Groundwater hydrochemical signatures, nitrate sources, and potential health risks in a typical karst catchment of North China using hydrochemistry and multiple stable isotopes

Wang, Shou; Chen, Jing; Zhang, Shuxuan; Bai, Yanjie; Zhang, Xiaoyan; Chen, Dan; Hu, Jiahong

doi:10.1007/s10653-024-01964-x

Environ Geochem Health

2024

DOI: 10.1007/s10653-024-01964-x

|View full text |Cite

Groundwater hydrochemical signatures, nitrate sources, and potential health risks in a typical karst catchment of North China using hydrochemistry and multiple stable isotopes

Shou Wang,

Jing Chen,

Shuxuan Zhang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

Liu,

Li,

Zhang

et al. 2024

Water

View full text Add to dashboard Cite

This study employed hydrochemical data, traditional hydrogeochemical methods, inverse hydrogeochemical modeling, and unsupervised machine learning techniques to explore the hydrogeochemical traits and origins of groundwater in the Changbai Mountain region. (1) Findings reveal that predominant hydrochemical types include HCO3−Ca·Mg, HCO3−Ca·Na·Mg, HCO3−Mg·Na, and HCO3−Na·Mg. The average metasilicic acid content was found to be at 49.13 mg/L. (2) Rock weathering mechanisms, particularly silicate mineral weathering, primarily shape groundwater chemistry, followed by carbonate dissolution. (3) Water-rock interactions involve volcanic mineral dissolution and cation exchange adsorption. Inverse hydrogeochemical modeling, alongside analysis of the widespread volcanic lithology, underscores the complexity of groundwater reactions, influenced not only by water-rock interactions but also by evaporation and precipitation. (4) Unsupervised machine learning, integrating SOM, PCA, and K-means techniques, elucidates hydrochemical types. SOM component maps reveal a close combination of various hydrochemical components. Principal component analysis (PCA) identifies the first principal component (PC1), explaining 48.15% of the variance. The second (PC2) and third (PC3) principal components, explain 13.2% and 10.8% of the variance, respectively. K clustering categorized samples into three main clusters: one less influenced by basaltic geological processes, another showing strong igneous rock weathering characteristics, and the third affected by other geological processes or anthropogenic factors.

show abstract

Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

Liu,

Li,

Zhang

et al. 2024

Water

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Groundwater hydrochemical signatures, nitrate sources, and potential health risks in a typical karst catchment of North China using hydrochemistry and multiple stable isotopes

Cited by 1 publication

References 68 publications

Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

Contact Info

Product

Resources

About