Nature-based solutions can represent beneficial tools in the field of urban transformation for their contribution to important environmental services such as air quality improvement. To evaluate the impact on urban air pollution of a CityTree (CT), an innovative wall-type green infrastructure in passive (deposition) and active (filtration) modes of operation, a study was conducted in a real urban setting in Modena (Italy) during 2017 and 2018, combining experimental measurements with modelling system evaluations. In this work, relying on the computational resources of CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID High Performance Computing infrastructure, we used the air pollution microscale model PMSS (Parallel Micro-SWIFT-Micro SPRAY) to simulate air quality during the experimental campaigns. The spatial characteristics of the impact of the CT on local air pollutants concentrations, specifically nitrogen oxides (NOx) and particulate matter (PM₁₀), were assessed. In particular, we used prescribed bulk deposition velocities provided by the experimental campaigns, which tested the CT both in passive (deposition) and in active (filtration) mode of operation. Our results showed that the PM₁₀ and NOx concentration reductions reach from more than 0.1% up to about 0.8% within an area of 10 × 20 m² around the infrastructure, when the green infrastructure operates in passive mode. In filtration mode the CT exhibited higher performances in the abatement of PM₁₀ concentrations (between 1.5% and 15%), within approximately the same area. We conclude that CTs may find an application in air quality hotspots within specific urban settings (i.e., urban street canyons) where a very localized reduction of pollutants concentration during rush hours might be of interest to limit population exposure. The optimization of the spatial arrangement of CT modules to increment the “clean air zone” is a factor to be investigated in the ongoing development of the CT technology.

A comparison between a puff atmospheric dispersion model (hereafter: PuM) and a Lagrangian particle model (hereafter: LPM) was conducted for a real case of emissions from an industrial plant, in the context of a complex and coastal site. The PuM’s approach is well-known and widely adopted worldwide, thanks to the authoritative suggestions by the US-EPA for regulatory use as, according to the definitions included in its guidelines, an “alternative” to “preferred” models; LPMs are more advanced models and have gained reliability over the last two decades. Therefore, it is of interest to provide insights into the decision to adopt or recommend, in the field of atmospheric impact assessment, a more advanced, but more knowledge- and resource-intensive, modeling tool, rather than an established albeit less accurate one. An inter-comparison of the two approaches is proposed based on the use of various statistical and comparative parameters with the goal of studying their differences in reproducing maps of ground-level ambient concentration statistics for assessment purposes (annual means, hourly peaks). The models were tested under a year-long simulation. The dispersion from both a point and a volume source, belonging to an existing industrial plant, was analyzed separately. The inter-comparison was performed through the analysis of 2D ground concentration maps, scatterplots, and three classical indices from the 2D maps of annual concentration statistics. To correlate the differences among models with site characteristics, the statistics were analyzed not only globally, but also according to distance from the source, the elevation, and the land-use classification. The analysis shows that around-its-axis plume dispersion in LPM is lower than in PuM over all the land-use types except water surfaces, in agreement with the theoretical basis provided by the models. Because of its more advanced theoretical formulation, e.g., in the interaction of the plume with the complex terrain and the three-dimensional wind field, an LPM used as a comparison term allowed us to highlight the weaknesses of a more traditional approach, such as PuM, in reproducing effects such as plume up-sloping, deflection, channeling, confinement, and wind shear diffusion.

Urban air quality in cities is strongly influenced by road traffic emissions. Micro-scale models have often been used to evaluate the pollutant concentrations at the scale of the order of meters for estimating citizen exposure. Nonetheless, retrieving emissions information with the required spatial and temporal details is still not an easy task. In this work, we use our modelling system PMSS (Parallel Micro Swift Spray) with an emission dataset based on Floating Car Data (FCD), containing hourly data for a large number of road links within a 1 × 1 km² domain in the city of Rome for the month of May 2013. The procedures to obtain both the emission database and the PMSS simulations are hosted on CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID HPC facilities managed by ENEA. The possibility of using such detailed emissions, coupled with HPC performance, represents a desirable goal for microscale modeling that can allow such modeling systems to be employed in quasi-real time and nowcasting applications. We compute NOx concentrations obtained by: (i) emissions coming from prescribed hourly modulations of three types of roads, based on vehicle flux data in the FCD dataset, and (ii) emissions from the FCD dataset integrated into our modelling chain. The results of the simulations are then compared to concentrations measured at an urban traffic station.