Assessing transport timescales in Lake Huron's Hammond Bay: The crucial role of the Straits of Mackinac's exchange flows.

The oscillating bidirectional exchange flows between Lakes Michigan and Huron in the Straits of Mackinac generate complex hydrodynamics and the exchange flows are known to alter hydrodynamics in regions as far down as 50-60 km from the Straits modulating physical, chemical, and biological processes in the region. Although previous research examined the effects of exchange flows on hydrodynamics, their impacts on transport time scales, including residence and flushing times, have not been quantified. We used observations and a three-dimensional hydrodynamic model to simulate bidirectional exchange flows in the Straits and their effects on hydrodynamics, temperature, and transport timescales in the Hammond Bay area, Lake Huron for the summers of 2018 and 2019. Comparisons with field observations showed that hydrodynamics can only be accurately described when the bidirectional flows are included in the modeling of the bays close to the Straits. Spectral analysis showed that the exchange flows play an important role in controlling conservative solute transport in bays close to the Straits. The residence time in the Hammond Bay area was calculated using a dye release approach with (without) the effects of bidirectional exchange flows producing estimates of 9.87 (16.00) and 13.75 (23.62) days for years 2018 and 2019 respectively based on a combined model of the two lakes and a model of Lake Huron only. Similarly, flushing times in the Hammond Bay area were estimated as 12.14 (14.38) and 8.96 (10.80) days for 2018 and 2019, respectively with (without) the exchange flows. Ignoring the exchange flows in the Straits was found to overestimate the residence time and flushing time in the Hammond Bay area by roughly 74 and 20 %, respectively. These results highlight the importance of including the bidirectional exchange flows in biophysical models of bays in Lake Huron closer to the Straits and in similar systems elsewhere.

An integrated watershed-scale framework to model nitrogen transport and transformations.

Excess nitrogen in water bodies is associated with a number of environmental problems, including hypoxia and eutrophication. Originating from anthropogenic activities such as fertilizer application, and influenced by watershed characteristics such as the structure of the drainage network, stream discharge, temperature, and soil moisture, factors influencing nitrogen transport and transformation are many and interconnected. This paper describes the development and application of a process-oriented nitrogen model based on the modeling framework of PAWS (Process-based Adaptive Watershed Simulator) that can describe coupled hydrologic, thermal and nutrient processes. The integrated model was tested for an agricultural watershed with complex land use, namely the Kalamazoo River watershed in Michigan, USA. Nitrogen transport and transformations on the landscape were modeled by representing multiple sources and processes (fertilizer/manure application, point sources, atmospheric deposition, nitrogen retention and removal in wetlands and other lowland storage, etc.) across multiple hydrologic domains (streams, groundwater, soil water). The coupled model provides a tool to examine nitrogen budgets and to quantify the impacts of human activities and agricultural practices on the riverine export of nitrogen species. Model results indicate that the river network removed approximately 5.96 % of the total anthropogenic nitrogen input to the watershed, and that the riverine export of nitrogen accounted for 29.22 % of the total anthropogenic inputs during 2004-2009 while the groundwater contribution of nitrogen to the rivers during the same period was found to be 18.53 % highlighting the important role of groundwater within the watershed.

Improving safe sanitation practices using groundwater transport modelling and water quality monitoring data.

In many developing countries, poorly constructed pit latrines are the primary source of groundwater contamination. Approaches are needed to identify site-specific separation distances between domestic wells and pit latrines. In this study, tracer transport simulations are combined with water quality monitoring data to identify separation distances in peri-urban Dar es Salaam. Bivariate correlation and linear regression models were used to find the relationships between (1) simulated tracer and distances from the wells to the nearest pit latrines (2) simulated tracer and observed contaminants (nitrate, E. coli, and total dissolved solids). The results showed a strong correlation between tracer with nitrate and E. coli, with Pearson coefficient (r) values of 0.80 and 0.79, but a weak correlation with total dissolved solids (TDS) (r = 0.23). A strong correlation between tracer and distance for shallow and deep wells (r = -0.96, -0.76) was found. Based on the soil type and aquifer properties in the area, wells must be placed at least 34 m from a pit latrine to minimize contamination. With recent advances in sensor technologies and the availability of low-cost sensors, linking simulated tracer with observed contaminant levels may provide an alternative first approach to quickly assess human health risks associated with groundwater contamination.

