Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations – A case study from a city-boulevard

Karttunen, Sasu; Kurppa, Mona; Auvinen, Mikko; Hellsten, Antti; Järvi, Leena

doi:10.1016/j.aeaoa.2020.100073

Cited by 26 publications

(38 citation statements)

References 61 publications

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“…For example, variation in the tree height has been shown to be effective in increasing turbulence, compared to homogenous canopy height, and therefore leading to a smaller deterioration of the ventilation and air quality at the pedestrian level. 30 In addition, it is important to understand the effect of trees on the spatial variation of pollutant accumulation/removal and on the spatial variation of BVOC emissions, in order to understand better the spatial variation of new particle formation in cities. 1 The correlations in the linear regression analyses were rather weak even for the statistically signicant cases (from 0.029 to 0.49, Table 2).…”

Section: Discussionmentioning

confidence: 99%

“…Trees are porous and exible, and can therefore absorb momentum and reduce the turbulent intensity and the velocity of the wind ow, even during the winter when there are no leaves, and therefore trees can deteriorate the air quality locally. [28][29][30][31][32] Furthermore, certain tree species can release signicant amounts of biogenic volatile organic compound (BVOC), 33 contributing to ozone formation in the atmosphere. However, there are still considerable uncertainties in the net impact of trees on air quality in different urban areas, for example in urban parks and street canyons.…”

Section: Introductionmentioning

confidence: 99%

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The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing

Kokkonen

Xie

Paasonen

et al. 2021

Environ. Sci.: Atmos.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing

Kokkonen

Xie

Paasonen

et al. 2021

Environ. Sci.: Atmos.

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“…In order to obtain a turbulent inflow, we applied a turbulence recycling method according to Kataoka and Mizuno (2002), where the inflow mean vertical profiles of u and v are superimposed by turbulent fluctuations sampled at a recycling plane, which is placed at x rc = 1.5 km downstream from the inflow boundary. The recycling plane is placed sufficiently far apart from the inflow boundary to allow for statistically independent turbulence but also sufficiently far apart from the location of the child domain to avoid any feedback between the grid nesting and the inflow conditions.…”

Section: Neutrally Stratified Boundary Layer Tests Initialization and Inflow Conditionsmentioning

confidence: 99%

A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0

et al. 2021

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Abstract. 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.

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“…The smaller scales, which should represent less than 10% of the turbulence energy (Heus et al, 2010), are parametrised using a sub-grid scale model. This study uses output data from two studies to train and evaluate the regression methods: Kurppa et al (2018) and Karttunen et al (2020). Both studies apply the LES model PALM (Maronga et al, 2015(Maronga et al, , 2019 to assess the impact of city planning on the pedestrian-level air quality.…”

Section: Large-eddy Simulation Datasetsmentioning

confidence: 99%

“…Figure 1. Simulation domains of the LES output data applied in the study: a) Four city-planning alternatives V1-4 investigated in KU18 (Kurppa et al, 2018), and b) City-boulevard scenario S1 and its surroundings studied in KA20 (Karttunen et al, 2020). Green dots illustrate trees.…”

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confidence: 99%

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

Lange

Suominen

Kurppa

et al. 2020

Preprint

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Abstract. Running large-eddy simulations (LES) can be burdensome and computationally too expensive from the application point-of-view for example to support urban planning. In this study, regression models are used to replicate modelled air pollutant concentrations from LES in urban boulevards. We study the performance of regression models and discuss how to detect situations where the models are applied outside their training domain and their outputs cannot be trusted. Regression models from 10 different model families are trained and a cross-validation methodology is used to evaluate their performance and to find the best set of features needed to reproduce the LES outputs. We also test the regression models on an independent testing dataset. Our results suggest that in general, log-linear regression gives the best and most robust performance on new independent data. It clearly outperforms the dummy model which would predict constant concentrations for all locations (mRMSE of 0.76 vs 1.78 of the dummy model). Furthermore, we demonstrate that it is possible to detect concept drift, i.e., situations where the model is applied outside its training domain and a new LES run may be necessary to obtain reliable results. Regression models can be used to replace LES simulations in estimating air pollutant concentrations, unless higher accuracy is needed. In order to have reliable results, it is however important to do the model and feature selection carefully to avoid over-fitting and to use methods to detect the concept drift.

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Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations – A case study from a city-boulevard

Cited by 26 publications

References 61 publications

The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing

The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing

A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

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Large-eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations – A case study from a city-boulevard

Cited by 26 publications

References 61 publications

The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing

The effect of urban morphological characteristics on the spatial variation of PM2.5 air quality in downtown Nanjing

A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0

Machine learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets

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Product

Resources

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The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing

The effect of urban morphological characteristics on the spatial variation of PM_2.5 air quality in downtown Nanjing