QSPR modeling for the prediction of partitioning of VOCs and SVOCs to indoor fabrics: Integrating environmental factors,Journal of Hazardous Materials

当前位置： X-MOL 学术 › J. Hazard. Mater. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

QSPR modeling for the prediction of partitioning of VOCs and SVOCs to indoor fabrics: Integrating environmental factors
Journal of Hazardous Materials ( IF 12.2 ) Pub Date : 2024-03-02 , DOI: 10.1016/j.jhazmat.2024.133945
Xiaojun Zhou ₁ , Weipeng Fang ₁ , Xuejiao Dong ₁ , Wenlong Li ₁ , Jialu Liu ₁ , Xinke Wang ₁

Affiliation

Porous fabrics have a significant impact on indoor air quality by adsorbing and emitting chemical substances, such as volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs). Understanding the partition behavior between organic compound molecules and indoor fabrics is crucial for assessing their environmental fate and associated human exposure. The physicochemical properties of fabrics and compounds are fundamental in determining the free energy of partitioning. Moreover, environmental factors like temperature and humidity critically affect the partition process by modifying the thermal and moisture conditions of the fabric. However, existing methods for determining the fabric-air partition coefficient are limited to specific fabric-chemical combinations and lack a comprehensive consideration of indoor environmental factors. In this study, large amounts of experimental data on fabric-air partition coefficients () of (S)VOCs were collected for silk, polyester, and cotton fabrics. Key molecular descriptors were identified, integrating the influences of physicochemical properties, temperature, and humidity. Subsequently, two typical quantitative structure-property relationship (QSPR) models were developed to correlate the values with the molecular descriptors. The fitting performance, robustness, and predictive ability of the two QSPR models were evaluated through statistical analysis and internal/external validation. This research provides insights for the high-throughput prediction of the environmental behaviors of indoor organic compounds.

中文翻译：

用于预测室内织物中 VOC 和 SVOC 分配的 QSPR 模型：整合环境因素

多孔织物通过吸附和释放挥发性有机化合物（VOC）和半挥发性有机化合物（SVOC）等化学物质，对室内空气质量产生重大影响。了解有机化合物分子和室内织物之间的分配行为对于评估其环境命运和相关的人类暴露至关重要。织物和化合物的物理化学性质是决定分配自由能的基础。此外，温度和湿度等环境因素会改变织物的热和湿度条件，从而严重影响分区过程。然而，现有确定织物-空气分配系数的方法仅限于特定的织物-化学组合，缺乏对室内环境因素的综合考虑。在这项研究中，收集了大量关于丝绸、涤纶和棉织物的(S)VOCs织物-空气分配系数()的实验数据。确定了关键分子描述符，整合了物理化学性质、温度和湿度的影响。随后，开发了两种典型的定量结构-性质关系（QSPR）模型，将这些值与分子描述符相关联。通过统计分析和内部/外部验证评估了两个 QSPR 模型的拟合性能、稳健性和预测能力。这项研究为室内有机化合物环境行为的高通量预测提供了见解。

更新日期：2024-03-02

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南