Impact of urban canopy characteristics on turbulence dynamics: Insights from large eddy simulation

Tian, Geng; Ma, Yongfeng; Chen, Yuanqing; Wan, Minping; Chen, Shiyi

doi:10.1016/j.buildenv.2024.111183

Cited by 7 publications

(1 citation statement)

References 84 publications

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“…The ever-increasing process of urbanization (United Nations, 2022) presses the issue of addressing urban environmental hazards, e.g. air pollution, heat waves, increased heat stress or disrupted thermal comfort (Geletič et al, 2023;Tian et al, 2024) and obliges cities across the world to move their focus towards environmental sustainability and human well-being (Gál and Kántor,…”

Section: Introductionmentioning

confidence: 99%

Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions

Resler,

Bauerová,

Belda

et al. 2024

Preprint

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Abstract. The urban air quality is an important part of human well-being and its detailed and precise modeling is important for efficient urban planning. In this study the potential sources of errors in LES runs of the PALM model in stable conditions for a high-traffic residential area in Prague, Czech Republic with focus to street canyon ventilation are investigated. The evaluation of the PALM model simulations against observations obtained during a dedicated campaign revealed unrealistically high concentrations of modeled air pollutants for a short period during a winter inversion episode. To identify potential reasons, the sensitivities of the model to changes of meteorological boundary conditions and adjustments of model parameters were tested. The model adaptations included adding the anthropogenic heat from cars, setting a bottom limit of the subgrid-scale TKE, adjusting the profiles of parameters of the Synthetic Turbulence Generator in PALM and limiting the model time step. The study confirmed the crucial role of the correct meteorological boundary conditions for realistic air quality modeling during stable conditions. Besides this, the studied adjustments of the model parameters proved to have a significant impact in these stable conditions, resulting in a decrease of concentration overestimation in range 30–66 % while exhibiting negligible influence on model results during the rest of the episode. This suggested that the inclusion or improvement of these processes in PALM is desirable despite their negligible impact in most other conditions. Moreover, the time step limitation test revealed numerical inaccuracies caused by discretization errors which occurred during such extremely stable conditions.

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Section: Introductionmentioning

confidence: 99%