Influence of Filter Pore Size on Composition and Relative Abundance of Bacterial Communities and Select Host-Specific MST Markers in Coastal Waters of Southern Lake Michigan.

Water clarity is often the primary guiding factor in determining whether a prefiltration step is needed to increase volumes processed for a range of microbial endpoints. In this study, we evaluate the effect of filter pore size on the bacterial communities detected by 16S rRNA gene sequencing and incidence of two host-specific microbial source tracking (MST) markers in a range of coastal waters from southern Lake Michigan, using two independent data sets collected in 2015 (bacterial communities) and 2016-2017 (MST markers). Water samples were collected from river, shoreline, and offshore areas. For bacterial communities, each sample was filtered through a 5.0-µm filter, followed by filtration through a 0.22-µm filter, resulting in 70 and 143 filter pairs for bacterial communities and MST markers, respectively. Following DNA extraction, the bacterial communities were compared using 16S rRNA gene amplicons of the V3-V4 region sequenced on a MiSeq Illumina platform. Presence of human (Bacteroides HF183) and gull (Gull2, Catellicoccus marimammalium) host-specific MST markers were detected by qPCR. Actinobacteriota, Bacteroidota, and Proteobacteria, collectively represented 96.9% and 93.9% of the relative proportion of all phyla in the 0.22- and 5.0-µm pore size filters, respectively. There were more families detected in the 5.0-µm pore size filter (368) than the 0.22-µm (228). There were significant differences in the number of taxa between the two filter sizes at all levels of taxonomic classification according to linear discriminant analysis (LDA) effect size (LEfSe) with as many as 986 taxa from both filter sizes at LDA effect sizes greater than 2.0. Overall, the Gull2 marker was found in higher abundance on the 5.0-µm filter than 0.22 µm with the reverse pattern for the HF183 marker. This discrepancy could lead to problems with identifying microbial sources of contamination. Collectively, these results highlight the importance of analyzing pre- and final filters for a wide range of microbial endpoints, including host-specific MST markers routinely used in water quality monitoring programs. Analysis of both filters may increase costs but provides more complete genomic data via increased sample volume for characterizing microbial communities in coastal waters.

Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach.

Beaches along the Great Lakes shorelines are important recreational and economic resources. However, contamination at the beaches can threaten their usage during the swimming season, potentially resulting in beach closures and/or advisories. Thus, understanding the dynamics that control nearshore water quality is integral to effective beach management. There have been significant improvements in this effort, including incorporating modeling (empirical, mechanistic) in recent years. Mechanistic modeling frameworks can contribute to this understanding of dynamics by determining sources and interactions that substantially impact fecal indicator bacteria concentrations, an index routinely used in water quality monitoring programs. To simulate E. coli concentrations at Jeorse Park beaches in southwest Lake Michigan, a coupled hydrodynamic and wave-current interaction model was developed that progressively added contaminant sources from river inputs, avian presence, bacteria-sediment interactions, and bacteria-sand-sediment interactions. Results indicated that riverine inputs affected E. coli concentrations at Jeorse Park beaches only marginally, while avian, shoreline sand, and sediment sources were much more substantial drivers of E. coli contamination at the beach. By including avian and riverine inputs, as well as bacteria-sand-sediment interactions at the beach, models can reasonably capture the variability in observed E. coli concentrations in nearshore water and bed sediments at Jeorse Park beaches. Consequently, it will be crucial to consider avian contamination sources and water-sand-sediment interactions in effective management of the beach for public health and as a recreational resource and to extend these findings to similar beaches affected by shoreline embayment.

Modeling the photoinactivation and transport of somatic and F-specific coliphages at a Great Lakes beach.

Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as surrogates of contamination in the context of beach management; however, bacteriophages may be more reliable indicators than FIO due to their similarity to viral pathogens in terms of size and persistence in the environment. In the past, mechanistic modeling of environmental contamination has focused on FIOs, with virus and bacteriophage modeling efforts remaining limited. In this paper, we describe the development and application of a fate and transport model of somatic and F-specific coliphages for the Washington Park beach in Lake Michigan, which is affected by riverine outputs from the nearby Trail Creek. A three-dimensional model of coliphage transport and photoinactivation was tested and compared with a previously reported E. coli fate and transport model. The light-based inactivation of the phages was modeled using organism-specific action spectra. Results indicate that the coliphage models outperformed the E. coli model in terms of reliably predicting observed E. coli/coliphage concentrations at the beach. This is possibly due to the presence of additional E. coli sources that were not accounted for in the modeling. The coliphage models can be used to test hypotheses about potential sources and their behavior and for predictive modeling.

