NC0003433_App C Geochem_20160229

Analysis of Geochemical Phenomena Controlling Mobility of Ions
from Coal Ash Basins
Brian A. Powell, Ph.D.
112 Cherry Street
Pendleton, SC 29670
[phone]
bpowellnclemson.edu
February 23, 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.
GEOCHEMICAL MODELING of COBALT....................................................................
46
7.1.
Pourbaix diagram analysis..............................................................................................
46
7.2.
Comparison between modeled and experimental Kd values for cobalt ...........................
49
8.
GEOCHEMICAL MODELING of SELENIUM................................................................
51
8.1.
Pourbaix diagram analysis..............................................................................................
51
8.2.
Comparison between modeled and experimental Kd values for selenium ...................... 56
9.
GEOCHEMICAL MODELING of VANADIUM.............................................................. 58
9.1.
Pourbaix diagram analysis..............................................................................................
58

10. SUMMARY........................................................................................................................63
11. REFERENCES................................................................................................................... 66

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),
and a concentration of sorption sites. The field data from seven sites was compared and it was
demonstrated that each of these parameters were relatively consistent between the seven sites. Therefore,
an average range of geochemical conditions were used to predict the minimum and maximum distribution
coefficients (Kd) for each constituent of interest. The initial concentrations of ions were determined by
examining the minimum, average, and maximum concentrations observed by compiling the data from all
seven sites under consideration. A range of pH and EH values were selected to capture the range of
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 soil 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-, Se032-)
which can form neutrally charged H3ASO4 and H2SeO3 species at low pH. At higher pH values, these
Page i

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., As043-, Se032-, 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 Cr042- 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 As(V) from the ground water resulting in As(V) values
reported as non detect and/or 2) a kinetic limitation with respect to the As(III)/As(V)
oxidation/reduction reactions which prevents the system from reaching chemical equilibrium.
However, the observation of As(III) is consistent with the relatively lower Kd values required in
the reactive transport modeling efforts compared with the higher Kd values predicted by
PHREEQC. Therefore, the reactive transport model represents a conservative estimate. Due to the
stronger sorption of As(V), the tendency of the element to move in the subsurface, will decrease
as As(III) becomes oxidized to As(V) and sorbs to mineral surfaces. 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 all seven sites indicate Cr(III) is the dominant
oxidation state which is in agreement with the PHREEQC model. The sorption of Cr(III) is
significantly stronger than Cr(VI) because Cr(III) persists as a highly charged cation (Cr3+) which
readily sorbs to mineral surfaces as the pH increases from acidic to basic conditions. This
behavior is in stark contrast to that of Cr(VI) which persists as a weakly sorbing anion (Cr04)
and decreases sorption from acidic to basic conditions. This high charge density of Cr3+ also
Page ii

causes a propensity to form aqueous complexes with anions such as S042- 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 µg/L. This concentration range is similar to what was modeled in
PHREEQC and indicates that formation of mineral phases containing Cr may occur under high
pH conditions with relatively high Cr concentrations.
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.
• Cobalt: The dominant cobalt species predicted by the PHREEQC model is Coe+. Essentially no
oxidized Co(III) is expected to be present within the EH range of the groundwater from the seven
sites. Thus, the behavior of cobalt exhibits relatively little influence on redox potential except for
the few cases where reaction between Co and some other reduced species (such as Se( -II) occurs.
The predicted Kd value of 4.7 L/kg using all average groundwater values (i.e., average pH,
average EH, and average ion concentrations from all seven sites) agrees well with the value of 2.5
L/kg used in the reactive transport model of the Asheville site. Note that Asheville is the only site
where the reactive transport model considered cobalt. The experimentally derived Kd values from
batch experiments are generally higher than the value used in reactive transport modeling.
However, there is good agreement between the average value of 465 L/kg from the experimental
systems and the average value of 533 L/kg from the PHREEQC modeling effort [11]. Overall
cobalt is expected to exhibit minimal transport in these systems relative to more mobile species
such as manganese and boron.
• Selenium: Selenium geochemical behavior is highly dependent on the EH of the groundwater.
Under reducing conditions, selenium can persist as the reduced Se( -II) or Se(0) species which are
generally insoluble. Therefore, selenium is expected to be relatively immobile under very
reducing conditions. Under mildly reducing to oxidizing conditions Se(IV) and Se(VI) exist as
anionic species SC03-2 and Se04 2. The sorption of these anions decreases with increasing pH.
However, sorption is also influenced by competition with other sorbing ions like sulfate.
Therefore, selenium Kd values for either Se(IV) or Se(VI) exhibits a wide range due to the
influence of pH and ion competition. The PHREEQC model predictions show Se(IV) as the
dominant species under approximately neutral pH conditions but the fraction of Se(VI) increases
with increasing pH and EH. This behavior is consistent with the selenium oxidation state
Page iii

speciation analysis measured in field samples. Overall the range of Kd values predicted by
PHREEQC agrees with the values determined experimentally from batch sorption tests.
Vanadium: Vanadium can exist in multiple oxidation states including V(I11), V(IV), and V(V)
under the groundwater conditions at each of the seven sites. The tetravalent V(IV) species only
exists at low pH and the trivalent V(1I1) species only exists under reducing conditions. These low
pH or low EH conditions do occur in site groundwater but generally represent extreme values.
Therefore, the majority of vanadium is expected to exist as pentavalent V(V) which exhibits
moderate sorption. Predicted Kd values are highly dependent on the pH of the groundwater
simulant and the concentration of other ions which can compete for sorption sites. The predicted
Kd value from the average groundwater simulant was 652 L/kg. This value is approximately 2x
the average value of 345 L/kg determined from batch sorption experiments with solids from each
of the seven sites. However, given the strong influence of ion competition on the predicted Kd
values, the range of experimental values is captured by the predicted values.
Page iv

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 as 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 I

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 g,oi,d/L must also be assumed.
Page 2

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.,
[SOH2+] Fyf
develops positive surface
=SOH + H � =SOH2
_
K_ [SOH] { - exp(RT
charge at low pH)
Surface deprotonation (i.e.,
[SO-] {H+} Fyr
develops negative surface
=SOH � =SO-+ H
K = exp -
charge at high pH)
[SOH] RT
Cation sorption
=SOH + Mn+ q=SOMn-i + H+
g = [SOMn-i ] {H+} exp C(n —1) Fy
M [SOH] {Mn+} RT
=SOH + H+ + A- <* =SOH2+A_
or
[SOHZ tY A-]
FI
Anion Sorption
=SOH + A- � =SA + OH-
_ r
KA [SOH] [H+] [A-] exp \— RT 1
However, this assumed value essentially is canceled out when back calculating a Kdvalue from the
geochemical modeling data. Therefore, the concentration of sorption sites based on extractable Fe
concentrations is based on the following equation:
[- FeOH] _ [Solid] * [Fe]extr
19Fe molFe 0.2 mol = FeOH
1000m9Fe 55.8459Fe mol Feextractable
where [Solid] is the solid phase suspension concentration in g/L assumed for the model, [Fe],, is the
extractable iron concentration in mgFe/g,,Iid, 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:
a
[SS] * SA * 1018;rr _M_ * SD
[- FeOH] = atoms
6.022 x 1023 (mol
Page 3

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_Ai/
kgsolid
mo1Fe_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 4

In previous geochemical models developed for each of the seven sites [28-34], 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.
4000
,3500
.o
3000
00
2500
w
0 2000
1500
1000
W 500
Average Extractable Fe
■ Average Extractable Al
Figure 2.1: Average extractable Al and Fe concentrations and standard deviation in solids from all
seven sites.
Page 5

6000
5000
0
on
w 4000
Pro3000
a
2000
U
W 1000
n
MIN GEOMEAN AVG MAX
■ Asheville ■ Sutton ■ Lee ■ Mayo ■ Roxboro ■ Cape Fear ■ Weatherspoon
6000
9
7
5000
00
4000
nn
3000
d
y
2000
U
C�
W
1000
MIN GEOMEAN
AVG
MAX
■ Asheville ■ Sutton ■ Mayo ® Roxboro ■ Cape Fear ■ Weatherspoon
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 have been measured at each of the
seven sites along with other relevant geochemical parameters including the dissolved ion concentrations
and the oxidation state speciation ofredox 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.
Page 6

650
450
j 250
8
s
w
•
50 •. • j
•
-150 •
-350 `
2 4 6
pH
12
•
Weatherspoon
•
Sutton
•
Lee
•
Cape Fear
•
Asheville
•
Mayo
•
Roxboro
- - - - Water Oxidation
- - - - Water Reduction
O
Selected Values
Figure 2.3: Measured pH and Ex 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 02(g)). The inset box represents an approximate range of pH and Ex
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
Page 7

