تحلیل شوری و پهنه بندی کیفیت آبهای زیرزمینی با استفاده از تکنیک تجزیه به مولفه های اصلی، مطالعه موردی : دشت خفر
محورهای موضوعی : مقالات برگرفته از پایان نامه
هما رزمخواه
1
(استادیار گروه مهندسی آب، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران.)
عیسی محمدی
2
(دانش آموخته کارشناسی ارشد رشته آبیاری و زهکشی، گروه مهندسی آب، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران.)
امین رستمی راوری
3
(استادیار گروه مهندسی آب، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران.)
علیرضا فرارویی
4
(استادیار گروه مهندسی آب، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران.)
کلید واژه: کریجینگ, درون یابی, تجزیه به مولفه های اصلی, پهنه بندی کیفیت آب زیرزمینی,
چکیده مقاله :
چکیده
مقدمه: توجه به تغییرات کمی وکیفی منابع آب زیرزمینی، به خصوص در مناطق خشک و نیمه خشک ضروری است. این تحقیق با هدف تحلیل علل شوری و پهنه بندی کیفی آب زیرزمینی دشت خفر، تعیین نقاط آلوده و بررسی دلایل آن انجام گرفت.
روش: بدین منظور برای میانگین سالانه داده های فصل های بهار و پاییز مولفه های اصلی آلودگی آب زیرزمینی استخراج شد. سپس مدل های منحنی پوش (Splin) ، عکس فاصله (IDW) و کریجینگ (Kriging) جهت ارزیابی درون یابی استفاده و مناسب ترین مدل با استفاده از تکنیک اعتبارسنجی متقاطع و معیارهای ارزیابی میانگین مطلق مربعات خطا و ریشه دوم میانگین مربعات خطا تعیین و نقشه های پهنه بندی کیفیت تهیه شد.
یافته ها: در ارزیابی روشهای درون یابی پاییز مدل کریجینگ رتبه اول دقت درون یابی را به خود اختصاص داد. نقشه پهنه بندی مولفه های اصلی استخراجی پاییز بالاترین مقدار مولفه اول (سختی، پتاسیم، منیزیم، کلسیم، سولفات، TDS و EC) را در جنوب شرقی و مرکز دشت نشان داد. بیشترین مقدار مولفه دوم (سدیم قابل جذب، کلر و سدیم) نیز در جنوب شرقی دشت دیده شد. مولفه سوم (PH) از مرکز دشت به سمت شمال و شمال غربی افزایش، و از مرکز به طرف جنوب و جنوب شرق کاهش داشت. مولفه چهارم (بیکربنات) بیشترین مقدار را در شمال و کمترین میزان را در جنوب شرقی دشت نشان داد. در بین روشهای درون یابی فصل بهار مدل منحنی پوش به عنوان مدل برتر شناخته شد. نقشه پهنه بندی بیشترین مقدار مولفه اول (کلر، سدیم و سدیم قابل جذب) را در جنوب شرقی دشت نشان داد. مولفه دوم (کلسیم و منیزیم و سختی) مقادیر بالایی را عمدتا در مرکز دشت، مولفه سوم (PH) بیشترین مقادیر را در غرب دشت، و مولفه چهارم (بیکربنات) بیشترین مقادیر را در مرکز و شمال غربی دشت نشان داد.
نتیجه گیری: تغییرات مکانی پهنه بندی آلاینده ها در فصول مختلف می تواند به علت تغییرات فصلی عوامل هیدروکلیماتولوژی نظیر بارندگی و تبخیر، بهره برداری از چاه ها و یا نفوذ پسابهای صنعتی باشد. بررسی نسبت کلر به بی کربنات نمونه ها موید ورود رواناب شور گنبدهای نمکی خاوران به جنوب شرقی منطقه است.
Abstract
Introduction: Assessment of groundwater quality and quantity variation in drought and semi-drought region are of most interest especially in Iran. This research is going to analysis salinity reasons by ground water quality mapping in the Khafr plain.
Methods: To do this the principal components of water quality extracted in spring and autumn seasons. Then Spline, IDW and Kriging interpolation techniques were fitted and evaluated for mapping. Finally, the best model was determined and ground water quality mapped using principle components.
Findings: Kriging recognized as the best model for autumn. The first component with high coefficients on TH, K, Mg, Ca, SO4, TDS, EC showed maximum values in the center and eastern south of the region. For the second component (SAR, Cl, Na) maximum values observed at the eastern south as well. The third component (PH) increased from center to the north and western north and decreased from center to the south and eastern south, and for the Forth (HCO3) maximum observed at the north and minimum in the eastern south. Spline method recognized as the best model in spring season. The first component (SAR, Cl, Na) showed maximum values in the eastern south of the region. The second (Th, Mg, Ca) maximum values observed at the center and the third component (PH) maximums in the west. Forth component (HCO3) maximum valued observed in the center and western north. Finally, the reasons of spatial and temporal variation of the components analyzed.
