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The materials efficiently emitting photoluminescence (PL) under solar irradiation could find broad applications in passive radiative cooling of buildings, urban heat mitigation, and solar energy harvesting. In this work, we discuss the limitations of common laboratory characterization of such materials (using tunable light sources or solar simulators) and propose a methodology for direct outdoor characterization of PL efficiency under full solar irradiation. A simple, portable radiometry setup with an integrating sphere is described, and its capability of rapid determination of the spectral distribution of the absorbed and emitted power, as well as the overall PL power efficiency and quantum yield, is demonstrated. By characterization of three materials developed for daytime radiative cooling applications, we reveal deviations of obtained parameters caused by the replacement of the real solar irradiation by that of solar simulators. The described method is suitable for studies of long-term stability, photo-induced degradation, or thermal effects.

By 2030 Cyprus committed to reduce greenhouse gas emissions by 40% and increase renewables’ energy share by 19% according to the European engagements. Difficulties appear due to the continuous increase in domestic energy consumption, the large dependency on fossil fuels and the adverse climate. A number of macroeconomic, demographic and climatic indicators that influence Cyprus’s electricity consumption (EC) was analysed for years 2000–2018 using augmented Dickey–Fuller unit root test and the autoregressive distributed lag model (ARDL). The ARDL model revealed that an increase in population could radically increase EC in the long-run and short-run. Results posit that a 1% increase in urban population, electricity price and unemployment decrease domestic EC in the long-run by 17.25%, 0.48% and 0.30%, respectively. In the short-run smaller elasticities were found because time was too short for adjustments to be made. However, their decrease could not outweigh the population growth effect on increasing domestic EC.

Abstract Photoluminescent materials are advanced cutting-edge heat-rejecting materials capable of reemitting a part of the absorbed light through radiative/non-thermal recombination of excited electrons to their ground energy state. Photoluminescent materials have recently been developed and tested as advanced non-white heat-rejecting materials for urban heat mitigation application. Photoluminescent materials has shown promising cooling potential for urban heat mitigation application, but further developments should be made to achieve optimal photoluminescence cooling potential. In this paper, an advanced mathematical model is developed to explore the most efficient methods to enhance the photoluminescence cooling potential through estimation of contribution of non-radiative mechanisms. The non-radiative recombination mechanisms include: (1) Transmission loss and (2) Thermal losses including thermalization, quenching, and Stokes shift. The results on transmission and thermal loss mechanisms could be used for systems solely relying on photoluminescence cooling, while the thermal loss estimations can be helpful to minimize the non-radiative losses of both integrated photoluminescent-near infrared (NIR) reflective and stand-alone photoluminescent systems. As per our results, the transmission loss is higher than thermal loss in photoluminescent materials with an absorption edge wavelength (λ AE ) shorter than 794 nm and quantum yield (QY) of 50%. Our predictions show that thermalization loss overtakes quenching in photoluminescent materials with λAE longer than 834 nm and QY of 50%. The results also show that thermalization, quenching, and Stokes shift constitute around 56.8%, 35%, and 8.2% of the overall thermal loss. Results of this research can be used as a guide for the future research to enhance the photoluminescence cooling potential for urban heat mitigation application.

Abstract Urban overheating (UO) may interact with synoptic-scale weather conditions. The association between meteorological parameters and UO has already been a subject of considerable research, however, the impact of synoptic-scale weather conditions on UO magnitude, particularly in a coastal city that is also near the desert landmass (Sydney) has never been investigated before. The present research examines the influence of synoptic-scale weather conditions on UO magnitude in Sydney by utilizing the newly developed gridded weather typing classification (GWTC). The diurnal, and seasonal variations in suburban-urban temperature contrast (ΔT) in association with synoptic-scale weather conditions, and ΔT response to synoptic air-masses during extreme heat events are investigated in three zones of Sydney. Generally, an exacerbation in UO magnitude was reported at daytime over the years, whereas the nocturnal UO magnitude was alleviated over time. The humid warm (HW), and warm (W) air-masses were found primarily responsible for exacerbated daytime UO during extreme heat events and in all other seasons, raising the mean daily maximum ΔT to 8–10.5 °C in Western Sydney, and 5–6.5 °C in inner Sydney. The dry warm (DW), and W conditions were mainly responsible for urban cooling (UC) at nighttime, bringing down the mean daily minimum ΔT to − 7.5 to − 10 °C in Western Sydney, and − 6 to − 7.5 °C in inner Sydney. The appropriate mitigation technologies can be planned based on this study to alleviate the higher daytime temperatures in the Sydney suburbs.

