کاربرد کریجینگ بیزین تجربی در پهنه‌بندی آلودگی خاک به فلزات سنگین (مطالعه موردی: شهرستان اسفراین)
محورهای موضوعی :
آلودگی های محیط زیست (آب، خاک و هوا)
صفیه تیمورزاده
1
,
روح اله میرزایی محمدآبادی
2
,
محسن محمدی
3
1 - کارشناس ارشد مهندسی محیط‌زیست گرایش آلودگی، دانشکده منابع طبیعی، دانشگاه گیلان، رشت، ایران (نویسنده مسؤول)
2 - دانشیار گروه محیط‌زیست، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران
3 - استادیار گروه محیط‌زیست، دانشکده منابع طبیعی، دانشگاه گیلان، رشت، ایران
تاریخ دریافت : 1394/11/15
تاریخ پذیرش : 1395/03/19
تاریخ انتشار : 1398/07/01
کلید واژه:
ارزیابی متقابل,
درون‌یابی,
کریجینگ بیزین تجربی,
مجتمع صنعتی اسفراین,
عناصر سنگین,
چکیده مقاله :
چکیده زمینه و هدف: روش‌های کریجینگ به‌ویژه کریجینگ معمولی در بین روش‌های درون‌یابی به دلیل ایجاد خطای کم‌تر و تولید حداقل واریانس ممکن، محبوبیت بیش‌تری نسبت به سایر روش‌ها دارند؛ بااین‌وجود، پیچیدگی‌های مختلف موجود در این روش، استفاده از آن را محدود می‌کند؛ از این رو کریجینگ بیزین تجربی برای غلبه بر مشکلات کریجینگ معمولی معرفی شده است. روش بررسی: در این پژوهش، کارایی کریجینگ معمولی و بیزین تجربی برای تعیین الگوی مکانی عناصر نیکل، مس و روی در خاک سطحی اطراف مجتمع صنعتی اسفراین بررسی شده است. بدین منظور ۳۵ نمونه خاک سطحی (cm۲۰-0) در منطقه‌ای به وسعت تقریبی 87 کیلومترمربع برداشت شد و غلظت فلزات در نمونه ها با استفاده از دستگاه جذب اتمی شعله تعیین گردید. دو روش کریجینگ معمولی و بیزین تجربی برای درون یابی استفاده شد و ارزیابی متقابل به همراه آماره های ارزیابیRMSE ، NSE و PBIASبرای سنجش صحت و مقایسه کارایی دو روش استفاده شد. یافته ها: نتایج حاصل از توصیف آماری نشان داد که میانگین غلظت عناصر نیکل، روی و مس در منطقه به ترتیب 61/23، 47/58 و 51/12 میلی‌گرم بر کیلوگرم بوده و در مقایسه با غلظت زمینه طبیعی، میانگین غلظت دو عنصر روی و مس بیش‌تر و در مورد عنصر نیکل این مقدار کم‌تر از غلظت طبیعی آن‌ها می‌باشد. بحث و نتیجه گیری: در روش کریجینگ معمولی مدل‌های نمایی (مس و روی) و کروی (نیکل) بهترین مدل‌هایی بودند که به داده ها برازش داده شدند. نتایج ارزیابی متقابل نشان داد که روش کریجینگ بیزین تجربی نسبت به کریجینگ معمولی دارای کارایی بیش‌تری در برآورد غلظت عناصر مورد مطالعه بوده است اگرچه این تفاوت بسیار زیاد نبود.
چکیده انگلیسی:
Background and purpose: Kriging methods especially ordinary kriging are more popular than other interpolation methods because of less uncertainty and the least possible variance. However, various complications in the procedure of this method limit its uses. Therefore, empirical Bayesian kriging has been introduced to overcome the problems of ordinary kriging. Materials and methods: In this study, the efficiency of ordinary kriging and empirical Bayesian kriging was investigated to determine the spatial pattern of concentrations of Ni, Cu and Zn in the soils surrounding the Esfarayen industrial complex. For this purpose, 35 surface soil samples (0-20 cm) were collected in area of nearly 87 Km2 and the metal concentrations were determined in the soil samples using a Flame Atomic Absorption Spectrophotometer (FAAS). The ordinary and empirical Bayesian kriging were utilized for interpolating and cross validation including RMSE, NSE and PBIAS were used to assess and compare the efficiency of two methods. Results: The results showed the mean concentration of Ni, Zn and Cu were 23.61, 58.47 and 12.51 mg/kg respectively. Based on the results the mean concentration of Zn and Cu were more than background concentrations of the metals, whereas Ni concentration was less than background concentration. Discussion and conclusion: The experimental vario-gram of Cu, Zn and Ni concentrations were best-fitted by exponential, exponential and spherical models respectively. The results of cross validation indicated that the empirical Bayesian kriging was more accurate than ordinary kriging to estimate the elements concentration, though this difference was not considerable.
