http://hdl.handle.net/10174/37677 2026-05-22T12:31:30Z 2026-05-22T12:31:30Z Almeida, Domingas de Oliveira Duarte, Edicle de Souza Fernandes Gomes, Ana Carla dos Santos Batalha, Sarah Suely Alves Mandú, Tiago Bentes Nascimento, Fernanda Souza do Silva, Glauce Vitor da Costa, Maria João http://hdl.handle.net/10174/42008 2026-05-13T16:12:02Z 2026-03-01T00:00:00Z

Title: Spatiotemporal assessment of wildfire smoke exposure using a low-cost air quality monitoring system in a developing Amazonian city Authors: Almeida, Domingas de Oliveira; Duarte, Edicle de Souza Fernandes; Gomes, Ana Carla dos Santos; Batalha, Sarah Suely Alves; Mandú, Tiago Bentes; Nascimento, Fernanda Souza do; Silva, Glauce Vitor da; Costa, Maria João Abstract: Biomass burning is the dominant source of seasonal air pollution in the Amazon, yet local-scale exposure remains poorly characterized due to sparse monitoring. This study aims to quantify the spatiotemporal dynamics and drivers of PM2.5 pollution in Santarém, Brazilian Amazon, by integrating measurements from a dense network of low-cost sensors, satellite-derived fire radiative power (FRP), and reanalysis meteorology throughout 2023. We applied Generalized Estimating Equations (GEE) to evaluate the daily influence of fire activity and meteorological conditions on local PM2.5. Mean PM2.5 concentrations increased from ∼5 μg/m3 in the rainy season to ∼16 μg/m3 in the dry season, with 94% of exceedances occurring from July–December and a fine-particle dominance (PM2.5/PM10 ≈ 0.79). Peri-urban communities experienced earlier-season pollution peaks, whereas the urban core showed more persistent late-season accumulation. FRP emerged as the primary driver of PM2.5, with effect sizes strengthening from 10% (wet season) to 25% (dry season) per standard deviation, while meteorological factors such as wind speed and boundary-layer height played secondary but modulating roles. A negligible weekend–weekday contrast confirmed that smoke overwhelmingly dominates over local traffic emissions. Finally, we operationalized these relationships into a low-computational-cost FRP–Meteo–PM2.5 polar radar tool for identifying high-risk smoke transport corridors. These results provide actionable evidence for early warning and highlight the urgent need for targeted fire management to reduce public health risks in developing Amazonian cities.

2026-03-01T00:00:00Z Reis, Jean Souza dos Duarte, Edicle de Souza Fernandes Costa, Rafaela Lisboa Silva, Fabricio Daniel dos Santos Cortes, Taisa Rodrigues Coelho, Rachel Helena Velasco, Sofia Rafaela Maito Neves, Danielson Jorge Delgado Filho, José Firmino Sousa Barreto, Cairo Eduardo Carvalho Cabral Júnior, Jório Bezerra Reis, Herald Souza dos Mendes, Keila Rêgo Lins, Mayara Christine Correia Ferreira, Thomás Rocha Vanderlei, Mário Henrique Guilherme dos Santos Alonso, Marcelo Felix Mariano, Glauber Lopes Gomes, Heliofábio Barros Gomes, Helber Barros http://hdl.handle.net/10174/42000 2026-05-13T16:08:40Z 2025-01-24T00:00:00Z

Title: Predicting Asthma Hospitalizations from Climate and Air Pollution Data: A Machine Learning-Based Approach Authors: Reis, Jean Souza dos; Duarte, Edicle de Souza Fernandes; Costa, Rafaela Lisboa; Silva, Fabricio Daniel dos Santos; Cortes, Taisa Rodrigues; Coelho, Rachel Helena; Velasco, Sofia Rafaela Maito; Neves, Danielson Jorge Delgado; Filho, José Firmino Sousa; Barreto, Cairo Eduardo Carvalho; Cabral Júnior, Jório Bezerra; Reis, Herald Souza dos; Mendes, Keila Rêgo; Lins, Mayara Christine Correia; Ferreira, Thomás Rocha; Vanderlei, Mário Henrique Guilherme dos Santos; Alonso, Marcelo Felix; Mariano, Glauber Lopes; Gomes, Heliofábio Barros; Gomes, Helber Barros Abstract: This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series.

