Within-day variability in microbial concentrations at a UK designated bathing water: Implications for regulatory monitoring and the application of predictive modelling based on historical compliance data

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.

A new Bayesian approach for managing bathing water quality at river bathing locations vulnerable to short-term pollution

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.

Enhancing hydro-epidemiological modelling of nearshore coastal waters with source-receptor connectivity study

Faecal Indicator Organism (FIO) concentrations in nearshore coastal waters may lead to significant public health concerns and economic loss. A three-dimensional numerical source-receptor connectivity study was conducted to improve the modelling of FIO transport and decay processes and identify major FIO sources impacting sensitive receptors (source apportionment). The study site was Swansea Bay, UK and the effects of wind, density, and tracer microbe (surrogate FIO) decay models were investigated by comparing the model simulations to microbial tracer field studies. The relevance of connectivity tests to source apportionment was demonstrated by hindcasting FIO concentration in Swansea Bay with the identified FIO source and the Impulse Response Function (IRF) in Control System theory. This is the first time the IRF approach has been applied for FIO modelling in bathing waters. Results show the importance of density, widely ignored in fully mixed water bodies, and the potential for biphasic decay models to improve prediction accuracy. The microbe-carrying riverine freshwater, having a smaller hydrostatic pressure, could not intrude on the heavier seawater and remained in the nearshore areas. The freshwater and the associated tracer microbes then travelled along the shoreline and reached bathing water sites. This effect cannot be faithfully modelled without the inclusion of the density effect. Biphasic decay models improved the agreement between measured and modelled microbe concentrations. The IRF hindcasted and measured FIO concentrations for Swansea Bay agreed reasonably, demonstrating the importance of connectivity tests in identifying key FIO sources. The findings of this study, namely enhancing hydro-epidemiological modelling and highlighting the effectiveness of connectivity studies in identifying key FIO sources, directly benefit hydraulics and water quality modellers, regulatory authorities, water resource managers and policy.

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

AIMS

While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors.

METHODS AND RESULTS

Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls).

CONCLUSIONS

This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.

Microbe-sediment interactions in Great Lakes recreational waters: Implications for human health risk

Microbial assessments of recreational water have traditionally focused on culturing or DNA-based approaches of the planktonic water column, omitting influence from microbe-sediment relationships. Sediment (bed and suspended) has been shown to often harbour levels of bacteria higher than the planktonic phase. The fate of suspended sediment (SS) bacteria is extensively related to transport dynamics (e.g., deposition) of the associated sediment/floc. When hydraulic energy allows, SS will settle, introducing new (potentially pathogenic) organisms to the bed. With turbulence, including waves, currents and swimmers, the risk of human ingestion is elevated due to resuspension of bed sediment and associated microbes. This research used multiplex nanofluidic reverse transcriptase quantitative PCR on RNA of bacteria associated with bed and SS to explore the active bacteria in freshwater shorelines. Bacterial genes of human health concern regarding recreational water use were targeted, such as faecal indicator bacteria (FIB), microbial source tracking genes and virulence factors from waterborne pathogens. Results indicate avian sources (i.e., gulls, geese) to be the largest nonpoint source of FIB associated with sediment in Great Lakes shorelines. This research introduces a novel approach to microbial water quality assessments and enhances our understanding of microbe-sediment dynamics and the quality of freshwater beaches.

A Longitudinal Survey of Antibiotic-Resistant Enterobacterales in the Irish Environment, 2019-2020

