Principal component and canonical correlation analysis for air pollutants and meteorological data in Guangzhou

Four years data on SO2, NO2, CO and PM10 concentrations recorded at air-pollution monitoring stations in the city of Guangzhou and meteorological data concerning temperature, relative humidity, wind velocity, duration of precipitation and atmos- pheric pressure were analyzed using principal component analysis (PCA). Separate analyses were undertaken for summer, winter and year periods. It was found that the main principal components extracted from the air pollution data were related to gasoline combus- tion (automobiles) and coal or oil combustion (industrial pollution). The most prominent principal components from the meteoro- logical data were related to air temperature and air humidity. Finally, canonical correlation analysis determined relationships between the two different data sets. The air pollution data showed a remarkable correlation with the meteorological data, the main relationship was between gaseous pollutants with temperature and wind velocity.