AIR QUALITY ASSESSMENT BY MULTIVARIATE STATISTICS

The present communication deals with the application of several chemometrical methods (cluster and principal components analysis, source apportioning on absolute principal components scores) to an aerosol data collection from Arnoldstein, Austria. It is convincingly shown that six latent factors explaining almost 80 % of the total variance are responsible for the data structure and are conditionally identified as "fertilizer", secondary emission", "lead smelter", "traffic", "salt" and "soil dust". Further more, the contribution of each identified source to the formation of the particle total mass and chemical compounds total concentration is calculated. Thus, a reliable assessment of the air quality in the region of observation is achieved. The apportioning models obtained are checked for adequateness and validated. It is explained why for sodium and magnesium non-adequate models are obtained. The latent factor contribution models can be further used for risk assessment and respective decision making. Additionally, it is commented why chemometrics could be successfully applied as sustainability metrics in various aspects of interpretation of the state of "sustainable development"