Landscape 05

Urban Heat

Cities are experiencing increasing climate-related risk, particularly urban heat and heat waves which are affecting all citizens, especially the elderly and those without capacity to cope with those events. This chapter explores urban heat through the estimation of the urban heat island intensity and the distribution of this hazard throughout the cities.

The Urban Heat category includes two indicators: Surface Urban Heat Island (SUHI) intensity and Canopy Urban Heat Island (CUHI) intensity. The UHI intensity measures the differences in  surface or near-surface temperatures between urban land cover and non-urban land cover within the city, in degrees Celsius (°C). The SUHI uses daytime and nighttime land surface temperature derived from MODIS LST products1, while the CUHI uses maximum and minimum Near-Surface Air Temperature derived from machine learning (ML) methods2. Both indicators are calculated for each neighborhood in the UESI cities. 

Description

The UHI effect is one of the oldest known consequences of urbanization. The phenomenon was observed for the first time over a century ago, and is currently one of the major research themes in urban climatology11. The act of urbanization replaces natural surfaces with built-up structures. This conversion changes the radiative, thermal, and aerodynamic properties of the surface, which modifies the energy balance over urban surfaces12. Urbanization involves the replacement of vegetated land surface with built-up structures, which are predominantly composed of asphalt (for highways) and concrete (for buildings). Concrete is slightly lighter in color (has a higher albedo) than vegetation, while asphalt is darker and has a lower albedo than vegetation. Thus, concrete reflects a higher percentage of solar radiation than vegetation, while asphalt absorbs more radiation than natural surfaces. Depending on the percentage of concrete and asphalt in an urban area, it might absorb less or more radiation than it would in its natural state. Urbanization also leads to the replacement of vegetation with built-up structures. This shift reduces the evaporative cooling vegetation provides, further increasing heat build-up in urban areas. There are other city-specific factors that modulate UHI intensity, including: the higher thermal mass of built-up structures; the ability of urban canyons and urban haze to trap outgoing longwave radiation; and the difference in surface roughness between the city and its surroundings, illustrated in Figure 1 13 14 15.  

Figure 1. Factors causing UHI. Adapted from (Kleerekoper et al., 2012).

There are two primary approaches to measuring UHI intensity. The Canopy UHI (CUHI) measures the difference in near-surface air temperature between the urban region and its surrounding “background” or non-urban region. The Surface UHI (SUHI) measures the difference in the surface temperature between the urban and “background” regions, often using satellite data. Our UHI indicator calculates both measures of UHI using a simplified algorithm to compare a cities urban areas against the non-urban areas within the city boundaries (see Box 1, The Rural Reference: Defining the Urban Heat Island Magnitude, and Box 2, Canopy versus Surface Urban Heat Islands: Heat Stress Implications, for additional discussion of the implications of different approaches to measuring UHI). Urban Heat Island has multiple environmental and economic consequences. Due to the overall increase in global temperatures17, heat waves are expected to become more frequent, and UHI amplifies this effect in urban areas, exacerbating heat stress and accounting 18 for a large proportion of deaths during heat waves19 20 .Hot and arid communities are frequently water-stressed, and higher temperatures in these regions can aggravate water scarcity by spurring residents’ increased water consumption21. By enhancing chemical reaction rates, urban heat can also increase production of secondary pollutants22, such as ground-level ozone, worsening local air quality23 24.  Since ground-level ozone is a precursor to photochemical smog, UHI can have a particularly detrimental impact in cities already struggling with this issue. However, warmer surfaces can also increase convective mixing, which can reduce the concentration of primary pollutants; put another way, UHI can help prevent temperature inversions, which trap warm air – and pollutants – beneath a layer of cold air25.

