It has been suggested that physical education (PE) and active transport can make a meaningful contribution to children's physical activity (PA) levels. However, data on the contribution these activities to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE, whereas active transportation had a much larger contribution.
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Uit het rapport: "Deze onderzoeksagenda is tot stand gebracht door de lectoren die samenwerken in het Nationaal Lectoren Platform Urban Energy. Alle betrokkenen bij het platform zijn in staat gesteld om bij te dragen aan de tekst, speciale dank daarbij voor de bijdragen en commentaren vanuit de TKI Urban Energy en de HCA topsector Energie."
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Technological development from horse-drawn carriages to the new Airbus A380 has led to a remarkable increase in both the capacity and speed of tourist travel. This development has an endogenous systemic cause and will continue to increase carbon dioxide emissions/energy consumption if left unchecked. Another stream of technological research and development aims at reducing pollution and will reduce emissions per passenger-kilometer, but suffers from several rebound effects. The final impact on energy consumption depends on the strength of the positive and negative feedback in the technology system of tourism transport. However, as the core tourism industry including tour operators, travel agencies, and, accommodation has a strong link with air transport, it is unlikely that technological development without strong social and political control will result in delivering the emission reductions required for avoiding dangerous climate change.
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Introduction: It has been suggested that physical education (PE) can make a meaningful contribution to children's physical activity (PA) levels. The amount of moderate-to-vigorous physical activity (MVPA) in PE has been quantified in various manners, including heart rate monitoring and direct observation (Fairclough & Stratton, 2005). However, data on the contribution of PE to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). However, the fairly recent emergence of combined sensing methodology allows for low-invasive measurement of PAEE in free-living conditions. In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. We assessed the contribution of PE and other school-related activity to PAEE and MVPA. Methods: Nineteen secondary school students (16 ± 0,7 yrs, BMI 22 ± 4) were included after they and their parents had consented. All had 100 minutes of scheduled PE per week. Actiheart monitors (CamNtech, Cambridge, UK) were used to determine PAEE on four weekdays and two weekend days consecutively. Actiheart monitors combine a heart rate monitor and an uniaxial accelerometer in a single 10 gram unit, that is applied to the chest with electrodes. Using a step test, an individual heart rate-energy expenditure relationship was determinded in each subject. Through a validated branched equation model (Brage, S. et al., 2007), energy expenditure was calculated. In addition, subjects kept an activity diary for the same six-day period. They recorded predefined activities including PE and active transport. These activities were then retraced to the Actiheart data by visual inspection. Results: Table 1 shows the (contribution of) PE, and school-related active transport to PAEE, while table 2 shows similar data for MVPA. Data are mean (± SD). Table 1: PAEE for PE, and active transport (AT). Table 2: MVPA for PE and active transport (AT). PAEE (KJ) % of total % of school PE 805(474) 5(4) 16(7) AT 1698(1033) 11(6) 31(11) MVPA (min) % of total % of school PE 36(19) 9(8) 22(11) AT 90(56) 20(11) 48(14) Over all six days, the physical activity level (PAL, which is total EE/Resting EE) was 1,54 ± 0,12; total MVPA was 472 min ± 179, and total PAEE 16262 KJ ± 5267. PAEE at school (4 days, including AT) was 5311 ± 3065 KJ, amounting to 34 % of total PAEE during the six measurement days. Students accumulated 179 ± 77 minutes of MVPA at school, which was 38% of total MVPA. Discussion: To our knowledge, this is the first study to present data on PE's contribution to total physical activity energy expenditure. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE. This was substantially less than the amount of energy expended for active transport to and from school. However, it should be noted that in the Netherlands, the vast majority of secondary school students cycle to school. And while PE was scheduled on one day per week in all of the measured students, active transport takes place on all school days. The total amount of MVPA accumulated at school was 179 minutes. With adolescent physical activity guidelines generally recommending 60 min of MVPA per day, i.e. 420 minutes per week, this means that school-related PA covered ~43% of this. PE provided 36 minutes to this total, all on one day. It could be argued that daily PE could potentially provide a substantial amount of MVPA. But with current time allocated to PE in the curriculum, its contribution to physical activity guidelines and PAEE is quite modest. The preliminary data presented here reflect a small subsample of a larger study that is still in progress. Therefore, care should be taken not to interpret these outcomes as representative for the whole of the Netherlands. However, they do provide a first indication for the order of magnitude of the contribution of PE and school-related activity to total PAEE. References: Fairclough, S. J. & Stratton, G. (2005) Physical Activity Levels in Middle and High School Physical Education: A Review. Pediatric Exercise Science, 17, 217. Welk, G. J. (2002) Physical activity assessments for health-related research, Champaign, Ill.; United States, Human Kinetics. Brage, S., Ekelund, U., Brage, N. Hennings, M.A., Froberg, K., Franks, P.W., Wareham. N.J. (2007). Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. J Appl Physiol, 103, (682-692)
