Enhancing Climate-Driven Urban Tree Cooling with Targeted Nonclimatic Interventions.

Journal: Environmental science & technology
Published Date:

Abstract

Urban trees play a pivotal role in mitigating heat, yet the global determinants and patterns of their cooling efficiency (CE) remain elusive. Here, we quantify the diel CE of 229 cities across four climatic zones and employ a machine-learning model to assess the influence of variables on CE. We found that for every 10% increase in tree cover, surface temperatures are reduced by 0.25 °C during the day and 0.04 °C at night. Trees in humid regions exhibit the highest daytime CE, while those in arid zones demonstrate the greatest cooling effect at night. This can be explained by the difference in canopy density between the humid and arid zones. During the day, the high canopy density in the humid zone converts more solar radiation into latent heat flux. At night, the low canopy density in the arid zone intercepts less longwave radiation, which favors surface cooling. While climatic factors contribute nearly twice as much to CE as nonclimatic ones, our findings suggest that optimizing CE is possible by managing variables within specific thresholds due to their nonlinear effects. For instance, we revealed that in arid regions, an impervious surface coverage of approximately 60% is optimal, whereas in humid areas, reducing it to around 40% maximizes cooling benefits. These insights underscore the need for targeted management of nonclimatic factors to sustain tree cooling benefits and offer practical guidance for designing climate-resilient, nature-based urban strategies.

Authors

  • Zhaowu Yu
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China.
  • Siheng Li
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China.
  • Wenjun Yang
    Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
  • Jiquan Chen
    Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences / Key Laboratory of Stem-fiber Biomass and Engineering Microbiology, Ministry of Agriculture, Changsha, 410205, China.
  • Mohammad A Rahman
    The University of Melbourne, Burnley, Victoria 3010, Australia.
  • Chenghao Wang
    Department of Applied Linguistics, Xi'an Jiaotong-Liverpool University, Suzhou, China.
  • Wenjuan Ma
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China. Electronic address: mawenjuan2008@163.com.
  • Xihan Yao
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China.
  • Junqi Xiong
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China.
  • Chi Xu
    Hamlyn Centre of Robotic Surgery, Department of Surgery and Cancer Imperial College London London UK.
  • Yuyu Zhou
    Department of Geography, The University of Hong Kong, Hong Kong 999077, China.
  • Jike Chen
    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
  • Kangning Huang
    New York University Shanghai, Shanghai 200124, China.
  • Xiaojiang Gao
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China.
  • Rasmus Fensholt
    Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark.
  • Qihao Weng
    Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong 999077, China.
  • Weiqi Zhou
    College of Software, Jiangxi Agricultural University, Nanchang 330045, People's Republic of China.