UNDERSTANDING VARIATION IN PARTITION COEFFICIENT, Kd ...

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al., 1992; and others). These references include derivation of the surface complexation constants for surface coordination sites determined to be important. In addition to these laboratory studies, there are numerous examples in the literature of the application of surface complexation models and published binding constants to predict and evaluate the migration of uranium in soil/groundwater systems. For example, Koß (1988) describes the use of a surface complexation adsorption model to calculate the sorption of uranium for soil-groundwater systems associated with the proposed site for a German geologic radioactive waste repository at Gorleben. An apparent constant (i.e., apparent surface complex formation constant based on bulk solution concentrations, K app ) was derived for uranium sorption using the MINEQL geochemical code and site-specific geochemical data for soil CEC values, groundwater compositions, and measured uranium K d values. Quartz (SiO 2) was the main constituent in the soils considered in this study. Because the model incorporates the aqueous speciation of uranium, it may be used tor compare K d values for different soil systems having equal sorption sites. The modeling results indicated that CEC, pH, ionic strength, and dissolved carbonate concentrations were the main geochemical parameters affecting the sorption of uranium in groundwater systems. Puigdomènech and Bergström (1994) evaluated the use of surface complexation models for calculating radionuclide sorption and K d values in support of performance assessments studies of geologic repositories for radioactive wastes. They used a triple layer surface complexation model to predict the amount of uranium sorbed to a soil as a function of various environmental parameters. They then derived K d values based on the concentrations of adsorbed and dissolved uranium predicted by the model. For the surface complexation modeling, they assumed (1) a total uranium concentration of 10 -5 mol/l, and (2) the adsorption of uranium on soil was controlled by the soil concentration of iron oxyhydroxide solid, which was assumed to be 5 percent goethite ["-FeO(OH)]. Their modeling results indicated that pH, inorganic carbon (i.e., dissolved carbonate), and Eh (redox conditions) are major parameters that affect uranium K d values. Under oxidizing conditions at pH values greater than 6, their derived K d values were approximately 100 ml/g. At high concentrations of dissolved carbonate, and pH values greater than 6, the K d values for uranium decrease considerably. Their results indicate that the triple layer surface complexation model using constants obtained under well controlled laboratory conditions on well characterized minerals can easily be applied to estimate the dependence of uranium adsorption and uranium K d values as a function of a variety of important site environmental conditions. Efforts have also been made to compile site binding constants for radionuclides and other metals to create “sorption databases” for use with geochemical codes such as MINTEQA2. For example, Turner et al. (1993) and Turner (1993, 1995) describe the application of the surfacecomplexation models (SCMs) [i.e., the diffuse layer model (DLM), constant capacitance model (CCM), and triple layer model (TLM)] in the geochemical reaction code MINTEQA2 to simulate potentiometric titration and adsorption data published for U(VI) and other radionuclides on several single mineral phases. Their studies were conducted in support of developing a uniform approach to using surface complexation models to predict radionuclide migration behavior associated with disposal of high-level radioactive waste in a geologic repository. The parameter J.24
optimization code FITEQL was used for fitting and optimization of the adsorption binding constants that were used in conjunction with MINTEQA2 and its thermodynamic database. For those radionuclides having sufficient data, the surface-complexation models were used to examine the effects of changing geochemical conditions (e.g., pH) on radionuclide adsorption. Turner et al. (1993) and Turner (1993, 1995) include a detailed listing and documentation of the adsorption reactions and associated binding constants used for the MINTEQA2 DLM, CCM, and TLM calculations. Although all 3 models proved capable of simulating the available adsorption data, the DLM was able to do so using the fewest parameters (Turner, 1995). Compared to empirical approaches (e.g., K d) for predicting contaminant adsorption, Turner notes that surface complexation models based on geochemical principles have the advantage of being used to extrapolate contaminant adsorption to environmental conditions beyond the range measured experimentally. J.5.0 Other Studies of Uranium The following studies and adsorption reviews were identified during the course of this study. Although they typically do not contain uranium K d data, they discuss aspects of uranium adsorption behavior in soils that might be useful to some readers searching for similar site conditions. These studies and reviews are briefly discussed below. Ames and Rai (1978) reviewed and evaluated the processes influencing the mobility and retention of radionuclides. Their review for uranium discussed the following published adsorption studies. The following descriptions are paraphrased from in their report. 1 · Dementyev and Syromyatnikov (1968) determined that the maximum adsorption observed for uranium in the pH 6 region is due to the boundary between the dominant uranium aqueous species being cationic and anionic at lower and higher pH values, respectively. · Goldsztaub and Wey (1955) determined that 7.5 and 2.0 g uranium could be adsorbed per 100 g of calcined montmorillonite and kaolinite, respectively. · Horráth (1960) measured an average enrichment factor of 200 to 350 for the adsorption of uranium on peat. · Kovalevskii (1967) determined that the uranium content of western Siberian noncultivated soils increased as a function of their clay content and that clay soils contained at least 3 times more uranium than sands. 1 The full citations listed for these references at the end of this appendix are provided exactly as given by Ames and Rai (1978). J.25
