1National Bureau of Soil Survey and Land Use Planning, Jamuguri Road, Jorhat, Assam, India
2National Bureau of Soil Survey and Land Use Planning, Nagpur, Maharashtra, India
National Bureau of Soil Survey and Land Use Planning, Sector-II, DK-Block, Salt Lake, Kolkata, West Bengal, India
*Corresponding author: reza_ssac@yahoo.co.in
Online published on 9 January, 2015.
The present paper aims to maps Cr, Cd, Ni and Pb concentration and assess the hazard in the soils of surrounding agricultural fields affected by oil refinery drainage of Digboi refinery of Tinsukia district, Assam using statistics, geostatistics and GIS techniques. The amounts of Cr, Cd, Ni and Pb were determined from 97 samples collected within the contaminated area. Among the heavy metals studied, the mean concentration of Pb was high. The greatest and the smallest standard deviation were observed in the Ni (44.1) and pH (0.47), respectively. Analysis of the isotropic variogram indicated that the Cr and Cd semivariograms were well described with the circular model, with the distance of spatial dependence being 1240 and 1022 m, respectively, while the Pb and Ni were well describe with Gaussain model, with the distance of spatial dependence being 1930 and 2321 m, respectively. The ordinary kriging maps of Cr, Cd, Ni and Pb showed that high concentrations of heavy metals were located in the low lying area. Indicator kriged probability maps of soil Cr, Cd, Ni and Pb were prepared based on the concentrations to exceed the respective Food and Agriculture Organization maximum permissible limit (MPL) value of 100, 3, 30 and 50 mg kg−1, respectively. It was seen that whole studied area had a higher than 0.99% probability to exceed the MPL value of Pb. About 10% area of the study site was having higher concentration than MPL value of Cd and Ni concentrated at the centre and north-west corner of the study area, respectively.
Apart from transport and municipal services, industrial plants constitute the main source of heavy metals released to environment.
A good variogram structure of heavy metals was observed, showing that there are clear spatial patterns of heavy metals on the distribution map and also that the current sampling density is sufficient to indicate such spatial patterns.
The kriging interpolated map showed areas with high values of heavy metal concentrations. The probability map produced based on indicator kriging provided useful information for hazard assessment.
Heavy metals, geostatistics, spatial variability, accuracy assessment, risk assessment
