1. bookVolume 61 (2016): Issue 1 (March 2016)
Journal Details
License
Format
Journal
eISSN
1508-5791
First Published
25 Mar 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

Preliminary PM2.5 and PM10 fractions source apportionment complemented by statistical accuracy determination

Published Online: 17 Mar 2016
Volume & Issue: Volume 61 (2016) - Issue 1 (March 2016)
Page range: 75 - 83
Received: 12 Aug 2015
Accepted: 26 Jan 2016
Journal Details
License
Format
Journal
eISSN
1508-5791
First Published
25 Mar 2014
Publication timeframe
4 times per year
Languages
English
Abstract

Samples of PM10 and PM2.5 fractions were collected between the years 2010 and 2013 at the urban area of Krakow, Poland. Numerous types of air pollution sources are present at the site; these include steel and cement industries, traffic, municipal emission sources and biomass burning. Energy dispersive X-ray fluorescence was used to determine the concentrations of the following elements: Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, As and Pb within the collected samples. Defining the elements as indicators, airborne particulate matter (APM) source profiles were prepared by applying principal component analysis (PCA), factor analysis (FA) and multiple linear regression (MLR). Four different factors identifying possible air pollution sources for both PM10 and PM2.5 fractions were attributed to municipal emissions, biomass burning, steel industry, traffic, cement and metal industry, Zn and Pb industry and secondary aerosols. The uncertainty associated with each loading was determined by a statistical simulation method that took into account the individual elemental concentrations and their corresponding uncertainties. It will be possible to identify two or more sources of air particulate matter pollution for a single factor in case it is extremely difficult to separate the sources.

Keywords

1. Thurston, G. D., & Spengler, J. D. (1985). A quantitative assessment of source contributions to inhalable particulate matter pollution in Metropolitan Boston. Atmos. Environ., 19, 9–25.10.1016/0004-6981(85)90132-5Search in Google Scholar

2. Thurston, G. D., & Spengler, J. D. (1985). A multivariate assessment of meteorological influences on inhalable particle source impacts. J. Clim. Appl. Meteorol., 24, 1245–1256.10.1175/1520-0450(1985)024<1245:AMAOMI>2.0.CO;2Search in Google Scholar

3. Song, Y., Xie, S., Zhang, Y., Zeng, L., Salmon, L. G., & Zheng, M. (2006). Source apportionment of PM2.5 in Beijing using Principal Component Analysis/Absolute Principal Component scores and UNMIX. Sci. Total Environ., 372, 278–286. DOI: 10.1016/j.scitotenv.2006.08.041.10.1016/j.scitotenv.2006.08.041Search in Google Scholar

4. Samek, L. (2012). Source apportionment of PM10 fraction of particulate matter collected in Krakow, Poland. Nukleonika, 57(4), 601–606.Search in Google Scholar

5. Samek, L., Gdowik, A., Ogarek, J., & Furman, L. (2016). Elemental composition and rough source apportionment of fine particulate matter in Krakow, Poland. Environ. Prot. Eng. (in press).Search in Google Scholar

6. Almeida, S. M., Pio, C. A., Freitas, M. C., Reis, M. A., & Trancoso, M. A. (2006). Approaching PM2.5 and PM2.5-10 source apportionment by mass balance analysis, principal component analysis and particle size distribution. Sci. Total Environ., 368, 663–674. DOI: 10.1016/j.scitotenv.2006.03.031.10.1016/j.scitotenv.2006.03.031Search in Google Scholar

7. Pandolfi, M., Viana, M., Minguillon, M. C., Querol, X., Alastuey, A., Amato, F., Celades, I., Escrig, A., & Monfort, E. (2008). Receptor models application to multi-year ambient PM10 measurements in an industrialized ceramic area: Comparison of source apportionment results. Atmos. Environ., 42, 9007–9017. DOI: 10.1016/j.atmosenv.2008.09.029.10.1016/j.atmosenv.2008.09.029Search in Google Scholar

8. Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, M., Szidat, S., Prevot, A. S. H., Hueglin, C., Bloemen, H., Wahlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., & Hitzenberger, R. (2008). Source apportionment of particulate matter in Europe: A review of methods and results. Aerosol Sci., 39, 827–849. DOI: 10.1016/j.jaerosci.2008.05.007.10.1016/j.jaerosci.2008.05.007Search in Google Scholar

9. Almeida, M., Pio, C. A., Freitas, M. C., Reis, M. A., & Trancoso, M. A. (2005). Source apportionment of fine and coarse particulate matter in sub-urban area at Western European Coast. Atmos. Environ., 39, 3127–3138. DOI: 10.1016/j.atmosenv.2005.01.048.10.1016/j.atmosenv.2005.01.048Search in Google Scholar

