Blue-green roofs have been utilized and studied for their enhanced water storage capacity compared to conventional roofs or extensive green roofs. Nonetheless, research about the thermal effect of blue-green roofs is lacking. The goal of this research is to study the thermal effect of blue-green roofs in order to assess their potential for shielding the indoor environment from outdoor temperature extremes (cold- and heat-waves). In this field study, we examined the differences between blue-green roofs and conventional gravel roofs from the perspective of the roof surface temperatures and the indoor temperatures in the city of Amsterdam for late 20th century buildings. Temperature sensor (iButtons) values indicate that outside surface temperatures for blue-green roofs are lower in summer and fluctuate less during the whole year than temperatures of conventional roofs. Results show that for three warm periods during summer in 2021 surface substrate temperatures peaked on average 5°C higher for gravel roofs than for blue-green roofs. Second, during both warm and cold periods, the temperature inside the water crate layer was more stable than the roof surface temperatures. During a cold period in winter, minimum water crate layer temperatures remained 3.0 o C higher than other outdoor surface temperatures. Finally, also the variation of the indoor temperature fluctuations of locations with and without blue-green roofs have been studied. Locations with blue-green roofs are less sensitive to outside air temperature changes, as daily temperature fluctuations (standard deviations) were systematically lower compared to conventional roofs for both warm and cold periods.
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Green roofs received increased scientific attention with respect to climate adaptation in urban environments for their hydrological, biodiversity and insulative capacities. Yet, the thermal properties of roofs with an additional water layer underneath the vegetation substrate (blue-green roofs) are not well represented in scientific research. In this field study, we examined the impact of surface temperatures, indoor temperature and insulative properties of blue-green, green, and conventional gravel/bitumen roofs in the city of Amsterdam for early 20th century buildings. Temperature sensor (IButtons) results indicate that outside surface temperatures of blue-green roofs were more stable than for conventional roofs. For instance, for three warm periods during summer (2021) surface substrate temperatures peaked much higher for gravel roofs (+8 oC) or bitumen roofs (+18 oC) than for blue-green roofs. On top of that, during a cold period in winter average water crate layer temperatures remained 3.0 oC higher and much more stable than substrate temperatures of blue-green roofs and conventional roofs, implicating that the blue layer functions as an extra temperature buffer. The effect of lower daily variation of surface temperatures in winter and summer is also reflected by inside air temperatures. Inside temperatures showed that locations with blue-green roofs are less sensitive to outside air temperatures, as daily temperature fluctuations (standard deviations) were 0.19 and 0.23 oC lower for warm and cold periods, respectively, compared to conventional roofs. This effect seems rather small but comprises a relatively large proportion of the total daily variation of 24% and 64% of warm and cold periods respectively.
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Many nature-based solutions are seen as favourable and effective measures to increase urban resilience during more extreme weather events, by for example decrease high temperatures in summer. Since space is often scarce in urban environments, roofs have received increased attention in mitigating the consequences of climate change in urban areas. This resulted in a variety of roof systems of green and blue-green roofs designed as an integral part of the built environment due to their hydrological, insulative and biodiverse capacities. This study examined the impact of blue-green, and conventional roofs on roof surface temperatures, indoor temperatures and insulative properties of the building. Temperature sensors (IButtons) have been used for summer and winter measurements on roofs for early 20th century buildings in the city of Amsterdam (NL).The results indicate the strongest effect of blue-green roofs on surface temperatures in summer, with significantly lower surface temperatures (2-3°C) than for conventional roofs. During winter days, the surface temperatures were not significantly different on blue-green roofs than on conventional roofs. The measurements in the water crate layers of blue-green roofs show an all year-round temperature buffering effect. During hot summer days, the temperature in the water storage of the blue-green roof was much lower than other measured surfaces (up to 12 °C and 7 °C compared to gravel roofs and the blue-green roof substrate, respectively) and also experienced the least diurnal variation. Similarly, the empty water crate layer showed up to 3 °C higher minimum temperatures during cold winter nights. The measurements also show a small positive systematic effect on the indoor environment under a blue-green roof compared to traditional gravel roof type. The variation in indoor temperature is smaller underneath the blue-green roofs compared to the reference roofs during both warm and cold periods (0.19 – 0.35 °C reduction in STD). This suggests that rooms located under a blue-green roof are less sensitive to the outside air temperature and its natural diurnal variation.Although the effect on indoor thermal comfort seems to be small, blue-green roofs contribute to overall greening of the city. Second, thanks to the water storage the potential for growing biodiverse vegetation is higher than on extensive green roofs.
