Environ. Sci. Technol. 2006, 40, 7481-7487
Lanthanide-Humic Substances Complexation. II. Calibration of Humic Ion-Binding Model V† JEROEN E. SONKE Laboratoire des Me´canismes et Transferts en Ge´ologie, CNRS/ IRD/Universite´ Paul Sabatier Toulouse III, 14 avenue Edouard Be´lin, 31400 Toulouse, France
The experimental complexation of the lanthanides (Sc, Y, and rare earth elements) with Suwannee river fulvic acid, Leonardite coal humic acid, and Elliot soil humic acid is described with Humic Ion-Binding Model V. The fitted intrinsic equilibrium constants for metal-proton exchange, pKMHA, for Eu3+ are similar to previously published experimental fits, and linear free energy relationship (LFER) estimated values. The experimentally observed lanthanide contraction effect in REE-humic complex stability is reflected in the gradual decrease in pKMHA from La to Lu. In Model V, a decrease in pKMHA from La to Lu indicates an increase in complex stability. Fitted pKMHA values for heavy REE are lower than those estimated by LFERs. Consequently, REE fractionation by humic substances complexation could be more pronounced than previously thought. Recommended pKMHA values for lanthanide-fulvic and -humic acid complexation are derived by superimposing the fitted trends in pKMHA for all REE, i.e., the decrease in pKMHA from La to Lu, on the average Eu pKMHA value for all literature datasets. These results will allow modeling assessments of organic matter induced REE fractionation in aquatic environments, taking into account changes in pH, ionic strength, and ion competition. A simulation of dissolved REE speciation in an average world river suggests that organic matter outcompetes carbonate complexation, even under alkaline conditions.
Introduction The lanthanides, including scandium, yttrium, and the rare earth elements (REEs), constitute a group of elements of diverse geochemical and environmental interests. Several radioactive REE isotopes are short-lived constituents of highlevel radioactive waste, and based on their similar physicochemical properties, Nd and Eu3+ have been studied intensively as analogues for Am3+ and Cm3+ (1, 2). Recent interests include the impact of nonnuclear and agricultural use of REEs (3, 4). The gradual change in physicochemical properties due to the lanthanide contraction, a regular decrease in ionic radii from 1.04 Å (La) to 0.86 Å (Lu), has made the REE an often-applied tool in quantifying geochemical fluxes between the Earth’s reservoirs (5). At the Earth’s surface several important changes in the REE concentration patterns take place during weathering, river transport, estuarine mixing, and marine scavenging (6-10). Filtration studies of fresh waters indicate that colloids, an operationally defined mixture (1 nm < colloids < 1µm) of dissolved humic
substances (HS), Mn,Fe,Al-(oxy)hydroxides, clays, carbonates, and polysaccharides (11), dominate dissolved REE speciation (12-14). Dissolved REE speciation in alkaline waters, including seawater, is thought to be dominated by carbonate complexes (15, 16). From the modeling perspective, only Eu3+ and select Tb3+ and Dy3+ experimental data have been available to calibrate state-of-the-art ion-humic binding models, such as Humic Ion-Binding Model V (Model V (17)), its successor Humic Ion-Binding Model VI (Model VI (18)), and the Non Ideal Competitive Adsorption model (NICA-Donnan) (19). We lack, however, these model’s intrinsic equilibrium binding parameters to accurately describe REE-HS binding for all 14 naturally occurring REEs as a function of pH, ionic strength, and ion competition. This in turn prevents the observed REE fractionation patterns in soils and rivers from being interpreted mechanistically in the context of colloidal complexation. Tipping (20) illustrated early on in the application of Model V that linear free energy relationships (LFER) existed between thermodynamic bindings constants for metal-carboxylic acid complexation and the Model V “pKMHA” intrinsic binding parameters for the same set of metals. These LFERs were subsequently used to estimate pKMHA values for actinides that lacked experimental data. Recently, Tang and Johannesson (21) used similar LFERs to predict pKMHA values for all 14 stable REEs. It was stressed that for an accurate estimation of pKMHA values for REE, other than Eu3+, additional experimental data was needed. Such data were recently provided by Sonke and Salters in a comprehensive experimental REE-HS binding study as a function of pH (6-9), ionic strength (0.001-0.1 mol L-1) for all 14 REE, Sc, and Y, and three sources of HS (22). The data were obtained with EDTA as a competitive ligand and a new speciation technique based on capillary electrophoresis coupled to a sector field ICP-MS detector (23-25). In summary, strong Ln-HS complexation was observed under all conditions, with conditional binding constants, Kc, in the range 8.9 < logKc < 16.5. A pronounced lanthanide contraction effect, ∆HSKc, defined as logKc,LuHS - logKc,LaHS, was observed in the conditional binding constants, i.e., a gradual increase in complex stability from La to Lu by 2-3 orders of magnitude. ∆HSKc was also found to increase with pH, and possibly decrease with ionic strength. These new results suggested that humic substances may play a role in fractionating the REEs at the Earth’s surface. In this contribution Humic Ion-Binding Model V is calibrated with the previously published experimental dataset for REE-HS complexation. Results are compared to experimentally and LFER derived pKMHA values, and implications for REE speciation are discussed in the context of pH and ion competition effects.
