Abstract
Exploring the impact of complex urban morphology on the urban heat island (UHI) effect is essential for sustainable environmental management and enhancing human well-being. This study explored the combined cooling effect of street canyon geometry and the surrounding built environment using a CatBoost model and the Shapley method. The findings indicated that in streets with low building height and density, a high proportion of sky and vegetation and a flatter skyline are conductive to mitigate UHI effect. In streets with high building height and density, a lower proportion of sky and vegetation, and a well-proportioned skyline, can effectively mitigate UHI effect. Regardless of the building density and height around the street, street trees are the optimal choice for greening construction and improvement of large and medium-sized cities in China, given their high controllability and the current urban stock background. Therefore, reasonable control and allocation of street trees can effectively adjust the street canyon geometry, providing suitable cooling strategies for streets with different surrounding built environments. This study proposed a method to mitigate the UHI effect through street canyon geometry, which can be extended to other high-density urban thermal environment studies and guide policymakers on street construction and urban design.
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References
Aflaki A, Mirnezhad M, Ghaffarianhoseini A et al (2017) Urban heat island mitigation strategies: a state-of-the-art review on Kuala Lumpur, Singapore and Hong Kong. Cities 62:131–145. https://doi.org/10.1016/j.cities.2016.09.003
Akbari H, Cartalis C, Kolokotsa D et al (2016) Local climate change and urban heat island mitigation techniques - the state of the art. J Civ Eng Manag 22:1–16. https://doi.org/10.3846/13923730.2015.1111934
Berger C, Rosentreter J, Voltersen M et al (2017) Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature. Remote Sens Environ 193:225–243. https://doi.org/10.1016/j.rse.2017.02.020
Cao Q, Yu D, Georgescu M et al (2018) Impacts of future urban expansion on summer climate and heat-related human health in eastern China. Environ Int 112:134–146. https://doi.org/10.1016/j.envint.2017.12.027
Carrasco-Hernandez R, Smedley ARD, Webb AR (2015) Using urban canyon geometries obtained from Google Street View for atmospheric studies: potential applications in the calculation of street level total shortwave irradiances. Energy and Buildings 86:340–348. https://doi.org/10.1016/j.enbuild.2014.10.001
Chen G, Yang X, Yang H et al (2020) The influence of aspect ratios and solar heating on flow and ventilation in 2D street canyons by scaled outdoor experiments. Build Environ 185:107159
Chen Q, Cheng Q, Chen Y et al (2021) The influence of sky view factor on daytime and nighttime urban land surface temperature in different spatial-temporal scales: a case study of Beijing. Remote Sensing 13:4117. https://doi.org/10.3390/rs13204117
Cheng Q, Chen Q, Li Y, Cao B (2021) Analysis of the influence of sky view factor on urban surface temperature based on multi-source data. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, Brussels, Belgium, p 6952–6955
Coutts A, Beringer J, Tapper N (2007) Impact of increasing urban density on local climate: spatial and temporal variations in the surface energy balance in Melbourne, Australia. J Appl Meteorol Climatol 46:477–493. https://doi.org/10.1175/JAM2462.1
Dorogush AV, Ershov V, Gulin A (2018) CatBoost: gradient boosting with categorical features support. https://doi.org/10.48550/arXiv.1810.11363
Gong F-Y, Zeng Z-C, Zhang F et al (2018) Mapping sky, tree, and building view factors of street canyons in a high-density urban environment. Build Environ 134:155–167. https://doi.org/10.1016/j.buildenv.2018.02.042
Guo A, Yang J, Xiao X et al (2020) Influences of urban spatial form on urban heat island effects at the community level in China. Sustain Cities Soc 53:101972. https://doi.org/10.1016/j.scs.2019.101972
Hang J, Chen G (2022) Experimental study of urban microclimate on scaled street canyons with various aspect ratios. Urban Climate 46:101299
Hou H, Estoque RC (2020) Detecting cooling effect of landscape from composition and configuration: an urban heat island study on Hangzhou. Urban Forestry & Urban Greening 53:126719. https://doi.org/10.1016/j.ufug.2020.126719
Hu Y, Dai Z, Guldmann J-M (2020) Modeling the impact of 2D/3D urban indicators on the urban heat island over different seasons: a boosted regression tree approach. J Environ Manage 266:110424. https://doi.org/10.1016/j.jenvman.2020.110424
Huang X, Wang Y (2019) Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: a case study of Wuhan, Central China. ISPRS J Photogramm Remote Sens 152:119–131. https://doi.org/10.1016/j.isprsjprs.2019.04.010
