NC0000396_App C Geochemical Model_20160219

Revision 0, 1/27/2016

Analysis of Geochemical Phenomena Controlling Mobility of Ions
from Coal Ash Basins

Brian A. Powell, Ph.D.
112 Cherry Street
Pendleton, SC 29670
[phone]
[e-mail]
January 27, 2016

Revision 0, 1/27/2016
TABLE OF CONTENTS
EXECUTIVE SUMMARY .............................................................................................................. i 
1. INTRODUCTION ................................................................................................................ 1 
2. GEOCHEMICAL MODEL DEVELOPMENT .................................................................... 2 
2.1. Sorption model development ............................................................................................ 2 
2.2. Geochemical model parameterization ............................................................................... 6 
2.3. Pourbaix diagram modeling ............................................................................................ 15 
2.4. The use of Kd values ....................................................................................................... 16 
3. GEOCHEMICAL MODELING OF ARSENIC ................................................................. 18 
3.1. Pourbaix diagram analysis .............................................................................................. 18 
3.2. PHREEQC model analysis .............................................................................................. 19 
3.3. Comparison of measured and calculated As speciation .................................................. 26 
3.4. Comparison modeled and experimental Kd values for arsenic ........................................ 29 
4. GEOCHEMICAL MODELING OF BORON .................................................................... 30 
4.1. Pourbaix diagram analysis .............................................................................................. 30 
4.2. PHREEQC speciation analysis ....................................................................................... 30 
4.3. Comparison between modeled and experimental Kd values for boron ........................... 32 
5. GEOCHEMICAL MODELING of CHROMIUM .............................................................. 34 
5.1. Pourbaix diagram analysis .............................................................................................. 34 
5.2. PHREEQC speciation analysis ....................................................................................... 34 
5.3. Comparison between modeled and experimental Kd values for chromium .................... 39 
6. GEOCHEMICAL MODELING of MANGANESE ........................................................... 41 
6.1. Pourbaix diagram analysis .............................................................................................. 41 
6.2. PHREEQC speciation analysis ....................................................................................... 43 
6.3. Comparison between modeled and experimental Kd values for manganese ................... 45 
7. SUMMARY ........................................................................................................................ 46 
8. REFERENCES ................................................................................................................... 48 

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 describes the
geochemical behavior relevant to 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. 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 hydrous ferric oxide (HFO) and hydrous aluminum oxide (HAO) were the dominant sorbing
surfaces based on the databases developed by Dzomback and Morel [1] and Karamalidis and Dzomback
[2].
The model input requires initial concentrations of all ions of interest, pH, redox potential (EH), as
well as 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 was selected to capture the range of
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. Parameterization of the model in terms of the
sorption site concentrations has a direct and linear relationship to the predicted Kd values. The average
extractable iron and aluminum concentrations from the solid samples was converted to a sorption site
concentration by assuming site densities of 0.2 moles of Fe sites per mole of solid phase Fe and 0.4 moles
of Al sites per mole of solid phase Al. Essentially assuming an increase in the iron or aluminum site
density will cause a corresponding increase in the sorption site concentration. The major emphasis in
describing the predicted Kd values should be on comparing the trends with changing system parameters
(i.e. EH, pH, ion concentrations) rather than comparing specific Kd values.
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

Page ii
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 is highly soluble and 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, Fe, V, Mn, Cr, Co, S) 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 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. 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 0.34 L/kg). These values are slightly 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: The ground water measurements from Weatherspoon 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

Page iii
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 g/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.
 Manganese: Manganese is predominantly present as Mn2+ in the PHREEQC model output which
is in agreement with the measurements of Mn(II) in groundwater from the Weatherspoon, Lee,
and Sutton sites. Sorption of Mn(II) is generally weak and yields Kd values ranging from 5 x 10-7
to 5 L/kg calculated from the PHREEQC model. The mean Kd value of 7 x 10-3 L/kg and the Kd
value of 0.15 L/kg for the average groundwater conditions from the PHREEQC model are in
reasonable agreement with the range of 0 to 0.10 L/kg used in reactive transport models [3, 4, 8]
and the value of 9 x 10-3 L/kg determined from batch sorption experiments with solids from the
Cape Fear site. Analysis of the saturation index of mineral phases containing Mn from the
PHREEQC model indicates that several common Mn bearing soil minerals (manganite,
hausmannite, and birnessite) are near saturation under high pH and high EH conditions. Therefore,
precipitation of Mn mineral phases could occur given sufficiently high Mn concentrations and
high pH/EH conditions. Based on the above analysis, it is expected that physical attenuation,
sorption, and chemical precipitation could all play a role on controlling the movement of Mn in
the subsurface.

Page 1
1. INTRODUCTION
A geochemical modeling effort was undertaken to describe the chemical speciation expected
under the variable conditions at seven Duke Energy Progress power plant sites: Sutton, Weatherspoon,
H.F. Lee, Mayo, Cape Fear, Asheville, and Roxboro. 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. In the previous models, a wide range of pH and EH
conditions at each site was modeled using site relevant data [17-23]. 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]. This model assumed sorption occurred only to iron oxide surfaces based on sorption reactions to
HFO described by Dzomback and Morel [1]. In this revised model, sorption to HAO was also modeled
based on the reactions compiled by Karamalidis and Dzombak [2]. The concentrations of HFO and HAO
were constrained based on extractable Al and Fe concentrations from solid phases recovered from each
site 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.

