Microbial water quality in freshwater lakes with different land use

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Fecal indicators and antibiotic resistance genes exhibit diurnal trends in the Chattahoochee River: Implications for water quality monitoring

Water bodies that serve as sources of drinking or recreational water are routinely monitored for fecal indicator bacteria (FIB) by state and local agencies. Exceedances of monitoring thresholds set by those agencies signal likely elevated human health risk from exposure, but FIB give little information about the potential source of contamination. To improve our understanding of how within-day variation could impact monitoring data interpretation, we conducted a study at two sites along the Chattahoochee River that varied in their recreational usage and adjacent land-use (natural versus urban), collecting samples every 30 min over one 24-h period. We assayed for three types of microbial indicators: FIB (total coliforms and Escherichia coli); human fecal-associated microbial source tracking (MST) markers (crAssphage and HF183/BacR287); and a suite of clinically relevant antibiotic resistance genes (ARGs; blaCTX-M, blaCMY, MCR, KPC, VIM, NDM) and a gene associated with antibiotic resistance (intl1). Mean levels of FIB and clinically relevant ARGs (blaCMY and KPC) were similar across sites, while MST markers and intI1 occurred at higher mean levels at the natural site. The human-associated MST markers positively correlated with antibiotic resistant-associated genes at both sites, but no consistent associations were detected between culturable FIB and any molecular markers. For all microbial indicators, generalized additive mixed models were used to examine diurnal variability and whether this variability was associated with environmental factors (water temperature, turbidity, pH, and sunlight). We found that FIB peaked during morning and early afternoon hours and were not associated with environmental factors. With the exception of HF183/BacR287 at the urban site, molecular MST markers and intI1 exhibited diurnal variability, and water temperature, pH, and turbidity were significantly associated with this variability. For blaCMY and KPC, diurnal variability was present but was not correlated with environmental factors. These results suggest that differences in land use (natural or urban) both adjacent and upstream may impact overall levels of microbial contamination. Monitoring agencies should consider matching sample collection times with peak levels of target microbial indicators, which would be in the morning or early afternoon for the fecal associated indicators. Measuring multiple microbial indicators can lead to clearer interpretations of human health risk associated with exposure to contaminated water.

A comparison of soil Staphylococcus aureus and fecal indicator bacteria concentrations across land uses in a Hawaiian watershed

Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), and fecal indicator bacteria (FIB; Enterococcus spp., Clostridium perfringens) concentrations increase in Hawaiian streams and estuaries following storms and pose a health threat to recreational water users. To reduce this risk, watershed bacteria sources need to be identified for management actions. This study's goals were to identify soil bacteria sources among different land uses and to determine if their concentrations were associated with different soil properties. Soil samples were collected three times on 24 d between October 2017 and November 2018 at urban, agriculture, and native-forest land uses in the Hilo Bay watershed, Hawai'i Island, Hawai'i. Soil bacteria concentrations were quantified using culturing techniques with selective media. Staphylococcus aureus, MRSA, and FIB were present in soil from all land uses. Bacteria concentrations were highest in urban soils and lowest in native-forest soils, with up to three orders of magnitude differences among land uses. Staphylococcus aureus, MRSA, and FIB soil concentrations were positively correlated with each other and with soil temperature and pH, but inversely correlated with soil moisture and organic matter content. Our results demonstrate that soils are a watershed bacteria source and that some soil properties affect their concentrations. Identifying these sources is critical for implementing management actions to reduce pathogen loads to estuaries and transmission to recreational water users.

Next-Generation High-Throughput Sequencing to Evaluate Bacterial Communities in Freshwater Ecosystem in Hydroelectric Reservoirs

The quality of aquatic ecosystems is a major public health concern. The assessment and management of a freshwater system and the ecological monitoring of microorganisms that are present in it can provide indicators of the environment and water quality to protect human and animal health. with bacteria is. It is a major challenge to monitor the microbiological bacterial contamination status of surface water associated with anthropogenic activities within rivers and freshwater reservoirs. Understanding the composition of aquatic microbial communities can be beneficial for the early detection of pathogens, improving our knowledge of their ecological niches, and characterizing the assemblages of microbiota responsible for the degradation of contaminants and microbial substrates. The present study aimed to characterize the bacterial microbiota of water samples collected alongside the Madeira River and its small tributaries in rural areas near the Santo Antonio Energia hydroelectric power plant (SAE) reservoir in the municipality of Porto Velho, Rondonia state, Western Brazil. An Illumina 16s rRNA metagenomic approach was employed and the physicochemical characteristics of the water sample were assessed. We hypothesized that both water metagenomics and physicochemical parameters would vary across sampling sites. The most abundant genera found in the study were Acinetobacter, Deinococcus, and Pseudomonas. PERMANOVA and ANCOM analysis revealed that collection points sampled at the G4 location presented a significantly different microbiome compared to any other group, with the Chlamidomonadaceae family and Enhydrobacter genus being significantly more abundant. Our findings support the use of metagenomics to assess water quality standards for the protection of human and animal health in this microgeographic region.

