Determining hot spots of fecal contamination in a tropical watershed by combining land-use information and meteorological data with source-specific assays

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.

Investigating the Relationship between Surface Water Pollution and Onsite Wastewater Treatment Systems

Onsite wastewater treatment systems (OWTSs) are important nonpoint sources (NPSs) of pollution to consider in watershed management. However, limited OWTS data availability makes it challenging to account for them as an NPS of water pollution. In this study, we succeeded in obtaining OWTS permits and integrated them with environmental data to model the pollution potential from OWTSs at the watershed scale using GIS-based multicriteria decision analysis. Then, in situ water quality parameters─Escherichia coli (E. coli), total nitrogen, total phosphorus, temperature, and pH─were measured along the main tributary at base-flow conditions. Three general linear models were developed to relate E. coli to water quality parameters and OWTS pollution indicators. It was found that the model with the OWTS pollution potential had the lowest corrected Akaike information criterion (AICc) value (35.01) compared to the models that included classified OWTS pollution potential input criteria (AICc = 36.76) and land cover (AICc = 36.74). These results demonstrate that OWTSs are a significant contributor to surface water pollution, and future efforts should be made to improve access to OWTS data (i.e., location and age) to account for these systems as an NPS of water pollution.

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.

Quantifying the effects of land use change and aggregate stormwater management practices on fecal coliform dynamics in a temperate catchment

Changes in land use and land cover (LULC) due to land development can lead to an increase in diffuse microbial pollutions and, consequently, degradation of the receiving aquatic ecosystem. However, the mechanisms underlying these phenomena are rarely considered in hydrological models. Therefore, in this study, fecal indicator bacteria (FIB) and total suspended solids (TSS) in a temperate catchment were simulated using a well-established water quality model (Personal Computer Storm Water Management Model) to systematically quantify the factors influencing their dynamics and the effects of stormwater management. Additionally, high-resolution data (e.g., water quality variables and LULC changes) were used to calibrate the model, which accurately reproduced the physical and biological features of the catchment. The results showed that increases in bare land areas and impervious cover in the catchment exceeded the Korean (as well as the USEPA-based) standard recreational water quality criteria for fecal contamination and TSS. Dissolved organic compounds (only during storm events), TSS, and total nitrogen (except during the pre-development phase) were the strongest predictors in shaping FIB dynamics. The multiple control of stormwater management reduced the FIB and TSS concentrations by approximately 65% in the catchment. The results of this study not only provide conclusions on the drivers of FIB and TSS dynamics and their quantitative contribution but also help in designing a methodology for empirical and ecological predictions of diffuse microbial and TSS pollution in a catchment with ongoing land development.

Septic system-groundwater-surface water couplings in waterfront communities contribute to harmful algal blooms in Southwest Florida

As human population growth has expanded in Southwest Florida, water quality has become degraded with an increased occurrence of harmful algal blooms (HABs). Red tide (Karenia brevis) originating offshore, intensifies in nearshore waters along Florida's Gulf Coast, and blue-green algae (Microcystis spp.) originating in Lake Okeechobee is discharged into the Caloosahatchee River. These HABs could be enhanced by anthropogenic nitrogen (N) and phosphorus (P) from adjacent watersheds. North Fort Myers is a heavily developed, low-lying city on the Caloosahatchee River Estuary serviced by septic systems with documented nutrient and bacterial pollution. To identify sources of pollution within North Fort Myers and determine connections with downstream HABs, this multiyear (2017-2020) study examined septic system- groundwater- surface water couplings through the analysis of water table depth, nutrients (N, P), fecal indicator bacteria (FIB), molecular markers (HF183, GFD, Gull2), chemical tracers (sucralose, pharmaceuticals, herbicides, pesticides), stable isotopes of groundwater (δ¹⁵N-NH₄, δ¹⁵N-NO₃) and particulate organic matter (POM; δ¹⁵N, δ¹³C), and POM elemental composition (C:N:P). POM samples were also collected during K. brevis and Microcystis spp. HAB events. Most (>80%) water table depth measurements were too shallow to support septic system functioning (<1.07 m). High concentrations of NH₄⁺ and NO_x, up to 1094 μM and 482 μM respectively, were found in groundwater and surface water. δ¹⁵N values of groundwater (+4.7‰) were similar to septic effluent (+4.9‰), POM (+4.7‰), and downstream HABs (+4.8 to 6.9‰), indicating a human waste N source. In surface water, FIB were elevated and HF183 was detected, while in groundwater and surface water sucralose, carbamazepine, primidone, and acetaminophen were detected. These data suggest that groundwater and surface water in North Fort Myers are coupled and contaminated by septic system effluent, which is negatively affecting water quality and contributing to the maintenance and intensification of downstream HABs.

