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整合PMF和可解释的机器学习,用于城市绿色空间中微塑料的来源分配和生态风险

Jianhao Song¹, Xingnian Ren¹, Jinjiang Duan¹

¹School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu 611700, China.

Journal of hazardous materials

|October 15, 2025

概括

微塑料 (MPs) 的污染威胁着城市绿地. 这项研究显示,城市土壤中的MP主要来自农业,工业和交通,预计到2030年风险将增加.

科学领域:

环境科学环境科学
生态毒理学生态毒理学
城市生态城市生态学

背景情况:

微塑料 (MP) 是一个日益严重的全球环境问题,带来生态风险.
对于生活质量至关重要的城市绿地,在PM污染方面没有得到充分的研究.
现有的MP研究主要集中在农业土壤上,城市绿地未被充分探索.

研究的目的:

调查议员在城市绿地中的空间分布.
确定这些环境中MPs污染的来源.
评估MP在城市绿地中的当前和未来的生态风险.

主要方法:

量化MP的丰度,从141到1258个项目/kg.
按尺寸 (近一半<100微米) 和类型 (聚烯,聚乙烯,PET占主导地位) 进行特征MP.
应用源分配模型来识别来源 (农业,国内工业,交通).

主要成果:

议员的丰富度在城市绿地之间有很大差异.
碎片构成了大多数 (54.3%) 的国会议员,其中很大一部分是非常小的 (<100微米).
聚烯,聚乙烯和PET是最常见的MP类型.

结论:

城市绿地中的MP主要来自农业,家庭工业和与交通有关的活动.
目前的生态风险较低,但预计的经济和运输增长可能会在2030年之前将风险提升到适度水平.
调查结果为城市生态系统中针对性PM污染管理策略提供了关键数据.

关键词:

生态风险评估生态风险评估机器学习是机器学习.微塑料是一种微塑料.来源分配的分配方式城市绿色空间城市绿色空间

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