تحلیل جغرافیایی و آماری تغییرات کیفیت آب زیرزمینی دشت بم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 دانشکده مهندسی انرژی‌ و منابع پایدار، دانشکدگان علوم و فناوری‌های بین رشته‌ای، دانشگاه تهران، تهران، ایران.

10.22059/jwim.2024.373180.1151

چکیده

افزایش جمعیت و در نتیجه آن افزایش بهره‌برداری بی‌رویه از منابع آب زیرزمینی نه‌تنها باعث کاهش کمیت بلکه سبب تخریب کیفیت این منابع ارزشمند شده ‌‌است. لذا ضرورت مطالعه و بررسی کیفیت آب‌های زیر‌زمینی می‌تواند به مدیریت صحیح استفاده از این منابع آبی کمک نماید. لذا هدف از این مطالعه، تعیین مهم‌ترین متغیر‌های کیفی آب زیر‌زمینی با استفاده از آنالیز مؤلفه‌های اصلی و سپس، بررسی عملکرد سه مدل کریجینگ ساده، جهانی و معمولی در درون‌یابی مهم‌ترین متغیر‌های کیفی تعیین‌شده در دشت بم می‌باشد. بدین منظور از60 حلقه چاه موجود، 40 حلقه چاه به‌صورت تصادفی و با پراکنش مناسب در منطقه موردمطالعه به‌عنوان چاه آموزشی و مابقی چاه‌ها برای آزمایش مدل‌ها استفاده شد. نتایج آزمون مؤلفه‌های اصلی نشان داد که دو متغیر EC و TDS به‌عنوان متغیر‌های اصلی، بیش‌ترین تغییرات واریانس موجود در سایر متغیر‌ها کیفی آب را توجیه می‌کنند. نتایج درون‌یابی براساس این دو پارامتر نشان داد که روش کریجینگ معمولی و جهانی در تخمین شوری در مرحله‌ی آموزش دارای عملکرد نسبتا مشابهی می‌باشند، اما در مرحله آزمایش، در روش KO ضرایب RMSE و MAE به‌ترتیب برابر 24/422 و 35/153 میکروزیمنس بر سانتی‌متر بوده که به‌ترتیب دارای اختلاف 22/1 و 52/0 میکروزیمنس بر سانتی‌متر کم‌تر از روش KU می‌باشد و در نتیجه بهتر از کریجینگ جهانی است. در درون‌یابی متغیر TDS روش کریجینگ معمولی در هر دو مرحله‌ی آموزش و آزمایش بهترین عملکرد را نسبت به دو روش دیگر داشته است. هم‌چنین نتایج درون‌یابی براساس این دو متغیر نشان داد که میزان شوری در قسمت شمال و شمال شرقی دشت در دو روش کریجنگ معمولی و جهانی بیش‌تر از سایر نقاط بوده، که با  تغییرات کاربری اراضی منطقه انطباق مناسبی داشته است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Geographical and statistical analysis of groundwater quality changes in Bam Plain

نویسندگان [English]

  • Moslem Borji 1
  • Hossein Yousefi 2
  • Ali Mahmoudi Aznaveh 2
1 Department of Arid and Mountainous Reclamation Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
2 School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
چکیده [English]

Increasing population growth and thereby increasing exploitation of ground water resources has led to not only decreased quantity but also reduced quality of these valuable resources. Therefore, necessity of studying the quality of water in these areas can help proper management of these water resources. The aim of this study was to determine the groundwater quality variables using principal component analysis and then to evaluate the efficacy of the three kriging models namely simple kriging, universal kriging, and ordinary kriging in interpolation of the most important qualitative variables defined in Bam plain. For this purpose, of the 60 existing wells, 40 wells with good distribution in the study area were selected randomly as for training and the remaining wells were used to test the models. Results of principal component analysis showed that the two variables EC and TDS as the main variables explained the highest changes in variance of other water quality variables. Results of interpolation based on these two parameters showed that ordinary and universal kriging were relatively same in estimating the salinity in the training step, but in the testing step, in the KO method, the RMSE and MAE coefficients are 24.422 and 35.153 microsiemens per centimeter, respectively. These values have differences of 1.22 and 0.52 µs/cm less than the KU method, and consequently, they are superior to Universal Kriging. In interpolation of variable TDS in both the training and testing steps, ordinary kriging had the best performance compared to the two other methods. Interpolation results based on these two variables also showed that the salinity in the north and northeastern parts of the plain in two ordinary and universal kriging was higher than other places indicating a good conformity with changes in land use.

