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

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A review of multivariate analysis: is there a relationship between airborne particulate matter and meteorological variables? Danilo Covaes Nogarotto

&

Simone Andrea Pozza

Received: 31 January 2020 / Accepted: 2 August 2020 # Springer Nature Switzerland AG 2020

Abstract 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 MetaAnalyses) 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 D. C. Nogarotto (*) : S. A. Pozza School of Technology (FT), University of Campinas (Unicamp), Limeira, Brazil e-mail: [email protected]

S. A. Pozza e-mail: [email protected]

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. Keywords Particulate matter . Air mass trajectories . Cluster analysis . Principal component analysis

Introduction Evaluation, monitoring, and prediction of the levels of atmospheric pollution are especially important. Air pollutants directly affect the properties of the atmosphere in several ways: reduced visibility and solar radiation, rainfall formation and precipitation, temperature changes, and wind distribution (Seinfeld 1986). Among the main atmospheric pollutants, there are sulfur dioxide (SO2), carbon monoxide (CO), photochemical oxidants (such as ozone, O3), nitrogen oxides (NOx), and airborne particulate matter (PM). PM is all solid and liquid material that, due to their small size, becomes suspended in the air. Total suspended particles (TSP), inhalable particles (PM10), fine inhalable particles (PM2.5 and PM1), and smoke (SMK) are some types of PM (Hinds 1998; Revuelta et al. 2014; CETESB 2016). PM10 and PM2.5 are two of the major pollutants monitored nowadays. PM10 are particles whose aerodynamic diameter is less than or equal to 10 μm, and which may be retained in the upper