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SOIL CHEMISTRY

Competitive Adsorption of Heavy Metals in Humic Substances by a Simple Ligand Model

^a Dep. of Renewable Resources, 317 Hamilton Hall, Univ. of Louisiana, Lafayette, LA 70504
^b School of Biosciences, Biology Building, Univ. of Nottingham, University Park, Nottingham NG7 2RD, UK

^* Corresponding author (cjeong{at}louisiana.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS APPENDIX REFERENCES

 
Proton and metal ion binding to humic acids has been recognized as an important factor in controlling metal speciation and mobility in aqueous and soil environments. The binding and competition behavior of humics has not been fully described, however, due to the polydisperse mixtures of natural organic polyelectrolytes with different functional groups. The simplified discrete binding group type of model (Model A) was used in this study. Model A was applied to describe three single metals and their competitive binding on the humic acids. Model A considers only a single carboxyl group type and a single phenolic hydroxyl group type with a variable electrostatic interaction factor that is expressed as a polynomial equation. Metal binding experiments were performed using a batch-type dialysis equilibration method, and mass and charge balance expressions were used to calculate individual components of the system. The fit of Model A suggested that the major binding site for the three metal ions tested (Cd, Zn, and Cu) was a bidentate chelate of the carboxyl groups. Model A described two- and three-metal competition on the humic acids using the intrinsic stability constant for the single metal ion. The model used in this study adequately described the competition of two metals; however, it showed weaknesses when applied to more complex systems, especially when metals with substantially different binding characteristics were present. Model A presented a better prediction with adjusted Cu concentration (2:1:1 Cu/Cd/Zn).

Abbreviations: HA, humic acid.

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS APPENDIX REFERENCES

 
Humic substances from soil and water are major controlling materials for metal speciation, pollutant binding, and nutrient availability. An understanding of metal ion binding and competition remains difficult because of the complexity of humic substances. Humic substances are proposed to be irregular polymers with a number of chemically unidentical acidic functional groups having a variable electric potential (Reuter and Perdue, 1977; Saar and Weber, 1982). The acidic functional groups of humic substances have been considered to be the reactive groups for protons and metal ions in acid–base equilibrium reactions. The acidic character of humic substances is usually attributed to the ionization of carboxylic and phenolic hydroxyl groups (Schnitzer and Skinner, 1965; Murray and Linder, 1984). The degree of acidity, or acid strength, of the colloid is dependent on the nature of the reactive group involved and associated structures on the molecule (Visser, 1982). The carboxyl and phenolic hydroxyl groups thus are considered to be the dominant metal binding sites, although other functional groups (e.g., amines, thiols) might be involved to some extent (Tipping, 1998). Interactions between metal ions and humic substances are of major importance in the migration and redistribution of potentially toxic metal ions.

To describe metal binding with humic substances, it is necessary to develop models in which various simplifications are made because of the complex heterogeneous nature of humic materials and the variable electrostatic interaction between functional groups (Dzombak et al., 1986a, 1986b; Marinsky and Ephraim, 1986; Bartschat et al., 1992; van Riemsdijk and Koopal, 1992; Tipping and Hurley, 1992; Marinsky et al., 1995; Marshall et al., 1995; Milne et al., 1995a, 1995b; Tipping et al., 1995; Jeong, 1998). Several approaches have been used in modeling humic–metal complexation.

Discrete ligand nonelectrostatic models attempt to describe humic molecules as a mixture of a small number of sites and site types, each of which can be defined in terms of a binding reaction. These binding sites are regarded as completely independent of each other, however, and interactions between binding groups are ignored (Turner et al., 1986; Cabaniss and Shuman, 1988; Pinheiro et al., 1994). Nevertheless, such an approach has met with some success. Mountney and Williams (1992) simulated the interaction between humic acid (HA) and metal ions using the coupled model RANDOM-PHREEQE (Murray and Linder, 1983, 1984; Parkhurst et al., 1980), which is based on a discrete ligand model and a general geochemical speciation code. They neglected the effect of macromolecular electrostatic forces.

