Seasonal relationships among indicator bacteria, pathogenic bacteria, Cryptosporidium oocysts, Giardia cysts, and hydrological indices for surface waters within an agricultural landscape

Quantitative microbial risk assessment of acute gastrointestinal illness attributable to freshwater recreation in Ontario

OBJECTIVES

The burden of disease associated with acute gastrointestinal illness (AGI) in Canada is estimated to be ~ 20 million cases/year. One known risk factor for developing AGI is recreation in freshwater bodies such as lakes. The proportion of cases attributable to freshwater recreation in Canada, however, is currently unknown. The study objective was to estimate the risk of developing AGI from exposure to Giardia, Cryptosporidium, Campylobacter, Escherichia coli O157:H7, norovirus, and Salmonella during freshwater recreation in Ontario, Canada.

METHODS

A quantitative microbial risk assessment (QMRA) was conducted to estimate the number of AGI cases per 1000 recreational events associated with freshwater recreation. QMRA utilizes four steps: hazard identification, exposure assessment, dose-response modelling, and risk characterization. A probabilistic model was developed using the following inputs accounting for uncertainty and variability: published data on pathogen prevalence and concentration in freshwaters in Ontario (hazard identification), recreator water ingestion volumes (exposure), pathogen-specific dose-response models, and ratios between numbers of infections and symptomatic disease cases to estimate illness risks (risk characterization).

RESULTS

The mean estimated AGI risk associated with recreation ranged from 0.8 to 36.7 cases per 1000 swimmers (5th-95th probability interval: 0-226.3 cases/1000) which is in line with previous studies conducted in Lake Ontario, as well as prior QMRAs of freshwater recreation. Upper range predicted values exceeded the Health Canada guideline of less than 20 cases per 1000 recreators.

CONCLUSION

This study shows that QMRA can be used to estimate disease risk in the absence of large-scale epidemiological studies. The results demonstrate a range of risk that is in line with exposure to pristine (low risk estimates) and more contaminated waters (high risk estimates) and capture the potential risk to vulnerable populations.

Differential microbiome features in lake-river systems of Taihu basin in response to water flow disturbance

Introduction

In riverine ecosystems, dynamic interplay between hydrological conditions, such as flow rate, water level, and rainfall, significantly shape the structure and function of bacterial and microeukaryotic communities, with consequences for biogeochemical cycles and ecological stability. Lake Taihu, one of China's largest freshwater lakes, frequently experiences cyanobacterial blooms primarily driven by nutrient over-enrichment and hydrological changes, posing severe threats to water quality, aquatic life, and surrounding human populations. This study explored how varying water flow disturbances influence microbial diversity and community assembly within the interconnected river-lake systems of the East and South of Lake Taihu (ET&ST). The Taipu River in the ET region accounts for nearly one-third of Lake Taihu's outflow, while the ST region includes the Changdougang and Xiaomeigang rivers, which act as inflow rivers. These two rivers not only channel water into Lake Taihu but can also cause the backflow of lake water into the rivers, creating distinct river-lake systems subjected to different intensities of water flow disturbances.

Methods

Utilizing high-throughput sequencing, we selected 22 sampling sites in the ET and ST interconnected river-lake systems and conducted seasonally assessments of bacterial and microeukaryotic community dynamics. We then compared differences in microbial diversity, community assembly, and co-occurrence networks between the two regions under varying hydrological regimes.

Results and discussion

This study demonstrated that water flow intensity and temperature disturbances significantly influenced diversity, community structure, community assembly, ecological niches, and coexistence networks of bacterial and eukaryotic microbes. In the ET region, where water flow disturbances were stronger, microbial richness significantly increased, and phylogenetic relationships were closer, yet variations in community structure were greater than in the ST region, which experienced milder water flow disturbances. Additionally, migration and dispersal rates of microbes in the ET region, along with the impact of dispersal limitations, were significantly higher than in the ST region. High flow disturbances notably reduced microbial niche width and overlap, decreasing the complexity and stability of microbial coexistence networks. Moreover, path analysis indicated that microeukaryotic communities exhibited a stronger response to water flow disturbances than bacterial communities. Our findings underscore the critical need to consider the effects of hydrological disturbance on microbial diversity, community assembly, and coexistence networks when developing strategies to manage and protect river-lake ecosystems, particularly in efforts to control cyanobacterial blooms in Lake Taihu.

Machine learning and explainable artificial intelligence for the prevention of waterborne cryptosporidiosis and giardiosis

Cryptosporidium and Giardia are important parasitic protozoa due to their zoonotic potential and impact on human health, and have often caused waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and extremely costly, thus only few countries have legislated for regular monitoring of drinking water for their presence. Several attempts have been made trying to investigate the association between the presence of such (oo)cysts in waters with other biotic or abiotic factors, with inconclusive findings. In this regard, the aim of this study was the development of an holistic approach leveraging Machine Learning (ML) and eXplainable Artificial Intelligence (XAI) techniques, in order to provide empirical evidence related to the presence and prediction of Cryptosporidium oocysts and Giardia cysts in water samples. To meet this objective, we initially modelled the complex relationship between Cryptosporidium and Giardia (oo)cysts and a set of parasitological, microbiological, physicochemical and meteorological parameters via a model-agnostic meta-learner algorithm that provides flexibility regarding the selection of the ML model executing the fitting task. Based on this generic approach, a set of four well-known ML candidates were, empirically, evaluated in terms of their predictive capabilities. Then, the best-performed algorithms, were further examined through XAI techniques for gaining meaningful insights related to the explainability and interpretability of the derived solutions. The findings reveal that the Random Forest achieves the highest prediction performance when the objective is the prediction of both contamination and contamination intensity with Cryptosporidium oocysts in a given water sample, with meteorological/physicochemical and microbiological markers being informative, respectively. For the prediction of contamination with Giardia, the eXtreme Gradient Boosting with physicochemical parameters was the most efficient algorithm, while, the Support Vector Regression that takes into consideration both microbiological and meteorological markers was more efficient for evaluating the contamination intensity with cysts. The results of the study designate that the adoption of ML and XAI approaches can be considered as a valuable tool for unveiling the complicated correlation of the presence and contamination intensity with these zoonotic parasites that could constitute, in turn, a basis for the development of monitoring platforms and early warning systems for the prevention of waterborne disease outbreaks.

Accumulation of antibiotics in the environment: Have appropriate measures been taken to protect Canadian human and ecological health?

In Canada, every day, contaminants of emerging concern (CEC) are discharged from waste treatment facilities into freshwaters. CECs such as pharmaceutical active compounds (PhACs), personal care products (PCPs), per- and polyfluoroalkyl substances (PFAS), and microplastics are legally discharged from sewage treatment plants (STPs), water reclamation plants (WRPs), hospital wastewater treatment plants (HWWTPs), or other forms of wastewater treatment facilities (WWTFs). In 2006, the Government of Canada established the Chemicals Management Plan (CMP) to classify chemicals based on a risk-priority assessment, which ranked many CECs such as PhACs as being of low urgency, therefore permitting these substances to continue being released into the environment at unmonitored rates. The problem with ranking PhACs as a low priority is that CMP's risk management assessment overlooks the long-term environmental and synergistic effects of PhAC accumulation, such as the long-term risk of antibiotic CEC accumulation in the spread of antibiotic resistance genes. The goal of this review is to specifically investigate antibiotic CEC accumulation and associated environmental risks to human and environmental health, as well as to determine whether appropriate legislative strategies are in place within Canada's governance framework. In this research, secondary data on antibiotic CEC levels in Canadian and international wastewaters, their potential to promote antibiotic-resistant residues, associated environmental short- and long-term risks, and synergistic effects were all considered. Unlike similar past reviews, this review employed an interdisciplinary approach to propose new strategies from the perspectives of science, engineering, and law.

