Quantitative predictive models for octanol–air partition coefficients of polybrominated diphenyl ethers at different temperatures

Abstract

Quantitative predictive models for octanol–air partition coefficients of polybrominated diphenyl ethers at different environmental temperatures ( T) were developed. Partial least squares (PLS) regression was used for model development. A list of 18 theoretical molecular structural descriptors was screened by PLS analysis. The optimal model was selected from the one containing nine theoretical molecular descriptors and 1/ T as predictor variables. The cross-validated Q 2 cum value for the optimal model is 0.975, indicating a good predictive ability and stability of the model. Intermolecular dispersive interactions play a leading role in governing the magnitude of log K OA. The lower the E LUMO (the energy of the lowest unoccupied molecular orbital), the greater the intermolecular interactions between octanol and PCB molecules, and thus the greater the log K OA values.