Mon, May 12, 2025
[Archive]
.png)
Volume 11, Issue 2 (4-2025)
jhehp 2025, 11(2): 129-137 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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: (206 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
References
1. Akhoni Pourhosseini, F., & Ghorbani, M. A. (2016). Application of Shannon entropy in determining the most effective chemical parameter in surface water quality (case study: Sofi Chay watershed). Environment and Water Engineering, 2(4), 322-332.
2. Alili, A., & Krstev, D. (2019). Using spss for research and data analysis. Knowledge-International Journal, 32(3), 363-368. [Crossref]
3. Alkarkhi, A. F., Ahmad, A., Ismail, N., Easa, A., & Omar, K. (2008). Assessment of surface water through multivariate analysis. Journal of Sustainable Development, 1(3), 27-33. [Crossref]
4. Arain, M. B., Ullah, I., Niaz, A., Shah, N., Shah, A., Hussain, Z., . . . & Kazi, T. G. (2014). Evaluation of water quality parameters in drinking water of district Bannu, Pakistan: multivariate study. Sustainability of Water Quality and Ecology, 3, 114-123. [Crossref]
5. Babolhakami, A., & Gholami Sefidkouhi, M. A. (2019). Analyze of Talar River water quality using multivariate techniques. Journal of Watershed Management Research, 9(18), 250-259. [Crossref]
6. Bhardwaj, V., Singh, D. S., & Singh, A. K. (2010). Water quality of the Chhoti Gandak River using principal component analysis, Ganga Plain, India. Journal of Earth System Science, 119(1), 117-127. [Crossref]
7. Bierman, P., Lewis, M., Ostendorf, B., & Tanner, J. (2011). A review of methods for analysing spatial and temporal patterns in coastal water quality. Ecological Indicators, 11(1), 103-114. [Crossref]
8. Bu, H., Tan, X., Li, S., & Zhang, Q. (2010). Temporal and spatial variations of water quality in the Jinshui River of the South Qinling Mts., China. Ecotoxicology and Environmental Safety, 73(5), 907-913. [Crossref]
9. Ebadati, N. (2017). Statistical analysis of Dez River water quality, southwest of Iran. Anthropogenic Pollution, 1(1), 46-60.
10. Fan, X., Cui, B., Zhao, H., Zhang, Z., & Zhang, H. (2010). Assessment of River water quality in Pearl River Delta using multivariate statistical techniques. Procedia Environmental Sciences, 2, 1220-1234. [Crossref]
11. Faryadi, S., Shahedi, K., & Nabatpoor, M. (2013). Investigation of water quality parameters in Tadjan River using multivariate statistical techniques. Journal of Watershed Management Research, 3(6), 75-92.
12. Fataei, E., & Shiralipoor, S. (2011). Evaluation of surface water quality using cluster analysis: a case study. World Journal of Fish and Marine Sciences, 3(5), 366-370.
13. Fataei, E., Mosavi, S., & Imani, A. A. (2012). Identification of anthropogenic influences on water quality of Aras River by multivariate statistical techniques. 2nd International Conference on Biotechnology and Environment Management IPCBEE, 42(1), 1-8.
14. Hajigholizadeh, M., & Melesse, A. M. (2017). Assortment and spatiotemporal analysis of surface water quality using cluster and discriminant analyses. Catena, 151, 247-258. [Crossref]
15. Ishtiyaq, A. N., Anisa, B. K., & Abdul, H. (2017). Evaluation of seasonal variability in surface water quality of Shallow Valley Lake, Kashmir, India, using multivariate statistical techniques. Pollution, 3(3), 349-362.
16. Jalili, S. (2020). Water quality assessment based on HFB I& BMWP index in Karoon River, Khouzestan Provience, (Northwest of Persian Gulf). Anthropogenic Pollution Journal, 4(1), 36-49.
17. Kazemzadeh, M., & Malekian A. (2017). Analysis of the surface water quality parameters in the Aji-Chai watershed based on the multivariate statistical techniques. Journal of Range and Watershed Management, 70(2), 465-478.
18. Kiyani, M., Kiyani, S., & Sadeghi, H. (2016). Analysis of spatial and temporal variation in water quality in Karoon basin using multivariate statistical techniques. Journal of Irrigation and Water Engineering, 6(3), 34-48.