2025-01-24T00:00:00Z Esposito, Marcelo Prestes Kunz, Matheus Mesquita Bruel, Gabriela Belén Cristóbal López, Ana Luis André Pereira Fialho, Luis Mesbahi, Oumaima http://hdl.handle.net/10174/41960 2026-05-06T13:43:40Z 2025-01-01T00:00:00Z

Title: Dry Transformer Specification for Photovoltaic Power Plants: Investigation Into the K-Factor and the Use of 800VAC Disconnectors Authors: Esposito, Marcelo; Prestes Kunz, Matheus; Mesquita Bruel, Gabriela; Belén Cristóbal López, Ana; Luis André Pereira Fialho, Luis; Mesbahi, Oumaima Abstract: Although there are several transformer manufacturers in Brazil dedicated to photovoltaic power generation, the connection between inverters with a nominal output voltage of 800VAC and the protection system installed before the step-up transformer is still complex. In this paper, issues related to design, installation and supply of equipment and preventive and corrective maintenance are explored. Operating data is presented for two photovoltaic plants (PV), one has a power of 444kWp and the other 406kWp, which share the same installation design. Both plants experienced faults including, in the worst case, a short circuit followed by a fire caused by the general disconnector. The transformers of 500kVA and K1 factor were assessed after the accidents and showed no faults in the measurements taken at the substations. The installation of disconnect switches with a nominal operating voltage (Ue) equal to 690VAC was identified as the cause of the short circuit. The quality of the equipment was investigated and an analysis of the operating temperature of the transformers was carried out. Possible causes and effects, such as the presence of harmonics and inverter failures, were explored. Given the high cost of circuit breakers for 800VAC/400A Ue and transformers with a K4 factor, the solution found was to repair them and replace the faulty cables and switch-disconnectors with products dedicated to photovoltaic systems. The use of fuses in the AC circuit made the disconnection difficult and impaired the safety in several aspects.

2025-01-01T00:00:00Z Couto, Flavio Tiago Santos, Filippe L. M. Campos, Cátia Purificação, Carolina Andrade, Nuno López-Vega, Juan Manuel Lacroix, Matthieu http://hdl.handle.net/10174/41952 2026-05-05T14:06:56Z 2025-08-19T23:00:00Z

Title: ANÁLISE EXPLORATÓRIA DO USO DA MODELAGEM ATMOSFÉRICA SOBRE OS INCÊNDIOS FLORESTAIS NO PANTANAL Authors: Couto, Flavio Tiago; Santos, Filippe L. M.; Campos, Cátia; Purificação, Carolina; Andrade, Nuno; López-Vega, Juan Manuel; Lacroix, Matthieu Abstract: O estudo discute de forma exploratória as condições atmosféricas favoráveis à evolução dos incêndios florestais no Pantanal em 12 de novembro de 2023. Esses episódios foram marcados por dois períodos de rápida expansão do fogo, primeiro no início da tarde e outro à noite. O estudo usa um conjunto de observações de satélite e estações meteorológicas, as quais ajudaram a identificar o fogo e algumas condições meteorológicas na superfície. Além disso, o Fire Weather Index (FWI) no Pantanal foi analisado para um período de 44 anos. No entanto, esse conjunto de dados não foi suficiente para explicar completamente o comportamento do fogo naquele dia. Nesse contexto, a modelagem atmosférica foi aplicada para encontrar as possíveis causas do comportamento do fogo em dois períodos. O modelo Meso-NH foi configurado com dois domínios aninhados e resoluções horizontais de 2500 m e 500 m. Os resultados mostraram uma tendência positiva do FWI nas últimas décadas, bem como uma clara sazonalidade para os valores máximos no ano de 2023. A simulação indicou condições favoráveis à ignição do fogo, e o campo de rajadas de vento mostrou ventos moderados em ambos os períodos, mas causados por diferentes forçantes. No início da tarde, a circulação em grande escala favoreceu a propagação do fogo, enquanto à noite uma frente de rajada foi observada. O estudo destaca o papel das condições meteorológicas na escala sub-diária, em particular para mudanças repentinas do vento à superfície ao longo do dia. Esse resultado deve ser considerado ao examinar o perigo de fogo e o planejamento das ações de combate aos incêndios na região.

2025-08-19T23:00:00Z