The natural environment represents a complex reservoir of antibiotic-resistant bacteria as a consequence of different wastewater discharges including anthropogenic and agricultural. Therefore, the aim of this study was to examine sewage and waters across Ireland for the presence of antibiotic-resistant Enterobacterales. Samples were collected from the West, East and South of Ireland. Two periods of sampling took place between July 2019 and November 2020, during which 118 water (30 L) and 36 sewage samples (200 mL) were collected. Waters were filtered using the CapE method, followed by enrichment and culturing. Sewage samples were directly cultured on selective agars. Isolates were identified by MALDI-TOF and antibiotic susceptibility testing was performed in accordance with EUCAST criteria. Selected isolates were examined for bla_CTX-M, bla_VIM, bla_IMP, bla_OXA-48, bla_NDM, and bla_KPC by real time PCR and whole genome sequencing (n = 146). A total of 419 Enterobacterales (348 water, 71 sewage) were isolated from all samples. Hospital sewage isolates displayed the highest percentage resistance to many beta-lactam and aminoglycoside antibiotics. Extended-spectrum beta-lactamase-producers were identified in 78% of water and 50% of sewage samples. One or more carbapenemase-producing Enterobacterales were identified at 23 individual sampling sites (18 water, 5 sewage). This included the detection of bla_OXA-48 (n = 18), bla_NDM (n = 14), bla_KPC (n = 4) and bla_OXA-484 (n = 1). All NDM-producing isolates harbored the ble-MBL bleomycin resistance gene. Commonly detected sequence types included Klebsiella ST323, ST17, and ST405 as well as E. coli ST131, ST38 and ST10. Core genome MLST comparisons detected identical E. coli isolates from wastewater treatment plant (WWTP) influent and nursing home sewage, and the surrounding waters. Similarly, one Klebsiella pneumoniae isolated from WWTP influent and the surrounding estuarine water were identical. These results highlight the need for regular monitoring of the aquatic environment for the presence of antibiotic-resistant organisms to adequately inform public health policies.

Intra-daily variation of Escherichia coli concentrations in agricultural irrigation ponds

Microbial water quality is determined by comparing observed Escherichia coli concentrations with regulatory thresholds. Measured concentrations can be expected to change throughout the course of a day in response to diurnal variation in environmental conditions, such as solar radiation and temperature. Therefore, the time of day at which samples are taken is an important factor within microbial water quality measurements. However, little is known about the diurnal variations of E. coli concentrations in surface sources of irrigation water. The objectives of this work were to evaluate the intra-daily dynamics of E. coli in three irrigation ponds in Maryland over several years and to determine the water quality parameters to which E. coli populations are most sensitive. Water sampling was conducted across the ponds at 0900, 1200, and 1500 h on a total of 17 dates in the summers of 2019-2021. One-way ANOVA revealed significant diurnal variability in E. coli concentrations in Pond (P)1 and P2, whereas no significant effects were observed in P3. Escherichia coli die-off rates calculated between sampling time points in the same day were significantly higher in P2 than in P1 and P3, and these rates ranged from 0.005 to 0.799 h^-1 across ponds. Concentrations of dissolved oxygen, pH, conductivity, and turbidity exerted the most control over E. coli populations. Results of this work demonstrate that sampling in the early-morning hours provides the most conservative assessment of the microbial quality of irrigation waters.

Evaluating the potential for exposure to organisms of public health concern in naturally occurring bathing waters in Europe: A scoping review

Globally, water-based bathing pastimes are important for both mental and physical health. However, exposure to waterborne organisms could present a substantial public health issue. Bathing waters are shown to contribute to the transmission of illness and disease and represent a reservoir and pathway for the dissemination of antimicrobial resistant (AMR) organisms. Current bathing water quality regulations focus on enumeration of faecal indicator organisms and are not designed for detection of specific waterborne organisms of public health concern (WOPHC), such as antimicrobial resistant (AMR)/pathogenic bacteria, or viruses. This investigation presents the first scoping review of the occurrence of waterborne organisms of public health concern (WOPHC) in identified natural bathing waters across the European Union (EU), which aimed to critically evaluate the potential risk of human exposure and to assess the appropriateness of the current EU bathing water regulations for the protection of public health. Accordingly, this review sought to identify and synthesise all literature pertaining to a selection of bacterial (Campylobacter spp., Escherichia coli, Salmonella spp., Shigella spp., Vibrio spp., Pseudomonas spp., AMR bacteria), viral (Hepatitis spp., enteroviruses, rotavirus, adenovirus, norovirus), and protozoan (Giardia spp., and Cryptosporidium spp.) contaminants in EU bathing waters. Sixty investigations were identified as eligible for inclusion and data was extracted. Peer-reviewed investigations included were from 18 countries across the EU, totalling 87 investigations across a period of 35 years, with 30% published between 2011 and 2015. A variety of water bodies were identified, with 27 investigations exclusively assessing coastal waters. Waterborne organisms were classified into three categories; bacteria, viruses, and protozoa; amounting to 58%, 36% and 17% of the total investigations, respectively. The total number of samples across all investigations was 8,118, with detection of one or more organisms in 2,449 (30%) of these. Viruses were detected in 1281 (52%) of all samples where WOPHC were found, followed by bacteria (865(35%)) and protozoa (303(12%)). Where assessed (442 samples), AMR bacteria had a 47% detection rate, emphasising their widespread occurrence in bathing waters. Results of this scoping review highlight the potential public health risk of exposure to WOPHC in bathing waters that normally remain undetected within the current monitoring parameters.

