Environ. Sci. Technol. 1997, 31, 448-453
Modeling Sorption of Trace Metals on Natural Sediments by Surface Complexation Model FEIYUE WANG* AND JINGSHENG CHEN Department of Urban & Environmental Sciences, Peking University, Beijing 100871, People’s Republic of China WILLIS FORSLING Division of Inorganic Chemistry, Lulea˚ University of Technology, S-971 87 Lulea˚, Sweden
The potential possibility of the surface complexation models to describe the sorption of trace metals on natural sediments has been suggested since the late 1980s, which should be of natural importance to predict the bioavailability of trace metals in aquatic sediments. This possibility was tested based on 11 geographically and hydrologically diverse natural aquatic sediment samples. The sorption of Cu(II), Zn(II), Pb(II), and Cd(II) on these sediments was studied by both sorption isotherm and pH-edge sorption experiments. The experimental sorption data were fit well by the surface complexation model (the double-layer model). The linear free energy relationship (LFER) between the surface complexation constants (Ks) of trace metals on the sediments and the corresponding first-hydrolysis constants (*K1) was observed, which could be expressed as log Ks ) a log *K1 + b. The slope of the linear regression, a, was dependent on the sediment composition: a ) 0.19TOC - 0.09Oxides + 1.31 (n ) 11), where TOC (%) is the total organic carbon and Oxides (%) include reactive iron oxide, amorphous iron oxide, cryptocrystalline manganese oxide, and aluminum oxide. The results strongly suggest that the sorption of trace metals on natural sediments could be described reasonably by the surface complexation model and predicted potentially from the relationships between Ks and *K1 and the sediment composition.
Introduction The accumulation of trace metals in aquatic sediments has been studied intensively since the 1970s. It is now well documented that a prominent fraction of trace metals introduced into the aquatic environment is associated with suspended or bottom sediments (1-3). To describe the distribution of trace metals between sediment and water, the difficulties lie not only in the complexity and diversity of aquatic sediments but also in the surface charge carried by the colloidal particles, which may influence the sorption significantly. The currently existing models, including partition coefficient model, Langmuir model, Freundlich model, and ion exchange model, all neglect the electrostatic influences of charged surfaces of aquatic sediments. To incorporate the electrostatic influences, the surface complexation model was introduced in the 1980s (4, 5). * Corresponding author present address: INRS-Eau, Universite´ du Que´bec, 2700 rue Einstein, C.P. 7500, Sainte-Foy, Que´bec, Canada G1V 4C7. Fax: 1-418-654-2600; E-mail:
[email protected].
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The surface complexation model was originally proposed by Stumm and Schindler (6, 7) to describe the adsorption of H+ and metal ions at the pure oxide-water interface in terms of ion complexation by specific sites on the adsorbents. Since then, a number of modifications involving different descriptions of the electrical double layer have been suggested, among which the most common ones are the constant capacitance model (6-8), double-layer model (6, 9) and triple-layer model (10, 11). Experimental studies have also indicated that the surface complexation model can also be applied to the surfaces of carbonates, sulfides, disulfides, phosphates, and biological materials in solution as well as to the hydrous oxides (12). The surfaces of the natural sediments contain some similar functional groups, e.g., -OH, -COOH, -SH, and -NH2, that can interact with H+ and metal ions, which are analogous to the hydrous oxide surfaces (12). Therefore the surface complexation model may also be applied to the sorption of trace metals in the natural sediment-water systems (4, 5). However, compared to the pure oxides or other minerals, the natural sediment is a mixture of impure amorphous oxides, clays, and humic materials characterized by its extreme complexity and diversity. To verify the possibility of applying the surface complexation model, further studies based upon a large set of representative sediment samples are urgently needed. In this paper, the surface complexation model (the doublelayer model) was used to interpret the Cu(II), Zn(II), Pb(II), and Cd(II) sorption on 11 geographically