Predictive modeling of fluoride and nitrate health risks using artificial neural networks

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL
Sidique Gawusu , Mahamuda Abu
{"title":"Predictive modeling of fluoride and nitrate health risks using artificial neural networks","authors":"Sidique Gawusu ,&nbsp;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.
基于人工神经网络的氟化物和硝酸盐健康风险预测模型
地下水中氟化物(F -)和硝酸盐(NO3 -)含量高所带来的健康风险使全球地下水不适于饮用。这一关切为对地下水进行持续研究和健康风险评估提供了理由。据报道,在加纳博莱地区的一些地区,F -和NO3 -的含量很高。然而,他们的健康风险是未知的。因此,该研究使用索引代理和机器学习技术评估了F -和NO3 -的健康风险。水化学分析显示,虽然大多数参数符合世卫组织标准,但某些社区存在氟化物、硝酸盐和总溶解固体(TDS)水平升高的情况。氟化物风险分类显示,83.3%的地区面临极低风险,3.3%的地区面临极端氟化物风险。使用危害商数(HQ)进行的健康风险评估表明,成年人更容易受到氟化物的影响,53.3%的样本超过安全水平,而40%的儿童面临硝酸盐污染的风险。使用人工神经网络(ANN)模型预测氟化物和硝酸盐暴露相关的健康风险。该模型在氟化物预测方面表现良好,训练和测试数据集的R2值均高于0.97。然而,它在硝酸盐预测方面的表现不太可靠,尤其是对儿童,测试R2降至0.60。敏感性分析发现,SO42−、Cl−、Na+和Mg2+是影响健康风险预测的关键离子。这些结果突出表明,需要对高风险地区的地下水资源进行更严格的管理,并推广低成本的水处理技术来减轻污染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Groundwater for Sustainable Development
Groundwater for Sustainable Development Social Sciences-Geography, Planning and Development
CiteScore
11.50
自引率
10.20%
发文量
152
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信