A preliminary evaluation of microbial water quality in the irrigation pond

This study aimed to determine the microbial water quality of Imrahor Pond by enumerating the coliform bacteria levels in the area. Water samples were collected biweekly from the surface and bottom waters at seven points in the pond. Samples were analyzed for total coliforms, Escherichia coli, physicochemical parameters (water temperature, conductivity, pH, turbidity, dissolved oxygen, nitrate) and 1-day rainfall. The average values of TC and E. coli were 1487.4 and 36.3 MPN/100 ml, respectively. TC concentrations/physicochemical parameters were met at least 2nd class water quality class and E. coli results were met "guideline value" (E. coli < 250 MPN/100 ml) of national regulation. Overall, among measured physicochemical parameters, rainfall had the strongest positive correlation (r = 0.377 for total coliforms and r = 0.466 for E. coli, p < 0.05) with both indicators, indicted that surface runoff due to rainfall is the main factor which effects microbial water quality in the study area. This study demonstrated the preliminary microbial water quality results (TC and E. coli) in the Imrahor Pond and can serve as a basis for developing more precise water quality monitoring and management studies in the future. PRACTITIONER POINTS: Prevalence of TC and E. coli in the surface and bottom waters of Imrahor Pond were investigated for the first time. Imrahor Pond was met guideline value of national regulations based on E. coli concentrations, in the study period. Surface runoff after rainfall was the main environmental factor which influenced the microbial water quality of the pond.

An Overview of Microbial Source Tracking Using Host-Specific Genetic Markers to Identify Origins of Fecal Contamination in Different Water Environments

Fecal contamination of water constitutes a serious health risk to humans and environmental ecosystems. This is mainly due to the fact that fecal material carries a variety of enteropathogens, which can enter and circulate in water bodies through fecal pollution. In this respect, the prompt identification of the polluting source(s) is pivotal to guiding appropriate target-specific remediation actions. Notably, microbial source tracking (MST) is widely applied to determine the host origin(s) contributing to fecal water pollution through the identification of zoogenic and/or anthropogenic sources of fecal environmental DNA (eDNA). A wide array of host-associated molecular markers have been developed and exploited for polluting source attribution in various aquatic ecosystems. This review is intended to provide the most up-to-date overview of genetic marker-based MST studies carried out in different water types, such as freshwaters (including surface and groundwaters) and seawaters (from coasts, beaches, lagoons, and estuaries), as well as drinking water systems. Focusing on the latest scientific progress/achievements, this work aims to gain updated knowledge on the applicability and robustness of using MST for water quality surveillance. Moreover, it also provides a future perspective on advancing MST applications for environmental research.

Modelling the impact of weather parameters on the microbial quality of water in distribution systems

In this study, a framework for integrating weather variables and seasons into the modelling and prediction of the microbial quality in drinking water distribution networks is presented. Statistical analysis and Bayesian network (BN) modelling were used to evaluate relationships among water quality parameters in distribution pipes and their dependencies on weather parameters. Two robust predictive models for Total Bacteria in the network were built based on a deep learning approach (Long Short-Term Memory (LSTM)). The first model included water quality parameters alone as inputs while the second model included weather parameters. The seven-year dataset used in this study constituted water quality parameters measured at seven location in the water distribution network for the city of Ålesund in Norway, and weather data for the same period. Results of the initial statistical analysis and the BN models showed that, air temperature, the summer season, precipitation, as well as water quality parameters namely, residual chlorine, water temperature, alkalinity and electrical conductivity have strong relations with the counts of Total Bacteria in the distribution networks studied. It was found that the integration of the weather parameters in the Total Bacteria prediction models significantly improved the quality of the predictions. Compared to the LSTM 1, LSTM 2 achieved MAE and MSE values as high as to 6.8 and 4.9 times respectively when the model was tested on the seven locations. In addition, the R² values were marginally higher in LSTM 2 (0.92-0.95) than in LSTM (0.81-0.86). The prediction results demonstrate the relevance of integrating weather parameters such as air temperature seasons in predicting bacteria levels in water distribution systems. This suggests that changes in the microbial quality of water in distribution systems and potentially drinking water sources could be reliably assessed by integrating online sensors of water quality and weather parameters with efficient models such as the LSTM. Applying this efficient modelling approach in the management of water supply systems could offer immense support in addressing current challenges in assessing the microbial quality of water and minimizing associated health risks.

