Pm10-pm2.5 Time Series and Fractal Analysis

Several epidemiological studies have shown an association between particulate air pollution and health effects. Suspended particulates and more specifically the inhalable PM10 fraction appear to cause respiratory health effects and heart diseases. Furthermore, particulate pollution is of paramount importance in areas with open-pit mines and especially when it is combined with raw lignite transfer and combustion in power stations through the suspension of particles and stack emissions, respectively. The penetration of particles into respiratory track is a function of the size of the particles and thus, it is more likely for the finer PM2.5 fraction to reach the deepest of the lugs. The fast economic growth the last decades has resulted in an increase of the sources of pollution not only in large metropolitan areas but also in medium-sized urban areas like the city of Kozani, Greece. It is the most densely populated city in the area of West Macedonia affected by urban particulate matter originated from local and stationary sources, from regional and long-range transport, and from street dust resuspension. Kozani is located a few kilometers away from lignite power stations that contribute to about 70% of the total electrical energy produced in Greece. Dust emissions seem to be the most serious problem in the area, as the measured ambient concentrations of suspended particles are at high levels and exceed local and international standards. In this study PM10 and PM2.5 concentrations are presented. The measurements have been carried out, from April to December 2002, by the Lab of Atmospheric Pollution and Environmental Physics (LAPEP) of Technological Education Institute of West Macedonia in the commercial centre of the city of Kozani. The temporal variation of PM10 and PM2.5 concentrations was studied and allowed a further insight on the factors affecting the measured ambient particulate levels. PM2.5 – PM10 correlation and PM2.5/PM10 ratios were investigated and compared to those in the literature together with the factors affecting their diurnal variation. The pollution levels were also detected in process of the experimental time series data by fractal dimension. Generally, fractal analysis is able to detect the data set complexity by scaling empirical data using threshold values. These values define the levels of air pollution episodes. The method presented in this study, is the transformation of PM10 and PM2.5 concentrations into a set of points whose dimension was estimated by box counting. This technique has estimated the fractal dimension of both the time series by the relationship between data variance and time scale.