Page 2
2. GEOCHEMICAL MODEL DEVELOPMENT
2.1. Sorption model development
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 [24].
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 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.
Using surface complexation models, the sorption of an element is written as a standard chemical
reaction such as those shown in Table 2.1. 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, 25-27]. 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.
To constrain the number of sorption sites to be used in this model, a concentration of surface sites
(SOH) must be calculated in units of mol/L for application in the aforementioned chemical equations.
Such a concentration is conceptually difficult because SOH 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 batch laboratory data, a solid phase concentration in gsolid/L must also be assumed.

Page 3
Table 2.1: Example reactions used in surface complexation modeling
(where SOH represents a sorption site).
Reaction Type Reaction Expression Stability constant
Surface protonation (i.e.
develops positive surface
charge at low pH)
SOH + H+  SOH2+

Surface deprotonation (i.e.
develops negative surface
charge at high pH)
SOH  SO- + H+

Cation sorption SOH + Mn+  SOMn-1 + H+

Anion Sorption
SOH + H+ + A-  SOH2+A-
or
SOH + A-  SA + OH-

K୅ ൌ ሾSOHଶା Aି ሿ
ሾSOHሿሾH ା ሿሾAି ሿ exp ൬െ ܨΨ
ܴܶ ൰

However, this assumed value essentially is canceled out when back calculating a Kd value from the
geochemical modeling data. Therefore, the concentration of sorption sites based on extractable Fe
concentrations is based on the following equation:

ሾ≡ ܨܱ݁ܪ
ሿ ൌ ሾܵ݋݈݅݀ሿ ∗ሾܨ݁ሿ௘௫௧௥ ∗1݃ி௘
1000݉݃ி௘
∗݉݋݈ி௘
55.845݃ி௘
∗0.2 ݉݋݈ ≡ ܨܱ݁ܪ
݉݋݈ ܨ݁௘௫௧௥௔௖௧௔௕௟௘

where [Solid] is the solid phase suspension concentration in g/L assumed for the model, [Fe]extr is the
extractable Fe concentration in mgFe/gsolid, and [≡FeOH] is the concentration of iron surface sites in the
model input. 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 alternative approach when the site density is either
known or assumed (i.e. sites per nm2 of mineral surface area), a molar concentration of surface sites can
be determined using the equation:

ሾ≡ ܨܱ݁ܪ
ሿ ൌ
ሾܵܵሿ ∗ܵܣ∗10ଵ଼ ݊݉ଶ
݉݉ଶ ∗ܵܦ
6.022 ݔ 10ଶଷ ሺܽݐ݋݉ݏ
݉݋݈ ሻ

+
2
++
[SOH ]FψK= exp[SOH]{H } RT

-+
-
[SO ]{H } FψK= exp -[SOH] RT

+
n-1 +
M
[SOM ]{H } FψK= exp( 1)[SOH]{M } RT
n n


Page 4

where [≡FeOH] is the concentration of iron sorption sites in mol/L, [SS] is the suspension of solids in
g/L, SA is the surface area of the solid in m2/g, and SD is the site density of the solid (sites/nm2).
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 [25]. 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 [25]. 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.
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 all seven sites. The average
concentrations of extractable Al and Fe from solids obtained from all seven sites are shown in Figure 2.1.
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 2.2. 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 2.2: 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 3.59E-06 1.79E-04
Extractable Al 762 2.83E-05 1.16E-05 5.79E-04

Page 5
In previous geochemical models, the site densities of HAO and HFO sorbents were used to
determine the capacity of the solid phases at each of the seven sites. Using the same method described
above, site densities available for sorption were calculated based on the average extractable Fe and Al
concentrations from each site [28-34]. Then modeling groundwater concentrations of each constituent of
interest at the NC2L Standard Level and 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] . Therefore, solid phases
from each site were shown to have the capacity to sorb all of the available constituents of interest
considering the above assumptions. However, it is important to note that these calculations assume 100%
sorption which will not be the case for constituents with low Kd values such as boron and manganese.
Therefore, although this calculation shows that it is unlikely the capacity of the aquifer solids would be
exceeded, the actual aqueous phases of each constituent would be based on the Kd value for that
constituent under the geochemical conditions of the pore water. A similar result occurs when using the
average sorption site concentrations from the average extractable Fe and Al concentrations listed in Table
2.2 and used in this report. Therefore, in the PHREEQC model output discussed in this report, saturation
of available surface sorption sites is not expected to occur.

Figure 2.1: Average extractable Al and Fe concentrations 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 6

Figure 2.2: Minimum, mean, and maximum extractable Fe (top) and Al (bottom)
concentrations in solids from all seven sites. Based on data from [10-16]

2.2. 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 has 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 [17-23]. 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 2.3.
0
1000
2000
3000
4000
5000
6000
7000
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
7000
8000
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 7

Figure 2.3: 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 [17-23].

