The search for renewable energy sources has driven the desire for knowledge about the energy source of photovoltaic systems and the factors that can influence it. This study applies powerful machine learning techniques to identify the best model for predicting photovoltaic energy generation, using meteorological variables as key inputs. The energy generated data were collected in a photovoltaic plant installed in the city of Pontes e Lacerda, while the meteorological variables were collected from nearby INMET stations. Four different techniques were employed, including SVR (Support Vector Machine), Random Forest, LSTM Neural Network and SARIMAX. The results showed that the Random Forest technique presented the best performance, with calculated values for the coefficient of determination (R²) and Willmott index of 0.909 and 0.972, respectively, standing out for accuracy and efficiency in scenarios where data is available. On the other hand, it was revealed that the model generated by the SARIMAX technique had great potential for applications where there is little data availability, presenting satisfactory estimates. This study highlights the practical applications of machine learning in optimizing photovoltaic power generation plant design and management, including improving energy prediction accuracy, enabling better decision-making, and supporting the expansion of renewable energy sources, especially in areas with scarce data. The findings also reinforce the critical role of meteorological variables in influencing the performance of photovoltaic systems, offering valuable insights for future applications in energy systems planning and operation.

A temperatura e a umidade relativa do ar são de grande importância para o entendimento da dinâmica dos fluxos de CO2, taxas de evapotranspiração e estudos de microclima. Nesse sentido, o presente trabalho analisou a variação mensal da evapotranspiração de referência (ET0), temperatura e umidade relativa do ar na região do Pantanal Mato-grossense. Os dados foram obtidos de uma torre micrometeorológica instalada na Base Avançada de Pesquisas do Pantanal (BAPP) da Universidade Federal de Mato Grosso (UFMT), localizada no Parque SESC Baía das Pedras em Porto Cercado, Poconé – MT. A partir dos dados analisados no período de 2017 a 2019, percebeu-se a ocorrência de oscilações mensais e anuais na evapotranspiração. Em relação às análises dos dados de temperatura e umidade relativa do ar, concluiu-se que no período de seca, que acontece no inverno do Pantanal, os dados de temperatura e umidade do ar são baixas. Enquanto no período chuvoso e de cheia, que acontecem durante o verão, as temperaturas e umidade relativa do ar são altas.

The Brazilian Pantanal region experiences intense biomass burning during the dry season, releasing large quantities of gasses and particles into the atmosphere, which have serious implications on both the climate system and public health. Understanding the dynamics of these emissions is crucial for mitigating the impact on the ecosystem, its functioning, and potential anthropogenic disturbances. This study focused on analyzing emissions in the northern Pantanal during the 2022 drought. Concentrations of fine particulate matter (PM_2.5), black carbon (BC), and 25 chemical elements were measured using gravimetry, reflectance analysis, and X-Ray fluorescence, respectively, from samples collected between August and October 2022. The average concentrations of PM_2.5 and BC increased approximately 4-fold and 2.5-fold, respectively, compared to averages from a decade ago. Significant increases were also observed in elements such as sulfur (S), potassium (K), iron (Fe), and silicon (Si). The maximum concentrations were comparable to values typical of the southern Amazon, a region known for high deforestation rates and land use changes. Elemental analysis revealed substantial shifts in concentrations, primarily associated with biomass burning (BB) and soil suspension. Additionally, enrichment factor (Ef) analysis showed that lead (Pb) levels, correlated with human activities, were 200 times higher than those found under clean atmospheric conditions.

Facilitar o entendimento da física tem sido um desafio constante ao longo do tempo, exigindo cada vez mais a busca por maneiras de tornar o aprendizado de física mais acessível. Neste trabalho, nosso objetivo é demonstrar a física teórica e experimental das ondas estacionárias. Para isso, utilizamos uma abordagem inovadora que combinou um instrumento musical amplamente conhecido, o violão, com o aplicativo PhyPhox, que permite a realização de experimentos de física por meio dos sensores presentes em smartphones. Realizando o experimento de autocorrelação de áudio disponível no aplicativo, os resultados obtidos mostraram-se satisfatórios, com as frequências medidas para cada nota muito próximas das esperadas em uma escala temperada. A análise dessas frequências possibilitou o cálculo da velocidade de propagação das ondas nas cordas do violão, um valor relevante que está diretamente relacionado à tensão e à densidade linear de cada corda. Observou-se que as cordas mais grossas geram sons mais graves devido à menor velocidade de propagação, enquanto as cordas mais finas produzem sons mais agudos. Além disso, as variações nas intensidades dos picos de autocorrelação sugerem flutuações no som, refletindo o comportamento realista de atenuação do som ao longo do tempo

Evapotranspiration is crucial for the water balance, especially in irrigated agriculture. It can be measured directly using lysimeters or estimated through models such as the Penman-Monteith (PM) model, recommended by the FAO. This study aimed to analyze evapotranspiration measurements from a constant water table lysimeter and verify their validity by comparing the measurements, taken between May and November 2023, with the estimates from the PM model. The experiment was conducted in Botucatu, SP, which has a tropical savanna climate (Aw), characterized by hot, rainy summers and cold, dry winters. For comparison, the following validation indicators were used: root mean square error (RMSE), mean absolute error (MAE), Willmott's index of agreement (d), mean bias error (MBE), Pearson’s correlation coefficient (r), relative mean bias error (rMBE), relative root mean square error (rRMSE), and the confidence coefficient (c). The results showed that in August, the PM model underestimated evapotranspiration measurements by 2.28%, the smallest variation in the analyzed period. In October, which had the greatest variation, the model overestimated the measurements by 46.39%. This highlights the need for seasonal adjustments when using the PM model in the region.

The Brazilian Pantanal region suffers intensely from biomass burning during the dry season. Large quantities of gases and particles are emitted into the atmosphere during this period, with serious implications for fauna and flora. Understanding the dynamics of these emissions is es-sential for mitigating the ecosystem, its functioning, and possible anthropogenic disturbances. In this sense, this study analyzed emissions over the north of the Brazilian Pantanal during the 2022 drought. Measurements of concentration of fine particulate matter (PM2.5), Black carbon (BC), and elemental concentration (25 chemical elements) were determined by gravimetry, reflectance analysis, and X-ray fluorescence, respectively, for samples collected between August and Octo-ber 2022. The average concentrations of PM2.5 and BC had their values increased by approxi-mately 4 and 2.5 times, respectively, when compared to the averages from a decade ago. The maximum concentrations found are close to values typical of the southern Amazon region, a re-gion with high rates of deforestation and changes in land use. The elemental analysis showed large changes in concentrations, mainly associated with biomass burning emission (BB) and soil resuspension. Furthermore, analyses of lead (Pb) concentrations showed strong anthropization at the study site.