A Computational Fluid Dynamic (CFD) Simulation of PM10 Dispersion Caused by Rail Transit Construction Activity: A Real Urban Street Canyon Model

Abstract

Particle emissions derived from construction activities have a significant impact on the local air quality, while the canyon effect with reduced natural ventilation contributes to the highest particulate pollution in urban environments. This study attempted to examine the effect of PM10 emissions derived from the construction of a rail transit system in an urban street canyon. Using a 3D computational fluid dynamic (CFD) model based on a real street canyon with different height ratios, this study formulates the impact of height ratio and wind directions on the dispersion and concentration of PM10. The results indicate that parallel flow would cause the concentration of PM10 at the end of the street canyons in all height ratios, and the trends in horizontal, vertical and lateral planes in all street canyons are similar. While in the condition of perpendicular flow, double-eddy circulations occur and lead to the concentration of PM10 in the middle part of the street canyon and leeward of backwind buildings in all height ratios. Furthermore, perpendicular flow will cause the concentration of PM10 to increase if the upwind buildings are higher than the backwind ones. This study also shows that the dispersion of PM10 is strongly associated with wind direction in and the height ratios of the street canyons. Certain measures could, therefore, be taken to prevent the impact on people in terms of the PM10 concentration and the heights of street canyons identified in this research. Potential mitigation strategies are suggested, include measurements below 4 m according to governmental regulations, dust shields, and atomized water.

Exposure to Secondhand Tobacco Smoke at Airport Terminals

“…Another study in the restaurant setting found that overhead coverage outdoors indeed increased average SHS exposure by around 50% [36]. Several studies have investigated the utilization of computational fluid dynamics using k-ε turbulence models and models for particle transport to describe particle pollution in partially enclosed environments with good agreement with empirical data [37, 38]; such approaches potentially may be of use in modeling SHS in the airport setting.…”

Spatial Distribution of Fine Particulate Matter in Underground Passageways

“…The standard κ - ε turbulence model is suitable for simulating airflows and turbulence in semi-closed buildings, such as rooms with windows and underground passageways. The κ - ε equation is described below [24]:$\frac{\partial }{\partial t}(\rho \kappa )+\frac{\partial }{\partial x_i}(\rho \kappa {\mu }_i)=\frac{\partial }{\partial x_j}\left[\left(\mu +\frac{{\mu }_t}{{\sigma }_k}\right)\frac{\partial \kappa }{\partial x_j}\right]+G_{\kappa }+G_b-\rho \epsilon$…”

Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station

“…Airpak3.0 and ANSYS Fluent19.0 together form a CFD simulation model, in which the former is used to build a subway station model and mesh division while solving the continuous-phase airflow field; while the latter is used to simulate the particle concentration field based on the Airpak model, and the particles in the simulation are selected as PM2.5. On the choice of the numerical simulation model, the standard κ-ε turbulence model is used to simulate airflows and turbulence in this semi-closed building, which can be described as follows [ 22 ], where κ and ε denote kinetic energy of turbulence flow and the rate of turbulent dissipation transport, respectively; ρ , t , and μ i denote the density, time, and the velocity of flow, respectively; μ is kinetic viscosity; G k is turbulence production item; G b is the turbulent kinetic energy generation caused by Buoyancy; σ k and σ ε are the turbulence coefficients, σ k = 1.0, σ ε = 1.3; Cμ = 0.09; C 1 ε = 1.44, C 2 ε = 1.92, and C 3 ε = 1.44.…”