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.
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