بررسی تغییرات برخی پارامترهای کیفی آب زیرزمینی به روش زمین‌آمار در حوزه ‌آبخیز شهرستان مرند- آذربایجان‌شرقی

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه سمنان

2 موسسه تحقیقات جنگلها و مراتع کشور

چکیده

آب‌های زیرزمینی تنها منبع اصلی آب برای مصارف شرب، کشاورزی و صنعتی در شهرستان مرند می‌باشد و اهمیت حیاتی آن موجب می‌شود تضمین کیفیت آن به طور جدی مورد توجه قرار گیرد. در این مطالعه به منظور ارزیابی روند تغییرات پارامترهای کیفی آب زیرزمینی شامل EC, TDS, Cl-, SAR  و SO42-، اطلاعات مربوط به چاه‌های پیزومتری طی سال‌های آماری 84، 88 و 91 استفاده شدند و با روش‌های زمین آمار شامل روش کریجینگ معمولی در حالت‌های کروی، گوسی و نمایی و روش فاصله معکوس وزن‌دار (IDW) با توان‌های 1 تا 3 مورد بررسی قرار گرفتند. بر اساس روش ارزیابی متقاطع، روش کریجینگ در مقایسه با روش فاصله معکوس وزن‌دار دارای RMSE  و ME کمتری بود. نقشه پهنه‌بندی آنیون SO42- در سال 91 با مقادیر آماره G و شاخص موران به ترتیب برابر 41/21 و 59/0 درصد دارای بیشترین تعامل در ساختار فضایی و نقشه پهنه‌بندی EC در سال 84 با شاخص موران و آماره G به ترتیب 16/0 و 45/3  درصد از کمترین تعامل ساختار فضایی برخوردار می‌باشد. با ترسیم نمودارهای روند تغییرات پارامترهای کیفی در طول و عرض جغرافیایی معلوم شد مقادیر آنیون Cl EC, SAR, و TDS و SO42 بین سالهای 84 تا 88 در جهت غربی –شرقی روند افزایشی نامحسوسی  داشتند و در سال 91 با شیب بیشتری افزایش یافتند. این عوامل در جهت شمالی-جنوبی از سال 84 تا 91 در قسمت شمال حوزه افزایش، در میانه حوزه آبخیز روند کاهشی و در جنوب حوزه آبخیز دوباره افزایش یافته‌اند. در نهایت با قطع دادن نقشه کاربری اراضی و زمین‌شناسی حوزه آبخیز با نقشه‌های پهنه‌بندی هر یک از این پارامترها چنین استنباط می‌شود که با توجه به پراکنش روستاها، مناطق مسکونی و اراضی کشاورزی اطراف آن­ها در مرکز و شرق حوزه آبخیز، روند تغییرات این پارامترهای کیفی در آب زیرزمینی حوزه آبخیز شهرستان مرند تحت تأثیر فعالیت‌های انسانی هستند. همچنین برخی سازندهای زمین‌شناسی و کانی‌های ژیپس‌دار و دولومیتی حوزه در کیفیت آب زیرزمینی سبب بالا رفتن مقادیر TDS و سولفاته شدن منابع آب در بخش‌های شرقی حوزه شده است. 

کلیدواژه‌ها


عنوان مقاله [English]

Assessment Changes of Some Parameters of Groundwater Quality in the Marand Country Watershed- East Azarbayejan

نویسندگان [English]

  • Leila Kashi Zenouzi 1
  • Mohammadreza Yazdani 1
  • Mohammad Khosroshahi 2
  • Mohammad Rahimi 1
1 Semnan University
2 Research Institute of Forests and Rangelands
چکیده [English]

