AMTD Atmospheric Measurement Techniques Discussions AMTD Atmos. Meas. Tech. Discuss. 1867-8610 Göttingen, Germany 10.5194/amtd-6-4941-2013 Applying receptor models Unmix and PMF on real data set of elements in PM for sources evaluation of the sea coastal side region (Southeast Adriatic Sea) Đorđević D. 1 Petrović S. 2 Relić D. 3 Mihajlidi-Zelić A. 3 ICTM – Centre of Chemistry, University of Belgrade, Studentski trg 12–16, 11000 Belgrade, Serbia ICTM – Centre of Catalysis, University of Belgrade, Studentski trg 12–16, 11000 Belgrade, Serbia Faculty of Chemistry, University of Belgrade, Studentski trg 12–16, 11000 Belgrade, Serbia 04 06 2013 6 3 4941 4969 Copyright: © 2013 D. Đorđević et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://amt.copernicus.org/preprints/6/4941/2013/amtd-6-4941-2013.html The full text article is available as a PDF file from https://amt.copernicus.org/preprints/6/4941/2013/amtd-6-4941-2013.pdf

Two receptor modeling techniques were applied to a common data set of daily measurements of 11 elements in particulate matter (PM). Samples of PM were collected in the 5 yr period at an urban site located at the sea coast. In the vicinity of the sampling site traffic is a permanent but not significant anthropogenic source. In this study we used both the Unmix and PMF receptor models for evaluation of the sources contribution. Unmix found thirteen solutions for several combinations of species, but four solutions satisfy the criteria Min <i>R</i><sup>2</sup> > 0.8 and Min <i>S</i>/<i>N</i> > 2. Unmix identified three and four sources in satisfactory solutions. The PMF model has given 3 possible solutions and by further analysis the best solution of four sources was selected. <i>F</i> peak refinement enabled finding a more realistic solution that includes re-suspension and traffic as dominant source contributions. The results given in this study are in accordance with the results of Enrichment Factors analysis presented in our previous work.

 References Chen, L.-W. A., Watson, J. G., Chow J. C., and Magliano, K. L.: Quantifying PM<sub>2.5</sub> Source Contributions for the San Joaquin Valley with Multivariate Receptor Models, Environ. Sci. Technol., 41, 2818–2826, 2007. \DJor{\dj}ević, D., Vukmirović, Z., Tosić, I., and Unkasević, M.: Contribution of dust transport and resuspension to particulate matter levels in the Mediterranean atmosphere, Atmos. Environ., 38, 3637–3645, 2004. \DJor{\dj}ević, D., Mihajlidi-Zelić, A., and Relić, D.: Differentiation of the contribution of local resuspension from that of regional and remote sources on trace elements content in the atmospheric aerosol in the Mediterranean area, Atmos. Environ., 39, 6271–6281, 2005. Eberly, S.: EPA&nbsp;PMF&nbsp;1.1 user's guide, <a href="http://www.epa.gov/heasd/products/pmf/users_guide old.pdf">http://www.epa.gov/heasd/products/pmf/users_guide old.pdf</a>, Prepared by the US&nbsp;Environmental Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC, 2005. Hegg, D. A., Covert, D. S., Jonsson, H. H., and Woods, R. K.: The contribution of anthropogenic aerosols to aerosol light-scattering and CCN activity in the California coastal zone, Atmos. Chem. Phys., 10, 7341–7351, <a href="http://dx.doi.org/10.5194/acp-10-7341-2010">https://doi.org/10.5194/acp-10-7341-2010</a>, 2010. Henry, R. C.: Current Factor Analysis Receptor Models are Ill-Posed, Atmos. Environ., 21, 1815–1820, 1987. Henry, R. C.: History and Fundamentals of Multivariate Air Quality Receptor Models, Chemometr. Intell. Lab., 37, 525–530, 1997. Henry, R. C.: UNMIX Version&nbsp;2.4 Manual, US&nbsp;Environmental Protection Agency, Research Triangle Park, NC, 2001. Henry, R. C.: Multivariate receptor