An electrostatic model was originally applied to the dissociation of acid groups on protein molecules (Tanford, 1961). The simplest form of the electrostatic model considers all functional groups on the humic molecule to be identical with the same intrinsic acid dissociation constant (pK_int), which strictly applies only to the uncharged molecule. The pK_int was modified by an electrostatic interaction factor (W) that was related to the surface electrostatic free energy arising from the surface charge on the molecule. Several researchers have applied electrostatic models to Cu, Al, Mn, Zn, and Cd adsorption by humic substances and synthetic polycarboxylates (Wilson and Kinney, 1977; Young et al., 1982; Young and Bache, 1985; Backes and Tipping, 1987). This simple approach has been criticized on various grounds, however. For example, Masini (1993) evaluated the effects of ignoring electrostatic interactions on ionizable sites in HA. He concluded that the chemical heterogeneity among the titratable groups was the major factor governing the acid–base properties of HAs rather than electrostatic interactions between groups.

Discrete ligand models with electrostatic effects have been suggested (Ephraim et al., 1986; Ephraim and Marinsky, 1986; Tipping and Hurley, 1992; Tipping, 1993a; Marshall et al., 1995; Gustafsson and van Schaik, 2003). Several researchers have applied this type of model to describe ion binding to humic substances by making a variety of arbitrary assumptions concerning the number of functional group types and humic molecule size and structure. For example, Ephraim et al. (1986) and Ephraim and Marinsky (1986) used four functional group types on humic surfaces and electrostatic effects that were suppressed completely at ionic strengths >1.0 M. Tipping and Hurley (1992) and Tipping (1993a) assumed four types of carboxyl and phenolic hydroxyl groups in their Model V. Crawford (1996) produced PHREEQEV that incorporated Model V into PHREEQE (Parkhurst et al., 1980), a geochemical speciation model. Tipping (1994, 1998) developed a hydrochemistry model (WHAM and WHAM-6), which is based on Model V and Model IV, an improved version of Model V. Gustafsson and van Schaik (2003) developed a Windows-based version of Model V (WinHumicV) that is similar to WHAM. These various models describe metal binding by humic substances under a wide range of chemical conditions.

There have been extensive modeling studies on single metal–humic substance binding and the competition between alkaline earth cations and heavy metal ions (Tipping, 1993b; Cabaniss and Shuman, 1988; Kinniburgh et al., 1999). The competition effect was observed between Pb and Al on humics (Mota et al., 1996), Cu binding on fulvic acids in the presence and absence of Mg (Cabaniss and Shuman, 1988), and Cu and Cd binding on humics with and without Ca (Kinniburgh et al., 1999). Until now, few studies have evaluated multimetal competitive binding in humic substances.

The main goal of this research was to describe multimetal competitive binding on the humic surface using the model designated Model A. In many respects, Model A and Model V/VI (Tipping and Hurley, 1992; Tipping, 1998) are similar, based on the discrete ligand model including electrostatics. Model A, however, involves different features such as a fitted P_CC parameter (the proportion of carboxyl groups capable of forming bidentate chelates) instead of a fixed value in Model V and the description of a proportionality factor (F_W) between the electrostatic interaction factor for hydrogen (W_H) and the electrostatic interaction factor for specific metal ions (W_M). In addition, Model A expresses an electrostatic interaction factor using the function of ionic strength with a polynomial-type equation in proton binding rather than the empirical equation in Model V and VI. The model was applied to the binding of single metal ions, and extended to describe metal competition for binding sites. In contrast to single metal binding studies, multimetal competitive binding on humics has not been modeled extensively. We have studied proton dissociation as a necessary prerequisite to modeling metal binding reactions. This model was developed and extended to describe competition between two and three metals (Cd, Zn, and Cu) for binding sites on humics.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS APPENDIX REFERENCES

 
Humic Acid Extraction and Purification
The HA sample used in this study was extracted from soil taken from Benscliffe Wood (map reference: SK519123), near Sutton Bonington, UK. On the same day, the samples were sieved to pass through a 2-mm sieve. One hundred grams of sieved soil was transferred into each of 24 500-mL bottles and the bottles were completely filled with 0.1 M NaOH and sealed to avoid air entry during the extraction process. The reason for exclusion of air is the possibility of structural change at high pH in the presence of O₂. The bottles were shaken for 24 h, after which the supernatant was centrifuged at 10000 rpm for 15 min. The supernatant was filtered with a fine plastic sieve to remove lightweight or floating materials (plant roots and parts of dried foliage, etc.) and immediately acidified to pH 2 with concentrated HCl to precipitate the HA. The humic and fulvic acid fractions were separated by centrifugation at 2500 rpm for 15 min. The supernatant, containing the fulvic acid fraction, was removed and the HA fraction was packed into bags of Visking dialysis membrane (100 mL volume). The dialysis bags were transferred into a mixture of 1% (v/v) HCl and 1% (v/v) HF to remove metallic contaminants and silica. The acidic solution was changed twice at weekly intervals. The acid treatment was followed by dialysis against deionized water, with repeated changes of deionized water at 2-d intervals, until the conductivity of this solution was <20 µS cm^–1 (compared with fresh deionized water, which was 16 µS cm^–1). The purified HA was freeze-dried and stored in an evacuated desiccator in the dark. Measurement of the ash content of the purified HA was by ashing in a muffle furnace at 500°C for 8 h. The ash content of the purified HA sample was 0.93%.

