Modeling fate and transport of fecally-derived microorganisms at the watershed scale: State of the science and future opportunities

Modelling Escherichia coli concentrations: 45.6 %-78.1 % of China's rivers show poor microbial water quality

Water quality is under threat due to the presence of pathogenic and antibiotic-resistant bacteria. Escherichia coli (E. coli) serves as an indicator of faecal contamination and the potential presence of other harmful pathogens. Understanding E. coli concentrations helps in assessing the overall health risks associated with waterborne diseases and developing effective water management strategies. Therefore, we developed the first large-scale model, GloWPa-Ecoli C1 to simulate E. coli loads and concentrations in rivers and apply this model to China. The model provides the first comprehensive overview of microbial water quality across China's rivers. The model simulates E. coli concentrations in 2020 to range from 10-1.2 to 106.3 CFU/L, with 45.6 %-78.1 % of rivers exhibiting poor microbial water quality. Major hotspots of E. coli pollution are Haihe, Huaihe and Pearl River Basins. Direct discharge of human faecal waste contributes 80.2 % of the total E. coli load, while directly discharged livestock waste accounts for 13.1 %. To mitigate E. coli pollution in rivers in China, we recommend increasing human faecal waste collection rates, expanding wastewater treatment plant (WWTP) coverage, phasing out primary treatment WWTPs and eliminating direct livestock faecal waste discharge, particularly from smallholder farms. The study underscores the urgent need to improve microbial water quality in China's rivers. The findings provide actionable insights to inform policy development aimed at safeguarding water quality and public health. Furthermore, the modelling approach is applicable to other regions and microorganisms, offering a foundation for developing models to address antibiotic-resistant bacteria and other emerging water quality challenges.

The use of faecal indicator organisms to manage microbial health risks in recreational waterways not impacted by point sources of sewage: a systematic review of the epidemiological evidence

This PRISMA review investigated the extent to which epidemiological evidence supports the use of faecal indicator organisms (FIOs) to manage microbial health risks in recreational waters without point sources of sewage. The quality of papers meeting the inclusion criteria was appraised using the Office of Health Assessment and Translation (OHAT) Risk of Bias tool and low-bias studies were synthesised. Studies consistently reported elevated illness risks (particularly gastrointestinal) among bathers compared with non-bathers. However, no FIOs or pathogens were associated consistently with any health outcomes. While enterococci most frequently correlated with a variety of illnesses, the relatively even split of positive and negative associations suggests an overall lack of association. Consequently, applying FIO guidelines derived from epidemiological studies with point sources of sewage could result in type I and type II errors. Overall, results suggest that the sources and drivers of health risks are site-specific. Tools including sanitary surveys, microbial source tracking, epidemiology and quantitative microbial risk assessment provide avenues for characterising site-specific health risks, for those who can afford them. Meanwhile, characterising the site-specific sources/drivers of contamination seems pragmatic as the limited evidence so far suggests that FIO monitoring may not be sufficient to protect health in these waters.

Changing climate and socio-economic conditions as part of quantitative microbial risk assessment of surface drinking water sources: a review

Climate and socio-economic changes are expected to significantly impact waterborne pathogens and associated health risks, yet the full extent of these effects remains unclear. Accurate quantification of these risks is crucial for informing effective interventions and policy decisions. Quantitative microbial risk assessment (QMRA) serves as a valuable tool for estimating the risk of infection caused by microorganisms in drinking water. This study reviews existing QMRA studies and tools in the context of surface water and drinking water provision. Most studies have implemented various steps of the QMRA framework but often without the application of specific QMRA tools. Although several QMRA tools address climatic factors, there are currently no tools that integrate socio-economic factors into their risk assessments. This study proposes an approach for incorporating both climatic and socio-economic factors into QMRA tools. Specifically, we suggest enhancements to the Swedish QMRA tool - an open-source tool that currently does not incorporate climate and socio-economic changes. Our proposed advancements aim to systematically account for future climatic and socio-economic impacts on health risks, providing a more comprehensive microbial risk assessment tool. These recommendations are also applicable to other QMRA tools, offering a pathway for their development and improving the overall assessment of microbial health risks.

Extreme rainfall disproportionately impacts E. coli concentrations in Texas recreational waterbodies

Waterborne pathogen contamination poses a significant threat to water resources globally and the exposure to waterborne pathogen contamination is widely recognized as unevenly distributed. Extreme weather events could exacerbate inequalities in waterborne disease as climate variability continues to escalate. However, there is a limited understanding of how extreme rainfall affects E. coli levels and whether disadvantaged communities experience disproportionate impacts from extreme rainfall on E. coli contamination. Leveraging 21 years of E. coli data along with climate data and watershed characteristics, this study employed Multiscale Geographically Weighted Regression (MGWR) models to quantify the seasonal and spatial impacts of extreme rainfall on E. coli concentrations in Texas. Our results indicate that during dry seasons, regions in northern and eastern Texas exhibit significantly higher impacts of extreme rainfall on E. coli concentrations, which is associated with high proportions of pastures, wetlands, and silt. However, during wet seasons, western and southern regions experience slightly higher extreme rainfall induced E. coli contamination risks likely due to significantly increased runoff from the rainfall together with higher slopes and clay-rich soil. In addition, we found census tracts with higher proportions of Black and Latino populations experience greater extreme rainfall impacts on E. coli levels in different months. Furthermore, an analysis of historical trends in extreme rainfall intensity indicates that climate variability could further amplify the existing inequalities in exposure to E. coli contamination. Our findings highlight the disproportionate impacts of extreme rainfall induced E. coli contamination on disadvantaged communities and emphasize the critical need for targeted intervention strategies to mitigate these risks effectively and equitably in Texas.

Modeling the fate and transport of E. coli pathogens in the Tano River Basin of Ghana under climate change and socioeconomic scenarios

Surface water contamination by fecal matter threatens human health due to human and biological processes within a watershed, making socioeconomic development crucial for predicting and improving microbiological water quality. Consequently, climate change alters climatic parameters that affect flow regimes and the movement and fate of microorganisms. This study assessed the fate and transport of microbial Escherichia coli (E. coli) concentrations and their sources in the Tano River Basin in Ghana. Additionally, the study predicted future E. coli concentrations using climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC)'s most recent representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs). Scenario_1 featured planned urbanization, enhanced manure and wastewater treatment, moderate population, livestock density growth, and climate change. Scenario_2 involved higher population growth, minimal improvements in wastewater management, zero manure treatment, higher livestock population, urbanization, and substantial climate change. Calibration and validation using E. coli data from June 2022 to April 2023 showed good agreement with observed concentrations (R2, 0.75 and 0.89; NSE, 0.69 and 0.68; PBIAS, 3.4 and 1.9, respectively). The measured and modeled E. coli concentrations were high, with the highest recording at 2.39 log cfu/100 ml during the rainy season. The study finds that the main causes of E. coli concentrations (44%) are point sources, primarily from human feces and livestock manure, followed by upstream pollution (34%) and non-point sources (22%). Non-point sources became the predominant contributors during periods of maximum discharge due to runoff from land and the dilution of point sources. Again Scenario_1 E. coli dropped to 68% and 97% of reference point levels by the 2050s and 2100s, respectively. E. coli concentrations decrease even more with subsequent treatment, such as tertiary treatment, manure treatment, or both. The scenario analysis demonstrates the potential for E. coli reduction through wastewater and manure treatment, driven by socioeconomic and climate change scenarios.

Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Village Settlements in Mountainous Tropical Areas, Hotspots of Fecal Contamination as Evidenced by Escherichia coli and Stanol Concentrations in Stormwater Pulses

Fecal bacteria in surface water may indicate threats to human health. Our hypothesis is that village settlements in tropical rural areas are major hotspots of fecal contamination because of the number of domestic animals usually roaming in the alleys and the lack of fecal matter treatment before entering the river network. By jointly monitoring the dynamics of Escherichia coli and of seven stanol compounds during four flood events (July-August 2016) at the outlet of a ditch draining sewage and surface runoff out of a village of Northern Lao PDR, our objectives were (1) to assess the range of E. coli concentration in the surface runoff washing off from a village settlement and (2) to identify the major contributory sources of fecal contamination using stanol compounds during flood events. E. coli pulses ranged from 4.7 × 104 to 3.2 × 106 most probable number (MPN) 100 mL-1, with particle-attached E. coli ranging from 83 to 100%. Major contributory feces sources were chickens and humans (about 66 and 29%, respectively), with the highest percentage switching from the human pole to the chicken pole during flood events. Concentrations indicate a severe fecal contamination of surface water during flood events and suggest that villages may be considered as major hotspots of fecal contamination pulses into the river network and thus as point sources in hydrological models.

From source to house: unraveling the seasonal effect of water distribution system on drinking water quality of poultry farms under Egyptian environmental condition

Improvements in drinking water quality (DWQ) can lead, according to some estimates, to a 10% reduction of the world's disease load. The drinking water distribution system (DWDS) plays a crucial role in influencing DWQ and can contribute to the emergence of poultry-related epidemics. This study aims to monitor the variations in DWQ throughout the seasons within the DWDS of Egyptian poultry farms experiencing epidemics. The study assessed DWQ at four different points along the DWDS, including the water source (WS), water tank (WT), broiler drinker (BD), and layer drinker (LD), across 86 farms. Statistical analysis was employed to establish correlations between DWQ and the sampling points within the DWDS, as well as between water temperature (Tw.C°), ambient temperature (Ta.C°), and microbial DWQ. The survey revealed significant differences between Tw.C° and Ta.C°, with notable effect sizes (d = 0.89-1). Additionally, the results revealed significant differences in physicochemical DWQ between WS and house drinkers (HD), with medium to large effect sizes (d = 0.56-0.85). Furthermore, significant differences were identified in microbial DWQ between winter and summer, with a small to large effect size (d = 0.40-0.87). Notably, we recorded significant differences in microbial DWQ between WS and WT, with a small to medium effect size (d = 0.40-0.61), and between WT and BD, with a small to medium effect size (d = 0.48-0.53). Additionally, we found significant differences in microbial DWQ between WS and LD, with a medium effect size (d = 0.59-0.68). In conclusion, Tw.C° is influenced by seasonal variations in Ta.C°. While the physicochemical DWQ was unaffected by seasonal temperature variations, it was significantly impacted by the DWDS from WS to HD. In contrast, the microbial DWQ was strongly influenced by both seasonal temperature changes and DWDS.

Fate and transport modelling for evaluating antibiotic resistance in aquatic environments: Current knowledge and research priorities

Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments.

Environmental and Anthropogenic Influences on Coliform Concentrations in the Octopus insularis Production Chain in the Veracruz Reef System, Gulf of Mexico

Coliforms are relatively common in aquatic environments, but their concentrations can be increased by environmental changes and anthropogenic activities, thus impacting fisheries resources. To determine the microbiological quality in the octopus production chain (capture, post-capture, processing and commercialization), total (TC) and fecal (FC) coliforms were quantified in sea water, fresh octopus, fresh water, ice and octopus in two presentations: packed in ice and boiled. Samples came from fishing zones Enmedio, Chopa and La Gallega at the Veracruz Reef System (VRS) during dry, rainy and windy seasons. The coliforms were determined using the most probable number technique (MPN). The most relevant results indicated that octopus packed in ice coming from the commercialization stage had FC levels >540 MPN/100 g, which exceeded the permissible limits (230 MPN/100 g). Therefore, these products present a risk for human consumption. Differences in FC were observed in octopuses between the three fishing zones (H = 8.697; p = 0.0129) and among the three climatic seasons, increasing during the rainy season, highlighting La Gallega with 203.33 ± 63 MPN (H = 7.200; p = 0.0273). The results provide evidence of the environmental and anthropogenic influences on coliform concentrations and the urgent need to implement an efficient cold chain throughout octopus production stages with adequate handling practices to reverse this situation.

Persistence of E. coli in Streambed Sediment Contaminated with Faeces from Dairy Cows, Geese, and Deer: Legacy Risks to Environment and Health

Legacy stores of faecal pollution in streambed sediments can result in delayed impacts on environmental quality and human health if resuspended into the overlying water column. Different catchment sources of faecal pollution can contribute to a legacy store of microbial pollutants, with size of stores influenced by microbial die-off and faecal accrual rates in the streambed. The aim of this study was to use a mesocosm experiment to characterise the persistence of E. coli derived from faeces of dairy cows, deer, and geese once introduced to streambed sediment under different temperature regimes. The settling rate of solid constituents of faecal material into streambed sediment once delivered into an aquatic environment was also quantified. The persistence patterns of E. coli in streambed sediment were found to vary as a function of faecal source and temperature; die-off of E. coli in sediment contaminated with goose faeces was more rapid than in sediments contaminated with dairy cow or deer faeces. Goose faeces also recorded a more rapid settling rate of faecal particles through the water column relative to dairy cow and deer faeces, suggesting a more efficient delivery of E. coli to streambed sediments associated with this faecal source. Our findings provide new evidence to improve understanding of the potential longer-term risks to both the environment and public health posed by sediments when contaminated with livestock, wildlife, and wildfowl faeces.

Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA

Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Watershed characteristics, such as urban land, are often associated with impaired microbial water quality. Within the proximity of the New York-New Jersey-Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act's 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The log₁₀ geometric means of E. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. The results indicate that upstream urban land was positively and significantly associated with the log₁₀ geometric mean concentrations of E. coli for all sampling cases and during storm events, respectively (p < 0.05). Prediction of E. coli concentrations by SSN models identified potential hot spots prone to water quality deterioration. The results emphasize that anthropogenic sources were the main threats to microbial water quality in the suburban Musconetcong River watershed. The SSN modeling approaches from this study can serve as a novel microbial water quality modeling framework for other watersheds to identify key land use stressors to guide future urban and suburban water quality restoration directions in the USA and beyond.

