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

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 Q2cum 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 logKOA. The lower the ELUMO (the energy of the lowest unoccupied molecular orbital), the greater the intermolecular interactions between octanol and PCB molecules, and thus the greater the logKOA values.