Improving the identification of the source of faecal pollution in water using a modelling approach: From multi-source to aged and diluted samples

CrAss-like phages are suitable indicators of antibiotic resistance genes found in abundance in fecally polluted samples

Antibiotic resistance genes (ARGs) have been extensively observed in bacterial DNA, and more recently, in phage particles from various water sources and food items. The pivotal role played by ARG transmission in the proliferation of antibiotic resistance and emergence of new resistant strains calls for a thorough understanding of the underlying mechanisms. The aim of this study was to assess the suitability of the prototypical p-crAssphage, a proposed indicator of human fecal contamination, and the recently isolated crAssBcn phages, both belonging to the Crassvirales group, as potential indicators of ARGs. These crAss-like phages were evaluated alongside specific ARGs (blaTEM, blaCTX-M-1, blaCTX-M-9, blaVIM, blaOXA-48, qnrA, qnrS, tetW and sul1) within the total DNA and phage DNA fractions in water and food samples containing different levels of fecal pollution. In samples with high fecal load (>103 CFU/g or ml of E. coli or somatic coliphages), such as wastewater and sludge, positive correlations were found between both types of crAss-like phages and ARGs in both DNA fractions. The strongest correlation was observed between sul1 and crAssBcn phages (rho = 0.90) in sludge samples, followed by blaCTX-M-9 and p-crAssphage (rho = 0.86) in sewage samples, both in the phage DNA fraction. The use of crAssphage and crAssBcn as indicators of ARGs, considered to be emerging environmental contaminants of anthropogenic origin, is supported by their close association with the human gut. Monitoring ARGs can help to mitigate their dissemination and prevent the emergence of new resistant bacterial strains, thus safeguarding public health.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Global development and future trends of artificial sweetener research based on bibliometrics

Artificial sweeteners have sparked a heated debate worldwide due to their ambiguous impacts on public and environmental health and food safety and quality. Many studies on artificial sweeteners have been conducted; however, none scientometric studies exist in the field. This study aimed to elaborate on the knowledge creation and development of the field of artificial sweeteners and predict the frontiers of knowledge based on bibliometrics. In particular, this study combined VOSviewer, CiteSpace, and Bibliometrix to visualize the mapping of knowledge production, covered 2389 relevant scientific publications (1945-2022), and systematically analyzed articles and reviews (n = 2101). Scientific publications on artificial sweeteners have been growing at an annual rate of 6.28% and globally attracting 7979 contributors. Susan J. Brown with total publications (TP) of 17, average citation per article (AC) of 36.59, and Hirsch (h)-index of 12 and Robert F. Margolskee (TP = 12; AC = 2046; h-index = 11) were the most influential scholars. This field was clustered into four groups: eco-environment and toxicology, physicochemical mechanisms, public health and risks, and nutrition metabolism. The publications about environmental issues, in particular, "surface water," were most intensive during the last five years (2018-2022). Artificial sweeteners are gaining importance in the monitoring and assessment of environmental and public health. Results of the dual-map overlay showed that the future research frontiers tilt toward molecular biology, immunology, veterinary and animal sciences, and medicine. Findings of this study are conducive to identifying knowledge gaps and future research directions for scholars.

Performance of bacterial and mitochondrial qPCR source tracking methods: A European multi-center study

