4.1 Article

Multifractal Patterns in 17-Year PM10 Time Series in Athens, Greece

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ENVIRONMENTS
卷 10, 期 1, 页码 -

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MDPI
DOI: 10.3390/environments10010009

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air pollution; PM10; MFDFA; sliding windows

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This paper explores the multifractal characteristics of PM10 time series in the Greater Athens Area, Greece. The study utilizes a novel method called multifractal detrended fluctuation analysis (MFDFA) to analyze raw and shuffled series in 74 segments. The results show multifractality in all segments, with generalized and classical Hurst exponents falling within specific ranges. A new parameter, FWHM / f(max), is used to identify important multifractal behavior. The findings provide strong evidence of the multifractality of the PM10 time series.
This paper reports the multifractal characteristics of lengthy PM10 time series from five stations in the Greater Athens Area (GAA), Greece. A novel methodology based on the multifractal detrended fluctuation analysis (MFDFA) is applied to raw and shuffled series in 74 segments in 11 date-periods, previously located, with very strong self-organised critical (SOC) and fractal properties. The MFDFA identified multifractality in all segments. Generalised and classical Hurst exponents are in the range 0.8-1.5 and 9-4.5 for the raw and shuffled series, while the multifractal f(a) - a is within 0.5-1.2 and 0.1-2, respectively. The f(a) - a data are fitted to polynomials to calculate the multifractal parameters W, FWHM and f(max). While these are bimodal, a new parameter, FWHM / f(max), is normally distributed, and due to this, it is employed to locate the important multifractal behaviour via the FWHM / f(max) outliers. Five date-periods are found. The date-period 8 January 2015 has extraordinary multifractality for raw and shuffled series for both the AGP and LYK stations. This date-period is one of the three reported in the most recent combination study. Finally, sliding window MFDFA evolution plots of all the series are given. The results provide very strong evidence of the multifractality of the PM10 time series.

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