10. Almeida, S. M., Reis, M. A., Freitas, M. C., & Pio, C. A. (2007). Quality assurance in elemental analysis of airborne particles. Nucl. Instrum. Methods Phys. Res. Sect. B-Beam Interact. Mater. Atoms, 207, 434–446. DOI: 10.1016/so168-583x(03)01119-4.Search in Google Scholar

11. Vallius, M., Janssen, N. A. H., Heinrich, J., Hoek, G., Ruuskanen, J., Cyrys, J., Van Grieken, R., de Hartog, J. J., Kreyling, W. G., & Pekkanen, J. (2005). Sources and elemental composition of ambient PM2.5 in three European cities. Sci. Total Environ., 337, 147–162. DOI: 10.1016/j.scitotenv.2004.06.018.10.1016/j.scitotenv.2004.06.018Search in Google Scholar

12. Hasheminassab, S., Daher, N., Ostro, B. D., & Siontas, C. (2014). Long term source apportionment of ambient fine particulate matter (PM2.5) in the Los Angeles basin: A focus on emissions reduction from vehicular sources. Environ. Pollut., 193, 54–64. DOI: 10.1016/j.envpol.2014.06.012.10.1016/j.envpol.2014.06.012Search in Google Scholar

13. Callen, M. S., Iturmendi, A., & Lopez, J. M. (2014). Source apportionment of atmospheric PM2.5 bound polycyclic aromatic hydrocarbons by a PMF receptor model. Assessment of potential risk for human health. Environ. Pollut., 195, 167–177. DOI: 10.1016/j.envpol.2014.08.025.10.1016/j.envpol.2014.08.025Search in Google Scholar

14. Paatero, P., & Tapper, U. (1994). Positive matrix factorization: non negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5, 111–126. DOI: 10.1002/env3170050203.Search in Google Scholar

15. Paatero, P., & Hopke, P. K. (2003). Discarding or downweighting high noise variables in factor analytic models. Anal. Chim. Acta, 490, 277–289. DOI: 10.1016/S0003-2670(02)01643-4.10.1016/S0003-2670(02)01643-4Search in Google Scholar

16. Kim, E., Hopke, P. K., & Qin, Y. (2005). Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionment. J. Air Waste Manag. Assoc., 55, 1190–1199.10.1080/10473289.2005.1046470516187588Search in Google Scholar

17. Hopke, P. K., Ito, K., Mar, T., Christensen, W. F., Eatough, D. J., Henry, R. C., Kim, E., Laden, F., Lall, R., Larson, T. V., Liu, H., Neas, L., Pinto, J., Stölzel, M., Suh, H., Paatero, P., & Thurston, G. D. (2006). PM source apportionment and health effects: Intercomparison of source apportionment results. J. Expo. Sci. Environ. Epidemiol., 16, 275–286. DOI: 10.1038/sj.jea.7500458.10.1038/sj.jea.750045816249798Search in Google Scholar

18. PN-EN 12341. (2006). Air quality-determination of the PM10 fraction of suspended particulate matter – reference method and field test procedure to demonstrate reference equivalence of measurement methods. 19. PN-EN 14907. (2006). Ambient air quality-standard gravimetric measurement method for the determination of the PM2.5 mass fraction of suspended particular matter.Search in Google Scholar

20. http://www.canberra.com (accessed 12 August 2015).Search in Google Scholar

21. Vekemans, B., Janssens, K., Vincze, L., Adams, F., & Van Espin, P. (1994). Analysis of X-ray spectra by iterative least squares (AXIL): new developments. X-Ray Spectrom., 23, 278–285.10.1002/xrs.1300230609Search in Google Scholar

22. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008.Search in Google Scholar

23. Begun, B. A., Hopke, P. K., & Zhao, W. (2005). Source identification of fine particles in Washington, DC, by expanded factor analysis modelling. Environ. Sci. Technol., 39, 1129–1137. DOI: 10.1021/es049804v.10.1021/es049804v15773486Search in Google Scholar

24. Samek, L., Zwozdziak, A., & Sowka, I. (2013). Chemical characterization and source identification of particulate matter PM10 in a rural and urban site in Poland. EPE, 39, 91–103. DOI: 10.5277/epe130408.Search in Google Scholar

25. Lammel, G., Rohrl, A., & Schreiber, H. (2002). Atmospheric lead and bromine in Germany. Post-abatement levels, variabilities and trends. Environ. Sci. Pollut., 9, 397–404.10.1007/BF0298758912515348Search in Google Scholar

26. Laugh, G. C., Schauer, J. J., Park, J. S., Shafer, M. M., Deminter, J. T., & Weinstein, J. P. (2005). Emissions of metals associated with motor vehicle roadways. Environ. Sci. Technol., 39, 826–836. DOI: 10.1021/es048715f.10.1021/es048715f15757346Search in Google Scholar

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