The Effects of Seasonal Variability and Weather on Microbial Fecal Pollution and Enteric Pathogens in a Subtropical Estuary

The Charlotte Harbor estuary in southwest Florida was sampled monthly for one year at twelve stations, in the lower reaches of the Myakka and Peace Rivers. The objectives of the study were to address the distribution and seasonal changes in microbial indicators and human pathogen levels in Charlotte...

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Veröffentlicht in:Estuaries 2001-04, Vol.24 (2), p.266-276
Hauptverfasser: Lipp, Erin K., Kurz, Raymond, Vincent, Robert, Rodriguez-Palacios, Cesar, Farrah, Samuel R., Rose, Joan B.
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Sprache:eng
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Zusammenfassung:The Charlotte Harbor estuary in southwest Florida was sampled monthly for one year at twelve stations, in the lower reaches of the Myakka and Peace Rivers. The objectives of the study were to address the distribution and seasonal changes in microbial indicators and human pathogen levels in Charlotte Harbor shellfish and recreational waters, and to determine those factors that may be important in the transport and survival of pathogens. Monthly water samples and quarterly sediment samples were analyzed for fecal coliform bacteria, enterococci, Clostridium perfringens, and coliphage. Quarterly samples also were analyzed for the enteric human pathogens, Cryptosporidium spp., Giardia spp., and enteroviruses. Fecal indicator organisms were generally concentrated in areas of low salinity and high densities of septic systems; however, pollution became widespread during wet weather in the late fall and winter of 1997-1998, coincident with a strong El Niño event. Between December 1997 and February 1998, enteroviruses were detected at 75% of the sampling stations; none were detected in other months. Enteric protozoa were detected infrequently and were not related to seasonal influences. Fecal indicators and enteroviruses were each significantly associated with rainfall, streamflow, and temperature. Regression models suggest that temperature and rainfall can predict the occurrence of enteroviruses in 93.7% of the cases. Based on findings in this watershed, factors such as variability in precipitation, streamflow, and temperature show promise in modeling and forecasting periods of poor coastal water quality.
ISSN:0160-8347
1559-2723
1559-2758
1559-2731
DOI:10.2307/1352950