A predictive model for microbial counts on beaches where intertidal sand is the primary source. Issue 1 (15th May 2015)

• Record Type: Journal Article
• Authors: Feng, Zhixuan; Reniers, Ad; Haus, Brian K.; Solo-Gabriele, Helena M.; Wang, John D.; Fleming, Lora E.
• Is Part Of: Marine pollution bulletin. Volume 94:Issue 1/2(2015:May)
• Page Start: 37
• Page End: 47
• Publication Date: 2015-05-15
• Subjects: Water quality model -- Fecal indicator bacteria -- Microbial mass balance -- Multivariable linear regression -- Nonpoint source beach -- Beach advisory; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2015.03.019
• Languages: English
• ISSNs: 0025-326X

Abstract

Graphical abstract: Highlights: A balance model of enterococci was developed for nonpoint source beaches. Wave, tide, and sunlight were major factors influencing enterococci levels. Balance model showed similar skills to regression in issuing beach advisories. Balance model simulated transient cross-shore distributions of enterococci. Spatiotemporal patterns are useful when making beach management decisions. Abstract: Human health protection at recreational beaches requires accurate and timely information on microbiological conditions to issue advisories. The objective of this study was to develop a new numerical mass balance model for enterococci levels on nonpoint source beaches. The significant advantage of this model is its easy implementation, and it provides a detailed description of the cross-shore distribution of enterococci that is useful for beach management purposes. The performance of the balance model was evaluated by comparing predicted exceedances of a beach advisory threshold value to field data, and to a traditional regression model. Both the balance model and regression equation predicted approximately 70% the advisories correctly at the knee depth and over 90% at the waist depth. The balance model has the advantage over the regression equation in its ability to simulate spatiotemporal variations of microbial levels, and it is recommended for making more informed management decisions.