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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 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.

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The authors thank the Division of Agriculture and Natural Resources (ANR) and Veterinary Medicine Extension, University of California, Davis, and National Science Foundation (award No. CBET-0967845) for supporting this work.

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Correspondence to Pramod K. Pandey.

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Pandey, P.K., Pasternack, G.B., Majumder, M. et al. A neighborhood statistics model for predicting stream pathogen indicator levels. Environ Monit Assess 187, 124 (2015).

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  • Stream water
  • E. coli
  • Neighborhood structures
  • Markov random field model