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| Enrichment patterns of cadmium and arsenic in rice from seven major regions in China based on machine learning recognition of minor and trace elements |
| Received:April 05, 2023 |
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| KeyWord:cadmium;arsenic;decision tree algorithm;random forest;bioavailability model |
| Author Name | Affiliation | E-mail | | MOU Liyan | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Xiangtan Experimental Station of Agro-environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan 411100, China | | | LIU Chunxiang | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Xiangtan Experimental Station of Agro-environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan 411100, China | | | CHEN Min | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Xiangtan Experimental Station of Agro-environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan 411100, China | | | QIN Li | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Xiangtan Experimental Station of Agro-environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan 411100, China | ql-tj@163.com | | LIN Dasong | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
Xiangtan Experimental Station of Agro-environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan 411100, China | | | Batsaikhan Bayartungalag | Institute of Geography and Ecological Geology, Mongolian Academy of Sciences, Ulaanbaatar 1568683, Mongolia | |
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| Abstract: |
| This study identified important influencing factors of cadmium(Cd)and arsenic(As)enrichment in rice based on machine learning on a large spatial scale nationwide, explored the contribution rate of medium and trace elements to rice Cd and As exceeding standards, and constructed a bioavailability model. First, a prediction model was constructed using a decision tree algorithm to identify trace elements that exceeded Cd and As limits, with prediction accuracies of 95.55% and 97.55%, respectively. This indicates that trace elements were essential for identifying excessive Cd and As in rice. Second, the random forest algorithm was used to screen the main control factors affecting rice Cd and As enrichment, and the main control factors showed significant differences in different regions. Cd enrichment differences, mainly driven by a single factor in different regions, were as follows:the contribution of pH in East China was dominant, exchangeable calcium in South China, and soil organic matter in Northeast China accounted for the main contribution. Effective iron exhibited a specific regional contribution to As enrichment(such as in the East China, South China, and Southwest regions). The main control factors determined in each region were introduced to construct a soil rice bioavailability model; overall, the nine-factor models for Cd and As bioavailability had the highest determination coefficients in different regions, with 0.680 and 0.664(P<0.05), respectively. The models quantified the explanatory power of different factors on Cd and As enrichment patterns in rice from rice-producing areas. This study provides the scientific basis and decision-making support for preventing and controlling Cd and As heavy metal pollution in rice and environmental management at a large-scale regional level. |
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