Fecal pollution source characterization at non-point source impacted beaches under dry and wet weather conditions

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under 'wet' and 'dry' conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

Quantitative fecal source characterization of urban municipal storm sewer system outfall 'wet' and 'dry' weather discharges

Urban areas are built environments containing substantial amounts of impervious surfaces (e.g., streets, sidewalks, roof tops). These areas often include elaborately engineered drainage networks designed to collect, transport, and discharge untreated stormwater into local surface waters. When left uncontrolled, these discharges may contain unsafe levels of fecal waste from sources such as sanitary sewage and wildlife even under dry weather conditions. This study evaluates paired measurements of host-associated genetic markers (log10 copies per reaction) indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), canine (DG3), and avian (GFD) fecal sources, 12-hour cumulative precipitation (mm), four catchment land use metrics determined by global information system (GIS) mapping, and Escherichia coli (MPN/100 ml) from seven municipal separate storm sewer system outfall locations situated at the southern portion of the Anacostia River Watershed (District of Columbia, U.S.A.). A total of 231 discharge samples were collected twice per month (n = 24 sampling days) and after rain events (n = 9) over a 13-month period. Approximately 50 % of samples (n = 116) were impaired, exceeding the local E. coli single sample maximum of 2.613 log10 MPN/100 ml. Genetic quality controls indicated the absence of amplification inhibition in 97.8 % of samples, however 14.7 % (n = 34) samples showed bias in DNA recovery. Of eligible samples, quantifiable levels were observed for avian (84.1 %), human (57.4 % for HF183/BacR287 and 40 % for HumM2), canine (46.7 %), and ruminant (15.9 %) host-associated genetic markers. Potential links between paired measurements are explored with a recently developed Bayesian qPCR censored data analysis approach. Findings indicate that human, pet, and urban wildlife all contribute to storm outfall discharge water quality in the District of Columbia, but pollutant source contributions vary based on 'wet' and 'dry' conditions and catchment land use, demonstrating that genetic-based fecal source identification methods combined with GIS land use mapping can complement routine E. coli monitoring to improve stormwater management in urban areas.

A fecal score approximation model for analysis of real-time quantitative PCR fecal source identification measurements

Numerous qPCR-based methods are available to estimate the concentration of fecal pollution sources in surface waters. However, qPCR fecal source identification data sets often include a high proportion of non-detections (reactions failing to attain a prespecified minimal signal intensity for detection) and measurements below the assay lower limit of quantification (minimal signal intensity required to estimate target concentration), making it challenging to interpret results in a quantitative manner while accounting for error. In response, a Bayesian statistic based Fecal Score (FS) approach was developed that estimates the weighted average concentration of a fecal source identification genetic marker across a defined group of samples, mathematically incorporating qPCR measurements from all samples. Yet, implementation is technically demanding and computationally intensive requiring specialized training, the use of expert software, and access to high performance computing. To address these limitations, this study reports a novel approximation model for FS determination based on a frequentist approach. The performance of the Bayesian and Frequentist models are compared using fecal source identification qPCR data representative of different 'censored' data scenarios from a recently published study focusing on the impact of stormwater discharge in urban streams. In addition, data set eligibility recommendations for the responsible use of these models are presented. Findings indicate that the Frequentist model can generate similar average concentrations and uncertainty estimates for FS, compared to the original Bayesian approach. The Frequentist model should make calculations less computationally and technically intensive, allowing for the development of easier to use data analysis tools for fecal source identification applications.

