Performance of polycarbonate, cellulose nitrate and polyethersulfone filtering membranes for culture-independent microbiota analysis of clean waters

Demineralized and disinfected waters may have very low microbial loads, requiring that large volumes of water are filtered to recover enough biomass for further analysis. Extended filtration periods, often interrupted by clogging, are a major limiting factor to concentrate samples' microbiota for further examination, besides hindering the work pace. In this study, we investigated the performance of three types of filtering membranes - polycarbonate (PC), cellulose nitrate (CN), and polyethersulfone (PES) with 0.22 μm pore size for culture-independent microbiological analysis (quantitative PCR of seven housekeeping and integrase genes) of tap water, recirculating tap water in a bottle washing loop, and of demineralized water. Compared to PC membranes, CN or PES required lower filtration periods, although had slightly lower DNA extraction yields. However, genes abundance per volume of water was, in general, not significantly different. The exception was observed for bottle washing water in which PC membranes supported significantly higher quantification values than PES membranes. These differences were lower than ∼0.5 log-units and did not hamper the distinction of the types of water based on genes profile. Also, the type of membrane did not significantly affect the profile of the bacterial community determined for tap and demineralized water. A major conclusion is that CN membranes, cheaper, allowing shorter filtration periods, and producing results that are not significantly different from those obtained with PC or PES, can be a good alternative to analyze waters with low biomass loads.info: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 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

Monitorization and optimization of a SBR in organic matter and nitrogen removals, with step-feed and a real-time control perspective.

Dissertação de Mestrado Integrado em Engenharia Química apresentada à Faculdade de Ciências e TecnologiaA indústria vinícola tem, cultural e economicamente, uma importância significativa em Portugal. Este trabalho, realizado no âmbito de um estágio curricular na empresa Adventech, Lda, dedicou-se à otimização do tratamento de efluente vinícola em reatores descontínuos sequenciais (RDS). Teve como objetivos melhorar as remoções de azoto e CQO, numa perspetiva de controlo em tempo real (CTR), fazendo a monitorização de parâmetros indiretos, modificando o esquema do ciclo reacional e aplicado alimentação faseada. A perspetiva de CTR foi investigada no âmbito da redução do tempo de ciclo RDS e, consequentemente, da redução dos custos com arejamento. Para tal, usaram-se as sondas de pH, potencial oxidação-redução e oxigénio dissolvido (OD), com o objetivo de pesquisar pontos de controlo relativos às reações biológicas do azoto e do carência química de oxigénio (CQO), nomeadamente o ponto do vale da amónia, o ponto de quebra do OD, o ponto de pico do nitrato e o ponto de joelho do nitrato. Foram utilizados dois RDS de 30 L, e foram monitorizadas as concentrações de CQO, azoto total, amónia e nitratos. As variáveis de teste foram a duração de cada fase aeróbia e anóxica e a aplicação de alimentação faseada.Apenas 3 pontos de controlo relacionados com as reações biológicas do azoto foram identificados, o que inviabiliza a aplicação de CTR no âmbito deste nutriente. No entanto, foram facilmente identificáveis os pontos de degradação da carga orgânica, viabilizando CTR em relação à remoção de CQO. Foi observado que para efluentes com menor CQO e menor razão carbono/azoto existiram problemas na remoção de azoto, não sendo estes verificados em efluentes sem estas características. Concluiu-se que o aumento de CQO do efluente inicial levou a melhorias na remoção de azoto e CQO. O aumento da duração das fases aeróbias e anóxicas teve um impacto positivo nas remoções de azoto e CQO no efluente estudado. O desenvolvimento deste ponto do estudo é uma proposta de trabalho futuro, aconselhando-se o uso de efluente de vindima e também de efluente vinícola com razão C/N mais baixa. A aplicação de alimentação faseada teve um impacto negativo nas remoções de CQO e azoto, esta última com uma queda de 20 pontos percentuais em relação ao reator de controlo, obtendo-se efluentes que violaram os parâmetros de descarga. Estimou-se uma redução global do tempo de arejamento de 56 % mediante a aplicação de CTR. O sensor mais fiável aparentou ser o de oxigénio dissolvido, mas a melhor estratégia de controlo seria o uso combinado dos sensores de potencial oxidação-redução e oxigénio dissolvido. O desenvolvimento e teste de um sistema de controlo são também proposta de trabalho futuro. Sugere-se ainda a realização do estudo apresentado com efluentes diferentes, com maiores quantidades de azoto e razões C/N muito mais baixas.Wine industry has a significant impact in Portugal's economy and culture. The present work, which was part of a curricular internship at Adventech, focus winery wastewater treatment in sequential batch reactors (SBR). It had the objective of enhancing nitrogen and COD removal, in a real-time control (RTC) perspective. The RTC strategy was investigated with the aim of reducing cycle time, thus reducing aeration costs. For that, indirect parameter probes (oxidation-reduction potential - ORP -, pH and dissolved oxygen - DO) were used with the aim of identifying control points relative to nitrogen and chemical oxygen demand (COD) removals, such as ammonia valley point, DO break point, nitrate apex point or nitrate knee point.Two 30 liter SBR were used, and COD, total nitrogen, ammonia and nitrate concentrations were monitored. The test variables were the duration of each aerobic/anoxic phase and the use of a step-feed approach. Only 3 nitrogen control points were found in all the study, invalidating the possibility for RTC on nitrogen removal. Nevertheless, COD removal points were easily identified, allowing RTC on this parameter removal. It was revealed that for wastewaters with less COD and lower carbon/nitrogen ratios nitrogen removal was troubled, not being that verified for wastewaters without those characteristics. Initial COD increase led to bigger nitrogen and COD removals. The results revealed that increasing the duration of the aerobic and anoxic phases possibly allows a more efficient nitrogen removal, as well as COD removal, on the analysed wastewater. Further study is proposed on this matter, using harvest wastewater and also winery wastewater with lower C/N ratio. The use of step-feed decreased both nitrogen and COD removal, with 20 percentual points less removal on nitrogen basis, obtaining effluents inadequate for discharge. The results showed strong evidences that CTR based on COD removal is possible by tracking DO breakthrough point, reducing global aeration time in 56 %. The most reliable probe would be DO, but the best strategy would be the combined use of ORP and DO probes. The development and testing of a control system are a further study proposal. In this matter, is also recommended the study of the influence of all the referred variables in different wastewaters with higher nitrogen concentrations and much lower C/N ratio