The Santa Filomena meteorite shower: Trajectory,classification, and opaque phases as indicators of metamorphic conditions

On August 19, 2020, at 13:18—UTC, a meteor event ended as a meteorite shower in Santa Filomena, a city in the Pernambuco State, northeast Brazil. The heliocentric orbital parameters resulting from images by cameras of the weather broadcasting system were semimajor axis a = 2.1 ± 0.1 au, eccentricity e = 0.55 ± 0.03, and inclination i = 0.15^o ± 0.05. The data identified the body as an Apollo object, an Earth-crossing object with a pericenter interior to the Earth's orbit. The chemical, mineralogical, and petrological evaluations, as well as the physical analysis, followed several traditional techniques. The meteorite was identified as a H5-6 S4 W0 ordinary chondrite genomict breccia. The large amount of metal in the meteorite made a metallographic evaluation based on the opaque phases possible. The monocrystalline kamacite crystals suggest a higher petrological type and the distorted Neumann lines imply at least two different shock events. The absence of the plessite phase shows that the meteorite did not reach the highest shock levels S5 and S6. The well-defined polycrystalline taenite is indicative of petrologic types 4 and 5 due to the conserved internal tetrataenite rim at the boundaries. The presence of polycrystalline taenites and the characteristics of the Agrell Effect suggest that the Santa Filomena meteorite did not reheat above 700°C. The absence of martensite confirms reheating temperatures <800°C and a slow cooling rate. The Ni contents and sizes of the zoned taenite particles indicate a slow cooling rate ranging from 1 to 10 K Myr⁻¹.     

Object-based image analysis supported by data mining to discriminate large areas of soybean

This research aimed to analyze the possibility to estimate and automatically map large areas of soybean cultivation through the use of MODIS (Moderate-Resolution Imaging Spectroradiometer) images. Two major techniques were used: GEOgraphic-Object-Based Image Analysis (GEOBIA) and Data Mining (DM). In order to obtain the images, the segmentation algorithm implemented by Definiens Developer was used. A decision tree (DT) was created from a training set previously prepared. Time-series of images from the MODIS sensor aboard the Terra satellite were acquired in order to represent the wide variation of the vegetation pattern along the soybean crop cycle. The time-series data were used only for the CEI index. Furthermore, to compare the results obtained from GEOBIA, the slicing technique was used at the CEI level. After the training, the DT was applied to the vegetation indices generating the thematic map of the spatial distribution of soybean. In accordance with the error matrix and kappa parameter analysis, tests for statistical significance were created. Results indicate that the classification achieved by Kappa coefficients is 0.76. In short, the obtained results proved that combining vegetation indices and time-series data using GEOBIA return promising results for mapping soybean plantation on a regional scale.     

Towards a spatial data infrastructure for technological disasters: an approach for the road transportation of hazardous materials

Spatial data have been used for the environmental monitoring of the consequences of accidents that involve the transportation of hazardous chemical products. This spatial data infrastructure (SDI), which was created for the sharing and use of spatial data, is limited by the absence of policies to support its establishment. The main objective of this study was to explore the use of social network analysis (SNA) as a tool to identify spatial data sharing between organizations involved in the management of accidents related to road transport of hazardous materials (RTHM). In addition, to discuss the existing policies and institutional agreements, and to initiate a conceptual SDI framework for RTHM sector. In this context, the institutions that are involved with RTHM were identified and information concerning their interest in the use and sharing of spatial data via a SDI was collected through interviews and consolidated. The interviews were at 39 institutions with representative employees. The interview data were tabulated and entered into the UCINET software (2000 version) to calculate metrics of centrality. From the SNA, the flow of data among the participating institutions was identified through the visual representation of the spatial data sharing and use networks. Subsequently, the existing institutional agreements for spatial data sharing were analyzed and discussed. The compiled results enabled the proposal of a conceptual SDI framework to support the management of disasters involving RTHM, based on the application of SNA theory, and the development of a methodology that supports the analysis of interactions among the various actors of an SDI. The purpose is to facilitate the formulation of policies for the sharing of spatial data for decision-making and preventive disaster management. The results indicate that the 39 institutions share spatial data, but this sharing is not always predetermined by formal agreements. Furthermore, there is a strong demand, by the institutions involved in the management of RTHM accidents, regarding legal mechanisms governing the sharing of data for the purpose of producing maps that help to describe actions of preparedness, prevention, management and immediate relief involving RTHM incidents. Finally, it was possible to propose a conceptual framework with data that is considered essential for creating an SDI for RTHM.     