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Integrating fecal pollution markers and fluorescence analysis for water quality assessment of urban river

Human fecal contamination in urban rivers poses significant health risks, but their potential connections with other substances like dissolved organic matter (DOM) remain underexplored. In this study, five fecal pollution markers related to fecal Bacteroides or human fecal contamination (AllBac, HF183, BacH, Hum2, and Hum163) and DOM along an urban river were analyzed using quantitative polymerase chain reaction (qPCR) and three-dimensional excitation-emission (3D EEM) fluorescence spectrometry. All five markers were detected with average absolute abundance ranging from 2.51 to 6.28 lg gene copies/100 mL, showing a progressive increase along the river (R2 = 0.29-0.92, p < 0.05). Parallel factor analysis identified three dominant DOM components (humic acid-like, fulvic acid-like, and protein-like), with strong positive correlations between protein-like components and all fecal markers (R2 = 0.59-0.66, p < 0.001). Both fecal and DOM distributions consistently showed significant differences between upstream and downstream areas (p < 0.001), suggesting their complementary assessment. While DOM was more sensitive to environmental variables such as rainfall, rubber dam, and tidal dynamic, the combination of fecal pollution markers and 3D EEM analysis allowed a more comprehensive assessment of contamination levels, mitigating potential biases caused by the influence of multiple factors on a single method. Furthermore, due to the strong correlation between protein-like and fecal markers in the DOM, 3D EEM can be used as a pre-detection means for qPCR detection, reducing testing time and costs.

Meta-analysis of microbial source tracking for the identification of fecal contamination in aquatic environments based on data-mining

Microbial source tracking (MST) technology represents an innovative approach employed to trace fecal contamination in environmental water systems. The performance of primers may be affected by amplification techniques, target primer categories, and regional differences. To investigate the influence of these factors on primer recognition performance, a meta-analysis was conducted on the application of MST in water environments using three databases: Web of Science, Scopus, and PubMed (n = 2291). After data screening, 46 studies were included in the final analysis. The investigation encompassed Polymerase Chain Reaction (PCR)/quantitative PCR (qPCR) methodologies, dye-based (SYBR)/probe-based (TaqMan) techniques, and geographical differences of a human host-specific (HF183) primer and other 21 additional primers. The results indicated that the primers analyzed were capable of differentiating host specificity to a certain degree. Nonetheless, by comparing sensitivity and specificity outcomes, it was observed that virus-based primers exhibited superior specificity and recognition capacity, as well as a stronger correlation with human pathogenicity in water environments compared to bacteria-based primers. This finding highlights an important direction for future advancements. Moreover, within the same category, qPCR did not demonstrate significant benefits over conventional PCR amplification methods. In comparing dye-based and probe-based techniques, it was revealed that the probe-based method's advantage lay primarily in specificity, which may be associated with the increased propensity of dye-based methods to produce false positives. Furthermore, the heterogeneity of the HF183 primer was not detected in China, Canada, and Singapore respectively, indicating a low likelihood of regional differences. The variation among the 21 other primers may be attributable to regional differences, sample sources, detection techniques, or alternative factors. Finally, we identified that economic factors, climatic conditions, and geographical distribution significantly influence primer performance.

Using watershed characteristics to enhance fecal source identification

Fecal pollution is one of the most prevalent forms of pollution affecting waterbodies worldwide, threatening public health and negatively impacting aquatic environments. Microbial source tracking (MST) applies polymerase chain reaction (PCR) technology to help identify the source of fecal pollution. In this study, we combine spatial data for two watersheds with general and host-associated MST markers to target human (HF183/BacR287), bovine (CowM2), and general ruminant (Rum2Bac) sources. Concentrations of MST markers in samples were determined with droplet digital PCR (ddPCR). The three MST markers were detected at all sites (n = 25), but bovine and general ruminant markers were significantly associated with watershed characteristics. MST results, combined with watershed characteristics, suggest that streams draining areas with low-infiltration soil groups and high agricultural land use are at an increased risk for fecal contamination. Microbial source tracking has been applied in numerous studies to aid in identifying the sources of fecal contamination, but these studies usually lack information on the involvement of watershed characteristics. Our study combined watershed characteristics with MST results to provide more comprehensive insight into the factors that influence fecal contamination in order to implement the most effective best management practices.

