Articles | Volume 26, issue 16
https://doi.org/10.5194/acp-26-11683-2026
© Author(s) 2026. 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-26-11683-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Aug 2026
Research article |
| 19 Aug 2026
| 19 Aug 2026
Quantitative insights into regime-dependent aerosol pH variability in ammonia-rich urban Beijing from explainable machine learning
Jing Duan
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Ting Wang
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Ru-Jin Huang
CORRESPONDING AUTHOR
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an 710049, China
University of Chinese Academy of Sciences, Beijing 100049, China
Jingye Ren
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Haobin Zhong
School of Advanced Materials Engineering, Jiaxing Nanhu University, Jiaxing 314001, China
State Key Laboratory of Advanced Environmental Technology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Chunshui Lin
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Yanan Zhan
State Key Laboratory of Loess Science, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an 710049, China
Huabin Huang
College of Environment and Public Health, Xiamen Huaxia University, Xiamen 361024, China
Yongjie Li
Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR 999078, China
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Preprint archived
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Short summary
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Secondary organic aerosol (SOA) can be produced by various pathways, but its formation mechanisms are unclear. Observations were conducted in the North China Plain during a highly oxidizing atmosphere in summer. We found that fast photochemistry dominated SOA formation during daytime. Two types of aqueous-phase chemistry (nocturnal and daytime processing) take place at high relative humidity. The potential transformation from primary organic aerosol (POA) to SOA was also an important pathway.
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Short summary
Short summary
The complex interaction between O3 and PM2.5, coupled with the topology of the Fenwei Plain and the evolution of the boundary layer height, highlights the challenges in further reducing particulate pollution in winter despite years of efforts to reduce emissions. Through scenario analysis in a chemical box model constrained by observation, we show the co-benefits of reducing NOx and VOCs simultaneously in reducing ozone and SOA.
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This work presents the differences resulting from two techniques (rolling and seasonal) of the positive matrix factorisation model that can be run for organic aerosol source apportionment. The current state of the art suggests that the rolling technique is more accurate, but no proof of its effectiveness has been provided yet. This paper tackles this issue in the context of a synthetic dataset and a multi-site real-world comparison.
Jing Duan, Ru-Jin Huang, Yifang Gu, Chunshui Lin, Haobin Zhong, Wei Xu, Quan Liu, Yan You, Jurgita Ovadnevaite, Darius Ceburnis, Thorsten Hoffmann, and Colin O'Dowd
Atmos. Chem. Phys., 22, 10139–10153, https://doi.org/10.5194/acp-22-10139-2022, https://doi.org/10.5194/acp-22-10139-2022, 2022
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Biomass-burning-influenced oxygenated organic aerosol (OOA-BB), formed from the photochemical oxidation and aging of biomass burning OA (BBOA), was resolved in urban Xi’an. The aqueous-phase processed oxygenated OA (aq-OOA) concentration was more dependent on secondary inorganic aerosol (SIA) content and aerosol liquid water content (ALWC). The increased aq-OOA contribution during SIA-enhanced periods likely reflects OA evolution due to the addition of alcohol or peroxide groups
Haobin Zhong, Ru-Jin Huang, Chunshui Lin, Wei Xu, Jing Duan, Yifang Gu, Wei Huang, Haiyan Ni, Chongshu Zhu, Yan You, Yunfei Wu, Renjian Zhang, Jurgita Ovadnevaite, Darius Ceburnis, and Colin D. O'Dowd
Atmos. Chem. Phys., 22, 9513–9524, https://doi.org/10.5194/acp-22-9513-2022, https://doi.org/10.5194/acp-22-9513-2022, 2022
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To investigate the physico-chemical properties of aerosol transported from major pollution regions in China, observations were conducted ~200 m above the ground at the junction location of the two key pollution areas. We found that the formation efficiency, oxidation state and production rate of secondary aerosol were different in the transport sectors from different pollution regions, and they were largely enhanced by the regional long-distance transport.
Youwei Hong, Xinbei Xu, Dan Liao, Taotao Liu, Xiaoting Ji, Ke Xu, Chunyang Liao, Ting Wang, Chunshui Lin, and Jinsheng Chen
Atmos. Chem. Phys., 22, 7827–7841, https://doi.org/10.5194/acp-22-7827-2022, https://doi.org/10.5194/acp-22-7827-2022, 2022
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Secondary organic aerosol (SOA) simulation remains uncertain, due to the unknown SOA formation mechanisms. Aerosol samples with a 4 h time resolution were collected, along with online measurements of aerosol chemical compositions and meteorological parameters. We found that anthropogenic emissions, atmospheric oxidation capacity and halogen chemistry have significant effects on the formation of biogenic SOA (BSOA). The findings of this study are helpful to better explore the missed SOA sources.
Chunshui Lin, Darius Ceburnis, Anna Trubetskaya, Wei Xu, William Smith, Stig Hellebust, John Wenger, Colin O'Dowd, and Jurgita Ovadnevaite
Atmos. Meas. Tech., 14, 6905–6916, https://doi.org/10.5194/amt-14-6905-2021, https://doi.org/10.5194/amt-14-6905-2021, 2021
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Source apportionment of solid-fuel-burning emissions can be complicated by the use of different fuels, stoves, and burning conditions. Here, the organic aerosol mass spectra produced from burning a range of solid fuels in several stoves were compared. This study accounts for the source variability and provides better constraints on the primary factor contributions to the ambient organic aerosol estimations, holding significant implications for public health and policymakers.
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Short summary
This study combines explainable machine learning with thermodynamic modeling to quantitatively assess how meteorological conditions and chemical composition jointly contribute to aerosol pH variation in an ammonia-rich urban atmosphere. The analysis highlights regime-dependent interactions, threshold behaviors, and sample-specific variability, providing a data-driven framework for interpreting aerosol acidity under diverse environmental conditions.
This study combines explainable machine learning with thermodynamic modeling to quantitatively...
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