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 industrial and domestic sewage. Furthermore, the HP region was polluted by chemical industries, and the LP region was influenced by agricultural and livestock sewage.
Highlights
More and more attention has been paid to surface water quality, as it is strongly relevant to human lives and public health [1,2]
Water quality of class I and II was regarded as low pollution status, while water quality of class III, IV and V was regarded as high pollution status
Spatio-temporal variations of water quality and potential pollution sources in Danjiangkou Reservoir Basin were analyzed based on fuzzy comprehensive evaluation method (FCA), cluster analysis (CA), discriminant analysis (DA), principal component analysis/factor analysis (PCA/FA) and APCS-MLR
Summary
More and more attention has been paid to surface water quality, as it is strongly relevant to human lives and public health [1,2]. As surface water quality is controlled by both natural factors (hydrological and meteorological conditions) and human influences (urban, industrial and agricultural activities), decision makers are facing with significant difficulties the problem of how to manage surface water quality [3,4,5]. Datasets of water quality are usually complex containing huge amounts of information with internal relationships among variables, which make it difficult to interpret and draw meaningful conclusions [9].
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