Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangj...

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Veröffentlicht in:	PloS one 2021-01, Vol.16 (1), p.e0245525-e0245525
Hauptverfasser:	Liu, Junzhao, Zhang, Dong, Tang, Qiuju, Xu, Hongbin, Huang, Shanheng, Shang, Dan, Liu, Ruxue
Format:	Artikel
Sprache:	eng
Schlagworte:	Alkylbenzenes Ammonia Analysis Arsenic Cadmium Chemical oxygen demand Chromium Copper Discriminant analysis Dissolved oxygen Earth Sciences Ecology Ecology and Environmental Sciences Engineering and Technology Environmental aspects Environmental monitoring Fluorides Groundwater Groundwater flow Groundwater runoff Hexavalent chromium Hydrocarbons Mercury Mercury (metal) Nitrogen Oxygen Petroleum hydrocarbons Phenols Phosphorus Physical Sciences Pollutants Pollution Precipitation Principal components analysis Pulp & paper industry Pumping Quality assessment Quality control Quality management Research and Analysis Methods River flow Rivers Runoff Sampling Seasonal variations Selenium Sewage treatment plants Statistical analysis Statistical methods Sulfonates Surface runoff Surface water Surface water quality Tributaries Water pollution Water quality Water quality assessments Water quality management Water quality monitoring Water shortages Zinc
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