Spatial mapping and modeling of arsenic contamination of groundwater and risk assessment through geospatial interpolation technique
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Spatial interpolation technique is useful for spatial mapping with sparse data procured from vantage in situ sampling sources. Through spatial interpolation, wall-to-wall mapping of the arsenic concentration in groundwater was accomplished for the whole of the study area by using known concentration value at nearby locations (aquifers) under homogenous terrain conditions. This present study proposes an empirical methodology through interpolation approach for spatial mapping of seasonal and annual groundwater arsenic contamination in the district North 24 Parganas, which happens to be the one of the worst arsenic-affected districts of West Bengal, India, in Bengal Basin. Two types of interpolation approach, Thiessen polygon and Kriging, have been used for spatial mapping of arsenic distribution. On the basis of spatial distribution map, classification has been done for the entire district into seven arsenic concentration zones with various levels of contaminations from arsenic in groundwater (0.01 mg/L as WHO-declared maximum limit for safe zone). In this study, a total of six seasonal (pre-/post-monsoon) data from 2006 to 2008 have been interpreted to examine temporal changes of arsenic concentration in groundwater, and finally, the future trend is projected. Future trend assessment of arsenic contamination has been performed through statistical analysis fitting a linear regression equation. Through this study, it is revealed that the unaffected blocks in the pre-monsoon season (March–April–May) of year 2006 became significantly affected by the end of year 2008. From regression model, it has been predicted that if this trend continues, then, after ten years 2/3 blocks of the said districts will be arsenic affected.
KeywordsArsenic (As) Hydraulic station Spatial interpolation Thiessen polygon Kriging Regression model
We would like to thank SWID (State Water Investigation Directorate, Govt. of West Bengal, India) for chemical testing of data samples and providing arsenic concentration value of each sample. It is a great pleasure to acknowledge Dr. Pijush Kanti Jana, Head of the Dept. of Library and Information Science, Vidyasagar University, who guided us to make related statistical analysis of this research study by allotting us valuable time from his very busy schedule.
- Chang, Kt. (2002). Introduction to geographical information systems. India: Tata McGraw Hill.Google Scholar
- Davis, J. C. (1996). Statistics and data analysis in geology. New York: Wiley.Google Scholar
- Deutsch, C., & Journel, A. (1998). GSLIB: Geostatistical software library and user’s guide (p. 340). Oxford: Oxford University Press.Google Scholar
- Ghosh, M., Pal, D. K., & Santra, S. C. (2012). Different land use and other physical and socio-economic parameters in ground water arsenic concentration. International Journal of Science & Emerging Technologies, 3, 89–101.Google Scholar
- Groundwater Arsenic Contamination in West Bengal-India (20 years study). www.soesju.org. Accessed May 2009.
- Heine, G. W. (1986). A controlled study of some two-dimensional interpolation methods. COGS Computer Contributions, 3(2), 60–72.Google Scholar
- Kinniburg, D. G., Smedley, P. L. (2001). Arsenic contamination of groundwater in Bangladesh. Ministry of Local Government, Rural Government and Cooperatives, Government of Bangladesh, BGS Technical Report WC/00/19 (Vol 1).Google Scholar
- Levin, R. I., Rastogi, S., Rubin, D. S., & Siddiqui, M. H. (2013). Statistics for management. Delhi: Dorling Kindersley pvt. Ltd.Google Scholar
- Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1988). Numerical recipes in C/; The art of scientific computing. New York: Cambridge University Press.Google Scholar
- Royle, A. G., Clausen, F. L., & Frederiksen, P. (1981). Practical universal Kriging and automatic contouring. Geoprocessing, 1, 377–394.Google Scholar
- Sibson, R. (1981). A brief description of natural neighbor interpolation (pp. 21–36)., Chapter 2 in interpolating multivariate data New York: Wiley.Google Scholar
- The World Bank. (2005). Environment and social unit—South Asia region, water and sanitation programme (WSP) South and East Asia. Arsenic Contamination of Groundwater in South and East Asian Countries. Volume II Technical Report No. 31303.Google Scholar
- Tseng, T., Babazono, A., Yamamoto, E., Kurumatani, N., Mino, Y., Ogawa, T., et al. (1968). Ingested arsenic and internal cancer in an endemic area of chronic arsenicism in Taiwan. Journal of the National Cancer Institute, 40, 453–463.Google Scholar
- Watson, D. (1992). Countouring: A guide to the analysis and display of spatial data. London: Pergamon Press.Google Scholar
- WHO. (2001). Environmental health criteria. Geneva, 224, Arsenic and Arsenic Compounds.Google Scholar