Cover Image

Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis

The overexploitation of groundwater due to increasing irrigation, domestic demands, and industry has led to the degradation of groundwater resources worldwide. Therefore, rigorous statistical techniques are required to simplify groundwater evaluation and monitoring. In this paper, we consider the ap...

Full description

Saved in:
Bibliographic Details
Main Authors: Wali, Saadu Umar, Alias, Noraliani, Harun, Sobri, Umar, Kabiru Jega, Abor, Isa Garba, Abba, Aminu, Buba, Adamu
Format: Article
Published: Gheorghe Asachi Technical University of Iasi, Romania 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/106870/
https://www.eemj.eu/index.php/EEMJ/article/view/4656
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.106870
record_format eprints
spelling my.utm.1068702024-08-01T05:42:55Z http://eprints.utm.my/106870/ Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis Wali, Saadu Umar Alias, Noraliani Harun, Sobri Umar, Kabiru Jega Abor, Isa Garba Abba, Aminu Buba, Adamu TA Engineering (General). Civil engineering (General) The overexploitation of groundwater due to increasing irrigation, domestic demands, and industry has led to the degradation of groundwater resources worldwide. Therefore, rigorous statistical techniques are required to simplify groundwater evaluation and monitoring. In this paper, we consider the application of Principal Component Analysis (PCA) to identify sources of ions in groundwater. Two data sets were derived from the Western Sokoto Basin, comprising hydrochemical data from shallow and deep aquifers. We show how PCA can be applied to identify sources of ions. Along with Hierarchical Cluster Analysis, PCA enables groundwater classification based on the mechanisms controlling the hydrochemistry of groundwater. The exploratory PCA and associated PCs and Biplots offer insights that can provide water quality analysts with an understanding of the experiential water composition influenced by natural and anthropogenic processes. PCA offers a comprehensive, straightforward, and cost-effective tool for standard and innovative analysis that helps water quality analysts discover, recognize, and analyze the accumulating volumes of water quality data by reducing data and retaining the major components characterizing the hydrochemistry of aquifers or surface water. Therefore, this study provides the basis for PCA application in groundwater quality analysis and monitoring for pollutant source identification, especially in data-poor regions where historical data is lacking. Theoretically, this review further enriches the literature on the systematic approach to assessing groundwater quality analysis, monitoring, and management using PCA. Practically, PCA is a robust statistical tool for identifying complex relationships between different physicochemical parameters and their links to natural and human processes. This review presents a new approach to hydrogeochemical analysis different from the conventional PCA application in the existing literature, which concerns hydrochemical data reduction. Thus, the PCA model used in this study can be used by environmental agencies seeking long-lasting and cost-effective improvement of their water quality assessment and monitoring. Gheorghe Asachi Technical University of Iasi, Romania 2023 Article PeerReviewed Wali, Saadu Umar and Alias, Noraliani and Harun, Sobri and Umar, Kabiru Jega and Abor, Isa Garba and Abba, Aminu and Buba, Adamu (2023) Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis. Environmental Engineering and Management Journal, 22 (2). pp. 321-337. ISSN 1582-9596 https://www.eemj.eu/index.php/EEMJ/article/view/4656 NA
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Wali, Saadu Umar
Alias, Noraliani
Harun, Sobri
Umar, Kabiru Jega
Abor, Isa Garba
Abba, Aminu
Buba, Adamu
Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
description The overexploitation of groundwater due to increasing irrigation, domestic demands, and industry has led to the degradation of groundwater resources worldwide. Therefore, rigorous statistical techniques are required to simplify groundwater evaluation and monitoring. In this paper, we consider the application of Principal Component Analysis (PCA) to identify sources of ions in groundwater. Two data sets were derived from the Western Sokoto Basin, comprising hydrochemical data from shallow and deep aquifers. We show how PCA can be applied to identify sources of ions. Along with Hierarchical Cluster Analysis, PCA enables groundwater classification based on the mechanisms controlling the hydrochemistry of groundwater. The exploratory PCA and associated PCs and Biplots offer insights that can provide water quality analysts with an understanding of the experiential water composition influenced by natural and anthropogenic processes. PCA offers a comprehensive, straightforward, and cost-effective tool for standard and innovative analysis that helps water quality analysts discover, recognize, and analyze the accumulating volumes of water quality data by reducing data and retaining the major components characterizing the hydrochemistry of aquifers or surface water. Therefore, this study provides the basis for PCA application in groundwater quality analysis and monitoring for pollutant source identification, especially in data-poor regions where historical data is lacking. Theoretically, this review further enriches the literature on the systematic approach to assessing groundwater quality analysis, monitoring, and management using PCA. Practically, PCA is a robust statistical tool for identifying complex relationships between different physicochemical parameters and their links to natural and human processes. This review presents a new approach to hydrogeochemical analysis different from the conventional PCA application in the existing literature, which concerns hydrochemical data reduction. Thus, the PCA model used in this study can be used by environmental agencies seeking long-lasting and cost-effective improvement of their water quality assessment and monitoring.
format Article
author Wali, Saadu Umar
Alias, Noraliani
Harun, Sobri
Umar, Kabiru Jega
Abor, Isa Garba
Abba, Aminu
Buba, Adamu
author_facet Wali, Saadu Umar
Alias, Noraliani
Harun, Sobri
Umar, Kabiru Jega
Abor, Isa Garba
Abba, Aminu
Buba, Adamu
author_sort Wali, Saadu Umar
title Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
title_short Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
title_full Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
title_fullStr Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
title_full_unstemmed Application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
title_sort application of principal component analysis in the context of multivariate statistics and its use for hydrogeochemical analysis
publisher Gheorghe Asachi Technical University of Iasi, Romania
publishDate 2023
url http://eprints.utm.my/106870/
https://www.eemj.eu/index.php/EEMJ/article/view/4656
_version_ 1806442420889452544
score 13.211869

Similar Items