با همکاری انجمن آبخیزداری ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه محیط زیست دانشکده منابع طبیعی و محیط زیست دانشگاه ملایر

2 دانشیار گروه محیط زیست دانشکده منابع طبیعی و محیط زیست دانشگاه ملایر

3 دانشیار دانشکده علوم زمین دانشگاه شهید چمران اهواز

4 استادیار پژوهشی مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی لرستان

چکیده

رسوبات می­‌توانند نمایانه­‌های حساسی برای پایش آلاینده‌­ها در محیط­‌های آبی باشند. بنابراین، شناخت ساز و کارهای انباشت و توزیع ژئوشیمیایی فلزات سنگین در سامانه­‌های آبزی و فراهم‌­سازی اطلاعات اولیه برای قضاوت ریسک­‌های سلامت محیطی، بررسی فضایی غلظت فلزات در رسوبات و مقایسه آن‌­ها با مقادیر پایه­‌های غیر آلوده از اهمیت بسزایی برخوردار می­‌باشد. یکی از روش­‌های مرسوم ارزیابی آلودگی خاک و رسوب استفاده از شاخص عامل آلودگی (CF) است. محاسبه این شاخص نیازمند تعیین پس زمینه عناصر مورد بررسی است. برای این منظور، به­‌طور معمول از روش‌­های آماری مبتنی بر میانگین و انحراف معیار داده‌­ها استفاده می­‌شود. با توجه به چوله بودن توزیع بیشتر داده­‌های ژئوشیمیایی، استفاده از این روش پارامتریک را با محدودیت مواجه می­‌سازد، لذا، استفاده از روش‌­های مقاوم به داده‌­های پرت به­‌عنوان رویکردی جایگزین می‌­تواند مطرح شود. در یکی دو دهه اخیر، روش فرکتال نیز برای جداسازی جوامع در داده­‌های مربوط به بسیاری از رشته‌­های علوم زمینی به­‌کار گرفته شده است. به­‌منظور ارزیابی کاربرد این دو روش در شاخص CF، 771 نمونه رسوب آبراهه‌­ای حاوی شش عنصر فلزی As، Cr، Cu، Ni، Pb و Zn در منطقه­‌ای به وسعت برگه نقشه زمین­‌شناسی یکصد هزارم الشتر (واقع در شمال شهر خرم­آباد) برداشت شد. سپس، با هر دو روش (روش آماری مقاوم به داده­‌های پرت و روش فرکتالی) پس‌زمینه عناصر تعیین و در نهایت، شاخص CF محاسبه شد. این شاخص مبتنی بر روش آماری به‌ترتیب برای عناصر آرسنیک، کرم، مس، نیکل، سرب و روی 7.5، 0.8، 5.1، 0.5، 4.7 و 9.2 درصد نمونه­‌های مورد بررسی را در سطح متوسط ارزیابی کرده است. شاخص CF مبتنی بر روش فرکتال برای عناصر مذکور به‌ترتیب 39، 28.9، 88.6، 39.4، 45.7 و 73.4 درصد در سطح متوسط است و برای عناصر مس، سرب و روی سه، 0.1 و 4.3 درصد در سطح قابل ملاحظه ارزیابی کرده است. نتایج به‌­دست آمده نشان می­‌دهد که استفاده از روش­‌های آماری در تعیین پس‌زمینه عناصر با وجود انتخاب روش آماری متناسب با توزیع داده‌­ها، حساسیت شاخص CF را کاهش داده، در جداسازی طبقات آلودگی، کارایی شاخص را پائین ­آورده است، در حالی‌­که استفاده از روش فرکتال به ­علت در نظر گرفتن بعد فضایی (مساحت) در جداسازی جوامع مختلف پس‌زمینه از آنومالی تفکیک طبقات آلودگی به‌­وسیله شاخص CF را قوت بخشیده، در نتیجه برآوردهای آلودگی منطقه­‌ای را بهبود می­‌بخشد.

