Submitted:
14 November 2025
Posted:
17 November 2025
Read the latest preprint version here
Abstract
Keywords:
1. Introduction
2. Materials and Methods
3. Results
3.1. Nature-Based Solutions (NBS) for UHI Mitigation
3.2. Health Impacts of UHI
| Author/s | Methodology | Outcomes |
| [8] - Arellano and Roca, 2022 |
Remote sensing study using Landsat 8 and Sentinel 2 to analyse urban greenery and its impact on nighttime UHI in Barcelona. | Found that urban vegetation significantly reduces nighttime temperatures, mitigating heatwaves and improving public health by decreasing respiratory and cardiovascular risks during extreme heat events. |
| [9] - Aznarez, C. et al. (2024) | Analysed inequalities in urban heat vulnerability by assessing mismatches in ecosystem services using GIS and environmental datasets. | Low-income and marginalized communities have reduced access to cooling ecosystem services, making them more vulnerable to heat-related health issues. |
| [10] - Buchin, O. et al. (2016) | Modelled and evaluated health-risk reduction potential of UHI countermeasures. | Increasing green infrastructure and reflective surfaces significantly reduce heat stress and related health risks in urban areas. |
| [11] - Capari, L. et al. (2022) | Scientometric study analysing urban green/blue infrastructure and climate-induced public health effects. | Strong correlation between green & blue infrastructure and reduced heat-related illnesses and mortality. |
| [15] - Hoeven, F. van der and Wandl, A. (2015) | Mapped land use, UHI, and health implications using GIS and spatial analysis in Amsterdam. | Higher urban temperatures impact maternal and infant health by leading to lower birth weights in newborns. Birth weight is a critical health indicator, and exposure to excessive heat during pregnancy can have long-term developmental consequences for infants. |
| [21] - Mosca, F. et al. (2021) | Explored the impact of urban heat islands (UHIs) on health, focusing on both thermal comfort and psychological well-being in Genoa, Italy. | Highlighted that UHIs contribute to heat-related health issues, including heat stress and exacerbation of pre-existing conditions. |
| [27] - Vasconcelos, L. et al. (2024) | Investigated urban green spaces as cooling solutions for older adults. | Elderly populations benefit significantly from green spaces, reinforcing the role of nature-based climate shelters. |
3.3. Vulnerable Population and Socioeconomic Factors
| Author/s | Methodology | Outcomes | Suggested Solutions |
| [9] - Aznarez, C. et al. (2024) | Used GIS and environmental datasets to analyse inequalities in urban heat vulnerability. | Found that low-income and marginalized communities have reduced access to cooling ecosystem services, making them more vulnerable to heat-related health issues. | Suggested that better urban planning and green infrastructure are needed to reduce UHI-related health disparities. |
| [11] - Capari, L. et al. (2022) | Conducted a scientometric study analysing urban green/blue infrastructure and climate-induced public health effects, including impacts on vulnerable populations. | Identified a strong correlation between green/blue infrastructure and reduced heat-related illnesses and mortality, particularly in vulnerable groups. | Recommended increasing urban greenery to reduce health inequalities in cities. |
| [15] - Hoeven, F. van der and Wandl, A. (2015) | Mapped land use, UHI, and health implications using GIS and spatial analysis in Amsterdam. | Found that elderly and low-income populations are disproportionately affected by UHI. Also highlighted that mothers exposed to higher temperatures give birth to children with lower body weights, a key health risk for infants. | Emphasised the need for targeted cooling strategies. |
| [26] - Sánchez, C.S.-G., Peiró, M.N. and González, Fco.J.N. (2017) | Examined the relationship between UHI and vulnerable populations in Madrid, using socioeconomic and climate data. | Determined that elderly and low-income communities are more susceptible to heat-related illnesses due to limited access to cooling infrastructure and urban greenery. | Stressed the importance of targeted interventions. |
