This study uses and compares stationary and non-stationary Generalised Extreme Value Distribution (GEVD) to model the behaviour of nitrogen dioxide (NO2) emission maxima from each of 13 Eskom’s coal-fuelled power stations. The pollutant is modelled to facilitate monitoring and regulation in order to protect public health and the environment. The Maximum Likelihood Estimate (MLE) and Generalised Maximum Likelihood Estimate (GMLE) parameter estimation methods are used and compared in finding the best-fitting model per power station. The results show that a non-stationary model with time-dependent location and/or scale parameter(s) produced the best fit for ten of the power stations, while a stationary model gave the best fit for three, as confirmed by the diagnostic tools. Future extremely high NO2 emissions were estimated by making use of the 40 and 100 quarter return levels based on the best-fitting models. This study shows how stationarity may not hold for all NO2 emission data from Eskom’s coal-fired power stations. Modelling data using time-dependent non-stationary GEVD models can be useful, especially in identifying and predicting trends or patterns in worsening high NO2 emissions with time. This modelling approach is important in providing information for planning and policy formulation of extreme emissions from coal-fired electricity-generating power stations at Eskom (South Africa).
Mamba et al. (Tue,) studied this question.