Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China

Abstract

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.

Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas

“…Generally, heavy metal pollution is considered to be point source pollution, and is primarily discharged from smelting and heavy industrial enterprises (Zhang et al 2009); however, nutrient and organic pollution includes point source and nonpoint source pollution, such as domestic wastewater, effluent from wastewater treatment plants and agricultural run-off (Shrestha and Kazama 2007). Industrial, agricultural and other anthropogenic activities often lead to an increased input of metals in soils and natural waters (Cundy et al 2003).…”

Water quality index to determine the surface water quality of Sankey tank and Mallathahalli lake, Bangalore urban district, Karnataka, India

“…Most urban and rural lakes have vanished under this pressure with worldwide environmental concerns (Iscen et al 2008;Prasanna et al 2010). However, in those lakes that could endure, drinking water supply is either substantially reduced or is non-potable, flood absorption capacity impaired, biodiversity threatened and there is diminished fish production (Zhang et al 2009). The main reasons which resulted in impaired conditions of the lakes could be categorized into two classes namely, (a) pollutants entering from fixed point sources (viz., nutrients from wastewater, from municipal and domestic effluents; organic, inorganic and toxic pollutants from industrial effluents and storm water runoff) (b) pollutants entering from non-point sources (viz., nutrients through fertilizers, toxic pesticides and other chemicals, mainly from agriculture runoff; organic pollution from human settlements spread over areas along the periphery of the lakes and reservoirs).…”

Drinking Water Quality of a Rice Land in Turkey by Statistical and GIS Perspectives

“…Cluster Analysis (CA) that enables us to classify objects according to similar characteristics is also a multivariate statistical technique being used widely in water quality assessment studies [1,[19][20][21][22].…”