Abstract
This work examines if chaos and long memory exist in PM10 concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months’ duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM10 time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz’s algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM10 time series were combined in this manner.
Highlights
Urban air pollution is a problem related to a sustainable environment, and human society and economy [1,2]
Since no forecast method is solid, reliable, and accurate enough to sufficiently match all air-contamination time series [37], it is a challenging task to achieve credible estimations. Their main limitations are related to a lack in measurements, gaps in knowledge on the interaction of air pollutants with the atmosphere, and the fact that it is not fully understood if urban air-pollution variations are governed by randomness or dynamical complexity
The reader may recall that the PM10 time series (subfigures (a) of Figures 2–7), are lengthy since they refer to all years between 2001 and 2016
Summary
Urban air pollution is a problem related to a sustainable environment, and human society and economy [1,2]. Since no forecast method is solid, reliable, and accurate enough to sufficiently match all air-contamination time series [37], it is a challenging task to achieve credible estimations. Their main limitations are related to a lack in measurements, gaps in knowledge on the interaction of air pollutants with the atmosphere, and the fact that it is not fully understood if urban air-pollution variations are governed by randomness or dynamical complexity. The exploration of the latter topic has recently gained particular attention
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