Articles | Volume 21, issue 18
https://doi.org/10.5194/acp-21-13747-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-13747-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2021
Research article |
| 16 Sep 2021
| 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
CORRESPONDING AUTHOR
State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Zilong Hao
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China
Zengliang Zang
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China
Zhiquan Liu
National Center for Atmospheric Research, Boulder, CO, USA
Xiangde Xu
State Key Lab of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Shuisheng Wang
GZ Source Clear Tech. Co., Ltd., Guangzhou 510630, China
Yuelin Liu
College of Architecture and Environment, Sichuan University, Chengdu 610065, China
Yiwen Hu
Nanjing University of Information Science and Technology, Nanjing 210044, China
Xiaodan Ma
Nanjing University of Information Science and Technology, Nanjing 210044, China
Related authors
Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Xiangde Xu, Wenqian Zhang, Jinguang Lv, Gang Bai, Bing Chen, Siying Ma, Steffen Ziegler, Sebastian Donner, and Thomas Wagner
Atmos. Chem. Phys., 23, 3655–3677, https://doi.org/10.5194/acp-23-3655-2023, https://doi.org/10.5194/acp-23-3655-2023, 2023
Short summary
Short summary
We made mobile MAX-DOAS measurements in the background atmosphere over the Tibetan Plateau in summer 2021. We retrieved the tropospheric NO2 and HCHO vertical column densities (VCDs) along extended driving routes and found a decreasing trend of the VCDs with altitude. Elevated NO2 VCDs along the driving routes could be attributed to enhanced traffic emissions from the towns crossed. The spatio-temporal distribution of the HCHO VCDs correlated strongly with the surface temperature.
Tao Sun, Jonathan J. Guerrette, Zhiquan Liu, Junmei Ban, Byoung-Joo Jung, Ivette Hernández Baños, and Chris Snyder
EGUsphere, https://doi.org/10.5194/egusphere-2025-2079, https://doi.org/10.5194/egusphere-2025-2079, 2025
Short summary
Short summary
We evaluated a new ensemble data assimilation system that uses satellite observations in all weather conditions for global weather forecasts. The results show that including cloud- and precipitation-affected satellite data improves forecasts of moisture, wind, and clouds, especially in the tropics. This work highlights the potential of this new ensemble data assimilation system to enhance global weather forecasts.
Lang Liu, Xin Long, Yi Li, Zengliang Zang, Fengwen Wang, Yan Han, Zhier Bao, Yang Chen, Tian Feng, and Jinxin Yang
Atmos. Chem. Phys., 25, 1569–1585, https://doi.org/10.5194/acp-25-1569-2025, https://doi.org/10.5194/acp-25-1569-2025, 2025
Short summary
Short summary
This study uses WRF-Chem to assess how meteorological conditions and emission reductions affected fine particulate matter (PM2.5) in the North China Plain (NCP). It highlights regional disparities: in the northern NCP, adverse weather negated emission reduction effects. In contrast, the southern NCP featured a PM2.5 decrease due to favorable weather and emission reductions. The research highlighted the interaction between emissions, meteorology, and PM2.5.
Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-12, https://doi.org/10.5194/egusphere-2025-12, 2025
Short summary
Short summary
Ground-based microwave radiometers (MWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from MWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Jing Wang, Yanju Liu, Fei Cheng, Chengyu Song, Qiaoping Li, Yihui Ding, and Xiangde Xu
Atmos. Chem. Phys., 24, 5099–5115, https://doi.org/10.5194/acp-24-5099-2024, https://doi.org/10.5194/acp-24-5099-2024, 2024
Short summary
Short summary
Based on long-term observational, reanalysis, and numerical model simulation datasets from 1901 through 2014, this study shows that precipitation over the East Asian monsoon boundary zone featured prominent interdecadal changes, with dry summers during the periods preceding 1927, 1939–1945, 1968–1982, and 1998–2010 and wet summers during 1928–1938, 1946–1967, and 2011 onwards. The Indian Ocean basin mode is an important oceanic modulator responsible for its interdecadal variations.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Siyang Cheng, Xinghong Cheng, Jianzhong Ma, Xiangde Xu, Wenqian Zhang, Jinguang Lv, Gang Bai, Bing Chen, Siying Ma, Steffen Ziegler, Sebastian Donner, and Thomas Wagner
Atmos. Chem. Phys., 23, 3655–3677, https://doi.org/10.5194/acp-23-3655-2023, https://doi.org/10.5194/acp-23-3655-2023, 2023
Short summary
Short summary
We made mobile MAX-DOAS measurements in the background atmosphere over the Tibetan Plateau in summer 2021. We retrieved the tropospheric NO2 and HCHO vertical column densities (VCDs) along extended driving routes and found a decreasing trend of the VCDs with altitude. Elevated NO2 VCDs along the driving routes could be attributed to enhanced traffic emissions from the towns crossed. The spatio-temporal distribution of the HCHO VCDs correlated strongly with the surface temperature.
Xiangde Xu, Yi Tang, Yinjun Wang, Hongshen Zhang, Ruixia Liu, and Mingyu Zhou
Atmos. Chem. Phys., 23, 3299–3309, https://doi.org/10.5194/acp-23-3299-2023, https://doi.org/10.5194/acp-23-3299-2023, 2023
Short summary
Short summary
The vertical motion over the Tibetan Plateau (TP) is associated with the anomalous convective activities. The diurnal variations and formation mechanisms of low clouds over the TP, Rocky Mountains and low-elevation regions are analyzed. We further discuss whether there exists a
high-efficiencytriggering mechanism for convection over the TP and whether there is an association among low air density and strong turbulence and ubiquitous popcorn-like cumulus clouds.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
Short summary
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Yiwen Hu, Zengliang Zang, Xiaoyan Ma, Yi Li, Yanfei Liang, Wei You, Xiaobin Pan, and Zhijin Li
Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, https://doi.org/10.5194/acp-22-13183-2022, 2022
Short summary
Short summary
This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840, https://doi.org/10.5194/gmd-15-1821-2022, https://doi.org/10.5194/gmd-15-1821-2022, 2022
Short summary
Short summary
This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Xiangde Xu, Chan Sun, Deliang Chen, Tianliang Zhao, Jianjun Xu, Shengjun Zhang, Juan Li, Bin Chen, Yang Zhao, Hongxiong Xu, Lili Dong, Xiaoyun Sun, and Yan Zhu
Atmos. Chem. Phys., 22, 1149–1157, https://doi.org/10.5194/acp-22-1149-2022, https://doi.org/10.5194/acp-22-1149-2022, 2022
Short summary
Short summary
A vertical transport window of tropospheric vapor exists on the Tibetan Plateau (TP). The TP's thermal forcing drives the vertical transport
windowof vapor in the troposphere. The effects of the TP's vertical transport window of vapor are of importance in global climate change.
Xiangde Xu, Wenyue Cai, Tianliang Zhao, Xinfa Qiu, Wenhui Zhu, Chan Sun, Peng Yan, Chunzhu Wang, and Fei Ge
Atmos. Chem. Phys., 21, 14131–14139, https://doi.org/10.5194/acp-21-14131-2021, https://doi.org/10.5194/acp-21-14131-2021, 2021
Short summary
Short summary
We found that the structure of atmospheric thermodynamics in the troposphere can be regarded as a strong forewarning signal for variations of surface PM2.5 concentration in heavy air pollution.
