可解释的人工智能用于预测细颗粒和关键化学驱动因素的氧化潜力
Seunghye Lee1, Minhan Park1, Jingyu Lee1
1Department of Environment and Energy Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea.
Journal of hazardous materials
|September 19, 2025
概括
微粒中的氧化潜力 (OP) 可以使用机器学习模型进行预测. 和是关键预测因素,在它们对OP的影响方面观察到区域差异.
科学领域:
- 环境化学环境化学
- 大气科学 大气科学
- 毒理学 毒理学 毒理学
背景情况:
- 氧化潜力 (OP) 是评估环境细颗粒的关键健康指标.
- 了解OP的化学驱动因素对于有效的空气质量管理至关重要.
研究的目的:
- 开发一种使用化学组成的细颗粒OP的预测模型.
- 确定影响OP的关键化学成分及其相互作用.
- 为了利用可解释的人工智能 (XAI) 来实现模型的解释性.
主要方法:
- 从中国和韩国收集了细颗粒样本.
- 分析的化学成分包括Mn,Cu,Zn,Pb和水溶性有机碳 (WSOC).
- 通过XAI技术训练和评估各种机器学习模型,包括投票回归,随机森林和梯度增强.
主要成果:
- 一个投票回归模型在预测OP变化方面取得了74.9%的准确性.
- (Mn) 是最重要的预测因素,其次是Pb,WSOC,Cu和 Zn.
- 观察到区域差异:Pb主导城市的广州,而WSOC在农业的吉姆杰是关键.
- 确定了Cu (>0.004μg/m3) 和WSOC在OP上的对抗作用.
结论:
- 机器学习模型,特别是投票回归,有效地预测细颗粒OP.
- XAI方法为化学成分及其对OP的相互作用的影响提供了宝贵的见解.
- 确定关键的贡献者和相互作用有助于制定有针对性的空气污染控制战略.
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