Interaction of bacterial communities and indicators of water quality in shoreline sand, sediment, and water of Lake Michigan.

Shoreline sand harbors high concentrations of fecal indicator bacteria (FIB) that may be resuspended into the water column through washing and resuspension. Studies have explored coastal processes that influence this sand-water flux for FIB, but little is known about how microbial markers of contamination or the bacterial community interact in the sand-water interface. In this study, we take a three-tiered approach to explore the relationship between bacteria in sand, sediment, and overlying water at three shoreline sites and two associated rivers along an extended freshwater shoreline. Samples were collected over two years and analyzed for FIB, two microbial source tracking (MST) markers (Catellicoccus marimammalium, Gull2; Bacteroides HF183), and targeted metagenomic 16S rRNA gene analysis. FIB was much higher in sand than in water at all three sites. Gull2 marker was abundant in shoreline sand and water while HF183 marker was mostly present in rivers. Overall bacterial communities were dissimilar between sand/sediment and water, indicating little interaction. Sediment composition was generally unfavorable to bacterial resuspension. Results show that FIB and MST markers were effective estimates of short-term conditions at these locations, and bacterial communities in sand and sediment reflected longer-term conditions. Findings are useful for locating contamination sources and targeting restoration by evaluating scope of shoreline degradation.

Impact of domestic wells and hydrogeologic setting on water quality in peri-urban Dar es Salaam, Tanzania.

In densely populated urban areas of many low-income countries, water scarcity, poor water quality, and inadequate wastewater management present complex challenges to ensuring health and wellbeing. This study was conducted in an impoverished peri-urban community in Dar es Salaam, Tanzania that experiences water scarcity and relies on domestic wells for drinking water. The objective of this study was to identify the sources of domestic well water contamination and assess the relationship and association of water contamination with three variables 1) the proximity of the well to a sanitation system, 2) well age, and 3) well depth. Out of the 71 wells tested, samples from >80% of wells contained Escherichia coli (E. coli) and 58% had nitrate levels above WHO guidelines. The average concentration of total dissolved solids (TDS) was 882 mg/L, which exceeded the WHO guideline of 600 mg/L. Bivariate correlation analysis showed a strong correlation between water contamination and proximity of the well to a sanitation system along with well depth. Univariate regression analysis confirmed the association of contaminants with distance of a well from a sanitation system and well depth (p < 0.05) but age of the well did not show any significant influence on water quality. Our findings indicate significant contamination of wells from nearby septic tanks and pit latrines. New regulatory mandates for the distance of domestic wells from sanitation systems are essential to prevent groundwater contamination and to protect human health.

Impacts of a changing earth on microbial dynamics and human health risks in the continuum between beach water and sand.

Although infectious disease risk from recreational exposure to waterborne pathogens has been an active area of research for decades, beach sand is a relatively unexplored habitat for the persistence of pathogens and fecal indicator bacteria (FIB). Beach sand, biofilms, and water all present unique advantages and challenges to pathogen introduction, growth, and persistence. These dynamics are further complicated by continuous exchange between sand and water habitats. Models of FIB and pathogen fate and transport at beaches can help predict the risk of infectious disease from beach use, but knowledge gaps with respect to decay and growth rates of pathogens in beach habitats impede robust modeling. Climatic variability adds further complexity to predictive modeling because extreme weather events, warming water, and sea level change may increase human exposure to waterborne pathogens and alter relationships between FIB and pathogens. In addition, population growth and urbanization will exacerbate contamination events and increase the potential for human exposure. The cumulative effects of anthropogenic changes will alter microbial population dynamics in beach habitats and the assumptions and relationships used in quantitative microbial risk assessment (QMRA) and process-based models. Here, we review our current understanding of microbial populations and transport dynamics across the sand-water continuum at beaches, how these dynamics can be modeled, and how global change factors (e.g., climate and land use) should be integrated into more accurate beachscape-based models.

Quantifying the space - time variability of water balance components in an agricultural basin using a process-based hydrologic model and the Budyko framework.

