Microbial Source Tracking Techniques Research Articles

Applying Microbial Source Tracking Techniques for Identification of Pathways of Faecal Pollution from Water Sources to Point of Use in Vhembe District, South Africa

A safe water supply is a necessity, but it remains one of the backlogs of services rendered in rural areas of developing countries. This leads to vulnerable communities using water from available sources that is unsafe as it is contaminated with faecal matter. Microbial source tracking (MST) methods are gold-standard techniques that detect the exact sources of faecal contamination. This study, therefore, tracked and identified the exact sources of faecal contamination from the catchment to the point of use in rural areas of Vhembe District Municipality. Collected water samples (n = 1048) were concentrated by membrane filtration for the enumeration and detection of E. coli, followed by DNA extraction. The extracted DNA was subjected to a quantitative polymerase chain reaction (qPCR) to track target host-specific Bacteroidales genetic markers from the water source to the point of use. Rivers and dams exhibited maximum E. coli counts of up to 90 CFU/100 mL during the wet season and up to 50 CFU/100 mL during the dry season. Due to the effective treatment of these water sources, no E. coli bacteria were detected in any of the sampled municipal drinking water treatment plants at the point of treatment, while this indicator bacterium was detected at the point of use (households), with a maximum of 4 CFU/100 mL recorded during both the wet and dry seasons. Overall, the most prevalent MST marker exhibited during the wet season was BacCan (dog-associated, 6.87%), followed by BacCow (cow-associated, 5.53%), while Pig-2-Bac (pig-associated, 2.48%) was the least prevalent. The most prevalent marker exhibited during the dry season was BacCan (5.34%), followed by BacCow, with Pig-2-Bac (1.72%) being the least prevalent. A positive correlation (r = 0.31, p = 0.001) was established between the presence of the MST markers and detected E. coli from water sources to the point of use. The knowledge of the faecal contamination attributes in both public and domestic domains will assist in developing prevention and control strategies.

Seems fishy: environmental DNA impacts on sketa22 quality control in salmonidae dominated waterbodies using qPCR and ddPCR

Globally, water resources used for recreation and drinking water are threatened by fecal pollution. These pollutants can cause gastrointestinal illness and environmental degradation. Additionally, most sources of fecal pollution are non-point sources stemming from multiple species. Identifying these sources is vital to categorizing the exposure risk from contact and improving remediation efforts. A common technique to provide species-specific information for fecal source identification is microbial source tracking (MST). MST quantifies DNA of host or host-associated microorganisms through polymerase chain reaction (PCR) technologies such as quantitative PCR (qPCR) or droplet digital PCR (ddPCR). MST techniques have been implemented globally and are used for routine monitoring. In the United States (US), the US Environmental Protection Agency has provided several approved standard PCR methods for MST and other recreational water quality applications. These methods have specified quality controls including sample processing controls (SPC) and assessments for sample inhibition. A standard SPC used in EPA methods involves spiking samples with salmon testes DNA (nominally originating from Chum Salmon, Oncorhynchus keta and quantifying them using Sketa22, a genus specific TaqManTM assay). This quality control (QC) behaves similarly to the microbial species being monitored. MST testing in Fall 2022 indicated elevated Sketa22 recoveries and re-analysis of samples indicated the detection of external Salmonidae DNA on both qPCR and ddPCR platforms. Our research was designed to identify the cause of this interference. Results indicate that the primer probe set may react with wild Salmonidae DNA. Analyzing the Sketa22 sequence using BLAST indicated matches with many species of Salmonidae present in the sampled stream system. Consequently, further research is required to identify the effectiveness of Sketa22 as a QC when native and migratory Salmonidae are present. General recommendations are provided to account for excess ambient Salmonidae DNA.

