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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Bibliographic Details
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0245525