A new steady-state gas–particle partitioning model of polycyclic aromatic hydrocarbons: implication for the influence of the particulate proportion in emissions

Abstract

Abstract. Gas–particle (G–P) partitioning is a crucial atmospheric process for semi-volatile organic compounds (SVOCs), particularly polycyclic aromatic hydrocarbons (PAHs). However, accurately predicting the G–P partitioning of PAHs has remained a challenge. In this study, we established a new steady-state G–P partitioning model based on the level-III multimedia fugacity model, with a particular focus on the particulate proportion (ϕ0) of PAHs in emissions. Similar to previous steady-state models, our new model divided the G–P partitioning behavior into three domains based on the threshold values of log KOA (octanol–air partitioning coefficient), with slopes of 1, from 1 to 0, and 0 for the three domains. However, our model differed significantly from previous models in different domains. We found that deviations from the equilibrium-state G–P partitioning models were caused by both gaseous interference and particulate interference, with ϕ0 determining the influence of this interference. Different forms of the new steady-state model were observed under different values of ϕ0, highlighting its significant impact on the G–P partitioning of PAHs. Comparison of the G–P partitioning of PAHs between the prediction results of our new steady-state model and monitored results from 11 cities in China suggested varying prediction performances under different values of ϕ0, with the lowest root mean square error observed when ϕ0 was set to 0.9 or 0.99. The results indicated that the ϕ0 was a crucial factor for the G–P partitioning of PAHs. Furthermore, our new steady-state model also demonstrated excellent performance in predicting the G–P partitioning of PAHs with entirely gaseous emission and polybrominated diphenyl ethers with entirely particulate emission. Therefore, we concluded that the ϕ0 should be considered in the study of G–P partitioning of PAHs, which also provided a new insight into other SVOCs.