Built to Breathe? Modeling Air Quality with Features of the Built and Natural Environment - Furtado, Lara S.; Gurjão, Nayara O.; Monteiro, Nicolas C.; Filho, Edilson; Ferreira, Carlos Matheus; Nunes, Jarbas A.; Soares, Jorge B.; Macêdo, José A. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2025)
Although air quality data is often limited by the cost and complexity of sensor networks, open geospatial data provides detailed information on the built environment, which can be used to estimate concentrations of pollutants. Using point-based sensor data and urban features from a pilot city, the research presented herein has trained and validated multiple supervised regression models finding that features such as tree density, building height, street connectivity, and infrastructure coverage can effectively predict spatial variation in Particulate Matter size 2.5µm, even in areas without direct measurements. This scalable and data-driven solution supports environmental monitoring and sustainable planning in cities worldwide with minimal reliance on primary sensor data.