Jamei E, Rajagopalan P, Seyedmahmoudian M, Jamei Y (2016) Review on the impact of urban geometry and pedestrian level greening on outdoor thermal comfort. Renew Sustain Energy Rev 54:1002–1017. https://doi.org/10.1016/j.rser.2015.10.104
Jamei Y, Rajagopalan P, Sun Q, Chayn (2019) Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Sci Total Environ 659:1335–1351. https://doi.org/10.1016/j.scitotenv.2018.12.308
Jiang Y, Huang J, Shi T, Wang H (2021) Interaction of urban rivers and green space morphology to mitigate the urban heat island effect: case-based comparative analysis. Int J Environ Res Public Health 18:11404. https://doi.org/10.3390/ijerph182111404
Jiao M, Zhou W, Zheng Z et al (2021) Optimizing the shade potential of trees by accounting for landscape context. Sustain Cities Soc 70:102905. https://doi.org/10.1016/j.scs.2021.102905
Johansson E (2006) Influence of urban geometry on outdoor thermal comfort in a hot dry climate: a study in Fez, Morocco. Build Environ 41:1326–1338. https://doi.org/10.1016/j.buildenv.2005.05.022
Lee H, Mayer H, Kuttler W (2020) Impact of the spacing between tree crowns on the mitigation of daytime heat stress for pedestrians inside E-W urban street canyons under Central European conditions. Urban Forestry & Urban Greening 48:126558. https://doi.org/10.1016/j.ufug.2019.126558
Li X, Ratti C (2019) Mapping the spatio-temporal distribution of solar radiation within street canyons of Boston using Google Street View panoramas and building height model. Landsc Urban Plan 191:103387. https://doi.org/10.1016/j.landurbplan.2018.07.011
Li J, Song C, Cao L et al (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115:3249–3263. https://doi.org/10.1016/j.rse.2011.07.008
Li X, Ratti C, Seiferling I (2018) Quantifying the shade provision of street trees in urban landscape: a case study in Boston, USA, using Google Street View. Landsc Urban Plan 169:81–91. https://doi.org/10.1016/j.landurbplan.2017.08.011
Li G, Ren Z, Zhan C (2020) Sky View Factor-based correlation of landscape morphology and the thermal environment of street canyons: a case study of Harbin. China Building and Environment 169:106587. https://doi.org/10.1016/j.buildenv.2019.106587
Liang J, Gong J, Sun J et al (2017) Automatic sky view factor estimation from street view photographs-a big data approach. Remote Sensing 9(5):411. https://doi.org/10.3390/rs9050411
Liu Z, Ma X, Hu L et al (2022a) Nonlinear cooling effect of street green space morphology: evidence from a gradient boosting decision tree and explainable machine learning approach. Land 11:2220. https://doi.org/10.3390/land11122220
Liu Z, Ma X, Hu L et al (2022b) Information in Streetscapes—research on visual perception information quantity of street space based on information entropy and machine learning. ISPRS Int J Geo Inf 11:628. https://doi.org/10.3390/ijgi11120628
Lobaccaro G, Acero JA (2015) Comparative analysis of green actions to improve outdoor thermal comfort inside typical urban street canyons. Urban Climate 14:251–267. https://doi.org/10.1016/j.uclim.2015.10.002
Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30
Lundberg SM, Erion GG, Lee S-I (2018) Consistent individualized feature attribution for tree ensembles. https://doi.org/10.48550/arXiv.1802.03888
Middel A, Lukasczyk J, Zakrzewski S et al (2019) Urban form and composition of street canyons: a human-centric big data and deep learning approach. Landsc Urban Plan 183:122–132. https://doi.org/10.1016/j.landurbplan.2018.12.001
Morakinyo TE, Lam YF (2016) Simulation study on the impact of tree-configuration, planting pattern and wind condition on street-canyon’s micro-climate and thermal comfort. Build Environ 103:262–275. https://doi.org/10.1016/j.buildenv.2016.04.025
Oke TR (2002) Boundary layer climates. Routledge
Pal NR, Pal SK (1989) Object-background segmentation using new definitions of entropy. IEE Proc E Comput Digit Tech UK 136:284. https://doi.org/10.1049/ip-e.1989.0039
Peng F, Wong MS, Ho HC et al (2017) Reconstruction of historical datasets for analyzing spatiotemporal influence of built environment on urban microclimates across a compact city. Build Environ 123:649–660. https://doi.org/10.1016/j.buildenv.2017.07.038
Säumel I, Weber F, Kowarik I (2016) Toward livable and healthy urban streets: roadside vegetation provides ecosystem services where people live and move. Environ Sci Policy 62:24–33
Scarano M, Sobrino JA (2015) On the relationship between the sky view factor and the land surface temperature derived by Landsat-8 images in Bari, Italy. Int J Remote Sens 36:4820–4835. https://doi.org/10.1080/01431161.2015.1070325
Shapley LS (2016) 17. A value for n-person games. In: 17. A Value for n-Person Games. Princeton University Press, pp 307–318
Soltani A, Sharifi E (2017) Daily variation of urban heat island effect and its correlations to urban greenery: a case study of Adelaide. Front Archit Res 6:529–538. https://doi.org/10.1016/j.foar.2017.08.001