کلیدواژه‌ها

عنوان مقاله [English]

Using fractal method in determining the background of toxic metals pollution in order to calculate the index of contamination factor in stream sediments of Aleshtar area

نویسندگان [English]

  • Kianfar payamani 1
  • Eisa Solgi 2
  • Kazem Rangzan 3
  • Taher Farhadinejad 4

1 PhD Student, Department of Environment Faculty of Natural Resources and Environment, Malayer University

2 Associate Professor, Department of Environment, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran

3 Associate Professor, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Iran

4 Assistant Professor of Research, Soil Conservation and Watershed Management Research Department, Lorestan Agricultural and Natural Research and Education Center, AREEO, Khoramabad, Iran

چکیده [English]

Sediments can be sensitive indicators for monitoring contaminants in aquatic environments. Therefore, understanding the mechanisms of accumulation and geochemical distribution of heavy metals in aquatic systems and providing basic information for judging environmental health risks, spatial study of metal concentrations in sediments and their comparison with non-contaminated bases is of great importance. One of the common methods of soil pollution assessment is the use of pollution factor index (CF). The calculation of this index requires determining the background of the elements under study. For this purpose, statistical methods based on mean and standard deviation of data are commonly used. Due to the skuwness of the distribution of geochemical data, the use of this parametric method is limited, so the use of methods resistant to outlier data can be proposed as an alternative approach. In the last two decades, the fractal method has been used to separate communities in data related to many disciplines of earth sciences. In order to evaluate the two mentioned methods, 770 samples of waterway sediments containing six metal elements of As, Cr, Cu, Ni, Pb and Zn were collected in an area that is located in the north of Khorramabad. The background of the elements was determined by both methods (statistical method resistant to outlier data and fractal method) and finally the CF index was calculated. This index based on statistical methods assessed at the intermediate level for arsenic, chromium, copper, nickel, lead and zinc 7.5, 0.8, 5.5, 0.5, 4.7 and 9.2 percent, respectively. For the mentioned elements based on fractal method, the medium level of contamination were 39%, 28.9%, 88.6%, 39.4%, 45.7%, 73.4% of the total sample, respectively. In addition, with the second method, 3% (copper), 0.1% (lead) and 4.3% (zinc) of the number of samples have been evaluated at a considerable level. The results showed that the use of statistical methods in determining the background of the elements, despite the selection of a statistical method appropriate to the distribution of data, has reduced the sensitivity of the pollution index and reduced the efficiency of the index in the separation of pollution classes. While the use of fractal method due to considering the spatial dimension (area) in separating different background communities from anomalies leads to better efficiency of CF index and thus improves regional pollution estimates.    

کلیدواژه‌ها [English]

