Bacterial survival in a dilute environment

Sjogren, Robert E.; Gibson, Michael J.

doi:10.1128/aem.41.6.1331-1336.1981

Cited by 44 publications

(13 citation statements)

References 28 publications

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“…Nontarget confirmation was low for tropical waters and not significantly different from temperate-water studies (25). Stressed or injured E. coli are known to give false-negative reactions (2,4,5,10,11,17,19,23,24,26,28); thus, tropical fresh waters seem to exert very little injury on allochthonous E. coli or autochthonous E. coli do give typical fecal coliform reactions. For all methods, 60% of the false-positive target isolates were Klebsiella spp., whereas Enterobacter spp.…”

Section: Resultsmentioning

confidence: 99%

Comparison of four membrane filter methods for fecal coliform enumeration in tropical waters

Santiago-Mercado

Hazen

1987

Appl Environ Microbiol

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Four membrane filter methods for the enumeration of fecal coliforms were compared for accuracy, specificity, and recovery. Water samples were taken several times from 13 marine, 1 estuarine, and 4 freshwater sites around Puerto Rico, from pristine waters and waters receiving treated and untreated sewage and effluent from a tuna cannery and a rum distillery. Differences of 1 to 3 orders of magnitude in the levels of fecal coliforms were observed in some samples by different recovery techniques. Marine water samples gave poorer results, in terms of specificity, selectivity, and comparability, than freshwater samples for all four fecal coliform methods used. The method using Difco m-FC agar with a resuscitation step gave the best overall results; however, even this method gave higher false-positive error, higher undetected-target error, lower selectivity, and higher recovery of nontarget organisms than the method using MacConkey membrane broth, the worst method for temperate waters. All methods tested were unacceptable for the enumeration of fecal coliforms in tropical fresh and marine waters. Thus, considering the high densities of fecal coliforms observed at most sites in Puerto Rico by all these methods, it would seem that these density estimates are, in many cases, grossly overestimating the degree of recent fecal contamination. Since Escherichia coli appears to be a normal inhabitant of tropical waters, fecal contamination may be indicated when none is present. Using fecal coliforms as an indicator is grossly inadequate for the detection of recent human fecal contamination and associated pathogens in both marine and fresh tropical waters.

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Section: Resultsmentioning

confidence: 99%

Comparison of four membrane filter methods for fecal coliform enumeration in tropical waters

Santiago-Mercado

Hazen

1987

Appl Environ Microbiol

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“…One is physico-chemical, and the other a biological explanation. The first ascribes the bactericidal activity to various physico-chemical parameters such as: pH [8] conditions. This could easily be a major reason for the seemingly contradictory results found in the literature.…”

Section: I61mentioning

confidence: 99%

Survival of Escherichia coli K12 in seawater

Sørensen¹

1991

FEMS Microbiology Ecology

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“…However, the indicator organisms are monitored due to the relative ease and lesser cost involved in their measurements. It is well established that the fate of E. coli in surface water systems is governed by multiple physical (e.g., temperature [Flint, 1987]), chemical (e.g., pH [Sjogren and Gibson, 1981], nutrients [Lessard and Sieburth, 1983], sulfate [Robakis et al, 1983], and nitrate [Noguchi et al, 1997]), and biological (chlorophyll [Nevers and Whitman, 2005]) factors. The relationship among these factors and E. coli loads gets complicated by flow rate [Whitman et al, 2004;McKergow and Davies-Colley, 2009].…”

Section: Introductionmentioning

confidence: 99%

Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

Dwivedi

Mohanty

Lesikar

2013

Water Resources Research

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Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.

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Bacterial survival in a dilute environment

Cited by 44 publications

References 28 publications

Comparison of four membrane filter methods for fecal coliform enumeration in tropical waters

Comparison of four membrane filter methods for fecal coliform enumeration in tropical waters

Survival of Escherichia coli K12 in seawater

Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

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