given set of conditions. Thus,
while these Pourbaix diagrams are useful tools to identify the major species, it is important to note some
limitations:

Page 27
· The dividing lines between boxes are where species may be equal, but there is no information in
the diagram regarding the uncertainty of the simulation or the change in speciation as pH and Eh
move away from the boundary lines. So there may be significant concentrations of other species
present which cannot be seen on the diagrams.
· The speciation is also considered only for the conditions given (listed in Table 3.11). Altering the
concentrations of aqueous constituents may influence the data.
· The Pourbaix diagrams report the activity of species, not molar concentrations. So corrections
must be made to get molar units or mass units that are typical measures of concentration.
· These Pourbaix diagrams show only the aqueous species and precipitates with no consideration of
sorption. Therefore, when comparing these with ground water measurements at the site, some
consideration must be made regarding the potential for a species to be present in the subsurface
but sorbed to the solid phase and not present in the ground water. A notable example of the
significance of this is discussed below with regard to As speciation. The Pourbaix diagrams
predict that As(V) will be the dominant oxidation state in many waters. However ground water
speciation measurements indicate that As(III) is the dominant aqueous oxidation state. Since
As(V) sorbs strongly to mineral surfaces under the pH of the ground waters, the As(V) may
indeed be present in the system but sorbed to the mineral surface and not measured in ground
water samples.
3.6. Flow transect model
Three flow transects were chosen to investigate the ash basin’s influence on the subsurface
environment along hydrogeologically significant flow paths. Each transect begins at ABMW-01S/D, a
well location set within the Old Ash Basin Area, which is centrally located in the Sutton Plant, between
the New and Former Ash Disposal Areas. ABMW-01S is screened within the ash pore water of the
source area and ABMW-01D is screened within the Pee Dee Upper geologic zone (geozone) beneath the
source area. From ABMW-01S/D the flow paths chosen for the transect flow north, east, and southeast as
follows:
· North Transect
o ABMW-01S/D à MW-36B/C àMW-27B/C à MW-38B/C/D à AW-08B/C
· East Transect
o ABMW-01S/D à MW-23B/C/D/E àAW-06B/D/E & MW-12 à SMW-06B/C/D
o with an additional branch from MW-23B/C/D/E à AW-04B/C à SMW-01B/C/D
· Southeast Transect
o ABMW-01S/D à MW-18 àMW-21C àMW-28B/C àMW-07A/B/C à AW-09B/C/D
With the pH and redox potential as the most influential geochemical conditions influencing the
partitioning of constituents of interest, an understanding of how the pH and redox potential Eh change
along flow transects away from the source area is a crucial component to understanding the geochemical
behavior of each constituent. In general, along transects at the Sutton Plant, pH decreased and pe
increased with distance from the source area. A notable exception in this trend is in the Pee Dee Upper
geozone, where the pattern seems to be either nonexistent or reversed (Figure 3.9). The decrease in pH
will generally cause a decrease in the mobility of anions due to a greater electrostatic attraction as the
mineral surfaces become increasingly positively charged with decreasing pH. Conversely, the pH
decrease will potentially increase the mobility of cations due to a weakened electrostatic interaction with

Page 28

Figure 3.9: pH values along the north (top), east (middle), and southeast (bottom) transects
beginning at the Ash Basin wells ABMW-01S/D.
0.0
2.0
4.0
6.0
8.0
10.0
0 1000 2000 3000 4000 5000
pH
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
4.0
5.0
6.0
7.0
8.0
9.0
10.0
0 1000 2000 3000 4000 5000
pH
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
4.0
5.0
6.0
7.0
8.0
9.0
10.0
0 1000 2000 3000 4000 5000
pH
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower

Page 29

Figure 3.10: Redox potential along the north (top), east (middle), and southeast (bottom) transects
beginning at the Ash Basin wells ABMW-01S/D.
-100
0
100
200
300
400
500
600
700
0 1000 2000 3000 4000 5000
Eh

(
m
V
)
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
-600
-400
-200
0
200
400
600
800
0 1000 2000 3000 4000 5000
Eh

(
m
V
)
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
-300
-200
-100
0
100
200
300
400
500
600
0 1000 2000 3000 4000 5000
Eh

(
m
V
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower

Page 30
the mineral surfaces. The increase in Eh could lead to the formation of more oxidized species (Figure
3.10). The impact of this is dependent on the species which is formed and the pH of the system. For
example, oxidation of the sparsely soluble cation Cr3+ to the soluble chromate anion CrO4-2 has the
potential to enhance the mobility of chromium. But this behavior is dependent on the sorption affinity of
Cr(VI) which is dependent on the pH of the pore water. Therefore, the influences of Eh will be discussed
for specific cases below.

Page 31
3.7. The use of Kd values
In this report the PHEEQC model, which predicts both aqueous and solid phase speciation based
on thermochemical principles, is used to calculate Kd values and examine how pH, Eh, and ground water
ion concentrations influence the predicted Kd values. The stability constants used in the PHREEQC
database to describe chemical reactions are on a log scale. Therefore, small differences in the stability
constants can have a large impact on the predicted Kd values. As an example of this phenomenon, a plot
of the Kd versus fraction sorbed (assuming a 50 g/L suspension of sorbent) is shown in Figure 3.11. The
Kd values were calculated using:

∗

where [M]solid is the sorbed concentration of a constituent M in units of mol/kgsorbent, [M]aqueous is the
aqueous concentration of the constituent in units of mol/L, [M]Total is the total initial concentration of the
constituent in units of mol/L, V is the volume of the sample in L, and m is the mass of sorbent in kg. Thus
V/m is the inverse of the suspended sorbent concentration (50 g/L in the simulation below). The Kd
equation above can be rearranged to estimate the sorbed fraction of the constituent as:
1 1

The figure is meant to illustrate the fact that at Kd values less than 1 or greater than 1000, only small
increase in the concentration of sorbed ions can cause orders of magnitude differences in the predicted Kd
values. Such small differences would be difficult to determine experimentally based on analytical
equipment resolution or detection limits. Thus, in many cases discussed below, very low or very high Kd
values are reached which could not be determined in many laboratory studies.

Figure 3.11: Theoretical relationship between Kd values and the predicted sorbed fraction within a
hypothetical 50 g/L suspension of sorbent. Numerical values are provided to the right to
demonstrate the small change in the fraction sorbed with increasing Kd.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.0E-03 1.0E-01 1.0E+01 1.0E+03 1.0E+05 1.0E+07
Fr
a
c
t
i
o
n

S
o
r
b
e
d
Kd (L/kg)

Page 32
4. GEOCHEMICAL MODELING of ARSENIC
4.1. Pourbaix diagram analysis
The pH and Eh values from the global model and the site-specific values are plotted over a
pourbaix diagram in Figure 4.1 to gain an understanding of the expected speciation under equilibrium
conditions. Under mildly oxidizing to strongly oxidizing conditions, arsenic can exist as the arsenate
(AsO4
-3) and arsenite (AsO3
-3) oxoanions. Both are weak acids and persist in solution as HxAsOy
x-2y
species [37]. Under the pH and Eh conditions expected at each site, both As(III) and As(V) may be
present as evident by the range of values in the Pourbaix diagram below. Relatively low pH values will
favor the protonated forms of As(III) and As(V) as H3AsO3 and H2AsO4-, respectively. As the pH
increases and the redox potential decreases, the arsenite species (As(III)) could persist as the neutrally
charged H3AsO3 or anionic H2AsO3-. Such changes in redox speciation or protonation state can have
profound impacts on the mobility of arsenic. Changes in ionic charge will alter the strength of interactions
with mineral surfaces. Generally as the pH decreases and mineral surfaces develop increasingly positive
net surface charges, sorption of As(III) and As(V) oxoanions will increase [18, 38]. Reduction of As(V)
to As(III) will cause greater overall mobility of As because of the lower sorption affinity of As(III)
relative to As(V) [17]. As discussed above, the stronger sorption of As(V) would preferentially remove
As from the ground waters and thus comparison of the groundwater speciation and the modeled
speciation in this work must also consider the influence of sorption that the model accounts for but is not
determined in groundwater samples

Figure 4.1: Pourbaix diagram of arsenic species along with pH and Eh values examined in
PHREEQC modeling. The round symbols represent the arsenic species that correspond to the range
of pH and Eh values used in the global geochemical model (left) and the Sutton, site-specific model
(right). The error bars represent the standard deviation of the average pH and Eh value calculated
from the combined measurements at all seven sites for the average.

Page 33
4.2. Transect model analysis
The two wells whose arsenic concentration exceeds the 15A NCAC 02L Standard were ABMW-
01S and MW-21C. MW-21C fell into a moderate pH range of 6.5 to 6.7 with a relatively low to moderate
range of redox potentials, while ABMW-01S had a relatively high pH range of 8.0 to 9.0 and a moderate
redox potential. Based on these Eh and pH values, MW-21C is likely the neutrally charged arsenite
As(III) species, H3AsO3, and ABMW-01S is either the neutrally charged H3AsO3 or ionic H2AsO4
- (refer
to pourbaix diagrams in Figure 4.1).
Along the transects there is a general decrease in arsenic concentration with distance
from the ash basin, except for the notable increase with distance seen in the Pee Dee Upper geozone
(Figure 4.2). The Pee Dee Upper was also the only geozone to have an increase in pH with distance from
the ash basin. As pH increases and redox potential decreases, the conditions for the formation and
stability of arsenite species (As(III)) become increasingly favorable. Additionally, with an increase in pH
the net surface charge of mineral surfaces becomes increasingly negative, resulting in lower sorption
potential for As(III) and As(V) oxoanions. In the case of the Pee Dee Upper geozone, the increase in pH
along the transect with low to moderate Eh values likely resulted in an increase in As(III) in the aqueous
phase. Reduction of As(V) to As(III) will cause greater overall mobility of As because of the lower
sorption affinity of As(III) relative to As(V) [17].
The PHREEQC predicted Kd values along each transect are shown in Figure 4.3. There is
generally an increase in the Kd value as a function of distance which is indicative of a decrease in the
aqueous concentration noted in the well measurements in Figure 4.2. Thus there is a qualitative agreement
between the geochemical model and the site measured values. Interestingly, the Pee Dee Upper zone
exhibits a slight increase in pH as a function of distance along the transect. The geochemical model
predicts a slight decrease in the Kd value as a function of distance which is in good agreement with the
field observations.
In all cases, As(V) is predicted to be the dominant oxidation state in these systems. For example,
a plot of the concentrations of As(V) and As(III) as a function of pH in the East transect is shown in
Figure 4.4. The concentration of As(V) makes up greater than 99% of the total arsenic in the system at
almost all pH values. This is consistent with the speciation expected from the Pourbaix diagram in Figure
4.1. Therefore, it is not expected that under the pH and Eh conditions modeled in these transects that
reduction of As(V) to As(III) will occur. Understanding this phenomenon may influence the interpretation
of remediation activities such as basin removal which could introduce oxygen into the subsurface. Based
on these model results, for the case of arsenic this will have no impact because the majority of arsenic is
already present as an oxidized species.
There are several noteworthy points in Figure 4.2 where the Kd dramatically decreases
particularly for the surficial lower geozone in the east and southeast transects. There is a resulting
increase in the predicted aqueous phase concentration at these points where the Kd decreases. These
changes in the arsenic partitioning are linked to the availability of HFO surfaces for sorption in the model.
As discussed above, the concentration of HFO is determined by the saturation state of ferrihydrate in
these systems. Furthermore, the saturation of ferrihydrate is determined by the total amount of Fe in the
system based on the solid phase Fe measurements performed on solids from these wells. For the model
iterations where there is a decrease in the Kd value, the ferrihydrate was no longer saturated. Plots of the
ferrihydrate saturation state as a function of distance in each transect are shown in Figure 4.5.

Page 34

Figure 4.2: Arsenic concentrations along the north (top), east (middle), and southeast (bottom)
transects beginning at the Ash Basin wells ABMW-01S/D.
1
10
100
1000
0 1000 2000 3000 4000 5000
As

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
1
10
100
1000
0 1000 2000 3000 4000 5000
As

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
1
10
100
1000
0 1000 2000 3000 4000 5000
As

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*

Page 35

Figure 4.3: PHREEQC predicted Kd values along the north (top), east (middle), and southeast
(bottom) transects beginning at the Ash Basin wells ABMW-01S/D

1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
1.0E+10
0 1000 2000 3000 4000 5000
To
t
a
l

A
s

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Sutton, North Transect
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
0 1000 2000 3000 4000
As

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Sutton, East Transect
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
0 1000 2000 3000 4000 5000
As

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 36

Figure 4.4: Predicted arsenic oxidation state speciation from the PHREEQC model along the East
Transect.

In cases where ferrihydrate is no longer saturated, the mineral was formed and as a result the model
predicted there would be no mineral phase for sorption to occur. The loss of the sorbing mineral phase
could have a major impact on the mobility of ions in these and explain some locally high concentrations
in wells. If ferrihydrate completely dissolves within some local well area, there will be a resulting
increase in the concentrations of ions which were adsorbed to the ferrihydrate. These dissolved ions
would then be mobile and if the ions were transported to a region where the ferrihydrate was saturated
and present, readsorption could occur. Note, this discussion is based on the mineral ferrihydrate. In all
cases gibbsite remained saturated and sorption to gibbsite would occur. Had gibbsite also been
unsaturated, the Kd values would have essentially gone to zero because there would have been no sorbing
phase. To demonstrate this impact, the predicted concentrations of HAO and HFO surfaces (which are
based on the availability of gibbsite and ferrihydrate) are shown in Figure 4.6Figure 4.7.
It is noteworthy that ferrihydrate and gibbsite are the assumed mineral phases for this modeling
effort due to their common occurrence as well as the availability of a self-consistent thermochemical
modeling database for these two minerals and the constituents of interest. However, the exact mineralogy
controlling sorption within each well is not known. So these models are best used to provide a qualitative
conceptual description of the system behavior.

1.00E-23
1.00E-22
1.00E-21
1.00E-20
1.00E-19
1.00E-18
1.00E-17
1.00E-16
1.00E-15
1.00E-14
1.00E-13
1.00E-12
1.00E-11
1.00E-10
1.00E-09
0 2 4 6 8 10
Aq
u
e
o
u
s

A
s

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH
As(III)
As(V)

Page 37

Figure 4.5: Predicted ferrihydrate saturation state as a function of distance along the north (top), east
(middle), and southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D.
-1
-0.5
0
0.5
1
0 1000 2000 3000 4000 5000
Fe
(
O
H
)
3
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
Distance along transect from well ABMW-01 (feet)
Ferrihydrate saturation index, North Transect
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0 1000 2000 3000 4000
Fe
(
O
H
)
3
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
Distance along transect from well ABMW-01 (feet)
Ferrihydrate saturation index, East Transect
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0 1000 2000 3000 4000 5000
Fe
(
O
H
)
3
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
Distance along transect from well ABMW-01 (feet)
Ferrihydrate saturation index, Southeast transect
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 38

Figure 4.6: Predicted HAO concentrations as a function of distance along the north (top), east
(middle), and southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D. Note
concentrations along North transect are essentially constant.
0.32915
0.3292
0.32925
0.3293
0.32935
0.3294
0.32945
0.3295
0.32955
0.3296
0.32965
0.3297
0 1000 2000 3000 4000 5000To
t
a
l

H
A
O

C
o
n
c
e
n
t
r
a
t
i
o
n
,

m
o
l
/
L
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
0.0014
0.0016
0.0018
0 1000 2000 3000 4000
To
t
a
l

H
A
O

C
o
n
c
e
n
t
r
a
t
i
o
n
,

m
o
l
/
L
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
0
0.00001
0.00002
0.00003
0.00004
0.00005
0.00006
0.00007
0.00008
0.00009
0 1000 2000 3000 4000 5000
To
t
a
l

H
A
O

C
o
n
c
e
n
t
r
a
t
i
o
n
,

m
o
l
/
L
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 39

Figure 4.7: Predicted HFO concentrations as a function of distance along the north (top), east
(middle), and southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D. Note
the values <10-23 mol/L in the North and East transect essentially represent zero atoms of HFO in
the system.
1.0E-31
1.0E-28
1.0E-25
1.0E-22
1.0E-19
1.0E-16
1.0E-13
1.0E-10
1.0E-07
1.0E-04
1.0E-01
0 2000 4000 6000
To
t
a
l

H
F
O

C
o
n
c
e
n
t
r
a
t
i
o
n
,

m
o
l
/
L
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-27
1.0E-24
1.0E-21
1.0E-18
1.0E-15
1.0E-12
1.0E-09
1.0E-06
1.0E-03
1.0E+00
0 1000 2000 3000 4000
To
t
a
l

H
F
O

C
o
n
c
e
n
t
r
a
t
i
o
n
,

m
o
l
/
L
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E-06
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
0 1000 2000 3000 4000 5000To
t
a
l

H
F
O

C
o
n
c
e
n
t
r
a
t
i
o
n
,

m
o
l
/
L
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 40

4.3. PHREEQC global model analysis
Changes in ion concentrations, pH, and Eh were found to have a significant impact on As
sorption. The calculated Kd values from the three modeled ion concentration conditions and the range of
pH and Eh values are shown in Figure 4.8. There is a wide variation in the Kd values which is a
manifestation of 1) a change in arsenic redox speciation between As(III) and As(V) and 2) competition
for sorption sites from other anions such as sulfate. The redox speciation changes can be examined by
monitoring the changes in the aqueous phase oxidation states as shown in Figure 4.9. The pH/Eh
condition 5.1/-20 mV is the only condition where arsenic is predominantly in the aqueous phase as
As(III). Under other conditions the As(V) ion is the dominant oxidation state. Similar behavior is
observed on the solid phase primarily due to the greater sorption affinity of As(V) as compared with
As(III). For example, using the speciation output from PHREEQC, separate Kd diagrams could be
generated for As(V) and As(III) as shown in Figure 4.10. The Kd values for As(III) range from 10-1 to 102
L/kg and those for As(V) range from 103-106 L/kg. The low values for As(V) at pH/Eh 5.1/-20 mV are
somewhat erroneous because As(III) is the dominant oxidation state in both solid and aqueous phase
samples. This is demonstrated through analysis of the solid phase speciation in Figure 4.11. The solid
phase concentrations are plotted in units of mol/L based on the PHREEQC model output despite the
apparent conflict of reporting an “aqueous” concentration of a sorbed species. It is clear that As(V) is the
dominant sorbed species under almost all conditions, consistent with the high Kd values for As(V) shown
in Figure 4.11. Based on these values, it is clear that the oxidation of As(III) to As(V) will result in
greater immobility of arsenic. The potential for changes in As speciation are significant because the
majority of speciation measurements for arsenic in site groundwater indicate As(III) is the dominant
oxidation state. Thus, any remedial activity in which more oxidizing conditions are introduced into the
system, which causes oxidation of As(III) to As(V), would likely result in a decrease in arsenic mobility
at the site.

