Desafios e oportunidades da aplicação de Sistemas Ciberfísicos no monitoramento da poluição urbana
Resumo
A ação do homem tem provocado mudanças no meio ambiente, sendo a poluição urbana uma das consequências negativas aplicadas nesse ecossistema. Com o desenvolvimento tecnológico, novas perspectivas computacionais afloram para o monitoramento ambiental. Este artigo apresenta pontos chaves da fenomenologia ambiental que podem se beneficiar da evolução promovida pela computação aplicada nos estudos da poluição, assim como realiza um levantamento de pesquisas e tecnologias utilizadas nesse contexto.
Palavras-chave:
Poluição urbana, monitoramento ambiental, sistemas ciberfísicos, computação aplicada
Referências
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Goel, A. and Kumar, P. (2015). Characterisation of nanoparticle emissions and exposureat traffic intersections through fast-response mobile and sequential measurements. At-mospheric Environment, 107:374 — 390.
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Horst, J., Welty, N., Schnobrich, M., Sinha, P., and Kulkarni, P. (2017). Digital innovation:The next disruptive but transformative remediation frontier. Groundwater Monitoringand Remediation, 37(3):19-27.
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Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., and Chi, T. (2017). Long short-term me-mory neural network for air pollutant concentration predictions: Method developmentand evaluation. Environmental pollution, 231:997-1004.
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Morawska, L. (2018). Applications of low-cost sensing technologies for air quality moni-toring and exposure assessment: How far have they gone? Environment international,116:286-299.
Navarro, J. M., Martínez-Espafia, R., Bueno-Crespo, A., Martínez, R., and Cecilia, J. M.(2020). Sound levels forecasting in an acoustic sensor network using a deep neuralnetwork. Sensors, 20(3):903.
Ni, X., Huang, H., and Du, W. (2017). Relevance analysis and short-term prediction of PM2. 5 concentrations in Beijing based on multi-source data. Atmospheric environment, 150:146-161.
Nourani, V., Gôkçekus, H., and Umar, 1. K. (2020). Artificial intelligence based ensemblemodel for prediction of vehicular traffic noise. Environmental research, 180:108852.
Raza, U., Kulkarni, P., and Sooriyabandara, M. (2017). Low power wide area networks:An overview. IEEE Communications Surveys & Tutorials, 19(2):855-873.
Rybarczyk, Y. and Zalakeviciute, R. (2016). Machine learning approach to forecastingurban pollution. In 2016 IEEE Ecuador Technical Chapters Meeting (ETCM), pages1-6. IEEE.
Sales, E. and Aquilino, M. (2017). Desenvolvimento de método para mapeamento sonoroda cidade de São Paulo. Technical report, IPT, São Paulo, SP, Brasil.
Sanislav, T., Mois, G., and Miclea, L. (2016). An approach to model dependability ofcyber-physical systems. Microprocessors and Microsystems, 41:67-76.
Santos, A. S. D. (2018). Análise espaçotemporal da qualidade do ar em vias urbanaspor meio de Redes de Sensores com nós embarcados em ônibus coletivos. PhD thesis,Universidade de São Paulo.
Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution.International Journal of Applied Mathematics and Computer Science, 26(2):467-478.
Teixeira, C. E., Motta, F. G., and de Moraes, S. L. (2016). Panorama GAC: mapeamentoda cadeia de gerenciamento de áreas contaminadas. IPT.
Torija, A. J. and Ruiz, D. P. (2015). A general procedure to generate models for urbanenvironmental-noise pollution using feature selection and machine learning methods.Science of the Total Environment, 505:680-693.
Torija, A. J. and Ruiz, D. P. (2016). Automated classification of urban locations forenvironmental noise impact assessment on the basis of road-traffic content. ExpertSystems with Applications, 53:1-13.
Vasconcellos, P. A. C., Carvalho, L. R. F., and Pool, C. S. (2005). Volatile organic com-pounds inside urban tunnels of São Paulo City, Brazil. Journal of the Brazilian Che-mical Society, 16:1210 — 1216.
Wang, D., Wei, S., Luo, H., Yue, C., and Grunder, O. (2017). A novel hybrid model for airquality index forecasting based on two-phase decomposition technique and modifiedextreme learning machine. Science of The Total Environment, 580:719-733.
Weichenthal, S., Van Ryswyk, K., Goldstein, A., Bagg, S., Shekkarizfard, M., and Hat-zopoulou, M. (2016). A land use regression model for ambient ultrafine particles inMontreal, Canada: A comparison of linear regression and a machine learning appro-ach. Environmental research, 146:65-72.
