Premium
A quantitative structure‐property relationship ( QSPR ) for estimating solid material‐air partition coefficients of organic compounds
Author(s) -
Huang Lei,
Jolliet Olivier
Publication year - 2019
Publication title -
indoor air
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.387
H-Index - 99
eISSN - 1600-0668
pISSN - 0905-6947
DOI - 10.1111/ina.12510
Subject(s) - quantitative structure–activity relationship , partition coefficient , biological system , material properties , materials science , biochemical engineering , computer science , chemistry , organic chemistry , machine learning , engineering , composite material , biology
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 R 2 of 0.93 and has been verified by internal and external validations to be robust, stable and has good predicting ability ( R ext 2 > 0.78). A generic QSPR is also developed to predict K ma from chemical properties and temperature only (adjusted R 2 = 0.84), without the need to assign a specific material type. These QSPR s 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.