[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 20, Issue 6 (Iranian South Medical Journal 2018) ::
Iran South Med J 2018, 20(6): 584-594 Back to browse issues page
Modelling Spatial Variation of Arsenic Pollutant Using Empirical Bayesian Kriging in the Southern Part of Kerman Province
Maliheh Abbaszadeh * 1
1- Department of Mining Engineering, University of Kashan, Kashan, Iran
Abstract:   (785 Views)
Background: Arsenic is one of the elements that exist naturally in the earth crust. This element has several applications in the areas such as industrial, agricultural, medicinal. However, it could be seriously harmful because of its toxic effects on live organisms, including humans. Arsene could spread into the environment via two sources, including anthropogenic (produced directly by human activities) and natural (weathering and leaching from rocks and mineral layers of the earth and sedimentation) sources. This study investigates the probable sources of arsenic contaminations by producing the arsenic distribution map in the south of Kerman Province, Iran.
Materials and Methods: We studied an area of 5000 km2 in the south of Kerman Province. The results of assaying the 1804 stream sediment samples for arsenic contamination were used for spatially modeling. In other non-sampling areas, Empirical Bayesian Kriging (EBK) modeling was applied to assess the prediction of arsenic contamination.
Results: The level of arsenic concentration is more than the standard value (12 ppm) in the north-western part of the area. This contamination can cause several diseases such as cancer of the skin, bladder, and lung. It also could be a risk factor for diabetes and cardiovascular disorders. Therefore it is necessary to evaluate the prevalence of these diseases in the polluted areas.
Conclusion: Comparison of contaminated area map with the coordination of mineral deposits, mines, chemical industries and agricultural areas shows that the natural sources are more probable to be a source of this contamination compared to anthropogenic sources. The spatial modeling is produced based on the stream sediment samples, hence for the future studies, sampling from the soil, surface and underground water of the contaminated areas seems necessary.
 
