research paper on urban heat island

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• Published: 04 September 2019

Magnitude of urban heat islands largely explained by climate and population

• Gabriele Manoli 1   nAff6 ,
• Simone Fatichi 1 ,
• Markus Schläpfer 2 ,
• Kailiang Yu 3 ,
• Thomas W. Crowther 3 ,
• Naika Meili 1 , 2 ,
• Paolo Burlando 1 ,
• Gabriel G. Katul 4 &
• Elie Bou-Zeid 5  

Nature volume  573 ,  pages 55–60 ( 2019 ) Cite this article

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• Atmospheric science
• Attribution
• Climate-change mitigation

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (Δ T s ) worldwide and find a nonlinear increase in Δ T s with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of Δ T s with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban–rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

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Urbanization-induced warming amplifies population exposure to compound heatwaves but narrows exposure inequality between global North and South cities

Increased heat risk in wet climate induced by urban humid heat

Widespread global exacerbation of extreme drought induced by urbanization

Data availability.

The Global Urban Heat Island Data Set 2013 is available at https://doi.org/10.7927/H4H70CRF (accessed on 7 December 2017). MERRA data were retrieved from https://disc.gsfc.nasa.gov/daac-bin/FTPSubset2.pl (downloaded on 4 March 2018) while GPCC data are available at https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html (accessed on 13 September 2016). MODIS albedo data are available at https://gcmd.nasa.gov/records/GCMD_MCD43B3.html (accessed on 15 July 2018). Urban green cover data for EU and SEA cities are available, respectively, at https://ec.europa.eu/eurostat/statistics-explained/index.php/Urban_Europe_-_statistics_on_cities,_towns_and_suburbs_-_green_cities#Further_Eurostat_information (accessed on 14 June 2017) and https://doi.org/10.1016/j.landurbplan.2016.09.005 (accessed on 29 September 2017). A summary table containing the urban and climate characteristics of the cities analysed is also available on Code Ocean ( https://doi.org/10.24433/CO.9808462.v1 ).

Code availability

The MATLAB code ( https://www.mathworks.com/products/matlab.html ) of the coarse-grained UHI model is available on Code Ocean ( https://doi.org/10.24433/CO.9808462.v1 ).

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Acknowledgements

G.M. was supported by the The Branco Weiss Fellowship—Society in Science administered by ETH Zurich. E.B.-Z. acknowledges support by the US National Science Foundation under grant no. ICER 1664091, the SRN under cooperative agreement no. 1444758, and the Army Research Office under contract W911NF-15-1-0003 (program manager J. Barzyk). M.S. was supported by the Future Cities Laboratory at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation (FI 370074016), under its Campus for Research Excellence and Technological Enterprise programme. We thank P. Edwards, J. Carmeliet, C. Küffer, and D. Richards for help and discussions at the beginning of this research.

Author information

Gabriele Manoli

Present address: Department of Civil, Environmental and Geomatic Engineering, University College London, London, UK

Authors and Affiliations

Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland

Gabriele Manoli, Simone Fatichi, Naika Meili & Paolo Burlando

Future Cities Laboratory, Singapore-ETH Centre, ETH Zurich, Singapore, Singapore

Markus Schläpfer & Naika Meili

Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

Kailiang Yu & Thomas W. Crowther

Nicholas School of the Environment, Duke University, Durham, NC, USA

Gabriel G. Katul

Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

Elie Bou-Zeid

You can also search for this author in PubMed   Google Scholar

Contributions

G.M. designed the study, developed the model and conducted the analysis with contributions from S.F., G.G.K. and E.B.-Z. K.Y. and T.W.C. analysed albedo remote sensing observations. G.M. wrote the original draft of the manuscript with input from S.F., G.G.K. and E.B.-Z. M.S., K.Y., T.W.C., N.M. and P.B. reviewed and edited the manuscript. All authors discussed the results and contributed to the final version of the manuscript.

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Correspondence to Gabriele Manoli .

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Manoli, G., Fatichi, S., Schläpfer, M. et al. Magnitude of urban heat islands largely explained by climate and population. Nature 573 , 55–60 (2019). https://doi.org/10.1038/s41586-019-1512-9

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Received : 12 February 2019

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DOI : https://doi.org/10.1038/s41586-019-1512-9

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Urban heat island and its interaction with heatwaves: a review of studies on mesoscale.

1. Introduction

• How does HW affect UHI?
• What are the main drivers controlling the synergies between UHI and HW?
• What can be done to mitigate UHI under HW conditions?

2. Current Status of Research

2.1. methods for retrieving uhi, 2.1.1. in situ measurement, 2.1.2. remote sensing, 2.1.3. numerical modeling, 2.2. classifications of urban heat island, 2.3. features, causes and definitions of heatwaves, 2.4. interactions between urban heat island and heatwaves, 2.4.1. urban heat island intensity, 2.4.2. energy balance models, 2.4.3. variation in energy budgets and corresponding drivers.

• UHII increases during HWs
• UHII decreases during HWs
• UHII remains unchanged during HWs

2.5. Mitigation of Urban Heat Island during Heatwaves

2.5.1. increasing albedo, 2.5.2. increasing vegetation coverage, 2.5.3. irrigation, 2.5.4. combined strategies, 3. discussion, 3.1. addressing the important questions, 3.2. research gaps and suggestions for future research.

