Built to Breathe? Modeling Air Quality with Features of the Built and Natural Environment
Resumo
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.
Palavras-chave:
Air Quality, Geospatial Data, Supervised Regression Models, Sustainable Urban Planning
Referências
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Vardoulakis, S., Fisher, B. E. A., Pericleous, K., & Gonzalez-Flesca, N. (2003). Modelling air quality in street canyons: A review. Atmospheric Environment, 37(2), 155–182. DOI: 10.1016/S1352-2310(02)00857-9.
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Xie, M., Lu, X., Ding, F., Cui, W., Zhang, Y., & Feng, W. (2022). Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling. Environmental Pollution, 314, 120273. DOI: 10.1016/j.envpol.2022.120273.
Yang, L., Zhang, L., Stettler, M. E. J., Sukitpaneenit, M., Xiao, D., & Dam, K. H. (2020). Supporting an integrated transportation infrastructure and public space design: A coupled simulation method for evaluating traffic pollution and microclimate. Sustainable Cities and Society, 52, 101796. DOI: 10.1016/j.scs.2019.101796.
Zheng, T., Jia, Y., Zhang, S., Li, X., Wu, Y., Wu, C., He, H., & Peng, Z. (2021). Impacts of vegetation on particle concentrations in roadside environments. Environmental Pollution, 282, 117067. DOI: 10.1016/j.envpol.2021.117067.
Dapper, S. N., Spohr, C., & Zanini, R. R. (2016). Poluição do ar como fator de risco para a saúde: Uma revisão sistemática no estado de São Paulo. Estudos Avançados, 30, 83–97. DOI: 10.1590/S0103-40142016.00100006.
Faraji, M., Nadi, S., Ghaffarpasand, O., Homayoni, S., & Downey, K. (2022). An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment. Science of the Total Environment, 834, 155324. DOI: 10.1016/j.scitotenv.2022.155324.
Furtado, L. S., Monteiro, N., Gurjão, N., Cavalcante, R. M., Silva Filho, J. E., da Silveira, J. A. N., ... & de Macedo, J. A. F. (2024). Low-Cost Smart Sensing Pipeline: Assembly, Calibration, and Interpretation of Air Quality Data. In 2024 IEEE International Smart Cities Conference (ISC2) (pp. 1-6). IEEE.
Gurjão, N. O. (2024). Estudo sobre a contribuição do tráfego e de fatores urbanos na poluição atmosférica de Fortaleza/CE utilizando um equipamento de baixo custo. Dissertação de Mestrado, Programa de Pós-Graduação em Engenharia de Transportes, Universidade Federal do Ceará, Fortaleza, Brasil. Available at: [link].
Gurjão, N. O., Lucas Júnior, J. L. O., Furtado, L. S., & Soares, J. B. (2024). Air pollution dynamics in Fortaleza, Brazil: Exploring the interplay of traffic and high-rise development. Urban Climate, 58, 102176. DOI: 10.1016/j.uclim.2024.102176.
Ji, C., Zhang, C., Hua, L., Ma, H., Nazir, M. S., & Peng, T. (2022). A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction. Environmental Research, 215, 114228. DOI: 10.1016/j.envres.2022.114228.
Li, G., Wu, Z., Liu, N., Liu, X., Wang, Y., & Zhang, L. (2023). A variational Bayesian blind calibration approach for air quality sensor deployments. IEEE Sensors Journal, 23(7), 7129–7141. DOI: 10.1109/JSEN.2022.3212009.
Liang, H., Zhou, X., Zhu, Y., Li, D., Jing, D., Su, X., & Zhang, Y. (2023). Association of outdoor air pollution, lifestyle, genetic factors with the risk of lung cancer: A prospective cohort study. Environmental Research, 218, 114996. DOI: 10.1016/j.envres.2022.114996.
Liu, X., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., ChristenseN, B., Lamont, R., Dunbabin, M., Zhu, S., & Gao, J. (2020). Low-cost sensors as an alternative for long-term air quality monitoring. Environmental Research, 185, 109438. DOI: 10.1016/j.envres.2020.109438.
