Predição de Poluentes Atmosféricos via Métodos de Aprendizado de Máquina
Resumo
A exposição a partículas finas em suspensão (PM2,5) representa um risco à saúde em centros urbanos, exigindo sistemas de previsão confiáveis. Este artigo propõe um modelo preditivo baseado em aprendizado de máquina aplicado a dados reais com 730.558 registros coletados por sensores de baixo custo na cidade de Fortaleza/CE. Testamos os algoritmos Random Forest, XGBoost, MLP e SVR, após pré-processamento e calibração dos dados. O modelo Random Forest obteve o melhor desempenho, com R2 de 0,988 e RMSE de 0,125. A análise SHAP revelou PM10 e O3 como as variáveis mais relevantes para a predição. Os resultados indicam que técnicas de inteligência artificial podem melhorar o monitoramento ambiental urbano, com potencial para integrar plataformas de e-Ciência orientadas a dados.
Palavras-chave:
aprendizado de máquina, predição, poluentes
Referências
Alves, M. L. A. (2023). Repoair: Desenvolvimento do repositório de dados para monitoramento da qualidade do ar. Trabalho de conclusão de curso (graduação em engenharia civil), Universidade Federal do Ceará, Fortaleza. Orientador: Prof. Dr. Bruno Vieira Bertoncini.
Asgari, M., Yang, W., and Farnaghi, M. (2022). Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatio-temporal data on spark distributed framework. Environmental Technology Innovation, 27:102776.
Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Tommaso, S. D., Colangeli, C., Rosatelli, G., and Carlo, P. D. (2017). Recursive neural network model for analysis and forecast of pm10 and pm2.5. Atmospheric Pollution Research, 8:652–659.
Cengil, E. (2025). The power of machine learning methods and pso in air quality prediction. Applied Sciences, 15:2546.
Chojer, H., Branco, P., Martins, F., Alvim-Ferraz, M., and Sousa, S. (2020). Development of low-cost indoor air quality monitoring devices: Recent advancements. Science of The Total Environment, 727:138385.
DESA, U. (2023). The sustainable development goals report 2023: Special edition - july 2023. Technical report, Disponível em: [link]. Acesso em: 11 març 2025.
Deveer, L. and Minet, L. (2025). Real-time air quality prediction using traffic videos and machine learning. Transportation Research Part D, 142:104688.
Galli, L., Galvan, G., Sciandrone, M., Cantù, M., and Tomaselli, G. (2018). Machine learning methods for short-term bid forecasting in the renewable energy market: A case study in italy. Windy Energy, 21.
Goudarzi, G., Shirmardi, M., Naimabadi, A., Ghadiri, A., and Sajedifar, J. (2019). Chemical and organic characteristics of pm2.5 particles and their in-vitro cytotoxic effects on lung cells: The middle east dust storms in ahvaz, iran. Science of The Total Environment, 655:434–445.
Jairi, I., Ben-Othman, S., Canivet, L., and Zgaya-Biau, H. (2024). Enhancing air pollution prediction: A neural transfer learning approach across different air pollutants. Environmental Technology Innovation, 36.
Kawichai, S., Sripan, P., Rerkasem, A., Rerkasem, K., and Srisukkham, W. (2025). Long-term retrospective predicted concentration of pm2.5 in upper northern thailand using machine learning models. Toxics, 13:170.
Lakra, A. R., Gautam, S., Samuel, C., and Blaga, R. (2025). College bus commuter exposures to air pollutants in indian city: The urban-rural transportation exposure study. Geosystems and Geoenvironment, 4:100346.
Li, Y. and Sun, Y. (2021). Modeling and predicting city-level co2 emissions using open access data and machine learning. Environmental Science and Pollution Research, 28:19260–19271.
Rahman, M., Nayeem, E. H., Ahmed, S., Tanha, K. A., Sakib, S. A., Hafiz, K. M. M. U. ., and Babu, H. (2024). Airnet: predictive machine learning model for air quality forecasting using web interface. Environmental Systems Research, 13:1–19.
Zou, Y., Tian, H., Huang, Z., Liu, L., Xuan, Y., Dai, J., and Nie, L. (2025). Study on prediction models of oxygenated components content in biomass pyrolysis oil based on neural networks and random forests. Biomass and Bioenergy, 193:107601.
Asgari, M., Yang, W., and Farnaghi, M. (2022). Spatiotemporal data partitioning for distributed random forest algorithm: Air quality prediction using imbalanced big spatio-temporal data on spark distributed framework. Environmental Technology Innovation, 27:102776.
Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Tommaso, S. D., Colangeli, C., Rosatelli, G., and Carlo, P. D. (2017). Recursive neural network model for analysis and forecast of pm10 and pm2.5. Atmospheric Pollution Research, 8:652–659.
Cengil, E. (2025). The power of machine learning methods and pso in air quality prediction. Applied Sciences, 15:2546.
Chojer, H., Branco, P., Martins, F., Alvim-Ferraz, M., and Sousa, S. (2020). Development of low-cost indoor air quality monitoring devices: Recent advancements. Science of The Total Environment, 727:138385.
DESA, U. (2023). The sustainable development goals report 2023: Special edition - july 2023. Technical report, Disponível em: [link]. Acesso em: 11 març 2025.
Deveer, L. and Minet, L. (2025). Real-time air quality prediction using traffic videos and machine learning. Transportation Research Part D, 142:104688.
Galli, L., Galvan, G., Sciandrone, M., Cantù, M., and Tomaselli, G. (2018). Machine learning methods for short-term bid forecasting in the renewable energy market: A case study in italy. Windy Energy, 21.
Goudarzi, G., Shirmardi, M., Naimabadi, A., Ghadiri, A., and Sajedifar, J. (2019). Chemical and organic characteristics of pm2.5 particles and their in-vitro cytotoxic effects on lung cells: The middle east dust storms in ahvaz, iran. Science of The Total Environment, 655:434–445.
Jairi, I., Ben-Othman, S., Canivet, L., and Zgaya-Biau, H. (2024). Enhancing air pollution prediction: A neural transfer learning approach across different air pollutants. Environmental Technology Innovation, 36.
Kawichai, S., Sripan, P., Rerkasem, A., Rerkasem, K., and Srisukkham, W. (2025). Long-term retrospective predicted concentration of pm2.5 in upper northern thailand using machine learning models. Toxics, 13:170.
Lakra, A. R., Gautam, S., Samuel, C., and Blaga, R. (2025). College bus commuter exposures to air pollutants in indian city: The urban-rural transportation exposure study. Geosystems and Geoenvironment, 4:100346.
Li, Y. and Sun, Y. (2021). Modeling and predicting city-level co2 emissions using open access data and machine learning. Environmental Science and Pollution Research, 28:19260–19271.
Rahman, M., Nayeem, E. H., Ahmed, S., Tanha, K. A., Sakib, S. A., Hafiz, K. M. M. U. ., and Babu, H. (2024). Airnet: predictive machine learning model for air quality forecasting using web interface. Environmental Systems Research, 13:1–19.
Zou, Y., Tian, H., Huang, Z., Liu, L., Xuan, Y., Dai, J., and Nie, L. (2025). Study on prediction models of oxygenated components content in biomass pyrolysis oil based on neural networks and random forests. Biomass and Bioenergy, 193:107601.
Publicado
29/09/2025
Como Citar
SOUZA, Geovana R. P.; SOUZA, Thiago I. A.; BATISTA, Nicole S.; BERTONCINI, Bruno V.; GOMES, Danielo G..
Predição de Poluentes Atmosféricos via Métodos de Aprendizado de Máquina. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 65-72.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2025.248215.
