Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7573-2021
https://doi.org/10.5194/gmd-14-7573-2021
Model evaluation paper
 | 
15 Dec 2021
Model evaluation paper |  | 15 Dec 2021

High-resolution modeling of the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source–receptor model (GNAQPMS-SM)

Qian Ye, Jie Li, Xueshun Chen, Huansheng Chen, Wenyi Yang, Huiyun Du, Xiaole Pan, Xiao Tang, Wei Wang, Lili Zhu, Jianjun Li, Zhe Wang, and Zifa Wang

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Cited articles

Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Hoglund-Isaksson, L., Klimont, Z., Nguyen, B., Posch, M., Rafaj, P., Sandler, R., Schopp, W., Wagner, F., and Winiwarter, W.: Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications, Environ. Modell. Softw., 26, 1489–1501, https://doi.org/10.1016/j.envsoft.2011.07.012, 2011. 
Andreae, M. O. and Crutzen, P. J.: Atmospheric aerosols: Biogeochemical sources and role in atmospheric chemistry, Science, 276, 1052–1058, https://doi.org/10.1126/science.276.5315.1052, 1997. 
Badia, A., Jorba, O., Voulgarakis, A., Dabdub, D., Pérez García-Pando, C., Hilboll, A., Gonçalves, M., and Janjic, Z.: Description and evaluation of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH) version 1.0: gas-phase chemistry at global scale, Geosci. Model Dev., 10, 609–638, https://doi.org/10.5194/gmd-10-609-2017, 2017. 
Bauer, S. E. and Koch, D.: Impact of heterogeneous sulfate formation at mineral dust surfaces on aerosol loads and radiative forcing in the Goddard Institute for Space Studies general circulation model, J. Geophys. Res.-Atmos., 110, D17202, https://doi.org/10.1029/2005jd005870, 2005. 
Byun, D. W. and Dennis, R.: Design artifacts in eulerian air-quality models – evaluation of the effects of layer thickness and vertical profile correction on surface ozone concentrations, Atmos. Environ., 29, 105–126, https://doi.org/10.1016/1352-2310(94)00225-a, 1995. 
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Short summary
We developed a global tropospheric atmospheric chemistry source–receptor model. This model can quantify the contributions of multiple air pollutants from various source regions in one simulation without introducing the nonlinear error of atmospheric chemistry. The S-R relationships of PM2.5 and O3 from a global high-resolution (0.5° × 0.5°) simulation were given and compared with previous studies. This model will be useful for creating a link between the scientific community and policymakers.
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