THERMAL SCIENCE

International Scientific Journal

Bogdana B. Vujić

,

Srđan Vukmirović

,

Goran V. Vujić

,

Nebojša M. Jovičić

,

Gordana Jovičić

,

Milun J. Babić

EXPERIMENTAL AND ARTIFICIAL NEURAL NETWORK APPROACH FOR FORECASTING OF TRAFFIC AIR POLLUTION IN URBAN AREAS: THE CASE OF SUBOTICA

ABSTRACT
In the recent years, artificial neural networks (ANNs) have been used to predict the concentrations of various gaseous pollutants in ambient air, mainly to forecast mean daily particle concentrations. The data on traffic air pollution, irrespective of whether they are obtained by measuring or modelling, represent an important starting point for planning effective measures to improve air quality in urban areas. The aim of this study was to develop a mathematical model for predicting daily concentrations of air pollution caused by the traffic in urban areas. For the model development, experimental data have been collected for 10 months, covering all four seasons. The data about hourly concentration levels of suspended particles with aerodynamic diameter less than 10 μm (PM10) and meteorological data (temperature, air humidity, speed and direction of wind), measured at the measuring station in the town of Subotica from June 2008 to March 2009, served as the basis for developing an ANN-based model for forecasting mean daily concentrations of PM10. The quality of the ANN model was assessed on the basis of the statistical parameters, such as RMSE, MAE, MAPE, and r.
KEYWORDS
PM10, meteorological parameters, forecasting PM10 concentrations, artificial neural networks
PAPER SUBMITTED: 2010-05-07
PAPER REVISED: 2010-06-25
PAPER ACCEPTED: 2010-07-01
DOI REFERENCE: https://doi.org/10.2298/TSCI100507032V
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2010, VOLUME 14, ISSUE Supplement 1, PAGES [S79 - S87]
REFERENCES
  1. Zickus, M., Greig, A., J., Niranjan, M. Comparison of Four Machine Learning Methods for Predicting PM10 Concentrations in Helsinki, Finland. Water, Air, and Soil Pollution: focus 2: 717-729, 2002.
  2. Directive 2008/50/EC of the European Parliament and of the Council 0f 21 May 2008 on Ambient Air Quality and Cleaner Air for Europe, Official Journal of the European Communities, No. L 152/1. EC, 2008.
  3. Council Decision 97/101/EC of 27 January 1997 Establishing a Reciprocal Exchange of Information and Data from Networks and Individual Stations Measuring Ambient Air Pollution within the Member States, Official Journal of the European Communities, No. L 35/14. EC, 1997.
  4. Boy, M., Kulmala, M. Influence of Spectral Solar Irradiance on the Formation of New Partiles in the Continental Boundary Layer, Atmos. Chem. Phys. Discuss., 2 (2002) pp. 1317-1350
  5. U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, S. Vitabile Two-days Ahead Prediction of Daily Maximum Concentrations of SO2, O3, PM10, NO2, CO in the Urban Area of Palermo, Italy. Atmospheric Environment, 41(2007), 14, pp. 2967-2995
  6. The Mathworks, www.mathworks.com/products/ (15.07.2009)
  7. Papanastasiou, D. K., Melas, D., Kioutsioukis, I. Development and Assessment of Neural Network and Multiple Regression Models in Order to Predict PM10 Levels in a Medium - Sized Mediterranean City. Water Air Soil Pollution, 182 (2007), pp.325-334.
  8. Asha. B. C., Devotta, S., (2005). Air Quality Forecasting Using a Hibrid Autoregressive and Nonlinear Model, Atmospheric Environment, 40 (2005), pp. 1774-1780
  9. Luis A. Díaz-Robles, Juan C. Ortega, Joshua S. Fu, Gregory D. Reed, Judith C. Chow, John G., Watson, Juan A., Moncada-Herrera (2008). A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Particulate Matter in Urban Areas: The Case of Temuco, Chile Atmospheric Environment, 42 (2008), 35 pp. 8331-8340
  10. Goyal, P., Chan, T., Neeru, J., Statistical Models for the Prediction of Respirable Suspended Particulate Matter in Urban Cities. Atmospheric Environment, 40 (2006) 11, pp. 2068-2077
  11. Casseli, M.,Trizio, L., de Gennaro, G., Ielpo, P., A Simple Feedforward Neural Network for the PM10 Forecasting: Comparasion with a Radial Basis Function Network and a Multivariate Linear Regression Model, Water Air Soil Pollutant, 201 (2008), pp. 365-377
  12. Perez, P., Reyes, J., An Integrated Neural Network Model For PM10 Forecasting. Atmospheric Environment, 40 (2006), pp. 2845-2851
  13. Kolehmainen, M., Martikainen, H., Ruuskanen, J., (2001). Neural Networks and Periodic Components Used in Air Quality Forecasting. Atmospheric Environment, 35 (2001), pp. 815-825
  14. Hubbard, M., Cobourn, G., Development of a Regression Model to Forecast Ground-Level Ozone Concentration in Louisville, KY. Atmospheric Environment, 32 (1998) 14-15, pp. 2637-2647
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Experimental and artificial neural network approach for forecasting of traffic air pollution in urban areas: The case of Subotica

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence