Presentations and Publications

Zhou, L., Hopke, P.K., Stanier, C., Pandis, S.N., Ondov, J.M., and Pancras, P., “Investigation of the relationship between chemical composition and size distribution of airborne particles by Partial Least Squares (PLS) and Positive Matrix Factorization (PMF)”, Journal of Geophysical Research – Atmospheres, Vol. 110(D07S18), 2005, doi:10.1029/2004JD005050.

Two multivariate data analysis methods, partial least square (PLS) and positive matrix
factorization (PMF), were used to analyze aerosol size distribution data and omposition
data. The relationships between the size distribution data and composition data were
investigated by PLS. Three latent variables summarized chemical composition data and
most variations in size distribution data especially for large particles and proved the
existence of the linearity between the two data sets. The three latent variables were
associated with traffic and local combustion sources, secondary aerosol, and coal-fired
power plants. The size distribution, particle composition, and gas composition data were combined and analyzed by PMF. Source information was obtained for each source using size distribution and chemical composition simultaneously. Eleven sources were identified: secondary nitrate 1 and 2, remote traffic, secondary sulfate, lead, diesel traffic, coal-fired power plant, steel mill, nucleation, local traffic, and coke plant.