Alexandre Cacheffo

Driven by regional demand for stringent emission controls and the associated public health impacts, this study investigates tropospheric NO₂ concentrations in Volta Redonda, a major metallurgical hub in Rio de Janeiro State. Initially, TROPOMI data were used to establish spatiotemporal patterns from 2019 to 2024, revealing that Volta Redonda maintains higher NO₂ levels than similarly sized urban centers. A detailed analysis of the ‘weekend effect’ showed a minimal 3% reduction in NO₂ concentrations during the weekend in Volta Redonda, compared with a 23% drop in Rio de Janeiro city, suggesting that industrial sources of this pollutant predominate over vehicular traffic. This finding is further supported by an analysis of Climate TRACE emissions data, which shows that industrial NO_x emissions exceed transportation emissions in this industrial-urban cluster. Local ground station measurements, combined with satellite observations, indicate that the nearby industrial zone is likely the primary source of NO₂. Finally, the study quantifies the human cost through a Relative Risk Index (RRI) analysis. Results indicate that a 1 µg/m³ increase in minimum NO₂ concentrations leads to a 34% rise in pediatric hospitalizations and a 25% increase among older people, offering critical evidence for regional emission regulations and public health strategies.

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Although the atmospheric boundary layer height (ABLH) is a highly relevant parameter for various meteorological studies, the analysis of its behavior remains undersampled in South America, especially in Brazil. In this context, this work presents a monthly characterization of the ABLH during the convective period (Convective Boundary Layer Height-CBLH) using radiosonde data and a comparison between the monthly patterns obtained from ERA5 and COSMIC-2 data. The results demonstrate that, based on radiosonde data, the CBLH can be grouped into six regions (Northern Amazon, North, Northeast, Midwest, Southeast, and South), with seasonality varying according to the continentality and the climate to which they are exposed. The ERA5 and COSMIC-2 data show considerable agreement for most of the year [average absolute difference of [362 ± 182] m] and demonstrate the same seasonality observed in radiosondes for the North Amazon, North, Northeast, Southeast, and South regions. The highest discrepancies between ERA5 and COSMIC-2 occur during the fire season, mainly at Midwest region, reaching 802 m in July, likely linked to the sensitivity of the COSMIC-2 to fire plumes.

In 2024, Brazil experienced record-breaking wildfire activity, underscoring the escalating influence of climate change. This study examines the long-range transport of wildfire-generated aerosol plumes to São Paulo, combining multi-platform observations to trace their origin and properties. During August and September—a period marked by intense fire outbreaks in Pará and Mato Grosso do Sul—lidar measurements performed at São Paulo detected pronounced aerosol plumes. To investigate their source and characteristics, we integrated data from the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite, HYSPLIT back-trajectory modeling, and ground-based AERONET and Raman lidar measurements. Aerosol properties were derived from aerosol optical depth (AOD), Ångström exponent, and lidar ratio (LR) retrievals. Back-trajectory analysis identified three transport pathways originating from active fire zones, with coinciding AOD values (0.7–1.1) and elevated LR (60–100 sr), indicative of dense smoke plumes. Compositional analysis revealed a significant black carbon component, implicating wildfires near Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as probable emission sources. These findings highlight the efficacy of satellite-based lidar systems, such as Atmospheric Lidar (ATLID) onboard EarthCARE, in atmospheric monitoring, particularly in data-sparse regions where ground instrumentation is limited.

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.

Atmospheric lidars can simultaneously measure clouds and aerosols with high temporal and spatial resolution and hence help understand cloud–aerosol interactions, which are the source of major uncertainties in future climate projections. However, atmospheric lidars are typically custom-built, with significant differences between them. In this sense, lidar networks play a crucial role as they coordinate the efforts of different groups, provide guidelines for quality-assured routine measurements and opportunities for side-by-side instrument comparisons, and enforce algorithm validation, all aiming to homogenize the physical retrievals from heterogeneous instruments in a network. Here we provide a high-level overview of the Lidar Processing Pipeline (LPP), an ongoing, collaborative, and open-source coordinated effort in Latin America. The LPP is a collection of tools with the ultimate goal of handling all the steps of a typical analysis of lidar measurements. The modular and configurable framework is generic enough to be applicable to any lidar instrument. The first publicly released version of the LPP produces data files at levels 0 (raw and metadata), 1 (averaging and layer mask), and 2 (aerosol optical properties). We assess the performance of the LPP through quantitative and qualitative analyses of simulated and measured elastic lidar signals. For noiseless synthetic 532 nm elastic signals with a constant lidar ratio (LR), the root mean square error (RMSE) in aerosol extinction within the boundary layer is about 0.1 %. In contrast, retrievals of aerosol backscatter from noisy elastic signals with a variable LR have an RMSE of 11 %, mostly due to assuming a constant LR in the inversion. The application of the LPP for measurements in São Paulo, further constrained by co-located AERONET data, retrieved a lidar ratio of 69.9 ± 5.2 sr at 532 nm, in agreement with reported values for urban aerosols. Over the Amazon, analysis of a 6 km thick multi-layer cirrus found a cloud optical depth of about 0.46, also in agreement with previous studies. From this exercise, we identify the need for new features and discuss a roadmap to guide future development, accommodating the needs of our community.

Lidars can simultaneously measure clouds and aerosols with high temporal and spatial resolution and hence help understand their interactions, which are the source of the largest uncertainties in current climate projections. However, lidars are typically custom-built, so there are significant differences between them. In this sense, lidar networks play a crucial role as they coordinate the efforts of different groups, providing the guidelines for quality-assured routine measurements aiming to homogenize the physical retrievals. With that in mind, this work describes an ongoing effort to develop a lidar processing pipeline (LPP) collaboratively. The LPP is a collection of tools developed in C/C++, python, and Linux script that handle all the steps of a typical lidar analysis. The first publicly released version of LPP produces data files at levels 0 (raw and metadata), 1 (averaging and layer-mask), and 2 (aerosol optical properties). We discussed the application of LPP for two case studies for Sao Paulo and Amazon, which shows the capabilities of the current release but also highlights the need for new features. From this exercise, we developed and presented a roadmap to guide future development, accommodating the needs of our community.

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.