Model V Background Detailed descriptions of Model V can be found elsewhere (17, 18, 26). In brief, Model V is a discrete site model for humic-ion complexation, with two main types of functional groups (type A, carboxylic; type B, phenolic), and a Donnan model to estimate counterion accumulation in the diffuse layer (nonspecific binding). Proton dissociation is described as
RAHZ ) RAZ-1 + H+
(1)
†
This manuscript is part of a focus group on Modeling Natural Organic Matter. 10.1021/es060490g CCC: $33.50 Published on Web 06/16/2006
2006 American Chemical Society
with a variable equilibrium quotient K(Z) described as VOL. 40, NO. 24, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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K(Z) )
[RAZ-1]{H+} RAH
Z
) Kint exp(2wZ)
(2)
where w is an ionic strength (I) dependent electrostatic interaction factor, empirically defined as w ) PlogI, with P being an adjustable parameter (Table 2). The 2wZ term approximates the electrostatic effect of the humic charge on site specific binding. HS heterogeneity is described by introducing 4 type A and 4 type B groups in a systematic and fixed manner (shown here for site A)
pKH(i) ) pKA +
(2i - 5) ∆pKA 6
(3)
The site density for type B sites is fixed at half the density of type A sites. Metal binding takes place at single protonbinding sites (monodentate) and at bi-dentate sites by combining two monodentate sites. Monodentate metal binding is described with intrinsic equilibrium constants, pKMH, denoted as pKMHA for type A and pKMHB for type B sites, for the metal-proton exchange reaction
RAHZ + Mz+ ) RAM(Z+z) + H+
(4)
The smaller the value of pKMH, the stronger the intrinsic binding of M. For bidentate binding the pKMH values are added together for the two sites involved. For computational reasons, only twelve different bi-dentate sites are allowed (17). The binding site heterogeneity in Model V is illustrated in Figure 1 for the twenty different binding sites with their respective logK values for protons, La and Lu, and with their site densities. These logK values reflect conventional metalhumic association reactions and can be derived by subtracting eq 1 from eq 4. Also shown in Figure 1 is the parameter ∆FAK, defined as logKLuFA - logKLaFA, which represents the lanthanide contraction effect of the stability constants for each of the 20 different sites. Note how ∆FAK increases from type A to type B to the bidentate sites. Model V includes the binding of the first hydrolysis product (i.e., LnOH+2), using the same pKMHA values. A summary and explanation of all parameters involving K’s and ∆K is given in Table S1 in the Supporting Information. Linear Free Energy Relationships. Tang and Johannesson (21) used the following strategy to approximate pKMHA values for REE-HS complexation. A suite of metals (non-REE) was selected for which model V pKMHA parameters are available. LFERs between these metals’ pKMHA values and first hydrolysis constants, acetic acid (AA) and lactic acid (LA) stability constants were determined. These LFER’s were then inversely applied to the REE: well-known constants for REE hydrolysis and complexation by AA and LA were used to calculate pKMHA for the REE. These LFER approximated pKMHA values were then compared with pKMHA values fitted on experimental dataset of REE-fulvic acid complexation: Eu-FA (n ) 6), Dy-HA (n ) 1), and Tb-HA (n ) 1). It was concluded that hydrolysis based pKMHA values were too high compared to the experimental values for Eu. AA and LA based pKMHA values agreed better and their average was subsequently selected for REE speciation calculations. These values are summarized in Table 1 and shown in Figure 2. The particular curvature of LFER derived pKMHA values for the REE series (Figure 2) shows a minimum at Sm and is inherited directly from the comparative ligand(s) used in the LFER, i.e., in this case the average of stability constant trends for REE complexation by AA and LA.
Model V Fitting The experimental dataset has been published elsewhere (22). The data (n ) 171) consist of 11 sub-datasets of Ln speciation 7482
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in the presence of HS and EDTA and at a particular pH and I: 5 sets for Suwannee river FA (SRFA), 5 sets for Leonardite coal HA (LHA), and 1 set for Elliot soil HA (EHA). Ln and HS concentrations were 100 nmol L-1 and 10-20 mg L-1, respectively. The data also include two adsorption isotherms for Nd at pH 7 and at 0.1 and 0.001 mol L-1 I. The isotherm experiments had Nd concentrations between 1 nmol L-1 and 1 µmol L-1, the former being similar to the average world river Nd concentration (see Table S4). Current strategies in ion-HS data fitting involve the use of so-called generic or universal binding parameters (Table 2). Generic parameters for proton binding are typically obtained by fitting large numbers of HS protonation datasets and they describe the pH and I dependency of the fulvic or humic acid binding environment. The number of adjustable parameters for LnHS fitting was reduced to one, by making use of relationships between pKMHA and pKMHB (20, 21) (Table 2). The experimental EDTA-HS competition data were fitted after applying a nonlinearization procedure that suppresses experimental noise in the original data (eq 9 in ref 22). This approach was mainly taken for computational reasons, and justified based on the overall observed smooth inter-REE speciation changes. pKMHA parameters for Sc, Y, and REE were subsequently optimized for each individual pH, I, and HS sub-dataset. The optimum pKMHA values for SRFA, LHA, and EHA were calculated by weighted averaging over their respective subdatasets (Table S2, Supporting Information). Goodness-offit indicators for metal ion-binding are typically given by the root-mean-square error (rmse) and correlation coefficient, r 2, using “log bound metal”, the dependent variable. However, in the CE-ICP-MS ligand competition technique the quantified independent variables are [LnHS] and [LnEDTA-] and therefore the “log free metal” concentration is treated here as dependent variable for rmse calculations. Rmse values are expressed relative to the original experimental data, rather than the linearized data. Dissolved REE speciation simulations with model V were done with Win Humic V (27). Model V is also available in the aquatic speciation code WHAM (28) and the software code is available in BASIC (29), and as a FORTRAN implementation into PHREEQE (30).
