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On the quantitative relationships between environmental parameters and heavy metals pollution in Mediterranean soils using GIS regression-trees: The case study of Lebanon
Ist Teil von
Journal of geochemical exploration, 2014-12, Vol.147, p.250-259
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2014
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Soil heavy metal pollution has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, the present study used Geographic Information Systems (GIS) and regression-tree modeling (196 trees) to precisely quantify the relationships between four toxic heavy metals (Ni, Cr, Cd and As) and sixteen environmental parameters (e.g., parent material, slope gradient, proximity to roads, etc.) in the soils of northern Lebanon (as a case study of Mediterranean landscapes), and to detect the most important parameters that can be used as weighted input data in soil pollution prediction models. The developed strongest relationships were associated with Cd and As, variance being equal to 82%, followed by Ni (75%) and Cr (73%) as the weakest relationship. This study also showed that nearness to cities (with a relative importance varying between 68% and 100%), surroundings of waste areas (48–92%), proximity to roads (45–82%) and parent materials (57–73%) considerably influenced all investigated heavy metals, which is not the case of hydromorphological and soil properties. For instance, hydraulic conductivity (18–41%) and pH (23–37%) control the distribution of the investigated heavy metals more than soil type (21–32%), soil depth (5–17%), organic matter content (2–7%), and stoniness ratio (0–7%). Slope gradient affected Ni/Cr/Cd /As accumulation (10–13%), while slope length, slope aspect and slope curvature did not interfere in the building of soil heavy metals’ regression-trees and associated relationships. The latter can be extrapolated to other areas sharing similar geo-environmental conditions.
•We quantify the relationships between environmental parameters and soil heavy metals using GIS regression-trees.•The probabilistic and nonparametric regression-tree method appears useful for detecting the most important parameters influencing heavy metals’ pollution.•Nearness to cities, surroundings of waste areas, proximity to roads and parent materials influence highly all investigated metals (i.e., Ni, Cr, Cd and As).•Among the morphological parameters, slope gradient appears to have a certain impact in building soil heavy metals’ regression-trees.•Among the pedological parameters, hydraulic conductivity and soil pH have a higher impact on heavy metals’ pollution.