Tracking Major Sources of Water Contamination Using Machine Learning

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

Fecal pollution source characterization at non-point source impacted beaches under dry and wet weather conditions

Though Lake Michigan beaches in Chicago are not impacted by stormwater or wastewater outfalls, several of those beaches often exceed USEPA Beach Action Values (BAVs). We investigated the role of microbial source tracking (MST) as a complement to routine beach monitoring at Chicago beaches. In summer 2016, water samples from nine Chicago beaches were analyzed for E. coli by culture and enterococci by qPCR. A total of 195 archived samples were then tested for human (HF183/BacR287, HumM2), canine (DG3, DG37), and avian (GFD) microbial source tracking (MST) markers. Associations between MST and general fecal indicator bacteria (FIB) measures were evaluated and stratified based on wet and dry weather definitions. Among the 195 samples, HF183/BacR287 was quantifiable in 4%, HumM2 in 1%, DG3 in 6%, DG37 in 2%, and GFD in 23%. The one beach with a dog area was far more likely to have DG3 present in the quantifiable range than other beaches. Exceedance of general FIB BAVs increased the odds of human, dog and avian marker detection. MST marker weighted-average fecal scores for DG3 was 2.4 times, DG37 was 2.1 times, and GFD was 1.6 times higher during wet compared to dry weather conditions. HF183/BacR287 weighted-average fecal scores were not associated with precipitation. Associations between FIB BAV exceedance and MST marker detection were generally stronger in wet weather. Incorporating MST testing into routine beach water monitoring can provide information that beach managers can use when developing protection plans for beaches not impacted by point sources.

Identifying the primary sources of fecal contamination along the beaches and rivers of Trinidad

The aim of this study was to identify the main sources of fecal pollution at popular beaches and rivers in the island of Trinidad. Escherichia coli enumeration and microbial source tracking (MST) were used to identify the primary sources of fecal bacteria contamination at the sites. Nineteen sites exceeded USEPA water quality standards for safe recreational use. Highest levels of fecal contamination were recorded on the central and west coasts of the island and included Brickfield River (4,839 MPN 100 ml^-1), Orange Valley Bay (2,406.6 MPN 100 ml^-1) and Chaguaramas Bay (1,921.2 MPN 100 ml^-1). MST detected human (HF183) fecal pollution at ∼63%, birds at ∼67%, chicken at ∼36% and cattle (BacCow) at ∼34% of the sites. MST is a useful and rapid method for identifying major sources of fecal pollution in rivers and beaches. In Trinidad water bodies, the main sources of fecal pollution were humans and birds. The large number of sites with elevated levels of fecal pollution detected is particularly alarming and represents a serious public health risk.

Next-generation sequencing reveals fecal contamination and potentially pathogenic bacteria in a major inflow river of Taihu Lake

Taihu Lake is one of the largest freshwater lakes in China and serves as an important source for drinking water. This lake is suffering from eutrophication, cyanobacterial blooms and fecal pollution, and the inflow Tiaoxi River is one of the main contributors. The goal here was to characterize the bacterial community structure of Tiaoxi River water by next-generation sequencing (NGS), paying attention to bacteria that are either fecal-associated or pathogenic, and to examine the relationship between environmental parameters and bacterial community structure. Water samples collected from 15 locations in three seasons, and fecal samples collected from different hosts and wastewater samples were used for bacterial community analysis. The phyla Proteobacteria, Actinobacteria, Bacteroidetes, and Cyanobacteria were predominant in most of the water samples tested. In fecal samples, Bacteroidetes, Firmicutes, and Proteobacteria were abundant, while wastewater samples were dominated by Proteobacteria, Bacteroidetes, Acidobacteria, and Chloroflexi. The cluster analysis and principal coordinate analysis indicated that bacterial community structure was significantly different between water, fecal and sewage samples. Shared OTUs between water samples and chicken, pig, and human fecal samples ranged from 4.5 to 9.8% indicating the presence of avian, pig and human fecal contamination in Tiaoxi River. At genus level, five bacterial genera of fecal origin and sequences of seven potential pathogens were detected in many locations and their presence was correlated well with the land use pattern. The sequencing data revealed that Faecalibacterium could be a potential target for human-associated microbial source-tracking qPCR assays. Our results suggest that pH, conductivity, and temperature were the main environmental factors in shaping the bacterial community based on redundancy analysis. Overall, NGS is a valuable tool for preliminary investigation of environmental samples to identify the potential human health risk, providing specific information about fecal and potentially pathogenic bacteria that can be followed up by specific methods.

