Management of biogenic taste and odour: From source water, through treatment processes and distribution systems, to consumers.

Biogenic taste and odour (T&O) have become a global concern for water utilities, due to the increasing frequency of algal blooms and other microbial events arising from the combined effects of climate change and eutrophication. Microbially-produced T&O compounds impact source waters, drinking water treatment plants, and drinking water distribution systems. It is important to manage across the entire biogenic T&O pathway to identify key risk factors and devise strategies that will safeguard the quality of drinking water in a changing world, since the presence of T&O impacts consumer confidence in drinking water safety. This study provides a critical review of current knowledge on T&O-causing microbes and compounds for proactive management, including the identification of abiotic risk factors in source waters, a discussion on the effectiveness of existing T&O barriers in drinking water treatment plants, an analysis of risk factors for biofilm growth in water distribution systems, and an assessment of the impacts of T&O on consumers. The fate of biogenic T&O in drinking water systems is tracked from microbial production pathways, through the release of intracellular T&O by cell lysis, to the treatment of microbial cells and dissolved T&O. Based on current knowledge, five impactful research and management directions across the T&O pathway are recommended.

Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO4, FeCl3, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R2 of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.

Wastewater-based monitoring reveals geospatial-temporal trends for antibiotic-resistant pathogens in a large urban community.

Antimicrobial resistance (AMR) is one of the top ten global health threats, and current surveillance programs rarely monitor it outside healthcare settings. This limits our ability to understand and manage the spread of AMR. Wastewater testing has the potential to simply, reliably and continuously survey trends in AMR outside the healthcare settings, as it captures biological material from the entire community. To establish and evaluate such a surveillance, we monitored wastewater for four clinically significant pathogens across the urban area of Greater Sydney, Australia. Untreated wastewater from 25 wastewater treatment plants (WWTPs) covering distinct catchment regions of 5.2 million residents was sampled between 2017 and 2019. Isolates for extended-spectrum ß-lactamases-producing Enterobacteriaceae (ESBL-E) were consistently detected, suggesting its endemicity in the community. Isolates for carbapenem-resistant Enterobacteriaceae (CRE), vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) were only occasionally detected. The flow normalized relative (FNR) ESBL-E load was positively correlated with the proportion of the population between 19 and 50 years of age, completion of vocational education and the average length of hospital stay. Collectively, these variables explained only a third of the variance of the FNR ESBL-E load, indicating further, yet-unidentified factors as a contributor to the distribution. About half of the variation in the FNR CRE load was explained by the average length of hospital stay, showing healthcare-related drivers. Interestingly, variation in the FNR VRE load was not correlated to healthcare-related parameters but to the number of schools per 10,000 population. Our study provides insight into how routine wastewater surveillance can be used to understand the factors driving the distribution of AMR in an urban community. Such information can help to manage and mitigate the emergence and spread of AMR in important human pathogens.

Novel crAssphage marker genes ascertain sewage pollution in a recreational lake receiving urban stormwater runoff.

Considerable efforts have been made in recent years in developing novel marker genes for fecal pollution tracking in environmental waters. CrAssphage are recently discovered DNA bacteriophage that are highly abundant in human feces and untreated sewage. In this study, we evaluated the host-sensitivity and -specificity of the newly designed crAssphage qPCR assays (Stachler et al., 2017) CPQ_056 and CPQ_064 (i.e., marker genes) in fecal samples collected from various human and several animal host groups in Australia. We also investigated the utility of these marker genes to detect sewage pollution in an urban recreational lake (i.e., Lake Parramatta) in Sydney, NSW. The mean concentrations of CPQ_056 and CPQ_064 marker genes in untreated sewage were 9.43 ±â€¯0.14 log10 GC/L and 8.91 ±â€¯0.17 log10 GC/L, respectively, 2 to 3 orders of magnitude higher than other sewage-associated viruses used in microbial source tracking studies. Among 177 animal fecal samples tested from 11 species, the host-specificity values for CPQ_056 and CPQ_064 marker genes were 0.95 and 0.93, respectively. Limited cross-reactivity was observed with cat fecal and cattle wastewater samples. Abundance of crAssphage markers were monitored in an urban lake that receives stormwater runoff. The concentrations of both markers were higher (CPQ_056 ranging from 3.40 to 6.04 log10 GC/L and CPQ_064 ranging from 2.90 to 5.47 log10 GC/L) in 20 of 20 (for CPQ_056) and 18 of 20 (for CPQ_064) samples collected after storm events with gauged sewer overflows compared to dry weather event (10 of 10 samples were qPCR negative for the CPQ_056 and 8 of 10 were negative for the CPQ_064 marker genes) suggesting sewage pollution was transported by urban stormwater runoff to Lake Parramatta. The results of the study may provide context for management of sewage pollution from gauged overflow points of the sewerage system in the catchment.