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United States Office of Air and Rad
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NOTICE The following two-volume rep
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This publication is the result of a
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TO COMMENT ON THIS GUIDE OR PROVIDE
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CONTENTS NOTICE ...................
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Concentrations Extractable Iron Con
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Appendix A - Acronyms and Abbreviat
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Figure E.1. Variation of K d for Cr
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Table 5.16. Uranium(VI) aqueous spe
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Table H.3. Simple and multiple regr
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1.0 Introduction The objective of t
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discussed. The geochemical modeling
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2.0 The K d Model The simplest and
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track many more parameters and some
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of interest. The retardation factor
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(e.g., soil). An increasing body of
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complexation constants for the cont
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the soil column. Additionally, the
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Element Table 5.2. Concentrations o
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5.2.3 Aqueous Speciation Cadmium fo
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5.2.4 Dissolution/Precipitation/Cop
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cadmium, indicating that cadmium an
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5.2.6.2.2 Limits of K d Values with
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organic chelates (e.g., EDTA). Init
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(tarapacaite) in chromium sludge fr
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K d). Soils containing Mn oxides ox
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Table 5.7. Estimated range of Kd va
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most common valence state of lead e
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Table 5.8. Lead aqueous species inc
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Under reducing conditions, galena (
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C Adsorption of lead increases with
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(Choppin, 1983). Plutonium hydrolyt
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Table 5.10. Plutonium aqueous speci
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5.6.5 Sorption/Desorption Plutonium
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content in the system. Additionally
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much greater concentrations. Thus,
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exchanged which they attributed to
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up tables was based in part on thei
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organic complexes likely predominat
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Thorium undergoes hydrolysis in aqu
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The distribution of thorium aqueous
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from about 10 -8.5 mol/l (0.0007 mg
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Based on the assumptions and limita
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5.10 Tritium Geochemistry And K d V
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uranium. Uranium(VI) species domina
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gives 0.1 to 10 µg/l as the range
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mineral in reducing ore zones (Fron
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Percent Distribution 100 80 60 40 2
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5.11.6 Partition Coefficient, K d ,
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No attempt was made to statisticall
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Table 5.18. Selected chemical and t
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Another objective of this report is
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6.0 REFERENCES Adriano, D. C. 1992.
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Bensen, D. W. 1960. Review of Soil
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Metals by Geomedia. Variables, Mech
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EPA (U.S. Environmental Protection
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Griffin, R. A., A. K. Au, and R. R.
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+ Keeney-Kennicutt, W. L., and J. W
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Mattigod, S. V., A. L. Page, and I.
