ICP-MS-Based Assessment of Heavy Metals in Green Tea Leaves from Gilan: Multi-Element Determination, Health Risk Evaluation, and Monte Carlo Uncertainty Analysis.

This study quantifies multiple trace elements in green tea leaves from Gilan to establish leaves as a reliable environmental indicator of metal pollution and to integrate environmental surveillance with human health risk assessment for tea consumption. Sampling covered major tea-growing municipalities across three seasons (2022-2023). Statistical methods included Kruskal-Wallis, Mann-Whitney U, Spearman correlation, and principal component analysis (PCA) to reveal regional, seasonal, and metal interrelationships. Monte Carlo simulations quantified uncertainty and supported risk interpretation. Health risk assessment used average daily dose (ADD), hazard quotient (HQ), hazard index (HI) for non-carcinogenic effects, and incremental lifetime cancer risk (ILCR) for carcinogenic risk. The seasonal analysis showed variations in heavy metal concentrations in tea leaves from Gilan Province across spring, summer, and autumn. Mn and Al were the most abundant metals, while Hg, As, and Cd occurred at low concentrations. Summer had higher metal levels due to plant growth and transpiration; autumn showed lower absorption with cooler temperatures. Normality tests indicated non-normal distributions, justifying non-parametric analyses. PCA revealed four principal components explaining 91.63% of variance, clustering by natural soil inputs, agricultural practices, and industrial sources. Risk assessments indicated low non-carcinogenic risks across seasons, with Mn as the primary contributor, and carcinogenic risks from Ni below acceptable thresholds, suggesting minimal health risks from tea consumption. The findings underscore environmental factors' influence on heavy metals in tea leaves and advocate for continuous monitoring and sustainable agricultural practices to protect consumers.