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Volume 11, Issue 2 (4-2025)
jhehp 2025, 11(2): 129-137 |
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Gholami Deljomanesh E, Hakimi Abed M, Fataei E, Shariati Feyzabadi F, Imani A A. Classification of Water Quality in the Shafarood River Based on Physicochemical Parameters and Heavy Metal Concentration
Using Multivariate Analysis Methods. jhehp 2025; 11 (2) :129-137
URL: http://jhehp.zums.ac.ir/article-1-652-en.html
URL: http://jhehp.zums.ac.ir/article-1-652-en.html
Elham Gholami Deljomanesh1 
, Mahsa Hakimi Abed *1 , Ebrahim Fataei2 , Fatemeh Shariati Feyzabadi1 , Ali Akbar Imani3
, Mahsa Hakimi Abed *1 , Ebrahim Fataei2 , Fatemeh Shariati Feyzabadi1 , Ali Akbar Imani3
1- Department of Environment, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
2- Department of Environment, Ardabil Branch, Islamic Azad University, Ardabil, Iran.
3- Department of Agriculture, Ardabil Branch, Islamic Azad University, Ardabil, Iran.
2- Department of Environment, Ardabil Branch, Islamic Azad University, Ardabil, Iran.
3- Department of Agriculture, Ardabil Branch, Islamic Azad University, Ardabil, Iran.
Abstract: (331 Views)
Background: The present study aimed to classify the water quality of the Shafarood River in Gilan Province, Iran, using multivariate analysis methods.
Methods: This research employed Cluster Analysis (CA), Discriminant Analysis (DA), Principal Component Analysis (PCA), and Factor Analysis (FA) as effective methodologies for decision-making in river water quality management. The analysis was based on selected data from physicochemical parameters and heavy metal concentrations measured at five water sampling stations over a six-year period (2017-2022).
Results: The CA results classified the sampling stations into two clusters: medium pollution (Stations 1 and 2) and high pollution (Stations 3, 4, and 5). PCA results confirmed the quality clustering of CA data. The findings of CA and FA methods facilitated the reduction of parameters identified in the first cluster including arsenic (As), lead (Pb), cadmium (Ca), chromium (Cr), nitrate (NO3-), and phosphate (PO43-). In contrast, the second cluster was characterized by total suspended solids, turbidity, hardness, ammonia, fecal coliform, electrical conductivity, biological oxygen demand, and chemical oxygen demand. PCA analysis revealed that the first principal component, accounting for 69.5% of the total variance, identified Pb, As, Cd, and Cr as the most important factors influencing changes in water quality. The second principal component, explaining 15.8% of the total variance, identified ammonia, nitrate, turbidity, and total suspended solids as the main parameters affecting the water quality of the Shafarood River.
Conclusion: The findings suggest that multivariate statistical techniques are valuable for interpreting large data sets, assessing water quality, and elucidating relationships between parameters and pollutant sources. These methodologies provide essential information regarding water quality and represent an effective approach to decision-making in the management of the Shafarood River’s water quality.
Methods: This research employed Cluster Analysis (CA), Discriminant Analysis (DA), Principal Component Analysis (PCA), and Factor Analysis (FA) as effective methodologies for decision-making in river water quality management. The analysis was based on selected data from physicochemical parameters and heavy metal concentrations measured at five water sampling stations over a six-year period (2017-2022).
Results: The CA results classified the sampling stations into two clusters: medium pollution (Stations 1 and 2) and high pollution (Stations 3, 4, and 5). PCA results confirmed the quality clustering of CA data. The findings of CA and FA methods facilitated the reduction of parameters identified in the first cluster including arsenic (As), lead (Pb), cadmium (Ca), chromium (Cr), nitrate (NO3-), and phosphate (PO43-). In contrast, the second cluster was characterized by total suspended solids, turbidity, hardness, ammonia, fecal coliform, electrical conductivity, biological oxygen demand, and chemical oxygen demand. PCA analysis revealed that the first principal component, accounting for 69.5% of the total variance, identified Pb, As, Cd, and Cr as the most important factors influencing changes in water quality. The second principal component, explaining 15.8% of the total variance, identified ammonia, nitrate, turbidity, and total suspended solids as the main parameters affecting the water quality of the Shafarood River.
Conclusion: The findings suggest that multivariate statistical techniques are valuable for interpreting large data sets, assessing water quality, and elucidating relationships between parameters and pollutant sources. These methodologies provide essential information regarding water quality and represent an effective approach to decision-making in the management of the Shafarood River’s water quality.
Type of Study: Original Article |
Subject:
Environmental Health, Sciences, and Engineering
Received: 2025/01/21 | Accepted: 2025/04/3 | Published: 2025/04/15
Received: 2025/01/21 | Accepted: 2025/04/3 | Published: 2025/04/15
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