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Stepwise Calculation for the Determination of the Number of Transfer Units in Countercurrent Extraction Columns

Description: Report discussing a method for the stepwise calculation of the number of transfer units utilized in a given solvent extraction operation. "Use of the method results in a very appreciable saving in time of calculation with an error of 1.5% for the runs tested. The error tends to become smaller with increasing total numbers of transfer units involved."
Date: September 12, 1949
Creator: Burns, W. A. & Eschbach, E. A.
Partner: UNT Libraries Government Documents Department

Comparing Candidate Hospital Report Cards

Description: We present graphical and analytical methods that focus on multivariate outlier detection applied to the hospital report cards data. No two methods agree which hospitals are unusually good or bad, so we also present ways to compare the agreement between two methods. We identify factors that have a significant impact on the scoring.
Date: December 31, 1997
Creator: Burr, T.L.; Rivenburgh, R.D.; Scovel, J.C. & White, J.M.
Partner: UNT Libraries Government Documents Department

The Multi-Isotope Process (MIP) Monitor Project: FY12 Progress and Accomplishments

Description: The Multi-Isotope Process (MIP) Monitor, being developed at Pacific Northwest National Laboratory (PNNL), provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of "...(minimization of) the risks of nuclear proliferation and terrorism." The MIP Monitor measures distributions of a suite of indicator (radioactive) isotopes present within product and waste streams of a nuclear reprocessing facility. These indicator isotopes are monitored on-line by gamma spectrometry and compared, in near-real-time, to spectral patterns representing "normal" process conditions using multivariate pattern recognition software. The monitor utilizes this multivariate analysis and gamma spectroscopy of reprocessing streams to detect small changes in the gamma spectrum, which may indicate changes in process conditions. Multivariate analysis methods common in chemometrics, such as principal component analysis (PCA) and partial least squares regression (PLS), act as pattern recognition techniques, which can detect small deviations from the expected, nominal condition. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting. Development of the MIP Monitor approach continues to evaluate the efficacy of the monitor for automated, real-time or near-real-time application. This report details follow-on research and development efforts sponsored by the U.S. Department of Energy Fuel Cycle Research and Development related to the MIP Monitor for fiscal year 2012 (FY12).
Date: September 27, 2012
Creator: Coble, Jamie B.; Orton, Christopher R.; Jordan, David V.; Schwantes, Jon M.; Bender, Sarah; Dayman, Kenneth J. et al.
Partner: UNT Libraries Government Documents Department

CATDAT : A Program for Parametric and Nonparametric Categorical Data Analysis : User's Manual Version 1.0, 1998-1999 Progress Report.

Description: Natural resource professionals are increasingly required to develop rigorous statistical models that relate environmental data to categorical responses data. Recent advances in the statistical and computing sciences have led to the development of sophisticated methods for parametric and nonparametric analysis of data with categorical responses. The statistical software package CATDAT was designed to make some of these relatively new and powerful techniques available to scientists. The CATDAT statistical package includes 4 analytical techniques: generalized logit modeling; binary classification tree; extended K-nearest neighbor classification; and modular neural network.
Date: December 1, 1999
Creator: Peterson, James T.
Partner: UNT Libraries Government Documents Department

An Application of Multivariate Statistical Analysis for Query-Driven Visualization

Description: Abstract?Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.
Date: March 1, 2010
Creator: Gosink, Luke J.; Garth, Christoph; Anderson, John C.; Bethel, E. Wes & Joy, Kenneth I.
Partner: UNT Libraries Government Documents Department

Detection and Classification of Individual Airborne Microparticles using Laser Ablation Mass Spectroscopy and Multivariate Analysis

Description: We are developing a method for the real-time analysis of airborne microparticles based on laser ablation mass spectroscopy. Airborne particles enter an ion trap mass spectrometer through a differentially-pumped inlet, are detected by light scattered from two CW laser beams, and sampled by a 10 ns excimer laser pulse at 308 nm as they pass through the center of the ion trap electrodes. After the laser pulse, the stored ions are separated by conventional ion trap methods. In this work thousands of positive and negative ion spectra were collected for eighteen different species: six bacteria, six pollen, and six particulate samples. The data were then averaged and analyzed using the Multivariate Patch Algorithm (MPA), a variant of traditional multivariate anal ysis. The MPA correctly identified all of the positive ion spectra and 17 of the 18 negative ion spectra. In addition, when the average positive and negative spectra were combined the MPA correctly identified all 18 species. Finally, the MPA is also able to identify the components of computer synthesized mixtures of the samples studied
Date: April 27, 1999
Creator: Gieray, R.A.; Lazar, A.; Parker, E.P.; Ramsey, J. M.; Reilly, P.T.A.; Rosenthal, S.E. et al.
Partner: UNT Libraries Government Documents Department

