Healthy Environment: The Need to Mitigate Urban Heat Island Effects on Human Health

Shahmohamadi, P.; Che‐Ani, Adi Irfan; Etessam, I.; Maulud, Khairul Nizam Abdul; Tawil, Norngainy Mohd

doi:10.1016/j.proeng.2011.11.139

Cited by 107 publications

(47 citation statements)

References 7 publications

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“…For example, Kim and Joh (2006) examined potential vulnerabilities of low-income elderly and suggest an awareness to increased vulnerability to EHE events in Seoul. This is also clearly the case in other urban areas where such social factors have been evaluated (Harlan et al 2006;Shahmohamadi et al 2011;Perera et al 2012). Other than social factors, we tend to believe that some of the response difference, in fact, may be attributed to meteorological factors and probably relates to the differences in summer temperature standard deviations.…”

Section: Discussionmentioning

confidence: 77%

The application of the European heat wave of 2003 to Korean cities to analyze impacts on heat-related mortality

et al. 2015

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The goal of this research is to transpose the unprecedented 2003 European excessive heat event to six Korean cities and to develop meteorological analogs for each. Since this heat episode is not a model but an actual event, we can use a plausible analog to assess the risk of increasing heat on these cities instead of an analog that is dependent on general circulation (GCM) modeling or the development of arbitrary scenarios. Initially, the 2003 summer meteorological conditions from Paris are characterized statistically and these characteristics are transferred to the Korean cites. Next, the new meteorological dataset for each Korean city is converted into a daily air mass calendar. We can then determine the frequency and character of "offensive" air masses in the Korean cities that are historically associated with elevated heat-related mortality. One unexpected result is the comparative severity of the very hot summer of 1994 in Korea, which actually eclipsed the 2003 analog. The persistence of the offensive air masses is considerably greater for the summer of 1994, as were dew point temperatures for a majority of the Korean cities. For all the Korean cities but one, the summer of 1994 is associated with more heat-related deaths than the analog summer, in some cases yielding a sixfold increase over deaths in an average summer. The Korean cities appear less sensitive to heat-related mortality problems during very hot summers than do large eastern and Midwestern US cities, possibly due to a lesser summer climate variation and efficient social services available during extreme heat episodes.

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Section: Discussionmentioning

confidence: 77%

The application of the European heat wave of 2003 to Korean cities to analyze impacts on heat-related mortality

et al. 2015

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“…Because urban structures and vegetation are variable, a spatial distribution calculation model based on micro-climate prediction with varying urban structures is required to determine the temperature variability around buildings [32]. With a high resolution that can resolve building-scale forecasting information, the operations and management department of a city must plan to implement such a system to reduce the heat stress on public health [33,34].…”

Section: Of 21mentioning

confidence: 99%

Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling

Shin

Roh

2018

Atmosphere

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Abstract:The Korean peninsula has complex and diverse weather phenomena, and the Korea Meteorological Administration has been working on various numerical models to produce better forecasting data. The Unified Model Local Data Assimilation and Prediction System is a limited-area working model with a horizontal resolution of 1.5 km for estimating local-scale weather forecasts on the Korean peninsula. However, in order to numerically predict the detailed temperature characteristics of the urban space, in which surface characteristics change rapidly in a small spatial area, a city temperature prediction model with higher resolution spatial decomposition capabilities is required. As an alternative to this, a building-scale temperature model was developed, and a 25 m air temperature resolution was determined for the Seoul area. The spatial information was processed using statistical methods, such as linear regression models and machine learning. By comparing the accuracy of the estimated air temperatures with observational data during the summer, the machine learning was improved. In addition, horizontal and vertical characteristics of the urban space were better represented, and the air temperature was better resolved spatially. Air temperature information can be used to manage the response to heat-waves and tropical nights in administrative districts of urban areas.

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“…In this study a 7 ¢ 7 low pass filter was therefore applied to both the SVF derived using the Zakšek model [27], and SP, allowing a comparison between SP and the average SVF in the filtered area. Other filter sizes were also examined (3,5,7,9,11), and a trade-off between SVF prediction accuracy and spatial resolution was found to exist, for which the 7 ¢ 7 filter was chosen as an acceptable compromise. For Landsat TM's 30 m spatial resolution, the 7 ¢ 7 filter extends between 105 m and 148 m from the central point, which is adequate to account for shadows cast by the majority of buildings in the cities used in this study when illuminated at a solar elevation of 40 ¥ , and still small enough to preserve some fine-scale spatial variations in urban SVF.…”

Section: Development Of Theoretical and Empirical Relationships Betwementioning

confidence: 99%

“…The Urban Heat Island effect is a well-established phenomenon [1] with clear negative health impacts, especially during summer heat waves [2,3]. Sky View Factor (SVF) is defined as the ratio of radiation received from the sky by a planar surface to that received from the entire hemispheric radiating environment [4] and is typically simplified as a dimensionless value between 0 and 1, representing a completely obscured and unobscured sky, respectively [5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection

Hodúl

Knudby

2016

Remote Sensing

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Sky View Factor (SVF, a dimensionless value between 0 and 1 representing obstructed and unobstructed sky, respectively) has an important influence on urban energy balance, and is a key contributor to the Urban Heat Island (UHI) effect experienced by heavily built up regions. Continuous urban SVF maps used in modeling the spatial distribution of UHI can be derived analytically using Lidar data; however, Lidar data are costly to obtain and often lack complete coverage of large cities or metropolitan areas. This study develops and validates a method for estimating continuous urban SVF from globally available Landsat TM data, based on the presence of shadows cast by SVF-reducing urban features. SVF and per-pixel shadow proportion (SP) were first calculated for synthetic grid cities to confirm a logarithmic relationship between the two properties; then Lidar data from four US cities were used to determine an empirical regression relating SP to SVF. Spectral Mixture Analysis was then used to estimate per-pixel SP in a Landsat 5 TM image covering the Greater Vancouver Area, Canada, and the empirical regression was used to calculate SVF from per-pixel SP. The accuracy of the resulting SVF map was validated using independent Lidar-derived SVF data (R 2 = 0.78; RMSE = 0.056).

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Healthy Environment: The Need to Mitigate Urban Heat Island Effects on Human Health

Cited by 107 publications

References 7 publications

The application of the European heat wave of 2003 to Korean cities to analyze impacts on heat-related mortality

The application of the European heat wave of 2003 to Korean cities to analyze impacts on heat-related mortality

Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling

Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection

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