Bayesian Chemical Mass Balance Method for Surface Water Contaminant Source Apportionment

Abstract

A Bayesian chemical mass balance (CMB) source apportionment method is developed using the Markov Chain Monte Carlo (MCMC) approach. Compared with deterministic approaches, the Bayesian method is capable of accounting for the measurement errors and the impact of variability of the source elemental compositions resulting from the heterogeneities and estimate the uncertainties associated with the estimated source contributions. The method estimates the joint probability densities and consequently, the credible intervals and correlation matrices of source contributions of various sources into a receiving water using observed elemental profiles of samples from both potential sources and the receiving surface waters. The model is applied to samples collected from possible sources and runoff and stream flow from two stream crossing sites along Highway 89 in the Lake Tahoe Basin. The contributing sources of total dissolved nitrogen, total dissolved phosphorus concentrations, and microparticles (