Multifractal Characteristics on Temporal Maximum of Air Pollution Series

Abstract

Presenting and describing a temporal series of air pollution data with longer time lengths provides more concise information and is, in fact, one of the simplest techniques of data reduction in a time series. However, this process can result in the loss of important information related to data features. Thus, the purpose of this study is to determine the type of data characteristics that might be lost when describing data with different time lengths corresponding to a process of data reduction. In parallel, this study proposes the application of a multifractal technique to investigate the properties on an air pollution series with different time lengths. A case study has been carried out using an air pollution index data in Klang, Malaysia. Results show that hourly air pollution series contain the most informative knowledge regarding the behaviors and characteristics of air pollution, particularly in terms of the strength of multifractality, long-term persistent correlations, and heterogeneity of variations. On the other hand, the statistical findings found that data reduction corresponding to a longer time length will change the multifractal properties of the original data.

Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach

“…Furthermore, flexible multivariate modeling via the copula approach was also used to examine the dynamic dependence structure between PM 10 and other air pollutants [23]. Using a multifractal technique on API in Klang, Masseran [24] showed that hourly API data contains the most information on air pollution and that the data reduction process is affected by the data duration. Moreover, survey data obtained from the cross-sectional survey is used to study Malaysians' awareness of air pollution and its related impacts on human health, where the surveys pointed out the need to increase the awareness of air pollution among Malaysian [25,26].…”