Food chain contamination by soil cadmium (Cd) through vegetable consumption poses a threat to human health. It is imperative to understand the Cd uptake and transfer in different soil-vegetable systems. The aims of this study were to understand the effect of soil characteristics on Cd accumulation and transfer in pepper and to derive regression models to predict Cd concentrations in the vegetable grown on a wide range of soils with different properties. The accumulation and transfer of Cd in the root, stem, and fruit of pepper (Capsicum annuum L.) grown in 18 typical soils of China were investigated through pot experiments. The bioavailability of Cd in soil was evaluated by using EDTA and HNO3 extraction methods. The pot experiments included a control and two concentration levels of Cd salt added to soils according to Soil Environmental Quality Standards of China. The results showed that the Cd content in pepper fruits ranged from 0.007 to 0.049 for the control, 0.045 to 0.260 for the low Cd treatment, and 0.076 to 0.345 mg/kg for the high Cd treatment, respectively. The concentrations of Cd in the different parts of pepper decreased in the order of root > stem > fruit, and there were significant correlations among the Cd concentrations in pepper root, stem, and fruit tissues. Bioaccumulation factor (BCF) and transfer factor (TF) of Cd in pepper fruits exhibited a low accumulation of Cd in the fruit of pepper. The Cd accumulation in pepper fruit could be quantitatively predicted by EDTA-extractable Cd content in soils. Multiple linear regression models proved functional in predicting Cd accumulation in different parts of pepper. The Cd content in pepper tissues was well predicted using EDTA-extractable Cd and soil variables, such as pH, EC, CEC, total phosphorus, and CaCO3 content. Soil pH and EC were major soil factors influencing Cd transfer from soil to pepper fruits, whereas total phosphorus content presented a negative effect on Cd accumulation in stem and root parts of pepper.
Keywords: Accumulation; EDTA; Heavy metals; Multiple linear regression model; Soil–crop system.