Multivariate Statistical Analysis for Water Quality Assessment: A Review of Research Published between 2001 and 2020

Abstract

Research on water quality is a fundamental step in supporting the maintenance of environmental and human health. The elements involved in water quality analysis are multidimensional, because numerous characteristics can be measured simultaneously. This multidimensional character encourages researchers to statistically examine the data generated through multivariate statistical analysis (MSA). The objective of this review was to explore the research on water quality through MSA between the years 2001 and 2020, present in the Web of Science (WoS) database. Annual results, WoS subject categories, conventional journals, most cited publications, keywords, water sample types analyzed, country or territory where the study was conducted and most used multivariate statistical analyses were topics covered. The results demonstrate a considerable increase in research using MSA in water quality studies in the last twenty years, especially in developing countries. River, groundwater and lake were the most studied water sample types. In descending order, principal component analysis (PCA), hierarchical cluster analysis (HCA), factor analysis (FA) and discriminant analysis (DA) were the most used techniques. This review presents relevant information for researchers in choosing the most appropriate methods to analyze water quality data.

Optimising multimodal learning in higher education: a case study of a selected South African institution

“…The study design was quantitative of categorical form, requiring use of categorical data analysis (CDA). CDA involves examining of data that classifies observations into several categories (Muniz & Oliveira-Filho, 2023;Tang, He & Tu, 2023). These of dataset types are shared in various fields of applications where numbers need to be (analysed).…”

Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis

“…Statistical multivariate techniques applied to water quality studies have successfully characterized a variety of natural and anthropogenic contaminants under a variety of scenarios [19,20]. Multivariate analysis studies have multiplied in the past few decades because of their ability to examine numerous parameters simultaneously and to detect the source and spread of contaminants in groundwater [20].…”

“…Statistical multivariate techniques applied to water quality studies have successfully characterized a variety of natural and anthropogenic contaminants under a variety of scenarios [19,20]. Multivariate analysis studies have multiplied in the past few decades because of their ability to examine numerous parameters simultaneously and to detect the source and spread of contaminants in groundwater [20]. Commonly utilized multivariate techniques include principal component analysis (PCA), factor analysis (FA), hierarchical cluster analysis (HCA), k-means clustering, self-organized maps (SOM), and fuzzy C-mean clustering (FCM) for grouping analyses [20][21][22][23][24].…”

“…Multivariate analysis studies have multiplied in the past few decades because of their ability to examine numerous parameters simultaneously and to detect the source and spread of contaminants in groundwater [20]. Commonly utilized multivariate techniques include principal component analysis (PCA), factor analysis (FA), hierarchical cluster analysis (HCA), k-means clustering, self-organized maps (SOM), and fuzzy C-mean clustering (FCM) for grouping analyses [20][21][22][23][24]. We utilized these techniques in an aquifer naturally contaminated with arsenic (As) and fluoride (F − ) located in north-central Mexico at the foot of the Sierra Madre Occidental.…”