Dialysis Equilibrium Procedure
The dialysis equilibrium experiment was set up by placing each dialysis membrane bag inside glass boiling tubes. The metal ion and HA solution was placed inside the boiling tube but outside of the dialysis bag (external solution). The dialysis bag (internal solution) consisted of the background ionic solution only (Fig. 1 ).

View larger version (14K): [in this window] [in a new window]   Fig. 1. Schematic diagram for the dialysis membrane tube.

Metal Assay by Atomic Absorption Spectrophotometer
The equilibrium concentration of metal in the dialysis bags was measured by flame atomic absorption spectrometry (Varian Spectra AA20, Palo Alto, CA). Standard metal solutions were prepared from 1000 mg L^–1 concentration stocks and were adjusted to the same ionic background of the sample solutions.

Metal Ion Binding Calculation
The batch experimental procedure was used to measure metal ion binding to the HA by the dialysis equilibrium. The dialysis membrane (molecular weight cutoff = 12000) has small pores through which hydrated and free metal ions may migrate into the dialysis bag, while complexed metal ions are excluded. Thus, the concentration of free metal ions in the internal solution is the same as that in the external solution. Therefore, the concentration of free metal ions in the internal solution can be measured easily. Mass and charge balance expressions were used to calculate individual components of the system. The metal ions in this study were always added as nitrate salts. Thus, the concentration of NO₃^– in the bulk solution may be calculated from

[1]

where [M]_B is the concentration of the metal ion (in this case, NO₃^–) outside the double layer, [M]_T is the total concentration of the specified ions in the system, z is the ion charge valence, V_T is the total solution volume, and V_D is the volume of the diffuse double layer (DDL).

The concentration of Na⁺ in the bulk solution was calculated by charge balance, which included consideration of metal hydrous speciation:

[2]

The amount of Na⁺ in the bulk solution may be calculated from

[3]

where Na⁺_B is the amount of Na⁺ in the solution outside the double layer (mol_c).

The amount of Na⁺ in the DDL (Na⁺_D) is deduced by mass balance:

[4]

Therefore, the concentration of Na⁺ in the DDL is as follows:

[5]

The concentration of metal ions in the DDL, [M^z+]_D, was calculated from a Donnan-type equation:

[6]

and the concentration of metal hydroxyl ions from

[7]

The amount (mol) of M^z+ in the DDL may then be calculated from

[8]

and the amount of metal hydroxide MOH^z–1 in the DDL from

[9]

Therefore, the humic surface charge (mol_c kg^–1) that balances the DDL counterions is

[10]

The measured metal concentration is [M]_bag, the concentration of total metal within the dialysis bag. The concentration of metal outside the bag is therefore

[11]

In batch experiments of this kind, there is always some leakage of HA across the dialysis membrane into the bag. Therefore, to calculate the total inorganic metal concentration ([M]_B), we have to correct [M]_bag with the measured ratio R_HA, where

[12]

Thus:

[13]

The concentration (mol_c L^–1) of humic-bound metal outside the dialysis bag is calculated by difference:

[14]

The concentration term [M]_bound includes both chemically complexed metal and metal ions in the DDL.

Speciation of Free Metal
Speciation of total inorganic metal (not humically bound) is described as M^z+ and four monomeric hydrolysis species, M(OH)_i^(z–i)+, where i is an integer such that 1 i 4. Lindsay (1979) summarized the equilibrium constants of metal complexes. The equilibrium constants can be used to speciate metal ions in solution.