Growth and Decay of Fecal Indicator Bacteria and Changes in the Coliform Composition on the Top Surface Sand of Coastal Beaches during the Rainy Season

High counts of bacteria are present in beach sand, and human health threats attributable to contact with sand have been reported. In this study, we investigated fecal indicator bacteria in the top surface sand of coastal beaches. Monitoring investigations were performed during a monsoon when rainfall occurs randomly, and the composition of the coliforms was analyzed. The coliform count in the top surface sand (depth < 1 cm) increased by approximately 100 fold (26-2.23 × 10³ CFU/100 g) with increasing water content because of precipitation. The composition of the coliforms in the top surface sand changed within 24 h of rainfall, with Enterobacter comprising more than 40% of the coliforms. Estimation of factors that changed the bacterial counts and composition revealed that coliform counts tended to increase with increasing water content in the top surface sand. However, the abundance of Enterobacter was independent of the sand surface temperature and water content. Coliform counts in the top surface sand rapidly increased and the composition showed remarkable variations because of the supply of water to the beach following rainfall. Among them, some bacteria with suspected pathogenicity were present. Controlling bacteria in coastal beaches is important for improving public health for beachgoers.

Farmland nutrient pollution and its evolutionary relationship with plantation economic development in China

Contradiction between growing plantation economic demand and agro-ecological degradation has always restricted sustainable development of agricultural countries. This study applied the unit inventory analysis to evaluate the productions and discharges of farmland non-point source (FNPS) nitrogen (TN) and phosphorus (TP) among China's nine national-level agricultural districts over 1999-2019. On this basis, we quantified the evolutionary relationship between plantation economic output and FNPS pollution based on optimal regression fitting. The results showed that over 1999-2019, farmland cumulative TN and TP discharges for the whole China were approximately 15807 × 10⁴ t and 1312 × 10⁴ t, with prominent district heterogeneity. According to FNPS discharge magnitudes, China's agricultural districts can be classified into three categories: high, moderate and slight discharge zones. Huang-Huai-Hai Plain and Middle-lower Yangtze Plain were identified as the main severely-polluted districts. Mineral fertilizer is the primary contributor to FNPS pollution. Annual FNPS load showed a trend of increasing followed by decreasing, and the peak interval was recorded in 2014-2016. Spatiotemporal dynamics in FNPS discharge intensities were disparate from that in discharge magnitudes. SC has the highest TN discharge intensity, with an annual average intensity of 0.068 t/ha, followed by MLYP (0.044 t/ha) and HHHP (0.041 t/ha). HHHP has the highest TP discharge intensity, with an annual average intensity of 0.0051 t/ha, followed by SC (0.0038 t/ha) and MLYP (0.0031 t/ha). District-based agro-ecological restoration strategies were accordingly proposed considering FNPS discharge magnitude and intensity concurrently. In most agricultural districts, with the growing economic output in plantation, the FNPS load showed an increase followed by a decrease or to leveling off. Furthermore, with the increasing TN/TP economic partial productivity, the FNPS TN/TP discharge intensities reached the climax, then declined or tended to be flattening out.

Genetic fecal source identification in urban streams impacted by municipal separate storm sewer system discharges

Municipal stormwater systems are designed to collect, transport, and discharge precipitation from a defined catchment area into local surface waters. However, these discharges may contain unsafe levels of fecal waste. Paired measurements of Escherichia coli, precipitation, three land use metrics determined by geographic information system (GIS) mapping, and host-associated genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), dog (DG3), and avian (GFD) fecal sources were assessed in 231 urban stream samples impacted by two or more municipal stormwater outfalls. Receiving water samples were collected twice per month (n = 24) and after rain events (n = 9) from seven headwaters of the Anacostia River in the District of Columbia (United States) exhibiting a gradient of impervious surface, residential, and park surface areas. Almost 50% of stream samples (n = 103) were impaired, exceeding the local E. coli single sample maximum assessment level (410 MPN/100 ml). Fecal scores (average log10 copies per 100 ml) were determined to prioritize sites by pollution source and to evaluate potential links with land use, rainfall, and E. coli levels using a recently developed censored data analysis approach. Dog, ruminant, and avian fecal scores were almost always significantly increased after rain or when E. coli levels exceeded the local benchmark. Human fecal pollution trends showed the greatest variability with detections ranging from 9.1% to 96.7% across sites. Avian fecal scores exhibited the closest connection to land use, significantly increasing in catchments with larger residential areas after rain events (p = 0.038; R2 = 0.62). Overall, results demonstrate that combining genetic fecal source identification methods with GIS mapping complements routine E. coli monitoring to improve management of urban streams impacted by stormwater outfalls.