Cryptosporidium and agriculture: A review

Cryptosporidiosis is a significant contributor to global foodborne and waterborne disease burden. It is a widespread cause of diarrheal diseases that affect humans and animals worldwide. Agricultural environments can become a source of contamination with Cryptosporidium species through faecal material derived from humans and animals. This review aims to report the main findings of scientific research on Cryptosporidium species related to various agricultural sectors, and highlights the risks of cryptosporidiosis in agricultural production, the contamination sources, the importance of animal production in transmission, and the role of farmed animals as hosts of the parasites. Agricultural contamination sources can cause water pollution in groundwater and different surface waters used for drinking, recreational purposes, and irrigation. The application of contaminated manure, faecal sludge management, and irrigation with inadequately treated water are the main concerns associated with foodborne and waterborne cryptosporidiosis related to agricultural activities. The review emphasizes the public health implications of agriculture concerning the transmission risk of Cryptosporidium parasites and the urgent need for a new concept in the agriculture sector. Furthermore, the findings of this review provide valuable information for developing appropriate measures and monitoring strategies to minimize the risk of infection.

Occurrence, transmission and risks assessment of pathogens in aquatic environments accessible to humans

Pathogens are ubiquitously detected in various natural and engineered water systems, posing potential threats to public health. However, it remains unclear which human-accessible waters are hotspots for pathogens, how pathogens transmit to these waters, and what level of health risk associated with pathogens in these environments. This review collaboratively focuses and summarizes the contamination levels of pathogens on the 5 water systems accessible to humans (natural water, drinking water, recreational water, wastewater, and reclaimed water). Then, we showcase the pathways, influencing factors and simulation models of pathogens transmission and survival. Further, we compare the health risk levels of various pathogens through Quantitative Microbial Risk Assessment (QMRA), and assess the limitations of water-associated QMRA application. Pathogen levels in wastewater are consistently higher than in other water systems, with no significant variation for Cryptosporidium spp. among five water systems. Hydraulic conditions primarily govern the transmission of pathogens into human-accessible waters, while environmental factors such as temperature impact pathogens survival. The median and mean values of computed public health risk levels posed by pathogens consistently surpass safety thresholds, particularly in the context of recreational waters. Despite the highest pathogens levels found in wastewater, the calculated health risk is significantly lower than in other water systems. Except pathogens concentration, variables like the exposure mode, extent, and frequency are also crucial factors influencing the public health risk in water systems. This review shares valuable insights to the more accurate assessment and comprehensive management of public health risk in human-accessible water environments.

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.

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

Environmental Factors Associated with Cryptosporidium and Giardia

Environmental factors significantly influence the transmission of intestinal protozoan diseases. Cryptosporidiosis and giardiasis are important zoonotic diseases characterized by diarrhea, and are mainly water or foodborne diseases caused by fecal-borne oocysts. The One Health approach effectively addresses environmentally influenced zoonotic diseases. However, the impact of environmental factors on the survival of Cryptosporidium/Giardia (oo)cysts or disease transmission is mostly uncharacterized. Associations between cryptosporidiosis and giardiasis incidence and environmental variables (e.g., climatic conditions, soil characteristics, and water characteristics) have been reported; however, the identified relationships are not consistently reported. Whether these are country-specific or global observations is unclear. Herein, we review the evidence for the influence of environmental factors on Cryptosporidium/Giardia and corresponding diseases from three perspectives: climatic, soil, and water characteristics. The (oo)cyst concentration or survival of Cryptosporidium/Giardia and the incidence of corresponding diseases are related to environmental variables. The associations identified varied among studies and have different levels of importance and lag times in different locations. This review summarizes the influence of relevant environmental factors on Cryptosporidium/Giardia from the One Health perspective and provides recommendations for future research, monitoring, and response.

Spatial Versus Nonspatial Variance in Fecal Indicator Bacteria Differs Within and Between Ponds

Surface water environments are inherently heterogenous, and little is known about variation in microbial water quality between locations. This study sought to understand how microbial water quality differs within and between Virginia ponds. Grab samples were collected twice per week from 30 sampling sites across nine Virginia ponds (n = 600). Samples (100 mL) were enumerated for total coliform (TC) and Escherichia coli (EC) levels, and physicochemical, weather, and environmental data were collected. Bayesian models of coregionalization were used to quantify the variance in TC and EC levels attributable to spatial (e.g., site, pond) versus nonspatial (e.g., date, pH) sources. Mixed-effects Bayesian regressions and conditional inference trees were used to characterize relationships between data and TC or EC levels. Analyses were performed separately for each pond with ≥3 sampling sites (5 intrapond) while one interpond model was developed using data from all sampling sites and all ponds. More variance in TC levels were attributable to spatial opposed to nonspatial sources for the interpond model (variance ratio [VR] = 1.55) while intrapond models were pond dependent (VR: 0.65-18.89). For EC levels, more variance was attributable to spatial sources in the interpond model (VR = 1.62), compared to all intrapond models (VR < 1.0) suggesting that more variance is attributable to nonspatial factors within individual ponds and spatial factors when multiple ponds are considered. Within each pond, TC and EC levels were spatially independent for sites 56-87 m apart, indicating that different sites within the same pond represent different water quality for risk management. Rainfall was positively and pH negatively associated with TC and EC levels in both inter- and intrapond models. For all other factors, the direction and strength of associations varied. Factors driving microbial dynamics in ponds appear to be pond-specific and differ depending on the spatial scale considered.

Salmonella Prevalence Is Strongly Associated with Spatial Factors while Listeria monocytogenes Prevalence Is Strongly Associated with Temporal Factors on Virginia Produce Farms

The heterogeneity of produce production environments complicates the development of universal strategies for managing preharvest produce safety risks. Understanding pathogen ecology in different produce-growing regions is important for developing targeted mitigation strategies. This study aimed to identify environmental and spatiotemporal factors associated with isolating Salmonella and Listeria from environmental samples collected from 10 Virginia produce farms. Soil (n = 400), drag swab (n = 400), and irrigation water (n = 120) samples were tested for Salmonella and Listeria, and results were confirmed by PCR. Salmonella serovar and Listeria species were identified by the Kauffmann-White-Le Minor scheme and partial sigB sequencing, respectively. Conditional forest analysis and Bayesian mixed models were used to characterize associations between environmental factors and the likelihood of isolating Salmonella, Listeria monocytogenes (LM), and other targets (e.g., Listeria spp. and Salmonella enterica serovar Newport). Surrogate trees were used to visualize hierarchical associations identified by the forest analyses. Salmonella and LM prevalence was 5.3% (49/920) and 2.3% (21/920), respectively. The likelihood of isolating Salmonella was highest in water samples collected from the Eastern Shore of Virginia with a dew point of >9.4°C. The likelihood of isolating LM was highest in water samples collected in winter from sites where <36% of the land use within 122 m was forest wetland cover. Conditional forest results were consistent with the mixed models, which also found that the likelihood of detecting Salmonella and LM differed between sample type, region, and season. These findings identified factors that increased the likelihood of isolating Salmonella- and LM-positive samples in produce production environments and support preharvest mitigation strategies on a regional scale. IMPORTANCE This study sought to examine different growing regions across the state of Virginia and to determine how factors associated with pathogen prevalence may differ between regions. Spatial and temporal data were modeled to identify factors associated with an increased pathogen likelihood in various on-farm sources. The findings of the study show that prevalence of Salmonella and L. monocytogenes is low overall in the produce preharvest environment but does vary by space (e.g., region in Virginia) and time (e.g., season), and the likelihood of pathogen-positive samples is influenced by different spatial and temporal factors. Therefore, the results support regional or scale-dependent food safety standards and guidance documents for controlling hazards to minimize risk. This study also suggests that water source assessments are important tools for developing monitoring programs and mitigation measures, as spatiotemporal factors differ on a regional scale.

