Associations among pathogenic bacteria, parasites, and environmental and land use factors in multiple mixed-use watersheds

Two risk assessments: Evaluating the use of indicator HF183 Bacteroides versus pathogen measurements for modelling recreational illness risks in an urban watershed

The purpose of this study was to evaluate the performance of HF183 Bacteroides for estimating pathogen exposures during recreational water activities. We compared the use of Bacteroides-based exposure assessment to exposure assessment that relied on pathogen measurements. We considered two types of recreational water sites: those impacted by combined sewer overflows (CSOs) and those not impacted by CSOs. Samples from CSO-impacted and non-CSO-impacted urban creeks were analysed by quantitative polymerase chain reaction (qPCR) for HF183 Bacteroides and eight human gastrointestinal pathogens. Exposure assessment was conducted two ways for each type of site (CSO-impacted vs. non-CSO impacted): 1) by estimating pathogen concentrations from HF183 Bacteroides concentrations using published ratios of HF183 to pathogens in sewage and 2) by estimating pathogen concentrations from qPCR measurements. QMRA (quantitative microbial risk assessment) was then conducted for swimming, wading, and fishing exposures. Overall, mean risk estimates varied from 0.27 to 53 illnesses per 1,000 recreators depending on exposure assessment, site, activity, and norovirus dose-response model. HF183-based exposure assessment identified CSO-impacted sites as higher risk, and the recommended HF183 risk-based threshold of 525 genomic copies per 100 mL was generally protective of public health at the CSO-impacted sites but was not as protective at the non-CSO-impacted sites. In the context of our urban watershed, HF183-based exposure assessment over- and under-estimated risk relative to exposure assessment based on pathogen measurements, and the etiology of predicted pathogen-specific illnesses differed significantly. Across all sites, the HF183 model overestimated risk for norovirus, adenovirus, and Campylobacter jejuni, and it underestimated risk for E. coli and Cryptosporidium. To our knowledge, this study is the first to directly compare health risk estimates using HF183 and empirical pathogen measurements from the same waterways. Our work highlights the importance of site-specific hazard identification and exposure assessment to decide whether HF183 is applicable for monitoring risk.

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.

Investigating the relationship between extreme weather and cryptosporidiosis and giardiasis in Colorado: A multi-decade study using distributed-lag nonlinear models

Environmentally-mediated protozoan diseases like cryptosporidiosis and giardiasis are likely to be highly impacted by extreme weather, as climate-related conditions like temperature and precipitation have been linked to their survival, distribution, and overall transmission success. Our aim was to investigate the relationship between extreme temperature and precipitation and cryptosporidiosis and giardiasis infection using monthly weather data and case reports from Colorado counties over a twenty-one year period. Data on reportable diseases and weather among Colorado counties were collected using the Colorado Electronic Disease Reporting System (CEDRS) and the Daily Surface Weather and Climatological Summaries (Daymet) Version 3 dataset, respectively. We used a conditional Poisson distributed-lag nonlinear modeling approach to estimate the lagged association (between 0 and 12-months) between relative temperature and precipitation extremes and the risk of cryptosporidiosis and giardiasis infection in Colorado counties between 1997 and 2017, relative to the risk found at average values of temperature and precipitation for a given county and month. We found distinctly different patterns in the associations between temperature extremes and cryptosporidiosis, versus temperature extremes and giardiasis. When maximum or minimum temperatures were high (90th percentile) or very high (95th percentile), we found a significant increase in cryptosporidiosis risk, but a significant decrease in giardiasis risk, relative to risk at the county and calendar-month mean. Conversely, we found very similar relationships between precipitation extremes and both cryptosporidiosis and giardiasis, which highlighted the prominent role of long-term (>8 months) lags. Our study presents novel insights on the influence that extreme temperature and precipitation can have on parasitic disease transmission in real-world settings. Additionally, we present preliminary evidence that the standard lag periods that are typically used in epidemiological studies to assess the impacts of extreme weather on cryptosporidiosis and giardiasis may not be capturing the entire relevant period.

A multi-marker assessment of sewage contamination in streams using human-associated indicator bacteria, human-specific viruses, and pharmaceuticals

Human sewage contaminates waterways, delivering excess nutrients, pathogens, chemicals, and other toxic contaminants. Contaminants and various sewage indicators are measured to monitor and assess water quality, but these analytes vary in their representation of sewage contamination and the inferences about water quality they support. We measured the occurrence and concentration of multiple microbiological (n = 21) and chemical (n = 106) markers at two urban stream locations in Milwaukee, Wisconsin, USA over two years. Five-day composite water samples (n = 98) were collected biweekly, and sewage influent samples (n = 25) were collected monthly at a Milwaukee, WI water reclamation facility. We found the vast majority of markers were not sensitive enough to detect sewage contamination. To compare analytes for monitoring applications, five consistently detected human sewage indicators were used to evaluate temporal patterns of sewage contamination, including microbiological (pepper mild mottle virus, human Bacteroides, human Lachnospiraceae) and chemical (acetaminophen, metformin) markers. The proportion of human sewage in each stream was estimated using the mean influent concentration from the water reclamation facility and the mean concentration of all stream samples for each sewage indicator marker. Estimates of instream sewage pollution varied by marker, differing by up to two orders of magnitude, but four of the five sewage markers characterized Underwood Creek (mean proportions of human sewage ranged 0.0025 % - 0.075 %) as less polluted than Menomonee River (proportions ranged 0.013 % - 0.14 %) by an order of magnitude more. Chemical markers correlated with each other and yielded higher estimates of sewage pollution than microbial markers, which exhibited greater temporal variability. Transport, attenuation, and degradation processes can influence chemical and microbial markers differently and cause variation in human sewage estimates. Given the range of potential human and ecological health effects of human sewage contamination, robust characterization of sewage contamination that uses multiple lines of evidence supports monitoring and research applications.

Spatiotemporal variations in the occurrence of Campylobacter species in the Bloukrans and Swartkops rivers, Eastern Cape, South Africa

An increase in the incidence of Campylobacter species in rivers raises concerns on the safety of river water for humans who get exposed to river water. This study examines the spatiotemporal dynamics of Campylobacter species in the Bloukrans and Swartkops rivers, analysing patterns of its occurrence in relation to meteorological conditions, physicochemical parameters, seasons, and sampling sites. Physico-chemical parameters and meteorological conditions were measured during water sampling from various sites along the rivers over a year, while Polymerase Chain Reaction (PCR) was utilised to detect Campylobacter genus-specific genes and selected antibiotic-resistant genes. Campylobacter was detected in 66.67% (Bloukrans River) and 58.33% (Swartkops River). In the Bloukrans River, multi-drug resistance genes cmeA (20%), cmeB (65%), cmeC (10%), were detected while and tetO was detected at 70%. In the Swartkops River, the corresponding prevalence were 28%, 66.67%, 28.56%, and 76%. The study indicates that sampling season did not significantly impact Campylobacter prevalence. However, variation in Campylobacter occurrence exists among different sites along the rivers, reflecting the influence of site proximity to potential contamination sources. The study suggests that Campylobacter infection may be endemic in South Africa, with rivers serving as potential sources of exposure to humans, thereby contributing to the epidemiology of campylobacteriosis.

