Abstract
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.
Highlights
Recent epidemiological findings stress the need to resolve the variability of pollutant concentrations at the urban scale
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 and by 10 % by adopting the annual emission totals of the local inventory
Through a number of test cases we study the ability of the model to predict present-time decadal air-quality with respect to emission and meteorological input as well as the chemistry transport models (CTMs)’s horizontal and vertical resolution
Summary
Recent epidemiological findings stress the need to resolve the variability of pollutant concentrations at the urban scale. More recent studies have integrated advanced chemistry schemes capable of resolving the variability of pollutant concentrations at regional scale, which spans from several hours up to a few days, with chemistry transport models (CTMs) (Colette et al, 2012, 2013; Forkel and Knoche, 2006, 2007; Hogrefe et al, 2004; Katragkou et al, 2011; Kelly et al, 2012; Knowlton et al, 2004; Lam et al, 2011; Langner et al, 2005, 2012; Nolte et al, 2008; Szopa and Hauglustaine, 2007; Tagaris et al, 2009; Zanis et al, 2011). Global models with a typical resolution of a few hundreds of kilometers and regional CTMs used at resolutions of a few tens of kilometers, and their parameterization of physical and chemical processes make them inadequate for modeling airquality at the urban scale (Cohan et al, 2006; Forkel and Knoche, 2007; Markakis et al, 2014; Sillman et al, 1990; Tie et al, 2010; Valari and Menut, 2008; Valin et al, 2011; Vautard et al, 2007)
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