Understanding Variation in Partition Coefficient, Kd, Values Volume II

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4.0 Application of Chemical Reaction Models Computerized chemical reaction models based on thermodynamic principles may be used to calculate processes such as aqueous complexation, oxidation/reduction, adsorption/desorption, and mineral precipitation/dissolution for contaminants in soil-water systems. The capabilities of a chemical reaction model depend on the models incorporated into its computer code and the availability of thermodynamic and/or adsorption data for aqueous and mineral constituents of interest. Chemical reaction models, their utility to understanding the solution chemistry of contaminants, and the MINTEQA2 model in particular are described in detail in Chapter 5 of Volume I. The MINTEQA2 computer code is an equilibrium chemical reaction model. It was developed with EPA funding by originally combining the mathematical structure of the MINEQL code with the thermodynamic database and geochemical attributes of the WATEQ3 code. The MINTEQA2 code includes submodels to calculate aqueous speciation/complexation, oxidation-reduction, gas-phase equilibria, solubility and saturation state (i.e., saturation index), precipitation/dissolution of solid phases, and adsorption. The most current version of MINTEQA2 available from EPA is compiled to execute on a personal computer (PC) using the MS-DOS computer operating system. The MINTEQA2 software package includes PRODEFA2, a computer code used to create and modify input files for MINTEQA2. The MINTEQA2 code contains an extensive thermodynamic database for modeling the speciation and solubility of contaminants and geologically significant constituents in low-temperature, soil-water systems. Of the contaminants selected for consideration in this project [chromium, cadmium, cesium, tritium ( 3 H), lead, plutonium, radon, strontium, thorium, and uranium], the MINTEQA2 thermodynamic database contains speciation and solubility reactions for chromium, including the valence states Cr(II), Cr(III), and Cr(VI); cadmium; lead; strontium; and uranium, including the valence states U(III), U(IV), U(V), and U(VI). Some of the thermodynamic data in the EPA version have been superseded in other users’ databases by more recently published data. The MINTEQA2 code includes 7 adsorption model options. The non-electrostatic adsorption models include the activity K d act , activity Langmuir, activity Freundlich, and ion exchange models. The electrostatic adsorption models include the diffuse layer, constant capacitance, and triple layer models. The MINTEQA2 code does not include an integrated database of adsorption constants and reactions for any of the 7 models. These data must be supplied by the user as part of the input file information. Chemical reaction models, such as the MINTEQA2 code, cannot be used a priori to predict a partition coefficient, K d, value. The MINTEQA2 code may be used to calculate the chemical changes that result in the aqueous phase from adsorption using the more data intensive, electrostatic adsorption models. The results of such calculations in turn can be used to back calculate a K d value. The user however must make assumptions concerning the composition and mass of the dominant sorptive substrate, and supply the adsorption parameters for surface-complexation constants for the 4.1
contaminants of interest and the assumed sorptive phase. The EPA (EPA 1992, 1996) has used the MINTEQA2 model and this approach to estimate K d values for several metals under a variety of geochemical conditions and metal concentrations to support several waste disposal issues. The EPA in its “Soil Screening Guidance” determined MINTEQA2-estimated K d values for barium, beryllium, cadmium, Cr(III), Hg(II), nickel, silver, and zinc as a function of pH assuming adsorption on a fixed mass of iron oxide (EPA, 1996; RTI, 1994). The calculations assumed equilibrium conditions, and did not consider redox potential or metal competition for the adsorption sites. In addition to these constraints, EPA (1996) noted that this approach was limited by the potential sorbent surfaces that could be considered and availability of thermodynamic data. Their calculations were limited to metal adsorption on iron oxide, although sorption of these metals to other minerals, such as clays and carbonates, is well known. Typically, the data required to derive the values of adsorption parameters that are needed as input for adsorption submodels in chemical reaction codes are more extensive than information reported in a typical laboratory batch K d study. If the appropriate data are reported, it is likely that a user could hand calculate a composition-based K d