12 resultados para Exposure scenarios

em Instituto Politécnico do Porto, Portugal


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This study reports the levels of nitrate and nitrite of 34 vegetable samples, including different varieties of cabbage, lettuce, spinaches, parsley and turnips, collected in several locations of an intensive agricultural area (Modivas, Vila do Conde, northern Portugal). Nitrate levels ranged between 54 and 2440 mg NO-3 kg-1, while nitrite levels ranged between 1.1 and 57 mg NO-2 kg-1. The maximum residue levels established for nitrate in spinach and lettuce samples were not exceeded. Nitrate and nitrite levels reported in the literature for the same type of samples are reviewed, as well as the contribution of vegetables to nitrate and nitrite dietary exposure of populations.

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Pesticide exposure during brain development could represent an important risk factor for the onset of neurodegenerative diseases. Previous studies investigated the effect of permethrin (PERM) administered at 34 mg/kg, a dose close to the no observable adverse effect level (NOAEL) from post natal day (PND) 6 to PND 21 in rats. Despite the PERM dose did not elicited overt signs of toxicity (i.e. normal body weight gain curve), it was able to induce striatal neurodegeneration (dopamine and Nurr1 reduction, and lipid peroxidation increase). The present study was designed to characterize the cognitive deficits in the current animal model. When during late adulthood PERM treated rats were tested for spatial working memory performances in a T-maze-rewarded alternation task they took longer to choose for the correct arm in comparison to age matched controls. No differences between groups were found in anxiety-like state, locomotor activity, feeding behavior and spatial orientation task. Our findings showing a selective effect of PERM treatment on the T-maze task point to an involvement of frontal cortico-striatal circuitry rather than to a role for the hippocampus. The predominant disturbances concern the dopamine (DA) depletion in the striatum and, the serotonin (5-HT) and noradrenaline (NE) unbalance together with a hypometabolic state in the medial prefrontal cortex area. In the hippocampus, an increase of NE and a decrease of DA were observed in PERM treated rats as compared to controls. The concentration of the most representative marker for pyrethroid exposure (3-phenoxybenzoic acid) measured in the urine of rodents 12 h after the last treatment was 41.50 µ/L and it was completely eliminated after 96 h.

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Remote laboratories are an emergent technological and pedagogical tool at all education levels, and their widespread use is an important part of their own improvement and evolution. This paper describes several issues encountered on laboratorial classes, on higher education courses, when using remote laboratories based on PXI systems, either using the VISIR system or an alternate in-house solution. Three main issues are presented and explained, all reported by teachers, that gave support to students' use of remote laboratories. The first issue deals with the need to allow students to select the actual place where an ammeter is to be inserted on electric circuits, even incorrectly, therefore emulating real-world difficulties. The second one deals with problems with timing when several measurements are required at short intervals, as in the discharge cycle of a capacitor. In addition, the last issue deals with the use of a multimeter in dc mode when reading ac values, a use that collides with the lab settings. All scenarios are presented and discussed, including the solution found for each case. The conclusion derived from the described work is that the remote laboratories area is an expanding field, where practical use leads to improvement and evolution of the available solutions, requiring a strict cooperation and information-sharing between all actors, i.e., developers, teachers, and students.

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Coal contains trace quantities of natural radionuclides such as Th-232, U-235, U-238, as well as their radioactive decay products and 40K. These radionuclides can be released as fly ash in atmospheric emissions from coal-fired power plants, dispersed into the environment and deposited on the surrounding top soils. Therefore, the natural radiation background level is enhanced and consequently increase the total dose for the nearby population. A radiation monitoring programme was used to assess the external dose contribution to the natural radiation background, potentially resulting from the dispersion of coal ash in past atmospheric emissions. Radiation measurements were carried out by gamma spectrometry in the vicinity of a Portuguese coal-fired power plant. The radiation monitoring was achieved both on and off site, being the boundary delimited by a 20 km circle centered in the stacks of the coal plant. The measured radionuclides concentrations for the uranium and thorium series ranged from 7.7 to 41.3 Bq/kg for Ra-226 and from 4.7 to 71.6 Bq/kg for Th-232, while K-40 concentrations ranged from 62.3 to 795.1 Bq/kg. The highest values were registered near the power plant and at distances between 6 and 20 km from the stacks, mainly in the prevailing wind direction. The absorbed dose rates were calculated for each sampling location: 13.97-84.00 ηGy/h, while measurements from previous studies carried out in 1993 registered values in the range of 16.6-77.6 ηGy/h. The highest values were registered at locations in the prevailing wind direction (NW-SE). This study has been primarily done to assess the radiation dose rates and exposure to the nearby population in the surroundings of a coal-fired power plant. The results suggest an enhancement or at least an influence in the background radiation due to the coal plant past activities.