models-current practice and future trends, Chemometr. Intell. Lab., 60, 43–48, 2002. Henry, R. C.: Multivariate receptor modeling by N-dimensional edge detection, Chemometr. Intell. Lab., 65, 179–189, 2003. Henry, R. C. and Christensen, E. R.: Selecting an Appropriate Multivariate Source Apportionment Model Result, Environ. Sci. Technol., 44, 2474–2481, 2010. Kim, E., Hopke, P. K., Larson, T. V., and Covert, D. S.: Analysis of ambient particle size distributions using Unmix and positive matrix factorization, Environ. Sci. Technol., 38, 202–209, 2004. Lee, E., Chan, C. K., and Paatero, P.: Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong, Atmos. Environ., 33, 3201–3212, 1999. Lee, J. H. and Hopke, P. K.: Apportioning sources of PM<sub>2.5</sub> in St.&nbsp;Luis, MO using speciation trends network data, Atmos. Environ., 40, S360–S377, 2006. Lee, J. H., Lim, J. M., Kim, K. H., Chung, Y. S., and Lee, K. Y.: Trace element levels of aerosols at an urban area of Korea by instrumental neutron activation analysis, J. Radioanal. Nucl. Ch., 256, 553–560, 2003. Lee, D., Balachandran, S., Pachon, J., Shankaran, R., Lee, S., Mulholland, J. A., and Russell, A. G.: Ensemble-Trained PM<sub>2.5</sub> Source Apportionment Approach for Health Studies, Environ. Sci. Technol., 43, 7023-7031, 2009. Maykut, N. N., Lewtas, J., Kim, E., and Larson, T. V.: Source apportionment of PM<sub>2.5</sub> at an urban IMPROVE site in Seattle, Washington, Environ. Sci. Technol., 37, 5135–5142, 2003. Norris, G. A., Vedantham, R., and Duvall, R. M.: EPA UNMIX&nbsp;6.0 User Guuide, EPA/600/R-07/089 (NTIS PB2007-112630), US&nbsp;Environmental Protection Agency, Washington, DC, 2007. Paatero, P.: Least squares formulation of robust non-negative factor analysis, Chemometr. Intell. Lab., 37, 23–35, 1997. Paatero, P. and Tapper, U.: Analysis of different modes of factor analysis as least square fit problem, Chemometr. Intell. Lab., 18, 183–194, 1993. Paatero, P. and Tapper, U.: Positive Matrix Factorization: a non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, 5, 111–126, 1994. Park, E. S., Spiegelman, C. H., and Henry, R. C.: Bilinear estimation of pollution source profiles and amounts by using multivariate receptor models, Environmetics, 13, 775–798, 2002. Pekney, N. J., Davidson, C. I., Robinson, A., Zhou, L., Hopke, P., Eatough, D., and Rogge, W. F.: Major source categories for PM<sub>2.5</sub> in Pittsburgh using PMF and UNMIX, Aerosol Sci. Tech., 40, 910–924, 2006. Poirot, R. L., Wishinski, P. R., Hopke, P. K., and Polissar, A. V.: Comparative application of multiple receptor methods to identify aerosol sources in Northern Vermont, Environ. Sci. Technol., 35, 4622–4636, 2001. Polissar, A. V., Hopke, P. K., Paatero, P., Malm, W. C., and Sisler, J. F.: Atmospheric aerosol over Alaska 2.&nbsp;Elemental composition and sources, J. Geophys. Res., 103, 19045–19057, 1998. US&nbsp;Environmental Protection Agency, EPA Unmix&nbsp;6.0 Model, <a href="http://www.epa.gov/heasd/products/Unmix/Unmix.htm">http://www.epa.gov/heasd/products/Unmix/Unmix.htm</a>, last access: June&nbsp;2007. US&nbsp;Environmental Protection Agency, EPA Positive Matrix Factorisation&nbsp;3.0, <a href="http://www.epa.gov/heasd/products/pmf/pmf_registration.htm.htm">http://www.epa.gov/heasd/products/pmf/pmf_registration.htm.htm</a>, last access: July&nbsp;2008. Valentin, A. L., Rami Alfarra, M., Urs, B., Brigitte, B., Christoph, H., Sönke, S., Miriam, N. W., Lukas, W., Silke, W., Alexandre, C., Puxbaum, H., and Andre, S. H. P.: Source Attribution of Submicron Organic Aerosols during Wintertime Inversions by Advanced Factor Analysis of Aerosol Mass Spectra, Environ. Sci. Technol., 42, 214–220, 2008.