Abstract

Frequent extreme air pollution episodes in China accompanied with high concentrations of particulate matters (PM2.5) and ozone (O-3) are partly supported by meteorological conditions. However, the relationships between meteorological variables and pollution extremes can be poorly estimated solely... Show moreFrequent extreme air pollution episodes in China accompanied with high concentrations of particulate matters (PM2.5) and ozone (O-3) are partly supported by meteorological conditions. However, the relationships between meteorological variables and pollution extremes can be poorly estimated solely based on mean pollutant level. In this study, we use quantile regression to investigate meteorological sensitivities of PM2.5 and O-3 extremes, benefiting from nationwide observations of air pollutants over 2013-2019 in China. Results show that surface winds and humidity are identified as key drivers for high PM(2.5 )events during both summer and winter, with greater sensitivities at higher percentiles. Higher humidity favors the hydroscopic growth of particles during winter, but it tends to decrease PM(2.5 )through wet scavenging during summer. Surface temperature play dominant role in summer O-3 extremes, especially in VOC-limited regime, followed by surface winds and radiation. Sensitivities of O-3 to meteorological conditions are relatively unchanging across percentiles. Under the fossil-fueled development pathway (SSP5-8.5) scenario, meteorological conditions are projected to favor winter PM2.5 extremes in North China Plain (NCP), Yangtze River Delta (YRD) and Sichuan Basin (SCB), mainly due to enhanced surface specific humidity. Summer O-3 extremes are likely to occur more frequently in the NCP and YRD, associated with warmer temperature and stronger solar radiation. Besides, meteorological conditions over a relatively longer period play a more important role in the formation of pollution extremes. These results improve our understanding of the relationships between extreme PM2.5 and O-3 pollution and meteorology, and can be used as a valuable reference of model predicted air pollution extremes. Show less