and hydrologically diverse natural aquatic sediment samples. The model was validated and tested by a large set of experimental sorption data (both isotherm sorption and pH-edge sorption). The linear free energy relationships (LFER), relating the surface complexation constants of trace metals on natural sediments to the corresponding first-hydrolysis constants, were presented and compared with those observed for the pure minerals. Model Consideration. Although the constant capacitance model (6-8), double-layer model (6, 9), and triple-layer model (10, 11) are differed in the types of surface species that are allowed within specific physical locations or layers extending away from the surface and in the parameters of the electrostatic model that each employs, it has been reported that all the models can represent the experimental data equally well (13). Compared with the other two models, the doublelayer model fixes the relationship between the surface charge and potential by the electric double-layer theory. Because of its simplicity, fewer model parameters and subjective factors, the diffuse layer model is chosen to fit the experimental data in this work. According to the double-layer model, the sorption of trace metal ions (M2+) on the sediment surface may be considered as the following surface complexation reaction (we prefer to use tSH rather than tSOH to represent the sediment surface because of the possible existence of -SH and -NH2 as well as -OH and -COOH in natural sediments): int Ks,M
tSH + M2+ y\z tS-M+ + H+ int The intrinsic surface complexation constant, Ks,M , then is defined as
int Ks,M )
{tS-M+}{H+} {tSH}[M2+] {tS-M+}[H+]eφF/RT
)
{tSH}[M2+]
S0013-936X(96)00270-2 CCC: $14.00
1997 American Chemical Society
3.75 3.88 4.90 5.12 4.60 4.80 4.85 5.08 5.27 5.34 5.58 2.29 2.32 1.60 1.56 2.44 2.50 2.35 2.00 1.93 2.16 2.18 92.1 66.0 20.5 10.3 51.0 59.5 92.0 95.7 110.8 103.1 148.8 12.9 11.7 8.8 6.9 10.6 11.2 13.2 13.4 14.2 13.8 16.0 8.2 8.0 30.2 20.2 21.7 13.0 7.5 5.8 5.2 7.5 6.4 13.5 17.4 5.9 3.6 8.6 10.7 16.9 20.7 25.1 17.0 20.7 2.33 1.11 0.44 0.18 0.33 0.42 0.83 1.62 2.31 1.90 3.34 5.59 5.12 5.29 5.36 5.47 6.16 7.18 7.36 11.94 9.20 12.24 0.10 0.08 0.05 0.05 0.06 0.07 0.10 0.11 0.14 0.07 0.05 0.029 0.003 0.005 0.003 0.021 0.022 0.042 0.054 0.052 0.021 0.008 3.19 2.72 2.68 2.63 3.02 3.67 5.12 3.07 4.92 3.60 4.43 0.46 0.11 0.14 0.06 0.24 0.29 0.41 0.36 0.56 0.21 0.89 0.12 0.07 0.06 0.03 0.04 0.07 0.14 0.10 0.09 0.05 0.17 0.35 0.20 0.23 0.28 0.21 0.39 0.47 0.78 0.71 1.07 0.86
TOC (%) total Al (Al %) total Mn (Mn %) crypt MnOx (Mn %) total Fe (Fe %) am FeOx (Fe %) react FeOx (Fe %) Cd (mg/kg) Pb (mg/kg) Zn (mg/kg) river
Amur Nen Luanhe Yellow Huaihe Han Yangtze Qiantang Min Bei Pearl
sample
HLa1 NNa1 LNa1 HGa3 HIa1 HNa1 CGa4 QTa1 MNa1 BEa1 ZHa1
Cu (mg/kg)
TABLE 1. Characteristics of Sediments Used in This Study
Sediment Samples. Eleven relatively unpolluted surficial sediment samples were collected from 11 geographically and hydrologically diverse rivers in eastern China in the summer of 1992 to provide a diverse range of geographic, geologic, and hydrologic conditions and a wide range in chemical and physical parameters. The samples were scooped directly below the water and contained in polyethylene bags. The sorption capacity and intensity of trace metals on sediments are dependent on their grain size distributions. To reduce the grain size effect, from consideration of factors such as reasonability of sediment-contaminant chemistry, mineralogy, ease of sampling, and separation (14, 15), we select the Zn(II) > Cd(II), the same order as that observed on sorption on the sediment surfaces. Good correlations between surface complexation constants and the first-hydrolysis constants have been reported for SiO2 (26), γ-Al2O3 and amorphous iron oxide (11, 27), R-FeOOH and marine humics (28), and the diagenetic iron and manganese oxyhydroxides in lacustrine sediments (29). Mouvet and Bourg (4) have also reported a correlation between the surface complexation constants and the hydrolysis constants of Cu, Zn, and Cd on the Meuse River sediments. For the 11 diverse natural river sediments from the rivers in eastern China, the similar linear free energy relationships between the surface complexation constants of Cu, Zn, Pb, and Cd (from Table 2) and the corresponding first-hydrolysis constants (*K1, data from ref 30) are also observed, which can be expressed as
TABLE 3. LFERs between Log Ks and Log *K1 of Trace Metals (Log Ks ) a Log *K1 + b) a
