Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations

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Abstract

Data on the concentrations of seven environmental pollutants (CH 4, NMHC, CO, CO2, NO, NO2 and SO2) and meteorological variables (wind speed and direction, air temperature, relative humidity and solar radiation) were employed to predict the concentration of ozone in the atmosphere using both multiple linear and principal component regression methods. Separate analyses were carried out for day light and night time periods. For both periods the pollutants were highly correlated, but were all negatively correlated with ozone. Multiple regression analysis was used to fit the ozone data using the pollutant and meteorological variables as predictors. A variable selection method based on high loadings of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the regression model of the logarithm of the ozone data. It was found that while high temperature and high solar energy tended to increase the day time ozone concentrations, the pollutants NO and SO2 being emitted to the atmosphere were being depleted. Night time ozone concentrations were influenced predominantly by the nitrogen oxides (NO+NO 2), with the meteorological variables playing no significant role. However, the model did not predict the night time ozone concentrations as accurately as it did for the day time. This could be due to other factors that were not explicitly considered in this study.

Original languageEnglish
Pages (from-to)1263-1271
Number of pages9
JournalEnvironmental Modelling and Software
Volume20
Issue number10
DOIs
Publication statusPublished - Oct 2005

Fingerprint

Regression analysis
Ozone
multiple regression
regression analysis
ozone
modeling
pollutant
atmosphere
Nitrogen oxides
nitrogen oxides
Solar radiation
wind direction
Solar energy
relative humidity
solar radiation
Atmospheric humidity
air temperature
wind velocity
Temperature
Air

Keywords

  • Principal component analysis
  • Regression analysis
  • Statistical analysis
  • Variable selection methods

ASJC Scopus subject areas

  • Ecological Modelling
  • Environmental Science(all)

Cite this

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title = "Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations",
abstract = "Data on the concentrations of seven environmental pollutants (CH 4, NMHC, CO, CO2, NO, NO2 and SO2) and meteorological variables (wind speed and direction, air temperature, relative humidity and solar radiation) were employed to predict the concentration of ozone in the atmosphere using both multiple linear and principal component regression methods. Separate analyses were carried out for day light and night time periods. For both periods the pollutants were highly correlated, but were all negatively correlated with ozone. Multiple regression analysis was used to fit the ozone data using the pollutant and meteorological variables as predictors. A variable selection method based on high loadings of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the regression model of the logarithm of the ozone data. It was found that while high temperature and high solar energy tended to increase the day time ozone concentrations, the pollutants NO and SO2 being emitted to the atmosphere were being depleted. Night time ozone concentrations were influenced predominantly by the nitrogen oxides (NO+NO 2), with the meteorological variables playing no significant role. However, the model did not predict the night time ozone concentrations as accurately as it did for the day time. This could be due to other factors that were not explicitly considered in this study.",
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AB - Data on the concentrations of seven environmental pollutants (CH 4, NMHC, CO, CO2, NO, NO2 and SO2) and meteorological variables (wind speed and direction, air temperature, relative humidity and solar radiation) were employed to predict the concentration of ozone in the atmosphere using both multiple linear and principal component regression methods. Separate analyses were carried out for day light and night time periods. For both periods the pollutants were highly correlated, but were all negatively correlated with ozone. Multiple regression analysis was used to fit the ozone data using the pollutant and meteorological variables as predictors. A variable selection method based on high loadings of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the regression model of the logarithm of the ozone data. It was found that while high temperature and high solar energy tended to increase the day time ozone concentrations, the pollutants NO and SO2 being emitted to the atmosphere were being depleted. Night time ozone concentrations were influenced predominantly by the nitrogen oxides (NO+NO 2), with the meteorological variables playing no significant role. However, the model did not predict the night time ozone concentrations as accurately as it did for the day time. This could be due to other factors that were not explicitly considered in this study.

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