Environmental health risks of trace elements in sediment using multivariate approaches and contamination indices

This paper  presents a study that aims to investigate the application of multivariate analysis and correlational analysis in examining trace elements (TEs) within sediment samples. The study involved the collection of sixty samples from diverse surface water sources in Bayelsa State, Nigeria, and their quantification using atomic absorption spectrophotometry. The study utilized these TE levels to compute 18 pollution determinant indices, categorized into individual contamination indices (ICIx) and complex contamination indices (CCIx), which varied in their requirement for reference values. The ICIx and CCIx that necessitated background values were evaluated across three scenarios: geometric (GeoM), median (MeM), and control (ControlM) means. TE concentrations ranged from 5800.20–6567.92 mg/kg for iron, 3.47–7.21 mg/kg for copper, 13.22–19.81 mg/kg for zinc, 3.58–13.60 mg/kg for lead, below detection limit—0.45 mg/kg for cadmium, 4.20–9.21 mg/kg for nickel, and 4.21–9.29 mg/kg for cobalt. There were significant deviations (p < 0.05) among sampling locations for each element. The indices exhibited strong positive correlations across scenarios, with exceptions noted for the Potential Ecological Hazard Index (PEHIx) and Ecological Hazard (EH). Cluster Analysis indicated that 3 out of 4 CCIx do not require reference values, 5 out of 7 ICIx, and 4 out of 5 CCIx requiring background values were essential under each scenario. Principal Component Analysis (PCA) elucidated that over 80% of the total variance was explained, with all indices except EH and PEHIx predominantly distributed in the first principal component for each background scenario. This suggests that the selected principal components effectively capture a significant portion of the variability inherent in the original dataset of contamination indices.