PSCF method for source identification of particulate matter in an agricultural background region in Brazil.

The use of mathematical and statistical models to investigate potential sources of pollutants that have been transported by air masses to a study site is important for establishing control and monitoring measures for air pollutants such as PM10 and PM2.5. During the study period, from 2018 to 2021, the concentrations of PM10 and PM2.5 recorded in Ribeirão Preto (SP, Brazil) were higher during spring and winter, with a tendency to increase the amplitude and its maximum values relative to daily averages. The source-receptor model, Potential Source Contribution Function (PSCF), was used to identify probable sources of these pollutants, and the regions known as Triângulo Mineiro and Intermediate Geographic Region of Juiz de Fora (MG, Brazil) were the main regions associated with high PSCF probability values (> 0.5) as sources of PM. These regions indicate that the possible sources of PM emissions are associated with industrial complexes and agriculture, especially coffee production.

Occurrence of polar organic compounds in atmospheric particulate matter: a system review in South America.

Particulate matter (PM) is one of the existing air pollutants, which can cause damages to human health, public property, and the environment. The chemical composition of this pollutant greatly varies, mainly its organic fraction. Thus, our objective was to carry out a literature review based on articles, considering studies conducted in South America, whose authors address the characterization of the polar organic fraction of PM. We performed the review using the Scopus, SciELO, and Web of Science databases, considering publications dated from the years 2010 to 2019. A total of 14,575 articles were found, of which only 12 met the predefined selection criteria. According to our research, the most studied compound is levoglucosan, a biomass burning marker belonging to the group of anhydrous sugars. Besides, nitro-PAHs, which usually originate from vehicular sources and are compounds with mutagenic and carcinogenic characteristics, have also been found. Moreover, we concluded that, currently, there are few studies on the subject in South America, requiring more research on polar organic compounds present in PM in countries of this region. These studies are of great importance because some compounds can cause great damage to human health, such as the nitro-PAHs; furthermore, PM may still have unknown compounds that need identification and elucidation of their toxicity.

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

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.