What on earth? The impact of digestates and composts from farm effluent management on fluxes of foodborne pathogens in agricultural lands

The recycling of biomass is the cornerstone of sustainable development in the bioeconomy. In this context, digestates and composts from processed agricultural residues and biomasses are returned to the soil. Whether or not the presence of pathogenic microorganisms in these processed biomasses is a threat to the sustainability of the current on-farm practices is still the subject of debate. In this review, we describe the microbial pathogens that may be present in digestates and composts. We then provide an overview of the current European regulation designed to mitigate health hazards linked to the use of organic fertilisers and soil improvers produced from farm biomasses and residues. Finally, we discuss the many factors that underlie the fate of microbial pathogens in the field. We argue that incorporating land characteristics in the management of safety issues connected with the spreading of organic fertilisers and soil improvers can improve the sustainability of biomass recycling.

Infectious Pepper Mild Mottle Virus and Human Adenoviruses as Viral Indices in Sewage and Water Samples

The objective of this study was to compare human adenoviruses (HAdVs) genome and infectivity, polyomaviruses (JC and BK) genome (JCPyVs) and (BKPyVs), Pepper Mild Mottle Virus (PMMoV) genome and infectivity, and infectious bacteriophages as viral indices for sewage and water samples. One hundred and forty-four samples were collected from inlets and outlets of water and wastewater treatment plants (WTPs), and WWTPs within Greater Cairo from October 2015 till March 2017. Two methods of viral concentration [Aluminium hydroxide (Al(OH)₃) precipitation method and adsorption-elution technique followed by organic flocculation method] were compared to determine which of them was the best method to concentrate viruses from sewage and water. Although samples with only one litre volume were concentrated using Al(OH)₃ precipitation method and the same samples with larger volumes (5-20 L) were concentrated using the adsorption-elution technique followed by the organic flocculation method, a non-significant difference was observed between the efficiency of the two methods in all types of samples except for the drinking water samples. Based on the qualitative prevalence of studied viruses in water and wastewater samples, the number of genome copies and infectious units in the same samples, resistance to treatment processes in water and wastewater treatment plants, higher frequency of both adenoviruses and PMMoV genomes as candidate viral indices in treated sewage and drinking water was observed. The problem of having a viral genome as indices of viral pollution is that it does not express the recent viral pollution because of the longer survivability of the viral genome than the infectious units in water and wastewater. Both infectious adenovirus and infectious phiX174 bacteriophage virus showed similar efficiencies as indices for viral pollution in drinking water and treated sewage samples. On the other hand, qualitative detection of infectious PMMoV failed to express efficiently the presence/absence of infectious enteric viruses in drinking water samples. Infectious adenoviruses and infectious bacteriophage phiX174 virus may be better candidates than adenoviruses genome, polyomaviruses genome, and PMMoV genome and infectivity as viral indices for water and wastewater.

Development of a qPCR assay for the detection of naturalized wastewater E. coli strains

We recently demonstrated the presence of naturalized populations of Escherichia coli in municipal sewage. We wanted to develop a quantitative polymerase chain reaction (qPCR) assay targeting the uspC-IS30-flhDC marker of naturalized wastewater E. coli and assess the prevalence of these naturalized strains in wastewater. The limit of detection for the qPCR assay was 3.0 × 10^-8 ng of plasmid DNA template with 100% specificity. This strain was detected throughout the wastewater treatment process, including treated effluents. We evaluated the potential of this marker for detecting municipal sewage/wastewater contamination in water by comparing it to other human and animal markers of fecal pollution. Strong correlations were observed between the uspC-IS30-flhDC marker and the human fecal markers Bacteroides HF183 and HumM2, but not animal fecal markers, in surface and stormwater samples. The uspC-IS30-flhDC marker appears to be a potential E. coli-based marker for human wastewater contamination.

Microbial pollution in inland recreational freshwaters of Quetta, Pakistan: an initial report

Parasitic contamination of surface waters, especially recreational waters, is a serious problem for under-developed nations like Pakistan, where numerous outbreaks of parasitic diseases are reported each year. In the current study, parasitic presence in two surface waters (Hanna Lake and Wali-Tangi Dam) of Quetta was monitored quarterly for 1 year. The methodology involved the pre-concentration of the water samples and the subsequent preparation for the microscopic search of parasites. Physico-chemical and bacteriological variables were also studied. Wet staining, modified Trichrome staining, and modified acid-fast staining methods were used to identify various parasitic forms (cysts, oocysts, eggs, trophozoites). Collectively 11 parasitic elements (10 in Lake and 8 in Dam) belonging to 10 species were recorded, many of which are potential human pathogens. The species identified include Trichomonas sp., Isospora sp., Balantidium coli, Cryptosporidium sp., Entamoeba spp., amoebas, Microsporidium sp., Endolimax nana, Ascaris lumbricoides, and Giardia spp. Parasitic contamination remained persistent in both locations throughout the year independent of physico-chemical parameters (temperature, EC, pH, turbidity, and DO) and bacterial concentration of water. Reliance on bacterial presence for monitoring of recreational waters can be a risk for tourists. Entamoeba spp. and A. lumbricoides may be used for surface water monitoring in these waters.

Detection and correlation analysis of shellfish pathogens in Dadeng Island, Xiamen

Food poisoning is caused by pathogenic bacteria in water and aquatic products, especially bivalves (e.g., oysters, clams), which can bioaccumulate pathogenic bacteria. Polluted water and aquatic products thus pose a serious threat to human health and safety. In this study, the types of pathogenic bacteria in water samples and shellfish collected from the Dadeng offshore area in Xiamen were examined. We also analyzed the relationships between dominant pathogens and major climate and water quality parameters. Our objective was to provide reference data that may be used to help prevent bacterial infections and to improve aquatic food hygiene in Xiamen and its surrounding areas to safe levels, thus ensuring the health of Xiamen residents. We found that the main pathogenic bacteria were Vibrio and Bacillus, with the dominant pathogen being Vibrio parahaemolyticus. Physical and chemical indexes (water temperature, salinity, pH, dissolved oxygen, and turbidity) of water bodies and the 3-day accumulated rainfall were found to be important factors affecting the occurrence and abundance of V. parahaemolyticus.

The Persistence of Bacterial Pathogens in Surface Water and Its Impact on Global Food Safety

Water is vital to agriculture. It is essential that the water used for the production of fresh produce commodities be safe. Microbial pathogens are able to survive for extended periods of time in water. It is critical to understand their biology and ecology in this ecosystem in order to develop better mitigation strategies for farmers who grow these food crops. In this review the prevalence, persistence and ecology of four major foodborne pathogens, Shiga toxin-producing Escherichia coli (STEC), Salmonella, Campylobacter and closely related Arcobacter, and Listeria monocytogenes, in water are discussed. These pathogens have been linked to fresh produce outbreaks, some with devastating consequences, where, in a few cases, the contamination event has been traced to water used for crop production or post-harvest activities. In addition, antimicrobial resistance, methods improvements, including the role of genomics in aiding in the understanding of these pathogens, are discussed. Finally, global initiatives to improve our knowledge base of these pathogens around the world are touched upon.