With the advent of molecular biology diagnostics, different quantitative PCR assays have been developed for use in Source Tracking (ST), with none of them showing 100% specificity and sensitivity. Most studies have been conducted at a regional level and mainly in fecal slurry rather than in animal wastewater. The use of a single molecular assay has most often proven to fall short in discriminating with precision the sources of fecal contamination. This work is a multicenter European ST study to compare bacterial and mitochondrial molecular assays and was set to evaluate the efficiency of nine previously described qPCR assays targeting human-, cow/ruminant-, pig-, and poultry-associated fecal contamination. The study was conducted in five European countries with seven fecal indicators and nine ST assays being evaluated in a total of 77 samples. Animal fecal slurry samples and human and non-human wastewater samples were analyzed. Fecal indicators measured by culture and qPCR were generally ubiquitous in the samples. The ST qPCR markers performed at high levels in terms of quantitative sensitivity and specificity demonstrating large geographical application. Sensitivity varied between 73% (PLBif) and 100% for the majority of the tested markers. On the other hand, specificity ranged from 53% (CWMit) and 97% (BacR). Animal-associated ST qPCR markers were generally detected in concentrations greater than those found for the respective human-associated qPCR markers, with mean concentration for the Bacteroides qPCR markers varying between 8.74 and 7.22 log10 GC/10 mL for the pig and human markers, respectively. Bacteroides spp. and mitochondrial DNA qPCR markers generally presented higher Spearman's rank coefficient in the pooled fecal samples tested, particularly the human fecal markers with a coefficient of 0.79. The evaluation of the performance of Bacteroides spp., mitochondrial DNA and Bifidobacterium spp. ST qPCR markers support advanced pollution monitoring of impaired aquatic environments, aiming to elaborate strategies for target-oriented water quality management.

Using Data-Driven Methods and Aging Information to Quantitatively Identify Microplastic Environmental Sources and Establish a Comprehensive Discrimination Index

The global distribution of microplastics (MPs) across various environmental compartments has garnered significant attention. However, the differences in the characteristics of MPs in different environments remain unclear, and there is still a lack of quantitative analysis of their environmental sources. In addition, the inclusion of aging in source apportionment is a novel approach that has not been widely explored. In this study, we conducted a meta-analysis of the literature from the past 10 years and extracted conventional and aging characteristic data of MPs from 321 sampling points across 7 environmental compartments worldwide. We established a data-driven analysis framework using these data sets to identify different MP communities across environmental compartments, screen key MP features, and develop an environmental source analysis model for MPs. Our results indicate significant differences in the characteristics of MP communities across environments. The key features of differentiation were identified using the LEfSe method and include the carbonyl index, hydroxyl index, fouling index, proportions of polypropylene, white, black/gray, and film/sheet. These features were screened for each environmental compartment. An environmental source identification model was established based on these features with an accuracy of 75.1%. In order to accurately represent the single/multisource case in a more probabilistic manner, we proposed the MP environmental source index (MESI) to provide a probability estimation of the sample having multiple sources. Our findings contribute to a better understanding of MP migration trends and fluxes in the plastic cycle and inform effective prevention and control strategies for MP pollution.

The microbial diversity in industrial effluents makes high-throughput sequencing-based source tracking of the effluents possible

In order to explore the microbial diversity in industrial effluents, and on this basis, to verify the feasibility of tracking industrial effluents in sewer networks based on sequencing data, we collected 28 sewage samples from the industrial effluents relative to four factories in Shenzhen, China, and sequenced the 16S rRNA genes to profile the microbial compositions. We identified 5413 operational taxonomic units (OTUs) in total, and found that microbial compositions were highly diverse among samples from different locations in the sewer system, with only 107 OTUs shared by 90% of the samples. These shared OTUs were enriched in the phylum of Proteobacteria, the families of Comamonadaceae and Pseudomonadaceae, as well as the genus of Pseudomonas, with both degradation related and pathogenic bacteria. More importantly, we found differences in microbial composition among samples relevant to different factories, and identified microbial markers differentiating effluents from these factories, which can be used to track the sources of the effluents. This study improved our understanding of microbial diversity in industrial effluents, proved the feasibility of industrial effluent source tracking based on sequencing data, and provided an alternative technique solution for environmental surveillance and management.

