A mixture model approach to analyzing major element chemistry data of the Changjiang (Yangtze River)

Abstract

In this article we study the statistical distributions of major chemical compositions (HCO3, Ca; charges are neglected for simplicity) and the total dissolved solid (TDS) concentration in the river water of the Changjiang (Yangtze River) of China. We propose a Bayesian finite mixture model with an unknown number of components for the multi-year averages of continuously monitored data over the period 1958–1990 at 191 stations in the drainage basin. A discretization-based Monte Carlo sampling approach is used to estimate the posterior distributions of the parameters in the model. Two sub-populations are identified for the levels of TDS, HCO3 and Ca, and observations from the 191 stations are classified into two groups using the posterior classification probabilities. Copyright © 2005 John Wiley & Sons, Ltd.