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.
60
50
Qn
40
30
c>
0 20
it
10
0
1
0.9
0.8
0
0.7
0.6 w
0.5
0.4
0.3
0.2 U
0.1
0
-400 -300 -200 -100 0 100 200 300 400 500 600 700 800
EH (mV)
70
60
50
U
M
40
u
30
w
0
:* 20
10
♦ Histogram ♦ ■
■ Cumulative
Fraction ■
■
♦ Histogram
EH + 1 SD
EH mean value
—EH -1SD
♦
♦
0
-400 -300 -200 -100 0 100 200 300 400 500 600 700 800
EH (mV)
Figure 2A Histograms of EH values measured at all seven sites. The bottom figure shows the mean
EH values and +/- 1 standard deviation in the blue and black lines, respectively. Values at
approximately 25% and 75% for each for each pH and EH condition were included in geochemical
modeling at each site (Table 2.3). Based on data from [17-23].
Page 8

4J
40
35 ♦ Histogram
30 ■ Cumulative
Fraction
25
O 20 ■
5 15 N ■
10 ■
5 e ■
_
2 3 4 5 6
50
45
40
35
30
25
p 20
0 15
10
5
0
1
0.9
0.8
0
0.7
0.6 w
0.5
0.4 V
0.3 E
0.2 U
0.1
0
7
8
9
10
11
12
pH
♦ Histogram
pH + I SD
♦
pH mean value
♦♦♦
♦♦
pH -I SD
♦N
♦ N ♦
♦♦♦N ♦♦♦ ♦N
2 3 4 5 6 7 8 9 10 11 12
pH
Figure 2.5: Histograms of pH values measured at all seven sites. The bottom
figure shows the mean pH values and +/- 1 standard deviation in the blue
and black lines, respectively. Values at approximately 25% and 75% for each
for each pH and EH condition were included in geochemical modeling at
each site (Table 2.3). Based on data from [17-23].
Table 2.3: pH and EH Values for Global Model Input. Note that EH was
entered into PHREEQC using pe (-1og(e) based on the equation EH = 59
mV*pe
pH
EH (mV)
Pe
Notes
4
482
8.16
low pH, high EH value
5.6
-21
-0.35
low pH, low EH value
6.47
220
3.72
global average pH and EH
6.9
514
8.69
high pH, high EH value
9.1
-104
-1.75
high pH, low EH value
5.1
372
6.29
pH range covering 25-75% of sites from Figure 2.4
7.1
75.5
1.28
Page 9

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-, S042-) 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 Fee+ and S042- 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 As04 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 10

1000
0.1
10000
1
100000
a
on
?. 10000
0
1000
u
C
U 100
A
10
In
1
Aluminum Antimony Arsenic Barium Boron Cadmium Chromium Cobalt
■ Sutton
■ Weatherspoon
■ Lee
- Cape Fear
■ Roxboro
■ Mayo
■ Asheville
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
Aluminum Antimony Arsenic Barium Boron Cadmium Chromium Cobalt
Figure 2.6: Minimum, average, and maximum Al, Sb, As, Ba, B, Cd, Cr, and Co concentrations in
groundwater at all seven sites. Based on data from [17-231.
Page I I

100
a
�; 0.001
10000
�bA
1000
U
Q
0.1
Goo °���*�
100000
nn
° 10000
0
e�
1000
V
C."
�j 100
10
e�
1
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
■ Sutton
■ Weatherspoon
■ Lee
Cape Fear
■ Roxboro
■ Mayo
■ Asheville
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].
Page 12

1000
wo
100
a
0
d
0.01
10000000
1000000
100000
0 10000
1000
100
u
c 10
U 1
0.1
0.01
�e
a
0.001
1000
100
E
0.001
G 40 q
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
■ Sutton
■ Weatherspoon
■ Lee
■ Cape Fear
■ Roxboro
■ Mayo
■ Asheville
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].
Page 13

Table 2.4: Constituents to hold constant at average
concentrations in PHREEQC geochemical model
Constituent
Molecular
Weight
Average
OWL)
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
(moUL)
Average
(mol/L)
Maximum
(moUL)
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 14

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
WATEQ417 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
data points. 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)
CaSO4.21-120
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 15

• The dividing lines between boxes are where species may be equal, but there is no information in
the diagram regarding the uncertainty of the simulation or the change in speciation as pH and EH
move away from the boundary lines. So there may be significant concentrations of other species
present which cannot be seen on the diagrams.
• The speciation is also considered only for the conditions given (listed in Table 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.
Kd _ [Mlsolid — [M]Total'IM]aqueous * v Equation 2.1
[Mlaqueous [Mlaqueous M
where [M]solid is the sorbed concentration of a constituent M in units of mol/kgsorbent, [MJapeous 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:
fsorbed — 1 — faqueous — 1 "t Equation 2.2
1+K
dV
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 16

1.00
0.90
0.80
"a 0.70
a 0.60
0
0 0.50
0
0.40
0.30
0.20
0.10
0.00 *--
1.0E-03
11
•
•
1.0E-01 1.0E+01 1.0E+03 1.0E+05 1.0E+07
Kd (L/kg)
Ka
IFraction
Sorbed
1.00E-03
0.000050
1.00E-02
0.000500
LOGE-01
0.004975
1.00E+00
0.0,47619
1 AOE+01
0.333333
1.00E+02
0.833333
1.00E+03
0.980392
LOOE+04
0.998004
1.00E-05
0.999800
1.00E-606
0.999980
1.00E+07
0.999998
1.00E+08
1.000000
Figure 2.9: Theoretical relationship between & 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 &.
Page 17

3. GEOCHEMICAL MODELING of ARSENIC
3.1. Pourbaix diagram analysis
Under mildly oxidizing to strongly oxidizing conditions, arsenic can exist as the arsenate (As04 3)
and arsenite (As03-3) oxoanions (Figure 3.1). Both are weak acids and persist in solution as HXAsOy"-2y
species [35]. Under the pH and EH conditions expected at each site, both As(III) and As(V) may be
present as evident by the range of values in the Pourbaix diagram below. Relatively low pH values will
favor the protonated forms of As(III) and As(V) as H3As03 and H2As04 , 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.
1
.5
W
O
W 0
—.5
H3AsO¢
H2As0-4
•
HAs04
H3AsC)3
`As3SOS)2
AsO4
AsS(OH)( s
250C
0 2 4 6 8 10 12 14
pH
Figure 3.1: Pourbaix diagram of arsenic species along with the range of pH and Ex values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and Ex value calculated from the combined
measurements at all 7 sites for the average.
Page 18

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-' to 102
L/kg and those for As(V) range from 103-106 L/kg. The low values for As(V) at pH/EH 5.1/-20 mV are
somewhat erroneous because As(III) is the dominant oxidation state in both solid and aqueous phase
samples. This is demonstrated through analysis of the solid phase speciation in Figure 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 in As speciation are significant because the majority of
speciation measurements for arsenic in site groundwater indicate As(III) is the dominant oxidation state.
Thus, any remedial activity in which more oxidizing conditions are introduced into the system, which
causes oxidation of As(III) to As(V), would likely result in a decrease in arsenic mobility at the site.
1.00E+07
1.00E+06
1.00E+05
a
1.00E+04
x
d 1.00E+03
F 1.00E+02
1.00E+01
1.00E+00
l
J
4.0/ 5.6/-20 6.5/ 6.9/ 9.1/- 5.11 7.1/76
482 220 513 103 372
pH / EH (mV)
■ Total As Kd, Min
GW Values
■ Total As Kd, Avg
GW Values
■ Total As Kd, Max
GW Values
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 in redox speciation are shown by
the decrease or increase in Kd values.
Page 19

1.00E-06
a 1.00E-07
a
1.00E-08
1.00E-09
0
1.00E-10
1.00E-11
1.00E-12
1.00E-13
U 1.00E-14
0
1.00E-15
1.00E-16
d 1.00E-17
1.00E-18
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
pH / EH (mV)
1.00E-06
a
1.00E-07
a
0
e
1.00E-08
0
c
1.00E-09
U
1.00E-10
d
1.00E-11
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372
7.1/76
pH / EH (mV)
1.00E-06
d 1.00E-09
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1/76
pH / EH (mV)
■ Aqueous As(V),
Min GW Values
■ Aqueous As(III),
Min GW Values
Aqueous As(V),
Avg GW Values
Aqueous As(III),
Avg GW Values
Aqueous As(V),
Max GW Values
■ Aqueous As(III),
Max GW Values
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.
Page 20