Urban heat increases cooling and reduces heating requirements, which, in turn, may increase or decrease electricity use. Reported UHI impacts range from a 10% - 120% increase in cooling energy consumption, and a 3% - 45% decrease in heating energy consumption26, and a literature review of similar studies shows the UHI effect increases cities’ average energy load by 11 percent, accounting for both the decreased heating and increased cooling loads27. Higher urban temperatures can also reduce the efficiency and life span of devices, such as cooling systems, automobile engines, and electronic appliances, creating costs of hundreds of million dollars for some cities28. Additionally, UHI can exacerbate the contributions of heat stress to work absenteeism and productivity loss, an impact that could take place in developing regions like Southeast and South Asia or Sub-Saharan Africa 29 30 31. In short, UHI can dramatically shape the lives of its urban residents, but these impacts are heavily dependent on the city in question and can vary substantially across urban areas.

The UHI effect, and by extension, its consequences, all stem from urban-scale changes. Thus, these effects can be prevented through more-informed policymaking and city planning. There are various ways to engineer urban areas to lessen the magnitude of the UHI. For instance, urban temperatures can be reduced by increasing the reflectivity of the city or by increasing evaporative cooling over urban surfaces. Overall four main category of methods have been explored in the literature as relevant for UHI mitigation:

  • Cool roofs: Roofs with high solar reflectance and high thermal emittance, meaning they reflect most of the solar radiation during daytime and radiate the energy stored mostly during nighttime32, including high-albedo (White) roofs. 
  • Green spaces: Green spaces can increase evaporative cooling over the surface as well as promoting heat transfer to the air and the convection of heat away from the ground33. Urban trees in particular outperform other vegetation in terms of evapotranspiration and shading, making them effective in reducing local temperature34. 
  • Green roofs: Green roofs are similar to green spaces, but can directly reduce the temperature over built-up structures by increasing evapotranspiration through soil and plants in rooftops35.
  • Cool pavements: This umbrella term includes permeable and reflective pavements, as well as other innovative pavement technologies. Like white roofs, reflective pavements increase surface albedo, reducing urban radiation absorption. Permeable pavements retain water, which can lower temperatures through evaporative cooling 36 37.  

Numerous studies have quantified the impact of these methods on the UHI magnitude for individual cities. For example, a recent study in Europe’s Urban Functional Areas found that green areas cool European cities by 1.07 °C on average, and up to 2.9°C38. Similarly, a study of the effects of green and cool roofs during a Berlin heatwave estimated that both green and cool roofs reduced the duration of strong heat stress from 7 to 5 hours per day39.

While solving climate change requires global effort and cooperation between different countries, it is possible to temporarily shield urban residents from some of the consequences of climate change by enacting policies designed to curb the UHI. With around 66 percent of the global population expected to live in urban areas by 2050, mitigating urban heat can provide large benefits for both human health and the economic growth of cities. In this regard, city-level policies, enacted in tandem with multi-scale adaptation strategies, will become important to ensuring the sustainable growth of urban areas in a rapidly warming world.  

Results

The performance of the UESI cities for both SUHI and CUHI show a large range of daytime UHI values, from approximately -6 °C to above 12 °C for SUHI and -2 °C to 6 °C for CUHI (see Figure 2). These results are consistent with previous studies40, which highlighted that CUHI will have less intensity than CUHI. The significant variation among both the top and bottom 10 cities highlights the critical intraurban differences in this outcome, closely linked to the unique physical characteristics of each neighborhood For example, neighborhoods on the outskirts of Madrid (ESP), which have more green space and a greater rural or suburban land use compared to the rest of the city, exhibit negative UHI values, indicating they are cooler than the surrounding rural areas. In contrast, neighborhoods near the urban core of the city have higher UHI values. In line with previous research, CUHI shows less intra-urban variation and less overall variability, while being consistent with the SUHI distribution, as in the cities of Quito (ECU) and Bogota (COL), which  have the highest range of SUHI and CUHI among the bottom 10 performers, highlighting the strong interdependence of both indicators.

Figure 2. Surface UHI intensity of top and bottom 10 cities. The number in the circle is the mean daytime UHI for the city, while the error bars represent the standard deviation of UHI values across different neighborhoods of the city.

Figure 2. Canopy UHI intensity of top and bottom 10 cities. The number in the circle is the mean daytime UHI for the city, while the error bars represent the standard deviation of UHI values across different neighborhoods of the city.