Bacterial Indicators Are Ubiquitous Members of Pelagic Microbiome in Anthropogenically Impacted Coastal Ecosystem

Coastal zones are exposed to various anthropogenic impacts, such as different types of wastewater pollution, e.g., treated wastewater discharges, leakage from sewage systems, and agricultural and urban runoff. These various inputs can introduce allochthonous organic matter and microbes, including pathogens, into the coastal marine environment. The presence of fecal bacterial indicators in the coastal environment is usually monitored using traditional culture-based methods that, however, fail to detect their uncultured representatives. We have conducted a year-around in situ survey of the pelagic microbiome of the dynamic coastal ecosystem, subjected to different anthropogenic pressures to depict the seasonal and spatial dynamics of traditional and alternative fecal bacterial indicators. To provide an insight into the environmental conditions under which bacterial indicators thrive, a suite of environmental factors and bacterial community dynamics were analyzed concurrently. Analyses of 16S rRNA amplicon sequences revealed that the coastal microbiome was primarily structured by seasonal changes regardless of the distance from the wastewater pollution sources. On the other hand, fecal bacterial indicators were not affected by seasons and accounted for up to 34% of the sequence proportion for a given sample. Even more so, traditional fecal indicator bacteria (Enterobacteriaceae) and alternative wastewater-associated bacteria (Lachnospiraceae, Ruminococcaceae, Arcobacteraceae, Pseudomonadaceae and Vibrionaceae) were part of the core coastal microbiome, i.e., present at all sampling stations. Microbial source tracking and Lagrangian particle tracking, which we employed to assess the potential pollution source, revealed the importance of riverine water as a vector for transmission of allochthonous microbes into the marine system. Further phylogenetic analysis showed that the Arcobacteraceae in our data set was affiliated with the pathogenic Arcobacter cryaerophilus, suggesting that a potential exposure risk for bacterial pathogens in anthropogenically impacted coastal zones remains. We emphasize that molecular analyses combined with statistical and oceanographic models may provide new insights for environmental health assessment and reveal the potential source and presence of microbial indicators, which are otherwise overlooked by a cultivation approach.

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms

The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml-1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

Modeling human pollution in water bodies using somatic coliphages and bacteriophages that infect Bacteroides thetaiotaomicron strain GA17

The ability to detect human fecal pollution in water is of great importance when assessing the associated health risks. Many microbial source tracking (MST) markers have been proposed to determine the origin of fecal pollution, but their application remains challenging. A range of factors, not yet sufficiently analyzed, may affect MST markers in the environment, such as dilution and inactivation processes. In this work, a statistical framework based on Monte Carlo simulations and non-linear regression was used to develop a classification procedure for use in MST studies. The predictive model tested uses only two parameters: somatic coliphages (SOMCPH), as an index of general fecal pollution, and human host-specific bacteriophages that infect Bacteroides thetaiotaomicron strain GA17 (GA17PH). Taking into account bacteriophage dilution and differential inactivation, the threshold concentration of SOMCPH was calculated to be around 500 PFU/100 mL for a limit of detection of 10 PFU/100 mL. However, this threshold can be lowered by increasing the analyzed volume sample, which in turn lowers the limit of detection. The resulting model is sufficiently accurate for application in practical cases involving MST and could be easily used with markers other than those tested here.

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Elucidation of fecal inputs into the River Tagus catchment (Portugal) using source-specific mitochondrial DNA, HAdV, and phage markers

