Fractal analysis of the time series of particulate material

Abstract

The air quality index shows the pollution of the air we breathe, a very low index indicates that we can have respiratory, eye or heart conditions. An index below the minimum levels declared, requires generating strategies such as days without the presence of private vehicles, among others, to reduce pollution. The city of Medellin, Colombia, has shown very low levels in the air quality index due to several factors, mainly the emission produced by vehicles and companies. Due to this, activities have been initiated to reduce the emission of 2.5 micron and 10 microns particulate material, classified as highly harmful to health. The city installed 12 air quality monitoring stations and designed a decontamination proposal for the year 2030. Conducting a study on the persistence of particulate matter in the air according to the data recorded by the stations, will strengthen the proposed strategy or modify it to achieve the goal. In this work, the data collected by a station from July to December 2018 in which a high persistence in the presence of particulate material was analyzed.

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

“…Multi-scale framework is employed to study the air quality time series to show long memory and scale invariance. Prada et al (2020) analysed the PM 10 and PM 2.5 using rescaled range method to evaluate the H for understanding the persistence and volatility of time series. Alvarez-Mendoza et al (2019) carried out the analysis of PM 10 over the city of Quito, Ecuador, using remote sensing data.…”