Examining the geographical distribution of both CUHI and SUHI (Figure 4a), a similar relationship between the two indicators is evident. CUHI is consistently lower for most UESI cities across all regions, except in the Middle East and North Africa, where the mean Canopy UHI is slightly higher than the Surface UHI, although the difference is minimal. Furthermore there is not a consistently top-performing region, since the range of both CUHI and SUHI overlap for most regions. However, the Middle East and North Africa stand out, with cities showing the lowest UHI values and thus the highest scores. 

Figure 3: Surface and Canopy UHI intensity distribution by Regions.

Figure 3: Surface and Canopy UHI intensity distribution by Climate Zones.

The distribution across climate zones, which is arguably more relevant for modulating the background climate of urban areas, also reveals relevant information. On the one hand, there is an important distinction between CUHI and SUHI for cities located in Polar regions, where only  two cities are located and where further exploration with an increased sample size might be warranted. Similarly, Cold, Temperate, and Tropical cities also exhibit lower CUHI values compared to SUHI. The latter shows some of the lowest variability among climate regions, likely due to high humidity, which helps regulate near-surface air temperatures. In contrast, Temperate cities display higher variability, reflecting the broader temperature fluctuations experienced throughout the year in that climate zone. Finally, cities in Arid regions demonstrate that the mean CUHI and SUHI values are quite similar, though, as expected, the range of SUHI is broader. 

Overall, while there is an important overlap between CUHI and SUHI for some cities, urban areas in arid regions have both the lowest mean value and range for both CUHI and SUHI, indicating that they perform better in the overall urban heat component. Further investigation, particularly regarding the implementation of urban heat mitigation strategies in these regions, could help clarify whether this performance is due to policy measures aimed at reducing urban heat or to favorable climatic conditions, such as in desert cities, where temperatures are likely lower than the surrounding environment. 

When examining the distribution of UHI exposure across neighborhoods, Figure 4 illustrates the UESI’s equity typology, which includes both SUHI and CUHI Concentration Scores. The inequality analysis indicates that most of the UESI cities (153 cities) are located in the left side of the plot, indicating that UHI exposure in those cities is disproportionately affecting the less affluent citizens in those cities. Particularly noteworthy is the group of 44 cities in the bottom left quadrant, indicating that in these cities, not only are less wealthy citizens more exposed to UHI intensity, but the inequality in UHI exposure may exacerbate already higher-than-average levels of income inequality41. This result is consistent with previous analysis on heat exposure inequality, which revealed that US cities disproportionately burden their lowest income earners with higher SUHI intensity. This result is evident in the location of a group of North American cities located primarily in the bottom left quadrant. Furthermore, we can see that performance is not associated with equity, since both the top and bottom 10 cities in the performance score are distributed across the quadrants with some notable cases, including Denver (USA) and Madrid (ESP).

Figure 4. UHI typology quadrant plot considers the Income Gini and UHI Concentration Index to define four quadrants. The Income Gini Values represent the distribution of wealth across the population and range in value from 0 to 1. A Gini value of 0 indicates a perfectly equal distribution of income across the population, while a high Gini value (out of a maximum of 1) suggests a highly unequal distribution of wealth. The Environmental Concentration Index (ECI) measures the variation in UHI in response to income. Negative ECI values indicate that the environmental burden is allocated on the poorest citizens, while a positive ECI indicates that the environmental burden is allocated on the wealthier citizens. (See the Equity and Social Inclusion Profile for a more detailed description of this plot).

Figure 5 presents a more detailed view of the distribution of UHI intensity across a selection of cities. The Environmental Concentration Curves (ECI) for CUHI Intensity allows us to visually gauge the way in which CUHI is allocated with respect to population and income. The position of the CUHI Curve (Orange) above the 45 degree line indicates that this environmental outcome is allocated towards the poor, as in the case of Chicago (USA) or Berlin (DEU), while a curve below the 45 degree line indicates that the more affluent are exposed to higher intensity of CUHI, such as São Paulo (BRA). Although the Concentration Index provides an overall summary of the distribution, examining the curve can reveal specific population segments that may be disproportionately affected by CUHI, even if the overall index suggests low inequity. For instance, in Singapore, CUHI disproportionately impacts middle-income earners, while leaving the lowest and highest income earners relatively unaffected. 

