Humberto Alves Barbosa

A escassez e a contaminação da água constituem desafios crônicos no Semiárido brasileiro, exigindo soluções de tratamento no ponto de uso que sejam acessíveis e sustentáveis. Este estudo investiga o potencial do tecido de xilema da planta nativa Spondias tuberosa (umbuzeiro) como filtro biomimético para purificação da água. A microestrutura anatômica do xilema foi caracterizada por meio de Microscopia Eletrônica de Varredura (MEV), revelando uma rede porosa de canais vasculares adequados à filtração. A eficácia do sistema foi avaliada experimentalmente por meio da análise de parâmetros físico-químicos e microbiológicos em amostras de água de chuva e água salobra antes e após a filtração por gravidade. Os resultados demonstraram remoção completa (100% de eficiência) da bactéria indicadora de contaminação fecal Escherichia coli em ambas as fontes hídricas, reduzindo as concentrações iniciais de 4,0 e 8,0 NMP/100 mL para níveis não detectáveis (0,0 NMP/100 mL), atendendo, assim, a um critério essencial de potabilidade. Embora os níveis de coliformes totais tenham permanecido elevados — característica comum em águas superficiais não tratadas —, a eliminação específica de E. coli evidencia a seletividade do filtro. Os parâmetros físico-químicos, incluindo metais, mantiveram-se dentro dos limites seguros. Esses achados indicam que o xilema de S. tuberosa é um material promissor, de baixo custo e biodegradável, para o desenvolvimento de tecnologias de filtração baseadas na natureza. Essa abordagem representa uma via viável para aprimorar a qualidade da água e promover a saúde pública em comunidades rurais vulneráveis.

The growing integration of artificial intelligence into mineral exploration has created new opportunities for improving target selection and decision-making in geologically complex regions. This study presents an integrated multiple machine learning framework designed to address the assessment of exploration data quality. The analysis was conducted using an extensive geophysical and geochemical dataset comprising 221 exploration sites distributed across the Brazilian Shield. Six widely adopted algorithms were comparatively evaluated, including Random Forest, XGBoost, AdaBoost, Decision Trees, K-Nearest Neighbors, and Logistic Regression. The results demonstrate that Random Forest achieved the highest accuracy in data quality classification (accuracy = 0.82, AUC = 0.85). Cross-validation confirmed model robustness (5-fold CV R² = 0.80 ± 0.02; accuracy = 0.82 ± 0.02). Feature importance and explainability analyses revealed that magnetic anomaly intensity, copper concentration, and alteration-related indices are the most influential predictors, reinforcing both the geological plausibility and the computational reliability of the models. This proposed methodology offers practical support for mineral exploration strategies across the Brazilian Shield and provides a scalable framework for future applications involving critical mineral systems.

The growing integration of artificial intelligence into mineral exploration has created new opportunities for improving target selection and decision-making in geologically complex regions. This study presents an integrated multiple machine learning frame-work designed to address the assessment of exploration data quality. The analysis was conducted using an extensive geophysical and geochemical dataset comprising 221 ex-ploration sites distributed across the Brazilian Shield. Six widely adopted algorithms were comparatively evaluated, including Random Forest, XGBoost, AdaBoost, Decision Trees, K-Nearest Neighbors, and Logistic Regression. The results demonstrate that Random Forest achieved the highest accuracy in data quality classification (accuracy = 0.82, AUC = 0.85). Cross-validation confirmed model robustness (5-fold CV R² = 0.80 ± 0.02; accuracy = 0.82 ± 0.02). Feature importance and explainability analyses revealed that magnetic anomaly intensity, copper concentration, and alteration-related indices are the most influential predictors, reinforcing both the geological plausibility and the com-putational reliability of the models. This proposed methodology offers practical support for mineral exploration strategies across the Brazilian Shield and provides a scalable framework for future applications involving critical mineral systems.

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability.