The concentration of free M^z+ is calculated from the mass balance for total inorganic (not humically bound) metal:

[15]

Since

[16]

where K_i° is the ith overall metal hydrolysis constant. Substituting for [M(OH)_i^(z–i)+] in Eq. [15] gives

[17]

So that

[18]

Theoretical Consideration
Metal ion binding to HA is considered as a metal proton exchange reaction. The electrostatic interaction factor in Model A is considered specific to the binding ion. A mechanistic explanation for this may be that different ions specifically adsorb in slightly different "planes" relative to the HA surface. Therefore, a given surface charge may correspond to a range of "ion-specific" potentials in several adsorption planes. We therefore denote the electrostatic interaction factor as W_H or W_M for protons or specific metal ions. These are related by a proportionality factor F_W, such that

[19]

The competition between protons and metal ions may be described as the combination of the dissociation constant for the single carboxyl group and the stability constants for metal ion binding. Metal ion binding may be represented by

[20]

where C is a carboxyl group. The apparent and intrinsic stability constants for metal ion binding are then

[21]

Thus, the combination of the dissociation constant for the single carboxyl group and the stability constants for metal ion binding may be described as

[22]

Rearrange this:

[23]

Rearrange using the relationship between W_M and W_H (Eq. [19]):

[24]

Model A can describe four different types of metal binding sites: one carboxyl group; one phenolic hydroxyl group; two carboxyl groups; and one carboxyl group and one phenolic hydroxyl group. The concentration (mol_c kg^–1) of bound metal complexes is denoted as [M] with the subscripts C or to signify a combination with one or more carboxyl or phenolic hydroxyl groups, respectively. Thus, a bidentate chelate with two carboxyl groups is given as [M]_CC, for example.

The number of bidentate carboxyl group sites is calculated as P_CCT_C. The number of carboxyl groups capable of forming single linkages with metals is (1 – P_CC)T_C, where T_C is the total concentration of carboxyl groups. In the case of one carboxyl and one phenolic hydroxyl group binding with a metal ion, we need one more parameter (P_C), which is the proportion of the quantity (1 – P_CC)T_C capable of forming bidentate chelates with a cooperating phenolic hydroxyl group. Therefore the number of (potential) metal–carboxylic and phenolic hydroxyl chelate sites is P_C(1 – P_CC)T_C. The number of carboxyl groups capable of forming exclusively monodentate complexes then becomes (1 – P_C)(1 – P_CC)T_C and the number of phenolic hydroxyl groups capable of only monodentate binding is T – [P_C(1 – P_CC)T_C, where T is the total concentration of phenolic hydroxyl groups. Alternatively, the factors (1 – P_CC)T_C and (1 – P_C)T are used to calculate monodentate binding sites when chelate formation involving both carboxyl and phenolic hydroxyl groups are not considered.

The proportion of free carboxyl groups present in the protonated form, P_CH is given by

[25]

When one carboxyl and one phenolic hydroxyl group binding is considered, the concentration of metal bound to monodentate sites may be calculated from

[26]

The concentration of metal ion bidentate binding sites may be calculated from

[27]

The concentration of metal bound to bidentate sites consisting of one carboxyl and one phenolic hydroxyl group may be calculated from

[28]

When binding of one carboxyl and one phenolic hydroxyl group is considered, the concentration of metal bound to monodentate binding sites in phenolic hydroxyl groups may be calculated from

[29]

where T_A is total acidity. Similarly, the concentration of metal hydroxide ions considered only in monodentate binding sites is calculated as the equations for metal bivalent ions (Eq. [26] and [29]).

The concentration of metal counter-ions in the DDL was included in Model A:

[30]

The total concentration of bound metal may be calculated from the sum of all occupied binding sites and the DDL metal counter-ions:

[31]

The values used throughout for total acidity, total carboxylic acid concentration, molecular radius, molecular weight of the HA, intrinsic acidity constants, and the electrostatic factor are given in Table 1.

[32]

where n is the number of data points.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS APPENDIX REFERENCES

 
Proton Equilibria with Humic Acid
Proton equilibria and ionic strength effect information were required for all metal bindings on the HA using the ion charge balance, which was derived from the titration data (Fig. 2 ). The details of the surface charge equation have been presented elsewhere (Marshall et al., 1995; Jeong, 1998). The third-order polynomial expressions described the intrinsic acidity constants with various ionic strengths in Model A (Table 1).

View larger version (25K): [in this window] [in a new window]   Fig. 2. Plots of humic charge against pH as a function of ionic strength (NaNO3). The points are experimental values, and the lines are Model A fits. Model A was fitted to all six titrations as a single data set.

View this table: [in this window] [in a new window]   Table 2. Summary of constants for Cd binding resolved using Model A.

