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Water contamination across the European Mediterranean-Middle East, and North Africa (MENA) countries represents a persistent public health crisis driven by infrastructure deficiencies, regulatory gaps, and multi-pollutant exposure pathways affecting millions of vulnerable populations. Egypt was selected as a case study due to extreme contamination levels documented across chemical, biological, and emerging pollutant categories, with cadmium exposure causing 35,000 annual kidney failures, lead contamination responsible for 500 deaths per million population, and 97% of groundwater wells containing herbicide residues. This study aimed to discriminate regional water quality patterns through machine learning (ML) algorithms applied to 13 pollutant categories across 13 countries, establish definitive country pollution rankings, and stratify 22 Egyptian contaminants into validated severity-based risk groups, enabling monitoring optimization. Unsupervised principal component (PCA) analysis, K-Means clustering with silhouette validation, and multidimensional scaling (MDS) were applied to comprehensive datasets spanning heavy metals, pesticides, microplastics, pharmaceuticals, and biological pathogens. Regional analysis revealed a persistent European-MENA dichotomy, with Egypt and Iraq occupying extreme contamination positions while European Mediterranean countries clustered at minimal pollution levels with silhouette coefficients of 0.60–0.67. The Egyptian three-stage assessment identified nine very high-risk pollutants requiring immediate intervention, grouped contaminants into four monitoring frequency categories validated through tri-method convergence (silhouette values: 0.732–1.000), and confirmed cadmium as an exceptional outlier demanding emergency response. This integrated ML framework provides transferable methodologies applicable to developing regions experiencing resource-constrained water quality management challenges while generating evidence-based prioritization strategies optimizing limited monitoring and intervention resources across geographically diverse contamination contexts.

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Machine Learning Assessment of Water Pollution and Public Health Impacts Across European Mediterranean and MENA Countries: A Multivariate Analysis Considering Egypt as a Case Study

  • 章节

  • 发表日期：

    2026年

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  • Moustafa Gamal Snousy,
  • Ali Mahmoud Hussain,
  • Ashraf M. T. Elewa,
  • Jianhua Wu,
  • Alia A. El Shahawy,
  • Ahmed M. Saqr

摘要

Water contamination across the European Mediterranean-Middle East, and North Africa (MENA) countries represents a persistent public health crisis driven by infrastructure deficiencies, regulatory gaps, and multi-pollutant exposure pathways affecting millions of vulnerable populations. Egypt was selected as a case study due to extreme contamination levels documented across chemical, biological, and emerging pollutant categories, with cadmium exposure causing 35,000 annual kidney failures, lead contamination responsible for 500 deaths per million population, and 97% of groundwater wells containing herbicide residues. This study aimed to discriminate regional water quality patterns through machine learning (ML) algorithms applied to 13 pollutant categories across 13 countries, establish definitive country pollution rankings, and stratify 22 Egyptian contaminants into validated severity-based risk groups, enabling monitoring optimization. Unsupervised principal component (PCA) analysis, K-Means clustering with silhouette validation, and multidimensional scaling (MDS) were applied to comprehensive datasets spanning heavy metals, pesticides, microplastics, pharmaceuticals, and biological pathogens. Regional analysis revealed a persistent European-MENA dichotomy, with Egypt and Iraq occupying extreme contamination positions while European Mediterranean countries clustered at minimal pollution levels with silhouette coefficients of 0.60–0.67. The Egyptian three-stage assessment identified nine very high-risk pollutants requiring immediate intervention, grouped contaminants into four monitoring frequency categories validated through tri-method convergence (silhouette values: 0.732–1.000), and confirmed cadmium as an exceptional outlier demanding emergency response. This integrated ML framework provides transferable methodologies applicable to developing regions experiencing resource-constrained water quality management challenges while generating evidence-based prioritization strategies optimizing limited monitoring and intervention resources across geographically diverse contamination contexts.