Have genetic targets for faecal pollution diagnostics and source tracking revolutionized water quality analysis yet?

The impacts of nucleic acid-based methods - such as PCR and sequencing - to detect and analyze indicators, genetic markers or molecular signatures of microbial faecal pollution in health-related water quality research were assessed by rigorous literature analysis. A wide range of application areas and study designs has been identified since the first application more than 30 years ago (>1100 publications). Given the consistency of methods and assessment types, we suggest defining this emerging part of science as a new discipline: genetic faecal pollution diagnostics (GFPD) in health-related microbial water quality analysis. Undoubtedly, GFPD has already revolutionized faecal pollution detection (i.e., traditional or alternative general faecal indicator/marker analysis) and microbial source tracking (i.e., host-associated faecal indicator/marker analysis), the current core applications. GFPD is also expanding to many other research areas, including infection and health risk assessment, evaluation of microbial water treatment, and support of wastewater surveillance. In addition, storage of DNA extracts allows for biobanking, which opens up new perspectives. The tools of GFPD can be combined with cultivation-based standardized faecal indicator enumeration, pathogen detection, and various environmental data types, in an integrated data analysis approach. This comprehensive meta-analysis provides the scientific status quo of this field, including trend analyses and literature statistics, outlining identified application areas, and discusses the benefits and challenges of nucleic acid-based analysis in GFPD.

Probabilistic fecal pollution source profiling and microbial source tracking for an urban river catchment.

We developed an innovative approach to estimate the occurrence and extent of fecal pollution sources for urban river catchments. The methodology consists of 1) catchment surveys complemented by literature data where needed for probabilistic estimates of daily produced fecal indicator (FIBs, E. coli, enterococci) and zoonotic reference pathogen numbers (Campylobacter, Cryptosporidium and Giardia) excreted by human and animal sources in a river catchment, 2) generating a hypothesis about the dominant sources of fecal pollution and selecting a source targeted monitoring design, and 3) verifying the results by comparing measured concentrations of the informed choice of parameters (i.e. chemical tracers, C. perfringensspores, and host-associated genetic microbial source tracking (MST) markers) in the river, and by multi-parametric correlation analysis. We tested the approach at a study area in Vienna, Austria. The daily produced microbial particle numbers according to the probabilistic estimates indicated that, for the dry weather scenario, the discharge of treated wastewater (WWTP) was the primary contributor to fecal pollution. For the wet weather scenario, 80-99 % of the daily produced FIBs and pathogens resulted from combined sewer overflows (CSOs) according to the probabilistic estimates. When testing our hypothesis in the river, the measured concentrations of the human genetic fecal marker were log10 4 higher than for selected animal genetic fecal markers. Our analyses showed for the first-time statistical relationships between C. perfringens spores (used as conservative microbial tracer for communal sewage) and a human genetic fecal marker (i.e. HF183/BacR287) with the reference pathogen Giardia in river water (Spearman rank correlation: 0.78-0.83, p < 0.05. The developed approach facilitates urban water safety management and provides a robust basis for microbial fate and transport models and microbial infection risk assessment.

Transport and removal of spores of Bacillus subtilis in an alluvial gravel aquifer at varying flow rates and implications for setback distances.

To guarantee proper protection from fecally transmitted pathogen infections, drinking water wells should have a sufficiently large setback distance from potential sources of contamination, e.g. a nearby river. The aim of this study was to provide insight in regards to microbial contamination of groundwater under different flow velocities, which can vary over time due to changes in river stage, season or pumping rate. The effects of these changes, and how they affect removal parameters, are not completely understood. In this study, field tracer tests were carried out in a sandy gravel aquifer near Vienna, Austria to evaluate the ability of subsurface media to attenuate Bacillus subtilis spores, used as a surrogate for Cryptosporidium and Campylobacter. The hydraulic gradient between injection and extraction was controlled by changing the pumping rate (1, 10 l/s) of a pumping well at the test site, building upon previously published work in which tracer tests with a 5 l/s pumping rate were carried out. Attachment and detachment rate coefficients were determined using a HYDRUS-3D model and ranged from 0.12 to 0.76 and 0-0.0013 h-1, respectively. Setback distances were calculated based on the 60-day travel time, as well as a quantitative microbial risk assessment (QMRA) approach, which showed similar results at this site; around 700 m at the highest pumping rate. Removal rates (λ) in the field tests ranged from 0.2 to 0.3 log/m, with lower pumping rates leading to higher removal. It was shown that scale must be taken into consideration when determining λ for the calculation of safe setback distances.

