The Use of Chemical and Physical Properties for Characterization of Strontium Distribution Coefficients at the Idaho National Engineering and Environmental Laboratory, Idaho
The U.S. Geological Survey and Idaho State University, in cooperation with the U.S. Department of Energy, conducted a study to determine strontium distribution coefficients (Kds) of surficial sediments at the Idaho National Engineering and Environmental Laboratory (INEEL). Batch experimental techniques were used to determine experimental Kds of 20 surficial-sediment samples from the INEEL. The Kds describe the distribution of a solute between the solution and solid phase. A best-fit model was obtained using a four-variable data set consisting of surface area, manganese oxide concentration, specific conductance, and pH. Application of the model to an independent split of the data resulted in an average relative error of prediction of 20 percent and a correlation coefficient of 0.921 between predicted and observed strontium Kds. Chemical and physical characteristics of the solution and sediment that could successfully predict the Kd values were identified. Prediction variable select ion was limited to variables which are either easily determined or have available tabulated characteristics. The selection criterion could circumvent the need for time- and labor-intensive laboratory experiments and provide an alternate faster method for estimating strontium Kds.
- Research Organization:
- U.S. Geological Survey, Idaho Falls, ID (US)
- Sponsoring Organization:
- US Department of Energy (US)
- OSTI ID:
- 765563
- Report Number(s):
- DOE/ID-22157; TRN: AH200102%%472
- Resource Relation:
- Other Information: PBD: 1 Jun 1999
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
DISTRIBUTION
FORECASTING
GEOLOGIC SURVEYS
IDAHO
MANGANESE OXIDES
PHYSICAL PROPERTIES
SEDIMENTS
SOLUTES
STRONTIUM
SURFACE AREA
STRONTIUM DISTRIBUTION COEFFICIENTS (KDS)
SURFICIAL SEDIMENTS
SOLUTE
EQUILIBRIUM CONCENTRATIONS
PH
SPECIFIC CONDUCTANCE
GRAIN-SIZE DISTRIBUTION
MINERALOGY
MULTI-VARIATE-REGRESSION
EMPIRICAL MODEL