Česky
English
Výbor pro spolupráci ČR se Spojeným ústavem jaderných výzkumů Dubna Přihlášení
Multivariate statistical methods applied on organic marker species as an effective tool in source identification studies at a local scale

Autor
Štrbová Kristína, Ing. VŠB - Technická univerzita Ostrava, SÚJV Dubna
et al.  různé instituce

Rok
2018

Časopis
Proceedings of Air Quality Science and Application, 16

Web


Obsah
In the Proceedings of Abstracts 11th International Conference on Air Quality Science and Application

The aim of the study is to demonstrate application of multivariate statistical techniques such as principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) on organic pollutant species as an effective and reliable tool in source identification studies at a local scale. Analytical technique PyGC/MS was used to identify organic species in PM10 samples. HCPC produced three clusters of measurements which were clearly characterized by the predominant source of pollution at the measurement sites: plastic manufacturing sources; wood combustion and no specific source (background). Use of the HCPC was, thus, justified in the organic pollutant species based identification of the pollution sources.

Příklad citace článku:
K. Štrbová, . et al., "Multivariate statistical methods applied on organic marker species as an effective tool in source identification studies at a local scale", Proceedings of Air Quality Science and Application, 16 (2018)