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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
  • 22 Downloads

Abstract

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).

Keywords

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

Notes

Funding

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.

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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

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