title: Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.
abstract: This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.
authors.affiliation: Physics Department, University of Isfahan, Isfahan 81746, Iran. shz@sci.ui.ac.ir
authors.forename: S
authors.initials: S
authors.lastname: Hassanzadeh
authors.forename: F
authors.initials: F
authors.lastname: Hosseinibalam
authors.forename: R
authors.initials: R
authors.lastname: Alizadeh
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting / Q000379:methods*; D007492:Iran; D015233:Models, Statistical; D013458:Sulfur Dioxide / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D013458:Sulfur Dioxide