On the application and grid-size sensitivity of the urban dispersion model CAIRDIO v2.0 under real city weather conditions

Abstract

Abstract. There is a gap between the need for city-wide air-quality simulations considering the intra-urban variability and mircoscale dispersion features and the computational capacities that conventional urban microscale models require. This gap can be bridged by targeting model applications on the gray zone situated between the mesoscale and large-eddy scale. The urban dispersion model CAIRDIO is a new contribution to the class of computational-fluid dynamics models operating in this scale range. It uses a diffuse-obstacle boundary method to represent buildings as physical obstacles at gray-zone resolutions in the order of tens of meters. The main objective of this approach is to find an acceptable compromise between computationally inexpensive grid sizes for spatially comprehensive applications and the required accuracy in the description of building and boundary-layer effects. In this paper, CAIRDIO is applied on the simulation of black carbon and particulate matter dispersion for an entire mid-size city using a uniform horizontal grid spacing of 40 m. For model evaluation, measurements from five operational air monitoring stations representative for the urban background and high-traffic roads are used. The comparison also includes the mesoscale host simulation, which provides the boundary conditions. The measurements show a dominant influence of the mixing layer evolution at background sites, and therefore both the mesoscale and large-eddy simulation (LES) results are in good agreement with the observed air pollution levels. In contrast, at the high-traffic sites the proximity to emissions and the interactions with the building environment lead to a significantly amplified diurnal variability in pollutant concentrations. These urban road conditions can only be reasonably well represented by CAIRDIO while the meosocale simulation indiscriminately reproduces a typical urban-background profile, resulting in a large positive model bias. Remaining model discrepancies are further addressed by a grid-spacing sensitivity study using offline-nested refined domains. The results show that modeled peak concentrations within street canyons can be further improved by decreasing the horizontal grid spacing down to 10 m, but not beyond. Obviously, the default grid spacing of 40 m is too coarse to represent the specific environment within narrow street canyons. The accuracy gains from the grid refinements are still only modest compared to the remaining model error, which to a large extent can be attributed to uncertainties in the emissions. Finally, the study shows that the proposed gray-scale modeling is a promising downscaling approach for urban air-quality applications. The results, however, also show that aspects other than the actual resolution of flow patterns and numerical effects can determine the simulations at the urban microscale.

Air pollution trapping in the Dresden Basin induced by natural and urban topography

“…However, terrain height variations can be regarded as only moderate compared to, for example, some much steeper Alpine valleys studied in a similar framework (Harnisch et al, 2009). This study expands upon an earlier simulation case study presented in Weger et al (2022a) by focusing on the natural topographic impact on the dispersion of local black carbon (BC) emissions. In Weger et al (2022a) the effect of urban topography on the intra-urban variability of BC and particulate matter was investigated.…”

“…This study expands upon an earlier simulation case study presented in Weger et al (2022a) by focusing on the natural topographic impact on the dispersion of local black carbon (BC) emissions. In Weger et al (2022a) the effect of urban topography on the intra-urban variability of BC and particulate matter was investigated. Therein, natural topography was technically represented but played no significant role as it was basically flat.…”

“…The comparisons of modeled wind speed with hourly wind speed measurements for the two simulation periods are shown in (v2.0), the surface fields were obtained by a diagnostic downscaling of respective prognostic data from the hosting COSMO simulation (Weger et al, 2022a). The downscaling consisted of fitting the COSMO data into a linear regression model, which was based on land use as the only predictor.…”