Quantifying Differences in the Impact of Variable Chemistry on Equilibrium Uranium(VI) Adsorption Properties of Aquifer Sediments
Author(s) -
Deborah L. Stoliker,
Douglas B. Kent,
John M. Zachara
Publication year - 2011
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/es202677v
Subject(s) - adsorption , uranium , sediment , chemistry , desorption , mineralogy , environmental chemistry , geology , materials science , metallurgy , geomorphology , organic chemistry
Uranium adsorption-desorption on sediment samples collected from the Hanford 300-Area, Richland, WA varied extensively over a range of field-relevant chemical conditions, complicating assessment of possible differences in equilibrium adsorption properties. Adsorption equilibrium was achieved in 500-1000 h although dissolved uranium concentrations increased over thousands of hours owing to changes in aqueous chemical composition driven by sediment-water reactions. A nonelectrostatic surface complexation reaction, >SOH + UO₂²⁺ + 2CO₃²⁻ = >SOUO₂(CO₃HCO₃)²⁻, provided the best fit to experimental data for each sediment sample resulting in a range of conditional equilibrium constants (logK(c)) from 21.49 to 21.76. Potential differences in uranium adsorption properties could be assessed in plots based on the generalized mass-action expressions yielding linear trends displaced vertically by differences in logK(c) values. Using this approach, logK(c) values for seven sediment samples were not significantly different. However, a significant difference in adsorption properties between one sediment sample and the fines (< 0.063 mm) of another could be demonstrated despite the fines requiring a different reaction stoichiometry. Estimates of logK(c) uncertainty were improved by capturing all data points within experimental errors. The mass-action expression plots demonstrate that applying models outside the range of conditions used in model calibration greatly increases potential errors.
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