Drinking water quality assessment using water quality index: A case study of the Shahrekord, Iran

Document Type : Research Paper

Authors

1 Department of Chemistry, Faculty of Science, Shahrekord University, P.O. Box: 115, Shahrekord, Iran

2 Department of Chemistry, Faculty of Science, University of Jiroft, P. O. Box: 7867161167, Jiroft, Iran

Abstract

In recent years, the use of water quality indices (WQI) to ensure the safety of drinking water has expanded. In this study, the quality of drinking water in Shahrekord was investigated. This study involves measuring eight physicochemical parameters (pH, EC, TDS, TH, SO42-, PO43-, NO3-, Turbidity) of drinking water taken from the urban water network and calculating the water quality index. The obtained water quality index obtained for all water samples was below 50, indicating excellent and very good quality of Shahrekord's drinking water. After applying principal component analysis, the results indicated that first component, with the highest eigenvalue, is influenced by parameters such as sulfate, nitrate, electrical conductivity (EC), and TDS, confirming the salinity and chemical water quality are dominant. The second component, driven by phosphate, total hardness (TH), and turbidity, reflects the physical properties and hardness of water. The third component is associated with the contribution of phosphate and hardness. EC and TDS exhibit a high correlation.

Graphical Abstract

Drinking water quality assessment using water quality index: A case study of the Shahrekord, Iran

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References

[1] Kiteto, M., Vidija, B., Mecha, C. A., Mrosso, R., & Chollom, M. N. (2024). Advances in metal–organic frameworks as adsorbents, photocatalysts and membranes: a new frontier in water purification. Discover Water, 4(1), 54.
https://doi.org/10.1007/s43832-024-00119-4
[2] Zhang, S., Chen, J., Wan, Z., Yu, M., Shu, Y., Tan, Z., & Liu, J. (2021). Challenges and countermeasures for international ship waste management: IMO, China, United States, and EU. Ocean & Coastal Management, 213, 105836.
https://doi.org/10.1016/j.ocecoaman.2021.105836
[3] Fortes, A. C. C., Barrocas, P. R. G., & Kligerman, D. C. (2023). Water quality indices: Construction, potential, and limitations. Ecological Indicators, 157, 111187.
https://doi.org/10.1016/j.ecolind.2023.111187
[4] Patel, P. S., Pandya, D. M., & Shah, M. (2023). A systematic and comparative study of Water Quality Index (WQI) for groundwater quality analysis and assessment. Environmental Science and Pollution Research, 30(19), 54303-54323.
https://doi.org/10.1007/s11356-023-25936-3
[5] Manna, A., & Biswas, D. (2023). Assessment of drinking water quality using water quality index: a review. Water conservation science and engineering, 8(1), 6.
https://doi.org/10.1007/s41101-023-00185-0
[6] Chidiac, S., El Najjar, P., Ouaini, N., El Rayess, Y., & El Azzi, D. (2023). A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives. Reviews in Environmental Science and Bio/Technology, 22(2), 349-395.
https://doi.org/10.1007/s11157-023-09650-7
[7] Khouri, L., & Al-Moufti, M. B. (2022). Selection of suitable aggregation function for estimation of water quality index for the Orontes River. Ecological Indicators, 142, 109290.
https://doi.org/10.1016/j.ecolind.2022.109290
[8] Wu, Z., Lai, X., & Li, K. (2021). Water quality assessment of rivers in Lake Chaohu Basin (China) using water quality index. Ecological Indicators, 121, 107021.
https://doi.org/10.1016/j.ecolind.2020.107021
[9] Kouadri, S., Elbeltagi, A., Islam, A. R. M. T., & Kateb, S. (2021). Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast). Applied Water Science, 11(12), 190.
https://doi.org/10.1007/s13201-021-01528-9
[10] Han, Y., Liu, J., Li, J., Jiang, Z., Ma, B., Chu, C., & Geng, Z. (2023). Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight. Science of The Total Environment, 877, 162730.
https://doi.org/10.1016/j.scitotenv.2023.162730
[11] Khan, M. H. R. B., Ahsan, A., Imteaz, M., Shafiquzzaman, M., & Al-Ansari, N. (2023). Evaluation of the surface water quality using global water quality index (WQI) models: perspective of river water pollution. Scientific Reports, 13(1), 20454.
https://doi.org/10.1038/s41598-023-47137-1
[12] Liang, J., Zou, G., Gu, C., Tao, S., Guo, L., Tang, C., & Chen, Y. (2023). Study on skin infection model of Staphylococcus aureus based on analytic hierarchy process and Delphi method. Heliyon, 9(6).
https://doi.org/10.1016/j.heliyon.2023.e16327
[13] Marselina, M., Afifa, R., & Wulandari, S. (2025). Integrating analytical hierarchy process and Delphi technique in enhancing water quality assessment: development of a tailored water quality index for the Upper Citarum River, West Java, Indonesia. Environmental Monitoring and Assessment, 197(7), 708.
https://doi.org/10.1007/s10661-025-14133-z
[14] Kothari, V., Vij, S., Sharma, S., & Gupta, N. (2021). Correlation of various water quality parameters and water quality index of districts of Uttarakhand. Environmental and Sustainability Indicators, 9, 100093.
https://doi.org/10.1016/j.indic.2020.100093
[15] Mahammad, S., Islam, A., & Shit, P. K. (2023). Geospatial assessment of groundwater quality using entropy-based irrigation water quality index and heavy metal pollution indices. Environmental Science and Pollution Research, 30(55), 116498-116521.
https://doi.org/10.1007/s11356-022-20665-5
[16] Frasca, M., La Torre, D., Pravettoni, G., & Cutica, I. (2024). Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review. Discover Artificial Intelligence, 4(1), 15.
https://doi.org/10.1007/s44163-024-00114-7
[17] Alcantara, G. M., Dresch, D., & Melchert, W. R. (2021). Use of non-volatile compounds for the classification of specialty and traditional Brazilian coffees using principal component analysis. Food Chemistry, 360, 130088.
https://doi.org/10.1016/j.foodchem.2021.130088
[18] Shafizadeh-Moghadam, H. (2021). Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance. Expert Systems with Applications, 186, 115678.
https://doi.org/10.1016/j.eswa.2021.115678
 
 
Advances in Environmental Technology
Volume 12, Issue 2
April 2026
Pages 198-207

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History

  • Receive Date: 10 October 2025
  • Revise Date: 24 February 2026
  • Accept Date: 16 March 2026

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