Transmittal of the deliverables 'Thermodynamic Modeling of ...manuscripts examines the geochemical...
Transcript of Transmittal of the deliverables 'Thermodynamic Modeling of ...manuscripts examines the geochemical...
471 I'J'H A center of excellence in earth sciences and engineering A Division of Southwest Research Institute 6220 Culebra Road • San Antonio, Texas, U.S.A. 78228-5166 (210) 522-5160 ° Fax (210) 522-5155
September. 1, 2000 Contract No. NRC-02-97-009 Account No. 20.01402.871
U.S. Nuclear Regulatory Commission ATTN: Dr. John W. Bradbury Division of Waste Management Two White Flint North Mail Stop TD-13 Washington, DC 20555
Subject: Transmittal of the deliverables "Thermodynamic Modeling of Radionuclide Adsportion Part I"
(IM 01402.871.060) and "Thermodynamic Modeling of Radionuclide Adsorption Part II" (IM 01402.871.070)
Dear Dr. Bradbury:
This letter transmits the subject Intermediate Milestones (IM) under the publication titles "Thermodynamic Modeling
of the Adsorption of Radionuclide on Selected Minerals I: Cations" (IM 01402.871.060) and "Thermodynamic
Modeling ofthe Adsorption ofRadionuclide on Selected Minerals II: Anions" (IM 01402.871.070). These manuscripts
summarize the application of thermodynamic models for sorption of both cationic (IM 01402.871.060) and anionic
(IM 01402.871.070) radionuclides. These deliverables were identified in table 1 of the Periodic Management Progress
Report for Period 10. These manuscripts will be submitted for publication in the journal Geochimica et Cosmochimica Acta.
This work was conducted as an activity under the Radionuclide Transport (RT) Key Technical Issue (KTI). The
U.S. Department of Energy (DOE) Repository Safety Strategy, Revision 3 identifies RTthrough the unsaturated and
saturated zones as principal factors influencing the performance of the proposed repository at Yucca Mountain (YM),
Nevada. In the performance assessment (PA) analyses presented by DOE, repository performance was sensitive
to the retardation properties of the natural barrier system. In developing PA transport parameters for the license
application, DOE will need to demonstrate that it has provided an adequate abstraction ofthe sorption behavior ofthe
minerals in the YM environment. One means for doing this, as identified in the RT Issue Resolution Status Report,
is to use of process-level models similarto the ones developed in the subject deliverables. The work presented in these
manuscripts examines the geochemical controls on radionuclide sorption and applies a methodology for simulating the
effects of geochemistry on sorption of a number of key radionuclides, including neptunium, plutonium, americium,
technetium, iodine, and selenium. This provides an independent basis for evaluating DOE reliance on these processes.
SWa gton Office • Twinbrook Metro Plaza #210 12300 Twinbrook Parkway ° Rockville, Maryland 20852-1606
Dr. John W. Bradbury September. 1, 2000 Page 2
These manuscripts were prepared to document ongoing issue resolution activities in the RT KTI, particularly in the
subissues on transport through porous rock, transport through fractured rock, and transport through alluvium. These
analyses will also be used to develop the technical bases for acceptance criteria and review methods in the
Yucca Mountain Review Plan.
If you have any questions about this deliverable, please call me 210.522.5540 or Dr. David Turner 210.522.2139.
Sincerely,
English C. Pearcy, Manager Geohydrology & Geochemistry
d:\gh&gcefiscal year\letters\bradbury\thermodynamic modeling of the adsportion...
Enclosures
cc: J. Linehan D. DeMarco B. Meehan J. Greeves J. Holonich W. Reamer K. Stablein D. Brooks N. Coleman J. Ciocco T. Essig
W. Patrick CNWRA Directors CNWRA Managers CNWRA ISI Leads R. Pabalan P. Bertetti L. Browning M. Nugent T. Nagy (SwRI Contracts)
Prepared for submission to Geochimica et Cosmochimica Acta
Thermodynamic Modeling of the Adsorption of Radionuclides on Selected Minerals. H: Anions
PEIMING WANG* and ANDRZEJ ANDERKO
OLI Systems, Inc. 108 American Road, Morris Plains, NJ 07950
DAVID R. TURNER
Center for Nuclear Waste Regulatory Analyses, Southwest Research Institute
6220 Culebra Road, San Antonio, TX 78238-5166
* Author to whom correspondence should be addressed (E-mail: [email protected]).
Abstract- Because of their high solubility and reduced interaction with aquifer minerals, radionuclide
anions (I, 103", SeO 3"2, SeO4 "2, and TcO4 ") are typically identified as potentially significant contributors to
dose in high-level nuclear waste performance assessments. Adsorption of radionuclide anions on a
number of mineral surfaces has been studied using a thermodynamically consistent model that couples the
diffuse layer theory of surface complexation with an aqueous activity coefficient model based on the
B-dot equation for aqueous speciation calculations. The adsorbents include iron-containing minerals
(ferrihydrite, goethite, and hematite), silica, 7-alumina, 8-MnO 2, metal sulfides (cinnabar and chalcocite),
and aluminosilicate minerals (kaolinite, montmorillonite, and bentonite). Binding constants for sorption
of selected radionuclides on minerals have been determined. For aluminosilicate minerals, the diffuse
layer model uses the surface species --SiOH and -AIOH, in proportion to the stoichiometric formula of
the mineral. Comparison between the experimental data and the calculated results shows that the model
accurately represents radionuclide adsorption data over wide ranges of pH, solid-mass to solution-volume
ratio (m/V), and radionuclide concentration (CrN). Using binding constants determined from this study,
radionuclide adsorption has been predicted over extended ranges of system parameters such as pH, m/V,
and CRN. The surface complexes formed are largely related to the predicted aqueous speciation and the
surface site distribution, especially when stepwise protonation of the anion occurs under varying pH
conditions.
2
1. INTRODUCTION
In the geologic disposal of high-level nuclear waste (HLW), migration of radionuclides through
the geosphere dissolved in groundwater is one of the principal measures of repository performance.
Radionuclide sorption onto minerals is one type of process that may contribute to the delay of
radionuclide arrival or reduce groundwater concentrations at a receptor group location, and is of particular
interest in performance assessment (PA) calculations. Radionuclide sorption is strongly dependent on the
physicochemical conditions along the groundwater flow path. Understanding radionuclide sorption
behavior on various minerals is necessary for rationalizing parameter selection in PA and predicting the
behavior of radionuclides in natural environments.
A number of studies have provided data on radionuclide sorption over a wide range of chemical
conditions (e.g. Balistrieri and Chao, 1987, 1990; Balsley et al., 1996; Couture and Seitz, 1983; Ghosh et
al., 1994; Goldberg and Glaubig, 1988; Ikeda et al., 1994; Kang et al., 1996; Palmer and Meyer, 1981;
Parida et al., 1997; Saeki et al., 1990; Shade et al., 1984; Kent et al., 1986; Davis and Kent, 1990;
Dzombak and Morel, 1990; Payne and Waite, 1991; Venkataramani and Gupta, 1991; Bradbury and
Baeyens, 1993; Turner, 1995; Turner et al., 1996; Turner and Sassman, 1996, Pabalan et al., 1998; Turner
et al., 1998). These studies provide a strong basis for developing a uniform approach to developing
detailed geochemical sorption models to support PA calculations.
Recently, we have interpreted sorption data for radionuclides, selected for their significance in
high-level nuclear waste (HLW) disposal, using a thermodynamically consistent model that couples the
diffuse layer theory for surface complexation with an aqueous activity coefficient model based on the
"B-dot" method of Helgeson (1969) for aqueous speciation calculations (Wang et al., submitted). A
model has been developed based on the work of Turner and Sassman (1996) on defining the acid-base
behavior of minerals. In this paper, we report the application of this model to study the sorption of
radionuclides typically present in groundwater as anions. Negatively charged anions typically interact
weakly with silicate and oxide minerals; this limited sorption makes many anionic contaminants more
mobile and of particular concern in transport analyses.
