Particulate matter source identification using receptor models is one of the tools applied in air quality management. These models have limitations such as the collinearity effects, hindering their application and interpretation. Positive Matrix Factorization (PMF) models use chemical markers for the definition of likely sources, leaving to users the factors interpretation.
View Article and Find Full Text PDFParticulate matter driven health problems are strongly associated with its chemical composition. Despite the benefits of using source apportionment models for air quality management, limitations such as collinearity effects, restrict their application or compromise the accurate separation of sources, particularly for particulate matter with similar chemical profiles. Receptors models also depend on the operator expertise to appropriately classified sources, a subjective process that can lead to biased results.
View Article and Find Full Text PDFEpidemiological studies have shown the association of airborne particulate matter (PM) size and chemical composition with health problems affecting the cardiorespiratory and central nervous systems. PM also act as cloud condensation nuclei (CNN) or ice nuclei (IN), taking part in the clouds formation process, and therefore can impact the climate. There are several works using different analytical techniques in PM chemical and physical characterization to supply information to source apportionment models that help environmental agencies to assess damages accountability.
View Article and Find Full Text PDFSpeciation and the influence on the ozone formation potential (OFP) from volatile organic compounds (VOCs) have been studied between February June 2013 in Vitória, ES, Brazil. Passive samplers were installed at three air-quality monitoring stations and a total of 96 samplings were collected. A total of 78 VOCs were characterized by gas chromatograph-mass spectrometer.
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