Direct and indirect hydrological controls on E. coli concentration and loading in midwestern streams

Short-term forecasting of fecal coliforms in shellfish growing waters

This study sought to develop models for predicting near-term (1-3 day) fecal contamination events in coastal shellfish growing waters. Using Random Forest regression, we (1) developed fecal coliform (FC) concentration models for shellfish growing areas using watershed characteristics and antecedent hydrologic and meteorologic observations as predictors, (2) tested the change in model performance associated when forecasted, as opposed to measured, rainfall variables were used as predictors, and (3) evaluated model predictor importance in relation to shellfish sanitation management criteria. Models were trained to 10 years of coastal FC measurements (n = 1285) for 5 major shellfish management areas along the Florida (USA) coast. Model performance varied between the 5 management areas with R2 ranging from 0.36 to 0.72. Antecedent precipitation variables were among the most important predictors in the day-of forecast models in all management areas. When forecasted rainfall was included in the models, wind components became increasingly important.

The impact of combined sewer outflows on urban water quality: Spatio-temporal patterns of fecal coliform in indianapolis

Many urban waterways with older stormwater drainage systems receive a significant amount of untreated or poorly treated waste from Combined Sewer Outflow (CSO) systems during precipitation events. The input of effluent waste from CSO to urban water streams during storm events often leads to elevated fecal coliform, specifically Escherichia Coli (E. Coli) in these waterways. The aim of the study is to examine fecal coliform concentration, water chemistry, and water quality parameters to better understand spatio-temporal patterns of fecal coliform associated with CSO events in three waterways from Indianapolis, Indiana (USA). The waterways are Pleasant Run Creek (PRW), Fall Creek (FC) and White River (WR). The sampling occurred biweekly over one year for PRW, nine months for FC, and an intense (∼every three days) sub-analysis of the presumed peak period of fecal coliform growth (July) for WR. All PRW and FC sampling sites significantly exceeded the EPA contact standard limit of 200 CFU/100 mL for fecal coliform concentrations during the sampling period. We found no relationship between fecal coliform levels and the number or density of CSO outfalls above a given site. The most significant predictors of increased fecal coliform concentrations were precipitation on the sampling day and cumulative degree days. The most significant predictors of decreased fecal coliform were maximum precipitation during the ten-day window prior to sampling and median discharge during a three-day window prior to sampling. These findings suggest a push-pull balance within the system where CSO activation and seasonal gradients replenish and promote fecal coliform growth. At the same time, large hydrologic events act to flush and dilute fecal coliform concentrations. The results from this study help us to better understand how different drivers influence fecal coliform growth and how this information can be potentially used to predict and remediate the conditions of urban water streams.

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM) clustering were also used to develop models for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although the majority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the other models. The ANFIS models have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season.

Demonstration of Tryptophan-Like Fluorescence Sensor Concepts for Fecal Exposure Detection in Drinking Water in Remote and Resource Constrained Settings

Low-cost, field-deployable, near-time methods for assessing water quality are not available when and where waterborne infection risks are greatest. We describe the development and testing of a novel device for the measurement of tryptophan-like fluorescence (TLF), making use of recent advances in deep-ultraviolet light emitting diodes (UV-LEDs) and sensitive semiconductor photodiodes and photomultipliers. TLF is an emerging indicator of water quality that is associated with members of the coliform group of bacteria and therefore potential fecal contamination. Following the demonstration of close correlation between TLF and E. coli in model waters and proof of principle with sensitivity of 4 CFU/mL for E. coli, we further developed a two-LED flow-through configuration capable of detecting TLF levels corresponding to “high risk” fecal contamination levels (>10 CFU/100 mL). Findings to date suggest that this device represents a scalable solution for remote monitoring of drinking water supplies to identify high-risk drinking water in near-time. Such information can be immediately actionable to reduce risks.

