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基于机器学习的来源分配和以来源为导向的概率性生态风险评估重金属在城市绿色空间

Jun Li¹, Jia-Yi Lu¹, Xin-Ying Tuo¹

¹School of Environment and Urban Construction, Lanzhou City University, Lanzhou 730070, China.

Ecotoxicology and environmental safety

|July 22, 2025

概括

城市化增加了城市绿地 (UGS) 的重金属 (HM) 污染. 兰州兰州兰州

科学领域:

环境科学环境科学
城市生态城市生态学
地质化学地质化学

背景情况:

全球城市化推动了城市绿地 (UGS) 中的重金属 (HM) 污染,造成生态风险.
中国主要的河谷城市兰州在UGS中面临着重大的HM污染挑战.
了解HM源和风险对于有效的环境管理至关重要.

研究的目的:

评估兰州UGS中HM的概率污染水平和生态风险.
确定和量化这些地区的HM污染的主要来源.
为有针对性的整治策略提供见解.

主要方法:

使用污染指数 (Igeo,EF,NIEF) 和蒙特卡洛模拟 (MCS) 来进行概率污染评估.
集成的自我组织地图 (SOM) 超级集群,随机森林 (RF) 和正矩阵因数分解 (PMF) 用于HM源分配.
结合生态风险指数 (RI) 与MCS和PMF用于以来源为导向的概率生态风险评估.

主要成果:

平均度Cd,Cu,Hg,Pb和Zn超过了当地的背景值,Cd (90.91%),Hg (94.95%),Pb (80.81%) 和Zn (87.88%) 的超值率很高.
高度的HM度,特别是Zn,Cd,Pb和Hg,显示出与工业活动和城市发展相关的空间模式.
观察到总体中等污染,主要由 (Cd) 和 (Hg) 驱动.

结论:

交通排放,工业活动和煤炭燃烧是兰州UGS的HM污染的主要来源.
Cd和Hg构成最大的生态风险,主要来自工业活动和煤炭燃烧.
研究结果支持制定源特定的修复策略,用于城市环境中的HM污染管理.

关键词:

绿色空间是绿色的空间.蒙特卡洛模拟蒙特卡洛模拟随机的森林随机的森林自组织地图的自我组织地图微量元素中的微量元素.

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