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

• Published in: PloS one 2021-01, Vol.16 (1), p.e0245525-e0245525
• Main Authors: Liu, Junzhao, Zhang, Dong, Tang, Qiuju, Xu, Hongbin, Huang, Shanheng, Shang, Dan, Liu, Ruxue
• Format: Article
• Language: English
• Subjects: 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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0245525

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 Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018-2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.