A neighborhood statistics model for predicting stream pathogen indicator levels

Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degre...

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Bibliographic Details
Published in:Environmental monitoring and assessment 2015-03, Vol.187 (3), p.124-124, Article 124
Main Authors: Pandey, Pramod K., Pasternack, Gregory B., Majumder, Mahbubul, Soupir, Michelle L., Kaiser, Mark S.
Format: Article
Language:English
Subjects:
Atmospheric Protection/Air Quality Control/Air Pollution
Bacteria
Cross-Sectional Studies
Diarrhea
E coli
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental monitoring
Escherichia coli - growth & development
Geographic information systems
Hepatitis
Hydrology
Iowa
Models, Statistical
Monitoring/Environmental Analysis
Neighborhoods
Pathogens
Public health
Rivers - microbiology
Sediments
Statistical models
Stream water
Topography
Tropical diseases
Water column
Water depth
Water Pollution - statistics & numerical data
Water quality
Water Quality - standards
Water temperature
Watershed management
Watersheds
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Summary:Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degree of unchecked uncertainty or simpler statistical models. To assess spatio-temporal patterns of instream E. coli levels, herein we measured E. coli , a pathogen indicator, at 16 sites (at four different times) within the Squaw Creek watershed, Iowa, and subsequently, the Markov Random Field model was exploited to develop a neighborhood statistics model for predicting instream E. coli levels. Two observed covariates, local water temperature (degrees Celsius) and mean cross-sectional depth (meters), were used as inputs to the model. Predictions of E. coli levels in the water column were compared with independent observational data collected from 16 in-stream locations. The results revealed that spatio-temporal averages of predicted and observed E. coli levels were extremely close. Approximately 66 % of individual predicted E. coli concentrations were within a factor of 2 of the observed values. In only one event, the difference between prediction and observation was beyond one order of magnitude. The mean of all predicted values at 16 locations was approximately 1 % higher than the mean of the observed values. The approach presented here will be useful while assessing instream contaminations such as pathogen/pathogen indicator levels at the watershed scale.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-014-4228-1