1.00E+06
1.00E+05
1.00E+04
0 1.00E+03
�l 1.00E+02
1.00E+01
1.00E+00
d 1.00E-01
1.00E-02
1.00E-03
1.00E-04
1.00E+06
1.00E+05
1.00E+04
n^n 1.00E+03
a 1.00E+02
b
x 1.00E+01
1.00E+00
1.00E-01
1.00E-02
1.00E-03
1.00E-04
4.0/482 5.6/-20 6.5/220 6.9/513 9.1 /-103 5.1 /372 7.1 /76
pH / EH (mV)
JI I k
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1/76
pH / EH (mV)
■ As(V) Kd, Min
GW Values
■ As(V) Kd,. Avg
GW Values
■ As(V) Kd, Max
GW Values
■ As(III) Kd, Min
GW Values
■ As(III) Kd,. Avg
GW Values
■ As(III) Kd, Max
GW Values
Figure 3A 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.
Page 21

1.00E-05
1.00E-06
a 1.00E-07
E 1.00E-08
1.00E-09
1.00E-10
1.00E-I I
d 1.00E-12
c 1.00E-13
U 1.00E-14
1.00E-15
0 1.00E-16
1.00E-17
1.00E-18
1.00E-05
S� 1.00E-06
0
c 1.00E-07
1.00E-08
u
z j 1.00E-09
b
a 1.00E-10
0
91
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
pH / EH (mV)
1.00E-11 1 t EE = POM
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1;'76
pH / EH (mV)
1.00E-06
1.00E-09
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1/76
pH / EH (mV)
Sorbed As(V),
Min GW Values
■ Sorbed As(III),
Min GW Values
Sorbed As(V),
Avg GW Values
■ Sorbed As(III),
Avg GW Values
■ Sorbed As(V),
Max GW Values
■ Sorbed As(III),
Max GW Values
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.
Page 22

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 Fez+ and SO4 z. This is predominantly due to
competition for sorption sites between As(V) and As(III) with other anions such as S042-. As the
concentration of SO42 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-FeOHz+ and-AIOHz+, 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 ,-AIOFe+, and-Al-SO4 all increase as the
initial Fez+ is raised from 1.79 x 10-7 to 3.83 x 10-2 mol/L and the concentration of S042- is raised from
1.15 x 10"6 to 1.87 x 10-1 mol/L. These increased Fez+ and S042- 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
(FeAs04 H20) 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 (AlAsO4-2H20) 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 23

HFO Surface speciation, minimum GW ion concentrations
1.00E+00
'u
a 1.00E-01
0
a
u 1.00E-02
L
w
1.00E-03
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1/76
pH / EH (mV)
■ HFO OH2+ ■ HFO OH ■ HFO 0- ■ HFO OFe+ ■ HFO OFeOH
■ HFO_504- ■ HFO_OHSO4-2 ■ HFO OCa+ ■ HFO OMn+ ■ HFO_OMg+
HFO Surface speciation, average GW ion concentrations
1.00E+00
■
a 1.00E-01
0
C
O
1.00E-02
w
1.00E-03
4.0/482 5.6 / -20 6.5/220 6.9/513 9.1 / 403 5.1 / 372 7.1/76
pH / Ea (mV)
■ HFO OH2+ ■ HFO OH ■ HFO 0- ■ HFO OFe+ ■ HFO OFeOH
■ HFO_SO4- ■ HFO_OHSO4-2 ■ HFO_OCa+ ■ HFO_OMn+ ■ HFO_OMg+
HFO Surface speciation, maximum GW ion concentrations
1.00E+00
d
'u
1.00E-01
0
a
1.00E-02
w
1.00E-03
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1/76
pH / EH (mV)
■ HFO OH2+ ■ HFO OH ■ HFO 0- ■ HFO OFe+ ■ HFO OFeOH
■ HFO_SO4- ■ HFO_OHSO4-2 ■ HFO_OCa+ ■ HFO_OMn+ ■ HFO_OMg+
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-, Fez+, and S042- dominated surfaces with
increasing dissolved ion concentrations is shown by changes in relative color distributions.
Page 24

HAO Surface speciation, average GW ion concentrations
100%
U
10%
4.
Qr
i 1%
u
e�
L
0%
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/-103 5.1/372 7.1/76
pH / EH (mV)
■ HAO OH2+ ■ HAO OH ■ HAO O- ■ HAO OFe+ ■ HAO OHSO4-2
HAO Surface speciation, average GW ion concentrations
100%
d
10%
w
0
0
++ 1 %
Grr
0%
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
pH/EH (mV)
■ HAO OH2+ ■ HAO OH ■ HAO-0- ■ HAO OFe+ ■ HAO OHSO4-2
HAO Surface speciation, maximum GW ion concentrations
100%
U
10%
O
C
O
1 %
U
L
0% —
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
pH/EH (mV)
■ HAO OH2+ HAO OH ■ HAO-0- ■ HAO OFe+ ■ HAO OHSO4-2
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-, Fez+, and SOa-2
dominated surfaces with increasing dissolved ion concentrations is shown by changes in
relative color distributions.
Page 25

k
0
.Y
-2
-4
-6
-8
-10
-12
-14
-16
-18
-20
pH 5.6, pH 6.5, pH 6.9, pH 9.1, pH 5.1, pH 7.1,
EH -20 EH 220 EH 513 EH -103 EH 372 EH 76
■ Scorodite (FeAs04.2H2O)
■ AIM04:2H2O
■ Mn3(ASO4)2:8H2O
■ Ca3(ASO4)2:4W
■ Arsenolite (As203)
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 all seven sites and As(III) is
found to be the dominant oxidation state in almost all measurements (in which the arsenic concentration is
above detection limits). The fraction of As(IIl) and As(V) measured at each site as a function of EH and
pH are shown in Figure 3.9 for selected data from the Sutton, Lee, and Weatherspoon sites. 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.
Page 26

1.2
1.0
0.8
0.6
a�
d
w
0.4
0
s 0.2
w
M,
1.2
d
1.0
a
d 0.8
0.6
0.4
0
0.2
w
00
4.0 5.0 6.0 7.0 8.0
pH
■
*Weatherspoon, Fraction As(III)
O Weatherspoon, Fraction As(V)
♦Lee, Fraction As(III)
0 Lee, Fraction As(V)
■ Sutton, Fraction As(III)
❑ ❑ Sutton, Fraction As(V)
9.0 10.0 11.0
♦ Weatherspoon, Fraction As(III)
O Weatherspoon, Fraction As(V)
♦ Lee, Fraction As(III)
A Lee, Fraction As(V)
■ Sutton, Fraction As(III)
❑ Sutton, Fraction As(V)
-200 -100 0 100 200 300 400
Ex (mV)
A AA AA
♦ ♦ d
❑ r
■ ❑ 41
❑ 0 [A A
❑� �0 ❑ AL
n �
Figure 3.9: Arsenic redox speciation measured in aqueous samples from Weatherspoon, Lee, and
Sutton sites as a function of pH (top) and Ex (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:
[Fe(OH)aj[H+]z[e] —
[Fe++] — log —18.78
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)Z+ 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
Page 27

systems. The pe can be converted to EH by multiplying by 16.9 V-'. 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:
[Fe(OH)Z]
log [Fe++] — 2pH + 18.78 = pe
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.
800
700
600
S 500
x
W
400
s.
300
911
100
0
0 100 200 300 400 500 600 700 800
Estimated EH(mV) based on Fe(III)/Fe(II) Couple
■ Weatherspoon
♦ HF Lee
♦ Sutton
Figure 3.10: Estimated Ex 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:
[H2AsO4 ][H+]3[e]2
= log—21.197
[H3AsO3] [H2O]
which can be transformed to the following by taking the log form:
[HZAsO4 ]
log [H sOs ] — 3pH + 2pe — 21.197
Page 28

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 further 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(IIl):.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 29

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 1-131303, or as a borate anion 1-121303- (also
noted as 1302-) 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.
6i
Cn
O
L
W 0
0
• or
B(OH)3(a "
1250C
B 0-2
0 2 4 6 S 10 12 14
pH
Figure 4.1: Pourbaix diagram of boron species along with the range of pH and Ex values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and Ex value calculated from the combined
measurements at all seven 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
1-131303 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 30