1. Khalilian, S. and Mousavi, S. 2005. Assessment of pressure irrigation systems risk, Case study: Shahrekord. Agricultural Economics and Development, Productivity and efficiency, special issue: 113-119 [In Persian].
2. Abbasi, N. and Abbasi, F. 2020. Perspective of water resources and its consumption in Iran Report. Agricultural engineering research institute, Agricultural research education and extension organization, Ministry of Jahad-e Agriculture. [In Persian].
3. Abbasi, F., Sohrab, F. and Abbasi N. 2016. Irrigation efficiency and Spatio-temporal variation in Iran Report. Engineering and Technology Research center of Iran. [In Persian].
4. Jager, N. 1990, Hydrogeology and groundwater simulation, Lewis Publishers.
5. Kresic N. 1997, Hydrogeology and Groundwater Modeling, Lewis Pub.
6. Dagostino, V., Greene, E.A., Passarella, G. and Vurro, M. 1998. Spatial and temporal study of nitrate concentration in groundwater by means of coregionalizatio. Environmental geology, 36: 285-295.
7. Mohammadi, G. and Meroone, V. 1999. Using geostatistical tools to prepare hazard zonation maps of ecological pollution, Tarbite Modaress University. [In Persian].
8. Ahmed, S. 2002. Groundwater monitoring network design: Application of geostatistics with a few case studies from a granitic aquifer in a semi-arid region, in: Groundwater Hydrology, Sherif, M.M., Singh, V.P. and Al- Rashed, M. (Eds.), Balkema, Tokyo, Japan.
9. Lowe, M., and Butler, M. 2003. Ground water sensitivity and vulnerability to pesticides, Heberand Round Valleys, Wasatch County, Utah. Miscellaneous Publication, Utah Geological Survey.
10. Connell, L.D. and Daele, G.V.D. 2003. Aquantitative approach to aquifer vulnerability mapping. Journal of hydrology, 276 (71): 88-94.
11. Marengo, E., Gennaro, M.C., Robotti, E., Maiocchi, A., Pavese, G., Indaco, A. and Rainero, A. 2008. Statistical analysis of ground water distribution in Alessandria Province (Piedmont—Italy. Microchemical Journal, 88: 167–177.
12. Jalili, M. 2007. Assessment of the chemical components of Farming groundwater, western Iran. Environmental Geochemistry and Health, 29(5): 357-374.
13. Fetouani, S., Sbaa, M., Vanclooster, M. and Bendra, B. 2008. Assessing groundwater quality in the irrigated plain of Triffa (Nnorth-east Morocco). Journal of Agricultural Water Management, 95: 133-142.
14. Barcae, E. and Passarella, G. 2008. Spatial evaluation of the risk of groundwater quality degradation: A comparison between disjunctive kriging and geostatistical simulation. Journal of Environmental Monitoring and Assessment, 133: 261-273.
15. Mahapatra, S.S., Sahu, M., Patel, R.K. and Panda, B.N. 2012. Prediction of water quality using principal component analysis. Water Qual. Expo. Health, 4: 93-104.
16. Magesh, N.S., Krishnakumar, S., Chandrasekar, N. and Soundranayagam, J.P. 2013. Groundwater quality assessment using WQI and GIS techniques, Dindigul district, Tamil Nadu, India. Arab J Geosci, 6: 4179-4189.
17. Belkhiri, L. and Narany, T.SH. 2015. Using multivariate statistical analysis, geostatistical techniques and structural equation modeling to identify spatial variability of groundwater quality. Water Resour. Manage, 29: 2073-2089.
18. Bhuiyan, M.A.H., Bordud-Doza, Md., Towfiqul Islam, A.R.M., Rakib, M.A., Safiur Rahman, M. and Ramanathan, A.L. 2016. Assessment of groundwater quality of Lakshimpur district and Bangladesh using water quality indices, geostatistical methods, and multivariate analysis. Environ. Earth Sci., 75: 1020.
19. Kawo, N.S. and Karuppannan, Sh. 2018. Groundwater quality assessment using water quality index and GIS technique in Modjo river basin, central Ethiopia. J African Earth Sci, 147: 300-311.
20. Rao, N.S., Srihari, Ch., Deepthi Spandana B., Sravanthi, M., Kamalesh, T. and Jayadeep, V.A. 2019. Comprehensive understanding of groundwater quality and hydrochemistry for the sustainable development of suburban area of Visakhapatnam, Andhra Pradesh, India. Human and Ecological Risk Assessment, 25(1-2): 1-29.
21. Yang, W., Zhao, Y., Wang, D., Wu, H., Lin, A. and He, L. 2020. Using principal component analysis and IDW interpolation to determine spatial and temporal changes of surface water quality of Xin'anjiang river in Huangshan, China. Int. J Environmental Research and Public Health, 17, 2942.
22. Misaghi, F. and Mohammadi, K. 2005. Groundwater level estimation using usual interpolation methods, comparing with geostatistics. 21st Conference of geology and mine exploration of the country. [In Persian].
