Lin Lu, Zhao Xiaopeng, Li Yumeng, Ling Jingbo, Ren Jinghua, Liao Qilin, Zhou Dongmei, Gu Xueyuan
State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
Columbia University, New York, NY 10027, USA.
Eco Environ Health. 2025 May 14;4(2):100154. doi: 10.1016/j.eehl.2025.100154. eCollection 2025 Jun.
The high cadmium (Cd) accumulation ability of wheat has garnered significant attention in China. It is crucial to identify the key factors affecting Cd accumulation in wheat and to develop predictive models to derive the threshold concentration of Cd in soil for safe wheat production. A total of 311 soil-wheat paired datasets were collected from both literature and field surveys in China, in which the ranges of Cd in soil and wheat grain were 0.068-13.500 mg/kg and 0.006-2.190 mg/kg, respectively. Correlation analyses and Partial Least Squares Path Model indicated that soil Cd, soil pH, and CEC together controlled the transfer of Cd from soil to wheat. Multiple linear regression models were successfully established using soil Cd contents or bioavailable Cd (extracted by CaCl or calculated using a multi-surface speciation model), pH, and CEC as input variables to predict wheat Cd (RMSE = 0.242-0.327, MAE = 0.188-0.249). Furthermore, the Extreme Random Tree model (RMSE = 0.221, MAE = 0.165) outperformed the other seven machine learning algorithms. The thresholds for both soil total Cd and bioavailable Cd for safe wheat production were further back-calculated according to the permissible value of Cd in wheat grain, which demonstrated enhanced protection accuracy compared to the current soil quality standard. Our findings facilitate a quantitative assessment of Cd accumulation risk in wheat, offering a valuable reference for the safe production of wheat.
小麦对镉(Cd)的高积累能力在中国已引起广泛关注。识别影响小麦镉积累的关键因素并建立预测模型以推导安全小麦生产的土壤镉阈值浓度至关重要。通过对中国文献和田间调查收集的311组土壤-小麦配对数据集,土壤和小麦籽粒中镉的范围分别为0.068-13.500毫克/千克和0.006-2.190毫克/千克。相关分析和偏最小二乘路径模型表明,土壤镉、土壤pH值和阳离子交换量共同控制了镉从土壤到小麦的转移。以土壤镉含量或生物有效镉(用氯化钙提取或用多表面形态模型计算)、pH值和阳离子交换量作为输入变量,成功建立了多元线性回归模型来预测小麦镉含量(均方根误差=0.242-0.327,平均绝对误差=0.188-0.249)。此外,极端随机树模型(均方根误差=0.221,平均绝对误差=0.165)优于其他七种机器学习算法。根据小麦籽粒中镉的允许值,进一步反算安全小麦生产的土壤总镉和生物有效镉阈值,与现行土壤质量标准相比,其保护精度有所提高。我们的研究结果有助于对小麦镉积累风险进行定量评估,为小麦安全生产提供有价值的参考。