The use of multivariate statistical methods for the classification of groundwater quality: a case study of aqueducts in the east of Tehran, Iran

محورهای موضوعی : Research paper

Mina Mackialeagha ¹ , Mohammad Bagher Salarian ² , Azita Behbahaninia ³

1 - Department of Environmental Sciences, Department of Environmental Sciences and Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
2 - Department of Environmental Sciences and Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
3 - Assistant Professor, Department of Environmental Sciences, Department of Environmental Sciences and Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

تاریخ دریافت : 1401/05/24 تاریخ پذیرش : 1401/07/30 تاریخ انتشار : 1401/09/10

کلید واژه: principal component analysis, Cluster analysis, Tehran, Pollutant sources, qanat, water quality classification,

چکیده مقاله :

Natural and human factors have always threatened the health of Qanats, valuable water sources for arid and semi-arid regions. The present study decided to qualitatively classify eight selected Qanats of East Tehran, Iran, using two multivariate statistical methods, cluster analysis (CA) and principal component analysis (PCA) based on parameters includind pH, TDS, EC, Na2+, Ca2+, Mg2+, K+, Cl-, NO3 and SO42- according to standard methods during the summer of 2020. Data were analyzed by CA and PCA methods, the results of which based on the degree of pollution divided the studied stations into three groups, high pollution (anthropogenic origin), moderate pollution (natural and anthropogenic origin) and low pollution (natural origin). The stations close to each other for quality status were placed in the same group. The eigenvalues obtained from PCA based on the evaluated parameters showed that the first and second components explained more than 58% of changes between the stations. Analyzing the coefficients of each parameter (eigenvectors) for the first and second components revealed that the main causes for the difference between the stations were Cl-, Na2+, Mg2+ and SO42-, TDS and NO3. The two-dimensional display of the stations based on the first two main components confirmed the grouping resulting from the cluster analysis and was able to separate the investigated stations from each other like cluster analysis. The findings of this research highlighted the usefulness and efficiency of two multivariate statistical techniques, CA and PCA, to effectively manage Qanat water quality.

منابع و مأخذ:

1. Abbaspour M, Javid AH, Jalilzadeh Yengjeh R, Hassani AH, Mostafavi P G, (2013) The biodegradation of methyl tert-butyl ether (MTBE) by

indigenous Bacillus cereus strain RJ1 isolated from soil. Petroleum Science and Technology, 31(18): 1835-1841.

2. Babaei A. A, Ghanbari F, Yengejeh, R. J, (2017). Simultaneous use of iron and copper anodes in photoelectro-Fenton process: concurrent removals of dye and cadmium. Water Science and Technology, 75(7), 1732-1742.


3. Babaei S, Hamdami G, Ghasemieh H, (2017) Identify the Effective Wells in Determination of Groundwater Depth in Urmia Plain Using Principle Component Analysis. Journal of Water and Soil, 31(1): 40- 50.


4. Bhuiyan MAH, Bodrud-Doza M, Islam ARMT et al (2016) Assessment of groundwater quality of Lakshimpur district of Bangladesh using water quality indices, geostatistical methods, and multivariate analysis. Environ Earth Sci 75, 1020. https://doi.org/10.1007/s12665- 016-5823-y


5. Camdevyren H, Demyr N, Kanik A, Keskyn S, (2005) Use of principal component scores in multiple linear regression models for prediction of chlorophyll-a in reservoirs. Ecological Modeling, 181: 581-589.


6. Farhadi H, Fataei E, Kharrat Sadeghi M, (2020) The Relationship Between Nitrate Distribution in Groundwater and Agricultural Landuse (Case study: Ardabil Plain, Iran). Anthropogenic Pollution, 4(1): 50-56.


7. Fataei E, Mosavi S, Imani AA, (2012) Identification of anthropogenic influences on water quality of Aras river by multivariate statistical techniques, 2nd International Conference on Biotechnology and Environment Management IPCBEE, Pukit, Thiland.


8. Fataei E, Monavari SM, Hasani AH, Karbasi AR, Mirbagheri SA, (2010) Salarian MB . et al. Anthropogenic Pollut J. Vol 6 (2), 2022: 1-9 9 Heavy metal and agricultural toxics monitoring in Garasou River in Iran for water quality assessment. Asian Journal of Chemistry, 22 (4): 2991.


9. Fataei E, Monavari SM, Hasani H, Mirbagheri A, Karbasi A, (2011) Surface Water Quality Assessment Using Cluster Analysis. Environmental sciences, 8 (4): 137-146.


10. 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.


11. Fataei E, Tolou I, Nasehi F, Imani A, (2013) Qualitative classification and determination of Karaj River pollutant sources using multivariate statistical methods. Adv Environ Biol, 7 (11): 3517-3521.


12. Gangopadhyay S, Gupta A, Nachabe MH, (2001) Evaluation of ground water monitoring network by principal component analysis. Ground Water, 39(2): 181-191.


13. Gurunathan K, Ravichandran S, (1994) Analysis of water quality data using a multivariate statistical technique - a case study. IAHS Pub, 219.


14. Hajjabbari S, Fataei E, (2016) Determination cadmium and lead pollution resources of Ardabil Plain underground waters. Open Journal of Ecology, 6(9): 554-561.


