Articles | Volume 15, issue 15
Geosci. Model Dev., 15, 6143–6164, 2022
https://doi.org/10.5194/gmd-15-6143-2022
Geosci. Model Dev., 15, 6143–6164, 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 et al.

Related authors

The impacts of dust aerosol and convective available potential energy on precipitation vertical structure in eastern China as seen from multiple source observations
Hongxia Zhu, Rui Li, Shuping Yang, Chun Zhao, Zhe Jiang, and Chen Huang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-492,https://doi.org/10.5194/acp-2022-492, 2022
Preprint under review for ACP
Short summary
Using aircraft measurements to characterize subgrid-scale variability of aerosol properties near the Atmospheric Radiation Measurement Southern Great Plains site
Jerome D. Fast, David M. Bell, Gourihar Kulkarni, Jiumeng Liu, Fan Mei, Georges Saliba, John E. Shilling, Kaitlyn Suski, Jason Tomlinson, Jian Wang, Rahul Zaveri, and Alla Zelenyuk
Atmos. Chem. Phys., 22, 11217–11238, https://doi.org/10.5194/acp-22-11217-2022,https://doi.org/10.5194/acp-22-11217-2022, 2022
Short summary
Aging impact on sources, volatility, and viscosity of organic aerosols in the Chinese outflows
Tingting Feng, Yingkun Wang, Weiwei Hu, Ming Zhu, Wei Song, Wei Chen, Yanyan Sang, Zheng Fang, Wei Deng, Hua Fang, Xu Yu, Cheng Wu, Bin Yuan, Shan Huang, Min Shao, Xiaofeng Huang, Lingyan He, Young Ro Lee, L. Gregory Huey, Francesco Canonaco, Andre S. H. Prevot, and Xinming Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-575,https://doi.org/10.5194/acp-2022-575, 2022
Preprint under review for ACP
Short summary
A comprehensive study about the in-cloud processing of nitrate through coupled measurements of individual cloud residuals and cloud water
Guohua Zhang, Xiaodong Hu, Wei Sun, Yuxiang Yang, Ziyong Guo, Yuzhen Fu, Haichao Wang, Shengzhen Zhou, Lei Li, Mingjin Tang, Zongbo Shi, Duohong Chen, Xinhui Bi, and Xinming Wang
Atmos. Chem. Phys., 22, 9571–9582, https://doi.org/10.5194/acp-22-9571-2022,https://doi.org/10.5194/acp-22-9571-2022, 2022
Short summary
Budget of nitrous acid (HONO) at an urban site in the fall season of Guangzhou, China
Yihang Yu, Peng Cheng, Huirong Li, Wenda Yang, Baobin Han, Wei Song, Weiwei Hu, Xinming Wang, Bin Yuan, Min Shao, Zhijiong Huang, Zhen Li, Junyu Zheng, Haichao Wang, and Xiaofang Yu
Atmos. Chem. Phys., 22, 8951–8971, https://doi.org/10.5194/acp-22-8951-2022,https://doi.org/10.5194/acp-22-8951-2022, 2022
Short summary

Related subject area

Atmospheric sciences
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022,https://doi.org/10.5194/gmd-15-7139-2022, 2022
Short summary
Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5)
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022,https://doi.org/10.5194/gmd-15-7031-2022, 2022
Short summary
Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917, https://doi.org/10.5194/gmd-15-6891-2022,https://doi.org/10.5194/gmd-15-6891-2022, 2022
Short summary
Downscaling atmospheric chemistry simulations with physically consistent deep learning
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022,https://doi.org/10.5194/gmd-15-6677-2022, 2022
Short summary
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022,https://doi.org/10.5194/gmd-15-6341-2022, 2022
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.