You are here:
Optical Properties of Three Beach Waters: Implications for Predictive Modeling of Enterococci
WHITE, E. M., R. G. ZEPP, M. MOLINA, AND M. J. CYTERSKI. Optical Properties of Three Beach Waters: Implications for Predictive Modeling of Enterococci. Presented at US EPA National Beach Conference, Huntington Beach, CA, April 20 - 22, 2009.
The overall objective of the proposed study is to evaluate the loadings, fate and transport of bacterial contaminants from agricultural non-point sources in surface waters through the use of DNA-based technology that can quantify and track fecal contamination back to its source.
Sunlight plays an important role in the inactivation of fecal indicator bacteria in recreational waters. Solar radiation can explain temporal trends in bacterial counts and is commonly used as an explanatory variable in predictive models. Broadband surface radiation provides a basic measure of sunlight exposure. However, the amount and quality of light that bacteria are exposed to is largely dependent on the optical properties of the water. In this study, we investigated the optical properties of waters at a temperate freshwater beach (Milwaukee, WI), sub-tropical marine beach (Miami, FL), and a tropical marine beach (Luquillo, PR), during summer 2008. UV sensors were deployed in the water column to model light attenuation as a function of depth. Surface solar (300-1100 nm) and photosynthetically active radiation (PAR, 400-700 nm), turbidity, chlorophyll, suspended sediments, dissolved organic carbon, and chromophoric dissolved organic matter were also measured and compared with respect to culturable enterococci levels. The tropical marine beach had the most intense solar irradiance and the clearest water compared to the sub-tropical marine and temperate freshwater beaches. Observed differences between beaches were complicated due to variations in extent of contamination, water temperature, salinity, and tidal influence. Bacterial counts were better correlated with UV irradiance (325 nm), compared to PAR and solar radiation, suggesting that the inclusion of more detailed light data will help improve the accuracy of predictive models.