Spatial distribution characteristics and prediction of fluorine concentration in groundwater based on driving factors analysis

Lu, Muyuan; Liu, Yuan; Liu, Guijian; Li, Yongli; Xu, Jingyun; Wang, Guanyu

doi:10.1016/j.scitotenv.2022.159415

Cited by 11 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combination of SOM with different clustering methods can surpass single analysis methods and provide superior classification results, such as K-means clustering (Zhang et al, 2023b), hierarchical clustering (Kim et al, 2020) and fuzzy C-means clustering (Yao et al, 2022). Based on the classification results, conventional groundwater chemical characterization methods such as Piper diagrams, Gibbs diagrams, ion ratio analysis and mineral saturation indices were used to elucidate the hydrochemical characteristics and hydrochemical processes (Gao et al, 2022, Lu et al, 2023. The spatial distribution of water hydrochemical components based on inverse distance weight interpolation is essential to understand the distribution and evolution of groundwater contamination at spatial scales (Nsabimana and Li, 2023).…”

Section: Self-organizing Map (Som) With K-means Clusteringmentioning

confidence: 99%

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, health risk model

Yan,

Zhang,

Yao

et al. 2024

Preprint

View full text Add to dashboard Cite

An in-depth understanding of nitrate-contaminated groundwater quality and risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices and human health risk model. Groundwater samples were subdivided into two clusters by SOM analysis. Cluster Ⅰ including three samples were Ca-SO4 type and cluster Ⅱ of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for Cluster I samples, and (2) calcite dissolution, silicate weathering and positive cation exchange for Cluster Ⅱ samples. Nitrate contamination occurred in both Cluster I and Ⅱ samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis.

show abstract

Section: Self-organizing Map (Som) With K-means Clusteringmentioning

confidence: 99%

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, health risk model

Yan,

Zhang,

Yao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Hydrochemistry, quality, and integrated health risk assessments of groundwater in the Huaibei Plain, China

Xu,

Liu,

Liu

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model

Yan,

Zhang,

Yao

et al. 2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

An in-depth understanding of nitrate-contaminated groundwater quality and risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices and human health risk model. Groundwater samples were subdivided into two clusters by SOM analysis. Cluster Ⅰ including three samples were Ca-SO4 type and cluster Ⅱ of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for Cluster I samples, and (2) calcite dissolution, silicate weathering and positive cation exchange for Cluster Ⅱ samples. Nitrate contamination occurred in both Cluster I and Ⅱ samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children.Exposure frequency was the most sensitive factor (86.33% in total) influencing the total noncarcinogenic risk, indicated by sensitivity analysis.

show abstract

Spatial distribution characteristics and prediction of fluorine concentration in groundwater based on driving factors analysis

Cited by 11 publications

References 52 publications

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, health risk model

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, health risk model

Hydrochemistry, quality, and integrated health risk assessments of groundwater in the Huaibei Plain, China

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model

Contact Info

Product

Resources

About