Publicações

O presente estudo investiga a climatologia de 10 anos (2013-2022) das concentrações de poluentes atmosféricos na Região Metropolitana do Vale do Paraíba e Litoral Norte (RMVP). Utilizando dados de satélites e reanálises, foram analisados os padrões sazonais de monóxido de carbono (CO), dióxido de enxofre (SO2), material particulado fino (MP2.5), carbono orgânico (OC), carbono negro (BC) e profundidade óptica de aerossóis (AOD). Os resultados indicam que a poluição na RMVP apresenta padrões sazonais distintos, sendo influenciada por fatores como queimadas, emissões industriais e transporte de poluentes. O CO e MP2.5 exibem picos nos meses de agosto a setembro, associados às queimadas, enquanto o SO2 apresenta maiores concentrações entre maio e agosto, devido a emissões industriais e veiculares. O produto de maior resolução do AGAC para MP2.5 revela uma intensificação da pluma de MP2.5 na região durante a estação seca, destacando a importância de dados de alta resolução para monitoramento da qualidade do ar. A redução das emissões e o aprimoramento dos sistemas de monitoramento da qualidade do ar são essenciais para mitigar os impactos da poluição atmosférica na saúde pública. Sugere-se a inclusão de NO2 e FRP em futuras análises para uma compreensão mais completa da dinâmica dos poluentes na região.

iatoms represent the most abundant group of microorganisms carried by ballast water; they present great invasive potential and remain viable after long periods of darkness. This is likely the dispersal route of Coscinodiscus wailesii, a species native to the Indian Ocean or Pacific Ocean, which has become invasive in various global regions. Considering that this species is invasive and potentially harmful, we carried out potential distribution modeling with the aim of assessing the effects of global warming on the geographical distribution of C. wailesii. In this study, we also present the first record of a C. wailesii bloom on the coast of Northeast Brazil, generating low phytoplankton diversity. The distribution models indicated an expansion of the climatic niches for the occurrence of C. wailesii in the SSP2-4.5 scenario (intermediate pathway of greenhouse gas emissions), there was a more marked increase in the climatic niches from the present until 2030, followed by slower growth until 2090. These expansions are more evident in the SSP5-8.5 scenario (greater greenhouse gas emissions) under conditions of more intense global warming. We highlight that these predictions indicate a potential increase in the distribution and frequency of C. wailesii blooms, with important ecological effects. Algal blooms promote greater availability of dissolved oxygen in their initial phase, in addition to representing a temporary carbon sink. However, because C. wailesii is a potentially harmful species, their blooms can cause damage to the ecosystem, such as loss of planktonic diversity and anoxic conditions.

Deforestation in Brazilian Amazon impacts ecosystem services, affects the Amazonian population, and contributes to global warming. Public policies promoting highway construction pose a major threat to a critical area of undesignated public forests in Brazilian Amazonia: the Trans-Purus region-a vast forest area west of the Purus River in Amazonas state. The forest in this region is largely intact, as its inaccessibility by road makes it less attractive to land grabbers, but it could become a new deforestation hotspot if planned highways are built. We projected the potential impact of planned highways on deforestation and the advance of illegal land occupation under a business-as-usual scenario in the Trans-Purus region and its surrounding areas, including the BR-319 highway region to the east, the Humait & aacute; and L & aacute;brea areas to the south, the Juru & aacute; area to the west, and the Manaus influence region to the north. A baseline scenario (without highways) was also simulated for comparison. The business-as-usual scenario showed a reduction of 15% of remaining forest from 2022 to 2070. The increase in deforestation between the business-as-usual and baseline scenarios was greater in the Trans-Purus region than in any of the four surrounding regions we simulated. In the Trans-Purus region, the mean annual deforestation increased from 23 to 483 km2 with the highways, and undesignated public forests showed substantial deforestation, demonstrating the role of highways in facilitating the access of actors from Brazil’s “arc of deforestation.” The magnitude of potential impacts implies the need to reconsider government policies on Amazon development that rely on highway projects.

