Prediction of ozone concentration in ambient air using multivariate methods

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

Research output: Contribution to journalArticle

52 Citations (Scopus)

Abstract

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
JournalChemosphere
Volume57
Issue number8
DOIs
Publication statusPublished - Nov 2004

Fingerprint

Passive Cutaneous Anaphylaxis
Ozone
ambient air
Air
ozone
Polymerase Chain Reaction
pattern recognition
prediction
Linear regression
Principal component analysis
Pattern recognition
Statistical methods
principal component analysis
Hungary
Principal Component Analysis
Least-Squares Analysis
Linear Models
modeling
method
meteorological parameter

Keywords

  • 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 Chemistry
  • Environmental Science(all)

Cite this

Prediction of ozone concentration in ambient air using multivariate methods. / Lengyel, A.; Heberger, K.; Paksy, L.; Bánhidi, O.; Rajkó, R.

In: Chemosphere, Vol. 57, No. 8, 11.2004, p. 889-896.

Research output: Contribution to journalArticle

Lengyel, A. ; Heberger, K. ; Paksy, L. ; Bánhidi, O. ; Rajkó, R. / Prediction of ozone concentration in ambient air using multivariate methods. In: Chemosphere. 2004 ; Vol. 57, No. 8. pp. 889-896.
@article{b80d45462bbd458292836489077b7118,
title = "Prediction of ozone concentration in ambient air using multivariate methods",
abstract = "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.",
keywords = "Ambient air, Environmental data, Modeling of ozone concentration, Partial least squares (PLS), Principal component analysis (PCA), Principal component regression (PCR)",
author = "A. Lengyel and K. Heberger and L. Paksy and O. B{\'a}nhidi and R. Rajk{\'o}",
year = "2004",
month = "11",
doi = "10.1016/j.chemosphere.2004.07.043",
language = "English",
volume = "57",
pages = "889--896",
journal = "Chemosphere",
issn = "0045-6535",
publisher = "Elsevier Limited",
number = "8",

}

TY - JOUR

T1 - Prediction of ozone concentration in ambient air using multivariate methods

AU - Lengyel, A.

AU - Heberger, K.

AU - Paksy, L.

AU - Bánhidi, O.

AU - Rajkó, R.

PY - 2004/11

Y1 - 2004/11

N2 - 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.

AB - 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.

KW - Ambient air

KW - Environmental data

KW - Modeling of ozone concentration

KW - Partial least squares (PLS)

KW - Principal component analysis (PCA)

KW - Principal component regression (PCR)

UR - http://www.scopus.com/inward/record.url?scp=5544251157&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=5544251157&partnerID=8YFLogxK

U2 - 10.1016/j.chemosphere.2004.07.043

DO - 10.1016/j.chemosphere.2004.07.043

M3 - Article

C2 - 15488579

AN - SCOPUS:5544251157

VL - 57

SP - 889

EP - 896

JO - Chemosphere

JF - Chemosphere

SN - 0045-6535

IS - 8

ER -