Assessment of seasonal variations of chemical characteristics in surface water using multivariate statistical methods

Abstract

ABSTRACT:Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995-2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.

Heavy metals contamination in water and sediments of an urban river in a developing country

“…PCA was employed to evaluate the extent of metal contamination and infer the hypothetical location (Persuad et al, 1993); e Zakir et al (2006) and f Datta and Subramanian (1998), respectively of sources of heavy metals (Shin and Lam, 2001;Franco-Uria et al, 2009;Kikuchi et al, 2009;Zare Garizi et al, 2011). Initially data were normalized using Fe to compensate for both granulometric and mineralogical variability of metal concentration in sediments (Daskalakis and O'Connor, 1995;Schiff and Weisberg, 1999;Seshan et al, 2010); and then PCA with varimax rotation was applied to the data matrix.…”

Environmental Assessment of Water Quality in Kuwait Bay

“…When the eigenvalue of a principal component is equal to, or greater than, 1, the result of the principal component analysis is considered significant [24], [25]. To minimize the variations among the variables for each factor, the factor axes were varimax-rotated.…”

Spatio-temporal variability of hydro-chemical characteristics of coastal waters of Gulf of Mannar Marine Biosphere Reserve (GoMMBR), South India

“…The multivariate statistical technique is useful in asserting the temporal and spatial variations caused by natural and anthropogenic factors (Shrestha and Kazama 2007;Garizi et al 2011). Cluster analysis (CA) based on euclidean similarity measure is used to group sites recording similarity in environmental parameters (Arumugam et al 2013).…”

“…Cluster analysis (CA) based on euclidean similarity measure is used to group sites recording similarity in environmental parameters (Arumugam et al 2013). Principal component analysis (PCA) assists to recognize the factors or origin responsible for seasonal water quality variations (Zare et al 2011). As the first principal component accounts for the covariation shared by all attributes, this may be a better estimate than simple or weighted averages of the original variables (Babu .…”

“…In this present study, multivariate statistical techniques such as CA, PCA and FA were applied to the interpretation of complex data matrices to better understand the water quality. It helps in identifying the possible factors/sources that influence water quality (Reghunath et al 2002;Panda et al 2006;Bu et al 2009;Garizi et al 2011).…”