Skip to main content
SpringerLink
Log in

Combined Use of Principal Component Analysis (PCA) and Chemical Mass Balance (CMB) for Source Identification and Source Apportionment in Air Pollution Modeling Studies

  • Published:
Water, Air, & Soil Pollution Aims and scope Submit manuscript

Abstract

Chemical mass balance (CMB) and principal component analysis (PCA) are used together for source identification and source apportionment in this air pollution modeling study. Source profile sets, each of which contains five source profiles based on ten pollutant species, were generated using a computer program. Another algorithm was implemented to produce ten random data sets, which was composed of 100 simulated measurement results for all of ten pollutant species. Ten source profile sets were selected. Five of them contained sources of dissimilar characteristics, whereas the other five were chosen from those of similar emission profiles. Ten simulated data sets for each source profile set were used in the analyses. PCA was applied to all simulated data sets; a number of principal factors were extracted and interpreted. The identified sources for each data set were used in fitting with CMB analyses, and source contributions were estimated. The performance of PCA–CMB combination was evaluated in the aspect of percent variance explained, percent apportionment, R 2, and χ 2. PCA was able to explain 89.6% to 100% of the variance within the data sets used. Two to five sources were extracted depending on the characteristics of source profile sets used. CMB was found to be successful in the aspect of percent apportionment since 95.4% to 100% of mass concentrations were apportioned. The values of R 2 and χ 2 were found out to range from 0.981 to 1.000 and from 0.000 to 29.947, respectively. Evaluating overall results from the analyses, PCA–CMB combination produced satisfactory results in the aspect of source identification and source apportionment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Adgate, J. L., Willis, R. D., Buckley, T. J., Chow, J. C., Watson, J. G., Rhoads, G. G., et al. (1998). Chemical mass balance source apportionment of lead in house dust. Enviromental Science and Technology, 32, 108–114.

    Article  CAS  Google Scholar 

  • Coulter, C. T. (2004). EPA CMB8.2 users manual. Resource document. US Environmental Protection Agency, Air Quality Modeling Group. http://www.epa.gov/scram001/models/receptor/EPA-CMB82Manual.pdf.

  • EC (2008). European Commission Legislations for Ambient air Quality. http://ec.europa.eu/environment/air/legis.htm.

  • EPA (1990). Clean Air Act Amendments of 1990. EPA 620/R-03/001. Resource document. US Environmental Protection Agency. http://www.epa.gov/air/caa/.

  • Ertürk, F. (1986). Investigation of strategies for the control of air pollution in the Golden Horn Region, Istanbul, using a simple dispersion model. Environmental Pollution Series B-Chemical and Physical, 11(3), 161–168.

    Article  Google Scholar 

  • ETKHKKY (2006). Control of air pollution due to industrial facilities legislation. Turkish Ministry of Environment and Forests. Resource document. http://www.cevreorman.gov.tr/yasa/y/26236.doc (official print in Turkish).

  • Fujita, E. M., & Lu, Z. (1998). Analysis of data from the 1995 NARSTO Northeast study, vol. III: Chemical mass balance receptor modeling. Reno: Desert Research Institute.

    Google Scholar 

  • Guo, H., Wang, T., & Louie, P. K. K. (2004). Source apportionment of ambient non-methane hydrocarbons in Hong Kong: Application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model. Environmental Pollution, 129, 489–498.

    Article  CAS  Google Scholar 

  • Guo, H., Wang, T. R., Blake, D. R., Simpson, I. J., Kwok, Y. H., & Li, Y. S. (2006). Regional and local contributions to ambient non-methane volatile organic compounds at a polluted rural/coastal site in Pearl River Delta, China. Atmospheric Environment, 40, 2345–2359.

    Article  CAS  Google Scholar 

  • Henry, C. G., D’Abreton, P. C., Ormerod, R. J., Galvin, G. G., Hoff, S. J., Jacobsen, L. D., et al. (2007). Downwind odor predictions from four swine finishing barns using CALPUFF. ASABE, Proceedings of the International Symposium on Air Quality and Waste Management for Agriculture, 2007.

  • HKDYY (2008). Legislation of air quality evaluation and management. Turkish Ministry of Environment and Forests. Resource document. http://www.cevreorman.gov.tr/yasa/y/26898.doc (official print in Turkish).

  • Hopke, P. K. (1985). Receptor modeling in environmental chemistry. New York: Wiley-Interscience.

    Google Scholar 

  • Hopke, P. K. (1991). Receptor modeling for air quality management, data handling in science and technology (Vol. 7). New York: Elsevier.

    Google Scholar 

  • Indumati, S., Oza, R. B., Mayya, Y. S., Puranic, V. D., & Kushwaha, H. S. (2009). Dispersion of pollutants over land–water–land interface: Study using CALPUFF model. Atmospheric Environment, 43(2), 473–478.

    Article  CAS  Google Scholar 

  • Kaiser, H. F. (1959). Computer program for varimax rotation in factor analysis. Educational and Psychological Measurement, 19, 413–420.

    Article  Google Scholar 

  • Ke, L., Liu, W., Wang, Y., Russell, A. G., Edgerton, E. S., & Zheng, M. (2008). Comparison of PM2.5 source apportionment using positive matrix factorization and molecular marker-based chemical mass balance. Science of the Total Environment, 394, 290–302.

