Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

摘要

Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18-24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichia coli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R2 were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R~2 were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical predictive models appropriate for assessment of both recreational and shellfish harvesting water quality in estuarine waters.