A simple model for online forecasting of ammonium (NH4 +) concentrations in sewer systems is proposed. The forecast model utilizes a simple representation of daily NH4 + profiles and the dilution approach combined with information from online NH4 + and flow sensors. The method utilizes an ensemble approach based on past observations to create model prediction bounds. The forecast model was tested against observations collected at the inlet of two wastewater treatment plants (WWTPs) over an 11-month period. NH4 + data were collected with ion-selective sensors. The model performance evaluation focused on applications in relation to online control strategies. The results of the monitoring campaigns highlighted a high variability in daily NH4 + profiles, stressing the importance of an uncertainty-based modelling approach. The maintenance of the NH4 + sensors resulted in important variations of the sensor signal, affecting the evaluation of the model structure and its performance. The forecast model succeeded in providing outputs that potentially can be used for integrated control of wastewater systems. This study provides insights on full scale application of online water quality forecasting models in sewer systems. It also highlights several research gaps which - if further investigated - can lead to better forecasts and more effective real-time operations of sewer and WWTP systems.