The use of principal component factor analysis to interpret particulate compositional data sets
- Univ. of Illinois, Urbana
The study of urban aerosols often yields large data bases consisting of elemental and meteorological data for a multitude of samples collected at various locations and times. Several statistical techniques are currently being used to identify the nature of aerosol sources using these data. However, errors present in the data base may make the interpretation difficult. In addition to its value as a receptor model, principal component factor analysis has the ability to identify the errors present in the data so that appropriate corrections may be made before detailed data interpretation is attempted. Thus the technique can save much time and effort. Principal component factor analysis can yield interpretations of aerosol data and a better understanding of the airshed being studied.
- DOE Contract Number:
- AC02-80EV10403
- OSTI ID:
- 6519415
- Journal Information:
- J. Air Pollut. Control Assoc.; (United States), Vol. 32:6
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
AEROSOLS
STATISTICAL MODELS
DATA ANALYSIS
ERRORS
METEOROLOGY
URBAN AREAS
COLLOIDS
DISPERSIONS
MATHEMATICAL MODELS
SOLS
500200* - Environment
Atmospheric- Chemicals Monitoring & Transport- (-1989)
990200 - Mathematics & Computers