Spatial mapping and modeling of arsenic contamination of groundwater and risk assessment through geospatial interpolation technique

Merina Ghosh (), Dilip Kumar Pal () and Subhash Chandra Santra ()
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Merina Ghosh: Geospatial Delhi Ltd, A Government of NCT of Delhi Company
Dilip Kumar Pal: The Papua New Guinea University of Technology
Subhash Chandra Santra: University of Kalyani

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2020, vol. 22, issue 4, No 8, 2880 pages

Abstract: Abstract 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.

Keywords: Arsenic (As); Hydraulic station; Spatial interpolation; Thiessen polygon; Kriging; Regression model (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10668-019-00322-7

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