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Predictive Modeling of a Fecal Indicator at a Subtropical Marine Beach
Citation:
ZEPP, R. G., E. M. WHITE, M. MOLINA, AND M. J. CYTERSKI. Predictive Modeling of a Fecal Indicator at a Subtropical Marine Beach. Presented at US EPA National Beach Conference, Huntington Beach, CA, April 20 - 22, 2009.
Impact/Purpose:
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.
Description:
The Virtual Beach Model Builder (VBMB) is a software tool that can be used to develop predictive models at beaches based on microbial data and observations (explanatory variables) that describe hydrometeorological and biogeochemical conditions. During the summer of 2008, a study was conducted to evaluate the use of VBMB in developing models for predicting enterococci concentrations at Hobie Beach, Miami FL. Water samples were collected at shin-deep and waist deep sites in the swim area (three times a day, four days a week) and the samples were processed to determine culturable enterococci and filtered for enterococci qPCR analysis and archiving. Instruments were deployed at the beach to log data that were used to develop the models. The instruments included: a weather station; an acoustic doppler current meter (ADCP); a sonde for water quality parameters; a UV radiometer system equipped with two optical sensors, each measuring UV-A and UV-B irradiance, positioned at two different depths underwater. The microbial and explanatory variables data were used by VBMB to produce multilinear regression models that optimally fit the data. Using backward elimination with Mallows Cp, VBMB was also used to develop the most parsimonious model for fitting the culturable enterococci data and also to identify the best explanatory variables from the array of potential variables available for fitting. Preliminary analysis with VMBM (using multiple linear regression without transformation of explanatory variables) identified water depth, wave height, photosynthetically active radiation, wind speed, and water temperature as the most important predictors for this beach.