Published In

Urban Climate

Document Type

Post-Print

Publication Date

1-14-2025

Subjects

City planning -- Climatic factors, City planning -- Environmental aspects, Geographic Information systems, Turbulence -- Remote sensing, Computational fluid dynamics, Pollution

Abstract

This study explores the potential of architecture morphology to mitigate PM2.5 pollution through enhanced urban ventilation. Despite the recognized influence of building designs on wind and air pollution distribution at the urban block scale, specific mechanisms remain poorly understood. To address this gap, we have developed an integrative approach combining in-situ observations, Geographic Information System (GIS), Remote Sensing (RS), Computational Fluid Dynamics (CFD), and statistical modeling. Here, CFD simulates the flow field and PM2.5 dispersion, using in-situ observations for turbulence model selection. GIS quantify architecture morphology parameters to establish accurate CFD boundary conditions, ensuring simulations reflect real-world conditions and precise architectural impacts on air quality. In addition, statistical models and controlled experiments are adopted to detect the influence of architecture morphology on wind fields and PM2.5 dispersion. Our findings reveal that increased Floor Area Ratio (FAR) tends to create stagnant wind areas behind structures, reducing air circulation and trapping pollutants. Higher Building Density (BD) correlates with expanded zones of low wind speed, which further contributes to PM2.5 accumulation. In contrast, promoting open spaces and reducing architectural density can significantly enhance ventilation at the block level, facilitating pollutant dispersal. This research not only underscores the importance of architectural planning in pollution control but also offers practical guidelines for urban design aimed at fostering sustainable cities and communities.

Rights

This is the accepted manuscript available after 24 month embargo with this license:
https://creativecommons.org/licenses/by-nc-nd/4.0/

The final version, © Elsevier, is available from the publisher:
https://doi.org/10.1016/j.uclim.2025.102443

DOI

10.1016/j.uclim.2025.102443

Persistent Identifier

https://archives.pdx.edu/ds/psu/43649

Available for download on Friday, May 21, 2027

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