Wong, C. S. C., Li, X., and Thornton, I. (2006). Urban environmental geochemistry oftrace metals. Environmental Pollution, 142:1-16.
Zimmerman, N. e. a. (2018). A machine learning calibration model using random foreststo improve sensor performance for lower-cost air quality monitoring. AtmosphericMeasurement Techniques, 11(1).
Alías, F., Alsina-Pagês, R. M., Orga, F., and Socoró, J. C. (2018). Detection of anomalousnoise events for real-time road-traffic noise mapping: The Dynamap's project casestudy. Noise Mapping, 5(1):71-85.
Bello, J. P., Silva, C., Nov, O., DuBois, R. L., Arora, A., Salamon, J., Mydlarz, C., andDoraiswamy, H. (2018). SONYC: A system for the monitoring, analysis and mitigationof urban noise pollution. arXiv preprint arXiv:1805.00889.
Bravo-Moncayo, L., Lucio-Naranjo, J., Chávez, M., Pavón-García, I., and Garzón, C.(2019). A machine learning approach for traffic-noise annoyance assessment. AppliedAcoustics, 156:262-270.
Cardoso, J. (2009). Introdução à Química Ambiental. Artmed Editora.
CETESB (2001). Manual de gerenciamento de Áreas contaminadas. Technical report,CETESB.
CETESB (2015). Qualidade do ar no estado de SP 2014. Technical report, CETESB.
CETESB (2018). Emissões veiculares no estado de SP 2017. Technical report, CETESB.
CETESB (2020). Relatório de áreas contaminadas e reabilitadas no estado de São Paulo.Technical report, CETESB.
Council, N. R. (2004). Air Quality Management in the United States. The NationalAcademies Press, Washington, DC.
de Hoogh, K., Héritier, H., Stafoggia, M., Kiinzli, N., and Kloog, I. (2018). Modellingdaily PM2. 5 concentrations at high spatio-temporal resolution across Switzerland.Environmental Pollution, 233:1147-1154.
Giannakopoulos, T., Orfanidi, M., and Perantonis, S. (2019). Athens urban soundscape(ATHUS): A dataset for urban soundscape quality recognition. In International Conference on Multimedia Modeling, pages 338-348. Springer.
Goel, A. and Kumar, P. (2015). Characterisation of nanoparticle emissions and exposureat traffic intersections through fast-response mobile and sequential measurements. At-mospheric Environment, 107:374 — 390.
Günther, W. M. R. (2006). Áreas contaminadas no contexto da gestão urbana. São Pauloem Perspectiva, 20:105-117.
Horst, J., Welty, N., Schnobrich, M., Sinha, P., and Kulkarni, P. (2017). Digital innovation:The next disruptive but transformative remediation frontier. Groundwater Monitoringand Remediation, 37(3):19-27.
Hu, K., Rahman, A., Bhrugubanda, H., and Sivaraman, V. (2017). HazeEst: Machinelearning based metropolitan air pollution estimation from fixed and mobile sensors.JEEE Sensors Journal, 17(11):3517-3525.
Huang, C.-J. and Kuo, P.-H. (2018). A deep CNN-LSTM model for particulate matter(PM2. 5) forecasting in smart cities. Sensors, 18(7):2220.
IEMA (2014). 1º diagnóstico da rede de monitoramento da qualidade do ar no Brasil.Technical report, Instituto de Energia e Meio Ambiente, São Paulo, SP, Brasil.
Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N.,Crunaire, S., and Borowiak, A. (2019). Review of the performance of low-cost sensorsfor air quality monitoring. Atmosphere, 10(9):506.
Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Sabatino, S. D., Bell,M., Norford, L., and Britter, R. (2015). The rise of low-cost sensing for managing airpollution in cities. Environment International, 75:199 — 205.
Kumari, S., Roy, D., Cartwright, M., Bello, J. P., and Arora, A. (2019). EdgeL” 3: Com-pressing L” 3-Net for mote scale urban noise monitoring. In 2019 IEEE InternationalParallel and Distributed Processing Symposium Workshops (IPDPSW), pages 877-884. IEEE.
Lewis, A., von Schneidemesser, E., and Peltier, R. (2018). Low-cost sensors for themeasurement of atmospheric composition: overview of topic and future applications.Technical report, World Meteorological Organization.
Li, X., Peng, L., Yao, X., Cui, S., Hu, Y., You, C., and Chi, T. (2017). Long short-term me-mory neural network for air pollutant concentration predictions: Method developmentand evaluation. Environmental pollution, 231:997-1004.