• Time duration: Some studies, such as [ 25 , 108 ], only consider a short time period or even a single heatwave event, which may not be sufficiently representative of long-term behavior. However, the evolution of climate change is a long-term and complex process, and HWs are reported to become more intense and last for longer durations in the future [ 57 ]. Therefore, future studies exploring the synergies between UHI and HW and mitigation strategies should consider multi-year datasets or even future weather datasets.
• Study area: The synergistic effect between UHI and HW has been quantified mostly for metropolitan cities, but few studies have considered the different responses of urban and non-urban sites to HWs. Future studies should focus more on developing countries and the comparison between urban and rural sites. More detailed classifications such as low-intensity urban areas, high-intensity urban areas, forest, grassland and croplands, may be considered and compared. Moreover, although individual cities in different regions are worth investigating due to their unique geographies and climates [ 163 ], further studies may pay more attention to the comparisons among cities of different sizes or with different climates to reveal the general trend.
• Research method: In terms of numerical simulation, more complex UCMs such as MLUCMs may be preferred over the SLUCM for better prediction [ 25 , 84 , 164 , 165 ], but the latter is computationally inexpensive. In addition, high-resolution downscaling methods [ 166 ] may be considered in further studies. In terms of onsite observations, unified and dense weather stations can be established to improve spatial resolution. The data obtained through onsite measurements can be used to validate the numerical simulations. It is also advisable that a hybrid method incorporating both numerical simulations and onsite observations may be adopted alongside machine learning methods to construct accurate prediction models [ 167 ].
• Data collection: Some studies, such as [ 25 , 89 , 105 ], do not consider the anthropogenic heat flux or its variations during HWs. Future studies should include this as it plays an important role in the interactions between UHI and HW.
• UHI types: Despite a great deal of studies on UHIs during HWs, few studies have considered the differences in the thermal conditions, such as the temperature between urban and rural areas in the entire urban boundary layer. In addition, the relationships between the different UHI types also need to be investigated as studying the connections between air temperature and surface temperature may enable us to better understand the heat transfer in the atmospheric boundary layer and propose effective mitigation methods.
• Efficacy of mitigation strategies: When considering vegetation as a strategy, factors such as the efficiency of water use for different irrigation methods must be considered. Moreover, the choice of optimal vegetation type for different urban morphologies should be studied for cities in different climates. The sensitivity to soil moisture is worth investigating to provide more information about variations in latent heat flux. Mitigation by highly reflective surfaces may be effective in summer but may be undesirable in winter. The efficacy of these strategies for the mitigation of the HW effect and their impact in various seasons and in future climates should be investigated. More studies on the efficiency of combined mitigation strategies involving various physical mechanisms and processes should be conducted. In addition, studies on the efficiency of lightweight building materials may be conducted as they have a smaller heat capacity, which may reduce heat release at night. It is also worth noting that the efficacy of mitigation strategies may need to be assessed with reference to the ‘absolute’ urban thermal climate in addition to UHII due to the large diurnal variation in UHII and other factors influencing thermal sensation, as suggested by Martilli et al. [ 168 ].
• Health, economy and environmental considerations: Future work may consider other environmental and socio-economic impacts of the mitigation strategies, such as their impact on air quality, storm water, energy use, health, thermal comfort, visual comfort (albedo change), social aspects as wellbeing, costs and so on.
• Other considerations: A holistic approach considering changes in both climate and urbanization (horizontal and vertical spatial extent, building size, building material) may be adopted in future studies. Ramamurthy and Bou-Zeid [ 89 ] pointed out that the soil state and properties in urban areas are different from natural soils, which leads to variability in their thermal and hydrological characteristics. Accordingly, more research effort may be devoted to this topic.

4. Concluding Remark

Supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Kong, J.; Zhao, Y.; Carmeliet, J.; Lei, C. Urban Heat Island and Its Interaction with Heatwaves: A Review of Studies on Mesoscale. Sustainability 2021 , 13 , 10923. https://doi.org/10.3390/su131910923

Kong J, Zhao Y, Carmeliet J, Lei C. Urban Heat Island and Its Interaction with Heatwaves: A Review of Studies on Mesoscale. Sustainability . 2021; 13(19):10923. https://doi.org/10.3390/su131910923

Kong, Jing, Yongling Zhao, Jan Carmeliet, and Chengwang Lei. 2021. "Urban Heat Island and Its Interaction with Heatwaves: A Review of Studies on Mesoscale" Sustainability 13, no. 19: 10923. https://doi.org/10.3390/su131910923

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The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005–2050) of Tianjin City, China

Nadeem ullah.

1 School of Architecture, Tianjin University, Tianjin 300272, China

Muhammad Amir Siddique

Mengyue ding, sara grigoryan, irshad ahmad khan.

2 Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, 08193 Barcelona, Spain

Zhihao Kang

Shangen tsou, tianlin zhang, yazhuo zhang.

3 School of Civil Engineering, Tianjin University, Tianjin 300272, China

Associated Data

On request, the authors will provide the data from this study.

Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city’s land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area’s temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

1. Introduction

The rapid growth of urban areas worldwide has been observed over the past few decades [ 1 ]. The main factors contributing to urbanization are the lack of economic development and the increasing population [ 2 ]. Despite the slow growth of the global population, it is still expected that the number of people will continue to increase by around 2030 [ 1 ]. According to estimates, the world’s urban area is expected to grow by over a million kilometers by 2030 [ 1 , 3 , 4 , 5 , 6 ]. Urbanization is most prevalent in developing countries due to rapid economic development. China is one of the most prominent in the world regarding urbanization. It has been estimated that the country’s urban land area expanded at an annual rate of 13.3% [ 7 ].

Urbanization positively impacts people’s lives, as it allows them to improve their living standards and reduce their energy consumption. It can also help mitigate climate change by reducing vehicle miles travelled and greenhouse gas emissions [ 8 ]. Unfortunately, there are still negative impacts of urbanization. Due to the human activities that have occurred in the past few decades, the city has expanded. This process has caused both positive and negative effects [ 9 ].

Urbanization is a complex process involving multiple modelling variables and mechanisms involved in its development. The various aspects of this process must be thoroughly studied to understand its effects. One of the most effective ways to predict an urban area’s characteristics is through Land Use Cover change (LUCC) analysis [ 8 , 10 , 11 ]. A comprehensive simulation of the urban development process is necessary in today’s world [ 12 , 13 ]. With the help of spatial data, such as land area and development characteristics, urban models can be used to study the patterns of urbanization. These models can also simulate the conditions affecting the city’s development. Urban models use mathematical equations to describe the urban system [ 14 , 15 ]. They can also deal with the various factors that affect the development of a city. The study results are based on the interactions between different strategies and aspects [ 9 , 16 ]. Urban models are becoming more effective at predicting future changes in the LUCC due to the complexity of the process. They can use the available data and conditions to model the different factors affecting the city’s development. Numerous studies have been conducted on the use of LUCC in policy formulation and decision making.

