Prediction of ozone concentration in ambient air using multivariate methods

A. Lengyel, K. Héberger, L. Paksy, O. Bánhidi, R. Rajkó

Research output: Contribution to journalArticle

53 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)889-896
Number of pages8
Issue number8
Publication statusPublished - Nov 2004


  • Ambient air
  • Environmental data
  • Modeling of ozone concentration
  • Partial least squares (PLS)
  • Principal component analysis (PCA)
  • Principal component regression (PCR)

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Chemistry(all)
  • Pollution
  • Health, Toxicology and Mutagenesis

Fingerprint Dive into the research topics of 'Prediction of ozone concentration in ambient air using multivariate methods'. Together they form a unique fingerprint.

  • Cite this