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

Abstract

ABSTRACT 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.