M,

C

The analysis above was based on a parsimonious approach to model parameterization, in which bidentate complex formation constants were taken as the product of their monodentate components. Figure 3 shows the result of fitting Model A (Option 3, Table 4) to the Cd data set (n = 102). The data were presented as the concentration ratio of complexed Cd (chemically bound + Cd²⁺ and CdOH⁺ in the DDL) to free Cd (all unbound inorganic species). Measured data are compared with values produced by Model A. The eight individual data subsets are represented by different symbols. This clearly illustrats a good agreement between the model and the experimental values of Cd.

View larger version (16K): [in this window] [in a new window]   Fig. 3. Model A applied to metal binding data; the bound species considered included: monodentate Cd–carboxyl complex (CdC), bidentate Cd–carboxyl complex (CdCC), and bidentate Cd–(carboxyl + phenolic hydroxyl) complex (CdC). The background electrolyte was 0.1 M NaNO3. Data are presented as the natural log of the ratio of bound to free Cd concentrations ([Cd]bound/[Cd]free), modeled vs. measured. The concentration of Cdbound includes all Cd–humic acid complex forms and Cd held as a counter ion; Cdfree includes all free inorganic species. Different symbols show individual data subsets. The solid line indicates a 1:1 relationship. The values of the optimized Model A parameters were: –log intrinsic stability constant for metal complexation to a monodentate carboxyl binding site = 0.290, –log intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site = 4.76, proportion of carboxyl groups capable of forming bidentate chelates = 0.051, proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group = 0.950, and proportionality factor FW = 0.169; residual standard deviation = 0.519, n = 102.

View this table: [in this window] [in a new window]   Table 4. Summary of Cu constants resolved using Model A.

View this table: [in this window] [in a new window]   Table 3. Summary of Zn constants resolved using Model A.

The description of the data was improved (Options 7 and 8 vs. 5 and 6) by adding the species ZnC (RSD of 3.37 x 10^–1 [Option 7] and 3.73 x 10^–1 [Option 8], compared with 4.52 x 10^–1 [Option 5] and 4.78 x 10^–1 [Option 6]). The Zn model responded with the data, thus showed a close agreement with Cd in the significant species, including MC, MCC, and MC. Monodentate binding with phenolic hydroxide groups and complexation of monovalent metal hydroxide ions were apparently not important. The results from Option 3 were selected for further examination by comparison with Cd. Figure 4 represents the results of fitting Model A (Option 3) to the Zn data set (n = 42). The data are presented as the ratio of complexed Zn [chemically bound + Zn²⁺ and Zn(OH)⁺ in the DDL] to free Zn (all unbound inorganic species). Measured data are compared with values produced by Model A; the three individual data subsets are represented by different symbols.

View larger version (13K): [in this window] [in a new window]   Fig. 4. Model A applied to metal binding data; the bound species considered included: monodentate Zn–carboxyl complex (ZnC), bidentate Zn–carboxyl complex (ZnCC), and bidentate Zn–(carboxyl + phenolic hydroxyl) complex (ZnC). The background electrolyte was 0.1 M NaNO3. Data are presented as the natural log of the ration of bound to free Zn concentrations ([Zn]bound/[Zn]free), modeled vs. measured. The concentration of Znbound includes all Zn–humic acid complex forms and Zn held as a counter ion; Znfree includes all free inorganic species. Different symbols show individual data subsets. The solid line indicates a 1:1 relationship. The values of the optimized Model A parameters were: –log intrinsic stability constant for metal complexation to a monodentate carboxyl binding site = 0.968, –log intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site = 4.52, proportion of carboxyl groups capable of forming bidentate chelates = 0.171, proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group = 0.883, and proportionality factor FW = 0.259; residual standard deviation = 0.326, n = 42.

M,

View larger version (13K): [in this window] [in a new window]   Fig. 5. Model A applied to metal binding data; the bound species considered included: monodentate Cu–carboxyl complex (CuC), bidentate Cu–carboxyl complex (CuCC), and bidentate Cu–(carboxyl + phenolic hydroxyl) complex (CuC). The background electrolyte was 0.1 M NaNO3. Data are presented as the natural log of the ratio of bound to free Cu concentrations ([Cu]bound/[Cu]free), modeled vs. measured. The concentration of Cubound includes all Cu–humic acid complex forms and Cu held as a counter ion; Cufree includes all free inorganic species. Different symbols show individual data subsets. The solid line indicates a 1:1 relationship. The values of the optimized Model A parameters were: –log intrinsic stability constant for metal complexation to a monodentate carboxyl binding site = 0.042, –log intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site = 4.04, proportion of carboxyl groups capable of forming bidentate chelates = 0.126, proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group = 0.585, and proportionality factor FW = 0.397; residual standard deviation = 0.219, n = 26.