Surface Waters and Urban Brown Rats as Potential Sources of Human-Infective Cryptosporidium and Giardia in Vienna, Austria

Cryptosporidium and Giardia are waterborne protozoa that cause intestinal infections in a wide range of warm-blooded animals. Human infections vary from asymptomatic to life-threatening in immunocompromised people, and can cause growth retardation in children. The aim of our study was to assess the prevalence and diversity of Cryptosporidium and Giardia in urban surface water and in brown rats trapped in the center of Vienna, Austria, using molecular methods, and to subsequently identify their source and potential transmission pathways. Out of 15 water samples taken from a side arm of the River Danube, Cryptosporidium and Giardia (oo)cysts were detected in 60% and 73% of them, with concentrations ranging between 0.3-4 oocysts/L and 0.6-96 cysts/L, respectively. Cryptosporidium and Giardia were identified in 13 and 16 out of 50 rats, respectively. Eimeria, a parasite of high veterinary importance, was also identified in seven rats. Parasite co-ocurrence was detected in nine rats. Rat-associated genotypes did not match those found in water, but matched Giardia previously isolated from patients with diarrhea in Austria, bringing up a potential role of rats as sources or reservoirs of zoonotic pathogenic Giardia. Following a One Health approach, molecular typing across potential animal and environmental reservoirs and human cases gives an insight into environmental transmission pathways and therefore helps design efficient surveillance strategies and relevant outbreak responses.

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studies have indicated that E. coli testing can present an economic burden to growers and that time lags between sampling and obtaining results may reduce the utility of these data. Models that predict E. coli levels in agricultural water may provide a mechanism for overcoming these obstacles. Thus, this proof-of-concept study uses previously published datasets to train, test, and compare E. coli predictive models using multiple algorithms and performance measures. Since the collection of different feature data carries specific costs for growers, predictive performance was compared for models built using different feature types [geospatial, water quality, stream traits, and/or weather features]. Model performance was assessed against baseline regression models. Model performance varied considerably with root-mean-squared errors and Kendall's Tau ranging between 0.37 and 1.03, and 0.07 and 0.55, respectively. Overall, models that included turbidity, rain, and temperature outperformed all other models regardless of the algorithm used. Turbidity and weather factors were also found to drive model accuracy even when other feature types were included in the model. These findings confirm previous conclusions that machine learning models may be useful for predicting when, where, and at what level E. coli (and associated hazards) are likely to be present in preharvest agricultural water sources. This study also identifies specific algorithm-predictor combinations that should be the foci of future efforts to develop deployable models (i.e., models that can be used to guide on-farm decision-making and risk mitigation). When deploying E. coli predictive models in the field, it is important to note that past research indicates an inconsistent relationship between E. coli levels and foodborne pathogen presence. Thus, models that predict E. coli levels in agricultural water may be useful for assessing fecal contamination status and ensuring compliance with regulations but should not be used to assess the risk that specific pathogens of concern (e.g., Salmonella, Listeria) are present.

Quantitative detection of human adenovirus from river water by monolithic adsorption filtration and quantitative PCR

Water contaminated with fecally derived viruses, also known as enteric viruses, represents a particularly high risk for human health. However, they have not been included in water quality regulations yet. The detection of these viruses is often more expensive and time-consuming compared to the analysis of conventional fecal indicator organisms. In addition, most methods are not sensitive enough to detect small viral loads that may already cause serious health issues if present in water. In this study, we established a workflow for the successful and direct enrichment of human adenovirus (HAdV) from artificially contaminated river water based on monolithic adsorption filtration (MAF) and quantitative polymerase reaction (qPCR). With a clear focus on efficiency, we used targeted synthetic DNA fragments as standard for the quantification of HAdV by qPCR, leading to accurate and robust results with a qPCR efficiency of 95 %, a broad working range over 6 orders of magnitude and an LOD of 1 GU/μL. We carried out a cascade of spiking experiments, enhancing the complexity of the spiking matrix with each step to progressively evaluate MAF for the direct concentration of HAdV. We found that negatively charged MAF using monoliths with hydroxyl groups (MAF-OH) showed a better reproducibility and a significantly faster turnaround time than skimmed milk flocculation (SMF) when concentrating HAdV35 from artificially contaminated, acidified mineral water. We then validated positively charged MAF using monoliths with diethyl aminoethyl groups (MAF-DEAE) for the direct concentration of HAdV5 without pre-conditioning of water samples using tap water as spiking matrix with a less defined and controlled water chemistry. Finally, we evaluated MAF-DEAE for the direct concentration of HAdV5 from surface water using river water as representative matrix with an undefined water chemistry. We found, that MAF-DEAE achieved reproducible recoveries of HAdV5, independently of the spiked concentration level or sample volume. Furthermore, we showed, that MAF-DEAE drastically reduced the limit of detection (LOD) of HAdV5 by a factor of 115 from 6.0 ∙ 10³ GU/mL before to 5.2 ∙ 10¹ GU/mL after MAF-DEAE. We identified that recoveries increased for smaller processing volumes with a peak at 0.5 L of 84.0 % and showed that recovery efficiency depends on sample volume and matrix type. The here presented workflow based on MAF-DEAE and qPCR offers an easy-to-implement and highly efficient alternative to existing approaches and allows for a fast detection of HAdV in water.

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.

The Prevalence of Virulent and Multidrug-Resistant Enterococci in River Water and in Treated and Untreated Municipal and Hospital Wastewater

The aim of this study is to describe the drug resistance and virulence of enterococci in river water sampled downstream (DRW) and upstream (URW) from the wastewater discharge point, to determine the pool of virulent and drug-resistant enterococci in untreated wastewater (UWW) and the extent to which these bacteria are eliminated from hospital wastewater (HWW) and municipal wastewater treated (TWW) by biological and mechanical methods in a wastewater treatment plant (WWTP). A total of 283 strains were identified with the use of culture-dependent methods and PCR, including seven different species including E. faecalis and E. faecium which were predominant in all analyzed samples. Majority of the strains were classified as multidrug resistant (MDR), mostly on streptomycin and trimethoprim. Strains isolated from wastewater and DRW harbored van genes conditioning phenotypic resistance to vancomycin, the highest percentage of vancomycin-resistant strains (57.0%), mostly strains harboring vanC1 genes (27.6%), was noted in TWW. More than 65.0% of the isolated strains had different virulence genes, the highest number of isolates were positive for cell wall adhesin efaA and sex pheromones cob, cpd, and ccf which participate in the induction of virulence. Many of the strains isolated from TWW were resistant to a higher number of drugs and were more virulent than those isolated from UWW and HWW. The enterococci isolated from DRW and wastewater were characterized by similar multidrug resistance and virulence profiles, and significant correlations were observed between these groups of isolates. These findings suggest that pathogenic enterococci are released with TWW and can spread in the river, pose a serious epidemiological threat and a risk to public health.

Loop-mediated isothermal amplification: Development, validation and application of simple and rapid assays for quantitative detection of species of Arcobacteraceae family- and species-specific Aliarcobacter faecis and Aliarcobacter lanthieri

Aim

The family Arcobacteraceae formerly genus Arcobacter has recently been reclassified into six genera. Among nine species of the genus Aliarcobacter, Aliarcobacter faecis and Aliarcobacter lanthieri have been identified as emerging pathogens potentially cause health risks to humans and animals. This study was designed to develop/optimize, validate and apply Arcobacteraceae family‐ and two species‐specific (A. faecis and A. lanthieri) loop‐mediated isothermal amplification (LAMP) assays to rapidly detect and quantify total number of cells in various environmental niches.

Methods and Results

Three sets of LAMP primers were designed from conserved and variable regions of 16S rRNA (family‐specific) and gyrB (species‐specific) genes. Optimized Arcobacteraceae family‐specific LAMP assay correctly amplified and detected 24 species, whereas species‐specific LAMP assays detected A. faecis and A. lanthieri reference strains as well as 91 pure and mixed culture isolates recovered from aquatic and faecal sources. The specificity of LAMP amplification of A. faecis and A. lanthieri was further confirmed by restriction fragment length polymorphism analysis. Assay sensitivities were tested using variable DNA concentrations extracted from simulated target species cells in an autoclaved agricultural water sample by achieving a minimum detection limit of 10 cells mL⁻¹ (10 fg). Direct DNA‐based quantitative detection, from agricultural surface water, identified A. faecis (17%) and A. lanthieri (1%) at a low frequency compared to family‐level (93%) with the concentration ranging from 2·1 × 10¹ to 2·2 × 10⁵ cells 100 mL⁻¹.