Molecular microbiological approaches reduce ambiguity about the sources of faecal pollution and identify microbial hazards within an urbanised coastal environment

Urbanised beaches are regularly impacted by faecal pollution, but management actions to resolve the causes of contamination are often obfuscated by the inability of standard Faecal Indicator Bacteria (FIB) analyses to discriminate sources of faecal material or detect other microbial hazards, including antibiotic resistance genes (ARGs). We aimed to determine the causes, spatial extent, and point sources of faecal contamination within Rose Bay, a highly urbanised beach within Sydney, Australia's largest city, using molecular microbiological approaches. Sampling was performed across a network of transects originating at 9 stormwater drains located on Rose Bay beach over the course of a significant (67.5 mm) rainfall event, whereby samples were taken 6 days prior to any rain, on the day of initial rainfall (3.8 mm), three days later after 43 mm of rain and then four days after any rain. Quantitative PCR (qPCR) was used to target marker genes from bacteria (i.e., Lachnospiraceae and Bacteroides) that have been demonstrated to be specific to human faeces (sewage), along with gene sequences from Heliobacter and Bacteriodes that are specific to bird and dog faeces respectively, and ARGs (sulI, tetA, qnrS, dfrA1 and vanB). 16S rRNA gene amplicon sequencing was also used to discriminate microbial signatures of faecal contamination. Prior to the rain event, low FIB levels (mean: 2.4 CFU/100 ml) were accompanied by generally low levels of the human and animal faecal markers, with the exception of one transect, potentially indicative of a dry weather sewage leak. Following 43 mm of rain, levels of both human faecal markers increased significantly in stormwater drain and seawater samples, with highest levels of these markers pinpointing several stormwater drains as sources of sewage contamination. During this time, sewage contamination was observed up to 1000 m from shore and was significantly and positively correlated with often highly elevated levels of the ARGs dfrA1, qnrS, sulI and vanB. Significantly elevated levels of the dog faecal marker in stormwater drains at this time also indicated that rainfall led to increased input of dog faecal material from the surrounding catchment. Using 16S rRNA gene amplicon sequencing, several indicator taxa for stormwater contamination such as Arcobacter spp. and Comamonadaceae spp. were identified and the Bayesian SourceTracker tool was used to model the relative impact of specific stormwater drains on the surrounding environment, revealing a heterogeneous contribution of discrete stormwater drains during different periods of the rainfall event, with the microbial signature of one particular drain contributing up to 50% of bacterial community in the seawater directly adjacent. By applying a suite of molecular microbiological approaches, we have precisely pinpointed the causes and point-sources of faecal contamination and other associated microbiological hazards (e.g., ARGs) at an urbanised beach, which has helped to identify the most suitable locations for targeted management of water quality at the beach.

An Overview of Microbial Source Tracking Using Host-Specific Genetic Markers to Identify Origins of Fecal Contamination in Different Water Environments

Fecal contamination of water constitutes a serious health risk to humans and environmental ecosystems. This is mainly due to the fact that fecal material carries a variety of enteropathogens, which can enter and circulate in water bodies through fecal pollution. In this respect, the prompt identification of the polluting source(s) is pivotal to guiding appropriate target-specific remediation actions. Notably, microbial source tracking (MST) is widely applied to determine the host origin(s) contributing to fecal water pollution through the identification of zoogenic and/or anthropogenic sources of fecal environmental DNA (eDNA). A wide array of host-associated molecular markers have been developed and exploited for polluting source attribution in various aquatic ecosystems. This review is intended to provide the most up-to-date overview of genetic marker-based MST studies carried out in different water types, such as freshwaters (including surface and groundwaters) and seawaters (from coasts, beaches, lagoons, and estuaries), as well as drinking water systems. Focusing on the latest scientific progress/achievements, this work aims to gain updated knowledge on the applicability and robustness of using MST for water quality surveillance. Moreover, it also provides a future perspective on advancing MST applications for environmental research.

