Characterizing vertical distribution patterns of PM2.5 in low troposphere of Shanghai city, China: Implications from the perspective of unmanned aerial vehicle observations

The vertical distribution patterns of PM_2.5 (particulate matters with diameters ≤2.5 μm) are crucial for understanding the aggregation, dispersion, and regional transport of PM_2.5. However, due to the measurement limitation, the vertical observational data o is difficult to be obtained and is relatively insufficient at present stage. With this consideration, two electric-fueled rotary-wing unmanned aerial vehicles (UAVs) are used in this study to reveal the vertical distribution of PM_2.5 in Shanghai of China. Totally 53 vertical profiles of PM_2.5 concentration and meteorological factors are collected to reveal the vertical distribution pattern of PM_2.5. The results indicate that PM_2.5 vertical concentration, especially the maximum of the PM_2.5 vertical concentration, shows a good correlation with surface PM_2.5 concentration, which means it is possible to use surface PM_2.5 concentration to extrapolate PM_2.5 vertical concentration. In terms of meteorological factors, low wind speed and northerly (or northwesterly) wind direction significantly contribute to the accumulation of high concentration PM_2.5 in Shanghai during the winter. Furthermore, the low height of the planetary boundary layer (PBL) facilitates the accumulation of pollutants near the ground, but it is not imperative for the vertically high concentration of PM_2.5, since vertically high PM_2.5 concentration exists within high PBL as well. It has been concluded that low PBL height is not mandatory for well vertical mixing within it, for the vertically even distribution of PM_2.5 occurred under both low and high PBL. In terms of the effect the temperature, although temperature makes a significant contribution to the inverse distribution of PM_2.5, no significant linear correlation was found between temperature and PM_2.5 vertical distribution. The findings in this study could help to understand the vertical distribution patterns of PM_2.5 during the winter, and provide more evidence of how other factors impact these patterns.