Figure 4.8: Distribution coefficient (Kd) for summed As(III) and As(V) species from PHREEQC
model. The impact of changing ion concentrations and changes in redox speciation are shown by
the decrease or increase in Kd values.
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
4.0 /
482
5.6 / -20 6.5 /
220
6.9 /
513
9.1 / -
103
5.1 /
372
7.1 / 76
To
t
a
l

A
s

K
d
(L
/
k
g
)
pH / Eh (mV)
Total As Kd, Min
GW Values
Total As Kd, Avg
GW Values
Total As Kd, Max
GW Values

Page 41

Figure 4.9: Redox speciation of aqueous As from PHREEQC modeling under minimum (top),
average (middle), and maximum (bottom) ion concentrations listed in Table 3.10. Note that the
aqueous concentrations of As vary due to the extent of sorption at the pH/Eh conditions noted.

1.00E-18
1.00E-16
1.00E-14
1.00E-12
1.00E-10
1.00E-08
1.00E-06
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Aq
u
e
o
u
s

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Aqueous As(V),
Min GW Values
Aqueous As(III),
Min GW Values
1.00E-11
1.00E-10
1.00E-09
1.00E-08
1.00E-07
1.00E-06
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Aq
u
e
o
u
s

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Aqueous As(V),
Avg GW Values
Aqueous As(III),
Avg GW Values
1.00E-09
1.00E-08
1.00E-07
1.00E-06
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Aq
u
e
o
u
s

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Aqueous
As(V), Max
GW Values
Aqueous
As(III), Max
GW Values

Page 42

Figure 4.10: Separate distribution coefficients calculated for As(III) and As(V) from PHREEQC
model output. To allow easy comparison, the plots are shown on similar y-axes.

1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
As
(
V
)

K
d
(L
/
k
g
)
pH / Eh (mV)
As(V) Kd, Min
GW Values
As(V) Kd,. Avg
GW Values
As(V) Kd, Max
GW Values
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
As
(
I
I
I
)

K
d
(L
/
k
g
)
pH / Eh (mV)
As(III) Kd, Min
GW Values
As(III) Kd,. Avg
GW Values
As(III) Kd, Max
GW Values

Page 43

Figure 4.11: Redox speciation of sorbed As from PHREEQC modeling under minimum (top),
average (middle), and maximum (bottom) ion concentrations listed in Table 3.10.
1.00E-18
1.00E-17
1.00E-16
1.00E-15
1.00E-14
1.00E-13
1.00E-12
1.00E-11
1.00E-10
1.00E-09
1.00E-08
1.00E-07
1.00E-06
1.00E-05
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
So
r
b
e
d

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Sorbed As(V),
Min GW Values
Sorbed As(III),
Min GW Values
1.00E-11
1.00E-10
1.00E-09
1.00E-08
1.00E-07
1.00E-06
1.00E-05
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
So
r
b
e
d

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Sorbed As(V),
Avg GW Values
Sorbed As(III),
Avg GW Values
1.00E-09
1.00E-08
1.00E-07
1.00E-06
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
So
r
b
e
d

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Sorbed As(V),
Max GW Values
Sorbed As(III),
Max GW Values

Page 44

The Kd values reported in Figure 4.10 are also significantly changed by the concentration of other
ions in solution. Note that the total concentration of arsenic in all simulations was 1.13 x 10-6 mol/L as
listed in Table 3.9. The model was run in three simulated groundwater conditions containing the
minimum, average, and maximum ion concentrations listed in Table 3.10. Generally, the Kd values
decrease with increasing concentrations of other ions such as Fe2+ and SO4-2. This is predominantly due to
competition for sorption sites between As(V) and As(III) with other anions such as SO4
2-. As the
concentration of SO4-2 in the groundwater simulant increases from 1.15 x 10-6 to 1.87 x 10-1 mol/L, based
on the values in Table 3.10, the sulfate ion may compete with other anions like arsenate for sorption sites.
This behavior can be seen in Figure 4.12 and Figure 4.13 for HFO and HAO, respectively. The dominant
surface species in both systems is the protonated surface site ≡FeOH2+ and ≡AlOH2+, due to the relatively
low pH of these systems and the relatively high surface protonation constants for HFO and HAO [1,2].
The result of this speciation is that the surfaces generally have a net positive surface charge which can
result in greater sorption of anions such as the arsenate, borate, and chromate ions of interest to these
sites. The two primary competing ions in these systems are ferrous iron and sulfate. In Figure 4.11 and
Figure 4.13, the concentrations of the surface species ≡FeOFe+, ≡Fe-SO4-, ≡AlOFe+, and ≡Al-SO4- all
increase as the initial Fe2+ is raised from 1.79 x 10-7 to 3.83 x 10-2 mol/L and the concentration of SO42- is
raised from 1.15 x 10-6 to 1.87 x 10-1 mol/L. These increased Fe2+ and SO42- levels cause increased
sorption and take up additional sorption sites which otherwise were occupied by trace ions such as the
arsenate and arsenite ions examined in this section.
There are no indications in the geochemical model that precipitates containing arsenic will form.
However, there is some circular logic to this argument because the arsenic concentrations used in the
model were based upon measured values in ground waters at the coal ash basin sites. Thus, if the
measured arsenic concentrations in those systems were indeed controlled by solubility, the reported
values used here would be at or below those levels and would not necessarily indicate a saturated system
was present. Therefore, a more accurate comparison is how close potential solid phases are to saturation.
A plot of the saturation indices of some relevant phases out of the 300+ possible mineral phases
considered in the PHREEQC model is shown in Figure 4.14. The saturation index is a measure of the
concentration of an element in solution relative to the maximum possible concentration under equilibrium
solubility conditions. Therefore, a saturation index of 1 or greater indicates that the solution is saturated
with respect to that ion and will precipitate. A value less than 1 indicates the ion is not saturated and the
concentration can be increased before saturation will occur. The values are generally reported in log units.
Thus, based on the log saturation indices reported in Figure 4.14, the concentration of arsenic could be
increased several orders of magnitude before precipitation would be expected. The mineral scorodite
(FeAsO4.H2O) has the highest saturation index of log -2.6 at a pH and Eh of 9.1 and -103 mV,
respectively. The dominance of scorodite under these conditions is primarily due to the high Fe(II)
content facilitated by the low redox conditions (though not sufficiently low to predict reduction of As(III)
to As(V)). The mineral mansfieldite (AlAsO4.2H2O) also has high saturation indices. However, none of
these phases are saturated even under the “maximum groundwater ion” conditions. Therefore, it is highly
unlikely that these precipitates would form a solubility controlling phase in these systems. It is also
noteworthy that both scorodite and mansfieldite commonly form in hydrothermal deposits and/or under
acidic oxidizing conditions. Therefore, these are not expected to form under site conditons and are
unlikely to influence the behavior of As.

Page 45

Figure 4.12: Distribution of HFO surface site speciation under minimum (top), average (middle), and
maximum (bottom) total groundwater ion concentrations from Table 3.10. A shift from the dominance of
H+ and OH- dominated surfaces to a mixture of H+, OH-, Fe2+, and SO42- dominated surfaces with
increasing dissolved ion concentrations is shown by changes in relative color distributions.
1.00E-03
1.00E-02
1.00E-01
1.00E+00
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Fr
a
c
t
i
o
n

o
f

S
p
e
c
i
e
s
pH / Eh (mV)
HFO Surface speciation, minimum GW ion concentrations
HFO_OH2+HFO_OH HFO_O-HFO_OFe+HFO_OFeOH
HFO_SO4-HFO_OHSO4-2 HFO_OCa+HFO_OMn+HFO_OMg+
1.00E-03
1.00E-02
1.00E-01
1.00E+00
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Fr
a
c
t
i
o
n

o
f

S
p
e
c
i
e
s
pH / Eh (mV)
HFO Surface speciation, average GW ion concentrations
HFO_OH2+HFO_OH HFO_O-HFO_OFe+HFO_OFeOH
HFO_SO4-HFO_OHSO4-2 HFO_OCa+HFO_OMn+HFO_OMg+
1.00E-03
1.00E-02
1.00E-01
1.00E+00
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Fr
a
c
t
i
o
n

o
f

S
p
e
c
i
e
s
pH / Eh (mV)
HFO Surface speciation, maximum GW ion concentrations
HFO_OH2+HFO_OH HFO_O-HFO_OFe+HFO_OFeOH
HFO_SO4-HFO_OHSO4-2 HFO_OCa+HFO_OMn+HFO_OMg+

Page 46

Figure 4.13: Distribution of HAO surface site speciation under minimum (top), average
(middle), and maximum (bottom) total ion concentrations from Table 3.10. A shift from the
dominance of H+ and OH- dominated surfaces to a mixture of H+, OH-, Fe2+, and SO4-2
dominated surfaces with increasing dissolved ion concentrations is shown by changes in
relative color distributions.
0%
1%
10%
100%
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Fr
a
c
t
i
o
n

o
f

S
p
e
c
i
e
s
pH / Eh (mV)
HAO Surface speciation, average GW ion concentrations
HAO_OH2+HAO_OH HAO_O-HAO_OFe+HAO_OHSO4-2
0%
1%
10%
100%
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Fr
a
c
t
i
o
n

o
f

S
p
e
c
i
e
s
pH / Eh (mV)
HAO Surface speciation, average GW ion concentrations
HAO_OH2+HAO_OH HAO_O-HAO_OFe+HAO_OHSO4-2
0%
1%
10%
100%
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Fr
a
c
t
i
o
n

o
f

S
p
e
c
i
e
s
pH / Eh (mV)
HAO Surface speciation, maximum GW ion concentrations
HAO_OH2+HAO_OH HAO_O-HAO_OFe+HAO_OHSO4-2

Page 47

Figure 4.14: Saturation indices for five relevant As bearing solid phases considered in the
PHREEQC model. Other species for which the saturation index never reached a value > -12 are not
shown.

4.4. Comparison of measured and calculated As speciation
Arsenic redox speciation data is available for several samples from all seven sites and As(III) is
found to be the dominant oxidation state in almost all measurements (in which the arsenic concentration is
above detection limits). The fraction of As(III) and As(V) measured at each site as a function of Eh and
pH are shown in Figure 4.15 for selected data from the Sutton site. While pH and Eh values are
correlated, the data are shown separately for clarity. These samples generally show As(III) as the
dominant oxidation state in the ground water. There is no clear indication that the pH or Eh of the system
has influenced the observed aqueous As speciation and also little difference in the observed speciation at
each site. This is not in agreement with the PHREEQC model which predicts As(V) as the dominant
aqueous oxidation state under the majority of Eh and pH conditions considered. There are three potential
reasons for this discrepancy. First the equilibrium constants for the aqueous and sorbed species could be
incorrect. While this is always a possibility the constants used in this model are from reliable databases
and data compilations and are considered the best available values. The second possibility is that there are
redox couples controlling the arsenic speciation which have not been considered in the model. This is also
a possibility but all available redox active species have been included in the model based on field
measurements. The final and most plausible reason for the discrepancy is that arsenic is not present under
equilibrium conditions. A fundamental assumption of the PHREEQC model is that all chemical reactions
are at equilibriu m. Thus, the modeled speciation is predicting equilibrium conditions.
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
pH 5.6,
EH -20
pH 6.5,
EH 220
pH 6.9,
EH 513
pH 9.1,
EH -103
pH 5.1,
EH 372
pH 7.1,
EH 76
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
Scorodite (FeAsO4.2H2O)
AlAsO4:2H2O
Mn3(AsO4)2:8H2O
Ca3(AsO4)2:4w
Arsenolite (As2O3)

Page 48

Figure 4.15: Arsenic redox speciation measured in aqueous samples from Sutton well samples as a
function of pH (top) and Eh (bottom).

To evaluate the potential for redox disequilibrium in the field samples, the expected redox
potential for each groundwater sample was calculated based on the equation:

where [e] is the electron concentration in the system which is more commonly noted as pe (pe = -log[e]).
This reaction has been written in terms of Fe++ oxidation to Fe(OH)2+ which are the expected aqueous
species at the pH under consideration. While linking this explicitly to the ferrihydrate concentration in the
system could provide a more realistic estimation of the redox potential due to the low solubility of Fe(III)
species, Fe(OH)2+ was used as the aqueous species because the exact ferrihydrate concentration is not
0.0
0.2
0.4
0.6
0.8
1.0
1.2
4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0
Fr
a
c
t
i
o
n

o
f

A
q
u
e
o
u
s

A
s

S
p
e
c
i
e
s
pH
Sutton, Fraction As(III)
Sutton, Fraction As(V)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
-200 -100 0 100 200 300 400
Fr
a
c
t
i
o
n

o
f

A
q
u
e
o
u
s

A
s

S
p
e
c
i
e
s
Eh (mV)
Sutton, Fraction As(III)
Sutton, Fraction As(V)
( ) [ ] [ ]
[ ]=log −18.78

Page 49
known (note that extractable Fe concentrations are available but the fraction of a specific iron mineral is
unknown). The value log -18.78 is the value of the equilibrium constant for this reaction. Iron speciation
is used for this calculation because the dissolved Fe concentrations are relatively high compared to As and
it is likely that Fe would be a significant redox buffer in these systems. The pe can be converted to Eh by
multiplying by 16.9 V-1. Taking the log of the above equation, the expected pe value based on the ratio of
Fe(II) to Fe(III) can be calculated as:

The calculated and measured Eh values are shown in Figure 4.16. From these data, it is clear that the
expected Eh values based on the Fe redox couple are higher than the measured values. Thus, Fe is either
1) not the dominant redox buffer in this system causing the measured Eh values or 2) Fe speciation is not
present under equilibrium conditions.

Figure 4.16: Estimated Eh values based on the Fe(II)/Fe(III) redox couple compared with the
measured values in groundwater samples from the Sutton site. The solid black line represents
perfect agreement between the measured and estimated values.
A similar analysis can be done to evaluate the expected As speciation. In this case rather than predicting
the Eh of the water, the ratio of As(V) to As(III) was predicted using the measured pH and Eh values
based on the equation:

which can be transformed to the following by taking the log form:
0
100
200
300
400
500
600
700
800
0 100 200 300 400 500 600 700 800
Me
a
s
u
r
e
d

E
h

(
m
V
)
Estimated Eh(mV) based on Fe(III)/Fe(II) Couple
( )
[ ]−2 +18.78 =
[ ] [ ]
[ ]=log −21.197

Page 50

Using this equation, As(V) is predicted as the dominant oxidation state under all pH and Eh conditions for
which data are available, consistent with the PHREEQC model values.