Liu, Y., Goudreau, S., Oiamo, T., Rainham, D., Hatzopoulou, M., Chen, H., Davies,H., Tremblay, M., Johnson, J., Bockstael, A., et al. (2020). Comparison of land useregression and random forests models on estimating noise levels in five Canadian cities.Environmental Pollution, 256:113367.
Ma, J., Ding, Y., Cheng, J. C., Jiang, F., Tan, Y., Gan, V. J., and Wan, Z. (2020). Iden-tification of high impact factors of air quality on a national scale using big data andmachine learning techniques. Journal of Cleaner Production, 244:118955.
Morawska, L. (2018). Applications of low-cost sensing technologies for air quality moni-toring and exposure assessment: How far have they gone? Environment international,116:286-299.
Navarro, J. M., Martínez-Espafia, R., Bueno-Crespo, A., Martínez, R., and Cecilia, J. M.(2020). Sound levels forecasting in an acoustic sensor network using a deep neuralnetwork. Sensors, 20(3):903.
Ni, X., Huang, H., and Du, W. (2017). Relevance analysis and short-term prediction of PM2. 5 concentrations in Beijing based on multi-source data. Atmospheric environment, 150:146-161.
Nourani, V., Gôkçekus, H., and Umar, 1. K. (2020). Artificial intelligence based ensemblemodel for prediction of vehicular traffic noise. Environmental research, 180:108852.
Raza, U., Kulkarni, P., and Sooriyabandara, M. (2017). Low power wide area networks:An overview. IEEE Communications Surveys & Tutorials, 19(2):855-873.
Rybarczyk, Y. and Zalakeviciute, R. (2016). Machine learning approach to forecastingurban pollution. In 2016 IEEE Ecuador Technical Chapters Meeting (ETCM), pages1-6. IEEE.
Sales, E. and Aquilino, M. (2017). Desenvolvimento de método para mapeamento sonoroda cidade de São Paulo. Technical report, IPT, São Paulo, SP, Brasil.
Sanislav, T., Mois, G., and Miclea, L. (2016). An approach to model dependability ofcyber-physical systems. Microprocessors and Microsystems, 41:67-76.
Santos, A. S. D. (2018). Análise espaçotemporal da qualidade do ar em vias urbanaspor meio de Redes de Sensores com nós embarcados em ônibus coletivos. PhD thesis,Universidade de São Paulo.
Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution.International Journal of Applied Mathematics and Computer Science, 26(2):467-478.
Teixeira, C. E., Motta, F. G., and de Moraes, S. L. (2016). Panorama GAC: mapeamentoda cadeia de gerenciamento de áreas contaminadas. IPT.
Torija, A. J. and Ruiz, D. P. (2015). A general procedure to generate models for urbanenvironmental-noise pollution using feature selection and machine learning methods.Science of the Total Environment, 505:680-693.
Torija, A. J. and Ruiz, D. P. (2016). Automated classification of urban locations forenvironmental noise impact assessment on the basis of road-traffic content. ExpertSystems with Applications, 53:1-13.
Vasconcellos, P. A. C., Carvalho, L. R. F., and Pool, C. S. (2005). Volatile organic com-pounds inside urban tunnels of São Paulo City, Brazil. Journal of the Brazilian Che-mical Society, 16:1210 — 1216.
Wang, D., Wei, S., Luo, H., Yue, C., and Grunder, O. (2017). A novel hybrid model for airquality index forecasting based on two-phase decomposition technique and modifiedextreme learning machine. Science of The Total Environment, 580:719-733.
Weichenthal, S., Van Ryswyk, K., Goldstein, A., Bagg, S., Shekkarizfard, M., and Hat-zopoulou, M. (2016). A land use regression model for ambient ultrafine particles inMontreal, Canada: A comparison of linear regression and a machine learning appro-ach. Environmental research, 146:65-72.
Wong, C. S. C., Li, X., and Thornton, I. (2006). Urban environmental geochemistry oftrace metals. Environmental Pollution, 142:1-16.
Zimmerman, N. e. a. (2018). A machine learning calibration model using random foreststo improve sensor performance for lower-cost air quality monitoring. AtmosphericMeasurement Techniques, 11(1).
Publicado
10/12/2020
Como Citar
SANTOS, Alessandro Santiago dos; DE FREITAS, Leandro Gomes; TEIXEIRA, Igor Cunha; GAVA, Vagner Luiz; TAIRA, Gustavo Ryuji; ENCINAS QUILLE, Rosa Virginia; BRAGHETTO, Kelly Rosa.
Desafios e oportunidades da aplicação de Sistemas Ciberfísicos no monitoramento da poluição urbana. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 4. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 276-289.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2020.12369.
