Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Jannat, Jannatun Nahar; Islam, Abu Reza Md Towfiqul; Mia, Md Yousuf; Pal, Subodh Chandra; Biswas, Tanmoy; Jion, Most Mastura Munia Farjana; Islam, Md Saiful; Siddique, Md Abu Bakar; Idris, Abubakr M.; Khan, Rahat; Islam, Aznarul; Kormoker, Tapos; Senapathi, Venkatramanan

doi:10.1016/j.chemosphere.2024.141217

Chemosphere

2024

DOI: 10.1016/j.chemosphere.2024.141217

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Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Jannatun Nahar Jannat,

Abu Reza Md Towfiqul Islam,

Md Yousuf Mia

et al.

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Cited by 11 publications

References 128 publications

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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

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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.

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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

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Geospatial mapping and entropy-based analysis for groundwater evaluation with estimation of potential health risks due to nitrate and fluoride exposure

Kumar,

Singh

2024

Environ Sci Pollut Res

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Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks

Yang,

Jia,

Yao

et al. 2024

Chemosphere

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Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Cited by 11 publications

References 128 publications

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

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

Geospatial mapping and entropy-based analysis for groundwater evaluation with estimation of potential health risks due to nitrate and fluoride exposure

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks

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