Performance of NIST SRM® 2917 with 13 recreational water quality monitoring qPCR assays

Fecal pollution remains a significant challenge for recreational water quality management worldwide. In response, there is a growing interest in the use of real-time quantitative PCR (qPCR) methods to achieve same-day notification of recreational water quality and associated public health risk as well as to characterize fecal pollution sources for targeted mitigation. However, successful widespread implementation of these technologies requires the development of and access to a high-quality standard control material. Here, we report a single laboratory qPCR performance assessment of the National Institute of Standards and Technology Standard Reference Material 2917 (NIST SRM® 2917), a linearized plasmid DNA construct that functions with 13 recreational water quality qPCR assays. Performance experiments indicate the generation of standard curves with amplification efficiencies ranging from 0.95 ± 0.006 to 0.99 ± 0.008 and coefficient of determination values (R2) ≥ 0.980. Regardless of qPCR assay, variability in repeated measurements at each dilution level were very low (quantification threshold standard deviations ≤ 0.657) and exhibited a heteroscedastic trend characteristic of qPCR standard curves. The influence of a yeast carrier tRNA added to the standard control material buffer was also investigated. Findings demonstrated that NIST SRM® 2917 functions with all qPCR methods and suggests that the future use of this control material by scientists and water quality managers should help reduce variability in concentration estimates and make results more consistent between laboratories.

Interpretable tree-based ensemble model for predicting beach water quality

Tree-based machine learning models based on environmental features offer low-cost and timely solutions for predicting microbial fecal contamination in beach water to inform the public of the health risk. However, many of these models are black boxes that are difficult for humans to understand, which may cause severe consequences such as unexplained decisions and failure in accountability. To develop interpretable predictive models for beach water quality, we evaluate five tree-based models, namely classification tree, random forest, CatBoost, XGBoost, and LightGBM, and employ a state-of-the-art explanation method SHAP to explain the models. When tested on the Escherichia coli (E. coli) concentration data collected from three beach sites along Lake Erie shores, LightGBM, followed by XGBoost, achieves the highest averaged precision and recall scores. For all three sites, both models suggest lake turbidity as the most important predictor, and elucidate the crucial role of accurate local data of wave height and rainfall in the model development. Local SHAP values further reveal the robustness of the importance of lake turbidity as its SHAP value increases nearly monotonically with its value and is minimally affected by other environmental factors. Moreover, we found an intriguing interaction between lake turbidity and day-of-year. This work suggests that the combination of LightGBM and SHAP has a promising potential to develop interpretable models for predicting microbial water quality in freshwater lakes.

Analysis of Sea Pollution by Sewage from Vessels

In this study, we analysed the sea pollution caused by sewage from vessels. The Dubrovnik aquatorium was chosen as a typical sea area that accommodates a variety of vessels in different locations. We sampled the sea at eight different coastal locations over 14 months and then analysed the samples to determine the presence of the indicators of fecal pollution. Simultaneous with the sampling of the sea, we recorded the number and type of vessels accommodated at the port. These data were applied in chi-square tests, which were used to determine the existence of the relationship of certain types of vessels with fecal coliform bacteria in the sea for each location. The correlation was determined between smaller vessels such as boats, yachts, megayachts, and smaller cruise ships in national navigation with bacteria at sea at the sampling locations. The results can provide an improved understanding of sea pollution due to sewage from vessels.

