Identifying the various sources responsible for the composition of airborne particles at a given location is an important prerequisite to developing effective regulatory policies for air quality. During the past decade, there has been increasing use of elemental and chemical analytical methods to characterized airborne particulate matter and collected precipitation. With the availability of these large data sets and new multivariate of statistical methods that have been made practical by the continuing increases in computer size and speed, there is an enhanced opportunity to extract information about atmospheric processes from data characterizing collected particles or precipitation. Since some elements are strongly geochemically related, there is redundancy in the elemented data that limits the number of parameters that are influential in identifying sources. Another source of data is physical characterization (size, shape, texture) of individual particles by computer-controlled scanning electron microscopy. This project will examine several eigenvector-based statistical methods to investigate regional scale transport of airborne particulate matter. By using principal components analysis, empirical orthogonal functions, and three-mode factor analysis, is it expected that the patterns of spatial and temporal variations in the aerosol compositions can be better understood and relationships with the regional meteorology defined. In addition, methods will be developed for extracting quantitative measures of particle texture from images of individual particles obtained with a Scanning Electron Microscope. Fractal dimension, eigenvector and Fourier analysis methods will be explored. The effects of chemical modification of the particle surface by reaction with gaseous species on the visual surface texture and results of the image analysis methods will be examined.
