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.

Microbes in Beach Sands: Integrating Environment, Ecology and Public Health.

Beach sand is a habitat that supports many microbes, including viruses, bacteria, fungi and protozoa (micropsammon). The apparently inhospitable conditions of beach sand environments belie the thriving communities found there. Physical factors, such as water availability and protection from insolation; biological factors, such as competition, predation, and biofilm formation; and nutrient availability all contribute to the characteristics of the micropsammon. Sand microbial communities include autochthonous species/phylotypes indigenous to the environment. Allochthonous microbes, including fecal indicator bacteria (FIB) and waterborne pathogens, are deposited via waves, runoff, air, or animals. The fate of these microbes ranges from death, to transient persistence and/or replication, to establishment of thriving populations (naturalization) and integration in the autochthonous community. Transport of the micropsammon within the habitat occurs both horizontally across the beach, and vertically from the sand surface and ground water table, as well as at various scales including interstitial flow within sand pores, sediment transport for particle-associated microbes, and the large-scale processes of wave action and terrestrial runoff. The concept of beach sand as a microbial habitat and reservoir of FIB and pathogens has begun to influence our thinking about human health effects associated with sand exposure and recreational water use. A variety of pathogens have been reported from beach sands, and recent epidemiology studies have found some evidence of health risks associated with sand exposure. Persistent or replicating populations of FIB and enteric pathogens have consequences for watershed/beach management strategies and regulatory standards for safe beaches. This review summarizes our understanding of the community structure, ecology, fate, transport, and public health implications of microbes in beach sand. It concludes with recommendations for future work in this vastly under-studied area.

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.

In vitro and in vivo antioxidant activity of a water-soluble polysaccharide from Dendrobium denneanum.

The water-soluble crude polysaccharide (DDP) obtained from the aqueous extracts of the stem of Dendrobium denneanum through hot water extraction followed by ethanol precipitation, was found to have an average molecular weight (Mw) of about 484.7 kDa. Monosaccharide analysis revealed that DDP was composed of arabinose, xylose, mannose, glucose and galactose in a molar ratio of 1.00:2.66:8.92:34.20:10.16. The investigation of antioxidant activity both in vitro and in vivo showed that DDP is a potential antioxidant.

Significance testing for wavelet bicoherence and its application in analyzing nonlinearity in turbulent shear flows.

Wavelet-based bispectral analysis has been applied in various physical and engineering fields in recent years, but discussion of its significance testing, which distinguishes statistically meaningful results from those due to random noise, has been scarce and incomplete in the literature. Previously derived significance levels for wavelet bicoherence were either preliminary or based on numerical simulations of a limited sample size. The present study reviewed relevant previous works analytically identified the sampling distribution of the estimated wavelet bicoherence and derived expressions for its significance levels for any given probability value. Its application in analyzing a turbulent shear flow around a bluff body was demonstrated in detail. The significance testing developed here helped to identify significant quadratic couplings among a triad of scales in a separated turbulent shear layer over very short time intervals. The results obtained here can be applied to a wide variety of research topics for detecting nonlinearity in physical systems.

Coastal loading and transport of Escherichia coli at an embayed beach in Lake Michigan.

A Chicago beach in southwest Lake Michigan was revisited to determine the influence of nearshore hydrodynamic effects on the variability of Escherichia coli (E. coli) concentration in both knee-deep and offshore waters. Explanatory variables that could be used for identifying potential bacteria loading mechanisms, such as bed shear stress due to a combined wave-current boundary layer and wave runup on the beach surface, were derived from an existing wave and current database. The derived hydrodynamic variables, along with the actual observed E. coli concentrations in the submerged and foreshore sands, were expected to reveal bacteria loading through nearshore sediment resuspension and swash on the beach surface, respectively. Based on the observation that onshore waves tend to result in a more active hydrodynamic system at this embayed beach, multiple linear regression analysis of onshore-wave cases further indicated the significance of sediment resuspension and the interaction of swash with gull-droppings in explaining the variability of E. coli concentration in the knee-deep water. For cases with longshore currents, numerical simulations using the Princeton Ocean Model revealed current circulation patterns inside the embayment, which can effectively entrain bacteria from the swash zone into the central area of the embayed beach water and eventually release them out of the embayment. The embayed circulation patterns are consistent with the statistical results that identified that 1) the submerged sediment was an additional net source of E. coli to the offshore water and 2) variability of E. coli concentration in the knee-deep water contributed adversely to that in the offshore water for longshore-current cases. The embayed beach setting and the statistical and numerical methods used in the present study have wide applicability for analyzing recreational water quality at similar marine and freshwater sites.

Relationship and variation of qPCR and culturable Enterococci estimates in ambient surface waters are predictable.

The quantitative polymerase chain reaction (qPCR) method provides rapid estimates of fecal indicator bacteria densities that have been indicated to be useful in the assessment of water quality. Primarily because this method provides faster results than standard culture-based methods, the U.S. Environmental Protection Agency is currently considering its use as a basis for revised ambient water quality criteria. In anticipation of this possibility, we sought to examine the relationship between qPCR-based and culture-based estimates of enterococci in surface waters. Using data from several research groups, we compared enterococci estimates by the two methods in water samples collected from 37 sites across the United States. A consistent linear pattern in the relationship between cell equivalents (CCE), based on the qPCR method, and colony-forming units (CFU), based on the traditional culturable method, was significant (P < 0.05) at most sites. A linearly decreasing variance of CCE with increasing CFU levels was significant (P < 0.05) or evident for all sites. Both marine and freshwater sites under continuous influence of point-source contamination tended to reveal a relatively constant proportion of CCE to CFU. The consistency in the mean and variance patterns of CCE versus CFU indicates that the relationship of results based on these two methods is more predictable at high CFU levels (e.g., log(10)CFU > 2.0/100 mL) while uncertainty increases at lower CFU values. It was further noted that the relative error in replicated qPCR estimates was generally higher than that in replicated culture counts even at relatively high target levels, suggesting a greater need for replicated analyses in the qPCR method to reduce relative error. Further studies evaluating the relationship between culture and qPCR should take into account analytical uncertainty as well as potential differences in results of these methods that may arise from sample variability, different sources of pollution, and environmental factors.