Figure 5. Environmental and Income distribution curves for selected cities. These plots display the concentration distributions of UHI intensity (represented by the concentration curve) and income (represented by the Lorenz curve) across neighborhoods. Deviations from the dotted line (e.g., the line of perfect equity) illustrate cities that are less equitable in their distribution of UHI intensity. Concentration curves above the line of equity indicate the environmental burden is more heavily allocated to those with less income; concentration curves below the line of equity indicate that the environmental burden is more heavily allocated to those with greater income. (See the Equity Indicator Profile for a more detailed description of this plot).

Box 1. Canopy versus surface urban heat islands: Heat stress implications

While urban heat island (UHI) is a standard term in urban studies, the UHI can have two distinct meanings. Traditionally, researchers observed higher air temperatures in urban areas, which were based on measurements collected at a standard height (between 1.5 to 2 m above ground)42. Because this height falls within the urban canopy, it is referred to as the canopy UHI. Later, with the advent of satellite data, differences in surface temperature between the urban core and its surroundings became observable—this is known as the surface UHI43.

Surface and air temperatures show similar annual values. However, there are significant differences at the diurnal and seasonal scales due to how fast the surface and air respond to the incoming solar radiation44. The canopy air temperature maxima lags behind the surface temperature maxima by a couple of hours every day – in other words, the surface responds first to incoming radiation. Furthermore, land cover can influence these lags. The surface’s response to incoming radiation depends on the specific heat capacity of the material; built-up structures have a higher capacity, causing urban areas to heat up and cool down more slowly than vegetated landscapes. Additionally, the response of the canopy air depends on how the surface dissipates the heat, and whether this dissipation is predominantly through evaporation or convection. Evaporation leads to an increase in the moisture content (but not temperature) of the air above the surface, while convection leads to a transfer of heat from the surface to the air near the surface. This process causes dissimilarities between the canopy and surface UHI, as seen in a few recent studies 45 46 47. Finally, the horizontal movement of air across the land surface disperses warmer air, helping to mix the canopy and surface UHI intensities48. For cities in maritime climates, cooling by sea breezes may reduce the canopy UHI further than the surface UHI.

A major consequence of the UHI is the additional heat stress in urban areas affecting a rapidly increasing urban population, which can translate into higher incidence of heat strokes, loss of productivity due to absenteeism from work, and even heat related mortality49. It is important to note that the human response to heat stress is due to higher air temperatures, and thus the canopy UHI50. On the other hand, the surface UHI has an effect on the local urban climate, including changes in precipitation patterns51.

A number of challenges make it difficult to assess canopy UHI at the neighborhood scale. Unfortunately, the World Meteorological Organization (WMO) standards do not typically result in routine measurements of air temperature within urban areas, since meteorological stations near urban areas are usually at airports, which are not representative of the urban core52, which adds uncertainty to inter-city comparisons. Moreover, it is difficult to set up a dense enough network of sensors to investigate the spatial variation in the canopy UHI, which prevents comparisons at the neighborhood scale53. 

As concern grows over UHI and its impacts, the range and accuracy of methods used to observe it has likewise grown. Today, there exists a much wider range of methods to measure canopy level UHI, including direct observation, remote sensing, modeling, and data reanalysis. Cities are increasingly adopting networks of air temperature sensors, a form of direct observation, to provide UHI canopy measurements with higher accuracy and resolution54 55, which should become routine measurements for cities to inform policy and planning decisions around UHI mitigation. 

Thus, recent advances in modeling and remote sensing have allowed us to leverage globally available near-surface temperature data to calculate Canopy UHI at the neighborhood level, albeit with some of the same limitations mentioned before. This variable, in combination with the Surface UHI will provide a more complete picture on heat stress at the neighborhood level and allow us to provide more specific estimations on intra-urban variability and inequity.

