Data evaluations and quantitative predictive models for vapor pressures of polycyclic aromatic hydrocarbons at different temperatures

Polycyclic aromatic hydrocarbons (PAHs) are typical and ubiquitous organic pollutants. Vapor pressures, which can be classified as solid vapor pressure (P_S) and (subcooled) liquid vapor pressure (P_L), are key physicochemical properties governing the environmental fate of organic pollutants. It is of great importance to develop predictive models of vapor pressures. In the present study, partial least squares (PLS) regression together with 15 theoretical molecular structural descriptors was used to develop quantitative predictive models for vapor pressures of PAHs at different temperatures. Two procedures were adopted to develop the optimal predictive models by eliminating redundant molecular structural descriptors. The cross-validated Q_cum² values for the obtained models have been found higher than 0.975, indicating good predictive ability and robustness of the models. It has been shown that the intermolecular dispersive interactions played a leading role in governing the values of logP_L. In addition to dispersive interactions, dipole-dipole interactions also played a secondary role in determining the magnitude of log P_S. In view of the scarceness of chemical standards for some PAHs, the difficulty in experimental determinations, and the high cost involved in experimental determinations, the obtained models should serve as a fast and simple first approximation of the vapor pressure values for PAHs at different environmental temperatures.