Xiaodan Ma, Jianping Huang, Tianliang Zhao, Cheng Liu, Kaihui Zhao, Jia Xing, and Wei Xiao
Atmos. Chem. Phys., 21, 1–16, https://doi.org/10.5194/acp-21-1-2021, https://doi.org/10.5194/acp-21-1-2021, 2021
Short summary
Short summary
The present work aims at identifying and quantifying the relative contributions of the key factors in driving a rapid increase in summertime surface O3 over the North China Plain during 2013–2019. In addition to anthropogenic emission reduction and meteorological variabilities, our study highlights the importance of inclusion of aerosol absorption and scattering properties rather than aerosol abundance only in accurate assessment of aerosol radiative effect on surface O3 formation and change.
Yanfei Liang, Zengliang Zang, Dong Liu, Peng Yan, Yiwen Hu, Yan Zhou, and Wei You
Geosci. Model Dev., 13, 6285–6301, https://doi.org/10.5194/gmd-13-6285-2020, https://doi.org/10.5194/gmd-13-6285-2020, 2020
Cited articles
Bai, N. B. and Zhou, X. J.: Estimation of CO2, SO2 and NOx gridded emission sources with a resolution of
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.
Cheng, Y. F., Zheng, G. J., Wei, C., Mu, Q., Zheng, B., Wang, Z. B, Gao, M.,
Zhang, Q., He, K. B., Carmichael, G., Pöschl, U., and Su, H.: Reactive
nitrogen chemistry in aerosol water as a source of sulfate during haze
events in China, Sci. Adv., 2, e1601530, https://doi.org/10.1126/sciadv.1601530, 2016.
Chen, D., Liu, Z., Ban, J., and Chen, M.: The 2015 and 2016 wintertime air pollution in China: SO2 emission changes derived from a WRF-Chem/EnKF coupled data assimilation system, Atmos. Chem. Phys., 19, 8619–8650, https://doi.org/10.5194/acp-19-8619-2019, 2019.
China National Environmental Monitoring Centre (CNEMC): SO2, NO2, and O3 measurements, CNEMC [data set], available at: http://www.cnemc.cn/en/ (last access: 8 September 2021), 2013.
Cohan, D., Hu, Y., Hakami, A., Odman, M. T., and Russell, A.: Implementation of a direct sensitivity method into CMAQ, Models-3 User's Workshop, RTP, North Carolina, 22 October 2002.
Corazza, M., Bergamaschi, P., Vermeulen, A. T., Aalto, T., Haszpra, L., Meinhardt, F., O'Doherty, S., Thompson, R., Moncrieff, J., Popa, E., Steinbacher, M., Jordan, A., Dlugokencky, E., Brühl, C., Krol, M., and Dentener, F.: Inverse modelling of European N2O emissions: assimilating observations from different networks, Atmos. Chem. Phys., 11, 2381–2398, https://doi.org/10.5194/acp-11-2381-2011, 2011.
Dai, T., Cheng, Y., Goto, D., Li, Y., Tang, X., Shi, G., and Nakajima, T.: Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem, Atmos. Chem. Phys., 21, 4357–4379, https://doi.org/10.5194/acp-21-4357-2021, 2021.
Dennis, R., Byun, D., and Novak, J.: The next generation of integrated air
quality modeling: EPA's Models-3, Atmos. Environ., 30, 1925–1938, 1996.
Elbern, H., Schmidt, H., Talagrand, O., and Ebel, A.: 4D-variational data
assimilation with an adjoint air quality model for emission analysis,
Environ. Modell. Softw., 15, 539–548, 2000.
Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, https://doi.org/10.5194/acp-7-3749-2007, 2007.
Enting, I. G.: Inverse Problems in Atmospheric Constituent Transport, Cambridge University Press, Cambridge, ISBN 9780521812108, 2002.
Frey, H. C., Zheng, J., Zhao, Y., Li, S., and Zhu, Y.: Technical Documentation of the AuvTool Software for Analysis of Variability and Uncertainty, North Carolina State University for the Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park,
NC, 2002.
Gilliland, A. B., Dennis, R. L., Roselle, S. J., and Pierce, T. E.: Seasonal NH3 emission estimates for the eastern United States based on ammonium wet
concentrations and an inverse modeling method, J. Geophys. Res., 108, 4477, https://doi.org/10.1029/2002JD003063, 2003.
