Authors: Yu.Ya. Bunyakova
An improved theoretical scheme for sensing temporal and spatial structure of the chemical pollution substances in the forested watersheds is theoretically investigated and applied to an analysis and modelling the concentrations of phosphates and nitrates. The effects of stochasticity and chaotic features in the chemical pollution structure of the watersheds are discovered on the basis of the correlation dimension approach to empirical time series data. As the concrete example, there are studied a dynamics of the daily values of the concentrations of phosphates and nitrates, water flows (forested watershed Maleno, Small Carpathians, Slovakia) in 1991/1992 years and the relationship between the correlation dimension and embedding dimension is computed. The finite correlation dimensions obtained for the two series indicate that they all exhibit chaotic behaviour. The presence of the deterministic chaos elements at each of the two studied scales suggests that the dynamics of transformation of the chemical pollution component between these scales may also exhibit chaotic behaviour. This, in turn, may imply the applicability (or suitability) of a chaotic approach for transformation of the the pollution component data from one scale to another. Thus, for hydroecological systems it can be principally possible a scenario of so-called automodelity.
Tags: chaos; chemical pollution substances; concentrations of phosphates and nitrates; correlation dimension; forested watersheds; stochastic elements
- Bunyakova Yu.Ya., Glushkov A.V. Analysis and forecast of the impact of anthropogenic factors on air basin of an industrial city. Odessa: Ecology, 2010. 256 p. (In Russian)
- Glushkov A.V., Khokhlov V.N., Serbov N.G., Bunyakova Yu.Ya., Balan A.K., Balanyuk E.P. Low-dimensional chaos in the time series of concentrations of pollutants in an atmosphere and hydrosphere. Vìsn. Odes. derž. ekol. unìv.– Bulletin of Odessa state environmental university, 2007, no. 4, pp. 337-348. (In Russian)
- Glushkov A.V., Svinarenko A.A., Buyadzhi V.V., Zaichko P.A., Ternovsky V.B. Adv.in Neural Networks, Fuzzy Systems and Artificial Intelligence, Series: Recent Adv. in Computer Engineering. Gdansk: WSEAS, 2014., vol.21, pp. 143-150. (Ed.: J. Balicki)
- Khokhlov V.N., Glushkov A.V., Loboda N.S., Bunyakova Yu.Ya. Short-range forecast of atmospheric pollutants using non-linear prediction method. Atmospheric Environment. The Netherlands:
Elsevier; 2008, vol.42, pp. 7284–7292.
- Glushkov A.V., Khokhlov V.N., Prepelitsa G.P., Tsenenko I.A. Temporal changing of the atmosphere methane content: an influence of the NAO. Optics of atmosphere and ocean, 2004, vol.4, №7, pp. 593-598.
- Glushkov A.V., Khokhlov V.N., Bunyakova Yu.Ya. Renormgroup approach to studying spectrum of the turbulence in atmosphere. Meteor.Climat.Hydrol, 2004, no. 48, pp. 286-292.
- Glushkov A.V., Khokhlov V.N., Tsenenko I.A. Atmospheric teleconnection patterns and eddy kinetic energy content: wavelet analysis. Nonlinear Processes in Geophysics, 2004, vol. 11, no. 3, pp. 285-293.
- Glushkov A.V., Khokhlov V.N., Serbov N.G, Bunyakova Yu.Ya., Balan A.K., Balanjuk E.P. Low-dimensional chaos in the time series of the pollution substances concentrations in atmosphere and hydrosphere. Herald of Odessa State Environmental University, 2007, no. 4, pp. 337-348.
- Serga E.N., Bunyakova Yu.Ya., Loboda A.V., Mansarliysky V.F., Dudinov A.A. Multifractal analysis of time series of indices of the Arctic, the Antarctic and the Southern Oscillation. Ukr. gìdrometeorol. ž – Ulrainian Hydrometeorology Journal, 2013, no. 13, pp. 41-45.
- Schreiber T. Interdisciplinary application of nonlinear time series methods. Phys. Rep., 1999, vol. 308, pp. 1-64.
