Modelling NO2 Emissions at Eskom’s Coal-Fired Power Station: Application of Statistical Distributions at Arnot

The combustion of coal comes with a heavy price of pollutant emissions. To assist in the planning and management of these emissions and to protect human health, the current study uses the relatively heavy-tailed distributions, namely, the Weibull, Lognormal and Pareto distributions to analyse and characterise the distribution of NO2 emission (in tons) from Arnot, a coal-fired power station of South Africa’s power utility, Eskom. Quantile–quantile (QQ) plots and their corresponding derivative plots for the three distributions are used to characterise the statistical distribution of NO2 emissions. The strength and advantage of using derivative plots of the three distributions, in particular, for characterising NO2 emissions from a coal-fuelled power station, is that they are able to better capture and explain the behaviour of the data across different components of this data. Although this method possesses flexible ways of characterisation of data, it is not commonly applied to emissions data, especially NO2 emissions from a coal-fuelled power station belonging to Eskom, such as Arnot. The choice of the distributions of this study is motivated by their ability to cater to varied tails relative to the exponential distribution. Thus, the tail heaviness ranks of the distributions from lighter to heavier tail, that is, Weibull, Lognormal and Pareto, are taken into consideration in order to arrive at the best-fitting distribution(s). The Weibull distribution with a lighter tail than the Exponential distribution gave the best-fitting distribution over the Lognormal and Pareto distributions for the main body of the data. The Pareto distribution, however, captures the extreme emission tail behaviour much better than the other two distributions. The Kolmogorov–Smirnov and Vasicek–Song (VS) goodness of fit statistics were used to further assess the appropriateness of the fitted distributions. The selection of the Weibull distribution implies that milder high values and less frequent very high NO2 emission data are expected, showing the weakness of such criteria when extremes are present. For authorities to plan and draw policies for the reduction and management of emissions, these findings may be of interest to them and can assist in better understanding their behaviour and the planning to reduce the impact on humans and the environment. This may also assist practitioners in air quality modelling before other, more sophisticated methods can be explored.