Análise espacial do black carbon e material particulado oriundos de emissões veiculares no município de Curitiba/PR

Abstract

Due to the increasing concentration of population in urban centres, especially in recent decades, the emission of air pollutants has intensified, resulting in problems for the population and cities across the globe. So, studies that seek to quantify the exposure of individuals to air pollutants, in particular the Black Carbon and particulate matter, studies which aim to quantify the human exposition to these air pollutants in order to comprehend the spatial behavior of these concentrations in the urban network are extremely important. In this work, the data of air pollution in the Centre of Curitiba (PR) were measured by means of portable equipment installed on bicycles adapted, during August 2016, for the period of seven consecutive days. The overall objective of this study was to determine the spatial variability of concentrations of fine particles (Black Carbon, Fine Particulate Matter) for the entire region of the study area in order to observe your dispersion. The descriptive statistics were analyzed to observe data distribution, besides the analysis of autocorrelation. In order to spatialize the pollutants distribution throughout the study area, several methods were used, such as trend and interpolation analysis (Simple Average, Inverse Distance Weighted – IDW, Ordinary Kriging Method). As results, maps of air pollutant have been generated and areas where there are higher concentrations have been identified, which may pose a risk to human health. It was found that the studied area was more affected by these pollutants in the morning. In addition, it was found that the minimum and maximum concentration values of Black Carbon in the morning are respectively 70% and 84% higher than those observed in the afternoon. Concerning Particulate Matter, the same minimum concentration was obtained in both periods; however, the maximum value obtained in the morning was 14% higher than this pollutant concentration in the afternoon. Lastly, using the Mean Square Deviation as a way of comparing methods, it was concluded that Simple Average presents a map which represents the measured data distribution better than the others.