Escherichia coli runoff from sheep and dairy cow grazed pasture: A plot scale simulation

Animal agriculture is recognized as a key source of fecal microbial impacts on water quality and associated risks to human health. Most of the research effort has focused on losses of fecal microbes from cow/cattle feces with little research effort on sheep fecal risks. The literature on fecal microbial risks from pasture is complicated by the fact that experiments are carried out in different environments leading to difficulties in making direct comparisons between sheep and cow/cattle losses from pasture areas. In this study, a plot scale simulation was conducted on the same pasture plots, using simulated rainfall to generate comparable runoff conditions, and using simulated grazing to create similar relative stocking rates. The Escherichia coli concentrations in the runoff were similar from simulated or natural rainfall events. At an equivalent stocking rate, the E. coli runoff concentrations from the sheep grazed pastures were four times higher than the cow grazed pasture. These results show that at an equivalent stocking rate, the E. coli runoff risk from sheep grazed pasture is higher than for cow grazed pasture. Further research is needed to understand the relative impacts of different grazing species of animals as well as stocking rate or management effects on these relative risks to water quality.

Predicting in-stream water quality constituents at the watershed scale using machine learning

Predicting in-stream water quality is necessary to support the decision-making process of protecting healthy waterbodies and restoring impaired ones. Data-driven modeling is an efficient technique that can be used to support such efforts. Our objective was to determine if in-stream concentrations of contaminants, nutrients-total phosphorus (TP) and total nitrogen (TN) -total suspended solids (TSS), dissolved oxygen (DO), and fecal coliform bacteria (FC) can be predicted satisfactorily using machine learning (ML) algorithms based on publicly available datasets. To achieve this objective, we evaluated four modeling scenarios, differing in terms of the required inputs (i.e., publicly available datasets (e.g., land-use/land cover)), antecedent conditions, and additional in-stream water quality observations (e.g., pH and turbidity). We implemented five ML algorithms-Support Vector Machines, Random Forest (RF), eXtreme Gradient Boost (XGB), ensemble RF-XGB, and Artificial Neural Network (ANN) -and demonstrated our modeling framework in an inland stream-Bullfrog Creek, located near Tampa, Florida. The results showed that, while including additional water quality drivers improved overall model performance for all target constituents, TP, TN, DO, and TSS could still be predicted satisfactorily using only publicly available datasets (Nash-Sutcliffe efficiency [NSE] > 0.75 and percent bias [PBIAS] < 10%), whereas FC could not (NSE < 0.49 and PBIAS >25%). Additionally, antecedent conditions slightly improved predictions and reduced the predictive uncertainty, particularly when paired with other water quality observations (6.9% increase in NSE for FC, and 2.7% for TP, TN, DO, and TSS). Also, comparable model performances of all water quality constituents in wet and dry seasons suggest minimal season-dependence of the predictions (<4% difference in NSE and < 10% difference in PBIAS). Our developed modeling framework is generic and can serve as a complementary tool for monitoring and predicting in-stream water quality constituents.

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.

Difficulties in the Modeling of E. coli Spreading from Various Sources in a Coastal Marine Area

Coastal and transitional waters are often used as bathing waters. In many regions, such activities play an important economic role. According to the European Union Bathing Water Directive (2006/7/EC) (BWD) the concentration of Escherichia coli in bathing water exceeding 500 CFU·100 mL⁻¹ poses a high risk for bathers’ health. In order to safeguard public health, microbiological environmental monitoring is carried out, which has recently been supported or replaced by mathematical models detailing the spread of sanitary contamination. This study focuses on the problems and limitations that can be encountered in the process of constructing a mathematical model describing the spread of biological contamination by E. coli bacteria in coastal seawater. This and other studies point to the following problems occurring during the process of building and validating a model: the lack of data on loads of sanitary contamination (often connected with multiple sources of biological contamination inflow) makes the model more complex; E. coli concentrations higher than 250 CFU·100 mL⁻¹ (low hazard for health) are observed very rarely, and are associated with great uncertainty; the impossibility of predicting the time and intensity of precipitation as well as stronger winds and rougher sea, which may be a significant source of E. coli. However, there is universal agreement that such models will be useful in managing bathing water quality and protecting public health, especially during big failures of the wastewater network.

Intra-daily variation of Escherichia coli concentrations in agricultural irrigation ponds

Microbial water quality is determined by comparing observed Escherichia coli concentrations with regulatory thresholds. Measured concentrations can be expected to change throughout the course of a day in response to diurnal variation in environmental conditions, such as solar radiation and temperature. Therefore, the time of day at which samples are taken is an important factor within microbial water quality measurements. However, little is known about the diurnal variations of E. coli concentrations in surface sources of irrigation water. The objectives of this work were to evaluate the intra-daily dynamics of E. coli in three irrigation ponds in Maryland over several years and to determine the water quality parameters to which E. coli populations are most sensitive. Water sampling was conducted across the ponds at 0900, 1200, and 1500 h on a total of 17 dates in the summers of 2019-2021. One-way ANOVA revealed significant diurnal variability in E. coli concentrations in Pond (P)1 and P2, whereas no significant effects were observed in P3. Escherichia coli die-off rates calculated between sampling time points in the same day were significantly higher in P2 than in P1 and P3, and these rates ranged from 0.005 to 0.799 h^-1 across ponds. Concentrations of dissolved oxygen, pH, conductivity, and turbidity exerted the most control over E. coli populations. Results of this work demonstrate that sampling in the early-morning hours provides the most conservative assessment of the microbial quality of irrigation waters.

Relationships between extreme flows and microbial contamination in inland recreational swimming areas

Inland recreational swimming sites provide significant social value globally. This study focused on public recreational swimming sites across the Murrumbidgee River and its tributaries in the Australian Capital Territory (ACT) throughout the swimming season (September-April) from 2009 to 2020 to determine whether high intestinal enterococci concentrations could be predicted with flow exceedance and routinely monitored physical and chemical parameters of water quality. Enterococci concentrations were positively correlated with the turbidity associated with high-flow conditions. The predictive accuracy of high enterococci levels during high-flow conditions was good (mean percentage correctly classified, 60%). The prediction of high enterococci levels at low flows was significantly less reliable (mean percentage correctly classified, 12-15%). As the ACT is expected to experience decreases in rainfall overall but increases in extreme rainfall events due to climate change, understanding the drivers of elevated intestinal enterococci under extreme flow conditions remains important from a public health perspective.