Antibiotic resistance patterns of Escherichia coli isolates from the clinic through the wastewater pathway

Antimicrobial resistance (AMR) remains one of the leading global health threats. This study compared antimicrobial resistance patterns among E. coli isolates from clinical uropathogenic Escherichia coli (UPEC) to hospital wastewater populations and throughout an urban wastewater treatment facility - influent, pre- and post-chlorinated effluents. Antibiotic susceptibility of 201 isolates were analyzed against eleven different antibiotics, and the presence of twelve antibiotic resistant genes and type 1 integrase were identified. AMR exhibited the following pattern: UPEC (46.8%) > hospital wastewater (37.8%) > urban post-chlorinated effluent (27.6%) > pre-chlorinated effluent (21.4%) > urban influent wastewater (13.3%). However, multi-drug resistance against three or more antimicrobial classes was more prevalent among hospital wastewater populations (29.7%) compared to other sources. E. coli from wastewaters disinfected with chlorine were significantly correlated with increased trimethoprim-sulfamethoxazole resistance in E. coli compared to raw and treated wastewater populations. bla_CTX-M-1 group was the most common extended spectrum beta-lactamase in E. coli from hospital wastewater (90%), although UPEC strains also encoded bla_CTX-M-1 group (50%) and bla_TEM (100%) genes. Among tetracycline-resistant populations, tetA and tetB were the only resistance genes identified throughout wastewater populations that were associated with increased phenotypic resistance. Further characterization of the E. coli populations identified phylogroup B2 predominating among clinical UPEC populations and correlated with the highest AMR, whereas the elevated rate of multi-drug resistance among hospital wastewater was mostly phylogroup A. Together, our findings highlight hospital wastewater as a rich source of AMR and multi-drug resistant bacterial populations.

Integrated analyses of fecal indicator bacteria, microbial source tracking markers, and pathogens for Southeast Asian beach water quality assessment

The degradation of coastal water quality from fecal pollution poses a health risk to visitors at recreational beaches. Fecal indicator bacteria (FIB) are a proxy for fecal pollution; however the accuracy of their representation of fecal pollution health risks at recreational beaches impacted by non-point sources is disputed due to non-human derivation. This study aimed to investigate the relationship between FIB and a range of culturable and molecular-based microbial source tracking (MST) markers and pathogenic bacteria, and physicochemical parameters and rainfall. Forty-two marine water samples were collected from seven sampling stations during six events at two tourist beaches in Thailand. Both beaches were contaminated with fecal pollution as evident from the GenBac3 marker at 88%-100% detection and up to 8.71 log₁₀ copies/100 mL. The human-specific MST marker human polyomaviruses JC and BK (HPyVs) at up to 4.33 log₁₀ copies/100 mL with 92%-94% positive detection indicated that human sewage was likely the main contamination source. CrAssphage showed lower frequencies and concentrations; its correlations with the FIB group (i.e., total coliforms, fecal coliforms, and enterococci) and GenBac3 diminished its use as a human-specific MST marker for coastal water. Human-specific culturable AIM06 and SR14 bacteriophages and general fecal indicator coliphages also showed less sensitivity than the human-specific molecular assays. The applicability of the GenBac3 endpoint PCR assay as a lower-cost prescreening step prior to the GenBac3 qPCR assay was supported by its 100% positive predictive value, but its limited negative predictive values required subsequent qPCR confirmation. Human enteric adenovirus and Vibrio cholerae were not found in any of the samples. The HPyVs related to Vibrio parahaemolyticus, Vibrio vulnificus, and 5-d rainfall records, all of which were more prevalent and concentrated during the wet season. More monitoring is therefore recommended during wet periods. Temporal differences but no spatial differences were observed, suggesting the need for a sentinel site at each beach for routine monitoring. The exceedance of FIB water quality standards did not indicate increased prevalence or concentrations of the HPyVs or Vibrio spp. pathogen group, so the utility of FIB as an indicator of health risks at tropical beaches maybe challenged. Accurate assessment of fecal pollution by incorporating MST markers could lead to developing a more effective water quality monitoring plan to better protect human health risks in tropical recreational beaches.

