Evaluating the quality of river water is a critical process due to pollution and variations of natural or anthropogenic origin. For the Soan River (Pakistan), seven sampling sites were selected in the urban area of Rawalpindi/Islamabad, and 18 major chemical parameters were examined over two seasons, i.e., premonsoon and postmonsoon 2019. Multivariate statistical approaches such as the Spearman correlation coefficient, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the water quality of the Soan River based on temporal and spatial patterns. Analytical results obtained by PCA show that 92.46% of the total variation in the premonsoon season and 93.11% in the postmonsoon season were observed by only two loading factors in both seasons. The PCA and CA made it possible to extract and recognize the origins of the factors responsible for water quality variations during the year 2019. The sampling stations were grouped into specific clusters on the basis of the spatiotemporal pattern of water quality data. The parameters dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and total suspended solids (TSS) are among the prominent contributing variations in water quality, indicating that the water quality of the Soan River deteriorates gradually as it passes through the urban areas, receiving domestic and industrial wastewater from the outfalls. This study indicates that the adopted methodology can be utilized effectively for effective river water quality management.

中文翻译：

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用多元统计方法研究苏安河水质

由于污染以及自然或人为来源的变化，评估河水的质量是一个关键过程。对于巴基斯坦的Soan河，在拉瓦尔品第/伊斯兰堡市区选择了七个采样点，并在两个季节（即2019年季风前后）中检查了18个主要化学参数。多元统计方法，例如Spearman相关系数，聚类分析（CA）和主成分分析（PCA）用于基于时空格局评估苏安河的水质。PCA的分析结果表明，季风前季节总变化的92.46％和季风后季节总变化的93.11％在两个季节中仅观察到两个负荷因子。PCA和CA使得提取和识别造成2019年水质变化的因素的来源成为可能。根据水质数据的时空模式，将采样站分为特定的簇。溶解氧（DO），生化需氧量（BOD），化学需氧量（COD），浊度和总悬浮固体（TSS）的参数是水质的主要贡献变量，表明苏安河的水质污水通过市区逐渐恶化，从排污口接收生活和工业废水。这项研究表明，所采用的方法可以有效地用于有效的河流水质管理。根据水质数据的时空格局，将采样站分为特定的簇。溶解氧（DO），生化需氧量（BOD），化学需氧量（COD），浊度和总悬浮固体（TSS）的参数是水质的主要贡献变量，表明苏安河的水质污水通过市区逐渐恶化，从排污口接收生活和工业废水。这项研究表明，所采用的方法可以有效地用于有效的河流水质管理。根据水质数据的时空分布，将采样站分为特定的簇。溶解氧（DO），生化需氧量（BOD），化学需氧量（COD），浊度和总悬浮固体（TSS）的参数是水质的主要贡献变量，表明苏安河的水质污水通过市区逐渐恶化，从排污口接收生活和工业废水。这项研究表明，所采用的方法可以有效地用于有效的河流水质管理。总悬浮固体（TSS）是水质的主要贡献变量，表明Soan河的水质在流经市区时逐渐恶化，从排污口接收生活和工业废水。这项研究表明，所采用的方法可以有效地用于有效的河流水质管理。总悬浮固体（TSS）是水质的主要贡献变量，表明Soan河的水质在流经市区时逐渐恶化，从排污口接收生活和工业废水。这项研究表明，所采用的方法可以有效地用于有效的河流水质管理。