Few studies have analyzed the health effects of temperature variability (TV) accounting for both interday and intraday variations in ambient temperature. In this study, TV was defined as the standard deviations of the daily minimum and maximum temperature during different exposure days. Distributed lag non-linear Poisson regression model was used to examine the city-specific effect of TV on mortality in 31 Chinese municipalities and provincial capital cities. The national estimate was pooled through a meta-analysis based on the restricted maximum likelihood estimation. To assess effect modification on TV-mortality association by individual characteristics, stratified analyses were further fitted. Potential effect modification by city characteristics was performed through a meta-regression analysis. In total, 259 million permanent residents and 4,481,090 non-accidental deaths were covered in this study. The effect estimates of TV on mortality were generally increased by longer exposure days. A 1 °C increase in TV at 0–7 days' exposure was associated with a 0.60% (95% CI: 0.25–0.94%), 0.65% (0.24–1.05%), 0.82% (0.29–1.36%), 0.86% (0.42–1.31%), 0.98% (0.57–1.39%) and 0.54% (−0.11-1.20%) increase in non-accidental, cardiovascular, IHD, stroke, respiratory and COPD mortalities, respectively. Those with lower levels of educational attainment were significantly susceptible to TV. Cities with dense population, higher mean temperatures, and relative humidity and lower diurnal temperature ranges also had higher mortality risks caused by TV. This study demonstrated that TV had considerable health effects. An early warning system to alert residents about large temperature variations is recommended, which may have a significant impact on the community awareness and public health.

Increasing maritime traffic in the Arctic has heightened the oil spill-related risks in this highly sensitive environment. To quantitatively assess these risks, we need knowledge about both the vulnerability and sensitivity of the key Arctic functional groups that may be affected by spilled oil. However, in the Arctic these data are typically scarce or lacking altogether. To compensate for this limited data availability, we propose the use of a probabilistic expert elicitation methodology, which we apply to seals, anatids, and seabirds. Our results suggest that the impacts of oil vary between functional groups, seasons, and oil types. Overall, the impacts are least for seals and greatest for anatids. Offspring seem to be more sensitive than adults, the impact is greatest in spring, and medium and heavy oils are the most harmful oil types. The elicitation process worked well, yet finding enough skilled and motivated experts proved to be difficult.