Share This Article:

Modeling the Surface Ozone Concentration in Campo Grande (MS)—Brazil Using Neural Networks

Abstract Full-Text HTML XML Download Download as PDF (Size:567KB) PP. 171-178
DOI: 10.4236/ns.2015.74020    3,441 Downloads   3,880 Views   Citations


The estimation of the surface ozone concentration promotes the creation of data useful for planning the air quality forecast, which is a key element for the management of public health. The aim of this study is to develop an Artificial Neural Network (ANN) to estimate the concentration of surface ozone from daily climate data. ANN is an equivalent form of Feedforward Multilayer Perceptron whose data has been inserted from the daily concentration of measured ozone. In the intermediate and output layers activation functions like tan-sigmoid and linear have been used, respectively. The performance of the developed ANN is actually very good and it can be considered like part of the set of indirect methods to estimate the concentration of surface ozone. The proposed model may be used by governmental agencies as a tool to enable the public interventional actions during the period of atmospheric stagnation, when ozone levels in the atmosphere represent risks to the public health.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

de Souza, A. , Aristone, F. and Sabbah, I. (2015) Modeling the Surface Ozone Concentration in Campo Grande (MS)—Brazil Using Neural Networks. Natural Science, 7, 171-178. doi: 10.4236/ns.2015.74020.