Box 2. Utilizing crowdsourced data to examine urban heat

Combined environmental hazards of the UHI and climate change underscores a growing imperative to protect urban residents from the adverse impacts of urban heat. Recent studies recognize the burden of heat is not distributed equally, with income, race, and ethnicity connected to experiencing disproportionately higher temperatures56. To understand and explore the inequities in this heat burden, dense air temperature sensor networks are required, capable of discerning intra-urban temperature variations. Recognizing this challenge, researchers have turned to innovative solutions, such as crowdsourcing as a means of data generation57 58,  including personal weather stations. 

Building on recent developments, we compared the gridded daily air temperature data (GDAT)59, used as the basis data of the CUHI indicator, against data from Netatmo weather stations for 62 European UESI cities over the month of July 2019, during which there was a heatwave in Europe. Comparison of these data sources shows a strong correlation in daily maximum and minimum temperatures, although the crowdsourced data tends to report highest temperatures. Several factors could contribute to this bias60, one such being that the Netatmo stations do not always have radiation shields. Previous studies of the same period found the station positive bias is reduced during days with cloud cover, indicating the lack of radiation shields is a strong contributor to the observed bias. Given the bias is systematic, this means the data remains highly useful for examining intra-neighborhood variations in urban air temperatures, as the hotspots will still be identified. 

Figure A. Scatter plot comparison of urban Netatmo air temperature versus the gridded daily air temperature (GDAT) measurements. Each point represents a European UESI neighborhood.

Figure A. Scatter plot comparison of urban Netatmo air temperature versus the gridded daily air temperature (GDAT) measurements. Each point represents a European UESI neighborhood.

 

We also analyzed a specific city, Berlin (DEU), which contains the highest number of Netatmo stations totalling 245 in Figure 2. Based on their location and aggregated at the neighborhoods level, Netatmo stations located on built-up land on average exhibit higher temperatures in comparison to those situated on non-built-up land across all neighborhoods, which is expected due to the role of non impervious surface particularly tree cover in reducing temperatures by providing shade and evaporative cooling. While we can observe a typical city configuration, with a more built-up urban core with higher temperatures surrounded by greener and cooler neighborhoods, looking at the distribution of individual weather stations, we observe a much higher variability within each neighborhood.

These results demonstrate how heat disparities are present within neighborhoods, largely due to the effect of different urban forms in shaping local microclimates. For example, even within less central neighborhoods, there can still be pockets of warmer areas as high as 4 °C higher than the average temperature of the neighborhood, and within the urban core areas with lower temperatures as low as 2 °C compared to the mean temperature of the neighborhood. The variability in air temperatures across the city and its sensitivity to land cover types reveals the critical need for high-resolution air temperature data, and its importance for addressing inequities in urban heat.  

Figure B. Intra-urban air temperature based on Netatmo station data within Berlin. Neighborhood means for all land cover types are shown as a background for a) land cover types and b) differences between each station temperature and its corresponding neighborhood.

Figure B. Intra-urban air temperature based on Netatmo station data within Berlin. Neighborhood means for all land cover types are shown as a background for a) land cover types and b) differences between each station temperature and its corresponding neighborhood.

 

Footnotes

  1. Chakraborty, T. & Lee, X. (2018). A Simplified Urban-Extent Algorithm to Characterize Surface Urban Heat Islands on a Global S.cale and Examine Vegetation Control on their Spatiotemporal Variability, International Journal of Applied Earth Observation and Geoinformation

  2. Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., & Wang, L. (2022). A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020). Earth System Science Data Discussions, 2022, 1-18.

  3. https://climate.copernicus.eu/copernicus-2023-hottest-year-record

  4. Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C., Liniger, M. A., & Appenzeller, C. (2004). The role of increasing temperature variability in European summer heatwaves. Nature, 427(6972), 332-336.

