北京秋季一次降雪前污染天气的激光雷达观测研究

以2009年11月5~8日北京地区发生的一次特殊天气形势下的重污染天气过程为例,研究分析本次污染特点和大气边界层结构特征以及此天气过程的大气温度和相对湿度结构特点。激光雷达是探测大气边界层及气溶胶的一个高效工具,利用ALS300激光雷达系统测量信号,应用Fernald方法反演大气消光系数,根据反演的气溶胶消光系数的最大突变,即最大递减率的高度来确定大气边界层的高度。利用其观测的退偏比分析大气污染物特性。利用微波辐射计数据,确定大气温度和湿度时空特征。研究结果表明:在本次污染天气下,大气具有很强的逆温结构,逆温最大可达近1 K(100 m)-1,500 m以上的大气相对湿度很低,在这种天气特征下的大气边界层高度在400 m左右,非常稳定。污染结束降雪开始前,大气逆温结构消失,大气湿度大幅度增加,接近饱和。根据lidar(light detection and ranging)退偏比的分析,本次污染天气是一次典型的烟尘类颗粒物的污染,污染具有区域性特点。PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物)与AOT(Aerosol Optical Thickness)之间有明显的线性关系,相关系数达到0.72。该lidar系统能够反演出秋季降雪前本次污染天气背景下北京城区上空的大气污染特性和大气边界层高度。

Study on Atmospheric Pollution Characteristics before a Snowfall Event in Autumn in the Beijing Urban Area Using Lidar

Lidar measurements were performed at a heavily polluted site between 5 Nov 2009 and 8 Nov 2009. Aerosol extinction coefficients, AOT (Aerosol Optical Thickness), and depolarization ratio were measured, temperature and relative humidity profiles were acquired from a microwave radiometer. Lidar is an efficient tool for detecting the ABL (Atmospheric Boundary Layer) and atmospheric aerosols. In this study, the aerosol extinction coefficient was retrieved from the measured signal of an ALS300 lidar system using the Fernald method. The ABL height was determined according to the maximum inflexion point of the inversed aerosol extinction coefficient (the height of the maximum decline rate). Atmospheric pollutant characteristics were analyzed using the depolarization ratio from the lidar. The temperature and relative humidity structure of air were obtained from microwave radiometer data. The results show that under the air pollution conditions, the atmosphere had a strong temperature inversion layer, with an inversion intensity up to 1 K (100 m)^-1, and a very low atmospheric relative humidity above 500 m; the atmosphere was highly stable. After the pollution events and before the start of snowfall, the inversion structure disappeared and the relative humidity increased significantly to reach saturation. Lidar depolarization ratio analysis indicated the pollution type to be a typical soot particulate matter pollution with regional characteristics. There was a significant linear relationship between PM2.5 and AOT, with a correlation coefficient of 0.72. Our results show that lidar systems can detect air pollution characteristics and ABL height in polluted weather before snowfall in autumn in urban Beijing.