Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters

abstract: One of the two objectives of this dissertation is an investigation into the possible correlation between rainfall events and increased levels of E. coli and Mycobacterium using an existing data set. The literature states that levels of microbial concentrations do increase after rainfall ev...

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Other Authors: Buell, Andrew (Author)
Format: Dissertation
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.51766
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spelling ndltd-asu.edu-item-517662019-02-02T03:01:23Z Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters abstract: One of the two objectives of this dissertation is an investigation into the possible correlation between rainfall events and increased levels of E. coli and Mycobacterium using an existing data set. The literature states that levels of microbial concentrations do increase after rainfall events, but there are no studies to indicate this correlation applies in any Arizona water systems. The data analyzed for the bacterial concentrations project suggested the possibility of a correlation along one river but it is not conclusive to state that any correlation exists between rainfall events and the microbial concentration for many other sites included in the analysis. This is most likely due to the highly engineered water delivery systems that are not directly impacted. The secondary objective was to determine if there are environmental variables collected from an ongoing project which would be a good candidate for making predictions about any of the project data parameters. Of the 79 possible opportunities for the model to accurately predict the dependent variable, it showed strong statistical favorability as well as experimentally favorable results towards Dissolved Organic Carbon as the best dependent variable from the data set, resulting in an accuracy of 41%. This is relevant since Dissolved Organic Carbon is one of the most important water quality parameters of concern for drinking water treatment plants where disinfection by-products are a limiting factor. The need for further analysis and additional data collection is an obvious result from both studies. The use of hydrograph data instead of rainfall would be a logical new direction for the heavily engineered water delivery systems. Dissertation/Thesis Buell, Andrew (Author) Fox, Peter (Advisor) Abbaszadegan, Morteza (Advisor) Alum, Absar (Committee member) Arizona State University (Publisher) Civil engineering Hydraulic engineering Environmental engineering Modeling Mycobacterium Surface Water eng 106 pages Masters Thesis Civil, Environmental and Sustainable Engineering 2018 Masters Thesis http://hdl.handle.net/2286/R.I.51766 http://rightsstatements.org/vocab/InC/1.0/ 2018
collection NDLTD
language English
format Dissertation
sources NDLTD
topic Civil engineering
Hydraulic engineering
Environmental engineering
Modeling
Mycobacterium
Surface Water
spellingShingle Civil engineering
Hydraulic engineering
Environmental engineering
Modeling
Mycobacterium
Surface Water
Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters
description abstract: One of the two objectives of this dissertation is an investigation into the possible correlation between rainfall events and increased levels of E. coli and Mycobacterium using an existing data set. The literature states that levels of microbial concentrations do increase after rainfall events, but there are no studies to indicate this correlation applies in any Arizona water systems. The data analyzed for the bacterial concentrations project suggested the possibility of a correlation along one river but it is not conclusive to state that any correlation exists between rainfall events and the microbial concentration for many other sites included in the analysis. This is most likely due to the highly engineered water delivery systems that are not directly impacted. The secondary objective was to determine if there are environmental variables collected from an ongoing project which would be a good candidate for making predictions about any of the project data parameters. Of the 79 possible opportunities for the model to accurately predict the dependent variable, it showed strong statistical favorability as well as experimentally favorable results towards Dissolved Organic Carbon as the best dependent variable from the data set, resulting in an accuracy of 41%. This is relevant since Dissolved Organic Carbon is one of the most important water quality parameters of concern for drinking water treatment plants where disinfection by-products are a limiting factor. The need for further analysis and additional data collection is an obvious result from both studies. The use of hydrograph data instead of rainfall would be a logical new direction for the heavily engineered water delivery systems. === Dissertation/Thesis === Masters Thesis Civil, Environmental and Sustainable Engineering 2018
author2 Buell, Andrew (Author)
author_facet Buell, Andrew (Author)
title Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters
title_short Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters
title_full Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters
title_fullStr Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters
title_full_unstemmed Predicting Water Quality Parameters and Investigating the Impacts of Rainfall on Bacterial Concentrations in Arizona Surface Waters
title_sort predicting water quality parameters and investigating the impacts of rainfall on bacterial concentrations in arizona surface waters
publishDate 2018
url http://hdl.handle.net/2286/R.I.51766
_version_ 1718970074638843904