Approaches to study Urban Heat Island – Abilities and limitations

Mirzaei, Parham A.; Haghighat, Fariborz

doi:10.1016/j.buildenv.2010.04.001

Cited by 593 publications

(363 citation statements)

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“…thermal stress is intensified in cities mainly due to the urban heat island (UHi) effect, that is, the existence of areas (frequently the CBD) where the temperature is higher than that of the periphery. the UHi is a widely studied urban climate feature with implications on thermal comfort (therefore on energy consumption, Mirzaei and Haghighat, 2010) and in human health (tan et al, 2010). there are essentially three types of UHi: (i) surface UHi, as urban surfaces are usually hotter than those of the suburbs and of the countryside (Lopes, 2003); (ii) urban boundary layer UHi, that is the atmospheric layer where temperature, humidity, turbulence, chemical composition are modified by the presence of the underlying city; it lays above the top of the buildings and spreads sometimes up to the free-atmosphere (Oke, 1996); (iii) Urban canopy layer UHI, that corresponds to the existence of hotter urban air between the ground and the top of the buildings of the urban area and will be dealt with in this paper.…”

Section: Mots-clésmentioning

confidence: 99%

“…We are aware that statistical analysis has weaknesses, particularly the inability "to present several physical phenomena" (Mirzaei and Haghighat, 2010). Moreover, there are generally a limited number of stations and of meteorological parameters measured, not always for a long period of time, necessary to "filter the unpredictable errors" (idem).…”

Section: Fig 5 -Blox Plots Da Intensidade Da Ilha De Calor Diurna (1mentioning

confidence: 99%

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Lisbon heat island statistical study (2004-2012)

Alcoforado¹,

Lopes²,

Alves

et al. 2015

Finisterra

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In memoriam Henrique andrade abstract -With the aim of implementing climatic guidelines for planning purposes, the urban climate of each particular city must be assessed, particularly the frequency of unwanted climatic features, such as the Urban Heat island (UHi). as the Lisbon "Mesoscale urban meteorological network" (CeG-iGOt-ULisboa) has been running since 2004, it is now possible to present statistical results about the UHi. it was calculated on an hourly basis as the difference between one of the "central" measurement points (restauradores or saldanha) and one of the eccentric points of the network (Carnide or Monsanto). UHi is more intense in summer (maximum hourly averages up to 6.3ºC) than in winter (up to 3.8ºC), and more intense during the night than during the day. in Lisbon, its causes are not only due to the modification of energy balance in urban areas, but also to the shelter effect from the prevailing and cold/cool north winds, due to the topography and the buildings. Alcoforado, A. Lopes, E. Lima Alves and P. Canário o clima urbano de cada cidade, nomeadamente no que diz respeito à frequência de ocorrência de fenómenos considerados indesejáveis, como a ilha de calor (iC) urbana. Depois de 8 anos de funcionamento da "rede de mesoscala" de monitorização meteorológica em Lisboa (CeG-iGOt-ULisboa), é agora possível apresentar resultados estatísticos sobre a iC. É expli cada metodologia de cálculo da intensidade da ilha de calor, pela diferença entre um dos postos "centrais" (restauradores ou saldanha) e um dos postos mais excêntricos (Carnide ou Monsanto). a iC é mais intensa e frequente no Verão (valor máximo horário: 6,3ºC) do que no inverno (valor máximo: 3,8ºC) e é mais intensa de noite do que de dia. em Lisboa, as causas da iC devem-se não só à modificação de parâmetros do balanço energético em meio urbano, como também ao efeito de abrigo de ventos dominantes frios ou frescos do quadrante norte, proporcionado pela topografia e pelos próprios edifícios. Key-words:Palavras-chave: ilha urbana de calor, padrões térmicos, rede mesoclimática, Lisboa, Portugal.résumé -étude StatiStique de l'Îlot de chaleur à liSBonne. La connaissance des climats urbains est nécessaire à une juste appréciation des décisions à prendre dans le cadre de l'aménagement du territoire, et surtout en ce qui concerne la fréquence des phénomènes dits indésirables, comme l'îlot de chaleur urbain (iC). Un réseau «d'échelle moyenne» d'observations météorologiques ayant fonctionné à Lisbonne de 2004 à 2012 (CeG-iGOt-ULisboa), on peut déjà présenter des résultats statistiques concernant l'iC. Le calcul de son intensité est basé sur la différence entre les données d'un poste «central» (restauradores ou saldanha) et celles d'un des postes les plus excentriques (Carnide ou Monsanto). L'îlot de chaleur est plus intense et plus fréquent en été (valeur maximale horaire de 6,3ºC), qu'en hiver (valeur maximale de 3,8ºC) et il est plus fort la nuit que le jour. À Lisbonne, les causes de l'iC sont dues non seulement aux modifications des ...