کلیدواژه‌ها [English]

  • Water quality assessment
  • Sustainable development
  • Groundwater chemistry
  • Water quality variables
  • Water resource management

مراجع

  1. Askari, M., Mosaedi, A., Dehghani, A. A., & Halghi, M. M. (2009). Application of Geostatistics and GIS analysis, in study of groundwater quality spatial variability, Case study: Qazvin aquifer, Iran. International Conference on Water Resources, Shahrood University of Technology, Shahrood, Iran. (In Persian).
  2. Azhdari, Z., & Hosseini, S. Z. (2018). Spatial variations of groundwater quality parameters using geostatistics (Case study: Segsi Plain, Isfahan). Journal of Environmental Science and Technology, 20(3), 63-80. (In Persian).
  3. Bakhshodeh, M., & Golestani Kermani, S. (2019). Evaluation of kriging and cokriging interpolation methods to determine the spatial changes of some groundwater quality parameters of Bardsir Plain. 15th National Conference on Irrigation and Evaporation Reduction .(In Persian).
  4. Bameri, A., Khormali, F., Kiani, F., & Dehghani, A. A. (2012). Spatial variability of soil organic carbon on different slope positions of loess hillslopes in Toshan area, Golestan Province. Journal of Water and Soil Conservation, 19(2), 43-61. (In Persian).
  5. Bordbar, M., Neshat, A., Javadi, S., Pradhan, B., Dixon, B., & Paryani, S. (2022). Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches. Natural Hazards, 110(3), 1799-1820. https://doi.org/10.1007/s11069-021-05013-z
  6. Davis, J. C., & Sampson, R. J. (1986). Statistics and data analysis in geology (Vol. 646). Wiley New York.
  7. Delbari, M., Afrasiab, P., & Salari, M. (2013). Mapping water salinity and sodicity using selected geostatistical methods, case study: Kerman plain. Water Resources Engineering, 6(No16/Spring 2013), 11-24.
  8. Eftekhari, M., Eslaminezhad, S. A., Haji Elyasi, A., & Akbari, M. (2021). Geostatistical Evaluation with Drinking Groundwater Quality Index (DGWQI) in Birjand Plain Aquifer. Environment and Water Engineering, 7(2), 267-278. (In Persian).
  9. Eghbalian, S., & Bahmani, O. (2020). Study of Local and Temporal Changes of Groundwater Quality Standards of Hamedan-Bahar Plain Using (GIS) over a 10 Year Period. Journal of Environmental Science and Technology, 22(3), 83-98. (In Persian).
  10. El-Hames, A. S., Hannachi, A., Al-Ahmadi, M., & Al-Amri, N. (2013). Groundwater quality zonation assessment using GIS, EOFs and hierarchical clustering. Water Resources Management, 27, 2465-2481.
  11. Hassan, J. (2014). A Geostatistical approach for mapping groundwater quality (Case Study: Tehsil Sheikhupura). Int J Sci Res, 3(4), 239-245.
  12. Hassanzadeh Nafooti, M., & Emami Maybodi, E. (2018). Temporal and spatial variations of groundwater quality of Marvast Plain. Watershed Engineering and Management, 10(1), 121-132. (In Persian).
  13. Hosnipak, A. (1999). Geostatistics (First). Tehran University Publications. (In Persian).
  14. Jahanshahi, A., Rohimoghaddam, E., & Dehvari, A. (2014). Investigating groundwater quality parameters using GIS and geostatistics (case study: Shahr-Babak plain aquifer). Water and Soil Science, 24(2), 183-197. (In Persian).
  15. Khan, M., Almazah, M. M. A., EIlahi, A., Niaz, R., Al-Rezami, A. Y., & Zaman, B. (2023). Spatial interpolation of water quality index based on Ordinary kriging and Universal kriging. Geomatics, Natural Hazards and Risk, 14(1). https://doi.org/10.1080/19475705.2023.2190853
  16. Kumar, M., Sunitha, V., & Reddy, M. (2013). Determination of an Optimal Interpolation Technique by Using Ordinary Kriging to Represent Spatial Distribution of Groundwater Quality Indices at Ysr District, Andhra Pradesh, India. Advances in Agriculture, Sciences and Engineering Research, 3, 1293-1310.