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 2.3, 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 are depicted in Table
2.3 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 2.4 and 2.5.
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
24681012
E H
(m
V
)
pH
Weatherspoon
Sutton
Lee
Cape Fear
Asheville
Mayo
Roxboro
Water Oxidation
Water Reduction
Selected Values

Page 8
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 2.4: Histograms of EH values measured at all seven sites. The bottom figure shows the mean
EH values at +/- 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 2.3). Based on data from [17-23].

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

O
c
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

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

Page 9

Figure 2.5: Histograms of pH values measured at all seven sites. The bottom figure shows
the mean pH values at +/- 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 2.3). Based on data from [17-23].

Table 2.3: pH and EH Values for Global Model Input. Note that EH was entered in
PHREEQC using pe 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 2.4 7.1 75.5 1.28
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

O
c
c
u
r
r
e
n
c
e
s
pH
Histogram
Cumulative
Fraction
0
5
10
15
20
25
30
35
40
45
50
23456789101112
#
o
f

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

Page 10

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 various geologic units at all seven sites were plotted together in the following series of figures
(Figures 2.6-2.8). 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 (Tables 2.4 and 2.5). 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 ion sorption, respectively. Therefore, by considering the ranges of
ferrous iron and sulfate listed below in Table 2.5, 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 Tables 2.4 and 2.5.

Page 11
Figure 2.6: Minimum, average, and maximum Al, Sb, As, Ba, B, Cd, and Cr concentrations in
groundwater at all seven sites. Based on data from [17-23].

0.1
1
10
100
1000
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
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 12
Figure 2.7: Minimum, average, and maximum Cu, Pb, Mn, Mo, Ni, Se, Sr, and V concentrations in
groundwater at all seven sites. Based on data from [17-23].
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 13
Figure 2.8: Minimum, average, and maximum Ca, carbonate, Cl, Fe, Mg, nitrate, K, Na, sulfate,
sulfide, and Zn concentrations in groundwater at all seven sites. Based on data from [17-23].

0.01
0.1
1
10
100
1000
Av
e
r
a
g
e

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

(
m
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville
0.001
0.01
0.1
1
10
100
1000
10000
100000
1000000
10000000
Ma
x
i
m
u
m

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

(
m
g
/
L
)
Sutton
Weatherspoon
Lee
Cape Fear
Roxboro
Mayo
Asheville
0.001
0.01
0.1
1
10
100
1000
Mi
n
i
m
u
m

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

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

Page 14
Table 2.4: 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 2.5: 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 15
2.3. 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 2.3 are shown as individual
datapoints. A generic groundwater chemistry containing 500 ppb of each constituent of concern was used
in the simulations (Table 2.6). These concentrations are generally higher than the concentrations observed
in groundwater samples from the sites considered in this report. Therefore, 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 2.6: 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 16
 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
moves 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 2.1). 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.
2.4. 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 2.9. The Kd
values were calculated using Equation 2.1.
ܭௗ ൌ ሾெሿೞ೚೗೔೏
ሾெሿೌ೜ೠ೐೚ೠೞ
ൌ ሾெሿ೅೚೟ೌ೗ିሾெሿೌ೜ೠ೐೚ೠೞ
ሾெሿೌ೜ೠ೐೚ೠೞ
∗௏
௠ Equation 2.1
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). Equation 2.1
can be rearranged to estimate the sorbed fraction of the constituent as:
݂௦௢௥௕௘ௗ ൌ1െ݂௔௤௨௘௢௨௦ ൌ1െ ଵ
ଵା௄೏ ೘
ೇ
Equation 2.2
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.

Page 17

Figure 2.9: 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 18
3. GEOCHEMICAL MODELING OF ARSENIC
3.1. Pourbaix diagram analysis
Under mildly oxidizing to strongly oxidizing conditions, arsenic can exist as the arsenate (AsO4-3)
and arsenite (AsO3-3) oxoanions (Figure 3.1). Both are weak acids and persist in solution as HxAsOyx-2y
species [35]. Under the pH an EH conditions expected at each site, both As(III) and As(V) may be present
as evidenced 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 H3AsO4
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 [36, 37]. 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)
[38]. As discussed above, the stronger sorption of As(V) would preferentially remove As from the ground
waters measured in Figure 2.5 above 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 3.1: Pourbaix diagram of arsenic species along with the range of pH and EH values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and EH value calculated from the combined
measurements at all 7 sites for the average.

Page 19
3.2. PHREEQC 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 3.2. 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 3.3. 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 3.4. The Kd values for As(III) range from 10-1 to 102
L/kg and those or 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 3.5. 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 3.5. 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 As speciation are significant because the majority of
speciation measurements for arsenic in site groundwater indicated 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 3.2: Distribution coefficient (Kd) for summed As(III) and As(V) species from PHREEQC
model. The impact of changing ion concentrations and changes 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 20
Figure 3.3: Redox speciation of aqueous As from PHREEQC modeling under minimum (top),
average (middle) and maximum (bottom) ion concentrations listed in Table 2.5. 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-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
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 21

Figure 3.4: 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 22

Figure 3.5: Redox speciation of sorbed As from PHREEQC modeling under minimum (top),
average (middle) and maximum (bottom) ion concentrations listed in Table 2.5.
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 23

The Kd values reported in Figure 3.2 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 2.4. The model was run in three simulated groundwater conditions containing the
minimum, average, and maximum ion concentrations listed in Table 2.5. 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 SO42-. 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 2.5, the sulfate ion may compete with other anions like arsenate for sorption sites.
This behavior can be seen in Figures 3.6 and 3.7 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 Figures 3.5 and 3.6, 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 3.7. 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 3.7, 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 and could
theoretically precipitate. However, it is noteworthy that both scorodite and mansfieldite commonly form
in hydrothermal deposits. Thus, precipitation of these minerals may have significant kinetic limitations.

