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希腊雅典17年颗粒物时间序列中的随机与自组织模式。

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

作者信息

Nikolopoulos Dimitrios, Alam Aftab, Petraki Ermioni, Papoutsidakis Michail, Yannakopoulos Panayiotis, Moustris Konstantinos P

机构信息

Department of Industrial Design and Production Engineering, University of West Attica, GR-12244 Aigaleo, Greece.

Centre for Earthquake Studies, National Centre for Physics, Islamabad 44000, Pakistan.

出版信息

Entropy (Basel). 2021 Mar 5;23(3):307. doi: 10.3390/e23030307.

Abstract

This paper utilises statistical and entropy methods for the investigation of a 17-year PM 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 PM 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 PM series and, importantly, in combination with results from previously published fractal methods.

摘要

本文运用统计和熵方法,对希腊雅典五个站点记录的17年颗粒物(PM)时间序列进行研究,以描绘现有的随机和自组织趋势。通过在不同长度的窗口中进行归并和滑动分析随机模式。对于所有站点,在窗口1与3500 - 4000之间发现了下降趋势。通过在选定部分运用滑动和符号动力学,借助玻尔兹曼熵和Tsallis熵研究自组织。在玻尔兹曼常数之上,有几个值低于 -2(玻尔兹曼熵)和1.18(Tsallis熵)。利用一种已发表的方法来确定PM系统偏离随机行为且同时呈现临界自组织趋势的区域。发现了不同日期的66个为期两个月的窗口。其中,有9个窗口至少在三个不同站点是共同的。结合先前的出版物,通过15种不同的分形和自组织临界性(SOC)分析技术的组合,确定了两个区域是非随机的,并且同时呈现分形、长记忆和自组织模式。在这些区域中,块熵(范围为0.650 - 2.924)显著低于非随机但有自组织趋势的其余区域。这是首次将熵分析用于PM序列,重要的是,还与先前发表的分形方法的结果相结合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9e92/7999766/d325850323a8/entropy-23-00307-g001.jpg

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