• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Norges miljø- og biovitenskapelige universitet
  • Faculty of Science and Technology (RealTek)
  • Master's theses (RealTek)
  • View Item
  •   Home
  • Norges miljø- og biovitenskapelige universitet
  • Faculty of Science and Technology (RealTek)
  • Master's theses (RealTek)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Multiple linear regression models for estimating microbial load in a drinking water source case from the Glomma river, Norway

Eregno, Fasil Ejigu
Master thesis
Thumbnail
View/Open
Fasil_thesis.pdf (2.722Mb)
URI
http://hdl.handle.net/11250/189167
Date
2014-02-18
Metadata
Show full item record
Collections
  • Master's theses (RealTek) [1401]
Abstract
The application of integrated study of water quality and statistics for environmental modelling

is considered as a powerful analytical tool that has been thrived significantly during recent

years. The present study was conducted to identify the significant physico-chemical factors

that affects the raw water quality, and to study statistical interrelationships amongst them.

Multiple linear regression models were developed to estimate microbial load in the raw water

source, using data from the NRV drinking water treatment plant published from 1999 to 2012

and also from Norwegian school of veterinary science through VISK project. The study was

conducted based on indicator microbial load which contain Total viable count "Kimtall",

Coliform bacteria, Escherichia coli, Clostridium perfringens, and Intestinal Enterococci. In

addition, microbial pathogen load of Noro virus, and Adeno virus were also incorporated. The

explanatory variables examined for regression analysis were monitored properties of raw

water and hyro-climatic data from the catchment which include; river discharge, raw water

temperature, rainfall, pH, turbidity, conductivity, colour, and total organic carbon. Each

indicator and pathogenic microbial loads have its own unique set of selected explanatory

variables. The statistical significance tests were applied to the coefficients of the multiple

linear regression models, and they are found to be significant. The regression equations were

evaluated using measures of variability, including adjusted R2, which ranges from 38.0 % for

Adeno virus concentration to 50.0 % for Ecoli concentration. The results revealed that the

regression analysis provide useful mean for rapid monitoring of microbial raw water quality

based on the physico-chemical parameters.
Description
Linear regression model is used to relate the physico-chemical parameters with microbial load of drinking water source
Publisher
Norwegian University of Life Sciences, Ås

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit