Enterococci Concentrations in Diverse Coastal Environments Exhibit Extreme Variability

Abstract

Fecal indicator bacteria (FIB) concentrations in a single grab sample of water are used to notify the public about the safety of swimming in coastal waters. If concentrations are over a single-sample standard, waters are closed or placed under an advisory. Previous work has shown that notification errors occur often because FIB vary more quickly than monitoring results can be obtained (typically 24 h). Rapid detection technologies (such as quantitative polymerase chain reaction) that allow FIB quantification in hours have been suggested as a solution to notification errors. In the present study, I explore variability of enterococci (ENT) over time scales less than a day that might affect interpretation of FIB concentrations from a single grab sample, even if obtained rapidly. Five new data sets of ENT collected at 10 and 1 min periodicities for 24 and 1 h, respectively, are presented. Data sets are collected in diverse marine environments from a turbulent surf zone to a quiescent bay. ENT vary with solar and tidal cycles, as has been observed in previous studies. Over short time scales, ENT are extremely variable in each environment even the quiescent bay. Changes in ENT concentrations between consecutive samples (1 or 10 min apart) greater than the single-sample standard (104 most probable number per 100 mL) are not unusual. Variability, defined as the change in concentration between consecutive samples, is not distinct between environments. ENT change by 60% on average between consecutive samples, and by as much as 700%. Spectral analyses reveal no spectral peaks, but power-law decline of spectral density with frequency. Power-law exponents are close to 1 suggesting ENT time series share properties with 1/f noise and are fractal in nature. Since fractal time series have no characteristic time scale associated with them, it is not obvious how the fractal nature of ENT can be exploited for adaptive sampling or management. Policy makers, as well as scientists designing field campaigns for microbial source tracking and epidemiology studies, are cautioned that a single sample of water reveals little about the true water quality at a beach. Multiple samples must be taken to gain a snapshot into the patchy structure of microbial water quality and associated human health risk.

Presence of Pathogens and Indicator Microbes at a Non-Point Source Subtropical Recreational Marine Beach

“…However, this would not have been the case if a different sample had been processed. Thus, these results emphasize that the spatial and temporal variability of indicator levels in recreational marine water could result in different management decisions at a beach site (8,21). The results for the supplemental fecal indicator bacteria (fecal coliforms, E. coli, and C. perfringens) were predominantly consistent with the enterococcus results for both the water and sand samples, indicating that the environmental factors and source conditions affecting enterococci may also affect these indicators.…”

“…Swimming-related illness is attributed predominantly to exposure to microbial pathogens, which enter the water through point sources, such as sewage outfalls. Water quality at beaches may also be impacted by non-point sources, such as storm water runoff, sand resuspension, animal fecal inputs, and human bather shedding (8,12,22,47,59).The concentration of indicator microorganisms in a body of recreational water is used to estimate the health risk to bathers. These microbes serve as surrogates for microbial pathogens.…”

Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study

“…In the prognostic phase, the models so obtained are used to predict bacteria concentrations either unknown at the time of prediction or deliberately excluded from the diagnoses (fits) for research purposes. The adjusted R 2 and other statistics can be used to assess and express both the diagnostic goodness of fit and the prognostic predictive capacities of the models, but in this paper all listed adjusted R 2 values quantify prognostic results. If real-time explanatory variables and microbial data are available, then empirical multiple linear regression (MLR) models may be fit to provide a basis for issuing health advisories.…”

Temporal Stability of the Microbial Community in Sewage-Polluted Seawater Exposed to Natural Sunlight Cycles and Marine Microbiota

“…FIB enumeration methods are timeconsuming (ϳ24 h), and the concentrations of FIB in coastal waters vary over hourly time scales, complicating their use for health risk assessment (6,7). In addition, FIB can be found in a variety of different animal feces (8) and nonfecal environments, such as sand, sediments, and soils (9)(10)(11)(12)(13), as well as lacustrine and marine vegetation (14)(15)(16)(17).…”