Conclusions

Overall, these three DNA‐based rapid and cost‐effective novel LAMP assays are sensitive and can be completed in less than 40 min. They have potential for on‐site quantitative detection of species of family Arcobacteraceae, A. faecis and A. lanthieri in food, environmental and clinical matrices.

Significance and Impact of the Study

The newly developed LAMP assays are specific, sensitive, accurate with higher reproducibility that have potential to facilitate in a less equipped lab setting and can help in early quantitative detection and rate of prevalence in environmental niches. The assays can be adopted in the diagnostic labs and epidemiological studies.

Does land use affect pathogen presence in New Zealand drinking water supplies?

Four microbes (Campylobacter spp., Escherichia coli, Cryptosporidium spp. and Giardia spp.) were monitored in 16 waterways that supply public drinking water for 13 New Zealand towns and cities. Over 500 samples were collected from the abstraction point at each study site every three months between 2009 and 2019. The waterways represent a range from small to large, free flowing to reservoir impoundments, draining catchments of entirely native vegetation to those dominated by pastoral agriculture. We used machine learning algorithms to explore the relative contribution of land use, catchment geology, vegetation, topography, and water quality characteristics of the catchment to determining the abundance and/or presence of each microbe. Sites on rivers draining predominantly agricultural catchments, the Waikato River, Oroua River and Waiorohi Stream had all four microbes present, often in high numbers, throughout the sampling interval. Other sites, such as the Hutt River and Big Huia Creek in Wellington which drain catchments of native vegetation, never had pathogenic microbes detected, or unsafe levels of E. coli. Boosted Regression Tree models could predict abundances and presence/absence of all four microbes with good precision using a wide range of potential environmental predictors covering land use, geology, vegetation, topography, and nutrient concentrations. Models were more accurate for protozoa than bacteria but did not differ markedly in their ability to predict abundance or presence/absence. Environmental drivers of microbe abundance or presence/absence also differed depending on whether the microbe was protozoan or bacterial. Protozoa were more prevalent in waterways with lower water quality, higher numbers of ruminants in the catchment, and in September and December. Bacteria were more abundant with higher rainfall, saturated soils, and catchments with greater than 35% of the land in agriculture. Although modern water treatment protocols will usually remove many pathogens from drinking water, several recent outbreaks of waterborne disease due to treatment failures, have highlighted the need to manage water supplies on multiple fronts. This research has identified potential catchment level variables, and thresholds, that could be better managed to reduce the potential for pathogens to enter drinking water supplies.

Assessment of dead-end ultrafiltration for the detection and quantification of microbial indicators and pathogens in the drinking water treatment processes

A safe water supply requires distinct treatments and monitoring to guarantee the absence of pathogens and substances potentially hazardous for human health. In this study we assessed the efficiency of the dead-end ultrafiltration (DEUF) method to concentrate faecal indicator organisms (FIO) and pathogens in water samples with different physicochemical characteristics. Water samples were collected at the treatment stages of two drinking water treatment plants to analyse the concentration of a variety of 7 FIO and 4 reference microbes which have some species that are pathogenic to humans: Campylobacter spp., enteroviruses, Cryptosporidium spp. and Giardia spp. The samples were analysed before and after concentration by DEUF, detecting FIO concentrations about 1 log₁₀ higher in non-concentrated samples from both catchments. Percent recoveries were highly variable with a mean of 43.8 ± 17.5%, depending on the FIO and inherent sample characteristics. However, DEUF enabled FIO concentration in high volumes of water (100-500 l), allowing a reduction in the detection limit compared to the non-concentrated samples due to the high volume processing capabilities of the method. As a consequence, the detection of FIO removal from water in the drinking water treatment process was 1.0-1.5 logarithms greater in DEUF-treated water compared to unfiltered samples. The DEUF method improved the detection of target indicators and allowed for the detection of pathogens in low concentrations in water after the treatment stages, confirming the suitability of DEUF to concentrate high volumes of different types of water. This method could be useful for microbial analysis in water treatment monitoring and risk assessment, allowing the identification of critical points during the water treatment process and potential hazards in water destined for several uses.

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers' ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. "Full models" were built using all four features types, while "nested models" were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and "black-box" (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree- and rule-based learners) for both outcomes. However, nested eaeA-stx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently out-performed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

Longitudinal Assessment of the Dynamics of Escherichia coli, Total Coliforms, Enterococcus spp., and Aeromonas spp. in Alternative Irrigation Water Sources: a CONSERVE Study

As climate change continues to stress freshwater resources, we have a pressing need to identify alternative (nontraditional) sources of microbially safe water for irrigation of fresh produce. This study is part of the center CONSERVE, which aims to facilitate the adoption of adequate agricultural water sources. A 26-month longitudinal study was conducted at 11 sites to assess the prevalence of bacteria indicating water quality, fecal contamination, and crop contamination risk (Escherichia coli, total coliforms [TC], Enterococcus, and Aeromonas). Sites included nontidal freshwater rivers/creeks (NF), a tidal brackish river (TB), irrigation ponds (PW), and reclaimed water sites (RW). Water samples were filtered for bacterial quantification. E. coli, TC, enterococci (∼86%, 98%, and 90% positive, respectively; n = 333), and Aeromonas (∼98% positive; n = 133) were widespread in water samples tested. Highest E. coli counts were in rivers, TC counts in TB, and enterococci in rivers and ponds (P < 0.001 in all cases) compared to other water types. Aeromonas counts were consistent across sites. Seasonal dynamics were detected in NF and PW samples only. E. coli counts were higher in the vegetable crop-growing (May-October) than nongrowing (November-April) season in all water types (P < 0.05). Only one RW and both PW sites met the U.S. Food Safety Modernization Act water standards. However, implementation of recommended mitigation measures of allowing time for microbial die-off between irrigation and harvest would bring all other sites into compliance within 2 days. This study provides comprehensive microbial data on alternative irrigation water and serves as an important resource for food safety planning and policy setting.IMPORTANCE Increasing demands for fresh fruit and vegetables, a variable climate affecting agricultural water availability, and microbial food safety goals are pressing the need to identify new, safe, alternative sources of irrigation water. Our study generated microbial data collected over a 2-year period from potential sources of irrigation (rivers, ponds, and reclaimed water sites). Pond water was found to comply with Food Safety Modernization Act (FSMA) microbial standards for irrigation of fruit and vegetables. Bacterial counts in reclaimed water, a resource that is not universally allowed on fresh produce in the United States, generally met microbial standards or needed minimal mitigation. We detected the most seasonality and the highest microbial loads in river water, which emerged as the water type that would require the most mitigation to be compliant with established FSMA standards. This data set represents one of the most comprehensive, longitudinal analyses of alternative irrigation water sources in the United States.

