Science of the Total Environment, cilt.949, 2024 (SCI-Expanded)
Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) are semi-volatile organic compounds (SVOCs) existing in the atmosphere in the gas and particulate phase, remain persistent for a long time and pose a high risk to the environment and human health. In this study, PCDD/F measurements were made in an urban area between June 2022 and April 2023. In order to understand the fate of PCDD/Fs, the gas/particle (G/P) partitioning was studied. Although various models have been developed to determine the G/P partitioning of SVOCs, only logKp-logPL0, Junge-Pankow and Harner-Bidleman models are generally used for PCDD/Fs. In this study, nine different models (Junge-Pankow, Harner-Bidleman, Dachs-Eisenreich, Li-Ma-Yang, pp-LFER, mp-pp-LFER, QSPR, logKp-logPL0, logKp-logKOA) were employed to determine the G/P partitioning. To the best of our knowledge, pp-LFER, mp-pp-LFER and QSPR models were evaluated for PCDD/Fs for the first time in this study. In addition, the performance of the models within the equilibrium (EQ), non-equilibrium (NE) and maximum partitioning (MP) domain was investigated for PCDD/Fs for the first time in this study. Accordingly, models based on absorption in the EQ domain, adsorption in the NE domain and adsorption and absorption mechanisms in the MP domain were found to be effective in explaining the G/P transitions. It was determined that there is no equilibrium situation in the G/P partitioning. The Junge-Pankow, pp-LFER, Li-Ma-Yang and QSPR models under-predicted the particle fraction values while the other models showed a high prediction profile. The Li-Ma-Yang model showed the closest results to the measured particle fraction values, and it determined that deposition mechanisms are of non-negligible importance in the G/P partitioning of PCDD/Fs. One of the new models, the pp-LFER model, has shown remarkable success at high logKOA values. The mp-pp-LFER model, which overestimated the contribution of the adsorption mechanism, showed a very high prediction profile compared to the measured values.