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<p>Urbanization accelerates the replacement of natural surfaces with impermeable materials, intensifying urban heat island effects and negatively impacting residents’ quality of life. Nature-based solutions, such as rainwater spraying systems, have emerged as practical interventions that can rapidly mitigate urban heat. This study quantitatively evaluates the cooling effects of rainwater spraying on the thermal environments around roads in Suwon City, South Korea, employing machine-learning-based thermal infrared imagery analysis and difference-in-differences statistical modeling. Ambient and surface temperature data from fixed thermometers and unmanned aerial vehicle-mounted thermal infrared sensors, respectively, were analyzed, with surface temperatures corrected based on a linear regression model. Results demonstrated a statistically significant cooling effect of rainwater spraying, with an average 0.12&#xa0;°C reduction in ambient temperatures overall. A spatial cluster analysis further revealed distinct cooling distributions extending beyond the directly sprayed road surfaces to adjacent vegetated areas and artificial structures. Our findings highlight the potential of combining rainwater spraying with urban greenery management as a cost-effective strategy for mitigating urban heat island effects and improving pedestrian thermal comfort, informing future climate-adaptive urban planning policies.</p>

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Evaluating the cooling benefits of rainwater spraying in urban environments using machine learning and UAV thermal imaging

  • 期刊论文

  • 发表日期：

    2025年6月30日

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  • Wheemoon Kim,
  • Eunyoung Kim,
  • Wonkyong Song

摘要

Urbanization accelerates the replacement of natural surfaces with impermeable materials, intensifying urban heat island effects and negatively impacting residents’ quality of life. Nature-based solutions, such as rainwater spraying systems, have emerged as practical interventions that can rapidly mitigate urban heat. This study quantitatively evaluates the cooling effects of rainwater spraying on the thermal environments around roads in Suwon City, South Korea, employing machine-learning-based thermal infrared imagery analysis and difference-in-differences statistical modeling. Ambient and surface temperature data from fixed thermometers and unmanned aerial vehicle-mounted thermal infrared sensors, respectively, were analyzed, with surface temperatures corrected based on a linear regression model. Results demonstrated a statistically significant cooling effect of rainwater spraying, with an average 0.12 °C reduction in ambient temperatures overall. A spatial cluster analysis further revealed distinct cooling distributions extending beyond the directly sprayed road surfaces to adjacent vegetated areas and artificial structures. Our findings highlight the potential of combining rainwater spraying with urban greenery management as a cost-effective strategy for mitigating urban heat island effects and improving pedestrian thermal comfort, informing future climate-adaptive urban planning policies.