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

摘要

While previous research helped to identify and prioritize the sources oferror in air-quality modeling due to anthropogenic emissions and spatialscale effects, our knowledge is limited on how these uncertainties affectclimate-forced air-quality assessments. Using as reference a 10-year modelsimulation over the greater Paris (France) area at 4 km resolution andanthropogenic emissions from a 1 km resolution bottom-up inventory, throughseveral tests we estimate the sensitivity of modeled ozone and PMconcentrations to different potentially influential factors with aparticular interest over the urban areas. These factors include the modelhorizontal and vertical resolution, the meteorological input from a climatemodel and its resolution, the use of a top-down emission inventory, theresolution of the emissions input and the post-processing coefficients usedto derive the temporal, vertical and chemical split of emissions. We showthat urban ozone displays moderate sensitivity to the resolution ofemissions (~ 8 %), the post-processing method (6.5 %) andthe horizontal resolution of the air-quality model (~ 5 %),while annual PM levels are particularly sensitive to changes intheir primary emissions (~ 32 %) and the resolution of theemission inventory (~ 24 %). The air-quality modelhorizontal and vertical resolution have little effect on model predictionsfor 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 rateby nitrogen oxides. Such consistency is not observed for PM. Incontrast this consistency is not observed for PM. In addition we usethe results of these sensitivities to explain and quantify the discrepancybetween a coarse (~ 50 km) and a fine (4 km) resolutionsimulation over the urban area. We show that the ozone bias of the coarserun (+9 ppb) is reduced by ~ 40 % by adopting a higherresolution emission inventory, by 25 % by using a post-processingtechnique based on the local inventory (same improvement is obtained byincreasing model horizontal resolution) and by 10 % by adopting the annualemission totals of the local inventory. The bias of PMconcentrations follows a more complex pattern, with the positive valuesassociated with the coarse run (+3.6 μg m), increasing ordecreasing depending on the type of the refinement. We conclude that in thecase of fine particles, the coarse simulation cannot selectively incorporatelocal-scale features in order to reduce its error.