中文摘要：

为提高空气污染物的可视化效果与对比度,更加直观地展示气象特征对空气污染物浓度的影响,首先对宁波市的PM2.5污染物浓度进行周期分析,提取PM_（2.5）污染物季节变化因素;然后对宁波市气象数据进行可信度检验,信度检验结果 〉90%的置信区间;接着对宁波市的风力、风向数据在python环境下进行极坐标变换并进行风向可视化分析;最后利用python对PM_（2.5）、PM_（10）、SO_2、NO_2、O_3以及CO进行三维可视化展示。结果表明,可以直接利用空气质量数据和气象数据,通过信度检验、极坐标变换和频率比对进行空气质量的可视化对比和相关性分析。

英文摘要：

In order to increase the visibility and contrast effect of air pollutants,and more visually demonstrate the impact of the meteorological characteristics on the air pollutants concentrations,this paper firstly conducts a periodical analysis of PM_（2. 5）pollutant concentration in Ningbo. The data are obtained by the extracting the seasonal variations of PM_（2. 5） pollutant concentration. Secondly,a reliability test on Ningbo meteorological data and air pollution data is carried out. The results show that the confidence interval is over 90%. Thirdly,the paper analyzes the data concerning the wind power and direction in Ningbo,using Python language to conduct a polar coordinate transformation,and also an analyzes the visibility test on wind power and direction. Finally,by using Python language,a demonstration of the PM_（2. 5）,PM_（10）,SO_2,NO_2,O_3 and CO concentrations through the 3D coordinate system has been completed. After the four steps,we conclude that through reliability test,polar coordinative transformation,frequency comparison,by directly using the air and meteorological data,visibility contrast and correlation analysis could be positively completed.