23. Latif, M., Mousavi, S.F. and Velayati, S. 2005. Assessment and source identification of Nitrate in Mashhad plain groundwater. J Agricultural Sciences and Natural Resources, 12(2): 21-32. [In Persian].
24. Tabatabaie, S.H., Tavasoli, M., Eslamian, S.S. and Ahmadzadeh, Gh. 2006. Study and assessment of Isfahan groundwater pollution, with emphasis on the urban water. Scientific J Agriculture, 29(2): 79-92. [In Persian].
25. Lalehzari, R., Tabatabaie, S.H. 2010. Assessment of Shahrekord plain groundwater chemical quality. J Environmental Studies, 36(53): 55-62. [In Persian].
26. Shaebani, M. 2009. Finding the most suitable geostatistic method in the mapping of Arsanjan plain groundwater PH and TDS variation, Water Resources Engineering, 1: 47-58. [In Persian].
27. Lalehzari, R., Tabatabaie, S.H. and Yar Ali, N. 2009. Assessment of Nitrate monthly variation in Shahrekord plain groundwater and mapping using GIS. Iranian Water Researches J, 3(4): 9-17. [In Persian].
28. Razmkhah, H., Abrishamchi, A. and 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: 852–860.
29. Naderianfar, M., Ghahreman, B., Ansari, H. and Salari. M. 2012. Using different geostatistical methods to interpolate EC and SAR values of groundwater, with emphasis on the basin infiltration variation. J Irrigation Sciences and Engineering, 35(1): 21-33. [In Persian].
30. Safavi, H.R., Ahmadi, A. and Rahmatnia M. 2014. River quality mapping using combination method of principal component analysis and fuzzy classification. Water and Wastewater, 5: 21-31. [In Persian].
31. Einlou, F., Maafi Rabary A., Malekian, A., Ghazavi R. and Mohseni Saravy, M. 2016. Assessing groundwater quality of Zanjan plain considering urban standards, using geostatistical method. Geography and Environment Planning, 62(2): 1-16. [In Pertsian].
32. Samadi, J. 2017. Modelling spatio-temporal variation of Kashan urban and rural groundwater level, using GIS techniques. J Environment Science and Technology, 19(1): 63-77. [In Persian].
33. Nosrati, K., Rajabi Eslami, A. and Sayadi, M. 2018. Analysis and classification of Malard-Tehran urban water using multivariate statistical methods. Hydrogeomorphology, 15: 171-190. [In Persian].
34. Ghareh Mohamoud Lou, M., Heshmat Pour A., Jandaghi, N., Zarea, A. and Mehrabi, H. 2019. Assessment of Seyedan-Farough groundwater quality for Irrigation and urban use. Environmental Sciences, 17(3): 89-106. [In Persian].
35. Seify, A. and Riahy Madvar, H. 2020. Uncertainty risk mapping and evaluation of copper mine heavy metals using beyesian and gaussian analysis. Environmental Sciences, 18(1): 165-186. [In Persian].
36. Mirzaee mahmoodabadi, R. 2019. Evaluation of water quality and bed load sediment contamination of Ghareaghaj river to heavy metals, Khafr watershed, Fars province. J Environmental Science Studies, 4(3): 1696-1706. [In Persian].
37. No name, 1986. Ghareh Aghaj-Khafr groundwater basin identification Report, Fars Water Organization. [In Persian].
38. Mohammadi, E. 2012. Groundwater quality mapping of Khafr plain. M.S. Thesis, Water and Science Engineering, Islamic Azad University, Marvdash., Iran. [In Persian].
39. Razmkhah, H. 2003. Pattern Recognition techniques in the evaluation of spatio-temporal variation of river water quality, Case study: Jajroud river. M.S. Thesis, Civil college, Sharif University of Technology. [In Persian].
40. Ghahroudi Tali, M. 2005. Geographic information system in GIS three dimensions environment. Research training center of Teacher Education Publication. [In Persian].
41. Hassani Pak, A.A. 1999. Geostatistics. Tehran University Press. [In Persian].
42. Rangzan, K., Mokhtari, M. and Shaygan M. 2005. Evaluation of IDW and Kriging models in the interpolation of groundwater level of Shoushtar Mian Ab plain, Geomatic Conference. [In Persian].
43. Willmott, C.j. 1984. On the Evaluation of Model Performance in Physical Geography, In: Spatial Statistics and Model, ed. C.J., Willmott, pp. 443-460.
44. Kardovani, P. 2007. The resources and problems of water in Iran, Second Volume, Tehran university publication and press. [In Persian].
45. Lusczynski, N. J. and Swarzenski, W.V. 1966. Salt Water encroachment in Southern Nassu and SE Queen Countries, Long Island, New York, U.S.G.P.O. Publisher.
46. Babiker, I.S., Mohamed, M.A.A., Hiyama, T. and Kato, K. 2005. A GIS based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Science of the Total Environment, 345(1-3): 127-140.
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