Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

Abstract

Understanding the spatio-temporal variation and the potential source of water pollution could greatly improve our knowledge of human impacts on the environment. In this work, data of 11 water quality indices were collected during 2012-2014 at 10 monitoring sites in the mainstream and major tributaries of the Danjiangkou Reservoir Basin, Central China. The fuzzy comprehensive assessment (FCA), the cluster analysis (CA) and the discriminant analysis (DA) were used to assess the water pollution status and analyze its spatio-temporal variation. Ten sites were classified by the high pollution (HP) region and the low pollution (LP) region, while 12 months were divided into the wet season and the dry season. It was found that the HP region was mainly in the small tributaries with small drainage areas and low average annual discharges, and it was also found that most of these rivers went through urban areas with industrial and domestic sewages input into the water body. Principal component analysis/factor analysis (PCA/FA) was applied to reveal potential pollution sources, whereas absolute principal component score-multiple linear regression (APCS-MLR) was used to identify their contributions to each water quality variable. The study area was found as being generally affected by OPEN ACCESSWater 2015, 7 2592 industrial and domestic sewage. Furthermore, the HP region was polluted by chemical industries, and the LP region was influenced by agricultural and livestock sewage.

Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods

“…Figure 6 shows the chosen discriminate parameters identified by spatial backward stepwise DA. The pH (Figure 6a) in low pollution regions was clearly less than in high pollution regions, which was not consistent with analyze results in Danjiangkou Reservoir Basin of China [41]. It maybe because river segments in this region receives a great deal of acidic mine drainage and waste effluents containing Cu and Zn discharged from the neighboring Dexing Copper Mine and from many smelters and mining/panning activities.…”

Water quality assessment and apportionment of pollution sources using APCS-MLR and PMF receptor modeling techniques in three major rivers of South Florida

“…In this study, PCA/FA was performed on the normalized variables in the wet and dry seasons. The varimax rotation method, which maximizes the sum of the squared loadings for each component, was popular among the researchers and was used in this study (Chen et al, 2015;Li et al, 2009;Mustapha and Abdu, 2012;Shrestha et al, 2008;Simeonov et al, 2003;Su et al, 2011). The FA can be expressed as:…”

“…The APCS-MLR method was primarily used for pollution source identification and apportionment in atmospheric environment studies. In recent years, the application of this technique to apportion the pollution sources in water environment has increased (Chen et al, 2013(Chen et al, , 2015Fisher and Mustard, 2004;Mustaffa et al, 2014;Nasir et al, 2011;Shrestha et al, 2008;Simeonov et al, 2003;Singh et al, 2005;Su et al, 2011;Yang et al, 2013;Zhao et al, 2013).…”

A novel SON2‐based similarity index and its application for the rationalization of river water quality monitoring network

“…This puts enhanced focus on the monitoring of the quality of drinking water and environmental waters, as well as municipal and industrial wastewaters (Dong, Wang, Yan, Xu, & Zhang, ). The problem of surface water quality is especially delicate because it is affected by natural factors and human activities (Chen, Li, & Zhang, ; Telci, Nam, Guan, & Aral, ). Because the design of the water quality monitoring network (WQMN) is a crucial process in surface water management, several countries have adopted guidelines which are based on subjective assessment by experts rather than having a mathematical basis for the selection of sampling sites (Varekar, Karmakar, & Jha, ).…”

“…Another integrated technique, which uses a genetic algorithm and geographic information system for the design of effective water quality monitoring network in a large river system was proposed (Park et al, ). Fuzzy comprehensive assessment, cluster analysis and discriminant analysis were used to assess the status of water pollution and analyse its spatial–temporal variation by Chen et al (). Also, Yang et al () used various multivariate statistical methods (cluster analysis, discriminant analysis, FA, and PCA) to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi in order to group monitoring sites with respect to their temporal and spatial similarity.…”