3
Iodine, technetium, and selenium are potentially significant contributors to dose in HLW. In
oxidizing natural waters, these radionuclides exist as anionic species such as F, 103", SeO 3"2, SeO4 "2, and
TcO4 that can form highly soluble salts that are poorly retarded by geological barriers. Recent PA
calculations of the proposed HLW repository at Yucca Mountain, Nevada identify 79Se, 99Tc, and 1291 as
critical radionuclides for repository performance. For a regulatory timeframe of 104 y, 99Tc and 1291 are
the major contributors to dose (U.S. Department of Energy, 1998; Nuclear Regulatory Commission,
1999a,b). The sorption behavior of these radionuclides is dependent on geochemical conditions such as
pH. In addition, the redox conditions of the groundwater system are important; for example, Tc exists in
a heptavalent (7+) state under nonreducing conditions as the pertechnetate (TcO 4-) anion. Under reducing
conditions, however, Tc is present in a tetravalent (4+) state, with a lower solubility and stronger sorption
characteristics (Lieser and Bauscher, 1988; Pabalan et al., inpress). Similar redox dependency has been
discussed for selenium sorption (Davis et al., 1993).
Due to its significance as a toxic metal in agricultural and industrial processes, a relatively large
amount of sorption data has been reported for selenium, while only limited data are available for iodine
and technetium. The available literature data constitute a sound database for the application of detailed
models to evaluate the sorption behavior of anionic species in radioactive wastewaters.
The objectives of this work are:
(1) to evaluate published data on the sorption behavior of anionic radionuclide species using a
thermodynamic model based on the diffuse layer theory and aqueous speciation computations;
(2) to develop consistent model parameters which can either be used to support PA radionuclide transport
parameters, or in hydrogeochemical surface complexation calculations to account for radionuclide
sorption; and
(3) to predict the effect of system chemistry on the sorption of anionic species.
It is also of interest to compare the results obtained from this study with those obtained for
radionuclide cation adsorption (Wang et al., submitted), to draw general conclusions with regard to the
differences and similarities in sorption behavior for different radionuclides.
4
2. MODELING OF RADIONUCLIDE ANION ADSORPTION USING SURFACE
COMPLEXATION MODELING
2.1. General Description
For consistency of approach, the surface complexation model (SCM) used in the study of
radionuclide cation sorption (Wang et al., submitted) has been applied here. The model couples a
diffuse-layer theory for surface complexation with an aqueous activity coefficient model based on the
"B-dot" method of Helgeson (1969) for aqueous speciation calculations (Wang et al., submitted).
Optimal values for surface complex binding constants were determined from published experimental
sorption data using the general non-linear least squares optimization program, FITEQL version 2.0
(Westall, 1982a,b), revised to incorporate the B-dot equation for aqueous activity coefficients (Wang et
al., submitted). As appropriate, the acid-base behavior of minerals has been based on the work of Turner
and Sassman (1996). Acidity constants for additional minerals not considered by Turner and Sassman
(1996) (e.g. cinnabar and chalcocite) have been determined using the diffuse-layer model (DLM) with
published potentiometric titration data. The aqueous speciation equilibrium constants used in the
chemical model were taken from the DataO.com.V8.R6 file of the EQ3/6 thermodynamic database
(Wolery, 1992a,b), and are listed in Table 1. Details of the model have been presented in Part I of this
study in a separate paper (Wang et al., submitted).
Sorption modeling involves adjusting the binding constants for one or more postulated surface
reactions to minimize the differences between experimental and calculated values of the sorbed amount of
radionuclide at each pH, under defined physical and chemical conditions such as ionic strength (1), total
radionuclide concentration (CRN), solid-mass to solution-volume ratio (m/V), and mineral specific surface
area (A.,p). Although spectroscopic techniques such as Extended X-Ray Absorption Fine Structure
(EXAFS) potentially can be used to identify surface species, data are not available on I, Tc, and Se and
the selection of surface species is based primarily on the analysis of aqueous speciation and the results of
the sorption data regression.
5
2.2. Evaluation of Radionuclide Sorption Data for Modeling
Selected from a variety of published sources, sorption data for F, 103, Se03 2, Se0 4-2, and TcO 4 "
that cover a wide range of chemical conditions were used in the modeling. These data were measured
under specified conditions of pH, I, CRN, m/V, and As,,p. While it is difficult to assess the uncertainties of
the reported sorption data, the guidelines described by Dzombak and Morel (1990) for error estimates are
used in the present study in the parameter regression.
Frequently only one data set is available at a single set of experimental conditions for a given
radioelement-mineral system, but there are cases where two or more sources are available for a specific
system. For example, sorption data for selenite on goethite have been reported by Balistrieri and Chao
(1987), Zhang and Sparks (1990), and Parida et al. (1997). In these cases, each data set has been treated
separately to determine the optimal binding constants. Generally, binding constants determined from
multiple data sets are in reasonably good agreement in this study. However, among the three data sources
for the sorption of selenite on goethite, the data of Parida et al. (1997) show considerably lower sorption
under conditions comparable to those of Balistrieri and Chao (1987) and of Zhang and Sparks (1990).
The Parida et al. (1997) data were not considered in the determination of the binding constants for the
selenium-goethite system.
Several sets of data for iodine sorption on minerals such as hematite (Couture and Seitz, 1983),
cinnabar (Balsley et al., 1996; Ikada et al., 1994), and chalcocite (Balsley et al., 1996) are among those
selected for this study. Couture and Seitz (1983) also measured sorption for iodine on kaolinite, but
sorption was too low (e.g. <5% for the sorption of I) and the measurements contained too few points to
constrain modeling parameters. Moreover, observed sorption of iodine on silicate minerals has been
suggested (e.g. Couture and Seitz, 1983) to be actually due to the sorption by iron oxide or other
impurities. For this reason, these sorption data for iodine on kaolinite were not analyzed in this study.
Sorption data were also reported for F on TiO2 by Hakem et al. (1996), but the initial iodide concentration
was not reported, making it impossible to use these data for developing a geochemical sorption model.
6
Sorption data for technetium are relatively few compared with those for the other radionuclides.
Two data sets found for sorption of TcO4 (Shade et al., 1984; Kang et al., 1996) contain only a few data
points each. Palmer and Meyer (1981) measured pertechnetate sorption on a number of synthetic
inorganic materials, including A120 3, as well as on some naturally occurring minerals, under various ionic
strength conditions. The sorption data were presented in terms of a distribution coefficient (Kd) vs. pH.
However, the original paper did not indicate the solid concentration (m/V) used in their experiments,
which makes it impossible to convert Kd to the amount of sorbed pertechnetate which would then be used
in the modeling. Therefore, the data of Palmer and Meter (1981) were not analyzed in this study.
2.3. Determination of Acidity Constants for Metal Sulfide Minerals
Metal sulfides have been recognized as important adsorbents in nature. Although iron sulfides
are the most abundant metal sulfides in the earth's crust, their relatively high solubility compared to other
metal sulfides, and the formation of a series of metastable iron sulfide species (Anderko and Shuler,
1997), have made it difficult to characterize and identify the chemical species at their surfaces. The
surface charge development at metal sulfide surfaces can be ascribed to multiple functional groups, such
as the thiol group (-SH) and the metal hydroxide group (--Me-OH). The extreme insolubility of cinnabar
(HgS) and chalcocite (Cu2S) make them good model surfaces for iron sulfides. The study of adsorption
of radionuclides on these metal sulfides may provide valuable information for the characterization and
identification of surface species that may form on iron sulfides.
