Assessment of the impact of a phosphatic fertilizer plant on the adjacent environment using fuzzy logic |
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| Authors: | K. Szczepaniak C. Sarbu A. Astel E. Raińska M. Biziuk O. Culicov M.V. Frontasyeva P. Bode |
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| Affiliation: | (1) Department of Analytical Chemistry, Chemical Faculty, Gdansk University of Technology, 11/12 G. Narutowicza Str., 80-952 Gdansk, Poland;(2) Department of Analytical Chemistry, Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University Cluj-Napoca, 11 Arany Janos Str., 400028 Cluj-Napoca, Poland;(3) Biology and Environmental Protection Institute, Environmental Chemistry Research Unit, Pomeranian Pedagogical Academy, 22a Arciszewskiego Str., 76-200 Słupsk, Poland;(4) Department of Activation Analysis and Radiation Research, Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research, 141980 Dubna Moscow Region, Russia;(5) National Institute for Research and Development in Electrical Engineering, “ICPE-CA”, 313 Splaiul Unirii, Sector 3, 030 138 Bucharest, Romania;(6) Interfaculty Reactor Institute, Department of Radiochemistry, Technical University of Delft, Mekelweg 15, NL-2629 JB Delft, The Netherlands |
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| Abstract: | ![]()
The impact of a phosphatic fertilizer plant on the adjacent environment was examined. Selected rare earth elements, heavy metals and metalloids were determined in substrates and products, waste by-product, and grass and soil samples. Concentration gradients of elements in grass and soil samples along the southerly and easterly directions were examined and compared with the content of interior soil and grass samples, substrates, and products. Results were compared with available data on soil permissible element concentration levels. Two fuzzy principal component analysis (FPCA) methods for robust estimation of principal components were applied and compared with classical PCA. The efficiency of the new algorithms is illustrated. The investigation explored the impact of the plant on the adjacent environment. The most reliable results, in good agreement with types of samples, were produced using the FPCA-O algorithm |
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| Keywords: | Principal components analysis robust principal components analysis heavy metals rare-earth elements pollution profile |
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