[1] Derwent, R.G. and Kay, P.J.A. (1988) Factors Influencing the Ground Level Distribution of Ozone in Europe. Environmental Pollution, 55, 191-219, 1988. See also Air Quality Criteria for Ozone and Related Photochemical Oxidants. Environmental Protection Agency, 3-6, 1993.
[2] Cartalis, C. and Varotsos, C. (1994) Surface Ozone in Athens, Greece, at the Beginning and at the End of the 20th Century. Atmospheric Environment, 28, 3-8.
[3] Lisac, I. and Grubisic, V. (1991) An Analysis of Surface Ozone Data Measured at the End of the 19th Century in Zagreb, Yugoslavia. Atmospheric Environment, 25, 481-486.
[4] Nolle, M., Ellul, R., Ventura, F. and Güsten, H. (2005) A Study of Historical Surface Ozone Measurements (1884- 1900) on the Island of Gozo in the Central Mediterranean. Atmospheric Environment, 39, 5608-5618.
[5] Vingarzan, R. (2004) A Review of Surface Ozone Background Levels and Trends. Atmospheric Environment, 38, 3431-3442.
[6] Fishman, J. (1991) The Global Consequences of Increasing Tropospheric Ozone Concentrations. Chemosphere, 22, 685-695.
[7] Fuhrer, J., Skarby, L. and Ashmore, M.R. (1997) Critical Levels for Ozone Effects on Vegetation in Europe. Environmental Pollution, 97, 91-106.
[8] Lippmann, M. (1991. Health Effects of Tropospheric Ozone. Environmental Science & Technology, 25, 1954-1962.
[9] Hanna, S.R., Chang, J.C. and Fernau, M.E. (1998) Monte Carlo Estimates of Uncertainties in Predictions by a Photochemical Grid Model (UAM-IV) Due to Uncertainties in Input Variables. Atmospheric Environment, 32, 3619-3628.
[10] Vautard, R., Beekmann, M., Roux, J. and Gombert, D. (2001) Validation of a Hybrid Forecasting System for the Ozone Concentrations over the Paris Area. Atmospheric Environment, 35, 2449-2461.
[11] Yi, J.S. and Prybutok, V.R. (1996) A Neural Network Model Forecasting for Prediction of Daily Maximum Ozone Concentration in an Industrialized Urban Area. Environmental Pollution, 92, 349-357.
[12] Pires, J.C.M. and Martins, F.G. (2011) Correction Methods for Statistical Models in Tropospheric Ozone Forecasting. Atmospheric Environment, 45, 2413-2417.
[13] Sousa, S.I.V., Pires, J.C.M., Pereira, M.C., Alvim-Ferraz, M.C.M. and Martins, F.G. (2009) Potentialities of Quantile Regression to Predict Ozone Concentrations. Environmetrics, 20, 147-158.
[14] Cannon, A.J. and Lord, E.R. (2000) Forecasting Summertime Surface-Level Ozone Concentrations in the Lower Fraser Valley of British Columbia: An Ensemble Neural Network Approach. Journal of the Air & Waste Management Association, 50, 322-339.
[15] Gardner, M. and Dorling, S. (2001) Artificial Neural Network-Derived Trends in Daily Maximum Surface Ozone Concentrations. Journal of the Air & Waste Management Association, 51, 1202-1210.
[16] Inal, F. (2010) Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey. CLEAN—Soil, Air, Water, 38, 897-908.
[17] Latini, G., Grifoni, R.C. and Passerini, G. (2002) The Importance of Meteorology in Determining Surface Ozone Concentrations—A Neural Network Approach. Ecology and the Environment, 8, 405-414.
[18] Lu, H.C., Hsieh, J.C. and Chang, T.S. (2006) Prediction of Daily Maximum Ozone Concentrations from Meteorological Conditions Using a Two Stage Neural Network. Atmospheric Research, 81, 124-139.
[19] Pires, J.C.M., Alvim-Ferraz, M.C.M., Pereira, M.C. and Martins, F.G. (2010) Evolutionary Procedure Based Model to Predict Ground-Level Ozone Concentrations. Atmospheric Pollution Research, 1, 215-219.
[20] Pires, J.C.M., Alvim-Ferraz, M.C.M., Pereira, M.C. and Martins, F.G. (2011) Prediction of Tropospheric Ozone Concentrations: Application of a Methodology Based on the Darwin’s Theory of Evolution. Expert Systems with Applications, 38, 1903-1908.
[21] Bowden, G.J., Maier, H.R. and Dandy, G.C. (2002) Optimal Division of Data for Neural Network Models in Water Resources Applications. Water Resources Research, 38, 2-1-2-11.
[22] Chaloulakou, A. and Grivas, G. (2006) Artificial Neural Network Models for Prediction of PM10 Hourly Concentrations, in the Greater Area of Athens, Greece. Atmospheric Environment, 40, 1216-1229.
[23] Garcia-Gimeno, R.M., Hervas-Martinez, C. and de Siloniz, M.I. (2002) Improving Artificial Neural Networks with a Pruning Methodology and Genetic Algorithms for Their Application in Microbial Growth Prediction in Food. International Journal of Food Microbiology, 72, 19-30.
[24] Hansen, J.V., McDonald, J.B. and Nelson, R.D. (1999) Time Series Prediction with Genetic Algorithm Designed Neural Networks: An Empirical Comparison with Modern Statistical Models. Computational Intelligence, 15, 171-184.
[25] Corne, S.A. (1996) Artificial Neural Networks for Pattern Recognition. Concepts in Magnetic Resonance, 8, 303-324.<303::AID-CMR1>3.0.CO;2-2
[26] Paliwal, M. and Kumar, U.A. (2009) Neural Networks and Statistical Techniques: A Review of Applications. Expert Systems with Applications, 36, 2-17.
[27] Schneider, G. and Wrede, P. (1998) Artificial Neural Networks for Computer-Based Molecular Design. Progress in Biophysics and Molecular Biology, 70, 175-222.
[28] Gupta, R.R. and Achenie, L.E.K. (2007) A Network Model for Gene Regulation. Computers & Chemical Engineering, 31, 950-961.
[29] Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998) Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Forecasting, 14, 35-62.
[30] Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., et al. (1985) Statistics for the Evaluation and Comparison of Models. Journal of Geophysical Research, 90, 8995-9005.
[31] Camargo, A.P. and Sentelhas, P.C. (1997) Avaliação do desempenho de diferentes métodos de estimativa da evapo- transpiração potencial no estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, 5, 89-97.
[32] Moreira, M.C., Cecilio, R.A. and Silva, K.R. (2007) Comparação de métodos para a estimativa das temperaturas do ar no Nordeste brasileiro. In: Congresso Brasileiro de Agrometeorologia, Anais SBAgro, Vol. 15 (CD-ROM).
[33] Nagendra, S.M.S. and Khare, M. (2005) Modelling Urban Air Quality Using Artificial Neural Network. Clean Technologies and Environmental Policy, 7, 116-126.
[34] de Souza Tadano, Y., Ugaya, C.M.L. and Franco, A.T. (2009) Método de regressão de Poisson: Metodologia para avaliação do impacto da poluição atmosférica na saúde populacional. Ambiente & Sociedade [online], 12, 241-255.

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.