SCM approaches have been generally used in the literature to model adsorption on hydrous oxide
minerals (Davis and Kent, 1990; Dzombak and Morel, 1990). For modeling sorption of radionuclides on
cinnabar and chalcocite, surface acidity constants for these two sulfides are needed. Using the
potentiometric titration data of Balsley et al. (1996), DLM surface acidity constants for these two sulfides
were determined using FITEQL (Figure 1). Cinnabar and chalcocite exhibit negative surface charge in
the pH ranges of the potentiometric measurements (pH < 9) (Balsley et al., 1996). Thus, only the
deprotonation constants, K, were obtained. The values of these constants, together with their standard
7
deviations, are given in Table 2. It should be noted that in addition to thiol groups, hydroxyl groups may
also exist at the hydroxylated surface sites and contribute to the surface charge. However, hydroxylated
Cu(I) and Hg(II) only deprotonate in solution at very alkaline pH (Baes and Mesmer, 1976). The
deprotonation of thiol groups may outweigh that of the hydroxylated sites under common groundwater
pH conditions. The deprotonation constants obtained from our DLM approach perhaps reflect primarily
the deprotonation of thiol groups on the metal sulfide surfaces. Deprotonation constants for cinnabar and
chalcocite, together with aqueous reaction equilibrium constants, were used to determine binding
constants for iodide on these two metal sulfides.
3. RESULTS AND DISCUSSION
3.1. Representation of Experimental Data
The binding constants determined in this study for I, Se, and Tc are given in Tables 2-4. Listed in
these tables are also surface complexation reactions corresponding to the equilibrium binding constants,
and the intrinsic surface acidity constants used in the model. Standard deviations (oa) calculated by
FITEQL for binding constants are also given.
In the case of multiple data sets, binding constants were first determined for each individual data
sets. The best estimates were then determined by a weighted average of the optimum log K determined
from each individual data set, as discussed by Dzombak and Morel (1990). The standard deviation of the
averaged log K was assigned to be equal to the largest o-from the individual data regressions. While
binding constants determined from multiple data sets are in reasonably good agreement with each other in
most cases in this study, there are cases where inconsistency exists among data sets from different
authors. For example, binding constants determined from the data of Balistrieri and Chao [log
K(-XOH 2-HSeO 3°)= 18.83 (u=0.298) and log K(--XOH-HSeO 3-)= 12.99 (a=0.068)] give excellent
representation of experimental results at various selenite and solid concentrations. The same binding
constants predict sorption that is about 10-15% lower than the experimental data of Zhang and Sparks
8
(1990). When determined using the Zhang and Sparks (1990) data, these binding constants are
significantly higher [log K(=XOH2-HSeO30)=19.99 (a=0.392) and log K(=XOH-HSeO3)=14.35
(Y=0.152)]. The difference in log K values determined from the two data sets may arise from sources of
error in the two measurements due to the different experimental techniques. Binding constants
determined from the Balistrieri and Chao data are given in Table 3.
Figures 2-14 compare the experimental and calculated sorption results for F, 103, SeO 3z, SeO 42 ,
and TcO4 ". All of the lines in these figures were calculated based on the binding constants obtained from
the present study as listed in Tables 2-4. The agreement between the experimental and calculated
sorption results is good in general.
The strength of the present DLM for predicting the effect of changing system chemistry on
anionic radionuclide sorption behavior has been demonstrated in Figs. 2, 5-7, 9 and 10, where a set of
binding constants can accurately represent sorption results under varying geochemical conditions (I, m/V,
and CRN).
3.2. pH
The trend of sorption with changing pH for anion adsorption is clearly shown, i.e., anion sorption
decreases with increasing pH. This trend is consistent with that of the formation of protonated surface
site, --XOH 2+ (Wang et al., submitted) at low pH, and increasing deprotonation with increasing pH. It is
also consistent with anion sorption behavior analyzed in Dzombak and Morel (1990). This indicates that
in addition to the chemical bonding, electrostatic interactions between the anions and the protonated
mineral surface play an important role in the adsorption process.
3.3. Effects of Aqueous Speciation on Adsorption
Assuming a single surface complex typically results in a poor model fit to the observed data.
Inclusion of multiple surface complexes in the model can usually give the best fit of the anion sorption
9
data. The complexes formed on the mineral surfaces are expected to be different at different pH values
due to changes in aqueous speciation. This effect is most significant for the sorption of SeO32 which has
large association constants for the formation of HSeO 3 (log KI1=7.3) and H2SeO 3 (log K&2=9.9). The
result of the speciation analysis in aqueous selenite solution is shown in Fig. 15. Protonation of SeO 4-2 to
form HSeO 4 (log K,1=1.9) may also affect the sorption of selenate in acidic solutions (pH<3).
Protonation of pertechnetate, iodide, and iodate is negligible [e.g. log K0(HTcO 4) _ -9 and log K0(HI0 3) =
0.5], and the primary aqueous species are F, 103-, and TcO4 in their corresponding solutions.
Surface complexation reactions that involve different numbers of protons can be included in the
model to adjust the pH dependencies of the predicted sorption curves and improve the model results. This
is especially the case when stepwise protonation occurs under varying pH conditions, e.g., in the case of
selenite. The appropriate selection of surface complexes is crucial to the success of the model in
representing sorption results. Including multiple surface complexes (>3) can improve the quality of the
prediction when sorption results are presented over a wide pH range extending to highly acidic (pH-1)
and alkaline (pH-13) conditions. This is demonstrated for the sorption of selenate on y-A120 3 (Fig. 8)
and selenite on kaolinite and montmorillonite (Figs. 11 and 12), where 3, 4 and 6 surface complexes have
been included, respectively. Direct information (EXAFS) is not available on surface complexes for the
radionuclide-mineral systems studied here, and the exact form of the surface reaction is postulated to
obtain the best fit to the data. Combined analysis of sorption behavior, aqueous speciation, and surface
site distribution can assist in the selection of surface complexes. For example, the increase of SeO3"2
sorption with pH in acidic solutions (pH<5) on kaolinite and montmorillonite (Figs. 11 and 12)
(Goldberg and Glaubig, 1988) follows the trend for the formation of HSeO3 in this pH range, as shown in
the speciation diagram for Se(IV) (Fig. 15). Analysis of mineral surface site distribution (Wang et al.,
submitted) suggests that aluminosilicate surface sites are protonated in the same pH range to form a
positively charged site, =XOH 2÷. Thus, surface complexes of the type =XOH2-HSeO3° have been
included in the model, and the sorption data have been well represented at the low pH range.
10
Contribution to the total amount of adsorption from the formation of each surface complex was localized
in a relatively small pH range.
A positive correlation is obtained between the calculated binding constants for the sorption of
radionuclide cations and the hydrolysis constants; larger hydrolysis constants result in an increase in the
estimated binding constants (Wang et al., submitted). A similar trend is also noted in the radionuclide
anion sorption. Figure 16 shows an increase in binding constants with increasing first protonation
constants of the adsorbing divalent anions, A2 (where A=SeO4 and SeO3 in this study). This suggests that
anions prone to protonation would also associate with surface sites to form surface complexes. It is not
surprising that the logarithms of binding constants decrease (become less positive or more negative) with
increasing negative charge on the postulated surface complexes. Such a trend is also noted by Dzombak
and Morel (1990) for selenite and selenate and other divalent anions. Binding constants obtained from
this study are consistent with the trend obtained by Dzombak and Morel (1990), also using DLM, for the
divalent anion, as shown in Fig. 16 for surface complexes of the types --XOH-A-2 and -XOH-HA'. Due
to the limited number of data available for the radionuclide univalent anions (103, F, and TcO 4 ) and
negligible protonation associated with these anions, it difficult to perform the correlation on these anions.
3.4. Radionuclide Concentration
The effect of Cmv on sorption is shown in Fig. 7(a) for SeO 3"2 sorption on goethite and in Fig. 9
for the sorption of selenite on y-A120 3. When the calculated total surface site concentration, TxOH, is of
the same order of magnitude as the radionuclide concentration, CRN has a large observable effect on the
adsorption. For example, for the adsorption of selenite on goethite (Fig. 7(a)) (Balistrieri and Chao,
1987), the TxOH value is 5.64x10-6 mole sites/L, as calculated based on A.,.=49 m2/g, m/V=0.03 g/L and
the assumed value of site density, N, (2.3 lx 1018 sites/m 2) (Turner and Sassman, 1996). The CRN values
for the three sets of sorption data are 6.5x10 7 M, 2.83x106 M, and 5.34x10 6 M. Increased Cmv results in
reduced adsorption of selenite on goethite. Alternatively, if the total surface site concentration TxoH is far
11
in excess of CRN, the effect of CRN on the sorption is negligible. This is shown in Fig. 9 for the sorption of
selenite on 'y-A120 3 (Ghosh et al., 1994) where the TxoH value is 5.98x10 3 while the CRv values are from
5.7x10-6 to 1.33x10-4 M. The sorption curves for the four data sets are identical.