Process-based distributed hydrologic models (PBHMs), which link watershed characteristics with process representations, are useful tools to evaluate distributed and ensemble hydrologic responses of a basin to climate inputs. However, complexities associated with parameter interactions and their spatial heterogeneity may introduce high uncertainty in the parameterization of a PBHM. The Budyko curve framework offers an effective approach for evaluating the variability in the water balance components from a PBHM and can be used to explore the link between model performance with parameter heterogeneities and the Budyko curve characteristics. In this work a PBHM was calibrated using a multi-site calibration strategy, which was built upon a step-wise calibration algorithm combined with multiple calibration targets including river discharges, evapotranspiration and ground water heads, to reduce compensation errors caused by component interactions. This strategy was used for the Kalamazoo River watershed in Michigan, USA, with obvious physiographic and land surface heterogeneities. The Budyko framework characterized the water balance variability at the sub-watershed scale; two empirical methods were used to evaluate the calibrated PBHM parameters using Budyko-estimated values and to assess the physical relevance of the parameters. The relative infiltration capacity is found to play an important role in affecting the spatial variability of the annual water balance of this watershed. This work brings out the importance of optimizing calibration strategies by linking catchment heterogeneities with processes reasoning in order to understand the underlying hydrologic controls.

Identifying and Eliminating Sources of Recreational Water Quality Degradation along an Urban Coast.

Restoration of highly degraded urban coastal waters often requires large-scale, complex projects, but in the interim, smaller-scale efforts can provide immediate improvements to water quality conditions for visitor use. We examined short-term efforts to improve recreational water quality near the Grand Calumet River (GC) in the Laurentian Great Lakes. Identified as an Area of Concern (AOC) by the International Joint Commission, the GC has experienced years of industrial and municipal waste discharges, and as a result, coastal beaches have some of the highest rates of beach closings (>70%) in the United States. Project objectives were to identify sources of microbial contamination and to evaluate a short-term management solution to decrease beach closings: during 2015 (partial) and 2016 (season-long), canines were used to deter gull presence. Water samples were analyzed for in 2015 and 2016, and fecal sources were evaluated using microbial source tracking markers (2015): human ( HF183, ), gull (Gull2), and dog (DogBact). Hydrometeorological conditions were simultaneously measured. Results indicated that human, gull, and canine fecal sources were present, with gulls being the dominant source. densities were highly correlated with number of gulls present, Gull2 marker, and turbidity. Gull deterrence decreased and Gull2 marker detection during 2015, but numbers rebounded after program completion. The full-season program in 2016 resulted in lower densities and fewer beach closings. Large-scale restoration efforts are underway at this location, but short-term, small-scale projects can be useful for reducing beach closings and restoring ecosystem services.

Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach.

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input-output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R2 values between data and the NARX models ( R2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

Comparative Evaluation of Statistical and Mechanistic Models of Escherichia coli at Beaches in Southern Lake Michigan.

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term "tracer" transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

Prototypic automated continuous recreational water quality monitoring of nine Chicago beaches.

Predictive empirical modeling is used in many locations worldwide as a rapid, alternative recreational water quality management tool to eliminate delayed notifications associated with traditional fecal indicator bacteria (FIB) culturing (referred to as the persistence model, PM) and to prevent errors in releasing swimming advisories. The goal of this study was to develop a fully automated water quality management system for multiple beaches using predictive empirical models (EM) and state-of-the-art technology. Many recent EMs rely on samples or data collected manually, which adds to analysis time and increases the burden to the beach manager. In this study, data from water quality buoys and weather stations were transmitted through cellular telemetry to a web hosting service. An executable program simultaneously retrieved and aggregated data for regression equations and calculated EM results each morning at 9:30 AM; results were transferred through RSS feed to a website, mapped to each beach, and received by the lifeguards to be posted at the beach. Models were initially developed for five beaches, but by the third year, 21 beaches were managed using refined and validated modeling systems. The adjusted R(2) of the regressions relating Escherichia coli to hydrometeorological variables for the EMs were greater than those for the PMs, and ranged from 0.220 to 0.390 (2011) and 0.103 to 0.381 (2012). Validation results in 2013 revealed reduced predictive capabilities; however, three of the originally modeled beaches showed improvement in 2013 compared to 2012. The EMs generally showed higher accuracy and specificity than those of the PMs, and sensitivity was low for both approaches. In 2012 EM accuracy was 70-97%; specificity, 71-100%; and sensitivity, 0-64% and in 2013 accuracy was 68-97%; specificity, 73-100%; and sensitivity 0-36%. Factors that may have affected model capabilities include instrument malfunction, non-point source inputs, and sparse calibration data. The modeling system developed is the most extensive, fully-automated system for recreational water quality developed to date. Key insights for refining and improving large-scale empirical models for beach management have been developed through this multi-year effort.