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.
1.00E+00
1.00E-01
■ Total B Kd, Min
x GW Values
04 1.00E-02
x ■ Total B Kd, Avg
1.00E-03 GW Values
0
E�
1.00E-04 ■ Total B Kd,
Max GW Values
1.00E-05
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1/- 5.1/372 7.1/76
103
pH / EH (mV)
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.
G
25
-30
P'�V4 Pks6 P6,6s PYV 69 P'V91 �PgSI P4 �
16 gv �
, �� zo , ���zo , Vs13 , VIlo 3
3;�? 6
■ Pb(B02)2 ■ Zn(B02)2 ■ Cd(B02)2 :Fi Co(B02)2
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
Page 31

1.00E-03
1.00E-04
1.00E-05
a�
c 1.00E-06
U
a
1.00E-07
1.00E-08
♦•
®
®
■
®■
99
Co
pH
9 10
♦ Aqueous H2BO3-
♦ HAO-MB03
■ HFO-1121303
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. "At" 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 leaching of boron into the
aqueous phase (i.e. the solid selected contained native boron which was desorbing) was observed for
many samples and/or a non -linear sorption isotherm with minimal sorption was observed. Therefore, the
low Kd values are expected. Considering the assumptions in the PHREEQC model regarding the sorption
site density, background ion concentrations, and a somewhat arbitrary solid phase concentration assumed
in the model, the PHREEQC model is generally in good agreement with the other values. The sorption
constants could be revised to provide specific Kd values but this would mainly be a "fitting" exercise.
This model does not consider alternative reactions for sequestration of boron such as isomorphic
substitution into mica[39]. However, the rates of isomorphic substitution are not known and there is no
field data to demonstrate if such a process is occurring at these sites. Therefore, this mechanism of
substitution is not included in the model.
Page 32

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.
Reactive transport
Mean Kd value measured
Range of Kd values from
Site:
modeling derived Kd
by UNCC batch
PHREEQC geochemical
value (L/kg)[3-9]
experiments (L/kg) [10-16]
model
Sutton
0
1.7
Range:
Lee
0 and 3.5
4
1.1 x 10-5 to 0.34
Weatherspoon
1 to 4
2
Geometric mean:
8.0 X 10 3
Roxboro
1
0
Asheville
0.1
2.7
Value for average GW
conditions: 0.013
Mayo
0.12
0
Cape Fear
1
0
Page 33

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. Cr203) provided the concentration of Cr is
sufficiently high. The hexavalent phase exists as the anions HCr04 and Cr04 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.
N
0
w 0
—.5
C ro4
G r+++ D 0
Cro
rO H+
JC-r(OH CrC4Fi) aq )
Cr(O H )4
25'°C
0 2 4 6 8 10 12 14
pH
Figure 5.1: Pourbaix diagram of boron species along with the range of pH and Ex 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 34

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
Table 5.1: Chromium redox speciation
determined in Weatherspoon water
samples [17-231.
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
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 CrHSO42+complexes increases and the extent of Cr
sorption decreases. As the pH increases, formation of aqueous chromium sulfate complexes becomes less
favorable. Instead neutrally charged Cr(OH)3(aq) and anionic Cr(OH)4 become the dominant aqueous
species. There is little difference in the fraction of these species at pH 9.1 with the changing groundwater
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 Fee+ and other ions that occurs at pH 9.1 as demonstrated in Figures 3.6 and 3.7.
Page 35

1.00E+09
1.00E+08
1.00E+07
1.00E+06
1.00E+05
1.00E+04
0 1.00E+03
F
1.00E+02
1.00E+01
1.00E+00
1.00E+09
1.00E+08
1.00E+07
1.00E+06
1.00E+05
x
11.00E+04
j 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
pH / EH (mV)
OL
7.1 / 76
4.0/482 5.6/-20 6.5/220 6.9/513 9.1/403 5.1/372 7.1/76
pH / EA (mV)
■ Total Cr Kd, Min
GW Values
■ Total Cr Kd, Avg
GW Values
■ Total Cr Kd, Max
GW Values
■ Cr(III) Kd, Min
GW Values
■ Cr(Ill) Kd, Avg
GW Values
■ Cr(Ill) Kd, Max
GW Values
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).
Page 36

1.0E-07
1.0E-08
1.0E-09
a
U 1.0E-13
1.0E-14
1.0E-15
■ 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
4.0 / 482 5.6 / -20 6.5 / 220 6.9 / 513 9.1 / -103 5.1 / 372 7.1 / 76
pH / EH (mV)
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.
1.0E+09
1.0E+08
1.0E+07
w
1.0E+06
1.0E+05
j 1.0E+04
c 1.0E+03
F
1.0E+02
1.0E+01
1 0E+00
•
s
•
•
3 4 5 6 7 8 9 10
pH
• Total Cr Kd,
Min GW Values
• Total Cr Kd,
Avg GW Values
• Total Cr Kd,
Max GW Values
Figure 5.4: Predicted chromium Kd values from PHREEQC modeling using the range of
ground water (GW) concentrations listed in Tables 2.4 and 2.5.
Page 37

NJ
v
U 0.8
0
0
0.6
a'
d
0 0.4
c
0
0.2
s.
w
0
1.2
a
1
U 0.8
0
0.6
d
0 0.4
c
0
0.2
s.
F�
0
1.2
�U I
U
S3.
� U 0.8
0
0.6
a'
d
w
° 0.4
0
u
0.2
w
0
MIN GW, pH 4.0 AVG GW, pH 4.0 MAX GW, pH 4.0
MIN GW, pH 5.6 AVG GW, pH 5.6 MAX GW, pH 5.6
MIN GW, pH 9.1 AVG GW, pH 9.1 MAX GW, pH 9.1
■ CrOHSO4
■ CrSO4+
■ Cr(OH)+2
■ Cr(OH)2+
■ Cr+3
■ Cr(OH)3
■ CrOHSO4
■ CrSO4+
■ Cr(OH)+2
■ Cr(OH)2+
■ Cr+3
■ Cr(OH)4-
■ Cr02-
■ Cr(OH)3
■ Cr(OH)2+
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 Ex Values are not
discussed here because >99% of the total chromium exists as Cr(III) under these conditions.
Page 38

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.
10
-15
-20
^�V ��69
�kg82 , �� 10 "'2?O , S13 103 2 6-
■ FeCr204
■ Cr(OH)3 (am)
■ Cr(OH)3
■ MgCr204
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 span the range of experimental
values. However, as noted in section 2.4, the strong sorption of any ion (chromium in this case) can result
in very high Kd values such as those shown in Table 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 Cr042- 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
Page 39

also have a major impact of the Ind 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 40

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
data point 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 (Mn304), manganite (MnOOH), and birnessite (Mn02). The minerals pyrolysite (6-Mn02)
and bixbyite (Mn,Fe03) 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)z , 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 41

U)
O
W
O
7
W
1
4
0
5
1d4•,_
Mn2(OH9
I
250C
0 2 4 6 8 10 12 1�
pH
1
5
0
.5
Todorokite
_ r
dochroslt
t �41abandite
M (O
Mr
250C
0 2 4 6 8 10 12 14
pH
Figure 6.1: Pourbaix diagram of manganese species along with the range of pH and Ex values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and Ex 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 42

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 from groundwater samples
obtained from all seven sites. Selected data from Weatherspoon, Lee, and Sutton groundwater are 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 Mn02(s) minerals birnessite or pyrolusite.
1.000
0.800
c
0.600
4.
0.400
w
0.200
0.000
■
AM
pH
7 8 9 10
♦ Weatherspoon
■ Lee
♦ Sutton
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 6.4 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 104 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
Page 43

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.
1.00E+01
1.00E+00
1.00E-01
1.00E-02
1.00E-03
1.00E-04
1.00E-05
1.00E-06
1.00E-07
•
•
•
•
pH
10
•Mn Kd, MIN GW Values
•Mn Kd, AVG GW Values
•Mn Kd, MAX GW Values
Figure 6.4: Predicted manganese Kd values from PHREEQC modeling using the range of
ground water (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 6.5). The saturation index for rhodochrosite is greater than zero
in many simulations indicating the mineral could precipitate from solution. However, rhodochrosite
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 6.5, 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.
Page 44

Therefore, approximately a IOx 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.
0
SF
-20
pH 4.0, pH 5.6, pH 6.5, pH 6.9, pH 9.1, pH 5.1, pH 7.1,
EH 482 EH -20 EH 220 EH 513 EH -103 EH 372 EH 76
■ MnCO3 (Rhodochrocite)
■ MnOOH (Manganite)
■ MnO2 (Pyrolysite)
■ Mn3O4 (Hausmannite)
■ Mn(OH)2 (Pyrochroite)
■ MnO2 (Birnessite)
Figure 6.5: 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.
Reactive transport
Mean value measured by
Site:
modeling derived
UNCC batch experiments
Range of values from PHREEQC
Kd values [3-9]
[10-16]
geochernical model
Sutton
-
-
Lee
0
Range: 5.2 x 10-7 to 5.2
Weatherspoon
-
-
Geometric mean: 7.0 x 10-3
Roxboro
0.01
-
Value for average groundwater
conditions: 0.15
Asheville
-
-
Mayo
0.1
-
Cape Fear
-
0.009
Page 45

