Avaliação estatística de duas áreas distintas na região metropolitana do Rio de Janeiro

Abstract

The air quality study in the large cities involves different aspects, such as emissions, physical and chemical processes, transformations and meteorology as well as topographical influences, which makes this type of study highly non-linear. The treatment of the air quality data of monitoring stations aided by statistical analysis software, facilitates the understanding of the interactions between the several variables simultaneously. This study compares the air quality of two distinct localities within the Rio de Janeiro Metropolitan Region (Taquara - TQ and Jardim Primavera - JP). The data used corresponds with the hourly averages from 2014 to 2016 for nitrogen oxides (NOx, NO2 and NO), ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2) and particulate matter (MP10), relative humidity (ur), as well as meteorological data such as direction and wind speed. To interpret the results, a descriptive and multivariate statistical approach was used to characterize the concentrations of each site relating them to the types of sources emitted from each location to understand the impact on air quality. The results show that the ratios between the median and maximum concentration values for O3 in JP (10.7 and 197 ppb) and TQ (5.4 and 146 ppb) were 1.91 and 1.35, respectively. It is noted in JP, that each year, the values for O3 decrease and this variation of the median was 97% less between 2014 and 1016. In TQ this decrease was only 2% between the years 2014 and 2016. While in TQ the median NO2 concentrations increased by 22%, there was a 13% decrease for JP. These values indicate that the contribution of vehicular sources in TQ is more significant in relation to JP, where there are vehicular sources and emissions from large industries. Through the correlation matrices, it was found that the relationship between CO and NOx in TQ (r = 0.75) and in JP (r = 0.73) were the second highest positive relations, showing that both compounds are derived from sources. The polar and pink graphs of the pollutants showed that O3 has a contribution of nearby regions in both seasons, due to its dependence on the wind speed. The study shows the importance of the use of statistical software for the handling and treatment of data and the presentation of results from air quality stations.