Long-memory traces in $$\hbox {PM}_{10}$$ time series in Athens, Greece: investigation through DFA and R/S analysis

Nikolopoulos, Dimitrios; Moustris, Konstantinos; Petraki, Ermioni; Cantzos, Demetrios

doi:10.1007/s00703-020-00744-3

Cited by 16 publications

(37 citation statements)

References 104 publications

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“…In particular, windows of length 32 (approximately one-month data) are moved forwards with a step of 32 samples (one-month analysis lumped one month forwards) and with a step of 1 (one-month analysis slid-glided one day forwards). The latter analysis provides very fine tuning of the PM 10 concentrations series, since every 32-length window contains 31 common samples with the previous and the following windows and only one different sample due to the sliding process [ 37 ]. The 32-length window with a one-step movement has been applied with success in a recent publication of the team for PM 10 air pollution system [ 37 ].…”

Section: Mathematical Methodsmentioning

confidence: 99%

“…Up to now, the reporting team has employed different chaotic techniques to investigate and delineate the critical inter-change phases of PM 10 pollutants in the Greater Athens Area (GAA) in Greece [ 36 , 37 ]. Detrended fluctuation analysis (DFA), rescaled-range (

) analysis and fractal-dimension (FD) analysis via three different methods (Higuchi, Katz and Sevcik) have been employed in lengthy 17-year PM 10 time series in GAA.…”

Section: Introductionmentioning

confidence: 99%

“…Detrended fluctuation analysis (DFA), rescaled-range (

) analysis and fractal-dimension (FD) analysis via three different methods (Higuchi, Katz and Sevcik) have been employed in lengthy 17-year PM 10 time series in GAA. Most importantly, a new novel method has been introduced and applied in PM 10 time series that is based on the combination of two or more chaotic techniques [ 36 , 37 ]. In [ 36 ], a combination of four techniques is reported (

analysis versus FD analysis via the methods of Katz, Higuchi and Sevcik).…”

Section: Introductionmentioning

confidence: 99%

“…In [ 36 ], a combination of four techniques is reported (

analysis versus FD analysis via the methods of Katz, Higuchi and Sevcik). In [ 37 ], a combination of two techniques is employed (DFA versus

analysis); however, most significantly, a combination of 13 different chaos analysis techniques is reported. The combination of many different chaos analysis techniques have been employed and checked in critical electromagnetic time series [ 38 ] and critical radon in groundwater series [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

“…The analysis is performed on measurements recorded by the Department of Environmental Protection of the Hellenic Ministry of Environment and Energy at a sampling frequency of 1

. As with the previous two related papers [ 36 , 37 ], this paper employs the same sub-series of the whole dataset, namely the average daily concentrations during 2001–2018 from five different PM 10 monitoring stations within the GAA [ 44 ]. This sub-series is considered more suitable for long-term analysis because it averages the intra-day variations and minimises, consequently, the overall bias to the estimations.…”

Section: Introductionmentioning

confidence: 99%

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

Nikolopoulos

Alam

Petraki

et al. 2021

Entropy

Self Cite

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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.

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Section: Mathematical Methodsmentioning

confidence: 99%

) analysis and fractal-dimension (FD) analysis via three different methods (Higuchi, Katz and Sevcik) have been employed in lengthy 17-year PM 10 time series in GAA.…”

Section: Introductionmentioning

confidence: 99%

“…Detrended fluctuation analysis (DFA), rescaled-range (

analysis versus FD analysis via the methods of Katz, Higuchi and Sevcik).…”

Section: Introductionmentioning

confidence: 99%

“…In [ 36 ], a combination of four techniques is reported (

analysis versus FD analysis via the methods of Katz, Higuchi and Sevcik). In [ 37 ], a combination of two techniques is employed (DFA versus

Section: Introductionmentioning

confidence: 99%

“…The analysis is performed on measurements recorded by the Department of Environmental Protection of the Hellenic Ministry of Environment and Energy at a sampling frequency of 1

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Nikolopoulos

Alam

Petraki

et al. 2021

Entropy

Self Cite

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NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Gao

Du²,

Lei³

et al. 2023

Water Resour Manage

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Normalized difference vegetation index(NDVI) is the most commonly used factor to re ect vegetation growth status, and improving the prediction accuracy of NDVI is of great signi cance to the development of regional ecology. In this study, a new NDVI forecasting model based on the combination of time series decomposition(TSD), convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In order to verify the performance of TSD-CNN-LSTM model and explore the response of NDVI to climatic factors, two forecasting models of temperature and precipitation based on its own historical information and four NDVI forecasting models that based on Temperature, precipitation and its own historical information were established. The results show that TSD-CNN-LSTM model based on its own historical information had the best forecasting performance, with the RMSE, NSE, MAE to be 0.4996, 0.9981, 0.4169 for temperature, 5.6941, 0.9822, 3.9855 for precipitation and 0.0573, 0.9617, 0.0447 for NDVI, respectively. Meanwhile, the NDVI forecasting models based on climatic factors show that the model based on the combination of temperature and precipitation has the better effect than that based on single temperature and single precipitation. Combined with the results of correlation analysis it can be inferred that NDVI changes are most signi cantly in uenced by the combination of temperature and precipitation, followed by temperature, and least in uenced by precipitation. The above ndings can provide a meaningful reference and guidance for the study of vegetation growth with climate changes.

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Fractality in PM2.5 Concentrations During the Dry and Wet Season over Indo-Gangetic Plain, India

Chelani

Gautam

2023

Water Air Soil Pollut

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Long-memory traces in $$\hbox {PM}_{10}$$ time series in Athens, Greece: investigation through DFA and R/S analysis

Cited by 16 publications

References 104 publications

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

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

NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM

Fractality in PM2.5 Concentrations During the Dry and Wet Season over Indo-Gangetic Plain, India

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