Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

GIS-based environmental risk assessment in the Ribeira Valley, Sao Paulo, Brazil

Full text
Author(s):
Filippini-Alba, Jose Maria [1] ; de Souza Filho, Carlos Roberto [2]
Total Authors: 2
Affiliation:
[1] Brazilian Agr Res Corp, Ctr Temperate Climate Agr Res, BR-96001970 Pelotas, RS - Brazil
[2] Univ Estadual Campinas, Inst Geosci, Dept Geol & Nat Resources, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: ENVIRONMENTAL EARTH SCIENCES; v. 59, n. 5, p. 1139-1147, JAN 2010.
Web of Science Citations: 2
Abstract

The Ribeira Valley region (State of Sao Paulo, Brazil) hosts the largest Brazilian Atlantic forest reserve. The region was an important mineral district during the `60s, from where several tons of lead and other metals were exploited. Residual materials produced by the mining are diffused in the environment until today. The area also encompasses a regional arsenium anomaly associated with unexplored gold deposits. The population presently living in this region performs basic farming activities, from which most of their food is yielded. These features coupled together imply in a complex relationship between natural and anthropogenic factors that are likely to affect the life and health of the local communities. The aim of this paper is to apply digital data integration techniques for environmental risk assessment in the Ribeira Valley using environmental geochemistry. Geochemical, digital elevation and remote sensing data (Landsat Thematic Mapper) were merged and analyzed using a geographical information system. The assumed model considered environmental mobilization through erosion and anomalous As-Pb areas. Data were analyzed through Boolean and fuzzy logic techniques. Fuzzy logic proved superior in this case study as it allowed not only the detection but also the distinction between low, moderate and high environmental risk areas. (AU)