| [27] - Vasconcelos, L. et al. (2024) | Investigated the role of urban green spaces as cooling solutions for older adults, particularly in warming cities. | Found that elderly populations benefit significantly from urban green spaces, reinforcing the role of nature-based climate shelters in reducing heat-related health risks. | Recommended policies to improve green space accessibility for vulnerable groups. |
3.4. Remote sensing and GIS approaches
| Author/s | Methodology | Outcomes |
| [6] - Alexander, C. (2021) | LiDAR data, NDVI analysis, daytime LST | Vegetation cover and height significantly influence land surface temperature (LST). Increasing tree height reduces LST by up to 5.75 °C, highlighting the cooling effect of vegetation in urban areas. |
| [7] - Ampatzidis, P. and Kershaw, T. (2020) | Urban microclimate analysis, atmospheric conditions, remote sensing | Highlights that blue spaces can contribute to cooling effects, particularly when combined with green infrastructure. |
| [8] - Arellano and Roca, 2022 |
Used Landsat 8 and Sentinel 2, GIS-based mapping of land surface temperature (LST). Assessed the cooling effect of green spaces using spatial analysis. | Nighttime UHI intensifies heat stress, especially in dense urban areas. Large parks reduce temperatures by 1–4 °C (day) and 2–5 °C (night). |
| [9] - Aznarez, C. et al. (2024) | Analyses the supply and demand of temperature regulating ecosystem service (TR-ES) with remote sensing, health, and socio-demographic data with Artificial Intelligence for Environment and Sustainability (ARIES) and GIS. | Show disparities in heat vulnerability, with increased exposure observed among socio-economically disadvantaged communities, the elderly, and people with health issues. |
| [13] - Eldesoky, A.H.M., Colaninno, N. and Morello, E. (2020) | GIS-based ventilation corridor mapping, spatial analysi. | Urban ventilation corridors play a significant role in nighttime cooling, as green corridors release stored heat more rapidly than built-up areas, providing localized cooling benefits. |
| [14] - Ge, X. et al. (2020) | Landsat 8 imagery to analyse land cover changes and LST variations in Geneva and Paris. | Green spaces help lower LST by providing shading and evapotranspiration effects. Impervious surfaces lead to higher LST, reinforcing the need for distributed greenery in urban areas. |
| [15] - Hoeven, F. van der and Wandl, A. (2015) | Use NASA LANDSAT 5, Diurnal LST and nocturnal air temperature UHI mapping to analyze land use, health, and energy efficiency in Amsterdam. | Mapped UHI variations in air temperature using Landsat 5 data and highlighted the stronger nocturnal UHI effect due to heat retention in built environments. |
| [16] - Isola, F., Leone, F. and Pittau, R. (2024) | Systematic review of remote sensing and GIS methodologies for analysing UHI and ecosystem services. | Identifies the most effective remote sensing approaches for studying urban climate and mitigation strategies. Landsat imagery being one of the most efficient. |
| [17] - Lauwaet, D. et al. (2024) | UHI modeling across 100 European cities using UrbClim model, Air temperature UHI, NDVI from Copernicus Land service | Demonstrates varying UHI intensities across different urban morphologies and provides a large-scale comparative analysis. |
| [18] - Marando, F. et al. (2019) | Analyse UHI features in a spatially explicit way and on a seasonal basis, through the Land Surface Temperature (LST) derived from Landsat-8. | Finally, the multiple-linear regression model have showed that firstly the NDVI, and then the surface covered by trees, that provision of the ES of climate regulation by GI. NBS have the highest potential to provide this ES in a Mediterranean city. |
| [22] - Olivieri, F., Sassenou, L.-N. and Olivieri, L. (2024) | ENVI-met microclimate simulation. | Finds that green infrastructure can significantly lower temperatures, with effectiveness dependent on urban context and climate. |
