摘要:为了深入理解边界层内气温、相对湿度对PM2.5垂直分布和近地面污染的影响，本文使用搭载了多参数大气环境探测传感器的无人机对南京2017年12月3～4日和12月23～24日的PM2.5浓度、气温和相对湿度进行垂直观测，结合对气象数据的分析及HYSPLIT4（Hybrid Single Particle Lagrangian Integrated Trajectory version 4）轨迹计算模式的应用，对这两次PM2.5的垂直分布特征及污染过程的成因进行了分析。结果表明，PM2.5浓度和相对湿度呈明显的正相关关系，在12月23～24日的6次观测中相关系数均值达到0.96。逆温层下部，PM2.5浓度和相对湿度高且垂直差异较小；逆温层以上，PM2.5浓度和相对湿度随高度升高而迅速降低。由于大气扩散条件较差，导致PM2.5在华北平原南部不断累积，之后受到高压系统的影响分别向南和东南转移。这两次PM2.5污染过程都明显受到外部输送的影响，大气逆温对PM2.5和水汽的向上输送有明显的抑制作用，外部输送和局部逆温是导致这两次PM2.5污染的主要原因。
关键词:多旋翼无人机/
边界层/
PM2.5污染/
垂直分布/
后向轨迹
Abstract:To completely understand the influence of temperature and relative humidity in the boundary layer on the vertical distribution of PM2.5 and near-surface pollution, in this study, an unmanned aerial vehicle (UVA) equipped with multiparameter atmospheric environment detector was used for the vertical observation of PM2.5 concentration, temperature, and relative humidity in Nanjing from 3 December to 4 December 2017 and from 23 December to 24 December 2017. Combined with the analysis of meteorological data and the application of HYSPLIT4 (Hybrid Single Particle Lagrangian Integrated Trajectory version 4) trajectory calculation model, the vertical distribution characteristics of PM2.5 and the causes of PM2.5 pollution processes from 3 December to 4 December 2017 and from 23 December to 24 December 2017 were analyzed. The results showed a significant positive correlation between PM2.5 concentration and relative humidity. Moreover, the average correlation coefficient reached 0.96 in the six observations from 23 December to 24 December , 2017. Under the inversion layer, the PM2.5 concentration and relative humidity were high and the vertical difference was small. Over the inversion layer, the PM2.5 concentration and relative humidity rapidly decreased with the increase in height. Because of the poor atmospheric diffusion conditions, PM2.5 continuously accumulates in the south of the North China Plain and moves to the south and southeast under the influence of the high-pressure system. Both PM2.5 pollution processes were significantly affected by external transport. Atmospheric inversion significantly inhibited the upward transport of PM2.5 and water vapor. Moreover, external transport and local inversion were the main causes of these two PM2.5 pollution processes.
Key words:Multi-rotor unmanned aerial vehicle (UAV)/
Boundary layer/
PM2.5 pollution/
Vertical distribution/
Backward trajectory
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