However, the application of cellular automata and the Markov process is relatively rare. Tianjin is considered one of the most prominent cities in China that has experienced sustained urbanization, industrialization, and urbanization in China [ 17 , 18 ]. As a result of its ongoing development, many cities are expected to continue to grow [ 19 ]. It is essential that the cities’ LUCC change be studied and analyzed to determine its future trend [ 20 , 21 ]. This study was conducted to comprehensively analyze the various factors that have affected the city’s development. The study examined the LUCC change in Tianjin from 1995 to 2015. It first created five maps with different classifications at different points in time. The analysis revealed that many areas were converted into built-up areas. The model was then analyzed to create a set of dynamic variables for the Cellular Automata Model (CA) [ 11 , 22 , 23 ]. These variables were then used to project the LUCC change in the city from 2025 to 2050.

2. Materials and Methods

2.1. study area.

The city of Tianjin, the largest city on China’s northern coast, is straddled at 38°34′ N to 40°15′ N and 116°43′ E to 118°04′ E ( Figure 1 ), having a thousand square kilometers. It is regarded as the fifth-largest city in the country after Shanghai, Beijing, Guangzhou, and Shenzhen [ 24 ]. With a warm, temperate, semi-humid monsoonal climate, it is characterized by four distinct seasons during the year [ 25 , 26 ]. Over the past few years, Tianjin has experienced massive urbanization, with its population increasing from 12.99 million in 2010 to 13.86 million in 2021 [ 1 ].The city of Tianjin has a gross domestic product of about 240 billion yuan, making it one of the most prominent economic centers in China’s northern region [ 25 , 27 , 28 ]. It is an international port city and has experienced rapid urbanization over the past few decades. Due to rapid urbanization, large areas of land, such as forests, farmland, and meadows, have been converted into built-up areas [ 29 , 30 ].

Geographic location and characterization of the study area: ( A ) People’s Republic of China; ( B ) Beijing–Tianjin–Hebei (TBH); and ( C ) multispectral satellite image of Tianjin city.

2.2. Acquisition of Spatial Dataset

The United States Geological Survey (USGS) provided cloud-free images of the study area, which were taken from path 170 and series 053, through its website ( http://earthexplorer.com ) [ 6 , 9 , 10 , 13 , 17 , 25 , 31 ]. Due to the varying time of day and night in the study area, the data collected by the Landsat 5 Thematic Mapper (TM) and the Landsat 7 Enhanced Thematic Mapper (ETM) were used to create the LUCC map [ 13 , 18 , 32 , 33 , 34 ]. The data collected by the two satellites ( Table 1 ) were also used to calculate the Normalized Difference of Vegetation Index (NDVI) and Land Surface Temperature (LST).

The Landsat data used in this study are outlined in detail.

2.3. Methodology

An integrated workflow template ( Figure 2 ) was used to perform a series of steps. We began by processing the information sets in GEE to create a false colour positive (FCC) [ 10 , 11 , 25 , 35 , 36 ]. A georeferenced map of the outer boundaries of Tianjin was used to extract and mask the study area from all spatial ideas. The support Vector Machine (SVM) classification method was applied to improve the supervised classification results obtained from Landsat imagery [ 13 , 30 , 37 , 38 ]. Then, the LST was calculated to determine the time zones in the city [ 17 ]. A Pearson correlation analysis was performed based on the land cover, average LST, and percentage of greened and non-greened areas from 2005, 2010, 2015, and 2020 [ 8 , 31 , 39 , 40 ]. The CA-Markov model was used to forecast future trends for LUCC and LST in 2035 and 2050 [ 41 ]. All spatial statistical analyses and maps were created using ArcGIS 10.7, and ggplot2, corrplot and psych packages used in RStudio [ 42 ].

Flowchart of the methodology for the present study.

2.4. Land Use Cover Change (LUCC) Calculation

Landsat imagery (Landsat-5 TM & Landsat-8 OLI) was used to map the LUCC of Tianjin city for a four-time frame (2005, 2010, 2015, and 2020). The Support Vector Machine (SVM) classification algorithm in GEE was used to classify land use and areas [ 43 , 44 ]. Five types of LUCC were identified: built-up land, cropland, lowland, forest, and water body ( Figure 3 A). Built-up land included artificial structures such as buildings, roads, and other impervious surfaces. Water included rice fields, reservoirs, and rivers [ 28 , 35 , 45 ].

( A ) Shows the land use land cover area, and ( B ) proportional changes of LUCC during 2005-2020.

At specified intervals, GEE was used to assess the accuracy of the classification results. Field reference points were collected using a Google Earth explorer, which collected field reference average of 250 points for 2005, 2010, 2015, and 2020.

The classification accuracy of the signatures and images was evaluated by creating a confusion matrix consisting of rows and columns that refer to the categories derived from the image. The matrix rows are labelled with the reference values, while the columns represent the categories identified using the same criteria. The total number of entries that formed the main diagonal was then divided by the number of pixels. The Kappa coefficient was calculated using Equations (1)–(3) [ 1 , 2 , 46 , 47 ]:

where r = the number of rows in the error matrix; P ij = The proportion of pixels in a row “ i ” and column “ j ”; and P i = the fraction of the marginal sum of row “ i ”.

2.5. Calculation of Land Surface Temperature (LST)

The Landsat-8 thermal infrared sensor (TIRS) of bands 10 and 11 and the OLI sensor of bands 2–5 were used individually to convert the raw image into a radiance spectral image (SR) by following the equations ( Table 2 ) step by step.

Stepwise process for Land surface temperature (LST) determination.

where λ is the effective wavelength (10.9 mm for a thermal band in Landsat 8 data), σ is the Boltz–Mann constant (1.38 × 10 −23 J/K), h is the Plank constant (6.626 × 10 −34 Js), and c is the speed of light in vacuum (2.998 × 10 −8 m/sec).

2.6. CA-Markov Prediction Model Analysis

This model uses a stochastic Markov probability matrix to predict the transition from one state to another [ 14 , 43 , 50 ]. The study aims to analyze the various effects of urbanization on the land use and development of the city of Tianjin using a computer model known as a Markov chain model. This model was used to predict land use and development trends [ 13 , 36 , 51 ]. A conditional probability formula was used to estimate trend lines from Equations (4)–(6).

