Extensive studies have been carried out on the impact of human activities on air pollution, but systematic investigation on the relationship between air pollutant and meteorological conditions is still insufficient, especially in the context of China’s site scale and recent comprehensive environmental pollution control. Here, we used a spatial interpolation technology to establish a set of data sets of pollutants and meteorological elements that are spatially matched at 896 stations in China to reveal the air pollutant-meteorological interactions between 2014 and 2019. We found that air pollution and meteorological elements have obvious seasonal and regional characteristics. Over the last few years, the concentration of most air pollutants in China has dropped significantly except for O₃. The increase in O₃ concentration was closely related to the decrease of particulate matter and NO₂ concentration. The concentration of most air pollutants was affected by meteorological conditions, but the level of impact depended on the type of pollutants and varied across regions. The concentration of air pollutants at most stations was significantly negatively correlated with wind speed, precipitation and relative humidity, but positively correlated with atmospheric pressure. As the latitude increases, the impact of temperature on the concentration of air pollutants becomes more obvious. To effectively control air pollution, it is further urgent to reveal the relationship between air pollution and meteorological conditions based on long-term daily or real-time data.

Maps of population exposure, vulnerability and risk to natural hazards are useful tools for designing and implementing disaster risk mitigation programs in China. The ranking of provinces by relative risk to natural hazards would provide a metric for prioritizing risk management strategies. Using provinces as our study unit, from the perspectives of hazard exposure, susceptibility, coping capacity and adaptive capacity, this study first constructed China’s disaster risk index for five types of major natural hazards: earthquakes, floods, droughts, low temperatures/snow and gale/hail. Then, the relative risk level at the provincial scale in China was assessed. Finally, the hotspots with the highest hazard exposure, vulnerability and risk were identified. The results showed that high exposure was a significant risk driver in China, whereas high vulnerability, especially social vulnerability, amplified the risk levels. Similar to the population exposure to disasters, the relative risk levels in the southwestern, central and northeastern regions of China were significantly higher than those in the eastern, northern and western regions. The high-risk regions or hotspots of multi-hazards were concentrated in southern China (less-developed regions), while the low-risk regions were mainly distributed in the eastern coastal areas (well-developed regions). Furthermore, a nonlinear relationship existed between the disaster risk level and poverty incidence as well as per capita GDP, demonstrating that disaster losses in middle-income areas are likely to increase if economic policies are not modified to account for the rising disaster risk. These findings further indicated that research on disaster risk should focus not only on hazards and exposure but also on the vulnerability to natural disasters. Thus, reducing vulnerability and population exposure to natural hazards would be an effective measure in mitigating the disaster risk at hotspots in China.