CFD modelling of air quality in Pamplona City (Spain): Assessment, stations spatial representativeness and health impacts valuation

Assessing 3-D Spatial Extent of Near-Road Air Pollution around a Signalized Intersection Using Drone Monitoring and WRF-CFD Modeling

“…Then, the NO x emission on road was estimated based on the traffic volumes of six vehicle types counted using the video records at the Toegye intersection. In the CFD simulations, NO x is regarded as the no-reactive primary pollutant and has the zero background concentration to identify the 3-D spatial extent of near-road air pollutant focusing on traffic emission and dynamical and thermal dispersion [ 38 , 39 ].…”

Modeling Emission Flow Pattern of a Single Cruising Vehicle on Urban Streets with CFD Simulation and Wind Tunnel Validation

“…The results of a CFD numerical simulation can be compared with corresponding data from field measurements and a wind tunnel experiment. For example, Rivas et al applied computational fluid dynamics Reynolds-averaged Navier-Stokes (CFD-RANS) simulations to compute the concentrations of NO 2 and NO X at the pedestrian level of Pamplona city, in 2016, and validated the results through a comparison with measurements provided by fixed and mobile air quality monitoring stations in the city, with a maximum relative error below 30% [7]. Li et al performed field measurements using instrumented unmanned aerial vehicles (UAVs) with a CFD simulation to investigate pollutant dispersion at a university and to understand the influence of wind fields on PM 2.5 diffusion patterns [8].…”

Performance evaluation of a multiscale modelling system applied to particulate matter dispersion in a real traffic hot spot in Madrid (Spain)

“…where C is PM10 concentration, D is the molecular viscosity, SC is the source term to represent emissions and Kc is the eddy diffusivity of pollutant that is Kc = μ t /Sct , being Sct the turbulent Schmidt number and μ t the turbulent eddy viscosity. The turbulent Schmidt number used has an impact on the pollutant dispersion simulations and the modelled concentrations depend on the selected value (Di Sabatino et al, 2007;Tominaga and Stathopoulos, 2007;Gromke et al, 2008;Vranckx et al, 2015;Gromke and Blocken, 2015;Rivas et al, 2019). Tominaga and Stathopoulos (2007) pointed out that its optimum value is between 0.2 and 1.3 depending on flow properties and geometries and, for a simple geometry, Vranckx et al (2015) found a different optimum Sct (from 0.3 to 1) depending on wind direction.…”

“…microscale meteorology, for example between thermally-driven flows and the dynamics of the atmospheric boundary layer (ABL) that can have an important effects on the dispersion of pollutants. Neutral inlet profiles of velocity, turbulent kinetic energy and its dissipation defined by Richards and Hoxey (1993) were widely used in CFD simulations (Buccolieri et al, 2011;Santiago et al, 2013;Jeanjean et al, 2017;Rivas et al, 2019) taking into account wind speed and direction re-corded in nearby meteorological stations. However, to consider all scales processes described above in CFD simulations seems to be ne-cessary accounting for outputs from mesoscale models.…”