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

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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 climateforced 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.

Figures

  • Figure 1. Overview of the coarse (D1 having 50 km resolution) and local scale (D2, illustrated by the red rectangle having 4 km resolution) simulation domains. In D2, the city of Paris in located in the area enclosed by the purple line. Circles correspond to sites of the local air-quality monitoring network (AIRPARIF), with red for urban, blue for suburban and black for rural.
  • Table 1. Parameterization of the different sets of simulations presented in the paper. Changes with respect to the REF case are marked in bold. Changes with respect to a simulation other than REF are marked in italics.
  • Figure 2. Domain-wide annual emissions of NOx , NMVOC (leftaxis) and PM2.5 (right-axis) from the local (bottom-up) and the regional (top down) inventory (summed across the vertical column).
  • Table 2. Observed and modeled daily average meteorological variables over the Île-de-France region. MET_CLIM data set stems from a climate model and MET_ERA05, MET_ERA01 from reanalysis data at 0.5 and 0.1◦ resolution, respectively. Absolute model bias is given in parenthesis.
  • Figure 3. (a) Scatter plots and scores of daily average ozone concentrations at urban, suburban and rural stations from the REF simulation. Odd oxygen (Ox) and daily maximum values at urban locations are also shown. textbf(b) daily average PM2.5 concentrations in wintertime (DJF), summertime (JJA) and on annual basis over urban stations.
  • Figure 4. Scatter plots and scores for the sensitivity test on climate-model-driven meteorology for ozone and PM2.5.
  • Table 3. Absolute difference (and percentage in parenthesis) between daily averaged ozone (ppb) and PM2.5 (µg m −3) from two climateforced air-quality runs. The most influential factor for each sensitivity test is marked in bold.
  • Figure 5. Scatter plots and scores for the sensitivity test on the resolution of meteorology for ozone and PM2.5.

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CITATION STYLE

APA

Markakis, K., Valari, M., Perrussel, O., Sanchez, O., & Honore, C. (2015). Climate-forced air-quality modeling at the urban scale: Sensitivity to model resolution, emissions and meteorology. Atmospheric Chemistry and Physics, 15(13), 7703–7723. https://doi.org/10.5194/acp-15-7703-2015

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