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

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. Defi ning 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.
Czasopismo
Rocznik
Strony
75--83
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
  • Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30 Mickiewicza Ave., 30-059 Krakow, Poland, Tel.: +48 12 617 2975, Fax: +48 12 617 3400
autor
  • Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30 Mickiewicza Ave., 30-059 Krakow, Poland, Tel.: +48 12 617 2975, Fax: +48 12 617 3400
autor
  • Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30 Mickiewicza Ave., 30-059 Krakow, Poland, Tel.: +48 12 617 2975, Fax: +48 12 617 3400
Bibliografia
  • 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.
  • 2. Thurston, G. D., & Spengler, J. D. (1985). A multivariate assessment of meteorological infl uences on inhalable particle source impacts. J. Clim. Appl. Meteorol., 24, 1245–1256.
  • 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.
  • 4. Samek, L. (2012). Source apportionment of PM10 fraction of particulate matter collected in Krakow, Poland. Nukleonika, 57(4), 601–606.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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. 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.
  • 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.
  • 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.
  • 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.
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  • 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.
  • 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.
  • 18. PN-EN 12341. (2006). Air quality-determination of the PM10 fraction of suspended particulate matter –reference method and fi eld 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.
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  • 22. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008.
  • 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.
  • 24. Samek, L., Zwozdziak, A., & Sowka, I. (2013). Chemical characterization and source identifi cation of particulate matter PM10 in a rural and urban site in Poland. EPE, 39, 91–103. DOI: 10.5277/epe130408.
  • 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.
  • 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.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę,
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f0beb5cb-4c3e-479c-80aa-1fd3b1a97c3d
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