Modeling Contaminant Microbes in Rivers During Both Baseflow and Stormflow

Rivers transport contaminant microorganisms (including fecal indicator bacteria and human pathogens) long distances downstream of diffuse and point sources, posing a human health risk. We present a mobile-immobile model that incorporates transport as well as immobilization and remobilization of contaminant microbes and other fine particles during baseflow and stormflow. During baseflow conditions, hyporheic exchange flow causes particles to accumulate in streambed sediments. Remobilization of stored particles from streambed sediments occurs slowly during baseflow via hyporheic exchange flow, while remobilization is vastly increased during stormflow. Model predictions are compared to observations over a range of artificial and natural flood events in the dairy contaminated Topehaehae Stream, New Zealand. The model outputs closely matched timing and magnitude of E. coli and turbidity observations through multiple high-flow events. By accounting for both state-of-flow and hyporheic exchange processes, the model provides a valuable framework for predicting particle and contaminant microbe behavior in streams.

Removal of Fecal Indicator Bacteria by River Networks

Fecal contamination is a significant source of water quality impairment globally. Aquatic ecosystems can provide an important ecosystem service of fecal contamination removal. Understanding the processes that regulate the removal of fecal contamination among river networks across flow conditions is critical. We applied a river network model, the Framework for Aquatic Modeling in the Earth System (FrAMES-Ecoli), to quantify removal of fecal indicator bacteria by river networks across flow conditions during summers in a series of New England watersheds of different characteristics. FrAMES-Ecoli simulates sources, transport, and riverine removal of Escherichia coli (E. coli). Aquatic E. coli removal was simulated in both the water column and the hyporheic zone, and is a function of hydraulic conditions, flow exchange rates with the hyporheic zone, and die-off in each compartment. We found that, at the river network scale during summers, removal by river networks can be high (19–99%) with variability controlled by hydrologic conditions, watershed size, and distribution of sources in the watershed. Hydrology controls much of the variability, with 68–99% of network scale inputs removed under base flow conditions and 19–85% removed during storm events. Removal by the water column alone could not explain the observed pattern in E. coli, suggesting that processes such as hyporheic removal must be considered. These results suggest that river network removal of fecal indicator bacteria should be taken into consideration in managing fecal contamination at critical downstream receiving waters.

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

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

Indexes for the assessment of bacterial pollution in bathing waters from point sources: The northern Adriatic Sea CADEAU service

This paper presents a novel set of water quality indexes to identify the area potentially affected by point sources of bacterial pollution in coastal bathing waters. The indexes, developed in the framework of the CADEAU service, are evaluated on the results of a modelling system based on the integration of a high-resolution ocean model, remote sensing observations and in situ monitoring data for the northern Adriatic Sea. In particular, the system is a downscaling of the Mediterranean Copernicus Marine Environment Monitoring Service and exploits data produced within the Bathing Waters Directive, the Water Framework Directive and the Urban Waste Water Treatment Directive to create added value products. The aim of the proposed indexes is to support the identification of areas of influence for bathing waters by identifying the potential threat from point sources of bacterial pollution, both in standard conditions and peculiar events such as a total bypass of wastewater treatment plants. The results for the Chioggia Municipality case study show the potential of the indexes to significantly improve the geographical identification and quantitative evaluation of the impacts of bacterial pollution sources on bathing waters, facilitating the design of mitigation measures. The proposed methodology represents a new management approach to support local authorities in defining the area of influence within the water bathing profile through the proper characterization of the point sources of bacterial pollution.

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

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

Parsimonious Mechanistic Modeling of Bacterial Runoff into Irrigation Ponds To Inform Food Safety Management of Agricultural Water Quality

Pond irrigation water comprises a major pathway of pathogenic bacteria to fresh produce. Current regulatory methods have been shown to be ineffective in assessing this risk when variability of bacterial concentrations is large. This paper proposes using mechanistic modeling of bacterial transport as a way to identify improved strategies for mitigating this risk pathway. If the mechanistic model is successfully tested against observed data, global sensitivity analysis (GSA) can identify important mechanisms to inform alternative, preventive bacterial control practices. Model development favored parsimony and prediction of peak bacterial concentration events. Data from two highly variable surface water irrigation ponds showed that the model performance was similar or superior to that of existing pathogen transport models, with a Nash-Sutcliffe efficiency of 0.48 and 0.18 for the two ponds. GSA quantified bacterial sourcing and hydrology as the most important processes driving pond bacterial contamination events. Model analysis has two main implications for improved regulatory methods: that peak concentration events are associated with runoff-producing rainfall events and that intercepting bacterial runoff transport may be the best option to prevent bacterial contamination of surface water irrigation ponds and thus fresh produce. This research suggests the need for temporal management strategies. IMPORTANCE Preventive management of agricultural waters requires understanding of the drivers of bacterial contamination events. We propose mechanistic modeling as a way forward to understand and predict such events and have developed and tested a parsimonious model for rain-driven surface runoff contributing to generic Escherichia coli contamination of irrigation ponds in Central Florida. While the model was able to predict the timing of peak events reasonably well, the highly variable magnitude of the peaks was less well predicted. This indicates the need to collect more data on the fecal contamination inputs of these ponds and the use of mechanistic modeling and global sensitivity analysis to identify the most important data needs.

Predicting algal blooms: Are we overlooking groundwater?

Significant advances in understanding and predicting freshwater algal bloom dynamics have emerged in response to both increased occurrence and financial burden of nuisance and harmful blooms. Several factors have been highlighted as key controls of bloom occurrence, including nutrient dynamics, local hydrology, climatic perturbations, watershed geomorphology, biogeochemistry, food-web control, and algal competition. However, a major research gap continues to be the degree to which groundwater inputs modulate microbial biomass production and food-web dynamics at the terrestrial-aquatic interface. We present a synthesis of groundwater related algal bloom literature, upon which we derive a foundational hypothesis: long residence times cause groundwater to be geochemically and biologically distinct from surface water, allowing groundwater inputs to modulate algal bloom dynamics (growth, decline, toxicity) through its control over in-stream water chemistry. Distinct groundwater chemistry can support or prevent algal blooms, depending on specific local conditions. We highlight three mechanisms that influence the impact of groundwater discharge on algal growth: 1) redox state of the subsurface, 2) extent of water-rock interactions, and 3) stability of groundwater discharge. We underscore that in testing hypotheses related to groundwater control over algal blooms, it is critical to understand how changes in land use, water management, and climate will influence groundwater dynamics and, thus, algal bloom probabilities. Given this challenge, we argue that advances in both modeling and data integration, including genomics data and integrated process-based models that capture groundwater dynamics, are needed to illuminate mechanistic controls and improve predictions of algal blooms.

