The spatiotemporal distribution characteristics of PM2.5 pollution in Yuncheng City were analyzed based on the ambient air quality monitoring data from March 2019 to February 2020, and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) and cluster analysis were used to discuss the contamination transportation pathway and the distribution of potential pollution sources affecting PM2.5 in Yuncheng City in different seasons. The results showed that the PM2.5 concentration in Yuncheng was the highest in winter (111.24 μg·m-3) and the lowest in summer (30.02 μg·m-3). PM2.5/PM10 was greater than 0.6 in autumn and winter, indicating that the particulate matter pollution in Yuncheng City in autumn and winter was mainly fine particulate matter; spatially, the annual mean value of PM2.5 presented distribution characteristics that were high in the northern and central regions and low in the eastern and western regions. The high-value areas of PM2.5 were significantly and strongly correlated with SO2, NO2, and CO, indicating that local emissions contributed to the high values. The highest values in spring and winter were located in Hejin City (58.50 μg·m-3) and Jishan County (142.33 μg·m-3), and the highest values in summer and autumn were located in Pinglu County (36.92 μg·m-3) and the Salt Lake area (62.94 μg·m-3), respectively. Transportation analysis showed that spring and winter were mainly affected by air masses transporting northwestward, accounting for 58.69% and 55.77%. In summer, air masses were mainly eastward and southward, and in autumn, pollution was from short-distance transport air masses from southwestern Henan. The largest ratio was 32.89%; the potential source area was the smallest in spring and summer; it then began to grow in autumn, and it was the widest in the winter and had the highest contribution. The main source area in spring and summer was located in western and central Henan, and in autumn and winter, source areas were concentrated at the junction of Shaanxi, Shanxi, and Henan and west of Shaanxi.
Keywords: PM2.5; backward trajectory; potential source contribution; spatiotemporal distribution; transport pathway.