| [26] - Sánchez, C.S.-G., Peiró, | GIS based mapping vulnerable population. | Existence of several neighbourhoods in Madrid with an important presence of vulnerable population that are in some of the hottest areas of the city, |
| [29] - Vulova, S. et al. (2023) | Evapotranspiration mapping, Sentinel-2, other remote sensing methods. | Shows that evapotranspiration plays a key role in mitigating UHI, reinforcing the importance of urban vegetation. |
4. Discussion
5. Conclusions
Abbreviations
| UHI | Urban Heat Island |
| LST | Land Surface Temperature |
| GIS | Geographic Information Systems |
| NBS | Nature-Based Solutions |
| MODIS | Moderate Resolution Imaging Spectroradiometer |
| NDVI | Normalized Difference Vegetation Index |
| ET | Evapotranspiration |
| UTCI | Universal Thermal Climate Index |
| UrbClim | Urban climate model |
| TR-ES | Temperature-Regulating Ecosystem Services |
| LiDAR | Light Detection and Ranging |
| PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
References
- Z. Kalantari, C. S. S. Ferreira, H. Pan, e P. Pereira, «Nature-based solutions to global environmental challenges», Sci. Total Environ., vol. 880, p. 163227, lug. 2023. [CrossRef]
- F. Grilo et al., «Using green to cool the grey: Modelling the cooling effect of green spaces with a high spatial resolution», Sci. Total Environ., vol. 724, p. 138182, lug. 2020. [CrossRef]
- Panno, G. Carrus, R. Lafortezza, L. Mariani, e G. Sanesi, «Nature-based solutions to promote human resilience and wellbeing in cities during increasingly hot summers», Environ. Res., vol. 159, pp. 249–256, nov. 2017. [CrossRef]
- M. Romanello et al., «The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action», The Lancet, vol. 404, fasc. 10465, pp. 1847–1896, nov. 2024. [CrossRef]
- K. R. Van Daalen et al., «The 2024 Europe report of the Lancet Countdown on health and climate change: unprecedented warming demands unprecedented action», Lancet Public Health, vol. 9, fasc. 7, pp. e495–e522, lug. 2024. [CrossRef]
- Alexander, «Influence of the proportion, height and proximity of vegetation and buildings on urban land surface temperature», Int. J. Appl. Earth Obs. Geoinformation, vol. 95, p. 102265, mar. 2021. [CrossRef]
- P. Ampatzidis e T. Kershaw, «A review of the impact of blue space on the urban microclimate», Sci. Total Environ., vol. 730, 2020. [CrossRef]
- Arellano e J. Roca, «EFFECTS OF URBAN GREENERY ON HEALTH. A STUDY FROM REMOTE SENSING», Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLIII-B3-2022, pp. 17–24, mag. 2022. [CrossRef]
- Aznarez, S. Kumar, A. Marquez-Torres, U. Pascual, e F. Baró, «Ecosystem service mismatches evidence inequalities in urban heat vulnerability», Sci. Total Environ., vol. 922, 2024. [CrossRef]
- Buchin, M.-T. Hoelscher, F. Meier, T. Nehls, e F. Ziegler, «Evaluation of the health-risk reduction potential of countermeasures to urban heat islands», Energy Build., vol. 114, pp. 27–37, 2016. [CrossRef]
- L. Capari, H. Wilfing, A. Exner, T. Höflehner, e D. Haluza, «Cooling the City? A Scientometric Study on Urban Green and Blue Infrastructure and Climate Change-Induced Public Health Effects», Sustain. Switz., vol. 14, fasc. 9, 2022. [CrossRef]
- M. A. Coombes e H. A. Viles, «Integrating nature-based solutions and the conservation of urban built heritage: Challenges, opportunities, and prospects», Urban For. Urban Green., vol. 63, 2021. [CrossRef]
- H. M. Eldesoky, N. Colaninno, e E. Morello, «Mapping Urban ventilation corridors and assessing their impact upon the cooling effect of greening solutions», presentato al International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2020, pp. 665–672. [CrossRef]
- X. Ge, D. Mauree, R. Castello, e J.-L. Scartezzini, «Spatio-Temporal Relationship between Land Cover and Land Surface Temperature in Urban Areas: A Case Study in Geneva and Paris», ISPRS Int. J. Geo-Inf., vol. 9, fasc. 10, p. 593, ott. 2020. [CrossRef]
- Van Der Hoeven e A. Wandl, «Amsterwarm: Mapping the landuse, health and energy-efficiency implications of the Amsterdam urban heat island», Build. Serv. Eng. Res. Technol., vol. 36, fasc. 1, pp. 67–88, 2015. [CrossRef]
- «Isola et al. - 2024 - Urban Heat Island Phenomenon and Ecosystem Service.pdf».
