近年来，随着城市经济的迅猛发展和城市服务质量的不断完善，大量人口涌向城市，城市规模随之扩大。在发展的同时，环境问题随之而来，空气污染是当下最严重、最受关注的问题之一。与较大粒径的颗粒物相比，PM2.5由于粒径小、在空气中停留时间长、能够进入人体呼吸系统等原因危害性更大，对大气环境质量、人体健康和人类生活等产生重要影响。在这种背景下，PM2.5已成为近年来社会关注的最热门问题之一。因此，深入分析PM2.5的变化特征与影响因素是当前研究的热点，对环境监测、政策制定和疾病防御都有着深远的指导意义。本文主要分析了北京、成都、上海、广州和沈阳五个城市PM2.5质量浓度的时间变化特征，利用小波分析对北京市PM2.5周期性变化特征及其与气象要素的相关性进行研究，分析5个典型城市PM2.5特征差异的潜在原因。PM2.5数据选用了能够公开下载的美国大使馆站点观测小时数据。 本文研究发现：（1）Morlet小波系数分析显示北京存在3~7、9~13、15~20和25~33天的4个尺度的周期变化。最显著的为25~33天，其中心尺度约为30天，并呈现“低-高-低-高”的交替特征。利用小波相干的方法分析北京地区PM2.5浓度与气象因素间相关性发现，在 0~20天时间尺度上，PM2.5与相对湿度呈正相关，与风速呈负相关；冬春季节，在20~60天时间尺度上，受风速和湿度的影响较大；在大于60天的时间尺度上，PM2.5与相对湿度呈正相关。（2）研究五座城市的PM2.5质量浓度季节变化、月变化及日变化特征发现，季节性差异较大的城市有北京、成都和沈阳，而上海和广州的季节差异较小；1月份成都PM2.5质量浓度比北京更高，沈阳PM2.5质量浓度具有与北京相似的时间变化趋势；北京白天污染物浓度比夜晚低，而成都和沈阳的浓度峰值发生在上午，广州和上海的日变化特征均存在双峰双谷，只是上海的峰值、谷值比广州提前了1-2个小时左右。（3）本论文通过对5个典型城市质量浓度变化特征的潜在原因解析发现，北京、沈阳、成都空气质量受周边环境的影响较大，风场会对污染物起到清除作用，但另一方面也可能会输送污染物；上海风场的作用与广州类似，较高的风速对城市中的大气污染物有着较好的清除作用。各大城市的城市机动车保有量在逐年升高，大量的机动车污染物排放对城市空气有着较大的潜在负面影响。不断扩大的城市规模与庞大的人口数量也增加了生活排放。北京、成都城市周围的山地会使气溶胶颗粒滞留，并导致空气质量变差；相比之下，其他三个城市的地形更加开阔、扩散条件更好，尤其是沿海城市上海和广州。

In recent years, with the rapid development of the urban economy and the continuous improvement of the quality of urban services, a large number of people have flocked to cities, and the scale of the city has expanded. At the same time, environmental problems have popped up, and air pollution has become one of the most serious and most concerned issues of the society. Compared with larger size particles, PM2.5 is more harmful due to its small particle size, long residence time in the air, and the ability to enter the human respiratory system. It has serious impacts on the atmospheric environment, human health and human life. Therefore, an in-depth analysis of the changing characteristics and influencing factors of PM2.5 is a hot topic of current research, and has far-reaching guiding significance for environmental monitoring, policy making and pollution control. This paper mainly analyzes the characteristics of PM2.5 mass concentration in five typical cities of Beijing, Chengdu, Shanghai, Guangzhou and Shenyang. The wavelet analysis method is used to study the cyclical variation characteristics of PM2.5 in Beijing and its correlation with meteorological elements. Potential causes for PM2.5 differences in these five typical cities are also discussed. Hourly PM2.5 data are obtained from the US embassy sites that can be downloaded publicly. The results of this paper are as follows: (1) Through the analysis of Morlet wavelet coefficients, it is found that there are 4 time periods of 3~7, 9~13, 15~20 and 25~33 days for the variation of PM2.5 in Beijing. The most prominent period for PM2.5 variation is 25~33 days with central time period around 30 days. With this periodic variations, it implies a temporal variation feature of “low-high-low-high”. The wavelet coherence method was used to analyze the correlation between PM2.5 concentration and meteorological factors in Beijing. On the 0~20 days’ time-scale, PM2.5 is positively correlated with relative humidity and negatively correlated with wind speed. In winter and spring, it is greatly affected by wind speed and humidity on the time- scale of 20~60 days. On the time-scale greater than 60 days, PM2.5 is positively correlated with relative humidity. (2) Studying the seasonal, monthly and diurnal variation characteristics of PM2.5 mass concentration in five cities. The cities with large seasonal variations were Beijing, Chengdu and Shenyang, while the seasonal variations of PM2.5 in Shanghai and Guangzhou were small. In January, the concentration of PM2.5 in Chengdu was higher than that in Beijing. PM2.5 in Shenyang has a similar temporal trend with that in Beijing. The PM2.5 mass concentration in Beijing is lower during day time than that at night, and the peak concentration of PM2.5 in Chengdu and Shenyang occurs in the morning. The daily variation characteristics of PM2.5 in Guangzhou and Shanghai both have double peaks and double valleys, with differences in time. (3)The potential causes for the variation characteristics of typical urban PM2.5 mass concentrations are discussed. In Beijing, Shenyang, and Chengdu, air quality is greatly affected by the surrounding environment, the wind field can remove pollutants, and on the other hand, it may also transport pollutants. The effect of wind field in Shanghai is similar to that in Guangzhou, which shows that the higher wind speed has a more positive effect on the removal of atmospheric pollutants in the city. The number of motor vehicles in major cities is increasing year by year. Plenty of motor vehicle pollutants have a great negative impact on urban air. The growing size of cities and large populations also increase the human emissions. The aerosol particles remain can be caused by the mountains around Chengdu and Beijing, which lead to poor air quality. By contrast, the other three cities, especially the coastal cities Shanghai and Guangzhou, have wider terrains and better diffusion conditions.