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Oscarson, D. W., and H. B. Hume. 19
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Relyea, J. F. and D. A. Brown. 1978
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Schultz, R. K., R. Overstreet, and
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Szalay, A. 1964. “Cation Exchange
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Yariv, S., and H. Cross. 1979. Geoc
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APPENDIX A Acronyms, Abbreviations,
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PC Personal computers operating und
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A.3.0 List of Symbols and Notation
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APPENDIX B Definitions
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Clay Content - particle size fracti
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Polynuclear Species - an aqueous sp
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C.1.0 Background Appendix C Partiti
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Cadmium K d Table C.2. Correlation
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C.3.0 Data Set for Soils Table C.4
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Cd K d (ml/g) Clay Cont. (wt%) pH C
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D.1.0 Background Appendix D Partiti
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A second data set (see Section D.4)
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Table D.3. Correlation coefficients
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By transposing the CEC and cesium K
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Table D.6. Cesium K d values measur
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eported in Table D.8 are described
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a 1 b 1 Range of Solution Cs Concen
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Figure D.4. Generalized cesium Freu
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Table D.10. Estimated range of K d
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D.3.0 K d Data Set for Soils and Pu
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Cesium Kd (ml/g) Clay (wt.% ) Mica
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Cesium K d (ml/g) Clay (wt%) Mica (
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Bruggenwert, M. G. M., and A. Kamph
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Shiao, S. Y., P. Rafferty, R. E. Me
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E.1.0 Background Appendix E Partiti
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chromium-reductive soils may stem f
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E.2.0 Approach The approach used to
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Table E.3. Estimated range of K d v
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Table E.4. Data from Rai et al. (19
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E.4.0 References Davis, J. A. and J
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APPENDIX F Partition Coefficients F
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(CEC) of the soils. Such an anomaly
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Table F.1. Summary of K d values fo
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Figure F.2. Variation of K d as a f
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F.4.0 References Abd-Elfattah, A.,
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APPENDIX G Partition Coefficients F
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A number of investigators have exam
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pH value, which is typical of the m
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These significant reductions in ads
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This is typically accomplished by t
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The scatterplots are typically disp
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Table G.2. Regression models for pl
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G.4.0 References Barney, G. S. 1984
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Nelson, D. M., R. P. Larson, and W.
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APPENDIX H Partition Coefficients F
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1.6 ml/g for a measurement made on
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Figure H.1. Relation between stront
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Figure H.2. Relation between stront
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H.2.4 Approach Figure H.4. Relation
Page 250 and 251: A second look-up table (Table H.5)
Page 252 and 253: Sr K d (ml/g) Clay Content (%) pH C
Page 254 and 255: Sr K d (ml/g) Clay Content (%) pH C
Page 256 and 257: Sr K d (ml/g) Clay Conten t (%) pH
Page 264 and 265: Konishi, M., K. Yamamoto, T. Yanagi
Page 266 and 267: APPENDIX I Partition Coefficients F
Page 268 and 269: descriptive statistics of the thori
Page 270 and 271: Figure I.1. Linear regression betwe
Page 272 and 273: The look-up table (Table I.5) for t
Page 274 and 275: Thorium K d (ml/g) pH Clay (wt.%) C
Page 276 and 277: Rai, D., A. R. Felmy, D. A. Moore,
Page 278 and 279: J.1.0 Background Appendix J Partiti
Page 280 and 281: J.2.2 Uranium K d Studies on Soils
Page 282 and 283: Washington. The studies included an
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Page 286 and 287: Kd (ml/g) 100,000 10,000 1,000 100
Page 288 and 289: papers. Warnecke et al. (1984) indi
Page 290 and 291: Anderson et al. (1982) summarize an
Page 292 and 293: McKinley and Scholtis (1993) compar
Page 294 and 295: In a similar comparison of sorption
Page 296 and 297: J.3.1 K d Values as a Function ff p
Page 298 and 299: discussed, and one would have to ma
Page 302 and 303: · Manskaya et al. (1956) studied a
Page 304 and 305: concentrations of humic acid, there
Page 306 and 307: pH U Kd (ml/g) Clay Cont. (wt.%) CE
Page 326 and 327: J.6.0 References Ames, L. L., J. E.
Page 328 and 329: Erikson, R. L., C. J. Hostetler, R.
Page 330 and 331: R. C. Ewing. Materials Research Soc
Page 332 and 333: Sheppard, M. I., and D. H. Thibault
Page 334: Yamamoto, T., E. Yunoki, M. Yamakaw
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