Articles | Volume 15, issue 5
https://doi.org/10.5194/amt-15-1511-2022
https://doi.org/10.5194/amt-15-1511-2022
Research article
 | 
16 Mar 2022
Research article |  | 16 Mar 2022

Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species

Junlei Zhan, Yongchun Liu, Wei Ma, Xin Zhang, Xuezhong Wang, Fang Bi, Yujie Zhang, Zhenhai Wu, and Hong Li

Related authors

Exploring the Crucial Role of Atmospheric Carbonyl Compounds in Regional Ozone heavy Pollution: Insights from Intensive Field Observations and Observation-based modelling in the Chengdu Plain Urban Agglomeration, China
Jiemeng Bao, Xin Zhang, Zhenhai Wu, Li Zhou, Jun Qian, Qinwen Tan, Fumo Yang, Junhui Chen, Yunfeng Li, Hefan Liu, Liqun Deng, and Hong Li
EGUsphere, https://doi.org/10.5194/egusphere-2024-1204,https://doi.org/10.5194/egusphere-2024-1204, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Non-negligible secondary contribution to brown carbon in autumn and winter: inspiration from particulate nitrated and oxygenated aromatic compounds in urban Beijing
Yanqin Ren, Zhenhai Wu, Yuanyuan Ji, Fang Bi, Junling Li, Haijie Zhang, Hao Zhang, Hong Li, and Gehui Wang
Atmos. Chem. Phys., 24, 6525–6538, https://doi.org/10.5194/acp-24-6525-2024,https://doi.org/10.5194/acp-24-6525-2024, 2024
Short summary
On the potential of Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles
Markku Kulmala, Santeri Tuovinen, Sander Mirme, Paap Koemets, Lauri Ahonen, Yongchun Liu, Heikki Junninen, Tuukka Petäjä, and Veli-Matti Kerminen
Aerosol Research Discuss., https://doi.org/10.5194/ar-2024-14,https://doi.org/10.5194/ar-2024-14, 2024
Preprint under review for AR
Short summary
Characterization of nitrous acid and its potential effects on secondary pollution in warm-season of Beijing urban areas
Junling Li, Chaofan Lian, Mingyuan Liu, Hao Zhang, Yongxin Yan, Yufei Song, Chun Chen, Haijie Zhang, Yanqin Ren, Yucong Guo, Weigang Wang, Yisheng Xu, Hong Li, Jian Gao, and Maofa Ge
EGUsphere, https://doi.org/10.5194/egusphere-2024-367,https://doi.org/10.5194/egusphere-2024-367, 2024
Short summary
Iodine oxoacids and their roles in sub-3 nm particle growth in polluted urban environments
Ying Zhang, Duzitian Li, Xu-Cheng He, Wei Nie, Chenjuan Deng, Runlong Cai, Yuliang Liu, Yishuo Guo, Chong Liu, Yiran Li, Liangduo Chen, Yuanyuan Li, Chenjie Hua, Tingyu Liu, Zongcheng Wang, Jiali Xie, Lei Wang, Tuukka Petäjä, Federico Bianchi, Ximeng Qi, Xuguang Chi, Pauli Paasonen, Yongchun Liu, Chao Yan, Jingkun Jiang, Aijun Ding, and Markku Kulmala
Atmos. Chem. Phys., 24, 1873–1893, https://doi.org/10.5194/acp-24-1873-2024,https://doi.org/10.5194/acp-24-1873-2024, 2024
Short summary

Related subject area

Subject: Gases | Technique: In Situ Measurement | Topic: Validation and Intercomparisons
Pico-Light H2O: intercomparison of in situ water vapour measurements during the AsA 2022 campaign
Mélanie Ghysels, Georges Durry, Nadir Amarouche, Dale Hurst, Emrys Hall, Kensy Xiong, Jean-Charles Dupont, Jean-Christophe Samake, Fabien Frérot, Raghed Bejjani, and Emmanuel D. Riviere
Atmos. Meas. Tech., 17, 3495–3513, https://doi.org/10.5194/amt-17-3495-2024,https://doi.org/10.5194/amt-17-3495-2024, 2024
Short summary
Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment
Andrew R. Whitehill, Melissa Lunden, Brian LaFranchi, Surender Kaushik, and Paul A. Solomon
Atmos. Meas. Tech., 17, 2991–3009, https://doi.org/10.5194/amt-17-2991-2024,https://doi.org/10.5194/amt-17-2991-2024, 2024
Short summary
Intercomparison of eddy-covariance software for urban tall-tower sites
Changxing Lan, Matthias Mauder, Stavros Stagakis, Benjamin Loubet, Claudio D'Onofrio, Stefan Metzger, David Durden, and Pedro-Henrique Herig-Coimbra
Atmos. Meas. Tech., 17, 2649–2669, https://doi.org/10.5194/amt-17-2649-2024,https://doi.org/10.5194/amt-17-2649-2024, 2024
Short summary
Preparation of low concentration H2 test gas mixtures in ambient air for calibration of H2 sensors
Niklas Karbach, Lisa Höhler, Peter Hoor, Heiko Bozem, Nicole Bobrwoski, and Thorsten Hoffmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-611,https://doi.org/10.5194/egusphere-2024-611, 2024
Short summary
Assessment of current methane emission quantification techniques for natural gas midstream applications
Yunsong Liu, Jean-Daniel Paris, Gregoire Broquet, Violeta Bescós Roy, Tania Meixus Fernandez, Rasmus Andersen, Andrés Russu Berlanga, Emil Christensen, Yann Courtois, Sebastian Dominok, Corentin Dussenne, Travis Eckert, Andrew Finlayson, Aurora Fernández de la Fuente, Catlin Gunn, Ram Hashmonay, Juliano Grigoleto Hayashi, Jonathan Helmore, Soeren Honsel, Fabrizio Innocenti, Matti Irjala, Torgrim Log, Cristina Lopez, Francisco Cortés Martínez, Jonathan Martinez, Adrien Massardier, Helle Gottschalk Nygaard, Paula Agregan Reboredo, Elodie Rousset, Axel Scherello, Matthias Ulbricht, Damien Weidmann, Oliver Williams, Nigel Yarrow, Murès Zarea, Robert Ziegler, Jean Sciare, Mihalis Vrekoussis, and Philippe Bousquet
Atmos. Meas. Tech., 17, 1633–1649, https://doi.org/10.5194/amt-17-1633-2024,https://doi.org/10.5194/amt-17-1633-2024, 2024
Short summary

Cited articles

Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Carter, W.: Updated maximum incremental reactivity scale and hydrocarbon bin reactivities for regulatory applications, California Air Resources Board Contract 07-339, 2010. 
Copernicus: https://www.copernicus.eu/en, last access: 4 March 2022. 
di Carlo, P., Brune, W. H., Martinez, M., Harder, H., Lesher, R., Ren, X., Thornberry, T., Carroll, M. A., Young, V., Shepson, P. B., Riemer, D., Apel, E., and Campbell, C.: Missing OH Reactivity in a Forest: Evidence for Unknown Reactive Biogenic VOCs, Science, 304, 722–725, https://doi.org/10.1126/science.1094392, 2004. 
Download
Short summary
Our study investigated the O3 formation sensitivity in Beijing using a random forest model coupled with the reactivity of volatile organic compound (VOC) species. Results found that random forest accurately predicted O3 concentration when initial VOCs were considered, and relative importance correlated well with O3 formation potential. The O3 isopleth curves calculated by the random forest model were generally comparable with those calculated by the box model.