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.

Esse relatório é produto do Projeto Capacidade adaptativa em perspectiva policêntrica: monitoramento, avaliação e impactos sinérgicos de Políticas Públicas de Educação Ambiental para o enfrentamento das Mudanças Climáticas, em múltiplas escalas – Projeto MonitoraEA-CIEA, selecionado pela Chamada CNPq/MCTI/FNDCT Nº 59/2022 na Linha 4 – Projetos de Conscientização e Aumento da Difusão do Conhecimento sobre as Mudanças Climáticas Globais e Avaliação do Impacto da Implementação de Políticas Públicas para Enfrentamento das Mudanças Climáticas. O Projeto é coordenado pelo Instituto Nacional de Pesquisas Espaciais – INPE e pela Articulação Nacional de Políticas Públicas de Educação Ambiental – ANPPEA, com apoio do Órgão Gestor da Política Nacional de Educação Ambiental,composto pelo Ministério do Meio Ambiente e Mudança do Clima – MMA e pelo Ministério da Educação – MEC, e apresenta como parceiros as seguintes instituições: Centro Nacional de Monitoramento e Alertas de Desastres Naturais Cemaden, Escola de Comunicações e Artes da Universidade de São Paulo – ECA/USP, Instituto do Meio Ambiente e Recursos Hídricos do Governo do Estado da Bahia – Inema, Rede Brasileira de Educação Ambiental – REBEA, Universidade de Brasília – UnB, Universidade do Vale do Itajaí – UNIVALI, Universidade Estadual do Amazonas – UEA, Universidade Estadual do Sudoeste da Bahia – UESB, Universidade Federal do Estado do Rio de Janeiro – UNIRIO, Universidade Federal do Mato Grosso do Sul – UFMS, e Universidade Federal do Pará – UFPA. O objetivo geral do Projeto é desenvolver um sistema de monitoramento e avaliação colaborativo, baseado em indicadores, para auto-avaliação das Comissões Interinstitucionais de Educação Ambiental – CIEA das 27 Unidades de Federação do Brasil; e de riscos climáticos para temas prioritários regionais, a partir da compreensão de que as Políticas Públicas de Educação Ambiental – PPEA podem representar elementos de ampliação de capacidade adaptativa nos territórios. O Projeto integra o Ecossistema MonitoraEA Sistema Brasileiro de Monitoramento e Avaliação de Políticas Públicas e Projetos de Educação Ambiental, concebido, coordenado e desenvolvido pela ANPPEA desde 2016.

Wildfire substantially affects Amazonian forest ecosystems, including a large and persistent loss of aboveground biomass. This study investigates the drivers of changes in aboveground biomass in recently burned forests in western Amazonia. We collected soil samples in eight terra firme forest plots with varying fire history (up to 19 years since the last fire and burn up to 3 times in the last 20 years) in Acre state (Brazil). The samples, up to 30 centimetres (0-5cm, 5-10cm, 10-20cm and 20-30cm), were analysed for bulk density, nitrogen and carbon stocks. These soil data, along with fire history variables, were used to evaluate their relationship with aboveground biomass, estimated by the EBA project map (Estimativa de Biomassa da Amazônia). Given the small number of samples (8) and relatively large number of covariates (number of fires, time since last fire, soil bulk density and soil stocks of nitrogen and carbon), we used ridge regression to explore relationships (coefficients presented in parenthesis below) and to minimize the effects of collinearity and overfitting, which is common in generalised linear models. Our results indicate that the number of fires (-37.33) and soil compaction (BD = -39.79) has significant negative impacts on aboveground biomass. In contrast, nitrogen has a positive effect (25.26), while time since last fire (0.86) and soil carbon (0.88) have minor influences on aboveground biomass (intercept = 54.98). Our findings are in line with previous research showing the substantial and negative impacts of recurrent fire events and soil compaction on aboveground biomass in Amazonian forests. These results suggest that twenty years is insufficient for these forests to recover and that preventing recurring fire is one of the most effective active ecosystem conservation measures in terra firme Amazonia forests. The PyroCarbon Project (FAPESP 2021/00976-4; 2023/15402-9) will further investigate these relationships at the Pan-Amazon scale.

