V. Gopalakrishnan, K. Srinivasamoorthy, A. Rajesh Kanna, C. Babu, K. Ramesh, D. Supriya Varshini, P. Muhammed Farsin, Aleena G. Raj
{"title":"解码沿海含水层的营养动态:印度南部海底地下水排放和海水入侵的机器学习见解","authors":"V. Gopalakrishnan, K. Srinivasamoorthy, A. Rajesh Kanna, C. Babu, K. Ramesh, D. Supriya Varshini, P. Muhammed Farsin, Aleena G. Raj","doi":"10.1016/j.chemosphere.2025.144521","DOIUrl":null,"url":null,"abstract":"<div><div>Coastal aquifers are vulnerable to natural and human-induced processes that impact their resilience and ecosystems. Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) play crucial roles in transporting nutrients and contaminants into coastal waters and threatening coastal aquifers, respectively. This study aims to characterize hydrogeochemical processes governing SGD and SWI using an integrated machine learning (ML) algorithm, overcoming limitations of traditional geochemical methods in analysing complex, nonlinear, and high-dimensional hydrogeochemical datasets. The ML framework, integrating statistical and geochemical analyses, was applied to Ramanathapuram and Rameswaram Island coastal aquifers. Spearman correlation analysis identified key indicators of seawater influence, anthropogenic inputs, redox reactions, dissolution, and ion exchange. Self-Organizing Maps (SOM) and Fuzzy C-Means (FCM) clustering revealed hydrogeochemical patterns in groundwater (GW) and porewater (PW). In GW, Group 1 (27 %) indicated pollution from agricultural NO<sub>3</sub><sup>−</sup>, Group 2 (60 %) represented long-residence freshwater with high DSi and reduced NO<sub>3</sub><sup>−</sup> under clay-layer redox conditions, and Group 3 (13 %) contained high-salinity GW impacted by SWI and saline traps. In PW, Group 1 (11 %) reflected fresh SGD with high DSi and NH<sub>4</sub><sup>+</sup>, Group 2 (68 %) showed SW dominance in intertidal zones, and Group 3 (21 %) represented recirculated SGD enriched in salinity and nutrients due to ion exchange and desorption reactions. Factor analysis clarified hydrogeochemical drivers such as anthropogenic inputs, silicate dissolution, redox reactions, and SW interactions, while ionic ratios (Na/Cl, NO<sub>3</sub><sup>−</sup>/Cl) and delta-analysis geochemically supported these findings. This ML-based approach enhances SGD identification and SWI assessment, offering a novel methodology for coastal aquifer management and ecosystem protection.</div></div>","PeriodicalId":276,"journal":{"name":"Chemosphere","volume":"385 ","pages":"Article 144521"},"PeriodicalIF":8.1000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Decoding nutrient dynamics in coastal aquifers: Machine learning insights into submarine groundwater discharge and seawater intrusion in south India\",\"authors\":\"V. Gopalakrishnan, K. Srinivasamoorthy, A. Rajesh Kanna, C. Babu, K. Ramesh, D. Supriya Varshini, P. Muhammed Farsin, Aleena G. Raj\",\"doi\":\"10.1016/j.chemosphere.2025.144521\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Coastal aquifers are vulnerable to natural and human-induced processes that impact their resilience and ecosystems. Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) play crucial roles in transporting nutrients and contaminants into coastal waters and threatening coastal aquifers, respectively. This study aims to characterize hydrogeochemical processes governing SGD and SWI using an integrated machine learning (ML) algorithm, overcoming limitations of traditional geochemical methods in analysing complex, nonlinear, and high-dimensional hydrogeochemical datasets. The ML framework, integrating statistical and geochemical analyses, was applied to Ramanathapuram and Rameswaram Island coastal aquifers. Spearman correlation analysis identified key indicators of seawater influence, anthropogenic inputs, redox reactions, dissolution, and ion exchange. Self-Organizing Maps (SOM) and Fuzzy C-Means (FCM) clustering revealed hydrogeochemical patterns in groundwater (GW) and porewater (PW). In GW, Group 1 (27 %) indicated pollution from agricultural NO<sub>3</sub><sup>−</sup>, Group 2 (60 %) represented long-residence freshwater with high DSi and reduced NO<sub>3</sub><sup>−</sup> under clay-layer redox conditions, and Group 3 (13 %) contained high-salinity GW impacted by SWI and saline traps. In PW, Group 1 (11 %) reflected fresh SGD with high DSi and NH<sub>4</sub><sup>+</sup>, Group 2 (68 %) showed SW dominance in intertidal zones, and Group 3 (21 %) represented recirculated SGD enriched in salinity and nutrients due to ion exchange and desorption reactions. Factor analysis clarified hydrogeochemical drivers such as anthropogenic inputs, silicate dissolution, redox reactions, and SW interactions, while ionic ratios (Na/Cl, NO<sub>3</sub><sup>−</sup>/Cl) and delta-analysis geochemically supported these findings. This ML-based approach enhances SGD identification and SWI assessment, offering a novel methodology for coastal aquifer management and ecosystem protection.</div></div>\",\"PeriodicalId\":276,\"journal\":{\"name\":\"Chemosphere\",\"volume\":\"385 \",\"pages\":\"Article 144521\"},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemosphere\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0045653525004655\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemosphere","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045653525004655","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Decoding nutrient dynamics in coastal aquifers: Machine learning insights into submarine groundwater discharge and seawater intrusion in south India
Coastal aquifers are vulnerable to natural and human-induced processes that impact their resilience and ecosystems. Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) play crucial roles in transporting nutrients and contaminants into coastal waters and threatening coastal aquifers, respectively. This study aims to characterize hydrogeochemical processes governing SGD and SWI using an integrated machine learning (ML) algorithm, overcoming limitations of traditional geochemical methods in analysing complex, nonlinear, and high-dimensional hydrogeochemical datasets. The ML framework, integrating statistical and geochemical analyses, was applied to Ramanathapuram and Rameswaram Island coastal aquifers. Spearman correlation analysis identified key indicators of seawater influence, anthropogenic inputs, redox reactions, dissolution, and ion exchange. Self-Organizing Maps (SOM) and Fuzzy C-Means (FCM) clustering revealed hydrogeochemical patterns in groundwater (GW) and porewater (PW). In GW, Group 1 (27 %) indicated pollution from agricultural NO3−, Group 2 (60 %) represented long-residence freshwater with high DSi and reduced NO3− under clay-layer redox conditions, and Group 3 (13 %) contained high-salinity GW impacted by SWI and saline traps. In PW, Group 1 (11 %) reflected fresh SGD with high DSi and NH4+, Group 2 (68 %) showed SW dominance in intertidal zones, and Group 3 (21 %) represented recirculated SGD enriched in salinity and nutrients due to ion exchange and desorption reactions. Factor analysis clarified hydrogeochemical drivers such as anthropogenic inputs, silicate dissolution, redox reactions, and SW interactions, while ionic ratios (Na/Cl, NO3−/Cl) and delta-analysis geochemically supported these findings. This ML-based approach enhances SGD identification and SWI assessment, offering a novel methodology for coastal aquifer management and ecosystem protection.
期刊介绍:
Chemosphere, being an international multidisciplinary journal, is dedicated to publishing original communications and review articles on chemicals in the environment. The scope covers a wide range of topics, including the identification, quantification, behavior, fate, toxicology, treatment, and remediation of chemicals in the bio-, hydro-, litho-, and atmosphere, ensuring the broad dissemination of research in this field.