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使用可解释机器学习影响大气颗粒物氧化潜力的关键有毒成分和来源:来自雾事件的洞察力

Ruiyu Li¹, Caiqing Yan¹, Qingpeng Meng¹

¹Environment Research Institute, Shandong University, Qingdao 266237, China.

Journal of hazardous materials

|December 12, 2023

概括

在雾期间的细颗粒物 (PM2.5) 显示出更高的氧化毒性. 关键成分如水溶性有机碳和煤炭燃烧等来源对这种毒性有很大影响,影响人类健康.

科学领域:

环境科学环境科学
公共卫生公共卫生
大气化学大气化学

背景情况:

雾对空气质量和人类健康产生重大影响.
大气中的细颗粒物 (PM2.5) 在雾事件中构成风险.
了解PM2.5对健康的影响和机制至关重要.

研究的目的:

调查雾期间的PM2.5对健康的影响和机制.
分析PM2.5.5的化学成分和氧化潜力 (OP).
通过可解释机器学习 (IML) 来识别影响OP的关键PM2.5组件和来源.

主要方法:

在2021-2022年四季期间在青岛收集PM2.5样本.
通过dithiothreitol (DTT) 方法分析了PM2.5的化学成分并确定了氧化潜力 (OP).
应用正矩阵分解和可解释机器学习 (IML) 来识别影响OP的PM2.5来源和组件.

主要成果:

在雾期间,PM2.5呈现出更高的氧化毒性.
水溶性有机碳 (WSOC),NH4+,K+和水溶性Fe与增强的DTT活动 (DTTv) 有正相关.
IML确定了WSOC和K+作为DTTv的重要贡献者,与煤炭燃烧和生物质燃烧一起作为主要来源.

结论:

雾加剧了PM2.5的氧化毒性.
煤炭燃烧和生物质燃烧是PM2.5氧化潜力的关键驱动因素.
在雾中,IML提供了有关PM2.5成分-来源-毒性关系的宝贵见解.

关键词:

化学成分化学成分微细颗粒物质的细颗粒物质的细颗粒物质机器学习是机器学习.阳性矩阵分解因子是正的.有反应性氧物种的反应性氧物种.

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