Explaining and modeling the concentration and loading of Escherichia coli in a stream-A case study

Escherichia coli (E. coli) level in streams is a public health indicator. Therefore, being able to explain why E. coli levels are sometimes high and sometimes low is important. Using citizen science data from Fall Creek in central NY we found that complementarily using principal component analysis (PCA) and partial least squares (PLS) regression provided insights into the drivers of E. coli and a mechanism for predicting E. coli levels, respectively. We found that stormwater, temperature/season and shallow subsurface flow are the three dominant processes driving the fate and transport of E. coli. PLS regression modeling provided very good predictions under stormwater conditions (R² = 0.85 for log (E. coli concentration) and R² = 0.90 for log (E. coli loading)); predictions under baseflow conditions were less robust. But, in our case, both E. coli concentration and E. coli loading were significantly higher under stormwater condition, so it is probably more important to predict high-flow E. coli hazards than low-flow conditions. Besides previously reported good indicators of in-stream E. coli level, nitrate-/nitrite-nitrogen and soluble reactive phosphorus were also found to be good indicators of in-stream E. coli levels. These findings suggest management practices to reduce E. coli concentrations and loads in-streams and, eventually, reduce the risk of waterborne disease outbreak.

A Watershed Analysis of Seasonal Concentration- and Loading-based Results for Escherichia coli in Inland Waters

Fecal indicator bacteria, such as Escherichia coli, are frequently monitored in recreational waterbodies as indicators of potential fecal pathogen presence and exposure. The present watershed analysis investigated the influence of season on E. coli concentration (MPN/100 mL) and loading (MPN/day) measurements for inland waters at baseflow conditions. The master dataset collected during 2007-2012 for three watersheds included 896 E. coli (Colilert) samples with simultaneous flow taken for a subset (39 %) of samples. The outcomes for grouped watersheds were reflected in most cases for individual watersheds. Concentration- and loading-based results were highest in summer and spring, and lowest in the winter and fall, respectively. The comparison of these two measurement techniques (concentration and loading) highlight the impact of flow data during baseflow conditions for inland waters and reveal that caution should be used when inferring one method's results from another.

Cumulative effects of fecal contamination from combined sewer overflows: Management for source water protection

The quality of a drinking water source depends largely on upstream contaminant discharges. Sewer overflows can have a large influence on downstream drinking water intakes as they discharge untreated or partially treated wastewaters that may be contaminated with pathogens. This study focuses on the quantification of Escherichia coli discharges from combined sewer overflows (CSOs) and the dispersion and diffusion in receiving waters in order to prioritize actions for source water protection. E. coli concentrations from CSOs were estimated from monitoring data at a series of overflow structures and then applied to the 42 active overflow structures between 2009 and 2012 using a simple relationship based upon the population within the drainage network. From these estimates, a transport-dispersion model was calibrated with data from a monitoring program from both overflow structures and downstream drinking water intakes. The model was validated with 15 extreme events such as a large number of overflows (n > 8) or high concentrations at drinking water intakes. Model results demonstrated the importance of the cumulative effects of CSOs on the degradation of water quality downstream. However, permits are typically issued on a discharge point basis and do not consider cumulative effects. Source water protection plans must consider the cumulative effects of discharges and their concentrations because the simultaneous discharge of multiple overflows can lead to elevated E. coli concentrations at a drinking water intake. In addition, some CSOs have a disproportionate impact on peak concentrations at drinking water intakes. As such, it is recommended that the management of CSOs move away from frequency based permitting at the discharge point to focus on the development of comprehensive strategies to reduce cumulative and peak discharges from CSOs upstream of drinking water intakes.

