Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems

This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across...

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Published inPloS one Vol. 17; no. 1; p. e0262117
Main Authors Saalidong, Benjamin M., Aram, Simon Appah, Otu, Samuel, Lartey, Patrick Osei
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 25.01.2022
Public Library of Science (PLoS)
Subjects
Alkalinity
Biology and Life Sciences
Calcium
Carbonates
Chemical properties
Chemicals
Confined groundwater
Copper
Creeks & streams
Earth Sciences
Ecology and Environmental Sciences
Environment
Environmental aspects
Environmental Monitoring
Fluid dynamics
Ghana
Groundwater
Groundwater - chemistry
Groundwater - microbiology
Health aspects
Heavy metals
Hydrogen-Ion Concentration
Interaction parameters
Manganese
Medicine and Health Sciences
Metals
Metals, Heavy - analysis
Mining
pH effects
Physical Sciences
Physical simulation
Physicochemical properties
Regression analysis
Regression models
Robustness
Surface water
Surface-groundwater relations
Systems analysis
Turbidity
Water chemistry
Water Pollutants, Chemical - analysis
Water Quality
Water sampling
Water supply
Water, Underground
Zinc
Ghana
China
Online AccessGet full text

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Abstract This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO 4 2- , total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO 3 , Mn and total coliform were associated with optimal water pH while SO 4 2- , EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.
AbstractList This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO42-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO3, Mn and total coliform were associated with optimal water pH while SO42-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.
This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO42-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO3, Mn and total coliform were associated with optimal water pH while SO42-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO42-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO3, Mn and total coliform were associated with optimal water pH while SO42-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.
This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO 4 2- , total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO 3 , Mn and total coliform were associated with optimal water pH while SO 4 2- , EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.
This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO.sub.4 .sup.2-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO.sub.3, Mn and total coliform were associated with optimal water pH while SO.sub.4 .sup.2-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.
Audience Academic
Author Otu, Samuel
Aram, Simon Appah
Saalidong, Benjamin M.
Lartey, Patrick Osei
AuthorAffiliation 3 Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Socorro, NM, United States of America
1 Department of Geosciences, Taiyuan University of Technology, Taiyuan, People’s Republic of China
2 College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan, People’s Republic of China
Bangabandhu Sheikh Mujibur Rahman Agricultural University, BANGLADESH
4 Ministry of Education Key Laboratory of Interface and Engineering in Advanced Materials, Research Center of Advanced Materials Science and Technology, Taiyuan University of Technology, Taiyuan, People’s Republic of China
AuthorAffiliation_xml – name: 3 Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Socorro, NM, United States of America
– name: 1 Department of Geosciences, Taiyuan University of Technology, Taiyuan, People’s Republic of China
– name: 4 Ministry of Education Key Laboratory of Interface and Engineering in Advanced Materials, Research Center of Advanced Materials Science and Technology, Taiyuan University of Technology, Taiyuan, People’s Republic of China
– name: 2 College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan, People’s Republic of China
– name: Bangabandhu Sheikh Mujibur Rahman Agricultural University, BANGLADESH
Author_xml – sequence: 1
  givenname: Benjamin M.
  orcidid: 0000-0001-9696-3028
  surname: Saalidong
  fullname: Saalidong, Benjamin M.
– sequence: 2
  givenname: Simon Appah
  surname: Aram
  fullname: Aram, Simon Appah
– sequence: 3
  givenname: Samuel
  surname: Otu
  fullname: Otu, Samuel
– sequence: 4
  givenname: Patrick Osei
  surname: Lartey
  fullname: Lartey, Patrick Osei
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35077475$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2022 Public Library of Science
2022 Saalidong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2022 Saalidong et al 2022 Saalidong et al
Copyright_xml – notice: COPYRIGHT 2022 Public Library of Science
– notice: 2022 Saalidong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and...
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StartPage e0262117
SubjectTerms Alkalinity
Biology and Life Sciences
Calcium
Carbonates
Chemical properties
Chemicals
Confined groundwater
Copper
Creeks & streams
Earth Sciences
Ecology and Environmental Sciences
Environment
Environmental aspects
Environmental Monitoring
Fluid dynamics
Ghana
Groundwater
Groundwater - chemistry
Groundwater - microbiology
Health aspects
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Title Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems
URI https://www.ncbi.nlm.nih.gov/pubmed/35077475
https://www.proquest.com/docview/2622762044
https://www.proquest.com/docview/2622964367
https://pubmed.ncbi.nlm.nih.gov/PMC8789185
https://doaj.org/article/d804fb491f7645debebd2952900a6c59
http://dx.doi.org/10.1371/journal.pone.0262117
Volume 17
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