使用可解释的机器学习模型揭示欧洲地表水中的PFAS危险
Li Zhao1, Jian Chen2, Jiaqi Wen3
1Guangdong Institute for Drug Control, Guangzhou 510006, China; School of Environment, South China Normal University, Guangzhou 510006, China.
Environment international
|May 6, 2025
概括
这项研究绘制了欧洲地表水中的per-和polyfluoroalkyl物质 (PFAS),揭示了广泛的污染和生态风险. 一个关键的发现是,接近PFAS来源显著影响污染水平.
科学领域:
- 环境化学环境化学
- 生态毒理学 生态毒理学
- 数据科学数据科学数据科学
背景情况:
- 和多醇基物质 (PFAS) 或"永久化学品"在地表水中广泛存在,对人类健康和生态系统构成风险.
- 目前的监测正在努力捕捉欧洲地表水中的PFAS污染的全部程度.
- 了解PFAS空间分布对于有效的风险评估和管理至关重要.
研究的目的:
- 开发机器学习模型,用于绘制欧洲地表水中的PFAS度和生态风险.
- 在44个国家确定高PFAS水平和生态关注的地区.
- 确定影响PFAS污染的关键因素,并建立与风险相关的距离值.
主要方法:
- 开发和应用两个机器学习模型.
- 创建PFAS水平和风险的高分辨率 (2公里) 空间地图.
- 分析影响PFAS度的因素,包括距离点源的距离.
主要成果:
- 据估计,近8000人暴露在地表水中,超过了欧洲饮用水指南的100 ng/L.
- 确定了高风险地区,主要位于德国,荷兰,葡萄牙,西班牙和芬兰.
- 量化了PFAS点源的距离作为关键因素 (14%-19%),并确定了4.1-4.9公里内的升高危险值.
结论:
- 机器学习为欧洲地表水的PFAS污染提供了前所未有的空间洞察力.
- 靠近工业来源是PFAS污染的主要驱动因素.
- 调查结果提供了一个关键距离值,以指导PFAS缓解的监管行动.
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