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Using Unsupervised Machine Learning Models To Drive Groundwater Chemistry and Associated Health Risks in Indo-Bangla Sundarban Region

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dc.contributor.author Jannat, Jannatun Nahar
dc.contributor.author Islam, Abu Reza Md Towfiqul
dc.contributor.author Mia, Md Yousuf
dc.contributor.author Pal, Subodh Chandra
dc.contributor.author Biswas, Tanmoy
dc.contributor.author Jion, Most Mastura Munia Farjana
dc.contributor.author Islam, Md Saiful
dc.contributor.author Siddique, Md Abu Bakar
dc.contributor.author Idris, Abubakr M.
dc.contributor.author Khan, Rahat
dc.contributor.author Islam, Aznarul
dc.contributor.author Kormoker, Tapos
dc.contributor.author Senapathi, Venkatramanan
dc.date.accessioned 2024-10-03T06:28:56Z
dc.date.available 2024-10-03T06:28:56Z
dc.date.issued 2024-03-20
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/13483
dc.description.abstract Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl−-Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3− poses a higher PN-CHR risk to human health than F− and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3− emissions in the Indo-Bangla Sundarbans region. en_US
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Groundwater en_US
dc.subject Sundarban en_US
dc.subject Health risks en_US
dc.title Using Unsupervised Machine Learning Models To Drive Groundwater Chemistry and Associated Health Risks in Indo-Bangla Sundarban Region en_US
dc.type Article en_US


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