A análise e a apresentação de dados representam etapas cruciais em pesquisas científicas. No entanto, o volume e a complexidade dos dados coletados nesse tipo de estudo frequentemente impõem desafios técnicos significativos. O processamento de grandes quantidades de dados demanda tempo, capacidade computacional e estratégias adequadas de organização e análise. Visando contribuir nesse sentido, essa pesquisa teve como finalidade criar ferramentas tecnológicas para otimizar e facilitar a análise de dados em pesquisas socioambientais. Para isso, foram realizadas duas frentes de desenvolvimento. A primeira focou em agilizar a sistematização dos resultados de aplicação de um sistema de indicadores. Estes indicadores têm como objetivo auxiliar escolas que atuam com educação do campo na visualização e análise das percepções da comunidade escolar quanto ao papel das práticas da instituição no fortalecimento da resiliência socioecológica de comunidades rurais. No modelo anterior, a visualização dos resultados de forma agregada demandava um processo manual e trabalhoso, baseado no cruzamento de dados de múltiplas planilhas, o que tornava este instrumento de gestão pouco acessível para quem não tem familiaridade com determinadas ferramentas computacionais. Para superar este obstáculo, a primeira etapa da solução consistiu na criação de um notebook Python, no ambiente Google Colab. Essa abordagem gerou ganhos importantes em agilidade. Com base nessa experiência inicial, o programa evoluiu para uma plataforma web autônoma, na qual as instituições podem carregar os dados coletados e gerar um painel de visualizações interativas com possibilidade de edição e personalização dos gráficos, além da geração de um relatório para impressão e compartilhamento, que compila as visualizações e dá orientações para um processo de análise dos resultados junto à comunidade escolar. Essa transformação facilita a análise dos resultados, promove maior autonomia das escolas no monitoramento dos indicadores e estimula o aprimoramento de suas práticas pedagógicas. Paralelamente, a segunda frente foi concebida para suprir as limitações dos softwares para análise de dados qualitativos. Softwares comerciais possuem custos elevados, enquanto alternativas gratuitas carecem de funcionalidades como transcrição automática integrada e buscas que capturem o significado contextual. Como solução, foi desenvolvido um notebook interativo em Python, estruturado com elementos de interface para dispensar a interação direta com o código. A ferramenta integra o modelo Whisper para transcrição de áudios e vídeos e emprega embeddings representações numéricas de palavras e frases para permitir uma análise que leve em consideração o contexto semântico. Dessa forma, a ferramenta oferece uma gama de análises, desde métodos exploratórios até um sistema de busca interativa por termos-chave. Adicionalmente, por ser uma solução de código aberto, a ferramenta não se prende à rigidez de softwares comerciais, podendo ser constantemente expandida e adaptada pela comunidade de pesquisadores para atender a novas necessidades de análise. As soluções desenvolvidas em ambas as frentes entregam ferramentas que mitigam barreiras relacionadas ao custo, tempo e complexidade técnica. Assim, elas oferecem um meio para alcançar uma compreensão mais ágil, aprofundada e acessível de dados socioambientais, tanto na gestão dos indicadores pelas escolas, quanto na análise de conteúdo em pesquisas qualitativas.

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 risk of fire occurrence and the frequency of extreme fire events have been increasing globally due to climate change. As a result, greenhouse gas (GHG) emissions are higher worldwide, including in the Brazilian Cerrado. The Cerrado is a fire-prone biome in central Brazil, where fire is essential for maintaining the biomes diversity and integrity. The Cerrado presents distinct rainy and dry seasons. In the dry season, the accumulated biomass available for burning becomes highly flammable and fire can rapidly spread from grasslands and savannas to forests. In fire-prone ecosystems globally, prescribed burning prevents intense and frequent wildfires in the drier months by intentionally applying fire under controlled conditions at the end of the rainy season or the beginning of the dry season. In the Cerrado, prescribed burning is applied in the early dry season (EDS; April-June) to avoid intense wildfires in the late dry season (LDS; August-October), but so far there are few estimates of the effect of prescribed burning on carbon emissions. In this thesis, we evaluate the impacts of prescribed burning on burned area and fire carbon emissions in the Brazilian Cerrado. We modelled burned area and fire emissions in the Cerrado with JULES-INFERNO over a 30-year period (1990-2019), using the ISIMIP3a simulations. We adjust JULES-INFERNO to represent the Cerrado, and then simulate prescribed burning by setting an additional ignition to C4 grass during EDS. The prescribed burning additional set-up can be adapted to other parts of the world. To our knowledge, this is the first time that prescribed burning is included in a global fire model, and that a global model is adjusted to better represent the Cerrados fire emissions. Over the 30 years, prescribed burning applied in the EDS reduced burned area and fire emissions in the LDS by 9.28% year-1 and 11.24% year-1, respectively, when compared to the non-prescribed burning scenario. In years with intense fire events, the reductions are higher than the average. Our results are not effective on the annual balance, due to the high increase in burned area and fire emissions in the EDS to provoke reductions in the LDS. This indicates that prescribed burning can be used in the Cerrado to reduce the impacts of uncontrolled fires in the drier months. We also observe that the effectiveness of prescribed burning in reducing burned area and emissions depends on the C4 grass recovery rate in the JULES-INFERNO model. Prospective studies are recommended to improve the understanding of the models performance, including analysis of modelled parameters such as the C4 grass post-fire recovery, and the integration between observational and modelled data.