Cryptosporidium and Giardia in surface water and drinking water: Animal sources and towards the use of a machine-learning approach as a tool for predicting contamination

Cryptosporidium and Giardia are important parasites due to their zoonotic potential and impact on human health, often causing waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and few countries have legislated water monitoring for their presence. The aim of this study was to investigate the presence and origin of these parasites in different water sources in Northern Greece and identify interactions between biotic/abiotic factors in order to develop risk-assessment models. During a 2-year period, using a longitudinal, repeated sampling approach, 12 locations in 4 rivers, irrigation canals, and a water production company, were monitored for Cryptosporidium and Giardia, using standard methods. Furthermore, 254 faecal samples from animals were collected from 15 cattle and 12 sheep farms located near the water sampling points and screened for both parasites, in order to estimate their potential contribution to water contamination. River water samples were frequently contaminated with Cryptosporidium (47.1%) and Giardia (66.2%), with higher contamination rates during winter and spring. During a 5-month period, (oo)cysts were detected in drinking-water (<1/litre). Animals on all farms were infected by both parasites, with 16.7% of calves and 17.2% of lambs excreting Cryptosporidium oocysts and 41.3% of calves and 43.1% of lambs excreting Giardia cysts. The most prevalent species identified in both water and animal samples were C. parvum and G. duodenalis assemblage AII. The presence of G. duodenalis assemblage AII in drinking water and C. parvum IIaA15G2R1 in surface water highlights the potential risk of waterborne infection. No correlation was found between (oo)cyst counts and faecal-indicator bacteria. Machine-learning models that can predict contamination intensity with Cryptosporidium (75% accuracy) and Giardia (69% accuracy), combining biological, physicochemical and meteorological factors, were developed. Although these prediction accuracies may be insufficient for public health purposes, they could be useful for augmenting and informing risk-based sampling plans.

Human Enteric Pathogens in Eight Rivers Used as Rural Household Drinking Water Sources in the Northern Region of South Africa

People living in rural areas still rely on the use of environmental water that is contaminated by human and animal activities. This study assessed the occurrence of human enteric pathogens in rivers that are used by rural communities Vhembe District of South Africa as a source of drinking water covering two seasons (winter and summer) over a one-year period. Water quality was assessed using physico characteristics and indicator organisms (total coliforms, E. coli, Clostridium perfringens). Pathogens tested included bacteria (Pathogenic E. coli, Salmonella-, Shigella- and Vibrio spp.), protozoa (Cryptosporidium- and Giardia spp.), and enteric viruses (Rota-, Noro-, Entero-, and Adenoviruses) while using published molecular protocols. The results showed that the indicator bacteria counts exceeded South African drinking water quality guideline limits and pathogenic E. coli was detected in the samples. No Shigella spp. were isolated, while Vibrio spp. and Salmonella spp. were present; parasites were detected in four rivers and Enteric viruses were predominantly detected in the winter season. The results indicated the poor condition of water and the potential health risks to consumers highlighting the need for implementing river catchment management strategies for continued sustainability in these rivers.

Evaluating the risks associated with Shiga-toxin-producing Escherichia coli (STEC) in private well waters in Canada

Shiga-toxin-producing Escherichia coli (STEC) represent a major concern for waterborne disease outbreaks associated with consumption of contaminated groundwater. Over 4 million people rely on private groundwater systems as their primary drinking water source in Canada; many of these systems do not meet current standards for water quality. This manuscript provides a scoping overview of studies examining STEC prevalence and occurrence in groundwater, and it includes a synopsis of the environmental variables affecting survival, transport, persistence, and overall occurrence of these important pathogenic microbes in private groundwater wells used for drinking purposes.

Complex Interactions Between Weather, and Microbial and Physicochemical Water Quality Impact the Likelihood of Detecting Foodborne Pathogens in Agricultural Water

Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between Escherichia coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR] = 2.18), and eaeA-stx codetection (OR = 6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR = 50.2) and NY (OR = 18.4), and Salmonella detection in AZ (OR = 4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.

Temporal stability of E. coli and Enterococci concentrations in a Pennsylvania creek

Microbial quality of irrigation waters is a substantial food safety factor. Escherichia coli (E. coli) and Enterococci are used as the fecal indicator bacteria (FIB) to assess microbial water quality. Analysis of temporally stable patterns of FIB can facilitate effective monitoring of microbial water quality. The objectives of this study were (1) to investigate the spatiotemporal variation of E. coli and Enterococci concentrations in a large creek traversing diverse land use areas and (2) to explore the presence of temporally stable FIB concentration patterns along the creek. Concentrations of both FIB were measured weekly at five water monitoring locations along the 20-km long creek reach in Pennsylvania at baseflow for three years. The temporal stability was assessed using mean relative deviations of logarithms of FIB concentration from the average across the reach measured at the same time. The Spearman rank correlation coefficients between logarithms of FIB concentrations on consecutive sampling times was another metric used to assess the temporal stability of FIB concentration patterns. Logarithms of FIB concentrations had sinusoidal dependence on time and significantly correlated with temperature at all locations Both FIB exhibited temporal stability of concentrations. The two most downstream locations in urbanized areas tended to have logarithms of concentrations higher than the average along the observation reach. The location in the upstream forested area had mostly lower concentrations (log E. coli 1.59, log Enterococci 1.69) than average (log E. coli 2.07, log Enterococci 2.20). concentrations in colony-forming units (CFU) (100 mL)^-1. Two locations in the agricultural and sparsely urbanized area had these logarithm values close to the average. The temporal stability was more pronounced in cold seasons than in warm seasons. No significant difference was found between pattern determined for each of three observation years and for the entire three-year observation period. The Spearman rank correlations between observations on consecutive dates showed moderate to very strong relationships in most cases. Existence of the temporal stability of FIB concentrations in the creek indicates locations that inform about the average logarithm of concentrations or the geometric mean concentrations along the entire observation reach.

Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production

There is a need for science-based tools to (i) help manage microbial produce safety hazards associated with preharvest surface water use, and (ii) facilitate comanagement of agroecosystems for competing stakeholder aims. To develop these tools an improved understanding of foodborne pathogen ecology in freshwater systems is needed. The purpose of this study was to identify (i) sources of potential food safety hazards, and (ii) combinations of factors associated with an increased likelihood of pathogen contamination of agricultural water Sixty-eight streams were sampled between April and October 2018 (196 samples). At each sampling event separate 10-L grab samples (GS) were collected and tested for Listeria, Salmonella, and the stx and eaeA genes. A 1-L GS was also collected and used for Escherichia coli enumeration and detection of four host-associated fecal source-tracking markers (FST). Regression analysis was used to identify individual factors that were significantly associated with pathogen detection. We found that eaeA-stx codetection [Odds Ratio (OR) = 4.2; 95% Confidence Interval (CI) = 1.3, 13.4] and Salmonella isolation (OR = 1.8; CI = 0.9, 3.5) were strongly associated with detection of ruminant and human FST markers, respectively, while Listeria spp. (excluding Listeria monocytogenes) was negatively associated with log10 E. coli levels (OR = 0.50; CI = 0.26, 0.96). L. monocytogenes isolation was not associated with the detection of any fecal indicators. This observation supports the current understanding that, unlike enteric pathogens, Listeria is not fecally-associated and instead originates from other environmental sources. Separately, conditional inference trees were used to identify scenarios associated with an elevated or reduced risk of pathogen contamination. Interestingly, while the likelihood of isolating L. monocytogenes appears to be driven by complex interactions between environmental factors, the likelihood of Salmonella isolation and eaeA-stx codetection were driven by physicochemical water quality (e.g., dissolved oxygen) and temperature, respectively. Overall, these models identify environmental conditions associated with an enhanced risk of pathogen presence in agricultural water (e.g., rain events were associated with L. monocytogenes isolation from samples collected downstream of dairy farms; P = 0.002). The information presented here will enable growers to comanage their operations to mitigate the produce safety risks associated with preharvest surface water use.

