Classification and Spatial-Temporal Monitoring of Groundwater Quality for Drinking, Agricultural, and Industrial Uses (Case Study: Qazvin Plain) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| Volume 26, Autumn - Serial Number 100, September 2025, Pages 55-34 PDF (2.05 M) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/idser.2025.371470.1633 | ||
| Authors | ||
| Shima Azadeh Ranjbar1; Majid Kholghi2; Afshin َAshrafzadeh* 3 | ||
| 1MSc of Water Resources, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 2Professor of Water Resources, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 3Associate Professor of Water Resources, Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| Abstract | ||
| Extended Abstract Introduction In countries such as Iran, where drinking water, agriculture, and industry heavily rely on groundwater resources, management and protection are fundamental priorities. Excessive extraction has caused severe decline in groundwater levels and water quality reduction. Given increasing population and surface water shortage, dependence on quality groundwater has intensified significantly. The Qazvin Plain, as a major agricultural and industrial hub, plays an effective role in regional food security and economic development. Agricultural and industrial activities have imposed increasing pressure on water resources. Although part of water demand is supplied through the Taleghan Dam and seasonal rivers, this plain has severe dependence on groundwater with increasing extraction, leading to level decline and serious quality challenges. The Qazvin Plain is located within 49°10' to 50°40' East longitude and 35°20' to 36°31' North latitude. The plain area is 5059.3 square kilometers with elevation ranging between 1131 and 2902 meters. With semi-arid climate, it receives average annual precipitation of 317 millimeters. With 250,000 hectares of agricultural land, the agricultural sector accounts for more than 85 percent of water consumption. The objective is to classify groundwater quality for agricultural, drinking, and industrial uses, and investigate spatial and temporal variations over a 20-year period from 2001 to 2021. Methodology Groundwater quality data were obtained from Qazvin Regional Water Company. Water type and facies were determined using the Piper diagram. Qualitative classification for agricultural purposes was conducted according to Wilcox classification, for drinking according to Schoeller classification, and for industrial purposes using Langelier Saturation Index (LSI). The Wilcox method classifies water based on electrical conductivity (EC) and sodium adsorption ratio (SAR) into four classes: excellent, good, average, and unsuitable. Schoeller method classifies water into six classes based on total dissolved solids (TDS) and total hardness (TH). Langelier index categorizes water as corrosive (LSI<0), balanced (LSI=0), or scaling (LSI>0). Spatial and temporal zoning maps were prepared using Kriging interpolation in GIS at five-year intervals. Results and Discussion Piper diagrams analysis over five periods (2001-2021) revealed continuous aquifer degradation. In 2001, most samples showed calcium-bicarbonate facies indicating fresh water. By 2006, samples shifted toward calcium-sulfate facies. In 2011, degradation accelerated with dramatic dispersion increase and sodium-chloride facies emergence indicating serious salinization. By 2016, critical condition became evident with high facies diversity. In 2021, crisis peaked with maximum dispersion, where calcium-bicarbonate facies appeared only in limited samples, and significant portions showed saline facies dominated by sodium-chloride and sodium-sulfate. For agricultural water quality, no sample was excellent quality. Good quality decreased from 25.25% in 2001 to 17.91% in 2021 (29% reduction). Average quality increased from 46.46% to 58.95%, and unsuitable samples fluctuated between 14.23% and 33.33%. Medium and unsuitable categories comprised 75 to 92 percent. For drinking water quality, about 70% of samples remained in good to average categories, but good quality decreased from 29% to 22%. Completely inappropriate category increased from zero