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.

Assessing the Socio-Environmental Risk of Onsite Wastewater Treatment Systems to Inform Management Decisions

Quantifying the risk that failing onsite waste treatment systems (OWTS), such as septic systems, present to human health and the environment is a key component in natural resource management. We integrated environmental and socio-demographic data to assess the potential environmental risk and environmental justice concerns related to septic infrastructure. We used this process to develop a framework that can be applied in other jurisdictions. We found only 8% of the registered OWTS presented potential environmental risk due to the topographic, hydrologic, or edaphic characteristics of their placement. In contrast, almost 70% of the OWTS presented potential environmental risk due to their age (25 years or older). Approximately 60% of the OWTS we estimated to be at risk from age or placement were found in census blocks with more than 30% of the population living below the poverty line, had a population that was more than 50% nonwhite, or was predominantly nonwhite and impoverished. Our work suggests that jurisdictions with limited information about septic infrastructure may be able to use geospatial data that they do have to predict the parcel-level locations of OWTS. These locations can then be used to inform environmental monitoring to proactively address environmental justice concerns.

Climate and Land Use Influences on Bacteria Levels in Stormwater

The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Linear Regression (LR) and Multivariate Logistic Regression (MLR). Kendall’s τ-b demonstrated that FC levels were significantly and positively correlated with the amount of residential area within a drainage catchment generating the runoff, and that FC levels were location dependent. Climatic variables of temperature and antecedent dry period length were significantly and positively correlated with FC levels at both the sampling location level and across the region overall. Precipitation and flowrates were negatively correlated with FC levels. Fourier analysis showed that monthly FC levels shared the same 12 month cycle (peaking in July) as precipitation and temperature. MLR modelling was applied by aggregating the LogFC data by order of magnitude. The MLR model shows that the data are subject to different influences depending on the season and as well, the month of the year. The land use and climate analyses suggest that future climate change impact studies attempted on nearshore bacterial water quality should be conducted at the urban catchment scale.

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.

Human and animal microbial source tracking in a tropical river with multiple land use activities

The enhancement and restoration of the water quality of deteriorating surface water resources can be challenging, particularly for rivers with multiple usages, such as agriculture, animal husbandry, human residence, and industries. Recently, the performance of DNA-based microbial source tracking (MST) indicators detected by end-point and quantitative PCR assays for identifying sources of fecal pollution from human sewage, swine, and cattle and non-host-specific (universal) fecal pollution in the Tha Chin River basin, Thailand, was evaluated. The present study monitored these validated MST markers and various physicochemical and microbial water quality parameters in samples collected from twelve stations along the Tha Chin River during four sampling events in the wet and dry seasons. No significant difference in precipitation was observed between the wet and dry samplings. Universal markers (both PCR and qPCR) were detected in all 48 samples, indicating persistent and continuing fecal contamination. The sewage- and swine-specific qPCR marker concentrations did not vary among the sampling events, whereas cattle-specific qPCR markers were detected only in the wet season. Animal-specific markers were detected in the lower Tha Chin River section, which is characterized by intensive animal farming. Sewage-specific markers were also found in the lower section and near an upstream residential area. The high agreement (87.5-100%) between the PCR and qPCR results suggested that PCR could serve as a lower-cost MST screening test that requires less technical expertise. A multivariate analysis conducted using the survival analysis procedure to include censored data also emphasized the high pollution in the lower section of the river at all sampling events. Universal and swine-specific markers showed moderate correlations with microbial indicators, including total coliforms, fecal coliforms, E. coli, and enterococci. None of the MST markers or microbial parameters were associated with the measured physicochemical parameters. This study provides the first evaluation of MST markers for monitoring surface freshwater in Thailand, and the findings might aid the pollution surveillance of impaired water bodies and the development of strategies for improving their water quality.