Investigations from Northern Greece on mussels cultivated in areas proximal to wastewaters discharges, as a potential source for human infection with Giardia and Cryptosporidium

Marine bivalves are usually cultivated in shallow, estuarine waters where there is a high concentration of nutrients. Many micro-pollutants, including the protozoan parasites Giardia duodenalis and Cryptosporidium spp., which also occur in such environments, may be concentrated in shellfish tissues during their feeding process. Shellfish can thus be considered as vehicles for foodborne infections, as they are usually consumed lightly cooked or raw. Therefore, the main objective of this study was to investigate the presence of both parasites in Mediterranean mussels, Mytilus galloprovincialis that are cultivated in Thermaikos Gulf, North Greece, which is fed by four rivers that are contaminated with both protozoa. Moreover, the occurrence of these protozoa was monitored in treated wastewaters from 3 treatment plants that discharge into the gulf. In order to identify potential sources of contamination and to estimate the risk for human infection, an attempt was made to genotype Giardia and Cryptosporidium in positive samples. Immunofluorescence was used for detection and molecular techniques were used for both detection and genotyping of the parasites. In total, 120 mussel samples, coming from 10 farms, were examined for the presence of both protozoa over the 6-month farming period. None of them were found positive by immunofluorescence microscopy for the presence of parasites. Only in 3 mussel samples, PCR targeting the GP60 gene detected Cryptosporidium spp. DNA, but sequencing was not successful. Thirteen out of 18 monthly samples collected from the 3 wastewater treatment plants, revealed the presence of Giardia duodenalis cysts belonging to sub-assemblage AII, at relatively low counts (up to 11.2 cysts/L). Cryptosporidium oocysts (up to 0.9 oocysts/L) were also detected in 4 out of 8 samples, although sequencing was not successful at any of the target genes. At the studied location and under the sampling conditions described, mussels tested were not found to be harboring Giardia cysts and the presence of Cryptosporidium was found only in few cases (by PCR detection only). Our results suggest that the likelihood that mussels from these locations act as vehicles of human infection for Giardia and Cryptosporidium seems low.

Bathing Activities and Microbiological River Water Quality in the Paris Area: A Long-Term Perspective

This chapter presents the historical aspects regarding swimming in rivers in the Paris region since the seventeenth century, including the concept of fecal contamination indicator bacteria (FIBs) developed at the very beginning of the twentieth century, and historical contamination data covering more than one century in the Paris agglomeration. The sources of microbiological contamination of river waters are quantified, showing the importance of rain events. The present contamination levels are presented with reference to the European Directive for bathing water quality. FIB levels show that the sufficient quality for bathing is not reached yet in any of the monitored stations. A comprehensive data set regarding waterborne pathogens (viruses, Giardia, Cryptosporidium) in the Seine and Marne rivers is presented as a necessary complement to the regulatory FIB data to better evaluate health risks. The last section concludes on the actions to be conducted to improve the rivers’ microbiological quality in the coming years.

Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada

Optical sensing of chlorophyll-a (chl-a), turbidity, and fluorescent dissolved organic matter (fDOM) is often used to characterize the quality of water. There are many site-specific factors and environmental conditions that can affect optically sensed readings; notwithstanding the comparative implication of different procedures used to measure these properties in the laboratory. In this study, we measured these water quality properties using standard laboratory methods, and in the field using optical sensors (sonde-based) at water quality monitoring sites located in four watersheds in Canada. The overall objective of this work was to explore the relationships among sonde-based and standard laboratory measurements of the aforementioned water properties, and evaluate associations among these eco-hydrological properties and land use, environmental, and ancillary water quality variables such as dissolved organic carbon (DOC) and total suspended solids (TSS). Differences among sonde versus laboratory relationships for chl-a suggest such relationships are impacted by laboratory methods and/or site specific conditions. Data mining analysis indicated that interactive site-specific factors predominately impacting chl-a values across sites were specific conductivity and turbidity (variables with positive global associations with chl-a). The overall linear regression predicting DOC from fDOM was relatively strong (R² = 0.77). However, slope differences in the watershed-specific models suggest laboratory DOC versus fDOM relationships could be impacted by unknown localized water quality properties affecting fDOM readings, and/or the different standard laboratory methods used to estimate DOC. Artificial neural network analyses (ANN) indicated that higher relative chl-a concentrations were associated with low to no tree cover around sample sites and higher daily rainfall in the watersheds examined. Response surfaces derived from ANN indicated that chl-a concentrations were higher where combined agricultural and urban land uses were relatively higher.

Fecal indicator dynamics at the watershed scale: Variable relationships with land use, season, and water chemistry

Tracking fecal contamination in surface waters is critical to remediating water quality; however, general and source-specific fecal indicators often provide conflicting results. To understand the spatial and temporal dynamics of multiple fecal indicators and the sources they represent, we measured weekly concentrations of two general fecal indicator bacteria (FIB), a genetic indicator of human-associated Bacteroides (HF183), and surface water chemistry in nine mixed land-use watersheds in southwest Virginia, USA. At the watershed scale, general and source-specific indicators were decoupled, with distinct spatial, temporal, and chemical patterns. Random Forest analysis of individual sample variability identified temperature, watershed, nutrients, and cations as top predictors of indicator concentrations. However, these patterns - and the specific nutrients and cations identified - varied by indicator type. Among watersheds, FIB increased with developed land cover and during the summer months, while HF183 increased during the winter and only in urban watersheds. Nutrients generally related poorly to FIB and HF183, except E. coli, which correlated with total nitrogen. In contrast, all fecal indicators showed strong correlations with cations. FIB were more strongly related to calcium, magnesium, and potassium concentrations, while HF183 was related to sodium. These results suggest that, even at the watershed scale, 1) HF183 detects mainly human fecal contamination, while FIB detect broader ecosystem fecal inputs, and 2) poor correlation between specific and generalist fecal indicators is caused by unique spatial, temporal, and transport dynamics of different fecal sources in watersheds.

Anaerobic digestion of agricultural manure and biomass - Critical indicators of risk and knowledge gaps

Anaerobic digestion (AD) has been identified as a potential green technology to treat food and municipal waste, agricultural residues, including farmyard manure and slurry (FYM&S), to produce biogas. FYM&S and digestate can act as soil conditioners and provide valuable nutrients to plants; however, it may also contain harmful pathogens. This study looks at the critical indicators in determining the microbial inactivation potential of AD and the possible implications for human and environmental health of spreading the resulting digestate on agricultural land. In addition, available strategies for risk assessment in the context of EU and Irish legislation are assessed. Storage time and process parameters (including temperature, pH, organic loading rate, hydraulic retention time), feedstock recipe (carbon-nitrogen ratio) to the AD plant (both mesophilic and thermophilic) were all assessed to significantly influence pathogen inactivation. However, complete inactivation of all pathogens is unlikely. There are limited studies evaluating risks from FYM&S as a feedstock in AD and the spreading of resulting digestate. The lack of process standardisation and varying feedstocks between AD farms means risk must be evaluated on a case by case basis and calls for a more unified risk assessment methodology. In addition, there is a need for the enhancement of AD farm-based modelling techniques and datasets to help in advancing knowledge in this area.

