Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7621-2021
https://doi.org/10.5194/gmd-14-7621-2021
Model description paper
 | 
16 Dec 2021
Model description paper |  | 16 Dec 2021

Reduced-complexity air quality intervention modeling over China: the development of InMAPv1.6.1-China and a comparison with CMAQv5.2

Ruili Wu, Christopher W. Tessum, Yang Zhang, Chaopeng Hong, Yixuan Zheng, Xinyin Qin, Shigan Liu, and Qiang Zhang

Related authors

Direct measurements of black carbon fluxes in central Beijing using the eddy covariance method
Rutambhara Joshi, Dantong Liu, Eiko Nemitz, Ben Langford, Neil Mullinger, Freya Squires, James Lee, Yunfei Wu, Xiaole Pan, Pingqing Fu, Simone Kotthaus, Sue Grimmond, Qiang Zhang, Ruili Wu, Oliver Wild, Michael Flynn, Hugh Coe, and James Allan
Atmos. Chem. Phys., 21, 147–162, https://doi.org/10.5194/acp-21-147-2021,https://doi.org/10.5194/acp-21-147-2021, 2021
Short summary
Decadal changes in anthropogenic source contribution of PM2.5 pollution and related health impacts in China, 1990–2015
Jun Liu, Yixuan Zheng, Guannan Geng, Chaopeng Hong, Meng Li, Xin Li, Fei Liu, Dan Tong, Ruili Wu, Bo Zheng, Kebin He, and Qiang Zhang
Atmos. Chem. Phys., 20, 7783–7799, https://doi.org/10.5194/acp-20-7783-2020,https://doi.org/10.5194/acp-20-7783-2020, 2020
Short summary
Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, Michael Hollaway, David Carruthers, Jie Li, Qiang Zhang, Ruili Wu, Simone Kotthaus, Sue Grimmond, Freya A. Squires, James Lee, and Zongbo Shi
Atmos. Chem. Phys., 20, 2755–2780, https://doi.org/10.5194/acp-20-2755-2020,https://doi.org/10.5194/acp-20-2755-2020, 2020
Short summary

Related subject area

Atmospheric sciences
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
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025,https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025,https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025,https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary

Cited articles

Appel, K. W., Napelenok, S. L., Hogrefe, C., Foley, K. M., Pouliot, G. A., Murphy, B., Heath, N., Roselle, S., Pleim, J., Bash, J. O., Pye, H. O. T., and Mathur, R.: Overview and evaluation of the Community Multiscale Air Quality (CMAQ) modelling system version 5.2, Air Pollution Modeling and its Application XXV, ITM 2016, Springer Proceedings in Complexity, edited by: Mensink, C. and Kallos, G., Springer, Cham, 69–73, https://doi.org/10.1007/978-3-319-57645-9_11, 2018. 
Baker, K. R., Amend, M., Penn, S., Bankert, J., Simon, H., Chan, E., Fann, N., Zawacki, M., Davidson, K., and Roman, H.: A database for evaluating the InMAP, APEEP, and EASIUR reduced complexity air-quality modelling tools, Data in Brief, 28, 104886, https://doi.org/10.1016/j.dib.2019.104886, 2020. 
Chang, X., Wang, S., Zhao, B., Xing, J., Liu, X., Wei, L., Song, Y., Wu, W., Cai, S., Zheng, H., Ding, D., and Zheng, M.: Contributions of inter-city and regional transport to PM2.5 concentrations in the Beijing-Tianjin-Hebei region and its implications on regional joint air pollution control, Sci. Total Environ., 660, 1191–1200, https://doi.org/10.1016/j.scitotenv.2018.12.474, 2019. 
Download
Short summary
Reduced-complexity air quality models are less computationally intensive and easier to use. We developed a reduced-complexity air quality Intervention Model for Air Pollution over China (InMAP-China) to rapidly predict the air quality and estimate the health impacts of emission sources in China. We believe that this work will be of great interest to a broad audience, including environmentalists in China and scientists in relevant fields at both national and local institutes.
Share