Determining the source of fecal contamination in a water body is important for the application of appropriate remediation measures. However, it has been suggested in the extant literature that this can best be achieved using a 'toolbox' of molecular- and culture-based methods. In response, this study deployed three indicators (Escherichia coli (EC), intestinal enterococci (IE) and somatic coliphages (SC)), one culture-dependent human marker (Bacteroides (GB-124) bacteriophage) and five culture-independent markers (human adenovirus (HAdV), human (HMMit), cattle (CWMit), pig (PGMit) and poultry (PLMit) mitochondrial DNA markers (mtDNA)) within the River Tagus catchment (n = 105). Water samples were collected monthly over a 13-month sampling campaign at four sites (impacted by significant specific human and non-human inputs and influenced by differing degrees of marine and freshwater mixing) to determine the dominant fecal inputs and assess geographical, temporal, and meteorological (precipitation, UV, temperature) fluctuations. Our results revealed that all sampling sites were not only highly impacted by fecal contamination but that this contamination originated from human and from a range of agricultural animal sources. HMMit was present in a higher percentage (83%) and concentration (4.20 log GC/100 mL) than HAdV (32%, 2.23 log GC/100 mL) and GB-124 bacteriophage with the latter being detected once. Animal mtDNA markers were detected, with CWMit found in 73% of samples with mean concentration of 3.74 log GC/100 mL. Correlation was found between concentrations of fecal indicators (EC, IE and SC), CWMit and season. Levels of CWMit were found to be related to physico-chemical parameters, such as temperature and UV radiation, possibly as a result of the increasing presence of livestock outside in warmer months. This study provides the first evaluation of such a source-associated 'toolbox' for monitoring surface water in Portugal, and the conclusions may inform future implementation of surveillance and remediation strategies for improving water quality.

Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments

Microbial pollution in rivers poses known ecological and health risks, yet causal and mechanistic linkages to sources remain difficult to establish. Host-associated microbial source tracking (MST) markers help to assess the microbial risks by linking hosts to contamination but do not identify the source locations. Land-use regression (LUR) models have been used to screen the source locations using spatial predictors but could be improved by characterizing transport (i.e., hauling, decay overland, and downstream). We introduce the microbial Find, Inform, and Test (FIT) framework, which expands previous LUR approaches and develops novel spatial predictor models to characterize the transported contributions. We applied FIT to characterize the sources of BoBac, a ruminant Bacteroides MST marker, quantified in riverbed sediment samples from Kewaunee County, Wisconsin. A 1 standard deviation increase in contributions from land-applied manure hauled from animal feeding operations (AFOs) was associated with a 77% (p-value <0.05) increase in the relative abundance of ruminant Bacteroides (BoBac-copies-per-16S-rRNA-copies) in the sediment. This is the first work finding an association between the upstream land-applied manure and the offsite bovine-associated fecal markers. These findings have implications for the sediment as a reservoir for microbial pollution associated with AFOs (e.g., pathogens and antibiotic-resistant bacteria). This framework and application advance statistical analysis in MST and water quality modeling more broadly.

Coliphages as a Complementary Tool to Improve the Management of Urban Wastewater Treatments and Minimize Health Risks in Receiving Waters

Even in countries with extensive sanitation systems, outbreaks of waterborne infectious diseases are being reported. Current tendencies, such as the growing concentration of populations in large urban conurbations, climate change, aging of existing infrastructures, and emerging pathogens, indicate that the management of water resources will become increasingly challenging in the near future. In this context, there is an urgent need to control the fate of fecal microorganisms in wastewater to avoid the negative health consequences of releasing treated effluents into surface waters (rivers, lakes, etc.) or marine coastal water. On the other hand, the measurement of bacterial indicators yields insufficient information to gauge the human health risk associated with viral infections. It would therefore seem advisable to include a viral indicator—for example, somatic coliphages—to monitor the functioning of wastewater treatments. As indicated in the studies reviewed herein, the concentrations of somatic coliphages in raw sewage remain consistently high throughout the year worldwide, as occurs with bacterial indicators. The removal process for bacterial indicators and coliphages in traditional sewage treatments is similar, the concentrations in secondary effluents remaining sufficiently high for enumeration, without the need for cumbersome and costly concentration procedures. Additionally, according to the available data on indicator behavior, which is still limited for sewers but abundant for surface waters, coliphages persist longer than bacterial indicators once outside the gut. Based on these data, coliphages can be recommended as indicators to assess the efficiency of wastewater management procedures with the aim of minimizing the health impact of urban wastewater release in surface waters.

