Evaluation Of Cadmium Transfer From Soil To The Human Body Through Maize Consumption In A Cadmium Anomaly Area Of Southwestern China

ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY(2021)

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摘要
Evaluating the bioavailability, bioaccessibility, and transferability of cadmium (Cd) in soil-grain-human systems is essential in areas with a Cd anomaly in the karst region of southwestern China. In the present study, the main controlling factors and prediction models for Cd transfer in a soil-grain-human system were investigated in a typical area where natural processes and anthropogenic activities interact in the karst region of southwestern China. The environmental availability of Cd (diethylenetriaminepentaacetic acid- and CaCl2-extractable Cd [ Cd CaCl 2]) in the soil varies significantly because of the diversity of soil properties. However, Cd concentrations in the maize grain were significantly related only to the Cd CaCl 2 concentrations in the soil (r = 0.595, p < 0.01), indicating that soil Cd CaCl 2 is a good indicator for evaluating Cd uptake by maize grain. Of all the measured soil properties, the soil cation exchange capacity (CEC) and the soil calcium (Ca-soil) were the most important factors influencing Cd accumulation in the soil-maize grain system. A transfer model combining Cd CaCl 2, soil CEC, and Ca-soil was sufficiently reliable for predicting Cd accumulation in the maize grain (R-2 = 0.505). Although there is room for improvement regarding the prediction performance of the chain model combining soil Cd CaCl 2 with Ca-soil to predict the bioaccessible Cd concentration in maize grain (R-2 = 0.344 for the gastric phase and R-2 = 0.356 for the gastrointestinal phase), our findings provide a useful reference to further explore a model that can be used for a relatively rapid and reliable estimation of dietary Cd exposure for specific regions prior to crop harvest. Environ Toxicol Chem 2021;00:1-12. (c) 2021 SETAC.
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关键词
Cadmium anomaly area, Maize grain, Environmental availability, Bioaccessibility, Influential factors, Prediction models
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