Assessment of PTEs in water resources by integrating HHRISK code, water quality indices, multivariate statistics, and ANNs

The use of contaminated water for drinking and sanitary purposes can be detrimental to human health. In this article, the Human Health Risk (HHRISK) code was applied, alongside the modified heavy metal index (MHMI), synthetic pollution index (SPI), and entropy-weighted water quality index (EWQI), to investigate the pollution status, ingestion, and dermal health risks of potentially toxic elements (PTEs) (Fe, Zn, Mn, Pb, Cr, and Ni) in water resources from the Umunya area, Nigeria. Physicochemical measurements followed standard methods. Results of the MHMI, SPI, and EWQI revealed that about 60% of the water samples had low pollution and were considered suitable for human consumption, while 40% were unsuitable. Further, cumulative non-carcinogenic health risk scores indicated that 60% of the water samples pose low-medium risks while 40% pose high risks to child and adult populations. Contrarily, results of cumulative carcinogenic health risk showed that 6.67% of the samples expose water users to low risks, whereas 93.33% expose them to high risks. Although there are agreements between the results for both adult and child populations (regarding non-carcinogenic and carcinogenic risks), it is worth highlighting that the risk scores for children were higher. Therefore, children in the study area are more vulnerable to both carcinogenic and non-carcinogenic health risks. Also, it was revealed that the risk due to ingestion was higher than that due to dermal contact. Linear regression analysis showed strong agreement between the indexical models and the cumulative health risks. While artificial neural networks and multiple linear regression models accurately predicted the water quality indices, hierarchical dendrograms efficiently classed the water samples into various spatiotemporal water quality groups.

Automated summary

The study revealed that 60% of water samples in Umunya, Nigeria were suitable for human consumption while 40% were not. Non-carcinogenic health risk scores indicated that 60% of the samples posed low to medium risks, while 40% posed high risks. Conversely, 6.67% of samples posed low carcinogenic risks, while 93.33% posed high risks, with children being more vulnerable to health risks than adults. Linear regression analysis showed strong agreement between index models and health risks, and artificial neural networks accurately predicted water quality indices.