A baseline study of spatial variability of bacteria (total coliform, E. coli, and Enterococcus spp.) as biomarkers of pollution in ten tropical Atlantic beaches: concern for environmental and public health

Coastal water quality in urban cities is increasingly impacted by human activities such as agricultural runoff, sewage discharges, and poor sanitation. However, environmental factors controlling bacteria abundance remain poorly understood. The study employed multiple indicators to assess ten beach water qualities in Ghana during minor wet seasons. Environmental parameters (e.g. temperature, electrical conductivity, total dissolved solids) were measured in situ using the Horiba multiple parameter probe. Surface water samples were collected to measure total suspended solids, nutrients, and chlorophyll-a via standard methods and bacteria determination through membrane filtration. Environmental parameters measured showed no significant variation for the sample period. However, bacteria loads differ significantly (p = 0.024) among the beaches and influenced significantly by nitrate (55.3%, p = 0.02) and total dissolved solids (17.1%, p = 0.017). The baseline study detected an increased amount of total coliforms and faecal indicator bacteria (Escherichia coli and Enterococcus spp.) in beach waters along the coast of Ghana, suggesting faecal contamination, which can pose health risks. The mean ± standard deviations of bacteria loads in beach water are total coliforms (4.06 × 10³ ± 4.16 × 10³ CFU/100 mL), E. coli (7.06 × 10² ± 1.72 × 10³ CFU/100 mL), and Enterococcus spp. (6.15 × 10² ± 1.75 × 10³ CFU/100 mL). Evidence of pollution calls for public awareness to prevent ecological and health-related risks and policy reforms to control coastal water pollution. Future research should focus on identifying the sources of contamination in the tropical Atlantic region.

Critical source areas' identification for non-point source pollution related to nitrogen and phosphorus in an agricultural watershed based on SWAT model

Water eutrophication caused by the extensive expansion of slope farming has caused the high attention of the Chinese government. We choose Lake Tianmu basin as the study area because it can represent vast majority of basins plagued by water eutrophication derived from slope tillage in southern China. The water ecosystem in the reservoir Daxi and Shahe within the basin has been seriously threatened by multiple pollution sources related to many intricate human activities especially agricultural production. For the first time, we identified the critical source areas (CSAs) within the basin based on nutrient load and nutrient load intensity (NLI), and on this basis, we further excavated the main causes of pollution and proposed pertinent remediation measures. The results based on the calibrated Soil and Water Assessment Tool model indicated that the TN load of each reservoir remarkably exceeded their respective water environmental capacity from 2014 to 2018. Accordingly, six main tributaries with great nutrient contributions and their corresponding sub-basins were then identified. Overall, tea and rice plantations appear to be the major nutrient contributors to reservoir Daxi. And the main nutrient sources for reservoir Shahe are tea plantations, orchards, farmland, forestland, and point sources. Regarding the CSAs identified only by nutrient load, agronomic measures such as reducing fertilizer amount, biochar application, straw incorporation, and plastic mulch coverage can be employed to improve soil water retention and curb soil erosion. Regarding the CSAs identified by nutrient load intensity (NLI), the CSAs with narrow areas should be turned directly into forestland. For the CSAs with large areas, engineering measures such as constructing ecological riparian zone, filtration, and sedimentation tank can be employed to prevent pollutants from entering downstream reaches. Overall, the present results can provide the decision-making support for the safe and efficient management of watershed land use in southern China.