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In today’s city environments, extreme weather conditions are a fact oflife. Amsterdam, Mumbai, Nairobi or Sydney… climate change issuesneed to be tackled all around the world.In the last couple of decades, Amsterdam has dealt with largeramounts of rainwater, severe heat stress and a decreased biodiversity.In order to strengthen urban resilience to climate change, blue-green(BG) roofs are increasingly being introduced. BG roofs placean additional water layer underneath the green layer. The idea is thatthese roofs reduce runoff after rainfall by retaining precipitation andmitigate heat stress, caused by increased evapotranspiration (the sumof evaporation from the land surface and transpiration from plants)and a higher albedo effect (the ability of surfaces to reflect sunlight).
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Despite increased interest in applying psychological theory to the practice of designing behavioral change interventions, design professionals often lack adequate knowledge and resources to do so. In this paper, we present a tool to help professionals in the creative industries design evidence-based health interventions, the Persuasive by Design model. This paper describes the contents and application of the model as well as plans for further development and testing.
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Online knowledge-sharing platforms could potentially contribute to an accelerated climate adaptation by promoting more green and blue spaces in urban areas. The implementation of small-scale nature-based solutions (NBS) such as bio(swales), green roofs, and green walls requires the involvement and enthusiasm of multiple stakeholders. This paper discusses how online citizen science platforms can stimulate stakeholder engagement and promote NBS, which is illustrated with the case of ClimateScan. Three main concerns related to online platforms are addressed: the period of relevance of the platform, the lack of knowledge about the inclusiveness and characteristics of the contributors, and the ability of sustaining a well-functioning community with limited resources. ClimateScan has adopted a “bottom–up” approach in which users have much freedom to create and update content. Within six years, this has resulted in an illustrated map with over 5000 NBS projects around the globe and an average of more than 100 visitors a day. However, points of concern are identified regarding the data quality and the aspect of community-building. Although the numbers of users are rising, only a few users have remained involved. Learning from these remaining top users and their motivations, we draw general lessons and make suggestions for stimulating long-term engagement on online knowledge-sharing platforms
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As Vehicle-to-Everything (V2X) communication technologies gain prominence, ensuring human safety from radiofrequency (RF) electromagnetic fields (EMF) becomes paramount. This study critically examines human RF exposure in the context of ITS-5.9 GHz V2X connectivity, employing a combination of numerical dosimetry simulations and targeted experimental measurements. The focus extends across Road-Side Units (RSUs), On-Board Units (OBUs), and, notably, the advanced vehicular technologies within a Tesla Model S, which includes Bluetooth, Long Term Evolution (LTE) modules, and millimeter-wave (mmWave) radar systems. Key findings indicate that RF exposure levels for RSUs and OBUs, as well as from Tesla’s integrated technologies, consistently remain below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) exposure guidelines by a significant margin. Specifically, the maximum exposure level around RSUs was observed to be 10 times lower than ICNIRP reference level, and Tesla’s mmWave radar exposure did not exceed 0.29 W/m2, well below the threshold of 10 W/m2 set for the general public. This comprehensive analysis not only corroborates the effectiveness of numerical dosimetry in accurately predicting RF exposure but also underscores the compliance of current V2X communication technologies with exposure guidelines, thereby facilitating the protective advancement of intelligent transportation systems against potential health risks.
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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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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