Results and Discussion Goodness of Model V fits are illustrated in Figure 3 for La, Nd, Eu, and Lu binding to SRFA. Model V reproduces the pH and I variation in [Ln+3]free for La-, Eu-, and LuSRFA experiments within the propagated error of measurements, with rmse values of 0.35, 0.48, and 0.61 and r 2 values of 0.91, 0.85, and 0.92, respectively. Similar results were obtained for the other Ln. Nd is the only REE for which adsorption isotherms were determined over a concentration range of 1 nM to 1 µM total Nd at two different I values (0.001 and 0.1 mol L-1). Figure 3 shows that Model V reasonably well approximates the slope and I dependence of the Nd isotherms with an overall rmse of 0.50 and r 2 of 0.87. Table 3 summarizes published pKMHA values for FA and HA that are based on experimental data. The fitted pKMHA for EuSRFA of 0.01 ( 0.06 falls within the range of -0.11-0.76. The observed strong Eu binding by SRFA agrees qualitatively with the strong proton binding by SRFA (ranking 3rd among 25 different FA origins (31)). The published pKMHA for Tb Aquatic FA of 0.75 is larger than the value for TbSRFA of -0.05, yet the relative Eu vs Tb difference in pKMHA between the two datasets is similar: 0.76 vs 0.75 for “Aquatic FA” (32), and 0.01 vs -0.05 for SRFA (this study). The difference between the Aquatic FA and SRFA fits for Eu and Tb could reflect the origins of the FAs, the equilibrium windows of the experimental techniques, or the different metal loadings (Tb ) 20 µM (32), and 100 nM (this study)). The latter relates to Model V’s lack of strong binding sites that were earlier exposed
TABLE 1. Summary of pKMHA Values for REE-HS Binding, Based on Published LFERs (21), and New Experimental Data for SRFA, LHA, and EHA (22)a LFER (21) FA Sc Y La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu ∆HSpKMHAd
HA
experimental (this study) SRFA -0.58b
0.66 0.56 0.53 0.52 0.42 0.44 0.48 0.50 0.53 0.52 0.51 0.50 0.48 0.44 0.22
1.68 1.58 1.54 1.53 1.41 1.45 1.50 1.54 1.56 1.56 1.56 1.54 1.52 1.49 0.19
-0.03 0.21 0.18 0.14 0.11 0.04 0.01 -0.02 -0.05 -0.09 -0.12 -0.15 -0.19 -0.22 -0.25 0.46
LFER (this study)
LHA 0.03 0.09 0.09 0.08 0.08 0.07 0.06 0.06 0.05 0.05 0.04 0.04 0.03 0.03 0.02
-0.24 0.70 0.99 0.95 0.90 0.86 0.77 0.73 0.68 0.64 0.59 0.55 0.50 0.46 0.41 0.37 0.62
0.06 0.21 0.24 0.23 0.23 0.22 0.22 0.21 0.21 0.21 0.20 0.20 0.20 0.19 0.19 0.19
recommended
EHA
FA citrate
NTA
HA EDTA
FA
HA
1.23c 1.58 1.53 1.47 1.42 1.31 1.26 1.21 1.15 1.10 1.04 0.99 0.94 0.88 0.83 0.75
0.28 0.44 0.35 0.28 0.25 0.22 0.22 0.25 0.24 0.22 0.21 0.20 0.16 0.14 0.13 0.31
0.53 0.81 1.01 0.96 0.93 0.88 0.83 0.83 0.82 0.79 0.76 0.73 0.70 0.67 0.64 0.61 0.40
0.82 1.68 2.15 2.05 1.99 1.95 1.86 1.83 1.81 1.72 1.65 1.60 1.54 1.47 1.44 1.40 0.75
-0.23 0.32 0.56 0.52 0.49 0.46 0.39 0.36 0.32 0.29 0.26 0.23 0.19 0.16 0.13 0.09 0.47
0.23 1.17 1.49 1.44 1.39 1.34 1.24 1.19 1.14 1.09 1.05 1.00 0.95 0.90 0.85 0.80 0.69
a LFER based pK MHA values for citrate, NTA, and EDTA as comparative ligand are included. The last two columns contain the recommended pKMHA values for FA and HA, with 1 sigma standard deviations of 0.36 and 0.35, respectively. b Ill constrained as only experimental data for pH 8, 0.1 mol L-1 I were available. c Estimated based on resemblance of Y and Eu/Gd binding behavior. d ∆HSpKMHA is defined as |pKLuHA - pKLaHA|, the decrease in pKMHA values across the REE series.
