On the quantitative relationships between environmental parameters and heavy metals pollution in Mediterranean soils using GIS regression-trees: The case study of Lebanon

R. Bou Kheir, Basem Shomar, M. B. Greve, M. H. Greve

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)250-259
Number of pages10
JournalJournal of Geochemical Exploration
Volume147
Issue numberPB
DOIs
Publication statusPublished - 1 Jan 2014

Fingerprint

Mediterranean soil
Heavy Metals
Geographic information systems
Pollution
heavy metal
Soils
pollution
parent material
road
Soil pollution
soil pollution
soil
soil depth
Poisons
Hydraulic conductivity
curvature
soil type
hydraulic conductivity
soil property
Biological materials

Keywords

  • GIS regression-trees
  • Heavy metals
  • Lebanon
  • Mediterranean environments
  • Pollution quantitative relationships
  • Soil pollution

ASJC Scopus subject areas

  • Economic Geology
  • Geochemistry and Petrology

Cite this

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title = "On the quantitative relationships between environmental parameters and heavy metals pollution in Mediterranean soils using GIS regression-trees: The case study of Lebanon",
abstract = "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.",
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