  • Aquatic environment
  • Heavy metals
  • Geochemical
  • Khorramabad
  • Monitoring contaminants
  1. Abdel-Baki, A.S., M.A. Dkhil and S. Al-Quraishy. 2011. Bioaccumulation of some heavy metals in tilapia fish relevant to their concentration in water and sediment of Wadi Hanifah, Saudi Arabia. African Journal of Biotechnology, 10: 2541–2547.
  2. Alahabadi, A. and H. Malvandi. 2018. Contamination and ecological risk assessment of heavy metals and metalloids in surface sediments of the Tajan River, Iran. Marine Pollution Bulletin, 133: 741-749.
  3. Afzal, P., Y.F. Alghalandis, A. Khakzad, P. Moarefvand and N.R. Omran. 2011. Delineation of mineralization zones in porphyry Cu deposits by fractal concentration-volume modeling. Journal of Geochemical Exploration, 108: 220-232.
  4. Almasi1, A., A. Jafarirad, P. Afzal and M. Rahimi. 2015. Orogenic gold prospectivity mapping using geospatial data integration, region of Saqez, Iran. Bulletin of the Mineral Research and Exploration, 150: 65-76.
  5. Bai, J., A. Porwal, C. Hart, A. For and L. Yu. 2010. Mapping geochemical singularity using multifractal analysis: application to anomaly definition on stream sediments data from Funin Sheet, Yunnan, China. Journal of Geochemical Exploration, 104: 1-11.
  6. Banat, K.M., F.M. Howari and A.A. Al-Hamada. 2005. Heavy metals in urban soils of central Jordan: should we worry about their environmental risks? Environmental Research, 97: 258–273.
  7. Cheng, Q. 1999. Markov processes and discrete Multifractals. Mathematical Geology, 31: 455–469.
  8. Cabrera, F., L. Clemente and D.E. Barrientos. 1999. Heavy metal pollution of soils affected by the Guadiamar toxic flood. The Science of the Total Environment, 242(1–3): 117–129.
  9. Carranza, E.J.M. 2011. Analysis and mapping of geochemical anomalies using logration transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110: 167–185.
  10. Cheng, Q. 1999 Spatial and scaling modeling for geochemical anomaly separation. Journal of Geochemical Exploration, 63(3): 175–194.
  11. Cheng, Q., F.P. Agterberg and G.F. Bonham-Carter. 1996. A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration, 56: 183–195.
  12. Cheng, Q. 2007. Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32: 314–324.
  13. Cheng, Q., Q. Xia, W. Li, S. Zhang, Z. Chen, Zuo and W. Wang. 2010. Density/areapower-law models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China. Biogeosciences, 7: 3019–3025.
  14. Cheng, Q. and F.P. Agterberg. 2009. Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Computers and Geosciences, 35: 234–244.
  15. Fedele, L., J.A. Plant, B. De Vivo and A. Lima. 2008. The rare earth element distribution over Europe: geogenic and anthropogenic sources. Geochemistry: Exploration, Environment, Analysis 8: 3–18.
  16. Findik, Ö. and M.A. Turan. 2012. Metal concentrations in surface sediments of Beyler Reservoir (Turkey). Bulletin of Environmental Contamination and Toxicology, 88(2): 193-197.
  17. Frattini, P., B. De Vivo, A. Lima and D. Cicchella. 2006. Elemental and gamma-ray surveys in the volcanic soils of Ischia Island, Italy. Geochemistry: Exploration, Environment, Analysis, 6: 325–339.
  18. Goncalves, M.A., A. Mateus and V. Oliveria. 2001. Geochemical anomaly separation by Multifractal modeling. Journal of Geochemical Exploration, 72: 91-114.
  19. Hawkes, H.E. 1976. The downstream dilution of stream sediment anomalies. Journal of Geochemical Exploration, 6(1-2): 345-358.
  20. Hernández-Crespo, C. and M. Martín. 2015. Determination of background levels and pollution assessment for seven metals (Cd, Cu, Ni, Pb, Zn, Fe, Mn) in sediments of a Mediterranean coastal lagoon. Catena, 133: 206–214.
  21. Huang, Z., C. Liu, X. Zhao, J. Dong and B. Zheng. 2020. Risk assessment of heavy metals in the surface sediment at the drinking water source of the Xiangjiang River in South China. Environmental Sciences Europe, 32: 23-39.
  22. Iordache, M., R.L. Popescu, F.L. Pascu and I. Iordache. 2015. Environmental risk assessment in sediments from Jiu River, Romania. Revista De Chimie, 66(8): 1247-1252.
  23. Li, C., T. Ma and J. Shi. 2003. Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical Exploration, 77: 167-175.
  24. Li, Q. and Q. Cheng. 2006. Visual anomaly: a GIS-based Multifractal Method for geochemical and geophysical anomaly separation in Walsh domain. Computers and Geosciences, 32: 663-672.
  25. Long, E.R., L.J. Field and D.D. MacDonald. 1998. Predicting toxicity in marine sediments with numerical sediment quality guidelines. Environmental Toxicology and Chemistry, 17: 714–727.
  26. Long, E.R. and D.D. MacDonald. 1998. Perspective: recommended uses of empirically derived, sediment quality guidelines for marine and estuarine ecosystems. Human and Ecological Risk Assessment, 4: 1019–1039.
  27. Long, E.R., D.D. MacDonald and S.L. Smith. 1995. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environmental Management, 19: 81–97.
  28. Öğlü, B., B. Yorulmaz, T.O. Genç and F. Yilmaz. 2015. The assessment of heavy metal content by using bioaccumulation indices in European chub, Squalius cephalus (Linnaeus, 1758). Carpathian Journal of Earth and Environmental Sciences, 10(2): 85-94.