Impacts of a changing earth on microbial dynamics and human health risks in the continuum between beach water and sand

Although infectious disease risk from recreational exposure to waterborne pathogens has been an active area of research for decades, beach sand is a relatively unexplored habitat for the persistence of pathogens and fecal indicator bacteria (FIB). Beach sand, biofilms, and water all present unique advantages and challenges to pathogen introduction, growth, and persistence. These dynamics are further complicated by continuous exchange between sand and water habitats. Models of FIB and pathogen fate and transport at beaches can help predict the risk of infectious disease from beach use, but knowledge gaps with respect to decay and growth rates of pathogens in beach habitats impede robust modeling. Climatic variability adds further complexity to predictive modeling because extreme weather events, warming water, and sea level change may increase human exposure to waterborne pathogens and alter relationships between FIB and pathogens. In addition, population growth and urbanization will exacerbate contamination events and increase the potential for human exposure. The cumulative effects of anthropogenic changes will alter microbial population dynamics in beach habitats and the assumptions and relationships used in quantitative microbial risk assessment (QMRA) and process-based models. Here, we review our current understanding of microbial populations and transport dynamics across the sand-water continuum at beaches, how these dynamics can be modeled, and how global change factors (e.g., climate and land use) should be integrated into more accurate beachscape-based models.

Application of high-throughput 16S rRNA sequencing to identify fecal contamination sources and to complement the detection of fecal indicator bacteria in rural groundwater

Residents in rural communities across Canada collect potable water from aquifers. Fecal contaminants from sewage and agricultural runoffs can penetrate aquifers, posing a public health risk. Standard methods for detecting fecal contamination test for fecal indicator bacteria (FIB), but the presence of these do not identify sources of contamination. In contrast, DNA-based diagnostic tools can achieve this important objective. We employed quantitative polymerase chain reaction (qPCR) and high-throughput DNA sequencing to trace fecal contamination sources in Wainfleet, a rural Ontario township that has been under the longest active boil water advisory in Canada due to FIB contamination in groundwater wells. Using traditional methods, we identified FIBs indicating persistent fecal pollution in well waters. We used 16S rRNA sequencing to profile groundwater microbial communities and identified Campylobacteraceae as a fecal contamination DNA marker in septic tank effluents (STEs). We also identified Turicibacter and Gallicola as a potential cow and chicken fecal contamination marker, respectively. Using human specific Bacteroidales markers, we identified leaking septic tanks as the likely primary fecal contamination source in some of Wainfleet's groundwater. Overall, the results support the use of sequencing-based methods to augment traditional water quality testing methods and help end-users assess fecal contamination levels and identify point and non-point pollution sources.

Linking landscape patterns to sources of water contamination: Implications for tracking fecal contaminants with geospatial and Bayesian approaches

Microbial source tracking (MST) techniques have been designed to identify the host source of fecal contamination in water. However, current MST techniques cannot provide geographic origins of particular sources because they do not provide any spatial information beyond the points of observation. In this study, the associations between landscape patterns and the major sources of microbial contamination were examined and the application of geospatial techniques (e.g., remote sensing and geographic information systems) and Bayesian modeling was explored to track microbial sources over the landscape. The land cover information of three watersheds (the lower Dungeness Watershed, the Middle Rio Grande Watershed, and the Arroyo Burro Watershed) in the United States was obtained either by classifying high resolution satellite images or directly using land cover datasets (e.g., National Land Cover Dataset, 2006 and 2011). Then, the relationship between land use/land cover (LULC) and microbial sources from these three geographically disparate watersheds were analyzed using Bayesian hierarchical models. The results showed the predictive positive associations between human sources of fecal contamination and developed area, between dog sources and grassland, and between bird sources and water, but negative associations between human sources and forest and water areas. Furthermore, the diversity of microbial sources had positive associations with landscape fragmentation and diversity indices. This study demonstrates associations between landscape patterns and major microbial sources and offers new insight in tracking the dominant sources of fecal contamination in water using geospatial and Bayesian techniques.