Time-frequency analysis of beach bacteria variations and its implication for recreational water quality modeling.

This paper exploited the potential of the wavelet analysis in resolving beach bacteria concentration and candidate explanatory variables across multiple time scales with temporal information preserved. The wavelet transform of E. coli concentration and its explanatory variables observed at Huntington Beach, Ohio in 2006 exhibited well-defined patterns of different time scales, phases, and durations, which cannot be clearly shown in conventional time-domain analyses. If linear regression modeling is to be used for the ease of implementation and interpretation,the wavelet-transformed regression model reveals that low model residual can be realized through matching major patterns and their phase angles between E. coli concentration and its explanatory variables. The property of pattern matching for linear regression models can be adopted as a criterion for choosing useful predictors, while phase matching further explains why intuitively good variables such as wave height and onshore wind speed were excluded from the optimal models by model selection processes in Frick et al. (Environ. Sci. Technol. 2008, 42,4818-4824). The phase angles defined by the wavelet analysis in the time-frequency domain can help identify the physical processes and interactions occurring between bacteria concentration and its explanatory variables. It was deduced, for this particular case, that wind events resulted in elevated E. coli concentration, wave height, and turbidity at the beach with a periodicity of 7-8 days. Wind events also brought about increased beach bacteria concentrations through large-scale current circulations in the lake with a period of 21 days. The time length for linear regression models with statistical robustness can also be deduced from the periods of the major patterns in bacteria concentration and explanatory variables, which explains and supplements the modeling efforts performed in (1).

Nowcasting and forecasting concentrations of biological contaminants at beaches: a feasibility and case study.

Public concern over microbial contamination of recreational waters has increased in recent years. A common approach to evaluating beach water quality has been to use the persistence model which assumes that day-old monitoring results provide accurate estimates of current concentrations. This model is frequently incorrect Recent studies have shown that statistical regression models based on least-squares fitting often are more accurate. To make such models more generally available, the Virtual Beach (VB) tool was developed. VB is public-domain software that prescribes site-specific predictive models. In this study we used VB as a tool to evaluate statistical modeling for predicting Escherichia coli (E. coli levels at Huntington Beach, on Lake Erie. The models were based on readily available weather and environmental data, plus U.S. Geological Service onsite data. Although models for Great Lakes beaches have frequently been fitted to multiyear data sets, this work demonstrates that useful statistical models can be based on limited data sets collected over much shorter time periods, leading to dynamic models that are periodically refitted as new data become available. Comparisons of the resulting nowcasts (predictions of current, but yet unknown, bacterial levels) with observations verified the effectiveness of VB and showed that dynamic models are about as accurate as long-term static models. Finally, fitting models to forecasted explanatory variables, bacteria forecasts were found to compare favorably to nowcasts, yielding adjusted coefficients of determination (adjusted R2) of about 0.40.

Interaction and influence of two creeks on Escherichia coli concentrations of nearby beaches: exploration of predictability and mechanisms.

The impact of river outfalls on beach water quality depends on numerous interacting factors. The delivery of contaminants by multiple creeks greatly complicates understanding of the source contributions, especially when pollution might originate up- or down-coast of beaches. We studied two beaches along Lake Michigan that are located between two creek outfalls to determine the hydrometeorologic factors influencing near-shore microbiologic water quality and the relative impact of the creeks. The creeks continuously delivered water with high concentrations of Escherichia coli to Lake Michigan, and the direction of transport of these bacteria was affected by current direction. Current direction reversals were associated with elevated E. coli concentrations at Central Avenue beach. Rainfall, barometric pressure, wave height, wave period, and creek specific conductance were significantly related to E. coli concentration at the beaches and were the parameters used in predictive models that best described E. coli variation at the two beaches. Multiple inputs to numerous beaches complicates the analysis and understanding of the relative relationship of sources but affords opportunities for showing how these complex creek inputs might interact to yield collective or individual effects on beach water quality.

Some statistical issues related to multiple linear regression modeling of beach bacteria concentrations.

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those issues include the value and use of interaction terms, the serial correlation, the criteria for model selection, and model assessment. The present work shows that serial correlations, as often present in sequentially observed data records, deserve full attention from the modeler. The testing and adjustment for the time-series effect should be implemented in a statistically rigorous framework. The R(2) and Cp-statistic as joint criteria are recommended for the model selection process, while using the t-statistics associated with the full model is erroneous. During model selection, using interaction terms can often help to decrease the bias in reduced models, although the resulting improvement in the numerical performance may be limited. For the assessment of the model predictive capacity, which is different from testing the goodness of fit, a comprehensive set of statistics are advocated to allow for an objective evaluation of different models. Results obtained from the data at Huntington Beach, OH, show that erroneous conclusions could be drawn if only the model R(2) and the count of type I and type II errors are considered. In this sense, several previous works deserve further investigation.