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Modeling Cadmium Contents in a Soil–Rice System and Identifying Potential Controls

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  • Yingfan Zhang

    (Department of Computer Science, Swansea University, Fabian Way, Swansea SA1 8EN, UK
    Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Tingting Fu

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Xueyao Chen

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Hancheng Guo

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Hongyi Li

    (Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Bifeng Hu

    (Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
    Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    Key Laboratory of Agricultural Remote Sensing and Information Systems, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

Abstract

Cadmium (Cd) pollution in a soil–rice system is closely related to widely concerning issues, such as food security and health risk due to exposure to heavy metals. Therefore, modeling the Cd content in a soil–rice system and identifying related controls could provide critical information for ensuring food security and reducing related health risks. To archive this goal, in this study, we collected 217 pairs of soil–rice samples from three subareas in Zhejiang Province in the Yangtze River Delta of China. All soil–rice samples were air-dried and conducted for chemical analysis. The Pearson’s correlation coefficient, ANOVA, co-occurrence network, multiple regression model, and nonlinear principal component analysis were then used to predict the Cd content in rice and identify potential controls for the accumulation of Cd in rice. Our results indicate that although the mean total concentration of Cd in soil samples was higher than that of the background value in Zhejiang Province, the mean concentration of Cd in rice was higher than that of the national regulation value. Furthermore, a significant difference was detected for Cd content in rice planted in different soil groups derived from different parental materials. In addition, soil organic matter and total Cd in the soil are essential factors for predicting Cd concentrations in rice. Additionally, specific dominant factors resulting in Cd accumulation in rice planted at different subareas were identified via nonlinear principal component analysis. Our study provides new insights and essential implications for policymakers to formulate specific prevention and control strategies for Cd pollution and related health risks.

Suggested Citation

  • Yingfan Zhang & Tingting Fu & Xueyao Chen & Hancheng Guo & Hongyi Li & Bifeng Hu, 2022. "Modeling Cadmium Contents in a Soil–Rice System and Identifying Potential Controls," Land, MDPI, vol. 11(5), pages 1-13, April.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:617-:d:799297
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    References listed on IDEAS

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    1. Fang Xia & Youwei Zhu & Bifeng Hu & Xueyao Chen & Hongyi Li & Kejian Shi & Liuchang Xu, 2021. "Pollution Characteristics, Spatial Patterns, and Sources of Toxic Elements in Soils from a Typical Industrial City of Eastern China," Land, MDPI, vol. 10(11), pages 1-20, October.
    2. Volker Liermann & Sangmeng Li, 2021. "Methods of Machine Learning," Springer Books, in: Volker Liermann & Claus Stegmann (ed.), The Digital Journey of Banking and Insurance, Volume III, pages 225-238, Springer.
    3. Modian Xie & Hongyi Li & Youwei Zhu & Jie Xue & Qihao You & Bin Jin & Zhou Shi, 2021. "Predicting Bioaccumulation of Potentially Toxic Element in Soil–Rice Systems Using Multi-Source Data and Machine Learning Methods: A Case Study of an Industrial City in Southeast China," Land, MDPI, vol. 10(6), pages 1-17, May.
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