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Hospitals are considered as a special and important type of indoor public place where air quality has significant impacts on potential health outcomes. Information on indoor air quality of these environments, concerning exposures to particulate matter (PM) and related toxicity, is limited though. This work aims to evaluate risks associated with inhalation exposure to ten toxic metals and chlorine (As, Ni, Cr, Cd, Pb, Mn, Se, Ba, Al, Si, and Cl) in coarse (PM2.5–10) and fine (PM2.5) particles in a Portuguese hospital in comparison with studies representative of other countries. Samples were collected during 1 month in one urban hospital; elemental PM characterization was determined by proton-induced X-ray emission. Noncarcinogenic and carcinogenic risks were assessed according to the methodology provided by the United States Environmental Protection Agency (USEPA; Region III Risk-Based Concentration Table) for three different age categories of hospital personnel (adults, >20, and <65 years) and patients (considering nine different age groups, i.e., children of 1–3 years to seniors of >65 years). The estimated noncarcinogenic risks due to occupational inhalation exposure to PM2.5-bound metals ranged from 5.88×10−6 for Se (adults, 55–64 years) to 9.35×10−1 for As (adults, 20–24 years) with total noncarcinogenic risks (sum of all metals) above the safe level for all three age categories. As and Cl (the latter due to its high abundances) were the most important contributors (approximately 90 %) to noncarcinogenic risks. For PM2.5–10, noncarcinogenic risks of all metals were acceptable to all age groups. Concerning carcinogenic risks, for Ni and Pb, they were negligible (<1×10−6) in both PM fractions for all age groups of hospital personnel; potential risks were observed for As and Cr with values in PM2.5 exceeding (up to 62 and 5 times, respectively) USEPA guideline across all age groups; for PM2.5–10, increased excess risks of As and Cr were observed particularly for long-term exposures (adults, 55–64 years). Total carcinogenic risks highly (up to 67 times) exceeded the recommended level for all age groups, thus clearly showing that occupational exposure to metals in fine particles pose significant risks. If the extensive working hours of hospital medical staff were considered, the respective noncarcinogenic and carcinogenic risks were increased, the latter for PM2.5 exceeding the USEPA cumulative guideline of 10−4. For adult patients, the estimated noncarcinogenic and carcinogenic risks were approximately three times higher than for personnel, with particular concerns observed for children and adolescents.

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The aim of this work was to assess ultrafine particles (UFP) number concentrations in different microenvironments of Portuguese preschools and to estimate the respective exposure doses of UFP for 3–5-year-old children (in comparison with adults). UFP were sampled both indoors and outdoors in two urban (US1, US2) and one rural (RS1) preschool located in north of Portugal for 31 days. Total levels of indoor UFP were significantly higher at the urban preschools (mean of 1.82x104 and 1.32x104 particles/cm3 at US1 an US2, respectively) than at the rural one (1.15x104 particles/cm3). Canteens were the indoor microenvironment with the highest UFP (mean of 5.17x104, 3.28x104, and 4.09x104 particles/cm3 at US1, US2, and RS1), whereas the lowest concentrations were observed in classrooms (9.31x103, 11.3x103, and 7.14x103 particles/cm3 at US1, US2, and RS1). Mean indoor/outdoor ratios (I/O) of UFP at three preschools were lower than 1 (0.54–0.93), indicating that outdoor emissions significantly contributed to UFP indoors. Significant correlations were obtained between temperature, wind speed, relative humidity, solar radiation, and ambient UFP number concentrations. The estimated exposure doses were higher in children attending urban preschools; 3–5-year-old children were exposed to 4–6 times higher UFP doses than adults with similar daily schedules.

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This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

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The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

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This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

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This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

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This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

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Metal contamination of the environment is frequently associated to the presence of two or more metals. This work aimed to study the impact of a mixture of metals (Cd, Pb and Zn) on the physiology of the non-conventional yeast Pichia kudriavzevii. The incubation of yeast cells with 5 mg/l Cd, 10 mg/l Pb and 5 mg/l Zn, for 6 h, induced a loss of metabolic activity (assessed by FUN-1 staining) and proliferation capacity (evaluated by a clonogenic assay), with a small loss of membrane integrity (measured by trypan blue exclusion assay). The staining of yeast cells with calcofluor white revealed that no modification of chitin deposition pattern occurred during the exposure to metal mixture. Extending for 24 h, the exposure of yeast cells to metal mixture provoked a loss of membrane integrity, which was accompanied by the leakage of intracellular components. A marked loss of the metabolic activity and the loss of proliferation capacity were also observed. The analysis of the impact of a single metal has shown that, under the conditions studied, Pb was the metal responsible for the toxic effect observed in the metal mixture. Intracellular accumulation of Pb seems to be correlated with the metals' toxic effects observed.