Research Article Open Access

Heavy Metals Contamination of Road-Deposited Sediments

Jonathan Yisa1
  • 1 ,
American Journal of Applied Sciences
Volume 7 No. 9, 2010, 1231-1236

DOI: https://doi.org/10.3844/ajassp.2010.1231.1236

Submitted On: 20 June 2010 Published On: 30 September 2010

How to Cite: Yisa, J. (2010). Heavy Metals Contamination of Road-Deposited Sediments. American Journal of Applied Sciences, 7(9), 1231-1236. https://doi.org/10.3844/ajassp.2010.1231.1236

Abstract

Problem statement: Impact of anthropogenic activities on man and his environment as a result of the growing rate of urbanization in Bida, Nigeria is of a great concern. Street sediments that accumulate along pavements in urban environments have the potential to provide considerable loadings of heavy metals to receiving waters and water bodies, particularly with changing environmental conditions. The objective of this research was to evaluate the streets sediment contamination in Bida, Nigeria. Approach: Fifty five sediment samples were collected from four roads that experience intense traffic conditions and analyzed in the laboratory for some heavy metals by atomic absorption Spectrophotometric method and multivariate statistical techniques. Results: The overall decreasing metal concentration order was: Pb > Mn > Fe > Zn > Cu > Cr > Ni > Cd. Significantly positive correlation was only found between Cd and organic matter (r = 0.580). Factor analysis shows that road deposited sediment quality data consists of four major components accounting for 77.11% of cumulative variance of the contamination: Ni, pH and silt + clay; Cr, Fe and organic matter; Mn and Zn and finally Cu and Pb. Discriminant analysis revealed that the first two Discriminate Functions (DF1 and DF2) contain 90.61% information for Cu, Pb and Ni accumulation. Conclusion: This study concluded that the concentrations of all metals measured in Bida can be considered to present a low level of contamination and that multivariate statistical analysis is a useful tool in understanding contaminants relationships.

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Keywords

  • Anthropogenic
  • environment
  • multivariate
  • discriminant analysis
  • heavy metals