Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3151-2015
https://doi.org/10.5194/gmd-8-3151-2015
Development and technical paper
 | 
07 Oct 2015
Development and technical paper |  | 07 Oct 2015

Development of a chlorine chemistry module for the Master Chemical Mechanism

L. K. Xue, S. M. Saunders, T. Wang, R. Gao, X. F. Wang, Q. Z. Zhang, and W. X. Wang

Related authors

High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data
Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang
Atmos. Chem. Phys., 25, 5537–5555, https://doi.org/10.5194/acp-25-5537-2025,https://doi.org/10.5194/acp-25-5537-2025, 2025
Short summary
Spatial–temporal patterns in anthropogenic and biomass burning emission contributions to air pollution and mortality burden changes in India from 1995 to 2014
Bin Luo, Yuqiang Zhang, Tao Tang, Hongliang Zhang, Jianlin Hu, Jiangshan Mu, Wenxing Wang, and Likun Xue
Atmos. Chem. Phys., 25, 4767–4783, https://doi.org/10.5194/acp-25-4767-2025,https://doi.org/10.5194/acp-25-4767-2025, 2025
Short summary
Measurement report: Optical and structural properties of atmospheric water-soluble organic carbon in China – insights from multi-site spectroscopic measurements
Haibiao Chen, Caiqing Yan, Liubin Huang, Lin Du, Yang Yue, Xinfeng Wang, Qingcai Chen, Mingjie Xie, Junwen Liu, Fengwen Wang, Shuhong Fang, Qiaoyun Yang, Hongya Niu, Mei Zheng, Yan Wu, and Likun Xue
Atmos. Chem. Phys., 25, 3647–3667, https://doi.org/10.5194/acp-25-3647-2025,https://doi.org/10.5194/acp-25-3647-2025, 2025
Short summary
Explainable ensemble machine learning revealing enhanced anthropogenic emissions of particulate nitro-aromatic compounds in eastern China
Min Li, Xinfeng Wang, Tianshuai Li, Yujia Wang, Yueru Jiang, Yujiao Zhu, Wei Nie, Rui Li, Jian Gao, Likun Xue, Qingzhu Zhang, and Wenxing Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-165,https://doi.org/10.5194/egusphere-2025-165, 2025
Short summary
Surface and tropospheric ozone over East Asia and Southeast Asia from observations: distributions, trends, and variability
Ke Li, Rong Tan, Wenhao Qiao, Taegyung Lee, Yufen Wang, Danyuting Zhang, Minglong Tang, Wenqing Zhao, Yixuan Gu, Shaojia Fan, Jinqiang Zhang, Xiaopu Lyu, Likun Xue, Jianming Xu, Zhiqiang Ma, Mohd Talib Latif, Teerachai Amnuaylojaroen, Junsu Gil, Mee-Hye Lee, Juseon Bak, Joowan Kim, Hong Liao, Yugo Kanaya, Xiao Lu, Tatsuya Nagashima, and Ja-Ho Koo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3756,https://doi.org/10.5194/egusphere-2024-3756, 2025
Short summary

Related subject area

Atmospheric sciences
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025,https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025,https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025,https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025,https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary

Cited articles

Atkinson, R., Baulch, D. L., Cox, R. A., Hampson, R. F., Kerr, J. A., Rossi, M. J., and Troe, J.: Evaluated kinetic and photochemical data for atmospheric chemistry, organic species, Supplement VII, J. Phys. Chem. Ref. Data, 28, 191–393, 1999.
Carter, W. P. L.: Development of the SAPRC-07 chemical mechanism, Atmos. Environ., 44, 5324–5335, 2010.
Chang, S. Y., McDonald-Buller, E., Kimura, Y., Yarwood, G., Neece, J., Russell, M., Tanaka, P., and Allen, D.: Sensitivity of urban ozone formation to chlorine emission estimates, Atmos. Environ., 36, 4991–5003, 2002.
Elshorbany, Y. F., Kurtenbach, R., Wiesen, P., Lissi, E., Rubio, M., Villena, G., Gramsch, E., Rickard, A. R., Pilling, M. J., and Kleffmann, J.: Oxidation capacity of the city air of Santiago, Chile, Atmos. Chem. Phys., 9, 2257–2273, https://doi.org/10.5194/acp-9-2257-2009, 2009.
Finlayson-Pitts, B. J.: Chlorine Atoms as a Potential Tropospheric Oxidant in the Marine Boundary-Layer, Res. Chem. Intermediat., 19, 235–249, 1993.
Download
Short summary
A detailed chemical mechanism including 205 reactions is developed for use in the Master Chemical Mechanism. With this new chlorine mechanism, it was found that the nocturnal formation of ClNO2 has high potential to perturb the next day's atmospheric photochemistry, by enhancing the radical production and cycling, VOC oxidation and O3 formation, in the polluted coastal environments.
Share