منابع و مأخذ:
Matheron, G., 1963. Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Dayani, M., Mohammadi, J., and Naderi, M., 2009. Geostatistical Analysis of Pb, Zn and Cd concentration in soil of Sepahanshahr suburb (south of Esfahan). Water and Soil, 23(4), 67-76.
Dayani, M., and Mohammadi, J., 2010. Geostatistical assessment of Pb, Zn and Cd contamination in near-surface soils of the Urban-Mining transitional region of Isfahan, Iran. Pedosphere, 20(5), pp.568–577.
Taghipour, M., Khademi, and H., Ayoubi, Sh., 2010. Spatial variability of Pb and Zn concentration and its relationship with land useand parent materials in selected surface soils of Hamadan province. Water and Soil, 24(1), pp.132-144, (in persian).
Baghaei, AH., Khademi, H., and Mohammadi, J., 1997. Geostatistical analysis of spatial variability of Lead and Nickel around two industrial factories in Isfahan province. J. Agric. Sci. Natur. Resour, 14(2), pp.11-19, (in persian).
Zhang, X. Y., Yue-Yu, S. U. I., Zhang, X. D., Kai, M. E. N. G., and Herbert, S. J., 2007. Spatial variability of nutrient properties in black soil of northeast China. Pedosphere, 17(1), 19-29.
Isaaks, E.H. and Srivastava, R.M., 1989. Applied geostatistics (Vol. 2). New York: Oxford University Press.
Loska, K., Wiechuła, D., and Korus, I., 2004. Metal contamination of farming soils affected by industry. Environment International, 30(2), pp.159-165.
Bi, X., Feng, X., Yang, Y., Qiu, G., Li, G., Li, F., Liu, T., Fu, Z. and Jin, Z., 2006. Environmental contamination of heavy metals from zinc smelting areas in Hezhang County, western Guizhou, China. Environment international, 32(7), pp.883-890.
Jiachun, S., Hazian, W., Jianming, X., Jinjun, W., Xingmei, L., Haiping, Z., and Shunlan, J., 2006. Spatial distribution of heavy metal in soil: A case study of Changing, China. Environmental Geology Geol, 52(1), pp.245-264.
Khodakarami, L., Soffianian, A., Mohammadi Towfigh, E., and Mirghaffari, N., 2014. Study of heavy metals concentration Copper, Zinc and Arsenic soil using GIS and RS techniques (Case study: Kaboudarahang, Razan and Khonjin- Talkhab catchment in Hamedan province). RS & GIS Techniques in Natural Resource Science, 5(3), pp.45-55, (in persian).
Juang, K.W., Lee, D.Y., and Ellsworth, T.R., 2001. Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site. Journal of Environmental Quality, 30(3), pp.894-903.
Oliver, M.A., and Webster, R., 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena, 113, pp.56-69.
Ostovari, Y., Beigi, H., and Davoodian, AR., 2015. Land-Scale Processing of sedimentation potential and groundwater corrosion in Lordegan plain. Environmental Science and Technology, 17(2), pp45-61, (in persian).
Shahbazi, A., Soffianian, AR., Afraz, R., and Khodakarami, L., 2011. The spatial distribution of heave metals cadmium, copper and lead in soil and sourcesof these metals (Case study: Nahavand city).RS & GIS Techniques in Natural Resource Science, 2(2), pp97-109, (in persian).
Mirzaei, R., Esmaeelisari, A., Ghorbani, H., Hafezimoghadas, N., Homami, M. R., and Rezaei, H. R., 2013. Predicting the spatial distribution of Cd, Ar, Cr and Cu In surface soil of Golestan Province. Environmental Research, 4(7), pp.35-44.
Abdollahi, S., Delavar, MA., and Shekari, P., 2012. Spatial Distribution Mapping of Pb, Zn and Cd and Soil Pollution Assessment in Anguran Area of Zanjan Province. Soil and Water, 26(6), pp1410-1420, (in persian).