Investigation of relationships between fecal contamination, cattle grazing, human recreation, and microbial source tracking markers in a mixed-land-use rangeland watershed

The United States National Forests are mixed-use lands that support human recreation and cattle grazing. Overuse by humans or cattle, however, can lead to the fecal contamination of local waterways. Until recently, the source of these contaminants was a subject of conjecture; however, microbial source tracking tools have become widely used and are proving to be a valid methodology to identify the contamination source. This study aims to analyze and model the quantity and sources of fecal contamination in the Mink Creek watershed in southeastern Idaho. The U.S. Forest Service Caribou-Targhee National Forest (USFS) manages this watershed. Previous research has indicated that some localities within the watershed exceed US EPA standards for coliform bacteria. In 2019, water samples were collected before livestock began grazing and throughout the spring, summer, and fall after livestock grazing had ended. Fourteen sites were sampled seven times during the field season, allowing the water to be analyzed for total coliforms and E. coli bacteria. Microbial source tracking techniques using Bacteroides bacteria, which are known to live in specific digestive tracks, were used to identify the source of E. coli at each sampling location. The analysis indicated that E. coli counts exceeded state regulatory limits 35% of the time. These exceedances were associated with DNA source tracking markers for humans (58.8%), cattle (5.9%), or both cattle and humans (5.9%). Unknown sources were responsible for the Bacteroides bacteria 29.4% of the time. A statistical model was developed to estimate E. coli using the datasets of microbial source tracking measures, the presence or absence of humans, cattle, the proximity of the sampling date to a holiday, and other seasonal factors. The resulting model showed good performance indices at all the 14 sites based on a K-fold cross-validation scheme (R2 = 0.83 and NSE = 0.69). The results demonstrated that E. coli exceedances have a close association with human recreation and unknown sources and negatively influenced by dissolved oxygen.

Tracking Major Sources of Water Contamination Using Machine Learning.

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

Evaluating Microbial Water Quality and Potential Sources of Fecal Contamination in the Musconetcong River Watershed in New Jersey, USA

Microbial pathogens and indicators have contributed to major part of water quality degradation in the United States. Located in the northwestern New Jersey, the Musconetcong River has been included in the New Jersey Impaired Waters List or the 303(d) List due to high concentrations of fecal indicator bacteria. Hence, a Total Maximum Daily Load plan was established to address microbial water quality issues in the watershed. The objectives of this study were to assess the current status of microbial water quality and to determine potential sources of fecal contamination in the Musconetcong River Watershed using microbial source tracking techniques. Fifteen sampling events in total were carried out at nine sites throughout the Musconetcong River Watershed in August 2016, July and August 2017. E. coli enumeration was performed to determine the possible presence of fecal contaminations. Microbial source tracking techniques, specifically Canada goose, cow, deer, horse, and human-specific molecular markers, were used for real-time polymerase chain reaction (qPCR) analysis in order to identify and quantify potential sources of fecal contamination. The results indicated that E. coli was found present at all nine study sites. Two of the nine sites violated the New Jersey Surface Water Quality Standards in August 2016, while all of the nine sites exceeded the standards in both July and August 2017. Water temperature, dissolved oxygen (DO), and specific conductance at the study sites ranged from 13.5˚C to 25.3˚C, from 7.7 mg/L to 13.0 mg/L, and 278.5 μS/cm to 1335.0 μS/cm, respectively, at the time of sample collection. E. coli counts were found to be negatively correlated with temperature and specific conductance (p p < 0.05). Higher percentage of presence of human, Canada goose and deer markers were observed at all fifteen sampling events indicating human and wildlife were the two major sources of fecal contaminations in the Musconetcong River Watershed. The study suggested applying restoration measures to reduce fecal contaminations from anthropogenic and wildlife sources in order to improve microbial water quality of the Musconetcong River. However, more frequent and strategic sampling plan is recommended to supply more comprehensive data to aid in future planning of best management efforts on controlling fecal contaminations.

Microbial Source Tracking Using Quantitative and Digital PCR To Identify Sources of Fecal Contamination in Stormwater, River Water, and Beach Water in a Great Lakes Area of Concern.

