There are many outliers in air pollution time series data for various reasons. It has a serious impact on the data analysis and use. There are three main ways to identify anomalies but they each have definite limitations, especially when identifying and correcting the first category and the second category of outlier at the same time. In order to solve this problem, this paper presents a new way to identify anomalies based on wavelet transform and identify outlier by the use of the wavelet transform modulus maxima , then pass the amendment of the outlier through inverse transform the wavelet transform coefficient. Evidence shows that this method can be used to identify and correct the two types of outlier simultaneously and the results are obvious.