在泰国Bang Pakong流域的土壤微塑料分布的机器学习驱动分析,泰国
Ugochukwu Ihezukwu1, Chawalit Charoenpong2, Srilert Chotpantarat3
1International Program in Hazardous Substance and Environmental Management, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand.
Environmental pollution (Barking, Essex : 1987)
|May 3, 2025
概括
微塑料污染很普遍,城市土壤的度最高. 机器学习准确地预测了微塑料的分布,突出了城市化和河流的近距离作为有针对性的管理的关键因素.
科学领域:
- 环境科学 环境科学
- 环境化学环境化学
- 数据科学数据科学数据科学
背景情况:
- 微塑料 (MP) 是持续存在的全球污染物,但它们与环境共变量之间的关系未得到充分研究.
- 了解影响MP发生和分布的因素对于有效的环境管理至关重要.
研究的目的:
- 调查影响Bang Pakong流域微塑料发生和分布的因素.
- 评估用于预测微塑料空间分布的机器学习模型.
主要方法:
- 收集了40个土壤样本,涉及各种土地使用类型.
- 使用密度分离,H2O2消化和FTIR进行MP分析和聚合物识别.
- 使用随机森林 (RF) 和反向距离权重 (IDW) 进行预测和空间分布建模.
主要成果:
- 微塑料平均为1121±2465.6件/公斤干土,其中较小的颗粒 (<0.5毫米),碎片,透明聚合物和聚烯 (PP) 主导.
- 城市土壤的MP度最高 (2331±4114个物品/公斤).
- 射频模型实现了高精度 (R2 = 0.82),确定了泥含量和距离河流的距离作为关键预测指标.
结论:
- 城市化和水文因素显著推动了微塑料的分布和沉积.
- 空间分析显示,一个度梯度朝着Bang Pakong河.
- 研究结果支持在高风险地区制定有针对性的缓解策略,这些策略由城市化和水文影响来决定.
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