Abstract
Multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA) and component analysis/factor analysis (PCA/FA), were applied to explore the surface water quality datasets including 14 parameters at 28 sites of the Eastern Poyang Lake Basin, Jiangxi Province of China, from January 2012 to April 2015, characterize spatiotemporal variation in pollution and identify potential pollution sources. The 28 sampling stations were divided into two periods (wet season and dry season) and two regions (low pollution and high pollution), respectively, using hierarchical CA method. Four parameters (temperature, pH, ammonia-nitrogen (NH4-N), and total nitrogen (TN)) were identified using DA to distinguish temporal groups with close to 97.86% correct assignations. Again using DA, five parameters (pH, chemical oxygen demand (COD), TN, Fluoride (F), and Sulphide (S)) led to 93.75% correct assignations for distinguishing spatial groups. Five potential pollution sources including nutrients pollution, oxygen consuming organic pollution, fluorine chemical pollution, heavy metals pollution and natural pollution, were identified using PCA/FA techniques for both the low pollution region and the high pollution region. Heavy metals (Cuprum (Cu), chromium (Cr) and Zinc (Zn)), fluoride and sulfide are of particular concern in the study region because of many open-pit copper mines such as Dexing Copper Mine. Results obtained from this study offer a reasonable classification scheme for low-cost monitoring networks. The results also inform understanding of spatio-temporal variation in water quality as these topics relate to water resources management.
Highlights
Water scarcity is a growing threat to economic and social development and widespread water pollution in recent decades further complicates the threat, especially in developing countries [1,2,3]
Spatio-temporal analysis of the water quality in the Eastern Poyang Lake Basin was analyzed by using cluster analysis (CA), discriminant analysis (DA), and PCA/FA techniques
Temporal variation of surface water quality was significantly affected by local climate seasons and hydrological conditions
Summary
Water scarcity is a growing threat to economic and social development and widespread water pollution in recent decades further complicates the threat, especially in developing countries [1,2,3]. Sophisticated data-driven analytical approaches (e.g., the projection pursuit technique [20] and neural networks [21,22]), multivariate statistical techniques [23] (e.g., discriminant analysis (DA), cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA)), fuzzy theory[24] and hydrological models [11,25,26,27] have substantially improved water quality assessments Among these methods, multivariate statistical techniques including CA, PCA/FA, and DA can be applied to extract important information in large water quality datasets and are used widely to evaluate water quality and identify potential pollution sources [28]
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