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

A multi-criteria decision framework for circular wastewater systems in emerging megacities of the Global South

Lima faces increasing water stress due to demographic growth, climate change and outdated water management infrastructure. Moreover, its highly centralized wastewater management system is currently unable to recover water or other resources. Hence, the primary aim of this study is to identify suitable wastewater treatment alternatives for both eutrophication mitigation and indirect potable reuse (IPR). For eutrophication mitigation, we examined MLE, Bardenpho, Step-feed, HF-MBR, and FS-MBR. For IPR, we considered secondary treatment+UF + RO + AOP or MBR + RO + AOP. These alternatives form part of a WWTP network at a district level, aiding Lima's pursuit of a circular economy approach. This perspective allows reducing environmental impacts through resource recovery, making the system more resilient to disasters and future water shortages. The methods used to assess these scenarios were Life Cycle Assessment for the environmental dimension; Life Cycle Costing for the economic perspective; and Multi-Criteria Decision Analysis to integrate both the quantitative tools aforementioned and qualitative criteria for social and techno-operational dimensions, which combined, strengthen the decision-making process. The decision-making steered towards Bardenpho for eutrophication abatement when environmental and economic criteria were prioritized or when the four criteria were equally weighted, while HF-MBR was the preferred option when techno-operational and social aspects were emphasized. In this scenario, global warming (GW) impacts ranged from 0.23 to 0.27 kg CO2eq, eutrophication mitigation varied from 6.44 to 7.29 g PO4- equivalent, and costs ranged between 0.12 and 0.17 €/m3. Conversely, HF-MBR + RO + AOP showed the best performance when IPR was sought from the outset. In the IPR scenario, GW impacts were significantly higher, at 0.46-0.51 kg CO2eq, eutrophication abatement was above 98 % and costs increased to ca. 0.44 €/m3.

Modeling and Life Cycle Assessment of a Membrane Bioreactor–Membrane Distillation Wastewater Treatment System for Potable Reuse

Wastewater treatment for indirect potable reuse (IPR) is a possible approach to address water scarcity. In this study, a novel membrane bioreactor–membrane distillation (MBR-MD) system was evaluated to determine the environmental impacts of treatment compared to an existing IPR facility (“Baseline”). Physical and empirical models were used to obtain operational data for both systems and inform a life cycle inventory. Life cycle assessment (LCA) was used to compare the environmental impacts of each system. Results showed an average 53.7% reduction in environmental impacts for the MBR-MD system when waste heat is used to operate MD; however, without waste heat, the environmental impacts of MBR-MD are significantly higher, with average impacts ranging from 218% to 1400% greater than the Baseline, depending on the proportion of waste heat used. The results of this study demonstrate the effectiveness of the novel MBR-MD system for IPR and the reduced environmental impacts when waste heat is available to power MD.

On Bayesian modeling of censored data in JAGS

Background

Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() for censored data misspecifies the default computation of deviance function, which limits likelihood-based Bayesian model comparison.

Results

To establish an automatic approach to specifying the correct deviance function in JAGS, we propose a simple and generic alternative modeling strategy for the analysis of censored outcomes. The two illustrative examples demonstrate that the alternative strategy not only properly draws posterior samples in JAGS, but also automatically delivers the correct deviance for model assessment. In the survival data application, our proposed method provides the correct value of mean deviance based on the exact likelihood function. In the drug safety data application, the deviance information criterion and penalized expected deviance for seven Bayesian models of censored data are simultaneously computed by our proposed approach and compared to examine the model performance.

Conclusions

We propose an effective strategy to model censored data in the Bayesian modeling framework in JAGS with the correct deviance specification, which can simplify the calculation of popular Kullback–Leibler based measures for model selection. The proposed approach applies to a broad spectrum of censored data types, such as survival data, and facilitates different censored Bayesian model structures.

Make it clean, make it safe: A review on virus elimination via adsorption

Recently, the potential dangers of viral infection transmission through water and air have become the focus of worldwide attention, via the spread of COVID-19 pandemic. The occurrence of large-scale outbreaks of dangerous infections caused by unknown pathogens and the isolation of new pandemic strains require the development of improved methods of viruses' inactivation. Viruses are not stable self-sustaining living organisms and are rapidly inactivated on isolated surfaces. However, water resources and air can participate in the pathogens' diffusion, stabilization, and transmission. Viruses inactivation and elimination by adsorption are relevant since they can represent an effective and low-cost method to treat fluids, and hence limit the spread of pathogen agents. This review analyzed the interaction between viruses and carbon-based, oxide-based, porous materials and biological materials (e.g., sulfated polysaccharides and cyclodextrins). It will be shown that these adsorbents can play a relevant role in the viruses removal where water and air purification mostly occurring via electrostatic interactions. However, a clear systematic vision of the correlation between the surface potential and the adsorption capacity of the different filters is still lacking and should be provided to achieve a better comprehension of the global phenomenon. The rationalization of the adsorption capacity may be achieved through a proper physico-chemical characterization of new adsorbents, including molecular modeling and simulations, also considering the adsorption of virus-like particles on their surface. As a most timely perspective, the results on this review present potential solutions to investigate coronaviruses and specifically SARS-CoV-2, responsible of the COVID-19 pandemic, whose spread can be limited by the efficient disinfection and purification of closed-spaces air and urban waters.