  29. Mohd Kusin, F., N.N. Mohd Azani, S.N.N. Syed Hasan and N. Aqilah Sulong. 2018. Distribution of heavy metals and metalloid in surface sediments of heavilymined area for bauxite ore in Pengerang, Malaysia and associated risk assessment. Catena, 165: 454–464
  30. Palma, P., L. Ledo and P. Alvarenga. 2015. Assessment of trace element pollution and its environmental risk to freshwater sediments influenced by anthropogenic contributions: the case study of Alqueva Reservoir (Guadiana Basin). Catena, 128: 174–184.
  31. Panahi, A. and Q. Cheng. 2004. Multifractality as a measure of spatial distribution of geochemical patterns. Mathematical Geology, 36: 827–846.
  32. Panahi, A., Q. Cheng and G.F. Bonham-Carter. 2004. Modelling lake sediment geochemical distribution using principal component, indicator kriging and multifractal powerspectrum analysis: a case study from Gowganda, Ontario. Geochemistry: Exploration, Environment, Analysis, 4: 59–70.
  33. Panahi, A., Q. Cheng and Z. Chen. 2007. Multifractal power spectrum and singularity analysis for modelling stream sediment geochemical distribution patterns to identify anomalies related to gold mineralization in Yunnan Province, South China. Geochemistry: Exploration, Environment, Analysis, 7: 293–301.
  34. Pazand, K., A. Hezarkhani, M. Ataei and Y. Ghanbari. 2011. Application of Multifractal modelling technique in systematic geochemical stream sediment survey to identify copper anomalies: a case study from Ahar, Azarbaijan, Northwest Iran. Chemieder Erde-Geochemistry, 71: 397–402.
  35. Pekey, H., D. Karakaş and S. Ayberk. 2004. Ecological risk assessment using trace elements from surface sediments of İzmit Bay (Northeastern Marmara Sea) Turkey. Marine Pollution Bulletin, 48: 946–953.
  36. Shamseddin Meigoony, M., P. Afzal, M. Gholinejad, A.B. Yasrebi and B. Sadeghi. 2013. Delineation of geochemical anomalies using factor analysis and multifractal modeling based on stream sediments data in Sarajeh 1:100,000 sheet, Central Iran. Arabian Journal of Geosciences, 7: 5333–5343.
  37. Simpson, S.L., G.E. Batley, A.A. Chariton, J.L. Stauber, C.K. King, J.C. Chapman, C. Hyne, R.V. Gale, S.A. Roach, A.C. Maher and W.A. 2005. Handbook for sediment quality assessment. CSIRO: Bangor, NSW, 117 pages.
  38. Smal, H., S. Ligęza, A. Wójcikowska-Kapusta, S. Baran, D. Urban, R. Obroślak and A. Pawłowski. 2015. Spatial distribution and risk assessment of heavy metals in bottom sediments of two small dam reservoirs (south-east Poland). Archives of Environmental Protection, 41(4): 67–80.
  39. Roach, A.C. 2005. Assessment of metals in sediments from Lake Macquarie, New South Wales, Australia, using normalization models and sediment quality guidelines. Marine Environmental Research, 59(5): 453-72.
  40. Rose, A.W., E.C. Dahlberg and M.L. Keith. 1970. Multiple regression technique for adjusting background values in stream sediment geochemistry. Economic Geology, 65(2): 156-165.
  41. Uluturhan, E., A. Kontas and E. Can. 2011. Sediment concentrations of heavy metals in the Homa Lagoon (Eastern Aegean Sea): assessment of contamination and ecological risks. Marine Pollution Bulletin, 62: 1989–1997.
  42. Varol, M. and B. Şen. 2012. Assessment of nutrient and heavy metal contamination in surface water and sediments of the upper Tigris River, Turkey. Catena, 92: 1-10.
  43. Xie, S. and Z. Bao. 2004. Fractal and multifractal properties of geochemical fields. Mathematical Geology, 36: 847–864.
  44. Lima, A., B. De Vivo, D. Cicchella, M. Cortini and S. Albanese. 2003. Multifractal IDW interpolationand fractal filtering method in environmental studies: an application on regional stream sediments of Italy, Campania region. Applied Geochemistry, 18: 1853–1865.
  45. Lima, A., J.A. Plant, B. De Vivo, T. Tarvainen, S. Albanese and D. Cicchella. 2008. Interpolation methods for geochemical maps: a comparative study using arsenic data from European stream waters. Geochemistry: Exploration, Environment, Analysis, 8: 41–48.
  46. Wang, W., J. Zhao and Q. Cheng. 2011. Analysis and integration of geo-information to identify granitic intrusions as exploration targets in southeastern Yunnan District, China. Computers and Geosciences, 37: 1946–1957.
  47. Xu, J., Y. Chen, L. Zheng, B. Liu, J. Liu and X. Wang. 2018. Assessment of heavy metal pollution in the sediment of the main tributaries of Dongting Lake, China. Water, 10(8): 1060-1083.
  48. Zhao, J., S. Chen, R. Zuo and E.J.M. Carranza. 2011. Mapping complexity of spatial distribution of faults using fractal and multifractal models: vectoring towards exploration targets. Computers and Geosciences, 37: 1958–1966.
  49. Zuo, R. 2012. Exploring the effects of cell size in geochemical mapping. Journal of Geochemical Exploration, 112: 357–367.
  50. Zuo, R. and Q. Cheng. 2008. Mapping singularities-a technique to identify potential Cu mineral deposits using sediment geochemical data, an example for Tibet, West China. Mineralogical Magazine, 72: 531–534.
  51. Zuo, R., Q. Xia and D. Zhang. 2013. A comparison study of the Cechnnd Sal deposits using sediment geochemical data, an example for Tibet, towards exploration targets. Computers mp, 33: 1651-1675.