7. GEOCHEMICAL MODELING of COBALT
7.1. Pourbaix diagram analysis
Despite having an accessible Co(III) oxidation state, the dominant oxidation state of cobalt under
all conditions is Co(II). The dominant ions at neutral pH are Coe+ and HCo02 (Figure 2.5). As a cationic
species, Coe+ sorption will increase with increasing pH. This is a manifestation of the attraction of Coe+ to
mineral surfaces as the surface transitions from a net positive charge to a net negative charge with
increasing pH. Cobalt can form a wide range of mineral phases including CoS2 (cattierite), CoSe
(freboldite), CoAs2 (safflorite), and CoFe204 (cobalt ferrite). Relatively high concentrations of sulfide,
selenium, and arsenic are required to form cattierire, freboldite, and safflorite and in general these are not
expected, except under some anomalously high concentrations of Co and Se (discussed below in Chapter
8 on selenium geochemistry). While not shown in Figure 7.1, because of the relatively low iron
concentration used in the model to generate the EH -pH diagrams, cobalt ferrite is predicted to precipitate
under conditions with high ferric iron concentrations. This is discussed below with regards to the
PHREEQC sorption models.
While the ground water measurements have only determined the total Co(II) concentrations
instead of individual Co(II) and Co(III) fractions, the observation in the PHREEQC model of Co(II)
dominance and the modeled behavior of Co(II) is consistent with the field measurements. Co(II) generally
persists as the divalent cation Coe+ which is expected to increase sorption with increasing pH. This is
exactly the behavior predicted by the PHREEQC model. The predicted Kd values are shown in Figure 7.2
and this trend with respect to pH is clearly demonstrated by the model. The modeled Kd values decrease
as the concentration of other ions (primarily iron) increases in the groundwater simulants with the
"maximum" groundwater simulant from Table 2.5 yielding the lowest Kd values for Co. This is primarily
due to competition with other major ions such as Fe, Mn, and Al as discussed above. This behavior
shows that the predicted Kd values are not only a function of the behavior of Co but also a competition
with other ions for sorption sites. Therefore, the concentration of all ions in the system must be
considered when selecting a Kd value. Due to the profound complexity of this, the approach of taking
average Kd values for each site to account for this potential competition is reasonable. To demonstrate the
sensitivity of the predicted Kd values to the ground water ion concentrations, the predicted Kd values for
each ground water simulant are shown in Table 7.1. The Kd values decrease as the concentrations of ions
in the ground water simulant increase from the minimum, average, and maximum values from Table 2.5.
The predicted Kd values only account for aqueous speciation and sorption reactions and do not
take potential precipitation into account. However, the saturation index is monitored in the model. The
saturation index is a measure of the degree that an ion is saturation in solution with respect to a given
mineral phases. If the saturation index is greater than 1, then the system is saturated and formation of a
precipitated is thermodynamically favorable. It is important to note that this does not mean a solid will
form, it means that it can form. In many cases, precipitation of a mineral is kinetically limited and could
occur slowly despite thermodynamic favorability.
The saturation indices for Co are shown in Figure 7.3 and show that CoFe204 is saturated under
all conditions of the "maximum" groundwater simulant from Table 2.5. It is noteworthy that the
"maximum" groundwater simulant has Fe concentrations representing the maximum values observed in
the groundwater from all seven sites. CoSe is also predicted to form but only under reducing conditions
which facilitate formation of reduced Se( -II). This only occurs under the extreme reducing conditions
considered in this model (EH values of -20 and -103 mV).
Page 46

R
7�
0
U
w 0
—5
CoNC3
CO{' •
�� HC O02
S�(
250 C
pH
Figure 7.1: Pourbaix diagram of cobalt species along with the range of pH and Ex values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and Ex value calculated from the combined
measurements at all seven sites for the average.
1.00E+05
x 1.00E+04
1.00E+03
.E 1.00E+02
� 1.00E+01
x 1.00E+00
U 1.00E-01
1.00E-02
0
1.00E-03
1.00E-04
1.00E-05
0. 1 00E-06
•• 4
♦ Co Kd, MIN GW
Values
■ Co Kd, AVG GW
Values
♦ Co Kd, MAX
GW Values
3 4 5 6 7 8 9 10
pH
Figure 7.2: Predicted cobalt Kd values from PHREEQC modeling using the range of ground
water (GW) concentrations listed in Tables 2.4 and 2.5.
Page 47

Table 7.1: Predicted Kd values in each groundwater simulant and the average pH
and EH for the seven sites. NA = not applicable because only one Kd value is used.
Average Se Kd Values
Average
Standard
Kd L/k
Deviation
Minimum GW Simulant
5310
14042
Average GW Simulant
553
1431
Maximum GW Simulant
0.9
1.1
Value for Average Groundwater Conditions (pH 6.47,
EH 220 mV, all ions at average concentrations from
Tables 2.4 and 2.5).
4.7
NA
The saturation indices for Co shown in Figure 7.3 are for the "maximum" groundwater simulant
from Table 2.5. As discussed below, high iron concentrations in the "average" and "maximum" ground
water simulant models facilitate formation of CoFe2O4, While the model inputs for iron and cobalt
concentrations are sufficiently high to predict formation of CoFe2O4, kinetic limitations could prevent
formation of this mineral. Therefore, if reactive transport models rely on precipitation of CoFe2O4 as a
means of controlling Co mobility, the presence of this phase must be verified in field samples. It is
unlikely that this phase is completely controlling the aqueous Co concentrations because it is profoundly
insoluble and could effectively remove all Co from the groundwater. To demonstrate this, the PHREEQC
model was re -run but in this instance precipitation of CoFe2O4 was allowed. The concentration of aqueous
Co in all three groundwater simulants is shown in Figure 7.4. An important point in this figure is that any
aqueous concentration less than 6.022 x 1023 mol/L represents less than one atom of the element in
solution. So aspects of this plot are purely theoretical. However, the profound influence of solubility is
demonstrated. In the "minimum" groundwater simulant from Table 2.5, the total dissolved iron and cobalt
concentrations are sufficiently low and the system is not saturated with respect to CoFe2O4. However,
when the iron concentrations are increased in the "average" and "maximum" groundwater simulants,
precipitation of CoFe2O4 occurs and the aqueous cobalt concentrations drastically decrease, effectively
removing all Co from the aqueous phase. The break in the model predictions at pH 5.6 is due to predicted
formation of CoSe at the low EH of that model run. Since there have been measurements of Co in
groundwater at each site, this is clearly not happening. Therefore, formation of this phase may be
influencing the aqueous Co concentrations to some degree and CoFe2O4 is not a solubility controlling
species.
Page 48

40
35
30
25
20
0 15
.y
10
v� 5
0
-5
-10
pH 4.0, pH 5.6, pH 6.5, pH 6.9, pH 9.1, pH 5.1, pH 7.1,
EH 482 EH -20 EH 220 EH 513 EH -103 EH 372 EH 76
pH and EH
■ CoFe2O4
■ CoSe
Figure 7.3: Cobalt saturation indices predicted using the maximum ground water ion
concentrations listed in Tables 2.4 and 2.5.
1.00E-02
1.00E-05
� j ^ 1.00E-08
0 1.00E-11
o 1.00E-14
CIO
1.00E-17
0
' 1.00E-20
1.00E-23
1.00E-26
0
U 1.00E-29
a
1.00E-32
1.00E-35
1.00E-38
3 9 11
pri
*MIN GW Values
•AVG GW Values
*MAX GW Values
Figure 7.4: Predicted aqueous cobalt concentrations using the three groundwater simulants
from Tables 2.4 and 2.5.
7.2. Comparison between modeled and experimental Id values for cobalt
The Kd values from the PHREEQC model agree well with the data available from batch sorption
experiments and reactive transport modeling. Table 7.2 summarizes the various Kd values. The Kd values
from the PHREEQC model have a wide range due to the strong influence of pH (Figure 7.2). The
experimental values also vary by approximately 100 times. However, the average experimentally derived
value of 453 L/kg for all seven sites compares favorably with the average predicted value of 533 L/kg
from the "average" groundwater simulant. The experimentally determined values are all above the value
Page 49

