Resumen
The data from six air quality stations in California were subjected to a principal components analysis involving elemental, gas, and meteorological variables. The highly intercorrelated nature of many of the variables can make the results of ordinary regresssion techniques uncertain. The application of principal component analysis to the data set produces statistically independent linear combinations of the original variables. The factor which represents soil-derived particulate matter usually accounts for most of the observed variability. The visibility factor is present at every location and intermediate size sulfur-containing aerosols (sulfur size mode 0.6 µm < Dp < 3.6/µm) are major contributors to visibility reduction at all sites: coastal, urban, and interior valley. The rest of the principal factors were tentatively assigned to automotive pollution, fuel burning, and agricultural burning on the basis of their chemical composition.