Estudo físico-químico, tóxico e microbiológico associados à biorremediação nas águas do Riacho Reginaldo em Maceió / Physicochemical, toxic and microbiological study associated with bioremediation in Riacho Reginaldo waters in Maceió

Nos últimos anos, Maceió tem evidenciado um crescimento populacional muito acelerado que acarretou na formação de aglomerados em grandes partes da cidade e a maioria desses sofrem por falta de infraestrutura local, incluindo a falta esgotamento sanitário. A carência desse atendimento em boa parte da população levou alguns riachos urbanos a exercerem um papel de esgotos. O riacho Reginaldo ou popularmente chamado de Salgadinho nem sempre foi poluído como hoje em dia, mas acabou se tornando um esgotamento sanitário devido à falta de saneamento de muitos bairros. Neste artigo iremos relatar os resultados das análises físico-químicas (pH, temperatura, acidez, sólidos dissolvidos e cloreto), microbiológica (coliformes totais e fecais) e tóxica (cádmio, chumbo e mercúrio) que foram realizadas em três escolhidos estrategicamente. Com os resultados das análises e de acordo com a literatura, serão pesquisadas plantas e bactérias que se adequeriam de forma comensal a situação atual do riacho, ajudando na biorremediação do mesmo. 

Impactos sócio-ambientais da atividade hoteleira na orla urbana de Maceió / Social and environmental impacts of hotel activity in the urban water of Maceió

Este artigo analisou os Impactos advindos da atividade hoteleira com influências no Meio Ambiente, mais precisamente no espaço compreendido entre as praias de Ponta Verde e Cruz das Almas – Maceió/AL. A metodologia utilizada foi de um estudo transversal descritivo, com abordagem quali-qualitativa, a natureza da investigação, a abordagem foi do tipo não probabilística por conveniência, com aplicações de questionários nos meios de hospedagem que aceitaram participar da pesquisa de forma voluntária. Os meios de hospedagens exercem grande influência sobre a população autóctone e turística, sendo responsáveis por inúmeros aspectos positivos e negativos, onde o tratamento dispensado ao destino dos resíduos sólidos e líquidos foram objetos de pesquisa, bem como a Identificação das formas de descartes dos resíduos sólidos e líquidos, o grau de interesse dos turistas pela destinação dos resíduos produzidos pelos meios de hospedagens, Identificação das ações de preservação, conservação e sustentabilidade ambiental, desenvolvimento de palestras proferidas ao trade hoteleiro com objetivo de contribuir para um maior esclarecimento e divulgação das formas adequadas de consumo, tratamento e descarte de resíduos oriundos das atividades desenvolvidas pelos meios de hospedagens, com a elaboração de uma cartilha ilustrativa com informações relevantes ao tema pesquisado e classificar os principais tipos de resíduos líquidos e sólidos. 

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