APFoam 1.0: integrated computational fluid dynamics simulation of O<sub>3</sub>–NO<sub><i>x</i></sub>–volatile organic compound chemistry and pollutant dispersion in a typical street canyon

Abstract

Abstract. Urban air quality issues are closely related to human health and economic development. In order to investigate street-scale flow and air quality, this study developed the atmospheric photolysis calculation framework (APFoam 1.0), an open-source computational fluid dynamics (CFD) code based on OpenFOAM, which can be used to examine microscale reactive pollutant formation and dispersion in an urban area. The chemistry module of APFoam has been modified by adding five new types of reactions, which can implement the atmospheric photochemical mechanism (full O3–NOx–volatile organic compound chemistry) coupled with a CFD model. Additionally, the model, including the photochemical mechanism (CS07A), air flow, and pollutant dispersion, has been validated and shows good agreement with SAPRC modeling and wind tunnel experimental data, indicating that APFoam has sufficient ability to study urban turbulence and pollutant dispersion characteristics. By applying APFoam, O3–NOx–volatile organic compound (VOC) formation processes and dispersion of the reactive pollutants were analyzed in an example of a typical street canyon (aspect ratio H/W=1). The comparison of chemistry mechanisms shows that O3 and NO2 are underestimated, while NO is overestimated if the VOC reactions are not considered in the simulation. Moreover, model sensitivity cases reveal that 82 %–98 % and 75 %–90 % of NO and NO2, respectively, are related to the local vehicle emissions, which is verified as the dominant contributor to local reactive pollutant concentration in contrast to background conditions. In addition, a large amount of NOx emissions, especially NO, is beneficial to the reduction of O3 concentrations since NO consumes O3. Background precursors (NOx/VOCs) from boundary conditions only contribute 2 %–16 % and 12 %–24 % of NO and NO2 concentrations and raise O3 concentrations by 5 %–9 %. Weaker ventilation conditions could lead to the accumulation of NOx and consequently a higher NOx concentration but lower O3 concentration due to the stronger NO titration effect, which would consume O3. Furthermore, in order to reduce the reactive pollutant concentrations under the odd–even license plate policy (reduce 50 % of the total vehicle emissions), vehicle VOC emissions should be reduced by at least another 30 % to effectively lower O3, NO, and NO2 concentrations at the same time. These results indicate that the examination of the precursors (NOx and VOCs) from both traffic emissions and background boundaries is the key point for understanding O3–NOx–VOCs chemistry mechanisms better in street canyons and providing effective guidelines for the control of local street air pollution.

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

“…For simplicity, only the simulated concentration based on OF is presented and discussed in the following sections. specific time points is considered a practical method for evaluating street-level pollutant concentrations (Wu et al, 2021;Zhang et al, 2020). The transport (advection and diffusion) and chemical processes reached equilibrium, and the simulated concentrations reached quasi-stable values.…”

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

“…This simulation method is called the transientcondition method (TCM) in this study. However, because time-varying flow fields and concentration fields are expensive to compute in terms of computational time, conducting CFD simulations with fixed boundary conditions and emission rates at specific time points is considered a practical method for evaluating street-level pollutant concentrations (Wu et al, 2021;Zhang et al, 2020). The transport (advection and diffusion) and chemical processes will reach equilibrium, and the simulated concentrations will reach quasi-stable values.…”

MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling

“…Computational fluid dynamics (CFD) models, e.g. Code_Saturne (Milliez and Carissimo, 2007;Thouron et al, 2019), OpenFOAM (Jeanjean et al, 2015;Wu et al, 2021), STAR-CCM+ (Santiago et al, 2017), and the PALM model (Wolf et al, 2020;Zhang et al, 2021), finely describe the urban geometry, the air flow, and the pollutant concentrations. However, the computational cost is too high for operational purposes if they are applied to predict the pollutant concentrations in a city district with a large street network (at least hundreds of street segments) (Vardoulakis et al, 2003).…”