Detection of coliphages and human adenoviruses in a subtropical estuarine lake

Fecal indicator bacteria (FIB) have been used to assess fecal contamination in recreational water. However, enteric viruses have been shown to be more persistent in the environment and resistant to wastewater treatment than bacteria. Recently, U.S Environmental Protection Agency has proposed the use of coliphages as viral indicators to better protect against viral waterborne outbreaks. This study aimed to detect and determine correlation between coliphages (F-specific and somatic), fecal indicator bacteria (enterococci and fecal coliforms), and human enteric viruses (human adenovirus) in a subtropical brackish estuarine lake. Water samples were collected from 9 estuarine recreation sites on Lake Pontchartrain in southeast Louisiana. Water samples (n = 222, collected weekly) were analyzed for coliphages and fecal indicator bacteria using culture-based methods and large volume water samples (n = 54, collected monthly) were analyzed for human adenovirus using quantitative PCR. Somatic coliphage and F-specific coliphage were found in 93.7 and 65.2% of samples with geometric mean concentrations of 30 and 3 plaque forming units (PFU) per 100 mL, respectively. Enterococci, fecal coliforms, and adenovirus were found in all samples with geometric mean concentrations of 27 most probable number (MPN), 77 MPN, and 3.0 × 10⁴ gene copies per 100 mL, respectively. Watersheds in suburban areas exhibited significantly higher concentrations of coliphages and fecal indicator bacteria, indicating potential fecal contamination from septic systems. There was no significant correlation (p > 0.05) observed between the presence of adenoviruses and fecal indicator bacteria and coliphages. The presence of human adenovirus in Lake Pontchartrain poses a significant public health problem for both recreational use and seafood harvesting as it increases exposure risks. This study demonstrated the lack of relationship between fecal indicators and human viral pathogen in Lake Pontchartrain supporting an alternative microbial surveillance system such as direct pathogen detection.

Determination of wild animal sources of fecal indicator bacteria by microbial source tracking (MST) influences regulatory decisions

Fecal indicator bacteria (FIB) are used to assess fecal pollution levels in surface water and are among the criteria used by regulatory agencies to determine water body impairment status. While FIB provide no information about pollution source, microbial source tracking (MST) does, which contributes to more direct and cost effective remediation efforts. We studied a watershed in Florida managed for wildlife conservation that historically exceeded the state regulatory guideline for fecal coliforms. We measured fecal coliforms, enterococci, a marker gene for avian feces (GFD), and a marker gene for human-associated Bacteroides (HF183) in sediment, vegetation, and water samples collected monthly from six sites over two years to: 1) assess the influence of site, temporal factors, and habitat (sediment, vegetation, and water) on FIB and MST marker concentrations, 2) test for correlations among FIB and MST markers, and 3) determine if avian feces and/or human sewage contributed to FIB levels. Sediment and vegetation had significantly higher concentrations of FIB and GFD compared to water and thus may serve as microbial reservoirs, providing unreliable indications of recent contamination. HF183 concentrations were greatest in water samples but were generally near the assay limit of detection. HF183-positive results were attributed to white-tailed deer (Odocoileus virginianus) feces, which provided a false indication of human sewage in this water body. FIB and GFD were positively correlated while FIB and HF183 were negatively correlated. We demonstrated that birds, not sewage, were the main source of FIB, thus avoiding implementation of a total maximum daily load program (TMDL). Our results demonstrate that the concomitant use of FIB and MST can improve decision-making and provide direction when water bodies are impaired, and provides a strategy for natural source exclusion in water bodies impacted by wild animal feces.

Fecal source tracking and eDNA profiling in an urban creek following an extreme rain event

Fecal contamination of recreational waters (i.e. lakes, rivers, beaches) poses an on-going problem for environmental and public health. Heavy rainfall can exacerbate existing problems with fecal contamination. As there could be variable sources of fecal contamination, identifying the source is critical for remediation efforts. This study utilized microbial source tracking (MST), chemical source tracking (CST) markers and environmental DNA (eDNA) metabarcoding to profile sampling areas and identify sources of fecal contamination in creek, stormwater outfall and beach sites in the Etobicoke Creek watershed (Toronto, ON). Water samples were collected before and immediately following an extreme rain event. MST and CST identified stormwater outfalls as an important source of human fecal contamination during dry and wet conditions. eDNA metabarcoding allowed for potential identification of additional sources of fecal contamination and provided additional evidence of human fecal contamination. The extreme rainfall event altered the eDNA profiles, causing creek and beach sites to reflect a greater diversity of mammal and bird eDNA sequences. The profiles provided by eDNA metabarcoding provide a proof of concept suggesting that eDNA metabarcoding can be a useful tool to complement MST and CST methods for profiling sources of fecal contamination and studying impacts of extreme rain events.

Human polyomavirus: Advantages and limitations as a human-specific viral marker in aquatic environments

Human polyomaviruses (HPyVs) cause persistent infections in organs such as kidney, brain, skin, liver, respiratory tract, etc., and some types of HPyV are constantly excreted in the urine and/or feces of infected and healthy individuals. The use of an enteric virus as an indicator for human sewage/waste contamination in aquatic environments has been proposed; HPyVs are a good candidate since they are routinely found in environmental water samples from different geographical areas with relatively high abundance. HPyVs are highly human specific, having been detected in human waste from all age ranges and undetected in animal waste samples. In addition, HPyVs show a certain degree of resistance to high temperature, chlorine, UV, and low pH, with molecular signals (i.e., DNA) persisting in water for several months. Recently, various concentration methods (electronegative/positive filtration, ultrafiltration, skim-milk flocculation) and detection methods (immunofluorescence assay, cell culture, polymerase chain reaction (PCR), integrated cell culture PCR (ICC-PCR), and quantitative PCR) have been developed and demonstrated for HPyV, which has enabled the identification and quantification of HPyV in various environmental samples, such as sewage, surface water, seawater, drinking water, and shellfish. In this paper, we summarize these recent advancements in detection methods and the accumulation of environmental surveillance and laboratory-scale experiment data, and discuss the potential advantages as well as limitations of HPyV as a human-specific viral marker in aquatic environments.