Introduction: Groundwater is the only major source of water for drinking, agricultural and industrial purposes in the Marand city, and its vital importance makes sure that its quality is seriously considered. With qualitative zoning, the process of underground water quality changes is determined at any time, place, and condition. It is possible to save time and cost by removing the stations with similar quality status and install new stations at times that are different or critical. In this paper, using the observational data of wells in Marand watershed, the spatial distribution of some groundwater quality parameters has been studied and analyzed using land-based methods. Geostatistical methods for estimating the unknown are remarkably effective.
Materials and Methods: In order to predict the spatial distribution of groundwater quality, data was collected from 48 water wells, semi-deep wells, springs, and others from the Water Resources Management Company. In this research, the spatial variation process of five qualitative parameters of water include EC, electrical conductivity, chlorine and sulfate (SO42-) anions, and Sodium Rate Absorption (SAR) and soluble solids (TDS): Total Dissolved Salts) were studied. After reviewing, some of them were omitted due to statistical deficiencies. Common time base was selected for studying the Blue Years 2003-2005, and the years 1388-88 and 1391-1391. Data homogeneity was evaluated for the statistical period between 1384-1384 by the sequencing test method. According to the mentioned method, there was no heterogeneity in the data. Statistical deficits were determined according to the correlation coefficient of a variable. Data were normalized using SPSS 18.0 software using logarithmic transformation method and their elongation and bending values were obtained in the range -2 and 2. In this study, for estimation of groundwater quality parameters including EC, TDS, Cl-, SAR and SO42-, piezometric wells data were used during the years 84, 88 and 91. Statistical analysis methods consisted of conventional Kriging method in Spherical, Gaussian and exponential modes and Weighted Inverse Distance (IDW) methods with power from 1 to 3 were studied. Cross-validation, G statistics (GetisOrd General G) and Morans Index were used to select the best and most suitable interpolation method. The values of all three evaluation methods were calculated and analyzed using Arc / GIS 10.3 software.
Results and Discussion: Based on the cross-evaluation method, the Kriging method is less effective than RMSE and ME in comparison with the Inverse Distance Weighting method. The zonation map of anion SO42- in year 2012 with G statistics and Moran index was 21.41 and 0.99 %, had the highest interaction in spatial structure and EC zonation map in year 2005 with Moran index and G statistic was 0.16 and 45 respectively has the least interaction of spatial structure. Charts of Changes in Quality Parameters showed that, water quality in latitude and longitude, values which were Cl, EC, SAR, and TDS and SO42 anions between the years 2005-2009 in the western-eastern part have been intangible and have been steeply sloping in the year 91. But in the North-South direction of 84 to 91 increased and then decreased in the middle of basin. Finally, by disconnecting the map of land use and geology of the watershed with the zoning maps of each of the parameters, it is concluded that due to the distribution of villages, residential areas and agricultural lands around them in the center and east of the watershed, the trend of groundwater quality parameters had been changed. The underground waters of Marand country watershed were influenced by human activities. Also, some geological formations and gypsum and dolomite minerals in the area in groundwater quality have led to an increase in TDS values and sulfidation of water resources in the eastern parts of the basin.
Conclusion: Groundwater quality is always influenced by various factors such as flow direction, groundwater level, climatic factors (precipitation, evapotranspiration, etc.), type and composition of geological formations of the region and human factors (land use, extraction of groundwater resources, Entry of household wastewater and agriculture into groundwater resources, etc.). Therefore, due to the importance of the use of groundwater resources and the limitations of its use, it is suggested that continuous monitoring of groundwater quality changes should be carried out using ground-based methods and in order to evaluate the effective factors of water quality parameters spatial distribution maps was prepared and analyzed. In the present study, based on the previous studies, two geology formation and land use types were selected to prepare map of water quality parameters and it turned out that both of these factors are the most important factors affecting the groundwater quality in the Marand country watershed.
 

کلیدواژه‌ها [English]

