A Multifaceted Approach Assessing Heavy Metal Contamination and Health Risk in Tea Garden Soils: Insight Through Hot-Spot Analysis and Machine Learning Techniques

Abstract

Tea is globally esteemed for its economic worth and health benefits, yet heavy metal (HMs) pollution in tea garden soil poses a severe threat to the environment. Implementing multimodal statistical approach, the current study has provided insight into contaminations, risk indices, and health hazards associated with HMs pollution in tea soil. 100 surface soil (0–15 cm) samples were collected from four geographically distinct zones, i.e., zone 1 (North Dinajpur), zone 2 (Cooch Behar), zone 3 (Jalpaiguri), and zone 4 (Darjeeling). The findings revealed that the total HMs concentration exceeded permissible limits in all four zones, highlighting zone 1 as the most contaminated area with a pollution index of 2.06 and a contamination index of 5.06. The acidic pH (3.91–5.08) was identified as a crucial factor causing the accumulation of HMs in the soil. The health risk indices showed that exposure to Cr, Ni, and Pb had a more detrimental impact on children than adults, with the risk progressively reducing from zone 1 to zone 4. The Monte Carlo simulations model with sensitivity analysis identified the ingestion pathway to be the chief contributor as the chief carcinogenic risk contributor. Positive matrix factorization and self-organizing maps revealed Cr, Ni, and Pb as major pollutants in tea plantation soils, stemming from lithogenic and anthropogenic activities. Hotspot analysis aided with geostatistical approaches identified locations with elevated levels of HMs pollution. The findings from machine-learning approaches will offer insights into pollution levels in tea gardens, assisting researchers in implementing effective mitigation strategies.