used in reactive transport modeling. There is reasonable agreement between the value of 2.5 L/kg used in
the reactive transport model and the value of 4.7 which is derived from the single PHREEQC model
simulation using all average values (i.e. average pH, average EH, and average ion concentrations from all
seven sites). Therefore, the PHREEQC modeled values appear to be capturing the range of behavior that
is possible and targeting the average values that are appropriate for the reactive transport modeling
efforts. Since an average Kd value is used across the site for reactive transport modeling, agreement
between the average PHREEQC values and the reactive transport models demonstrates circumstantial
agreement between the two modeling approaches.
Table 7.2: Cobalt Kd values used in reactive transport modeling, measured in the laboratory, and
modeled using PHREEQC. Units in L/kg. The hash symbol indicates Co was not included in the
reactive trans ort model.
Mean value
Site:
Reactive transport
measured by UNCC
Range of values from PHREEQC
modeling derived
batch experiments
geochemical model
Kd value 3-9]
10-16
Sutton
-
141
Values varied with
Lee
-
858
GW Simulants
Weatherspoon
-
11
Minimum Simulant GW: 5309
Average Simulant GW 533
Roxboro
-
1182
Asheville
2.5
67
Maximum Simulant GW 0.9
All Average GW 4.7
Mayo
-
718
Cape Fear
-
196
Page 50

8. GEOCHEMICAL MODELING of SELENIUM
8.1. Pourbaix diagram analysis
Selenium exists as oxyanionic species under oxidizing conditions as selenite (Se032-) or selenate
(Se042-). Under the EH -pH conditions of the seven sites under consideration, Se(IV) present as HSe03-,
and Se(0) present as elemental Se° are expected to be the dominant species (Figure 8.1). However, it is
noteworthy that these Pourbaix diagrams show only the dominant species. The neutral pH and high
reduction potential conditions (noted as pH = 6.9 and EH = 514 mV in Figure 8.1), is close to the dividing
line between Se(IV) and Se(VI). Thus is can be expected that Se(VI) will be present in the site
groundwater under high reduction potential conditions. Similar to arsenic's behavior, both selenite and
selenate sorb to mineral surfaces (primarily iron oxides) [37, 38, 41]. Thus, oxidation of Se(IV) to Se(VI)
could influence the mobility of selenium in the subsurface. However, both are subject to competition from
other oxoanions such as phosphate and sulfate. Selenium is also readily reduced to zero valent Se° which
is relatively stable under mildly reducing conditions. As discussed above with respect to cobalt
geochemistry, formation of reduced Se( -II) species can have an impact of other metal ion concentrations
such as through the precipitation of CoSe(s).
Page 51

H2SeO,3
.Se04
5
HSeOq3-
0 _ H2Se
SeO
HSe-
-.5
250C
0 2 4 6 8 10 12 14
pH
1
.5
0
w 0
—.5
H2SeO3
SeO�
• HSeO�3- -
• r
L • '
SeO3
Se(s) •
25 C
0 2 4 E 8 10 12 14
pH
Figure 8.1: Pourbaix diagram of selenium species along with the range of pH and Ex values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the
standard deviation of the average pH and Ex 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 52

The PHREEQC model predicts Se(IV) as the dominant aqueous species with Se(VI) only
becoming the dominant aqueous species under oxidizing conditions (Figure 8.2). This model is consistent
with the Poubaix diagram shown in Figure 8.1. Measurements of the oxidation state speciation of
selenium from groundwater at several sites (Asheville, Roxboro, Cape Fear, and Sutton) indicate that
selenium is present as a mixture of Se(IV) and Se(VI). The total selenium aqueous concentrations in most
groundwater measurements are very low making accurate determination of the dominant oxidation state
difficult. To provide a comparison of the results, the groundwater measurements where either Se(IV) or
Se(VI) were measured above the background level are shown in Table 8.1. The data are plotted in Figure
8.2 as a function of EH with the measurements where either Se(IV) or Se(VI) were below detection
plotted as a fraction of 0. Two notable observations can be made from these data.
1. Generally, oxidizing conditions yield sufficiently high selenium concentrations in order to
determine the speciation, though in many case the values are still at or near detection limits.
This may be an indication of reduced Se(0) or Se( -II) species which form insoluble phases.
2. The fraction of Se(VI) generally increases with increasing EH (mV). This is not as clear of a
trend as would be expected in Figure 8.2 primarily because there is also an influence of pH
which is not demonstrated in this figure.
0
r.
2 0.2
-200 0 200 400 600
EH (mV)
• Se( -II) Fraction
• Se(IV) fraction
• Se(VI) fraction
Figure 8.2: The fraction of Se( -II), Se(IV), and Se(VI) predicted from the PHREEQC model as
a function of EH. The model predicts Se(IV) as the dominant oxidation state for most systems
except for those with high EH values (i.e. oxidizing conditions) where Se(VI) is dominant.
There is an influence of pH on the exact speciation in these plots which is not shown.
Page 53

Table 8.1: Selenium speciation measurements from
several sites. Note that additional species such as
methylated -Se were also measured but below detection
limits. Values with " < " before the value were below
detection.
Site
pH
En
(mV)
Se(IV)
/L)
Se(VI)
/L)
Sutton
9.0
-36
0.211
<0.065
Sutton
6.3
239
<0.08
5.29
Sutton
6.6
231
0.267
0.288
Sutton
5.1
409
<0.08
0.19
Sutton
9.8
-127
<0.08
0.142
Sutton
10.0
32
<0.08
0.169
Cape Fear
8.8
46
0.414
<0.085
Roxboro
9.9
487
3.62
<0.17
Roxboro
9.1
47
2.67
<0.17
Roxboro
7.0
464
0.182
<0.17
Roxboro
9.5
-66
0.401
<0.091
Roxboro
6.6
364
<0.082
0.464
Roxboro
6.4
361
<0.082
70.2
Roxboro
<0.082
67.7
Roxboro
6.6
509
<0.082
23.9
Roxboro
6.8
210
0.435
3.76
Roxboro
9.2
486
0.374
0.95
Roxboro
7.3
503
<0.082
0.33
Asheville
7.4
336
0.181
<0.043
Asheville
8.0
436
0.13
<0.16
Asheville
4.1
327
0.166
1.53
Asheville
6.3
170
0.879
0.46
Asheville
5.9
313
0.171
0.965
Asheville
5.5
384
<0.296
24.1
Asheville
4.0
298
0.097
1.42
Asheville
5.3
313
<0.082
0.93
Asheville
7.1
-59
<0.082
0.315
Asheville
5.6
272
<0.069
0.69
Asheville
5.7
207
<0.12
0.72
Asheville
6.4
75
<0.082
0.309
Page 54

0
0
d
0
0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
•
•
•
•
•
•
•
•
•
-200
200 400 600
EH (mV)
Se(IV)
Se(VI)
Figure 8.3: The fraction of Se(IV), and Se(VI) as a function of EH measured in groundwater
samples from Sutton, Cape Fear, Asheville, and Roxboro. Data are not available for other sites.
The PHREEQC model predicted Kd values consider formation of all oxidation states of selenium
and therefore average out some of the influences of oxidation or reduction reactions. The PHREEQC
model predicted values are shown in Figure 8.4 as a function of pH. For the groundwater simulant with
the minimum ion concentrations, selenium exhibits the expected behavior of most anions with sorption
decreasing with increasing pH. This is a manifestation of the fact that mineral surface charge on metal
oxide minerals transitions from a net positive to a net negative charge with increasing pH. Therefore, as
the pH increases, the sorption affinity of anionic selenium species to the more negatively charged surface
decreases. The dominant selenium species in this model (Se(IV)) exists as H2SeO3, HSeO3-, and Se032-
and changes speciation as the pH increases. This is demonstrated by the model output shown in Figure
8.5. The H2SeO3 species is only dominant under very low pH conditions and does not influence this
model. The HSeO3- species is dominant up until pH 7 after which Se032- becomes the dominant species.
Thus, Se(IV) persists as either a monovalent or divalent anion across the entire pH range considered
which causes the decrease in sorption with increasing pH observed in Figure 8.4.
Under the average and maximum groundwater simulant conditions (from Table 2.5), there is
significant scatter in the predicted Kd values with respect to pH (Figure 8.4). Generally the trend
discussed above reverses, and sorption increases with increasing pH. This could be due to changes in
selenium speciation at the low pH region or competition between selenium and other ions for sorption
sites. The speciation analysis in Figure 8.5 demonstrates that the aqueous species of selenium are
relatively simple and selenium is unlikely to form aqueous complexes which can influences sorption.
Therefore, the decrease in Kd values in Figure 8.4 is likely the result of competition from other ions such
as S042- and P043- for sorption sites. This behavior is shown in Figures 3.6 and 3.7 for HFO and HAO
sorption sites, respectively. Such competition with other anions for sorption sites would be diminished at
higher pH values were anion sorption is minimal. This is indeed the case in the model output where above
pH 7, all Kd values decrease with increasing pH.
As with the other models discussed in this report, the potential formation of mineral phases was
monitored but not included in the model. For selenium, elemental Se and CoSe are both saturated in the
Page 55