value from the data reported in the adsorption study without the need of a chemical reaction model. Chemical reaction models can be used, however, to support evaluations of K d values and related contaminant migration and risk assessment modeling predictions. Chemical reaction codes can be used to calculate aqueous complexation to determine the ionic state and composition of the dominant species for a dissolved contaminant present in a soil-water system. This information may in turn be used to substantiate the conceptual model being used for calculating the adsorption of a particular contaminant. Chemical reaction models can be used to predict bounding, technically defensible maximum concentration limits for contaminants as a function of key composition parameters (e.g., pH) for any specific soil-water system. These values may provide more realistic bounding values for the maximum concentration attainable in a soil-water system when doing risk assessment calculations. Chemical reaction models can also be used to analyze initial and final geochemical conditions associated with laboratory K d measurements to determine if the measurement had been affected by processes such as mineral precipitation which might have compromised the derived K d values. Although chemical reaction models cannot be used to predict K d values, they can provide aqueous speciation and solubility information that is exceedingly valuable in the evaluation of K d values selected from the literature and/or measured in the laboratory. 4.2
Page 1 and 2: United States Office of Air and Rad
Page 3 and 4: NOTICE The following two-volume rep
Page 5 and 6: This publication is the result of a
Page 7 and 8: TO COMMENT ON THIS GUIDE OR PROVIDE
Page 9 and 10: CONTENTS NOTICE ...................
Page 11 and 12: 5.6 Plutonium Geochemistry and K d
Page 13 and 14: Appendix C - Partition Coefficients
Page 15 and 16: Figure E.1. Variation of K d for Cr
Page 17 and 18: Table 5.16. Uranium(VI) aqueous spe
Page 19 and 20: Table H.4. Look-up table for estima
Page 21 and 22: 1.0 Introduction The objective of t
Page 23 and 24: eviewed. The main focus of Volume I
Page 25 and 26: 2.0 The Kd Model The simplest and m
Page 27 and 28: no longer be used to perform the in
Page 29 and 30: of the velocity of the contaminant
Page 31: An increasing body of evidence indi
Page 35 and 36: dissolved gases and complexing liga
Page 37 and 38: Element Table 5.2. Concentrations o
Page 39 and 40: 5.6
Page 41 and 42: 5.8
Page 43 and 44: 5.2.4 Dissolution/Precipitation/Cop
Page 45 and 46: 1980). In contrast, zinc almost com
Page 47 and 48: 5.2.6.2.2 Limits of K d Values with
Page 49 and 50: 5.3 Cesium Geochemistry and K d Val
Page 51 and 52: Cesium may also adsorb to iron oxid
Page 53 and 54: Table 5.5. Estimated range of K d v
Page 55 and 56: ore processing, plating operations,
Page 57 and 58: elow the isoelectric point, the min
Page 59 and 60: Among all available data for Cr(VI)
Page 61 and 62: 5.5 Lead Geochemistry and K d Value
Page 63 and 64: on dissolution/precipitation of lea
Page 65 and 66: Percent Distribution 100 80 60 40 2
Page 67 and 68: A number of studies have confirmed
Page 69 and 70: Solid organic matter such as humic
Page 71 and 72: Plutonium is produced by fissioning
Page 73 and 74: The pe is related to Eh by the equa
Page 75 and 76: Table 5.10. Plutonium aqueous speci
Page 77 and 78: 5.6.5 Sorption/Desorption Plutonium
Page 79 and 80: conditions depended on the colloida
Page 81 and 82: 5.6.6.2.2 Limits of K d Values with
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present in vadose zone pore water w
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There is little tendency for stront
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strontium partitions from the disso
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greater than about 10 -4 M, humic s
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Table 5.13. Look-up table for estim
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1976; Cotton and Wilkinson, 1980).
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Table 5.14. Thorium aqueous species
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Figure 5.5 changes slightly in the
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to less than 10 -9 mol/l (0.0002 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(VI) species dominate in oxi
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most natural waters. For waters ass
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U(IV) mineral, is a primary mineral
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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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strong anionic carbonato complexes.
