http://hdl.handle.net/10174/37677 Sun, 14 Jun 2026 11:39:53 GMT 2026-06-14T11:39:53Z http://hdl.handle.net/10174/42117 Title: Energy Sustainability in the Ripening of Traditional Cheese: Renewable Energy Sources and Internet of Things Based Energy Monitoring Authors: Santos, João Miguel; Garcia, João M.; Dias, João; Martins, João; Alvarenga, Nuno; Gonçalves, Elsa; Freitas, Daniela; Silvério, Karina; Fernandes, Jaime; Gomes, Sandra; Lageiro, Manuel; Potes, Miguel; Caeiro, José Abstract: Improving the energy efficiency of traditional production methods while preserving their cultural and economic value is a challenge aligned with the Sustainable Development Goals of the 2030 agenda. Refrigeration during cheese maturation is particularly energy-intensive, contributing significantly to greenhouse gas emissions and operating costs. An approach to make traditional cheese production more sustainable, through the development of a prototype ripening chamber with a natural refrigerant-based refrigeration system powered by renewable energy was studied. A dedicated system based on an Internet of Things architecture was developed using low-cost sensors, microcontroller units, and single-board computers to enable real-time measurement and monitoring of environmental variables and energy consumption throughout the ripening process. A comparative analysis was conducted using ewe’s milk cheese, produced and ripened with Protected Designation of Origin conditions, in both the prototype and the conventional chambers over four weeks, quantifying energy consumption and evaluating product quality. Results demonstrate the technical feasibility of energy efficient and sustainable refrigeration systems, as well as the possibility of retrofitting installed cheese ripening chambers with affordable IoT monitoring systems, while maintaining traditional cheese quality standards. Thu, 30 Oct 2025 00:00:00 GMT http://hdl.handle.net/10174/42117 2025-10-30T00:00:00Z http://hdl.handle.net/10174/42008 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. Sun, 01 Mar 2026 00:00:00 GMT http://hdl.handle.net/10174/42008 2026-03-01T00:00:00Z http://hdl.handle.net/10174/42000 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. Fri, 24 Jan 2025 00:00:00 GMT http://hdl.handle.net/10174/42000 2025-01-24T00:00:00Z http://hdl.handle.net/10174/41960 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. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10174/41960 2025-01-01T00:00:00Z