Environmental Science and Pollution Research

, Volume 26, Issue 9, pp 8748–8757 | Cite as

A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations—a case study of Xuzhou, China

  • Lijiang Lu
  • Baozhang ChenEmail author
  • Lifeng Guo
  • Huifang Zhang
  • Yanpeng Li
Research Article


Carbon monoxide (CO) emission inventory data are crucial for air quality control. However, the emission inventories are labor-intensive and time-consuming and generally have large uncertainties. In this study, we developed a new regional data assimilation system (TracersTracker) for estimating the surface CO emission flux from continuous mixing ratio observations using the proper orthogonal decomposition (POD)-based four-dimensional variational (4D-VAR) data assimilation method (POD-4DVar) and a coupled regional model (Weather Research and Forecasting model (WRF) with the Models-3 Community Multi-scale Air Quality (CMAQ) model). This system was applied to estimate CO emissions in Xuzhou city, China. An experiment was conducted with the continuous hourly surface CO mixing ratio observations from 21 monitoring towers in January and July of 2016. The experimental results of the system were examined and compared with the continuous surface CO observations (a priori emission). We found that the retrieved CO emission fluxes were higher than the a priori emission and were mainly distributed in urban and industrial areas, which were 104% higher in January (winter) and 44% higher in July (summer).


Regional data assimilation system Four-dimensional variational assimilation (4D-VAR) Proper orthogonal decomposition (POD) Carbon monoxide (CO) emissions 



This research was funded by the international partnership program of the Chinese Academy of Sciences (Grant #131A11KYSB20170025), research grants (O88RA901YA) funded by the State Key Laboratory of Resources and Environment Information System, and a research grant (41771114) funded by the National Natural Science Foundation of China.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.