Background Climate change and urban expansion pose significant challenges to controlling
Aedes aegypti mosquito populations, a primary vector of arboviruses such as dengue, Zika, and
chikungunya. This study aims assess how climate and anthropogenic factors will jointly shape Ae.
aegypti densities in Brazil, which is crucial to forecasting transmission risks and informing public health
strategies. Methods This study combined a biologically informed, stage-structured delay-differential
equation model with climate and anthropogenic data. Climate projections from the Coupled Model
Intercomparison Project Phase 6 under different Shared Socioeconomic Pathways (SSPs) were used to
forecast future climate scenarios from 2024 to 2080. Boosted Regression Trees integrated anthropogenic
factors like urbanisation, population growth, and urban accessibility. Model outputs were validated with
entomological surveillance data, and the basic reproductive number for dengue fever was used to assess
changes in disease transmission potential. Findings Our findings predicted that Ae. aegypti mosquito
density will increase nationally, but unevenly, exceeding thermal limits in North Brazil while rising
substantially in the South and Southeast. Increases in density were particularly pronounced under high
greenhouse gas emission scenario SSP5-8.5 (up to 92% in the Southeast). These trends were projected to
elevate the transmission potential for dengue fever, with Southeast Brazil facing the biggest increases due
to mosquito population growth outpacing human population expansion. Validation against historical data
confirmed model robustness. Interpretation By directly linking mosquito abundance to SSP-specific
emissions trajectories, our results show that climate mitigation can markedly reduce disease risk. Shifting
from SSP5-8.5 to SSP1-2.6 could cut projected mosquito density increases from 31% to 11% nationally
by 2080. The models spatial granularity and integration of local administrative boundaries support its
utility for national and sub-national health planning. Addressing compounded risks in vulnerable peri-
urban and rural populations will require coordinated interventions that span climate policy, vector control,
and health equity. Author summary Aedes aegypti mosquitoes transmit viruses like dengue, Zika, and
chikungunya, which are increasing in many parts of the world, including Brazil. Climate change and rapid
urban growth are likely to increase the risk of mosquito-borne disease outbreaks in the future. In this
study, we developed a model that combines mosquito biology with climate and urbanisation data to
estimate how mosquito populations might change in Brazil under different future climate and
development scenarios. Our model predicts that mosquito populations will increase significantly,
especially in the South and Southeast of Brazil, where conditions are becoming more favourable for
mosquito survival and reproduction. We also found that the risk of disease transmission is expected to rise
as mosquito numbers grow faster than the human population. These findings suggest that both climate
change and human development patterns are likely to increase the burden of mosquito-borne diseases. By
showing where and when mosquito risks are likely to be greatest, our study can help public health
authorities prepare and respond more effectively. It also highlights the importance of climate action to
reduce future health risks.

The number of reported drought events per year and their impacts have signifcantly increased in the last two decades. In addition to monitoring drought conditions, forecasting is essential for planning activities. Various Machine Learning (ML) algorithms have experienced a substantial increase in popularity in geoscience applications. This study presents a Systematic Literature Review on drought forecasting utilizing Machine Learning models. Following the PRISMA 2020 protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), the total number of papers was reduced from approximately a thousand to a hundred. The majority of the papers found study areas from Asia and Oceania. Meteorological drought was the most studied event in the articles evaluated due to the greater ease of its estimation using only rainfall data. The Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index are the most widely used indices in research relating to drought and Machine Learning. Precipitation is the most commonly used input among the various input data used in ML models. Remote sensing has yet to be widely used in drought forecasting, with less than 20% of papers utilizing remote sensing data. What still needs to be addressed is drought forecasting in the time scale of days, which is less utilized compared to the monthly scale. The regression method is the most commonly used, with 77% of papers utilizing it. In conclusion, we formulated fve recommendations based on the critical evidence and insights from our review: (1) it is essential to foster interdisciplinary collaborations among experts in ML, climatology, and hydrology while investing in initiatives that promote the sharing of data and code repositories; (2) satellite remote sensing technologies and crowd-sourced data collection methods should be considered in ML studies while enhancing existing monitoring infrastructure to increase the spatial and temporal coverage of datasets for validation of ML methods; (3) it is recommended to increase the availability of additional environmental variables, such as soil moisture and vegetation health, to promote more studies of agricultural drought and ML methods; (4) it is crucial to prioritize the integration of daily-scale climate data into drought modeling and forecasting for developing efective adaptation and mitigation measures to fash drought events; and fnally (5) ethical considerations of using Artifcial Intelligence (AI) for drought forecasting, emphasizing the environmental impact, issues of digital sovereignty, and the urgent need for a broader dialogue on AIs role in sustainable climate solutions.