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.

Pathogenicity assessment of Arcobacter butzleri isolated from Canadian agricultural surface water

BACKGROUND

Water is considered a source for the transmission of Arcobacter species to both humans and animals. This study was conducted to assess the prevalence, distribution, and pathogenicity of A. butzleri strains, which can potentially pose health risks to humans and animals. Cultures were isolated from surface waters of a mixed-use but predominately agricultural watershed in eastern Ontario, Canada. The detection of antimicrobial resistance (AMR) and virulence-associated genes (VAGs), as well as enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) assays were performed on 913 A. butzleri strains isolated from 11 agricultural sampling sites.

RESULTS

All strains were resistant to one or more antimicrobial agents, with a high rate of resistance to clindamycin (99%) and chloramphenicol (77%), followed by azithromycin (48%) and nalidixic acid (49%). However, isolates showed a significantly (p < 0.05) high rate of susceptibility to tetracycline (1%), gentamycin (2%), ciprofloxacin (4%), and erythromycin (5%). Of the eight VAGs tested, ciaB, mviN, tlyA, and pldA were detected at high frequency (> 85%) compared to irgA (25%), hecB (19%), hecA (15%), and cj1349 (12%) genes. Co-occurrence analysis showed A. butzleri strains resistant to clindamycin, chloramphenicol, nalidixic acid, and azithromycin were positive for ciaB, tlyA, mviN and pldA VAGs. ERIC-PCR fingerprint analysis revealed high genetic similarity among strains isolated from three sites, and the genotypes were significantly associated with AMR and VAGs results, which highlight their potential environmental ubiquity and potential as pathogenic.

CONCLUSIONS

The study results show that agricultural activities likely contribute to the contamination of A. butzleri in surface water. The findings underscore the importance of farm management practices in controlling the potential spread of A. butzleri and its associated health risks to humans and animals through contaminated water.

Fecal bacteria contamination in the Adriatic Sea: Investigating environmental factors and modeling to manage recreational coastal waters

This study is based on assessing fecal indicator bacteria contamination along meteorological, hydrological and physical-chemical variables after high rainy events during the summer period. The study focused on four different coastal sites in the western and eastern Adriatic coast characterized by various geomorphological and hydrological features, levels of urbanization and anthropogenic pressures, with the aim of finding appropriate and effective solutions to ensure the safety and sustainability of tourism and public health. Detailed in-situ survey revealed a wide range of fecal indicator bacterial (FIB) across the different river mouths with concentrations of E. coli ranging from 165 to 6700 CFU 100 mL-1. It was found that nitrogen compounds track microbial load and acted as tracers for fecal contaminants. Further, a modelling tool was also used to analyze the spatial and temporal distribution of fecal pollution at these coastal sites. The integrated monitoring through high frequent survey in river waters and modeling framework allowed for the estimation of fecal indicator bacterial load at the river mouth and examination of fecal pollutant dispersion in recreational waters, considering different scenarios of fecal dispersion along the coast. This study formed the basis of a robust decision support system aimed at improving the management of recreational areas and ensuring the protection of water bodies through efficient management of bathing areas.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Integrating culture-based and molecular methods provides an improved assessment of microbial quality in a coastal lagoon

Faecal pollution in aquatic environments is a worldwide public health concern, yet the reliability and comprehensiveness of the methods used to assess faecal contamination are still debated. We compared three approaches, namely a culture-based method to enumerate Faecal Indicator Bacteria (FIB), a FIB-targeting qPCR assay, and High-Throughput Sequencing (HTS) to detect faeces- and sewage-associated taxa in water and sediment samples of an impacted model lagoon and its adjacent sea across one year. Despite at different levels, all approaches agreed in showing a higher contamination in the lagoon than in the sea, and higher in sediments than water. FIB significantly correlated when considering separately sediment and water, and when using both cultivation and qPCR. Similarly, FIB correlated between cultivation and qPCR, but qPCR provided consistently higher estimates of FIB. Faeces-associated bacteria positively correlated with cultivated FIB in both compartments, whereas sewage-associated bacteria did only in water. Considering their benefits and limitations, we conclude that, in our study site, improved quali-quantitative information on contamination is provided when at least two approaches are combined (e.g., cultivation and qPCR or HTS data). Our results provide insights to move beyond the use of FIB to improve faecal pollution management in aquatic environments and to incorporate HTS analysis into routine monitoring.

Core and conditionally rare taxa as indicators of agricultural drainage ditch and stream health and function

BACKGROUND

The freshwater microbiome regulates aquatic ecological functionality, nutrient cycling, pathogenicity, and has the capacity to dissipate and regulate pollutants. Agricultural drainage ditches are ubiquitous in regions where field drainage is necessary for crop productivity, and as such, are first-line receptors of agricultural drainage and runoff. How bacterial communities in these systems respond to environmental and anthropogenic stressors are not well understood. In this study, we carried out a three year study in an agriculturally dominated river basin in eastern Ontario, Canada to explore the spatial and temporal dynamics of the core and conditionally rare taxa (CRT) of the instream bacterial communities using a 16S rRNA gene amplicon sequencing approach. Water samples were collected from nine stream and drainage ditch sites that represented the influence of a range of upstream land uses.

RESULTS

The cross-site core and CRT accounted for 5.6% of the total number of amplicon sequence variants (ASVs), yet represented, on average, over 60% of the heterogeneity of the overall bacterial community; hence, well reflected the spatial and temporal microbial dynamics in the water courses. The contribution of core microbiome to the overall community heterogeneity represented the community stability across all sampling sites. CRT was primarily composed of functional taxa involved in nitrogen (N) cycling and was linked to nutrient loading, water levels, and flow, particularly in the smaller agricultural drainage ditches. Both the core and the CRT were sensitive responders to changes in hydrological conditions.

CONCLUSIONS

We demonstrate that core and CRT can be considered as holistic tools to explore the temporal and spatial variations of the aquatic microbial community and can be used as sensitive indicators of the health and function of agriculturally dominated water courses. This approach also reduces computational complexity in relation to analyzing the entire microbial community for such purposes.