  1. Altaf MU, Ambrozic M, McCabe MF, Hoteit I (2016) A study of reduced-order 4DVAR with a finite element shallow water model. Int J Numer Methods Fluids 80(11):631–647CrossRefGoogle Scholar
  2. Attia A, Ştefănescu R, Sandu A (2017) The reduced-order hybrid Monte Carlo sampling smoother. Int J Numer Methods Fluids 83(1):28–51CrossRefGoogle Scholar
  3. Caiazzo F, Ashok A, Waitz IA, Yim SHL, Barrett SRH (2013) Air pollution and early deaths in the United States. Part I: quantifying the impact of major sectors in 2005. Atmos Environ 79:198–208CrossRefGoogle Scholar
  4. Cao Y, Zhu SC (2010) Current situations of environmental air quality and countermeasures for pollution in Xuzhou. Environmental Science & TechnologyGoogle Scholar
  5. Cao Y, Zhu J, Navon IM, Luo Z (2007) A reduced-order approach to four-dimensional variational data assimilation using proper orthogonal decomposition. Int J Numer Methods Fluids 53(10):1571–1583CrossRefGoogle Scholar
  6. Chen, B., Kan, H., Chen, R., Jiang, S., & Hong, C. (2011) Air Pollution and Health Studies in China-Policy Implications 61(11):1292–1299Google Scholar
  7. Constantinescu EM, Sandu A, Chai T, Carmichael GR (2006) Ensemble-based chemical data assimilation I: general approach. Q J R Meteorol Soc 128:1–21Google Scholar
  8. De Foy B, W. L. M. Z., Grutter M, A. R. A. L. (2007) Modelling constraints on the emission inventory and on vertical dispersion for CO and SO2 in the Mexico City Metropolitan Area using Solar FTIR and zenith sky UV spectroscopy. Atmos Chem Phys 7:781–801CrossRefGoogle Scholar
  9. Elbern H, Schmidt H, Talagrand O, Ebel A (2000) 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environ Model Softw 15(6):539–548CrossRefGoogle Scholar
  10. Elbern, H., Strunk, A., Schmidt, H., & Talagrand, O. (2007) Emission rate and chemical state estimation by 4-dimensional variational inversion 7:1725–1783Google Scholar
  11. Gabrielle Pe Tron CGBK, Valery Yudin JMLJ (2002) Inverse modeling of carbon monoxide surface emissions using CMDL network observations. J Geophys Res 107:1–23Google Scholar
  12. Gou T, Sandu A (2011) Continuous versus discrete advection adjoints in chemical data assimilation with CMAQ. Atmos Environ 15:1–37Google Scholar
  13. Greally BR, Manning AJ, Reimann S, McCulloch A, Huang J, Dunse BL (2007) Observations of 1,1-difluoroethane (HFC-152a) at AGAGE and SOGE monitoring stations in 1994–2004 and derived global and regional emission estimates. J Geophys Res 112:D6308CrossRefGoogle Scholar
  14. Gurjar BR, Jain A, Sharma A, Agarwal A, Gupta P, Nagpure AS, Lelieveld J (2010) Human health risks in megacities due to air pollution. Atmos Environ 44(36):4606–4613CrossRefGoogle Scholar
  15. Hakami A, HenzeDK, Seinfeld JH, Singh K, Sandu A, Kim S, Byun D, Li Q (2007) The adjoint of cmaq. Environ Sci Technol 41(22):7807–7817Google Scholar
  16. Kilmont ZS (2002) Anthropogenic emissions of non-methane volatile organic compounds in China. Atmos Environ 36:1309–1322CrossRefGoogle Scholar
  17. Kim H, Kim HM, Kim J, Cho C (2018) Effect of data assimilation parameters on the optimized surface CO2 flux in Asia. Asia-Pac J Atmos Sci 54(1):1–17CrossRefGoogle Scholar
  18. Kurokawa J, Yumimoto K, Uno I, Ohara T (2009) Adjoint inverse modeling of NOx emissions over eastern China using satellite observations of NO2 vertical column densities. Atmos Environ 43(11):1878–1887CrossRefGoogle Scholar
  19. Lu S, Lin HX, Heemink AW, Fu G, Segers AJ (2015) Estimation of volcanic ash emissions using trajectory-based 4D-Var data assimilation. Mon Weather Rev 144:575–589CrossRefGoogle Scholar
  20. Ma YT (2007) The compilation of vehicle emission inventory in Pearl River Delta region and its uncertainty analysis. Peking University, BeijingGoogle Scholar
  21. Maione M, Giostra U, Arduini J, Belfiore L, Furlani F, Geniali A, Mangani G, Vollmer MK, Reimann S (2008) Localization of source regions of selected hydrofluorocarbons combining data collected at two European mountain stations. Sci Total Environ 391(2–3):232–240CrossRefGoogle Scholar
  22. Manning AJ, Ryall DB, Derwent RG (2003a) Estimating European emissions of ozone-depleting and greenhouse gases using observations and a modeling back-attribution technique. J Geophys Res 108(14):4405CrossRefGoogle Scholar
  23. Manning AJ, Ryall DB, Derwent RG, Simmonds PG, O'Doherty S (2003b) Estimating European emissions of ozone-depleting and greenhouse gases using observations and a modeling back-attribution technique. Journal of Geophysical Research Atmospheres 108(D14):10–23CrossRefGoogle Scholar
  24. MEPC (2017) 2016 report on the state of the environment in ChinaGoogle Scholar
  25. Ng MK, Zhu Z (2018) Sparse matrix computation for air quality forecast data assimilation. Numerical AlgorithmsGoogle Scholar