Multivariate statistical analysis of low-voltage EDS spectrum images

Description: Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.
Date: March 1, 1998
Creator: Anderson, I.M.
Partner: UNT Libraries Government Documents Department

Handwriting Examination: Moving from Art to Science

Description: In this document, we present a method for validating the premises and methodology of forensic handwriting examination. This method is intuitively appealing because it relies on quantitative measurements currently used qualitatively by FDE's in making comparisons, and it is scientifically rigorous because it exploits the power of multivariate statistical analysis. This approach uses measures of both central tendency and variation to construct a profile for a given individual. (Central tendency and variation are important for characterizing an individual's writing and both are currently used by FDE's in comparative analyses). Once constructed, different profiles are then compared for individuality using cluster analysis; they are grouped so that profiles within a group cannot be differentiated from one another based on the measured characteristics, whereas profiles between groups can. The cluster analysis procedure used here exploits the power of multivariate hypothesis testing. The result is not only a profile grouping but also an indication of statistical significance of the groups generated.
Date: April 12, 1999
Creator: Jarman, K.H.; Hanlen, R.C. & Manzolillo, P.A.
Partner: UNT Libraries Government Documents Department

Multivariate statistical analysis of spectrum lines and images

Description: Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The information that can be extracted by MSA from a series of spectra is illustrated by an application to a TEM spectrum-line acquired with a Gatan Imaging Filter (GIF) at the Co-L edge for a phase boundary between the periclase- (CoO) and spinel- (Co{sub 3}O{sub 4}) structured phases of cobalt oxide. A series of 64 spectra, each of 512 channels, has been analyzed with MSA. The following MSA information is given: (1) logarithmic plot of the information content of the MSA-identified principle components of the series of spectra; (2) the spectrum line of the Co-L edge acquired with the GIF; (3) the first component of the variation; (4) the amplitude of the first component in each spectrum of raw data; (5) a second component; and (6) its amplitudes.
Date: April 1, 1997
Creator: Anderson, I.M. & Bentley, J.
Partner: UNT Libraries Government Documents Department

Adaptable Multivariate Calibration Models for Spectral Applications

Description: Multivariate calibration techniques have been used in a wide variety of spectroscopic situations. In many of these situations spectral variation can be partitioned into meaningful classes. For example, suppose that multiple spectra are obtained from each of a number of different objects wherein the level of the analyte of interest varies within each object over time. In such situations the total spectral variation observed across all measurements has two distinct general sources of variation: intra-object and inter-object. One might want to develop a global multivariate calibration model that predicts the analyte of interest accurately both within and across objects, including new objects not involved in developing the calibration model. However, this goal might be hard to realize if the inter-object spectral variation is complex and difficult to model. If the intra-object spectral variation is consistent across objects, an effective alternative approach might be to develop a generic intra-object model that can be adapted to each object separately. This paper contains recommendations for experimental protocols and data analysis in such situations. The approach is illustrated with an example involving the noninvasive measurement of glucose using near-infrared reflectance spectroscopy. Extensions to calibration maintenance and calibration transfer are discussed.
Date: December 20, 1999
Partner: UNT Libraries Government Documents Department

Multivariate Geographic Clustering Using a Beowulf-Style Parallel Computer

Description: The authors present an application of multivariate non-hierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity called ecoregions. Nine input variables thought to aflect the growth of vegetation are clustered at a resolution of one square kilometer. These data represent over 7.8 million map cells in a g-dimensional data space. For the analysis, the authors built a 126-node heterogeneous cluster--aptly named the Stone SouperComputer--out of surplus PCs. The authors developed a parallel iterative statistical clustering algorithm which uses the MPI message pawing routines, employs a classical master/slave single program multiple data (SPMD) organization, performs dynamic load balancing, and provides fault tolerance. In addition to being run on the Stone Souper-Computer, the parallel algorithm was tested on other parallel platforms without code modification. Finally, the results of the geographic clustering are presented.
Date: June 28, 1999
Creator: Hargrove, W.W. & Hoffman, F.M.
Partner: UNT Libraries Government Documents Department

The Generalization of the Logistic Discriminant Function Analysis and Mantel Score Test Procedures to Detection of Differential Testlet Functioning

