Traces of self-organisation and long-range memory in variations of environmental radon in soil: comparative results from monitoring in Lesvos Island and Ileia (Greece)

Radon transport carried by geogas: prediction model

This paper provides an overview and information on radon migration in the crust. In the past several decades, numerous studies on radon migration have been published. However, there is no there is no comprehensive review of large-scale radon transport in the earth crust. A literature review was conducted to present the research on the mechanism of radon migration, geogas theory, investigation of multiphase flow, and modeling method of fractures. Molecular diffusion was long considered the primary mechanism for radon migration in the crust. However, a molecular diffusion mechanism cannot explain the understanding of anomalous radon concentrations. In contrast with early views, the process of radon migration and redistribution within the Earth may be determined by geogas (mainly CO2 and CH4). Microbubbles rising in fractured rocks may be a rapid and efficient way of radon migration, as reported by recent studies. All these hypotheses on the mechanisms of geogas migration are summarized into a theoretical framework, defined as "geogas theory." According to geogas theory, fractures are the principal channel of gas migration. The development of the discrete fracture network (DFN) method is expected to supply a new tool for fracture modeling. It is hoped that this paper will contribute to a deeper understanding of radon migration and fracture modeling.

Stochastic and Self-Organisation Patterns in a 17-Year PM10 Time Series in Athens, Greece

This paper utilises statistical and entropy methods for the investigation of a 17-year PM₁₀ time series recorded from five stations in Athens, Greece, in order to delineate existing stochastic and self-organisation trends. Stochastic patterns are analysed via lumping and sliding, in windows of various lengths. Decreasing trends are found between Windows 1 and 3500-4000, for all stations. Self-organisation is studied through Boltzmann and Tsallis entropy via sliding and symbolic dynamics in selected parts. Several values are below -2 (Boltzmann entropy) and 1.18 (Tsallis entropy) over the Boltzmann constant. A published method is utilised to locate areas for which the PM₁₀ system is out of stochastic behaviour and, simultaneously, exhibits critical self-organised tendencies. Sixty-six two-month windows are found for various dates. From these, nine are common to at least three different stations. Combining previous publications, two areas are non-stochastic and exhibit, simultaneously, fractal, long-memory and self-organisation patterns through a combination of 15 different fractal and SOC analysis techniques. In these areas, block-entropy (range 0.650-2.924) is significantly lower compared to the remaining areas of non-stochastic but self-organisation trends. It is the first time to utilise entropy analysis for PM₁₀ series and, importantly, in combination with results from previously published fractal methods.

Fractal and Long-Memory Traces in PM10 Time Series in Athens, Greece

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.