Epidemiological studies have documented that elevated airborne particulate matter (PM) concentrations, especially those with an aerodynamic diameter less than 10 microm (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 microg/m3) were observed, with a mean concentration of 27.6 microg/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.
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