Universal predictive models on octanol-air partition coefficients at different temperatures for persistent organic pollutants.

Owing to the importance of octanol-air partition coefficients (KOA) in describing the partition of organic pollutants from air to environmental organic phases, the paucity of KOA data at different environmental temperatures, and the difficulty or high expenditures involved in experimental determination, the development of predictive models for KOA is necessary. Approaches such as this are greatly needed to evaluate the environmental fate of the ever-increasing list of production chemicals. Partial least squares (PLS) regression with 18 molecular structural descriptors was used to develop predictive models based on directly measured KOA values of selected chlorobenzenes, polychlorinated biphenyls (PCBs), polychlorinated naphthalenes, polychlorinated dibenzo-p-dioxins/dibenzofurans, polybrominated diphenyl ethers, polycyclic aromatic hydrocarbons, and organochlorine pesticides (OPs). An optimization procedure resulted in two temperature-dependent universal predictive models that explained at least 91 % of the variance of log KOA. Model 1 was the more general of the two models that could be used for all the persistent organic pollutant (POP) classes investigated. Although model 1 performed poorly for select OPs, this was attributed to wide variability in structural types within this subset of POPs and their diversity compared to the other POP classes that were investigated. The exclusion of the structurally complex OP subset resulted in a more precise model, model 5. Intermolecular dispersive interactions (induced dipole-induced dipole forces) between octanol and solute molecules play a decisive role in governing KOA and its temperature dependence. Further investigations are needed to better characterize the steric structures of the POPs under study, especially of OPs.