Impacts of an Urban Sanitation Intervention on Fecal Indicators and the Prevalence of Human Fecal Contamination in Mozambique

Fecal source tracking (FST) may be useful to assess pathways of fecal contamination in domestic environments and to estimate the impacts of water, sanitation, and hygiene (WASH) interventions in low-income settings. We measured two nonspecific and two human-associated fecal indicators in water, soil, and surfaces before and after a shared latrine intervention from low-income households in Maputo, Mozambique, participating in the Maputo Sanitation (MapSan) trial. Up to a quarter of households were impacted by human fecal contamination, but trends were unaffected by improvements to shared sanitation facilities. The intervention reduced Escherichia coli gene concentrations in soil but did not impact culturable E. coli or the prevalence of human FST markers in a difference-in-differences analysis. Using a novel Bayesian hierarchical modeling approach to account for human marker diagnostic sensitivity and specificity, we revealed a high amount of uncertainty associated with human FST measurements and intervention effect estimates. The field of microbial source tracking would benefit from adding measures of diagnostic accuracy to better interpret findings, particularly when FST analyses convey insufficient information for robust inference. With improved measures, FST could help identify dominant pathways of human and animal fecal contamination in communities and guide the implementation of effective interventions to safeguard health.

A Point Prevalence Survey of Antibiotic Resistance in the Irish Environment, 2018-2019

Water bodies worldwide have proven to be vast reservoirs of clinically significant antibiotic resistant organisms. Contamination of waters by anthropogenic discharges is a significant contributor to the widespread dissemination of antibiotic resistance. The aim of this research was to investigate multiple different anthropogenic sources on a national scale for the role they play in the environmental propagation of antibiotic resistance. A total of 39 water and 25 sewage samples were collected across four local authority areas in the West, East and South of Ireland. In total, 211 Enterobacterales were isolated (139 water, 72 sewage) and characterised. A subset of isolates (n=60) were chosen for whole genome sequencing. Direct comparisons of the water versus sewage isolate collections revealed a higher percentage of sewage isolates displayed resistance to cefoxitin (46%) and ertapenem (32%), while a higher percentage of water isolates displayed resistance to tetracycline (55%) and ciprofloxacin (71%). Half of all isolates displayed extended spectrum beta-lactamase (ESBL) production phenotypically (n = 105/211; 50%), with bla_CTX-M detected in 99/105 isolates by PCR. Carbapenemase genes were identified in 11 isolates (6 sewage, 5 water). The most common variant was bla_OXA-48 (n=6), followed by bla_NDM-5 (n=2) and bla_KPC-2 (n=2). Whole genome sequencing analysis revealed numerous different sequence types in circulation in both waters and sewage including E. coli ST131 (n=15), ST38 (n=8), ST10 (n=4) along with Klebsiella ST405 (n=3) and ST11 (n=2). Core genome MLST (cgMLST) comparisons uncovered three highly similar Klebsiella isolates originating from hospital sewage and two nearby waters. The Klebsiella isolates from an estuary and seawater displayed 99.1% and 98.8% cgMLST identity to the hospital sewage isolate respectively. In addition, three pairs of E. coli isolates from different waters also revealed cgMLST similarities, indicating widespread dissemination and persistence of certain strains in the aquatic environment. These findings highlight the need for routine monitoring of water bodies used for recreational and drinking purposes for the presence of multi-drug resistant organisms.

