Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China; National Engineering and Technology Research Center for Red Soil Improvement, Red Soil Ecological Experiment Station, Chinese Academy of Sciences, Yingtan, 335211, China.
Chemosphere. 2023 Apr;319:138028. doi: 10.1016/j.chemosphere.2023.138028. Epub 2023 Jan 31.
Identification the sources of heavy metals can effectively control and prevent agricultural soil pollution. Here we performed a three-year mass balance study along a gradient of soil pollution near a smelter to quantify the potential contribution and net cadmium (Cd) fluxes and predict Cd concentration in rice grains by multiple regression (MR) and back propagation (BP) neural network. The Cd inputs were mainly from the irrigation water (54.6-60.8%) in the moderately polluted and background sites but from atmospheric deposition (90.9%) in the highly polluted site. The Cd outputs were mainly from the surface runoff (55.8-59.5%) in the moderately polluted and background sites, but from Sedum plumbizincicola phytoextraction (83.6%) in the highly polluted site. The soil Cd concentrations, the annual fluxes of atmospheric deposition, pesticides and fertilizers, irrigation water, surface runoff, and leaching water were selected as the dependent factors to predict Cd concentrations in rice grains. The genetic algorithms (GA)-BP neural network model gives the best prediction accuracy compared to the BP neural network model and multivariate regression analysis. The major implication is that the health risks through the consumption of rice can be rapidly assessed based on the Cd concentrations in rice grains predicted by the model.
鉴定重金属的来源可以有效地控制和预防农业土壤污染。在这里，我们进行了一项为期三年的质量平衡研究，沿着一家冶炼厂附近的土壤污染梯度，通过多元回归（MR）和反向传播（BP）神经网络来量化潜在的贡献和净镉（Cd）通量，并预测稻米中的 Cd 浓度。Cd 的输入主要来自中度污染和背景地区的灌溉水（54.6-60.8%），而在高度污染地区则主要来自大气沉降（90.9%）。Cd 的输出主要来自中度污染和背景地区的地表径流（55.8-59.5%），但在高度污染地区则主要来自Sedum plumbizincicola 的植物提取（83.6%）。选择土壤 Cd 浓度、大气沉降、农药和化肥、灌溉水、地表径流和淋溶水的年通量作为预测稻米中 Cd 浓度的因变量。与 BP 神经网络模型和多元回归分析相比，遗传算法（GA）-BP 神经网络模型具有更好的预测精度。这意味着可以根据模型预测的稻米中 Cd 浓度，快速评估通过食用稻米带来的健康风险。
