Wastewater surveillance (WWS) of viruses can aid public health officials in monitoring community infection dynamics and act as an early warning system for the introduction of viral infectious diseases. In recent years, agile, low-cost devices called passive samplers have proven to be indispensable for targeted wastewater surveillance. However, the viral uptake kinetics are unexplored for most viruses, limiting the understanding of optimal deployment times and the representativeness of this sampling method for assessing community viral shedding. This study investigates the uptake kinetics of CrAssphage, Pepper Mild Mottle Virus, Human Adenovirus 40/41, Human Norovirus genogroup II, Enterovirus, and SARS-CoV-2 on electronegative membrane passive samplers. Viral uptake was modeled by linear and pseudo-first-order uptake models for up to 48 h (adjusted R²: 0.89–0.99), with minimal saturation for 48 h. Bench-scale experiments revealed enrichment of Human Adenoviruses 40/41 on membranes compared to all other viral targets for 24–48 h deployment (p < 0.05), while differences were less pronounced with shorter deployment durations. This work highlights virus-specific interactions with passive samplers and how deployment times can affect the relative concentrations of viruses detected. Understanding these kinetics is critical for selecting appropriate sampling strategies and normalization methodologies for WWS of viral infectious diseases.

Rapid and effective decision-making is crucial during drinking water contamination events to ensure public safety. This paper examines a case study where a water utility, responding to customer complaints, suspected wastewater contamination in its network. We compare the traditional expert judgement approach to a model-based approach using the PathoINVEST tool. The tool performs simulations of contamination events informed by sensor measurements, identifies contamination sources using sampling results, and suggests optimal valve closures for mitigation. Our findings show that the model-based approach significantly enhances response efficiency and accuracy. It identified the contamination source with four samples in 1.3 h, compared to 11 samples in 3.7 h for the traditional approach, and resulted in a lower infection risk (12% versus 20%) at the time of source identification. Regarding valve closure, the model-based approach performed better, resulting in a 3%-point reduction in infection risk compared to the traditional approach. Modeling uncertainty is addressed by considering valve settings uncertainty; despite a 0.7% discrepancy in valve settings compared to the model, the tool accurately pinpointed the contamination vicinity 75% of the time. These findings support the claim that integrating modeling and sensor tools into emergency response protocols for drinking water contamination events can improve early identification and mitigation, potentially safeguarding public health in urban water supply systems.

This book addresses significant gaps in our understanding of how watershed microbial structures and functions respond to the pressures of urbanization. It consolidates rapidly evolving, yet often fragmented, global data on watershed microbiomes, providing critical insights into the impacts of urban pollution, human health risks, and long-term ecological consequences.Featuring the latest technological advancements in biological monitoring, microbial source-tracking technologies, and strategies for effective watershed management, this volume serves as a comprehensive and reliable source for the current state of urban watershed microbiology. It examines the unique challenges urbanization presents to ecosystems, especially in terms of atmospheric chemistry, hydrology, vegetation, and how these factors disrupt microbial communities that mediate essential ecosystem services like water purification, nutrient recycling, and degradation of pollutants.Together the two-volume work delves into the mechanisms behind the urban watershed syndrome—ecological degradation of streams—and explores many unanswered questions that hinder progress in understanding urban watershed microbiomes. With original research articles, reviews, and perspectives, this collection is a key resource for microbiologists, environmental scientists, engineers, and professionals involved in water quality, ecosystem management, and land-use planning. The hope is that this volume will inspire new breakthroughs, drive further research, and ultimately help shape effective strategies for addressing the ecological challenges of urban watersheds.

This book addresses significant gaps in our understanding of how watershed microbial structures and functions respond to the pressures of urbanization. It consolidates rapidly evolving, yet often fragmented, global data on watershed microbiomes, providing critical insights into the impacts of urban pollution, human health risks, and long-term ecological consequences.Featuring the latest technological advancements in biological monitoring, microbial source-tracking technologies, and strategies for effective watershed management, this volume serves as a comprehensive and reliable source for the current state of urban watershed microbiology. It examines the unique challenges urbanization presents to ecosystems, especially in terms of atmospheric chemistry, hydrology, vegetation, and how these factors disrupt microbial communities that mediate essential ecosystem services like water purification, nutrient recycling, and degradation of pollutants.Together this two-volume work delves into the mechanisms behind the urban watershed syndrome—ecological degradation of streams—and explores many unanswered questions that hinder progress in understanding urban watershed microbiomes. With original research articles, reviews, and perspectives, this collection is a key resource for microbiologists, environmental scientists, engineers, and professionals involved in water quality, ecosystem management, and land-use planning. The hope is that this volume will inspire new breakthroughs, drive further research, and ultimately help shape effective strategies for addressing the ecological challenges of urban watersheds.

