A mobile sensor-based Approach for Analyzing and Mitigating Urban Heat Hazards

2020 ◽  
Author(s):  
Yanzhe Yin ◽  
Andrew Grundstein ◽  
Deepak Mishra ◽  
Navid Hashemi ◽  
Lakshmish Lakshmish
Keyword(s):  
Machine Learning ◽  
Air Temperature ◽  
Heat Exposure ◽  
Ambient Air ◽  
Temperature Data ◽  
Mobile Sensors ◽  
Mobile Sensor ◽  
High Quality ◽  
Urban Heat ◽  
Heat Hazard

<p>High-quality temperature data at a finer spatial-temporal scale is critical for analyzing the risk of heat hazards in urban environments. The variability of urban landscapes makes cities a challenging landscape for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts: the spatial-temporal granularities are too coarse and the ability to track the actual ambient air temperature instead of land surface temperature. Overcoming these limitations requires radically different research approaches, both the paradigms for collecting the temperature data and developing models for high-resolution heat mapping. We present a comprehensive approach for studying urban heat hazards by harnessing a high-quality hyperlocal temperature dataset from a network of mobile sensors and using it to refine the satellite-based temperature products. We mounted vehicle-borne mobile sensors on thirty city buses to collect high-frequency (5 sec) temperature data from June 2018 to Nov 2019. The vehicle-borne data clearly show significant temperature differences across the city, with the largest differences of up to 10℃ and morning-afternoon diurnal changes at a magnitude around 20℃. Then we developed a machine learning approach to derive a hyperlocal ambient air temperature (AAT) product by combining the mobile-sensor temperature data, satellite LST data, and other influential biophysical parameters to map the variability of heat hazard over areas not covered by the buses. The machine learning model output highlighted the high spatio-temporal granularity in AAT within an urban heat island. The seasonal AAT maps derived from the model show a well-defined hyperlocal variability of heat hazards which are not evident from other research approaches. The findings from this study will be beneficial for understanding the heat exposure vulnerabilities for individual communities. It may also create a pathway for policymakers to devise targeted hazard mitigation efforts such as increasing green space and developing better heat-safety policies for workers.</p>

scholarly journals Ambient Air Temperature Assisted Crystallization for Inorganic CsPbI2Br Perovskite Solar Cells

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3398
Author(s):  
Yi Long ◽  
Kun Liu ◽  
Yongli Zhang ◽  
Wenzhe Li
Keyword(s):  
Solar Cells ◽  
Air Temperature ◽  
High Performance ◽  
Defect Density ◽  
Perovskite Solar Cells ◽  
Ambient Air ◽  
Dark Phase ◽  
High Quality ◽  
Ambient Air Temperature ◽  
Air Atmosphere

Inorganic cesium lead halide perovskites, as alternative light absorbers for organic–inorganic hybrid perovskite solar cells, have attracted more and more attention due to their superb thermal stability for photovoltaic applications. However, the humid air instability of CsPbI2Br perovskite solar cells (PSCs) hinders their further development. The optoelectronic properties of CsPbI2Br films are closely related to the quality of films, so preparing high-quality perovskite films is crucial for fabricating high-performance PSCs. For the first time, we demonstrate that the regulation of ambient temperature of the dry air in the glovebox is able to control the growth of CsPbI2Br crystals and further optimize the morphology of CsPbI2Br film. Through controlling the ambient air temperature assisted crystallization, high-quality CsPbI2Br films are obtained, with advantages such as larger crystalline grains, negligible crystal boundaries, absence of pinholes, lower defect density, and faster carrier mobility. Accordingly, the PSCs based on as-prepared CsPbI2Br film achieve a power conversion efficiency of 15.5% (the maximum stabilized power output of 15.02%). Moreover, the optimized CsPbI2Br films show excellent robustness against moisture and oxygen and maintain the photovoltaic dark phase after 3 h aging in an air atmosphere at room temperature and 35% relative humidity (R.H.). In comparison, the pristine films are completely converted to the yellow phase in 1.5 h.

scholarly journals Historical and future temporal trends in the summer Urban Heat Island of Athens (Greece) 

2021 ◽  
Author(s):  
Tim van der Schriek ◽  
Konstantinos V. Varotsos ◽  
Dimitra Founda ◽  
Christos Giannakopoulos
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Air Temperature ◽  
Temporal Trends ◽  
Temperature Data ◽  
Climate Change Scenarios ◽  
Heat Island ◽  
Hot Days ◽  
The Future ◽  
Urban Heat

