Application of Optimal Nonstationary Time Series Analysis to Water Quality Data and Pollutant Transport Modelling

Abstract

Water quality and pollutant transport modelling play an important role in assessing the risk from pollution incidents. They are also important for the sustainable management of water resources. An ability to predict the concentrations of a pollutant travelling along the river is necessary in assessing the ecological impact of the pollutant and to plan a remedy against possible damage to humans and the environment. The risk from a pollutant at a given location along the river depends on the maximum concentration of any toxic component, the travel times of the pollutant from the release point and the duration over which its concentration exceeds feasible threshold levels. These tasks require modelling pollutant transport under varying flow conditions, i.e. unsteady flow. The aim is to outline on-going research on data-based models and to compare the results with physically-based approaches using worked case examples from pollutant transport modelling. In addition to steady-state examples, a Stochastic Transfer pollutant transport model in varying flow conditions is presented. A case study on water quality modelling has the form of a tutorial on the application of a multi-rate Stochastic Transfer Function to the identification of environmental processes.