Classification of Urban Solid Waste Collected with the Use of Ecobarriers in Watercourses in the Municipality of Caçapava do Sul, RS

Urban solid waste is a serious problem in cities when disposed in inappropriate places or when there is a deficiency in its collection, which can cause several environmental problems. In periods of rain, these problems become more evident when these residues are transported to drainage networks and water courses, accumulating and creating obstacles to the flow, causing floods, floods, etc. In this regard, this work aimed to collect and classify urban solid waste in two water courses in the municipality of Caçapava do Sul, an ecological barrier made with recyclable materials called Ecobarrier, placed across water courses in order to retain floating waste that is transported by water. As a result, the vast majority of residues retained in the eco-barriers correspond to the category of organic matter, this is due to the vegetation around the water bodies studied, followed by the plastic, metal and textile category respectively. Thus, it can be seen that the presence of floating garbage in the water courses of the municipality partially reflects the lack of concern on the part of the population and governments with its effects on human and environmental health. It is in this context, to avoid the generation of floating waste, that the integration of public policies can play a fundamental role, with the help of environmental education

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Energy for Bahia in 2030

The publication "Energy for Bahia in 2030" was prepared by the Academy of Sciences of Bahia (ACB). It is part of a set of eight publications called "Science, Technology and Innovation for the Development of Bahia", whose objective is the dissemination of a series of scientific articles based on studies and research carried out by some renowned Bahian academics, professors and researchers, with a view to addressing priority issues for the development of Bahia in different aspects. The publication "Energy for Bahia in 2030" presents information on wind, solar, biomass, hydroelectric, fossil, nuclear, among others. It analyzes strategic issues related to S&T research and the training of human resources, in addition to pointing out the main limitations and challenges of project development in Bahia. The document also provides an assessment of trends in the energy scenario and proposals that should be useful in guiding public policies for the State of Bahia.A publicação "Energia para a Bahia em 2030" foi elaborada pela Academia de Ciências da Bahia (ACB). Ela faz parte de um conjunto de oito publicações denominado "Ciência ,Tecnologia e Inovação para o Desenvolvimento da Bahia", cujo objetivo é a divulgação de uma série de artigos científicos baseados em estudos e pesquisas realizados por alguns acadêmicos, professores e pesquisadores baianos renomados, com vistas a abordar questões prioritárias para o desenvolvimento da Bahia em diferentes aspectos. A publicação "Energia para a Bahia em 2030" apresenta informações sobre as energias eólica, solar, da biomassa, hidrelétrica, fóssil, nuclear entre outras. Ela analisa questões estratégicas ligadas à pesquisa em C&T e à formação de recursos humanos, além de apontar as principais limitações e desafios do desenvolvimento de projetos na Bahia. O documento traz, ainda, uma avaliação das tendências do cenário energético e propostas que devem ser úteis na orientação de políticas públicas para o Estado da Bahia

Energia para a Bahia em 2030

<p>Em uma era cada vez mais digital e com população crescente, é mais do que claro que a demanda energética brasileira tem tendência de aumento. E, com a urgência de mitigar a crise ambiental, as fontes renováveis de energia ganham mais relevância. Com grande contribuição à matriz energética nacional, a atual produção de energia da Bahia tem forte predominância de fontes não renováveis (66,8%), com grande con- centração em derivados de petróleo. As fontes renováveis representam apenas 19,5%, e o restante é composto por outras fontes fósseis — estas tiveram crescimento signifi- cativo entre 1999 e 2015, principalmente por conta do gás natural e do petróleo e seus derivados.</p&gt