The multivariate statistical techniques principal component analysis (PCA), Q-mode factor analysis (QFA), and correspondence analysis (CA) were applied to a dataset containing trace element concentrations in groundwater samples collected from a number of wells located downgradient from the potential nuclear waste repository at Yucca Mountain, Nevada. PCA results reflect the similarities in the concentrations of trace elements in the water samples resulting from different geochemical processes. QFA results reflect similarities in the trace element compositions, whereas CA reflects similarities in the trace elements that are dominant in the waters relative to all other groundwater samples included in the dataset. These differences are mainly due to the ways in which data are preprocessed by each of the three methods. The highly concentrated, and thus possibly more mature (i.e. older), groundwaters are separated from the more dilute waters using principal component 1 (PC 1). PC 2, as well as dimension 1 of the CA results, describe differences in the trace element chemistry of the groundwaters resulting from the different aquifer materials through which they have flowed. Groundwaters thought to be representative of those flowing through an aquifer composed dominantly of volcanic rocks are characterized by elevated concentrations of Li, Be, Ge, Rb, Cs, and Ba, whereas those associated with an aquifer dominated by carbonate rocks exhibit greater concentrations of Ti, Ni, Sr, Rh, and Bi. PC 3, and to a lesser extent dimension 2 of the CA results, show a strong monotonic relationship with the percentage of As(III) in the groundwater suggesting that these multivariate statistical results reflect, in a qualitative sense, the oxidizing/reducing conditions within the groundwater. Groundwaters that are relatively more reducing exhibit greater concentrations of Mn, Cs, Co, Ba, Rb, and Be, and those that are more oxidizing are characterized by greater concentrations of V, Cr, Ga, As, W, and U.
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