Lou, C., Jiang, F., Tian, X., Zou, Q., Zheng, Y., Shen, Y., Feng, S., Chen, J., Zhang, L., & Jia, M. (2023). Modeling the biogenic isoprene emission and its impact on ozone pollution in Zhejiang Province, China. Science of the Total Environment, 865, 161212. DOI: 10.1016/j.scitotenv.2022.161212.
Sakti, A. D., Anggraini, T. S., Ihsan, K. Y., Misra, P., Trang, N. T. Q., Pradhan, B., Wenten, I. G., Hadi, P. O., & Wikantika, K. (2023). Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products. Science of the Total Environment, 854, 158825. DOI: 10.1016/j.scitotenv.2022.158825.
Seaton, M., O'Neill, J., Bien, B., Hood, C., Jackson, M., Jackson, R., Johnson, K., Oades, M., Stidworthy, A., & Stocker, J. (2022). A multi-model air quality system for health research: Road model development and evaluation. Environmental Modelling & Software, 155, 105455. DOI: 10.1016/j.envsoft.2022.105455.
Secretaria Municipal das Finanças de Fortaleza. (2016). GeoServiços - IDE Fortaleza. Infraestrutura de Dados Espaciais (IDE). Available at: [link].
Silva, L. T., & Mendes, J. F. G. (2006). Determinação do índice de qualidade do ar numa cidade de média dimensão. In Anais do 2º Congresso Luso-Brasileiro de Planeamento Urbano Regional Integrado Sustentável, Braga. [CD-ROM]. ISBN 85-85205-67-9. [link].
Stache, E., Schilperoort, B., Ottelé, M., & Jonkers, H. M. (2022). Comparative analysis in thermal behaviour of common urban building materials and vegetation and consequences for urban heat island effect. Building and Environment, 213, 108489. DOI: 10.1016/j.buildenv.2021.108489.
Tella, A., & Balogun, A. (2021). GIS-based air quality modelling: Spatial prediction of PM10 for Selangor state, Malaysia using machine learning algorithms. Environmental Science and Pollution Research, 29(57), 86109–86125. DOI: 10.1007/s11356-021-16150-0.
Vardoulakis, S., Fisher, B. E. A., Pericleous, K., & Gonzalez-Flesca, N. (2003). Modelling air quality in street canyons: A review. Atmospheric Environment, 37(2), 155–182. DOI: 10.1016/S1352-2310(02)00857-9.
World Health Organization (WHO). (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. [link].
Xie, M., Lu, X., Ding, F., Cui, W., Zhang, Y., & Feng, W. (2022). Evaluating the influence of constant source profile presumption on PMF analysis of PM2.5 by comparing long- and short-term hourly observation-based modeling. Environmental Pollution, 314, 120273. DOI: 10.1016/j.envpol.2022.120273.
Yang, L., Zhang, L., Stettler, M. E. J., Sukitpaneenit, M., Xiao, D., & Dam, K. H. (2020). Supporting an integrated transportation infrastructure and public space design: A coupled simulation method for evaluating traffic pollution and microclimate. Sustainable Cities and Society, 52, 101796. DOI: 10.1016/j.scs.2019.101796.
Zheng, T., Jia, Y., Zhang, S., Li, X., Wu, Y., Wu, C., He, H., & Peng, Z. (2021). Impacts of vegetation on particle concentrations in roadside environments. Environmental Pollution, 282, 117067. DOI: 10.1016/j.envpol.2021.117067.
Publicado
29/09/2025
Como Citar
FURTADO, Lara S.; GURJÃO, Nayara O.; MONTEIRO, Nicolas C.; FILHO, Edilson; FERREIRA, Carlos Matheus; NUNES, Jarbas A.; SOARES, Jorge B.; MACÊDO, José A..
Built to Breathe? Modeling Air Quality with Features of the Built and Natural Environment. In: DATA SCIENCE FOR SOCIAL GOOD BRAZILIAN WORKSHOP (DS4SG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 40. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 352-362.
DOI: https://doi.org/10.5753/sbbd_estendido.2025.248228.
