The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s10661-023-11911-5.pdf
Reference59 articles.
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2. Agudelo-Castañeda, D. M., Teixeira, E. C., Schneider, I. L., Pereira, F. N., Oliveira, M. L. S., Taffarel, S. R., Sehn, J. L., Ramos, C. G., & Silva, L. F. O. (2016). Potential utilization for the evaluation of particulate and gaseous pollutants at an urban site near a major highway. Science of the Total Environment, 543, 161–170. https://doi.org/10.1016/j.scitotenv.2015.11.030
3. Bai, Y., Li, Y., Wang, X., Xie, J., & Li, C. (2016). Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmospheric Pollution Research, 7, 1–10. https://doi.org/10.1016/j.apr.2016.01.004
4. Borsato, V. A., & Mendonça, F. A. (2015). Participação da Massa Polar Atlântica na dinâmica dos sistemas atmosféricos no Centro Sul do Brasil. Mercator (fortaleza), 14(1), 113–130. https://doi.org/10.4215/RM2015.1401.0008
5. Braga, A. L., Saldiva, P. H., Pereira, L. A., Menezes, J. J., Conceição, G. M., Lin, C. A., Zanobetti, A., Schwartz, J., & Dockery, D. W. (2001). Health effects of air pollution exposure on children and adolescents in São Paulo, Brazil. Pediatric Pulmonology, 31, 106–113. https://doi.org/10.1002/1099-0496(200102)31:2%3c106
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