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

• Published in: Environmental technology Vol. 34; no. 17; pp. 2453 - 2463
• Main Authors: Hoinaski, L, Franco, D, Stuetz, R. M, Sivret, E. C, de Melo Lisboa, H
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.09.2013; Taylor & Francis Ltd
• Subjects: adverse effects; Air pollution; Air Pollution - analysis; air quality; Airborne particulates; Applied sciences; Atmospheric pollution; Brazil; burning; coal; dust emissions; Environmental Monitoring - methods; environmental technology; epidemiological studies; Exact sciences and technology; Industrial plant emissions; Models, Statistical; Outdoor air quality; Particulate Matter - analysis; particulates; PMF; Pollution; power plants; samplers; Seasons; soil; statistical exploratory analysis; UNMIX; Brazil; Receptor model; Power plant; PMF; Air quality; Combustion; Matrix factorization; Identification; Pollutant emission; Modeling; Coarse particle; UNMIX; Dust; Health and environment; Coal; Sampler; Aerosols; Air pollution; statistical exploratory analysis; PM; Public health
• ISSN: 1479-487X; 0959-3330; 1479-487X
• DOI: 10.1080/21622515.2013.772659

Epidemiological studies have documented that elevated airborne particulate matter (PM) concentrations, especially those with an aerodynamic diameter less than 10 μm (PM ₁₀), are associated with adverse health effects. Two receptor models, UNMIX and positive matrix factorization (PMF), were used to identify and quantify the sources of PM ₁₀ 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. PM ₁₀ samples were collected using high volume samplers at two sites in the region and statistical exploratory analysis techniques were applied to identify and assess PM ₁₀ sources. The two primary PM ₁₀ sources were identified as soil re-suspension/road dust emissions and coal burning emissions, contributing 65–75% and 15–25% of the PM ₁₀, respectively. The study confirmed the significance of the influence of local PM ₁₀ emissions (power plants, soil re-suspension and road dust emissions) on regional air quality, although no violations of the Brazilian PM ₁₀ standards (limit of 150 μg/m ³) were observed, with a mean concentration of 27.6 μg/m ³ 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.