Accumulation and Risk Assessment of Heavy Metals in Sediments from Dredged Tributaries and Creeks of River Ethiope, South-South, Nigeria


The presence of heavy metals in rivers in the Niger Delta region has become a source of concern due to its associated health challenges. The present study was conducted to assess the risk of heavy metal accumulation in surface sediments obtained from creeks and dredged tributaries of the River Ethiope, Delta State, South-South, Nigeria. Heavy metals in the sediments were extracted using the three-step sequential extraction method of the European Commission Standard Measurement and Testing Program. The heavy metals; magnesium (Mn), iron (Fe), zinc (Zn), lead (Pb), copper (Cu), cobalt (Co), arsenic (As), chromium (Cr), cadmium (Cd) and barium (Ba) were quantified by employing inductively coupled plasma-mass spectrometry (ICP-MS). Assessment of the extent of sediment contamination was carried out by determining the contamination factor (CF), degree of contamination (Cd), modified degree of contamination (mCd), pollution load index (PLI), ecological risk factor (Er), potential ecological risk index (PI) and geo accumulation index (1 geo). Pearson’s correlation coefficient and principal component analysis (PCA) were used to determine the sources and the relationship between pollutants across sediments. The values of heavy metals ranged from 12.5 mg kg–1–116 mg kg–1 and 21.6 mg kg–1–71.1 mg kg–1 in the wet and dry seasons, respectively. The trend of heavy metals for risk index (RI) in this study is Cd > Pb > Cr > Co > Zn > Cu > Mn (wet season) and Cu > Cd > Pb > Zn > Cr > Mn > Co = As (dry season). It showed that heavy metal pollution was a result of Cd for extreme contamination, while moderate to high contamination levels were due to Pb and Cu. The Pearson’s correlation coefficient analysis and PCA displayed strong positive loadings for Mn, Fe, Zn, Pb, Cu and Cd across seasons as a result of high contamination levels in the study sites. The pollution load index revealed that the sediments were polluted by the metals, and the mean and median analyses revealed that the metals datasets were normally distributed, except for Cu with an irregular distribution.