Using Probabilities of Enterococci Exceedance and Logistic Regression to Evaluate Long Term Weekly Beach Monitoring Data

Authors

Diana Ixchel Aranda, Nova Southeastern University; University of Miami
Jose V. Lopez, Nova Southeastern UniversityFollow
Helena M. Solo-Gabriele, University of Miami
Jay Fleisher, Nova Southeastern University; University of Miami

ORCID

0000-0002-1637-4125

ResearcherID

F-8809-2011

Document Type

Article

Publication Title

Journal of Water & Health

ISSN

1477-8920

Publication Date

2-2016

Keywords

Water quality, Water sampling, Beaches, Environmental monitoring, Florida, Enterococcus, Logistic regression analysis

Abstract

Recreational water quality surveillance involves comparing bacterial levels to set threshold values to determine beach closure. Bacterial levels can be predicted through models which are traditionally based upon multiple linear regression. The objective of this study was to evaluate exceedance probabilities, as opposed to bacterial levels, as an alternate method to express beach risk. Data were incorporated into a logistic regression for the purpose of identifying environmental parameters most closely correlated with exceedance probabilities. The analysis was based on 7,422 historical sample data points from the years 2000-2010 for 15 South Florida beach sample sites. Probability analyses showed which beaches in the dataset were most susceptible to exceedances. No yearly trends were observed nor were any relationships apparent with monthly rainfall or hurricanes. Results from logistic regression analyses found that among the environmental parameters evaluated, tide was most closely associated with exceedances, with exceedances 2.475 times more likely to occur at high tide compared to low tide. The logistic regression methodology proved useful for predicting future exceedances at a beach location in terms of probability and modeling water quality environmental parameters with dependence on a binary response. This methodology can be used by beach managers for allocating resources when sampling more than one beach.

DOI

10.2166/wh.2015.030

Volume

14

Issue

1

First Page

81

Last Page

89

Comments

©IWA Publishing 2016

NSUWorks Citation

Diana Ixchel Aranda, Jose V. Lopez, Helena M. Solo-Gabriele, and Jay Fleisher. 2016. Using Probabilities of Enterococci Exceedance and Logistic Regression to Evaluate Long Term Weekly Beach Monitoring Data .Journal of Water & Health , (1) : 81 -89. https://nsuworks.nova.edu/occ_facarticles/741.