Source Identification and Quantification of Coarse and Fine Particles by TTFA and PMF

  • Hwang, In-Jo (Department of Environmental Science and Engineering, College of Environment and Applied Chemistry and Institute of Environmental Studies, Kyung Hee University) ;
  • Bong, Choon-Keun (Industrial Liaison Research Institute, Kyung Hee University) ;
  • Lee, Tae-Jung (LIDAR Tech., Nonhyun-Dong, Gangnam-Gu) ;
  • Kim, Dong-Sool (Department of Environmental Science and Engineering, College of Environment and Applied Chemistry and Institute of Environmental Studies, Kyung Hee University)
  • Published : 2002.12.01

Abstract

Receptor modeling is one of statistical methods to achieve reasonable air pollution strategies. In order to maintain and manage ambient air quality, it is necessary to identify sources and to apportion its sources for ambient particulate matters. The main purpose of the study was to survey seasonal trends of inorganic elements in the coarse and fine particles. Second, this study has attempted emission sources qualitatively by a receptor method, the PMF mo-del. After that. both PMF (positive matrix factorization) model and TTFA (target transformation factor analysis) model were applied to compare and to estimate mass contribution of coarse and fine particle sources at the receptor. A total of 138 sets of samples was collected from 1989 to 1996 by a low volume cascade impactor with 9 size fraction stages at Kyung Hee University in Korea. Sixteen chemical species (Si, Ca, Fe, K, Pb, Na, Zn, Mg, Ba, Ni, V, Mn, Cr, Br, Cu. Co) were characterized by XRF. The study result showed that the weighted arithmetic mean of coarse and fine particles were 51.3 and 54.4 $\mu\textrm{g}$/㎥, respectively. Contribution of both particle fractions were esti-mated using TTFA and PMF models. The number of estimated sources was seven according to TTFA model and 8 according to PMF model. Comparison of TTFA and PMF revealed that both methodologies exhibited similar trends in their contribution pattern. However, large differences between contributions were observed in some sour-ces. The results of this study may help to suggest control strategies in local countries where known source profiles do not exist.

Keywords

References

  1. Chueinta, W., P.K Hopke, and P. Paatero (2000) Investigation of source of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization, Atmospheric Environment, 34(20),3319-3329
  2. Friedlander, S.K (1973) Chemical element balances and identification of air pollution sources, Environ. Sci. & Techno!., 7, 235-240
  3. Hien, P.D., N.T. Binh, Y. Truong, and N.T. Ngo(1999) Temporal variations of source impacts at the receptor, as derived from air particulate monitoring data in Ho Chi Minh City, Vietnam, Atmospheric Environment, 33(19), 3133-3142
  4. Hopke, P.K (1988) Target transformation factor analysis as an aerosol mass apportionment method, A review and sensitive study, Atmospheric Environment, 9, 1777-1792
  5. Hopke, P.K, D.J. Alpert, and B.A. Roscore (1982) FANTASIA program for target transformation factor analysis to apportion source in environmental samples, Computers & Chemistry, 7(3), 149-155
  6. Huang, S., K.A. Rahn, and R. Arimoto (1999) Testing and optimizing two factor-analysis techniques on aerosol at Narragansett Rhode Island, Atmospheric Environment, 33(14), 2169-2185
  7. Hwang, I.J. and D.S. Kim (1998) Studies on the chemical compositions and distributions of ambient submicron aerosols, (in Korean), Journal of Korean Society for Atmospheric Environment, 14(1), 11-23
  8. Hwang, I.J., T.O. Kim, and D.S. Kim (2001) Source identification of Pm-10 in Suwon using the method of positive matrix factorization, (in Korean), Journal of Korean Society for Atmospheric Environment, 17(2),133-145
  9. Juntto, S. and P. Paatero (1994) Analysis of daily precipitation data by positive matrix factorization, Environmetries, 5, 127-144
  10. Kim, K.S., I.J. Hwang, and D.S. Kim (2001) Development of a receptor methodology for quantitative assessment of ambient PM-10 sources in Suwon area, (in Korean), Journal of Korean Society for Atmospheric Environment, 17(2), 119-131
  11. Kyunggi statistical year book (1998) Kyunggi-Do
  12. Lee, E., C.K. Chan, and P. Paatero (1999) Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong, Atmospheric Environment, 33(19), 3201-3212
  13. Lee, TJ. and D.S. Kim (1997) Estimation of source contribution for ambient particulate matters in Suwon area, (in Korean), Journal of Korean Society for Atmospheric Environment, 13(4),285-296
  14. Miller, M.S., S.K. Friedlander, and G.M. Hindy (1972) A chemical element balance for the Pasadena aerosol, J. Colloid and Inter. Science, 39(1), 165- I76
  15. Paatero, P. (1998) User's guide for positive matrix factorization program PMF2 and PMF3, part 1: tutorial, University of Helsinki
  16. Paatero, P. and U. Tapper (1994) Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, 5, 111-126
  17. Paterson, K.G., J.L. Sagady, D.L. Hooper, S.T. Bertman, M.A. Carroll, and P.B. Shepson (1999) Analysis of air quality data using positive matrix factorization, Environ. Sci. & Techno!., 33(4), 635-641
  18. Poissant, L., J.W. Bottenheim, P. Roussel, N.W. Reid, and H. Niki (1996) Multivariate analysis of a 1992 SONTOS data subset, Atmospheric Environment, 30(12), 2133-2144
  19. Simcik, M.F., S.J. Eisenreich, and P.J. Lioy (1999) Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan, Atmospheric Environment, 33(30), 5071-5079