TABLE 2. Generic Model V Parameters for Proton Binding Used in This Study (17, 20) generic Model V parameters
symbol
FA
HA
abundance of type A sites [mol g-1] abundance of type B sites [mol g-1] intrinsic proton dissociation constant for type A sites intrinsic proton dissociation constant for type B sites intrinsic equilibrium constant for metal binding at type A sites intrinsic equilibrium constant for metal binding at type B sites distribution term that modifies pKA distribution term that modifies pKB electrostatic parameter proximity factor for bi- and tri-dentate sites approximate molecular weight [Da] approximate molecular radius [nm]
nA nB p KA p KB pKMHA pKMHB ∆pKA ∆pKB P fpr
4.7 10-3 0.5 × nA 3.3 9.6 adjustable 3.96pKMHA 3.3 5.5 -103 0.4 1500 0.8
3.3 10-3 0.5 × nA 4.0 8.5 adjustable 3pKMHB-3 1.8 3.4 -374 0.5 15000 1.72
FIGURE 1. Summary of Model V binding site heterogeneity for a universal fulvic acid: Site numbers 1-4 represent type A sites, 5-8 represent type B sites, and 9-20 represent bidentate sites. LogK values for complex formation were calculated using generic parameters for protons, and new recommended pKMHA values for La (0.56) and Lu (0.09). Note how site densities decrease with increasing logK values. as a flaw in describing nonlinear Cu-HS isotherms over a large range of experimental Cu concentrations (18). Averaging all literature pKMHA values for EuFA, including those for SRFA obtained here, yields 0.36 ( 0.35. For HA, the fitted pKMHA value for EuEHA of 1.19 falls within the limited literature range of 0.94-1.63, whereas the EuLHA value of 0.73 ( 0.21 indicates stronger Eu binding than thus far observed. DyEHA binding in this study has a
pKMHA of 1.10, which agrees with the same value of 1.1 for Dy binding by Aldrich HA (33). Averaging all literature pKMHA values for EuHA, including those for EHA and LHA obtained here, yields 1.19 ( 0.36. Both these revised literature averages for FA and HA of 0.36 and 1.19 justify the LFER approach that produced Eu pKMHA values of 0.44 and 1.45, respectively (21). Yttrium pKMHA values are more similar to those of Eu and Gd, rather than Ho, despite the similar ionic radii of Y and VOL. 40, NO. 24, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Summary of pKMHA values for REE fulvic and humic acid complexation. Published values are mainly for Eu. LFER values are from ref 21. Recommended values are based on average published pKMHA values for Eu on which are superimposed the inter-REE trends for SRFA and EHA-LHA.
FIGURE 3. Model V fits (open symbols) of LnSRFA experimental data (closed symbols). Left panel: free [La3+], [Eu3+], [Lu3+] as a function of pH. Right panel: Nd isotherm plots for 0.001 (squares) and 0.1 (circles) mol L-1 I (single data point at 0.01 I is included (triangles)). Ho. HS may therefore induce Y-Ho fractionation in natural waters. Scandium pKMHA values are less well constrained for SRFA than for LHA, but overall display the strongest of LnHS complexes, due to its small ionic radius. Inter-REE Variations. The best-fit pKMHA values for all REE with SRFA, LHA, and EHA are summarized in Table 1 and visualized in Figure 2. In accordance with the experimentally observed increase in Ln-HS complex stability, the pKMHA values, which represent proton exchange reactions, decrease from La to Lu. The decrease in pKMHA across the Ln series from La to Lu is stronger for both LHA (0.62) and EHA (0.75) than for SRFA (0.46). Importantly, all of these experimentally based decreases in pKMHA are larger than their LFER estimated counterparts of 0.22 (FA) and 0.19 (HA). It appears therefore that relative to Eu, for which LFER approximated values agree well with the literature averages, the LFERs underestimate HREE binding. This result is clearly visible in the slope of pKMHA values across the REE series in Figure 2, which is steeper for experimental than for LFER values. Recommended pKMHA Values. Europium is by far the lanthanide for which the most experimental data, as well as fitted FA and HA pKMHA values for Model V, are available (Table 3). The comprehensive dataset discussed in this study covers all lanthanides, agrees with published Eu studies, yet lacks in HS source diversity. Therefore, to arrive at best estimates of universal lanthanide pKMHA values for both FA and HA, it is proposed that the inter-REE variation observed by Sonke and Salters (22) be superimposed onto the overall 7484
