Principal component and canonical correlation analysis for examining air pollution and meteorological data

Abstract

Five years data on CO, NO, NO 2, O 3, smoke and SO 2 concentrations recorded at oneair-pollution monitoring station in the city of Athens were analysed using principal component analysis(PCA). Separate analyses were undertaken for summer and winter periods. PCA was also applied tometeorological data concerning relative humidity, temperature, sunshine duration, wind velocity and winddirection. It was found that the main principal components extracted from the air pollution data wererelated to gasoline combustion, oil combustion and ozone interactions. The most prominent principalcomponents from the meteorological data were related to dry conditions (summer period) and high-speedsothwestern winds (for both periods). Finally, canonical correlation analysis determined relationshipsbetween the two different data sets. The main relationship was between total pollution and high humidity incombination with the low-velocity wind.