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Authors
# Name
1 Lara Furtado(lara.furtado@insightlab.ufc.br)
2 Nayara Gurjão(nayara.gurjao@det.ufc.br)
3 Nicolas Monteiro(nicolas.eng.comp@gmail.com)
4 Edilson Filho(edilsonfilho@lesc.ufc.br)
5 Carlos Matheus Ferreira(cmatheuslf@alu.ufc.br)
6 Carlos Matheus Ferreira(cmatheuslf@alu.ufc.br)
7 Jarbas Nunes(jarbas@lesc.ufc.br)
8 Jarbas Nunes(jarbas@lesc.ufc.br)
9 Jorge Soares(jsoares@det.ufc.br)
10 Jorge Soares(jsoares@det.ufc.br)
11 José Macêdo(jose.macedo@insightlab.ufc.br)
12 José Macêdo(jose.macedo@insightlab.ufc.br)
13 José Macêdo(jose.macedo@insightlab.ufc.br)

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Reference
# Reference
1 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.
2 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. https://doi.org/10.1109/JSEN.2022.3212009.
3 Che, W., Zhang, Y., Lin, C., Fung, Y. H., Fung, J. C. H., & Lau, A. K. H. (2023). Impacts of pollution heterogeneity on population exposure in dense urban areas using ultra-fine resolution air quality data. Journal of Environmental Sciences, 125, 513–523. https://doi.org/10.1016/j.jes.2022.02.041.
4 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. https://doi.org/10.1016/j.envres.2022.114996.
5 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. https://doi.org/10.1590/S0103-40142016.00100006.
6 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: https://repositorio.ufc.br/handle/riufc/80631.
7 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. https://doi.org/10.1016/j.envres.2020.109438.
8 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. http://dx.doi.org/10.1016/j.uclim.2024.102176.
9 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. https://doi.org/10.1016/j.scitotenv.2022.155324.
10 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. https://doi.org/10.1016/j.scitotenv.2022.161212.
11 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. https://doi.org/10.1016/j.envres.2022.114228.
12 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. https://doi.org/10.1016/j.scitotenv.2022.158825.
13 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.
14 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. https://doi.org/10.1109/JSEN.2022.3212009.
15 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. https://doi.org/10.1016/j.envsoft.2022.105455.
16 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. https://doi.org/10.1016/j.envres.2022.114996.
17 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: https://repositorio.ufc.br/handle/riufc/80631.
18 Secretaria Municipal das Finanças de Fortaleza. (2016). GeoServiços - IDE Fortaleza. Infraestrutura de Dados Espaciais (IDE). Available at: https://ide.sefin.fortaleza.ce.gov.br/geoservicos.
19 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. http://dx.doi.org/10.1016/j.uclim.2024.102176.
20 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. https://doi.org/10.1016/j.envres.2020.109438.
21 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. https://doi.org/10.1016/j.envres.2022.114228.
22 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. https://doi.org/10.1016/j.scitotenv.2022.161212.
23 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. https://hdl.handle.net/1822/7177.
24 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. https://doi.org/10.1109/JSEN.2022.3212009.
25 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. https://doi.org/10.1016/j.scitotenv.2022.158825.
26 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. https://doi.org/10.1016/j.envres.2022.114996.
27 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. http://dx.doi.org/10.1016/j.buildenv.2021.108489.
28 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. https://doi.org/10.1016/j.envsoft.2022.105455.
29 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. https://doi.org/10.1007/s11356-021-16150-0.
30 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. https://doi.org/10.1016/j.envres.2020.109438.
31 Secretaria Municipal das Finanças de Fortaleza. (2016). GeoServiços - IDE Fortaleza. Infraestrutura de Dados Espaciais (IDE). Available at: https://ide.sefin.fortaleza.ce.gov.br/geoservicos.
32 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. http://dx.doi.org/10.1016/S1352-2310(02)00857-9.
33 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. https://doi.org/10.1016/j.scitotenv.2022.161212.
34 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. https://hdl.handle.net/1822/7177.
35 World Health Organization (WHO). (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. https://apps.who.int/iris/handle/10665/345329.
36 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. https://doi.org/10.1016/j.scitotenv.2022.158825.
37 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. http://dx.doi.org/10.1016/j.buildenv.2021.108489.
38 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. https://doi.org/10.1016/j.envpol.2022.120273.
39 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. https://doi.org/10.1016/j.envsoft.2022.105455.
40 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. https://doi.org/10.1007/s11356-021-16150-0.
41 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. https://doi.org/10.1016/j.scs.2019.101796.
42 Secretaria Municipal das Finanças de Fortaleza. (2016). GeoServiços - IDE Fortaleza. Infraestrutura de Dados Espaciais (IDE). Available at: https://ide.sefin.fortaleza.ce.gov.br/geoservicos.
43 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. http://dx.doi.org/10.1016/S1352-2310(02)00857-9.
44 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. http://dx.doi.org/10.1016/j.envpol.2021.117067.
45 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. https://hdl.handle.net/1822/7177.
46 World Health Organization (WHO). (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. https://apps.who.int/iris/handle/10665/345329.
47 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. http://dx.doi.org/10.1016/j.buildenv.2021.108489.
48 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. https://doi.org/10.1016/j.envpol.2022.120273.
49 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. https://doi.org/10.1007/s11356-021-16150-0.
50 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. https://doi.org/10.1016/j.scs.2019.101796.
51 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. http://dx.doi.org/10.1016/S1352-2310(02)00857-9.
52 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. http://dx.doi.org/10.1016/j.envpol.2021.117067.
53 World Health Organization (WHO). (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. https://apps.who.int/iris/handle/10665/345329.
54 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. https://doi.org/10.1016/j.envpol.2022.120273.
55 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. https://doi.org/10.1016/j.scs.2019.101796.
56 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. http://dx.doi.org/10.1016/j.envpol.2021.117067.