Occurrence and distributions of human-associated markers in an impacted urban watershed

Numerous genetic markers for microbial source tracking (MST) have been evaluated by testing a panel of target and nontarget faecal samples. However, the performance of MST markers may vary between faecal and water samples, thereby resulting in inaccurate water quality assessment. In this study, a 30-day sampling study was conducted in an urban river impacted by human- and sewage-associated pollution to evaluate the performance of different human-associated markers in environmental water. Additionally, marker decay was assessed via a microcosms approach. Overall, Bacteroidales 16sRNA and crAssphage markers exhibited higher prevalence in the study area, and their detection frequencies exceeded 90%. In contrast, Bacteroidales protein markers exhibited poor detection frequencies compared to other markers, with the prevalence of Hum2 and Hum163 reaching only 63% and 84%, respectively. Regarding marker abundance, there was no significant difference in the detection concentrations between Bacteroidales 16sRNA and crAssphage markers (p > 0.05); however, the concentrations of Bacteroidales protein markers were nearly 1 order of magnitude lower than those of other MST markers. The microcosm experiments indicated that the decay rate of crAssphage markers was significantly lower than that of other bacterial target markers, which may improve their detectability when the pollution source is located far from the sampling site. Due to the observed differences in performance and decay patterns among Bacteroidales 16sRNA, crAssphage, and Bacteroidales protein markers, we recommend the simultaneous use of multiple markers from different target microorganisms to obtain a more comprehensive understanding of the pollution sources. This approach would also provide an accurate assessment of pollution levels and health risks.

Combining multi-isotopic and molecular source tracking methods to identify nitrate pollution sources in surface and groundwater

Nitrate (NO₃^-) pollution adversely impacts surface and groundwater quality. In recent decades, many countries have implemented measures to control and reduce anthropogenic nitrate pollution in water resources. However, to effectively implement mitigation measures at the origin of pollution,the source of nitrate must first be identified. The stable nitrogen and oxygen isotopes of NO₃^- (ẟ¹⁵N and ẟ¹⁸O) have been widely used to identify NO₃^- sources in water, and their combination with other stable isotopes such as boron (ẟ¹¹B) has further improved nitrate source identification. However, the use of these datasets has been limited due to their overlapping isotopic ranges, mixing between sources, and/or isotopic fractionation related to physicochemical processes. To overcome these limitations, we combined a multi-isotopic analysis with fecal indicator bacteria (FIB) and microbial source tracking (MST) techniques to improve nitrate origin identification. We applied this novel approach on 149 groundwater and 39 surface water samples distributed across Catalonia (NE Spain). A further 18 wastewater treatment plant (WWTP) effluents were also isotopically and biologically characterized. The groundwater and surface water results confirm that isotopes and MST analyses were complementary and provided more reliable information on the source of nitrate contamination. The isotope and MST data agreed or partially agreed in most of the samples evaluated (79 %). This approach was especially useful for nitrate pollution tracing in surface water but was also effective in groundwater samples influenced by organic nitrate pollution. Furthermore, the findings from the WWTP effluents suggest that the use of literature values to define the isotopic ranges of anthropogenic sources can constrain interpretations. We therefore recommend that local sources be isotopically characterized for accurate interpretations. For instance, the detection of MST inferred animal influence in some WWTP effluents, but the ẟ¹¹B values were higher than those reported in the literature for wastewater. The results of this study have been used by local water authorities to review uncertain cases and identify new vulnerable zones in Catalonia according to the European Nitrate Directive (91/676/CEE).