Addressing and reducing parameter uncertainty in quantitative microbial risk assessment by incorporating external information via Bayesian hierarchical modeling

Probabilistic quantitative microbial risk assessment (QMRA) studies define model inputs as random variables and use Monte-Carlo simulation to generate distributions of potential risk outcomes. If local information on important QMRA model inputs is missing, it is widely accepted to justify assumptions about these model inputs by using external literature information. A question, which remains unexplored, is the extent to which previously published external information should influence local estimates in cases of nonexistent, scarce, and moderate local data. This question can be addressed by employing Bayesian hierarchical modeling (BHM). Thus, we study the effects and potential benefits of BHM on risk and performance target calculations at three wastewater treatment plants (WWTP) in comparison to alternative statistical modeling approaches (separate modeling, no-pooling, complete pooling). The treated wastewater from the WWTPs is used for restricted irrigation, potable reuse, or influences recreational waters, respectively. We quantify the extent to which external data affects local risk estimations in each case depending on the statistical modeling approach applied. Modeling approaches are compared by calculating the pointwise expected log-predictive density for each model. As reference pathogens and example data, we use locally collected Norovirus genogroup II data with varying sample sizes (n = 4, n = 7, n = 27), and complement local information with external information from 44 other WWTPs (n = 307). Results indicate that BHM shows the highest predictive accuracy and improves estimates by reducing parameter uncertainty when data are scarce. In such situations, it may affect risk and performance target calculations by orders of magnitude in comparison to using local data alone. Furthermore, it allows making generalizable inferences about new WWTPs, while providing the necessary flexibility to adjust for different levels of information contained in the local data. Applying this flexible technique more widely may contribute to improving methods and the evidence base for decision-making in future QMRA studies.

Evaluating membrane performance in recycled water treatment plants for assets replacement strategy

Membranes are an important barrier used in recycled water treatment plants for pathogen removal. Understanding performance over operational life is important to inform membrane replacement. In this study, full scale virus challenge testing was conducted on newly commissioned membranes to validate virus log removal values for accreditation. After six years of operation, the membrane integrity was repeated to ensure compliance with the state regulatory health authority and gain an understanding of the asset's condition. Membrane performance was assessed using a combination of complementary tests including membrane autopsy and chemical tolerance testing to assess individual modules and selected membrane fibres, followed by a full scale virus challenge for whole of unit assessment. The results demonstrated that the aged membrane fibres were intact and had not been affected by long-term exposure to chlorine, which provides valuable information for membrane asset replacement strategies.

Robust evaluation of performance monitoring options for ozone disinfection in water recycling using Bayesian analysis

Ozonation of wastewater has gained popularity because of its effectiveness in removing colour, UV absorbance, trace organic chemicals, and pathogens. Due to the rapid reaction of ozone with organic compounds, dissolved ozone is often not measurable and therefore, the common disinfection controlling parameter, concentration integrated over contact time (CT) cannot be obtained. In such cases, alternative parameters have been shown to be useful as surrogate measures for microbial removal including change in UV₂₅₄ absorbance (ΔUVA), change in total fluorescence (ΔTF), or O₃:TOC (or O₃:DOC). Although these measures have shown promise, a number of caveats remain. These include uncertainties in the associations between these measurements and microbial inactivation. Furthermore, previous use of seeded microorganisms with higher disinfection sensitivity compared to autochthonous microorganisms could lead to overestimation of appropriate log credits. In our study, secondary treated wastewater from a full-scale plant was ozonated in a bench-scale reactor using five increasing ozone doses. During the experiments, removal of four indigenous microbial indicators representing viruses, bacteria and protozoa were monitored concurrent with ΔUVA, ΔTF, O₃:DOC and PARAFAC derived components. Bayesian methods were used to fit linear regression models, and the uncertainty in the posterior predictive distributions and slopes provided a comparison between previously reported results and those reported here. Combined results indicated that all surrogate parameters were useful in predicting the removal of microorganisms, with a better fit to the models using ΔUVA, ΔTF in most cases. Average adjusted determination coefficients for fitted models were high (R²_adjusted>0.47). With ΔUVA, one unit decrease in LRV corresponded with a UVA mean reduction of 15-20% for coliforms, 59% for C. perfringens spores, and 11% for somatic coliphages. With ΔTF, a one unit decrease in LRV corresponded with a TF mean reduction of 18-23% for coliforms, 71% for C. perfringens spores, and 14% for somatic coliphages. Compared to previous studies also analysed, our results suggest that microbial reductions were more conservative for autochthonous than for seeded microorganisms. The findings of our study suggested that site-specific analyses should be conducted to generate models with lower uncertainty and that indigenous microorganisms are useful for the measurement of system performance even when censored observations are obtained.