  5. Miner, M. J., Taylor, R. A., Jones, C., & Phelan, P. E. (2017). Efficiency, economics, and the urban heat island. Environment and Urbanization, 29(1), 183-194.

  6. Zhao, M., Lee, J. K. W., Kjellstrom, T., & Cai, W. (2021). Assessment of the economic impact of heat-related labor productivity loss: A systematic review. Climatic Change, 167(1), 22. https://doi.org/10.1007/s10584-021-03160-7

  7. Wan, Z. (1999). MODIS land-surface temperature algorithm theoretical basis document (LST ATBD). Institute for Computational Earth System Science, Santa Barbara, 75.

  8. Strahler, A. S., Moody, A., Lambin, E., Huete, A., Justice, C. D., Muller, J. P., ... & Wan, Z. (1994). MODIS land cover product: Algorithm theoretical basis document.

  9. Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., & Wang, L. (2022). A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020). Earth System Science Data Discussions, 2022, 1-18.

  10. Chakraborty, T. & Lee, X. (2018). A Simplified Urban-Extent Algorithm to Characterize Surface Urban Heat Islands on a Global Scale and Examine Vegetation Control on their Spatiotemporal Variability, International Journal of Applied Earth Observation and Geoinformation.

  11. Howard, L. (1833). The climate of London, vols. I–III. London: Harvey and Dorton.

  12. Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1-24.

  13. Oke, T. R. (1981). Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations. International Journal of Climatology, 1(3), 237-25

  14. Cao, C., Lee, X., Liu, S., Schultz, N., Xiao, W., Zhang, M., & Zhao, L. (2016). Urban heat islands in China enhanced by haze pollution. Nature communications, 7.

  15. Zhao, L., Lee, X., Smith, R. B., & Oleson, K. (2014). Strong contributions of local background climate to urban heat islands. Nature, 511(7508), 216-219.

  16. Kleerekoper, L., Van Esch, M., & Salcedo, T. B. (2012). How to make a city climate-proof, addressing the urban heat island effect. Resources, Conservation and Recycling, 64, 30-38.

  17. https://climate.copernicus.eu/copernicus-2023-hottest-year-record

  18. Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C., Liniger, M. A., & Appenzeller, C. (2004). The role of increasing temperature variability in European summer heatwaves. Nature, 427(6972), 332-336.

  19. Heaviside, C., Vardoulakis, S., & Cai, X. M. (2016). Attribution of mortality to the Urban Heat Island during heatwaves in the West Midlands, UK. Environmental Health, 15(1), S27.

  20. Ho, J. Y., Shi, Y., Lau, K. K., Ng, E. Y., Ren, C., & Goggins, W. B. (2023). Urban heat island effect-related mortality under extreme heat and non-extreme heat scenarios: A 2010–2019 case study in Hong Kong. Science of the Total Environment, 858, 159791.

  21. Aggarwal, R. M., Guhathakurta, S., Grossman-Clarke, S., & Lathey, V. (2012). How do variations in Urban Heat Islands in space and time influence household water use? The case of Phoenix, Arizona. Water Resources Research, 48(6). https://doi.org/10.1029/2011WR010924

  22. A primary pollutant is an air pollutant emitted directly from a source, such as a car’s tailpipe. A secondary pollutant forms when other pollutants (primary pollutants) react chemically in the atmosphere. Source: European Union. Glossary. Available at: https://ec.europa.eu/health/scientific_committees/opinions_layman/en/indoor-air-pollution/glossary/pqrs/primary-pollutant-secondary-pollutant.htm (accessed 5 August 2018). 

  23. Sarrat, C., Lemonsu, A., Masson, V., & Guedalia, D. (2006). Impact of urban heat island on regional atmospheric pollution. Atmospheric environment, 40(10), 1743-1758.

  24. Piracha, A., & Chaudhary, M. T. (2022). Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature. Sustainability, 14(15), Article 15. https://doi.org/10.3390/su14159234

  25. Li, H., Meier, F., Lee, X., Chakraborty, T., Liu, J., Schaap, M., & Sodoudi, S. (2018). Interaction between urban heat island and urban pollution island during summer in Berlin. Science of the Total Environment, 636, 818-828.

