Predicting recreational water quality and public health safety in urban estuaries using Bayesian Networks.

To support the reactivation of urban rivers and estuaries for bathing while ensuring public safety, it is critical to have access to real-time information on microbial water quality and associated health risks. Predictive modelling can provide this information, though challenges concerning the optimal size of training data, model transferability, and communication of uncertainty still need attention. Further, urban estuaries undergo distinctive hydrological variations requiring tailored modelling approaches. This study assessed the use of Bayesian Networks (BNs) for the prediction of enterococci exceedances and extrapolation of health risks at planned bathing sites in an urban estuary in Sydney, Australia. The transferability of network structures between sites was assessed. Models were validated using a novel application of the k-fold walk-forward validation procedure and further tested using independent compliance and event-based sampling datasets. Learning curves indicated the model's sensitivity reached a minimum performance threshold of 0.8 once training data included ≥ 400 observations. It was demonstrated that Semi-Naïve BN structures can be transferred while maintaining stable predictive performance. In all sites, salinity and solar exposure had the greatest influence on Posterior Probability Distributions (PPDs), when combined with antecedent rainfall. The BNs provided a novel and transparent framework to quantify and visualise enterococci, stormwater impact, health risks, and associated uncertainty under varying environmental conditions. This study has advanced the application of BNs in predicting recreational water quality and providing decision support in urban estuarine settings, proposed for bathing, where uncertainty is high.

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 (R2adjusted>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.

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.

Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater.

Chlorine disinfection of biologically treated wastewater is practiced in many locations prior to environmental discharge or beneficial reuse. The effectiveness of chlorine disinfection processes may be influenced by several factors, such as pH, temperature, ionic strength, organic carbon concentration, and suspended solids. We investigated the use of Bayesian multilayer perceptron (BMLP) models as efficient and practical tools for compiling and analysing free chlorine and monochloramine virus disinfection performance as a multivariate problem. Corresponding to their relative susceptibility, Adenovirus 2 was used to assess disinfection by monochloramine and Coxsackievirus B5 was used for free chlorine. A BMLP model was constructed to relate key disinfection conditions (CT, pH, turbidity) to observed Log Reduction Values (LRVs) for these viruses at constant temperature. The models proved to be valuable for incorporating uncertainty in the chlor(am)ination performance estimation and interpolating between operating conditions. Various types of queries could be performed with this model including the identification of target CT for a particular combination of LRV, pH and turbidity. Similarly, it was possible to derive achievable LRVs for combinations of CT, pH and turbidity. These queries yielded probability density functions for the target variable reflecting the uncertainty in the model parameters and variability of the input variables. The disinfection efficacy was greatly impacted by pH and to a lesser extent by turbidity for both types of disinfections. Non-linear relationships were observed between pH and target CT, and turbidity and target CT, with compound effects on target CT also evidenced. This work demonstrated that the use of BMLP models had considerable ability to improve the resolution and understanding of the multivariate relationships between operational parameters and disinfection outcomes for wastewater treatment.

Enhanced antagonism of BST-2 by a neurovirulent SIV envelope.

Current antiretroviral therapy (ART) is not sufficient to completely suppress disease progression in the CNS, as indicated by the rising incidence of HIV-1-associated neurocognitive disorders (HAND) among infected individuals on ART. It is not clear why some HIV-1-infected patients develop HAND, despite effective repression of viral replication in the circulation. SIV-infected nonhuman primate models are widely used to dissect the mechanisms of viral pathogenesis in the CNS. Here, we identified 4 amino acid substitutions in the cytoplasmic tail of viral envelope glycoprotein gp41 of the neurovirulent virus SIVsm804E that enhance replication in macrophages and associate with enhanced antagonism of the host restriction factor BM stromal cell antigen 2 (BST-2). Rhesus macaques were inoculated with a variant of the parental virus SIVsmE543-3 that had been engineered to contain the 4 amino acid substitutions present in gp41 of SIVsm804E. Compared with WT virus-infected controls, animals infected with mutant virus exhibited higher viral load in cerebrospinal fluid. Together, these results are consistent with a potential role for BST-2 in the CNS microenvironment and suggest that BST-2 antagonists may serve as a possible target for countermeasures against HAND.

Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models.

Risk management for wastewater treatment and reuse have led to growing interest in understanding and optimising pathogen reduction during biological treatment processes. However, modelling pathogen reduction is often limited by poor characterization of the relationships between variables and incomplete knowledge of removal mechanisms. The aim of this paper was to assess the applicability of Bayesian belief network models to represent associations between pathogen reduction, and operating conditions and monitoring parameters and predict AS performance. Naïve Bayes and semi-naïve Bayes networks were constructed from an activated sludge dataset including operating and monitoring parameters, and removal efficiencies for two pathogens (native Giardia lamblia and seeded Cryptosporidium parvum) and five native microbial indicators (F-RNA bacteriophage, Clostridium perfringens, Escherichia coli, coliforms and enterococci). First we defined the Bayesian network structures for the two pathogen log10 reduction values (LRVs) class nodes discretized into two states (< and ≥ 1 LRV) using two different learning algorithms. Eight metrics, such as Prediction Accuracy (PA) and Area Under the receiver operating Curve (AUC), provided a comparison of model prediction performance, certainty and goodness of fit. This comparison was used to select the optimum models. The optimum Tree Augmented naïve models predicted removal efficiency with high AUC when all system parameters were used simultaneously (AUCs for C. parvum and G. lamblia LRVs of 0.95 and 0.87 respectively). However, metrics for individual system parameters showed only the C. parvum model was reliable. By contrast individual parameters for G. lamblia LRV prediction typically obtained low AUC scores (AUC < 0.81). Useful predictors for C. parvum LRV included solids retention time, turbidity and total coliform LRV. The methodology developed appears applicable for predicting pathogen removal efficiency in water treatment systems generally.

Occurrence of ectoparasiticides in Australian beef cattle feedlot wastes.

A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the simultaneous analysis of 6 ectoparasiticides - 2 synthetic pyrethroids (deltamethrin, cypermethrin) and 4 macrocyclic lactones (abamectin, doramectin, ivermectin and eprinomectin) in biosolids. The method was used to investigate the occurrence of these ectoparasiticides in beef cattle feedlot wastes in Australia from 5 commercial feedlot operations which employ varying waste management practices. Deltamethrin and cypermethrin were not detected in any of the samples while abamectin, ivermectin, doramectin and eprinomectin were detected in some of the samples with concentrations ranging from 1 to 36 µg/kg dry weight (d.w.) freeze dried feedlot waste. Levels of macrocyclic lactones detected in the feedlot wastes varied and were dependent on sample type. The effect of seasonal variations and waste management practices were also investigated in this study.

Inactivation of indicators and pathogens in cattle feedlot manures and compost as determined by molecular and culture assays.

Accurate and conservative information about pathogen inactivation rates is needed as the basis for safe manure management on beef cattle feedlots. The survival of indicators and pathogens in faecal pen manure, stockpiled manure and manure compost was measured with autochthonous indicator bacteria (Escherichia coli, Clostridium perfringens, enterococci, total coliforms) and pathogens (Listeria monocytogenes, Campylobacter jejuni) using culture and/or real-time quantitative PCR (qPCR) methods. Additionally, the manures were incubated at 20, 37, 50 and 60 °C in microcosms to quantify the persistence of autochthonous microorganisms and selected process performance surrogates (Clostridium sporogenes, green fluorescent protein-labelled E. coli and L. monocytogenes). Estimated qPCR cell counts indicated that up to four orders of magnitude more target cells were present compared with the culturable counts. Corresponding T(90) estimates were up to sixfold higher. This study demonstrates the benefits of nucleic acid-based quantification of pathogen inactivation in cattle manures and concludes that the concurrent analysis of microorganisms by molecular and culture methods provides complementary value.

Diversity and abundance of zoonotic pathogens and indicators in manures of feedlot cattle in Australia.