Gilliland, A. B., Wyat, A. K., Pinder, R. W., and Dennis, R. L.: Seasonal NH3 emissions for the continental united states: Inverse model estimation and evaluation, Atmos. Environ., 40, 4986–4998, 2006.
Heald, C. L., Jacob, D. J., Jones, D. B. A., Palmer, P. I., Logan, J. A.,
Streets, D. G., Sachse, G. W., Gille, J. C., Hoffman, R. N., and Nehrkorn, T.: Comparative inverse analysis of satellite (MOPITT) and aircraft (TRACE-P)
observations to estimate Asian sources of carbon monoxide, J. Geophys. Res.,
109, D23306, https://doi.org/10.1029/2004JD005185, 2004.
Henze, D. K., Seinfeld, J. H., and Shindell, D. T.: Inverse modeling and mapping US air quality influences of inorganic PM2.5 precursor emissions using the adjoint of GEOS-Chem, Atmos. Chem. Phys., 9, 5877–5903, https://doi.org/10.5194/acp-9-5877-2009, 2009.
Hong, C., Zhang, Q., He, K., Guan, D., Li, M., Liu, F., and Zheng, B.: Variations of China's emission estimates: response to uncertainties in energy statistics, Atmos. Chem. Phys., 17, 1227–1239, https://doi.org/10.5194/acp-17-1227-2017, 2017.
Hu, Y., Odman, M. T., and Russell, A. G.: Top-down analysis of the elemental
carbon emissions inventory in the United States by inverse modeling using
Community Multiscale Air Quality model with decoupled direct method
(CMAQ-DDM), J. Geophys. Res., 114, D24302, https://doi.org/10.1029/2009JD011987, 2009.
Huang, R. J., Zhang, Y., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y., Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., Haddad, I. E., and Prévôt, A. S.: High secondary aerosol contribution to particulate pollution during haze events in China, Nature, 514, 218–222, 2014.
Jiang, Z., Jones, D. B. A., Kopacz, M., Liu, J., Henze, D. K., and Heald, C.: Quantifying the impact of model errors on top-down estimates of carbon
monoxide emissions using satellite observations, J. Geophys. Res., 116, D15306, https://doi.org/10.1029/2010JD015282, 2011.
Kerry, A., Gerhard, R., and Mike, D. F.: Fire-growth modelling using
meteorological data with random and systematic perturbations, Int. J. Wildland Fire, 16, 174–182, 2007.
Koohkan, M. R., Bocquet, M., Roustan, Y., Kim, Y., and Seigneur, C.: Estimation of volatile organic compound emissions for Europe using data assimilation, Atmos. Chem. Phys., 13, 5887–5905, https://doi.org/10.5194/acp-13-5887-2013, 2013.
Kopacz, M., Jacob, D. J., Henze, D. K., Heald, C. L., Streets, D. G., and Zhang, Q.: Comparison of adjoint and analytical Bayesian inversion methods for constraining Asian sources of carbon monoxide using satellite (MOPITT)
measurements of CO columns, J. Geophys. Res., 114, D04305, https://doi.org/10.1029/2007JD009264, 2009.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B.,
Cui, H., Man, H., Zhang, Q., He, K.: Anthropogenic emission inventories in
China: a review, Natl. Sci. Rev., 4, 834–866, 2017.
Li, Z., Zang, Z., Li, Q. B., Chao, Y., Chen, D., Ye, Z., Liu, Y., and Liou, K. N.: A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction, Atmos. Chem. Phys., 13, 4265–4278, https://doi.org/10.5194/acp-13-4265-2013, 2013.
Liu, F., Hu, F., and Zhu, J.: Solving the optimal layout problem of multiple
industrial pollution sources using the adjoint method, Science China: Earth
Sciences, 35, 64–71, 2005.