Because of Markov chain and cellular automata modelling, LUCC and LST’s future scenarios are calculated by projecting 2035 and 2050 using Terrset’s land use change modeler (LCM) (Clark Labs TerrSet 18.31).

3.1. Changes in LUCC between 2005 and 2020

According to the LUCC distribution values for 2005, 2010, 2015, and 2020, the built-up area in cities has increased ( Figure 3 ). Built-up area increased from 15.46% in 2005 to 17.80% in 2010, 19.56% in 2020, and 22.72% in 2050. In 2005, the study area included 18.43% of the lowlands; this number decreased to 12.52% by 2010, 11.89% by 2015, and 10.21% by 2020. Arable land increased rapidly from 26.10% in 2005 to 28.95% in 2020, while other land decreased from 26.47% to 26.10%. Increasing migration from villages to cities has led to an expansion of cultivated land outside prime locations. The cultivated area decreased from 10.72% in 2005 to 7.98% in 2020. Water covered 1.21% of the site in 2005, 0.92% in 2010, 0.87% in 2015, and 0.68% in 2020. An assessment of land use changes during 2005–2020 showed that farmland in the northeastern study area was converted to urban areas (mainly industrial areas). Between 2005 and 2020, built-up urban land and cropland increased by 15.45% and 1.64%, respectively, while lowland land decreased by 13.73%. These results show that about 11.45% of the lowlands have been converted into built-up areas. The LUCC changes were classified into five categories LUC with corresponding definitions ( Table 3 ).

Land use cover (LUC) statistics in 2035 and 2050.

The results of all studies show that urban built-up has changed significantly over two decades. In recent decades, Tianjin has gone from a village to a city residential settlement. This transition happens between agricultural land to residential areas. Urban growth and LST are sensitive to accuracy assessment [ 29 ]. According to [ 52 , 53 ], a method was defined for assessing the accuracy of the classification of maps. According to the LUCC maps, the overall accuracy was 84.39% in 2005, 90.43% in 2010, and 94.11% in 2020. Kappa coefficients for the LUCC maps were 0.79, 0.87, and 0.92. The kappa coefficient should be greater than 0.75 or 0.80 to show compatibility between the classification and the reference data [ 54 ]. The United States Geological Survey (USGS) recommends using Landsat satellite images for LUCC mapping if the accuracy level is 85% [ 55 ]. Our accuracy evaluation results are consistent with those recommended in the literature.

3.2. Relationship between LUCC and LST

LST is significantly affected by land use changes (LUCC). The number and distribution of hotspots increase with LUCC types (especially urban expansion) [ 56 ]. A map of LST distribution was created using Landsat TM/ETM+/OLI imagery for the study area ( Figure 4 ). There were temperature variations from 21 °C to 43 °C in 2005, 21.8 °C to 44.3 °C in 2010, 22.1 °C to 44.9 °C in 2010, and 22.5 °C to 45.9 °C in 2020. During 2005–2020, built-up urban areas had the highest average temperatures, followed by lowland, cropland, vegetation, and water. In 2005, all LUCC categories had the most elevated average temperatures ( Figure 5 A). In 2005, urban built-up areas had an average LST of 38.43 °C, 38.99 °C in 2010, 41.86 °C in 2015, and 44.80 °C in 2020. During 2005–2020, the temperature in built-up urban areas LST decreased by 4.12 °C but increased by a maximum of 6.82 °C from 2010 to 2020. Lowlands had the second-highest LST for all LUCC categories during the study period. The LST for wasteland decreased by 3.38 °C from 2005 to 2010 but did not change significantly between 2010 and 2020. The LST for cropland was 31.04 °C in 2015 and increased to 31.98 °C, 32.63 °C, and 33.75 °C in 2010, 2015, and 2020, respectively.

( A ) Maps for land use land cover (LUCC) classes: (i) water, (ii) vegetation, (iii) forest, (iv) urban, (v) barren land, (vi) cropland; ( B ) land surface temperature (LST) was divided into five thermal categories: (i) <20 °C, (ii) 20–25 °C, (iii) 25–30 °C, (iv) 30–35 °C, and (v) >35 °C of Tianjin between the study period of 2005 to 2020, at 5-year intervals.

( A ) Distribution of land surface temperature, and ( B ) proportional changes of LST during 2004–2019.

From 2005 to 2020, the temperature of cropland LST decreased by 3.85 °C, while it increased by 15.35% in developed areas. Vegetated areas recorded a decrease of 5.32 °C between 2005 and 2010 LST but an increase of 6.83 °C between 1999 and 2015. All LUCC categories recorded the lowest LST in 2010, and the average temperature in water bodies and vegetated areas was the lowest overall. According to statistics from LST for 2005–2020, the maximum difference between urban areas and water bodies is 14.35 °C.

LST has remained relatively stable between 2005 and 2020. These areas are also referred to as lowlands. Compared to urban areas, lowlands have a higher LST value. Our study came to similar conclusions. High LST values may be found in these areas due to the soil composition (sand, clay, etc.). The average daily air temperature may influence the LST values at the satellite imagery data on the day the satellite imagery was taken rather than the spatial values of the land use classes. To verify that LST results calculated from the Landsat TM/ETM heat band are comparable to actual field temperatures, temperatures of the various LUCC properties must be measured from field observations [ 57 ]. Considering the values reported at LST, the daily mean air temperatures of the reported data (the daily mean air temperature on 19 June 2005 is 27.5 °C; on 10 July 2010, it is 23.03 °C; on 23 July 2015, it is 26.86 °C; and the daily mean air temperature on 11 August 2020 is 29.53 °C) are all parallel to each other (See Figure 6 ).

Spatial distribution map for change detection of ( A ) LUCC (km 2 ) and ( B ) LST (°C) during 2005–2020.

Pearson’s correlation analysis shows LST is statistically associated with populated/developed areas. Even though LST is bad for water and plants and does not have much to do with them, it is strongly linked to forested areas. In the same way, LSTs in cities have a negative and insignificant effect on water and plants. LST and urban/built-up areas have a significant and favourable relationship, as shown by the simple correlation coefficient [ 51 ]. In urban areas, a temperature rise may also be caused by the construction of new buildings, highways, businesses, and industrial regions. Negative and insignificant correlations are observed with barren land, while optimistic and negligible correlations are marked with arable and cropland. Pearson correlation analysis results are reported for all LUC variables and LST indices ( Figure 7 ).

