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Mathematical model to identify nitrogen variability in large rivers

Authors

  • E.-C. Ani,

    Corresponding author
    1. Department of Chemical Technology, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland
    2. Faculty of Chemistry and Chemical Engineering, ‘Babes-Bolyai’ University Cluj-Napoca, No. 11, Arany Janos Street, 400028 Cluj-Napoca, Cluj, Romania
    • Faculty of Chemistry and Chemical Engineering, 11, Arany Janos, 400028 Cluj-Napoca, Cluj, Romania.
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  • M. Hutchins,

    1. Centre for Ecology and Hydrology Wallingford, Maclean Building, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK
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  • A. Kraslawski,

    1. Department of Chemical Technology, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland
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  • P. S. Agachi

    1. Faculty of Chemistry and Chemical Engineering, ‘Babes-Bolyai’ University Cluj-Napoca, No. 11, Arany Janos Street, 400028 Cluj-Napoca, Cluj, Romania
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Abstract

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. Copyright © 2010 John Wiley & Sons, Ltd.

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