The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arj...The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin,South India.Fluoride levels in the study area vary between 0.1 and 3.10 mg/L,with 32 samples exceeding the World Health Organization(WHO)standard of 1.5 mg/L.Hydrogeochemical analyses(Durov and Gibbs)clearly show that the overall water chemistry is primarily influenced by simple dissolution,mixing,and rock-water interactions,indicating that geogenic sources are the predominant contributors to fluoride in the study area.Around 446.5 km^(2)is considered at risk.In predictive analysis,five Machine Learning(ML)models were used,with the AdaBoost model performing better than the other models,achieving 96%accuracy and 4%error rate.The Traditional Health Risk Assessment(THRA)results indicate that 65%of samples pose highly susceptible for dental fluorosis,while 12%of samples pose highly susceptible for skeletal fluorosis in young age groups.The Fuzzy Inference System(FIS)model effectively manages ambiguity and linguistic factors,which are crucial when addressing health risks linked to groundwater fluoride contamination.In this model,input variables include fluoride concentration,individual age,and ingestion rate,while output variables consist of dental caries risk,dental fluorosis,and skeletal fluorosis.The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis,along with very young and young age groups.This study is an essential resource for local authorities,healthcare officials,and communities,aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment,aligning with Sustainable Development Goals(SDGs)3 and 6,thereby contributing to a cleaner and healthier society.展开更多
This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)perio...This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)periods.Seasonal variations significantly influence the groundwater quality,particularly fluoride(F−)concentrations,which can fluctuate due to changes in recharge,evaporation,and anthropogenic activities.This study assesses the dynamics of F−levels in PRM and POM seasons,and identifies elevated health risks using USEPA guidelines and Monte Carlo Simulations(MCS).Groundwater in the study area exhibits alkaline pH,with NaCl and Ca-Na-HCO_(3) facies increasing in the POM season due to intensified ion exchange and rock-water interactions,as indicated in Piper and Gibb’s diagrams.Correlation and dendrogram analyses indicate that F−contamination is from geogenic and anthropogenic sources.F−levels exceed the WHO limit(1.5 mg/L)in 51 PRM and 28 POM samples,affecting 371.74 km^(2) and 203.05 km^(2),respectively.Geochemical processes,including mineral weathering,cation exchange,evaporation,and dilution,are identified through CAI I&II.Health risk assessments reveal that HQ values>1 in 78%of children,73%of teens,and 68%of adults during PRM,decreasing to 45%,40%,and 38%,respectively,in POM.MCS show maximum HQ values of 5.67(PRM)and 4.73(POM)in children,with all age groups facing significant risks from fluoride ingestion.Managed Aquifer Recharge(MAR)is recommended in this study to minimize F−contamination,ensuring safe drinking water for the community.展开更多
基金the Anusandhan National Research Foundation(ANRF),New Delhi[Erstwhile,Science and Engineering Research Board(SERB)]Department of Science and Technology(DST)(Government of India)(File No.:CRG/2022/002618 Dated:22.08.2023)for providing the grant and support to carry out this work effectively.
文摘The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin,South India.Fluoride levels in the study area vary between 0.1 and 3.10 mg/L,with 32 samples exceeding the World Health Organization(WHO)standard of 1.5 mg/L.Hydrogeochemical analyses(Durov and Gibbs)clearly show that the overall water chemistry is primarily influenced by simple dissolution,mixing,and rock-water interactions,indicating that geogenic sources are the predominant contributors to fluoride in the study area.Around 446.5 km^(2)is considered at risk.In predictive analysis,five Machine Learning(ML)models were used,with the AdaBoost model performing better than the other models,achieving 96%accuracy and 4%error rate.The Traditional Health Risk Assessment(THRA)results indicate that 65%of samples pose highly susceptible for dental fluorosis,while 12%of samples pose highly susceptible for skeletal fluorosis in young age groups.The Fuzzy Inference System(FIS)model effectively manages ambiguity and linguistic factors,which are crucial when addressing health risks linked to groundwater fluoride contamination.In this model,input variables include fluoride concentration,individual age,and ingestion rate,while output variables consist of dental caries risk,dental fluorosis,and skeletal fluorosis.The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis,along with very young and young age groups.This study is an essential resource for local authorities,healthcare officials,and communities,aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment,aligning with Sustainable Development Goals(SDGs)3 and 6,thereby contributing to a cleaner and healthier society.
文摘This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)periods.Seasonal variations significantly influence the groundwater quality,particularly fluoride(F−)concentrations,which can fluctuate due to changes in recharge,evaporation,and anthropogenic activities.This study assesses the dynamics of F−levels in PRM and POM seasons,and identifies elevated health risks using USEPA guidelines and Monte Carlo Simulations(MCS).Groundwater in the study area exhibits alkaline pH,with NaCl and Ca-Na-HCO_(3) facies increasing in the POM season due to intensified ion exchange and rock-water interactions,as indicated in Piper and Gibb’s diagrams.Correlation and dendrogram analyses indicate that F−contamination is from geogenic and anthropogenic sources.F−levels exceed the WHO limit(1.5 mg/L)in 51 PRM and 28 POM samples,affecting 371.74 km^(2) and 203.05 km^(2),respectively.Geochemical processes,including mineral weathering,cation exchange,evaporation,and dilution,are identified through CAI I&II.Health risk assessments reveal that HQ values>1 in 78%of children,73%of teens,and 68%of adults during PRM,decreasing to 45%,40%,and 38%,respectively,in POM.MCS show maximum HQ values of 5.67(PRM)and 4.73(POM)in children,with all age groups facing significant risks from fluoride ingestion.Managed Aquifer Recharge(MAR)is recommended in this study to minimize F−contamination,ensuring safe drinking water for the community.