Comparison of Microbial and Chemical Source Tracking Markers To Identify Fecal Contamination Sources in the Humber River (Toronto, Ontario, Canada) and Associated Storm Water Outfalls

Storm water runoff is a major source of pollution, and understanding the components of storm water discharge is essential to remediation efforts and proper assessment of risks to human and ecosystem health. In this study, culturable Escherichia coli and ampicillin-resistant E. coli levels were quantified and microbial source tracking (MST) markers (including markers for general Bacteroidales spp., human, ruminant/cow, gull, and dog) were detected in storm water outfalls and sites along the Humber River in Toronto, Ontario, Canada, and enumerated via endpoint PCR and quantitative PCR (qPCR). Additionally, chemical source tracking (CST) markers specific for human wastewater (caffeine, carbamazepine, codeine, cotinine, acetaminophen, and acesulfame) were quantified. Human and gull fecal sources were detected at all sites, although concentrations of the human fecal marker were higher, particularly in outfalls (mean outfall concentrations of 4.22 log₁₀ copies, expressed as copy numbers [CN]/100 milliliters for human and 0.46 log₁₀ CN/100 milliliters for gull). Higher concentrations of caffeine, acetaminophen, acesulfame, E. coli, and the human fecal marker were indicative of greater raw sewage contamination at several sites (maximum concentrations of 34,800 ng/liter, 5,120 ng/liter, 9,720 ng/liter, 5.26 log₁₀ CFU/100 ml, and 7.65 log₁₀ CN/100 ml, respectively). These results indicate pervasive sewage contamination at storm water outfalls and throughout the Humber River, with multiple lines of evidence identifying Black Creek and two storm water outfalls with prominent sewage cross-connection problems requiring remediation. Limited data are available on specific sources of pollution in storm water, though our results indicate the value of using both MST and CST methodologies to more reliably assess sewage contamination in impacted watersheds.

IMPORTANCE

Storm water runoff is one of the most prominent non-point sources of biological and chemical contaminants which can potentially degrade water quality and pose risks to human and ecosystem health. Therefore, identifying fecal contamination in storm water runoff and outfalls is essential for remediation efforts to reduce risks to public health. This study employed multiple methods of identifying levels and sources of fecal contamination in both river and storm water outfall sites, evaluating the efficacy of using culture-based enumeration of E. coli, molecular methods of determining the source(s) of contamination, and CST markers as indicators of fecal contamination. The results identified pervasive human sewage contamination in storm water outfalls and throughout an urban watershed and highlight the utility of using both MST and CST to identify raw sewage contamination.

Comparison of Microbial and Chemical Source Tracking Markers To Identify Fecal Contamination Sources in the Humber River (Toronto, Ontario, Canada) and Associated Storm Water Outfalls

Storm water runoff is a major source of pollution, and understanding the components of storm water discharge is essential to remediation efforts and proper assessment of risks to human and ecosystem health. In this study, culturable Escherichia coli and ampicillin-resistant E. coli levels were quantified and microbial source tracking (MST) markers (including markers for general Bacteroidales spp., human, ruminant/cow, gull, and dog) were detected in storm water outfalls and sites along the Humber River in Toronto, Ontario, Canada, and enumerated via endpoint PCR and quantitative PCR (qPCR). Additionally, chemical source tracking (CST) markers specific for human wastewater (caffeine, carbamazepine, codeine, cotinine, acetaminophen, and acesulfame) were quantified. Human and gull fecal sources were detected at all sites, although concentrations of the human fecal marker were higher, particularly in outfalls (mean outfall concentrations of 4.22 log₁₀ copies, expressed as copy numbers [CN]/100 milliliters for human and 0.46 log₁₀ CN/100 milliliters for gull). Higher concentrations of caffeine, acetaminophen, acesulfame, E. coli, and the human fecal marker were indicative of greater raw sewage contamination at several sites (maximum concentrations of 34,800 ng/liter, 5,120 ng/liter, 9,720 ng/liter, 5.26 log₁₀ CFU/100 ml, and 7.65 log₁₀ CN/100 ml, respectively). These results indicate pervasive sewage contamination at storm water outfalls and throughout the Humber River, with multiple lines of evidence identifying Black Creek and two storm water outfalls with prominent sewage cross-connection problems requiring remediation. Limited data are available on specific sources of pollution in storm water, though our results indicate the value of using both MST and CST methodologies to more reliably assess sewage contamination in impacted watersheds.