A case study characterizing animal fecal sources in surface water using a mitochondrial DNA marker

Water quality impairment by fecal waste in coastal watersheds is a public health issue. The present study provided evidence for the use of a mitochondrial (mtDNA) marker to detect animal fecal sources in surface water. The accurate identification of fecal pollution is based on the notion that fecal microorganisms preferentially inhabit a host animal's gut environment. In contrast, mtDNA host-specific markers are inherent to eukaryotic host cells, which offers the advantage by detecting DNA from the host rather than its fecal bacteria. The present study focused on sampling water presumably from non-point sources (NPS), which can increase bacterial and nitrogen concentrations to receiving water bodies. Stream sampling sites located within the Piscataqua River Watershed (PRW), New Hampshire, USA, were sampled from a range of sites that experienced nitrogen inputs such as sewer and septic systems and suburban runoff. Three mitochondrial (mtDNA) gene marker assays (human, bovine, and canine) were tested from surface water. Nineteen sites were sampled during an 18-month period. Analyses of the combined single and multiplex assay results showed that the proportion of occurrence was highest for bovine (15.6%; n = 77) compared to canine (5.6%; n = 70) and human (5.7%; n = 107) mtDNA gene markers. For the human mtDNA marker, there was a statistically significant relationship between presence vs. absence and land use (Fisher's test p = 0.0031). This result was evident particularly for rural suburban septic, which showed the highest proportion of presence (19.2%) compared to the urban sewered (3.3%), suburban sewered (0%), and agricultural (0%) as well as forested septic (0%) sites. Although further testing across varied land use is needed, our study provides evidence for using the mtDNA marker in large watersheds.

Isolating the impact of septic systems on fecal pollution in streams of suburban watersheds in Georgia, United States

The presence of multiple sources of fecal pollution at the watershed level presents challenges to efforts aimed at identifying the influence of septic systems. In this study multiple approaches including targeted sampling and monitoring of host-specific Bacteroidales markers were used to identify the impact of septic systems on microbial water quality. Twenty four watersheds with septic density ranging from 8 to 373 septic units/km² were monitored for water quality under baseflow conditions over a 3-year period. The levels of the human-associated HF183 marker, as well as total and ruminant Bacteroidales, were quantified using quantitative polymerase chain reaction. Human-associated Bacteroidales yield was significantly higher in high density watersheds compared to low density areas and was negatively correlated (r = -0.64) with the average distance of septic systems to streams in the spring season. The human marker was also positively correlated with the total Bacteroidales marker, suggesting that the human source input was a significant contributor to total fecal pollution in the study area. Multivariable regression analysis indicates that septic systems, along with forest cover, impervious area and specific conductance could explain up to 74% of the variation in human fecal pollution in the spring season. The results suggest septic system impact through contributions to groundwater recharge during baseflow or failing septic system input, especially in areas with >87 septic units/km². This study supports the use of microbial source tracking approaches along with traditional fecal indicator bacteria monitoring and land use characterization in a tiered approach to isolate the influence of septic systems on water quality in mixed-use watersheds.

