Geostatistical Analysis of Groundwater Data in a Mining Area in Greece

Abstract

Geostatistical prediction methods are increasingly used in earth sciences and engineering to improve upon our knowledge of attributes in space and time. During mining activities, it is very important to have an estimate of any contamination of the soil and groundwater in the area for environmental reasons and to guide the reclamation once mining operations are finished. In this paper, we present the geostatistical analysis of the water content in certain pollutants (Cd and Mn) in a group of mines in Northern Greece. The monitoring points that were studied are 62. The aim of this work is to create a contamination prediction map that better represents the values of Cd and Mn, which is challenging based on the small sample size. The correlation between Cd and Mn concentration in the groundwater is investigated during the preliminary analysis of the data. The logarithm of the data values was used, and after removing a linear trend, the variogram parameters were estimated. In order to create the necessary maps of contamination, we employed the method of ordinary Kriging (OK) and inversed the transformations using bias correction to adjust the results for the inverse transform. Cross-validation shows promising results (ρ=65% for Cd and ρ=52% for Mn, RMSE = 25.9 ppb for Cd and RMSE = 25.1 ppm for Mn). As part of this work, the Spartan Variogram model was compared with the other models and was found to perform better for the data of Mn.

Keywords

• geostatistics
• environmental monitoring groundwater
• ordinary Kriging
• spatial statistics
• pollution
• heavy metals