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.

[1]  K. Héberger,et al.  Principal component analysis of Kovats indices for carbonyl compounds in capillary gas chromatography , 1999 .

[2]  T. Næs,et al.  Principal component regression in NIR analysis: Viewpoints, background details and selection of components , 1988 .

[3]  P. Hopke,et al.  Multiple regression for environmental data: nonlinearities and prediction bias , 1999 .

[4]  H. M. Heise,et al.  Calibration modeling by partial least-squares and principal component regression and its optimization using an improved leverage correction for prediction testing , 1990 .

[5]  Paul B. Shepson,et al.  Analysis of Air Quality Data Using Positive Matrix Factorization , 1999 .

[6]  John H. Kalivas,et al.  Examination of Criteria for Local Model Principal Component Regression , 1997 .

[7]  N. B. Vogt.  Polynomial Principal Component Regression: An approach to Analysis and Interpretation of Complex Mixture Relationships in Multivariate Environmental Data , 1989 .

[8]  J. Heicklen.  Atmospheric chemistry. , 1982, Environmental science & technology.

[9]  F. X. Rius,et al.  Assessing the validity of principal component regression models in different analytical conditions , 1997 .

[10]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[11]  I. Jolliffe.  A Note on the Use of Principal Components in Regression , 1982 .

[12]  B. Finlayson‐Pitts,et al.  Atmospheric chemistry : fundamentals and experimental techniques , 1986 .

[13]  K. Héberger.  Evaluation of polarity indicators and stationary phases by principal component analysis in gas–liquid chromatography , 1999 .

[14]  Matthias Otto,et al.  Chemometrics: Statistics and Computer Application in Analytical Chemistry , 1999 .

[15]  Richard Kramer.  Principal Component Regression , 1998 .

[16]  David D. Parrish,et al.  Review of observation-based analysis of the regional factors influencing ozone concentrations , 2000 .

[17]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[18]  K. Héberger,et al.  Thermodynamic significance of boiling point correlations for alkylbenzenes in gas chromatography: Extension of Trouton’s rule , 1999 .

[19]  D. K. Pissimanis,et al.  On the Spatial Distribution of the Daily Maximum Hourly Ozone Concentrations in the Athens Basin in Summer , 2000 .

[20]  C. Tomás,et al.  Spatial and temporal variability of ground-level ozone in Castilla-León (Spain) , 2000, International journal of biometeorology.