Multivariate linear regression models for predicting metal content and sources in leafy vegetables and human health risk assessment in metal mining areas of Southern Jharkhand, India.

Abstract

The present study was intended to investigate the metal concentrations in the leafy vegetables, irrigation water, soil, and atmospheric dust deposition in the iron and copper mining areas of Southern Jharkhand, India. The study aimed to develop a multivariate linear regression (MVLR) model to predict the concentration of metals in leafy vegetables from the metals in associated environmental factors and assessment of the risk to the local population through the consumption of leafy vegetables and other allied pathways. The developed species-specific MVLR models were well fitted to predict the concentration of metals in the leafy vegetables. The coefficient of determination values (R2) was greater than 0.8 for all the species-specific models. Risk assessment was carried out considering multiple pathways of ingestion, inhalation, and dermal contact of vegetables, soil, water, and free-fall dust. Consumption of leafy vegetables was the major route of metal exposure to the local population in both the metal mining areas. The average hazard index (HI) value considering all the metals and pathways was calculated to be 5.13 and 12.1, respectively for iron and copper mining areas suggesting considerable risk to the local residents. Fe, As, and Cu were the major contributors to non-carcinogenic risk in the Iron mining areas while in the case of copper mining areas, the main contributors were Co, As, and Cu.