Page 24
Figure 3.6: Distribution of HFO surface site speciation under minimum (top), average (middle), and
maximum (bottom) total groundwater ion concentrations from Table 2.5. 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 then 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 25
Figure 3.7: Distribution of HAO surface site speciation under minimum (top), average
(middle), and maximum (bottom) total ion concentrations from Table 2.5. 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 then 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 specation, 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 specation, 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 specation, maximum GW ion concentrations
HAO_OH2+HAO_OH HAO_O-HAO_OFe+HAO_OHSO4-2

Page 26

Figure 3.8: 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.

3.3. Comparison of measured and calculated As speciation
Arsenic redox speciation data is available for several samples from the Sutton, Lee, and
Weatherspoon sites. The fraction of As(III) and As(V) measured at each site as a function of EH and pH
are shown in Figure 3.9. 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 equilibrium. 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 27

Figure 3.9: Arsenic redox speciation measured in aqueous samples from Weatherspoon, Lee, and
Sutton sites 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. 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
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
Weatherspoon, Fraction As(III)
Weatherspoon, Fraction As(V)
Lee, Fraction As(III)
Lee, Fraction As(V)
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)
Weatherspoon, Fraction As(III)
Weatherspoon, Fraction As(V)
Lee, Fraction As(III)
Lee, Fraction As(V)
Sutton, Fraction As(III)
Sutton, Fraction As(V)
ሾܨ݁ሺܱܪሻଶା ሿሾܪା ሿଶሾ݁ሿ
ሾܨ݁ ାାሿ ൌlogെ18.78

Page 28
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 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. 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 3.10: Estimated EH values based on the Fe(II)/Fe(III) redox couple compared with the
measured values in groundwater samples from Lee, Weatherspoon, and Sutton sites. 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
0 200 400 600 800 1000
Me
a
s
u
r
e
d

E
H
(m
V
)
Estimated EH(mV) based on Fe(III)/Fe(II) Couple
Weatherspoon
HF Lee
Sutton
݈݋݃ ሾܨ݁ሺܱܪሻଶା ሿ
ሾܨ݁ ାା ሿ െ2݌ܪ൅18.78ൌ ݌݁
ሾܪଶ ܣݏܱସି ሿሾܪା ሿଷ ሾ݁ሿଶ
ሾܪଷ ܣݏܱଷ ሿሾܪଶܱሿ ൌ logെ21.197
log ሾܪଶܣݏܱସି ሿ
ሾܪଷ ܣݏܱଷሿ ൌ 3݌ܪ ൅ 2݌݁ െ 21.197

Page 29
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 (discussed in section 3.4 below). Thus
the lower Kd values required for the model are consistent with the dominance of the more mobile As(III)
species in groundwater measurements.

3.4. 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 3.1. 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 reduce the overall mobility of arsenic in these systems.

Table 3.1 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 geochemical
model (L/kg)
Sutton 9 48 Total As: 15 to 4.5 x 106
As(V): 11.6 to 4.5 x 106
As(III): .03 to 680

Value for Average GW
conditions:
As(III): 363; As(V) 2.24 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

Page 30
4. GEOCHEMICAL MODELING OF BORON
4.1. Pourbaix diagram analysis
As shown in Figure 4.1, 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.

Figure 4.1: Pourbaix diagram of boron species along with the range of pH and EH values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and EH value calculated from the combined
measurements at all 7 sites for the average.

4.2. PHREEQC speciation analysis
The PHREEQC model predicts relatively low sorption of boron as expected based on the
observed mobility of boron in field samples (Figure 4.2). 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 4.1 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 2.5,
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 figures 3.6

Page 31
and 3.7. 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 4.3 and all are well below 0. Therefore, the changes in Kd
shown in Figure 4.2 are only due to changes in pH and the influence of competing ions.
As discussed in section 2, sorption was modeled assuming aluminum and iron hydroxide minerals
were the dominant sorbing surfaces. Sorption of boron to aluminum hydroxides was predicted to be
significantly higher than iron oxides as shown in Figure 4.4. This is consistent with the higher sorption
constant for boron interactions with aluminum hydroxides relative to iron oxides [1, 2] and may also
indicate the potential for greater sorption to solids containing higher extractable aluminum concentrations.

Figure 4.2: Predicted boron Kd values from PHREEQC modeling using the range of ground water
(GW) concentrations listed in Tables 2.4 and 2.5.