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

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

Role of Environmental Variables in the Transport of Microbes in Stormwater

Microbial pathogens present in stormwater, which originate from human sewage and animal faecal matters, are one of the major impediments in stormwater reuse. The transport of microbes in stormwater is more than just a physical process. The mobility of microbes in stormwater is governed by many factors, such as dissolved organic matter, cations, pH, temperature and water flow. This paper examined the roles of three environmental variables, namely: dissolved organic matter, positive cations and stormwater flow on the transport of two faecal indicator bacteria (FIB), Enterococcus spp. and Escherichia coli. Stormwater runoff samples were collected during twelve wet weather events and one dry weather event from a medium density residential urban catchment in Brisbane. Enterococcus spp. numbers as high as 3 × 104 cfu/100 mL were detected in the stormwater runoff, while Escherichia coli numbers up to 3.6 × 103 cfu/100 mL were observed. The dissolved organic carbon (DOC) in the stormwater samples was in the range of 2.2–5.9 mg/L with an average concentration of 4.5 mg/L in which the hydrophilic carbon constituted the highest mass fraction of 60–80%. The results also showed that the transport of FIB in stormwater was reduced with an increasing concentration of the hydrophilic organic fraction, especially the humic fraction. On the contrary, the concentration of trivalent cations and stormwater flow rate showed a positive correlation with the FIB numbers. These findings indicated the potentiality to make a good use and measurement of simple environmental variables to reflect the degree of microbe transport in stormwater from residential/suburban catchments.

Integrating Metagenomic and Bayesian Analyses to Evaluate the Performance and Confidence of CrAssphage as an Indicator for Tracking Human Sewage Contamination in China

Recently, crAssphage has been proposed as a human-specific marker for tracking fecal contamination. However, its performance has always been validated in a limited number of host samples, which may obscure our understanding of its utility. Furthermore, few studies have quantified confidence of fecal contamination when using crAssphage. Here, we evaluate the performance and confidence of crAssphage by analyzing a large panel of metagenomic data sets combined with Bayesian analyses. Results demonstrate that crAssphage exhibits superior performance with high host sensitivity and specificity, indicating its suitability for tracking human fecal sources. With the marker, a high confidence (>90%) can be obtained and particularly, multiple samples with positive results provide a near certainty of confidence. The application of crAssphage in the sediments of three Chinese urban rivers shows a high confidence of >97% of human fecal contamination, suggesting the serious challenge of sewage pollution in these environments. Additionally, significant correlation is observed between crAssphage and antibiotic resistance genes (ARGs), expanding the utilization of crAssphage for pollution management of ARGs. This study highlights the benefit of using metagenomic-based analysis for evaluating the performance and confidence of microbial source tracking markers in the coming era of big data with increasing resources in available metagenomic data.

Experimental and modelling evidence of splash effects on manure borne Escherichia coli washoff

In tropical montane South-East Asia, recent changes in land use have induced increased runoff, soil erosion and in-stream suspended sediment loads. Land use change is also contributing to increased microbial pathogen dissemination and contamination of stream waters. Escherichia coli (E. coli) is frequently used as an indicator of faecal contamination. Field rain simulations were conducted to examine how E. coli is exported from the surface of upland, agricultural soils during runoff events. The objectives were to characterize the loss dynamics of this indicator from agricultural soils contaminated with livestock waste, and to identify the effect of splash on washoff. Experiments were performed on nine 1 m2 plots, amended or not with pig or poultry manure. Each plot was divided into two 0.5 m2 sub-plots. One of the two sub-plots was protected with a mosquito net for limiting the raindrop impact effects. Runoff, soil detachment by raindrop impact and its entrainment by runoff, and E. coli loads and discharge were measured for each sub-plot. The results show that raindrop impact strongly enhances runoff generation, soil detachment and entrainment and E. coli export. When the impact of raindrops was reduced with a mosquito net, total runoff was reduced by more than 50%, soil erosion was on average reduced by 90% and E. coli export from the amended soil surface was on average 3 to 8 times lower. A coupled physics-based approach was performed using the Cast3M platform for modelling the time evolutions of runoff, solid particles detachment and transfer and bacteria transport that were measured for one of the nine plots. After estimation of the saturated hydraulic conductivity, soil erodibility and attachment rate of bacteria, model outputs were consistent with measured runoff coefficients, suspended sediment and E. coli loads. This work therefore underlines the need to maintain adequate vegetation at the soil surface to avoid the erosion and export of soil borne potential pathogens towards downstream aquatic systems.

Evaluation of the soil and water assessment tool (SWAT) for simulating E. coli concentrations at the watershed-scale

Water quality management at the watershed level requires a framework to identify sources, apportion water quality risks and develop mitigation strategies to reduce health risks. Watershed-scale models have been used as a support tool to understand the sources, fate and transport of fecal bacteria and pathogens in the environment. The Soil and Water Assessment Tool (SWAT) model was applied in this study to understand the sources and drivers of microbial water quality in the Clouds Creek watershed in Georgia, USA. A criterion to evaluate the performance of the SWAT bacterial model was also developed in this study using the Nash-Sutcliffe Efficiency (NSE) performance measure. The SWAT model was successfully calibrated and validated for flow with Nash-Sutcliffe Efficiency (NSE) of 0.81 and 0.55, respectively. Escherichia coli (E. coli) predictions were good with NSE of 0.32 and 0.34 for the calibration and validation timeframes, respectively. Based on the criteria developed in this study, SWAT bacterial model for E. coli and fecal coliform can be judged as "satisfactory" when NSE > 0.20. The contribution of sources followed this order: in-stream cattle manure deposition > cattle manure application > poultry manure application > septic systems > wildlife manure, suggesting that a reduction in livestock access to streams would be the most effective approach to reduce fecal bacterial loads in this watershed and others impacted by fecal contamination. Finally, our results suggest that the SWAT model is capable of simulating E. coli dynamics in the Clouds Creek watershed and can provide insights into source impacts for risk management.

Chronic urban hotspots and agricultural drainage drive microbial pollution of karst water resources in rural developing regions

Contamination of surface and groundwater systems with human and animal faecal matter leads to exposure of reliant populations to disease causing micro-organisms. This exposure route remains a major cause of infection and mortality in developing countries, particularly rural regions. To meet the UN's sustainable development goal 6: Ensure availability and sustainable management of water and sanitation for all, we need to identify the key controls on faecal contamination across relevant settings. We conducted a high-resolution spatial study of E. coli concentration in catchment drainage waters over 6 months in a mixed land-use catchment in the extensive karst region extending across impoverished southwest China. Using a mixed effects modelling framework, we tested how land-use, karst hydrology, antecedent meteorological conditions, agricultural cycles, hydrochemistry, and position in the catchment system affected E. coli concentrations. Land-use was the best predictor of faecal contamination levels. Sites in urban areas were chronically highly contaminated, but water draining from agricultural land was also consistently contaminated and there was a catchment wide pulse of higher E. coli concentrations, turbidity, and discharge during paddy field drainage. E. coli concentration increased with increasing antecedent rainfall across all land-use types and compartments of the karst hydrological system (underground and surface waters), but decreased with increasing pH. This is interpreted to be a result of processes affecting pH, such as water residence time, rather than the direct effect of pH on E. coli survival. Improved containment and treatment of human waste in areas of higher population density would likely reduce contamination hotspots, and further research is needed to identify the nature and distribution of sources in agricultural land.

