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Geostatistical and multivariate statistical analysis of heavily and manifoldly contaminated soil samples

Schaefer, Kristin, Einax, Jürgen W., Simeonov, Vasil, Tsakovski, Stefan
Analytical and bioanalytical chemistry 2010 v.396 no.7 pp. 2675-2683
arsenic, emissions, environmental assessment, factor analysis, geostatistics, iron, kriging, lead, mechanism of action, models, multivariate analysis, pollutants, polluted soils, risk assessment, soil sampling, spectroscopy, steel, surveys, Bulgaria
The surroundings of the former Kremikovtzi steel mill near Sofia (Bulgaria) are influenced by various emissions from the factory. In addition to steel and alloys, they produce different products based on inorganic compounds in different smelters. Soil in this region is multiply contaminated. We collected 65 soil samples and analyzed 15 elements by different methods of atomic spectroscopy for a survey of this field site. Here we present a novel hybrid approach for environmental risk assessment of polluted soil combining geostatistical methods and source apportionment modeling. We could distinguish areas with heavily and slightly polluted soils in the vicinity of the iron smelter by applying unsupervised pattern recognition methods. This result was supported by geostatistical methods such as semivariogram analysis and kriging. The modes of action of the metals examined differ significantly in such a way that iron and lead account for the main pollutants of the iron smelter, whereas, e.g., arsenic shows a haphazard distribution. The application of factor analysis and source-apportionment modeling on absolute principal component scores revealed novel information about the composition of the emissions from the different stacks. It is possible to estimate the impact of every element examined on the pollution due to their emission source. This investigation allows an objective assessment of the different spatial distributions of the elements examined in the soil of the Kremikovtzi region. The geostatistical analysis illustrates this distribution and is supported by multivariate statistical analysis revealing relations between the elements.