This study investigates the effect of roof and façade geometry on the mean wind flow and turbulence in street canyons, as well as the ability of numerical simulation techniques in capturing the flow features. Numerical experiments, using the large eddy simulation FLUENT code, have been conducted under neutral stability conditions to test 5 building geometries: i) flat roof, ii) pitched roof, iii) round roof, iv) terraced building and v) building with balconies. Wind tunnel experiments were also conducted for the first three geometries. The simulation and experimental setups were closely matched and both featured configurations consisting of seven building arrays. The results from the physical and numerical experiments concur that (i) in-canyon vortex dynamics and over-canopy flow conditions, are strongly dependent on the geometric features of the buildings, and (ii) pitched and round roof geometries increase in-canyon mean and turbulent velocities, as well as the depth of the shear layer. The findings provide novel insight on the sensitivity of the flow and turbulence fields, as well as the simulation quality, to urban topography, inflow conditions, and the Reynolds number. They also underline the influence on the flow of small-scale features such as balconies, which are often ignored in prior literature. Keywords Building Geometry • Large Eddy Simulations • Street Canyon Design • Urban Ventilation • Wind Tunnel experiments
We define the new concept of an environmental neighborhood as the surrounding area influencing the environmental quality at a given point in a city, and propose a novel methodology to measure its spatial extent. We compute the spatial correlation of air quality and urban parameters from high spatial resolution datasets for New York City, where the urban characteristics are averaged over variable urban footprint sizes, ranging from 25 m × 5 m to 5000 m × 5000 m. The scale at which these correlations peak indicates the extent of the neighboring area that influences pollutant concentrations deviations from the city-wide average. The results indicate that the scale of these environmental neighborhoods ranges from ∼1000 m (for attributes such as road area or building footmark) down to ∼200 m (for building use or green area). Selecting this optimal neighborhood scale is thus critical for identifying the urban fabric and activity attributes that have the largest influence on air quality; smaller footprints do not contain all the pertinent urban surface information while larger footprints contain irrelevant, potentially misleading information. The quantification of this scale of influence therefore enables more effective and localized policies and interventions to improve urban environmental quality and reduce urban health disparities. More broadly, the findings indicate that, in a wide range of environmental and ecological applications where surface heterogeneity is a primary driver, the scale of analysis is not an external parameter to be chosen, but rather an internal parameter dictated by the problem physics.
Given the benefits of fine mapping of large urban areas affordably, mobile environmental sensing technologies are becoming increasingly popular to complement the traditional stationary weather and air quality sensing stations. However the reliability and accuracy of low-cost mobile urban technologies is often questioned. This paper presents the design of a fast-response, autonomous and affordable Mobile Urban Sensing Technology (MUST) for the acquisition of high spatial resolution environmental data. Only when accurate neighborhood scale environmental data is affordable and accessible for architects, urban planners and policy makers, can design strategies to enhance urban health be effectively implemented. The results of an experimental air quality sensing campaign developed within Princeton University Campus is presented.
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