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.

Transmission Scenarios of Listeria monocytogenes on Small Ruminant On-Farm Dairies

Listeria monocytogenes can cause severe foodborne infections in humans and invasive diseases in different animal species, especially in small ruminants. Infection of sheep and goats can occur via contaminated feed or through the teat canal. Both infection pathways result in direct (e.g., raw milk from an infected udder or fresh cheese produced from such milk) or indirect exposure of consumers. The majority of dairy farmers produces a high-risk product, namely fresh cheese made from raw ewe's and goat's milk. This, and the fact that L. monocytogenes has an extraordinary viability, poses a significant challenge to on-farm dairies. Yet, surprisingly, almost no scientific studies have been conducted dealing with the hygiene and food safety aspects of directly marketed dairy products. L. monocytogenes prevalence studies on small ruminant on-farm dairies are especially limited. Therefore, it was our aim to focus on three main transmission scenarios of this important major foodborne pathogen: (i) the impact of caprine and ovine listerial mastitis; (ii) the significance of clinical listeriosis and outbreak scenarios; and (iii) the impact of farm management and feeding practices.

Asymptomatic Carriage of Listeria monocytogenes by Animals and Humans and Its Impact on the Food Chain

Humans and animals can become asymptomatic carriers of Listeria monocytogenes and introduce the pathogen into their environment with their feces. In turn, this environmental contamination can become the source of food- and feed-borne illnesses in humans and animals, with the food production chain representing a continuum between the farm environment and human populations that are susceptible to listeriosis. Here, we update a review from 2012 and summarize the current knowledge on the asymptomatic carrier statuses in humans and animals. The data on fecal shedding by species with an impact on the food chain are summarized, and the ways by which asymptomatic carriers contribute to the risk of listeriosis in humans and animals are reviewed.

First Data on Campylobacter spp. Presence in Shellfish in Croatia

This study aimed to assess the presence of thermotolerant Campylobacter spp., as one of the most important foodborne zoonotic pathogens, in three shellfish species: mussels (Mytilus galloprovincialis), oysters (Ostrea edulis) and queen scallops (Aequipecten opercularis). The samples were collected from nine locations in the Istrian aquatory, Croatia. Isolation of Campylobacter was done according to standard ISO method, and species were identified using multiplex PCR. Isolates identified as C. jejuni and C. lari were genotyped using multilocus sequence typing (MLST) to determine the potential source of contamination. Among 108 examined samples of bivalve molluscs, mussels dominated and were the only ones found positive for the presence of Campylobacter (25.6%). In total, 19 C. lari and 1 C. jejuni strains were isolated. C. lari isolates found in this study belong to 13 sequence types (STs), and 9 of them are newly described in this paper. Two out of the four previously described C. lari STs that were found in this study were previously found in human stool. The only C. jejuni isolate was found to be sequence type 1268, which belongs to ST-1275 clonal complex that is almost exclusively found in seabirds and can sporadically cause infection in humans. Regarding the obtained results, introducing surveillance of thermotolerant Campylobacter in shellfish in the Republic of Croatia is advised as an improvement for public health safety.

Persistent Spatial Patterns of Listeria monocytogenes and Salmonella enterica Concentrations in Surface Waters: Empirical Orthogonal Function Analysis of Data from Maryland

High spatiotemporal variability of pathogen concentrations in surface waters complicates the design and interpretation of microbial water quality monitoring. Empirical orthogonal function (EOF) analysis can provide spatial patterns (EOFs) of variability in deviations of concentrations in specific locations from the average concentration across the study area. These patterns can be interpreted to assess the effect of environmental factors on pathogen levels in the water. The first and the second EOFs for Listeria monocytogenes explained 84.4% and 9.7% of the total variance of deviations from average, respectively. That percentage was 50.8% and 45.0% for Salmonella enterica. The precipitation also had a strong explanatory capability (79%) of the first EOF. The first EOFs of Listeria and precipitation were similar at pond sites but were opposite to the precipitation at the stream sites. The first EOF of S. enterica and precipitation demonstrated opposite trends, whereas the second S. enterica EOF pattern had similar signs with the precipitation EOF at pond sites, indicating a relationship between rainfall and Salmonella at these sites. Overall, the rainfall data could inform on persistent spatial patterns in concentrations of the two pathogens at the pond sites in farm settings but not at stream sites located in forested areas.

Humans and Hoofed Livestock Are the Main Sources of Fecal Contamination of Rivers Used for Crop Irrigation: A Microbial Source Tracking Approach

Freshwater bodies receive waste, feces, and fecal microorganisms from agricultural, urban, and natural activities. In this study, the probable sources of fecal contamination were determined. Also, antibiotic resistant bacteria (ARB) were detected in the two main rivers of central Chile. Surface water samples were collected from 12 sampling sites in the Maipo (n = 8) and Maule Rivers (n = 4) every 3 months, from August 2017 until April 2019. To determine the fecal contamination level, fecal coliforms were quantified using the most probable number (MPN) method and the source of fecal contamination was determined by Microbial Source Tracking (MST) using the Cryptosporidium and Giardia genotyping method. Separately, to determine if antimicrobial resistance bacteria (AMB) were present in the rivers, Escherichia coli and environmental bacteria were isolated, and the antibiotic susceptibility profile was determined. Fecal coliform levels in the Maule and Maipo Rivers ranged between 1 and 130 MPN/100-ml, and 2 and 30,000 MPN/100-ml, respectively. Based on the MST results using Cryptosporidium and Giardia host-specific species, human, cattle, birds, and/or dogs hosts were the probable sources of fecal contamination in both rivers, with human and cattle host-specific species being more frequently detected. Conditional tree analysis indicated that coliform levels were significantly associated with the river system (Maipo versus Maule), land use, and season. Fecal coliform levels were significantly (p < 0.006) higher at urban and agricultural sites than at sites immediately downstream of treatment centers, livestock areas, or natural areas. Three out of eight (37.5%) E. coli isolates presented a multidrug-resistance (MDR) phenotype. Similarly, 6.6% (117/1768) and 5.1% (44/863) of environmental isolates, in Maipo and Maule River showed and MDR phenotype. Efforts to reduce fecal discharge into these rivers should thus focus on agriculture and urban land uses as these areas were contributing the most and more frequently to fecal contamination into the rivers, while human and cattle fecal discharges were identified as the most likely source of this fecal contamination by the MST approach. This information can be used to design better mitigation strategies, thereby reducing the burden of waterborne diseases and AMR in Central Chile.

