Microbial Water Quality: Monitoring and Modeling

Village Settlements in Mountainous Tropical Areas, Hotspots of Fecal Contamination as Evidenced by Escherichia coli and Stanol Concentrations in Stormwater Pulses

Fecal bacteria in surface water may indicate threats to human health. Our hypothesis is that village settlements in tropical rural areas are major hotspots of fecal contamination because of the number of domestic animals usually roaming in the alleys and the lack of fecal matter treatment before entering the river network. By jointly monitoring the dynamics of Escherichia coli and of seven stanol compounds during four flood events (July-August 2016) at the outlet of a ditch draining sewage and surface runoff out of a village of Northern Lao PDR, our objectives were (1) to assess the range of E. coli concentration in the surface runoff washing off from a village settlement and (2) to identify the major contributory sources of fecal contamination using stanol compounds during flood events. E. coli pulses ranged from 4.7 × 104 to 3.2 × 106 most probable number (MPN) 100 mL-1, with particle-attached E. coli ranging from 83 to 100%. Major contributory feces sources were chickens and humans (about 66 and 29%, respectively), with the highest percentage switching from the human pole to the chicken pole during flood events. Concentrations indicate a severe fecal contamination of surface water during flood events and suggest that villages may be considered as major hotspots of fecal contamination pulses into the river network and thus as point sources in hydrological models.

Fate and transport modelling for evaluating antibiotic resistance in aquatic environments: Current knowledge and research priorities

Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments.

Selection of a diagnostic tool for microbial water quality monitoring and management of faecal contamination of water sources in rural communities

The aim of the current study was to evaluate, validate and select microbial water quality monitoring tools to establish their suitability and feasibility for use in rural communities. The monitoring of water quality was performed at three different levels: i) basic level focusing on sanitary inspection and hydrogen sulphide (H2S) test; ii) intermediate level via enumeration of faecal indicator bacteria (faecal coliforms, Escherichia coli, Enterococcus spp. and Clostridium perfringens); and iii) advanced level based on qPCR detecting host-associated genetic markers (BacHum, BacCow, Cytb, Pig-2-Bac, and BacCan) and pathogens (Vibrio cholerae, Escherichia coli O157:H7, and Shiga toxin-producing Escherichia coli). A positive correlation was recorded between sanitary risk and faecal coliforms (r = 0.613 and p < 0.002), E. coli (r = 0.589 and p < 0.003), and Enterococcus spp. (r = 0.625 and p < 0.003). The H2S test showed positive correlations with sanitary risk score (r = 0.623; p < 0.003), faecal coliforms (r = 0.809; p < 0.001), E. coli (r = 0.779; p < 0.001) and Enterococcus spp. (r = 0.799; p < 0.001). Similar correlation patterns were also found with advanced techniques used for detecting host-associated genetic markers, excepted between Clostridium perfringens, and Pig-2-Bac (pig), BacCan (dog), and V. cholerae. The H2S test and sanitary inspections are therefore suitable and cost-effective tools to capacitate rural areas at household level for the monitoring of faecal contamination and management of water sources.

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.

Microbial source tracking to elucidate the impact of land-use and physiochemical water quality on fecal contamination in a mixed land-use watershed

Escherichia coli has been widely used as a fecal indicator bacterium (FIB) for monitoring water quality in drinking water sources and recreational water. However, fecal contamination sources remain difficult to identify and mitigate, as millions of cases of infectious diseases are reported yearly due to swimming and bathing in recreational water. The objective of this study was to apply molecular techniques for microbial source tracking (MST) to identify sources of fecal contamination in a representative mixed land-use watershed located in the Appalachian Mountains of the United States of America (USA). Monthly samples were collected over one year at 11 sites, including the confluence of key first-order streams in the study watershed representing distinct land-use types and anticipated fecal sources. Results indicated that coupled monitoring of host-specific MST markers with the FIB E. coli effectively identified sources and quantified fecal contamination in the study watershed. Human-associated MST markers were abundant primarily at developed sites, suggesting septic or sewer failure is a key source of fecal input to the watershed. Across the dataset, samples positive for E. coli and human MST markers were associated with a higher pH than those samples from which each target was not detected, thereby suggesting that acid mine drainage in the watershed likely contributed to inactivation or loss of culturability in E. coli. In addition, this research provides the first evidence that the BacCan-UCD marker is present in fox feces and can influence MST results in areas where substantial wildlife activity is present. Identifying the sources of fecal contamination and better understanding the impact of in-stream physiochemistry throughout this study will help to develop sustainable and effective watershed management plans to control fecal contamination to protect drinking water sources and recreational water.