Description: Two procedures for detection of differential item functioning (DIF) for polytomous items were generalized to detection of differential testlet functioning (DTLF). The methods compared were the logistic discriminant function analysis procedure for uniform and non-uniform DTLF (LDFA-U and LDFA-N), and the Mantel score test procedure. Further analysis included comparison of results of DTLF analysis using the Mantel procedure with DIF analysis of individual testlet items using the Mantel-Haenszel (MH) procedure. Over 600 chi-squares were analyzed and compared for rejection of null hypotheses. Samples of 500, 1,000, and 2,000 were drawn by gender subgroups from the NELS:88 data set, which contains demographic and test data from over 25,000 eighth graders. Three types of testlets (totalling 29) from the NELS:88 test were analyzed for DTLF. The first type, the common passage testlet, followed the conventional testlet definition: items grouped together by a common reading passage, figure, or graph. The other two types were based upon common content and common process. as outlined in the NELS test specification.
Date: August 1994
Creator: Kinard, Mary E.
Partner: UNT Libraries

A Comparison of Multivariate Normal and Elliptical Estimation Methods in Structural Equation Models

Description: In the present study, parameter estimates, standard errors and chi-square statistics were compared using normal and elliptical estimation methods given three research conditions: population data contamination (10%, 20%, and 30%), sample size (100, 400, and 1000), and kurtosis (kappa =1,10, 20).
Date: August 1999
Creator: Cheevatanarak, Suchittra
Partner: UNT Libraries

Assessing Measurement Equivalence of the English and Spanish Versions on an Employee Attitude Survey Using Multigroup Analysis in Structural Equation Modeling.

Description: The study utilized the covariance structure comparison methodology - Multigroup Analysis in Structural Equation Modeling - evaluating measurement equivalence of English and Spanish versions of an employee opinion survey. The concept of measurement equivalence was defined as consisting of four components: sample equivalence, semantic equivalence, conceptual equivalence and scalar equivalence. The results revealed that the two language versions of the survey exhibited acceptable measurement equivalence across five survey dimensions Communications, Supervision, Leadership, Job Content & Satisfaction and Company Image & Commitment. Contrary to the study second hypothesis, there was no meaningful difference in opinion scores between English-speaking and Spanish-speaking respondents on the latent construct of Job Content & Satisfaction.
Date: August 2003
Creator: Koulikov, Mikhail
Partner: UNT Libraries

Bias and Precision of the Squared Canonical Correlation Coefficient under Nonnormal Data Conditions

Description: This dissertation: (a) investigated the degree to which the squared canonical correlation coefficient is biased in multivariate nonnormal distributions and (b) identified formulae that adjust the squared canonical correlation coefficient (Rc2) such that it most closely approximates the true population effect under normal and nonnormal data conditions. Five conditions were manipulated in a fully-crossed design to determine the degree of bias associated with Rc2: distribution shape, variable sets, sample size to variable ratios, and within- and between-set correlations. Very few of the condition combinations produced acceptable amounts of bias in Rc2, but those that did were all found with first function results. The sample size to variable ratio (n:v)was determined to have the greatest impact on the bias associated with the Rc2 for the first, second, and third functions. The variable set condition also affected the accuracy of Rc2, but for the second and third functions only. The kurtosis levels of the marginal distributions (b2), and the between- and within-set correlations demonstrated little or no impact on the bias associated with Rc2. Therefore, it is recommended that researchers use n:v ratios of at least 10:1 in canonical analyses, although greater n:v ratios have the potential to produce even less bias. Furthermore,because it was determined that b2 did not impact the accuracy of Rc2, one can be somewhat confident that, with marginal distributions possessing homogenous kurtosis levels ranging anywhere from -1 to 8, Rc2 will likely be as accurate as that resulting from a normal distribution. Because the majority of Rc2 estimates were extremely biased, it is recommended that all Rc2 effects, regardless of which function from which they result, be adjusted using an appropriate adjustment formula. If no rationale exists for the use of another formula, the Rozeboom-2 would likely be a safe choice given that it produced the greatest ...
Date: August 2006
Creator: Leach, Lesley Ann Freeny
Partner: UNT Libraries

Fluorescence measurements for evaluating the application of multivariate analysis techniques to optically thick environments.

