TY - JOUR
T1 - Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations
AU - Abdul-Wahab, Sabah A.
AU - Bakheit, Charles S.
AU - Al-Alawi, Saleh M.
PY - 2005/10
Y1 - 2005/10
N2 - 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.
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.
KW - Principal component analysis
KW - Regression analysis
KW - Statistical analysis
KW - Variable selection methods
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U2 - 10.1016/j.envsoft.2004.09.001
DO - 10.1016/j.envsoft.2004.09.001
M3 - Article
AN - SCOPUS:16244417840
SN - 1364-8152
VL - 20
SP - 1263
EP - 1271
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
IS - 10
ER -