Spatial and Statistical Characterization of Arsenic Contamination in Soil–Water–Tea Systems of the Titabor Tea Belt, Assam, India

IJEP 46(9): 870-876 : Vol. 46 Issue 9 (September 2026)

Full Text

Akash Dipta Thakur1, Saibal Ghosh2, Bishwapran Kashyap2, Jibon Saikia1 and Ajanto Kumar Hazarika3*

1. Jagannath Barooah College, Department of Physics, Jorhat – 785 001, Assam, India
2. Tocklai Tea Research Institute, Analytical Services Department, Jorhat – 785 008, Assam, India
3. Tocklai Tea Research Institute, Tea Processing and Manufacturing Advisory Department, Jorhat – 785 008, Assam, India

Abstract

This study examines arsenic (As) contamination in soil, groundwater and tea leaves across tea-growing regions of the Titabor block in Jorhat district, Assam, India. Arsenic concentrations were measured in groundwater, soil and tea leaves (mature and two-leaf-and-bud samples) to assess contamination levels and potential impacts on tea quality. The mean As levels were 0.069 mg/L in groundwater and 0.31 mg/kg in soil, with soil showing significantly higher concentrations. In tea leaves, mean As levels were 0.74 mg/kg in mature leaves and 0.46 mg/kg in two-leaf-and-bud samples, indicating greater accumulation in older leaves. Positive correlations were observed among As levels in groundwater, soil and tea leaves. Sensitivity analysis using Sobol’s method and artificial neural network (ANN) models identified soil arsenic as the dominant factor influencing As uptake in tea plants. Spatial mapping using inverse distance weighting (IDW) showed elevated groundwater As in southwestern regions and higher soil As in northwestern and northeastern areas. The findings highlight the need for region-specific soil management and water monitoring strategies to ensure tea safety and protect the sustainability of Assam’s tea industry.

Keywords

Arsenic contamination, Soil and groundwater pollution, Tea quality, Artificial neural network, Sobol spatial distribution

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