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average pKMHA for EuFA and EuHA binding. This approach amounts to a graphical translation of the entire REE-SRFA trend in pKMHA and anchoring it onto the average pKMHA for EuFA (Figure 2). For LnHA binding, two source materials were investigated (LHA and EHA), and universal pKMHA values are obtained by superimposing the averaged inter-REE trend for LHA and EHA onto the the “literature averaged” EuHA anchorpoint (Figure 2). Univeral Sc and Y pKMHA values have been derived similarly and are included in Table 1. As with any other ion, future datasets on LnHS binding may allow further refining of universal binding parameters. LFERs Revisited. Based on the carboxylic acid-HS analogy, it was suggested earlier that the magnitude of ∆HSKc (1.9-3.1) for conditional binding constants Kc, reflects a corresponding average denticity of REE-HS coordination of 2.8-4.2 (22). Within this range, FA had a denticity of ∼3 and HA had a denticity of ∼4. Rather than using the two monocarboxylic acids AA and LA as comparative LFER ligands (21), it is therefore of interest to explore LFERs based on triand tetra-dentate ligands such as citric acid or NTA and EDTA. Using the same suite of metals as in the previous LFER study, pKMHA values have been approximated for FA by using citric acid and NTA model ligands and for HA by using EDTA. The relevant stability constants are given in Table S3 (Supporting Information) and LFERs are shown in Figure S1-3 (Supporting Information). The LFERs obtained are as follows (r values rather than r 2 are given for consistency with ref 21):
TABLE 3. Summary of Published pKMHA Values for REE (sd ) Standard Deviation) dataset codea
material
FEu-01 FEu-02 FEu-03 FEu-03 FEu-05 FEu-06 FEu-10 FEu average 1sd
Suwannee river FA Armadale podzol Bh FA Aquatic FA Aquatic FA Whitray Beck FA Whitray Beck FA Suwannee river FA
FTb-01 FTb-02
source reference
fitting reference
pK(MHA)
rmseb
42 43 32 32 44 44 22
20 20 21 21 21 21 this study
0.49 -0.11 0.65 0.76 0.60 0.10 0.01 0.36 0.35
0.19 0.57 0.04 0.06 0.08 0.33 0.48
Aquatic FA Suwannee river FA
32 (32)
21 this study
0.75 -0.05
0.07 0.37
HEu-01 HEu-02 HEu-03 HEu-05 HEu-06 HEu average 1sd
EGA H1 HA Aldrich HA Podzol B1 HA Elliot soil HA Leonardite coal HA
45 46 47 22 22
20 20 20 this study this study
1.63 1.41 0.94 1.26 0.73 1.19 0.36
0.20 0.16 0.15
HDy-01 HDy-02 HDy-02
Aldrich HA Elliot soil HA Leonardite coal HA
33 33 33
21 this study this study
1.1 1.10 0.59
0.84
0.06 0.79
a
Dataset codes are those used in refs 18 and 48. New codes have been assigned to this study, FEu-10, HEu-05 and HEu-06 and to FTb-01. values are in log[Ln]bound, except for this study which has rmse in log[Ln+3]free.
pKMHA(FA) ) 0.32pK(citrate) + 3.37 r ) 0.83
(5)
pKMHA(FA) ) 0.22pK(NTA) + 3.71 r ) 0.84
(6)
pKMHA(HA) ) 0.17pK(EDTA) + 5.24 r ) 0.86
(7)
The alternative LFER pKMHA values, based on eqs 5-7, are given in Table 1. Citric acid based pKMHA values for FA are nearly identical to the recommended experimental pKMHA values, yet have a ∆FAK of 0.30, wheras NTA based pKMHA values for FA are larger but better approximate ∆FAK at 0.41 (experimental ∆SRFAK ) 0.46). EDTA based pKMHA values for HA are larger by 0.65 log units than the recommended values, yet duplicate exactly the experimental ∆EHAK of 0.75. Therefore, multi-dentate ligands such as NTA and EDTA approximate better the ∆HSK characteristic of LnHS complexation, but do not necessarily improve the estimation of absolute pKMHA values over those estimated with monocarboxylic ligands such as AA and LA. The variation in pKMHA based on single ligand LFERs essentially carries over too much ligand specific characteristics to be representative of average HS binding sites. Although it could be considered to determine multiple (multi-dentate based) LFERs, and average over the obtained pKMHA values to introduce some degree of heterogeneity, it is preferred in this study to base recommended pKMHA values (Table 1) on experimental data. Lanthanide Contraction Effect. Unlike for model organic ligands, L, where ∆LK directly reflects the difference in binding strength between LuL and LaL, humic substances in Model V have variable ∆HSK values among the various mono- and bi-dentate sites. For example, ∆FAK for type A and type B sites are 0.47 and 1.86 and the strongest bi-dentate site has a ∆FAK that is the sum of these, 2.33 (Figure 1). ∆HSKc, the observed increase in conditional binding constants, was found to be dependent on experimental conditions: ∆HSKc increases with pH, and possibly decreases with I (22). Does Model V reproduce this subtle influence of pH on the lanthanide contraction effect, ∆HSKc? The observed pH variation can be explained by considering the following two factors: (1) HS heterogeneity, and (2) denticity of the REEHS complex.