Bathing Water Quality Monitoring Practices in Europe and the United States

Many countries including EU Member States (EUMS) and the United States (U.S.) regularly monitor the microbial quality of bathing water to protect public health. This study comprehensively evaluates the EU bathing water directive (BWD) and the U.S. recreational water quality criteria (RWQC) as regulatory frameworks for monitoring microbial quality of bathing water. The major differences between these two regulatory frameworks are the provision of bathing water profiles, classification of bathing sites based on the pollution level, variations in the sampling frequency, accepted probable illness risk, epidemiological studies conducted during the development of guideline values, and monitoring methods. There are also similarities between the two approaches given that both enumerate viable fecal indicator bacteria (FIB) as an index of the potential risk to human health in bathing water and accept such risk up to a certain level. However, enumeration of FIB using methods outlined within these current regulatory frameworks does not consider the source of contamination nor variation in inactivation rates of enteric microbes in different ecological contexts, which is dependent on factors such as temperature, solar radiation, and salinity in various climatic regions within their geographical areas. A comprehensive "tool-box approach", i.e., coupling of FIB and viral pathogen indicators with microbial source tracking for regulatory purposes, offers potential for delivering improved understanding to better protect the health of bathers.

A Day at the Beach: Enabling Coastal Water Quality Prediction with High-Frequency Sampling and Data-Driven Models

To reduce the incidence of recreational waterborne illness, fecal indicator bacteria (FIB) are measured to assess water quality and inform beach management. Recently, predictive FIB models have been used to aid managers in making beach posting and closure decisions. However, those predictive models must be trained using rich historical data sets consisting of FIB and environmental data that span years, and many beaches lack such data sets. Here, we investigate whether water quality data collected during discrete short duration, high-frequency beach sampling events (e.g., samples collected at sub-hourly intervals for 24-48 h) are sufficient to train predictive models that can be used for beach management. We use data collected during six high-frequency sampling events at three California marine beaches and train a total of 126 models using common data-driven techniques. Tide, solar irradiation, water temperature, significant wave height, and offshore wind speed were found to be the most important environmental variables in the models. We validate the predictive performance of models using withheld data. Random forests are consistently the top performing model type. Overall, we find that data-driven models trained using high-frequency FIB and environmental data perform well at predicting water quality and can be used to inform public health decisions at beaches.

A comparison of E. coli concentration estimates quantified by the EPA and a Michigan laboratory network using EPA Draft Method C

We evaluated data from 10 laboratories that analyzed water samples from 82 recreational water sites across the state of Michigan between 2016 and 2018. Water sample replicates were analyzed by experienced U.S. Environmental Protection Agency (EPA) analysts and Michigan laboratories personnel, many of whom were newly trained, using EPA Draft Method C-a rapid quantitative polymerase chain reaction (qPCR) technique that provides same day Escherichia coli (E. coli) concentration results. Beach management decisions (i.e. remain open or issue an advisory or closure) based on E. coli concentration estimates obtained by Michigan labs and by the EPA were compared; the beach management decision agreed in 94% of the samples analyzed. We used the Wilcoxon one-sample signed rank test and nonparametric quantile regression to assess (1) the degree of agreement between E. coli concentrations quantified by Michigan labs versus the EPA and (2) Michigan lab E. coli measurement precision, relative to EPA results, in different years and water body types. The median quantile regression curve for Michigan labs versus EPA approximated the 1:1 line of perfect agreement more closely as years progressed. Similarly, Michigan lab E. coli estimates precision also demonstrated yearly improvements. No meaningful difference was observed in the degree of association between Michigan lab and EPA E. coli concentration estimates for inland lake and Great Lakes samples (median regression curve average slopes 0.93 and 0.95, respectively). Overall, our study shows that properly trained laboratory personnel can perform Draft Method C to a degree comparable with experienced EPA analysts. This allows health departments that oversee recreational water quality monitoring to be confident in qPCR results generated by the local laboratories responsible for analyzing the water samples.

Microbial Indicators of Fecal Pollution: Recent Progress and Challenges in Assessing Water Quality

PURPOSE OF REVIEW

Fecal contamination of water is a major public health concern. This review summarizes recent developments and advancements in water quality indicators of fecal contamination.

RECENT FINDINGS

This review highlights a number of trends. First, fecal indicators continue to be a valuable tool to assess water quality and have expanded to include indicators able to detect sources of fecal contamination in water. Second, molecular methods, particularly PCR-based methods, have advanced considerably in their selected targets and rigor, but have added complexity that may prohibit adoption for routine monitoring activities at this time. Third, risk modeling is beginning to better connect indicators and human health risks, with the accuracy of assessments currently tied to the timing and conditions where risk is measured. Research has advanced although challenges remain for the effective use of both traditional and alternative fecal indicators for risk characterization, source attribution and apportionment, and impact evaluation.