While this disagreement between the observed and predicted As redox speciation is concerning,
the model remains conservative. If the systems approach equilibrium as expected, oxidation of As(III) to
As(V) is expected based on the PHREEQC model. Since As(V) sorbs much stronger than As(III), this
would result in a decreased mobility of As and attenuation of As in the subsurface. It is also noteworthy
that the predicted Kd values for As from PHREEQC are significantly higher than the values required to
describe the observed field data using reactive transport modeling. Thus, the lower Kd values required for
the model are consistent with the dominance of the more mobile As(III) species in groundwater
measurements.

4.5. Comparison modeled and experimental Kd values for arsenic
The range of Kd values determined from PHREEQC modeling is significantly broader than the
range of values measured under laboratory conditions or used in reactive transport modeling. A
comparison of these values is shown in Table 4.1. The low values used in the reactive transport model
could be an indication of the predominance of As(III) measured in ground water samples. Based on the
measured Eh values in the ground waters, As(V) is thermodynamically predicted to be the dominant
species in most site ground waters. However, redox disequilibrium in natural systems is common and
discrepancies between measured redox conditions using platinum electrodes and values calculated based
on measured redox speciation are known to exist [12]. Therefore, the PHREEQC model can be
considered a conservative estimate. Any remedial action taken in the coal ash basins to promote more
oxidizing conditions or simply the allowance of time to approach a redox equilibrium would result in
oxidation of As(III) to As(V) which would further reduce the overall mobility of arsenic in these systems.

Table 4.1: Arsenic Kd values used in reactive transport modeling, measured in the laboratory, and
modeled using PHREEQC
Site
Reactive transport
modeling derived Kd
value (L/kg)[3-9]
Mean Kd value measured by
UNCC batch experiments
(L/kg) [10-16]
Range of values from
PHREEQC global
geochemical model (L/kg)
Sutton 9 48 Total As: 14.6 to 2.9 x 106
As(V): 14.6 to 2.9 x 106
As(III): 0.03 to 697
Value for Average GW
conditions:
As(III): 369;
As(V) 2.22 x 104
Lee 14 175
Weatherspoon 45 96.9
Roxboro 15 36.6
Asheville 0.1 1242
Mayo 0.12 83
Cape Fear 0 341.8

log
= 3 + 2 −21.197

Page 51
5. GEOCHEMICAL MODELING of BORON
5.1. Pourbaix diagram analysis
The pH and Eh values from the global model and the site-specific values are plotted over a
pourbaix diagram in Figure 5.1 to gain an understanding of the expected speciation under equilibrium
conditions. As shown in Figure 5.1, boron exhibits relatively simple chemistry existing as either neutrally
charged boric acid, noted in the literature as either B(OH)3 or H3BO3, or as a borate anion H2BO3
- (also
noted as BO2-) which persists above pH 9. Borate exhibits no redox reactions and solely exists as B(III).
The relatively simple aqueous speciation of borate is due to lack of affinity to form complexes with other
ions. This lack of chemical reactivity also limits borate sorption to mineral surfaces. Thus boron behaves
as a highly mobile ion in the subsurface.

5.2. Transect model analysis
As shown in Figure 5.2, boron exhibits only a slight decrease in concentration with distance from
the ash basin along the flow transects. Borate exhibits no redox reactions and solely exists as B(III). The
relatively simple aqueous speciation of borate is due to lack of affinity to form complexes with other ions.
This lack of chemical reactivity also limits borate sorption to mineral surfaces. Thus boron is essentially
inert and behaves as a highly mobile ion in the subsurface. The slight to no decrease in boron
concentrations along the flow transects are likely a result of dispersion rather than sorption or
precipitation onto mineral surfaces.

Figure 5.1: Pourbaix diagram of boron species along with pH and Eh values examined in
PHREEQC modeling. The round symbols represent the arsenic species that correspond to the
range of pH and Eh values used in the global geochemical model (left) and the Sutton, site-
specific model (right). The error bars represent the standard deviation of the average pH and Eh
value calculated from the combined measurements at all seven sites for the average.

Page 52

Figure 5.2: Boron concentrations along the north (top), east (middle), and southeast (bottom)
transects beginning at the Ash Basin wells ABMW-01S/D.

1
10
100
1000
10000
0 1000 2000 3000 4000 5000
B
C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
1
10
100
1000
10000
0 1000 2000 3000 4000 5000
B
C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
1
10
100
1000
10000
0 1000 2000 3000 4000 5000
B
C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*

Page 53
The PHREEQC modeled Kd values for boron along the three transects are shown in Figure 5.3.
There is essentially no adsorption of boron and the aqueous phase concentrations remain at >99% of the
total boron concentration in the system. While boron is an anion and will be more attracted to mineral
surfaces as the pH decreases, sorption still remains minimal (this is manifested as the low Kd values in
Figure 5.3). The lack of sorption in these systems and the lack of redox reactions which boron may
undergo indicate that boron is essentially a non-reactive species and is likely to be transported in the
subsurface with little to no retardation. Thus, the use of zero or low Kd values in reactive transport
modeling simulations is warranted.

Page 54

Figure 5.3 PHREEQC predicted boron Kd values along the north (top), east (middle), and southeast
(bottom) transects beginning at the Ash Basin wells ABMW-01S/D.

1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
0 1000 2000 3000 4000 5000
B
K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
0 500 1000 1500 2000 2500 3000
B
K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Pee Dee Upper
Pee Dee Lower
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
0 1000 2000 3000 4000 5000
B
K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 55
5.3. PHREEQC global model analysis
The PHREEQC model predicts relatively low sorption of boron as expected based on the
observed mobility of boron in field samples (Figure 5.4). There is relatively little change in the predicted
Kd values as a function of pH for the “minimum” ground water containing relatively low concentrations
of major ions. This limited influence of pH is consistent with the persistence of boron as the neutral
H3BO3 species as shown in Figure 5.1 and the relatively low competition with other major ions for
sorption sites. In the models of the average and maximum major ion concentrations from Table 3.10,
competition for sorption sites by other major ions results in a decrease in the observed Kd values. The
nature of competition is similar to the decrease in arsenic sorption discussed above regarding Figure
4.12Figure 4.13. There are no changes in boron aqueous speciation across this pH range. Furthermore,
there are no precipitates containing boron expected to form. The saturation indices for the four boron
bearing minerals considered in the model are shown in Figure 5.5 and all are well below 0. Therefore, the
changes in Kd shown in Figure 5.4 are only due to changes in pH and the influence of competing ions.
As discussed in section 3, sorption was modeled assuming aluminum and iron hydroxide minerals
were the dominant sorbing surfaces. Sorption of boron to ferrihydrate was predicted to be higher than
gibbsite for pH values up to 9 as shown in Figure 5.6. This is consistent with the higher site density (and
resulting higher surface site concentration) of HFO relative to HAO. However, boron sorption to HAO
has a higher stability constant compared with HFO and therefore, has the potential for greater sorption to
solids containing higher extractable aluminum concentrations [1, 2].

Figure 5.4: Predicted boron Kd values from PHREEQC modeling using the range of ground water
(GW) concentrations listed in Tables 3.9 and 3.10.
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -
103
5.1 / 372 7.1 / 76
To
t
a
l

B

K
d
(L
/
k
g
)
pH / Eh (mV)
Total B Kd, Min
GW Values
Total B Kd, Avg
GW Values
Total B Kd,
Max GW Values

Page 56

Figure 5.5: Saturation indices for four relevant boron bearing solid phases considered in the
PHREEQC model. Other species for which the saturation index never reached a value > -30 are not
shown

Figure 5.6: Predicted boron speciation from PHREEQC modeling showing aqueous species and
sorption to aluminum and iron hydroxides. Data are from model output using the minimum set of
groundwater concentrations listed in Tables 3.9 and 3.10.

5.4. Comparison between modeled and experimental Kd values for boron
The Kd values predicted from the PHREEQC model along with experimentally measured batch
values and values used in reactive transport modeling are shown in Table 5.1. There is some discrepancy
between the highest value predicted by PHREEQC (0.031 L/kg) and the highest values used in the
-30
-25
-20
-15
-10
-5
0
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
Pb(BO2)2 Zn(BO2)2 Cd(BO2)2 Co(BO2)2
1.00E-08
1.00E-07
1.00E-06
1.00E-05
1.00E-04
1.00E-03
4 5 6 7 8 9 10
Sp
e
c
i
e
s

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH
Aqueous H2BO3-
HAO-H2BO3
HFO-H2BO3

Page 57
reactive transport modeling and measured by batch sorption (4 L/kg). An earlier version of the
PHREEQC model which only assumed sorption to iron oxides significantly under predicted the boron Kd
with values near 1 x 10-3 L/kg. The stronger sorption of boron to aluminum bearing solids and the
inclusion of HAO sorption reactions in the current model are responsible for the higher Kd values
predicted by this updated PHREEQC model. It is noteworthy that in the batch sorption experiments
leaching of boron into the aqueous phase (i.e. the solid selected contained native boron which was
desorbing) was observed for many samples and/or a non-linear sorption isotherm with minimal sorption
was observed. Therefore, the low Kd values are expected. Considering the assumptions in the PHREEQC
model regarding the sorption site density, background ion concentrations, and a somewhat arbitrary solid
phase concentration assumed in the model, the PHREEQC model is generally in good agreement with the
other values. The sorption constants could be revised to provide specific Kd values but this would mainly
be a “fitting” exercise. This model does not consider alternative reactions for sequestration of boron such
as isomorphic substitution into mica [39]. However, the rates of isomorphic substitution are not known
and there is no field data to demonstrate if such a process is occurring at these sites. Therefore, this
mechanism of substitution is not included in the model.

Table 5.1: Boron Kd values used in reactive transport modeling, measured in the laboratory, and
modeled using PHREEQC. Units in each column are L/kg.
Site: Reactive transport
modeling derived Kd
value (L/kg)[3-9]
Mean Kd value measured
by UNCC batch
experiments (L/kg) [10-16]
Range of Kd values
from PHREEQC
global geochemical
model
Sutton 0 1.7
Range:
1.1 x 10-5 to 0.031

Geometric mean:
2.4 x 10-3

Value for average GW
conditions: 0.006
Lee 0 and 3.5 4
Weatherspoon 1 to 4 2
Roxboro 1 0
Asheville 0.1 2.7
Mayo 0.12 0
Cape Fear 1 0

Page 58
6. GEOCHEMICAL MODELING of CHROMIUM
6.1. Pourbaix diagram analysis
Under the range of Eh-pH conditions at the Sutton site chromium is dominated by the trivalent
oxidation state (Cr(III), Figure 6.1) with only a few Eh-pH values falling within the zone of Cr(VI)
stability. The trivalent state exists as Cr3+ at low pH and undergoes hydrolysis to form cationic CrOH+2
and Cr(OH)2
+, neutrally charged Cr(OH)3(aq), and anionic Cr(OH)4
- species with increasing pH. These
hydrolysis reactions resulting in cationic species have the potential to sorb to mineral surfaces with
increasing pH and/or form discrete precipitates (i.e. Cr2O3) provided the concentration of Cr is
sufficiently high. The hexavalent phase exists as the anions HCrO4- and CrO4-2 at environmentally
relevant pH values. These are generally soluble states but exhibit moderately strong sorption affinity to
metal oxide minerals such as iron oxides [1, 17] which is strong at low pH and decreases with increasing
pH as the mineral surface develops an increasingly negative charge.

Figure 6.1 : Pourbaix diagram of chromium species along with pH and Eh values examined in
PHREEQC modeling. The round symbols represent the arsenic species that correspond to the
range of pH and Eh values used in the global geochemical model (left) and the Sutton, site-
specific model (right). The error bars represent the standard deviation of the average pH and
Eh value calculated from the combined measurements at all seven sites for the average.

Page 59
6.2. Transect model analysis
The concentration of chromium slightly decreases with distance from the ash basin, with the
exception the Pee Dee Upper in the East transect (Figure 6.2). Note that many values are not plotted
because they were below the detection limit of 1 ug/L. Along the east transect the concentration of
chromium in the Pee Dee Upper geozone increased with increasing pH (pH values 7.8 to 8.6). The redox
potential in these wells were all relatively low, ranging from -85 to 30 mV. According to the pourbaix
diagrams shown in Figure 6.1, at these Eh values, Cr(III) should be the dominate species, having likely
undergone hydrolysis to form either the neutrally charged Cr(OH)3(aq) and anionic Cr(OH)4- species with
increasing pH. Thus, the increase in pH of the Pee Dee Upper geozone could have caused a decrease in
the sorption of anionic Cr(OH)4- due to electrostatic repulsion of the anion with a negatively charged
mineral surface. However, this is based on very limited data and a relatively small change in the aqueous
Cr concentration. Therefore, further analysis is necessary to confirm if pH is indeed influencing the
system in this way.
The PHREEQC predicted Kd values are shown in Figure 6.3. These simulated data clearly show
how the Kd value for Cr decreases with decreasing pH. This is due to the desorption of Cr(VI) and Cr(III)
as the pH increases and due to repulsion of anionic Cr(OH)4- and CrO4-2 species from the mineral surface
as an increasingly negative surface charge is developed at higher pH values. This is most clearly
demonstrated by plotting the predicted Kd values versus pH (Figure 6.4). The increase in Kd value with
increasing pH is a manifestation of changes in Cr speciation and sorption of the Cr(III) and Cr(VI) ions.
The PHREEQC simulations predict Cr(III) as the dominant aqueous phase oxidation state in almost all
conditions (Figure 6.5). While Cr(III) is predicted to be the dominant aqueous oxidation state of Cr, the
small amount of Cr(VI) present exhibits strong sorption affinity. Therefore, a change in the oxidation
state of the sorbed complexes can be observed with changing pH (Figure 6.5). At low pH values, Cr(VI)
is the dominant oxidation state on the solid phase but at higher pH values, anionic CrO4-2 exhibits
relatively weak sorption and Cr(III) is the dominant oxidation state of the sorbed Cr. This phenomenon
may help to explain the observation of mostly Cr(III) in the few ground water samples above detection
limits.
It is noteworthy that both Cr oxidation states have predicted Kd values greater than 10 in all
simulations. Therefore, neither oxidation state is expected to be particularly mobile in the subsurface.
However, a remedial action such as basin removal which has the potential to promote more oxidizing
redox conditions through the introduction of oxygen, could oxidize Cr(III) to Cr(VI). If the pH is also
high, Cr(VI) will have a higher aqueous concentration. Thus, changes in pH during any remedial effort
must be carefully considered so as to not unintentionally mobilize Cr.

Page 60

Figure 6.2: Chromium concentrations along the north (top), east (middle), and southeast (bottom)
transects beginning at the Ash Basin wells ABMW-01S/D.
0.1
1
10
100
0 1000 2000 3000 4000 5000
Cr

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
0.1
1
10
100
0 1000 2000 3000 4000 5000
Cr

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
0.1
1
10
100
0 1000 2000 3000 4000 5000
Cr

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*

Page 61

Figure 6.3: PHREEQC predicted chromium Kd values along the north (top), east (middle), and
southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D. Note that values on
the order of 10-23 represent only a few atoms and such low Kd values can be assumed to represent
100% aqueous ions.

1.0E+00
1.0E+02
1.0E+04
1.0E+06
1.0E+08
1.0E+10
1.0E+12
0 1000 2000 3000 4000 5000To
t
a
l

C
r

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-23
1.0E-20
1.0E-17
1.0E-14
1.0E-11
1.0E-08
1.0E-05
1.0E-02
1.0E+01
1.0E+04
0 500 1000 1500 2000 2500 3000 3500
Cr

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
1.0E+10
1.0E+11
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Cr

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 62

Figure 6.4: Comparison of PHREEQC predicted chromium Kd as a function of pH for north (top),
east (middle), and southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D.
Note that values on the order of 10-23 represent only a few atoms and such low Kd values can be
assumed to represent 100% aqueous ions.
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+08
1.E+09
1.E+10
1.E+11
1.E+12
3 4 5 6 7 8 9 10
Cr

K
d
(L
/
k
g
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.00E-23
1.00E-20
1.00E-17
1.00E-14
1.00E-11
1.00E-08
1.00E-05
1.00E-02
1.00E+01
1.00E+04
3 4 5 6 7 8 9 10 11
Cr

K
d
(L
/
k
g
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
1.0E+10
1.0E+11
3 4 5 6 7 8 9 10
Cr

K
d
(L
/
k
g
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 63

Figure 6.5: PHREEQC predicted Cr oxidation state distributions as a function of pH in the aqueous
(top) and solid (bottom) phases of the North transect. Note that concentration values less than
represent small or even fractional numbers of atoms and should not be considered a real result.
Under such conditions, there is essentially no aqueous Cr.