Listeria monocytogenes in Irrigation Water: An Assessment of Outbreaks, Sources, Prevalence, and Persistence

As more fresh fruits and vegetables are needed to meet the demands of a growing population, growers may need to start depending on more varied sources of water, including environmental, recycled, and reclaimed waters. Some of these sources might be susceptible to contamination with microbial pathogens, such as Listeria monocytogenes. Surveys have found this pathogen in water, soil, vegetation, and farm animal feces around the world. The frequency at which this pathogen is present in water sources is dependent on multiple factors, including the season, surrounding land use, presence of animals, and physicochemical water parameters. Understanding the survival duration of L. monocytogenes in specific water sources is important, but studies are limited concerning this environment and the impact of these highly variable factors. Understanding the pathogen’s ability to remain infectious is key to understanding how L. monocytogenes impacts produce outbreaks and, ultimately, consumers’ health.

Revisiting the Biological Behavior of Salmonella enterica in Hydric Resources: A Meta-Analysis Study Addressing the Critical Role of Environmental Water on Food Safety and Public Health

The increasing number of studies reporting the presence of Salmonella in environmental water sources suggests that it is beyond incidental findings originated from sparse fecal contamination events. However, there is no consensus on the occurrence of Salmonella as its relative serovar representation across non-recycled water sources. We conducted a meta-analysis of proportions by fitting a random-effects model using the restricted maximum-likelihood estimator to obtain the weighted average proportion and between-study variance associated with the occurrence of Salmonella in water sources. Moreover, meta-regression and non-parametric supervised machine learning method were performed to predict the effect of moderators on the frequency of Salmonella in non-recycled water sources. Three sequential steps (identification of information sources, screening and eligibility) were performed to obtain a preliminary selection from identified abstracts and article titles. Questions related to the frequency of Salmonella in aquatic environments, as well as putative differences in the relative frequencies of the reported Salmonella serovars and the role of potential variable moderators (sample source, country, and sample volume) were formulated according to the population, intervention, comparison, and outcome method (PICO). The results were reported according to the Preferred Reporting Items for Systematic Review and Meta-Analyzes statement (PRISMA). A total of 26 eligible papers reporting 148 different Salmonella serovars were retrieved. According to our model, the Salmonella frequency in non-recycled water sources was 0.19 [CI: 0.14; 0.25]. The source of water was identified as the most import variable affecting the frequency of Salmonella, estimated as 0.31 and 0.17% for surface and groundwater, respectively. There was a higher frequency of Salmonella in countries with lower human development index (HDI). Small volume samples of surface water resulted in lower detectable Salmonella frequencies both in high and low HDI regions. Relative frequencies of the 148 serovars were significantly affected only by HDI and volume. Considering that serovars representation can also be affected by water sample volume, efforts toward the standardization of water samplings for monitoring purposes should be considered. Further approaches such as metagenomics could provide more comprehensive insights about the microbial ecology of fresh water and its importance for the quality and safety of agricultural products.

Assessment of Spatio-Temporal Variability of Faecal Pollution along Coastal Waters during and after Rainfall Events

More than 80% of wastewaters are discharged into rivers or seas, with a negative impact on water quality along the coast due to the presence of potential pathogens of faecal origin. Escherichia coli and enterococci are important indicators to assess, monitor, and predict microbial water quality in natural ecosystems. During rainfall events, the amount of wastewater delivered to rivers and coastal systems is increased dramatically. This study implements measures capable of monitoring the pathways of wastewater discharge to rivers and the transport of faecal bacteria to the coastal area during and following extreme rainfall events. Spatio-temporal variability of faecal microorganisms and their relationship with environmental variables and sewage outflow in an area located in the western Adriatic coast (Fano, Italy) was monitored. The daily monitoring during the rainy events was carried out for two summer seasons, for a total of five sampling periods. These results highlight that faecal microbial contaminations were related to rainy events with a high flow of wastewater, with recovery times for the microbiological indicators varying between 24 and 72 h and influenced by a dynamic dispersion. The positive correlation between ammonium and faecal bacteria at the Arzilla River and the consequences in seawater can provide a theoretical basis for controlling ammonium levels in rivers as a proxy to monitor the potential risk of bathing waters pathogen pollution.

Microbial water quality at contrasting recreational areas in a mixed-use watershed in eastern Canada

Recreational water use is an important source of human enteric illness. Enhanced (episodic) surveillance of natural recreational waters as a supplement to beach monitoring can enrich our understanding of human health risks. From 2011 to 2013, water sampling was undertaken at recreational sites on a watershed in eastern Canada. This study compared the prevalence and associations of human enteric pathogens and fecal indicator organisms. Beach water samples had lower pathogen presence than those along the main river, due to different pollution sources and the hydrological disposition. Pathogen profiles identified from the beach sites suggested a more narrow range of sources, including birds, indicating that wild bird management could help reduce public health risks at these sites. The presence and concentration of indicator organisms did not differ significantly between beaches and the river. However, higher concentrations of generic Escherichia coli were observed when Salmonella and Cryptosporidium were present at beach sites, when Salmonella was present at the river recreational site, and when verotoxigenic E. coli were present among all sites sampled. In this watershed, generic E. coli concentrations were good indicators of potential contamination, pathogen load, and elevated human health risk, supporting their use for routine monitoring where enhanced pathogen testing is not possible.

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land-use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. PRACTITIONER POINTS: The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R² value, RMSE and MAE indicate the effectiveness of DTs in prediction.

Occurrence and distribution of microbial pollutants in coastal areas of the Adriatic Sea influenced by river discharge

The transport of a variety of pollutants from agricultural, industrial and urbanised areas makes rivers major contributors to the contamination of coastal marine environments. Too little is known of their role in carrying pathogens to the coast. We used DNA-based metabarcoding data to describe the microbial community composition in seawater and sediment collected in front of the estuary of the Tronto, the Chienti and the Esino, three Italian rivers with different pollution levels that empty into the north-central Adriatic Sea, and to detect and measure within these communities the relative abundance of microbial pollutants, including traditional faecal indicators and alternative faecal and sewage-associated pollutants. We then applied the FORENSIC algorithm to distinguish human from non-human sources of microbial pollution and FAPROTAX to map prokaryotic clades to established metabolic or other ecologically relevant functions. Finally, we searched the dataset for other common pathogenic taxa. Seawater and sediment contained numerous potentially pathogenic bacteria, mainly faecal and sewage-associated. The samples collected in front of the Tronto estuary showed the highest level of contamination, likely sewage-associated. The pathogenic signature showed a weak but positive correlation with some nutrients and strong correlations with some polycyclic aromatic hydrocarbons. This study confirms that rivers transport pathogenic bacteria to the coastal sea and highlights the value of expanding the use of HTS data, source tracking and functional identification tools to detect microbial pollutants and identify their sources with a view to gaining a better understanding of the pathways of sewage-associated discharges to the sea.

