Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar 16;19(6):3524.
doi: 10.3390/ijerph19063524.

Numerical Investigations of Urban Pollutant Dispersion and Building Intake Fraction with Various 3D Building Configurations and Tree Plantings

Affiliations

Numerical Investigations of Urban Pollutant Dispersion and Building Intake Fraction with Various 3D Building Configurations and Tree Plantings

Qingman Li et al. Int J Environ Res Public Health. .

Abstract

Rapid urbanisation and rising vehicular emissions aggravate urban air pollution. Outdoor pollutants could diffuse indoors through infiltration or ventilation, leading to residents’ exposure. This study performed CFD simulations with a standard k-ε model to investigate the impacts of building configurations and tree planting on airflows, pollutant (CO) dispersion, and personal exposure in 3D urban micro-environments (aspect ratio = H/W = 30 m, building packing density λp = λf = 0.25) under neutral atmospheric conditions. The numerical models are well validated by wind tunnel data. The impacts of open space, central high-rise building and tree planting (leaf area density LAD= 1 m2/m3) with four approaching wind directions (parallel 0° and non-parallel 15°, 30°, 45°) are explored. Building intake fraction <P_IF> is adopted for exposure assessment. The change rates of <P_IF> demonstrate the impacts of different urban layouts on the traffic exhaust exposure on residents. The results show that open space increases the spatially-averaged velocity ratio (VR) for the whole area by 0.40−2.27%. Central high-rise building (2H) can increase wind speed by 4.73−23.36% and decrease the CO concentration by 4.39−23.00%. Central open space and high-rise building decrease <P_IF> under all four wind directions, by 6.56−16.08% and 9.59−24.70%, respectively. Tree planting reduces wind speed in all cases, raising <P_IF> by 14.89−50.19%. This work could provide helpful scientific references for public health and sustainable urban planning.

Keywords: CFD simulation; open space; personal intake fraction; pollutant dispersion; urban tree planting; ventilation.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Flow validation by wind tunnel data. (a) Geometry of the UCL model in wind tunnel dataset. (b) Setting of the computational domain and boundary conditions. (ch) Vertical profiles of monitored and modelled stream-wise velocity (u¯) at Point P1–P6. (i,j) Vertical profiles of monitored and modelled turbulence kinetic energy (k) at Point P1 and P5.
Figure A1
Figure A1
Flow validation by wind tunnel data. (a) Geometry of the UCL model in wind tunnel dataset. (b) Setting of the computational domain and boundary conditions. (ch) Vertical profiles of monitored and modelled stream-wise velocity (u¯) at Point P1–P6. (i,j) Vertical profiles of monitored and modelled turbulence kinetic energy (k) at Point P1 and P5.
Figure A1
Figure A1
Flow validation by wind tunnel data. (a) Geometry of the UCL model in wind tunnel dataset. (b) Setting of the computational domain and boundary conditions. (ch) Vertical profiles of monitored and modelled stream-wise velocity (u¯) at Point P1–P6. (i,j) Vertical profiles of monitored and modelled turbulence kinetic energy (k) at Point P1 and P5.
Figure A2
Figure A2
Validation for pollutant dispersion. (a) Configurations of the wind tunnel experiment with top view. (b) Configurations of the wind tunnel experiment with lateral view. (c) Vertical profiles of normalized inert gas concentration K at the roof top of the model. (d) Vertical profiles of K at the leeward and windward wall.
Figure A3
Figure A3
Validation for the vegetation modelling. (a) Configurations of the wind tunnel experiment with vegetation model. (b) Vertical profiles of K at the leeward wall. (c) Vertical profiles of K at the windward wall.
Figure 1
Figure 1
Computational domain of (a) Case [Base, 0°] and (b) Case [Base, θ] (θ = 15°, 30°, 45°). (c) 3D model description of open-space cases, base cases and high-rise-building cases. (d) Model description and (e) grid arrangements from top view in base cases. (f) Setups of building, tree planting and pollutant source.
Figure 1
Figure 1
Computational domain of (a) Case [Base, 0°] and (b) Case [Base, θ] (θ = 15°, 30°, 45°). (c) 3D model description of open-space cases, base cases and high-rise-building cases. (d) Model description and (e) grid arrangements from top view in base cases. (f) Setups of building, tree planting and pollutant source.
Figure 2
Figure 2
Streamline and velocity ratio (VR) at z = 2m in (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 2
Figure 2
Streamline and velocity ratio (VR) at z = 2m in (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 3
Figure 3
Streamline and velocity ratio (VR) at z = 2m in Region A2 in (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 3
Figure 3
Streamline and velocity ratio (VR) at z = 2m in Region A2 in (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 4
Figure 4
Normalised velocity (V/Vδ, Vδ = 4.34 m/s) at vertical plane (y = 135m) in Region A2 in (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 5
Figure 5
Spatially-averaged VR in different scenarios at z = 2 m in (a) Region A1 and (b) Region A2.
Figure 6
Figure 6
CO concentration (C) at z = 2 m in (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 7
Figure 7
Vertical profile of C in Region A2 at y = 135 m: (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 8
Figure 8
Spatially-averaged CO concentration in different scenarios at z = 2 m in (a) Region A1 and (b) Region A2.
Figure 9
Figure 9
CO concentration (C) at building walls in 3D models: (a) Case [Base, 0°], (b) Case [Base-tree, 0°], (c) Case [Open, 0°], (d) Case [Open-tree, 0°], (e) Case [High, 0°] and (f) Case [High-tree, 0°].
Figure 10
Figure 10
Building intake fraction <P_IF> in all cases.

Similar articles

Cited by

References

    1. Chan C.K., Yao X. Air pollution in mega cities in China. Atmos. Environ. 2008;42:1–42. doi: 10.1016/j.atmosenv.2007.09.003. - DOI
    1. Fenger J. Urban air quality. Atmos. Environ. 1999;33:4877–4900. doi: 10.1016/S1352-2310(99)00290-3. - DOI
    1. Pu Y., Yang C. Estimating urban roadside emissions with an atmospheric dispersion model based on in-field measurements. Environ. Pollut. 2014;192:300–307. doi: 10.1016/j.envpol.2014.05.019. - DOI - PubMed
    1. Ji W., Zhao B. Estimating mortality derived from indoor exposure to particles of outdoor origin. PLoS ONE. 2015;10:e0124238. doi: 10.1371/journal.pone.0124238. - DOI - PMC - PubMed
    1. Peters A., Pope III C.A. Cardiopulmonary mortality and air pollution. Lancet. 2002;360:1184–1185. doi: 10.1016/S0140-6736(02)11289-X. - DOI - PubMed

Publication types