Analysis and prediction of anthropogenic impact on the atmospheric air of an industrial city with the use of new geographic and mathematical approaches
DOI:
https://doi.org/10.24028/gzh.0203-3100.v42i4.2020.210679Keywords:
chaos theory, time series of concentrations, atmospheric pollution, nitrogen dioxide, analysis and prediction, method of correlation dimensionAbstract
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.
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