Microbiological quality of irrigation water for cultivation of fruits and vegetables: An overview of available guidelines, water testing strategies and some factors that influence compliance

Contaminated irrigation water is among many potential vehicles of human pathogens to food plants, constituting significant public health risks especially for the fresh produce category. This review discusses some available guidelines or regulations for microbiological safety of irrigation water, and provides a summary of some common methods used for characterizing microbial contamination. The goal of such exploration is to understand some of the considerations that influence formulation of water testing guidelines, describe priority microbial parameters particularly with respect to food safety risks, and attempt to determine what methods are most suitable for their screening. Furthermore, the review discusses factors that influence the potential for microbiologically polluted irrigation water to pose substantial risks of pathogenic contamination to produce items. Some of these factors include type of water source exploited, irrigation methods, other agro ecosystem features/practices, as well as pathogen traits such as die-off rates. Additionally, the review examines factors such as food safety knowledge, other farmer attitudes or inclinations, level of social exposure and financial circumstances that influence adherence to water testing guidelines and other safe water application practices. A thorough understanding of relevant risk metrics for the application and management of irrigation water is necessary for the development of water testing criteria. To determine sampling and analytical approach for water testing, factors such as agricultural practices (which differ among farms and regionally), as well as environmental factors that modulate how water quality may affect the microbiological safety of produce should be considered. Research and technological advancements that can improve testing approach and the determination of target levels for hazard characterization or description for the many different pollution contexts as well as farmer adherence to testing requirements, are desirable.

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.

Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.

Antimicrobial properties of silver nanoparticles may interfere with fecal indicator bacteria detection in pathogen impaired streams

Silver nanoparticles (AgNPs) are expected to enter aquatic systems, but there are limited data on how they might affect microbial communities in pathogen impaired streams. We examined microbial community responses to citrate-AgNP (10.9 ± 0.7 nm) and polyvinylpyrrolidone (PVP)-AgNP (11.0 ± 0.7 nm) based on microbial concentration and enzyme activity in sediment from a pathogen impaired stream. Addition of each nanoparticle to sediment caused at least a 69% decrease in microbial concentration (1,264 ± 93.6 to 127 ± 29.5 CFU/g) and a 62% decrease in β-glucosidase activity (11.7 ± 2.1 to 1.3 ± 0.3 μg/g/h). Each AgNP reduced alkaline phosphatase activity but their effects were not statistically significant. Sediment exposed to 0.108 mg Ag/kg of AgNO₃ resulted in a 92% decrease in microbial concentration and a reduced enzyme activity which was not statistically significant. Measured total silver in sediments treated with AgNPs which exhibited significant inhibition effects on the microbial community ranged from 0.19 ± 0.02 to 0.39 ± 0.13 mg Ag/kg. These concentrations tested in this study are much lower than the expected concentrations (2-14 mg Ag/kg) in freshwater sediments. The results of this study demonstrate that AgNPs can alter microbial community activity and population size, which may lead to false negative fecal indicator bacteria detection and enumeration using methods that rely on β-glucosidase activity. We conclude that the presence of AgNPs in impaired streams and recreational waters can influence pathogen detection methods, potentially affecting public health risk estimates.

Evaluating multiple predictive models for beach management at a freshwater beach in the Great Lakes region

Recreational water quality is currently monitored at Sandpoint Beach on Lake St. Clair using culture-based enumeration of Escherichia coli. Using water quality and weather data collected over 4 yr, several multiple linear regression (MLR)-based models were developed for near real-time prediction of E. coli concentration and were tested using independent data from the fifth year. Model performance was assessed by the determination of metrics such as RMSE, accuracy, specificity, sensitivity, and area under the receiver operating characteristic curve (AUROC). Each of the developed MLR models described herein resulted in increased correct responses for both exceedance and non-exceedance of the applicable standard as compared to predictions based on E. coli measurements (persistence models, using the previous day's E. coli concentration), which is the method currently being used. The AUROC values for persistence models are between 0.5 and 0.6, as compared to >0.7 for all the MLR models described herein. Among the MLR models, model performance improved when qualitative sky weather condition, which is commonly reported but was not previously used in similar models, was included. To select the best model, a principal coordinate analysis was used to combine multiple model performance metrics and provide a more sensitive tool for model comparison. Although models developed using 2, 3, and 4 yr of monitoring data provided reasonable performance, the model developed using the most recent 2-yr data was marginally better. Thus, data from the most recent 2 yr are likely sufficient as a training dataset for updating the MLR model for Sandpoint Beach in the future.

