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Area of Science:

  • Environmental Science
  • Microbiology
  • Statistical Modeling

Background:

  • Recreational water monitoring is crucial for public health.
  • Fecal indicator bacteria (FIB) are commonly used to assess water quality.
  • Current FIB assessments often fail to differentiate between analytical, spatial, and temporal variability.

Purpose of the Study:

  • To compare two methods for quantifying FIB concentration variability.
  • To assess the significance of analytical, spatial, and temporal factors on FIB levels.
  • To evaluate the utility of Bayesian hierarchical models in water quality assessments.

Main Methods:

  • Compared conventional FIB quantification methods (most probable number [MPN] vs. colony-forming unit [CFU]).
  • Employed a Bayesian hierarchical model using raw data to account for analytical variability.
  • Analyzed FIB concentration variability at a Lake Huron beach over small spatial and temporal scales.

Main Results:

  • In situ FIB concentrations showed no significant variation over small spatial and temporal scales.
  • Observed differences between MPN and CFU values were primarily attributed to laboratory analysis variability.
  • The Bayesian model effectively distinguished analytical variability from in situ environmental factors.

Conclusions:

  • Laboratory analytical procedures introduce significant variability into FIB measurements.
  • Spatial and temporal variations in FIB concentrations are less significant than previously assumed.
  • Bayesian statistical models offer a promising approach for improving routine water quality assessments and protecting public health.