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可解释的机器学习模型揭示了在一个世纪的规模上沉积物中微塑料和 perfluoroalkyl 物质的伙伴关系

Ligang Deng¹, Kai Liu¹, Yifan Fan²

¹School of Environment, Nanjing Normal University, Nanjing 210023, China; State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China.

Journal of hazardous materials

|December 31, 2024

概括

自20世纪60年代以来,甲基物质 (PFAS) 和微塑料在太湖沉积物中共同演变,自20世纪90年代以来观察到显著增加. 机器学习揭示了影响它们度的相互作用和关键环境因素.

科学领域:

环境科学环境科学
环境化学环境化学
机器学习应用机器学习应用

背景情况:

perfluoroalkyl物质 (PFAS) 和微塑料是水生生态系统中复杂相互作用的新兴污染物.
实验确定它们在湖泊沉积物中的共发生和协同效应是具有挑战性的.
了解它们的历史趋势和影响因素对于环境风险评估至关重要.

研究的目的:

为了重建PFAS和微塑料在太湖沉积物中的共存历史.
通过可解释的机器学习揭示PFAS和微塑料之间的协作相互作用.
确定影响其度的关键环境因素和值.

主要方法:

对太湖沉积物核心的分析,以建立历史记录.
应用后期可解释的机器学习模型,包括极端梯度增强.
利用特征重要性和沙普利的附加解释来阐明污染物相互作用和影响因素.

主要成果:

微塑料和PFAS在20世纪60年代出现,自20世纪90年代以来大幅增加,特别是在0-10厘米顶部沉积层.
透明和颗粒微塑料被发现是PFAS度的重要驱动因素,而 perfluorooctane sulfonate (PFOS) 和总PFAS影响了微塑料水平.
湿度含量,氧化还原潜力和磁性易感性 (χfd, χ_ARM) 被确定为关键的环境因素. 确定了PFAS和微塑料度的具体值.

结论:

这项研究为湖泊沉积物中PFAS和微塑料之间的相互作用提供了一个世纪规模的阐明.
可解释机器学习为了解复杂的环境污染物动态提供了一种强大的方法.
这些发现强调了新出现的污染物及其环境驱动因素的相互联系.

关键词:

共存是一种共存.新出现的污染物新出现的污染物可以解释的机器学习沉积物的核心是沉积物.沉积物磁性是一种沉积物磁性.

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