Uma das maneiras que a água pode ser conservada na atmosfera é pelo fluxo de umidade, o qual pode ser integrado verticalmente em toda a coluna atmosférica. Com o aumento da umidade na baixa troposfera devido ao aquecimento global ocorre uma intensificação do transporte horizontal de vapor na atmosfera e amplificação dos padrões de diferença entre a precipitação e a evaporação. Segundo o último relatório do IPCC em 2023, as emissões antropogênicas dos gases do efeito estufa aumentaram nas últimas décadas e as projeções climáticas indicam que a temperatura global continuará aumentando no futuro próximo. Em particular, o aquecimento diferenciado dos oceanos tropicais pode amplificar o transporte de umidade e complicar os processos de interação entre bacias oceânicas. Portanto, compreender o aumento da disponibilidade da umidade atmosférica e principalmente seu transporte entre bacias se torna fundamental para entender e explicar possíveis impactos nos padrões de precipitação no clima presente e no futuro. O objetivo da presente tese é investigar o fluxo de umidade entre o Oceano Índico e Atlântico, e suas implicações para o Oceano Atlântico tropical-sul e América do Sul. Foram utilizados dados de reanálise do ERA5 para as variáveis atmosféricas, e dados do ORAS5 para salinidade e temperatura da superfície do mar, compreendendo o período de 1959 a 2020. Foram realizados os cálculos de climatologias e anomalias sazonais, tendências ao longo do período estudado, correlações, regressões, e análise de compostos representativos de eventos extremos. Os resultados climatológicos foram coerentes com a literatura, e os somatórios do fluxo de umidade entre os segmentos identificados ao longo das bacias oceânicas identificaram valores significativos para alguns segmentos, principalmente para o segmento chave, entre o Índico e o Atlântico, sobre a África. O transporte de umidade neste segmento se mostrou intenso para todas as estações do ano, exceto DJF, e com tendência de aumento ao longo dos anos. As bacias do Atlântico e do Índico especificamente também mostraram uma tendência de aumento na exportação de umidade ao longo dos anos. Análise das correlações e dos compostos entre as variáveis se mostraram significativas em regiões com alta variabilidade de precipitação, como por exemplo, sobre a Zona de Convergência Intertropical e monção da América do Sul. E a região identificada para a análise dos extremos de precipitação na região norte do Brasil e oceano adjacente e a região sul do Brasil se mostraram influenciadas pela temperatura da superfície do mar do Atlântico e do Índico, e principalmente, pelo fluxo de umidade vindo do Oceano Índico. Finalmente, o estudo de caso validou todas as análises apresentadas neste trabalho e, pela primeira vez, demonstrou que o fluxo de umidade que atravessa a África pode contribuir para a precipitação em regiões específicas da América do Sul.

The chapter examines the forms and possibilities of progressive politics in the context of climate change. Environmental movement actors have often advanced mainstream green progressive discourse in political and media spheres. At the same time, grassroots actors and more radical versions of green progressive discourse and actions have a harder time gaining traction in policy networks and achieving mass media visibility. We provide an overview of the emergence and globalization of the environmental coalition regarding climate change. Then, drawing on the cases of India, Finland, Brazil, and Canada, we examine the diverse politics of actors that make up the environmental coalition. Our analysis illustrates how progressive coalitions and politics emerge in varied political and cultural contexts, how these alignments are shaped by the global climate regime and critical events, and how and why these alignments gain or lose strength over time.

The Brazilian Forest Code plays a crucial role in balancing environmental conservation and agricultural development. Recent debates challenge its implementation, particularly concerning the legal reserve areas. This study evaluates the potential impact of a legal precedent that could redefine property size classifications by disregarding legal reserve in the calculation of fiscal modules. Using geospatial analysis of Rural Environmental Registry data from the Amazon biome, we assess how this change would alter land classification and its implications. Our findings indicate a significant increase in small properties, potentially granting more properties access to policy benefits designed for smallholders. Such a shift could create legal uncertainties, affecting land tenure regularization, environmental policies, and agricultural financing. This study highlights the need for a careful, evidence-based assessment of legislative changes to ensure sustainable land management and regulatory stability in Brazil.

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.