Article metrics

  • citations in SCindeks: 0
  • citations in CrossRef:0
  • citations in Google Scholar:[=>]
  • visits in previous 30 days:0
  • full-text downloads in 30 days:0
article: 1 from 34  
Back back to result list
Zaštita materijala
2020, vol. 61, iss. 1, pp. 19-30
article language: Bosnian
document type: Scientific Paper
published on: 27/03/2020
doi: 10.5937/zasmat2001019A
Creative Commons License 4.0
Modeling of suspended particles concentrations in the urban area using artificial intelligence methods
aCentar za ekologiju i energiju,Tuzla, BiH
bUniversity of Bihać, Biotechnical Faculty, Federation of B&H
cUniversity of East Sarajevo, Faculty of Technology, Zvornik, Republic of Srpska, B&H
dElektrotehnička škola, Tuzla, BiH

e-mail: dzemila.agic@bih.net.ba

Abstract

The paper develops unique and reliable models for predicting PM2,5 for the City of Tuzla based on the existing monitoring results of PM2,5 and meteorological data (pressure, temperature, wind and humidity) using statistical methods, neural network modeling and genetic programming methods. A correlation between the concentration of pollutants and the influence factors such as temperature and wind has been demonstrated. The developed models can be used for the prediction of PM2,5 concentrations for the early warnings and public protection from the harmful effects of polluted air on human health. The obtained results can be used in the process of making strategic decisions and activities related to air quality control and management. Designing of suspended materials concentration in urban areas is very significant when regular measurements are performed, but the measurements of polluting materials are often lacking. In case of the interruption of the pollutants concentration measurements in Tuzla City for a short or longer time, appliance of the model that is resulting from this work can predict the concentration of pollutants and plan actions based on them.

Keywords

References

Bai, L., Wang, J., Ma, X., Lu, H. (2018) Air pollution forecasts: An overview. International Journal of Environmental Research and Public Health, 15(4), 780-787
Brandt, J., Silver, J.D., Christensenm, J.H.S., Andersen,, Bønløkke, J., Sigsgaard, T., Geels, C., Gross, A., Hansen, A.B., Hansen, K.M., Hedegaard, G.B., Kaas, E., Frohn, L.M. (2013) Contribution from the ten major emission sectors in Evrope to the health-cost externalities of air pollution using the EVA model system: An integrated modelling approach. Atmos. Chem. Phys, 13, 7725-7746
Gargava, A., Aggarwal, A.L. (1999) Emission inventory for an industrial area of India prashant. Environmental Monitoring and Assessment, 55, 299-304
Tong, Y., Wan, B. (2001) Methods of forecasting air pollution and their development at home and abroad. in: Sixth National Academic Conference on Environmental Monitoring BT, Chengdu, Sichuan, China, Proceedings, 10-12
Vukadinović, S. (1990) Elements of theory of probability and mathematical statistics. Belgrade: Privredni pregled
Zhongshan, Y., Jian, W. (2017) A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction. Environmental Research, 158, 105-117