b
natural river sediments: Amur 1.25 7.94 Nen 1.14 6.85 Luanhe 0.80 2.55 Yellow 0.80 2.46 Huaihe 0.85 3.19 Han 0.84 3.24 Yangtze 0.87 3.51 Qiantang 0.87 3.59 Min 0.73 2.86 Bei 0.67 2.82 Pearl 0.71 3.35 Meuse 0.95 5.60 diagenetic iron oxyhydroxides McFarlane Lake 1.28 17.81 Clearwater Lake 0.52 8.09 diagenetic manganese oxyhydroxides McFarlane Lake 1.25 17.84 marine particulates as oxides 0.87 6.60 as organic matter 1.61 13.60 seawater humic acid 1.61 18.61 pure minerals SiO2 0.66 0.13 R-FeOOH 0.75 3.97 am Fe2O3‚H2O 0.56 0.66 γ-Al2O3 0.57 -0.29
R2
source
0.99 0.99 0.94 0.94 0.97 0.96 0.99 0.99 0.97 0.98 0.98
this study this study this study this study this study this study this study this study this study this study this study 4
0.93 0.87
29 29
0.94
29 28 28 28 26 28 11, 27 11,27
log Ks ) a log *K1 + b where a is the slope of the line and b is the y-axis intercept. For four trace metals studied, the coefficients of determination, R2, for the 11 sediments are all above 0.90. The LFERs of the 11 sediments in this study and those of pure solids and other aquatic particulates available from literature are all listed in Table 3 and shown in Figures 4 and 5. This correlation suggests that the surface complexation reactions of trace metals with the sediment surface are somewhat similar as and related to the solution complexation reactions with H2O, and the tSH surface ligand is slightly stronger than the H-OH solution ligand. The LFER between them makes it possible to predict the sorption of other trace metals on natural sediments when their hydrolysis constants are available.
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FIGURE 4. Correlations between log Ks and log *K1 for the natural sediments from 11 rivers in eastern China. Extension to Other Sediments. A close insight on the slope of the linear regression between log Ks and log *K1, a, reveals that the minerals and organic matter have very different a values (Table 3). All the pure inorganic minerals (oxides) that appear in Table 3 (SiO2, amorphous iron oxide,
Laboratory of the U.S. EPA are gratefully acknowledged for their support on the FITEQL and MINTEQA2 programs. We thank A. Tessier, H. Tang, and three anonymous reviewers for their critical comments on the manuscript.
Literature Cited
FIGURE 5. Correlations between log Ks and log *K1 for the various solids-water systems. Data for SiO2 are taken from ref 26; γ-Al2O3 and amorphous Fe2O3 are from refs 11 and 27; r-FeOOH, seawater humic acid, and marine particulates are from ref 28; diagenetic iron and manganese oxyhydroxides are from ref 29. The shadow area represents the natural sediments from this study. R-FeOOH, and γ-Al2O3) have quite low slopes (a e 0.87) as compared with humics (a ) 1.61), while for the naturally occurring iron and manganese oxyhydroxides, the slope can be very low (0.52) or very high (1.28), probably dependent on the quantity of humic substances coated on their surfaces (29). The slopes for the natural sediments vary in a wide range (0.67-1.25), which is among those of the pure oxides, diagenetic iron and manganese oxyhydroxides, and organic matter. It could be expected that the slope of the linear regression between log Ks of trace metals on the natural sediment and log *K1 is controlled by the composition (e.g., the contents of oxides and organic matter) of the sediment. The multi-regression analysis confirmed that with the following equation:
a ) 0.19TOC - 0.09Oxides + 1.31
(n ) 11)
where TOC is the total organic carbon in sediment (%) and Oxides represents the total content of reactive iron oxide, amorphous iron oxide, cryptocrystalline manganese oxide and aluminum oxide (%). The coefficient of determination, R 2, is 0.76, and the adjusted R′2 is 0.70. Although for a given solid with constant composition it has been suggested the y-intercept of the linear regression between log Ks and log *K1, b, is related to the surface site density of the solid (28), no significant correlation was found in this study, probably because the 11 sediments have very different compositions (Table 1). Based on the 11 diverse natural sediments, our results suggest that the sorption of trace metals on the natural sediments could be well described by the surface complexation model and potentially predicted from the relation between the surface complexation constants and the firsthydrolysis constants as well as the sediment compositions, although the detailed mechanism still remains unknown.
