Reúso industrial de efluentes petroquímicos: Um estudo de caso da ultrafiltração e osmose reversa

The petrochemical industry uses high volumes of water in its production processes and generates effluents that have a great potential for reuse in production processes. The reuse of these effluents is, therefore, an alternative for the sustainable development of the sector. This study aimed at evaluating the application of ultrafiltration (UF) and reverse osmosis (RO) in the treatment of petrochemical effluents for the production of industrial reuse water, reducing environmental impacts caused by the disposal of effluents in the soil. The experiments were carried out with effluents named waste stabilization pond 1 (WSP-1), waste stabilization pond 8 (WSP-8) and an inorganic effluent (INO), which was used as feedwater in the pilot unit with a treatment capacity of 1 m³.h–1. The parameters chosen for evaluation in the treated effluents were calcium, magnesium, chloride, sulfate, electrical conductivity (EC), total organic carbon, color, chemical oxygen demand (COD), pH, total suspended solids and turbidity. Membrane permeate fluxes were determined to evaluate the performance of the pilot system. After treatment and characterization of each effluent, the results were compared to define the most suitable effluent to achieve the quality required for industrial reuse. The results showed that the proposed UF/RO treatment provided a stable flux for the WSP-8 effluent. Conversely, the other streams showed an accentuated decrease in flux, which indicates fouling processes of the UF and RO membranes. As for the efficiency of the treatment, the process removed compounds of interest such as COD above 90%, salts and EC above 92% for the three assessed effluents. Thus, considering all aspects evaluated in this study, WSP-8 was the most suitable to be used as feed in the pilot system with UF and RO. The permeate produced presented the necessary quality for reuse in the industries of the Southern Petrochemical Complex, presenting equivalent characteristics to those of clarified water. Thus, the reuse of treated petrochemical effluents may be an important alternative source of water resources in face of availability and scarcity restrictions in industries in southern Brazil.A indústria petroquímica utiliza elevados volumes de água em seus processos produtivos e geram efluentes que apresentam grande potencial para a reutilização nos processos produtivos. Neste cenário o reúso destes efluentes é uma alternativa para o desenvolvimento sustentável do setor. Portanto, este estudo teve como objetivo avaliar a aplicação da ultrafiltração (UF) e osmose reversa (OR) no tratamento dos efluentes petroquímicos para produção de água de reúso industrial reduzindo impactos ambientais com a disposição de efluentes no solo. Os experimentos foram realizados com os efluentes da Lagoa 1 (LE-1), efluente da Lagoa 8 (LE-8) e efluente inorgânico (INO), os quais foram utilizados como água de alimentação na unidade piloto com capacidade de tratamento de 1 m³.h–1. Os parâmetros avaliados nos efluentes tratados foram o cálcio, magnésio, cloreto, sulfato, condutividade, carbono orgânico total, cor, demanda química de oxigênio, pH, sólidos suspensos totais e turbidez. Foram determinados os fluxos dos permeados das membranas para avaliar o desempenho do sistema piloto. Após tratamento e caracterização de cada efluente, os resultados foram comparados para a definição do efluente mais adequado para alcançar a qualidade requerida para reúso industrial. Os resultados mostraram que o tratamento UF/OR proposto forneceu um fluxo estável para o efluente da LE-8, e para as demais correntes houve queda acentuada de fluxo que indicam processos de incrustação das membranas de UF e OR. Quanto à eficiência do tratamento, o processo apresentou a remoção de compostos de interesse como a demanda química de oxigênio (DQO) acima de 90%, remoção de sais e condutividade elétrica (CE) acima de 92% para os efluentes da Lagoa 1, efluente da Lagoa 8 e efluente inorgânico. Assim, considerando todos os aspectos avaliados neste estudo, o efluente LE-8 foi o mais adequado para ser utilizado como alimentação no sistema piloto com UF e OR, de tal modo, que o permeado produzido apresentou a qualidade necessária para reúso nas indústrias do Polo Petroquímico do Sul, atingindo qualidade equivalente à água clarificada. Desta forma, o reúso de efluentes petroquímicos tratados poderá ser uma importante fonte alternativa de recursos hídricos frente às restrições de disponibilidade e escassez nas indústrias no Sul do Brasil

Linguagem: primeira infância x implicações da otite média / Language: early childhood x implications of otite media

O artigo trata-se de um estudo que mostra o comprometimento da audição decorrente da otite média interferindo na linguagem na primeira infância. Destacamos, tendo por base um referencial que aborda de forma ampliada o problema e suas implicações na aprendizagem do indivíduo, justificando a realização deste trabalho. O objetivo geral tem como base oportunizar um aprofundamento na doença otológica, e os objetivos específicos, observar as consequências que a privação auditiva altera na aquisição da linguagem e da fala em crianças na fase pré-escolar e ressaltar a importância da avaliação auditiva precocemente. A metodologia trata-se de uma revisão bibliográfica, onde diversos autores renomados foram pesquisados em sites, livros, artigos e revistas acadêmicas. Concluímos que, esta doença otológica é considerada na população pediátrica de maior prevalência e sem a devida atenção e tratamento das áreas de saúde e afins poderá complicar-se levando a criança a surdez e o seu comprometimento com a aprendizagem, sociabilidade e comunicação. Isto serve de alerta para reforçarmos a importância de realizar precocemente a avaliação auditiva

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