Original Research Article

Groundwater Hydrogeochemistry, Uranium Mobility and Human Health Risk in the Mikir Hills, Assam, India: An Integrated Multivariate, Machine-Learning and Risk-Assessment Framework

ISSN 2979-8582  ·  Article No. 015

Ashokaditya P. Dhurandhar✉

Publication Details

Publication Date
10/11/2026
Volume / Issue
Vol 1, Issue 6 (2026)
Article No.
015
Journal
British Journal of Contemporary Research
Received
10 Oct 2026
Views
22
Downloads
10
Affiliations

Ashokaditya P. Dhurandhar: Orion Geohytech India, G-10 Brahmaputra Apartment, Aakar Nagar, Katol Road, Nagpur, India

ORCID

Ashokaditya P. Dhurandhar 0000-0002-9948-1937

Abstract

Groundwater in the Mikir Hills of Assam, India, occurs within a complexly layered lithostratigraphic framework spanning Archaean basement gneisses to Quaternary alluvium. To characterise the hydrogeochemical controls on major ions, trace metals, and uranium (U) behaviour, a dataset of 143 groundwater samples across 16 core parameters (U, Na, K, Ca, Mg, Cl, SO₄²⁻, HCO₃⁻ , pH, electrical conductivity, TDS, Cu, Pb, Zn, Ni, and Fe) was evaluated using an integrated multivariate, geochemical, and machine-learning framework. Hydrochemical facies analysis demonstrates that groundwaters are predominantly of Ca–Mg–HCO₃⁻ (52%) and mixed Ca–Na–HCO₃⁻ (30%) types, with Gibbs formulations confirming rock-weathering dominance alongside negative chloro-alkaline indices denoting reverse ion exchange. Speciation modeling indicates widespread supersaturation with respect to calcite (SI=+0.42) and dolomite (SI=+0.77), confirming that carbonate buffering establishes circumneutral-to-alkaline conditions. Nonparametric correlation and principal component analysis (PCA; four components explaining 67.2% of total variance) show that U is decoupled from bulk mineralisation (loading −0.04 on PC1) and instead loads on PC3 (0.64) and PC4 (0.35) alongside Cu, Fe, and Zn. Random Forest (RF) regression reveals negative cross-validated performance (R2=−0.077), whereas RF classification effectively discriminates upper-quartile U concentrations (>0.50 μg/L) with a ROC-AUC of 0.794. The Entropy-Weighted Water Quality Index (EWQI) identifies 38% of samples within poor-to-very-poor categories, driven predominantly by Fe and localized trace elements. Chemical health-risk assessment establishesnon-carcinogenic hazard quotients (HQ) exceeding 1.0 in 19% of adult and 31% of child cohorts, with cumulative hazard indices (HI>1.0) reaching 47% and 58%, respectively. These findings demonstrate that U migration is governed by redox-mediated Fe–Mn cycling, carbonate ligand complexation, and structural fracture conduits rather than bulk salinity. Identification of overlapping anomalies in high-strain corridors and fault zones—specifically at the Akashiganga–Udganga Convergence—host a significant alignment of peak gaseous Radon (>1100 counts/50 s) alongside Aqueous Radium (Ra) and Aqueous Uranium (U) anomalies, designating this convergence zone as the prime vector target for concealed mineralization.

Keywords

Groundwater Hydrogeochemistry Uranium Mobility Hydrochemical Facies Mineral Saturation Index Entropy-Weighted Water Quality Index Human Health Risk Assessment Random Forest Classification Mikir Hills

License

CC BY 4.0

This article is published under the Creative Commons Attribution 4.0 International License . Free to read, share, and adapt with attribution.

Cite This Article

Ashokaditya P. Dhurandhar (2026). Groundwater Hydrogeochemistry, Uranium Mobility and Human Health Risk in the Mikir Hills, Assam, India: An Integrated Multivariate, Machine-Learning and Risk-Assessment Framework. British Journal of Contemporary Research, 1(6), Article 015. https://doi.org/10.67693/BJCR-6UTHFG7U
Ashokaditya P. Dhurandhar. “Groundwater Hydrogeochemistry, Uranium Mobility and Human Health Risk in the Mikir Hills, Assam, India: An Integrated Multivariate, Machine-Learning and Risk-Assessment Framework.” British Journal of Contemporary Research, vol. 1, no. 6, 2026.
Ashokaditya P. Dhurandhar. “Groundwater Hydrogeochemistry, Uranium Mobility and Human Health Risk in the Mikir Hills, Assam, India: An Integrated Multivariate, Machine-Learning and Risk-Assessment Framework.” British Journal of Contemporary Research 1, no. 6.

Metadata

ISSN 2979-8582
DOI Prefix 10.67693
Tracking ID BEX_OCT_26_089

British Journal of Contemporary Research

Open Access · Peer Reviewed · Published by Bexford Publishing Ltd

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