to 2.74%. In total hardness, good quality declined from 39% to 24%, while acceptable water increased to 44%. For industrial water quality, significant changes occurred. Corrosive water decreased from 96.97% to 70.1%, while scaling water increased dramatically from 2.02% to 23.1%. Balanced water comprised only 1 to 6.7%. More than 93% falls into corrosive or scaling categories, requiring treatment before use. Spatial-temporal analysis revealed northern areas had best quality while southern areas faced worst quality, with degradation advancing from south to north. For agriculture, medium and unsuitable water increased from 75% to over 90%. For drinking, good quality decreased from 29-39% to 22-24%. For industry, over 93% are either corrosive or scaling. Conclusions Groundwater resources show concerning degradation trends. Facies evolution from fresh to saline types, dramatic diversity increase, and significant reduction in suitable quality represent serious warnings. For agriculture, unsuitable water increased from 75% to over 90%, necessitating urgent strategies including extraction control, cropping pattern modification, and drainage systems development. For drinking, good quality decreased from 29-39% to 22-24%, with southern areas requiring advanced treatment. For industry, more than 93% are corrosive or scaling, requiring treatment. The spatial pattern indicates degradation advancing from south to north. The trend shows increasing salinity and hardness resulting from excessive extraction, reduced recharge, and likely saline water intrusion. This requires urgent actions including extraction control, artificial recharge enhancement, agricultural return flows treatment, and pollution prevention. Without fundamental measures, sustainability will be severely threatened. Keywords: Langelier index, Piper, Qazvin aquifer, Quality zoning, Schoeller, Wilcox. Acknowledgments The authors wish to express their sincere gratitude to the Editor and the two anonymous reviewers for their insightful comments and constructive feedback, which significantly improved the quality of this manuscript. We also thank the Regional Water Company of Qazvin for providing the necessary data for this research. Conflict of Interest The authors declared no potential conflicts of interest concerning the research, authorship, and publication of this article. Confirmation. Funding The authors received no financial support for the research, authorship, and publication of this article. Data Availability Statements The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Authors’ contribution All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts. Sh. AR. handled the methodology, formal analysis, investigation, data curation, visualization, and initial draft preparation. A.A. and M. Kh. provided supervision and validation for the study. Resources were provided by M. Kh., A. A., and M. Kh. shared responsibility for the final review and editing of the manuscript. All authors have read and agreed to the published version of the manuscript. | ||
| Keywords | ||
| Langelier index; Piper; Qazvin aquifer; Quality zoning; Schoeller; Wilcox | ||
| References | ||
|
Abu-Khalaf, N., Khayat, S., & Natsheh, B. (2013). Multivariate Data Analysis to Identify the Groundwater Pollution Sources in Tulkarm Area / Palestine. In Science and Technology (Vol. 3, Issue 4, pp. 99–104). https://doi.org/10.5923/j.scit.20130304.01 Amini, H., Ashrafzadeh, A., & Khaledian, M. (2024). Enhancing groundwater salinity estimation through integrated GMDH and geostatistical techniques to minimize Kriging interpolation error. Earth Science Informatics, 17(1), 283–297. https://doi.org/10.1007/s12145-023-01157-7 Amiri-Bourkhani, M., Khaledian, M. R., Ashrafzadeh, A., & Shahnazari, A. (2017). The temporal and spatial variations in groundwater salinity in mazandaran plain, Iran, during a long-term period of 26 years. Geofizika, 34(1). https://doi.org/10.15233/gfz.2017.34.4 Azadeh Ranjbar, S. (2025). Groundwater Management Framework Based on Artificial Intelligence. University of Tehran. Crittenden, J. C., Trussell, R. R., Hand, D. W., Howe, K. J., & Tchobanoglous, G. (2012). MWH’s water treatment: principles and design (3rd ed.). John Wiley & Sons. El Bilali, A., Taleb, A., & Brouziyne, Y. (2021). Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agricultural Water Management, 245, 106625. https://doi.org/10.1016/j.agwat.2020.106625 Faraji, Z., Kaviani, A., & Ashrafzadeh, A. (2017). Assessment of GRACE satellite data for estimating the groundwater level changes in Qazvin province. Iranian Journal of Ecohydrology, 4(2), 463–476. https://doi.org/10.22059/ije.2017.61482 Farnia, E., Ghorbani, K., & Salarijazi, M. (2018). Evaluation of the Empirical Bayesian Kriging method in ground water level zoning. Water and Soil Conservation, 25(1), 165–182. https://doi.org/10.22069/jwsc.2017.12528.2723 (in Persian) Fetter, C. W. (2001). Applied Hydrogeology (4th ed.). Prentice-Hall, Inc. Fotamy, M. J., Kholghi, M., Abdeh, A., & Roostaei, M. (2023). The Performance of the Evidence Weighting in GIS for Determining the Effective Factors on the Land Subsidence in Qazvin Plain. Iran-Water Resources Research, 19(3). Gökçekuş, H., Kassem, Y., & Rizza, T. (2025). Groundwater quality assessment for irrigation in coastal region (Güzelyurt), Northern Cyprus and importance of empirical model for predicting groundwater quality (electric conductivity). Environmental Earth Sciences, 84(8), 224. https://doi.org/10.1007/s12665-025-12190-8 Hussein, E. E., Derdour, A., Zerouali, B., Almaliki, A., Wong, Y. J., Ballesta-de los Santos, M., Minh Ngoc, P., Hashim, M. A., & Elbeltagi, A. (2024). Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms. Water (Switzerland), 16(2). https://doi.org/10.3390/w16020264 Jalali, M., Karami, S., & Marj, A. F. (2016). Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer, Iran. Environmental Modeling & Assessment, 21(6), 707–719. https://doi.org/10.1007/s10666-016-9506-6 Mahmoudpour, H., Janatrostami, S., & Ashrafzadeh, A. (2023). Optimal Design of Groundwater Quality Monitoring Network Using Aquifer Vulnerability Map. Water Resources Management, 37(2), 797–818. https://doi.org/10.1007/S11269-022-03404-W Mohammed, M. A. A., Szabó, N. P., Mikita, V., & Szűcs, P. (2025). Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods. Environmental Science and Pollution Research, 32(11), 6884–6903. https://doi.org/10.1007/s11356-025-36175-z Mohammed, M. A., Khleel, N. A., Szabó, N. P., & Szűcs, P. (2023). s40808-022-01638-6.pdf. Modeling Earth Systems and Environment, 9(2), 2501–2516. https://doi.org/10.1007/s40808-022-01638-6 Pappaka, R. K., Somagouni, S. G., Chinthala, K., & Nakkala, A. B. (2024). Appraisal of groundwater quality for suitability of drinking and irrigation purposes of pandameru river basin, anantapur district, AP, India. Arabian Journal of Geosciences, 17(1), 23. https://doi.org/10.1007/s12517-023-11827-x Piper, A. M. (1944). A graphic procedure in the geochemical interpretation of water analysis. Transactions of the American Geophysical Union (یا Trans. American Geophysical Union), 25(6), 914–928. Piroozfar, P., Alipour, S., Modabberi, S., & Cohen, D. (2018). Hydrogeochemical investigation and water quality assessment in the Sarough watershed, Takab minig district. Geosciences, 106(February), 13–28. Salari, M. (2024). Investigating groundwater quality using water quality indicators for drinking, agriculture and industry (Case study: Shiraz plain). Journal of Environmental Science Studies, 8(4), 7574–7586. (in Persian) Schoeller, H. (1962). L’eaux souterraines. Masson. Sunkari, E. D., Abu, M., Bayowobie, P. S., & Dokuz, U. E. (2019). Hydrogeochemical appraisal of groundwater quality in the Ga west municipality, Ghana: Implication for domestic and irrigation purposes. Groundwater for Sustainable Development, 8, 501–511. https://doi.org/10.1016/j.gsd.2019.02.002 Torkamanitombeki, H., & Khamehchiyan, M. (2025). Investigating the Quality of Surface Water at Hydrometric Stations in the Study Area of Sarni Dam, Minab County, for Various Uses Using Aq.QA Software. Journal of Environmental Studies, 50(4), 413–434. https://doi.org/10.22059/jes.2025.372054.1008479 (in Persian) Wilcox, L. (1955). Classification and use of irrigation waters (No. 969). US Department of Agriculture. | ||
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