Rigorous uncertainty assessment in contaminant transport inverse modelling: a case study of fluoride diffusion through clay liners

Abstract

Inverse methods used in assessing landfill liner design have not yet taken advantage of current developments in inverse procedures. Here, a method for inverting contaminant transport models is presented including a general error model and procedures for differentially weighted multiple response regression. General error models are employed in cases where the residuals are heteroscedastic and correlated, and lead to valid inference on model parameter and predictive uncertainty. The Shuffled Complex Evolution algorithm is used to optimise model parameters. Model parameter uncertainty is assessed by exploring the posterior probability distribution with the Metropolis algorithm, a Markov chain Monte Carlo sampling method. The inverse method is applied to simultaneously determine the sorption and diffusion parameters from laboratory diffusion cell experiments. In these experiments, fluoride migration through kaolin clays was measured by sampling the source and collector cells over time. To uniquely determine the transport model parameters, it was necessary to simultaneously fit the observed data from two independent diffusion cell experiments with different initial concentrations. The jointly fitted transport model parameters compared well with those fitted to independent batch experiments.