IMPORTANCE

Storm water runoff is one of the most prominent non-point sources of biological and chemical contaminants which can potentially degrade water quality and pose risks to human and ecosystem health. Therefore, identifying fecal contamination in storm water runoff and outfalls is essential for remediation efforts to reduce risks to public health. This study employed multiple methods of identifying levels and sources of fecal contamination in both river and storm water outfall sites, evaluating the efficacy of using culture-based enumeration of E. coli, molecular methods of determining the source(s) of contamination, and CST markers as indicators of fecal contamination. The results identified pervasive human sewage contamination in storm water outfalls and throughout an urban watershed and highlight the utility of using both MST and CST to identify raw sewage contamination.

Using generalized additive mixed models to assess spatial, temporal, and hydrologic controls on bacteria and nitrate in a vulnerable agricultural aquifer

The Abbotsford-Sumas aquifer (ASA) has a history of nitrate contamination from agricultural land use and manure application to soils, yet little is known about its microbial groundwater quality. The goal of this study was to investigate the spatiotemporal distribution of pathogen indicators (Escherichia coli [E. coli] and total coliform [TC]) and nitrate in groundwater, and their potential relation to hydrologic drivers. Sampling of 46 wells over an 11-month period confirmed elevated nitrate concentrations, with more than 50% of samples exceeding 10 mg-N/L. E. coli detections in groundwater were infrequent (4 of 385 total samples) and attributed mainly to surface water-groundwater connections along Fishtrap Creek, which tested positive for E. coli in every sampling event. TC was detected frequently in groundwater (70% of samples) across the ASA. Generalized additive mixed models (GAMMs) yielded valuable insights into relationships between TC or nitrate and a range of spatial, temporal, and hydrologic explanatory variables. Increased TC values over the wetter fall and winter period were most strongly related to groundwater temperatures and levels, while precipitation and well location were weaker (but still significant) predictors. In contrast, the moderate temporal variability in nitrate concentrations was not significantly related to hydrologic forcings. TC was relatively widespread across the ASA and spatial patterns could not be attributed solely to surface water connectivity. Varying nitrate concentrations across the ASA were significantly related to both well location and depth, likely due to spatially variable nitrogen loading and localized geochemical attenuation (i.e., denitrification). Vulnerability of the ASA to bacteria was clearly linked to hydrologic conditions, and was distinct from nitrate, such that a groundwater management strategy specifically for bacterial contaminants is warranted.

Microbial Source Tracking in Adjacent Karst Springs

Modern man-made environments, including urban, agricultural, and industrial environments, have complex ecological interactions among themselves and with the natural surroundings. Microbial source tracking (MST) offers advanced tools to resolve the host source of fecal contamination beyond indicator monitoring. This study was intended to assess karst spring susceptibilities to different fecal sources using MST quantitative PCR (qPCR) assays targeting human, bovine, and swine markers. It involved a dual-time monitoring frame: (i) monthly throughout the calendar year and (ii) daily during a rainfall event. Data integration was taken from both monthly and daily MST profile monitoring and improved identification of spring susceptibility to host fecal contamination; three springs located in close geographic proximity revealed different MST profiles. The Giach spring showed moderate fluctuations of MST marker quantities amid wet and dry samplings, while the Zuf spring had the highest rise of the GenBac3 marker during the wet event, which was mirrored in other markers as well. The revelation of human fecal contamination during the dry season not connected to incidents of raining leachates suggests a continuous and direct exposure to septic systems. Pigpens were identified in the watersheds of Zuf, Shefa, and Giach springs and on the border of the Gaaton spring watershed. Their impact was correlated with partial detection of the Pig-2-Bac marker in Gaaton spring, which was lower than detection levels in all three of the other springs. Ruminant and swine markers were detected intermittently, and their contamination potential during the wet samplings was exposed. These results emphasized the importance of sampling design to utilize the MST approach to delineate subtleties of fecal contamination in the environment.