Effects of CpR on sorption behavior can also be predicted using parameters determined in this
study. For example, using parameters in Table 3, the sorption of Se(IV) as SeO 32 on goethite is
calculated as a function of the total selenite concentration at various pH values and is shown in Fig. 17.
This figure shows a reduction in adsorption with increasing radionuclide concentration, i.e., the same
trend that has been predicted for radionuclide cation adsorption (Wang et al., submitted). In contrast to
radionuclide cations for which the adsorption is more affected by Clv at higher pH, the effect of CRN on
the anion adsorption is more significant at lower pH.
Decreased adsorption with increasing CRv is attributed to a reduction in the number of open
sorption sites to levels insufficient for sorbing radionuclides as these sites are filled. When the total
concentration of available sorption sites is far in excess of the radionuclide concentration, this effect is
negligible. Part of this insensitivity is due to the assumption of a single site type in contrast to the strong
site/weak site approach of Dzombak and Morel (1990).
In HLW repository systems, concentrations for cationic radionuclides may be dilute (U.S.
Department of Energy, 1998; Nuclear Regulatory Commission, 1999a,b). Depending on the number of
sorption sites available along the flowpath, CR may have little effect on the radionuclide adsorption in
these systems. Anionic radionuclides, however, have a much higher solubility (U.S. Department of
Energy, 1998; Nuclear Regulatory Commission, 1999a,b), and may be comparable to TxOH, and the
effects of available sorption sites may be more significant.
3.5. Solid-Mass to Solution Volume Ratio
For a specific mineral, the amount (percent) of adsorbed radionuclide increases as m/V becomes
larger. This is demonstrated in Figs. 5 and 6 (selenate and selenite on ferrihydrite), Fig. 7(b) (selenite on
goethite), and Fig. 10 (selenite on 8-MnO 2). This trend is reasonable because the value of m/V is directly
12
proportional to the total available sorption sites. The greater the number of available sites, the more
effective the solid will be in adsorbing the trace concentration of radionuclides.
Using the parameters determined in this study, the present DLM predicts a sorption edge as a
function of m/V (Fig. 18). As pH increases, the sorption edge shifts to higher m/V values for the sorption
of anionic species. It has been predicted that this sorption edge shifts to lower m/Vvalues for the sorption
of cationic species, such as NpO 2+ (Wang et al., submitted). Pabalan et al. (1998) have noted that when
the sorption data are normalized to m/V and presented in terms of a distribution coefficient, Kd, sorption
becomes relatively insensitive to the change in solid concentration. This was also seen in our study for
modeling sorption of radionuclide cations (Wang et al., submitted). In the present study, the Kd values for
the sorption of selenite on goethite are predicted to be insensitive to the change in m/V above neutral pH,
but an increase of Kd with m/V becomes obvious at each lower pH where it appears to approach a stable
value, as shown in Fig. 19. As m/V increases, the total concentration of available sorption sites, TxoH,
increases. When m/V is small and the TxOH values are of the same order of magnitude as the total
radionuclide concentration (6.5x10-7 M for the case shown in Fig. 19), the greater m/V(or TxoH) makes
the solid more effective in adsorbing the radionuclide, resulting in increased Kd. As m/V increases and
TxOH becomes far in excess of the radionuclide concentration (e.g., TxoH>lO"5 mole sites/L), the increased
m/V has little influence on the sorption. A similar "threshold" effect has also been noted for cation
sorption by Pabalan et al. (1993). The greater sensitivity of the anion adsorption at lower pH to changing
m/V is consistent with the result obtained for the effect of CRN on the anion sorption, as described in the
previous section. The predicted insensitivity of the cation [e.g., Np(V)] sorption to m/V in our recent
study (Fig. 18 in Wang et al., submitted) is due to the extremely small concentration of radionuclide
(5.5x10 1 4 M) compared to the total concentration of available sorption sites, which ranges from 10-8 to
10-4 mole sites/L, and is far beyond the threshold value of the m/V. For PA, even though m/V values are
not known with precision in a radionuclide repository system, the concept of a threshold value may be
useful to constrain sorption in applying SCM approaches to field transport calculations. In the saturated
zone, values of m/V are expected to be large in rock-dominated systems to yield TxOH values that are far
13
greater than the trace amount of radionuclides. The rn/Vmay have little effect on Kd values in such
systems. In the unsaturated zone, however, wetting may be incomplete, reducing contact with the aquifer
minerals (e.g., reducing TxoH). Under these conditions, m/V may be more pronounced.
3.6. Valence States
For the radionuclides present in nuclear waste such as Np, Tc, Pu, and U, it has been found
(Meyer et al., 1985) that the formation of lower valence states could significantly increase the
retardation/adsorption of the nuclide by the host rock. Shown in Fig. 20 is the sorption of Se on
ferrihydrite (Balistrieri and Chao, 1990). It is seen that the lower valence states [Se(IV)] adsorb more
strongly than the higher valence states [Se(VI)] (Davis et al., 1993). Similar behavior is also observed for
the sorption of Se(IV) and Se(VI) on y-A120 3 (Figs. 8 and 9) (Ghosh et al., 1994). More importantly,
reducing conditions for Tc can result in changing of speciation from TcO 4 to TcO÷2 (anionic to cationic
form) which can have a significant enhancing effect on Tc-sorption (Lieser and Bauscher, 1988; Pabalan,
et al., in press). Moderately reducing conditions have been observed in saturated zone groundwaters near
Yucca Mountain (Ogard and Kerrisk, 1984; U.S. Department of Energy, 1999), but no data are available
on Tc(IV) sorption to allow model calculation.
3.7. Dependence of Binding Constants on Surface Acidity Constants and Aqueous Thermodynamic
Data
As described in a previous paper (Wang et al., submitted), DLM parameters obtained in this work
are dependent on the data used in constructing the geochemical equilibrium model for the optimization
runs. If acidity constants or aqueous thermodynamic parameters are modified, the radionuclide binding
constants must be reevaluated. For example, in the analysis of the results for selenite adsorption on
anatase (TiO2) (Gruebel et al., 1995), it was found that the use of DLM intrinsic acidity constants for
anatase (log K+=5.37 and log K_=-5.92), as determined by Turner and Sassman (1996), could not obtain a
satisfactory fit to the experimental results. Instead, when intrinsic acidity constants for rutile (TiO 2)
14
(logK+=4.23 and logK_=-7.49) were used in the model, the fit was excellent for data measured at two
different selenite concentrations. The pH corresponding to the zero point of charge, pHzpc, for anatase is
6.1 (Sprycha, 1984), while the relationship
pHzpc = (log K+ - log K.)12 (1)
predicts a much lower value of pHzpc (-5.6) based on Turner and Sassman's values for anatase. Sprycha
(1984) obtained log K, and log K_ values in the vicinity of 3.1 and -8.9, respectively; and Gruebel et al.
(1995) determined these values to be 3.6 and -7.6. It is clear that the intrinsic acidity constants for anatase
are largely uncertain, and it is reasonable to use an average of values reported by different authors before
the uncertainty is resolved. The results of the modeling for the sorption of SeO3"2 on anatase are shown in
Fig. 21. Radionuclide binding constants determined from this study will be recalculated when the
existing acidity constants are modified by incorporating new potentiometric titration data. Likewise, any
significant changes in the aqueous thermodynamic data will require a re-calculation of the necessary
parameters.
4. IMPLICATION OF THE MODELING RESULTS FOR PERFORMANCE ASSESSMENT
Anionic radionuclides are potentially important contributors to dose in HLW PA calculations.
Anion sorption may reduce radionuclide concentrations and delay arrival times at the point of exposure.
Because anion sorption behavior is strongly dependent on geochemical conditions along groundwater
flow paths, detailed process-level models can provide a level of understanding that is necessary to support
transport simulations in PA.
