Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6143-2022
https://doi.org/10.5194/gmd-15-6143-2022
Model evaluation paper
 | 
04 Aug 2022
Model evaluation paper |  | 04 Aug 2022

Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes

Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng

Related authors

WRF-Chem simulations of snow nitrate and other physicochemical properties in northern China
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025,https://doi.org/10.5194/gmd-18-651-2025, 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

Ahrens, L., Harner, T., Shoeib, M., Lane, D. A., and Murphy, J. G.: Improved Characterization of Gas-Particle Partitioning for Per- and Polyfluoroalkyl Substances in the Atmosphere Using Annular Diffusion Denuder Samplers, Environ. Sci. Technol., 46, 7199–7206, https://doi.org/10.1021/es300898s, 2012. 
Battaglia Jr., M. A., Douglas, S., and Hennigan, C. J.: Effect of the Urban Heat Island on Aerosol pH, Environ. Sci. Technol., 51, 13095–13103, https://doi.org/10.1021/acs.est.7b02786, 2017. 
Chen, D., Liu, Z., Fast, J., and Ban, J.: Simulations of sulfate–nitrate–ammonium (SNA) aerosols during the extreme haze events over northern China in October 2014, Atmos. Chem. Phys., 16, 10707–10724, https://doi.org/10.5194/acp-16-10707-2016, 2016. 
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:Caalsh>2.0.Co;2, 2001. 
Chen, Y., Wang, Y., Nenes, A., Wild, O., Song, S., Hu, D., Liu, D., He, J., Hildebrandt Ruiz, L., Apte, J. S., Gunthe, S. S., and Liu, P.: Ammonium Chloride Associated Aerosol Liquid Water Enhances Haze in Delhi, India, Environ. Sci. Technol., 56, 7163–7173, https://doi.org/10.1021/acs.est.2c00650, 2022. 
Download
Short summary
Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Share