C

A comparison of several model options in Model A suggests that bidentate carboxyl binding sites dominated metal adsorption on the HA (Tables 2, 3, and 4). There was a mechanistic deficiency inherent in Model A, however, because it included a very restricted range of chemical heterogeneity. The simple model assumed just two binding group types (carboxyl and phenolic hydroxyl groups) with three to seven adjustable parameters. Much of the "flexibility" for parameterization in Model A was contained within the relationship between W_H and W_M through the proportionality factor, F_W. The resolved values of F_W were quite low (less than unity), so that W_M < W_H. This suggests that metal ions may be subject to a lower electrostatic potential than protons. In simple mechanistic terms, the metals were adsorbed at a "plane" that was slightly farther from the humic surface than the plane of proton adsorption. In addition, the resolved values of F_W for each metal were also slightly different.

Competition Between Cadmium and Zinc
Model A was applied to Cd and Zn competition using the competition data set. The complex species considered were MC, MCC, and MC, as suggested from the previous studies of single-metal complexation. The values of model parameters used were also obtained from the single-metal complexation studies using Model A (Tables 2 and 3), and were applied to the Cd and Zn competition data set without further optimization. The parameter used to illustrate the goodness of model fit was the same as that for single metal studies: the natural log of the ratio of complexed metal to free metal ion concentrations. Measured values were compared with the predicted values produced by the parameterized Model A (Fig. 6 ).

View larger version (13K): [in this window] [in a new window]   Fig. 6. Model applied to Cd and Zn competition data; Cd and Zn were present in solution. The background electrolyte was 0.1 M NaNO3. Data are presented as the natural log of the ratio of bound to free metal ions ([M]bound/[M]free), modeled vs. measured. The bound species considered in Model A included: monodentate Cd–carboxyl complex (CdC), bidentate Cd–carboxyl complex (CdCC), bidentate Cd–(carboxyl + phenolic hydroxyl) complex (CdC), monodentate Zn–carboxyl complex (ZnC), bidentate Zn–carboxyl complex (ZnCC), and bidentate Zn–(carboxyl + phenolic hydroxyl) complex (ZnC). The values of constants and parameters used in Model A were: for Cd, –log intrinsic stability constant for metal complexationto a monodentate carboxyl binding site (pßintM,C)=0.290, –log intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site (pßintM,)=4.76, proportion of carboxyl groups capable of forming bidentate chelates (PCC) = 0.051, proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group (PC) = 0.951, and proportionality factor FW = 0.169; for Zn,pßintM,C=0.968, pßintM,=4.52, PCC = 0.171, PC = 0.883, and FW = 0.259.

View this table: [in this window] [in a new window]   Table 5. Analysis of competition for adsorption between Cd and Zn using Model A. The range of (total) concentration of metal was 5.00 x 10–5 to 5.00 x 10–4 M; ln[RM] represents the natural log of the ratio of [M]bound and [M]free where [M] is the metal ion concentration; predicted ln[RM] indicates the difference between the predicted ln[RCd] and the predicted ln[RZn].

View larger version (12K): [in this window] [in a new window]   Fig. 7. Model applied to Cd, Zn, and Cu competition; total concentration of each metal ion ([M]) was 2.50 x 10–4 M and the mole ratio of Cd/Zn/Cu was 1:1:1. The background electrolyte was 0.1 M NaNO3. The bound species considered included: monodentate Cd–carboxyl complex (CdC), bidentate Cd–carboxyl complex (CdCC), bidentate Cd–(carboxyl + phenolic hydroxyl) complex (CdC), monodentate Zn–carboxyl complex (ZnC), bidentate Zn–carboxyl complex (ZnCC), bidentate Zn–(carboxyl + phenolic hydroxyl) complex (ZnC), monodentate Cu–carboxyl complex (CuC), bidentate Cu–carboxyl complex (CuCC), and bidentate Cu–(carboxyl + phenolic hydroxyl) complex (CuC). The values of constants and parameters used were: for Cd, intrinsic stability constant for metal complexation to a monodentate carboxyl binding site (pßintM,C) = 0.290, intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site (pßintM,)=4.76, proportion of carboxyl groups capable of forming bidentate chelates (PCC) = 0.051, proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group (PC) = 0.951, and proportionality factor FW = 0.169; for Zn,pßintM,C=0.969,pßintM,=4.52, PCC = 0.172, PC = 0.887, and FW = 0.260; for Cu,pßintM,C=0.042, pßintM,=4.04, PCC = 0.126, PC = 0.585, and FW = 0.397.

