Interaction between urban heat island and urban pollution island during summer in Berlin

Li, Huidong; Meier, Fred; Lee, Xuhui; Chakraborty, TC; Liu, Junfeng; Schaap, Martijn; Sodoudi, Sahar

doi:10.1016/j.scitotenv.2018.04.254

Cited by 265 publications

(140 citation statements)

References 60 publications

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“…For example, Tan et al 28 used data from 11 AWSs to examine the UHI effect on human health in Shanghai. Similarly, Li et al 29 used data from four AWSs to analyze the relation between UHI intensity and summertime air pollution in Berlin. However, in the present study, we used data from 54 AWSs spread across Seoul city.…”

Section: Discussionmentioning

confidence: 99%

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Ngarambe

Duhirwe

et al. 2020

Sci Rep

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Urban heat island (UHi), a phenomenon involving increased air temperature of a city compared to the surrounding rural area, results in increased energy use and escalated health problems. to understand the magnitude and characteristics of UHi in Seoul and to accommodate for the high temporal variability and spatial heterogeneity of the UHi which make it inherently challenging to analyze using conventional statistical methods, we developed two deep learning models, a temporal UHi-model and a spatial UHi model, using a feed-forward deep neural network (Dnn) architecture. Data related to meteorological elements (e.g. air temperature) and urban texture (e.g. surface albedo) were used to train and test the temporal UHi-model and the Spatial UHi-model respectively. Also, we develop and propose a new metric, UHI-hours, that quantifies the total number of hours that UHI exists in a given area. our results show that UHi-hours is a better indicator of seasonal UHi than the commonly used index, UHi-intensity. consequently, UHi-hours is likely to provide a better measure of the cumulative effects of UHI over time than UHI-intensity. UHI-hours will help us to better quantify the effect of UHI on, for example, the overall daily productivity of outdoor workers or heat-related mortality rates.The world's population is increasing dramatically; new cities are being built, while existing cities are getting overpopulated. Currently, more than 50% of the world population resides in cities 1 . This increased development in urban areas has resulted in changes in urban morphology and surfaces, as well as an increase in the amount of anthropogenic heat released to the atmosphere 2 . The combined effect of such changes leads to higher air temperature in urban areas than in the surrounding rural or suburban areas, hence the formation of heat islands 3 . These heat islands, especially in summers, considerably decrease the outdoor air quality and trigger heat-related diseases and deaths in urban areas 4,5 . Moreover, extreme temperatures have been reported to be the leading cause of weather-related deaths 6 . This is particularly prevalent among the elderly (people of 65 years of age and above) and those with cardiovascular and respiratory health issues. For example, Paravantis et al. 7 analyzed the impact of temperature and heat waves on the number of deaths caused by cardiovascular and respiratory health issues in people above 65 years of age in Athens, Greece. They reported a U-shaped exposure-response curve, indicating reduced mortality rates at moderate temperatures and 20% and 35% increase in mortality rates at very low and very high temperatures, respectively. Furthermore, due to the increase in urban temperatures, there has been an increase in air-conditioning system usage, which in turn has led to high electricity demands and overall increased building energy consumption 8 . At the same time, passive cooling methods, such as natural and night ventilation, have become ineffective for the thermal comfort of building occupants 9 .Due to...

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Section: Discussionmentioning

confidence: 99%

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Ngarambe

Duhirwe

et al. 2020

Sci Rep

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“…While many others factors, like anthropogenic heat flux, longwave trapping by urban canyons, aerosols, etc can modulate the UHI (Taha 1997, Zhao et al 2014, Li et al 2018, two of the factors considered in this study account for the vast majority of UHI mitigation measures; green roofs and green spaces modulate NDVI, while white roofs and reflective pavements alter α (Rizwan et al 2008, Zhao et al 2017. The third factor, NDBI, is the UHI's most direct cause since it is a proxy for the degree of urbanization.…”

Section: Data Processingmentioning

confidence: 93%

Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective

Chakraborty

Hsu

Manya

et al. 2019

Environ. Res. Lett.

Self Cite

158

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A growing literature documents the effects of heat stress on premature mortality and other adverse health outcomes. Urban heat islands (UHI) can exacerbate these adverse impacts in cities by amplifying heat exposure during the day and inhibiting the body's ability to recover at night. Since the UHI intensity varies not only across, but also within cities, intra-city variation may lead to differential impact of urban heat stress on different demographic groups. To examine these differential impacts, we combine satellite observations with census data to evaluate the relationship between distributions of both UHI and income at the neighborhood scale for 25 cities around the world. We find that in most (72%) cases, poorer neighborhoods experience elevated heat exposure, an incidental consequence of the intra-city distribution of income in cities. This finding suggests that policymakers should consider designing city-specific UHI reduction strategies to mitigate its impacts on the most socioeconomically vulnerable populations who may be less equipped to adapt to environmental stressors. Since the strongest contributor of intra-urban UHI variability among the physical characteristics considered in this study is a neighborhood's vegetation density, increasing green space in lower income neighborhoods is one strategy urban policymakers can adopt to ameliorate some of UHI's inequitable burden on economically disadvantaged residents.

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“…This stronger change pattern in the summer months is also visible for specific humidity throughout the decades and to a lesser extent for temperature, underpinning the RH results. The strong change in the summer months can be explained by the increase in incoming solar radiation in the summer compared to winter, resulting in higher summer temperatures [69,70]. This increase in temperature exacerbates the air moisture deficit in summer, leading to a stronger drying effect in the urban areas in the summer months.…”

Section: Humidity Under Climate Changementioning

confidence: 99%

“…In spring, around April-May, increased vegetation growth removes moisture from the soil through intensive evapotranspiration, increasing RH in the atmosphere [70,71]. solar radiation in the summer compared to winter, resulting in higher summer temperatures [69,70]. This increase in temperature exacerbates the air moisture deficit in summer, leading to a stronger drying effect in the urban areas in the summer months.…”

Section: Humidity Under Climate Changementioning

confidence: 99%

Urban Areas and Urban–Rural Contrasts under Climate Change: What Does the EURO-CORDEX Ensemble Tell Us?—Investigating near Surface Humidity in Berlin and Its Surroundings

2019

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Climate change will impact urban areas. Decision makers need useful climate information to adapt adequately. This research aims to improve understanding of changes in moisture and temperature projected under climate change in Berlin compared to its surroundings. Simulations for the Representative Concentration Pathway (RCP) 8.5 scenario from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) 0.11 • are analyzed, showing a difference in moisture and temperature variables between Berlin and its surroundings. The running mean over 30 years shows a divergence throughout the twenty-first century for relative humidity between Berlin and its surroundings. Under this scenario, Berlin gets drier over time. The Mann-Kendall test quantifies a robust decreasing trend in relative humidity for the multi-model ensemble throughout the twenty-first century. The Mann-Whitney-Wilcoxon test for relative humidity indicates a robust climate change signal in Berlin. It is drier and warmer in Berlin compared to its surroundings for all months with the largest difference existing in summer. Additionally, the change in humidity for the period 2070-2099 compared to 1971-2000 is larger in the summer months. This study presents results to better understand near surface moisture change and related variables under long-term climate change in urban areas compared to their rural surroundings using a regional climate multi-model ensemble.

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Interaction between urban heat island and urban pollution island during summer in Berlin

Cited by 265 publications

References 60 publications

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective

Urban Areas and Urban–Rural Contrasts under Climate Change: What Does the EURO-CORDEX Ensemble Tell Us?—Investigating near Surface Humidity in Berlin and Its Surroundings

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