Biofilm formation in drinking water distribution systems is primarily managed by disinfectants such as free chlorine (FC) and monochloramine (MC). However, there is limited understanding of their long-term and dynamic effects on biofilm development. To address this, a 56-week study was conducted to comprehensively assess biofilm development in terms of microbial quantity and community under different disinfection regimes: no chlorine (NC), FC (0.1 mg/L), and MC (0.4 mg/L). The results showed that both FC and MC significantly inhibited biofilm growth compared to the NC condition while shaping distinct biofilm communities. Notably, FC drastically reduced biofilm biomass and community diversity, resulting in a more uniform biofilm community predominantly composed of Proteobacteria (e.g., Rhizobacter spp., Pseudomonas spp., and Hyphomicrobium spp.), indicating stronger selection pressures on the microbial population. In contrast, though MC effectively reduced the biofilm biomass to a level comparable to that of FC, it maintained a high diversity comparable to that of NC (dominated by Sphingobium spp. and Nocardioides spp.), reflecting weaker selection pressure on bacterial community. Temporally, biofilm communities under all conditions started from nearly identical states. From week-19 and week-36 onwards, deterministic processes predominantly governed biofilm formation under FC and NC conditions, signifying that these biofilms reached a stable state. Differently, under MC condition, the community assembly was continually influenced by stochastic processes, with the biofilm not achieving stability until week-56. Overall, this study provides valuable insights into the long-term dynamics of biofilm development and evidenced that FC is better than MC in controlling biofilm formation, particularly from the community diversity perspective. This challenges classical views that MC is more effective than FC in penetrating and controlling biofilm, which may change the popularity of MC as a disinfectant in water utilities.

The risk of infection by enteric pathogens in bathing waters is generally monitored by using fecal indicator bacteria (FIB). Mechanistic models are efficient tools to predict FIB concentrations in bathing waters, both in near-future forecasting and in long-term climate change projections. However, most existing mechanistic FIB models are limited by the availability of observations for validation and incorporation of all relevant physical, biological, and chemical (physico-biochemical) processes. Therefore, the quantitative influence of different physio-biochemical processes and impact factors is missing. To enhance the understanding of FIB fate in different aquatic systems, we developed a comprehensive yet generically applicable physico-biochemical model, focused on Escherichia coli (E. coli). It includes a die-off module and a sediment interaction module. Separate validation of the two sub-modules demonstrated the reliability of our modeling approach. The die-off module shows a higher R² value (0.88) and lower RMSE value (1.1 day^-1) than the existing models (0.48–0.79, and 1.8–7.2 day ^-1). This demonstrated an improvement by adding Ultraviolet-A and Ultraviolet-B (UVB) inactivation and UV spectrum extinction due to colored dissolved organic matter (CDOM) absorption. According to our sediment module validation, considering the impact of sediment composition on E. coli attachment can improve the allocation of E. coli between waters and sediments. Sensitivity analysis showed that 1) photo-inactivation is important in low CDOM waters, but not in high CDOM waters, where the UV penetration is limited; 2) the impact of sediment interaction can extend the duration of a peak event in high turbid waters. This work demonstrated the dominant impact factors in different aquatic systems for E. coli prediction. The new generic model enables better simulation of bathing water quality across different types of aquatic environments, which can be a useful tool to inform management at bathing sites. Future applications can choose processes selectively from the new FIB physico-biochemical model and couple it with appropriate hydrological/hydrodynamic models to address specific environmental conditions and research purposes.

Drinking water distribution systems are increasingly vulnerable to sewage intrusion due to aging water infrastructure and intensifying water stress. While the health risks associated with sewage intrusion have been extensively studied, little is known about the impacts of intruded bacteria and dissolved organic matter (DOM) on microbiology in drinking water. In this dynamic study, we demonstrate that the intrusion of 1 % sewage into tap water resulted in immediate contamination, including an 8-fold increase in biomass (TCC), a 48.9 % increase in bacterial species (ASVs), a 12.5 % increase in organic carbon content (DOC), and a 13.5 % increase in unique DOM molecular formulae. Over time, sewage intrusion altered tap water microbiology by accelerating bacterial growth rates (5-fold faster), selectively promoting ASVs in community succession, and producing 998 more unique DOM formulae. More significantly, statistical analysis revealed that the intrusion of 1 % sewage shifted the driving force of bacterial and DOM composition covariance from a DOM-dependent process in tap water to a bacterial-governed process post-intrusion. Our results clearly demonstrate the disruptive effects of sewage intrusion into tap water, emphasizing the urgent need to consider the long-lasting impacts of sewage intrusion in drinking water distribution systems, in addition to its immediate health risks.

