This study investigated whether there was an effective discriminant function that substantially depicted two-group seasonal variation and predicted metals’ posterior seasonal variability group membership. It also sought to quantify the degree of sediment contamination by both single and multiple metals. The analytical method involved discriminant analysis for the derivation of the discriminant function and adoption of single and integrated contamination indices for the determination of the degree of sediment contamination. The sediment samples were analysed using inductively coupled plasma mass spectrometry. The results from multivariate Wilks’ Lambda, canonical correlations and functions at group centroids provided clear evidence of significant discriminant functions that signified substantial dry and wet seasonal variations. In terms of relative contribution, Al, Fe, Pb, Na and Ni had the highest impact on the discriminant function. With 76% predictive accuracy, Al, Fe, Pb, Na were classified under the greater probability of dry season variability, while Ni was predicted to have the posterior probability of greater wet season variability. Based on the contamination factors, the level of sediment contamination by Pb and Na ranged from heavy to very heavy but Al and Fe showed moderate contamination. Based on the modified degree of contamination, the overall degree of sediment contamination by all the heavy metals analysed in the study ranged from moderate to heavy degrees of contamination. An assessment with the pollution load index, using trace metals (Pb, Mn, Cu, Ni, Cr), showed that the overall status of the degree of contamination was greater than the baseline level, indicating that the sediments were typically polluted and consequently in a progressive state of deterioration caused by the metals. In general, the degree of contamination was greater in the dry season and in the northern part of the lake.

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