  26. Li, X., Zhou, Y., Yu, S., Jia, G., Li, H., & Li, W. (2019). Urban heat island impacts on building energy consumption: A review of approaches and findings. Energy, 174, 407–419. https://doi.org/10.1016/j.energy.2019.02.183

  27. Santamouris, M. (2014). On the energy impact of urban heat island and global warming on buildings. Energy and Buildings, 82, 100-113.

  28. Miner, M. J., Taylor, R. A., Jones, C., & Phelan, P. E. (2017). Efficiency, economics, and the urban heat island. Environment and Urbanization, 29(1), 183-194.

  29. Zander, K. K., Botzen, W. J., Oppermann, E., Kjellstrom, T., & Garnett, S. T. (2015). Heat stress causes substantial labour productivity loss in Australia. Nature Climate Change, 5(7), 647-651.

  30. Estrada, F., Botzen, W. W., & Tol, R. S. (2017). A global economic assessment of city policies to reduce climate change impacts. Nature Climate Change, 7(6), 403-40

  31. Zhao, M., Lee, J. K. W., Kjellstrom, T., & Cai, W. (2021). Assessment of the economic impact of heat-related labor productivity loss: A systematic review. Climatic Change, 167(1), 22. https://doi.org/10.1007/s10584-021-03160-7

  32. Zinzi, M., & Agnoli, S. (2012). Cool and green roofs. An energy and comfort comparison between passive cooling and mitigation urban heat island techniques for residential buildings in the Mediterranean region. Energy and buildings, 55, 66-76.

  33. Mackey, C. W., Lee, X., & Smith, R. B. (2012). Remotely sensing the cooling effects of city scale efforts to reduce urban heat island. Building and Environment, 49, 348-358.

  34. Du, M., Li, N., Hu, T., Yang, Q., Chakraborty, T. C., Venter, Z., & Yao, R. (2024). Daytime cooling efficiencies of urban trees derived from land surface temperature are much higher than those for air temperature. Environmental Research Letters, 19(4), 044037.

  35. Li, D., Bou-Zeid, E., & Oppenheimer, M. (2014). The effectiveness of cool and green roofs as urban heat island mitigation strategies. Environmental Research Letters, 9(5), 055002.

  36. U.S. Environmental Protection Agency. (2012). Reducing Urban Heat Islands: Compendium of Strategies, Cool Pavements. https://www.epa.gov/heat-islands/heat-island-compendium

  37. Santamouris, M. (2013). Using cool pavements as a mitigation strategy to fight urban heat island—A review of the actual developments. Renewable and Sustainable Energy Reviews, 26, 224-240.

  38. Marando, F., Heris, M. P., Zulian, G., Udías, A., Mentaschi, L., Chrysoulakis, N., ... & Maes, J. (2022). Urban heat island mitigation by green infrastructure in European Functional Urban Areas. Sustainable Cities and Society, 77, 103564.

  39. Wang, X., Li, H., & Sodoudi, S. (2022). The effectiveness of cool and green roofs in mitigating urban heat island and improving human thermal comfort. Building and Environment, 217, 109082.

  40. Du, H., Zhan, W., Voogt, J., Bechtel, B., Chakraborty, T. C., Liu, Z., ... & Miao, S. (2023). Contrasting trends and drivers of global surface and canopy urban heat islands. Geophysical Research Letters, 50(15), e2023GL104661.

  41. Hsu, A., Sheriff, G., Chakraborty, T., & Manya, D. (2021). Disproportionate exposure to urban heat island intensity across major US cities. Nature communications, 12(1), 2721.

  42. Voogt, J. (2007, July). How researchers measure urban heat islands. In United States Environmental Protection Agency (EPA), State and Local Climate and Energy Program, Heat Island Effect, Urban Heat Island Webcasts and Conference Calls.

  43. Price, J. C. (1979). Assessment of the urban heat island effect through the use of satellite data. Monthly Weather Review, 107(11), 1554-1557.