Geographic information systems and multivariate analysis to evaluate fecal bacterial pollution in coastal waters of Andaman, India

Urbanization of coastal areas in recent years has driven us to consider a new approach for visually delineating sites that are contaminated with fecal bacteria (FB) in the coastal waters of the Andaman Islands in India. Geo-spatial analysis demarcated harbor, settlement, and freshwater/discharge influenced zones as hot spots for FB, while the open sea was demarcated as a cold spot. The land use types, such as developed and agriculture, with more anthropogenic activities increasing the FB counts while open sea showed the least FB. Box whisker plot indicated an increasing FB trend in the coastal waters during monsoon. Furthermore, principal component analysis revealed 67.35%, 78.62% and 70.43% of total variance at Port Blair, Rangat and Aerial bays, respectively. Strong factor loading was observed for depth (0.95), transparency (0.93), dissolved oxygen (0.93) and fecal streptococci (0.85). Distance proximity analysis revealed that fecal contaminations diluted significantly (P < 0.05) at the distance of 2.1 km toward the deeper or open sea water. This study demonstrates the effectiveness of an integrated approach in identifying the sources of fecal contamination and thus helping in better monitoring and management of coastal waters.

Sources identification of antibiotic pollution combining land use information and multivariate statistics

To quantify the extent of antibiotic contamination and to identity the dominant pollutant sources in the Tiaoxi River Watershed, surface water samples were collected at eight locations and analyzed for four tetracyclines and three sulfonamides using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The observed maximum concentrations of tetracycline (623 ng L(-1)), oxytetracycline (19,810 ng L(-1)), and sulfamethoxazole (112 ng L(-1)) exceeded their corresponding Predicted No Effect Concentration (PNEC) values. In particular, high concentrations of antibiotics were observed in wet summer with heavy rainfall. The maximum concentrations of antibiotics appeared in the vicinity of intensive aquaculture areas. High-resolution land use data were used for identifying diffuse source of antibiotic pollution in the watershed. Significant correlations between tetracycline and developed (r = 0.93), tetracycline and barren (r = 0.87), oxytetracycline and barren (r = 0.82), and sulfadiazine and agricultural facilities (r = 0.71) were observed. In addition, the density of aquaculture significantly correlated with doxycycline (r = 0.74) and oxytetracycline (r = 0.76), while the density of livestock significantly correlated with sulfadiazine (r = 0.71). Principle Component Analysis (PCA) indicated that doxycycline, tetracycline, oxytetracycline, and sulfamethoxazole were from aquaculture and domestic sources, whereas sulfadiazine and sulfamethazine were from livestock wastewater. Flood or drainage from aquaculture ponds was identified as a major source of antibiotics in the Tiaoxi watershed. A hot-spot map was created based on results of land use analysis and multi-variable statistics, which provided an effective management tool of sources identification in watersheds with multiple diffuse sources of antibiotic pollution.

Characterization of sources and loadings of fecal pollutants using microbial source tracking assays in urban and rural areas of the Grand River Watershed, Southwestern Ontario

Sources of fecal water pollution were assessed in the Grand River and two of its tributaries (Ontario, Canada) using total and host-specific (human and bovine) Bacteroidales genetic markers in conjunction with reference information, such as land use and weather. In-stream levels of the markers and culturable Escherichia coli were also monitored during multiple rain events to gain information on fecal loadings to catchment from diffuse sources. Elevated human-specific marker levels were accurately identified in river water impacted by a municipal wastewater treatment plant (WWTP) effluent and at a downstream site in the Grand River. In contrast, the bovine-specific marker showed high levels of cattle fecal pollution in two tributaries, both of which are characterized as intensely farmed areas. The bovine-specific Bacteroidales marker increased with rainfall in the agricultural tributaries, indicating enhanced loading of cattle-derived fecal pollutants to river from non-point sources following rain events. However, rain-triggered fecal loading was not substantiated in urban settings, indicating continuous inputs of human-originated fecal pollutants from point sources, such as WWTP effluent. This study demonstrated that the Bacteroidales source tracking assays, in combination with land use information and hydrological data, may provide additional insight into the spatial and temporal distribution of source-specific fecal contamination in streams impacted by varying land uses. Using the approach described in this study may help to characterize impacted water sources and to design targeted land use management plans in other watersheds in the future.