A review of multivariate analysis: is there a relationship between airborne particulate matter and meteorological...

Among statistical tools for the study of atmospheric pollutants, trajectory regression analysis (TRA), cluster analysis (CA), and principal component analysis (PCA) can be highlighted. Therefore, this article presents a systematic review of such techniques based on (i) air mass influences on particulate matter (PM) and (ii) the study of the relationship between PM and meteorological variables. This article aims to review studies that use TRA and to review studies that adopt CA and/or PCA to identify the associations and relationship between meteorological variables and atmospheric pollutants. Papers published between 2006 and 2018 and indexed by five of the main scientific databases were considered (ScienceDirect, Web of Science, PubMed, SciELO, and Scopus databases). PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations supported this systematic review. From the resulting most relevant papers, eight studies analyzed the influence of air mass trajectories on PM using TRA and twenty-one studies searched for the relationship between meteorological variables and PM using CA and/or PCA. A combination of TRA and time series models was identified as the possibility of future works. Besides, studies that simultaneously combine the three techniques to identify both the influence of air masses on PM and its relationship with meteorological variables are a possibility of future papers, because it can lead to a better comprehension of such a phenomenon.