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基于优化的 DGT 和 BCR 耦合模型预测土壤-水稻-人体系统中镉的生物可给性和积累。

Prediction of the bioaccessibility and accumulation of cadmium in the soil-rice-human system based on optimized DGT and BCR coupled models.

机构信息

Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China.

Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing 100044, China.

出版信息

Ecotoxicol Environ Saf. 2024 Jul 15;280:116509. doi: 10.1016/j.ecoenv.2024.116509. Epub 2024 Jun 3.

DOI:10.1016/j.ecoenv.2024.116509
PMID:38833979
Abstract

Cadmium, as a typical heavy metal, has the potential to induce soil pollution and threaten human health through the soil-plant-human pathway. The conventional evaluation method based on the total content in soil cannot accurately represent the content migrated from the food chain to plants and the human body. Previous studies focused on the process of plant enrichment of heavy metals in soil, and very few studies directly predicted human exposure or risk through the labile state of Cd in soil. Hence, a relatively accurate and convenient prediction model of Cd release and translocation in the soil-rice-human system was developed. This model utilizes available Cd and soil parameters to predict the bioavailability of Cd in soil, as well as the in vitro bioaccessibility of Cd in cooked rice. The bioavailability of Cd was determined by the Diffusive Gradients in Thin-films technology and BCR sequential extraction procedure, offering in-situ quantification, which presents a significant advantage over traditional monitoring methods and aligns closely with the actual uptake of heavy metals by plants. The experimental results show that the prediction model based on the concentration of heavy metal forms measured by BCR sequential extraction procedure and diffusive gradients in thin-films technique can accurately predict the Cd uptake in rice grains, gastric and gastrointestinal phase (R=0.712, 0.600 and 0.629). This model accurately predicts Cd bioavailability and bioaccessibility across the soil-rice-human pathway, informing actual human Cd intake, offering scientific support for developing more effective risk assessment methods.

摘要

镉作为一种典型的重金属,通过土壤-植物-人类途径,有可能引发土壤污染并威胁人类健康。传统的基于土壤中总含量的评价方法不能准确地代表从食物链迁移到植物和人体的含量。以前的研究主要集中在植物对土壤中重金属的富集过程上,很少有研究直接通过土壤中镉的不稳定态来预测人类暴露或风险。因此,开发了一种相对准确和方便的土壤-水稻-人类系统中镉释放和迁移的预测模型。该模型利用可用的镉和土壤参数来预测土壤中镉的生物利用度,以及烹饪后大米中镉的体外生物可及性。镉的生物利用度通过薄膜扩散梯度技术和 BCR 连续提取程序来确定,提供了原位定量,这与传统监测方法相比具有显著优势,并且与植物对重金属的实际吸收非常吻合。实验结果表明,基于 BCR 连续提取程序和薄膜扩散梯度技术测量的重金属形态浓度的预测模型可以准确预测水稻籽粒中镉的吸收、胃和胃肠道阶段(R=0.712、0.600 和 0.629)。该模型准确地预测了土壤-水稻-人类途径中的镉生物利用度和生物可及性,为实际的人类镉摄入量提供了信息,为开发更有效的风险评估方法提供了科学支持。

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