6.3. PHREEQC global model analysis
Plots of the Kd values from the PHREEQC global model as a function of the pH and Eh are
shown in Figure 6.6. The pH and Eh values used in the model are from the range provided in Table 3.8.
Under the range of redox conditions examined, there is almost no dependence on the reduction potential
of these systems. The lack of dependence on Eh is because trivalent Cr(III) is the dominant oxidation state
for the majority of systems examined.

1.00E-37
1.00E-34
1.00E-31
1.00E-28
1.00E-25
1.00E-22
1.00E-19
1.00E-16
1.00E-13
1.00E-10
1.00E-07
1.00E-04
1.00E-01
3 4 5 6 7 8 9 10
Aq
u
e
o
u
s

C
r

C
o
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH
Total aqueous
Cr(III), mol/L
Total aqueous
Cr(VI), mol/L
0.00E+00
2.00E-07
4.00E-07
6.00E-07
8.00E-07
1.00E-06
1.20E-06
1.40E-06
1.60E-06
3 4 5 6 7 8 9 10
So
r
b
e
d

C
r
(
I
I
I
)

o
r

C
r
(
V
I
)

co
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
k
g
)
pH
Total sorbed
Cr(III), mol/kg
Total sorbed
Cr(VI), mol/kg

Page 64
The lack of a relationship with Eh is consistent with the dominance of Cr(III) in field samples
from the Sutton site presented in Table 6.1 and discussed above with regard to the transect models. It is
noteworthy that Table 6.1 represents only a few of many measurements and the data not shown was
below detection limits for both total Cr and Cr(VI). Data for other sites is either not available or the
concentrations of chromium are below detection limits required for speciation analysis. Therefore, Kd
values calculated for the total chromium in the system are almost exactly the same as those calculated for
only the Cr(III) fraction. (Figure 6.7). The only model output containing a significant fraction of HCrO4
-
or CrO4- is the relatively high Eh system (pH 6.9, Eh 513 mV). However, due to the strong sorption of
Cr(VI), the predicted Kd values remain high. The prevalence of Cr(III) can be seen from the plot in Figure
6.7 comparing the total aqueous chromium and the fraction of Cr(III). Due to the dominance of Cr(III)
across almost all Eh values under consideration, the sorption of chromium can be more clearly
demonstrated by considering the profound influence of pH. As shown in Figure 6.8, the sorption of
chromium increases by several orders of magnitude as the pH increases. This behavior is a characteristic
of a positively charged cation (in this case the Cr(III) species CrOH+2, and Cr(OH)2+) forming stronger
surface complexes as the pH increases and the mineral surfaces develop an increasingly negative surface
charge. While Cr(III) remains the dominant oxidation state of Cr at high pH values, sorption decreases as
the concentrations of other groundwater ions (such as SO4-2) increase in the groundwater simulants listed
in Tables 3.8 and 3.9. This decrease in chromium sorption is shown by the increasing aqueous phase
concentrations in Figure 6.7 and the decrease in Kd values from Figure 6.8.

Table 6.1: Chromium redox speciation determined in Sutton water samples[35].
Well Geozone
Total Chromium
(ug/L)
Cr(VI)
(ug/L)
MW-36C Surficial Upper 2.38 <1
MW-36C Surficial Upper 1.24 <1
MW-38C Surficial Upper <1 0.27
ABMW-1D Ash Pore Water 1.18 <1
ABMW-1S Pee Dee Upper 1.22 <1

The predicted Kd values for chromium sorption exhibit a strong dependence on pH and a
relatively small dependence on other ion concentrations. There is relatively little difference between the
sorption of chromium under the three groundwater concentrations examined, which is primarily due to the
strong sorption of chromium under all conditions to the point that other ions are not capable of
outcompeting chromium for sorption sites. The notable exceptions to this observation are the decreased
sorption of chromium at low pH values (4.0 and 5.1) and pH 9.1 under the maximum ion concentration
system and the general decrease in sorption under the “maximum” groundwater ion concentrations. The
decreased sorption at pH 4.0 and 5.1 appear to be due to increased formation of aqueous CrSO4
+ and
CrHSO4+2 aqueous species (shown in Figure 6.9). These chromium sulfate complexes do not sorb and
thus essentially compete with the mineral surfaces for Cr. As the total groundwater sulfate concentrations
increase (Table 3.9), the concentration of CrSO4+ and CrHSO42+ complexes increases and the extent of Cr
sorption decreases. As the pH increases, formation of aqueous chromium sulfate complexes becomes less
favorable. Instead neutrally charged Cr(OH)3(aq) and anionic Cr(OH)4- become the dominant aqueous
species. There is little difference in the fraction of these species at pH 9.1 with the changing groundwater

Page 65
ion concentrations going from minimum to maximum values listed in Table 3.9 (shown in Figure 6.9).
Therefore, the decrease in the Kd values at pH 9.1 reported in Figure 6.4 is not due to a change in the
aqueous chromium speciation. Rather, this decrease is likely due to competition for mineral surface
sorption sites from Fe2+ and other ions that occurs at pH 9.1 as demonstrated in Figures 4.12 and 4.13.

Figure 6.6: Predicted chromium Kd values from PHREEQC modeling using the range of ground
water (GW) concentrations listed in Tables 3.9 and 3.10. The Kd values of the total chromium
fraction (Cr(III) + Cr(VI)) are shown in the top panel and the bottom panel shows only the Cr(III)
Kd values. The similarity between the two graphs indicates the Kd values for Cr(VI) are relatively
small (average of 36 L/kg).

1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
To
t
a
l

C
r

K
d
(L
/
k
g
)
pH / Eh (mV)
Total Cr Kd, Min
GW Values
Total Cr Kd, Avg
GW Values
Total Cr Kd, Max
GW Values
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Cr
(
I
I
I
)

K
d
(L
/
k
g
)
pH / Eh (mV)
Cr(III) Kd, Min
GW Values
Cr(III) Kd, Avg
GW Values
Cr(III) Kd, Max
GW Values

Page 66

Figure 6.7: Predicted concentrations of total aqueous chromium and trivalent chromium modeled
using the range of ground water (GW) concentrations listed in Tables 3.9 and 3.10.

Figure 6.8: Predicted chromium Kd values from PHREEQC modeling using the range of ground
water (GW) concentrations listed in Tables 3.9 and 3.10.

1.0E-15
1.0E-14
1.0E-13
1.0E-12
1.0E-11
1.0E-10
1.0E-09
1.0E-08
1.0E-07
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
Co
n
c
e
n
t
r
a
t
i
o
n

(
m
o
l
/
L
)
pH / Eh (mV)
Total Aqueous Cr,
Min GW Values
Aqueous Cr(III),
Min GW Values
Total Aqueous Cr,
Avg GW Values
Aqueous Cr(III),
Avg GW Values
Total Aqueous Cr,
Max GW Values
Aqueous Cr(III),
Max GW Values
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
3 4 5 6 7 8 9 10
To
t
a
l

C
r

K
d
(L
/
k
g
)
pH
Total Cr Kd,
Min GW Values
Total Cr Kd,
Avg GW Values
Total Cr Kd,
Max GW Values

Page 67

Figure 6.9: Predicted chromium aqueous speciation at pH 4.0, 5.6, and 9.1 using the range of ground
water (GW) concentrations listed in Tables 3.9 and 3.10. Note, the Eh values are not discussed here
because >99% of the total chromium exists as Cr(III) under these conditions.

0
0.2
0.4
0.6
0.8
1
1.2
MIN GW, pH 4.0 AVG GW, pH 4.0 MAX GW, pH 4.0
Fr
a
c
t
i
o
n

o
f

A
q
u
e
o
u
s

C
r

S
p
e
c
i
e
s
CrOHSO4
CrSO4+
Cr(OH)+2
Cr(OH)2+
Cr+3
0
0.2
0.4
0.6
0.8
1
1.2
MIN GW, pH 5.6 AVG GW, pH 5.6 MAX GW, pH 5.6
Fr
a
c
t
i
o
n

o
f

A
q
u
e
o
u
s

C
r

S
p
e
c
i
e
s
Cr(OH)3
CrOHSO4
CrSO4+
Cr(OH)+2
Cr(OH)2+
Cr+3
0
0.2
0.4
0.6
0.8
1
1.2
MIN GW, pH 9.1 AVG GW, pH 9.1 MAX GW, pH 9.1
Fr
a
c
t
i
o
n

o
f

A
q
u
e
o
u
s

C
r

S
p
e
c
i
e
s
Cr(OH)4-
CrO2-
Cr(OH)3
Cr(OH)2+

Page 68
In addition to sorption reactions discussed above, Cr may be removed from solution by
precipitation. Based on the groundwater concentrations listed in Tables 3.8 and 3.9, only Fe2CrO4 is
predicted to have a solubility product greater than zero (indicating precipitation is possible). This value is
only at relatively high pH values where precipitation will be favored due to charge neutralization of the
aqueous species. However, it is noteworthy that the ion concentrations used in this model were
determined from aqueous measurements of the ions in pore waters. Thus, unless the systems were
supersaturated and precipitation was kinetically hindered, prediction of a saturated system would be
unlikely. Therefore, a more useful comparison is to note how close each phase is to reaching saturation.
Generally, Cr(OH)3 has a saturation index near -6. Thus, the aqueous chromium concentration could
increase several orders of magnitude before additional precipitates may form. Formation of such
precipitates is more favored at higher pH values.

Figure 6.10: Chromium saturation indices predicted using the maximum ground water ion
concentrations listed in Tables 3.9 and 3.10.

6.4. Comparison between modeled and experimental Kd values for chromium
A comparison of the PHREEQC model predicted Kd values and Kd values measured from batch
sorption experiments are provided in Table 6.2. Experimental values are only available for the sorbents
from the Roxboro and Asheville sites. The wide range of modeled values span the range of experimental
values. However, as noted in section 2.4, the strong sorption of any ion (chromium in this case) can result
in very high Kd values such as those shown in Table 6.2. The range of values for both Cr(III) and Cr(VI)
are indicative of the profound influences of pH on sorption of chromium. Sorption of Cr(III) increases
with increasing pH owing to its existence as cationic and neutral species. Conversely, Cr(VI) sorption
decreases with increasing pH due to the predominance of anionic CrO42- species. Based on the speciation
analysis of Cr from both the PHREEQC model and ground water measurements, trivalent Cr(III) is the
primary species expected in the aqueous phase. Thus, the Kd range 23 to 6.9 x 108 is the more appropriate
range to consider. While pH is the primary variable of concern, complexing anions in ground water can
-20
-15
-10
-5
0
5
10
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
FeCr2O4
Cr(OH)3(am)
Cr(OH)3
MgCr2O4

Page 69
also have a major impact of the Kd values as demonstrated by the ~103 change in Kd from the three
groundwater simulants.

Table 6.2: Chromium Kd values (L/kg) from batch laboratory studies and modeled using
PHREEQC. NM = not measured. Leaching = leaching or no sorption observed.
Site: Mean value measured by UNCC
batch experiments [10-16]
Range of values from PHREEQC
geochemical model
Sutton NM

Total Cr: 23 to 6.9 x 108,
Mean 1.6 x 106

Cr(III): 23 to 6.9 x 108,
Mean 1.8 x 106

Cr(VI): 7.45 x 10-4 to 2.7 x 105,
Mean 22

Average GW conditions:
Total Cr: 7.15 x 107

Lee NM
Weatherspoon NM
Roxboro 139, Maximum of 830
Asheville 655, Maximum of 20,490
Mayo Leaching
Cape Fear NM

Page 70
7. GEOCHEMICAL MODELING of COBALT
7.1. Pourbaix diagram analysis
Despite having an accessible Co(III) oxidation state, the dominant oxidation state of cobalt under
all conditions is Co(II). The dominant ions at neutral pH are Co2+ and HCoO2- (Figure 7.1). As a cationic
species, Co2+ sorption will increase with increasing pH. This is a manifestation of the attraction of Co2+ to
mineral surfaces as the surface transitions from a net positive charge to a net negative charge with
increasing pH. Cobalt can form a wide range of mineral phases including CoS2 (cattierite), CoSe
(freboldite), CoAs2 (safflorite), and CoFe2O4 (cobalt ferrite). Relatively high concentrations of sulfide,
selenium, and arsenic are required to form cattierite, freboldite, and safflorite and in general these are not
expected, except under some anomalously high concentrations of Co and Se (discussed below in Chapter
8 on selenium geochemistry). While not shown in Figure 7.1, because of the relatively low iron
concentration used in the model to generate the Eh-pH diagrams, cobalt ferrite is predicted to precipitate
under conditions with high ferric iron concentrations. This is discussed below with regards to the
PHREEQC sorption models.

Figure 7.1: Pourbaix diagram of cobalt species along with pH and Eh values examined in
PHREEQC modeling. The round symbols represent the arsenic species that correspond to the range
of pH and Eh values used in the global geochemical model (left) and the Sutton, site-specific model
(right). The error bars represent the standard deviation of the average pH and Eh value calculated
from the combined measurements at all seven sites for the average.

Page 71

7.2. Transect model analysis
Along the three primary flow transects, cobalt is only measured at values above the detection
limit at wells screened within the Surficial Upper and Surficial Lower geozones. As the redox potential in
these wells covers a large range of Eh values, from -347 to 556, the redox potential is not the dominant
controlling factor in the mobility of cobalt. The pH values were generally low to moderate (4.2-7.4), with
the highest Co concentrations appearing in wells with pH values ranging from 5.0 to 6.6 and Eh values
between 150 and 450 mV. One notable exception is at Surficial Lower Well AW-04C with the highest
Co concentration of 24.5 ug/L resulting at a pH of 7.4 and an Eh of -347. Over all these values indicate
that the dominant cobalt species is Co(II), with the majority of the mobile Co persisting as the cationic
Co2+ at pH values below 7, and as the anionic HCoO2- at pH values above 7.
The PHREEQC model of these transects supports these observations of the aqueous phase
concentrations. Cobalt almost exclusively present at Co(II) with Co(III) concentration predicted to be less
than 10-20 mol/L. As shown in Figure 7.3, the Kd values generally decrease with increasing distance from
the ash basin well. This is consistent with decreasing sorption of Co2+ with decreasing pH which is
demonstrated by plotting the Co Kd values in these transects as a function of pH in Figure 7.4. Due to the
extreme influence of pH on Co mobility, any remediation efforts which lower the pH of the pore waters
are likely to cause an increase in the aqueous concentration of Co and therefore must be carefully
considered.

Page 72

Figure 7.2: Cobalt concentrations along the north (top), east (middle), and southeast (bottom)
transects beginning at the Ash Basin wells ABMW-01S/D.

0
5
10
15
20
25
0 1000 2000 3000 4000 5000
Co

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit**
0
5
10
15
20
25
30
0 1000 2000 3000 4000 5000
Co

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit**
0
2
4
6
8
10
12
0 1000 2000 3000 4000 5000
Co

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit**

Page 73

Figure 7.3: PHREEQC predicted cobalt Kd values along the north (top), east (middle), and
southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D.