Microbial Water Quality Conditions Associated with Livestock Grazing, Recreation, and Rural Residences in Mixed-Use Landscapes

Contamination of surface waters with microbial pollutants from fecal sources is a significant human health issue. Identification of relative fecal inputs from the mosaic of potential sources common in rural watersheds is essential to effectively develop and deploy mitigation strategies. We conducted a cross-sectional longitudinal survey of fecal indicator bacteria (FIB) concentrations associated with extensive livestock grazing, recreation, and rural residences in three rural, mountainous watersheds in California, USA during critical summer flow conditions. Overall, we found that 86% to 87% of 77 stream sample sites across the study area were below contemporary Escherichia coli-based microbial water quality standards. FIB concentrations were lowest at recreation sites, followed closely by extensive livestock grazing sites. Elevated concentrations and exceedance of water quality standards were highest at sites associated with rural residences, and at intermittently flowing stream sites. Compared to national and state recommended E. coli-based water quality standards, antiquated rural regional policies based on fecal coliform concentrations overestimated potential fecal contamination by as much as four orders of magnitude in this landscape, hindering the identification of the most likely fecal sources and thus the efficient targeting of mitigation practices to address them.

Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Waterborne disease outbreaks are a persistent and serious threat to public health according to reported incidents across the globe. Online drinking water quality monitoring technologies have evolved substantially and have become more accurate and accessible. However, using online measurements alone is unsuitable for detecting microbial regrowth, potentially including harmful species, ahead of time in the distribution systems. Alternatively, observational data could be collected periodically, e.g. once per week or once per month and it could include a representative set of variables: physicochemical water characteristics, disinfectant concentrations, and bacterial abundances, which would be a valuable source of knowledge for predictive modelling that aims to reveal pathogen-related threats. In this study, we utilised data collected from a pilot-scale drinking water distribution system. A data-driven random forest model was used for predictive modelling and was trained for nowcasting and forecasting abundances of bacterial groups. In all the experiments, we followed the realistic crossline scenario, which means that when training and testing the models the data is collected from different pipelines. In spite of the more accurate results of the nowcasting, the 1-week forecasting still provided accurate predictions of the most abundant bacteria, their rapid increase and decrease. In the future predictive modelling might be used as a tool in designing control measures for opportunistic pathogens which are able to multiply in the favourable conditions in drinking water distribution systems (DWDS). Eventually, the forecasting information will be able to produce practically helpful data for controlling the DWDS regrowth.

Intraseasonal variation of E. coli and environmental covariates in two irrigation ponds in Maryland, USA

The microbial quality of irrigation water is typically assessed by measuring the concentrations of E. coli in irrigation water reservoirs that are variable in space and time. E. coli concentrations are affected by water quality parameters that co-vary with E. coli concentrations and may be easily measured with currently available sensors. The objective of this work was to identify the most influential environmental covariates affecting E. coli concentrations during a three-month biweekly monitoring period within two irrigation ponds in Maryland during the summer of 2017. E. coli levels as well as sensor-based water quality parameters including turbidity, pH, dissolved oxygen, dissolved fluorescent organic matter, conductivity, and chlorophyll were measured at 23 and 34 locations in ponds 1 and 2, respectively. Regression tree analyses were used to determine the most influential water quality parameters for the prediction of E. coli levels. Correlations between E. coli and water quality covariates were not strong and were inconsistently significant. Shoreline sample locations had higher E. coli concentrations than interior pond samples and significant differences were observed when comparing these two groups. Regression trees provided fairly accurate predictions of E. coli levels based on water quality parameters with R² values ranging from 0.70 to 0.93. Factors identified via the regression trees varied by sampling date but common leading covariates included cyanobacteria, organic matter, and turbidity. Results indicated environmental covariates, sensed either remotely or in situ, could be useful to delineate areas with different E. coli survival conditions across irrigation ponds and potentially other water bodies such as lakes, rivers, or bays.