Urban Heat Island and Mitigation Technologies in Asian and Australian Cities—Impact and Mitigation

Yang, Junjing; Santamouris, Mat

doi:10.3390/urbansci2030074

Cited by 20 publications

(8 citation statements)

References 40 publications

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“…These results confirm that the roles played by the difference in energy consumption between inner city, suburban, and urban fringe areas are mainly because the cooling load of urban buildings is 13% higher than that of similar buildings in the rural-urban fringe areas. Thus, this relationship between electricity consumption and temperature has been clearly established [20,[54][55][56].…”

Section: Relationship Between Lst and Uhii With High-rise Buildings In Different Three Zonesmentioning

confidence: 93%

“…Anthropogenic parameters such as industrial heat emissions, heating, transport, or air conditioning can contribute as well to heat intensification, as the cities consume 78% of the world's energy and produce more than 60% of greenhouse gas emissions [18,19]. This increasing urbanization has a significant impact on urban microclimates and leads to warmer temperatures in cities [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Urban Land Surface Temperature and Vertical City Associated with Dengue Incidences

et al. 2020

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Rapid population and urban growth in Bangkok increases the need for vertical city development because of the limited territory. This might lead to increasing land surface temperatures (LST), which makes some urban areas significantly warmer and leads to hot spots known as urban heat islands. It is known that climatic factors, such as rainfall and temperature, influence increases in dengue incidences. Thus, this research uses spatial statistical analysis to consider the association of urban LST with dengue incidences. The LST calculation methods are based on LANDSAT imageries in 2009 and 2014. Pearson correlation and Bayesian hierarchical modeling were used for predicting dengue incidences. This study found the highest correlation between the density of high-rise buildings, which had a significant influence on LST, and dengue incidences. Both the number of high-rise buildings and the surface temperature of low-rise buildings increased dengue incidence between 2009 and 2014. Overall, it was found that for every increase of 1000 high-rise buildings, the dengue incidence increased 2.19 on average during that period.

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Section: Relationship Between Lst and Uhii With High-rise Buildings In Different Three Zonesmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Assessment of Urban Land Surface Temperature and Vertical City Associated with Dengue Incidences

et al. 2020

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“…A recent modeling study based on a building located in the center of Rome, Italy, indicated that the energy consumption of cooling would be underestimated by 35%-50% if the climatic effect of the heat island is not considered (Ciancio et al, 2018). More studies related to the urban microclimate and the UHI effect and its mitigation technology solutions can be tracked down in a recent special issue of Urban Science (Yang and Santamouris, 2018).…”

Section: Question 6: How Can Local Weather Data Be Generated and Usedmentioning

confidence: 99%

Ten questions on urban building energy modeling

Hong

Chen

Luo

et al. 2020

Building and Environment

289

121

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Buildings in cities consume up to 70% of all primary energy. To achieve cities' energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings. Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers. This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

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“…Several researches have used the Moran's I index (Deilami et al, 2018;Mavrakou et al, 2018 andYang andSantamouris, 2018) to analyze the adverse effects of UHI and under the perspective of appropiate urban development. It is noteworthy that the main object of the technique is based on the spatial heterogeneity of the distribution of LST (for example, high or low clustering pattern and dispersed spatial distribution).…”

Section: Moran's I Indexmentioning

confidence: 99%

Application of Spatial Modeling of Biophysical Variables in an Urbanized Area in the Amazon: The Case of the Metropolitan Area of Belém-Pará

Silva

Margalho

Serrão

et al. 2021

Rev. bras. meteorol.

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The type of land use and land cover plays a decisive role in land surface temperature (LST). As cities are composed of varied covers, including vegetation, built-up areas, buildings, roads and areas without vegetation, understanding LST patterns in complex urban spaces is becoming increasingly important. The present study investigated the relationship between LST and albedo, NDVI, NDWI, NDBI and NDBaI in the period between 1994 and 2017. Images from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) onboard the Landsat 5 and 8 satellites, respectively, were used in the study. The images were processed, resampled (spatial resolution of 120 m) in the environment of the QGIS 3.0 software and, finally, centroids were extracted resulting in a total of 1252 points. A classical regression (CR) model was applied to the variables, followed by spatial autoregressive (SARM) and spatial error (SEM) models, and the results were compared using accuracy indices. The results showed that the highest correlation coefficient was found between albedo and NDBaI (r = 0.88). The relationship between albedo and LST (r = 0.7) was also positive and significant at р < 0.05. The global Moran's I index showed spatial dependence and non-stationarity of the LST (I = 0.44). The SEM presented the best accuracy metrics (AIC = 3307.15 and R2 = 0.65) for the metropolitan region of Belém, explaining considerably more variations in the relationship between explanatory factors and LST when compared to conventional CR models.

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Urban Heat Island and Mitigation Technologies in Asian and Australian Cities—Impact and Mitigation

Abstract: n/a

Cited by 20 publications

References 40 publications

Assessment of Urban Land Surface Temperature and Vertical City Associated with Dengue Incidences

Assessment of Urban Land Surface Temperature and Vertical City Associated with Dengue Incidences

Ten questions on urban building energy modeling

Application of Spatial Modeling of Biophysical Variables in an Urbanized Area in the Amazon: The Case of the Metropolitan Area of Belém-Pará

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