View larger version (13K): [in this window] [in a new window]   Fig. 8. Model applied to Cd, Zn, and Cu competition; total metal concentrations ([M] = 2.00 x 10–4 M for Cd and Zn, 1.00 x 10–4 M for Cu) were in the ratio Cd/Zn/Cu = 2:2:1. The background electrolyte was 0.1 M NaNO3. The bound species considered included: monodentate Cd–carboxyl complex (CdC), bidentate Cd–carboxyl complex (CdCC), bidentate Cd–(carboxyl + phenolic hydroxyl) complex (CdC), monodentate Zn–carboxyl complex (ZnC), bidentate Zn–carboxyl complex (ZnCC), bidentate Zn–(carboxyl + phenolic hydroxyl) complex (ZnC), monodentate Cu–carboxyl complex (CuC), bidentate Cu–carboxyl complex (CuCC), and bidentate Cu–(carboxyl + phenolic hydroxyl) complex (CuC). The values of constants and parameters used were: for Cd, intrinsic stability constant for metal complexation to a monodentate carboxyl binding site (pßintM,C)=0.290, intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site (pßintM,)=4.76, proportion of carboxyl groups capable of forming bidentate chelates (PCC) = 0.051, proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group (PC) = 0.951, and proportionality factor FW = 0.169; for Zn,pßintM,C=0.969, pßintM,=4.52, PCC = 0.172, PC = 0.887, and FW = 0.260; for Cu,pßintM,C=0.042, pßintM,=4.04, PCC = 0.126, PC = 0.585, and FW = 0.397.

Model A clearly predicted a higher affinity of Cu for HA binding sites but there was little difference between the affinities for Cd and Zn. The binding affinity of Cd (both measured and modeled values) was slightly higher than that of Zn in the case of three-metal competition, as demonstrated in Fig. 8.

Implications of the Electrostatic Factors in Model A
Specific adsorption of ions by HA was affected by the type of binding site present at the surface, and the electrostatic potential in the vicinity of the adsorption site. In the model investigated here, the effect of the electrostatic potential was represented by an electrostatic interaction factor, W. Our knowledge, however, about the relationship between the electrostatic interaction factor for protons (W_H) and that for metal ions (W_M) is at present very limited. Hence, this section evaluated the sensitivity of Model A to the values of F_W, which may partly determine the ability of the model to fit experimental data during parameterization.

Humic substances exhibit a variable surface charge, which gives rise to an electric field around the ion binding sites. This electric field exerts an influence on the affinity of the humic binding sites for ions in solution. Proton and metal ions may be assumed to be specifically bound in different "planes of adsorption" close to charged surfaces (Barrow, 1985, 1986a, 1986b). Thus, metal ions may experience a different electrostatic field strength than those in which protons are exposed. It therefore follows that the relationship between surface charge and electrostatic potential differs for individual specifically adsorbed cations. Some models such as the NICA–Donnan model (Milne et al., 1995b; Benedetti et al., 1995, 1996; Kinniburgh et al., 1996) also contained descriptions of dissimilar behavior by metals and protons with differences in the chemical adsorption constant. Their heterogeneity parameter, N_i, could partly represent differences in the electrostatic model parameter, W .

The relationship between the electrostatic factor for metal ions (W_M) and that for protons (W_H) may be described with a proportionality factor, F_W where W_M = F_WW_H. The equation describing the influence of electrostatic interaction on ion binding may be written as

[33]

which describes the relationship between the apparent exchange constant (log₁₀ß_app^M,H) and surface charge (Z). Plotting log₁₀ß_app^M,H against Z will produce a straight line with intercept log₁₀ß_int^M,H and gradient 2W_H(1 – F_Wz) (Young et al., 1981, 1982). Thus, if the surface charge is zero, the apparent metal–proton exchange constant is coincident with the intrinsic exchange constant.