1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
0 1000 2000 3000 4000 5000
To
t
a
l

C
o

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-06
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
0 500 1000 1500 2000 2500 3000 3500
Co

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
0 1000 2000 3000 4000 5000
Co

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 74

Figure 7.4: Comparison of PHREEQC predicted cobalt Kd as a function of pH for north (top), east
(middle), and southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D.
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
3 4 5 6 7 8 9 10
Co

K
d
(L
/
k
g
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-06
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
3 4 5 6 7 8 9 10
Co

K
d
(L
/
k
g
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1.0E+07
1.0E+08
1.0E+09
3 4 5 6 7 8 9 10
Co

K
d
(L
/
k
g
)
pH
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 75
7.3. PHREEQC global model analysis
While the ground water measurements have only determined the total Co(II) concentrations
instead of individual Co(II) and Co(III) fractions, the observation in the PHREEQC model of Co(II)
dominance and the modeled behavior of Co(II) is consistent with the field measurements. Co(II) generally
persists as the divalent cation Co2+ which is expected to increase sorption with increasing pH. This is
exactly the behavior predicted by the PHREEQC model. The predicted Kd values are shown in Figure 7.5
and this trend with respect to pH is clearly demonstrated by the model. The modeled Kd values decrease
as the concentration of other ions (primarily iron) increases in the groundwater simulants with the
“maximum” groundwater simulant from Table 3.10 yielding the lowest Kd values for Co. This is
primarily due to competition with other major ions such as Fe, Mn, and Al as discussed above. This
behavior shows that the predicted Kd values are not only a function of the behavior of Co but also a
competition with other ions for sorption sites. Therefore, the concentration of all ions in the system must
be considered when selecting a Kd value. Due to the profound complexity of this, the approach of taking
average Kd values for each site to account for this potential competition is reasonable. To demonstrate the
sensitivity of the predicted Kd values to the ground water ion concentrations, the predicted Kd values for
each ground water simulant are shown in Table 7.1. The Kd values decrease as the concentrations of ions
in the ground water simulant increase from the minimum, average, and maximum values from Table 3.10.
The predicted Kd values only account for aqueous speciation and sorption reactions and do not
take potential precipitation into account. However, the saturation index is monitored in the model. The
saturation index is a measure of the degree that an ion is saturation in solution with respect to a given
mineral phases. If the saturation index is greater than 1, then the system is saturated and formation of a
precipitated is thermodynamically favorable. It is important to note that this does not mean a solid will
form, it means that it can form. In many cases, precipitation of a mineral is kinetically limited and could
occur slowly despite thermodynamic favorability.
The saturation indices for Co are shown in Figure 7.6 and show that CoFe2O4 is saturated under
all conditions of the “maximum” groundwater simulant from Table 3.10. It is noteworthy that the
“maximum” groundwater simulant has Fe concentrations representing the maximum values observed in
the groundwater from all seven sites. CoSe is also predicted to form but only under reducing conditions
which facilitate formation of reduced Se(-II). This only occurs under the extreme reducing conditions
considered in this model (Eh values of -20 and -103 mV).

Page 76

Figure 7.5: Predicted cobalt Kd values from PHREEQC modeling using the range of ground water
(GW) concentrations listed in Tables 3.8 and 3.9.

Table 7.1: Predicted Kd values in each groundwater simulant and the average pH and Eh for the
seven sites. NA = not applicable because only one Kd value is used.

Average Co Kd Values
Average
Kd (L/kg)
Standard
Deviation
Minimum GW Simulant 2226 5887
Average GW Simulant 32.5 81.0
Maximum GW Simulant 0.11 0.16
Value for Average Groundwater Conditions (pH 6.47,
Eh 220 mV, all ions at average concentrations from
Table 3.9 andTable 3.10). 0.79 NA

The saturation indices for Co shown in Figure 7.6 are for the “maximum” groundwater simulant
from Table 3.10. As discussed below, high iron concentrations in the “average” and “maximum” ground
water simulant models facilitate formation of CoFe2O4. While the model inputs for iron and cobalt
concentrations are sufficiently high to predict formation of CoFe2O4, kinetic limitations could prevent
formation of this mineral. Therefore, if reactive transport models rely on precipitation of CoFe2O4 as a
means of controlling Co mobility, the presence of this phase must be verified in field samples. It is
unlikely that this phase is completely controlling the aqueous Co concentrations because it is profoundly
insoluble and could effectively remove all Co from the groundwater. To demonstrate this, the PHREEQC
model was re-run but in this instance precipitation of CoFe2O4 was allowed. The concentration of aqueous
Co in all three groundwater simulants is shown in Figure 7.7. An important point in this figure is that any
aqueous concentration less than 6.022 x 1023 mol/L represents less than one atom of the element in
solution. So aspects of this plot are purely theoretical. However, the profound influence of solubility is
demonstrated. In the “minimum” groundwater simulant from Table 3.10, the total dissolved iron and
cobalt concentrations are sufficiently low and the system is not saturated with respect to CoFe2O4.
However, when the iron concentrations are increased in the “average” and “maximum” groundwater
1.00E-06
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
3 4 5 6 7 8 9 10
Pr
e
d
i
c
t
e
d

T
o
t
a
l

C
o

K
d
Va
l
u
e

(
L
/
k
g
)

pH
Co Kd, MIN GW
Values
Co Kd, AVG GW
Values
Co Kd, MAX
GW Values

Page 77
simulants, precipitation of CoFe2O4 occurs and the aqueous cobalt concentrations drastically decrease,
effectively removing all Co from the aqueous phase. The break in the model predictions at pH 5.6 is due
to predicted formation of CoSe at the low Eh of that model run. Since there have been measurements of
Co in groundwater at each site, this is clearly not happening. Therefore, formation of this phase may be
influencing the aqueous Co concentrations to some degree and CoFe2O4 is not a solubility controlling
species.

Figure 7.6: Cobalt saturation indices predicted using the maximum ground water ion concentrations
listed in Tables 3.9 and 3.10.

Figure 7.7: Predicted aqueous cobalt concentrations using the three groundwater simulants from
Tables 3.9 and 3.10.

-10
-5
0
5
10
15
20
25
30
35
40
pH 4.0,
EH 482
pH 5.6,
EH -20
pH 6.5,
EH 220
pH 6.9,
EH 513
pH 9.1,
EH -103
pH 5.1,
EH 372
pH 7.1,
EH 76
Sa
t
u
r
a
t
i
o
n

I
n
d
e
x
pH and Eh
CoFe2O4
CoSe
1.00E-38
1.00E-35
1.00E-32
1.00E-29
1.00E-26
1.00E-23
1.00E-20
1.00E-17
1.00E-14
1.00E-11
1.00E-08
1.00E-05
1.00E-02
3 4 5 6 7 8 9 10
Pr
e
d
i
c
t
e
d

t
o
t
a
l

d
i
s
s
o
l
v
e
d

C
o

Co
n
c
e
n
t
r
a
t
i
o
n
s

(
m
o
l
/
L
)
pH
MIN GW Values
AVG GW Values
MAX GW Values

Page 78
7.4. Comparison between modeled and experimental Kd values for cobalt
The Kd values from the PHREEQC model agree well with the data available from batch sorption
experiments and reactive transport modeling. The Kd values from the PHREEQC model have a wide
range due to the strong influence of pH (Figure 7.4). The experimental values also vary by approximately
100x. However, the average experimentally derived value of 453 L/kg for all seven sites is approximately
a factor of 10 above the average predicted value of 32.5 L/kg from the “average” groundwater simulant.
However, given the high standard deviation of the experimentally measured values, this is satisfactory
agreement. The experimentally determined values are all above the value used in reactive transport
modeling. There is reasonable agreement between the value of 2.5 L/kg used in the reactive transport
model and the value of 0.79 which is derived from the single PHREEQC model simulation using all
average values (i.e. average pH, average Eh, and average ion concentrations from all seven sites).
Therefore, the PHREEQC modeled values appear to be capturing the range of behavior that is possible
and targeting the average values that are appropriate for the reactive transport modeling efforts. Since an
average Kd value is used across the site for reactive transport modeling, agreement between the average
PHREEQC values and the reactive transport models demonstrates circumstantial agreement between the
two modeling approaches.

Table 7.2: Cobalt Kd values used in reactive transport modeling, measured in the laboratory, and
modeled using PHREEQC. Units in L/kg. The hash symbol indicates Co was not included in the
reactive transport model.

Site:
Reactive
transport
modeling derived
Kd value [3-9]
Mean value
measured by UNCC
batch experiments
[10-16]
Range of values from PHREEQC
geochemical model
Sutton - 141 Values varied with
GW Simulants

Minimum Simulant GW: 2226
Average Simulant GW 32.5
Maximum Simulant GW 0.11
All Average GW 0.79

Lee - 858
Weatherspoon - 11
Roxboro - 1182
Asheville 2.5 67
Mayo - 718
Cape Fear - 196

Page 79
8. GEOCHEMICAL MODELING of SELENIUM
8.1. Pourbaix diagram analysis
Selenium exists as oxyanionic species under oxidizing conditions as selenite (SeO32-) or selenate
(SeO42-). Under the Eh-pH conditions of the seven sites under consideration, Se(IV) present as HSeO3-,
and Se(0) present as elemental Se0 are expected to be the dominant species (Figure 8.1). However, it is
noteworthy that these Pourbaix diagrams show only the dominant species. The neutral pH and high
reduction potential conditions (noted as pH = 6.9 and Eh = 514 mV in Figure 8.1), is close to the dividing
line between Se(IV) and Se(VI). Thus is can be expected that Se(VI) will be present in the site
groundwater under high reduction potential conditions. Similar to arsenic’s behavior, both selenite and
selenate sorb to mineral surfaces (primarily iron oxides) [17-19]. Thus, oxidation of Se(IV) to Se(VI)
could influence the mobility of selenium in the subsurface. However, both are subject to competition from
other oxoanions such as phosphate and sulfate. Selenium is also readily reduced to zero valent Se0 which
is relatively stable under mildly reducing conditions. As discussed above with respect to cobalt
geochemistry, formation of reduced Se(-II) species can have an impact of other metal ion concentrations
such as through the precipitation of CoSe(s).

Figure 8.1: Pourbaix diagram of selenium species along with pH and Eh values examined in
PHREEQC modeling. The round symbols represent the arsenic species that correspond to the
range of pH and Eh values used in the global geochemical model (left) and the Sutton, site-specific
model (right). The error bars represent the standard deviation of the average pH and Eh value
calculated from the combined measurements at all seven sites for the average. The top plots neglects
precipitation of any solid phases which are shown with yellow shading in the bottom plot.

Page 80
8.2. Transect model analysis
Selenium concentrations along the three flow transects were only measured above the detection
limit in the Surficial Upper and the Surficial Lower geozones, with no pattern evident with distance from
the source area (Figure 8.2). At the wells that measured selenium concentrations above the detection
limit, the pH range was low to moderate (4.5 to 6.7) and relatively high redox potential, ranging from 311
to 528 mV. In general, as the pH decreases and Eh increases, the concentration of selenium increases
indicating that selenite (Se(IV)) is the dominant species in the groundwater at the Sutton site. Under these
Eh and pH conditions, Se(-II)(s) may also occur (Figure 8.1). The highest selenium concentrations are
north of the ash basin pond with much lower, but detectable, values observed east the ash basins at or
beyond the compliance boundary.
The PHREEQC model along the three transects indicates that sorption of Se increases slightly as
a function of distance from the ash basin well. This is consistent with the decrease in pH observed along
the transect which would cause greater sorption of the anionic HSeO3- and SeO3-2 species. It is noteworthy
that in the sorbed selenium is >99% Se(IV) in almost all cases. This is due to the generally stronger
adsorption of Se(IV) compared to Se(VI). As discussed below, the measurements of aqueous Se show
mixtures of Se(IV) and Se(VI) with Se(VI) generally being observed at higher concentrations in most
samples (though admittedly only few sample are above detection limits to allow for this determination).
However, since only the water is being sampled and not the mineral phases, strong sorption of Se(IV)
would tend to limit the aqueous concentrations and leave only trace amounts of Se(VI) in solution. The
dominance of Se(IV) on the solid phase is shown in Figure 8.4 for each of the three transects.

Page 81

Figure 8.2: Selenium concentrations along the north (top), east (middle), and southeast (bottom)
transects beginning at the Ash Basin wells ABMW-01S/D.
0
10
20
30
40
50
60
70
0 1000 2000 3000 4000 5000
Se

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, North transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
0
5
10
15
20
25
0 1000 2000 3000 4000 5000
Se

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, East transect
Surficial Aquifer
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*
0
5
10
15
20
25
0 1000 2000 3000 4000 5000
Se

C
o
n
c
e
n
t
r
a
t
i
o
n

(
u
g
/
L
)
Distance along transect from well ABMW-01 (feet)
Sutton, Southeast transect
Surficial Upper
Ash Pore Water
Surficial Lower
Pee Dee Upper
Pee Dee Lower
Detection Limit
GW Standard*

Page 82

Figure 8.3: PHREEQC predicted selenium Kd values along the north (top), east (middle), and
southeast (bottom) transects beginning at the Ash Basin wells ABMW-01S/D.
1.0E-06
1.0E-04
1.0E-02
1.0E+00
1.0E+02
1.0E+04
1.0E+06
1.0E+08
0 1000 2000 3000 4000 5000To
t
a
l

S
e

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
1.0E-16
1.0E-14
1.0E-12
1.0E-10
1.0E-08
1.0E-06
1.0E-04
1.0E-02
1.0E+00
1.0E+02
1.0E+04
0 500 1000 1500 2000 2500 3000 3500
Se

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper
Pee Dee Lower
1.0E-09
1.0E-08
1.0E-07
1.0E-06
1.0E-05
1.0E-04
1.0E-03
1.0E-02
1.0E-01
1.0E+00
1.0E+01
1.0E+02
1.0E+03
1.0E+04
0 1000 2000 3000 4000 5000
Se

K
d
(L
/
k
g
)
Distance along transect from well ABMW-01 (feet)
Ash Pore Water
Surficial Upper
Surficial Lower
Pee Dee Upper

Page 83

Figure 8.4: PHREEQC predicted selenium speciation on HFO and HAO as a function of pH for the
north (top), east (middle), and southeast (bottom) transects. Note that values on the order of 10-23
represent only a few atoms and such low concentrations likely represent a system with essentially no
sorption.
1E-23
1E-21
1E-19
1E-17
1E-15
1E-13
1E-11
1E-09
1E-07
1E-05
0.001
0.1
3 4 5 6 7 8 9 10
So
r
b
e
d

S
e

C
o
n
c
e
n
t
r
a
t
i
o
n

(m
o
l
/
k
g
)
pH
Total sorbed Se(IV), mol/kg
Total sorbed Se(VI), mol/kg
1E-23
1E-21
1E-19
1E-17
1E-15
1E-13
1E-11
1E-09
1E-07
1E-05
0.001
0.1
3 4 5 6 7 8 9 10
So
r
b
e
d

S
e

C
o
n
c
e
n
t
a
t
i
o
n

(
m
o
l
/
k
g
)
pH
Total Sorbed Se(IV), mol/kg
Total Sorbed Se(VI), mol/kg
1E-23
1E-21
1E-19
1E-17
1E-15
1E-13
1E-11
1E-09
1E-07
1E-05
0.001
0.1
3 4 5 6 7 8 9 10
So
r
b
e
d

S
e

C
o
n
c
e
n
t
r
a
t
i
o
n

(m
o
l
/
k
g
)
pH
Total Sorbed Se(IV), mol/kg
Total Sorbed Se(VI), mol/kg

Page 84
8.3. PHREEQC global model analysis
The PHREEQC model predicts Se(IV) as the dominant aqueous species with Se(VI) only
becoming the dominant aqueous species under oxidizing conditions (Figure 8.5). This model is consistent
with the Pourbaix diagram shown in Figure 8.1. Measurements of the oxidation state speciation of
selenium from groundwater at several sites (Asheville, Roxboro, Cape Fear, and Sutton) indicate that
selenium is present as a mixture of Se(IV) and Se(VI). The total selenium aqueous concentrations in most
groundwater measurements are very low making accurate determination of the dominant oxidation state
difficult. To provide a comparison of the results, the groundwater measurements where either Se(IV) or
Se(VI) were measured above the background level are shown in Table 8.1. The data are plotted in Figure
8.6 as a function of Eh with the measurements where either Se(IV) or Se(VI) were below detection
plotted using the detection limit to provide a quantitative measure. Two notable observations can be made
from these data.

1. Generally, oxidizing conditions yield sufficiently high selenium concentrations in order to
determine the speciation, though in many case the values are still at or near detection limits.
This may be an indication of reduced Se(0) or Se(-II) species which form insoluble phases.
2. The fraction of Se(VI) generally increases with increasing Eh (mV). This is not as clear of a
trend as would be expected in Figure 8.6 primarily because there is also an influence of pH
where Se(VI) may be more prevalent at lower pH values.

Figure 8.5:The fraction of Se(-II), Se(IV), and Se(VI) predicted from the PHREEQC model as a
function of Eh. The model predicts Se(IV) as the dominant oxidation state for most systems except
for those with high Eh values (i.e. oxidizing conditions) where Se(VI) is dominant. There is an
influence of pH on the exact speciation in these plots which is not shown.