- D. Lauwaet et al., «High resolution modelling of the urban heat island of 100 European cities», Urban Clim., vol. 54, 2024. [CrossRef]
- Marando, E. Salvatori, A. Sebastiani, L. Fusaro, e F. Manes, «Regulating Ecosystem Services and Green Infrastructure: assessment of Urban Heat Island effect mitigation in the municipality of Rome, Italy», Ecol. Model., vol. 392, pp. 92–102, 2019. [CrossRef]
- Mihalakakou et al., «Green roofs as a nature-based solution for improving urban sustainability: Progress and perspectives», Renew. Sustain. Energy Rev., vol. 180, 2023. [CrossRef]
- M. Vaz Monteiro, K. J. Doick, P. Handley, e A. Peace, «The impact of greenspace size on the extent of local nocturnal air temperature cooling in London», Urban For. Urban Green., vol. 16, pp. 160–169, 2016. [CrossRef]
- «Mosca et al. - 2021 - Nature-Based Solutions Thermal Comfort Improvemen.pdf».
- F. Olivieri, L.-N. Sassenou, e L. Olivieri, «Potential of Nature-Based Solutions to Diminish Urban Heat Island Effects and Improve Outdoor Thermal Comfort in Summer: Case Study of Matadero Madrid», Sustain. Switz., vol. 16, fasc. 7, 2024. [CrossRef]
- F. Isola, F. Leone, e R. Pittau, «Evaluating the urban heat island phenomenon from a spatial planning viewpoint. A systematic review», TeMA J. Land Use Mobil. Environ., vol. 2023, fasc. Special Issue 2, pp. 75–93, 2023. [CrossRef]
- K. Perini, C. Calise, P. Castellari, e E. Roccotiello, «Microclimatic and Environmental Improvement in a Mediterranean City through the Regeneration of an Area with Nature-Based Solutions: A Case Study», Sustain. Switz., vol. 14, fasc. 10, 2022. [CrossRef]
- T. Rötzer, M. A. Rahman, A. Moser-Reischl, S. Pauleit, e H. Pretzsch, «Process based simulation of tree growth and ecosystem services of urban trees under present and future climate conditions», Sci. Total Environ., vol. 676, pp. 651–664, ago. 2019. [CrossRef]
- C. Sánchez-Guevara Sánchez, M. Núñez Peiró, e F. J. Neila González, «Urban heat Island and vulnerable population. The case of Madrid», in Sustainable Development and Renovation in Architecture, Urbanism and Engineering, 2017, pp. 3–13. [CrossRef]
- L. Vasconcelos, J. Langemeyer, H. V. S. Cole, e F. Baró, «Nature-based climate shelters? Exploring urban green spaces as cooling solutions for older adults in a warming city», Urban For. Urban Green., vol. 98, 2024. [CrossRef]
- J. Vieira et al., «Green spaces are not all the same for the provision of air purification and climate regulation services: The case of urban parks», Environ. Res., vol. 160, pp. 306–313, 2018. [CrossRef]
- S. Vulova et al., «City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning», Remote Sens. Environ., vol. 287, 2023. [CrossRef]
- F. Mosca, G. M. Dotti Sani, A. Giachetta, e K. Perini, «Nature-Based Solutions: Thermal Comfort Improvement and Psychological Wellbeing, a Case Study in Genoa, Italy», Sustainability, vol. 13, fasc. 21, p. 11638, ott. 2021. [CrossRef]
- D. Zendeli et al., «From heatwaves to ‘healthwaves’: A spatial study on the impact of urban heat on cardiovascular and respiratory emergency calls in the city of Milan», Sustain. Cities Soc., vol. 124, p. 106181, apr. 2025. [CrossRef]