Areas of concern (AOCs) around the Great Lakes are characterized by historic and ongoing problems with microbial water quality, leading to beneficial use impairments (BUIs) such as beach postings and closures. In this study, we assessed river and beach sites within the Rouge River watershed, associated stormwater outfalls, and at Rouge Beach. The concentrations of Escherichia coli as well as human- and gull-specific qPCR microbial source tracking (MST) markers were assessed at all sites. A preliminary comparison of digital PCR (dPCR) methodologies for both MST markers was conducted regarding sensitivity and specificity. Within the watershed, the outfalls were found to be a prominent source of human fecal contamination, with two outfalls particularly affected by sewage cross-connections. However, the occurrence of human fecal contamination along Rouge Beach and in the lower portions of the watershed was largely dependent on rain events. Gull fecal contamination was the predominant source of contamination at the beach, particularly during dry weather. The multiplex human/gull dPCR methodology used in this study tended to be more sensitive than the individual quantitative PCR (qPCR) assays, with only a slight decrease in specificity. Both dPCR and qPCR methodologies identified the same predominance of human and gull markers in stormwater and beach locations, respectively; however, the dPCR multiplex assay was more sensitive and capable of detecting fecal contamination that was undetected by qPCR assays. These results demonstrate the dPCR assay used in this study could be a viable tool for MST studies to increase the ability to identify low levels of fecal contamination.IMPORTANCE Fecal contamination of recreational water poses a persistent and ongoing problem, particularly in areas of concern around the Great Lakes. The identification of the source(s) of fecal contamination is essential for safeguarding public health as well as guiding remediation efforts; however, fecal contamination may frequently be present at low levels and remain undetectable by certain methodologies. In this study, we utilized microbial source tracking techniques using both quantitative and digital PCR assays to identify sources of contamination. Our results indicated high levels of human fecal contamination within stormwater outfalls, while lower levels were observed throughout the watershed. Additionally, high levels of gull fecal contamination were detected at Rouge Beach, particularly during drier sampling events. Furthermore, our results indicated an increased sensitivity of the digital PCR assay to detect both human and gull contamination, suggesting it could be a viable tool for future microbial source tracking studies.

Linking landscape patterns to sources of water contamination: Implications for tracking fecal contaminants with geospatial and Bayesian approaches

Microbial source tracking (MST) techniques have been designed to identify the host source of fecal contamination in water. However, current MST techniques cannot provide geographic origins of particular sources because they do not provide any spatial information beyond the points of observation. In this study, the associations between landscape patterns and the major sources of microbial contamination were examined and the application of geospatial techniques (e.g., remote sensing and geographic information systems) and Bayesian modeling was explored to track microbial sources over the landscape. The land cover information of three watersheds (the lower Dungeness Watershed, the Middle Rio Grande Watershed, and the Arroyo Burro Watershed) in the United States was obtained either by classifying high resolution satellite images or directly using land cover datasets (e.g., National Land Cover Dataset, 2006 and 2011). Then, the relationship between land use/land cover (LULC) and microbial sources from these three geographically disparate watersheds were analyzed using Bayesian hierarchical models. The results showed the predictive positive associations between human sources of fecal contamination and developed area, between dog sources and grassland, and between bird sources and water, but negative associations between human sources and forest and water areas. Furthermore, the diversity of microbial sources had positive associations with landscape fragmentation and diversity indices. This study demonstrates associations between landscape patterns and major microbial sources and offers new insight in tracking the dominant sources of fecal contamination in water using geospatial and Bayesian techniques.

Remediation of a Beneficial Use Impairment at Bluffer’s Park Beach in the Toronto Area of Concern

Bluffer’s Park Beach in the Toronto and Region Area of Concern had a history of beach postings often exceeding 80% of the beach season since the 1980s. A study applied expanded E. coli surveillance and microbial source tracking techniques in 2005–2007 to identify fecal pollution sources contributing to beach postings. Expanded surveillance in the beach vicinity identified significant E. coli hotspots in the foreshore beach sand (pore water max E. coli = 255,000 CFU 100 ml−1) and associated with a marsh inland of the beach. During rain events, streams from the marsh (max E. coli = 173,000 CFU 100 ml−1) and runoff from the parking lot (max E. coli = 4100 CFU 100 ml−1) were observed to overflow across the beach to contaminate beach waters. Microbial source tracking using library-dependent (antibiotic resistance and rep-PCR DNA fingerprinting of E. coli isolates) and library-independent (human HF183 bacterial DNA marker) methods indicated the prevalence of animal fecal pollution sources at the beach rather than human sewage. These results were consistent with sanitary survey information, observations of wildlife in the marsh area, and Gulls and Canada Geese on the beach. In 2006, a bird management program was initiated, and remedial actions continued in advance of the 2008 bathing season to engineer a berm to prevent marsh runoff into beach water and re-direct parking lot drainage into the marsh. Since these remediation actions, Bluffer’s Park Beach has been posted less than 20% of each beach season, and it was awarded a Blue Flag accreditation in 2011.