Farzaneh, P., Soffianian, AR., and Moattar, F., 2001. Spatial distribution of (Ni, Cr, Pb, Cu and Co) in the Surface (Superficial) Soil of Hamadan county with Geostatistic & GIS. Human and Environment, 9(4), pp39-48, (in persian).
Khodakarami, L., Soffianian, A., Mirghafari, N., Afyuni, M. and Golshahi, A., 2012. Concentration zoning of chromium, cobalt and nickel in the soils of three sub-basin of the Hamadan province using GIS technology and the geostatistics. JWSS-Isfahan University of Technology, 15(58), pp.243-254.
Mahmoudi, Sh., Mohammadi, J., and Naderi, M., 2013. Statistical and spatial distribution of some heavy metals in surface soil particle size fractions in South of Isfahan. Water and Soil Conservation, 20(2), pp.1-22.
Krige, D. G., 1951. A statistical approach to some basic mine problems on the Witwatersrand. Journal of the Chemical. Metallurgical and Mining Society of South Africa, 52, pp.119–139.
Krige, D.G., 1966. Two-dimensional weighted moving average trend surfaces for ore evaluation. Journal of the South African Institute of Mining and Metallurgy, 66, pp.13–38.
Deutsch, C.V., and Journel, A. G., 1998. GSLIB: Geostatistical Software and User's Guide, second edition. Oxford University Press. New York. 340 pages.
Payne, R. W., (Ed.), 2013. The Guide to GenStat Release 16, Part 2: Statistics. VSN International Ltd. Hemel Hempstead. UK.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers and Geosciences. 30(7), pp.683–691.
Krivoruchko, K., and Gribov, A., 2014. Pragmatic Bayesian kriging for non-stationary and moderately non-Gaussian data. In Mathematics of Planet Earth. Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences. Eds: Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J.J., Vargas-Guzmán, J.A., Springer, pp.61-64. also available online at http://www.esri.com/news/arcuser/1012/empirical-byesian-kriging.html.
Mirzaei, R. and Sakizadeh, M., 2016. Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environmental Science and Pollution Research, 23(3), pp.2758-2769.
Jones, C.L., Hodge, V. F., and Schoengold, D.M., 1987. An Interlaboratory Study of Inductively Coupled Plasma Atomic Emission Spectroscopy Method 6010 and Digestion Method 3050. Publication/US. Environmental protection agency. Also available online at http://www2.epa.gov/region8/method-6010b-inductively-coupled-plasma-atomic-emission-spectrometry.
_||_
Matheron, G., 1963. Principles of geostatistics. Economic Geology, 58(8), 1246–1266.
Dayani, M., Mohammadi, J., and Naderi, M., 2009. Geostatistical Analysis of Pb, Zn and Cd concentration in soil of Sepahanshahr suburb (south of Esfahan). Water and Soil, 23(4), 67-76.
Dayani, M., and Mohammadi, J., 2010. Geostatistical assessment of Pb, Zn and Cd contamination in near-surface soils of the Urban-Mining transitional region of Isfahan, Iran. Pedosphere, 20(5), pp.568–577.
Taghipour, M., Khademi, and H., Ayoubi, Sh., 2010. Spatial variability of Pb and Zn concentration and its relationship with land useand parent materials in selected surface soils of Hamadan province. Water and Soil, 24(1), pp.132-144, (in persian).
Baghaei, AH., Khademi, H., and Mohammadi, J., 1997. Geostatistical analysis of spatial variability of Lead and Nickel around two industrial factories in Isfahan province. J. Agric. Sci. Natur. Resour, 14(2), pp.11-19, (in persian).
Zhang, X. Y., Yue-Yu, S. U. I., Zhang, X. D., Kai, M. E. N. G., and Herbert, S. J., 2007. Spatial variability of nutrient properties in black soil of northeast China. Pedosphere, 17(1), 19-29.
Isaaks, E.H. and Srivastava, R.M., 1989. Applied geostatistics (Vol. 2). New York: Oxford University Press.
Loska, K., Wiechuła, D., and Korus, I., 2004. Metal contamination of farming soils affected by industry. Environment International, 30(2), pp.159-165.
Bi, X., Feng, X., Yang, Y., Qiu, G., Li, G., Li, F., Liu, T., Fu, Z. and Jin, Z., 2006. Environmental contamination of heavy metals from zinc smelting areas in Hezhang County, western Guizhou, China. Environment international, 32(7), pp.883-890.
Jiachun, S., Hazian, W., Jianming, X., Jinjun, W., Xingmei, L., Haiping, Z., and Shunlan, J., 2006. Spatial distribution of heavy metal in soil: A case study of Changing, China. Environmental Geology Geol, 52(1), pp.245-264.
