Prediction of ozone concentration in ambient air using multivariate methods.
Multivariate statistical methods including pattern recognition (Principal Component Analysis--PCA) and modeling (Multiple Linear Regression--MLR, Partial Least Squares--PLS, as well as Principal Component Regression--PCR) methods were carried out to evaluate the state of ambient air in Miskolc (second largest city in Hungary). Samples were taken from near the ground at a place with an extremely heavy traffic. Although PCA is not able to determine the significance of variables, it can uncover their similarities and classify the cases. PCA revealed that it is worth to separate day and night data because different factors influence the ozone concentrations during all day. Ozone concentration was modeled by MLR and PCR with the same efficiency if the conditions of meteorological parameters were not changed (i.e. morning and afternoon). Without night data, PCR and PLS suggest that the main process is not a photochemical but a chemical one.
Lengyel
A
A
Department of Analytical Chemistry, Institute of Chemistry, University of Miskolc, H-3515 Miskolc-Egyetemváros, Hungary. akmla@gold.uni-miskolc.hu
Héberger
K
K
Paksy
L
L
Bánhidi
O
O
Rajkó
R
R
D000388:Air / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D006814:Hungary; D016014:Linear Models; D015999:Multivariate Analysis; D010126:Ozone / Q000032:analysis*; D025341:Principal Component Analysis
D010126:Ozone