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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/31347
Title: Using magnetic susceptibility data, showing the anomaly of anthropogenic pollution sources
Authors: Aydin, A.
Karagenc, N.
Keywords: материалы конференций;Magnetic susceptibility;Sediment;Pollution;Heavy metals
Issue Date: 2018
Publisher: БГУИР
Citation: Aydin, A. Using magnetic susceptibility data, showing the anomaly of anthropogenic pollution sources / A. Aydin, N. Karagenc // BIG DATA Advanced Analytics: collection of materials of the fourth international scientific and practical conference, Minsk, Belarus, May 3 – 4, 2018 / editorial board: М. Batura [etc.]. – Minsk, BSUIR, 2018. – Р. 99.
Abstract: The magnetic susceptibility (MS) methods is a very useful tool for giving us very easy processing and analysis towards fast discrimination of anthropogenic heavy metal loads in the sediment deposits by field measurements. The magnetic susceptibility distribution anomaly in the sediment deposits are caused by natural and anthropogenic influences in urban areas. Magnetic susceptibility measurements were taken by using the field probe polluted and less polluted in the agricultural areas in different cities, Turkey. Using the statistical method on the mag- netic susceptibility data and their results show us that the values from the polluted areas or unpolluted areas searching sites. It was showed the pollutant distributions after mapping the data of magnetic susceptibility and showed their sources to assess environmental threats. Magnetic susceptibility method is cheaper and less time-consuming against chemical methods. We showed that it is enough only using magnetic susceptibility measurements could provide heavy metal pollution distribution in such areas. To show the distributions of heavy metal pollution in the sediment deposit areas, 400 field measurements were collected. This work is supported by PAU-BAP 2018KRM002-094.
URI: https://libeldoc.bsuir.by/handle/123456789/31347
Appears in Collections:BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2018)

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