PHREEQC model with pH 5.3 and EH -20 mV. These are the only conditions under which any selenium
phase is saturated. Since these values represent the extreme low pH and low EH conditions of the seven
sites under consideration, it can be reasonably concluded that formation of selenium bearing mineral
phases is unlikely to be a major factor controlling selenium mobility.
1.00E+04
1.00E+03
on
a 1.00E+02
x 1.00E+01
Q
CA
1.00E+00
1.00E-01
1.00E-02
1.00E-03
3
■
■
■
5 7 9
pH
♦ Se Kd, MIN GW Values
■ Se Kd, AVG GW Values
♦ Se Kd, MAX GW Values
Figure 8.4: Predicted selenium Kd values from PHREEQC modeling using the range of
ground water (GW) concentrations listed in Tables 2.4 and 2.5.
1.2
1.0
ar
0.8
0.6
a
0.4
0
0.2
w
3 4 5 6 7 8 9 10
pH
Figure 8.5: Predicted Se(IV) species as a function of pH.
■ H2SeO3 Fraction
♦ HSeO3- Fraction
♦ SeO3-- Fraction
8.2. Comparison between modeled and experimental Kd values for selenium
The Kd values from the PHREEQC model are shown in Table 8.2 and are wide ranging due to the
strong influence of both pH and EH on selenium speciation and sorption behavior. The influence of ion
competition for sorption sites is demonstrated by the decreasing Kd for the minimum, average, and
maximum groundwater simulants. With the exception of the high Kd values predicted for the minimum
groundwater simulant, the data compare favorably with experimentally measured values from batch
Page 56

sorption experiments. Reactive transport models for the seven sites under consideration did not consider
selenium. Therefore, there are no values from a reactive transport model for comparison.
Table 8.2: Predicted Kd values in each groundwater simulant and the average pH and Ex
for the seven sites. NA = not applicable because only one Kd value is used.
Standard
Groundwater condition
Average Kd (L/kg)
Deviation
Minimum GW Simulant
3417
4057
Average GW Simulant
181
270
Maximum GW Simulant
S
9
Value for Average Groundwater Conditions (pH
6.47, EH 220 mV, all ions at average
160
NA
concentrations from Tables 2.4 and 2.5).
Table 8.3: Selenium Kd values measured in the laboratory and modeled using PHREEQC. Units
in L/kg. Reactive transport models did not consider Se, so no values are available for comparison.
Site:
Mean value measured by UNCC batch
experiments [10-16]
Range of values from PHREEQC
geochemical model
Sutton
10.2
Values varied with GW Simulants
Minimum GW simulant: 3417
Average GW simulant: 181
Maximum GW simulant: 4.9
Average groundwater conditions: 160
Lee
69.0
Weatherspoon
-
Roxboro
8.3
Asheville
228.1
Mayo
8.3
Cape Fear
-
Page 57

9. GEOCHEMICAL MODELING of VANADIUM
9.1. Pourbaix diagram analysis
Vanadium exhibits very complex chemical speciation in aqueous systems because of its
possibility to exist in multiple oxidation states: V(I11), V(IV), and V(V) (Figure 9.1). Under the EH -pH
conditions of the seven sites under consideration, pentavalent H2VO4 and V030H2- and tetravalent V02+
and VO(OH)+ appear to be the dominant aqueous species in all zones. Reduction of V(V) to V(IV) and
precipitation of V204 may also be a means of controlling the mobility of vanadium. Similar to selenium
and arsenic, sorption of H2VO4 is relatively strong and increases with decreasing pH. However, the lower
pH also generally stabilizes lower vanadium oxidation states. Thus, vanadium could become mobile as
V(I1I) and V(1V) at low pH values if oxidizing conditions are not maintained.
11-1
0
W
—.5
vat
VO( H (aq)
W2v©4
vo` !
V++
vc3oK
cam+ •
V(OH)2
250C
0 2 4 6 8 10 12 14
PH
Figure 9.1: Pourbaix diagram of selenium species along with the range of pH and Ex values
examined in PHREEQC modeling (shown by blue symbols). The error bars represent the standard
deviation of the average pH and Ex value calculated from the combined measurements at all seven
sites.
Reactions describing sorption of vanadium to hydrous ferric oxide or hydrous aluminum oxide
are only available for the V(V) species HV042 Sorption of V(I11) or V(IV) species is not considered
because the necessary sorption equilibrium constants are not available in the literature. Therefore,
predictions of aqueous V(1I1) and V(IV) presented here could underestimate the amount of sorption.
Assuming the species V(OH)2+ and V02+ behave similar to other cations, V(I11) and V(IV) sorption
should be moderate and increase with increasing pH. Since V(IV) only persists at low pH values (based
Page 58

on the range of stability in Figure 9.1), the lack of V(IV) sorption equilibrium constants likely will not
influence the model. V(III) does persist across a wide pH range but only under reducing conditions.
Therefore, the lack of sorption constants will have the greatest impact on predictions of vanadium
sorption under reducing conditions.
The predicted Kd values from the PHEEQC model are shown in Figure 9.2. Similar to the
sorption of other cations, vanadium sorption increases with increasing pH. Relatively low Kd values are
predicted for the low pH groundwater simulants. Under these low pH conditions, V(IV) and V(III) can
exist and influence the model output based on the lack of sorption constants as discussed above. This is
demonstrated in Figure 9.3 where the fraction of each vanadium oxidation state from the PHREEQC
model output is shown. In groundwater simulants with the most reducing conditions, V(III) is predicted
to form and contributes to the aqueous vanadium concentration. Under oxidizing conditions, the vast
majority of vanadium is present as V(V) which sorbs strongly. To demonstrate this influence of pH and
EH on the oxidation state speciation of vanadium, the predicted aqueous phase oxidation states of
vanadium (i.e. the aqueous species remaining in solution after sorption has been accounted for in the
PHREEQC model) are shown in Figure 9.4. The aqueous speciation of vanadium is dominated by V(III)
and V(V) with the tetravalent V(IV) state only persisting at low pH (4.0). This is consistent with the
narrow range of stability of V(IV) shown in Figure 9.1.
Similar to other constituents discussed above, there is a competition between vanadium and other
ions present in the groundwater simulant for sorption sites. This causes a change in the predicted Kd
values for each of the three groundwater simulants. The V(V) oxidation state primarily exists as anionic
HV042- species and sorption is expected to decrease with increasing pH. This behavior is observed in
Figure 9.2 except for the lowest pH simulation. Similar to the discussion of selenium above, competition
between sulfate and other anions for sorption sites reduces the sorption of vanadium at low pH values.
1.00E+05
1.00E+03
a 1.00E+01
x 1.00E-01
1.00E-03
1.00E-05
^0 1.00E-07
1.00E-09
d
a 1.00E-11
1.00E-13
3 4 5 6 7 8 9 10
pH
♦ V Kd, MIN GW Values
■ V Kd, AVG GW Values
♦ V Kd, MAX GW Values
Figure 9.2: Predicted vanadium Kd values from PHREEQC modeling using the range of ground
water (GW) concentrations listed in Tables 2.4 and 2.5.
Page 59

1.00
0.90
b
0.80
0.70
0.60
w y
'E; 0.50
0.40
u
w 0.30
0.20
u
0.10
a 0.00
pH 4.0, pH 5.6, pH 6.4, pH 6.9, pH 9.1, pH 5.1, pH 7.1,
EH 480 EH -21 EH 219 EH 512 EH -103 EH 371 EH 76
■ Fraction sorbed V(V)
■ Fraction V(V)
■ Fraction V(IV)
■ Fraction V(11I)
■ Fraction V(II)
Figure 9.3: Predicted vanadium speciation including sorption of V(V) for the minimum groundwater
simulant from Table 2.5.
Page 60

