Air quality in the mid-21st century for the city of Paris under two climate scenarios; from the regional to local scale

Markakis, K.; Valari, Myrto; Colette, Augustin; Sanchez, Olivier; Perrussel, Olivier; Honoré, Cécile; Vautard, Robert; Klimont, Zbigniew; Rao, Shilpa

doi:10.5194/acp-14-7323-2014

Cited by 30 publications

(64 citation statements)

References 77 publications

(72 reference statements)

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“…It has been shown that these inventories cannot adequately portray the plethora and complexity of the anthropogenic emissions over large cities (Gilliland et al, 2003;Markakis et al, 2010Markakis et al, , 2012Russell and Dennis, 2000). In Markakis et al (2014) we showed that ozone formation occurs under a VOC-limited chemical regime in the 10-year simulations that used the bottom-up emission inventory. This result is consistent with previous studies over the Paris area (Beekmann and Derognat, 2003;Beekmann and Vautard, 2010;Deguillaume et al, 2008).…”

Section: Introductionmentioning

confidence: 93%

“…For the reference run at the urban scale (hereafter REF), we use the same model setup as in Markakis et al (2014): the modeling domain has a horizontal resolution of 4 km and consists of 39 grid cells in the west-east direction, 32 grid cells in the north-south direction and 8σ -p hybrid vertical layers from the surface (999 hPa) up to approximately 5.5 km (500 hPa) with the surface layer being 25 m thick. The configuration of the reference run represents the best compromise between local-scale emission data and the high computational demand of a long-term simulation at fine resolution.…”

Section: Meteorological and Air-quality Models' Setupmentioning

confidence: 99%

“…Such a discrepancy is critical when mitigation scenarios are investigated because they may lead to controversy when studying the ozone response in the future. As shown in Markakis et al (2014), regional-scale modeling and the use of top-down emissions can result to higher future reductions than the urban-scale modeling using bottom-up emissions. Other challenges stem from the fact that emission projections are mostly based on scenarios developed to represent changes at the global scale and are rarely suited for assessment at the regional let alone urban scale.…”

Section: Introductionmentioning

confidence: 99%

“…This study builds on the previous work of Markakis et al (2014) where a qualitative comparison was accomplished between an urban (local) and a regional-scale simulation over Paris. The aim of the present study is to disentangle modeling errors of climate-forced air-quality studies over finer scales due to different factors such as emission and meteorological input as well as the CTM's horizontal and vertical resolution.…”

Section: Introductionmentioning

confidence: 99%

“…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, 2012Knoche, 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, 2005Langner et al, , 2012Nolte 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).…”

Section: Introductionmentioning

confidence: 99%

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

Markakis

Valari

Perrussel³

et al. 2015

Atmos. Chem. Phys.

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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 PM 2.5 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 PM 2.5 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 PM 2.5 . In contrast this consistency is not observed for PM 2.5 . 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 PM 2.5 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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