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

A geochemical and statistical approach for assessing heavy metal pollution in sediments from the southern Caspian coast

The nearshore marine environment of the Caspian sea is a major repository for toxic metals originating from various sources. Since the persistent toxic metals pose serious health risks this research concentrated on investigating the concentrations and spatial distribution of metals in the nearshore sediments along the Iranian coast of the Caspian sea. Fourteen sampling sites were selected along the coast and approximately 400 g of surficial sediments were obtained. Samples were sieved and three grain size fractions from each sample plus fourteen bulk samples were selected for the analysis of metals. Laboratory analysis of the samples utilized the Cold Acetic protocol, followed by Inductively coupled plasma optical emission spectroscopy. The statistical techniques were used to analyze all obtained data. Linear regression analysis demonstrated that grain size of the sediments was not a major factor controlling the concentrations and spatial distributions of heavy metals. Box and Whisker plots emphasized that metal concentrations were not homogeneously distributed. Discriminant analysis was also proved to be useful in identifying geographic areas where heavy metal concentrations occur along the coast

Multivariate statistical assessment of heavy metal pollution sources of groundwater around a lead and zinc plant

<p>Abstract</p> <p>The contamination of groundwater by heavy metal ions around a lead and zinc plant has been studied. As a case study groundwater contamination in Bonab Industrial Estate (Zanjan-Iran) for iron, cobalt, nickel, copper, zinc, cadmium and lead content was investigated using differential pulse polarography (DPP). Although, cobalt, copper and zinc were found correspondingly in 47.8%, 100.0%, and 100.0% of the samples, they did not contain these metals above their maximum contaminant levels (MCLs). Cadmium was detected in 65.2% of the samples and 17.4% of them were polluted by this metal. All samples contained detectable levels of lead and iron with 8.7% and 13.0% of the samples higher than their MCLs. Nickel was also found in 78.3% of the samples, out of which 8.7% were polluted. In general, the results revealed the contamination of groundwater sources in the studied zone. The higher health risks are related to lead, nickel, and cadmium ions. Multivariate statistical techniques were applied for interpreting the experimental data and giving a description for the sources. The data analysis showed correlations and similarities between investigated heavy metals and helps to classify these ion groups. Cluster analysis identified five clusters among the studied heavy metals. Cluster 1 consisted of Pb, Cu, and cluster 3 included Cd, Fe; also each of the elements Zn, Co and Ni was located in groups with single member. The same results were obtained by factor analysis. Statistical investigations revealed that anthropogenic factors and notably lead and zinc plant and pedo-geochemical pollution sources are influencing water quality in the studied area.</p