  44. Jin, M., & Dickinson, R. E. (2010). Land surface skin temperature climatology: benefitting from the strengths of satellite observations. Environmental Research Letters, 5(4), 044004

  45. Chakraborty, T., Sarangi, C., & Tripathi, S. N. (2017). Understanding Diurnality and Inter-Seasonality of a Sub-tropical Urban Heat Island. Boundary-Layer Meteorology, 163(2), 287-309.

  46. Wang, K., Jiang, S., Wang, J., Zhou, C., Wang, X., & Lee, X. (2017). Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network. Journal of Geophysical Research: Atmospheres, 122(4), 2131-2154.

  47. Du, H., Zhan, W., Voogt, J., Bechtel, B., Chakraborty, T. C., Liu, Z., ... & Miao, S. (2023). Contrasting trends and drivers of global surface and canopy urban heat islands. Geophysical Research Letters, 50(15), e2023GL104661.

  48. Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote sensing of environment, 86(3), 370-384.

  49. Estrada, F., Botzen, W. W., & Tol, R. S. (2017). A global economic assessment of city policies to reduce climate change impacts. Nature Climate Change, 7(6), 403-406.

  50. Matthews, T. K., Wilby, R. L., & Murphy, C. (2017). Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences, 114(15), 3861-3866.

  51. Dixon, P. G., & Mote, T. L. (2003). Patterns and causes of Atlanta's urban heat island–initiated precipitation. Journal of Applied Meteorology, 42(9), 1273-1284.

  52. Venter, Z. S., Chakraborty, T., & Lee, X. (2021). Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Science Advances, 7(22), eabb9569..

  53. Li, Y., Yang, T., Zhao, G., Ma, C., Yan, Y., Xu, Y., Wang, L., & Wang, L. (2024). A systematic review of studies involving canopy layer urban heat island: Monitoring and associated factors. Ecological Indicators, 158, 111424. https://doi.org/10.1016/j.ecolind.2023.111424

  54. Muller, C. L., Chapman, L., Grimmond, C. S. B., Young, D. T., & Cai, X. (2013). Sensors and the city: a review of urban meteorological networks. International Journal of Climatology, 33(7), 1585-1600.

  55. Li, Y., Yang, T., Zhao, G., Ma, C., Yan, Y., Xu, Y., Wang, L., & Wang, L. (2024). A systematic review of studies involving canopy layer urban heat island: Monitoring and associated factors. Ecological Indicators, 158, 111424. https://doi.org/10.1016/j.ecolind.2023.111424

  56. Hsu, A., Sheriff, G., Chakraborty, T., & Manya, D. (2021). Disproportionate exposure to urban heat island intensity across major US cities. Nature Communications, 12(1), 2721. https://doi.org/10.1038/s41467-021-22799-5

  57. Chakraborty, T., Venter, Z. S., Qian, Y., & Lee, X. (2022). Lower Urban Humidity Moderates Outdoor Heat Stress. AGU Advances, 3(5), 1–19. https://doi.org/10.1029/2022AV000729

  58. Chapman, L., Bell, S., & Randall, S. (2023). Can crowdsourcing increase the durability of an urban meteorological network? Urban Climate, 49, 101542. https://doi.org/10.1016/j.uclim.2023.101542

  59. Zhang, T., Zhou, Y., Zhao, K., Zhu, Z., Chen, G., Hu, J., & Wang, L. (2022). A global dataset of daily maximum and minimum near-surface air temperature at 1km resolution over land (2003-2020). Earth System Science Data, 14(12), 5637–5649. https://doi.org/10.5194/essd-14-5637-2022

  60. Venter, Z. S., Chakraborty, T., & Lee, X. (2021). Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Science Advances, 7(22), 1–10. https://doi.org/10.1126/sciadv.abb9569

Contact us

Data-Driven EnviroLab

West Franklin Street

Chapel Hill

United States

Orange County

27516

ddl@unc.edu

Discover more

Follow us

Urban Environment & Social Inclusion Index logo
Powered byData Driven Envirolab logo