Pearson correlation analysis of land use change and land surface temperature during 2005-2020.

3.3. Variations of LST Changes over Different LUCC

We estimated the mean LST distributions for LUCC classes over 2005–2020. During the study period, mean values of LST increased significantly in all LUCC classes, but matters of LST were substantially higher in built-up areas and bare ground. The importance of LST in the built-up area increased from 28.86 °C to 37.23 °C between 2005 and 2020, while in the empty ground area, they increased from 21.56 °C to 25.01 °C. Over the past two decades, the average LST distribution in built-up and bare-ground regions has risen by about 9 °C and 4 °C, respectively. The LST distribution in water bodies and vegetated areas have also changed. In 2000, the mean LST for vegetated areas was 21.31 °C, but it is expected to reach 25.98 °C by 2020. The LST of water bodies increased from 20 to 24.45 °C. The following figure ( Figure 8 ) briefly describes the changes in LUCC types and their relative impacts on land surface temperature.

Shows classified maps of LUC and LST for the year 2020, and predicted maps of 2020, 2035, and 2050.

3.4. Validation of Predicted LUCC and LST Scenarios

To validate the accuracy of the predicted values, we first used the CA-Markov model to estimate the LUCC and LST for 2020 ( Figure 8 ). Based on various kappa parameters, the predicted and estimated maps were compared using the land use Change Modeler in Clark Lab’s Terrset software. The average error value for all parameters during the comparison was about 12.86%, and all kappa parameters, percentage of accuracy, and total kappa values were above 0.80.

3.5. Predicted LUCC for 2035 and 2050

We could predict the scenario for 2035 and 2050 based on the classified maps for the study period. According to the predicted LUCC map, the growth of urban areas will be concentrated by 37% in the northwestern and central regions if the trend of the building continues without planned actions. Urban areas will replace the lowlands and vegetation cover. Vegetation cover has decreased by 9.62% from 12.82% in 2020. Based on the study scenario, LUCC would face a 20.51% increase in developed land, followed by a significant decrease in lowlands, vegetation cover, and water bodies of 10.87%, 9.62%, 8.32%, and 2.45%, respectively ( Figure 8 ). The category-wise land use statistics for the forecast years are shown in the following table: Ecosystem services, urban health, and thermal characteristics may be affected by decreased vegetation cover and increased urbanization. If unplanned urban expansion continues, the environmental, economic, and medical problems will increase significantly. A proper land use plan, the protection of water bodies, and the reforestation of forests are needed to make Tianjin city more environmentally sustainable.

By forecasting LST for 2035 and 2050, the simulation showed that higher temperatures will occur in the built-up areas in the northwest and central parts of the country ( Figure 8 ), ranging from 41.56 °C to 44.34 °C in 2035 and 2050, respectively. We divide the temperature zone into five classes to estimate how much area is covered by each temperature range ( Table 4 ). Based on the projections, LST has increased over the past two decades (2005–2020), with urban areas influencing the prevalence of LST. UHI effects will increase as urban areas and vegetation cover decrease. It would be possible to explain the temperature increase without urbanization by climate change, greenhouse effects, and surface features. The LST prediction highlights the real risks of the temperature rise in the trend, including higher UHI effects. A combination of energy use, greenhouse gas emissions, and air pollution contribute to the UHI effect. It threatens aquatic systems (rivers, lakes, ponds, streams, and oceans) and human health. Human health is primarily harmed by increased greenhouse gas emissions, which affect urban health and reduce the urban environment’s sustainability [ 58 ].

Land surface temperature (LST) statistics in 2035 and 2050.

3.6. Limitations of the CA-Markov Model

The prediction of LUCC and LST can be improved using the CA-Markov model if the previous LUCC and LST patterns are consistent. As a result, CA-Markov models do not provide accurate spatial predictions for raster datasets [ 59 ]. Since influential factors can be directly determined between CA-Markov and other factors, CA-Markov is based on a probability matrix [ 60 ]. Given the relative importance of the different variables in identifying the most important variables, it is essential to note that the CA-Markov model generates training patterns and automatically begins training after receiving inputs from the strata. The input parameters are not individually weighted according to established standards [ 61 ]. Since urbanization, the loss of green space and increase in surface temperatures are primarily influenced by human activities and conscious decisions at regional to metropolitan scales; it is impossible to predict them accurately. It is essential to recognize that dynamic models have some limitations. Still, they help develop hypotheses and make decisions about changes in land cover or surface temperatures in any given area, regardless of their rules. In recent years, LUCC and LST variability and predictive maps have emerged as one of the best tools for managing and mitigating vital natural resources.

4. Discussion

Tianjin’s rapid urbanization and development between the 1990s and 2020s significantly altered the LUCC landscapes caused by farmland separation and reduced total vegetation cover [ 25 , 33 ]. The city’s urban development also resulted in the establishment of new industries and residential areas. Rapid vegetation cover loss affects an area’s natural cooling effect [ 29 , 62 ]. Some factors contributing to this phenomenon are vegetation shading and transpiration. To amplify this, LST and NDVI have shown that VC, due to its cooling effect, serves as a sink in an urban heat island [ 11 , 63 ]. Rapid vegetation cover loss has several consequences for an area’s natural cooling effect. It has the potential to eventually eliminate the processes that regulate surface transpiration and evaporation [ 11 , 17 , 56 , 64 ]. Urbanization leads to distorted construction, reducing soil infiltration and increasing surface runoff. As a result, the water table and groundwater table decrease. Evapotranspiration is not adequately realized due to these two factors. Climate change leads to a deterioration of the water balance [ 49 ]. Climate variables such as daily maximum and minimum temperatures are affected by changes in land use. Surface albedo changes due to changes in land use. Therefore, land use changes disturb the balance of Earth’s radiation [ 65 ]. An important factor in reducing air temperatures is the conversion of wetlands to agricultural land with high albedo [ 66 ].

