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Volume 17, issue 6 | Copyright

Special issue: East Asia emissions assessment (EA2)

Atmos. Chem. Phys., 17, 4131-4145, 2017
https://doi.org/10.5194/acp-17-4131-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Mar 2017

Research article | 28 Mar 2017

Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

Guannan Geng1, Qiang Zhang1, Randall V. Martin2,3, Jintai Lin4, Hong Huo5, Bo Zheng6, Siwen Wang6,7, and Kebin He6 Guannan Geng et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
  • 2Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
  • 3Smithsonian Astrophysical Observatory, Harvard–Smithsonian Center for Astrophysics, Cambridge, MA, USA
  • 4Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
  • 5Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China
  • 6State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
  • 7Max Planck Institute for Chemistry, Mainz, Germany

Abstract. Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled tropospheric NO2 vertical columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 vertical columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 vertical columns. Using vehicle population and an updated road network for the on-road transport sector could substantially enhance urban emissions and improve the model performance. When further applying industrial gross domestic product (IGDP) values for the industrial sector, modeled NO2 vertical columns could better capture pollution hotspots in urban areas and exhibit the best performance of the six cases compared to satellite-based NO2 vertical columns (slope  =  1.01 and R2 = 0. 85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

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We investigated the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled NO2 columns from the six gridded emissions are compared with satellite-based columns from OMI. Results show that differences between modeled and satellite-based NO2 columns are sensitive to the spatial proxies used in the gridded emission inventories.
We investigated the impact of spatial proxies on the representation of gridded emissions by...
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