Abstract
The digital terrain model (DTM), the representation of earth's surface at regularly spaced intervals, is the first input in the computational modelling of atmospheric flows. The ability of computational meshes based on high- (2 m; airborne laser scanning, ASL), medium- (10 m; military maps, Mil) and low-resolution (30 m; Shuttle Radar Topography Mission, SRTM) DTMs to replicate the Perdigao experiment site was appraised in two ways: by their ability to replicate the two main terrain attributes, elevation and slope, and by their effect on the wind flow computational results. The effect on the flow modelling was evaluated by comparing the wind speed, wind direction and turbulent kinetic energy using VENTOS (R)/2 at three locations, representative of the wind flow in the region. It was found that the SRTM was not an accurate representation of the Perdigao site. A 40m mesh based on the highest-resolution data yielded an elevation error of less than 1.4m and an RMSE of less than 2.5m at five reference points compared to 5.0m in the case of military maps and 7.6m in the case of the SRTM. Mesh refinement beyond 40m yielded no or insignificant changes on the flow field variables, wind speed, wind direction and turbulent kinetic energy. At least 40m horizontal resolution - threshold resolution based on topography available from aerial surveys is recommended in computational modelling of the flow over Perdigao.

Abstract
Elite swimmers and swimming pool employees are likely to be at greater health risk due to their regular and intense exposure to air stressors in the indoor swimming pool environment. Since data on the real long-term exposure is limited, a long-term monitoring and sampling plan (22 non-consecutive days, from March to July 2017) was carried out in an indoor Olympic-size pool with a chlorine-based disinfection method to characterize indoor environments to which people involved in elite swimming and maintenance staff may be exposed to. A comprehensive set of parameters related with comfort and environmental conditions (temperature, relative humidity (RH), carbon dioxide (CO2) and monoxide and ultrafine particles (UFP)) were monitored both indoors and outdoors in order to determine indoor-to-outdoor (I/O) ratios. Additionally, an analysis of volatile organic compounds (VOC) concentration and its dynamics was implemented in three 1-hr periods: early morning, evening elite swimmers training session and late evening. Samplings were simultaneously carried out in the air layer above the water surface and in the air surrounding the pool, selected to be representative of swimmers and coaches/employees' breathing zones, respectively. The results of this work showed that the indoor climate was very stable in terms of air temperature, RH and CO 2 . In terms of the other measured parameters, mean indoor UFP number concentrations (5158 pt/cm(3)) were about 50% of those measured outdoors whereas chloroform was the predominant substance detected in all samples collected indoors (13.0-369.3 mu g/m(3)), among a varied list of chemical compounds. An I/O non-trihalomethanes (THM) VOC concentration ratio of 2.7 was also found, suggesting that, beyond THM, other potentially hazardous VOC have also their source(s) indoors. THM and non-THM VOC concentration were found to increase consistently during the evening training session and exhibited a significant seasonal pattern. Compared to their coaches, elite swimmers seemed to be exposed via inhalation to significantly higher total THM levels, but to similar concentrations of non-THM VOC, during routine training activities. Regarding swimming employees, the exposure to THM and other VOC appeared to be significantly minimized during the early morning period. The air/water temperature ratio and RH were identified as important parameters that are likely to trigger the transfer processes of volatile substances from water to air and of their accumulation in the indoor environment of the swimming pool, respectively.

Abstract
Conducting epidemiological and risk assessment research that considers the exposome concept, as in the case of HEALS project, requires the acquisition of higher dimension data sets of an increased complexity. In this context, new methods that provide accurate and interpretable data summary on relevant environmental factors are of major importance. In this work, a questionnaire was developed to collect harmonized data on potential pollutant sources to air in the indoor environment where children spend an important part of their early life. The questionnaire was designed in a user friendly checklist format to be filled out at the maternity in ten European cities. This paper presents and discusses the rationale for the selection of the questionnaire contents and the results obtained from its application in the households of 309 HEALS-enrolled families with babies recently born in Porto, Portugal. The tool was very effective in providing data on the putative air pollution sources in homes, with special focus on the bedroom of the newborns. The data collected is part of a wider effort to build the databases and risk assessment models of the HEALS project. The results of the analysis of the collected data suggest that, for the population under study, the main concerns on early life exposures at home can be related to emissions from the use of household solid fuels, indoor tobacco, household cleaning products, fragranced consumer products (e.g. air fresheners, incense and candles), moisture-related pathologies and traffic-related outdoor pollution. Furthermore, it is anticipated that the tool can be a valuable means to empower citizens to actively participate in the control of their own exposures at home. Within this context, the application of the checklist will also allow local stakeholders to identify buildings presenting most evident IAQ problems for sampling or intervention as well as to guide them in preparing evidence-based educational/awareness campaigns to promote public health through creating healthy households.

Abstract
Prediction of the energy consumption is a key aspect of home energy management systems, whose aim is to increase the occupant’s comfort while reducing the energy consumption. This work, employing three years measured data, uses radial basis function neural networks, designed using a multi-objective genetic algorithm (MOGA) framework, for the prediction of total electric power consumption, HVAC demand and other loads demand. The prediction horizon desired is 12 h, using 15 min step ahead model, in a multi-step ahead fashion. To reduce the uncertainty, making use of the preferred set MOGA output, a model ensemble technique is proposed which achieves excellent forecast results, comparing additionally very favorably with existing approaches. © 2020, Springer Nature Switzerland AG.

Abstract
This study addresses the thermal and energy performance assessment of a Building Integrated Photovoltaic Thermal (BIPVT) system installed on the facade of a test room in Solar XXI, a Net Zero Energy Building (NZEB) located in Lisbon, Portugal. A numerical analysis using the dynamic simulation tool EnergyPlus was carried out for assessing the performance of the test room with the BIPVT integrated on its facade through a parametric analysis of 14 scenarios in two conditions: a) receiving direct solar gains on the glazing surface and b) avoiding direct solar gains on the glazing surface. Additionally, a computational fluid dynamics (CFD) analysis of the BIPVT system was performed using ANSYS Fluent. The findings of this work demonstrate that the BIPVT has a good potential to improve the sustainability of the building by reducing the nominal energy needs to achieve thermal comfort, reducing up to 48% the total energy needs for heating and cooling compared to the base case. The operation mode must be adjusted to the other strategies already implemented in the room (e.g., the presence of windows and blinds to control direct solar gains), and the automatic operation mode has proven to have a better performance in the scope of this work.

Abstract
In this paper, Lagrangian dual formulation is used to solve the Environmental/Economic Dispatch problem. The proposed method, that results quite different from the metaheuristic methods employed in literature, was tested on a six generating units system. The results obtained improve others reported in previous investigations, by simultaneously diminishing the total fuel cost and pollutants emissions.