Occurrence and distribution of fecal indicators and pathogenic bacteria in seawater and Perna perna mussel in the Gulf of Annaba (Southern Mediterranean)

The identification of fecal contamination in coastal marine ecosystems is one of the main requirements for evaluation of potential risks to human health. The objective of this study was to investigate the occurrence and distribution of fecal indicators and pathogenic bacteria in seawaters and mussels collected monthly during a period of 1 year from four different sites in Northeastern Algeria (sites S1 to S4), through biochemical and molecular analyses. Our research is the first to use molecular analysis to unambiguously identify the potentially pathogenic bacteria present in Algerian Perna perna mussels. The obtained results revealed that the levels of fecal indicator bacteria (FIB) from both P. perna and seawater samples largely exceeded the permissible limits at S2 and S3. This is mainly related to their location close to industrial and coastal activity zones, which contain a mixture of urban, agricultural, and industrial pollutants. Besides, P. perna collected from all sites were severalfold more contaminated by FIB than seawater samples, primarily during the warm season of the study period. Biochemical and molecular analyses showed that isolated bacteria from both seawater and mussels were mainly potentially pathogenic species such as E. coli, Salmonella spp., Staphylococcus spp., Klebsiella spp., Pseudomonas spp., and Proteus spp.

Land use intensification destabilizes stream microbial biodiversity and decreases metabolic efficiency

Urbanization and agricultural intensification can transform landscapes. Changes in land-use can lead to increases in storm runoff and nutrient loadings which can impair the health and function of stream ecosystems. Microorganisms are an integral component of stream ecosystems. Due to the sensitivity of microorganisms to perturbations, changes in hydrology and water chemistry may alter microbial activity and structure. These shifts in microbial community dynamics may alter stream metabolism and water quality, potentially impacting higher trophic levels. Here we examine the effects of land-use and associated changes in water chemistry on sediment microbial communities by studying the West Run Watershed (WRW) a mixed-land-use system in West Virginia, USA. Streams were sampled throughout the growing season at six sites within the WRW spanning different levels of land use intensification. The proportion of land impacted by agricultural and urban development was positively correlated with temporal variation in stream sediment microbial community composition (adj R² = 0.65), suggesting development can destabilize microbial communities. Moreover, streams in developed watersheds had an increased metabolic quotient (20-50% higher), this indicates that microorganisms have greater respiration per unit biomass and signifies reduced metabolic efficiency. Further, our results suggest that land use associated changes in water chemistry alter microbial function both directly and indirectly via changes in microbial community composition and biomass. Taken together our results suggest that highly developed watersheds with elevated conductivity, metal ion concentration, and pH impose stress on microbial communities resulting in reduced microbial efficiency and elevated respiration.

Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrhoeal disease in Ifanadiana, rural Madagascar

Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.

Adjacent Terrestrial Landscapes Impact the Biogeographical Pattern of Soil Escherichia coli Strains in Produce Fields by Modifying the Importance of Environmental Selection and Dispersal

High-quality habitats for wildlife (e.g., forest) provide essential ecosystem services while increasing species diversity and habitat connectivity. Unfortunately, the presence of such habitats adjacent to produce fields may increase risk for contamination of fruits and vegetables by enteric bacteria, including Escherichia coliE. coli survives in extrahost environments (e.g., soil) and could be dispersed across landscapes by wildlife. Understanding how terrestrial landscapes impact the distribution of soil E. coli strains is of importance in assessing the contamination risk of agricultural products. Here, using multilocus sequence typing, we characterized 938 E. coli soil isolates collected from two watersheds with different landscape patterns in New York State, USA, and compared the distribution of E. coli and the influence that environmental selection and dispersal have on the distribution between these two watersheds. Results showed that for the watershed with widespread produce fields, sparse forests, and limited interaction between the two land use types, E. coli composition was significantly different between produce field sites and forest sites; this distribution appears to be shaped by relatively strong environmental selection, likely from soil phosphorus, and slight dispersal limitation. For the watershed with more forested areas and stronger interaction between produce field sites and forest sites, E. coli composition between these two land use types was relatively homogeneous; this distribution appeared to be a consequence of wildlife-driven dispersal, inferred by competing models. Collectively, our results suggest that terrestrial landscape attributes could impact the biogeographic pattern of enteric bacteria by adjusting the importance of environmental selection and dispersal.IMPORTANCE Understanding the ecology of enteric bacteria in extrahost environments is important for the development and implementation of strategies to minimize preharvest contamination of produce with enteric pathogens. Our findings suggest that watershed landscape is an important factor influencing the importance of ecological drivers and dispersal patterns of E. coli Agricultural areas in such watersheds may have a higher risk of produce contamination due to fewer environmental constraints and higher potential of dispersal of enteric bacteria between locations. Thus, there is a perceived trade-off between priorities of environmental conservation and public health in on-farm food safety, with limited ecological data supporting or refuting the role of wildlife in dispersing pathogens under normal operating conditions. By combining field sampling and spatial modeling, we explored ecological principles underlying the biogeographic pattern of enteric bacteria at the regional level, which can benefit agricultural, environmental, and public health scientists who aim to reduce the risk of food contamination by enteric bacteria while minimizing negative impacts on wildlife habitats.

Small Produce Farm Environments Can Harbor Diverse Listeria monocytogenes and Listeria spp. Populations

ABSTRACT

A comprehensive understanding of foodborne pathogen diversity in preharvest environments is necessary to effectively track pathogens on farms and identify sources of produce contamination. As such, this study aimed to characterize Listeria diversity in wildlife feces and agricultural water collected from a New York state produce farm over a growing season. Water samples were collected from a pond (n = 80) and a stream (n = 52). Fecal samples (n = 77) were opportunistically collected from areas <5 m from the water sources; all samples were collected from a <0.5-km2 area. Overall, 86 (41%) and 50 (24%) of 209 samples were positive for Listeria monocytogenes and Listeria spp. (excluding L. monocytogenes), respectively. For each positive sample, one L. monocytogenes or Listeria spp. isolate was speciated by sequencing the sigB gene, thereby allowing for additional characterization based on the sigB allelic type. The 86 L. monocytogenes and 50 Listeria spp. isolates represented 8 and 23 different allelic types, respectively. A subset of L. monocytogenes isolates (n = 44) from pond water and pond-adjacent feces (representing an ∼5,000-m2 area) were further characterized by pulsed-field gel electrophoresis (PFGE); these 44 isolates represented 22 PFGE types, which is indicative of considerable diversity at a small spatial scale. Ten PFGE types were isolated more than once, suggesting persistence or reintroduction of PFGE types in this area. Given the small spatial scale, the prevalence of L. monocytogenes and Listeria spp., as well as the considerable diversity among isolates, suggests traceback investigations may be challenging. For example, traceback of finished product or processing facility contamination with specific subtypes to preharvest sources may require collection of large sample sets and characterization of a considerable number of isolates. Our data also support the adage "absence of evidence does not equal evidence of absence" as applies to L. monocytogenes traceback efforts at the preharvest level.