Figure 4.3: 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

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 / 2206.9 / 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
-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

Page 32

Figure 4.4: 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 2.4 and 2.5. “Al” and “Fe” in the legend represent the
HAO and HFO

4.3. 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 4.1. There is some discrepancy
between the highest value predicted by PHREEQC (0.34 L/kg) and the highest values used in the 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 either leaching of boron into
the aqueous phase (i.e. the solid selected contained native boron which was desorbing) or demonstrated a
non-linear sorption isotherm with minimal sorption. 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.

1.00E-08
1.00E-07
1.00E-06
1.00E-05
1.00E-04
1.00E-03
45678910
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 33
Table 4.2: 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 geochemical
model
Sutton 0 1.7
Range:
1.1 x 10-5 to 0.34

Geometric mean: 8.0
x 10-3

Value for average GW
conditions: 0.013
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 34
5. GEOCHEMICAL MODELING of CHROMIUM
5.1. Pourbaix diagram analysis
Under the range of EH-pH conditions representative of the seven sites, chromium is dominated by
the trivalent oxidation state (Cr(III), Figure 5.1) with only one EH-pH value 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, 38] which is strong at low pH and decreases with increasing
pH as the mineral surface develops an increasingly negative charge.

Figure 5.1: Pourbaix diagram of boron species along with the range of pH and EH values
examined in PHREEQC modeling (shown by blue symbols).

5.2. PHREEQC speciation analysis
Plots of the Kd values from the PHREEQC model as a function of the pH and EH are shown in
Figure 5.2. The pH and EH values used in the model are from the range provided in Table 2.3. Under the
range of redox conditions examined, there is almost no dependence on the reduction potential (EH) 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.

Page 35
This is consistent with the dominance of Cr(III) in field
samples from the Weatherspoon site presented in Table
5.1. 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 5.2). 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 5.3 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 5.4, 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 2.4 and 2.5. This decrease in chromium sorption is shown by the increasing aqueous phase
concentrations in Figure 5.3 and the decrease in Kd values from Figure 5.2.
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 5.5). 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 2.5), the concentration of CrSO4+ and CrHSO4+2 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
ion concentrations going from minimum to maximum values listed in Table 2.5 (shown in Figure 5.5).
Therefore, the decrease in the Kd values at pH 9.1 reported in Figure 5.2 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 3.6 and 3.7.

Table 5.1: Chromium redox speciation
determined in Weatherspoon water
samples [17-23].
pH EH Fraction
Cr(VI)
Fraction
Cr(III)
3.6 531.1 0.002 0.998
5.8 129.0 0.015 0.985
5.5 97.0 0.016 0.984
5.0 235.0 0.013 0.987
6.2 99.8 0.014 0.986
5.3 119.0 0.013 0.987

Page 36

Figure 5.2: Predicted chromium Kd values from PHREEQC modeling using the range of
ground water (GW) concentrations listed in Tables 2.4 and 2.5. 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 37

Figure 5.3: Predicted concentrations of total aqueous chromium and trivalent chromium
modeled using the range of ground water (GW) concentrations listed in Tables 2.4 and 2.5.

Figure 5.4: Predicted concentrations of total aqueous chromium modeled using the range of
ground water (GW) concentrations listed in Tables 2.4 and 2.5.

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
345678910
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 38

Figure 5.5: Predicted chromium aqueous speciation at pH 4.0, 5.6, and 9.1 using the range of
ground water (GW) concentrations listed in Tables 2.4 and 2.5. 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 39
In addition to sorption reactions discussed above, Cr may be removed from solution by
precipitation. 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). 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 5.6: Chromium saturation indices predicted using the maximum ground water ion
concentrations listed in Tables 2.4 and 2.5.

5.3. 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 5.2. Experimental values are only available for the sorbents
from the Roxboro and Asheville sites. The wide range of modeled values spans 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 5.2. 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 40
also have a major impact of the Kd values as demonstrated by the ~103 change in Kd from the three
groundwater simulants.

Table 5.2: 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): 1.3 x 10-3 to 1.9 x 105
Mean 36

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

Page 41
6. GEOCHEMICAL MODELING of MANGANESE
6.1. Pourbaix diagram analysis
Manganese can exist in multiple oxidation states ranging from Mn(II) to Mn(VII). Under the EH
and pH conditions of the groundwater at each of the seven sites under consideration, Mn(II) is the
dominant oxidation state (Figure 6.1). The selected EH and pH values used for PHREEQC modeling,
shown in the Pourbaix diagrams in Figure 6.1) all fall within the region where Mn(II) is dominant. One
datapoint lies within the region where the mineral rhodochrosite (MnCO3) is saturated. It is noteworthy
that several other Mn mineral phases are possible including relatively common soil minerals such as
hausmannite (Mn3O4), manganite (MnOOH), and birnessite (MnO2). The minerals pyrolysite (-MnO2)
and bixbyite (Mn,FeO3) were also predicted to form but were removed from consideration in the model
due their rarity. While these mineral phases are possible, they preferentially occur at high pH and are not
expected under the conditions modeled in Figure 6.1 (based on the concentrations in Table 2.6).
Therefore, should the groundwater conditions shift to higher pH values, sequestration of manganese via
precipitation could occur. These mineral phases typically form under higher pH regions where hydrolysis
of Mn2+ can lead to reduced or neutrally charged species MnOH+ and Mn(OH)20, which can facilitate
precipitation. However, hydrolysis of Mn2+ to form MnOH+ is not expected below pH 10. Thus, Mn2+
remains the dominant aqueous ion to be considered in these systems.

