Literature DB >> 12201126

Faecal contamination over flood events in a pastoral agricultural stream in New Zealand.

J W Nagels1, R J Davies-Colley, A M Donnison, R W Muirhead.   

Abstract

Faecal bacterial dynamics during flood events were studied in the Topehaehae Stream near Morrinsville, New Zealand, in a catchment used for grazing dairy and beef cattle. During the rising limb of a natural flood event, E. coli bacterial concentration rose by more than 2 orders of magnitude and peaked at 41,000 cfu/100 mL. E. coli correlated closely with turbidity over the flood event, and both variables peaked close to the time of maximum flow acceleration rather than peak flow. An artificial flood on the same stream, created by releasing water from a supply reservoir during fine weather with no wash-in from the catchment, produced a broadly similar pattern of faecal contamination (peak E. coli = 12,500 cfu/100 mL). This and other evidence suggests that direct deposition of faecal matter by cattle in the stream channel may be of similar or greater importance than wash-in from land. The flood experiments have been useful for constructing a model of faecal bacterial yields, and they imply that exclusion of livestock from stream channels may appreciably improve water quality.

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Year:  2002        PMID: 12201126

Source DB:  PubMed          Journal:  Water Sci Technol        ISSN: 0273-1223            Impact factor:   1.915


  9 in total

1.  Estimating the microbiological risks associated with inland flood events: Bridging theory and models of pathogen transport.

Authors:  Philip A Collender; Olivia C Cooke; Lee D Bryant; Thomas R Kjeldsen; Justin V Remais
Journal:  Crit Rev Environ Sci Technol       Date:  2016-12-09       Impact factor: 12.561

2.  A neighborhood statistics model for predicting stream pathogen indicator levels.

Authors:  Pramod K Pandey; Gregory B Pasternack; Mahbubul Majumder; Michelle L Soupir; Mark S Kaiser
Journal:  Environ Monit Assess       Date:  2015-02-20       Impact factor: 2.513

3.  Faecal contamination of water and sediment in the rivers of the Scheldt drainage network.

Authors:  Nouho Koffi Ouattara; Julien Passerat; Pierre Servais
Journal:  Environ Monit Assess       Date:  2011-02-19       Impact factor: 2.513

4.  Modeling Contaminant Microbes in Rivers During Both Baseflow and Stormflow.

Authors:  J D Drummond; T Aquino; R J Davies-Colley; R Stott; S Krause
Journal:  Geophys Res Lett       Date:  2022-04-18       Impact factor: 5.576

5.  High prevalence of multiple-antibiotic-resistant (MAR) Escherichia coli in river bed sediments of the Apies River, South Africa.

Authors:  Akebe Luther King Abia; Eunice Ubomba-Jaswa; Maggy Ndombo Benteke Momba
Journal:  Environ Monit Assess       Date:  2015-09-30       Impact factor: 2.513

6.  Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water.

Authors:  Daniel L Weller; Tanzy M T Love; Martin Wiedmann
Journal:  Front Artif Intell       Date:  2021-05-14

7.  Spatiotemporal Variation and the Role of Wildlife in Seasonal Water Quality Declines in the Chobe River, Botswana.

Authors:  J Tyler Fox; Kathleen A Alexander
Journal:  PLoS One       Date:  2015-10-13       Impact factor: 3.240

8.  Assessing the impact of COVID-19 restrictions on the microbial quality of an urban water catchment and the associated probability of waterborne infections.

Authors:  Akebe Luther King Abia; Memory Tekere
Journal:  Sci Total Environ       Date:  2022-09-28       Impact factor: 10.753

9.  Contamination of water resources by pathogenic bacteria.

Authors:  Pramod K Pandey; Philip H Kass; Michelle L Soupir; Sagor Biswas; Vijay P Singh
Journal:  AMB Express       Date:  2014-06-28       Impact factor: 4.126

  9 in total

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