Antibodies Against Hepatitis E Virus (HEV) in European Moose and White-Tailed Deer in Finland

The main animal reservoirs of zoonotic hepatitis E virus (HEV) are domestic pigs and wild boars, but HEV also infects cervids. In this study, we estimated the prevalence of HEV in Finnish cervid species that are commonly hunted for human consumption. We investigated sera from 342 European moose (Alces alces), 70 white-tailed deer (Odocoileus virginianus), and 12 European roe deer (Capreolus capreolus). The samples had been collected from legally hunted animals from different districts of Finland during 2008-2009. We analysed the samples for total anti-HEV antibodies using a double-sandwich ELISA assay. Seropositive sera were analysed with RT-qPCR for HEV RNA. HEV seroprevalence was 9.1% (31/342) in moose and 1.4% (1/70) in white-tailed deer. None of the European roe deer were HEV seropositive (0/12). No HEV RNA was detected from samples of seropositive animals. HEV seropositive moose were detected in all districts. Statistically, HEV seroprevalence in moose was significantly higher (p < 0.05) in the North-East area compared to the South-West area. The highest HEV seroprevalence (20.0%) in district level was more than six times higher than the lowest (3.1%). We demonstrated the presence of total anti-HEV antibodies in European moose and white-tailed deer in Finland. Our results suggest that HEV is circulating among the moose population. Infections may occur also in white-tailed deer. We were the first to report a HEV seropositive white-tailed deer from Europe. Further studies are needed to demonstrate the HEV genotypes in cervids in Finland and to evaluate the importance of the findings in relation to food safety.

Occurrence of antibiotic resistance genes and microbial source tracking markers in the water of a karst spring in Germany

The emergence of antimicrobial resistances causes serious public health concerns worldwide. In recent years, the aquatic ecosystem has been recognized as a reservoir for antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). The prevalence of 11 ARGs, active against six antibiotic classes (β-lactams, aminoglycosides, tetracycline, macrolides, trimethoprim, and sulfonamides), was evaluated at a karst spring (Gallusquelle) in Germany, using molecular biological methods. In addition, fecal indicator bacteria (FIB), turbidity, electrical conductivity, spring discharge, and microbial source tracking markers specific for human, horse, chicken, and cow were determined. The ARGs most frequently detected were ermB (42.1%), tet(C) (40.8%), sul2 (39.5%), and sul1 (36.8%), which code for resistance to macrolides, tetracycline and sulfonamides, respectively. After a heavy rain event, the increase in FIB in the spring water was associated with the increase in ARGs and human-specific microbial source tracking (MST) markers. The determined correlations of the microbiological parameters, the observed overflow of a combined sewer overflow basin a few days before the increase of these parameters, and the findings of previous studies indicate that the overflow of this undersized basin located 9 km away from the spring could be a factor affecting the water quality of the karst spring. Our results provide a scientific basis for minimization of the input of fecal pollution and thus ARGs into the karst spring.

Implementation and integration of microbial source tracking in a river watershed monitoring plan

Fecal pollution of water bodies poses a serious threat for public health and ecosystems. Microbial source tracking (MST) is used to track the source of this pollution facilitating better management of pollution at the source. In this study we tested 12 MST markers to track human, ruminant, sheep, horse, pig and gull pollution to assess their usefulness as an effective management tool of water quality. First, the potential of the selected markers to track the source was evaluated using fresh fecal samples. Subsequently, we evaluated their performance in a catchment with different impacts, considering land use and environmental conditions. All MST markers showed high sensitivity and specificity, although none achieved 100% for both. Although some of the MST markers were detected in hosts other than the intended ones, their abundance in the target group was always several orders of magnitude higher than in the non-target hosts, demonstrating their suitability to distinguish between sources of pollution. The MST analysis matched the land use in the watershed allowing an accurate assessment of the main sources of pollution, in this case mainly human and ruminant pollution. Correlating environmental parameters including temperature and rainfall with MST markers provided insight into the dynamics of the pollution in the catchment. The levels of the human marker showed a significant negative correlation with rainfall in human polluted areas suggesting a dilution of the pollution, whereas at agricultural areas the ruminant marker increased with rainfall. There were no seasonal differences in the levels of human marker, indicating human pollution as a constant pressure throughout the year, whereas the levels of the ruminant marker was influenced by the seasons, being more abundant in summer and autumn. MST analysis integrated with land use and environmental data can improve the management of fecal polluted areas and set up best practice.

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.