<p>Historical changes, spanning 1971–2016, in the Athens Urban Heat Island (UHI) over summer were assessed by contrasting two air temperature records from established meteorological stations in urban and rural settings. When contrasting two 20-year historical periods (1976–1995 and 1996–2015), there is a significant difference in summer UHI regimes. The stronger UHI-intensity of the second period (1996–2015) is likely linked to increased pollution and heat input. Observations suggest that the Athens summer UHI characteristics even fluctuate on multi-annual basis. Specifically, the reduction in air pollution during the Greek Economic Recession (2008-2016) probable subtly changed the UHI regime, through lowering the frequencies of extremely hot days (T<sub>max</sub> > 37 °C) and nights (T<sub>min</sub> > 26 °C).</p><p>Subsequently, we examined the future temporal trends of two different UHIs in Athens (Greece) under three climate change scenarios. A five-member regional climate model (RCM) sub-ensemble from EURO-CORDEX with a horizontal resolution of 0.11° (~12 × 12 km) simulated air temperature data, spanning the period 1976–2100, for the two station sites. Three future emissions scenarios (RCP2.6, RCP4.5 and RCP8.5) were implanted in the simulations after 2005. The observed daily maximum and minimum air temperature data (T<sub>max</sub> and T<sub>min</sub>) from two historical UHI regimes (1976–1995 and 1996–2015, respectively) were used, separately, to bias-adjust the model simulations thus creating two sets of results.</p><p>This novel approach allowed us to assess future temperature developments in Athens under two different UHI intensity regimes. We found that the future frequency of days with T<sub>max</sub> > 37 °C in Athens was only different from rural background values under the intense UHI regime. There is a large increase in the future frequency of nights with T<sub>min</sub> > 26 °C in Athens under all UHI regimes and climate scenarios; these events remain comparatively rare at the rural site.</p><p>This study shows a large urban amplification of the frequency of extremely hot days and nights which is likely forced by increasing air pollution and heat input. Consequently, local mitigation policies aimed at decreasing urban atmospheric pollution are expected to be also effective in reducing urban temperatures during extreme heat events in Athens under all future climate change scenarios. Such policies therefore have multiple benefits, including: reducing electricity (energy) needs, improving living quality and decreasing heat- and pollution related illnesses/deaths.</p><p> </p>

scholarly journals Evaluating the Effectiveness of Mitigation Options on Heat Stress for Sydney, Australia

2018 ◽  
Vol 57 (2) ◽  
pp. 209-220 ◽  
Author(s):  
Shaoxiu Ma ◽  
Andy Pitman ◽  
Jiachuan Yang ◽  
Claire Carouge ◽  
Jason P. Evans ◽  
...  
Keyword(s):  
Heat Stress ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Ambient Air ◽  
Mitigation Strategies ◽  
Heat Island ◽  
Human Thermal Comfort ◽  
Ambient Air Temperature ◽  
Populations At Risk ◽  
Urban Heat

AbstractGlobal warming, in combination with the urban heat island effect, is increasing the temperature in cities. These changes increase the risk of heat stress for millions of city dwellers. Given the large populations at risk, a variety of mitigation strategies have been proposed to cool cities—including strategies that aim to reduce the ambient air temperature. This paper uses common heat stress metrics to evaluate the performance of several urban heat island mitigation strategies. The authors found that cooling via reducing net radiation or increasing irrigated vegetation in parks or on green roofs did reduce ambient air temperature. However, a lower air temperature did not necessarily lead to less heat stress because both temperature and humidity are important factors in determining human thermal comfort. Specifically, cooling the surface via evaporation through the use of irrigation increased humidity—consequently, the net impact on human comfort of any cooling was negligible. This result suggests that urban cooling strategies must aim to reduce ambient air temperatures without increasing humidity, for example via the deployment of solar panels over roofs or via cool roofs utilizing high albedos, in order to combat human heat stress in the urban environment.

scholarly journals Estimasi Dampak Urban Heat Island terhadap Laju Evapotranspirasi: Studi Kasus di Kota Palembang

2021 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Ari Sugiarto ◽  
Budi Indra Setiawan ◽  
Chusnul Arif ◽  
Satyanto Krido Saptomo
Keyword(s):  
Urban Heat Island ◽  
Air Temperature ◽  
Meteorological Station ◽  
Temperature Data ◽  
Water Evaporation ◽  
Heat Island ◽  
National Agency ◽  
Evapotranspiration Rate ◽  
Urban Heat ◽  
The Difference

A review of air temperature in the Palembang city by reviewing data from the National Agency for Meteorology, Climatology, and Geophysics/BMKG (Kenten Climatology Station and the SMB II Meteorological Station) shows a difference in air temperature can indicate the occurrence of Urban Heat Island (UHI). The difference in air temperature affects the evapotranspiration rate (ET) because air temperature very influencing water evaporation. ET rate estimation with air temperature data is the first step to prove this hypothesis. Hargreaves and Samani, Blaney and Criddle, Linacre, and Kharuffa models is the ET model that using air temperature as the variable was used to estimate the ET rate. Air temperature data used in the period 2011-2020 by reviewing data from the Kenten Climatology Station and the SMB II Meteorological Station. The results of this study of air temperature data from the Kenten Climatology Station and the SMB II Meteorology Station showed a difference in air temperature with the minimum ∆T of 0.42 oC, the maximum of 0.43 oC, and the daily average of 0.41 oC. This difference in air temperature has an impact on the difference in the ET rate with the average ∆ET of the Hargreaves and Samani model of 0.05 mm/day, the Blaney and Criddle model of 0.05 mm/day, the Linacre model of 0.06 mm/day, and the Kharuffa model of 0.14 mm/day. The results of this study predicted that an increase in air temperature causes an increase in the ET rate of ± 10-30%.