In this paper we analyze the evolution of atmospheric and surface physical properties on that portion of the Earth covered by the Meteosat Second Generation (MSG) satellites over a period of 20 years (from 2005 to 2024). The radiances show significant changes over time in the solar (–1.3 %) and infrared (+0.4%) domains, consistent with data from similar radiometers (e.g. CERES) on other satellite platforms. The outgoing solar radiance (OSR) mainly decreases as a result of low-level cloud reduction in the nominal Meteosat (at 0° longitude) field of view (MFoV) in the geostationary orbit. The increased CO2 content in the atmosphere also plays a decisive role in the radiation imbalance at the top of the atmosphere (ToA). For 60 MFoV subareas we describe regional changes in the cloud amount at high and low levels in the atmosphere, and show the connection between the imbalance at the ToA and the observed variation in the sea surface temperature (SST) in the Atlantic. We also did not find any significant cirrus variation in the study period. Our study provides a better spectral resolution in its conclusions compared with other analyses, such as those of CERES. It also introduces the SST as a major representative of the climate. For example, the radiance values in the atmospheric split window measurements around 11 µm can be used as a proxy for the humidity column at low levels, a key variable in climate projections, which shows a decadal increase. Meteosat data suggests a so-far unpredictable contribution to climate from the deep ocean and its heat reserve. The recent decrease in the total cloud cover (TCC) at low levels in the MFoV is also validated by the ECMWF ERA-5 reanalysis.

The Bay of Bengal is home to a significant number of tropical cyclones. Tropical cyclones have been the major attention to the meteorological communities due to their varied socio-economic impacts. Recent studies have shown that tropical cyclones can feedback to the large-scale air-sea interactions. Since the Bay of Bengal is a part of the Indo-Pacific warm-pool region, small changes in the thermal structure of the Bay could alter the air-sea interactions. Because the Bay of Bengal is part of the Indo-Pacific warm pool zone, it is vulnerable to large-scale distant forcing from El Niño Southern Oscillation (ENSO) and other natural disasters. This study particularly deals with the impact of tropical cyclones (pre and post-monsoon seasons) on the Bay of Bengal. The role of mesoscale eddies in the intensity and movement of tropical cyclones has been studied in detail as case studies. Different satellite-based oceanographic observations are used in the analysis. The study concludes that distinct ocean processes have a relative role in impacting mixed layer temperature. It is examined distinct physical oceanographic parameters and conducted a thorough oceanic mixed layer heat budget analysis. Among which, for the warm case of warm-core eddies, pre-existing barrier layer plays a crucial role in impacting the mixed layer temperature.

In the Amazon region, the impact of fire on the region’s biogeochemical processes remains poorly understood. In this study, we examined the relationship of seasonal fire on greenhouse gas (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. Results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early- and middle-dry-season fires (July–September). We found a strong positive correlation (r² = 0.71) between NO₂ and burned biomass when a small percentage of burned biomass (around ≤ 0.5%) is released during a fire. However, in the event of a large percentage of burned biomass (>0.8%), the correlation coefficient between NO₂ and burned biomass was weak (r² = 0.41). New models need to be developed that incorporate the substantial existing knowledge on the seasonal dynamics of fire-derived GHG emissions. This information should be utilized to make effective decisions about how to manage fire in the Amazon ecosystems and to drive further data collection campaigns and modelling initiatives.

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines.

Flash droughts, characterized by a rapid onset and severe intensity, pose a serious threat to water resource management. Extensive research has indicated that drought has lagged impacts on streamflow. Nevertheless, the hydrometeorological conditions by which streamflow dynamics respond to drought within the São Francisco River Basin (SFRB) remain ambiguous. To bridge this gap, we conducted a study on long-term streamflow responses to flash drought in the SFRB from 1991 to 2020, combining the Standardized Antecedent Precipitation Evapotranspiration Index (SAPEI) and quantile streamflow with a trend analysis. This study employed the SAPEI, a daily drought index, to identify flash droughts and assess the response of streamflow to the identified events across the SFRB. Our findings reveal insights into the direct response of streamflow to flash drought conditions, stimulated by the application of the SAPEI for analysis of flash drought events. The interannual flash droughts fluctuated over the years, with the middle part of the SFRB experiencing frequent, longer flash droughts, while the south part encountered shorter but less frequent events. About 55% of the study area is trended towards drying conditions. A comparative analysis of the SAPEI and streamflow identified a synchronized response to the onset of flash drought events, but the recovery timescale for the SAPEI and streamflow varied among the events. This study enhances understanding of the flash-drought–streamflow relationship in the SFRB and provides theoretical support for addressing drought risks under climate change.

Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts.