Water quality management of the Aras River in Iran using two modeling methods: canonical correlation analysis and principal component analysis

被引：0
作者：
Shabarang, Mohammad Mostafa [1 ]
Fataei, Ebrahim [1 ]
Imani, Ali Akbar [2 ]
Bahmanpour, Hooman [3 ]
Shabani, Mohammad [4 ]
机构：
[1] Department of Environment, Ardabil Branch, Islamic Azad University, Ardabil
[2] Department of Agriculture, Ardabil Branch, Islamic Azad University, Ardabil
[3] Department of Environment, Shahrood Branch, Islamic Azad University, Shahrood
[4] Department of Water Engineering, Shiraz Branch, Islamic Azad University, Shiraz
来源：
Water Supply | 2024年 / 24卷 / 08期
关键词：
anthropogenic pollution; Aras River; canonical correlation analysis; principal component analysis; water quality management;
D O I：
10.2166/ws.2024.179
中图分类号：
学科分类号：
摘要：
This research aimed to determine the pollution resources and the main parameters affecting the Aras River water quality, as one of the important rivers of the Caspian Sea watershed, within the borders of Iran, and to present control solutions. Hence, canonical correlation analysis (CCA) and principal component analysis (PCA) were used to reduce data dimensions and determine the pollution criteria based on four physical and six chemical parameters premeasured at 19 stations between 2020 and 2022. The CCA results showed that pH and dissolved oxygen (as physical variables) and also mercury, nitrate, and sulfate (as chemical variables) had a significant role in predicting the canonical variables of chemical and physical parameters, respectively. These pollutants predominantly originated from anthropogenic pollution sources, including runoff infiltration from surrounding vast agricultural lands (based on the first principle component results accounted for 39.3% of the variations), and also the soil erosion in the watershed (based on the second principle component results accounted for 25.7% of the variations). To conclude, the Aras River water quality management programs should focus more on controlling anthropogenic pollution sources to monitor the status of effective physicochemical parameters using multivariate statistical methods, especially CCA and PCA methods. © 2024 The Authors.
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页码：2882 / 2895
页数：13
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