In this work, hourly primary and secondary organic carbon were estimated by a novel Bayesian inference approach in suburban Hong Kong. Their multi-temporal-scale variations and evolution characteristics during PM_2.5 episodes were examined. The methodology could serve as a guide for other locations with similar monitoring capabilities. The observation-based results are helpful for understanding the evolving nature of secondary organic aerosols and refining the accuracy of model simulations.