Articles | Volume 20, issue 11
https://doi.org/10.5194/acp-20-6631-2020
https://doi.org/10.5194/acp-20-6631-2020
Research article
 | 
05 Jun 2020
Research article |  | 05 Jun 2020

Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis

Yi Wang, Jun Wang, Xiaoguang Xu, Daven K. Henze, Zhen Qu, and Kai Yang

Related authors

Aggravated surface O3 pollution primarily driven by meteorological variation in China during the early COVID-19 pandemic lockdown period
Zhendong Lu, Jun Wang, Yi Wang, Daven K. Henze, Xi Chen, Tong Sha, and Kang Sun
EGUsphere, https://doi.org/10.5194/egusphere-2023-2723,https://doi.org/10.5194/egusphere-2023-2723, 2023
Short summary
Background nitrogen dioxide (NO2) over the United States and its implications for satellite observations and trends: effects of nitrate photolysis, aircraft, and open fires
Ruijun Dang, Daniel J. Jacob, Viral Shah, Sebastian D. Eastham, Thibaud M. Fritz, Loretta J. Mickley, Tianjia Liu, Yi Wang, and Jun Wang
Atmos. Chem. Phys., 23, 6271–6284, https://doi.org/10.5194/acp-23-6271-2023,https://doi.org/10.5194/acp-23-6271-2023, 2023
Short summary
Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
Yi Wang, Jun Wang, Meng Zhou, Daven K. Henze, Cui Ge, and Wei Wang
Atmos. Chem. Phys., 20, 6651–6670, https://doi.org/10.5194/acp-20-6651-2020,https://doi.org/10.5194/acp-20-6651-2020, 2020
Short summary
A Tale of Two Dust Storms: analysis of a complex dust event in the Middle East
Steven D. Miller, Louie D. Grasso, Qijing Bian, Sonia M. Kreidenweis, Jack F. Dostalek, Jeremy E. Solbrig, Jennifer Bukowski, Susan C. van den Heever, Yi Wang, Xiaoguang Xu, Jun Wang, Annette L. Walker, Ting-Chi Wu, Milija Zupanski, Christine Chiu, and Jeffrey S. Reid
Atmos. Meas. Tech., 12, 5101–5118, https://doi.org/10.5194/amt-12-5101-2019,https://doi.org/10.5194/amt-12-5101-2019, 2019
Short summary
The influence of simulated surface dust lofting and atmospheric loading on radiative forcing
Stephen M. Saleeby, Susan C. van den Heever, Jennie Bukowski, Annette L. Walker, Jeremy E. Solbrig, Samuel A. Atwood, Qijing Bian, Sonia M. Kreidenweis, Yi Wang, Jun Wang, and Steven D. Miller
Atmos. Chem. Phys., 19, 10279–10301, https://doi.org/10.5194/acp-19-10279-2019,https://doi.org/10.5194/acp-19-10279-2019, 2019
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Investigating the contribution of grown new particles to cloud condensation nuclei with largely varying preexisting particles – Part 2: Modeling chemical drivers and 3-D new particle formation occurrence
Ming Chu, Xing Wei, Shangfei Hai, Yang Gao, Huiwang Gao, Yujiao Zhu, Biwu Chu, Nan Ma, Juan Hong, Yele Sun, and Xiaohong Yao
Atmos. Chem. Phys., 24, 6769–6786, https://doi.org/10.5194/acp-24-6769-2024,https://doi.org/10.5194/acp-24-6769-2024, 2024
Short summary
Technical note: Influence of different averaging metrics and temporal resolutions on the aerosol pH calculated by thermodynamic modeling
Haoqi Wang, Xiao Tian, Wanting Zhao, Jiacheng Li, Haoyu Yu, Yinchang Feng, and Shaojie Song
Atmos. Chem. Phys., 24, 6583–6592, https://doi.org/10.5194/acp-24-6583-2024,https://doi.org/10.5194/acp-24-6583-2024, 2024
Short summary
Dual roles of the inorganic aqueous phase on secondary organic aerosol growth from benzene and phenol
Jiwon Choi, Myoseon Jang, and Spencer Blau
Atmos. Chem. Phys., 24, 6567–6582, https://doi.org/10.5194/acp-24-6567-2024,https://doi.org/10.5194/acp-24-6567-2024, 2024
Short summary
Global source apportionment of aerosols into major emission regions and sectors over 1850–2017
Yang Yang, Shaoxuan Mou, Hailong Wang, Pinya Wang, Baojie Li, and Hong Liao
Atmos. Chem. Phys., 24, 6509–6523, https://doi.org/10.5194/acp-24-6509-2024,https://doi.org/10.5194/acp-24-6509-2024, 2024
Short summary
Modeling atmospheric brown carbon in the GISS ModelE Earth system model
Maegan A. DeLessio, Kostas Tsigaridis, Susanne E. Bauer, Jacek Chowdhary, and Gregory L. Schuster
Atmos. Chem. Phys., 24, 6275–6304, https://doi.org/10.5194/acp-24-6275-2024,https://doi.org/10.5194/acp-24-6275-2024, 2024
Short summary

Cited articles

Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteor. Soc., 134, 1951–1970, https://doi.org/10.1002/qj.339, 2008. 
Benedetti, A. and Fisher, M.: Background error statistics for aerosols, Q. J. Roy. Meteor. Soc., 133, 391–405, https://doi.org/10.1002/qj.37, 2007. 
Bucsela, E. J., Celarier, E. A., Gleason, J. L., Krotkov, N. A., Lamsal, L. N., Marchenko, S. V., and Swartz, W. H.: OMNO2 README Document Data Product Version 3.0, available at: https://acdisc.gesdisc.eosdis.nasa.gov/data/Aura_OMI_Level3/OMNO2d.003/doc/README.OMNO2.pdf (last access: 11 August 2018), 2016. 
Byrd, R., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm for Bound Constrained Optimization, SIAM J. Sci. Comput., 16, 1190–1208, https://doi.org/10.1137/0916069, 1995. 
Calkins, C., Ge, C., Wang, J., Anderson, M., and Yang, K.: Effects of meteorological conditions on sulfur dioxide air pollution in the North China plain during winters of 2006–2015, Atmos. Environ., 147, 296–309, https://doi.org/10.1016/j.atmosenv.2016.10.005, 2016. 
Download
Short summary
The use of OMPS satellite observations to inverse-model SO2 and NO2 emissions is presented through the GEOS-Chem adjoint modeling framework. The work is illustrated over China. The robustness of the results is studied through separate and joint inversions of SO2 and NO2 and the consideration of NH3 uncertainty. Independent validation is performed with OMI SO2 and NO2 data. It is shown that simultaneous inversion of NO2 and SO2 from OMPS provides an effective way to rapidly update emissions.
Altmetrics
Final-revised paper
Preprint