Login / Signup

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

Qingman LiJie LiangQun WangYuntong ChenHongyu YangHong LingZhiwen LuoJian Hang
Published in: International journal of environmental research and public health (2022)
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 λ p = λ f = 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 m 2 /m 3 ) 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 (2 H ) 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.
Keyphrases
  • air pollution
  • public health
  • minimally invasive
  • particulate matter
  • cystic fibrosis
  • machine learning
  • deep learning
  • heavy metals
  • artificial intelligence
  • quantum dots
  • clinical evaluation