title: A neighborhood statistics model for predicting stream pathogen indicator levels.
abstract: 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.
authors.affiliation: Department of Population Health and Reproduction, University of California, Davis, CA, 95616, USA, pkpandey@ucdavis.edu.
authors.forename: Pramod K
authors.initials: PK
authors.lastname: Pandey
authors.forename: Gregory B
authors.initials: GB
authors.lastname: Pasternack
authors.forename: Mahbubul
authors.initials: M
authors.lastname: Majumder
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Soupir
authors.forename: Mark S
authors.initials: MS
authors.lastname: Kaiser
mesh_terms: D003430:Cross-Sectional Studies; D004926:Escherichia coli / Q000254:growth & development*; D007484:Iowa; D015233:Models, Statistical; D045483:Rivers / Q000382:microbiology*; D014876:Water Pollution / Q000706:statistics & numerical data*; D060753:Water Quality / Q000592:standards*