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Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China
Hong Zhang1, Sheng Zhang1, Ping Wang2
1a College of Environment and Resource , Shanxi University , Taiyuan , People's Republic of China.
Abstract:
Particulate matter with aerodynamic diameter below 10 μm (PM10) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction.
Implications:
Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM10 forecasting field.
