An integrated methodology for improving heavy metal risk management in soil-rice system. (10th November 2020)

• Record Type: Journal Article
• Authors: Jia, Zhenyi; Wang, Junxiao; Li, Baojie; Li, Yan; Zhou, Yujie; Tong, Guijie; Yan, Daohao; Zhou, Shenglu
• Is Part Of: Journal of cleaner production. Volume 273(2020)
• Publication Date: 2020-11-10
• Subjects: Bayesian decision tree -- Heavy metal -- LISA -- Risk management -- Soil-rice system; BCF bioconcentration factor -- BDT Bayesian decision tree -- BL-LISA bivariate local indicators of spatial association -- CIA chemical alteration index -- dis_factory distance from the factory -- dis_residential area distance from the residential area -- dis_road distance from the road -- EC electrical conductivity -- HM heavy metal -- ID3 iterative dichotomizer 3 -- MAE mean absolute error -- OK ordinary kriging -- RDA redundancy analysis -- RMSE root mean square error -- RNS ratio of nugget coefficients -- saCd available Cd content in soil -- SGS sequential gaussian simulation -- SOM soil organic matter -- stCd total Cd content in soil -- TSP total suspended particles; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.122797
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The lack of linear correlation between regional soil and rice heavy metal (HM) content aggravates the difficulty of risk management in farmland. Hence, an effective HM contamination risk control strategy is urgently required for rice paddy. Here, a novel integrated spatial interaction and risk identification methodology was proposed. Bivariate local indicators of spatial association (BL-LISA) was used to analyze the spatial interaction between soil and rice HM. The mechanism of the spatial interaction pattern was elucidated with lead isotope ratios and redundancy analysis. The rice HM risk was predicted via a Bayesian decision tree (BDT). The spatial interaction patterns were mainly High-Low and Low-High. Thus, there was antagonism between soil and rice HM. Emission sources and sinks accounted for the observed spatial interaction patterns. The parent material contributed 69% to the soil HM content but only 10.2% to that of rice. Seven risk rules and 13 security rules were identified by BDT. The risk identification accuracy of these rules was 96.8% for the overall sample. BL-LISA mapping was combined with BDT to demarcate and classify the risk zones and project differentiated and refined management modes. The risk, potential risk and clean zones comprised 7.8%, 14.1% and 46.6% of the farmland, respectively. The integrated method was superior to other traditional techniques in terms of farm HM risk management and may enhance decision-making in HM risk management for soil-rice system. Graphical abstract: Image 1 Highlights: An integrated method was proposed for managing HM risk in soil-rice systems. BL-LISA mapping revealed the spatial HM interactions in soil-rice system. Bayesian decision tree improved rice risk identification accuracy. 7.8% of farmland including certain HH cluster regions was identified as risk zone. Integrated method was superior to other methods for soil-rice HM risk management.