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Classification of Anthroposols in areas of old urban solid waste deposits in Presidente Prudente - SP: methodological contribution

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Janaina Natali Antonio
Total Authors: 1
Document type: Doctoral Thesis
Press: Presidente Prudente. 2018-02-16.
Institution: Universidade Estadual Paulista (Unesp). Faculdade de Ciências e Tecnologia. Presidente Prudente
Defense date:
Advisor: José Tadeu Garcia Tommaselli

The expansion of urban areas and different types of land use in these areas cause modifications in the natural characteristics of the soils, since in the urban environment there are several mechanical processes, such as the removal, transportation, and deposition of materials of various origins and compositions. The alterations in the soil are significant, with many countries inserting new soil categories into their classification systems. In Brazil, soils resulting from anthropic actions are considered as Anthroposols, characterized by changes in the landscape and physical and chemical composition, including the presence of artifacts and contaminating materials. In order to carry out the characterization and mapping of the Anthroposols, initially a survey of the characteristics of the natural soils was carried out, including their relation with the attributes of the relief in the municipality of Presidente Prudente - SP, and the predictive mapping of soils, using artificial neural networks. Understanding of the relationship between the soils and relief attributes derived from a digital elevation model provided information to relate the main patterns of occurrence of soil types to the attributes of elevation, slope, relief curvature, geoforms, and aspect. Among the variables used for the predictive mapping of soils, those that stood out were slope and geoforms, presenting similarities in patterns of occurrence. The areas of occurrence of the Neosols are mainly related to the slope areas of 20% or above, the Oxisols present a pattern of occurrence in areas with slopes lower than 8%, the Argisols vary in average slopes above 3% and below 20%, and the hydromorphic soils (Gleysols and Planosols) are located in valley bottoms with gradients below 3%. Artificial neural networks proved to be an efficient technique for the delineation of soil units and allowed identification of landscape components by establishing patterns generated from sample collections. Based on the attributes of soil relief and predictive mapping, characterization of areas of former urban solid waste deposits in the urban area of Presidente Prudente – SP was carried out, predominantly located in valley bottom compartments, in concave curves, slopes of undulating relief and strong undulation and in clayey and hydromorphic soils (Gleysols and Planosols). Anthroposols in former USW disposal areas are classified in the second level as Líxicos, due to the presence of organic materials and artifacts of various origins and compositions, which may contain contaminating substances. For classification in the third and fourth levels, an area of impact from one of the former USW disposal areas was delimited, located in Parque Furquim. Granulometric analyzes and evaluation of the presence of heavy metals in the soils were carried out. The analyzed elements were Arsenic (As), Cadmium (Cd), Lead (Pb), and Chromium (Cr). The results were compared with the Quality Reference Values used in the state of São Paulo and indicated the presence of Lead (Pb) and Chromium (Cr) above the Quality Reference Values at some points, located upstream of the delimited area, in valley bottoms near the watercourse. The area was classified by the occurrence of Líxicos Áquicos and Órticos Anthroposols with the presence of toxic elements and potential Anthroposols. As it is a recent concept, studies aiming to increase knowledge on the characteristics of Anthroposols are fundamental to the understanding of their dynamics and to propose measures of recovery and planning for appropriate use. (AU)

FAPESP's process: 13/03505-6 - Predictive mapping of soils in Presidente Prudente - SP, using artificial neural networks (ANN): a contribution to the analysis of the landscape
Grantee:Janaina Natali Antonio
Support Opportunities: Scholarships in Brazil - Doctorate