Khodakarami, L., Soffianian, A., Mohammadi Towfigh, E., and Mirghaffari, N., 2014. Study of heavy metals concentration Copper, Zinc and Arsenic soil using GIS and RS techniques (Case study: Kaboudarahang, Razan and Khonjin- Talkhab catchment in Hamedan province). RS & GIS Techniques in Natural Resource Science, 5(3), pp.45-55, (in persian).
Juang, K.W., Lee, D.Y., and Ellsworth, T.R., 2001. Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site. Journal of Environmental Quality, 30(3), pp.894-903.
Oliver, M.A., and Webster, R., 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena, 113, pp.56-69.
Ostovari, Y., Beigi, H., and Davoodian, AR., 2015. Land-Scale Processing of sedimentation potential and groundwater corrosion in Lordegan plain. Environmental Science and Technology, 17(2), pp45-61, (in persian).
Shahbazi, A., Soffianian, AR., Afraz, R., and Khodakarami, L., 2011. The spatial distribution of heave metals cadmium, copper and lead in soil and sourcesof these metals (Case study: Nahavand city).RS & GIS Techniques in Natural Resource Science, 2(2), pp97-109, (in persian).
Mirzaei, R., Esmaeelisari, A., Ghorbani, H., Hafezimoghadas, N., Homami, M. R., and Rezaei, H. R., 2013. Predicting the spatial distribution of Cd, Ar, Cr and Cu In surface soil of Golestan Province. Environmental Research, 4(7), pp.35-44.
Abdollahi, S., Delavar, MA., and Shekari, P., 2012. Spatial Distribution Mapping of Pb, Zn and Cd and Soil Pollution Assessment in Anguran Area of Zanjan Province. Soil and Water, 26(6), pp1410-1420, (in persian).
Farzaneh, P., Soffianian, AR., and Moattar, F., 2001. Spatial distribution of (Ni, Cr, Pb, Cu and Co) in the Surface (Superficial) Soil of Hamadan county with Geostatistic & GIS. Human and Environment, 9(4), pp39-48, (in persian).
Khodakarami, L., Soffianian, A., Mirghafari, N., Afyuni, M. and Golshahi, A., 2012. Concentration zoning of chromium, cobalt and nickel in the soils of three sub-basin of the Hamadan province using GIS technology and the geostatistics. JWSS-Isfahan University of Technology, 15(58), pp.243-254.
Mahmoudi, Sh., Mohammadi, J., and Naderi, M., 2013. Statistical and spatial distribution of some heavy metals in surface soil particle size fractions in South of Isfahan. Water and Soil Conservation, 20(2), pp.1-22.
Krige, D. G., 1951. A statistical approach to some basic mine problems on the Witwatersrand. Journal of the Chemical. Metallurgical and Mining Society of South Africa, 52, pp.119–139.
Krige, D.G., 1966. Two-dimensional weighted moving average trend surfaces for ore evaluation. Journal of the South African Institute of Mining and Metallurgy, 66, pp.13–38.
Deutsch, C.V., and Journel, A. G., 1998. GSLIB: Geostatistical Software and User's Guide, second edition. Oxford University Press. New York. 340 pages.
Payne, R. W., (Ed.), 2013. The Guide to GenStat Release 16, Part 2: Statistics. VSN International Ltd. Hemel Hempstead. UK.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers and Geosciences. 30(7), pp.683–691.
Krivoruchko, K., and Gribov, A., 2014. Pragmatic Bayesian kriging for non-stationary and moderately non-Gaussian data. In Mathematics of Planet Earth. Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences. Eds: Pardo-Igúzquiza, E., Guardiola-Albert, C., Heredia, J., Moreno-Merino, L., Durán, J.J., Vargas-Guzmán, J.A., Springer, pp.61-64. also available online at http://www.esri.com/news/arcuser/1012/empirical-byesian-kriging.html.
Mirzaei, R. and Sakizadeh, M., 2016. Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran. Environmental Science and Pollution Research, 23(3), pp.2758-2769.
Jones, C.L., Hodge, V. F., and Schoengold, D.M., 1987. An Interlaboratory Study of Inductively Coupled Plasma Atomic Emission Spectroscopy Method 6010 and Digestion Method 3050. Publication/US. Environmental protection agency. Also available online at http://www2.epa.gov/region8/method-6010b-inductively-coupled-plasma-atomic-emission-spectrometry.