Liu, J., Mauzerallb, D. L., Chen. Q., Zhang, Q., Song, Y., Peng, W., Klimont, Z., Qiu, X. H., Zhang, S. Q., Hu, M., Lin, W. L., Smith, K. R., and Zhu, T.: Air pollutant emissions from Chinese households: A major and underappreciated ambient pollution source, P. Natl. Acad. Sci. USA, 113, 7756–7761, 2016.
Liu, Z., Liu, Q., Lin, H. C., Schwartz, C. S., Lee, Y. H., and Wang, T.:
Three-dimensional variational assimilation of MODIS aerosol optical depth:
implementation and application to a dust storm over East Asia, J. Geophys.
Res., 116, D23206, https://doi.org/10.1029/2011JD016159, 2011.
Lucas, D. D., Simpson, M., Cameron-Smith, P., and Baskett, R. L.: Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant, Atmos. Chem. Phys., 17, 13521–13543, https://doi.org/10.5194/acp-17-13521-2017, 2017.
Ma, J. and van Aardenne, J. A.: Impact of different emission inventories on simulated tropospheric ozone over China: a regional chemical transport model evaluation, Atmos. Chem. Phys., 4, 877–887, https://doi.org/10.5194/acp-4-877-2004, 2004.
Manning, A. J., Doherty, S. O., Jones, A. R., Simmonds, P. G., Derwent, R.
G.: Estimating UK methane and nitrous oxide emissions from 1990 to 2007
using an inversion modeling approach, J. Geophys. Res., 116, D02305,
https://doi.org/10.1029/2010JD014763, 2011.
Martin, R. V., Jacob, D. J., Chance, K., Palmer, P. I., and Evans, M. J.: Global inventory of nitrogen oxide emissions constrained by space-based
observations of NO2 columns, J. Geophys. Res., 108, 4537, https://doi.org/10.1029/2003JD003453, 2003.
Michalakes, J., Dudhia, J., Gill, D., Henderson, T., Klemp, J., Skamarock, W., and Wang, W.: The Weather Reseach and Forecast Model: Software Architecture and Performance, in: Proceedings of the 11th ECMWF Workshop on the Use of High Performance Computing In Meteorology, Reading, UK, edited by:
Mozdzynski, G., 25–29 October 2004.
Mijling, B. and van der A, R. J.: Using daily satellite observations to
estimate emissions of short-lived air pollutants on a mesoscopic scale, J.
Geophys. Res., 117, D17302, https://doi.org/10.1029/2012JD017817, 2012.
Miyazaki, K., Eskes, H. J., and Sudo, K.: Global NOx emission estimates derived from an assimilation of OMI tropospheric NO2 columns, Atmos. Chem. Phys., 12, 2263–2288, https://doi.org/10.5194/acp-12-2263-2012, 2012.
Napelenok, S. L., Pinder, R. W., Gilliland, A. B., and Martin, R. V.: A method for evaluating spatially-resolved NOx emissions using Kalman filter inversion, direct sensitivities, and space-based NO2 observations, Atmos. Chem. Phys., 8, 5603–5614, https://doi.org/10.5194/acp-8-5603-2008, 2008.
National Center for Atmospheric Research: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2021.
Otte, T. L. and Pleim, J. E.: The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci. Model Dev., 3, 243–256, https://doi.org/10.5194/gmd-3-243-2010, 2010.
Pagowski, M., Grell, G. A., McKeen, S. A., Peckham, S. E., and Devenyi, D.:
Threedimensional variational data assimilation of ozone and fine particulate
matter observations: some results using the weather research and
forecasting-chemistry model and grid-point statistical interpolation, Q. J.
Roy. Meteor. Soc., 136, 2013–2024, 2010.
Parrish, D. F. and Derber, J. C.: The National Meteorological Center's spectral statistical interpolation analysis scheme, Mon. Weather Rev., 120,
1747–1763, 1992.
