农田土壤重金属污染定量源解析方法的建立与应用验证

Agricultural soil is a type of soil that is subjected to the most significant influence of human activities, and its contents of heavy metals are commonly affected by a range of anthropogenic sources, including farming activities, industrial production, and transportation. Identifying sources of heavy metals in soils and quantifying their contributions is crucial for developing effective pollution prevention and control policies. Source apportionment is also one of the most challenging hot topics in heavy metal pollution research. Based on the principal investigator’s previous research experience, this project aims to develop a novel method for apportioning the contributions of natural and anthropogenic source by combining stochastic models, including decision tree, random forest and factor analysis. This method will be applied to investigate the heavy metal pollution in a typical region impacted by non-ferrous metal smeltering (Yitan city of Jiangxi province), and the validity of its results will be evaluated in multiple ways, including comparing the heavy metal contents in the soil samples from the surface layer and deep below, and investigating results from other methods such as multivariate statistical analyses and geospatial analyses. The feasibility of applying this method for apportioning the sources of heavy metal pollution in major agricultural products (rice and vegetables) will be examined, the transfer of heavy metals from environmental media to the food chain will be studied based on correlation analysis, and the correponding health risk from heavy metal exposure through chronic consumption of the local agricultural products will be assessed. Overall, the project will develop a novel and practical method for heavy metal source apportionment, which can serve as a valuable tool for regional soil heavy metal pollution control and prevention. Meanwhile, quantifying the health risk associated with the dietary exposure to heavy metals in the major agricultural products has important implications for the protection of public health.

农田土壤是受人类活动影响最为强烈的一类土壤，其重金属含量通常受到农业、工业以及交通运输等多种活动的影响。对土壤重金属的来源进行定量解析是有的放矢地开展污染防控的前提与基础，也是重金属污染研究领域的热点与难点。申请人拟在前期工作基础上，综合运用决策树、随机森林和因子分析等方法，发展和建立基于随机模型的土壤重金属污染定量源解析新方法；并将该方法应用于受有色金属冶炼加工活动影响典型区域（江西省鹰潭市）的农田土壤污染研究，结合表层与深层土壤对比、不同源解析技术识别结果对比等手段，验证其结果的可靠性和准确性；探讨使用该方法解析农产品重金属污染源的可行性，考察重金属从环境介质向食物链的传递，并评估相应的人群健康风险。本项目将发展和创新切实可行的重金属污染源解析方法，为区域土壤重金属污染的防控提供科学工具；同时，厘清食用主要农产品带来的重金属暴露健康风险，对保护人群健康具有重要的现实意义。

农田土壤重金属含量通常受到农业、工业以及交通运输等多种人类活动的影响，并通过在农作物以及食物链中的累积威胁人群健康。对农田土壤重金属的来源进行定量解析是有的放矢地开展污染防控的前提与基础，也是重金属污染研究领域的热点与难点。本项目以我国典型工业化地区为研究区域，构建了APCS-MLR, PMF, Unmix等受体模型与线性回归、空间制图、潜在污染源清单等方法相结合的定量源解析方法，并探讨了它们在区域农田土壤中的应用；基于虚拟数据和真实数据集，系统研究了典型源解析模型应用于土壤重金属源解析领域的准确性与可靠性，识别了噪音、共线性、源贡献分布、元素数量等因素对模型解析结果的影响，并探讨了样本量与模型解析结果可靠性之间的关联；建立了水稻和蔬菜中重金属含量的分析方法，识别了重金属在土壤-水稻系统的迁移特征与关键影响因素，评估了居民通过多种途径暴露于土壤和农作物重金属导致的健康风险。结果表明：基于受体模型的源解析方法能够有效区别区域内具有不同特征元素的潜在污染源，并较为准确的量化自然源与人为源的贡献；源解析结果具有一定的不确定性，采样点分布和样本量对模型解析结果具有显著影响；拥有能够充分反映被调查区域内所有潜在污染源的污染特征的样本量，对于受体模型产生可靠的结果至关重要；共线性、非正态分布的源贡献等因素显著影响源解析结果的准确性与可靠性，而模型的内置不确定性分析往往难以正确反映模型的准确性；农田土壤污染造成的农作物污染是导致居民人群健康风险的主要因素，而重金属在土壤-作物中的迁移受土壤性质、重金属有效态含量和矿物元素含量等多因素影响。相关研究成果可为区域土壤采样方案的确定、源解析模型的选取、模型可靠性与准确性的评价等提供科学依据与技术支持，并为区域土壤重金属污染防控和保障人群健康提供科学工具，具有重要的理论与应用价值。