Modeling anthropogenic trends in air quality data

Rao and Zurbenko suggest an effective method for detecting changes in air quality due to changes in emissions in the presence of meteorological fluctuations.(1) On the other hand, it is well known that air quality time series display long-range dependence (LRD),(2) and various methods have been suggested for modeling this component. This paper attempts to show that the LRD component can be used to model the anthropogenic changes in air quality data. We estimate the anthropogenic trend component using the Rao-Zurbenko method and the LRD component using the Haslett-Raftery algorithm in an ozone time series and an NO2 time series. These two methods produce nearly identical results.