Acknowledgments This work was supported by the National Science Foundation of China. J. C. Westall and Athens Environmental Research
(1) Fo¨rstner, U.; Wittmann, G. Metal Pollution in the Aquatic Environment, 2nd ed.; Springer-Verlag: Berlin, 1981; 486 pp. (2) Salomons, W.; Fo¨rstner, U. Metals in the Hydrocycle; SpringerVerlag: New York, 1984; 349 pp. (3) Fo¨rstner, U. Contaminated Sediments; Lecture Notes in Earth Sciences Vol. 91; Springer-Verlag: New York, 1989; 157 pp. (4) Mouvet, C.; Bourg, A. C. M. Water Res. 1983, 17, 641. (5) Loux, N. T.; Brown, D. S.; Chafin, C. R.; Allison, J. D.; Hassan, S. M J. Chem. Speciation Bioavailability 1989, 1, 111. (6) Stumm, W.; Huang, C. P.; Jenkins, S. R. Croat. Chem. Acta 1970, 42, 223. (7) Schindler, P. W.; Gamsja¨ger H. Kolloid Z. Z. Polym. 1972, 250, 759. (8) Hohl, H.; Stumm, W. J. Colloid Interface Sci. 1976, 55, 281. (9) Dzombak, D. A.; Morel, F. M. M. Surface Complexation Modeling, Hydrous Ferric Oxide; John Wiley & Sons: New York, 1990; p 43. (10) Yates, D. E.; Levine, S.; Healy, T. W. J. Chem. Soc. Faraday Trans. 1974, 70, 1807. (11) Davis, J. A.; James, R. O.; Leckie, J. O. J. Colloid Interface Sci. 1978, 63, 480. (12) Stumm, W. Chemistry of the Solid-Water Interface; John Wiley & Sons: New York, 1992; 428 pp. (13) Westall, J.; Hohl, H. Adv. Colloid Interface Sci. 1980, 12, 265. (14) Horowitz, A. J.; Elrick, K. A. Appl. Geochem. 1987, 2, 437. (15) Horowitz, A. J.; Elrick, K. A. In Chemical and Biological Characterization of Sludges, Sediments, Dredge Spoils, and Drilling Muds; Lichtenberg, J. J.; Winter, J. A., Weber, C. I., Fradkin, L., Eds.; ASTM STP 976; ASTM: Philadelphia, 1988; p 114. (16) Chao, T. T.; Zhou, L. Soil Sci. Soc. Am. J. 1983, 47, 225. (17) Chao, T. T. J. Geochem. Explor. 1984, 20, 101. (18) Huang, C. P. In Adsorption of Inorganics at Solid-Liquid Interfaces; Anderson, M. A., Rubin, A. J., Eds.; Ann Arbor Science: Ann arbor, MI, 1981; p 183. (19) Westall, J. FITEQL: A Computer Program for Determination of Chemical Equilibrium Constants from experimental Data, Version 2.0; Technical Report 82-02; Dept. of Chemistry, Oregon State University: Corvallis, OR, 1982. (20) Wang, F. Ph.D. Dissertation, Peking University, 1995. (21) Wang, F.; Chen, J.; Chen, J.; Forsling, W. Water Res. In press. (22) Meybeck, M.; Friedrich, G.; Thomas, R.; Chapman, D. In Water Quality Assessments; Chapman, D., Ed.; Chapman & Hall: London, 1992; p 239. (23) Quality Criteria for Water (Gold Book); U.S. Environmental Protection Agency: Washington, DC, 1986; EPA-440/5-86-001. (24) Abdullah, M. I.; Reusch B. B.; Klimek, R. Anal. Chim. Acta 1976, 84, 307. (25) Allison, J. D.; Brown, D. S.; Novo-Gradac, K. J. MINTEQA2/ PRODEFA2, A Geochemical Assessment Model for Environmental Systems, Version 3.0 User’s Manual; Environmental Research Laboratory, U.S. EPA: Washington, DC, 1991. (26) Schindler, P. W. Thalassia Jugosl. 1975, 11 (1/2), 101. (27) Davis, J. A.; Leckie, J. O. Environ. Sci. Technol. 1978, 12, 1309. (28) Balistrieri, L.; Brewer, P. G.; Murray, J. W. Deep-Sea Res. 1981, 28A, 101. (29) Tessier, A.; Fortin, D.; Belzile, N.; De Vitre, R. R.; Leppard, G. G. Geochim. Cosmochim. Acta 1996, 60, 387. (30) Lindsay, W. L. Chemical Equlibria in Soils; Wiley Interscience: New York, 1979.
Received for review March 25, 1996. Revised manuscript received September 24, 1996. Accepted October 8, 1996.X ES960270A X
Abstract published in Advance ACS Abstracts, December 15, 1996.
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