    Article  CAS  Google Scholar 

  • Komp, M. J., Elias, D. F., Swain, T. M., & Myers, M. R. (1984). Comparison of two air quality models (CDM and ISC) predicted concentrations for surface mining. Air Pollution Control Association Annual Meeting Proceedings, 1, 20.

    Google Scholar 

  • Marmur, A., Park, S. K., Mulholland, J. A., Tolbert, P. E., & Russell, A. G. (2006). Source apportionment of PM2.5 in the southeastern United States using receptor and emissions-based models: conceptual differences and implications for time-series health studies. Atmospheric Environment, 40, 2533–2551.

    Article  CAS  Google Scholar 

  • Na, K., & Kim, Y. P. (2007). Chemical mass balance receptor model applied to ambient C2–C9 VOC concentration in Seoul, Korea: Effect of chemical reaction losses. Atmospheric Environment, 41, 6715–6728.

    Article  CAS  Google Scholar 

  • Nail, J. M., Pearson, R. L., & Yansura, M. B. (2007). Accounting for natural obscuration in CALPUFF visibility analyses. Air and Waste Management Association Guideline on air quality models: Applications and FLAG Developments. An A and WMA Specialty Conference, pp. 25–44.

  • Pancras, J. P., Ondov, J. M., Poor, N., Landis, M. S., & Stevens, R. K. (2006). Identification of sources and estimation of emission profiles from highly time-resolved pollutant measurements in Tampa, FL. Atmospheric Environment, 40, 467–481.

    Article  Google Scholar 

  • Parra, M. A., Elustondo, D., Bermejo, R., & Santamaria, J. M. (2009). Ambient air levels of volatile organic compounds (VOC) and nitrogen dioxide (NO2) in a medium size city in Northern Spain. Science of the Total Environment, 407, 999–1009.

    CAS  Google Scholar 

  • Samara, C., Kouimtzis, Th, Tsitouridou, R., Kanias, G., & Simeonov, V. (2003). Chemical mass balance source apportionment of PM10 in an industrialized urban area of Northern Greece. Atmospheric Environment, 37, 41–54.

    Article  CAS  Google Scholar 

  • Saral,A., Ertürk, F. (2001). Prediction of ground level SO2 concentration using artificial neural networks. 2nd International Symposium on Air Quality Management at Urban, Regional and Global Scales , September 25–28, 2001, Istanbul.

  • Seinfeld, J. H., & Pandis, S. N. (1998). Atmospheric chemistry and physics: From air pollution to climate change. USA: Wiley-Interscience.

    Google Scholar 

  • Srivastava, A., Sengupta, B., & Dutta, S. A. (2005). Source apportionment of ambient VOCs in Delhi City. Science of the Total Environment, 343, 207–220.

    Article  CAS  Google Scholar 

  • Tiris, M., Ekinci, M., & Okutan, H. (1996). Modelling of SO2 pollution changes with fuel shifting in Gebze, Turkey. Energy, 21(5), 371–375.

    Article  CAS  Google Scholar 

  • Viana, M., Querol, X., Alastuey, A., Gil, J. I., & Menendez, M. (2006). Identification of PM sources by principal component analysis (PCA) coupled with wind direction data. Chemosphere, 65, 2411–2418.

    Article  CAS  Google Scholar 

  • Wall, M. E., Rechtsteiner, A., & Luis, M. R. (2003). Singular value decomposition and principal component analysis. In D. P. Berrar, W. Dubitzky, & M. Granzow (Eds.), A practical approach to microarray data analysis (pp. 91–109). Norwell: Kluwer. LANL LA-UR-02-4001.

    Chapter  Google Scholar 

  • Wang, T., Poon, C. N., Kwok, Y. H., & Li, Y. S. (2003). Characterizing the temporal variability and emission patterns of pollution plumes in the Pearl River Delta of China. Atmospheric Environment, 37, 3539–3550.

    Article  CAS  Google Scholar 

  • Wang, T., Guo, H., Blake, D. R., Kwok, Y. H., Simpson, I. J., & Li, Y. S. (2005). Measurements of trace gases in the inflow of South China Sea background air and outflow of regional pollution at Tai O, southern China. Journal of Atmospheric Chemistry, 52, 295–317.

    Article  CAS  Google Scholar 

  • Watson, J. G. (1988). Receptor models in air resources management, air and waste management association’s TP-5 receptor source apportionment committee—An International Specialty Conference Proceedings, San Francisco, California.

Download references

Author information

Authors and Affiliations

  1. Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, 34220, Davutpasa, Esenler, Istanbul, Turkey

    Selami Demir, Arslan Saral, Ferruh Ertürk & Levent Kuzu

Authors
  1. Selami Demir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arslan Saral
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ferruh Ertürk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Levent Kuzu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Selami Demir.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Demir, S., Saral, A., Ertürk, F. et al. Combined Use of Principal Component Analysis (PCA) and Chemical Mass Balance (CMB) for Source Identification and Source Apportionment in Air Pollution Modeling Studies. Water Air Soil Pollut 212, 429–439 (2010). https://doi.org/10.1007/s11270-010-0358-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11270-010-0358-4

Keywords

Search

Navigation