To illustrate the sensitivity of Model A to the value of F_W, the complexation of Cd was described using a distribution coefficient (K_d) that represented the relative binding affinity of the humic surface. The distribution coefficient was given by the ratio of the concentration of bound Cd to the concentration of free Cd ions in solution (mol_c kg^–1/mol_c L^–1),

[34]

Figure 9 shows the relationship between log₁₀(K_d) and F_W for Cd at a number of hypothetical values for surface charge expressed in relation to the total carboxyl content (T_C), including zero, ±T_C and ±T_C/2 (where T_C had a value of 3.13 mol_c kg^–1). Ionic strength was fixed at 0.01 M, pH at 5.50, and [Cd²⁺ ] at 10^–8 M. The range of variation in log₁₀(K_d) across the range 0 F_W 1 was 1 to 7. The various trends shown in Fig. 9 represent the relationship between the values of F_W and that of log₁₀(K_d) under all likely surface charge conditions.

View larger version (13K): [in this window] [in a new window]   Fig. 9. Testing the sensitivity of Model A [through the distribution coefficient log10(Kd)] to the value of proportionality factor FW at various values of surface charge (Z). The free Cd ion concentration was 1.00 x 10–8 M; pH = 5.50; ionic strength was 0.01 M (NaNO3); total carboxyl group concentration (TC) was 3.13 molc kg–1. The values of constants and parameters used for Cd were: –log intrinsic stability constant for metal complexation to a monodentate carboxyl binding site = 0.290, –log intrinsic stability constant for metal complexation to a monodentate phenolic hydroxyl binding site = 4.76, proportion of carboxyl groups capable of forming bidentate chelates = 0.051, and proportion of monodentate binding carboxyl groups capable of forming bidentate chelates to a phenolic hydroxyl group = 0.951.

It is useful to examine the trends with respect to the resolved values of F_W (Cd = 0.169, Zn = 0.260, Cu = 0.397). For Cd and Zn, it appears that these should have greater binding when Z = T_C compared with a surface charge equivalent to –T_C. Considering only the effect of surface charge, this would seem to contradict the normal expectation that a negative value for Z should increase metal binding. Figure 9 represents somewhat contrived conditions, however, in that the pH was constant at 5.50. The contrasts shown (Z = –T_C vs. Z = T_C) arise because of greater competitive proton binding, when Z was negative, under conditions of constant (H⁺) activity.

	CONCLUSIONS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS APPENDIX REFERENCES

 
The analysis of proton equilibrium was the first step in the characterization of specifically adsorbing ions on the humic surfaces because the proton is the primary adsorbing species for the HAs under environmental conditions (van Riemsdijk and Koopal, 1992). Model A predicted that the dominant adsorption site for Cd, Zn, and Cu was a bidentate binding site comprising only carboxyl groups. The preference for bidentate binding sites in metal complexation may depend on the metal/HA ratio. At lower metal/HA ratios, there is a greater possibility for bidentate metal complexation to occur. Theoretically this should not affect the resolution of the binding models. Higher metal/HA ratios might be unrealistic, however, in comparison with the natural environment. The concentration of dissolved HA in estuarine waters typically ranges from undetectable to 2 mg L^–1 in estuaries of the northeastern USA (Mayer, 1985). The concentration of Cd in fresh water was found by Bowen (1979) to range from 1 x 10^–5 to 3 x 10^–3 mg L^–1. Thus, gravimetric ratios of HA/Cd in the natural environment are up to 200000. Tipping and Hurley (1992) used Cd data for their Model V, with a concentration range from 0.112 to 44.8 mg L^–1 and HA at 80.0 mg L^–1. The ratios of HA/Cd thus ranged from 1.79 to 714.

The metal ion concentration in Tipping and Hurley's (1992) study was in the upper range of values for natural waters, and a lower concentration of metal ions may give rise to an unacceptable scatter in the binding data (Backes and Tipping, 1987). Our study used metal concentrations (Fig. 3) that ranged from 0.336 to 112 mg L^–1. The ratios of HA/Cd varied from 4.46 to 1488. Thus, the data set used in this study was of similar HA/metal ratio to that used by other researchers and natural environmental conditions.

The adaptability of Model A was associated with the flexibility derived from a variable electrostatic term (W_M and W_H) even though there was a simplified number of binding group types. For example, Model V and VI (Tipping and Hurley, 1992; Tipping, 1998) considered a greater number of binding group types than Model A.

To test the validity of the model parameterization, the resolved values obtained from single-metal studies were applied to metal competition trials without further optimization. Model A demonstrated weaknesses when applied to multimetal systems, especially when metals with substantially different binding characteristics were present. Studies with Zn and Cd gave good predictions with or without reparameterization of the model. Introduction of Cu, however, which has a greater binding affinity than Zn and Cd, produced a poor fit to Cd and Zn binding.

	APPENDIX

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS APPENDIX REFERENCES

 

	NOTES

TOP NOTES