Peng, L., Zhang, Q., Yao, Z., Mauzerall, D. L., Kang, S., Du, Z., Zheng, Y.,
Xue, T., and He, K.: Underreported coal in statistics: A survey-based solid fuel consumption and emission inventory for the rural residential sector in
China, Appl. Energ., 235, 1169–1182, 2019.
Peng, Z., Liu, Z., Chen, D., and Ban, J.: Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter, Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, 2017.
Sportisse, B.: A review of current issues in air pollution modeling and
simulation, Computat. Geosci., 11, 159–181, 2007.
Stavrakou, T., Müller, J.-F., De Smedt, I., Van Roozendael, M., Kanakidou, M., Vrekoussis, M., Wittrock, F., Richter, A., and Burrows, J. P.: The continental source of glyoxal estimated by the synergistic use of spaceborne measurements and inverse modelling, Atmos. Chem. Phys., 9, 8431–8446, https://doi.org/10.5194/acp-9-8431-2009, 2009.
Streets, D. G., Bond, T. C., Carmichael, G. R., Fernandes, S. D., Fu, Q., He,
D., Klimont, Z., Nelson, S. M., Tsai, N. Y., Wang, M. Q., Woo, J. H., and Yarber, K. F.: An inventory of gaseous and primary aerosol emissions in Asia in the year 2000, J. Geophys. Res., 108, 8809–8820, 2003.
Tang, X., Zhu, J., Wang, Z. F., and Gbaguidi, A.: Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions, Atmos. Chem. Phys., 11, 12901–12916, https://doi.org/10.5194/acp-11-12901-2011, 2011.
Tang, X., Zhu, J., Wang, Z., Gbaguidi, A., Lin, C., Xin, J., Song, T., and Hu, B.: Limitations of ozone data assimilation with adjustment of NOx emissions: mixed effects on NO2 forecasts over Beijing and surrounding areas, Atmos. Chem. Phys., 16, 6395–6405, https://doi.org/10.5194/acp-16-6395-2016, 2016.
Tang, X. Y., Zhang, Y. H., and Shao, M.: Atmospheric Environmental Chemistry,
2nd edn., Higher Education Press, Beijing, 447–449, 2006 (in Chinese).
The University of North Carolina (UNC): SMOKE v3.6 user's manual, The institute for the Environment, Chapel Hill, 520 pp., 2014.
Wang, C., An, X. Q., Zhai, S. X., Hou, Q., and Sun, Z. B.: Tracking sensitive
source areas of different weather pollution types using GRAPES-CUACE adjoint
model, Atmos. Environ., 175, 154–166, 2018.
Wang, L. T., Zhang, P., Yang, J., Zhao, X. J., Wei, W., Su, J., Cheng, D.
D., Liu X., Y., Han, G., G., and Wang, H. J.: Application of CMAQ-DDM-3D in the source analysis of fine particulate matter (PM2.5), Acta Scientiae
Circumstantiae, 33, 1355–1361, 2013 (in Chinese).
Wang, P., Wang, H., Wang, Y. Q., Zhang, X. Y., Gong, S. L., Xue, M., Zhou, C. H., Liu, H. L., An, X. Q., Niu, T., and Cheng, Y. L.: Inverse modeling of black carbon emissions over China using ensemble data assimilation, Atmos. Chem. Phys., 16, 989–1002, https://doi.org/10.5194/acp-16-989-2016, 2016.
Wang, Y., McElroy, M. B., Martin, R. V., Streets, D. G., Zhang, Q., and Fu,
T.-M.: Seasonal variability of NOx emissions over east China constrained by satellite observations: Implications for combustion and microbial sources, J. Geophys. Res., 112, D06301, https://doi.org/10.1029/2006JD007538, 2007.