Trophic Conditions Influence Widespread Distribution of Aster-Like Nanoparticles Within Aquatic Environments

Aster-like nanoparticles (ALNs) are newly described femto-entities. Their ecology (e.g., geographic distribution, spatial dynamic, preferences, forcing factors) is still unknown. Here, we report that these entities, which have largely been ignored until now, can develop or maintain themselves in most aquatic environments in the Loire River catchment, France. We observed a significant influence of the trophic state on ALN ecological distributions. A positive relationship between prokaryotic abundance and ALN (r² = 0.72, p < 0.01) has been identified, but its exact nature remains to be clarified. Combined with their ubiquitous distribution and high abundances (up to 7.9 × 10⁶ ALNs mL^-1) recorded in our samples, this probably makes ALNs an overlooked functional component in aquatic ecosystems.

Data assimilation in surface water quality modeling: A review

Data assimilation (DA) techniques are powerful means of dynamic natural system modeling that allow for the use of data as soon as it appears to improve model predictions and reduce prediction uncertainty by correcting state variables, model parameters, and boundary and initial conditions. The objectives of this review are to explore existing approaches and advances in DA applications for surface water quality modeling and to identify future research prospects. We first reviewed the DA methods used in water quality modeling as reported in literature. We then addressed observations and suggestions regarding various factors of DA performance, such as the mismatch between both lateral and vertical spatial detail of measurements and modeling, subgrid heterogeneity, presence of temporally stable spatial patterns in water quality parameters and related biases, evaluation of uncertainty in data and modeling results, mismatch between scales and schedules of data from multiple sources, selection of parameters to be updated along with state variables, update frequency and forecast skill. The review concludes with the outlook section that outlines current challenges and opportunities related to growing role of novel data sources, scale mismatch between model discretization and observation, structural uncertainty of models and conversion of measured to simulated vales, experimentation with DA prior to applications, using DA performance or model selection, the role of sensitivity analysis, and the expanding use of DA in water quality management.

The impact of manure and soil texture on antimicrobial resistance gene levels in farmlands and adjacent ditches

Manure application can spread antimicrobial resistance (AMR) from manure to soil and surface water. This study evaluated the role of the soil texture on the dynamics of antimicrobial resistance genes (ARGs) in soils and surrounding surface waters. Six dairy farms with distinct soil textures (clay, sand, and peat) were sampled at different time points after the application of manure, and three representative ARGs sul1, erm(B), and tet(W) were quantified with qPCR. Manuring initially increased levels of erm(B) by 1.5 ± 0.5 log copies/kg of soil and tet(W) by 0.8 ± 0.4 log copies/kg across soil textures, after which levels gradually declined. In surface waters from clay environments, regardless of the ARG, the gene levels initially increased by 2.6 ± 1.6 log copies/L, after which levels gradually declined. The gene decay in soils was strongly dependent on the type of ARG (erm(B) < tet(W) < sul1; half-lives of 7, 11, and 75 days, respectively), while in water, the decay was primarily dependent on the soil texture adjacent to the sampled surface water (clay < peat < sand; half-lives of 2, 6, and 10 days, respectively). Finally, recovery of ARG levels was predicted after 29-42 days. The results thus showed that there was not a complete restoration of ARGs in soils between rounds of manure application. In conclusion, this study demonstrates that rather than showing similar dynamics of decay, factors such as the type of ARG and soil texture drive the ARG persistence in the environment.

Impact of Freeze-Thaw Cycles on Die-Off of E. coli and Intestinal Enterococci in Deer and Dairy Faeces: Implications for Landscape Contamination of Watercourses

Characterising faecal indicator organism (FIO) survival in the environment is important for informing land management and minimising public health risk to downstream water users. However, key gaps in knowledge include understanding how wildlife contribute to catchment-wide FIO sources and how FIO survival is affected by low environmental temperatures. The aim of this study was to quantify E. coli and intestinal enterococci die-off in dairy cow versus red deer faecal sources exposed to repeated freeze–thaw cycles under controlled laboratory conditions. Survival of FIOs in water exposed to freeze–thaw was also investigated to help interpret survival responses. Both E. coli and intestinal enterococci were capable of surviving sub-freezing conditions with the faeces from both animals able to sustain relatively high FIO concentrations, as indicated by modelling, and observations revealing persistence in excess of 11 days and in some cases confirmed beyond 22 days. Die-off responses of deer-derived FIOs in both faeces and water exposed to low temperatures provide much needed information to enable better accounting of the varied catchment sources of faecal pollution and results from this study help constrain the parameterisation of die-off coefficients to better inform more integrated modelling and decision-making for microbial water quality management.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 1: Background and model description

A new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is developed that attempts to overcome limitations in existing faecal indicator organism (FIO) models arising from coarse spatial discretisations and poorly-constrained hydrological processes. MAFIO is a spatially-distributed, process-based model presently designed to simulate the fate and transport of agents representing FIOs shed by livestock at the sub-field scale in small (<10 km²) agricultural catchments. Specifically, FIO loading, die-off, detachment, surface routing, seepage and channel routing are modelled on a regular spatial grid. Central to MAFIO is that hydrological transfer mechanisms are simulated based on a hydrological environment generated by an external model for which it is possible to robustly determine the accuracy of simulated catchment hydrological functioning. The spatially-distributed, tracer-aided ecohydrological model EcH₂O-iso is highlighted as a possible hydrological environment generator. The present paper provides a rationale for and description of MAFIO, whilst a companion paper applies the model in a small agricultural catchment in Scotland to provide a proof-of-concept.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 2: Application to a small agricultural catchment

The new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is applied to a small (0.42 km²) Scottish agricultural catchment to simulate the dynamics of E. coli arising from sheep and cattle farming, in order to provide a proof-of-concept. The hydrological environment for MAFIO was simulated by the "best" ensemble run of the tracer-aided ecohydrological model EcH₂O-iso, obtained through multi-criteria calibration to stream discharge (MAE: 1.37 L s^-1) and spatially-distributed stable isotope data (MAE: 1.14-3.02‰) for the period April-December 2017. MAFIO was then applied for the period June-August for which twice-weekly E. coli loads were quantified at up to three sites along the stream. Performance in simulating these data suggested the model has skill in capturing the transfer of faecal indicator organisms (FIOs) from livestock to streams via the processes of direct deposition, transport in overland flow and seepage from areas of degraded soil. Furthermore, its agent-based structure allowed source areas, transfer mechanisms and host animals contributing FIOs to the stream to be quantified. Such information is likely to have substantial value in the context of designing and spatially-targeting mitigation measures against impaired microbial water quality. This study also revealed, however, that avenues exist for improving process conceptualisation in MAFIO (e.g. to include FIO contributions from wildlife) and highlighted the need to quantitatively assess how uncertainty in the spatial extent of surface flow paths in the simulated hydrological environment may affect FIO simulations. Despite the consequent status of MAFIO as a research-level model, its encouraging performance in this proof-of-concept study suggests the model has significant potential for eventual incorporation into decision support frameworks.

