Potsdam Institute for Climate Impact Research, Potsdam, Germany.
Potsdam Institute for Climate Impact Research, Potsdam, Germany; Carbon Delta, Zurich, Switzerland.
Sci Total Environ. 2019 Dec 10;695:133560. doi: 10.1016/j.scitotenv.2019.07.366. Epub 2019 Aug 9.
Human mortality shows a pronounced temperature dependence. The minimum mortality temperature (MMT) as a characteristic point of the temperature-mortality relationship is influenced by many factors. As MMT estimates are based on case studies, they are sporadic, limited to data-rich regions, and their drivers have not yet been clearly identified across case studies. This impedes the elaboration of spatially comprehensive impact studies on heat-related mortality and hampers the temporal transfer required to assess climate change impacts. Using 400 MMTs from cities, we systematically establish a generalised model that is able to estimate MMTs (in daily apparent temperature) for cities, based on a set of climatic, topographic and socio-economic drivers. A sigmoid model prevailed against alternative model setups due to having the lowest Akaike Information Criterion (AICc) and the smallest RMSE. We find the long-term climate, the elevation, and the socio-economy to be relevant drivers of our MMT sample within the non-linear parametric regression model. A first model application estimated MMTs for 599 European cities (>100 000 inhabitants) and reveals a pronounced decrease in MMTs (27.8-16 °C) from southern to northern cities. Disruptions of this pattern across regions of similar mean temperatures can be explained by socio-economic standards as noted for central eastern Europe. Our alternative method allows to approximate MMTs independently from the availability of daily mortality records. For the first time, a quantification of climatic and non-climatic MMT drivers has been achieved, which allows to consider changes in socio-economic conditions and climate. This work contributes to the comparability among MMTs beyond location-specific and regional limits and, hence, towards a spatially comprehensive impact assessment for heat-related mortality.
人类死亡率表现出明显的温度依赖性。作为温度-死亡率关系的特征点，最小死亡率温度 (MMT) 受到许多因素的影响。由于 MMT 的估计是基于案例研究的，因此它们是零星的，仅限于数据丰富的地区，而且它们的驱动因素在案例研究中尚未明确确定。这阻碍了对与热相关的死亡率进行空间综合影响研究的开展，并阻碍了评估气候变化影响所需的时间转移。我们使用来自城市的 400 个 MMT 值，系统地建立了一个通用模型，该模型能够根据一组气候、地形和社会经济驱动因素来估算城市的 MMT（以日明显温度为单位）。由于具有最低的 Akaike 信息准则 (AICc) 和最小的 RMSE，因此 sigmoid 模型优于替代模型设置。我们发现，长期气候、海拔和社会经济是我们的 MMT 样本中非线性参数回归模型中的相关驱动因素。首次模型应用估计了 599 个欧洲城市（>100,000 名居民）的 MMT 值，结果表明 MMT 值（27.8-16°C）从南向北的城市明显降低。在平均温度相似的地区，这种模式的中断可以用社会经济标准来解释，如中欧东部地区所示。我们的替代方法允许在没有每日死亡率记录的情况下独立估算 MMT 值。首次实现了对气候和非气候 MMT 驱动因素的量化，从而可以考虑社会经济条件和气候的变化。这项工作有助于超越特定地点和区域限制，实现 MMT 之间的可比性，从而为与热相关的死亡率进行空间综合影响评估做出贡献。