The success of the DLM in predicting the experimental sorption results suggests that it may be
possible to use the simple conceptual models developed in this study to extrapolate to a variety of
chemical conditions from a relatively limited data set. This is in contrast to typical empirical approaches,
where the lack of a strong theoretical basis frequently makes extrapolation beyond experimental
conditions uncertain. The DLM may be used to support Kd selection for HLW PA under site-specific
hydrochemical conditions (e.g., Turner and Pabalan, 1999). Where detailed information is not available,
15
it may also be possible to use the modeling approach outlined here to provide bounding constraints on
radionuclide anion sorption. While this is not an explicit incorporation of geochemistry in the transport
calculations, it does provide a step toward a more sound theoretical basis for sorption modeling in HLW
performance assessment.
Acknowledgment - This manuscript was prepared to document work performed for the Center for Nuclear
Waste Regulatory Analyses (CNWRA) and the U.S. Nuclear Regulatory Commission (NRC). The
manuscript is an independent product of the authors and does not necessarily reflect the views or
regulatory position of the NRC. CNWRA-generated original data contained here meet quality assurance
requirements described in the CNWRA Quality Assurance Manual. The assistance provided by Dr.
Roberto T. Pabalan of CNWRA is greatly appreciated.
REFERENCES
Anderko A. and Shuler P. (1997) A computational approach to predicting the formation of iron sulfide
species using stability diagrams. Comput. Geosci. 23, 647-658.
Baes C.F. and Mesmer R.E. (1976) The Hydrolysis of Cations. John Wiley & Sons
Balistrieri L.S. and Chao T.T. (1987) Selenium adsorption by goethite, Soil Sci. Soc. Am. J. 51, 1145
1151.
Balistrieri L.S. and Chao T.T. (1990) Adsorption of selenium by amorphous iron oxyhydroxide and
manganese dioxide. Geochim. Cosmochim. Acta 54, 739-751.
Balsley S.D., Brady P.V., Krumhansl J.L., and Anderson H.L. (1996) Iodide retention by metal sulfides:
Cinnabar and chalcocite. Environ. Sci. Technol. 30, 3025-3027.
Bradbury M.H. and Baeyens B. (1993) A general application of surface complexation to modeling
radionuclide sorption in natural systems. J Colloid Interface Sci. 158, 364-371.
Couture R.A. and Seitz M.G. (1983) Sorption of anions of iodine by iron oxides and kaolinite. Nuclear
and Chemical Waste Management 4, 301-306.
16
Davis J.A., Kent D.B., Rea B.A., Maest A.S., and Garabedian S.P. (1993) Influence of redox environment
and aqueous speciation on metal transport in groundwater: Preliminary results of trace injection
studies. In Metals in Groundwater (eds. H.E. Allen, E.M. Perdue, and D.S. Brown), pp. 223-273.
Lewis Publishers.
Davis J.A. and Kent D.B. (1990) Surface complexation modeling in aqueous geochemistry. In Mineral
water Interface Geochemistry (eds. M.F. Hochella, Jr. and A.F. White); Reviews in Mineralogy 23,
pp. 177-260. Mineralogical Society of America, Washington DC.
Dzombak D.A. and Morel F.M.M. (1990) Surface Complexation Modeling: Hydrous Ferric Oxide. John
Wiley & Sons.
Ghosh M.M., Cox C.D., and Yuan-Pan J.R. (1994) Adsorption of selenium on hydrous alumina. Environ.
Prog. 13, 79-88.
Goldberg S. and Glaubig R.A. (1988) Anion sorption on a calcareous, montmorillonitic soil - Selenium.
Soil Sci. Soc. Am. J. 52, 954-958.
Gruebel K.A., Davis J.A., and Leckie J.O. (1995) Kinetics of oxidation of selenite to selenate in the
presence of oxygen, titania, and light. Environ. Sci. Technol. 29, 5 86-594.
Hakem N., Fourest B., Guillaumont R., and Marmier N. (1996) Sorption of iodine and cesium on some
mineral oxide colloids. Radiochim. Acta 74, 225-230.
Helgeson H.C. (1969) Thermodynamics of hydrothermal systems at elevated temperatures and pressures.
Am. J. Sci. 267, 729-804.
Ikeda Y., Sazarashi M., Tsuji M., Seki R., and Yoshikawa H. (1994) Adsorption of I ions on cinnabar for
1291 waste management. Radiochim. Acta 65, 195-198.
Kang M.J., Rhee S.W., Moon H., Neck V., and Fanghanel T. (1996) Sorption of M0 4 (M=Tc, Re) on
Mg/Al layered double hydroxide by anion exchange. Radiochim. Acta 75, 169-173.
Kent D.B., Tripathi V.S., Ball N.B., and Leckie J.O. (1986) Surface-complexation modeling of
radionuclide adsorption in sub-surface environments. Department of Civil Engineering, Stanford
University, Technical Report No. 294.
17
Lieser K.H. and Bauscher C.H. (1988) Technetium in the hydrosphere and in the geosphere. II. Influence
of pH, of comp lexing agents and of some minerals on the sorption of technetium. Radiochimica Acta
44/45, 125-128.
Meyer R.E., Arnold W.D., and Case F.I. (1985) Valence effects on the sorption of nuclides on rocks and
minerals. II. Oak Ridge National Laboratory, Report No. NUREG/CR-4114, ORNL-6137.
Nuclear Regulatory Commission (1999a) NRC sensitivity and uncertainty analyses for a proposed HLW
repository at Yucca Mountain, Nevada, using TPA 3.1. Conceptual models and data. U.S. Nuclear
Regulatory Commission, Report No. NUREG-1668, Vol. 1.
Nuclear Regulatory Commission (1999b) NRC sensitivity and uncertainty analyses for a proposed HLW
repository at Yucca Mountain, Nevada using TPA 3.1. Results and conclusions. U.S. Nuclear
Regulatory Commission, Report No. NUREG-1668, Vol. 2.
Ogard A.E. and Kerrisk J.F. (1984) Groundwater chemistry along flow paths between a proposed
repository site and the accessible environment. Los Alamos National Laboratory, Report No.
LA-10188-MS.
Pabalan R.T. and Turner D.R. (1993) Sorption modeling for HLW performance assessment In NRC
High-Level Radioactive Waste Research at CNWRA (ed. B. Sagar), pp. 6-1 to 6-23. Center for
Nuclear Waste Regulatory Analyses, Report No. CNWRA 93-02S.
Pabalan R.T., Turner D.R., and Miklas M.P., Jr. (in press) Technetium-99 chemistry In reduced
groundwaters: Implications for the performance of a proposed high-level nuclear waste repository at
Yucca Mountain, Nevada. In Materials Research Society Symposium Proceedings: Scientific Basis
for Nuclear Waste Management - XXIII (eds. D. Shoesmith and R. Smith), Materials Research
Society (in press).
Pabalan R.T., Turner D.R., Bertetti F.P., and Prikryl J.D. (1998) UraniumvI sorption onto selected mineral
surfaces: Key geochemical parameters. In Adsorption of Metals by Geomedia (ed. E.A. Jenne), pp.
99-130. Academic Press, Inc.
18
Palmer D.A. and Meyer R.E. (1981) Adsorption of technetium on selected inorganic ion-exchange
materials and on a range of naturally occurring minerals using oxic conditions. J. Inorg. Nucl. Chem.
43, 2979-2984.
Parida K.M., Gorai B., Das N.N., and Rao S.B. (1997) Studies on ferric oxide hydroxides. III.
Adsorption of selenite on different forms of iron oxyhydroxides. J Colloid Interface Sci. 185, 355
362.
Payne T.E. and Waite T.D. (1991) Surface complexation modeling of uranium sorption data obtained by
isotope exchange techniques. Radiochim. Acta 52/53, 487-493.
Saeki K., Matsumoto S., and Tatsukawa R. (1990) Selenite adsorption by manganese oxides. Soil Sci.
160, 265-272.
Shade J.W., Ames L.L., and McGrarrah J.E. (1984) Actinide and technetium sorption on iron-silicate and
dispersed clay colloids. ACS Sym. Ser. pp. 67-77. American Chemical Society, Washington DC.