b
Rmse
HS heterogeneity indicates the variety of binding sites that are present in HS, notably the range of carboxylic and phenolic functional groups. By analogy with organic acids where weaker acidic groups (strong proton binders) display stronger metal binding as well as larger ∆LK, the same trend is expected for HS: an increase in pH invokes the stronger binding sites to participate in metal binding and thus increases ∆HSKc. Model V was specifically developed to take into account HS heterogeneity and will therefore, as pH increases, invoke a greater proportion of the weakly acidic type B sites. As mentioned above and illustrated in Figure 1 for FA, the type B sites have a larger ∆FAK than type A sites and will therefore reproduce the observed effect of pH on ∆HSKc. Similarly, Model V’s strongest multi-dentate sites have the largest ∆FAK values and become increasingly involved in metal binding as proton competition decreases with increasing pH. REE Speciation Simulations. Select dissolved REE equilibrium speciation simulations with Model V and the recommended pKMHA values for REE are illustrated here. An average world river scenario (Table S4, Supporting Information) and well-accepted stability constants (25 °C, infinite dilution) for Cl-, SO42-, CO32-, PO43-, and hydrolysis were used to produce a pH speciation diagram for La and Lu, the two end members of the REE series (Figure 4) (16, 34-37). The “active” humic substances concentration was set at 65% and assumed to resemble FA exclusively (26). Fe and Al concentration are ∼1.2 µmol L-1 each and resulted in hydrous ferric oxide (HFO) and gibbsite precipitation at pH > 7 and 6.5 < pH < 7.5, respectively. The outcome suggests that La speciation is dominated by humic complexes from pH 4 upward (Figure 4). Free La3+ and LaSO4+ complexes are significant at acidic pH levels, such as those found in black rivers or bogwaters. However, the higher DOC levels in bogwaters may compensate for this pH effect (38). Lu speciation is dominated by humics over the entire pH range 3-10, and other Lns follow intermediate trends. These results are different from LFER based world river simulations that suggested dominant REE-humic complexation in the neutral pH range only (21), because (1) recommended Model V pKMHA values for HREE result in stronger binding, and (2) the LFER simulation invoked stronger Fe and Al competition effects at high pH VOL. 40, NO. 24, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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(3)
(4)
(5) (6)
(7) (8) (9) (10)
(11) (12)
FIGURE 4. pH diagrams for La (black lines) and Lu (grey lines) speciation in an “average world river” (Table S4, Supporting Information); sum(LnCO3) ) LnCO3+ + Ln(CO3)2- + LnHCO32+. The upper panel has a linear percentage scale, while the lower panel has log [mol L-1] units to illustrate the insignificance of carbonate complexes at alkaline pH.
(13) (14)
(15)
due to different simulation assumptions: (i) 10-fold higher total Fe, Al concentrations, (ii) HFO precipitation suppression, and (iii) mixed ligand complexes of second and third hydrolysis complexes. A similar sensitivity of trace metalHS complexation to Fe and Al competition was observed in other studies (26, 39, 40). The simulation results in Figure 4 suggest that HS out-compete carbonate complexes in natural alkaline freshwaters. This is a departure from the widely held view that dissolved REE speciation in such waters is dominated by carbonate complexes (15, 16, 41).
(16) (17) (18) (19)
Acknowledgments Preliminary results of this study were presented at the 1st International Workshop on Organic Matter Modeling (WOMM), held November 16-18 in Toulon, France. I thank Patricia Merdy and Sandrine Huclier for their organizational efforts at the WOMM, Jianwu Tang for valuable discussions, and one anonymous reviewer and Karen Johannesson for their constructive comments.
(20) (21)
(22)
Supporting Information Available Explanation of symbols and abbreviations, data summary tables, LFER figure, and additional references. This material is available free of charge via the Internet at http:// pubs.acs.org.
