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使用传统的统计技术和机器学习数据驱动模型,在沿海土壤中追踪重金属的来源足迹

Abu Reza Md Towfiqul Islam¹, Memet Varol², Javed Mallick³

¹Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh; Department of Earth and Environmental Science, College of Science, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

Marine pollution bulletin

|September 13, 2025

概括

这项研究使用先进的统计和机器学习方法追踪了孟加拉国沿海土壤中的重金属来源. 自然和废船行业被确定为关键来源,为污染控制战略提供信息.

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:

沿海土壤中的重金属 (HM) 污染对生态系统和人类健康构成风险.
了解HM源对于有效的环境管理和污染控制至关重要.

研究的目的:

在孟加拉国沿海土壤中检测和追踪重金属来源.
整合传统的统计和机器学习方法来识别HM源.

主要方法:

采用了主要组件分析 (PCA),主要坐标分析 (PCoA),自我组织地图 (SOM),条件推理树 (CIT),脊柱回归 (RR) 和夏普利添加式扩展 (SHAP).
分析了土壤样本中的Pb,Cd,Mn,As,Ni和Fe度.
评估影响HM分布的空间模式和物理化学因素.

主要成果:

Pb,Cd,Mn和As的度超过了页岩平均值.
PCoA解释了HM含量的75.46%的空间变化.
SOM确定了As,Ni,Fe,Mn的自然来源,以及Cu,Zn,Pb的破船工业来源.
CIT表示地质过程影响Fe,Cd,As,Mn和Ni.
RR模型显示了Pb,Zn,Mn的高预测性能.
SHAP分析强调了土壤pH值,泥和有机物质是HM积累的关键因素.

结论:

综合传统和机器学习方法有效地追踪了沿海土壤中的HM来源.
确定了自然和人为 (破船行业) 导致HM污染的来源.
物理化学因素显著影响HM积累,为沿海地区的目标土壤污染控制提供了基础.

关键词:

沉重金属 (重金属) 类型孟加拉国东北部海岸坡回归的回归方法自组织地图的自我组织地图土壤污染土壤污染

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