Soil contamination by heavy metals has become a widespread dangerous problem in many parts of the world, including the Mediterranean environments. This is closely related to the increase irrigation by waste waters, to the uncontrolled application of sewage sludge, industrial effluents,
pesticides and fertilizers, to the rapid urbanization, to the atmospheric deposition of dust and aerosols, to the vehicular emissions and to many other negative human activities. In this context, this paper predicts the spatial distribution and concentration level of copper (Cu) in the 195 km2
of Nahr el-Jawz watershed coastal area situated in northern Lebanon using a geographic information system (GIS) and regression-tree analysis. The chosen area represents a typical case study of Mediterranean coastal landscape with deteriorating environment. Fifteen environmental parameters
(parent material, soil type, pH, hydraulical conductivity, organic matter, stoniness ratio, soil depth, slope gradient, slope aspect, slope curvature, land cover/use, distance to drainage line, proximity to roads, nearness to cities, and surroundings to waste areas) were generated from satellite
imageries, Digital Elevation Models (DEMs), ancillary data and/or field observations to statistically explain Cu laboratory measurements. A large number of tree-based regression models (214) were developed using (1) all parameters, (2) all soil parameters only, and (3) selected pairs of parameters.
The best regression tree model (with the lowest number of terminal nodes) combined soil pH and surroundings to waste areas, and explained 77% of the variability in Cu laboratory measurements. The overall accuracy of the predictive quantitative copper map produced using this model (at 1 : 50,000
cartographic scale) was estimated to be ca. 80%. Applying the proposed tree model is relatively simple, and may be used in other coastal areas. It is certainly of significant interest to local governments and municipalities. It will serve several development projects concerned with improving
the environmental conditions and the quality of living in coastal areas.
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GIS regression trees;
Document Type: Research Article
Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF),Aarhus University, Blichers Allé 20, P.O. Box 50DK-8830 Tjele, Denmark
Université de Reims Champagne-Ardenne, EA 3795 GEGENAA, 2 esplanade Roland Garros51100 Reims, France
Faculté des Sciences de Tunis, Campus universitaire,Université El Tunis Manar, Avenue 7 Novembre 2092, Tunis
January 1, 2013
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