Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Abstract

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.

Quality characterization and pollution source identification of surface water using multivariate statistical techniques, Nalagarh Valley, Himachal Pradesh, India

“…Multivariate statistical techniques are considered trustworthy and authentic approaches to characterize and evaluate surface water quality for efficient management and effective solution to pollution problems (Helena et al 2000;Singh et al 2004Singh et al , 2005Simeonova 2012, 2013;Okiongbo and Douglas 2015;Hamid et al 2016, Le et al 2017. Noori et al (2012) suggested that PCA and CA techniques are useful tools for identifying the importance of water quality monitoring stations.…”

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

“…Multivariate techniques are frequently used to evaluate water quality along rivers, reservoirs, groundwater and other AEs (Abgarie and Obi, 2009; Cruz et al 2012; Farmaki et al 2012; Hong et al 2010; Noori et al 2010; Noori et al 2012; Ouyang et al 2006; Poma et al 2012; Shrestha and Kazama, 2007; Shin et al 2012; Samsudin et al 2011; Wunderlin et al 2001). They have been applied to large amounts of data but in some cases, they were used to analyze the behavior of a unique AE and/or considering one or two microbiological parameters as indicators to evaluate microbial pollution.…”

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

“…PCA (Hamill, Zhao, Mészáros, Bryce, & Arenz, ; Noori, Karbassi, Khakpour et al, ; Noori, Sabahi, Karbassi, Baghvand, & Zadeh, ) can be used to obtain the linearly uncorrelated feature vectors (principal components) to represent the original high‐dimensional possibly correlated variables ( and are the number of observations and variables, respectively) based on the techniques of orthogonal linear transformation: where denotes the score matrix ( is the number of principal component factors), denotes the loading matrix, and denotes the residual matrix. To reduce the dimensionality of data in the subsequent identification process, not all the principal components need to be calculated.…”

“…PCA (Hamill, Zhao, Mészáros, Bryce, & Arenz, 2018;Noori, Karbassi, Khakpour et al, 2012b;Noori, Sabahi, Karbassi, Baghvand, & Zadeh, 2010) can be used to obtain the linearly uncorrelated feature vectors (principal components) to represent the original high-dimensional possibly correlated variables X n m × (n and m are the number of observations and variables, respectively) based on the techniques of orthogonal linear transformation:…”