Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.

Ferric (Fe3+) salt dosing is an efficient sulfide control strategy in the sewer network, with potential for multiple benefits including phosphorus removal in the biological reactors and sulfide emission control in the anaerobic digesters of wastewater treatment plant (WWTP). This paper extends the knowledge on the benefit of iron dosing by exploring its impact on the fate of organic micropollutants (MPs) in the wastewater using sewer reactors simulating a rising main sewer pipe. The sulfide produced by the sewer biofilms reacted with Fe3+ forming black colored iron sulfide (FeS). Among the selected MPs, morphine, methadone, and atenolol had >90% initial rapid removal within 5 min of ferric dosing in the sewer reactor. The ultimate removal after 6 h of retention time in the reactor reached 93-97%. Other compounds, ketamine, codeine, carbamazepine, and acesulfame had 30-70% concentration decrease. The ultimate removal varied between 35 and 70% depending on the biodegradability of those MPs. In contrast, paracetamol had no initial removal. The rapid removal of MPs was likely due to adsorption to the FeS surface, which is further confirmed by batch tests with different FeS concentrations. The results showed a direct relationship between the removal of MPs and FeS concentration. The transformation kinetics of these compounds in the reactor without Fe3+ dosing is in good agreement with biodegradation associated with the sewer biofilms in the reactor. This study revealed a significant additional benefit of dosing ferric salts in sewers, that is, the removal of MPs before the sewage enters the WWTP.

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