Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Water Pollutants, Chemical*/analysis ; Groundwater*/chemistry ; Arsenic*/analysis; Humans ; Environmental Monitoring ; Fluorides

Determining the degree of high groundwater arsenic (As) and fluoride (F ^- ) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F ^- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F ^- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.
(© 2024. The Author(s).)

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Grant: 3,422 King Faisal University

0 (Water Pollutants, Chemical)
N712M78A8G (Arsenic)
Q80VPU408O (Fluorides)

Date Created: 20240113 Date Completed: 20240115 Latest Revision: 20240116

20240116

PMC10787756

10.1038/s41598-024-51917-8

38218993