Wang, Q., Huo, H., He, K., Yao, Z., and Zhang, Q.: Characterization of vehicle driving patterns and development of driving cycles in Chinese cities, Transport. Res. D-Tr. E., 13, 289–297, 2008.
Wang, Y. S., Yao, L., Wang, L., L., Liu, Z. R., Ji, D. S., Tang, G. Q.,
Zhang, J. K., Sun, Y., Hu, B., and Xin, J. Y.: Mechanism for the formation of
the January 2013 heavy haze pollution episode over central and eastern
China, Science China, Earth Sciences, 57, 14–25, 2014 (in Chinese).
Willmott, C. J., Robeson, S. M., and Matsuura, K. A.: A refined index of
model performance, Int. J. Climatol., 32, 2088–2094, 2012.
Xu, X. D., Xie, L. A., Cheng, X. H., Xu., J. M., Zhou, X. J., and Ding, G. A.: Application of an Adaptive Nudging Scheme in Air Quality Forecasting in
China, J. Appl. Meteorol. Climatol., 47, 2105–2114, 2008.
Yang, Q., Wang, Y. H., Zhao, C., Liu, Z., William, I., Gustafson, J., and Shao, M.: NOx emission reduction and its effects on ozone during the 2008 Olympic Games, Environ. Sci. Technol., 45, 6404–6410, 2011.
Zang, Z. L., Li, Z. J., Pan, X. B., Hao, Z. L., and You, W.: Aerosol data
assimilation and forecasting experiments using aircraft and surface
observations during CalNex, Tellus B, 68, 29812, https://doi.org/10.3402/tellusb.v68.29812, 2016.
Zhang, Q., Streets, D. G., He, K., Wang, Y., Richter, A., Burrows, J. P., Uno, I., Jang, C. J., Chen, D., Yao, Z., and Lei Y.: NOx emission trends for China, 1995–2004: The view from the ground and the view from space, J. Geophys. Res., 112, D22306, https://doi.org/10.1029/2007JD008684, 2007.
Zhang, L., Shao, J. Y., Lu, X., Zhao, Y. H., Hu, Y. Y., Henze, D., Liao, H.,
Gong, S., L., and Zhang, Q.: Sources and processes affecting fine particulate
matter pollution over North China: An adjoint analysis of the Beijing APEC
period, Environ. Sci. Technol., 50, 8731–8740, 2016.
Zhang, Q., Geng, G. N., Wang, S. W., Andreas, R., and He, K. B.: Satellite
remote sensing of changes in NOx emissions over China during 1996–2010, Chin. Sci. Bull., 57, 2857–2864, 2012.
Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z., Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L. T., and Yao, Z. L.: Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153, https://doi.org/10.5194/acp-9-5131-2009, 2009.
Zhao, B., Wang, P., Ma, J. Z., Zhu, S., Pozzer, A., and Li, W.: A high-resolution emission inventory of primary pollutants for the Huabei region, China, Atmos. Chem. Phys., 12, 481–501, https://doi.org/10.5194/acp-12-481-2012, 2012.
Zhao, H. Y., Zhang, Q., Guan, D. B., Davis, S. J., Liu, Z., Huo, H., Lin, J. T., Liu, W. D., and He, K. B.: Assessment of China's virtual air pollution transport embodied in trade by using a consumption-based emission inventory, Atmos. Chem. Phys., 15, 5443–5456, https://doi.org/10.5194/acp-15-5443-2015, 2015.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zhu, J. and Wang, P.: Ensemble kalman smoother and ensemble kalman filter
approaches to the joint air quality state and emission estimation problem,
Chinese Journal of Atmospheric Sciences, 30, 871–882, 2006.
Zhu, J., Tang, X., Wang, Z. F., and Wu, L.: A review of air quality data
assimilation methods and their application, Chinese Journal of Atmospheric
Sciences, 42, 607–620, 2018.
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.
We develop a new inversion method of emission sources based on sensitivity analysis and the...
Altmetrics
Final-revised paper
Preprint