Utility of QMRA to compare health risks associated with alternative urban sewer overflow management strategies

Wet weather sewer overflows pose potential short-term public health risks. With increasing populations, aging infrastructure and climate change, utilities are challenged with managing sewerage infrastructure to provide optimum outcomes. This study compared how modelled public health risk profiles could change under alternative sewer overflow management strategies during 12 and 24-month rainfall-runoff events. Specifically, existing conditions were compared with both a 'business-as-usual' (BAU) sewer upgrade and a more holistic 'effects-based planning' (EBP) approach based on pumped wet weather sewage overflows directed to a local receiving waterway. Options were compared based on their efficacy to reduce manhole overflows, recreational waterway guideline exceedances and downstream recreational waterway health risks estimated through a screening-level Quantitative Microbial Risk Assessment (QMRA). Results indicated that the two management strategies would be equally effective in reducing the frequency, duration and volume of manhole sewer overflows, eliminating them in the 12-month scenarios and reducing them from >5000 m³ for the 24-month baseline scenario, to 23 and 35 m³ for BAU and EBP, respectively. Baseline, BAU and EBP scenarios produced similar hours of enterococci guideline exceedances, ranging from 1 to 4 h difference. The QMRA produced similar health risk profiles for downstream recreational waterway users for all design events, suggesting that sewer overflows are not the primary driver of public health risks during and immediately following high rainfall events. As such, QMRA provided evidence that an EBP strategy may be used to manage wet weather sewer overflows in lieu of an expensive BAU upgrade, without exacerbating the public health of downstream waterway users. Further investigation of the broader environmental health impacts of implementing this type of innovative approach is warranted. Nonetheless, this work highlights the value of integrating QMRA with other modelling approaches to guide and inform sewer overflow management.

Modelling the seasonal impacts of a wastewater treatment plant on water quality in a Mediterranean stream using microbial indicators

Faecal pollution modelling is a valuable tool to evaluate and improve water management strategies, especially in a context of water scarcity. The reduction dynamics of five faecal indicator organisms (E. coli, spores of sulphite-reducing clostridia, somatic coliphages, GA17 bacteriophages and a human-specific Bifidobacterium molecular marker) were assessed in an intermittent Mediterranean stream affected by a wastewater treatment plant (WWTP). Using Bayesian inverse modelling, the decay rates of each indicator were correlated with two environmental drivers (temperature and streamflow downstream of the WWTP) and the generated model was used to evaluate the self-depuration distance (SDD) of the stream. A consistent increase of 1-2 log₁₀ in the concentration of all indicators was detected after the discharge of the WWTP effluent. The decay rates showed seasonal variation, reaching a maximum in the dry season, when SDDs were also shorter and the stream had a higher capacity to self-depurate. High seasonality was observed for all faecal indicators except for the spores of sulphite-reducing clostridia. The maximum SDD ranged from 3 km for the spores of sulphite-reducing clostridia during the dry season and 15 km for the human-specific Bifidobacterium molecular marker during the wet season. The SDD provides a single standardized metric that integrates and compares different contamination indicators. It could be extended to other Mediterranean drainage basins and has the potential to integrate changes in land use and catchment water balance, a feature that will be especially useful in the transient climate conditions expected in the coming years.

Spatial and Temporal Characterization of Escherichia coli, Suspended Particulate Matter and Land Use Practice Relationships in a Mixed-Land Use Contemporary Watershed

Understanding land use practice induced increases in Escherichia (E.) coli and suspended particulate matter (SPM) concentrations is necessary to improve water quality. Weekly stream water samples were collected from 22 stream gauging sites with varying land use practices in a representative contemporary mixed-land use watershed of the eastern USA. Over the period of one annual year, Escherichia (E.) coli colony forming units (CFU per 100 mL) were compared to suspended particulate matter (SPM) concentrations (mg/L) and land use practices. Agricultural land use sub-catchments comprised elevated E. coli concentrations (avg. 560 CFU per 100 mL) compared to proximate mixed development (avg. 330 CFU per 100 mL) and forested (avg. 206 CFU per 100 mL) sub-catchments. Additionally, agricultural land use showed statistically significant relationships (p < 0.01) between annual E. coli and SPM concentration data. Quarterly PCA biplots displayed temporal variability in land use impacts on E. coli and SPM concentrations, with agricultural land use being closely correlated with both pollutants during Quarters 2 and 3 but not Quarters 1 and 4. The data collected during this investigation advance the understanding of land use impacts on fecal contamination in receiving waters, thereby informing land use managers on the best management practices to reduce exposure risks.

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

The last decades have seen the development of several source tracking (ST) markers to determine the source of pollution in water, but none of them show 100% specificity and sensitivity. Thus, a combination of several markers might provide a more accurate classification. In this study Ichnaea® software was improved to generate predictive models, taking into account ST marker decay rates and dilution factors to reflect the complexity of ecosystems. A total of 106 samples from 4 sources were collected in 5 European regions and 30 faecal indicators and ST markers were evaluated, including E. coli, enterococci, clostridia, bifidobacteria, somatic coliphages, host-specific bacteria, human viruses, host mitochondrial DNA, host-specific bacteriophages and artificial sweeteners. Models based on linear discriminant analysis (LDA) able to distinguish between human and non-human faecal pollution and identify faecal pollution of several origins were developed and tested with 36 additional laboratory-made samples. Almost all the ST markers showed the potential to correctly target their host in the 5 areas, although some were equivalent and redundant. The LDA-based models developed with fresh faecal samples were able to differentiate between human and non-human pollution with 98.1% accuracy in leave-one-out cross-validation (LOOCV) when using 2 molecular human ST markers (HF183 and HMBif), whereas 3 variables resulted in 100% correct classification. With 5 variables the model correctly classified all the fresh faecal samples from 4 different sources. Ichnaea® is a machine-learning software developed to improve the classification of the faecal pollution source in water, including in complex samples. In this project the models were developed using samples from a broad geographical area, but they can be tailored to determine the source of faecal pollution for any user.

Decay of infectious adenovirus and coliphages in freshwater habitats is differentially affected by ambient sunlight and the presence of indigenous protozoa communities

BACKGROUND

Sanitary quality of recreational waters worldwide is assessed using fecal indicator bacteria (FIB), such as Escherichia coli and enterococci. However, fate and transport characteristics of FIB in aquatic habitats can differ from those of viral pathogens which have been identified as main etiologic agents of recreational waterborne illness. Coliphages (bacteriophages infecting E. coli) are an attractive alternative to FIB because of their many morphological and structural similarities to viral pathogens.

METHODS

In this in situ field study, we used a submersible aquatic mesocosm to compare decay characteristics of somatic and F+ coliphages to those of infectious human adenovirus 2 in a freshwater lake. In addition, we also evaluated the effect of ambient sunlight (and associated UV irradiation) and indigenous protozoan communities on decay of somatic and F+ coliphage, as well as infectious adenovirus.