Keywords: Geographical information systems, Bayesian kriging, uncertainty, Arsenic, contamination
Full-Text [PDF 1406 kb]   (246 Downloads)    
Type of Study: Original | Subject: General
Received: 2018/01/13 | Accepted: 2018/01/13 | Published: 2018/01/13
References
1. Li, MS,Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: a review of research and practice. Sci. Total Environ.2006;357(1): 38–53. [PubMed] [Google Scholar]
2. The European environment – state and outlook 2010: Soil. European Environment Agency, Copenhagen. (https://www.eea.europa.eu/soer/europe/soil)
3. European Soil Bureau Network of the European Commission, 2005. Soil atlas of Europe. Office for Official Publications of the European Communities, Luxembourg.
4. Finzgar N, Jez E, Voglar D, et al.Spatial distribution of metal contamination before and after remediation in the Meza Valley, Slovenia.Geoderma 2014; 217-218: 135-43. [Google Scholar]
5. Butcher DJ. Environmental Applications of Arsenic Speciation Using Atomic Spectrometry Detection". Applied Spectroscopy Reviews 2007; 42(1): 1-22. [Google Scholar]
6. Antunes, I.M.H.R, Albuquerque, M.T.D. Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal). Science of the Total Environment 2013; 442. 545–52. [PubMed] [Google Scholar]
7. Pais I, Jones JB. The Handbook of Trace Elements. CRC press, Boca Raton, USA. 2000; 240.
8. DuanL, SongJ, Yuan H, et al. Spatio-temporal distribution and environmental risk of arsenic in sediments of the East China Sea. Chemical Geology 2013; 340: 21-31. [Google Scholar]
9. Mosaferi M, Taghipour H, Hassani AM, et al. Study of Arsenic Presence in Drinking Water Sources: A Case Study. Iran. J. Health & Environ 2008; 15: 1(1): 19-28. (Persian) [Google Scholar]
10. Mosaferi M, Yunesian M, Dastgiri S, et al. Prevalence of skin lesions and exposure to arsenic in drinking water in Iran. Science of The Total Environment. 2008; 390(1): 69 -76. [PubMed] [Google Scholar]
11. Babaei Y, AlaviMoghaddam MR, Ghasem Zadeh F, et al. Study of arsenic contamination of surface waters in the area of Kashmar Koohsorkh. Environ Sci Technol. 2008;10(3):29–35.
12. Hatami Manesh, M, Mirzayi M, Bandegani M, et al. Determination of mercury, lead, arsenic, cadmium and chromium in salt and water of Maharloo Lake, Iran, in different seasons. J-Mazand-Univ-Med-Sci2014; 23(108): 91-8.(Persian) [Google Scholar]
13. Bell FG. Environmental geology:principles and practice. Wiley-Blackwell, 1998; 487–500.
14. Nealson KH. Sediment bacteria: who’s there, what are they doing, andwhat’s new? Annu Rev Earth Planet Sci 1997;25:403– 34. [PubMed] [Google Scholar]
15. Nriagu Jo, Bhattacharya P, Mukhergee AB, et al. Arsenic in soil and groundwater: an overview. Trace Metals and other contamints in the Environment 2007; 9: 3-60 [Google Scholar]
16. Duker AA, Carranza EJM, Hale M. Arsenic geochemistry and health. Environment International 31(5): 631-41. [PubMed] [Google Scholar]
17. Webster R. Oliver MA. Geostatistics for environmental scientists. Wiley, Chichester, 2001; 89-96. [Google Scholar]
18. Diggle PJ, Ribeiro JrPJ. Bayesian inference in Gaussian model-based geostatistics. Geogr. Environ.Model 2002;6(2):129–146. [Google Scholar]
19. Environmental Systems Research Institute (ESRI), (2014). ArcGIS Desktop Help 10.2 Geostatistical Analyst. http://resources.arcgis.com/en/help/main/10.2/index.html
20. Aelion CM, Davis HT, Lawson AB, et al. Validation of Bayesian Kriging of Arsenic, Chromium, Lead, and Mercury Surface Soil Concentrations Based on Internode Sampling.Environmental Science & Technology 2009; 43(12): 4432-38. [PubMed] [Google Scholar]
21. Pilz J, Pluch P, Spock G. Bayesian Kriging with lognormal data and uncertain variogram parameters. Geostatistics for Environmental Applications 2005; 51-62. [Google Scholar]
22. Pilz, J. Spöck G. Why do we need and how should we implement Bayesian kriging methods. Stochastic Environmental Research and Risk Assessment2008; 22(5): 621-32. [Google Scholar]
23. Yousefi M, Kamkar-Rouhani A, Carranza EJM. Geochemical mineralization probability index (GMPI): A new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping". Journal of Geochemical Exploration 2012; 115. 24–35. [Google Scholar]
24. Ranjbar H., HonarmandM, Moezifar Z. Application of the Crosta technique for porphyry copper alteration mapping, using ETM data in the southern part of the Iranian volcanic sedimentary belt. Journal of Asian Earth Sciences 2004; 24(2), 237–43. [Google Scholar]
25. Biglari H, Saeidi M, Alipour V, et al. Review onhydrochemical and health effects of it inSistan and Baluchistan groundwater’s, Iran.International Journal of Pharmacy andTechnology. 2016;8(3):17900-20. (Persian) [Google Scholar]
26. Ebrahimpoor S, Mohammadzadeh H, Nasseri N,. Arsenic contamination of ground waters and its effects on human health.; Proceedings of the First National Conference of Applied Research on Water Resources of Iran.; 2010; Iran. Kermanshah Regional Water Company; pp. 269–82. (Persian)
27. Chandrasekaran VRM, Muthaiyan I, HuangPC, et al. Using iron precipitants toremove arsenic from water: Is it safe? WaterResearch. 2010;44(19):5823-7. [PubMed] [Google Scholar]
28. Ahuja S. Arsenic contamination of groundwater: Mechanism, Analysis, and Remediation. John Wiley & Sons, INC. 2008; 367-376.
29. Chen CJ, Wang SH L. Chiou JM et al.Arsenic and diabetes and hypertension in human populations: a review, Toxicology and Applied Pharmacology, 2007; 222(3), 298–304. [Google Scholar]
30. Kwok RK., Kaufmann RB., Jakariya M. Arsenic in drinking-water and reproductive health outcomes: a study of participants in the Bangladesh Integrated Nutrition Programme, J Health Popul Nutr. 2006;24(2):190-205. [PubMed] [Google Scholar]
31. Vahter M, Health effects of early life exposure to arsenic. Basic Clin Pharmacol Toxicol 2008; 102(2):204-11 [Google Scholar]
32. Rodrı VM, Capdeville MEJ, Giordani M. The effects of arsenic exposure on the nervous system.Toxicology Letters 2003; 145(1): 1-18. [PubMed] [Google Scholar]
33. Dobaradaran S, Mohamadzadeh F. Servey of the oil and gas pollutant impacts on the human and environment. Iran South Med J 2014; 17 (1):85-98 [Google Scholar]
Add your comments about this article
Your username or Email:

CAPTCHA code


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Abbaszadeh M. Modelling Spatial Variation of Arsenic Pollutant Using Empirical Bayesian Kriging in the Southern Part of Kerman Province. Iran South Med J. 2018; 20 (6) :584-594
URL: http://ismj.bpums.ac.ir/article-1-909-en.html


Volume 20, Issue 6 (Iranian South Medical Journal 2018) Back to browse issues page
دانشگاه علوم پزشکی بوشهر، طب جنوب ISMJ

Iranian South Medical Journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License which allows users to read,
copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly

Copyright © 2017, Iranian South Medical Journal| All Rights Reserved

Persian site map - English site map - Created in 0.05 seconds with 31 queries by YEKTAWEB 3764