Abstract (EN):
The number of sites that constitute the air quality monitoring network (AQMN) of Oporto Metropolitan Area (Oporto-MA) should be optimised, aiming to reduce significant associated expenses. Ideally, only one monitoring site should operate in an area characterized by specific air pollution behaviour. The global aim of this study was to evaluate the performance of statistical methods for the more efficient management of AQMNs. The specific objectives were: (i) to identify city areas with similar air pollution behaviours; and (ii) to locate emission sources. Two statistical techniques, principal components analysis (PCA) and cluster analysis (CA), were applied to the mass concentrations of sulphur dioxide (SO2) and particulate matter with an aerodynamic diameter less than 10 mu m (PM10), collected in the AQMN of Oporto-MA from January 2003 to December 2005. Main results showed that the 10 monitoring sites of the AQMN could be coupled in six and no more than two groups for SO2 and PM10, respectively. It was found also that several monitoring sites covered city areas characterized by the same specific air pollution behaviour; suggesting then an ineffective management of the air quality monitoring system. The redundant equipment should be transferred to other monitoring sites allowing the enlargement of the monitored area. Only one main emission source of SO2 was located and the six groups needed to characterize SO2 mass concentrations were justified by the geographical location of that main emission source and by the variability of wind direction. Three main emission sources of PM10 were located: (i) one inside the region defined by the AQMN, which significantly affected only two monitoring sites; and (ii) two outside that region, which affected all monitoring sites. One emission source located outside of the region affected significantly only one monitoring site in a short period of the day. Additionally this emission source decreased its impact in 2005. These findings indicate that only two monitoring sites are needed to characterize the observed PM10 mass concentrations.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
12