Description: Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.
Date: September 1, 2010
Creator: Reichardt, Thomas A.; Timlin, Jerilyn Ann; Jones, Howland D. T.; Sickafoose, Shane M. & Schmitt, Randal L.
Partner: UNT Libraries Government Documents Department

Decreased expression of RNA interference machinery, Dicer and Drosha, is associated with poor outcome in ovarian cancer patients

Description: The clinical and functional significance of RNA interference (RNAi) machinery, Dicer and Drosha, in ovarian cancer is not known and was examined. Dicer and Drosha expression was measured in ovarian cancer cell lines (n=8) and invasive epithelial ovarian cancer specimens (n=111) and correlated with clinical outcome. Validation was performed with previously published cohorts of ovarian, breast, and lung cancer patients. Anti-Galectin-3 siRNA and shRNA transfections were used for in vitro functional studies. Dicer and Drosha mRNA and protein levels were decreased in 37% to 63% of ovarian cancer cell lines and in 60% and 51% of human ovarian cancer specimens, respectively. Low Dicer was significantly associated with advanced tumor stage (p=0.007), and low Drosha with suboptimal surgical cytoreduction (p=0.02). Tumors with both high Dicer and Drosha were associated with increased median patient survival (>11 years vs. 2.66 years for other groups; p<0.001). In multivariate analysis, high Dicer (HR=0.48; p=0.02), high-grade histology (HR=2.46; p=0.03), and poor chemoresponse (HR=3.95; p<0.001) were identified as independent predictors of disease-specific survival. Findings of poor clinical outcome with low Dicer expression were validated in separate cohorts of cancer patients. Galectin-3 silencing with siRNA transfection was superior to shRNA in cell lines with low Dicer (78-95% vs. 4-8% compared to non-targeting sequences), and similar in cell lines with high Dicer. Our findings demonstrate the clinical and functional impact of RNAi machinery alterations in ovarian carcinoma and support the use of siRNA constructs that do not require endogenous Dicer and Drosha for therapeutic applications.
Date: May 6, 2008
Creator: Merritt, William M.; Lin, Yvonne G.; Han, Liz Y.; Kamat, Aparna A.; Spannuth, Whitney A.; Schmandt, Rosemarie et al.
Partner: UNT Libraries Government Documents Department

Meeting Report--NASA Radiation Biomarker Workshop

Description: A summary is provided of presentations and discussions from the NASA Radiation Biomarker Workshop held September 27-28, 2007, at NASA Ames Research Center in Mountain View, California. Invited speakers were distinguished scientists representing key sectors of the radiation research community. Speakers addressed recent developments in the biomarker and biotechnology fields that may provide new opportunities for health-related assessment of radiation-exposed individuals, including for long-duration space travel. Topics discussed include the space radiation environment, biomarkers of radiation sensitivity and individual susceptibility, molecular signatures of low-dose responses, multivariate analysis of gene expression, biomarkers in biodefense, biomarkers in radiation oncology, biomarkers and triage following large-scale radiological incidents, integrated and multiple biomarker approaches, advances in whole-genome tiling arrays, advances in mass-spectrometry proteomics, radiation biodosimetry for estimation of cancer risk in a rat skin model, and confounding factors. Summary conclusions are provided at the end of the report.
Date: May 1, 2008
Creator: Straume, Tore; Amundson, Sally A,; Blakely, William F.; Burns, Frederic J.; Chen, Allen; Dainiak, Nicholas et al.
Partner: UNT Libraries Government Documents Department

Exploration of new multivariate spectral calibration algorithms.

Description: A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.
Date: March 1, 2004
Creator: Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J. (The Dow Chemical Company, Midland, MI) et al.
Partner: UNT Libraries Government Documents Department

Measurement issues in assessing employee performance: A generalizability theory approach

Description: Increasingly, organizations are assessing employee performance through the use of rating instruments employed in the context of varied data collection strategies. For example, the focus may be on obtaining multiple perspectives regarding employee performance (360{degree} evaluation). From the standpoint of evaluating managers, upward assessments and ``peer to peer`` evaluations are perhaps two of the more common examples of such a multiple perspective approach. Unfortunately, it is probably fair to say that the increased interest and use of such data collection strategies has not been accompanied by a corresponding interest in addressing both validity and reliability concerns that have traditionally been associated with other forms of employee assessment (e.g., testing, assessment centers, structured interviews). As a consequence, many organizations may be basing decisions upon information collected under less than ideal measurement conditions. To the extent that such conditions produce unreliable measurements, the process may be both dysfunctional to the organization and/or unfair to the individual(s) being evaluated. Conversely, the establishment of reliable and valid measurement processes may in itself support the utilization of results in pursuit of organizational goals and enhance the credibility of the measurement process (see McEvoy (1990), who found the acceptance of subordinate ratings to be related to perceived accuracy and fairness of the measurement process). The present paper discusses a recent ``peer to peer`` evaluation conducted in our organization. The intent is to focus on the design of the study and present a Generalizability Theory (GT) approach to assessing the overall quality of the data collection strategy, along with suggestions for improving future designs. 9 refs., 3 tabs.
Date: August 1, 1996
Creator: Stephenson, B.O.
Partner: UNT Libraries Government Documents Department

