Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection

Wastewater reclamation and reuse have been practically applied to water-stressed regions, but water-borne pathogens remaining in insufficiently treated wastewater are of concern. Sanitation Safety Plan-ning adopts the hazard analysis and critical control point (HACCP) approach to manage human health risks upon exposure to reclaimed wastewater. HACCP requires a predetermined reference value (critical limit: CL) at critical control points (CCPs), in which specific parameters are monitored and recorded in real time. A disinfection reactor of a wastewater treatment plant (WWTP) is regarded as a CCP, and one of the CCP parameters is the disinfection intensity (e.g., initial disinfectant concentration and contact time), which is proportional to the log reduction value (LRV) of waterborne pathogens. However, the achievable LRVs are not always stable because the disinfection intensity is affected by water quality parameters, which vary among WWTPs. In this study, we established models for projecting virus LRVs using ozone, in which water quality and operational parameters were used as explanatory variables. For the model construction, we used five machine learning algorithms and found that automatic relevance determi-nation with interaction terms resulted in better prediction performances for norovirus and rotavirus LRVs. Poliovirus and coxsackievirus LRVs were predicted well by a Bayesian ridge with interaction terms and lasso with quadratic terms, respectively. The established models were relatively robust to predict LRV using new datasets that were out of the range of the training data used here, but it is important to collect LRV datasets further to make the models more predictable and flexible for newly obtained datasets. The modeling framework proposed here can help WWTP operators and risk assessors deter-mine the appropriate CL to protect human health in wastewater reclamation and reuse. (C) 2021 The Author(s). Published by Elsevier Ltd.

Automated summary

Models were established to project virus log reduction values using ozone, with water quality and operational parameters as explanatory variables. Machine learning algorithms were used, showing different prediction performances for different viruses, but overall robustness of the models.