In the pursuit of ever more efficient built environments, able to resiliently respond to the many implications of climate change, near room-temperature caloric cooling could be a game changer from multiple standpoints. In this paper, perspectives and challenges of successful implementation of elastocaloric devices in the built environment are explored by contrasting the current readiness level with the envisaged potentiality. Material-level and device-level criticalities are identified and potential solutions are discussed. The roadmap towards an informed and efficient use of this environmentally friendly technology is eventually proposed aiming at an increase of building’s energy efficiency, but also at counteracting the urban heat island effect.

Summary: A primary contributor to urban overheating is the urban heat island (UHI) formed due to increased urbanization. The adverse effects of UHI on building energy use are substantial and well documented. However, such effects are typically demonstrated through numerical simulations which are susceptible to modeling uncertainties and lack of validation resulting in a pressing research gap. Here, for the first time, we conduct a large-scale assessment to demonstrate the devastating impact of UHI on building energy consumption using real building energy use data. We find empirical evidence correlating UHI with building energy use; changes in average UHI intensity of 0.5 K correspond to an increase in monthly cooling energy consumption in a range of 0.17 kWh/m2–1.84 kWh/m2. The study validates theoretical evidence on the impact of UHI on building energy and proposes a highly innovative methodology to assess the impact of overheating on the energy balance of cities.

The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.

Variations in urban form lead to the development of distinctive intra-urban surface thermal patterns. Previous assessment of the relation between urban structure and satellite-based Land Surface Temperature (LST) has generally been limited to single-city cases. Here, examining 25 European cities (June–August 2017), we estimated the statistical association between surface parameters—the impervious fraction (λimp), the building fraction (λb), and the building height (H)—and the neighborhood scale (1000 × 1000 m) LST variations, as captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Correlation analysis, multiple linear regression, and spatial regression were used. As expected, λimp had a consistent positive influence on LSTs. In contrast, the relation of LST with λb and H was generally weaker or negative in the daytime, whereas at night it shifted to a robust positive effect. In particular, daytime LSTs of densely built, high-rise European districts tended to have lower values. This was especially the case for the city of Athens, Greece, where a more focused analysis was conducted, using further surface parameters and the Local Climate Zone (LCZ) scheme. For the urban core of the city, the canyon aspect ratio H/W had a statistically significant (p rho = −0.68) and positive during nighttime (rho = 0.45). The prevailing intra-urban surface thermal variability in Athens was well reproduced by a 5-day numerical experiment using the meteorological Weather Research and Forecasting Model (WRF) model and a modified urban parameterization scheme. Although the simulation resulted in some systematic errors, the overall accuracy of the model was adequate, regarding the surface temperature (RMSE = 2.4 K) and the near-surface air temperature (RMSE = 1.7 K) estimations.

Urban greenery is a natural solution to cool cities and provide comfort, clean air and significant social, health and economic benefits. This paper aims to present the latest progress on the field of greenery urban mitigation techniques including aspects related to the theoretical and experimental assessment of the greenery cooling potential, the impact on urban vegetation on energy, health and comfort and the acquired knowledge on the best integration of the various types of greenery in the urban frame. Also to present the recent knowledge on the impact of climate change on the cooling performance of urban vegetation and investigate and analyse possible technological solutions to face the impact of high ambient temperatures.