HIGHLIGHTS

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.

Prevalence, Distribution, and Diversity of Salmonella Strains Isolated From a Subtropical Lake

This study investigated the prevalence, serovar distribution, antimicrobial resistance, and pulsed field gel electrophoresis (PFGE) typing of Salmonella enterica isolated from Lake Zapotlán, Jalisco, Mexico. Additionally, the association of the presence of Salmonella with physicochemical and environmental parameters was analyzed using Pearson correlation analysis and principal component analysis (PCA). Salmonella spp. were identified in 19 of 63 (30.15%) samples. The prevalence of Salmonella was positively correlated with air temperature, electrical conductivity, pH, and dissolved oxygen and negatively correlated with relative humidity, water temperature, turbidity, and precipitation. The predominant serotype identified was Agona (68.48%), followed by Weltevreden (5.26%), Typhimurium (5.26%), and serogroup B (21.05%). Overall, the highest detected antimicrobial resistance was toward colistin (73.68%), followed by sulfamethoxazole (63.15%), tetracycline (57.89%), nalidixic acid (52.63%), and trimethoprim (52.63%). All Salmonella strains were genetically diverse, with a total of 11 XbaI and four BlnI profiles on PFGE. The use of these two enzymes allowed differentiate strains of Salmonella of the same serotype. The results obtained in this study contribute to a better understanding of the Salmonella spp. ecology in an endorheic subtropical lake and provide information for decision makers to propose and implement effective strategies to control point and non-point sources of pathogen contamination.

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.

Real-time quantitative PCR assay development and application for assessment of agricultural surface water and various fecal matter for prevalence of Aliarcobacter faecis and Aliarcobacter lanthieri

BACKGROUND

Aliarcobacter faecis and Aliarcobacter lanthieri are recently identified as emerging human and animal pathogens. In this paper, we demonstrate the development and optimization of two direct DNA-based quantitative real-time PCR assays using species-specific oligonucleotide primer pairs derived from rpoB and gyrA genes for A. faecis and A. lanthieri, respectively. Initially, the specificity of primers and amplicon size of each target reference strain was verified and confirmed by melt curve analysis. Standard curves were developed with a minimum quantification limit of 100 cells mL^- 1 or g^- 1 obtained using known quantities of spiked A. faecis and A. lanthieri reference strains in autoclaved agricultural surface water and dairy cow manure samples.

RESULTS

Each species-specific qPCR assay was validated and applied to determine the rate of prevalence and quantify the total number of cells of each target species in natural surface waters of an agriculturally-dominant and non-agricultural reference watershed. In addition, the prevalence and densities were determined for human and various animal (e.g., dogs, cats, dairy cow, and poultry) fecal samples. Overall, the prevalence of A. faecis for surface water and feces was 21 and 28%, respectively. The maximum A. faecis concentration for water and feces was 2.3 × 10⁷ cells 100 mL^{- 1} and 1.2 × 10⁷ cells g^- 1, respectively. A. lanthieri was detected at a lower frequency (2%) with a maximum concentration in surface water of 4.2 × 10⁵ cells 100 mL^- 1; fecal samples had a prevalence and maximum density of 10% and 2.0 × 10⁶ cells g^- 1, respectively.

CONCLUSIONS

The results indicate that the occurrence of these species in agricultural surface water is potentially due to fecal contamination of water from livestock, human, or wildlife as both species were detected in fecal samples. The new real-time qPCR assays can facilitate rapid and accurate detection in < 3 h to quantify total numbers of A. faecis and A. lanthieri cells present in various complex environmental samples.

Verification of Bacteroidales 16S rRNA markers as a complementary tool for detecting swine fecal pollution in the Yangtze Delta

To correctly assess and properly manage the public health risks associated with exposure to contaminated water, it is necessary to identify the source of fecal pollution in a watershed. In this study, we evaluated the efficacy of our two previously developed real time-quantitative PCR (qPCR) assays for the detection of swine-associated Bacteroidales genetic markers (gene 1-38, gene 3-53) in the Yangtze Delta watershed of southeastern China. The results indicated that the gene 1-38 and 3-53 markers exhibited high accuracy (92.5%, 91.7% conditional probability, respectively) in detecting Bacteroidales spp. in water samples. According to binary logistic regression (BLR), these two swine-associated markers were well correlated (P < 0.05) with fecal indicators (Escherichia coli and Enterococci spp.) and zoonotic pathogens (E. coli O157: H7, Salmonella spp. and Campylobacter spp.) in water samples. In contrast, concentrations of conventional fecal indicator bacteria (FIB) were not correlated with zoonotic pathogens, suggesting that they are noneffective at detecting fecal pollution events. Collectively, the results obtained in this study demonstrated that a swine-targeted qPCR assay based on two Bacteroidales genes markers (gene 1-38, gene 3-53) could be a useful tool in determining the swine-associated impacts of fecal contamination in a watershed.

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.

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.

Potential pathogen communities in highly polluted river ecosystems: Geographical distribution and environmental influence

Risks of pathogenic bacteria to the health of both human beings and water ecosystems have been widely acknowledged. However, traditional risk assessment methods based on fecal indicator bacteria and/or pure culture are not comprehensive at the community level, mainly owing to the limited taxonomic coverage. Here, we combined the technique of high-throughput sequencing and the concept of metacommunity to assess the potential pathogenic bacterial communities in an economically and ecologically crucial but highly polluted river-the North Canal River (NCR) in Haihe River Basin located in North China. NCR presented a significant environmental gradient, with the highest, moderate, and lowest levels of pollution in the up-, middle, and downstream. After multiple analyses, we successfully identified 48 genera, covering nine categories of potential pathogens (mainly human pathogens). The most abundant genus was Acinetobacter, which was rarely identified as a pathogen bacterium in previous studies of NCR. At the community level, we observed significant geographical variation of community composition and structure. Such a high level of geographical variation was mainly derived from differed abundance of species among sections along the river, especially the top seven Operational Taxonomic Units (OTUs). For example, relative abundance of OTU1 (Gammaproteobacteria/Acinetobacter) increased significantly from upstream towards downstream. Regarding the underlying mechanisms driving community geographical variation, environmental filtering was identified as the dominant ecological process and total nitrogen as the most influential environmental variable. Altogether, this study provided a comprehensive profile of potential pathogenic bacteria in NCR and revealed the underlying mechanisms of community succession. Owing to their high abundance and wide geographical distribution, we suggest that potential pathogens identified in this study should be incorporated into future monitoring and management programs in NCR. By revealing the correlation between environmental factors and community composition, the results obtained in this study have significant implications for early warning and risk assessment of potential pathogen bacteria, as well as management practices in highly polluted river ecosystems.

