Application of multivariate statistics and geostatistical techniques to identify the spatial variability of heavy metals in groundwater resources

The performance of geostatistical and spatial interpolation techniques were investigated for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in Ramiyan district (Golestan province, Iran). 24 spring/well water samples were collected and the concentration of heavy metals (Ni, Co, Pb, Cd and Cu) was determined using differential pulse polarography. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities as the sources of heavy metal pollutants in groundwater across the study area. The results of the cluster analysis and factor analysis show that Ni and Co are grouped in the factor F1, whereas, Pb and Cd in F2 and Zn and Cu in F3. The probability of presence of elevated levels for the three factors was predicted by utilizing the most appropriate Variogram Model, whilst the performance of methods, was evaluated using mean absolute error, mean bias error and root mean square error. The spatial structure results show that the variograms and cross-validation of the six variables can be modeled with three methods, namely, the radial basis fraction, inverse distance weight and ordinary kriging. Moreover, the results illustrated that radial basis fraction method was the best due to its highest precision and lowest error. The geographic information system can fully display spatial patterns of heavy metal concentrations in groundwater resources of the study area.