| NBS for UHI mitigation | Health Impacts of UHI | Vulnerable Populations and Socioeconomic Factors | Remote Sensing and GIS Approaches | |
| [6] - Alexander, C. (2021) | x | |||
| [7] - Ampatzidis, P. and Kershaw, T. (2020) | x | x | ||
| [8] - Arellano, B. and Roca, J. (2022) | x | x | ||
| [9] - Aznarez, C. et al. (2024) | x | x | x | x |
| [10] - Buchin, O. et al. (2016) | x | x | ||
| [11] - Capari, L. et al. (2022) | x | x | x | |
| [12] - Coombes, M.A. and Viles, H. (2021) | x | |||
| [13] - Eldesoky, A.H.M., Colaninno, N. and Morello, E. (2020) | x | x | ||
| [14] - Ge, X. et al. (2020) | x | x | ||
| [15] - Hoeven, F. van der and Wandl, A. (2015) | x | x | x | |
| [16] - Isola, F., Leone, F. and Pittau, R. (2024) | x | x | ||
| [17] - Lauwaet, D. et al. (2024) | x | |||
| [18] - Marando, F. et al. (2019) | x | x | ||
| [19] - Mihalakakou, G. et al. (2023 | x | |||
| [20] - Monteiro, M.V. et al. (2016) | x | |||
| [21] Mosca, F. et al. (2021) | x | x | ||
| [22] - Olivieri, F., Sassenou, L.-N. and Olivieri, L. (2024) | x | x | ||
| [23] - Isola, Leone and Pittau (2023) | x | |||
| [24] - Perini, K. et al. (2022) | x | |||
| [25] - Rötzer, T. et al. (2019) | x | |||
| [26] - Sánchez, C.S.-G., Peiró, | x | x | ||
| [27] - Vasconcelos, L. et al. (2024) | x | x | x | |
| [28] - Vieira, J. et al. (2017) | x | |||
| [29] - Vulova, S. et al. (2023) | x |
| Author/s | Methodology | Outcomes |
| [7] - Ampatzidis, P. and Kershaw, T. (2020) | Conducted a comparative analysis of blue spaces’ impact on urban climate adaptation, using remote sensing and climate models. |
Found that blue spaces provide a cooling effect, but their effectiveness varies depending on surrounding land use and urban density. Suggested combining blue and green infrastructure for optimal UHI mitigation. |
| [30] - Mosca et al., 2021 | Conducted a case study in Genoa, using microclimate modelling, field measurements, and public perception surveys to assess NBS benefits. |
Demonstrated that NBS not only reduce UHI effects but also improve psychological well-being, enhancing both thermal comfort and mental health. Emphasized the need for integrating NBS in urban planning for liveability. |
| [22] - Olivieri, Sassenou and Olivieri, 2024 | Used GIS and remote sensing to quantify NBS effects on UHI, focusing on Matadero Madrid. |
Found that green infrastructure significantly lowers urban temperatures, with effectiveness dependent on urban morphology and climate conditions. Recommended integrating NBS into urban design policies for better climate resilience. |
| [27] - Vasconcelos, L. et al. (2024) | Investigated urban green spaces as climate shelters for older adults, using GIS mapping and social analysis. |
Confirmed that elderly populations benefit significantly from urban green spaces, reinforcing their role in reducing heat-related health risks. Recommended policies to improve green space accessibility for vulnerable groups. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).