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.

Estimating cumulative wastewater treatment plant discharge influences on acesulfame and Escherichia coli in a highly impacted watershed with a fully-integrated modelling approach

Wastewater treatment plant (WWTP) discharge is often considered a principal source of surface water contamination. In this study, a three-dimensional fully-integrated groundwater-surface water model was used to simulate the transport characteristics and cumulative loading of an artificial sweetener (acesulfame) and fecal indicator bacteria (Escherichia coli) from WWTPs within a 6800 km² mixed-use, highly impacted watershed in Ontario, Canada. The model, which employed 3.5 × 10⁶ computational nodes and 15 layers, facilitated a comprehensive assessment of groundwater-surface water interactions under high and low flow conditions; processes typically not accounted for in WWTP cumulative effects models. Simulations demonstrate that the model had significant capacity in reproducing the average and transient multi-year groundwater and surface water flow conditions in the watershed. As a proxy human-specific conservative tracer, acesulfame was useful for model validation and to help inform the representation of watershed-scale transport processes. Using a uniform WWTP acesulfame loading rate of 7.14 mg person^-1 day^-1, the general spatial trends and magnitudes of the acesulfame concentration profile along the main river reach within the watershed were reproduced; however, model performance was improved by tuning individual WWTP loading rates. Although instream dilution and groundwater-surface water interactions were strongly dependent on flow conditions, the main reach primarily consisted of groundwater discharge zones. For this reason, hydrodynamic dispersion in the hyporheic zone is shown as the predominant mechanism driving acesulfame into near-stream shallow groundwater, while under high flow conditions, the simulations demonstrate the potential for advective flushing of the shallow groundwater. Regarding the cumulative impact of the WWTPs on E. coli concentrations in the surface flow system, simulated transient E. coli levels downstream of WWTPs in the watershed were significantly lower than observed values, thus highlighting the potential importance of other sources of E. coli in the watershed.

Storm runoff differentially influences the nutrient concentrations and microbial contamination at two distinct beaches in northern China

With the escalating coastal development and loss of vegetated landscape, the volume of storm runoff increases significantly in Chinese coastal cities. To protect human health and valuable recreational resources, it is necessary to develop a quantitative understanding of coastal pollution. Here we studied the influence of storm runoff on the nutrients and microbial pathogens at two popular bathing beaches in northern China. Dongshan Beach, located near the mouth of an urban river, is influenced by non-point source pollution while Tiger-Rock Beach, a coastal beach, is primarily influenced by a point source from a storm drain outfall. Storm runoff significantly (P < 0.001) decreased the salinity and Chl a post-storm at both the beaches, but only reduced the concentration of dissolved inorganic N at Tiger-Rock Beach. Escherichia coli decreased by 68.7% at Dongshan Beach, possibly due to the dilution effect of the stormflow, contradicting the notion of elevated fecal contamination in coastal beaches from storm runoff. Vibrio parahaemolyticus increased at both beaches post-storm, by 155.7% at Dongshan Beach and 136.7% at Tiger-Rock Beach. Regardless of storm impact, both E. coli and V. parahaemolyticus were much higher at Dongshan Beach than that at Tiger-Rock, suggesting the influence of different surrounding topographies. Lastly, the statistical models developed based on the environmental and microbial parameters regression showed predictive power (adjusted R² > 0.5) to estimate the concentration of E. coli at Dongshan Beach and V. parahaemolyticus at Tiger-Rock Beach. Overall, the results suggest the unique role of the individual beaches in attenuating the effect of rainfall on the concentration of microbial pathogens in bathing water quality and provide unique predictive models for recreational water management and public health protection.

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.

The Influence of Climate and Livestock Reservoirs on Human Cases of Giardiasis

Giardia duodenalis is an intestinal parasite which causes diarrhoeal illness in people. Zoonotic subtypes found in livestock may contribute to human disease occurrence through runoff of manure into multi-use surface water. This study investigated temporal associations among selected environmental variables and G. duodenalis occurrence in livestock reservoirs on human giardiasis incidence using data collected in the Waterloo Health Region, Ontario, Canada. The study objectives were to: (1) evaluate associations between human cases and environmental variables between 1 June 2006 and 31 December 2013, and (2) evaluate associations between human cases, environmental variables and livestock reservoirs using a subset of this time series, with both analyses controlling for seasonal and long-term trends. Human disease incidence exhibited a seasonal trend but no annual trend. A Poisson multivariable regression model identified an inverse association with water level lagged by 1 month (IRR = 0.10, 95% CI 0.01, 0.85, P < 0.05). Case crossover analysis found varying associations between lagged variables including livestock reservoirs (1 week), mean air temperature (3 weeks), river water level (1 week) and flow rate (1 week), and precipitation (4 weeks). This study contributes to our understanding of epidemiologic relationships influencing human giardiasis cases in Ontario, Canada.

Weather, environmental conditions, and waterborne Giardia and Cryptosporidium in Iqaluit, Nunavut

Indigenous communities in the Arctic often face unique drinking water quality challenges related to inadequate infrastructure and environmental contamination; however, limited research exists on waterborne parasites in these communities. This study examined Giardia and Cryptosporidium in untreated surface water used for drinking in Iqaluit, Canada. Water samples (n = 55) were collected weekly from June to September 2016 and tested for the presence of Giardia and Cryptosporidium using microscopy and polymerase chain reaction (PCR). Exact logistic regressions were used to examine associations between parasite presence and environmental exposure variables. Using microscopy, 20.0% of samples tested positive for Giardia (n = 11) and 1.8% of samples tested positive for Cryptosporidium (n = 1). Low water temperatures (1.1 to 6.7 °C) and low air temperatures (-0.1 to 4.5 °C) were significantly associated with an increased odds of parasite presence (p = 0.047, p = 0.041, respectively). These results suggest that surface water contamination with Giardia and Cryptosporidium may be lower in Iqaluit than in other Canadian regions; however, further research should examine the molecular characterization of waterborne parasites to evaluate the potential human health implications in Northern Canada.

