Estudo sobre águas residuárias do agronegócio brasileiro: composição, caracterização físico-química, produção volumétrica e recuperação de recursos

Brazil is a significant producer of agricultural and agro-industrial waste, which can be used to recover valuable resources, such as struvite, hydroxyapatite, methane gas, hydrogen gas, and carboxylic acids, to mitigate the environmental impacts of the agro-industrial sector, add economic value to organic waste, and promote the sustainability of natural resources. Thus, this work’s objective was to compile and analyze data on the composition, physical-chemical characterization, and volumetric production of six agricultural and agro-industrial wastewaters (AWWs) from activities of paramount importance in Brazilian agribusiness and to report studies on resource recovery from those liquid wastes. The literature review was carried out by analyzing scientific works obtained by searching for keywords in different databases. It was concluded that swine wastewaters (SWs), slaughterhouse wastewaters (SHWs), and dairy wastewaters (DWs) are the most promising for struvite recovery. DWs also stand out for the recovery of hydroxyapatite. SWs and brewery wastewaters (BWs) are commonly used for prospecting for algae or bacterial biomass and their derivative products. All AWWs analyzed are considered promising for biogas, methane and hydrogen, while the most soluble AWWs are more valuable for carboxylic acid production.  O Brasil é um grande produtor de resíduos agrícolas e agroindustriais, os quais podem ser utilizados para a recuperação de recursos valiosos, como a estruvita, a hidroxiapatita, o gás metano, o gás hidrogênio e os ácidos carboxílicos, visando mitigar os impactos ambientais do setor agroindustrial, agregar valor econômico aos resíduos orgânicos e promover a sustentabilidade dos recursos naturais. Assim, o objetivo deste trabalho foi compilar e analisar dados de composição, de caracterização físicoquímica e de produção volumétrica de seis águas residuárias agrícolas e agroindustriais (ARA) provenientes de atividades de suma importância ao agronegócio brasileiro e reportar estudos sobre recuperação de recursos a partir desses resíduos líquidos. A revisão de literatura foi elaborada por meio da análise de trabalhos científicos obtidos mediante à busca de palavras-chave em diferentes bancos de dados. Concluiu-se que as águas residuárias da criação de suínos (ARCS), as águas residuárias de abate bovino (ARAB) e as águas residuárias do beneficiamento de leite (ARBL) são as mais promissoras para a recuperação de estruvita. As ARBL também se destacam para a recuperação de hidroxiapatita. As ARCS e as águas residuárias da produção de cerveja (ARPC) são comumente utilizadas para a prospecção de biomassa algácea ou bacteriana e seus produtos derivados. Todas as ARA analisadas são adequadas para a prospecção de biogás, metano e hidrogênio, enquanto as ARA mais solúveis são as mais promissoras para a produção de ácidos carboxílicos. &nbsp

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

Growing knowledge: an overview of Seed Plant diversity in Brazil

Abstract An updated inventory of Brazilian seed plants is presented and offers important insights into the country's biodiversity. This work started in 2010, with the publication of the Plants and Fungi Catalogue, and has been updated since by more than 430 specialists working online. Brazil is home to 32,086 native Angiosperms and 23 native Gymnosperms, showing an increase of 3% in its species richness in relation to 2010. The Amazon Rainforest is the richest Brazilian biome for Gymnosperms, while the Atlantic Rainforest is the richest one for Angiosperms. There was a considerable increment in the number of species and endemism rates for biomes, except for the Amazon that showed a decrease of 2.5% of recorded endemics. However, well over half of Brazillian seed plant species (57.4%) is endemic to this territory. The proportion of life-forms varies among different biomes: trees are more expressive in the Amazon and Atlantic Rainforest biomes while herbs predominate in the Pampa, and lianas are more expressive in the Amazon, Atlantic Rainforest, and Pantanal. This compilation serves not only to quantify Brazilian biodiversity, but also to highlight areas where there information is lacking and to provide a framework for the challenge faced in conserving Brazil's unique and diverse flora