Evidence suggesting the association between the atmospheric particulate matter (PM) and health problems stress the need for the establishment of policies and actions aiming the improvement of air quality. As a start point, the knowledge of the main PM contributors is fundamental. Receptor models are frequently used for the identification and apportionment of local sources, nevertheless, some features of these models must be considered. For instance, whether the region has sources with similar chemical profiles and/or whether there is source temporal or spatial similarity, which can generate collinearity, affecting the sensibility of the models. In this work, it is presented some study of cases showing some strengths of the chemical mass balance model (CMB), such as to infer specific sources acting over specific locations in a same region, and its weaknesses for separating collinear sources. Besides, this work shows some study of cases reporting that the identification of specific PM markers (organic, inorganic, and crystallographic) and determined in the receptor samples can lead to better sources separation and improvements in the interpretation of the results using positive matrix factorization model. This work also highlights for the importance of the information provided by receptor models, in which should be carefully considered by the environmental agencies for decision-making concerning air quality management.
Keywords: Air quality management; Particulate matter; Receptor models; Source apportionment; Source markers.