通过可解释的工作流程,探索影响湖泊沉积物中重金属的化学分数的主要磁性参数
Ligang Deng1, Yifan Fan2, Kai Liu3
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
|February 25, 2024
概括
湖泊沉积物的磁性特性有效地预测了重金属 (HMs) 的风险. 支持矢量机 (SVM) 和可解释机器学习 (IML) 确定了用于评估湖泊沉积物中HM生态风险的关键磁性指标.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 湖泊沉积物与重金属化物 (HMs) 呈现强烈的相关性.
- 了解沉积物的磁性特性与HM化学分数 (CFs) 之间的关系对于风险评估至关重要.
- 使用磁性测量和机器学习 (ML) 预测HM风险仍然是一个挑战.
研究的目的:
- 用磁性参数评估九种ML方法的效率,以预测使用磁性参数的HM风险评估代码 (RAC) 和二次阶段与初级阶段 (RSP) 的比例.
- 在浅湖沉积物中确定HM风险评估的关键磁性指标.
- 建立一个有效的工作流程,结合磁性测量和可解释的ML用于HM风险评估.
主要方法:
- 从一个浅湖收集和分析沉积物核心.
- 确定了总HM含量,HM CFs和磁性参数 (例如 χARM,HIRM).
- 应用了9个ML模型,包括支持矢量机 (SVM) 和可解释ML (IML) 用于预测和指标识别.
主要成果:
- 在预测RAC和RSP方面,SVM表现出卓越的性能,确定系数 (R2>0.8) 在预测RAC和RSP方面.
- 关键磁性参数 (χARM,HIRM, χARM/χ, χARM/SIRM) 被确定为HM风险的重要指标.
- 磁性参数的特定范围表明了 (Cd), (Hg) 和 (Sb) 的潜在生态风险.
结论:
- 磁性测量与ML相结合,特别是SVM和IML,为评估湖泊沉积物中的HM风险提供了一种有效的方法.
- 这种集成的工作流提供了对HM污染监测和生态风险评估的新见解.
- 该研究强调了利用沉积物的磁性特性来预测HM生物可用性和环境影响的潜力.
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