Abstract
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.

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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.