期刊论文详细信息
MARINE POLLUTION BULLETIN 卷:94
A predictive model for microbial counts on beaches where intertidal sand is the primary source
Article
Feng, Zhixuan1,2  Reniers, Ad1,2,3  Haus, Brian K.1,2  Solo-Gabriele, Helena M.2,4  Wang, John D.1,2  Fleming, Lora E.2,5 
[1] Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, Dept Ocean Sci, Miami, FL 33149 USA
[2] Univ Miami, NSF NIEHS Oceans & Human Hlth Ctr, Rosenstiel Sch Marine & Atmospher Sci, Miami, FL 33149 USA
[3] Delft Univ Technol, Dept Hydraul Engn, Environm Fluid Mech Sect, NL-2628 CN Delft, Netherlands
[4] Univ Miami, Coll Engn, Dept Civil Environm & Architectural Engn, Coral Gables, FL 33146 USA
[5] Univ Exeter, Royal Cornwall Hosp, European Ctr Environm & Human Hlth, Sch Med, Truro TR1 3HD, Cornwall, England
关键词: Water quality model;    Fecal indicator bacteria;    Microbial mass balance;    Multivariable linear regression;    Nonpoint source beach;    Beach advisory;   
DOI  :  10.1016/j.marpolbul.2015.03.019
来源: Elsevier
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【 摘 要 】

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. (C) 2015 Elsevier Ltd. All rights reserved.

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