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

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 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.

The influence of model spatial resolution on simulated ozone and fine particulate matter for Europe: implications for health impact assessments

“…PM 2.5 concentrations have also been found to be sensitive to the model horizontal resolution (Arunachalam et al, 2011;Punger and West, 2013;Markakis et al, 2015;Neal et al, 2017). In the US, Punger and West (2013) found populationweighted annual mean PM 2.5 concentrations to be 6 % higher at 36 km compared to 12 km, but 27 % lower when simulated at 408 km compared to 12 km.…”

“…Investigating the impact of increasing model horizontal resolution from 48 to 6 km on O 3 concentrations in Paris, Valari and Menut (2008) found modelled surface O 3 to be more sensitive to the resolution of input emissions than to meteorology. A number of other studies note the sensitivity of simulated O 3 to simulated nitrogen oxide (NO x ) concentrations that determine the extent of titration of O 3 by nitrogen monoxide (NO) (Stock et al, 2014;Markakis et al, 2015;Schaap et al, 2015). Furthermore, Stock et al (2014) found the impact of spatial resolution (150 km vs. 40 km) on simulated O 3 concentrations to vary with season across Europe.…”

“…Amongst a number of factors, simulated air pollutant concentrations may vary depending on the three-dimensional chemistry model used, its set-up and the model resolution (e.g. Markakis et al, 2015;Schaap et al, 2015;Yu et al, 2016;Neal et al, 2017). Although the same model processes are represented at different model resolutions, simulated pollutant concentrations can vary due to differences in (i) the resolution of emissions, which may have a nonlinear effect on the chemical formation of pollutants, and (ii) the resolution of the driving meteorology (Valari and Menut, 2008;Tie et al, 2010;Arunachalam et al, 2011;Colette et al, 2013;Markakis et al, 2015;Schaap et al, 2015).…”

On the long-term impact of emissions from central European cities on regional air quality

“…On regional scales, many studies focused on European urban centers, especially those in the Mediterranean region (e.g. Im et al, 2011a, b;Escudero et al, 2014;Finardi et al, 2014), but also covering London and the Ruhr area (Hodnebrog et al, 2011), or Paris (Skyllakou et al, 2014Markakis et al, 2015). The importance of multi-model modeling approach for investigating the megacities impact on air quality and climate was analyzed in detail by Baklanov et al (2010) in the framework of the European FP7 project MEGAPOLI.…”

“…Therefore, we have proposed to conduct continuous, 10-year long simulations which decreases the uncertainty originating in the driving meteorological conditions. This choice was preferred also by Katragkou et al (2010) and Zanis et al (2011) or Markakis et al (2015). (3) Most of the above-listed regional studies focused on only one or two megacities (and their impact).…”