Song J, Wang Z-H, Myint SW, Wang C (2017) The hysteresis effect on surface-air temperature relationship and its implications to urban planning: an examination in Phoenix, Arizona, USA. Landsc Urban Plan 167:198–211. https://doi.org/10.1016/j.landurbplan.2017.06.024
Stewart I, Oke T (2012) Local climate zones for urban temperature studies. Bull Am Meteor Soc 93:1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1
Tan Z, Lau KK-L, Ng E (2017) Planning strategies for roadside tree planting and outdoor comfort enhancement in subtropical high-density urban areas. Build Environ 120:93–109. https://doi.org/10.1016/j.buildenv.2017.05.017
Teshnehdel S, Akbari H, Di Giuseppe E, Brown RD (2020) Effect of tree cover and tree species on microclimate and pedestrian comfort in a residential district in Iran. Build Environ 178:106899. https://doi.org/10.1016/j.buildenv.2020.106899
Wang M, Vermeulen F (2021) Life between buildings from a street view image: what do big data analytics reveal about neighbourhood organisational vitality? Urban Studies 58:3118–3139. https://doi.org/10.1177/0042098020957198
Ward K, Lauf S, Kleinschmit B, Endlicher W (2016) Heat waves and urban heat islands in Europe: a review of relevant drivers. Sci Total Environ 569:527–539. https://doi.org/10.1016/j.scitotenv.2016.06.119
Watson I, Johnson G (1987) Graphical estimation of sky view-factors in urban environments. J Climatol 7:193–197
Wong NH, Tan CL, Kolokotsa DD et al (2021) Greenery as a mitigation and adaptation strategy to urban heat. Nat Rev Earth Environ 2(3):166–181
Wu W-B, Yu Z-W, Ma J, Zhao B (2022) Quantifying the influence of 2D and 3D urban morphology on the thermal environment across climatic zones. Landsc Urban Plan 226:104499. https://doi.org/10.1016/j.landurbplan.2022.104499
Xia Y, Yabuki N, Fukuda T (2021) Sky view factor estimation from street view images based on semantic segmentation. Urban Climate 40:100999. https://doi.org/10.1016/j.uclim.2021.100999
Xu C, Chen G, Huang Q et al (2022) Can improving the spatial equity of urban green space mitigate the effect of urban heat islands? An empirical study. Sci Total Environ 841:156687. https://doi.org/10.1016/j.scitotenv.2022.156687
Xue X, He T, Xu L et al (2022) Quantifying the spatial pattern of urban heat islands and the associated cooling effect of blue–green landscapes using multisource remote sensing data. Sci Total Environ 843:156829. https://doi.org/10.1016/j.scitotenv.2022.156829
Yang F, Qian F, Lau SSY (2013) Urban form and density as indicators for summertime outdoor ventilation potential: a case study on high-rise housing in Shanghai. Build Environ 70:122–137. https://doi.org/10.1016/j.buildenv.2013.08.019
Yang B, Meng F, Ke X, Ma C (2015) The impact analysis of water body landscape pattern on urban heat island: a case study of Wuhan city. Advances in Meteorology 2015:416728. https://doi.org/10.1155/2015/416728
Yang J, Yang Y, Sun D et al (2021) Influence of urban morphological characteristics on thermal environment. Sustain Cities Soc 72:103045. https://doi.org/10.1016/j.scs.2021.103045
Yin C, Yuan M, Lu Y et al (2018) Effects of urban form on the urban heat island effect based on spatial regression model. Sci Total Environ 634:696–704. https://doi.org/10.1016/j.scitotenv.2018.03.350
Zhang Y, Middel A, Turner BL (2019) Evaluating the effect of 3D urban form on neighborhood land surface temperature using Google Street View and geographically weighted regression. Landscape Ecol 34:681–697. https://doi.org/10.1007/s10980-019-00794-y
Zhang J, Cui P, Song H (2020) Impact of urban morphology on outdoor air temperature and microclimate optimization strategy base on Pareto optimality in Northeast China. Build Environ 180:107035. https://doi.org/10.1016/j.buildenv.2020.107035
Zheng Z, Zhou W, Yan J et al (2019) The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing. Phys Chem Earth, Parts a/b/c 110:149–156. https://doi.org/10.1016/j.pce.2019.01.008
Funding
This work is support by Science Foundation of Zhejiang Sci-Tech University (ZSTU) under Grant No.21052290-Y and Team Construction and Talent training Fund Project of First-class Discipline (B) of Civil Engineering in Zhejiang Province No.11140031281901.
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Ziyi Liu: Conceptualization, Methodology, Software, Formal analysis, Writing—Original Draft, Writing—Review & Editing. Lihui Hu: Conceptualization, Methodology, Writing—Review & Editing, Supervision. Huilin Chen: Conceptualization, Writing—Original Draft, Writing—Review & Editing. Zexun Li: Investigation, Data Curation. Ling Jiang: Investigation, Data Curation.
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Liu, Z., Hu, L., Chen, H. et al. Exploring the combined cooling effect of street canyon geometry and the surrounding built environment. Environ Sci Pollut Res 31, 28507–28524 (2024). https://doi.org/10.1007/s11356-024-33012-7
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DOI: https://doi.org/10.1007/s11356-024-33012-7