0.0
0.2
0.4
0.6
0.8
1.0
1.2
-200 0 200 400 600
Fr
a
c
t
i
o
n

o
f

E
a
c
h

O
x
i
d
a
t
i
o
n

S
t
a
t
e

i
n

th
e

A
q
u
e
o
u
s

P
h
a
s
e

Eh (mV)
Se(-II) Fraction
Se(IV) fraction
Se(VI) fraction

Page 85

Table 8.1: Selenium speciation measurements from Sutton water samples. Note that additional
species such as methylated-Se were also measured but below detection limits. Values with "<"
before the value were below detection.
Site pH Eh
(mV)
Se(IV)
(mg/L)
Se(VI)
(mg/L)
Sutton 9.0 -36 0.211 <0.065
Sutton 6.3 239 <0.08 5.29
Sutton 6.6 231 0.267 0.288
Sutton 5.1 409 <0.08 0.19
Sutton 9.8 -127 <0.08 0.142
Sutton 10.0 32 <0.08 0.169

Figure 8.6: The fraction of Se(IV), and Se(VI) as a function of Eh measured in groundwater samples
from Sutton as a function of pH (top) and Eh (bottom). Only values where a positive measurement
of either Se(IV) or Se(VI) are shown (Table 8.1) and the detection limit was used to enable a
quantitative value for plotting.
0
0.2
0.4
0.6
0.8
1
1.2
4 5 6 7 8 9 10 11
Fr
a
c
t
i
o
n

o
f

S
e

o
x
i
d
a
t
i
o
n

s
t
a
t
e
pH
Fraction Se(IV)
Fraction Se(VI)
0
0.2
0.4
0.6
0.8
1
1.2
-200 -100 0 100 200 300 400 500
Fr
a
c
t
i
o
n

o
f

S
e

o
x
i
d
a
t
i
o
n

s
t
a
t
e
Eh (mV)
Fraction Se(IV)
Fraction Se(VI)

Page 86

The PHREEQC model predicted Kd values consider formation of all oxidation states of selenium
and therefore average out some of the influences of oxidation or reduction reactions. The PHREEQC
model predicted values are shown in Figure 8.7 as a function of pH. For the groundwater simulant with
the minimum ion concentrations, selenium exhibits the expected behavior of most anions with sorption
decreasing with increasing pH. This is a manifestation of the fact that mineral surface charge on metal
oxide minerals transitions from a net positive to a net negative charge with increasing pH. Therefore, as
the pH increases, the sorption affinity of anionic selenium species to the more negatively charged surface
decreases. The dominant selenium species in this model (Se(IV)) exists as H2SeO3, HSeO3-, and SeO32-
and changes speciation as the pH increases. This is demonstrated by the model output shown in Figure
8.8. The H2SeO3 species is only dominant under very low pH conditions and does not influence this
model. The HSeO3- species is dominant up until pH 7 after which SeO32- becomes the dominant species.
Thus, Se(IV) persists as either a monovalent or divalent anion across the entire pH range considered
which causes the decrease in sorption with increasing pH observed in Figure 8.7.
Under the average and maximum groundwater simulant conditions (from Table 3.10), there is
significant scatter in the predicted Kd values with respect to pH (Figure 8.7). Generally the trend
discussed above reverses, and sorption increases with increasing pH. This could be due to changes in
selenium speciation at the low pH region or competition between selenium and other ions for sorption
sites. The speciation analysis in Figure 8.8 demonstrates that the aqueous species of selenium are
relatively simple and selenium is unlikely to form aqueous complexes which can influences sorption.
Therefore, the decrease in Kd values in Figure 8.7 is likely the result of competition from other ions such
as SO42- and PO43- for sorption sites. This behavior is shown in Figures 4.12 and 4.13 for HFO and HAO
sorption sites, respectively. Such competition with other anions for sorption sites would be diminished at
higher pH values were anion sorption is minimal. This is indeed the case in the model output where above
pH 7, all Kd values decrease with increasing pH.
As with the other models discussed in this report, the potential formation of mineral phases was
monitored but not included in the model. For selenium, elemental Se and CoSe are both saturated in the
PHREEQC model with pH 5.3 and Eh -20 mV. These are the only conditions under which any selenium
phase is saturated. Since these values represent the extreme low pH and low Eh conditions of the seven
sites under consideration, it can be reasonably concluded that formation of selenium bearing mineral
phases is unlikely to be a major factor controlling selenium mobility.

Page 87

Figure 8.7: Predicted selenium Kd values from PHREEQC modeling using the range of ground
water (GW) concentrations listed in Table 3.9Table 3.10.

Figure 8.8: Predicted Se(IV) species as a function of pH.
8.4. Comparison between modeled and experimental Kd values for selenium
The Kd values from the PHREEQC model are shown in Table 8.2 and are wide ranging due to the
strong influence of both pH and Eh on selenium speciation and sorption behavior. The influence of ion
competition for sorption sites is demonstrated by the decreasing Kd for the minimum, average, and
maximum groundwater simulants. With the exception of the high Kd values predicted for the minimum
groundwater simulant, the data compare favorably with experimentally measured values from batch
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
3 5 7 9
Pr
e
d
i
c
t
e
d

S
e

K
d
(L
/
k
g
)
pH
Se Kd, MIN GW Values
Se Kd, AVG GW Values
Se Kd, MAX GW Values
0.0
0.2
0.4
0.6
0.8
1.0
1.2
3 4 5 6 7 8 9 10
Fr
a
c
t
i
o
n

o
f

S
e
(
I
V
)

A
q
u
e
o
u
s

S
p
e
c
i
e
s
pH
H2SeO3 Fraction
HSeO3- Fraction
SeO3-- Fraction

Page 88
sorption experiments (Table 8.3). Reactive transport models for the seven sites under consideration did
not consider selenium. Therefore, there are no values from a reactive transport model for comparison.

Table 8.2: Predicted Kd values in each groundwater simulant and the average pH and Eh for the
seven sites. NA = not applicable because only one Kd value is used.
Groundwater condition Average Kd (L/kg) Standard
Deviation
Minimum GW Simulant 3415 4031
Average GW Simulant 96 98
Maximum GW Simulant 3.1 4.4
Value for Average Groundwater Conditions (pH
6.47, Eh 220 mV, all ions at average
concentrations from Tables 2.4 and 2.5).
160 NA

Table 8.3: Selenium Kd values measured in the laboratory and modeled using PHREEQC. Units in
L/kg. Reactive transport models did not consider Se, so no values are available for comparison.
Site: Mean value measured by UNCC
batch experiments [10-16]
Range of values from PHREEQC
geochemical model
Sutton 10.2
Values varied with GW Simulants
Minimum GW simulant: 3415
Average GW simulant: 96
Maximum GW simulant: 3.1

Average groundwater conditions: 160
Lee 69.0
Weatherspoon -
Roxboro 8.3
Asheville 228.1
Mayo 8.3
Cape Fear -

Page 89
9. EXAMINATION of SORPTION CAPACITY
A simulation was run to examine the capacity of the solid phases to sequester constituents of
concern over long periods of time in which groundwater will pass through the system. This was done by
approximating the pore water chemistry in well MW-23B/C/D/E. The availability hydrous ferric oxide
(HFO) and hydrous aluminum oxide (HAO) sorption sites was determined by adding a total ferrihydrate
and gibbsite mass to the PHREEQC input file based on the measurements of solid phase Fe and Al in
solids obtained from the well. The ferrihydrate and gibbsite were allowed to dissolved to reach saturation
within the pore fluid. Then the ADVECTION function within PHREEQC was used to simulate an
exchange between the MW-23B/C/D/E porewater with ABMW-1S porewater 500 times. This is
essentially allowing the ferrrihydrate and gibbsite phases to initially form based on the measured
chemistry of the MW-23B/C/D/E pore waters and solid phase followed by an exchange with the ABMW-
1S porewater 500 times. The input values are listed in Table 9.1 below. The total mass of ferrihydrate and
gibbsite initially present in MW-23B/C/D/E was determined using the solid phase Fe and Al
concentrations from solids obtained from the well and a bulk density of 1.6 g/cm3 and porosity of 0.2.
This approach is described in section 3 above. For convenient comparison, Kd values were determined
from the output data as a function of pore volumes exchanged. This effectively shows the change in
aqueous and solid phase concentrations of the constituents in MW-23 well waters as ABMW-1S
porewater passes through.

Table 9.1: Input values for wells for capacity simulations. Ion concentration units are in ug/L.
Well ABMW-1S MW-23B MW-23C MW-23D MW-23E
pH 8 6.3 6.4 8.5 10
pe -0.169 6.625 6.320 0.300 0.547
O(0) 0.34 0.52 0.1 0 1.55
Al 143 35 27 84 41
As 634 1 1 1.15 1
Ba 594 25 42 5 5
B 3940 50 938 886 2500
Ca 128000 11700 25400 10800 4960
Cl 83000 4000 21000 160000 500000
Cr 5 5 5 5 5
Co 6.3 6.3 6.3 6.3 6.3
Fe 152 16 61 145 48
Mg 51000 1230 4910 6740 9040
Mn 88 8 86 17 5
K 16300 1050 6730 9440 88200
Se 10.3 10.3 10.3 10.3 10.3
Na 54500 1290 14700 190000 519000
S(6) 83 12 52 35 110
V 3.45 0.457 0.691 0.423 0.993
Zn 14 5 5 5 5

Page 90

The pH and Eh changed as the two porewaters mixed and the aqueous ions and solid phase
obtained a new equilibrium state after mixing (Figure 9.1). Furthermore, the sorbent phases HFO and
HAO also remained stable (Figure 9.2). The HAO concentrations essentially remained unchanged. This is
primarily due to the relatively high Al concentrations in the pore waters of both wells which maintains
gibbsite in a saturated state. There is a slight decrease in the HFO concentration with continued pore
volume exchanges due to the decreasing pH and the relatively low concentrations of influent dissolved Fe
which limit the saturation of ferrihydrate. While relatively small, these changes in pH have a pronounced
influence on the distribution of ions in the system. The Kd values for anionic species of B, As, and V
decrease with increasing pore volume exchanges (Figure 9.3). This is proposed to be due to the decrease
in HFO concentrations which is a major sorbent for these ions. The Se Kd values remain relatively
constant and the values for Cr, Co, and Zn show slight increases. This increase is a manifestation of an
increasing solid phase concentration of these ions as the incoming pore water adds more Cr, Co, and Zn to
the simulation.
These data indicate that continued flushing of pore waters from ABMW-1S can have an impact
on the aqueous concentrations of these ions. In many cases there is a stable or increasing solid phase
concentration after passing 500 pore volumes through the system. The accumulation of ions on the
surface does not appear to have reached a saturation state. While there is some decrease in the total
amount of HFO in the system, sufficient iron and aluminum appears to be available to maintain a high
concentration of HFO and HAO sorption sites. The Al and Fe could to be either 1) present in the initial
conditions or 2) added to the system in the form of soluble Al and Fe from the ABMW-1S pore waters.
Regardless of the source, this simulation demonstrates the stability of the HFO and HAO sorbents despite
500 pore volumes of fluid exchanged.

Page 91

Figure 9.1: PHREEQC modeled changes in MW-23B (top) and MW-23D (bottom) pore waters
following exchange with ABMW-1S pore waters.

0
1
2
3
4
5
6
7
8
9
0
50
100
150
200
250
300
350
0 100 200 300 400 500 600
pHEh
Exchanged pore volumes
East Transect, Well MW23-B
EH
pH
0
1
2
3
4
5
6
7
8
9
0
20
40
60
80
100
120
140
160
180
0 100 200 300 400 500 600
pHEh
Exchanged pore volumes
East Transect, Well MW23-D
EH
pH

Page 92

Figure 9.2: PHREEQC modeled changes in HFO and HAO concentrations in MW-23B (top) and
MW-23D (bottom) pore waters following exchange with ABMW-1S pore waters.
1.00E-03
1.00E-02
1.00E-01
1.00E+00
0 100 200 300 400 500 600
Co
n
c
e
n
t
r
a
t
i
o
n

o
f

H
F
O

a
n
d

H
A
O

S
i
t
e
s

(m
o
l
/
L
)
Exchanged Pore Volumes
East Transect, MW-23B
HFO
HAO
1.00E-03
1.00E-02
1.00E-01
1.00E+00
0 100 200 300 400 500 600
Co
n
c
e
n
t
r
a
t
i
o
n

o
f

H
F
O

a
n
d

H
A
O

S
i
t
e
s

(m
o
l
/
L
)
Exchanged Pore Volumes
East Transect, MW-23D
HFO
HAO

Page 93

Figure 9.3: PHREEQC modeled changes in Kd for various ions in MW-23B (top) and MW-23D
(bottom) pore waters following exchange with ABMW-1S pore waters.

1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
0 100 200 300 400 500 600
K d
(L
/
k
g
)
Exchanged Pore Volumes
East Transect, Well MW-23B
As
B
Co
Cr
Se
V
Zn
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
1.00E+09
0 100 200 300 400 500 600
K d
(L
/
k
g
)
Exchanged Pore Volumes
East Transect, Well MW-23D
As
B
Co
Cr
Se
V
Zn

Page 94
10. POTENTIAL EFFECTS of ACCELERATED REMEDIATION
The goals of this modeling exercise are to 1) provide a qualitative conceptual model of the
behavior of several constituents of interest at the L. V. Sutton site and 2) provide a qualitative assesment
of the potential effects of any accelerated remediation efforts at the site. The intent is to evaluate if
accelerated remediation efforts may inadvertently increase the mobility of any constituent. The two
primary remediation techniques planned for the site are coal ash removal from the basin and installation
of pump and treat systems. As discussed above for both the flow transect models and the global model,
the pH and redox potential (Eh) of these systems are the primary factors which could increase or decrease
the mobility of a constituent. Removal of ash from the basin has the potential to increase the redox
potential of the system through the introduction of oxygen. It is unclear how ash removal could influence
the pH. However, based on the general observation along the flow transects that the pH decreases with
distance when moving away from the ash basin, it is assumed in the discussion below that the pH will
decrease after ash removal from the basin. Using these assumptions the following qualitative conclusions
can be drawn.
· Arsenic: Increasing the redox potential could result in oxidation of As(III) to As(V). If the pH of
the system is low, then this will result in greater sorption of anioic As(V) species. If the pH of the
system increases, As(V) sorption will decrease. However, based on the transect and global
geochemical modeling excercises discussed above, the Kd for As(V) will remain high across the
pH range at the site.
· Boron: Boron exists in only one oxidation state in aqueous systems. Therefore, an increase in Eh
is not expected to directly influence boron sorption. Furthermore, the Kd values for boron are
esesntially zero. So there is no potential to enhance the pore water concentrations of boron during
ash basin removal because boron is inherently non-reactive (i.e., does not form complexes and
only weakly sorbs.
· Cobalt: The transect and global geochemical models both predict Co(II) is the only relevant
oxidaiton state of cobalt under the site conditons. Therefore, oxidation to Co(III) is unlikely so
there is no expected change with respect to oxidation state. A decrease pH would likely cause
greater pore water Co(II) concentrations due to the decreasing Kd with decreasing pH as shown in
Figure 7.3Figure 7.4.
· Chromium: The transect and global models both predict Cr(III) as the dominant oxiation state
which has a strong sorption affinity. An increase in Eh could facilitate oxidation of Cr(III) to
Cr(VI) which has a lower sorption affinity and thus could cause an increase in the pore water
chromium concentration. However, based on the geochemical modeling output and
experimentally determined Kd values, both Cr(III) and Cr(VI) are expected to stay strongly
associated with the mineral phases.
· Selenium: Se(IV) is predicted as the dominant oxidaiton state of selenium in both the global and
transect geochemical models. Oxidation of Se(IV) to Se(VI) due to an increase in Eh would likely
result in a decrease in pore water concentraitons of selenium. Oxidation to Se(VI) would result in
formation of anionic SeO4-2 which exhibits stronger sorption with decreasing pH. Therefore, the
oxidaiton coupled with the decreased pH along the flow transect could decrease selenium
mobility.

A pump and treat system will result in enhanced pore water removal which will generate a
hydraulic gradient which will bring in waters from further up the flow path at a faster rate. Assuming the

Page 95
newly introduced water equilibrates with the subsurface solids and that the solids are the primary redox
and pH buffer, no change in the pH or redox potential is expected. Furthermore, the intended goal of the
pump and treat system is to promote removal of pore waters containing the constituents of interset, which
will induce a concentration gradient causing desorption of sorbed constituents and lower the solid phase
concentration. One limitation in this discussion is that a re-equilibration is assumed but if the kinetics of
pH, redox and sorption/desorption equilibration are not sufficient to reach equilibrium then the model
predicted values (which inherently assume equilibrium is achieved) may not be accurate. However, based
on the relative consistency of measured pH and Eh values in Sutton site wells over time, assumption of a
rapid equilibration seems appropriate. Therefore, it is assumed that the pump and treat system not impact
the pH and Eh of the pore waters and is unlikely to result in enhanced mobilization of an constituents of
concern. However, this can be verified with measurements once the systems are operating.