Relationship between viral detection and turbidity in a watershed contaminated with group A rotavirus

Enteric viruses are present in aquatic environments due to contamination by raw sewage, even in the absence of fecal coliforms, which are considered to be significant indicators when it comes to microbial water quality assessment. This study investigated the presence of group A rotavirus (RVA) in surface water from a river basin in Minas Gerais, Brazil, assessing the influence of the urbanization, the rainfall, and the microbiological and physico-chemical parameters regarding water quality. Forty-eight surface water samples collected in urbanized and non-urbanized areas, both in dry and rainy periods, were obtained throughout the study. The viral particles were concentrated by adsorption-elution in a negatively charged membrane. Fecal coliforms, as well as physico-chemical water parameters were determined at each point in all collections. The RVA was detected in 62.5 % (30/48) of the water samples through a real-time PCR assay. All the sequenced RVA strains belonged to genotype I1. The RVA was detected in 50.0 % (11/22) of the water samples regarded as being acceptable by current microbiological standards. The presence of the RVA and the viral load were influenced by the collection area (p < 0.05). It was also observed a significant association between the RVA and detecting the turbidity of water (p < 0.05). The collected data showed a high level of contamination in this watershed by the discharge of raw sewage, highlighting the need for urgent measures to improve water quality, ensuring the safe use of this matrix.

Effects of future climate and land use scenarios on riverine source water quality

Surface water quality is particularly sensitive to land use practices and climatic events that affect its catchment. The relative influence of a set of watershed characteristics (climate, land use, morphology and pedology) and climatic variables on two key water quality parameters (turbidity and fecal coliforms (FC)) was examined in 24 eastern Canadian catchments at various spatial scales (1 km, 5 km, 10 km and the entire catchment). A regression analysis revealed that the entire catchment was a better predictor of water quality. Based on this information, linear mixed effect models for predicting turbidity and FC levels were developed. A set of land use and climate scenarios was considered and applied within the water quality models. Four land use scenarios (no change, same rate of variation, optimistic and pessimistic) and three climate change scenarios (B1, A1B and A2) were tested and variations for the near future (2025) were assessed and compared to the reference period (2000). Climate change impacts on water quality remained low annually for this time horizon (turbidity: +1.5%, FC: +1.6%, A2 scenario). On the other hand, the influence of land use changes appeared to predominate. Significant benefits for both parameters could be expected following the optimistic scenario (turbidity: -16.4%, FC: -6.3%; p < 0.05). However, pessimistic land use scenario led to significant increases on an annual basis (turbidity: +11.6%, FC: +15.2%; p < 0.05). Additional simulations conducted for the late 21st century (2090) revealed that climate change impacts could become equivalent to those modeled for land use for this horizon.

Biotic interactions and sunlight affect persistence of fecal indicator bacteria and microbial source tracking genetic markers in the upper Mississippi river

The sanitary quality of recreational waters that may be impacted by sewage is assessed by enumerating fecal indicator bacteria (FIB) (Escherichia coli and enterococci); these organisms are found in the gastrointestinal tracts of humans and many other animals, and hence their presence provides no information about the pollution source. Microbial source tracking (MST) methods can discriminate between different pollution sources, providing critical information to water quality managers, but relatively little is known about factors influencing the decay of FIB and MST genetic markers following release into aquatic environments. An in situ mesocosm was deployed at a temperate recreational beach in the Mississippi River to evaluate the effects of ambient sunlight and biotic interactions (predation, competition, and viral lysis) on the decay of culture-based FIB, as well as molecularly based FIB (Entero1a and GenBac3) and human-associated MST genetic markers (HF183 and HumM2) measured by quantitative real-time PCR (qPCR). In general, culturable FIB decayed the fastest, while molecularly based FIB and human-associated genetic markers decayed more slowly. There was a strong correlation between the decay of molecularly based FIB and that of human-associated genetic markers (r(2), 0.96 to 0.98; P < 0.0001) but not between culturable FIB and any qPCR measurement. Overall, exposure to ambient sunlight may be an important factor in the early-stage decay dynamics but generally was not after continued exposure (i.e., after 120 h), when biotic interactions tended to be the only/major influential determinant of persistence.

Relationship between water quality parameters and bacterial indicators in a large prairie reservoir: Lake Diefenbaker, Saskatchewan, Canada

Lake Diefenbaker (LD) is a large reservoir on the South Saskatchewan River used for agricultural irrigation, drinking water, and recreation. Our objectives were to determine the distribution and abundance of bacterial indicators in embayments and the main channel of LD and to relate these to environmental factors. Total coliforms (TCs), fecal coliforms (FCs), and fecal indicator bacteria (i.e., Escherichia coli) were measured concurrently with water quality parameters. Although TCs, FCs, and E. coli were present in LD, they rarely exceeded the TC and FC Canadian Council of Ministers of the Environment (CCME) water quality standards for agricultural use (1000 colony-forming units (CFU) per 100 mL and 100 CFU per 100 mL, respectively). The correlation between the bacterial indicators in the sediments and the water column indicates that higher embayment abundances may be related to sediment loading and (or) resuspension events in these frequently mixed embayments. With higher water temperatures and water levels, as well as higher microbial activity, CCME bacterial limits may be exceeded. The greatest contributor to bacterial indicator abundance was water temperature. We predict that water quality standards will be exceeded more frequently with climate warming.