Advances in the Application of Spectroscopic Techniques in the Biofuel Area over the Last Few Decades

Guided by the instability of the oil market, as well as limited availability of and, especially, the environmental impacts of fossil fuels, the needs of the market for environmental-friendly energy sources have increased. However, as with any other product that is intended to place on the market, it is essential to ensure the quality of the fuel for successful marketing and acceptance by consumers. Spectroscopic techniques have been widely used for different purposes in the literature for the past decades, from biological applications to the measurement of the elemental composition of planets. From studies focused on biodiesel, bioethanol, biomass and biofuel in general, different spectroscopic techniques have also been applied in the area. The focus of this chapter is to elucidate what has been published in the last few decades over the subject, detailing the basic concepts of the main spectroscopic techniques applied and showing the results and developments over biofuel. The aim of the chapter is to achieve a set of information that can be used as a bigger compile of information of the state of the art regarding the theme

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

A simple method based on the application of a ccd camera as a sensor to detect low concentrations of barium sulfate in suspension

12 p. : il.The development of a simple, rapid and low cost method based on video image analysis and aimed at the detection of low concentrations of precipitated barium sulfate is described. The proposed system is basically composed of a webcam with a CCD sensor and a conventional dichroic lamp. For this purpose, software for processing and analyzing the digital images based on the RGB (Red, Green and Blue) color system was developed. The proposed method had shown very good repeatability and linearity and also presented higher sensitivity than the standard turbidimetric method. The developed method is presented as a simple alternative for future applications in the study of precipitations of inorganic salts and also for detecting the crystallization of organic compounds

Why Do Agroforestry Systems Enhance Biodiversity? Evidence From Habitat Amount Hypothesis Predictions

Considering the present ecological crisis, land use-biodiversity relationships have become a major topic in landscape planning, ecosystem management and ecological restoration. In this scope, consistent patterns of outstanding biodiversity have been identified in agroforestry systems within diverse biogeographic regions and types of management. Empirical work has revealed that agroforestry higher structural complexity, when compared with current simplified agricultural systems, might be partially responsible for the observed patterns. The recently developed Habitat Amount Hypothesis predicts diversity for a local habitat patch, from the amount of the same habitat within the local landscape. We have expanded the previous hypothesis to the landscape level, computing the influence of the dominant land uses on the diversity of coexisting guilds. As a case study, we have considered archetypal landscapes dominated (or co-dominated) by crops or trees, which were compared using normalized diversities. The results obtained show that agroforestry systems substantially increase functional diversity and overall biodiversity within landscapes. We highlight that the normalized values should be parametrized to real conditions where the type of crop, tree and agroecological management will make a difference. Most importantly, our findings provide additional evidence that agroforestry has a critical role in enhancing biodiversity in agricultural landscapes and, in this way, should be regarded as a priority measure in European Agri-environmental funding schemes