Wave-induced mass transport affects daily Escherichia coli fluctuations in nearshore water.

Characterization of diel variability of fecal indicator bacteria concentration in nearshore waters is of particular importance for development of water sampling standards and protection of public health. Significant nighttime increase in Escherichia coli (E. coli) concentration in beach water, previously observed at marine sites, has also been identified in summer 2000 from fixed locations in waist- and knee-deep waters at Chicago 63rd Street Beach, an embayed, tideless, freshwater beach with low currents at night (approximately 0.015 m s(-1)). A theoretical model using wave-induced mass transport velocity for advection was developed to assess the contribution of surface waves to the observed nighttime E. coli replenishment in the nearshore water. Using average wave conditions for the summer season of year 2000, the model predicted an amount of E. coli transported from water of intermediate depth, where sediment resuspension occurred intermittently, that would be sufficient to have elevated E. coli concentration in the surf and swash zones as observed. The nighttime replenishment of E. coli in the surf and swash zones revealed here is an important phase in the cycle of diel variations of E. coli concentration in nearshore water. According to previous findings in Ge et al. (Environ. Sci. Technol. 2010, 44, 6731-6737), enhanced current circulation in the embayment during the day tends to displace and deposit material offshore, which partially sets up the system by the early evening for a new period of nighttime onshore movement. This wave-induced mass transport effect, although facilitating a significant base supply of material shoreward, can be perturbed or significantly influenced by high currents (orders of magnitude larger than a typical wave-induced mass transport velocity), current-induced turbulence, and tidal forcing.

Budget analysis of Escherichia coli at a Southern Lake Michigan Beach.

Escherichia coli (EC) concentrations at two beaches impacted by river plume dynamics in southern Lake Michigan were analyzed using three-dimensional hydrodynamic and transport models. The relative importance of various physical and biological processes influencing the fate and transport of EC were examined via budget analysis and a first-order sensitivity analysis of model parameters. The along-shore advective flux of EC (CFU/m(2).s) was found to be higher compared to its cross-shore counterpart; however, the sum of diffusive and advective components was of a comparable magnitude in both directions showing the importance of cross-shore exchange in EC transport. Examination of individual terms in the EC mass balance equation showed that vertical turbulent mixing in the water column dominated the overall EC transport for the summer conditions simulated. Dilution due to advection and diffusion accounted for a large portion of the total EC budget in the nearshore, and the net EC loss rate within the water column (CFU/m(3).s) was an order of magnitude smaller compared to the horizontal and vertical transport rates. This result has important implications for modeling EC at recreational beaches; however, the assessment of the magnitude of EC loss rate is complicated due to the strong coupling between vertical exchange and depth-dependent EC loss processes such as sunlight inactivation and settling. Sensitivity analysis indicated that solar inactivation has the greatest impact on EC loss rates. Although these results are site-specific, they clearly bring out the relative importance of various processes involved.

Quantitative detection of human adenoviruses in wastewater and combined sewer overflows influencing a Michigan river.

Enteric viruses are important pathogens found in contaminated surface waters and have previously been detected in waters of the Great Lakes. Human adenoviruses were monitored because of their high prevalence and persistence in aquatic environments. In this study, we quantified adenoviruses in wastewater, surface water, and combined sewer overflows (CSOs) by real-time PCR. Between August 2005 and August 2006, adenovirus concentrations in raw sewage, primary-treated effluent, secondary-treated effluent, and chlorinated effluent from a wastewater treatment plant in Michigan were examined. CSO samples (n = 6) were collected from a CSO retention basin in Grand Rapids, MI. Adenoviruses were detected in 100% of wastewater and CSO discharge samples. Average adenovirus DNA concentrations in sewage and CSOs were 1.15 x 10(6) viruses/liter and 5.35 x 10(5) viruses/liter, respectively. Adenovirus removal was <2 log(10) (99%) at the wastewater treatment plant. Adenovirus type 41 (60% of clones), type 12 (29%), type 40 (3%), type 2 (3%), and type 3 (3%) were isolated from raw sewage and primary effluents (n = 28). Six of 20 surface water samples from recreational parks at the lower Grand River showed virus concentrations above the real-time PCR detection limit (average, 7.8 x 10(3) viruses/liter). This research demonstrates that wastewater effluents and wastewater-impacted surface waters in the lower Grand River in Michigan contain high levels of viruses and may not be suitable for full-body recreational activities. High concentrations of adenovirus in these waters may be due to inefficient removal during wastewater treatment and to the high persistence of these viruses in the environment.