Sprycha R. (1984) Surface charge and adsorption of background electrolyte ions at anatase/electrolyte
interface. J. Colloid Interface Sci. 102, 173-185.
Turner D.R. (1995) A uniform approach to surface complexation modeling of radionuclide sorption.
Center for Nuclear Waste Regulatory Analyses, Report No 95-001.
Turner D.R. and Pabalan R.T. (1999) Abstraction of mechanistic sorption model results for performance
assessment calculations at Yucca Mountain, Nevada. Waste Management 19: 375-388.
Turner D.R., Pabalan R.T., and Bertetti F.P. (1998) Neptunium(V) sorption on montmorillonite: An
experimental and surface complexation modeling study. Clays Clay Miner. 46, 256-269.
Turner D.R. and Sassman S.A. (1996) Approaches to sorption modeling for high-level waste performance
assessment. J. Contam. Hydrol. 21, 311-332.
Turner G.D., Zachara J.M., McKinley J.P., and Smith S.C. (1996) Surface-charge properties and UO 2' 2
adsorption of a surface smectite. Geochim. Cosmochim. Acta 60, 3399-3414.
U.S. Department of Energy (1999) Downhole Eh and pH Measurements for UE-25 WT#17, Available at
http://m-oext.ymp.gov/html/prod/db-tdp/atdt/internet/TDIF307214.html.
19
U.S. Department of Energy (1998) Viability assessment of a repository at Yucca Mountain, Nevada. U.S.
Department of Energy. Office of Civilian Radioactive Waste Management, Report No.
DOE/RW-0508.
Venkataramani B. and Gupta A.R. (1991) Effect of anions on the sorption of uranyl ions on hydrous
oxides: Application of the surface hydrolysis model. Colloids Surf 53, 1-19.
Wang P., Anderko A., and Turner D.R. (submitted) Thermodynamic modeling of adsorption of
radionuclide on selected minerals: I. Cations. Geochim. Cosmochim. Acta.
Westall J.C. (1982a) FITEQL: A computer program for determination of chemical equilibrium constants
from experimental data, Version 1.2. Department of Chemistry, Oregon State University, Report No.
82-01.
Westall J.C. (1982b) FITEQL: A computer program for determination of chemical equilibrium constants
from experimental data, Version 2.0. Department of Chemistry, Oregon State University, Report No.
82-02.
Wolery T.J. (1992a) EQ3/6, A software package for geochemical modeling of aqueous systems: Package
overview and installation guide, Version 7.0. Lawrence Livermore National Laboratory, Livermore,
CA.
Wolery T.J. (1992b) EQ3NR, A computer program for geochemical modeling of
aqueous-speciation-solubility calculations: Theoretical manual, user's guide, and related
documentation, Version 7.0. Lawrence Livermore National Laboratory, Livermore, CA.
Zhang P. and Sparks D.L. (1990) Kinetics of selenate and selenite adsorption/desorption at the
goethite/water interface. Environ. Sci. Technol. 24, 1848-11860.
FIGURE CAPTIONS
Figure 1. Potentiometric titration of (a) cinnabar and (b) chalcocite. The lines are calculated using
parameters listed in Table 2 and experimental data are from Balsley et al. (1996) (mlV=50g/L, A,,,=1.99
m2/g for cinnabar and Ap=1. 11 m2/g for chalcocite, I=0.00 1 M NaCI).
20
Figure 2. Sorption of Ion cinnabar. Experimental data are from Balsley et al. (1996) (A, ASP=1.99 m2/g,
m/V=1.Og/L, [I]=lxl0-5 M, and ionic strength = 0.01 M NaCI) and Ikada et al. (1994) (0, A.,V=1.99 m2/g,
m/V=10 g/L, [I-]=Ix10- 6 M, ionic strength = 0.0003 M). SCM fits to the data of Balsley et al. (1996)
(dashed line) and Ikada et al. (1994) (solid line) are calculated using the parameters listed in Table 2.
Surface complexes included in the model are =HgSH-I and -HgSH 2-I°.
Figure 3. Sorption of ron chalcocite. Experimental data are from Balsley et al. (1996) (A.T= 1.11 m2/g;
[I-]total=Ix 10-5 M; m/V=1.0 g/L; I=0.01 M NaC1) and the solid line is calculated using parameters listed in
Table 2. Surface complexes included in the model are -CuSH-I and -CuSH 2-I0. Dashed lines represent
contributions from the formation of various surface complexes to the total amount of adsorption.
Figure 4. Sorption ofIO3 on hematite. Experimental data are from Couture and Seitz (1983) (Apv=10
m2/g; [103"]total=1.2x10-3 M; m/V=58.31 g/L; I=0.1 12 M) and the lines are calculated using parameters
listed in Table 2. Surface complexes included in the model are =-FeOH-IO 3- and -FeOH 2-IO30. Dashed
lines represent contributions from the formation of various surface complexes to the total amount of
adsorption.
Figure 5. Sorption of selenate on ferrihydrite. The lines are calculated using parameters listed in Tables
1 and 3. Experimental data are from Balistrieri and Chao (1990) (Asp=600 m2/g; [Se(VI)]totai=6.7x10-7 M;
I=0.1 M KC1): 0, m/V=0.0264 g/L; A, mlV=0.264 g/L. Surface complexes included in the model are
-FeO-HSeO 42 and (=FeOH 2)2-SeO 4°.
Figure 6. Sorption of selenite on ferrihydrite. The lines are calculated using parameters listed in Tables
I and 3. Experimental data are from Balistrieri and Chao (1990) (A.,P=600 m2/g; [Se(IV)]total=6.8x10-7 M;
21
I=0.1 M KCl): 0, m/V=O.0264 g/L; A, m/V=0.264 g/L. Surface complexes included in the model are
(-FeOH2)2-SeO 30 and (-=FeO) 2-SeO3.
Figure 7. Sorption of selenite on goethite. The lines are calculated using parameters listed in Tables 1
and 3. Surface complexes included in the model are -FeOH 2-HSeO30 and -FeOH-HSeO 3-. Experimental
data are from Balistrieri and Chao (1987) (A,,p=49 m2/g; I=0.1 M KC1). (a) m/V=0.03 g/L; 1', [SeO3 2]totai=
6.5x1 0-7 M (solid line); 0, [SeO3"2]total = 2.83xl 0-6 M (dash-dot line); A, [Se03 2]total = 5.34x10-6 M
(dashed line). (b) [Se03-2]total = 6.5xi0 7 M; 0, m/V=O.03 g/L (solid line); 0, m/V=0.006 g/L (dashed line);
A, m/V=O.003 g/L (dotted line).
Figure 8. Sorption of selenate on y-A120 3. The lines are calculated using parameters listed in Tables 1
and 3. Experimental data are from Ghosh et al. (1994) (A.TP=250 m2/g; m/V=1.0 g/L; [Se04-2]tota[ =
1.42xl 04 M; I=0.1 M NaCl). Surface complexes included in the model are (-AIO)2-SeO 4"4,
-A1OH 2-SeO 4 , and -A1OHz-H 2 SeO4+. Dashed lines represent contributions from the formation of
various surface complexes to the total amount of adsorption.
Figure 9. Sorption of selenite on y'-A120 3. The lines are calculated using parameters listed in Tables 1
and 3. Experimental data are from Ghosh et al. (1994) (A4=250 m2/g; m/V=6.24 g/L; I=0.1 M NaCI). 0,
[SeO3"2]total=5.x10 6 M; ", [SeO3 2]tota= 2.74x10- M; 0, [SeO3-2 ]total= 5.2x10-5 M; A, [Se03"2]totaI=
1.33x10-4 M. Surface complexes included in the model are (=AIO) 2-SeO3"4 and =AIO-HseO3"2. Dashed
lines represent contributions from the formation of various surface complexes to the total amount of
adsorption.