In 2014, an extreme drought caused massive, unprecedented livestock and wildlife deaths in northwestern South Americas tropical savanna region. The causes of this drought remain unclear. Here, we present evidence that a weakening of the atmospheric teleconnection from the Amazon forests is a previously unknown mechanism behind this and other regional droughts over the last decades. To quantify this weakening, we introduce the concept of forest fraction anomaly (FFA) based on how the dominant patterns of atmospheric moisture transport pass over the forests. By combining climate records with FFAs computed for 1979-2019, we identify a decadal shift in the severity of droughts that coincides with an increasing weakening of the forest-savanna connection. Our results reveal a physically plausible mechanism linking the Amazon forests with the occurrence of droughts in neighboring savannas and provide an analytical framework for investigating forest-related droughts elsewhere.

This research paper presents the findings and lessons from the international CLIMAX project-Climate Services Through Knowledge Co-production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events. The project, engaging with the Brazilian National Electric System Operator (ONS), explores co-production as a method to implement climate services in the context of Brazil, particularly within the country’s hydroelectric power sector. Through interactive research and transdisciplinary collaboration, the CLIMAX project evaluates both the implementation of climate services and the concept of utility in knowledge co-production. The research identifies inherent diversity and “utility paradoxes” within the co-production process. These paradoxes involve the perceived relevance of climate information versus its integration into systems, and its instrumental use versus its justification for decisions. The study highlights the significance of stakeholder engagement, close and meaningful communication, and adaptability to context-specific needs. By sharing experiences from a five-year interactive research initiative, it offers insights into improving practices for future coproduction endeavors. This entails recognizing varied research contexts, managing co-design processes with an awareness of time and resources, and encouraging flexibility and personal transformation within co-design.

Coastal waters play a pivotal role in the global carbon cycle, showing increased short-term variability of dissolved oxygen saturation (DOsat) and partial pressure of greenhouse gases like carbon dioxide (pCO(2) ), especially in underrepresented tropical eutrophic environments. Here, we conducted high-frequency (1-min interval) diel measurements of surface DOsat and pCO(2) in Guanabara Bay, Brazil, a highly nutrient-enriched coastal ecosystem. The predominant metabolic controls on pCO(2) were revealed by its strong negative correlation with DOsat. Air-sea CO2 fluxes derived from high-frequency diel sampling showed emissions of 533 mmol C m(-2) annually. Conventional estimates based on daylight-only measurements were similar to 73% and 319% higher in the morning (10:00-12:00 h and sunrise-8:00 h, respectively) or similar to 172% and 244% lower in the afternoon (12:00-14:00 h and 14:00-16:00 h, respectively). Our findings indicate that rapid diel shifts between CO2 sinks and sources in eutrophic coastal waters can introduce significant uncertainty in estimating air-water CO2 fluxes from regional to global carbon budgets.

Hydropower systems are vulnerable to climate change effects, mainly in the Southern Hemisphere, risking energy security and investments in emerging markets. With Brazil as the representative case, this study explores strategies to enhance the power system’s reliability and opportunities amid water stress scenarios in 2050. This work presents a high-resolution power model, which optimizes capacity and dispatch simultaneously while minimizing the costs. Findings indicate a major role of wind energy and a decline in natural gas in water stress scenarios. However, extensive wind penetration is only possible when combined with storage systems, which remain mainly reservoir-based. Also, severe cases of water stress can increase the power system cost by up to 14.9% when combined with 100% renewable systems. The moderate cost scenarios have similar optimal results to advanced costs (less costly), indicating the high competition between wind and solar alternatives. Furthermore, expanding the wind farms portfolio may lead to surplus wind energy, particularly during the dry season when energy demand is lower. Such excess energy could potentially produce 0.46 Mt of green hydrogen annually, surpassing the current industry usage of 0.33 Mt in Brazil. Effective spatial planning is crucial, particularly considering that green hydrogen production requires water, and the surplus energy predominantly comes from the Northeast, where intense drought events are frequent.