ENVIRONMENTAL ASSESSMENT OF BACTERIAL FAECAL POLLUTION MARKERS IN OVERLYING WATERS FROM AN EARTHEN FISH POND

This study was carried out to examine the sanitary quality of overlying waters in an earthen pond containing African catfish (Clarias gariepinus) in both wet and dry seasons. Water samples were collected twice weekly at different times of the day (i.e., morning and afternoon) from the earthen pond in Akure, Nigeria in August, September 2016, and February, March 2017 (n=48). Levels of faecal indicator bacteria were determined using membrane filtration method and physicochemical properties (such as temperature, pH, salinity etc.) of the overlying water samples were determined using standard methods. The results revealed that the concentration of E. coli in the overlying waters ranged from 0.78 to 1.40 Log10 CFU 100 ml-1 in morning samples and 0.85 to 1.38 Log10 CFU 100 ml-1 in afternoon samples while the concentration of faecal coliforms in the overlying waters ranged from 1.30 to 1.86 Log10 CFU 100 ml-1 in morning samples and 1.26 to 1.88 Log10 CFU 100 ml-1 in afternoon samples. Faecal coliforms showed the highest prevalence in both seasons compared to E. coli and intestinal enterococci and their levels were greater in the wet periods than the dry periods. The findings from this study suggest that the sources of faecal contamination of the overlying waters in the earthen pond originate mainly from non-human sources and the adoption of microbial source tracking techniques would further elucidate the appropriate management and mitigation strategies to control faecal contamination of the overlying waters in the earthen pond especially for human health protection.

Tracking the downstream impacts of inadequate sanitation in central Appalachia.

Poor sanitation in rural infrastructure is often associated with high levels of fecal contamination in adjacent surface waters, which presents a community health risk. Although microbial source tracking techniques have been widely applied to identify primary remediation needs in urban and/or recreational waters, use of human-specific markers has been more limited in rural watersheds. This study quantified the human source tracking marker Bacteroides-HF183, along with more general fecal indicators (i.e. culturable Escherichia coli and a molecular Enterococcus marker), in two Appalachian streams above and below known discharges of untreated household waste. Although E. coli and Enterococcus were consistently recovered in samples collected from both streams, Bacteroides-HF183 was only detected sporadically in one stream. Multiple linear regression analysis demonstrated a positive correlation between the concentration of E. coli and the proximity and number of known waste discharge points upstream; this correlation was not significant with respect to Bacteroides-HF183, likely due to the low number of quantifiable samples. These findings suggest that, while the application of more advanced source targeting strategies can be useful in confirming the influence of substandard sanitation on surface waters to justify infrastructure improvements, they may be of limited use without concurrent traditional monitoring targets and on-the-ground sanitation surveys.

Multiple lines of evidence to identify sewage as the cause of water quality impairment in an urbanized tropical watershed

Indicator bacteria, which are conventionally used to evaluate recreational water quality, can originate from various non-human enteric and extra-enteric sources, hence they may not be indicative of human health risk nor do they provide information on the sources of contamination. In this study we utilized traditional (enterococci and Escherichia coli) and alternative (Clostridium perfringens) indicator bacteria, F+-specific coliphage, molecular markers for microorganisms associated with human sewage (human-associated Bacteroides and polyomaviruses), and microbial community analysis tools (16S rRNA gene fragment amplicon sequencing), to identify and evaluate human sewage-related impact in the Manoa watershed in Honolulu, Hawaii. Elevated concentrations of enterococci (geometric mean ranging from 1604 to 2575 CFU 100 mL−1) and C. perfringens (45–77 CFU 100 mL−1) indicated impairment of the urbanized section of the stream, while indicator bacteria concentrations decreased downstream in the tidally influenced Ala Wai Canal. The threshold values triggering water quality violation notifications in Hawaii were exceeded in 33.3–75.0% of samples collected at sites in the urbanized section of Manoa Stream, but were not exceeded in any of the samples collected at an upstream site located in a forested area. Correlation between indicator bacteria concentrations and rainfall amounts was weak to moderate but significant (E. coli R = 0.251, P = 0.009; enterococci R = 0.369, P < 0.001; C. perfringens R = 0.343, P < 0.001), while concentrations of human fecal-associated molecular markers were not significantly correlated with rainfall (human-associated Bacteroides, R = 0.131, P = 0.256; human-associated polyomaviruses, R = 0.213, P = 0.464). Presence of human sewage was confirmed by detection of human-associated Bacteroides and human polyomavirus in the urbanized section of Manoa Stream (83.3–100% and 41.7–66.7% positive samples respectively). It was further confirmed by microbial community analyses which suggested that an average 2.4–3.4% of the total bacterial population in this section was associated with sewage. Microbial community profiles were significantly influenced by rainfall (R2 = 0.4390, P < 0.001), pH (R2 = 0.3077, P = 0.006), salinity (R2 = 0.2614, P = 0.038), and conductivity (R2 = 0.2676, P = 0.031). Although microbial diversity fluctuated throughout the watershed, it was lower in the impaired section. Leaking sewer systems and illegal cross-connections are implicated in the impairment of the watershed, hence both the sewer and the storm water lines should be routinely inspected. Collectively, our data suggest that information derived from the analysis of microbial communities complements current marker-based microbial source tracking techniques and environmental monitoring programs.

The impact of point source pollution on shallow groundwater used for human consumption in a threshold country

Many developing and threshold countries rely on shallow groundwater wells for their water supply whilst pit latrines are used for sanitation. We employed a unified strategy involving satellite images and environmental monitoring of 16 physico-chemical and microbiological water quality parameters to identify significant land uses that can lead to unacceptable deterioration of source water, in a region with a subtropical climate and seasonally restricted torrential rainfall in Northern Argentina. Agricultural and non-agricultural sources of nitrate were illustrated in satellite images and used to assess the organic load discharged. The estimated human organic load per year was 28.5 BOD(5) tons and the N load was 7.5 tons, while for poultry farms it was 9940-BOD(5) tons and 1037-N tons, respectively. Concentrations of nitrates and organics were significantly different between seasons in well water (p values of 0.026 and 0.039, respectively). The onset of the wet season had an extraordinarily negative impact on well water due in part to the high permeability of soils made up of fine gravels and coarse sand. Discriminant analysis showed that land uses had a pronounced seasonal influence on nitrates and introduced additional microbial contamination, causing nitrification and denitrification in shallow groundwater. P-well was highly impacted by a poultry farm while S-well was affected by anthropogenic pollution and background load, as revealed by Principal Component Analysis. The application of microbial source tracking techniques is recommended to corroborate local sources of human versus animal origin.

Evaluating the operational utility of a Bacteroidales quantitative PCR-based MST approach in determining the source of faecal indicator organisms at a UK bathing water

