Izabel da Silva Andrade

This study uses in situ measurements, satellite data, and modeling techniques to investigate methane (CH₄) emissions from the Caieiras landfill in São Paulo, Brazil. Methane is the second most significant greenhouse gas due to its high heating potential. Previous studies have shown that landfills can be considered the super-emitters of methane. Quantifying its emissions is essential to comprehending the emissions patterns of this emitter, promoting an improvement in data from inventories. Multiple platforms were used to obtain the data and better characterize the landfill. Satellite data from TROPOMI and EMIT were analyzed during the present study to characterize emission feathers. The results showed consistent emissions over 4 years, as shown by the inversions obtained using TROPOMI data. EMIT could identify one plume originating in the landfill dispersed over the city’s populated area. The in situ data were acquired near the Caieiras landfill using a greenhouse gas analyzer with integrated off-axis cavity output spectroscopy (OA-ICOS), a high-precision method to measure gases in the atmosphere. Three campaigns were conducted in 2023 on February 14, July 6, and November 22. The results obtained in all three campaigns showed median concentration values above 2 ppm, reaching values close to 35 ppm. These data were integrated into the AERMOD dispersion model, combined with meteorological data, and estimated methane emission rates, revealing variability of concentrations, where emission rates were 12,974.4 kg/h, 11,284.92 kg/h and 23,472 kg/h, respectively, for 3 days of sampling. The integrated approach in this study promotes valuable insights into landfill emissions and emphasizes targeted strategies for mitigating greenhouse gases. Results support the elaboration of policies to enhance waste management and reduce the climate impact produced by waste.

The emissions of CH₄ and CO₂, the primary greenhouse gases, have a significant impact on radiative forcing. This study investigated these gases along the Cananéia-Iguape estuarine system on the southern coast of the State of São Paulo, Brazil, which is a mangrove region characterized by low anthropogenic impact and a sparse population. As such, this area provides an ideal location for identifying natural emissions and background concentrations. The data for this study were collected using a portable gas analyzer (LGR-ICOS^TM GLA131), known for its high sensitivity and precision in detecting gases, mounted on a research boat. The results obtained were promising for both gases. A small variability in CH₄ concentrations was observed along the route, ranging from 1.84 ppm to 1.95 ppm, while CO₂, showed greater variation in values obtained during routes, ranging from approximately 411 ppm to 575 ppm. This study underscores the importance of investigating areas with minimal environmental impact. Together with future analyses, this research should help improve Greenhouse Gas (GHG) inventories in Brazil by providing valuable baseline data for comparisons with more impacted areas.

This 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.

This 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, PM_2.5, 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 (PM_2.5 and PM₁₀) exhibited minimal variation from 2018 to 2021; thus, their measured concentration remained consistent with the prediction model.

Atualmente, existem vários métodos de monitoramento de poluentes atmosféricos, porém poucos permitem uma grande cobertura espacial e temporal. O Sentinel-5P é um satélite dedicado ao monitoramento atmosférico com alta resolução espacial-temporal, oferecendo um grande volume de dados de diversas espécies químicas. Os óxidos de nitrogênio (NO e NO2), emitidos por atividades antrópicas na atmosfera - nos grandes centros urbanos sua principal fonte emissora são os veículos - merecem atenção especial, além de serem poluentes primários, são precursores da formação de outras espécies químicas devido à reações fotoquímicas, principalmente o ozônio troposférico. Essas interações fotoquímicas de NOx estimulam a redução de seu tempo de vida na atmosfera. Além disso, esses poluentes são usados como índices de qualidade do ar. A Região Metropolitana de São Paulo (RMSP) possui mais de 30 cidades, sendo um importante polo econômico do estado de São Paulo. A RMSP possui rodovias com grande circulação de veículos leves e pesados, indústrias e também alta densidade populacional. Tais fatores tornam esta região uma área favorável para um estudo de satélite. Assim, o presente trabalho utiliza dados do Sentinel-5P NO2 L2 para analisar a evolução das concentrações ao longo de 2019.

Severe biomass burning (BB) events have become increasingly common in South America in the last few years, mainly due to the high number of wildfires observed recently. Such incidents can negatively influence the air quality index associated with PM_2.5 (particulate matter, which is harmful to human health). A study performed in the Metropolitan Area of São Paulo (MASP) took place on selected days of July 2019, evaluated the influence of a BB event on air quality. Use of combined remote sensing, a surface monitoring system and data modeling and enabled detection of the BB plume arrival (light detection and ranging (lidar) ratio of (50 ± 34) sr at 532 nm, and (72 ± 45) sr at 355 nm) and how it affected the Ångström exponent (>1.3), atmospheric optical depth (>0.7), PM_2.5 concentrations (>25 µg.m⁻³), and air quality classification. The utilization of high-order statistical moments, obtained from elastic lidar, provided a new way to observe the entrainment process, allowing understanding of how a decoupled aerosol layer influences the local urban area. This new novel approach enables a lidar system to obtain the same results as a more complex set of instruments and verify how BB events contribute from air masses aloft towards near ground ones.