Page 96
11. SUMMARY
Based on the model predicted Kd values and the aqueous speciation underlying the models as well
as the observational data from field measurements, a list of potential attenuation mechanisms for several
constituents was compiled (Table 11.1). The list includes physical attenuation in the form of flow through
a system which will cause dilution and which is expected for all elements. Sorption and precipitation are
also considered. Sorption is defined broadly and is proposed to account for any process removing aqueous
ions via chemical interactions with a surface. Thus, sorption reactions can include ion exchange, surface
complexation, and sorption to metal oxides, metal sulfides, and organic matter. Precipitation broadly
includes both homogenous mineral precipitation as well as co-precipitation.

Table 11.1 : Listing of primary attenuation mechanisms and general geochemical considerations for
several constituents of concern
Constituent Physical attenuation Chemical Precipitation Sorption
Arsenic
√ √
The PHREEQC transect and global models predict As(V) as the dominant oxidation state of
arsenic under the field measured Eh and pH conditions but As(III) is the dominant species
measured in ground waters. The reason for this discrepancy is proposed to be due to 1)
increased sorption of As(V) relative to As(III) which would remove all As(V) from the ground
water and prevent As(V) measurements in samples and/or 2) a kinetic limitation with respect to
the As(III)/As(V) oxidation/reduction reactions which prevents the system from reaching
chemical equilibrium. However, the observation of As(III) is consistent with the relatively lower
Kd values required in the reactive transport modeling efforts compared with the higher Kd values
predicted by PHREEQC which is primarily due to As(V) sorption. Therefore, the reactive
transport model represents a conservative estimate. Due to the stronger sorption of As(V), the
tendency of the element to move in the subsurface, will decrease as As(III) becomes oxidized to
As(V) and sorbs to mineral surfaces. Additionally, the minerals scorodite (FeAsO4.2H2O) and
mansfieldite (AlAsO4.2H2O) are near saturation under some pH and Eh conditions examined in
this model and measured in the field. Thus these minerals may theoretically form but generally
are unlikely mineral phases to form in the shallow subsurface.
Boron
√ √
Boron exists only in the B(III) oxidation state and generally persists as the neutrally charged
chemical species boric acid (H3BO3), which is a weak acid and exhibits minimal sorption to
mineral surfaces. As the system pH increases, H3BO3 will deprotonate (i.e. release a H+ ion) to
form H2BO3- which also sorbs weakly. Boric acid and H2BO3- are the only two aqueous species
of boron predicted to occur in this model. Thus, the PHREEQC predicted Kd values for boron
are low (1.1 x 10-5 to 0.031 L/kg). These values are slightly lower but generally consistent with
the values chosen for reactive transport modeling and those measured in batch laboratory
experiments. Precipitation of any boron containing mineral phases is not expected to occur.
Therefore, physical attenuation and sorption are the two primary processes which will control
the movement of boron in the subsurface.

Page 97
Constituent Physical attenuation Chemical Precipitation Sorption
Chromium
√ √ √
The ground water measurements from all seven sites monitoring Cr oxidation state speciation
indicate Cr(III) is the dominant oxidation state which is in agreement with the PHREEQC
model. The sorption of Cr(III) is significantly stronger than Cr(VI) because Cr(III) persists as a
highly charged cation (Cr3+) which readily sorbs to mineral surfaces as the pH increases from
acidic to basic conditions. This behavior is in stark contrast to that of Cr(VI) which persists as a
weakly sorbing anion (CrO4-) and decreases sorption from acidic to basic conditions. This high
charge density of Cr3+ also causes a propensity to form aqueous complexes with anions such as
SO4-2 and Cl- which can influence sorption behavior. For example, formation of CrSO4+ appears
to be responsible for a decreased Kd relative to baseline conditions in the PHREEQC model
presented in this work. The measured aqueous concentrations in groundwater from the seven
sites range from below detection to approximately 100 mg/L. This concentration range is similar
to what was modeled in PHREEQC and is indicates that formation of mineral phases containing
Cr may occur under high pH conditions with relatively high Cr concentrations.
Cobalt
√ √ √
The dominant cobalt species predicted by the PHREEQC model is Co2+. Essentially no oxidized
Co(III) is expected to be present within the Eh range of the groundwater from the seven sites.
Thus, the behavior of cobalt exhibits relatively little influence on redox potential except for the
few cases where reaction between Co and some other reduced species (such as Se(-II) occurs.
The predicted Kd value of 0.79 L/kg using all average groundwater values (i.e. average pH,
average Eh, and average ion concentrations from all seven sites) agrees well with the value of
2.5 L/kg used in the reactive transport model of the Asheville site. Note that Asheville is the
only site where the reactive transport model considered cobalt. The experimentally derived Kd
values from batch experiments are generally higher than the value used in reactive transport
modeling. However, there is good agreement between the average value of 32.5 L/kg from the
experimental systems and the average value of 533 L/kg from the PHREEQC modeling effort.
Overall cobalt is expected to exhibit minimal transport in these systems relative to more mobile
species such as manganese and boron.
Selenium
√ √
Selenium geochemical behavior is highly dependent on the Eh of the groundwater. Under
reducing conditions, selenium can persist as the reduced Se(-II) or Se(0) species which are
generally insoluble. Therefore, selenium is expected to be relatively immobile under very
reducing conditions. Under mildly reducing to oxidizing conditions Se(IV) and Se(VI) exist as
anionic species SeO3-2 and SeO4-2. The sorption of these anions decreases with increasing pH.
However, sorption is also influenced by competition with other sorbing ions like sulfate.
Therefore, selenium Kd values for either or Se(IV) or Se(VI) exhibits a wide range due to the
influence of pH and ion competition. The PHREEQC model predictions show Se(IV) as the
dominant species under approximately neutral pH conditions but the fraction of Se(VI)
increases with increasing pH and Eh. This behavior is consistent with the selenium oxidation
state speciation measured in field samples. Overall the range of Kd values predicted by
PHREEQC agrees with the values determined experimentally from batch sorption tests.

Page 98
12. REFERENCES

1. Dzombak, D.A. and F.M.M. Morel, Surface complexation modeling : hydrous ferric oxide. 1990,
New York: Wiley. xvii, 393.
2. Karamalidis, A.K. and D.A. Dzombak, Surface Complexation Modeling: Gibbsite. 2010,
Hoboken, NJ: John Wiley and Sons, Inc.
3. Brame, S.E., et al., Groundwater flow and transport modeling report for the H.F. Lee Energy
Complex, Goldsboro, NC. 2015: Greenville, SC.
4. Murdoch, L.C., et al., Groundwater Flow and Transport Modeling Report for Mayo Steam
Electric Plant, Roxboro, NC. 2015.
5. Graziano, R., et al., Groundwater Flow and Transport Modeling Report for Cape Fear Steam
Electric Plant, Moncure, NC. 2015: Greenville, SC.
6. Falta, R.W., et al., Groundwater Flow and Transport Modeling Report for L. V. Sutton Energy
Complex, Wilmington, NC. 2015: Greenville, SC.
7. Falta, R.W., et al., Groundwater Flow and Transport Modeling Report for the W. H.
Weatherspoon Power Plant, Lumberton, NC. 2015: Greenville, SC.
8. Murdoch, L.C., et al., Groundwater Flow and Transport Modeling Report for Roxboro Steam
Electric Plant, Semora, NC. 2015: Greenville, SC.
9. Falta, R.W., et al., Groundwater Flow and Transport Modeling Report for Asheville Steam
Electric Plant, Arden, NC. 2015: Greenville, SC.
10. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: Mayo Steam Electric Plant. 2015:
Charlotte, NC.
11. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: Asheville Steam Electric Plant.
2015: Charlotte, NC.
12. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: Cape Fear Steam Station. 2015:
Charlotte, NC.
13. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: H. F. Lee Steam Station. 2015:
Charlotte, NC.
14. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: Roxboro Steam Electric Plant.
2015: Charlotte, NC.
15. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: L. V. Sutton Energy Complex.
2015: Charlotte, NC.
16. Langley, W.G. and O. Shubhashini, Soil Sorption Evaluation: W. H. Weatherspoon Steam
Station. 2015: Charlotte, NC.
17. USEPA, Monitored Natural Attenuation of Inorganic Contaminants in Ground Water. Volume 2.
Assessment for Non-Radionuclides Including Arsenic, Cadmium, Chromium, Copper, Lead,
Nickel, Nitrate, Perchlorate, and Selenium.2007. 2007.
18. Strawn, D., et al., Microscale investigation into the geochemistry of arsenic, selenium, and iron in
soil developed in pyritic shale materials. Geoderma, 2002. 108(3-4): p. 237-257.
19. Zawislanski, P.T. and M. Zavarin, Nature and rates of selenium transformations: A laboratory
study of Kesterson Reservoir soils. Soil Science Society of America Journal, 1996. 60(3): p. 791-
800.
20. Emerson, H.P., K.A. Hickok, and B.A. Powell, Experimental evidence for ternary colloid-
facilitated transport of Th(IV) with hematite (α-Fe2O3) colloids and Suwannee River fulvic acid.
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22. Martell, A.E. and R.K. Smith, Critical Stability Constants, Standard Reference Database 46,
Version 6.30. 2001, National Institute of Standards, Gaithersburg, MD.

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23. Coup, K.M. and P.J. Swedlund, Demystifying the interfacial aquatic geochemistry of thallium(I):
New and old data reveal just a regular cation. Chemical Geology, 2015. 398: p. 97-103.
24. Casiot, C., et al., Predominance of Aqueous Tl(I) Species in the River System Downstream from
the Abandoned Carnoules Mine (Southern France). Environmental Science & Technology, 2011.
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25. Casiot, C., et al., Response to Comment on "Predominance of Aqueous Tl(I) Species in the River
System Downstream from the Abandoned Carnoules Mine (Southern France)". Environmental
Science & Technology, 2012. 46(4): p. 2475-2476.
26. Smeaton, C.M., C.G. Weisener, and B.J. Fryer, Comment on "Predominance of Aqueous Tl(I)
Species in the River System Downstream from the Abandoned Carnoules Mine (Southern
France)". Environmental Science & Technology, 2012. 46(4): p. 2473-2474.
27. Davis, J.A., et al., Application of the surface complexation concept to complex mineral
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29. Goldberg, S., Use of Surface Complexation Models in Soil Chemical Systems, in Advances in
Agronomy, D.L. Sparks, Editor. 1990?, Academic Press, Inc. p. 233-329
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32. SynTerra, Comprehensive Site Assessment Report – Roxboro Steam Electric Plant, Semora, NC.
September 2, 2015: Greenville, SC.
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Lumberton, NC. August 5, 2015: Greenville, SC.
35. SynTerra, Comprehensive Site Assessment Report –L.V. Sutton Electric Plant, Wilmington, NC.
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Acta., 1977. 41: p. 527-538.
38. Stachowicz, M., T. Hiemstra, and W.H. van Riemsdijk, Surface speciation of As(III) and As(V) in
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Refined Geochemical Model Report October 2016
L.V. Sutton Energy Complex, Wilmington, NC SynTerra

P:\Duke Energy Progress.1026\108. Sutton Ash Basin GW Assessment Plan\20.EG_CAP\CAP Site Specific
Modeling Supplment\Final\FINAL Sutton Geochemical Rpt Oct 2016.docx
APPENDIX B
CSA & CSA SUPPLEMENT NO. 1
ISOCONCENTRATION MAPS

