Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Testing for the Multifractality in Air Pollutants Time Series Dynamics and Application to Tabuk Region Case Study

Abdulrahman F. Aljohani
ORCID logo ,
Anouar Ben Ben Mabrouk
* ORCID logo ,
Mounirah Areshi
,
Jamel Bouslimi
,
Ali H. Laatar
,
Abdallah A. Shaltout
Version 1 : Received: 27 December 2023 / Approved: 28 December 2023 / Online: 28 December 2023 (15:00:13 CET)

How to cite: Aljohani, A.F.; Ben Mabrouk, A.B.; Areshi, M.; Bouslimi, J.; Laatar, A.H.; Shaltout, A.A. Testing for the Multifractality in Air Pollutants Time Series Dynamics and Application to Tabuk Region Case Study. Preprints 2023, 2023122168. https://doi.org/10.20944/preprints202312.2168.v1 Aljohani, A.F.; Ben Mabrouk, A.B.; Areshi, M.; Bouslimi, J.; Laatar, A.H.; Shaltout, A.A. Testing for the Multifractality in Air Pollutants Time Series Dynamics and Application to Tabuk Region Case Study. Preprints 2023, 2023122168. https://doi.org/10.20944/preprints202312.2168.v1

Abstract

Fractal analysis of time series aims to extract the hidden or complex structures issued from the associated real data, such as scaling laws, non-stationary, dynamics, and so on. In the present work, the air quality index and its components are considered as time series owing fractal structures. To understand their underlying dynamics, wavelet multifractal approach is applied starting with a wavelet multifractal spectrum test to confirm the multifractality of the underlying data. Next, a new model is introduced improving the classical minmax one, provided with a statistical measure for the adequacy of the new model. The theoretical findings are applied to the north west Tabuk region of Saudi Arabia for environmental and climate data related to pollutants and factors involved in the estimation of the air quality index.

Keywords

wavelets; multifractals; time series; air quality

Subject

Environmental and Earth Sciences, Pollution

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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