Li Cheng, Zhang Chaosheng, Yu Tao, Liu Xu, Yang Yeyu, Hou Qingye, Yang Zhongfang, Ma Xudong, Wang Lei
School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, PR China.
School of Geography, Archaeology & Irish Studies, National University of Ireland, Galway, University Road, Galway, H91 CF50, Ireland.
Environ Pollut. 2022 Jul 1;304:119234. doi: 10.1016/j.envpol.2022.119234. Epub 2022 Mar 30.
In recent years, the naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil Cd content and bioavailability among different parent materials, the previous land classification scheme based on total soil Cd content as the classification standard, has certain shortcomings. This study aims to explore the factors influencing soil Cd bioavailability in typical karst areas of Guilin and to suggest a scientific and effective farmland use management plan based on the prediction model. A total of 9393 and 8883 topsoil samples were collected from karst and non-karst areas, respectively. Meanwhile, 149 and 145 rice samples were collected together with rhizosphere soil in karst and non-karst areas, respectively. The results showed that the higher CaO level in the karst area was a key factor leading to elevated soil pH value. Although Cd was highly enriched in karst soils, the higher pH value and adsorption of Mn oxidation inhibited Cd mobility in soils. Conversely, the Cd content in non-karst soils was lower, whereas the Cd level in rice grains was higher. To select the optimal prediction model based on the correlation between Cd bioaccumulation factors and geochemical parameters of soil, artificial neural network (ANN) and linear regression prediction models were established in this study. The ANN prediction model was more accurate than the traditional linear regression model according to the evaluation parameters of the test set. Furthermore, a new land classification scheme based on an ANN prediction model and soil Cd concentration is proposed in this study, making full use of the spatial resources of farmland to ensure safe rice consumption.
近年来，源自碳酸盐风化的镉自然高背景值区域受到了广泛关注。由于不同母质间土壤镉含量和生物有效性存在显著差异，以往基于土壤总镉含量作为分类标准的土地分类方案存在一定缺陷。本研究旨在探究桂林典型岩溶地区土壤镉生物有效性的影响因素，并基于预测模型提出科学有效的农田利用管理方案。分别从岩溶区和非岩溶区采集了9393个和8883个表层土壤样本。同时，在岩溶区和非岩溶区分别采集了149个和145个水稻样本及其根际土壤。结果表明，岩溶区较高的氧化钙水平是导致土壤pH值升高的关键因素。尽管镉在岩溶土壤中高度富集，但较高的pH值和锰氧化物的吸附抑制了镉在土壤中的迁移。相反，非岩溶土壤中的镉含量较低，而水稻籽粒中的镉含量较高。为基于镉生物累积因子与土壤地球化学参数的相关性选择最优预测模型，本研究建立了人工神经网络（ANN）和线性回归预测模型。根据测试集的评估参数，ANN预测模型比传统线性回归模型更准确。此外，本研究提出了一种基于ANN预测模型和土壤镉浓度的新土地分类方案，充分利用农田空间资源以确保水稻消费安全。