Corrigendum: Genetic microbial source tracking support QMRA modeling for a riverine wetland drinking water resource.

[This corrects the article DOI: 10.3389/fmicb.2021.668778.].

Genetic Microbial Source Tracking Support QMRA Modeling for a Riverine Wetland Drinking Water Resource.

Riverine wetlands are important natural habitats and contain valuable drinking water resources. The transport of human- and animal-associated fecal pathogens into the surface water bodies poses potential risks to water safety. The aim of this study was to develop a new integrative modeling approach supported by microbial source tracking (MST) markers for quantifying the transport pathways of two important reference pathogens, Cryptosporidium and Giardia, from external (allochthonous) and internal (autochthonous) fecal sources in riverine wetlands considering safe drinking water production. The probabilistic-deterministic model QMRAcatch (v 1.1 python backwater) was modified and extended to account for short-time variations in flow and microbial transport at hourly time steps. As input to the model, we determined the discharge rates, volumes and inundated areas of the backwater channel based on 2-D hydrodynamic flow simulations. To test if we considered all relevant fecal pollution sources and transport pathways, we validated QMRAcatch using measured concentrations of human, ruminant, pig and bird associated MST markers as well as E. coli in a Danube wetland area from 2010 to 2015. For the model validation, we obtained MST marker decay rates in water from the literature, adjusted them within confidence limits, and simulated the MST marker concentrations in the backwater channel, resulting in mean absolute errors of < 0.7 log10 particles/L (Kruskal-Wallis p > 0.05). In the scenarios, we investigated (i) the impact of river discharges into the backwater channel (allochthonous sources), (ii) the resuspension of pathogens from animal fecal deposits in inundated areas, and (iii) the pathogen release from animal fecal deposits after rainfall (autochthonous sources). Autochthonous and allochthonous human and animal sources resulted in mean loads and concentrations of Cryptosporidium and Giardia (oo)cysts in the backwater channel of 3-13 × 109 particles/hour and 0.4-1.2 particles/L during floods and rainfall events, and in required pathogen treatment reductions to achieve safe drinking water of 5.0-6.2 log10. The integrative modeling approach supports the sustainable and proactive drinking water safety management of alluvial backwater areas.

Upscaling Transport of Bacillus subtilis Endospores and Coliphage phiX174 in Heterogeneous Porous Media from the Column to the Field Scale.

Groundwater contamination and transport of viruses and bacteria in aquifers are a major concern worldwide. To ascertain the ability of these aquifers to remove pathogens, tracer tests with microbial surrogates are carried out. These tests are laborious and may require special permits, and therefore, column tests are often done instead. Unfortunately, results from column tests tend to grossly overestimate removal rates when compared to the field scale, which can lead to an underestimation of groundwater contamination risks. Scale is an important consideration when examining pathogen transport through porous media, as pathogen removal is rarely a linear process. In this study, field tests were carried out with endospores of Bacillus subtilis and coliphage phiX174 over a distance of 25 m in an alluvial gravel aquifer near Vienna, Austria. The sandy gravel material from the field site was also used in column tests with the same tracers. Both attachment-detachment and colloid filtration theory were used to model these tests, as well as log-removal rates per meter. The results show that the spatial removal rate (log/m) is approximately 2 orders of magnitude higher on the column scale, when compared to the field. A comparison with the literature showed a correlation between the heterogeneity of the porous media and the difference in removal rates between the column and field scale.

