Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NOx NO2 and O3 and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NOx and NO2 concentrations in London. Primary emissions and wind speed are the most important factors influencing NOx concentrations; in addition to these two, reaction of NO with O3 is also a major factor influencing NO2 concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NOx and NO2 regression based on a whole yearʹs data, and is capable of predicting NO2 (R = 0.83) and NOx (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO2 source, other than primary emission and reaction of NO with O3, in the air pollution episode in London in December 1991.