Literature DB >> 18490046

Modeling the relationship between most probable number (MPN) and colony-forming unit (CFU) estimates of fecal coliform concentration.

Andrew D Gronewold1, Robert L Wolpert.   

Abstract

Most probable number (MPN) and colony-forming-unit (CFU) estimates of fecal coliform bacteria concentration are common measures of water quality in coastal shellfish harvesting and recreational waters. Estimating procedures for MPN and CFU have intrinsic variability and are subject to additional uncertainty arising from minor variations in experimental protocol. It has been observed empirically that the standard multiple-tube fermentation (MTF) decimal dilution analysis MPN procedure is more variable than the membrane filtration CFU procedure, and that MTF-derived MPN estimates are somewhat higher on average than CFU estimates, on split samples from the same water bodies. We construct a probabilistic model that provides a clear theoretical explanation for the variability in, and discrepancy between, MPN and CFU measurements. We then compare our model to water quality samples analyzed using both MPN and CFU procedures, and find that the (often large) observed differences between MPN and CFU values for the same water body are well within the ranges predicted by our probabilistic model. Our results indicate that MPN and CFU intra-sample variability does not stem from human error or laboratory procedure variability, but is instead a simple consequence of the probabilistic basis for calculating the MPN. These results demonstrate how probabilistic models can be used to compare samples from different analytical procedures, and to determine whether transitions from one procedure to another are likely to cause a change in quality-based management decisions.

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Year:  2008        PMID: 18490046     DOI: 10.1016/j.watres.2008.04.011

Source DB:  PubMed          Journal:  Water Res        ISSN: 0043-1354            Impact factor:   11.236


  6 in total

1.  Real or perceived: the environmental health risks of urban sack gardening in Kibera slums of Nairobi, Kenya.

Authors:  Courtney Maloof Gallaher; Dennis Mwaniki; Mary Njenga; Nancy K Karanja; Antoinette M G A WinklerPrins
Journal:  Ecohealth       Date:  2013-03-20       Impact factor: 3.184

2.  Anaerobic soil disinfestation, amendment-type, and irrigation regimen influence Salmonella survival and die-off in agricultural soils.

Authors:  Claire M Murphy; Daniel L Weller; Mark S Reiter; Cameron A Bardsley; Joseph Eifert; Monica Ponder; Steve L Rideout; Laura K Strawn
Journal:  J Appl Microbiol       Date:  2021-10-26       Impact factor: 3.772

3.  Muddying the waters: a new area of concern for drinking water contamination in Cameroon.

Authors:  Jessica M Healy Profitós; Arabi Mouhaman; Seungjun Lee; Rebecca Garabed; Mark Moritz; Barbara Piperata; Joe Tien; Michael Bisesi; Jiyoung Lee
Journal:  Int J Environ Res Public Health       Date:  2014-11-28       Impact factor: 3.390

4.  A Semi-distributed Model for Predicting Faecal Coliform in Urban Stormwater by Integrating SWMM and MOPUS.

Authors:  Xiaoshu Hou; Lei Chen; Jiali Qiu; Yali Zhang; Zhenyao Shen
Journal:  Int J Environ Res Public Health       Date:  2019-03-08       Impact factor: 3.390

Review 5.  Microbial monitoring of surface water in South Africa: an overview.

Authors:  Catherine D Luyt; Roman Tandlich; Wilhelmine J Muller; Brendan S Wilhelmi
Journal:  Int J Environ Res Public Health       Date:  2012-07-30       Impact factor: 3.390

Review 6.  Considerations for estimating microbial environmental data concentrations collected from a field setting.

Authors:  Erin E Silvestri; Cynthia Yund; Sarah Taft; Charlena Yoder Bowling; Daniel Chappie; Kevin Garrahan; Eletha Brady-Roberts; Harry Stone; Tonya L Nichols
Journal:  J Expo Sci Environ Epidemiol       Date:  2016-02-17       Impact factor: 5.563

  6 in total

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