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从机器学习的角度来看,通过环境磁性来表征湖泊沉积物中的 perfluoroalkyl 物质的季节性变化

Ligang Deng¹, Yuteng Zhang¹, Jialu Shi²

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

Environmental research

|September 11, 2025

概括

湖泊沉积物中的季节性per-和多基物质 (PFAS) 受环境因素和粒子输入的影响. 机器学习模型显示,湖泊的化学因素和磁性特性显著影响PFAS水平,春季显示度更高.

科学领域:

环境化学环境化学
地质化学地质化学
生态毒理学生态毒理学

背景情况:

和多基基物质 (PFAS) 是持久的环境污染物.
了解湖泊沉积物中PFAS的季节性动态对于环境管理至关重要.
目前关于湖泊沉积物中季节性PFAS控制机制的知识有限.

研究的目的:

用可解释的机器学习分析PFAS在一个大浅湖的季节性变化.
量化环境和磁性参数对PFAS分布的影响.
阐明控制季节性PFAS动态的生命周期机制.

主要方法:

可解释机器学习 (ML),包括随机森林,XGBoost,CatBoost,SVM和贝叶斯神经网络 (BNN).
冗余分析 (RDA) 和多重线性回归 (MLR) 来评估环境因素的影响.
分析季节性变化,空间分布和PFAS的特征重要性.

主要成果:

春季PFAS度显著上升 (p < 0.01),没有显著的空间分布 (p > 0.05).
环境因素解释了47%的PFAS分布;特定的PFAS与颗粒物或工业来源有关.
随机森林模型实现了PFAS模拟的平均R2为0.85,通过空间数据的整合增强了>20%.
湖泊环境的化学因素 (58.87%) 和磁性因素 (32.57%) 是PFAS模拟的关键驱动因素.

结论:

湖泊沉积物中的季节性PFAS动态是复杂的,由环境因素,磁性和粒子输入驱动.
外源粒子输入增加了PFAS水平,而秋冬降低可能反映了内部转变.
该研究强调了考虑生命周期机制管理季节性PFAS分配的重要性.

关键词:

当地的摩兰斯 I.机器学习是机器学习.磁性测量测量方法磁性测量方法金融基金管理系统 (PFAS) 是一个.这是一种事后解释.

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