• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

小麦籽粒中的镉积累:积累模型与安全生产的土壤阈值

Cadmium accumulation in wheat grain: Accumulation models and soil thresholds for safe production.

作者信息

Lin Lu, Zhao Xiaopeng, Li Yumeng, Ling Jingbo, Ren Jinghua, Liao Qilin, Zhou Dongmei, Gu Xueyuan

机构信息

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.

出版信息

Eco Environ Health. 2025 May 14;4(2):100154. doi: 10.1016/j.eehl.2025.100154. eCollection 2025 Jun.

DOI:10.1016/j.eehl.2025.100154
PMID:40503387
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12152888/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/00544dc32c2a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/6d812ce1ed71/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/9c267f26a8d1/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/8c6051e6748c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/44dc8328c6dd/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/00544dc32c2a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/6d812ce1ed71/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/9c267f26a8d1/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/8c6051e6748c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/44dc8328c6dd/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d9c/12152888/00544dc32c2a/gr4.jpg

相似文献

1
Cadmium accumulation in wheat grain: Accumulation models and soil thresholds for safe production.小麦籽粒中的镉积累:积累模型与安全生产的土壤阈值
Eco Environ Health. 2025 May 14;4(2):100154. doi: 10.1016/j.eehl.2025.100154. eCollection 2025 Jun.
2
Cadmium accumulation in wheat and maize grains from China: Interaction of soil properties, novel enrichment models and soil thresholds.中国小麦和玉米籽粒中镉的积累:土壤特性、新型富集模型和土壤阈值的相互作用。
Environ Pollut. 2021 Apr 15;275:116623. doi: 10.1016/j.envpol.2021.116623. Epub 2021 Feb 2.
3
Predictive statistical modelling of cadmium content in durum wheat grain based on soil parameters.基于土壤参数的硬质小麦籽粒中镉含量的预测统计建模。
Environ Sci Pollut Res Int. 2017 Sep;24(25):20641-20654. doi: 10.1007/s11356-017-9712-z. Epub 2017 Jul 15.
4
Predicting accumulation of Cd in rice (Oryza sativa L.) and soil threshold concentration of Cd for rice safe production.预测水稻(Oryza sativa L.)中 Cd 的积累和土壤中 Cd 的浓度阈值,以确保水稻安全生产。
Sci Total Environ. 2020 Oct 10;738:139805. doi: 10.1016/j.scitotenv.2020.139805. Epub 2020 Jun 1.
5
The determination of regulating thresholds of soil pH under different cadmium stresses using a predictive model for rice safe production.利用水稻安全生产预测模型确定不同镉胁迫下土壤pH值调控阈值
Environ Sci Pollut Res Int. 2022 Dec;29(58):88008-88017. doi: 10.1007/s11356-022-21751-4. Epub 2022 Jul 12.
6
Source-specific risk assessment for cadmium in wheat and maize: Towards an enrichment model for China.针对小麦和玉米中镉的来源特定风险评估:建立中国的富集模型。
J Environ Sci (China). 2023 Mar;125:723-734. doi: 10.1016/j.jes.2022.02.024. Epub 2022 Feb 27.
7
Bayesian risk prediction model: An accessible strategy to predict cadmium contamination risk in wheat grain grown in alkaline soils.贝叶斯风险预测模型:一种预测碱性土壤中种植的小麦中镉污染风险的可行策略。
Environ Pollut. 2024 Aug 1;354:124169. doi: 10.1016/j.envpol.2024.124169. Epub 2024 May 16.
8
A field study to predict Cd bioaccumulation in a soil-wheat system: Application of a geochemical model.田间研究预测土壤-小麦系统中 Cd 的生物积累:地球化学模型的应用。
J Hazard Mater. 2020 Dec 5;400:123135. doi: 10.1016/j.jhazmat.2020.123135. Epub 2020 Jun 11.
9
Phytoavailability, translocation and soil thresholds derivation of cadmium for food safety through soil-wheat (Triticum aestivum L.) system.通过土壤-小麦(普通小麦)系统评估镉的植物有效性、转运及食品安全土壤阈值推导
Environ Sci Pollut Res Int. 2021 Jul;28(28):37716-37726. doi: 10.1007/s11356-021-13385-9. Epub 2021 Mar 15.
10
Prediction of Cd Accumulation in Wheat (Triticum aestivum L.) and Simulation Calculation of Lime or Zn Fertilizer Remediated Soil.小麦(普通小麦)镉积累预测及石灰或锌肥修复土壤的模拟计算
Bull Environ Contam Toxicol. 2022 Dec 21;110(1):19. doi: 10.1007/s00128-022-03660-x.

