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Volume 15, issue 13
Atmos. Chem. Phys., 15, 7703–7723, 2015
https://doi.org/10.5194/acp-15-7703-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 15, 7703–7723, 2015
https://doi.org/10.5194/acp-15-7703-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Jul 2015

Research article | 14 Jul 2015

Climate-forced air-quality modeling at the urban scale: sensitivity to model resolution, emissions and meteorology

K. Markakis1, M. Valari1, O. Perrussel2, O. Sanchez2, and C. Honore2 K. Markakis et al.
  • 1Laboratoire de Meteorologie Dynamique, IPSL Laboratoire CEA/CNRS/UVSQ, Ecole Polytechnique, 91128 Palaiseau CEDEX, France
  • 2AIRPARIF, Association de surveillance de qualité de l'air en Île-de-France, 7 rue Crillon, 75004, Paris, France

Abstract. While previous research helped to identify and prioritize the sources of error in air-quality modeling due to anthropogenic emissions and spatial scale effects, our knowledge is limited on how these uncertainties affect climate-forced air-quality assessments. Using as reference a 10-year model simulation over the greater Paris (France) area at 4 km resolution and anthropogenic emissions from a 1 km resolution bottom-up inventory, through several tests we estimate the sensitivity of modeled ozone and PM2.5 concentrations to different potentially influential factors with a particular interest over the urban areas. These factors include the model horizontal and vertical resolution, the meteorological input from a climate model and its resolution, the use of a top-down emission inventory, the resolution of the emissions input and the post-processing coefficients used to derive the temporal, vertical and chemical split of emissions. We show that urban ozone displays moderate sensitivity to the resolution of emissions (~ 8 %), the post-processing method (6.5 %) and the horizontal resolution of the air-quality model (~ 5 %), while annual PM2.5 levels are particularly sensitive to changes in their primary emissions (~ 32 %) and the resolution of the emission inventory (~ 24 %). The air-quality model horizontal and vertical resolution have little effect on model predictions for the specific study domain. In the case of modeled ozone concentrations, the implementation of refined input data results in a consistent decrease (from 2.5 up to 8.3 %), mainly due to inhibition of the titration rate by nitrogen oxides. Such consistency is not observed for PM2.5. In contrast this consistency is not observed for PM2.5. In addition we use the results of these sensitivities to explain and quantify the discrepancy between a coarse (~ 50 km) and a fine (4 km) resolution simulation over the urban area. We show that the ozone bias of the coarse run (+9 ppb) is reduced by ~ 40 % by adopting a higher resolution emission inventory, by 25 % by using a post-processing technique based on the local inventory (same improvement is obtained by increasing model horizontal resolution) and by 10 % by adopting the annual emission totals of the local inventory. The bias of PM2.5 concentrations follows a more complex pattern, with the positive values associated with the coarse run (+3.6 μg m−3), increasing or decreasing depending on the type of the refinement. We conclude that in the case of fine particles, the coarse simulation cannot selectively incorporate local-scale features in order to reduce its error.

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The efficacy of emission policies is explored by coarse resolution modeling applications. These were shown to be biased, overestimating that efficacy indicated in simulations with refined resolution. In order to improve our assessments, we need to quantify those biases. In this study we show that the ozone bias of the coarse run is reduced by 40% by adopting higher resolution emissions. For PM2.5, the coarse run cannot selectively incorporate local scale features in order to reduce model error.
The efficacy of emission policies is explored by coarse resolution modeling applications. These...
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