Multivariate techniques as alternative statistical tools applied to magnetic proxies for pollution: a case study from Argentina and Antarctica

Several statistical analyses—as alternative tools—were applied to magnetic monitoring studies. Magnetic and chemical data from two environments have been gathered from previous papers and studied separately. Univariate and multivariate analyses were first examined, revealing a link between magnetic and chemical variables. The latter analyses, in particular, canonical correlation analysis, showed very good canonical correlations: R = 0.950 (Antarctica) and R = 0.891 (Argentina). On the other hand, in order to classify the data according to the degree of contamination, principal coordinates and discriminant analyses, as well as the comparison of several multivariate means were performed. Three groups were distinguished in both case studies, which were well classified at a low margin of error and quite different from each other at a significant level: 0.01 (Antarctica) and 0.05 (Argentina). The joint use of these statistical analyses also showed, in agreement with previous studies, that the relevant variables in order to identify atmospheric pollution are: magnetic susceptibility, saturation of isothermal remanent magnetisation, anhysteric susceptibility/magnetic susceptibility, remanent coercivity, and copper, lead, zinc and chromium contents.