Microbial source tracking techniques are used in the UK to provide an evidence-base to guide major expenditure decisions and/or regulatory action relating to sewage disposal. Consequently, it is imperative that the techniques used robustly index faecal indicator organisms (FIOs) that are the regulatory parameters for bathing and shellfish harvesting areas. This study reports a ‘field-scale’ test of microbial source tracking (MST) based on the quantitative PCR analyses of Bacteroidales 16S rRNA genetic marker sequences. The project acquired data to test the operational utility of quantitative Bacteroidales MST data, comparing it with FIO concentrations in streams, effluents and bathing waters. Overall, the data did not exhibit a consistent pattern of significant correlations between Bacteroidales MST parameters and FIOs within the different sample matrices (i.e. rivers, bathing waters and/or effluents). Consequently, there was little evidence from this study that reported concentrations and/or percentages of human and/or ruminant faecal loadings (that are based on Bacteroidales MST gene copy numbers) offer a credible evidence-base describing FIO contributions to receiving water ‘non-compliance’. The study also showed (i) there was no significant attenuation of the Bacteroidales gene copy number ‘signal’ through the UV disinfection process; and (ii) single non-compliant samples submitted for Bacteroidales MST analysis, do not reliably characterise the balance of faecal loadings due to the high variability in the MST signal observed. At this stage in the development of the MST tool deployed, it would be imprudent to use the percentage human and/or ruminant contributions (i.e. as indicated by MST data acquired at a bathing water) as the sole or principal element in the evidence-base used to guide major expenditure decisions and/or regulatory action.

Microbial source tracking: a forensic technique for microbial source identification?

As the requirements of the Water Framework Directive (WFD) and the US Clean Water Act (USCWA) for the maintenance of microbiological water quality in 'protected areas' highlight, there is a growing recognition that integrated management of point and diffuse sources of microbial pollution is essential. New information on catchment microbial dynamics and, in particular, the sources of faecal indicator bacteria found in bathing and shellfish harvesting waters is a pre-requisite for the design of any 'programme of measures' at the drainage basin scale to secure and maintain compliance with existing and new health-based microbiological standards. This paper reports on a catchment-scale microbial source tracking (MST) study in the Leven Estuary drainage basin, northwest England, an area for which quantitative faecal indicator source apportionment empirical data and land use information were also collected. Since previous MST studies have been based on laboratory trials using 'manufactured' samples or analyses of spot environmental samples without the contextual microbial flux data (under high and low flow conditions) and source information, such background data are needed to evaluate the utility of MST in USCWA total maximum daily load (TMDL) assessments or WFD 'Programmes of Measures'. Thus, the operational utility of MST remains in some doubt. The results of this investigation, using genotyping of Bacteroidetes using polymerase chain reaction (PCR) and male-specific ribonucleic acid coliphage (F + RNA coliphage) using hybridisation, suggest some discrimination is possible between livestock- and human-derived faecal indicator concentrations but, in inter-grade areas, the degree to which the tracer picture reflected the land use pattern and probable faecal indicator loading were less distinct. Interestingly, the MST data was more reliable on high flow samples when much of the faecal indicator flux from catchment systems occurs. Whilst a useful supplementary tool, the MST information did not provide quantitative source apportionment for the study catchment. Thus, it could not replace detailed empirical measurement of microbial flux at key catchment outlets to underpin faecal indicator source apportionment. Therefore, the MST techniques reported herein currently may not meet the standards required to be a useful forensic tool, although continued development of the methods and further catchment scale studies could increase confidence in such methods for future application.

Locating Escherichia coli contamination in a rural South Carolina watershed

One of the problems associated with the use of ambient water quality standards in surface water regulation is the difficulty of identifying and regulating nonpoint source pollution, making such standards unenforceable, especially at the local level. We used the Escherichia coli indicator to locate the most contaminated reaches in rural South Carolina's Bush River watershed (297 km 2, 186 stream-km). We divided the watershed into 20 smaller reaches and sampled each reach multiple times, but restricted each sampling round to one day. We located four low order creek reaches, representing just nine stream-km, where we observed geometric mean E. coli densities of over 1250 E. coli/100 mL; in each case, the source of the contamination (riparian grazing of cattle) was easily identifiable. On the Bush River itself, we observed a step change in one reach where geometric means increased from 106 E. coli/100 mL to 565 E. coli/100 mL over the reach's 10 km length. In this case, the sources of contamination were not as obvious as in the lower order streams; in this case, more advanced Microbial Source Tracking techniques will be required to identify the sources. Nevertheless, this sampling protocol helped locate polluted reaches and provided decision-makers with reasonable justifications for concrete action in deciding where (or where not) to install conservation practices and where more sophisticated (and expensive) MST techniques were warranted.