小波变换在大气污染物时间序列分析中的应用

Application of wavelet transform in air pollutant time series

以西安市PM10日平均浓度时间序列为例,根据小波分析的基本原理,应用小波变换的时频局部化功能,对大气污染物时间序列的变化进行了分析,清楚地给出了大气污染物的年变化趋势及突变特征。这些特征对大气污染物的治理、预报和控制具有十分重要的实际意义。研究结果表明,将小波变换应用于大气污染物时间序列的分析是可行的。

In illustration of the PMIO time series in Xi＇an city, PMIO time series trend and jump features of the variations are analyzed using the wavelet transform. These features are very important for PMIO control and forecasting. The study shows that wavelet transform can clearly demonstrate features of air pollutant time series.