A Loggamma Generalised Linear Model for NO2 Emissions Data from South Africa’s Eskom’s Coal-Fired Power Stations When the Data Are Non-Normal and the Variance Is Non-Constant

The aim of this paper is to determine if the Loggamma distribution model in a Generalised Linear Model (GLM) setup is a better model than the traditional simple linear regression model and the Lognormal-based GLM when fitted to nitrogen dioxide (NO2) emissions data generated during the production of electricity from 13 Eskom’s coal-fuelled power stations in South Africa. The variables explaining the NO2 emissions data are selected using backward stepwise variable selection techniques. The variables considered include the power station itself, the amount of electricity generated from the power station, the age in years of the power station, the abatement technology (filter) used at the particular power station, and the month of the year. Interaction terms between the variables are also considered. The maximum likelihood estimation (MLE) method is used to estimate parameters of the GLM, and ordinary least squares is used to estimate parameters for the regression model. The Normal, Lognormal, and Loggamma distribution models with identity link function are fitted to the NO2 emissions data. The variance of the NO2 emissions increases with mean emissions and the Loggamma model plots, and the explained variance metrics (the variance-function-based R2 and adjusted R2) confirm the best fit to the data over the Normally distributed regression model and Lognormal-based GLM. Thus, NO2 emissions at Eskom in South Africa can be explained and predicted by employing the Loggamma-based GLM model. The findings will assist in providing information for the development of effective strategies for mitigating air pollution and promoting sustainable practices within the energy sector in South Africa.