Modelling the interplay of future changes and wastewater management measures on the microbiological river water quality considering safe drinking water production.

Rivers are important for drinking water supply worldwide. However, they are often impacted by pathogen discharges via wastewater treatment plants (WWTP) and combined sewer overflows (CSO). To date, accurate predictions of the effects of future changes and pollution control measures on the microbiological water quality of rivers considering safe drinking water production are hindered due to the uncertainty of the pathogen source and transport variables. The aim of this study was to test an integrative approach for an improved understanding of these effects, i.e. climate change and population growth as well as enhanced treatment at WWTPs and/or prevention of CSOs. We applied a significantly extended version of QMRAcatch (v1.0 Python), a probabilistic-deterministic model that combines fate and transport modelling with quantitative microbial infection risk assessment. The impact of climatic changes until the period 2035-2049 was investigated by a conceptual semi-distributed hydrological model, based on regional climate model outputs. QMRAcatch was calibrated and validated using site- and source-specific data (human-associated genetic microbial source tracking marker and enterovirus). The study showed that the degree to which future changes affect drinking water safety strongly depends on the type and magnitude of faecal pollution sources and are thus highly site- and scenario-specific. For example, if the load of pathogens from WWTPs is reduced through enhanced treatment, climate-change driven increases in CSOs had a considerable impact. Preventing CSOs and installing enhanced treatment at the WWTPs together had the most significant positive effect. The simultaneous consideration of source apportionment and concentrations of reference pathogens, focusing on human-specific viruses (enterovirus, norovirus) and cross-comparison with bacterial and protozoan pathogens (Campylobacter, Cryptosporidium), was found crucial to quantify these effects. While demonstrated here for a large, wastewater-impacted river, the approach is applicable at other catchments and pollution sources. It allows assessing future changes and selecting suitable pollution control measures for long-term water safety planning.

Elucidating fecal pollution patterns in alluvial water resources by linking standard fecal indicator bacteria to river connectivity and genetic microbial source tracking.

A novel concept for fecal pollution analysis was applied at alluvial water resources to substantially extend the information provided by fecal indicator bacteria (FIB). FIB data were linked to river connectivity and genetic microbial source tracking (MST). The concept was demonstrated at the Danube River and its associated backwater area downstream of the city of Vienna, using a comprehensive 3-year data set (10 selected sites, n = 317 samples). Enumeration of Escherichia coli (ISO 16649-2), intestinal enterococci (ISO 7899-2) and Clostridium perfringens (ISO 14189) revealed a patchy distribution for the investigation area. Based on these parameters alone a clear interpretation of the observed fecal contamination patterns was not possible. Comparison of FIB concentrations to river connectivity allowed defining sites with dominating versus rare fecal pollution influence from the River Danube. A strong connectivity gradient at the selected backwater sites became obvious by 2D hydrodynamic surface water modeling, ranging from 278 days (25%) down to 5 days (<1%) of hydraulic connectivity to the River Danube within the 3-year study period. Human sewage pollution could be identified as the dominating fecal source at the highly connected sites by adding information from MST analysis. In contrast, animal fecal pollution proofed to be dominating in areas with low river connectivity. The selection of genetic MST markers was focusing on potentially important pollution sources in the backwater area, using human (BacHum, HF183II), ruminant (BacR) and pig (Pig2Bac) -associated quantitative PCR assays. The presented approach is assumed to be useful to characterize alluvial water resources for water safety management throughout the globe, by allocating fecal pollution to autochthonous, allochthonous, human or animal contamination components. The established river connectivity metric is not limited to bacterial fecal pollution, but can be applied to any type of chemical and microbiological contamination.

Spatiotemporal resolved sampling for the interpretation of micropollutant removal during riverbank filtration.

