The cotton-air partition coefficient (Kca) is a key parameter governing the concentration and human exposure to volatile organic compounds (VOCs) in indoor environments. We experimentally determined Kca for 46 diverse VOCs, including carbonyls, aromatics, amines, amides, alkenes, acids, alcohols, furan, and siloxanes. Results showed that compounds with a lower vapor pressure and higher molecular weight exhibited stronger sorption. Water sorbed in cotton contributed less than half an order of magnitude to Kca for most VOCs, except for some highly water-soluble species. Octanol-air partition coefficients (log Koa) correlate well with log Kca for homologous compounds but demonstrate limited predictive capacity across different VOC classes. The predictions of log Kca using methyl cellulose as a cotton surrogate led to deviations of up to one log unit. We developed a polyparameter linear free energy relationship (pp-LFER) model for log Kca prediction based on the experimental data. The model shows good agreement with experimental data (adjusted R2 = 0.73, root-mean-square error ∼ 0.55 log units), providing a useful tool for chemical distribution and exposure assessment. Field investigations further demonstrated that environmental factors (temperature, humidity, particle deposition, and interfering compounds) can induce 1-2 orders of magnitude variation in Kca values compared to controlled laboratory measurements.
Yang et al. (Fri,) studied this question.