Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-5803-2024
https://doi.org/10.5194/acp-24-5803-2024
Research article
 | 
22 May 2024
Research article |  | 22 May 2024

Bayesian inference-based estimation of hourly primary and secondary organic carbon in suburban Hong Kong: multi-temporal-scale variations and evolution characteristics during PM2.5 episodes

Shan Wang, Kezheng Liao, Zijing Zhang, Yuk Ying Cheng, Qiongqiong Wang, Hanzhe Chen, and Jian Zhen Yu

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Cited articles

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Short summary
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 PM2.5 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.
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