22
Figure 10. Sorption of selenite on 8-MnO 2. The lines are calculated using parameters listed in Tables 1
and 3. Surface complexes included in the model are =MnOH 2-SeO 3- and -MnO-HSeO 3"2. Experimental
data are from: (i) Balistrieri and Chao (1990) (A.,P=290 m2/g, [SeO 3"2]total = 6.5x10"7 M, 1=0.1 M KC1). 10,
m/V=0.03 g/L (solid line); A, m/V=0.3 g/L (dotted line); (ii) Saeki et al. (1995) (A.P=10.2 m2/g,
[Se03-2]total = 1.0x0-6 M, I=0.1 M NaCl). 0, m/V=3.33 g/L (dashed line).
Figure 11. Sorption of selenite on kaolinite. The lines are calculated using parameters listed in Tables 1
and 3. Experimental data are from Goldberg and Glaubig (1988) (A,,=20.5 m2/g; m/V=40 g/L;
[Se0 32]total=.9xl 0 M; 1=0.1 M NaCI). Surface complexes included in the model are -AIOH2-HSeO 3°,
-SiOH 2-HSeO3°, -SiO-HSeO3" 2, and -A1O-SeO 3"3. Dashed lines represent contributions from the
formation of various surface complexes to the total amount of adsorption.
Figure 12. Sorption of selenite on montmorillonite. The lines are calculated using parameters listed in
Tables 1 and 3. Experimental data are from Goldberg and Glaubig (1988) (A.P=18.6 m2/g; m/V=40 g/L;
[Se03-2]total= 1.9x 10- 5 M; I=0.1 M NaCI). Surface complexes included in the model are -AIOH 2-HSeO 30,
=SiOH2-HSeO3°, =AIO-HSeO32, -SiO-HSeO32, =AIO-SeO3
3, and (=-A1) 2 -SeO3 4. Dashed lines
represent contributions from the formation of various surface complexes to the total amount of
adsorption.
Figure 13. Sorption of pertechnetate on Mg/Al layered double hydroxide (Mg2AI20 9). The line is
calculated using parameters listed in Table 4. Surface complexes included in the model are -=AIOH-TcO 4
and =AlOH2-TcO4. Experimental data are from Kang et al. (1996) (Asp=70 m2/g; m/V=0.59 g/L;
[TcO4"]total=1.4x10- 5 M; I=0.01 M). Dashed lines represent contributions from various surface complexes
to the total amount of adsorption.
23
Figure 14. Sorption of pertechnetate on bentonite. The lines are calculated using parameters listed in
Table 4. Surface complexes included in the model are =AIO-TcO 4"2 and -SiO-TcO 4-2. Experimental data
are from Shade et al. (1984) (A.VP= 10 m2/g; m/V=0.0682 g/L; [TcO4"]total=4.2xl0"14 M; I=0.0 1 M). Dashed
lines represent contributions from various surface complexes to the total amount of adsorption.
Figure 15. Distribution of Se(IV). Lines are calculated based on thermodynamic data given in Table 1.
Figure 16. Plot of the binding constants, log K, vs. first protonation constants, log Ku,, for divalent
anions. Open symbols are from this study, solid symbols are taken from Dzombak and Morel (1990).
Figure 17. Sorption of Se(IV) on goethite as a function of total selenite concentration at various pH
(A~p=49 m2/g; m/V=0.03 g/L, 1=0.1 M KC1). All lines are calculated using parameters in Table 3.
Figure 18. Sorption of selenite on ferrihydrite as a function of W/V at various pH (A.,p=600 m2/g;
[SeO3-2]tota'=6.8x1 0-7 M; 1=0.1 M KCI). All lines are calculated using parameters in Table 3.
Figure 19. Plot of Kd vs. m/V for the sorption of selenite on goethite (A.S,=49 m2/g; ([Se03"2]total=6.5x 10-7
M; I=0.1 M KCI). The lines are calculated based on parameters in Table 3.
Figure 20. Sorption of Se(IV) and Se(VI) on ferrihydrite. Experimental data are from Balistrieri and
Chao (1990) (A.-P=600 m2/g; m/V=0.0264 g/L; [Se]totar=6.7x10- 7 M; I=0.1 M KC1). The lines are
calculated using parameters given in Table 3.
Figure 21. Sorption of selenite on anatase (TiO 2). The lines are calculated using parameters listed in
Table 1 and 3. Experimental data are from Gruebel et al. (1995) (A4,=8.6 m2/g; m/V=10 g/L; 1=0.01 M
24
NaC1): 0, [SeO 3-2 ]totai = 1.25xl 04 M (solid line); A, [SeO3-2]tota, =2.5x10-5 M (dashed line). Surface
complexes included in the model are =TiO-HSeO 3"2 and -TiOH-HSeO3.
25
1.2E-04
I.-
4 5 6 7 8
pH
Figure 1.
100
80
0 0)
60
40
20
0
.
1.2E-04
8.OE-05
4.0E-05
O.OE+O0(
-4.0E-05
-8.0E-05
-1.2E-04
6 7 8 9
pH
3 5 7
pH
Figure 2.
(b)
9 11
3 4 5 6 7 8 9 10 11
pH
Figure 3.
2 3 4 5 6 7 8 9 10
pH
Figure 4.
100
80
0D 60
o
S40
20
0 exp - cal
- -- - CuSH-I
-.........CuSH2-1
100
80
60 o 6 2.0
40
20
0
©
100
80
L0
0 4
20
0
4 5 6
pH
7 8
Figure 5.
100
80
*• 60 0
• 40
20
06 7 8 9 10 11 12
pH
Figure 6.
100
80
.~60
40 • 40
20
0
100
80
*% 60
00 •t 40
20
0
9 11
9 11
Figure 7.
5 7
pH
3 5 7
pH
0 exp -cal
--- AIOH2-H2SeO4+ -----AIOH2-SeO4------(AIO)2-SeO4---
2 3 4 5 6 7 8 9 10
pH
Figure 8.
o selenite=5.7E-6M
E3 selenite=2.74E-5M
Sselenite=5.2E-5M
A selenite=1 .33E-4M -Cal
------AlO-HSeO3
--- (AIO)2-SeO3----
3 4 5 6 7 8 9 10 11
pH
Figure 9.
100
80
60
4
20
40
100
80
n 60
40
20
20
100
80 ,
. e60
40
20
20!
4 5 6 7 8 9 10 11 12
pH
Figure 10.
100
80o exp
" ,-cal )60 ... AIOH2-HSeO3
.. .SiOH2-HSeO3 - 40 --------- SiO-HSeO3
S,,AIO-SeO3-
20
1 3 5 7 9 11 13
pH
Figure 11.
o exp -cal
-------. SiOH2-HSeO3
- -- -AIOH2-HSeO3
----------SiO-HSeO3-
AIO-HSeO3
AIO-SeO3--
-...... (AIO)2-SeO3---
1 3 5 7 9 11 13
pH
Figure 12.
0 exp -cal
- - -- XOH-TcO4
-..........XOH2-TcO4
11 12 13
0
100
80
60
40
20
0
0
100
80
0 n 0
60
40
20
010
pH
Figure 13.
0 exp -cal
-.... AIO-TcO4-
-..........SiO-TcO4--
4 5 6 7 8 9 10 11 12
pH
Figure 14.
100 \N / \ oo
80 \ /
60
40
20 / \ *
0o \
1 3 5 7 9 11 13
pH
------.SeO3--- - HSeO3
S.... H2SeO3
Figure 15.
100
80
"a) O0
60
40
20
U
30
ýIja (XOI-2)-A(0)
20 0 F- X, -H(l
A XOHI-A(-2) 10 * (XO)2-A(-4)
0O 0 A D&M; XOH-A(-2)
a D&M; XOH-HA(-1)
V~ Linear (D&M; XOH-HA(-1))
-20 0 2 4 6 8 10
log K.,
Figure 16
100
AE pH=3
80 -- H=
W pH=6.75 (D
m 60 -C"H
~ 40 ~pH=7.5
20 - pH=8.50 0 0 pH=1 1
0
1.E-09 1.E-08 1.E-07 1.E-06 1.E-05 1.E-04
[selenite]total, M
Figure 17.
