article · Water
Surface water quality was evaluated across 55 sites in the northern Nile Delta using a combination of the drinking water quality index, four pollution indices, and multivariate statistical modelling. Laboratory analysis of 22 physicochemical parameters revealed that only 33 percent of the sampled water was suitable for drinking, whilst 67 percent ranged from poor to unsuitable. Heavy metal contamination was largely driven by lead and manganese, alongside lesser impacts from iron and chromium. Statistical analysis incorporated principal component analysis, partial least squares regression, and stepwise multiple linear regression. Stepwise multiple linear regression models, incorporating major ions and heavy metals, provided the most precise predictions of water quality and pollution indices, achieving an R-squared value of one. Integrating these indices with chemometric modelling provides a robust methodology for monitoring and managing surface water contamination.
Access to safe drinking water depends on accurate surface water monitoring and management, particularly in developing regions. Identifying that the majority of sampled local water is unsuitable for drinking, alongside highlighting specific toxic contaminants such as lead, provides environmental managers and public health bodies with clear evidence to prioritise targeted water treatment and pollution remediation strategies.
The chemometric modelling approaches, specifically stepwise multiple linear regression and partial least squares regression, could be integrated into environmental monitoring software used by municipal water utilities and environmental regulators. Because the predictive models demonstrated high statistical accuracy when tested on regional water samples, the method represents applied research that could be developed into automated water quality assessment tools.
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Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.
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DOI: 10.3390/w12082142
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