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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This century, greenhouse gas emissions such as carbon dioxide, methane and nitrogen oxides must be significantly reduced. Greenhouse gases absorb and emit infrared radiation that contributes to global warming, which can lead to irreversible negative consequences for humans and the environment. Greenhouse gases are caused by the burning of fossil fuels such as crude oil, coal, and natural gas, but livestock farming, and agriculture are also to blame. In addition, deforestation contributes to more greenhouse gases. Of the natural greenhouse gases, water vapor is the main cause of the greenhouse effect, accounting for 90%. The remaining 10% is caused from high to low by carbon dioxide, methane, nitrogen oxides, chlorofluorocarbons, and ozone. In addition, there are industrial greenhouse gases such as fluorinated hydrocarbons, sulphurhexafluoride and nitrogen trifluoride that contribute to the greenhouse effect too. Greenhouse gases are a major cause of climate change, with far-reaching consequences for the welfare of humans and animals. In some regions, extreme weather events like rainfall are more common, while others are associated with more extreme heat waves and droughts. Sea level rise caused by melting ice and an increase in forest fires are undesirable effects of climate change. Countries in low lying areas fear that sea level rise will force their populations to move to the higher lying areas. Climate change is affecting the entire world. An estimated 30-40% o f the carbon dioxide released by the combustion of fossil fuels dissolves into the surface water resulting in an increased concentration of hydrogen ions. This causes the seawater to become more acidic, resulting in a decreasing of carbonate ions. Carbonate ions are an important building block for forming and maintaining calcium carbonate structures of organisms such as oysters, mussels, sea urchins, shallow water corals, deep sea corals and calcareous plankton.
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The Interreg Europe eBussed project supports the transition of European regions towards low carbon mobility and more efficient transport. The regions involved are Turku (Finland), Hamburg (Germany), Utrecht (The Netherlands), Livorno (Italy), South Transdanubia (Hungary) and Gozo island in Malta. It promotes the uptake of e-busses in new regions and supports the expansion of existing e-fleets. Within the project, there are four thematic working groups formed that aim at delivering a best practices report and policy recommendations to be used in the partner regions. Thematic Working Group 4 (TWG4) focusses on the topics of Procurement, Tendering and Costs of e-busses. As a starting point for TWG4, the value chain for e-bus public transport per region has been mapped. By mapping how the value chain for e-bus public transport works and defining the nature of the issues, problems or maybe challenges per region can be better understood.
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Abstract: Climate change is related with weather extremes, which may cause damages to infrastructure used by freight transport services. Heavy rainfall may lead to flooding and damage to railway lines, roads and inland waterways. Extreme drought may lead to extremely low water levels, which prevent safe navigation by inland barges. Wet and dry periods may alternate, leaving little time to repair damages. In some Western and Middle-European countries, barges have a large share in freight transport. If a main waterway is out of service, then alternatives are called for. Volume- and price-wise, trucking is not a viable alternative. Could railways be that alternative? The paper was written after the unusually long dry summer period in Europe in 2022. It deals with the question: If the Rhine, a major European waterway becomes locally inaccessible, could railways (temporarily) play a larger role in freight transport? It is a continuation of our earlier research. It contains a case study, the data of which was fed into a simulation model. The model deals with technical details like service specification route length, energy consumption and emissions. The study points to interesting rail services to keep Europe’s freight on the move. Their realization may be complex especially in terms of logistics and infrastructure, but is there an alternative?
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The volume of biogas produced in agricultural areas is expected to increase in coming years. An increasing number of local and regional initiatives show a growing interest in decentralized energy production, wherein biogas can play a role. Biogas transport from production sites to user, i.e. a CHP, boiler or an upgrading installation, induces a scale advantage and an efficiency increase. Therefore the exploration of the costs and energy use of biogas transport using a dedicated infrastructure is needed. A model was developed to describe a regional biogas grid that is used to collect biogas from several digesters and deliver it to a central point. The model minimizes transport costs per volumetric unit of biogas in a region. Results are presented for different digester scales, different sizes of the biomass source area and two types of grid lay-out: a star lay-out and a fishbone lay-out. The model shows that transport costs in a fishbone lay-out are less than 10 Vct m3 for a digester scale of 100 m3 h1; for the star lay-outcosts can go up to 45 Vct m3. For 1800 m3 h1 digesters, these values are 4.0 Vct m3 and 6.1 Vct m3, respectively. The results indicate that cooperation between biogas producers in collecting biogas by means of a fishbone lay-out reduces the biogas transport costs relative to using a star lay-out. Merging smaller digesters into a smaller number of larger ones reduces the costs of biogas transport for the same biomass source area.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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