Research Article Open Access

Estimating Soil Contamination with Kriging Interpolation Method

Fatima Zohra Benmostefa Largueche

Abstract

The main objective of this study was to investigate whether Kriging is a useful tool to estimate the spatial distribution of ground pollutants in contaminated land. The second objective of this work was a more practical one. It consists on the identification of areas that should be subjected to remedial actions and also on deciding which contaminant needs to be considered when remediation processes are taken. To achieve the described objectives, a contaminated site has been studied and the following steps have been followed: The contamination concentration limits beyond which action needs to be taken to remediate the ground contamination, in which case it is important to determine the areas that should be subjected to the appropriate remediation measures. A presentation of a case study will follow. A brief site description is given. Next, a spatial analysis of the site has been carried out. It consists essentially of: Firstly a primary process of the data which means that histograms and an unprocessed representation of the pollutant's distribution has been plotted for each contaminant. Secondly a graphic presentation of the pollution using Kriging interpolation technique is shown. Finally conclusions concerning Kriging applications are given. An assessment concerning Kriging is presented and a balance between the advantages and disadvantages of its use is discussed.

American Journal of Applied Sciences
Volume 3 No. 6, 2006, 1894-1898

DOI: https://doi.org/10.3844/ajassp.2006.1894.1898

Submitted On: 3 July 2006 Published On: 30 June 2006

How to Cite: Largueche, F. Z. B. (2006). Estimating Soil Contamination with Kriging Interpolation Method. American Journal of Applied Sciences, 3(6), 1894-1898. https://doi.org/10.3844/ajassp.2006.1894.1898

  • 4,106 Views
  • 4,038 Downloads
  • 40 Citations

Download

Keywords

  • Ground contaminants
  • remediation
  • assessment
  • regionalised variables
  • semivariogramms
  • geostatistics interpolations techniques