Coupling artificial neural network and fluorescence spectroscopy to control CEC removal during AOPs

Contaminants of emerging concern (CEC) include anthropogenic compounds frequently detected in natural and engineered water systems at trace concentrations. CEC are relevant due to their high persistence and mobility and adverse effects on humans, wildlife, ecosystems. One of the main challenges is the lack of real-time monitoring systems of CEC and process parameters at wastewater treatment plants (WWTPs). In this study, fluorescence indexes and artificial neural networks (ANNs) were used to track the removal of CEC from secondary and tertiary WWTP effluents during O3- and UV-based advanced oxidation processes operated at pilot scale. Results show that indexes served as effective surrogate parameters to monitor CEC removal within individual wastewater types. The application of an ANN model improved the correlation (R2 = 0.87) between CEC and fluorescence indexes, highlighting the potential for fluorescence-based monitoring of CEC removal regardless of WWTP effluent type.