Multidimensional modeling of aerosol monitoring data.
The present study deals with the application of N-way factor analysis for modeling and interpretation of a three-dimensional environmental data set acquired from monitoring of particulate matter (PM) collected at four different sampling locations in Lower Austria region (Central Europe). In the study the Tucker3 algorithm for N-way modeling was used. It was statistically validated that the Tucker3 model offered having the dimensionality [222] is appropriate for correct interpretation of the relationships between chemical parameters, sampling locations and sampling period. The Tucker3 model allowed to distinguish three major sources of pollution in the region of interest conditionally named "soil dust", "combustion" and "street dust" latent factors as responsible for chemical profile of PM and to identify seasonal variability. Additionally, some specificity of the sampling locations was also pointed out.
Astel
Aleksander
A
Pomeranian Academy, Biology and Environmental Protection Institute, Environmental Chemistry Research Unit, 22a Arciszewskiego Str., 76-200 Słupsk, Poland. astel@apsl.edu.pl
Simeonov
Vasil
V
Bauer
Heidi
H
Puxbaum
Hans
H
D000336:Aerosols / Q000032:analysis*; D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000379:methods*; D008956:Models, Chemical; D052638:Particulate Matter / Q000032:analysis
D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter