Analysis and prediction of anthropogenic impact on the atmospheric air of an industrial city with the use of new geographic and mathematical approaches


  • Yu.Ya. Bunyakova Odessa State Ecological University, Ukraine
  • A.V. Glushkov Odessa State Ecological University, Ukraine



chaos theory, time series of concentrations, atmospheric pollution, nitrogen dioxide, analysis and prediction, method of correlation dimension


In the last two decades, there have been quite successful implementation of various mathematical and cybernetic approaches to solving geographic and ecological problems. It is important to note that the ecological dynamic system is nonlinear, the use of linear methods of analysis, Fourier transform, etc. can not always give a satisfactory result, as in the case of a linear system. This is due to the fact that the processes that lead to the chaotic regime are fundamentally multidimensional. These circumstances are characteristic of the dynamics of the distribution of harmful impurities in the air pool of an industrial city. The article is devoted to the development of in-depth, improved analysis, modeling and forecasting of time dynamics of concentrations of pollutants for specific industrial cities. A new method of analysis and prediction of the field structure of the concentrations of ingredients in the atmosphere of an industrial city is proposed, based on the provisions of chaos theory. Hourly observations of nitrogen dioxide at monitoring posts in Gdańsk — Gdynia and Sopot (Poland) throughout the year were used for the analysis. Stochastic features and chaos effect in dynamics and structure of time series of impurity concentrations are revealed on the basis of the analysis of empirical data of concentrations of pollutants in the air basin. The results presented here can be considered as an example of a quite satisfactory short-term prediction of concentrations of pollutants in the atmosphere. It may be noted here that the nonlinear prediction method works quite well in cases where there is an increase in concentrations, at least, almost all tendencies to such an increase are detected in the prediction. The latter allows it to be used as an alternative to traditional methods.


Bunyakova, Yu.Ya. (2015). Analysis and prediction of changes in concentrations of sulfur dioxide in the atmosphere of an industrial city (on the example of Gdansk region, Poland) by chaos theory. Visnyk Kyyivsʹkoho natsionalʹnoho universytetu im. Tarasa Shevchenka. Heohrafiya, (1), 37—40 (in Ukrainian).

Mandelbrot, B. (2002). Fractal geometry of nature. Moscow: Ed. of the Institute for Computer Research, 656 p. (in Russian).

Brock, W.A., Hsieh, D.A., & LeBaron, B. (1991). Nonlinear dynamics, chaos, and instability: statistical theory and economic evidence. MIT Press, 346 p.

Bunyakova, Yu.Ya., Glushkov, A.V., & Dudinov, A.A. (2011). Short-range forecast of atmospheric pollutants using non-linear prediction method. Abstracts of the European Geosciences Union General Assembly. Vienna, Austria. P. A3.4.

Glushkov, A.V., Khokhlov, V.N., Loboda, N.S., & Bunyakova, Yu.Ya. (2010). Modeling greenhouse gas concentration fields using chaos theory. 18th. Intern. Symp. Transport and Air Pollution. May 18—19, 2010. Dubendorf, Switzerland. Р06.

Chelani, A.B. (2005). Predicting chaotic time series of PM10 concentration using artificial neural network. International Journal of Environmental Studies, 62(2), 181—191.

European program «Air pollution observation data: Gdansk region, 2003». (2004). Institute of Chemistry and Environmental Protection, Technical University of Szczecin. Poland.

Islam, M.N., & Sivakumar, B. (2002). Characterization and prediction of runoff dynamics: a nonlinear dynamical view. Advances in Water Resources, 25(2), 179—190.

Khokhlov, V.N., Glushkov, A.V., Loboda, N.S., & Bunyakova, Yu.Ya. (2008). Short-range forecast of atmospheric pollutants using non-linear prediction method. Atmospheric Environment, 42(31), 7284—7292.

Lanfredi, M., & Machhiato, M. (1997). Searching for low dimensionality in air pollution time series. Europhysics Letters, 40(6), 589—594.

Paluš, M., Pelikán, E., Eben, K., Krejčíř, P., & Juruš, P. (2001). Nonlinearity and prediction of air pollution. Artificial neural nets and genetic algorithms. In: V. Kurkova, N.C. Steele, R. Neruda, & M. Karny (Eds.), Artificial Neural Nets and Genetic Algorithms (pp. 473—476). Springer Verlag, Vienna, Austria.

Rusov, V.D., Glushkov, A.V., Vaschenko, V.N., Myhalus, O.T., Bondartchuk, Yu.A., Smolyar, V.P., Linnik, E.P., Mavrodiev, S.Cht., & Vachev B.I. (2010). Galactic cosmic rays-clouds effect and bifurcation model of the earth global climate. Part 1. Theory. Journal of Atmospheric and Solar-Terrestrial Physics, 72, 398—408. doi: 10.1016/j.jastp.2009.12.007.

Sprott, J.C., Vano, J.A., Wildenberg, J.C., Anderson, M.B., & Noel, J.K. (2005). Coexistence and chaos in complex ecologies. Physics Letters A., 335(2-3), 207—212.



How to Cite

Bunyakova, Y., & Glushkov, A. (2020). Analysis and prediction of anthropogenic impact on the atmospheric air of an industrial city with the use of new geographic and mathematical approaches. Geofizicheskiy Zhurnal, 42(4), 165–173.