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Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection
- Source :
- Applied and Environmental Microbiology. 79:1676-1688
- Publication Year :
- 2013
- Publisher :
- American Society for Microbiology, 2013.
-
Abstract
- Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium , adenovirus, eaeA ( E. coli ), ipaH ( Shigella ), and spvC ( Salmonella ) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.
- Subjects :
- Pathogen detection
Climate
Cryptosporidium
Fresh Water
Public Health Microbiology
medicine.disease_cause
Sensitivity and Specificity
Applied Microbiology and Biotechnology
Adenoviridae
Salmonella
Escherichia coli
medicine
Turbidity
Ohio
Hydrology
Models, Statistical
Ecology
biology
Sampling (statistics)
biology.organism_classification
Bacterial Load
Lakes
Water temperature
Environmental science
Shigella
Water quality
Predictive variables
Food Science
Biotechnology
Subjects
Details
- ISSN :
- 10985336 and 00992240
- Volume :
- 79
- Database :
- OpenAIRE
- Journal :
- Applied and Environmental Microbiology
- Accession number :
- edsair.doi.dedup.....1e48f09ee2f8bc554d5649b17a878ab9