Page 42

Figure 6.1: Pourbaix diagram of manganese species along with the range of pH and EH values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and EH value calculated from the combined
measurements at all 7 sites for the average. The top plot neglects precipitation of any solid
phases which are shown with yellow shading in the bottom plot.

Page 43
6.2. PHREEQC speciation analysis
As expected from the Pourbaix diagrams shown in Figure 6.1, the dominant aqueous species of
manganese is Mn2+ under the range of groundwater conditions examined in the PHREEQC model. This
observation is consistent with measurements of Mn oxidation state speciation in Weatherspoon, Lee, and
Sutton groundwater as shown in Figure 6.2. Divalent Mn2+ is a weakly sorbing cation and forms weak
complexes with groundwater anions such as Cl-, SO4-2, and CO3-2. Due to the weak complexation affinity
of Mn2+, it persists as the free ion Mn2+ despite relatively high concentrations of anions used in the model.
Though manganese can also exist as Mn(III), Mn(IV), and Mn(VII), >99.99% of the manganese in the
model output is present as Mn(II). Based on the region of stability for other manganese oxidation states in
Figure 6.1, only very high pH and EH conditions will facilitate formation of Mn(VII). Aqueous species of
Mn(III) and Mn(IV) are not expected because trivalent Mn(III) is unstable in aqueous systems and
Mn(IV) is generally insoluble and will precipitate from solution as MnO2(s) minerals birnessite or
pyrolusite.

Figure 6.3: Fraction of Mn(II) relative to total aqueous Mn in Weatherspoon, Lee, and Sutton
groundwater as a function of pH.

Due to the prevalence of Mn(II), the system EH has little influence over the sorption behavior of
manganese in the model output. Thus, the modeled Kd values are shown in Figure 6.3 as a function of pH
to demonstrate the major influence of pH on the system. The Kd values are generally low and, as expected
based on the low complexation affinity of Mn(II) discussed above, show little change with respect to the
changing groundwater ion concentrations. As the pH increases, the mineral surface changes from a net
positive to a net negative charge which causes increasing attraction of the Mn2+ cation with increasing pH
(exhibited by the increasing Kd values). Unlike the models discussed above for As, B, and Cr, the Mn
concentration in the model varied between the minimum, average, and maximum concentrations observed
at the seven sites based on the values listed in Table 2.5. The similarity in Kd values from the minimum
and average groundwater concentration systems demonstrates the linearity of a Kd model where a change
in the total concentration does not cause a change in the observed distribution coefficient. However, at the
maximum groundwater Mn concentration of 45.5 ppm (8.28 x 10-4 mol/L), the surfaces may become
saturated with Mn and other sorbing ions which leads to a decrease in the overall Kd value. At the average
0.000
0.200
0.400
0.600
0.800
1.000
345678910
Fr
a
c
t
i
o
n

o
f

M
n
(
I
I
)
pH
Weatherspoon
Lee
Sutton

Page 44
pH and EH conditions of groundwater from all seven sites (pH 6.47, EH 222 mV as listed in Table 2.3), the
Kd values are 0.10, 0.15, and 0.02 L/kg for the minimum, average, and maximum groundwater simulants,
respectively. Thus, increasing both the groundwater ion concentrations and total Mn to the maximum
values (Table 2.5) results in relatively consistent Kd values with approximately a 5x difference in the
range of Kd values.

Figure 6.3: Predicted concentrations of total aqueous chromium modeled using the range of
groundwater (GW) concentrations listed in Tables 2.4 and 2.5.

As expected based on the relatively high Mn aqueous concentrations and the prevalence of
mineral phases in the Pourbaix diagrams in Figure 6.1, there are multiple minerals containing Mn which
have saturation indices close to 0 (Figure 6.4). The saturation index for rhodochrosite is greater than zero
in many simulations indicating the mineral could precipitate from solution. However, rhodocrocite
generally occurs in hydrothermal systems and is unexpected to form under these site conditions. The
common soil minerals manganite, hausmannite, and birnessite are more likely to dominate these systems
[40]. The saturation indices for these minerals are well below zero in many cases with the notable
exceptions being the systems with higher pH values (9.1 and 7.1) and the pH 6.9, EH 513 mV system. The
minerals hausmannite, birnessite, and manganite have higher saturation indices in the pH 6.9, EH 513 mV
system because aqueous Mn(II) can be oxidized to higher oxidation state Mn(III) and Mn(IV) under the
higher EH conditions (relative to the other models). Due to the higher concentrations of Mn(III) and
Mn(IV) under the high EH condition, the saturation indices of hausmannite, manganite, and birnessite are
increased. Increasing the pH could increase the saturation index of these minerals further. Therefore,
while these model conditions do not predict the formation of a pure Mn mineral phase, increased Mn
groundwater concentrations coupled with increases in pH and EH could result in the formation of
hausmannite, manganite, and/or birnessite. While pyrolusite is also shown in Figure 6.3, birnessite is the
more common form of MnO2 and thus has been used in the discussion above. Without high EH conditions,
Mn(II) will persist and the Mn(OH)2(s) mineral pyrochroite may form. The saturation index increases
with increasing pH. Therefore, higher pH values could facilitate formation of pyrochroite. The model at
pH 9.1 was the highest considered in this model and yielded a pyrochroite saturation index of -1.23.
1.00E-07
1.00E-06
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00E+01
345678910
To
t
a
l

