Modeling and Forecasting Air Pollution of Tehran Application of Autoregressive Model with Long Memory Properties

Subject Areas : environmental management

reza  akhbari ¹ (MSc of Environmental economics, University of Allameh Tabataba’I, Tehran, Iran.* (Corresponding Author))
Hamid  Amadeh ² (Department of agricultural and environmental Economics, Faculty of economics, University of Allameh Tabataba’I, Tehran, Iran.)

Received: 2014-11-10 Accepted : 2015-03-18 Published : 2018-03-21

Keywords: Forecasting, Long-Memory, ARFIMA, GPH test, Modified R/S Test,

Abstract :

Background and Objective: Environmental pollution modeling is one of the essential requirements in the field of air quality monitoring which with using the output of the model, improvement of future situation can be possible. The existing literature of the modeling of environmental pollution –especially air pollutants- could be divided to two whole categories. First, those researches that in addition of pollutants data, they used some factors such as temperature, wind direction, wind speed and humidity. The second one –which this study belong to- with using time series regression models and by usage of the existing data about each pollutant, the future situation was forecasted. Method: In this study, we forecast future pollutants (CO,PM10,NO2,SO2,O3,PM2.5) status with ARIMA, ARFIMA and ARIMA-GARCH models with Box-Jenkins approach, then the best model is determined with MSE, RMSE, MAE and MAPE. Findings: Results indicate that the assumption of existence of long-memory is acceptable but the hypothesis that always ARFIMA models prepare the best forecast is rejected. Discussion and Conclusion: This study proves the application of econometric models to predict the pollutants state. Based on the high social costs of pollutant emissions, it is recommended that using these models, identify the pollutants affecting the future of the city and reduce the level of their dissemination of efficiency plans.  

References:

1. Tehran Air Quality Annual Report, 2012. Technical Report of the Air Quality Control Company, No. QM92/03/03/(U)/01. (In Persian)


2. Bodaghpour, S., Charkestani, A., 2011. Prediction of gas pollutant's concentration by means of artificial neural network in Tehran urban air. Journal of environmental science and technology Volume 13 , Number 1 (48); 1-10.


3. Ibarra-Berastegi, G., Elias, A., Barona, A., Saenz, J., Ezcurra, A., Diaz de Argandona, J., 2008. From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao. Environmental modeling and software, 23, 622-637.


4. Pires, J. C. M., Martins, F. G., Sousa, S. I. V., Alvim-Ferraz, M. C. M., Pereira, M. C., 2008. Prediction of the daily mean PM₁₀ concentrations using linear models. American Journal of Environmental Sciences, 4(5), 445-453.


5. Ibrahim, M. Z., Zailan, R., Marzuki, I., Lola, M. S., 2009. Forcasting and time series analysis of air pollutants in several area of Malaysia. American Journal of Environmental Sciences, 5(5), 625-632.


6. Marzuki, I., Ibrahim, M. Z., Ibrahim, A., Ahmad Makmon, A., 2011. Time series analysis of surface ozone monitoring records in Kemaman, Malaysia. Sains Malaysiana, 40(5), 411-417.


7. Kumar, U., Ridder, K. D., 2010. GARCH modelling in association with FFT-ARIMA to forcast ozone episodes. Atmospheric Environment, 44, 4252-4265.


8. Diaz-Robles, L. A., Juan, C. O., Joshua, S. Fu., Gregory, D. R., Judith, C. C., Watson, J. G., Moncada-Herrea, J. A., 2008. A hybrid ARIMA and artificial neural networks model to forecast particulate mater in urban areas: The case of Temuco. Chile. Atmospheric Environment, 42, 8331-8340


9. Siew, L. Y., Chin, L. Y., Wee, P. M. J., 2008. ARIMA and integrated ARFIMA models for forecasting air pollution index in Shah Alam, Selangor. The Malaysian Journal of Analytical Sciences, 12(1), 257-263.


10. Bagheri Soltanabadi, H., 2009. comparison of genetic planning and ARIMA time series models in Short Term Forecasting of Crude Oil Prices in Iran. MSc thesis. Faculty of economics, Ferdowsi University of Mashhad, Iran. (In Persian).


11. Kumar, A., Goyal, P., 2011. Forecasting of daily air quality index in Delhi. Sience of the total environment, 409, 5517-5523.


12. Saffarini, G., Odat, S., 2008. Time series analysis of air pollution in AL-Hashimeya Town Zarqa, Jordan. Jordan Journal of Earth and Environmental Sciences, 1(2), 63-72.


13. Hurst, H. E., 1951. Long-term storage capacity of reservoirs. Transactions of the American society of civil engineers, 116, 770-779.


14. Mandelbrot, B. B., Wallis, J., 1968. Noah, Joseph and operational hydrology. Water resources research, 4(5), 909-918.


15. Baillie, R. T., Bollerslev, T., 1994. The long memory of the forward premium. Journal of international money and finance, 13(5), 565-571.


16. Baillie, R. T., 1996. Long memory processes and fractional integration in econometrics. Journal of Econometrics, 73: 5-59.


17. Festic, M., Kavkler, A., Dajcman, S., 2012. Long memory in the Croatian and Hungurian stock market returns. Zb. Rad. Ekon. Fak. Rij, 30(1), 115-139.


18. Erfani, A., Jafari samimi, A., 2009. Long memory forecasting of stock price index using a fractionally differenced ARMA model. Journal of Applied sciences research, 5(10), 1721-1731.


19. Bollereslev, T., 1986. Generalized autoregressive conditional Hetrokedasticity. Journal of Econometrics, 31, 307-328.


20. Mussadiq, T., 2012. Modeling and forecasting the volatility of oil futures using the ARCH family models. The Lahore Journal of Business, 1, 79-108.


21. Alem shiferaw, Y., 2012. Modeling volatility of price of some selected Agricultural products in Ethiopia: ARIMA-GARCH applications. Availible at SSRN: http://ssrn.com/abstract=2125712 or http://dx.doi.org/10.2139/ssrn.2125712.


22. Noferesti, M., 2010. Unit root and cointegration in econometrics, 3ed. Rasa publication. (In Persian).


23. Granger, C. W. J., Joyeux, R., 1980. An introduction to long memory time series models and fractional differencing. Journal of time series analysis, 1(1), 15-29.


24. Hosking, J. R. M., 1981. Fractional differencing. Biometrika, 68(1), 165-176.


25. Baillie, R. T., Chung, S-K., 2012. Modeling and forecasting from trend-stationary long memory models with applications to climatology. International Journal of Forecasting, 18, 215-226.


26. Varotsos, C., Kirk-Davidoff, D., 2006. long-memory processes in ozone and temperature variations at the region 60 ͘S-60 ͘N. Atmospheric Chemistry and Physics, 6(12), 4093-4100.


27. Geweke, J., Porter-Hudak, S., 1983. The estimation and application of long memory time series models. Journal of time series analysis, 4(4), 221-238.


28. Hurst, H. E., 1957. A suggested statistical model of some time series that occur in nature. Nature, 180-494.


29. Lee, D., Schmidt, P., 1996. on the power of the KPSS test of stationary against fractionally-integrated alternatives. Journal of Econometrics, 73, 285-302.


30. Baillie, R. T., Chung, C-F., Tieslau, M. A., 1996. Analysing inflation by the fractionally integrated ARFIMA-GARCH model. Journal of applied econometrics, 11, 23-40.