The effects of seasonal variability and weather on microbial fecal pollution and enteric pathogens in a subtropical estuary

Citation
Ek. Lipp et al., The effects of seasonal variability and weather on microbial fecal pollution and enteric pathogens in a subtropical estuary, ESTUARIES, 24(2), 2001, pp. 266-276
Citations number
44
Categorie Soggetti
Aquatic Sciences
Journal title
ESTUARIES
ISSN journal
01608347 → ACNP
Volume
24
Issue
2
Year of publication
2001
Pages
266 - 276
Database
ISI
SICI code
0160-8347(200104)24:2<266:TEOSVA>2.0.ZU;2-B
Abstract
The Charlotte Harbor estuary in southwest Florida was sampled monthly for o ne year at twelve stations, in the lower reaches of the Myakka and Peace Ri vers. The objectives of the study were to address the distribution and seas onal changes in microbial indicators and human pathogen levels in Charlotte Harbor shellfish and recreational waters, and to determine those factors t hat map be important in the transport and survival of pathogens. Monthly wa ter samples and quarterly sediment samples were analyzed for fecal coliform bacteria, enterococci, Clostridium perfringens, and coliphage. Quarterly s amples also were analyzed for the enteric human pathogens, Cryptosporidium spp., Giardia spp., and enteroviruses. Fecal indicator organisms were gener ally concentrated in areas of low salinity and high densities of septic sys tems; however, pollution became widespread during wet weather in the late f all and winter of 1997-1998, coincident with a strong El Nino event. Betwee n December 1997 and February 1998, enteroviruses were detected at 75% of th e sampling stations; none were detected in other months. Enteric protozoa w ere detected infrequently and were not related to seasonal influences. Feca l indicators and enteroviruses were each significantly associated with rain fall, streamflow, and temperature. Regression models suggest that temperatu re and rainfall can predict the occurrence of enteroviruses in 98.7% of the cases. Based on findings in this watershed, factors such as variability in precipitation, streamflow, and temperature show promise in modeling and fo recasting periods of poor coastal water quality.