MONOFRACTAL AND MULTIFRACTAL APPROACHES IN INVESTIGATING TEMPORAL VARIATION OF AIR POLLUTION INDEXES
Abstract
It seems evident that the understanding of the complex temporal evolution dynamic characteristics of air pollution indexes (APIs) can contribute to developing advanced techniques for air pollution forecasting. In this work, monofractal and multifractal methods have been successfully used to characterize the temporal fluctuations of APIs in Shanghai. It shows that APIs in Shanghai are characterized by scale invariance, long range dependence and multifractal scaling. Introducing a kind of variable window sizes method, we analyze the temporal evolution of the monofractal and multifractal behaviors of APIs. Some information about the temporal evolution dynamics of APIs is revealed. Our study suggests that the variable window sizes method can be help the analysis of air pollution time series temporal evolution dynamical mechanisms. This work could play an important role in the research of air pollution.
