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<p>Groundwater is a major source of domestic water in coastal Ghana, but its quality is increasingly threatened by salinisation, nutrient enrichment, geogenic mineralisation, and localised anthropogenic contamination. In addition to hydrochemical indices and multivariate statistics that have been used over the years to assess coastal water quality, this study has incorporated nonlinear machine learning and probabilistic risk assessment to enhance source discrimination and uncertainty-based health risk characterisation of groundwater along central coastal zone of Ghana. This study aimed to evaluate groundwater quality, understand the predominant hydrogeochemical and anthropogenic controls on it, and assess the non-carcinogenic health risks to adults, children, and infants. This has become necessary as more indigens are now relying on groundwater for their domestic use due to fluctuations in the national water delivery. 432 groundwater samples from wells were examined for physicochemical parameters, major ions, nutrients and trace metals. Hydrochemical indices, Principal Component Analysis, Self-Organising Maps and probabilistic Human Health Risk Assessment with Monte Carlo simulation were employed. The results revealed high hydrochemical variability in EC (94.6–52,700 µS/cm), TDS (5–53,925&#xa0;mg/l), and chloride concentration (1–22,433&#xa0;mg/l) among samples collected from the study area, indicating that hydrochemical salinisation and mineralisation are very pronounced. Some sites had nitrate and fluoride above the WHO drinking-water guidelines. The Weighted Average Water Quality Index classified 73.49% of samples as excellent to good, while the Comprehensive Pollution Index indicated that 76.10% of the samples were slightly to heavily polluted. The Water Nutrient Pollution Index classified 70.48% of samples as considerably to extremely polluted. In contrast, the Heavy Metal Evaluation Index classified 91.37% of the samples as having low heavy metal contamination. Five components were extracted by PCA, explaining 64.25% of the total variance; salinity and mineralisation explained 32.51%. SOM recognised two hydrogeochemical clusters, reflecting the major water–rock interaction and the minor anthropogenic and redox effects. The probabilistic health risk assessment indicated low non-carcinogenic risk for adults (mean HI = 0.38, 3.05% exceedance) and high risk for children (mean HI = 1.04, 32.11% exceedance) and infants (mean HI = 1.10, 36.49% exceedance). The most important factor in risk variability was fluoride. Results indicate that groundwater quality is primarily affected by geogenic mineralisation, coastal salinisation, and increasing anthropogenic nutrient inputs, and that the risk to groundwater is higher for vulnerable populations. In high-risk communities, groundwater should be monitored routinely, nutrient-source control measures should be implemented, wellhead protection measures should be applied, and targeted defluoridation measures should be implemented.</p>

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Determining groundwater quality and its associated human health risk using hydrochemical signatures and some machine learning techniques

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Maxwell Anim-Gyampo,
  • Millicent Obeng Addai,
  • Raymond Webrah Kazapoe,
  • Musah Saeed Zango,
  • Stanley Yaw Blankson,
  • Ebenezer Ebo Yahans Amuah,
  • Samuel Dzidefo Sagoe,
  • Belinda Seyram Berdie

摘要

Groundwater is a major source of domestic water in coastal Ghana, but its quality is increasingly threatened by salinisation, nutrient enrichment, geogenic mineralisation, and localised anthropogenic contamination. In addition to hydrochemical indices and multivariate statistics that have been used over the years to assess coastal water quality, this study has incorporated nonlinear machine learning and probabilistic risk assessment to enhance source discrimination and uncertainty-based health risk characterisation of groundwater along central coastal zone of Ghana. This study aimed to evaluate groundwater quality, understand the predominant hydrogeochemical and anthropogenic controls on it, and assess the non-carcinogenic health risks to adults, children, and infants. This has become necessary as more indigens are now relying on groundwater for their domestic use due to fluctuations in the national water delivery. 432 groundwater samples from wells were examined for physicochemical parameters, major ions, nutrients and trace metals. Hydrochemical indices, Principal Component Analysis, Self-Organising Maps and probabilistic Human Health Risk Assessment with Monte Carlo simulation were employed. The results revealed high hydrochemical variability in EC (94.6–52,700 µS/cm), TDS (5–53,925 mg/l), and chloride concentration (1–22,433 mg/l) among samples collected from the study area, indicating that hydrochemical salinisation and mineralisation are very pronounced. Some sites had nitrate and fluoride above the WHO drinking-water guidelines. The Weighted Average Water Quality Index classified 73.49% of samples as excellent to good, while the Comprehensive Pollution Index indicated that 76.10% of the samples were slightly to heavily polluted. The Water Nutrient Pollution Index classified 70.48% of samples as considerably to extremely polluted. In contrast, the Heavy Metal Evaluation Index classified 91.37% of the samples as having low heavy metal contamination. Five components were extracted by PCA, explaining 64.25% of the total variance; salinity and mineralisation explained 32.51%. SOM recognised two hydrogeochemical clusters, reflecting the major water–rock interaction and the minor anthropogenic and redox effects. The probabilistic health risk assessment indicated low non-carcinogenic risk for adults (mean HI = 0.38, 3.05% exceedance) and high risk for children (mean HI = 1.04, 32.11% exceedance) and infants (mean HI = 1.10, 36.49% exceedance). The most important factor in risk variability was fluoride. Results indicate that groundwater quality is primarily affected by geogenic mineralisation, coastal salinisation, and increasing anthropogenic nutrient inputs, and that the risk to groundwater is higher for vulnerable populations. In high-risk communities, groundwater should be monitored routinely, nutrient-source control measures should be implemented, wellhead protection measures should be applied, and targeted defluoridation measures should be implemented.