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• W1970069948 abstract "Computer-controlled scanning electron microscopy (CCSEM) has proven to be a powerful tool in the characterization of individual particles and the source apportionment of ambient aerosol mass. The method can measure both physical properties of the particle including maximum and minimum diameters and area, as well as determine the major elemental composition of each particle. The particle volume and density can be estimated and the particle mass calculated. There are also powerful multivariate statistical procedures that can be directly applied to the individual particle data to assign any particular particle to the identified classes. The purpose of the current study was to explore the use of various hierarchical and non-hierarchical cluster analysis methods to define the number of distinct particle classes within an auto emission source sample. Each class was then separately modeled using a disjoint principal component procedure and membership of each particle in defined classes was tested. Mass fractions of each particle class and their uncertainties were calculated. These methods are being tested using auto emission source data from El Paso Quantitative Microscopy Study to identify sources of TSP and lead." @default.
• W1970069948 created "2016-06-24" @default.
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• W1970069948 date "1987-01-01" @default.
• W1970069948 modified "2023-10-16" @default.
• W1970069948 title "Multivariate analysis of CCSEM auto emission data" @default.
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• W1970069948 doi "https://doi.org/10.1016/0048-9697(87)90438-4" @default.
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