- Grassberger P, Procaccia I. Measuring the strangeness of strange attractors. Physica D, 1983, vol. 9, pp. 189–208.
- Havstad J.W., Ehlers C.L. Attractor dimension of nonstationary dynamical systems from small data sets. Phys. Rev. A., 1989, vol. 39, pp. 845–853.
- Berndtsson R., Jinno K., Kawamura A., Olsson J., Xu S. Dynamical systems theory applied to long-term temperature and precipitation time series. Trends in Hydrol, 1994, vol. 1, pp. 291–297.
- Barnston A.G., Livezey R.E. Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 1987, vol. 115, pp. 1083-1126.
- Morlet J., Arens G., Fourgeau E., Giard D. Wave propagation and sampling theory. Geophysics, 1982, vol. 47, pp. 203-236.
- Nason G., von Sachs R., Kroisand G. Wavelet processes and adaptive estimation of the evolutionary wavelet spectrum. J. Royal Stat. Soc., 2000, vol. B-62, pp. 271-292.
- Glushkov A.V., Khokhlov V.N., Loboda N.S., Ponomarenko E.L. Computer modelling the global cycle of carbon dioxide in system of atmosphere-ocean and environmental consequences of climate change. Environmental Informatics Arch, 2003, vol.1, pp. 125-130.
- Mandelbrot B.B. Fractal Geometry of Nature. N.-Y.: W.H. Freeman, 1982.
- Bunyakova Yu.Ya., Glushkov A.V. Laser emission analysis of the fractal dusty atmosphere parameters. Preprint of the I.I. Mechnikov Odessa Nat.Univ., NIIF., N4.-Odessa, 2004.
- Inhaber H. A set of suggested air quality indices for Canada. Atmos. Environ., 1975, vol. 9, pp. 353–364.
- Ott W.R., Thom G.A. Critical review of air pollution index systems in the United States and Canada. J. Air Pollut. Contr. Assoc., 1976, vol. 26, pp. 460–470.
- Bunyakova Yu.Ya. Air pollution field structure in the industrial city’s atmosphere: New data on stochasticity and chaos. Ukr. gìdrometeorol. Ž – Ulrainian Hydrometeorology Journal, 2014, no. 15, pp. 22-26.
- Glushkov A.V., Serbov N.G., Balan A.K., Shakhman I.A., Solyanikova E.P. Chaos-geometric approach to modeling the temporal fluctuations of the concentration of pollutants in the river water. Ukr. gìdrometeorol. ž – Ulrainian Hydrometeorology Journal, 2014, no. 15, pp. 183-187. (In Russian).
- Serbov N.G., Balan A.K., Solyanikova E.P. Multivariate system and multifractal approaches in modeling extreme high floods (for example, r. Danube) and temporal fluctuations in the concentrations of pollutants in the river water. Vìsn. Odes. derž. ekol. unìv.– Bulletin of Odessa state environmental university, 2008, no. 6, pp. 7-13. (In Russian).
- Serbov N.G., Sukharev D.E., Balan A.K. Multivariate system and multifractal approach to modeling an extremely high floods on the example of the river. Danube. Ukr. gìdrometeorol. Ž -Ulrainian Hydrometeorology Journal, 2010, no. 7, pp. 167-171. (In Russian).
- Glushkov A.V., Khetselius O.Yu., Bunyakova Yu.Ya., Grushevsky O.N., Solyanikova E.P. Studying and forecasting the atmospheric and hydroecological systems dynamics by using chaos theory methods. Dynamical Systems Theory. Polland, 2013, vol. T1, pp. 249-258. (Eds.: J. Awrejcewicz et al).
- Svoboda A., Pekarova P., Miklanek P. Flood hydrology of Danube between Devin and Nagymaros in Slovakia. Nat. Rep.2000 of the UNESKO. Project 4.1. Intern.Water Systems, 2000, 96 p.
- Pekarova P., Miklanek P., Konicek A., Pekar J. Water quality in experimental basins.- Nat. Rep.1999 of the UNESKO.-Project 1.1. Intern.Water Systems, 1999, 98 p.