Antimicrobial Resistance in Listeria Species

For nearly a century the use of antibiotics to treat infectious diseases has benefited human and animal health. In recent years there has been an increase in the emergence of antibiotic-resistant bacteria, in part attributed to the overuse of compounds in clinical and farming settings. The genus Listeria currently comprises 17 recognized species found throughout the environment. Listeria monocytogenes is the etiological agent of listeriosis in humans and many vertebrate species, including birds, whereas Listeria ivanovii causes infections mainly in ruminants. L. monocytogenes is the third-most-common cause of death from food poisoning in humans, and infection occurs in at-risk groups, including pregnant women, newborns, the elderly, and immunocompromised individuals.

Aquatic Bacterial Communities Associated With Land Use and Environmental Factors in Agricultural Landscapes Using a Metabarcoding Approach

This study applied a 16S rRNA gene metabarcoding approach to characterize bacterial community compositional and functional attributes for surface water samples collected within, primarily, agriculturally dominated watersheds in Ontario and Québec, Canada. Compositional heterogeneity was best explained by stream order, season, and watercourse discharge. Generally, community diversity was higher at agriculturally dominated lower order streams, compared to larger stream order systems such as small to large rivers. However, during times of lower relative water flow and cumulative 2-day rainfall, modestly higher relative diversity was found in the larger watercourses. Bacterial community assemblages were more sensitive to environmental/land use changes in the smaller watercourses, relative to small-to-large river systems, where the proximity of the sampled water column to bacteria reservoirs in the sediments and adjacent terrestrial environment was greater. Stream discharge was the environmental variable most significantly correlated (all positive) with bacterial functional groups, such as C/N cycling and plant pathogens. Comparison of the community structural similarity via network analyses helped to discriminate sources of bacteria in freshwater derived from, for example, wastewater treatment plant effluent and intensity and type of agricultural land uses (e.g., intensive swine production vs. dairy dominated cash/livestock cropping systems). When using metabarcoding approaches, bacterial community composition and coexisting pattern rather than individual taxonomic lineages, were better indicators of environmental/land use conditions (e.g., upstream land use) and bacterial sources in watershed settings. Overall, monitoring changes and differences in aquatic microbial communities at regional and local watershed scales has promise for enhancing environmental footprinting and for better understanding nutrient cycling and ecological function of aquatic systems impacted by a multitude of stressors and land uses.

Occurrence of Bacterial Pathogens and Human Noroviruses in Shellfish-Harvesting Areas and Their Catchments in France

During a 2-year study, the presence of human pathogenic bacteria and noroviruses was investigated in shellfish, seawater and/or surface sediments collected from three French coastal shellfish-harvesting areas as well as in freshwaters from the corresponding upstream catchments. Bacteria isolated from these samples were further analyzed. Escherichia coli isolates classified into the phylogenetic groups B2, or D and enterococci from Enterococcus faecalis and E. faecium species were tested for the presence of virulence genes and for antimicrobial susceptibility. Salmonella members were serotyped and the most abundant serovars (Typhimurium and its monophasic variants and Mbandaka) were genetically characterized by high discriminative subtyping methods. Campylobacter and Vibrio were identified at the species level, and haemolysin-producing Vibrio parahaemolyticus were searched by tdh- and trh- gene detection. Main results showed a low prevalence of Salmonella in shellfish samples where only members of S. Mbandaka were found. Campylobacter were more frequently isolated than Salmonella and a different distribution of Campylobacter species was observed in shellfish compared to rivers, strongly suggesting possible additional inputs of bacteria. Statistical associations between enteric bacteria, human noroviruses (HuNoVs) and concentration of fecal indicator bacteria revealed that the presence of Salmonella was correlated with that of Campylobacter jejuni and/or C. coli as well as to E. coli concentration. A positive correlation was also found between the presence of C. lari and the detection of HuNoVs. This study highlights the importance of simultaneous detection and characterization of enteric and marine pathogenic bacteria and human noroviruses not only in shellfish but also in catchment waters for a hazard assessment associated with microbial contamination of shellfish.

Maxent estimation of aquatic Escherichia coli stream impairment

BACKGROUND

The leading cause of surface water impairment in United States' rivers and streams is pathogen contamination. Although use of fecal indicators has reduced human health risk, current approaches to identify and reduce exposure can be improved. One important knowledge gap within exposure assessment is characterization of complex fate and transport processes of fecal pollution. Novel modeling processes can inform watershed decision-making to improve exposure assessment.

METHODS

We used the ecological model, Maxent, and the fecal indicator bacterium Escherichia coli to identify environmental factors associated with surface water impairment. Samples were collected August, November, February, and May for 8 years on Sinking Creek in Northeast Tennessee and analyzed for 10 water quality parameters and E. coli concentrations. Univariate and multivariate models estimated probability of impairment given the water quality parameters. Model performance was assessed using area under the receiving operating characteristic (AUC) and prediction accuracy, defined as the model's ability to predict both true positives (impairment) and true negatives (compliance). Univariate models generated action values, or environmental thresholds, to indicate potential E. coli impairment based on a single parameter. Multivariate models predicted probability of impairment given a suite of environmental variables, and jack-knife sensitivity analysis removed unresponsive variables to elicit a set of the most responsive parameters.

RESULTS

Water temperature univariate models performed best as indicated by AUC, but alkalinity models were the most accurate at correctly classifying impairment. Sensitivity analysis revealed that models were most sensitive to removal of specific conductance. Other sensitive variables included water temperature, dissolved oxygen, discharge, and NO3. The removal of dissolved oxygen improved model performance based on testing AUC, justifying development of two optimized multivariate models; a 5-variable model including all sensitive parameters, and a 4-variable model that excluded dissolved oxygen.

DISCUSSION

Results suggest that E. coli impairment in Sinking Creek is influenced by seasonality and agricultural run-off, stressing the need for multi-month sampling along a stream continuum. Although discharge was not predictive of E. coli impairment alone, its interactive effect stresses the importance of both flow dependent and independent processes associated with E. coli impairment. This research also highlights the interactions between nutrient and fecal pollution, a key consideration for watersheds with multiple synergistic impairments. Although one indicator cannot mimic theplethora of existing pathogens in water, incorporating modeling can fine tune an indicator's utility, providing information concerning fate, transport, and source of fecal pollution while prioritizing resources and increasing confidence in decision making.