Learning Principal Component Analysis by Using Data from Air Quality Networks

With the final objective of using computational and chemometrics tools in the chemistry studies, this paper shows the methodology and interpretation of the Principal Component Analysis (PCA) using pollution data from different cities. This paper describes how students can obtain data on air quality and process such data for additional information related to the pollution sources, climate effects, and social aspects over pollution levels by using a powerful chemometrics tool such as principal component analysis (PCA). The paper could also be useful for students interested in environmental chemistry and pollution interpretation; this statistical method is a simple way to display visually as much as possible of the total variation of the data in a few dimensions, and it is an excellent tool for looking into the normal pollution patterns.