The reaction rate constants of ozone with 95 alkenes (-logkO3) and the hydroxyl radical (•OH) with 98 alkenes (-logkOH) in the atmosphere were predicted by quantitative structure-activity relationship (QSAR) models. Density functional theory (DFT) calculations were carried out on respective ground-state alkenes and transition-state structures of degradation processes in the atmosphere. Stepwise multiple linear regression (MLR) and general regression neural network (GRNN) techniques were used to develop the models. The GRNN model of -logkO3 based on three descriptors and the optimal spread σ of 0.09 has the mean root mean square (rms) error of 0.344; the GRNN model of -logkOH having four descriptors and the optimal spread σ of 0.14 produces the mean rms error of 0.097. Compared with literature models, the GRNN models in this article show better statistical characteristics. The importance of transition state descriptors in predicting kO3 and kOH of atmospheric degradation processes has been demonstrated.
atmospheric degradation; general regression neural network; quantitative structure-activity relationship; reaction rate constant; transition states
