Mitigating the Urban Heat Island Effect in Megacity Tehran

Sodoudi, Sahar; Shahmohamadi, P.; Vollack, Ken; Cubasch, Ulrich; Che‐Ani, Adi Irfan

doi:10.1155/2014/547974

Cited by 82 publications

(51 citation statements)

References 36 publications

(34 reference statements)

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“…These results go with other recently published study, which suggests that the combination of vegetation and green roofs can reduce the temperature from around 295K to 294.20K [51]. Additionally, M. Burse and J. Carol found that combining green-roofs with street-level vegetation can reduce the air temperature uniform ally with a maximum of 2.4K.…”

Section: E Green-roof Casesupporting

confidence: 91%

“…Several studies show that the maximum temperature difference between the inside and outside of the small green area can be 3K [51]. Accordingly, we have compared the temperature and the relative humidity of the measuring site between the two measuring points in the site to estimate green-vegetation cover impact on the temperature reduction.…”

Section: B Measurements Resultsmentioning

confidence: 99%

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Quantifying the Impact of Green-Roofs on Urban Heat Island Mitigation

Sahnoune¹,

Benhassine²

2017

IJESD

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Abstract-Climate change is one of the main concerns of our times, and it became even more noticeable due to its impact on global temperature, precipitation patterns, and even arctic sea ice melting. Moreover, it has been proven that climate change is one of the main factors affecting the Urban Heat Island (UHI); which in its turn, increases the energy demand in sustainable cities, where reducing power costs still a major challenge. Furthermore, in order to keep a balanced environment, and contribute to temperature mitigation, the use of vegetation cover is definitely one remarkable strategy. It helps decreasing the temperature down to the values required in sustainable development, reducing energy consumption, and lowering Greenhouse Emission (GHE). In this work, we assess the impact of green-roofs on UHI, and we try to define a decision model that helps calculating the best green-roof/green-infrastructure ratio. Besides, we aim to identify the best practices and measures to take in order to mitigate the UHI in semi-arid climate areas.Our work involves the use of a Geographic Information System (GIS), which provides a support to estimate the greenery conditions. Our methodology is based on a probabilistic and comparative approach, and evaluated using different green-roof models. We select a residential area in Constantine, Algeria as our study subject, and we use ENVI-met as the software support. Among the findings of this study, providing proper, yet experimentally proven recommendations on how, how much and where to use green roofs, was our main contribution. In addition, the results of our study indicate not only the impact of green-roofs on UHI, but also the effect of large-scale green-roof installation on the liveable areas in sustainable cities.Index Terms-Climate change, geographic information system, green-roofs, sustainable development, urban heat island.

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Section: E Green-roof Casesupporting

confidence: 91%

Section: B Measurements Resultsmentioning

confidence: 99%

Quantifying the Impact of Green-Roofs on Urban Heat Island Mitigation

Sahnoune¹,

Benhassine²

2017

IJESD

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“…In recent decades, Tehran has been experiencing environmental problems, such as UHI [31][32][33] and urban air pollution (UAP) [34,35] that has impacted the health and even endangered the lives of local residents. UHI and UAP have a common source and interact with each other [4,8].…”

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confidence: 99%

Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City

et al. 2016

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Abstract:The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of´4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient:´0.76), night LST and IS in spring (0.60), night LST and albedo in winter (´0.53) and day LST with fractional vegetation cover in summer (´0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.

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“…This megacity has a large, densely populated area of 750 km 2 (Sodoudi et al 2014). It is located in valleys and is surrounded to the north, northwest, east and southeast by mountains with heights between 1000 and 3800 m (Madanipour 2006).…”

Section: Case Studymentioning

confidence: 99%

Air Quality Risk Index (AQRI) and its application for a megacity

Ahmadi

Abbaspour

Arjmandi

et al. 2015

Int. J. Environ. Sci. Technol.

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Urban air quality is a major concern throughout the world. The concentration of human activities in a relatively small area puts enormous pressure on urban systems and has led to numerous environmental problems which have created major problems for urban air quality management. The complex nature of air pollution, especially with respect to health impacts in urban areas, has prompted attempts to define the so-called indicators that condense and simplify the available monitoring data to make them suitable for public reporting and decision makers. Several concepts and indicators exist to measure and rank urban areas in terms of their infrastructural, socioeconomic and environment-related parameters. In past four decades different indices have been introduced, such as Pollutant Standards Index, Air Quality Index (AQI), Air Quality Health Index and Aggregate Risk Index, but none of them have considered importance of the integrated health, environment and safety factors relevant to the extended risk factors involved, such as diverse economic losses and negative socioeconomic aspects. Therefore an integrated AQI, so-called, Air Quality Risk Index which considers environmental conditions, and economic losses involved were applied for the city of Tehran as a megacity. The results indicated that this index can be applied to define the actual condition(s) of urban air quality and to achieve sustainability and resiliency.

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Mitigating the Urban Heat Island Effect in Megacity Tehran

Cited by 82 publications

References 36 publications

Quantifying the Impact of Green-Roofs on Urban Heat Island Mitigation

Quantifying the Impact of Green-Roofs on Urban Heat Island Mitigation

Seasonal Variations of the Surface Urban Heat Island in a Semi-Arid City

Air Quality Risk Index (AQRI) and its application for a megacity

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