Source identification in river pollution incidents using a cellular automata model and Bayesian MCMC method

Abstract

Abstract Identification of contaminant sources in rivers is crucial to river protection and emergency response. A general Bayesian framework combining the forward transport model with observed data is proposed to identify unknown sources of river pollutions in this paper. The computational effectiveness of the Bayesian inference will be significantly influenced by the efficiency and accuracy of the forward transport model. Therefore, a forward cellular automata (CA) contaminant transport model is developed to efficiently simulates the concentration values of pollutant rivers in Bayesian estimation. These simulated concentration values are used to calculate the likelihood function of available measurements. A Markov chain Monte-Carlo (MCMC) method is used to produce the posterior distribution of contaminant source parameters. The suggested methodology is tested on a real case study drawn from the publicly available records of the Fen River. The research indicates that the proposed methodology is an effective and flexible way to identify the location and concentrations of river contaminant sources.