C A P E F E A R RIVER
U
S
-
4
2
1
S
R
-
2
1
6
9
PRJ-I
-
1
4
0
SR-1394
SR-2779
SR-2145
PRJ-I-140
PRJ-I-1
4
0
10
10
1
0
1
0
10
100
100
FORMER ASHDISPOSAL AREA
1984 ASHBASIN(LINED)
NEW ASHBASIN AREA(LINED)
1971 ASHBASIN
MW-12< 1 (< 1)
MW-23C< 1 (< 1)
MW-24C3.42 (< 1)
MW-31C< 1 (< 1)
MW-07C< 1 (< 1)
AW-01C< 1 (< 1)
AW-02C< 1 (< 1)
AW-05C< 1 (< 1)
MW-32C< 1 (NS)
MW-33C< 1 (NS)ABMW-02D170 (167)
AW-09C< 1 (< 1)
SMW-02C< 1 (< 1)
SMW-03C< 1 (< 1)
SMW-06C2.79 (2.55)
AW-04C< 1 (< 1)
SMW-01C< 1 (< 1)
SMW-04C2.56 (2.09)
SMW-05C< 1 (< 1)
AW-03C1.54 (1.54)
MW-15D< 1 (< 1)
MW-16D< 1 (< 1)MW-20D2.25 (1.8)
AW-08C< 1 (< 1)
MW-37C< 1 (2.56)
MW-11< 1 (NS)
MW-21C53.8 (NS)
MW-22C< 1 (NS)
MW-28C< 1 (NS)
MW-04B< 1 (NS)
MW-05C< 1 (NS)
ABMW-01S634 (654)
COOLINGPOND
COOLINGPOND
COOLINGPOND
COOLINGPOND
CAPE FEARRIVER
DRAINAGECHANNEL
COOLINGPOND
COOLINGPOND
COOLINGPOND
GRAPHIC SCALE
500 0 500 1,000 1,500
(IN FEET)
NOTES:1 CON CEN TRATION S ARE FROM THE JUN E 2015SAMPLIN G EVEN T.
2 THE N ORTH CAROLIN A 2L FOR ARSEN IC IS 10 μg/L.
3 SITE AERIAL ORTHOPHOTOGRAPHY IS DATED APRIL17, 2014. IT W AS OBTAIN ED FROM W SP.
4 ADDITION AL OFFSITE AERIAL ORTHOPHOTOGRAPHY ISDATED 2012. IT W AS OBTAIN ED FROM THE N C CEN TERFOR GEOGRAPHIC IN FORMATION AN D AN ALYSIS.(http://servic es.nc onem ap.gov/)
5 PARCEL BOUN DARY W AS OBTAIN EDFROM THE N CCEN TER FOR GEOGRAPHIC IN FORMATION AN DAN ALYSIS. (http://services.nconem ap.gov/)
6 DRAW IN G HAS BEEN SET W ITH A PROJECTION OFN ORTH CAROLIN A STATE PLAN E COORDIN ATE SYSTEMFIPS 3200 (N AD83/2011).
P:\Duke Energy Progress.1026\00 GIS BASE DATA\Sutton\Ma p_ Docs\Draft_ CSA_ v07152015\Figure 10-5 to 10-44 - Isoc onc entra tion Ma ps_ v20150801.m xd
PROJECT MAN AGER: P. W ALDREP
DRAW N BY: T. KIN G
DATE: 07/31/2015
DATE: 07/31/2015
CHECKED BY: J. MEADOW S
FIGURE 10-15JUNE 2015 ISOCONCENTRATION MAPARSENIC IN LOWER SURFICIAL WELLSL. V. SUTTON EN ERGY COMPLEX801 SUTTON STEAM PLAN T RDW ILMIN GTON , N ORTH CAROLIN A
148 RIVER STREET, SUITE 220 GREEN VILLE, SC 29601 864-421-9999www.synterra corp.c om
ASH BASIN BOUN DARY
ASH BASIN COMPLIAN CE BOUN DARY
DUKE EN ERGY PROGRESS SUTTON PLAN T SITE BOUN DARY
(10)(10)
1010
DISSOLVED ARSEN IC CON CEN TRATION (μg/L)BOLD VALUES IN DICATE AN EX CEEDAN CE
2.
LEGEND
TOTAL ARSEN IC CON CEN TRATION
1 (μg/L)BOLD VALUES IN DICATE AN EX CEEDAN CE
2.
ISOCON CEN TRATION CON TOUR (DASHED W HERE ESTIMATED)
N ORTH CAROLIN A 2L CON TOUR
1 (DASHED W HERE ESTIMATED)(HASH MARKS IN DICATE DIRECTION OF LOW ER CON CEN TRATION S)
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
U
S H
w
y 17
I - 1 4 0
Sutton Lake Rd
U
S H
w
y 421
FORMERASH DISPOSALAREA
1971 ASHBASIN
1984 ASHBASIN(LINED)
1984 ASHBASIN(LINED)
1983EXTENSION
1 0 0 0
2
0
0
0
6 0
2000
60
1 0 0 0
60
1 0 0 0
6 0
7
0
0
7 0 0
60
ABMW-2D677
AW-4C1260
AW-5CND
AW-8CND
AW-9C152
MW-4BND
MW-5CND
MW-7C279
MW-11ND
MW-121210
MW-16D444
MW-21C1070
MW-22C2650
MW-23C938
MW-24C1030
MW-27C459
MW-28CND
MW-31C1260
MW-32C87
MW-33CND
MW-36C529
MW-37CND
MW-38C384
MW-39CND
MW-40C557
S-1NewWell24
S-1 OldWell15
S-2 Pumphouse690
S-2Well 1160
S-3600
S-4180
S-612
S-720.7
S-8Well117
S-8Well 2210
S-1234
S-1762
S-1826
SMW-1C706
SMW-2C225
SMW-5C345
SMW-6C298
MW-191620
ABMW-1S3500
SMW-4C85
AW-1C98
SMW-3C417
AW-3C1370
AW-2CND
CAPE FEARRIVER
COOLINGPOND
COOLINGPOND
DRAINAGECHANNEL
COOLINGPOND
COOLINGPOND
COOLINGPOND
NOTES:CONCENTRATIONS SHOWN ARE FROM THE MOST RECENT SAMPLINGEVENT FOR EACH MONITORING WELL (JUNE 2015, JANUARY 2016, ORJUNE 2016).
WATER SUPPLY WELL SAMPLED WHERE NOTED IN JANUARY, MARCH,OR JULY 2016.
THE NORTH CAROLINA 2L FOR BORON IS 700 μg/L.
THE PROVISIONAL BACKGROUND LEVEL FOR BORON IS 60 μg/L.
2014 AERIAL ORTHOPHOTOGRAPHY OBTAINED FROM USDA NRCSGEOSPATIAL DATA GATEWAY(https://gdg.sc.egov.usda.gov/GDGOrder.aspx).
DRAWING HAS BEEN SET WITH A PROJECTION OF NORTH CAROLINASTATE PLANE COORDINATE SYSTEM FIPS 3200 (NAD83/2011).
LEGEND
FIGURE 1-11ISOCONCENTRATION MAPBORON IN SURFICIAL LOWER GROUNDWATERL.V. SUTTON ENERGY COMPLEXWILMINGTON, NORTH CARO LINA
DRAWN BY: J. ROBERTSPROJECT M ANAGER: P. WALDREPCHECKED BY: B. WYLIE
DATE: 08/17 /2016
148 RIVER STREET, SUITE 220GREENVILLE, SOUTH CAROLINA 29601PHONE [phone]www.synterracorp.com
P:\Duke Energy Progress.1026\00 GIS BASE DATA\Sutton\Map_Docs\CSA_Supplement\Sutton - Isoconcentration Maps.mxd
600 0 600 1,200300
IN FEET
GRAPHIC SCALE
&<MONITORING WELL
PRIVATE WATER SUPPLY WELL
ASH BASIN BOUNDARY
ASH BASIN COMPLIANCE BOUNDARY
DUKE ENERGY PROGRESS SUTTON PLANT SITE BOUNDARY
NOT DETECTED ABOVE REPORTING LIMITND
NORTH CAROLINA 2L CONTOUR INFERRED
NORTH CAROLINA 2L CONTOUR
HIGHEST CONCENTRATION AREA CONTOUR
HIGHEST CONCENTRATION AREA CONTOUR INFERRED
INTERMEDIATE CONTOUR
INTERMEDIATE CONTOUR INFERRED
TOTAL BORON CONCENTRATION (μg/L)1
APPROX. PROVISIONAL BACKGROUND CONTOUR INFERRED
APPROX. PROVISIONAL BACKGROUND CONTOUR
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
U
S H
w
y 17
I - 1 4 0
Sutton Lake Rd
U
S H
w
y 421
FORMER ASHDISPOSALAREA
1971 ASHBASIN
1984 ASHBASIN(LINED)
1984 ASHBASIN(LINED)
1983EXTENSION
5
5
5
5
5 5
5
ABMW-2D2.69
AW-4C7.09AW-5CND
AW-8CND
AW-9CND
MW-4BND
MW-5CND
MW-7CND
MW-11ND
MW-121.08
MW-16D5.71
MW-21C5.02
MW-22C9.43
MW-23C6.3
MW-24C15.5
MW-27C1.76
MW-28CND
MW-31C2.27
MW-32CND
MW-33CND
MW-36C17.2
MW-37C2.04
MW-38C3.58
MW-39C2.93
MW-40C8.39
S-1 NewWellND
S-1 OldWellND
S-2Pumphouse2
S-2 Well12
S-32 S-4ND
S-6ND
S-7ND
S-8 Well13 S-8 Well24
S-12ND
S-17ND
S-18ND
SMW-1C2.58
SMW-2C12.3
SMW-5C1.25
SMW-6C6.5
MW-192.41
ABMW-1SND
AW-3C6.91
SMW-3C9.21
AW-1CND
AW-2CND
SMW-4CND
CAPE FEARRIVER
COOLINGPOND
COOLINGPOND
DRAINAGECHANNEL
COOLINGPOND
COOLINGPOND
COOLINGPOND
NOTES:CONCENTRATIONS SHOWN ARE FROM THE MOST RECENT SAMPLINGEVENT FOR EACH MONITORING WELL (JUNE 2015, JANUARY 2016, ORJUNE 2016).
WATER SUPPLY WELL SAMPLED WHERE NOTED IN JANUARY, MARCH,OR JULY 2016.
THE INTERIM MAXIMUM ALLOWABLE CONCENTRATION (IMAC) FORCOBALT IS 1 μg/L.
THE PROVISIONAL BACKGROUND LEVEL FOR COBALT IS 5 μg/L.
2014 AERIAL ORTHOPHOTOGRAPHY OBTAINED FROM USDA NRCSGEOSPATIAL DATA GATEWAY(https://gdg.sc.egov.usda.gov/GDGOrder.aspx).
DRAWING HAS BEEN SET WITH A PROJECTION OF NORTH CAROLINASTATE PLANE COORDINATE SYSTEM FIPS 3200 (NAD83/2011).
LEGEND
FIGURE 1-12ISOCONCENTRATION MAPCOBALT IN SURFICIAL LOW ER GROUNDWATERL.V. SUTTON ENERGY COMPLEXWILMINGTON, NORTH CARO LINA
DRAWN BY: J. ROBERTSPROJECT M ANAGER: P. WALDREPCHECKED BY: B. WYLIE
DATE: 08/17 /2016
148 RIVER STREET, SUITE 220GREENVILLE, SOUTH CAROLINA 29601PHONE [phone]www.synterracorp.com
P:\Duke Energy Progress.1026\00 GIS BASE DATA\Sutton\Map_Docs\CSA_Supplement\Sutton - Isoconcentration Maps.mxd
600 0 600 1,200300
IN FEET
GRAPHIC SCALE
&<MONITORING WELL
PRIVATE WATER SUPPLY WELL
ASH BASIN BOUNDARY
ASH BASIN COMPLIANCE BOUNDARY
DUKE ENERGY PROGRESS SUTTON PLANT SITE BOUNDARY
NOT DETECTED ABOVE REPORTING LIMITND
IMAC CONTOUR INFERRED
IMAC CONTOUR
HIGHEST CONCENTRATION AREA CONTOUR
HIGHEST CONCENTRATION AREA CONTOUR INFERRED
INTERMEDIATE CONTOUR
INTERMEDIATE CONTOUR INFERRED
TOTAL COBALT CONCENTRATION (μg/L)1
APPROX. PROVISIONAL BACKGROUND CONTOUR INFERRED
APPROX. PROVISIONAL BACKGROUND CONTOUR
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
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&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
&<
U
S H
w
y 17
I - 1 4 0
Sutton Lake Rd
U
S H
w
y 421
FORMER ASHDISPOSALAREA
1971 ASHBASIN
1984 ASHBASIN(LINED)
1984 ASHBASIN(LINED)
1983EXTENSION
2.5
2 .5
2.5
2 .5
2
0
ABMW-2DND
AW-4CND
AW-5CND
AW-8CND
AW-9CND
MW-4BND
MW-5CND
MW-7CND
MW-11ND
MW-12ND
MW-16DND
MW-21CND
MW-22CND
MW-23C10.3
MW-24CND
MW-27C61.9
MW-28CND
MW-31CND
MW-32CND
MW-33CND
MW-36C49.1
MW-37CND
MW-38C21.8
MW-39CND
MW-40C26.6
S-1 NewWellND
S-1 OldWellND
S-2PumphouseND
S-2Well 1ND
S-3ND S-4ND
S-6ND S-7ND
S-8Well 1ND S-8Well 2ND
S-12ND
S-17ND
S-18ND
SMW-1CND
SMW-2CND
SMW-5CND
SMW-6CND
MW-19ND
ABMW-1SND
AW-1C2.6
AW-2CND
AW-3CND
SMW-3CND
SMW-4CND
CAPE FEARRIVER
COOLINGPOND
COOLINGPOND
DRAINAGECHANNEL
COOLINGPOND
COOLINGPOND
COOLINGPOND
NOTES:CONCENTRATIONS SHOWN ARE FROM THE MOST RECENT SAMPLINGEVENT FOR EACH MONITORING WELL (JUNE 2015, JANUARY 2016, ORJUNE 2016).
WATER SUPPLY WELL SAMPLED WHERE NOTED IN JANUARY, MARCH,OR JULY 2016.
THE NORTH CAROLINA 2L FOR SELENIUM IS 20 mg/L.
THE PROVISIONAL BACKGROUND LEVEL FOR SELENIUM IS 2.5 μg/L.
2014 AERIAL ORTHOPHOTOGRAPHY OBTAINED FROM USDA NRCSGEOSPATIAL DATA GATEWAY(https://gdg.sc.egov.usda.gov/GDGOrder.aspx).
DRAWING HAS BEEN SET WITH A PROJECTION OF NORTH CAROLINASTATE PLANE COORDINATE SYSTEM FIPS 3200 (NAD83/2011).
LEGEND
FIGURE 1-15ISOCONCENTRATION MAPSELENIUM IN SURFICIAL LOWERGROUNDWATERL.V. SUTTON ENERGY COMPLEXWILMINGTON, NORTH CARO LINADRAWN BY: J. ROBERTSPROJECT M ANAGER: P. WALDREPCHECKED BY: B. WYLIE
DATE: 08/17 /2016
148 RIVER STREET, SUITE 220GREENVILLE, SOUTH CAROLINA 29601PHONE [phone]www.synterracorp.com
P:\Duke Energy Progress.1026\00 GIS BASE DATA\Sutton\Map_Docs\CSA_Supplement\Sutton - Isoconcentration Maps.mxd
600 0 600 1,200300
IN FEET
GRAPHIC SCALE
&<MONITORING WELL
PRIVATE WATER SUPPLY WELL
ASH BASIN BOUNDARY
ASH BASIN COMPLIANCE BOUNDARY
DUKE ENERGY PROGRESS SUTTON PLANT SITE BOUNDARY
NOT DETECTED ABOVE REPORTING LIMITND
NORTH CAROLINA 2L CONTOUR INFERRED
NORTH CAROLINA 2L CONTOUR
HIGHEST CONCENTRATION AREA CONTOUR
HIGHEST CONCENTRATION AREA CONTOUR INFERRED
INTERMEDIATE CONTOUR
INTERMEDIATE CONTOUR INFERRED
TOTAL SELENIUM CONCENTRATION (mg/L)1
APPROX. PROVISIONAL BACKGROUND CONTOUR INFERRED
APPROX. PROVISIONAL BACKGROUND CONTOUR
C A P E F E A R RIVER
U
S-421
S
R-2
1
6
9
P R J -I -1 4 0
SR-1394
S R -2 7 7 9
S R -2 1 4 5
PRJ-I-140
P R J -I -1 4 0
0 .2
0.2
FORMER ASHDISPOSALAREA
1984 ASHBASIN(LINED)
NEW ASHBASIN AREA(LINED)
1971 ASHBASIN
MW-12< 0.2 (< 0.2)MW-23C0.21 (< 0.2)
MW-24C< 0.2 (< 0.2)
MW-31C< 0.2 (< 0.2)
MW-07C0.405 (< 0.2)
AW-01C< 0.2 (< 0.2)
AW-02C< 0.2 (< 0.2)
AW-05C< 0.2 (< 0.2)
MW-32C< 0.2 (NS)
MW-33C< 0.2 (NS)ABMW-02D< 0.2 (< 0.2)
AW-09C< 0.2 (< 0.2)
SMW-02C< 0.2 (< 0.2)
SMW-03C< 0.2 (< 0.2)
SMW-06C< 0.2 (< 0.2)
AW-04C< 0.2 (< 0.2)
SMW-01C< 0.2 (< 0.2)
SMW-04C< 0.2 (< 0.2)
SMW-05C< 0.2 (0.26)
AW-03C< 0.2 (< 0.2)
MW-15D< 0.2 (< 0.2)
MW-16D< 0.2 (< 0.2)
MW-20D< 0.2 (< 0.2)
AW-08C< 0.2 (< 0.2)
MW-37C< 0.2 (< 0.2)
MW-11< 0.2 (NS)
MW-21C< 0.2 (NS)
MW-22C0.398 (NS)
MW-28C< 0.2 (NS)
MW-04B< 0.2 (NS)
MW-05C< 0.2 (NS)
ABMW-01S< 0.2 (< 0.2)
COOLINGPOND
COOLINGPOND
COOLINGPOND
COOLINGPOND
CAPE FEARRIVER
DRAINAGECHANNEL
COOLINGPOND
COOLINGPOND
COOLINGPOND
GRAPHIC SCALE
500 0 500 1,000 1,500
(IN FEET)
NOTES:1 CONCENTRATIONS ARE FROM THE JUNE 2015SAMPLING EVENT.
2 THE INTERIM MAXIMUM ALLOWABLE CONCENTRATION(IMAC) THALLIUM IS 0.2 μg/L.
3 SITE AERIAL ORTHOPHOTOGRAPHY IS DATED APRIL17, 2014. IT WAS OBTAINED FROM WSP.
4 ADDITIONAL OFFSITE AERIAL ORTHOPHOTOGRAPHY ISDATED 2012. IT WAS OBTAINED FROM THE NC CENTERFOR GEOGRAPHIC INFORMATION AND ANALYSIS.(http://services.nconemap.gov/)
5 PARCEL BOUNDARY WAS OBTAINEDFROM THE NCCENTER FOR GEOGRAPHIC INFORMATION ANDANALYSIS. (http://services.nconemap.gov/)
6 DRAWING HAS BEEN SET WITH A PROJECTION OFNORTH CAROLINA STATE PLANE COORDINATE SYSTEM
P:\D uke E nergy P rogress.1026\00 GIS B AS E DATA \Sutton\Map_Docs\D raft_CS A_v07152015\Figure 10-5 to 10-44 - Isoconcentration Maps_v20150801.mxd
PROJECT MANAGER: P. WALDREP
DRAWN BY: T. KING
DATE: 07/31/2015
DATE: 07/31/2015
CHECKED BY: J. MEADOWS
FIG URE 10-22JUNE 2015 ISOCONCENTRATION MAPTHALLIUM IN LOWER SURFICIAL WELLSL. V. SUTTON ENERGY COMPLEX801 SUTTON STEAM PLANT RDWILMINGTON, NORTH CAROLINA
148 RIVER STREET, SUITE 220GREENVILLE, SC 29601864-421-9999www.synterracorp.com
ASH BASIN BOUNDARY
ASH BASIN COMPLIANCE BOUNDARY
DUKE ENERGY PROGRESS SUTTON PLANT SITE BOUNDARY
(0.2)(0.2)
0.20.2
DISSOLVED THALLIUM CONCENTRATION (μg/L)BOLD VALUES INDICATE AN EXCEEDANCE2.
LEGEND
TOTAL THALLIUM CONCENTRATION1 (μg/L)BOLD VALUES INDICATE AN EXCEEDANCE2.
ISOCONCENTRATION CONTOUR (DASHED WHERE ESTIMATED)
NORTH CAROLINA IMAC CONTOUR1 (DASHED WHERE ESTIMATED)(HASH MARKS INDICATE DIRECTION OF LOWER CONCENTRATIONS)
Refined Geochemical Model Report October 2016
L.V. Sutton Energy Complex, Wilmington, NC SynTerra

P:\Duke Energy Progress.1026\108. Sutton Ash Basin GW Assessment Plan\20.EG_CAP\CAP Site Specific
Modeling Supplment\Final\FINAL Sutton Geochemical Rpt Oct 2016.docx
APPENDIX C
HISTORICAL GROUND WATER DATA
(MICROSOFT EXCEL FORMAT)