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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

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  • Modian Xie

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Hongyi Li

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

  • Youwei Zhu

    (Protection and Monitoring Station of Agricultural Environment, Bureau of Agriculture of Department of Rural and Agriculture of Zhejiang Province, Hangzhou 310020, China)

  • Jie Xue

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Qihao You

    (Eco-Environmental Science & Research Institute of Zhejiang Province, Hangzhou 310012, China)

  • Bin Jin

    (Ningbo Agricultural Food Safety Management Station, Ningbo 315000, China)

  • Zhou Shi

    (Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
    Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

Abstract

Potentially toxic element (PTE) pollution in farmland soils and crops is a serious cause of concern in China. To analyze the bioaccumulation characteristics of chromium (Cr), zinc (Zn), copper (Cu), and nickel (Ni) in soil-rice systems, 911 pairs of top soil (0–0.2 m) and rice samples were collected from an industrial city in Southeast China. Multiple linear regression (MLR), support vector machines (SVM), random forest (RF), and Cubist were employed to construct models to predict the bioaccumulation coefficient (BAC) of PTEs in soil–rice systems and determine the potential dominators for PTE transfer from soil to rice grains. Cr, Cu, Zn, and Ni contents in soil of the survey region were higher than corresponding background contents in China. The mean Ni content of rice grains exceeded the national permissible limit, whereas the concentrations of Cr, Cu, and Zn were lower than their thresholds. The BAC of PTEs kept the sequence of Zn (0.219) > Cu (0.093) > Ni (0.032) > Cr (0.018). Of the four algorithms employed to estimate the bioaccumulation of Cr, Cu, Zn, and Ni in soil–rice systems, RF exhibited the best performance, with coefficient of determination (R 2 ) ranging from 0.58 to 0.79 and root mean square error (RMSE) ranging from 0.03 to 0.04 mg kg −1 . Total PTE concentration in soil, cation exchange capacity (CEC), and annual average precipitation were identified as top 3 dominators influencing PTE transfer from soil to rice grains. This study confirmed the feasibility and advantages of machine learning methods especially RF for estimating PTE accumulation in soil–rice systems, when compared with traditional statistical methods, such as MLR. Our study provides new tools for analyzing the transfer of PTEs from soil to rice, and can help decision-makers in developing more efficient policies for regulating PTE pollution in soil and crops, and reducing the corresponding health risks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:6:p:558-:d:562883
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    References listed on IDEAS

    as
    1. Bifeng Hu & Songchao Chen & Jie Hu & Fang Xia & Junfeng Xu & Yan Li & Zhou Shi, 2017. "Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    2. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    3. Piao Liu & Zhenhua Liu & Yueming Hu & Zhou Shi & Yuchun Pan & Lu Wang & Guangxing Wang, 2019. "Integrating a Hybrid Back Propagation Neural Network and Particle Swarm Optimization for Estimating Soil Heavy Metal Contents Using Hyperspectral Data," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    4. Kyoko Ono & Tetsuo Yasutaka & Takehiko I Hayashi & Masashi Kamo & Yuichi Iwasaki & Taizo Nakamori & Yoshikazu Fujii & Takafumi Kamitani, 2019. "Model construction for estimating potential vulnerability of Japanese soils to cadmium pollution based on intact soil properties," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-17, June.
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    Cited by:

    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. Ping Li & Tao Wu & Guojun Jiang & Lijie Pu & Yan Li & Jianzhen Zhang & Fei Xu & Xuefeng Xie, 2021. "An Integrated Approach for Source Apportionment and Health Risk Assessment of Heavy Metals in Subtropical Agricultural Soils, Eastern China," Land, MDPI, vol. 10(10), pages 1-17, September.
    3. 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.

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