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Anthropogenic and demographic processes cause worldwide air problems, giving rise to focus on exhaust air
purification to counteract these effects. Due to the large number of substances found in exhaust air and the various
operational parameters needed, a huge amount of often high dimensional data has to be analyzed. The ultimate goal is to
finally reduce data complexity in terms of information reflecting the substances' characteristics.
The Cluster Analysis (CA) of data from 30 exhaust air compounds with 11 indices representing both structural
characteristics and physicochemical data resulted in 7 clusters. The Principal Component Analysis (PCA) led to the
identification of 6 Principal Components (PCs) and therefore to a dimensional reduction compared to the originally used
11 indices. After re-gathering the total information of the original data-set upon the 6 PCs only, a re-clustering showed
that we were able to restore the same cluster structure as in the original CA based on the 11 indices. This process is a first
proof of principle in successful re-clustering after dimensional data reduction by our proposed combined CA-PCA method
and hence a step towards a possible development of an adsorption method to selectively remove malodorous/toxic
components from the exhaust air.