1
0 3 0.9
0.8
E 0.7
0.6
0.5
0.4
0 .5; 0.3
0 0.2
o a� 0.1
C 0
w
1.0
0 0.9
6 C37 0.8
ao 0.7
0.6
as
0.5
ti 0.4
o 0.3
o 0.2
0.1
w 0.0
1.0
o 0.9
6 0.8
0.7
X 0.6
0.5
0.4
o �
0.3
o i 0.2
'E, 0.1
w ¢ 0.0
pH 4.0, EH pH 5.6, EH - pH 6.4, EH pH 6.9, EH pH 9.1, EH - pH 5.1, EH pH 7.1, EH
480 21 219 512 103 371 76
■ V(II) ■ V(III) ■ V(IV) ■ V(V)
pH 4.0, EH pH 5.6, EH - pH 6.4, EH pH 6.9, EH pH 9.1, EH - pH 5.1, EH pH 7.1, EH 76
480 21 219 512 103 371
■ V(II) ■ V(III) ■ V(IV) ■ V(V)
pH 4.0, EH pH 5.6, EH - pH 6.4, EH pH 6.9, EH pH 9.1, EH - pH 5.1, EH pH 7.1, EH 76
480 21 219 512 103 371
■ V(II) ■ V(1II) ■ V(IV) ■ V(V)
Figure 9A Predicted fraction of vanadium oxidation states in the range of ground water (GW)
concentrations listed in Tables 2.4 and 2.5. These values were calculated using the PHREEQC
output after sorption of V(V) had been determined. Therefore, the fraction is relative to the
concentration of vanadium remaining in the aqueous phase after sorption has occurred.
Page 61

Due to the strong influence of pH and EH on vanadium oxidation state speciation and sorption, a
wide range of Kd values is predicted from the PHREEQC model. The average values for the three
groundwater simulants as well as the average groundwater condition are shown in Table 9.1. The
decreasing Kd is due to the competition for sorption sites as the groundwater ion concentrations increases
as discussed above. The reactive transport models for the seven sites under consideration did not model
vanadium transport; therefore, there are no Kd values for comparison. The experimentally determined Kd
values from batch sorption experiments are shown in Table 9.2. These values also cover a relatively wide
range but are generally consistent with the values determined from the PHREEQC model. The range of
experimentally determined values is captured by the average and maximum groundwater simulants,
except for the value of 1229 L/kg measured using Asheville materials. The average Kd value from the
batch sorption experiments is 345 L/kg which compares favorably with the value of 452 L/kg from the
average groundwater conditions.
Table 9.1: Predicted Kd values in each groundwater simulant and the average pH and Ex for the
seven sites. NA = not applicable because only one Kd value is used.
Groundwater Condition
Average Kd
LL/ky,
Standard
Deviation
Minimum GW Simulant
29500
36700
Average GW Simulant
672
1050
Maximum GW Simulant
3
8
Value for Average Groundwater Conditions (pH 6.47, EH 220
mV, all ions at average concentrations from Tables 2.4 and 2.5).
452
NA
Table 9.2: Vanadium Kd values measured in the laboratory and modeled using PHREEQC. Units
in L/kg. Reactive transport models did not consider vanadium so no values are available for
comnarison.
Site:
Mean value measured
by UNCC batch
experiments 10-16
Range of values from PHREEQC
eochemical model
Sutton
36.8
Values varied with GW Simulants
Minimum GW simulant: 29500
Average GW simulant: 672
Maximum GW simulant: 3
Average groundwater conditions: 452
Lee
138.0
Weatherspoon
75.9
Roxboro
_
Asheville
1229.3
Mayo
222 0
Cape Fear
366.4
Page 62

10. 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 10.1). The list includes physical attenuation in the form of flow through
a system which will cause dilution and which is expected for all elements. Sorption and precipitation are
also considered. Sorption is defined broadly and is proposed to account for any process removing aqueous
ions via chemical interactions with a surface. Thus, sorption reactions can include ion exchange, surface
complexation, and sorption to metal oxides, metal sulfides, and organic matter. Precipitation broadly
includes both homogenous mineral precipitation as well as co -precipitation.
Table 10.1: Listing of primary attenuation mechanisms and general geochemical
considerations for several constituents of concern
Constituent
Physical attenuation
Chemical Precipitation
Sorption
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
Arsenic
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(IIl) becomes oxidized to As(V) and sorbs to mineral surfaces. Additionally, the
minerals scorodite (FeAsO4,2H2O) and mansfieldite (AlAs04 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 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
Boron
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 63

Constituent
Physical attenuation
Chemical Precipitation
Sorption
The ground water measurements from all seven sites monitoring Cr oxidation state speciation
indicate Cr(IIl) 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
Chromium
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 µg/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 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
Manganese
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.
The dominant cobalt species predicted by the PHREEQC model is Co2+. Essentially no
oxidized Co(III) is expected to be present within the EH range of the groundwater from the
seven sites. Thus, the behavior of cobalt exhibits relatively little influence on redox potential
except for the few cases where reaction between Co and some other reduced species (such as
Se( -II) occurs. The predicted Kd value of 4.7 L/kg using all average groundwater values (i.e.
average pH, average EH, and average ion concentrations from all seven sites) agrees well with
Cobalt
the value of 2.5 L/kg used in the reactive transport model of the Asheville site. Note that
Asheville is the only site where the reactive transport model considered cobalt. The
experimentally derived Kd values from batch experiments are generally higher than the value
used in reactive transport modeling. However, there is good agreement between the average
value of 465 L/kg from the experimental systems and the average value of 533 L/kg from the
PHREEQC modeling effort. Overall cobalt is expected to exhibit minimal transport in these
systems relative to more mobile species such as manganese and boron.
Page 64

Constituent
Physical attenuation
Chemical Precipitation
Sorption
Selenium geochemical behavior is highly dependent on the EH of the groundwater. Under
reducing conditions, selenium can persist as the reduced Se( -II) or Se(0) species which are
generally insoluble. Therefore, selenium is expected to be relatively immobile under very
reducing conditions. Under mildly reducing to oxidizing conditions Se(IV) and Se(VI) exist as
anionic species Se03 2 and Se04 z. The sorption of these anions decreases with increasing pH.
Selenium
However, sorption is also influenced by competition with other sorbing ions like sulfate.
Therefore, selenium Kd values for either or Se(IV) or Se(VI) exhibits a wide range due to the
influence of pH and ion competition. The PHREEQC model predictions show Se(IV) as the
dominant species under approximately neutral pH conditions but the fraction of Se(VI)
increases with increasing pH and EH. This behavior is consistent with the selenium oxidation
state speciation measured in field samples. Overall the range of Kd values predicted by
PHREEQC agrees with the values determined experimentally from batch sorption tests.
Vanadium can exist in multiple oxidation states including V(111), V(IV), and V(V) under the
groundwater conditions at each of the seven sites. The tetravalent V(IV) species only exists at
low pH and the trivalent V(111) species only exists under reducing conditions. These low pH or
low EH conditions do occur in site groundwater but generally represent extreme values.
Therefore, the majority of vanadium is expected to exist as pentavalent V(V) which exhibits
Vanadium
moderate sorption. Predicted Kd values are highly dependent on the pH of the groundwater
simulant and the concentration of other ions which can compete for sorption sites. The predicted
Kd value from the average groundwater simulant was 652 L/kg. This value is approximately 2x
the average value of 345 L/kg determined from batch sorption experiments with solids from
each of the seven sites. However, given the strong influence of ion competition on the predicted
Kd values, the range of experimental values is captured by the predicted values.
Page 65

11. REFERENCES
1. Dzombak, D.A. and F.M.M. Morel, Surface complexation modeling: hydrous ferric oxide. 1990,
New York: Wiley. xvii, 393.
2. Karamalidis, A.K. and D.A. Dzombak, Surface Complexation Modeling: Gibbsite. 2010,
Hoboken, NJ: John Wiley and Sons, Inc..
3. Brame, S.E., et al., Groundwater flow and transport modeling report for the H.F. Lee Energy
Complex, Goldsboro, NC. 2015: Greenville, SC.
4. Murdoch, L.C., et al., Groundwater Flow and Transport Modeling Report for Mayo Steam
Electric Plant, Roxboro, NC. 2015.
5. Graziano, R., et al., Groundwater Flow and Transport Modeling Report for Cape Fear Steam
Electric Plant, Moncure, NC. 2015: Greenville, SC.
6. Falta, R.W., et al., Groundwater Flow and Transport Modeling Report for L. V. Sutton Energy
Complex, Wilmington, NC. 2015: Greenville, SC.
7. Falta, R.W., et al., Groundwater Flow and Transport Modeling Report for the W. H.
Weatherspoon Power Plant, Lumberton, NC. 2015: Greenville, SC.
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