本文引用的文献

1
Wheat tends to accumulate higher levels of cadmium in the grains than rice under a wide range of soil pH and Cd concentrations: A field study on rice-wheat rotation farmland.在广泛的土壤pH值和镉浓度条件下,小麦籽粒中镉的积累水平往往高于水稻:稻麦轮作农田的田间研究。
Environ Pollut. 2025 Feb 15;367:125574. doi: 10.1016/j.envpol.2024.125574. Epub 2024 Dec 24.
2
Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.基于机器学习模型预测作物中 Cd 的积累并识别多种环境因素的非线性效应。
Sci Total Environ. 2024 Nov 15;951:175787. doi: 10.1016/j.scitotenv.2024.175787. Epub 2024 Aug 24.
3
Available heavy metals concentrations in agricultural soils: Relationship with soil properties and total heavy metals concentrations in different industries.
农业土壤中可利用重金属浓度:与土壤性质及不同行业总重金属浓度的关系
J Hazard Mater. 2024 Jun 5;471:134410. doi: 10.1016/j.jhazmat.2024.134410. Epub 2024 Apr 25.
4
How exogenous ligand enhances the efficiency of cadmium phytoextraction from soils?外源配体如何提高土壤中镉的植物提取效率?
J Hazard Mater. 2024 Mar 5;465:133188. doi: 10.1016/j.jhazmat.2023.133188. Epub 2023 Dec 6.
5
Predicting Cd accumulation in rice and identifying nonlinear effects of soil nutrient elements based on machine learning methods.基于机器学习方法预测水稻镉积累及识别土壤养分元素的非线性效应。
Sci Total Environ. 2024 Feb 20;912:168721. doi: 10.1016/j.scitotenv.2023.168721. Epub 2023 Nov 24.
6
Prediction of cadmium and zinc phytoextraction by the hyperaccumulator Sedum plumbizincicola using a dynamic geochemical mechanical combination model.利用动态地球化学力学组合模型预测超积累植物垂盆草对镉锌的提取。
Sci Total Environ. 2024 Jan 1;906:167627. doi: 10.1016/j.scitotenv.2023.167627. Epub 2023 Oct 6.
7
New insights into the sustainable use of soluble straw humic substances for the remediation of multiple heavy metals in contaminated soil.可溶性秸秆腐殖物质用于修复污染土壤中多种重金属的可持续利用新见解。
Sci Total Environ. 2023 Dec 10;903:166274. doi: 10.1016/j.scitotenv.2023.166274. Epub 2023 Aug 13.
8
Machine Learning in Environmental Research: Common Pitfalls and Best Practices.机器学习在环境研究中的应用:常见陷阱与最佳实践。
Environ Sci Technol. 2023 Nov 21;57(46):17671-17689. doi: 10.1021/acs.est.3c00026. Epub 2023 Jun 29.
9
[Prediction of Cadmium Uptake Factor in Wheat Based on Machine Learning].基于机器学习的小麦镉吸收因子预测
Huan Jing Ke Xue. 2023 Jun 8;44(6):3619-3626. doi: 10.13227/j.hjkx.202207237.
10
Accurate derivation and modelling of criteria of soil extractable and total cadmium for safe wheat production.准确推导和建立土壤可提取态和总镉指标,以保障小麦安全生产。
Ecotoxicol Environ Saf. 2023 Aug;261:115092. doi: 10.1016/j.ecoenv.2023.115092. Epub 2023 Jun 6.