15. Hamidian A, Ghorbani M, Abdolshahnejad M, Abdolshahnejad A, (2015) Qanat, Traditional Eco-Technology for Irrigation and Water Management. Agriculture and Agricultural Science Procedia, 4: 119- 125. doi: 10.1016/j.aaspro.2015.03.014


16. Jafari Aval Y, Ebadti N, Yousefi H, Kalantari B, Mirzaei M, (2017) Distribution and monitoring of aqueducts water quality for water resources management. Iranian Journal of Ecohydrology, 4(1): 39-52.


17. Kahvaei S, Takdastan A, Jalilzadeh Yengejeh R, (2021) Evaluating the Efficiency of Clarifier Returned Sludge with Poly-aluminum Chloride Coagulant (PAC) for Improving the Removal of Turbidity, COD and PVC. Journal of Health Sciences & Surveillance System, 9(4), 298- 304.


18. Majidi H, Mahmoudi Qaraei MH, Minaei M, Malekzadeh Shafaroudi A, (2019) Investigating groundwater nitrate pollution in Qanat hydraulic structure: Jalmabadan and Ramshin villages, north of Sabzevar, 19th Iranian Hydraulic Conference in date 2021-02-14 by Ferdowsi University of Mashhad, Iranian Hydraulic Association in City Mashhad.


19. Manly BF (1986) Multivariate Statistical Methods: A Primer. London, UK: Chapman and Hall.


20. Mirzade Ahari S, Jalilzadeh Yangejeh R, Mahvi A.H, Dadban Shahamat Y, Takdastan A, (2019) A new method for the removal of ammonium

from drinking water using hybrid method of modified zeolites/ catalytic ozonation, 170 : 148–157 . doi: 10.5004/dwt.2019.24619

21. Mohammadzadeh H, Heydarizad M, (2011) Hydrochemical and stable isotopes study (O18 and H2 surface and groundwater resources) Andarkh Karstic region (north of Mashhad). Earth science research, 2(5): 59-69. (in Persian).


22. Motiee H, Mcbean E, Semsar A, Gharabaghi B, Ghomashchi V, (2006) Assessment of the contributions of traditional qanats in sustainable water resources management. International Journal of Water Resources Development, 22(4): 575-588.


23. Noori R, Farokhnia A, Riahi Medvar H, (2009) Effects of preprocessing the inlet parameters to artificial neural network (ANN) for predicting the monthly flow by PCA and wavelet. Journal of Water & Wastewater, 20(69): 13-22. [In Persian].


24. Ouyang Y (2005) Evaluation of river water quality monitoring stations by principal component analysis. Water Res, 39(12): 2621-2635. 25. Radaei M, Salehi E, Faryadi Sh, Masnavi MR, Zebardast L, (2020) Ecological wisdom, a social–ecological approach to environmental planning with an emphasis on water resources: the case of Qanat Hydraulic Structure (QHS) in a desert city of Iran. Environment, Development and Sustainability, 23(7): 10490-10511.


25. Ranjan Naik M, Barik M, Jha V, Sahoo SK, Sahoo NK, (2021) Spatial distribution and probabilistic health risk assessment of fluoride in groundwater of Angul district, Odisha, India. Groundwater for Sustainable Development, 14: 100604.


26. Rasi Nezami S, Nazariha M, Baghvand A, Moridi A, (2013) Karkheh River Water Quality Using Multivariate Statistical Analysis and Qualitative Data Variations. J Health Syst Res, 8(7): 1280-1292.


27. Sánchez-Martos F, Jiménez-Espinosa R, Pulido A, (2001) Mapping groundwater quality variables using PCA and geostatistics: A case study of Bajo Andarax, Southeastern Spain. Environmental Management, 30(5): 716-726.


28. Siyue L, (2009) Water quality in the upper Han River, China: The impacts of land use/land cover in riparian buffer zone. Hazardous Materials, 165(1): 317-324.


29. Toossab Consulting Engineers Company (2011) Study of Qantas water quality of Tehran, Report Number 430752-5139, Tehran, 221. 31. Zare Aghbolagh J, Fataei E, (2016) The study of changes in Ardabil plain groundwater level using GIS , Advances in Science and Technology Research Journal, 10(29): 109-115.


30. Duan, F., Song, F., Chen, S., Khayatnezhad, M. & Ghadimi, N. 2022. Model parameters identification of the PEMFCs using an improved design of Crow Search Algorithm. International Journal of Hydrogen Energy, 47, 33839-33849.


31. Elsayied Abdein, A. A. 2022. The efficiency of Nitrogen utilization and root nodules' life cycle in Alfalfa after various mineral fertilization and

cultivation of soil. Water and Environmental Sustainability, 2, 13-20.

34. Guo, H., Gu, W., Khayatnezhad, M. & Ghadimi, N. 2022. Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. International Journal of Hydrogen Energy, 47, 24059-24068.


35. Lin, H. 2022. Levafix blue color's visible light degradation utilizing Fenton and photo-Fenton procedures. Water and Environmental Sustainability, 2, 1-8.


36. Mobar, S. & Bhatnagar, P. 2022. ling women by Greenhouse plan as illustrated in the Post-Feminist Tamil Film 36 Vayadhinile. Water and Environmental Sustainability, 2, 9-12.


37. Zhang, J., Khayatnezhad, M. & Ghadimi, N. 2022. Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African Vulture Optimization Algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44, 287-305.