Storm loads of culturable and molecular fecal indicators in an inland urban stream

Elevated concentrations of fecal indicator bacteria in receiving waters during wet-weather flows are a considerable public health concern that is likely to be exacerbated by future climate change and urbanization. Knowledge of factors driving the fate and transport of fecal indicator bacteria in stormwater is limited, and even less is known about molecular fecal indicators, which may eventually supplant traditional culturable indicators. In this study, concentrations and loading rates of both culturable and molecular fecal indicators were quantified throughout six storm events in an instrumented inland urban stream. While both concentrations and loading rates of each fecal indicator increased rapidly during the rising limb of the storm hydrographs, it is the loading rates rather than instantaneous concentrations that provide a better estimate of transport through the stream during the entire storm. Concentrations of general fecal indicators (both culturable and molecular) correlated most highly with each other during storm events but not with the human-associated HF183 Bacteroides marker. Event loads of general fecal indicators most strongly correlated with total runoff volume, maximum discharge, and maximum turbidity, while event loads of HF183 most strongly correlated with the time to peak flow in a hydrograph. These observations suggest that collection of multiple samples during a storm event is critical for accurate predictions of fecal indicator loading rates and total loads during wet-weather flows, which are required for effective watershed management. In addition, existing predictive models based on general fecal indicators may not be sufficient to predict source-specific genetic markers of fecal contamination.

Bacterial Pathogen Indicator Transport from Livestock Mortality Biopiles

Biopiles can be used to dispose of slaughterhouse residuals (SLRs); however, the fate of pathogenic bacteria (e.g., pathogenic strains of , ) in these systems is not well understood. The transport of these bacteria in water leaching from the biopile could represent a significant contamination source. This research examined the transport of Enterobacteriaceae and Enterococcaceae indicator bacteria from SLR biopiles. Three biopiles (2.6 m wide by 4.6 m long by 1.8 m high) were formed on soil layers in concrete cells that allowed for real-time monitoring of environmental parameters, hydrologic flux, and indicator bacteria levels in effluent leaching from the piles. In biopile effluent, indicator bacteria populations decreased exponentially following biopile formation. Indicator bacteria loads in effluent constituted <0.01% of the initial indicator bacteria levels in the biopiles, which was attributed to retention, inactivation, and death. Nearly 90% of the total indicator bacteria loads coincided with large precipitation events (>15 mm d). Movement of the indicator bacteria through the biopiles and underlying soil appeared to be consistent with preferential flow phenomena. The populations of the Enterobacteriaceae indicators remained low in conditions of higher soil water content and lower biopile temperatures, whereas the Enterococcaceae indicator appeared to regrow in these conditions. This indicated that bacterial pathogen transport from a biopile could be a concern after the disappearance of conventional bacterial indicators, such as . Management considerations should attempt to divert excess water from entering a biopile, such as locating a biopile under a roof. Unsaturated biopile and soil conditions should be maintained to impede water flow through preferential pathways in the soil underneath a biopile.

A short review of fecal indicator bacteria in tropical aquatic ecosystems: knowledge gaps and future directions

Given the high numbers of deaths and the debilitating nature of diseases caused by the use of unclean water it is imperative that we have an understanding of the factors that control the dispersion of water borne pathogens and their respective indicators. This is all the more important in developing countries where significant proportions of the population often have little or no access to clean drinking water supplies. Moreover, and notwithstanding the importance of these bacteria in terms of public health, at present little work exists on the persistence, transfer and proliferation of these pathogens and their respective indicator organisms, e.g., fecal indicator bacteria (FIB) such as Escherichia coli and fecal coliforms in humid tropical systems, such as are found in South East Asia or in the tropical regions of Africa. Both FIB and the waterborne pathogens they are supposed to indicate are particularly susceptible to shifts in water flow and quality and the predicted increases in rainfall and floods due to climate change will only exacerbate the problems of contamination. This will be furthermore compounded by the increasing urbanization and agricultural intensification that developing regions are experiencing. Therefore, recognizing and understanding the link between human activities, natural process and microbial functioning and their ultimate impacts on human health are prerequisites for reducing the risks to the exposed populations. Most of the existing work in tropical systems has been based on the application of temperate indicator organisms, models and mechanisms regardless of their applicability or appropriateness for tropical environments. Here, we present a short review on the factors that control FIB dynamics in temperate systems and discuss their applicability to tropical environments. We then highlight some of the knowledge gaps in order to stimulate future research in this field in the tropics.

