RESUMO
The Brazilian coast is rich in monazite which is found in beach sand deposits. In this study, the composition of the monazite sands from beaches of State of Espírito Santo, Brazil, was investigated. The concentrations of rare earth elements (REEs), Th, and U were determined by inductively coupled plasma mass spectrometry (ICP-MS). In the studied region, the mean concentration of investigated elements increased in the following order: Tm < Yb < Ho < Lu < Eu < Er < Tb < Dy < U < Y < Th < Gd < Sm < Pr < Nd < La < Ce. The sampling sites were classified into three clusters and discriminated by the concentrations of REEs, Th, and U found. In general, the radiological risk indices were higher than the established limits, and the risk of developing cancer was estimated to be higher than the world average.
Assuntos
Metais Terras Raras , Areia , Metais Terras Raras/análise , Medição de Risco , BrasilRESUMO
In the present study the distribution of chemical elements in beaches adjacent to the Doce River mouth hit by the tailings mud from a mining accident were assessed. Sedimentological and morphological coastal aspects were also considered. The results indicate that wave-exposed delta plain beaches exhibit high resiliency, despite their proximity to potential pollution sources. On the other hand, shore platform beaches tend to accumulate chemical elements, mainly due to limited cross-shore sediment exchanges. Arsenic concentrations in the evaluated shore platform beaches were significantly higher than the delta plain beach. Shore platform beaches are more susceptible to frequent flooding and to higher elemental concentrations at the berm and beach face. Thus, the morphological characteristics of the assessed shore platform beaches, and input from the mud plume must be considered in a joint assessment strategy in order to obtain a broad understanding of the actual scenario regarding beach contamination.
Assuntos
Praias , Monitoramento Ambiental , Acidentes , Geologia , MineraçãoRESUMO
Every day, new applications arise relying on the use of high-resolution road maps in both academic and industrial environments. Autonomous vehicles rely on digital maps to navigate when optical sensors cannot be trusted, such as heavy rainfalls, snowy conditions, fog, and other situations. These situations increase the risks of accidents and disable the potentials of real-time mapping sensors. To tackle those problems, we present a methodology to automatically map anomalies on the road, namely speed bumps in this study, using an off-the-shelf camera (GoPro) and Machine Learning (ML) algorithms. We acquired data over a series of differently shaped speed bumps and applied three classification techniques: Naive Bayes, Multi-Layer Perceptron, and Random Forest (RF). With over 96% of classification accuracy, then RF was able to identify speed bumps on a GoPro dataset automatically. The results show a potential of the proposed methodology to be developed in surveying vehicles to produce highly-detailed maps of vertical road anomalies with a fast and accurate update rate.