(23)
(24)
Literature Cited (1) Choppin, G. R. Comparison of the solution chemistry of the actinides and lanthanides. J. Less-Common Met. 1983, 93, 323330. (2) Wood, S. A. The aqueous geochemistry of the rare earth elements: critical stability constants for complexes with simple 7486
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carboxylic acids at 25 °C and 1 bar and their application to nuclear waste management. J. Geol. Eng. 1993, 34, 229-259. Verplanck, P. L.; Taylor, H. E.; Nordstrom, D. K.; Barber, L. B. Aqueous stability of gadolinium in surface waters receiving sewage treatment plant effluent, Boulder Creek, Colorado. Environ. Sci. Technol. 2005, 39, 6923-6929. Hu, Z. Y.; Richter, H.; Sparovek, G.; Schnug, E. Physiological and biochemical effects of rare earth elements on plants and their agricultural significance: A review. J. Plant Nutr. 2004, 27, 183-220. Henderson, P. Rare Earth Element Geochemistry; Elsevier: Amsterdam, 1984. Elderfield, H.; Upstill-Goddard, R.; Sholkovitz, E. R. The RareEarth Elements in Rivers, Estuaries, and Coastal Seas and Their Significance to the Composition of Ocean Waters. Geochim. Cosmochim. Acta 1990, 54, 971-991. Sholkovitz, E. R.; Shen, G. T. The incorporation of rare earth elements in modern coral. Geochim. Cosmochim. Acta 1995, 59, 2749-2756. Shiller, A. M. Seasonality of dissolved rare earth elements in the lower Mississippi river. Geochem., Geophys., Geosyst. 2003, 3, 1068. Gaillardet, J.; Dupre, B.; Allegre, C. J.; Negrel, P. Chemical and physical denudation in the Amazon River basin. Chem. Geol. 1997, 142, 141-173. Braun, J. J.; Pagel, M.; Herbillon, A.; Rosin, C. Mobilization and redistribution of REEs and thorium in a seyenitic lateritic profile: A mass balance study. Geochim. Cosmochim. Acta 1993, 57, 4419-4434. Buffle, J.; Leppard, G. G. Characterization of aquatic colloids and macromolecules. 1. Structure and behavior of colloidal matter. Environ. Sci. Technol. 1995, 29, 2169-2175. Gaillardet, J.; Viers, J.; Dupre, B., Trace Elements in River Waters. In Treatise on Geochemistry; Holland, E. D., Turekian, K. K., Eds.; Elsevier-Pergamon: Oxford, 2004; 5, pp 225-263. Sholkovitz, E. R. The aquatic chemistry of the rare earth elements in rivers and estuaries. Aquat. Geochem. 1995, 1, 1-34. Viers, J.; Dupre, B.; Polve, M.; Schott, J.; Dandurand, J. L.; Braun, J. J. Chemical weathering in the drainage basin of a tropical watershed (Nsimi-Zoetele site, Cameroon): Comparison between organic-poor and organic-rich waters. Chem. Geol. 1997, 140, 181-206. Lee, J. H.; Byrne, R. H. Complexation of Trivalent Rare-Earth Elements (Ce, Eu, Gd, Tb, Yb) By Carbonate Ions. Geochim. Cosmochim. Acta 1993, 57, 295-302. Millero, F. J. Stability constants for the formation of rare earth inorganic complexes as a function of ionic strength. Geochim. Cosmochim. Acta 1992, 56, 3123-3132. Tipping, E.; Hurley, M. A. A Unifying Model of Cation Binding By Humic Substances. Geochim. Cosmochim. Acta 1992, 56, 3627-3641. Tipping, E. Humic ion-binding model VI: An improved description of the interactions of protons and metal ions with humic substances. Aquat. Geochem. 1998, 4, 3-48. Kinniburgh, D. G.; van Riemsdijk, W. H.; Koopal, L. K.; Borkovec, M.; Benedetti, M. F.; Avena, M. J. Ion binding to natural organic matter: competition, heterogeneity, stoichiometry and thermodynamic consistency. Colloids Surf., A 1999, 151, 147-166. Tipping, E. Modelling the Binding of Europium and the Actinides By Humic Substances. Radiochim. Acta 1993, 62, 141-152. Tang, J.; Johannesson, K. H. Speciation of the rare earth elements in natural terrestrial waters: assessing the role of dissolved organic matter from the modeling approach. Geochim. Cosmochim. Acta 2003, 67, 2321-2339. Sonke, J. E.; Salters, V. J. M. Lanthanide - humic substances complexation. I. Experimental evidence for a lanthanide contraction effect. Geochim. Cosmochim. Acta 2006, 70, 14951506. Sonke, J. E.; Furbish, D. J.; Salters, V. J. M. Dispersion effects of laminar flow and spray chamber volume in Capillary Electrophoresis-Inductively Coupled Plasma-Mass Spectrometry: a numerical and experimental approach. J. Chromatogr. A 2003, 1015, 205-218. Sonke, J. E.; Salters, V. J. M. Determination of NeodymiumFulvic Acid binding constants by Capillary Electrophoresis Inductively Coupled Plasma Mass Spectrometry (CE-ICP-MS). J. Anal. At. Spectrom. 2004, 19, 235-240. Sonke, J. E.; Salters, V. J. M. Disequilibrium artifacts in metal speciation by Capillary Electrophoresis Inductively Coupled Plasma Mass Spectrometry (CE-ICP-MS); theory, simulations and experiments. The Analyst 2004, 129, 731-738.
