This study investigates the impact of urban surface albedo and emissivity on Mean Radiant Temperature (MRT), a key parameter for assessing thermal comfort and urban heat island (UHI) dynamics. Results show that MRT generally follows the diurnal cycle of air temperature, with notable deviations in highly exposed areas, particularly on horizontal surfaces, which are more sensitive due to their orientation, thermal capacity, and roughness. High-albedo scenarios effectively reduce daytime MRT and summer heat stress but may also enhance nocturnal radiative cooling. The influence of emissivity is more subtle, as most urban materials already exhibit high values, yet variations can still affect longwave exchanges, especially at night. Intermediate albedo-emissivity configurations provide a more balanced performance across seasons by maintaining moderate MRT levels both day and night. Findings further indicate that horizontal surfaces exert a stronger control on MRT than vertical ones under open-sky conditions, though facades can become dominant in dense urban canyons due to radiative trapping. Additionally, integrating urban vegetation—through evapotranspiration and shading—proves highly effective in lowering MRT and mitigating UHI. Optimizing the radiative properties of urban surfaces, combined with green infrastructure, can improve outdoor thermal comfort, reduce cooling energy demand, and indirectly contribute to lowering greenhouse gas (GHG) emissions. These results provide operational insights for incorporating MRT into energy-climate planning and guiding urban transition strategies towards more resilient and sustainable cities.
References
[1]
GIEC-IPCC (2019) Réchauffement planétaire de 1,5 °C. https://www.ipcc.ch/site/assets/uploads/sites/2/2019/09/SR15_Summary_Volume_french.pdf
[2]
IPCC AR6 WGII Chapter 6 (2022) Chapter 6: Cities, Settlements and Key Infrastructure. Climate Change 2022: Impacts, Adaptation and Vulnerability.
[3]
Bocquier, P. (2005) World Urbanization Prospects: An Alternative to the UN Model of Projection Compatible with the Mobility Transition Theory. DemographicResearch, 12, 197-236. https://doi.org/10.4054/demres.2005.12.9
[4]
Stewart, I.D. and Oke, T.R. (2012) Local Climate Zones for Urban Temperature Studies. BulletinoftheAmericanMeteorologicalSociety, 93, 1879-1900. https://doi.org/10.1175/bams-d-11-00019.1
[5]
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I.D., et al. (2022) A Global Map of Local Climate Zones to Support Earth System Modelling and Urban-Scale Environmental Science. EarthSystemScienceData, 14, 3835-3873. https://doi.org/10.5194/essd-14-3835-2022
[6]
Santamouris, M. (2020) Recent Progress on Urban Overheating and Heat Island Research. Integrated Assessment of the Energy, Environmental, Vulnerability and Health Impact. Synergies with the Global Climate Change. EnergyandBuildings, 207, Article ID: 109482. https://doi.org/10.1016/j.enbuild.2019.109482
[7]
Fraisse, P. (2017) Délos: études de morphologie urbaine I Objectifs et méthodes. Bulletin de correspondancehellénique, 144, 357-371.
[8]
Mirzabeigi, S. and Razkenari, M. (2022) Design Optimization of Urban Typologies: A Framework for Evaluating Building Energy Performance and Outdoor Thermal Comfort. SustainableCitiesandSociety, 76, Article ID: 103515. https://doi.org/10.1016/j.scs.2021.103515
[9]
Acero, J.A., Koh, E.J.Y., Ruefenacht, L.A. and Norford, L.K. (2021) Modelling the Influence of High-Rise Urban Geometry on Outdoor Thermal Comfort in Singapore. UrbanClimate, 36, Article ID: 100775. https://doi.org/10.1016/j.uclim.2021.100775
[10]
Aghamolaei, R., Fallahpour, M. and Mirzaei, P.A. (2021) Tempo-spatial Thermal Comfort Analysis of Urban Heat Island with Coupling of CFD and Building Energy Simulation. EnergyandBuildings, 251, Article ID: 111317. https://doi.org/10.1016/j.enbuild.2021.111317
[11]
Mariani, L., Parisi, S.G., Cola, G., Lafortezza, R., Colangelo, G. and Sanesi, G. (2016) Climatological Analysis of the Mitigating Effect of Vegetation on the Urban Heat Island of Milan, Italy. ScienceoftheTotalEnvironment, 569, 762-773. https://doi.org/10.1016/j.scitotenv.2016.06.111
[12]
Santamouris, M. (2016) Cooling the Buildings—Past, Present and Future. EnergyandBuildings, 128, 617-638. https://doi.org/10.1016/j.enbuild.2016.07.034
[13]
Watson, I.D. and Johnson, G.T. (1987) Graphical Estimation of Sky View‐factors in Urban Environments. JournalofClimatology, 7, 193-197. https://doi.org/10.1002/joc.3370070210
[14]
Middel, A., Lukasczyk, J., Maciejewski, R., Demuzere, M. and Roth, M. (2018) Sky View Factor Footprints for Urban Climate Modeling. UrbanClimate, 25, 120-134. https://doi.org/10.1016/j.uclim.2018.05.004
[15]
Lindberg, F., Grimmond, C.S.B., Gabey, A., Huang, B., Kent, C.W., Sun, T., et al. (2018) Urban Multi-Scale Environmental Predictor (UMEP): An Integrated Tool for City-Based Climate Services. EnvironmentalModelling&Software, 99, 70-87. https://doi.org/10.1016/j.envsoft.2017.09.020
[16]
Prata, A. (1996) A New Long-Wave Formula for Estimating Downward Clear-Sky Radiation at the Surface. QuarterlyJournaloftheRoyalMeteorologicalSociety, 122, 1127-1151. https://doi.org/10.1256/smsqj.53305
[17]
Robinson, D. and Stone, A. (2004) Solar Radiation Modelling in the Urban Context. SolarEnergy, 77, 295-309. https://doi.org/10.1016/j.solener.2004.05.010
[18]
Holmer, B., Lindberg, F., Rayner, D., et al. (2015) How to Transform the Standing Man from a Box to a Cylinder—A Modified Methodology To calculate Mean Radiant Temperature in Field Studies and Models. http://www.meteo.fr/cic/meetings/2015/ICUC9/LongAbstracts/bph5-2-3271344_a.pdf
[19]
Kirschbaum, M.U.F., Whitehead, D., Dean, S.M., Beets, P.N., Shepherd, J.D. and Ausseil, A.E. (2011) Implications of Albedo Changes Following Afforestation on the Benefits of Forests as Carbon Sinks. Biogeosciences, 8, 3687-3696. https://doi.org/10.5194/bg-8-3687-2011