Real time control (RTC) is generally viewed as a viable method for optimising the performance of urban wastewater systems. A literature review on the performance evaluation of RTC demonstrated a lack of consensus on how to do this. Two main deficiencies were identified: omitting uncertainty analysis and applying limited evaluation periods. A general methodology to evaluate the performance of RTC in practice, that takes into account these deficiencies, is proposed. The methodology is either data or model driven and the (dis)advantages of each are discussed. In a case study for a combined sewer system with limited discharge to a WWTP, it is demonstrated that the successful application of RTC and the possibility to determine a significant effect is very much dependent on the goal. It also clearly illustrates the need for taking uncertainties into account and that careful consideration in the chosen evaluation period is required.

UV/Vis spectrophotometers have been used for one decade to monitor water quality in various locations: sewers, rivers, wastewater treatment plants (WWTPs), tap water networks, etc. Resulting equivalent concentrations of interest can be estimated by three ways: i) by manufacturer global calibration; ii) by local calibration based on the provided global calibration and grab sampling; iii) by advanced calibration looking for relations between UV/Vis spectra and corresponding concentrations from grab sampling. However, no study has compared the applied methods so far. This collaborative work presents a comparison between five different methods. A Linear Regression (LR), Support Vector Machine (SVM), EVOlutionary algorithm method (EVO) and Partial Least Squares (PLS) have been applied on various data sets (sewers, rivers, WWTPs under dry, wet and all weather conditions) and for three water quality parameters: TSS, COD total and dissolved. Two criteria (r² and Root Mean Square Error RMSE) have been calculated - on calibration and verification data subsets - to evaluate accuracy and robustness of the applied methods. Values of criteria have then been statistically analysed for all and separated data sets. Non-consistent outcomes come through this study. According to the Kruskal-Wallis test and RMSEs, PLS and SVM seem to be the best methods. According to uncertainties in laboratory analysis and ranking of methods, LR and EVO appear more robust and sustainable for concentration estimations. Conclusions are mostly independent of water matrices, weather conditions or concentrations investigated.