Contribution of polycyclic aromatic hydrocarbon (PAH) sources to the urban environment: A comparison of receptor models Elba Calesso Teixeira a,b, ⁎, Dayana Milena Agudelo-Castañeda b , Camila Dalla Porta Mattiuzi c a Research Department, Fundação Estadual de Proteção Ambiental Henrique Luís Roessler, Av. Borges de Medeiros, 261, Porto Alegre, RS 90020-021, Brazil b Postgraduate Program in Remote Sensing and Meteorology, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre, RS 91501-970, Brazil c Postgraduate Institute research hydraulic, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre, RS 91501-970, Brazil HIGHLIGHTS • Identify source contributions of PAHs associated to PM2.5 • PAH sources were determined using re- ceptor models (PMF and CMB) • Source contributions were compared for both models • Both models indicated that diesel and gasoline emissions were the major sources. • Source apportionment methods were compared analyzing their advantages and disadvantages. GRAPHICAL ABSTRACT abstract article info Article history: Received 15 May 2015 Received in revised form 13 July 2015 Accepted 14 July 2015 Available online xxxx Editor: D. Barcelo Keywords: Polycyclic aromatic hydrocarbons Source apportionment Emission sources Receptor model PMF 3.0 model CMB model The aim of this study was to evaluate the contribution of the main emission sources of PAHs associated with PM 2.5 , in an urban area of the Rio Grande do Sul state. Source apportionment was conducted using both the US EPA Positive Matrix Factorization (PMF) model and the Chemical Mass Balance (CMB) model. The two models were compared to analyze the source contributions similarities and differences, their advantages and disadvantages. PM 2.5 samples were collected continuously over 24 h using a stacked filter unit at 3 sam- pling sites of the urban area of the Rio Grande do Sul state every 15 days between 2006 and 2008. Both models identified the main emission sources of PAHs in PM 2.5 : vehicle fleet (diesel and gasoline), coal com- bustion, wood burning, and dust. Results indicated similar source contribution amongst the sampling sites, as expected because of the proximity amongst the sampling sites, which are under the influence of the same pollutants emitting sources. Moreover, differences were observed in obtained sources contributions for the same data set of each sampling site. The PMF model attributed a slightly greater amount of PAHs to the gas- oline and diesel sources, while diesel contributed more in the CMB results. The results were comparable with previous works of the region and in accordance with the characteristics of the study area. Comparison Science of the Total Environment 538 (2015) 212–219 ⁎ Corresponding author at: Research Department, Fundação Estadual de Proteção Ambiental Henrique Luís Roessler, Av. Borges de Medeiros, 261, Porto Alegre, RS 90020-021, Brazil. E-mail addresses: gerpro.pesquisa@fepam.rs.gov.br, ecalessoteixeira@gmail.com (E.C. Teixeira). http://dx.doi.org/10.1016/j.scitotenv.2015.07.072 0048-9697/© 2015 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv