Investigation of PM10 sources in Santa Catarina, Brazil through graphical interpretation analysis combined with receptor modelling

Epidemiological studies have documented that elevated airborne particulate matter (PM) concentrations, especially those with an aerodynamic diameter less than 10 μm (PM₁₀), are associated with adverse health effects. Two receptor models, UNMIX and positive matrix factorization (PMF), were used to identify and quantify the sources of PM₁₀ concentrations in Tubarão and Capivari de Baixo, Santa Catarina, Brazil. This region is known for its high pollution levels due to intense industrial activity and exploitation of natural resources. PM₁₀ samples were collected using high volume samplers at two sites in the region and statistical exploratory analysis techniques were applied to identify and assess PM₁₀ sources. The two primary PM₁₀ sources were identified as soil re-suspension/road dust emissions and coal burning emissions, contributing 65–75% and 15–25% of the PM₁₀, respectively. The study confirmed the significance of the influence of local PM₁₀ emissions (power plants, soil re-suspension and road dust emissions) on regional air quality, although no violations of the Brazilian PM₁₀ standards (limit of 150 μg/m³) were observed, with a mean concentration of 27.6 μg/m³ measured in this study. This study demonstrated the usefulness of statistical exploratory analysis techniques in assessing the validity of modelling results and contributing to the interpretation of ambient air quality data.