Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD

Abstract

Abstract. In the urban environment, gas and particles impose adverse impacts on the health of pedestrians. The conventional computational fluid dynamics (CFD) methods that regard pollutants as passive scalars cannot reproduce the formation of secondary pollutants and lead to uncertain prediction. In this study, SSH-aerosol, a modular box model that simulates the evolution of gas, primary and secondary aerosols, is coupled with the CFD software, OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon is simulated using the unsteady Reynolds-averaged Navier–Stokes equations (RANS) model. The simulated concentrations of NO2, PM10, and black carbon (BC) are compared with field measurements on a street of Greater Paris. The simulated NO2 and PM10 concentrations based on the coupled model achieved better agreement with measurement data than the conventional CFD simulation. Meanwhile, the black carbon concentration is underestimated, probably partly because of the underestimation of non-exhaust emissions (tire and road wear). Aerosol dynamics lead to a large increase of ammonium nitrate and anthropogenic organic compounds from precursor gas emitted in the street canyon.

Modelling concentration heterogeneities in streets using the street-network model MUNICH

“…The lower concentration difference for PM 10 than for the other compounds reflects the fact that non-traffic sources are more important for PM 10 , and inversely, they are small for BC. Despite the higher concentrations in MUNICH-hete-l1, BC concentrations remain strongly underestimated compared to observations, in agreement with the CFD simulations of Lin et al (2023). For PM 10 , NO 2 , and NO x , the concentrations compare well to observations; e.g.…”

“…To represent these concentrations, local-scale models are thus developed with different approaches of variable complexity and computational cost. Models based on computational fluid dynamics (CFD), such as code_saturne (Archambeau et al, 2004;Gao et al, 2018), OpenFoam (Lin et al, 2023), and PALM (Wolf et al, 2020), are able to represent the dispersion of pollutants and the physicochemical processes taking place in urban districts and streets with a fine spatial resolution by solving the Navier-Stokes equations and mass conservation equations for pollutants. However, they suffer from high computational cost as they use fine meshes to describe the morphology of buildings and streets.…”

“…In the current version of MUNICH (v2.0) (Kim et al, 2022), concentrations are considered to be homogeneous in each street segment. However, as shown by several on-site and modelling studies, the concentrations are very heterogeneous in streets with traffic emissions (Xie et al, 2003;Vardoulakis et al, 2011;Lateb et al, 2016;Sanchez et al, 2016;Amato et al, 2019;Lin et al, 2023). They are higher near the ground than at the top of the street, especially for primary pollutants.…”

Large-eddy simulations on pollutant reduction effects of road-center hedge and solid barriers in an idealized street canyon

“…(4) Chemical reactions and aerosol dynamics were not considered during the pollutant dispersion in the current study, whereas they have been found to significantly influence the distribution of traffic pollutants in the street canyon [58,59] and they should be considered to simulate the air quality more accurately.…”

To what extent is the description of streets important in estimating local air-quality? A case study over Paris

“…Additionally, CFD models incorporate advanced approaches to take into account turbulence, making it well-suited for applications involving small-scale dispersion of pollutants. This type of model is widely used to analyse specific urban configuration on a neighbourhood scale (Sabatino et al, 2013;Zhang et al, 2020;Pantusheva et al, 2022;Lin et al, 2023), but the computing times virtually requested to simulate a large urban area over periods of time of the order of a year are currently unattainable. With this type of temporal and spatial constraint, compatible with regulatory objectives (for example, compliance with a threshold for an annual average concentration) or a health impact study, methods with lower computational burdens are currently mandatory.…”