Geochemical Assessment and Modelling of Groundwater Arsenic with Land Surface Features in Ballia District using Satellite Data and GIS

Abstract

The study examines the spatial variation of arsenic (As) level in groundwater of deep aquifers with respect to surface elevation, slope, and water-table, in Ballia, the eastern most districts of Uttar Pradesh. Total 33 water samples from deep aquifer and their site locations were taken from the 17 blocks of the district during pre and post-monsoon seasons during 2011. Atomic absorption spectrophotometer (AAS) was used for the analyses of water samples. Geographic information system (GIS) is used to show the spatial variation of As and Global positioning system (GPS) for mapping the sampling site. Inverse distance weighted (IDW) interpolation model was used for the preparation of As concentrations maps. Descriptive statistical analysis was applied to examine the nature of relationships and statistical association amongst the variables. The result shows inverse relationships between groundwater As, slopes and water-table in deep aquifers i.e. high As is found in areas of low surface elevation, low slopes, and low water-table in the district. It is also observed that As concentration is controlled by the regional topography rainfall, withdrawal of water and the local-scale groundwater level in the district. The correlation of coefficient (r²) value between As and surface elevation is 0.75 for both pre and post-monsoon. The r² value between As and slope were 0.70 and 0.69 pre-monsoon and post-monsoon respectively. The r² value between As and water-table is 0.79 and 0.76 in pre and post-monsoon respectively. The results show that As in most of the deep aquifer was under the permissible limit.

Keywords: Groundwater Arsenic, geomorphic features, SRTM data, GIS, Ballia-India

Cite this Article

Atiqur Rahman, Joheb Khan, Tabrez Alam Khan, et al. Geochemical Assessment and Modelling of Groundwater Arsenic with Land Surface Features in Ballia District using Satellite Data and GIS. Journal of Remote Sensing & GIS. 2015; 6(2): 18–32p.