A Markov-renewal extension of a deterministic state-variable model of the transport and fate of a toxic chemical in the environment

Abstract

Specifications for stochastic Markov-renewal (MR) models are compared with those for deterministic, dynamic, state-variable (SV) models. Numerical predictions provided by MR models are qualified by probabilities. A case study is demonstrated in which the fate of the herbicide atrazine upon application in the watershed of an Iowa lake is tracked. Comparisons of numerical results on an MR model with those obtained in an earlier study employing an SV model show that explicit allowance for statistical variability in measured concentrations of a target chemical species assist substantially in interpreting differences between predicted and measured concentrations of the species. It is concluded that MR models provide a feasible alternative to SV models for predicting the fate of chemical species in aquatic environments in cases involving zero- or first-order kinetics of transfer and transformation of those species.