Toxicology Reports (Jan 2018)

Assessing heavy metal toxicity in sediments of Chennai Coast of Tamil Nadu using Energy Dispersive X-Ray Fluorescence Spectroscopy (EDXRF) with statistical approach

  • M. Tholkappian,
  • R. Ravisankar,
  • A. Chandrasekaran,
  • J. Prince Prakash Jebakumar,
  • K.V. Kanagasabapathy,
  • M.V.R. Prasad,
  • K.K. Satapathy

Journal volume & issue
Vol. 5
pp. 173 – 182

Abstract

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The concentration of some heavy metals: Al, Ca, K, Fe, Ti, Mg, Mn, V, Cr, Zn, Ni and Co in sediments from Pulicat Lake to Vadanemmeli along Chennai Coast, Tamil Nadu has been determined using EDXRF technique. The mean concentrations of Mg, Al, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni, and Zn were found to be 1918, 25436, 9832, 9859, 2109, 8209, 41.58, 34.14, 160.80, 2.85. 18.79 and 29.12 mg kg−1 respectively. These mean concentrations do not exceed the world crustal average. The level of pollution attributed to heavy metals was evaluated using several pollution indicators in order to determine anthropogenically derived contaminations. Enrichment Factor (EF), Geoaccumulation Index (Igeo), Contamination Factor (CF) and Pollution Load Index (PLI) were used in evaluating the contamination status of sediments. Enrichment Factors (EF) reveal the anthropogenic sources of V, Cr, Ni and Zn Geoaccumulation Index (Igeo) results reveal that the study area is not contaminated by the heavy metals. Similar results were also obtained by using pollution load index (PLI). The results of pollution indices indicates that most of the locations were not polluted by heavy metals. Multivariate statistical analysis performed using principal components and clustering techniques were used to identify the source of the heavy metals. The result of statistical procedures indicate that heavy metals in sediments are mainly of natural origin. This study provides a relatively novel technique for identifying and mapping the distribution of metal pollutants and their sources in sediment. Keywords: Sediments, EDXRF, Heavy metals, Pollution indices, Multivariate statistical methods