Although the impact of this phenomenon on the LST of various types of plants is less than that of urban tree cover and gardens, studies have shown that it still contributes to the overall reduction of the area’s natural cooling effect [ 32 , 67 ]. The impact of various types of urban vegetation, water bodies, and forests on the LST varies according to their proportional area [ 23 , 37 ]. In urban areas, vegetation plays a vital role in controlling or mitigating temperature. Evaporation from urban water bodies contributes to moisture accumulation in the surrounding air. According to studies, these bodies regulate the LST in residential areas. It is also known that urban areas contribute to the development of intricate heat flows within these regions [ 46 , 68 ]. Various private and public entities have worked together to revitalize large tracts of land for industrial, commercial, and residential development. Traditional wooden structures have been demolished and replaced with tall structures made of non-evaporative materials such as glass, concrete, and aluminium. These materials can directly impact heat flows in urban areas [ 8 , 10 ]. According to studies, urban areas in China are more vulnerable to severe LST than rural areas. LST has risen due to the government’s decision to convert agricultural and forest land into urban areas [ 38 , 69 ]. The government has relocated factories and businesses to the outskirts of cities to improve their efficiency. These facilities are typically found in developed areas. Before the development of urban areas, forests and vegetation were regarded as buffer zones between rural and urban areas, absorbing excess heat generated by factories and automobiles [ 3 , 29 , 40 ]. According to the scientific literature, the cooling effect of LUCC is well-matched to the expected warming effect caused by the physical interaction of the Indian region and its surroundings [ 32 ]. For example, the maximum cooling contribution from forested areas is 0.27%, while the minimum cooling effect is 0.06%. The most negligible difference between the surface temperature and the impervious surface is the primary reason why vegetation contributes the least to the cooling effect. The greatest cooling effect, on the other hand, is observed when forested areas are converted into water bodies. This is due to the fact that the contribution of land cover to cooling is negligible in various areas, such as urban areas, water bodies, and vegetation. The results of the study revealed that the built-up area in the southeastern and central port areas will continue growing. The paper discussed the various effects of the LUCC on the Tianjin city’s development. The study used the CA model and Geographic Information Systems to analyze the data. The results of the analysis helped improve the Tianjin city’s planning process. In addition, the paper discussed the use of remote sensing tools for improving the urban planning process.

5. Conclusions

The objective of this study was to analyze the influence of LUCC on land surface temperature (LST) in a large urban area of Tianjin. Data from RS were used to observe the area’s various socioeconomic and development parameters. The study also used the CA-Markov model and Pearson correlation coefficient to evaluate the contribution of landscape dynamics to temperature. A 5.94% increase in built-up area was found to increase the temperature by 1.5%. However, the increase in vegetation cover by 10% showed a negative correlation. In addition, the study concluded that LUCC has a cooling effect of about 1.40 °C in the city. The average warming effect of LUCC on the UHI is about 0.5%.

On the other hand, the cooling effect of LUCC compared to the shifts in the reverse direction is 0.11%. The positive contribution of LUCC to the UHI was higher than the negative one. Urban development and infrastructure planning should be further targeted to minimize the impacts of climate change. In addition to improving water bodies and parks, other measures, such as the establishment of green spaces and linear planting of woody plants, should also be implemented. The study found that further research is needed to analyze the impact of land use change on the climate of regions and cities. As more areas are affected by climate change, the government and private sector must work together to develop effective cooling strategies. Environmental education should be made accessible to promote the development of ecological resources. This needs effective urban planning and green policies to address the increasing thermal stress. In addition, a quantitative analysis of these parameters needs to be conducted. Although the study found that urbanization directly impacts land surface temperature, it is not yet clear how the effects of this process are related to the other factors. The practical application of the study provides essential guidance for urban landscape planning. It shows how landscape connectivity between impervious and green areas can affect LST. Future research should also address infrastructure stress and public health issues associated with rapid urbanization.

Acknowledgments

The authors wish to express his appreciation and gratitude to the anonymous reviewers and editors for their insightful comments and suggestions to improve the paper’s quality.

Funding Statement

Reconstructing the Architecture System based on the coherence mechanism of “Architecture-human-environment” in the Chinese context, Key project of National Natural Science Foundation of China, grant number 52038007, 2021-01-2025-12.

Author Contributions

Conceptualization, N.U., M.A.S., Y.Z. and Y.H.; data curation, N.U., M.A.S., M.D., S.G., S.T. and T.Z.; formal analysis, N.U., M.A.S., S.T., Z.K. and M.D.; funding acquisition, Y.H. and Y.Z.; methodology, N.U., M.A.S., S.T., I.A.K. and S.G.; project administration, Y.H. and Y.Z.; software, N.U. and M.A.S.; supervision, Y.H. and Y.Z.; visualization, N.U., M.A.S., T.Z., Z.K. and Y.H.; writing—original draft, N.U. and M.A.S.; writing—review and editing, N.U., M.A.S., I.A.K. and Y.H. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

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Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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Landscapes of Urban Thermal and Green Inequities: Insights into Australian Capital Cities’ Urban Heat Island and Green Infrastructure Accessibility

33 Pages Posted: 22 Aug 2024

Queensland University of Technology

Ruihong Jiao

Tan yigitcanlar, mirko guaralda.

Climate change has intensified urban heat islands (UHIs) in recent decades. While green infrastructure (GI) can mitigate UHIs, access to GI varies due to socioeconomic factors and location, leading to green inequity. This study addresses this gap by analysing UHI and green infrastructure accessibility (GIA) in these cities. It uses urban heat island intensity (UHII) to capture thermal inequity and network analysis to identify green inequity. A green infrastructure accessibility index (GIAI) is proposed alongside UHII to analyse their spatial relationship. Key findings reveal that: (a) UHII varies across capitals, with high UHII in urban and coastal zones of Sydney, sparse distribution in Darwin’s eastern rural areas, and extensive coverage in Melbourne, Perth, and Canberra. It is broadly distributed in Brisbane, Adelaide, and Hobart; (b) Urban areas have less GI but greater accessibility due to advanced road networks, while most cities offer 85% GI access within 20 minutes by car and over 60% within a 15-minute walk in urban areas; (c) Melbourne, Adelaide, and Perth have high GIAI despite sparse GI, while Sydney, Darwin, and Canberra show a clear urban-rural divide; (d) Sydney, Hobart, and Canberra exhibit balanced UHII and GIAI in both urban and rural areas.

