College of Computer, National University of Defense Technology, Changsha 410003, PR China.
College of Resources and Environment, Hunan Agricultural University, Changsha 410128, PR China; Risk Assessment Laboratory for Environmental Factors of Agro-product Quality Safety (Changsha), Ministry of Agriculture and Rural Affairs, Changsha 410005, PR China.
Sci Total Environ. 2022 Sep 10;838(Pt 4):156466. doi: 10.1016/j.scitotenv.2022.156466. Epub 2022 Jun 9.
The long-term consumption of heavy metal-rich rice can cause serious harm to human health. However, the existing health risk assessment (HRA) can only be performed after the rice has been harvested, and this approach belongs to a passive and lagging pattern. This study is the first to explore the feasibility of health risk (HR) prediction by proposing the indirect model CNNHR-IND and the direct model CNNHR-DIR based on the convolutional neural network (CNN) technology. The dataset included 390 pairs of soil-rice samples collected from You County, China, with 17 environmental covariates. The R values for CNNHR-IND for non-carcinogenic and carcinogenic risks were 0.578 and 0.554, respectively, and those for CNNHR-DIR were 0.647 and 0.574, respectively. The results demonstrated that both models performed well, especially CNNHR-DIR had a higher estimation accuracy. The spatial autocorrelation analysis indicated that CNNHR-DIR exerted no systematic bias in the prediction results for health risks, confirming the rationality of the CNNHR-DIR model. The sensitivity analysis further confirmed the generalizability and robustness of CNNHR-DIR. This study proved the feasibility of HR prediction and the potential of CNN technology in HRA, and is significant regarding early risk warnings of rice planting and the sustainable development of public health.
长期食用重金属含量高的大米会对人体健康造成严重危害。然而，现有的健康风险评估（HRA）只能在水稻收获后进行，这种方法属于被动和滞后的模式。本研究首次探索了通过提出基于卷积神经网络（CNN）技术的间接模型 CNNHR-IND 和直接模型 CNNHR-DIR 来预测健康风险（HR）的可行性。该数据集包括从中国尤县采集的 390 对土壤-水稻样本，共有 17 个环境协变量。CNNHR-IND 对非致癌和致癌风险的 R 值分别为 0.578 和 0.554，CNNHR-DIR 的 R 值分别为 0.647 和 0.574。结果表明，这两个模型都表现良好，尤其是 CNNHR-DIR 的估计精度更高。空间自相关分析表明，CNNHR-DIR 对健康风险预测结果没有系统偏差，证实了 CNNHR-DIR 模型的合理性。敏感性分析进一步证实了 CNNHR-DIR 的泛化性和稳健性。本研究证明了 HR 预测的可行性和 CNN 技术在 HRA 中的潜力，对于水稻种植的早期风险预警和公共卫生的可持续发展具有重要意义。