Partial least squares, conjugate gradient and the fisher discriminant

Description: The theory of multivariate regression has been extensively studied and is commonly used in many diverse scientific areas. A wide variety of techniques are currently available for solving the problem of multivariate calibration. The volume of literature on this subject is so extensive that understanding which technique to apply can often be very confusing. A common class of techniques for solving linear systems, and consequently applications of linear systems to multivariate analysis, are iterative methods. While common linear system solvers typically involve the factorization of the coefficient matrix A in solving the system Ax = b, this method can be impractical if A is large and sparse. Iterative methods such as Gauss-Seidel, SOR, Chebyshev semi-iterative, and related methods also often depend upon parameters that require calibration and which are sometimes hard to choose properly. An iterative method which surmounts many of these difficulties is the method of conjugate gradient. Algorithms of this type find solutions iteratively, by optimally calculating the next approximation from the residuals.
Date: December 1996
Creator: Faber, V.
Partner: UNT Libraries Government Documents Department

Dynamical system modeling via signal reduction and neural network simulation

Description: Many dynamical systems tested in the field and the laboratory display significant nonlinear behavior. Accurate characterization of such systems requires modeling in a nonlinear framework. One construct forming a basis for nonlinear modeling is that of the artificial neural network (ANN). However, when system behavior is complex, the amount of data required to perform training can become unreasonable. The authors reduce the complexity of information present in system response measurements using decomposition via canonical variate analysis. They describe a method for decomposing system responses, then modeling the components with ANNs. A numerical example is presented, along with conclusions and recommendations.
Date: November 1, 1997
Creator: Paez, T.L. & Hunter, N.F.
Partner: UNT Libraries Government Documents Department

Multivariate statistical analysis of spectrum lines from Si{sub 3}N{sub 4} grain boundaries

Description: It is well known that the high-temperature properties of polycrystalline Si{sub 3}N{sub 4} ceramics are strongly influenced by the nanometer-scale glassy phase at the grain boundaries. The authors have recently analyzed the variation of the near-edge fine structure (ELNES) of the Si-L{sub 2,3} edges using a combination of TEM spectrum-line acquisition with an imaging filter and multivariate statistical analysis. The glassy phase at the Si{sub 3}N{sub 4} grain boundaries is easily damaged by the fine probes usually used in scanning transmission electron microscopy to acquire ELNES data. Thus an alternative method using a Gatan imaging filter (GIF), called TEM spectrum-line analysis, was used. This technique will be used to correlate variations in grain boundary chemistry and bonding with the observed performance of Si{sub 3}N{sub 4} ceramics.
Date: April 1, 1997
Creator: Rice, P.M.; Alexander, K.B. & Anderson, I.M.
Partner: UNT Libraries Government Documents Department

A detailed examination of the chemical, hydrological, and geological properties influencing the mobility of {sup 222}radon and parent radionuclides in groundwater

Description: This study examines hydrological, geological and geochemical controls on {sup 222}Rn variability in groundwater in the Front Range of Colorado. Specific objectives of the study are: (1) to determine if there are any correlations or spatial relationships between {sup 222}Rn and the geological, geochemical and hydrogeological data; and (2) to determine whether it is geochemically reasonable for observed {sup 222}Rn levels to be the result of U and {sup 226}Ra accumulation by fracture filling minerals. Domestic-water wells were sampled and tested to determine the local aquifer characteristics and aqueous geochemistry. A multivariate and staged approach was used in the data analyses. Analysis of variance tests were used to test for relationships between {sup 222}Rn and the lithology of the study wells. The effects of rock-type were then removed from the chemical and hydrological variables by subtracting the mean value for each rock-type from each of the measured values within that rock-type (a residual transformation). Linear and linear multiple regression techniques were used to test for expected relationships between residual {sup 222}Rn levels and these variables, and stepwise linear regressions were used to test for any unforeseen multivariate relationships in the data. Correlograms, distance-weighted average and inverse-distance-weighted average predictions were used to look for spatial relationships in the data.
Date: December 31, 1996
Creator: Sexsmith, K.S.
Partner: UNT Libraries Government Documents Department