Keywords: thermal inequity, green inequity, urban heat island, green infrastructure accessibility, climate change, Australia

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Queensland University of Technology ( email )

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Tan Yigitcanlar (Contact Author)

2 George Street Brisbane, Queensland 4000 Australia

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Urban Heat Island studies: Current status in India and a comparison with the International studies

• Published: 04 March 2020
• Volume 129 , article number  85 , ( 2020 )

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• K Veena 1 , 2 ,
• K M Parammasivam 1 &
• T N Venkatesh 2  

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Urbanization has resulted in many critical issues like increase in pollution levels, sudden climatic changes and the rise of temperature in the urban area, that is the formation of Urban Heat Islands (UHI). As the density of population rises, most of the land areas are being converted into cities and cities grows very rapidly. Due to the UHI effect, the cities are becoming hotter day by day. In India, all the metropolitan cities are victims of UHI effect and the severity of heat formation, necessitates research in this area. The present paper evaluates the trends of UHI studies in Indian cities and its out reach till 2018. Heat Island classification, methods of studying UHI in India and their limitation are discussed. Eventually a comparison of new trends of UHI studies in the world and where India lacks its growth in UHI research are included in this paper. One of the findings is that numerical modelling studies are very limited in India in this field and more focus in this area is required.

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The first author is financially supported by CSIR (Grant No. Ack.No.141410/2K15/1) through Direct SRF Scheme and it is gratefully acknowledged.

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Veena, K., Parammasivam, K.M. & Venkatesh, T.N. Urban Heat Island studies: Current status in India and a comparison with the International studies. J Earth Syst Sci 129 , 85 (2020). https://doi.org/10.1007/s12040-020-1351-y

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Annual Review of Environment and Resources

Volume 40, 2015, review article, urban heat island: mechanisms, implications, and possible remedies.

• Patrick E. Phelan 1 , Kamil Kaloush 2 , Mark Miner 1 , Jay Golden 3 , Bernadette Phelan 4 , Humberto Silva III 5 , and Robert A. Taylor 6
• View Affiliations Hide Affiliations Affiliations: 1 School for Engineering of Matter, Transport & Energy, Arizona State University, Tempe, Arizona 85287; email: [email protected] , [email protected] 2 School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona 85281; email: [email protected] 3 Division of Earth and Ocean Sciences, Nicholas School of the Environment and Pratt School of Engineering, Duke University, Durham, North Carolina 27708; email: [email protected] 4 Synectics for Management Decisions, Inc., Arlington, Virginia 22209; email: [email protected] 5 Mechanical Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131; email: [email protected] 6 School of Mechanical and Manufacturing Engineering and the School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, NSW 2052, Australia; email: [email protected]
• Vol. 40:285-307 (Volume publication date November 2015) https://doi.org/10.1146/annurev-environ-102014-021155
• First published as a Review in Advance on September 02, 2015
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Urban heat island (UHI) manifests as the temperature rise in built-up urban areas relative to the surrounding rural countryside, largely because of the relatively greater proportion of incident solar energy that is absorbed and stored by man-made materials. The direct impact of UHI can be significant on both daytime and night-time temperatures, and the indirect impacts include increased air conditioning loads, deteriorated air and water quality, reduced pavement lifetimes, and exacerbated heat waves. Modifying the thermal properties and emissivity of roofs and paved surfaces and increasing the vegetated area within the city are potential mitigation strategies. A quantitative comparison of their efficacies and costs suggests that so-called cool roofs are likely the most cost-effective UHI mitigation strategy. However, additional research is needed on how to modify surface emissivities and dynamically control surface and material properties, as well as on the health and socioeconomic impacts of UHI.

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April 01, 2024

Exploring Urban Heat Islands and Temperature Inequality in U.S. Cities

Using satellite data to understand how city design impacts temperature variations.

Land cover of the six largest cities analyzed. The white line delineates the boundary between urban and nonurban areas, determined by applying Gaussian smoothing (a-f). Distribution of annual and seasonal surface urban heat island intensity (SUHII) across all 120 cities (g). A spatial depiction of the cities with their corresponding annual mean SUHII values (h).

[Reprinted under a Creative Commons Attribution 4.0 International License ( CC BY 4.0 ) from Lee, J. "Assessment of U.S. Urban Surface Temperature Using GOES-16 and GOES-17 Data: Urban Heat Island and Temperature Inequality." Weather, Climate, and Society 16 (2), 315–29 (2024). DOI: 10.1175/WCAS-D-23-0129.1 .]

The Science

A team of researchers used satellite data to study how temperatures vary in 120 large U.S. cities. By examining how heat changes throughout the day and year, the study found green spaces and reflective surfaces help cool cities and urban areas get hotter as they become more developed. Vulnerable communities often live in these hotter areas, facing more extreme heat. This research provides insight into how city planning can reduce these temperature differences.

Certain areas in cities are much hotter than others, especially where vulnerable communities live. By understanding these temperature patterns, city planners can design better strategies to cool hot spots. This can lead to fairer living conditions and reduce health risks associated with extreme heat.

The study used hourly land surface temperature (LST) data from the Geostationary Operational Environmental Satellite–16 (GOES-16) and GOES-17 to analyze the surface urban heat island intensity (SUHII) in the 120 largest U.S. cities and their surrounding areas. The analysis reveals distinct patterns in seasonal and diurnal SUHII, with the enhanced vegetation index and albedo significantly influencing temperature variations. SUHII’s diurnal cycle shows climate conditions, urban and nonurban land covers, and nighttime human activities affect SUHII peaks differently.

Examining intracity LST dynamics revealed a strong correlation between urban intensity (UI) and LST, indicating LST rises with increasing UI. Vulnerable populations, identified using the social vulnerability index, are often located in high UI regions, resulting in notable LST inequality. These communities face higher LST conditions, potentially leading to greater heat exposure risks. This study highlights the need for city-specific climate change mitigation strategies addressing LST variations and their societal implications, aiming to create cooler and more equitable urban environments.