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Section: Mots-clésmentioning

confidence: 99%

Section: Fig 5 -Blox Plots Da Intensidade Da Ilha De Calor Diurna (1mentioning

confidence: 99%

Lisbon heat island statistical study (2004-2012)

Alcoforado¹,

Lopes²,

Alves

et al. 2015

Finisterra

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“…Unlike air temperature, LST has a higher spatial resolution and relates more closely to surface land use or landscape pattern, which can easily be derived from remote sensing data (Imhoff et al, 2010;Voogt and Oke, 2003;Weng, 2009). Thus, surface UHI and its associated data source, LST, have been increasingly used in the study of urban climate in recent decades (EPA, 2009;Mirzaei and Haghighat, 2010). Previous studies have shown that both air and surface UHIs are associated with heat waves (Changnon et al, 1996;Fischer et al, 2007), disease (Liu and Weng, 2009) and pose a health risk to more than 50% of the world's population (Frumkin and McMichael, 2008;Wu, 2010).…”

Section: Introductionmentioning

confidence: 99%

Application of a new integrated landscape index to predict potential urban heat islands

Chen

Zhao

Yao

et al. 2016

Ecological Indicators

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a b s t r a c tStudies on the interactions between urban landscape patterns and land surface temperatures are the key to urban heat island (UHI) mitigation. However, the landscape pattern indices used in previous studies were inconsistent in type and number. Furthermore, few studies combined the composition and configuration indices into one integrated indicator. The description of landscape pattern is thus a great yet rewarding challenge. In this study, we used the integrated location weighted landscape index (LWLI) proposed by Chen et al. (2009) and revised it to indicate potential UHIs. Fifty-six circular landscape samples along four transects were created and the LWLI was derived based on the theory of the GINI index. The LWLI considers the type, composition and configuration of different land covers inside a landscape, where configuration is treated as location weights. The potential UHI was represented by the land surface temperature (LST) of each circle landscape center. The correlation analysis results showed that the LWLI was significantly positively correlated with summer potential UHIs, with a Pearson R equaling 0.736, and barely correlated with winter potential UHI. Moreover, the LWLI was slightly more strongly correlated with potential UHI than composition alone. These correlations weakened as the landscapes grew larger. The linear regression results further revealed that the LWLI explained about 53% of the summer potential UHI, which was slightly better than composition alone (49%). This indicated that the LWLI was as effective in predicting the potential UHI as the combination of several pattern indicators, echoing Tobler's first law of geography, which states that "all things are related, but nearby things are more related than distant things." This study also leaves room for improvement of the index by integrating more environmental/ecological parameters as weights, and for further application of the index in other fields.

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“…Researchers has been interested in understanding the various aspects of this phenomenon including its causes (Huang et al, 2011), impacts (Imhoff et al, 2010) and complexity (Mirzaei and Haghighat, 2010). Additionally, UHI has the tendency to elevate heat wave intensities as observed in case of Chicago in 1995, Russia in 2003 (Sailor and Lu, 2004), Delhi, India in 2014 (Sharma et al, 2014), Shanghai, and Beijing, China (Chen et al, 2014) and recently in Bangkok, Thailand.…”

Section: Introductionmentioning

confidence: 99%

Effects of Agricultural Suburb on Urban Heat Island of Bangkok Metropolitan City, Thailand

2017

CAFES-17, ICFCT-2017, SDMCAE-17 April 18-19, 2017 Kyoto (Japan)

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Approaches to study Urban Heat Island – Abilities and limitations

Cited by 593 publications

References 77 publications

Lisbon heat island statistical study (2004-2012)

Lisbon heat island statistical study (2004-2012)

Application of a new integrated landscape index to predict potential urban heat islands

Effects of Agricultural Suburb on Urban Heat Island of Bangkok Metropolitan City, Thailand

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