为研究北京城区PM2.5浓度不同时间尺度的周期及其演变特征,利用2010~2015年PM2.5浓度和常规气象资料,对其进行Morlet小波和交叉小波分析.结果表明,北京城区PM2.5浓度存在显著的周期性变化,主要周期包括24h左右,8d左右和14d左右.14d左右的周期主要受大气准双周振荡的影响,8d左右周期不仅与天气尺度系统周期有关,此外可能还与人类活动引起“星期效应”有关,其中天气尺度系统的影响可能居于主要地位.通过交叉小波分析,PM2.5与平均风速在8d和14d左右存在显著的共振现象,并且二者表征为负位相关系.气象要素日变化、城市居民行为习惯导致的污染物排放差异可能是造成24h左右振荡周期的重要因素.北京城区PM2.5浓度的各周期在秋冬季较为显著,与北京地区秋冬季低层大气更多受强天气系统的影响有关;春夏季PM2.5浓度较低和影响因素较多以及局地中尺度热力环流对于低层大气的影响更为显著是该时期周期性较弱的主要原因;季节内振荡(40~60d)在2014年后减弱可能与北京市开展的减排措施有关.尽管本研究利用小波分析方法得到一些关于北京城区 PM2.5浓度振荡周期及其演变特征的有效信息,但所用资料时长较短且站点相对单一,所得结论还需要大量的实测数据或其他分析方法的验证.
The variation period of PM2.5 concentration and its evolution feature in Beijing urban area were investigated using the Morlet wavelet analysis and Cross wavelet transform (XWT) method. Theobservation data of PM2.5 concentration and meteorological elements from 2010 to 2015 were applied in this study. The results showed that there were significant periodic variations in PM2.5 in Beijing urban, with major period of 24h, 8d and 14d. The 14d period was mainly influenced by quasi-two-week atmosphericoscillation. The 8d period was not only related to the synoptic scale of weather system, but also related to the "weekend effect" caused by human activities, in which the synoptic scale system was probably playing the leading role. There were obvious sympathetic vibrationsin PM2.5 and average wind speed in the frequency period of 8d and 14d, and the anti-phase relationbetween them was also found. Diurnal variations of meteorological condition and anthropogenic emissions might be important factors causing the oscillation period of 24h. These oscillation periods were strongly significant in autumn and winter, because the flow patterns of lower atmosphere was more frequently influenced by strong synoptic systems. Weak oscillation in spring and summer was mainly attributedto low concentration level of PM2.5, multiple influencing factors and intensivemeso-scale thermal circulation induced by topography. The emission reduction measuresmight bethe important factor in weakening Madden-Julian Oscillation after 2014. Although this study achieved some conclusions about oscillation period of PM2.5 in Beijing urban and its evolution feature more measured data and other analytic methods should be verify in future.