South America is a large continent with a wide diversity of weather and climate features, including tropical, subtropical and extratropical regimes interacting within a complex landscape. Simulations by global climate models, as well as their downscaling through regional circulation models or statistical methods, are important tools, particularly when assessing the impacts of climate change in the continent. This work evaluates 57 models of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) in their simulation of various spatial patterns and circulation features over South America. Our evaluation aims to provide useful input for the selection of climate models to force regional simulations in South America. Therefore, we focus on spatial fields that are relevant for regional simulations, such as horizontal winds, sea level pressure, sea surface temperature (SST), and moisture and energy fluxes across the domain boundaries. Additionally, we evaluated different circulation features influencing the regional climate of South America that have not been widely evaluated in these models. Several indices are studied to assess the main low-level and upper-level continental-scale circulation patterns, the regional Walker and Hadley cells, the subtropical highs and the boundary SST patterns. Our results show that no single model performs best across all evaluated features, highlighting the importance of in-depth model evaluation for the region concerning the features of interest.

The increase in aridity has been reported in different regions of the world in recent decades. This study analyzes changes in aridity in Brazil over the period 19612020, both in terms of their spatial distributions and across different main Brazilian biomes, using the index recommended by the desertification convention applied to a high-resolution observational gridded dataset. Results revealed a trend towards drier conditions in the center and northeast of the country, while the southern region showed changes to wetter conditions. Over the study period, an expansion of semi-arid areas at the expense of dry subhumid and humid regions was observed. These trends accelerated over the period 19912020, indicating an intensification. In this period, an area of >4200 km2 within the arid category was observed for the first time according to the convention classification. In addition, novel areas, with sizes ranging from 1200 to 11,500 km2, included in the dry subhumid category were detected in the center of Brazil. Moreover, all the scenarios revealed by the observations are fully consistent with the projection of future climate change. Regardless of the observed trends in aridification, a preliminary analysis based on the time series of the vegetation health index (VHI) shows negative trends in all Brazilian biomes, suggesting an increase in vegetation stress. Current trends may significantly impact the country’s economy in the near future, affecting production systems, including agricultural activities and the availability of water resources for multiple uses. This study points out areas in the country that should be prioritized in soil and vegetation recovery actions and regional environmental development policies.

Background Understanding the role of space weather, specifically Geomagnetic Disturbances (GMDs) caused by solar activity, on health outcomes is unclear. One emerging link includes the impact of space weather on myocardial infarctions (MI). In this study we examined the correlation between MI and GMDs in Brazil. Methods We used a database from the public health in Brazil, focusing on the city of São José dos Campos (23° 10′ 44″ S, 45° 53′ 13″ W), located in the state of São Paulo, during the period of 19982005. We focused on admissions for MIs, which included a total of 871 men and 469 women. We categorized the MI data into three age groups: age 30 and younger, age 3160, and age over 60. Additionally, we incorporated Planetary Index (Kp) data as an indicator of variations in the Earths geomagnetic field resulting from solar disturbances, categorized as quiet, moderate, or disturbed days. In our analysis, we employed two methods: statistical counting and the unsupervised clustering known as K-Means, considering the attributes of age, sex, and geomagnetic condition. Results Here we show that geomagnetic conditions have an impact on MI cases, particularly for women. The rate of relative frequency of MI cases during disturbed geomagnetic conditions is almost three times greater compared to quiet geomagnetic conditions. Using the unsupervised K-Means algorithm, the results indicate that the group associated with disturbed geomagnetic conditions has a higher incidence of MIs in women. Conclusions Overall, our results provide evidence that women may exhibit a higher susceptibility to the effects of geomagnetic disturbances caused by solar activity on MI.