Abstract
Heavy metal (HM) concentrations in edible plants can develop many serious health risks to humans. The precise prediction of plant uptake of HMs is highly important. Thus, the present investigation was carried out to develop regression models for predicting the concentrations of HMs in cucumbers (Cucumis sativus L.) from their concentration in the soil and using the organic matter (OM) content and soil pH as co-factors. The results showed that cucumber roots had the highest significant concentrations of all HMs at P < 0.001, except Cd, Cu, and Zn were in fruits. The lowest concentrations of Cd, Co, Cr, Mn, Ni, and Pb were recorded in stems. HM concentrations in cucumbers were strongly correlated with soil HM, pH, and OM content. Soil pH and OM content had negative and positive correlations with all HMs in cucumber tissues, respectively. Regression analysis indicated that soil HM, pH, and OM contents were good predictors for HM concentrations in cucumbers. The regression models for root Co, Cr, Fe, and Zn were described by high model efficiency values that explain 48-58% variability. The best regression models for cucumber stem were for Cu, Mn, Ni, and Zn that are characterized by high R (2) and model efficiency values. For cucumber fruits, R (2) values were ranged from 54 to 82%, with best models for Cr, Pb, Cd, Cu, Ni, and Co in the fruit. We expect that these models will be beneficial for risk assessment studies on sewage sludge utilization in agriculture.