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使用机器学习预测药品和个人护理产品在长期老化的微塑料上的吸附能力

Jingjing Yao¹, Haipu Li², Hui Ying Yang³

¹Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China; Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.

Journal of hazardous materials

|July 5, 2023

概括

长期衰老显著增加了药品和个人护理产品 (PPCP) 的微塑料吸附. PPCP的表面化学和分子特性是关键因素,可以通过光谱和机器学习模型进行预测.

科学领域:

环境化学环境化学
材料科学材料科学材料科学
分析化学分析化学

背景情况:

微塑料 (MP) 是一种持久性环境污染物,可以吸附各种污染物.
药品和个人护理产品 (PPCPs) 是水生环境中令人担忧的新兴污染物.
国会议员的老化过程改变了它们的表面特性,可能会影响污染物吸附.

研究的目的:

研究66种PPCP在不同条件下老化的微塑料上的吸附机制.
为了确定PPCP分子特性对吸附能力的影响.
使用光谱和机器学习技术,开发PPCP在微塑料上吸附的预测模型.

主要方法:

微塑料使用硫酸盐,氧化和芬顿试剂进行了人工老化,持续时间不同 (54,110,500天).
在原始和老化微塑料上进行了66个PPCP的吸附实验.
使用量子化学计算 (B3LYP/6-31G*) 来评估PPCP的分子特性.
减弱总反射率里叶变换红外光谱学 (ATR-FTIR) 和机器学习模型 (最大信息系数,梯度增强决策树回归) 被用于预测建模.

主要成果:

与原始微塑料相比,长期老化的微塑料对PPCP具有显著更高的总吸附能力 (q_e).
吸附能力主要受到PPCP分子特性的影响,包括总能量,最高占有分子轨道能量和能量差距.
基于ATR-FTIR数据的预测模型在预测个体和总PPCP吸附方面取得了高准确性 (R²>0.96).
表面功能组变化和特定的光谱区域 (15281700 cm-1) 被确定为吸附过程的关键.

结论:

长期的老化大大提高了微塑料对PPCP的吸附潜力.
综合光谱学和机器学习的预测模型为评估各种微塑料类型的PPCP吸附提供了强大的工具.
了解微塑料表面化学,PPCP分子特性和衰老之间的相互作用对于环境风险评估至关重要.

关键词:

吸附能力吸附能力吸附能力里埃变换红外光谱法红外光谱法长期老化的微塑料机器学习机器学习药品和个人护理产品.

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