Evaluation of public health risks at recreational beaches in Lake Michigan via detection of enteric viruses and a human-specific bacteriological marker.

UNLABELLED: Each year the National Resource Defense Council addresses the quality of US beaches by routine bacterial indicators. In the Great Lakes region the indicator used is Escherichia coli and for 2007 more beaches were closed and impacted than ever before. In this study, water quality was addressed at two Lake Michigan Beaches over the 2004 swimming season by monitoring infectious enteric viruses by cell culture and integrated PCR and for a human sewage marker based on the Enterococcal Surface Protein (esp). Our goals for this study were to 1) examine the occurrence and variety of human enteric viruses present during peak usage of the beaches 2) determine key variables for development of predictive models for viruses; and 3) use quantitative risk assessment to estimate the potential health impact. Our results demonstrate that for both beaches predictive models of virus pollution were best described utilizing physical parameters like wind speed, wind direction and water temperature. The esp marker was not predictive of human viruses. The daily risk of acquiring a viral infection at either of the beaches ranged from 0.2 to 2.4/1000 swimmers using a quantitative microbial risk assessment model, with three swims during a day at the beach for children and over the season, the risk was 9-15/1000 swimmers using adenovirus as the model. CONCLUSIONS: Lake Michigan recreational beaches are being adversely impacted by human fecal pollution. Monitoring for the traditional indicators of water quality does not address viral risks and models can be developed and potentially used as real-time water quality forecasting tools.

Evaluating bacteriophage P22 as a tracer in a complex surface water system: the Grand River, Michigan.

Viruses are important pathogens in both marine and fresh water environments. There is a strong interest in using bacteriophages as tracers because of their role as model viruses, since dissolved chemical tracers may not adequately describe the behavior of viruses that are suspended colloids. Despite a large number of studies that examined the transport of bacteriophages in the subsurface environment, few studies examined phage transport in large and complex surface water systems. In this paper we report the results of a dual tracer study on a 40 km reach of the Grand River, the longest river in Michigan, and we examine the performance of bacteriophage P22 relative to a chemical tracer (Rhodamine WT). Our analysis based on the transient storage (TS) model indicated that P22 can be successfully used as a tracer in complex surface water environments. Estimated P22 inactivation rates were found to be in the range 0.27-0.57 per day (0.12-0.25 log10 per day). The highest inactivation rate was found in a reach with high suspended solids concentration, relatively low dissolved organic carbon content, and sediment with high clay content. Estimated TS model parameters for both tracers were found to be consistent with surficial geology and land use patterns. Maximum storage zone sizes for the two tracers were found in different river reaches, indicating that different processes contributed to TS within the same reach for the two tracers. This model can be used to examine the arrival times and concentrations of human viral pathogens released from untreated sewage at recreational areas.

Modeling the transport and inactivation of E. coli and enterococci in the near-shore region of Lake Michigan.

To investigate the transport and fate of fecal pollution at Great Lakes beaches and the health risks associated with swimming, the near-shore waters of Lake Michigan and two tributaries discharging into it were examined for bacterial indicators of human fecal pollution. The enterococcus human fecal pollution marker, which targets a putative virulence factor--the enterococcal surface protein (esp) in Enterococcus faecium, was detected in 2/28 samples (7%) in the tributaries draining into Lake Michigan and in 6/30 samples (20%) in Lake Michigan beaches. This was indicative of human fecal pollution being transported in the tributaries and occurrence at Lake Michigan beaches. To understand the relative importance of different processes influencing pollution transport and inactivation, a finite-element model of surf-zone hydrodynamics (coupled with models for temperature, E. coli and enterococci) was used. Enterococci appear to survive longer than E. coli, which was described using an overall first-order inactivation coefficient in the range 0.5-2.0 per day. Our analysis suggests that the majority of fecal indicator bacteria variation can be explained based on loadings from the tributaries. Sunlight is a major contributor to inactivation in the surf-zone and the formulation based on sunlight, temperature and sedimentation is preferred over the first-order inactivation formulation.