Bathing Water Quality Monitoring Practices in Europe and the United States

Many countries including EU Member States (EUMS) and the United States (U.S.) regularly monitor the microbial quality of bathing water to protect public health. This study comprehensively evaluates the EU bathing water directive (BWD) and the U.S. recreational water quality criteria (RWQC) as regulatory frameworks for monitoring microbial quality of bathing water. The major differences between these two regulatory frameworks are the provision of bathing water profiles, classification of bathing sites based on the pollution level, variations in the sampling frequency, accepted probable illness risk, epidemiological studies conducted during the development of guideline values, and monitoring methods. There are also similarities between the two approaches given that both enumerate viable fecal indicator bacteria (FIB) as an index of the potential risk to human health in bathing water and accept such risk up to a certain level. However, enumeration of FIB using methods outlined within these current regulatory frameworks does not consider the source of contamination nor variation in inactivation rates of enteric microbes in different ecological contexts, which is dependent on factors such as temperature, solar radiation, and salinity in various climatic regions within their geographical areas. A comprehensive "tool-box approach", i.e., coupling of FIB and viral pathogen indicators with microbial source tracking for regulatory purposes, offers potential for delivering improved understanding to better protect the health of bathers.

Variable fecal source prioritization in recreational waters routinely monitored with viral and bacterial general indicators

Somatic and F+ coliphage methods are under consideration as potential routine surface water quality monitoring tools to identify unsafe levels of fecal pollution in recreational waters. However, little is known about the cooccurrence of these virus-based fecal indicators and host-associated genetic markers used to prioritize key pollution sources for remediation. In this study, paired measurements of cultivated coliphage (somatic and F+) and bacterial (E. coli and enterococci) general fecal indicators and genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), canine (DG3), and avian (GFD) fecal pollution sources were assessed in 365 water samples collected from six Great Lakes Basin beach and river sites over a 15-week recreational season. Water samples were organized into groups based on defined viral and bacterial fecal indicator water quality thresholds and average log10 host-associated genetic marker fecal score ratios were estimated to compare pollutant source inferences based on variable routine water quality monitoring practices. Eligible log10 fecal score ratios ranged from -0.051 (F+ coliphage, GFD) to 2.08 (enterococci, Rum2Bac). Using a fecal score ratio approach, findings suggest that general fecal indicator selection for routine water quality monitoring can influence the interpretation of host-associated genetic marker measurements, in some cases, prioritizing different pollutant sources for remediation. Variable trends were also observed between Great Lake beach and river sites suggesting disparate management practices may be useful for each water type.

Metagenomic Sequencing and Quantitative Real-Time PCR for Fecal Pollution Assessment in an Urban Watershed

Microbial contamination of recreation waters is a major concern globally, with pollutants originating from many sources, including human and other animal wastes often introduced during storm events. Fecal contamination is traditionally monitored by employing culture methods targeting fecal indicator bacteria (FIB), namely E. coli and enterococci, which provides only limited information of a few microbial taxa and no information on their sources. Host-associated qPCR and metagenomic DNA sequencing are complementary methods for FIB monitoring that can provide enhanced understanding of microbial communities and sources of fecal pollution. Whole metagenome sequencing (WMS), quantitative real-time PCR (qPCR), and culture-based FIB tests were performed in an urban watershed before and after a rainfall event to determine the feasibility and application of employing a multi-assay approach for examining microbial content of ambient source waters. Cultivated E. coli and enterococci enumeration confirmed presence of fecal contamination in all samples exceeding local single sample recreational water quality thresholds (E. coli, 410 MPN/100 mL; enterococci, 107 MPN/100 mL) following a rainfall. Test results obtained with qPCR showed concentrations of E. coli, enterococci, and human-associated genetic markers increased after rainfall by 1.52-, 1.26-, and 1.11-fold log₁₀ copies per 100 mL, respectively. Taxonomic analysis of the surface water microbiome and detection of antibiotic resistance genes, general FIB, and human-associated microorganisms were also employed. Results showed that fecal contamination from multiple sources (human, avian, dog, and ruminant), as well as FIB, enteric microorganisms, and antibiotic resistance genes increased demonstrably after a storm event. In summary, the addition of qPCR and WMS to traditional surrogate techniques may provide enhanced characterization and improved understanding of microbial pollution sources in ambient waters.