Articles | Volume 21, issue 18
https://doi.org/10.5194/acp-21-13747-2021
https://doi.org/10.5194/acp-21-13747-2021
Research article
 | 
16 Sep 2021
Research article |  | 16 Sep 2021

A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region

Xinghong Cheng, Zilong Hao, Zengliang Zang, Zhiquan Liu, Xiangde Xu, Shuisheng Wang, Yuelin Liu, Yiwen Hu, and Xiaodan Ma

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Cited articles

Bai, N. B. and Zhou, X. J.: Estimation of CO2, SO2 and NOx gridded emission sources with a resolution of 1×1 in Changes of atmospheric ozone and its impact on climate and environment in China, China Meteorological Press, Beijing, 145–150, 1996. 
Barbu, A. L., Segers, A. J., Schaap, M., Heemink, A. W., and Builtjes, P. J. H.: A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe, Atmos. Environ., 43, 1622–1631, 2009. 
Bergamaschi, P., Hein, R., Heimann, M., and Crutzen, M., P. J.: Inverse modeling of the global CO cycle: 1. Inversion of CO mixing ratios, J. Geophys. Res., 105, 1909–1927, 2000. 
Cao, G. L., Zhang, X. Y., Gong, S. L., Gong, S. L., An, X. Q., and Wang, Y. Q.: Emission inventories of primary particles and pollutant gases for China, Chin. Sci. Bull., 56, 781, https://doi.org/10.1007/s11434-011-4373-7, 2011. 
Cheng, X., Xu, X., and Ding, G.: An emission source inversion model based on satellite data and its application in air quality forecasts, Science China: Earth Sciences, 53, 752–762, 2010. 
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Short summary
We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
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