This paper presents a mathematical model for the prediction of pollutant transport in rivers. The model has been developed and calibrated using the Matlab software and relying on field data from Romanian Someş River (collected in case of accidental cyanide release). Field data has been used to estimate model parameters (e.g. dispersion coefficient, velocity) as variable in time and space in order account for river features non-uniformity in time and also in space. These initial parameter values have been employed for the calculation of pollutant distribution along the river based on explicit analytical solutions of the advectiondispersion fundamental equation for mass transport in rivers (Fickian approach). Further, parameters optimization has been carried out during model calibration involving a custom tailored optimization algorithm. Results of the comparison between simulated and experimental data show that calibrated model is capable to predict satisfactory the dynamic distribution of pollutant concentration along the river stretch. Peaks travel times are the best predicted features compared to trails, revealing accurate velocity estimation along the stretch. Consequently the model can be employed in case of accidental pollutant releases and also in case of pollutant releases under customary conditions (for cyanides and other pollutants), in order to offer decision support in river water quality management.

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The river Swale in Yorkshire, northern England has been the subject of many studies concerning water quality. This paper builds on existing data resources and previous 1D river water quality modelling applications at daily resolution (using QUESTOR) to provide a different perspective on understanding pollution, through simulation of the short-term dynamics of nutrient transport along the river. The two main objectives are (1) building, calibration and evaluation of a detailed mathematical model (Advection-Dispersion Model: ADModel), for nutrient transport under unsteady flow conditions and (2) the development of methods for estimating key parameters characterizing pollutant transport (velocity, dispersion coefficient and transformation rates) as functions of hydrological parameters and/or seasonality.The study of ammonium and nitrate has highlighted temporal variability in processes, with maximum nitrification and denitrification rates during autumn. Results show that ADModel is able to predict the main trend of measured concentration with reasonable accuracy and accounts for temporal changes in water flow and pollutant load along the river. Prediction accuracy could be improved through more detailed modelling of transformation processes by taking into account the variability of factors for which existing data were insufficient to allow representation. For example, modelling indicates that interactions with bed sediment may provide an additional source of nutrients during high spring flows.