A quantitative structure-property relationship (QSPR) for estimating solid material-air partition coefficients of...

The material-air partition coefficient (K_ma ) is a key parameter to estimate the release of chemicals incorporated in solid materials and resulting human exposures. Existing correlations to estimate K_ma are applicable for a limited number of chemical-material combinations without considering the effect of temperature. The present study develops a quantitative structure-property relationship (QSPR) to predict K_ma for a large number of chemical-material combinations. We compiled a dataset of 991 measured K_ma for 179 chemicals in 22 consolidated material types. A multiple linear regression model predicts K_ma as a function of chemical's K_oa , enthalpy of vaporization (∆H_v ), temperature, and material type. The model shows good fitting of the experimental dataset with adjusted R² of 0.93 and has been verified by internal and external validations to be robust, stable and has good predicting ability ( $$  > 0.78). A generic QSPR is also developed to predict K_ma from chemical properties and temperature only (adjusted R²  = 0.84), without the need to assign a specific material type. These QSPRs provide correlation methods to estimate K_ma for a wide range of organic chemicals and materials, which will facilitate high-throughput estimates of human exposures for chemicals in solid materials, particularly building materials and furniture.