RESULTS

Our results show that decay of coliphages and adenovirus was similar (p = 0.0794), indicating that both of these bacteriophage groups are adequate surrogates for decay of human adenoviruses. Overall, after 8 days the greatest log10 reductions were observed when viruses were exposed to a combination of biotic and abiotic factors (2.92 ± 0.39, 4.48 ± 0.38, 3.40 ± 0.19 for somatic coliphages, F+ coliphages and adenovirus, respectively). Both, indigenous protozoa and ambient sunlight, were important contributors to decay of all three viruses, although the magnitude of that effect differed over time and across viral targets.

CONCLUSIONS

While all viruses studied decayed significantly faster (p < 0.0001) when exposed to ambient sunlight, somatic coliphages were particularly susceptible to sunlight irradiation suggesting a potentially different mechanism of UV damage compared to F+ coliphages and adenoviruses. Presence of indigenous protozoan communities was also a significant contributor (p value range: 0.0016 to < 0.0001) to decay of coliphages and adenovirus suggesting that this rarely studied biotic factor is an important driver of viral reductions in freshwater aquatic habitats.

Modeling the Effects of Future Hydroclimatic Conditions on Microbial Water Quality and Management Practices in Two Agricultural Watersheds

Anticipated future hydroclimatic changes are expected to alter the transport and survival of fecally-sourced waterborne pathogens, presenting an increased risk of recreational water quality impairments. Managing future risk requires an understanding of interactions between fecal sources, hydroclimatic conditions and best management practices (BMPs) at spatial scales relevant to decision makers. In this study we used the Hydrologic Simulation Program FORTRAN to quantify potential fecal coliform (FC - an indicator of the potential presence of pathogens) responses to a range of mid-century climate scenarios and assess different BMP scenarios (based on reduction factors) for reducing the risk of water quality impairment in two, small agricultural watersheds - the Chippewa watershed in Minnesota, and the Tye watershed in Virginia. In each watershed, simulations show a wide range of FC responses, driven largely by variability in projected future precipitation. Wetter future conditions, which drive more transport from non-point sources (e.g. manure application, livestock grazing), show increases in FC loads. Loads typically decrease under drier futures; however, higher mean FC concentrations and more recreational water quality criteria exceedances occur, likely caused by reduced flow during low-flow periods. Median changes across the ensemble generally show increases in FC load. BMPs that focus on key fecal sources (e.g., runoff from pasture, livestock defecation in streams) within a watershed can mitigate the effects of hydroclimatic change on FC loads. However, more extensive BMP implementation or improved BMP efficiency (i.e., higher FC reductions) may be needed to fully offset increases in FC load and meet water quality goals, such as total maximum daily loads and recreational water quality standards. Strategies for managing climate risk should be flexible and to the extent possible include resilient BMPs that function as designed under a range of future conditions.

Is returning farmland to forest an effective measure to reduce phosphorus delivery across distinct spatial scales?

As one typical land use change, the mechanism of returning farmland to forests (RFF) on nonpoint source pollution (NPS) is not clear, especially at multiple spatial scales. In this study, by using the Soil and Water Assessment Tool (SWAT), the changes in several flow-related and NPS-related indicators across several nested catchments were quantified and compared in the Three Gorges Reservoir Region, China. The results indicated that RFF could reduce the total flow and total phosphorus (TP), which are higher in the dry season (41% and 79%, respectively) than in the wet season (21% and 47%, respectively) at the watershed with a total area of 2423.74 km². In comparison, RFF has a larger impact on the baseflow index during the wet season (367.02%) than during the dry season (166.54%). The results also indicated that a spatial scaling effect did exist, while the reduction in TP increased from 24.57% to 48.46% as the drainage area increased from 65.92 km² to 2104.35 km². Specific thresholds of RFF efficiency were also observed (approximately 2000 km² for the study area). It is suggested that other source control measures could supplement RFF by stabilizing the efficiency of RFF across different spatial scales. The results of this study could provide valuable suggestions for land use development and water quality protection, especially for large, complex watersheds.

Rainfall-driven E. coli transfer to the stream-conduit network observed through increasing spatial scales in mixed land-use paddy farming karst terrain

Karst aquifers have distinctive hydrology and supply 25% of the world's population with drinking water, making them a critical geological setting for understanding and managing microbial water pollution. Rainfall causes elevated concentrations and loading of faecal microorganisms, e.g. E. coli, in catchment surface and groundwater systems, increasing the risk of human exposure to faecally-contaminated water. However, effective management of microbial water quality in complex karst catchments is constrained by limited understanding of E. coli - discharge responses to rainfall. We analysed how rainfall events of varying magnitude (2.4-100 mm) control E. coli-discharge dynamics at increasing spatial scales in a mixed land-use karst catchment in southwest China. During the wet season, hourly water sampling was undertaken throughout five storm events to characterise in high detail E. coli emergence with resulting flow across multiple sites of varying catchment area, stream order, and land-use. E. coli concentration was found to increase by 1-3 orders of magnitude following rainfall events. Maximum E. coli concentration and speed of E. coli recession were influenced by rainfall (amount, intensity), timing of agricultural activities, and position in the hydrological system. For high intensity events ∼90% of the cumulative E. coli export occurred within 48 h. E. coli concentration increased with increasing discharge at all sites. E. coli concentration at low discharge was higher in the headwaters than at the catchment outlet, while the rate of increase in E. coli concentration with increasing discharge appears to follow the opposite trend, being higher at the catchment outlet than the headwaters. This was attributed to the decreasing flow path gradient and increasing degree of development of the fissure network, but further event monitoring at varying catchment scales is required to confirm this relationship. The results provide novel insight into how rainfall characteristics combine with land-use and catchment hydrology to control E. coli export in karst landscapes.

Assessing Risk of E. coli Resuspension from Intertidal Estuarine Sediments: Implications for Water Quality

Estuarine sediments are a reservoir for faecal bacteria, such as E. coli, where they reside at greater concentrations and for longer periods than in the overlying water. Faecal bacteria in sediments do not usually pose significant risk to human health until resuspended into the water column, where transmission routes to humans are facilitated. The erosion resistance and corresponding E. coli loading of intertidal estuarine sediments was monitored in two Scottish estuaries to identify sediments that posed a risk of resuspending large amounts of E. coli. In addition, models were constructed in an attempt to identify sediment characteristics leading to higher erosion resistance. Sediments that exhibited low erosion resistance and a high E. coli loading occurred in the upper- and mid-reaches of the estuaries where sediments had higher organic content and smaller particle sizes, and arose predominantly during winter and autumn, with some incidences during summer. Models using sediment characteristics explained 57.2% and 35.7% of sediment shear strength and surface stability variance respectively, with organic matter content and season being important factors for both. However large proportions of the variance remained unexplained. Sediments that posed a risk of resuspending high amounts of faecal bacteria could be characterised by season and sediment type, and this should be considered in the future modelling of bathing water quality.