Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-5803-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-5803-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2024
Research article |
| 22 May 2024
| 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
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Kezheng Liao
Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Zijing Zhang
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Yuk Ying Cheng
Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Qiongqiong Wang
School of Environmental Studies, China University of Geosciences, Wuhan, China
Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Hanzhe Chen
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong SAR, China
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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.
In this work, hourly primary and secondary organic carbon were estimated by a novel Bayesian...
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