Tracking the contribution of multiple raw and treated wastewater discharges at an urban drinking water supply using near real-time monitoring of β-d-glucuronidase activity

Past waterborne outbreaks have demonstrated that informed vulnerability assessment of drinking water supplies is paramount for the provision of safe drinking water. Although current monitoring frameworks are not designed to account for short-term peak concentrations of fecal microorganisms in source waters, the recent development of online microbial monitoring technologies is expected to fill this knowledge gap. In this study, online near real-time monitoring of β-d-glucuronidase (GLUC) activity was conducted for 1.5 years at an urban drinking water intake impacted by multiple point sources of fecal pollution. Parallel routine and event-based monitoring of E. coli and online measurement of physico-chemistry were performed at the intake and their dynamics compared over time. GLUC activity fluctuations ranged from seasonal to hourly time scales. All peak contamination episodes occurred between late fall and early spring following intense rainfall and/or snowmelt. In the absence of rainfall, recurrent daily fluctuations in GLUC activity and culturable E. coli were observed at the intake, a pattern otherwise ignored by regulatory monitoring. Cross-correlation analysis of time series retrieved from the drinking water intake and an upstream Water Resource Recovery Facility (WRRF) demonstrated a hydraulic connection between the two sites. Sewage by-passes from the same WRRF were the main drivers of intermittent GLUC activity and E. coli peaks at the drinking water intake following intense precipitation and/or snowmelt. Near real-time monitoring of fecal pollution through GLUC activity enabled a thorough characterization of the frequency, duration and amplitude of peak contamination periods at the urban drinking water intake while providing crucial information for the identification of the dominant upstream fecal pollution sources. To the best of our knowledge, this is the first characterization of a hydraulic connection between a WRRF and a downstream drinking water intake across hourly to seasonal timescales using high frequency microbial monitoring data. Ultimately, this should help improve source water protection through catchment mitigation actions, especially in a context of de facto wastewater reuse.

Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory

Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of 'permeability' from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Drinking Water Safety: Role of Hand Hygiene, Sanitation Facility, and Water System in Semi-Urban Areas of India

Poor drinking water quality is one of the main causes of acute diarrheal disease in developing countries. The study investigated the relationship between fecal contamination of hands, stored drinking water, and source waters in India. We further evaluated the environmental and behavioral factors associated with recontamination of water between collection and consumption. The bacterial contamination, that is, Escherichia coli (log₁₀ most probable number per two hands), found on mothers' hands (mean = 1.11, standard deviation [SD] = 1.2, N = 152) was substantially higher than that on their children younger than 5 years (mean = 0.64, SD = 1.0, and N = 152). We found a low level of E. coli (< 1 per 100 mL) in the source water samples; however, E. coli contamination in stored drinking water was above the recommended guidelines of the World Health Organization. The study also found that E. coli on hands was significantly associated with E. coli in the stored drinking water (P < 0.001). Moreover, E. coli was positively associated with gastrointestinal symptoms (odds ratio 1.42, P < 0.05). In the households with elevated levels (> 100 E. coli/100 mL) of fecal contamination, we found that 43.5% had unimproved sanitation facilities, poor water handling practices, and higher diarrheal incidences. The water quality deterioration from the source to the point of consumption is significant. This necessitates effective interventions in collection, transport, storage, and extraction practices when hand-water contact is likely to occur. These findings support the role of hands in the contamination of stored drinking water and suggest that clean source water does not guarantee safe water at the point of consumption.

Infectious rain? Evaluation of human pathogen concentrations in stormwater in separate sewer systems

Separate sewer systems collect and discharge rainwater directly into surface water bodies. In residential areas covering moderate traffic load these are alternative drainage routes to avoid combined sewer overflow discharge and to keep rivers clean as required by the EU Water Framework Directive. This overflow's microbial quality, however, needs to be evaluated, since stormwater run-offs are potential pathways for pathogens into river systems. Between 2010 and 2016, two separate sewer systems in North Rhine-Westphalia (Germany) were investigated. The stormwater outflow was sampled during discharge events and microbiologically analysed. The results showed high concentrations of Escherichia coli (1,100-1,100,000 CFU/100 mL) and Clostridium perfringens (20-13,000 CFU/100 mL). Campylobacter and Salmonella were detected in 97% and 43% of the samples. Giardia cysts were more often detected (31.6%) than Cryptosporidium oocysts (10.5%). The sources of human pathogens in rainwater run-off are heterogeneous. While roads have already been declared as chemical polluters via rainwater run-off, our study detected supplementary pollution of mainly faecal microorganisms. Presumably, failed connections in the sewer system itself are important sources of human pathogens. We suggest treatment of stormwater run-offs before being discharged into the river system.

Bacterial Abundance and Physicochemical Characteristics of Water and Sediment Associated with Hydroelectric Dam on the Lancang River China

Research on bacterial abundance in water column and sediment of dammed rivers remain poorly understood, despite their importance to biogeochemical processes, benthic ecology, and bioremediation. The present study investigates the water and sediment bacteria by epifluorescence microscopy in the reservoir (above the dam site), as well as in the downstream river stretches (below-dam site) at the middle reach of Lancang River during the wet, the normal and the dry seasons. The results demonstrated that the reservoir operating regime (water discharge variations) and strong precipitation promoted significant differences in the conditions of the river below the dam, especially for the concentration of dissolved oxygen, redox potential, electric conductivity, turbidity, and total dissolved solids in water and concentration of microbial activity in sediment. The seasonal variations were also key factors influencing water quality at the below-dam sampling sites. Nutrients concentration did not induce a significant response in bacterial abundance when inorganic nutrients were sufficient. Bacterial density in sediment was regulated by hydropower-related discharge, particle size, and type of sediments, while bacterial abundances in water were strongly linked with the physicochemical characteristics of the water, such as total dissolved solids and conductivity.

Detection of Clostridium perfringens in tsunami deposits after the Great East Japan Earthquake

The Great East Japan Earthquake struck off the Tohoku and caused a tsunami in 2011. Most of the microbial characteristics of tsunami-affected soil remain unknown and no published study has shown how a tsunami affects the risk of infection by Clostridium perfringens living in soil. In 2011 and 2015, C. perfringens was assessed in deposits in soil from tsunami-damaged areas and undamaged areas of Miyagi. It was found that the number of C. perfringens was overwhelmingly greater in 2011 than in 2015 in the tsunami-damaged areas. According to real-time PCR, the prevalence C. perfringens organisms (%) was 10³ fold greater in the damaged than in the undamaged areas.

Do reductions in agricultural field drainage during the growing season impact bacterial densities and loads in small tile-fed watersheds?

Predicting bacterial levels in watersheds in response to agricultural beneficial management practices (BMPs) requires understanding the germane processes at both the watershed and field scale. Controlling subsurface tile drainage (CTD) is a highly effective BMP at reducing nutrient losses from fields, and watersheds when employed en masse, but little work has been conducted on CTD effects on bacterial loads and densities in a watershed context. This study compared fecal indicator bacteria (FIB) [E. coli, Enterococcus, Fecal coliform, Total coliform, Clostridium perfringens] densities and unit area loads (UAL) from a pair of flat tile-drained watersheds (∼250-467 ha catchment areas) during the growing season over a 10-year monitoring period, using a before-after-control-impact (BACI) design (i.e., test CTD watershed vs. reference uncontrolled tile drainage (UCTD) watershed during a pre CTD intervention period and a CTD-intervention period where the test CTD watershed had CTD deployed on over 80% of the fields). With no tile drainage management, upstream tile drainage to ditches comprised ∼90% of total ditch discharge. We also examined FIB loads from a subset of tile drained fields to determine field load contributions to the watershed drainage ditches. Statistical evidence of a CTD effect on FIB UAL in the surface water systems was not strong; however, there was statistical evidence of increased FIB densities [pronounced when E. coli >200 most probable number (MPN) 100 mL^-1] in the test CTD watershed during the CTD-intervention period. This was likely a result of reduced dilution/flushing in the test CTD watershed ditch due to CTD significantly decreasing the amount of tile drainage water entering the surface water system. Tile E. coli load contributions to the ditches were low; for example, during the 6-yr CTD-intervention period they amounted to on average only ∼3 and ∼9% of the ditch loads for the test CTD and reference UCTD watersheds, respectively. This suggests in-stream, or off-field FIB reservoirs and bacteria mobilization drivers, dominated ditch E. coli loads in the watersheds during the growing season. Overall, this study suggested that decision making regarding deployment of CTD en masse in tile-fed watersheds should consider drainage practice effects on bacterial densities and loads, as well as CTD's documented capacity to boost crop yields and reduce seasonal nutrient pollution.