Comparing exposure metrics for classifying ‘dangerous heat’ in heat wave and health warning systems

• Published in: Environment international Vol. 46; pp. 23 - 29
• Main Authors: Zhang, Kai, Rood, Richard B., Michailidis, George, Oswald, Evan M., Schwartz, Joel D., Zanobetti, Antonella, Ebi, Kristie L., O'Neill, Marie S.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2012; Elsevier
• Subjects: Air mass; Bayes Theorem; Biological and medical sciences; Climate; Cluster Analysis; Ergonomics. Workplace layout. Occupational psychology; Forecasting; Heat health warning system; Heat Stress Disorders - mortality; Heat Stress Disorders - prevention & control; Heat wave; Hot Temperature; Humans; Likelihood Functions; Medical sciences; Michigan; Model-based clustering; Occupational medicine; Occupational psychology; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Public Health - methods; Public health. Hygiene-occupational medicine; Temperature; Weather; Michigan; USA, Michigan, Detroit; Temperature; Air mass; Heat wave; Model-based clustering; Heat health warning system; Human; Health system; Atmospheric condition; Dog days; Heatwave; Exposure; Modeling; Heat; Classification; Health hazards; Models; Warning system; Public health
• ISSN: 0160-4120; 1873-6750
• DOI: 10.1016/j.envint.2012.05.001

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 10day 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. ► Our multi-level hybrid clustering method is a new way to identify hot days. ► We compared this method to other triggers used in heat and health warning systems. ► The days identified as ‘hot’ differed moderately or greatly among trigger methods. ► Our new method is relevant for prevention programs and pollutant mixture research.