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JNC TJ7420 2000-001, 14 Pages, 2000/02

JNC-TJ7420-2000-001.pdf:0.27MB

no abstracts in English

JAEA Reports

QA issues for site hydrochemical data used for groundwater evolution models

Sasamoto, Hiroshi; Yui, Mikazu; Savage, D.*; Bille, B.*

JNC TN8400 99-025, 32 Pages, 1999/06

JNC-TN8400-99-025.pdf:1.94MB

Groundwater data used for modelling site or repository evolution need to be assessed for their quality and whether they are "fit for purpose", prior to utilization. This report discuss factors and issues which impinge upon the quality of such data. It is recommended that geochemical modelleres : (1)are aware of how groundwater samples were collected, whether during drilling, during hydraulic testing, or thereafter, by in-situ measurement, pumped from boreholes, or by pressurised sampler ; (2)are aware of what procedures (if any) were used to "correct" samples for drill fluid contamination and what errors were associated with those methods ; (3)are aware of whether samples were subject to de-pressurisation during sampling, and whether geochemical modelling techniques were applied to correct the compositions of samples for that process ; (4)request different measures of redox activity (e.g., electrode measurements of Eh, concentrations of different redox-sensitive aqueous species) to be applied to key groundwater samples to investigate the extent of redox equilibrium ; (5)are aware of how groundwater samples were filtered and preserved for off-site analysis ; (6)ensure that adequate methods of groundwater filtration ( 0.1m) and chemical analysis are applied to ensure accurate and reproducible analyses for dissolved aluminum at low levels of concentration (generally less than 0.2 mg/L) ; (7)are aware of elemental errors and detection limits in chemical analysis of groundwater samples and assess the quality of groundwater analyses via ion exchange balances and via a comparison of measured and calculated values for total dissolved solids contents. (8)ensure that detailed mineralogical analysis is carried out on rock samples from locations where key groundwater samples have been extracted.

JAEA Reports

Geostatistical Analysis of Groundwater Chemistry in Japan; Evaluation of the Base Case Groundwater Data Set

PATRICIA F SALTE*; Sasamoto, Hiroshi; Apted, M. J.*; Yui, Mikazu

JNC TN8400 99-023, 231 Pages, 1999/05

JNC-TN8400-99-023.pdf:63.86MB

The groundwater chemistry is one of important geological environment for performance assessment of high level radioactive disposal system. This report describes the results of geostatistical analysis of groundwater chemistry in Japan. Over 15,000 separate groundwater analyses have been collected of deep Japanese groundwaters for the purpose of evaluating the range of geochemical conditions for geological radioactive waste repositories in Japan. The significance to issues such as radioelement solubility limits, sorption, corrosion of overpack, behavior of compacted clay buffers, and many other factors involved in safety assessment. It is important therefore, that a small, but representative set of groundwater types be identified so that defensible models and data for generic repository performance assessment can be established. Principal component analysis (PCA) is used to categorize representative deep groundwater types from this extensive data set. PCA is a multi-variate statistical analysis technique, similar to factor analysis or eigenvector analysis, designed to provide the best possible resolution of the variability within multi-variate data sets. PCA allows the graphical inspection of the most important similarities (clustering) and differences among samples, based on simultaneous consideration of all variables in the dataset, in a low dimensionality plot. It also allows the analyst to determine the reasons behind any pattern that is observed. In this study, PCA has been aided by hierarchical cluster analysis (HCA), in which statistical indices of similarity among multiple samples are used to distinguish distinct clusters of samples. HCA allows the natural, a priori, grouping of data into clusters showing similar attributes and is graphically represented in a dendrogram Pirouette is the multivariate statistical software package used to conduct the PCA and HCA for the Japanese groundwater dataset. An audit of the initial 15,000 sample dataset on the ...

JAEA Reports