Riverbank filtration (RBF) systems along rivers are widely used as public water supplies. In these systems, many organic micropollutants (OMPs) are attenuated, but some compounds have shown to be rather persistent. Their fate and transport has been studied in RBF sites along lakes and small rivers, but not extensively along large and dynamic rivers. Therefore, the influence of flood events on OMP behavior in these large and dynamic RBF sites was investigated. Monthly samples were taken from surface- and groundwater up to a distance of 900 m from the riverbank of the Danube from March 2014 till May 2016. Two flood events were sampled more extensively nearby the river. Results showed that changes in flow conditions in the river not only caused changes in OMP concentrations, but also in their load. It was seen that the load of benzotriazole, carbamazepine and sulfamethoxazole in the river increased with increasing river discharges. After a relatively long, oxic groundwater passage, several OMPs were reduced. In contrast to previous work, we found that benzotriazole was almost fully removed under oxic conditions. When entering the aquifer, benzotriazole concentrations were significantly reduced and at a distance of 550 m from the river, >97% was degraded. Carbamazepine and sulfamethoxazole showed relatively persistent behavior in the aquifer. The concentrations measured during flood events were in the same range as seasonal sampling. Furthermore concentrations in the groundwater were higher during these events than in the Danube and can reach further into the aquifer. During flood events some highly degradable compounds (i.e. diclofenac) were found up to a distance of 24 m from the river. These results implied that drinking water utilities with RBF wells in oxic, alluvial aquifers located close to highly dynamic rivers need to consider a potential reduction in groundwater quality during and directly after flood events.

Poikilothermic Animals as a Previously Unrecognized Source of Fecal Indicator Bacteria in a Backwater Ecosystem of a Large River.

Quantitative information regarding the presence of Escherichia coli, intestinal enterococci, and Clostridium perfringens in poikilotherms is notably scarce. Therefore, this study was designed to allow a systematic comparison of the occurrence of these standard fecal indicator bacteria (SFIB) in the excreta of wild homeothermic (ruminants, boars, carnivores, and birds) and poikilothermic (earthworms, gastropods, frogs, and fish) animals inhabiting an alluvial backwater area in eastern Austria. With the exception of earthworms, the average concentrations of E. coli and enterococci in the excreta of poikilotherms were equal to or only slightly lower than those observed in homeothermic excreta and were 1 to 4 orders of magnitude higher than the levels observed in the ambient soils and sediments. Enterococci reached extraordinarily high concentrations in gastropods. Additional estimates of the daily excreted SFIB (E. coli and enterococcus) loads (DESL) further supported the importance of poikilotherms as potential pollution sources. The newly established DESL metric also allowed comparison to the standing stock of SFIB in the sediment and soil of the investigated area. In agreement with its biological characteristics, the highest concentrations of C. perfringens were observed in carnivores. In conclusion, the long-standing hypothesis that only humans and homeothermic animals are primary sources of SFIB is challenged by the results of this study. It may be necessary to extend the fecal indicator concept by additionally considering poikilotherms as potential important primary habitats of SFIB. Further studies in other geographical areas are needed to evaluate the general significance of our results. We hypothesize that the importance of poikilotherms as sources of SFIB is strongly correlated with the ambient temperature and would therefore be of increased significance in subtropical and tropical habitats and water resources.IMPORTANCE The current fecal indicator concept is based on the assumption that the standard fecal indicator bacteria (SFIB) Escherichia coli, intestinal enterococci, and Clostridium perfringens multiply significantly only in the guts of humans and other homeothermic animals and can therefore indicate fecal pollution and the potential presence of pathogens from those groups. The findings of the present study showed that SFIB can also occur in high concentrations in poikilothermic animals (i.e., animals with body temperatures that vary with the ambient environmental temperature, such as fish, frogs, and snails) in an alluvial backwater area in a temperate region, indicating that a reconsideration of this long-standing indicator paradigm is needed. This study suggests that poikilotherms must be considered to be potential primary sources of SFIB in future studies.

Global Distribution of Human-Associated Fecal Genetic Markers in Reference Samples from Six Continents.

