ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO)

In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has been adapted to forecast daily mean ambient air pollutants (O3, CO, NO and NO2) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions, namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion), and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function) inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q) models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts, one step (i.e., one day) ahead MAPE for CO, NO2, NO and O3, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning purposes.

[1]  Krishan Kumar,et al.  Forecasting Daily Maximum Surface Ozone Concentrations in Brunei Darussalam—An ARIMA Modeling Approach , 2004, Journal of the Air & Waste Management Association.

[2]  Sung Eun Kim,et al.  Accounting seasonal nonstationarity in time series models for short-term ozone level forecast , 2005 .

[3]  H. Akaike.  A new look at the statistical model identification , 1974 .

[4]  P. Guttorp,et al.  A review of statistical methods for the meteorological adjustment of tropospheric ozone , 2001 .

[5]  Sung Eun Kim,et al.  Tree-based threshold modeling for short-term forecast of daily maximum ozone level , 2010 .

[6]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[7]  Sukumar Devotta,et al.  Air quality forecasting using a hybrid autoregressive and nonlinear model , 2006 .

[8]  R. Shibata.  Selection of the order of an autoregressive model by Akaike's information criterion , 1976 .

[9]  Anatoly B. Schmidt.  Quantitative Finance for Physicists , 2004 .

[10]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[11]  Charles R. Nelson,et al.  The Interpretation of R 2 in Autoregressive-Moving Average Time Series Models , 1976 .

[12]  G. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[13]  J. Seinfeld,et al.  Atmospheric Chemistry and Physics: From Air Pollution to Climate Change , 1997 .

[14]  B. G. Quinn,et al.  The determination of the order of an autoregression , 1979 .

[15]  Lennart Ljung,et al.  System identification (2nd ed.): theory for the user , 1999 .

[16]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[17]  A. A. Argiriou,et al.  Use of neural networks for tropospheric ozone time series approximation and forecasting ? a review , 2007 .

[18]  Lennart Ljung,et al.  System identification toolbox for use with MATLAB , 1988 .

[19]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[20]  P. W. Strike,et al.  Forecasting and control , 1991 .

[21]  Yafeng Yin,et al.  Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach , 2009 .

[22]  Scott M. Robeson,et al.  Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations , 1990 .