Cities play a crucial role in climate change: More than 50% of the growing population lives in cities producing most of the global GDP but also 78% of greenhouse gases (GHG) responsible for climate change. Moreover, due to their highly modified land-use and intensive activities, cities are at the forefront of the most rapid environmental and climatic change ever experienced by mankind. Yet, cities’ potential to mitigate both climate change and their own environment is underexploited. This paper explores ideas related to the potential of urban environments to modify their microclimates, reflecting on the overlapping potential between mitigative and adaptive actions. These actions in cities can not only tackle some of the largest contributing factors to global climate change but offer short- to medium-term benefits that could drive more immediate socioeconomic and behavioral changes. This review proposes and discusses a new preliminary definition of urban environments as microclimate modifiers—Mitigative urban Environments and Microclimates (MitEM)—and calls for further research into: (a) inter-connecting the full range of mitigative and adaptive initiatives already being undertaken in many cities and maximizing their input systemically; (b) developing a common and holistic definition of MitEM; (c) promoting its uptake at policy level and amongst the key stakeholders, based on its social and public value beyond the environmental.

Retrofitting is widely explored as one of the energy conserving opportunities for existing buildings, in which both passive and active solutions are carefully evaluated. However, when different retrofitting solutions are combined and applied to a building, the total energy savings potential, which is less than the sum of the savings from applying the various individual retrofitting solutions, is considerably reduced and the synergies among the various technologies need to be understood and evaluated. In this study, the concept of utilizability is employed for the analysis of multiple energy retrofitting solutions in buildings and is defined as the ratio of energy savings derived from applying combined solutions to a building over the sum of individual energy savings from applying individual solutions. It is aimed at providing a better understanding of the combined retrofitting solutions. The sensitivity analysis on the utilizability value further pro­vides a selection criterion for retrofitting solution selection.

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top−down and bottom−up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.

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Continuous monitoring systems have been regarded as a very useful tool to provide continuous high-frequency measurements of many parameters. Here, we analyze high-frequency time series of air temperature measurements, recorded every 10 min during 2003 in Athens (Greece) by an online monitoring system for the urban environment. We propose a set of time series analysis techniques, where missing data are well respected and information concerning the system’s dynamics is preserved. A power spectral density analysis is performed over time scales spanning from 10 min to several days. A scale-invariant behavior of the form E ( f ) ≈ f − β is revealed for scales below 9 h. Over this scaling range, we have performed structure functions analysis, and shown that air temperature data exhibit turbulent-like intermittent properties with multi-fractal statistics. The multifractal exponents obtained possess some similarities with passive scalar turbulence results. Although we illustrate the proposed approach using air temperature data, the method can be used as an efficient tool to analyse other environmental parameters monitored in urban environment.

Mitigation plans to counteract overheating in urban areas need to be based on a thorough knowledge of the state of the thermal environment, most importantly on the presence of areas which consistently demonstrate higher or lower urban land surface temperatures (hereinafter referred to as “hot spots” or “cold spots”, respectively). The main objective of this research study is to develop a methodological approach for the recognition of thermal “hot spots” and “cold spots” in urban areas during summer; this is accomplished with (a) the combined use of high and medium spatial resolution satellite data (Landsat 8 and Terra-MODIS, respectively); (b) the downscaling of the Terra-MODIS satellite data so as to acquire spatial resolution similar to the Landsat one and at the same time take advantage of the high revisit time as compared to the respective one of Landsat (16 days); and (c) the application of a statistical clustering technique to recognize “hot spots” and “cold spots”. The methodological approach was applied as a case study for the urban area of Athens, Greece for a summer period. Results demonstrated the capacity of the methodological approach to recognize “hot spots” and “cold spots”, revealed a strong relationship between land use and “hot spots” and “cold spots”, and showed that the average land surface temperature (LST) difference between the “hot spots” and “cold spots” can reach 9.1 °K.