A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations—a case study of Xuzhou, China
- 116 Downloads
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).
KeywordsRegional 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.
- Cao Y, Zhu SC (2010) Current situations of environmental air quality and countermeasures for pollution in Xuzhou. Environmental Science & TechnologyGoogle Scholar
- 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
- 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
- Elbern, H., Strunk, A., Schmidt, H., & Talagrand, O. (2007) Emission rate and chemical state estimation by 4-dimensional variational inversion 7:1725–1783Google Scholar
- 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
- Gou T, Sandu A (2011) Continuous versus discrete advection adjoints in chemical data assimilation with CMAQ. Atmos Environ 15:1–37Google Scholar
- 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
- Ma YT (2007) The compilation of vehicle emission inventory in Pearl River Delta region and its uncertainty analysis. Peking University, BeijingGoogle Scholar
- MEPC (2017) 2016 report on the state of the environment in ChinaGoogle Scholar
- Ng MK, Zhu Z (2018) Sparse matrix computation for air quality forecast data assimilation. Numerical AlgorithmsGoogle Scholar
- 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
- 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
- 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
- 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
- Tian X, Xie Z, Dai A (2008) An ensemble-based explicit four-dimensional variational assimilation method. J Geophys Res 113(D21124)Google Scholar
- 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