Hydrometeorological and physicochemical drivers of fecal indicator bacteria in urban stream bottom sediments

High levels of fecal indicator bacteria (FIB) are the leading cause of surface water quality impairments in the United States. Watershed-scale models are commonly used to identify relative contributions of watershed sources and to evaluate the effectiveness of remediation strategies. However, most existing models simplify FIB transport behavior as equivalent to that of dissolved-phase contaminants, ignoring the impacts of sediment on the fate and transport of FIB. Implementation of sediment-related processes within existing models is limited by minimal available monitoring data on sediment FIB concentrations for model development, calibration, and validation purposes. The purpose of the present study is to evaluate FIB levels in the streambed sediments as compared to those in the water column and to identify environmental variables that influence water and underlying sediment FIB levels. Concentrations of and enterococci in the water column and sediments of an urban stream were monitored weekly for 1 yr and correlated with a variety of potential hydrometeorological and physicochemical variables. Increased FIB concentrations in both the water column and sediments were most strongly correlated with increased antecedent 24-h rainfall, increased stream water temperature, decreased dissolved oxygen, and decreased specific conductivity. These observations will support future efforts to incorporate sediment-related processes in existing models through the identification of key FIB relationships with other model inputs, and the provision of sediment FIB concentrations for direct model calibration. In addition, identified key variables can be used in quick evaluation of the effectiveness of potential remediation strategies.

Roadside ditches as conduits of fecal indicator organisms and sediment: implications for water quality management

Roadside ditches are ubiquitous, yet their role in water pollution conveyance has largely been ignored, especially for bacteria and sediment. The goal of this study was to determine if roadside ditches are conduits for fecal indicator organisms and sediment, and if land use, specifically manure amendment, affects the concentrations and loadings. Seven roadside ditches in central New York, adjacent to either manure amended fields or predominately forested land, were monitored for one year for Escherichia coli (E. coli), total suspended solids (TSS) and flow. E. coli concentrations in water samples following storms averaged 4616 MPN of E. coli/100 mL. Concentrations reached as high as >241,960 MPN of E. coli/100 mL and frequently exceeded New York State and US EPA recommendations. Concentrations peaked in both summers following manure spreading, with declining levels thereafter. However, viable organisms were detected throughout the year. The concentrations were also high in the forested sites, with possible sources including wildlife, pets, septic wastes and livestock. E. coli concentrations and loadings were related to TSS concentrations and loadings, whether manure had been spread in the last 30 days and for concentrations only, antecedent rainfall. Viable E. coli were also present in ditch sediment between storm events and were available for resuspension and transport. Total suspended solids concentrations averaged 0.51 g/L and reached as high as 52.2 g/L. Loads were similarly high, at an average of 631.6 kg/day. Both concentrations and loads tended to be associated with discharge and rainfall parameters. The cumulative pollutant contribution from the ditch network was estimated to be large enough to produce detectable and sometimes high concentrations in a receiving stream in a small, rural watershed. Roadside drainage networks need to be actively managed for water quality improvements, because they capture and rapidly shunt stormwater and associated contaminants to streams.

Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.

Modern space/time geostatistics using river distances: data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan River in New Jersey

Escherichia coli (E. coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E. coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E. coli at unmonitored locations. We obtained all E. coli and turbidity monitoring data available from existing monitoring networks for the 2000-2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements, we developed a predictive model of E. coli from turbidity data. Using this model, soft data are constructed for E. coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/time geostatistics was used for the data integration of monitored and predicted E. coli data to produce maps showing E. coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E coli impairment greater than 90% on the most polluted day of the study period.