Multivariate analysis of stream water chemical data: The use of principal components analysis for the end‐member mixing problem

Abstract

Traditional multivariate data analysis techniques, such as principal components analysis (PCA), have often been used in an attempt to identify source solutions from potential mixtures, such as stream water. Artificial data, generated from conservative mixing of known source solutions in random proportions, are employed to demonstrate that PCA should be used only to determine the rank of the mixture and not to determine the composition of the source solutions. The rank of the mixture is related to the number of source solutions. Unambiguous identification of the source solution compositions from the mixture alone is impossible; thus it is necessary that potential source solutions be derived from independent measurements. In the case of stream water, possible source solutions are groundwater and soil water from different horizons. A multivariate screening procedure is presented for the evaluation of these potential source solutions.