Buildings and vegetation alter the wind and pollutant transport in urban environments. This comparative study investigates the role of orientation and shape of perimeter blocks on the dispersion and ventilation of traffic-related air pollutants, and the street-level concentrations along a planned city boulevard. A large-eddy simulation (LES) model PALM is employed over a highly detailed representation of the urban domain including street trees and forested areas. Air pollutants are represented by massless and passive particles (non-reactive gases), which are released with traffic-related emission rates. High-resolution simulations for four different city-block-structures are conducted over a 8.2 km ${}^2$ domain under two contrasting inflow conditions with neutral and stable atmospheric stratification corresponding the general and wintry meteorological conditions. Variation in building height together with multiple cross streets along the boulevard improves ventilation, resulting in 7–9% lower mean concentrations at pedestrian level. The impact of smaller scale variability in building shape was negligible. Street trees further complicate the flow and dispersion. Notwithstanding the surface roughness, atmospheric stability controls the concentration levels with higher values under stably stratified inflow. Little traffic emissions are transported to courtyards. The results provide urban planners direct information to reduce air pollution by proper structural layout of perimeter blocks.

Large-eddy simulation (LES) provides a physically sound approach to study complex turbulent processes within the atmospheric boundary layer including urban boundary layer flows. However, such flow problems often involve a large separation of turbulent scales, requiring a large computational domain and very high grid resolution near the surface features, leading to prohibitive computational costs. To overcome this problem, an online LES–LES nesting scheme is implemented into the PALM model system 6.0. The hereby documented and evaluated nesting method is capable of supporting multiple child domains, which can be nested within their parent domain either in a parallel or recursively cascading configuration. The nesting system is evaluated by first simulating a purely convective boundary layer flow system and then three different neutrally stratified flow scenarios with increasing order of topographic complexity. The results of the nested runs are compared with corresponding non-nested high- and low-resolution results. The results reveal that the solution accuracy within the high-resolution nest domain is clearly improved as the solutions approach the non-nested high-resolution reference results. In obstacle-resolving LES, the two-way coupling becomes problematic as anterpolation introduces a regional discrepancy within the obstacle canopy of the parent domain. This is remedied by introducing canopy-restricted anterpolation where the operation is only performed above the obstacle canopy. The test simulations make evident that this approach is the most suitable coupling strategy for obstacle-resolving LES. The performed simulations testify that nesting can reduce the CPU time up to 80 % compared to the fine-resolution reference runs, while the computational overhead from the nesting operations remained below 16 % for the two-way coupling approach and significantly less for the one-way alternative.

This study examines the statistical predictability of local wind conditions in a real urban environment under realistic atmospheric boundary layer conditions by means of Large-Eddy Simulation (LES). The computational domain features a highly detailed description of a densely built coastal downtown area, which includes vegetation. A multi-scale nested LES modelling approach is utilized to achieve a setup where a fully developed boundary layer flow, which is also allowed to form and evolve very large-scale turbulent motions, becomes incident with the urban surface. Under these nonideal conditions, the local scale predictability and result sensitivity to central modelling choices are scrutinized via comparative techniques. Joint time–frequency analysis with wavelets is exploited to aid targeted filtering of the problematic large-scale motions, while concepts of information entropy and divergence are exploited to perform a deep probing comparison of local urban canopy turbulence signals. The study demonstrates the utility of wavelet analysis and information theory in urban turbulence research while emphasizing the importance of grid resolution when local scale predictability, particularly close to the pedestrian level, is sought. In densely built urban environments, the level of detail of vegetation drag modelling description is deemed most significant in the immediate vicinity of the trees.

High-resolution large-eddy simulation (LES) is exploited to study indoor air turbulence and its effect on the dispersion of respiratory virus-laden aerosols and subsequent transmission risks. The LES modeling is carried out with unprecedented accuracy and subsequent analysis with novel mathematical robustness. To substantiate the physical relevance of the LES model under realistic ventilation conditions, a set of experimental aerosol concentration measurements are carried out, and their results are used to successfully validate the LES model results. The obtained LES dispersion results are subjected to pathogen exposure and infection probability analysis in accordance with the Wells–Riley model, which is here mathematically extended to rely on LES-based space- and time-dependent concentration fields. The methodology is applied to assess two dissimilar approaches to reduce transmission risks: a strategy to augment the indoor ventilation capacity with portable air purifiers and a strategy to utilize partitioning by exploiting portable space dividers. The LES results show that use of air purifiers leads to greater reduction in absolute risks compared to the analytical Wells–Riley model, which fails to predict the original risk level. However, the two models do agree on the relative risk reduction. The spatial partitioning strategy is demonstrated to have an undesirable effect when employed without other measures, but may yield desirable outcomes with targeted air purifier units. The study highlights the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies.

Conventional footprint models cannot account for the heterogeneity of the urban landscape imposing a pronounced uncertainty on the spatial interpretation of eddy-covariance (EC) flux measurements in urban studies. This work introduces a computational methodology that enables the generation of detailed footprints in arbitrarily complex urban flux measurements sites. The methodology is based on conducting high-resolution large-eddy simulation (LES) and Lagrangian stochastic (LS) particle analysis on a model that features a detailed topographic description of a real urban environment. The approach utilizes an arbitrarily sized target volume set around the sensor in the LES domain, to collect a dataset of LS particles which are seeded from the potential source area of the measurement and captured at the sensor site. The urban footprint is generated from this dataset through a piecewise postprocessing procedure, which divides the footprint evaluation into multiple independent processes that each yield an intermediate result. These results are ultimately selectively combined to produce the final footprint. The strategy reduces the computational cost of the LES–LS simulation and incorporates techniques to account for the complications that arise when the EC sensor is mounted on a building instead of a conventional flux tower. The presented computational framework also introduces a result assessment strategy which utilizes the obtained urban footprint together with a detailed land cover type dataset to estimate the potential error that may arise if analytically derived footprint models were employed instead. The methodology is demonstrated with a case study that concentrates on generating the footprint for a building-mounted EC measurement station in downtown Helsinki, Finland, under the neutrally stratified atmospheric boundary layer.

This article reviews explicit algebraic Reynolds stress models and other non-linear eddy-viscosity turbulence models utilised in the context of Reynolds-Averaged Navier-Stokes simulations of turbulent flows. Since the 1990s, these modelling classes have become important intermediate classes between the linear eddy viscosity models and full Reynolds stress transport models. The derivation of explicit algebraic Reynolds stress models from the Reynolds stress transport models, and the required simplifications are discussed. The most important simplification is the weak-equilibrium assumption. Properties of the basic weak-equilibrium assumption and its extended form are discussed. Differences between explicit solutions based on complete tensor representation and those based on reduced representation as well as other non-linear eddy-viscosity models are pointed out. The scale-determining models are also discussed briefly.