Summary

The objective of this study was to interpret a multidimensional data set of variables measured in urban air by using multivariate techniques such as a principal component analysis (PCA). An advantage of PC A is that it allows a multidimensional data set to be projected onto two or three dimensions (compo- nents). Statistical and graphical methods can be used to investigate the existence and nature of associa- tions between the concentrations of variables. In this study, PCA was applied to the arrays of airborne pollutant variables in order to graphically represent the interrelationships between the measured vari- ables. The data used in this study was collected in Kuopio, a city of 80 000 inhabitants in Middle Fin- land. During the sampling period between 24 January and 9 April 1994 the ground was covered by snow and the temperature ranged from -30 °C to +5°C. The sampling site was located in a square close to the city centre and at the distance from the surrounding streets was at least 50 metres. The main pollution source in that area was traffic. Particles (12 consecutive size classes ranging from 0.01 μm to 10 μm, in PCA calculations two nearest size classes were summed together in order to get six size classes) were monitored at the height of 3 meters above the ground level by using an aerosol spectrometer based on the analysis of the electrical mobility of the aerosol particles, Mirme (1994). Black carbon (BC) was measured with an aethalometer. From gaseous pollutants NO, NO₂, SO₂ and CO concentrations were monitored at the same time. The measured data set consisted of the concentration values of one hour av- erages giving eleven variables and 1524 objects. Wind speed, wind direction and temperature were also recorded but they were not used in modelling in this study; they were used, however, for plotting and in- terpretation of the results.