scholarly journals Retrofitting solutions for a campus building to mitigate urban heat island in a hot humid climate

2021 ◽  
Vol 2042 (1) ◽  
pp. 012062
Author(s):  
Vajreshwari Patil ◽  
Maite Bizcarguenaga ◽  
Katherine Lieberknecht ◽  
Juliana Felkner
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Ambient Air ◽  
Cooling Effect ◽  
Heat Island ◽  
Humid Climate ◽  
Temperature Reduction ◽  
Urban Heat ◽  
Hot Humid Climate

Abstract In this study we examine the summer cooling effects of trees and green facades on reducing urban heat island effects. Using ENVI-met model simulations, we investigate the influence of added greenery on the surface and ambient air temperature and its role on air fluctuations in the hot humid climate of Austin, TX, at pedestrian height. Under the specific conditions considered in this model, the results show the combination of trees and green facades has a greater cooling effect. Added greenery to the building mostly impacts the building's surface temperature during the hottest hours of the day, registering a maximum surface temperature reduction of 20.33°C. Simulations also show a maximum overall potential air temperature reduction of 0.54°C, and a maximum potential air temperature cooling effect near the building of 0.91°C. Future research should be conducted to address this study's limitations. Nevertheless, these findings can provide architects, designers, planners, and policymakers with a better understanding of the many benefits trees and green facades have, and provide them with the necessary tools to implement new solutions across sectors and scales to reduce the impacts urban areas have on the environment and provide a better living for all.

scholarly journals GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning

2021 ◽  
Author(s):  
Qian He ◽  
Ming Wang ◽  
Kai Liu ◽  
Kaiwen Li ◽  
Ziyu Jiang
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Air Temperature ◽  
Spatial Interpolation ◽  
Gaussian Process Regression ◽  
Temperature Data ◽  
Air Temperatures ◽  
Maximum Air Temperature ◽  
Maximum Minimum

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

scholarly journals A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4378
Author(s):  
Anastasiia Grishina ◽  
Marta Chinnici ◽  
Ah-Lian Kor ◽  
Eric Rondeau ◽  
Jean-Philippe Georges
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Air Temperature ◽  
Data Center ◽  
Waste Heat ◽  
Cooling System ◽  
Thermal Characteristics ◽  
Ambient Air ◽  
Characteristics Analysis ◽  
Temperature Ranges

The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data.

scholarly journals Ilhas de calor urbano em cidade do semiárido nordestino

GeoTextos ◽  
2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Juliana Maria Oliveira Silva ◽  
Marcelo De Oliveira Moura ◽  
Vinicius Ferreira Luna
Keyword(s):  
Data Collection ◽  
Air Temperature ◽  
Urban Climate ◽  
Temperature Data ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Heat Islands ◽  
Intensity Category ◽  
Urban Heat ◽  
The City

<p>A pesquisa pautou-se nas concepções do Sistema Clima Urbano de Monteiro (1976) e procurou identificar e mapear as ilhas de calor urbano na cidade do Crato-CE, em dois períodos sazonais do ano. Para isso, foram selecionados 10 pontos experimentais distribuídos em bairros na zona urbana da cidade, e aferiram-se dados de temperatura do ar com termohigrômetros instalados em abrigos meteorológicos durante os meses de abril (mês chuvoso) e outubro (mês seco). A partir da coleta de dados, a categoria predominante de intensidade das Ilhas de Calor intra e interurbana para a cidade do Crato foi o de ‘Média magnitude’. O horário que ocorre a maior intensidade da ilha de calor é pela tarde, 14h, com valores superiores a 5ºC de diferença de um local para o outro. Os bairros mais densamente ocupados e com baixa cobertura vegetal apresentaram os maiores valores de temperatura, enquanto que, nos que se localizam mais próximos da encosta da chapada e com vegetação mais densa, ocorreram as temperaturas mais amenas.</p><p>Abstract</p><p>URBAN HEAT ISLANDS IN CITY OF THE NORTHEAST SEMIARID</p><p>The research was based on the conceptions of the Monteiro Urban Climate System (1976) and sought to identify and map the urban heat islands in the city of Crato/ Ce in two seasonal periods of the year. For this, 10 experimental points were selected and distributed in neighborhoods in the urban area of the city and air temperature data was measured with thermohygrometers installed in meteorological shelters during the months of April (rainy month) and October (dry month). From the data collection, the predominant intensity category of the intra and interurban Heat Islands for the city of Crato was that of ‘Medium magnitude’. The time that occurs the greatest intensity of the heat island is in the afternoon, 14h, with values above 5ºC of difference from one place to another. The most densely occupied neighborhoods and with low vegetation cover had the highest temperature values, while those located closer to the slope of the plateau and with more dense vegetation, the milder temperatures occurred.</p>

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