The GREAT-ER software was used to calculate linear alkylbenzene sulphonate [LAS] and boron concentrations in the Itter stream in North Rhine-Westfalia, Germany. The aim was to investigate the predictive strength of this newly developed tool and to compare its results to measured data. Substance-specific input data which were used in this scenario were partly generic (e.g. LAS and sodium perborate tetrahydrate consumption figures for Germany) and partly (site-)specific data (e.g. half-life time for LAS elimination). The comparison with the measured data reveals that the model predictions for LAS and boron are correct at least within a factor of two, only if generic German consumption data is applied. By using refined input data, the accuracy can be increased further.