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

摘要

The vertical distribution patterns of PM2.(5) (particulate matters with diameters = 2.5 mu m) are crucial for understanding the aggregation, dispersion, and regional transport of PM2.(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 PM2.5 in Shanghai of China. Totally 53 vertical profiles of PM2.5 concentration and meteorological factors are collected to reveal the vertical distribution pattern of PM2.5. The results indicate that PM2.5 vertical concentration, especially the maximum of the PM2.5 vertical concentration, shows a good correlation with surface PM2.5 concentration, which means it is possible to use surface PM2.5 concentration to extrapolate PM2.5 vertical concentration. In terms of meteorological factors, low wind speed and northerly (or northwesterly) wind direction significantly contribute to the accumulation of high concentration PM2.5 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 PM2.5, since vertically high PM2.5 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 PM2.(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 PM2.5, no significant linear correlation was found between temperature and PM2.5 vertical distribution. The findings in this study could help to understand the vertical distribution patterns of PM2.5 during the winter, and provide more evidence of how other factors impact these patterns.