Institute of Island and Coastal Ecosystems, Ocean College, Zhejiang University , Zhoushan 316021, China.
Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China.
Environ Sci Technol. 2017 Feb 7;51(3):1498-1507. doi: 10.1021/acs.est.6b04355. Epub 2017 Jan 25.
Extreme heat events, a leading cause of weather-related fatality worldwide, are expected to intensify, last longer, and occur more frequently in the near future. In heat health risk assessments, a spatiotemporal mismatch usually exists between hazard (heat stress) data and exposure (population distribution) data. Such mismatch is present because demographic data are generally updated every couple of years and unavailable at the subcensus unit level, which hinders the ability to diagnose human risks. In the present work, a human settlement index based on multisensor remote sensing data, including nighttime light, vegetation index, and digital elevation model data, was used for heat exposure assessment on a per-pixel basis. Moreover, the nighttime urban heat island effect was considered in heat hazard assessment. The heat-related health risk was spatially explicitly assessed and mapped at the 250 m × 250 m pixel level across Zhejiang Province in eastern China. The results showed that the accumulated heat risk estimates and the heat-related deaths were significantly correlated at the county level (Spearman's correlation coefficient = 0.76, P ≤ 0.01). Our analysis introduced a spatially specific methodology for the risk mapping of heat-related health outcomes, which is useful for decision support in preparation and mitigation of heat-related risk and potential adaptation.
极端高温事件是全球与天气相关的死亡主要原因之一，预计在不久的将来，此类事件的强度将加大、持续时间将更长且发生频率将更高。在热健康风险评估中，危害（热压力）数据与暴露（人口分布）数据之间通常存在时空不匹配的情况。出现这种不匹配是因为人口数据通常每两年更新一次，而且无法在细分普查单位层面获得，这阻碍了对人类风险进行诊断的能力。在本工作中，使用了一种基于多传感器遥感数据（包括夜间灯光、植被指数和数字高程模型数据）的人类住区指数，以便在像素级别上进行热暴露评估。此外，在热危害评估中还考虑了夜间城市热岛效应。在中国东部的浙江省，以 250 m×250 m 的像素为单位，对热相关健康风险进行了空间明确的评估和制图。结果表明，在县级水平上，累积的热风险估计值与热相关死亡具有显著相关性（斯皮尔曼相关系数=0.76，P≤0.01）。我们的分析为热相关健康结果的风险制图引入了一种空间特异性方法，这对于热相关风险的准备和缓解以及潜在的适应措施的决策支持非常有用。