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Elemen t Table 5.18. Selected chemi
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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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Billon, A. 1982. “Fixation D’el
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Chisholm-Brause, C., S. D. Conradso
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EPA/DOE/NRC (Cooperative Effort by
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Griffin, R. A., and N. F. Shimp. 19
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+ Keeney-Kennicutt, W. L., and J. W
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McBride, M. B. 1980. “Chemisorpti
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Östhols, E. 1995. “Thorium Sorpt
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Rhoads, K., B. N. Bjornstad, R. E.
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Schwertmann, U., and R. M. Taylor.
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Tanner, A. B., 1980. “Radon Migra
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Zimdahl, R. L., and J. J. Hassett.
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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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Cation Exchange - reversible adsorp
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subbituminous coal. Marl - an earth
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APPENDIX C Partition Coefficients F
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Mean Standard Error Table C.1. Desc
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Figure C.1. Relation between cadmiu
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Cd K d (ml/g) Clay Cont. (wt%) pH C
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APPENDIX D Partition Coefficients F
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Two separate data sets were compile
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The soil-only data set was frequent
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The high correlations between mica
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D.2.4 Cesium Adsorption onto Mica-L
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Table D.7. Approximate upper limits
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Table D.8. Freundlich equations ide
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Table D.9. Descriptive statistics o
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D.2.6 Approach to Selecting K d Val
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Mica Concentration in Clay Fraction
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Cesium Kd (ml/g) 15200 8440 143 73
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Cesium Kd (ml/g) Clay (wt.%) Mica (
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Cesium Kd (ml/g) Clay (wt.%) Mica (
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D.4.0 Data Set for Soils Table D.14
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Cesium K d (ml/g) Clay (wt%) Mica (
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Li, Y., L. Burkhardt, M. Buchholtz,
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APPENDIX E Partition Coefficients F
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Windsor, and Olivier soils with pH
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Table E.1. Summary of K d values fo
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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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influenced significantly by the typ
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The Pu(IV) and Pu(V) adsorption dat
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that Pu(IV) adsorption on soils wou
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Table G.1. Plutonium adsorption dat
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KD PH CEC EC DCARB Figure G.1. Scat
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Figure G.2. Variation of K d for pl
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Delegard, C. H. , G. S. Barney, and
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Seattle, Washington, pp. 291-497. B
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H.1.0 Background Appendix H Partiti
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H.2.0 Approach and Regression Model
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strontium K d values, which are cle
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Table H.3. Simple and multiple regr
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Table H.4. Look-up table for estima
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H.3.0 K d Data Set for Soils Table
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Sr K d (ml/g) Clay Content (%) pH C
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H.4.0 K d Data Set for Pure Mineral
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H.5.0 References Adeleye, S. A., P.
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Satmark, B., and Y. Albinsson. 1991
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I.1.0 BACKGROUND Appendix I Partiti
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Table I.2. Correlation coefficients
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The regression equation between the
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I.3.0 K d Data Set for Soils Figure
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I.5.0 References Ames, L. L., and D
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APPENDIX J Partition Coefficients F
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J.2.1 Sources of Error and Variabil
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Erikson et al. (1993) determined th
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26.1 wt.% plagioclase feldspar, and
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Kd (ml/g) 100,000 10,000 1,000 100
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Sheppard and Thibault (1988) invest
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Warnecke et al. (1986) present an o
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Borovec (1981) investigated the ads
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Table J.2. Uranium K d values liste
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J.3.0 Approach in Developing K d Lo
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at any pH. These estimated K d valu
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The minimum and maximum K d values
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ehavior associated with disposal of
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accompanied by unreacted zeolite. T
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pH Table J.5. Uranium K d values se
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pH U Kd (ml/g) Clay Cont. (wt.%) CE
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pH U Kd (ml/g) 6.90 1,739,87 7 6.90
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J.6.0 References Ames, L. L., J. E.
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Erikson, R. L., C. J. Hostetler, R.
Page 337 and 338:
Lindenmeier, C. W., R. J. Serne, J.
Page 339 and 340:
Sheppard, M. I., and D. H. Thibault
Page 341:
Zachara, J. M., C. C. Ainsworth, J.
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Understanding Variation in Partition Coefficient, Kd, Values Volume II

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