Water quality index, ecotoxicology and human health risk modelling of surface and groundwater along illegal crude oil refining sites in a developing economy.

Water quality index, ecotoxicology and human health risk models were applied to surface and groundwater samples along illegal crude oil refining sites in Rivers State, Nigeria. Eight (8) surface water and four (4) groundwater sampling points were identified along illegal refining sites. Thirty-six (36) samples in triplicates were collected monthly from each of the twelve (12) sampling points over a three (3) month period. Water samples were collected and analyzed using standard methods as prescribed by the American Public Health Association. The mean pH for surface and groundwater ranged from 5.61 ± 0.15 to 7.34 ± 0.10 and 5.80 ± 0.10 to 6.39 ± 0.13, respectively. Turbidity, TDS, and BOD data for surface water samples exceeded the WHO guideline values. The ionic dominance pattern of anions for both surface and groundwater water samples were the same and in the order Cl > SO > NO > PO. Mean heavy metal concentration was in the order Pb > Ni > Fe > Cd > Mn > Cu for surface water and Pb > Cd > Fe > Mn > Ni > Cu for groundwater. Cd and Pb concentrations in both sources were generally high, with Cd exceeding the WHO guideline value (GV). The CCME water quality index model ranked 62.5% of surface water as marginal, 12.5% as good, 12.5% as poor, and 12.5% as fair. The impact of heavy metals on public health was in the order Pb > Cd > Ni > Fe > Mn, with 83% of samples seriously affected by Pb pollution. The potential ecological risk index ranged from 1.61 × 10 to 2.64 × 10 for surface water and 8.10 × 10 to 2.21 × 10 for groundwater. Heavy metal contamination was very high, and the ecological risk effect was extremely high. The health risk through oral ingestion was in the order of adults > infants > children. Two principal components, PC1 and PC2, explained 50.51% and 16.00% of the variations in surface water quality, respectively. For groundwater quality data, three principal components explained the observed variations in water quality data, of which 51.39% is attributed to PC1, 26.29% to PC2, and 16.58% to PC3.