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Analysis of disinfection data from dilution count experiments
Journal article   Peer reviewed

Analysis of disinfection data from dilution count experiments

Charles N. Haas
Water research (Oxford), v 23(3), pp 345-349
1989
DOI: https://doi.org/10.1016/0043-1354(89)90101-2
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

disinfection Monte Carlo simulation MPN methods statistics
When disinfection experiments are conducted using dilution count methods (e.g. MPN tests or animal infectivity assays), inactivation parameters may be assessed in three distinct ways. First, the density of survivors may be determined by MPN tables and two-step linear regression (or “eyeball”) methods used to estimate Chick-Watson parameters. Second, the survivor density vs concentration and time may be used in a non-linear least squares procedure to estimate inactivation parameters. Third, a maximum likelihood estimation in which the actual numbers of positive and negative tubes as a function of experimental conditions and sample volume may be executed. Using Monte Carlo simulation, it is determined that the third method is optimal (in terms of bias and standard error) for Poisson, or nearly Poisson error distribution. For more overdisperse error structure, however, nonlinear regression performs more satisfactorily. An empirical test of overdispersion is proposed as a diagnostic tool for assessing the optimum method for data analysis.

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4 citations in Web of Science
3 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#6 Clean Water and Sanitation

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Web of Science research areas
Engineering, Environmental
Environmental Sciences
Water Resources
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