{"title":"Predictive modeling of fluoride and nitrate health risks using artificial neural networks","authors":"Sidique Gawusu , Mahamuda Abu","doi":"10.1016/j.gsd.2025.101464","DOIUrl":null,"url":null,"abstract":"<div><div>The health risk associated with high levels of fluoride (F<sup>−</sup>) and nitrate (NO<sub>3</sub><sup>−</sup>) in groundwater is making groundwater unsafe for drinking globally. This concern has justified a continuous study and health risk assessment of groundwater. High levels of F<sup>−</sup> and NO<sub>3</sub><sup>−</sup> have been reported in some parts of the Bole District of Ghana. However, their health risks are unknown. Hence, the study assesses the health risks of F<sup>−</sup> and NO<sub>3</sub><sup>−</sup> using indexical proxies and machine learning techniques. Hydrochemical analysis reveals that while most parameters meet WHO standards, elevated fluoride, nitrate, and total dissolved solids (TDS) levels are present in certain communities. Fluoride risk classification shows that 83.3 % of the area faces very low risk, but 3.3 % is exposed to extreme fluoride risk. Health risk assessments using hazard quotient (HQ) indicate that adults are more vulnerable to fluoride exposure, with 53.3 % of samples exceeding safe levels, while 40 % of children are at risk from nitrate contamination. An artificial neural network (ANN) model was used to predict health risks associated with fluoride and nitrate exposure. The model performed well for fluoride predictions, achieving R<sup>2</sup> values above 0.97 for both training and testing datasets. However, its performance was less reliable for nitrate predictions, particularly for children, where the testing R<sup>2</sup> dropped to 0.60. Sensitivity analysis identified SO<sub>4</sub><sup>2−</sup>, Cl<sup>−</sup>, Na<sup>+</sup>, and Mg<sup>2+</sup> as key ions influencing health risk predictions. These results highlight the need for stricter regulation of groundwater sources in high-risk areas and the promotion of low-cost water treatment technologies to mitigate contamination.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"30 ","pages":"Article 101464"},"PeriodicalIF":4.9000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Groundwater for Sustainable Development","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352801X2500061X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
The health risk associated with high levels of fluoride (F−) and nitrate (NO3−) in groundwater is making groundwater unsafe for drinking globally. This concern has justified a continuous study and health risk assessment of groundwater. High levels of F− and NO3− have been reported in some parts of the Bole District of Ghana. However, their health risks are unknown. Hence, the study assesses the health risks of F− and NO3− using indexical proxies and machine learning techniques. Hydrochemical analysis reveals that while most parameters meet WHO standards, elevated fluoride, nitrate, and total dissolved solids (TDS) levels are present in certain communities. Fluoride risk classification shows that 83.3 % of the area faces very low risk, but 3.3 % is exposed to extreme fluoride risk. Health risk assessments using hazard quotient (HQ) indicate that adults are more vulnerable to fluoride exposure, with 53.3 % of samples exceeding safe levels, while 40 % of children are at risk from nitrate contamination. An artificial neural network (ANN) model was used to predict health risks associated with fluoride and nitrate exposure. The model performed well for fluoride predictions, achieving R2 values above 0.97 for both training and testing datasets. However, its performance was less reliable for nitrate predictions, particularly for children, where the testing R2 dropped to 0.60. Sensitivity analysis identified SO42−, Cl−, Na+, and Mg2+ as key ions influencing health risk predictions. These results highlight the need for stricter regulation of groundwater sources in high-risk areas and the promotion of low-cost water treatment technologies to mitigate contamination.
期刊介绍:
Groundwater for Sustainable Development is directed to different stakeholders and professionals, including government and non-governmental organizations, international funding agencies, universities, public water institutions, public health and other public/private sector professionals, and other relevant institutions. It is aimed at professionals, academics and students in the fields of disciplines such as: groundwater and its connection to surface hydrology and environment, soil sciences, engineering, ecology, microbiology, atmospheric sciences, analytical chemistry, hydro-engineering, water technology, environmental ethics, economics, public health, policy, as well as social sciences, legal disciplines, or any other area connected with water issues. The objectives of this journal are to facilitate: • The improvement of effective and sustainable management of water resources across the globe. • The improvement of human access to groundwater resources in adequate quantity and good quality. • The meeting of the increasing demand for drinking and irrigation water needed for food security to contribute to a social and economically sound human development. • The creation of a global inter- and multidisciplinary platform and forum to improve our understanding of groundwater resources and to advocate their effective and sustainable management and protection against contamination. • Interdisciplinary information exchange and to stimulate scientific research in the fields of groundwater related sciences and social and health sciences required to achieve the United Nations Millennium Development Goals for sustainable development.