Faecal contamination across the lettuce value chain was assessed in Maputo, Mozambique. Escherichia coli was used as an indicator of faecal contamination, with concentrations ranging from 3.4 to 5.7 log units/100 ml in groundwater, river water and partially treated wastewater. Municipal tap water used to wash lettuce heads in the markets had lower than 1 log unit/100 ml. Irrespective of the source of irrigation water, the lettuce heads were contaminated throughout the value chain, with concentrations ranging between 6.5 and 7.8 log units/100 g. Interventions and awareness raising should be applied at every stage of the value chain.

Although simulated studies have provided valuable knowledge regarding the communities of planktonic bacteria and biofilms, the lack of systematic field studies have hampered the understanding of microbiology in real-world service lines and premise plumbing. In this study, the bacterial communities of water and biofilm were explored, with a special focus on the lifetime development of biofilm communities and their key influencing factors. The 16S rRNA gene sequencing results showed that both the planktonic bacteria and biofilm were dominated by Proteobacteria. Among the 15,084 observed amplicon sequence variants (ASVs), the 33 core ASVs covered 72.8 %, while the 12 shared core ASVs accounted for 62.2 % of the total sequences. Remarkably, it was found that the species richness and diversity of biofilm communities correlated with pipe age. The relative abundance of ASV2 (f_Sphingomonadaceae) was lower for pipe ages 40–50 years (7.9 %) than for pipe ages 10–20 years (59.3 %), while the relative abundance of ASV10 (f_Hyphomonadaceae) was higher for pipe ages 40–50 years (19.5 %) than its presence at pipe ages 20–30 years (1.9 %). The community of the premise plumbing biofilm had significantly higher species richness and diversity than that of the service line, while the steel-plastics composite pipe interior lined with polyethylene (S-PE) harbored significantly more diverse biofilm than the galvanized steel pipes (S-Zn). Interestingly, S-PE was enriched with ASV27 (g_Mycobacterium), while S-Zn pipes were enriched with ASV13 (g_Pseudomonas). Moreover, the network analysis showed that five rare ASVs, not core ASVs, were keystone members in biofilm communities, indicating the importance of rare members in the function and stability of biofilm communities. This manuscript provides novel insights into real-world service lines and premise plumbing microbiology, regarding lifetime dynamics (pipe age 10–50 years), and the influences of pipe types (premise plumbing vs. service line) and pipe materials (S-Zn vs. S-PE).

In a desktop exercise, a water utility’s emergency response to suspected wastewater contamination in a drinking water network was compared with a model-based approach using PathoINVEST. This tool simulates contamination scenarios and assists with locating the source of contamination using sampling results. The sampling procedure used a portable sensor that offers rapid (20 min time-to-result) screening of fecal contamination. Preliminary results show that the model-based approach is able to find the contamination source faster and with fewer samples than current practices. Integrating modeling and rapid sensor tools in emergency responses improves decision-making and public health protection in drinking water networks.

Pathogen intrusion in drinking water systems can pose severe health risks. To better prepare in planning and responding to such events, computational models that capture the intrusion and health impact dynamics are needed. This study presents a novel benchmark testbed that integrates current knowledge on pathogen transport and fate in chlorinated systems and can assess infection risk from contamination events. The model considers organic matter degradation, chlorine decay mechanisms, pathogen inactivation kinetics, as well as stochastic water demands. We studied modeling of wastewater intrusion events that can occur anywhere within a chlorinated and non-chlorinated network. We applied the Quantitative Microbial Risk Assessment framework focusing on three pathogens: enterovirus, Campylobacter, and Cryptosporidium, and their respective dose-response models. Synthetic household-level water demand time series were used to model the individual water consumption timing and calculate the infection risk (exposure via ingestion). Model outcomes indicate that while chlorination aids mitigation, larger contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. In our example scenarios, chlorine-susceptible pathogens infected 0.78–26.6% of the downstream population, while chlorine-resistant ones infected the entire downstream population. Enterovirus infection risk is higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. In non-chlorinated networks, the modeled wastewater contamination events led to 11–46% infection risk in the total population, depending on the contamination location. Hydraulic uncertainty had a limited influence on infection risk. Furthermore, Campylobacter's infection risk is more sensitive to the initial concentration in the contamination source whereas enterovirus infection risk to the inactivation rate. The model further indicates that the time window for effective mitigation of the magnitude of a waterborne outbreak is short (within hours).

Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.