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This study was carried out to determine the groundwater quality of Türkmen Mountain, which provides drinking water to about 250,000 people, and to evaluate the water quality by using some multivariate statistical techniques. In this study, groundwater samples were collected from 18 stations on Türkmen Mountain in summer 2011. Some lymnological parameters and element levels in groundwater of the mountain were determined. Factor analysis (FA), cluster analysis (CA), and Pearson Correlation Index were applied to the results in order to estimate the data properly. The ArcGIS package program was used to make distribution maps of arsenic, boron, and total phosphorus (which were detected as the most critical parameters of the mountain) in order to provide visual summaries of element accumulations. Also, water samples were evaluated according to the criteria of SKKY (water pollution control regulation in Turkey) and evaluated as drinking water according to the criteria of TS266 (Turkish Standards Institute), the EC (European Communities), and WHO (World Health Organization). It was determined that arsenic accumulations of some stations exceeded the limit values specified by TS266, WHO, and the EC. Significant positive correlations were determined between arsenic and boron levels (p<0.01), and according to the FA results, the “Boron Works Factor,” which was strongly positive related to the variables of arsenic and boron, was identified as the most effective component for Türkmen Mountain (25.88% of total variance). As a result, in addition to the geological structure of the mountain, mining activities and mineral recovery processes are significant effective factors of groundwater quality of Türkmen Mountain.
The article presents an example of applying multidimensional statistical methods in the ranking of provinces in terms of the amount of funds received from the selected programs implemented under the Rural Development Programme (RDP).The purpose of this paper is to build the ranking and specify the homogeneous groups of provinces in regard to the analysed attributes. On the basis of volatility analysis and a matrix of correlation coefficients, six variables characterizing this issue were chosen for the final calculation. The paper uses three methods which enable multidimensional statistical analysis, i.e. the measure of Hellwig, the Czekanowski Diagram and Prim’s dendrite. All the mentioned methods made it possible to draw the same conclusion, namely that the provinces which have been using EU funds supporting agricultural activities to the greatest extent are zachodnipomorskie, pomorskie and warminsko-mazurskie. The lowest positions are occupied by podkarpackie, małopolskie and śląskie.
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