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Artigo IPEN-doc 30209 Comparison of PBL heights from ceilometer measurements and greenhouse gases concentrations in São Paulo2023 - SANTOS, AMANDA V. dos; ARAUJO, ELAINE C.; ANDRADE, IZABEL da S.; CORREA, THAIS; MARQUES, MARCIA T.A.; SOUTO-OLIVEIRA, CARLOS E.; LEONARDO, NOELE F.; MACEDO, FERNANDA de M.; SOUZA, GIOVANNI; LOPES, PEROLA P. de Q.; MOREIRA, GREGORI de A.; ANDRADE, MARIA de F.; LANDULFO, EDUARDOThis paper presents a study conducted in São Paulo, Brazil, where the planetary boundary layer height (PBLH) was determined using ceilometer data and the wavelet covariance transform method. The retrieved PBLH values were subsequently compared with the concentrations of CO2 and CH4 measured at three distinct experimental sites in the city. The period of study was July 2021. This study also included a comparison between ceilometer data and lidar data, which demonstrated the favorable applicability of the ceilometer data for PBLH estimation. An examination of the correlation between changes in average CO2 concentrations and PBLH values revealed stronger correlations for the IAG and UNICID stations, with correlation coefficients (ρ) of approximately −0.86 and −0.85, respectively, in contrast to the Pico do Jaraguá station, which exhibited a lower correlation coefficient of −0.42. When assessing changes in CH4 concentrations against variations in PBL height, the retrieved correlation coefficients were approximately −0.78 for IAG, −0.66 for UNICID, and −0.38 for Pico do Jaraguá. The results indicated that CO2/CH4 concentrations are negatively correlated with PBL heights, with CO2 concentrations showing more significant correlation than CH4 . Additionally, among the three measurement stations, IAG measurements displayed the most substantial correlation. The results from this study contribute to the understanding of the relationship between PBLH and greenhouse gas concentrations, emphasizing the potential of remote sensing systems like ceilometers in monitoring and studying atmospheric processes.Artigo IPEN-doc 29836 Analyzing the influence of vehicular traffic on the concentration of pollutants in the city of São Paulo2023 - MOREIRA, GREGORI de A.; CACHEFFO, ALEXANDRE; ANDRADE, IZABEL da S.; LOPES, FABIO JULIANO da S.; GOMES, ANTONIO A.; LANDULFO, EDUARDOThis study employs surface and remote sensing data jointly with deep learning techniques to examine the influence of vehicular traffic in the seasonal patterns of CO, NO2 , PM2.5, and PM10 concentrations in the São Paulo municipality, as the period of physical distancing (March 2020 to December 2021), due to SARS-CoV-2 pandemic and the resumption of activities, made it possible to observe significant variations in the flow of vehicles in the city of São Paulo. Firstly, an analysis of the planetary boundary layer height and ventilation coefficient was performed to identify the seasons’ patterns of pollution dispersion. Then, the variations (from 2018 to 2021) of the seasonal average values of air temperature, relative humidity, precipitation, and thermal inversion occurrence/position were compared to identify possible variations in the patterns of such variables that would justify (or deny) the occurrence of more favorable conditions for pollutants dispersion. However, no significant variations were found. Finally, the seasonal average concentrations of the previously mentioned pollutants were compared from 2018 to 2021, and the daily concentrations observed during the pandemic period were compared with a model based on an artificial neural network. Regarding the concentration of pollutants, the primarily sourced from vehicular traffic (CO and NO2 ) exhibited substantial variations, demonstrating an inverse relationship with the rate of social distancing. In addition, the measured concentrations deviated from the predictive model during periods of significant social isolation. Conversely, pollutants that were not primarily linked to vehicular sources (PM2.5 and PM10) exhibited minimal variation from 2018 to 2021; thus, their measured concentration remained consistent with the prediction model.