A unit process log reduction database for water reuse practitioners

Pathogen reduction for the purpose of human health protection is a critical function provided by water reuse systems. Pathogen reduction performance potential is dependent on a wide range of design and operational parameters. Poor understanding of pathogen reduction performance has important consequences-under treatment can jeopardize human health, while over treatment can lead to unnecessary costs and environmental impacts. Documented pathogen reduction potential of the unit processes that make up water reuse treatment trains is based on a highly dispersed and unstructured literature, creating an impediment to practitioners looking to design, model or simply better understand these systems. This review presents a database of compiled log reduction values (LRVs) and log reduction credits (LRCs) for unit processes capable of providing some level of pathogen reduction, with a focus on processes suitable for onsite non-potable water reuse systems. Where reported, we have also compiled all relevant design and operational factors associated with the LRVs and LRCs. Overall, we compiled over 1100 individual LRV data entries for 31 unit processes, and LRCs for 8 unit processes. Results show very inconsistent reporting of influencing parameters, representing a limitation to the use of some of the data. As a standalone resource, the database (included as Supplemental Information) provides water reuse practitioners with easy access to LRV and LRC data. The database is also part of a longer-term effort to optimize the balance between human health protection, potential environmental impacts and cost of water reuse treatment trains.

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 waterborne pathogens remaining in insufficiently treated wastewater are of concern. Sanitation Safety Planning 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 determination 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 determine the appropriate CL to protect human health in wastewater reclamation and reuse.

Helminth log reduction values for recycling water from sewage for the protection of human and stock health

The LRVs required to decrease HE concentrations in raw sewage to an acceptable level to manage the risk to human and livestock health were determined. An LRV of 3.0 was required to meet the HBT of 1 μDALY pppy in SE Australia where human helminth infections are not endemic. In comparison, a similar exposure volume and LRV in endemic regions would result in a HBT of 100 μDALY pppy. The risks posed by cattle- and pig-related helminths were also managed acceptably with the treatment of sewage providing an LRV of 3.0. New design equations were derived to determine LRVs based on hydraulic residence times (HRTs) in an activated sludge plant (ASP) and lagoons. The new equation for lagoons indicated that an LRV of 3.0 could be achieved with a HRT of 18 days or less.

Virus removal by ultrafiltration: Understanding long-term performance change by application of Bayesian analysis

Ultrafiltration is an effective barrier to waterborne pathogens including viruses. Challenge testing is commonly used to test the inherent reliability of such systems. Performance validation seeks to demonstrate the adequate reliability of the treatment system. Appropriate and rigorous data analysis is an essential aspect of validation testing. In this study we used Bayesian analysis to assess the performance of a full-scale ultrafiltration system which was validated and revalidated after five years of operation. A hierarchical Bayesian model was used to analyse a number of similar ultrafiltration membrane skids working in parallel during the two validation periods. This approach enhanced our ability to obtain accurate estimations of performance variability, especially when the sample size of some system skids was limited. This methodology enabled the quantitative estimation of uncertainty in the performance parameters and generation of predictive distributions incorporating those uncertainties. The results indicated that there was a decrease in the mean skid performance after five years of operation of approximately 1 log reduction value (LRV). Interestingly, variability in the LRV also reduced, with standard deviations from the revalidation data being decreased by a mean 0.37 LRV compared with the original validation data. The model was also useful in comparing the operating performance of the various parallel skids within the same year. Evidence of differences was obtained in 2015 for one of the membrane skids. A hierarchical Bayesian analysis of validation data provides robust estimations of performance and the incorporation of probabilistic analysis which is increasingly important for comprehensive quantitative risk assessment purposes.