Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials Pub Date : 2024-10-05 Epub Date: 2024-08-22 DOI:10.1016/j.jhazmat.2024.135555
Zhimin Liu, Weijun Wang, Yibo Geng, Yuting Zhang, Xuan Gao, Junfeng Xu, Xiaolu Liu
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Abstract

The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this study, traditional machine learning (ML) and H2O automated machine learning (H2O AutoML) were utilized to offer a powerful framework for detecting PE-MPs (0.1 %, 1 %, and 2 % by dry soil weight) and the co-contamination of PE-MPs and fomesafen (a common herbicide) in soil. The development of the framework was based on the results of the metabolic reprogramming of soybean plants. Our study stated that traditional ML exhibits lower accuracy due to the challenges associated with optimizing complex parameters. H2O AutoML can accurately distinguish between clean soil and contaminated soil. Notably, H2O AutoML can detect PE-MPs as low as 0.1 % (with 100 % accuracy) and co-contamination of PE-MPs and fomesafen (with 90 % accuracy) in soil. The VIP and SHAP analyses of the H2O AutoML showed that PE-MPs and the co-contamination of PE-MPs and fomesafen significantly interfered with the antioxidant system and energy regulation of soybean. We hope this study can provide a reliable scientific basis for sustainable development of the environment.

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整合自动机器学习和代谢重编程,识别土壤中的微塑料:大豆案例研究。
聚乙烯微塑料(PE-MPs)在土壤中的积累会严重影响植物的质量和产量,也会影响人类健康和食物链循环。因此,开发快速有效的检测方法至关重要。本研究利用传统的机器学习(ML)和 H2O 自动机器学习(H2O AutoML)提供了一个强大的框架,用于检测土壤中的 PE-MPs(按土壤干重计算分别为 0.1%、1% 和 2%)以及 PE-MPs 和福美双(一种常见的除草剂)的共污染。该框架是根据大豆植物代谢重编程的结果制定的。我们的研究表明,由于优化复杂参数所面临的挑战,传统 ML 的准确度较低。H2O AutoML 可以准确区分清洁土壤和污染土壤。值得注意的是,H2O AutoML 可以检测出土壤中低至 0.1%的 PE-MPs(准确率为 100%),以及 PE-MPs 和福美沙芬的共污染(准确率为 90%)。H2O AutoML 的 VIP 和 SHAP 分析表明,PE-MPs 以及 PE-MPs 和福美双的共污染会显著干扰大豆的抗氧化系统和能量调节。我们希望这项研究能为环境的可持续发展提供可靠的科学依据。
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