Time-series analysis of air pollution data

Time-series analysis of air pollution environmental levels involves the ide ntification of long-term variation in the mean (trend) and of cyclical or p eriodic components. A model based on a stepwise approach to time-series ana lysis was applied to the daily average concentrations of strong acidity (SA ) and black smoke (BS) in the Oporto area, using an available computer prog ram. Each step is completed by a correlation analysis of the residuals, all owing the identification of an optimal structure with a residual white nois e. A periodic component with harmonics defined through "peaks" of concentra tion on week middle days and "troughs" on weekends was observed. SA concent ration behaviour can be related with industrial activities, mainly through fossil-fuel burning in discontinuous working cycles. The observed evolution for BS is most probably related with weekly patterns of motor traffic, wit h observed minimum values during weekends. The periodic components represen t, on the average, about 5% of the total variance for the SA series and 15% for the BS series. However, the weekly cycles are predominant in the SA se ries, representing on the average 75% of the periodic variance, against 46% for the BS series. Statistically significant higher frequency (approximate to 2-4 day) periodic components were observed for both pollutant indicator s and for all collection sites analysed. This may be due to synoptic weathe r variations of minimum and maximum daily temperature and precipitation, wh ich show similar periods in the study area. (C) 1999 Elsevier Science Ltd. All rights reserved.