Principal Investigator

Max Berkelhammer University of Illinois Chicago [email protected]

Program Manager

Sally McFarlane U.S. Department of Energy, Biological and Environmental Research (SC-33) Urban Integrated Field Laboratories [email protected]

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research program’s Urban Integrated Field Laboratories Community Research on Climate and Urban Science research activity under Award Number DE-SC0023226.

Lee, J. "Assessment of U.S. Urban Surface Temperature Using GOES-16 and GOES-17 Data: Urban Heat Island and Temperature Inequality." Weather, Climate, and Society 16 (2), 315–29  (2024). https://doi.org/10.1175/WCAS-D-23-0129.1 .

Impact of urbanization on urban heat island intensity-a case study of Larkana City, Sindh, Pakistan

• Lanjwani, Muhammad Umar
• Lanjwani, Muhammad Farooque
• Hussain, Muhammad
• Sodhar, Khalida

The climate change is one of the important problem of the current situation in the world. The urban heat island intensity is a major problem of increasing the climate condition in the developed and developing countries. In current situation, the growth of population in the Pakistan causes over population in the cities. The population of Larkana is increasing rapidly day by day. The purpose of this research was to investigate the impact of Urbanization on the Climate. In this proposed research study, two types of data were collected (i) satellite data of Thematic Mapper (TM) Landsat 5 which was downloaded from the United States Geological Survey (USGS) of 1990, 2000, and 2010, furthermore satellite data of 2023 downloaded the Landsat 8 from USGS. (ii) Second data used from secondary sources of population census report of Pakistan 1981, 1998, 2017 and 2023 was collected from the Pakistan Statistics Bureau. The land surface temperature was found from satellite data of 1990, 2000, 2010 and 2023. The average temperature in 1990 was 4.25 0 C greater than 2000 in summer season and average temperature of 2010 was 4.73 0 C less than from 2023 in summer season. The average temperature in 1990 was 3.15 0 C less than from 2000 in winter season and in 2022 was 1 0 C higher than 2010 in winter season average temperature. Recently census reported above 579,000 populations lived in the urban city of Larkana. The shape file of the Larkana classification total area showed 41 Square kilometers. The supervised classification showed that settlement increased from 8.16 Square kilometers in 1990 to 23.98 Square kilometers in 2023. The correlation was shown between urban expansion and the growth of population strongly positive to each other. Another finding relationship between urban heat islands with urban expansion that correlation showed a positive relationship between each other.

• Environmental Health
• Why This Matters

Everything you need to know about the urban heat island effect

Photo by Pixabay via Pexels

With 80% of Americans living in urban areas , understanding the heat island effect is crucial when reporting on environmental health as our climate gets hotter . 

The heat island effect occurs in urban areas where buildings, roads and other infrastructure absorb and re-emit heat from the sun. City temperatures can be 1–7°F higher than in greener outlying areas ; the effect is especially extreme in heavily urbanized or industrial areas with sparse greenery. 

The relentlessness of heat islands from day to night can be dangerous. Even after sunset, the heat can cause dehydration and heat exhaustion. Since man-made structures hold onto heat, especially if they have dark surfaces, nighttime temperatures in cities remain higher by about 2-5°F . These temperature averages can seem relatively small, but there can be enormous differences felt in cities like Las Cruces, NM, where there was a 44.5 °F difference between shaded grass and exposed pavement on a hot day. 

The heat experienced in a heat island is often described as atmospheric or surface urban heat islands . The former varies less and describes the warmer air felt in cities compared to its surroundings. The latter changes more significantly when the sun is shining and describes the heat felt from roads, buildings, and non-green surfaces absorbing heat and re-emitting it over time. 

Research is limited on chronic exposure to high temperatures and humidity . But, the acute effects of a heat wave exacerbated by the heat island effect can be the difference between life and death. Portland, Oregon, experienced a record high temperature of 116°F during a heat dome in the summer of 2021, a 42°F spike above average. As a result of the event, 69 people died. According to the county’s official report, the heat dome accounted for 61% of all heat-related visits to emergency departments and urgent care centers.

Research has shown that low-income communities of color often experience significantly higher heat conditions than surrounding areas. Low-income communities are also at greater risk of heat-related illness and death , which can be due to inadequate housing conditions without working air conditioning. Also, low-income communities don’t have proper resources to find alternative shelter during heat waves . 

In formerly redlined neighborhoods, a discriminatory practice used to deny housing loans to minorities, research has shown that these neighborhoods were 4.6°F warmer than non-redlined areas . Running air conditioning during high temperatures can be a financial burden, and between a quarter and one-third of US households experience some form of energy insecurity .

Here are some health equity-related resources to inform your reporting on urban heat islands:

• Mapping Inequality is an online resource for understanding the history of racial and ethnic discrimination in housing policy.
• The National League of Cities created a tool to mitigate heat wave risk by visualizing tree canopy data and bus stops.
• The EPA created a webpage that helps communities identify and assess heat islands . 

Satellite imaging and redline maps

Extreme heat is a silent killer and can be challenging to visualize compared to other disasters like flooding or wildfires. However, satellite imaging of heat maps can help readers visualize and understand heat islands when layered with vegetation and redlined maps . 

A city’s redlining history can illuminate health disparities related to extreme heat. Journalists can help readers understand the causes of the heat island effect in redlined areas by covering the inequality of urban tree cover , the percentage of cityscape covered by tree canopy, and how vegetation can be used to reduce heat-related health impacts . 

While the urban heat island effect doesn’t contribute significantly to climate change by increasing global temperatures, the phenomenon is affected by it. 

The summer of 2023 was 0.41°F warmer than any other summer in NASA records, and NOAA’s latest projections gave 2024 a 61% chance of beating 2023 . The earth is warming quickly, and people in low-income urban neighborhoods are disproportionately affected.

Additional resources

• Dangerous urban heat exposure has tripled since the 1980s, with the poor most at risk — The Conversation.
• As Rising Heat Bakes U.S. Cities, The Poor Often Feel It Most — NPR.
• NOAA is crowdsourcing a national urban heat map — Grist.
• The danger of urban ‘heat islands’ — High Country News.
• The surprisingly simple way cities could save people from extreme heat — Grist.

Recent Posts

Paul Gordon

• climate change
• heat island
• urban heat island

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