Seasonal and vertical distributional patterns of siphonophores and medusae in the Chiloé Interior Sea,Chile

The horizontal and vertical distribution of jellyfish was assessed in the Chiloé Inland sea, in the northern area of the Chilean Patagonia. A total of 41 species of cnidarians (8 siphonophores, 31 hydromedusae, 2 scyphomedusae) were collected. Eleven jellyfish species were recorded for the first time in the area. Species richness was higher in spring than in winter (37 vs. 25 species, respectively). Species such as Muggiaea atlantica, Solmundella bitentaculata, and Clytia simplex were extremely abundant in spring. The total abundance (408,157 ind 1000 m^?3) was 18 times higher in spring than in winter (22,406 ind 1000 m^?3).The horizontal distribution of the most abundant species (four in winter, five in spring) showed decreasing abundances in the north–south direction in winter and spring. Peak abundances occurred in the northern microbasins (Reloncaví Fjord, Reloncaví and Ancud gulfs), where the water column stability, phytoplankton and zooplankton abundance were higher, compared with the southern microbasins (Corcovado Gulf, Boca del Guafo). During the spring higher jellyfish abundance season, the vertical distribution of the dominant species (except M. atlantica) showed peak values at mid-depth (30–50 m) and in the deepest sampled layer (50–200 m). This vertical distribution pattern reduced seaward transport in the shallowest layer through estuarine circulation and also limited mortality by predation in the more illuminated shallow layers. Thus, jellyfish were able to remain in the interior waters during the season of maximum biological production.     

Climate change projections over three metropolitan regions in Southeast Brazil using the non-hydrostatic Eta regional climate model at 5-km resolution

Theoretical and Applied Climatology - The objective of this work is to assess changes in three metropolitan regions of Southeast Brazil (Rio de Janeiro, São Paulo, and Santos) based on the...     

新一代天气雷达产品在阿克苏大降水预报中的应用

利用新疆阿克苏CINRAD／CC多普勒天气雷达的探测资料，在新疆气象局新一代天气雷达网络应用软件和CINRAD PUP软件产品的基础上，分析研究了2003年11月5～7日发生在阿克苏地区的大降水天气，得出：此次天气的强度场、速度场、垂直剖面结构具有积层混合性降水回波与层状云降水回波的特征，低空偏东急流、暖平流中尺度系统和速度场分布对强降水的定时、定量、定点预报具有明确的指示意义。     

Environmental influences on soil CO2 degassing at Furnas and Fogo volcanoes (São Miguel Island,Azores archipelago)

Since October 2001, four soil CO₂ flux stations were installed in the island of São Miguel (Azores archipelago), at Fogo and Furnas quiescent central volcanoes. These stations perform measurements by the accumulation chamber method and, as the gas flux may be influenced by external variables, the stations are equipped with several meteorological sensors. Multivariate regression analysis applied to the large datasets obtained allowed observing that the meteorological variables may influence the soil CO₂ flux oscillations from 18% to 50.5% at the different monitoring sites. Additionally, it was observed that meteorological variables (mainly soil water content, barometric pressure, wind speed and rainfall) play a different role in the control of the gas flux, depending on the selected monitoring site and may cause significant short-term (spike-like) fluctuations. These divergences may be potentially explained by the porosity and hydraulic conductivity of the soils, topographic effects, drainage area and different exposure of the monitoring sites to the weather conditions. Seasonal effects are responsible for long-term oscillations on the gas flux.