19. Koklu, R., Sengorur, B., & Topal, B. (2010). Water quality assessment using multivariate statistical methods-a case study: Melen River system (Turkey). Water Resources Management, 24, 959-978. [Crossref]
20. Mirzaei, M., Riahi-Bakhtiari, A., Salman-Mahini, A., & Gholamalifard, M. (2014). Analysis of the physical and chemical quality of Mazandaran province (Iran) rivers using multivariate statistical methods. Journal of Mazandaran University of Medical Sciences, 23(108), 41-52.
21. Mohammadi, J., Fataei, E., Ojaghi Aghchekandi, A., & Taghavi, L. (2023). Investigation and determination of land use effects on surface water quality in semi-arid areas: case study on Qarasu River in Iran. Anthropogenic Pollution, 7(2), 1-7.
22. Mohammadi Ghaleni, M., & Kardan Moghaddam, H. (2022). Introducing a new drinking water quality index for surface water resources using multivariate analysis (case study: Sefidroud River). Journal of Water and Soil, 36(4), 439-458.
23. Mostafavi, H., & Teimori, A. (2018). Investigating multiple human pressure types in the southern Caspian Sea Basin Rivers at different spatial scales toward integrating water resource management (IWRM) in Iran. Anthropogenic Pollution Journal, 2(1), 38-47.
24. Neissi, L., & Tishehzan, P. (2019). Dez River water quality assessment by using multivariate statistical methods. Journal of Irrigation and Water Engineering, 9(1), 139-150.
25. Noori, R., Karbassi, A., Khakpour, A., Shahbazbegian, M., Mohammadi Khalf Badam, H., & Vesali-Naseh, M. (2012). Chemometric analysis of surface water quality data: case study of the Gorganrud River basin, Iran. Environmental Modeling & Assessment, 17, 411-420. [Crossref]
26. Nosrati, K., Derafshi, Kh., Ghrachahi, S., & Ghrachahi, Kh. (2011). Assessment of surface water quality in Haraz-Ghara Soo watershed using multivariate statistical techniques. Researches in Earth Sciences, 2(1), 41-55.
27. Ouyang, Y. (2005). Evaluation of river water quality monitoring stations by principal component analysis. Water Research, 39(12), 2621-2635. [Crossref]
28. Rahnama, S., & Sayari, N. (2019). Survey and trends of chemical water quality parameters of Tajan River water quality using principal component analysis and Aqua Chem software. Human & Environment, 17(1), 13-25.
29. Ravanbakhsh, M., Tahmasebi Birgani, Y., Dastoorpoor, M., & Ahmadi Angali, K. (2019). Evaluation of temporal and spatial variations of water quality parameters in Zohreh River, Iran. Avicenna Journal of Environmental Health Engineering, 6(2), 75-82. [Crossref]
30. Razmkhah, H., Abrishamchi, A., & Torkian, A. (2010). Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: a case study on Jajrood River (Tehran, Iran). Journal of Environmental Management, 91(4), 852-860. [Crossref]
31. Rodger, B. B., Andrew, D. E., & Eugene, W. R. (2017). Standard methods for the examination of water and wastewater, 23rd edition. American Public Health Association, American Water Works Association, Water Environment Federation, 1545.
32. Safizadeh, E., Karimi, D., Gahfarzadeh, H. R., & Pourhashemi, S. A. (2021). Investigation of physicochemical properties of water in downstream areas of selected dams in Aras catchment and water quality assessment. Anthropogenic Pollution Journal, 5(1), 41-48.
33. Sajjadi, N., Davoodi, M., & Jozi, S. A. (2019). The quality assessment of Kan River's resources in terms of agricultural and drinking purposes. Anthropogenic Pollution Journal, 3(1), 46-53.
34. Salarian, M. B., Mackialeagha, M., & Behbahaninia, A. (2022). The use of multivariate statistical methods for the classification of groundwater quality: a case study of aqueducts in the east of Tehran, Iran. Anthropogenic Pollution Journal, 6(2), 1-9.
35. Soltani, S. H., Ghohroudi, M., & Sadoogh, S. H. (2019). Evaluation of the surface water quality using statistical multi-variate techniques, case study: Aras watershed. Iran-Water Resources Research, 15(2), 319-328.
36. Yidana, S. M., Ophori, D., & Banoeng-Yakubo, B. (2008). A multivariate statistical analysis of surface water chemistry data-the Ankobra Basin, Ghana. Journal of Environmental Management, 86(1), 80-87. [Crossref]
37. Zhao, Z. W., & Cui, F. Y. (2009). Multivariate statistical analysis for the surface water quality of the Luan River, China. Journal of Zhejiang University-Science A, 10(1), 142-148. [Crossref]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
© 2024 The Author(s)
