织物影响环境中的PFAS跨媒体动态和优先级风险:使用地理空间机器学习
Fan Yang1, Minrui Liu1, Shuren Liu2
1College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China.
Environment international
|December 20, 2025
概括
机器学习模型揭示了织行业对中国的和多基物质 (PFAS) 污染影响.
科学领域:
- 环境化学环境化学
- 环境科学 环境科学
- 毒理学 毒理学 毒理学
背景情况:
- 和多基基物质 (PFAS) 构成环境风险,特别是来自织工业.
- 了解PFAS在土壤和树皮之间的交叉媒体动态受到传统模型的限制.
研究的目的:
- 通过机器学习框架,阐明中国织中心的土壤和树皮之间的PFAS相互作用.
- 评估环境风险并确定高优先级的PFAS用于缓解策略.
主要方法:
- 在48个地点应用的地理空间信息机器学习 (ML) 框架.
- 在土壤和树皮样本中对31个PFAS的目标分析.
- 开发一个新的多标准风险评估框架 (ToxPi,风险指数,EHPi).
主要成果:
- 在树皮中比在土壤中发现的PFAS度更高.
- 超短链和新兴的PFAS占主导地位,其中TFA和8:2 FTS被确定为高风险.
- 轻GBM ML模型显示出优异的预测性能 (R2 = 0.962-0.974).
- 树皮PFAS显著预测了土壤污染,突出了跨区间的影响.
结论:
- 机器学习有效地模拟了织品影响环境中的PFAS动态.
- 织行业的排放大大增加了土壤和树皮的PFAS负担.
- 综合风险评估框架有助于优先考虑PFAS的污染减缓战略.
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