Wavelet Self-Similar Models for Air Pollutants Dynamics and Application

Abstract

Self-similar and generally scaling laws are pointed out in time series issued from different types of data. The analysis of these structures has been conducted via many advanced mathematical tools such as wavelets. In this paper, we propose to exploit some self-similar type models for the modeling of time series issued from air quality and/or pollution data, by using wavelet multifractal techniques. The applied models are shown to involve self-similar, multi-scaling and also noised structures. The resulting models are applied empirically on a sample of data issued from air pollution factors in the northwestern region of Tabuk governorate in Saudi Arabia. Some of the chemicals and materials are essential components of many air pollution factors such as PM10 and PM2.5 particles.