  17. Lashkaripour, G., & Salari, H. (2007). An investigation on the quantitative effect of the 2003 Bam earthquake on Bam plain aquifer. Geotechnical Geology, 3(1), 52-59. (In Persian).
  18. Li, J., Zhang, Z., Feng, Y., & Zhang, X. (2013). Use of genetic-algorithm-optimized back propagation neural network and ordinary kriging for predicting the spatial distribution of groundwater quality parameter (Z. Zhu, Ed.; p. 87684V). https://doi.org/10.1117/12.2011857
  19. Malakootian, M., & Nouri, J. (2010). Chemical Variations of Ground Water Affected by the Earthquake in bam region Malakootian, M. International Journal of Environmental Research, 4(3), 443-454. https://doi.org/10.22059/ijer.2010.229 80 (In Persian).
  20. Marofi, S., A. Toranjeyan, & Zare Abyaneh. H. (2010). Evaluation of geostatistical methods for estimating electrical conductivity and pH of stream drained water in Hamedan-Bahar Plain. Journal of Water and Soil Conservation, 16(2), 169-187. (In Persian).
  21. Mohammadi, A., Yousefi, H., & Mahmoudi Aznaveh, A. (2024). Evaluation of Tehran Province Livestock Production from Water Footprint Prospective. Environmental Energy and Economic Research, 1-16.
  22. OUYANG, Y. (2005). Evaluation of river water quality monitoring stations by principal component analysis. Water Research, 39(12), 2621–2635. https://doi.org/10.1016/j.watres.2005.04.024
  23. Paryani, S., Neshat, A., Pourghasemi, H. R., Ntona, M. M., & Kazakis, N. (2022). A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping. Science of The Total Environment, 807, 151055. https://doi.org/10.1016/j.scitotenv.2021.151055
  24. Pirzadeh, B., & Asvar, T. (2020). Determining spatial and temporal variations of groundwater quality parameters using GIS and interpolation methods (case study: Sirjan Plain). Irrigation and Water Engineering, 11(2), 266-275. (In Persian).
  25. Rasi Nezami, S., Nazariha, M., Baghvand, A., & Moridi, A. (2013). Karkheh River Water Quality Using Multivariate Statistical Analysis and Qualitative Data Variations. Mui-Jhsr, 8(7), 1280-1292. http://hsr.mui.ac.ir/article-1-486-en.html (In Persian).
  26. Saghazadeh, N. (2022). Comparison and Applications of Geostatistical and Determined Methods for Groundwater Quality Zonation of Drinking. Desert Ecosystem Engineering, 2(3), 55-66. (In Persian).
  27. Soleimani, K., Zandi, J., & Zandi, S. (2012). Evaluating the efficiency of geostatistical methods for mapping spatial distributions of springs TDS and pH (Case Study: Mirdeh Watershed, Kurdistan). Environmental Study, 38(4), 57-66. (In Persian).
  28. Taghizadeh-Mehrjardi, rohollah, Zareian-Jahromi, mojtaba, Mahmoodi, S., Heidari, A., & Sarmadian, freedon. (2009). Investigation of Interpolation Methods to Determine Spatial Distribution of Groundwater Quality in Rafsanjan. Ijwmse, 2(5), 63-70. http://jwmsei.ir/article-1-94-fa.html (In Persian).
  29. Yousefi, H., Khademi, V., & Mahmoudi Aznaveh,A. (2023). Feasibility of wetland technology in biological wastewater treatment Tehran Province villages. Iranian Journal of Ecohydrology, 10(2), 215-229. https://doi.org/10.22059/ije.2023.362170.1748 (In Persian).
  30. Zare-Chahouki, M. A. (2010). Data analysis in natural resources research with SPSS software. Academic Center of Education Culture and Research-Tehran Branch: Tehran. Iran. (In Persian).
  31. Zehtabian, G. H., Janfaza, E., & Nematollahi, M. J. (2010). Modeling of ground water spatial distribution for some chemical properties (case study in Garmsar watershed). Iranian Journal of Range and Desert Research, 17(1), 61-73. (In Persian).
  32. Zimmerman, D., Pavlik, C., Ruggles, A., & Armstrong, M. P. (1999). An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting. Mathematical Geology, 31(4), 375- https://doi.org/10.1023/A:1007586507433
مدیریت آب و آبیاری
دوره 14، شماره 2
مهر 1403
صفحه 527-541

فایل ها

هم رسانی

ارجاع به این مقاله

آمار