Variabilidade da coluna total de gases (SO2, NO2, CH4 e CO) sobre regiões metropolitanas sul-americanas

Abstract

Air pollution is a problem faced daily in regions where there is an intensification of pollutant emissions and is directly related to the reduction in the quality of human life, due to the increase in cases of respiratory diseases. Among the sources of the main gases emitted, those of anthropogenic origin are those that most impact in metropolitan areas through industrial activities and vehicle traffic where sulfur dioxide (SO2) and nitrogen (NO2), methane (CH4) and monoxide are highlighted. carbon (CO). The metropolitan regions of the present study have different characteristics (topography, vegetation and climate) and the pollution problems are attributed to anthropogenic concentrations close to the Planetary Boundary Layer as well as in some regions it is inserted in locations close to mountain ranges (RMLP and RMS). The main objective of this work is to carry out analysis of the monthly concentrations of the total pollutant column under five metropolitan South American regions through sensors coupled to satellites. The methodology consists in the use of interquantial analysis methods, which will investigate monthly trends in three quantiles, and Mann Kendall, for annual and monthly trends, from 2005 to 2019. The data used in this study were obtained directly from AIRS sensors and OMI with different spatial and temporal resolutions (CH4 and CO - 1 ° x1 ° and 12h, NO2 - 0.25 °x0.25 ° and daily, SO2 - 0.125 ° x0.125 and daily). The results showed that for the interquartile analysis there was a greater amount of trends (negative and positive) under maximum (Q90) and minimum (Q10) values in the monthly concentrations of CH4, CO and SO2, finally in NO2 growth seasonality was detected (March to June) and decrease (July to February) in RMBA, RMS and RMSP. For the Mann Kendall test, the results indicated that there was a reduction in the concentrations of the four pollutants in the five metropolitan regions (annual and monthly) and an increase in growth trends, in 90% of the data, some regions can be attributed can be influenced by a presence of negative trends under extreme values.