Climate change triggered more environmental extremes. The joint events of air pollution wave and cold wave showed higher health risks than independent events, but little evidence is available for the spatiotemporal features of their co-occurrence. To better understand and forecast the joint events, a method framework was developed in this study. The temporal trend and spatial distribution of count and duration for joint events were measured at each grid cell (0.5°×0.5°) by integrating the PM2.5 air pollution wave and cold wave. The generalized linear mixed model was used to screen influencing variables that took into account socioeconomic characteristics, meteorological variables, and annual PM2.5 levels. During 2000 and 2018, the average annual count of joint events was 4.1 ± 6.8 days and the average duration ranged from 1.0 to 9.7 days. High spatial heterogeneity was observed throughout China, with a significant increase in joint events observed in Xinjiang area (the largest province in China). The most average count of joint events was observed in Henan province (one of the most populous provinces), while the longest duration was in Chongqing (a municipality, one of the megacities). Areas with higher PM2.5 levels, prolonged air pollution wave, and cold wave durations would experience more joint events. These findings can assist China in locating vulnerable areas and establishing effective local early warning systems. The method framework offers broader perspectives on mitigating health risks associated with extreme events in other countries and regions.
Keywords: Air pollution wave; Cold wave; Influencing factor analysis; Joint event; PM(2.5); Spatiotemporal characteristics.
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