Assessing the potential threat of fecal contamination in surface water often depends on model forecasts which assume that fecal indicator bacteria (FIB, a proxy for the concentration of pathogens found in fecal contamination from warm-blooded animals) are lost or removed from the water column at a certain rate (often referred to as an "inactivation" rate). In efforts to reduce human health risks in these water bodies, regulators enforce limits on easily-measured FIB concentrations, commonly reported as most probable number (MPN) and colony forming unit (CFU) values. Accurate assessment of the potential threat of fecal contamination, therefore, depends on propagating uncertainty surrounding "true" FIB concentrations into MPN and CFU values, inactivation rates, model forecasts, and management decisions. Here, we explore how empirical relationships between FIB inactivation rates and extrinsic factors might vary depending on how uncertainty in MPN values is expressed. Using water samples collected from the Neuse River Estuary (NRE) in eastern North Carolina, we compare Escherichia coli (EC) and Enterococcus (ENT) dark inactivation rates derived from two statistical models of first-order loss; a conventional model employing ordinary least-squares (OLS) regression with MPN values, and a novel Bayesian model utilizing the pattern of positive wells in an IDEXX Quanti-Tray®/2000 test. While our results suggest that EC dark inactivation rates tend to decrease as initial EC concentrations decrease and that ENT dark inactivation rates are relatively consistent across different ENT concentrations, we find these relationships depend upon model selection and model calibration procedures. We also find that our proposed Bayesian model provides a more defensible approach to quantifying uncertainty in microbiological assessments of water quality than the conventional MPN-based model, and that our proposed model represents a new strategy for developing robust relationships between environmental factors and FIB inactivation rates, and for reducing uncertainty in water resource management decisions.
Published by Elsevier Ltd.