Numerous bacterial genetic markers are available for the molecular detection of human sources of fecal pollution in environmental waters. However, widespread application is hindered by a lack of knowledge regarding geographical stability, limiting implementation to a small number of well-characterized regions. This study investigates the geographic distribution of five human-associated genetic markers (HF183/BFDrev, HF183/BacR287, BacHum-UCD, BacH, and Lachno2) in municipal wastewaters (raw and treated) from 29 urban and rural wastewater treatment plants (750-4 400 000 population equivalents) from 13 countries spanning six continents. In addition, genetic markers were tested against 280 human and nonhuman fecal samples from domesticated, agricultural and wild animal sources. Findings revealed that all genetic markers are present in consistently high concentrations in raw (median log10 7.2-8.0 marker equivalents (ME) 100 mL-1) and biologically treated wastewater samples (median log10 4.6-6.0 ME 100 mL-1) regardless of location and population. The false positive rates of the various markers in nonhuman fecal samples ranged from 5% to 47%. Results suggest that several genetic markers have considerable potential for measuring human-associated contamination in polluted environmental waters. This will be helpful in water quality monitoring, pollution modeling and health risk assessment (as demonstrated by QMRAcatch) to guide target-oriented water safety management across the globe.

QMRAcatch: Human-Associated Fecal Pollution and Infection Risk Modeling for a River/Floodplain Environment.

Protection of drinking water resources requires addressing all relevant fecal pollution sources in the considered catchment. A freely available simulation tool, QMRAcatch, was recently developed to simulate concentrations of fecal indicators, a genetic microbial source tracking (MST) marker, and intestinal pathogens in water resources and to conduct a quantitative microbial risk assessment (QMRA). At the same time, QMRAcatch was successfully applied to a region of the Danube River in Austria, focusing on municipal wastewater emissions. Herein, we describe extension of its application to a Danube River floodplain, keeping the focus on fecal sources of human origin. QMRAcatch was calibrated to match measured human-associated MST marker concentrations for a dry year and a wet year. Appropriate performance characteristics of the human-associated MST assay were proven by simulating correct and false-positive marker concentrations, as determined in human and animal feces. With the calibrated tool, simulated and measured enterovirus concentrations in the rivers were compared. Finally, the calibrated tool allowed demonstrating that 4.5 log enterovirus and 6.6 log norovirus reductions must be achieved to convert current surface water to safe drinking water that complies with a health-based target of 10 infections person yr. Simulations of the low- and high-pollution scenarios showed that the required viral reductions ranged from 0 to 8 log. This study has implications for water managers with interests in assessing robust catchment protection measures and water treatment criteria by considering the fate of fecal pollution from its sources to the point of abstraction.

QMRAcatch: Microbial Quality Simulation of Water Resources including Infection Risk Assessment.

Given the complex hydrologic dynamics of water catchments and conflicts between nature protection and public water supply, models may help to understand catchment dynamics and evaluate contamination scenarios and may support best environmental practices and water safety management. A catchment model can be an educative tool for investigating water quality and for communication between parties with different interests in the catchment. This article introduces an interactive computational tool, QMRAcatch, that was developed to simulate concentrations in water resources of , a human-associated microbial source tracking (MST) marker, enterovirus, norovirus, , and as target microorganisms and viruses (TMVs). The model domain encompasses a main river with wastewater discharges and a floodplain with a floodplain river. Diffuse agricultural sources of TMVs that discharge into the main river are not included in this stage of development. The floodplain river is fed by the main river and may flood the plain. Discharged TMVs in the river are subject to dilution and temperature-dependent degradation. River travel times are calculated using the Manning-Gauckler-Strickler formula. Fecal deposits from wildlife, birds, and visitors in the floodplain are resuspended in flood water, runoff to the floodplain river, or infiltrate groundwater. Fecal indicator and MST marker data facilitate calibration. Infection risks from exposure to the pathogenic TMVs by swimming or drinking water consumption are calculated, and the required pathogen removal by treatment to meet a health-based quality target can be determined. Applicability of QMRAcatch is demonstrated by calibrating the tool for a study site at the River Danube near Vienna, Austria, using field TMV data, including a sensitivity analysis and evaluation of the model outcomes.