4pH=4
UpH=5
*PH=6
9 G-pH=7.12
0pH-=9.12 m-+ pH=10
-pH11l
"- *- pH=12
0.001 0.01 0.1 1
inN, gIL
Figure 18.
0.1
1.9x10-5
~PH=5 ~pH=6
e pH=7
E3- pH=8
-- pH=9
*pH=10
1inN, gIL
qx10o4 TxOH, moleIL
Figure 19.
100
80
60
40
(D
0 U)
20~
0.0001
7.0
6.0
5.0
4.0
3.0
2.0,
1.0-
0,
E
0)
3 B E3B B E
1 1 1 11C14
0 Q; 0 I0.0 T
0.001
1.9x10 7,
0.01
1.9x106, 1.
100
80Se(IV)
S60 0 U) o 40 Se(VI)
20
0 4 5 6 7 8 9 10 1
pH
Figure 20.
100 A
80
0 60-]
,- 40
20
0 3 4 5 6 7 8 9 10 11 12
pH
Figure 2 1.
1
Table 1. Aqueous Reactions Associated with Iodate, Se(VI), and Se(IV) and Their Equilibrium Constants at 25°Ca
Aqueous Reactions log K
103- + H+ = H10 30 0.49
SeO4-2 + H+ = HSeO4" 1.91
SeO 3-2 + H+ = HSeO3- 7.29
SeO 3"2 + 2H+ = H2SeO 3
0 9.86
aEquilibrium constants were based on thermodynamic data from
Data0.com.V8.R6 file of the EQ3/6 database (Wolery, 1992a, 1992b).
Table 2. Summary of Parameters Used for the Determination of Binding Constants for the Sorption of I and 103" on Various Mineralsa
Radionuclide/Solid AP, m2/g m/V, g/L CN, M I, M log K, log K. log KIb log K2b ref.
IO3"/Hematite (a-Fe 20 3) 10 58.31 1.2x10"3 0.112c 7.35d -9.17d 2.00 (0.04) 8.43 (0.09) Courture and Seitz, 1983 IF/Cinnabar (HgS) 1.99 1.0 l.Oxl05- 0.01 (NaCI) n.c.0 -7.27 (0.06)' 6.86 (0.06) 9.05 (0.11) Balsley et al., 1996 I'/Chalcocite (Cu2S) 1.11 1.0 1.0xl0"5 0.01 (NaCI) n.c.' -6.95 (0.03)r 7.58 (0.05) 11.73 (0.14) Balsley et al., 1996
aA site density of 2.31 sites/nm2 (from Dzombak and Morel, 1990) was assigned to all minerals.
bBinding constants K, and K2 correspond to the following surface reactions:
-XPH° + A-z = =-XPH-Az (K1) =-XPH0 + A- '+ H+= •-XPH 2-AXý' (K2)
where A` = Ir or 103"; P=O for hematite and P=S for cinnabar and chalcocite. Values in the parenthesis are the standard deviations of the binding constants. 00.1 M NaC10 4 + 0.011 M NaHCO3 + 0.0012 M K10 3. d Acidity constants from Turner and Sassman (1996). 'Not considered. fLog K_ values determined in this study. The value in the parenthesis is the standard deviation of log K..
Table 3. Summary of Parameters Used for the Determination of Binding Constants for the Sorption of Se on Various Mineralsa
Radionuclide/Solid Asm 2/g m/VgIL CXR, M I, M log KVb log K. log K,' log K2' log K3' log KV log K5' log K6' log K7' ref.
SeO4-2/Ferrihydrite 600 0.0264,0.264 6.7x10-7 0.1 (KCI) 7.29 -8.93 0.65 (0.04) - 19.46 (0.10) - Balistrieri & Chao, 1990
SeO3-2/Ferrihydrite 600 0.0264,0.264 6.8x10-7 0.1 (KCI) 7.29 -8.93 26.06 (1.17) -6.96 (0.64) Balistrieri &Chao, 1990
SeO 3"2/Goethite 49 0.03 2.83x106 0.1 (KCI) 7.35 -9.17 12.99 (0.07) 18.83 (0.30) - - Balistrieri & Chao, 1987
SeO 4-2/Y-AI20 3 250 1.0 1.42x104 0.1 (NaCl) 6.85 -9.05 6.70 (0.15) - 16.72 (1.00) -9.32 (0.09) Ghosh etal., 1994
SeO 3"2/y-AI20 3 250 6.24 2.74x10"5 0.1 (NaCI) 6.85 -9.05 4.74 (0.10) - - - -3.70 (0.09) Ghosh etal., 1994
SeO3 2/8-MnO 2 290 0.03,0.3 6.5x10 7 0.1 (KCI) - -3.27 12.20 (0.05) 16.89 (0.66) - - - Balistrieri & Chao, 1990
SeO 3"2/Anatase 8.6 10 1.25x10 4,2.5x10 5 0.01 (NaCI) 4 .2 3d -7.49d 7.44 (0.04) 11.78 (0.47) - Gruebel etal., 1995
SeO32/Kaolinite' 20.5 40 1.9x10 5 0.1 (NaCI) 8.33 -9.73 - - 16.84 (0.08) -3.80 (0.50) - Goldberg & Glaubig, 1988 - -7.20 2.37 (0.17) - 12.54 (0.08) -
SeO32/Montmorillonite' 18.6 40 1. 9x10 5 0.1 (NaCI) 8.33 -9.73 2.65 (0.12) - 16.51 (0.07) -4.35 (0.09) -8.80 (0.05) Goldberg & Glaubig, 1988 - -7.20 1.50 (0.30) - 12.20 (0.09) -
'A site density of 2.31 sites/nm 2 (from Dzombak and Morel, 1990) was assigned to all minerals.
b Acidity constants from Turner and Sassman (1996).
'Binding constants K, through K7 correspond to the following surface reactions: --XOH0 + A' = --XOH-A' (Ki)
=-XOH + A- + WT= mXOH2 -A-"'l (K2) -XOH0 + A: + 2I-= -XOH 2-HANz+ 2
(K3) --XOH° + A-z + 3HI= =-XOH 2-H2AN3
(K 4)
=-XOH0 + A-'= =-XO-A~z' + H+ (Ks) 2(-XOH6 ) + A-z + 2H+ = (-XOH 2)2-A -+2 (K6) 2(-XOH°) + Az = (-XO) 2-Az 2 + 2H+ (K7)
where A` = SeO; 2 or SeO4"2. Values in the parenthesis are the standard deviations of the binding constants. d Acidity constants for rutile (Turner and Sassman, 1996) are used. See text in section 4.7. d Log K values in the first line for the aluminosilicate minerals are for aluminol-radionuclide surface complexes, those in the second line are for silanol-radionuclide surface complexes.
Table 4. Summary of Parameters Used for the Determination of Binding Constants for the Sorption of TcO 4- on Mineralsa
Solid Asp,m 2/g mnV,g/L CN, M 1, M log K.b log K.b log KI' log K2c log K3' ref.
Mg6A1209d 70 0.59 1.4x10"5 0.01 6.85 -9.05 5.97 (0.61) 15.33 (0.23) - Kang etal., 1996 Bentonite' 10 0.0682 4.2xI0' 4 0.01 8.33 -9.73 - -1.70 (0.08) Shade et al., 1984
- -7.20 -3.50 (0.35)
'A site density of 2.31 sites/nm2 (from Dzombak and Morel, 1990) was assigned to all minerals. b Acidity constants from Turner and Sassman (1996). 'Binding constants K, through K3 correspond to the following surface reactions:
=XOH° + TcO4 XOH-TcO4 (Kl) -=XOH0 + TcO; + HW = -=XOH2-TcO 4
0 (K2) --XOH° + TcO 4 = =-XO-TcO 4"2
+ W (K3) Values in the parenthesis are the standard deviations of the binding constants.
C 0.1 M NaC1O 4 + 0.011 M NaHCO3 + 0.0012 M KIO 3. d Mg/Al Layered double hydroxide. 'Log K values in the first line for bentonite are for aluminol-radionuclide surface species, those in the second line are for silanol-radionuclide surface species.