(26) Tipping, E. Ion Binding by Humic Substances; Cambridge University Press: Cambridge, U.K., 2002. (27) Gustafsson, J. P. WinHumicV For Win95/98/NT; http://www.lwr.kth.se/Vara%20Datorprogram/WinHumicV. (28) Tipping, E. WHAM; http://www.windermere.ceh.ac.uk/Aquatic_Processes/wham. (29) Tipping, E. WHAM - A chemical equilibrium model and computer code for waters, sediments, and soils incorporating a discrete site/electrostatic model of ion-binding by humic substances. Computers and Geosciences 1994, 20, 973-1023. (30) Crawford, M. B. PHREEQEV: The incorporation of a version of Model V for organic complexation in aqueous solutions into the speciation code PHREEQE. Comput. Geosci. 1996, 22, 109116. (31) Milne, C. J.; Kinniburgh, D. G.; Tipping, E. Generic NICA-Donnan model parameters for proton binding by humic substances. Environ. Sci. Technol. 2003, 35, 2049-1059. (32) Bidoglio, G.; Grenthe, I.; Qi, P.; Robouch, P.; Omenetto, N. Complexation of Eu and Tb With Fulvic-Acids As Studied By Time-Resolved Laser-Induced Fluorescence. Talanta 1991, 38, 999-1008. (33) Moulin, V.; Tits, J.; Moulin, C.; Decambox, P.; Mauchien, P.; Deruty, O. Complexation Behavior of Humic Substances Towards Actinides and Lanthanides Studied By Time-Resolved Laser-Induced Spectrofluorometry. Radiochim. Acta 1992, 589, 121-128. (34) Klungness, G. D.; Byrne, R. H. Comparative hydrolysis behavior of the rare earths and yttrium: the influence of termperature and ionic strength. Polyhedron 2000, 19, 99-107. (35) Luo, Y. R.; Byrne, R. H. Carbonate Complexation of Yttrium and the Rare Earth Elements in Natural Waters. Geochim. Cosmochim. Acta 2004, 68, 691-699. (36) Schijf, J.; Byrne, R. H. Determination of SO4b1 for yttrium and the rare earth elements at I ) 0.66 m and T ) 25 C - implications for YREE solution speciation in sulfate-rich waters. Geochim. Cosmochim. Acta 2004, 68, 2825-2837. (37) Morel, F. M. M.; Hering, J. G. Principles and Applications of Aquatic Chemistry; John Wiley & Sons: New York, 1993. (38) Pokrovsky, O. S.; Dupre, B.; Schott, J. Fe-Al-organic colloids control of trace elements in peat soil solutions: results of ultrafiltration and dialysis. Aquat. Geochem. 2005, 11, 241-278. (39) Unsworth, E. R. Warnken, K. W.; Zhang, H.; Davison, W.; Black, F.; Buffle, J.; Cao, J.; Cleven, R.; Galceran, J.; Gunkel, P.; Kalis, E.; Kistler, D.; van Leeuwen, H. P.; Martin, M.; Noel, S.; Nur, Y.;
(40)
(41)
(42)
(43) (44) (45)
(46) (47)
(48)
Odzak, N.; Puy, J.; van Riemsdijk, W.; Sigg, L.; Temminghoff, E.; Tercier-Waeber, M.-L.; Toepperwien, S.; Town, R. M.; Weng, L.; Xue, H. Model Predictions of Metal Speciation in Freshwaters Compared to Measurements by In Situ Techniques. Environ. Sci. Technol. 2006, 40, 1942-1949. Tipping, E.; Rey-Castro, C.; Bryan, S. E.; Hamilton-Taylor, J. Al(III) and Fe(III) binding by humic substances in freshwaters, and implications for trace metal speciation. Geochim. Cosmochim. Acta 2002, 66, 3211-3224. Johannesson, K. H.; Stetzenbach, K. J.; Hodge, V. F.; Lyons, W. B. Rare earth element complexation behavior in circumneutral pH groundwaters: Assessing the role of carbonate and phosphate ions. Earth Planet Sci. Lett. 1996, 139, 305-319. Dobbs, J. C.; Susetyo, W.; Carreira, L. A.; Azarraga, L. V. Competitive-Binding of Protons and Metal-Ions in Humic Substances By Lanthanide Ion Probe Spectroscopy. Anal. Chem. 1989, 61, 1519-1524. Ephraim, J. H.; Marinsky, J. A.; Cramer, S. J. Complex-Forming Properties of Natural Organic-Acids - Fulvic- Acid Complexes With Cobalt, Zinc and Europium. Talanta 1989, 36, 437-443. Lead, J. R.; Hamilton-Taylor, J.; Peters, A.; Reiner, S.; Tipping, E. Europium binding by fulvic acids. Anal. Chim. Acta 1998, 369, 171-180. Carlsen, L.; Bo, P.; Larsen, G. Radionulcide-humic acid interactions dtudied by dialysis. In Geochemical Behavior of Radioactive Waste; Barney, G. S., Navratil, J. D., Schulz, W. W., Eds.; American Chemical Society: Washington, DC, 1984; p 167. Caceci, M. S. The Interaction of Humic-Acid With Europium(Iii) - Complexation Strength As a Function of Load and pH. Radiochim. Acta 1985, 39, 51-56. Maes, A.; Debrabandere, J.; Cremers, A. A Modified Schubert Method For the Measurement of the Stability of Europium Humic-Acid Complexes in Alkaline Conditions. Radiochim. Acta 1988, 44-5, 51-57. Milne, C. J.; Kinniburgh, D. G.; Van Riemsdijk, W. H.; Tipping, E. Generic NICA-Donnan model parameters for metal-ion binding by humic substances. Environ. Sci. Technol. 2003, 37, 958-971.
Received for review March 1, 2006. Revised manuscript received April 23, 2006. Accepted May 8, 2006. ES060490G
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