  • Ground water quality
  • Geostatistics
  • Marand country watershed
1. Arsalan H. 2012. Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: The case of Bafra Plain, Turkey. Agricultural Water Management, 113: 57-63.
2. Bahrami Jovein E., and Hosseini S. M. 2015. A systematic comparison of geostatistical methods for estimation of groundwater salinity in desert areas. Iran-Water Resources, 11(2): 1-15. (In Persian).
3. Dagostino V. Greene E. A. Passarella B., and Vurro G. 1998. Spatial and Temporal study of nitrate concentration in ground water by means of co regionalization. Environmental Geology, 36: 285-295.
4. Foster S. D. 1987. Fundamental concepts in aquifer vulnerability, pollution risk. H. G. W.V. Van (Eds), Waegeningh, The Hague. Vulnerability of Soil and Groundwater to Pollution, 38: 69–86.
5. Ghasemi R. Ziatabar Ahmadi M. Kh. Karimi V., and Abbasi E. 2016. Estimation of quantitative and qualitative changes of groundwater using ground statistics (Case study: Khormoj plain-Booshehr Province). 2nd National Conference of Water Crisis in Iran and the Middle East, Shiraz, 7p. (In Persian).
6. Getis A., and Ord J. K. 1992. Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27: 286 – 306.
7. Gohroudi Tali M. 2005. Geographic Information System in 3D. Jihad-e- Daneshghahi Tarbiat Moallem Press, No 49.
8. Gong G. Mattevada S., and O’Bryant S. E. 2014. Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environmental Researches, 130: 59– 69.
9. Hamidian M. Salighe M., and Fallh Ghalhari Gh. 2013. Application of various interpolation methods for drought spatial monitoring and analysis (Case study: Khorasan Razavi province). Gography and Development Iranian Journal, 30(1): 57-70.
10. Hasani Pak A. 2007. Geostatistics. Tehran University Press, Iran, 268p. (In Persian).
11. Fakhri M. S. Asghari Moghaddam A. and Najib M. 2016. Application of statistical methods and saturation indices in groundwater quality assessment of the Marand plain. Journal of Water and Soil Conservation, 22(6): 117-133. (In Persian).
12. Fakhri M. S. Asghari Moghaddam A. Najib M., and Barzgar R. 2016. Investigation of Nitrate Concentration in Groundwater Resources of Marand Plain and Evaluation of Groundwater Vulnerability by Methods of AVI and GODS. Ecology, 41(1): 49-66. (In Persian).
13. Fakhri M. S. Asghari Moghaddam A. Barzgar R. Kazemian N., and Najib M. 2017. Investigating the Origin of Some Heavy Metals in Groundwater of Marand Plain Using Multivariate Statistical Methods. Soil & Water Science, 26(2/2): 237-253.
14. Forests, Rangeland and Wartershed Management Organization of Iran. 2010. Detailed planning of the Zilberchay watershed, 8:146p. (In Persian).
15. Lee Jay Wong., and David. W. S. 2001. Statistical analysis with ArcView GIS, John Wiley and sons, New York, 135-137.
16. Ma R. Wang Y. Sun Z. Zheng C. Ma T., and Prommer H. 2011. Geochemical evolution of groundwater in carbonate aquifers in Taiyuan, northern China. Applied Geochemistry, 26: 884-897.
17. Merati A. Tizro A., and Parsafar N. 2017. Qualitative zoning of groundwater resources using land statistics and GIS methods (case study: Solymanshah watershed). Soil and Water Science, 27(2): 237-248. (In Persian).
18. Momeni Damaneh J. Joulaie F. Alidadi H., and Peirovi R. 2015. Evaluation of interpolation methods to determine spatial variations of groundwater qualitative parameters (Case study: Gonabad plain). Journal of Research in Environmental Health, 165-176. (In Persian).
19. Osati Kh., and Nahvinia M. J. 2016. Spatial Variations of Ground Water Quality in Birjand Plain for Agriculture. Journal of Environment and Water Engineering, 2(1): 25-36. (In Persian).
20. Ozelkan E. Bagis S. Ozelkan O. C. Ustandag B. Yucel M., and Ormeci C. 2015. Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting. International Journal of Remote Sensing, 36(4): 697-113.
21. Khaleghi F., and Shahinfar H. 2008. Investigating the effective factors in water hydrogeology of Marand city with the aim of sensitization and groundwater quality assessment. Journal of Environmental Geology, 2(1): 82-94.
22. Kashi Zenouzi L. Saadat, H., and Namdar M. 2011. Investigation of the relationship between vegetation density and soil ecosystems in arid and semiarid regions (case study: Marand watershed). 7th Conference on Watershed Management Sciences and Engineering, 27-28 April, 10p.
23. Khazaei S. Abbasi Tabar H., and Taghizadeh Mehrjardi R. 2011. Spatial Distribution of Nitrate Contamination in Groundwater Using Geostatistic in Fars Province (Case study: Siakh Darengoun area). Journal of Natural Environment, Iranian Journal of Natural Resources, 64(3): 267-279.
24. Khosroshahi M. 2017. Threat and escalation of desertification risk in Iran from the perspective of water. Iranian Journal of Nature, Research Institute of Forests and Rangelands of Iran, 2(2): 6-13 (In Persian).
25. Khosroshahi M. 2014. The lack of water crisis and desertification. Forest and Rangeland Journal, 100:38-43 (In Persian).
26. Samani S. and Asghari Moghaddam A. 2017. Evaluation of the groundwater pollution potential using the Akipro method, and validation and validation of the method by examining the distribution of nitrate concentration. Journal of Water Enginering System, 10: 13-23. (In Persian).
27. Shen Ch. Li Ch., and Si Y. 2016. Spatio-temporal autocorrelation measures for nonstationary series: A new temporally detrended spatio-temporal Moran's index. Physics Letters A, 380(1-2): 106-116.
28. Tesoriero A. J. E. L, Inkpen., and F. D. Voss. 1998. Assessing ground-water vulnerability using logistic regression. Proceeding Source, Water Assessment and Protect, 98 Conference, Dallas, TX; 157–65.
29. Torabi Potkale S. 2009. Drought management: Drought analysis and forecasting and its impact on water resources management. Ph.D. Thesis, Faculty of Civil and Environment, Amir Kabir University, 148p. (In Persian).
30. Zaiming Z. Guanghui Z. Mingjiang Y., and Jinzhe W. 2012. Spatial variability of the shallow groundwater level and its chemistry characteristics in the low plain around the Bohai Sea, North China. Environmental Monitoring and Assessment, 184(6): 3697-3710.