M
n

K
d

v
a
l
u
e

(
L
/
k
g
)
pH
Mn Kd, MIN GW Values
Mn Kd, AVG GW Values
Mn Kd, MAX GW Values

Page 45
Therefore, approximately a 10x increase in the aqueous Mn(II) concentration at pH 9.1 could cause
saturation of the system with respect to pyrochroite and lead to precipitation.

Figure 6.4: Manganese saturation indices predicted using the maximum ground water ion
concentrations listed in Tables 2.4 and 2.5.

6.3. Comparison between modeled and experimental Kd values for manganese
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. However, the PHREEQC Kd value of 0.15 L/kg for the average
groundwater conditions (i.e. average pH, EH, and ion concentrations from Tables 2.3 and 2.5) is very
similar to the values of 0.1 and 0.01 L/kg used to model the transport of Mn at Roxboro and Mayo,
respectively.

Table 6.1: Manganese Kd values used in reactive transport modeling, measured in the laboratory, and
modeled using PHREEQC. Units in L/kg. The hash symbol indicates either Mn was not included in the
reactive transport model or was not measured in batch sorption tests.
Site:
Reactive transport
modeling derived
Kd values [3-9]
Mean value measured by
UNCC batch experiments
[10-16]
Range of values from PHREEQC
geochemical model
Sutton - -
Range : 5.2 x 10-7 to 5.2

Geometric mean: 7.0 x 10-3

Value for average groundwater
conditions: 0.15
Lee 0 -
Weatherspoon - -
Roxboro 0.01 -
Asheville - -
Mayo 0.1 -
Cape Fear - 0.009
-20
-15
-10
-5
0
5
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
MnCO3 (Rhodochrocite)
MnOOH (Manganite)
MnO2 (Pyrolysite)
Mn3O4 (Hausmannite)
Mn(OH)2 (Pyrochroite)
MnO2 (Birnessite)

Page 46

7. 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 7.1). 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, sorption to metal oxides, sorption to metal sulfides, and sorption to organic matter.
Precipitation broadly includes both homogenous mineral precipitation as well as co-precipitation.

Table 7.1: Listing of primary attenuation mechanisms and general geochemical
considerations for several constituents of concern 
Constituent Physical attenuation Chemical Precipitation Sorption
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 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. 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 0.34 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 47
Constituent Physical attenuation Chemical Precipitation Sorption
Chromium
√ √ √
The PHREEQC model output indicated that Cr(III) is the dominant oxidation state in agreement
with the Pourbaix diagram in Figure 5.1. 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 g/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.

 √ √ √ 
Manganese 
Manganese is predominantly present as Mn2+ in the PHREEQC model output which is in
agreement with the measurements of Mn(II) in groundwater from all seven sites. Sorption of
Mn(II) is generally weak and yields Kd values ranging from 5 x 10-7 to 5 L/kg calculated from
the PHREEQC model. The mean Kd value of 7 x 10-3 L/kg and the Kd value of 0.15 L/kg for the
average groundwater conditions from the PHREEQC model are in reasonable agreement with
the range of 0 to 0.10 L/kg used in reactive transport models [3, 4, 8] and the value of 9 x 10-3
L/kg determined from batch sorption experiments with solids from the Cape Fear site. Analysis
of the saturation index of mineral phases containing Mn from the PHREEQC model indicates
that several common Mn bearing soil minerals (manganite, hausmannite, and birnessite) are
near saturation under high pH and high EH conditions. Therefore, precipitation of Mn mineral
phases could occur given sufficiently high Mn concentrations and high pH/EH conditions. Based
on the above analysis, it is expected that physical attenuation, sorption, and chemical
precipitation could all play a role on controlling the movement of Mn in the subsurface.

