Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109-2029, USA.
Environ Int. 2012 Oct 1;46:23-9. doi: 10.1016/j.envint.2012.05.001. Epub 2012 Jun 5.
Heat waves have been linked to excess mortality and morbidity, and are projected to increase in frequency and intensity with a warming climate. This study compares exposure metrics to trigger heat wave and health warning systems (HHWS), and introduces a novel multi-level hybrid clustering method to identify potential dangerously hot days. Two-level and three-level hybrid clustering analysis as well as common indices used to trigger HHWS, including spatial synoptic classification (SSC), and the 90th, 95th, and 99th percentiles of minimum and relative minimum temperature (using a 10 day reference period), were calculated using a summertime weather dataset in Detroit from 1976 to 2006. The days classified as 'hot' with hybrid clustering analysis, SSC, minimum and relative minimum temperature methods differed by method type. SSC tended to include the days with, on average, 2.5 °C lower daily minimum temperature and 5.3 °C lower dew point than days identified by other methods. These metrics were evaluated by comparing their performance in predicting excess daily mortality. The 99th percentile of minimum temperature was generally the most predictive, followed by the three-level hybrid clustering method, the 95th percentile of minimum temperature, SSC and others. Our proposed clustering framework has more flexibility and requires less substantial meteorological prior information than the synoptic classification methods. Comparison of these metrics in predicting excess daily mortality suggests that metrics thought to better characterize physiological heat stress by considering several weather conditions simultaneously may not be the same metrics that are better at predicting heat-related mortality, which has significant implications in HHWSs.
热浪与超额死亡率和发病率有关，预计随着气候变暖，热浪的频率和强度将会增加。本研究比较了触发热浪和健康预警系统（HHWS）的暴露指标，并引入了一种新的多层次混合聚类方法来识别潜在的危险炎热天气。使用 1976 年至 2006 年夏季在底特律的天气数据集，计算了两层次和三层次混合聚类分析以及用于触发 HHWS 的常用指标，包括空间天气分类（SSC），以及 90、95 和 99 百分位数的最低和相对最低温度（使用 10 天参考期）。用混合聚类分析、SSC、最低和相对最低温度方法分类的“炎热”天数因方法类型而异。SSC 平均每天包含 2.5°C 更低的最低温度和 5.3°C 更低的露点，比其他方法确定的天数要多。通过比较它们在预测超额日死亡率方面的性能来评估这些指标。最低温度的 99 百分位数通常是最具预测性的，其次是三层次混合聚类方法、最低温度的 95 百分位数、SSC 和其他方法。我们提出的聚类框架比天气分类方法更灵活，所需的气象先验信息更少。这些指标在预测超额日死亡率方面的比较表明，通过同时考虑几种天气条件来更好地描述生理热应激的指标可能与更好地预测与热相关的死亡率的指标并不相同，这在 HHWS 中具有重要意义。
