Speaker
Description
As cities continue to face the challenges of climate change and environmental degradation, understanding the spatial variability of urban air quality is essential for fostering sustainable and resilient development. Air pollution, a critical determinant of public health and environmental well-being, is especially problematic in high-density urban environments where complex interactions between the built and natural landscape drive its dynamics (Liang & Gong 2020). Yet, traditional air quality monitoring methods often fall short, relying on sparse or elevated sensor placements that fail to capture pollution at the "human level" (Hoek et al. 2008). This study addresses these limitations by developing a novel methodology for high-resolution spatial mapping of air quality in Songdo, South Korea, using an integrated approach that combines low-cost sensor networks (Jayaratne et al. 2020), Gaussian Mixture Models (GMMs), and Computational Fluid Dynamics (CFD) simulations.
Key pollutants, including PM₂.₅, PM₁₀, and NO₂, are measured through a dense network of strategically deployed sensors covering diverse urban contexts such as residential zones, traffic corridors, and green spaces. Advanced spatial interpolation techniques are used to identify nuanced patterns in pollutant concentrations, while CFD simulations model the interaction between urban morphology, wind dynamics, and pollutant dispersion (Guo et al. 2021). The resulting high-resolution maps reveal localized pollution hotspots and the factors driving air quality disparities at a granular level.
Preliminary findings highlight the significant influence of urban design on air quality. For instance, dense building configurations and narrow streets can trap pollutants, creating "canyon effects" (Guo et al. 2021), whereas strategically placed green spaces and open corridors enhance airflow and reduce pollutant concentrations (Łowicki 2019). These insights underscore the critical role of urban planning in mitigating pollution exposure and improving public health outcomes.
By combining advanced computational tools with cost-effective, scalable monitoring systems, this study overcomes the limitations of traditional air quality assessment methods. The detailed maps generated provide actionable insights for policymakers and urban planners, enabling targeted interventions to mitigate air pollution and enhance urban livability. The methodology also emphasizes the importance of addressing environmental justice by identifying and alleviating pollution burdens that disproportionately affect vulnerable communities (Manisalidis et al. 2020).
This research advances the understanding of urban air pollution dynamics and provides a scalable framework that other cities can adopt to assess and manage air quality challenges. Bridging scientific innovation with practical application, the findings offer a valuable resource for creating healthier, more equitable, and more resilient urban environments.
References
Guo, X. et al., 2021. Effect of greening on pollutant dispersion and ventilation at urban street intersections. Building and environment, 203(108075)
Hoek, G. et al., 2008. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric environment (Oxford, England: 1994), 42(33)
Jayaratne, R. et al., 2020. Low-cost PM2.5 Sensors: An Assessment of Their Suitability for Various Applications. Aerosol and Air Quality Research,
Liang, L. & Gong, P., 2020. Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Scientific reports, 10(1)
Łowicki, D., 2019. Landscape pattern as an indicator of urban air pollution of particulate matter in Poland. Ecological indicators, 97
Manisalidis, I. et al., 2020. Environmental and health impacts of air pollution: A review. Frontiers in public health, 8
| Keywords | Urban air; Spatial mapping; Computational Fluid Dynamics; Gaussian Mixture Models |
|---|---|
| Best Congress Paper Award | Yes |
