预测水培植物对全氟和多氟烷基物质 (PFAS) 的吸收和转移的机器学习方法

IF 7.4 Q1 ENGINEERING, ENVIRONMENTAL
Olatunbosun Adu, Michael Taylor Bryant, Xingmao Ma* and Virender K. Sharma*, 
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引用次数: 0

摘要

利用机器学习(ML)模型,从 19 种 PFAS 化合物和 9 种植物物种的实验数据中预测了植物对全氟化烃和多氟化烃(PFAS)的吸收和积累,以根浓度因子(RCF)、芽浓度因子(SCF)和易位因子(TF)表示。首先使用无监督主成分分析(PCA)对输入数据进行分类,然后应用有监督的 ML 模型(包括多元线性回归模型(MLR)、人工神经网络(ANN)、随机森林(RF)和支持向量机(SVM)算法)预测所选的输出参数。在所有测试模型中,RF 的预测准确率最高。使用 RF 进行的特征重要性分析表明,分子量、暴露时间和植物种类是预测水培系统中 RCF、SCF 和 TF 的最重要参数。我们进一步将 RF 应用于估算两种最普遍的全氟辛烷磺酸化合物(全氟辛酸和全氟辛烷磺酸)及其常见替代品的 RCF、SCF 和 TF,结果发现它们的常见替代化合物在植物根部和芽中的累积量相当或更高。我们的研究结果表明,ML 方法可以深入了解植物对全氟辛烷磺酸的吸收和积累情况,并揭示全氟辛烷磺酸及其替代品可能带来的食品安全问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Machine Learning Approach for Predicting Plant Uptake and Translocation of Per- and Polyfluoroalkyl Substances (PFAS) from Hydroponics

A Machine Learning Approach for Predicting Plant Uptake and Translocation of Per- and Polyfluoroalkyl Substances (PFAS) from Hydroponics

Plant uptake and accumulation of per- and polyfluoroalkyl substances (PFAS), represented by the root concentration factor (RCF), shoot concentration factor (SCF), and translocation factor (TF), were predicted using machine learning (ML) models from experimental data with 19 PFAS compounds and nine plant species. Unsupervised principal component analysis (PCA) was first used to classify the input data, and then supervised ML models, including multiple linear regression model (MLR), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) algorithms, were applied for predicting the chosen output parameters. RF displayed the highest prediction accuracy among the tested models. Feature importance analysis performed using RF showed that the molecular weight, exposure time, and plant species are the most important parameters for predicting RCF, SCF, and TF in hydroponic systems. RF was further applied to estimate RCF, SCF, and TF of the two most prevalent PFAS compounds, perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), and their common alternatives and the results revealed that their common replacing compounds have either comparable or higher accumulation in plant roots and shoots. Our results demonstrated that the ML approach could generate critical insight into PFAS plant uptake and accumulation and shed light on the potential food safety concerns from PFAS and their replacements.

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来源期刊
ACS ES&T engineering
ACS ES&T engineering ENGINEERING, ENVIRONMENTAL-
CiteScore
8.50
自引率
0.00%
发文量
0
期刊介绍: ACS ES&T Engineering publishes impactful research and review articles across all realms of environmental technology and engineering, employing a rigorous peer-review process. As a specialized journal, it aims to provide an international platform for research and innovation, inviting contributions on materials technologies, processes, data analytics, and engineering systems that can effectively manage, protect, and remediate air, water, and soil quality, as well as treat wastes and recover resources. The journal encourages research that supports informed decision-making within complex engineered systems and is grounded in mechanistic science and analytics, describing intricate environmental engineering systems. It considers papers presenting novel advancements, spanning from laboratory discovery to field-based application. However, case or demonstration studies lacking significant scientific advancements and technological innovations are not within its scope. Contributions containing experimental and/or theoretical methods, rooted in engineering principles and integrated with knowledge from other disciplines, are welcomed.
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