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Volume 18, issue 6
Atmos. Chem. Phys., 18, 4171–4186, 2018
https://doi.org/10.5194/acp-18-4171-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 18, 4171–4186, 2018
https://doi.org/10.5194/acp-18-4171-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Mar 2018

Research article | 27 Mar 2018

Evaluation of modeling NO2 concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

Fei Liu1,2,3, Ronald J. van der A1, Henk Eskes1, Jieying Ding1,4, and Bas Mijling1 Fei Liu et al.
  • 1Royal Netherlands Meteorological Institute (KNMI), Department of Satellite Observations, De Bilt, the Netherlands
  • 2Universities Space Research Association (USRA), GESTAR, Columbia, MD, USA
  • 3NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4Department of Geoscience and Remote Sensing (GRS), Delft University of Technology, Delft, the Netherlands

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope  =  0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope  =  1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of −30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

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Short summary
We used ground measurements from the recently developed air quality monitoring network in China to validate modeling surface NO2 concentrations from the regional chemical transport model (CTM). The CTM simulations driven by satellite-derived and bottom-up inventories show negative and positive differences against the ground measurements, respectively. Our study suggests an improvement of the distribution of emissions between urban and rural areas in the satellite-derived inventory.
We used ground measurements from the recently developed air quality monitoring network in China...
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