scholarly journals Assessment of Spatio-Temporal Variations in Water Quality of Shailmari River, Khulna (Bangladesh) Using Multivariate Statistical Techniques

2017 ◽  
Vol 05 (01) ◽  
pp. 1-26 ◽  
Author(s):  
Md. Muhyminul Islam ◽  
Olaf K. Lenz ◽  
Abul Kalam Azad ◽  
Mosummath Hosna Ara ◽  
Masudur Rahman ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Variations ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Spatio Temporal

scholarly journals Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2764
Author(s):  
Xiaoxue Ma ◽  
Lachun Wang ◽  
Hong Yang ◽  
Na Li ◽  
Chang Gong
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Wastewater Discharge ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Qinhuai River

Monitoring water quality is indispensable for the identification of threats to water environment and later management of water resources. Accurate monitoring and assessment of water quality have been long-term challenges. In this study, multivariate statistical techniques (MST) and water quality identification index (WQII) were applied to analyze spatiotemporal variation in water quality and determine the major pollution sources in the Qinhuai River, East China. A rotated principal component analysis (PCA) identified three potential pollution sources during the wet season (mixed pollution, physicochemical, and nonpoint sources of nutrients) and the dry season (nutrient, primary environmental, and organic sources) and they explained 81.14% of the total variances in the wet season and 78.42% of total variances in the dry season. The result of redundancy analysis (RDA) showed that population density, urbanization, and wastewater discharge are the main sources of organic pollution, while agricultural fertilizer consumption and industrial wastewater discharge are the main sources of nutrients such as nitrogen and phosphorus. The water quality of the Qinhuai River basin was determined to be mainly Class III (slightly polluted) and Class IV (moderately polluted) based on WQII. Temporally, the change trend of WQII showed that water quality gradually deteriorated between 1990 and 2005, improved between 2006 and 2010, and then deteriorated again. Spatially, the WQII distribution map showed that areas with more developed urbanization were relatively more polluted. Our results show that MST and WQII are useful tools to help the public and decision makers to evaluate the water quality of aquatic environment.

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