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      Soil Contamination Interpretation by the Use of Monitoring Data Analysis

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          Abstract

          The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0–20 cm) and subsurface (20–40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition—one cluster for coastal and mountain sites and another—for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm.

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          Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy.

          The possibility to adapt chemometrics approaches for the quantitative estimation of heavy metals in soils polluted by a mining accident was explored. In April 1998, the dam of a mine tailings pond in Aznalcóllar (Spain) collapsed and flooded an area of more than 4000 ha with pyritic sludge contaminated with high concentrations of heavy metals. Six months after the end of the first remediation campaign, soil samples were collected for chemical analysis and measurement of visible to near-infrared reflectance (0.35-2.4 microm). Concentrations for As, Cd, Cu, Fe, Hg, Pb, S, Sb, and Zn were well above background values. Prediction of heavy metals was achieved by stepwise multiple linear regression analysis (MLR) and an artificial neural network (ANN) approach. It was possible to predict six out of nine elements with high accuracy. Best R2 between predicted and chemically analyzed concentrations were As, 0.84; Fe, 0.72; Hg, 0.96; Pb, 0.95; S, 0.87; and Sb, 0.93. Results for Cd (0.51), Cu (0.43), and Zn (0.24) were not significant. MLR and ANN both achieved similar results. Correlation analysis revealed that most wavelengths important for prediction could be attributed to absorptions features of iron and iron oxides. These results indicate that it is feasible to predict heavy metals in soils contaminated by mining residuals using the rapid and cost-effective reflectance spectroscopy.
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            An assessment of soil contamination due to heavy metals around a coal-fired thermal power plant in India

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              How geostatistics can help you

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                Author and article information

                Contributors
                astel@apsl.edu.pl , AliAst@poczta.fm
                vsimeonov@chem.uni-sofia.bg
                Journal
                Water Air Soil Pollut
                Water, Air, and Soil Pollution
                Springer Netherlands (Dordrecht )
                0049-6979
                1573-2932
                13 July 2010
                13 July 2010
                March 2011
                : 216
                : 1-4
                : 375-390
                Affiliations
                [1 ]Biology and Environmental Protection Institute, Environmental Chemistry Research Unit Pomeranian Academy, Arciszewskego Str, 22a, 76200 Slupsk, Poland
                [2 ]Regional Laboratory Burgas, Environmental Executive Agency, Ministry of Environment and Waters, Burgas, Bulgaria
                [3 ]Analytical Chemistry, Faculty of Chemistry, University of Sofia “St. Kl. Okhridski”, J. Bourchier Blvd. 1, 1164 Sofia, Bulgaria
                Article
                539
                10.1007/s11270-010-0539-1
                3038224
                21423336
                d032f28a-aa83-48ff-850f-c8b7412c3aa5
                © The Author(s) 2010
                History
                : 23 February 2010
                : 29 June 2010
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media B.V. 2011

                General environmental science
                environmetrics,soil contamination,cluster analysis,principal components analysis,spatial variation

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