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 (Kfa) 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 Kfa 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.
Keywords: Humidity; Indoor air quality; Organic compounds; Partition coefficient; Temperature.
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