Assessing pollutant ventilation in a city-boulevard using large-eddy simulation This dataset contains input and output information for a computational fluid dynamics study in an urban environment. The simulations were conducted using the large-eddy simulation (LES) model PALM. In short: Model: PALM modelling system (https://palm.muk.uni-hannover.de) Revision: 1904 The modified code parts of PALM revision 1904 used in this study are available online at https://www.mdpi.com/2073-4433/9/2/65/s1. More details about the study can be found in Kurppa et al. (2018): Ventilation and Air Quality in City Blocks Using Large-Eddy Simulation—Urban Planning Perspective, Atmosphere 9(2), 27. Description: This study utilizes LES (Large-Eddy Simulation) modeling to examine how the structural layout of different urban perimeter block arrangements influences the air quality within street canyons. The objective is to reveal whether changes in the perimeter block orientation and shape can alter the wind conditions enough to result in meaningful differences in the local pollutant dispersion and ventilation characteristics. This numerical investigation considers four alternative city boulevard designs for the Hämeenlinnanväylä area that forms part of the new Helsinki City Plan. Each design facilitates an equal amount of gross floor area, but features a distinctly different city block plan layout. The efficiency with which each boulevard design is capable of dispersing traffic emissions from its street canyons is assessed by performing high-resolution LES analysis with a model that features a detailed representation of the urban landscape including trees planted on the boulevard and surrounding forests. The LES simulations yield detailed descriptions of the atmospheric turbulence and how it interacts with the urban structures in each boulevard alternative. The solution also incorporates a simultaneously evolution of air parcels, representing elements of traffic emissions, which are continuously discharged from the streets. Air parcels are modelled using a Lagrangian particle model within PALM. The ventilation and dispersion performance of each design package is evaluated and compared by analyzing the distribution of these air parcel concentrations and their rate of vertical transport and dilution. Two contrasting meteorological scenarios are considered in the simulations: south-westerly wind with a neutral stratification and easterly wind with a stable stratification. The data has been partitioned as follows: input_data --> wd{wind direction in degrees} --> {atmospheric stability} --> V{number of city-plan version}: • CANOPY_HEIGHT{_number of the sub-domain}: raster map on the height of vegetation • PARIN{_number of the sub-domain}_batch{batch number}: parameter file to run the simulation • PARTICLE_SOURCE_02: source map of air parcels for the sub-domain 2 • TOPOGRAPHY_DATA{_number of the sub-domain}: land-surface elevation and buildings raw_output_data --> wd{wind direction in degrees} --> {atmospheric stability} --> V{number of city-plan version}: • V{number of city-plan version}_M01_orig.nc: 40-minute time series with a time step of 5 seconds. Includes the three wind components (u, v and w) and the concentration of air parcels (pr) over nearly the entire sub-domain 2. • V{number of city-plan version}_M02_orig.nc: 5-minute time series with a time step of 0.07/0.15 seconds. Includes the three wind components (u, v and w) and the concentration of air parcels (pr) over a small area in the street canyon. • V{number of city-plan version}_M03_orig.nc: 5-minute time series with a time step of 5 seconds. Includes the three wind components (u, v and w) and the concentration of air parcels (pr) over nearly the entire sub-domain 2. calculated_output_data (for the definitions of all variables, see Kurppa et al. (2018)): • dilution: dilution of particles away from the street canyons ◦ per_column: column-averaged dilution rate ▪ D_col_wd{wind direction}_{atmospheric stability}_V{number of city-plan version} ◦ total_volume: volume-averaged dilution rate over the whole domain ▪ D_vol_wd{wind direction}_{atmospheric stability}_V{number of city-plan version} • flux_high_frequency: flux on air parcels calculated at frequency every 0.07 (neutral)/0.15 (stable) seconds. ◦ flux_HF_wd{wind direction}_{atmospheric stability}_V{number of city-plan version} • flux_low_frequency: flux on air parcels calculated at frequency every five seconds. ◦ flux_LF_wd{wind direction}_{atmospheric stability}_V{number of city-plan version} • surface_following: surface following datasets calculated from the raw data. Similar folder partitioning and naming. scripts: Python-scripts to analyse the data. Naming conversion for the number of city-plan version between Kurppa et al. (2018) and the dataset provided here: V_par = V1 V_per = V2 V_perHV = V3 V_J-J = V4

2020