• 中文核心期刊
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土壤-葡萄体系中重金属的迁移富集与风险评估研究进展

Research Progress on the Migration and Enrichment Mechanism, Influencing Factors, and Risk Assessment of Heavy Metals in Soil Grape Systems

  • 摘要: 重金属元素作为潜在有毒元素,在葡萄园土壤中的污染水平直接影响生态系统平衡及人类健康。本文通过对国内外现有研究的分析,着重探讨了葡萄园土壤-葡萄体系中重金属的含量特征,及其在体系中的迁移、富集行为和污染风险。结果显示,Cd、Cu、Zn含量会对葡萄品质存在主要影响,其余重金属须重点对照国标限量进行监测;重金属由土壤向葡萄叶片的迁移率最高可达到向果实部分的32倍,在迁移机制作用下积累水平多表现为叶片≈根部>茎部>果实。其中Zn在叶片和根部中的积累量最高可分别达到93mg/kg和51mg/kg,显著高于果实的0.53mg/kg。作为影响重金属迁移积累行为的关键因素,土壤酸碱度与体系中重金属的生物可利用度呈负相关,有机质含量通常与其呈正相关关系。当前进展还揭示了重金属因不同品种而产生的迁移差异性,但针对不同气候条件、土壤类型及生理特性之间的影响机制仍缺乏系统性研究。未来建议基于区域环境特征,全面地探究与重金属迁移能力及含量相关的影响因素,以及构建机器学习模型来预测和评估,在不同污染水平下该体系中的重金属之间产生的相互作用及其生态风险。

     

    Abstract: Heavy metal elements, as potential toxic elements, directly impact ecosystems balance and human health through their pollution levels in vineyard soils. This study analyzes the current related literatures, focusing on the characteristics of heavy metals content in the soil to grape system of vineyards, their migration behavior, and the associated pollution risk assessment. The findings reveal that cadmium (Cd), copper(Cu), and zinc(Zn) significantly influence grape quality, while other heavy metals require close monitoring against national standards. The migration rate of heavy metals from soil to grape leaves can be up to 32 times higher than to the pulp. Accumulation levels typically follow the order: leaves≈roots>stems>pulp. Specifically, Zn accumulation in leaves and roots can reach as high as 93mg/kg and 51mg/kg, respectively, far exceeding the 0.53mg/kg observed in pulp. Soil pH, as a critical factor affecting heavy metal migration and accumulation, shows a negative correlation with the bioavailability of heavy metals, while organic matter content typically exhibits a positive correlation. Current findings also highlight the varietal differences in heavy metal migration; however, systematic studies on the interaction mechanisms among climatic conditions, soil types, and physiological traits remain limited. Future research should aim to comprehensively explore the factors influencing heavy metal migration and content within the system under regional environmental characteristics. The application of machine learning models is recommended to predict and evaluate interactions among heavy metals and their ecological risks under different pollution levels.

     

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