Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.
Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.
Black
L E
LE
Civil Engineering, College of Engineering, 161 Raymond Building, University of Kentucky, Lexington, KY 40506-0281, USA.
Brion
G M
GM
Freitas
S J
SJ
D002777:Cholestanols / Q000032:analysis; D004755:Enterobacteriaceae / Q000302:isolation & purification; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D005243:Feces / Q000737:chemistry / Q000382:microbiology; D007629:Kentucky; D016015:Logistic Models; D008962:Models, Theoretical; D015999:Multivariate Analysis; D045483:Rivers / Q000821:virology*; D014881:Water Supply
D002777:Cholestanols; C523170:epicoprostanol