European Commission, Joint Research Centre (JRC), Ispra, Italy.
University of Turin, Turin, Italy.
Nat Commun. 2023 May 22;14(1):2903. doi: 10.1038/s41467-023-38596-1.
The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment.
由于人为气候变化、住区扩张和人口增长，城市中经历高温的人口数量正在增加。然而，评估潜在干预策略以减少人口暴露于地表温度（LST）极端情况的有效工具仍然缺乏。在这里，我们实施了一种基于遥感数据的空间回归模型，该模型能够根据城市环境中表面特性（如植被覆盖和与水体的距离）评估 200 个城市中人口对 LST 极端情况的暴露程度。我们将暴露定义为每年 LST 超过给定阈值的天数乘以暴露的城市总人口（人 ⋅ 天）。我们的研究结果表明，城市植被在降低城市人口对 LST 极端情况的暴露方面发挥了重要作用。我们表明，与均匀处理相比，针对高暴露区域减少植被可以达到相同的减少暴露效果。
