Prediction of recreational water safety using Escherichia coli as an indicator: case study of the Passaic and Pompton rivers, New Jersey

Abstract

As contact with high concentrations of pathogens in a waterbody can cause waterborne diseases, Escherichia coli is commonly used as an indicator of water quality in routine public health monitoring of recreational freshwater ecosystems. However, traditional processes of detection and enumeration of pathogen indicators can be costly and are not time-sensitive enough to alarm recreational users. The predictive models developed to produce real-time predictions also have various methodological challenges, including arbitrary selection of explanatory variables, deterministic statistical approach, and heavy reliance on correlation instead of the more rigorous multivariate regression analyses, among others. The objective of this study is to address these challenges and develop a cost-effective and timely alternative for estimating pathogen indicators using real-time water quality and quantity data. As a case study we use New Jersey, where pathogens represent the most common cause of impairment for water quality, and Passaic and Pompton rivers, which are among the largest in the state and the country. We used Membrane Filtration Method and mColiblue24 media to enumerate Escherichia coli in a total of 69 water samples collected from April to November 2016 from the two rivers. We also collected data on environmental variables concurrently and performed stepwise and logistic regression analyses to address the said methodological challenges and determine the variables significantly predicting whether or not the Escherichia coli count was above prescribed levels for recreation activities. The results show that source water, higher specific conductance, lower pH, and cumulative rainfall for the 72 h antecedent the sampling significantly impacted the density of Escherichia coli. In addition to using the Bagging technique to validate the results, we also assessed Whole Model Tests, R², Entropy R², and Misclassification Rates. This approach improves the prediction of bacteria counts and their use in informing the potential safety/hazard of that waterbody for recreational activities.

Keywords: E. coli; Fecal Indicator Bacteria; Logistic regression; Model; Multivariate regression; mColiblue24.