APPLICATION OF SPATIAL ANALYSIS FUNCTIONS FOR AUTOMATIC CREATION OF A VULNERABILITY MAP OF BRAZILIANS SOILS TO 137Cs.
ISBN 978-85-88783-11-9
Authors
1Picanco Jr, P.; 2Wasserman, M.A.; 3Araujo, J.
1FEDERAL UNIVERSITY OF PARANA - UFPR Email: pericles.picanco@outlook.com
2INSTITUTO DE ENGENHARIA NUCLEAR/CNEN Email: mwasserman@ien.gov.br
3UERJ Email: joao.araujo@gmail.com
Abstract
This research aims the development of an automatic process to create a vulnerability map of soil to 137Cs contamination. Achieving this, we used previous radioecological studies that have identified some soil properties showed to be more relevant than other in 137Cs soil contamination, so based on pedological analyses for a given class of soil, was theoretically possible to classify the area according to their potential vulnerability and estimate soil to plant transfer factor (TF). TF is the possibility of radionuclides be transferred from the soil to the plant through a chemical or physical process. The main risk with the radionuclide transfer is the contamination of crops and pasture areas. The vulnerability of soils to 137Cs contamination was categorized: 1) extremely vulnerable soils; 2) highly vulnerable soil; 3) vulnerable soils; 4) mildly vulnerable soils and 5) unvulnerable ageing soils. The analysis took place through a graphic model builder interface, from Esri’s ArcGis for desktop, to construct a Geographic Information System basing on: 1) Brazilian soil classes; 2) soil to plant transfer factor values for reference species 3) soil parameters that interferes on 137Cs behavior in soil such as exchangeable K (Potassium), cation exchange capacity, pH and organic matter content. A test map was built using a real area with some Brazilian soils with known physico-chemical properties, but unknown TF and areas representing soils where values reported for 137Cs TF and soil properties were available in the literature originated from experimental essays studies. The results showed the use of spatial analysis functions through a graphical process combination is efficient to automate the map construction, since the TF inference, the soil vulnerability classification and the proper symbolization. The vulnerability of the studied soil from the rural area (Cambisol and Ferralsol) varied from the highly vulnerable and vulnerable, what was very consistent with experimental results obtained in other soil classes from Brazil (Histisol, Ferralsol, Acrisol and Nitisol). This automatic mapping can be an important tool to improve planning emergency actions in rural areas identifying vulnerable areas and suitable remediation.
Keywords
GIS; Radio vulnerability; Soil vulnerbility