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Publication | Open Access

An evaluation of climate/mortality relationships in large U.S. cities and the possible impacts of a climate change.

472

Citations

15

References

1997

Year

TLDR

Daily winter mortality is higher than summer, yet the leading causes of winter deaths are largely temperature‑independent. The study applies a new air‑mass synoptic procedure to assess current climate‑mortality links and project how future global warming could modify them. Forty‑four U.S. cities with populations over one million were analyzed using the air‑mass procedure to quantify mortality responses to specific weather patterns.

Abstract

A new air mass-based synoptic procedure is used to evaluate climate/mortality relationships as they presently exist and to estimate how a predicted global warming might alter these values. Forty-four large U.S. cities with metropolitan areas exceeding 1 million in population are analyzed. Sharp increases in mortality are noted in summer for most cities in the East and Midwest when two particular air masses are present. A very warm air mass of maritime origin is most important in the eastern United States, which when present can increase daily mortality by as many as 30 deaths in large cities. A hot, dry air mass is important in many cities, and, although rare in the East, can increase daily mortality by up to 50 deaths. Cities in the South and Southwest show lesser weather/mortality relationships in summer. During winter, air mass-induced increases in mortality are considerably less than in summer. Although daily winter mortality is usually higher than summer, the causes of death that are responsible for most winter mortality do not vary much with temperature. Using models that estimate climate change for the years 2020 and 2050, it is estimated that summer mortality will increase dramatically and winter mortality will decrease slightly, even if people acclimatize to the increased warmth. Thus, a sizable net increase in weather-related mortality is estimated if the climate warms as the models predict.

References

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