  26. T. Ohara, H. A. J. K. (2007) An Asian emission inventory of anthropogenic emission sources for the period 1980–2020. Atmos. Chem Phys (7):4419–4444Google Scholar
  27. Pan XL, Kanaya Y, Wang ZF, Tang X, Takigawa M, Pakpong P, Taketani F, Akimoto H (2014) Using Bayesian optimization method and FLEXPART tracer model to evaluate CO emission in East China in springtime. Environ Sci Pollut Res 21(5):3873–3879CrossRefGoogle Scholar
  28. Park S, Lee S, Lee HW (2014) Assimilation of wind profiler observations and its impact on three-dimensional transport of ozone over the Southeast Korean Peninsula. Atmos Environ 99:660–672CrossRefGoogle Scholar
  29. Park S, Kim D, Lee S, Lee HW (2016) Variational data assimilation for the optimized ozone initial state and the short-time forecasting. Atmos Chem Phys 16(5):3631–3649CrossRefGoogle Scholar
  30. Peng Z, Zhang M, Kou X, Tian X, Ma X (2015) A regional carbon data assimilation system and its preliminary evaluation in East Asia. Atmos Chem Phys 15:1087–1104CrossRefGoogle Scholar
  31. Qian S, Lv X, Cao Y, Shao F (2016) Parameter estimation for a 2D tidal model with POD 4D-VAR data assimilation. Math Probl Eng 2016:1–14Google Scholar
  32. Quélo D, Mallet V, Sportisse B (2005) Inverse modeling of NOx emissions at regional scale over northern France: preliminary investigation of the second-order sensitivity. J Geophys Res 110(D24)Google Scholar
  33. Rivier L, Ciais P, Hauglustaine DA, Bakwin P, Bousquet P, Peylin P et al (2006) Evaluation of SF6, C2Cl4, and CO to approximate fossil fuel CO2 in the Northern Hemisphere using a chemistry transport model. Journal of Geophysical Research Atmospheres 111(D16):21–43CrossRefGoogle Scholar
  34. Ryall DB, Derwent RG, Manning AJ, Simmonds PG, O'Doherty S (2000) Estimating source regions of European emissions of trace gases from observations at Mace Head. Atmos Environ 35:2507–2523CrossRefGoogle Scholar
  35. Saide P, Osses A, Gallardo L, Osses M (2009) Adjoint inverse modeling of a CO emission inventory at the city scale: Santiago de Chile’s case. Atmospheric Chemistry and Physics Discussions 9(2):6325–6361CrossRefGoogle Scholar
  36. Saroj Kumar Sahu GBNP (2015) High resolution emission inventory of NOx and CO for mega City Delhi, India. Aerosol Air Qual Res 15:1137–1144CrossRefGoogle Scholar
  37. Siade AJ, Putti M, Yeh WWG (2010) Snapshot selection for groundwater model reduction using proper orthogonal decomposition. Water Resour Res 46(8):1–24CrossRefGoogle Scholar
  38. Stohl A, Seibert P, Arduini J, Eckhardt S, Fraser P, Greally BR, Lunder C, Maione M, Mühle J, O'Doherty S, Prinn RG, Reimann S, Saito T, Schmidbauer N, Simmonds PG, Vollmer MK, Weiss RF, Yokouchi Y (2009) An analytical inversion method for determining regional and global emissions of greenhouse gases: sensitivity studies and application to halocarbons. Atmos Chem Phys 9(5):1597–1620CrossRefGoogle Scholar
  39. Tian X, Feng X (2015) A non-linear least squares enhanced POD-4DVar algorithm for data assimilation. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 67:25340CrossRefGoogle Scholar
  40. Tian X, ZhengHui X (2009) An explicit four-dimensional variational data assimilation method based on the proper orthogonal decomposition: theoretics and evaluation. Sci China Ser D Earth Sci 52(2):279–286CrossRefGoogle Scholar
  41. Tian X, Xie Z, Dai A (2008) An ensemble-based explicit four-dimensional variational assimilation method. J Geophys Res 113(D21124)Google Scholar
  42. Tian X, Xie Z, Liu Y, Cai Z, Fu Y, Zhang H, Feng L (2014) A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO2 fluxes and 3-D atmospheric CO2 concentrations from observations. Atmos Chem Phys 14(23):13281–13293CrossRefGoogle Scholar
  43. Wang KY, Lary DJ, Shallcross DE, Hall SM, Pyle JA (2001) A review on the use of the adjoint method in four-dimensional. Q J R Meteoml SOC 127:2181–2204CrossRefGoogle Scholar
  44. Zhang L, Constantinescu EM, Sandu A, Tang Y, Chai T (2008) An adjoint sensitivity analysis and 4D-Var data assimilation study of Texas air quality. Atmos Environ (23):1–25Google Scholar
  45. Zheng J, Zhang L, Che W, Zheng Z, Yin S (2009) A highly resolved temporal and spatial air pollutant emission inventory for the Pearl River Delta region, China and its uncertainty assessment. Atmos Environ 43(32):5112–5122CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lijiang Lu
    • 1
  • Baozhang Chen
    • 1
    • 2
    • 3
    Email author
  • Lifeng Guo
    • 2
    • 3
  • Huifang Zhang
    • 2
    • 3
  • Yanpeng Li
    • 1
  1. 1.School of Environment Science and Spatial InformationChina University of Mining and TechnologyXuzhouChina
  2. 2.Chinese Academy of SciencesInstitute of Geographic Sciences & Nature Resources ResearchBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations