Ocean and Coastal ResearchPockmarks are circular or elliptical structures formed at the seabed by the expulsion of gas from the subsurface. They are widely distributed along the continental margin off southeastern Brazil and can be over a kilometer wide and 100 meters deep. However, studies concerning the organic characteristics of these pockmark areas are scarce. This study sought to evaluate the organic composition of the sedimentary matter in pockmark areas located in the continental slope region of the southern Brazilian coast. Hydrocarbons, sterols, long-chain alcohols, stable isotopes of C and N, total organic carbon, and total nitrogen were assessed to provide an organic molecular characterization of the pockmarks located in the Southwestern Atlantic Ocean. These compounds did not reflect the organic characteristics of the scape of fluids that generate pockmark structures.
Esta nota apresenta a validação de um método para realizar a determinação de lítio emconcentrações menores do que 40 μg L‑1 em amostras de águas de abastecimento público, utilizando‑se cromatografia de íons e calibração externa, com a curva analítica obtida por regressão linear(mínimos quadrados ordinários). O método é seletivo, e apresenta limite de detecção igual a 1,0 μg L‑1e limite de quantificação igual a 2,0 μg L‑1. Os ensaios de recuperação em três níveis de concentraçãoapresentaram resultados entre 99,4 e 101,9%. Na avaliação da precisão nos mesmos três níveis deconcentração, os coeficientes de variação exibiram valores entre 1,1 e 4,0%.
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%.
The continental margin off the southeastern Brazilian coast is punctuated by a series of geological-geomorphological features, such as subsurface saline diapirs and pockmarks at the seafloor interface, which evidence the abundant presence of oil and gas in the region. In several of these sites, hydrocarbons can be naturally released into the water column, areas are cold seep areas. These are marked by the presence of oil- and gas-dependent ecosystems, where specific organisms are able to fix carbon from hydrocarbon chemosynthesis. In addition, light hydrocarbon fluid flow through the sediment may build up authigenic carbonates that can be further colonized by cold-water corals, generating large carbonate mounds over geological time, normally positioned at the border of these pockmark features. The present work reports on a multidisciplinary oceanographic cruise carried out in the Santos Basin, SW Atlantic, to seek, map, and collect geological, chemical, and biological data from different deep-sea habitats. The cruise occurred in November 2019 on the R/V Alpha Crucis of the Oceanographic Institute of the University of São Paulo (IOUSP). We intended to discover and detail different geomorphological features, characterize free-living and symbiotic microorganisms, determine the chemosynthetic rates in relation to heterotrophic microbial production, and characterize the fauna and study their ecological and evolutionary links within and across ocean basins. All discoveries made during the cruise and their respective results will be presented separately in several papers that comprise this special volume.
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