Mapping soil pollution by spatial analysis and fuzzy classification

• Published in: Environmental earth sciences 2010-04, Vol.60 (3), p.495-504
• Main Authors: Lourenço, Roberto Wagner, Landim, Paulo M. Barbosa, Rosa, André Henrique, Roveda, José Arnaldo F., Martins, Antonio César Germano, Fraceto, Leonardo Fernandes
• Format: Article
• Language: English
• Subjects: Biogeosciences; Classification; Coastal zone; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental risk; Environmental Science and Engineering; Exact sciences and technology; Fuzzy logic; Geochemistry; Geology; Heavy metals; Hydrology/Water Resources; Lead; Mapping; Metal concentrations; Original Article; Pollution levels; Pollution, environment geology; Risk; Risk assessment; Soil; Soil contamination; Soil pollution; Soils; Spatial analysis; Spatial distribution; Surficial geology; Terrestrial Pollution; Topsoil; Zinc
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-009-0190-6

Spatial analysis and fuzzy classification techniques were used to estimate the spatial distributions of heavy metals in soil. The work was applied to soils in a coastal region that is characterized by intense urban occupation and large numbers of different industries. Concentrations of heavy metals were determined using geostatistical techniques and classes of risk were defined using fuzzy classification. The resulting prediction mappings identify the locations of high concentrations of Pb, Zn, Ni, and Cu in topsoils of the study area. The maps show that areas of high pollution of Ni and Cu are located at the northeast, where there is a predominance of industrial and agricultural activities; Pb and Zn also occur in high concentrations in the northeast, but the maps also show significant concentrations of Pb and Zn in other areas, mainly in the central and southeastern parts, where there are urban leisure activities and trade centers. Maps were also prepared showing levels of pollution risk. These maps show that (1) Cu presents a large pollution risk in the north–northwest, midwest, and southeast sectors, (2) Pb represents a moderate risk in most areas, (3) Zn generally exhibits low risk, and (4) Ni represents either low risk or no risk in the studied area. This study shows that combining geostatistics with fuzzy theory can provide results that offer insight into risk assessment for environmental pollution.