Page 48

8. REFERENCES

1. Dzombak, D. A., Morel, F. M. M.: Surface complexation modeling : hydrous ferric oxide1990,
New York: Wiley. xvii.
2. Karamalidis, A. K., Dzombak, D. A.: Surface Complexation Modeling: Gibbsite2010, Hoboken,
NJ: John Wiley and Sons, Inc. .
3. Brame, S. E., Graziano, R., Falta, R. W., Murdoch, L. C.: Groundwater flow and transport
modeling report for the H.F. Lee Energy Complex, Goldsboro, NC, 2015: Greenville, SC.
4. Murdoch, L. C., Brame, S. E., Falta, R. W., Graziano, R.: Groundwater Flow and Transport
Modeling Report for Mayo Steam Electric Plant, Roxboro, NC, 2015.
5. Graziano, R., Falta, R. W., Brame, S. E., Murdoch, L. C.: Groundwater Flow and Transport
Modeling Report for Cape Fear Steam Electric Plant, Moncure, NC, 2015: Greenville, SC.
6. Falta, R. W., Brame, S. E., Graziano, R., Murdoch, L. C.: Groundwater Flow and Transport
Modeling Report for L. V. Sutton Energy Complex, Wilmington, NC, 2015: Greenville, SC.
7. Falta, R. W., Brame, S. E., Graziano, R., Murdoch, L. C.: Groundwater Flow and Transport
Modeling Report for the W. H. Weatherspoon Power Plant, Lumberton, NC, 2015: Greenville,
SC.
8. Murdoch, L. C., Brame, S. E., Falta, R. W., Graziano, R.: Groundwater Flow and Transport
Modeling Report for Roxboro Steam Electric Plant, Semora, NC, 2015: Greenville, SC.
9. Falta, R. W., Brame, S. E., Graziano, R., Murdoch, L. C.: Groundwater Flow and Transport
Modeling Report for Asheville Steam Electric Plant, Arden, NC, 2015: Greenville, SC.
10. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: Mayo Steam Electric Plant, 2015:
Charlotte, NC.
11. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: Asheville Steam Electric Plant, 2015:
Charlotte, NC.
12. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: Cape Fear Steam Station, 2015:
Charlotte, NC.
13. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: H. F. Lee Steam Station, 2015:
Charlotte, NC.
14. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: Roxboro Steam Electric Plant, 2015:
Charlotte, NC.
15. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: L. V. Sutton Energy Complex, 2015:
Charlotte, NC.
16. Langley, W. G., Shubhashini, O.: Soil Sorption Evaluation: W. H. Weatherspoon Steam Station,
2015: Charlotte, NC.
17. Synterra: Comprehensive Site Assessment Report – Mayo Steam Electric Plant, Roxboro,
NCSeptember 2, 2015: Greenville, SC.
18. Synterra: Comprehensive Site Assessment Report – Cape Fear Steam Electric Plant, Moncure,
NC, September 2, 2015: Greenville, SC.
19. Synterra: Comprehensive Site Assessment Report – Roxboro Steam Electric Plant, Semora, NC,
September 2, 2015: Greenville, SC.
20. Synterra: Comprehensive Site Assessment Report – Asheville Steam Electric Plant, Arden, NC,
August 23, 2015: Greenville, SC.
21. Synterra: Comprehensive Site Assessment Report – W.H. Weatherspoon Power Plant,
Lumberton, NC, August 5, 2015: Greenville, SC.

Page 49
22. Synterra: Comprehensive Site Assessment Report –L.V. Sutton Electric Plant, Wilmington, NC,
August 5, 2015: Greenville, SC.
23. Synterra: Comprehensive Site Assessment Report – H.F. Lee Electric Plant, Goldsboro, NC,
August 5, 2015: Greenville, SC.
24. Martell, A. E., Smith, R. K.: Critical Stability Constants, Standard Reference Database 46,
Version 6.30, 2001, National Institute of Standards, Gaithersburg, MD.
25. Davis, J. A., Coston, J. A., Kent, D. B., Fuller, C. C.: Application of the surface complexation
concept to complex mineral assemblages. Environmental Science and Technology 32, 2820
(1998).
26. Davis, J. A., James, R. O., Leckie, J. O.: Surface ionization and complexation at the oxide-water
interface.1. Computation of electrical double layer properties in simple electrolytes. Journal of
Colloid and Interface Science 63, 480 (1978).
27. 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.
28. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy Asheville Power Plant, 2015: Clemson, SC.
29. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy Cape Fear Steam Electric Plant, 2015: Clemson, SC.
30. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy H. F. Lee Energy Complex, 2015: Clemson, SC
31. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy Mayo Steam Electric Plant, 2015: Clemson, SC.
32. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy Roxboro Steam Electric Plant, 2015: Clemson, SC.
33. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy L. V. Sutton Energy Complex, 2015: Clemson, SC.
34. Powell, B. A.: Analysis of geochemical phenomena controlling mobility of ions from coal ash
basins at the Duke Energy W. H. Weatherspoon Power Plant, 2015: Clemson, SC.
35. Hem, J. D.: Reactions of metal ions at surfaces of hydrous iron oxide. Geochem. Cosmochim.
Acta. 41, 527 (1977).
36. Stachowicz, M., Hiemstra, T., Van Riemsdijk, W. H.: Surface speciation of As(III) and As(V) in
relation to charge distribution. Journal of Colloid and Interface Science 302, 62 (2006).
37. Strawn, D., Doner, H., Zavarin, M., Mchugo, S.: Microscale investigation into the geochemistry
of arsenic, selenium, and iron in soil developed in pyritic shale materials. Geoderma 108, 237
(2002).
38. 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.
39. Sposito, G.: The chemistry of soils1989, Oxford: Oxford University Press.
40. Mckenzie, R. M.: Manganese Oxides and Hydroxides, in Minerals in Soil Environments, 2nd
Edition, J. B. DixonWeed, S. B., Editors. 1989, Soil Science Society of America: Madison, WI. p.
439.