Arabian Journal of Geosciences

, Volume 8, Issue 3, pp 1691–1703 | Cite as

Heavy metals in the soils of the Arabian Gulf coast affected by industrial activities: analysis and assessment using enrichment factor and multivariate analysis

  • Fahad I. Almasoud
  • Adel R. Usman
  • Abdullah S. Al-Farraj
Original Paper
First Online:
Received:
Accepted:

Abstract

The concentrations of nine heavy metals (Fe, Mn, Zn, Cu, Ni, Cd, Cr, Co, and Pb) in soil samples of Arabian Gulf coast, Saudi Arabia, were investigated. Sediment Quality Guideline (SQGs), SQG-Quotient (SQG-Q), toxicity degree index (TDI), enrichment factor (EF), and multivariate analysis, including principle component analysis (PCA) and hierarchical cluster analysis (HCA), were used to measure heavy metals of concern in the study area and to identify their possible sources. The results showed that the concentrations of different heavy metals were 530–5,700 mg kg? 1 for Fe; 9–150 mg kg−1 for Mn and 8–69 mg kg−1 for Zn; 1–21 mg kg−1 for Cu and not detectable—17 mg kg−1 for Ni; 6.9–130 mg kg−1 for Cr and not detectable—5 mg kg−1 for Co; and not detectable—24 mg kg−1 for Pb. Based on SQGs, only the maximum and mean Cr concentrations of study area were in heavy and moderate rates of pollution, respectively. The values of SQG-Q and TDI revealed that the investigated sampling points showed the lowest potential of adverse biological effects. The considerable number of collected soil samples has relatively higher EF values of 5–20 for Zn, Cu, Cr, and Pb, suggesting that these four metals may be derived from anthropogenic origin. Multivariate analysis also confirmed this finding that the sources of Zn, Cu, and Pb resulted primarily from anthropogenic sources, whereas Co, Ni, Fe, and Mn were mainly attributed to lithogenic sources. It could be generally concluded that it is possible to use multivariate analyses in combination with EF values as useful tools to identify the natural or anthropogenic sources of heavy metals in soils.

Keywords

Heavy metals Sediment Quality Guidelines (SQGs) Principal component analysis (PCA) Hierarchical cluster analysis (HCA) 

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Copyright information

© Saudi Society for Geosciences 2014

Authors and Affiliations

  • Fahad I. Almasoud
    • 1
  • Adel R. Usman
    • 2
    • 3
  • Abdullah S. Al-Farraj
    • 2
  1. 1.King Abdulaziz City for Science and TechnologyAERIRiyadhSaudi Arabia
  2. 2.Department of Soil Sciences, College of Food and Agricultural SciencesKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Department of Soils and Water, Faculty of AgricultureAssiut UniversityAssiutEgypt

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