The occurrence of 10 pathogens and three fecal indicators was assessed by quantitative PCR in manures of Australian feedlot cattle. Most samples tested positive for one or more pathogens. For the dominant pathogens Campylobacter jejuni, Listeria monocytogenes, Giardia spp., Cryptosporidium spp., and eaeA-positive Escherichia coli, 10² to 107 genome copies g⁻¹ (dry weight) manure were recovered.

Predicting pathogen risks to aid beach management: the real value of quantitative microbial risk assessment (QMRA).

There has been an ongoing dilemma for agencies that set criteria for safe recreational waters in how to provide for a seasonal assessment of a beach site versus guidance for day-to-day management. Typically an overall 'safe' criterion level is derived from epidemiologic studies of sewage-impacted beaches. The decision criterion is based on a percentile value for a single sample or a moving median of a limited number (e.g. five per month) of routine samples, which are reported at least the day after recreator exposure has occurred. The focus of this paper is how to better undertake day-to-day recreational site monitoring and management. Internationally, good examples exist where predictive empirical regression models (based on rainfall, wind speed/direction, etc.) may provide an estimate of the target faecal indicator density for the day of exposure. However, at recreational swimming sites largely impacted by non-sewage sources of faecal indicators, there is concern that the indicator-illness associations derived from studies at sewage-impacted beaches may be inappropriate. Furthermore, some recent epidemiologic evidence supports the relationship to gastrointestinal (GI) illness with qPCR-derived measures of Bacteroidales/Bacteroides spp. as well as more traditional faecal indicators, but we understand less about the environmental fate of these molecular targets and their relationship to bather risk. Modelling pathogens and indicators within a quantitative microbial risk assessment framework is suggested as a way to explore the large diversity of scenarios for faecal contamination and hydrologic events, such as from waterfowl, agricultural animals, resuspended sediments and from the bathers themselves. Examples are provided that suggest that more site-specific targets derived by QMRA could provide insight, directly translatable to management actions.

Monitoring bacterial indicators and pathogens in cattle feedlot waste by real-time PCR.

Quantitative microbial health risk assessment requires accurate enumeration of pathogens in hazard-containing matrices as part of the risk characterization process. As part of a risk management-oriented study of cattle feedlot waste contaminants, we investigated the utility of quantitative real-time PCR (qPCR) for surveying the microbial constituents of different faecal wastes. The abundance of Escherichia coli and enterococci were first estimated in five cattle feedlot waste types from five localities. Bacteria were quantified using two culture methods and compared to the number of genome copies detected by qPCR targeted at E. coli and Enterococcus faecalis. Bacterial numbers detected in the different wastes (fresh faeces, pen manure, aged manure, composted manure, carcass manure compost) ranged from 10-(7) to 10(2)g(-1) (dry weight). Both indicator groups were detected by qPCR with a comparable sensitivity to culture methods across this range. qPCR measurements of E. coli and E. faecalis correlated well with MPN and spread plate data. As a second comparison, we inoculated green fluorescent protein (GFP) labeled reference bacteria into manure samples. GFP labeled E. coli and Listeria monocytogenes were detected by qPCR in concentrations corresponding to between 18% and 71% of the initial bacterial numbers, compared to only 2.5-16% by plating. Our results supported our selection of qPCR as a fast, accurate and reliable system for surveying the presence and abundance of pathogens in cattle waste.

Fate and transport of viruses during sewage treatment in a mound system.

Studies undertaken to assess the performance of filter materials to remove phosphorus in decentralised sewage systems have not reported on the broader performance of these systems. This study aimed to identify virus fate and transport mechanisms at the laboratory scale for comparison with field experiments on a mound system amended with blast furnace slag. Inactivation was a significant removal mechanism for MS2 bacteriophage, but not for PRD1 bacteriophage. Column studies identified rapid transport of PRD1. Laboratory studies predicted lower removal of PRD1 in a full scale system than was experienced in the field study, highlighting the importance of considering pH and flow rate in pathogen removal estimates. The results highlight the necessity for studying a range of organisms when assessing the potential for pathogen transport.