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

  • Merina GhoshEmail author
  • Dilip Kumar Pal
  • Subhash Chandra Santra
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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 
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Notes

Acknowledgements

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.

Supplementary material

10668_2019_322_MOESM1_ESM.docx (58 kb)
Supplementary material 1 (DOCX 58 kb)

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Merina Ghosh
    • 1
    • 2
    Email author
  • Dilip Kumar Pal
    • 3
  • Subhash Chandra Santra
    • 4
  1. 1.Geospatial Delhi Ltd, A Government of NCT of Delhi CompanyNew DelhiIndia
  2. 2.Department of Geography and Environment ManagementVidyasagar UniversityMidnaporeIndia
  3. 3.Department of Surveying and Land StudiesThe Papua New Guinea University of TechnologyLaePapua New Guinea
  4. 4.Department of Environment ScienceUniversity of KalyaniKalyani, NadiaIndia

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